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import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union
import torch
from .tensor_utils import tensor_tree_map, tree_map
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE ):
A_ : List[Any] = []
if isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
for v in tree.values():
shapes.extend(_fetch_dims(SCREAMING_SNAKE_CASE ) )
elif isinstance(SCREAMING_SNAKE_CASE , (list, tuple) ):
for t in tree:
shapes.extend(_fetch_dims(SCREAMING_SNAKE_CASE ) )
elif isinstance(SCREAMING_SNAKE_CASE , torch.Tensor ):
shapes.append(tree.shape )
else:
raise ValueError('''Not supported''' )
return shapes
@torch.jit.ignore
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
A_ : str = []
for d in reversed(SCREAMING_SNAKE_CASE ):
idx.append(flat_idx % d )
A_ : List[str] = flat_idx // d
return tuple(reversed(SCREAMING_SNAKE_CASE ) )
@torch.jit.ignore
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = None , SCREAMING_SNAKE_CASE = None , ):
# start_edges and end_edges both indicate whether, starting from any given
# dimension, the start/end index is at the top/bottom edge of the
# corresponding tensor, modeled as a tree
def reduce_edge_list(SCREAMING_SNAKE_CASE ) -> None:
A_ : Dict = True
for i in range(len(SCREAMING_SNAKE_CASE ) ):
A_ : Optional[Any] = -1 * (i + 1)
l[reversed_idx] &= tally
A_ : Any = l[reversed_idx]
if start_edges is None:
A_ : Tuple = [s == 0 for s in start]
reduce_edge_list(SCREAMING_SNAKE_CASE )
if end_edges is None:
A_ : Union[str, Any] = [e == (d - 1) for e, d in zip(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )]
reduce_edge_list(SCREAMING_SNAKE_CASE )
# Base cases. Either start/end are empty and we're done, or the final,
# one-dimensional tensor can be simply sliced
if len(SCREAMING_SNAKE_CASE ) == 0:
return [()]
elif len(SCREAMING_SNAKE_CASE ) == 1:
return [(slice(start[0] , end[0] + 1 ),)]
A_ : List[Tuple[slice, ...]] = []
A_ : List[slice] = []
# Dimensions common to start and end can be selected directly
for s, e in zip(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
if s == e:
path_list.append(slice(SCREAMING_SNAKE_CASE , s + 1 ) )
else:
break
A_ : Tuple[slice, ...] = tuple(SCREAMING_SNAKE_CASE )
A_ : Optional[int] = len(SCREAMING_SNAKE_CASE )
# start == end, and we're done
if divergence_idx == len(SCREAMING_SNAKE_CASE ):
return [path]
def upper() -> Tuple[Tuple[slice, ...], ...]:
assert start_edges is not None
assert end_edges is not None
A_ : List[Any] = start[divergence_idx]
return tuple(
path + (slice(SCREAMING_SNAKE_CASE , sdi + 1 ),) + s
for s in _get_minimal_slice_set(
start[divergence_idx + 1 :] , [d - 1 for d in dims[divergence_idx + 1 :]] , dims[divergence_idx + 1 :] , start_edges=start_edges[divergence_idx + 1 :] , end_edges=[True for _ in end_edges[divergence_idx + 1 :]] , ) )
def lower() -> Tuple[Tuple[slice, ...], ...]:
assert start_edges is not None
assert end_edges is not None
A_ : Union[str, Any] = end[divergence_idx]
return tuple(
path + (slice(SCREAMING_SNAKE_CASE , edi + 1 ),) + s
for s in _get_minimal_slice_set(
[0 for _ in start[divergence_idx + 1 :]] , end[divergence_idx + 1 :] , dims[divergence_idx + 1 :] , start_edges=[True for _ in start_edges[divergence_idx + 1 :]] , end_edges=end_edges[divergence_idx + 1 :] , ) )
# If both start and end are at the edges of the subtree rooted at
# divergence_idx, we can just select the whole subtree at once
if start_edges[divergence_idx] and end_edges[divergence_idx]:
slices.append(path + (slice(start[divergence_idx] , end[divergence_idx] + 1 ),) )
# If just start is at the edge, we can grab almost all of the subtree,
# treating only the ragged bottom edge as an edge case
elif start_edges[divergence_idx]:
slices.append(path + (slice(start[divergence_idx] , end[divergence_idx] ),) )
slices.extend(lower() )
# Analogous to the previous case, but the top is ragged this time
elif end_edges[divergence_idx]:
slices.extend(upper() )
slices.append(path + (slice(start[divergence_idx] + 1 , end[divergence_idx] + 1 ),) )
# If both sides of the range are ragged, we need to handle both sides
# separately. If there's contiguous meat in between them, we can index it
# in one big chunk
else:
slices.extend(upper() )
A_ : int = end[divergence_idx] - start[divergence_idx]
if middle_ground > 1:
slices.append(path + (slice(start[divergence_idx] + 1 , end[divergence_idx] ),) )
slices.extend(lower() )
return slices
@torch.jit.ignore
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
A_ : Tuple = t.shape[:no_batch_dims]
A_ : Tuple = list(_flat_idx_to_idx(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) )
# _get_minimal_slice_set is inclusive
A_ : List[str] = list(_flat_idx_to_idx(flat_end - 1 , SCREAMING_SNAKE_CASE ) )
# Get an ordered list of slices to perform
A_ : List[Any] = _get_minimal_slice_set(
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , )
A_ : Tuple = [t[s] for s in slices]
return torch.cat([s.view((-1,) + t.shape[no_batch_dims:] ) for s in sliced_tensors] )
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = False , SCREAMING_SNAKE_CASE = None , SCREAMING_SNAKE_CASE = False , ):
if not (len(SCREAMING_SNAKE_CASE ) > 0):
raise ValueError('''Must provide at least one input''' )
A_ : int = [shape[:no_batch_dims] for shape in _fetch_dims(SCREAMING_SNAKE_CASE )]
A_ : int = tuple([max(SCREAMING_SNAKE_CASE ) for s in zip(*SCREAMING_SNAKE_CASE )] )
def _prep_inputs(SCREAMING_SNAKE_CASE ) -> torch.Tensor:
if not low_mem:
if not sum(t.shape[:no_batch_dims] ) == no_batch_dims:
A_ : Any = t.expand(orig_batch_dims + t.shape[no_batch_dims:] )
A_ : List[Any] = t.reshape(-1 , *t.shape[no_batch_dims:] )
else:
A_ : Optional[int] = t.expand(orig_batch_dims + t.shape[no_batch_dims:] )
return t
A_ : Dict[str, Any] = tensor_tree_map(_prep_inputs , SCREAMING_SNAKE_CASE )
A_ : Optional[Any] = None
if _out is not None:
A_ : Optional[int] = tensor_tree_map(lambda SCREAMING_SNAKE_CASE : t.view([-1] + list(t.shape[no_batch_dims:] ) ) , _out )
A_ : Dict = 1
for d in orig_batch_dims:
flat_batch_dim *= d
A_ : Optional[Any] = flat_batch_dim // chunk_size + (flat_batch_dim % chunk_size != 0)
def _select_chunk(SCREAMING_SNAKE_CASE ) -> torch.Tensor:
return t[i : i + chunk_size] if t.shape[0] != 1 else t
A_ : Union[str, Any] = 0
A_ : Optional[int] = prepped_outputs
for _ in range(SCREAMING_SNAKE_CASE ):
# Chunk the input
if not low_mem:
A_ : Optional[Any] = _select_chunk
else:
A_ : Dict = partial(
_chunk_slice , flat_start=SCREAMING_SNAKE_CASE , flat_end=min(SCREAMING_SNAKE_CASE , i + chunk_size ) , no_batch_dims=len(SCREAMING_SNAKE_CASE ) , )
A_ : Dict[str, Any] = tensor_tree_map(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
# Run the layer on the chunk
A_ : List[str] = layer(**SCREAMING_SNAKE_CASE )
# Allocate space for the output
if out is None:
A_ : Dict = tensor_tree_map(lambda SCREAMING_SNAKE_CASE : t.new_zeros((flat_batch_dim,) + t.shape[1:] ) , SCREAMING_SNAKE_CASE )
# Put the chunk in its pre-allocated space
if isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
def assign(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> None:
for k, v in da.items():
if isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
assign(SCREAMING_SNAKE_CASE , da[k] )
else:
if _add_into_out:
v[i : i + chunk_size] += da[k]
else:
A_ : Union[str, Any] = da[k]
assign(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
elif isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
for xa, xa in zip(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
if _add_into_out:
xa[i : i + chunk_size] += xa
else:
A_ : Any = xa
elif isinstance(SCREAMING_SNAKE_CASE , torch.Tensor ):
if _add_into_out:
out[i : i + chunk_size] += output_chunk
else:
A_ : Tuple = output_chunk
else:
raise ValueError('''Not supported''' )
i += chunk_size
A_ : Optional[Any] = tensor_tree_map(lambda SCREAMING_SNAKE_CASE : t.view(orig_batch_dims + t.shape[1:] ) , SCREAMING_SNAKE_CASE )
return out
class _lowerCamelCase :
"""simple docstring"""
def __init__( self , _SCREAMING_SNAKE_CASE = 512 , )->Any:
'''simple docstring'''
A_ : Any = max_chunk_size
A_ : Optional[int] = None
A_ : Optional[tuple] = None
def _snake_case ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )->int:
'''simple docstring'''
logging.info('''Tuning chunk size...''' )
if min_chunk_size >= self.max_chunk_size:
return min_chunk_size
A_ : List[int] = [2**l for l in range(int(math.log(self.max_chunk_size , 2 ) ) + 1 )]
A_ : int = [c for c in candidates if c > min_chunk_size]
A_ : Dict = [min_chunk_size] + candidates
candidates[-1] += 4
def test_chunk_size(_SCREAMING_SNAKE_CASE ) -> bool:
try:
with torch.no_grad():
fn(*_SCREAMING_SNAKE_CASE , chunk_size=_SCREAMING_SNAKE_CASE )
return True
except RuntimeError:
return False
A_ : List[str] = 0
A_ : Union[str, Any] = len(_SCREAMING_SNAKE_CASE ) - 1
while i > min_viable_chunk_size_index:
A_ : List[str] = test_chunk_size(candidates[i] )
if not viable:
A_ : Any = (min_viable_chunk_size_index + i) // 2
else:
A_ : Any = i
A_ : int = (i + len(_SCREAMING_SNAKE_CASE ) - 1) // 2
return candidates[min_viable_chunk_size_index]
def _snake_case ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )->bool:
'''simple docstring'''
A_ : List[Any] = True
for aa, aa in zip(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
assert type(_SCREAMING_SNAKE_CASE ) == type(_SCREAMING_SNAKE_CASE )
if isinstance(_SCREAMING_SNAKE_CASE , (list, tuple) ):
consistent &= self._compare_arg_caches(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
elif isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
A_ : List[Any] = [v for _, v in sorted(aa.items() , key=lambda _SCREAMING_SNAKE_CASE : x[0] )]
A_ : int = [v for _, v in sorted(aa.items() , key=lambda _SCREAMING_SNAKE_CASE : x[0] )]
consistent &= self._compare_arg_caches(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
else:
consistent &= aa == aa
return consistent
def _snake_case ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , )->int:
'''simple docstring'''
A_ : List[Any] = True
A_ : tuple = tree_map(lambda _SCREAMING_SNAKE_CASE : a.shape if isinstance(_SCREAMING_SNAKE_CASE , torch.Tensor ) else a , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
if self.cached_arg_data is not None:
# If args have changed shape/value, we need to re-tune
assert len(self.cached_arg_data ) == len(_SCREAMING_SNAKE_CASE )
A_ : Tuple = self._compare_arg_caches(self.cached_arg_data , _SCREAMING_SNAKE_CASE )
else:
# Otherwise, we can reuse the precomputed value
A_ : Union[str, Any] = False
if not consistent:
A_ : List[Any] = self._determine_favorable_chunk_size(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , )
A_ : Union[str, Any] = arg_data
assert self.cached_chunk_size is not None
return self.cached_chunk_size
| 186 |
import unittest
import numpy as np
import torch
from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device
from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class _lowerCamelCase ( UpperCamelCase , unittest.TestCase ):
"""simple docstring"""
snake_case = DDIMPipeline
snake_case = UNCONDITIONAL_IMAGE_GENERATION_PARAMS
snake_case = PipelineTesterMixin.required_optional_params - {
"num_images_per_prompt",
"latents",
"callback",
"callback_steps",
}
snake_case = UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
snake_case = False
def _snake_case ( self )->List[str]:
'''simple docstring'''
torch.manual_seed(0 )
A_ : List[str] = UNetaDModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=3 , out_channels=3 , down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''') , up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''') , )
A_ : Optional[Any] = DDIMScheduler()
A_ : str = {'''unet''': unet, '''scheduler''': scheduler}
return components
def _snake_case ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=0 )->Optional[Any]:
'''simple docstring'''
if str(_SCREAMING_SNAKE_CASE ).startswith('''mps''' ):
A_ : Any = torch.manual_seed(_SCREAMING_SNAKE_CASE )
else:
A_ : Optional[int] = torch.Generator(device=_SCREAMING_SNAKE_CASE ).manual_seed(_SCREAMING_SNAKE_CASE )
A_ : Any = {
'''batch_size''': 1,
'''generator''': generator,
'''num_inference_steps''': 2,
'''output_type''': '''numpy''',
}
return inputs
def _snake_case ( self )->List[Any]:
'''simple docstring'''
A_ : Optional[int] = '''cpu'''
A_ : Dict = self.get_dummy_components()
A_ : str = self.pipeline_class(**_SCREAMING_SNAKE_CASE )
pipe.to(_SCREAMING_SNAKE_CASE )
pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE )
A_ : str = self.get_dummy_inputs(_SCREAMING_SNAKE_CASE )
A_ : Any = pipe(**_SCREAMING_SNAKE_CASE ).images
A_ : int = image[0, -3:, -3:, -1]
self.assertEqual(image.shape , (1, 32, 32, 3) )
A_ : List[Any] = np.array(
[1.000e00, 5.717e-01, 4.717e-01, 1.000e00, 0.000e00, 1.000e00, 3.000e-04, 0.000e00, 9.000e-04] )
A_ : str = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(_SCREAMING_SNAKE_CASE , 1e-3 )
def _snake_case ( self )->Union[str, Any]:
'''simple docstring'''
super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3 )
def _snake_case ( self )->Optional[int]:
'''simple docstring'''
super().test_save_load_local(expected_max_difference=3e-3 )
def _snake_case ( self )->Optional[int]:
'''simple docstring'''
super().test_save_load_optional_components(expected_max_difference=3e-3 )
def _snake_case ( self )->Any:
'''simple docstring'''
super().test_inference_batch_single_identical(expected_max_diff=3e-3 )
@slow
@require_torch_gpu
class _lowerCamelCase ( unittest.TestCase ):
"""simple docstring"""
def _snake_case ( self )->Union[str, Any]:
'''simple docstring'''
A_ : int = '''google/ddpm-cifar10-32'''
A_ : Tuple = UNetaDModel.from_pretrained(_SCREAMING_SNAKE_CASE )
A_ : str = DDIMScheduler()
A_ : str = DDIMPipeline(unet=_SCREAMING_SNAKE_CASE , scheduler=_SCREAMING_SNAKE_CASE )
ddim.to(_SCREAMING_SNAKE_CASE )
ddim.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE )
A_ : Optional[int] = torch.manual_seed(0 )
A_ : Any = ddim(generator=_SCREAMING_SNAKE_CASE , eta=0.0 , output_type='''numpy''' ).images
A_ : Any = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
A_ : Any = np.array([0.1_7_2_3, 0.1_6_1_7, 0.1_6_0_0, 0.1_6_2_6, 0.1_4_9_7, 0.1_5_1_3, 0.1_5_0_5, 0.1_4_4_2, 0.1_4_5_3] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def _snake_case ( self )->List[str]:
'''simple docstring'''
A_ : Tuple = '''google/ddpm-ema-bedroom-256'''
A_ : int = UNetaDModel.from_pretrained(_SCREAMING_SNAKE_CASE )
A_ : Any = DDIMScheduler.from_pretrained(_SCREAMING_SNAKE_CASE )
A_ : Optional[Any] = DDIMPipeline(unet=_SCREAMING_SNAKE_CASE , scheduler=_SCREAMING_SNAKE_CASE )
ddpm.to(_SCREAMING_SNAKE_CASE )
ddpm.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE )
A_ : Dict = torch.manual_seed(0 )
A_ : List[str] = ddpm(generator=_SCREAMING_SNAKE_CASE , output_type='''numpy''' ).images
A_ : Any = image[0, -3:, -3:, -1]
assert image.shape == (1, 256, 256, 3)
A_ : Tuple = np.array([0.0_0_6_0, 0.0_2_0_1, 0.0_3_4_4, 0.0_0_2_4, 0.0_0_1_8, 0.0_0_0_2, 0.0_0_2_2, 0.0_0_0_0, 0.0_0_6_9] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
| 186 | 1 |
class _UpperCAmelCase :
'''simple docstring'''
def __init__( self : Optional[Any] , lowercase_ : Dict) -> str:
"""simple docstring"""
_UpperCamelCase = arr.split(",")
def __UpperCAmelCase ( self : Optional[int]) -> List[str]:
"""simple docstring"""
_UpperCamelCase = [int(self.array[0])] * len(self.array)
_UpperCamelCase = [int(self.array[0])] * len(self.array)
for i in range(1 , len(self.array)):
_UpperCamelCase = max(
int(self.array[i]) + sum_value[i - 1] , int(self.array[i]))
_UpperCamelCase = max(sum_value[i] , rear[i - 1])
return rear[len(self.array) - 1]
if __name__ == "__main__":
lowerCamelCase__ = input('''please input some numbers:''')
lowerCamelCase__ = SubArray(whole_array)
lowerCamelCase__ = array.solve_sub_array()
print(('''the results is:''', re))
| 361 | from __future__ import annotations
import random
import unittest
from transformers import TransfoXLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
TFTransfoXLForSequenceClassification,
TFTransfoXLLMHeadModel,
TFTransfoXLModel,
)
class _UpperCAmelCase :
'''simple docstring'''
def __init__( self : Optional[Any] , lowercase_ : Optional[Any] , ) -> Optional[Any]:
"""simple docstring"""
_UpperCamelCase = parent
_UpperCamelCase = 13
_UpperCamelCase = 7
_UpperCamelCase = 30
_UpperCamelCase = self.seq_length + self.mem_len
_UpperCamelCase = 15
_UpperCamelCase = True
_UpperCamelCase = True
_UpperCamelCase = 99
_UpperCamelCase = [10, 50, 80]
_UpperCamelCase = 32
_UpperCamelCase = 32
_UpperCamelCase = 4
_UpperCamelCase = 8
_UpperCamelCase = 128
_UpperCamelCase = 2
_UpperCamelCase = 2
_UpperCamelCase = None
_UpperCamelCase = 1
_UpperCamelCase = 0
_UpperCamelCase = 3
_UpperCamelCase = self.vocab_size - 1
_UpperCamelCase = 0.01
def __UpperCAmelCase ( self : Dict) -> Optional[int]:
"""simple docstring"""
_UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size)
_UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size)
_UpperCamelCase = None
if self.use_labels:
_UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size)
_UpperCamelCase = TransfoXLConfig(
vocab_size=self.vocab_size , mem_len=self.mem_len , clamp_len=self.clamp_len , cutoffs=self.cutoffs , d_model=self.hidden_size , d_embed=self.d_embed , n_head=self.num_attention_heads , d_head=self.d_head , d_inner=self.d_inner , div_val=self.div_val , n_layer=self.num_hidden_layers , eos_token_id=self.eos_token_id , pad_token_id=self.vocab_size - 1 , init_range=self.init_range , num_labels=self.num_labels , )
return (config, input_ids_a, input_ids_a, lm_labels)
def __UpperCAmelCase ( self : Union[str, Any]) -> Tuple:
"""simple docstring"""
random.seed(self.seed)
tf.random.set_seed(self.seed)
def __UpperCAmelCase ( self : int , lowercase_ : Optional[int] , lowercase_ : Tuple , lowercase_ : Optional[Any] , lowercase_ : Optional[Any]) -> Union[str, Any]:
"""simple docstring"""
_UpperCamelCase = TFTransfoXLModel(lowercase_)
_UpperCamelCase , _UpperCamelCase = model(lowercase_).to_tuple()
_UpperCamelCase = {"input_ids": input_ids_a, "mems": mems_a}
_UpperCamelCase , _UpperCamelCase = model(lowercase_).to_tuple()
self.parent.assertEqual(hidden_states_a.shape , (self.batch_size, self.seq_length, self.hidden_size))
self.parent.assertEqual(hidden_states_a.shape , (self.batch_size, self.seq_length, self.hidden_size))
self.parent.assertListEqual(
[mem.shape for mem in mems_a] , [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers , )
self.parent.assertListEqual(
[mem.shape for mem in mems_a] , [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers , )
def __UpperCAmelCase ( self : Dict , lowercase_ : str , lowercase_ : str , lowercase_ : Dict , lowercase_ : List[Any]) -> Union[str, Any]:
"""simple docstring"""
_UpperCamelCase = TFTransfoXLLMHeadModel(lowercase_)
_UpperCamelCase , _UpperCamelCase = model(lowercase_).to_tuple()
_UpperCamelCase = {"input_ids": input_ids_a, "labels": lm_labels}
_UpperCamelCase , _UpperCamelCase = model(lowercase_).to_tuple()
_UpperCamelCase , _UpperCamelCase = model([input_ids_a, mems_a]).to_tuple()
_UpperCamelCase = {"input_ids": input_ids_a, "mems": mems_a, "labels": lm_labels}
_UpperCamelCase , _UpperCamelCase = model(lowercase_).to_tuple()
self.parent.assertEqual(lm_logits_a.shape , (self.batch_size, self.seq_length, self.vocab_size))
self.parent.assertListEqual(
[mem.shape for mem in mems_a] , [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers , )
self.parent.assertEqual(lm_logits_a.shape , (self.batch_size, self.seq_length, self.vocab_size))
self.parent.assertListEqual(
[mem.shape for mem in mems_a] , [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers , )
def __UpperCAmelCase ( self : Optional[Any] , lowercase_ : List[Any] , lowercase_ : List[Any] , lowercase_ : Optional[Any] , lowercase_ : Dict) -> str:
"""simple docstring"""
_UpperCamelCase = TFTransfoXLForSequenceClassification(lowercase_)
_UpperCamelCase = model(lowercase_)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels))
def __UpperCAmelCase ( self : Dict) -> List[Any]:
"""simple docstring"""
_UpperCamelCase = self.prepare_config_and_inputs()
((_UpperCamelCase) , (_UpperCamelCase) , (_UpperCamelCase) , (_UpperCamelCase)) = config_and_inputs
_UpperCamelCase = {"input_ids": input_ids_a}
return config, inputs_dict
@require_tf
class _UpperCAmelCase ( lowerCAmelCase, lowerCAmelCase, unittest.TestCase ):
'''simple docstring'''
__A = (
(TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
)
__A = () if is_tf_available() else ()
__A = (
{
'''feature-extraction''': TFTransfoXLModel,
'''text-classification''': TFTransfoXLForSequenceClassification,
'''text-generation''': TFTransfoXLLMHeadModel,
'''zero-shot''': TFTransfoXLForSequenceClassification,
}
if is_tf_available()
else {}
)
# TODO: add this test when TFTransfoXLLMHead has a linear output layer implemented
__A = False
__A = False
__A = False
__A = False
def __UpperCAmelCase ( self : List[Any] , lowercase_ : Dict , lowercase_ : Tuple , lowercase_ : Dict , lowercase_ : Any , lowercase_ : List[str]) -> Any:
"""simple docstring"""
if pipeline_test_casse_name == "TextGenerationPipelineTests":
# Get `ValueError: AttributeError: 'NoneType' object has no attribute 'new_ones'` or `AssertionError`.
# `TransfoXLConfig` was never used in pipeline tests: cannot create a simple
# tokenizer.
return True
return False
def __UpperCAmelCase ( self : Optional[Any]) -> int:
"""simple docstring"""
_UpperCamelCase = TFTransfoXLModelTester(self)
_UpperCamelCase = ConfigTester(self , config_class=lowercase_ , d_embed=37)
def __UpperCAmelCase ( self : Dict) -> Optional[int]:
"""simple docstring"""
self.config_tester.run_common_tests()
def __UpperCAmelCase ( self : Union[str, Any]) -> List[str]:
"""simple docstring"""
self.model_tester.set_seed()
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_transfo_xl_model(*lowercase_)
def __UpperCAmelCase ( self : Optional[Any]) -> List[Any]:
"""simple docstring"""
self.model_tester.set_seed()
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_transfo_xl_lm_head(*lowercase_)
def __UpperCAmelCase ( self : List[str]) -> List[Any]:
"""simple docstring"""
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*lowercase_)
def __UpperCAmelCase ( self : Dict) -> int:
"""simple docstring"""
_UpperCamelCase , _UpperCamelCase = self.model_tester.prepare_config_and_inputs_for_common()
_UpperCamelCase = [TFTransfoXLForSequenceClassification]
for model_class in self.all_model_classes:
_UpperCamelCase = model_class(lowercase_)
assert isinstance(model.get_input_embeddings() , tf.keras.layers.Layer)
if model_class in list_other_models_with_output_ebd:
_UpperCamelCase = model.get_output_embeddings()
assert isinstance(lowercase_ , tf.keras.layers.Layer)
_UpperCamelCase = model.get_bias()
assert name is None
else:
_UpperCamelCase = model.get_output_embeddings()
assert x is None
_UpperCamelCase = model.get_bias()
assert name is None
def __UpperCAmelCase ( self : Optional[int]) -> Any:
"""simple docstring"""
pass
@slow
def __UpperCAmelCase ( self : List[str]) -> Tuple:
"""simple docstring"""
for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCamelCase = TFTransfoXLModel.from_pretrained(lowercase_)
self.assertIsNotNone(lowercase_)
@unittest.skip(reason="This model doesn't play well with fit() due to not returning a single loss.")
def __UpperCAmelCase ( self : Union[str, Any]) -> Tuple:
"""simple docstring"""
pass
@require_tf
class _UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
@unittest.skip("Skip test until #12651 is resolved.")
@slow
def __UpperCAmelCase ( self : Optional[Any]) -> Dict:
"""simple docstring"""
_UpperCamelCase = TFTransfoXLLMHeadModel.from_pretrained("transfo-xl-wt103")
# fmt: off
_UpperCamelCase = tf.convert_to_tensor([[33,1297,2,1,1009,4,1109,11739,4762,358,5,25,245,22,1706,17,20098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,71477,20098,104447,2,20961,1,2604,4,1,329,3,6224,831,16002,2,8,603,78967,29546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,29546,54,8,3609,5,57211,49,4,1,277,18,8,1755,15691,3,341,25,416,693,42573,71,17,401,94,31,17919,2,29546,7873,18,1,435,23,11011,755,5,5167,3,7983,98,84,2,29546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,29546,824,1400,1868,2,19,160,2,311,8,5496,2,20920,17,25,15097,3,24,24,0]] , dtype=tf.intaa) # noqa: E231
# fmt: on
# In 1991 , the remains of Russian Tsar Nicholas II and his family
# ( except for Alexei and Maria ) are discovered .
# The voice of Nicholas's young son , Tsarevich Alexei Nikolaevich , narrates the
# remainder of the story . 1883 Western Siberia ,
# a young Grigori Rasputin is asked by his father and a group of men to perform magic .
# Rasputin has a vision and denounces one of the men as a horse thief . Although his
# father initially slaps him for making such an accusation , Rasputin watches as the
# man is chased outside and beaten . Twenty years later , Rasputin sees a vision of
# the Virgin Mary , prompting him to become a priest . Rasputin quickly becomes famous ,
# with people , even a bishop , begging for his blessing . <eod> </s> <eos>
# fmt: off
_UpperCamelCase = [33,1297,2,1,1009,4,1109,11739,4762,358,5,25,245,22,1706,17,20098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,71477,20098,104447,2,20961,1,2604,4,1,329,3,6224,831,16002,2,8,603,78967,29546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,29546,54,8,3609,5,57211,49,4,1,277,18,8,1755,15691,3,341,25,416,693,42573,71,17,401,94,31,17919,2,29546,7873,18,1,435,23,11011,755,5,5167,3,7983,98,84,2,29546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,29546,824,1400,1868,2,19,160,2,311,8,5496,2,20920,17,25,15097,3,24,24,0,33,1,1857,2,1,1009,4,1109,11739,4762,358,5,25,245,28,1110,3,13,1041,4,24,603,490,2,71477,20098,104447,2,20961,1,2604,4,1,329,3,0] # noqa: E231
# fmt: on
# In 1991, the remains of Russian Tsar Nicholas II and his family (
# except for Alexei and Maria ) are discovered. The voice of young son,
# Tsarevich Alexei Nikolaevich, narrates the remainder of the story.
# 1883 Western Siberia, a young Grigori Rasputin is asked by his father
# and a group of men to perform magic. Rasputin has a vision and
# denounces one of the men as a horse thief. Although his father initially
# slaps him for making such an accusation, Rasputin watches as the man
# is chased outside and beaten. Twenty years later, Rasputin sees a vision
# of the Virgin Mary, prompting him to become a priest.
# Rasputin quickly becomes famous, with people, even a bishop, begging for
# his blessing. <unk> <unk> <eos> In the 1990s, the remains of Russian Tsar
# Nicholas II and his family were discovered. The voice of <unk> young son,
# Tsarevich Alexei Nikolaevich, narrates the remainder of the story.<eos>
_UpperCamelCase = model.generate(lowercase_ , max_length=200 , do_sample=lowercase_)
self.assertListEqual(output_ids[0].numpy().tolist() , lowercase_)
| 63 | 0 |
'''simple docstring'''
import unittest
from transformers import (
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TextaTextGenerationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_available
from .test_pipelines_common import ANY
if is_torch_available():
import torch
@is_pipeline_test
class lowercase_ ( unittest.TestCase ):
__UpperCAmelCase = MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
__UpperCAmelCase = TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
def __a ( self , a , a , a ):
UpperCamelCase__ = TextaTextGenerationPipeline(model=a , tokenizer=a )
return generator, ["Something to write", "Something else"]
def __a ( self , a , a ):
UpperCamelCase__ = generator("Something there" )
self.assertEqual(a , [{"generated_text": ANY(a )}] )
# These are encoder decoder, they don't just append to incoming string
self.assertFalse(outputs[0]["generated_text"].startswith("Something there" ) )
UpperCamelCase__ = generator(["This is great !", "Something else"] , num_return_sequences=2 , do_sample=a )
self.assertEqual(
a , [
[{"generated_text": ANY(a )}, {"generated_text": ANY(a )}],
[{"generated_text": ANY(a )}, {"generated_text": ANY(a )}],
] , )
UpperCamelCase__ = generator(
["This is great !", "Something else"] , num_return_sequences=2 , batch_size=2 , do_sample=a )
self.assertEqual(
a , [
[{"generated_text": ANY(a )}, {"generated_text": ANY(a )}],
[{"generated_text": ANY(a )}, {"generated_text": ANY(a )}],
] , )
with self.assertRaises(a ):
generator(4 )
@require_torch
def __a ( self ):
UpperCamelCase__ = pipeline("text2text-generation" , model="patrickvonplaten/t5-tiny-random" , framework="pt" )
# do_sample=False necessary for reproducibility
UpperCamelCase__ = generator("Something there" , do_sample=a )
self.assertEqual(a , [{"generated_text": ""}] )
UpperCamelCase__ = 3
UpperCamelCase__ = generator(
"Something there" , num_return_sequences=a , num_beams=a , )
UpperCamelCase__ = [
{"generated_text": "Beide Beide Beide Beide Beide Beide Beide Beide Beide"},
{"generated_text": "Beide Beide Beide Beide Beide Beide Beide Beide"},
{"generated_text": ""},
]
self.assertEqual(a , a )
UpperCamelCase__ = generator("This is a test" , do_sample=a , num_return_sequences=2 , return_tensors=a )
self.assertEqual(
a , [
{"generated_token_ids": ANY(torch.Tensor )},
{"generated_token_ids": ANY(torch.Tensor )},
] , )
UpperCamelCase__ = generator.model.config.eos_token_id
UpperCamelCase__ = "<pad>"
UpperCamelCase__ = generator(
["This is a test", "This is a second test"] , do_sample=a , num_return_sequences=2 , batch_size=2 , return_tensors=a , )
self.assertEqual(
a , [
[
{"generated_token_ids": ANY(torch.Tensor )},
{"generated_token_ids": ANY(torch.Tensor )},
],
[
{"generated_token_ids": ANY(torch.Tensor )},
{"generated_token_ids": ANY(torch.Tensor )},
],
] , )
@require_tf
def __a ( self ):
UpperCamelCase__ = pipeline("text2text-generation" , model="patrickvonplaten/t5-tiny-random" , framework="tf" )
# do_sample=False necessary for reproducibility
UpperCamelCase__ = generator("Something there" , do_sample=a )
self.assertEqual(a , [{"generated_text": ""}] )
| 80 |
'''simple docstring'''
import enum
import warnings
from .. import MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING
from ..utils import add_end_docstrings, is_tf_available
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
class lowercase_ ( enum.Enum ):
__UpperCAmelCase = 0
__UpperCAmelCase = 1
__UpperCAmelCase = 2
@add_end_docstrings(a__ )
class lowercase_ ( a__ ):
__UpperCAmelCase = '\n In 1991, the remains of Russian Tsar Nicholas II and his family (except for Alexei and Maria) are discovered. The\n voice of Nicholas\'s young son, Tsarevich Alexei Nikolaevich, narrates the remainder of the story. 1883 Western\n Siberia, a young Grigori Rasputin is asked by his father and a group of men to perform magic. Rasputin has a vision\n and denounces one of the men as a horse thief. Although his father initially slaps him for making such an\n accusation, Rasputin watches as the man is chased outside and beaten. Twenty years later, Rasputin sees a vision of\n the Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous, with people, even a bishop,\n begging for his blessing. <eod> </s> <eos>\n '
def __init__( self , *a , **a ):
super().__init__(*a , **a )
self.check_model_type(
TF_MODEL_FOR_CAUSAL_LM_MAPPING if self.framework == "tf" else MODEL_FOR_CAUSAL_LM_MAPPING )
if "prefix" not in self._preprocess_params:
# This is very specific. The logic is quite complex and needs to be done
# as a "default".
# It also defines both some preprocess_kwargs and generate_kwargs
# which is why we cannot put them in their respective methods.
UpperCamelCase__ = None
if self.model.config.prefix is not None:
UpperCamelCase__ = self.model.config.prefix
if prefix is None and self.model.__class__.__name__ in [
"XLNetLMHeadModel",
"TransfoXLLMHeadModel",
"TFXLNetLMHeadModel",
"TFTransfoXLLMHeadModel",
]:
# For XLNet and TransformerXL we add an article to the prompt to give more state to the model.
UpperCamelCase__ = self.XL_PREFIX
if prefix is not None:
# Recalculate some generate_kwargs linked to prefix.
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = self._sanitize_parameters(prefix=a , **self._forward_params )
UpperCamelCase__ = {**self._preprocess_params, **preprocess_params}
UpperCamelCase__ = {**self._forward_params, **forward_params}
def __a ( self , a=None , a=None , a=None , a=None , a=None , a=None , a=None , a=None , **a , ):
UpperCamelCase__ = {}
if prefix is not None:
UpperCamelCase__ = prefix
if prefix:
UpperCamelCase__ = self.tokenizer(
a , padding=a , add_special_tokens=a , return_tensors=self.framework )
UpperCamelCase__ = prefix_inputs["input_ids"].shape[-1]
if handle_long_generation is not None:
if handle_long_generation not in {"hole"}:
raise ValueError(
f'''{handle_long_generation} is not a valid value for `handle_long_generation` parameter expected'''
" [None, 'hole']" )
UpperCamelCase__ = handle_long_generation
preprocess_params.update(a )
UpperCamelCase__ = generate_kwargs
UpperCamelCase__ = {}
if return_full_text is not None and return_type is None:
if return_text is not None:
raise ValueError("`return_text` is mutually exclusive with `return_full_text`" )
if return_tensors is not None:
raise ValueError("`return_full_text` is mutually exclusive with `return_tensors`" )
UpperCamelCase__ = ReturnType.FULL_TEXT if return_full_text else ReturnType.NEW_TEXT
if return_tensors is not None and return_type is None:
if return_text is not None:
raise ValueError("`return_text` is mutually exclusive with `return_tensors`" )
UpperCamelCase__ = ReturnType.TENSORS
if return_type is not None:
UpperCamelCase__ = return_type
if clean_up_tokenization_spaces is not None:
UpperCamelCase__ = clean_up_tokenization_spaces
if stop_sequence is not None:
UpperCamelCase__ = self.tokenizer.encode(a , add_special_tokens=a )
if len(a ) > 1:
warnings.warn(
"Stopping on a multiple token sequence is not yet supported on transformers. The first token of"
" the stop sequence will be used as the stop sequence string in the interim." )
UpperCamelCase__ = stop_sequence_ids[0]
return preprocess_params, forward_params, postprocess_params
def __a ( self , *a , **a ):
# Parse arguments
if self.model.__class__.__name__ in ["TransfoXLLMHeadModel"]:
kwargs.update({"add_space_before_punct_symbol": True} )
return super()._parse_and_tokenize(*a , **a )
def __call__( self , a , **a ):
return super().__call__(a , **a )
def __a ( self , a , a="" , a=None , **a ):
UpperCamelCase__ = self.tokenizer(
prefix + prompt_text , padding=a , add_special_tokens=a , return_tensors=self.framework )
UpperCamelCase__ = prompt_text
if handle_long_generation == "hole":
UpperCamelCase__ = inputs["input_ids"].shape[-1]
if "max_new_tokens" in generate_kwargs:
UpperCamelCase__ = generate_kwargs["max_new_tokens"]
else:
UpperCamelCase__ = generate_kwargs.get("max_length" , self.model.config.max_length ) - cur_len
if new_tokens < 0:
raise ValueError("We cannot infer how many new tokens are expected" )
if cur_len + new_tokens > self.tokenizer.model_max_length:
UpperCamelCase__ = self.tokenizer.model_max_length - new_tokens
if keep_length <= 0:
raise ValueError(
"We cannot use `hole` to handle this generation the number of desired tokens exceeds the"
" models max length" )
UpperCamelCase__ = inputs["input_ids"][:, -keep_length:]
if "attention_mask" in inputs:
UpperCamelCase__ = inputs["attention_mask"][:, -keep_length:]
return inputs
def __a ( self , a , **a ):
UpperCamelCase__ = model_inputs["input_ids"]
UpperCamelCase__ = model_inputs.get("attention_mask" , a )
# Allow empty prompts
if input_ids.shape[1] == 0:
UpperCamelCase__ = None
UpperCamelCase__ = None
UpperCamelCase__ = 1
else:
UpperCamelCase__ = input_ids.shape[0]
UpperCamelCase__ = model_inputs.pop("prompt_text" )
# If there is a prefix, we may need to adjust the generation length. Do so without permanently modifying
# generate_kwargs, as some of the parameterization may come from the initialization of the pipeline.
UpperCamelCase__ = generate_kwargs.pop("prefix_length" , 0 )
if prefix_length > 0:
UpperCamelCase__ = "max_new_tokens" in generate_kwargs or (
"generation_config" in generate_kwargs
and generate_kwargs["generation_config"].max_new_tokens is not None
)
if not has_max_new_tokens:
UpperCamelCase__ = generate_kwargs.get("max_length" ) or self.model.config.max_length
generate_kwargs["max_length"] += prefix_length
UpperCamelCase__ = "min_new_tokens" in generate_kwargs or (
"generation_config" in generate_kwargs
and generate_kwargs["generation_config"].min_new_tokens is not None
)
if not has_min_new_tokens and "min_length" in generate_kwargs:
generate_kwargs["min_length"] += prefix_length
# BS x SL
UpperCamelCase__ = self.model.generate(input_ids=a , attention_mask=a , **a )
UpperCamelCase__ = generated_sequence.shape[0]
if self.framework == "pt":
UpperCamelCase__ = generated_sequence.reshape(a , out_b // in_b , *generated_sequence.shape[1:] )
elif self.framework == "tf":
UpperCamelCase__ = tf.reshape(a , (in_b, out_b // in_b, *generated_sequence.shape[1:]) )
return {"generated_sequence": generated_sequence, "input_ids": input_ids, "prompt_text": prompt_text}
def __a ( self , a , a=ReturnType.FULL_TEXT , a=True ):
UpperCamelCase__ = model_outputs["generated_sequence"][0]
UpperCamelCase__ = model_outputs["input_ids"]
UpperCamelCase__ = model_outputs["prompt_text"]
UpperCamelCase__ = generated_sequence.numpy().tolist()
UpperCamelCase__ = []
for sequence in generated_sequence:
if return_type == ReturnType.TENSORS:
UpperCamelCase__ = {"generated_token_ids": sequence}
elif return_type in {ReturnType.NEW_TEXT, ReturnType.FULL_TEXT}:
# Decode text
UpperCamelCase__ = self.tokenizer.decode(
a , skip_special_tokens=a , clean_up_tokenization_spaces=a , )
# Remove PADDING prompt of the sequence if XLNet or Transfo-XL model is used
if input_ids is None:
UpperCamelCase__ = 0
else:
UpperCamelCase__ = len(
self.tokenizer.decode(
input_ids[0] , skip_special_tokens=a , clean_up_tokenization_spaces=a , ) )
if return_type == ReturnType.FULL_TEXT:
UpperCamelCase__ = prompt_text + text[prompt_length:]
else:
UpperCamelCase__ = text[prompt_length:]
UpperCamelCase__ = {"generated_text": all_text}
records.append(a )
return records
| 80 | 1 |
from multiprocessing import Lock, Pipe, Process
# lock used to ensure that two processes do not access a pipe at the same time
__UpperCAmelCase : Tuple = Lock()
def A__ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__) -> List[Any]:
global process_lock
# we perform n swaps since after n swaps we know we are sorted
# we *could* stop early if we are sorted already, but it takes as long to
# find out we are sorted as it does to sort the list with this algorithm
for i in range(0 , 10):
if (i + position) % 2 == 0 and r_send is not None:
# send your value to your right neighbor
process_lock.acquire()
r_send[1].send(SCREAMING_SNAKE_CASE__)
process_lock.release()
# receive your right neighbor's value
process_lock.acquire()
__snake_case: Union[str, Any] = rr_cv[0].recv()
process_lock.release()
# take the lower value since you are on the left
__snake_case: Optional[Any] = min(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__)
elif (i + position) % 2 != 0 and l_send is not None:
# send your value to your left neighbor
process_lock.acquire()
l_send[1].send(SCREAMING_SNAKE_CASE__)
process_lock.release()
# receive your left neighbor's value
process_lock.acquire()
__snake_case: int = lr_cv[0].recv()
process_lock.release()
# take the higher value since you are on the right
__snake_case: int = max(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__)
# after all swaps are performed, send the values back to main
result_pipe[1].send(SCREAMING_SNAKE_CASE__)
def A__ ( SCREAMING_SNAKE_CASE__) -> Union[str, Any]:
__snake_case: List[Any] = []
__snake_case: List[Any] = []
# initialize the list of pipes where the values will be retrieved
for _ in arr:
result_pipe.append(Pipe())
# creates the processes
# the first and last process only have one neighbor so they are made outside
# of the loop
__snake_case: str = Pipe()
__snake_case: Any = Pipe()
process_array_.append(
Process(
target=SCREAMING_SNAKE_CASE__ , args=(0, arr[0], None, temp_rs, None, temp_rr, result_pipe[0]) , ))
__snake_case: Optional[Any] = temp_rs
__snake_case: Optional[int] = temp_rr
for i in range(1 , len(SCREAMING_SNAKE_CASE__) - 1):
__snake_case: Optional[Any] = Pipe()
__snake_case: int = Pipe()
process_array_.append(
Process(
target=SCREAMING_SNAKE_CASE__ , args=(i, arr[i], temp_ls, temp_rs, temp_lr, temp_rr, result_pipe[i]) , ))
__snake_case: Any = temp_rs
__snake_case: List[Any] = temp_rr
process_array_.append(
Process(
target=SCREAMING_SNAKE_CASE__ , args=(
len(SCREAMING_SNAKE_CASE__) - 1,
arr[len(SCREAMING_SNAKE_CASE__) - 1],
temp_ls,
None,
temp_lr,
None,
result_pipe[len(SCREAMING_SNAKE_CASE__) - 1],
) , ))
# start the processes
for p in process_array_:
p.start()
# wait for the processes to end and write their values to the list
for p in range(0 , len(SCREAMING_SNAKE_CASE__)):
__snake_case: Tuple = result_pipe[p][0].recv()
process_array_[p].join()
return arr
def A__ ( ) -> Union[str, Any]:
__snake_case: List[str] = list(range(10 , 0 , -1))
print("""Initial List""")
print(*SCREAMING_SNAKE_CASE__)
__snake_case: int = odd_even_transposition(SCREAMING_SNAKE_CASE__)
print("""Sorted List\n""")
print(*SCREAMING_SNAKE_CASE__)
if __name__ == "__main__":
main()
| 293 |
from typing import Dict, List, Optional, Union
import numpy as np
from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy
__UpperCAmelCase : str = logging.get_logger(__name__)
class __snake_case ( __lowerCamelCase ):
'''simple docstring'''
def __init__( self : Any , A : int , A : int , A : float , **A : Optional[int] ):
__snake_case: List[str] = feature_size
__snake_case: Optional[int] = sampling_rate
__snake_case: Any = padding_value
__snake_case: Dict = kwargs.pop("""padding_side""" , """right""" )
__snake_case: Union[str, Any] = kwargs.pop("""return_attention_mask""" , A )
super().__init__(**A )
def UpperCAmelCase__ ( self : Optional[Any] , A : Union[
BatchFeature,
List[BatchFeature],
Dict[str, BatchFeature],
Dict[str, List[BatchFeature]],
List[Dict[str, BatchFeature]],
] , A : Union[bool, str, PaddingStrategy] = True , A : Optional[int] = None , A : bool = False , A : Optional[int] = None , A : Optional[bool] = None , A : Optional[Union[str, TensorType]] = None , ):
# If we have a list of dicts, let's convert it in a dict of lists
# We do this to allow using this method as a collate_fn function in PyTorch Dataloader
if isinstance(A , (list, tuple) ) and isinstance(processed_features[0] , (dict, BatchFeature) ):
__snake_case: Optional[int] = {
key: [example[key] for example in processed_features] for key in processed_features[0].keys()
}
# The model's main input name, usually `input_values`, has be passed for padding
if self.model_input_names[0] not in processed_features:
raise ValueError(
"""You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`"""
f''' to this method that includes {self.model_input_names[0]}, but you provided'''
f''' {list(processed_features.keys() )}''' )
__snake_case: List[str] = processed_features[self.model_input_names[0]]
__snake_case: Any = (
return_attention_mask if return_attention_mask is not None else self.return_attention_mask
)
if len(A ) == 0:
if return_attention_mask:
__snake_case: Union[str, Any] = []
return processed_features
# If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
# and rebuild them afterwards if no return_tensors is specified
# Note that we lose the specific device the tensor may be on for PyTorch
__snake_case: int = required_input[0]
if isinstance(A , (list, tuple) ):
# first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
__snake_case: Optional[int] = 0
while len(required_input[index] ) == 0:
index += 1
if index < len(A ):
__snake_case: Optional[int] = required_input[index][0]
if return_tensors is None:
if is_tf_tensor(A ):
__snake_case: str = """tf"""
elif is_torch_tensor(A ):
__snake_case: str = """pt"""
elif isinstance(A , (int, float, list, tuple, np.ndarray) ):
__snake_case: List[str] = """np"""
else:
raise ValueError(
f'''type of {first_element} unknown: {type(A )}. '''
"""Should be one of a python, numpy, pytorch or tensorflow object.""" )
for key, value in processed_features.items():
if isinstance(value[0] , (int, float) ):
__snake_case: List[Any] = to_numpy(A )
else:
__snake_case: Union[str, Any] = [to_numpy(A ) for v in value]
# Convert padding_strategy in PaddingStrategy
__snake_case: Union[str, Any] = self._get_padding_strategies(padding=A , max_length=A )
__snake_case: Any = processed_features[self.model_input_names[0]]
__snake_case: int = len(A )
if not all(len(A ) == batch_size for v in processed_features.values() ):
raise ValueError("""Some items in the output dictionary have a different batch size than others.""" )
__snake_case: Union[str, Any] = []
for i in range(A ):
__snake_case: List[Any] = {k: v[i] for k, v in processed_features.items()}
# truncation
__snake_case: Tuple = self._truncate(
A , max_length=A , pad_to_multiple_of=A , truncation=A , )
truncated_inputs.append(A )
if padding_strategy == PaddingStrategy.LONGEST:
# make sure that `max_length` cannot be longer than the longest truncated length
__snake_case: Optional[Any] = max(len(input_slice[self.model_input_names[0]] ) for input_slice in truncated_inputs )
__snake_case: List[str] = PaddingStrategy.MAX_LENGTH
__snake_case: List[Any] = {}
for i in range(A ):
# padding
__snake_case: Any = self._pad(
truncated_inputs[i] , max_length=A , padding_strategy=A , pad_to_multiple_of=A , return_attention_mask=A , )
for key, value in outputs.items():
if key not in batch_outputs:
__snake_case: Optional[Any] = []
if value.dtype is np.dtype(np.floataa ):
__snake_case: str = value.astype(np.floataa )
batch_outputs[key].append(A )
return BatchFeature(A , tensor_type=A )
def UpperCAmelCase__ ( self : int , A : Union[Dict[str, np.ndarray], BatchFeature] , A : Optional[int] = None , A : PaddingStrategy = PaddingStrategy.DO_NOT_PAD , A : Optional[int] = None , A : Optional[bool] = None , ):
__snake_case: List[Any] = processed_features[self.model_input_names[0]]
if padding_strategy == PaddingStrategy.LONGEST:
__snake_case: List[str] = len(A )
if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
__snake_case: List[Any] = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
__snake_case: Dict = padding_strategy != PaddingStrategy.DO_NOT_PAD and len(A ) < max_length
if return_attention_mask and "attention_mask" not in processed_features:
__snake_case: List[str] = np.ones(len(A ) , dtype=np.intaa )
if needs_to_be_padded:
__snake_case: Any = max_length - len(A )
if self.padding_side == "right":
if return_attention_mask:
__snake_case: Optional[int] = np.pad(
processed_features["""attention_mask"""] , (0, difference) )
__snake_case: Any = ((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
__snake_case: Union[str, Any] = np.pad(
A , A , """constant""" , constant_values=self.padding_value )
elif self.padding_side == "left":
if return_attention_mask:
__snake_case: Dict = np.pad(
processed_features["""attention_mask"""] , (difference, 0) )
__snake_case: Union[str, Any] = ((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
__snake_case: str = np.pad(
A , A , """constant""" , constant_values=self.padding_value )
else:
raise ValueError("""Invalid padding strategy:""" + str(self.padding_side ) )
return processed_features
def UpperCAmelCase__ ( self : Optional[Any] , A : Union[Dict[str, np.ndarray], BatchFeature] , A : Optional[int] = None , A : Optional[int] = None , A : Optional[bool] = None , ):
if not truncation:
return processed_features
elif truncation and max_length is None:
raise ValueError("""When setting ``truncation=True``, make sure that ``max_length`` is defined.""" )
__snake_case: List[str] = processed_features[self.model_input_names[0]]
# find `max_length` that fits `pad_to_multiple_of`
if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
__snake_case: List[Any] = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
__snake_case: Tuple = len(A ) > max_length
if needs_to_be_truncated:
__snake_case: List[Any] = processed_features[self.model_input_names[0]][:max_length]
if "attention_mask" in processed_features:
__snake_case: int = processed_features["""attention_mask"""][:max_length]
return processed_features
def UpperCAmelCase__ ( self : int , A : int=False , A : int=None ):
# Get padding strategy
if padding is not False:
if padding is True:
__snake_case: Optional[int] = PaddingStrategy.LONGEST # Default to pad to the longest sequence in the batch
elif not isinstance(A , A ):
__snake_case: Optional[int] = PaddingStrategy(A )
elif isinstance(A , A ):
__snake_case: Any = padding
else:
__snake_case: Any = PaddingStrategy.DO_NOT_PAD
# Set max length if needed
if max_length is None:
if padding_strategy == PaddingStrategy.MAX_LENGTH:
raise ValueError(
f'''When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined''' )
# Test if we have a padding value
if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
raise ValueError(
"""Asking to pad but the feature_extractor does not have a padding value. Please select a value to use"""
""" as `padding_value`. For example: `feature_extractor.padding_value = 0.0`.""" )
return padding_strategy
| 293 | 1 |
"""simple docstring"""
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F
from transformers import DetrImageProcessor, TableTransformerConfig, TableTransformerForObjectDetection
from transformers.utils import logging
logging.set_verbosity_info()
lowerCAmelCase__ : Optional[int] = logging.get_logger(__name__)
# here we list all keys to be renamed (original name on the left, our name on the right)
lowerCAmelCase__ : Any = []
for i in range(6):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append(
(F"""transformer.encoder.layers.{i}.self_attn.out_proj.weight""", F"""encoder.layers.{i}.self_attn.out_proj.weight""")
)
rename_keys.append(
(F"""transformer.encoder.layers.{i}.self_attn.out_proj.bias""", F"""encoder.layers.{i}.self_attn.out_proj.bias""")
)
rename_keys.append((F"""transformer.encoder.layers.{i}.linear1.weight""", F"""encoder.layers.{i}.fc1.weight"""))
rename_keys.append((F"""transformer.encoder.layers.{i}.linear1.bias""", F"""encoder.layers.{i}.fc1.bias"""))
rename_keys.append((F"""transformer.encoder.layers.{i}.linear2.weight""", F"""encoder.layers.{i}.fc2.weight"""))
rename_keys.append((F"""transformer.encoder.layers.{i}.linear2.bias""", F"""encoder.layers.{i}.fc2.bias"""))
rename_keys.append(
(F"""transformer.encoder.layers.{i}.norm1.weight""", F"""encoder.layers.{i}.self_attn_layer_norm.weight""")
)
rename_keys.append((F"""transformer.encoder.layers.{i}.norm1.bias""", F"""encoder.layers.{i}.self_attn_layer_norm.bias"""))
rename_keys.append((F"""transformer.encoder.layers.{i}.norm2.weight""", F"""encoder.layers.{i}.final_layer_norm.weight"""))
rename_keys.append((F"""transformer.encoder.layers.{i}.norm2.bias""", F"""encoder.layers.{i}.final_layer_norm.bias"""))
# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
rename_keys.append(
(F"""transformer.decoder.layers.{i}.self_attn.out_proj.weight""", F"""decoder.layers.{i}.self_attn.out_proj.weight""")
)
rename_keys.append(
(F"""transformer.decoder.layers.{i}.self_attn.out_proj.bias""", F"""decoder.layers.{i}.self_attn.out_proj.bias""")
)
rename_keys.append(
(
F"""transformer.decoder.layers.{i}.multihead_attn.out_proj.weight""",
F"""decoder.layers.{i}.encoder_attn.out_proj.weight""",
)
)
rename_keys.append(
(
F"""transformer.decoder.layers.{i}.multihead_attn.out_proj.bias""",
F"""decoder.layers.{i}.encoder_attn.out_proj.bias""",
)
)
rename_keys.append((F"""transformer.decoder.layers.{i}.linear1.weight""", F"""decoder.layers.{i}.fc1.weight"""))
rename_keys.append((F"""transformer.decoder.layers.{i}.linear1.bias""", F"""decoder.layers.{i}.fc1.bias"""))
rename_keys.append((F"""transformer.decoder.layers.{i}.linear2.weight""", F"""decoder.layers.{i}.fc2.weight"""))
rename_keys.append((F"""transformer.decoder.layers.{i}.linear2.bias""", F"""decoder.layers.{i}.fc2.bias"""))
rename_keys.append(
(F"""transformer.decoder.layers.{i}.norm1.weight""", F"""decoder.layers.{i}.self_attn_layer_norm.weight""")
)
rename_keys.append((F"""transformer.decoder.layers.{i}.norm1.bias""", F"""decoder.layers.{i}.self_attn_layer_norm.bias"""))
rename_keys.append(
(F"""transformer.decoder.layers.{i}.norm2.weight""", F"""decoder.layers.{i}.encoder_attn_layer_norm.weight""")
)
rename_keys.append(
(F"""transformer.decoder.layers.{i}.norm2.bias""", F"""decoder.layers.{i}.encoder_attn_layer_norm.bias""")
)
rename_keys.append((F"""transformer.decoder.layers.{i}.norm3.weight""", F"""decoder.layers.{i}.final_layer_norm.weight"""))
rename_keys.append((F"""transformer.decoder.layers.{i}.norm3.bias""", F"""decoder.layers.{i}.final_layer_norm.bias"""))
# convolutional projection + query embeddings + layernorm of encoder + layernorm of decoder + class and bounding box heads
rename_keys.extend(
[
('input_proj.weight', 'input_projection.weight'),
('input_proj.bias', 'input_projection.bias'),
('query_embed.weight', 'query_position_embeddings.weight'),
('transformer.encoder.norm.weight', 'encoder.layernorm.weight'),
('transformer.encoder.norm.bias', 'encoder.layernorm.bias'),
('transformer.decoder.norm.weight', 'decoder.layernorm.weight'),
('transformer.decoder.norm.bias', 'decoder.layernorm.bias'),
('class_embed.weight', 'class_labels_classifier.weight'),
('class_embed.bias', 'class_labels_classifier.bias'),
('bbox_embed.layers.0.weight', 'bbox_predictor.layers.0.weight'),
('bbox_embed.layers.0.bias', 'bbox_predictor.layers.0.bias'),
('bbox_embed.layers.1.weight', 'bbox_predictor.layers.1.weight'),
('bbox_embed.layers.1.bias', 'bbox_predictor.layers.1.bias'),
('bbox_embed.layers.2.weight', 'bbox_predictor.layers.2.weight'),
('bbox_embed.layers.2.bias', 'bbox_predictor.layers.2.bias'),
]
)
def a_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase ):
UpperCAmelCase__ = state_dict.pop(lowerCamelCase )
UpperCAmelCase__ = val
def a_ ( lowerCamelCase ):
UpperCAmelCase__ = OrderedDict()
for key, value in state_dict.items():
if "backbone.0.body" in key:
UpperCAmelCase__ = key.replace('backbone.0.body' , 'backbone.conv_encoder.model' )
UpperCAmelCase__ = value
else:
UpperCAmelCase__ = value
return new_state_dict
def a_ ( lowerCamelCase ):
UpperCAmelCase__ = ''
# first: transformer encoder
for i in range(6 ):
# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
UpperCAmelCase__ = state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight''' )
UpperCAmelCase__ = state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias''' )
# next, add query, keys and values (in that order) to the state dict
UpperCAmelCase__ = in_proj_weight[:2_5_6, :]
UpperCAmelCase__ = in_proj_bias[:2_5_6]
UpperCAmelCase__ = in_proj_weight[2_5_6:5_1_2, :]
UpperCAmelCase__ = in_proj_bias[2_5_6:5_1_2]
UpperCAmelCase__ = in_proj_weight[-2_5_6:, :]
UpperCAmelCase__ = in_proj_bias[-2_5_6:]
# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
for i in range(6 ):
# read in weights + bias of input projection layer of self-attention
UpperCAmelCase__ = state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight''' )
UpperCAmelCase__ = state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias''' )
# next, add query, keys and values (in that order) to the state dict
UpperCAmelCase__ = in_proj_weight[:2_5_6, :]
UpperCAmelCase__ = in_proj_bias[:2_5_6]
UpperCAmelCase__ = in_proj_weight[2_5_6:5_1_2, :]
UpperCAmelCase__ = in_proj_bias[2_5_6:5_1_2]
UpperCAmelCase__ = in_proj_weight[-2_5_6:, :]
UpperCAmelCase__ = in_proj_bias[-2_5_6:]
# read in weights + bias of input projection layer of cross-attention
UpperCAmelCase__ = state_dict.pop(
f'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight''' )
UpperCAmelCase__ = state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias''' )
# next, add query, keys and values (in that order) of cross-attention to the state dict
UpperCAmelCase__ = in_proj_weight_cross_attn[:2_5_6, :]
UpperCAmelCase__ = in_proj_bias_cross_attn[:2_5_6]
UpperCAmelCase__ = in_proj_weight_cross_attn[2_5_6:5_1_2, :]
UpperCAmelCase__ = in_proj_bias_cross_attn[2_5_6:5_1_2]
UpperCAmelCase__ = in_proj_weight_cross_attn[-2_5_6:, :]
UpperCAmelCase__ = in_proj_bias_cross_attn[-2_5_6:]
def a_ ( lowerCamelCase , lowerCamelCase ):
UpperCAmelCase__ , UpperCAmelCase__ = image.size
UpperCAmelCase__ = max(lowerCamelCase , lowerCamelCase )
UpperCAmelCase__ = 8_0_0 if 'detection' in checkpoint_url else 1_0_0_0
UpperCAmelCase__ = target_max_size / current_max_size
UpperCAmelCase__ = image.resize((int(round(scale * width ) ), int(round(scale * height ) )) )
return resized_image
def a_ ( lowerCamelCase ):
UpperCAmelCase__ = F.to_tensor(lowerCamelCase )
UpperCAmelCase__ = F.normalize(lowerCamelCase , mean=[0.485, 0.456, 0.406] , std=[0.229, 0.224, 0.225] )
return image
@torch.no_grad()
def a_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase ):
logger.info('Converting model...' )
# load original state dict
UpperCAmelCase__ = torch.hub.load_state_dict_from_url(lowerCamelCase , map_location='cpu' )
# rename keys
for src, dest in rename_keys:
rename_key(lowerCamelCase , lowerCamelCase , lowerCamelCase )
UpperCAmelCase__ = rename_backbone_keys(lowerCamelCase )
# query, key and value matrices need special treatment
read_in_q_k_v(lowerCamelCase )
# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
UpperCAmelCase__ = 'model.'
for key in state_dict.copy().keys():
if not key.startswith('class_labels_classifier' ) and not key.startswith('bbox_predictor' ):
UpperCAmelCase__ = state_dict.pop(lowerCamelCase )
UpperCAmelCase__ = val
# create HuggingFace model and load state dict
UpperCAmelCase__ = TableTransformerConfig(
backbone='resnet18' , mask_loss_coefficient=1 , dice_loss_coefficient=1 , ce_loss_coefficient=1 , bbox_loss_coefficient=5 , giou_loss_coefficient=2 , eos_coefficient=0.4 , class_cost=1 , bbox_cost=5 , giou_cost=2 , )
if "detection" in checkpoint_url:
UpperCAmelCase__ = 1_5
UpperCAmelCase__ = 2
UpperCAmelCase__ = {0: 'table', 1: 'table rotated'}
UpperCAmelCase__ = idalabel
UpperCAmelCase__ = {v: k for k, v in idalabel.items()}
else:
UpperCAmelCase__ = 1_2_5
UpperCAmelCase__ = 6
UpperCAmelCase__ = {
0: 'table',
1: 'table column',
2: 'table row',
3: 'table column header',
4: 'table projected row header',
5: 'table spanning cell',
}
UpperCAmelCase__ = idalabel
UpperCAmelCase__ = {v: k for k, v in idalabel.items()}
UpperCAmelCase__ = DetrImageProcessor(
format='coco_detection' , max_size=8_0_0 if 'detection' in checkpoint_url else 1_0_0_0 )
UpperCAmelCase__ = TableTransformerForObjectDetection(lowerCamelCase )
model.load_state_dict(lowerCamelCase )
model.eval()
# verify our conversion
UpperCAmelCase__ = 'example_pdf.png' if 'detection' in checkpoint_url else 'example_table.png'
UpperCAmelCase__ = hf_hub_download(repo_id='nielsr/example-pdf' , repo_type='dataset' , filename=lowerCamelCase )
UpperCAmelCase__ = Image.open(lowerCamelCase ).convert('RGB' )
UpperCAmelCase__ = normalize(resize(lowerCamelCase , lowerCamelCase ) ).unsqueeze(0 )
UpperCAmelCase__ = model(lowerCamelCase )
if "detection" in checkpoint_url:
UpperCAmelCase__ = (1, 1_5, 3)
UpperCAmelCase__ = torch.tensor(
[[-6.7897, -16.9985, 6.7937], [-8.0186, -22.2192, 6.9677], [-7.3117, -21.0708, 7.4055]] )
UpperCAmelCase__ = torch.tensor([[0.4867, 0.1767, 0.6732], [0.6718, 0.4479, 0.3830], [0.4716, 0.1760, 0.6364]] )
else:
UpperCAmelCase__ = (1, 1_2_5, 7)
UpperCAmelCase__ = torch.tensor(
[[-18.1430, -8.3214, 4.8274], [-18.4685, -7.1361, -4.2667], [-26.3693, -9.3429, -4.9962]] )
UpperCAmelCase__ = torch.tensor([[0.4983, 0.5595, 0.9440], [0.4916, 0.6315, 0.5954], [0.6108, 0.8637, 0.1135]] )
assert outputs.logits.shape == expected_shape
assert torch.allclose(outputs.logits[0, :3, :3] , lowerCamelCase , atol=1e-4 )
assert torch.allclose(outputs.pred_boxes[0, :3, :3] , lowerCamelCase , atol=1e-4 )
print('Looks ok!' )
if pytorch_dump_folder_path is not None:
# Save model and image processor
logger.info(f'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...''' )
Path(lowerCamelCase ).mkdir(exist_ok=lowerCamelCase )
model.save_pretrained(lowerCamelCase )
image_processor.save_pretrained(lowerCamelCase )
if push_to_hub:
# Push model to HF hub
logger.info('Pushing model to the hub...' )
UpperCAmelCase__ = (
'microsoft/table-transformer-detection'
if 'detection' in checkpoint_url
else 'microsoft/table-transformer-structure-recognition'
)
model.push_to_hub(lowerCamelCase )
image_processor.push_to_hub(lowerCamelCase )
if __name__ == "__main__":
lowerCAmelCase__ : Dict = argparse.ArgumentParser()
parser.add_argument(
'--checkpoint_url',
default='https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth',
type=str,
choices=[
'https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth',
'https://pubtables1m.blob.core.windows.net/model/pubtables1m_structure_detr_r18.pth',
],
help='URL of the Table Transformer checkpoint you\'d like to convert.',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
)
parser.add_argument(
'--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
)
lowerCAmelCase__ : Optional[int] = parser.parse_args()
convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)
| 98 |
import unittest
from transformers import SPIECE_UNDERLINE
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.tokenization_utils import AddedToken
from ...test_tokenization_common import TokenizerTesterMixin
lowerCAmelCase__ = get_tests_dir("""fixtures/test_sentencepiece_bpe_char.model""")
@require_sentencepiece
@require_tokenizers
class a__ ( snake_case , unittest.TestCase ):
"""simple docstring"""
__lowerCamelCase = SpeechTaTokenizer
__lowerCamelCase = False
__lowerCamelCase = True
def UpperCamelCase ( self ) -> Any:
'''simple docstring'''
super().setUp()
# We have a SentencePiece fixture for testing
A__ = SpeechTaTokenizer(lowercase )
A__ = AddedToken("<mask>" , lstrip=lowercase , rstrip=lowercase )
A__ = mask_token
tokenizer.add_special_tokens({"mask_token": mask_token} )
tokenizer.add_tokens(["<ctc_blank>"] )
tokenizer.save_pretrained(self.tmpdirname )
def UpperCamelCase ( self , lowercase ) -> Union[str, Any]:
'''simple docstring'''
A__ = "this is a test"
A__ = "this is a test"
return input_text, output_text
def UpperCamelCase ( self , lowercase , lowercase=False , lowercase=20 , lowercase=5 ) -> Optional[Any]:
'''simple docstring'''
A__ , A__ = self.get_input_output_texts(lowercase )
A__ = tokenizer.encode(lowercase , add_special_tokens=lowercase )
A__ = tokenizer.decode(lowercase , clean_up_tokenization_spaces=lowercase )
return text, ids
def UpperCamelCase ( self ) -> Union[str, Any]:
'''simple docstring'''
A__ = "<pad>"
A__ = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowercase ) , lowercase )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowercase ) , lowercase )
def UpperCamelCase ( self ) -> List[str]:
'''simple docstring'''
A__ = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , "<s>" )
self.assertEqual(vocab_keys[1] , "<pad>" )
self.assertEqual(vocab_keys[-4] , "œ" )
self.assertEqual(vocab_keys[-2] , "<mask>" )
self.assertEqual(vocab_keys[-1] , "<ctc_blank>" )
self.assertEqual(len(lowercase ) , 81 )
def UpperCamelCase ( self ) -> Dict:
'''simple docstring'''
self.assertEqual(self.get_tokenizer().vocab_size , 79 )
def UpperCamelCase ( self ) -> Optional[int]:
'''simple docstring'''
A__ = self.get_tokenizers(do_lower_case=lowercase )
for tokenizer in tokenizers:
with self.subTest(F'{tokenizer.__class__.__name__}' ):
A__ = tokenizer.vocab_size
A__ = len(lowercase )
self.assertNotEqual(lowercase , 0 )
# We usually have added tokens from the start in tests because our vocab fixtures are
# smaller than the original vocabs - let's not assert this
# self.assertEqual(vocab_size, all_size)
A__ = ["aaaaa bbbbbb", "cccccccccdddddddd"]
A__ = tokenizer.add_tokens(lowercase )
A__ = tokenizer.vocab_size
A__ = len(lowercase )
self.assertNotEqual(lowercase , 0 )
self.assertEqual(lowercase , lowercase )
self.assertEqual(lowercase , len(lowercase ) )
self.assertEqual(lowercase , all_size + len(lowercase ) )
A__ = tokenizer.encode("aaaaa bbbbbb low cccccccccdddddddd l" , add_special_tokens=lowercase )
self.assertGreaterEqual(len(lowercase ) , 4 )
self.assertGreater(tokens[0] , tokenizer.vocab_size - 1 )
self.assertGreater(tokens[-3] , tokenizer.vocab_size - 1 )
A__ = {"eos_token": ">>>>|||<||<<|<<", "pad_token": "<<<<<|||>|>>>>|>"}
A__ = tokenizer.add_special_tokens(lowercase )
A__ = tokenizer.vocab_size
A__ = len(lowercase )
self.assertNotEqual(lowercase , 0 )
self.assertEqual(lowercase , lowercase )
self.assertEqual(lowercase , len(lowercase ) )
self.assertEqual(lowercase , all_size_a + len(lowercase ) )
A__ = tokenizer.encode(
">>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l" , add_special_tokens=lowercase )
self.assertGreaterEqual(len(lowercase ) , 6 )
self.assertGreater(tokens[0] , tokenizer.vocab_size - 1 )
self.assertGreater(tokens[0] , tokens[1] )
self.assertGreater(tokens[-3] , tokenizer.vocab_size - 1 )
self.assertGreater(tokens[-3] , tokens[-4] )
self.assertEqual(tokens[0] , tokenizer.eos_token_id )
self.assertEqual(tokens[-3] , tokenizer.pad_token_id )
def UpperCamelCase ( self ) -> Tuple:
'''simple docstring'''
pass
def UpperCamelCase ( self ) -> Any:
'''simple docstring'''
pass
def UpperCamelCase ( self ) -> List[Any]:
'''simple docstring'''
A__ = self.get_tokenizer()
A__ = tokenizer.tokenize("This is a test" )
# fmt: off
self.assertListEqual(lowercase , [SPIECE_UNDERLINE, "T", "h", "i", "s", SPIECE_UNDERLINE, "i", "s", SPIECE_UNDERLINE, "a", SPIECE_UNDERLINE, "t", "e", "s", "t"] )
# fmt: on
self.assertListEqual(
tokenizer.convert_tokens_to_ids(lowercase ) , [4, 32, 11, 10, 12, 4, 10, 12, 4, 7, 4, 6, 5, 12, 6] , )
A__ = tokenizer.tokenize("I was born in 92000, and this is falsé." )
self.assertListEqual(
lowercase , [SPIECE_UNDERLINE, "I", SPIECE_UNDERLINE, "w", "a", "s", SPIECE_UNDERLINE, "b", "o", "r", "n", SPIECE_UNDERLINE, "i", "n", SPIECE_UNDERLINE, "92000", ",", SPIECE_UNDERLINE, "a", "n", "d", SPIECE_UNDERLINE, "t", "h", "i", "s", SPIECE_UNDERLINE, "i", "s", SPIECE_UNDERLINE, "f", "a", "l", "s", "é", "."] )
A__ = tokenizer.convert_tokens_to_ids(lowercase )
# fmt: off
self.assertListEqual(lowercase , [4, 30, 4, 20, 7, 12, 4, 25, 8, 13, 9, 4, 10, 9, 4, 3, 23, 4, 7, 9, 14, 4, 6, 11, 10, 12, 4, 10, 12, 4, 19, 7, 15, 12, 73, 26] )
# fmt: on
A__ = tokenizer.convert_ids_to_tokens(lowercase )
self.assertListEqual(
lowercase , [SPIECE_UNDERLINE, "I", SPIECE_UNDERLINE, "w", "a", "s", SPIECE_UNDERLINE, "b", "o", "r", "n", SPIECE_UNDERLINE, "i", "n", SPIECE_UNDERLINE, "<unk>", ",", SPIECE_UNDERLINE, "a", "n", "d", SPIECE_UNDERLINE, "t", "h", "i", "s", SPIECE_UNDERLINE, "i", "s", SPIECE_UNDERLINE, "f", "a", "l", "s", "é", "."] )
@slow
def UpperCamelCase ( self ) -> int:
'''simple docstring'''
A__ = [
"Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides "
"general-purpose architectures (BERT, GPT, RoBERTa, XLM, DistilBert, XLNet...) for Natural "
"Language Understanding (NLU) and Natural Language Generation (NLG) with over thirty-two pretrained "
"models in one hundred plus languages and deep interoperability between Jax, PyTorch and TensorFlow.",
"BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly "
"conditioning on both left and right context in all layers.",
"The quick brown fox jumps over the lazy dog.",
]
# fmt: off
A__ = {
"input_ids": [
[4, 32, 13, 7, 9, 12, 19, 8, 13, 18, 5, 13, 12, 4, 64, 19, 8, 13, 18, 5, 13, 15, 22, 4, 28, 9, 8, 20, 9, 4, 7, 12, 4, 24, 22, 6, 8, 13, 17, 11, 39, 6, 13, 7, 9, 12, 19, 8, 13, 18, 5, 13, 12, 4, 7, 9, 14, 4, 24, 22, 6, 8, 13, 17, 11, 39, 24, 13, 5, 6, 13, 7, 10, 9, 5, 14, 39, 25, 5, 13, 6, 63, 4, 24, 13, 8, 27, 10, 14, 5, 12, 4, 21, 5, 9, 5, 13, 7, 15, 39, 24, 16, 13, 24, 8, 12, 5, 4, 7, 13, 17, 11, 10, 6, 5, 17, 6, 16, 13, 5, 12, 4, 64, 40, 47, 54, 32, 23, 4, 53, 49, 32, 23, 4, 54, 8, 40, 47, 54, 32, 7, 23, 4, 69, 52, 43, 23, 4, 51, 10, 12, 6, 10, 15, 40, 5, 13, 6, 23, 4, 69, 52, 48, 5, 6, 26, 26, 26, 63, 4, 19, 8, 13, 4, 48, 7, 6, 16, 13, 7, 15, 4, 52, 7, 9, 21, 16, 7, 21, 5, 4, 61, 9, 14, 5, 13, 12, 6, 7, 9, 14, 10, 9, 21, 4, 64, 48, 52, 61, 63, 4, 7, 9, 14, 4, 48, 7, 6, 16, 13, 7, 15, 4, 52, 7, 9, 21, 16, 7, 21, 5, 4, 53, 5, 9, 5, 13, 7, 6, 10, 8, 9, 4, 64, 48, 52, 53, 63, 4, 20, 10, 6, 11, 4, 8, 27, 5, 13, 4, 6, 11, 10, 13, 6, 22, 39, 6, 20, 8, 4, 24, 13, 5, 6, 13, 7, 10, 9, 5, 14, 4, 18, 8, 14, 5, 15, 12, 4, 10, 9, 4, 8, 9, 5, 4, 11, 16, 9, 14, 13, 5, 14, 4, 24, 15, 16, 12, 4, 15, 7, 9, 21, 16, 7, 21, 5, 12, 4, 7, 9, 14, 4, 14, 5, 5, 24, 4, 10, 9, 6, 5, 13, 8, 24, 5, 13, 7, 25, 10, 15, 10, 6, 22, 4, 25, 5, 6, 20, 5, 5, 9, 4, 58, 7, 37, 23, 4, 49, 22, 32, 8, 13, 17, 11, 4, 7, 9, 14, 4, 32, 5, 9, 12, 8, 13, 55, 15, 8, 20, 26, 2],
[4, 40, 47, 54, 32, 4, 10, 12, 4, 14, 5, 12, 10, 21, 9, 5, 14, 4, 6, 8, 4, 24, 13, 5, 39, 6, 13, 7, 10, 9, 4, 14, 5, 5, 24, 4, 25, 10, 14, 10, 13, 5, 17, 6, 10, 8, 9, 7, 15, 4, 13, 5, 24, 13, 5, 12, 5, 9, 6, 7, 6, 10, 8, 9, 12, 4, 19, 13, 8, 18, 4, 16, 9, 15, 7, 25, 5, 15, 5, 14, 4, 6, 5, 37, 6, 4, 25, 22, 4, 46, 8, 10, 9, 6, 15, 22, 4, 17, 8, 9, 14, 10, 6, 10, 8, 9, 10, 9, 21, 4, 8, 9, 4, 25, 8, 6, 11, 4, 15, 5, 19, 6, 4, 7, 9, 14, 4, 13, 10, 21, 11, 6, 4, 17, 8, 9, 6, 5, 37, 6, 4, 10, 9, 4, 7, 15, 15, 4, 15, 7, 22, 5, 13, 12, 26, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[4, 32, 11, 5, 4, 45, 16, 10, 17, 28, 4, 25, 13, 8, 20, 9, 4, 19, 8, 37, 4, 46, 16, 18, 24, 12, 4, 8, 27, 5, 13, 4, 6, 11, 5, 4, 15, 7, 57, 22, 4, 14, 8, 21, 26, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
],
"attention_mask": [
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
]
}
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=lowercase , model_name="microsoft/speecht5_asr" , revision="c5ef64c71905caeccde0e4462ef3f9077224c524" , sequences=lowercase , )
| 68 | 0 |
'''simple docstring'''
from ...utils import deprecate
from ..controlnet.pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline # noqa: F401
deprecate(
"""stable diffusion controlnet""",
"""0.22.0""",
"""Importing `FlaxStableDiffusionControlNetPipeline` from diffusers.pipelines.stable_diffusion.flax_pipeline_stable_diffusion_controlnet is deprecated. Please import `from diffusers import FlaxStableDiffusionControlNetPipeline` instead.""",
standard_warn=False,
stacklevel=3,
)
| 351 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase = logging.get_logger(__name__)
lowerCamelCase = {
"""microsoft/wavlm-base""": """https://huggingface.co/microsoft/wavlm-base/resolve/main/config.json""",
# See all WavLM models at https://huggingface.co/models?filter=wavlm
}
class _UpperCamelCase ( A ):
'''simple docstring'''
lowerCAmelCase__ = """wavlm"""
def __init__( self : List[str] , _lowerCAmelCase : List[Any]=3_2 , _lowerCAmelCase : int=7_6_8 , _lowerCAmelCase : Any=1_2 , _lowerCAmelCase : Union[str, Any]=1_2 , _lowerCAmelCase : List[Any]=3_0_7_2 , _lowerCAmelCase : Dict="gelu" , _lowerCAmelCase : Any=0.1 , _lowerCAmelCase : Any=0.1 , _lowerCAmelCase : Optional[Any]=0.1 , _lowerCAmelCase : List[Any]=0.0 , _lowerCAmelCase : str=0.1 , _lowerCAmelCase : Dict=0.1 , _lowerCAmelCase : List[Any]=0.02 , _lowerCAmelCase : Dict=1e-5 , _lowerCAmelCase : List[Any]="group" , _lowerCAmelCase : Optional[Any]="gelu" , _lowerCAmelCase : Dict=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2) , _lowerCAmelCase : Any=(5, 2, 2, 2, 2, 2, 2) , _lowerCAmelCase : Optional[Any]=(1_0, 3, 3, 3, 3, 2, 2) , _lowerCAmelCase : Optional[int]=False , _lowerCAmelCase : int=1_2_8 , _lowerCAmelCase : Tuple=1_6 , _lowerCAmelCase : Optional[int]=3_2_0 , _lowerCAmelCase : Union[str, Any]=8_0_0 , _lowerCAmelCase : Optional[Any]=False , _lowerCAmelCase : Union[str, Any]=True , _lowerCAmelCase : Any=0.05 , _lowerCAmelCase : List[Any]=1_0 , _lowerCAmelCase : Any=2 , _lowerCAmelCase : List[Any]=0.0 , _lowerCAmelCase : Union[str, Any]=1_0 , _lowerCAmelCase : List[Any]=3_2_0 , _lowerCAmelCase : int=2 , _lowerCAmelCase : Dict=0.1 , _lowerCAmelCase : Optional[int]=1_0_0 , _lowerCAmelCase : Tuple=2_5_6 , _lowerCAmelCase : Union[str, Any]=2_5_6 , _lowerCAmelCase : Any=0.1 , _lowerCAmelCase : Tuple="mean" , _lowerCAmelCase : Any=False , _lowerCAmelCase : Union[str, Any]=False , _lowerCAmelCase : Any=2_5_6 , _lowerCAmelCase : Tuple=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 1_5_0_0) , _lowerCAmelCase : Dict=(5, 3, 3, 1, 1) , _lowerCAmelCase : Dict=(1, 2, 3, 1, 1) , _lowerCAmelCase : int=5_1_2 , _lowerCAmelCase : Optional[int]=8_0 , _lowerCAmelCase : Any=0 , _lowerCAmelCase : int=1 , _lowerCAmelCase : Tuple=2 , _lowerCAmelCase : List[str]=False , _lowerCAmelCase : Any=3 , _lowerCAmelCase : List[Any]=2 , _lowerCAmelCase : List[Any]=3 , _lowerCAmelCase : List[str]=None , **_lowerCAmelCase : List[str] , ):
'''simple docstring'''
super().__init__(**_lowerCAmelCase , pad_token_id=_lowerCAmelCase , bos_token_id=_lowerCAmelCase , eos_token_id=_lowerCAmelCase)
__lowercase =hidden_size
__lowercase =feat_extract_norm
__lowercase =feat_extract_activation
__lowercase =list(_lowerCAmelCase)
__lowercase =list(_lowerCAmelCase)
__lowercase =list(_lowerCAmelCase)
__lowercase =conv_bias
__lowercase =num_buckets
__lowercase =max_bucket_distance
__lowercase =num_conv_pos_embeddings
__lowercase =num_conv_pos_embedding_groups
__lowercase =len(self.conv_dim)
__lowercase =num_hidden_layers
__lowercase =intermediate_size
__lowercase =hidden_act
__lowercase =num_attention_heads
__lowercase =hidden_dropout
__lowercase =attention_dropout
__lowercase =activation_dropout
__lowercase =feat_proj_dropout
__lowercase =final_dropout
__lowercase =layerdrop
__lowercase =layer_norm_eps
__lowercase =initializer_range
__lowercase =num_ctc_classes
__lowercase =vocab_size
__lowercase =do_stable_layer_norm
__lowercase =use_weighted_layer_sum
__lowercase =classifier_proj_size
if (
(len(self.conv_stride) != self.num_feat_extract_layers)
or (len(self.conv_kernel) != self.num_feat_extract_layers)
or (len(self.conv_dim) != self.num_feat_extract_layers)
):
raise ValueError(
'Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='
' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='
f""" {len(self.conv_dim)}`, `len(config.conv_stride) = {len(self.conv_stride)}`,"""
f""" `len(config.conv_kernel) = {len(self.conv_kernel)}`.""")
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
__lowercase =apply_spec_augment
__lowercase =mask_time_prob
__lowercase =mask_time_length
__lowercase =mask_time_min_masks
__lowercase =mask_feature_prob
__lowercase =mask_feature_length
# parameters for pretraining with codevector quantized representations
__lowercase =num_codevectors_per_group
__lowercase =num_codevector_groups
__lowercase =contrastive_logits_temperature
__lowercase =num_negatives
__lowercase =codevector_dim
__lowercase =proj_codevector_dim
__lowercase =diversity_loss_weight
# ctc loss
__lowercase =ctc_loss_reduction
__lowercase =ctc_zero_infinity
# adapter
__lowercase =add_adapter
__lowercase =adapter_kernel_size
__lowercase =adapter_stride
__lowercase =num_adapter_layers
__lowercase =output_hidden_size or hidden_size
# SequenceClassification-specific parameter. Feel free to ignore for other classes.
__lowercase =classifier_proj_size
# XVector-specific parameters. Feel free to ignore for other classes.
__lowercase =list(_lowerCAmelCase)
__lowercase =list(_lowerCAmelCase)
__lowercase =list(_lowerCAmelCase)
__lowercase =xvector_output_dim
@property
def __lowerCamelCase ( self : Optional[int]):
'''simple docstring'''
return functools.reduce(operator.mul , self.conv_stride , 1)
| 48 | 0 |
'''simple docstring'''
import os
lowerCAmelCase__ = {'''I''': 1, '''V''': 5, '''X''': 10, '''L''': 50, '''C''': 100, '''D''': 500, '''M''': 1000}
def _A ( A__ ):
"""simple docstring"""
__lowercase = 0
__lowercase = 0
while index < len(A__ ) - 1:
__lowercase = SYMBOLS[numerals[index]]
__lowercase = SYMBOLS[numerals[index + 1]]
if current_value < next_value:
total_value -= current_value
else:
total_value += current_value
index += 1
total_value += SYMBOLS[numerals[index]]
return total_value
def _A ( A__ ):
"""simple docstring"""
__lowercase = ''''''
__lowercase = num // 1000
numerals += m_count * "M"
num %= 1000
__lowercase = num // 100
if c_count == 9:
numerals += "CM"
c_count -= 9
elif c_count == 4:
numerals += "CD"
c_count -= 4
if c_count >= 5:
numerals += "D"
c_count -= 5
numerals += c_count * "C"
num %= 100
__lowercase = num // 10
if x_count == 9:
numerals += "XC"
x_count -= 9
elif x_count == 4:
numerals += "XL"
x_count -= 4
if x_count >= 5:
numerals += "L"
x_count -= 5
numerals += x_count * "X"
num %= 10
if num == 9:
numerals += "IX"
num -= 9
elif num == 4:
numerals += "IV"
num -= 4
if num >= 5:
numerals += "V"
num -= 5
numerals += num * "I"
return numerals
def _A ( A__ = "/p089_roman.txt" ):
"""simple docstring"""
__lowercase = 0
with open(os.path.dirname(A__ ) + roman_numerals_filename ) as filea:
__lowercase = filea.readlines()
for line in lines:
__lowercase = line.strip()
__lowercase = parse_roman_numerals(A__ )
__lowercase = generate_roman_numerals(A__ )
savings += len(A__ ) - len(A__ )
return savings
if __name__ == "__main__":
print(f'{solution() = }')
| 104 |
'''simple docstring'''
import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.utils import write_basic_config
from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, torch_device
from transformers.utils import is_apex_available
logging.basicConfig(level=logging.DEBUG)
lowerCAmelCase__ = logging.getLogger()
def _A ( ):
"""simple docstring"""
__lowercase = argparse.ArgumentParser()
parser.add_argument('''-f''' )
__lowercase = parser.parse_args()
return args.f
def _A ( A__ ):
"""simple docstring"""
__lowercase = {}
__lowercase = os.path.join(A__ , '''all_results.json''' )
if os.path.exists(A__ ):
with open(A__ , '''r''' ) as f:
__lowercase = json.load(A__ )
else:
raise ValueError(F"can't find {path}" )
return results
def _A ( ):
"""simple docstring"""
__lowercase = torch.cuda.is_available() and torch_device == '''cuda'''
return is_using_cuda and is_apex_available()
lowerCAmelCase__ = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
class lowercase_ (lowerCamelCase__ ):
"""simple docstring"""
@classmethod
def SCREAMING_SNAKE_CASE ( cls : List[str] ):
# Write Accelerate config, will pick up on CPU, GPU, and multi-GPU
__lowercase = tempfile.mkdtemp()
__lowercase = os.path.join(cls.tmpdir ,'''default_config.yml''' )
write_basic_config(save_location=cls.configPath )
__lowercase = ['''accelerate''', '''launch''', '''--config_file''', cls.configPath]
@classmethod
def SCREAMING_SNAKE_CASE ( cls : str ):
shutil.rmtree(cls.tmpdir )
@mock.patch.dict(os.environ ,{'''WANDB_MODE''': '''offline'''} )
def SCREAMING_SNAKE_CASE ( self : Any ):
__lowercase = self.get_auto_remove_tmp_dir()
__lowercase = F"\n {self.examples_dir}/pytorch/text-classification/run_glue_no_trainer.py\n --model_name_or_path distilbert-base-uncased\n --output_dir {tmp_dir}\n --train_file ./tests/fixtures/tests_samples/MRPC/train.csv\n --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv\n --per_device_train_batch_size=2\n --per_device_eval_batch_size=1\n --learning_rate=1e-4\n --seed=42\n --checkpointing_steps epoch\n --with_tracking\n ".split()
if is_cuda_and_apex_available():
testargs.append('''--fp16''' )
run_command(self._launch_args + testargs )
__lowercase = get_results(lowercase__ )
self.assertGreaterEqual(result['''eval_accuracy'''] ,0.7_5 )
self.assertTrue(os.path.exists(os.path.join(lowercase__ ,'''epoch_0''' ) ) )
self.assertTrue(os.path.exists(os.path.join(lowercase__ ,'''glue_no_trainer''' ) ) )
@mock.patch.dict(os.environ ,{'''WANDB_MODE''': '''offline'''} )
def SCREAMING_SNAKE_CASE ( self : Optional[int] ):
__lowercase = self.get_auto_remove_tmp_dir()
__lowercase = F"\n {self.examples_dir}/pytorch/language-modeling/run_clm_no_trainer.py\n --model_name_or_path distilgpt2\n --train_file ./tests/fixtures/sample_text.txt\n --validation_file ./tests/fixtures/sample_text.txt\n --block_size 128\n --per_device_train_batch_size 5\n --per_device_eval_batch_size 5\n --num_train_epochs 2\n --output_dir {tmp_dir}\n --checkpointing_steps epoch\n --with_tracking\n ".split()
if torch.cuda.device_count() > 1:
# Skipping because there are not enough batches to train the model + would need a drop_last to work.
return
run_command(self._launch_args + testargs )
__lowercase = get_results(lowercase__ )
self.assertLess(result['''perplexity'''] ,1_0_0 )
self.assertTrue(os.path.exists(os.path.join(lowercase__ ,'''epoch_0''' ) ) )
self.assertTrue(os.path.exists(os.path.join(lowercase__ ,'''clm_no_trainer''' ) ) )
@mock.patch.dict(os.environ ,{'''WANDB_MODE''': '''offline'''} )
def SCREAMING_SNAKE_CASE ( self : Optional[int] ):
__lowercase = self.get_auto_remove_tmp_dir()
__lowercase = F"\n {self.examples_dir}/pytorch/language-modeling/run_mlm_no_trainer.py\n --model_name_or_path distilroberta-base\n --train_file ./tests/fixtures/sample_text.txt\n --validation_file ./tests/fixtures/sample_text.txt\n --output_dir {tmp_dir}\n --num_train_epochs=1\n --checkpointing_steps epoch\n --with_tracking\n ".split()
run_command(self._launch_args + testargs )
__lowercase = get_results(lowercase__ )
self.assertLess(result['''perplexity'''] ,4_2 )
self.assertTrue(os.path.exists(os.path.join(lowercase__ ,'''epoch_0''' ) ) )
self.assertTrue(os.path.exists(os.path.join(lowercase__ ,'''mlm_no_trainer''' ) ) )
@mock.patch.dict(os.environ ,{'''WANDB_MODE''': '''offline'''} )
def SCREAMING_SNAKE_CASE ( self : Tuple ):
# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
__lowercase = 7 if get_gpu_count() > 1 else 2
__lowercase = self.get_auto_remove_tmp_dir()
__lowercase = F"\n {self.examples_dir}/pytorch/token-classification/run_ner_no_trainer.py\n --model_name_or_path bert-base-uncased\n --train_file tests/fixtures/tests_samples/conll/sample.json\n --validation_file tests/fixtures/tests_samples/conll/sample.json\n --output_dir {tmp_dir}\n --learning_rate=2e-4\n --per_device_train_batch_size=2\n --per_device_eval_batch_size=2\n --num_train_epochs={epochs}\n --seed 7\n --checkpointing_steps epoch\n --with_tracking\n ".split()
run_command(self._launch_args + testargs )
__lowercase = get_results(lowercase__ )
self.assertGreaterEqual(result['''eval_accuracy'''] ,0.7_5 )
self.assertLess(result['''train_loss'''] ,0.5 )
self.assertTrue(os.path.exists(os.path.join(lowercase__ ,'''epoch_0''' ) ) )
self.assertTrue(os.path.exists(os.path.join(lowercase__ ,'''ner_no_trainer''' ) ) )
@unittest.skip(reason='''Fix me @muellerzr''' )
@mock.patch.dict(os.environ ,{'''WANDB_MODE''': '''offline'''} )
def SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
__lowercase = self.get_auto_remove_tmp_dir()
__lowercase = F"\n {self.examples_dir}/pytorch/question-answering/run_qa_no_trainer.py\n --model_name_or_path bert-base-uncased\n --version_2_with_negative\n --train_file tests/fixtures/tests_samples/SQUAD/sample.json\n --validation_file tests/fixtures/tests_samples/SQUAD/sample.json\n --output_dir {tmp_dir}\n --seed=42\n --max_train_steps=10\n --num_warmup_steps=2\n --learning_rate=2e-4\n --per_device_train_batch_size=2\n --per_device_eval_batch_size=1\n --checkpointing_steps epoch\n --with_tracking\n ".split()
run_command(self._launch_args + testargs )
__lowercase = get_results(lowercase__ )
# Because we use --version_2_with_negative the testing script uses SQuAD v2 metrics.
self.assertGreaterEqual(result['''eval_f1'''] ,2_8 )
self.assertGreaterEqual(result['''eval_exact'''] ,2_8 )
self.assertTrue(os.path.exists(os.path.join(lowercase__ ,'''epoch_0''' ) ) )
self.assertTrue(os.path.exists(os.path.join(lowercase__ ,'''qa_no_trainer''' ) ) )
@mock.patch.dict(os.environ ,{'''WANDB_MODE''': '''offline'''} )
def SCREAMING_SNAKE_CASE ( self : Dict ):
__lowercase = self.get_auto_remove_tmp_dir()
__lowercase = F"\n {self.examples_dir}/pytorch/multiple-choice/run_swag_no_trainer.py\n --model_name_or_path bert-base-uncased\n --train_file tests/fixtures/tests_samples/swag/sample.json\n --validation_file tests/fixtures/tests_samples/swag/sample.json\n --output_dir {tmp_dir}\n --max_train_steps=20\n --num_warmup_steps=2\n --learning_rate=2e-4\n --per_device_train_batch_size=2\n --per_device_eval_batch_size=1\n --with_tracking\n ".split()
run_command(self._launch_args + testargs )
__lowercase = get_results(lowercase__ )
self.assertGreaterEqual(result['''eval_accuracy'''] ,0.8 )
self.assertTrue(os.path.exists(os.path.join(lowercase__ ,'''swag_no_trainer''' ) ) )
@slow
@mock.patch.dict(os.environ ,{'''WANDB_MODE''': '''offline'''} )
def SCREAMING_SNAKE_CASE ( self : List[str] ):
__lowercase = self.get_auto_remove_tmp_dir()
__lowercase = F"\n {self.examples_dir}/pytorch/summarization/run_summarization_no_trainer.py\n --model_name_or_path t5-small\n --train_file tests/fixtures/tests_samples/xsum/sample.json\n --validation_file tests/fixtures/tests_samples/xsum/sample.json\n --output_dir {tmp_dir}\n --max_train_steps=50\n --num_warmup_steps=8\n --learning_rate=2e-4\n --per_device_train_batch_size=2\n --per_device_eval_batch_size=1\n --checkpointing_steps epoch\n --with_tracking\n ".split()
run_command(self._launch_args + testargs )
__lowercase = get_results(lowercase__ )
self.assertGreaterEqual(result['''eval_rouge1'''] ,1_0 )
self.assertGreaterEqual(result['''eval_rouge2'''] ,2 )
self.assertGreaterEqual(result['''eval_rougeL'''] ,7 )
self.assertGreaterEqual(result['''eval_rougeLsum'''] ,7 )
self.assertTrue(os.path.exists(os.path.join(lowercase__ ,'''epoch_0''' ) ) )
self.assertTrue(os.path.exists(os.path.join(lowercase__ ,'''summarization_no_trainer''' ) ) )
@slow
@mock.patch.dict(os.environ ,{'''WANDB_MODE''': '''offline'''} )
def SCREAMING_SNAKE_CASE ( self : List[Any] ):
__lowercase = self.get_auto_remove_tmp_dir()
__lowercase = F"\n {self.examples_dir}/pytorch/translation/run_translation_no_trainer.py\n --model_name_or_path sshleifer/student_marian_en_ro_6_1\n --source_lang en\n --target_lang ro\n --train_file tests/fixtures/tests_samples/wmt16/sample.json\n --validation_file tests/fixtures/tests_samples/wmt16/sample.json\n --output_dir {tmp_dir}\n --max_train_steps=50\n --num_warmup_steps=8\n --num_beams=6\n --learning_rate=3e-3\n --per_device_train_batch_size=2\n --per_device_eval_batch_size=1\n --source_lang en_XX\n --target_lang ro_RO\n --checkpointing_steps epoch\n --with_tracking\n ".split()
run_command(self._launch_args + testargs )
__lowercase = get_results(lowercase__ )
self.assertGreaterEqual(result['''eval_bleu'''] ,3_0 )
self.assertTrue(os.path.exists(os.path.join(lowercase__ ,'''epoch_0''' ) ) )
self.assertTrue(os.path.exists(os.path.join(lowercase__ ,'''translation_no_trainer''' ) ) )
@slow
def SCREAMING_SNAKE_CASE ( self : str ):
__lowercase = logging.StreamHandler(sys.stdout )
logger.addHandler(lowercase__ )
__lowercase = self.get_auto_remove_tmp_dir()
__lowercase = F"\n {self.examples_dir}/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py\n --dataset_name huggingface/semantic-segmentation-test-sample\n --output_dir {tmp_dir}\n --max_train_steps=10\n --num_warmup_steps=2\n --learning_rate=2e-4\n --per_device_train_batch_size=2\n --per_device_eval_batch_size=1\n --checkpointing_steps epoch\n ".split()
run_command(self._launch_args + testargs )
__lowercase = get_results(lowercase__ )
self.assertGreaterEqual(result['''eval_overall_accuracy'''] ,0.1_0 )
@mock.patch.dict(os.environ ,{'''WANDB_MODE''': '''offline'''} )
def SCREAMING_SNAKE_CASE ( self : Tuple ):
__lowercase = self.get_auto_remove_tmp_dir()
__lowercase = F"\n {self.examples_dir}/pytorch/image-classification/run_image_classification_no_trainer.py\n --model_name_or_path google/vit-base-patch16-224-in21k\n --dataset_name hf-internal-testing/cats_vs_dogs_sample\n --learning_rate 1e-4\n --per_device_train_batch_size 2\n --per_device_eval_batch_size 1\n --max_train_steps 2\n --train_val_split 0.1\n --seed 42\n --output_dir {tmp_dir}\n --with_tracking\n --checkpointing_steps 1\n ".split()
if is_cuda_and_apex_available():
testargs.append('''--fp16''' )
run_command(self._launch_args + testargs )
__lowercase = get_results(lowercase__ )
# The base model scores a 25%
self.assertGreaterEqual(result['''eval_accuracy'''] ,0.6 )
self.assertTrue(os.path.exists(os.path.join(lowercase__ ,'''step_1''' ) ) )
self.assertTrue(os.path.exists(os.path.join(lowercase__ ,'''image_classification_no_trainer''' ) ) )
| 104 | 1 |
import dataclasses
import re
import string
from typing import Any, Dict, Iterator, List, Mapping, Optional, Sequence, Tuple
import numpy as np
from . import residue_constants
lowercase_ = Mapping[str, np.ndarray]
lowercase_ = Mapping[str, Any] # Is a nested dict.
lowercase_ = 0.01
@dataclasses.dataclass(frozen=UpperCAmelCase )
class SCREAMING_SNAKE_CASE :
_UpperCamelCase : np.ndarray # [num_res, num_atom_type, 3]
# Amino-acid type for each residue represented as an integer between 0 and
# 20, where 20 is 'X'.
_UpperCamelCase : np.ndarray # [num_res]
# Binary float mask to indicate presence of a particular atom. 1.0 if an atom
# is present and 0.0 if not. This should be used for loss masking.
_UpperCamelCase : np.ndarray # [num_res, num_atom_type]
# Residue index as used in PDB. It is not necessarily continuous or 0-indexed.
_UpperCamelCase : np.ndarray # [num_res]
# B-factors, or temperature factors, of each residue (in sq. angstroms units),
# representing the displacement of the residue from its ground truth mean
# value.
_UpperCamelCase : np.ndarray # [num_res, num_atom_type]
# Chain indices for multi-chain predictions
_UpperCamelCase : Optional[np.ndarray] = None
# Optional remark about the protein. Included as a comment in output PDB
# files
_UpperCamelCase : Optional[str] = None
# Templates used to generate this protein (prediction-only)
_UpperCamelCase : Optional[Sequence[str]] = None
# Chain corresponding to each parent
_UpperCamelCase : Optional[Sequence[int]] = None
def __UpperCamelCase (_SCREAMING_SNAKE_CASE ) -> Protein:
lowercase__ = R'(\[[A-Z]+\]\n)'
lowercase__ = [tag.strip() for tag in re.split(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) if len(_SCREAMING_SNAKE_CASE ) > 0]
lowercase__ = zip(tags[0::2] , [l.split('\n' ) for l in tags[1::2]] )
lowercase__ = ["N", "CA", "C"]
lowercase__ = None
lowercase__ = None
lowercase__ = None
for g in groups:
if "[PRIMARY]" == g[0]:
lowercase__ = g[1][0].strip()
for i in range(len(_SCREAMING_SNAKE_CASE ) ):
if seq[i] not in residue_constants.restypes:
lowercase__ = 'X' # FIXME: strings are immutable
lowercase__ = np.array(
[residue_constants.restype_order.get(_SCREAMING_SNAKE_CASE , residue_constants.restype_num ) for res_symbol in seq] )
elif "[TERTIARY]" == g[0]:
lowercase__ = []
for axis in range(3 ):
tertiary.append(list(map(_SCREAMING_SNAKE_CASE , g[1][axis].split() ) ) )
lowercase__ = np.array(_SCREAMING_SNAKE_CASE )
lowercase__ = np.zeros((len(tertiary[0] ) // 3, residue_constants.atom_type_num, 3) ).astype(np.floataa )
for i, atom in enumerate(_SCREAMING_SNAKE_CASE ):
lowercase__ = np.transpose(tertiary_np[:, i::3] )
atom_positions *= PICO_TO_ANGSTROM
elif "[MASK]" == g[0]:
lowercase__ = np.array(list(map({'-': 0, '+': 1}.get , g[1][0].strip() ) ) )
lowercase__ = np.zeros(
(
len(_SCREAMING_SNAKE_CASE ),
residue_constants.atom_type_num,
) ).astype(np.floataa )
for i, atom in enumerate(_SCREAMING_SNAKE_CASE ):
lowercase__ = 1
atom_mask *= mask[..., None]
assert aatype is not None
return Protein(
atom_positions=_SCREAMING_SNAKE_CASE , atom_mask=_SCREAMING_SNAKE_CASE , aatype=_SCREAMING_SNAKE_CASE , residue_index=np.arange(len(_SCREAMING_SNAKE_CASE ) ) , b_factors=_SCREAMING_SNAKE_CASE , )
def __UpperCamelCase (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = 0 ) -> List[str]:
lowercase__ = []
lowercase__ = prot.remark
if remark is not None:
pdb_headers.append(F"""REMARK {remark}""" )
lowercase__ = prot.parents
lowercase__ = prot.parents_chain_index
if parents is not None and parents_chain_index is not None:
lowercase__ = [p for i, p in zip(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) if i == chain_id]
if parents is None or len(_SCREAMING_SNAKE_CASE ) == 0:
lowercase__ = ['N/A']
pdb_headers.append(F"""PARENT {" ".join(_SCREAMING_SNAKE_CASE )}""" )
return pdb_headers
def __UpperCamelCase (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> str:
lowercase__ = []
lowercase__ = pdb_str.split('\n' )
lowercase__ = prot.remark
if remark is not None:
out_pdb_lines.append(F"""REMARK {remark}""" )
lowercase__ = 42
if prot.parents is not None and len(prot.parents ) > 0:
lowercase__ = []
if prot.parents_chain_index is not None:
lowercase__ = {}
for p, i in zip(prot.parents , prot.parents_chain_index ):
parent_dict.setdefault(str(_SCREAMING_SNAKE_CASE ) , [] )
parent_dict[str(_SCREAMING_SNAKE_CASE )].append(_SCREAMING_SNAKE_CASE )
lowercase__ = max([int(_SCREAMING_SNAKE_CASE ) for chain_idx in parent_dict] )
for i in range(max_idx + 1 ):
lowercase__ = parent_dict.get(str(_SCREAMING_SNAKE_CASE ) , ['N/A'] )
parents_per_chain.append(_SCREAMING_SNAKE_CASE )
else:
parents_per_chain.append(list(prot.parents ) )
else:
lowercase__ = [['N/A']]
def make_parent_line(_SCREAMING_SNAKE_CASE ) -> str:
return F"""PARENT {" ".join(_SCREAMING_SNAKE_CASE )}"""
out_pdb_lines.append(make_parent_line(parents_per_chain[0] ) )
lowercase__ = 0
for i, l in enumerate(_SCREAMING_SNAKE_CASE ):
if "PARENT" not in l and "REMARK" not in l:
out_pdb_lines.append(_SCREAMING_SNAKE_CASE )
if "TER" in l and "END" not in lines[i + 1]:
chain_counter += 1
if not chain_counter >= len(_SCREAMING_SNAKE_CASE ):
lowercase__ = parents_per_chain[chain_counter]
else:
lowercase__ = ['N/A']
out_pdb_lines.append(make_parent_line(_SCREAMING_SNAKE_CASE ) )
return "\n".join(_SCREAMING_SNAKE_CASE )
def __UpperCamelCase (_SCREAMING_SNAKE_CASE ) -> str:
lowercase__ = residue_constants.restypes + ['X']
def res_atoa(_SCREAMING_SNAKE_CASE ) -> str:
return residue_constants.restype_atoa.get(restypes[r] , 'UNK' )
lowercase__ = residue_constants.atom_types
lowercase__ = []
lowercase__ = prot.atom_mask
lowercase__ = prot.aatype
lowercase__ = prot.atom_positions
lowercase__ = prot.residue_index.astype(np.intaa )
lowercase__ = prot.b_factors
lowercase__ = prot.chain_index
if np.any(aatype > residue_constants.restype_num ):
raise ValueError('Invalid aatypes.' )
lowercase__ = get_pdb_headers(_SCREAMING_SNAKE_CASE )
if len(_SCREAMING_SNAKE_CASE ) > 0:
pdb_lines.extend(_SCREAMING_SNAKE_CASE )
lowercase__ = aatype.shape[0]
lowercase__ = 1
lowercase__ = 0
lowercase__ = string.ascii_uppercase
lowercase__ = None
# Add all atom sites.
for i in range(_SCREAMING_SNAKE_CASE ):
lowercase__ = res_atoa(aatype[i] )
for atom_name, pos, mask, b_factor in zip(_SCREAMING_SNAKE_CASE , atom_positions[i] , atom_mask[i] , b_factors[i] ):
if mask < 0.5:
continue
lowercase__ = 'ATOM'
lowercase__ = atom_name if len(_SCREAMING_SNAKE_CASE ) == 4 else F""" {atom_name}"""
lowercase__ = ''
lowercase__ = ''
lowercase__ = 1.0_0
lowercase__ = atom_name[0] # Protein supports only C, N, O, S, this works.
lowercase__ = ''
lowercase__ = 'A'
if chain_index is not None:
lowercase__ = chain_tags[chain_index[i]]
# PDB is a columnar format, every space matters here!
lowercase__ = (
F"""{record_type:<6}{atom_index:>5} {name:<4}{alt_loc:>1}"""
F"""{res_name_a:>3} {chain_tag:>1}"""
F"""{residue_index[i]:>4}{insertion_code:>1} """
F"""{pos[0]:>8.3f}{pos[1]:>8.3f}{pos[2]:>8.3f}"""
F"""{occupancy:>6.2f}{b_factor:>6.2f} """
F"""{element:>2}{charge:>2}"""
)
pdb_lines.append(_SCREAMING_SNAKE_CASE )
atom_index += 1
lowercase__ = i == n - 1
if chain_index is not None:
if i != n - 1 and chain_index[i + 1] != prev_chain_index:
lowercase__ = True
lowercase__ = chain_index[i + 1]
if should_terminate:
# Close the chain.
lowercase__ = 'TER'
lowercase__ = (
F"""{chain_end:<6}{atom_index:>5} {res_atoa(aatype[i] ):>3} {chain_tag:>1}{residue_index[i]:>4}"""
)
pdb_lines.append(_SCREAMING_SNAKE_CASE )
atom_index += 1
if i != n - 1:
# "prev" is a misnomer here. This happens at the beginning of
# each new chain.
pdb_lines.extend(get_pdb_headers(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) )
pdb_lines.append('END' )
pdb_lines.append('' )
return "\n".join(_SCREAMING_SNAKE_CASE )
def __UpperCamelCase (_SCREAMING_SNAKE_CASE ) -> np.ndarray:
return residue_constants.STANDARD_ATOM_MASK[prot.aatype]
def __UpperCamelCase (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None , ) -> Protein:
return Protein(
aatype=features['aatype'] , atom_positions=result['final_atom_positions'] , atom_mask=result['final_atom_mask'] , residue_index=features['residue_index'] + 1 , b_factors=b_factors if b_factors is not None else np.zeros_like(result['final_atom_mask'] ) , chain_index=_SCREAMING_SNAKE_CASE , remark=_SCREAMING_SNAKE_CASE , parents=_SCREAMING_SNAKE_CASE , parents_chain_index=_SCREAMING_SNAKE_CASE , )
| 269 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"""transfo-xl-wt103""": """https://huggingface.co/transfo-xl-wt103/resolve/main/config.json""",
}
class SCREAMING_SNAKE_CASE (UpperCAmelCase ):
_UpperCamelCase : Optional[Any] = 'transfo-xl'
_UpperCamelCase : Any = ['mems']
_UpperCamelCase : Any = {
'n_token': 'vocab_size',
'hidden_size': 'd_model',
'num_attention_heads': 'n_head',
'num_hidden_layers': 'n_layer',
}
def __init__( self : Optional[Any] , a : Optional[int]=267_735 , a : str=[20_000, 40_000, 200_000] , a : str=1_024 , a : str=1_024 , a : int=16 , a : Optional[int]=64 , a : Optional[int]=4_096 , a : int=4 , a : Tuple=False , a : Any=18 , a : Tuple=1_600 , a : Union[str, Any]=1_000 , a : str=True , a : Dict=True , a : Any=0 , a : List[Any]=-1 , a : List[Any]=True , a : Tuple=0.1 , a : List[Any]=0.0 , a : Optional[Any]=True , a : int="normal" , a : Optional[Any]=0.01 , a : str=0.01 , a : List[Any]=0.02 , a : List[Any]=1E-5 , a : Optional[Any]=0 , **a : Optional[int] , )-> Optional[int]:
"""simple docstring"""
lowercase__ = vocab_size
lowercase__ = []
self.cutoffs.extend(a )
if proj_share_all_but_first:
lowercase__ = [False] + [True] * len(self.cutoffs )
else:
lowercase__ = [False] + [False] * len(self.cutoffs )
lowercase__ = d_model
lowercase__ = d_embed
lowercase__ = d_head
lowercase__ = d_inner
lowercase__ = div_val
lowercase__ = pre_lnorm
lowercase__ = n_layer
lowercase__ = n_head
lowercase__ = mem_len
lowercase__ = same_length
lowercase__ = attn_type
lowercase__ = clamp_len
lowercase__ = sample_softmax
lowercase__ = adaptive
lowercase__ = dropout
lowercase__ = dropatt
lowercase__ = untie_r
lowercase__ = init
lowercase__ = init_range
lowercase__ = proj_init_std
lowercase__ = init_std
lowercase__ = layer_norm_epsilon
super().__init__(eos_token_id=a , **a )
@property
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] )-> Union[str, Any]:
"""simple docstring"""
logger.info(f"""The model {self.model_type} is one of the few models that has no sequence length limit.""" )
return -1
@max_position_embeddings.setter
def SCREAMING_SNAKE_CASE_ ( self : Any , a : Optional[int] )-> Optional[int]:
"""simple docstring"""
raise NotImplementedError(
f"""The model {self.model_type} is one of the few models that has no sequence length limit.""" )
| 269 | 1 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_vision_available():
import PIL
snake_case : Any = logging.get_logger(__name__)
def __lowerCamelCase ( UpperCAmelCase_ : Union[str, Any] , UpperCAmelCase_ : Tuple ):
"""simple docstring"""
a :Any = b.T
a :Tuple = np.sum(np.square(UpperCAmelCase_ ) , axis=1 )
a :Union[str, Any] = np.sum(np.square(UpperCAmelCase_ ) , axis=0 )
a :Tuple = np.matmul(UpperCAmelCase_ , UpperCAmelCase_ )
a :List[str] = aa[:, None] - 2 * ab + ba[None, :]
return d
def __lowerCamelCase ( UpperCAmelCase_ : List[str] , UpperCAmelCase_ : int ):
"""simple docstring"""
a :Union[str, Any] = x.reshape(-1 , 3 )
a :List[Any] = squared_euclidean_distance(UpperCAmelCase_ , UpperCAmelCase_ )
return np.argmin(UpperCAmelCase_ , axis=1 )
class _snake_case ( _snake_case ):
SCREAMING_SNAKE_CASE__ = ['pixel_values']
def __init__( self , _lowerCamelCase = None , _lowerCamelCase = True , _lowerCamelCase = None , _lowerCamelCase = PILImageResampling.BILINEAR , _lowerCamelCase = True , _lowerCamelCase = True , **_lowerCamelCase , ):
super().__init__(**_lowerCamelCase )
a :int = size if size is not None else {'''height''': 256, '''width''': 256}
a :Any = get_size_dict(_lowerCamelCase )
a :List[Any] = np.array(_lowerCamelCase ) if clusters is not None else None
a :Optional[Any] = do_resize
a :Any = size
a :Dict = resample
a :Optional[int] = do_normalize
a :Optional[int] = do_color_quantize
def SCREAMING_SNAKE_CASE__ ( self , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase = PILImageResampling.BILINEAR , _lowerCamelCase = None , **_lowerCamelCase , ):
a :Optional[Any] = get_size_dict(_lowerCamelCase )
if "height" not in size or "width" not in size:
raise ValueError(F'''Size dictionary must contain both height and width keys. Got {size.keys()}''' )
return resize(
_lowerCamelCase , size=(size['''height'''], size['''width''']) , resample=_lowerCamelCase , data_format=_lowerCamelCase , **_lowerCamelCase )
def SCREAMING_SNAKE_CASE__ ( self , _lowerCamelCase , _lowerCamelCase = None , ):
a :int = rescale(image=_lowerCamelCase , scale=1 / 127.5 , data_format=_lowerCamelCase )
a :List[Any] = image - 1
return image
def SCREAMING_SNAKE_CASE__ ( self , _lowerCamelCase , _lowerCamelCase = None , _lowerCamelCase = None , _lowerCamelCase = None , _lowerCamelCase = None , _lowerCamelCase = None , _lowerCamelCase = None , _lowerCamelCase = None , _lowerCamelCase = ChannelDimension.FIRST , **_lowerCamelCase , ):
a :Optional[int] = do_resize if do_resize is not None else self.do_resize
a :Optional[Any] = size if size is not None else self.size
a :Optional[int] = get_size_dict(_lowerCamelCase )
a :Union[str, Any] = resample if resample is not None else self.resample
a :int = do_normalize if do_normalize is not None else self.do_normalize
a :Tuple = do_color_quantize if do_color_quantize is not None else self.do_color_quantize
a :List[str] = clusters if clusters is not None else self.clusters
a :List[str] = np.array(_lowerCamelCase )
a :List[Any] = make_list_of_images(_lowerCamelCase )
if not valid_images(_lowerCamelCase ):
raise ValueError(
'''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
'''torch.Tensor, tf.Tensor or jax.ndarray.''' )
if do_resize and size is None or resample is None:
raise ValueError('''Size and resample must be specified if do_resize is True.''' )
if do_color_quantize and clusters is None:
raise ValueError('''Clusters must be specified if do_color_quantize is True.''' )
# All transformations expect numpy arrays.
a :Tuple = [to_numpy_array(_lowerCamelCase ) for image in images]
if do_resize:
a :List[str] = [self.resize(image=_lowerCamelCase , size=_lowerCamelCase , resample=_lowerCamelCase ) for image in images]
if do_normalize:
a :List[Any] = [self.normalize(image=_lowerCamelCase ) for image in images]
if do_color_quantize:
a :Union[str, Any] = [to_channel_dimension_format(_lowerCamelCase , ChannelDimension.LAST ) for image in images]
# color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
a :Union[str, Any] = np.array(_lowerCamelCase )
a :List[Any] = color_quantize(_lowerCamelCase , _lowerCamelCase ).reshape(images.shape[:-1] )
# flatten to (batch_size, height*width)
a :List[str] = images.shape[0]
a :Optional[Any] = images.reshape(_lowerCamelCase , -1 )
# We need to convert back to a list of images to keep consistent behaviour across processors.
a :Optional[int] = list(_lowerCamelCase )
else:
a :Tuple = [to_channel_dimension_format(_lowerCamelCase , _lowerCamelCase ) for image in images]
a :Any = {'''input_ids''': images}
return BatchFeature(data=_lowerCamelCase , tensor_type=_lowerCamelCase )
| 94 |
import numpy as np
from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch
from .test_feature_extraction_common import FeatureExtractionSavingTestMixin
class __lowerCAmelCase ( lowerCamelCase__ ):
# to overwrite at feature extractactor specific tests
__lowerCamelCase = None
__lowerCamelCase = None
@property
def snake_case ( self ):
"""simple docstring"""
return self.feat_extract_tester.prepare_feat_extract_dict()
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.feature_extraction_class(**self.feat_extract_dict )
self.assertTrue(hasattr(_snake_case , """feature_size""" ) )
self.assertTrue(hasattr(_snake_case , """sampling_rate""" ) )
self.assertTrue(hasattr(_snake_case , """padding_value""" ) )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.feat_extract_tester.prepare_inputs_for_common()
_lowerCAmelCase = self.feature_extraction_class(**self.feat_extract_dict )
_lowerCAmelCase = feat_extract.model_input_names[0]
_lowerCAmelCase = BatchFeature({input_name: speech_inputs} )
self.assertTrue(all(len(_snake_case ) == len(_snake_case ) for x, y in zip(_snake_case , processed_features[input_name] ) ) )
_lowerCAmelCase = self.feat_extract_tester.prepare_inputs_for_common(equal_length=_snake_case )
_lowerCAmelCase = BatchFeature({input_name: speech_inputs} , tensor_type="""np""" )
_lowerCAmelCase = processed_features[input_name]
if len(batch_features_input.shape ) < 3:
_lowerCAmelCase = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )
@require_torch
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.feat_extract_tester.prepare_inputs_for_common(equal_length=_snake_case )
_lowerCAmelCase = self.feature_extraction_class(**self.feat_extract_dict )
_lowerCAmelCase = feat_extract.model_input_names[0]
_lowerCAmelCase = BatchFeature({input_name: speech_inputs} , tensor_type="""pt""" )
_lowerCAmelCase = processed_features[input_name]
if len(batch_features_input.shape ) < 3:
_lowerCAmelCase = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )
@require_tf
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.feat_extract_tester.prepare_inputs_for_common(equal_length=_snake_case )
_lowerCAmelCase = self.feature_extraction_class(**self.feat_extract_dict )
_lowerCAmelCase = feat_extract.model_input_names[0]
_lowerCAmelCase = BatchFeature({input_name: speech_inputs} , tensor_type="""tf""" )
_lowerCAmelCase = processed_features[input_name]
if len(batch_features_input.shape ) < 3:
_lowerCAmelCase = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )
def snake_case ( self , _snake_case=False ):
"""simple docstring"""
def _inputs_have_equal_length(_snake_case ):
_lowerCAmelCase = len(input[0] )
for input_slice in input[1:]:
if len(_snake_case ) != length:
return False
return True
def _inputs_are_equal(_snake_case , _snake_case ):
if len(_snake_case ) != len(_snake_case ):
return False
for input_slice_a, input_slice_a in zip(_snake_case , _snake_case ):
if not np.allclose(np.asarray(_snake_case ) , np.asarray(_snake_case ) , atol=1e-3 ):
return False
return True
_lowerCAmelCase = self.feature_extraction_class(**self.feat_extract_dict )
_lowerCAmelCase = self.feat_extract_tester.prepare_inputs_for_common(numpify=_snake_case )
_lowerCAmelCase = feat_extract.model_input_names[0]
_lowerCAmelCase = BatchFeature({input_name: speech_inputs} )
_lowerCAmelCase = self.feat_extract_tester.seq_length_diff
_lowerCAmelCase = self.feat_extract_tester.max_seq_length + pad_diff
_lowerCAmelCase = self.feat_extract_tester.min_seq_length
_lowerCAmelCase = self.feat_extract_tester.batch_size
_lowerCAmelCase = self.feat_extract_tester.feature_size
# test padding for List[int] + numpy
_lowerCAmelCase = feat_extract.pad(_snake_case , padding=_snake_case )
_lowerCAmelCase = input_a[input_name]
_lowerCAmelCase = feat_extract.pad(_snake_case , padding="""longest""" )
_lowerCAmelCase = input_a[input_name]
_lowerCAmelCase = feat_extract.pad(_snake_case , padding="""max_length""" , max_length=len(speech_inputs[-1] ) )
_lowerCAmelCase = input_a[input_name]
_lowerCAmelCase = feat_extract.pad(_snake_case , padding="""longest""" , return_tensors="""np""" )
_lowerCAmelCase = input_a[input_name]
# max_length parameter has to be provided when setting `padding="max_length"`
with self.assertRaises(_snake_case ):
feat_extract.pad(_snake_case , padding="""max_length""" )[input_name]
_lowerCAmelCase = feat_extract.pad(
_snake_case , padding="""max_length""" , max_length=_snake_case , return_tensors="""np""" )
_lowerCAmelCase = input_a[input_name]
self.assertFalse(_inputs_have_equal_length(_snake_case ) )
self.assertTrue(_inputs_have_equal_length(_snake_case ) )
self.assertTrue(_inputs_have_equal_length(_snake_case ) )
self.assertTrue(_inputs_are_equal(_snake_case , _snake_case ) )
self.assertTrue(len(input_a[0] ) == pad_min_length )
self.assertTrue(len(input_a[1] ) == pad_min_length + pad_diff )
self.assertTrue(input_a.shape[:2] == (batch_size, len(input_a[0] )) )
self.assertTrue(input_a.shape[:2] == (batch_size, pad_max_length) )
if feature_size > 1:
self.assertTrue(input_a.shape[2] == input_a.shape[2] == feature_size )
# test padding for `pad_to_multiple_of` for List[int] + numpy
_lowerCAmelCase = feat_extract.pad(_snake_case , pad_to_multiple_of=10 )
_lowerCAmelCase = input_a[input_name]
_lowerCAmelCase = feat_extract.pad(_snake_case , padding="""longest""" , pad_to_multiple_of=10 )
_lowerCAmelCase = input_a[input_name]
_lowerCAmelCase = feat_extract.pad(
_snake_case , padding="""max_length""" , pad_to_multiple_of=10 , max_length=_snake_case )
_lowerCAmelCase = input_a[input_name]
_lowerCAmelCase = feat_extract.pad(
_snake_case , padding="""max_length""" , pad_to_multiple_of=10 , max_length=_snake_case , return_tensors="""np""" , )
_lowerCAmelCase = input_a[input_name]
self.assertTrue(all(len(_snake_case ) % 10 == 0 for x in input_a ) )
self.assertTrue(_inputs_are_equal(_snake_case , _snake_case ) )
_lowerCAmelCase = pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
self.assertTrue(all(len(_snake_case ) == expected_mult_pad_length for x in input_a ) )
self.assertEqual(input_a.shape[:2] , (batch_size, expected_mult_pad_length) )
if feature_size > 1:
self.assertTrue(input_a.shape[2] == feature_size )
# Check padding value is correct
_lowerCAmelCase = (np.ones(self.feat_extract_tester.feature_size ) * feat_extract.padding_value).sum()
self.assertTrue(
abs(np.asarray(input_a[0] )[pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length) )
< 1e-3 )
self.assertTrue(
abs(
np.asarray(input_a[1] )[pad_min_length + pad_diff :].sum()
- padding_vector_sum * (pad_max_length - pad_min_length - pad_diff) )
< 1e-3 )
self.assertTrue(
abs(
np.asarray(input_a[2] )[pad_min_length + 2 * pad_diff :].sum()
- padding_vector_sum * (pad_max_length - pad_min_length - 2 * pad_diff) )
< 1e-3 )
self.assertTrue(
abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length) ) < 1e-3 )
self.assertTrue(
abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (expected_mult_pad_length - pad_min_length) )
< 1e-3 )
def snake_case ( self , _snake_case=False ):
"""simple docstring"""
def _inputs_have_equal_length(_snake_case ):
_lowerCAmelCase = len(input[0] )
for input_slice in input[1:]:
if len(_snake_case ) != length:
return False
return True
def _inputs_are_equal(_snake_case , _snake_case ):
if len(_snake_case ) != len(_snake_case ):
return False
for input_slice_a, input_slice_a in zip(_snake_case , _snake_case ):
if not np.allclose(np.asarray(_snake_case ) , np.asarray(_snake_case ) , atol=1e-3 ):
return False
return True
_lowerCAmelCase = self.feature_extraction_class(**self.feat_extract_dict )
_lowerCAmelCase = self.feat_extract_tester.prepare_inputs_for_common(numpify=_snake_case )
_lowerCAmelCase = feat_extract.model_input_names[0]
_lowerCAmelCase = BatchFeature({input_name: speech_inputs} )
# truncate to smallest
_lowerCAmelCase = feat_extract.pad(
_snake_case , padding="""max_length""" , max_length=len(speech_inputs[0] ) , truncation=_snake_case )
_lowerCAmelCase = input_a[input_name]
_lowerCAmelCase = feat_extract.pad(_snake_case , padding="""max_length""" , max_length=len(speech_inputs[0] ) )
_lowerCAmelCase = input_a[input_name]
self.assertTrue(_inputs_have_equal_length(_snake_case ) )
self.assertFalse(_inputs_have_equal_length(_snake_case ) )
# truncate to smallest with np
_lowerCAmelCase = feat_extract.pad(
_snake_case , padding="""max_length""" , max_length=len(speech_inputs[0] ) , return_tensors="""np""" , truncation=_snake_case , )
_lowerCAmelCase = input_a[input_name]
_lowerCAmelCase = feat_extract.pad(
_snake_case , padding="""max_length""" , max_length=len(speech_inputs[0] ) , return_tensors="""np""" )
_lowerCAmelCase = input_a[input_name]
self.assertTrue(_inputs_have_equal_length(_snake_case ) )
self.assertTrue(input_a.shape[1] == len(speech_inputs[0] ) )
# since truncation forces padding to be smaller than longest input
# function can't return `np.ndarray`, but has to return list
self.assertFalse(_inputs_have_equal_length(_snake_case ) )
# truncate to middle
_lowerCAmelCase = feat_extract.pad(
_snake_case , padding="""max_length""" , max_length=len(speech_inputs[1] ) , truncation=_snake_case , return_tensors="""np""" , )
_lowerCAmelCase = input_a[input_name]
_lowerCAmelCase = feat_extract.pad(
_snake_case , padding="""max_length""" , max_length=len(speech_inputs[1] ) , truncation=_snake_case )
_lowerCAmelCase = input_a[input_name]
_lowerCAmelCase = feat_extract.pad(
_snake_case , padding="""max_length""" , max_length=len(speech_inputs[1] ) , return_tensors="""np""" )
_lowerCAmelCase = input_a[input_name]
self.assertTrue(input_a.shape[1] == len(speech_inputs[1] ) )
self.assertTrue(_inputs_have_equal_length(_snake_case ) )
self.assertTrue(_inputs_have_equal_length(_snake_case ) )
self.assertTrue(_inputs_are_equal(_snake_case , _snake_case ) )
# since truncation forces padding to be smaller than longest input
# function can't return `np.ndarray`, but has to return list
self.assertFalse(_inputs_have_equal_length(_snake_case ) )
self.assertTrue(len(input_a[-1] ) == len(speech_inputs[-1] ) )
# padding has to be max_length when setting `truncation=True`
with self.assertRaises(_snake_case ):
feat_extract.pad(_snake_case , truncation=_snake_case )[input_name]
# padding has to be max_length when setting `truncation=True`
with self.assertRaises(_snake_case ):
feat_extract.pad(_snake_case , padding="""longest""" , truncation=_snake_case )[input_name]
# padding has to be max_length when setting `truncation=True`
with self.assertRaises(_snake_case ):
feat_extract.pad(_snake_case , padding="""longest""" , truncation=_snake_case )[input_name]
# max_length parameter has to be provided when setting `truncation=True` and padding="max_length"
with self.assertRaises(_snake_case ):
feat_extract.pad(_snake_case , padding="""max_length""" , truncation=_snake_case )[input_name]
# test truncation for `pad_to_multiple_of` for List[int] + numpy
_lowerCAmelCase = 12
_lowerCAmelCase = feat_extract.pad(
_snake_case , padding="""max_length""" , max_length=len(speech_inputs[0] ) , pad_to_multiple_of=_snake_case , truncation=_snake_case , )
_lowerCAmelCase = input_a[input_name]
_lowerCAmelCase = feat_extract.pad(
_snake_case , padding="""max_length""" , max_length=len(speech_inputs[0] ) , pad_to_multiple_of=_snake_case , )
_lowerCAmelCase = input_a[input_name]
# retrieve expected_length as multiple of pad_to_multiple_of
_lowerCAmelCase = len(speech_inputs[0] )
if expected_length % pad_to_multiple_of != 0:
_lowerCAmelCase = ((len(speech_inputs[0] ) // pad_to_multiple_of) + 1) * pad_to_multiple_of
self.assertTrue(len(input_a[0] ) == expected_length )
self.assertTrue(_inputs_have_equal_length(_snake_case ) )
self.assertFalse(_inputs_have_equal_length(_snake_case ) )
def snake_case ( self ):
"""simple docstring"""
self._check_padding(numpify=_snake_case )
def snake_case ( self ):
"""simple docstring"""
self._check_padding(numpify=_snake_case )
def snake_case ( self ):
"""simple docstring"""
self._check_truncation(numpify=_snake_case )
def snake_case ( self ):
"""simple docstring"""
self._check_truncation(numpify=_snake_case )
@require_torch
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.feature_extraction_class(**self.feat_extract_dict )
_lowerCAmelCase = self.feat_extract_tester.prepare_inputs_for_common()
_lowerCAmelCase = feat_extract.model_input_names[0]
_lowerCAmelCase = BatchFeature({input_name: speech_inputs} )
_lowerCAmelCase = feat_extract.pad(_snake_case , padding="""longest""" , return_tensors="""np""" )[input_name]
_lowerCAmelCase = feat_extract.pad(_snake_case , padding="""longest""" , return_tensors="""pt""" )[input_name]
self.assertTrue(abs(input_np.astype(np.floataa ).sum() - input_pt.numpy().astype(np.floataa ).sum() ) < 1e-2 )
@require_tf
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.feature_extraction_class(**self.feat_extract_dict )
_lowerCAmelCase = self.feat_extract_tester.prepare_inputs_for_common()
_lowerCAmelCase = feat_extract.model_input_names[0]
_lowerCAmelCase = BatchFeature({input_name: speech_inputs} )
_lowerCAmelCase = feat_extract.pad(_snake_case , padding="""longest""" , return_tensors="""np""" )[input_name]
_lowerCAmelCase = feat_extract.pad(_snake_case , padding="""longest""" , return_tensors="""tf""" )[input_name]
self.assertTrue(abs(input_np.astype(np.floataa ).sum() - input_tf.numpy().astype(np.floataa ).sum() ) < 1e-2 )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.feat_extract_dict
_lowerCAmelCase = True
_lowerCAmelCase = self.feature_extraction_class(**_snake_case )
_lowerCAmelCase = self.feat_extract_tester.prepare_inputs_for_common()
_lowerCAmelCase = [len(_snake_case ) for x in speech_inputs]
_lowerCAmelCase = feat_extract.model_input_names[0]
_lowerCAmelCase = BatchFeature({input_name: speech_inputs} )
_lowerCAmelCase = feat_extract.pad(_snake_case , padding="""longest""" , return_tensors="""np""" )
self.assertIn("""attention_mask""" , _snake_case )
self.assertListEqual(list(processed.attention_mask.shape ) , list(processed[input_name].shape[:2] ) )
self.assertListEqual(processed.attention_mask.sum(-1 ).tolist() , _snake_case )
def snake_case ( self ):
"""simple docstring"""
_lowerCAmelCase = self.feat_extract_dict
_lowerCAmelCase = True
_lowerCAmelCase = self.feature_extraction_class(**_snake_case )
_lowerCAmelCase = self.feat_extract_tester.prepare_inputs_for_common()
_lowerCAmelCase = [len(_snake_case ) for x in speech_inputs]
_lowerCAmelCase = feat_extract.model_input_names[0]
_lowerCAmelCase = BatchFeature({input_name: speech_inputs} )
_lowerCAmelCase = min(_snake_case )
_lowerCAmelCase = feat_extract.pad(
_snake_case , padding="""max_length""" , max_length=_snake_case , truncation=_snake_case , return_tensors="""np""" )
self.assertIn("""attention_mask""" , _snake_case )
self.assertListEqual(
list(processed_pad.attention_mask.shape ) , [processed_pad[input_name].shape[0], max_length] )
self.assertListEqual(
processed_pad.attention_mask[:, :max_length].sum(-1 ).tolist() , [max_length for x in speech_inputs] )
| 82 | 0 |
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import (
BitConfig,
ViTHybridConfig,
ViTHybridForImageClassification,
ViTHybridImageProcessor,
ViTHybridModel,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging
logging.set_verbosity_info()
lowercase = logging.get_logger(__name__)
def lowerCamelCase_ ( UpperCamelCase__ : Any, UpperCamelCase__ : Optional[Any]=False ):
'''simple docstring'''
UpperCamelCase__ = []
# fmt: off
# stem:
rename_keys.append(('''cls_token''', '''vit.embeddings.cls_token''') )
rename_keys.append(('''pos_embed''', '''vit.embeddings.position_embeddings''') )
rename_keys.append(('''patch_embed.proj.weight''', '''vit.embeddings.patch_embeddings.projection.weight''') )
rename_keys.append(('''patch_embed.proj.bias''', '''vit.embeddings.patch_embeddings.projection.bias''') )
# backbone
rename_keys.append(('''patch_embed.backbone.stem.conv.weight''', '''vit.embeddings.patch_embeddings.backbone.bit.embedder.convolution.weight''') )
rename_keys.append(('''patch_embed.backbone.stem.norm.weight''', '''vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.weight''') )
rename_keys.append(('''patch_embed.backbone.stem.norm.bias''', '''vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.bias''') )
for stage_idx in range(len(config.backbone_config.depths ) ):
for layer_idx in range(config.backbone_config.depths[stage_idx] ):
rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv1.weight""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv1.weight""") )
rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.weight""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.weight""") )
rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.bias""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.bias""") )
rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv2.weight""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv2.weight""") )
rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.weight""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.weight""") )
rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.bias""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.bias""") )
rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv3.weight""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv3.weight""") )
rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.weight""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.weight""") )
rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.bias""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.bias""") )
rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.conv.weight""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.conv.weight""") )
rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.weight""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.weight""") )
rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.bias""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.bias""") )
# transformer encoder
for i in range(config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((F"""blocks.{i}.norm1.weight""", F"""vit.encoder.layer.{i}.layernorm_before.weight""") )
rename_keys.append((F"""blocks.{i}.norm1.bias""", F"""vit.encoder.layer.{i}.layernorm_before.bias""") )
rename_keys.append((F"""blocks.{i}.attn.proj.weight""", F"""vit.encoder.layer.{i}.attention.output.dense.weight""") )
rename_keys.append((F"""blocks.{i}.attn.proj.bias""", F"""vit.encoder.layer.{i}.attention.output.dense.bias""") )
rename_keys.append((F"""blocks.{i}.norm2.weight""", F"""vit.encoder.layer.{i}.layernorm_after.weight""") )
rename_keys.append((F"""blocks.{i}.norm2.bias""", F"""vit.encoder.layer.{i}.layernorm_after.bias""") )
rename_keys.append((F"""blocks.{i}.mlp.fc1.weight""", F"""vit.encoder.layer.{i}.intermediate.dense.weight""") )
rename_keys.append((F"""blocks.{i}.mlp.fc1.bias""", F"""vit.encoder.layer.{i}.intermediate.dense.bias""") )
rename_keys.append((F"""blocks.{i}.mlp.fc2.weight""", F"""vit.encoder.layer.{i}.output.dense.weight""") )
rename_keys.append((F"""blocks.{i}.mlp.fc2.bias""", F"""vit.encoder.layer.{i}.output.dense.bias""") )
if base_model:
# layernorm + pooler
rename_keys.extend(
[
('''norm.weight''', '''layernorm.weight'''),
('''norm.bias''', '''layernorm.bias'''),
('''pre_logits.fc.weight''', '''pooler.dense.weight'''),
('''pre_logits.fc.bias''', '''pooler.dense.bias'''),
] )
# if just the base model, we should remove "vit" from all keys that start with "vit"
UpperCamelCase__ = [(pair[0], pair[1][4:]) if pair[1].startswith('''vit''' ) else pair for pair in rename_keys]
else:
# layernorm + classification head
rename_keys.extend(
[
('''norm.weight''', '''vit.layernorm.weight'''),
('''norm.bias''', '''vit.layernorm.bias'''),
('''head.weight''', '''classifier.weight'''),
('''head.bias''', '''classifier.bias'''),
] )
# fmt: on
return rename_keys
def lowerCamelCase_ ( UpperCamelCase__ : str, UpperCamelCase__ : Optional[int], UpperCamelCase__ : Optional[Any]=False ):
'''simple docstring'''
for i in range(config.num_hidden_layers ):
if base_model:
UpperCamelCase__ = ''''''
else:
UpperCamelCase__ = '''vit.'''
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
UpperCamelCase__ = state_dict.pop(F"""blocks.{i}.attn.qkv.weight""" )
UpperCamelCase__ = state_dict.pop(F"""blocks.{i}.attn.qkv.bias""" )
# next, add query, keys and values (in that order) to the state dict
UpperCamelCase__ = in_proj_weight[
: config.hidden_size, :
]
UpperCamelCase__ = in_proj_bias[: config.hidden_size]
UpperCamelCase__ = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
UpperCamelCase__ = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
UpperCamelCase__ = in_proj_weight[
-config.hidden_size :, :
]
UpperCamelCase__ = in_proj_bias[-config.hidden_size :]
def lowerCamelCase_ ( UpperCamelCase__ : str ):
'''simple docstring'''
UpperCamelCase__ = ['''head.weight''', '''head.bias''']
for k in ignore_keys:
state_dict.pop(UpperCamelCase__, UpperCamelCase__ )
def lowerCamelCase_ ( UpperCamelCase__ : Dict, UpperCamelCase__ : int, UpperCamelCase__ : Dict ):
'''simple docstring'''
UpperCamelCase__ = dct.pop(UpperCamelCase__ )
UpperCamelCase__ = val
def lowerCamelCase_ ( ):
'''simple docstring'''
UpperCamelCase__ = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
UpperCamelCase__ = Image.open(requests.get(UpperCamelCase__, stream=UpperCamelCase__ ).raw )
return im
@torch.no_grad()
def lowerCamelCase_ ( UpperCamelCase__ : Any, UpperCamelCase__ : int, UpperCamelCase__ : Optional[Any]=False ):
'''simple docstring'''
UpperCamelCase__ = BitConfig(
global_padding='''same''', layer_type='''bottleneck''', depths=(3, 4, 9), out_features=['''stage3'''], embedding_dynamic_padding=UpperCamelCase__, )
UpperCamelCase__ = ViTHybridConfig(backbone_config=UpperCamelCase__, image_size=384, num_labels=1000 )
UpperCamelCase__ = False
# load original model from timm
UpperCamelCase__ = timm.create_model(UpperCamelCase__, pretrained=UpperCamelCase__ )
timm_model.eval()
# load state_dict of original model, remove and rename some keys
UpperCamelCase__ = timm_model.state_dict()
if base_model:
remove_classification_head_(UpperCamelCase__ )
UpperCamelCase__ = create_rename_keys(UpperCamelCase__, UpperCamelCase__ )
for src, dest in rename_keys:
rename_key(UpperCamelCase__, UpperCamelCase__, UpperCamelCase__ )
read_in_q_k_v(UpperCamelCase__, UpperCamelCase__, UpperCamelCase__ )
UpperCamelCase__ = '''huggingface/label-files'''
UpperCamelCase__ = '''imagenet-1k-id2label.json'''
UpperCamelCase__ = json.load(open(hf_hub_download(UpperCamelCase__, UpperCamelCase__, repo_type='''dataset''' ), '''r''' ) )
UpperCamelCase__ = {int(UpperCamelCase__ ): v for k, v in idalabel.items()}
UpperCamelCase__ = idalabel
UpperCamelCase__ = {v: k for k, v in idalabel.items()}
# load HuggingFace model
if vit_name[-5:] == "in21k":
UpperCamelCase__ = ViTHybridModel(UpperCamelCase__ ).eval()
else:
UpperCamelCase__ = ViTHybridForImageClassification(UpperCamelCase__ ).eval()
model.load_state_dict(UpperCamelCase__ )
# create image processor
UpperCamelCase__ = create_transform(**resolve_data_config({}, model=UpperCamelCase__ ) )
UpperCamelCase__ = transform.transforms
UpperCamelCase__ = {
'''bilinear''': PILImageResampling.BILINEAR,
'''bicubic''': PILImageResampling.BICUBIC,
'''nearest''': PILImageResampling.NEAREST,
}
UpperCamelCase__ = ViTHybridImageProcessor(
do_resize=UpperCamelCase__, size={'''shortest_edge''': timm_transforms[0].size}, resample=pillow_resamplings[timm_transforms[0].interpolation.value], do_center_crop=UpperCamelCase__, crop_size={'''height''': timm_transforms[1].size[0], '''width''': timm_transforms[1].size[1]}, do_normalize=UpperCamelCase__, image_mean=timm_transforms[-1].mean.tolist(), image_std=timm_transforms[-1].std.tolist(), )
UpperCamelCase__ = prepare_img()
UpperCamelCase__ = transform(UpperCamelCase__ ).unsqueeze(0 )
UpperCamelCase__ = processor(UpperCamelCase__, return_tensors='''pt''' ).pixel_values
# verify pixel values
assert torch.allclose(UpperCamelCase__, UpperCamelCase__ )
# verify logits
with torch.no_grad():
UpperCamelCase__ = model(UpperCamelCase__ )
UpperCamelCase__ = outputs.logits
print('''Predicted class:''', logits.argmax(-1 ).item() )
if base_model:
UpperCamelCase__ = timm_model.forward_features(UpperCamelCase__ )
assert timm_pooled_output.shape == outputs.pooler_output.shape
assert torch.allclose(UpperCamelCase__, outputs.pooler_output, atol=1e-3 )
else:
UpperCamelCase__ = timm_model(UpperCamelCase__ )
assert timm_logits.shape == outputs.logits.shape
assert torch.allclose(UpperCamelCase__, outputs.logits, atol=1e-3 )
print('''Looks ok!''' )
if pytorch_dump_folder_path is not None:
Path(UpperCamelCase__ ).mkdir(exist_ok=UpperCamelCase__ )
print(F"""Saving model {vit_name} to {pytorch_dump_folder_path}""" )
model.save_pretrained(UpperCamelCase__ )
print(F"""Saving processor to {pytorch_dump_folder_path}""" )
processor.save_pretrained(UpperCamelCase__ )
if push_to_hub:
print(F"""Pushing model and processor to the hub {vit_name}""" )
model.push_to_hub(F"""ybelkada/{vit_name}""" )
processor.push_to_hub(F"""ybelkada/{vit_name}""" )
if __name__ == "__main__":
lowercase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--vit_name""",
default="""vit_base_r50_s16_384""",
type=str,
help="""Name of the hybrid ViT timm model you'd like to convert.""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
)
parser.add_argument(
"""--push_to_hub""", action="""store_true""", help="""Whether to upload the model to the HuggingFace hub."""
)
lowercase = parser.parse_args()
convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 35 | from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokenizers
lowercase = [
"""python""",
"""tqdm""",
"""regex""",
"""requests""",
"""packaging""",
"""filelock""",
"""numpy""",
"""tokenizers""",
"""huggingface-hub""",
"""safetensors""",
"""accelerate""",
"""pyyaml""",
]
for pkg in pkgs_to_check_at_runtime:
if pkg in deps:
if pkg == "tokenizers":
# must be loaded here, or else tqdm check may fail
from .utils import is_tokenizers_available
if not is_tokenizers_available():
continue # not required, check version only if installed
elif pkg == "accelerate":
# must be loaded here, or else tqdm check may fail
from .utils import is_accelerate_available
# Maybe switch to is_torch_available in the future here so that Accelerate is hard dep of
# Transformers with PyTorch
if not is_accelerate_available():
continue # not required, check version only if installed
require_version_core(deps[pkg])
else:
raise ValueError(f'can\'t find {pkg} in {deps.keys()}, check dependency_versions_table.py')
def lowerCamelCase_ ( UpperCamelCase__ : Union[str, Any], UpperCamelCase__ : Dict=None ):
'''simple docstring'''
require_version(deps[pkg], UpperCamelCase__ )
| 35 | 1 |
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
"files" , [
["full:README.md", "dataset_infos.json"],
["empty:README.md", "dataset_infos.json"],
["dataset_infos.json"],
["full:README.md"],
] , )
def _snake_case ( lowerCAmelCase : str , lowerCAmelCase : Any ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Tuple = tmp_path_factory.mktemp("dset_infos_dir" )
if "full:README.md" in files:
with open(dataset_infos_dir / "README.md" , "w" ) as f:
f.write("---\ndataset_info:\n dataset_size: 42\n---" )
if "empty:README.md" in files:
with open(dataset_infos_dir / "README.md" , "w" ) as f:
f.write("" )
# we want to support dataset_infos.json for backward compatibility
if "dataset_infos.json" in files:
with open(dataset_infos_dir / "dataset_infos.json" , "w" ) as f:
f.write("{\"default\": {\"dataset_size\": 42}}" )
SCREAMING_SNAKE_CASE_ : str = DatasetInfosDict.from_directory(lowerCAmelCase )
assert dataset_infos
assert dataset_infos["default"].dataset_size == 4_2
@pytest.mark.parametrize(
"dataset_info" , [
DatasetInfo(),
DatasetInfo(
description="foo" , features=Features({"a": Value("int32" )} ) , builder_name="builder" , config_name="config" , version="1.0.0" , splits=[{"name": "train"}] , download_size=4_2 , ),
] , )
def _snake_case ( lowerCAmelCase : Optional[Any] , lowerCAmelCase : DatasetInfo ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[str] = str(lowerCAmelCase )
dataset_info.write_to_directory(lowerCAmelCase )
SCREAMING_SNAKE_CASE_ : List[str] = DatasetInfo.from_directory(lowerCAmelCase )
assert dataset_info == reloaded
assert os.path.exists(os.path.join(lowerCAmelCase , "dataset_info.json" ) )
def _snake_case ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[Any] = DatasetInfo(
description="foo" , citation="bar" , homepage="https://foo.bar" , license="CC0" , features=Features({"a": Value("int32" )} ) , post_processed={} , supervised_keys=() , task_templates=[] , builder_name="builder" , config_name="config" , version="1.0.0" , splits=[{"name": "train", "num_examples": 4_2}] , download_checksums={} , download_size=1_3_3_7 , post_processing_size=4_4_2 , dataset_size=1_2_3_4 , size_in_bytes=1_3_3_7 + 4_4_2 + 1_2_3_4 , )
SCREAMING_SNAKE_CASE_ : Union[str, Any] = dataset_info._to_yaml_dict()
assert sorted(lowerCAmelCase ) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML )
for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
assert key in dataset_info_yaml_dict
assert isinstance(dataset_info_yaml_dict[key] , (list, dict, int, str) )
SCREAMING_SNAKE_CASE_ : Optional[Any] = yaml.safe_dump(lowerCAmelCase )
SCREAMING_SNAKE_CASE_ : Optional[Any] = yaml.safe_load(lowerCAmelCase )
assert dataset_info_yaml_dict == reloaded
def _snake_case ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : str = DatasetInfo()
SCREAMING_SNAKE_CASE_ : List[str] = dataset_info._to_yaml_dict()
assert dataset_info_yaml_dict == {}
@pytest.mark.parametrize(
"dataset_infos_dict" , [
DatasetInfosDict(),
DatasetInfosDict({"default": DatasetInfo()} ),
DatasetInfosDict({"my_config_name": DatasetInfo()} ),
DatasetInfosDict(
{
"default": DatasetInfo(
description="foo" , features=Features({"a": Value("int32" )} ) , builder_name="builder" , config_name="config" , version="1.0.0" , splits=[{"name": "train"}] , download_size=4_2 , )
} ),
DatasetInfosDict(
{
"v1": DatasetInfo(dataset_size=4_2 ),
"v2": DatasetInfo(dataset_size=1_3_3_7 ),
} ),
] , )
def _snake_case ( lowerCAmelCase : Dict , lowerCAmelCase : DatasetInfosDict ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[int] = str(lowerCAmelCase )
dataset_infos_dict.write_to_directory(lowerCAmelCase )
SCREAMING_SNAKE_CASE_ : int = DatasetInfosDict.from_directory(lowerCAmelCase )
# the config_name of the dataset_infos_dict take over the attribute
for config_name, dataset_info in dataset_infos_dict.items():
SCREAMING_SNAKE_CASE_ : Any = config_name
# the yaml representation doesn't include fields like description or citation
# so we just test that we can recover what we can from the yaml
SCREAMING_SNAKE_CASE_ : Tuple = DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict() )
assert dataset_infos_dict == reloaded
if dataset_infos_dict:
assert os.path.exists(os.path.join(lowerCAmelCase , "README.md" ) )
| 18 | from collections import defaultdict
def _snake_case ( lowerCAmelCase : int ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Any = 1
SCREAMING_SNAKE_CASE_ : Tuple = True
for v in tree[start]:
if v not in visited:
ret += dfs(lowerCAmelCase )
if ret % 2 == 0:
cuts.append(lowerCAmelCase )
return ret
def _snake_case ( ):
"""simple docstring"""
dfs(1 )
if __name__ == "__main__":
__lowerCamelCase , __lowerCamelCase : Union[str, Any] = 10, 9
__lowerCamelCase : Optional[int] = defaultdict(list)
__lowerCamelCase : dict[int, bool] = {}
__lowerCamelCase : list[int] = []
__lowerCamelCase : Optional[Any] = 0
__lowerCamelCase : Any = [(2, 1), (3, 1), (4, 3), (5, 2), (6, 1), (7, 2), (8, 6), (9, 8), (10, 8)]
for u, v in edges:
tree[u].append(v)
tree[v].append(u)
even_tree()
print(len(cuts) - 1)
| 18 | 1 |
import argparse
import os
import torch
from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint
def __UpperCamelCase (_SCREAMING_SNAKE_CASE ) -> List[Any]:
# encoder.embeddings are double copied in original FLAVA
return sum(param.float().sum() if 'encoder.embeddings' not in key else 0 for key, param in state_dict.items() )
def __UpperCamelCase (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Dict:
lowercase__ = {}
for key, value in state_dict.items():
if "text_encoder.embeddings" in key or "image_encoder.embeddings" in key:
continue
lowercase__ = key.replace('heads.cmd.mim_head.cls.predictions' , 'mmm_image_head' )
lowercase__ = key.replace('heads.cmd.mlm_head.cls.predictions' , 'mmm_text_head' )
lowercase__ = key.replace('heads.cmd.itm_head.cls' , 'itm_head' )
lowercase__ = key.replace('heads.cmd.itm_head.pooler' , 'itm_head.pooler' )
lowercase__ = key.replace('heads.cmd.clip_head.logit_scale' , 'flava.logit_scale' )
lowercase__ = key.replace('heads.fairseq_mlm.cls.predictions' , 'mlm_head' )
lowercase__ = key.replace('heads.imagenet.mim_head.cls.predictions' , 'mim_head' )
lowercase__ = key.replace('mm_text_projection' , 'flava.text_to_mm_projection' )
lowercase__ = key.replace('mm_image_projection' , 'flava.image_to_mm_projection' )
lowercase__ = key.replace('image_encoder.module' , 'flava.image_model' )
lowercase__ = key.replace('text_encoder.module' , 'flava.text_model' )
lowercase__ = key.replace('mm_encoder.module.encoder.cls_token' , 'flava.multimodal_model.cls_token' )
lowercase__ = key.replace('mm_encoder.module' , 'flava.multimodal_model' )
lowercase__ = key.replace('text_projection' , 'flava.text_projection' )
lowercase__ = key.replace('image_projection' , 'flava.image_projection' )
lowercase__ = value.float()
for key, value in codebook_state_dict.items():
lowercase__ = value
return upgrade
@torch.no_grad()
def __UpperCamelCase (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None ) -> List[Any]:
if config_path is not None:
lowercase__ = FlavaConfig.from_pretrained(_SCREAMING_SNAKE_CASE )
else:
lowercase__ = FlavaConfig()
lowercase__ = FlavaForPreTraining(_SCREAMING_SNAKE_CASE ).eval()
lowercase__ = convert_dalle_checkpoint(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , save_checkpoint=_SCREAMING_SNAKE_CASE )
if os.path.exists(_SCREAMING_SNAKE_CASE ):
lowercase__ = torch.load(_SCREAMING_SNAKE_CASE , map_location='cpu' )
else:
lowercase__ = torch.hub.load_state_dict_from_url(_SCREAMING_SNAKE_CASE , map_location='cpu' )
lowercase__ = upgrade_state_dict(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
hf_model.load_state_dict(_SCREAMING_SNAKE_CASE )
lowercase__ = hf_model.state_dict()
lowercase__ = count_parameters(_SCREAMING_SNAKE_CASE )
lowercase__ = count_parameters(_SCREAMING_SNAKE_CASE ) + count_parameters(_SCREAMING_SNAKE_CASE )
assert torch.allclose(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , atol=1E-3 )
hf_model.save_pretrained(_SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
lowercase_ = argparse.ArgumentParser()
parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to flava checkpoint""")
parser.add_argument("""--codebook_path""", default=None, type=str, help="""Path to flava codebook checkpoint""")
parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
lowercase_ = parser.parse_args()
convert_flava_checkpoint(args.checkpoint_path, args.codebook_path, args.pytorch_dump_folder_path, args.config_path)
| 361 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"""transfo-xl-wt103""": """https://huggingface.co/transfo-xl-wt103/resolve/main/config.json""",
}
class SCREAMING_SNAKE_CASE (UpperCAmelCase ):
_UpperCamelCase : Optional[Any] = 'transfo-xl'
_UpperCamelCase : Any = ['mems']
_UpperCamelCase : Any = {
'n_token': 'vocab_size',
'hidden_size': 'd_model',
'num_attention_heads': 'n_head',
'num_hidden_layers': 'n_layer',
}
def __init__( self : Optional[Any] , a : Optional[int]=267_735 , a : str=[20_000, 40_000, 200_000] , a : str=1_024 , a : str=1_024 , a : int=16 , a : Optional[int]=64 , a : Optional[int]=4_096 , a : int=4 , a : Tuple=False , a : Any=18 , a : Tuple=1_600 , a : Union[str, Any]=1_000 , a : str=True , a : Dict=True , a : Any=0 , a : List[Any]=-1 , a : List[Any]=True , a : Tuple=0.1 , a : List[Any]=0.0 , a : Optional[Any]=True , a : int="normal" , a : Optional[Any]=0.01 , a : str=0.01 , a : List[Any]=0.02 , a : List[Any]=1E-5 , a : Optional[Any]=0 , **a : Optional[int] , )-> Optional[int]:
"""simple docstring"""
lowercase__ = vocab_size
lowercase__ = []
self.cutoffs.extend(a )
if proj_share_all_but_first:
lowercase__ = [False] + [True] * len(self.cutoffs )
else:
lowercase__ = [False] + [False] * len(self.cutoffs )
lowercase__ = d_model
lowercase__ = d_embed
lowercase__ = d_head
lowercase__ = d_inner
lowercase__ = div_val
lowercase__ = pre_lnorm
lowercase__ = n_layer
lowercase__ = n_head
lowercase__ = mem_len
lowercase__ = same_length
lowercase__ = attn_type
lowercase__ = clamp_len
lowercase__ = sample_softmax
lowercase__ = adaptive
lowercase__ = dropout
lowercase__ = dropatt
lowercase__ = untie_r
lowercase__ = init
lowercase__ = init_range
lowercase__ = proj_init_std
lowercase__ = init_std
lowercase__ = layer_norm_epsilon
super().__init__(eos_token_id=a , **a )
@property
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] )-> Union[str, Any]:
"""simple docstring"""
logger.info(f"""The model {self.model_type} is one of the few models that has no sequence length limit.""" )
return -1
@max_position_embeddings.setter
def SCREAMING_SNAKE_CASE_ ( self : Any , a : Optional[int] )-> Optional[int]:
"""simple docstring"""
raise NotImplementedError(
f"""The model {self.model_type} is one of the few models that has no sequence length limit.""" )
| 269 | 0 |
'''simple docstring'''
from __future__ import annotations
def A__ ( UpperCAmelCase_ ):
_UpperCamelCase : Union[str, Any] = 0.00
_UpperCamelCase : Any = 0
for resistor in resistors:
if resistor <= 0:
_UpperCamelCase : Optional[int] = f'Resistor at index {index} has a negative or zero value!'
raise ValueError(UpperCAmelCase_ )
first_sum += 1 / float(UpperCAmelCase_ )
index += 1
return 1 / first_sum
def A__ ( UpperCAmelCase_ ):
_UpperCamelCase : str = 0.00
_UpperCamelCase : Tuple = 0
for resistor in resistors:
sum_r += resistor
if resistor < 0:
_UpperCamelCase : Optional[Any] = f'Resistor at index {index} has a negative value!'
raise ValueError(UpperCAmelCase_ )
index += 1
return sum_r
if __name__ == "__main__":
import doctest
doctest.testmod()
| 83 |
'''simple docstring'''
def A__ ( UpperCAmelCase_ ):
if num < 0:
return False
_UpperCamelCase : int = num
_UpperCamelCase : int = 0
while num > 0:
_UpperCamelCase : str = rev_num * 1_0 + (num % 1_0)
num //= 1_0
return num_copy == rev_num
if __name__ == "__main__":
import doctest
doctest.testmod()
| 83 | 1 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase__ :str = '''▁'''
lowerCAmelCase__ :int = {'''vocab_file''': '''spiece.model'''}
lowerCAmelCase__ :Optional[Any] = {
'''vocab_file''': {'''google/pegasus-xsum''': '''https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'''}
}
lowerCAmelCase__ :List[str] = {
'''google/pegasus-xsum''': 5_1_2,
}
lowerCAmelCase__ :List[str] = logging.get_logger(__name__)
class __a ( UpperCAmelCase ):
_a : Any = VOCAB_FILES_NAMES
_a : Tuple = VOCAB_FILES_NAMES
_a : str = PRETRAINED_VOCAB_FILES_MAP
_a : Optional[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_a : List[Any] = ['input_ids', 'attention_mask']
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE="<pad>" , _SCREAMING_SNAKE_CASE="</s>" , _SCREAMING_SNAKE_CASE="<unk>" , _SCREAMING_SNAKE_CASE="<mask_2>" , _SCREAMING_SNAKE_CASE="<mask_1>" , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=103 , _SCREAMING_SNAKE_CASE = None , **_SCREAMING_SNAKE_CASE , ) -> None:
"""simple docstring"""
_UpperCAmelCase = offset
if additional_special_tokens is not None:
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
raise TypeError(
f'''additional_special_tokens should be of type {type(_SCREAMING_SNAKE_CASE )}, but is'''
f''' {type(_SCREAMING_SNAKE_CASE )}''' )
_UpperCAmelCase = (
([mask_token_sent] + additional_special_tokens)
if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
else additional_special_tokens
)
# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
additional_special_tokens_extended += [
f'''<unk_{i}>''' for i in range(len(_SCREAMING_SNAKE_CASE ) , self.offset - 1 )
]
if len(set(_SCREAMING_SNAKE_CASE ) ) != len(_SCREAMING_SNAKE_CASE ):
raise ValueError(
'Please make sure that the provided additional_special_tokens do not contain an incorrectly'
f''' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.''' )
_UpperCAmelCase = additional_special_tokens_extended
else:
_UpperCAmelCase = [mask_token_sent] if mask_token_sent is not None else []
additional_special_tokens += [f'''<unk_{i}>''' for i in range(2 , self.offset )]
_UpperCAmelCase = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
eos_token=_SCREAMING_SNAKE_CASE , unk_token=_SCREAMING_SNAKE_CASE , mask_token=_SCREAMING_SNAKE_CASE , pad_token=_SCREAMING_SNAKE_CASE , mask_token_sent=_SCREAMING_SNAKE_CASE , offset=_SCREAMING_SNAKE_CASE , additional_special_tokens=_SCREAMING_SNAKE_CASE , sp_model_kwargs=self.sp_model_kwargs , **_SCREAMING_SNAKE_CASE , )
_UpperCAmelCase = mask_token_sent
_UpperCAmelCase = vocab_file
_UpperCAmelCase = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(_SCREAMING_SNAKE_CASE )
# add special tokens to encoder dict
_UpperCAmelCase = {
0: self.pad_token,
1: self.eos_token,
}
if self.mask_token_sent is not None:
self.encoder.update(
{
2: self.mask_token_sent,
3: self.mask_token,
} )
if self.offset > 0:
# entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
# mask_token_sent is already added to list -> so start at 1
self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1 , self.offset - 1 )} )
_UpperCAmelCase = {v: k for k, v in self.encoder.items()}
@property
def UpperCAmelCase__ ( self ) -> int:
"""simple docstring"""
return len(self.sp_model ) + self.offset
def UpperCAmelCase__ ( self ) -> Dict[str, int]:
"""simple docstring"""
_UpperCAmelCase = {self.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self ) -> List[str]:
"""simple docstring"""
_UpperCAmelCase = self.__dict__.copy()
_UpperCAmelCase = None
return state
def __setstate__( self , _SCREAMING_SNAKE_CASE ) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase = d
# for backward compatibility
if not hasattr(self , 'sp_model_kwargs' ):
_UpperCAmelCase = {}
_UpperCAmelCase = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def UpperCAmelCase__ ( self , _SCREAMING_SNAKE_CASE ) -> List[str]:
"""simple docstring"""
return self.sp_model.encode(_SCREAMING_SNAKE_CASE , out_type=_SCREAMING_SNAKE_CASE )
def UpperCAmelCase__ ( self , _SCREAMING_SNAKE_CASE ) -> int:
"""simple docstring"""
if token in self.decoder:
return self.decoder[token]
elif token in self.added_tokens_decoder:
return self.added_tokens_decoder[token]
_UpperCAmelCase = self.sp_model.piece_to_id(_SCREAMING_SNAKE_CASE )
return sp_id + self.offset
def UpperCAmelCase__ ( self , _SCREAMING_SNAKE_CASE ) -> str:
"""simple docstring"""
if index in self.encoder:
return self.encoder[index]
elif index in self.added_tokens_encoder:
return self.added_tokens_encoder[index]
else:
_UpperCAmelCase = self.sp_model.IdToPiece(index - self.offset )
return token
def UpperCAmelCase__ ( self , _SCREAMING_SNAKE_CASE ) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase = []
_UpperCAmelCase = ''
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
out_string += self.sp_model.decode(_SCREAMING_SNAKE_CASE ) + token
_UpperCAmelCase = []
else:
current_sub_tokens.append(_SCREAMING_SNAKE_CASE )
out_string += self.sp_model.decode(_SCREAMING_SNAKE_CASE )
return out_string.strip()
def UpperCAmelCase__ ( self , _SCREAMING_SNAKE_CASE=False ) -> List[str]:
"""simple docstring"""
return 1
def UpperCAmelCase__ ( self , _SCREAMING_SNAKE_CASE ) -> Dict:
"""simple docstring"""
_UpperCAmelCase = set(self.all_special_ids ) # call it once instead of inside list comp
all_special_ids.remove(self.unk_token_id ) # <unk> is only sometimes special
return [1 if x in all_special_ids else 0 for x in seq]
def UpperCAmelCase__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = False ) -> List[int]:
"""simple docstring"""
if already_has_special_tokens:
return self._special_token_mask(_SCREAMING_SNAKE_CASE )
elif token_ids_a is None:
return self._special_token_mask(_SCREAMING_SNAKE_CASE ) + [1]
else:
return self._special_token_mask(token_ids_a + token_ids_a ) + [1]
def UpperCAmelCase__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None ) -> List[int]:
"""simple docstring"""
if token_ids_a is None:
return token_ids_a + [self.eos_token_id]
# We don't expect to process pairs, but leave the pair logic for API consistency
return token_ids_a + token_ids_a + [self.eos_token_id]
def UpperCAmelCase__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None ) -> Tuple[str]:
"""simple docstring"""
if not os.path.isdir(_SCREAMING_SNAKE_CASE ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
_UpperCAmelCase = os.path.join(
_SCREAMING_SNAKE_CASE , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_SCREAMING_SNAKE_CASE ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , _SCREAMING_SNAKE_CASE )
elif not os.path.isfile(self.vocab_file ):
with open(_SCREAMING_SNAKE_CASE , 'wb' ) as fi:
_UpperCAmelCase = self.sp_model.serialized_model_proto()
fi.write(_SCREAMING_SNAKE_CASE )
return (out_vocab_file,)
| 351 |
import copy
import tempfile
import unittest
from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MaMaaaForConditionalGeneration, MaMaaaModel, MaMaaaTokenizer
from transformers.models.mam_aaa.modeling_mam_aaa import MaMaaaDecoder, MaMaaaEncoder
def lowerCAmelCase__ ( a__: Dict , a__: Dict , a__: Any , a__: Optional[int]=None , a__: str=None , a__: List[Any]=None , a__: Optional[int]=None , a__: Union[str, Any]=None , ) -> Tuple:
'''simple docstring'''
if attention_mask is None:
_UpperCAmelCase = input_ids.ne(config.pad_token_id )
if decoder_attention_mask is None:
_UpperCAmelCase = decoder_input_ids.ne(config.pad_token_id )
if head_mask is None:
_UpperCAmelCase = torch.ones(config.encoder_layers , config.encoder_attention_heads , device=a__ )
if decoder_head_mask is None:
_UpperCAmelCase = torch.ones(config.decoder_layers , config.decoder_attention_heads , device=a__ )
if cross_attn_head_mask is None:
_UpperCAmelCase = torch.ones(config.decoder_layers , config.decoder_attention_heads , device=a__ )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
class __a :
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=13 , _SCREAMING_SNAKE_CASE=7 , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=False , _SCREAMING_SNAKE_CASE=99 , _SCREAMING_SNAKE_CASE=16 , _SCREAMING_SNAKE_CASE=2 , _SCREAMING_SNAKE_CASE=4 , _SCREAMING_SNAKE_CASE=4 , _SCREAMING_SNAKE_CASE="relu" , _SCREAMING_SNAKE_CASE=0.1 , _SCREAMING_SNAKE_CASE=0.1 , _SCREAMING_SNAKE_CASE=0.0 , _SCREAMING_SNAKE_CASE=0.0 , _SCREAMING_SNAKE_CASE=20 , _SCREAMING_SNAKE_CASE=2 , _SCREAMING_SNAKE_CASE=1 , _SCREAMING_SNAKE_CASE=0 , ) -> Any:
"""simple docstring"""
_UpperCAmelCase = parent
_UpperCAmelCase = batch_size
_UpperCAmelCase = seq_length
_UpperCAmelCase = is_training
_UpperCAmelCase = use_labels
_UpperCAmelCase = vocab_size
_UpperCAmelCase = hidden_size
_UpperCAmelCase = num_hidden_layers
_UpperCAmelCase = num_attention_heads
_UpperCAmelCase = intermediate_size
_UpperCAmelCase = hidden_act
_UpperCAmelCase = hidden_dropout_prob
_UpperCAmelCase = attention_probs_dropout_prob
_UpperCAmelCase = encoder_layerdrop
_UpperCAmelCase = decoder_layerdrop
_UpperCAmelCase = max_position_embeddings
_UpperCAmelCase = eos_token_id
_UpperCAmelCase = pad_token_id
_UpperCAmelCase = bos_token_id
def UpperCAmelCase__ ( self ) -> str:
"""simple docstring"""
_UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_UpperCAmelCase = self.eos_token_id # Eos Token
_UpperCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
# we need to clamp the input ids here to avoid having pad token in between
# this is because for M2M100 the position_ids are prepared such that
# all pad tokens have pos id = 2 and rest are between 2..seq_length
# and the seq_length here is seq_length - num_pad_tokens
# but when using past, there is no way of knowing if the past input ids had
# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
# position_ids being off by num_pad_tokens in past input
_UpperCAmelCase = input_ids.clamp(self.pad_token_id + 1 )
_UpperCAmelCase = decoder_input_ids.clamp(self.pad_token_id + 1 )
_UpperCAmelCase = self.get_config()
_UpperCAmelCase = prepare_mam_aaa_inputs_dict(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
return config, inputs_dict
def UpperCAmelCase__ ( self ) -> Optional[Any]:
"""simple docstring"""
return MaMaaaConfig(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , encoder_layerdrop=self.encoder_layerdrop , decoder_layerdrop=self.decoder_layerdrop , max_position_embeddings=self.max_position_embeddings , eos_token_id=self.eos_token_id , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , )
def UpperCAmelCase__ ( self ) -> str:
"""simple docstring"""
_UpperCAmelCase , _UpperCAmelCase = self.prepare_config_and_inputs()
return config, inputs_dict
def UpperCAmelCase__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase = MaMaaaModel(config=_SCREAMING_SNAKE_CASE ).get_decoder().to(_SCREAMING_SNAKE_CASE ).eval()
_UpperCAmelCase = inputs_dict['input_ids']
_UpperCAmelCase = inputs_dict['attention_mask']
_UpperCAmelCase = inputs_dict['head_mask']
# first forward pass
_UpperCAmelCase = model(_SCREAMING_SNAKE_CASE , attention_mask=_SCREAMING_SNAKE_CASE , head_mask=_SCREAMING_SNAKE_CASE , use_cache=_SCREAMING_SNAKE_CASE )
_UpperCAmelCase , _UpperCAmelCase = outputs.to_tuple()
# create hypothetical multiple next token and extent to next_input_ids
_UpperCAmelCase = ids_tensor((self.batch_size, 3) , config.vocab_size )
_UpperCAmelCase = ids_tensor((self.batch_size, 3) , 2 )
# append to next input_ids and
_UpperCAmelCase = torch.cat([input_ids, next_tokens] , dim=-1 )
_UpperCAmelCase = torch.cat([attention_mask, next_attn_mask] , dim=-1 )
_UpperCAmelCase = model(_SCREAMING_SNAKE_CASE , attention_mask=_SCREAMING_SNAKE_CASE )['last_hidden_state']
_UpperCAmelCase = model(_SCREAMING_SNAKE_CASE , attention_mask=_SCREAMING_SNAKE_CASE , past_key_values=_SCREAMING_SNAKE_CASE )[
'last_hidden_state'
]
# select random slice
_UpperCAmelCase = ids_tensor((1,) , output_from_past.shape[-1] ).item()
_UpperCAmelCase = output_from_no_past[:, -3:, random_slice_idx].detach()
_UpperCAmelCase = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , atol=1e-2 ) )
def UpperCAmelCase__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> List[str]:
"""simple docstring"""
_UpperCAmelCase = MaMaaaModel(config=_SCREAMING_SNAKE_CASE ).to(_SCREAMING_SNAKE_CASE ).eval()
_UpperCAmelCase = model(**_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = outputs.encoder_last_hidden_state
_UpperCAmelCase = outputs.last_hidden_state
with tempfile.TemporaryDirectory() as tmpdirname:
_UpperCAmelCase = model.get_encoder()
encoder.save_pretrained(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = MaMaaaEncoder.from_pretrained(_SCREAMING_SNAKE_CASE ).to(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = encoder(inputs_dict['input_ids'] , attention_mask=inputs_dict['attention_mask'] )[
0
]
self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1e-3 )
with tempfile.TemporaryDirectory() as tmpdirname:
_UpperCAmelCase = model.get_decoder()
decoder.save_pretrained(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = MaMaaaDecoder.from_pretrained(_SCREAMING_SNAKE_CASE ).to(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = decoder(
input_ids=inputs_dict['decoder_input_ids'] , attention_mask=inputs_dict['decoder_attention_mask'] , encoder_hidden_states=_SCREAMING_SNAKE_CASE , encoder_attention_mask=inputs_dict['attention_mask'] , )[0]
self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1e-3 )
@require_torch
class __a ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , unittest.TestCase ):
_a : List[Any] = (
(
MaMaaaModel,
MaMaaaForConditionalGeneration,
)
if is_torch_available()
else ()
)
_a : List[str] = (MaMaaaForConditionalGeneration,) if is_torch_available() else ()
_a : int = (
{
'conversational': MaMaaaForConditionalGeneration,
'feature-extraction': MaMaaaModel,
'summarization': MaMaaaForConditionalGeneration,
'text2text-generation': MaMaaaForConditionalGeneration,
'translation': MaMaaaForConditionalGeneration,
}
if is_torch_available()
else {}
)
_a : str = True
_a : Union[str, Any] = True
_a : Optional[int] = False
_a : Union[str, Any] = False
def UpperCAmelCase__ ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> int:
"""simple docstring"""
if pipeline_test_casse_name == "TranslationPipelineTests":
# Get `ValueError: Translation requires a `src_lang` and a `tgt_lang` for this model`.
# `M2M100Config` was never used in pipeline tests: cannot create a simple tokenizer.
return True
return False
def UpperCAmelCase__ ( self ) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase = MaMaaaModelTester(self )
_UpperCAmelCase = ConfigTester(self , config_class=_SCREAMING_SNAKE_CASE )
def UpperCAmelCase__ ( self ) -> List[Any]:
"""simple docstring"""
self.config_tester.run_common_tests()
def UpperCAmelCase__ ( self ) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
_UpperCAmelCase = model_class(_SCREAMING_SNAKE_CASE )
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase , _UpperCAmelCase = model_class.from_pretrained(_SCREAMING_SNAKE_CASE , output_loading_info=_SCREAMING_SNAKE_CASE )
self.assertEqual(info['missing_keys'] , [] )
def UpperCAmelCase__ ( self ) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_past_large_inputs(*_SCREAMING_SNAKE_CASE )
def UpperCAmelCase__ ( self ) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_encoder_decoder_model_standalone(*_SCREAMING_SNAKE_CASE )
def UpperCAmelCase__ ( self ) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in (MaMaaaModel, MaMaaaForConditionalGeneration):
_UpperCAmelCase = model_class(_SCREAMING_SNAKE_CASE )
model.to(_SCREAMING_SNAKE_CASE )
model.eval()
_UpperCAmelCase = copy.deepcopy(self._prepare_for_class(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) )
if not self.is_encoder_decoder:
_UpperCAmelCase = inputs['input_ids']
del inputs["input_ids"]
else:
_UpperCAmelCase = inputs['input_ids']
_UpperCAmelCase = inputs.get('decoder_input_ids' , _SCREAMING_SNAKE_CASE )
del inputs["input_ids"]
inputs.pop('decoder_input_ids' , _SCREAMING_SNAKE_CASE )
_UpperCAmelCase = model.get_input_embeddings()
if not self.is_encoder_decoder:
_UpperCAmelCase = wte(_SCREAMING_SNAKE_CASE )
else:
_UpperCAmelCase = wte(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = wte(_SCREAMING_SNAKE_CASE )
with torch.no_grad():
model(**_SCREAMING_SNAKE_CASE )[0]
def UpperCAmelCase__ ( self ) -> str:
"""simple docstring"""
_UpperCAmelCase , _UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
_UpperCAmelCase = input_dict['input_ids']
_UpperCAmelCase = input_ids.ne(1 ).to(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = MaMaaaForConditionalGeneration(_SCREAMING_SNAKE_CASE ).eval().to(_SCREAMING_SNAKE_CASE )
if torch_device == "cuda":
model.half()
model.generate(_SCREAMING_SNAKE_CASE , attention_mask=_SCREAMING_SNAKE_CASE )
model.generate(num_beams=4 , do_sample=_SCREAMING_SNAKE_CASE , early_stopping=_SCREAMING_SNAKE_CASE , num_return_sequences=3 )
def lowerCAmelCase__ ( a__: Tuple ) -> Optional[int]:
'''simple docstring'''
return torch.tensor(a__ , dtype=torch.long , device=a__ )
lowerCAmelCase__ :str = 1e-4
@require_torch
@require_sentencepiece
@require_tokenizers
@slow
class __a ( unittest.TestCase ):
@cached_property
def UpperCAmelCase__ ( self ) -> List[str]:
"""simple docstring"""
return MaMaaaTokenizer.from_pretrained('facebook/m2m100_418M' )
def UpperCAmelCase__ ( self ) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase = MaMaaaModel.from_pretrained('facebook/m2m100_418M' ).to(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = _long_tensor([[128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38, 2]] )
_UpperCAmelCase = _long_tensor([[2, 128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38]] )
_UpperCAmelCase = prepare_mam_aaa_inputs_dict(model.config , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
with torch.no_grad():
_UpperCAmelCase = model(**_SCREAMING_SNAKE_CASE )[0]
_UpperCAmelCase = torch.Size((1, 11, 1024) )
self.assertEqual(output.shape , _SCREAMING_SNAKE_CASE )
# change to expected output here
_UpperCAmelCase = torch.tensor(
[[-0.7780, -0.1676, 0.1038], [-6.7556, -1.3992, 0.0567], [-7.5383, -0.5920, -0.2779]] , device=_SCREAMING_SNAKE_CASE )
self.assertTrue(torch.allclose(output[:, :3, :3] , _SCREAMING_SNAKE_CASE , atol=_SCREAMING_SNAKE_CASE ) )
def UpperCAmelCase__ ( self ) -> Any:
"""simple docstring"""
_UpperCAmelCase = MaMaaaForConditionalGeneration.from_pretrained('facebook/m2m100_418M' ).to(_SCREAMING_SNAKE_CASE )
# change to intended input
_UpperCAmelCase = _long_tensor([[128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38, 2]] )
_UpperCAmelCase = _long_tensor([[2, 128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38]] )
_UpperCAmelCase = prepare_mam_aaa_inputs_dict(model.config , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
with torch.no_grad():
_UpperCAmelCase = model(**_SCREAMING_SNAKE_CASE )[0]
_UpperCAmelCase = torch.Size((1, 11, model.config.vocab_size) )
self.assertEqual(output.shape , _SCREAMING_SNAKE_CASE )
# change to expected output here
_UpperCAmelCase = torch.tensor(
[[-1.0448, -1.0411, 3.7992], [-3.2191, -3.2386, -1.3451], [-3.6210, -3.5993, 0.4925]] , device=_SCREAMING_SNAKE_CASE )
self.assertTrue(torch.allclose(output[:, :3, :3] , _SCREAMING_SNAKE_CASE , atol=_SCREAMING_SNAKE_CASE ) )
def UpperCAmelCase__ ( self ) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase = MaMaaaForConditionalGeneration.from_pretrained('facebook/m2m100_418M' ).to(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = MaMaaaTokenizer.from_pretrained('facebook/m2m100_418M' , src_lang='fr' , tgt_lang='en' )
_UpperCAmelCase = [
'L\'affaire NSA souligne l\'absence totale de débat sur le renseignement',
'Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.',
'Lorsque François Hollande téléphone à Barack Obama ou quand le ministre des affaires étrangères Laurent'
' Fabius convoque l\'ambassadeur des Etats-Unis, ils réagissent à une vraie découverte, qui est celle de'
' l\'ampleur de la surveillance américaine sur l\'ensemble des communications en France.',
]
# The below article tests that we don't add any hypotheses outside of the top n_beams
_UpperCAmelCase = tokenizer(_SCREAMING_SNAKE_CASE , padding=_SCREAMING_SNAKE_CASE , return_tensors='pt' )
_UpperCAmelCase = model.generate(
input_ids=dct['input_ids'].to(_SCREAMING_SNAKE_CASE ) , attention_mask=dct['attention_mask'].to(_SCREAMING_SNAKE_CASE ) , num_beams=5 , forced_bos_token_id=tokenizer.get_lang_id('en' ) , )
_UpperCAmelCase = [
'The NSA case highlights the total absence of intelligence debate',
'I think there are two levels of response from the French government.',
'When François Hollande calls Barack Obama or when Foreign Minister Laurent Fabius calls the U.S.'
' Ambassador, they respond to a real discovery, which is that of the scale of U.S. surveillance on all'
' communications in France.',
]
_UpperCAmelCase = tokenizer.batch_decode(
hypotheses_batch.tolist() , clean_up_tokenization_spaces=_SCREAMING_SNAKE_CASE , skip_special_tokens=_SCREAMING_SNAKE_CASE )
assert generated == expected_en
| 185 | 0 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowercase__ :str = "▁"
lowercase__ :List[str] = {"vocab_file": "spiece.model"}
lowercase__ :List[Any] = {
"vocab_file": {"google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"}
}
lowercase__ :Optional[int] = {
"google/pegasus-xsum": 512,
}
lowercase__ :str = logging.get_logger(__name__)
class lowercase ( SCREAMING_SNAKE_CASE__ ):
lowercase_ : Dict =VOCAB_FILES_NAMES
lowercase_ : Optional[Any] =VOCAB_FILES_NAMES
lowercase_ : List[str] =PRETRAINED_VOCAB_FILES_MAP
lowercase_ : List[Any] =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase_ : List[str] =['''input_ids''', '''attention_mask''']
def __init__( self ,A__ ,A__="<pad>" ,A__="</s>" ,A__="<unk>" ,A__="<mask_2>" ,A__="<mask_1>" ,A__=None ,A__=1_0_3 ,A__ = None ,**A__ ,):
lowercase = offset
if additional_special_tokens is not None:
if not isinstance(A__ ,A__):
raise TypeError(
f'additional_special_tokens should be of type {type(A__)}, but is'
f' {type(A__)}')
lowercase = (
([mask_token_sent] + additional_special_tokens)
if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
else additional_special_tokens
)
# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
additional_special_tokens_extended += [
f'<unk_{i}>' for i in range(len(A__) ,self.offset - 1)
]
if len(set(A__)) != len(A__):
raise ValueError(
'''Please make sure that the provided additional_special_tokens do not contain an incorrectly'''
f' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.')
lowercase = additional_special_tokens_extended
else:
lowercase = [mask_token_sent] if mask_token_sent is not None else []
additional_special_tokens += [f'<unk_{i}>' for i in range(2 ,self.offset)]
lowercase = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
eos_token=A__ ,unk_token=A__ ,mask_token=A__ ,pad_token=A__ ,mask_token_sent=A__ ,offset=A__ ,additional_special_tokens=A__ ,sp_model_kwargs=self.sp_model_kwargs ,**A__ ,)
lowercase = mask_token_sent
lowercase = vocab_file
lowercase = spm.SentencePieceProcessor(**self.sp_model_kwargs)
self.sp_model.Load(A__)
# add special tokens to encoder dict
lowercase = {
0: self.pad_token,
1: self.eos_token,
}
if self.mask_token_sent is not None:
self.encoder.update(
{
2: self.mask_token_sent,
3: self.mask_token,
})
if self.offset > 0:
# entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
# mask_token_sent is already added to list -> so start at 1
self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1 ,self.offset - 1)})
lowercase = {v: k for k, v in self.encoder.items()}
@property
def A__ ( self):
return len(self.sp_model) + self.offset
def A__ ( self):
lowercase = {self.convert_ids_to_tokens(A__): i for i in range(self.vocab_size)}
vocab.update(self.added_tokens_encoder)
return vocab
def __getstate__( self):
lowercase = self.__dict__.copy()
lowercase = None
return state
def __setstate__( self ,A__):
lowercase = d
# for backward compatibility
if not hasattr(self ,'''sp_model_kwargs'''):
lowercase = {}
lowercase = spm.SentencePieceProcessor(**self.sp_model_kwargs)
self.sp_model.Load(self.vocab_file)
def A__ ( self ,A__):
return self.sp_model.encode(A__ ,out_type=A__)
def A__ ( self ,A__):
if token in self.decoder:
return self.decoder[token]
elif token in self.added_tokens_decoder:
return self.added_tokens_decoder[token]
lowercase = self.sp_model.piece_to_id(A__)
return sp_id + self.offset
def A__ ( self ,A__):
if index in self.encoder:
return self.encoder[index]
elif index in self.added_tokens_encoder:
return self.added_tokens_encoder[index]
else:
lowercase = self.sp_model.IdToPiece(index - self.offset)
return token
def A__ ( self ,A__):
lowercase = []
lowercase = ''''''
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
out_string += self.sp_model.decode(A__) + token
lowercase = []
else:
current_sub_tokens.append(A__)
out_string += self.sp_model.decode(A__)
return out_string.strip()
def A__ ( self ,A__=False):
return 1
def A__ ( self ,A__):
lowercase = set(self.all_special_ids) # call it once instead of inside list comp
all_special_ids.remove(self.unk_token_id) # <unk> is only sometimes special
return [1 if x in all_special_ids else 0 for x in seq]
def A__ ( self ,A__ ,A__ = None ,A__ = False):
if already_has_special_tokens:
return self._special_token_mask(A__)
elif token_ids_a is None:
return self._special_token_mask(A__) + [1]
else:
return self._special_token_mask(token_ids_a + token_ids_a) + [1]
def A__ ( self ,A__ ,A__=None):
if token_ids_a is None:
return token_ids_a + [self.eos_token_id]
# We don't expect to process pairs, but leave the pair logic for API consistency
return token_ids_a + token_ids_a + [self.eos_token_id]
def A__ ( self ,A__ ,A__ = None):
if not os.path.isdir(A__):
logger.error(f'Vocabulary path ({save_directory}) should be a directory')
return
lowercase = os.path.join(
A__ ,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''])
if os.path.abspath(self.vocab_file) != os.path.abspath(A__) and os.path.isfile(self.vocab_file):
copyfile(self.vocab_file ,A__)
elif not os.path.isfile(self.vocab_file):
with open(A__ ,'''wb''') as fi:
lowercase = self.sp_model.serialized_model_proto()
fi.write(A__)
return (out_vocab_file,)
| 101 |
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import torch
class snake_case__ (TensorFormatter[Mapping, """torch.Tensor""", Mapping] ):
"""simple docstring"""
def __init__( self : Tuple , __lowerCamelCase : Union[str, Any]=None , **__lowerCamelCase : Any ) -> Optional[Any]:
super().__init__(features=__lowerCamelCase )
a = torch_tensor_kwargs
import torch # noqa import torch at initialization
def __UpperCAmelCase ( self : List[str] , __lowerCamelCase : Dict ) -> Dict:
import torch
if isinstance(__lowerCamelCase , __lowerCamelCase ) and column:
if all(
isinstance(__lowerCamelCase , torch.Tensor ) and x.shape == column[0].shape and x.dtype == column[0].dtype
for x in column ):
return torch.stack(__lowerCamelCase )
return column
def __UpperCAmelCase ( self : Optional[Any] , __lowerCamelCase : List[Any] ) -> str:
import torch
if isinstance(__lowerCamelCase , (str, bytes, type(__lowerCamelCase )) ):
return value
elif isinstance(__lowerCamelCase , (np.character, np.ndarray) ) and np.issubdtype(value.dtype , np.character ):
return value.tolist()
a = {}
if isinstance(__lowerCamelCase , (np.number, np.ndarray) ) and np.issubdtype(value.dtype , np.integer ):
a = {"dtype": torch.intaa}
elif isinstance(__lowerCamelCase , (np.number, np.ndarray) ) and np.issubdtype(value.dtype , np.floating ):
a = {"dtype": torch.floataa}
elif config.PIL_AVAILABLE and "PIL" in sys.modules:
import PIL.Image
if isinstance(__lowerCamelCase , PIL.Image.Image ):
a = np.asarray(__lowerCamelCase )
return torch.tensor(__lowerCamelCase , **{**default_dtype, **self.torch_tensor_kwargs} )
def __UpperCAmelCase ( self : List[str] , __lowerCamelCase : Tuple ) -> List[str]:
import torch
# support for torch, tf, jax etc.
if hasattr(__lowerCamelCase , "__array__" ) and not isinstance(__lowerCamelCase , torch.Tensor ):
a = data_struct.__array__()
# support for nested types like struct of list of struct
if isinstance(__lowerCamelCase , np.ndarray ):
if data_struct.dtype == object: # torch tensors cannot be instantied from an array of objects
return self._consolidate([self.recursive_tensorize(__lowerCamelCase ) for substruct in data_struct] )
elif isinstance(__lowerCamelCase , (list, tuple) ):
return self._consolidate([self.recursive_tensorize(__lowerCamelCase ) for substruct in data_struct] )
return self._tensorize(__lowerCamelCase )
def __UpperCAmelCase ( self : int , __lowerCamelCase : dict ) -> str:
return map_nested(self._recursive_tensorize , __lowerCamelCase , map_list=__lowerCamelCase )
def __UpperCAmelCase ( self : Optional[Any] , __lowerCamelCase : pa.Table ) -> Mapping:
a = self.numpy_arrow_extractor().extract_row(__lowerCamelCase )
a = self.python_features_decoder.decode_row(__lowerCamelCase )
return self.recursive_tensorize(__lowerCamelCase )
def __UpperCAmelCase ( self : int , __lowerCamelCase : pa.Table ) -> "torch.Tensor":
a = self.numpy_arrow_extractor().extract_column(__lowerCamelCase )
a = self.python_features_decoder.decode_column(__lowerCamelCase , pa_table.column_names[0] )
a = self.recursive_tensorize(__lowerCamelCase )
a = self._consolidate(__lowerCamelCase )
return column
def __UpperCAmelCase ( self : Optional[Any] , __lowerCamelCase : pa.Table ) -> Mapping:
a = self.numpy_arrow_extractor().extract_batch(__lowerCamelCase )
a = self.python_features_decoder.decode_batch(__lowerCamelCase )
a = self.recursive_tensorize(__lowerCamelCase )
for column_name in batch:
a = self._consolidate(batch[column_name] )
return batch
| 107 | 0 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionSAGPipeline,
UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class SCREAMING_SNAKE_CASE (a__ , a__ , unittest.TestCase ):
lowerCAmelCase = StableDiffusionSAGPipeline
lowerCAmelCase = TEXT_TO_IMAGE_PARAMS
lowerCAmelCase = TEXT_TO_IMAGE_BATCH_PARAMS
lowerCAmelCase = TEXT_TO_IMAGE_IMAGE_PARAMS
lowerCAmelCase = TEXT_TO_IMAGE_IMAGE_PARAMS
lowerCAmelCase = False
def SCREAMING_SNAKE_CASE ( self):
'''simple docstring'''
torch.manual_seed(0)
__A : Tuple = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D') , up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D') , cross_attention_dim=32 , )
__A : Dict = DDIMScheduler(
beta_start=0.00085 , beta_end=0.012 , beta_schedule='scaled_linear' , clip_sample=_UpperCAmelCase , set_alpha_to_one=_UpperCAmelCase , )
torch.manual_seed(0)
__A : int = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D'] , up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D'] , latent_channels=4 , )
torch.manual_seed(0)
__A : Dict = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1e-0_5 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , )
__A : Any = CLIPTextModel(_UpperCAmelCase)
__A : str = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip')
__A : Tuple = {
'unet': unet,
'scheduler': scheduler,
'vae': vae,
'text_encoder': text_encoder,
'tokenizer': tokenizer,
'safety_checker': None,
'feature_extractor': None,
}
return components
def SCREAMING_SNAKE_CASE ( self , _UpperCAmelCase , _UpperCAmelCase=0):
'''simple docstring'''
if str(_UpperCAmelCase).startswith('mps'):
__A : int = torch.manual_seed(_UpperCAmelCase)
else:
__A : List[Any] = torch.Generator(device=_UpperCAmelCase).manual_seed(_UpperCAmelCase)
__A : str = {
'prompt': '.',
'generator': generator,
'num_inference_steps': 2,
'guidance_scale': 1.0,
'sag_scale': 1.0,
'output_type': 'numpy',
}
return inputs
def SCREAMING_SNAKE_CASE ( self):
'''simple docstring'''
super().test_inference_batch_single_identical(expected_max_diff=3e-3)
@slow
@require_torch_gpu
class SCREAMING_SNAKE_CASE (unittest.TestCase ):
def SCREAMING_SNAKE_CASE ( self):
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def SCREAMING_SNAKE_CASE ( self):
'''simple docstring'''
__A : Optional[int] = StableDiffusionSAGPipeline.from_pretrained('CompVis/stable-diffusion-v1-4')
__A : Dict = sag_pipe.to(_UpperCAmelCase)
sag_pipe.set_progress_bar_config(disable=_UpperCAmelCase)
__A : Union[str, Any] = '.'
__A : Any = torch.manual_seed(0)
__A : List[Any] = sag_pipe(
[prompt] , generator=_UpperCAmelCase , guidance_scale=7.5 , sag_scale=1.0 , num_inference_steps=20 , output_type='np')
__A : List[str] = output.images
__A : int = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
__A : str = np.array([0.1568, 0.1738, 0.1695, 0.1693, 0.1507, 0.1705, 0.1547, 0.1751, 0.1949])
assert np.abs(image_slice.flatten() - expected_slice).max() < 5e-2
def SCREAMING_SNAKE_CASE ( self):
'''simple docstring'''
__A : int = StableDiffusionSAGPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base')
__A : int = sag_pipe.to(_UpperCAmelCase)
sag_pipe.set_progress_bar_config(disable=_UpperCAmelCase)
__A : Union[str, Any] = '.'
__A : Optional[int] = torch.manual_seed(0)
__A : Tuple = sag_pipe(
[prompt] , generator=_UpperCAmelCase , guidance_scale=7.5 , sag_scale=1.0 , num_inference_steps=20 , output_type='np')
__A : Dict = output.images
__A : Dict = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
__A : Optional[int] = np.array([0.3459, 0.2876, 0.2537, 0.3002, 0.2671, 0.2160, 0.3026, 0.2262, 0.2371])
assert np.abs(image_slice.flatten() - expected_slice).max() < 5e-2
def SCREAMING_SNAKE_CASE ( self):
'''simple docstring'''
__A : Optional[Any] = StableDiffusionSAGPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base')
__A : List[str] = sag_pipe.to(_UpperCAmelCase)
sag_pipe.set_progress_bar_config(disable=_UpperCAmelCase)
__A : Any = '.'
__A : List[str] = torch.manual_seed(0)
__A : Optional[Any] = sag_pipe(
[prompt] , width=768 , height=512 , generator=_UpperCAmelCase , guidance_scale=7.5 , sag_scale=1.0 , num_inference_steps=20 , output_type='np' , )
__A : List[Any] = output.images
assert image.shape == (1, 512, 768, 3) | 190 |
'''simple docstring'''
from collections.abc import Iterable
from typing import Any
class SCREAMING_SNAKE_CASE :
def __init__( self , _UpperCAmelCase = None):
'''simple docstring'''
__A : str = value
__A : Node | None = None # Added in order to delete a node easier
__A : Node | None = None
__A : Node | None = None
def __repr__( self):
'''simple docstring'''
from pprint import pformat
if self.left is None and self.right is None:
return str(self.value)
return pformat({F'{self.value}': (self.left, self.right)} , indent=1)
class SCREAMING_SNAKE_CASE :
def __init__( self , _UpperCAmelCase = None):
'''simple docstring'''
__A : int = root
def __str__( self):
'''simple docstring'''
return str(self.root)
def SCREAMING_SNAKE_CASE ( self , _UpperCAmelCase , _UpperCAmelCase):
'''simple docstring'''
if new_children is not None: # reset its kids
__A : Optional[int] = node.parent
if node.parent is not None: # reset its parent
if self.is_right(_UpperCAmelCase): # If it is the right children
__A : int = new_children
else:
__A : Union[str, Any] = new_children
else:
__A : Tuple = new_children
def SCREAMING_SNAKE_CASE ( self , _UpperCAmelCase):
'''simple docstring'''
if node.parent and node.parent.right:
return node == node.parent.right
return False
def SCREAMING_SNAKE_CASE ( self):
'''simple docstring'''
return self.root is None
def SCREAMING_SNAKE_CASE ( self , _UpperCAmelCase):
'''simple docstring'''
__A : Dict = Node(_UpperCAmelCase) # create a new Node
if self.empty(): # if Tree is empty
__A : Union[str, Any] = new_node # set its root
else: # Tree is not empty
__A : Dict = self.root # from root
if parent_node is None:
return
while True: # While we don't get to a leaf
if value < parent_node.value: # We go left
if parent_node.left is None:
__A : Tuple = new_node # We insert the new node in a leaf
break
else:
__A : str = parent_node.left
else:
if parent_node.right is None:
__A : List[str] = new_node
break
else:
__A : int = parent_node.right
__A : int = parent_node
def SCREAMING_SNAKE_CASE ( self , *_UpperCAmelCase):
'''simple docstring'''
for value in values:
self.__insert(_UpperCAmelCase)
def SCREAMING_SNAKE_CASE ( self , _UpperCAmelCase):
'''simple docstring'''
if self.empty():
raise IndexError('Warning: Tree is empty! please use another.')
else:
__A : Any = self.root
# use lazy evaluation here to avoid NoneType Attribute error
while node is not None and node.value is not value:
__A : List[Any] = node.left if value < node.value else node.right
return node
def SCREAMING_SNAKE_CASE ( self , _UpperCAmelCase = None):
'''simple docstring'''
if node is None:
if self.root is None:
return None
__A : str = self.root
if not self.empty():
while node.right is not None:
__A : Dict = node.right
return node
def SCREAMING_SNAKE_CASE ( self , _UpperCAmelCase = None):
'''simple docstring'''
if node is None:
__A : Optional[Any] = self.root
if self.root is None:
return None
if not self.empty():
__A : Optional[int] = self.root
while node.left is not None:
__A : Tuple = node.left
return node
def SCREAMING_SNAKE_CASE ( self , _UpperCAmelCase):
'''simple docstring'''
__A : Optional[Any] = self.search(_UpperCAmelCase) # Look for the node with that label
if node is not None:
if node.left is None and node.right is None: # If it has no children
self.__reassign_nodes(_UpperCAmelCase , _UpperCAmelCase)
elif node.left is None: # Has only right children
self.__reassign_nodes(_UpperCAmelCase , node.right)
elif node.right is None: # Has only left children
self.__reassign_nodes(_UpperCAmelCase , node.left)
else:
__A : str = self.get_max(
node.left) # Gets the max value of the left branch
self.remove(tmp_node.value) # type: ignore
__A : List[Any] = (
tmp_node.value # type: ignore
) # Assigns the value to the node to delete and keep tree structure
def SCREAMING_SNAKE_CASE ( self , _UpperCAmelCase):
'''simple docstring'''
if node is not None:
yield node # Preorder Traversal
yield from self.preorder_traverse(node.left)
yield from self.preorder_traverse(node.right)
def SCREAMING_SNAKE_CASE ( self , _UpperCAmelCase=None):
'''simple docstring'''
if traversal_function is None:
return self.preorder_traverse(self.root)
else:
return traversal_function(self.root)
def SCREAMING_SNAKE_CASE ( self , _UpperCAmelCase , _UpperCAmelCase):
'''simple docstring'''
if node:
self.inorder(_UpperCAmelCase , node.left)
arr.append(node.value)
self.inorder(_UpperCAmelCase , node.right)
def SCREAMING_SNAKE_CASE ( self , _UpperCAmelCase , _UpperCAmelCase):
'''simple docstring'''
__A : list[int] = []
self.inorder(_UpperCAmelCase , _UpperCAmelCase) # append all values to list using inorder traversal
return arr[k - 1]
def _lowerCAmelCase ( __snake_case : Node | None ) -> list[Node]:
__A : Tuple = []
if curr_node is not None:
__A : Optional[Any] = postorder(curr_node.left ) + postorder(curr_node.right ) + [curr_node]
return node_list
def _lowerCAmelCase ( ) -> None:
__A : Optional[int] = (8, 3, 6, 1, 10, 14, 13, 4, 7)
__A : str = BinarySearchTree()
for i in testlist:
t.insert(__snake_case )
# Prints all the elements of the list in order traversal
print(__snake_case )
if t.search(6 ) is not None:
print('The value 6 exists' )
else:
print('The value 6 doesn\'t exist' )
if t.search(-1 ) is not None:
print('The value -1 exists' )
else:
print('The value -1 doesn\'t exist' )
if not t.empty():
print('Max Value: ' , t.get_max().value ) # type: ignore
print('Min Value: ' , t.get_min().value ) # type: ignore
for i in testlist:
t.remove(__snake_case )
print(__snake_case )
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True) | 190 | 1 |
"""simple docstring"""
import copy
import random
from transformers import CLIPTokenizer
class __snake_case ( SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
def __init__( self , *__lowerCamelCase , **__lowerCamelCase ):
'''simple docstring'''
super().__init__(*__lowerCamelCase , **__lowerCamelCase )
__A : Dict = {}
def UpperCamelCase__( self , __lowerCamelCase , *__lowerCamelCase , **__lowerCamelCase ):
'''simple docstring'''
__A : str = super().add_tokens(__lowerCamelCase , *__lowerCamelCase , **__lowerCamelCase )
if num_added_tokens == 0:
raise ValueError(
F"""The tokenizer already contains the token {placeholder_token}. Please pass a different"""
''' `placeholder_token` that is not already in the tokenizer.''' )
def UpperCamelCase__( self , __lowerCamelCase , *__lowerCamelCase , __lowerCamelCase=1 , **__lowerCamelCase ):
'''simple docstring'''
__A : Tuple = []
if num_vec_per_token == 1:
self.try_adding_tokens(__lowerCamelCase , *__lowerCamelCase , **__lowerCamelCase )
output.append(__lowerCamelCase )
else:
__A : Dict = []
for i in range(__lowerCamelCase ):
__A : Tuple = placeholder_token + F"""_{i}"""
self.try_adding_tokens(__lowerCamelCase , *__lowerCamelCase , **__lowerCamelCase )
output.append(__lowerCamelCase )
# handle cases where there is a new placeholder token that contains the current placeholder token but is larger
for token in self.token_map:
if token in placeholder_token:
raise ValueError(
F"""The tokenizer already has placeholder token {token} that can get confused with"""
F""" {placeholder_token}keep placeholder tokens independent""" )
__A : Optional[int] = output
def UpperCamelCase__( self , __lowerCamelCase , __lowerCamelCase=False , __lowerCamelCase=1.0 ):
'''simple docstring'''
if isinstance(__lowerCamelCase , __lowerCamelCase ):
__A : Tuple = []
for i in range(len(__lowerCamelCase ) ):
output.append(self.replace_placeholder_tokens_in_text(text[i] , vector_shuffle=__lowerCamelCase ) )
return output
for placeholder_token in self.token_map:
if placeholder_token in text:
__A : List[str] = self.token_map[placeholder_token]
__A : int = tokens[: 1 + int(len(__lowerCamelCase ) * prop_tokens_to_load )]
if vector_shuffle:
__A : Tuple = copy.copy(__lowerCamelCase )
random.shuffle(__lowerCamelCase )
__A : List[Any] = text.replace(__lowerCamelCase , ''' '''.join(__lowerCamelCase ) )
return text
def __call__( self , __lowerCamelCase , *__lowerCamelCase , __lowerCamelCase=False , __lowerCamelCase=1.0 , **__lowerCamelCase ):
'''simple docstring'''
return super().__call__(
self.replace_placeholder_tokens_in_text(
__lowerCamelCase , vector_shuffle=__lowerCamelCase , prop_tokens_to_load=__lowerCamelCase ) , *__lowerCamelCase , **__lowerCamelCase , )
def UpperCamelCase__( self , __lowerCamelCase , *__lowerCamelCase , __lowerCamelCase=False , __lowerCamelCase=1.0 , **__lowerCamelCase ):
'''simple docstring'''
return super().encode(
self.replace_placeholder_tokens_in_text(
__lowerCamelCase , vector_shuffle=__lowerCamelCase , prop_tokens_to_load=__lowerCamelCase ) , *__lowerCamelCase , **__lowerCamelCase , )
| 179 |
"""simple docstring"""
def __lowercase ( snake_case_ : int ) ->int:
'''simple docstring'''
assert (
isinstance(snake_case_ ,snake_case_ ) and number_of_steps > 0
), F"""number_of_steps needs to be positive integer, your input {number_of_steps}"""
if number_of_steps == 1:
return 1
__A , __A : List[Any] = 1, 1
for _ in range(number_of_steps - 1 ):
__A , __A : List[str] = current + previous, current
return current
if __name__ == "__main__":
import doctest
doctest.testmod()
| 179 | 1 |
class _A : # Public class to implement a graph
def __init__( self : List[Any] , _A : int , _A : int , _A : list[list[bool]] ) -> None:
"""simple docstring"""
lowercase : Tuple = row
lowercase : Union[str, Any] = col
lowercase : int = graph
def __a ( self : List[Any] , _A : int , _A : int , _A : list[list[bool]] ) -> bool:
"""simple docstring"""
return (
0 <= i < self.ROW
and 0 <= j < self.COL
and not visited[i][j]
and self.graph[i][j]
)
def __a ( self : int , _A : int , _A : int , _A : list[list[bool]] ) -> None:
"""simple docstring"""
lowercase : List[str] = [-1, -1, -1, 0, 0, 1, 1, 1] # Coordinate order
lowercase : Dict = [-1, 0, 1, -1, 1, -1, 0, 1]
lowercase : Dict = True # Make those cells visited
for k in range(8 ):
if self.is_safe(i + row_nbr[k] , j + col_nbr[k] , _A ):
self.diffs(i + row_nbr[k] , j + col_nbr[k] , _A )
def __a ( self : List[str] ) -> int: # And finally, count all islands.
"""simple docstring"""
lowercase : List[str] = [[False for j in range(self.COL )] for i in range(self.ROW )]
lowercase : Optional[Any] = 0
for i in range(self.ROW ):
for j in range(self.COL ):
if visited[i][j] is False and self.graph[i][j] == 1:
self.diffs(_A , _A , _A )
count += 1
return count | 365 |
class _A : # Public class to implement a graph
def __init__( self : List[Any] , _A : int , _A : int , _A : list[list[bool]] ) -> None:
"""simple docstring"""
lowercase : Tuple = row
lowercase : Union[str, Any] = col
lowercase : int = graph
def __a ( self : List[Any] , _A : int , _A : int , _A : list[list[bool]] ) -> bool:
"""simple docstring"""
return (
0 <= i < self.ROW
and 0 <= j < self.COL
and not visited[i][j]
and self.graph[i][j]
)
def __a ( self : int , _A : int , _A : int , _A : list[list[bool]] ) -> None:
"""simple docstring"""
lowercase : List[str] = [-1, -1, -1, 0, 0, 1, 1, 1] # Coordinate order
lowercase : Dict = [-1, 0, 1, -1, 1, -1, 0, 1]
lowercase : Dict = True # Make those cells visited
for k in range(8 ):
if self.is_safe(i + row_nbr[k] , j + col_nbr[k] , _A ):
self.diffs(i + row_nbr[k] , j + col_nbr[k] , _A )
def __a ( self : List[str] ) -> int: # And finally, count all islands.
"""simple docstring"""
lowercase : List[str] = [[False for j in range(self.COL )] for i in range(self.ROW )]
lowercase : Optional[Any] = 0
for i in range(self.ROW ):
for j in range(self.COL ):
if visited[i][j] is False and self.graph[i][j] == 1:
self.diffs(_A , _A , _A )
count += 1
return count | 116 | 0 |
'''simple docstring'''
def _a( UpperCamelCase__ : int = 2_0_0_0_0_0_0 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : List[str] =[0 for i in range(n + 1 )]
SCREAMING_SNAKE_CASE__ : Union[str, Any] =1
SCREAMING_SNAKE_CASE__ : Tuple =1
for i in range(2, int(n**0.5 ) + 1 ):
if primality_list[i] == 0:
for j in range(i * i, n + 1, UpperCamelCase__ ):
SCREAMING_SNAKE_CASE__ : Optional[int] =1
SCREAMING_SNAKE_CASE__ : List[str] =0
for i in range(UpperCamelCase__ ):
if primality_list[i] == 0:
sum_of_primes += i
return sum_of_primes
if __name__ == "__main__":
print(F'''{solution() = }''') | 152 |
'''simple docstring'''
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, PLBartTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
from ...test_tokenization_common import TokenizerTesterMixin
a_ = get_tests_dir('fixtures/test_sentencepiece.model')
if is_torch_available():
from transformers.models.plbart.modeling_plbart import shift_tokens_right
a_ = 5_0_0_0_3
a_ = 5_0_0_0_2
@require_sentencepiece
@require_tokenizers
class __SCREAMING_SNAKE_CASE ( lowerCamelCase , unittest.TestCase ):
snake_case_ = PLBartTokenizer
snake_case_ = None
snake_case_ = False
def __magic_name__ ( self : Optional[Any] ) -> Union[str, Any]:
super().setUp()
# We have a SentencePiece fixture for testing
SCREAMING_SNAKE_CASE__ : List[Any] =PLBartTokenizer(__lowercase , language_codes='''base''' , keep_accents=__lowercase )
tokenizer.save_pretrained(self.tmpdirname )
def __magic_name__ ( self : Union[str, Any] ) -> List[Any]:
SCREAMING_SNAKE_CASE__ : List[Any] =PLBartTokenizer(__lowercase , language_codes='''base''' , keep_accents=__lowercase )
SCREAMING_SNAKE_CASE__ : List[str] =tokenizer.tokenize('''This is a test''' )
self.assertListEqual(__lowercase , ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(__lowercase ) , [value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]] , )
SCREAMING_SNAKE_CASE__ : str =tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
self.assertListEqual(
__lowercase , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''9''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''é''',
'''.''',
] , )
SCREAMING_SNAKE_CASE__ : Any =tokenizer.convert_tokens_to_ids(__lowercase )
self.assertListEqual(
__lowercase , [
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
] , )
SCREAMING_SNAKE_CASE__ : Optional[int] =tokenizer.convert_ids_to_tokens(__lowercase )
self.assertListEqual(
__lowercase , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''<unk>''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''<unk>''',
'''.''',
] , )
SCREAMING_SNAKE_CASE__ : Optional[int] =tokenizer.vocab_size
SCREAMING_SNAKE_CASE__ : Dict =[tokenizer.convert_ids_to_tokens(__lowercase ) for x in range(end - 4 , __lowercase )]
self.assertListEqual(__lowercase , ['''__java__''', '''__python__''', '''__en_XX__''', '''<mask>'''] )
SCREAMING_SNAKE_CASE__ : str ='''java.lang.Exception, python.lang.Exception, javascript, php, ruby, go'''
SCREAMING_SNAKE_CASE__ : List[Any] =tokenizer(__lowercase ).input_ids
self.assertEqual(
tokenizer.decode(__lowercase , skip_special_tokens=__lowercase , clean_up_tokenization_spaces=__lowercase ) , __lowercase , )
def __magic_name__ ( self : Dict ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE__ : int =PLBartTokenizer(__lowercase , language_codes='''multi''' , keep_accents=__lowercase )
SCREAMING_SNAKE_CASE__ : Tuple =tokenizer.tokenize('''This is a test''' )
self.assertListEqual(__lowercase , ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(__lowercase ) , [value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]] , )
SCREAMING_SNAKE_CASE__ : List[str] =tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
self.assertListEqual(
__lowercase , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''9''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''é''',
'''.''',
] , )
SCREAMING_SNAKE_CASE__ : List[Any] =tokenizer.convert_tokens_to_ids(__lowercase )
self.assertListEqual(
__lowercase , [
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
] , )
SCREAMING_SNAKE_CASE__ : Optional[Any] =tokenizer.convert_ids_to_tokens(__lowercase )
self.assertListEqual(
__lowercase , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''<unk>''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''<unk>''',
'''.''',
] , )
SCREAMING_SNAKE_CASE__ : Tuple =tokenizer.vocab_size
SCREAMING_SNAKE_CASE__ : List[str] =[tokenizer.convert_ids_to_tokens(__lowercase ) for x in range(end - 7 , __lowercase )]
self.assertListEqual(
__lowercase , ['''__java__''', '''__python__''', '''__en_XX__''', '''__javascript__''', '''__php__''', '''__ruby__''', '''__go__'''] )
SCREAMING_SNAKE_CASE__ : Any ='''java.lang.Exception, python.lang.Exception, javascript, php, ruby, go'''
SCREAMING_SNAKE_CASE__ : Tuple =tokenizer(__lowercase ).input_ids
self.assertEqual(
tokenizer.decode(__lowercase , skip_special_tokens=__lowercase , clean_up_tokenization_spaces=__lowercase ) , __lowercase , )
@require_torch
@require_sentencepiece
@require_tokenizers
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
snake_case_ = """uclanlp/plbart-python-en_XX"""
snake_case_ = [
"""def maximum(a,b,c):NEW_LINE_INDENTreturn max([a,b,c])""",
"""def sum(a,b,c):NEW_LINE_INDENTreturn sum([a,b,c])""",
]
snake_case_ = [
"""Returns the maximum value of a b c.""",
"""Sums the values of a b c.""",
]
snake_case_ = [
134,
5452,
3_3460,
3_3441,
3_3463,
3_3465,
3_3463,
3_3449,
988,
20,
3_3456,
19,
3_3456,
771,
39,
4258,
889,
3318,
3_3441,
3_3463,
3_3465,
3_3463,
3_3449,
2471,
2,
PYTHON_CODE,
]
@classmethod
def __magic_name__ ( cls : List[str] ) -> Tuple:
SCREAMING_SNAKE_CASE__ : PLBartTokenizer =PLBartTokenizer.from_pretrained(
cls.checkpoint_name , language_codes='''base''' , src_lang='''python''' , tgt_lang='''en_XX''' )
SCREAMING_SNAKE_CASE__ : int =1
return cls
def __magic_name__ ( self : int ) -> Tuple:
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''__java__'''] , 5_00_01 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''__python__'''] , 5_00_02 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''__en_XX__'''] , 5_00_03 )
def __magic_name__ ( self : str ) -> Dict:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =self.tokenizer.batch_encode_plus(self.src_text ).input_ids[0]
self.assertListEqual(self.expected_src_tokens , __lowercase )
def __magic_name__ ( self : List[str] ) -> Tuple:
self.assertIn(__lowercase , self.tokenizer.all_special_ids )
SCREAMING_SNAKE_CASE__ : Tuple =[EN_CODE, 90_37, 3_34_42, 57, 7_52, 1_53, 14, 56, 18, 9, 2]
SCREAMING_SNAKE_CASE__ : int =self.tokenizer.decode(__lowercase , skip_special_tokens=__lowercase )
SCREAMING_SNAKE_CASE__ : Tuple =self.tokenizer.decode(generated_ids[1:] , skip_special_tokens=__lowercase )
self.assertEqual(__lowercase , __lowercase )
self.assertNotIn(self.tokenizer.eos_token , __lowercase )
def __magic_name__ ( self : List[str] ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE__ : Tuple =['''def sum(a,b,c):NEW_LINE_INDENTreturn sum([a,b,c])''' * 20]
self.assertIsInstance(src_text[0] , __lowercase )
SCREAMING_SNAKE_CASE__ : Tuple =10
SCREAMING_SNAKE_CASE__ : Tuple =self.tokenizer(__lowercase , max_length=__lowercase , truncation=__lowercase ).input_ids[0]
self.assertEqual(ids[-2] , 2 )
self.assertEqual(ids[-1] , __lowercase )
self.assertEqual(len(__lowercase ) , __lowercase )
def __magic_name__ ( self : Any ) -> Tuple:
self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['''<mask>''', '''__java__'''] ) , [5_00_04, 5_00_01] )
def __magic_name__ ( self : str ) -> List[str]:
SCREAMING_SNAKE_CASE__ : Optional[Any] =tempfile.mkdtemp()
SCREAMING_SNAKE_CASE__ : Optional[Any] =self.tokenizer.fairseq_tokens_to_ids
self.tokenizer.save_pretrained(__lowercase )
SCREAMING_SNAKE_CASE__ : Any =PLBartTokenizer.from_pretrained(__lowercase )
self.assertDictEqual(new_tok.fairseq_tokens_to_ids , __lowercase )
@require_torch
def __magic_name__ ( self : Dict ) -> List[str]:
SCREAMING_SNAKE_CASE__ : List[str] =self.tokenizer(self.src_text , text_target=self.tgt_text , padding=__lowercase , return_tensors='''pt''' )
SCREAMING_SNAKE_CASE__ : str =shift_tokens_right(batch['''labels'''] , self.tokenizer.pad_token_id )
# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
self.assertEqual(batch.input_ids[1][-2:].tolist() , [2, PYTHON_CODE] )
self.assertEqual(batch.decoder_input_ids[1][0] , __lowercase )
self.assertEqual(batch.decoder_input_ids[1][-1] , 2 )
self.assertEqual(batch.labels[1][-2:].tolist() , [2, EN_CODE] )
@require_torch
def __magic_name__ ( self : Optional[Any] ) -> Optional[int]:
SCREAMING_SNAKE_CASE__ : Dict =self.tokenizer(
self.src_text , text_target=self.tgt_text , padding=__lowercase , truncation=__lowercase , max_length=len(self.expected_src_tokens ) , return_tensors='''pt''' , )
SCREAMING_SNAKE_CASE__ : Dict =shift_tokens_right(batch['''labels'''] , self.tokenizer.pad_token_id )
self.assertIsInstance(__lowercase , __lowercase )
self.assertEqual((2, 26) , batch.input_ids.shape )
self.assertEqual((2, 26) , batch.attention_mask.shape )
SCREAMING_SNAKE_CASE__ : Optional[Any] =batch.input_ids.tolist()[0]
self.assertListEqual(self.expected_src_tokens , __lowercase )
self.assertEqual(2 , batch.decoder_input_ids[0, -1] ) # EOS
# Test that special tokens are reset
self.assertEqual(self.tokenizer.prefix_tokens , [] )
self.assertEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id, PYTHON_CODE] )
def __magic_name__ ( self : Tuple ) -> int:
SCREAMING_SNAKE_CASE__ : Optional[Any] =self.tokenizer(self.src_text , padding=__lowercase , truncation=__lowercase , max_length=3 , return_tensors='''pt''' )
SCREAMING_SNAKE_CASE__ : Optional[Any] =self.tokenizer(
text_target=self.tgt_text , padding=__lowercase , truncation=__lowercase , max_length=10 , return_tensors='''pt''' )
SCREAMING_SNAKE_CASE__ : Dict =targets['''input_ids''']
SCREAMING_SNAKE_CASE__ : Union[str, Any] =shift_tokens_right(__lowercase , self.tokenizer.pad_token_id )
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.decoder_input_ids.shape[1] , 10 )
@require_torch
def __magic_name__ ( self : Tuple ) -> str:
SCREAMING_SNAKE_CASE__ : str =self.tokenizer._build_translation_inputs(
'''A test''' , return_tensors='''pt''' , src_lang='''en_XX''' , tgt_lang='''java''' )
self.assertEqual(
nested_simplify(__lowercase ) , {
# A, test, EOS, en_XX
'''input_ids''': [[1_50, 2_42, 2, 5_00_03]],
'''attention_mask''': [[1, 1, 1, 1]],
# java
'''forced_bos_token_id''': 5_00_01,
} , ) | 152 | 1 |
"""simple docstring"""
def lowerCamelCase_ ( _lowerCamelCase ):
lowerCamelCase__ : Any = [1]
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ : List[str] = 0, 0, 0
lowerCamelCase__ : Optional[Any] = ugly_nums[ia] * 2
lowerCamelCase__ : Any = ugly_nums[ia] * 3
lowerCamelCase__ : int = ugly_nums[ia] * 5
for _ in range(1 , _lowerCamelCase ):
lowerCamelCase__ : List[str] = min(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
ugly_nums.append(_lowerCamelCase )
if next_num == next_a:
ia += 1
lowerCamelCase__ : Optional[Any] = ugly_nums[ia] * 2
if next_num == next_a:
ia += 1
lowerCamelCase__ : List[str] = ugly_nums[ia] * 3
if next_num == next_a:
ia += 1
lowerCamelCase__ : Optional[int] = ugly_nums[ia] * 5
return ugly_nums[-1]
if __name__ == "__main__":
from doctest import testmod
testmod(verbose=True)
print(f"{ugly_numbers(2_00) = }")
| 316 |
"""simple docstring"""
# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position
A_ : Union[str, Any] = "2.13.1"
import platform
import pyarrow
from packaging import version
if version.parse(platform.python_version()) < version.parse("3.7"):
raise ImportWarning(
"To use `datasets`, Python>=3.7 is required, and the current version of Python doesn't match this condition."
)
if version.parse(pyarrow.__version__).major < 8:
raise ImportWarning(
"To use `datasets`, the module `pyarrow>=8.0.0` is required, and the current version of `pyarrow` doesn't match this condition.\n"
"If you are running this in a Google Colab, you should probably just restart the runtime to use the right version of `pyarrow`."
)
del platform
del pyarrow
del version
from .arrow_dataset import Dataset
from .arrow_reader import ReadInstruction
from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
from .combine import concatenate_datasets, interleave_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .download import *
from .features import *
from .fingerprint import disable_caching, enable_caching, is_caching_enabled, set_caching_enabled
from .info import DatasetInfo, MetricInfo
from .inspect import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
list_datasets,
list_metrics,
)
from .iterable_dataset import IterableDataset
from .load import load_dataset, load_dataset_builder, load_from_disk, load_metric
from .metric import Metric
from .splits import (
NamedSplit,
NamedSplitAll,
Split,
SplitBase,
SplitDict,
SplitGenerator,
SplitInfo,
SubSplitInfo,
percent,
)
from .tasks import *
from .utils import *
from .utils import logging
# deprecated modules
from datasets import arrow_dataset as _arrow_dataset # isort:skip
from datasets import utils as _utils # isort:skip
from datasets.utils import download_manager as _deprecated_download_manager # isort:skip
A_ : int = concatenate_datasets
A_ : Any = DownloadConfig
A_ : List[Any] = DownloadManager
A_ : Optional[Any] = DownloadMode
A_ : List[str] = DownloadConfig
A_ : Optional[int] = DownloadMode
A_ : Dict = DownloadManager
del _arrow_dataset, _utils, _deprecated_download_manager
| 316 | 1 |
'''simple docstring'''
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class UpperCamelCase_ ( __magic_name__ ):
lowercase = (UniPCMultistepScheduler,)
lowercase = (('num_inference_steps', 25),)
def _lowercase( self , **A ) -> int:
UpperCAmelCase : int = {
"""num_train_timesteps""": 1000,
"""beta_start""": 0.0_0_0_1,
"""beta_end""": 0.0_2,
"""beta_schedule""": """linear""",
"""solver_order""": 2,
"""solver_type""": """bh2""",
}
config.update(**A )
return config
def _lowercase( self , A=0 , **A ) -> Tuple:
UpperCAmelCase : int = dict(self.forward_default_kwargs )
UpperCAmelCase : Optional[Any] = kwargs.pop("""num_inference_steps""" , A )
UpperCAmelCase : Optional[int] = self.dummy_sample
UpperCAmelCase : Optional[Any] = 0.1 * sample
UpperCAmelCase : int = [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
for scheduler_class in self.scheduler_classes:
UpperCAmelCase : Any = self.get_scheduler_config(**A )
UpperCAmelCase : int = scheduler_class(**A )
scheduler.set_timesteps(A )
# copy over dummy past residuals
UpperCAmelCase : Any = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(A )
UpperCAmelCase : List[Any] = scheduler_class.from_pretrained(A )
new_scheduler.set_timesteps(A )
# copy over dummy past residuals
UpperCAmelCase : List[Any] = dummy_past_residuals[: new_scheduler.config.solver_order]
UpperCAmelCase , UpperCAmelCase : List[Any] = sample, sample
for t in range(A , time_step + scheduler.config.solver_order + 1 ):
UpperCAmelCase : Any = scheduler.step(A , A , A , **A ).prev_sample
UpperCAmelCase : Dict = new_scheduler.step(A , A , A , **A ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
def _lowercase( self , A=0 , **A ) -> List[str]:
UpperCAmelCase : List[str] = dict(self.forward_default_kwargs )
UpperCAmelCase : Union[str, Any] = kwargs.pop("""num_inference_steps""" , A )
UpperCAmelCase : Any = self.dummy_sample
UpperCAmelCase : Dict = 0.1 * sample
UpperCAmelCase : Union[str, Any] = [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
for scheduler_class in self.scheduler_classes:
UpperCAmelCase : List[Any] = self.get_scheduler_config()
UpperCAmelCase : Any = scheduler_class(**A )
scheduler.set_timesteps(A )
# copy over dummy past residuals (must be after setting timesteps)
UpperCAmelCase : Dict = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(A )
UpperCAmelCase : int = scheduler_class.from_pretrained(A )
# copy over dummy past residuals
new_scheduler.set_timesteps(A )
# copy over dummy past residual (must be after setting timesteps)
UpperCAmelCase : Dict = dummy_past_residuals[: new_scheduler.config.solver_order]
UpperCAmelCase : Dict = scheduler.step(A , A , A , **A ).prev_sample
UpperCAmelCase : List[Any] = new_scheduler.step(A , A , A , **A ).prev_sample
assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"
def _lowercase( self , A=None , **A ) -> Dict:
if scheduler is None:
UpperCAmelCase : List[str] = self.scheduler_classes[0]
UpperCAmelCase : Optional[Any] = self.get_scheduler_config(**A )
UpperCAmelCase : Union[str, Any] = scheduler_class(**A )
UpperCAmelCase : Optional[Any] = self.scheduler_classes[0]
UpperCAmelCase : Union[str, Any] = self.get_scheduler_config(**A )
UpperCAmelCase : Tuple = scheduler_class(**A )
UpperCAmelCase : Optional[Any] = 10
UpperCAmelCase : Optional[Any] = self.dummy_model()
UpperCAmelCase : Tuple = self.dummy_sample_deter
scheduler.set_timesteps(A )
for i, t in enumerate(scheduler.timesteps ):
UpperCAmelCase : Dict = model(A , A )
UpperCAmelCase : Union[str, Any] = scheduler.step(A , A , A ).prev_sample
return sample
def _lowercase( self ) -> int:
UpperCAmelCase : List[Any] = dict(self.forward_default_kwargs )
UpperCAmelCase : Dict = kwargs.pop("""num_inference_steps""" , A )
for scheduler_class in self.scheduler_classes:
UpperCAmelCase : List[str] = self.get_scheduler_config()
UpperCAmelCase : Tuple = scheduler_class(**A )
UpperCAmelCase : Optional[Any] = self.dummy_sample
UpperCAmelCase : Any = 0.1 * sample
if num_inference_steps is not None and hasattr(A , """set_timesteps""" ):
scheduler.set_timesteps(A )
elif num_inference_steps is not None and not hasattr(A , """set_timesteps""" ):
UpperCAmelCase : Any = num_inference_steps
# copy over dummy past residuals (must be done after set_timesteps)
UpperCAmelCase : Optional[int] = [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
UpperCAmelCase : Optional[Any] = dummy_past_residuals[: scheduler.config.solver_order]
UpperCAmelCase : str = scheduler.timesteps[5]
UpperCAmelCase : str = scheduler.timesteps[6]
UpperCAmelCase : Optional[int] = scheduler.step(A , A , A , **A ).prev_sample
UpperCAmelCase : Optional[int] = scheduler.step(A , A , A , **A ).prev_sample
self.assertEqual(output_a.shape , sample.shape )
self.assertEqual(output_a.shape , output_a.shape )
def _lowercase( self ) -> List[Any]:
# make sure that iterating over schedulers with same config names gives same results
# for defaults
UpperCAmelCase : List[str] = UniPCMultistepScheduler(**self.get_scheduler_config() )
UpperCAmelCase : Union[str, Any] = self.full_loop(scheduler=A )
UpperCAmelCase : Optional[int] = torch.mean(torch.abs(A ) )
assert abs(result_mean.item() - 0.2_4_6_4 ) < 1e-3
UpperCAmelCase : int = DPMSolverSinglestepScheduler.from_config(scheduler.config )
UpperCAmelCase : Tuple = DEISMultistepScheduler.from_config(scheduler.config )
UpperCAmelCase : Optional[int] = DPMSolverMultistepScheduler.from_config(scheduler.config )
UpperCAmelCase : Optional[Any] = UniPCMultistepScheduler.from_config(scheduler.config )
UpperCAmelCase : str = self.full_loop(scheduler=A )
UpperCAmelCase : Union[str, Any] = torch.mean(torch.abs(A ) )
assert abs(result_mean.item() - 0.2_4_6_4 ) < 1e-3
def _lowercase( self ) -> Tuple:
for timesteps in [25, 50, 100, 999, 1000]:
self.check_over_configs(num_train_timesteps=A )
def _lowercase( self ) -> Dict:
self.check_over_configs(thresholding=A )
for order in [1, 2, 3]:
for solver_type in ["bh1", "bh2"]:
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
thresholding=A , prediction_type=A , sample_max_value=A , solver_order=A , solver_type=A , )
def _lowercase( self ) -> Tuple:
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=A )
def _lowercase( self ) -> Dict:
for solver_type in ["bh1", "bh2"]:
for order in [1, 2, 3]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
solver_order=A , solver_type=A , prediction_type=A , )
UpperCAmelCase : Optional[int] = self.full_loop(
solver_order=A , solver_type=A , prediction_type=A , )
assert not torch.isnan(A ).any(), "Samples have nan numbers"
def _lowercase( self ) -> Dict:
self.check_over_configs(lower_order_final=A )
self.check_over_configs(lower_order_final=A )
def _lowercase( self ) -> Optional[Any]:
for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
self.check_over_forward(num_inference_steps=A , time_step=0 )
def _lowercase( self ) -> Optional[int]:
UpperCAmelCase : Optional[int] = self.full_loop()
UpperCAmelCase : Optional[int] = torch.mean(torch.abs(A ) )
assert abs(result_mean.item() - 0.2_4_6_4 ) < 1e-3
def _lowercase( self ) -> Union[str, Any]:
UpperCAmelCase : Tuple = self.full_loop(prediction_type="""v_prediction""" )
UpperCAmelCase : Optional[int] = torch.mean(torch.abs(A ) )
assert abs(result_mean.item() - 0.1_0_1_4 ) < 1e-3
def _lowercase( self ) -> List[Any]:
UpperCAmelCase : Dict = self.scheduler_classes[0]
UpperCAmelCase : Dict = self.get_scheduler_config(thresholding=A , dynamic_thresholding_ratio=0 )
UpperCAmelCase : Dict = scheduler_class(**A )
UpperCAmelCase : Optional[int] = 10
UpperCAmelCase : List[str] = self.dummy_model()
UpperCAmelCase : List[Any] = self.dummy_sample_deter.half()
scheduler.set_timesteps(A )
for i, t in enumerate(scheduler.timesteps ):
UpperCAmelCase : Tuple = model(A , A )
UpperCAmelCase : int = scheduler.step(A , A , A ).prev_sample
assert sample.dtype == torch.floataa
def _lowercase( self , **A ) -> Optional[int]:
for scheduler_class in self.scheduler_classes:
UpperCAmelCase : int = self.get_scheduler_config(**A )
UpperCAmelCase : Optional[Any] = scheduler_class(**A )
scheduler.set_timesteps(scheduler.config.num_train_timesteps )
assert len(scheduler.timesteps.unique() ) == scheduler.num_inference_steps
| 265 |
'''simple docstring'''
import unittest
from parameterized import parameterized
from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
GPTNeoXForCausalLM,
GPTNeoXForQuestionAnswering,
GPTNeoXForSequenceClassification,
GPTNeoXForTokenClassification,
GPTNeoXModel,
)
class UpperCamelCase_ :
def __init__( self , A , A=13 , A=7 , A=True , A=True , A=True , A=True , A=99 , A=64 , A=5 , A=4 , A=37 , A="gelu" , A=0.1 , A=0.1 , A=512 , A=16 , A=2 , A=0.0_2 , A=3 , A=4 , A=None , ) -> Optional[int]:
UpperCAmelCase : List[Any] = parent
UpperCAmelCase : Optional[int] = batch_size
UpperCAmelCase : Union[str, Any] = seq_length
UpperCAmelCase : Optional[Any] = is_training
UpperCAmelCase : Dict = use_input_mask
UpperCAmelCase : str = use_token_type_ids
UpperCAmelCase : List[Any] = use_labels
UpperCAmelCase : List[Any] = vocab_size
UpperCAmelCase : Dict = hidden_size
UpperCAmelCase : Dict = num_hidden_layers
UpperCAmelCase : Optional[int] = num_attention_heads
UpperCAmelCase : int = intermediate_size
UpperCAmelCase : List[str] = hidden_act
UpperCAmelCase : List[str] = hidden_dropout_prob
UpperCAmelCase : int = attention_probs_dropout_prob
UpperCAmelCase : str = max_position_embeddings
UpperCAmelCase : Optional[Any] = type_vocab_size
UpperCAmelCase : List[str] = type_sequence_label_size
UpperCAmelCase : int = initializer_range
UpperCAmelCase : str = num_labels
UpperCAmelCase : Optional[int] = num_choices
UpperCAmelCase : Dict = scope
UpperCAmelCase : Union[str, Any] = vocab_size - 1
def _lowercase( self ) -> Union[str, Any]:
UpperCAmelCase : List[str] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCAmelCase : Any = None
if self.use_input_mask:
UpperCAmelCase : int = random_attention_mask([self.batch_size, self.seq_length] )
UpperCAmelCase : List[str] = None
if self.use_labels:
UpperCAmelCase : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
UpperCAmelCase : Optional[int] = self.get_config()
return config, input_ids, input_mask, token_labels
def _lowercase( self ) -> Optional[Any]:
return GPTNeoXConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=A , initializer_range=self.initializer_range , pad_token_id=self.pad_token_id , )
def _lowercase( self ) -> Optional[Any]:
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : List[str] = self.prepare_config_and_inputs()
UpperCAmelCase : Any = True
return config, input_ids, input_mask, token_labels
def _lowercase( self , A , A , A ) -> int:
UpperCAmelCase : str = GPTNeoXModel(config=A )
model.to(A )
model.eval()
UpperCAmelCase : List[str] = model(A , attention_mask=A )
UpperCAmelCase : List[str] = model(A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _lowercase( self , A , A , A ) -> Optional[int]:
UpperCAmelCase : str = True
UpperCAmelCase : Optional[Any] = GPTNeoXModel(A )
model.to(A )
model.eval()
UpperCAmelCase : List[Any] = model(A , attention_mask=A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _lowercase( self , A , A , A , A ) -> List[str]:
UpperCAmelCase : Tuple = GPTNeoXForCausalLM(config=A )
model.to(A )
model.eval()
UpperCAmelCase : str = model(A , attention_mask=A , labels=A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def _lowercase( self , A , A , A , A ) -> Tuple:
UpperCAmelCase : List[str] = self.num_labels
UpperCAmelCase : Any = GPTNeoXForQuestionAnswering(A )
model.to(A )
model.eval()
UpperCAmelCase : str = model(A , attention_mask=A )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def _lowercase( self , A , A , A , A ) -> int:
UpperCAmelCase : Tuple = self.num_labels
UpperCAmelCase : List[str] = GPTNeoXForSequenceClassification(A )
model.to(A )
model.eval()
UpperCAmelCase : List[Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCAmelCase : Tuple = model(A , attention_mask=A , labels=A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _lowercase( self , A , A , A , A ) -> str:
UpperCAmelCase : List[Any] = self.num_labels
UpperCAmelCase : Tuple = GPTNeoXForTokenClassification(A )
model.to(A )
model.eval()
UpperCAmelCase : int = model(A , attention_mask=A , labels=A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def _lowercase( self , A , A , A ) -> Union[str, Any]:
UpperCAmelCase : Optional[int] = True
UpperCAmelCase : str = GPTNeoXForCausalLM(config=A )
model.to(A )
model.eval()
# first forward pass
UpperCAmelCase : List[str] = model(A , attention_mask=A , use_cache=A )
UpperCAmelCase : List[Any] = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
UpperCAmelCase : Optional[int] = ids_tensor((self.batch_size, 3) , config.vocab_size )
UpperCAmelCase : Any = ids_tensor((self.batch_size, 3) , vocab_size=2 )
# append to next input_ids and
UpperCAmelCase : str = torch.cat([input_ids, next_tokens] , dim=-1 )
UpperCAmelCase : Any = torch.cat([input_mask, next_mask] , dim=-1 )
UpperCAmelCase : Dict = model(A , attention_mask=A , output_hidden_states=A )
UpperCAmelCase : Any = output_from_no_past["""hidden_states"""][0]
UpperCAmelCase : List[str] = model(
A , attention_mask=A , past_key_values=A , output_hidden_states=A , )["""hidden_states"""][0]
# select random slice
UpperCAmelCase : Tuple = ids_tensor((1,) , output_from_past.shape[-1] ).item()
UpperCAmelCase : List[Any] = output_from_no_past[:, -3:, random_slice_idx].detach()
UpperCAmelCase : List[str] = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(A , A , atol=1e-3 ) )
def _lowercase( self ) -> int:
UpperCAmelCase : Tuple = self.prepare_config_and_inputs()
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : Optional[Any] = config_and_inputs
UpperCAmelCase : Union[str, Any] = {"""input_ids""": input_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_torch
class UpperCamelCase_ ( __magic_name__ , __magic_name__ , __magic_name__ , unittest.TestCase ):
lowercase = (
(
GPTNeoXModel,
GPTNeoXForCausalLM,
GPTNeoXForQuestionAnswering,
GPTNeoXForSequenceClassification,
GPTNeoXForTokenClassification,
)
if is_torch_available()
else ()
)
lowercase = (GPTNeoXForCausalLM,) if is_torch_available() else ()
lowercase = (
{
'feature-extraction': GPTNeoXModel,
'question-answering': GPTNeoXForQuestionAnswering,
'text-classification': GPTNeoXForSequenceClassification,
'text-generation': GPTNeoXForCausalLM,
'token-classification': GPTNeoXForTokenClassification,
'zero-shot': GPTNeoXForSequenceClassification,
}
if is_torch_available()
else {}
)
lowercase = False
lowercase = False
lowercase = False
lowercase = False
def _lowercase( self ) -> Union[str, Any]:
UpperCAmelCase : str = GPTNeoXModelTester(self )
UpperCAmelCase : Optional[Any] = ConfigTester(self , config_class=A , hidden_size=64 , num_attention_heads=8 )
def _lowercase( self ) -> Optional[Any]:
self.config_tester.run_common_tests()
def _lowercase( self ) -> str:
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(A , A , A )
def _lowercase( self ) -> str:
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_model_as_decoder(A , A , A )
def _lowercase( self ) -> Optional[Any]:
# This regression test was failing with PyTorch < 1.3
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_decoder()
UpperCAmelCase : Optional[Any] = None
self.model_tester.create_and_check_model_as_decoder(A , A , A )
def _lowercase( self ) -> str:
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_past_large_inputs(A , A , A )
def _lowercase( self ) -> int:
UpperCAmelCase : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_causal_lm(*A )
def _lowercase( self ) -> Optional[int]:
UpperCAmelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*A )
def _lowercase( self ) -> Any:
UpperCAmelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*A )
def _lowercase( self ) -> Optional[Any]:
UpperCAmelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*A )
@unittest.skip(reason="""Feed forward chunking is not implemented""" )
def _lowercase( self ) -> Optional[int]:
pass
@parameterized.expand([("""linear""",), ("""dynamic""",)] )
def _lowercase( self , A ) -> str:
UpperCAmelCase , UpperCAmelCase : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
UpperCAmelCase : int = ids_tensor([1, 10] , config.vocab_size )
UpperCAmelCase : Optional[Any] = ids_tensor([1, int(config.max_position_embeddings * 1.5 )] , config.vocab_size )
set_seed(42 ) # Fixed seed at init time so the two models get the same random weights
UpperCAmelCase : Dict = GPTNeoXModel(A )
original_model.to(A )
original_model.eval()
UpperCAmelCase : List[str] = original_model(A ).last_hidden_state
UpperCAmelCase : Any = original_model(A ).last_hidden_state
set_seed(42 ) # Fixed seed at init time so the two models get the same random weights
UpperCAmelCase : Any = {"""type""": scaling_type, """factor""": 1_0.0}
UpperCAmelCase : str = GPTNeoXModel(A )
scaled_model.to(A )
scaled_model.eval()
UpperCAmelCase : Optional[Any] = scaled_model(A ).last_hidden_state
UpperCAmelCase : Optional[Any] = scaled_model(A ).last_hidden_state
# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
# maximum sequence length, so the outputs for the short input should match.
if scaling_type == "dynamic":
self.assertTrue(torch.allclose(A , A , atol=1e-5 ) )
else:
self.assertFalse(torch.allclose(A , A , atol=1e-5 ) )
# The output should be different for long inputs
self.assertFalse(torch.allclose(A , A , atol=1e-5 ) )
@require_torch
class UpperCamelCase_ ( unittest.TestCase ):
@slow
def _lowercase( self ) -> List[Any]:
UpperCAmelCase : str = AutoTokenizer.from_pretrained("""EleutherAI/pythia-410m-deduped""" )
for checkpointing in [True, False]:
UpperCAmelCase : int = GPTNeoXForCausalLM.from_pretrained("""EleutherAI/pythia-410m-deduped""" )
if checkpointing:
model.gradient_checkpointing_enable()
else:
model.gradient_checkpointing_disable()
model.to(A )
UpperCAmelCase : List[Any] = tokenizer("""My favorite food is""" , return_tensors="""pt""" ).to(A )
# The hub repo. is updated on 2023-04-04, resulting in poor outputs.
# See: https://github.com/huggingface/transformers/pull/24193
UpperCAmelCase : List[str] = """My favorite food is a good old-fashioned, old-fashioned, old-fashioned.\n\nI'm not sure"""
UpperCAmelCase : Union[str, Any] = model.generate(**A , do_sample=A , max_new_tokens=20 )
UpperCAmelCase : Tuple = tokenizer.batch_decode(A )[0]
self.assertEqual(A , A )
| 265 | 1 |
from argparse import ArgumentParser
from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
UpperCamelCase = logging.get_logger(__name__) # pylint: disable=invalid-name
def __lowerCamelCase ( snake_case__ ) -> List[Any]:
"""simple docstring"""
if not path:
return "pipe"
for ext in PipelineDataFormat.SUPPORTED_FORMATS:
if path.endswith(snake_case__ ):
return ext
raise Exception(
F'Unable to determine file format from file extension {path}. '
F'Please provide the format through --format {PipelineDataFormat.SUPPORTED_FORMATS}' )
def __lowerCamelCase ( snake_case__ ) -> Any:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = pipeline(
task=args.task ,model=args.model if args.model else None ,config=args.config ,tokenizer=args.tokenizer ,device=args.device ,)
_SCREAMING_SNAKE_CASE = try_infer_format_from_ext(args.input ) if args.format == """infer""" else args.format
_SCREAMING_SNAKE_CASE = PipelineDataFormat.from_str(
format=snake_case__ ,output_path=args.output ,input_path=args.input ,column=args.column if args.column else nlp.default_input_names ,overwrite=args.overwrite ,)
return RunCommand(snake_case__ ,snake_case__ )
class __UpperCAmelCase (_UpperCAmelCase ):
def __init__( self: Optional[Any] , UpperCAmelCase_: Pipeline , UpperCAmelCase_: PipelineDataFormat ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = nlp
_SCREAMING_SNAKE_CASE = reader
@staticmethod
def UpperCamelCase ( UpperCAmelCase_: ArgumentParser ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = parser.add_parser("""run""" , help="""Run a pipeline through the CLI""" )
run_parser.add_argument("""--task""" , choices=get_supported_tasks() , help="""Task to run""" )
run_parser.add_argument("""--input""" , type=UpperCAmelCase_ , help="""Path to the file to use for inference""" )
run_parser.add_argument("""--output""" , type=UpperCAmelCase_ , help="""Path to the file that will be used post to write results.""" )
run_parser.add_argument("""--model""" , type=UpperCAmelCase_ , help="""Name or path to the model to instantiate.""" )
run_parser.add_argument("""--config""" , type=UpperCAmelCase_ , help="""Name or path to the model's config to instantiate.""" )
run_parser.add_argument(
"""--tokenizer""" , type=UpperCAmelCase_ , help="""Name of the tokenizer to use. (default: same as the model name)""" )
run_parser.add_argument(
"""--column""" , type=UpperCAmelCase_ , help="""Name of the column to use as input. (For multi columns input as QA use column1,columns2)""" , )
run_parser.add_argument(
"""--format""" , type=UpperCAmelCase_ , default="""infer""" , choices=PipelineDataFormat.SUPPORTED_FORMATS , help="""Input format to read from""" , )
run_parser.add_argument(
"""--device""" , type=UpperCAmelCase_ , default=-1 , help="""Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)""" , )
run_parser.add_argument("""--overwrite""" , action="""store_true""" , help="""Allow overwriting the output file.""" )
run_parser.set_defaults(func=UpperCAmelCase_ )
def UpperCamelCase ( self: Union[str, Any] ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = self._nlp, []
for entry in self._reader:
_SCREAMING_SNAKE_CASE = nlp(**UpperCAmelCase_ ) if self._reader.is_multi_columns else nlp(UpperCAmelCase_ )
if isinstance(UpperCAmelCase_ , UpperCAmelCase_ ):
outputs.append(UpperCAmelCase_ )
else:
outputs += output
# Saving data
if self._nlp.binary_output:
_SCREAMING_SNAKE_CASE = self._reader.save_binary(UpperCAmelCase_ )
logger.warning(F'Current pipeline requires output to be in binary format, saving at {binary_path}' )
else:
self._reader.save(UpperCAmelCase_ )
| 125 |
import json
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from transformers import OneFormerImageProcessor
from transformers.models.oneformer.image_processing_oneformer import binary_mask_to_rle
from transformers.models.oneformer.modeling_oneformer import OneFormerForUniversalSegmentationOutput
if is_vision_available():
from PIL import Image
def __lowerCamelCase ( snake_case__ ,snake_case__="shi-labs/oneformer_demo" ) -> Union[str, Any]:
"""simple docstring"""
with open(hf_hub_download(snake_case__ ,snake_case__ ,repo_type="""dataset""" ) ,"""r""" ) as f:
_SCREAMING_SNAKE_CASE = json.load(snake_case__ )
_SCREAMING_SNAKE_CASE = {}
_SCREAMING_SNAKE_CASE = []
_SCREAMING_SNAKE_CASE = []
for key, info in class_info.items():
_SCREAMING_SNAKE_CASE = info["""name"""]
class_names.append(info["""name"""] )
if info["isthing"]:
thing_ids.append(int(snake_case__ ) )
_SCREAMING_SNAKE_CASE = thing_ids
_SCREAMING_SNAKE_CASE = class_names
return metadata
class __UpperCAmelCase (unittest.TestCase ):
def __init__( self: List[Any] , UpperCAmelCase_: List[Any] , UpperCAmelCase_: Optional[Any]=7 , UpperCAmelCase_: Union[str, Any]=3 , UpperCAmelCase_: Optional[int]=30 , UpperCAmelCase_: List[str]=400 , UpperCAmelCase_: List[str]=None , UpperCAmelCase_: List[Any]=True , UpperCAmelCase_: Tuple=True , UpperCAmelCase_: Union[str, Any]=[0.5, 0.5, 0.5] , UpperCAmelCase_: int=[0.5, 0.5, 0.5] , UpperCAmelCase_: List[str]=10 , UpperCAmelCase_: Optional[int]=False , UpperCAmelCase_: Optional[int]=255 , UpperCAmelCase_: Tuple="shi-labs/oneformer_demo" , UpperCAmelCase_: Union[str, Any]="ade20k_panoptic.json" , UpperCAmelCase_: Union[str, Any]=10 , ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = parent
_SCREAMING_SNAKE_CASE = batch_size
_SCREAMING_SNAKE_CASE = num_channels
_SCREAMING_SNAKE_CASE = min_resolution
_SCREAMING_SNAKE_CASE = max_resolution
_SCREAMING_SNAKE_CASE = do_resize
_SCREAMING_SNAKE_CASE = {"""shortest_edge""": 32, """longest_edge""": 1_333} if size is None else size
_SCREAMING_SNAKE_CASE = do_normalize
_SCREAMING_SNAKE_CASE = image_mean
_SCREAMING_SNAKE_CASE = image_std
_SCREAMING_SNAKE_CASE = class_info_file
_SCREAMING_SNAKE_CASE = prepare_metadata(UpperCAmelCase_ , UpperCAmelCase_ )
_SCREAMING_SNAKE_CASE = num_text
_SCREAMING_SNAKE_CASE = repo_path
# for the post_process_functions
_SCREAMING_SNAKE_CASE = 2
_SCREAMING_SNAKE_CASE = 10
_SCREAMING_SNAKE_CASE = 10
_SCREAMING_SNAKE_CASE = 3
_SCREAMING_SNAKE_CASE = 4
_SCREAMING_SNAKE_CASE = num_labels
_SCREAMING_SNAKE_CASE = do_reduce_labels
_SCREAMING_SNAKE_CASE = ignore_index
def UpperCamelCase ( self: Optional[int] ):
'''simple docstring'''
return {
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"num_labels": self.num_labels,
"do_reduce_labels": self.do_reduce_labels,
"ignore_index": self.ignore_index,
"class_info_file": self.class_info_file,
"metadata": self.metadata,
"num_text": self.num_text,
}
def UpperCamelCase ( self: int , UpperCAmelCase_: Union[str, Any] , UpperCAmelCase_: List[str]=False ):
'''simple docstring'''
if not batched:
_SCREAMING_SNAKE_CASE = image_inputs[0]
if isinstance(UpperCAmelCase_ , Image.Image ):
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = image.size
else:
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = image.shape[1], image.shape[2]
if w < h:
_SCREAMING_SNAKE_CASE = int(self.size["""shortest_edge"""] * h / w )
_SCREAMING_SNAKE_CASE = self.size["""shortest_edge"""]
elif w > h:
_SCREAMING_SNAKE_CASE = self.size["""shortest_edge"""]
_SCREAMING_SNAKE_CASE = int(self.size["""shortest_edge"""] * w / h )
else:
_SCREAMING_SNAKE_CASE = self.size["""shortest_edge"""]
_SCREAMING_SNAKE_CASE = self.size["""shortest_edge"""]
else:
_SCREAMING_SNAKE_CASE = []
for image in image_inputs:
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
_SCREAMING_SNAKE_CASE = max(UpperCAmelCase_ , key=lambda UpperCAmelCase_ : item[0] )[0]
_SCREAMING_SNAKE_CASE = max(UpperCAmelCase_ , key=lambda UpperCAmelCase_ : item[1] )[1]
return expected_height, expected_width
def UpperCamelCase ( self: Any ):
'''simple docstring'''
return OneFormerForUniversalSegmentationOutput(
# +1 for null class
class_queries_logits=torch.randn((self.batch_size, self.num_queries, self.num_classes + 1) ) , masks_queries_logits=torch.randn((self.batch_size, self.num_queries, self.height, self.width) ) , )
@require_torch
@require_vision
class __UpperCAmelCase (_UpperCAmelCase ,unittest.TestCase ):
__snake_case : Union[str, Any] = OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
# only for test_image_processing_common.test_image_proc_to_json_string
__snake_case : int = image_processing_class
def UpperCamelCase ( self: Optional[int] ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = OneFormerImageProcessorTester(self )
@property
def UpperCamelCase ( self: int ):
'''simple docstring'''
return self.image_processing_tester.prepare_image_processor_dict()
def UpperCamelCase ( self: int ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(UpperCAmelCase_ , """image_mean""" ) )
self.assertTrue(hasattr(UpperCAmelCase_ , """image_std""" ) )
self.assertTrue(hasattr(UpperCAmelCase_ , """do_normalize""" ) )
self.assertTrue(hasattr(UpperCAmelCase_ , """do_resize""" ) )
self.assertTrue(hasattr(UpperCAmelCase_ , """size""" ) )
self.assertTrue(hasattr(UpperCAmelCase_ , """ignore_index""" ) )
self.assertTrue(hasattr(UpperCAmelCase_ , """class_info_file""" ) )
self.assertTrue(hasattr(UpperCAmelCase_ , """num_text""" ) )
self.assertTrue(hasattr(UpperCAmelCase_ , """repo_path""" ) )
self.assertTrue(hasattr(UpperCAmelCase_ , """metadata""" ) )
self.assertTrue(hasattr(UpperCAmelCase_ , """do_reduce_labels""" ) )
def UpperCamelCase ( self: Optional[int] ):
'''simple docstring'''
pass
def UpperCamelCase ( self: Optional[Any] ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
_SCREAMING_SNAKE_CASE = prepare_image_inputs(self.image_processing_tester , equal_resolution=UpperCAmelCase_ )
for image in image_inputs:
self.assertIsInstance(UpperCAmelCase_ , Image.Image )
# Test not batched input
_SCREAMING_SNAKE_CASE = image_processor(image_inputs[0] , ["""semantic"""] , return_tensors="""pt""" ).pixel_values
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = self.image_processing_tester.get_expected_values(UpperCAmelCase_ )
self.assertEqual(
encoded_images.shape , (1, self.image_processing_tester.num_channels, expected_height, expected_width) , )
# Test batched
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = self.image_processing_tester.get_expected_values(UpperCAmelCase_ , batched=UpperCAmelCase_ )
_SCREAMING_SNAKE_CASE = image_processor(
UpperCAmelCase_ , ["""semantic"""] * len(UpperCAmelCase_ ) , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processing_tester.batch_size,
self.image_processing_tester.num_channels,
expected_height,
expected_width,
) , )
def UpperCamelCase ( self: int ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
_SCREAMING_SNAKE_CASE = prepare_image_inputs(self.image_processing_tester , equal_resolution=UpperCAmelCase_ , numpify=UpperCAmelCase_ )
for image in image_inputs:
self.assertIsInstance(UpperCAmelCase_ , np.ndarray )
# Test not batched input
_SCREAMING_SNAKE_CASE = image_processor(image_inputs[0] , ["""semantic"""] , return_tensors="""pt""" ).pixel_values
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = self.image_processing_tester.get_expected_values(UpperCAmelCase_ )
self.assertEqual(
encoded_images.shape , (1, self.image_processing_tester.num_channels, expected_height, expected_width) , )
# Test batched
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = self.image_processing_tester.get_expected_values(UpperCAmelCase_ , batched=UpperCAmelCase_ )
_SCREAMING_SNAKE_CASE = image_processor(
UpperCAmelCase_ , ["""semantic"""] * len(UpperCAmelCase_ ) , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processing_tester.batch_size,
self.image_processing_tester.num_channels,
expected_height,
expected_width,
) , )
def UpperCamelCase ( self: Tuple ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
_SCREAMING_SNAKE_CASE = prepare_image_inputs(self.image_processing_tester , equal_resolution=UpperCAmelCase_ , torchify=UpperCAmelCase_ )
for image in image_inputs:
self.assertIsInstance(UpperCAmelCase_ , torch.Tensor )
# Test not batched input
_SCREAMING_SNAKE_CASE = image_processor(image_inputs[0] , ["""semantic"""] , return_tensors="""pt""" ).pixel_values
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = self.image_processing_tester.get_expected_values(UpperCAmelCase_ )
self.assertEqual(
encoded_images.shape , (1, self.image_processing_tester.num_channels, expected_height, expected_width) , )
# Test batched
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = self.image_processing_tester.get_expected_values(UpperCAmelCase_ , batched=UpperCAmelCase_ )
_SCREAMING_SNAKE_CASE = image_processor(
UpperCAmelCase_ , ["""semantic"""] * len(UpperCAmelCase_ ) , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processing_tester.batch_size,
self.image_processing_tester.num_channels,
expected_height,
expected_width,
) , )
def UpperCamelCase ( self: Optional[Any] , UpperCAmelCase_: Tuple=False , UpperCAmelCase_: Any=False , UpperCAmelCase_: str="np" ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = self.image_processing_class(**self.image_processor_dict )
# prepare image and target
_SCREAMING_SNAKE_CASE = self.image_processing_tester.num_labels
_SCREAMING_SNAKE_CASE = None
_SCREAMING_SNAKE_CASE = None
_SCREAMING_SNAKE_CASE = prepare_image_inputs(self.image_processing_tester , equal_resolution=UpperCAmelCase_ )
if with_segmentation_maps:
_SCREAMING_SNAKE_CASE = num_labels
if is_instance_map:
_SCREAMING_SNAKE_CASE = list(range(UpperCAmelCase_ ) ) * 2
_SCREAMING_SNAKE_CASE = dict(enumerate(UpperCAmelCase_ ) )
_SCREAMING_SNAKE_CASE = [
np.random.randint(0 , high * 2 , (img.size[1], img.size[0]) ).astype(np.uinta ) for img in image_inputs
]
if segmentation_type == "pil":
_SCREAMING_SNAKE_CASE = [Image.fromarray(UpperCAmelCase_ ) for annotation in annotations]
_SCREAMING_SNAKE_CASE = image_processor(
UpperCAmelCase_ , ["""semantic"""] * len(UpperCAmelCase_ ) , UpperCAmelCase_ , return_tensors="""pt""" , instance_id_to_semantic_id=UpperCAmelCase_ , pad_and_return_pixel_mask=UpperCAmelCase_ , )
return inputs
def UpperCamelCase ( self: Union[str, Any] ):
'''simple docstring'''
pass
def UpperCamelCase ( self: Any ):
'''simple docstring'''
def common(UpperCAmelCase_: List[str]=False , UpperCAmelCase_: Optional[int]=None ):
_SCREAMING_SNAKE_CASE = self.comm_get_image_processor_inputs(
with_segmentation_maps=UpperCAmelCase_ , is_instance_map=UpperCAmelCase_ , segmentation_type=UpperCAmelCase_ )
_SCREAMING_SNAKE_CASE = inputs["""mask_labels"""]
_SCREAMING_SNAKE_CASE = inputs["""class_labels"""]
_SCREAMING_SNAKE_CASE = inputs["""pixel_values"""]
_SCREAMING_SNAKE_CASE = inputs["""text_inputs"""]
# check the batch_size
for mask_label, class_label, text_input in zip(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ ):
self.assertEqual(mask_label.shape[0] , class_label.shape[0] )
# this ensure padding has happened
self.assertEqual(mask_label.shape[1:] , pixel_values.shape[2:] )
self.assertEqual(len(UpperCAmelCase_ ) , self.image_processing_tester.num_text )
common()
common(is_instance_map=UpperCAmelCase_ )
common(is_instance_map=UpperCAmelCase_ , segmentation_type="""pil""" )
common(is_instance_map=UpperCAmelCase_ , segmentation_type="""pil""" )
def UpperCamelCase ( self: Any ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = np.zeros((20, 50) )
_SCREAMING_SNAKE_CASE = 1
_SCREAMING_SNAKE_CASE = 1
_SCREAMING_SNAKE_CASE = 1
_SCREAMING_SNAKE_CASE = binary_mask_to_rle(UpperCAmelCase_ )
self.assertEqual(len(UpperCAmelCase_ ) , 4 )
self.assertEqual(rle[0] , 21 )
self.assertEqual(rle[1] , 45 )
def UpperCamelCase ( self: str ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = self.image_processing_class(
num_labels=self.image_processing_tester.num_classes , max_seq_length=77 , task_seq_length=77 , class_info_file="""ade20k_panoptic.json""" , num_text=self.image_processing_tester.num_text , repo_path="""shi-labs/oneformer_demo""" , )
_SCREAMING_SNAKE_CASE = self.image_processing_tester.get_fake_oneformer_outputs()
_SCREAMING_SNAKE_CASE = fature_extractor.post_process_semantic_segmentation(UpperCAmelCase_ )
self.assertEqual(len(UpperCAmelCase_ ) , self.image_processing_tester.batch_size )
self.assertEqual(
segmentation[0].shape , (
self.image_processing_tester.height,
self.image_processing_tester.width,
) , )
_SCREAMING_SNAKE_CASE = [(1, 4) for i in range(self.image_processing_tester.batch_size )]
_SCREAMING_SNAKE_CASE = fature_extractor.post_process_semantic_segmentation(UpperCAmelCase_ , target_sizes=UpperCAmelCase_ )
self.assertEqual(segmentation[0].shape , target_sizes[0] )
def UpperCamelCase ( self: Union[str, Any] ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = self.image_processing_class(
num_labels=self.image_processing_tester.num_classes , max_seq_length=77 , task_seq_length=77 , class_info_file="""ade20k_panoptic.json""" , num_text=self.image_processing_tester.num_text , repo_path="""shi-labs/oneformer_demo""" , )
_SCREAMING_SNAKE_CASE = self.image_processing_tester.get_fake_oneformer_outputs()
_SCREAMING_SNAKE_CASE = image_processor.post_process_instance_segmentation(UpperCAmelCase_ , threshold=0 )
self.assertTrue(len(UpperCAmelCase_ ) == self.image_processing_tester.batch_size )
for el in segmentation:
self.assertTrue("""segmentation""" in el )
self.assertTrue("""segments_info""" in el )
self.assertEqual(type(el["""segments_info"""] ) , UpperCAmelCase_ )
self.assertEqual(
el["""segmentation"""].shape , (self.image_processing_tester.height, self.image_processing_tester.width) )
def UpperCamelCase ( self: List[Any] ):
'''simple docstring'''
_SCREAMING_SNAKE_CASE = self.image_processing_class(
num_labels=self.image_processing_tester.num_classes , max_seq_length=77 , task_seq_length=77 , class_info_file="""ade20k_panoptic.json""" , num_text=self.image_processing_tester.num_text , repo_path="""shi-labs/oneformer_demo""" , )
_SCREAMING_SNAKE_CASE = self.image_processing_tester.get_fake_oneformer_outputs()
_SCREAMING_SNAKE_CASE = image_processor.post_process_panoptic_segmentation(UpperCAmelCase_ , threshold=0 )
self.assertTrue(len(UpperCAmelCase_ ) == self.image_processing_tester.batch_size )
for el in segmentation:
self.assertTrue("""segmentation""" in el )
self.assertTrue("""segments_info""" in el )
self.assertEqual(type(el["""segments_info"""] ) , UpperCAmelCase_ )
self.assertEqual(
el["""segmentation"""].shape , (self.image_processing_tester.height, self.image_processing_tester.width) )
| 125 | 1 |
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
SCREAMING_SNAKE_CASE_:str = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_:Union[str, Any] = {"""vocab_file""": """spiece.model"""}
SCREAMING_SNAKE_CASE_:Optional[Any] = {
"""vocab_file""": {
"""albert-base-v1""": """https://huggingface.co/albert-base-v1/resolve/main/spiece.model""",
"""albert-large-v1""": """https://huggingface.co/albert-large-v1/resolve/main/spiece.model""",
"""albert-xlarge-v1""": """https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model""",
"""albert-xxlarge-v1""": """https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model""",
"""albert-base-v2""": """https://huggingface.co/albert-base-v2/resolve/main/spiece.model""",
"""albert-large-v2""": """https://huggingface.co/albert-large-v2/resolve/main/spiece.model""",
"""albert-xlarge-v2""": """https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model""",
"""albert-xxlarge-v2""": """https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model""",
}
}
SCREAMING_SNAKE_CASE_:List[Any] = {
"""albert-base-v1""": 512,
"""albert-large-v1""": 512,
"""albert-xlarge-v1""": 512,
"""albert-xxlarge-v1""": 512,
"""albert-base-v2""": 512,
"""albert-large-v2""": 512,
"""albert-xlarge-v2""": 512,
"""albert-xxlarge-v2""": 512,
}
SCREAMING_SNAKE_CASE_:int = """▁"""
class SCREAMING_SNAKE_CASE__ ( _lowercase ):
'''simple docstring'''
__lowerCamelCase : Optional[Any] = VOCAB_FILES_NAMES
__lowerCamelCase : Dict = PRETRAINED_VOCAB_FILES_MAP
__lowerCamelCase : Any = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
def __init__( self, lowerCamelCase__, lowerCamelCase__=True, lowerCamelCase__=True, lowerCamelCase__=False, lowerCamelCase__="[CLS]", lowerCamelCase__="[SEP]", lowerCamelCase__="<unk>", lowerCamelCase__="[SEP]", lowerCamelCase__="<pad>", lowerCamelCase__="[CLS]", lowerCamelCase__="[MASK]", lowerCamelCase__ = None, **lowerCamelCase__, ):
# Mask token behave like a normal word, i.e. include the space before it and
# is included in the raw text, there should be a match in a non-normalized sentence.
A : Any = (
AddedToken(_SCREAMING_SNAKE_CASE, lstrip=_SCREAMING_SNAKE_CASE, rstrip=_SCREAMING_SNAKE_CASE, normalized=_SCREAMING_SNAKE_CASE )
if isinstance(_SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE )
else mask_token
)
A : List[Any] = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
do_lower_case=_SCREAMING_SNAKE_CASE, remove_space=_SCREAMING_SNAKE_CASE, keep_accents=_SCREAMING_SNAKE_CASE, bos_token=_SCREAMING_SNAKE_CASE, eos_token=_SCREAMING_SNAKE_CASE, unk_token=_SCREAMING_SNAKE_CASE, sep_token=_SCREAMING_SNAKE_CASE, pad_token=_SCREAMING_SNAKE_CASE, cls_token=_SCREAMING_SNAKE_CASE, mask_token=_SCREAMING_SNAKE_CASE, sp_model_kwargs=self.sp_model_kwargs, **_SCREAMING_SNAKE_CASE, )
A : Tuple = do_lower_case
A : Optional[int] = remove_space
A : str = keep_accents
A : Optional[Any] = vocab_file
A : Optional[Any] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(_SCREAMING_SNAKE_CASE )
@property
def _lowerCAmelCase ( self ):
return len(self.sp_model )
def _lowerCAmelCase ( self ):
A : List[str] = {self.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self ):
A : int = self.__dict__.copy()
A : Tuple = None
return state
def __setstate__( self, lowerCamelCase__ ):
A : int = d
# for backward compatibility
if not hasattr(self, """sp_model_kwargs""" ):
A : Optional[Any] = {}
A : List[Any] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def _lowerCAmelCase ( self, lowerCamelCase__ ):
if self.remove_space:
A : Optional[int] = ''' '''.join(inputs.strip().split() )
else:
A : Any = inputs
A : List[Any] = outputs.replace("""``""", """\"""" ).replace("""\'\'""", """\"""" )
if not self.keep_accents:
A : int = unicodedata.normalize("""NFKD""", _SCREAMING_SNAKE_CASE )
A : List[str] = ''''''.join([c for c in outputs if not unicodedata.combining(_SCREAMING_SNAKE_CASE )] )
if self.do_lower_case:
A : Any = outputs.lower()
return outputs
def _lowerCAmelCase ( self, lowerCamelCase__ ):
A : int = self.preprocess_text(_SCREAMING_SNAKE_CASE )
A : int = self.sp_model.encode(_SCREAMING_SNAKE_CASE, out_type=_SCREAMING_SNAKE_CASE )
A : str = []
for piece in pieces:
if len(_SCREAMING_SNAKE_CASE ) > 1 and piece[-1] == str(""",""" ) and piece[-2].isdigit():
A : Optional[Any] = self.sp_model.EncodeAsPieces(piece[:-1].replace(_SCREAMING_SNAKE_CASE, """""" ) )
if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
if len(cur_pieces[0] ) == 1:
A : Optional[Any] = cur_pieces[1:]
else:
A : List[str] = cur_pieces[0][1:]
cur_pieces.append(piece[-1] )
new_pieces.extend(_SCREAMING_SNAKE_CASE )
else:
new_pieces.append(_SCREAMING_SNAKE_CASE )
return new_pieces
def _lowerCAmelCase ( self, lowerCamelCase__ ):
return self.sp_model.PieceToId(_SCREAMING_SNAKE_CASE )
def _lowerCAmelCase ( self, lowerCamelCase__ ):
return self.sp_model.IdToPiece(_SCREAMING_SNAKE_CASE )
def _lowerCAmelCase ( self, lowerCamelCase__ ):
A : str = []
A : Any = ''''''
A : str = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(_SCREAMING_SNAKE_CASE ) + token
A : int = True
A : Any = []
else:
current_sub_tokens.append(_SCREAMING_SNAKE_CASE )
A : Union[str, Any] = False
out_string += self.sp_model.decode(_SCREAMING_SNAKE_CASE )
return out_string.strip()
def _lowerCAmelCase ( self, lowerCamelCase__, lowerCamelCase__ = None ):
A : List[Any] = [self.sep_token_id]
A : str = [self.cls_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def _lowerCAmelCase ( self, lowerCamelCase__, lowerCamelCase__ = None, lowerCamelCase__ = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_SCREAMING_SNAKE_CASE, token_ids_a=_SCREAMING_SNAKE_CASE, already_has_special_tokens=_SCREAMING_SNAKE_CASE )
if token_ids_a is not None:
return [1] + ([0] * len(_SCREAMING_SNAKE_CASE )) + [1] + ([0] * len(_SCREAMING_SNAKE_CASE )) + [1]
return [1] + ([0] * len(_SCREAMING_SNAKE_CASE )) + [1]
def _lowerCAmelCase ( self, lowerCamelCase__, lowerCamelCase__ = None ):
A : Any = [self.sep_token_id]
A : str = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def _lowerCAmelCase ( self, lowerCamelCase__, lowerCamelCase__ = None ):
if not os.path.isdir(_SCREAMING_SNAKE_CASE ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
A : List[Any] = os.path.join(
_SCREAMING_SNAKE_CASE, (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_SCREAMING_SNAKE_CASE ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file, _SCREAMING_SNAKE_CASE )
elif not os.path.isfile(self.vocab_file ):
with open(_SCREAMING_SNAKE_CASE, """wb""" ) as fi:
A : Optional[Any] = self.sp_model.serialized_model_proto()
fi.write(_SCREAMING_SNAKE_CASE )
return (out_vocab_file,)
| 116 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_fnet import FNetTokenizer
else:
lowerCamelCase = None
lowerCamelCase = logging.get_logger(__name__)
lowerCamelCase = {'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}
lowerCamelCase = {
'''vocab_file''': {
'''google/fnet-base''': '''https://huggingface.co/google/fnet-base/resolve/main/spiece.model''',
'''google/fnet-large''': '''https://huggingface.co/google/fnet-large/resolve/main/spiece.model''',
},
'''tokenizer_file''': {
'''google/fnet-base''': '''https://huggingface.co/google/fnet-base/resolve/main/tokenizer.json''',
'''google/fnet-large''': '''https://huggingface.co/google/fnet-large/resolve/main/tokenizer.json''',
},
}
lowerCamelCase = {
'''google/fnet-base''': 512,
'''google/fnet-large''': 512,
}
lowerCamelCase = '''▁'''
class _a ( _lowercase):
_a : List[str] = VOCAB_FILES_NAMES
_a : Union[str, Any] = PRETRAINED_VOCAB_FILES_MAP
_a : str = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_a : Union[str, Any] = ['''input_ids''', '''token_type_ids''']
_a : Dict = FNetTokenizer
def __init__( self : Optional[Any] , _SCREAMING_SNAKE_CASE : str=None , _SCREAMING_SNAKE_CASE : str=None , _SCREAMING_SNAKE_CASE : Optional[Any]=False , _SCREAMING_SNAKE_CASE : Tuple=True , _SCREAMING_SNAKE_CASE : Optional[int]=True , _SCREAMING_SNAKE_CASE : List[Any]="<unk>" , _SCREAMING_SNAKE_CASE : str="[SEP]" , _SCREAMING_SNAKE_CASE : str="<pad>" , _SCREAMING_SNAKE_CASE : Union[str, Any]="[CLS]" , _SCREAMING_SNAKE_CASE : List[str]="[MASK]" , **_SCREAMING_SNAKE_CASE : str , )-> Any:
# Mask token behave like a normal word, i.e. include the space before it and
# is included in the raw text, there should be a match in a non-normalized sentence.
lowerCAmelCase__ : List[str] = (
AddedToken(_SCREAMING_SNAKE_CASE , lstrip=_SCREAMING_SNAKE_CASE , rstrip=_SCREAMING_SNAKE_CASE , normalized=_SCREAMING_SNAKE_CASE )
if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
else mask_token
)
super().__init__(
_SCREAMING_SNAKE_CASE , tokenizer_file=_SCREAMING_SNAKE_CASE , do_lower_case=_SCREAMING_SNAKE_CASE , remove_space=_SCREAMING_SNAKE_CASE , keep_accents=_SCREAMING_SNAKE_CASE , unk_token=_SCREAMING_SNAKE_CASE , sep_token=_SCREAMING_SNAKE_CASE , pad_token=_SCREAMING_SNAKE_CASE , cls_token=_SCREAMING_SNAKE_CASE , mask_token=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE , )
lowerCAmelCase__ : Optional[int] = do_lower_case
lowerCAmelCase__ : Any = remove_space
lowerCAmelCase__ : Union[str, Any] = keep_accents
lowerCAmelCase__ : int = vocab_file
lowerCAmelCase__ : List[str] = False if not self.vocab_file else True
def UpperCAmelCase__( self : Union[str, Any] , _SCREAMING_SNAKE_CASE : List[int] , _SCREAMING_SNAKE_CASE : Optional[List[int]] = None )-> List[int]:
lowerCAmelCase__ : Optional[int] = [self.sep_token_id]
lowerCAmelCase__ : Dict = [self.cls_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def UpperCAmelCase__( self : Any , _SCREAMING_SNAKE_CASE : List[int] , _SCREAMING_SNAKE_CASE : Optional[List[int]] = None )-> List[int]:
lowerCAmelCase__ : List[Any] = [self.sep_token_id]
lowerCAmelCase__ : Tuple = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def UpperCAmelCase__( self : Tuple , _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : Optional[str] = None )-> Tuple[str]:
if not os.path.isdir(_SCREAMING_SNAKE_CASE ):
logger.error(F'Vocabulary path ({save_directory}) should be a directory' )
return
lowerCAmelCase__ : Optional[Any] = os.path.join(
_SCREAMING_SNAKE_CASE , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_SCREAMING_SNAKE_CASE ):
copyfile(self.vocab_file , _SCREAMING_SNAKE_CASE )
return (out_vocab_file,)
| 131 | 0 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
torch.set_grad_enabled(False)
def lowerCamelCase_ ( UpperCamelCase__ : Dict , UpperCamelCase__ : Union[str, Any]=False ) -> List[str]:
"""simple docstring"""
__lowerCamelCase = []
for i in range(config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((F"""module.blocks.{i}.norm1.weight""", F"""vit.encoder.layer.{i}.layernorm_before.weight""") )
rename_keys.append((F"""module.blocks.{i}.norm1.bias""", F"""vit.encoder.layer.{i}.layernorm_before.bias""") )
rename_keys.append(
(F"""module.blocks.{i}.attn.proj.weight""", F"""vit.encoder.layer.{i}.attention.output.dense.weight""") )
rename_keys.append((F"""module.blocks.{i}.attn.proj.bias""", F"""vit.encoder.layer.{i}.attention.output.dense.bias""") )
rename_keys.append((F"""module.blocks.{i}.norm2.weight""", F"""vit.encoder.layer.{i}.layernorm_after.weight""") )
rename_keys.append((F"""module.blocks.{i}.norm2.bias""", F"""vit.encoder.layer.{i}.layernorm_after.bias""") )
rename_keys.append((F"""module.blocks.{i}.mlp.fc1.weight""", F"""vit.encoder.layer.{i}.intermediate.dense.weight""") )
rename_keys.append((F"""module.blocks.{i}.mlp.fc1.bias""", F"""vit.encoder.layer.{i}.intermediate.dense.bias""") )
rename_keys.append((F"""module.blocks.{i}.mlp.fc2.weight""", F"""vit.encoder.layer.{i}.output.dense.weight""") )
rename_keys.append((F"""module.blocks.{i}.mlp.fc2.bias""", F"""vit.encoder.layer.{i}.output.dense.bias""") )
# projection layer + position embeddings
rename_keys.extend(
[
('module.cls_token', 'vit.embeddings.cls_token'),
('module.patch_embed.proj.weight', 'vit.embeddings.patch_embeddings.projection.weight'),
('module.patch_embed.proj.bias', 'vit.embeddings.patch_embeddings.projection.bias'),
('module.pos_embed', 'vit.embeddings.position_embeddings'),
] )
if base_model:
# layernorm + pooler
rename_keys.extend(
[
('module.norm.weight', 'layernorm.weight'),
('module.norm.bias', 'layernorm.bias'),
] )
# if just the base model, we should remove "vit" from all keys that start with "vit"
__lowerCamelCase = [(pair[0], pair[1][4:]) if pair[1].startswith('vit' ) else pair for pair in rename_keys]
else:
# layernorm + classification head
rename_keys.extend(
[
('norm.weight', 'vit.layernorm.weight'),
('norm.bias', 'vit.layernorm.bias'),
('head.weight', 'classifier.weight'),
('head.bias', 'classifier.bias'),
] )
return rename_keys
def lowerCamelCase_ ( UpperCamelCase__ : Tuple , UpperCamelCase__ : List[Any] , UpperCamelCase__ : Tuple=False ) -> Tuple:
"""simple docstring"""
for i in range(config.num_hidden_layers ):
if base_model:
__lowerCamelCase = ''
else:
__lowerCamelCase = 'vit.'
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
__lowerCamelCase = state_dict.pop(F"""module.blocks.{i}.attn.qkv.weight""" )
__lowerCamelCase = state_dict.pop(F"""module.blocks.{i}.attn.qkv.bias""" )
# next, add query, keys and values (in that order) to the state dict
__lowerCamelCase = in_proj_weight[
: config.hidden_size, :
]
__lowerCamelCase = in_proj_bias[: config.hidden_size]
__lowerCamelCase = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
__lowerCamelCase = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
__lowerCamelCase = in_proj_weight[
-config.hidden_size :, :
]
__lowerCamelCase = in_proj_bias[-config.hidden_size :]
def lowerCamelCase_ ( UpperCamelCase__ : Optional[Any] ) -> List[Any]:
"""simple docstring"""
__lowerCamelCase = ['head.weight', 'head.bias']
for k in ignore_keys:
state_dict.pop(UpperCamelCase__ , UpperCamelCase__ )
def lowerCamelCase_ ( UpperCamelCase__ : Dict ) -> Dict:
"""simple docstring"""
__lowerCamelCase = [
'module.fc.fc1.weight',
'module.fc.fc1.bias',
'module.fc.bn1.weight',
'module.fc.bn1.bias',
'module.fc.bn1.running_mean',
'module.fc.bn1.running_var',
'module.fc.bn1.num_batches_tracked',
'module.fc.fc2.weight',
'module.fc.fc2.bias',
'module.fc.bn2.weight',
'module.fc.bn2.bias',
'module.fc.bn2.running_mean',
'module.fc.bn2.running_var',
'module.fc.bn2.num_batches_tracked',
'module.fc.fc3.weight',
'module.fc.fc3.bias',
]
for k in ignore_keys:
state_dict.pop(UpperCamelCase__ , UpperCamelCase__ )
def lowerCamelCase_ ( UpperCamelCase__ : Tuple , UpperCamelCase__ : int , UpperCamelCase__ : Union[str, Any] ) -> str:
"""simple docstring"""
__lowerCamelCase = dct.pop(UpperCamelCase__ )
__lowerCamelCase = val
def lowerCamelCase_ ( UpperCamelCase__ : List[str] , UpperCamelCase__ : int ) -> Any:
"""simple docstring"""
__lowerCamelCase = ViTMSNConfig()
__lowerCamelCase = 1000
__lowerCamelCase = 'datasets/huggingface/label-files'
__lowerCamelCase = 'imagenet-1k-id2label.json'
__lowerCamelCase = json.load(open(hf_hub_download(UpperCamelCase__ , UpperCamelCase__ ) , 'r' ) )
__lowerCamelCase = {int(UpperCamelCase__ ): v for k, v in idalabel.items()}
__lowerCamelCase = idalabel
__lowerCamelCase = {v: k for k, v in idalabel.items()}
if "s16" in checkpoint_url:
__lowerCamelCase = 384
__lowerCamelCase = 1536
__lowerCamelCase = 6
elif "l16" in checkpoint_url:
__lowerCamelCase = 1024
__lowerCamelCase = 4096
__lowerCamelCase = 24
__lowerCamelCase = 16
__lowerCamelCase = 0.1
elif "b4" in checkpoint_url:
__lowerCamelCase = 4
elif "l7" in checkpoint_url:
__lowerCamelCase = 7
__lowerCamelCase = 1024
__lowerCamelCase = 4096
__lowerCamelCase = 24
__lowerCamelCase = 16
__lowerCamelCase = 0.1
__lowerCamelCase = ViTMSNModel(UpperCamelCase__ )
__lowerCamelCase = torch.hub.load_state_dict_from_url(UpperCamelCase__ , map_location='cpu' )['target_encoder']
__lowerCamelCase = ViTImageProcessor(size=config.image_size )
remove_projection_head(UpperCamelCase__ )
__lowerCamelCase = create_rename_keys(UpperCamelCase__ , base_model=UpperCamelCase__ )
for src, dest in rename_keys:
rename_key(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
read_in_q_k_v(UpperCamelCase__ , UpperCamelCase__ , base_model=UpperCamelCase__ )
model.load_state_dict(UpperCamelCase__ )
model.eval()
__lowerCamelCase = 'http://images.cocodataset.org/val2017/000000039769.jpg'
__lowerCamelCase = Image.open(requests.get(UpperCamelCase__ , stream=UpperCamelCase__ ).raw )
__lowerCamelCase = ViTImageProcessor(
size=config.image_size , image_mean=UpperCamelCase__ , image_std=UpperCamelCase__ )
__lowerCamelCase = image_processor(images=UpperCamelCase__ , return_tensors='pt' )
# forward pass
torch.manual_seed(2 )
__lowerCamelCase = model(**UpperCamelCase__ )
__lowerCamelCase = outputs.last_hidden_state
# The following Colab Notebook was used to generate these outputs:
# https://colab.research.google.com/gist/sayakpaul/3672419a04f5997827503fd84079bdd1/scratchpad.ipynb
if "s16" in checkpoint_url:
__lowerCamelCase = torch.tensor([[-1.09_15, -1.48_76, -1.18_09]] )
elif "b16" in checkpoint_url:
__lowerCamelCase = torch.tensor([[14.28_89, -18.90_45, 11.72_81]] )
elif "l16" in checkpoint_url:
__lowerCamelCase = torch.tensor([[41.50_28, -22.86_81, 45.64_75]] )
elif "b4" in checkpoint_url:
__lowerCamelCase = torch.tensor([[-4.38_68, 5.29_32, -0.41_37]] )
else:
__lowerCamelCase = torch.tensor([[-0.17_92, -0.64_65, 2.42_63]] )
# verify logits
assert torch.allclose(last_hidden_state[:, 0, :3] , UpperCamelCase__ , atol=1E-4 )
print(F"""Saving model to {pytorch_dump_folder_path}""" )
model.save_pretrained(UpperCamelCase__ )
print(F"""Saving image processor to {pytorch_dump_folder_path}""" )
image_processor.save_pretrained(UpperCamelCase__ )
if __name__ == "__main__":
__A = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--checkpoint_url",
default="https://dl.fbaipublicfiles.com/msn/vits16_800ep.pth.tar",
type=str,
help="URL of the checkpoint you'd like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
)
__A = parser.parse_args()
convert_vit_msn_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
| 348 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Sequence, Value
from .base import TaskTemplate
@dataclass(frozen=__magic_name__ )
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
snake_case_ = field(default='''question-answering-extractive''' , metadata={'''include_in_asdict_even_if_is_default''': True} )
snake_case_ = Features({'''question''': Value('''string''' ), '''context''': Value('''string''' )} )
snake_case_ = Features(
{
'''answers''': Sequence(
{
'''text''': Value('''string''' ),
'''answer_start''': Value('''int32''' ),
} )
} )
snake_case_ = "question"
snake_case_ = "context"
snake_case_ = "answers"
@property
def lowercase_ ( self ) -> Dict[str, str]:
'''simple docstring'''
return {self.question_column: "question", self.context_column: "context", self.answers_column: "answers"}
| 348 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase = logging.get_logger(__name__)
lowerCAmelCase = {
"""facebook/timesformer""": """https://huggingface.co/facebook/timesformer/resolve/main/config.json""",
}
class A_ ( A__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ = """timesformer"""
def __init__( self :int , lowerCamelCase_ :str=224 , lowerCamelCase_ :Tuple=16 , lowerCamelCase_ :List[str]=3 , lowerCamelCase_ :List[str]=8 , lowerCamelCase_ :List[Any]=768 , lowerCamelCase_ :str=12 , lowerCamelCase_ :str=12 , lowerCamelCase_ :List[Any]=3_072 , lowerCamelCase_ :int="gelu" , lowerCamelCase_ :str=0.0 , lowerCamelCase_ :Tuple=0.0 , lowerCamelCase_ :Optional[int]=0.02 , lowerCamelCase_ :List[str]=1e-6 , lowerCamelCase_ :Optional[Any]=True , lowerCamelCase_ :Optional[int]="divided_space_time" , lowerCamelCase_ :Any=0 , **lowerCamelCase_ :Tuple , ):
"""simple docstring"""
super().__init__(**lowerCamelCase_ )
lowerCamelCase__ : List[Any] =image_size
lowerCamelCase__ : Any =patch_size
lowerCamelCase__ : Any =num_channels
lowerCamelCase__ : Optional[int] =num_frames
lowerCamelCase__ : int =hidden_size
lowerCamelCase__ : Any =num_hidden_layers
lowerCamelCase__ : Optional[int] =num_attention_heads
lowerCamelCase__ : int =intermediate_size
lowerCamelCase__ : Any =hidden_act
lowerCamelCase__ : List[Any] =hidden_dropout_prob
lowerCamelCase__ : Optional[int] =attention_probs_dropout_prob
lowerCamelCase__ : int =initializer_range
lowerCamelCase__ : List[Any] =layer_norm_eps
lowerCamelCase__ : Union[str, Any] =qkv_bias
lowerCamelCase__ : Tuple =attention_type
lowerCamelCase__ : List[str] =drop_path_rate | 126 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_DEFAULT_MEAN,
IMAGENET_DEFAULT_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_vision_available():
import PIL
lowerCAmelCase = logging.get_logger(__name__)
class A_ ( A__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ = ["""pixel_values"""]
def __init__( self :Union[str, Any] , lowerCamelCase_ :bool = True , lowerCamelCase_ :Dict[str, int] = None , lowerCamelCase_ :int = 0.9 , lowerCamelCase_ :PILImageResampling = PILImageResampling.BICUBIC , lowerCamelCase_ :bool = True , lowerCamelCase_ :Dict[str, int] = None , lowerCamelCase_ :Union[int, float] = 1 / 255 , lowerCamelCase_ :bool = True , lowerCamelCase_ :bool = True , lowerCamelCase_ :Optional[Union[float, List[float]]] = None , lowerCamelCase_ :Optional[Union[float, List[float]]] = None , **lowerCamelCase_ :Tuple , ):
"""simple docstring"""
super().__init__(**lowerCamelCase_ )
lowerCamelCase__ : str =size if size is not None else {'shortest_edge': 224}
lowerCamelCase__ : List[str] =get_size_dict(lowerCamelCase_ , default_to_square=lowerCamelCase_ )
lowerCamelCase__ : Union[str, Any] =crop_size if crop_size is not None else {'height': 224, 'width': 224}
lowerCamelCase__ : str =get_size_dict(lowerCamelCase_ , param_name='crop_size' )
lowerCamelCase__ : Tuple =do_resize
lowerCamelCase__ : List[Any] =size
lowerCamelCase__ : List[str] =crop_pct
lowerCamelCase__ : Union[str, Any] =resample
lowerCamelCase__ : List[str] =do_center_crop
lowerCamelCase__ : List[str] =crop_size
lowerCamelCase__ : List[Any] =do_rescale
lowerCamelCase__ : List[str] =rescale_factor
lowerCamelCase__ : Tuple =do_normalize
lowerCamelCase__ : int =image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
lowerCamelCase__ : List[Any] =image_std if image_std is not None else IMAGENET_DEFAULT_STD
def UpperCAmelCase__ ( self :Any , lowerCamelCase_ :np.ndarray , lowerCamelCase_ :Dict[str, int] , lowerCamelCase_ :Optional[float] = None , lowerCamelCase_ :PILImageResampling = PILImageResampling.BICUBIC , lowerCamelCase_ :Optional[Union[str, ChannelDimension]] = None , **lowerCamelCase_ :Any , ):
"""simple docstring"""
lowerCamelCase__ : Union[str, Any] =get_size_dict(lowerCamelCase_ , default_to_square=lowerCamelCase_ )
if "shortest_edge" not in size and ("height" not in size or "width" not in size):
raise ValueError(f"""size must contain 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}""" )
if crop_pct is not None:
if "shortest_edge" in size:
lowerCamelCase__ : Optional[int] =int(size['shortest_edge'] / crop_pct )
elif "height" in size and "width" in size:
if size["height"] == size["width"]:
lowerCamelCase__ : Union[str, Any] =int(size['height'] / crop_pct )
else:
lowerCamelCase__ : Any =(int(size['height'] / crop_pct ), int(size['width'] / crop_pct ))
else:
raise ValueError('Invalid size for resize: {}'.format(lowerCamelCase_ ) )
lowerCamelCase__ : Tuple =get_resize_output_image_size(lowerCamelCase_ , size=lowerCamelCase_ , default_to_square=lowerCamelCase_ )
else:
if "shortest_edge" in size:
lowerCamelCase__ : str =get_resize_output_image_size(lowerCamelCase_ , size=size['shortest_edge'] , default_to_square=lowerCamelCase_ )
elif "height" in size and "width" in size:
lowerCamelCase__ : Union[str, Any] =(size['height'], size['width'])
else:
raise ValueError('Invalid size for resize: {}'.format(lowerCamelCase_ ) )
return resize(lowerCamelCase_ , size=lowerCamelCase_ , resample=lowerCamelCase_ , data_format=lowerCamelCase_ , **lowerCamelCase_ )
def UpperCAmelCase__ ( self :Any , lowerCamelCase_ :np.ndarray , lowerCamelCase_ :Dict[str, int] , lowerCamelCase_ :Optional[Union[str, ChannelDimension]] = None , **lowerCamelCase_ :str , ):
"""simple docstring"""
lowerCamelCase__ : Tuple =get_size_dict(lowerCamelCase_ )
if "height" not in size or "width" not in size:
raise ValueError(f"""size must contain 'height' and 'width' as keys. Got {size.keys()}""" )
return center_crop(lowerCamelCase_ , size=(size['height'], size['width']) , data_format=lowerCamelCase_ , **lowerCamelCase_ )
def UpperCAmelCase__ ( self :int , lowerCamelCase_ :np.ndarray , lowerCamelCase_ :Union[int, float] , lowerCamelCase_ :Optional[Union[str, ChannelDimension]] = None , **lowerCamelCase_ :List[str] , ):
"""simple docstring"""
return rescale(lowerCamelCase_ , scale=lowerCamelCase_ , data_format=lowerCamelCase_ , **lowerCamelCase_ )
def UpperCAmelCase__ ( self :List[Any] , lowerCamelCase_ :np.ndarray , lowerCamelCase_ :Union[float, List[float]] , lowerCamelCase_ :Union[float, List[float]] , lowerCamelCase_ :Optional[Union[str, ChannelDimension]] = None , **lowerCamelCase_ :Tuple , ):
"""simple docstring"""
return normalize(lowerCamelCase_ , mean=lowerCamelCase_ , std=lowerCamelCase_ , data_format=lowerCamelCase_ , **lowerCamelCase_ )
def UpperCAmelCase__ ( self :Any , lowerCamelCase_ :ImageInput , lowerCamelCase_ :bool = None , lowerCamelCase_ :Dict[str, int] = None , lowerCamelCase_ :int = None , lowerCamelCase_ :PILImageResampling = None , lowerCamelCase_ :bool = None , lowerCamelCase_ :Dict[str, int] = None , lowerCamelCase_ :bool = None , lowerCamelCase_ :float = None , lowerCamelCase_ :bool = None , lowerCamelCase_ :Optional[Union[float, List[float]]] = None , lowerCamelCase_ :Optional[Union[float, List[float]]] = None , lowerCamelCase_ :Optional[Union[str, TensorType]] = None , lowerCamelCase_ :ChannelDimension = ChannelDimension.FIRST , **lowerCamelCase_ :List[str] , ):
"""simple docstring"""
lowerCamelCase__ : Dict =do_resize if do_resize is not None else self.do_resize
lowerCamelCase__ : Union[str, Any] =crop_pct if crop_pct is not None else self.crop_pct
lowerCamelCase__ : Tuple =resample if resample is not None else self.resample
lowerCamelCase__ : Any =do_center_crop if do_center_crop is not None else self.do_center_crop
lowerCamelCase__ : Optional[Any] =do_rescale if do_rescale is not None else self.do_rescale
lowerCamelCase__ : Optional[int] =rescale_factor if rescale_factor is not None else self.rescale_factor
lowerCamelCase__ : Optional[Any] =do_normalize if do_normalize is not None else self.do_normalize
lowerCamelCase__ : List[str] =image_mean if image_mean is not None else self.image_mean
lowerCamelCase__ : List[Any] =image_std if image_std is not None else self.image_std
lowerCamelCase__ : int =size if size is not None else self.size
lowerCamelCase__ : Tuple =get_size_dict(lowerCamelCase_ , default_to_square=lowerCamelCase_ )
lowerCamelCase__ : Dict =crop_size if crop_size is not None else self.crop_size
lowerCamelCase__ : str =get_size_dict(lowerCamelCase_ , param_name='crop_size' )
lowerCamelCase__ : Dict =make_list_of_images(lowerCamelCase_ )
if not valid_images(lowerCamelCase_ ):
raise ValueError(
'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
'torch.Tensor, tf.Tensor or jax.ndarray.' )
if do_resize and size is None or resample is None:
raise ValueError('Size and resample must be specified if do_resize is True.' )
if do_center_crop and crop_pct is None:
raise ValueError('Crop_pct must be specified if do_center_crop is True.' )
if do_rescale and rescale_factor is None:
raise ValueError('Rescale factor must be specified if do_rescale is True.' )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('Image mean and std must be specified if do_normalize is True.' )
# All transformations expect numpy arrays.
lowerCamelCase__ : List[str] =[to_numpy_array(lowerCamelCase_ ) for image in images]
if do_resize:
lowerCamelCase__ : Tuple =[self.resize(image=lowerCamelCase_ , size=lowerCamelCase_ , crop_pct=lowerCamelCase_ , resample=lowerCamelCase_ ) for image in images]
if do_center_crop:
lowerCamelCase__ : Union[str, Any] =[self.center_crop(image=lowerCamelCase_ , size=lowerCamelCase_ ) for image in images]
if do_rescale:
lowerCamelCase__ : str =[self.rescale(image=lowerCamelCase_ , scale=lowerCamelCase_ ) for image in images]
if do_normalize:
lowerCamelCase__ : Optional[Any] =[self.normalize(image=lowerCamelCase_ , mean=lowerCamelCase_ , std=lowerCamelCase_ ) for image in images]
lowerCamelCase__ : Optional[Any] =[to_channel_dimension_format(lowerCamelCase_ , lowerCamelCase_ ) for image in images]
lowerCamelCase__ : List[str] ={'pixel_values': images}
return BatchFeature(data=lowerCamelCase_ , tensor_type=lowerCamelCase_ ) | 126 | 1 |
'''simple docstring'''
from __future__ import annotations
import math
def SCREAMING_SNAKE_CASE__ ( snake_case : Optional[Any] , snake_case : List[str] ) -> List[Any]:
"""simple docstring"""
a : Optional[int] = u
for i in range(1 , __a ):
a : Optional[Any] = temp * (u - i)
return temp
def SCREAMING_SNAKE_CASE__ ( ) -> Tuple:
"""simple docstring"""
a : Dict = int(input('enter the numbers of values: ' ) )
a : list[list[float]] = []
for _ in range(__a ):
y.append([] )
for i in range(__a ):
for j in range(__a ):
y[i].append(__a )
a : str = 0
print('enter the values of parameters in a list: ' )
a : int = list(map(__a , input().split() ) )
print('enter the values of corresponding parameters: ' )
for i in range(__a ):
a : Union[str, Any] = float(input() )
a : int = int(input('enter the value to interpolate: ' ) )
a : List[Any] = (value - x[0]) / (x[1] - x[0])
# for calculating forward difference table
for i in range(1 , __a ):
for j in range(n - i ):
a : int = y[j + 1][i - 1] - y[j][i - 1]
a : str = y[0][0]
for i in range(1 , __a ):
summ += (ucal(__a , __a ) * y[0][i]) / math.factorial(__a )
print(F"""the value at {value} is {summ}""" )
if __name__ == "__main__":
main()
| 350 | '''simple docstring'''
import faiss # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import requests # noqa: F401 # Here to have a nice missing dependency error message early on
import sklearn # noqa: F401 # Here to have a nice missing dependency error message early on
import tqdm # noqa: F401 # Here to have a nice missing dependency error message early on
from mauve import compute_mauve # From: mauve-text
import datasets
UpperCamelCase : Optional[int] = """\
@inproceedings{pillutla-etal:mauve:neurips2021,
title={MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers},
author={Pillutla, Krishna and Swayamdipta, Swabha and Zellers, Rowan and Thickstun, John and Welleck, Sean and Choi, Yejin and Harchaoui, Zaid},
booktitle = {NeurIPS},
year = {2021}
}
"""
UpperCamelCase : Optional[Any] = """\
MAUVE is a library built on PyTorch and HuggingFace Transformers to measure the gap between neural text and human text with the eponymous MAUVE measure.
MAUVE summarizes both Type I and Type II errors measured softly using Kullback–Leibler (KL) divergences.
For details, see the MAUVE paper: https://arxiv.org/abs/2102.01454 (Neurips, 2021).
This metrics is a wrapper around the official implementation of MAUVE:
https://github.com/krishnap25/mauve
"""
UpperCamelCase : str = """
Calculates MAUVE scores between two lists of generated text and reference text.
Args:
predictions: list of generated text to score. Each predictions
should be a string with tokens separated by spaces.
references: list of reference for each prediction. Each
reference should be a string with tokens separated by spaces.
Optional Args:
num_buckets: the size of the histogram to quantize P and Q. Options: 'auto' (default) or an integer
pca_max_data: the number data points to use for PCA dimensionality reduction prior to clustering. If -1, use all the data. Default -1
kmeans_explained_var: amount of variance of the data to keep in dimensionality reduction by PCA. Default 0.9
kmeans_num_redo: number of times to redo k-means clustering (the best objective is kept). Default 5
kmeans_max_iter: maximum number of k-means iterations. Default 500
featurize_model_name: name of the model from which features are obtained. Default 'gpt2-large' Use one of ['gpt2', 'gpt2-medium', 'gpt2-large', 'gpt2-xl'].
device_id: Device for featurization. Supply a GPU id (e.g. 0 or 3) to use GPU. If no GPU with this id is found, use CPU
max_text_length: maximum number of tokens to consider. Default 1024
divergence_curve_discretization_size: Number of points to consider on the divergence curve. Default 25
mauve_scaling_factor: \"c\" from the paper. Default 5.
verbose: If True (default), print running time updates
seed: random seed to initialize k-means cluster assignments.
Returns:
mauve: MAUVE score, a number between 0 and 1. Larger values indicate that P and Q are closer,
frontier_integral: Frontier Integral, a number between 0 and 1. Smaller values indicate that P and Q are closer,
divergence_curve: a numpy.ndarray of shape (m, 2); plot it with matplotlib to view the divergence curve,
p_hist: a discrete distribution, which is a quantized version of the text distribution p_text,
q_hist: same as above, but with q_text.
Examples:
>>> # faiss segfaults in doctest for some reason, so the .compute call is not tested with doctest
>>> import datasets
>>> mauve = datasets.load_metric('mauve')
>>> predictions = [\"hello there\", \"general kenobi\"]
>>> references = [\"hello there\", \"general kenobi\"]
>>> out = mauve.compute(predictions=predictions, references=references) # doctest: +SKIP
>>> print(out.mauve) # doctest: +SKIP
1.0
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class UpperCamelCase ( datasets.Metric ):
"""simple docstring"""
def SCREAMING_SNAKE_CASE_ ( self : Optional[int]):
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , homepage='https://github.com/krishnap25/mauve' , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'predictions': datasets.Value('string' , id='sequence'),
'references': datasets.Value('string' , id='sequence'),
}) , codebase_urls=['https://github.com/krishnap25/mauve'] , reference_urls=[
'https://arxiv.org/abs/2102.01454',
'https://github.com/krishnap25/mauve',
] , )
def SCREAMING_SNAKE_CASE_ ( self : str , UpperCAmelCase_ : Union[str, Any] , UpperCAmelCase_ : Any , UpperCAmelCase_ : Optional[Any]=None , UpperCAmelCase_ : int=None , UpperCAmelCase_ : Any=None , UpperCAmelCase_ : Optional[int]=None , UpperCAmelCase_ : Tuple="auto" , UpperCAmelCase_ : Any=-1 , UpperCAmelCase_ : Optional[int]=0.9 , UpperCAmelCase_ : Union[str, Any]=5 , UpperCAmelCase_ : int=5_0_0 , UpperCAmelCase_ : int="gpt2-large" , UpperCAmelCase_ : Tuple=-1 , UpperCAmelCase_ : Dict=1_0_2_4 , UpperCAmelCase_ : List[str]=2_5 , UpperCAmelCase_ : int=5 , UpperCAmelCase_ : Any=True , UpperCAmelCase_ : str=2_5 , ):
"""simple docstring"""
a : List[str] = compute_mauve(
p_text=UpperCAmelCase_ , q_text=UpperCAmelCase_ , p_features=UpperCAmelCase_ , q_features=UpperCAmelCase_ , p_tokens=UpperCAmelCase_ , q_tokens=UpperCAmelCase_ , num_buckets=UpperCAmelCase_ , pca_max_data=UpperCAmelCase_ , kmeans_explained_var=UpperCAmelCase_ , kmeans_num_redo=UpperCAmelCase_ , kmeans_max_iter=UpperCAmelCase_ , featurize_model_name=UpperCAmelCase_ , device_id=UpperCAmelCase_ , max_text_length=UpperCAmelCase_ , divergence_curve_discretization_size=UpperCAmelCase_ , mauve_scaling_factor=UpperCAmelCase_ , verbose=UpperCAmelCase_ , seed=UpperCAmelCase_ , )
return out
| 345 | 0 |
def __lowercase ( __lowerCAmelCase : str ):
return "".join(chr(ord(__lowerCAmelCase ) - 3_2 ) if 'a' <= char <= 'z' else char for char in word )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 240 |
'''simple docstring'''
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class __SCREAMING_SNAKE_CASE (lowerCamelCase_ ):
"""simple docstring"""
__a =['image_processor', 'tokenizer']
__a ='LayoutLMv3ImageProcessor'
__a =('LayoutLMv3Tokenizer', 'LayoutLMv3TokenizerFast')
def __init__( self : Tuple , __a : int=None , __a : Union[str, Any]=None , **__a : Optional[Any] ):
_a = None
if "feature_extractor" in kwargs:
warnings.warn(
"The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
" instead." , __a , )
_a = kwargs.pop("feature_extractor" )
_a = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError("You need to specify an `image_processor`." )
if tokenizer is None:
raise ValueError("You need to specify a `tokenizer`." )
super().__init__(__a , __a )
def __call__( self : Any , __a : List[str] , __a : Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None , __a : Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None , __a : Union[List[List[int]], List[List[List[int]]]] = None , __a : Optional[Union[List[int], List[List[int]]]] = None , __a : bool = True , __a : Union[bool, str, PaddingStrategy] = False , __a : Union[bool, str, TruncationStrategy] = None , __a : Optional[int] = None , __a : int = 0 , __a : Optional[int] = None , __a : Optional[bool] = None , __a : Optional[bool] = None , __a : bool = False , __a : bool = False , __a : bool = False , __a : bool = False , __a : bool = True , __a : Optional[Union[str, TensorType]] = None , **__a : Dict , ):
# verify input
if self.image_processor.apply_ocr and (boxes is not None):
raise ValueError(
"You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True." )
if self.image_processor.apply_ocr and (word_labels is not None):
raise ValueError(
"You cannot provide word labels if you initialized the image processor with apply_ocr set to True." )
# first, apply the image processor
_a = self.image_processor(images=__a , return_tensors=__a )
# second, apply the tokenizer
if text is not None and self.image_processor.apply_ocr and text_pair is None:
if isinstance(__a , __a ):
_a = [text] # add batch dimension (as the image processor always adds a batch dimension)
_a = features["words"]
_a = self.tokenizer(
text=text if text is not None else features["words"] , text_pair=text_pair if text_pair is not None else None , boxes=boxes if boxes is not None else features["boxes"] , word_labels=__a , add_special_tokens=__a , padding=__a , truncation=__a , max_length=__a , stride=__a , pad_to_multiple_of=__a , return_token_type_ids=__a , return_attention_mask=__a , return_overflowing_tokens=__a , return_special_tokens_mask=__a , return_offsets_mapping=__a , return_length=__a , verbose=__a , return_tensors=__a , **__a , )
# add pixel values
_a = features.pop("pixel_values" )
if return_overflowing_tokens is True:
_a = self.get_overflowing_images(__a , encoded_inputs["overflow_to_sample_mapping"] )
_a = images
return encoded_inputs
def UpperCamelCase__ ( self : Optional[int] , __a : str , __a : List[Any] ):
# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
_a = []
for sample_idx in overflow_to_sample_mapping:
images_with_overflow.append(images[sample_idx] )
if len(__a ) != len(__a ):
raise ValueError(
"Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"
f' {len(__a )} and {len(__a )}' )
return images_with_overflow
def UpperCamelCase__ ( self : int , *__a : str , **__a : Tuple ):
return self.tokenizer.batch_decode(*__a , **__a )
def UpperCamelCase__ ( self : str , *__a : List[Any] , **__a : List[str] ):
return self.tokenizer.decode(*__a , **__a )
@property
def UpperCamelCase__ ( self : Tuple ):
return ["input_ids", "bbox", "attention_mask", "pixel_values"]
@property
def UpperCamelCase__ ( self : int ):
warnings.warn(
"`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead." , __a , )
return self.image_processor_class
@property
def UpperCamelCase__ ( self : List[str] ):
warnings.warn(
"`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead." , __a , )
return self.image_processor
| 63 | 0 |
import unittest
from transformers import XLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
XLMForMultipleChoice,
XLMForQuestionAnswering,
XLMForQuestionAnsweringSimple,
XLMForSequenceClassification,
XLMForTokenClassification,
XLMModel,
XLMWithLMHeadModel,
)
from transformers.models.xlm.modeling_xlm import XLM_PRETRAINED_MODEL_ARCHIVE_LIST
class SCREAMING_SNAKE_CASE__ :
def __init__( self : Optional[int] , SCREAMING_SNAKE_CASE__ : Any , SCREAMING_SNAKE_CASE__ : int=1_3 , SCREAMING_SNAKE_CASE__ : List[str]=7 , SCREAMING_SNAKE_CASE__ : str=True , SCREAMING_SNAKE_CASE__ : Optional[Any]=True , SCREAMING_SNAKE_CASE__ : int=True , SCREAMING_SNAKE_CASE__ : Tuple=True , SCREAMING_SNAKE_CASE__ : int=True , SCREAMING_SNAKE_CASE__ : List[Any]=False , SCREAMING_SNAKE_CASE__ : List[Any]=False , SCREAMING_SNAKE_CASE__ : Optional[Any]=False , SCREAMING_SNAKE_CASE__ : int=2 , SCREAMING_SNAKE_CASE__ : Union[str, Any]=9_9 , SCREAMING_SNAKE_CASE__ : Dict=0 , SCREAMING_SNAKE_CASE__ : List[str]=3_2 , SCREAMING_SNAKE_CASE__ : Dict=5 , SCREAMING_SNAKE_CASE__ : Tuple=4 , SCREAMING_SNAKE_CASE__ : Dict=0.1 , SCREAMING_SNAKE_CASE__ : Dict=0.1 , SCREAMING_SNAKE_CASE__ : str=5_1_2 , SCREAMING_SNAKE_CASE__ : Any=2 , SCREAMING_SNAKE_CASE__ : Any=0.02 , SCREAMING_SNAKE_CASE__ : Tuple=2 , SCREAMING_SNAKE_CASE__ : Optional[Any]=4 , SCREAMING_SNAKE_CASE__ : List[Any]="last" , SCREAMING_SNAKE_CASE__ : List[str]=True , SCREAMING_SNAKE_CASE__ : Union[str, Any]=None , SCREAMING_SNAKE_CASE__ : List[str]=0 , ) -> List[str]:
a_ : List[Any] = parent
a_ : int = batch_size
a_ : Union[str, Any] = seq_length
a_ : Union[str, Any] = is_training
a_ : Optional[int] = use_input_lengths
a_ : List[Any] = use_token_type_ids
a_ : str = use_labels
a_ : List[str] = gelu_activation
a_ : Tuple = sinusoidal_embeddings
a_ : Optional[Any] = causal
a_ : Union[str, Any] = asm
a_ : Dict = n_langs
a_ : Tuple = vocab_size
a_ : Optional[int] = n_special
a_ : Optional[Any] = hidden_size
a_ : int = num_hidden_layers
a_ : int = num_attention_heads
a_ : int = hidden_dropout_prob
a_ : Dict = attention_probs_dropout_prob
a_ : int = max_position_embeddings
a_ : Dict = type_sequence_label_size
a_ : List[Any] = initializer_range
a_ : List[Any] = num_labels
a_ : Dict = num_choices
a_ : Optional[int] = summary_type
a_ : int = use_proj
a_ : Dict = scope
a_ : Any = bos_token_id
def SCREAMING_SNAKE_CASE ( self : Tuple ) -> Optional[int]:
a_ : int = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
a_ : Optional[int] = random_attention_mask([self.batch_size, self.seq_length] )
a_ : List[Any] = None
if self.use_input_lengths:
a_ : Any = (
ids_tensor([self.batch_size] , vocab_size=2 ) + self.seq_length - 2
) # small variation of seq_length
a_ : Optional[Any] = None
if self.use_token_type_ids:
a_ : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.n_langs )
a_ : int = None
a_ : List[Any] = None
a_ : Tuple = None
if self.use_labels:
a_ : Dict = ids_tensor([self.batch_size] , self.type_sequence_label_size )
a_ : Any = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
a_ : List[Any] = ids_tensor([self.batch_size] , 2 ).float()
a_ : Any = ids_tensor([self.batch_size] , self.num_choices )
a_ : Tuple = self.get_config()
return (
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
)
def SCREAMING_SNAKE_CASE ( self : List[str] ) -> Union[str, Any]:
return XLMConfig(
vocab_size=self.vocab_size , n_special=self.n_special , emb_dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , gelu_activation=self.gelu_activation , sinusoidal_embeddings=self.sinusoidal_embeddings , asm=self.asm , causal=self.causal , n_langs=self.n_langs , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , summary_type=self.summary_type , use_proj=self.use_proj , num_labels=self.num_labels , bos_token_id=self.bos_token_id , )
def SCREAMING_SNAKE_CASE ( self : Any , SCREAMING_SNAKE_CASE__ : List[Any] , SCREAMING_SNAKE_CASE__ : Dict , SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : Union[str, Any] , SCREAMING_SNAKE_CASE__ : Dict , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : List[Any] , SCREAMING_SNAKE_CASE__ : Optional[Any] , ) -> Optional[int]:
a_ : Dict = XLMModel(config=SCREAMING_SNAKE_CASE__ )
model.to(SCREAMING_SNAKE_CASE__ )
model.eval()
a_ : int = model(SCREAMING_SNAKE_CASE__ , lengths=SCREAMING_SNAKE_CASE__ , langs=SCREAMING_SNAKE_CASE__ )
a_ : Tuple = model(SCREAMING_SNAKE_CASE__ , langs=SCREAMING_SNAKE_CASE__ )
a_ : Union[str, Any] = model(SCREAMING_SNAKE_CASE__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def SCREAMING_SNAKE_CASE ( self : List[str] , SCREAMING_SNAKE_CASE__ : Dict , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : Dict , SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : Optional[Any] , SCREAMING_SNAKE_CASE__ : List[Any] , SCREAMING_SNAKE_CASE__ : Optional[Any] , ) -> Any:
a_ : Tuple = XLMWithLMHeadModel(SCREAMING_SNAKE_CASE__ )
model.to(SCREAMING_SNAKE_CASE__ )
model.eval()
a_ : str = model(SCREAMING_SNAKE_CASE__ , token_type_ids=SCREAMING_SNAKE_CASE__ , labels=SCREAMING_SNAKE_CASE__ )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def SCREAMING_SNAKE_CASE ( self : Any , SCREAMING_SNAKE_CASE__ : Any , SCREAMING_SNAKE_CASE__ : Dict , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : List[Any] , SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : Union[str, Any] , SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : Optional[Any] , SCREAMING_SNAKE_CASE__ : str , ) -> List[str]:
a_ : Tuple = XLMForQuestionAnsweringSimple(SCREAMING_SNAKE_CASE__ )
model.to(SCREAMING_SNAKE_CASE__ )
model.eval()
a_ : int = model(SCREAMING_SNAKE_CASE__ )
a_ : int = model(SCREAMING_SNAKE_CASE__ , start_positions=SCREAMING_SNAKE_CASE__ , end_positions=SCREAMING_SNAKE_CASE__ )
a_ : str = outputs
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def SCREAMING_SNAKE_CASE ( self : List[str] , SCREAMING_SNAKE_CASE__ : Dict , SCREAMING_SNAKE_CASE__ : List[Any] , SCREAMING_SNAKE_CASE__ : Dict , SCREAMING_SNAKE_CASE__ : List[Any] , SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : List[Any] , SCREAMING_SNAKE_CASE__ : Optional[int] , SCREAMING_SNAKE_CASE__ : Union[str, Any] , SCREAMING_SNAKE_CASE__ : str , ) -> Dict:
a_ : str = XLMForQuestionAnswering(SCREAMING_SNAKE_CASE__ )
model.to(SCREAMING_SNAKE_CASE__ )
model.eval()
a_ : Optional[int] = model(SCREAMING_SNAKE_CASE__ )
a_ : Optional[int] = model(
SCREAMING_SNAKE_CASE__ , start_positions=SCREAMING_SNAKE_CASE__ , end_positions=SCREAMING_SNAKE_CASE__ , cls_index=SCREAMING_SNAKE_CASE__ , is_impossible=SCREAMING_SNAKE_CASE__ , p_mask=SCREAMING_SNAKE_CASE__ , )
a_ : Optional[Any] = model(
SCREAMING_SNAKE_CASE__ , start_positions=SCREAMING_SNAKE_CASE__ , end_positions=SCREAMING_SNAKE_CASE__ , cls_index=SCREAMING_SNAKE_CASE__ , is_impossible=SCREAMING_SNAKE_CASE__ , )
((a_) , ) : Dict = result_with_labels.to_tuple()
a_ : str = model(SCREAMING_SNAKE_CASE__ , start_positions=SCREAMING_SNAKE_CASE__ , end_positions=SCREAMING_SNAKE_CASE__ )
((a_) , ) : List[str] = result_with_labels.to_tuple()
self.parent.assertEqual(result_with_labels.loss.shape , () )
self.parent.assertEqual(result.start_top_log_probs.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(result.start_top_index.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(
result.end_top_log_probs.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(
result.end_top_index.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(result.cls_logits.shape , (self.batch_size,) )
def SCREAMING_SNAKE_CASE ( self : Optional[int] , SCREAMING_SNAKE_CASE__ : List[Any] , SCREAMING_SNAKE_CASE__ : List[Any] , SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : Any , SCREAMING_SNAKE_CASE__ : Dict , SCREAMING_SNAKE_CASE__ : List[Any] , SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : List[Any] , ) -> int:
a_ : str = XLMForSequenceClassification(SCREAMING_SNAKE_CASE__ )
model.to(SCREAMING_SNAKE_CASE__ )
model.eval()
a_ : Dict = model(SCREAMING_SNAKE_CASE__ )
a_ : Optional[Any] = model(SCREAMING_SNAKE_CASE__ , labels=SCREAMING_SNAKE_CASE__ )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def SCREAMING_SNAKE_CASE ( self : Optional[Any] , SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : Optional[Any] , SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : Any , SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : Dict , SCREAMING_SNAKE_CASE__ : Optional[Any] , SCREAMING_SNAKE_CASE__ : Tuple , ) -> List[Any]:
a_ : Optional[Any] = self.num_labels
a_ : Dict = XLMForTokenClassification(SCREAMING_SNAKE_CASE__ )
model.to(SCREAMING_SNAKE_CASE__ )
model.eval()
a_ : Tuple = model(SCREAMING_SNAKE_CASE__ , attention_mask=SCREAMING_SNAKE_CASE__ , labels=SCREAMING_SNAKE_CASE__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def SCREAMING_SNAKE_CASE ( self : str , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : List[Any] , SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : Optional[int] , SCREAMING_SNAKE_CASE__ : Optional[int] , SCREAMING_SNAKE_CASE__ : Dict , SCREAMING_SNAKE_CASE__ : Dict , SCREAMING_SNAKE_CASE__ : Optional[int] , ) -> Union[str, Any]:
a_ : List[Any] = self.num_choices
a_ : Optional[int] = XLMForMultipleChoice(config=SCREAMING_SNAKE_CASE__ )
model.to(SCREAMING_SNAKE_CASE__ )
model.eval()
a_ : Optional[Any] = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
a_ : List[str] = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
a_ : int = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
a_ : List[str] = model(
SCREAMING_SNAKE_CASE__ , attention_mask=SCREAMING_SNAKE_CASE__ , token_type_ids=SCREAMING_SNAKE_CASE__ , labels=SCREAMING_SNAKE_CASE__ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def SCREAMING_SNAKE_CASE ( self : Optional[int] ) -> Any:
a_ : List[Any] = self.prepare_config_and_inputs()
(
(
a_
) , (
a_
) , (
a_
) , (
a_
) , (
a_
) , (
a_
) , (
a_
) , (
a_
) , (
a_
) ,
) : Tuple = config_and_inputs
a_ : Optional[Any] = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'lengths': input_lengths}
return config, inputs_dict
@require_torch
class SCREAMING_SNAKE_CASE__ ( lowercase__ , lowercase__ , lowercase__ , unittest.TestCase ):
snake_case__ : int = (
(
XLMModel,
XLMWithLMHeadModel,
XLMForQuestionAnswering,
XLMForSequenceClassification,
XLMForQuestionAnsweringSimple,
XLMForTokenClassification,
XLMForMultipleChoice,
)
if is_torch_available()
else ()
)
snake_case__ : Optional[int] = (
(XLMWithLMHeadModel,) if is_torch_available() else ()
) # TODO (PVP): Check other models whether language generation is also applicable
snake_case__ : List[Any] = (
{
'''feature-extraction''': XLMModel,
'''fill-mask''': XLMWithLMHeadModel,
'''question-answering''': XLMForQuestionAnsweringSimple,
'''text-classification''': XLMForSequenceClassification,
'''text-generation''': XLMWithLMHeadModel,
'''token-classification''': XLMForTokenClassification,
'''zero-shot''': XLMForSequenceClassification,
}
if is_torch_available()
else {}
)
def SCREAMING_SNAKE_CASE ( self : Tuple , SCREAMING_SNAKE_CASE__ : List[Any] , SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : Any , SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : Optional[Any] ) -> Union[str, Any]:
if (
pipeline_test_casse_name == "QAPipelineTests"
and tokenizer_name is not None
and not tokenizer_name.endswith('Fast' )
):
# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
return True
return False
def SCREAMING_SNAKE_CASE ( self : Dict , SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : List[Any]=False ) -> List[Any]:
a_ : Optional[Any] = super()._prepare_for_class(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , return_labels=SCREAMING_SNAKE_CASE__ )
if return_labels:
if model_class.__name__ == "XLMForQuestionAnswering":
a_ : Any = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=SCREAMING_SNAKE_CASE__ )
a_ : Optional[Any] = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=SCREAMING_SNAKE_CASE__ )
return inputs_dict
def SCREAMING_SNAKE_CASE ( self : str ) -> Any:
a_ : Any = XLMModelTester(self )
a_ : List[str] = ConfigTester(self , config_class=SCREAMING_SNAKE_CASE__ , emb_dim=3_7 )
def SCREAMING_SNAKE_CASE ( self : str ) -> List[Any]:
self.config_tester.run_common_tests()
def SCREAMING_SNAKE_CASE ( self : Optional[int] ) -> List[str]:
a_ : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_model(*SCREAMING_SNAKE_CASE__ )
def SCREAMING_SNAKE_CASE ( self : Optional[Any] ) -> int:
a_ : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_lm_head(*SCREAMING_SNAKE_CASE__ )
def SCREAMING_SNAKE_CASE ( self : str ) -> str:
a_ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_simple_qa(*SCREAMING_SNAKE_CASE__ )
def SCREAMING_SNAKE_CASE ( self : List[str] ) -> str:
a_ : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_qa(*SCREAMING_SNAKE_CASE__ )
def SCREAMING_SNAKE_CASE ( self : Tuple ) -> int:
a_ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_sequence_classif(*SCREAMING_SNAKE_CASE__ )
def SCREAMING_SNAKE_CASE ( self : Optional[int] ) -> int:
a_ : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_token_classif(*SCREAMING_SNAKE_CASE__ )
def SCREAMING_SNAKE_CASE ( self : Tuple ) -> List[Any]:
a_ : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_for_multiple_choice(*SCREAMING_SNAKE_CASE__ )
def SCREAMING_SNAKE_CASE ( self : Tuple , SCREAMING_SNAKE_CASE__ : Dict , SCREAMING_SNAKE_CASE__ : Optional[int] , SCREAMING_SNAKE_CASE__ : Dict , SCREAMING_SNAKE_CASE__ : Optional[int] , SCREAMING_SNAKE_CASE__ : Optional[int] , SCREAMING_SNAKE_CASE__ : Optional[int]=False , SCREAMING_SNAKE_CASE__ : int=1 ) -> int:
self.assertIsInstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
self.assertListEqual(
[isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) for iter_attentions in attentions] , [True] * len(SCREAMING_SNAKE_CASE__ ) )
self.assertEqual(len(SCREAMING_SNAKE_CASE__ ) , (max_length - min_length) * num_beam_groups )
for idx, iter_attentions in enumerate(SCREAMING_SNAKE_CASE__ ):
# adds PAD dummy token
a_ : int = min_length + idx + 1
a_ : str = min_length + idx + 1
a_ : Any = (
batch_size * num_beam_groups,
config.num_attention_heads,
tgt_len,
src_len,
)
# check attn size
self.assertListEqual(
[layer_attention.shape for layer_attention in iter_attentions] , [expected_shape] * len(SCREAMING_SNAKE_CASE__ ) )
def SCREAMING_SNAKE_CASE ( self : Optional[int] , SCREAMING_SNAKE_CASE__ : Tuple , SCREAMING_SNAKE_CASE__ : Optional[Any] , SCREAMING_SNAKE_CASE__ : Any , SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : Optional[int] , SCREAMING_SNAKE_CASE__ : int=False , SCREAMING_SNAKE_CASE__ : Any=1 ) -> int:
self.assertIsInstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
self.assertListEqual(
[isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) for iter_hidden_states in hidden_states] , [True] * len(SCREAMING_SNAKE_CASE__ ) , )
self.assertEqual(len(SCREAMING_SNAKE_CASE__ ) , (max_length - min_length) * num_beam_groups )
for idx, iter_hidden_states in enumerate(SCREAMING_SNAKE_CASE__ ):
# adds PAD dummy token
a_ : List[str] = min_length + idx + 1
a_ : Dict = (batch_size * num_beam_groups, seq_len, config.hidden_size)
# check hidden size
self.assertListEqual(
[layer_hidden_states.shape for layer_hidden_states in iter_hidden_states] , [expected_shape] * len(SCREAMING_SNAKE_CASE__ ) , )
pass
@slow
def SCREAMING_SNAKE_CASE ( self : Optional[int] ) -> int:
for model_name in XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
a_ : List[Any] = XLMModel.from_pretrained(SCREAMING_SNAKE_CASE__ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE__ )
@require_torch
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
@slow
def SCREAMING_SNAKE_CASE ( self : str ) -> Dict:
a_ : Tuple = XLMWithLMHeadModel.from_pretrained('xlm-mlm-en-2048' )
model.to(SCREAMING_SNAKE_CASE__ )
a_ : Optional[Any] = torch.tensor([[1_4, 4_4_7]] , dtype=torch.long , device=SCREAMING_SNAKE_CASE__ ) # the president
a_ : List[str] = [
1_4,
4_4_7,
1_4,
4_4_7,
1_4,
4_4_7,
1_4,
4_4_7,
1_4,
4_4_7,
1_4,
4_4_7,
1_4,
4_4_7,
1_4,
4_4_7,
1_4,
4_4_7,
1_4,
4_4_7,
] # the president the president the president the president the president the president the president the president the president the president
# TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
a_ : Dict = model.generate(SCREAMING_SNAKE_CASE__ , do_sample=SCREAMING_SNAKE_CASE__ )
self.assertListEqual(output_ids[0].cpu().numpy().tolist() , SCREAMING_SNAKE_CASE__ )
| 120 |
def SCREAMING_SNAKE_CASE_ ( __A : int , __A : int ) -> int:
"""simple docstring"""
while b:
a_ , a_ : int = b, a % b
return a
def SCREAMING_SNAKE_CASE_ ( __A : int , __A : int ) -> int:
"""simple docstring"""
return a if b == 0 else euclidean_gcd_recursive(__A , a % b )
def SCREAMING_SNAKE_CASE_ ( ) -> str:
"""simple docstring"""
print(F"""euclidean_gcd(3, 5) = {euclidean_gcd(3 , 5 )}""" )
print(F"""euclidean_gcd(5, 3) = {euclidean_gcd(5 , 3 )}""" )
print(F"""euclidean_gcd(1, 3) = {euclidean_gcd(1 , 3 )}""" )
print(F"""euclidean_gcd(3, 6) = {euclidean_gcd(3 , 6 )}""" )
print(F"""euclidean_gcd(6, 3) = {euclidean_gcd(6 , 3 )}""" )
print(F"""euclidean_gcd_recursive(3, 5) = {euclidean_gcd_recursive(3 , 5 )}""" )
print(F"""euclidean_gcd_recursive(5, 3) = {euclidean_gcd_recursive(5 , 3 )}""" )
print(F"""euclidean_gcd_recursive(1, 3) = {euclidean_gcd_recursive(1 , 3 )}""" )
print(F"""euclidean_gcd_recursive(3, 6) = {euclidean_gcd_recursive(3 , 6 )}""" )
print(F"""euclidean_gcd_recursive(6, 3) = {euclidean_gcd_recursive(6 , 3 )}""" )
if __name__ == "__main__":
main()
| 120 | 1 |
'''simple docstring'''
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE = {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt'}
# See all BART models at https://huggingface.co/models?filter=bart
_SCREAMING_SNAKE_CASE = {
'vocab_file': {
'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/vocab.json',
'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/vocab.json',
'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json',
'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json',
'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json',
'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json',
},
'merges_file': {
'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/merges.txt',
'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/merges.txt',
'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt',
'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt',
'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt',
'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt',
},
}
_SCREAMING_SNAKE_CASE = {
'facebook/bart-base': 10_24,
'facebook/bart-large': 10_24,
'facebook/bart-large-mnli': 10_24,
'facebook/bart-large-cnn': 10_24,
'facebook/bart-large-xsum': 10_24,
'yjernite/bart_eli5': 10_24,
}
@lru_cache()
def __a():
'''simple docstring'''
_lowerCAmelCase = (
list(range(ord("!" ) , ord("~" ) + 1 ) ) + list(range(ord("¡" ) , ord("¬" ) + 1 ) ) + list(range(ord("®" ) , ord("ÿ" ) + 1 ) )
)
_lowerCAmelCase = bs[:]
_lowerCAmelCase = 0
for b in range(2**8 ):
if b not in bs:
bs.append(_UpperCAmelCase )
cs.append(2**8 + n )
n += 1
_lowerCAmelCase = [chr(_UpperCAmelCase ) for n in cs]
return dict(zip(_UpperCAmelCase , _UpperCAmelCase ) )
def __a(SCREAMING_SNAKE_CASE_ : List[Any] ):
'''simple docstring'''
_lowerCAmelCase = set()
_lowerCAmelCase = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
_lowerCAmelCase = char
return pairs
class lowerCAmelCase_ ( UpperCAmelCase__ ):
__lowerCamelCase : List[Any] = VOCAB_FILES_NAMES
__lowerCamelCase : List[str] = PRETRAINED_VOCAB_FILES_MAP
__lowerCamelCase : Optional[int] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__lowerCamelCase : Union[str, Any] = ["""input_ids""", """attention_mask"""]
def __init__( self , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase="replace" , _lowerCAmelCase="<s>" , _lowerCAmelCase="</s>" , _lowerCAmelCase="</s>" , _lowerCAmelCase="<s>" , _lowerCAmelCase="<unk>" , _lowerCAmelCase="<pad>" , _lowerCAmelCase="<mask>" , _lowerCAmelCase=False , **_lowerCAmelCase , ) -> Dict:
_lowerCAmelCase = AddedToken(snake_case_ , lstrip=snake_case_ , rstrip=snake_case_ ) if isinstance(snake_case_ , snake_case_ ) else bos_token
_lowerCAmelCase = AddedToken(snake_case_ , lstrip=snake_case_ , rstrip=snake_case_ ) if isinstance(snake_case_ , snake_case_ ) else eos_token
_lowerCAmelCase = AddedToken(snake_case_ , lstrip=snake_case_ , rstrip=snake_case_ ) if isinstance(snake_case_ , snake_case_ ) else sep_token
_lowerCAmelCase = AddedToken(snake_case_ , lstrip=snake_case_ , rstrip=snake_case_ ) if isinstance(snake_case_ , snake_case_ ) else cls_token
_lowerCAmelCase = AddedToken(snake_case_ , lstrip=snake_case_ , rstrip=snake_case_ ) if isinstance(snake_case_ , snake_case_ ) else unk_token
_lowerCAmelCase = AddedToken(snake_case_ , lstrip=snake_case_ , rstrip=snake_case_ ) if isinstance(snake_case_ , snake_case_ ) else pad_token
# Mask token behave like a normal word, i.e. include the space before it
_lowerCAmelCase = AddedToken(snake_case_ , lstrip=snake_case_ , rstrip=snake_case_ ) if isinstance(snake_case_ , snake_case_ ) else mask_token
super().__init__(
errors=snake_case_ , bos_token=snake_case_ , eos_token=snake_case_ , unk_token=snake_case_ , sep_token=snake_case_ , cls_token=snake_case_ , pad_token=snake_case_ , mask_token=snake_case_ , add_prefix_space=snake_case_ , **snake_case_ , )
with open(snake_case_ , encoding="utf-8" ) as vocab_handle:
_lowerCAmelCase = json.load(snake_case_ )
_lowerCAmelCase = {v: k for k, v in self.encoder.items()}
_lowerCAmelCase = errors # how to handle errors in decoding
_lowerCAmelCase = bytes_to_unicode()
_lowerCAmelCase = {v: k for k, v in self.byte_encoder.items()}
with open(snake_case_ , encoding="utf-8" ) as merges_handle:
_lowerCAmelCase = merges_handle.read().split("\n" )[1:-1]
_lowerCAmelCase = [tuple(merge.split() ) for merge in bpe_merges]
_lowerCAmelCase = dict(zip(snake_case_ , range(len(snake_case_ ) ) ) )
_lowerCAmelCase = {}
_lowerCAmelCase = add_prefix_space
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
_lowerCAmelCase = re.compile(r"\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+" )
@property
def _snake_case ( self ) -> int:
return len(self.encoder )
def _snake_case ( self ) -> Dict:
return dict(self.encoder , **self.added_tokens_encoder )
def _snake_case ( self , _lowerCAmelCase ) -> str:
if token in self.cache:
return self.cache[token]
_lowerCAmelCase = tuple(snake_case_ )
_lowerCAmelCase = get_pairs(snake_case_ )
if not pairs:
return token
while True:
_lowerCAmelCase = min(snake_case_ , key=lambda _lowerCAmelCase : self.bpe_ranks.get(snake_case_ , float("inf" ) ) )
if bigram not in self.bpe_ranks:
break
_lowerCAmelCase = bigram
_lowerCAmelCase = []
_lowerCAmelCase = 0
while i < len(snake_case_ ):
try:
_lowerCAmelCase = word.index(snake_case_ , snake_case_ )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
_lowerCAmelCase = j
if word[i] == first and i < len(snake_case_ ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
_lowerCAmelCase = tuple(snake_case_ )
_lowerCAmelCase = new_word
if len(snake_case_ ) == 1:
break
else:
_lowerCAmelCase = get_pairs(snake_case_ )
_lowerCAmelCase = ' '.join(snake_case_ )
_lowerCAmelCase = word
return word
def _snake_case ( self , _lowerCAmelCase ) -> List[Any]:
_lowerCAmelCase = []
for token in re.findall(self.pat , snake_case_ ):
_lowerCAmelCase = ''.join(
self.byte_encoder[b] for b in token.encode("utf-8" ) ) # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
bpe_tokens.extend(bpe_token for bpe_token in self.bpe(snake_case_ ).split(" " ) )
return bpe_tokens
def _snake_case ( self , _lowerCAmelCase ) -> int:
return self.encoder.get(snake_case_ , self.encoder.get(self.unk_token ) )
def _snake_case ( self , _lowerCAmelCase ) -> Optional[Any]:
return self.decoder.get(snake_case_ )
def _snake_case ( self , _lowerCAmelCase ) -> Any:
_lowerCAmelCase = ''.join(snake_case_ )
_lowerCAmelCase = bytearray([self.byte_decoder[c] for c in text] ).decode("utf-8" , errors=self.errors )
return text
def _snake_case ( self , _lowerCAmelCase , _lowerCAmelCase = None ) -> int:
if not os.path.isdir(snake_case_ ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
_lowerCAmelCase = os.path.join(
snake_case_ , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
_lowerCAmelCase = os.path.join(
snake_case_ , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"] )
with open(snake_case_ , "w" , encoding="utf-8" ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=snake_case_ , ensure_ascii=snake_case_ ) + "\n" )
_lowerCAmelCase = 0
with open(snake_case_ , "w" , encoding="utf-8" ) as writer:
writer.write("#version: 0.2\n" )
for bpe_tokens, token_index in sorted(self.bpe_ranks.items() , key=lambda _lowerCAmelCase : kv[1] ):
if index != token_index:
logger.warning(
f'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
" Please check that the tokenizer is not corrupted!" )
_lowerCAmelCase = token_index
writer.write(" ".join(snake_case_ ) + "\n" )
index += 1
return vocab_file, merge_file
def _snake_case ( self , _lowerCAmelCase , _lowerCAmelCase = None ) -> Any:
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
_lowerCAmelCase = [self.cls_token_id]
_lowerCAmelCase = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def _snake_case ( self , _lowerCAmelCase , _lowerCAmelCase = None , _lowerCAmelCase = False ) -> Tuple:
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=snake_case_ , token_ids_a=snake_case_ , already_has_special_tokens=snake_case_ )
if token_ids_a is None:
return [1] + ([0] * len(snake_case_ )) + [1]
return [1] + ([0] * len(snake_case_ )) + [1, 1] + ([0] * len(snake_case_ )) + [1]
def _snake_case ( self , _lowerCAmelCase , _lowerCAmelCase = None ) -> Any:
_lowerCAmelCase = [self.sep_token_id]
_lowerCAmelCase = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def _snake_case ( self , _lowerCAmelCase , _lowerCAmelCase=False , **_lowerCAmelCase ) -> Optional[int]:
_lowerCAmelCase = kwargs.pop("add_prefix_space" , self.add_prefix_space )
if (is_split_into_words or add_prefix_space) and (len(snake_case_ ) > 0 and not text[0].isspace()):
_lowerCAmelCase = ' ' + text
return (text, kwargs)
| 158 |
"""simple docstring"""
from heapq import heappop, heappush
import numpy as np
def UpperCAmelCase__ ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , ):
"""simple docstring"""
A_ , A_ : List[str] = grid.shape
A_ : Optional[int] = [-1, 1, 0, 0]
A_ : str = [0, 0, -1, 1]
if allow_diagonal:
dx += [-1, -1, 1, 1]
dy += [-1, 1, -1, 1]
A_ , A_ : List[Any] = [(0, source)], set()
A_ : Optional[Any] = np.full((rows, cols) , np.inf )
A_ : int = 0
A_ : Optional[int] = np.empty((rows, cols) , dtype=_UpperCAmelCase )
A_ : Optional[int] = None
while queue:
((A_) , (A_)) : str = heappop(_UpperCAmelCase )
if (x, y) in visited:
continue
visited.add((x, y) )
if (x, y) == destination:
A_ : int = []
while (x, y) != source:
path.append((x, y) )
A_ , A_ : List[Any] = predecessors[x, y]
path.append(_UpperCAmelCase ) # add the source manually
path.reverse()
return matrix[destination], path
for i in range(len(_UpperCAmelCase ) ):
A_ , A_ : Tuple = x + dx[i], y + dy[i]
if 0 <= nx < rows and 0 <= ny < cols:
A_ : Union[str, Any] = grid[nx][ny]
if next_node == 1 and matrix[nx, ny] > dist + 1:
heappush(_UpperCAmelCase , (dist + 1, (nx, ny)) )
A_ : Optional[Any] = dist + 1
A_ : Optional[Any] = (x, y)
return np.inf, []
if __name__ == "__main__":
import doctest
doctest.testmod() | 286 | 0 |
"""simple docstring"""
import argparse
import pickle
import numpy as np
import torch
from torch import nn
from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging
logging.set_verbosity_info()
def snake_case (A_ :Union[str, Any] , A_ :Dict , A_ :Dict=None ):
'''simple docstring'''
assert torch_layer.weight.shape == weight.shape, f'''{torch_layer} layer.weight does not match'''
a : int = nn.Parameter(snake_case_ )
if bias is not None:
assert torch_layer.bias.shape == bias.shape, f'''{torch_layer} layer.bias does not match'''
a : Optional[Any] = nn.Parameter(snake_case_ )
def snake_case (A_ :Optional[Any] , A_ :List[str] , A_ :Optional[Any] ):
'''simple docstring'''
a : str = np.asarray(weights[0] )
a : List[str] = np.asarray(weights[1] )
a : Optional[Any] = np.asarray(weights[2] )
set_param(
torch_layer.self_attention.query_key , torch.tensor(snake_case_ ).transpose(1 , 2 ).contiguous().view(-1 , snake_case_ ) , )
set_param(
torch_layer.self_attention.value , torch.tensor(snake_case_ ).transpose(1 , 2 ).contiguous().view(-1 , snake_case_ ) , )
set_param(
torch_layer.output.dense , torch.tensor(snake_case_ ).view(-1 , snake_case_ ).contiguous().transpose(0 , 1 ) , )
def snake_case (A_ :Union[str, Any] , A_ :Union[str, Any] , A_ :str ):
'''simple docstring'''
a : str = np.asarray(weights[0] )
a : int = np.asarray(weights[1] )
a : List[str] = np.asarray(weights[2] )
a : Any = np.asarray(weights[3] )
set_param(
torch_layer.self_attention.query , torch.tensor(snake_case_ ).transpose(1 , 2 ).contiguous().view(-1 , snake_case_ ) , )
set_param(
torch_layer.self_attention.key , torch.tensor(snake_case_ ).transpose(1 , 2 ).contiguous().view(-1 , snake_case_ ) , )
set_param(
torch_layer.self_attention.value , torch.tensor(snake_case_ ).transpose(1 , 2 ).contiguous().view(-1 , snake_case_ ) , )
set_param(
torch_layer.output.dense , torch.tensor(snake_case_ ).view(-1 , snake_case_ ).contiguous().transpose(0 , 1 ) , )
def snake_case (A_ :Optional[Any] , A_ :Optional[Any] , A_ :int ):
'''simple docstring'''
a : Optional[int] = weights[0][0][0]
a : Union[str, Any] = np.asarray(layer_norm_a[0] )
a : Optional[Any] = np.asarray(layer_norm_a[1] )
set_param(
torch_block.attention.layer_norm , torch.tensor(snake_case_ ) , torch.tensor(snake_case_ ) , )
# lsh weights + output
a : Any = weights[0][1]
if len(snake_case_ ) < 4:
set_layer_weights_in_torch_lsh(snake_case_ , torch_block.attention , snake_case_ )
else:
set_layer_weights_in_torch_local(snake_case_ , torch_block.attention , snake_case_ )
# intermediate weighs
a : Optional[Any] = weights[2][0][1][2]
# Chunked Feed Forward
if len(snake_case_ ) == 4:
a : int = intermediate_weights[2]
# layernorm 2
a : List[Any] = np.asarray(intermediate_weights[0][0] )
a : str = np.asarray(intermediate_weights[0][1] )
set_param(
torch_block.feed_forward.layer_norm , torch.tensor(snake_case_ ) , torch.tensor(snake_case_ ) , )
# intermediate dense
a : Union[str, Any] = np.asarray(intermediate_weights[1][0] )
a : str = np.asarray(intermediate_weights[1][1] )
set_param(
torch_block.feed_forward.dense.dense , torch.tensor(snake_case_ ).transpose(0 , 1 ).contiguous() , torch.tensor(snake_case_ ) , )
# intermediate out
a : Union[str, Any] = np.asarray(intermediate_weights[4][0] )
a : List[str] = np.asarray(intermediate_weights[4][1] )
set_param(
torch_block.feed_forward.output.dense , torch.tensor(snake_case_ ).transpose(0 , 1 ).contiguous() , torch.tensor(snake_case_ ) , )
def snake_case (A_ :int , A_ :Optional[int] , A_ :Tuple ):
'''simple docstring'''
a : List[str] = torch_model.reformer
# word embeds
a : Optional[int] = np.asarray(weights[1] )
set_param(
torch_model_reformer.embeddings.word_embeddings , torch.tensor(snake_case_ ) , )
if isinstance(weights[3] , snake_case_ ):
a : Union[str, Any] = torch_model_reformer.embeddings.position_embeddings
for emb_idx in range(len(position_embeddings.weights ) ):
a : List[Any] = np.asarray(weights[3][emb_idx][0] )
assert (
position_embeddings.weights[emb_idx].shape == emb_weights.shape
), f'''{position_embeddings[emb_idx]} emb does not match'''
a : List[str] = nn.Parameter(torch.tensor(snake_case_ ) )
a : Optional[Any] = weights[5]
assert len(torch_model_reformer.encoder.layers ) * 4 == len(
snake_case_ ), "HF and trax model do not have the same number of layers"
for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers ):
a : Optional[int] = trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
set_block_weights_in_torch(snake_case_ , snake_case_ , snake_case_ )
# output layer norm
a : Optional[Any] = np.asarray(weights[7][0] )
a : Optional[int] = np.asarray(weights[7][1] )
set_param(
torch_model_reformer.encoder.layer_norm , torch.tensor(snake_case_ ) , torch.tensor(snake_case_ ) , )
# output embeddings
a : Tuple = np.asarray(weights[9][0] )
a : Any = np.asarray(weights[9][1] )
set_param(
torch_model.lm_head.decoder , torch.tensor(snake_case_ ).transpose(0 , 1 ).contiguous() , torch.tensor(snake_case_ ) , )
def snake_case (A_ :Tuple , A_ :Optional[Any] , A_ :Optional[int] ):
'''simple docstring'''
a : str = ReformerConfig.from_json_file(snake_case_ )
print(f'''Building PyTorch model from configuration: {config}''' )
a : Dict = ReformerModelWithLMHead(snake_case_ )
with open(snake_case_ , 'rb' ) as f:
a : List[Any] = pickle.load(snake_case_ )['weights']
set_model_weights_in_torch(snake_case_ , snake_case_ , config.hidden_size )
# Save pytorch-model
print(f'''Save PyTorch model to {pytorch_dump_path}''' )
torch.save(model.state_dict() , snake_case_ )
if __name__ == "__main__":
_UpperCamelCase : Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--trax_model_pkl_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
)
parser.add_argument(
'--config_file',
default=None,
type=str,
required=True,
help=(
'The config json file corresponding to the pre-trained Reformer model. \n'
'This specifies the model architecture.'
),
)
parser.add_argument(
'--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
)
_UpperCamelCase : Any = parser.parse_args()
convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)
| 370 |
"""simple docstring"""
import inspect
import unittest
import numpy as np
from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor
if is_flax_available():
import jax
from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel
class snake_case ( unittest.TestCase ):
def __init__( self : List[str] , A : Union[str, Any] , A : Optional[Any]=1_3 , A : List[Any]=3_0 , A : List[Any]=2 , A : Optional[Any]=3 , A : Union[str, Any]=True , A : Union[str, Any]=True , A : Optional[int]=3_2 , A : Tuple=5 , A : List[str]=4 , A : List[Any]=3_7 , A : Optional[Any]="gelu" , A : Any=0.1 , A : Tuple=0.1 , A : Optional[int]=1_0 , A : Union[str, Any]=0.02 , ):
'''simple docstring'''
a : Optional[Any] = parent
a : Tuple = batch_size
a : int = image_size
a : str = patch_size
a : List[str] = num_channels
a : List[str] = is_training
a : List[str] = use_labels
a : Optional[int] = hidden_size
a : Optional[Any] = num_hidden_layers
a : Optional[int] = num_attention_heads
a : str = intermediate_size
a : List[str] = hidden_act
a : List[str] = hidden_dropout_prob
a : Union[str, Any] = attention_probs_dropout_prob
a : List[Any] = type_sequence_label_size
a : Optional[Any] = initializer_range
# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
a : Optional[int] = (image_size // patch_size) ** 2
a : List[Any] = num_patches + 1
def lowerCamelCase__ ( self : Tuple ):
'''simple docstring'''
a : List[Any] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
a : str = ViTConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=A , initializer_range=self.initializer_range , )
return config, pixel_values
def lowerCamelCase__ ( self : Union[str, Any] , A : str , A : Union[str, Any] ):
'''simple docstring'''
a : Tuple = FlaxViTModel(config=A )
a : int = model(A )
# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
a : Optional[Any] = (self.image_size, self.image_size)
a : List[str] = (self.patch_size, self.patch_size)
a : Optional[Any] = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, num_patches + 1, self.hidden_size) )
def lowerCamelCase__ ( self : Tuple , A : Dict , A : Optional[int] ):
'''simple docstring'''
a : Optional[Any] = self.type_sequence_label_size
a : List[Any] = FlaxViTForImageClassification(config=A )
a : Tuple = model(A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
a : Dict = 1
a : Tuple = FlaxViTForImageClassification(A )
a : List[Any] = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
a : Optional[int] = model(A )
def lowerCamelCase__ ( self : Optional[int] ):
'''simple docstring'''
a : Optional[int] = self.prepare_config_and_inputs()
(
(
a
), (
a
),
) : Dict = config_and_inputs
a : List[Any] = {'pixel_values': pixel_values}
return config, inputs_dict
@require_flax
class snake_case ( UpperCAmelCase , unittest.TestCase ):
__magic_name__ = (FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()
def lowerCamelCase__ ( self : Any ):
'''simple docstring'''
a : Any = FlaxViTModelTester(self )
a : List[str] = ConfigTester(self , config_class=A , has_text_modality=A , hidden_size=3_7 )
def lowerCamelCase__ ( self : Tuple ):
'''simple docstring'''
self.config_tester.run_common_tests()
def lowerCamelCase__ ( self : List[Any] ):
'''simple docstring'''
a : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A )
def lowerCamelCase__ ( self : List[Any] ):
'''simple docstring'''
a : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*A )
def lowerCamelCase__ ( self : Dict ):
'''simple docstring'''
a, a : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
a : Tuple = model_class(A )
a : str = inspect.signature(model.__call__ )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
a : List[str] = [*signature.parameters.keys()]
a : Dict = ['pixel_values']
self.assertListEqual(arg_names[:1] , A )
def lowerCamelCase__ ( self : Any ):
'''simple docstring'''
a, a : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
a : List[Any] = self._prepare_for_class(A , A )
a : Tuple = model_class(A )
@jax.jit
def model_jitted(A : Tuple , **A : int ):
return model(pixel_values=A , **A )
with self.subTest('JIT Enabled' ):
a : List[str] = model_jitted(**A ).to_tuple()
with self.subTest('JIT Disabled' ):
with jax.disable_jit():
a : List[str] = model_jitted(**A ).to_tuple()
self.assertEqual(len(A ) , len(A ) )
for jitted_output, output in zip(A , A ):
self.assertEqual(jitted_output.shape , output.shape )
@slow
def lowerCamelCase__ ( self : List[str] ):
'''simple docstring'''
for model_class_name in self.all_model_classes:
a : List[str] = model_class_name.from_pretrained('google/vit-base-patch16-224' )
a : Optional[Any] = model(np.ones((1, 3, 2_2_4, 2_2_4) ) )
self.assertIsNotNone(A )
| 186 | 0 |
def _A ( SCREAMING_SNAKE_CASE__ : list[int] , SCREAMING_SNAKE_CASE__ : list[int] ):
UpperCamelCase :Tuple = len(SCREAMING_SNAKE_CASE__ )
print('''The following activities are selected:''' )
# The first activity is always selected
UpperCamelCase :Dict = 0
print(SCREAMING_SNAKE_CASE__ , end=''',''' )
# Consider rest of the activities
for j in range(SCREAMING_SNAKE_CASE__ ):
# If this activity has start time greater than
# or equal to the finish time of previously
# selected activity, then select it
if start[j] >= finish[i]:
print(SCREAMING_SNAKE_CASE__ , end=''',''' )
UpperCamelCase :List[str] = j
if __name__ == "__main__":
import doctest
doctest.testmod()
__snake_case = [1, 3, 0, 5, 8, 5]
__snake_case = [2, 4, 6, 7, 9, 9]
print_max_activities(start, finish)
| 259 |
def _A ( ):
for n in range(1 , 1000000 ):
yield n * (n + 1) // 2
def _A ( SCREAMING_SNAKE_CASE__ : int ):
UpperCamelCase :Optional[int] = 1
UpperCamelCase :List[Any] = 2
while i * i <= n:
UpperCamelCase :str = 0
while n % i == 0:
n //= i
multiplicity += 1
divisors_count *= multiplicity + 1
i += 1
if n > 1:
divisors_count *= 2
return divisors_count
def _A ( ):
return next(i for i in triangle_number_generator() if count_divisors(SCREAMING_SNAKE_CASE__ ) > 500 )
if __name__ == "__main__":
print(solution())
| 259 | 1 |
'''simple docstring'''
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
snake_case_ : str = logging.get_logger(__name__)
snake_case_ : Optional[int] = {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}
snake_case_ : Union[str, Any] = {
'vocab_file': {
'allegro/herbert-base-cased': 'https://huggingface.co/allegro/herbert-base-cased/resolve/main/vocab.json'
},
'merges_file': {
'allegro/herbert-base-cased': 'https://huggingface.co/allegro/herbert-base-cased/resolve/main/merges.txt'
},
}
snake_case_ : Tuple = {'allegro/herbert-base-cased': 514}
snake_case_ : Tuple = {}
class lowercase__ ( lowercase ):
lowercase__ = VOCAB_FILES_NAMES
lowercase__ = PRETRAINED_VOCAB_FILES_MAP
lowercase__ = PRETRAINED_INIT_CONFIGURATION
lowercase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase__ = HerbertTokenizer
def __init__( self : Optional[int] ,lowerCamelCase__ : List[str]=None ,lowerCamelCase__ : Dict=None ,lowerCamelCase__ : Tuple=None ,lowerCamelCase__ : int="<s>" ,lowerCamelCase__ : Dict="<unk>" ,lowerCamelCase__ : List[str]="<pad>" ,lowerCamelCase__ : Union[str, Any]="<mask>" ,lowerCamelCase__ : str="</s>" ,**lowerCamelCase__ : Tuple ,):
'''simple docstring'''
super().__init__(
lowerCamelCase__ ,lowerCamelCase__ ,tokenizer_file=lowerCamelCase__ ,cls_token=lowerCamelCase__ ,unk_token=lowerCamelCase__ ,pad_token=lowerCamelCase__ ,mask_token=lowerCamelCase__ ,sep_token=lowerCamelCase__ ,**lowerCamelCase__ ,)
def UpperCamelCase_ ( self : int ,lowerCamelCase__ : List[int] ,lowerCamelCase__ : Optional[List[int]] = None ):
'''simple docstring'''
_UpperCamelCase : Optional[int] = [self.cls_token_id]
_UpperCamelCase : Optional[int] = [self.sep_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def UpperCamelCase_ ( self : Tuple ,lowerCamelCase__ : List[int] ,lowerCamelCase__ : Optional[List[int]] = None ,lowerCamelCase__ : bool = False ):
'''simple docstring'''
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=lowerCamelCase__ ,token_ids_a=lowerCamelCase__ ,already_has_special_tokens=lowerCamelCase__ )
if token_ids_a is None:
return [1] + ([0] * len(lowerCamelCase__ )) + [1]
return [1] + ([0] * len(lowerCamelCase__ )) + [1] + ([0] * len(lowerCamelCase__ )) + [1]
def UpperCamelCase_ ( self : List[str] ,lowerCamelCase__ : List[int] ,lowerCamelCase__ : Optional[List[int]] = None ):
'''simple docstring'''
_UpperCamelCase : Optional[Any] = [self.sep_token_id]
_UpperCamelCase : Dict = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def UpperCamelCase_ ( self : Optional[Any] ,lowerCamelCase__ : str ,lowerCamelCase__ : Optional[str] = None ):
'''simple docstring'''
_UpperCamelCase : List[str] = self._tokenizer.model.save(lowerCamelCase__ ,name=lowerCamelCase__ )
return tuple(lowerCamelCase__ )
| 236 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable
snake_case_ : Any = {
'configuration_gpt_neox_japanese': ['GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTNeoXJapaneseConfig'],
'tokenization_gpt_neox_japanese': ['GPTNeoXJapaneseTokenizer'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case_ : Optional[int] = [
'GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST',
'GPTNeoXJapaneseForCausalLM',
'GPTNeoXJapaneseLayer',
'GPTNeoXJapaneseModel',
'GPTNeoXJapanesePreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_gpt_neox_japanese import GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXJapaneseConfig
from .tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_gpt_neox_japanese import (
GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
GPTNeoXJapaneseForCausalLM,
GPTNeoXJapaneseLayer,
GPTNeoXJapaneseModel,
GPTNeoXJapanesePreTrainedModel,
)
else:
import sys
snake_case_ : Any = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 236 | 1 |
'''simple docstring'''
import heapq
def _lowerCAmelCase ( _UpperCamelCase : str ) -> set[int]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =[]
# for each node and his adjacency list add them and the rank of the node to queue
# using heapq module the queue will be filled like a Priority Queue
# heapq works with a min priority queue, so I used -1*len(v) to build it
for key, value in graph.items():
# O(log(n))
heapq.heappush(__UpperCamelCase , [-1 * len(__UpperCamelCase ), (key, value)] )
# chosen_vertices = set of chosen vertices
_SCREAMING_SNAKE_CASE =set()
# while queue isn't empty and there are still edges
# (queue[0][0] is the rank of the node with max rank)
while queue and queue[0][0] != 0:
# extract vertex with max rank from queue and add it to chosen_vertices
_SCREAMING_SNAKE_CASE =heapq.heappop(__UpperCamelCase )[1][0]
chosen_vertices.add(__UpperCamelCase )
# Remove all arcs adjacent to argmax
for elem in queue:
# if v haven't adjacent node, skip
if elem[0] == 0:
continue
# if argmax is reachable from elem
# remove argmax from elem's adjacent list and update his rank
if argmax in elem[1][1]:
_SCREAMING_SNAKE_CASE =elem[1][1].index(__UpperCamelCase )
del elem[1][1][index]
elem[0] += 1
# re-order the queue
heapq.heapify(__UpperCamelCase )
return chosen_vertices
if __name__ == "__main__":
import doctest
doctest.testmod()
lowerCamelCase : str = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
print(f'''Minimum vertex cover:\n{greedy_min_vertex_cover(graph)}''')
| 47 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase__ = {
"""configuration_mgp_str""": ["""MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MgpstrConfig"""],
"""processing_mgp_str""": ["""MgpstrProcessor"""],
"""tokenization_mgp_str""": ["""MgpstrTokenizer"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase__ = [
"""MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""MgpstrModel""",
"""MgpstrPreTrainedModel""",
"""MgpstrForSceneTextRecognition""",
]
if TYPE_CHECKING:
from .configuration_mgp_str import MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP, MgpstrConfig
from .processing_mgp_str import MgpstrProcessor
from .tokenization_mgp_str import MgpstrTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mgp_str import (
MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST,
MgpstrForSceneTextRecognition,
MgpstrModel,
MgpstrPreTrainedModel,
)
else:
import sys
lowercase__ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 241 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_torch_available,
)
__UpperCamelCase : Optional[int] = {
"configuration_speecht5": [
"SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP",
"SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP",
"SpeechT5Config",
"SpeechT5HifiGanConfig",
],
"feature_extraction_speecht5": ["SpeechT5FeatureExtractor"],
"processing_speecht5": ["SpeechT5Processor"],
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCamelCase : List[Any] = ["SpeechT5Tokenizer"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCamelCase : Optional[int] = [
"SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST",
"SpeechT5ForSpeechToText",
"SpeechT5ForSpeechToSpeech",
"SpeechT5ForTextToSpeech",
"SpeechT5Model",
"SpeechT5PreTrainedModel",
"SpeechT5HifiGan",
]
if TYPE_CHECKING:
from .configuration_speechta import (
SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
SpeechTaConfig,
SpeechTaHifiGanConfig,
)
from .feature_extraction_speechta import SpeechTaFeatureExtractor
from .processing_speechta import SpeechTaProcessor
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_speechta import SpeechTaTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_speechta import (
SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
SpeechTaForSpeechToSpeech,
SpeechTaForSpeechToText,
SpeechTaForTextToSpeech,
SpeechTaHifiGan,
SpeechTaModel,
SpeechTaPreTrainedModel,
)
else:
import sys
__UpperCamelCase : Optional[int] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 364 |
import unittest
import numpy as np
from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import DonutImageProcessor
class __magic_name__ ( unittest.TestCase):
def __init__( self : Optional[Any] , lowerCamelCase__ : Any , lowerCamelCase__ : Tuple=7 , lowerCamelCase__ : List[Any]=3 , lowerCamelCase__ : Optional[int]=18 , lowerCamelCase__ : Any=30 , lowerCamelCase__ : int=400 , lowerCamelCase__ : List[str]=True , lowerCamelCase__ : Optional[Any]=None , lowerCamelCase__ : Union[str, Any]=True , lowerCamelCase__ : int=False , lowerCamelCase__ : Union[str, Any]=True , lowerCamelCase__ : int=True , lowerCamelCase__ : Dict=[0.5, 0.5, 0.5] , lowerCamelCase__ : str=[0.5, 0.5, 0.5] , ) -> Optional[Any]:
'''simple docstring'''
UpperCamelCase__ : Optional[Any] = parent
UpperCamelCase__ : Dict = batch_size
UpperCamelCase__ : List[Any] = num_channels
UpperCamelCase__ : int = image_size
UpperCamelCase__ : str = min_resolution
UpperCamelCase__ : str = max_resolution
UpperCamelCase__ : Tuple = do_resize
UpperCamelCase__ : str = size if size is not None else {'''height''': 18, '''width''': 20}
UpperCamelCase__ : Optional[Any] = do_thumbnail
UpperCamelCase__ : int = do_align_axis
UpperCamelCase__ : List[Any] = do_pad
UpperCamelCase__ : List[Any] = do_normalize
UpperCamelCase__ : Dict = image_mean
UpperCamelCase__ : List[Any] = image_std
def UpperCAmelCase__ ( self : List[Any] ) -> Any:
'''simple docstring'''
return {
"do_resize": self.do_resize,
"size": self.size,
"do_thumbnail": self.do_thumbnail,
"do_align_long_axis": self.do_align_axis,
"do_pad": self.do_pad,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
}
@require_torch
@require_vision
class __magic_name__ ( __lowerCAmelCase , unittest.TestCase):
A: Tuple = DonutImageProcessor if is_vision_available() else None
def UpperCAmelCase__ ( self : str ) -> int:
'''simple docstring'''
UpperCamelCase__ : int = DonutImageProcessingTester(self )
@property
def UpperCAmelCase__ ( self : Tuple ) -> Optional[int]:
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def UpperCAmelCase__ ( self : Any ) -> Optional[Any]:
'''simple docstring'''
UpperCamelCase__ : Tuple = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(lowerCamelCase__ , '''do_resize''' ) )
self.assertTrue(hasattr(lowerCamelCase__ , '''size''' ) )
self.assertTrue(hasattr(lowerCamelCase__ , '''do_thumbnail''' ) )
self.assertTrue(hasattr(lowerCamelCase__ , '''do_align_long_axis''' ) )
self.assertTrue(hasattr(lowerCamelCase__ , '''do_pad''' ) )
self.assertTrue(hasattr(lowerCamelCase__ , '''do_normalize''' ) )
self.assertTrue(hasattr(lowerCamelCase__ , '''image_mean''' ) )
self.assertTrue(hasattr(lowerCamelCase__ , '''image_std''' ) )
def UpperCAmelCase__ ( self : Optional[Any] ) -> Tuple:
'''simple docstring'''
UpperCamelCase__ : Union[str, Any] = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'''height''': 18, '''width''': 20} )
UpperCamelCase__ : Any = self.image_processing_class.from_dict(self.image_processor_dict , size=42 )
self.assertEqual(image_processor.size , {'''height''': 42, '''width''': 42} )
# Previous config had dimensions in (width, height) order
UpperCamelCase__ : Dict = self.image_processing_class.from_dict(self.image_processor_dict , size=(42, 84) )
self.assertEqual(image_processor.size , {'''height''': 84, '''width''': 42} )
def UpperCAmelCase__ ( self : Any ) -> str:
'''simple docstring'''
pass
@is_flaky()
def UpperCAmelCase__ ( self : Optional[int] ) -> Any:
'''simple docstring'''
UpperCamelCase__ : Dict = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
UpperCamelCase__ : Any = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCamelCase__ )
for image in image_inputs:
self.assertIsInstance(lowerCamelCase__ , Image.Image )
# Test not batched input
UpperCamelCase__ : Dict = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['''height'''],
self.image_processor_tester.size['''width'''],
) , )
# Test batched
UpperCamelCase__ : List[str] = image_processing(lowerCamelCase__ , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['''height'''],
self.image_processor_tester.size['''width'''],
) , )
@is_flaky()
def UpperCAmelCase__ ( self : Optional[int] ) -> List[str]:
'''simple docstring'''
UpperCamelCase__ : List[str] = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
UpperCamelCase__ : Optional[Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCamelCase__ , numpify=lowerCamelCase__ )
for image in image_inputs:
self.assertIsInstance(lowerCamelCase__ , np.ndarray )
# Test not batched input
UpperCamelCase__ : List[str] = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['''height'''],
self.image_processor_tester.size['''width'''],
) , )
# Test batched
UpperCamelCase__ : List[Any] = image_processing(lowerCamelCase__ , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['''height'''],
self.image_processor_tester.size['''width'''],
) , )
@is_flaky()
def UpperCAmelCase__ ( self : str ) -> Tuple:
'''simple docstring'''
UpperCamelCase__ : Tuple = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
UpperCamelCase__ : List[str] = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCamelCase__ , torchify=lowerCamelCase__ )
for image in image_inputs:
self.assertIsInstance(lowerCamelCase__ , torch.Tensor )
# Test not batched input
UpperCamelCase__ : str = image_processing(image_inputs[0] , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['''height'''],
self.image_processor_tester.size['''width'''],
) , )
# Test batched
UpperCamelCase__ : List[str] = image_processing(lowerCamelCase__ , return_tensors='''pt''' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size['''height'''],
self.image_processor_tester.size['''width'''],
) , )
| 51 | 0 |
def UpperCamelCase_( _snake_case : int , _snake_case : list[int] , _snake_case : int ):
"""simple docstring"""
def count_of_possible_combinations(_snake_case : int ) -> int:
if target < 0:
return 0
if target == 0:
return 1
return sum(count_of_possible_combinations(target - item ) for item in array )
return count_of_possible_combinations(_snake_case )
def UpperCamelCase_( _snake_case : int , _snake_case : list[int] , _snake_case : int ):
"""simple docstring"""
def count_of_possible_combinations_with_dp_array(
_snake_case : int , _snake_case : list[int] ) -> int:
if target < 0:
return 0
if target == 0:
return 1
if dp_array[target] != -1:
return dp_array[target]
__a =sum(
count_of_possible_combinations_with_dp_array(target - item , _snake_case )
for item in array )
__a =answer
return answer
__a =[-1] * (target + 1)
return count_of_possible_combinations_with_dp_array(_snake_case , _snake_case )
def UpperCamelCase_( _snake_case : int , _snake_case : list[int] , _snake_case : int ):
"""simple docstring"""
__a =[0] * (target + 1)
__a =1
for i in range(1 , target + 1 ):
for j in range(_snake_case ):
if i - array[j] >= 0:
dp_array[i] += dp_array[i - array[j]]
return dp_array[target]
if __name__ == "__main__":
import doctest
doctest.testmod()
_lowerCAmelCase : Optional[Any] = 3
_lowerCAmelCase : Dict = 5
_lowerCAmelCase : Dict = [1, 2, 5]
print(combination_sum_iv(n, array, target))
| 218 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
_lowerCAmelCase : List[str] = logging.get_logger(__name__)
_lowerCAmelCase : Union[str, Any] = {
"Salesforce/instruct-blip-flan-t5": "https://huggingface.co/Salesforce/instruct-blip-flan-t5/resolve/main/config.json",
}
class __magic_name__ ( lowerCAmelCase_ ):
SCREAMING_SNAKE_CASE = 'instructblip_vision_model'
def __init__( self , __snake_case=1408 , __snake_case=6144 , __snake_case=39 , __snake_case=16 , __snake_case=224 , __snake_case=14 , __snake_case="gelu" , __snake_case=1e-6 , __snake_case=0.0 , __snake_case=1e-10 , __snake_case=True , **__snake_case , ) -> str:
'''simple docstring'''
super().__init__(**__snake_case )
__a =hidden_size
__a =intermediate_size
__a =num_hidden_layers
__a =num_attention_heads
__a =patch_size
__a =image_size
__a =initializer_range
__a =attention_dropout
__a =layer_norm_eps
__a =hidden_act
__a =qkv_bias
@classmethod
def __magic_name__ ( cls , __snake_case , **__snake_case ) -> "PretrainedConfig":
'''simple docstring'''
cls._set_token_in_kwargs(__snake_case )
__a , __a =cls.get_config_dict(__snake_case , **__snake_case )
# get the vision config dict if we are loading from InstructBlipConfig
if config_dict.get('model_type' ) == "instructblip":
__a =config_dict['vision_config']
if "model_type" in config_dict and hasattr(cls , 'model_type' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.' )
return cls.from_dict(__snake_case , **__snake_case )
class __magic_name__ ( lowerCAmelCase_ ):
SCREAMING_SNAKE_CASE = 'instructblip_qformer'
def __init__( self , __snake_case=3_0522 , __snake_case=768 , __snake_case=12 , __snake_case=12 , __snake_case=3072 , __snake_case="gelu" , __snake_case=0.1 , __snake_case=0.1 , __snake_case=512 , __snake_case=0.02 , __snake_case=1e-12 , __snake_case=0 , __snake_case="absolute" , __snake_case=2 , __snake_case=1408 , **__snake_case , ) -> List[str]:
'''simple docstring'''
super().__init__(pad_token_id=__snake_case , **__snake_case )
__a =vocab_size
__a =hidden_size
__a =num_hidden_layers
__a =num_attention_heads
__a =hidden_act
__a =intermediate_size
__a =hidden_dropout_prob
__a =attention_probs_dropout_prob
__a =max_position_embeddings
__a =initializer_range
__a =layer_norm_eps
__a =position_embedding_type
__a =cross_attention_frequency
__a =encoder_hidden_size
@classmethod
def __magic_name__ ( cls , __snake_case , **__snake_case ) -> "PretrainedConfig":
'''simple docstring'''
cls._set_token_in_kwargs(__snake_case )
__a , __a =cls.get_config_dict(__snake_case , **__snake_case )
# get the qformer config dict if we are loading from InstructBlipConfig
if config_dict.get('model_type' ) == "instructblip":
__a =config_dict['qformer_config']
if "model_type" in config_dict and hasattr(cls , 'model_type' ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.' )
return cls.from_dict(__snake_case , **__snake_case )
class __magic_name__ ( lowerCAmelCase_ ):
SCREAMING_SNAKE_CASE = 'instructblip'
SCREAMING_SNAKE_CASE = True
def __init__( self , __snake_case=None , __snake_case=None , __snake_case=None , __snake_case=32 , **__snake_case ) -> str:
'''simple docstring'''
super().__init__(**__snake_case )
if vision_config is None:
__a ={}
logger.info('vision_config is None. initializing the InstructBlipVisionConfig with default values.' )
if qformer_config is None:
__a ={}
logger.info('qformer_config is None. Initializing the InstructBlipQFormerConfig with default values.' )
if text_config is None:
__a ={}
logger.info('text_config is None. Initializing the text config with default values (`OPTConfig`).' )
__a =InstructBlipVisionConfig(**__snake_case )
__a =InstructBlipQFormerConfig(**__snake_case )
__a =text_config['model_type'] if 'model_type' in text_config else 'opt'
__a =CONFIG_MAPPING[text_model_type](**__snake_case )
__a =self.text_config.tie_word_embeddings
__a =self.text_config.is_encoder_decoder
__a =num_query_tokens
__a =self.vision_config.hidden_size
__a =self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
__a =1.0
__a =0.02
@classmethod
def __magic_name__ ( cls , __snake_case , __snake_case , __snake_case , **__snake_case , ) -> Optional[Any]:
'''simple docstring'''
return cls(
vision_config=vision_config.to_dict() , qformer_config=qformer_config.to_dict() , text_config=text_config.to_dict() , **__snake_case , )
def __magic_name__ ( self ) -> List[Any]:
'''simple docstring'''
__a =copy.deepcopy(self.__dict__ )
__a =self.vision_config.to_dict()
__a =self.qformer_config.to_dict()
__a =self.text_config.to_dict()
__a =self.__class__.model_type
return output
| 218 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE = {
"""unc-nlp/lxmert-base-uncased""": """https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config.json""",
}
class lowerCamelCase_ ( _A ):
'''simple docstring'''
a__ = "lxmert"
a__ = {}
def __init__( self : Optional[int] , __lowerCamelCase : List[Any]=3_05_22 , __lowerCamelCase : List[Any]=7_68 , __lowerCamelCase : Tuple=12 , __lowerCamelCase : str=95_00 , __lowerCamelCase : str=16_00 , __lowerCamelCase : Optional[Any]=4_00 , __lowerCamelCase : Tuple=30_72 , __lowerCamelCase : Any="gelu" , __lowerCamelCase : Optional[int]=0.1 , __lowerCamelCase : str=0.1 , __lowerCamelCase : str=5_12 , __lowerCamelCase : Union[str, Any]=2 , __lowerCamelCase : Dict=0.02 , __lowerCamelCase : Union[str, Any]=1e-12 , __lowerCamelCase : Union[str, Any]=9 , __lowerCamelCase : Dict=5 , __lowerCamelCase : Optional[int]=5 , __lowerCamelCase : List[Any]=20_48 , __lowerCamelCase : str=4 , __lowerCamelCase : str=6.67 , __lowerCamelCase : Optional[int]=True , __lowerCamelCase : Union[str, Any]=True , __lowerCamelCase : Any=True , __lowerCamelCase : Tuple=True , __lowerCamelCase : Optional[int]=True , __lowerCamelCase : Any=True , __lowerCamelCase : Optional[Any]=True , **__lowerCamelCase : Union[str, Any] , ) -> List[Any]:
A : Dict = vocab_size
A : Any = hidden_size
A : int = num_attention_heads
A : Union[str, Any] = hidden_act
A : List[Any] = intermediate_size
A : Optional[int] = hidden_dropout_prob
A : Dict = attention_probs_dropout_prob
A : Optional[int] = max_position_embeddings
A : Optional[int] = type_vocab_size
A : Optional[Any] = initializer_range
A : str = layer_norm_eps
A : List[str] = num_qa_labels
A : Optional[int] = num_object_labels
A : int = num_attr_labels
A : List[str] = l_layers
A : Tuple = x_layers
A : List[Any] = r_layers
A : Union[str, Any] = visual_feat_dim
A : List[Any] = visual_pos_dim
A : str = visual_loss_normalizer
A : Tuple = task_matched
A : Union[str, Any] = task_mask_lm
A : Tuple = task_obj_predict
A : List[Any] = task_qa
A : Tuple = visual_obj_loss
A : Dict = visual_attr_loss
A : Optional[int] = visual_feat_loss
A : Optional[Any] = {"vision": r_layers, "cross_encoder": x_layers, "language": l_layers}
super().__init__(**__lowerCamelCase ) | 256 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE = {
"""microsoft/resnet-50""": """https://huggingface.co/microsoft/resnet-50/blob/main/config.json""",
}
class lowerCamelCase_ ( _A ,_A ):
'''simple docstring'''
a__ = "resnet"
a__ = ["basic", "bottleneck"]
def __init__( self : Tuple , __lowerCamelCase : int=3 , __lowerCamelCase : Optional[int]=64 , __lowerCamelCase : Union[str, Any]=[2_56, 5_12, 10_24, 20_48] , __lowerCamelCase : Tuple=[3, 4, 6, 3] , __lowerCamelCase : Optional[Any]="bottleneck" , __lowerCamelCase : Dict="relu" , __lowerCamelCase : Tuple=False , __lowerCamelCase : List[Any]=None , __lowerCamelCase : Tuple=None , **__lowerCamelCase : Tuple , ) -> Optional[Any]:
super().__init__(**__lowerCamelCase )
if layer_type not in self.layer_types:
raise ValueError(F"""layer_type={layer_type} is not one of {",".join(self.layer_types )}""" )
A : Any = num_channels
A : Union[str, Any] = embedding_size
A : Any = hidden_sizes
A : List[str] = depths
A : Union[str, Any] = layer_type
A : Any = hidden_act
A : Any = downsample_in_first_stage
A : Any = ["stem"] + [F"""stage{idx}""" for idx in range(1 , len(__lowerCamelCase ) + 1 )]
A , A : int = get_aligned_output_features_output_indices(
out_features=__lowerCamelCase , out_indices=__lowerCamelCase , stage_names=self.stage_names )
class lowerCamelCase_ ( _A ):
'''simple docstring'''
a__ = version.parse("1.11" )
@property
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
] )
@property
def SCREAMING_SNAKE_CASE__ ( self : str ) -> float:
return 1e-3 | 256 | 1 |
"""simple docstring"""
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# this script dumps information about the environment
import os
import sys
import transformers
_a = '3'
print('Python version:', sys.version)
print('transformers version:', transformers.__version__)
try:
import torch
print('Torch version:', torch.__version__)
print('Cuda available:', torch.cuda.is_available())
print('Cuda version:', torch.version.cuda)
print('CuDNN version:', torch.backends.cudnn.version())
print('Number of GPUs available:', torch.cuda.device_count())
print('NCCL version:', torch.cuda.nccl.version())
except ImportError:
print('Torch version:', None)
try:
import deepspeed
print('DeepSpeed version:', deepspeed.__version__)
except ImportError:
print('DeepSpeed version:', None)
try:
import tensorflow as tf
print('TensorFlow version:', tf.__version__)
print('TF GPUs available:', bool(tf.config.list_physical_devices('GPU')))
print('Number of TF GPUs available:', len(tf.config.list_physical_devices('GPU')))
except ImportError:
print('TensorFlow version:', None)
| 17 |
import math
import os
import sys
def a__ ( snake_case ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Optional[int] = ''''''
try:
with open(snake_case , '''rb''' ) as binary_file:
__SCREAMING_SNAKE_CASE : int = binary_file.read()
for dat in data:
__SCREAMING_SNAKE_CASE : Optional[Any] = F'''{dat:08b}'''
result += curr_byte
return result
except OSError:
print('''File not accessible''' )
sys.exit()
def a__ ( snake_case , snake_case , snake_case , snake_case ):
"""simple docstring"""
lexicon.pop(snake_case )
__SCREAMING_SNAKE_CASE : List[str] = last_match_id
if math.loga(snake_case ).is_integer():
for curr_key in lexicon:
__SCREAMING_SNAKE_CASE : int = '''0''' + lexicon[curr_key]
__SCREAMING_SNAKE_CASE : List[str] = bin(snake_case )[2:]
def a__ ( snake_case ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : str = {'''0''': '''0''', '''1''': '''1'''}
__SCREAMING_SNAKE_CASE, __SCREAMING_SNAKE_CASE : Any = '''''', ''''''
__SCREAMING_SNAKE_CASE : Optional[Any] = len(snake_case )
for i in range(len(snake_case ) ):
curr_string += data_bits[i]
if curr_string not in lexicon:
continue
__SCREAMING_SNAKE_CASE : Any = lexicon[curr_string]
result += last_match_id
add_key_to_lexicon(snake_case , snake_case , snake_case , snake_case )
index += 1
__SCREAMING_SNAKE_CASE : Tuple = ''''''
while curr_string != "" and curr_string not in lexicon:
curr_string += "0"
if curr_string != "":
__SCREAMING_SNAKE_CASE : Dict = lexicon[curr_string]
result += last_match_id
return result
def a__ ( snake_case , snake_case ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Optional[Any] = os.path.getsize(snake_case )
__SCREAMING_SNAKE_CASE : Union[str, Any] = bin(snake_case )[2:]
__SCREAMING_SNAKE_CASE : int = len(snake_case )
return "0" * (length_length - 1) + file_length_binary + compressed
def a__ ( snake_case , snake_case ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : int = 8
try:
with open(snake_case , '''wb''' ) as opened_file:
__SCREAMING_SNAKE_CASE : Optional[int] = [
to_write[i : i + byte_length]
for i in range(0 , len(snake_case ) , snake_case )
]
if len(result_byte_array[-1] ) % byte_length == 0:
result_byte_array.append('''10000000''' )
else:
result_byte_array[-1] += "1" + "0" * (
byte_length - len(result_byte_array[-1] ) - 1
)
for elem in result_byte_array:
opened_file.write(int(snake_case , 2 ).to_bytes(1 , byteorder='''big''' ) )
except OSError:
print('''File not accessible''' )
sys.exit()
def a__ ( snake_case , snake_case ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Optional[Any] = read_file_binary(snake_case )
__SCREAMING_SNAKE_CASE : Optional[int] = compress_data(snake_case )
__SCREAMING_SNAKE_CASE : Dict = add_file_length(snake_case , snake_case )
write_file_binary(snake_case , snake_case )
if __name__ == "__main__":
compress(sys.argv[1], sys.argv[2])
| 303 | 0 |
"""simple docstring"""
import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey
def _a ( _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case ):
"""simple docstring"""
if (ksize % 2) == 0:
UpperCAmelCase = ksize + 1
UpperCAmelCase = np.zeros((ksize, ksize) , dtype=np.floataa )
# each value
for y in range(_snake_case ):
for x in range(_snake_case ):
# distance from center
UpperCAmelCase = x - ksize // 2
UpperCAmelCase = y - ksize // 2
# degree to radiant
UpperCAmelCase = theta / 180 * np.pi
UpperCAmelCase = np.cos(_theta )
UpperCAmelCase = np.sin(_theta )
# get kernel x
UpperCAmelCase = cos_theta * px + sin_theta * py
# get kernel y
UpperCAmelCase = -sin_theta * px + cos_theta * py
# fill kernel
UpperCAmelCase = np.exp(
-(_x**2 + gamma**2 * _y**2) / (2 * sigma**2) ) * np.cos(2 * np.pi * _x / lambd + psi )
return gabor
if __name__ == "__main__":
import doctest
doctest.testmod()
# read original image
_UpperCamelCase = imread("""../image_data/lena.jpg""")
# turn image in gray scale value
_UpperCamelCase = cvtColor(img, COLOR_BGR2GRAY)
# Apply multiple Kernel to detect edges
_UpperCamelCase = np.zeros(gray.shape[:2])
for theta in [0, 30, 60, 90, 120, 150]:
_UpperCamelCase = gabor_filter_kernel(10, 8, theta, 10, 0, 0)
out += filteraD(gray, CV_8UC3, kernel_aa)
_UpperCamelCase = out / out.max() * 255
_UpperCamelCase = out.astype(np.uinta)
imshow("""Original""", gray)
imshow("""Gabor filter with 20x20 mask and 6 directions""", out)
waitKey(0)
| 361 |
"""simple docstring"""
from __future__ import annotations
import json
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
_UpperCamelCase = {"""UserAgent""": UserAgent().random}
def _a ( _snake_case ):
"""simple docstring"""
UpperCAmelCase = script.contents[0]
UpperCAmelCase = json.loads(data[data.find("""{\"config\"""" ) : -1] )
return info["entry_data"]["ProfilePage"][0]["graphql"]["user"]
class lowerCamelCase__ :
def __init__( self ,A ):
UpperCAmelCase = F'''https://www.instagram.com/{username}/'''
UpperCAmelCase = self.get_json()
def _UpperCamelCase ( self ):
UpperCAmelCase = requests.get(self.url ,headers=A ).text
UpperCAmelCase = BeautifulSoup(A ,"""html.parser""" ).find_all("""script""" )
try:
return extract_user_profile(scripts[4] )
except (json.decoder.JSONDecodeError, KeyError):
return extract_user_profile(scripts[3] )
def __repr__( self ):
return F'''{self.__class__.__name__}(\'{self.username}\')'''
def __str__( self ):
return F'''{self.fullname} ({self.username}) is {self.biography}'''
@property
def _UpperCamelCase ( self ):
return self.user_data["username"]
@property
def _UpperCamelCase ( self ):
return self.user_data["full_name"]
@property
def _UpperCamelCase ( self ):
return self.user_data["biography"]
@property
def _UpperCamelCase ( self ):
return self.user_data["business_email"]
@property
def _UpperCamelCase ( self ):
return self.user_data["external_url"]
@property
def _UpperCamelCase ( self ):
return self.user_data["edge_followed_by"]["count"]
@property
def _UpperCamelCase ( self ):
return self.user_data["edge_follow"]["count"]
@property
def _UpperCamelCase ( self ):
return self.user_data["edge_owner_to_timeline_media"]["count"]
@property
def _UpperCamelCase ( self ):
return self.user_data["profile_pic_url_hd"]
@property
def _UpperCamelCase ( self ):
return self.user_data["is_verified"]
@property
def _UpperCamelCase ( self ):
return self.user_data["is_private"]
def _a ( _snake_case = "github" ):
"""simple docstring"""
import os
if os.environ.get("""CI""" ):
return # test failing on GitHub Actions
UpperCAmelCase = InstagramUser(_snake_case )
assert instagram_user.user_data
assert isinstance(instagram_user.user_data , _snake_case )
assert instagram_user.username == username
if username != "github":
return
assert instagram_user.fullname == "GitHub"
assert instagram_user.biography == "Built for developers."
assert instagram_user.number_of_posts > 150
assert instagram_user.number_of_followers > 12_0000
assert instagram_user.number_of_followings > 15
assert instagram_user.email == "[email protected]"
assert instagram_user.website == "https://github.com/readme"
assert instagram_user.profile_picture_url.startswith("""https://instagram.""" )
assert instagram_user.is_verified is True
assert instagram_user.is_private is False
if __name__ == "__main__":
import doctest
doctest.testmod()
_UpperCamelCase = InstagramUser("""github""")
print(instagram_user)
print(F"""{instagram_user.number_of_posts = }""")
print(F"""{instagram_user.number_of_followers = }""")
print(F"""{instagram_user.number_of_followings = }""")
print(F"""{instagram_user.email = }""")
print(F"""{instagram_user.website = }""")
print(F"""{instagram_user.profile_picture_url = }""")
print(F"""{instagram_user.is_verified = }""")
print(F"""{instagram_user.is_private = }""")
| 234 | 0 |
def lowerCAmelCase_ ( snake_case_,snake_case_ ):
_A : int = len(snake_case_ )
_A : int = len(snake_case_ )
_A : int = (
first_str_length if first_str_length > second_str_length else second_str_length
)
_A : list = []
for char_count in range(snake_case_ ):
if char_count < first_str_length:
output_list.append(first_str[char_count] )
if char_count < second_str_length:
output_list.append(second_str[char_count] )
return "".join(snake_case_ )
if __name__ == "__main__":
print(alternative_string_arrange("AB", "XYZ"), end=" ")
| 26 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
"google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/config.json",
"google/fnet-large": "https://huggingface.co/google/fnet-large/resolve/main/config.json"
# See all FNet models at https://huggingface.co/models?filter=fnet
}
class lowercase ( UpperCamelCase__ ):
_a = "fnet"
def __init__( self , _a=3_2000 , _a=768 , _a=12 , _a=3072 , _a="gelu_new" , _a=0.1 , _a=512 , _a=4 , _a=0.02 , _a=1e-12 , _a=False , _a=512 , _a=3 , _a=1 , _a=2 , **_a , ) -> int:
super().__init__(pad_token_id=_a , bos_token_id=_a , eos_token_id=_a , **_a )
_A : Any = vocab_size
_A : str = max_position_embeddings
_A : Optional[Any] = hidden_size
_A : List[str] = num_hidden_layers
_A : List[str] = intermediate_size
_A : List[Any] = hidden_act
_A : List[str] = hidden_dropout_prob
_A : List[str] = initializer_range
_A : List[Any] = type_vocab_size
_A : List[Any] = layer_norm_eps
_A : List[str] = use_tpu_fourier_optimizations
_A : str = tpu_short_seq_length
| 26 | 1 |
"""simple docstring"""
import json
import os
import unittest
from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors
@require_tokenizers
class lowercase_ ( lowerCamelCase_ , unittest.TestCase ):
'''simple docstring'''
UpperCAmelCase : int = MvpTokenizer
UpperCAmelCase : List[Any] = MvpTokenizerFast
UpperCAmelCase : Optional[int] = True
UpperCAmelCase : Tuple = filter_roberta_detectors
def lowerCAmelCase_ ( self : Optional[Any] ):
super().setUp()
_A = [
'l',
'o',
'w',
'e',
'r',
's',
't',
'i',
'd',
'n',
'\u0120',
'\u0120l',
'\u0120n',
'\u0120lo',
'\u0120low',
'er',
'\u0120lowest',
'\u0120newer',
'\u0120wider',
'<unk>',
]
_A = dict(zip(__snake_case , range(len(__snake_case ) ) ) )
_A = ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
_A = {'unk_token': '<unk>'}
_A = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
_A = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['merges_file'] )
with open(self.vocab_file , 'w' , encoding='utf-8' ) as fp:
fp.write(json.dumps(__snake_case ) + '\n' )
with open(self.merges_file , 'w' , encoding='utf-8' ) as fp:
fp.write('\n'.join(__snake_case ) )
def lowerCAmelCase_ ( self : Optional[Any] , **_UpperCAmelCase : Tuple ):
kwargs.update(self.special_tokens_map )
return self.tokenizer_class.from_pretrained(self.tmpdirname , **__snake_case )
def lowerCAmelCase_ ( self : Optional[Any] , **_UpperCAmelCase : Dict ):
kwargs.update(self.special_tokens_map )
return self.rust_tokenizer_class.from_pretrained(self.tmpdirname , **__snake_case )
def lowerCAmelCase_ ( self : Any , _UpperCAmelCase : Union[str, Any] ):
return "lower newer", "lower newer"
@cached_property
def lowerCAmelCase_ ( self : Tuple ):
return MvpTokenizer.from_pretrained('RUCAIBox/mvp' )
@cached_property
def lowerCAmelCase_ ( self : Optional[int] ):
return MvpTokenizerFast.from_pretrained('RUCAIBox/mvp' )
@require_torch
def lowerCAmelCase_ ( self : Optional[int] ):
_A = ['A long paragraph for summarization.', 'Another paragraph for summarization.']
_A = [0, 250, 251, 17_818, 13, 39_186, 1_938, 4, 2]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
_A = tokenizer(__snake_case , max_length=len(__snake_case ) , padding=__snake_case , return_tensors='pt' )
self.assertIsInstance(__snake_case , __snake_case )
self.assertEqual((2, 9) , batch.input_ids.shape )
self.assertEqual((2, 9) , batch.attention_mask.shape )
_A = batch.input_ids.tolist()[0]
self.assertListEqual(__snake_case , __snake_case )
# Test that special tokens are reset
@require_torch
def lowerCAmelCase_ ( self : Union[str, Any] ):
_A = ['A long paragraph for summarization.', 'Another paragraph for summarization.']
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
_A = tokenizer(__snake_case , padding=__snake_case , return_tensors='pt' )
# check if input_ids are returned and no labels
self.assertIn('input_ids' , __snake_case )
self.assertIn('attention_mask' , __snake_case )
self.assertNotIn('labels' , __snake_case )
self.assertNotIn('decoder_attention_mask' , __snake_case )
@require_torch
def lowerCAmelCase_ ( self : Any ):
_A = [
'Summary of the text.',
'Another summary.',
]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
_A = tokenizer(text_target=__snake_case , max_length=32 , padding='max_length' , return_tensors='pt' )
self.assertEqual(32 , targets['input_ids'].shape[1] )
@require_torch
def lowerCAmelCase_ ( self : int ):
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
_A = tokenizer(
['I am a small frog' * 1_024, 'I am a small frog'] , padding=__snake_case , truncation=__snake_case , return_tensors='pt' )
self.assertIsInstance(__snake_case , __snake_case )
self.assertEqual(batch.input_ids.shape , (2, 1_024) )
@require_torch
def lowerCAmelCase_ ( self : List[Any] ):
_A = ['A long paragraph for summarization.']
_A = [
'Summary of the text.',
]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
_A = tokenizer(__snake_case , text_target=__snake_case , return_tensors='pt' )
_A = inputs['input_ids']
_A = inputs['labels']
self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item() )
self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item() )
self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item() )
self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item() )
def lowerCAmelCase_ ( self : Union[str, Any] ):
pass
def lowerCAmelCase_ ( self : Tuple ):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
_A = self.rust_tokenizer_class.from_pretrained(__snake_case , **__snake_case )
_A = self.tokenizer_class.from_pretrained(__snake_case , **__snake_case )
_A = 'A, <mask> AllenNLP sentence.'
_A = tokenizer_r.encode_plus(__snake_case , add_special_tokens=__snake_case , return_token_type_ids=__snake_case )
_A = tokenizer_p.encode_plus(__snake_case , add_special_tokens=__snake_case , return_token_type_ids=__snake_case )
# token_type_ids should put 0 everywhere
self.assertEqual(sum(tokens_r['token_type_ids'] ) , sum(tokens_p['token_type_ids'] ) )
# attention_mask should put 1 everywhere, so sum over length should be 1
self.assertEqual(
sum(tokens_r['attention_mask'] ) / len(tokens_r['attention_mask'] ) , sum(tokens_p['attention_mask'] ) / len(tokens_p['attention_mask'] ) , )
_A = tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids'] )
_A = tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids'] )
# Rust correctly handles the space before the mask while python doesnt
self.assertSequenceEqual(tokens_p['input_ids'] , [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2] )
self.assertSequenceEqual(tokens_r['input_ids'] , [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2] )
self.assertSequenceEqual(
__snake_case , ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>'] )
self.assertSequenceEqual(
__snake_case , ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>'] )
| 371 |
"""simple docstring"""
import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation
def _snake_case ( _snake_case : int = 8 ) -> str:
'''simple docstring'''
_A = ascii_letters + digits + punctuation
return "".join(secrets.choice(_snake_case ) for _ in range(_snake_case ) )
def _snake_case ( _snake_case : str , _snake_case : int ) -> str:
'''simple docstring'''
i -= len(_snake_case )
_A = i // 3
_A = i % 3
# chars = chars_incl + random_letters(ascii_letters, i / 3 + remainder) +
# random_number(digits, i / 3) + random_characters(punctuation, i / 3)
_A = (
chars_incl
+ random(_snake_case , quotient + remainder )
+ random(_snake_case , _snake_case )
+ random(_snake_case , _snake_case )
)
_A = list(_snake_case )
shuffle(_snake_case )
return "".join(_snake_case )
# random is a generalised function for letters, characters and numbers
def _snake_case ( _snake_case : str , _snake_case : int ) -> str:
'''simple docstring'''
return "".join(secrets.choice(_snake_case ) for _ in range(_snake_case ) )
def _snake_case ( _snake_case : Dict , _snake_case : Optional[int] ) -> int:
'''simple docstring'''
pass # Put your code here...
def _snake_case ( _snake_case : Any , _snake_case : str ) -> Dict:
'''simple docstring'''
pass # Put your code here...
def _snake_case ( _snake_case : Union[str, Any] , _snake_case : int ) -> int:
'''simple docstring'''
pass # Put your code here...
def _snake_case ( _snake_case : str , _snake_case : int = 8 ) -> bool:
'''simple docstring'''
if len(_snake_case ) < min_length:
# Your Password must be at least 8 characters long
return False
_A = any(char in ascii_uppercase for char in password )
_A = any(char in ascii_lowercase for char in password )
_A = any(char in digits for char in password )
_A = any(char in punctuation for char in password )
return upper and lower and num and spec_char
# Passwords should contain UPPERCASE, lowerase
# numbers, and special characters
def _snake_case ( ) -> Optional[Any]:
'''simple docstring'''
_A = int(input('Please indicate the max length of your password: ' ).strip() )
_A = input(
'Please indicate the characters that must be in your password: ' ).strip()
print('Password generated:' , password_generator(_snake_case ) )
print(
'Alternative Password generated:' , alternative_password_generator(_snake_case , _snake_case ) , )
print('[If you are thinking of using this passsword, You better save it.]' )
if __name__ == "__main__":
main()
| 271 | 0 |
"""simple docstring"""
# Copyright 2022 The HuggingFace Team and The OpenBMB Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
A_ : Union[str, Any] = {
"configuration_cpmant": ["CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP", "CpmAntConfig"],
"tokenization_cpmant": ["CpmAntTokenizer"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : str = [
"CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST",
"CpmAntForCausalLM",
"CpmAntModel",
"CpmAntPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_cpmant import CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP, CpmAntConfig
from .tokenization_cpmant import CpmAntTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_cpmant import (
CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST,
CpmAntForCausalLM,
CpmAntModel,
CpmAntPreTrainedModel,
)
else:
import sys
A_ : Union[str, Any] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 165 |
"""simple docstring"""
def A ( snake_case__ = 10_00 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = 1, 1
SCREAMING_SNAKE_CASE__ = 2
while True:
SCREAMING_SNAKE_CASE__ = 0
SCREAMING_SNAKE_CASE__ = fa + fa
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = fa, f
index += 1
for _ in str(snake_case__ ):
i += 1
if i == n:
break
return index
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
| 165 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case : List[str] = logging.get_logger(__name__)
__snake_case : Optional[int] = {
'unc-nlp/lxmert-base-uncased': 'https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config.json',
}
class A__ ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = 'lxmert'
SCREAMING_SNAKE_CASE = {}
def __init__( self: Tuple , _SCREAMING_SNAKE_CASE: int=3_0522 , _SCREAMING_SNAKE_CASE: Any=768 , _SCREAMING_SNAKE_CASE: Optional[int]=12 , _SCREAMING_SNAKE_CASE: Tuple=9500 , _SCREAMING_SNAKE_CASE: List[Any]=1600 , _SCREAMING_SNAKE_CASE: Dict=400 , _SCREAMING_SNAKE_CASE: str=3072 , _SCREAMING_SNAKE_CASE: int="gelu" , _SCREAMING_SNAKE_CASE: str=0.1 , _SCREAMING_SNAKE_CASE: List[str]=0.1 , _SCREAMING_SNAKE_CASE: str=512 , _SCREAMING_SNAKE_CASE: List[str]=2 , _SCREAMING_SNAKE_CASE: List[str]=0.02 , _SCREAMING_SNAKE_CASE: Union[str, Any]=1e-12 , _SCREAMING_SNAKE_CASE: Union[str, Any]=9 , _SCREAMING_SNAKE_CASE: List[Any]=5 , _SCREAMING_SNAKE_CASE: Dict=5 , _SCREAMING_SNAKE_CASE: List[Any]=2048 , _SCREAMING_SNAKE_CASE: List[str]=4 , _SCREAMING_SNAKE_CASE: Any=6.67 , _SCREAMING_SNAKE_CASE: int=True , _SCREAMING_SNAKE_CASE: Any=True , _SCREAMING_SNAKE_CASE: Optional[Any]=True , _SCREAMING_SNAKE_CASE: List[Any]=True , _SCREAMING_SNAKE_CASE: str=True , _SCREAMING_SNAKE_CASE: Optional[Any]=True , _SCREAMING_SNAKE_CASE: Union[str, Any]=True , **_SCREAMING_SNAKE_CASE: List[str] , ) -> int:
"""simple docstring"""
__lowerCAmelCase : Union[str, Any] = vocab_size
__lowerCAmelCase : Any = hidden_size
__lowerCAmelCase : Tuple = num_attention_heads
__lowerCAmelCase : str = hidden_act
__lowerCAmelCase : List[str] = intermediate_size
__lowerCAmelCase : str = hidden_dropout_prob
__lowerCAmelCase : Optional[int] = attention_probs_dropout_prob
__lowerCAmelCase : Any = max_position_embeddings
__lowerCAmelCase : str = type_vocab_size
__lowerCAmelCase : List[Any] = initializer_range
__lowerCAmelCase : int = layer_norm_eps
__lowerCAmelCase : Dict = num_qa_labels
__lowerCAmelCase : Optional[Any] = num_object_labels
__lowerCAmelCase : List[Any] = num_attr_labels
__lowerCAmelCase : Tuple = l_layers
__lowerCAmelCase : List[str] = x_layers
__lowerCAmelCase : Any = r_layers
__lowerCAmelCase : Any = visual_feat_dim
__lowerCAmelCase : str = visual_pos_dim
__lowerCAmelCase : Optional[int] = visual_loss_normalizer
__lowerCAmelCase : List[Any] = task_matched
__lowerCAmelCase : Optional[int] = task_mask_lm
__lowerCAmelCase : Optional[int] = task_obj_predict
__lowerCAmelCase : Any = task_qa
__lowerCAmelCase : List[Any] = visual_obj_loss
__lowerCAmelCase : Optional[Any] = visual_attr_loss
__lowerCAmelCase : Union[str, Any] = visual_feat_loss
__lowerCAmelCase : List[Any] = {"vision": r_layers, "cross_encoder": x_layers, "language": l_layers}
super().__init__(**_SCREAMING_SNAKE_CASE) | 58 |
"""simple docstring"""
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import (
ImageTextPipelineOutput,
UniDiffuserPipeline,
)
else:
from .modeling_text_decoder import UniDiffuserTextDecoder
from .modeling_uvit import UniDiffuserModel, UTransformeraDModel
from .pipeline_unidiffuser import ImageTextPipelineOutput, UniDiffuserPipeline | 58 | 1 |
'''simple docstring'''
from __future__ import annotations
import math
def _UpperCamelCase ( __A , __A , __A , __A , __A ) -> int:
'''simple docstring'''
if depth < 0:
raise ValueError("Depth cannot be less than 0" )
if not scores:
raise ValueError("Scores cannot be empty" )
if depth == height:
return scores[node_index]
return (
max(
minimax(depth + 1 , node_index * 2 , __A , __A , __A ) , minimax(depth + 1 , node_index * 2 + 1 , __A , __A , __A ) , )
if is_max
else min(
minimax(depth + 1 , node_index * 2 , __A , __A , __A ) , minimax(depth + 1 , node_index * 2 + 1 , __A , __A , __A ) , )
)
def _UpperCamelCase ( ) -> None:
'''simple docstring'''
UpperCamelCase__ = [90, 23, 6, 33, 21, 65, 123, 34423]
UpperCamelCase__ = math.log(len(__A ) , 2 )
print(F'''Optimal value : {minimax(0 , 0 , __A , __A , __A )}''' )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 80 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
OPENAI_CLIP_MEAN,
OPENAI_CLIP_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
_snake_case = logging.get_logger(__name__)
if is_vision_available():
import PIL
class a__ ( lowerCamelCase_ ):
_SCREAMING_SNAKE_CASE : Dict = ['pixel_values']
def __init__( self , _UpperCamelCase = True , _UpperCamelCase = None , _UpperCamelCase = PILImageResampling.BICUBIC , _UpperCamelCase = True , _UpperCamelCase = None , _UpperCamelCase = True , _UpperCamelCase = 1 / 255 , _UpperCamelCase = True , _UpperCamelCase = None , _UpperCamelCase = None , _UpperCamelCase = True , **_UpperCamelCase , ):
"""simple docstring"""
super().__init__(**_UpperCamelCase )
_lowercase : Dict = size if size is not None else {"shortest_edge": 224}
_lowercase : List[Any] = get_size_dict(_UpperCamelCase , default_to_square=_UpperCamelCase )
_lowercase : Union[str, Any] = crop_size if crop_size is not None else {"height": 224, "width": 224}
_lowercase : Tuple = get_size_dict(_UpperCamelCase , default_to_square=_UpperCamelCase , param_name="crop_size" )
_lowercase : List[str] = do_resize
_lowercase : Dict = size
_lowercase : Any = resample
_lowercase : int = do_center_crop
_lowercase : Optional[Any] = crop_size
_lowercase : Tuple = do_rescale
_lowercase : Any = rescale_factor
_lowercase : Union[str, Any] = do_normalize
_lowercase : List[Any] = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
_lowercase : List[Any] = image_std if image_std is not None else OPENAI_CLIP_STD
_lowercase : Optional[int] = do_convert_rgb
def _lowerCamelCase ( self , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase = PILImageResampling.BICUBIC , _UpperCamelCase = None , **_UpperCamelCase , ):
"""simple docstring"""
_lowercase : int = get_size_dict(_UpperCamelCase , default_to_square=_UpperCamelCase )
if "shortest_edge" not in size:
raise ValueError(f'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}''' )
_lowercase : List[str] = get_resize_output_image_size(_UpperCamelCase , size=size["shortest_edge"] , default_to_square=_UpperCamelCase )
return resize(_UpperCamelCase , size=_UpperCamelCase , resample=_UpperCamelCase , data_format=_UpperCamelCase , **_UpperCamelCase )
def _lowerCamelCase ( self , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase = None , **_UpperCamelCase , ):
"""simple docstring"""
_lowercase : int = get_size_dict(_UpperCamelCase )
if "height" not in size or "width" not in size:
raise ValueError(f'''The `size` parameter must contain the keys (height, width). Got {size.keys()}''' )
return center_crop(_UpperCamelCase , size=(size["height"], size["width"]) , data_format=_UpperCamelCase , **_UpperCamelCase )
def _lowerCamelCase ( self , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase = None , **_UpperCamelCase , ):
"""simple docstring"""
return rescale(_UpperCamelCase , scale=_UpperCamelCase , data_format=_UpperCamelCase , **_UpperCamelCase )
def _lowerCamelCase ( self , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase = None , **_UpperCamelCase , ):
"""simple docstring"""
return normalize(_UpperCamelCase , mean=_UpperCamelCase , std=_UpperCamelCase , data_format=_UpperCamelCase , **_UpperCamelCase )
def _lowerCamelCase ( self , _UpperCamelCase , _UpperCamelCase = None , _UpperCamelCase = None , _UpperCamelCase = None , _UpperCamelCase = None , _UpperCamelCase = None , _UpperCamelCase = None , _UpperCamelCase = None , _UpperCamelCase = None , _UpperCamelCase = None , _UpperCamelCase = None , _UpperCamelCase = None , _UpperCamelCase = None , _UpperCamelCase = ChannelDimension.FIRST , **_UpperCamelCase , ):
"""simple docstring"""
_lowercase : Tuple = do_resize if do_resize is not None else self.do_resize
_lowercase : Union[str, Any] = size if size is not None else self.size
_lowercase : Optional[int] = get_size_dict(_UpperCamelCase , param_name="size" , default_to_square=_UpperCamelCase )
_lowercase : List[Any] = resample if resample is not None else self.resample
_lowercase : Union[str, Any] = do_center_crop if do_center_crop is not None else self.do_center_crop
_lowercase : Union[str, Any] = crop_size if crop_size is not None else self.crop_size
_lowercase : Tuple = get_size_dict(_UpperCamelCase , param_name="crop_size" , default_to_square=_UpperCamelCase )
_lowercase : Any = do_rescale if do_rescale is not None else self.do_rescale
_lowercase : Optional[Any] = rescale_factor if rescale_factor is not None else self.rescale_factor
_lowercase : List[str] = do_normalize if do_normalize is not None else self.do_normalize
_lowercase : Optional[int] = image_mean if image_mean is not None else self.image_mean
_lowercase : Dict = image_std if image_std is not None else self.image_std
_lowercase : Tuple = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
_lowercase : str = make_list_of_images(_UpperCamelCase )
if not valid_images(_UpperCamelCase ):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
"torch.Tensor, tf.Tensor or jax.ndarray." )
if do_resize and size is None:
raise ValueError("Size must be specified if do_resize is True." )
if do_center_crop and crop_size is None:
raise ValueError("Crop size must be specified if do_center_crop is True." )
if do_rescale and rescale_factor is None:
raise ValueError("Rescale factor must be specified if do_rescale is True." )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError("Image mean and std must be specified if do_normalize is True." )
# PIL RGBA images are converted to RGB
if do_convert_rgb:
_lowercase : List[Any] = [convert_to_rgb(_UpperCamelCase ) for image in images]
# All transformations expect numpy arrays.
_lowercase : List[Any] = [to_numpy_array(_UpperCamelCase ) for image in images]
if do_resize:
_lowercase : Optional[Any] = [self.resize(image=_UpperCamelCase , size=_UpperCamelCase , resample=_UpperCamelCase ) for image in images]
if do_center_crop:
_lowercase : Optional[int] = [self.center_crop(image=_UpperCamelCase , size=_UpperCamelCase ) for image in images]
if do_rescale:
_lowercase : Any = [self.rescale(image=_UpperCamelCase , scale=_UpperCamelCase ) for image in images]
if do_normalize:
_lowercase : List[Any] = [self.normalize(image=_UpperCamelCase , mean=_UpperCamelCase , std=_UpperCamelCase ) for image in images]
_lowercase : List[Any] = [to_channel_dimension_format(_UpperCamelCase , _UpperCamelCase ) for image in images]
_lowercase : Dict = {"pixel_values": images}
return BatchFeature(data=_UpperCamelCase , tensor_type=_UpperCamelCase )
| 250 | 0 |
from __future__ import annotations
import sys
from collections import deque
from typing import Generic, TypeVar
lowerCAmelCase__ = TypeVar('''T''')
class snake_case__(Generic[T] ):
"""simple docstring"""
lowercase_ = 42 # Cache store of keys
lowercase_ = 42 # References of the keys in cache
lowercase_ = 1_0 # Maximum capacity of cache
def __init__( self : List[Any] , SCREAMING_SNAKE_CASE : int ):
lowercase__ : List[str] = deque()
lowercase__ : List[str] = set()
if not n:
lowercase__ : List[str] = sys.maxsize
elif n < 0:
raise ValueError("n should be an integer greater than 0." )
else:
lowercase__ : List[Any] = n
def snake_case ( self : List[str] , SCREAMING_SNAKE_CASE : T ):
if x not in self.key_reference:
if len(self.dq_store ) == LRUCache._MAX_CAPACITY:
lowercase__ : List[Any] = self.dq_store.pop()
self.key_reference.remove(SCREAMING_SNAKE_CASE )
else:
self.dq_store.remove(SCREAMING_SNAKE_CASE )
self.dq_store.appendleft(SCREAMING_SNAKE_CASE )
self.key_reference.add(SCREAMING_SNAKE_CASE )
def snake_case ( self : Any ):
for k in self.dq_store:
print(SCREAMING_SNAKE_CASE )
def __repr__( self : Union[str, Any] ):
return f"""LRUCache({self._MAX_CAPACITY}) => {list(self.dq_store )}"""
if __name__ == "__main__":
import doctest
doctest.testmod()
lowerCAmelCase__ = LRUCache(4)
lru_cache.refer('''A''')
lru_cache.refer(2)
lru_cache.refer(3)
lru_cache.refer('''A''')
lru_cache.refer(4)
lru_cache.refer(5)
lru_cache.display()
print(lru_cache)
assert str(lru_cache) == "LRUCache(4) => [5, 4, 'A', 3]"
| 121 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface import HuggingFace
@pytest.mark.skipif(
literal_eval(os.getenv("""TEST_SAGEMAKER""" , """False""" ) ) is not True , reason="""Skipping test because should only be run when releasing minor transformers version""" , )
@pytest.mark.usefixtures("""sm_env""" )
@parameterized_class(
[
{
"""framework""": """pytorch""",
"""script""": """run_glue.py""",
"""model_name_or_path""": """distilbert-base-cased""",
"""instance_type""": """ml.g4dn.xlarge""",
"""results""": {"""train_runtime""": 6_5_0, """eval_accuracy""": 0.6, """eval_loss""": 0.9},
},
{
"""framework""": """tensorflow""",
"""script""": """run_tf.py""",
"""model_name_or_path""": """distilbert-base-cased""",
"""instance_type""": """ml.g4dn.xlarge""",
"""results""": {"""train_runtime""": 6_0_0, """eval_accuracy""": 0.3, """eval_loss""": 0.9},
},
] )
class snake_case__(unittest.TestCase ):
"""simple docstring"""
def snake_case ( self : Union[str, Any] ):
if self.framework == "pytorch":
subprocess.run(
f"""cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py""".split() , encoding="utf-8" , check=SCREAMING_SNAKE_CASE , )
assert hasattr(self , "env" )
def snake_case ( self : Tuple , SCREAMING_SNAKE_CASE : str=1 ):
# creates estimator
return HuggingFace(
entry_point=self.script , source_dir=self.env.test_path , role=self.env.role , image_uri=self.env.image_uri , base_job_name=f"""{self.env.base_job_name}-single""" , instance_count=SCREAMING_SNAKE_CASE , instance_type=self.instance_type , debugger_hook_config=SCREAMING_SNAKE_CASE , hyperparameters={**self.env.hyperparameters, "model_name_or_path": self.model_name_or_path} , metric_definitions=self.env.metric_definitions , py_version="py36" , )
def snake_case ( self : Union[str, Any] , SCREAMING_SNAKE_CASE : str ):
TrainingJobAnalytics(SCREAMING_SNAKE_CASE ).export_csv(f"""{self.env.test_path}/{job_name}_metrics.csv""" )
def snake_case ( self : str ):
# create estimator
lowercase__ : Optional[int] = self.create_estimator()
# run training
estimator.fit()
# result dataframe
lowercase__ : Union[str, Any] = TrainingJobAnalytics(estimator.latest_training_job.name ).dataframe()
# extract kpis
lowercase__ : str = list(result_metrics_df[result_metrics_df.metric_name == "eval_accuracy"]["value"] )
lowercase__ : Tuple = list(result_metrics_df[result_metrics_df.metric_name == "eval_loss"]["value"] )
# get train time from SageMaker job, this includes starting, preprocessing, stopping
lowercase__ : Any = (
Session().describe_training_job(estimator.latest_training_job.name ).get("TrainingTimeInSeconds" , 999_999 )
)
# assert kpis
assert train_runtime <= self.results["train_runtime"]
assert all(t >= self.results["eval_accuracy"] for t in eval_accuracy )
assert all(t <= self.results["eval_loss"] for t in eval_loss )
# dump tests result into json file to share in PR
with open(f"""{estimator.latest_training_job.name}.json""" , "w" ) as outfile:
json.dump({"train_time": train_runtime, "eval_accuracy": eval_accuracy, "eval_loss": eval_loss} , SCREAMING_SNAKE_CASE )
| 121 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = {
"facebook/data2vec-text-base": "https://huggingface.co/data2vec/resolve/main/config.json",
}
class _snake_case ( _a ):
_A : Any = '''data2vec-text'''
def __init__( self : List[Any] ,SCREAMING_SNAKE_CASE__ : Union[str, Any]=30_522 ,SCREAMING_SNAKE_CASE__ : List[Any]=768 ,SCREAMING_SNAKE_CASE__ : List[Any]=12 ,SCREAMING_SNAKE_CASE__ : Optional[int]=12 ,SCREAMING_SNAKE_CASE__ : int=3_072 ,SCREAMING_SNAKE_CASE__ : Dict="gelu" ,SCREAMING_SNAKE_CASE__ : Optional[Any]=0.1 ,SCREAMING_SNAKE_CASE__ : str=0.1 ,SCREAMING_SNAKE_CASE__ : Optional[int]=512 ,SCREAMING_SNAKE_CASE__ : Union[str, Any]=2 ,SCREAMING_SNAKE_CASE__ : Any=0.02 ,SCREAMING_SNAKE_CASE__ : List[str]=1e-12 ,SCREAMING_SNAKE_CASE__ : List[str]=1 ,SCREAMING_SNAKE_CASE__ : str=0 ,SCREAMING_SNAKE_CASE__ : List[str]=2 ,SCREAMING_SNAKE_CASE__ : Optional[Any]="absolute" ,SCREAMING_SNAKE_CASE__ : List[Any]=True ,SCREAMING_SNAKE_CASE__ : int=None ,**SCREAMING_SNAKE_CASE__ : Tuple ,):
super().__init__(pad_token_id=SCREAMING_SNAKE_CASE__ ,bos_token_id=SCREAMING_SNAKE_CASE__ ,eos_token_id=SCREAMING_SNAKE_CASE__ ,**SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE:Any = vocab_size
SCREAMING_SNAKE_CASE:str = hidden_size
SCREAMING_SNAKE_CASE:List[Any] = num_hidden_layers
SCREAMING_SNAKE_CASE:List[Any] = num_attention_heads
SCREAMING_SNAKE_CASE:Tuple = hidden_act
SCREAMING_SNAKE_CASE:Union[str, Any] = intermediate_size
SCREAMING_SNAKE_CASE:Any = hidden_dropout_prob
SCREAMING_SNAKE_CASE:List[str] = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE:Any = max_position_embeddings
SCREAMING_SNAKE_CASE:Optional[int] = type_vocab_size
SCREAMING_SNAKE_CASE:Any = initializer_range
SCREAMING_SNAKE_CASE:Optional[int] = layer_norm_eps
SCREAMING_SNAKE_CASE:List[str] = position_embedding_type
SCREAMING_SNAKE_CASE:List[Any] = use_cache
SCREAMING_SNAKE_CASE:int = classifier_dropout
class _snake_case ( _a ):
@property
def __UpperCamelCase ( self : Union[str, Any] ):
if self.task == "multiple-choice":
SCREAMING_SNAKE_CASE:Optional[Any] = {0: "batch", 1: "choice", 2: "sequence"}
else:
SCREAMING_SNAKE_CASE:str = {0: "batch", 1: "sequence"}
return OrderedDict(
[
("input_ids", dynamic_axis),
("attention_mask", dynamic_axis),
] )
| 139 |
'''simple docstring'''
# Lint as: python3
import dataclasses
import re
from dataclasses import dataclass
from functools import total_ordering
from typing import Optional, Union
A_ = re.compile(R"^(?P<major>\d+)" R"\.(?P<minor>\d+)" R"\.(?P<patch>\d+)$")
@total_ordering
@dataclass
class _snake_case :
_A : str
_A : Optional[str] = None
_A : Optional[Union[str, int]] = None
_A : Optional[Union[str, int]] = None
_A : Optional[Union[str, int]] = None
def __UpperCamelCase ( self : Dict ):
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE:List[str] = _str_to_version_tuple(self.version_str )
def __repr__( self : Optional[Any] ):
return F'''{self.tuple[0]}.{self.tuple[1]}.{self.tuple[2]}'''
@property
def __UpperCamelCase ( self : List[Any] ):
return self.major, self.minor, self.patch
def __UpperCamelCase ( self : Union[str, Any] ,SCREAMING_SNAKE_CASE__ : int ):
if isinstance(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ):
return Version(SCREAMING_SNAKE_CASE__ )
elif isinstance(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ):
return other
raise TypeError(F'''{other} (type {type(SCREAMING_SNAKE_CASE__ )}) cannot be compared to version.''' )
def __eq__( self : Optional[Any] ,SCREAMING_SNAKE_CASE__ : List[str] ):
try:
SCREAMING_SNAKE_CASE:List[str] = self._validate_operand(SCREAMING_SNAKE_CASE__ )
except (TypeError, ValueError):
return False
else:
return self.tuple == other.tuple
def __lt__( self : int ,SCREAMING_SNAKE_CASE__ : Union[str, Any] ):
SCREAMING_SNAKE_CASE:Tuple = self._validate_operand(SCREAMING_SNAKE_CASE__ )
return self.tuple < other.tuple
def __hash__( self : Union[str, Any] ):
return hash(_version_tuple_to_str(self.tuple ) )
@classmethod
def __UpperCamelCase ( cls : str ,SCREAMING_SNAKE_CASE__ : str ):
SCREAMING_SNAKE_CASE:str = {f.name for f in dataclasses.fields(cls )}
return cls(**{k: v for k, v in dic.items() if k in field_names} )
def __UpperCamelCase ( self : Tuple ):
return self.version_str
def A_ ( snake_case ):
SCREAMING_SNAKE_CASE:int = _VERSION_REG.match(snake_case )
if not res:
raise ValueError(F'''Invalid version \'{version_str}\'. Format should be x.y.z with {{x,y,z}} being digits.''' )
return tuple(int(snake_case ) for v in [res.group("major" ), res.group("minor" ), res.group("patch" )] )
def A_ ( snake_case ):
return ".".join(str(snake_case ) for v in version_tuple )
| 139 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowerCAmelCase : Union[str, Any] = {
'configuration_groupvit': [
'GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP',
'GroupViTConfig',
'GroupViTOnnxConfig',
'GroupViTTextConfig',
'GroupViTVisionConfig',
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase : Any = [
'GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
'GroupViTModel',
'GroupViTPreTrainedModel',
'GroupViTTextModel',
'GroupViTVisionModel',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase : Any = [
'TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFGroupViTModel',
'TFGroupViTPreTrainedModel',
'TFGroupViTTextModel',
'TFGroupViTVisionModel',
]
if TYPE_CHECKING:
from .configuration_groupvit import (
GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GroupViTConfig,
GroupViTOnnxConfig,
GroupViTTextConfig,
GroupViTVisionConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_groupvit import (
GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
GroupViTModel,
GroupViTPreTrainedModel,
GroupViTTextModel,
GroupViTVisionModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_groupvit import (
TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFGroupViTModel,
TFGroupViTPreTrainedModel,
TFGroupViTTextModel,
TFGroupViTVisionModel,
)
else:
import sys
lowerCAmelCase : List[str] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 362 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_realm import RealmTokenizer
lowerCAmelCase : Dict = logging.get_logger(__name__)
lowerCAmelCase : Any = {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}
lowerCAmelCase : int = {
'vocab_file': {
'google/realm-cc-news-pretrained-embedder': (
'https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/vocab.txt'
),
'google/realm-cc-news-pretrained-encoder': (
'https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/vocab.txt'
),
'google/realm-cc-news-pretrained-scorer': (
'https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/vocab.txt'
),
'google/realm-cc-news-pretrained-openqa': (
'https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/vocab.txt'
),
'google/realm-orqa-nq-openqa': 'https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/vocab.txt',
'google/realm-orqa-nq-reader': 'https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/vocab.txt',
'google/realm-orqa-wq-openqa': 'https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/vocab.txt',
'google/realm-orqa-wq-reader': 'https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/vocab.txt',
},
'tokenizer_file': {
'google/realm-cc-news-pretrained-embedder': (
'https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/tokenizer.jsont'
),
'google/realm-cc-news-pretrained-encoder': (
'https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/tokenizer.json'
),
'google/realm-cc-news-pretrained-scorer': (
'https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/tokenizer.json'
),
'google/realm-cc-news-pretrained-openqa': (
'https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/tokenizer.json'
),
'google/realm-orqa-nq-openqa': (
'https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/tokenizer.json'
),
'google/realm-orqa-nq-reader': (
'https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/tokenizer.json'
),
'google/realm-orqa-wq-openqa': (
'https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/tokenizer.json'
),
'google/realm-orqa-wq-reader': (
'https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/tokenizer.json'
),
},
}
lowerCAmelCase : Tuple = {
'google/realm-cc-news-pretrained-embedder': 5_12,
'google/realm-cc-news-pretrained-encoder': 5_12,
'google/realm-cc-news-pretrained-scorer': 5_12,
'google/realm-cc-news-pretrained-openqa': 5_12,
'google/realm-orqa-nq-openqa': 5_12,
'google/realm-orqa-nq-reader': 5_12,
'google/realm-orqa-wq-openqa': 5_12,
'google/realm-orqa-wq-reader': 5_12,
}
lowerCAmelCase : Union[str, Any] = {
'google/realm-cc-news-pretrained-embedder': {'do_lower_case': True},
'google/realm-cc-news-pretrained-encoder': {'do_lower_case': True},
'google/realm-cc-news-pretrained-scorer': {'do_lower_case': True},
'google/realm-cc-news-pretrained-openqa': {'do_lower_case': True},
'google/realm-orqa-nq-openqa': {'do_lower_case': True},
'google/realm-orqa-nq-reader': {'do_lower_case': True},
'google/realm-orqa-wq-openqa': {'do_lower_case': True},
'google/realm-orqa-wq-reader': {'do_lower_case': True},
}
class SCREAMING_SNAKE_CASE__ ( snake_case_):
lowerCAmelCase_ = VOCAB_FILES_NAMES
lowerCAmelCase_ = PRETRAINED_VOCAB_FILES_MAP
lowerCAmelCase_ = PRETRAINED_INIT_CONFIGURATION
lowerCAmelCase_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCAmelCase_ = RealmTokenizer
def __init__( self , A_=None , A_=None , A_=True , A_="[UNK]" , A_="[SEP]" , A_="[PAD]" , A_="[CLS]" , A_="[MASK]" , A_=True , A_=None , **A_ , )-> Tuple:
'''simple docstring'''
super().__init__(
A_ , tokenizer_file=A_ , do_lower_case=A_ , unk_token=A_ , sep_token=A_ , pad_token=A_ , cls_token=A_ , mask_token=A_ , tokenize_chinese_chars=A_ , strip_accents=A_ , **A_ , )
UpperCamelCase = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get('lowercase' , A_ ) != do_lower_case
or normalizer_state.get('strip_accents' , A_ ) != strip_accents
or normalizer_state.get('handle_chinese_chars' , A_ ) != tokenize_chinese_chars
):
UpperCamelCase = getattr(A_ , normalizer_state.pop('type' ) )
UpperCamelCase = do_lower_case
UpperCamelCase = strip_accents
UpperCamelCase = tokenize_chinese_chars
UpperCamelCase = normalizer_class(**A_ )
UpperCamelCase = do_lower_case
def UpperCAmelCase_ ( self , A_ , **A_ )-> Optional[int]:
'''simple docstring'''
UpperCamelCase = PaddingStrategy.MAX_LENGTH
UpperCamelCase = text
UpperCamelCase = kwargs.pop('text_pair' , A_ )
UpperCamelCase = kwargs.pop('return_tensors' , A_ )
UpperCamelCase = {
'input_ids': [],
'attention_mask': [],
'token_type_ids': [],
}
for idx, candidate_text in enumerate(A_ ):
if batch_text_pair is not None:
UpperCamelCase = batch_text_pair[idx]
else:
UpperCamelCase = None
UpperCamelCase = super().__call__(A_ , A_ , return_tensors=A_ , **A_ )
UpperCamelCase = encoded_candidates.get('input_ids' )
UpperCamelCase = encoded_candidates.get('attention_mask' )
UpperCamelCase = encoded_candidates.get('token_type_ids' )
if encoded_input_ids is not None:
output_data["input_ids"].append(A_ )
if encoded_attention_mask is not None:
output_data["attention_mask"].append(A_ )
if encoded_token_type_ids is not None:
output_data["token_type_ids"].append(A_ )
UpperCamelCase = {key: item for key, item in output_data.items() if len(A_ ) != 0}
return BatchEncoding(A_ , tensor_type=A_ )
def UpperCAmelCase_ ( self , A_ , A_=None )-> Any:
'''simple docstring'''
UpperCamelCase = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def UpperCAmelCase_ ( self , A_ , A_ = None )-> List[int]:
'''simple docstring'''
UpperCamelCase = [self.sep_token_id]
UpperCamelCase = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def UpperCAmelCase_ ( self , A_ , A_ = None )-> Tuple[str]:
'''simple docstring'''
UpperCamelCase = self._tokenizer.model.save(A_ , name=A_ )
return tuple(A_ )
| 251 | 0 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig
_UpperCamelCase = logging.get_logger(__name__)
_UpperCamelCase = {
'Intel/dpt-large': 'https://huggingface.co/Intel/dpt-large/resolve/main/config.json',
# See all DPT models at https://huggingface.co/models?filter=dpt
}
class __lowercase (_snake_case ):
_UpperCamelCase = """dpt"""
def __init__( self , A_=768 , A_=12 , A_=12 , A_=3072 , A_="gelu" , A_=0.0 , A_=0.0 , A_=0.02 , A_=1e-12 , A_=384 , A_=16 , A_=3 , A_=False , A_=True , A_=[2, 5, 8, 11] , A_="project" , A_=[4, 2, 1, 0.5] , A_=[96, 192, 384, 768] , A_=256 , A_=-1 , A_=False , A_=True , A_=0.4 , A_=255 , A_=0.1 , A_=[1, 1024, 24, 24] , A_=[0, 1] , A_=None , **A_ , ) ->Tuple:
'''simple docstring'''
super().__init__(**__snake_case )
__lowerCAmelCase : int = hidden_size
__lowerCAmelCase : Union[str, Any] = is_hybrid
if self.is_hybrid:
if backbone_config is None:
logger.info('''Initializing the config with a `BiT` backbone.''' )
__lowerCAmelCase : Optional[int] = {
"""global_padding""": """same""",
"""layer_type""": """bottleneck""",
"""depths""": [3, 4, 9],
"""out_features""": ["""stage1""", """stage2""", """stage3"""],
"""embedding_dynamic_padding""": True,
}
__lowerCAmelCase : Any = BitConfig(**__snake_case )
elif isinstance(__snake_case , __snake_case ):
logger.info('''Initializing the config with a `BiT` backbone.''' )
__lowerCAmelCase : Tuple = BitConfig(**__snake_case )
elif isinstance(__snake_case , __snake_case ):
__lowerCAmelCase : Optional[int] = backbone_config
else:
raise ValueError(
f"""backbone_config must be a dictionary or a `PretrainedConfig`, got {backbone_config.__class__}.""" )
__lowerCAmelCase : Optional[int] = backbone_featmap_shape
__lowerCAmelCase : Optional[int] = neck_ignore_stages
if readout_type != "project":
raise ValueError('''Readout type must be \'project\' when using `DPT-hybrid` mode.''' )
else:
__lowerCAmelCase : List[str] = None
__lowerCAmelCase : List[str] = None
__lowerCAmelCase : List[Any] = []
__lowerCAmelCase : Any = num_hidden_layers
__lowerCAmelCase : str = num_attention_heads
__lowerCAmelCase : int = intermediate_size
__lowerCAmelCase : int = hidden_act
__lowerCAmelCase : int = hidden_dropout_prob
__lowerCAmelCase : Optional[Any] = attention_probs_dropout_prob
__lowerCAmelCase : Optional[Any] = initializer_range
__lowerCAmelCase : int = layer_norm_eps
__lowerCAmelCase : Optional[Any] = image_size
__lowerCAmelCase : List[Any] = patch_size
__lowerCAmelCase : Optional[int] = num_channels
__lowerCAmelCase : str = qkv_bias
__lowerCAmelCase : List[str] = backbone_out_indices
if readout_type not in ["ignore", "add", "project"]:
raise ValueError('''Readout_type must be one of [\'ignore\', \'add\', \'project\']''' )
__lowerCAmelCase : List[str] = readout_type
__lowerCAmelCase : Union[str, Any] = reassemble_factors
__lowerCAmelCase : List[Any] = neck_hidden_sizes
__lowerCAmelCase : Dict = fusion_hidden_size
__lowerCAmelCase : int = head_in_index
__lowerCAmelCase : int = use_batch_norm_in_fusion_residual
# auxiliary head attributes (semantic segmentation)
__lowerCAmelCase : Optional[Any] = use_auxiliary_head
__lowerCAmelCase : Optional[int] = auxiliary_loss_weight
__lowerCAmelCase : Optional[Any] = semantic_loss_ignore_index
__lowerCAmelCase : List[Any] = semantic_classifier_dropout
def UpperCamelCase__ ( self ) ->Dict:
'''simple docstring'''
__lowerCAmelCase : Union[str, Any] = copy.deepcopy(self.__dict__ )
if output["backbone_config"] is not None:
__lowerCAmelCase : str = self.backbone_config.to_dict()
__lowerCAmelCase : Dict = self.__class__.model_type
return output
| 275 |
'''simple docstring'''
import copy
import inspect
import unittest
from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available
from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor
if is_torch_available():
import torch
from transformers import TimmBackbone, TimmBackboneConfig
from ...test_pipeline_mixin import PipelineTesterMixin
class lowercase__ :
'''simple docstring'''
def __init__( self , __snake_case , __snake_case=None , __snake_case=None , __snake_case=None , __snake_case="resnet50" , __snake_case=3 , __snake_case=32 , __snake_case=3 , __snake_case=True , __snake_case=True , ):
_SCREAMING_SNAKE_CASE : Tuple = parent
_SCREAMING_SNAKE_CASE : Optional[int] = out_indices if out_indices is not None else [4]
_SCREAMING_SNAKE_CASE : str = stage_names
_SCREAMING_SNAKE_CASE : List[str] = out_features
_SCREAMING_SNAKE_CASE : int = backbone
_SCREAMING_SNAKE_CASE : Any = batch_size
_SCREAMING_SNAKE_CASE : List[str] = image_size
_SCREAMING_SNAKE_CASE : Union[str, Any] = num_channels
_SCREAMING_SNAKE_CASE : int = use_pretrained_backbone
_SCREAMING_SNAKE_CASE : Optional[Any] = is_training
def UpperCAmelCase_ ( self ):
_SCREAMING_SNAKE_CASE : int = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_SCREAMING_SNAKE_CASE : List[Any] = self.get_config()
return config, pixel_values
def UpperCAmelCase_ ( self ):
return TimmBackboneConfig(
image_size=self.image_size , num_channels=self.num_channels , out_features=self.out_features , out_indices=self.out_indices , stage_names=self.stage_names , use_pretrained_backbone=self.use_pretrained_backbone , backbone=self.backbone , )
def UpperCAmelCase_ ( self , __snake_case , __snake_case ):
_SCREAMING_SNAKE_CASE : Optional[int] = TimmBackbone(config=__snake_case )
model.to(__snake_case )
model.eval()
with torch.no_grad():
_SCREAMING_SNAKE_CASE : List[Any] = model(__snake_case )
self.parent.assertEqual(
result.feature_map[-1].shape , (self.batch_size, model.channels[-1], 14, 14) , )
def UpperCAmelCase_ ( self ):
_SCREAMING_SNAKE_CASE : Tuple = self.prepare_config_and_inputs()
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE : Any = config_and_inputs
_SCREAMING_SNAKE_CASE : Optional[int] = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_torch
@require_timm
class lowercase__ ( _snake_case , _snake_case , _snake_case , unittest.TestCase ):
'''simple docstring'''
A_ : Optional[Any] = (TimmBackbone,) if is_torch_available() else ()
A_ : Tuple = {"""feature-extraction""": TimmBackbone} if is_torch_available() else {}
A_ : Optional[Any] = False
A_ : List[Any] = False
A_ : Dict = False
A_ : int = False
def UpperCAmelCase_ ( self ):
_SCREAMING_SNAKE_CASE : Any = TimmBackboneModelTester(self )
_SCREAMING_SNAKE_CASE : int = ConfigTester(self , config_class=__snake_case , has_text_modality=__snake_case )
def UpperCAmelCase_ ( self ):
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def UpperCAmelCase_ ( self ):
_SCREAMING_SNAKE_CASE : Optional[int] = """resnet18"""
_SCREAMING_SNAKE_CASE : Tuple = """microsoft/resnet-18"""
_SCREAMING_SNAKE_CASE : List[str] = AutoBackbone.from_pretrained(__snake_case , use_timm_backbone=__snake_case )
_SCREAMING_SNAKE_CASE : Tuple = AutoBackbone.from_pretrained(__snake_case )
self.assertEqual(len(timm_model.out_features ) , len(transformers_model.out_features ) )
self.assertEqual(len(timm_model.stage_names ) , len(transformers_model.stage_names ) )
self.assertEqual(timm_model.channels , transformers_model.channels )
# Out indices are set to the last layer by default. For timm models, we don't know
# the number of layers in advance, so we set it to (-1,), whereas for transformers
# models, we set it to [len(stage_names) - 1] (kept for backward compatibility).
self.assertEqual(timm_model.out_indices , (-1,) )
self.assertEqual(transformers_model.out_indices , [len(timm_model.stage_names ) - 1] )
_SCREAMING_SNAKE_CASE : Optional[Any] = AutoBackbone.from_pretrained(__snake_case , use_timm_backbone=__snake_case , out_indices=[1, 2, 3] )
_SCREAMING_SNAKE_CASE : Union[str, Any] = AutoBackbone.from_pretrained(__snake_case , out_indices=[1, 2, 3] )
self.assertEqual(timm_model.out_indices , transformers_model.out_indices )
self.assertEqual(len(timm_model.out_features ) , len(transformers_model.out_features ) )
self.assertEqual(timm_model.channels , transformers_model.channels )
@unittest.skip("""TimmBackbone doesn't support feed forward chunking""" )
def UpperCAmelCase_ ( self ):
pass
@unittest.skip("""TimmBackbone doesn't have num_hidden_layers attribute""" )
def UpperCAmelCase_ ( self ):
pass
@unittest.skip("""TimmBackbone initialization is managed on the timm side""" )
def UpperCAmelCase_ ( self ):
pass
@unittest.skip("""TimmBackbone models doesn't have inputs_embeds""" )
def UpperCAmelCase_ ( self ):
pass
@unittest.skip("""TimmBackbone models doesn't have inputs_embeds""" )
def UpperCAmelCase_ ( self ):
pass
@unittest.skip("""TimmBackbone model cannot be created without specifying a backbone checkpoint""" )
def UpperCAmelCase_ ( self ):
pass
@unittest.skip("""Only checkpoints on timm can be loaded into TimmBackbone""" )
def UpperCAmelCase_ ( self ):
pass
@unittest.skip("""model weights aren't tied in TimmBackbone.""" )
def UpperCAmelCase_ ( self ):
pass
@unittest.skip("""model weights aren't tied in TimmBackbone.""" )
def UpperCAmelCase_ ( self ):
pass
@unittest.skip("""Only checkpoints on timm can be loaded into TimmBackbone""" )
def UpperCAmelCase_ ( self ):
pass
@unittest.skip("""Only checkpoints on timm can be loaded into TimmBackbone""" )
def UpperCAmelCase_ ( self ):
pass
@unittest.skip("""TimmBackbone doesn't have hidden size info in its configuration.""" )
def UpperCAmelCase_ ( self ):
pass
@unittest.skip("""TimmBackbone doesn't support output_attentions.""" )
def UpperCAmelCase_ ( self ):
pass
@unittest.skip("""Safetensors is not supported by timm.""" )
def UpperCAmelCase_ ( self ):
pass
@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests.""" )
def UpperCAmelCase_ ( self ):
pass
def UpperCAmelCase_ ( self ):
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_SCREAMING_SNAKE_CASE : List[str] = model_class(__snake_case )
_SCREAMING_SNAKE_CASE : Tuple = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_SCREAMING_SNAKE_CASE : int = [*signature.parameters.keys()]
_SCREAMING_SNAKE_CASE : List[Any] = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , __snake_case )
def UpperCAmelCase_ ( self ):
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
_SCREAMING_SNAKE_CASE : Tuple = True
_SCREAMING_SNAKE_CASE : List[str] = self.has_attentions
# no need to test all models as different heads yield the same functionality
_SCREAMING_SNAKE_CASE : str = self.all_model_classes[0]
_SCREAMING_SNAKE_CASE : str = model_class(__snake_case )
model.to(__snake_case )
_SCREAMING_SNAKE_CASE : Tuple = self._prepare_for_class(__snake_case , __snake_case )
_SCREAMING_SNAKE_CASE : Tuple = model(**__snake_case )
_SCREAMING_SNAKE_CASE : Optional[Any] = outputs[0][-1]
# Encoder-/Decoder-only models
_SCREAMING_SNAKE_CASE : str = outputs.hidden_states[0]
hidden_states.retain_grad()
if self.has_attentions:
_SCREAMING_SNAKE_CASE : Optional[int] = outputs.attentions[0]
attentions.retain_grad()
output.flatten()[0].backward(retain_graph=__snake_case )
self.assertIsNotNone(hidden_states.grad )
if self.has_attentions:
self.assertIsNotNone(attentions.grad )
def UpperCAmelCase_ ( self ):
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_SCREAMING_SNAKE_CASE : str = model_class(__snake_case )
model.to(__snake_case )
model.eval()
_SCREAMING_SNAKE_CASE : List[str] = model(**__snake_case )
self.assertEqual(len(result.feature_maps ) , len(config.out_indices ) )
self.assertEqual(len(model.channels ) , len(config.out_indices ) )
# Check output of last stage is taken if out_features=None, out_indices=None
_SCREAMING_SNAKE_CASE : Union[str, Any] = copy.deepcopy(__snake_case )
_SCREAMING_SNAKE_CASE : Optional[Any] = None
_SCREAMING_SNAKE_CASE : Tuple = model_class(__snake_case )
model.to(__snake_case )
model.eval()
_SCREAMING_SNAKE_CASE : Optional[Any] = model(**__snake_case )
self.assertEqual(len(result.feature_maps ) , 1 )
self.assertEqual(len(model.channels ) , 1 )
# Check backbone can be initialized with fresh weights
_SCREAMING_SNAKE_CASE : str = copy.deepcopy(__snake_case )
_SCREAMING_SNAKE_CASE : Tuple = False
_SCREAMING_SNAKE_CASE : Optional[int] = model_class(__snake_case )
model.to(__snake_case )
model.eval()
_SCREAMING_SNAKE_CASE : List[Any] = model(**__snake_case )
| 200 | 0 |
from __future__ import annotations
import math
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE ):
if num <= 0:
A_ : Optional[int] = f'''{num}: Invalid input, please enter a positive integer.'''
raise ValueError(SCREAMING_SNAKE_CASE )
A_ : Union[str, Any] = [True] * (num + 1)
A_ : Tuple = []
A_ : Union[str, Any] = 2
A_ : Any = int(math.sqrt(SCREAMING_SNAKE_CASE ) )
while start <= end:
# If start is a prime
if sieve[start] is True:
prime.append(SCREAMING_SNAKE_CASE )
# Set multiples of start be False
for i in range(start * start , num + 1 , SCREAMING_SNAKE_CASE ):
if sieve[i] is True:
A_ : Union[str, Any] = False
start += 1
for j in range(end + 1 , num + 1 ):
if sieve[j] is True:
prime.append(SCREAMING_SNAKE_CASE )
return prime
if __name__ == "__main__":
print(prime_sieve(int(input("""Enter a positive integer: """).strip())))
| 65 |
import tempfile
import unittest
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
is_torch_available,
require_optimum,
require_torch,
slow,
)
if is_torch_available():
import torch
@require_torch
@require_optimum
@slow
class _lowerCamelCase ( unittest.TestCase ):
"""simple docstring"""
def _snake_case ( self )->Any:
'''simple docstring'''
A_ : Dict = '''hf-internal-testing/tiny-random-t5'''
A_ : str = AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE )
A_ : Union[str, Any] = AutoModelForSeqaSeqLM.from_pretrained(_SCREAMING_SNAKE_CASE )
A_ : Union[str, Any] = tokenizer('''This is me''' , return_tensors='''pt''' )
A_ : Tuple = model.to_bettertransformer()
self.assertTrue(any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model.named_modules() ) )
A_ : Dict = model.generate(**_SCREAMING_SNAKE_CASE )
A_ : Union[str, Any] = model.reverse_bettertransformer()
self.assertFalse(any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model.named_modules() ) )
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(_SCREAMING_SNAKE_CASE )
A_ : List[Any] = AutoModelForSeqaSeqLM.from_pretrained(_SCREAMING_SNAKE_CASE )
self.assertFalse(
any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model_reloaded.named_modules() ) )
A_ : str = model_reloaded.generate(**_SCREAMING_SNAKE_CASE )
self.assertTrue(torch.allclose(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) )
def _snake_case ( self )->Optional[Any]:
'''simple docstring'''
A_ : List[str] = '''hf-internal-testing/tiny-random-t5'''
A_ : Dict = AutoModelForSeqaSeqLM.from_pretrained(_SCREAMING_SNAKE_CASE )
A_ : List[Any] = model.to_bettertransformer()
with tempfile.TemporaryDirectory() as tmpdirname:
with self.assertRaises(_SCREAMING_SNAKE_CASE ):
model.save_pretrained(_SCREAMING_SNAKE_CASE )
A_ : List[str] = model.reverse_bettertransformer()
model.save_pretrained(_SCREAMING_SNAKE_CASE )
| 65 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A__ : Dict = {
"configuration_trajectory_transformer": [
"TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"TrajectoryTransformerConfig",
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ : Any = [
"TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
"TrajectoryTransformerModel",
"TrajectoryTransformerPreTrainedModel",
"load_tf_weights_in_trajectory_transformer",
]
if TYPE_CHECKING:
from .configuration_trajectory_transformer import (
TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
TrajectoryTransformerConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_trajectory_transformer import (
TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TrajectoryTransformerModel,
TrajectoryTransformerPreTrainedModel,
load_tf_weights_in_trajectory_transformer,
)
else:
import sys
A__ : Union[str, Any] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 185 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ....tokenization_utils_fast import PreTrainedTokenizerFast
from ....utils import logging
from .tokenization_retribert import RetriBertTokenizer
a_ : str = logging.get_logger(__name__)
a_ : Union[str, Any] = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}
a_ : List[str] = {
"vocab_file": {
"yjernite/retribert-base-uncased": (
"https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/vocab.txt"
),
},
"tokenizer_file": {
"yjernite/retribert-base-uncased": (
"https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/tokenizer.json"
),
},
}
a_ : Any = {
"yjernite/retribert-base-uncased": 5_1_2,
}
a_ : Tuple = {
"yjernite/retribert-base-uncased": {"do_lower_case": True},
}
class a ( _SCREAMING_SNAKE_CASE ):
_lowerCAmelCase = VOCAB_FILES_NAMES
_lowerCAmelCase = PRETRAINED_VOCAB_FILES_MAP
_lowerCAmelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_lowerCAmelCase = PRETRAINED_INIT_CONFIGURATION
_lowerCAmelCase = RetriBertTokenizer
_lowerCAmelCase = ["""input_ids""", """attention_mask"""]
def __init__( self , __magic_name__=None , __magic_name__=None , __magic_name__=True , __magic_name__="[UNK]" , __magic_name__="[SEP]" , __magic_name__="[PAD]" , __magic_name__="[CLS]" , __magic_name__="[MASK]" , __magic_name__=True , __magic_name__=None , **__magic_name__ , ) -> Tuple:
super().__init__(
__magic_name__ , tokenizer_file=__magic_name__ , do_lower_case=__magic_name__ , unk_token=__magic_name__ , sep_token=__magic_name__ , pad_token=__magic_name__ , cls_token=__magic_name__ , mask_token=__magic_name__ , tokenize_chinese_chars=__magic_name__ , strip_accents=__magic_name__ , **__magic_name__ , )
_a = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get('lowercase' , __magic_name__ ) != do_lower_case
or normalizer_state.get('strip_accents' , __magic_name__ ) != strip_accents
or normalizer_state.get('handle_chinese_chars' , __magic_name__ ) != tokenize_chinese_chars
):
_a = getattr(__magic_name__ , normalizer_state.pop('type' ) )
_a = do_lower_case
_a = strip_accents
_a = tokenize_chinese_chars
_a = normalizer_class(**__magic_name__ )
_a = do_lower_case
def __UpperCAmelCase ( self , __magic_name__ , __magic_name__=None ) -> Union[str, Any]:
_a = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def __UpperCAmelCase ( self , __magic_name__ , __magic_name__ = None ) -> List[int]:
_a = [self.sep_token_id]
_a = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def __UpperCAmelCase ( self , __magic_name__ , __magic_name__ = None ) -> Tuple[str]:
_a = self._tokenizer.model.save(__magic_name__ , name=__magic_name__ )
return tuple(__magic_name__ )
| 168 | 0 |
def __lowercase ( lowerCamelCase : int ):
UpperCamelCase_ : int = abs(lowerCamelCase )
UpperCamelCase_ : Any = 0
while n > 0:
res += n % 10
n //= 10
return res
def __lowercase ( lowerCamelCase : int ):
UpperCamelCase_ : Any = abs(lowerCamelCase )
return n if n < 10 else n % 10 + sum_of_digits(n // 10 )
def __lowercase ( lowerCamelCase : int ):
return sum(int(lowerCamelCase ) for c in str(abs(lowerCamelCase ) ) )
def __lowercase ( ):
from collections.abc import Callable
from timeit import timeit
def benchmark_a_function(lowerCamelCase : Callable , lowerCamelCase : int ) -> None:
UpperCamelCase_ : Any = F"{func.__name__}({value})"
UpperCamelCase_ : int = timeit(F"__main__.{call}" , setup='import __main__' )
print(F"{call:56} = {func(lowerCamelCase )} -- {timing:.4f} seconds" )
for value in (262144, 1125899906842624, 1267650600228229401496703205376):
for func in (sum_of_digits, sum_of_digits_recursion, sum_of_digits_compact):
benchmark_a_function(lowerCamelCase , lowerCamelCase )
print()
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| 50 | import argparse
import shutil
from pathlib import Path
from tqdm import tqdm
from transformers import AutoTokenizer
def __lowercase ( lowerCamelCase : Optional[Any] , lowerCamelCase : Optional[int] , lowerCamelCase : Union[str, Any] , lowerCamelCase : Union[str, Any]=1024 ):
UpperCamelCase_, UpperCamelCase_ : int = [], []
UpperCamelCase_ : Dict = list(zip(lowerCamelCase , lowerCamelCase ) )
UpperCamelCase_, UpperCamelCase_ : int = sorted_examples[0]
def is_too_big(lowerCamelCase : str ):
return tok(lowerCamelCase , return_tensors='pt' ).input_ids.shape[1] > max_tokens
for src, tgt in tqdm(sorted_examples[1:] ):
UpperCamelCase_ : Optional[Any] = new_src + ' ' + src
UpperCamelCase_ : int = new_tgt + ' ' + tgt
if is_too_big(lowerCamelCase ) or is_too_big(lowerCamelCase ): # cant fit, finalize example
finished_src.append(lowerCamelCase )
finished_tgt.append(lowerCamelCase )
UpperCamelCase_, UpperCamelCase_ : Dict = src, tgt
else: # can fit, keep adding
UpperCamelCase_, UpperCamelCase_ : Union[str, Any] = cand_src, cand_tgt
# cleanup
if new_src:
assert new_tgt
finished_src.append(lowerCamelCase )
finished_tgt.append(lowerCamelCase )
return finished_src, finished_tgt
def __lowercase ( lowerCamelCase : Dict , lowerCamelCase : Path , lowerCamelCase : Tuple , lowerCamelCase : Dict ):
UpperCamelCase_ : List[Any] = Path(lowerCamelCase )
save_path.mkdir(exist_ok=lowerCamelCase )
for split in ["train"]:
UpperCamelCase_, UpperCamelCase_ : Any = data_dir / F"{split}.source", data_dir / F"{split}.target"
UpperCamelCase_ : List[Any] = [x.rstrip() for x in Path(lowerCamelCase ).open().readlines()]
UpperCamelCase_ : Optional[int] = [x.rstrip() for x in Path(lowerCamelCase ).open().readlines()]
UpperCamelCase_, UpperCamelCase_ : Union[str, Any] = pack_examples(lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase )
print(F"packed {split} split from {len(lowerCamelCase )} examples -> {len(lowerCamelCase )}." )
Path(save_path / F"{split}.source" ).open('w' ).write('\n'.join(lowerCamelCase ) )
Path(save_path / F"{split}.target" ).open('w' ).write('\n'.join(lowerCamelCase ) )
for split in ["val", "test"]:
UpperCamelCase_, UpperCamelCase_ : Any = data_dir / F"{split}.source", data_dir / F"{split}.target"
shutil.copyfile(lowerCamelCase , save_path / F"{split}.source" )
shutil.copyfile(lowerCamelCase , save_path / F"{split}.target" )
def __lowercase ( ):
UpperCamelCase_ : int = argparse.ArgumentParser()
parser.add_argument('--tok_name' , type=lowerCamelCase , help='like facebook/bart-large-cnn,t5-base, etc.' )
parser.add_argument('--max_seq_len' , type=lowerCamelCase , default=128 )
parser.add_argument('--data_dir' , type=lowerCamelCase )
parser.add_argument('--save_path' , type=lowerCamelCase )
UpperCamelCase_ : Tuple = parser.parse_args()
UpperCamelCase_ : Optional[int] = AutoTokenizer.from_pretrained(args.tok_name )
return pack_data_dir(lowerCamelCase , Path(args.data_dir ) , args.max_seq_len , args.save_path )
if __name__ == "__main__":
packer_cli()
| 50 | 1 |
from .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HPSearchBackend,
default_hp_space_optuna,
default_hp_space_ray,
default_hp_space_sigopt,
default_hp_space_wandb,
)
from .utils import logging
_a = logging.get_logger(__name__)
class __lowerCamelCase :
"""simple docstring"""
UpperCamelCase__ = 42
UpperCamelCase__ = None
@staticmethod
def UpperCamelCase ( ):
"""simple docstring"""
raise NotImplementedError
def UpperCamelCase ( self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , **UpperCAmelCase ):
"""simple docstring"""
raise NotImplementedError
def UpperCamelCase ( self , UpperCAmelCase ):
"""simple docstring"""
raise NotImplementedError
def UpperCamelCase ( self ):
"""simple docstring"""
if not self.is_available():
raise RuntimeError(
F"""You picked the {self.name} backend, but it is not installed. Run {self.pip_install()}.""" )
@classmethod
def UpperCamelCase ( cls ):
"""simple docstring"""
return F"""`pip install {cls.pip_package or cls.name}`"""
class __lowerCamelCase ( lowercase_):
"""simple docstring"""
UpperCamelCase__ = "optuna"
@staticmethod
def UpperCamelCase ( ):
"""simple docstring"""
return is_optuna_available()
def UpperCamelCase ( self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , **UpperCAmelCase ):
"""simple docstring"""
return run_hp_search_optuna(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , **lowerCamelCase_ )
def UpperCamelCase ( self , UpperCAmelCase ):
"""simple docstring"""
return default_hp_space_optuna(lowerCamelCase_ )
class __lowerCamelCase ( lowercase_):
"""simple docstring"""
UpperCamelCase__ = "ray"
UpperCamelCase__ = "\'ray[tune]\'"
@staticmethod
def UpperCamelCase ( ):
"""simple docstring"""
return is_ray_available()
def UpperCamelCase ( self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , **UpperCAmelCase ):
"""simple docstring"""
return run_hp_search_ray(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , **lowerCamelCase_ )
def UpperCamelCase ( self , UpperCAmelCase ):
"""simple docstring"""
return default_hp_space_ray(lowerCamelCase_ )
class __lowerCamelCase ( lowercase_):
"""simple docstring"""
UpperCamelCase__ = "sigopt"
@staticmethod
def UpperCamelCase ( ):
"""simple docstring"""
return is_sigopt_available()
def UpperCamelCase ( self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , **UpperCAmelCase ):
"""simple docstring"""
return run_hp_search_sigopt(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , **lowerCamelCase_ )
def UpperCamelCase ( self , UpperCAmelCase ):
"""simple docstring"""
return default_hp_space_sigopt(lowerCamelCase_ )
class __lowerCamelCase ( lowercase_):
"""simple docstring"""
UpperCamelCase__ = "wandb"
@staticmethod
def UpperCamelCase ( ):
"""simple docstring"""
return is_wandb_available()
def UpperCamelCase ( self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , **UpperCAmelCase ):
"""simple docstring"""
return run_hp_search_wandb(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , **lowerCamelCase_ )
def UpperCamelCase ( self , UpperCAmelCase ):
"""simple docstring"""
return default_hp_space_wandb(lowerCamelCase_ )
_a = {
HPSearchBackend(backend.name): backend for backend in [OptunaBackend, RayTuneBackend, SigOptBackend, WandbBackend]
}
def __A ( )-> Union[str, Any]:
"""simple docstring"""
_UpperCAmelCase = [backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
if len(lowerCamelCase_ ) > 0:
_UpperCAmelCase = available_backends[0].name
if len(lowerCamelCase_ ) > 1:
logger.info(
F"""{len(lowerCamelCase_ )} hyperparameter search backends available. Using {name} as the default.""" )
return name
raise RuntimeError(
'No hyperparameter search backend available.\n'
+ '\n'.join(
F""" - To install {backend.name} run {backend.pip_install()}"""
for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() ) )
| 39 |
'''simple docstring'''
import hashlib
import unittest
from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_timm,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
else:
class UpperCamelCase__ :
"""simple docstring"""
@staticmethod
def lowerCamelCase_ ( *lowerCamelCase_ : Union[str, Any] , **lowerCamelCase_ : List[str] ):
'''simple docstring'''
pass
def __A ( lowerCamelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = hashlib.mda(image.tobytes() )
return m.hexdigest()
@is_pipeline_test
@require_vision
@require_timm
@require_torch
class UpperCamelCase__ ( unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = MODEL_FOR_DEPTH_ESTIMATION_MAPPING
def lowerCamelCase_ ( self : Any , lowerCamelCase_ : str , lowerCamelCase_ : int , lowerCamelCase_ : Union[str, Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Tuple = DepthEstimationPipeline(model=lowerCamelCase_ , image_processor=lowerCamelCase_ )
return depth_estimator, [
"./tests/fixtures/tests_samples/COCO/000000039769.png",
"./tests/fixtures/tests_samples/COCO/000000039769.png",
]
def lowerCamelCase_ ( self : Union[str, Any] , lowerCamelCase_ : List[Any] , lowerCamelCase_ : Any ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Tuple = depth_estimator("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
self.assertEqual({"""predicted_depth""": ANY(torch.Tensor ), """depth""": ANY(Image.Image )} , lowerCamelCase_ )
import datasets
SCREAMING_SNAKE_CASE : List[str] = datasets.load_dataset("""hf-internal-testing/fixtures_image_utils""" , """image""" , split="""test""" )
SCREAMING_SNAKE_CASE : Any = depth_estimator(
[
Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" ),
"""http://images.cocodataset.org/val2017/000000039769.jpg""",
# RGBA
dataset[0]["""file"""],
# LA
dataset[1]["""file"""],
# L
dataset[2]["""file"""],
] )
self.assertEqual(
[
{"""predicted_depth""": ANY(torch.Tensor ), """depth""": ANY(Image.Image )},
{"""predicted_depth""": ANY(torch.Tensor ), """depth""": ANY(Image.Image )},
{"""predicted_depth""": ANY(torch.Tensor ), """depth""": ANY(Image.Image )},
{"""predicted_depth""": ANY(torch.Tensor ), """depth""": ANY(Image.Image )},
{"""predicted_depth""": ANY(torch.Tensor ), """depth""": ANY(Image.Image )},
] , lowerCamelCase_ , )
@require_tf
@unittest.skip("""Depth estimation is not implemented in TF""" )
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
pass
@slow
@require_torch
def lowerCamelCase_ ( self : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[int] = """Intel/dpt-large"""
SCREAMING_SNAKE_CASE : Union[str, Any] = pipeline("""depth-estimation""" , model=lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Optional[int] = depth_estimator("""http://images.cocodataset.org/val2017/000000039769.jpg""" )
SCREAMING_SNAKE_CASE : str = hashimage(outputs["""depth"""] )
# This seems flaky.
# self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
self.assertEqual(nested_simplify(outputs["""predicted_depth"""].max().item() ) , 29.304 )
self.assertEqual(nested_simplify(outputs["""predicted_depth"""].min().item() ) , 2.662 )
@require_torch
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
self.skipTest("""There is not hf-internal-testing tiny model for either GLPN nor DPT""" )
| 323 | 0 |
"""simple docstring"""
from collections.abc import Iterable
from typing import Generic, TypeVar
_lowercase : List[Any] = TypeVar("_T")
class __SCREAMING_SNAKE_CASE ( Generic[_T] ):
'''simple docstring'''
def __init__( self : Any, lowerCamelCase : str = None )-> None:
lowerCamelCase__ : Tuple =list(iterable or [] )
lowerCamelCase__ : int =[]
def __len__( self : str )-> int:
return len(self._stacka ) + len(self._stacka )
def __repr__( self : List[str] )-> str:
return F'''Queue({tuple(self._stacka[::-1] + self._stacka )})'''
def snake_case ( self : List[Any], lowerCamelCase : str )-> None:
self._stacka.append(lowerCamelCase )
def snake_case ( self : Optional[Any] )-> _T:
lowerCamelCase__ : Optional[int] =self._stacka.pop
lowerCamelCase__ : List[str] =self._stacka.append
if not self._stacka:
while self._stacka:
stacka_append(stacka_pop() )
if not self._stacka:
raise IndexError('''Queue is empty''' )
return self._stacka.pop()
if __name__ == "__main__":
from doctest import testmod
testmod()
| 366 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import FunnelConfig, is_tf_available
from transformers.testing_utils import require_tf
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TFFunnelBaseModel,
TFFunnelForMaskedLM,
TFFunnelForMultipleChoice,
TFFunnelForPreTraining,
TFFunnelForQuestionAnswering,
TFFunnelForSequenceClassification,
TFFunnelForTokenClassification,
TFFunnelModel,
)
class __SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self : Dict, lowerCamelCase : str, lowerCamelCase : Dict=13, lowerCamelCase : Optional[Any]=7, lowerCamelCase : List[Any]=True, lowerCamelCase : Dict=True, lowerCamelCase : List[Any]=True, lowerCamelCase : Optional[int]=True, lowerCamelCase : int=99, lowerCamelCase : Optional[int]=[1, 1, 2], lowerCamelCase : str=1, lowerCamelCase : List[Any]=32, lowerCamelCase : str=4, lowerCamelCase : Dict=8, lowerCamelCase : List[Any]=37, lowerCamelCase : Optional[int]="gelu_new", lowerCamelCase : Union[str, Any]=0.1, lowerCamelCase : List[Any]=0.1, lowerCamelCase : List[Any]=0.0, lowerCamelCase : Dict=512, lowerCamelCase : Dict=3, lowerCamelCase : str=0.02, lowerCamelCase : str=3, lowerCamelCase : Optional[int]=4, lowerCamelCase : List[str]=None, lowerCamelCase : Tuple=False, )-> Union[str, Any]:
lowerCamelCase__ : int =parent
lowerCamelCase__ : Dict =batch_size
lowerCamelCase__ : Dict =seq_length
lowerCamelCase__ : Any =is_training
lowerCamelCase__ : int =use_input_mask
lowerCamelCase__ : Tuple =use_token_type_ids
lowerCamelCase__ : int =use_labels
lowerCamelCase__ : Tuple =vocab_size
lowerCamelCase__ : Union[str, Any] =block_sizes
lowerCamelCase__ : Any =num_decoder_layers
lowerCamelCase__ : Optional[Any] =d_model
lowerCamelCase__ : List[str] =n_head
lowerCamelCase__ : List[Any] =d_head
lowerCamelCase__ : Dict =d_inner
lowerCamelCase__ : Dict =hidden_act
lowerCamelCase__ : List[str] =hidden_dropout
lowerCamelCase__ : Union[str, Any] =attention_dropout
lowerCamelCase__ : Union[str, Any] =activation_dropout
lowerCamelCase__ : Dict =max_position_embeddings
lowerCamelCase__ : Dict =type_vocab_size
lowerCamelCase__ : Union[str, Any] =2
lowerCamelCase__ : Optional[int] =num_labels
lowerCamelCase__ : List[str] =num_choices
lowerCamelCase__ : Tuple =scope
lowerCamelCase__ : Optional[int] =initializer_std
# Used in the tests to check the size of the first attention layer
lowerCamelCase__ : List[str] =n_head
# Used in the tests to check the size of the first hidden state
lowerCamelCase__ : Tuple =self.d_model
# Used in the tests to check the number of output hidden states/attentions
lowerCamelCase__ : List[Any] =sum(self.block_sizes ) + (0 if base else self.num_decoder_layers)
# FunnelModel adds two hidden layers: input embeddings and the sum of the upsampled encoder hidden state with
# the last hidden state of the first block (which is the first hidden state of the decoder).
if not base:
lowerCamelCase__ : Union[str, Any] =self.num_hidden_layers + 2
def snake_case ( self : int )-> List[Any]:
lowerCamelCase__ : Dict =ids_tensor([self.batch_size, self.seq_length], self.vocab_size )
lowerCamelCase__ : Union[str, Any] =None
if self.use_input_mask:
lowerCamelCase__ : Any =random_attention_mask([self.batch_size, self.seq_length] )
lowerCamelCase__ : int =None
if self.use_token_type_ids:
lowerCamelCase__ : Union[str, Any] =ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size )
lowerCamelCase__ : List[str] =None
lowerCamelCase__ : Union[str, Any] =None
lowerCamelCase__ : List[str] =None
if self.use_labels:
lowerCamelCase__ : List[Any] =ids_tensor([self.batch_size], self.type_sequence_label_size )
lowerCamelCase__ : Optional[Any] =ids_tensor([self.batch_size, self.seq_length], self.num_labels )
lowerCamelCase__ : Union[str, Any] =ids_tensor([self.batch_size], self.num_choices )
lowerCamelCase__ : Optional[int] =FunnelConfig(
vocab_size=self.vocab_size, block_sizes=self.block_sizes, num_decoder_layers=self.num_decoder_layers, d_model=self.d_model, n_head=self.n_head, d_head=self.d_head, d_inner=self.d_inner, hidden_act=self.hidden_act, hidden_dropout=self.hidden_dropout, attention_dropout=self.attention_dropout, activation_dropout=self.activation_dropout, max_position_embeddings=self.max_position_embeddings, type_vocab_size=self.type_vocab_size, initializer_std=self.initializer_std, )
return (
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
)
def snake_case ( self : List[Any], lowerCamelCase : Optional[Any], lowerCamelCase : Optional[int], lowerCamelCase : int, lowerCamelCase : int, lowerCamelCase : str, lowerCamelCase : List[str], lowerCamelCase : Dict, )-> Union[str, Any]:
lowerCamelCase__ : Tuple =TFFunnelModel(config=lowerCamelCase )
lowerCamelCase__ : Dict ={'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
lowerCamelCase__ : Tuple =model(lowerCamelCase )
lowerCamelCase__ : Optional[Any] =[input_ids, input_mask]
lowerCamelCase__ : List[Any] =model(lowerCamelCase )
lowerCamelCase__ : Any =model(lowerCamelCase )
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.d_model) )
lowerCamelCase__ : int =False
lowerCamelCase__ : Any =TFFunnelModel(config=lowerCamelCase )
lowerCamelCase__ : str =model(lowerCamelCase )
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.d_model) )
lowerCamelCase__ : Dict =False
lowerCamelCase__ : Optional[int] =TFFunnelModel(config=lowerCamelCase )
lowerCamelCase__ : Tuple =model(lowerCamelCase )
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.d_model) )
def snake_case ( self : Tuple, lowerCamelCase : List[Any], lowerCamelCase : Union[str, Any], lowerCamelCase : Union[str, Any], lowerCamelCase : str, lowerCamelCase : Optional[Any], lowerCamelCase : Tuple, lowerCamelCase : Dict, )-> Optional[Any]:
lowerCamelCase__ : List[str] =TFFunnelBaseModel(config=lowerCamelCase )
lowerCamelCase__ : str ={'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
lowerCamelCase__ : Union[str, Any] =model(lowerCamelCase )
lowerCamelCase__ : Tuple =[input_ids, input_mask]
lowerCamelCase__ : Any =model(lowerCamelCase )
lowerCamelCase__ : Optional[Any] =model(lowerCamelCase )
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, 2, self.d_model) )
lowerCamelCase__ : List[Any] =False
lowerCamelCase__ : Dict =TFFunnelBaseModel(config=lowerCamelCase )
lowerCamelCase__ : int =model(lowerCamelCase )
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, 3, self.d_model) )
lowerCamelCase__ : Union[str, Any] =False
lowerCamelCase__ : Optional[Any] =TFFunnelBaseModel(config=lowerCamelCase )
lowerCamelCase__ : str =model(lowerCamelCase )
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, 2, self.d_model) )
def snake_case ( self : str, lowerCamelCase : Dict, lowerCamelCase : Dict, lowerCamelCase : List[Any], lowerCamelCase : Dict, lowerCamelCase : List[Any], lowerCamelCase : Union[str, Any], lowerCamelCase : List[Any], )-> List[Any]:
lowerCamelCase__ : List[str] =TFFunnelForPreTraining(config=lowerCamelCase )
lowerCamelCase__ : List[Any] ={'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
lowerCamelCase__ : Union[str, Any] =model(lowerCamelCase )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length) )
def snake_case ( self : str, lowerCamelCase : Tuple, lowerCamelCase : str, lowerCamelCase : List[Any], lowerCamelCase : List[Any], lowerCamelCase : str, lowerCamelCase : Tuple, lowerCamelCase : int, )-> List[Any]:
lowerCamelCase__ : Union[str, Any] =TFFunnelForMaskedLM(config=lowerCamelCase )
lowerCamelCase__ : Tuple ={'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
lowerCamelCase__ : List[Any] =model(lowerCamelCase )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size) )
def snake_case ( self : Optional[int], lowerCamelCase : Tuple, lowerCamelCase : Any, lowerCamelCase : List[str], lowerCamelCase : Union[str, Any], lowerCamelCase : str, lowerCamelCase : Optional[int], lowerCamelCase : Dict, )-> Union[str, Any]:
lowerCamelCase__ : Optional[Any] =self.num_labels
lowerCamelCase__ : Tuple =TFFunnelForSequenceClassification(config=lowerCamelCase )
lowerCamelCase__ : Tuple ={'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
lowerCamelCase__ : List[str] =model(lowerCamelCase )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels) )
def snake_case ( self : Union[str, Any], lowerCamelCase : str, lowerCamelCase : Dict, lowerCamelCase : Dict, lowerCamelCase : Dict, lowerCamelCase : Optional[Any], lowerCamelCase : int, lowerCamelCase : Tuple, )-> int:
lowerCamelCase__ : int =self.num_choices
lowerCamelCase__ : List[Any] =TFFunnelForMultipleChoice(config=lowerCamelCase )
lowerCamelCase__ : int =tf.tile(tf.expand_dims(lowerCamelCase, 1 ), (1, self.num_choices, 1) )
lowerCamelCase__ : Union[str, Any] =tf.tile(tf.expand_dims(lowerCamelCase, 1 ), (1, self.num_choices, 1) )
lowerCamelCase__ : Optional[Any] =tf.tile(tf.expand_dims(lowerCamelCase, 1 ), (1, self.num_choices, 1) )
lowerCamelCase__ : Union[str, Any] ={
'''input_ids''': multiple_choice_inputs_ids,
'''attention_mask''': multiple_choice_input_mask,
'''token_type_ids''': multiple_choice_token_type_ids,
}
lowerCamelCase__ : str =model(lowerCamelCase )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices) )
def snake_case ( self : str, lowerCamelCase : Dict, lowerCamelCase : Optional[Any], lowerCamelCase : Any, lowerCamelCase : str, lowerCamelCase : Union[str, Any], lowerCamelCase : Union[str, Any], lowerCamelCase : Dict, )-> Optional[int]:
lowerCamelCase__ : Optional[Any] =self.num_labels
lowerCamelCase__ : Optional[Any] =TFFunnelForTokenClassification(config=lowerCamelCase )
lowerCamelCase__ : Tuple ={'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
lowerCamelCase__ : Union[str, Any] =model(lowerCamelCase )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels) )
def snake_case ( self : Optional[int], lowerCamelCase : Dict, lowerCamelCase : str, lowerCamelCase : Union[str, Any], lowerCamelCase : Union[str, Any], lowerCamelCase : Union[str, Any], lowerCamelCase : str, lowerCamelCase : Optional[int], )-> Tuple:
lowerCamelCase__ : Tuple =TFFunnelForQuestionAnswering(config=lowerCamelCase )
lowerCamelCase__ : Union[str, Any] ={'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
lowerCamelCase__ : Optional[int] =model(lowerCamelCase )
self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length) )
def snake_case ( self : int )-> List[str]:
lowerCamelCase__ : List[Any] =self.prepare_config_and_inputs()
(
(
lowerCamelCase__
) , (
lowerCamelCase__
) , (
lowerCamelCase__
) , (
lowerCamelCase__
) , (
lowerCamelCase__
) , (
lowerCamelCase__
) , (
lowerCamelCase__
) ,
) : Tuple =config_and_inputs
lowerCamelCase__ : str ={'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_tf
class __SCREAMING_SNAKE_CASE ( lowerCAmelCase_ , lowerCAmelCase_ , unittest.TestCase ):
'''simple docstring'''
_a = (
(
TFFunnelModel,
TFFunnelForMaskedLM,
TFFunnelForPreTraining,
TFFunnelForQuestionAnswering,
TFFunnelForTokenClassification,
)
if is_tf_available()
else ()
)
_a = (
{
'feature-extraction': (TFFunnelBaseModel, TFFunnelModel),
'fill-mask': TFFunnelForMaskedLM,
'question-answering': TFFunnelForQuestionAnswering,
'text-classification': TFFunnelForSequenceClassification,
'token-classification': TFFunnelForTokenClassification,
'zero-shot': TFFunnelForSequenceClassification,
}
if is_tf_available()
else {}
)
_a = False
_a = False
def snake_case ( self : str )-> Tuple:
lowerCamelCase__ : Any =TFFunnelModelTester(self )
lowerCamelCase__ : Any =ConfigTester(self, config_class=lowerCamelCase )
def snake_case ( self : List[str] )-> Tuple:
self.config_tester.run_common_tests()
def snake_case ( self : str )-> List[Any]:
lowerCamelCase__ : Optional[Any] =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCamelCase )
def snake_case ( self : str )-> Dict:
lowerCamelCase__ : int =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*lowerCamelCase )
def snake_case ( self : int )-> List[Any]:
lowerCamelCase__ : Tuple =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*lowerCamelCase )
def snake_case ( self : Dict )-> Any:
lowerCamelCase__ : str =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*lowerCamelCase )
def snake_case ( self : Tuple )-> Optional[Any]:
lowerCamelCase__ : Optional[Any] =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*lowerCamelCase )
@require_tf
class __SCREAMING_SNAKE_CASE ( lowerCAmelCase_ , unittest.TestCase ):
'''simple docstring'''
_a = (
(TFFunnelBaseModel, TFFunnelForMultipleChoice, TFFunnelForSequenceClassification) if is_tf_available() else ()
)
_a = False
_a = False
def snake_case ( self : int )-> Tuple:
lowerCamelCase__ : Union[str, Any] =TFFunnelModelTester(self, base=lowerCamelCase )
lowerCamelCase__ : Tuple =ConfigTester(self, config_class=lowerCamelCase )
def snake_case ( self : Any )-> Any:
self.config_tester.run_common_tests()
def snake_case ( self : Optional[Any] )-> Optional[Any]:
lowerCamelCase__ : Any =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_base_model(*lowerCamelCase )
def snake_case ( self : Union[str, Any] )-> int:
lowerCamelCase__ : Tuple =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase )
def snake_case ( self : List[str] )-> Optional[int]:
lowerCamelCase__ : List[Any] =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*lowerCamelCase )
| 272 | 0 |
"""simple docstring"""
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import YolosImageProcessor
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
def __init__( self : Optional[Any] , lowerCAmelCase : Any , lowerCAmelCase : Optional[int]=7 , lowerCAmelCase : str=3 , lowerCAmelCase : int=30 , lowerCAmelCase : int=400 , lowerCAmelCase : Union[str, Any]=True , lowerCAmelCase : Tuple=None , lowerCAmelCase : Any=True , lowerCAmelCase : int=[0.5, 0.5, 0.5] , lowerCAmelCase : Any=[0.5, 0.5, 0.5] , lowerCAmelCase : Optional[Any]=True , lowerCAmelCase : List[Any]=1 / 255 , lowerCAmelCase : Tuple=True , ):
lowerCAmelCase = size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 1333}
lowerCAmelCase = parent
lowerCAmelCase = batch_size
lowerCAmelCase = num_channels
lowerCAmelCase = min_resolution
lowerCAmelCase = max_resolution
lowerCAmelCase = do_resize
lowerCAmelCase = size
lowerCAmelCase = do_normalize
lowerCAmelCase = image_mean
lowerCAmelCase = image_std
lowerCAmelCase = do_rescale
lowerCAmelCase = rescale_factor
lowerCAmelCase = do_pad
def __lowercase ( self : Optional[Any] ):
return {
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_rescale": self.do_rescale,
"rescale_factor": self.rescale_factor,
"do_pad": self.do_pad,
}
def __lowercase ( self : Any , lowerCAmelCase : List[str] , lowerCAmelCase : int=False ):
if not batched:
lowerCAmelCase = image_inputs[0]
if isinstance(lowerCAmelCase , Image.Image ):
lowerCAmelCase , lowerCAmelCase = image.size
else:
lowerCAmelCase , lowerCAmelCase = image.shape[1], image.shape[2]
if w < h:
lowerCAmelCase = int(self.size["""shortest_edge"""] * h / w )
lowerCAmelCase = self.size["""shortest_edge"""]
elif w > h:
lowerCAmelCase = self.size["""shortest_edge"""]
lowerCAmelCase = int(self.size["""shortest_edge"""] * w / h )
else:
lowerCAmelCase = self.size["""shortest_edge"""]
lowerCAmelCase = self.size["""shortest_edge"""]
else:
lowerCAmelCase = []
for image in image_inputs:
lowerCAmelCase , lowerCAmelCase = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
lowerCAmelCase = max(lowerCAmelCase , key=lambda lowerCAmelCase : item[0] )[0]
lowerCAmelCase = max(lowerCAmelCase , key=lambda lowerCAmelCase : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class SCREAMING_SNAKE_CASE__ ( _lowerCamelCase , unittest.TestCase ):
_a = YolosImageProcessor if is_vision_available() else None
def __lowercase ( self : Optional[int] ):
lowerCAmelCase = YolosImageProcessingTester(self )
@property
def __lowercase ( self : Optional[int] ):
return self.image_processor_tester.prepare_image_processor_dict()
def __lowercase ( self : Union[str, Any] ):
lowerCAmelCase = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(lowerCAmelCase , """image_mean""" ) )
self.assertTrue(hasattr(lowerCAmelCase , """image_std""" ) )
self.assertTrue(hasattr(lowerCAmelCase , """do_normalize""" ) )
self.assertTrue(hasattr(lowerCAmelCase , """do_resize""" ) )
self.assertTrue(hasattr(lowerCAmelCase , """size""" ) )
def __lowercase ( self : Tuple ):
lowerCAmelCase = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {"""shortest_edge""": 18, """longest_edge""": 1333} )
self.assertEqual(image_processor.do_pad , lowerCAmelCase )
lowerCAmelCase = self.image_processing_class.from_dict(
self.image_processor_dict , size=42 , max_size=84 , pad_and_return_pixel_mask=lowerCAmelCase )
self.assertEqual(image_processor.size , {"""shortest_edge""": 42, """longest_edge""": 84} )
self.assertEqual(image_processor.do_pad , lowerCAmelCase )
def __lowercase ( self : str ):
pass
def __lowercase ( self : str ):
lowerCAmelCase = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
lowerCAmelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase )
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase , Image.Image )
# Test not batched input
lowerCAmelCase = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
lowerCAmelCase , lowerCAmelCase = self.image_processor_tester.get_expected_values(lowerCAmelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
lowerCAmelCase , lowerCAmelCase = self.image_processor_tester.get_expected_values(lowerCAmelCase , batched=lowerCAmelCase )
lowerCAmelCase = image_processing(lowerCAmelCase , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def __lowercase ( self : Tuple ):
lowerCAmelCase = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
lowerCAmelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase , numpify=lowerCAmelCase )
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase , np.ndarray )
# Test not batched input
lowerCAmelCase = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
lowerCAmelCase , lowerCAmelCase = self.image_processor_tester.get_expected_values(lowerCAmelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
lowerCAmelCase = image_processing(lowerCAmelCase , return_tensors="""pt""" ).pixel_values
lowerCAmelCase , lowerCAmelCase = self.image_processor_tester.get_expected_values(lowerCAmelCase , batched=lowerCAmelCase )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def __lowercase ( self : str ):
lowerCAmelCase = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
lowerCAmelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase , torchify=lowerCAmelCase )
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase , torch.Tensor )
# Test not batched input
lowerCAmelCase = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
lowerCAmelCase , lowerCAmelCase = self.image_processor_tester.get_expected_values(lowerCAmelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
lowerCAmelCase = image_processing(lowerCAmelCase , return_tensors="""pt""" ).pixel_values
lowerCAmelCase , lowerCAmelCase = self.image_processor_tester.get_expected_values(lowerCAmelCase , batched=lowerCAmelCase )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def __lowercase ( self : str ):
lowerCAmelCase = self.image_processing_class(**self.image_processor_dict )
lowerCAmelCase = self.image_processing_class(do_resize=lowerCAmelCase , do_normalize=lowerCAmelCase , do_rescale=lowerCAmelCase )
# create random PyTorch tensors
lowerCAmelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase , torchify=lowerCAmelCase )
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase , torch.Tensor )
# Test whether the method "pad" and calling the image processor return the same tensors
lowerCAmelCase = image_processing_a.pad(lowerCAmelCase , return_tensors="""pt""" )
lowerCAmelCase = image_processing_a(lowerCAmelCase , return_tensors="""pt""" )
self.assertTrue(
torch.allclose(encoded_images_with_method["""pixel_values"""] , encoded_images["""pixel_values"""] , atol=1e-4 ) )
@slow
def __lowercase ( self : str ):
lowerCAmelCase = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt""" , """r""" ) as f:
lowerCAmelCase = json.loads(f.read() )
lowerCAmelCase = {"""image_id""": 3_9769, """annotations""": target}
# encode them
lowerCAmelCase = YolosImageProcessor.from_pretrained("""hustvl/yolos-small""" )
lowerCAmelCase = image_processing(images=lowerCAmelCase , annotations=lowerCAmelCase , return_tensors="""pt""" )
# verify pixel values
lowerCAmelCase = torch.Size([1, 3, 800, 1066] )
self.assertEqual(encoding["""pixel_values"""].shape , lowerCAmelCase )
lowerCAmelCase = torch.tensor([0.2796, 0.3138, 0.3481] )
self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3] , lowerCAmelCase , atol=1e-4 ) )
# verify area
lowerCAmelCase = torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""] , lowerCAmelCase ) )
# verify boxes
lowerCAmelCase = torch.Size([6, 4] )
self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape , lowerCAmelCase )
lowerCAmelCase = torch.tensor([0.5503, 0.2765, 0.0604, 0.2215] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0] , lowerCAmelCase , atol=1e-3 ) )
# verify image_id
lowerCAmelCase = torch.tensor([3_9769] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""] , lowerCAmelCase ) )
# verify is_crowd
lowerCAmelCase = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""] , lowerCAmelCase ) )
# verify class_labels
lowerCAmelCase = torch.tensor([75, 75, 63, 65, 17, 17] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""] , lowerCAmelCase ) )
# verify orig_size
lowerCAmelCase = torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""] , lowerCAmelCase ) )
# verify size
lowerCAmelCase = torch.tensor([800, 1066] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""] , lowerCAmelCase ) )
@slow
def __lowercase ( self : int ):
lowerCAmelCase = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
with open("""./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt""" , """r""" ) as f:
lowerCAmelCase = json.loads(f.read() )
lowerCAmelCase = {"""file_name""": """000000039769.png""", """image_id""": 3_9769, """segments_info""": target}
lowerCAmelCase = pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic""" )
# encode them
lowerCAmelCase = YolosImageProcessor(format="""coco_panoptic""" )
lowerCAmelCase = image_processing(images=lowerCAmelCase , annotations=lowerCAmelCase , masks_path=lowerCAmelCase , return_tensors="""pt""" )
# verify pixel values
lowerCAmelCase = torch.Size([1, 3, 800, 1066] )
self.assertEqual(encoding["""pixel_values"""].shape , lowerCAmelCase )
lowerCAmelCase = torch.tensor([0.2796, 0.3138, 0.3481] )
self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3] , lowerCAmelCase , atol=1e-4 ) )
# verify area
lowerCAmelCase = torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""] , lowerCAmelCase ) )
# verify boxes
lowerCAmelCase = torch.Size([6, 4] )
self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape , lowerCAmelCase )
lowerCAmelCase = torch.tensor([0.2625, 0.5437, 0.4688, 0.8625] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0] , lowerCAmelCase , atol=1e-3 ) )
# verify image_id
lowerCAmelCase = torch.tensor([3_9769] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""] , lowerCAmelCase ) )
# verify is_crowd
lowerCAmelCase = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""] , lowerCAmelCase ) )
# verify class_labels
lowerCAmelCase = torch.tensor([17, 17, 63, 75, 75, 93] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""] , lowerCAmelCase ) )
# verify masks
lowerCAmelCase = 82_2873
self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item() , lowerCAmelCase )
# verify orig_size
lowerCAmelCase = torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""] , lowerCAmelCase ) )
# verify size
lowerCAmelCase = torch.tensor([800, 1066] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""] , lowerCAmelCase ) )
| 155 |
"""simple docstring"""
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import YolosImageProcessor
class a ( unittest.TestCase ):
"""simple docstring"""
def __init__( self: Optional[Any] , UpperCamelCase: Any , UpperCamelCase: Optional[int]=7 , UpperCamelCase: str=3 , UpperCamelCase: int=30 , UpperCamelCase: int=4_00 , UpperCamelCase: Union[str, Any]=True , UpperCamelCase: Tuple=None , UpperCamelCase: Any=True , UpperCamelCase: int=[0.5, 0.5, 0.5] , UpperCamelCase: Any=[0.5, 0.5, 0.5] , UpperCamelCase: Optional[Any]=True , UpperCamelCase: List[Any]=1 / 2_55 , UpperCamelCase: Tuple=True , ):
"""simple docstring"""
A__ = size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 13_33}
A__ = parent
A__ = batch_size
A__ = num_channels
A__ = min_resolution
A__ = max_resolution
A__ = do_resize
A__ = size
A__ = do_normalize
A__ = image_mean
A__ = image_std
A__ = do_rescale
A__ = rescale_factor
A__ = do_pad
def UpperCamelCase ( self: Optional[Any] ):
"""simple docstring"""
return {
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_rescale": self.do_rescale,
"rescale_factor": self.rescale_factor,
"do_pad": self.do_pad,
}
def UpperCamelCase ( self: Any , UpperCamelCase: List[str] , UpperCamelCase: int=False ):
"""simple docstring"""
if not batched:
A__ = image_inputs[0]
if isinstance(UpperCamelCase , Image.Image ):
A__ , A__ = image.size
else:
A__ , A__ = image.shape[1], image.shape[2]
if w < h:
A__ = int(self.size["""shortest_edge"""] * h / w )
A__ = self.size["""shortest_edge"""]
elif w > h:
A__ = self.size["""shortest_edge"""]
A__ = int(self.size["""shortest_edge"""] * w / h )
else:
A__ = self.size["""shortest_edge"""]
A__ = self.size["""shortest_edge"""]
else:
A__ = []
for image in image_inputs:
A__ , A__ = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
A__ = max(UpperCamelCase , key=lambda UpperCamelCase : item[0] )[0]
A__ = max(UpperCamelCase , key=lambda UpperCamelCase : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class a ( _lowerCamelCase, unittest.TestCase ):
"""simple docstring"""
UpperCAmelCase = YolosImageProcessor if is_vision_available() else None
def UpperCamelCase ( self: Optional[int] ):
"""simple docstring"""
A__ = YolosImageProcessingTester(self )
@property
def UpperCamelCase ( self: Optional[int] ):
"""simple docstring"""
return self.image_processor_tester.prepare_image_processor_dict()
def UpperCamelCase ( self: Union[str, Any] ):
"""simple docstring"""
A__ = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(UpperCamelCase , """image_mean""" ) )
self.assertTrue(hasattr(UpperCamelCase , """image_std""" ) )
self.assertTrue(hasattr(UpperCamelCase , """do_normalize""" ) )
self.assertTrue(hasattr(UpperCamelCase , """do_resize""" ) )
self.assertTrue(hasattr(UpperCamelCase , """size""" ) )
def UpperCamelCase ( self: Tuple ):
"""simple docstring"""
A__ = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {"""shortest_edge""": 18, """longest_edge""": 13_33} )
self.assertEqual(image_processor.do_pad , UpperCamelCase )
A__ = self.image_processing_class.from_dict(
self.image_processor_dict , size=42 , max_size=84 , pad_and_return_pixel_mask=UpperCamelCase )
self.assertEqual(image_processor.size , {"""shortest_edge""": 42, """longest_edge""": 84} )
self.assertEqual(image_processor.do_pad , UpperCamelCase )
def UpperCamelCase ( self: str ):
"""simple docstring"""
pass
def UpperCamelCase ( self: str ):
"""simple docstring"""
A__ = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
A__ = prepare_image_inputs(self.image_processor_tester , equal_resolution=UpperCamelCase )
for image in image_inputs:
self.assertIsInstance(UpperCamelCase , Image.Image )
# Test not batched input
A__ = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
A__ , A__ = self.image_processor_tester.get_expected_values(UpperCamelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
A__ , A__ = self.image_processor_tester.get_expected_values(UpperCamelCase , batched=UpperCamelCase )
A__ = image_processing(UpperCamelCase , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def UpperCamelCase ( self: Tuple ):
"""simple docstring"""
A__ = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
A__ = prepare_image_inputs(self.image_processor_tester , equal_resolution=UpperCamelCase , numpify=UpperCamelCase )
for image in image_inputs:
self.assertIsInstance(UpperCamelCase , np.ndarray )
# Test not batched input
A__ = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
A__ , A__ = self.image_processor_tester.get_expected_values(UpperCamelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
A__ = image_processing(UpperCamelCase , return_tensors="""pt""" ).pixel_values
A__ , A__ = self.image_processor_tester.get_expected_values(UpperCamelCase , batched=UpperCamelCase )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def UpperCamelCase ( self: str ):
"""simple docstring"""
A__ = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
A__ = prepare_image_inputs(self.image_processor_tester , equal_resolution=UpperCamelCase , torchify=UpperCamelCase )
for image in image_inputs:
self.assertIsInstance(UpperCamelCase , torch.Tensor )
# Test not batched input
A__ = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
A__ , A__ = self.image_processor_tester.get_expected_values(UpperCamelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
A__ = image_processing(UpperCamelCase , return_tensors="""pt""" ).pixel_values
A__ , A__ = self.image_processor_tester.get_expected_values(UpperCamelCase , batched=UpperCamelCase )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def UpperCamelCase ( self: str ):
"""simple docstring"""
A__ = self.image_processing_class(**self.image_processor_dict )
A__ = self.image_processing_class(do_resize=UpperCamelCase , do_normalize=UpperCamelCase , do_rescale=UpperCamelCase )
# create random PyTorch tensors
A__ = prepare_image_inputs(self.image_processor_tester , equal_resolution=UpperCamelCase , torchify=UpperCamelCase )
for image in image_inputs:
self.assertIsInstance(UpperCamelCase , torch.Tensor )
# Test whether the method "pad" and calling the image processor return the same tensors
A__ = image_processing_a.pad(UpperCamelCase , return_tensors="""pt""" )
A__ = image_processing_a(UpperCamelCase , return_tensors="""pt""" )
self.assertTrue(
torch.allclose(encoded_images_with_method["""pixel_values"""] , encoded_images["""pixel_values"""] , atol=1e-4 ) )
@slow
def UpperCamelCase ( self: str ):
"""simple docstring"""
A__ = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt""" , """r""" ) as f:
A__ = json.loads(f.read() )
A__ = {"""image_id""": 3_97_69, """annotations""": target}
# encode them
A__ = YolosImageProcessor.from_pretrained("""hustvl/yolos-small""" )
A__ = image_processing(images=UpperCamelCase , annotations=UpperCamelCase , return_tensors="""pt""" )
# verify pixel values
A__ = torch.Size([1, 3, 8_00, 10_66] )
self.assertEqual(encoding["""pixel_values"""].shape , UpperCamelCase )
A__ = torch.tensor([0.2_796, 0.3_138, 0.3_481] )
self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3] , UpperCamelCase , atol=1e-4 ) )
# verify area
A__ = torch.tensor([5_887.9_600, 11_250.2_061, 489_353.8_438, 837_122.7_500, 147_967.5_156, 165_732.3_438] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""] , UpperCamelCase ) )
# verify boxes
A__ = torch.Size([6, 4] )
self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape , UpperCamelCase )
A__ = torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0] , UpperCamelCase , atol=1e-3 ) )
# verify image_id
A__ = torch.tensor([3_97_69] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""] , UpperCamelCase ) )
# verify is_crowd
A__ = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""] , UpperCamelCase ) )
# verify class_labels
A__ = torch.tensor([75, 75, 63, 65, 17, 17] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""] , UpperCamelCase ) )
# verify orig_size
A__ = torch.tensor([4_80, 6_40] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""] , UpperCamelCase ) )
# verify size
A__ = torch.tensor([8_00, 10_66] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""] , UpperCamelCase ) )
@slow
def UpperCamelCase ( self: int ):
"""simple docstring"""
A__ = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
with open("""./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt""" , """r""" ) as f:
A__ = json.loads(f.read() )
A__ = {"""file_name""": """000000039769.png""", """image_id""": 3_97_69, """segments_info""": target}
A__ = pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic""" )
# encode them
A__ = YolosImageProcessor(format="""coco_panoptic""" )
A__ = image_processing(images=UpperCamelCase , annotations=UpperCamelCase , masks_path=UpperCamelCase , return_tensors="""pt""" )
# verify pixel values
A__ = torch.Size([1, 3, 8_00, 10_66] )
self.assertEqual(encoding["""pixel_values"""].shape , UpperCamelCase )
A__ = torch.tensor([0.2_796, 0.3_138, 0.3_481] )
self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3] , UpperCamelCase , atol=1e-4 ) )
# verify area
A__ = torch.tensor([147_979.6_875, 165_527.0_469, 484_638.5_938, 11_292.9_375, 5_879.6_562, 7_634.1_147] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""] , UpperCamelCase ) )
# verify boxes
A__ = torch.Size([6, 4] )
self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape , UpperCamelCase )
A__ = torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0] , UpperCamelCase , atol=1e-3 ) )
# verify image_id
A__ = torch.tensor([3_97_69] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""] , UpperCamelCase ) )
# verify is_crowd
A__ = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""] , UpperCamelCase ) )
# verify class_labels
A__ = torch.tensor([17, 17, 63, 75, 75, 93] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""] , UpperCamelCase ) )
# verify masks
A__ = 82_28_73
self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item() , UpperCamelCase )
# verify orig_size
A__ = torch.tensor([4_80, 6_40] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""] , UpperCamelCase ) )
# verify size
A__ = torch.tensor([8_00, 10_66] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""] , UpperCamelCase ) )
| 335 | 0 |
from __future__ import annotations
_A =10
def lowerCamelCase__ ( a__ : list[int] ) -> list[int]:
UpperCamelCase_ = 1
UpperCamelCase_ = max(lowerCAmelCase__ )
while placement <= max_digit:
# declare and initialize empty buckets
UpperCamelCase_ = [[] for _ in range(lowerCAmelCase__ )]
# split list_of_ints between the buckets
for i in list_of_ints:
UpperCamelCase_ = int((i / placement) % RADIX )
buckets[tmp].append(lowerCAmelCase__ )
# put each buckets' contents into list_of_ints
UpperCamelCase_ = 0
for b in range(lowerCAmelCase__ ):
for i in buckets[b]:
UpperCamelCase_ = i
a += 1
# move to next
placement *= RADIX
return list_of_ints
if __name__ == "__main__":
import doctest
doctest.testmod()
| 366 |
import argparse
import re
from typing import Dict
import torch
from datasets import Audio, Dataset, load_dataset, load_metric
from transformers import AutoFeatureExtractor, pipeline
def lowerCamelCase__ ( a__ : Dataset , a__ : Dict[str, str] ) -> int:
UpperCamelCase_ = args.log_outputs
UpperCamelCase_ = """_""".join(args.dataset.split("""/""" ) + [args.config, args.split] )
# load metric
UpperCamelCase_ = load_metric("""wer""" )
UpperCamelCase_ = load_metric("""cer""" )
# compute metrics
UpperCamelCase_ = wer.compute(references=result["""target"""] , predictions=result["""prediction"""] )
UpperCamelCase_ = cer.compute(references=result["""target"""] , predictions=result["""prediction"""] )
# print & log results
UpperCamelCase_ = f'''WER: {wer_result}\nCER: {cer_result}'''
print(a__ )
with open(f'''{dataset_id}_eval_results.txt''' , """w""" ) as f:
f.write(a__ )
# log all results in text file. Possibly interesting for analysis
if log_outputs is not None:
UpperCamelCase_ = f'''log_{dataset_id}_predictions.txt'''
UpperCamelCase_ = f'''log_{dataset_id}_targets.txt'''
with open(a__ , """w""" ) as p, open(a__ , """w""" ) as t:
# mapping function to write output
def write_to_file(a__ : List[str] , a__ : Any ):
p.write(f'''{i}''' + """\n""" )
p.write(batch["""prediction"""] + """\n""" )
t.write(f'''{i}''' + """\n""" )
t.write(batch["""target"""] + """\n""" )
result.map(a__ , with_indices=a__ )
def lowerCamelCase__ ( a__ : str ) -> str:
UpperCamelCase_ = """[,?.!\-\;\:\"“%‘”�—’…–]""" # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training
UpperCamelCase_ = re.sub(a__ , """""" , text.lower() )
# In addition, we can normalize the target text, e.g. removing new lines characters etc...
# note that order is important here!
UpperCamelCase_ = ["""\n\n""", """\n""", """ """, """ """]
for t in token_sequences_to_ignore:
UpperCamelCase_ = """ """.join(text.split(a__ ) )
return text
def lowerCamelCase__ ( a__ : Optional[int] ) -> Union[str, Any]:
# load dataset
UpperCamelCase_ = load_dataset(args.dataset , args.config , split=args.split , use_auth_token=a__ )
# for testing: only process the first two examples as a test
# dataset = dataset.select(range(10))
# load processor
UpperCamelCase_ = AutoFeatureExtractor.from_pretrained(args.model_id )
UpperCamelCase_ = feature_extractor.sampling_rate
# resample audio
UpperCamelCase_ = dataset.cast_column("""audio""" , Audio(sampling_rate=a__ ) )
# load eval pipeline
if args.device is None:
UpperCamelCase_ = 0 if torch.cuda.is_available() else -1
UpperCamelCase_ = pipeline("""automatic-speech-recognition""" , model=args.model_id , device=args.device )
# map function to decode audio
def map_to_pred(a__ : Optional[Any] ):
UpperCamelCase_ = asr(
batch["""audio"""]["""array"""] , chunk_length_s=args.chunk_length_s , stride_length_s=args.stride_length_s )
UpperCamelCase_ = prediction["""text"""]
UpperCamelCase_ = normalize_text(batch["""sentence"""] )
return batch
# run inference on all examples
UpperCamelCase_ = dataset.map(a__ , remove_columns=dataset.column_names )
# compute and log_results
# do not change function below
log_results(a__ , a__ )
if __name__ == "__main__":
_A = argparse.ArgumentParser()
parser.add_argument(
'''--model_id''', type=str, required=True, help='''Model identifier. Should be loadable with 🤗 Transformers'''
)
parser.add_argument(
'''--dataset''',
type=str,
required=True,
help='''Dataset name to evaluate the `model_id`. Should be loadable with 🤗 Datasets''',
)
parser.add_argument(
'''--config''', type=str, required=True, help='''Config of the dataset. *E.g.* `\'en\'` for Common Voice'''
)
parser.add_argument('''--split''', type=str, required=True, help='''Split of the dataset. *E.g.* `\'test\'`''')
parser.add_argument(
'''--chunk_length_s''', type=float, default=None, help='''Chunk length in seconds. Defaults to 5 seconds.'''
)
parser.add_argument(
'''--stride_length_s''', type=float, default=None, help='''Stride of the audio chunks. Defaults to 1 second.'''
)
parser.add_argument(
'''--log_outputs''', action='''store_true''', help='''If defined, write outputs to log file for analysis.'''
)
parser.add_argument(
'''--device''',
type=int,
default=None,
help='''The device to run the pipeline on. -1 for CPU (default), 0 for the first GPU and so on.''',
)
_A = parser.parse_args()
main(args)
| 261 | 0 |
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image
from transformers import (
AutoTokenizer,
BlipaConfig,
BlipaForConditionalGeneration,
BlipaProcessor,
BlipaVisionConfig,
BlipImageProcessor,
OPTConfig,
TaConfig,
)
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD
def __lowercase ( ) -> List[str]:
__SCREAMING_SNAKE_CASE = 'https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png'
__SCREAMING_SNAKE_CASE = Image.open(requests.get(a__ , stream=a__ ).raw ).convert('RGB' )
return image
def __lowercase ( a__ ) -> Dict:
__SCREAMING_SNAKE_CASE = []
# fmt: off
# vision encoder
rename_keys.append(('visual_encoder.cls_token', 'vision_model.embeddings.class_embedding') )
rename_keys.append(('visual_encoder.pos_embed', 'vision_model.embeddings.position_embedding') )
rename_keys.append(('visual_encoder.patch_embed.proj.weight', 'vision_model.embeddings.patch_embedding.weight') )
rename_keys.append(('visual_encoder.patch_embed.proj.bias', 'vision_model.embeddings.patch_embedding.bias') )
rename_keys.append(('ln_vision.weight', 'vision_model.post_layernorm.weight') )
rename_keys.append(('ln_vision.bias', 'vision_model.post_layernorm.bias') )
for i in range(config.vision_config.num_hidden_layers ):
rename_keys.append((f"""visual_encoder.blocks.{i}.norm1.weight""", f"""vision_model.encoder.layers.{i}.layer_norm1.weight""") )
rename_keys.append((f"""visual_encoder.blocks.{i}.norm1.bias""", f"""vision_model.encoder.layers.{i}.layer_norm1.bias""") )
rename_keys.append((f"""visual_encoder.blocks.{i}.norm2.weight""", f"""vision_model.encoder.layers.{i}.layer_norm2.weight""") )
rename_keys.append((f"""visual_encoder.blocks.{i}.norm2.bias""", f"""vision_model.encoder.layers.{i}.layer_norm2.bias""") )
rename_keys.append((f"""visual_encoder.blocks.{i}.attn.qkv.weight""", f"""vision_model.encoder.layers.{i}.self_attn.qkv.weight""") )
rename_keys.append((f"""visual_encoder.blocks.{i}.attn.proj.weight""", f"""vision_model.encoder.layers.{i}.self_attn.projection.weight""",) )
rename_keys.append((f"""visual_encoder.blocks.{i}.attn.proj.bias""", f"""vision_model.encoder.layers.{i}.self_attn.projection.bias""") )
rename_keys.append((f"""visual_encoder.blocks.{i}.mlp.fc1.weight""", f"""vision_model.encoder.layers.{i}.mlp.fc1.weight""") )
rename_keys.append((f"""visual_encoder.blocks.{i}.mlp.fc1.bias""", f"""vision_model.encoder.layers.{i}.mlp.fc1.bias""") )
rename_keys.append((f"""visual_encoder.blocks.{i}.mlp.fc2.weight""", f"""vision_model.encoder.layers.{i}.mlp.fc2.weight""") )
rename_keys.append((f"""visual_encoder.blocks.{i}.mlp.fc2.bias""", f"""vision_model.encoder.layers.{i}.mlp.fc2.bias""") )
# QFormer
rename_keys.append(('Qformer.bert.embeddings.LayerNorm.weight', 'qformer.layernorm.weight') )
rename_keys.append(('Qformer.bert.embeddings.LayerNorm.bias', 'qformer.layernorm.bias') )
# fmt: on
return rename_keys
def __lowercase ( a__ , a__ , a__ ) -> int:
__SCREAMING_SNAKE_CASE = dct.pop(a__ )
__SCREAMING_SNAKE_CASE = val
def __lowercase ( a__ , a__ ) -> Optional[int]:
for i in range(config.vision_config.num_hidden_layers ):
# read in original q and v biases
__SCREAMING_SNAKE_CASE = state_dict.pop(f"""visual_encoder.blocks.{i}.attn.q_bias""" )
__SCREAMING_SNAKE_CASE = state_dict.pop(f"""visual_encoder.blocks.{i}.attn.v_bias""" )
# next, set bias in the state dict
__SCREAMING_SNAKE_CASE = torch.cat((q_bias, torch.zeros_like(a__ , requires_grad=a__ ), v_bias) )
__SCREAMING_SNAKE_CASE = qkv_bias
def __lowercase ( a__ , a__ ) -> int:
__SCREAMING_SNAKE_CASE = 3_64 if 'coco' in model_name else 2_24
__SCREAMING_SNAKE_CASE = BlipaVisionConfig(image_size=a__ ).to_dict()
# make sure the models have proper bos_token_id and eos_token_id set (important for generation)
# seems like flan-T5 models don't have bos_token_id properly set?
if "opt-2.7b" in model_name:
__SCREAMING_SNAKE_CASE = OPTConfig.from_pretrained('facebook/opt-2.7b' , eos_token_id=a__ ).to_dict()
elif "opt-6.7b" in model_name:
__SCREAMING_SNAKE_CASE = OPTConfig.from_pretrained('facebook/opt-6.7b' , eos_token_id=a__ ).to_dict()
elif "t5-xl" in model_name:
__SCREAMING_SNAKE_CASE = TaConfig.from_pretrained('google/flan-t5-xl' , dense_act_fn='gelu' , bos_token_id=1 ).to_dict()
elif "t5-xxl" in model_name:
__SCREAMING_SNAKE_CASE = TaConfig.from_pretrained('google/flan-t5-xxl' , dense_act_fn='gelu' , bos_token_id=1 ).to_dict()
__SCREAMING_SNAKE_CASE = BlipaConfig(vision_config=a__ , text_config=a__ )
return config, image_size
@torch.no_grad()
def __lowercase ( a__ , a__=None , a__=False ) -> Any:
__SCREAMING_SNAKE_CASE = (
AutoTokenizer.from_pretrained('facebook/opt-2.7b' )
if 'opt' in model_name
else AutoTokenizer.from_pretrained('google/flan-t5-xl' )
)
__SCREAMING_SNAKE_CASE = tokenizer('\n' , add_special_tokens=a__ ).input_ids[0]
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = get_blipa_config(a__ , eos_token_id=a__ )
__SCREAMING_SNAKE_CASE = BlipaForConditionalGeneration(a__ ).eval()
__SCREAMING_SNAKE_CASE = {
'blip2-opt-2.7b': ('blip2_opt', 'pretrain_opt2.7b'),
'blip2-opt-6.7b': ('blip2_opt', 'pretrain_opt6.7b'),
'blip2-opt-2.7b-coco': ('blip2_opt', 'caption_coco_opt2.7b'),
'blip2-opt-6.7b-coco': ('blip2_opt', 'caption_coco_opt6.7b'),
'blip2-flan-t5-xl': ('blip2_t5', 'pretrain_flant5xl'),
'blip2-flan-t5-xl-coco': ('blip2_t5', 'caption_coco_flant5xl'),
'blip2-flan-t5-xxl': ('blip2_t5', 'pretrain_flant5xxl'),
}
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = model_name_to_original[model_name]
# load original model
print('Loading original model...' )
__SCREAMING_SNAKE_CASE = 'cuda' if torch.cuda.is_available() else 'cpu'
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = load_model_and_preprocess(
name=a__ , model_type=a__ , is_eval=a__ , device=a__ )
original_model.eval()
print('Done!' )
# update state dict keys
__SCREAMING_SNAKE_CASE = original_model.state_dict()
__SCREAMING_SNAKE_CASE = create_rename_keys(a__ )
for src, dest in rename_keys:
rename_key(a__ , a__ , a__ )
# some keys can be renamed efficiently
for key, val in state_dict.copy().items():
__SCREAMING_SNAKE_CASE = state_dict.pop(a__ )
if key.startswith('Qformer.bert' ):
__SCREAMING_SNAKE_CASE = key.replace('Qformer.bert' , 'qformer' )
if "attention.self" in key:
__SCREAMING_SNAKE_CASE = key.replace('self' , 'attention' )
if "opt_proj" in key:
__SCREAMING_SNAKE_CASE = key.replace('opt_proj' , 'language_projection' )
if "t5_proj" in key:
__SCREAMING_SNAKE_CASE = key.replace('t5_proj' , 'language_projection' )
if key.startswith('opt' ):
__SCREAMING_SNAKE_CASE = key.replace('opt' , 'language' )
if key.startswith('t5' ):
__SCREAMING_SNAKE_CASE = key.replace('t5' , 'language' )
__SCREAMING_SNAKE_CASE = val
# read in qv biases
read_in_q_v_bias(a__ , a__ )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = hf_model.load_state_dict(a__ , strict=a__ )
assert len(a__ ) == 0
assert unexpected_keys == ["qformer.embeddings.position_ids"]
__SCREAMING_SNAKE_CASE = load_demo_image()
__SCREAMING_SNAKE_CASE = vis_processors['eval'](a__ ).unsqueeze(0 ).to(a__ )
__SCREAMING_SNAKE_CASE = tokenizer(['\n'] , return_tensors='pt' ).input_ids.to(a__ )
# create processor
__SCREAMING_SNAKE_CASE = BlipImageProcessor(
size={'height': image_size, 'width': image_size} , image_mean=a__ , image_std=a__ )
__SCREAMING_SNAKE_CASE = BlipaProcessor(image_processor=a__ , tokenizer=a__ )
__SCREAMING_SNAKE_CASE = processor(images=a__ , return_tensors='pt' ).pixel_values.to(a__ )
# make sure processor creates exact same pixel values
assert torch.allclose(a__ , a__ )
original_model.to(a__ )
hf_model.to(a__ )
with torch.no_grad():
if "opt" in model_name:
__SCREAMING_SNAKE_CASE = original_model({'image': original_pixel_values, 'text_input': ['']} ).logits
__SCREAMING_SNAKE_CASE = hf_model(a__ , a__ ).logits
else:
__SCREAMING_SNAKE_CASE = original_model(
{'image': original_pixel_values, 'text_input': ['\n'], 'text_output': ['\n']} ).logits
__SCREAMING_SNAKE_CASE = input_ids.masked_fill(input_ids == tokenizer.pad_token_id , -1_00 )
__SCREAMING_SNAKE_CASE = hf_model(a__ , a__ , labels=a__ ).logits
assert original_logits.shape == logits.shape
print('First values of original logits:' , original_logits[0, :3, :3] )
print('First values of HF logits:' , logits[0, :3, :3] )
# assert values
if model_name == "blip2-flan-t5-xl":
__SCREAMING_SNAKE_CASE = torch.tensor(
[[-41.5850, -4.4440, -8.9922], [-47.4322, -5.9143, -1.7340]] , device=a__ )
assert torch.allclose(logits[0, :3, :3] , a__ , atol=1E-4 )
elif model_name == "blip2-flan-t5-xl-coco":
__SCREAMING_SNAKE_CASE = torch.tensor(
[[-57.0109, -9.8967, -12.6280], [-68.6578, -12.7191, -10.5065]] , device=a__ )
else:
# cast to same type
__SCREAMING_SNAKE_CASE = logits.dtype
assert torch.allclose(original_logits.to(a__ ) , a__ , atol=1E-2 )
print('Looks ok!' )
print('Generating a caption...' )
__SCREAMING_SNAKE_CASE = ''
__SCREAMING_SNAKE_CASE = tokenizer(a__ , return_tensors='pt' ).input_ids.to(a__ )
__SCREAMING_SNAKE_CASE = original_model.generate({'image': original_pixel_values} )
__SCREAMING_SNAKE_CASE = hf_model.generate(
a__ , a__ , do_sample=a__ , num_beams=5 , max_length=30 , min_length=1 , top_p=0.9 , repetition_penalty=1.0 , length_penalty=1.0 , temperature=1 , )
print('Original generation:' , a__ )
__SCREAMING_SNAKE_CASE = input_ids.shape[1]
__SCREAMING_SNAKE_CASE = processor.batch_decode(outputs[:, prompt_length:] , skip_special_tokens=a__ )
__SCREAMING_SNAKE_CASE = [text.strip() for text in output_text]
print('HF generation:' , a__ )
if pytorch_dump_folder_path is not None:
processor.save_pretrained(a__ )
hf_model.save_pretrained(a__ )
if push_to_hub:
processor.push_to_hub(f"""nielsr/{model_name}""" )
hf_model.push_to_hub(f"""nielsr/{model_name}""" )
if __name__ == "__main__":
lowerCAmelCase__ : Dict =argparse.ArgumentParser()
lowerCAmelCase__ : Union[str, Any] =[
'''blip2-opt-2.7b''',
'''blip2-opt-6.7b''',
'''blip2-opt-2.7b-coco''',
'''blip2-opt-6.7b-coco''',
'''blip2-flan-t5-xl''',
'''blip2-flan-t5-xl-coco''',
'''blip2-flan-t5-xxl''',
]
parser.add_argument(
'''--model_name''',
default='''blip2-opt-2.7b''',
choices=choices,
type=str,
help='''Path to hf config.json of model to convert''',
)
parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
parser.add_argument(
'''--push_to_hub''',
action='''store_true''',
help='''Whether to push the model and processor to the hub after converting''',
)
lowerCAmelCase__ : int =parser.parse_args()
convert_blipa_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 257 |
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def __lowercase ( a__ ) -> Tuple:
__SCREAMING_SNAKE_CASE = [
'encoder.version',
'decoder.version',
'model.encoder.version',
'model.decoder.version',
'decoder.output_projection.weight',
'_float_tensor',
'encoder.embed_positions._float_tensor',
'decoder.embed_positions._float_tensor',
]
for k in ignore_keys:
state_dict.pop(a__ , a__ )
def __lowercase ( a__ ) -> int:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = emb.weight.shape
__SCREAMING_SNAKE_CASE = nn.Linear(a__ , a__ , bias=a__ )
__SCREAMING_SNAKE_CASE = emb.weight.data
return lin_layer
def __lowercase ( a__ ) -> Union[str, Any]:
__SCREAMING_SNAKE_CASE = torch.load(a__ , map_location='cpu' )
__SCREAMING_SNAKE_CASE = mam_aaa['args'] or mam_aaa['cfg']['model']
__SCREAMING_SNAKE_CASE = mam_aaa['model']
remove_ignore_keys_(a__ )
__SCREAMING_SNAKE_CASE = state_dict['encoder.embed_tokens.weight'].shape[0]
__SCREAMING_SNAKE_CASE = MaMaaaConfig(
vocab_size=a__ , max_position_embeddings=10_24 , encoder_layers=args.encoder_layers , decoder_layers=args.decoder_layers , encoder_attention_heads=args.encoder_attention_heads , decoder_attention_heads=args.decoder_attention_heads , encoder_ffn_dim=args.encoder_ffn_embed_dim , decoder_ffn_dim=args.decoder_ffn_embed_dim , d_model=args.encoder_embed_dim , encoder_layerdrop=args.encoder_layerdrop , decoder_layerdrop=args.decoder_layerdrop , dropout=args.dropout , attention_dropout=args.attention_dropout , activation_dropout=args.activation_dropout , activation_function='relu' , )
__SCREAMING_SNAKE_CASE = state_dict['decoder.embed_tokens.weight']
__SCREAMING_SNAKE_CASE = MaMaaaForConditionalGeneration(a__ )
model.model.load_state_dict(a__ , strict=a__ )
__SCREAMING_SNAKE_CASE = make_linear_from_emb(model.model.shared )
return model
if __name__ == "__main__":
lowerCAmelCase__ : Optional[int] =argparse.ArgumentParser()
# Required parameters
parser.add_argument('''fairseq_path''', type=str, help='''path to a model.pt on local filesystem.''')
parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
lowerCAmelCase__ : Optional[int] =parser.parse_args()
lowerCAmelCase__ : Tuple =convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
model.save_pretrained(args.pytorch_dump_folder_path)
| 257 | 1 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_mbart import MBartTokenizer
else:
_lowercase: Optional[int] = None
_lowercase: str = logging.get_logger(__name__)
_lowercase: Optional[int] = {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}
_lowercase: Tuple = {
"vocab_file": {
"facebook/mbart-large-en-ro": (
"https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model"
),
"facebook/mbart-large-cc25": (
"https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model"
),
},
"tokenizer_file": {
"facebook/mbart-large-en-ro": "https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json",
"facebook/mbart-large-cc25": "https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json",
},
}
_lowercase: Optional[int] = {
"facebook/mbart-large-en-ro": 1024,
"facebook/mbart-large-cc25": 1024,
}
# fmt: off
_lowercase: Union[str, Any] = ["ar_AR", "cs_CZ", "de_DE", "en_XX", "es_XX", "et_EE", "fi_FI", "fr_XX", "gu_IN", "hi_IN", "it_IT", "ja_XX", "kk_KZ", "ko_KR", "lt_LT", "lv_LV", "my_MM", "ne_NP", "nl_XX", "ro_RO", "ru_RU", "si_LK", "tr_TR", "vi_VN", "zh_CN"]
class _lowercase ( lowerCAmelCase ):
"""simple docstring"""
__A = VOCAB_FILES_NAMES
__A = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__A = PRETRAINED_VOCAB_FILES_MAP
__A = ["input_ids", "attention_mask"]
__A = MBartTokenizer
__A = []
__A = []
def __init__(self , lowerCamelCase_=None , lowerCamelCase_=None , lowerCamelCase_="<s>" , lowerCamelCase_="</s>" , lowerCamelCase_="</s>" , lowerCamelCase_="<s>" , lowerCamelCase_="<unk>" , lowerCamelCase_="<pad>" , lowerCamelCase_="<mask>" , lowerCamelCase_=None , lowerCamelCase_=None , lowerCamelCase_=None , **lowerCamelCase_ , ):
"""simple docstring"""
a = AddedToken(lowerCamelCase_ , lstrip=lowerCamelCase_ , rstrip=lowerCamelCase_ ) if isinstance(lowerCamelCase_ , lowerCamelCase_ ) else mask_token
super().__init__(
vocab_file=lowerCamelCase_ , tokenizer_file=lowerCamelCase_ , bos_token=lowerCamelCase_ , eos_token=lowerCamelCase_ , sep_token=lowerCamelCase_ , cls_token=lowerCamelCase_ , unk_token=lowerCamelCase_ , pad_token=lowerCamelCase_ , mask_token=lowerCamelCase_ , src_lang=lowerCamelCase_ , tgt_lang=lowerCamelCase_ , additional_special_tokens=lowerCamelCase_ , **lowerCamelCase_ , )
a = vocab_file
a = False if not self.vocab_file else True
a = FAIRSEQ_LANGUAGE_CODES.copy()
if additional_special_tokens is not None:
# Only add those special tokens if they are not already there.
_additional_special_tokens.extend(
[t for t in additional_special_tokens if t not in _additional_special_tokens] )
self.add_special_tokens({"additional_special_tokens": _additional_special_tokens} )
a = {
lang_code: self.convert_tokens_to_ids(lowerCamelCase_ ) for lang_code in FAIRSEQ_LANGUAGE_CODES
}
a = src_lang if src_lang is not None else "en_XX"
a = self.convert_tokens_to_ids(self._src_lang )
a = tgt_lang
self.set_src_lang_special_tokens(self._src_lang )
@property
def UpperCamelCase_ (self ):
"""simple docstring"""
return self._src_lang
@src_lang.setter
def UpperCamelCase_ (self , lowerCamelCase_ ):
"""simple docstring"""
a = new_src_lang
self.set_src_lang_special_tokens(self._src_lang )
def UpperCamelCase_ (self , lowerCamelCase_ , lowerCamelCase_ = None ):
"""simple docstring"""
if token_ids_a is None:
return self.prefix_tokens + token_ids_a + self.suffix_tokens
# We don't expect to process pairs, but leave the pair logic for API consistency
return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
def UpperCamelCase_ (self , lowerCamelCase_ , lowerCamelCase_ = None ):
"""simple docstring"""
a = [self.sep_token_id]
a = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def UpperCamelCase_ (self , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , **lowerCamelCase_ ):
"""simple docstring"""
if src_lang is None or tgt_lang is None:
raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model" )
a = src_lang
a = self(lowerCamelCase_ , add_special_tokens=lowerCamelCase_ , return_tensors=lowerCamelCase_ , **lowerCamelCase_ )
a = self.convert_tokens_to_ids(lowerCamelCase_ )
a = tgt_lang_id
return inputs
def UpperCamelCase_ (self , lowerCamelCase_ , lowerCamelCase_ = "en_XX" , lowerCamelCase_ = None , lowerCamelCase_ = "ro_RO" , **lowerCamelCase_ , ):
"""simple docstring"""
a = src_lang
a = tgt_lang
return super().prepare_seqaseq_batch(lowerCamelCase_ , lowerCamelCase_ , **lowerCamelCase_ )
def UpperCamelCase_ (self ):
"""simple docstring"""
return self.set_src_lang_special_tokens(self.src_lang )
def UpperCamelCase_ (self ):
"""simple docstring"""
return self.set_tgt_lang_special_tokens(self.tgt_lang )
def UpperCamelCase_ (self , lowerCamelCase_ ):
"""simple docstring"""
a = self.convert_tokens_to_ids(lowerCamelCase_ )
a = []
a = [self.eos_token_id, self.cur_lang_code]
a = self.convert_ids_to_tokens(self.prefix_tokens )
a = self.convert_ids_to_tokens(self.suffix_tokens )
a = processors.TemplateProcessing(
single=prefix_tokens_str + ["$A"] + suffix_tokens_str , pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str , self.prefix_tokens + self.suffix_tokens ) ) , )
def UpperCamelCase_ (self , lowerCamelCase_ ):
"""simple docstring"""
a = self.convert_tokens_to_ids(lowerCamelCase_ )
a = []
a = [self.eos_token_id, self.cur_lang_code]
a = self.convert_ids_to_tokens(self.prefix_tokens )
a = self.convert_ids_to_tokens(self.suffix_tokens )
a = processors.TemplateProcessing(
single=prefix_tokens_str + ["$A"] + suffix_tokens_str , pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str , special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str , self.prefix_tokens + self.suffix_tokens ) ) , )
def UpperCamelCase_ (self , lowerCamelCase_ , lowerCamelCase_ = None ):
"""simple docstring"""
if not self.can_save_slow_tokenizer:
raise ValueError(
"Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
"tokenizer." )
if not os.path.isdir(lowerCamelCase_ ):
logger.error(F'''Vocabulary path ({save_directory}) should be a directory.''' )
return
a = os.path.join(
lowerCamelCase_ , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(lowerCamelCase_ ):
copyfile(self.vocab_file , lowerCamelCase_ )
return (out_vocab_file,)
| 71 |
from .testing import (
are_the_same_tensors,
execute_subprocess_async,
require_bnb,
require_cpu,
require_cuda,
require_huggingface_suite,
require_mps,
require_multi_gpu,
require_multi_xpu,
require_safetensors,
require_single_gpu,
require_single_xpu,
require_torch_min_version,
require_tpu,
require_xpu,
skip,
slow,
)
from .training import RegressionDataset, RegressionModel, RegressionModelaXPU
from .scripts import test_script, test_sync, test_ops # isort: skip
| 71 | 1 |
import math
def UpperCamelCase( __UpperCamelCase : int ):
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 ,int(math.sqrt(__UpperCamelCase ) + 1 ) ,6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def UpperCamelCase( __UpperCamelCase : float = 0.1 ):
lowerCAmelCase_ : Optional[Any] = 3
lowerCAmelCase_ : List[str] = 3
while primes / (2 * j - 1) >= ratio:
for i in range(j * j + j + 1 ,(j + 2) * (j + 2) ,j + 1 ):
primes += is_prime(__UpperCamelCase )
j += 2
return j
if __name__ == "__main__":
import doctest
doctest.testmod()
| 103 |
import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset
from ..utils.generic import ModelOutput
class __a ( UpperCAmelCase ):
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Any:
"""simple docstring"""
_UpperCAmelCase = dataset
_UpperCAmelCase = process
_UpperCAmelCase = params
def __len__( self ) -> Union[str, Any]:
"""simple docstring"""
return len(self.dataset )
def __getitem__( self , _SCREAMING_SNAKE_CASE ) -> Any:
"""simple docstring"""
_UpperCAmelCase = self.dataset[i]
_UpperCAmelCase = self.process(_SCREAMING_SNAKE_CASE , **self.params )
return processed
class __a ( UpperCAmelCase ):
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None ) -> Union[str, Any]:
"""simple docstring"""
_UpperCAmelCase = loader
_UpperCAmelCase = infer
_UpperCAmelCase = params
if loader_batch_size == 1:
# Let's spare some time by deactivating altogether
_UpperCAmelCase = None
_UpperCAmelCase = loader_batch_size
# Internal bookkeeping
_UpperCAmelCase = None
_UpperCAmelCase = None
def __len__( self ) -> Any:
"""simple docstring"""
return len(self.loader )
def __iter__( self ) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase = iter(self.loader )
return self
def UpperCAmelCase__ ( self ) -> int:
"""simple docstring"""
if isinstance(self._loader_batch_data , torch.Tensor ):
# Batch data is simple tensor, just fetch the slice
_UpperCAmelCase = self._loader_batch_data[self._loader_batch_index]
else:
# Batch data is assumed to be BaseModelOutput (or dict)
_UpperCAmelCase = {}
for k, element in self._loader_batch_data.items():
if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
# Convert ModelOutput to tuple first
_UpperCAmelCase = element.to_tuple()
if isinstance(element[0] , torch.Tensor ):
_UpperCAmelCase = tuple(el[self._loader_batch_index].unsqueeze(0 ) for el in element )
elif isinstance(element[0] , np.ndarray ):
_UpperCAmelCase = tuple(np.expand_dims(el[self._loader_batch_index] , 0 ) for el in element )
continue
if k in {"hidden_states", "past_key_values", "attentions"} and isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
# Those are stored as lists of tensors so need specific unbatching.
if isinstance(element[0] , torch.Tensor ):
_UpperCAmelCase = tuple(el[self._loader_batch_index].unsqueeze(0 ) for el in element )
elif isinstance(element[0] , np.ndarray ):
_UpperCAmelCase = tuple(np.expand_dims(el[self._loader_batch_index] , 0 ) for el in element )
continue
if element is None:
# This can happen for optional data that get passed around
_UpperCAmelCase = None
elif isinstance(element[self._loader_batch_index] , torch.Tensor ):
# Take correct batch data, but make it looked like batch_size=1
# For compatibility with other methods within transformers
_UpperCAmelCase = element[self._loader_batch_index].unsqueeze(0 )
elif isinstance(element[self._loader_batch_index] , np.ndarray ):
# Take correct batch data, but make it looked like batch_size=1
# For compatibility with other methods within transformers
_UpperCAmelCase = np.expand_dims(element[self._loader_batch_index] , 0 )
else:
# This is typically a list, so no need to `unsqueeze`.
_UpperCAmelCase = element[self._loader_batch_index]
# Recreate the element by reusing the original class to make it look
# batch_size=1
_UpperCAmelCase = self._loader_batch_data.__class__(_SCREAMING_SNAKE_CASE )
self._loader_batch_index += 1
return result
def UpperCAmelCase__ ( self ) -> List[str]:
"""simple docstring"""
if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
# We are currently unrolling a batch so we just need to return
# the current item within a batch
return self.loader_batch_item()
# We're out of items within a batch
_UpperCAmelCase = next(self.iterator )
_UpperCAmelCase = self.infer(_SCREAMING_SNAKE_CASE , **self.params )
# We now have a batch of "inferred things".
if self.loader_batch_size is not None:
# Try to infer the size of the batch
if isinstance(_SCREAMING_SNAKE_CASE , torch.Tensor ):
_UpperCAmelCase = processed
else:
_UpperCAmelCase = list(processed.keys() )[0]
_UpperCAmelCase = processed[key]
if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
_UpperCAmelCase = len(_SCREAMING_SNAKE_CASE )
else:
_UpperCAmelCase = first_tensor.shape[0]
if 0 < observed_batch_size < self.loader_batch_size:
# could be last batch so we can't unroll as many
# elements.
_UpperCAmelCase = observed_batch_size
# Setting internal index to unwrap the batch
_UpperCAmelCase = processed
_UpperCAmelCase = 0
return self.loader_batch_item()
else:
# We're not unrolling batches
return processed
class __a ( UpperCAmelCase ):
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None ) -> Tuple:
"""simple docstring"""
super().__init__(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
def __iter__( self ) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase = iter(self.loader )
_UpperCAmelCase = None
return self
def UpperCAmelCase__ ( self ) -> int:
"""simple docstring"""
if self.subiterator is None:
_UpperCAmelCase = self.infer(next(self.iterator ) , **self.params )
try:
# Try to return next item
_UpperCAmelCase = next(self.subiterator )
except StopIteration:
# When a preprocess iterator ends, we can start lookig at the next item
# ChunkIterator will keep feeding until ALL elements of iterator
# all have created their subiterator and have been iterating against.
#
# Another way to look at it, is we're basically flattening lists of lists
# into a single list, but with generators
_UpperCAmelCase = self.infer(next(self.iterator ) , **self.params )
_UpperCAmelCase = next(self.subiterator )
return processed
class __a ( UpperCAmelCase ):
def __iter__( self ) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase = iter(self.loader )
return self
def UpperCAmelCase__ ( self ) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase = False
_UpperCAmelCase = []
if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
while self._loader_batch_index < self.loader_batch_size:
_UpperCAmelCase = self.loader_batch_item()
_UpperCAmelCase = item.pop('is_last' )
accumulator.append(_SCREAMING_SNAKE_CASE )
if is_last:
return accumulator
while not is_last:
_UpperCAmelCase = self.infer(next(self.iterator ) , **self.params )
if self.loader_batch_size is not None:
if isinstance(_SCREAMING_SNAKE_CASE , torch.Tensor ):
_UpperCAmelCase = processed
else:
_UpperCAmelCase = list(processed.keys() )[0]
_UpperCAmelCase = processed[key]
if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
_UpperCAmelCase = len(_SCREAMING_SNAKE_CASE )
else:
_UpperCAmelCase = first_tensor.shape[0]
if 0 < observed_batch_size < self.loader_batch_size:
# could be last batch so we can't unroll as many
# elements.
_UpperCAmelCase = observed_batch_size
_UpperCAmelCase = processed
_UpperCAmelCase = 0
while self._loader_batch_index < self.loader_batch_size:
_UpperCAmelCase = self.loader_batch_item()
_UpperCAmelCase = item.pop('is_last' )
accumulator.append(_SCREAMING_SNAKE_CASE )
if is_last:
return accumulator
else:
_UpperCAmelCase = processed
_UpperCAmelCase = item.pop('is_last' )
accumulator.append(_SCREAMING_SNAKE_CASE )
return accumulator
class __a ( UpperCAmelCase ):
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase = dataset
_UpperCAmelCase = key
def __len__( self ) -> Optional[int]:
"""simple docstring"""
return len(self.dataset )
def __getitem__( self , _SCREAMING_SNAKE_CASE ) -> List[str]:
"""simple docstring"""
return self.dataset[i][self.key]
class __a ( UpperCAmelCase ):
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> List[str]:
"""simple docstring"""
_UpperCAmelCase = dataset
_UpperCAmelCase = keya
_UpperCAmelCase = keya
def __len__( self ) -> Optional[int]:
"""simple docstring"""
return len(self.dataset )
def __getitem__( self , _SCREAMING_SNAKE_CASE ) -> Dict:
"""simple docstring"""
return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}
| 329 | 0 |
"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import InterpolationMode
import transformers
from transformers import (
HfArgumentParser,
Trainer,
TrainingArguments,
ViTImageProcessor,
ViTMAEConfig,
ViTMAEForPreTraining,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
lowercase__ = logging.getLogger(__name__)
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("""4.31.0""")
require_version("""datasets>=1.8.0""", """To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt""")
@dataclass
class lowerCAmelCase__ :
'''simple docstring'''
lowerCamelCase__ = field(
default="""cifar10""", metadata={"""help""": """Name of a dataset from the datasets package"""} )
lowerCamelCase__ = field(
default=lowercase, metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""} )
lowerCamelCase__ = field(
default=lowercase, metadata={"""help""": """The column name of the images in the files."""} )
lowerCamelCase__ = field(default=lowercase, metadata={"""help""": """A folder containing the training data."""} )
lowerCamelCase__ = field(default=lowercase, metadata={"""help""": """A folder containing the validation data."""} )
lowerCamelCase__ = field(
default=0.15, metadata={"""help""": """Percent to split off of train for validation."""} )
lowerCamelCase__ = field(
default=lowercase, metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of training examples to this """
"""value if set."""
)
}, )
lowerCamelCase__ = field(
default=lowercase, metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of evaluation examples to this """
"""value if set."""
)
}, )
def A_ ( self ):
_lowerCamelCase : List[str] = {}
if self.train_dir is not None:
_lowerCamelCase : Optional[Any] = self.train_dir
if self.validation_dir is not None:
_lowerCamelCase : Any = self.validation_dir
_lowerCamelCase : Optional[int] = data_files if data_files else None
@dataclass
class lowerCAmelCase__ :
'''simple docstring'''
lowerCamelCase__ = field(
default=lowercase, metadata={
"""help""": (
"""The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."""
)
}, )
lowerCamelCase__ = field(
default=lowercase, metadata={"""help""": """Pretrained config name or path if not the same as model_name_or_path"""} )
lowerCamelCase__ = field(
default=lowercase, metadata={
"""help""": (
"""Override some existing default config settings when a model is trained from scratch. Example: """
"""n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"""
)
}, )
lowerCamelCase__ = field(
default=lowercase, metadata={"""help""": """Where do you want to store the pretrained models downloaded from s3"""} )
lowerCamelCase__ = field(
default="""main""", metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""}, )
lowerCamelCase__ = field(default=lowercase, metadata={"""help""": """Name or path of preprocessor config."""} )
lowerCamelCase__ = field(
default=lowercase, metadata={
"""help""": (
"""Will use the token generated when running `huggingface-cli login` (necessary to use this script """
"""with private models)."""
)
}, )
lowerCamelCase__ = field(
default=0.75, metadata={"""help""": """The ratio of the number of masked tokens in the input sequence."""} )
lowerCamelCase__ = field(
default=lowercase, metadata={"""help""": """Whether or not to train with normalized pixel values as target."""} )
@dataclass
class lowerCAmelCase__ ( lowercase ):
'''simple docstring'''
lowerCamelCase__ = field(
default=1e-3, metadata={"""help""": """Base learning rate: absolute_lr = base_lr * total_batch_size / 256."""} )
def _snake_case ( lowercase__ ):
_lowerCamelCase : List[Any] = torch.stack([example['pixel_values'] for example in examples] )
return {"pixel_values": pixel_values}
def _snake_case ( ):
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
_lowerCamelCase : int = HfArgumentParser((ModelArguments, DataTrainingArguments, CustomTrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith('.json' ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
_lowerCamelCase, _lowerCamelCase, _lowerCamelCase : Optional[Any] = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
_lowerCamelCase, _lowerCamelCase, _lowerCamelCase : Optional[int] = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry('run_mae' , lowercase__ , lowercase__ )
# Setup logging
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s' , datefmt='%m/%d/%Y %H:%M:%S' , handlers=[logging.StreamHandler(sys.stdout )] , )
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
_lowerCamelCase : Tuple = training_args.get_process_log_level()
logger.setLevel(lowercase__ )
transformers.utils.logging.set_verbosity(lowercase__ )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
f'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
+ f'''distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}''' )
logger.info(f'''Training/evaluation parameters {training_args}''' )
# Detecting last checkpoint.
_lowerCamelCase : str = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
_lowerCamelCase : List[str] = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
f'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
'Use --overwrite_output_dir to overcome.' )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
f'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.' )
# Initialize our dataset.
_lowerCamelCase : Tuple = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , data_files=data_args.data_files , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
# If we don't have a validation split, split off a percentage of train as validation.
_lowerCamelCase : List[Any] = None if 'validation' in ds.keys() else data_args.train_val_split
if isinstance(data_args.train_val_split , lowercase__ ) and data_args.train_val_split > 0.0:
_lowerCamelCase : Optional[int] = ds['train'].train_test_split(data_args.train_val_split )
_lowerCamelCase : Any = split['train']
_lowerCamelCase : List[str] = split['test']
# Load pretrained model and image processor
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
_lowerCamelCase : int = {
'cache_dir': model_args.cache_dir,
'revision': model_args.model_revision,
'use_auth_token': True if model_args.use_auth_token else None,
}
if model_args.config_name:
_lowerCamelCase : Optional[int] = ViTMAEConfig.from_pretrained(model_args.config_name , **lowercase__ )
elif model_args.model_name_or_path:
_lowerCamelCase : Optional[int] = ViTMAEConfig.from_pretrained(model_args.model_name_or_path , **lowercase__ )
else:
_lowerCamelCase : Any = ViTMAEConfig()
logger.warning('You are instantiating a new config instance from scratch.' )
if model_args.config_overrides is not None:
logger.info(f'''Overriding config: {model_args.config_overrides}''' )
config.update_from_string(model_args.config_overrides )
logger.info(f'''New config: {config}''' )
# adapt config
config.update(
{
'mask_ratio': model_args.mask_ratio,
'norm_pix_loss': model_args.norm_pix_loss,
} )
# create image processor
if model_args.image_processor_name:
_lowerCamelCase : Tuple = ViTImageProcessor.from_pretrained(model_args.image_processor_name , **lowercase__ )
elif model_args.model_name_or_path:
_lowerCamelCase : Any = ViTImageProcessor.from_pretrained(model_args.model_name_or_path , **lowercase__ )
else:
_lowerCamelCase : List[Any] = ViTImageProcessor()
# create model
if model_args.model_name_or_path:
_lowerCamelCase : Optional[Any] = ViTMAEForPreTraining.from_pretrained(
model_args.model_name_or_path , from_tf=bool('.ckpt' in model_args.model_name_or_path ) , config=lowercase__ , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
else:
logger.info('Training new model from scratch' )
_lowerCamelCase : List[Any] = ViTMAEForPreTraining(lowercase__ )
if training_args.do_train:
_lowerCamelCase : Any = ds['train'].column_names
else:
_lowerCamelCase : Union[str, Any] = ds['validation'].column_names
if data_args.image_column_name is not None:
_lowerCamelCase : int = data_args.image_column_name
elif "image" in column_names:
_lowerCamelCase : int = 'image'
elif "img" in column_names:
_lowerCamelCase : int = 'img'
else:
_lowerCamelCase : List[Any] = column_names[0]
# transformations as done in original MAE paper
# source: https://github.com/facebookresearch/mae/blob/main/main_pretrain.py
if "shortest_edge" in image_processor.size:
_lowerCamelCase : Optional[int] = image_processor.size['shortest_edge']
else:
_lowerCamelCase : Any = (image_processor.size['height'], image_processor.size['width'])
_lowerCamelCase : Union[str, Any] = Compose(
[
Lambda(lambda lowercase__ : img.convert('RGB' ) if img.mode != "RGB" else img ),
RandomResizedCrop(lowercase__ , scale=(0.2, 1.0) , interpolation=InterpolationMode.BICUBIC ),
RandomHorizontalFlip(),
ToTensor(),
Normalize(mean=image_processor.image_mean , std=image_processor.image_std ),
] )
def preprocess_images(lowercase__ ):
_lowerCamelCase : Any = [transforms(lowercase__ ) for image in examples[image_column_name]]
return examples
if training_args.do_train:
if "train" not in ds:
raise ValueError('--do_train requires a train dataset' )
if data_args.max_train_samples is not None:
_lowerCamelCase : Optional[Any] = ds['train'].shuffle(seed=training_args.seed ).select(range(data_args.max_train_samples ) )
# Set the training transforms
ds["train"].set_transform(lowercase__ )
if training_args.do_eval:
if "validation" not in ds:
raise ValueError('--do_eval requires a validation dataset' )
if data_args.max_eval_samples is not None:
_lowerCamelCase : List[str] = (
ds['validation'].shuffle(seed=training_args.seed ).select(range(data_args.max_eval_samples ) )
)
# Set the validation transforms
ds["validation"].set_transform(lowercase__ )
# Compute absolute learning rate
_lowerCamelCase : Any = (
training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
)
if training_args.base_learning_rate is not None:
_lowerCamelCase : Tuple = training_args.base_learning_rate * total_train_batch_size / 256
# Initialize our trainer
_lowerCamelCase : Dict = Trainer(
model=lowercase__ , args=lowercase__ , train_dataset=ds['train'] if training_args.do_train else None , eval_dataset=ds['validation'] if training_args.do_eval else None , tokenizer=lowercase__ , data_collator=lowercase__ , )
# Training
if training_args.do_train:
_lowerCamelCase : Dict = None
if training_args.resume_from_checkpoint is not None:
_lowerCamelCase : Union[str, Any] = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
_lowerCamelCase : Any = last_checkpoint
_lowerCamelCase : Optional[Any] = trainer.train(resume_from_checkpoint=lowercase__ )
trainer.save_model()
trainer.log_metrics('train' , train_result.metrics )
trainer.save_metrics('train' , train_result.metrics )
trainer.save_state()
# Evaluation
if training_args.do_eval:
_lowerCamelCase : Union[str, Any] = trainer.evaluate()
trainer.log_metrics('eval' , lowercase__ )
trainer.save_metrics('eval' , lowercase__ )
# Write model card and (optionally) push to hub
_lowerCamelCase : List[str] = {
'tasks': 'masked-auto-encoding',
'dataset': data_args.dataset_name,
'tags': ['masked-auto-encoding'],
}
if training_args.push_to_hub:
trainer.push_to_hub(**lowercase__ )
else:
trainer.create_model_card(**lowercase__ )
def _snake_case ( lowercase__ ):
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main() | 12 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool
class lowerCAmelCase__ ( lowercase ):
'''simple docstring'''
lowerCamelCase__ = """philschmid/bart-large-cnn-samsum"""
lowerCamelCase__ = (
"""This is a tool that summarizes an English text. It takes an input `text` containing the text to summarize, """
"""and returns a summary of the text."""
)
lowerCamelCase__ = """summarizer"""
lowerCamelCase__ = AutoTokenizer
lowerCamelCase__ = AutoModelForSeqaSeqLM
lowerCamelCase__ = ["""text"""]
lowerCamelCase__ = ["""text"""]
def A_ ( self , lowercase ):
return self.pre_processor(lowercase , return_tensors='pt' , truncation=lowercase )
def A_ ( self , lowercase ):
return self.model.generate(**lowercase )[0]
def A_ ( self , lowercase ):
return self.pre_processor.decode(lowercase , skip_special_tokens=lowercase , clean_up_tokenization_spaces=lowercase ) | 12 | 1 |
import unittest
from parameterized import parameterized
from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import OpenLlamaForCausalLM, OpenLlamaForSequenceClassification, OpenLlamaModel
class UpperCAmelCase__ :
"""simple docstring"""
def __init__( self , A_ , A_=13 , A_=7 , A_=True , A_=True , A_=False , A_=True , A_=99 , A_=32 , A_=5 , A_=4 , A_=37 , A_="gelu" , A_=0.1 , A_=0.1 , A_=512 , A_=16 , A_=2 , A_=0.02 , A_=3 , A_=4 , A_=None , ) -> int:
__UpperCamelCase =parent
__UpperCamelCase =batch_size
__UpperCamelCase =seq_length
__UpperCamelCase =is_training
__UpperCamelCase =use_input_mask
__UpperCamelCase =use_token_type_ids
__UpperCamelCase =use_labels
__UpperCamelCase =vocab_size
__UpperCamelCase =hidden_size
__UpperCamelCase =num_hidden_layers
__UpperCamelCase =num_attention_heads
__UpperCamelCase =intermediate_size
__UpperCamelCase =hidden_act
__UpperCamelCase =hidden_dropout_prob
__UpperCamelCase =attention_probs_dropout_prob
__UpperCamelCase =max_position_embeddings
__UpperCamelCase =type_vocab_size
__UpperCamelCase =type_sequence_label_size
__UpperCamelCase =initializer_range
__UpperCamelCase =num_labels
__UpperCamelCase =num_choices
__UpperCamelCase =scope
def _a ( self ) -> Dict:
__UpperCamelCase =ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__UpperCamelCase =None
if self.use_input_mask:
__UpperCamelCase =random_attention_mask([self.batch_size, self.seq_length] )
__UpperCamelCase =None
if self.use_token_type_ids:
__UpperCamelCase =ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
__UpperCamelCase =None
__UpperCamelCase =None
__UpperCamelCase =None
if self.use_labels:
__UpperCamelCase =ids_tensor([self.batch_size] , self.type_sequence_label_size )
__UpperCamelCase =ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
__UpperCamelCase =ids_tensor([self.batch_size] , self.num_choices )
__UpperCamelCase =self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def _a ( self ) -> int:
return OpenLlamaConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=A_ , initializer_range=self.initializer_range , use_stable_embedding=A_ , )
def _a ( self , A_ , A_ , A_ , A_ , A_ , A_ , A_ ) -> Dict:
__UpperCamelCase =OpenLlamaModel(config=A_ )
model.to(A_ )
model.eval()
__UpperCamelCase =model(A_ , attention_mask=A_ )
__UpperCamelCase =model(A_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _a ( self , A_ , A_ , A_ , A_ , A_ , A_ , A_ , A_ , A_ , ) -> Optional[int]:
__UpperCamelCase =True
__UpperCamelCase =OpenLlamaModel(A_ )
model.to(A_ )
model.eval()
__UpperCamelCase =model(
A_ , attention_mask=A_ , encoder_hidden_states=A_ , encoder_attention_mask=A_ , )
__UpperCamelCase =model(
A_ , attention_mask=A_ , encoder_hidden_states=A_ , )
__UpperCamelCase =model(A_ , attention_mask=A_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _a ( self , A_ , A_ , A_ , A_ , A_ , A_ , A_ , A_ , A_ , ) -> Union[str, Any]:
__UpperCamelCase =OpenLlamaForCausalLM(config=A_ )
model.to(A_ )
model.eval()
__UpperCamelCase =model(A_ , attention_mask=A_ , labels=A_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def _a ( self , A_ , A_ , A_ , A_ , A_ , A_ , A_ , A_ , A_ , ) -> List[Any]:
__UpperCamelCase =True
__UpperCamelCase =True
__UpperCamelCase =OpenLlamaForCausalLM(config=A_ )
model.to(A_ )
model.eval()
# first forward pass
__UpperCamelCase =model(
A_ , attention_mask=A_ , encoder_hidden_states=A_ , encoder_attention_mask=A_ , use_cache=A_ , )
__UpperCamelCase =outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
__UpperCamelCase =ids_tensor((self.batch_size, 3) , config.vocab_size )
__UpperCamelCase =ids_tensor((self.batch_size, 3) , vocab_size=2 )
# append to next input_ids and
__UpperCamelCase =torch.cat([input_ids, next_tokens] , dim=-1 )
__UpperCamelCase =torch.cat([input_mask, next_mask] , dim=-1 )
__UpperCamelCase =model(
A_ , attention_mask=A_ , encoder_hidden_states=A_ , encoder_attention_mask=A_ , output_hidden_states=A_ , )['hidden_states'][0]
__UpperCamelCase =model(
A_ , attention_mask=A_ , encoder_hidden_states=A_ , encoder_attention_mask=A_ , past_key_values=A_ , output_hidden_states=A_ , )['hidden_states'][0]
# select random slice
__UpperCamelCase =ids_tensor((1,) , output_from_past.shape[-1] ).item()
__UpperCamelCase =output_from_no_past[:, -3:, random_slice_idx].detach()
__UpperCamelCase =output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(A_ , A_ , atol=1E-3 ) )
def _a ( self ) -> List[str]:
__UpperCamelCase =self.prepare_config_and_inputs()
(
(
__UpperCamelCase
) , (
__UpperCamelCase
) , (
__UpperCamelCase
) , (
__UpperCamelCase
) , (
__UpperCamelCase
) , (
__UpperCamelCase
) , (
__UpperCamelCase
) ,
) =config_and_inputs
__UpperCamelCase ={'input_ids': input_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class UpperCAmelCase__ ( A_ , A_ , A_ , unittest.TestCase ):
"""simple docstring"""
UpperCAmelCase__ : Optional[int] = (
(OpenLlamaModel, OpenLlamaForCausalLM, OpenLlamaForSequenceClassification) if is_torch_available() else ()
)
UpperCAmelCase__ : List[Any] = (OpenLlamaForCausalLM,) if is_torch_available() else ()
UpperCAmelCase__ : str = (
{
"feature-extraction": OpenLlamaModel,
"text-classification": OpenLlamaForSequenceClassification,
"text-generation": OpenLlamaForCausalLM,
"zero-shot": OpenLlamaForSequenceClassification,
}
if is_torch_available()
else {}
)
UpperCAmelCase__ : Optional[Any] = False
UpperCAmelCase__ : Optional[int] = False
def _a ( self ) -> List[str]:
__UpperCamelCase =OpenLlamaModelTester(self )
__UpperCamelCase =ConfigTester(self , config_class=A_ , hidden_size=37 )
def _a ( self ) -> Tuple:
self.config_tester.run_common_tests()
def _a ( self ) -> Any:
__UpperCamelCase =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A_ )
def _a ( self ) -> str:
__UpperCamelCase =self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
__UpperCamelCase =type
self.model_tester.create_and_check_model(*A_ )
def _a ( self ) -> Dict:
__UpperCamelCase , __UpperCamelCase =self.model_tester.prepare_config_and_inputs_for_common()
__UpperCamelCase =3
__UpperCamelCase =input_dict['input_ids']
__UpperCamelCase =input_ids.ne(1 ).to(A_ )
__UpperCamelCase =ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
__UpperCamelCase =OpenLlamaForSequenceClassification(A_ )
model.to(A_ )
model.eval()
__UpperCamelCase =model(A_ , attention_mask=A_ , labels=A_ )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def _a ( self ) -> Tuple:
__UpperCamelCase , __UpperCamelCase =self.model_tester.prepare_config_and_inputs_for_common()
__UpperCamelCase =3
__UpperCamelCase ='single_label_classification'
__UpperCamelCase =input_dict['input_ids']
__UpperCamelCase =input_ids.ne(1 ).to(A_ )
__UpperCamelCase =ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
__UpperCamelCase =OpenLlamaForSequenceClassification(A_ )
model.to(A_ )
model.eval()
__UpperCamelCase =model(A_ , attention_mask=A_ , labels=A_ )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def _a ( self ) -> Tuple:
__UpperCamelCase , __UpperCamelCase =self.model_tester.prepare_config_and_inputs_for_common()
__UpperCamelCase =3
__UpperCamelCase ='multi_label_classification'
__UpperCamelCase =input_dict['input_ids']
__UpperCamelCase =input_ids.ne(1 ).to(A_ )
__UpperCamelCase =ids_tensor(
[self.model_tester.batch_size, config.num_labels] , self.model_tester.type_sequence_label_size ).to(torch.float )
__UpperCamelCase =OpenLlamaForSequenceClassification(A_ )
model.to(A_ )
model.eval()
__UpperCamelCase =model(A_ , attention_mask=A_ , labels=A_ )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
@unittest.skip('Open-Llama buffers include complex numbers, which breaks this test' )
def _a ( self ) -> List[Any]:
pass
@parameterized.expand([('linear',), ('dynamic',)] )
def _a ( self , A_ ) -> Tuple:
__UpperCamelCase , __UpperCamelCase =self.model_tester.prepare_config_and_inputs_for_common()
__UpperCamelCase =ids_tensor([1, 10] , config.vocab_size )
__UpperCamelCase =ids_tensor([1, int(config.max_position_embeddings * 1.5 )] , config.vocab_size )
set_seed(42 ) # Fixed seed at init time so the two models get the same random weights
__UpperCamelCase =OpenLlamaModel(A_ )
original_model.to(A_ )
original_model.eval()
__UpperCamelCase =original_model(A_ ).last_hidden_state
__UpperCamelCase =original_model(A_ ).last_hidden_state
set_seed(42 ) # Fixed seed at init time so the two models get the same random weights
__UpperCamelCase ={'type': scaling_type, 'factor': 10.0}
__UpperCamelCase =OpenLlamaModel(A_ )
scaled_model.to(A_ )
scaled_model.eval()
__UpperCamelCase =scaled_model(A_ ).last_hidden_state
__UpperCamelCase =scaled_model(A_ ).last_hidden_state
# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
# maximum sequence length, so the outputs for the short input should match.
if scaling_type == "dynamic":
self.assertTrue(torch.allclose(A_ , A_ , atol=1E-5 ) )
else:
self.assertFalse(torch.allclose(A_ , A_ , atol=1E-5 ) )
# The output should be different for long inputs
self.assertFalse(torch.allclose(A_ , A_ , atol=1E-5 ) )
| 62 |
from __future__ import annotations
import math
import random
from collections.abc import Collection
from typing import overload
class UpperCAmelCase__ :
"""simple docstring"""
def __init__( self , A_ = None ) -> None:
if components is None:
__UpperCamelCase =[]
__UpperCamelCase =list(A_ )
def __len__( self ) -> int:
return len(self.__components )
def __str__( self ) -> str:
return "(" + ",".join(map(A_ , self.__components ) ) + ")"
def __add__( self , A_ ) -> Vector:
__UpperCamelCase =len(self )
if size == len(A_ ):
__UpperCamelCase =[self.__components[i] + other.component(A_ ) for i in range(A_ )]
return Vector(A_ )
else:
raise Exception('must have the same size' )
def __sub__( self , A_ ) -> Vector:
__UpperCamelCase =len(self )
if size == len(A_ ):
__UpperCamelCase =[self.__components[i] - other.component(A_ ) for i in range(A_ )]
return Vector(A_ )
else: # error case
raise Exception('must have the same size' )
@overload
def __mul__( self , A_ ) -> Vector:
...
@overload
def __mul__( self , A_ ) -> float:
...
def __mul__( self , A_ ) -> float | Vector:
if isinstance(A_ , (float, int) ):
__UpperCamelCase =[c * other for c in self.__components]
return Vector(A_ )
elif isinstance(A_ , A_ ) and len(self ) == len(A_ ):
__UpperCamelCase =len(self )
__UpperCamelCase =[self.__components[i] * other.component(A_ ) for i in range(A_ )]
return sum(A_ )
else: # error case
raise Exception('invalid operand!' )
def _a ( self ) -> Vector:
return Vector(self.__components )
def _a ( self , A_ ) -> float:
if isinstance(A_ , A_ ) and -len(self.__components ) <= i < len(self.__components ):
return self.__components[i]
else:
raise Exception('index out of range' )
def _a ( self , A_ , A_ ) -> None:
assert -len(self.__components ) <= pos < len(self.__components )
__UpperCamelCase =value
def _a ( self ) -> float:
if len(self.__components ) == 0:
raise Exception('Vector is empty' )
__UpperCamelCase =[c**2 for c in self.__components]
return math.sqrt(sum(A_ ) )
def _a ( self , A_ , A_ = False ) -> float:
__UpperCamelCase =self * other
__UpperCamelCase =self.euclidean_length() * other.euclidean_length()
if deg:
return math.degrees(math.acos(num / den ) )
else:
return math.acos(num / den )
def _UpperCAmelCase ( SCREAMING_SNAKE_CASE__ : int ):
assert isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
return Vector([0] * dimension )
def _UpperCAmelCase ( SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int ):
assert isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) and (isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ))
__UpperCamelCase =[0] * dimension
__UpperCamelCase =1
return Vector(SCREAMING_SNAKE_CASE__ )
def _UpperCAmelCase ( SCREAMING_SNAKE_CASE__ : float , SCREAMING_SNAKE_CASE__ : Vector , SCREAMING_SNAKE_CASE__ : Vector ):
assert (
isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
and isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
and (isinstance(SCREAMING_SNAKE_CASE__ , (int, float) ))
)
return x * scalar + y
def _UpperCAmelCase ( SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int ):
random.seed(SCREAMING_SNAKE_CASE__ )
__UpperCamelCase =[random.randint(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) for _ in range(SCREAMING_SNAKE_CASE__ )]
return Vector(SCREAMING_SNAKE_CASE__ )
class UpperCAmelCase__ :
"""simple docstring"""
def __init__( self , A_ , A_ , A_ ) -> None:
__UpperCamelCase =matrix
__UpperCamelCase =w
__UpperCamelCase =h
def __str__( self ) -> str:
__UpperCamelCase =''
for i in range(self.__height ):
ans += "|"
for j in range(self.__width ):
if j < self.__width - 1:
ans += str(self.__matrix[i][j] ) + ","
else:
ans += str(self.__matrix[i][j] ) + "|\n"
return ans
def __add__( self , A_ ) -> Matrix:
if self.__width == other.width() and self.__height == other.height():
__UpperCamelCase =[]
for i in range(self.__height ):
__UpperCamelCase =[
self.__matrix[i][j] + other.component(A_ , A_ )
for j in range(self.__width )
]
matrix.append(A_ )
return Matrix(A_ , self.__width , self.__height )
else:
raise Exception('matrix must have the same dimension!' )
def __sub__( self , A_ ) -> Matrix:
if self.__width == other.width() and self.__height == other.height():
__UpperCamelCase =[]
for i in range(self.__height ):
__UpperCamelCase =[
self.__matrix[i][j] - other.component(A_ , A_ )
for j in range(self.__width )
]
matrix.append(A_ )
return Matrix(A_ , self.__width , self.__height )
else:
raise Exception('matrices must have the same dimension!' )
@overload
def __mul__( self , A_ ) -> Matrix:
...
@overload
def __mul__( self , A_ ) -> Vector:
...
def __mul__( self , A_ ) -> Vector | Matrix:
if isinstance(A_ , A_ ): # matrix-vector
if len(A_ ) == self.__width:
__UpperCamelCase =zero_vector(self.__height )
for i in range(self.__height ):
__UpperCamelCase =[
self.__matrix[i][j] * other.component(A_ )
for j in range(self.__width )
]
ans.change_component(A_ , sum(A_ ) )
return ans
else:
raise Exception(
'vector must have the same size as the '
'number of columns of the matrix!' )
elif isinstance(A_ , (int, float) ): # matrix-scalar
__UpperCamelCase =[
[self.__matrix[i][j] * other for j in range(self.__width )]
for i in range(self.__height )
]
return Matrix(A_ , self.__width , self.__height )
return None
def _a ( self ) -> int:
return self.__height
def _a ( self ) -> int:
return self.__width
def _a ( self , A_ , A_ ) -> float:
if 0 <= x < self.__height and 0 <= y < self.__width:
return self.__matrix[x][y]
else:
raise Exception('change_component: indices out of bounds' )
def _a ( self , A_ , A_ , A_ ) -> None:
if 0 <= x < self.__height and 0 <= y < self.__width:
__UpperCamelCase =value
else:
raise Exception('change_component: indices out of bounds' )
def _a ( self , A_ , A_ ) -> float:
if self.__height != self.__width:
raise Exception('Matrix is not square' )
__UpperCamelCase =self.__matrix[:x] + self.__matrix[x + 1 :]
for i in range(len(A_ ) ):
__UpperCamelCase =minor[i][:y] + minor[i][y + 1 :]
return Matrix(A_ , self.__width - 1 , self.__height - 1 ).determinant()
def _a ( self , A_ , A_ ) -> float:
if self.__height != self.__width:
raise Exception('Matrix is not square' )
if 0 <= x < self.__height and 0 <= y < self.__width:
return (-1) ** (x + y) * self.minor(A_ , A_ )
else:
raise Exception('Indices out of bounds' )
def _a ( self ) -> float:
if self.__height != self.__width:
raise Exception('Matrix is not square' )
if self.__height < 1:
raise Exception('Matrix has no element' )
elif self.__height == 1:
return self.__matrix[0][0]
elif self.__height == 2:
return (
self.__matrix[0][0] * self.__matrix[1][1]
- self.__matrix[0][1] * self.__matrix[1][0]
)
else:
__UpperCamelCase =[
self.__matrix[0][y] * self.cofactor(0 , A_ ) for y in range(self.__width )
]
return sum(A_ )
def _UpperCAmelCase ( SCREAMING_SNAKE_CASE__ : int ):
__UpperCamelCase =[[0] * n for _ in range(SCREAMING_SNAKE_CASE__ )]
return Matrix(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
def _UpperCAmelCase ( SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int ):
random.seed(SCREAMING_SNAKE_CASE__ )
__UpperCamelCase =[
[random.randint(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) for _ in range(SCREAMING_SNAKE_CASE__ )] for _ in range(SCREAMING_SNAKE_CASE__ )
]
return Matrix(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
| 62 | 1 |
import math
import sys
def _a ( SCREAMING_SNAKE_CASE : int ):
"""simple docstring"""
if number != int(SCREAMING_SNAKE_CASE ):
raise ValueError('''the value of input must be a natural number''' )
if number < 0:
raise ValueError('''the value of input must not be a negative number''' )
if number == 0:
return 1
UpperCamelCase__ : Tuple = [-1] * (number + 1)
UpperCamelCase__ : Tuple = 0
for i in range(1 , number + 1 ):
UpperCamelCase__ : Optional[Any] = sys.maxsize
UpperCamelCase__ : Optional[Any] = int(math.sqrt(SCREAMING_SNAKE_CASE ) )
for j in range(1 , root + 1 ):
UpperCamelCase__ : int = 1 + answers[i - (j**2)]
UpperCamelCase__ : int = min(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
UpperCamelCase__ : str = answer
return answers[number]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 51 |
import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter
from transformers import HfArgumentParser
def _a ( SCREAMING_SNAKE_CASE : Optional[Any]=None , SCREAMING_SNAKE_CASE : int=None ):
"""simple docstring"""
return field(default_factory=lambda: default , metadata=SCREAMING_SNAKE_CASE )
@dataclass
class __magic_name__ :
A: str = field(
metadata={"help": "The csv file to plot."} , )
A: bool = field(
default=__lowerCAmelCase , metadata={"help": "Whether to plot along batch size or sequence length. Defaults to sequence length."} , )
A: bool = field(
default=__lowerCAmelCase , metadata={"help": "Whether the csv file has time results or memory results. Defaults to memory results."} , )
A: bool = field(
default=__lowerCAmelCase , metadata={"help": "Disable logarithmic scale when plotting"} , )
A: bool = field(
default=__lowerCAmelCase , metadata={
"help": "Whether the csv file has training results or inference results. Defaults to inference results."
} , )
A: Optional[str] = field(
default=__lowerCAmelCase , metadata={"help": "Filename under which the plot will be saved. If unused no plot is saved."} , )
A: Optional[List[str]] = list_field(
default=__lowerCAmelCase , metadata={"help": "List of model names that are used instead of the ones in the csv file."})
def _a ( SCREAMING_SNAKE_CASE : Any ):
"""simple docstring"""
try:
int(SCREAMING_SNAKE_CASE )
return True
except ValueError:
return False
def _a ( SCREAMING_SNAKE_CASE : Union[str, Any] ):
"""simple docstring"""
try:
float(SCREAMING_SNAKE_CASE )
return True
except ValueError:
return False
class __magic_name__ :
def __init__( self : Any , lowerCamelCase__ : Dict ) -> Dict:
'''simple docstring'''
UpperCamelCase__ : int = args
UpperCamelCase__ : Any = defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}} )
with open(self.args.csv_file , newline='''''' ) as csv_file:
UpperCamelCase__ : Union[str, Any] = csv.DictReader(lowerCamelCase__ )
for row in reader:
UpperCamelCase__ : Union[str, Any] = row['''model''']
self.result_dict[model_name]["bsz"].append(int(row['''batch_size'''] ) )
self.result_dict[model_name]["seq_len"].append(int(row['''sequence_length'''] ) )
if can_convert_to_int(row['''result'''] ):
# value is not None
UpperCamelCase__ : Any = int(row['''result'''] )
elif can_convert_to_float(row['''result'''] ):
# value is not None
UpperCamelCase__ : Any = float(row['''result'''] )
def UpperCAmelCase__ ( self : List[Any] ) -> List[Any]:
'''simple docstring'''
UpperCamelCase__ , UpperCamelCase__ : str = plt.subplots()
UpperCamelCase__ : Dict = '''Time usage''' if self.args.is_time else '''Memory usage'''
UpperCamelCase__ : int = title_str + ''' for training''' if self.args.is_train else title_str + ''' for inference'''
if not self.args.no_log_scale:
# set logarithm scales
ax.set_xscale('''log''' )
ax.set_yscale('''log''' )
for axis in [ax.xaxis, ax.yaxis]:
axis.set_major_formatter(ScalarFormatter() )
for model_name_idx, model_name in enumerate(self.result_dict.keys() ):
UpperCamelCase__ : Tuple = sorted(set(self.result_dict[model_name]['''bsz'''] ) )
UpperCamelCase__ : Tuple = sorted(set(self.result_dict[model_name]['''seq_len'''] ) )
UpperCamelCase__ : Dict = self.result_dict[model_name]['''result''']
((UpperCamelCase__) , (UpperCamelCase__)) : int = (
(batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
)
UpperCamelCase__ : Optional[int] = (
model_name if self.args.short_model_names is None else self.args.short_model_names[model_name_idx]
)
for inner_loop_value in inner_loop_array:
if self.args.plot_along_batch:
UpperCamelCase__ : Optional[Any] = np.asarray(
[results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results] , dtype=lowerCamelCase__ , )
else:
UpperCamelCase__ : Tuple = np.asarray(
[results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results] , dtype=np.floataa , )
((UpperCamelCase__) , (UpperCamelCase__)) : str = (
('''batch_size''', '''len''') if self.args.plot_along_batch else ('''in #tokens''', '''bsz''')
)
UpperCamelCase__ : Optional[Any] = np.asarray(lowerCamelCase__ , lowerCamelCase__ )[: len(lowerCamelCase__ )]
plt.scatter(
lowerCamelCase__ , lowerCamelCase__ , label=F"{label_model_name} - {inner_loop_label}: {inner_loop_value}" )
plt.plot(lowerCamelCase__ , lowerCamelCase__ , '''--''' )
title_str += F" {label_model_name} vs."
UpperCamelCase__ : Optional[Any] = title_str[:-4]
UpperCamelCase__ : List[Any] = '''Time in s''' if self.args.is_time else '''Memory in MB'''
# plot
plt.title(lowerCamelCase__ )
plt.xlabel(lowerCamelCase__ )
plt.ylabel(lowerCamelCase__ )
plt.legend()
if self.args.figure_png_file is not None:
plt.savefig(self.args.figure_png_file )
else:
plt.show()
def _a ( ):
"""simple docstring"""
UpperCamelCase__ : Optional[Any] = HfArgumentParser(SCREAMING_SNAKE_CASE )
UpperCamelCase__ : Dict = parser.parse_args_into_dataclasses()[0]
UpperCamelCase__ : Dict = Plot(args=SCREAMING_SNAKE_CASE )
plot.plot()
if __name__ == "__main__":
main()
| 51 | 1 |
'''simple docstring'''
def lowerCAmelCase_ ( snake_case__ ):
'''simple docstring'''
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
A : Optional[Any] = 1
A : Any = 1
while repunit:
A : Tuple = (10 * repunit + 1) % divisor
repunit_index += 1
return repunit_index
def lowerCAmelCase_ ( snake_case__ = 100_0000 ):
'''simple docstring'''
A : int = limit - 1
if divisor % 2 == 0:
divisor += 1
while least_divisible_repunit(snake_case__ ) <= limit:
divisor += 2
return divisor
if __name__ == "__main__":
print(f'''{solution() = }''')
| 3 |
import re
from filelock import FileLock
try:
import nltk
UpperCAmelCase__ : Tuple = True
except (ImportError, ModuleNotFoundError):
UpperCAmelCase__ : Optional[Any] = False
if NLTK_AVAILABLE:
with FileLock(""".lock""") as lock:
nltk.download("""punkt""", quiet=True)
def __lowercase ( _A ) -> str:
re.sub("""<n>""" , """""" , _A ) # remove pegasus newline char
assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
return "\n".join(nltk.sent_tokenize(_A ) )
| 245 | 0 |
"""simple docstring"""
from __future__ import annotations
from math import ceil, floor, sqrt
def lowerCamelCase (a_ :int = 200_0000) -> int:
lowercase :list[int] = [0]
lowercase :int
for idx in range(1 , ceil(sqrt(target * 2) * 1.1)):
triangle_numbers.append(triangle_numbers[-1] + idx)
# we want this to be as close as possible to target
lowercase :int = 0
# the area corresponding to the grid that gives the product closest to target
lowercase :int = 0
# an estimate of b, using the quadratic formula
lowercase :float
# the largest integer less than b_estimate
lowercase :int
# the largest integer less than b_estimate
lowercase :int
# the triangle number corresponding to b_floor
lowercase :int
# the triangle number corresponding to b_ceil
lowercase :int
for idx_a, triangle_a in enumerate(triangle_numbers[1:] , 1):
lowercase :Union[str, Any] = (-1 + sqrt(1 + 8 * target / triangle_a)) / 2
lowercase :Optional[int] = floor(a_)
lowercase :Optional[Any] = ceil(a_)
lowercase :List[Any] = triangle_numbers[b_floor]
lowercase :Dict = triangle_numbers[b_ceil]
if abs(target - triangle_b_first_guess * triangle_a) < abs(
target - best_product):
lowercase :Optional[Any] = triangle_b_first_guess * triangle_a
lowercase :Dict = idx_a * b_floor
if abs(target - triangle_b_second_guess * triangle_a) < abs(
target - best_product):
lowercase :str = triangle_b_second_guess * triangle_a
lowercase :Any = idx_a * b_ceil
return area
if __name__ == "__main__":
print(F"""{solution() = }""")
| 172 |
"""simple docstring"""
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler
from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.models.fsmt.configuration_fsmt import FSMTConfig
from transformers.optimization import (
Adafactor,
AdamW,
get_constant_schedule,
get_constant_schedule_with_warmup,
get_cosine_schedule_with_warmup,
get_cosine_with_hard_restarts_schedule_with_warmup,
get_linear_schedule_with_warmup,
get_polynomial_decay_schedule_with_warmup,
)
from transformers.trainer_pt_utils import get_tpu_sampler
from transformers.training_args import ParallelMode
from transformers.utils import is_torch_tpu_available
if is_fairscale_available():
from fairscale.optim import OSS
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {
'''linear''': get_linear_schedule_with_warmup,
'''cosine''': get_cosine_schedule_with_warmup,
'''cosine_w_restarts''': get_cosine_with_hard_restarts_schedule_with_warmup,
'''polynomial''': get_polynomial_decay_schedule_with_warmup,
'''constant''': get_constant_schedule,
'''constant_w_warmup''': get_constant_schedule_with_warmup,
}
class __magic_name__ ( __UpperCAmelCase ):
def __init__( self : int , snake_case__ : Dict=None , snake_case__ : List[str]=None , *snake_case__ : str , **snake_case__ : Optional[Any] ):
'''simple docstring'''
super().__init__(*snake_case__ , **snake_case__ )
if config is None:
assert isinstance(self.model , snake_case__ ), (
"If no `config` is passed the model to be trained has to be of type `PreTrainedModel`, but is"
f""" {self.model.__class__}"""
)
lowercase :int = self.model.config
else:
lowercase :str = config
lowercase :Dict = data_args
lowercase :int = self.config.tgt_vocab_size if isinstance(self.config , snake_case__ ) else self.config.vocab_size
if self.args.label_smoothing != 0 or (self.data_args is not None and self.data_args.ignore_pad_token_for_loss):
assert self.config.pad_token_id is not None, (
"Make sure that `config.pad_token_id` is correcly defined when ignoring `pad_token` for loss"
" calculation or doing label smoothing."
)
if self.config.pad_token_id is None and self.config.eos_token_id is not None:
logger.warning(
f"""The `config.pad_token_id` is `None`. Using `config.eos_token_id` = {self.config.eos_token_id} for"""
''' padding..''' )
if self.args.label_smoothing == 0:
lowercase :List[str] = torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id )
else:
# dynamically import label_smoothed_nll_loss
from utils import label_smoothed_nll_loss
lowercase :Union[str, Any] = label_smoothed_nll_loss
def __snake_case ( self : Union[str, Any] , snake_case__ : int ):
'''simple docstring'''
if self.optimizer is None:
lowercase :Optional[int] = ['''bias''', '''LayerNorm.weight''']
lowercase :int = [
{
'''params''': [p for n, p in self.model.named_parameters() if not any(nd in n for nd in no_decay )],
'''weight_decay''': self.args.weight_decay,
},
{
'''params''': [p for n, p in self.model.named_parameters() if any(nd in n for nd in no_decay )],
'''weight_decay''': 0.0,
},
]
lowercase :List[Any] = Adafactor if self.args.adafactor else AdamW
if self.args.adafactor:
lowercase :Union[str, Any] = Adafactor
lowercase :Dict = {'''scale_parameter''': False, '''relative_step''': False}
else:
lowercase :List[str] = AdamW
lowercase :Union[str, Any] = {
'''betas''': (self.args.adam_betaa, self.args.adam_betaa),
'''eps''': self.args.adam_epsilon,
}
lowercase :Tuple = self.args.learning_rate
if self.sharded_ddp:
lowercase :Union[str, Any] = OSS(
params=snake_case__ , optim=snake_case__ , **snake_case__ , )
else:
lowercase :Dict = optimizer_cls(snake_case__ , **snake_case__ )
if self.lr_scheduler is None:
lowercase :List[Any] = self._get_lr_scheduler(snake_case__ )
else: # ignoring --lr_scheduler
logger.warning('''scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored.''' )
def __snake_case ( self : Any , snake_case__ : List[str] ):
'''simple docstring'''
lowercase :Tuple = arg_to_scheduler[self.args.lr_scheduler]
if self.args.lr_scheduler == "constant":
lowercase :Dict = schedule_func(self.optimizer )
elif self.args.lr_scheduler == "constant_w_warmup":
lowercase :str = schedule_func(self.optimizer , num_warmup_steps=self.args.warmup_steps )
else:
lowercase :int = schedule_func(
self.optimizer , num_warmup_steps=self.args.warmup_steps , num_training_steps=snake_case__ )
return scheduler
def __snake_case ( self : Tuple ):
'''simple docstring'''
if isinstance(self.train_dataset , torch.utils.data.IterableDataset ):
return None
elif is_torch_tpu_available():
return get_tpu_sampler(self.train_dataset )
else:
if self.args.sortish_sampler:
self.train_dataset.make_sortish_sampler(
self.args.per_device_train_batch_size , distributed=(self.args.parallel_mode == ParallelMode.DISTRIBUTED) , )
return (
RandomSampler(self.train_dataset )
if self.args.local_rank == -1
else DistributedSampler(self.train_dataset )
)
def __snake_case ( self : Any , snake_case__ : Tuple , snake_case__ : Tuple , snake_case__ : Tuple ):
'''simple docstring'''
if self.args.label_smoothing == 0:
if self.data_args is not None and self.data_args.ignore_pad_token_for_loss:
# force training to ignore pad token
lowercase :List[Any] = model(**snake_case__ , use_cache=snake_case__ )[0]
lowercase :Dict = self.loss_fn(logits.view(-1 , logits.shape[-1] ) , labels.view(-1 ) )
else:
# compute usual loss via models
lowercase , lowercase :str = model(**snake_case__ , labels=snake_case__ , use_cache=snake_case__ )[:2]
else:
# compute label smoothed loss
lowercase :str = model(**snake_case__ , use_cache=snake_case__ )[0]
lowercase :Tuple = torch.nn.functional.log_softmax(snake_case__ , dim=-1 )
lowercase , lowercase :Optional[int] = self.loss_fn(snake_case__ , snake_case__ , self.args.label_smoothing , ignore_index=self.config.pad_token_id )
return loss, logits
def __snake_case ( self : Optional[Any] , snake_case__ : List[Any] , snake_case__ : Any ):
'''simple docstring'''
lowercase :List[str] = inputs.pop('''labels''' )
lowercase , lowercase :Union[str, Any] = self._compute_loss(snake_case__ , snake_case__ , snake_case__ )
return loss
def __snake_case ( self : List[str] , snake_case__ : nn.Module , snake_case__ : Dict[str, Union[torch.Tensor, Any]] , snake_case__ : bool , snake_case__ : Optional[List[str]] = None , ):
'''simple docstring'''
lowercase :List[str] = self._prepare_inputs(snake_case__ )
lowercase :Optional[Any] = {
'''max_length''': self.data_args.val_max_target_length
if self.data_args is not None
else self.config.max_length,
'''num_beams''': self.data_args.eval_beams if self.data_args is not None else self.config.num_beams,
}
if self.args.predict_with_generate and not self.args.prediction_loss_only:
lowercase :Optional[Any] = self.model.generate(
inputs['''input_ids'''] , attention_mask=inputs['''attention_mask'''] , **snake_case__ , )
# in case the batch is shorter than max length, the output should be padded
if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
lowercase :int = self._pad_tensors_to_max_len(snake_case__ , gen_kwargs['''max_length'''] )
lowercase :Any = inputs.pop('''labels''' )
with torch.no_grad():
# compute loss on predict data
lowercase , lowercase :List[str] = self._compute_loss(snake_case__ , snake_case__ , snake_case__ )
lowercase :List[Any] = loss.mean().detach()
if self.args.prediction_loss_only:
return (loss, None, None)
lowercase :Any = generated_tokens if self.args.predict_with_generate else logits
if labels.shape[-1] < gen_kwargs["max_length"]:
lowercase :Tuple = self._pad_tensors_to_max_len(snake_case__ , gen_kwargs['''max_length'''] )
return (loss, logits, labels)
def __snake_case ( self : int , snake_case__ : List[Any] , snake_case__ : Any ):
'''simple docstring'''
lowercase :Union[str, Any] = self.config.pad_token_id if self.config.pad_token_id is not None else self.config.eos_token_id
if pad_token_id is None:
raise ValueError(
'''Make sure that either `config.pad_token_id` or `config.eos_token_id` is defined if tensor has to be'''
f""" padded to `max_length`={max_length}""" )
lowercase :Optional[Any] = pad_token_id * torch.ones(
(tensor.shape[0], max_length) , dtype=tensor.dtype , device=tensor.device )
lowercase :Any = tensor
return padded_tensor
| 172 | 1 |
import argparse
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
lowerCamelCase : Union[str, Any] = "src/diffusers"
# Matches is_xxx_available()
lowerCamelCase : Dict = re.compile(r"is\_([a-z_]*)_available\(\)")
# Matches from xxx import bla
lowerCamelCase : Union[str, Any] = re.compile(r"\s+from\s+\S*\s+import\s+([^\(\s].*)\n")
lowerCamelCase : Any = "\n{0} = None\n"
lowerCamelCase : List[str] = "\nclass {0}(metaclass=DummyObject):\n _backends = {1}\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, {1})\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, {1})\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, {1})\n"
lowerCamelCase : str = "\ndef {0}(*args, **kwargs):\n requires_backends({0}, {1})\n"
def _SCREAMING_SNAKE_CASE ( lowercase : Optional[Any] ):
'''simple docstring'''
lowerCamelCase_ = _re_backend.findall(lowercase )
if len(lowercase ) == 0:
return None
return "_and_".join(lowercase )
def _SCREAMING_SNAKE_CASE ( ):
'''simple docstring'''
with open(os.path.join(lowercase , '__init__.py' ) , 'r' , encoding='utf-8' , newline='\n' ) as f:
lowerCamelCase_ = f.readlines()
# Get to the point we do the actual imports for type checking
lowerCamelCase_ = 0
lowerCamelCase_ = {}
# Go through the end of the file
while line_index < len(lowercase ):
# If the line contains is_backend_available, we grab all objects associated with the `else` block
lowerCamelCase_ = find_backend(lines[line_index] )
if backend is not None:
while not lines[line_index].startswith('else:' ):
line_index += 1
line_index += 1
lowerCamelCase_ = []
# Until we unindent, add backend objects to the list
while line_index < len(lowercase ) and len(lines[line_index] ) > 1:
lowerCamelCase_ = lines[line_index]
lowerCamelCase_ = _re_single_line_import.search(lowercase )
if single_line_import_search is not None:
objects.extend(single_line_import_search.groups()[0].split(', ' ) )
elif line.startswith(' ' * 8 ):
objects.append(line[8:-2] )
line_index += 1
if len(lowercase ) > 0:
lowerCamelCase_ = objects
else:
line_index += 1
return backend_specific_objects
def _SCREAMING_SNAKE_CASE ( lowercase : List[str] , lowercase : str ):
'''simple docstring'''
if name.isupper():
return DUMMY_CONSTANT.format(lowercase )
elif name.islower():
return DUMMY_FUNCTION.format(lowercase , lowercase )
else:
return DUMMY_CLASS.format(lowercase , lowercase )
def _SCREAMING_SNAKE_CASE ( lowercase : Optional[int]=None ):
'''simple docstring'''
if backend_specific_objects is None:
lowerCamelCase_ = read_init()
# For special correspondence backend to module name as used in the function requires_modulename
lowerCamelCase_ = {}
for backend, objects in backend_specific_objects.items():
lowerCamelCase_ = '[' + ', '.join(f"""\"{b}\"""" for b in backend.split('_and_' ) ) + ']'
lowerCamelCase_ = '# This file is autogenerated by the command `make fix-copies`, do not edit.\n'
dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
dummy_file += "\n".join([create_dummy_object(lowercase , lowercase ) for o in objects] )
lowerCamelCase_ = dummy_file
return dummy_files
def _SCREAMING_SNAKE_CASE ( lowercase : Optional[int]=False ):
'''simple docstring'''
lowerCamelCase_ = create_dummy_files()
# For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
lowerCamelCase_ = {'torch': 'pt'}
# Locate actual dummy modules and read their content.
lowerCamelCase_ = os.path.join(lowercase , 'utils' )
lowerCamelCase_ = {
backend: os.path.join(lowercase , f"""dummy_{short_names.get(lowercase , lowercase )}_objects.py""" )
for backend in dummy_files.keys()
}
lowerCamelCase_ = {}
for backend, file_path in dummy_file_paths.items():
if os.path.isfile(lowercase ):
with open(lowercase , 'r' , encoding='utf-8' , newline='\n' ) as f:
lowerCamelCase_ = f.read()
else:
lowerCamelCase_ = ''
for backend in dummy_files.keys():
if dummy_files[backend] != actual_dummies[backend]:
if overwrite:
print(
f"""Updating diffusers.utils.dummy_{short_names.get(lowercase , lowercase )}_objects.py as the main """
'__init__ has new objects.' )
with open(dummy_file_paths[backend] , 'w' , encoding='utf-8' , newline='\n' ) as f:
f.write(dummy_files[backend] )
else:
raise ValueError(
'The main __init__ has objects that are not present in '
f"""diffusers.utils.dummy_{short_names.get(lowercase , lowercase )}_objects.py. Run `make fix-copies` """
'to fix this.' )
if __name__ == "__main__":
lowerCamelCase : List[Any] = argparse.ArgumentParser()
parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
lowerCamelCase : Tuple = parser.parse_args()
check_dummies(args.fix_and_overwrite)
| 204 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import (
TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
FlaubertConfig,
TFFlaubertForMultipleChoice,
TFFlaubertForQuestionAnsweringSimple,
TFFlaubertForSequenceClassification,
TFFlaubertForTokenClassification,
TFFlaubertModel,
TFFlaubertWithLMHeadModel,
)
class A:
'''simple docstring'''
def __init__( self : str , A_ : Optional[Any] , ) -> str:
"""simple docstring"""
lowerCamelCase_ = parent
lowerCamelCase_ = 13
lowerCamelCase_ = 7
lowerCamelCase_ = True
lowerCamelCase_ = True
lowerCamelCase_ = True
lowerCamelCase_ = True
lowerCamelCase_ = True
lowerCamelCase_ = False
lowerCamelCase_ = False
lowerCamelCase_ = False
lowerCamelCase_ = 2
lowerCamelCase_ = 99
lowerCamelCase_ = 0
lowerCamelCase_ = 32
lowerCamelCase_ = 2
lowerCamelCase_ = 4
lowerCamelCase_ = 0.1
lowerCamelCase_ = 0.1
lowerCamelCase_ = 512
lowerCamelCase_ = 16
lowerCamelCase_ = 2
lowerCamelCase_ = 0.02
lowerCamelCase_ = 3
lowerCamelCase_ = 4
lowerCamelCase_ = 'last'
lowerCamelCase_ = True
lowerCamelCase_ = None
lowerCamelCase_ = 0
def a__ ( self : Dict ) -> Union[str, Any]:
"""simple docstring"""
lowerCamelCase_ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowerCamelCase_ = random_attention_mask([self.batch_size, self.seq_length] , dtype=tf.floataa )
lowerCamelCase_ = None
if self.use_input_lengths:
lowerCamelCase_ = (
ids_tensor([self.batch_size] , vocab_size=2 ) + self.seq_length - 2
) # small variation of seq_length
lowerCamelCase_ = None
if self.use_token_type_ids:
lowerCamelCase_ = ids_tensor([self.batch_size, self.seq_length] , self.n_langs )
lowerCamelCase_ = None
lowerCamelCase_ = None
lowerCamelCase_ = None
if self.use_labels:
lowerCamelCase_ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowerCamelCase_ = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
lowerCamelCase_ = ids_tensor([self.batch_size] , 2 , dtype=tf.floataa )
lowerCamelCase_ = ids_tensor([self.batch_size] , self.num_choices )
lowerCamelCase_ = FlaubertConfig(
vocab_size=self.vocab_size , n_special=self.n_special , emb_dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , gelu_activation=self.gelu_activation , sinusoidal_embeddings=self.sinusoidal_embeddings , asm=self.asm , causal=self.causal , n_langs=self.n_langs , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , summary_type=self.summary_type , use_proj=self.use_proj , bos_token_id=self.bos_token_id , )
return (
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
)
def a__ ( self : int , A_ : List[str] , A_ : List[Any] , A_ : str , A_ : List[Any] , A_ : int , A_ : Tuple , A_ : Optional[int] , A_ : Optional[int] , A_ : str , ) -> Union[str, Any]:
"""simple docstring"""
lowerCamelCase_ = TFFlaubertModel(config=A_ )
lowerCamelCase_ = {'input_ids': input_ids, 'lengths': input_lengths, 'langs': token_type_ids}
lowerCamelCase_ = model(A_ )
lowerCamelCase_ = [input_ids, input_mask]
lowerCamelCase_ = model(A_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def a__ ( self : Tuple , A_ : List[str] , A_ : int , A_ : List[Any] , A_ : Any , A_ : Any , A_ : Dict , A_ : str , A_ : List[Any] , A_ : Union[str, Any] , ) -> List[str]:
"""simple docstring"""
lowerCamelCase_ = TFFlaubertWithLMHeadModel(A_ )
lowerCamelCase_ = {'input_ids': input_ids, 'lengths': input_lengths, 'langs': token_type_ids}
lowerCamelCase_ = model(A_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def a__ ( self : str , A_ : Tuple , A_ : Any , A_ : Any , A_ : List[Any] , A_ : Dict , A_ : List[Any] , A_ : Union[str, Any] , A_ : Optional[int] , A_ : List[Any] , ) -> Union[str, Any]:
"""simple docstring"""
lowerCamelCase_ = TFFlaubertForQuestionAnsweringSimple(A_ )
lowerCamelCase_ = {'input_ids': input_ids, 'lengths': input_lengths}
lowerCamelCase_ = model(A_ )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def a__ ( self : Optional[int] , A_ : List[Any] , A_ : str , A_ : List[str] , A_ : Dict , A_ : Optional[Any] , A_ : Tuple , A_ : str , A_ : Optional[int] , A_ : Tuple , ) -> List[Any]:
"""simple docstring"""
lowerCamelCase_ = TFFlaubertForSequenceClassification(A_ )
lowerCamelCase_ = {'input_ids': input_ids, 'lengths': input_lengths}
lowerCamelCase_ = model(A_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def a__ ( self : Dict , A_ : Optional[Any] , A_ : List[Any] , A_ : int , A_ : Any , A_ : Union[str, Any] , A_ : str , A_ : Any , A_ : Union[str, Any] , A_ : List[str] , ) -> Union[str, Any]:
"""simple docstring"""
lowerCamelCase_ = self.num_labels
lowerCamelCase_ = TFFlaubertForTokenClassification(config=A_ )
lowerCamelCase_ = {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
lowerCamelCase_ = model(A_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def a__ ( self : List[Any] , A_ : Optional[int] , A_ : List[Any] , A_ : Optional[int] , A_ : Tuple , A_ : Union[str, Any] , A_ : int , A_ : str , A_ : Tuple , A_ : str , ) -> Optional[int]:
"""simple docstring"""
lowerCamelCase_ = self.num_choices
lowerCamelCase_ = TFFlaubertForMultipleChoice(config=A_ )
lowerCamelCase_ = tf.tile(tf.expand_dims(A_ , 1 ) , (1, self.num_choices, 1) )
lowerCamelCase_ = tf.tile(tf.expand_dims(A_ , 1 ) , (1, self.num_choices, 1) )
lowerCamelCase_ = tf.tile(tf.expand_dims(A_ , 1 ) , (1, self.num_choices, 1) )
lowerCamelCase_ = {
'input_ids': multiple_choice_inputs_ids,
'attention_mask': multiple_choice_input_mask,
'token_type_ids': multiple_choice_token_type_ids,
}
lowerCamelCase_ = model(A_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def a__ ( self : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
lowerCamelCase_ = self.prepare_config_and_inputs()
(
(
lowerCamelCase_
) , (
lowerCamelCase_
) , (
lowerCamelCase_
) , (
lowerCamelCase_
) , (
lowerCamelCase_
) , (
lowerCamelCase_
) , (
lowerCamelCase_
) , (
lowerCamelCase_
) , (
lowerCamelCase_
) ,
) = config_and_inputs
lowerCamelCase_ = {
'input_ids': input_ids,
'token_type_ids': token_type_ids,
'langs': token_type_ids,
'lengths': input_lengths,
}
return config, inputs_dict
@require_tf
class A( UpperCamelCase , UpperCamelCase , unittest.TestCase ):
'''simple docstring'''
UpperCamelCase = (
(
TFFlaubertModel,
TFFlaubertWithLMHeadModel,
TFFlaubertForSequenceClassification,
TFFlaubertForQuestionAnsweringSimple,
TFFlaubertForTokenClassification,
TFFlaubertForMultipleChoice,
)
if is_tf_available()
else ()
)
UpperCamelCase = (
(TFFlaubertWithLMHeadModel,) if is_tf_available() else ()
) # TODO (PVP): Check other models whether language generation is also applicable
UpperCamelCase = (
{
'''feature-extraction''': TFFlaubertModel,
'''fill-mask''': TFFlaubertWithLMHeadModel,
'''question-answering''': TFFlaubertForQuestionAnsweringSimple,
'''text-classification''': TFFlaubertForSequenceClassification,
'''token-classification''': TFFlaubertForTokenClassification,
'''zero-shot''': TFFlaubertForSequenceClassification,
}
if is_tf_available()
else {}
)
UpperCamelCase = False
UpperCamelCase = False
def a__ ( self : Union[str, Any] , A_ : Any , A_ : List[Any] , A_ : Union[str, Any] , A_ : str , A_ : List[str] ) -> Optional[Any]:
"""simple docstring"""
if (
pipeline_test_casse_name == "QAPipelineTests"
and tokenizer_name is not None
and not tokenizer_name.endswith('Fast' )
):
# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
return True
return False
def a__ ( self : List[str] ) -> Optional[int]:
"""simple docstring"""
lowerCamelCase_ = TFFlaubertModelTester(self )
lowerCamelCase_ = ConfigTester(self , config_class=A_ , emb_dim=37 )
def a__ ( self : List[str] ) -> str:
"""simple docstring"""
self.config_tester.run_common_tests()
def a__ ( self : int ) -> Optional[int]:
"""simple docstring"""
lowerCamelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_model(*A_ )
def a__ ( self : List[str] ) -> Any:
"""simple docstring"""
lowerCamelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_lm_head(*A_ )
def a__ ( self : Dict ) -> Tuple:
"""simple docstring"""
lowerCamelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_qa(*A_ )
def a__ ( self : Union[str, Any] ) -> Tuple:
"""simple docstring"""
lowerCamelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_sequence_classif(*A_ )
def a__ ( self : List[Any] ) -> Dict:
"""simple docstring"""
lowerCamelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_for_token_classification(*A_ )
def a__ ( self : int ) -> Optional[int]:
"""simple docstring"""
lowerCamelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_for_multiple_choice(*A_ )
@slow
def a__ ( self : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
for model_name in TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCamelCase_ = TFFlaubertModel.from_pretrained(A_ )
self.assertIsNotNone(A_ )
@require_tf
@require_sentencepiece
@require_tokenizers
class A( unittest.TestCase ):
'''simple docstring'''
@slow
def a__ ( self : Dict ) -> str:
"""simple docstring"""
lowerCamelCase_ = TFFlaubertModel.from_pretrained('jplu/tf-flaubert-small-cased' )
lowerCamelCase_ = tf.convert_to_tensor(
[[0, 158, 735, 2592, 1424, 6727, 82, 1]] , dtype=tf.intaa , ) # "J'aime flaubert !"
lowerCamelCase_ = model(A_ )[0]
lowerCamelCase_ = tf.TensorShape((1, 8, 512) )
self.assertEqual(output.shape , A_ )
# compare the actual values for a slice.
lowerCamelCase_ = tf.convert_to_tensor(
[
[
[-1.8768773, -1.566555, 0.27072418],
[-1.6920038, -0.5873505, 1.9329599],
[-2.9563985, -1.6993835, 1.7972052],
]
] , dtype=tf.floataa , )
self.assertTrue(np.allclose(output[:, :3, :3].numpy() , expected_slice.numpy() , atol=1E-4 ) )
| 204 | 1 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Iterator
from typing import Generic, TypeVar
SCREAMING_SNAKE_CASE : Union[str, Any] = TypeVar('''T''')
class __lowerCamelCase ( Generic[T] ):
def __init__(self , lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase = data
_lowerCAmelCase = None
def __str__(self ):
'''simple docstring'''
return f"""{self.data}"""
class __lowerCamelCase ( Generic[T] ):
def __init__(self ):
'''simple docstring'''
_lowerCAmelCase = None
def __iter__(self ):
'''simple docstring'''
_lowerCAmelCase = self.top
while node:
yield node.data
_lowerCAmelCase = node.next
def __str__(self ):
'''simple docstring'''
return "->".join([str(lowerCamelCase ) for item in self] )
def __len__(self ):
'''simple docstring'''
return len(tuple(iter(self ) ) )
def A__ (self ):
'''simple docstring'''
return self.top is None
def A__ (self , lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase = Node(lowerCamelCase )
if not self.is_empty():
_lowerCAmelCase = self.top
_lowerCAmelCase = node
def A__ (self ):
'''simple docstring'''
if self.is_empty():
raise IndexError("""pop from empty stack""" )
assert isinstance(self.top , lowerCamelCase )
_lowerCAmelCase = self.top
_lowerCAmelCase = self.top.next
return pop_node.data
def A__ (self ):
'''simple docstring'''
if self.is_empty():
raise IndexError("""peek from empty stack""" )
assert self.top is not None
return self.top.data
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = None
if __name__ == "__main__":
from doctest import testmod
testmod() | 317 |
"""simple docstring"""
from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
SCREAMING_SNAKE_CASE : str = (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
SCREAMING_SNAKE_CASE : list[int] = [ord(letter) for letter in string.ascii_lowercase]
SCREAMING_SNAKE_CASE : set[int] = {ord(char) for char in VALID_CHARS}
SCREAMING_SNAKE_CASE : list[str] = ["the", "be", "to", "of", "and", "in", "that", "have"]
def __UpperCAmelCase ( snake_case_ : list[int] , snake_case_ : tuple[int, ...] ) -> str | None:
"""simple docstring"""
_lowerCAmelCase = ""
_lowerCAmelCase = 42
_lowerCAmelCase = 42
_lowerCAmelCase = 42
for keychar, cipherchar in zip(cycle(snake_case_ ) , snake_case_ ):
_lowerCAmelCase = cipherchar ^ keychar
if decodedchar not in VALID_INTS:
return None
decoded += chr(snake_case_ )
return decoded
def __UpperCAmelCase ( snake_case_ : list[int] ) -> list[str]:
"""simple docstring"""
_lowerCAmelCase = []
for key in product(snake_case_ , repeat=3 ):
_lowerCAmelCase = try_key(snake_case_ , snake_case_ )
if encoded is not None:
possibles.append(snake_case_ )
return possibles
def __UpperCAmelCase ( snake_case_ : list[str] , snake_case_ : str ) -> list[str]:
"""simple docstring"""
return [possible for possible in possibles if common_word in possible.lower()]
def __UpperCAmelCase ( snake_case_ : str = "p059_cipher.txt" ) -> int:
"""simple docstring"""
_lowerCAmelCase = 42
_lowerCAmelCase = 42
_lowerCAmelCase = 42
_lowerCAmelCase = 42
_lowerCAmelCase = Path(snake_case_ ).parent.joinpath(snake_case_ ).read_text(encoding="""utf-8""" )
_lowerCAmelCase = [int(snake_case_ ) for number in data.strip().split(""",""" )]
_lowerCAmelCase = filter_valid_chars(snake_case_ )
for common_word in COMMON_WORDS:
_lowerCAmelCase = filter_common_word(snake_case_ , snake_case_ )
if len(snake_case_ ) == 1:
break
_lowerCAmelCase = possibles[0]
return sum(ord(snake_case_ ) for char in decoded_text )
if __name__ == "__main__":
print(F'{solution() = }') | 317 | 1 |
'''simple docstring'''
def _A ( A__ ):
"""simple docstring"""
if not isinstance(A__ , A__ ):
raise ValueError('''Input must be an integer''' )
if input_num <= 0:
raise ValueError('''Input must be positive''' )
return sum(
divisor for divisor in range(1 , input_num // 2 + 1 ) if input_num % divisor == 0 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 104 |
'''simple docstring'''
from heapq import heappop, heappush
import numpy as np
def SCREAMING_SNAKE_CASE_ (UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , ) -> tuple[float | int, list[tuple[int, int]]]:
lowerCamelCase__ , lowerCamelCase__ : Union[str, Any] = grid.shape
lowerCamelCase__ : List[str] = [-1, 1, 0, 0]
lowerCamelCase__ : Dict = [0, 0, -1, 1]
if allow_diagonal:
dx += [-1, -1, 1, 1]
dy += [-1, 1, -1, 1]
lowerCamelCase__ , lowerCamelCase__ : Any = [(0, source)], set()
lowerCamelCase__ : Tuple = np.full((rows, cols) , np.inf )
lowerCamelCase__ : List[str] = 0
lowerCamelCase__ : Optional[int] = np.empty((rows, cols) , dtype=UpperCamelCase )
lowerCamelCase__ : str = None
while queue:
((lowerCamelCase__) , (lowerCamelCase__)) : List[str] = heappop(UpperCamelCase )
if (x, y) in visited:
continue
visited.add((x, y) )
if (x, y) == destination:
lowerCamelCase__ : Optional[int] = []
while (x, y) != source:
path.append((x, y) )
lowerCamelCase__ , lowerCamelCase__ : List[Any] = predecessors[x, y]
path.append(UpperCamelCase ) # add the source manually
path.reverse()
return matrix[destination], path
for i in range(len(UpperCamelCase ) ):
lowerCamelCase__ , lowerCamelCase__ : Union[str, Any] = x + dx[i], y + dy[i]
if 0 <= nx < rows and 0 <= ny < cols:
lowerCamelCase__ : Any = grid[nx][ny]
if next_node == 1 and matrix[nx, ny] > dist + 1:
heappush(UpperCamelCase , (dist + 1, (nx, ny)) )
lowerCamelCase__ : Union[str, Any] = dist + 1
lowerCamelCase__ : List[str] = (x, y)
return np.inf, []
if __name__ == "__main__":
import doctest
doctest.testmod()
| 41 | 0 |
'''simple docstring'''
from jiwer import compute_measures
import datasets
lowerCAmelCase :List[Any] = '''\
@inproceedings{inproceedings,
author = {Morris, Andrew and Maier, Viktoria and Green, Phil},
year = {2004},
month = {01},
pages = {},
title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}
}
'''
lowerCAmelCase :Tuple = '''\
Word error rate (WER) is a common metric of the performance of an automatic speech recognition system.
The general difficulty of measuring performance lies in the fact that the recognized word sequence can have a different length from the reference word sequence (supposedly the correct one). The WER is derived from the Levenshtein distance, working at the word level instead of the phoneme level. The WER is a valuable tool for comparing different systems as well as for evaluating improvements within one system. This kind of measurement, however, provides no details on the nature of translation errors and further work is therefore required to identify the main source(s) of error and to focus any research effort.
This problem is solved by first aligning the recognized word sequence with the reference (spoken) word sequence using dynamic string alignment. Examination of this issue is seen through a theory called the power law that states the correlation between perplexity and word error rate.
Word error rate can then be computed as:
WER = (S + D + I) / N = (S + D + I) / (S + D + C)
where
S is the number of substitutions,
D is the number of deletions,
I is the number of insertions,
C is the number of correct words,
N is the number of words in the reference (N=S+D+C).
This value indicates the average number of errors per reference word. The lower the value, the better the
performance of the ASR system with a WER of 0 being a perfect score.
'''
lowerCAmelCase :Union[str, Any] = '''
Compute WER score of transcribed segments against references.
Args:
references: List of references for each speech input.
predictions: List of transcriptions to score.
concatenate_texts (bool, default=False): Whether to concatenate all input texts or compute WER iteratively.
Returns:
(float): the word error rate
Examples:
>>> predictions = [\"this is the prediction\", \"there is an other sample\"]
>>> references = [\"this is the reference\", \"there is another one\"]
>>> wer = datasets.load_metric(\"wer\")
>>> wer_score = wer.compute(predictions=predictions, references=references)
>>> print(wer_score)
0.5
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class _lowerCamelCase ( datasets.Metric ):
'''simple docstring'''
def __lowerCAmelCase ( self : Any ) -> Optional[Any]:
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'predictions': datasets.Value('string' , id='sequence' ),
'references': datasets.Value('string' , id='sequence' ),
} ) , codebase_urls=['https://github.com/jitsi/jiwer/'] , reference_urls=[
'https://en.wikipedia.org/wiki/Word_error_rate',
] , )
def __lowerCAmelCase ( self : str , _A : Optional[Any]=None , _A : Any=None , _A : Union[str, Any]=False ) -> Optional[Any]:
if concatenate_texts:
return compute_measures(__UpperCamelCase , __UpperCamelCase )["wer"]
else:
__magic_name__ : Optional[int] = 0
__magic_name__ : Tuple = 0
for prediction, reference in zip(__UpperCamelCase , __UpperCamelCase ):
__magic_name__ : List[str] = compute_measures(__UpperCamelCase , __UpperCamelCase )
incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
total += measures["substitutions"] + measures["deletions"] + measures["hits"]
return incorrect / total | 360 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class _lowerCamelCase ( unittest.TestCase ):
'''simple docstring'''
@property
def __lowerCAmelCase ( self : Dict ) -> List[str]:
torch.manual_seed(0 )
__magic_name__ : Dict = UNetaDModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=3 , out_channels=3 , down_block_types=('DownBlock2D', 'AttnDownBlock2D') , up_block_types=('AttnUpBlock2D', 'UpBlock2D') , )
return model
def __lowerCAmelCase ( self : str ) -> Any:
__magic_name__ : Union[str, Any] = self.dummy_uncond_unet
__magic_name__ : str = KarrasVeScheduler()
__magic_name__ : List[Any] = KarrasVePipeline(unet=_A , scheduler=_A )
pipe.to(_A )
pipe.set_progress_bar_config(disable=_A )
__magic_name__ : Dict = torch.manual_seed(0 )
__magic_name__ : int = pipe(num_inference_steps=2 , generator=_A , output_type='numpy' ).images
__magic_name__ : Any = torch.manual_seed(0 )
__magic_name__ : str = pipe(num_inference_steps=2 , generator=_A , output_type='numpy' , return_dict=_A )[0]
__magic_name__ : int = image[0, -3:, -3:, -1]
__magic_name__ : str = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
__magic_name__ : List[Any] = np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
@slow
@require_torch
class _lowerCamelCase ( unittest.TestCase ):
'''simple docstring'''
def __lowerCAmelCase ( self : List[Any] ) -> str:
__magic_name__ : Optional[int] = 'google/ncsnpp-celebahq-256'
__magic_name__ : List[str] = UNetaDModel.from_pretrained(_A )
__magic_name__ : int = KarrasVeScheduler()
__magic_name__ : str = KarrasVePipeline(unet=_A , scheduler=_A )
pipe.to(_A )
pipe.set_progress_bar_config(disable=_A )
__magic_name__ : Any = torch.manual_seed(0 )
__magic_name__ : Union[str, Any] = pipe(num_inference_steps=20 , generator=_A , output_type='numpy' ).images
__magic_name__ : List[str] = image[0, -3:, -3:, -1]
assert image.shape == (1, 256, 256, 3)
__magic_name__ : int = np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2 | 275 | 0 |
'''simple docstring'''
import requests
lowercase : List[str] = 'YOUR API KEY'
def lowerCAmelCase_ ( snake_case__ , snake_case__ = giphy_api_key ):
'''simple docstring'''
A : str = '''+'''.join(query.split() )
A : Optional[Any] = F'https://api.giphy.com/v1/gifs/search?q={formatted_query}&api_key={api_key}'
A : Any = requests.get(snake_case__ ).json()['''data''']
return [gif["url"] for gif in gifs]
if __name__ == "__main__":
print('\n'.join(get_gifs('space ship')))
| 3 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_tf_available,
is_torch_available,
)
lowerCamelCase_ : List[str] = {
"""configuration_speech_to_text""": ["""SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Speech2TextConfig"""],
"""processing_speech_to_text""": ["""Speech2TextProcessor"""],
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase_ : str = ["""Speech2TextTokenizer"""]
try:
if not is_speech_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase_ : Optional[Any] = ["""Speech2TextFeatureExtractor"""]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase_ : List[Any] = [
"""TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFSpeech2TextForConditionalGeneration""",
"""TFSpeech2TextModel""",
"""TFSpeech2TextPreTrainedModel""",
]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase_ : str = [
"""SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""Speech2TextForConditionalGeneration""",
"""Speech2TextModel""",
"""Speech2TextPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_speech_to_text import SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextConfig
from .processing_speech_to_text import SpeechaTextProcessor
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_speech_to_text import SpeechaTextTokenizer
try:
if not is_speech_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_speech_to_text import SpeechaTextFeatureExtractor
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_speech_to_text import (
TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFSpeechaTextForConditionalGeneration,
TFSpeechaTextModel,
TFSpeechaTextPreTrainedModel,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_speech_to_text import (
SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
SpeechaTextForConditionalGeneration,
SpeechaTextModel,
SpeechaTextPreTrainedModel,
)
else:
import sys
lowerCamelCase_ : Optional[Any] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__) | 81 | 0 |
'''simple docstring'''
from .constants import (
MODEL_NAME,
OPTIMIZER_NAME,
RNG_STATE_NAME,
SAFE_WEIGHTS_INDEX_NAME,
SAFE_WEIGHTS_NAME,
SCALER_NAME,
SCHEDULER_NAME,
TORCH_LAUNCH_PARAMS,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
)
from .dataclasses import (
BnbQuantizationConfig,
ComputeEnvironment,
CustomDtype,
DeepSpeedPlugin,
DistributedDataParallelKwargs,
DistributedType,
DynamoBackend,
FPaRecipeKwargs,
FullyShardedDataParallelPlugin,
GradientAccumulationPlugin,
GradScalerKwargs,
InitProcessGroupKwargs,
KwargsHandler,
LoggerType,
MegatronLMPlugin,
PrecisionType,
ProjectConfiguration,
RNGType,
SageMakerDistributedType,
TensorInformation,
TorchDynamoPlugin,
)
from .environment import get_int_from_env, parse_choice_from_env, parse_flag_from_env
from .imports import (
get_ccl_version,
is_abit_bnb_available,
is_abit_bnb_available,
is_aim_available,
is_bfaa_available,
is_bnb_available,
is_botoa_available,
is_ccl_available,
is_comet_ml_available,
is_datasets_available,
is_deepspeed_available,
is_fpa_available,
is_ipex_available,
is_megatron_lm_available,
is_mlflow_available,
is_mps_available,
is_npu_available,
is_rich_available,
is_safetensors_available,
is_sagemaker_available,
is_tensorboard_available,
is_tpu_available,
is_transformers_available,
is_wandb_available,
is_xpu_available,
)
from .modeling import (
check_device_map,
check_tied_parameters_in_config,
check_tied_parameters_on_same_device,
compute_module_sizes,
convert_file_size_to_int,
dtype_byte_size,
find_tied_parameters,
get_balanced_memory,
get_max_layer_size,
get_max_memory,
get_mixed_precision_context_manager,
id_tensor_storage,
infer_auto_device_map,
load_checkpoint_in_model,
load_offloaded_weights,
load_state_dict,
named_module_tensors,
retie_parameters,
set_module_tensor_to_device,
shard_checkpoint,
)
from .offload import (
OffloadedWeightsLoader,
PrefixedDataset,
extract_submodules_state_dict,
load_offloaded_weight,
offload_state_dict,
offload_weight,
save_offload_index,
)
from .operations import (
broadcast,
broadcast_object_list,
concatenate,
convert_outputs_to_fpaa,
convert_to_fpaa,
find_batch_size,
find_device,
gather,
gather_object,
get_data_structure,
honor_type,
initialize_tensors,
is_namedtuple,
is_tensor_information,
is_torch_tensor,
listify,
pad_across_processes,
recursively_apply,
reduce,
send_to_device,
slice_tensors,
)
from .versions import compare_versions, is_torch_version
if is_deepspeed_available():
from .deepspeed import (
DeepSpeedEngineWrapper,
DeepSpeedOptimizerWrapper,
DeepSpeedSchedulerWrapper,
DummyOptim,
DummyScheduler,
HfDeepSpeedConfig,
)
from .bnb import has_abit_bnb_layers, load_and_quantize_model
from .fsdp_utils import load_fsdp_model, load_fsdp_optimizer, save_fsdp_model, save_fsdp_optimizer
from .launch import (
PrepareForLaunch,
_filter_args,
prepare_deepspeed_cmd_env,
prepare_multi_gpu_env,
prepare_sagemager_args_inputs,
prepare_simple_launcher_cmd_env,
prepare_tpu,
)
from .megatron_lm import (
AbstractTrainStep,
BertTrainStep,
GPTTrainStep,
MegatronEngine,
MegatronLMDummyDataLoader,
MegatronLMDummyScheduler,
MegatronLMOptimizerWrapper,
MegatronLMSchedulerWrapper,
TaTrainStep,
avg_losses_across_data_parallel_group,
gather_across_data_parallel_groups,
)
from .megatron_lm import initialize as megatron_lm_initialize
from .megatron_lm import prepare_data_loader as megatron_lm_prepare_data_loader
from .megatron_lm import prepare_model as megatron_lm_prepare_model
from .megatron_lm import prepare_optimizer as megatron_lm_prepare_optimizer
from .megatron_lm import prepare_scheduler as megatron_lm_prepare_scheduler
from .memory import find_executable_batch_size, release_memory
from .other import (
extract_model_from_parallel,
get_pretty_name,
is_port_in_use,
merge_dicts,
patch_environment,
save,
wait_for_everyone,
write_basic_config,
)
from .random import set_seed, synchronize_rng_state, synchronize_rng_states
from .torch_xla import install_xla
from .tqdm import tqdm
from .transformer_engine import convert_model, has_transformer_engine_layers
| 21 | '''simple docstring'''
import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen
from ..table import array_cast
from ..utils.file_utils import is_local_path
from ..utils.py_utils import first_non_null_value, no_op_if_value_is_null, string_to_dict
if TYPE_CHECKING:
import PIL.Image
from .features import FeatureType
_lowercase : Optional[List[str]] = None
_lowercase : str = "<" if sys.byteorder == "little" else ">"
# Origin: https://github.com/python-pillow/Pillow/blob/698951e19e19972aeed56df686868f1329981c12/src/PIL/Image.py#L3126 minus "|i1" which values are not preserved correctly when saving and loading an image
_lowercase : Optional[int] = [
np.dtype("|b1"),
np.dtype("|u1"),
np.dtype("<u2"),
np.dtype(">u2"),
np.dtype("<i2"),
np.dtype(">i2"),
np.dtype("<u4"),
np.dtype(">u4"),
np.dtype("<i4"),
np.dtype(">i4"),
np.dtype("<f4"),
np.dtype(">f4"),
np.dtype("<f8"),
np.dtype(">f8"),
]
@dataclass
class __magic_name__ :
UpperCamelCase__ = True
UpperCamelCase__ = None
# Automatically constructed
UpperCamelCase__ = "PIL.Image.Image"
UpperCamelCase__ = pa.struct({'''bytes''': pa.binary(), '''path''': pa.string()})
UpperCamelCase__ = field(default='''Image''', init=_UpperCAmelCase, repr=_UpperCAmelCase)
def __call__( self : Tuple ):
return self.pa_type
def SCREAMING_SNAKE_CASE_ ( self : Tuple , lowercase_ : Union[str, bytes, dict, np.ndarray, "PIL.Image.Image"] ):
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("""To support encoding images, please install 'Pillow'.""" )
if isinstance(lowercase_ , lowercase_ ):
lowercase_ : int = np.array(lowercase_ )
if isinstance(lowercase_ , lowercase_ ):
return {"path": value, "bytes": None}
elif isinstance(lowercase_ , lowercase_ ):
return {"path": None, "bytes": value}
elif isinstance(lowercase_ , np.ndarray ):
# convert the image array to PNG/TIFF bytes
return encode_np_array(lowercase_ )
elif isinstance(lowercase_ , PIL.Image.Image ):
# convert the PIL image to bytes (default format is PNG/TIFF)
return encode_pil_image(lowercase_ )
elif value.get("""path""" ) is not None and os.path.isfile(value["""path"""] ):
# we set "bytes": None to not duplicate the data if they're already available locally
return {"bytes": None, "path": value.get("""path""" )}
elif value.get("""bytes""" ) is not None or value.get("""path""" ) is not None:
# store the image bytes, and path is used to infer the image format using the file extension
return {"bytes": value.get("""bytes""" ), "path": value.get("""path""" )}
else:
raise ValueError(
f'''An image sample should have one of \'path\' or \'bytes\' but they are missing or None in {value}.''' )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] , lowercase_ : dict , lowercase_ : List[str]=None ):
if not self.decode:
raise RuntimeError("""Decoding is disabled for this feature. Please use Image(decode=True) instead.""" )
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("""To support decoding images, please install 'Pillow'.""" )
if token_per_repo_id is None:
lowercase_ : Union[str, Any] = {}
lowercase_ , lowercase_ : List[Any] = value["""path"""], value["""bytes"""]
if bytes_ is None:
if path is None:
raise ValueError(f'''An image should have one of \'path\' or \'bytes\' but both are None in {value}.''' )
else:
if is_local_path(lowercase_ ):
lowercase_ : int = PIL.Image.open(lowercase_ )
else:
lowercase_ : str = path.split("""::""" )[-1]
try:
lowercase_ : Any = string_to_dict(lowercase_ , config.HUB_DATASETS_URL )["""repo_id"""]
lowercase_ : Optional[Any] = token_per_repo_id.get(lowercase_ )
except ValueError:
lowercase_ : str = None
with xopen(lowercase_ , """rb""" , use_auth_token=lowercase_ ) as f:
lowercase_ : Dict = BytesIO(f.read() )
lowercase_ : Optional[Any] = PIL.Image.open(bytes_ )
else:
lowercase_ : Any = PIL.Image.open(BytesIO(bytes_ ) )
image.load() # to avoid "Too many open files" errors
return image
def SCREAMING_SNAKE_CASE_ ( self : int ):
from .features import Value
return (
self
if self.decode
else {
"bytes": Value("""binary""" ),
"path": Value("""string""" ),
}
)
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] , lowercase_ : Union[pa.StringArray, pa.StructArray, pa.ListArray] ):
if pa.types.is_string(storage.type ):
lowercase_ : str = pa.array([None] * len(lowercase_ ) , type=pa.binary() )
lowercase_ : Any = pa.StructArray.from_arrays([bytes_array, storage] , ["""bytes""", """path"""] , mask=storage.is_null() )
elif pa.types.is_binary(storage.type ):
lowercase_ : str = pa.array([None] * len(lowercase_ ) , type=pa.string() )
lowercase_ : Any = pa.StructArray.from_arrays([storage, path_array] , ["""bytes""", """path"""] , mask=storage.is_null() )
elif pa.types.is_struct(storage.type ):
if storage.type.get_field_index("""bytes""" ) >= 0:
lowercase_ : Optional[int] = storage.field("""bytes""" )
else:
lowercase_ : Optional[Any] = pa.array([None] * len(lowercase_ ) , type=pa.binary() )
if storage.type.get_field_index("""path""" ) >= 0:
lowercase_ : Dict = storage.field("""path""" )
else:
lowercase_ : int = pa.array([None] * len(lowercase_ ) , type=pa.string() )
lowercase_ : Dict = pa.StructArray.from_arrays([bytes_array, path_array] , ["""bytes""", """path"""] , mask=storage.is_null() )
elif pa.types.is_list(storage.type ):
lowercase_ : Optional[int] = pa.array(
[encode_np_array(np.array(lowercase_ ) )["""bytes"""] if arr is not None else None for arr in storage.to_pylist()] , type=pa.binary() , )
lowercase_ : Tuple = pa.array([None] * len(lowercase_ ) , type=pa.string() )
lowercase_ : Tuple = pa.StructArray.from_arrays(
[bytes_array, path_array] , ["""bytes""", """path"""] , mask=bytes_array.is_null() )
return array_cast(lowercase_ , self.pa_type )
def SCREAMING_SNAKE_CASE_ ( self : Dict , lowercase_ : pa.StructArray ):
@no_op_if_value_is_null
def path_to_bytes(lowercase_ : Optional[Any] ):
with xopen(lowercase_ , """rb""" ) as f:
lowercase_ : int = f.read()
return bytes_
lowercase_ : Optional[Any] = pa.array(
[
(path_to_bytes(x["""path"""] ) if x["""bytes"""] is None else x["""bytes"""]) if x is not None else None
for x in storage.to_pylist()
] , type=pa.binary() , )
lowercase_ : Any = pa.array(
[os.path.basename(lowercase_ ) if path is not None else None for path in storage.field("""path""" ).to_pylist()] , type=pa.string() , )
lowercase_ : Dict = pa.StructArray.from_arrays([bytes_array, path_array] , ["""bytes""", """path"""] , mask=bytes_array.is_null() )
return array_cast(lowercase_ , self.pa_type )
def lowerCamelCase ( ) -> List[str]:
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("""To support encoding images, please install 'Pillow'.""" )
global _IMAGE_COMPRESSION_FORMATS
if _IMAGE_COMPRESSION_FORMATS is None:
PIL.Image.init()
lowercase_ : int = list(set(PIL.Image.OPEN.keys() ) & set(PIL.Image.SAVE.keys() ) )
return _IMAGE_COMPRESSION_FORMATS
def lowerCamelCase ( UpperCAmelCase__ : "PIL.Image.Image" ) -> bytes:
lowercase_ : Tuple = BytesIO()
if image.format in list_image_compression_formats():
lowercase_ : int = image.format
else:
lowercase_ : int = """PNG""" if image.mode in ["""1""", """L""", """LA""", """RGB""", """RGBA"""] else """TIFF"""
image.save(UpperCAmelCase__ , format=UpperCAmelCase__ )
return buffer.getvalue()
def lowerCamelCase ( UpperCAmelCase__ : "PIL.Image.Image" ) -> dict:
if hasattr(UpperCAmelCase__ , """filename""" ) and image.filename != "":
return {"path": image.filename, "bytes": None}
else:
return {"path": None, "bytes": image_to_bytes(UpperCAmelCase__ )}
def lowerCamelCase ( UpperCAmelCase__ : np.ndarray ) -> dict:
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("""To support encoding images, please install 'Pillow'.""" )
lowercase_ : List[Any] = array.dtype
lowercase_ : int = dtype.byteorder if dtype.byteorder != """=""" else _NATIVE_BYTEORDER
lowercase_ : Dict = dtype.kind
lowercase_ : List[Any] = dtype.itemsize
lowercase_ : Any = None
# Multi-channel array case (only np.dtype("|u1") is allowed)
if array.shape[2:]:
lowercase_ : int = np.dtype("""|u1""" )
if dtype_kind not in ["u", "i"]:
raise TypeError(
F'''Unsupported array dtype {dtype} for image encoding. Only {dest_dtype} is supported for multi-channel arrays.''' )
if dtype is not dest_dtype:
warnings.warn(F'''Downcasting array dtype {dtype} to {dest_dtype} to be compatible with \'Pillow\'''' )
# Exact match
elif dtype in _VALID_IMAGE_ARRAY_DTPYES:
lowercase_ : str = dtype
else: # Downcast the type within the kind (np.can_cast(from_type, to_type, casting="same_kind") doesn't behave as expected, so do it manually)
while dtype_itemsize >= 1:
lowercase_ : str = dtype_byteorder + dtype_kind + str(UpperCAmelCase__ )
lowercase_ : Optional[Any] = np.dtype(UpperCAmelCase__ )
if dest_dtype in _VALID_IMAGE_ARRAY_DTPYES:
warnings.warn(F'''Downcasting array dtype {dtype} to {dest_dtype} to be compatible with \'Pillow\'''' )
break
else:
dtype_itemsize //= 2
if dest_dtype is None:
raise TypeError(
F'''Cannot convert dtype {dtype} to a valid image dtype. Valid image dtypes: {_VALID_IMAGE_ARRAY_DTPYES}''' )
lowercase_ : Optional[int] = PIL.Image.fromarray(array.astype(UpperCAmelCase__ ) )
return {"path": None, "bytes": image_to_bytes(UpperCAmelCase__ )}
def lowerCamelCase ( UpperCAmelCase__ : Union[List[str], List[dict], List[np.ndarray], List["PIL.Image.Image"]] ) -> List[dict]:
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("""To support encoding images, please install 'Pillow'.""" )
if objs:
lowercase_ , lowercase_ : Dict = first_non_null_value(UpperCAmelCase__ )
if isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
return [{"path": obj, "bytes": None} if obj is not None else None for obj in objs]
if isinstance(UpperCAmelCase__ , np.ndarray ):
lowercase_ : Union[str, Any] = no_op_if_value_is_null(UpperCAmelCase__ )
return [obj_to_image_dict_func(UpperCAmelCase__ ) for obj in objs]
elif isinstance(UpperCAmelCase__ , PIL.Image.Image ):
lowercase_ : int = no_op_if_value_is_null(UpperCAmelCase__ )
return [obj_to_image_dict_func(UpperCAmelCase__ ) for obj in objs]
else:
return objs
else:
return objs
| 21 | 1 |
'''simple docstring'''
import json
import os
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase : str =logging.get_logger(__name__)
lowerCAmelCase : List[str] ={
'''vocab_file''': '''vocab.json''',
'''tokenizer_config_file''': '''tokenizer_config.json''',
'''merges_file''': '''merges.txt''',
}
lowerCAmelCase : List[Any] ={
'''vocab_file''': {
'''facebook/s2t-wav2vec2-large-en-de''': (
'''https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/vocab.json'''
),
},
'''tokenizer_config_file''': {
'''facebook/s2t-wav2vec2-large-en-de''': (
'''https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/tokenizer_config.json'''
),
},
'''merges_file''': {
'''facebook/s2t-wav2vec2-large-en-de''': (
'''https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/merges.txt'''
),
},
}
lowerCAmelCase : int ='''</w>'''
lowerCAmelCase : Union[str, Any] ='''@@ '''
def UpperCAmelCase_ ( __lowerCamelCase : Optional[int] ):
lowercase_ :List[str] = set()
lowercase_ :List[Any] = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
lowercase_ :int = char
return pairs
# Speech2Text2 has no max input length
lowerCAmelCase : List[str] ={'''facebook/s2t-wav2vec2-large-en-de''': 1_024}
class a_ ( _lowerCAmelCase ):
__A = VOCAB_FILES_NAMES
__A = PRETRAINED_VOCAB_FILES_MAP
__A = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__A = ["input_ids", "attention_mask"]
def __init__( self : Dict , lowercase : List[Any] , lowercase : Tuple="<s>" , lowercase : Union[str, Any]="<pad>" , lowercase : Optional[int]="</s>" , lowercase : Tuple="<unk>" , lowercase : int=False , lowercase : Tuple=None , **lowercase : Dict , ):
"""simple docstring"""
super().__init__(
unk_token=lowercase , bos_token=lowercase , eos_token=lowercase , pad_token=lowercase , do_lower_case=lowercase , **lowercase , )
lowercase_ :Any = do_lower_case
with open(lowercase , encoding="utf-8" ) as vocab_handle:
lowercase_ :Dict = json.load(lowercase )
lowercase_ :Tuple = {v: k for k, v in self.encoder.items()}
if merges_file is None:
logger.info(F'No merges files provided. {self.__class__.__name__} can only be used for decoding.' )
lowercase_ :Any = None
lowercase_ :int = None
else:
with open(lowercase , encoding="utf-8" ) as merges_handle:
lowercase_ :List[str] = merges_handle.read().split("\n" )[:-1]
lowercase_ :Optional[int] = [tuple(merge.split()[:2] ) for merge in merges]
lowercase_ :List[str] = dict(zip(lowercase , range(len(lowercase ) ) ) )
lowercase_ :str = {}
@property
def lowercase__ ( self : List[Any] ):
"""simple docstring"""
return len(self.decoder )
def lowercase__ ( self : int ):
"""simple docstring"""
return dict(self.encoder , **self.added_tokens_encoder )
def lowercase__ ( self : Union[str, Any] , lowercase : Tuple ):
"""simple docstring"""
lowercase_ :List[str] = tuple(token[:-1] ) + (token[-1] + BPE_TOKEN_MERGES,)
if token in self.cache:
return self.cache[token]
lowercase_ :Dict = get_pairs(lowercase )
if not pairs:
return token
while True:
lowercase_ :Any = min(lowercase , key=lambda lowercase : self.bpe_ranks.get(lowercase , float("inf" ) ) )
if bigram not in self.bpe_ranks:
break
lowercase_ , lowercase_ :Optional[int] = bigram
lowercase_ :Tuple = []
lowercase_ :Optional[int] = 0
while i < len(lowercase ):
try:
lowercase_ :List[Any] = word.index(lowercase , lowercase )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
lowercase_ :Union[str, Any] = j
if word[i] == first and i < len(lowercase ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
lowercase_ :Optional[Any] = tuple(lowercase )
lowercase_ :Tuple = new_word
if len(lowercase ) == 1:
break
else:
lowercase_ :Tuple = get_pairs(lowercase )
lowercase_ :Any = " ".join(lowercase )
if word == "\n " + BPE_TOKEN_MERGES:
lowercase_ :int = "\n" + BPE_TOKEN_MERGES
if word.endswith(lowercase ):
lowercase_ :int = word.replace(lowercase , "" )
lowercase_ :Dict = word.replace(" " , lowercase )
lowercase_ :int = word
return word
def lowercase__ ( self : Optional[int] , lowercase : List[str] ):
"""simple docstring"""
if self.bpe_ranks is None:
raise ValueError(
"This tokenizer was instantiated without a `merges.txt` file, so"
" that it can only be used for decoding, not for encoding."
"Make sure to provide `merges.txt` file at instantiation to enable "
"encoding." )
if self.do_lower_case:
lowercase_ :List[Any] = text.lower()
lowercase_ :Tuple = text.split()
lowercase_ :int = []
for token in text:
if token:
split_tokens.extend(list(self.bpe(lowercase ).split(" " ) ) )
return split_tokens
def lowercase__ ( self : Any , lowercase : str ):
"""simple docstring"""
return self.encoder.get(lowercase , self.encoder.get(self.unk_token ) )
def lowercase__ ( self : Tuple , lowercase : int ):
"""simple docstring"""
lowercase_ :Any = self.decoder.get(lowercase , self.unk_token )
return result
def lowercase__ ( self : int , lowercase : List[str] ):
"""simple docstring"""
lowercase_ :Optional[Any] = " ".join(lowercase )
# make sure @@ tokens are concatenated
lowercase_ :int = "".join(string.split(lowercase ) )
return string
def lowercase__ ( self : Tuple , lowercase : str , lowercase : Optional[str] = None ):
"""simple docstring"""
if not os.path.isdir(lowercase ):
logger.error(F'Vocabulary path ({save_directory}) should be a directory' )
return
lowercase_ :str = os.path.join(
lowercase , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
lowercase_ :List[Any] = os.path.join(
lowercase , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"] )
with open(lowercase , "w" , encoding="utf-8" ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=lowercase , ensure_ascii=lowercase ) + "\n" )
lowercase_ :Tuple = 0
if self.bpe_ranks is None:
return (vocab_file,)
with open(lowercase , "w" , encoding="utf-8" ) as writer:
for bpe_tokens, token_index in sorted(self.bpe_ranks.items() , key=lambda lowercase : kv[1] ):
if index != token_index:
logger.warning(
F'Saving vocabulary to {merges_file}: BPE merge indices are not consecutive.'
" Please check that the tokenizer is not corrupted!" )
lowercase_ :Optional[Any] = token_index
writer.write(" ".join(lowercase ) + "\n" )
index += 1
return (vocab_file, merges_file)
| 223 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase : Optional[Any] ={
'''configuration_nllb_moe''': [
'''NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''NllbMoeConfig''',
]
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase : Tuple =[
'''NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''NllbMoeForConditionalGeneration''',
'''NllbMoeModel''',
'''NllbMoePreTrainedModel''',
'''NllbMoeTop2Router''',
'''NllbMoeSparseMLP''',
]
if TYPE_CHECKING:
from .configuration_nllb_moe import (
NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP,
NllbMoeConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_nllb_moe import (
NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST,
NllbMoeForConditionalGeneration,
NllbMoeModel,
NllbMoePreTrainedModel,
NllbMoeSparseMLP,
NllbMoeTopaRouter,
)
else:
import sys
lowerCAmelCase : Tuple =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 223 | 1 |
"""simple docstring"""
import unittest
from parameterized import parameterized
from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
GPTNeoXForCausalLM,
GPTNeoXForQuestionAnswering,
GPTNeoXForSequenceClassification,
GPTNeoXForTokenClassification,
GPTNeoXModel,
)
class _lowercase :
"""simple docstring"""
def __init__( self : Dict , UpperCamelCase__ : str , UpperCamelCase__ : Dict=13 , UpperCamelCase__ : Tuple=7 , UpperCamelCase__ : Any=True , UpperCamelCase__ : Optional[Any]=True , UpperCamelCase__ : Tuple=True , UpperCamelCase__ : Optional[Any]=True , UpperCamelCase__ : Union[str, Any]=99 , UpperCamelCase__ : Dict=64 , UpperCamelCase__ : Optional[int]=5 , UpperCamelCase__ : Optional[int]=4 , UpperCamelCase__ : str=37 , UpperCamelCase__ : int="gelu" , UpperCamelCase__ : Optional[int]=0.1 , UpperCamelCase__ : int=0.1 , UpperCamelCase__ : Any=512 , UpperCamelCase__ : str=16 , UpperCamelCase__ : str=2 , UpperCamelCase__ : Optional[Any]=0.02 , UpperCamelCase__ : Optional[int]=3 , UpperCamelCase__ : Union[str, Any]=4 , UpperCamelCase__ : Optional[Any]=None , ) -> str:
'''simple docstring'''
__UpperCamelCase =parent
__UpperCamelCase =batch_size
__UpperCamelCase =seq_length
__UpperCamelCase =is_training
__UpperCamelCase =use_input_mask
__UpperCamelCase =use_token_type_ids
__UpperCamelCase =use_labels
__UpperCamelCase =vocab_size
__UpperCamelCase =hidden_size
__UpperCamelCase =num_hidden_layers
__UpperCamelCase =num_attention_heads
__UpperCamelCase =intermediate_size
__UpperCamelCase =hidden_act
__UpperCamelCase =hidden_dropout_prob
__UpperCamelCase =attention_probs_dropout_prob
__UpperCamelCase =max_position_embeddings
__UpperCamelCase =type_vocab_size
__UpperCamelCase =type_sequence_label_size
__UpperCamelCase =initializer_range
__UpperCamelCase =num_labels
__UpperCamelCase =num_choices
__UpperCamelCase =scope
__UpperCamelCase =vocab_size - 1
def UpperCAmelCase_ ( self : Union[str, Any] ) -> Union[str, Any]:
'''simple docstring'''
__UpperCamelCase =ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__UpperCamelCase =None
if self.use_input_mask:
__UpperCamelCase =random_attention_mask([self.batch_size, self.seq_length] )
__UpperCamelCase =None
if self.use_labels:
__UpperCamelCase =ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
__UpperCamelCase =self.get_config()
return config, input_ids, input_mask, token_labels
def UpperCAmelCase_ ( self : Dict ) -> List[Any]:
'''simple docstring'''
return GPTNeoXConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=UpperCamelCase__ , initializer_range=self.initializer_range , pad_token_id=self.pad_token_id , )
def UpperCAmelCase_ ( self : List[str] ) -> Optional[int]:
'''simple docstring'''
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase =self.prepare_config_and_inputs()
__UpperCamelCase =True
return config, input_ids, input_mask, token_labels
def UpperCAmelCase_ ( self : Union[str, Any] , UpperCamelCase__ : Dict , UpperCamelCase__ : Optional[int] , UpperCamelCase__ : int ) -> Any:
'''simple docstring'''
__UpperCamelCase =GPTNeoXModel(config=UpperCamelCase__ )
model.to(UpperCamelCase__ )
model.eval()
__UpperCamelCase =model(UpperCamelCase__ , attention_mask=UpperCamelCase__ )
__UpperCamelCase =model(UpperCamelCase__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def UpperCAmelCase_ ( self : Optional[int] , UpperCamelCase__ : Any , UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : List[Any] ) -> str:
'''simple docstring'''
__UpperCamelCase =True
__UpperCamelCase =GPTNeoXModel(UpperCamelCase__ )
model.to(UpperCamelCase__ )
model.eval()
__UpperCamelCase =model(UpperCamelCase__ , attention_mask=UpperCamelCase__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def UpperCAmelCase_ ( self : List[Any] , UpperCamelCase__ : Tuple , UpperCamelCase__ : Dict , UpperCamelCase__ : str , UpperCamelCase__ : Dict ) -> List[Any]:
'''simple docstring'''
__UpperCamelCase =GPTNeoXForCausalLM(config=UpperCamelCase__ )
model.to(UpperCamelCase__ )
model.eval()
__UpperCamelCase =model(UpperCamelCase__ , attention_mask=UpperCamelCase__ , labels=UpperCamelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def UpperCAmelCase_ ( self : int , UpperCamelCase__ : str , UpperCamelCase__ : int , UpperCamelCase__ : List[str] , UpperCamelCase__ : Tuple ) -> Tuple:
'''simple docstring'''
__UpperCamelCase =self.num_labels
__UpperCamelCase =GPTNeoXForQuestionAnswering(UpperCamelCase__ )
model.to(UpperCamelCase__ )
model.eval()
__UpperCamelCase =model(UpperCamelCase__ , attention_mask=UpperCamelCase__ )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def UpperCAmelCase_ ( self : Optional[Any] , UpperCamelCase__ : Dict , UpperCamelCase__ : Any , UpperCamelCase__ : Optional[int] , UpperCamelCase__ : int ) -> str:
'''simple docstring'''
__UpperCamelCase =self.num_labels
__UpperCamelCase =GPTNeoXForSequenceClassification(UpperCamelCase__ )
model.to(UpperCamelCase__ )
model.eval()
__UpperCamelCase =ids_tensor([self.batch_size] , self.type_sequence_label_size )
__UpperCamelCase =model(UpperCamelCase__ , attention_mask=UpperCamelCase__ , labels=UpperCamelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def UpperCAmelCase_ ( self : Union[str, Any] , UpperCamelCase__ : Optional[int] , UpperCamelCase__ : Any , UpperCamelCase__ : Any , UpperCamelCase__ : Tuple ) -> Optional[int]:
'''simple docstring'''
__UpperCamelCase =self.num_labels
__UpperCamelCase =GPTNeoXForTokenClassification(UpperCamelCase__ )
model.to(UpperCamelCase__ )
model.eval()
__UpperCamelCase =model(UpperCamelCase__ , attention_mask=UpperCamelCase__ , labels=UpperCamelCase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def UpperCAmelCase_ ( self : List[Any] , UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : Union[str, Any] , UpperCamelCase__ : List[str] ) -> Dict:
'''simple docstring'''
__UpperCamelCase =True
__UpperCamelCase =GPTNeoXForCausalLM(config=UpperCamelCase__ )
model.to(UpperCamelCase__ )
model.eval()
# first forward pass
__UpperCamelCase =model(UpperCamelCase__ , attention_mask=UpperCamelCase__ , use_cache=UpperCamelCase__ )
__UpperCamelCase =outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
__UpperCamelCase =ids_tensor((self.batch_size, 3) , config.vocab_size )
__UpperCamelCase =ids_tensor((self.batch_size, 3) , vocab_size=2 )
# append to next input_ids and
__UpperCamelCase =torch.cat([input_ids, next_tokens] , dim=-1 )
__UpperCamelCase =torch.cat([input_mask, next_mask] , dim=-1 )
__UpperCamelCase =model(UpperCamelCase__ , attention_mask=UpperCamelCase__ , output_hidden_states=UpperCamelCase__ )
__UpperCamelCase =output_from_no_past['''hidden_states'''][0]
__UpperCamelCase =model(
UpperCamelCase__ , attention_mask=UpperCamelCase__ , past_key_values=UpperCamelCase__ , output_hidden_states=UpperCamelCase__ , )['''hidden_states'''][0]
# select random slice
__UpperCamelCase =ids_tensor((1,) , output_from_past.shape[-1] ).item()
__UpperCamelCase =output_from_no_past[:, -3:, random_slice_idx].detach()
__UpperCamelCase =output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(UpperCamelCase__ , UpperCamelCase__ , atol=1E-3 ) )
def UpperCAmelCase_ ( self : Any ) -> Optional[Any]:
'''simple docstring'''
__UpperCamelCase =self.prepare_config_and_inputs()
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase =config_and_inputs
__UpperCamelCase ={'''input_ids''': input_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_torch
class _lowercase ( __a , __a , __a , unittest.TestCase ):
"""simple docstring"""
lowercase__ = (
(
GPTNeoXModel,
GPTNeoXForCausalLM,
GPTNeoXForQuestionAnswering,
GPTNeoXForSequenceClassification,
GPTNeoXForTokenClassification,
)
if is_torch_available()
else ()
)
lowercase__ = (GPTNeoXForCausalLM,) if is_torch_available() else ()
lowercase__ = (
{
'''feature-extraction''': GPTNeoXModel,
'''question-answering''': GPTNeoXForQuestionAnswering,
'''text-classification''': GPTNeoXForSequenceClassification,
'''text-generation''': GPTNeoXForCausalLM,
'''token-classification''': GPTNeoXForTokenClassification,
'''zero-shot''': GPTNeoXForSequenceClassification,
}
if is_torch_available()
else {}
)
lowercase__ = False
lowercase__ = False
lowercase__ = False
lowercase__ = False
def UpperCAmelCase_ ( self : int ) -> Optional[int]:
'''simple docstring'''
__UpperCamelCase =GPTNeoXModelTester(self )
__UpperCamelCase =ConfigTester(self , config_class=UpperCamelCase__ , hidden_size=64 , num_attention_heads=8 )
def UpperCAmelCase_ ( self : Union[str, Any] ) -> List[Any]:
'''simple docstring'''
self.config_tester.run_common_tests()
def UpperCAmelCase_ ( self : Dict ) -> List[Any]:
'''simple docstring'''
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
def UpperCAmelCase_ ( self : int ) -> Union[str, Any]:
'''simple docstring'''
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase =self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_model_as_decoder(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
def UpperCAmelCase_ ( self : Union[str, Any] ) -> List[str]:
'''simple docstring'''
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase =self.model_tester.prepare_config_and_inputs_for_decoder()
__UpperCamelCase =None
self.model_tester.create_and_check_model_as_decoder(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
def UpperCAmelCase_ ( self : Any ) -> Optional[int]:
'''simple docstring'''
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_past_large_inputs(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
def UpperCAmelCase_ ( self : List[Any] ) -> Tuple:
'''simple docstring'''
__UpperCamelCase =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_causal_lm(*UpperCamelCase__ )
def UpperCAmelCase_ ( self : List[str] ) -> Optional[Any]:
'''simple docstring'''
__UpperCamelCase =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*UpperCamelCase__ )
def UpperCAmelCase_ ( self : Union[str, Any] ) -> Optional[int]:
'''simple docstring'''
__UpperCamelCase =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*UpperCamelCase__ )
def UpperCAmelCase_ ( self : str ) -> Any:
'''simple docstring'''
__UpperCamelCase =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*UpperCamelCase__ )
@unittest.skip(reason='''Feed forward chunking is not implemented''' )
def UpperCAmelCase_ ( self : Union[str, Any] ) -> Optional[int]:
'''simple docstring'''
pass
@parameterized.expand([('''linear''',), ('''dynamic''',)] )
def UpperCAmelCase_ ( self : Union[str, Any] , UpperCamelCase__ : Optional[int] ) -> List[Any]:
'''simple docstring'''
__UpperCamelCase , __UpperCamelCase =self.model_tester.prepare_config_and_inputs_for_common()
__UpperCamelCase =ids_tensor([1, 10] , config.vocab_size )
__UpperCamelCase =ids_tensor([1, int(config.max_position_embeddings * 1.5 )] , config.vocab_size )
set_seed(42 ) # Fixed seed at init time so the two models get the same random weights
__UpperCamelCase =GPTNeoXModel(UpperCamelCase__ )
original_model.to(UpperCamelCase__ )
original_model.eval()
__UpperCamelCase =original_model(UpperCamelCase__ ).last_hidden_state
__UpperCamelCase =original_model(UpperCamelCase__ ).last_hidden_state
set_seed(42 ) # Fixed seed at init time so the two models get the same random weights
__UpperCamelCase ={'''type''': scaling_type, '''factor''': 10.0}
__UpperCamelCase =GPTNeoXModel(UpperCamelCase__ )
scaled_model.to(UpperCamelCase__ )
scaled_model.eval()
__UpperCamelCase =scaled_model(UpperCamelCase__ ).last_hidden_state
__UpperCamelCase =scaled_model(UpperCamelCase__ ).last_hidden_state
# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
# maximum sequence length, so the outputs for the short input should match.
if scaling_type == "dynamic":
self.assertTrue(torch.allclose(UpperCamelCase__ , UpperCamelCase__ , atol=1E-5 ) )
else:
self.assertFalse(torch.allclose(UpperCamelCase__ , UpperCamelCase__ , atol=1E-5 ) )
# The output should be different for long inputs
self.assertFalse(torch.allclose(UpperCamelCase__ , UpperCamelCase__ , atol=1E-5 ) )
@require_torch
class _lowercase ( unittest.TestCase ):
"""simple docstring"""
@slow
def UpperCAmelCase_ ( self : Optional[Any] ) -> Optional[int]:
'''simple docstring'''
__UpperCamelCase =AutoTokenizer.from_pretrained('''EleutherAI/pythia-410m-deduped''' )
for checkpointing in [True, False]:
__UpperCamelCase =GPTNeoXForCausalLM.from_pretrained('''EleutherAI/pythia-410m-deduped''' )
if checkpointing:
model.gradient_checkpointing_enable()
else:
model.gradient_checkpointing_disable()
model.to(UpperCamelCase__ )
__UpperCamelCase =tokenizer('''My favorite food is''' , return_tensors='''pt''' ).to(UpperCamelCase__ )
# The hub repo. is updated on 2023-04-04, resulting in poor outputs.
# See: https://github.com/huggingface/transformers/pull/24193
__UpperCamelCase ='''My favorite food is a good old-fashioned, old-fashioned, old-fashioned.\n\nI\'m not sure'''
__UpperCamelCase =model.generate(**UpperCamelCase__ , do_sample=UpperCamelCase__ , max_new_tokens=20 )
__UpperCamelCase =tokenizer.batch_decode(UpperCamelCase__ )[0]
self.assertEqual(UpperCamelCase__ , UpperCamelCase__ )
| 85 | """simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path
import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized
from transformers import AutoProcessor
from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.testing_utils import require_pyctcdecode, require_torch, require_torchaudio, slow
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_pyctcdecode_available, is_torch_available
from ..wavaveca.test_feature_extraction_wavaveca import floats_list
if is_pyctcdecode_available():
from huggingface_hub import snapshot_download
from pyctcdecode import BeamSearchDecoderCTC
from transformers.models.wavaveca_with_lm import WavaVecaProcessorWithLM
from transformers.models.wavaveca_with_lm.processing_wavaveca_with_lm import WavaVecaDecoderWithLMOutput
if is_torch_available():
from transformers import WavaVecaForCTC
@require_pyctcdecode
class _lowercase ( unittest.TestCase ):
"""simple docstring"""
def UpperCAmelCase_ ( self : int ) -> int:
'''simple docstring'''
__UpperCamelCase ='''| <pad> <unk> <s> </s> a b c d e f g h i j k'''.split()
__UpperCamelCase =dict(zip(UpperCamelCase__ , range(len(UpperCamelCase__ ) ) ) )
__UpperCamelCase ={
'''unk_token''': '''<unk>''',
'''bos_token''': '''<s>''',
'''eos_token''': '''</s>''',
}
__UpperCamelCase ={
'''feature_size''': 1,
'''padding_value''': 0.0,
'''sampling_rate''': 16000,
'''return_attention_mask''': False,
'''do_normalize''': True,
}
__UpperCamelCase =tempfile.mkdtemp()
__UpperCamelCase =os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
__UpperCamelCase =os.path.join(self.tmpdirname , UpperCamelCase__ )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write(json.dumps(UpperCamelCase__ ) + '''\n''' )
with open(self.feature_extraction_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write(json.dumps(UpperCamelCase__ ) + '''\n''' )
# load decoder from hub
__UpperCamelCase ='''hf-internal-testing/ngram-beam-search-decoder'''
def UpperCAmelCase_ ( self : Tuple , **UpperCamelCase__ : Tuple ) -> List[str]:
'''simple docstring'''
__UpperCamelCase =self.add_kwargs_tokens_map.copy()
kwargs.update(UpperCamelCase__ )
return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname , **UpperCamelCase__ )
def UpperCAmelCase_ ( self : Union[str, Any] , **UpperCamelCase__ : List[Any] ) -> Any:
'''simple docstring'''
return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname , **UpperCamelCase__ )
def UpperCAmelCase_ ( self : List[Any] , **UpperCamelCase__ : Union[str, Any] ) -> str:
'''simple docstring'''
return BeamSearchDecoderCTC.load_from_hf_hub(self.decoder_name , **UpperCamelCase__ )
def UpperCAmelCase_ ( self : Union[str, Any] ) -> int:
'''simple docstring'''
shutil.rmtree(self.tmpdirname )
def UpperCAmelCase_ ( self : Tuple ) -> Optional[int]:
'''simple docstring'''
__UpperCamelCase =self.get_tokenizer()
__UpperCamelCase =self.get_feature_extractor()
__UpperCamelCase =self.get_decoder()
__UpperCamelCase =WavaVecaProcessorWithLM(tokenizer=UpperCamelCase__ , feature_extractor=UpperCamelCase__ , decoder=UpperCamelCase__ )
processor.save_pretrained(self.tmpdirname )
__UpperCamelCase =WavaVecaProcessorWithLM.from_pretrained(self.tmpdirname )
# tokenizer
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer.get_vocab() )
self.assertIsInstance(processor.tokenizer , UpperCamelCase__ )
# feature extractor
self.assertEqual(processor.feature_extractor.to_json_string() , feature_extractor.to_json_string() )
self.assertIsInstance(processor.feature_extractor , UpperCamelCase__ )
# decoder
self.assertEqual(processor.decoder._alphabet.labels , decoder._alphabet.labels )
self.assertEqual(
processor.decoder.model_container[decoder._model_key]._unigram_set , decoder.model_container[decoder._model_key]._unigram_set , )
self.assertIsInstance(processor.decoder , UpperCamelCase__ )
def UpperCAmelCase_ ( self : Optional[int] ) -> str:
'''simple docstring'''
__UpperCamelCase =WavaVecaProcessorWithLM(
tokenizer=self.get_tokenizer() , feature_extractor=self.get_feature_extractor() , decoder=self.get_decoder() )
processor.save_pretrained(self.tmpdirname )
# make sure that error is thrown when decoder alphabet doesn't match
__UpperCamelCase =WavaVecaProcessorWithLM.from_pretrained(
self.tmpdirname , alpha=5.0 , beta=3.0 , score_boundary=-7.0 , unk_score_offset=3 )
# decoder
self.assertEqual(processor.language_model.alpha , 5.0 )
self.assertEqual(processor.language_model.beta , 3.0 )
self.assertEqual(processor.language_model.score_boundary , -7.0 )
self.assertEqual(processor.language_model.unk_score_offset , 3 )
def UpperCAmelCase_ ( self : Optional[int] ) -> List[str]:
'''simple docstring'''
__UpperCamelCase =self.get_tokenizer()
# add token to trigger raise
tokenizer.add_tokens(['''xx'''] )
with self.assertRaisesRegex(UpperCamelCase__ , '''include''' ):
WavaVecaProcessorWithLM(
tokenizer=UpperCamelCase__ , feature_extractor=self.get_feature_extractor() , decoder=self.get_decoder() )
def UpperCAmelCase_ ( self : List[Any] ) -> Dict:
'''simple docstring'''
__UpperCamelCase =self.get_feature_extractor()
__UpperCamelCase =self.get_tokenizer()
__UpperCamelCase =self.get_decoder()
__UpperCamelCase =WavaVecaProcessorWithLM(tokenizer=UpperCamelCase__ , feature_extractor=UpperCamelCase__ , decoder=UpperCamelCase__ )
__UpperCamelCase =floats_list((3, 1000) )
__UpperCamelCase =feature_extractor(UpperCamelCase__ , return_tensors='''np''' )
__UpperCamelCase =processor(UpperCamelCase__ , return_tensors='''np''' )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1E-2 )
def UpperCAmelCase_ ( self : List[str] ) -> Dict:
'''simple docstring'''
__UpperCamelCase =self.get_feature_extractor()
__UpperCamelCase =self.get_tokenizer()
__UpperCamelCase =self.get_decoder()
__UpperCamelCase =WavaVecaProcessorWithLM(tokenizer=UpperCamelCase__ , feature_extractor=UpperCamelCase__ , decoder=UpperCamelCase__ )
__UpperCamelCase ='''This is a test string'''
__UpperCamelCase =processor(text=UpperCamelCase__ )
__UpperCamelCase =tokenizer(UpperCamelCase__ )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def UpperCAmelCase_ ( self : Union[str, Any] , UpperCamelCase__ : List[str]=(2, 10, 16) , UpperCamelCase__ : Union[str, Any]=77 ) -> int:
'''simple docstring'''
np.random.seed(UpperCamelCase__ )
return np.random.rand(*UpperCamelCase__ )
def UpperCAmelCase_ ( self : int ) -> Union[str, Any]:
'''simple docstring'''
__UpperCamelCase =self.get_feature_extractor()
__UpperCamelCase =self.get_tokenizer()
__UpperCamelCase =self.get_decoder()
__UpperCamelCase =WavaVecaProcessorWithLM(tokenizer=UpperCamelCase__ , feature_extractor=UpperCamelCase__ , decoder=UpperCamelCase__ )
__UpperCamelCase =self._get_dummy_logits(shape=(10, 16) , seed=13 )
__UpperCamelCase =processor.decode(UpperCamelCase__ )
__UpperCamelCase =decoder.decode_beams(UpperCamelCase__ )[0]
self.assertEqual(decoded_decoder[0] , decoded_processor.text )
self.assertEqual('''</s> <s> </s>''' , decoded_processor.text )
self.assertEqual(decoded_decoder[-2] , decoded_processor.logit_score )
self.assertEqual(decoded_decoder[-1] , decoded_processor.lm_score )
@parameterized.expand([[None], ['''fork'''], ['''spawn''']] )
def UpperCAmelCase_ ( self : Dict , UpperCamelCase__ : Optional[Any] ) -> List[str]:
'''simple docstring'''
__UpperCamelCase =self.get_feature_extractor()
__UpperCamelCase =self.get_tokenizer()
__UpperCamelCase =self.get_decoder()
__UpperCamelCase =WavaVecaProcessorWithLM(tokenizer=UpperCamelCase__ , feature_extractor=UpperCamelCase__ , decoder=UpperCamelCase__ )
__UpperCamelCase =self._get_dummy_logits()
# note: pool should be instantiated *after* Wav2Vec2ProcessorWithLM.
# otherwise, the LM won't be available to the pool's sub-processes.
# manual logic used to allow parameterized test for both pool=None and pool=Pool(...)
if pool_context is None:
__UpperCamelCase =processor.batch_decode(UpperCamelCase__ )
else:
with get_context(UpperCamelCase__ ).Pool() as pool:
__UpperCamelCase =processor.batch_decode(UpperCamelCase__ , UpperCamelCase__ )
__UpperCamelCase =list(UpperCamelCase__ )
with get_context('''fork''' ).Pool() as p:
__UpperCamelCase =decoder.decode_beams_batch(UpperCamelCase__ , UpperCamelCase__ )
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase =[], [], []
for beams in decoded_beams:
texts_decoder.append(beams[0][0] )
logit_scores_decoder.append(beams[0][-2] )
lm_scores_decoder.append(beams[0][-1] )
self.assertListEqual(UpperCamelCase__ , decoded_processor.text )
self.assertListEqual(['''<s> <s> </s>''', '''<s> <s> <s>'''] , decoded_processor.text )
self.assertListEqual(UpperCamelCase__ , decoded_processor.logit_score )
self.assertListEqual(UpperCamelCase__ , decoded_processor.lm_score )
def UpperCAmelCase_ ( self : Dict ) -> List[str]:
'''simple docstring'''
__UpperCamelCase =self.get_feature_extractor()
__UpperCamelCase =self.get_tokenizer()
__UpperCamelCase =self.get_decoder()
__UpperCamelCase =WavaVecaProcessorWithLM(tokenizer=UpperCamelCase__ , feature_extractor=UpperCamelCase__ , decoder=UpperCamelCase__ )
__UpperCamelCase =self._get_dummy_logits()
__UpperCamelCase =15
__UpperCamelCase =-20.0
__UpperCamelCase =-4.0
__UpperCamelCase =processor.batch_decode(
UpperCamelCase__ , beam_width=UpperCamelCase__ , beam_prune_logp=UpperCamelCase__ , token_min_logp=UpperCamelCase__ , )
__UpperCamelCase =decoded_processor_out.text
__UpperCamelCase =list(UpperCamelCase__ )
with get_context('''fork''' ).Pool() as pool:
__UpperCamelCase =decoder.decode_beams_batch(
UpperCamelCase__ , UpperCamelCase__ , beam_width=UpperCamelCase__ , beam_prune_logp=UpperCamelCase__ , token_min_logp=UpperCamelCase__ , )
__UpperCamelCase =[d[0][0] for d in decoded_decoder_out]
__UpperCamelCase =[d[0][2] for d in decoded_decoder_out]
__UpperCamelCase =[d[0][3] for d in decoded_decoder_out]
self.assertListEqual(UpperCamelCase__ , UpperCamelCase__ )
self.assertListEqual(['''</s> <s> <s>''', '''<s> <s> <s>'''] , UpperCamelCase__ )
self.assertTrue(np.array_equal(UpperCamelCase__ , decoded_processor_out.logit_score ) )
self.assertTrue(np.allclose([-20.0_54, -18.4_47] , UpperCamelCase__ , atol=1E-3 ) )
self.assertTrue(np.array_equal(UpperCamelCase__ , decoded_processor_out.lm_score ) )
self.assertTrue(np.allclose([-15.5_54, -13.94_74] , UpperCamelCase__ , atol=1E-3 ) )
def UpperCAmelCase_ ( self : Any ) -> Optional[Any]:
'''simple docstring'''
__UpperCamelCase =self.get_feature_extractor()
__UpperCamelCase =self.get_tokenizer()
__UpperCamelCase =self.get_decoder()
__UpperCamelCase =WavaVecaProcessorWithLM(tokenizer=UpperCamelCase__ , feature_extractor=UpperCamelCase__ , decoder=UpperCamelCase__ )
__UpperCamelCase =self._get_dummy_logits()
__UpperCamelCase =2.0
__UpperCamelCase =5.0
__UpperCamelCase =-20.0
__UpperCamelCase =True
__UpperCamelCase =processor.batch_decode(
UpperCamelCase__ , alpha=UpperCamelCase__ , beta=UpperCamelCase__ , unk_score_offset=UpperCamelCase__ , lm_score_boundary=UpperCamelCase__ , )
__UpperCamelCase =decoded_processor_out.text
__UpperCamelCase =list(UpperCamelCase__ )
decoder.reset_params(
alpha=UpperCamelCase__ , beta=UpperCamelCase__ , unk_score_offset=UpperCamelCase__ , lm_score_boundary=UpperCamelCase__ , )
with get_context('''fork''' ).Pool() as pool:
__UpperCamelCase =decoder.decode_beams_batch(
UpperCamelCase__ , UpperCamelCase__ , )
__UpperCamelCase =[d[0][0] for d in decoded_decoder_out]
self.assertListEqual(UpperCamelCase__ , UpperCamelCase__ )
self.assertListEqual(['''<s> </s> <s> </s> </s>''', '''</s> </s> <s> </s> </s>'''] , UpperCamelCase__ )
__UpperCamelCase =processor.decoder.model_container[processor.decoder._model_key]
self.assertEqual(lm_model.alpha , 2.0 )
self.assertEqual(lm_model.beta , 5.0 )
self.assertEqual(lm_model.unk_score_offset , -20.0 )
self.assertEqual(lm_model.score_boundary , UpperCamelCase__ )
def UpperCAmelCase_ ( self : Dict ) -> Optional[int]:
'''simple docstring'''
__UpperCamelCase =WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm''' )
__UpperCamelCase =processor.decoder.model_container[processor.decoder._model_key]
__UpperCamelCase =Path(language_model._kenlm_model.path.decode('''utf-8''' ) ).parent.parent.absolute()
__UpperCamelCase =os.listdir(UpperCamelCase__ )
__UpperCamelCase =['''alphabet.json''', '''language_model''']
downloaded_decoder_files.sort()
expected_decoder_files.sort()
# test that only decoder relevant files from
# https://huggingface.co/hf-internal-testing/processor_with_lm/tree/main
# are downloaded and none of the rest (e.g. README.md, ...)
self.assertListEqual(UpperCamelCase__ , UpperCamelCase__ )
def UpperCAmelCase_ ( self : List[str] ) -> Optional[Any]:
'''simple docstring'''
__UpperCamelCase =snapshot_download('''hf-internal-testing/processor_with_lm''' )
__UpperCamelCase =WavaVecaProcessorWithLM.from_pretrained(UpperCamelCase__ )
__UpperCamelCase =processor.decoder.model_container[processor.decoder._model_key]
__UpperCamelCase =Path(language_model._kenlm_model.path.decode('''utf-8''' ) ).parent.parent.absolute()
__UpperCamelCase =os.listdir(UpperCamelCase__ )
__UpperCamelCase =os.listdir(UpperCamelCase__ )
local_decoder_files.sort()
expected_decoder_files.sort()
# test that both decoder form hub and local files in cache are the same
self.assertListEqual(UpperCamelCase__ , UpperCamelCase__ )
def UpperCAmelCase_ ( self : Optional[int] ) -> List[str]:
'''simple docstring'''
__UpperCamelCase =WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm''' )
__UpperCamelCase =AutoProcessor.from_pretrained('''hf-internal-testing/processor_with_lm''' )
__UpperCamelCase =floats_list((3, 1000) )
__UpperCamelCase =processor_wavaveca(UpperCamelCase__ , return_tensors='''np''' )
__UpperCamelCase =processor_auto(UpperCamelCase__ , return_tensors='''np''' )
for key in input_wavaveca.keys():
self.assertAlmostEqual(input_wavaveca[key].sum() , input_auto[key].sum() , delta=1E-2 )
__UpperCamelCase =self._get_dummy_logits()
__UpperCamelCase =processor_wavaveca.batch_decode(UpperCamelCase__ )
__UpperCamelCase =processor_auto.batch_decode(UpperCamelCase__ )
self.assertListEqual(decoded_wavaveca.text , decoded_auto.text )
def UpperCAmelCase_ ( self : List[Any] ) -> int:
'''simple docstring'''
__UpperCamelCase =self.get_feature_extractor()
__UpperCamelCase =self.get_tokenizer()
__UpperCamelCase =self.get_decoder()
__UpperCamelCase =WavaVecaProcessorWithLM(tokenizer=UpperCamelCase__ , feature_extractor=UpperCamelCase__ , decoder=UpperCamelCase__ )
self.assertListEqual(
processor.model_input_names , feature_extractor.model_input_names , msg='''`processor` and `feature_extractor` model input names do not match''' , )
@staticmethod
def UpperCAmelCase_ ( UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : Optional[Any] ) -> int:
'''simple docstring'''
__UpperCamelCase =[d[key] for d in offsets]
return retrieved_list
def UpperCAmelCase_ ( self : Dict ) -> List[str]:
'''simple docstring'''
__UpperCamelCase =WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm''' )
__UpperCamelCase =self._get_dummy_logits()[0]
__UpperCamelCase =processor.decode(UpperCamelCase__ , output_word_offsets=UpperCamelCase__ )
# check Wav2Vec2CTCTokenizerOutput keys for word
self.assertEqual(len(outputs.keys() ) , 4 )
self.assertTrue('''text''' in outputs )
self.assertTrue('''word_offsets''' in outputs )
self.assertTrue(isinstance(UpperCamelCase__ , UpperCamelCase__ ) )
self.assertEqual(''' '''.join(self.get_from_offsets(outputs['''word_offsets'''] , '''word''' ) ) , outputs.text )
self.assertListEqual(self.get_from_offsets(outputs['''word_offsets'''] , '''word''' ) , ['''<s>''', '''<s>''', '''</s>'''] )
self.assertListEqual(self.get_from_offsets(outputs['''word_offsets'''] , '''start_offset''' ) , [0, 2, 4] )
self.assertListEqual(self.get_from_offsets(outputs['''word_offsets'''] , '''end_offset''' ) , [1, 3, 5] )
def UpperCAmelCase_ ( self : List[Any] ) -> Optional[int]:
'''simple docstring'''
__UpperCamelCase =WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm''' )
__UpperCamelCase =self._get_dummy_logits()
__UpperCamelCase =processor.batch_decode(UpperCamelCase__ , output_word_offsets=UpperCamelCase__ )
# check Wav2Vec2CTCTokenizerOutput keys for word
self.assertEqual(len(outputs.keys() ) , 4 )
self.assertTrue('''text''' in outputs )
self.assertTrue('''word_offsets''' in outputs )
self.assertTrue(isinstance(UpperCamelCase__ , UpperCamelCase__ ) )
self.assertListEqual(
[''' '''.join(self.get_from_offsets(UpperCamelCase__ , '''word''' ) ) for o in outputs['''word_offsets''']] , outputs.text )
self.assertListEqual(self.get_from_offsets(outputs['''word_offsets'''][0] , '''word''' ) , ['''<s>''', '''<s>''', '''</s>'''] )
self.assertListEqual(self.get_from_offsets(outputs['''word_offsets'''][0] , '''start_offset''' ) , [0, 2, 4] )
self.assertListEqual(self.get_from_offsets(outputs['''word_offsets'''][0] , '''end_offset''' ) , [1, 3, 5] )
@slow
@require_torch
@require_torchaudio
def UpperCAmelCase_ ( self : Optional[int] ) -> Tuple:
'''simple docstring'''
import torch
__UpperCamelCase =load_dataset('''common_voice''' , '''en''' , split='''train''' , streaming=UpperCamelCase__ )
__UpperCamelCase =ds.cast_column('''audio''' , datasets.Audio(sampling_rate=16000 ) )
__UpperCamelCase =iter(UpperCamelCase__ )
__UpperCamelCase =next(UpperCamelCase__ )
__UpperCamelCase =AutoProcessor.from_pretrained('''patrickvonplaten/wav2vec2-base-100h-with-lm''' )
__UpperCamelCase =WavaVecaForCTC.from_pretrained('''patrickvonplaten/wav2vec2-base-100h-with-lm''' )
# compare to filename `common_voice_en_100038.mp3` of dataset viewer on https://huggingface.co/datasets/common_voice/viewer/en/train
__UpperCamelCase =processor(sample['''audio''']['''array'''] , return_tensors='''pt''' ).input_values
with torch.no_grad():
__UpperCamelCase =model(UpperCamelCase__ ).logits.cpu().numpy()
__UpperCamelCase =processor.decode(logits[0] , output_word_offsets=UpperCamelCase__ )
__UpperCamelCase =model.config.inputs_to_logits_ratio / processor.feature_extractor.sampling_rate
__UpperCamelCase =[
{
'''start_time''': d['''start_offset'''] * time_offset,
'''end_time''': d['''end_offset'''] * time_offset,
'''word''': d['''word'''],
}
for d in output['''word_offsets''']
]
__UpperCamelCase ='''WHY DOES MILISANDRA LOOK LIKE SHE WANTS TO CONSUME JOHN SNOW ON THE RIVER AT THE WALL'''
# output words
self.assertEqual(''' '''.join(self.get_from_offsets(UpperCamelCase__ , '''word''' ) ) , UpperCamelCase__ )
self.assertEqual(''' '''.join(self.get_from_offsets(UpperCamelCase__ , '''word''' ) ) , output.text )
# output times
__UpperCamelCase =torch.tensor(self.get_from_offsets(UpperCamelCase__ , '''start_time''' ) )
__UpperCamelCase =torch.tensor(self.get_from_offsets(UpperCamelCase__ , '''end_time''' ) )
# fmt: off
__UpperCamelCase =torch.tensor([1.41_99, 1.65_99, 2.25_99, 3.0, 3.24, 3.59_99, 3.79_99, 4.09_99, 4.26, 4.94, 5.28, 5.65_99, 5.78, 5.94, 6.32, 6.53_99, 6.65_99] )
__UpperCamelCase =torch.tensor([1.53_99, 1.89_99, 2.9, 3.16, 3.53_99, 3.72, 4.01_99, 4.17_99, 4.76, 5.15_99, 5.55_99, 5.69_99, 5.86, 6.19_99, 6.38, 6.61_99, 6.94] )
# fmt: on
self.assertTrue(torch.allclose(UpperCamelCase__ , UpperCamelCase__ , atol=0.01 ) )
self.assertTrue(torch.allclose(UpperCamelCase__ , UpperCamelCase__ , atol=0.01 ) )
| 85 | 1 |
"""simple docstring"""
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,
BertEmbeddings,
BertLayer,
BertPooler,
BertPreTrainedModel,
)
def _lowerCamelCase( a ):
__a = torch.exp(a )
__a = torch.sum(a , dim=1 ) # sum of exp(x_i)
__a = torch.sum(x * exp_x , dim=1 ) # sum of x_i * exp(x_i)
return torch.log(a ) - B / A
class snake_case__ ( nn.Module ):
def __init__( self , lowerCamelCase ):
super().__init__()
__a = config.output_attentions
__a = config.output_hidden_states
__a = nn.ModuleList([BertLayer(lowerCamelCase ) for _ in range(config.num_hidden_layers )] )
__a = nn.ModuleList([BertHighway(lowerCamelCase ) for _ in range(config.num_hidden_layers )] )
__a = [-1 for _ in range(config.num_hidden_layers )]
def a__ ( self , lowerCamelCase ):
if (type(lowerCamelCase ) is float) or (type(lowerCamelCase ) is int):
for i in range(len(self.early_exit_entropy ) ):
__a = x
else:
__a = x
def a__ ( self , lowerCamelCase ):
__a = pooler.state_dict()
for highway in self.highway:
for name, param in highway.pooler.state_dict().items():
param.copy_(loaded_model[name] )
def a__ ( self , lowerCamelCase , lowerCamelCase=None , lowerCamelCase=None , lowerCamelCase=None , lowerCamelCase=None , ):
__a = ()
__a = ()
__a = ()
for i, layer_module in enumerate(self.layer ):
if self.output_hidden_states:
__a = all_hidden_states + (hidden_states,)
__a = layer_module(
lowerCamelCase , lowerCamelCase , head_mask[i] , lowerCamelCase , lowerCamelCase )
__a = layer_outputs[0]
if self.output_attentions:
__a = all_attentions + (layer_outputs[1],)
__a = (hidden_states,)
if self.output_hidden_states:
__a = current_outputs + (all_hidden_states,)
if self.output_attentions:
__a = current_outputs + (all_attentions,)
__a = self.highway[i](lowerCamelCase )
# logits, pooled_output
if not self.training:
__a = highway_exit[0]
__a = entropy(lowerCamelCase )
__a = highway_exit + (highway_entropy,) # logits, hidden_states(?), entropy
__a = all_highway_exits + (highway_exit,)
if highway_entropy < self.early_exit_entropy[i]:
__a = (highway_logits,) + current_outputs[1:] + (all_highway_exits,)
raise HighwayException(lowerCamelCase , i + 1 )
else:
__a = all_highway_exits + (highway_exit,)
# Add last layer
if self.output_hidden_states:
__a = all_hidden_states + (hidden_states,)
__a = (hidden_states,)
if self.output_hidden_states:
__a = outputs + (all_hidden_states,)
if self.output_attentions:
__a = outputs + (all_attentions,)
__a = outputs + (all_highway_exits,)
return outputs # last-layer hidden state, (all hidden states), (all attentions), all highway exits
@add_start_docstrings(
"""The Bert Model transformer with early exiting (DeeBERT). """, snake_case_, )
class snake_case__ ( snake_case_ ):
def __init__( self , lowerCamelCase ):
super().__init__(lowerCamelCase )
__a = config
__a = BertEmbeddings(lowerCamelCase )
__a = DeeBertEncoder(lowerCamelCase )
__a = BertPooler(lowerCamelCase )
self.init_weights()
def a__ ( self ):
self.encoder.init_highway_pooler(self.pooler )
def a__ ( self ):
return self.embeddings.word_embeddings
def a__ ( self , lowerCamelCase ):
__a = value
def a__ ( self , lowerCamelCase ):
for layer, heads in heads_to_prune.items():
self.encoder.layer[layer].attention.prune_heads(lowerCamelCase )
@add_start_docstrings_to_model_forward(lowerCamelCase )
def a__ ( self , lowerCamelCase=None , lowerCamelCase=None , lowerCamelCase=None , lowerCamelCase=None , lowerCamelCase=None , lowerCamelCase=None , lowerCamelCase=None , lowerCamelCase=None , ):
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time" )
elif input_ids is not None:
__a = input_ids.size()
elif inputs_embeds is not None:
__a = inputs_embeds.size()[:-1]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds" )
__a = input_ids.device if input_ids is not None else inputs_embeds.device
if attention_mask is None:
__a = torch.ones(lowerCamelCase , device=lowerCamelCase )
if encoder_attention_mask is None:
__a = torch.ones(lowerCamelCase , device=lowerCamelCase )
if token_type_ids is None:
__a = torch.zeros(lowerCamelCase , dtype=torch.long , device=lowerCamelCase )
# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
# ourselves in which case we just need to make it broadcastable to all heads.
__a = self.get_extended_attention_mask(lowerCamelCase , lowerCamelCase , lowerCamelCase )
# If a 2D ou 3D attention mask is provided for the cross-attention
# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
if encoder_attention_mask.dim() == 3:
__a = encoder_attention_mask[:, None, :, :]
if encoder_attention_mask.dim() == 2:
__a = encoder_attention_mask[:, None, None, :]
__a = encoder_extended_attention_mask.to(
dtype=next(self.parameters() ).dtype ) # fp16 compatibility
__a = (1.0 - encoder_extended_attention_mask) * -1_0000.0
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
__a = self.get_head_mask(lowerCamelCase , self.config.num_hidden_layers )
__a = self.embeddings(
input_ids=lowerCamelCase , position_ids=lowerCamelCase , token_type_ids=lowerCamelCase , inputs_embeds=lowerCamelCase )
__a = self.encoder(
lowerCamelCase , attention_mask=lowerCamelCase , head_mask=lowerCamelCase , encoder_hidden_states=lowerCamelCase , encoder_attention_mask=lowerCamelCase , )
__a = encoder_outputs[0]
__a = self.pooler(lowerCamelCase )
__a = (
sequence_output,
pooled_output,
) + encoder_outputs[
1:
] # add hidden_states and attentions if they are here
return outputs # sequence_output, pooled_output, (hidden_states), (attentions), highway exits
class snake_case__ ( snake_case_ ):
def __init__( self , lowerCamelCase , lowerCamelCase ):
__a = message
__a = exit_layer # start from 1!
class snake_case__ ( nn.Module ):
def __init__( self , lowerCamelCase ):
super().__init__()
__a = BertPooler(lowerCamelCase )
__a = nn.Dropout(config.hidden_dropout_prob )
__a = nn.Linear(config.hidden_size , config.num_labels )
def a__ ( self , lowerCamelCase ):
# Pooler
__a = encoder_outputs[0]
__a = self.pooler(lowerCamelCase )
# "return" pooler_output
# BertModel
__a = (pooler_input, pooler_output) + encoder_outputs[1:]
# "return" bmodel_output
# Dropout and classification
__a = bmodel_output[1]
__a = self.dropout(lowerCamelCase )
__a = self.classifier(lowerCamelCase )
return logits, pooled_output
@add_start_docstrings(
"""Bert Model (with early exiting - DeeBERT) with a classifier on top,
also takes care of multi-layer training. """, snake_case_, )
class snake_case__ ( snake_case_ ):
def __init__( self , lowerCamelCase ):
super().__init__(lowerCamelCase )
__a = config.num_labels
__a = config.num_hidden_layers
__a = DeeBertModel(lowerCamelCase )
__a = nn.Dropout(config.hidden_dropout_prob )
__a = nn.Linear(config.hidden_size , self.config.num_labels )
self.init_weights()
@add_start_docstrings_to_model_forward(lowerCamelCase )
def a__ ( self , lowerCamelCase=None , lowerCamelCase=None , lowerCamelCase=None , lowerCamelCase=None , lowerCamelCase=None , lowerCamelCase=None , lowerCamelCase=None , lowerCamelCase=-1 , lowerCamelCase=False , ):
__a = self.num_layers
try:
__a = self.bert(
lowerCamelCase , attention_mask=lowerCamelCase , token_type_ids=lowerCamelCase , position_ids=lowerCamelCase , head_mask=lowerCamelCase , inputs_embeds=lowerCamelCase , )
# sequence_output, pooled_output, (hidden_states), (attentions), highway exits
__a = outputs[1]
__a = self.dropout(lowerCamelCase )
__a = self.classifier(lowerCamelCase )
__a = (logits,) + outputs[2:] # add hidden states and attention if they are here
except HighwayException as e:
__a = e.message
__a = e.exit_layer
__a = outputs[0]
if not self.training:
__a = entropy(lowerCamelCase )
__a = []
__a = []
if labels is not None:
if self.num_labels == 1:
# We are doing regression
__a = MSELoss()
__a = loss_fct(logits.view(-1 ) , labels.view(-1 ) )
else:
__a = CrossEntropyLoss()
__a = loss_fct(logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
# work with highway exits
__a = []
for highway_exit in outputs[-1]:
__a = highway_exit[0]
if not self.training:
highway_logits_all.append(lowerCamelCase )
highway_entropy.append(highway_exit[2] )
if self.num_labels == 1:
# We are doing regression
__a = MSELoss()
__a = loss_fct(highway_logits.view(-1 ) , labels.view(-1 ) )
else:
__a = CrossEntropyLoss()
__a = loss_fct(highway_logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
highway_losses.append(lowerCamelCase )
if train_highway:
__a = (sum(highway_losses[:-1] ),) + outputs
# exclude the final highway, of course
else:
__a = (loss,) + outputs
if not self.training:
__a = outputs + ((original_entropy, highway_entropy), exit_layer)
if output_layer >= 0:
__a = (
(outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
) # use the highway of the last layer
return outputs # (loss), logits, (hidden_states), (attentions), (highway_exits)
| 261 | """simple docstring"""
import itertools
import random
import unittest
import numpy as np
from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
SCREAMING_SNAKE_CASE__:Any = random.Random()
if is_torch_available():
import torch
def _lowerCamelCase( a , a=1.0 , a=None , a=None ):
if rng is None:
__a = global_rng
__a = []
for batch_idx in range(shape[0] ):
values.append([] )
for _ in range(shape[1] ):
values[-1].append(rng.random() * scale )
return values
class snake_case__ ( unittest.TestCase ):
def __init__( self , lowerCamelCase , lowerCamelCase=7 , lowerCamelCase=400 , lowerCamelCase=2000 , lowerCamelCase=1 , lowerCamelCase=0.0 , lowerCamelCase=16000 , lowerCamelCase=True , lowerCamelCase=True , ):
__a = parent
__a = batch_size
__a = min_seq_length
__a = max_seq_length
__a = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
__a = feature_size
__a = padding_value
__a = sampling_rate
__a = return_attention_mask
__a = do_normalize
def a__ ( self ):
return {
"feature_size": self.feature_size,
"padding_value": self.padding_value,
"sampling_rate": self.sampling_rate,
"return_attention_mask": self.return_attention_mask,
"do_normalize": self.do_normalize,
}
def a__ ( self , lowerCamelCase=False , lowerCamelCase=False ):
def _flatten(lowerCamelCase ):
return list(itertools.chain(*lowerCamelCase ) )
if equal_length:
__a = floats_list((self.batch_size, self.max_seq_length) )
else:
# make sure that inputs increase in size
__a = [
_flatten(floats_list((x, self.feature_size) ) )
for x in range(self.min_seq_length , self.max_seq_length , self.seq_length_diff )
]
if numpify:
__a = [np.asarray(lowerCamelCase ) for x in speech_inputs]
return speech_inputs
@require_torch
@require_torchaudio
class snake_case__ ( snake_case_, unittest.TestCase ):
_snake_case : str = ASTFeatureExtractor
def a__ ( self ):
__a = ASTFeatureExtractionTester(self )
def a__ ( self ):
# Tests that all call wrap to encode_plus and batch_encode_plus
__a = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
# create three inputs of length 800, 1000, and 1200
__a = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
__a = [np.asarray(lowerCamelCase ) for speech_input in speech_inputs]
# Test not batched input
__a = feat_extract(speech_inputs[0] , return_tensors="np" ).input_values
__a = feat_extract(np_speech_inputs[0] , return_tensors="np" ).input_values
self.assertTrue(np.allclose(lowerCamelCase , lowerCamelCase , atol=1E-3 ) )
# Test batched
__a = feat_extract(lowerCamelCase , padding=lowerCamelCase , return_tensors="np" ).input_values
__a = feat_extract(lowerCamelCase , padding=lowerCamelCase , return_tensors="np" ).input_values
for enc_seq_a, enc_seq_a in zip(lowerCamelCase , lowerCamelCase ):
self.assertTrue(np.allclose(lowerCamelCase , lowerCamelCase , atol=1E-3 ) )
# Test 2-D numpy arrays are batched.
__a = [floats_list((1, x) )[0] for x in (800, 800, 800)]
__a = np.asarray(lowerCamelCase )
__a = feat_extract(lowerCamelCase , return_tensors="np" ).input_values
__a = feat_extract(lowerCamelCase , return_tensors="np" ).input_values
for enc_seq_a, enc_seq_a in zip(lowerCamelCase , lowerCamelCase ):
self.assertTrue(np.allclose(lowerCamelCase , lowerCamelCase , atol=1E-3 ) )
@require_torch
def a__ ( self ):
import torch
__a = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
__a = np.random.rand(100 ).astype(np.floataa )
__a = np_speech_inputs.tolist()
for inputs in [py_speech_inputs, np_speech_inputs]:
__a = feature_extractor.pad([{"input_values": inputs}] , return_tensors="np" )
self.assertTrue(np_processed.input_values.dtype == np.floataa )
__a = feature_extractor.pad([{"input_values": inputs}] , return_tensors="pt" )
self.assertTrue(pt_processed.input_values.dtype == torch.floataa )
def a__ ( self , lowerCamelCase ):
from datasets import load_dataset
__a = load_dataset("hf-internal-testing/librispeech_asr_dummy" , "clean" , split="validation" )
# automatic decoding with librispeech
__a = ds.sort("id" ).select(range(lowerCamelCase ) )[:num_samples]["audio"]
return [x["array"] for x in speech_samples]
@require_torch
def a__ ( self ):
# fmt: off
__a = torch.tensor(
[-0.9894, -1.2776, -0.9066, -1.2776, -0.9349, -1.2609, -1.0386, -1.2776,
-1.1561, -1.2776, -1.2052, -1.2723, -1.2190, -1.2132, -1.2776, -1.1133,
-1.1953, -1.1343, -1.1584, -1.2203, -1.1770, -1.2474, -1.2381, -1.1936,
-0.9270, -0.8317, -0.8049, -0.7706, -0.7565, -0.7869] )
# fmt: on
__a = self._load_datasamples(1 )
__a = ASTFeatureExtractor()
__a = feature_extractor(lowerCamelCase , return_tensors="pt" ).input_values
self.assertEquals(input_values.shape , (1, 1024, 128) )
self.assertTrue(torch.allclose(input_values[0, 0, :30] , lowerCamelCase , atol=1E-4 ) )
| 261 | 1 |
'''simple docstring'''
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import warnings
from typing import List
from unittest.mock import Mock
import torch
from torch.utils.data import DataLoader, IterableDataset, TensorDataset
from accelerate.accelerator import Accelerator
from accelerate.utils.dataclasses import DistributedType
class __lowercase ( lowerCAmelCase__ ):
'''simple docstring'''
def __init__(self ,_lowerCamelCase ) -> Any:
'''simple docstring'''
__lowercase = data
def __iter__(self ) -> Optional[Any]:
'''simple docstring'''
for element in self.data:
yield element
def _lowerCAmelCase ( lowerCamelCase_ : List[Any]=True ):
__lowercase = Accelerator(even_batches=lowerCamelCase_ )
assert accelerator.num_processes == 2, "this script expects that two GPUs are available"
return accelerator
def _lowerCAmelCase ( lowerCamelCase_ : Accelerator , lowerCamelCase_ : int , lowerCamelCase_ : int , lowerCamelCase_ : bool = False ):
if iterable:
__lowercase = DummyIterableDataset(torch.as_tensor(range(lowerCamelCase_ ) ) )
else:
__lowercase = TensorDataset(torch.as_tensor(range(lowerCamelCase_ ) ) )
__lowercase = DataLoader(lowerCamelCase_ , batch_size=lowerCamelCase_ )
__lowercase = accelerator.prepare(lowerCamelCase_ )
return dl
def _lowerCAmelCase ( lowerCamelCase_ : Accelerator , lowerCamelCase_ : int , lowerCamelCase_ : int , lowerCamelCase_ : List[int] , lowerCamelCase_ : List[int] , ):
__lowercase = create_dataloader(accelerator=lowerCamelCase_ , dataset_size=lowerCamelCase_ , batch_size=lowerCamelCase_ )
__lowercase = [len(batch[0] ) for batch in dl]
if accelerator.process_index == 0:
assert batch_sizes == process_0_expected_batch_sizes
elif accelerator.process_index == 1:
assert batch_sizes == process_1_expected_batch_sizes
def _lowerCAmelCase ( ):
__lowercase = create_accelerator()
# without padding, we would expect a different number of batches
verify_dataloader_batch_sizes(
lowerCamelCase_ , dataset_size=3 , batch_size=1 , process_0_expected_batch_sizes=[1, 1] , process_1_expected_batch_sizes=[1, 1] , )
# without padding, we would expect the same number of batches, but different sizes
verify_dataloader_batch_sizes(
lowerCamelCase_ , dataset_size=7 , batch_size=2 , process_0_expected_batch_sizes=[2, 2] , process_1_expected_batch_sizes=[2, 2] , )
def _lowerCAmelCase ( ):
__lowercase = create_accelerator(even_batches=lowerCamelCase_ )
verify_dataloader_batch_sizes(
lowerCamelCase_ , dataset_size=3 , batch_size=1 , process_0_expected_batch_sizes=[1, 1] , process_1_expected_batch_sizes=[1] , )
verify_dataloader_batch_sizes(
lowerCamelCase_ , dataset_size=7 , batch_size=2 , process_0_expected_batch_sizes=[2, 2] , process_1_expected_batch_sizes=[2, 1] , )
def _lowerCAmelCase ( ):
__lowercase = create_accelerator(even_batches=lowerCamelCase_ )
__lowercase = torch.nn.Linear(1 , 1 )
__lowercase = accelerator.prepare(lowerCamelCase_ )
__lowercase = create_dataloader(lowerCamelCase_ , dataset_size=3 , batch_size=1 )
__lowercase = []
with accelerator.join_uneven_inputs([ddp_model] ):
for batch_idx, batch in enumerate(lowerCamelCase_ ):
__lowercase = ddp_model(batch[0].float() )
__lowercase = output.sum()
loss.backward()
batch_idxs.append(lowerCamelCase_ )
accelerator.wait_for_everyone()
if accelerator.process_index == 0:
assert batch_idxs == [0, 1]
elif accelerator.process_index == 1:
assert batch_idxs == [0]
def _lowerCAmelCase ( lowerCamelCase_ : List[str] ):
with warnings.catch_warnings(record=lowerCamelCase_ ) as w:
with accelerator.join_uneven_inputs([Mock()] ):
pass
assert issubclass(w[-1].category , lowerCamelCase_ )
assert "only supported for multi-GPU" in str(w[-1].message )
def _lowerCAmelCase ( ):
__lowercase = True
__lowercase = False
__lowercase = create_accelerator(even_batches=lowerCamelCase_ )
__lowercase = torch.nn.Linear(1 , 1 )
__lowercase = accelerator.prepare(lowerCamelCase_ )
__lowercase = create_dataloader(lowerCamelCase_ , dataset_size=3 , batch_size=1 )
__lowercase = create_dataloader(lowerCamelCase_ , dataset_size=3 , batch_size=1 )
with accelerator.join_uneven_inputs([ddp_model] , even_batches=lowerCamelCase_ ):
__lowercase = train_dl.batch_sampler.even_batches
__lowercase = valid_dl.batch_sampler.even_batches
assert train_dl_overridden_value == overridden_even_batches
assert valid_dl_overridden_value == overridden_even_batches
assert train_dl.batch_sampler.even_batches == default_even_batches
assert valid_dl.batch_sampler.even_batches == default_even_batches
def _lowerCAmelCase ( ):
__lowercase = True
__lowercase = False
__lowercase = create_accelerator(even_batches=lowerCamelCase_ )
__lowercase = torch.nn.Linear(1 , 1 )
__lowercase = accelerator.prepare(lowerCamelCase_ )
create_dataloader(lowerCamelCase_ , dataset_size=3 , batch_size=1 , iterable=lowerCamelCase_ )
__lowercase = create_dataloader(lowerCamelCase_ , dataset_size=3 , batch_size=1 )
with warnings.catch_warnings():
warnings.filterwarnings('''ignore''' )
try:
with accelerator.join_uneven_inputs([ddp_model] , even_batches=lowerCamelCase_ ):
__lowercase = batch_dl.batch_sampler.even_batches
except AttributeError:
# ensure attribute error is not raised when processing iterable dl
raise AssertionError
assert batch_dl_overridden_value == overridden_even_batches
assert batch_dl.batch_sampler.even_batches == default_even_batches
def _lowerCAmelCase ( ):
__lowercase = create_accelerator()
__lowercase = torch.nn.Linear(1 , 1 )
__lowercase = accelerator.prepare(lowerCamelCase_ )
create_dataloader(lowerCamelCase_ , dataset_size=3 , batch_size=1 , iterable=lowerCamelCase_ )
with warnings.catch_warnings(record=lowerCamelCase_ ) as w:
with accelerator.join_uneven_inputs([ddp_model] , even_batches=lowerCamelCase_ ):
pass
assert issubclass(w[-1].category , lowerCamelCase_ )
assert "only supported for map-style datasets" in str(w[-1].message )
def _lowerCAmelCase ( ):
__lowercase = create_accelerator()
accelerator.print('''Test that even_batches variable ensures uniform batches across processes''' )
test_default_ensures_even_batch_sizes()
accelerator.print('''Run tests with even_batches disabled''' )
test_can_disable_even_batches()
accelerator.print('''Test joining uneven inputs''' )
test_can_join_uneven_inputs()
accelerator.print('''Test overriding even_batches when joining uneven inputs''' )
test_join_can_override_even_batches()
accelerator.print('''Test overriding even_batches for mixed dataloader types''' )
test_join_can_override_for_mixed_type_dataloaders()
accelerator.print('''Test overriding even_batches raises a warning for iterable dataloaders''' )
test_join_raises_warning_for_iterable_when_overriding_even_batches()
accelerator.print('''Test join with non DDP distributed raises warning''' )
__lowercase = accelerator.state.distributed_type
__lowercase = DistributedType.FSDP
test_join_raises_warning_for_non_ddp_distributed(lowerCamelCase_ )
__lowercase = original_state
if __name__ == "__main__":
main()
| 217 |
'''simple docstring'''
_SCREAMING_SNAKE_CASE = {
'''A''': ['''B''', '''C''', '''E'''],
'''B''': ['''A''', '''D''', '''E'''],
'''C''': ['''A''', '''F''', '''G'''],
'''D''': ['''B'''],
'''E''': ['''A''', '''B''', '''D'''],
'''F''': ['''C'''],
'''G''': ['''C'''],
}
def _lowerCAmelCase ( lowerCamelCase_ : dict , lowerCamelCase_ : Optional[Any] , lowerCamelCase_ : List[str] ):
__lowercase = set()
# keep track of all the paths to be checked
__lowercase = [[start]]
# return path if start is goal
if start == goal:
return [start]
# keeps looping until all possible paths have been checked
while queue:
# pop the first path from the queue
__lowercase = queue.pop(0 )
# get the last node from the path
__lowercase = path[-1]
if node not in explored:
__lowercase = graph[node]
# go through all neighbour nodes, construct a new path and
# push it into the queue
for neighbour in neighbours:
__lowercase = list(lowerCamelCase_ )
new_path.append(lowerCamelCase_ )
queue.append(lowerCamelCase_ )
# return path if neighbour is goal
if neighbour == goal:
return new_path
# mark node as explored
explored.add(lowerCamelCase_ )
# in case there's no path between the 2 nodes
return []
def _lowerCAmelCase ( lowerCamelCase_ : dict , lowerCamelCase_ : str , lowerCamelCase_ : str ):
if not graph or start not in graph or target not in graph:
return -1
if start == target:
return 0
__lowercase = [start]
__lowercase = set(lowerCamelCase_ )
# Keep tab on distances from `start` node.
__lowercase = {start: 0, target: -1}
while queue:
__lowercase = queue.pop(0 )
if node == target:
__lowercase = (
dist[node] if dist[target] == -1 else min(dist[target] , dist[node] )
)
for adjacent in graph[node]:
if adjacent not in visited:
visited.add(lowerCamelCase_ )
queue.append(lowerCamelCase_ )
__lowercase = dist[node] + 1
return dist[target]
if __name__ == "__main__":
print(bfs_shortest_path(demo_graph, '''G''', '''D''')) # returns ['G', 'C', 'A', 'B', 'D']
print(bfs_shortest_path_distance(demo_graph, '''G''', '''D''')) # returns 4
| 217 | 1 |
import logging
import os
from logging import (
CRITICAL, # NOQA
DEBUG, # NOQA
ERROR, # NOQA
FATAL, # NOQA
INFO, # NOQA
NOTSET, # NOQA
WARN, # NOQA
WARNING, # NOQA
)
from typing import Optional
from tqdm import auto as tqdm_lib
_UpperCamelCase = {
'''debug''': logging.DEBUG,
'''info''': logging.INFO,
'''warning''': logging.WARNING,
'''error''': logging.ERROR,
'''critical''': logging.CRITICAL,
}
_UpperCamelCase = logging.WARNING
def lowerCAmelCase__( ) -> int:
__snake_case : Dict = os.getenv("DATASETS_VERBOSITY" , UpperCamelCase_ )
if env_level_str:
if env_level_str in log_levels:
return log_levels[env_level_str]
else:
logging.getLogger().warning(
f"""Unknown option DATASETS_VERBOSITY={env_level_str}, """
f"""has to be one of: { ", ".join(log_levels.keys() ) }""" )
return _default_log_level
def lowerCAmelCase__( ) -> str:
return __name__.split("." )[0]
def lowerCAmelCase__( ) -> logging.Logger:
return logging.getLogger(_get_library_name() )
def lowerCAmelCase__( ) -> None:
__snake_case : int = _get_library_root_logger()
library_root_logger.setLevel(_get_default_logging_level() )
def lowerCAmelCase__( ) -> None:
__snake_case : str = _get_library_root_logger()
library_root_logger.setLevel(logging.NOTSET )
def lowerCAmelCase__( lowercase : Optional[str] = None ) -> logging.Logger:
if name is None:
__snake_case : Any = _get_library_name()
return logging.getLogger(UpperCamelCase_ )
def lowerCAmelCase__( ) -> int:
return _get_library_root_logger().getEffectiveLevel()
def lowerCAmelCase__( lowercase : int ) -> None:
_get_library_root_logger().setLevel(UpperCamelCase_ )
def lowerCAmelCase__( ) -> Any:
return set_verbosity(UpperCamelCase_ )
def lowerCAmelCase__( ) -> Optional[int]:
return set_verbosity(UpperCamelCase_ )
def lowerCAmelCase__( ) -> Optional[Any]:
return set_verbosity(UpperCamelCase_ )
def lowerCAmelCase__( ) -> str:
return set_verbosity(UpperCamelCase_ )
def lowerCAmelCase__( ) -> None:
__snake_case : List[str] = False
def lowerCAmelCase__( ) -> None:
__snake_case : Dict = True
# Configure the library root logger at the module level (singleton-like)
_configure_library_root_logger()
class _lowerCamelCase :
"""simple docstring"""
def __init__( self , *UpperCAmelCase , **UpperCAmelCase ) -> Union[str, Any]: # pylint: disable=unused-argument
'''simple docstring'''
__snake_case : List[Any] = args[0] if args else None
def __iter__( self ) -> int:
'''simple docstring'''
return iter(self._iterator )
def __getattr__( self , UpperCAmelCase ) -> Optional[int]:
'''simple docstring'''
def empty_fn(*UpperCAmelCase , **UpperCAmelCase ): # pylint: disable=unused-argument
return
return empty_fn
def __enter__( self ) -> Tuple:
'''simple docstring'''
return self
def __exit__( self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) -> Tuple:
'''simple docstring'''
return
_UpperCamelCase = True
class _lowerCamelCase :
"""simple docstring"""
def __call__( self , *UpperCAmelCase , UpperCAmelCase=False , **UpperCAmelCase ) -> int:
'''simple docstring'''
if _tqdm_active and not disable:
return tqdm_lib.tqdm(*UpperCAmelCase__ , **UpperCAmelCase__ )
else:
return EmptyTqdm(*UpperCAmelCase__ , **UpperCAmelCase__ )
def UpperCAmelCase ( self , *UpperCAmelCase , **UpperCAmelCase ) -> Any:
'''simple docstring'''
__snake_case : Any = None
if _tqdm_active:
return tqdm_lib.tqdm.set_lock(*UpperCAmelCase__ , **UpperCAmelCase__ )
def UpperCAmelCase ( self ) -> Tuple:
'''simple docstring'''
if _tqdm_active:
return tqdm_lib.tqdm.get_lock()
_UpperCamelCase = _tqdm_cls()
def lowerCAmelCase__( ) -> bool:
global _tqdm_active
return bool(_tqdm_active )
def lowerCAmelCase__( ) -> Optional[int]:
global _tqdm_active
__snake_case : List[str] = True
def lowerCAmelCase__( ) -> List[Any]:
global _tqdm_active
__snake_case : str = False
| 326 |
"""simple docstring"""
import argparse
import json
import os
import re
import shutil
import torch
from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging
logging.set_verbosity_warning()
_a = 2
class _lowerCAmelCase :
"""simple docstring"""
def __init__( self : Dict, *, # begin keyword-only arguments
UpperCAmelCase__ : str="<s>", UpperCAmelCase__ : Tuple="<pad>", UpperCAmelCase__ : str="</s>", UpperCAmelCase__ : Optional[Any]="<unk>", UpperCAmelCase__ : List[Any]=None, ):
__lowercase ,__lowercase ,__lowercase ,__lowercase = bos, unk, pad, eos
__lowercase = []
__lowercase = []
__lowercase = {}
__lowercase = self.add_symbol(UpperCAmelCase__ )
__lowercase = self.add_symbol(UpperCAmelCase__ )
__lowercase = self.add_symbol(UpperCAmelCase__ )
__lowercase = self.add_symbol(UpperCAmelCase__ )
if extra_special_symbols:
for s in extra_special_symbols:
self.add_symbol(UpperCAmelCase__ )
__lowercase = len(self.symbols )
def __eq__( self : List[str], UpperCAmelCase__ : Dict ):
return self.indices == other.indices
def __getitem__( self : Optional[int], UpperCAmelCase__ : List[str] ):
if idx < len(self.symbols ):
return self.symbols[idx]
return self.unk_word
def __len__( self : str ):
return len(self.symbols )
def __contains__( self : Any, UpperCAmelCase__ : Optional[Any] ):
return sym in self.indices
@classmethod
def _lowercase ( cls : List[Any], UpperCAmelCase__ : Optional[Any] ):
__lowercase = cls()
d.add_from_file(UpperCAmelCase__ )
return d
def _lowercase ( self : Dict, UpperCAmelCase__ : Optional[Any], UpperCAmelCase__ : List[Any]=1, UpperCAmelCase__ : str=False ):
if word in self.indices and not overwrite:
__lowercase = self.indices[word]
__lowercase = self.count[idx] + n
return idx
else:
__lowercase = len(self.symbols )
__lowercase = idx
self.symbols.append(UpperCAmelCase__ )
self.count.append(UpperCAmelCase__ )
return idx
def _lowercase ( self : Any, UpperCAmelCase__ : str ):
return 0
def _lowercase ( self : Tuple, UpperCAmelCase__ : List[Any] ):
if isinstance(UpperCAmelCase__, UpperCAmelCase__ ):
try:
with open(UpperCAmelCase__, "r", encoding="utf-8" ) as fd:
self.add_from_file(UpperCAmelCase__ )
except FileNotFoundError as fnfe:
raise fnfe
except UnicodeError:
raise Exception("Incorrect encoding detected in {}, please rebuild the dataset".format(UpperCAmelCase__ ) )
return
__lowercase = f.readlines()
__lowercase = self._load_meta(UpperCAmelCase__ )
for line in lines[indices_start_line:]:
try:
__lowercase ,__lowercase = line.rstrip().rsplit(" ", 1 )
if field == "#fairseq:overwrite":
__lowercase = True
__lowercase ,__lowercase = line.rsplit(" ", 1 )
else:
__lowercase = False
__lowercase = int(UpperCAmelCase__ )
__lowercase = line
if word in self and not overwrite:
raise RuntimeError(
"Duplicate word found when loading Dictionary: '{}'. "
"Duplicate words can overwrite earlier ones by adding the "
"#fairseq:overwrite flag at the end of the corresponding row "
"in the dictionary file. If using the Camembert model, please "
"download an updated copy of the model file.".format(UpperCAmelCase__ ) )
self.add_symbol(UpperCAmelCase__, n=UpperCAmelCase__, overwrite=UpperCAmelCase__ )
except ValueError:
raise ValueError("Incorrect dictionary format, expected '<token> <cnt> [flags]'" )
def _A ( UpperCamelCase_ : int) -> str:
'''simple docstring'''
__lowercase = dict((re.sub(r"@@$", "", UpperCamelCase_), v) if k.endswith("@@") else (re.sub(r"$", "</w>", UpperCamelCase_), v) for k, v in d.items())
__lowercase = "<s> <pad> </s> <unk>".split()
# restore the special tokens
for k in keep_keys:
del da[F"""{k}</w>"""]
__lowercase = d[k] # restore
return da
def _A ( UpperCamelCase_ : str, UpperCamelCase_ : str) -> List[Any]:
'''simple docstring'''
if not os.path.exists(UpperCamelCase_):
raise ValueError(F"""path {biogpt_checkpoint_path} does not exist!""")
os.makedirs(UpperCamelCase_, exist_ok=UpperCamelCase_)
print(F"""Writing results to {pytorch_dump_folder_path}""")
# handle various types of models
__lowercase = os.path.join(UpperCamelCase_, "checkpoint.pt")
if not os.path.isfile(UpperCamelCase_):
raise ValueError(F"""path to the file {checkpoint_file} does not exist!""")
__lowercase = torch.load(UpperCamelCase_, map_location="cpu")
__lowercase = chkpt["cfg"]["model"]
# dicts
__lowercase = os.path.join(UpperCamelCase_, "dict.txt")
if not os.path.isfile(UpperCamelCase_):
raise ValueError(F"""path to the file {dict_file} does not exist!""")
__lowercase = Dictionary.load(UpperCamelCase_)
__lowercase = rewrite_dict_keys(src_dict.indices)
__lowercase = len(UpperCamelCase_)
__lowercase = os.path.join(UpperCamelCase_, VOCAB_FILES_NAMES["vocab_file"])
print(F"""Generating {src_vocab_file} of {src_vocab_size} records""")
with open(UpperCamelCase_, "w", encoding="utf-8") as f:
f.write(json.dumps(UpperCamelCase_, ensure_ascii=UpperCamelCase_, indent=UpperCamelCase_))
# merges_file (bpecodes)
__lowercase = os.path.join(UpperCamelCase_, "bpecodes")
if not os.path.isfile(UpperCamelCase_):
raise ValueError(F"""path to the file {bpecodes_file} does not exist!""")
__lowercase = os.path.join(UpperCamelCase_, VOCAB_FILES_NAMES["merges_file"])
shutil.copyfile(UpperCamelCase_, UpperCamelCase_)
# model config
__lowercase = os.path.join(UpperCamelCase_, "config.json")
__lowercase = {
"activation_dropout": args["activation_dropout"],
"architectures": ["BioGptForCausalLM"],
"attention_probs_dropout_prob": args["attention_dropout"],
"bos_token_id": 0,
"eos_token_id": 2,
"hidden_act": args["activation_fn"],
"hidden_dropout_prob": args["dropout"],
"hidden_size": args["decoder_embed_dim"],
"initializer_range": 0.02,
"intermediate_size": args["decoder_ffn_embed_dim"],
"layer_norm_eps": 1E-12,
"layerdrop": args["decoder_layerdrop"],
"max_position_embeddings": args["max_target_positions"],
"model_type": "biogpt",
"num_attention_heads": args["decoder_attention_heads"],
"num_hidden_layers": args["decoder_layers"],
"pad_token_id": 1,
"scale_embedding": not args["no_scale_embedding"],
"tie_word_embeddings": args["share_decoder_input_output_embed"],
"vocab_size": src_vocab_size,
}
# good hparam defaults to start with
print(F"""Generating {biogpt_model_config_file}""")
with open(UpperCamelCase_, "w", encoding="utf-8") as f:
f.write(json.dumps(UpperCamelCase_, ensure_ascii=UpperCamelCase_, indent=UpperCamelCase_))
# tokenizer config
__lowercase = os.path.join(UpperCamelCase_, UpperCamelCase_)
__lowercase = {
"bos_token": "<s>",
"eos_token": "</s>",
"model_max_length": 1024,
"pad_token": "<pad>",
"special_tokens_map_file": None,
"tokenizer_class": "BioGptTokenizer",
"unk_token": "<unk>",
}
print(F"""Generating {biogpt_tokenizer_config_file}""")
with open(UpperCamelCase_, "w", encoding="utf-8") as f:
f.write(json.dumps(UpperCamelCase_, ensure_ascii=UpperCamelCase_, indent=UpperCamelCase_))
# model
__lowercase = chkpt["model"]
# remove unneeded keys
__lowercase = [
"decoder.version",
]
for k in ignore_keys:
model_state_dict.pop(UpperCamelCase_, UpperCamelCase_)
__lowercase = list(model_state_dict.keys())
for layer_name in layer_names:
if layer_name.endswith("output_projection.weight"):
__lowercase = model_state_dict.pop(UpperCamelCase_)
else:
__lowercase = model_state_dict.pop(UpperCamelCase_)
__lowercase = BioGptConfig.from_pretrained(UpperCamelCase_)
__lowercase = BioGptForCausalLM(UpperCamelCase_)
# check that it loads ok
model_new.load_state_dict(UpperCamelCase_)
# save
__lowercase = os.path.join(UpperCamelCase_, UpperCamelCase_)
print(F"""Generating {pytorch_weights_dump_path}""")
torch.save(UpperCamelCase_, UpperCamelCase_)
print("Conversion is done!")
if __name__ == "__main__":
_a = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--biogpt_checkpoint_path',
default=None,
type=str,
required=True,
help=(
'Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,'
' bpecodes, etc.'
),
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
)
_a = parser.parse_args()
convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)
| 17 | 0 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..models.whisper import WhisperForConditionalGeneration, WhisperProcessor
from .base import PipelineTool
class UpperCamelCase ( __UpperCamelCase ):
'''simple docstring'''
lowercase : int ="""openai/whisper-base"""
lowercase : Optional[int] =(
"""This is a tool that transcribes an audio into text. It takes an input named `audio` and returns the """
"""transcribed text."""
)
lowercase : List[str] ="""transcriber"""
lowercase : str =WhisperProcessor
lowercase : Optional[Any] =WhisperForConditionalGeneration
lowercase : List[Any] =["""audio"""]
lowercase : List[str] =["""text"""]
def UpperCamelCase ( self , UpperCamelCase_ ):
return self.pre_processor(UpperCamelCase_ , return_tensors='''pt''' ).input_features
def UpperCamelCase ( self , UpperCamelCase_ ):
return self.model.generate(inputs=UpperCamelCase_ )
def UpperCamelCase ( self , UpperCamelCase_ ):
return self.pre_processor.batch_decode(UpperCamelCase_ , skip_special_tokens=UpperCamelCase_ )[0]
| 356 |
from operator import delitem, getitem, setitem
import pytest
from data_structures.hashing.hash_map import HashMap
def UpperCamelCase ( _a ) -> Union[str, Any]:
'''simple docstring'''
return getitem, k
def UpperCamelCase ( _a , _a ) -> int:
'''simple docstring'''
return setitem, k, v
def UpperCamelCase ( _a ) -> int:
'''simple docstring'''
return delitem, k
def UpperCamelCase ( _a , _a , *_a ) -> Any:
'''simple docstring'''
try:
return fun(_a , *_a ), None
except Exception as e:
return None, e
SCREAMING_SNAKE_CASE : List[Any] = (
_set("key_a", "val_a"),
_set("key_b", "val_b"),
)
SCREAMING_SNAKE_CASE : Tuple = [
_set("key_a", "val_a"),
_set("key_a", "val_b"),
]
SCREAMING_SNAKE_CASE : Any = [
_set("key_a", "val_a"),
_set("key_b", "val_b"),
_del("key_a"),
_del("key_b"),
_set("key_a", "val_a"),
_del("key_a"),
]
SCREAMING_SNAKE_CASE : Union[str, Any] = [
_get("key_a"),
_del("key_a"),
_set("key_a", "val_a"),
_del("key_a"),
_del("key_a"),
_get("key_a"),
]
SCREAMING_SNAKE_CASE : Any = [
*[_set(x, x) for x in range(5)], # guaranteed upsize
]
SCREAMING_SNAKE_CASE : int = [
*[_set(x, x) for x in range(5)], # guaranteed upsize
*[_del(x) for x in range(5)],
_set("key_a", "val_b"),
]
@pytest.mark.parametrize(
'''operations''' , (
pytest.param(_add_items , id='''add items''' ),
pytest.param(_overwrite_items , id='''overwrite items''' ),
pytest.param(_delete_items , id='''delete items''' ),
pytest.param(_access_absent_items , id='''access absent items''' ),
pytest.param(_add_with_resize_up , id='''add with resize up''' ),
pytest.param(_add_with_resize_down , id='''add with resize down''' ),
) , )
def UpperCamelCase ( _a ) -> List[str]:
'''simple docstring'''
lowercase_ :Optional[Any] = HashMap(initial_block_size=4 )
lowercase_ :Optional[int] = {}
for _, (fun, *args) in enumerate(_a ):
lowercase_ , lowercase_ :List[str] = _run_operation(_a , _a , *_a )
lowercase_ , lowercase_ :List[str] = _run_operation(_a , _a , *_a )
assert my_res == py_res
assert str(_a ) == str(_a )
assert set(_a ) == set(_a )
assert len(_a ) == len(_a )
assert set(my.items() ) == set(py.items() )
def UpperCamelCase ( ) -> Optional[Any]:
'''simple docstring'''
def is_public(_a ) -> bool:
return not name.startswith('''_''' )
lowercase_ :Dict = {name for name in dir({} ) if is_public(_a )}
lowercase_ :Dict = {name for name in dir(HashMap() ) if is_public(_a )}
assert dict_public_names > hash_public_names
| 252 | 0 |
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List
import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor
from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging
logging.set_verbosity_info()
UpperCAmelCase : Any = logging.get_logger()
@dataclass
class __lowerCAmelCase :
_lowercase : nn.Module
_lowercase : List[nn.Module] = field(default_factory=UpperCamelCase__)
_lowercase : list = field(default_factory=UpperCamelCase__)
def _lowercase ( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) -> str:
'''simple docstring'''
a__ : Tuple =len(list(m.modules() ) ) == 1 or isinstance(lowerCAmelCase__ , nn.Convad ) or isinstance(lowerCAmelCase__ , nn.BatchNormad )
if has_not_submodules:
self.traced.append(lowerCAmelCase__ )
def __call__( self , lowerCAmelCase__ ) -> List[Any]:
'''simple docstring'''
for m in self.module.modules():
self.handles.append(m.register_forward_hook(self._forward_hook ) )
self.module(lowerCAmelCase__ )
[x.remove() for x in self.handles]
return self
@property
def _lowercase ( self ) -> Optional[Any]:
'''simple docstring'''
return list(filter(lambda lowerCAmelCase__ : len(list(x.state_dict().keys() ) ) > 0 , self.traced ) )
@dataclass
class __lowerCAmelCase :
_lowercase : nn.Module
_lowercase : nn.Module
_lowercase : int = 0
_lowercase : List = field(default_factory=UpperCamelCase__)
_lowercase : List = field(default_factory=UpperCamelCase__)
def __call__( self , lowerCAmelCase__ ) -> Tuple:
'''simple docstring'''
a__ : Any =Tracker(self.dest )(lowerCAmelCase__ ).parametrized
a__ : List[str] =Tracker(self.src )(lowerCAmelCase__ ).parametrized
a__ : Tuple =list(filter(lambda lowerCAmelCase__ : type(lowerCAmelCase__ ) not in self.src_skip , lowerCAmelCase__ ) )
a__ : Any =list(filter(lambda lowerCAmelCase__ : type(lowerCAmelCase__ ) not in self.dest_skip , lowerCAmelCase__ ) )
if len(lowerCAmelCase__ ) != len(lowerCAmelCase__ ):
raise Exception(
F'''Numbers of operations are different. Source module has {len(lowerCAmelCase__ )} operations while'''
F''' destination module has {len(lowerCAmelCase__ )}.''' )
for dest_m, src_m in zip(lowerCAmelCase__ , lowerCAmelCase__ ):
dest_m.load_state_dict(src_m.state_dict() )
if self.verbose == 1:
print(F'''Transfered from={src_m} to={dest_m}''' )
def _A ( SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : ResNetConfig , SCREAMING_SNAKE_CASE : Path , SCREAMING_SNAKE_CASE : bool = True ):
"""simple docstring"""
print(f'''Converting {name}...''' )
with torch.no_grad():
a__ : Tuple =timm.create_model(SCREAMING_SNAKE_CASE , pretrained=SCREAMING_SNAKE_CASE ).eval()
a__ : str =ResNetForImageClassification(SCREAMING_SNAKE_CASE ).eval()
a__ : List[str] =ModuleTransfer(src=SCREAMING_SNAKE_CASE , dest=SCREAMING_SNAKE_CASE )
a__ : Optional[Any] =torch.randn((1, 3, 224, 224) )
module_transfer(SCREAMING_SNAKE_CASE )
assert torch.allclose(from_model(SCREAMING_SNAKE_CASE ) , our_model(SCREAMING_SNAKE_CASE ).logits ), "The model logits don't match the original one."
a__ : Union[str, Any] =f'''resnet{"-".join(name.split("resnet" ) )}'''
print(SCREAMING_SNAKE_CASE )
if push_to_hub:
our_model.push_to_hub(
repo_path_or_name=save_directory / checkpoint_name , commit_message="Add model" , use_temp_dir=SCREAMING_SNAKE_CASE , )
# we can use the convnext one
a__ : Optional[int] =AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k" )
image_processor.push_to_hub(
repo_path_or_name=save_directory / checkpoint_name , commit_message="Add image processor" , use_temp_dir=SCREAMING_SNAKE_CASE , )
print(f'''Pushed {checkpoint_name}''' )
def _A ( SCREAMING_SNAKE_CASE : Path , SCREAMING_SNAKE_CASE : str = None , SCREAMING_SNAKE_CASE : bool = True ):
"""simple docstring"""
a__ : int ="imagenet-1k-id2label.json"
a__ : List[str] =1_000
a__ : List[Any] =(1, num_labels)
a__ : str ="huggingface/label-files"
a__ : Optional[int] =num_labels
a__ : Union[str, Any] =json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , repo_type="dataset" ) , "r" ) )
a__ : str ={int(SCREAMING_SNAKE_CASE ): v for k, v in idalabel.items()}
a__ : int =idalabel
a__ : Dict ={v: k for k, v in idalabel.items()}
a__ : str =partial(SCREAMING_SNAKE_CASE , num_labels=SCREAMING_SNAKE_CASE , idalabel=SCREAMING_SNAKE_CASE , labelaid=SCREAMING_SNAKE_CASE )
a__ : Optional[Any] ={
"resnet18": ImageNetPreTrainedConfig(
depths=[2, 2, 2, 2] , hidden_sizes=[64, 128, 256, 512] , layer_type="basic" ),
"resnet26": ImageNetPreTrainedConfig(
depths=[2, 2, 2, 2] , hidden_sizes=[256, 512, 1_024, 2_048] , layer_type="bottleneck" ),
"resnet34": ImageNetPreTrainedConfig(
depths=[3, 4, 6, 3] , hidden_sizes=[64, 128, 256, 512] , layer_type="basic" ),
"resnet50": ImageNetPreTrainedConfig(
depths=[3, 4, 6, 3] , hidden_sizes=[256, 512, 1_024, 2_048] , layer_type="bottleneck" ),
"resnet101": ImageNetPreTrainedConfig(
depths=[3, 4, 23, 3] , hidden_sizes=[256, 512, 1_024, 2_048] , layer_type="bottleneck" ),
"resnet152": ImageNetPreTrainedConfig(
depths=[3, 8, 36, 3] , hidden_sizes=[256, 512, 1_024, 2_048] , layer_type="bottleneck" ),
}
if model_name:
convert_weight_and_push(SCREAMING_SNAKE_CASE , names_to_config[model_name] , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
else:
for model_name, config in names_to_config.items():
convert_weight_and_push(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
return config, expected_shape
if __name__ == "__main__":
UpperCAmelCase : str = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--model_name""",
default=None,
type=str,
help=(
"""The name of the model you wish to convert, it must be one of the supported resnet* architecture,"""
""" currently: resnet18,26,34,50,101,152. If `None`, all of them will the converted."""
),
)
parser.add_argument(
"""--pytorch_dump_folder_path""",
default=None,
type=Path,
required=True,
help="""Path to the output PyTorch model directory.""",
)
parser.add_argument(
"""--push_to_hub""",
default=True,
type=bool,
required=False,
help="""If True, push model and image processor to the hub.""",
)
UpperCAmelCase : str = parser.parse_args()
UpperCAmelCase : Path = args.pytorch_dump_folder_path
pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)
| 95 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a__ : List[Any] ={
'''configuration_timesformer''': ['''TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TimesformerConfig'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a__ : Optional[int] =[
'''TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TimesformerModel''',
'''TimesformerForVideoClassification''',
'''TimesformerPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_timesformer import TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, TimesformerConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_timesformer import (
TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TimesformerForVideoClassification,
TimesformerModel,
TimesformerPreTrainedModel,
)
else:
import sys
a__ : Optional[int] =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 53 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_UpperCAmelCase : List[str] = {
"configuration_tapas": ["TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP", "TapasConfig"],
"tokenization_tapas": ["TapasTokenizer"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCAmelCase : Tuple = [
"TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST",
"TapasForMaskedLM",
"TapasForQuestionAnswering",
"TapasForSequenceClassification",
"TapasModel",
"TapasPreTrainedModel",
"load_tf_weights_in_tapas",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCAmelCase : Union[str, Any] = [
"TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFTapasForMaskedLM",
"TFTapasForQuestionAnswering",
"TFTapasForSequenceClassification",
"TFTapasModel",
"TFTapasPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
from .tokenization_tapas import TapasTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tapas import (
TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasPreTrainedModel,
load_tf_weights_in_tapas,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_tapas import (
TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
TFTapasForMaskedLM,
TFTapasForQuestionAnswering,
TFTapasForSequenceClassification,
TFTapasModel,
TFTapasPreTrainedModel,
)
else:
import sys
_UpperCAmelCase : List[Any] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 110 |
from __future__ import annotations
def A ( lowercase , lowercase ) -> tuple[int, int]:
'''simple docstring'''
if b == 0:
return (1, 0)
((UpperCamelCase) , (UpperCamelCase)) = extended_euclid(lowercase , a % b )
UpperCamelCase = a // b
return (y, x - k * y)
def A ( lowercase , lowercase , lowercase , lowercase ) -> int:
'''simple docstring'''
((UpperCamelCase) , (UpperCamelCase)) = extended_euclid(lowercase , lowercase )
UpperCamelCase = na * na
UpperCamelCase = ra * x * na + ra * y * na
return (n % m + m) % m
def A ( lowercase , lowercase ) -> int:
'''simple docstring'''
((UpperCamelCase) , (UpperCamelCase)) = extended_euclid(lowercase , lowercase )
if b < 0:
UpperCamelCase = (b % n + n) % n
return b
def A ( lowercase , lowercase , lowercase , lowercase ) -> int:
'''simple docstring'''
UpperCamelCase , UpperCamelCase = invert_modulo(lowercase , lowercase ), invert_modulo(lowercase , lowercase )
UpperCamelCase = na * na
UpperCamelCase = ra * x * na + ra * y * na
return (n % m + m) % m
if __name__ == "__main__":
from doctest import testmod
testmod(name="chinese_remainder_theorem", verbose=True)
testmod(name="chinese_remainder_theorem2", verbose=True)
testmod(name="invert_modulo", verbose=True)
testmod(name="extended_euclid", verbose=True)
| 110 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__lowerCamelCase = logging.get_logger(__name__)
__lowerCamelCase = {
"""junnyu/roformer_chinese_small""": """https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json""",
"""junnyu/roformer_chinese_base""": """https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json""",
"""junnyu/roformer_chinese_char_small""": (
"""https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json"""
),
"""junnyu/roformer_chinese_char_base""": (
"""https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json"""
),
"""junnyu/roformer_small_discriminator""": (
"""https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json"""
),
"""junnyu/roformer_small_generator""": (
"""https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json"""
),
# See all RoFormer models at https://huggingface.co/models?filter=roformer
}
class UpperCAmelCase ( A_ ):
A__ : Tuple = "roformer"
def __init__(self : int , snake_case__ : Optional[Any]=5_00_00 , snake_case__ : Optional[int]=None , snake_case__ : Any=7_68 , snake_case__ : Optional[int]=12 , snake_case__ : Optional[int]=12 , snake_case__ : Union[str, Any]=30_72 , snake_case__ : Optional[int]="gelu" , snake_case__ : int=0.1 , snake_case__ : Dict=0.1 , snake_case__ : Tuple=15_36 , snake_case__ : List[str]=2 , snake_case__ : str=0.02 , snake_case__ : Dict=1e-12 , snake_case__ : Any=0 , snake_case__ : str=False , snake_case__ : Dict=True , **snake_case__ : Optional[Any] , ) -> Dict:
'''simple docstring'''
super().__init__(pad_token_id=snake_case__ , **snake_case__ )
snake_case : List[Any] = vocab_size
snake_case : Any = hidden_size if embedding_size is None else embedding_size
snake_case : Tuple = hidden_size
snake_case : str = num_hidden_layers
snake_case : int = num_attention_heads
snake_case : Optional[int] = hidden_act
snake_case : Optional[int] = intermediate_size
snake_case : int = hidden_dropout_prob
snake_case : List[str] = attention_probs_dropout_prob
snake_case : str = max_position_embeddings
snake_case : Union[str, Any] = type_vocab_size
snake_case : List[Any] = initializer_range
snake_case : Optional[int] = layer_norm_eps
snake_case : Optional[Any] = rotary_value
snake_case : Union[str, Any] = use_cache
class UpperCAmelCase ( A_ ):
@property
def _SCREAMING_SNAKE_CASE (self : Optional[int] ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
if self.task == "multiple-choice":
snake_case : Any = {0: "batch", 1: "choice", 2: "sequence"}
else:
snake_case : Tuple = {0: "batch", 1: "sequence"}
snake_case : str = {0: "batch", 1: "sequence"}
return OrderedDict(
[
("input_ids", dynamic_axis),
("attention_mask", dynamic_axis),
("token_type_ids", dynamic_axis),
] )
| 59 |
import warnings
from ...utils import logging
from .image_processing_dpt import DPTImageProcessor
snake_case_ = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE__ (__snake_case ):
def __init__( self , *a , **a):
warnings.warn(
'The class DPTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
' use DPTImageProcessor instead.' , a , )
super().__init__(*a , **a)
| 214 | 0 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_DEFAULT_MEAN,
IMAGENET_DEFAULT_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_vision_available():
import PIL
__UpperCAmelCase = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE ( a_ ):
"""simple docstring"""
lowerCamelCase : List[str] =["pixel_values"]
def __init__( self : Any , lowerCAmelCase : bool = True , lowerCAmelCase : Dict[str, int] = None , lowerCAmelCase : int = 0.9 , lowerCAmelCase : PILImageResampling = PILImageResampling.BICUBIC , lowerCAmelCase : bool = True , lowerCAmelCase : Dict[str, int] = None , lowerCAmelCase : Union[int, float] = 1 / 2_55 , lowerCAmelCase : bool = True , lowerCAmelCase : bool = True , lowerCAmelCase : Optional[Union[float, List[float]]] = None , lowerCAmelCase : Optional[Union[float, List[float]]] = None , **lowerCAmelCase : Union[str, Any] , ) -> None:
"""simple docstring"""
super().__init__(**lowerCAmelCase )
__lowerCAmelCase : List[str] = size if size is not None else {"""shortest_edge""": 2_24}
__lowerCAmelCase : str = get_size_dict(lowerCAmelCase , default_to_square=lowerCAmelCase )
__lowerCAmelCase : str = crop_size if crop_size is not None else {"""height""": 2_24, """width""": 2_24}
__lowerCAmelCase : Any = get_size_dict(lowerCAmelCase , param_name="""crop_size""" )
__lowerCAmelCase : Tuple = do_resize
__lowerCAmelCase : Tuple = size
__lowerCAmelCase : Union[str, Any] = crop_pct
__lowerCAmelCase : List[Any] = resample
__lowerCAmelCase : Any = do_center_crop
__lowerCAmelCase : int = crop_size
__lowerCAmelCase : Tuple = do_rescale
__lowerCAmelCase : int = rescale_factor
__lowerCAmelCase : int = do_normalize
__lowerCAmelCase : Any = image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
__lowerCAmelCase : Optional[Any] = image_std if image_std is not None else IMAGENET_DEFAULT_STD
def SCREAMING_SNAKE_CASE ( self : Tuple , lowerCAmelCase : np.ndarray , lowerCAmelCase : Dict[str, int] , lowerCAmelCase : Optional[float] = None , lowerCAmelCase : PILImageResampling = PILImageResampling.BICUBIC , lowerCAmelCase : Optional[Union[str, ChannelDimension]] = None , **lowerCAmelCase : List[str] , ) -> np.ndarray:
"""simple docstring"""
__lowerCAmelCase : int = get_size_dict(lowerCAmelCase , default_to_square=lowerCAmelCase )
if "shortest_edge" not in size and ("height" not in size or "width" not in size):
raise ValueError(f'''size must contain \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}''' )
if crop_pct is not None:
if "shortest_edge" in size:
__lowerCAmelCase : Any = int(size["""shortest_edge"""] / crop_pct )
elif "height" in size and "width" in size:
if size["height"] == size["width"]:
__lowerCAmelCase : str = int(size["""height"""] / crop_pct )
else:
__lowerCAmelCase : List[Any] = (int(size["""height"""] / crop_pct ), int(size["""width"""] / crop_pct ))
else:
raise ValueError("""Invalid size for resize: {}""".format(lowerCAmelCase ) )
__lowerCAmelCase : Any = get_resize_output_image_size(lowerCAmelCase , size=lowerCAmelCase , default_to_square=lowerCAmelCase )
else:
if "shortest_edge" in size:
__lowerCAmelCase : Tuple = get_resize_output_image_size(lowerCAmelCase , size=size["""shortest_edge"""] , default_to_square=lowerCAmelCase )
elif "height" in size and "width" in size:
__lowerCAmelCase : List[str] = (size["""height"""], size["""width"""])
else:
raise ValueError("""Invalid size for resize: {}""".format(lowerCAmelCase ) )
return resize(lowerCAmelCase , size=lowerCAmelCase , resample=lowerCAmelCase , data_format=lowerCAmelCase , **lowerCAmelCase )
def SCREAMING_SNAKE_CASE ( self : List[str] , lowerCAmelCase : np.ndarray , lowerCAmelCase : Dict[str, int] , lowerCAmelCase : Optional[Union[str, ChannelDimension]] = None , **lowerCAmelCase : int , ) -> np.ndarray:
"""simple docstring"""
__lowerCAmelCase : Any = get_size_dict(lowerCAmelCase )
if "height" not in size or "width" not in size:
raise ValueError(f'''size must contain \'height\' and \'width\' as keys. Got {size.keys()}''' )
return center_crop(lowerCAmelCase , size=(size["""height"""], size["""width"""]) , data_format=lowerCAmelCase , **lowerCAmelCase )
def SCREAMING_SNAKE_CASE ( self : Optional[Any] , lowerCAmelCase : np.ndarray , lowerCAmelCase : Union[int, float] , lowerCAmelCase : Optional[Union[str, ChannelDimension]] = None , **lowerCAmelCase : Optional[int] , ) -> Tuple:
"""simple docstring"""
return rescale(lowerCAmelCase , scale=lowerCAmelCase , data_format=lowerCAmelCase , **lowerCAmelCase )
def SCREAMING_SNAKE_CASE ( self : Tuple , lowerCAmelCase : np.ndarray , lowerCAmelCase : Union[float, List[float]] , lowerCAmelCase : Union[float, List[float]] , lowerCAmelCase : Optional[Union[str, ChannelDimension]] = None , **lowerCAmelCase : int , ) -> np.ndarray:
"""simple docstring"""
return normalize(lowerCAmelCase , mean=lowerCAmelCase , std=lowerCAmelCase , data_format=lowerCAmelCase , **lowerCAmelCase )
def SCREAMING_SNAKE_CASE ( self : int , lowerCAmelCase : ImageInput , lowerCAmelCase : bool = None , lowerCAmelCase : Dict[str, int] = None , lowerCAmelCase : int = None , lowerCAmelCase : PILImageResampling = None , lowerCAmelCase : bool = None , lowerCAmelCase : Dict[str, int] = None , lowerCAmelCase : bool = None , lowerCAmelCase : float = None , lowerCAmelCase : bool = None , lowerCAmelCase : Optional[Union[float, List[float]]] = None , lowerCAmelCase : Optional[Union[float, List[float]]] = None , lowerCAmelCase : Optional[Union[str, TensorType]] = None , lowerCAmelCase : ChannelDimension = ChannelDimension.FIRST , **lowerCAmelCase : int , ) -> PIL.Image.Image:
"""simple docstring"""
__lowerCAmelCase : int = do_resize if do_resize is not None else self.do_resize
__lowerCAmelCase : List[Any] = crop_pct if crop_pct is not None else self.crop_pct
__lowerCAmelCase : Any = resample if resample is not None else self.resample
__lowerCAmelCase : Union[str, Any] = do_center_crop if do_center_crop is not None else self.do_center_crop
__lowerCAmelCase : int = do_rescale if do_rescale is not None else self.do_rescale
__lowerCAmelCase : Optional[int] = rescale_factor if rescale_factor is not None else self.rescale_factor
__lowerCAmelCase : Dict = do_normalize if do_normalize is not None else self.do_normalize
__lowerCAmelCase : int = image_mean if image_mean is not None else self.image_mean
__lowerCAmelCase : Dict = image_std if image_std is not None else self.image_std
__lowerCAmelCase : Dict = size if size is not None else self.size
__lowerCAmelCase : Tuple = get_size_dict(lowerCAmelCase , default_to_square=lowerCAmelCase )
__lowerCAmelCase : Tuple = crop_size if crop_size is not None else self.crop_size
__lowerCAmelCase : Optional[Any] = get_size_dict(lowerCAmelCase , param_name="""crop_size""" )
__lowerCAmelCase : Optional[Any] = make_list_of_images(lowerCAmelCase )
if not valid_images(lowerCAmelCase ):
raise ValueError(
"""Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
"""torch.Tensor, tf.Tensor or jax.ndarray.""" )
if do_resize and size is None or resample is None:
raise ValueError("""Size and resample must be specified if do_resize is True.""" )
if do_center_crop and crop_pct is None:
raise ValueError("""Crop_pct must be specified if do_center_crop is True.""" )
if do_rescale and rescale_factor is None:
raise ValueError("""Rescale factor must be specified if do_rescale is True.""" )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError("""Image mean and std must be specified if do_normalize is True.""" )
# All transformations expect numpy arrays.
__lowerCAmelCase : Any = [to_numpy_array(lowerCAmelCase ) for image in images]
if do_resize:
__lowerCAmelCase : str = [self.resize(image=lowerCAmelCase , size=lowerCAmelCase , crop_pct=lowerCAmelCase , resample=lowerCAmelCase ) for image in images]
if do_center_crop:
__lowerCAmelCase : Any = [self.center_crop(image=lowerCAmelCase , size=lowerCAmelCase ) for image in images]
if do_rescale:
__lowerCAmelCase : int = [self.rescale(image=lowerCAmelCase , scale=lowerCAmelCase ) for image in images]
if do_normalize:
__lowerCAmelCase : List[str] = [self.normalize(image=lowerCAmelCase , mean=lowerCAmelCase , std=lowerCAmelCase ) for image in images]
__lowerCAmelCase : Union[str, Any] = [to_channel_dimension_format(lowerCAmelCase , lowerCAmelCase ) for image in images]
__lowerCAmelCase : Union[str, Any] = {"""pixel_values""": images}
return BatchFeature(data=lowerCAmelCase , tensor_type=lowerCAmelCase )
| 139 |
import argparse
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import (
RobertaTokenizer,
TrOCRConfig,
TrOCRForCausalLM,
TrOCRProcessor,
VisionEncoderDecoderModel,
ViTConfig,
ViTImageProcessor,
ViTModel,
)
from transformers.utils import logging
logging.set_verbosity_info()
__UpperCAmelCase = logging.get_logger(__name__)
def snake_case_ (__A : Optional[int] , __A : Any ) -> Any:
__lowerCAmelCase : Union[str, Any] = []
for i in range(encoder_config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append(
(f'''encoder.deit.blocks.{i}.norm1.weight''', f'''encoder.encoder.layer.{i}.layernorm_before.weight''') )
rename_keys.append((f'''encoder.deit.blocks.{i}.norm1.bias''', f'''encoder.encoder.layer.{i}.layernorm_before.bias''') )
rename_keys.append(
(f'''encoder.deit.blocks.{i}.attn.proj.weight''', f'''encoder.encoder.layer.{i}.attention.output.dense.weight''') )
rename_keys.append(
(f'''encoder.deit.blocks.{i}.attn.proj.bias''', f'''encoder.encoder.layer.{i}.attention.output.dense.bias''') )
rename_keys.append(
(f'''encoder.deit.blocks.{i}.norm2.weight''', f'''encoder.encoder.layer.{i}.layernorm_after.weight''') )
rename_keys.append((f'''encoder.deit.blocks.{i}.norm2.bias''', f'''encoder.encoder.layer.{i}.layernorm_after.bias''') )
rename_keys.append(
(f'''encoder.deit.blocks.{i}.mlp.fc1.weight''', f'''encoder.encoder.layer.{i}.intermediate.dense.weight''') )
rename_keys.append(
(f'''encoder.deit.blocks.{i}.mlp.fc1.bias''', f'''encoder.encoder.layer.{i}.intermediate.dense.bias''') )
rename_keys.append(
(f'''encoder.deit.blocks.{i}.mlp.fc2.weight''', f'''encoder.encoder.layer.{i}.output.dense.weight''') )
rename_keys.append((f'''encoder.deit.blocks.{i}.mlp.fc2.bias''', f'''encoder.encoder.layer.{i}.output.dense.bias''') )
# cls token, position embeddings and patch embeddings of encoder
rename_keys.extend(
[
("""encoder.deit.cls_token""", """encoder.embeddings.cls_token"""),
("""encoder.deit.pos_embed""", """encoder.embeddings.position_embeddings"""),
("""encoder.deit.patch_embed.proj.weight""", """encoder.embeddings.patch_embeddings.projection.weight"""),
("""encoder.deit.patch_embed.proj.bias""", """encoder.embeddings.patch_embeddings.projection.bias"""),
("""encoder.deit.norm.weight""", """encoder.layernorm.weight"""),
("""encoder.deit.norm.bias""", """encoder.layernorm.bias"""),
] )
return rename_keys
def snake_case_ (__A : List[str] , __A : str ) -> Optional[Any]:
for i in range(encoder_config.num_hidden_layers ):
# queries, keys and values (only weights, no biases)
__lowerCAmelCase : Optional[Any] = state_dict.pop(f'''encoder.deit.blocks.{i}.attn.qkv.weight''' )
__lowerCAmelCase : Tuple = in_proj_weight[
: encoder_config.hidden_size, :
]
__lowerCAmelCase : str = in_proj_weight[
encoder_config.hidden_size : encoder_config.hidden_size * 2, :
]
__lowerCAmelCase : str = in_proj_weight[
-encoder_config.hidden_size :, :
]
def snake_case_ (__A : Union[str, Any] , __A : str , __A : Optional[Any] ) -> Optional[Any]:
__lowerCAmelCase : Any = dct.pop(__A )
__lowerCAmelCase : str = val
def snake_case_ (__A : int ) -> Tuple:
if "handwritten" in checkpoint_url:
__lowerCAmelCase : Tuple = """https://fki.tic.heia-fr.ch/static/img/a01-122-02-00.jpg""" # industry
# url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02-12.jpg" # have
# url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02-10.jpg" # let
# url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02.jpg" #
# url = "https://fki.tic.heia-fr.ch/static/img/a01-122.jpg"
elif "printed" in checkpoint_url or "stage1" in checkpoint_url:
__lowerCAmelCase : Optional[Any] = """https://www.researchgate.net/profile/Dinh-Sang/publication/338099565/figure/fig8/AS:840413229350922@1577381536857/An-receipt-example-in-the-SROIE-2019-dataset_Q640.jpg"""
__lowerCAmelCase : Dict = Image.open(requests.get(__A , stream=__A ).raw ).convert("""RGB""" )
return im
@torch.no_grad()
def snake_case_ (__A : Any , __A : Union[str, Any] ) -> Optional[int]:
__lowerCAmelCase : List[Any] = ViTConfig(image_size=3_8_4 , qkv_bias=__A )
__lowerCAmelCase : List[Any] = TrOCRConfig()
# size of the architecture
if "base" in checkpoint_url:
__lowerCAmelCase : Union[str, Any] = 7_6_8
elif "large" in checkpoint_url:
# use ViT-large encoder
__lowerCAmelCase : Any = 1_0_2_4
__lowerCAmelCase : Any = 4_0_9_6
__lowerCAmelCase : Optional[int] = 2_4
__lowerCAmelCase : str = 1_6
__lowerCAmelCase : List[Any] = 1_0_2_4
else:
raise ValueError("""Should either find 'base' or 'large' in checkpoint URL""" )
# the large-printed + stage1 checkpoints uses sinusoidal position embeddings, no layernorm afterwards
if "large-printed" in checkpoint_url or "stage1" in checkpoint_url:
__lowerCAmelCase : Tuple = False
__lowerCAmelCase : Union[str, Any] = """relu"""
__lowerCAmelCase : List[Any] = 1_0_2_4
__lowerCAmelCase : Any = True
__lowerCAmelCase : List[Any] = False
__lowerCAmelCase : Dict = False
# load HuggingFace model
__lowerCAmelCase : Dict = ViTModel(__A , add_pooling_layer=__A )
__lowerCAmelCase : Union[str, Any] = TrOCRForCausalLM(__A )
__lowerCAmelCase : Any = VisionEncoderDecoderModel(encoder=__A , decoder=__A )
model.eval()
# load state_dict of original model, rename some keys
__lowerCAmelCase : Union[str, Any] = torch.hub.load_state_dict_from_url(__A , map_location="""cpu""" , check_hash=__A )["""model"""]
__lowerCAmelCase : Any = create_rename_keys(__A , __A )
for src, dest in rename_keys:
rename_key(__A , __A , __A )
read_in_q_k_v(__A , __A )
# remove parameters we don't need
del state_dict["encoder.deit.head.weight"]
del state_dict["encoder.deit.head.bias"]
del state_dict["decoder.version"]
# add prefix to decoder keys
for key, val in state_dict.copy().items():
__lowerCAmelCase : Tuple = state_dict.pop(__A )
if key.startswith("""decoder""" ) and "output_projection" not in key:
__lowerCAmelCase : str = val
else:
__lowerCAmelCase : Tuple = val
# load state dict
model.load_state_dict(__A )
# Check outputs on an image
__lowerCAmelCase : List[Any] = ViTImageProcessor(size=encoder_config.image_size )
__lowerCAmelCase : List[str] = RobertaTokenizer.from_pretrained("""roberta-large""" )
__lowerCAmelCase : List[Any] = TrOCRProcessor(__A , __A )
__lowerCAmelCase : List[str] = processor(images=prepare_img(__A ) , return_tensors="""pt""" ).pixel_values
# verify logits
__lowerCAmelCase : List[str] = torch.tensor([[model.config.decoder.decoder_start_token_id]] )
__lowerCAmelCase : List[str] = model(pixel_values=__A , decoder_input_ids=__A )
__lowerCAmelCase : Optional[Any] = outputs.logits
__lowerCAmelCase : Union[str, Any] = torch.Size([1, 1, 5_0_2_6_5] )
if "trocr-base-handwritten" in checkpoint_url:
__lowerCAmelCase : Dict = torch.tensor(
[-1.4502, -4.6683, -0.5347, -2.9291, 9.1435, -3.0571, 8.9764, 1.7560, 8.7358, -1.5311] )
elif "trocr-large-handwritten" in checkpoint_url:
__lowerCAmelCase : List[Any] = torch.tensor(
[-2.6437, -1.3129, -2.2596, -5.3455, 6.3539, 1.7604, 5.4991, 1.4702, 5.6113, 2.0170] )
elif "trocr-base-printed" in checkpoint_url:
__lowerCAmelCase : Tuple = torch.tensor(
[-5.6816, -5.8388, 1.1398, -6.9034, 6.8505, -2.4393, 1.2284, -1.0232, -1.9661, -3.9210] )
elif "trocr-large-printed" in checkpoint_url:
__lowerCAmelCase : List[Any] = torch.tensor(
[-6.0162, -7.0959, 4.4155, -5.1063, 7.0468, -3.1631, 2.6466, -0.3081, -0.8106, -1.7535] )
if "stage1" not in checkpoint_url:
assert logits.shape == expected_shape, "Shape of logits not as expected"
assert torch.allclose(logits[0, 0, :1_0] , __A , atol=1e-3 ), "First elements of logits not as expected"
Path(__A ).mkdir(exist_ok=__A )
print(f'''Saving model to {pytorch_dump_folder_path}''' )
model.save_pretrained(__A )
print(f'''Saving processor to {pytorch_dump_folder_path}''' )
processor.save_pretrained(__A )
if __name__ == "__main__":
__UpperCAmelCase = argparse.ArgumentParser()
parser.add_argument(
"""--checkpoint_url""",
default="""https://layoutlm.blob.core.windows.net/trocr/model_zoo/fairseq/trocr-base-handwritten.pt""",
type=str,
help="""URL to the original PyTorch checkpoint (.pth file).""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
)
__UpperCAmelCase = parser.parse_args()
convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
| 139 | 1 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch
from accelerate import PartialState
from accelerate.utils.operations import broadcast, gather, gather_object, pad_across_processes, reduce
def __lowerCamelCase ( lowerCamelCase__ : Optional[Any] ):
'''simple docstring'''
return (torch.arange(state.num_processes ) + 1.0 + (state.num_processes * state.process_index)).to(state.device )
def __lowerCamelCase ( lowerCamelCase__ : Tuple ):
'''simple docstring'''
lowerCamelCase = create_tensor(lowerCamelCase__ )
lowerCamelCase = gather(lowerCamelCase__ )
assert gathered_tensor.tolist() == list(range(1 , state.num_processes**2 + 1 ) )
def __lowerCamelCase ( lowerCamelCase__ : int ):
'''simple docstring'''
lowerCamelCase = [state.process_index]
lowerCamelCase = gather_object(lowerCamelCase__ )
assert len(lowerCamelCase__ ) == state.num_processes, f'{gathered_obj}, {len(lowerCamelCase__ )} != {state.num_processes}'
assert gathered_obj == list(range(state.num_processes ) ), f'{gathered_obj} != {list(range(state.num_processes ) )}'
def __lowerCamelCase ( lowerCamelCase__ : Optional[int] ):
'''simple docstring'''
lowerCamelCase = create_tensor(lowerCamelCase__ )
lowerCamelCase = broadcast(lowerCamelCase__ )
assert broadcasted_tensor.shape == torch.Size([state.num_processes] )
assert broadcasted_tensor.tolist() == list(range(1 , state.num_processes + 1 ) )
def __lowerCamelCase ( lowerCamelCase__ : Tuple ):
'''simple docstring'''
if state.is_main_process:
lowerCamelCase = torch.arange(state.num_processes + 1 ).to(state.device )
else:
lowerCamelCase = torch.arange(state.num_processes ).to(state.device )
lowerCamelCase = pad_across_processes(lowerCamelCase__ )
assert padded_tensor.shape == torch.Size([state.num_processes + 1] )
if not state.is_main_process:
assert padded_tensor.tolist() == list(range(0 , state.num_processes ) ) + [0]
def __lowerCamelCase ( lowerCamelCase__ : List[str] ):
'''simple docstring'''
if state.num_processes != 2:
return
lowerCamelCase = create_tensor(lowerCamelCase__ )
lowerCamelCase = reduce(lowerCamelCase__ , """sum""" )
lowerCamelCase = torch.tensor([4.0, 6] ).to(state.device )
assert torch.allclose(lowerCamelCase__ , lowerCamelCase__ ), f'{reduced_tensor} != {truth_tensor}'
def __lowerCamelCase ( lowerCamelCase__ : Any ):
'''simple docstring'''
if state.num_processes != 2:
return
lowerCamelCase = create_tensor(lowerCamelCase__ )
lowerCamelCase = reduce(lowerCamelCase__ , """mean""" )
lowerCamelCase = torch.tensor([2.0, 3] ).to(state.device )
assert torch.allclose(lowerCamelCase__ , lowerCamelCase__ ), f'{reduced_tensor} != {truth_tensor}'
def __lowerCamelCase ( lowerCamelCase__ : List[str] ):
'''simple docstring'''
main()
def __lowerCamelCase ( ):
'''simple docstring'''
lowerCamelCase = PartialState()
state.print(f'State: {state}' )
state.print("""testing gather""" )
test_gather(lowerCamelCase__ )
state.print("""testing gather_object""" )
test_gather_object(lowerCamelCase__ )
state.print("""testing broadcast""" )
test_broadcast(lowerCamelCase__ )
state.print("""testing pad_across_processes""" )
test_pad_across_processes(lowerCamelCase__ )
state.print("""testing reduce_sum""" )
test_reduce_sum(lowerCamelCase__ )
state.print("""testing reduce_mean""" )
test_reduce_mean(lowerCamelCase__ )
if __name__ == "__main__":
main()
| 252 |
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class __lowercase ( a_ ):
"""simple docstring"""
UpperCamelCase : Any = ["image_processor", "tokenizer"]
UpperCamelCase : Dict = "BridgeTowerImageProcessor"
UpperCamelCase : List[Any] = ("RobertaTokenizer", "RobertaTokenizerFast")
def __init__( self , A , A ) -> Optional[int]:
'''simple docstring'''
super().__init__(A , A )
def __call__( self , A , A = None , A = True , A = False , A = None , A = None , A = 0 , A = None , A = None , A = None , A = False , A = False , A = False , A = False , A = True , A = None , **A , ) -> BatchEncoding:
'''simple docstring'''
lowerCamelCase = self.tokenizer(
text=A , add_special_tokens=A , padding=A , truncation=A , max_length=A , stride=A , pad_to_multiple_of=A , return_token_type_ids=A , return_attention_mask=A , return_overflowing_tokens=A , return_special_tokens_mask=A , return_offsets_mapping=A , return_length=A , verbose=A , return_tensors=A , **A , )
# add pixel_values + pixel_mask
lowerCamelCase = self.image_processor(
A , return_tensors=A , do_normalize=A , do_center_crop=A , **A )
encoding.update(A )
return encoding
def __A ( self , *A , **A ) -> Optional[int]:
'''simple docstring'''
return self.tokenizer.batch_decode(*A , **A )
def __A ( self , *A , **A ) -> Optional[int]:
'''simple docstring'''
return self.tokenizer.decode(*A , **A )
@property
def __A ( self ) -> Dict:
'''simple docstring'''
lowerCamelCase = self.tokenizer.model_input_names
lowerCamelCase = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
| 252 | 1 |
"""simple docstring"""
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
)
from transformers.utils import is_sentencepiece_available
if is_sentencepiece_available():
from transformers.models.mam_aaa.tokenization_mam_aaa import VOCAB_FILES_NAMES, save_json
from ...test_tokenization_common import TokenizerTesterMixin
if is_sentencepiece_available():
__UpperCamelCase = get_tests_dir('''fixtures/test_sentencepiece.model''')
if is_torch_available():
from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right
__UpperCamelCase = 128022
__UpperCamelCase = 128028
@require_sentencepiece
class lowerCAmelCase ( lowerCamelCase_ , unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : str = MaMaaaTokenizer
SCREAMING_SNAKE_CASE_ : Optional[Any] = False
SCREAMING_SNAKE_CASE_ : Dict = False
SCREAMING_SNAKE_CASE_ : str = True
def __A ( self ) -> Tuple:
super().setUp()
SCREAMING_SNAKE_CASE = ['</s>', '<unk>', '▁This', '▁is', '▁a', '▁t', 'est', '\u0120', '<pad>']
SCREAMING_SNAKE_CASE = dict(zip(_a , range(len(_a ) ) ) )
SCREAMING_SNAKE_CASE = Path(self.tmpdirname )
save_json(_a , save_dir / VOCAB_FILES_NAMES['vocab_file'] )
if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
copyfile(_a , save_dir / VOCAB_FILES_NAMES['spm_file'] )
SCREAMING_SNAKE_CASE = MaMaaaTokenizer.from_pretrained(self.tmpdirname )
tokenizer.save_pretrained(self.tmpdirname )
def __A ( self , **lowerCAmelCase__ ) -> List[str]:
return MaMaaaTokenizer.from_pretrained(self.tmpdirname , **_a )
def __A ( self , lowerCAmelCase__ ) -> Optional[Any]:
return (
"This is a test",
"This is a test",
)
def __A ( self ) -> Optional[int]:
SCREAMING_SNAKE_CASE = '</s>'
SCREAMING_SNAKE_CASE = 0
self.assertEqual(self.get_tokenizer()._convert_token_to_id(_a ) , _a )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(_a ) , _a )
def __A ( self ) -> Dict:
SCREAMING_SNAKE_CASE = self.get_tokenizer()
SCREAMING_SNAKE_CASE = list(tokenizer.get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '</s>' )
self.assertEqual(vocab_keys[1] , '<unk>' )
self.assertEqual(vocab_keys[-1] , '<s>' )
self.assertEqual(len(_a ) , tokenizer.vocab_size + len(tokenizer.get_added_vocab() ) )
@unittest.skip('Skip this test while all models are still to be uploaded.' )
def __A ( self ) -> Optional[int]:
pass
def __A ( self ) -> str:
SCREAMING_SNAKE_CASE = self.get_tokenizer()
SCREAMING_SNAKE_CASE = tokenizer.tokenize('This is a test' )
self.assertListEqual(_a , ['▁This', '▁is', '▁a', '▁t', 'est'] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(_a ) , [2, 3, 4, 5, 6] , )
SCREAMING_SNAKE_CASE = tokenizer.convert_ids_to_tokens([2, 3, 4, 5, 6] )
self.assertListEqual(_a , ['▁This', '▁is', '▁a', '▁t', 'est'] )
SCREAMING_SNAKE_CASE = tokenizer.convert_tokens_to_string(_a )
self.assertEqual(_a , 'This is a test' )
@slow
def __A ( self ) -> Optional[Any]:
# fmt: off
SCREAMING_SNAKE_CASE = {'input_ids': [[128_022, 110_108, 397, 11, 38_272, 2_247, 124_811, 285, 18_105, 1_586, 207, 7, 39_534, 4_428, 397, 1_019, 18_105, 1_586, 207, 7, 41_337, 16_786, 241, 7, 20_214, 17, 125_690, 10_398, 7, 44_378, 58_069, 68_342, 7_798, 7_343, 11, 299, 33_310, 4, 158, 37_350, 94_077, 4_569, 299, 33_310, 90, 4, 52_840, 290, 4, 31_270, 112, 299, 682, 4, 52_840, 39_953, 14_079, 193, 52_519, 90_894, 17_894, 120_697, 11, 40_445, 551, 17, 1_019, 52_519, 90_894, 17_756, 963, 11, 40_445, 480, 17, 9_792, 1_120, 5_173, 1_393, 6_240, 16_786, 241, 120_996, 28, 1_245, 1_393, 118_240, 11_123, 1_019, 93_612, 2_691, 10_618, 98_058, 120_409, 1_928, 279, 4, 40_683, 367, 178, 207, 1_019, 103, 103_121, 506, 65_296, 5, 2], [128_022, 21_217, 367, 117, 125_450, 128, 719, 7, 7_308, 40, 93_612, 12_669, 1_116, 16_704, 71, 17_785, 3_699, 15_592, 35, 144, 9_584, 241, 11_943, 713, 950, 799, 2_247, 88_427, 150, 149, 118_813, 120_706, 1_019, 106_906, 81_518, 28, 1_224, 22_799, 397, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [128_022, 1_658, 123_311, 5_155, 5_578, 4_722, 279, 14_947, 2_366, 1_120, 1_197, 14, 1_348, 9_232, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=_a , model_name='facebook/m2m100_418M' , revision='c168bae485c864188cf9aa0e4108b0b6934dc91e' , )
@require_torch
@require_sentencepiece
@require_tokenizers
class lowerCAmelCase ( unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : Optional[Any] = '''facebook/m2m100_418M'''
SCREAMING_SNAKE_CASE_ : Any = [
'''In my opinion, there are two levels of response from the French government.''',
'''NSA Affair Emphasizes Complete Lack of Debate on Intelligence''',
]
SCREAMING_SNAKE_CASE_ : Any = [
'''Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.''',
'''L\'affaire NSA souligne l\'absence totale de débat sur le renseignement''',
]
# fmt: off
SCREAMING_SNAKE_CASE_ : int = [EN_CODE, 5_9_3, 1_9_4_9, 1_1_5_7_8_1, 4, 7_1_5_8_6, 4_2_3_4, 6_0_6_3_3, 1_2_6_2_3_3, 4_3_2, 1_2_3_8_0_8, 1_5_5_9_2, 1_1_9_7, 1_1_7_1_3_2, 1_2_0_6_1_8, 5, 2]
@classmethod
def __A ( cls ) -> str:
SCREAMING_SNAKE_CASE = MaMaaaTokenizer.from_pretrained(
cls.checkpoint_name , src_lang='en' , tgt_lang='fr' )
SCREAMING_SNAKE_CASE = 1
return cls
def __A ( self ) -> Dict:
self.assertEqual(self.tokenizer.get_lang_id('ar' ) , 128_006 )
self.assertEqual(self.tokenizer.get_lang_id('en' ) , 128_022 )
self.assertEqual(self.tokenizer.get_lang_id('ro' ) , 128_076 )
self.assertEqual(self.tokenizer.get_lang_id('mr' ) , 128_063 )
def __A ( self ) -> int:
SCREAMING_SNAKE_CASE = self.tokenizer.get_vocab()
self.assertEqual(len(_a ) , self.tokenizer.vocab_size )
self.assertEqual(vocab['<unk>'] , 3 )
self.assertIn(self.tokenizer.get_lang_token('en' ) , _a )
def __A ( self ) -> int:
SCREAMING_SNAKE_CASE = 'en'
SCREAMING_SNAKE_CASE = self.tokenizer.batch_encode_plus(self.src_text ).input_ids[0]
self.assertListEqual(self.expected_src_tokens , _a )
def __A ( self ) -> Any:
self.assertIn(_a , self.tokenizer.all_special_ids )
# fmt: off
SCREAMING_SNAKE_CASE = [FR_CODE, 5_364, 82, 8_642, 4, 294, 47, 8, 14_028, 136, 3_286, 9_706, 6, 90_797, 6, 144_012, 162, 88_128, 30_061, 5, 2]
# fmt: on
SCREAMING_SNAKE_CASE = self.tokenizer.decode(_a , skip_special_tokens=_a )
SCREAMING_SNAKE_CASE = self.tokenizer.decode(generated_ids[1:] , skip_special_tokens=_a )
self.assertEqual(_a , _a )
self.assertNotIn(self.tokenizer.eos_token , _a )
def __A ( self ) -> Dict:
SCREAMING_SNAKE_CASE = tempfile.mkdtemp()
SCREAMING_SNAKE_CASE = self.tokenizer.lang_token_to_id
self.tokenizer.save_pretrained(_a )
SCREAMING_SNAKE_CASE = MaMaaaTokenizer.from_pretrained(_a )
self.assertDictEqual(new_tok.lang_token_to_id , _a )
@require_torch
def __A ( self ) -> Optional[int]:
SCREAMING_SNAKE_CASE = 'en'
SCREAMING_SNAKE_CASE = 'fr'
SCREAMING_SNAKE_CASE = self.tokenizer(self.src_text , text_target=self.tgt_text , padding=_a , return_tensors='pt' )
SCREAMING_SNAKE_CASE = shift_tokens_right(
batch['labels'] , self.tokenizer.pad_token_id , self.tokenizer.eos_token_id )
for k in batch:
SCREAMING_SNAKE_CASE = batch[k].tolist()
# batch = {k: v.tolist() for k,v in batch.items()}
# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
# batch.decoder_inputs_ids[0][0] ==
assert batch.input_ids[1][0] == EN_CODE
assert batch.input_ids[1][-1] == 2
assert batch.labels[1][0] == FR_CODE
assert batch.labels[1][-1] == 2
assert batch.decoder_input_ids[1][:2] == [2, FR_CODE]
@require_torch
def __A ( self ) -> List[str]:
SCREAMING_SNAKE_CASE = 'mr'
self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id('mr' )] )
self.assertListEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id] )
SCREAMING_SNAKE_CASE = 'zh'
self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id('zh' )] )
self.assertListEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id] )
@require_torch
def __A ( self ) -> Tuple:
SCREAMING_SNAKE_CASE = 'mr'
self.tokenizer._switch_to_target_mode()
self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id('mr' )] )
self.assertListEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id] )
self.tokenizer._switch_to_input_mode()
self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id(self.tokenizer.src_lang )] )
SCREAMING_SNAKE_CASE = 'zh'
self.tokenizer._switch_to_target_mode()
self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id('zh' )] )
self.assertListEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id] )
self.tokenizer._switch_to_input_mode()
self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id(self.tokenizer.src_lang )] )
@require_torch
def __A ( self ) -> Optional[Any]:
SCREAMING_SNAKE_CASE = self.tokenizer._build_translation_inputs('A test' , return_tensors='pt' , src_lang='en' , tgt_lang='ar' )
self.assertEqual(
nested_simplify(_a ) , {
# en_XX, A, test, EOS
'input_ids': [[128_022, 58, 4_183, 2]],
'attention_mask': [[1, 1, 1, 1]],
# ar_AR
'forced_bos_token_id': 128_006,
} , )
| 363 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import floats_tensor, load_image, load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
class lowerCAmelCase ( lowerCamelCase_ , unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : Optional[Any] = ShapEImgaImgPipeline
SCREAMING_SNAKE_CASE_ : Any = ["""image"""]
SCREAMING_SNAKE_CASE_ : Optional[int] = ["""image"""]
SCREAMING_SNAKE_CASE_ : Any = [
"""num_images_per_prompt""",
"""num_inference_steps""",
"""generator""",
"""latents""",
"""guidance_scale""",
"""frame_size""",
"""output_type""",
"""return_dict""",
]
SCREAMING_SNAKE_CASE_ : Any = False
@property
def __A ( self ) -> Tuple:
return 32
@property
def __A ( self ) -> Optional[int]:
return 32
@property
def __A ( self ) -> List[str]:
return self.time_input_dim * 4
@property
def __A ( self ) -> Union[str, Any]:
return 8
@property
def __A ( self ) -> Any:
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE = CLIPVisionConfig(
hidden_size=self.text_embedder_hidden_size , image_size=64 , projection_dim=self.text_embedder_hidden_size , intermediate_size=37 , num_attention_heads=4 , num_channels=3 , num_hidden_layers=5 , patch_size=1 , )
SCREAMING_SNAKE_CASE = CLIPVisionModel(lowerCAmelCase__ )
return model
@property
def __A ( self ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE = CLIPImageProcessor(
crop_size=224 , do_center_crop=lowerCAmelCase__ , do_normalize=lowerCAmelCase__ , do_resize=lowerCAmelCase__ , image_mean=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73] , image_std=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11] , resample=3 , size=224 , )
return image_processor
@property
def __A ( self ) -> str:
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE = {
'num_attention_heads': 2,
'attention_head_dim': 16,
'embedding_dim': self.time_input_dim,
'num_embeddings': 32,
'embedding_proj_dim': self.text_embedder_hidden_size,
'time_embed_dim': self.time_embed_dim,
'num_layers': 1,
'clip_embed_dim': self.time_input_dim * 2,
'additional_embeddings': 0,
'time_embed_act_fn': 'gelu',
'norm_in_type': 'layer',
'embedding_proj_norm_type': 'layer',
'encoder_hid_proj_type': None,
'added_emb_type': None,
}
SCREAMING_SNAKE_CASE = PriorTransformer(**lowerCAmelCase__ )
return model
@property
def __A ( self ) -> List[Any]:
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE = {
'param_shapes': (
(self.renderer_dim, 93),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
),
'd_latent': self.time_input_dim,
'd_hidden': self.renderer_dim,
'n_output': 12,
'background': (
0.1,
0.1,
0.1,
),
}
SCREAMING_SNAKE_CASE = ShapERenderer(**lowerCAmelCase__ )
return model
def __A ( self ) -> Dict:
SCREAMING_SNAKE_CASE = self.dummy_prior
SCREAMING_SNAKE_CASE = self.dummy_image_encoder
SCREAMING_SNAKE_CASE = self.dummy_image_processor
SCREAMING_SNAKE_CASE = self.dummy_renderer
SCREAMING_SNAKE_CASE = HeunDiscreteScheduler(
beta_schedule='exp' , num_train_timesteps=1_024 , prediction_type='sample' , use_karras_sigmas=lowerCAmelCase__ , clip_sample=lowerCAmelCase__ , clip_sample_range=1.0 , )
SCREAMING_SNAKE_CASE = {
'prior': prior,
'image_encoder': image_encoder,
'image_processor': image_processor,
'renderer': renderer,
'scheduler': scheduler,
}
return components
def __A ( self , lowerCAmelCase__ , lowerCAmelCase__=0 ) -> List[str]:
SCREAMING_SNAKE_CASE = floats_tensor((1, 3, 64, 64) , rng=random.Random(lowerCAmelCase__ ) ).to(lowerCAmelCase__ )
if str(lowerCAmelCase__ ).startswith('mps' ):
SCREAMING_SNAKE_CASE = torch.manual_seed(lowerCAmelCase__ )
else:
SCREAMING_SNAKE_CASE = torch.Generator(device=lowerCAmelCase__ ).manual_seed(lowerCAmelCase__ )
SCREAMING_SNAKE_CASE = {
'image': input_image,
'generator': generator,
'num_inference_steps': 1,
'frame_size': 32,
'output_type': 'np',
}
return inputs
def __A ( self ) -> List[Any]:
SCREAMING_SNAKE_CASE = 'cpu'
SCREAMING_SNAKE_CASE = self.get_dummy_components()
SCREAMING_SNAKE_CASE = self.pipeline_class(**lowerCAmelCase__ )
SCREAMING_SNAKE_CASE = pipe.to(lowerCAmelCase__ )
pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
SCREAMING_SNAKE_CASE = pipe(**self.get_dummy_inputs(lowerCAmelCase__ ) )
SCREAMING_SNAKE_CASE = output.images[0]
SCREAMING_SNAKE_CASE = image[0, -3:, -3:, -1]
assert image.shape == (20, 32, 32, 3)
SCREAMING_SNAKE_CASE = np.array(
[
0.00_03_92_16,
0.00_03_92_16,
0.00_03_92_16,
0.00_03_92_16,
0.00_03_92_16,
0.00_03_92_16,
0.00_03_92_16,
0.00_03_92_16,
0.00_03_92_16,
] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def __A ( self ) -> Union[str, Any]:
# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
self._test_inference_batch_consistent(batch_sizes=[1, 2] )
def __A ( self ) -> List[str]:
SCREAMING_SNAKE_CASE = torch_device == 'cpu'
SCREAMING_SNAKE_CASE = True
self._test_inference_batch_single_identical(
batch_size=2 , test_max_difference=lowerCAmelCase__ , relax_max_difference=lowerCAmelCase__ , )
def __A ( self ) -> List[str]:
SCREAMING_SNAKE_CASE = self.get_dummy_components()
SCREAMING_SNAKE_CASE = self.pipeline_class(**lowerCAmelCase__ )
SCREAMING_SNAKE_CASE = pipe.to(lowerCAmelCase__ )
pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
SCREAMING_SNAKE_CASE = 1
SCREAMING_SNAKE_CASE = 2
SCREAMING_SNAKE_CASE = self.get_dummy_inputs(lowerCAmelCase__ )
for key in inputs.keys():
if key in self.batch_params:
SCREAMING_SNAKE_CASE = batch_size * [inputs[key]]
SCREAMING_SNAKE_CASE = pipe(**lowerCAmelCase__ , num_images_per_prompt=lowerCAmelCase__ )[0]
assert images.shape[0] == batch_size * num_images_per_prompt
@slow
@require_torch_gpu
class lowerCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def __A ( self ) -> Optional[Any]:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __A ( self ) -> Any:
SCREAMING_SNAKE_CASE = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/shap_e/corgi.png' )
SCREAMING_SNAKE_CASE = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/shap_e/test_shap_e_img2img_out.npy' )
SCREAMING_SNAKE_CASE = ShapEImgaImgPipeline.from_pretrained('openai/shap-e-img2img' )
SCREAMING_SNAKE_CASE = pipe.to(lowerCAmelCase__ )
pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
SCREAMING_SNAKE_CASE = torch.Generator(device=lowerCAmelCase__ ).manual_seed(0 )
SCREAMING_SNAKE_CASE = pipe(
lowerCAmelCase__ , generator=lowerCAmelCase__ , guidance_scale=3.0 , num_inference_steps=64 , frame_size=64 , output_type='np' , ).images[0]
assert images.shape == (20, 64, 64, 3)
assert_mean_pixel_difference(lowerCAmelCase__ , lowerCAmelCase__ )
| 38 | 0 |
'''simple docstring'''
from typing import Optional, Union
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreTrainedModel
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_mobilenet_va import MobileNetVaConfig
lowercase : List[Any] = logging.get_logger(__name__)
# General docstring
lowercase : List[str] = "MobileNetV1Config"
# Base docstring
lowercase : int = "google/mobilenet_v1_1.0_224"
lowercase : List[str] = [1, 1024, 7, 7]
# Image classification docstring
lowercase : Any = "google/mobilenet_v1_1.0_224"
lowercase : Tuple = "tabby, tabby cat"
lowercase : str = [
"google/mobilenet_v1_1.0_224",
"google/mobilenet_v1_0.75_192",
# See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
]
def SCREAMING_SNAKE_CASE__ ( __A , __A , __A=None ) -> Dict:
_snake_case = {}
if isinstance(lowercase__ , lowercase__ ):
_snake_case = model.mobilenet_va
else:
_snake_case = model
_snake_case = 'MobilenetV1/Conv2d_0/'
_snake_case = backbone.conv_stem.convolution.weight
_snake_case = backbone.conv_stem.normalization.bias
_snake_case = backbone.conv_stem.normalization.weight
_snake_case = backbone.conv_stem.normalization.running_mean
_snake_case = backbone.conv_stem.normalization.running_var
for i in range(13 ):
_snake_case = i + 1
_snake_case = i * 2
_snake_case = backbone.layer[pt_index]
_snake_case = F'MobilenetV1/Conv2d_{tf_index}_depthwise/'
_snake_case = pointer.convolution.weight
_snake_case = pointer.normalization.bias
_snake_case = pointer.normalization.weight
_snake_case = pointer.normalization.running_mean
_snake_case = pointer.normalization.running_var
_snake_case = backbone.layer[pt_index + 1]
_snake_case = F'MobilenetV1/Conv2d_{tf_index}_pointwise/'
_snake_case = pointer.convolution.weight
_snake_case = pointer.normalization.bias
_snake_case = pointer.normalization.weight
_snake_case = pointer.normalization.running_mean
_snake_case = pointer.normalization.running_var
if isinstance(lowercase__ , lowercase__ ):
_snake_case = 'MobilenetV1/Logits/Conv2d_1c_1x1/'
_snake_case = model.classifier.weight
_snake_case = model.classifier.bias
return tf_to_pt_map
def SCREAMING_SNAKE_CASE__ ( __A , __A , __A ) -> Optional[int]:
try:
import numpy as np
import tensorflow as tf
except ImportError:
logger.error(
'Loading a TensorFlow models in PyTorch, requires TensorFlow to be installed. Please see '
'https://www.tensorflow.org/install/ for installation instructions.' )
raise
# Load weights from TF model
_snake_case = tf.train.list_variables(lowercase__ )
_snake_case = {}
for name, shape in init_vars:
logger.info(F'Loading TF weight {name} with shape {shape}' )
_snake_case = tf.train.load_variable(lowercase__ , lowercase__ )
_snake_case = array
# Build TF to PyTorch weights loading map
_snake_case = _build_tf_to_pytorch_map(lowercase__ , lowercase__ , lowercase__ )
for name, pointer in tf_to_pt_map.items():
logger.info(F'Importing {name}' )
if name not in tf_weights:
logger.info(F'{name} not in tf pre-trained weights, skipping' )
continue
_snake_case = tf_weights[name]
if "depthwise_weights" in name:
logger.info('Transposing depthwise' )
_snake_case = np.transpose(lowercase__ , (2, 3, 0, 1) )
elif "weights" in name:
logger.info('Transposing' )
if len(pointer.shape ) == 2: # copying into linear layer
_snake_case = array.squeeze().transpose()
else:
_snake_case = np.transpose(lowercase__ , (3, 2, 0, 1) )
if pointer.shape != array.shape:
raise ValueError(F'Pointer shape {pointer.shape} and array shape {array.shape} mismatched' )
logger.info(F'Initialize PyTorch weight {name} {array.shape}' )
_snake_case = torch.from_numpy(lowercase__ )
tf_weights.pop(lowercase__ , lowercase__ )
tf_weights.pop(name + '/RMSProp' , lowercase__ )
tf_weights.pop(name + '/RMSProp_1' , lowercase__ )
tf_weights.pop(name + '/ExponentialMovingAverage' , lowercase__ )
logger.info(F'Weights not copied to PyTorch model: {", ".join(tf_weights.keys() )}' )
return model
def SCREAMING_SNAKE_CASE__ ( __A , __A ) -> Optional[Any]:
_snake_case , _snake_case = features.shape[-2:]
_snake_case , _snake_case = conv_layer.stride
_snake_case , _snake_case = conv_layer.kernel_size
if in_height % stride_height == 0:
_snake_case = max(kernel_height - stride_height , 0 )
else:
_snake_case = max(kernel_height - (in_height % stride_height) , 0 )
if in_width % stride_width == 0:
_snake_case = max(kernel_width - stride_width , 0 )
else:
_snake_case = max(kernel_width - (in_width % stride_width) , 0 )
_snake_case = pad_along_width // 2
_snake_case = pad_along_width - pad_left
_snake_case = pad_along_height // 2
_snake_case = pad_along_height - pad_top
_snake_case = (pad_left, pad_right, pad_top, pad_bottom)
return nn.functional.pad(lowercase__ , lowercase__ , 'constant' , 0.0 )
class __UpperCAmelCase ( nn.Module ):
def __init__( self , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ = 1 , lowerCAmelCase_ = 1 , lowerCAmelCase_ = False , lowerCAmelCase_ = True , lowerCAmelCase_ = True , ):
"""simple docstring"""
super().__init__()
_snake_case = config
if in_channels % groups != 0:
raise ValueError(F'Input channels ({in_channels}) are not divisible by {groups} groups.' )
if out_channels % groups != 0:
raise ValueError(F'Output channels ({out_channels}) are not divisible by {groups} groups.' )
_snake_case = 0 if config.tf_padding else int((kernel_size - 1) / 2 )
_snake_case = nn.Convad(
in_channels=__lowercase , out_channels=__lowercase , kernel_size=__lowercase , stride=__lowercase , padding=__lowercase , groups=__lowercase , bias=__lowercase , padding_mode='zeros' , )
if use_normalization:
_snake_case = nn.BatchNormad(
num_features=__lowercase , eps=config.layer_norm_eps , momentum=0.9997 , affine=__lowercase , track_running_stats=__lowercase , )
else:
_snake_case = None
if use_activation:
if isinstance(__lowercase , __lowercase ):
_snake_case = ACTaFN[use_activation]
elif isinstance(config.hidden_act , __lowercase ):
_snake_case = ACTaFN[config.hidden_act]
else:
_snake_case = config.hidden_act
else:
_snake_case = None
def lowerCamelCase ( self , lowerCAmelCase_ ):
"""simple docstring"""
if self.config.tf_padding:
_snake_case = apply_tf_padding(__lowercase , self.convolution )
_snake_case = self.convolution(__lowercase )
if self.normalization is not None:
_snake_case = self.normalization(__lowercase )
if self.activation is not None:
_snake_case = self.activation(__lowercase )
return features
class __UpperCAmelCase ( _lowerCamelCase ):
__lowercase = MobileNetVaConfig
__lowercase = load_tf_weights_in_mobilenet_va
__lowercase = """mobilenet_v1"""
__lowercase = """pixel_values"""
__lowercase = False
def lowerCamelCase ( self , lowerCAmelCase_ ):
"""simple docstring"""
if isinstance(__lowercase , (nn.Linear, nn.Convad) ):
module.weight.data.normal_(mean=0.0 , std=self.config.initializer_range )
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(__lowercase , nn.BatchNormad ):
module.bias.data.zero_()
module.weight.data.fill_(1.0 )
lowercase : List[str] = r"\n This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it\n as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n behavior.\n\n Parameters:\n config ([`MobileNetV1Config`]): Model configuration class with all the parameters of the model.\n Initializing with a config file does not load the weights associated with the model, only the\n configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n"
lowercase : Optional[Any] = r"\n Args:\n pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n [`MobileNetV1ImageProcessor.__call__`] for details.\n output_hidden_states (`bool`, *optional*):\n Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n more detail.\n return_dict (`bool`, *optional*):\n Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n"
@add_start_docstrings(
"""The bare MobileNetV1 model outputting raw hidden-states without any specific head on top.""" , _lowerCamelCase , )
class __UpperCAmelCase ( _lowerCamelCase ):
def __init__( self , lowerCAmelCase_ , lowerCAmelCase_ = True ):
"""simple docstring"""
super().__init__(__lowercase )
_snake_case = config
_snake_case = 32
_snake_case = max(int(depth * config.depth_multiplier ) , config.min_depth )
_snake_case = MobileNetVaConvLayer(
__lowercase , in_channels=config.num_channels , out_channels=__lowercase , kernel_size=3 , stride=2 , )
_snake_case = [1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1]
_snake_case = nn.ModuleList()
for i in range(13 ):
_snake_case = out_channels
if strides[i] == 2 or i == 0:
depth *= 2
_snake_case = max(int(depth * config.depth_multiplier ) , config.min_depth )
self.layer.append(
MobileNetVaConvLayer(
__lowercase , in_channels=__lowercase , out_channels=__lowercase , kernel_size=3 , stride=strides[i] , groups=__lowercase , ) )
self.layer.append(
MobileNetVaConvLayer(
__lowercase , in_channels=__lowercase , out_channels=__lowercase , kernel_size=1 , ) )
_snake_case = nn.AdaptiveAvgPoolad((1, 1) ) if add_pooling_layer else None
# Initialize weights and apply final processing
self.post_init()
def lowerCamelCase ( self , lowerCAmelCase_ ):
"""simple docstring"""
raise NotImplementedError
@add_start_docstrings_to_model_forward(__lowercase )
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC , output_type=__lowercase , config_class=_CONFIG_FOR_DOC , modality='vision' , expected_output=_EXPECTED_OUTPUT_SHAPE , )
def lowerCamelCase ( self , lowerCAmelCase_ = None , lowerCAmelCase_ = None , lowerCAmelCase_ = None , ):
"""simple docstring"""
_snake_case = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
_snake_case = return_dict if return_dict is not None else self.config.use_return_dict
if pixel_values is None:
raise ValueError('You have to specify pixel_values' )
_snake_case = self.conv_stem(__lowercase )
_snake_case = () if output_hidden_states else None
for i, layer_module in enumerate(self.layer ):
_snake_case = layer_module(__lowercase )
if output_hidden_states:
_snake_case = all_hidden_states + (hidden_states,)
_snake_case = hidden_states
if self.pooler is not None:
_snake_case = torch.flatten(self.pooler(__lowercase ) , start_dim=1 )
else:
_snake_case = None
if not return_dict:
return tuple(v for v in [last_hidden_state, pooled_output, all_hidden_states] if v is not None )
return BaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=__lowercase , pooler_output=__lowercase , hidden_states=__lowercase , )
@add_start_docstrings(
"""\n MobileNetV1 model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n ImageNet.\n """ , _lowerCamelCase , )
class __UpperCAmelCase ( _lowerCamelCase ):
def __init__( self , lowerCAmelCase_ ):
"""simple docstring"""
super().__init__(__lowercase )
_snake_case = config.num_labels
_snake_case = MobileNetVaModel(__lowercase )
_snake_case = self.mobilenet_va.layer[-1].convolution.out_channels
# Classifier head
_snake_case = nn.Dropout(config.classifier_dropout_prob , inplace=__lowercase )
_snake_case = nn.Linear(__lowercase , config.num_labels ) if config.num_labels > 0 else nn.Identity()
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(__lowercase )
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT , output_type=__lowercase , config_class=_CONFIG_FOR_DOC , expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT , )
def lowerCamelCase ( self , lowerCAmelCase_ = None , lowerCAmelCase_ = None , lowerCAmelCase_ = None , lowerCAmelCase_ = None , ):
"""simple docstring"""
_snake_case = return_dict if return_dict is not None else self.config.use_return_dict
_snake_case = self.mobilenet_va(__lowercase , output_hidden_states=__lowercase , return_dict=__lowercase )
_snake_case = outputs.pooler_output if return_dict else outputs[1]
_snake_case = self.classifier(self.dropout(__lowercase ) )
_snake_case = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
_snake_case = 'regression'
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
_snake_case = 'single_label_classification'
else:
_snake_case = 'multi_label_classification'
if self.config.problem_type == "regression":
_snake_case = MSELoss()
if self.num_labels == 1:
_snake_case = loss_fct(logits.squeeze() , labels.squeeze() )
else:
_snake_case = loss_fct(__lowercase , __lowercase )
elif self.config.problem_type == "single_label_classification":
_snake_case = CrossEntropyLoss()
_snake_case = loss_fct(logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
elif self.config.problem_type == "multi_label_classification":
_snake_case = BCEWithLogitsLoss()
_snake_case = loss_fct(__lowercase , __lowercase )
if not return_dict:
_snake_case = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return ImageClassifierOutputWithNoAttention(
loss=__lowercase , logits=__lowercase , hidden_states=outputs.hidden_states , )
| 42 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
is_valid_image,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_vision_available():
import PIL
UpperCAmelCase = logging.get_logger(__name__)
def __UpperCamelCase ( lowercase__ : List[Any] ):
'''simple docstring'''
if isinstance(lowercase__, (list, tuple) ) and isinstance(videos[0], (list, tuple) ) and is_valid_image(videos[0][0] ):
return videos
elif isinstance(lowercase__, (list, tuple) ) and is_valid_image(videos[0] ):
return [videos]
elif is_valid_image(lowercase__ ):
return [[videos]]
raise ValueError(F'''Could not make batched video from {videos}''' )
class lowerCAmelCase ( A ):
lowerCAmelCase_ = ["pixel_values"]
def __init__( self : Union[str, Any] , __lowercase : bool = True , __lowercase : Dict[str, int] = None , __lowercase : PILImageResampling = PILImageResampling.BILINEAR , __lowercase : bool = True , __lowercase : Dict[str, int] = None , __lowercase : bool = True , __lowercase : Union[int, float] = 1 / 255 , __lowercase : bool = True , __lowercase : Optional[Union[float, List[float]]] = None , __lowercase : Optional[Union[float, List[float]]] = None , **__lowercase : Optional[Any] , ):
"""simple docstring"""
super().__init__(**__lowercase )
__lowercase =size if size is not None else {'shortest_edge': 224}
__lowercase =get_size_dict(__lowercase , default_to_square=__lowercase )
__lowercase =crop_size if crop_size is not None else {'height': 224, 'width': 224}
__lowercase =get_size_dict(__lowercase , param_name='crop_size' )
__lowercase =do_resize
__lowercase =size
__lowercase =do_center_crop
__lowercase =crop_size
__lowercase =resample
__lowercase =do_rescale
__lowercase =rescale_factor
__lowercase =do_normalize
__lowercase =image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
__lowercase =image_std if image_std is not None else IMAGENET_STANDARD_STD
def snake_case ( self : int , __lowercase : np.ndarray , __lowercase : Dict[str, int] , __lowercase : PILImageResampling = PILImageResampling.BILINEAR , __lowercase : Optional[Union[str, ChannelDimension]] = None , **__lowercase : Optional[Any] , ):
"""simple docstring"""
__lowercase =get_size_dict(__lowercase , default_to_square=__lowercase )
if "shortest_edge" in size:
__lowercase =get_resize_output_image_size(__lowercase , size['shortest_edge'] , default_to_square=__lowercase )
elif "height" in size and "width" in size:
__lowercase =(size['height'], size['width'])
else:
raise ValueError(f'''Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}''' )
return resize(__lowercase , size=__lowercase , resample=__lowercase , data_format=__lowercase , **__lowercase )
def snake_case ( self : Dict , __lowercase : np.ndarray , __lowercase : Dict[str, int] , __lowercase : Optional[Union[str, ChannelDimension]] = None , **__lowercase : List[Any] , ):
"""simple docstring"""
__lowercase =get_size_dict(__lowercase )
if "height" not in size or "width" not in size:
raise ValueError(f'''Size must have \'height\' and \'width\' as keys. Got {size.keys()}''' )
return center_crop(__lowercase , size=(size['height'], size['width']) , data_format=__lowercase , **__lowercase )
def snake_case ( self : str , __lowercase : np.ndarray , __lowercase : Union[int, float] , __lowercase : Optional[Union[str, ChannelDimension]] = None , **__lowercase : Any , ):
"""simple docstring"""
return rescale(__lowercase , scale=__lowercase , data_format=__lowercase , **__lowercase )
def snake_case ( self : Dict , __lowercase : np.ndarray , __lowercase : Union[float, List[float]] , __lowercase : Union[float, List[float]] , __lowercase : Optional[Union[str, ChannelDimension]] = None , **__lowercase : Optional[Any] , ):
"""simple docstring"""
return normalize(__lowercase , mean=__lowercase , std=__lowercase , data_format=__lowercase , **__lowercase )
def snake_case ( self : Optional[Any] , __lowercase : ImageInput , __lowercase : bool = None , __lowercase : Dict[str, int] = None , __lowercase : PILImageResampling = None , __lowercase : bool = None , __lowercase : Dict[str, int] = None , __lowercase : bool = None , __lowercase : float = None , __lowercase : bool = None , __lowercase : Optional[Union[float, List[float]]] = None , __lowercase : Optional[Union[float, List[float]]] = None , __lowercase : Optional[ChannelDimension] = ChannelDimension.FIRST , ):
"""simple docstring"""
if do_resize and size is None or resample is None:
raise ValueError('Size and resample must be specified if do_resize is True.' )
if do_center_crop and crop_size is None:
raise ValueError('Crop size must be specified if do_center_crop is True.' )
if do_rescale and rescale_factor is None:
raise ValueError('Rescale factor must be specified if do_rescale is True.' )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('Image mean and std must be specified if do_normalize is True.' )
# All transformations expect numpy arrays.
__lowercase =to_numpy_array(__lowercase )
if do_resize:
__lowercase =self.resize(image=__lowercase , size=__lowercase , resample=__lowercase )
if do_center_crop:
__lowercase =self.center_crop(__lowercase , size=__lowercase )
if do_rescale:
__lowercase =self.rescale(image=__lowercase , scale=__lowercase )
if do_normalize:
__lowercase =self.normalize(image=__lowercase , mean=__lowercase , std=__lowercase )
__lowercase =to_channel_dimension_format(__lowercase , __lowercase )
return image
def snake_case ( self : Union[str, Any] , __lowercase : ImageInput , __lowercase : bool = None , __lowercase : Dict[str, int] = None , __lowercase : PILImageResampling = None , __lowercase : bool = None , __lowercase : Dict[str, int] = None , __lowercase : bool = None , __lowercase : float = None , __lowercase : bool = None , __lowercase : Optional[Union[float, List[float]]] = None , __lowercase : Optional[Union[float, List[float]]] = None , __lowercase : Optional[Union[str, TensorType]] = None , __lowercase : ChannelDimension = ChannelDimension.FIRST , **__lowercase : Tuple , ):
"""simple docstring"""
__lowercase =do_resize if do_resize is not None else self.do_resize
__lowercase =resample if resample is not None else self.resample
__lowercase =do_center_crop if do_center_crop is not None else self.do_center_crop
__lowercase =do_rescale if do_rescale is not None else self.do_rescale
__lowercase =rescale_factor if rescale_factor is not None else self.rescale_factor
__lowercase =do_normalize if do_normalize is not None else self.do_normalize
__lowercase =image_mean if image_mean is not None else self.image_mean
__lowercase =image_std if image_std is not None else self.image_std
__lowercase =size if size is not None else self.size
__lowercase =get_size_dict(__lowercase , default_to_square=__lowercase )
__lowercase =crop_size if crop_size is not None else self.crop_size
__lowercase =get_size_dict(__lowercase , param_name='crop_size' )
if not valid_images(__lowercase ):
raise ValueError(
'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
'torch.Tensor, tf.Tensor or jax.ndarray.' )
__lowercase =make_batched(__lowercase )
__lowercase =[
[
self._preprocess_image(
image=__lowercase , do_resize=__lowercase , size=__lowercase , resample=__lowercase , do_center_crop=__lowercase , crop_size=__lowercase , do_rescale=__lowercase , rescale_factor=__lowercase , do_normalize=__lowercase , image_mean=__lowercase , image_std=__lowercase , data_format=__lowercase , )
for img in video
]
for video in videos
]
__lowercase ={'pixel_values': videos}
return BatchFeature(data=__lowercase , tensor_type=__lowercase )
| 141 | 0 |
from __future__ import annotations
class UpperCamelCase :
def __init__( self , UpperCAmelCase__=None ):
A__ = data
A__ = None
def __repr__( self ):
A__ = []
A__ = self
while temp:
string_rep.append(F"""{temp.data}""" )
A__ = temp.next
return "->".join(UpperCAmelCase__ )
def UpperCamelCase ( _A : list )-> Dict:
"""simple docstring"""
if not elements_list:
raise Exception("The Elements List is empty" )
A__ = A__ = Node(elements_list[0] )
for i in range(1 , len(_A ) ):
A__ = Node(elements_list[i] )
A__ = current.next
return head
def UpperCamelCase ( _A : Node )-> None:
"""simple docstring"""
if head_node is not None and isinstance(_A , _A ):
print_reverse(head_node.next )
print(head_node.data )
def UpperCamelCase ( )-> Tuple:
"""simple docstring"""
from doctest import testmod
testmod()
A__ = make_linked_list([14, 52, 14, 12, 43] )
print("Linked List:" )
print(_A )
print("Elements in Reverse:" )
print_reverse(_A )
if __name__ == "__main__":
main()
| 198 |
from manim import *
class UpperCamelCase ( _UpperCAmelCase ):
def __A ( self ):
A__ = Rectangle(height=0.5 , width=0.5 )
A__ = Rectangle(height=0.46 , width=0.46 ).set_stroke(width=0 )
A__ = [mem.copy() for i in range(6 )]
A__ = [mem.copy() for i in range(6 )]
A__ = VGroup(*UpperCAmelCase__ ).arrange(UpperCAmelCase__ , buff=0 )
A__ = VGroup(*UpperCAmelCase__ ).arrange(UpperCAmelCase__ , buff=0 )
A__ = VGroup(UpperCAmelCase__ , UpperCAmelCase__ ).arrange(UpperCAmelCase__ , buff=0 )
A__ = Text("CPU" , font_size=24 )
A__ = Group(UpperCAmelCase__ , UpperCAmelCase__ ).arrange(UpperCAmelCase__ , buff=0.5 , aligned_edge=UpperCAmelCase__ )
cpu.move_to([-2.5, -0.5, 0] )
self.add(UpperCAmelCase__ )
A__ = [mem.copy() for i in range(4 )]
A__ = VGroup(*UpperCAmelCase__ ).arrange(UpperCAmelCase__ , buff=0 )
A__ = Text("GPU" , font_size=24 )
A__ = Group(UpperCAmelCase__ , UpperCAmelCase__ ).arrange(UpperCAmelCase__ , buff=0.5 , aligned_edge=UpperCAmelCase__ )
gpu.move_to([-1, -1, 0] )
self.add(UpperCAmelCase__ )
A__ = [mem.copy() for i in range(6 )]
A__ = VGroup(*UpperCAmelCase__ ).arrange(UpperCAmelCase__ , buff=0 )
A__ = Text("Model" , font_size=24 )
A__ = Group(UpperCAmelCase__ , UpperCAmelCase__ ).arrange(UpperCAmelCase__ , buff=0.5 , aligned_edge=UpperCAmelCase__ )
model.move_to([3, -1.0, 0] )
self.add(UpperCAmelCase__ )
A__ = []
for i, rect in enumerate(UpperCAmelCase__ ):
rect.set_stroke(UpperCAmelCase__ )
# target = fill.copy().set_fill(YELLOW, opacity=0.7)
# target.move_to(rect)
# self.add(target)
A__ = Rectangle(height=0.46 / 4 , width=0.46 / 3 ).set_stroke(width=0.0 ).set_fill(UpperCAmelCase__ , opacity=0.7 )
if i == 0:
cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT ) , buff=0.02 , direction=UpperCAmelCase__ )
cpu_target.set_x(cpu_target.get_x() + 0.1 )
elif i == 3:
cpu_target.next_to(cpu_targs[0] , direction=UpperCAmelCase__ , buff=0.0 )
else:
cpu_target.next_to(cpu_targs[i - 1] , direction=UpperCAmelCase__ , buff=0.0 )
self.add(UpperCAmelCase__ )
cpu_targs.append(UpperCAmelCase__ )
A__ = [mem.copy() for i in range(6 )]
A__ = VGroup(*UpperCAmelCase__ ).arrange(UpperCAmelCase__ , buff=0 )
A__ = Text("Loaded Checkpoint" , font_size=24 )
A__ = Group(UpperCAmelCase__ , UpperCAmelCase__ ).arrange(UpperCAmelCase__ , aligned_edge=UpperCAmelCase__ , buff=0.4 )
checkpoint.move_to([3, 0.5, 0] )
A__ = Square(side_length=2.2 )
key.move_to([-5, 2, 0] )
A__ = MarkupText(
F"""<b>Key:</b>\n\n<span fgcolor='{YELLOW}'>●</span> Empty Model""" , font_size=18 , )
key_text.move_to([-5, 2.4, 0] )
self.add(UpperCAmelCase__ , UpperCAmelCase__ )
A__ = MarkupText(
F"""<span fgcolor='{BLUE}'>●</span> Checkpoint""" , font_size=18 , )
blue_text.next_to(UpperCAmelCase__ , DOWN * 2.4 , aligned_edge=key_text.get_left() )
A__ = MarkupText(
F"""Next, a <i><span fgcolor=\"{BLUE}\">second</span></i> model is loaded into memory,\nwith the weights of a <span fgcolor=\"{BLUE}\">single shard</span>.""" , font_size=24 , )
step_a.move_to([2, 2, 0] )
self.play(Write(UpperCAmelCase__ ) , Write(UpperCAmelCase__ ) )
self.play(Write(UpperCAmelCase__ , run_time=1 ) , Create(UpperCAmelCase__ , run_time=1 ) )
A__ = []
A__ = []
for i, rect in enumerate(UpperCAmelCase__ ):
A__ = fill.copy().set_fill(UpperCAmelCase__ , opacity=0.7 )
target.move_to(UpperCAmelCase__ )
first_animations.append(GrowFromCenter(UpperCAmelCase__ , run_time=1 ) )
A__ = target.copy()
cpu_target.generate_target()
if i < 5:
cpu_target.target.move_to(cpu_left_col_base[i + 1] )
else:
cpu_target.target.move_to(cpu_right_col_base[i - 5] )
second_animations.append(MoveToTarget(UpperCAmelCase__ , run_time=1.5 ) )
self.play(*UpperCAmelCase__ )
self.play(*UpperCAmelCase__ )
self.wait()
| 198 | 1 |
'''simple docstring'''
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment
class UpperCamelCase__ ( unittest.TestCase ):
"""simple docstring"""
def A_ ( self ):
'''simple docstring'''
UpperCAmelCase : List[str] = inspect.getfile(accelerate.test_utils )
UpperCAmelCase : Tuple = os.path.sep.join(mod_file.split(os.path.sep )[:-1] + ["scripts", "test_script.py"] )
UpperCAmelCase : Optional[int] = os.path.sep.join(
mod_file.split(os.path.sep )[:-1] + ["scripts", "test_distributed_data_loop.py"] )
UpperCAmelCase : Tuple = os.path.sep.join(mod_file.split(os.path.sep )[:-1] + ["scripts", "test_ops.py"] )
@require_multi_gpu
def A_ ( self ):
'''simple docstring'''
print(f"Found {torch.cuda.device_count()} devices." )
UpperCAmelCase : Any = ["torchrun", f"--nproc_per_node={torch.cuda.device_count()}", self.test_file_path]
with patch_environment(omp_num_threads=1 ):
execute_subprocess_async(snake_case , env=os.environ.copy() )
@require_multi_gpu
def A_ ( self ):
'''simple docstring'''
print(f"Found {torch.cuda.device_count()} devices." )
UpperCAmelCase : Tuple = ["torchrun", f"--nproc_per_node={torch.cuda.device_count()}", self.operation_file_path]
print(f"Command: {cmd}" )
with patch_environment(omp_num_threads=1 ):
execute_subprocess_async(snake_case , env=os.environ.copy() )
@require_multi_gpu
def A_ ( self ):
'''simple docstring'''
UpperCAmelCase : Optional[Any] = ["torchrun", f"--nproc_per_node={torch.cuda.device_count()}", inspect.getfile(self.__class__ )]
with patch_environment(omp_num_threads=1 ):
execute_subprocess_async(snake_case , env=os.environ.copy() )
@require_multi_gpu
def A_ ( self ):
'''simple docstring'''
print(f"Found {torch.cuda.device_count()} devices, using 2 devices only" )
UpperCAmelCase : str = ["torchrun", f"--nproc_per_node={torch.cuda.device_count()}", self.data_loop_file_path]
with patch_environment(omp_num_threads=1 , cuda_visible_devices="0,1" ):
execute_subprocess_async(snake_case , env=os.environ.copy() )
if __name__ == "__main__":
a : Union[str, Any] = Accelerator()
a : str = (accelerator.state.process_index + 2, 10)
a : List[str] = torch.randint(0, 10, shape).to(accelerator.device)
a : Optional[int] = ""
a : int = accelerator.pad_across_processes(tensor)
if tensora.shape[0] != accelerator.state.num_processes + 1:
error_msg += F"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
if not torch.equal(tensora[: accelerator.state.process_index + 2], tensor):
error_msg += "Tensors have different values."
if not torch.all(tensora[accelerator.state.process_index + 2 :] == 0):
error_msg += "Padding was not done with the right value (0)."
a : List[Any] = accelerator.pad_across_processes(tensor, pad_first=True)
if tensora.shape[0] != accelerator.state.num_processes + 1:
error_msg += F"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
a : List[str] = accelerator.state.num_processes - accelerator.state.process_index - 1
if not torch.equal(tensora[index:], tensor):
error_msg += "Tensors have different values."
if not torch.all(tensora[:index] == 0):
error_msg += "Padding was not done with the right value (0)."
# Raise error at the end to make sure we don't stop at the first failure.
if len(error_msg) > 0:
raise ValueError(error_msg)
| 311 |
'''simple docstring'''
import argparse
import copy
def lowercase ( __magic_name__ ):
'''simple docstring'''
UpperCAmelCase : List[str] = {}
with open(__magic_name__ ) as f:
for line in f:
if line.split()[0] not in dict_of_neighbours:
UpperCAmelCase : List[Any] = []
_list.append([line.split()[1], line.split()[2]] )
UpperCAmelCase : Tuple = _list
else:
dict_of_neighbours[line.split()[0]].append(
[line.split()[1], line.split()[2]] )
if line.split()[1] not in dict_of_neighbours:
UpperCAmelCase : Any = []
_list.append([line.split()[0], line.split()[2]] )
UpperCAmelCase : int = _list
else:
dict_of_neighbours[line.split()[1]].append(
[line.split()[0], line.split()[2]] )
return dict_of_neighbours
def lowercase ( __magic_name__ , __magic_name__ ):
'''simple docstring'''
with open(__magic_name__ ) as f:
UpperCAmelCase : List[str] = f.read(1 )
UpperCAmelCase : List[Any] = start_node
UpperCAmelCase : Union[str, Any] = []
UpperCAmelCase : Any = start_node
UpperCAmelCase : Optional[Any] = 0
while visiting not in first_solution:
UpperCAmelCase : Optional[Any] = 1_0000
for k in dict_of_neighbours[visiting]:
if int(k[1] ) < int(__magic_name__ ) and k[0] not in first_solution:
UpperCAmelCase : Tuple = k[1]
UpperCAmelCase : Dict = k[0]
first_solution.append(__magic_name__ )
UpperCAmelCase : int = distance_of_first_solution + int(__magic_name__ )
UpperCAmelCase : str = best_node
first_solution.append(__magic_name__ )
UpperCAmelCase : int = 0
for k in dict_of_neighbours[first_solution[-2]]:
if k[0] == start_node:
break
position += 1
UpperCAmelCase : str = (
distance_of_first_solution
+ int(dict_of_neighbours[first_solution[-2]][position][1] )
- 1_0000
)
return first_solution, distance_of_first_solution
def lowercase ( __magic_name__ , __magic_name__ ):
'''simple docstring'''
UpperCAmelCase : Optional[Any] = []
for n in solution[1:-1]:
UpperCAmelCase : Any = solution.index(__magic_name__ )
for kn in solution[1:-1]:
UpperCAmelCase : Dict = solution.index(__magic_name__ )
if n == kn:
continue
UpperCAmelCase : Tuple = copy.deepcopy(__magic_name__ )
UpperCAmelCase : Optional[int] = kn
UpperCAmelCase : List[str] = n
UpperCAmelCase : str = 0
for k in _tmp[:-1]:
UpperCAmelCase : List[Any] = _tmp[_tmp.index(__magic_name__ ) + 1]
for i in dict_of_neighbours[k]:
if i[0] == next_node:
UpperCAmelCase : List[Any] = distance + int(i[1] )
_tmp.append(__magic_name__ )
if _tmp not in neighborhood_of_solution:
neighborhood_of_solution.append(_tmp )
UpperCAmelCase : List[str] = len(neighborhood_of_solution[0] ) - 1
neighborhood_of_solution.sort(key=lambda __magic_name__ : x[index_of_last_item_in_the_list] )
return neighborhood_of_solution
def lowercase ( __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ ):
'''simple docstring'''
UpperCAmelCase : List[Any] = 1
UpperCAmelCase : List[str] = first_solution
UpperCAmelCase : str = []
UpperCAmelCase : Union[str, Any] = distance_of_first_solution
UpperCAmelCase : Union[str, Any] = solution
while count <= iters:
UpperCAmelCase : int = find_neighborhood(__magic_name__ , __magic_name__ )
UpperCAmelCase : Any = 0
UpperCAmelCase : List[str] = neighborhood[index_of_best_solution]
UpperCAmelCase : Dict = len(__magic_name__ ) - 1
UpperCAmelCase : Dict = False
while not found:
UpperCAmelCase : List[Any] = 0
while i < len(__magic_name__ ):
if best_solution[i] != solution[i]:
UpperCAmelCase : int = best_solution[i]
UpperCAmelCase : Optional[int] = solution[i]
break
UpperCAmelCase : List[str] = i + 1
if [first_exchange_node, second_exchange_node] not in tabu_list and [
second_exchange_node,
first_exchange_node,
] not in tabu_list:
tabu_list.append([first_exchange_node, second_exchange_node] )
UpperCAmelCase : List[str] = True
UpperCAmelCase : List[Any] = best_solution[:-1]
UpperCAmelCase : str = neighborhood[index_of_best_solution][best_cost_index]
if cost < best_cost:
UpperCAmelCase : Union[str, Any] = cost
UpperCAmelCase : Tuple = solution
else:
UpperCAmelCase : Optional[Any] = index_of_best_solution + 1
UpperCAmelCase : str = neighborhood[index_of_best_solution]
if len(__magic_name__ ) >= size:
tabu_list.pop(0 )
UpperCAmelCase : int = count + 1
return best_solution_ever, best_cost
def lowercase ( __magic_name__=None ):
'''simple docstring'''
UpperCAmelCase : Dict = generate_neighbours(args.File )
UpperCAmelCase , UpperCAmelCase : Any = generate_first_solution(
args.File , __magic_name__ )
UpperCAmelCase , UpperCAmelCase : Any = tabu_search(
__magic_name__ , __magic_name__ , __magic_name__ , args.Iterations , args.Size , )
print(F"Best solution: {best_sol}, with total distance: {best_cost}." )
if __name__ == "__main__":
a : Union[str, Any] = argparse.ArgumentParser(description="Tabu Search")
parser.add_argument(
"-f",
"--File",
type=str,
help="Path to the file containing the data",
required=True,
)
parser.add_argument(
"-i",
"--Iterations",
type=int,
help="How many iterations the algorithm should perform",
required=True,
)
parser.add_argument(
"-s", "--Size", type=int, help="Size of the tabu list", required=True
)
# Pass the arguments to main method
main(parser.parse_args())
| 311 | 1 |
"""simple docstring"""
import os
import tempfile
import unittest
import numpy as np
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_utils import shard
from diffusers import FlaxDDIMScheduler, FlaxDiffusionPipeline, FlaxStableDiffusionPipeline
@require_flax
class _SCREAMING_SNAKE_CASE( unittest.TestCase ):
def _UpperCamelCase ( self ) -> Tuple:
"""simple docstring"""
with tempfile.TemporaryDirectory() as tmpdirname:
# pipeline has Flax weights
__SCREAMING_SNAKE_CASE :List[str] = FlaxDiffusionPipeline.from_pretrained(
'''hf-internal-testing/tiny-stable-diffusion-pipe''' ,safety_checker=_lowercase ,cache_dir=_lowercase )
__SCREAMING_SNAKE_CASE :int = [t[-1] for t in os.walk(os.path.join(_lowercase ,os.listdir(_lowercase )[0] ,'''snapshots''' ) )]
__SCREAMING_SNAKE_CASE :Any = [item for sublist in all_root_files for item in sublist]
# None of the downloaded files should be a PyTorch file even if we have some here:
# https://huggingface.co/hf-internal-testing/tiny-stable-diffusion-pipe/blob/main/unet/diffusion_pytorch_model.bin
assert not any(f.endswith('''.bin''' ) for f in files )
@slow
@require_flax
class _SCREAMING_SNAKE_CASE( unittest.TestCase ):
def _UpperCamelCase ( self ) -> Dict:
"""simple docstring"""
__SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE :Optional[Any] = FlaxStableDiffusionPipeline.from_pretrained(
'''hf-internal-testing/tiny-stable-diffusion-pipe''' ,safety_checker=_lowercase )
__SCREAMING_SNAKE_CASE :Optional[int] = (
'''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
''' field, close up, split lighting, cinematic'''
)
__SCREAMING_SNAKE_CASE :Optional[Any] = jax.random.PRNGKey(0 )
__SCREAMING_SNAKE_CASE :Dict = 4
__SCREAMING_SNAKE_CASE :Tuple = jax.device_count()
__SCREAMING_SNAKE_CASE :Any = num_samples * [prompt]
__SCREAMING_SNAKE_CASE :List[Any] = pipeline.prepare_inputs(_lowercase )
# shard inputs and rng
__SCREAMING_SNAKE_CASE :Any = replicate(_lowercase )
__SCREAMING_SNAKE_CASE :List[str] = jax.random.split(_lowercase ,_lowercase )
__SCREAMING_SNAKE_CASE :Optional[int] = shard(_lowercase )
__SCREAMING_SNAKE_CASE :Any = pipeline(_lowercase ,_lowercase ,_lowercase ,_lowercase ,jit=_lowercase ).images
assert images.shape == (num_samples, 1, 64, 64, 3)
if jax.device_count() == 8:
assert np.abs(np.abs(images[0, 0, :2, :2, -2:] ,dtype=np.floataa ).sum() - 4.1_5_1_4_7_4_5 ) < 1E-3
assert np.abs(np.abs(_lowercase ,dtype=np.floataa ).sum() - 4_99_47.8_75 ) < 5E-1
__SCREAMING_SNAKE_CASE :Any = pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:] ) ) )
assert len(_lowercase ) == num_samples
def _UpperCamelCase ( self ) -> Union[str, Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE :Any = FlaxStableDiffusionPipeline.from_pretrained(
'''CompVis/stable-diffusion-v1-4''' ,revision='''flax''' ,safety_checker=_lowercase )
__SCREAMING_SNAKE_CASE :Tuple = (
'''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
''' field, close up, split lighting, cinematic'''
)
__SCREAMING_SNAKE_CASE :int = jax.random.PRNGKey(0 )
__SCREAMING_SNAKE_CASE :Optional[Any] = 50
__SCREAMING_SNAKE_CASE :List[str] = jax.device_count()
__SCREAMING_SNAKE_CASE :int = num_samples * [prompt]
__SCREAMING_SNAKE_CASE :int = pipeline.prepare_inputs(_lowercase )
# shard inputs and rng
__SCREAMING_SNAKE_CASE :Union[str, Any] = replicate(_lowercase )
__SCREAMING_SNAKE_CASE :str = jax.random.split(_lowercase ,_lowercase )
__SCREAMING_SNAKE_CASE :Tuple = shard(_lowercase )
__SCREAMING_SNAKE_CASE :Tuple = pipeline(_lowercase ,_lowercase ,_lowercase ,_lowercase ,jit=_lowercase ).images
assert images.shape == (num_samples, 1, 5_12, 5_12, 3)
if jax.device_count() == 8:
assert np.abs((np.abs(images[0, 0, :2, :2, -2:] ,dtype=np.floataa ).sum() - 0.0_5_6_5_2_4_0_1) ) < 1E-3
assert np.abs((np.abs(_lowercase ,dtype=np.floataa ).sum() - 2_38_38_08.2) ) < 5E-1
def _UpperCamelCase ( self ) -> List[str]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE :Optional[Any] = FlaxStableDiffusionPipeline.from_pretrained(
'''CompVis/stable-diffusion-v1-4''' ,revision='''bf16''' ,dtype=jnp.bfloataa ,safety_checker=_lowercase )
__SCREAMING_SNAKE_CASE :int = (
'''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
''' field, close up, split lighting, cinematic'''
)
__SCREAMING_SNAKE_CASE :Dict = jax.random.PRNGKey(0 )
__SCREAMING_SNAKE_CASE :Optional[int] = 50
__SCREAMING_SNAKE_CASE :Optional[Any] = jax.device_count()
__SCREAMING_SNAKE_CASE :Optional[int] = num_samples * [prompt]
__SCREAMING_SNAKE_CASE :str = pipeline.prepare_inputs(_lowercase )
# shard inputs and rng
__SCREAMING_SNAKE_CASE :int = replicate(_lowercase )
__SCREAMING_SNAKE_CASE :Optional[int] = jax.random.split(_lowercase ,_lowercase )
__SCREAMING_SNAKE_CASE :Optional[int] = shard(_lowercase )
__SCREAMING_SNAKE_CASE :str = pipeline(_lowercase ,_lowercase ,_lowercase ,_lowercase ,jit=_lowercase ).images
assert images.shape == (num_samples, 1, 5_12, 5_12, 3)
if jax.device_count() == 8:
assert np.abs((np.abs(images[0, 0, :2, :2, -2:] ,dtype=np.floataa ).sum() - 0.0_4_0_0_3_9_0_6) ) < 1E-3
assert np.abs((np.abs(_lowercase ,dtype=np.floataa ).sum() - 2_37_35_16.75) ) < 5E-1
def _UpperCamelCase ( self ) -> List[Any]:
"""simple docstring"""
__SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE :Optional[Any] = FlaxStableDiffusionPipeline.from_pretrained(
'''CompVis/stable-diffusion-v1-4''' ,revision='''bf16''' ,dtype=jnp.bfloataa )
__SCREAMING_SNAKE_CASE :int = (
'''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
''' field, close up, split lighting, cinematic'''
)
__SCREAMING_SNAKE_CASE :Union[str, Any] = jax.random.PRNGKey(0 )
__SCREAMING_SNAKE_CASE :List[str] = 50
__SCREAMING_SNAKE_CASE :Tuple = jax.device_count()
__SCREAMING_SNAKE_CASE :List[Any] = num_samples * [prompt]
__SCREAMING_SNAKE_CASE :List[str] = pipeline.prepare_inputs(_lowercase )
# shard inputs and rng
__SCREAMING_SNAKE_CASE :Optional[Any] = replicate(_lowercase )
__SCREAMING_SNAKE_CASE :Dict = jax.random.split(_lowercase ,_lowercase )
__SCREAMING_SNAKE_CASE :Optional[int] = shard(_lowercase )
__SCREAMING_SNAKE_CASE :Union[str, Any] = pipeline(_lowercase ,_lowercase ,_lowercase ,_lowercase ,jit=_lowercase ).images
assert images.shape == (num_samples, 1, 5_12, 5_12, 3)
if jax.device_count() == 8:
assert np.abs((np.abs(images[0, 0, :2, :2, -2:] ,dtype=np.floataa ).sum() - 0.0_4_0_0_3_9_0_6) ) < 1E-3
assert np.abs((np.abs(_lowercase ,dtype=np.floataa ).sum() - 2_37_35_16.75) ) < 5E-1
def _UpperCamelCase ( self ) -> Any:
"""simple docstring"""
__SCREAMING_SNAKE_CASE :List[Any] = FlaxDDIMScheduler(
beta_start=0.0_0_0_8_5 ,beta_end=0.0_1_2 ,beta_schedule='''scaled_linear''' ,set_alpha_to_one=_lowercase ,steps_offset=1 ,)
__SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE :List[Any] = FlaxStableDiffusionPipeline.from_pretrained(
'''CompVis/stable-diffusion-v1-4''' ,revision='''bf16''' ,dtype=jnp.bfloataa ,scheduler=_lowercase ,safety_checker=_lowercase ,)
__SCREAMING_SNAKE_CASE :Any = scheduler.create_state()
__SCREAMING_SNAKE_CASE :Any = scheduler_state
__SCREAMING_SNAKE_CASE :Optional[Any] = (
'''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
''' field, close up, split lighting, cinematic'''
)
__SCREAMING_SNAKE_CASE :Optional[Any] = jax.random.PRNGKey(0 )
__SCREAMING_SNAKE_CASE :Optional[int] = 50
__SCREAMING_SNAKE_CASE :List[str] = jax.device_count()
__SCREAMING_SNAKE_CASE :Dict = num_samples * [prompt]
__SCREAMING_SNAKE_CASE :Dict = pipeline.prepare_inputs(_lowercase )
# shard inputs and rng
__SCREAMING_SNAKE_CASE :Any = replicate(_lowercase )
__SCREAMING_SNAKE_CASE :Optional[Any] = jax.random.split(_lowercase ,_lowercase )
__SCREAMING_SNAKE_CASE :Any = shard(_lowercase )
__SCREAMING_SNAKE_CASE :List[str] = pipeline(_lowercase ,_lowercase ,_lowercase ,_lowercase ,jit=_lowercase ).images
assert images.shape == (num_samples, 1, 5_12, 5_12, 3)
if jax.device_count() == 8:
assert np.abs((np.abs(images[0, 0, :2, :2, -2:] ,dtype=np.floataa ).sum() - 0.0_4_5_0_4_3_9_4_5) ) < 1E-3
assert np.abs((np.abs(_lowercase ,dtype=np.floataa ).sum() - 2_34_76_93.5) ) < 5E-1
def _UpperCamelCase ( self ) -> Dict:
"""simple docstring"""
__SCREAMING_SNAKE_CASE :Dict = (
'''A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'''
''' field, close up, split lighting, cinematic'''
)
__SCREAMING_SNAKE_CASE :Any = jax.device_count()
__SCREAMING_SNAKE_CASE :Optional[Any] = num_samples * [prompt]
__SCREAMING_SNAKE_CASE :Union[str, Any] = jax.random.split(jax.random.PRNGKey(0 ) ,_lowercase )
__SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE :List[Any] = FlaxStableDiffusionPipeline.from_pretrained(
'''CompVis/stable-diffusion-v1-4''' ,revision='''bf16''' ,dtype=jnp.bfloataa ,safety_checker=_lowercase ,)
__SCREAMING_SNAKE_CASE :Union[str, Any] = replicate(_lowercase )
__SCREAMING_SNAKE_CASE :Tuple = pipeline.prepare_inputs(_lowercase )
__SCREAMING_SNAKE_CASE :List[Any] = shard(_lowercase )
__SCREAMING_SNAKE_CASE :Tuple = pipeline(_lowercase ,_lowercase ,_lowercase ,jit=_lowercase ).images
assert images.shape == (num_samples, 1, 5_12, 5_12, 3)
__SCREAMING_SNAKE_CASE :str = images[2, 0, 2_56, 10:17, 1]
# With memory efficient attention
__SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE :Dict = FlaxStableDiffusionPipeline.from_pretrained(
'''CompVis/stable-diffusion-v1-4''' ,revision='''bf16''' ,dtype=jnp.bfloataa ,safety_checker=_lowercase ,use_memory_efficient_attention=_lowercase ,)
__SCREAMING_SNAKE_CASE :str = replicate(_lowercase )
__SCREAMING_SNAKE_CASE :int = pipeline.prepare_inputs(_lowercase )
__SCREAMING_SNAKE_CASE :Dict = shard(_lowercase )
__SCREAMING_SNAKE_CASE :Optional[Any] = pipeline(_lowercase ,_lowercase ,_lowercase ,jit=_lowercase ).images
assert images_eff.shape == (num_samples, 1, 5_12, 5_12, 3)
__SCREAMING_SNAKE_CASE :List[str] = images[2, 0, 2_56, 10:17, 1]
# I checked the results visually and they are very similar. However, I saw that the max diff is `1` and the `sum`
# over the 8 images is exactly `256`, which is very suspicious. Testing a random slice for now.
assert abs(slice_eff - slice ).max() < 1E-2
| 366 |
"""simple docstring"""
from itertools import product
def __lowerCamelCase ( a_ : int , a_ : int ) -> list[int]:
__SCREAMING_SNAKE_CASE :Tuple = sides_number
__SCREAMING_SNAKE_CASE :List[Any] = max_face_number * dice_number
__SCREAMING_SNAKE_CASE :List[Any] = [0] * (max_total + 1)
__SCREAMING_SNAKE_CASE :Optional[int] = 1
__SCREAMING_SNAKE_CASE :Tuple = range(a_ , max_face_number + 1 )
for dice_numbers in product(a_ , repeat=a_ ):
__SCREAMING_SNAKE_CASE :Any = sum(a_ )
totals_frequencies[total] += 1
return totals_frequencies
def __lowerCamelCase ( ) -> float:
__SCREAMING_SNAKE_CASE :Dict = total_frequency_distribution(
sides_number=4 , dice_number=9 )
__SCREAMING_SNAKE_CASE :Union[str, Any] = total_frequency_distribution(
sides_number=6 , dice_number=6 )
__SCREAMING_SNAKE_CASE :Optional[Any] = 0
__SCREAMING_SNAKE_CASE :Any = 9
__SCREAMING_SNAKE_CASE :List[str] = 4 * 9
__SCREAMING_SNAKE_CASE :Dict = 6
for peter_total in range(a_ , max_peter_total + 1 ):
peter_wins_count += peter_totals_frequencies[peter_total] * sum(
colin_totals_frequencies[min_colin_total:peter_total] )
__SCREAMING_SNAKE_CASE :List[str] = (4**9) * (6**6)
__SCREAMING_SNAKE_CASE :Union[str, Any] = peter_wins_count / total_games_number
__SCREAMING_SNAKE_CASE :str = round(a_ , ndigits=7 )
return rounded_peter_win_probability
if __name__ == "__main__":
print(f'{solution() = }') | 239 | 0 |
'''simple docstring'''
from argparse import ArgumentParser, Namespace
from ..utils import logging
from . import BaseTransformersCLICommand
def SCREAMING_SNAKE_CASE__ ( __A ) -> List[Any]:
return ConvertCommand(
args.model_type , args.tf_checkpoint , args.pytorch_dump_output , args.config , args.finetuning_task_name )
lowercase : List[str] = '\ntransformers can only be used from the commandline to convert TensorFlow models in PyTorch, In that case, it requires\nTensorFlow to be installed. Please see https://www.tensorflow.org/install/ for installation instructions.\n'
class __UpperCAmelCase ( _lowerCamelCase ):
@staticmethod
def lowerCamelCase ( lowerCAmelCase_ ):
"""simple docstring"""
_snake_case = parser.add_parser(
'convert' , help='CLI tool to run convert model from original author checkpoints to Transformers PyTorch checkpoints.' , )
train_parser.add_argument('--model_type' , type=_UpperCamelCase , required=_UpperCamelCase , help='Model\'s type.' )
train_parser.add_argument(
'--tf_checkpoint' , type=_UpperCamelCase , required=_UpperCamelCase , help='TensorFlow checkpoint path or folder.' )
train_parser.add_argument(
'--pytorch_dump_output' , type=_UpperCamelCase , required=_UpperCamelCase , help='Path to the PyTorch saved model output.' )
train_parser.add_argument('--config' , type=_UpperCamelCase , default='' , help='Configuration file path or folder.' )
train_parser.add_argument(
'--finetuning_task_name' , type=_UpperCamelCase , default=_UpperCamelCase , help='Optional fine-tuning task name if the TF model was a finetuned model.' , )
train_parser.set_defaults(func=_UpperCamelCase )
def __init__( self , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , *lowerCAmelCase_ , ):
"""simple docstring"""
_snake_case = logging.get_logger('transformers-cli/converting' )
self._logger.info(F'Loading model {model_type}' )
_snake_case = model_type
_snake_case = tf_checkpoint
_snake_case = pytorch_dump_output
_snake_case = config
_snake_case = finetuning_task_name
def lowerCamelCase ( self ):
"""simple docstring"""
if self._model_type == "albert":
try:
from ..models.albert.convert_albert_original_tf_checkpoint_to_pytorch import (
convert_tf_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(_UpperCamelCase )
convert_tf_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "bert":
try:
from ..models.bert.convert_bert_original_tf_checkpoint_to_pytorch import (
convert_tf_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(_UpperCamelCase )
convert_tf_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "funnel":
try:
from ..models.funnel.convert_funnel_original_tf_checkpoint_to_pytorch import (
convert_tf_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(_UpperCamelCase )
convert_tf_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "t5":
try:
from ..models.ta.convert_ta_original_tf_checkpoint_to_pytorch import convert_tf_checkpoint_to_pytorch
except ImportError:
raise ImportError(_UpperCamelCase )
convert_tf_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "gpt":
from ..models.openai.convert_openai_original_tf_checkpoint_to_pytorch import (
convert_openai_checkpoint_to_pytorch,
)
convert_openai_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "transfo_xl":
try:
from ..models.transfo_xl.convert_transfo_xl_original_tf_checkpoint_to_pytorch import (
convert_transfo_xl_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(_UpperCamelCase )
if "ckpt" in self._tf_checkpoint.lower():
_snake_case = self._tf_checkpoint
_snake_case = ''
else:
_snake_case = self._tf_checkpoint
_snake_case = ''
convert_transfo_xl_checkpoint_to_pytorch(
_UpperCamelCase , self._config , self._pytorch_dump_output , _UpperCamelCase )
elif self._model_type == "gpt2":
try:
from ..models.gpta.convert_gpta_original_tf_checkpoint_to_pytorch import (
convert_gpta_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(_UpperCamelCase )
convert_gpta_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
elif self._model_type == "xlnet":
try:
from ..models.xlnet.convert_xlnet_original_tf_checkpoint_to_pytorch import (
convert_xlnet_checkpoint_to_pytorch,
)
except ImportError:
raise ImportError(_UpperCamelCase )
convert_xlnet_checkpoint_to_pytorch(
self._tf_checkpoint , self._config , self._pytorch_dump_output , self._finetuning_task_name )
elif self._model_type == "xlm":
from ..models.xlm.convert_xlm_original_pytorch_checkpoint_to_pytorch import (
convert_xlm_checkpoint_to_pytorch,
)
convert_xlm_checkpoint_to_pytorch(self._tf_checkpoint , self._pytorch_dump_output )
elif self._model_type == "lxmert":
from ..models.lxmert.convert_lxmert_original_tf_checkpoint_to_pytorch import (
convert_lxmert_checkpoint_to_pytorch,
)
convert_lxmert_checkpoint_to_pytorch(self._tf_checkpoint , self._pytorch_dump_output )
elif self._model_type == "rembert":
from ..models.rembert.convert_rembert_tf_checkpoint_to_pytorch import (
convert_rembert_tf_checkpoint_to_pytorch,
)
convert_rembert_tf_checkpoint_to_pytorch(self._tf_checkpoint , self._config , self._pytorch_dump_output )
else:
raise ValueError(
'--model_type should be selected in the list [bert, gpt, gpt2, t5, transfo_xl, xlnet, xlm, lxmert]' )
| 42 |
import argparse
import torch
from transformers import BertForMaskedLM
if __name__ == "__main__":
snake_case__ : Optional[int] = argparse.ArgumentParser(
description=(
'Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned'
' Distillation'
)
)
parser.add_argument('--model_type', default='bert', choices=['bert'])
parser.add_argument('--model_name', default='bert-base-uncased', type=str)
parser.add_argument('--dump_checkpoint', default='serialization_dir/tf_bert-base-uncased_0247911.pth', type=str)
parser.add_argument('--vocab_transform', action='store_true')
snake_case__ : Optional[int] = parser.parse_args()
if args.model_type == "bert":
snake_case__ : Dict = BertForMaskedLM.from_pretrained(args.model_name)
snake_case__ : Union[str, Any] = 'bert'
else:
raise ValueError('args.model_type should be "bert".')
snake_case__ : Optional[int] = model.state_dict()
snake_case__ : List[Any] = {}
for w in ["word_embeddings", "position_embeddings"]:
snake_case__ : Tuple = state_dict[f'{prefix}.embeddings.{w}.weight']
for w in ["weight", "bias"]:
snake_case__ : Optional[Any] = state_dict[f'{prefix}.embeddings.LayerNorm.{w}']
snake_case__ : int = 0
for teacher_idx in [0, 2, 4, 7, 9, 11]:
for w in ["weight", "bias"]:
snake_case__ : Union[str, Any] = state_dict[
f'{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}'
]
snake_case__ : Dict = state_dict[
f'{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}'
]
snake_case__ : int = state_dict[
f'{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}'
]
snake_case__ : int = state_dict[
f'{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}'
]
snake_case__ : Optional[int] = state_dict[
f'{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}'
]
snake_case__ : Optional[Any] = state_dict[
f'{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}'
]
snake_case__ : List[str] = state_dict[
f'{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}'
]
snake_case__ : int = state_dict[
f'{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}'
]
std_idx += 1
snake_case__ : Optional[int] = state_dict['cls.predictions.decoder.weight']
snake_case__ : str = state_dict['cls.predictions.bias']
if args.vocab_transform:
for w in ["weight", "bias"]:
snake_case__ : int = state_dict[f'cls.predictions.transform.dense.{w}']
snake_case__ : Optional[int] = state_dict[f'cls.predictions.transform.LayerNorm.{w}']
print(f'N layers selected for distillation: {std_idx}')
print(f'Number of params transferred for distillation: {len(compressed_sd.keys())}')
print(f'Save transferred checkpoint to {args.dump_checkpoint}.')
torch.save(compressed_sd, args.dump_checkpoint)
| 117 | 0 |
from __future__ import annotations
from math import pi
def SCREAMING_SNAKE_CASE ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase) -> dict[str, float]:
if (inductance, frequency, reactance).count(0) != 1:
raise ValueError("One and only one argument must be 0")
if inductance < 0:
raise ValueError("Inductance cannot be negative")
if frequency < 0:
raise ValueError("Frequency cannot be negative")
if reactance < 0:
raise ValueError("Inductive reactance cannot be negative")
if inductance == 0:
return {"inductance": reactance / (2 * pi * frequency)}
elif frequency == 0:
return {"frequency": reactance / (2 * pi * inductance)}
elif reactance == 0:
return {"reactance": 2 * pi * frequency * inductance}
else:
raise ValueError("Exactly one argument must be 0")
if __name__ == "__main__":
import doctest
doctest.testmod()
| 180 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ : Any = logging.get_logger(__name__)
lowercase__ : int = {
"asapp/sew-d-tiny-100k": "https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json",
# See all SEW-D models at https://huggingface.co/models?filter=sew-d
}
class a__ ( UpperCamelCase__ ):
a : Optional[Any] = """sew-d"""
def __init__( self , A=32 , A=768 , A=12 , A=12 , A=3072 , A=2 , A=512 , A=256 , A=True , A=True , A=("p2c", "c2p") , A="layer_norm" , A="gelu_python" , A=0.1 , A=0.1 , A=0.1 , A=0.0 , A=0.1 , A=0.0_2 , A=1e-7 , A=1e-5 , A="group" , A="gelu" , A=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512) , A=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1) , A=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1) , A=False , A=128 , A=16 , A=True , A=0.0_5 , A=10 , A=2 , A=0.0 , A=10 , A=0 , A="mean" , A=False , A=False , A=256 , A=0 , A=1 , A=2 , **A , ) -> Dict:
'''simple docstring'''
super().__init__(**A , pad_token_id=A , bos_token_id=A , eos_token_id=A )
a = hidden_size
a = feat_extract_norm
a = feat_extract_activation
a = list(A )
a = list(A )
a = list(A )
a = conv_bias
a = num_conv_pos_embeddings
a = num_conv_pos_embedding_groups
a = len(self.conv_dim )
a = num_hidden_layers
a = intermediate_size
a = squeeze_factor
a = max_position_embeddings
a = position_buckets
a = share_att_key
a = relative_attention
a = norm_rel_ebd
a = list(A )
a = hidden_act
a = num_attention_heads
a = hidden_dropout
a = attention_dropout
a = activation_dropout
a = feat_proj_dropout
a = final_dropout
a = layer_norm_eps
a = feature_layer_norm_eps
a = initializer_range
a = vocab_size
if (
(len(self.conv_stride ) != self.num_feat_extract_layers)
or (len(self.conv_kernel ) != self.num_feat_extract_layers)
or (len(self.conv_dim ) != self.num_feat_extract_layers)
):
raise ValueError(
"Configuration for convolutional layers is incorrect."
"It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,"
F'''but is `len(config.conv_dim) = {len(self.conv_dim )}`, `len(config.conv_stride)'''
F'''= {len(self.conv_stride )}`, `len(config.conv_kernel) = {len(self.conv_kernel )}`.''' )
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
a = apply_spec_augment
a = mask_time_prob
a = mask_time_length
a = mask_time_min_masks
a = mask_feature_prob
a = mask_feature_length
a = mask_feature_min_masks
# ctc loss
a = ctc_loss_reduction
a = ctc_zero_infinity
# sequence classification
a = use_weighted_layer_sum
a = classifier_proj_size
@property
def lowerCAmelCase_ ( self ) -> List[str]:
'''simple docstring'''
return functools.reduce(operator.mul , self.conv_stride , 1 )
| 180 | 1 |
"""simple docstring"""
import argparse
import requests
import torch
from PIL import Image
from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor
def lowercase ( _SCREAMING_SNAKE_CASE : List[Any] ):
'''simple docstring'''
_UpperCAmelCase = SwinConfig(image_size=192 )
if "base" in model_name:
_UpperCAmelCase = 6
_UpperCAmelCase = 128
_UpperCAmelCase = (2, 2, 18, 2)
_UpperCAmelCase = (4, 8, 16, 32)
elif "large" in model_name:
_UpperCAmelCase = 12
_UpperCAmelCase = 192
_UpperCAmelCase = (2, 2, 18, 2)
_UpperCAmelCase = (6, 12, 24, 48)
else:
raise ValueError('''Model not supported, only supports base and large variants''' )
_UpperCAmelCase = window_size
_UpperCAmelCase = embed_dim
_UpperCAmelCase = depths
_UpperCAmelCase = num_heads
return config
def lowercase ( _SCREAMING_SNAKE_CASE : Optional[Any] ):
'''simple docstring'''
if "encoder.mask_token" in name:
_UpperCAmelCase = name.replace('''encoder.mask_token''' , '''embeddings.mask_token''' )
if "encoder.patch_embed.proj" in name:
_UpperCAmelCase = name.replace('''encoder.patch_embed.proj''' , '''embeddings.patch_embeddings.projection''' )
if "encoder.patch_embed.norm" in name:
_UpperCAmelCase = name.replace('''encoder.patch_embed.norm''' , '''embeddings.norm''' )
if "attn.proj" in name:
_UpperCAmelCase = name.replace('''attn.proj''' , '''attention.output.dense''' )
if "attn" in name:
_UpperCAmelCase = name.replace('''attn''' , '''attention.self''' )
if "norm1" in name:
_UpperCAmelCase = name.replace('''norm1''' , '''layernorm_before''' )
if "norm2" in name:
_UpperCAmelCase = name.replace('''norm2''' , '''layernorm_after''' )
if "mlp.fc1" in name:
_UpperCAmelCase = name.replace('''mlp.fc1''' , '''intermediate.dense''' )
if "mlp.fc2" in name:
_UpperCAmelCase = name.replace('''mlp.fc2''' , '''output.dense''' )
if name == "encoder.norm.weight":
_UpperCAmelCase = 'layernorm.weight'
if name == "encoder.norm.bias":
_UpperCAmelCase = 'layernorm.bias'
if "decoder" in name:
pass
else:
_UpperCAmelCase = 'swin.' + name
return name
def lowercase ( _SCREAMING_SNAKE_CASE : List[str] , _SCREAMING_SNAKE_CASE : str ):
'''simple docstring'''
for key in orig_state_dict.copy().keys():
_UpperCAmelCase = orig_state_dict.pop(__SCREAMING_SNAKE_CASE )
if "attn_mask" in key:
pass
elif "qkv" in key:
_UpperCAmelCase = key.split('''.''' )
_UpperCAmelCase = int(key_split[2] )
_UpperCAmelCase = int(key_split[4] )
_UpperCAmelCase = model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size
if "weight" in key:
_UpperCAmelCase = val[:dim, :]
_UpperCAmelCase = val[
dim : dim * 2, :
]
_UpperCAmelCase = val[-dim:, :]
else:
_UpperCAmelCase = val[
:dim
]
_UpperCAmelCase = val[
dim : dim * 2
]
_UpperCAmelCase = val[
-dim:
]
else:
_UpperCAmelCase = val
return orig_state_dict
def lowercase ( _SCREAMING_SNAKE_CASE : Dict , _SCREAMING_SNAKE_CASE : Optional[Any] , _SCREAMING_SNAKE_CASE : Optional[Any] , _SCREAMING_SNAKE_CASE : List[str] ):
'''simple docstring'''
_UpperCAmelCase = torch.load(__SCREAMING_SNAKE_CASE , map_location='''cpu''' )['model']
_UpperCAmelCase = get_swin_config(__SCREAMING_SNAKE_CASE )
_UpperCAmelCase = SwinForMaskedImageModeling(__SCREAMING_SNAKE_CASE )
model.eval()
_UpperCAmelCase = convert_state_dict(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
model.load_state_dict(__SCREAMING_SNAKE_CASE )
_UpperCAmelCase = 'http://images.cocodataset.org/val2017/000000039769.jpg'
_UpperCAmelCase = ViTImageProcessor(size={'''height''': 192, '''width''': 192} )
_UpperCAmelCase = Image.open(requests.get(__SCREAMING_SNAKE_CASE , stream=__SCREAMING_SNAKE_CASE ).raw )
_UpperCAmelCase = image_processor(images=__SCREAMING_SNAKE_CASE , return_tensors='''pt''' )
with torch.no_grad():
_UpperCAmelCase = model(**__SCREAMING_SNAKE_CASE ).logits
print(outputs.keys() )
print('''Looks ok!''' )
if pytorch_dump_folder_path is not None:
print(f'Saving model {model_name} to {pytorch_dump_folder_path}' )
model.save_pretrained(__SCREAMING_SNAKE_CASE )
print(f'Saving image processor to {pytorch_dump_folder_path}' )
image_processor.save_pretrained(__SCREAMING_SNAKE_CASE )
if push_to_hub:
print(f'Pushing model and image processor for {model_name} to hub' )
model.push_to_hub(f'microsoft/{model_name}' )
image_processor.push_to_hub(f'microsoft/{model_name}' )
if __name__ == "__main__":
__A : Tuple = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model_name",
default="swin-base-simmim-window6-192",
type=str,
choices=["swin-base-simmim-window6-192", "swin-large-simmim-window12-192"],
help="Name of the Swin SimMIM model you'd like to convert.",
)
parser.add_argument(
"--checkpoint_path",
default="/Users/nielsrogge/Documents/SwinSimMIM/simmim_pretrain__swin_base__img192_window6__100ep.pth",
type=str,
help="Path to the original PyTorch checkpoint (.pth file).",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
)
parser.add_argument(
"--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
)
__A : Any = parser.parse_args()
convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)
| 260 | """simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_mbart import MBartTokenizer
else:
__SCREAMING_SNAKE_CASE =None
__SCREAMING_SNAKE_CASE =logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE ={"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}
__SCREAMING_SNAKE_CASE ={
"vocab_file": {
"facebook/mbart-large-en-ro": (
"https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model"
),
"facebook/mbart-large-cc25": (
"https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model"
),
},
"tokenizer_file": {
"facebook/mbart-large-en-ro": "https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json",
"facebook/mbart-large-cc25": "https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json",
},
}
__SCREAMING_SNAKE_CASE ={
"facebook/mbart-large-en-ro": 1024,
"facebook/mbart-large-cc25": 1024,
}
# fmt: off
__SCREAMING_SNAKE_CASE =["ar_AR", "cs_CZ", "de_DE", "en_XX", "es_XX", "et_EE", "fi_FI", "fr_XX", "gu_IN", "hi_IN", "it_IT", "ja_XX", "kk_KZ", "ko_KR", "lt_LT", "lv_LV", "my_MM", "ne_NP", "nl_XX", "ro_RO", "ru_RU", "si_LK", "tr_TR", "vi_VN", "zh_CN"]
class UpperCamelCase ( lowercase_ ):
lowercase = VOCAB_FILES_NAMES
lowercase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase = PRETRAINED_VOCAB_FILES_MAP
lowercase = ['input_ids', 'attention_mask']
lowercase = MBartTokenizer
lowercase = []
lowercase = []
def __init__( self ,__UpperCamelCase=None ,__UpperCamelCase=None ,__UpperCamelCase="<s>" ,__UpperCamelCase="</s>" ,__UpperCamelCase="</s>" ,__UpperCamelCase="<s>" ,__UpperCamelCase="<unk>" ,__UpperCamelCase="<pad>" ,__UpperCamelCase="<mask>" ,__UpperCamelCase=None ,__UpperCamelCase=None ,__UpperCamelCase=None ,**__UpperCamelCase ,) -> List[str]:
'''simple docstring'''
lowercase_ : str = AddedToken(__UpperCamelCase ,lstrip=__UpperCamelCase ,rstrip=__UpperCamelCase ) if isinstance(__UpperCamelCase ,__UpperCamelCase ) else mask_token
super().__init__(
vocab_file=__UpperCamelCase ,tokenizer_file=__UpperCamelCase ,bos_token=__UpperCamelCase ,eos_token=__UpperCamelCase ,sep_token=__UpperCamelCase ,cls_token=__UpperCamelCase ,unk_token=__UpperCamelCase ,pad_token=__UpperCamelCase ,mask_token=__UpperCamelCase ,src_lang=__UpperCamelCase ,tgt_lang=__UpperCamelCase ,additional_special_tokens=__UpperCamelCase ,**__UpperCamelCase ,)
lowercase_ : str = vocab_file
lowercase_ : Optional[Any] = False if not self.vocab_file else True
lowercase_ : List[str] = FAIRSEQ_LANGUAGE_CODES.copy()
if additional_special_tokens is not None:
# Only add those special tokens if they are not already there.
_additional_special_tokens.extend(
[t for t in additional_special_tokens if t not in _additional_special_tokens] )
self.add_special_tokens({'additional_special_tokens': _additional_special_tokens} )
lowercase_ : List[Any] = {
lang_code: self.convert_tokens_to_ids(__UpperCamelCase ) for lang_code in FAIRSEQ_LANGUAGE_CODES
}
lowercase_ : Dict = src_lang if src_lang is not None else 'en_XX'
lowercase_ : Union[str, Any] = self.convert_tokens_to_ids(self._src_lang )
lowercase_ : int = tgt_lang
self.set_src_lang_special_tokens(self._src_lang )
@property
def _UpperCAmelCase ( self ) -> str:
'''simple docstring'''
return self._src_lang
@src_lang.setter
def _UpperCAmelCase ( self ,__UpperCamelCase ) -> None:
'''simple docstring'''
lowercase_ : Optional[int] = new_src_lang
self.set_src_lang_special_tokens(self._src_lang )
def _UpperCAmelCase ( self ,__UpperCamelCase ,__UpperCamelCase = None ) -> List[int]:
'''simple docstring'''
if token_ids_a is None:
return self.prefix_tokens + token_ids_a + self.suffix_tokens
# We don't expect to process pairs, but leave the pair logic for API consistency
return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
def _UpperCAmelCase ( self ,__UpperCamelCase ,__UpperCamelCase = None ) -> List[int]:
'''simple docstring'''
lowercase_ : List[Any] = [self.sep_token_id]
lowercase_ : str = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def _UpperCAmelCase ( self ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase ,**__UpperCamelCase ) -> Union[str, Any]:
'''simple docstring'''
if src_lang is None or tgt_lang is None:
raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model' )
lowercase_ : Dict = src_lang
lowercase_ : List[Any] = self(__UpperCamelCase ,add_special_tokens=__UpperCamelCase ,return_tensors=__UpperCamelCase ,**__UpperCamelCase )
lowercase_ : Optional[Any] = self.convert_tokens_to_ids(__UpperCamelCase )
lowercase_ : Dict = tgt_lang_id
return inputs
def _UpperCAmelCase ( self ,__UpperCamelCase ,__UpperCamelCase = "en_XX" ,__UpperCamelCase = None ,__UpperCamelCase = "ro_RO" ,**__UpperCamelCase ,) -> BatchEncoding:
'''simple docstring'''
lowercase_ : Union[str, Any] = src_lang
lowercase_ : List[Any] = tgt_lang
return super().prepare_seqaseq_batch(__UpperCamelCase ,__UpperCamelCase ,**__UpperCamelCase )
def _UpperCAmelCase ( self ) -> Union[str, Any]:
'''simple docstring'''
return self.set_src_lang_special_tokens(self.src_lang )
def _UpperCAmelCase ( self ) -> int:
'''simple docstring'''
return self.set_tgt_lang_special_tokens(self.tgt_lang )
def _UpperCAmelCase ( self ,__UpperCamelCase ) -> None:
'''simple docstring'''
lowercase_ : Any = self.convert_tokens_to_ids(__UpperCamelCase )
lowercase_ : Optional[Any] = []
lowercase_ : int = [self.eos_token_id, self.cur_lang_code]
lowercase_ : Any = self.convert_ids_to_tokens(self.prefix_tokens )
lowercase_ : int = self.convert_ids_to_tokens(self.suffix_tokens )
lowercase_ : Union[str, Any] = processors.TemplateProcessing(
single=prefix_tokens_str + ['$A'] + suffix_tokens_str ,pair=prefix_tokens_str + ['$A', '$B'] + suffix_tokens_str ,special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str ,self.prefix_tokens + self.suffix_tokens ) ) ,)
def _UpperCAmelCase ( self ,__UpperCamelCase ) -> None:
'''simple docstring'''
lowercase_ : Optional[int] = self.convert_tokens_to_ids(__UpperCamelCase )
lowercase_ : str = []
lowercase_ : Dict = [self.eos_token_id, self.cur_lang_code]
lowercase_ : str = self.convert_ids_to_tokens(self.prefix_tokens )
lowercase_ : List[Any] = self.convert_ids_to_tokens(self.suffix_tokens )
lowercase_ : int = processors.TemplateProcessing(
single=prefix_tokens_str + ['$A'] + suffix_tokens_str ,pair=prefix_tokens_str + ['$A', '$B'] + suffix_tokens_str ,special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str ,self.prefix_tokens + self.suffix_tokens ) ) ,)
def _UpperCAmelCase ( self ,__UpperCamelCase ,__UpperCamelCase = None ) -> Tuple[str]:
'''simple docstring'''
if not self.can_save_slow_tokenizer:
raise ValueError(
'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
'tokenizer.' )
if not os.path.isdir(__UpperCamelCase ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory.''' )
return
lowercase_ : Tuple = os.path.join(
__UpperCamelCase ,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(__UpperCamelCase ):
copyfile(self.vocab_file ,__UpperCamelCase )
return (out_vocab_file,)
| 213 | 0 |
class lowerCamelCase__ :
'''simple docstring'''
def __init__( self , __UpperCAmelCase , __UpperCAmelCase=None , __UpperCAmelCase=None ) -> Union[str, Any]:
_lowerCAmelCase =data
_lowerCAmelCase =previous
_lowerCAmelCase =next_node
def __str__( self ) -> str:
return f'''{self.data}'''
def _lowerCAmelCase ( self ) -> int:
return self.data
def _lowerCAmelCase ( self ) -> Union[str, Any]:
return self.next
def _lowerCAmelCase ( self ) -> Dict:
return self.previous
class lowerCamelCase__ :
'''simple docstring'''
def __init__( self , __UpperCAmelCase ) -> Optional[Any]:
_lowerCAmelCase =head
def __iter__( self ) -> Union[str, Any]:
return self
def _lowerCAmelCase ( self ) -> List[Any]:
if not self.current:
raise StopIteration
else:
_lowerCAmelCase =self.current.get_data()
_lowerCAmelCase =self.current.get_next()
return value
class lowerCamelCase__ :
'''simple docstring'''
def __init__( self ) -> Tuple:
_lowerCAmelCase =None # First node in list
_lowerCAmelCase =None # Last node in list
def __str__( self ) -> Union[str, Any]:
_lowerCAmelCase =self.head
_lowerCAmelCase =[]
while current is not None:
nodes.append(current.get_data() )
_lowerCAmelCase =current.get_next()
return " ".join(str(__UpperCAmelCase ) for node in nodes )
def __contains__( self , __UpperCAmelCase ) -> Optional[Any]:
_lowerCAmelCase =self.head
while current:
if current.get_data() == value:
return True
_lowerCAmelCase =current.get_next()
return False
def __iter__( self ) -> int:
return LinkedListIterator(self.head )
def _lowerCAmelCase ( self ) -> Optional[int]:
if self.head:
return self.head.get_data()
return None
def _lowerCAmelCase ( self ) -> int:
if self.tail:
return self.tail.get_data()
return None
def _lowerCAmelCase ( self , __UpperCAmelCase ) -> None:
if self.head is None:
_lowerCAmelCase =node
_lowerCAmelCase =node
else:
self.insert_before_node(self.head , __UpperCAmelCase )
def _lowerCAmelCase ( self , __UpperCAmelCase ) -> None:
if self.head is None:
self.set_head(__UpperCAmelCase )
else:
self.insert_after_node(self.tail , __UpperCAmelCase )
def _lowerCAmelCase ( self , __UpperCAmelCase ) -> None:
_lowerCAmelCase =Node(__UpperCAmelCase )
if self.head is None:
self.set_head(__UpperCAmelCase )
else:
self.set_tail(__UpperCAmelCase )
def _lowerCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase ) -> None:
_lowerCAmelCase =node
_lowerCAmelCase =node.previous
if node.get_previous() is None:
_lowerCAmelCase =node_to_insert
else:
_lowerCAmelCase =node_to_insert
_lowerCAmelCase =node_to_insert
def _lowerCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase ) -> None:
_lowerCAmelCase =node
_lowerCAmelCase =node.next
if node.get_next() is None:
_lowerCAmelCase =node_to_insert
else:
_lowerCAmelCase =node_to_insert
_lowerCAmelCase =node_to_insert
def _lowerCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase ) -> None:
_lowerCAmelCase =1
_lowerCAmelCase =Node(__UpperCAmelCase )
_lowerCAmelCase =self.head
while node:
if current_position == position:
self.insert_before_node(__UpperCAmelCase , __UpperCAmelCase )
return
current_position += 1
_lowerCAmelCase =node.next
self.insert_after_node(self.tail , __UpperCAmelCase )
def _lowerCAmelCase ( self , __UpperCAmelCase ) -> Node:
_lowerCAmelCase =self.head
while node:
if node.get_data() == item:
return node
_lowerCAmelCase =node.get_next()
raise Exception("""Node not found""" )
def _lowerCAmelCase ( self , __UpperCAmelCase ) -> Dict:
if (node := self.get_node(__UpperCAmelCase )) is not None:
if node == self.head:
_lowerCAmelCase =self.head.get_next()
if node == self.tail:
_lowerCAmelCase =self.tail.get_previous()
self.remove_node_pointers(__UpperCAmelCase )
@staticmethod
def _lowerCAmelCase ( __UpperCAmelCase ) -> None:
if node.get_next():
_lowerCAmelCase =node.previous
if node.get_previous():
_lowerCAmelCase =node.next
_lowerCAmelCase =None
_lowerCAmelCase =None
def _lowerCAmelCase ( self ) -> Optional[Any]:
return self.head is None
def _lowerCamelCase() -> None:
pass
if __name__ == "__main__":
import doctest
doctest.testmod()
| 351 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
'microsoft/cvt-13': 'https://huggingface.co/microsoft/cvt-13/resolve/main/config.json',
# See all Cvt models at https://huggingface.co/models?filter=cvt
}
class lowerCamelCase__ ( __magic_name__ ):
'''simple docstring'''
lowerCamelCase = '''cvt'''
def __init__( self , __UpperCAmelCase=3 , __UpperCAmelCase=[7, 3, 3] , __UpperCAmelCase=[4, 2, 2] , __UpperCAmelCase=[2, 1, 1] , __UpperCAmelCase=[64, 1_92, 3_84] , __UpperCAmelCase=[1, 3, 6] , __UpperCAmelCase=[1, 2, 10] , __UpperCAmelCase=[4.0, 4.0, 4.0] , __UpperCAmelCase=[0.0, 0.0, 0.0] , __UpperCAmelCase=[0.0, 0.0, 0.0] , __UpperCAmelCase=[0.0, 0.0, 0.1] , __UpperCAmelCase=[True, True, True] , __UpperCAmelCase=[False, False, True] , __UpperCAmelCase=["dw_bn", "dw_bn", "dw_bn"] , __UpperCAmelCase=[3, 3, 3] , __UpperCAmelCase=[1, 1, 1] , __UpperCAmelCase=[2, 2, 2] , __UpperCAmelCase=[1, 1, 1] , __UpperCAmelCase=[1, 1, 1] , __UpperCAmelCase=0.0_2 , __UpperCAmelCase=1e-12 , **__UpperCAmelCase , ) -> Optional[Any]:
super().__init__(**__UpperCAmelCase )
_lowerCAmelCase =num_channels
_lowerCAmelCase =patch_sizes
_lowerCAmelCase =patch_stride
_lowerCAmelCase =patch_padding
_lowerCAmelCase =embed_dim
_lowerCAmelCase =num_heads
_lowerCAmelCase =depth
_lowerCAmelCase =mlp_ratio
_lowerCAmelCase =attention_drop_rate
_lowerCAmelCase =drop_rate
_lowerCAmelCase =drop_path_rate
_lowerCAmelCase =qkv_bias
_lowerCAmelCase =cls_token
_lowerCAmelCase =qkv_projection_method
_lowerCAmelCase =kernel_qkv
_lowerCAmelCase =padding_kv
_lowerCAmelCase =stride_kv
_lowerCAmelCase =padding_q
_lowerCAmelCase =stride_q
_lowerCAmelCase =initializer_range
_lowerCAmelCase =layer_norm_eps
| 341 | 0 |
def lowerCAmelCase_ ():
"""simple docstring"""
UpperCAmelCase_: List[Any] = 0
for i in range(1 , 1_0_0_1 ):
total += i**i
return str(lowerCAmelCase__ )[-1_0:]
if __name__ == "__main__":
print(solution())
| 147 |
import math
from typing import Callable, List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale import StableDiffusionUpscalePipeline
from diffusers.schedulers import DDIMScheduler, DDPMScheduler, LMSDiscreteScheduler, PNDMScheduler
def lowerCAmelCase_ (lowerCAmelCase__: List[Any] , lowerCAmelCase__: List[str] , lowerCAmelCase__: Optional[Any]=[] ):
"""simple docstring"""
UpperCAmelCase_: Union[str, Any] = size[0] - overlap_pixels * 2
UpperCAmelCase_: Dict = size[1] - overlap_pixels * 2
for letter in ["l", "r"]:
if letter in remove_borders:
size_x += overlap_pixels
for letter in ["t", "b"]:
if letter in remove_borders:
size_y += overlap_pixels
UpperCAmelCase_: Union[str, Any] = np.ones((size_y, size_x) , dtype=np.uinta ) * 2_5_5
UpperCAmelCase_: Optional[int] = np.pad(lowerCAmelCase__ , mode="""linear_ramp""" , pad_width=lowerCAmelCase__ , end_values=0 )
if "l" in remove_borders:
UpperCAmelCase_: List[Any] = mask[:, overlap_pixels : mask.shape[1]]
if "r" in remove_borders:
UpperCAmelCase_: Optional[Any] = mask[:, 0 : mask.shape[1] - overlap_pixels]
if "t" in remove_borders:
UpperCAmelCase_: Optional[int] = mask[overlap_pixels : mask.shape[0], :]
if "b" in remove_borders:
UpperCAmelCase_: int = mask[0 : mask.shape[0] - overlap_pixels, :]
return mask
def lowerCAmelCase_ (lowerCAmelCase__: List[Any] , lowerCAmelCase__: str , lowerCAmelCase__: Union[str, Any] ):
"""simple docstring"""
return max(lowerCAmelCase__ , min(lowerCAmelCase__ , lowerCAmelCase__ ) )
def lowerCAmelCase_ (lowerCAmelCase__: [int] , lowerCAmelCase__: [int] , lowerCAmelCase__: [int] ):
"""simple docstring"""
return (
clamp(rect[0] , min[0] , max[0] ),
clamp(rect[1] , min[1] , max[1] ),
clamp(rect[2] , min[0] , max[0] ),
clamp(rect[3] , min[1] , max[1] ),
)
def lowerCAmelCase_ (lowerCAmelCase__: [int] , lowerCAmelCase__: int , lowerCAmelCase__: [int] ):
"""simple docstring"""
UpperCAmelCase_: str = list(lowerCAmelCase__ )
rect[0] -= overlap
rect[1] -= overlap
rect[2] += overlap
rect[3] += overlap
UpperCAmelCase_: int = clamp_rect(lowerCAmelCase__ , [0, 0] , [image_size[0], image_size[1]] )
return rect
def lowerCAmelCase_ (lowerCAmelCase__: List[Any] , lowerCAmelCase__: List[str] , lowerCAmelCase__: List[Any] , lowerCAmelCase__: int ):
"""simple docstring"""
UpperCAmelCase_: Optional[Any] = Image.new("""RGB""" , (tile.size[0] + original_slice, tile.size[1]) )
result.paste(
original_image.resize((tile.size[0], tile.size[1]) , Image.BICUBIC ).crop(
(slice_x, 0, slice_x + original_slice, tile.size[1]) ) , (0, 0) , )
result.paste(lowerCAmelCase__ , (original_slice, 0) )
return result
def lowerCAmelCase_ (lowerCAmelCase__: Dict , lowerCAmelCase__: Dict ):
"""simple docstring"""
UpperCAmelCase_: Dict = (original_image_slice * 4, 0, tile.size[0], tile.size[1])
UpperCAmelCase_: Optional[int] = tile.crop(lowerCAmelCase__ )
return tile
def lowerCAmelCase_ (lowerCAmelCase__: Tuple , lowerCAmelCase__: int ):
"""simple docstring"""
UpperCAmelCase_: str = n % d
return n - divisor
class _a ( _lowerCAmelCase ):
def __init__(self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ = 350, ) -> str:
super().__init__(
vae=SCREAMING_SNAKE_CASE_, text_encoder=SCREAMING_SNAKE_CASE_, tokenizer=SCREAMING_SNAKE_CASE_, unet=SCREAMING_SNAKE_CASE_, low_res_scheduler=SCREAMING_SNAKE_CASE_, scheduler=SCREAMING_SNAKE_CASE_, max_noise_level=SCREAMING_SNAKE_CASE_, )
def __snake_case (self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, **SCREAMING_SNAKE_CASE_ ) -> Dict:
torch.manual_seed(0 )
UpperCAmelCase_: Dict = (
min(image.size[0] - (tile_size + original_image_slice), x * tile_size ),
min(image.size[1] - (tile_size + original_image_slice), y * tile_size ),
min(image.size[0], (x + 1) * tile_size ),
min(image.size[1], (y + 1) * tile_size ),
)
UpperCAmelCase_: Tuple = add_overlap_rect(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, image.size )
UpperCAmelCase_: List[str] = image.crop(SCREAMING_SNAKE_CASE_ )
UpperCAmelCase_: Optional[Any] = ((crop_rect[0] + ((crop_rect[2] - crop_rect[0]) / 2)) / image.size[0]) * tile.size[0]
UpperCAmelCase_: List[Any] = translated_slice_x - (original_image_slice / 2)
UpperCAmelCase_: str = max(0, SCREAMING_SNAKE_CASE_ )
UpperCAmelCase_: Any = squeeze_tile(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ )
UpperCAmelCase_: Any = to_input.size
UpperCAmelCase_: Any = to_input.resize((tile_size, tile_size), Image.BICUBIC )
UpperCAmelCase_: str = super(SCREAMING_SNAKE_CASE_, self ).__call__(image=SCREAMING_SNAKE_CASE_, **SCREAMING_SNAKE_CASE_ ).images[0]
UpperCAmelCase_: Optional[int] = upscaled_tile.resize((orig_input_size[0] * 4, orig_input_size[1] * 4), Image.BICUBIC )
UpperCAmelCase_: int = unsqueeze_tile(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ )
UpperCAmelCase_: Optional[Any] = upscaled_tile.resize((tile.size[0] * 4, tile.size[1] * 4), Image.BICUBIC )
UpperCAmelCase_: Union[str, Any] = []
if x == 0:
remove_borders.append("""l""" )
elif crop_rect[2] == image.size[0]:
remove_borders.append("""r""" )
if y == 0:
remove_borders.append("""t""" )
elif crop_rect[3] == image.size[1]:
remove_borders.append("""b""" )
UpperCAmelCase_: Tuple = Image.fromarray(
make_transparency_mask(
(upscaled_tile.size[0], upscaled_tile.size[1]), tile_border * 4, remove_borders=SCREAMING_SNAKE_CASE_ ), mode="""L""", )
final_image.paste(
SCREAMING_SNAKE_CASE_, (crop_rect_with_overlap[0] * 4, crop_rect_with_overlap[1] * 4), SCREAMING_SNAKE_CASE_ )
@torch.no_grad()
def __call__(self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ = 75, SCREAMING_SNAKE_CASE_ = 9.0, SCREAMING_SNAKE_CASE_ = 50, SCREAMING_SNAKE_CASE_ = None, SCREAMING_SNAKE_CASE_ = 1, SCREAMING_SNAKE_CASE_ = 0.0, SCREAMING_SNAKE_CASE_ = None, SCREAMING_SNAKE_CASE_ = None, SCREAMING_SNAKE_CASE_ = None, SCREAMING_SNAKE_CASE_ = 1, SCREAMING_SNAKE_CASE_ = 128, SCREAMING_SNAKE_CASE_ = 32, SCREAMING_SNAKE_CASE_ = 32, ) -> Dict:
UpperCAmelCase_: int = Image.new("""RGB""", (image.size[0] * 4, image.size[1] * 4) )
UpperCAmelCase_: str = math.ceil(image.size[0] / tile_size )
UpperCAmelCase_: int = math.ceil(image.size[1] / tile_size )
UpperCAmelCase_: Dict = tcx * tcy
UpperCAmelCase_: Optional[Any] = 0
for y in range(SCREAMING_SNAKE_CASE_ ):
for x in range(SCREAMING_SNAKE_CASE_ ):
self._process_tile(
SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, prompt=SCREAMING_SNAKE_CASE_, num_inference_steps=SCREAMING_SNAKE_CASE_, guidance_scale=SCREAMING_SNAKE_CASE_, noise_level=SCREAMING_SNAKE_CASE_, negative_prompt=SCREAMING_SNAKE_CASE_, num_images_per_prompt=SCREAMING_SNAKE_CASE_, eta=SCREAMING_SNAKE_CASE_, generator=SCREAMING_SNAKE_CASE_, latents=SCREAMING_SNAKE_CASE_, )
current_count += 1
if callback is not None:
callback({"""progress""": current_count / total_tile_count, """image""": final_image} )
return final_image
def lowerCAmelCase_ ():
"""simple docstring"""
UpperCAmelCase_: Tuple = """stabilityai/stable-diffusion-x4-upscaler"""
UpperCAmelCase_: Union[str, Any] = StableDiffusionTiledUpscalePipeline.from_pretrained(lowerCAmelCase__ , revision="""fp16""" , torch_dtype=torch.floataa )
UpperCAmelCase_: str = pipe.to("""cuda""" )
UpperCAmelCase_: List[str] = Image.open("""../../docs/source/imgs/diffusers_library.jpg""" )
def callback(lowerCAmelCase__: Dict ):
print(F'progress: {obj["progress"]:.4f}' )
obj["image"].save("""diffusers_library_progress.jpg""" )
UpperCAmelCase_: Optional[int] = pipe(image=lowerCAmelCase__ , prompt="""Black font, white background, vector""" , noise_level=4_0 , callback=lowerCAmelCase__ )
final_image.save("""diffusers_library.jpg""" )
if __name__ == "__main__":
main()
| 147 | 1 |
'''simple docstring'''
import collections
import importlib.util
import os
import re
from pathlib import Path
a : Optional[int] = 'src/transformers'
# Matches is_xxx_available()
a : Optional[int] = re.compile(r'is\_([a-z_]*)_available()')
# Catches a one-line _import_struct = {xxx}
a : int = re.compile(r'^_import_structure\s+=\s+\{([^\}]+)\}')
# Catches a line with a key-values pattern: "bla": ["foo", "bar"]
a : Tuple = re.compile(r'\s+"\S*":\s+\[([^\]]*)\]')
# Catches a line if not is_foo_available
a : Optional[Any] = re.compile(r'^\s*if\s+not\s+is\_[a-z_]*\_available\(\)')
# Catches a line _import_struct["bla"].append("foo")
a : Optional[Any] = re.compile(r'^\s*_import_structure\["\S*"\]\.append\("(\S*)"\)')
# Catches a line _import_struct["bla"].extend(["foo", "bar"]) or _import_struct["bla"] = ["foo", "bar"]
a : Any = re.compile(r'^\s*_import_structure\[\S*\](?:\.extend\(|\s*=\s+)\[([^\]]*)\]')
# Catches a line with an object between quotes and a comma: "MyModel",
a : List[Any] = re.compile('^\s+"([^"]+)",')
# Catches a line with objects between brackets only: ["foo", "bar"],
a : List[str] = re.compile('^\s+\[([^\]]+)\]')
# Catches a line with from foo import bar, bla, boo
a : List[str] = re.compile(r'\s+from\s+\S*\s+import\s+([^\(\s].*)\n')
# Catches a line with try:
a : Union[str, Any] = re.compile(r'^\s*try:')
# Catches a line with else:
a : Any = re.compile(r'^\s*else:')
def __magic_name__ ( __UpperCAmelCase ) -> Dict:
'''simple docstring'''
if _re_test_backend.search(__UpperCAmelCase ) is None:
return None
snake_case_ = [b[0] for b in _re_backend.findall(__UpperCAmelCase )]
backends.sort()
return "_and_".join(__UpperCAmelCase )
def __magic_name__ ( __UpperCAmelCase ) -> Optional[Any]:
'''simple docstring'''
with open(__UpperCAmelCase, '''r''', encoding='''utf-8''', newline='''\n''' ) as f:
snake_case_ = f.readlines()
snake_case_ = 0
while line_index < len(__UpperCAmelCase ) and not lines[line_index].startswith('''_import_structure = {''' ):
line_index += 1
# If this is a traditional init, just return.
if line_index >= len(__UpperCAmelCase ):
return None
# First grab the objects without a specific backend in _import_structure
snake_case_ = []
while not lines[line_index].startswith('''if TYPE_CHECKING''' ) and find_backend(lines[line_index] ) is None:
snake_case_ = lines[line_index]
# If we have everything on a single line, let's deal with it.
if _re_one_line_import_struct.search(__UpperCAmelCase ):
snake_case_ = _re_one_line_import_struct.search(__UpperCAmelCase ).groups()[0]
snake_case_ = re.findall('''\[([^\]]+)\]''', __UpperCAmelCase )
for imp in imports:
objects.extend([obj[1:-1] for obj in imp.split(''', ''' )] )
line_index += 1
continue
snake_case_ = _re_import_struct_key_value.search(__UpperCAmelCase )
if single_line_import_search is not None:
snake_case_ = [obj[1:-1] for obj in single_line_import_search.groups()[0].split(''', ''' ) if len(__UpperCAmelCase ) > 0]
objects.extend(__UpperCAmelCase )
elif line.startswith(''' ''' * 8 + '''"''' ):
objects.append(line[9:-3] )
line_index += 1
snake_case_ = {'''none''': objects}
# Let's continue with backend-specific objects in _import_structure
while not lines[line_index].startswith('''if TYPE_CHECKING''' ):
# If the line is an if not is_backend_available, we grab all objects associated.
snake_case_ = find_backend(lines[line_index] )
# Check if the backend declaration is inside a try block:
if _re_try.search(lines[line_index - 1] ) is None:
snake_case_ = None
if backend is not None:
line_index += 1
# Scroll until we hit the else block of try-except-else
while _re_else.search(lines[line_index] ) is None:
line_index += 1
line_index += 1
snake_case_ = []
# Until we unindent, add backend objects to the list
while len(lines[line_index] ) <= 1 or lines[line_index].startswith(''' ''' * 4 ):
snake_case_ = lines[line_index]
if _re_import_struct_add_one.search(__UpperCAmelCase ) is not None:
objects.append(_re_import_struct_add_one.search(__UpperCAmelCase ).groups()[0] )
elif _re_import_struct_add_many.search(__UpperCAmelCase ) is not None:
snake_case_ = _re_import_struct_add_many.search(__UpperCAmelCase ).groups()[0].split(''', ''' )
snake_case_ = [obj[1:-1] for obj in imports if len(__UpperCAmelCase ) > 0]
objects.extend(__UpperCAmelCase )
elif _re_between_brackets.search(__UpperCAmelCase ) is not None:
snake_case_ = _re_between_brackets.search(__UpperCAmelCase ).groups()[0].split(''', ''' )
snake_case_ = [obj[1:-1] for obj in imports if len(__UpperCAmelCase ) > 0]
objects.extend(__UpperCAmelCase )
elif _re_quote_object.search(__UpperCAmelCase ) is not None:
objects.append(_re_quote_object.search(__UpperCAmelCase ).groups()[0] )
elif line.startswith(''' ''' * 8 + '''"''' ):
objects.append(line[9:-3] )
elif line.startswith(''' ''' * 12 + '''"''' ):
objects.append(line[13:-3] )
line_index += 1
snake_case_ = objects
else:
line_index += 1
# At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
snake_case_ = []
while (
line_index < len(__UpperCAmelCase )
and find_backend(lines[line_index] ) is None
and not lines[line_index].startswith('''else''' )
):
snake_case_ = lines[line_index]
snake_case_ = _re_import.search(__UpperCAmelCase )
if single_line_import_search is not None:
objects.extend(single_line_import_search.groups()[0].split(''', ''' ) )
elif line.startswith(''' ''' * 8 ):
objects.append(line[8:-2] )
line_index += 1
snake_case_ = {'''none''': objects}
# Let's continue with backend-specific objects
while line_index < len(__UpperCAmelCase ):
# If the line is an if is_backend_available, we grab all objects associated.
snake_case_ = find_backend(lines[line_index] )
# Check if the backend declaration is inside a try block:
if _re_try.search(lines[line_index - 1] ) is None:
snake_case_ = None
if backend is not None:
line_index += 1
# Scroll until we hit the else block of try-except-else
while _re_else.search(lines[line_index] ) is None:
line_index += 1
line_index += 1
snake_case_ = []
# Until we unindent, add backend objects to the list
while len(lines[line_index] ) <= 1 or lines[line_index].startswith(''' ''' * 8 ):
snake_case_ = lines[line_index]
snake_case_ = _re_import.search(__UpperCAmelCase )
if single_line_import_search is not None:
objects.extend(single_line_import_search.groups()[0].split(''', ''' ) )
elif line.startswith(''' ''' * 12 ):
objects.append(line[12:-2] )
line_index += 1
snake_case_ = objects
else:
line_index += 1
return import_dict_objects, type_hint_objects
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> Optional[Any]:
'''simple docstring'''
def find_duplicates(__UpperCAmelCase ):
return [k for k, v in collections.Counter(__UpperCAmelCase ).items() if v > 1]
if list(import_dict_objects.keys() ) != list(type_hint_objects.keys() ):
return ["Both sides of the init do not have the same backends!"]
snake_case_ = []
for key in import_dict_objects.keys():
snake_case_ = find_duplicates(import_dict_objects[key] )
if duplicate_imports:
errors.append(F"Duplicate _import_structure definitions for: {duplicate_imports}" )
snake_case_ = find_duplicates(type_hint_objects[key] )
if duplicate_type_hints:
errors.append(F"Duplicate TYPE_CHECKING objects for: {duplicate_type_hints}" )
if sorted(set(import_dict_objects[key] ) ) != sorted(set(type_hint_objects[key] ) ):
snake_case_ = '''base imports''' if key == '''none''' else F"{key} backend"
errors.append(F"Differences for {name}:" )
for a in type_hint_objects[key]:
if a not in import_dict_objects[key]:
errors.append(F" {a} in TYPE_HINT but not in _import_structure." )
for a in import_dict_objects[key]:
if a not in type_hint_objects[key]:
errors.append(F" {a} in _import_structure but not in TYPE_HINT." )
return errors
def __magic_name__ ( ) -> Tuple:
'''simple docstring'''
snake_case_ = []
for root, _, files in os.walk(__UpperCAmelCase ):
if "__init__.py" in files:
snake_case_ = os.path.join(__UpperCAmelCase, '''__init__.py''' )
snake_case_ = parse_init(__UpperCAmelCase )
if objects is not None:
snake_case_ = analyze_results(*__UpperCAmelCase )
if len(__UpperCAmelCase ) > 0:
snake_case_ = F"Problem in {fname}, both halves do not define the same objects.\n{errors[0]}"
failures.append('''\n'''.join(__UpperCAmelCase ) )
if len(__UpperCAmelCase ) > 0:
raise ValueError('''\n\n'''.join(__UpperCAmelCase ) )
def __magic_name__ ( ) -> Dict:
'''simple docstring'''
snake_case_ = []
for path, directories, files in os.walk(__UpperCAmelCase ):
for folder in directories:
# Ignore private modules
if folder.startswith('''_''' ):
directories.remove(__UpperCAmelCase )
continue
# Ignore leftovers from branches (empty folders apart from pycache)
if len(list((Path(__UpperCAmelCase ) / folder).glob('''*.py''' ) ) ) == 0:
continue
snake_case_ = str((Path(__UpperCAmelCase ) / folder).relative_to(__UpperCAmelCase ) )
snake_case_ = short_path.replace(os.path.sep, '''.''' )
submodules.append(__UpperCAmelCase )
for fname in files:
if fname == "__init__.py":
continue
snake_case_ = str((Path(__UpperCAmelCase ) / fname).relative_to(__UpperCAmelCase ) )
snake_case_ = short_path.replace('''.py''', '''''' ).replace(os.path.sep, '''.''' )
if len(submodule.split('''.''' ) ) == 1:
submodules.append(__UpperCAmelCase )
return submodules
a : Dict = [
'convert_pytorch_checkpoint_to_tf2',
'modeling_flax_pytorch_utils',
]
def __magic_name__ ( ) -> Union[str, Any]:
'''simple docstring'''
snake_case_ = importlib.util.spec_from_file_location(
'''transformers''', os.path.join(__UpperCAmelCase, '''__init__.py''' ), submodule_search_locations=[PATH_TO_TRANSFORMERS], )
snake_case_ = spec.loader.load_module()
snake_case_ = [
module
for module in get_transformers_submodules()
if module not in IGNORE_SUBMODULES and module not in transformers._import_structure.keys()
]
if len(__UpperCAmelCase ) > 0:
snake_case_ = '''\n'''.join(F"- {module}" for module in module_not_registered )
raise ValueError(
'''The following submodules are not properly registered in the main init of Transformers:\n'''
F"{list_of_modules}\n"
'''Make sure they appear somewhere in the keys of `_import_structure` with an empty list as value.''' )
if __name__ == "__main__":
check_all_inits()
check_submodules()
| 72 |
'''simple docstring'''
import os
from pathlib import Path
import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader
from transformers import AutoTokenizer
from transformers.models.mbart.modeling_mbart import shift_tokens_right
from transformers.testing_utils import TestCasePlus, slow
from utils import FAIRSEQ_AVAILABLE, DistributedSortishSampler, LegacySeqaSeqDataset, SeqaSeqDataset
a : int = 'bert-base-cased'
a : Optional[int] = 'google/pegasus-xsum'
a : Optional[int] = [' Sam ate lunch today.', 'Sams lunch ingredients.']
a : int = ['A very interesting story about what I ate for lunch.', 'Avocado, celery, turkey, coffee']
a : Dict = 'patrickvonplaten/t5-tiny-random'
a : Any = 'sshleifer/bart-tiny-random'
a : Union[str, Any] = 'sshleifer/tiny-mbart'
a : Optional[int] = 'sshleifer/tiny-marian-en-de'
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> Optional[Any]:
'''simple docstring'''
snake_case_ = '''\n'''.join(__UpperCAmelCase )
Path(__UpperCAmelCase ).open('''w''' ).writelines(__UpperCAmelCase )
def __magic_name__ ( __UpperCAmelCase ) -> str:
'''simple docstring'''
for split in ["train", "val", "test"]:
_dump_articles(os.path.join(__UpperCAmelCase, F"{split}.source" ), __UpperCAmelCase )
_dump_articles(os.path.join(__UpperCAmelCase, F"{split}.target" ), __UpperCAmelCase )
return tmp_dir
class a ( _lowerCamelCase ):
@parameterized.expand(
[
MBART_TINY,
MARIAN_TINY,
T5_TINY,
BART_TINY,
PEGASUS_XSUM,
] , )
@slow
def A_ ( self : int , lowercase_ : Optional[Any] ):
snake_case_ = AutoTokenizer.from_pretrained(lowercase_ )
snake_case_ = make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() )
snake_case_ = max(len(tokenizer.encode(lowercase_ ) ) for a in ARTICLES )
snake_case_ = max(len(tokenizer.encode(lowercase_ ) ) for a in SUMMARIES )
snake_case_ = 4
snake_case_ = 8
assert max_len_target > max_src_len # Will be truncated
assert max_len_source > max_src_len # Will be truncated
snake_case_ ,snake_case_ = '''ro_RO''', '''de_DE''' # ignored for all but mbart, but never causes error.
snake_case_ = SeqaSeqDataset(
lowercase_ , data_dir=lowercase_ , type_path='''train''' , max_source_length=lowercase_ , max_target_length=lowercase_ , src_lang=lowercase_ , tgt_lang=lowercase_ , )
snake_case_ = DataLoader(lowercase_ , batch_size=2 , collate_fn=train_dataset.collate_fn )
for batch in dataloader:
assert isinstance(lowercase_ , lowercase_ )
assert batch["attention_mask"].shape == batch["input_ids"].shape
# show that articles were trimmed.
assert batch["input_ids"].shape[1] == max_src_len
# show that targets are the same len
assert batch["labels"].shape[1] == max_tgt_len
if tok_name != MBART_TINY:
continue
# check language codes in correct place
snake_case_ = shift_tokens_right(batch['''labels'''] , tokenizer.pad_token_id )
assert batch["decoder_input_ids"][0, 0].item() == tokenizer.lang_code_to_id[tgt_lang]
assert batch["decoder_input_ids"][0, -1].item() == tokenizer.eos_token_id
assert batch["input_ids"][0, -2].item() == tokenizer.eos_token_id
assert batch["input_ids"][0, -1].item() == tokenizer.lang_code_to_id[src_lang]
break # No need to test every batch
@parameterized.expand([BART_TINY, BERT_BASE_CASED] )
def A_ ( self : Union[str, Any] , lowercase_ : Dict ):
snake_case_ = AutoTokenizer.from_pretrained(lowercase_ )
snake_case_ = make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() )
snake_case_ = max(len(tokenizer.encode(lowercase_ ) ) for a in ARTICLES )
snake_case_ = max(len(tokenizer.encode(lowercase_ ) ) for a in SUMMARIES )
snake_case_ = 4
snake_case_ = LegacySeqaSeqDataset(
lowercase_ , data_dir=lowercase_ , type_path='''train''' , max_source_length=20 , max_target_length=lowercase_ , )
snake_case_ = DataLoader(lowercase_ , batch_size=2 , collate_fn=train_dataset.collate_fn )
for batch in dataloader:
assert batch["attention_mask"].shape == batch["input_ids"].shape
# show that articles were trimmed.
assert batch["input_ids"].shape[1] == max_len_source
assert 20 >= batch["input_ids"].shape[1] # trimmed significantly
# show that targets were truncated
assert batch["labels"].shape[1] == trunc_target # Truncated
assert max_len_target > trunc_target # Truncated
break # No need to test every batch
def A_ ( self : Any ):
snake_case_ = AutoTokenizer.from_pretrained('''facebook/mbart-large-cc25''' )
snake_case_ = Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() ) )
snake_case_ = tmp_dir.joinpath('''train.source''' ).open().readlines()
snake_case_ = Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() ) )
pack_data_dir(lowercase_ , lowercase_ , 128 , lowercase_ )
snake_case_ = {x.name for x in tmp_dir.iterdir()}
snake_case_ = {x.name for x in save_dir.iterdir()}
snake_case_ = save_dir.joinpath('''train.source''' ).open().readlines()
# orig: [' Sam ate lunch today.\n', 'Sams lunch ingredients.']
# desired_packed: [' Sam ate lunch today.\n Sams lunch ingredients.']
assert len(lowercase_ ) < len(lowercase_ )
assert len(lowercase_ ) == 1
assert len(packed_examples[0] ) == sum(len(lowercase_ ) for x in orig_examples )
assert orig_paths == new_paths
@pytest.mark.skipif(not FAIRSEQ_AVAILABLE , reason='''This test requires fairseq''' )
def A_ ( self : Any ):
if not FAIRSEQ_AVAILABLE:
return
snake_case_ ,snake_case_ ,snake_case_ = self._get_dataset(max_len=64 )
snake_case_ = 64
snake_case_ = ds.make_dynamic_sampler(lowercase_ , required_batch_size_multiple=lowercase_ )
snake_case_ = [len(lowercase_ ) for x in batch_sampler]
assert len(set(lowercase_ ) ) > 1 # it's not dynamic batch size if every batch is the same length
assert sum(lowercase_ ) == len(lowercase_ ) # no dropped or added examples
snake_case_ = DataLoader(lowercase_ , batch_sampler=lowercase_ , collate_fn=ds.collate_fn , num_workers=2 )
snake_case_ = []
snake_case_ = []
for batch in data_loader:
snake_case_ = batch['''input_ids'''].shape
snake_case_ = src_shape[0]
assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
snake_case_ = np.product(batch['''input_ids'''].shape )
num_src_per_batch.append(lowercase_ )
if num_src_tokens > (max_tokens * 1.1):
failures.append(lowercase_ )
assert num_src_per_batch[0] == max(lowercase_ )
if failures:
raise AssertionError(F"too many tokens in {len(lowercase_ )} batches" )
def A_ ( self : List[str] ):
snake_case_ ,snake_case_ ,snake_case_ = self._get_dataset(max_len=512 )
snake_case_ = 2
snake_case_ = ds.make_sortish_sampler(lowercase_ , shuffle=lowercase_ )
snake_case_ = DataLoader(lowercase_ , batch_size=lowercase_ , collate_fn=ds.collate_fn , num_workers=2 )
snake_case_ = DataLoader(lowercase_ , batch_size=lowercase_ , collate_fn=ds.collate_fn , num_workers=2 , sampler=lowercase_ )
snake_case_ = tokenizer.pad_token_id
def count_pad_tokens(lowercase_ : Any , lowercase_ : int="input_ids" ):
return [batch[k].eq(lowercase_ ).sum().item() for batch in data_loader]
assert sum(count_pad_tokens(lowercase_ , k='''labels''' ) ) < sum(count_pad_tokens(lowercase_ , k='''labels''' ) )
assert sum(count_pad_tokens(lowercase_ ) ) < sum(count_pad_tokens(lowercase_ ) )
assert len(lowercase_ ) == len(lowercase_ )
def A_ ( self : List[str] , lowercase_ : Tuple=1000 , lowercase_ : Optional[Any]=128 ):
if os.getenv('''USE_REAL_DATA''' , lowercase_ ):
snake_case_ = '''examples/seq2seq/wmt_en_ro'''
snake_case_ = max_len * 2 * 64
if not Path(lowercase_ ).joinpath('''train.len''' ).exists():
save_len_file(lowercase_ , lowercase_ )
else:
snake_case_ = '''examples/seq2seq/test_data/wmt_en_ro'''
snake_case_ = max_len * 4
save_len_file(lowercase_ , lowercase_ )
snake_case_ = AutoTokenizer.from_pretrained(lowercase_ )
snake_case_ = SeqaSeqDataset(
lowercase_ , data_dir=lowercase_ , type_path='''train''' , max_source_length=lowercase_ , max_target_length=lowercase_ , n_obs=lowercase_ , )
return ds, max_tokens, tokenizer
def A_ ( self : Any ):
snake_case_ ,snake_case_ ,snake_case_ = self._get_dataset()
snake_case_ = set(DistributedSortishSampler(lowercase_ , 256 , num_replicas=2 , rank=0 , add_extra_examples=lowercase_ ) )
snake_case_ = set(DistributedSortishSampler(lowercase_ , 256 , num_replicas=2 , rank=1 , add_extra_examples=lowercase_ ) )
assert idsa.intersection(lowercase_ ) == set()
@parameterized.expand(
[
MBART_TINY,
MARIAN_TINY,
T5_TINY,
BART_TINY,
PEGASUS_XSUM,
] , )
def A_ ( self : List[str] , lowercase_ : Optional[Any] ):
snake_case_ = AutoTokenizer.from_pretrained(lowercase_ , use_fast=lowercase_ )
if tok_name == MBART_TINY:
snake_case_ = SeqaSeqDataset(
lowercase_ , data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() ) , type_path='''train''' , max_source_length=4 , max_target_length=8 , src_lang='''EN''' , tgt_lang='''FR''' , )
snake_case_ = train_dataset.dataset_kwargs
assert "src_lang" in kwargs and "tgt_lang" in kwargs
else:
snake_case_ = SeqaSeqDataset(
lowercase_ , data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() ) , type_path='''train''' , max_source_length=4 , max_target_length=8 , )
snake_case_ = train_dataset.dataset_kwargs
assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
assert len(lowercase_ ) == 1 if tok_name == BART_TINY else len(lowercase_ ) == 0
| 72 | 1 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
A__ : Dict = {'''tokenization_byt5''': ['''ByT5Tokenizer''']}
if TYPE_CHECKING:
from .tokenization_byta import ByTaTokenizer
else:
import sys
A__ : Optional[Any] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 103 |
import copy
from typing import Dict, List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
A__ : Optional[Any] = {
'''facebook/mask2former-swin-small-coco-instance''': (
'''https://huggingface.co/facebook/mask2former-swin-small-coco-instance/blob/main/config.json'''
)
# See all Mask2Former models at https://huggingface.co/models?filter=mask2former
}
A__ : Dict = logging.get_logger(__name__)
class __snake_case ( UpperCamelCase_ ):
_a = '''mask2former'''
_a = ['''swin''']
_a = {'''hidden_size''': '''hidden_dim'''}
def __init__( self : Any , A_ : Optional[Dict] = None , A_ : int = 2_5_6 , A_ : int = 2_5_6 , A_ : int = 2_5_6 , A_ : int = 1_0_2_4 , A_ : str = "relu" , A_ : int = 6 , A_ : int = 1_0 , A_ : int = 8 , A_ : float = 0.0 , A_ : int = 2_0_4_8 , A_ : bool = False , A_ : bool = False , A_ : int = 4 , A_ : int = 2_5_5 , A_ : int = 1_0_0 , A_ : float = 0.1 , A_ : float = 2.0 , A_ : float = 5.0 , A_ : float = 5.0 , A_ : int = 1_2_5_4_4 , A_ : float = 3.0 , A_ : float = 0.75 , A_ : float = 0.02 , A_ : float = 1.0 , A_ : bool = True , A_ : List[int] = [4, 8, 1_6, 3_2] , A_ : bool = None , **A_ : Dict , ):
if backbone_config is None:
logger.info('''`backbone_config` is `None`. Initializing the config with the default `Swin` backbone.''')
lowerCAmelCase_ : int = CONFIG_MAPPING['''swin'''](
image_size=2_2_4 , in_channels=3 , patch_size=4 , embed_dim=9_6 , depths=[2, 2, 1_8, 2] , num_heads=[3, 6, 1_2, 2_4] , window_size=7 , drop_path_rate=0.3 , use_absolute_embeddings=A_ , out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4'''] , )
if isinstance(A_ , A_):
lowerCAmelCase_ : List[Any] = backbone_config.pop('''model_type''')
lowerCAmelCase_ : Optional[Any] = CONFIG_MAPPING[backbone_model_type]
lowerCAmelCase_ : List[Any] = config_class.from_dict(A_)
# verify that the backbone is supported
if backbone_config.model_type not in self.backbones_supported:
logger.warning_once(
F"""Backbone {backbone_config.model_type} is not a supported model and may not be compatible with Mask2Former. """
F"""Supported model types: {",".join(self.backbones_supported)}""")
lowerCAmelCase_ : List[Any] = backbone_config
lowerCAmelCase_ : str = feature_size
lowerCAmelCase_ : Optional[Any] = mask_feature_size
lowerCAmelCase_ : int = hidden_dim
lowerCAmelCase_ : int = encoder_feedforward_dim
lowerCAmelCase_ : Optional[int] = activation_function
lowerCAmelCase_ : Any = encoder_layers
lowerCAmelCase_ : Optional[Any] = decoder_layers
lowerCAmelCase_ : Optional[Any] = num_attention_heads
lowerCAmelCase_ : Optional[int] = dropout
lowerCAmelCase_ : List[str] = dim_feedforward
lowerCAmelCase_ : Optional[Any] = pre_norm
lowerCAmelCase_ : List[str] = enforce_input_projection
lowerCAmelCase_ : Tuple = common_stride
lowerCAmelCase_ : Optional[Any] = ignore_value
lowerCAmelCase_ : Optional[Any] = num_queries
lowerCAmelCase_ : int = no_object_weight
lowerCAmelCase_ : Tuple = class_weight
lowerCAmelCase_ : int = mask_weight
lowerCAmelCase_ : Dict = dice_weight
lowerCAmelCase_ : str = train_num_points
lowerCAmelCase_ : Dict = oversample_ratio
lowerCAmelCase_ : Tuple = importance_sample_ratio
lowerCAmelCase_ : List[str] = init_std
lowerCAmelCase_ : List[str] = init_xavier_std
lowerCAmelCase_ : Optional[Any] = use_auxiliary_loss
lowerCAmelCase_ : List[Any] = feature_strides
lowerCAmelCase_ : int = output_auxiliary_logits
lowerCAmelCase_ : Optional[Any] = decoder_layers
super().__init__(**A_)
@classmethod
def UpperCAmelCase__ ( cls : List[str] , A_ : PretrainedConfig , **A_ : List[Any]):
return cls(
backbone_config=A_ , **A_ , )
def UpperCAmelCase__ ( self : List[Any]):
lowerCAmelCase_ : str = copy.deepcopy(self.__dict__)
lowerCAmelCase_ : Dict = self.backbone_config.to_dict()
lowerCAmelCase_ : Optional[int] = self.__class__.model_type
return output
| 103 | 1 |
def _lowerCAmelCase ( __lowerCAmelCase , __lowerCAmelCase = False ) -> bool:
"""simple docstring"""
if n == 2:
return True
if not n % 2 or n < 2:
return False
if n > 5 and n % 10 not in (1, 3, 7, 9): # can quickly check last digit
return False
if n > 3317044064679887385961981 and not allow_probable:
raise ValueError(
'''Warning: upper bound of deterministic test is exceeded. '''
'''Pass allow_probable=True to allow probabilistic test. '''
'''A return value of True indicates a probable prime.''' )
# array bounds provided by analysis
snake_case__ : List[Any] = [
2047,
1373653,
25326001,
3215031751,
2152302898747,
3474749660383,
341550071728321,
1,
3825123056546413051,
1,
1,
318665857834031151167461,
3317044064679887385961981,
]
snake_case__ : Optional[int] = [2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41]
for idx, _p in enumerate(_UpperCAmelCase , 1 ):
if n < _p:
# then we have our last prime to check
snake_case__ : Any = primes[:idx]
break
snake_case__ : Any = n - 1, 0
# break up n -1 into a power of 2 (s) and
# remaining odd component
# essentially, solve for d * 2 ** s == n - 1
while d % 2 == 0:
d //= 2
s += 1
for prime in plist:
snake_case__ : Optional[int] = False
for r in range(_UpperCAmelCase ):
snake_case__ : str = pow(_UpperCAmelCase , d * 2**r , _UpperCAmelCase )
# see article for analysis explanation for m
if (r == 0 and m == 1) or ((m + 1) % n == 0):
snake_case__ : Union[str, Any] = True
# this loop will not determine compositeness
break
if pr:
continue
# if pr is False, then the above loop never evaluated to true,
# and the n MUST be composite
return False
return True
def _lowerCAmelCase ( ) -> None:
"""simple docstring"""
assert not miller_rabin(561 )
assert miller_rabin(563 )
# 2047
assert not miller_rabin(838201 )
assert miller_rabin(838207 )
# 1_373_653
assert not miller_rabin(17316001 )
assert miller_rabin(17316017 )
# 25_326_001
assert not miller_rabin(3078386641 )
assert miller_rabin(3078386653 )
# 3_215_031_751
assert not miller_rabin(1713045574801 )
assert miller_rabin(1713045574819 )
# 2_152_302_898_747
assert not miller_rabin(2779799728307 )
assert miller_rabin(2779799728327 )
# 3_474_749_660_383
assert not miller_rabin(113850023909441 )
assert miller_rabin(113850023909527 )
# 341_550_071_728_321
assert not miller_rabin(1275041018848804351 )
assert miller_rabin(1275041018848804391 )
# 3_825_123_056_546_413_051
assert not miller_rabin(79666464458507787791867 )
assert miller_rabin(79666464458507787791951 )
# 318_665_857_834_031_151_167_461
assert not miller_rabin(552840677446647897660333 )
assert miller_rabin(552840677446647897660359 )
# 3_317_044_064_679_887_385_961_981
# upper limit for probabilistic test
if __name__ == "__main__":
test_miller_rabin()
| 350 |
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
A__ = {'''configuration_dpt''': ['''DPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DPTConfig''']}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ = ['''DPTFeatureExtractor''']
A__ = ['''DPTImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ = [
'''DPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''DPTForDepthEstimation''',
'''DPTForSemanticSegmentation''',
'''DPTModel''',
'''DPTPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_dpt import DPT_PRETRAINED_CONFIG_ARCHIVE_MAP, DPTConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_dpt import DPTFeatureExtractor
from .image_processing_dpt import DPTImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_dpt import (
DPT_PRETRAINED_MODEL_ARCHIVE_LIST,
DPTForDepthEstimation,
DPTForSemanticSegmentation,
DPTModel,
DPTPreTrainedModel,
)
else:
import sys
A__ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 44 | 0 |
from __future__ import annotations
from typing import Any
class UpperCAmelCase :
def __init__(self : int , snake_case__ : Tuple ) -> None:
'''simple docstring'''
snake_case : Tuple = num_of_nodes
snake_case : list[list[int]] = []
snake_case : dict[int, int] = {}
def _SCREAMING_SNAKE_CASE (self : Any , snake_case__ : List[Any] , snake_case__ : Any , snake_case__ : int ) -> None:
'''simple docstring'''
self.m_edges.append([u_node, v_node, weight] )
def _SCREAMING_SNAKE_CASE (self : int , snake_case__ : Optional[Any] ) -> int:
'''simple docstring'''
if self.m_component[u_node] == u_node:
return u_node
return self.find_component(self.m_component[u_node] )
def _SCREAMING_SNAKE_CASE (self : str , snake_case__ : Dict ) -> None:
'''simple docstring'''
if self.m_component[u_node] != u_node:
for k in self.m_component:
snake_case : str = self.find_component(lowerCAmelCase__ )
def _SCREAMING_SNAKE_CASE (self : str , snake_case__ : Dict , snake_case__ : List[str] , snake_case__ : Dict ) -> None:
'''simple docstring'''
if component_size[u_node] <= component_size[v_node]:
snake_case : Union[str, Any] = v_node
component_size[v_node] += component_size[u_node]
self.set_component(lowerCAmelCase__ )
elif component_size[u_node] >= component_size[v_node]:
snake_case : List[str] = self.find_component(lowerCAmelCase__ )
component_size[u_node] += component_size[v_node]
self.set_component(lowerCAmelCase__ )
def _SCREAMING_SNAKE_CASE (self : int ) -> None:
'''simple docstring'''
snake_case : Tuple = []
snake_case : int = 0
snake_case : list[Any] = [-1] * self.m_num_of_nodes
# A list of components (initialized to all of the nodes)
for node in range(self.m_num_of_nodes ):
self.m_component.update({node: node} )
component_size.append(1 )
snake_case : int = self.m_num_of_nodes
while num_of_components > 1:
for edge in self.m_edges:
snake_case : Optional[int] = edge
snake_case : Optional[int] = self.m_component[u]
snake_case : Dict = self.m_component[v]
if u_component != v_component:
for component in (u_component, v_component):
if (
minimum_weight_edge[component] == -1
or minimum_weight_edge[component][2] > w
):
snake_case : Optional[int] = [u, v, w]
for edge in minimum_weight_edge:
if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
snake_case : int = edge
snake_case : str = self.m_component[u]
snake_case : Optional[int] = self.m_component[v]
if u_component != v_component:
mst_weight += w
self.union(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
print(f"""Added edge [{u} - {v}]\nAdded weight: {w}\n""" )
num_of_components -= 1
snake_case : Dict = [-1] * self.m_num_of_nodes
print(f"""The total weight of the minimal spanning tree is: {mst_weight}""" )
def UpperCamelCase ( ):
pass
if __name__ == "__main__":
import doctest
doctest.testmod()
| 59 |
import math
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCAmelCase = logging.get_logger(__name__)
__lowerCAmelCase = {
'''facebook/data2vec-base-960h''': '''https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/main/config.json''',
# See all Data2VecAudio models at https://huggingface.co/models?filter=data2vec-audio
}
class __a ( __UpperCamelCase ):
__lowercase : Optional[int] = 'data2vec-audio'
def __init__( self , lowerCAmelCase__=32 , lowerCAmelCase__=768 , lowerCAmelCase__=12 , lowerCAmelCase__=12 , lowerCAmelCase__=3_072 , lowerCAmelCase__="gelu" , lowerCAmelCase__=0.1 , lowerCAmelCase__=0.1 , lowerCAmelCase__=0.1 , lowerCAmelCase__=0.0 , lowerCAmelCase__=0.1 , lowerCAmelCase__=0.1 , lowerCAmelCase__=0.0_2 , lowerCAmelCase__=1E-5 , lowerCAmelCase__="gelu" , lowerCAmelCase__=(512, 512, 512, 512, 512, 512, 512) , lowerCAmelCase__=(5, 2, 2, 2, 2, 2, 2) , lowerCAmelCase__=(10, 3, 3, 3, 3, 2, 2) , lowerCAmelCase__=False , lowerCAmelCase__=16 , lowerCAmelCase__=19 , lowerCAmelCase__=5 , lowerCAmelCase__=0.0_5 , lowerCAmelCase__=10 , lowerCAmelCase__=2 , lowerCAmelCase__=0.0 , lowerCAmelCase__=10 , lowerCAmelCase__=0 , lowerCAmelCase__="sum" , lowerCAmelCase__=False , lowerCAmelCase__=False , lowerCAmelCase__=256 , lowerCAmelCase__=(512, 512, 512, 512, 1_500) , lowerCAmelCase__=(5, 3, 3, 1, 1) , lowerCAmelCase__=(1, 2, 3, 1, 1) , lowerCAmelCase__=512 , lowerCAmelCase__=0 , lowerCAmelCase__=1 , lowerCAmelCase__=2 , lowerCAmelCase__=False , lowerCAmelCase__=3 , lowerCAmelCase__=2 , lowerCAmelCase__=3 , lowerCAmelCase__=None , **lowerCAmelCase__ , ) -> Optional[int]:
'''simple docstring'''
super().__init__(**lowerCAmelCase__ , pad_token_id=lowerCAmelCase__ , bos_token_id=lowerCAmelCase__ , eos_token_id=lowerCAmelCase__ )
lowercase__: int = hidden_size
lowercase__: str = feat_extract_activation
lowercase__: List[Any] = list(lowerCAmelCase__ )
lowercase__: Optional[int] = list(lowerCAmelCase__ )
lowercase__: int = list(lowerCAmelCase__ )
lowercase__: Union[str, Any] = conv_bias
lowercase__: int = num_conv_pos_embeddings
lowercase__: List[str] = num_conv_pos_embedding_groups
lowercase__: List[Any] = conv_pos_kernel_size
lowercase__: Optional[Any] = len(self.conv_dim )
lowercase__: List[str] = num_hidden_layers
lowercase__: List[str] = intermediate_size
lowercase__: Tuple = hidden_act
lowercase__: Any = num_attention_heads
lowercase__: Optional[int] = hidden_dropout
lowercase__: List[str] = attention_dropout
lowercase__: int = activation_dropout
lowercase__: Dict = feat_proj_dropout
lowercase__: str = final_dropout
lowercase__: List[str] = layerdrop
lowercase__: str = layer_norm_eps
lowercase__: Union[str, Any] = initializer_range
lowercase__: Union[str, Any] = vocab_size
lowercase__: Any = use_weighted_layer_sum
if (
(len(self.conv_stride ) != self.num_feat_extract_layers)
or (len(self.conv_kernel ) != self.num_feat_extract_layers)
or (len(self.conv_dim ) != self.num_feat_extract_layers)
):
raise ValueError(
'Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='
' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='
F' {len(self.conv_dim )}`, `len(config.conv_stride) = {len(self.conv_stride )}`,'
F' `len(config.conv_kernel) = {len(self.conv_kernel )}`.' )
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
lowercase__: List[str] = mask_time_prob
lowercase__: Tuple = mask_time_length
lowercase__: List[Any] = mask_time_min_masks
lowercase__: Optional[int] = mask_feature_prob
lowercase__: Union[str, Any] = mask_feature_length
lowercase__: List[str] = mask_feature_min_masks
# ctc loss
lowercase__: Union[str, Any] = ctc_loss_reduction
lowercase__: str = ctc_zero_infinity
# adapter
lowercase__: str = add_adapter
lowercase__: List[Any] = adapter_kernel_size
lowercase__: Tuple = adapter_stride
lowercase__: Dict = num_adapter_layers
lowercase__: Optional[Any] = output_hidden_size or hidden_size
# SequenceClassification-specific parameter. Feel free to ignore for other classes.
lowercase__: List[Any] = classifier_proj_size
# XVector-specific parameters. Feel free to ignore for other classes.
lowercase__: int = list(lowerCAmelCase__ )
lowercase__: Dict = list(lowerCAmelCase__ )
lowercase__: int = list(lowerCAmelCase__ )
lowercase__: str = xvector_output_dim
@property
def SCREAMING_SNAKE_CASE__ ( self ) -> Optional[int]:
'''simple docstring'''
return math.prod(self.conv_stride )
| 196 | 0 |
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device
_snake_case : Any = False
class _UpperCAmelCase ( unittest.TestCase ):
"""simple docstring"""
pass
@nightly
@require_torch_gpu
class _UpperCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def lowercase ( self : str ) -> Dict:
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowercase ( self : List[Any] ) -> Union[str, Any]:
__lowerCAmelCase = VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion' , torch_dtype=torch.floataa )
pipe.to(__lowerCamelCase )
pipe.set_progress_bar_config(disable=__lowerCamelCase )
__lowerCAmelCase = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg' )
__lowerCAmelCase = torch.manual_seed(0 )
__lowerCAmelCase = pipe.dual_guided(
prompt='first prompt' , image=__lowerCamelCase , text_to_image_strength=0.75 , generator=__lowerCamelCase , guidance_scale=7.5 , num_inference_steps=2 , output_type='numpy' , ).images
with tempfile.TemporaryDirectory() as tmpdirname:
pipe.save_pretrained(__lowerCamelCase )
__lowerCAmelCase = VersatileDiffusionPipeline.from_pretrained(__lowerCamelCase , torch_dtype=torch.floataa )
pipe.to(__lowerCamelCase )
pipe.set_progress_bar_config(disable=__lowerCamelCase )
__lowerCAmelCase = generator.manual_seed(0 )
__lowerCAmelCase = pipe.dual_guided(
prompt='first prompt' , image=__lowerCamelCase , text_to_image_strength=0.75 , generator=__lowerCamelCase , guidance_scale=7.5 , num_inference_steps=2 , output_type='numpy' , ).images
assert np.abs(image - new_image ).sum() < 1e-5, "Models don't have the same forward pass"
def lowercase ( self : Optional[Any] ) -> str:
__lowerCAmelCase = VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion' , torch_dtype=torch.floataa )
pipe.to(__lowerCamelCase )
pipe.set_progress_bar_config(disable=__lowerCamelCase )
__lowerCAmelCase = '''cyberpunk 2077'''
__lowerCAmelCase = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg' )
__lowerCAmelCase = torch.manual_seed(0 )
__lowerCAmelCase = pipe.dual_guided(
prompt=__lowerCamelCase , image=__lowerCamelCase , text_to_image_strength=0.75 , generator=__lowerCamelCase , guidance_scale=7.5 , num_inference_steps=5_0 , output_type='numpy' , ).images
__lowerCAmelCase = image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]
assert image.shape == (1, 5_1_2, 5_1_2, 3)
__lowerCAmelCase = np.array([0.14_48, 0.16_19, 0.17_41, 0.10_86, 0.11_47, 0.11_28, 0.11_99, 0.11_65, 0.10_01] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
__lowerCAmelCase = '''A painting of a squirrel eating a burger '''
__lowerCAmelCase = torch.manual_seed(0 )
__lowerCAmelCase = pipe.text_to_image(
prompt=__lowerCamelCase , generator=__lowerCamelCase , guidance_scale=7.5 , num_inference_steps=5_0 , output_type='numpy' ).images
__lowerCAmelCase = image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]
assert image.shape == (1, 5_1_2, 5_1_2, 3)
__lowerCAmelCase = np.array([0.33_67, 0.31_69, 0.26_56, 0.38_70, 0.47_90, 0.37_96, 0.40_09, 0.48_78, 0.47_78] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
__lowerCAmelCase = pipe.image_variation(__lowerCamelCase , generator=__lowerCamelCase , output_type='numpy' ).images
__lowerCAmelCase = image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]
assert image.shape == (1, 5_1_2, 5_1_2, 3)
__lowerCAmelCase = np.array([0.30_76, 0.31_23, 0.32_84, 0.37_82, 0.37_70, 0.38_94, 0.42_97, 0.43_31, 0.44_56] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
| 356 |
import unittest
import numpy as np
import torch
from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class _UpperCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def lowercase ( self : Any ) -> Optional[int]:
__lowerCAmelCase = 1_0
def lowercase ( self : int ) -> Union[str, Any]:
__lowerCAmelCase = [1, 2, 3, 4]
__lowerCAmelCase = [1, 2, 3, 4, 0, 0, 0, 0, 0, 0]
self.assertEqual(truncate_or_pad(lowerCAmelCase_ , self.block_size , 0 ) , lowerCAmelCase_ )
def lowercase ( self : Optional[Any] ) -> List[str]:
__lowerCAmelCase = [1, 2, 3, 4, 5, 6, 7, 8, 9, 1_0]
__lowerCAmelCase = [1, 2, 3, 4, 5, 6, 7, 8, 9, 1_0]
self.assertEqual(truncate_or_pad(lowerCAmelCase_ , self.block_size , 0 ) , lowerCAmelCase_ )
def lowercase ( self : Any ) -> Optional[Any]:
__lowerCAmelCase = [1, 2, 3, 4, 5, 6, 7, 8, 9, 1_0, 1_1, 1_2, 1_3]
__lowerCAmelCase = [1, 2, 3, 4, 5, 6, 7, 8, 9, 1_0]
self.assertEqual(truncate_or_pad(lowerCAmelCase_ , self.block_size , 0 ) , lowerCAmelCase_ )
def lowercase ( self : List[str] ) -> Any:
__lowerCAmelCase = 'It was the year of Our Lord one thousand seven hundred and\n seventy-five.\n\nSpiritual revelations were conceded to England at that\n favoured period, as at this.'
__lowerCAmelCase , __lowerCAmelCase = process_story(lowerCAmelCase_ )
self.assertEqual(lowerCAmelCase_ , [] )
def lowercase ( self : Any ) -> str:
__lowerCAmelCase = ''
__lowerCAmelCase , __lowerCAmelCase = process_story(lowerCAmelCase_ )
self.assertEqual(lowerCAmelCase_ , [] )
self.assertEqual(lowerCAmelCase_ , [] )
def lowercase ( self : int ) -> int:
__lowerCAmelCase = (
'It was the year of Our Lord one thousand seven hundred and '
'seventy-five\n\nSpiritual revelations were conceded to England '
'at that favoured period, as at this.\n@highlight\n\nIt was the best of times'
)
__lowerCAmelCase , __lowerCAmelCase = process_story(lowerCAmelCase_ )
__lowerCAmelCase = [
'It was the year of Our Lord one thousand seven hundred and seventy-five.',
'Spiritual revelations were conceded to England at that favoured period, as at this.',
]
self.assertEqual(lowerCAmelCase_ , lowerCAmelCase_ )
__lowerCAmelCase = ['It was the best of times.']
self.assertEqual(lowerCAmelCase_ , lowerCAmelCase_ )
def lowercase ( self : Dict ) -> Any:
__lowerCAmelCase = torch.tensor([1, 2, 3, 4] )
__lowerCAmelCase = torch.tensor([1, 1, 1, 1] )
np.testing.assert_array_equal(build_mask(lowerCAmelCase_ , 0 ).numpy() , expected.numpy() )
def lowercase ( self : List[Any] ) -> Optional[int]:
__lowerCAmelCase = torch.tensor([1, 2, 3, 4, 2_3, 2_3, 2_3] )
__lowerCAmelCase = torch.tensor([1, 1, 1, 1, 0, 0, 0] )
np.testing.assert_array_equal(build_mask(lowerCAmelCase_ , 2_3 ).numpy() , expected.numpy() )
def lowercase ( self : str ) -> List[Any]:
__lowerCAmelCase = torch.tensor([8, 2, 3, 4, 1, 1, 1] )
__lowerCAmelCase = torch.tensor([1, 1, 1, 1, 0, 0, 0] )
np.testing.assert_array_equal(build_mask(lowerCAmelCase_ , 1 ).numpy() , expected.numpy() )
def lowercase ( self : Optional[Any] ) -> Optional[int]:
__lowerCAmelCase = 1_0_1
__lowerCAmelCase = torch.tensor([[1, 2, 3, 4, 5, 6], [1, 2, 3, 1_0_1, 5, 6], [1, 1_0_1, 3, 4, 1_0_1, 6]] )
__lowerCAmelCase = torch.tensor([[1, 1, 1, 1, 1, 1], [1, 1, 1, 0, 0, 0], [1, 0, 0, 0, 1, 1]] )
__lowerCAmelCase = compute_token_type_ids(lowerCAmelCase_ , lowerCAmelCase_ )
np.testing.assert_array_equal(lowerCAmelCase_ , lowerCAmelCase_ )
| 207 | 0 |
'''simple docstring'''
import html
from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...utils import is_bsa_available, logging, requires_backends
if is_bsa_available():
import bsa
from bsa import BeautifulSoup
__lowerCAmelCase = logging.get_logger(__name__)
class _lowerCAmelCase ( __snake_case ):
'''simple docstring'''
def __init__(self , **UpperCAmelCase ) -> int:
requires_backends(self , ["""bs4"""] )
super().__init__(**UpperCAmelCase )
def lowercase (self , UpperCAmelCase ) -> List[str]:
_snake_case = []
_snake_case = []
_snake_case = element if element.name else element.parent
for parent in child.parents: # type: bs4.element.Tag
_snake_case = parent.find_all(child.name , recursive=UpperCAmelCase )
xpath_tags.append(child.name )
xpath_subscripts.append(
0 if 1 == len(UpperCAmelCase ) else next(i for i, s in enumerate(UpperCAmelCase , 1 ) if s is child ) )
_snake_case = parent
xpath_tags.reverse()
xpath_subscripts.reverse()
return xpath_tags, xpath_subscripts
def lowercase (self , UpperCAmelCase ) -> Tuple:
_snake_case = BeautifulSoup(UpperCAmelCase , """html.parser""" )
_snake_case = []
_snake_case = []
_snake_case = []
for element in html_code.descendants:
if type(UpperCAmelCase ) == bsa.element.NavigableString:
if type(element.parent ) != bsa.element.Tag:
continue
_snake_case = html.unescape(UpperCAmelCase ).strip()
if not text_in_this_tag:
continue
all_doc_strings.append(UpperCAmelCase )
_snake_case, _snake_case = self.xpath_soup(UpperCAmelCase )
stringaxtag_seq.append(UpperCAmelCase )
stringaxsubs_seq.append(UpperCAmelCase )
if len(UpperCAmelCase ) != len(UpperCAmelCase ):
raise ValueError("""Number of doc strings and xtags does not correspond""" )
if len(UpperCAmelCase ) != len(UpperCAmelCase ):
raise ValueError("""Number of doc strings and xsubs does not correspond""" )
return all_doc_strings, stringaxtag_seq, stringaxsubs_seq
def lowercase (self , UpperCAmelCase , UpperCAmelCase ) -> Optional[int]:
_snake_case = """"""
for tagname, subs in zip(UpperCAmelCase , UpperCAmelCase ):
xpath += f"""/{tagname}"""
if subs != 0:
xpath += f"""[{subs}]"""
return xpath
def __call__(self , UpperCAmelCase ) -> BatchFeature:
_snake_case = False
# Check that strings has a valid type
if isinstance(UpperCAmelCase , UpperCAmelCase ):
_snake_case = True
elif isinstance(UpperCAmelCase , (list, tuple) ):
if len(UpperCAmelCase ) == 0 or isinstance(html_strings[0] , UpperCAmelCase ):
_snake_case = True
if not valid_strings:
raise ValueError(
"""HTML strings must of type `str`, `List[str]` (batch of examples), """
f"""but is of type {type(UpperCAmelCase )}.""" )
_snake_case = bool(isinstance(UpperCAmelCase , (list, tuple) ) and (isinstance(html_strings[0] , UpperCAmelCase )) )
if not is_batched:
_snake_case = [html_strings]
# Get nodes + xpaths
_snake_case = []
_snake_case = []
for html_string in html_strings:
_snake_case, _snake_case, _snake_case = self.get_three_from_single(UpperCAmelCase )
nodes.append(UpperCAmelCase )
_snake_case = []
for node, tag_list, sub_list in zip(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ):
_snake_case = self.construct_xpath(UpperCAmelCase , UpperCAmelCase )
xpath_strings.append(UpperCAmelCase )
xpaths.append(UpperCAmelCase )
# return as Dict
_snake_case = {"""nodes""": nodes, """xpaths""": xpaths}
_snake_case = BatchFeature(data=UpperCAmelCase , tensor_type=UpperCAmelCase )
return encoded_inputs | 341 |
'''simple docstring'''
from scipy.stats import spearmanr
import datasets
__lowerCAmelCase = '\nThe Spearman rank-order correlation coefficient is a measure of the\nrelationship between two datasets. Like other correlation coefficients,\nthis one varies between -1 and +1 with 0 implying no correlation.\nPositive correlations imply that as data in dataset x increases, so\ndoes data in dataset y. Negative correlations imply that as x increases,\ny decreases. Correlations of -1 or +1 imply an exact monotonic relationship.\n\nUnlike the Pearson correlation, the Spearman correlation does not\nassume that both datasets are normally distributed.\n\nThe p-value roughly indicates the probability of an uncorrelated system\nproducing datasets that have a Spearman correlation at least as extreme\nas the one computed from these datasets. The p-values are not entirely\nreliable but are probably reasonable for datasets larger than 500 or so.\n'
__lowerCAmelCase = '\nArgs:\n predictions (`List[float]`): Predicted labels, as returned by a model.\n references (`List[float]`): Ground truth labels.\n return_pvalue (`bool`): If `True`, returns the p-value. If `False`, returns\n only the spearmanr score. Defaults to `False`.\nReturns:\n spearmanr (`float`): Spearman correlation coefficient.\n p-value (`float`): p-value. **Note**: is only returned if `return_pvalue=True` is input.\nExamples:\n Example 1:\n >>> spearmanr_metric = datasets.load_metric("spearmanr")\n >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5], predictions=[10, 9, 2.5, 6, 4])\n >>> print(results)\n {\'spearmanr\': -0.7}\n\n Example 2:\n >>> spearmanr_metric = datasets.load_metric("spearmanr")\n >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5],\n ... predictions=[10, 9, 2.5, 6, 4],\n ... return_pvalue=True)\n >>> print(results[\'spearmanr\'])\n -0.7\n >>> print(round(results[\'spearmanr_pvalue\'], 2))\n 0.19\n'
__lowerCAmelCase = r'\\n@book{kokoska2000crc,\n title={CRC standard probability and statistics tables and formulae},\n author={Kokoska, Stephen and Zwillinger, Daniel},\n year={2000},\n publisher={Crc Press}\n}\n@article{2020SciPy-NMeth,\n author = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and\n Haberland, Matt and Reddy, Tyler and Cournapeau, David and\n Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and\n Bright, Jonathan and {van der Walt}, St{\'e}fan J. and\n Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and\n Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and\n Kern, Robert and Larson, Eric and Carey, C J and\n Polat, {\.I}lhan and Feng, Yu and Moore, Eric W. and\n {VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and\n Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and\n Harris, Charles R. and Archibald, Anne M. and\n Ribeiro, Ant{\^o}nio H. and Pedregosa, Fabian and\n {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},\n title = {{{SciPy} 1.0: Fundamental Algorithms for Scientific\n Computing in Python}},\n journal = {Nature Methods},\n year = {2020},\n volume = {17},\n pages = {261--272},\n adsurl = {https://rdcu.be/b08Wh},\n doi = {10.1038/s41592-019-0686-2},\n}\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class _lowerCAmelCase ( datasets.Metric ):
'''simple docstring'''
def lowercase (self ) -> Optional[Any]:
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""predictions""": datasets.Value("""float""" ),
"""references""": datasets.Value("""float""" ),
} ) , reference_urls=["""https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.spearmanr.html"""] , )
def lowercase (self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase=False ) -> Optional[Any]:
_snake_case = spearmanr(UpperCAmelCase , UpperCAmelCase )
if return_pvalue:
return {"spearmanr": results[0], "spearmanr_pvalue": results[1]}
else:
return {"spearmanr": results[0]} | 341 | 1 |
'''simple docstring'''
import inspect
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
import torch.utils.checkpoint
from ...models import UNetaDModel, VQModel
from ...schedulers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
def snake_case_ (UpperCamelCase : Dict ):
'''simple docstring'''
_a , _a = image.size
_a , _a = (x - x % 32 for x in (w, h)) # resize to integer multiple of 32
_a = image.resize((w, h) , resample=PIL_INTERPOLATION['''lanczos'''] )
_a = np.array(lowerCAmelCase__ ).astype(np.floataa ) / 255.0
_a = image[None].transpose(0 , 3 , 1 , 2 )
_a = torch.from_numpy(lowerCAmelCase__ )
return 2.0 * image - 1.0
class A ( __SCREAMING_SNAKE_CASE ):
def __init__( self : List[Any] , lowerCAmelCase_ : str , lowerCAmelCase_ : str , lowerCAmelCase_ : Tuple , ) -> Optional[Any]:
"""simple docstring"""
super().__init__()
self.register_modules(vqvae=_a , unet=_a , scheduler=_a )
@torch.no_grad()
def __call__( self : List[str] , lowerCAmelCase_ : Optional[int] = None , lowerCAmelCase_ : List[Any] = 1 , lowerCAmelCase_ : Union[str, Any] = 1_00 , lowerCAmelCase_ : List[Any] = 0.0 , lowerCAmelCase_ : Optional[int] = None , lowerCAmelCase_ : Tuple = "pil" , lowerCAmelCase_ : int = True , ) -> Union[str, Any]:
"""simple docstring"""
if isinstance(_a , PIL.Image.Image ):
_a = 1
elif isinstance(_a , torch.Tensor ):
_a = image.shape[0]
else:
raise ValueError(F'`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(_a )}' )
if isinstance(_a , PIL.Image.Image ):
_a = preprocess(_a )
_a , _a = image.shape[-2:]
# in_channels should be 6: 3 for latents, 3 for low resolution image
_a = (batch_size, self.unet.config.in_channels // 2, height, width)
_a = next(self.unet.parameters() ).dtype
_a = randn_tensor(_a , generator=_a , device=self.device , dtype=_a )
_a = image.to(device=self.device , dtype=_a )
# set timesteps and move to the correct device
self.scheduler.set_timesteps(_a , device=self.device )
_a = self.scheduler.timesteps
# scale the initial noise by the standard deviation required by the scheduler
_a = latents * self.scheduler.init_noise_sigma
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
_a = '''eta''' in set(inspect.signature(self.scheduler.step ).parameters.keys() )
_a = {}
if accepts_eta:
_a = eta
for t in self.progress_bar(_a ):
# concat latents and low resolution image in the channel dimension.
_a = torch.cat([latents, image] , dim=1 )
_a = self.scheduler.scale_model_input(_a , _a )
# predict the noise residual
_a = self.unet(_a , _a ).sample
# compute the previous noisy sample x_t -> x_t-1
_a = self.scheduler.step(_a , _a , _a , **_a ).prev_sample
# decode the image latents with the VQVAE
_a = self.vqvae.decode(_a ).sample
_a = torch.clamp(_a , -1.0 , 1.0 )
_a = image / 2 + 0.5
_a = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
_a = self.numpy_to_pil(_a )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=_a )
| 361 |
'''simple docstring'''
import logging
import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, set_seed
class A ( _a ):
def __init__( self : Optional[int] , lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : Union[str, Any] , lowerCAmelCase_ : List[str]=10_24 , lowerCAmelCase_ : Optional[Any]=10_24 , lowerCAmelCase_ : Tuple=3.6 ) -> List[Any]:
"""simple docstring"""
_a = tokenizer
_a = tokenizer.bos_token_id
_a = dataset
_a = seq_length
_a = seq_length * chars_per_token * num_of_sequences
def __iter__( self : Any ) -> int:
"""simple docstring"""
_a = iter(self.dataset )
_a = True
while more_examples:
_a , _a = [], 0
while True:
if buffer_len >= self.input_characters:
break
try:
buffer.append(next(lowerCAmelCase_ )['''content'''] )
buffer_len += len(buffer[-1] )
except StopIteration:
_a = False
break
_a = tokenizer(lowerCAmelCase_ , truncation=lowerCAmelCase_ )['''input_ids''']
_a = []
for tokenized_input in tokenized_inputs:
all_token_ids.extend(tokenized_input + [self.concat_token_id] )
for i in range(0 , len(lowerCAmelCase_ ) , self.seq_length ):
_a = all_token_ids[i : i + self.seq_length]
if len(lowerCAmelCase_ ) == self.seq_length:
yield torch.tensor(lowerCAmelCase_ )
def snake_case_ (UpperCamelCase : int ):
'''simple docstring'''
_a = {'''streaming''': True}
_a = load_dataset(args.dataset_name , split='''train''' , **UpperCamelCase )
_a = ConstantLengthDataset(UpperCamelCase , UpperCamelCase , seq_length=args.seq_length )
_a = DataLoader(UpperCamelCase , batch_size=args.batch_size )
return eval_dataloader
def snake_case_ (UpperCamelCase : int ):
'''simple docstring'''
model.eval()
_a = []
for step, batch in enumerate(UpperCamelCase ):
with torch.no_grad():
_a = model(UpperCamelCase , labels=UpperCamelCase )
_a = outputs.loss.repeat(args.batch_size )
losses.append(accelerator.gather(UpperCamelCase ) )
if args.max_eval_steps > 0 and step >= args.max_eval_steps:
break
_a = torch.mean(torch.cat(UpperCamelCase ) )
try:
_a = torch.exp(UpperCamelCase )
except OverflowError:
_a = float('''inf''' )
return loss.item(), perplexity.item()
# Setup Accelerator
_snake_case : List[str] = Accelerator()
# Parse configuration
_snake_case : List[str] = HfArgumentParser(EvaluationArguments)
_snake_case : Optional[int] = parser.parse_args()
set_seed(args.seed)
# Logging
_snake_case : Any = logging.getLogger(__name__)
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging.INFO
)
# Load model and tokenizer
_snake_case : Dict = AutoModelForCausalLM.from_pretrained(args.model_ckpt)
_snake_case : Optional[Any] = AutoTokenizer.from_pretrained(args.model_ckpt)
# Load dataset and dataloader
_snake_case : List[str] = create_dataloader(args)
# Prepare everything with our `accelerator`.
_snake_case , _snake_case : Optional[int] = accelerator.prepare(model, eval_dataloader)
# Evaluate and save the last checkpoint
logger.info('Evaluating and saving model after training')
_snake_case , _snake_case : int = evaluate(args)
logger.info(F'''loss/eval: {eval_loss}, perplexity: {perplexity}''')
| 179 | 0 |
"""simple docstring"""
import doctest
import glob
import importlib
import inspect
import os
import re
from contextlib import contextmanager
from functools import wraps
from unittest.mock import patch
import numpy as np
import pytest
from absl.testing import parameterized
import datasets
from datasets import load_metric
from .utils import for_all_test_methods, local, slow
# mark all tests as integration
__magic_name__ = pytest.mark.integration
__magic_name__ = {"comet"}
__magic_name__ = importlib.util.find_spec("fairseq") is not None
__magic_name__ = {"code_eval"}
__magic_name__ = os.name == "nt"
__magic_name__ = {"bertscore", "frugalscore", "perplexity"}
__magic_name__ = importlib.util.find_spec("transformers") is not None
def _lowerCAmelCase ( UpperCamelCase_ ):
@wraps(_UpperCAmelCase )
def wrapper(self , UpperCamelCase_ ):
if not _has_fairseq and metric_name in REQUIRE_FAIRSEQ:
self.skipTest("""\"test requires Fairseq\"""" )
else:
test_case(self , _UpperCAmelCase )
return wrapper
def _lowerCAmelCase ( UpperCamelCase_ ):
@wraps(_UpperCAmelCase )
def wrapper(self , UpperCamelCase_ ):
if not _has_transformers and metric_name in REQUIRE_TRANSFORMERS:
self.skipTest("""\"test requires transformers\"""" )
else:
test_case(self , _UpperCAmelCase )
return wrapper
def _lowerCAmelCase ( UpperCamelCase_ ):
@wraps(_UpperCAmelCase )
def wrapper(self , UpperCamelCase_ ):
if _on_windows and metric_name in UNSUPPORTED_ON_WINDOWS:
self.skipTest("""\"test not supported on Windows\"""" )
else:
test_case(self , _UpperCAmelCase )
return wrapper
def _lowerCAmelCase ( ):
__SCREAMING_SNAKE_CASE = [metric_dir.split(os.sep )[-2] for metric_dir in glob.glob("""./metrics/*/""" )]
return [{"testcase_name": x, "metric_name": x} for x in metrics if x != "gleu"] # gleu is unfinished
@parameterized.named_parameters(get_local_metric_names() )
@for_all_test_methods(
__a , __a , __a )
@local
class SCREAMING_SNAKE_CASE_ ( parameterized.TestCase ):
"""simple docstring"""
__lowercase : Dict = {}
__lowercase : Optional[Any] = None
@pytest.mark.filterwarnings("""ignore:metric_module_factory is deprecated:FutureWarning""")
@pytest.mark.filterwarnings("""ignore:load_metric is deprecated:FutureWarning""")
def snake_case_ ( self , lowerCAmelCase__):
__SCREAMING_SNAKE_CASE = """[...]"""
__SCREAMING_SNAKE_CASE = importlib.import_module(
datasets.load.metric_module_factory(os.path.join("""metrics""" , lowerCAmelCase__)).module_path)
__SCREAMING_SNAKE_CASE = datasets.load.import_main_class(metric_module.__name__ , dataset=lowerCAmelCase__)
# check parameters
__SCREAMING_SNAKE_CASE = inspect.signature(metric._compute).parameters
self.assertTrue(all(p.kind != p.VAR_KEYWORD for p in parameters.values())) # no **kwargs
# run doctest
with self.patch_intensive_calls(lowerCAmelCase__ , metric_module.__name__):
with self.use_local_metrics():
try:
__SCREAMING_SNAKE_CASE = doctest.testmod(lowerCAmelCase__ , verbose=lowerCAmelCase__ , raise_on_error=lowerCAmelCase__)
except doctest.UnexpectedException as e:
raise e.exc_info[1] # raise the exception that doctest caught
self.assertEqual(results.failed , 0)
self.assertGreater(results.attempted , 1)
@slow
def snake_case_ ( self , lowerCAmelCase__):
__SCREAMING_SNAKE_CASE = """[...]"""
__SCREAMING_SNAKE_CASE = importlib.import_module(
datasets.load.metric_module_factory(os.path.join("""metrics""" , lowerCAmelCase__)).module_path)
# run doctest
with self.use_local_metrics():
__SCREAMING_SNAKE_CASE = doctest.testmod(lowerCAmelCase__ , verbose=lowerCAmelCase__ , raise_on_error=lowerCAmelCase__)
self.assertEqual(results.failed , 0)
self.assertGreater(results.attempted , 1)
@contextmanager
def snake_case_ ( self , lowerCAmelCase__ , lowerCAmelCase__):
if metric_name in self.INTENSIVE_CALLS_PATCHER:
with self.INTENSIVE_CALLS_PATCHER[metric_name](lowerCAmelCase__):
yield
else:
yield
@contextmanager
def snake_case_ ( self):
def load_local_metric(lowerCAmelCase__ , *lowerCAmelCase__ , **lowerCAmelCase__):
return load_metric(os.path.join("""metrics""" , lowerCAmelCase__) , *lowerCAmelCase__ , **lowerCAmelCase__)
with patch("""datasets.load_metric""") as mock_load_metric:
__SCREAMING_SNAKE_CASE = load_local_metric
yield
@classmethod
def snake_case_ ( cls , lowerCAmelCase__):
def wrapper(lowerCAmelCase__):
__SCREAMING_SNAKE_CASE = contextmanager(lowerCAmelCase__)
__SCREAMING_SNAKE_CASE = patcher
return patcher
return wrapper
@LocalMetricTest.register_intensive_calls_patcher("""bleurt""" )
def _lowerCAmelCase ( UpperCamelCase_ ):
import tensorflow.compat.va as tf
from bleurt.score import Predictor
tf.flags.DEFINE_string("""sv""" , """""" , """""" ) # handle pytest cli flags
class SCREAMING_SNAKE_CASE_ ( __a ):
"""simple docstring"""
def snake_case_ ( self , lowerCAmelCase__):
assert len(input_dict["""input_ids"""]) == 2
return np.array([1.03, 1.04])
# mock predict_fn which is supposed to do a forward pass with a bleurt model
with patch("""bleurt.score._create_predictor""" ) as mock_create_predictor:
__SCREAMING_SNAKE_CASE = MockedPredictor()
yield
@LocalMetricTest.register_intensive_calls_patcher("""bertscore""" )
def _lowerCAmelCase ( UpperCamelCase_ ):
import torch
def bert_cos_score_idf(UpperCamelCase_ , UpperCamelCase_ , *UpperCamelCase_ , **UpperCamelCase_ ):
return torch.tensor([[1.0, 1.0, 1.0]] * len(_UpperCAmelCase ) )
# mock get_model which is supposed to do download a bert model
# mock bert_cos_score_idf which is supposed to do a forward pass with a bert model
with patch("""bert_score.scorer.get_model""" ), patch(
"""bert_score.scorer.bert_cos_score_idf""" ) as mock_bert_cos_score_idf:
__SCREAMING_SNAKE_CASE = bert_cos_score_idf
yield
@LocalMetricTest.register_intensive_calls_patcher("""comet""" )
def _lowerCAmelCase ( UpperCamelCase_ ):
def load_from_checkpoint(UpperCamelCase_ ):
class SCREAMING_SNAKE_CASE_ :
"""simple docstring"""
def snake_case_ ( self , lowerCAmelCase__ , *lowerCAmelCase__ , **lowerCAmelCase__):
assert len(lowerCAmelCase__) == 2
__SCREAMING_SNAKE_CASE = [0.19, 0.92]
return scores, sum(lowerCAmelCase__) / len(lowerCAmelCase__)
return Model()
# mock load_from_checkpoint which is supposed to do download a bert model
# mock load_from_checkpoint which is supposed to do download a bert model
with patch("""comet.download_model""" ) as mock_download_model:
__SCREAMING_SNAKE_CASE = None
with patch("""comet.load_from_checkpoint""" ) as mock_load_from_checkpoint:
__SCREAMING_SNAKE_CASE = load_from_checkpoint
yield
def _lowerCAmelCase ( ):
__SCREAMING_SNAKE_CASE = load_metric(os.path.join("""metrics""" , """seqeval""" ) )
__SCREAMING_SNAKE_CASE = """ERROR"""
__SCREAMING_SNAKE_CASE = f"Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {wrong_scheme}"
with pytest.raises(_UpperCAmelCase , match=re.escape(_UpperCAmelCase ) ):
metric.compute(predictions=[] , references=[] , scheme=_UpperCAmelCase )
| 100 |
from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
UpperCAmelCase__ = datasets.utils.logging.get_logger(__name__)
class __lowerCAmelCase ( folder_based_builder.FolderBasedBuilderConfig ):
UpperCamelCase = None
UpperCamelCase = None
class __lowerCAmelCase ( folder_based_builder.FolderBasedBuilder ):
UpperCamelCase = datasets.Audio()
UpperCamelCase = '''audio'''
UpperCamelCase = AudioFolderConfig
UpperCamelCase = 42 # definition at the bottom of the script
UpperCamelCase = AudioClassification(audio_column='''audio''' , label_column='''label''' )
UpperCAmelCase__ = [
".aiff",
".au",
".avr",
".caf",
".flac",
".htk",
".svx",
".mat4",
".mat5",
".mpc2k",
".ogg",
".paf",
".pvf",
".raw",
".rf64",
".sd2",
".sds",
".ircam",
".voc",
".w64",
".wav",
".nist",
".wavex",
".wve",
".xi",
".mp3",
".opus",
]
UpperCAmelCase__ = AUDIO_EXTENSIONS
| 339 | 0 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
a = logging.get_logger(__name__)
a = {'''vocab_file''': '''sentencepiece.bpe.model'''}
a = {
'''vocab_file''': {
'''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model''',
}
}
a = {
'''camembert-base''': 512,
}
a = '''▁'''
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
UpperCAmelCase : int = VOCAB_FILES_NAMES
UpperCAmelCase : List[str] = PRETRAINED_VOCAB_FILES_MAP
UpperCAmelCase : Any = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCAmelCase : Dict = ['''input_ids''', '''attention_mask''']
def __init__( self : Any , _UpperCAmelCase : int , _UpperCAmelCase : Dict="<s>" , _UpperCAmelCase : List[Any]="</s>" , _UpperCAmelCase : Tuple="</s>" , _UpperCAmelCase : int="<s>" , _UpperCAmelCase : Optional[Any]="<unk>" , _UpperCAmelCase : List[str]="<pad>" , _UpperCAmelCase : Dict="<mask>" , _UpperCAmelCase : str=["<s>NOTUSED", "</s>NOTUSED"] , _UpperCAmelCase : Optional[Dict[str, Any]] = None , **_UpperCAmelCase : List[str] , ):
# Mask token behave like a normal word, i.e. include the space before it
_A = AddedToken(_UpperCAmelCase , lstrip=_UpperCAmelCase , rstrip=_UpperCAmelCase ) if isinstance(_UpperCAmelCase , _UpperCAmelCase ) else mask_token
_A = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=_UpperCAmelCase , eos_token=_UpperCAmelCase , unk_token=_UpperCAmelCase , sep_token=_UpperCAmelCase , cls_token=_UpperCAmelCase , pad_token=_UpperCAmelCase , mask_token=_UpperCAmelCase , additional_special_tokens=_UpperCAmelCase , sp_model_kwargs=self.sp_model_kwargs , **_UpperCAmelCase , )
_A = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(_UpperCAmelCase ) )
_A = vocab_file
# HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
# sentencepiece vocabulary (this is the case for <s> and </s>
_A = {'<s>NOTUSED': 0, '<pad>': 1, '</s>NOTUSED': 2, '<unk>': 3}
_A = len(self.fairseq_tokens_to_ids )
_A = len(self.sp_model ) + len(self.fairseq_tokens_to_ids )
_A = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
def lowerCAmelCase_ ( self : Dict , _UpperCAmelCase : List[int] , _UpperCAmelCase : Optional[List[int]] = None ):
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
_A = [self.cls_token_id]
_A = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def lowerCAmelCase_ ( self : Optional[int] , _UpperCAmelCase : List[int] , _UpperCAmelCase : Optional[List[int]] = None , _UpperCAmelCase : bool = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_UpperCAmelCase , token_ids_a=_UpperCAmelCase , already_has_special_tokens=_UpperCAmelCase )
if token_ids_a is None:
return [1] + ([0] * len(_UpperCAmelCase )) + [1]
return [1] + ([0] * len(_UpperCAmelCase )) + [1, 1] + ([0] * len(_UpperCAmelCase )) + [1]
def lowerCAmelCase_ ( self : str , _UpperCAmelCase : List[int] , _UpperCAmelCase : Optional[List[int]] = None ):
_A = [self.sep_token_id]
_A = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
@property
def lowerCAmelCase_ ( self : str ):
return len(self.fairseq_tokens_to_ids ) + len(self.sp_model )
def lowerCAmelCase_ ( self : Any ):
_A = {self.convert_ids_to_tokens(_UpperCAmelCase ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def lowerCAmelCase_ ( self : int , _UpperCAmelCase : str ):
return self.sp_model.encode(_UpperCAmelCase , out_type=_UpperCAmelCase )
def lowerCAmelCase_ ( self : Optional[int] , _UpperCAmelCase : Optional[int] ):
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
elif self.sp_model.PieceToId(_UpperCAmelCase ) == 0:
# Convert sentence piece unk token to fairseq unk token index
return self.unk_token_id
return self.fairseq_offset + self.sp_model.PieceToId(_UpperCAmelCase )
def lowerCAmelCase_ ( self : Any , _UpperCAmelCase : List[str] ):
if index in self.fairseq_ids_to_tokens:
return self.fairseq_ids_to_tokens[index]
return self.sp_model.IdToPiece(index - self.fairseq_offset )
def lowerCAmelCase_ ( self : Optional[int] , _UpperCAmelCase : List[str] ):
_A = []
_A = ''
_A = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(_UpperCAmelCase ) + token
_A = True
_A = []
else:
current_sub_tokens.append(_UpperCAmelCase )
_A = False
out_string += self.sp_model.decode(_UpperCAmelCase )
return out_string.strip()
def __getstate__( self : str ):
_A = self.__dict__.copy()
_A = None
return state
def __setstate__( self : str , _UpperCAmelCase : Optional[int] ):
_A = d
# for backward compatibility
if not hasattr(self , 'sp_model_kwargs' ):
_A = {}
_A = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def lowerCAmelCase_ ( self : str , _UpperCAmelCase : str , _UpperCAmelCase : Optional[str] = None ):
if not os.path.isdir(_UpperCAmelCase ):
logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
return
_A = os.path.join(
_UpperCAmelCase , (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_UpperCAmelCase ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , _UpperCAmelCase )
elif not os.path.isfile(self.vocab_file ):
with open(_UpperCAmelCase , 'wb' ) as fi:
_A = self.sp_model.serialized_model_proto()
fi.write(_UpperCAmelCase )
return (out_vocab_file,)
| 271 |
"""simple docstring"""
import os
from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home
a = HUGGINGFACE_HUB_CACHE
a = '''config.json'''
a = '''diffusion_pytorch_model.bin'''
a = '''diffusion_flax_model.msgpack'''
a = '''model.onnx'''
a = '''diffusion_pytorch_model.safetensors'''
a = '''weights.pb'''
a = '''https://huggingface.co'''
a = default_cache_path
a = '''diffusers_modules'''
a = os.getenv('''HF_MODULES_CACHE''', os.path.join(hf_cache_home, '''modules'''))
a = ['''fp16''', '''non-ema''']
a = '''.self_attn'''
| 271 | 1 |
'''simple docstring'''
import argparse
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline
if __name__ == "__main__":
__A =argparse.ArgumentParser()
parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')
parser.add_argument(
'--txt2img_unclip',
default='kakaobrain/karlo-v1-alpha',
type=str,
required=False,
help='The pretrained txt2img unclip.',
)
__A =parser.parse_args()
__A =UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)
__A =CLIPImageProcessor()
__A =CLIPVisionModelWithProjection.from_pretrained('openai/clip-vit-large-patch14')
__A =UnCLIPImageVariationPipeline(
decoder=txtaimg.decoder,
text_encoder=txtaimg.text_encoder,
tokenizer=txtaimg.tokenizer,
text_proj=txtaimg.text_proj,
feature_extractor=feature_extractor,
image_encoder=image_encoder,
super_res_first=txtaimg.super_res_first,
super_res_last=txtaimg.super_res_last,
decoder_scheduler=txtaimg.decoder_scheduler,
super_res_scheduler=txtaimg.super_res_scheduler,
)
imgaimg.save_pretrained(args.dump_path) | 163 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable
lowerCAmelCase__ = {
'''configuration_gpt_neox_japanese''': ['''GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoXJapaneseConfig'''],
'''tokenization_gpt_neox_japanese''': ['''GPTNeoXJapaneseTokenizer'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ = [
'''GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''GPTNeoXJapaneseForCausalLM''',
'''GPTNeoXJapaneseLayer''',
'''GPTNeoXJapaneseModel''',
'''GPTNeoXJapanesePreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_gpt_neox_japanese import GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXJapaneseConfig
from .tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_gpt_neox_japanese import (
GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
GPTNeoXJapaneseForCausalLM,
GPTNeoXJapaneseLayer,
GPTNeoXJapaneseModel,
GPTNeoXJapanesePreTrainedModel,
)
else:
import sys
lowerCAmelCase__ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 104 | 0 |
from __future__ import annotations
import pandas as pd
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = [0] * no_of_processes
SCREAMING_SNAKE_CASE_ = [0] * no_of_processes
# Copy the burst time into remaining_time[]
for i in range(__UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = burst_time[i]
SCREAMING_SNAKE_CASE_ = 0
SCREAMING_SNAKE_CASE_ = 0
SCREAMING_SNAKE_CASE_ = 9_9_9_9_9_9_9_9_9
SCREAMING_SNAKE_CASE_ = 0
SCREAMING_SNAKE_CASE_ = False
# Process until all processes are completed
while complete != no_of_processes:
for j in range(__UpperCamelCase ):
if arrival_time[j] <= increment_time and remaining_time[j] > 0:
if remaining_time[j] < minm:
SCREAMING_SNAKE_CASE_ = remaining_time[j]
SCREAMING_SNAKE_CASE_ = j
SCREAMING_SNAKE_CASE_ = True
if not check:
increment_time += 1
continue
remaining_time[short] -= 1
SCREAMING_SNAKE_CASE_ = remaining_time[short]
if minm == 0:
SCREAMING_SNAKE_CASE_ = 9_9_9_9_9_9_9_9_9
if remaining_time[short] == 0:
complete += 1
SCREAMING_SNAKE_CASE_ = False
# Find finish time of current process
SCREAMING_SNAKE_CASE_ = increment_time + 1
# Calculate waiting time
SCREAMING_SNAKE_CASE_ = finish_time - arrival_time[short]
SCREAMING_SNAKE_CASE_ = finar - burst_time[short]
if waiting_time[short] < 0:
SCREAMING_SNAKE_CASE_ = 0
# Increment time
increment_time += 1
return waiting_time
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = [0] * no_of_processes
for i in range(__UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = burst_time[i] + waiting_time[i]
return turn_around_time
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = 0
SCREAMING_SNAKE_CASE_ = 0
for i in range(__UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = total_waiting_time + waiting_time[i]
SCREAMING_SNAKE_CASE_ = total_turn_around_time + turn_around_time[i]
print(F'''Average waiting time = {total_waiting_time / no_of_processes:.5f}''' )
print("Average turn around time =" , total_turn_around_time / no_of_processes )
if __name__ == "__main__":
print("Enter how many process you want to analyze")
A : int = int(input())
A : Union[str, Any] = [0] * no_of_processes
A : Optional[int] = [0] * no_of_processes
A : List[Any] = list(range(1, no_of_processes + 1))
for i in range(no_of_processes):
print("Enter the arrival time and burst time for process:--" + str(i + 1))
A : Tuple = map(int, input().split())
A : Any = calculate_waitingtime(arrival_time, burst_time, no_of_processes)
A : List[str] = burst_time
A : Optional[Any] = no_of_processes
A : Any = waiting_time
A : List[str] = calculate_turnaroundtime(bt, n, wt)
calculate_average_times(waiting_time, turn_around_time, no_of_processes)
A : Any = pd.DataFrame(
list(zip(processes, burst_time, arrival_time, waiting_time, turn_around_time)),
columns=[
"Process",
"BurstTime",
"ArrivalTime",
"WaitingTime",
"TurnAroundTime",
],
)
# Printing the dataFrame
pd.set_option("display.max_rows", fcfs.shape[0] + 1)
print(fcfs)
| 355 | from collections import deque
class lowerCamelCase :
"""simple docstring"""
def __init__( self : str , __magic_name__ : str , __magic_name__ : int , __magic_name__ : int ) -> None:
SCREAMING_SNAKE_CASE_ = process_name # process name
SCREAMING_SNAKE_CASE_ = arrival_time # arrival time of the process
# completion time of finished process or last interrupted time
SCREAMING_SNAKE_CASE_ = arrival_time
SCREAMING_SNAKE_CASE_ = burst_time # remaining burst time
SCREAMING_SNAKE_CASE_ = 0 # total time of the process wait in ready queue
SCREAMING_SNAKE_CASE_ = 0 # time from arrival time to completion time
class lowerCamelCase :
"""simple docstring"""
def __init__( self : Tuple , __magic_name__ : int , __magic_name__ : list[int] , __magic_name__ : deque[Process] , __magic_name__ : int , ) -> None:
# total number of mlfq's queues
SCREAMING_SNAKE_CASE_ = number_of_queues
# time slice of queues that round robin algorithm applied
SCREAMING_SNAKE_CASE_ = time_slices
# unfinished process is in this ready_queue
SCREAMING_SNAKE_CASE_ = queue
# current time
SCREAMING_SNAKE_CASE_ = current_time
# finished process is in this sequence queue
SCREAMING_SNAKE_CASE_ = deque()
def __A ( self : Dict ) -> list[str]:
SCREAMING_SNAKE_CASE_ = []
for i in range(len(self.finish_queue ) ):
sequence.append(self.finish_queue[i].process_name )
return sequence
def __A ( self : List[str] , __magic_name__ : list[Process] ) -> list[int]:
SCREAMING_SNAKE_CASE_ = []
for i in range(len(__magic_name__ ) ):
waiting_times.append(queue[i].waiting_time )
return waiting_times
def __A ( self : List[str] , __magic_name__ : list[Process] ) -> list[int]:
SCREAMING_SNAKE_CASE_ = []
for i in range(len(__magic_name__ ) ):
turnaround_times.append(queue[i].turnaround_time )
return turnaround_times
def __A ( self : Tuple , __magic_name__ : list[Process] ) -> list[int]:
SCREAMING_SNAKE_CASE_ = []
for i in range(len(__magic_name__ ) ):
completion_times.append(queue[i].stop_time )
return completion_times
def __A ( self : str , __magic_name__ : deque[Process] ) -> list[int]:
return [q.burst_time for q in queue]
def __A ( self : Optional[Any] , __magic_name__ : Process ) -> int:
process.waiting_time += self.current_time - process.stop_time
return process.waiting_time
def __A ( self : Optional[Any] , __magic_name__ : deque[Process] ) -> deque[Process]:
SCREAMING_SNAKE_CASE_ = deque() # sequence deque of finished process
while len(__magic_name__ ) != 0:
SCREAMING_SNAKE_CASE_ = ready_queue.popleft() # current process
# if process's arrival time is later than current time, update current time
if self.current_time < cp.arrival_time:
self.current_time += cp.arrival_time
# update waiting time of current process
self.update_waiting_time(__magic_name__ )
# update current time
self.current_time += cp.burst_time
# finish the process and set the process's burst-time 0
SCREAMING_SNAKE_CASE_ = 0
# set the process's turnaround time because it is finished
SCREAMING_SNAKE_CASE_ = self.current_time - cp.arrival_time
# set the completion time
SCREAMING_SNAKE_CASE_ = self.current_time
# add the process to queue that has finished queue
finished.append(__magic_name__ )
self.finish_queue.extend(__magic_name__ ) # add finished process to finish queue
# FCFS will finish all remaining processes
return finished
def __A ( self : Any , __magic_name__ : deque[Process] , __magic_name__ : int ) -> tuple[deque[Process], deque[Process]]:
SCREAMING_SNAKE_CASE_ = deque() # sequence deque of terminated process
# just for 1 cycle and unfinished processes will go back to queue
for _ in range(len(__magic_name__ ) ):
SCREAMING_SNAKE_CASE_ = ready_queue.popleft() # current process
# if process's arrival time is later than current time, update current time
if self.current_time < cp.arrival_time:
self.current_time += cp.arrival_time
# update waiting time of unfinished processes
self.update_waiting_time(__magic_name__ )
# if the burst time of process is bigger than time-slice
if cp.burst_time > time_slice:
# use CPU for only time-slice
self.current_time += time_slice
# update remaining burst time
cp.burst_time -= time_slice
# update end point time
SCREAMING_SNAKE_CASE_ = self.current_time
# locate the process behind the queue because it is not finished
ready_queue.append(__magic_name__ )
else:
# use CPU for remaining burst time
self.current_time += cp.burst_time
# set burst time 0 because the process is finished
SCREAMING_SNAKE_CASE_ = 0
# set the finish time
SCREAMING_SNAKE_CASE_ = self.current_time
# update the process' turnaround time because it is finished
SCREAMING_SNAKE_CASE_ = self.current_time - cp.arrival_time
# add the process to queue that has finished queue
finished.append(__magic_name__ )
self.finish_queue.extend(__magic_name__ ) # add finished process to finish queue
# return finished processes queue and remaining processes queue
return finished, ready_queue
def __A ( self : Any ) -> deque[Process]:
# all queues except last one have round_robin algorithm
for i in range(self.number_of_queues - 1 ):
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = self.round_robin(
self.ready_queue , self.time_slices[i] )
# the last queue has first_come_first_served algorithm
self.first_come_first_served(self.ready_queue )
return self.finish_queue
if __name__ == "__main__":
import doctest
A : Dict = Process("P1", 0, 53)
A : str = Process("P2", 0, 17)
A : List[Any] = Process("P3", 0, 68)
A : List[str] = Process("P4", 0, 24)
A : Dict = 3
A : Any = [17, 25]
A : Dict = deque([Pa, Pa, Pa, Pa])
if len(time_slices) != number_of_queues - 1:
raise SystemExit(0)
doctest.testmod(extraglobs={"queue": deque([Pa, Pa, Pa, Pa])})
A : Union[str, Any] = Process("P1", 0, 53)
A : Any = Process("P2", 0, 17)
A : Dict = Process("P3", 0, 68)
A : List[str] = Process("P4", 0, 24)
A : Optional[int] = 3
A : int = [17, 25]
A : Union[str, Any] = deque([Pa, Pa, Pa, Pa])
A : Tuple = MLFQ(number_of_queues, time_slices, queue, 0)
A : Tuple = mlfq.multi_level_feedback_queue()
# print total waiting times of processes(P1, P2, P3, P4)
print(
f"waiting time:\
\t\t\t{MLFQ.calculate_waiting_time(mlfq, [Pa, Pa, Pa, Pa])}"
)
# print completion times of processes(P1, P2, P3, P4)
print(
f"completion time:\
\t\t{MLFQ.calculate_completion_time(mlfq, [Pa, Pa, Pa, Pa])}"
)
# print total turnaround times of processes(P1, P2, P3, P4)
print(
f"turnaround time:\
\t\t{MLFQ.calculate_turnaround_time(mlfq, [Pa, Pa, Pa, Pa])}"
)
# print sequence of finished processes
print(
f"sequence of finished processes:\
{mlfq.calculate_sequence_of_finish_queue()}"
)
| 305 | 0 |