



import argparse
import json
import os
import time
import zipfile

from get_ci_error_statistics import download_artifact, get_artifacts_links

from transformers import logging


_snake_case		=  logging.get_logger(__name__)






def 				A				(					_lowerCamelCase				, _lowerCamelCase  ):





							'''simple docstring'''




							_lowerCAmelCase     :							Tuple    =    set()
							_lowerCAmelCase     :							int    =    []

							def parse_line(_lowerCamelCase  ):
														for line in fp:
																					if isinstance(_lowerCamelCase				, _lowerCamelCase  ):
																												_lowerCAmelCase     :							List[str]    =    line.decode("UTF-8"  )
																					if "warnings summary (final)" in line:
																												continue
																					# This means we are outside the body of a warning
																					elif not line.startswith(" "  ):
																												# process a single warning and move it to `selected_warnings`.
																												if len(_lowerCamelCase  ) > 0:
																																			_lowerCAmelCase     :							Any    =    "\n".join(_lowerCamelCase  )
																																			# Only keep the warnings specified in `targets`
																																			if any(F": {x}: " in warning for x in targets  ):
																																										selected_warnings.add(_lowerCamelCase  )
																																			buffer.clear()
																												continue
																					else:
																												_lowerCAmelCase     :							Tuple    =    line.strip()
																												buffer.append(_lowerCamelCase  )

							if from_gh:
														for filename in os.listdir(_lowerCamelCase  ):
																					_lowerCAmelCase     :							Dict    =    os.path.join(_lowerCamelCase				, _lowerCamelCase  )
																					if not os.path.isdir(_lowerCamelCase  ):
																												# read the file
																												if filename != "warnings.txt":
																																			continue
																												with open(_lowerCamelCase  ) as fp:
																																			parse_line(_lowerCamelCase  )
							else:
														try:
																					with zipfile.ZipFile(_lowerCamelCase  ) as z:
																												for filename in z.namelist():
																																			if not os.path.isdir(_lowerCamelCase  ):
																																										# read the file
																																										if filename != "warnings.txt":
																																																	continue
																																										with z.open(_lowerCamelCase  ) as fp:
																																																	parse_line(_lowerCamelCase  )
														except Exception:
																					logger.warning(
																					    F"{artifact_path} is either an invalid zip file or something else wrong. This file is skipped."  )

							return selected_warnings






def 				A				(					_lowerCamelCase				, _lowerCamelCase  ):





							'''simple docstring'''




							_lowerCAmelCase     :							Optional[Any]    =    set()

							_lowerCAmelCase     :							List[str]    =    [os.path.join(_lowerCamelCase				, _lowerCamelCase  ) for p in os.listdir(_lowerCamelCase  ) if (p.endswith(".zip"  ) or from_gh)]
							for p in paths:
														selected_warnings.update(extract_warnings_from_single_artifact(_lowerCamelCase				, _lowerCamelCase  )  )

							return selected_warnings


if __name__ == "__main__":






						def 				A				(					_lowerCamelCase  ):





													'''simple docstring'''




													return values.split(","  )

						_snake_case		=  argparse.ArgumentParser()
						# Required parameters
						parser.add_argument("--workflow_run_id", type=str, required=True, help="A GitHub Actions workflow run id.")
						parser.add_argument(
						    "--output_dir",
						    type=str,
						    required=True,
						    help="Where to store the downloaded artifacts and other result files.",
						)
						parser.add_argument("--token", default=None, type=str, help="A token that has actions:read permission.")
						# optional parameters
						parser.add_argument(
						    "--targets",
						    default="DeprecationWarning,UserWarning,FutureWarning",
						    type=list_str,
						    help="Comma-separated list of target warning(s) which we want to extract.",
						)
						parser.add_argument(
						    "--from_gh",
						    action="store_true",
						    help="If running from a GitHub action workflow and collecting warnings from its artifacts.",
						)

						_snake_case		=  parser.parse_args()

						_snake_case		=  args.from_gh
						if from_gh:
												# The artifacts have to be downloaded using `actions/download-artifact@v3`
												pass
						else:
												os.makedirs(args.output_dir, exist_ok=True)

												# get download links
												_snake_case		=  get_artifacts_links(args.workflow_run_id, token=args.token)
												with open(os.path.join(args.output_dir, "artifacts.json"), "w", encoding="UTF-8") as fp:
																		json.dump(artifacts, fp, ensure_ascii=False, indent=4)

												# download artifacts
												for idx, (name, url) in enumerate(artifacts.items()):
																		print(name)
																		print(url)
																		print("=" * 80)
																		download_artifact(name, url, args.output_dir, args.token)
																		# Be gentle to GitHub
																		time.sleep(1)

    # extract warnings from artifacts
						_snake_case		=  extract_warnings(args.output_dir, args.targets)
						_snake_case		=  sorted(selected_warnings)
						with open(os.path.join(args.output_dir, "selected_warnings.json"), "w", encoding="UTF-8") as fp:
												json.dump(selected_warnings, fp, ensure_ascii=False, indent=4)








# 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.

from pathlib import Path

import torch

from ...utils import is_npu_available, is_xpu_available
from .config_args import ClusterConfig, default_json_config_file
from .config_utils import SubcommandHelpFormatter


UpperCAmelCase_ :						Any      =      '''Create a default config file for Accelerate with only a few flags set.'''

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  Optional[int]="no"	,			__magic_name__					:  str = default_json_config_file	,			__magic_name__					:  bool = False					)		->			str:






	"""simple docstring"""



	UpperCamelCase     :Any          =	Path(__magic_name__					)
	path.parent.mkdir(parents=__magic_name__	,			exist_ok=__magic_name__					)
	if path.exists():
		print(
		    f"""Configuration already exists at {save_location}, will not override. Run `accelerate config` manually or pass a different `save_location`."""					)
		return False
	UpperCamelCase     :Dict          =	mixed_precision.lower()
	if mixed_precision not in ["no", "fp16", "bf16", "fp8"]:
		raise ValueError(
		    f"""`mixed_precision` should be one of 'no', 'fp16', 'bf16', or 'fp8'. Received {mixed_precision}"""					)
	UpperCamelCase     :Optional[Any]          =	{
	    """compute_environment""": """LOCAL_MACHINE""",
	    """mixed_precision""": mixed_precision,
	}
	if torch.cuda.is_available():
		UpperCamelCase     :Union[str, Any]          =	torch.cuda.device_count()
		UpperCamelCase     :List[Any]          =	num_gpus
		UpperCamelCase     :Dict          =	False
		if num_gpus > 1:
			UpperCamelCase     :Any          =	"""MULTI_GPU"""
		else:
			UpperCamelCase     :Any          =	"""NO"""
	elif is_xpu_available() and use_xpu:
		UpperCamelCase     :Optional[Any]          =	torch.xpu.device_count()
		UpperCamelCase     :Optional[int]          =	num_xpus
		UpperCamelCase     :int          =	False
		if num_xpus > 1:
			UpperCamelCase     :Union[str, Any]          =	"""MULTI_XPU"""
		else:
			UpperCamelCase     :Union[str, Any]          =	"""NO"""
	elif is_npu_available():
		UpperCamelCase     :List[Any]          =	torch.npu.device_count()
		UpperCamelCase     :Optional[Any]          =	num_npus
		UpperCamelCase     :Tuple          =	False
		if num_npus > 1:
			UpperCamelCase     :Optional[Any]          =	"""MULTI_NPU"""
		else:
			UpperCamelCase     :List[Any]          =	"""NO"""
	else:
		UpperCamelCase     :Any          =	0
		UpperCamelCase     :Optional[Any]          =	True
		UpperCamelCase     :Optional[Any]          =	1
		UpperCamelCase     :List[str]          =	"""NO"""
	UpperCamelCase     :int          =	ClusterConfig(**__magic_name__					)
	config.to_json_file(__magic_name__					)
	return path

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  Dict	,			__magic_name__					:  Tuple					)		->			List[str]:






	"""simple docstring"""



	UpperCamelCase     :Dict          =	parser.add_parser("""default"""	,			parents=__magic_name__	,			help=__magic_name__	,			formatter_class=__magic_name__					)
	parser.add_argument(
	    """--config_file"""	,			default=__magic_name__	,			help=(
	        """The path to use to store the config file. Will default to a file named default_config.yaml in the cache """
	        """location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have """
	        """such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed """
	        """with 'huggingface'."""
	    )	,			dest="""save_location"""	,			)

	parser.add_argument(
	    """--mixed_precision"""	,			choices=["""no""", """fp16""", """bf16"""]	,			type=__magic_name__	,			help="""Whether or not to use mixed precision training. """
	    """Choose between FP16 and BF16 (bfloat16) training. """
	    """BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later."""	,			default="""no"""	,			)
	parser.set_defaults(func=__magic_name__					)
	return parser

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  List[Any]					)		->			List[str]:






	"""simple docstring"""



	UpperCamelCase     :Optional[Any]          =	write_basic_config(args.mixed_precision	,			args.save_location					)
	if config_file:
		print(f"""accelerate configuration saved at {config_file}"""					)
