S-Dreamer/PyCodeT5
Text Generation • Updated • 20 • 1
python_code stringlengths 0 290k | repo_name stringclasses 30
values | file_path stringlengths 6 125 |
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from setuptools import find_packages, setup
# PEP0440 compatible formatted version, see:
# https://www.python.org/dev/peps/pep-0440/
#
# release markers:
# X.Y
# X.Y.Z # For bugfix releases
#
# pre-release markers:
# X.YaN # Alpha release
# X.YbN # Beta release
# X.YrcN # Release Candidate
# X.Y ... | allennlp-master | setup.py |
allennlp-master | test_fixtures/__init__.py | |
from d.d import D
| allennlp-master | test_fixtures/plugins/d/__init__.py |
import argparse
from overrides import overrides
from allennlp.commands import Subcommand
def do_nothing(_):
pass
@Subcommand.register("d")
class D(Subcommand):
@overrides
def add_subparser(self, parser: argparse._SubParsersAction) -> argparse.ArgumentParser:
subparser = parser.add_parser(self.... | allennlp-master | test_fixtures/plugins/d/d.py |
import os
_MAJOR = "1"
_MINOR = "3"
# On master and in a nightly release the patch should be one ahead of the last
# released build.
_PATCH = "0"
# This is mainly for nightly builds which have the suffix ".dev$DATE". See
# https://semver.org/#is-v123-a-semantic-version for the semantics.
_SUFFIX = os.environ.get("ALLE... | allennlp-master | allennlp/version.py |
# Make sure that allennlp is running on Python 3.6.1 or later
# (to avoid running into this bug: https://bugs.python.org/issue29246)
import sys
if sys.version_info < (3, 6, 1):
raise RuntimeError("AllenNLP requires Python 3.6.1 or later")
# We get a lot of these spurious warnings,
# see https://github.com/Continu... | allennlp-master | allennlp/__init__.py |
#!/usr/bin/env python
import logging
import os
import sys
if os.environ.get("ALLENNLP_DEBUG"):
LEVEL = logging.DEBUG
else:
level_name = os.environ.get("ALLENNLP_LOG_LEVEL", "INFO")
LEVEL = logging._nameToLevel.get(level_name, logging.INFO)
sys.path.insert(0, os.path.dirname(os.path.abspath(os.path.join(__... | allennlp-master | allennlp/__main__.py |
allennlp-master | allennlp/tools/__init__.py | |
import os
from allennlp.common.file_utils import CACHE_DIRECTORY
from allennlp.common.file_utils import filename_to_url
def main():
print(f"Looking for datasets in {CACHE_DIRECTORY}...")
if not os.path.exists(CACHE_DIRECTORY):
print("Directory does not exist.")
print("No cached datasets found... | allennlp-master | allennlp/tools/inspect_cache.py |
#! /usr/bin/env python
"""
Helper script for modifying config.json files that are locked inside
model.tar.gz archives. This is useful if you need to rename things or
add or remove values, usually because of changes to the library.
This script will untar the archive to a temp directory, launch an editor
to modify the c... | allennlp-master | allennlp/tools/archive_surgery.py |
import argparse
import gzip
import os
import torch
from allennlp.common.checks import ConfigurationError
from allennlp.data import Token, Vocabulary
from allennlp.data.token_indexers import ELMoTokenCharactersIndexer
from allennlp.data.vocabulary import DEFAULT_OOV_TOKEN
from allennlp.modules.elmo import _ElmoCharact... | allennlp-master | allennlp/tools/create_elmo_embeddings_from_vocab.py |
"""
Assorted utilities for working with neural networks in AllenNLP.
"""
import copy
import json
import logging
from collections import defaultdict
from typing import Any, Dict, List, Optional, Sequence, Tuple, TypeVar, Union
import math
import numpy
import torch
from allennlp.common.checks import ConfigurationError... | allennlp-master | allennlp/nn/util.py |
"""
An `Activation` is just a function
that takes some parameters and returns an element-wise activation function.
For the most part we just use
[PyTorch activations](https://pytorch.org/docs/master/nn.html#non-linear-activations).
Here we provide a thin wrapper to allow registering them and instantiating them `from_pa... | allennlp-master | allennlp/nn/activations.py |
from allennlp.nn.activations import Activation
from allennlp.nn.initializers import Initializer, InitializerApplicator
from allennlp.nn.regularizers import RegularizerApplicator
| allennlp-master | allennlp/nn/__init__.py |
from inspect import signature
from typing import List, Callable, Tuple, Dict, cast, TypeVar
import warnings
from overrides import overrides
import torch
from allennlp.common import FromParams, Registrable
from allennlp.common.checks import ConfigurationError
from allennlp.nn.util import min_value_of_dtype
StateType... | allennlp-master | allennlp/nn/beam_search.py |
"""
An initializer is just a PyTorch function.
Here we implement a proxy class that allows us
to register them and supply any additional function arguments
(for example, the `mean` and `std` of a normal initializer)
as named arguments to the constructor.
The available initialization functions are
* ["normal"](https:/... | allennlp-master | allennlp/nn/initializers.py |
from typing import List, Set, Tuple, Dict
import numpy
from allennlp.common.checks import ConfigurationError
def decode_mst(
energy: numpy.ndarray, length: int, has_labels: bool = True
) -> Tuple[numpy.ndarray, numpy.ndarray]:
"""
Note: Counter to typical intuition, this function decodes the _maximum_
... | allennlp-master | allennlp/nn/chu_liu_edmonds.py |
import re
from typing import List, Tuple
import torch
from allennlp.common import FromParams
from allennlp.nn.regularizers.regularizer import Regularizer
class RegularizerApplicator(FromParams):
"""
Applies regularizers to the parameters of a Module based on regex matches.
"""
def __init__(self, re... | allennlp-master | allennlp/nn/regularizers/regularizer_applicator.py |
"""
This module contains classes representing regularization schemes
as well as a class for applying regularization to parameters.
"""
from allennlp.nn.regularizers.regularizer import Regularizer
from allennlp.nn.regularizers.regularizers import L1Regularizer
from allennlp.nn.regularizers.regularizers import L2Regular... | allennlp-master | allennlp/nn/regularizers/__init__.py |
import torch
from allennlp.nn.regularizers.regularizer import Regularizer
@Regularizer.register("l1")
class L1Regularizer(Regularizer):
"""
Represents a penalty proportional to the sum of the absolute values of the parameters
Registered as a `Regularizer` with name "l1".
"""
def __init__(self, ... | allennlp-master | allennlp/nn/regularizers/regularizers.py |
import torch
from allennlp.common import Registrable
class Regularizer(Registrable):
"""
An abstract class representing a regularizer. It must implement
call, returning a scalar tensor.
"""
default_implementation = "l2"
def __call__(self, parameter: torch.Tensor) -> torch.Tensor:
ra... | allennlp-master | allennlp/nn/regularizers/regularizer.py |
from typing import Optional, Iterable, Dict, Any
from allennlp.common.checks import ConfigurationError
class MetricTracker:
"""
This class tracks a metric during training for the dual purposes of early stopping
and for knowing whether the current value is the best so far. It mimics the PyTorch
`state... | allennlp-master | allennlp/training/metric_tracker.py |
import os
from contextlib import contextmanager
from typing import Any, Dict, Iterator, Tuple
from allennlp.models import Model
from allennlp.training.checkpointer import Checkpointer
from allennlp.training.trainer import Trainer
@Trainer.register("no_op")
class NoOpTrainer(Trainer):
"""
Registered as a `Tra... | allennlp-master | allennlp/training/no_op_trainer.py |
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