



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    :						Tuple  =						logging.get_logger(__name__)
def 	snake_case__       (					__lowercase							,     __lowercase       )		->		int:







						"""simple docstring"""

						A__	:							Optional[int]						    = set()
						A__	:							Optional[Any]						    = []

						def parse_line(__lowercase       ):
												for line in fp:
																		if isinstance(__lowercase							,     __lowercase       ):
																								A__	:							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(__lowercase       ) > 0:
																														A__	:							str						    = "\n".join(__lowercase       )
																														# Only keep the warnings specified in `targets`
																														if any(F': {x}: ' in warning for x in targets       ):
																																				selected_warnings.add(__lowercase       )
																														buffer.clear()
																								continue
																		else:
																								A__	:							Optional[int]						    = line.strip()
																								buffer.append(__lowercase       )

						if from_gh:
												for filename in os.listdir(__lowercase       ):
																		A__	:							Optional[Any]						    = os.path.join(__lowercase							,     __lowercase       )
																		if not os.path.isdir(__lowercase       ):
																								# read the file
																								if filename != "warnings.txt":
																														continue
																								with open(__lowercase       ) as fp:
																														parse_line(__lowercase       )
						else:
												try:
																		with zipfile.ZipFile(__lowercase       ) as z:
																								for filename in z.namelist():
																														if not os.path.isdir(__lowercase       ):
																																				# read the file
																																				if filename != "warnings.txt":
																																										continue
																																				with z.open(__lowercase       ) as fp:
																																										parse_line(__lowercase       )
												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 	snake_case__       (					__lowercase							,     __lowercase       )		->		int:







						"""simple docstring"""

						A__	:							Optional[int]						    = set()

						A__	:							Tuple						    = [os.path.join(__lowercase							,     __lowercase       ) for p in os.listdir(__lowercase       ) if (p.endswith(".zip"       ) or from_gh)]
						for p in paths:
												selected_warnings.update(extract_warnings_from_single_artifact(__lowercase							,     __lowercase       )       )

						return selected_warnings


if __name__ == "__main__":
		def 	snake_case__       (					__lowercase       )		->		Any:







								"""simple docstring"""

								return values.split(","       )

		snake_case    :						List[str]  =						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    :						int  =						parser.parse_args()

		snake_case    :						Optional[int]  =						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    :						int  =						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('=' * 8_0)
						download_artifact(name, url, args.output_dir, args.token)
						# Be gentle to GitHub
						time.sleep(1)

    # extract warnings from artifacts
		snake_case    :						Any  =						extract_warnings(args.output_dir, args.targets)
		snake_case    :						str  =						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)





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


snake_case    :						Optional[int]  =						logging.get_logger(__name__)

snake_case    :						Union[str, Any]  =						{
    'facebook/data2vec-text-base': 'https://huggingface.co/data2vec/resolve/main/config.json',
}




class 							lowerCAmelCase__  (	UpperCamelCase						):
					__A     :    Optional[Any]           =						'data2vec-text'







					def __init__(       self      :     Optional[Any]			,		_A      :     List[str]=3_0522			,		_A      :     Optional[int]=768			,		_A      :     Union[str, Any]=12			,		_A      :     str=12			,		_A      :     Optional[int]=3072			,		_A      :     Dict="gelu"			,		_A      :     List[str]=0.1			,		_A      :     Any=0.1			,		_A      :     str=512			,		_A      :     Any=2			,		_A      :     str=0.02			,		_A      :     List[str]=1e-12			,		_A      :     Optional[int]=1			,		_A      :     Any=0			,		_A      :     Any=2			,		_A      :     List[Any]="absolute"			,		_A      :     Tuple=True			,		_A      :     str=None			,		**_A      :     List[Any]			,		):
											super().__init__(pad_token_id=_A			,		bos_token_id=_A			,		eos_token_id=_A			,		**_A)

											A__	:							Optional[int]						    = vocab_size
											A__	:							Tuple						    = hidden_size
											A__	:							str						    = num_hidden_layers
											A__	:							Union[str, Any]						    = num_attention_heads
											A__	:							Dict						    = hidden_act
											A__	:							List[Any]						    = intermediate_size
											A__	:							int						    = hidden_dropout_prob
											A__	:							Dict						    = attention_probs_dropout_prob
											A__	:							Union[str, Any]						    = max_position_embeddings
											A__	:							Tuple						    = type_vocab_size
											A__	:							Union[str, Any]						    = initializer_range
											A__	:							str						    = layer_norm_eps
											A__	:							int						    = position_embedding_type
											A__	:							str						    = use_cache
											A__	:							int						    = classifier_dropout




class 							lowerCAmelCase__  (	UpperCamelCase						):







					@property
					def  _lowercase			(       self      :     Tuple):
											if self.task == "multiple-choice":
																	A__	:							List[str]						    = {0: "batch", 1: "choice", 2: "sequence"}
											else:
																	A__	:							List[str]						    = {0: "batch", 1: "sequence"}
											return OrderedDict(
											    [
											        ("input_ids", dynamic_axis),
											        ("attention_mask", dynamic_axis),
											    ])