import ast
import contextlib
import functools
import gc
import getpass
import hashlib
import inspect
import json
import os
import pathlib
import pickle
import platform
import random
import shutil
import subprocess
import sys
import threading
import time
import traceback
import zipfile
import tarfile
from concurrent.futures import ProcessPoolExecutor
from datetime import datetime
from typing import Tuple, Callable, Dict
from queue import Queue, Empty
from concurrent.futures import ThreadPoolExecutor
import filelock
import fire
import numpy as np
import pandas as pd
import requests
import uuid
import tabulate
from fire import inspectutils
from joblib import Parallel
from tqdm.auto import tqdm
def H2O_Fire(component=None):
config_prefix = "H2OGPT_"
args = sys.argv[1:]
query_args = [arg.split("=")[0].split(" ")[0].lstrip("-") for arg in args]
fn_spec = inspectutils.GetFullArgSpec(component)
for key, value in os.environ.items():
if not (
(key.startswith(config_prefix) or key.startswith(config_prefix.lower()))
and len(key) > len(config_prefix)
):
continue # ignore as non H2OGPT argument
new_key = key[len(config_prefix):].lower()
if new_key in query_args:
continue # ignore as already passed as script argument
if new_key not in fn_spec.args:
continue # ignore as not a valid H2OGPT argument
args.append(f"--{new_key}={value}")
fire.Fire(component=component, command=args)
def set_seed(seed: int):
"""
Sets the seed of the entire notebook so results are the same every time we run.
This is for REPRODUCIBILITY.
"""
import torch
np.random.seed(seed)
random_state = np.random.RandomState(seed)
random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
os.environ['PYTHONHASHSEED'] = str(seed)
return random_state
def flatten_list(lis):
"""Given a list, possibly nested to any level, return it flattened."""
new_lis = []
for item in lis:
if type(item) == type([]):
new_lis.extend(flatten_list(item))
else:
new_lis.append(item)
return new_lis
def clear_torch_cache(allow_skip=False):
if allow_skip and os.getenv('CLEAR_CLEAR_TORCH', '2') == '1' or os.getenv('CLEAR_CLEAR_TORCH', '2') == '0':
return
try:
import torch
if torch.cuda.is_available():
torch.cuda.empty_cache()
torch.cuda.ipc_collect()
gc.collect()
except RuntimeError as e:
print("clear_torch_cache error: %s" % ''.join(traceback.format_tb(e.__traceback__)), flush=True)
def ping():
try:
print('Ping: %s' % str(datetime.now()), flush=True)
except AttributeError:
# some programs wrap print and will fail with flush passed
pass
def ping_gpu():
try:
print('Ping_GPU: %s %s' % (str(datetime.now()), system_info()), flush=True)
except AttributeError:
# some programs wrap print and will fail with flush passed
pass
try:
ping_gpu_memory()
except Exception as e:
print('Ping_GPU memory failure: %s' % str(e), flush=True)
def ping_gpu_memory():
from models.gpu_mem_track import MemTracker
gpu_tracker = MemTracker() # define a GPU tracker
from torch.cuda import memory_summary
gpu_tracker.track()
def get_torch_allocated():
import torch
return torch.cuda.memory_allocated()
def get_device(n_gpus=None):
import torch
if torch.cuda.is_available() and n_gpus != 0:
device = "cuda"
elif torch.backends.mps.is_built():
device = "mps"
else:
device = "cpu"
return device
def system_info():
import psutil
system = {}
# https://stackoverflow.com/questions/48951136/plot-multiple-graphs-in-one-plot-using-tensorboard
# https://arshren.medium.com/monitoring-your-devices-in-python-5191d672f749
try:
temps = psutil.sensors_temperatures(fahrenheit=False)
if 'coretemp' in temps:
coretemp = temps['coretemp']
temp_dict = {k.label: k.current for k in coretemp}
for k, v in temp_dict.items():
system['CPU_C/%s' % k] = v
except AttributeError:
pass
# https://github.com/gpuopenanalytics/pynvml/blob/master/help_query_gpu.txt
try:
from pynvml.smi import nvidia_smi
nvsmi = nvidia_smi.getInstance()
gpu_power_dict = {'W_gpu%d' % i: x['power_readings']['power_draw'] for i, x in
enumerate(nvsmi.DeviceQuery('power.draw')['gpu'])}
for k, v in gpu_power_dict.items():
system['GPU_W/%s' % k] = v
gpu_temp_dict = {'C_gpu%d' % i: x['temperature']['gpu_temp'] for i, x in
enumerate(nvsmi.DeviceQuery('temperature.gpu')['gpu'])}
for k, v in gpu_temp_dict.items():
system['GPU_C/%s' % k] = v
gpu_memory_free_dict = {'MiB_gpu%d' % i: x['fb_memory_usage']['free'] for i, x in
enumerate(nvsmi.DeviceQuery('memory.free')['gpu'])}
gpu_memory_total_dict = {'MiB_gpu%d' % i: x['fb_memory_usage']['total'] for i, x in
enumerate(nvsmi.DeviceQuery('memory.total')['gpu'])}
gpu_memory_frac_dict = {k: gpu_memory_free_dict[k] / gpu_memory_total_dict[k] for k in gpu_memory_total_dict}
for k, v in gpu_memory_frac_dict.items():
system[f'GPU_M/%s' % k] = v
except (KeyError, ModuleNotFoundError):
pass
system['hash'] = get_githash()
debug_mem = False
if debug_mem:
try:
# pip install guppy3
from guppy import hpy
h = hpy()
print(h.heap())
print(h.heap().byvia)
print(h.heap().byid)
except:
pass
return system
def system_info_print():
try:
df = pd.DataFrame.from_dict(system_info(), orient='index')
# avoid slamming GPUs
time.sleep(1)
return df.to_markdown()
except Exception as e:
return "Error: %s" % str(e)
def zip_data(root_dirs=None, zip_file=None, base_dir='./', fail_any_exception=False):
try:
return _zip_data(zip_file=zip_file, base_dir=base_dir, root_dirs=root_dirs)
except Exception as e:
traceback.print_exc()
print('Exception in zipping: %s' % str(e))
if not fail_any_exception:
raise
def _zip_data(root_dirs=None, zip_file=None, base_dir='./'):
if isinstance(root_dirs, str):
root_dirs = [root_dirs]
if zip_file is None:
datetime_str = str(datetime.now()).replace(" ", "_").replace(":", "_")
host_name = os.getenv('HF_HOSTNAME', 'emptyhost')
zip_file = "data_%s_%s.zip" % (datetime_str, host_name)
assert root_dirs is not None
base_path = os.path.dirname(zip_file)
if not os.path.isdir(base_path) and os.path.dirname(zip_file):
base_path = makedirs(base_path, exist_ok=True, tmp_ok=True, use_base=True)
zip_file = os.path.join(base_path, os.path.basename(zip_file))
with zipfile.ZipFile(zip_file, "w") as expt_zip:
for root_dir in root_dirs:
if root_dir is None:
continue
for root, d, files in os.walk(root_dir):
for file in files:
file_to_archive = os.path.join(root, file)
assert os.path.exists(file_to_archive)
path_to_archive = os.path.relpath(file_to_archive, base_dir)
expt_zip.write(filename=file_to_archive, arcname=path_to_archive)
return zip_file, zip_file
def tar_data(root_dirs=None, tar_file=None, base_dir='./', fail_any_exception=False):
try:
return _tar_data(tar_file=tar_file, base_dir=base_dir, root_dirs=root_dirs)
except Exception as e:
traceback.print_exc()
print('Exception in tar archiving: %s' % str(e))
if not fail_any_exception:
raise
def _tar_data(root_dirs=None, tar_file=None, base_dir='./'):
if isinstance(root_dirs, str):
root_dirs = [root_dirs]
if tar_file is None:
datetime_str = str(datetime.now()).replace(" ", "_").replace(":", "_")
host_name = os.getenv('HF_HOSTNAME', 'emptyhost')
tar_file = "data_%s_%s.tar.gz" % (datetime_str, host_name)
assert root_dirs is not None
base_path = os.path.dirname(tar_file)
if not os.path.isdir(base_path) and os.path.dirname(tar_file):
base_path = makedirs(base_path, exist_ok=True, tmp_ok=True, use_base=True)
tar_file = os.path.join(base_path, os.path.basename(tar_file))
with tarfile.open(tar_file, "w:gz") as expt_tar:
for root_dir in root_dirs:
if root_dir is None:
continue
for root, d, files in os.walk(root_dir):
for file in files:
file_to_archive = os.path.join(root, file)
assert os.path.exists(file_to_archive)
path_to_archive = os.path.relpath(file_to_archive, base_dir)
expt_tar.add(name=file_to_archive, arcname=path_to_archive)
return tar_file, tar_file
def save_generate_output(prompt=None, output=None, base_model=None, save_dir=None, where_from='unknown where from',
extra_dict={}, error='', sources=[], which_api='', valid_key=None,
h2ogpt_key='', return_dict=False):
if not save_dir:
return
try:
return _save_generate_output(prompt=prompt, output=output, base_model=base_model, save_dir=save_dir,
where_from=where_from, extra_dict=extra_dict, error=error, sources=sources,
which_api=which_api, valid_key=valid_key, h2ogpt_key=h2ogpt_key,
return_dict=return_dict)
except Exception as e:
traceback.print_exc()
print('Exception in saving: %s' % str(e))
def _save_generate_tokens(response_no_refs, extra_dict):
# tokenize at end if need to, so doesn't block generation in multi-generator case
if extra_dict.get('ntokens') is None:
extra_dict['ntokens'] = FakeTokenizer().num_tokens_from_string(str(response_no_refs))
# only do below if didn't already compute ntokens, else assume also computed rate
if extra_dict.get('ntokens') is not None and extra_dict.get('t_generate') is not None:
extra_dict['tokens_persecond'] = extra_dict['ntokens'] / extra_dict['t_generate']
return extra_dict
def _save_generate_output(prompt=None, output=None, base_model=None, save_dir=None, where_from='unknown where from',
extra_dict={}, error='', sources=[], which_api='',
valid_key=None, h2ogpt_key='',
return_dict=False):
"""
Save conversation to .json, row by row.
json_file_path is path to final JSON file. If not in ., then will attempt to make directories.
Appends if file exists
"""
prompt = '<not set>' if prompt is None else prompt
output = '<not set>' if output is None else output
extra_dict = _save_generate_tokens(output, extra_dict)
dict_to_save = dict(prompt=prompt, text=output, time=time.ctime(),
base_model=base_model,
where_from=where_from,
error=error,
sources=sources,
which_api=which_api,
valid_key=valid_key,
h2ogpt_key=h2ogpt_key,
)
dict_to_save.update(extra_dict)
if return_dict:
return dict_to_save
if os.path.exists(save_dir) and not os.path.isdir(save_dir):
raise RuntimeError("save_dir already exists and is not a directory!")
makedirs(save_dir, exist_ok=True) # already should be made, can't change at this point
import json
with filelock.FileLock("%s.lock" % os.path.basename(save_dir)):
# lock logging in case have concurrency
with open(os.path.join(save_dir, "history.json"), "a") as f:
# just add [ at start, and ] at end, and have proper JSON dataset
f.write(
" " + json.dumps(
dict_to_save
) + ",\n"
)
def s3up(filename):
try:
return _s3up(filename)
except Exception as e:
traceback.print_exc()
print('Exception for file %s in s3up: %s' % (filename, str(e)))
return "Failed to upload %s: Error: %s" % (filename, str(e))
def _s3up(filename):
import boto3
aws_access_key_id = os.getenv('AWS_SERVER_PUBLIC_KEY')
aws_secret_access_key = os.getenv('AWS_SERVER_SECRET_KEY')
bucket = os.getenv('AWS_BUCKET')
assert aws_access_key_id, "Set AWS key"
assert aws_secret_access_key, "Set AWS secret"
assert bucket, "Set AWS Bucket"
s3 = boto3.client('s3',
aws_access_key_id=os.getenv('AWS_SERVER_PUBLIC_KEY'),
aws_secret_access_key=os.getenv('AWS_SERVER_SECRET_KEY'),
)
ret = s3.upload_file(
Filename=filename,
Bucket=os.getenv('AWS_BUCKET'),
Key=filename,
)
if ret in [None, '']:
return "Successfully uploaded %s" % filename
def get_githash():
try:
githash = subprocess.run(['git', 'rev-parse', 'HEAD'], stdout=subprocess.PIPE).stdout.decode('utf-8')[0:-1]
except:
try:
with open('git_hash.txt', 'rt') as f:
githash = f.read()
except:
githash = "GET_GITHASH"
return githash
def copy_code(run_id):
"""
copy code to track changes
:param run_id:
:return:
"""
rnd_num = str(random.randint(0, 2 ** 31))
run_id = 'run_' + str(run_id)
os.makedirs(run_id, exist_ok=True)
me_full = os.path.join(pathlib.Path(__file__).parent.resolve(), __file__)
me_file = os.path.basename(__file__)
new_me = os.path.join(run_id, me_file + '_' + get_githash())
if os.path.isfile(new_me):
new_me = os.path.join(run_id, me_file + '_' + get_githash() + '_' + rnd_num)
shutil.copy(me_full, new_me)
else:
shutil.copy(me_full, new_me)
class NullContext(threading.local):
"""No-op context manager, executes block without doing any additional processing.
Used as a stand-in if a particular block of code is only sometimes
used with a normal context manager:
"""
def __init__(self, *args, **kwargs):
pass
def __enter__(self):
return self
def __exit__(self, exc_type, exc_value, exc_traceback):
self.finally_act()
def finally_act(self):
pass
def wrapped_partial(func, *args, **kwargs):
"""
Give partial properties of normal function, like __name__ attribute etc.
:param func:
:param args:
:param kwargs:
:return:
"""
partial_func = functools.partial(func, *args, **kwargs)
functools.update_wrapper(partial_func, func)
return partial_func
class ThreadException(Exception):
pass
class EThread(threading.Thread):
# Function that raises the custom exception
def __init__(self, group=None, target=None, name=None,
args=(), kwargs=None, *, daemon=None, streamer=None, bucket=None):
self.bucket = bucket
self.streamer = streamer
self.exc = None
self._return = None
super().__init__(group=group, target=target, name=name, args=args, kwargs=kwargs, daemon=daemon)
def run(self):
# Variable that stores the exception, if raised by someFunction
try:
if self._target is not None:
self._return = self._target(*self._args, **self._kwargs)
except BaseException as e:
print("thread exception: %s" % str(sys.exc_info()))
self.bucket.put(sys.exc_info())
self.exc = e
if self.streamer:
print("make stop: %s" % str(sys.exc_info()), flush=True)
self.streamer.do_stop = True
finally:
# Avoid a refcycle if the thread is running a function with
# an argument that has a member that points to the thread.
del self._target, self._args, self._kwargs
def join(self, timeout=None):
threading.Thread.join(self)
# Since join() returns in caller thread
# we re-raise the caught exception
# if any was caught
if self.exc:
raise self.exc
return self._return
def import_matplotlib():
import matplotlib
matplotlib.use('agg')
# KEEP THESE HERE! START
import matplotlib.pyplot as plt
import pandas as pd
# to avoid dlopen deadlock in fork
import pandas.core.computation.expressions as pd_expressions
import pandas._libs.groupby as pd_libgroupby
import pandas._libs.reduction as pd_libreduction
import pandas.core.algorithms as pd_algorithms
import pandas.core.common as pd_com
import numpy as np
# KEEP THESE HERE! END
def get_sha(value):
return hashlib.md5(str(value).encode('utf-8')).hexdigest()
def sanitize_filename(name, file_length_limit=250):
"""
Sanitize file *base* names.
:param name: name to sanitize
:param file_length_limit: bit smaller than 256 for safety
:return:
"""
bad_chars = ['[', ']', ',', '/', '\\', '\\w', '\\s', '-', '+', '\"', '\'', '>', '<', ' ', '=', ')', '(', ':', '^']
for char in bad_chars:
name = name.replace(char, "_")
length = len(name)
sha_length = 32
real_length_limit = file_length_limit - (sha_length + 2)
assert real_length_limit > 0, "Bad file limit length: %s %s" % (file_length_limit, real_length_limit)
if length > file_length_limit:
sha = get_sha(name)
half_real_length_limit = max(1, int(real_length_limit / 2))
name = name[0:half_real_length_limit] + "_" + sha + "_" + name[length - half_real_length_limit:length]
return name
def shutil_rmtree(*args, **kwargs):
return shutil.rmtree(*args, **kwargs)
def remove(path: str):
try:
if path is not None and os.path.exists(path):
if os.path.isdir(path):
shutil_rmtree(path, ignore_errors=True)
else:
with contextlib.suppress(FileNotFoundError):
os.remove(path)
except:
pass
def makedirs(path, exist_ok=True, tmp_ok=False, use_base=False):
"""
Avoid some inefficiency in os.makedirs()
:param path:
:param exist_ok:
:param tmp_ok: use /tmp if can't write locally
:param use_base:
:return:
"""
if path is None:
return path
# if base path set, make relative to that, unless user_path absolute path
if use_base:
if os.path.normpath(path) == os.path.normpath(os.path.abspath(path)):
pass
else:
if os.getenv('H2OGPT_BASE_PATH') is not None:
base_dir = os.path.normpath(os.getenv('H2OGPT_BASE_PATH'))
path = os.path.normpath(path)
if not path.startswith(base_dir):
path = os.path.join(os.getenv('H2OGPT_BASE_PATH', ''), path)
path = os.path.normpath(path)
if os.path.isdir(path) and os.path.exists(path):
assert exist_ok, "Path already exists"
return path
try:
os.makedirs(path, exist_ok=exist_ok)
return path
except FileExistsError:
# e.g. soft link
return path
except PermissionError:
if tmp_ok:
path0 = path
path = os.path.join('/tmp/', path)
print("Permission denied to %s, using %s instead" % (path0, path), flush=True)
os.makedirs(path, exist_ok=exist_ok)
return path
else:
raise
def atomic_move_simple(src, dst):
try:
shutil.move(src, dst)
except (shutil.Error, FileExistsError):
pass
remove(src)
def download_simple(url, dest=None, overwrite=False, verbose=False):
if dest is None:
dest = os.path.basename(url)
base_path = os.path.dirname(dest)
if base_path: # else local path
base_path = makedirs(base_path, exist_ok=True, tmp_ok=True, use_base=True)
dest = os.path.join(base_path, os.path.basename(dest))
if os.path.isfile(dest):
if not overwrite:
print("Already have %s from url %s, delete file if invalid" % (dest, str(url)), flush=True)
return dest
else:
remove(dest)
if verbose:
print("BEGIN get url %s" % str(url), flush=True)
if url.startswith("file://"):
from requests_file import FileAdapter
s = requests.Session()
s.mount('file://', FileAdapter())
url_data = s.get(url, stream=True)
else:
url_data = requests.get(url, stream=True)
if verbose:
print("GOT url %s" % str(url), flush=True)
if url_data.status_code != requests.codes.ok:
msg = "Cannot get url %s, code: %s, reason: %s" % (
str(url),
str(url_data.status_code),
str(url_data.reason),
)
raise requests.exceptions.RequestException(msg)
url_data.raw.decode_content = True
uuid_tmp = str(uuid.uuid4())[:6]
dest_tmp = dest + "_dl_" + uuid_tmp + ".tmp"
with open(dest_tmp, "wb") as f:
shutil.copyfileobj(url_data.raw, f)
atomic_move_simple(dest_tmp, dest)
if verbose:
print("DONE url %s" % str(url), flush=True)
return dest
def download(url, dest=None, dest_path=None):
if dest_path is not None:
dest = os.path.join(dest_path, os.path.basename(url))
if os.path.isfile(dest):
print("already downloaded %s -> %s" % (url, dest))
return dest
elif dest is not None:
if os.path.exists(dest):
print("already downloaded %s -> %s" % (url, dest))
return dest
else:
uuid_tmp = "dl2_" + str(uuid.uuid4())[:6]
dest = uuid_tmp + os.path.basename(url)
print("downloading %s to %s" % (url, dest))
if url.startswith("file://"):
from requests_file import FileAdapter
s = requests.Session()
s.mount('file://', FileAdapter())
url_data = s.get(url, stream=True)
else:
url_data = requests.get(url, stream=True)
if url_data.status_code != requests.codes.ok:
msg = "Cannot get url %s, code: %s, reason: %s" % (
str(url), str(url_data.status_code), str(url_data.reason))
raise requests.exceptions.RequestException(msg)
url_data.raw.decode_content = True
dirname = os.path.dirname(dest)
if dirname != "" and not os.path.isdir(dirname):
base_path = os.path.dirname(dest)
base_path = makedirs(base_path, exist_ok=True, tmp_ok=True, use_base=True)
dest = os.path.join(base_path, os.path.basename(dest))
uuid_tmp = "dl3_" + str(uuid.uuid4())[:6]
dest_tmp = dest + "_" + uuid_tmp + ".tmp"
with open(dest_tmp, 'wb') as f:
shutil.copyfileobj(url_data.raw, f)
try:
shutil.move(dest_tmp, dest)
except FileExistsError:
pass
remove(dest_tmp)
return dest
def get_doc(x):
return x.page_content
def get_source(x):
return x.metadata.get('source', "UNKNOWN SOURCE")
def get_accordion_named(content, title, font_size=8):
return f"""<details><summary><font size="{font_size}">{title}</font></summary><font size="{font_size}">{content}</font></details>"""
def hyde_titles(level):
if level == 0:
title = "HYDE 0: LLM"
elif level == 1:
title = "HYDE 1: Prompt+LLM embedding"
elif level == 2:
title = "HYDE 2: Prompt+LLM+HYDE 1 embedding"
elif level == 3:
title = "HYDE 3: Prompt+LLM+HYDE 1&2 embedding"
else:
title = "HYDE 4: Prompt+LLM+HYDE 1&2&3 embedding"
return title
def get_accordion(x, font_size=2, head_acc=50):
title = x.page_content[:head_acc].replace("\n", ' ').replace("<br>", ' ').replace("<p>", ' ').replace("\r", ' ')
content = x.page_content
return f"""<details><summary><font size="{font_size}">{title}</font></summary><font size="{font_size}">{content}</font></details>"""
def get_url(x, from_str=False, short_name=False, font_size=2):
if not from_str:
source = x.metadata['source']
else:
source = x
if short_name:
source_name = get_short_name(source)
else:
source_name = source
if source.startswith('http://') or source.startswith('https://'):
return """<font size="%s"><a href="%s" target="_blank" rel="noopener noreferrer">%s</a></font>""" % (
font_size, source, source_name)
elif '<a href=' not in source:
return """<font size="%s"><a href="file/%s" target="_blank" rel="noopener noreferrer">%s</a></font>""" % (
font_size, source, source_name)
else:
# already filled
return source
def get_short_name(name, maxl=50):
if name is None:
return ''
length = len(name)
if length > maxl:
allow_length = maxl - 3
half_allowed = max(1, int(allow_length / 2))
name = name[0:half_allowed] + "..." + name[length - half_allowed:length]
return name
def cuda_vis_check(total_gpus):
"""Helper function to count GPUs by environment variable
Stolen from Jon's h2o4gpu utils
"""
cudavis = os.getenv("CUDA_VISIBLE_DEVICES")
which_gpus = []
if cudavis is not None:
# prune away white-space, non-numerics,
# except commas for simple checking
cudavis = "".join(cudavis.split())
import re
cudavis = re.sub("[^0-9,]", "", cudavis)
lencudavis = len(cudavis)
if lencudavis == 0:
total_gpus = 0
else:
total_gpus = min(
total_gpus,
os.getenv("CUDA_VISIBLE_DEVICES").count(",") + 1)
which_gpus = os.getenv("CUDA_VISIBLE_DEVICES").split(",")
which_gpus = [int(x) for x in which_gpus]
else:
which_gpus = list(range(0, total_gpus))
return total_gpus, which_gpus
def get_ngpus_vis(raise_if_exception=True):
ngpus_vis1 = None
shell = False
if shell:
cmd = "nvidia-smi -L 2> /dev/null"
else:
cmd = ["nvidia-smi", "-L"]
try:
timeout = 5 * 3
o = subprocess.check_output(cmd, shell=shell, timeout=timeout)
lines = o.decode("utf-8").splitlines()
ngpus_vis1 = 0
for line in lines:
if 'Failed to initialize NVML' not in line:
ngpus_vis1 += 1
except (FileNotFoundError, subprocess.CalledProcessError, OSError):
# GPU systems might not have nvidia-smi, so can't fail
pass
except subprocess.TimeoutExpired as e:
print('Failed get_ngpus_vis: %s' % str(e))
if raise_if_exception:
raise
if ngpus_vis1 is None:
import torch
if get_device() == 'cuda':
ngpus_vis1 = torch.cuda.device_count() if torch.cuda.is_available() else 0
else:
ngpus_vis1 = 0
ngpus_vis1, which_gpus = cuda_vis_check(ngpus_vis1)
return ngpus_vis1
def get_mem_gpus(raise_if_exception=True, ngpus=None):
totalmem_gpus1 = 0
usedmem_gpus1 = 0
freemem_gpus1 = 0
if ngpus == 0:
return totalmem_gpus1, usedmem_gpus1, freemem_gpus1
try:
cmd = "nvidia-smi -q 2> /dev/null | grep -A 3 'FB Memory Usage'"
o = subprocess.check_output(cmd, shell=True, timeout=15)
lines = o.decode("utf-8").splitlines()
for line in lines:
if 'Total' in line:
totalmem_gpus1 += int(line.split()[2]) * 1024 ** 2
if 'Used' in line:
usedmem_gpus1 += int(line.split()[2]) * 1024 ** 2
if 'Free' in line:
freemem_gpus1 += int(line.split()[2]) * 1024 ** 2
except (FileNotFoundError, subprocess.CalledProcessError, OSError):
# GPU systems might not have nvidia-smi, so can't fail
pass
except subprocess.TimeoutExpired as e:
print('Failed get_mem_gpus: %s' % str(e))
if raise_if_exception:
raise
return totalmem_gpus1, usedmem_gpus1, freemem_gpus1
n_gpus_global = get_ngpus_vis()
class ForkContext(threading.local):
"""
Set context for forking
Ensures state is returned once done
"""
def __init__(self, args=None, kwargs=None, forkdata_capable=True):
"""
:param args:
:param kwargs:
:param forkdata_capable: whether fork is forkdata capable and will use copy-on-write forking of args/kwargs
"""
self.forkdata_capable = forkdata_capable
if self.forkdata_capable:
self.has_args = args is not None
self.has_kwargs = kwargs is not None
forkdatacontext.args = args
forkdatacontext.kwargs = kwargs
else:
self.has_args = False
self.has_kwargs = False
def __enter__(self):
try:
# flush all outputs so doesn't happen during fork -- don't print/log inside ForkContext contexts!
sys.stdout.flush()
sys.stderr.flush()
except BaseException as e:
# exit not called if exception, and don't want to leave forkdatacontext filled in that case
print("ForkContext failure on enter: %s" % str(e))
self.finally_act()
raise
return self
def __exit__(self, exc_type, exc_value, exc_traceback):
self.finally_act()
def finally_act(self):
"""
Done when exception hit or exit is reached in context
first reset forkdatacontext as crucial to have reset even if later 2 calls fail
:return: None
"""
if self.forkdata_capable and (self.has_args or self.has_kwargs):
forkdatacontext._reset()
class _ForkDataContext(threading.local):
def __init__(
self,
args=None,
kwargs=None,
):
"""
Global context for fork to carry data to subprocess instead of relying upon copy/pickle/serialization
:param args: args
:param kwargs: kwargs
"""
assert isinstance(args, (tuple, type(None)))
assert isinstance(kwargs, (dict, type(None)))
self.__args = args
self.__kwargs = kwargs
@property
def args(self) -> Tuple:
"""returns args"""
return self.__args
@args.setter
def args(self, args):
if self.__args is not None:
raise AttributeError(
"args cannot be overwritten: %s %s" % (str(self.__args), str(self.__kwargs))
)
self.__args = args
@property
def kwargs(self) -> Dict:
"""returns kwargs"""
return self.__kwargs
@kwargs.setter
def kwargs(self, kwargs):
if self.__kwargs is not None:
raise AttributeError(
"kwargs cannot be overwritten: %s %s" % (str(self.__args), str(self.__kwargs))
)
self.__kwargs = kwargs
def _reset(self):
"""Reset fork arg-kwarg context to default values"""
self.__args = None
self.__kwargs = None
def get_args_kwargs(self, func, args, kwargs) -> Tuple[Callable, Tuple, Dict]:
if self.__args:
args = self.__args[1:]
if not func:
assert len(self.__args) > 0, "if have no func, must have in args"
func = self.__args[0] # should always be there
if self.__kwargs:
kwargs = self.__kwargs
try:
return func, args, kwargs
finally:
forkdatacontext._reset()
@staticmethod
def get_args_kwargs_for_traced_func(func, args, kwargs):
"""
Return args/kwargs out of forkdatacontext when using copy-on-write way of passing args/kwargs
:param func: actual function ran by _traced_func, which itself is directly what mppool treats as function
:param args:
:param kwargs:
:return: func, args, kwargs from forkdatacontext if used, else originals
"""
# first 3 lines are debug
func_was_None = func is None
args_was_None_or_empty = args is None or len(args) == 0
kwargs_was_None_or_empty = kwargs is None or len(kwargs) == 0
forkdatacontext_args_was_None = forkdatacontext.args is None
forkdatacontext_kwargs_was_None = forkdatacontext.kwargs is None
func, args, kwargs = forkdatacontext.get_args_kwargs(func, args, kwargs)
using_forkdatacontext = func_was_None and func is not None # pulled func out of forkdatacontext.__args[0]
assert forkdatacontext.args is None, "forkdatacontext.args should be None after get_args_kwargs"
assert forkdatacontext.kwargs is None, "forkdatacontext.kwargs should be None after get_args_kwargs"
proc_type = kwargs.get('proc_type', 'SUBPROCESS')
if using_forkdatacontext:
assert proc_type == "SUBPROCESS" or proc_type == "SUBPROCESS"
if proc_type == "NORMAL":
assert forkdatacontext_args_was_None, "if no fork, expect forkdatacontext.args None entering _traced_func"
assert forkdatacontext_kwargs_was_None, "if no fork, expect forkdatacontext.kwargs None entering _traced_func"
assert func is not None, "function should not be None, indicates original args[0] was None or args was None"
return func, args, kwargs
forkdatacontext = _ForkDataContext()
# Add user info
username = getpass.getuser()
current_working_directory = os.getcwd()
operating_system = platform.system()
def _traced_func(func, *args, **kwargs):
func, args, kwargs = forkdatacontext.get_args_kwargs_for_traced_func(func, args, kwargs)
return func(*args, **kwargs)
def call_subprocess_onetask(func, args=None, kwargs=None):
if platform.system() in ['Darwin', 'Windows']:
return func(*args, **kwargs)
if isinstance(args, list):
args = tuple(args)
if args is None:
args = ()
if kwargs is None:
kwargs = {}
args = list(args)
args = [func] + args
args = tuple(args)
with ForkContext(args=args, kwargs=kwargs):
args = (None,)
kwargs = {}
with ProcessPoolExecutor(max_workers=1) as executor:
future = executor.submit(_traced_func, *args, **kwargs)
return future.result()
class ProgressParallel(Parallel):
def __init__(self, use_tqdm=True, total=None, *args, **kwargs):
self._use_tqdm = use_tqdm
self._total = total
super().__init__(*args, **kwargs)
def __call__(self, *args, **kwargs):
with tqdm(disable=not self._use_tqdm, total=self._total) as self._pbar:
return Parallel.__call__(self, *args, **kwargs)
def print_progress(self):
if self._total is None:
self._pbar.total = self.n_dispatched_tasks
self._pbar.n = self.n_completed_tasks
self._pbar.refresh()
def get_kwargs(func, exclude_names=None, **kwargs):
func_names = list(inspect.signature(func).parameters)
missing_kwargs = [x for x in func_names if x not in kwargs]
if exclude_names:
for k in exclude_names:
if k in missing_kwargs:
missing_kwargs.remove(k)
if k in func_names:
func_names.remove(k)
assert not missing_kwargs, "Missing %s" % missing_kwargs
kwargs = {k: v for k, v in kwargs.items() if k in func_names}
return kwargs
from importlib.metadata import distribution, PackageNotFoundError
have_faiss = False
try:
assert distribution('faiss') is not None
have_faiss = True
except (PackageNotFoundError, AssertionError):
pass
try:
assert distribution('faiss_gpu') is not None
have_faiss = True
except (PackageNotFoundError, AssertionError):
pass
try:
assert distribution('faiss_cpu') is not None
have_faiss = True
except (PackageNotFoundError, AssertionError):
pass
have_chromamigdb = False
try:
assert distribution('chromamigdb') is not None
have_chromamigdb = True
except (PackageNotFoundError, AssertionError):
pass
have_serpapi = False
try:
assert distribution('google-search-results') is not None
have_serpapi = True
except (PackageNotFoundError, AssertionError):
pass
def hash_file(file):
try:
import hashlib
# BUF_SIZE is totally arbitrary, change for your app!
BUF_SIZE = 65536 # lets read stuff in 64kb chunks!
md5 = hashlib.md5()
# sha1 = hashlib.sha1()
if not os.path.isfile(file):
md5.update(file.encode(encoding='UTF-8'))
else:
with open(file, 'rb') as f:
while True:
data = f.read(BUF_SIZE)
if not data:
break
md5.update(data)
# sha1.update(data)
except BaseException as e:
print("Cannot hash %s due to %s" % (file, str(e)))
traceback.print_exc()
return ''
return md5.hexdigest()
def start_faulthandler():
# If hit server or any subprocess with signal SIGUSR1, it'll print out all threads stack trace, but wont't quit or coredump
# If more than one fork tries to write at same time, then looks corrupted.
import faulthandler
# SIGUSR1 in h2oai/__init__.py as well
faulthandler.enable()
if hasattr(faulthandler, 'register'):
# windows/mac
import signal
faulthandler.register(signal.SIGUSR1)
def get_hf_server(inference_server):
inf_split = inference_server.split(" ")
assert len(inf_split) == 1 or len(inf_split) == 3
inference_server = inf_split[0]
if len(inf_split) == 3:
headers = {"authorization": "%s %s" % (inf_split[1], inf_split[2])}
else:
headers = None
return inference_server, headers
class FakeTokenizer:
"""
1) For keeping track of model_max_length
2) For when model doesn't directly expose tokenizer but need to count tokens
"""
def __init__(self, model_max_length=2048,
encoding_name="cl100k_base",
is_openai=False,
is_anthropic=False,
tokenizer=None,
is_llama_cpp=False):
if model_max_length is None:
assert not (is_openai or is_anthropic), "Should have set model_max_length for OpenAI or Anthropic"
model_max_length = 2048
self.is_openai = is_openai
self.is_anthropic = is_anthropic
self.is_llama_cpp = is_llama_cpp
self.tokenizer = tokenizer
self.model_max_length = model_max_length
if not self.is_openai and not self.is_anthropic and not self.is_llama_cpp:
# don't push limit, since if using fake tokenizer, only estimate, and seen underestimates by order 250
self.model_max_length -= 250
self.encoding_name = encoding_name
# The first time this runs, it will require an internet connection to download. Later runs won't need an internet connection.
if not self.is_anthropic:
import tiktoken
self.encoding = tiktoken.get_encoding(self.encoding_name)
else:
self.encoding = None
def encode(self, x, *args, return_tensors="pt", **kwargs):
if self.is_llama_cpp: # and len(x) < 4 * 4 * self.model_max_length: # don't use llama.cpp if too much
input_ids = self.tokenizer.tokenize(b" " + x.encode("utf-8"))
elif self.is_anthropic:
from anthropic import Anthropic
client = Anthropic()
tokenizer = client.get_tokenizer()
input_ids = tokenizer.encode(x).ids
else:
input_ids = self.encoding.encode(x, disallowed_special=())
if return_tensors == 'pt' and isinstance(input_ids, list):
import torch
input_ids = torch.tensor(input_ids)
return dict(input_ids=input_ids)
def decode(self, x, *args, **kwargs):
if self.is_llama_cpp: # and len(x) < 4 * self.model_max_length: # don't use llama.cpp if too much
return self.tokenizer.detokenize(x)
elif self.is_anthropic:
from anthropic import Anthropic
client = Anthropic()
tokenizer = client.get_tokenizer()
return tokenizer.decode(x)
# input is input_ids[0] form
return self.encoding.decode(x)
def num_tokens_from_string(self, prompt: str) -> int:
"""Returns the number of tokens in a text string."""
if self.is_anthropic:
from anthropic import Anthropic
client = Anthropic()
return client.count_tokens(prompt)
num_tokens = len(self.encode(prompt)['input_ids'])
return num_tokens
def __call__(self, x, *args, **kwargs):
return self.encode(x, *args, **kwargs)
def get_local_ip():
import socket
s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
try:
# doesn't even have to be reachable
s.connect(('10.255.255.255', 1))
IP = s.getsockname()[0]
except Exception:
IP = '127.0.0.1'
finally:
s.close()
return IP
try:
assert distribution('langchain') is not None
have_langchain = True
except (PackageNotFoundError, AssertionError):
have_langchain = False
import distutils.spawn
have_tesseract = distutils.spawn.find_executable("tesseract")
have_libreoffice = distutils.spawn.find_executable("libreoffice")
try:
from weasyprint import HTML
import doctr
have_doctr = True
except:
have_doctr = False
try:
assert distribution('arxiv') is not None
assert distribution('pymupdf') is not None
have_arxiv = True
except (PackageNotFoundError, AssertionError):
have_arxiv = False
try:
assert distribution('pymupdf') is not None
have_pymupdf = True
except (PackageNotFoundError, AssertionError):
have_pymupdf = False
try:
assert distribution('selenium') is not None
have_selenium = True
except (PackageNotFoundError, AssertionError):
have_selenium = False
try:
assert distribution('pillow') is not None
have_pillow = True
except (PackageNotFoundError, AssertionError):
have_pillow = False
try:
assert distribution('playwright') is not None
have_playwright = True
except (PackageNotFoundError, AssertionError):
have_playwright = False
try:
assert distribution('jq') is not None
have_jq = True
except (PackageNotFoundError, AssertionError):
have_jq = False
try:
assert distribution('optimum') is not None
have_optimum = True
except (PackageNotFoundError, AssertionError):
have_optimum = False
try:
assert distribution('librosa') is not None
have_librosa = True
except (PackageNotFoundError, AssertionError):
have_librosa = False
try:
assert distribution('wavio') is not None
have_wavio = True
except (PackageNotFoundError, AssertionError):
have_wavio = False
try:
assert distribution('soundfile') is not None
have_soundfile = True
except (PackageNotFoundError, AssertionError):
have_soundfile = False
try:
assert distribution('deepspeed') is not None
have_deepspeed = True
except (PackageNotFoundError, AssertionError):
have_deepspeed = False
try:
assert distribution('emoji') is not None
have_emoji = True
except (PackageNotFoundError, AssertionError):
have_emoji = False
try:
assert distribution('langid') is not None
have_langid = True
except (PackageNotFoundError, AssertionError):
have_langid = False
try:
assert distribution('TTS') is not None
have_TTS = True
except (PackageNotFoundError, AssertionError):
have_TTS = False
try:
assert distribution('faster_whisper') is not None
have_use_faster = True
except (PackageNotFoundError, AssertionError):
have_use_faster = False
try:
assert distribution('flash_attn') is not None
have_flash_attention = True
have_flash_attention_2 = distribution('flash_attn').version.startswith('2.')
except (PackageNotFoundError, AssertionError):
have_flash_attention = False
have_flash_attention_2 = False
try:
assert distribution('gradio') is not None
have_gradio = True
is_gradio_version4 = distribution('gradio').version.startswith('4.')
except (PackageNotFoundError, AssertionError):
have_gradio = False
is_gradio_version4 = False
try:
assert distribution('gradio_pdf') is not None
have_gradio_pdf = is_gradio_version4
except (PackageNotFoundError, AssertionError):
have_gradio_pdf = False
try:
assert distribution('pyrubberband') is not None
have_pyrubberband = True
except (PackageNotFoundError, AssertionError):
have_pyrubberband = False
try:
assert distribution('fiftyone') is not None
have_fiftyone = True
except (PackageNotFoundError, AssertionError):
have_fiftyone = False
try:
assert distribution('diffusers') is not None
have_diffusers = True
except (PackageNotFoundError, AssertionError):
have_diffusers = False
only_unstructured_urls = os.environ.get("ONLY_UNSTRUCTURED_URLS", "0") == "1"
only_selenium = os.environ.get("ONLY_SELENIUM", "0") == "1"
only_playwright = os.environ.get("ONLY_PLAYWRIGHT", "0") == "1"
def set_openai(inference_server, model_name=None):
if inference_server.startswith('vllm'):
import openvllm
api_key = openvllm.api_key = "EMPTY"
inf_type = inference_server.split(':')[0].strip()
ip_port_vllm = ':'.join(inference_server.split(':')[1:])
if ip_port_vllm.startswith('https://') or ip_port_vllm.startswith('http://'):
api_base = openvllm.api_base = ip_port_vllm
else:
ip_vllm = inference_server.split(':')[1].strip()
port_vllm = inference_server.split(':')[2].strip()
api_base = openvllm.api_base = f"http://{ip_vllm}:{port_vllm}/v1"
from openvllm import vLLM, AsyncvLLM
client_args = dict(base_url=api_base, api_key=api_key)
client = vLLM(**client_args)
async_client = AsyncvLLM(**client_args)
if inf_type in ['vllm_chat']:
client = client.chat.completions
async_client = async_client.chat.completions
else:
client = client.completions
async_client = async_client.completions
return client, async_client, inf_type, None, api_base, None, api_key
else:
api_key = os.getenv("OPENAI_API_KEY")
base_url = None
deployment_type = None
api_version = None
inf_type = inference_server.split(':')[0].strip()
if len(inference_server.split(':')) >= 2:
deployment_type = inference_server.split(':')[1].strip()
if len(inference_server.split(':')) >= 3:
base_url = inference_server.split(':')[2].strip()
base_url = 'https://' + base_url
if len(inference_server.split(':')) >= 4:
api_version = inference_server.split(':')[3].strip()
if inference_server.startswith('openai_azure'):
if api_version in ['None', None]:
# for function tools support
# https://github.com/Azure/azure-rest-api-specs/tree/main/specification/cognitiveservices/data-plane/AzureOpenAI/inference/preview/2023-12-01-preview
api_version = "2023-12-01-preview"
if os.getenv('OPENAI_AZURE_KEY') is not None:
# use this instead if exists
api_key = os.getenv("OPENAI_AZURE_KEY")
if len(inference_server.split(':')) >= 5:
api_key0 = inference_server.split(':')[4].strip()
if api_key0 not in ['None', None]:
api_key = api_key0
if deployment_type == 'None':
deployment_type = None
if base_url == 'None':
base_url = None
if base_url == 'None':
base_url = None
# cannot use non-chat model, uses old openai. stuff if go through to H2OOpenAI with chat model
if model_name:
chat_model = (model_name.startswith("gpt-3.5-turbo") or model_name.startswith(
"gpt-4")) and "-instruct" not in model_name
if chat_model and inf_type == 'openai_azure':
inf_type = 'openai_azure_chat'
if chat_model and inf_type == 'openai':
inf_type = 'openai_chat'
from openai import OpenAI, AzureOpenAI, AsyncOpenAI, AsyncAzureOpenAI
if inf_type in ['openai_azure', 'openai_azure_chat']:
client_args = dict(azure_deployment=deployment_type, azure_endpoint=base_url, api_version=api_version,
api_key=api_key)
client = AzureOpenAI(**client_args)
async_client = AsyncAzureOpenAI(**client_args)
else:
client_args = dict(base_url=base_url, api_key=api_key)
client = OpenAI(**client_args)
async_client = AsyncOpenAI(**client_args)
if inf_type in ['openai_chat', 'openai_azure_chat']:
client = client.chat.completions
async_client = async_client.chat.completions
else:
client = client.completions
async_client = async_client.completions
return client, async_client, inf_type, deployment_type, base_url, api_version, api_key
def get_list_or_str(x):
if isinstance(x, list):
return x
elif isinstance(x, str):
try:
x1 = ast.literal_eval(x)
assert isinstance(x1, list)
return x1
except:
return x
else:
return x
def deepcopy_by_pickle_object(object):
"""
Faster deepcopy, can only work on things that are picklable. Naive Deepcopy is more general.
Same method as for class Individual
:param object:
:return:
"""
gc.disable()
new_object = pickle.loads(pickle.dumps(object, -1))
gc.enable()
return new_object
def url_alive(url):
try:
response = requests.head(url)
except Exception as e:
return False
else:
if response.status_code in [200, 301, 302, 307]:
return True
else:
return False
def return_good_url(url):
# ignore status code, just see if exists or not
for prefix in ['', 'http://', 'http://', 'https://www.', 'http://www.']:
try:
url_test = prefix + url
response = requests.head(url_test)
except Exception as e:
response = None
url_test = None
if response is not None:
# and response.status_code < 400:
# don't do status check, if got status, then is real URL regardless of goodness, not text
return url_test
return None
def dict_to_html(x, small=True, api=False):
df = pd.DataFrame(x.items(), columns=['Key', 'Value'])
df.index = df.index + 1
df.index.name = 'index'
if api:
return tabulate.tabulate(df, headers='keys')
else:
res = tabulate.tabulate(df, headers='keys', tablefmt='unsafehtml')
if small:
return "<small>" + res + "</small>"
else:
return res
def text_to_html(x, api=False):
if api:
return x
return """
<style>
pre {
overflow-x: auto;
white-space: pre-wrap;
white-space: -moz-pre-wrap;
white-space: -pre-wrap;
white-space: -o-pre-wrap;
word-wrap: break-word;
}
</style>
<pre>
%s
</pre>
""" % x
def lg_to_gr(
**kwargs,
):
# translate:
import torch
n_gpus = torch.cuda.device_count() if torch.cuda.is_available() else 0
n_gpus, _ = cuda_vis_check(n_gpus)
image_audio_loaders_options = ['Caption']
if n_gpus != 0:
image_audio_loaders_options.extend(['CaptionBlip2', 'Pix2Struct'])
if have_tesseract:
image_audio_loaders_options.append('OCR')
if have_doctr:
image_audio_loaders_options.append('DocTR')
if have_librosa:
image_audio_loaders_options.append('ASR')
if n_gpus != 0:
image_audio_loaders_options.append('ASRLarge')
if kwargs['enable_llava'] and kwargs['llava_model']:
image_audio_loaders_options.append('LLaVa')
image_audio_loaders_options0 = []
if have_tesseract and kwargs['enable_ocr']:
image_audio_loaders_options0.append('OCR')
if have_doctr and kwargs['enable_doctr']:
image_audio_loaders_options0.append('DocTR')
if kwargs['enable_captions']:
if kwargs['max_quality'] and n_gpus > 0:
# BLIP2 only on GPU
image_audio_loaders_options0.append('CaptionBlip2')
else:
image_audio_loaders_options0.append('Caption')
if have_librosa and kwargs['enable_transcriptions']:
if kwargs['max_quality'] and n_gpus > 0:
image_audio_loaders_options0.append('ASRLarge')
else:
image_audio_loaders_options0.append('ASR')
if kwargs['enable_llava'] and kwargs['llava_model']:
# and n_gpus > 0 # don't require local GPUs
# LLaVa better and faster if present
# and kwargs['max_quality']
image_audio_loaders_options0.append('LLaVa')
if 'Caption' in image_audio_loaders_options0:
image_audio_loaders_options0.remove('Caption')
if 'CaptionBlip2' in image_audio_loaders_options0:
image_audio_loaders_options0.remove('CaptionBlip2')
pdf_loaders_options = ['Unstructured', 'PyPDF', 'TryHTML']
if have_pymupdf:
pdf_loaders_options = ['PyMuPDF'] + pdf_loaders_options
if have_tesseract:
pdf_loaders_options.append('OCR')
if have_doctr:
pdf_loaders_options.append('DocTR')
pdf_loaders_options0 = []
if have_pymupdf and kwargs['use_pymupdf'] in [True, 'auto', 'on']:
pdf_loaders_options0.append('PyMuPDF')
if kwargs['enable_pdf_ocr'] in [True, 'on']:
pdf_loaders_options0.append('OCR')
if have_doctr and kwargs['enable_pdf_doctr'] in [True, 'on']:
pdf_loaders_options0.append('DocTR')
# in case my pymupdf, use pypdf as backup default
if kwargs['use_pypdf'] in [True, 'on'] and have_pymupdf or kwargs['use_pypdf'] in [True, 'auto', 'on'] and not have_pymupdf:
pdf_loaders_options0.append('PyPDF')
if kwargs['use_unstructured_pdf'] in [True, 'on']:
pdf_loaders_options0.append('Unstructured')
if kwargs['try_pdf_as_html'] in [True, 'on']:
pdf_loaders_options0.append('TryHTML')
url_loaders_options = []
if only_unstructured_urls:
url_loaders_options.append('Unstructured')
elif have_selenium and only_selenium:
url_loaders_options.append('Selenium')
elif have_playwright and only_playwright:
url_loaders_options.append('PlayWright')
else:
url_loaders_options.append('Unstructured')
if have_selenium:
url_loaders_options.append('Selenium')
if have_playwright:
url_loaders_options.append('PlayWright')
url_loaders_options.append('ScrapeWithPlayWright')
url_loaders_options.append('ScrapeWithHttp')
url_loaders_options0 = [url_loaders_options[0]]
assert set(image_audio_loaders_options0).issubset(image_audio_loaders_options), "%s %s" % (
image_audio_loaders_options0, image_audio_loaders_options)
assert set(pdf_loaders_options0).issubset(pdf_loaders_options), "%s %s" % (
pdf_loaders_options0, pdf_loaders_options)
assert set(url_loaders_options0).issubset(url_loaders_options), "%s %s" % (
url_loaders_options0, url_loaders_options)
return image_audio_loaders_options0, image_audio_loaders_options, \
pdf_loaders_options0, pdf_loaders_options, \
url_loaders_options0, url_loaders_options
def fix_json(s):
# Attempt to parse the string as-is.
try:
return json.loads(s)
except json.JSONDecodeError:
pass
# Initialize variables.
new_s = ""
stack = []
is_inside_string = False
escaped = False
# Process each character in the string one at a time.
for char in s:
if is_inside_string:
if char == '"' and not escaped:
is_inside_string = False
elif char == '\n' and not escaped:
char = '\\n' # Replace the newline character with the escape sequence.
elif char == '\\':
escaped = not escaped
else:
escaped = False
else:
if char == '"':
is_inside_string = True
escaped = False
elif char == '{':
stack.append('}')
elif char == '[':
stack.append(']')
elif char == '}' or char == ']':
if stack and stack[-1] == char:
stack.pop()
else:
# Mismatched closing character; the input is malformed.
return None
# Append the processed character to the new string.
new_s += char
# If we're still inside a string at the end of processing, we need to close the string.
if is_inside_string:
new_s += '"'
# Close any remaining open structures in the reverse order that they were opened.
for closing_char in reversed(stack):
new_s += closing_char
# Attempt to parse the modified string as JSON.
try:
return json.loads(new_s)
except json.JSONDecodeError:
# If we still can't parse the string as JSON, return None to indicate failure.
return None
def wrap_in_try_except(code):
# Add import traceback
code = "import traceback\n" + code
# Parse the input code into an AST
parsed_code = ast.parse(code)
# Wrap the entire code's AST in a single try-except block
try_except = ast.Try(
body=parsed_code.body,
handlers=[
ast.ExceptHandler(
type=ast.Name(id="Exception", ctx=ast.Load()),
name=None,
body=[
ast.Expr(
value=ast.Call(
func=ast.Attribute(value=ast.Name(id="traceback", ctx=ast.Load()), attr="print_exc",
ctx=ast.Load()),
args=[],
keywords=[]
)
),
]
)
],
orelse=[],
finalbody=[]
)
# Assign the try-except block as the new body
parsed_code.body = [try_except]
# Convert the modified AST back to source code
return ast.unparse(parsed_code)
def enqueue_output(file, queue):
for line in iter(file.readline, ''):
queue.put(line)
file.close()
def read_popen_pipes(p):
with ThreadPoolExecutor(2) as pool:
q_stdout, q_stderr = Queue(), Queue()
pool.submit(enqueue_output, p.stdout, q_stdout)
pool.submit(enqueue_output, p.stderr, q_stderr)
while True:
if p.poll() is not None and q_stdout.empty() and q_stderr.empty():
break
out_line = err_line = ''
try:
out_line = q_stdout.get_nowait()
except Empty:
pass
try:
err_line = q_stderr.get_nowait()
except Empty:
pass
yield out_line, err_line
def start_process(cmd):
start_cmd = sys.executable + " -i -q -u"
print_cmd = 'print("{}")'
cmd = [start_cmd] + [cmd]
process = subprocess.Popen(cmd, stdout=subprocess.PIPE)
for c in iter(lambda: process.stdout.read(1), b''):
sys.stdout.write(c)
def str_to_list(x, allow_none=False):
if isinstance(x, str):
if len(x.strip()) > 0:
if x.strip().startswith('['):
x = ast.literal_eval(x.strip())
else:
raise ValueError("Invalid str_to_list for %s" % x)
else:
x = []
elif x is None and not allow_none:
x = []
if allow_none:
assert isinstance(x, (type(None), list))
else:
assert isinstance(x, list)
return x
def str_to_dict(x):
if isinstance(x, str):
if len(x.strip()) > 0:
if x.strip().startswith('{'):
x = ast.literal_eval(x.strip())
else:
raise ValueError("Invalid str_to_dict for %s" % x)
else:
x = {}
elif x is None:
x = {}
assert isinstance(x, dict)
return x
def get_token_count(x, tokenizer, token_count_fun=None):
# NOTE: Somewhat duplicates H2OTextGenerationPipeline.get_token_count()
# handle ambiguity in if get dict or list
if tokenizer is not None:
if hasattr(tokenizer, 'encode'):
tokens = tokenizer.encode(x)
else:
tokens = tokenizer(x)
if isinstance(tokens, dict) and 'input_ids' in tokens:
tokens = tokens['input_ids']
if isinstance(tokens, list):
n_tokens = len(tokens)
elif len(tokens.shape) == 2:
n_tokens = tokens.shape[1]
elif len(tokens.shape) == 1:
n_tokens = tokens.shape[0]
else:
raise RuntimeError("Cannot handle tokens: %s" % tokens)
elif token_count_fun is not None:
assert callable(token_count_fun)
n_tokens = token_count_fun(x)
else:
tokenizer = FakeTokenizer()
n_tokens = tokenizer.num_tokens_from_string(x)
return n_tokens
def reverse_ucurve_list(lst):
if not lst:
return []
if len(lst) == 1:
return lst
if len(lst) == 2:
return [lst[1], lst[0]]
front_list = []
end_list = []
for i, item in enumerate(lst):
if i % 2 == 0:
end_list.append(item)
else:
front_list.append(item)
return front_list + end_list[::-1]
def undo_reverse_ucurve_list(lst):
if not lst:
return []
if len(lst) == 1:
return lst
if len(lst) == 2:
return [lst[1], lst[0]]
# Split the list into two halves: the first half and the second half (reversed)
mid = len(lst) // 2
first_half = lst[:mid]
second_half = lst[mid:][::-1]
# Merge the two halves by taking elements alternatively from the second half and then the first half
result = []
for i in range(mid):
result.append(second_half[i])
result.append(first_half[i])
# If the length of the list is odd, append the last element of the second half
if len(lst) % 2 != 0:
result.append(second_half[-1])
return result
def get_size(start_path='.'):
total_size = 0
for dirpath, dirnames, filenames in os.walk(start_path):
for f in filenames:
fp = os.path.join(dirpath, f)
# skip if it is symbolic link
if not os.path.islink(fp):
total_size += os.path.getsize(fp)
return total_size
def get_test_name_core():
tn = os.environ['PYTEST_CURRENT_TEST'].split(':')[-1]
tn = "_".join(tn.split(' ')[:-1]) # skip (call) at end
return sanitize_filename(tn)