
'''simple docstring'''






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


A			      =      logging.get_logger(__name__)





def    UpperCAmelCase							(  UpperCAmelCase__							:							List[str]       ,							UpperCAmelCase__							:							str):
				lowerCamelCase					:			Dict									= set()
				lowerCamelCase					:			Optional[int]									= []

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

				if from_gh:
								for filename in os.listdir(UpperCAmelCase__):
												lowerCamelCase					:			Optional[Any]									= os.path.join(UpperCAmelCase__       ,							UpperCAmelCase__)
												if not os.path.isdir(UpperCAmelCase__):
																# read the file
																if filename != "warnings.txt":
																				continue
																with open(UpperCAmelCase__) as fp:
																				parse_line(UpperCAmelCase__)
				else:
								try:
												with zipfile.ZipFile(UpperCAmelCase__) as z:
																for filename in z.namelist():
																				if not os.path.isdir(UpperCAmelCase__):
																								# read the file
																								if filename != "warnings.txt":
																												continue
																								with z.open(UpperCAmelCase__) as fp:
																												parse_line(UpperCAmelCase__)
								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    UpperCAmelCase							(  UpperCAmelCase__							:							str       ,							UpperCAmelCase__							:							int):
				lowerCamelCase					:			Tuple									= set()

				lowerCamelCase					:			List[Any]									= [os.path.join(UpperCAmelCase__       ,							UpperCAmelCase__) for p in os.listdir(UpperCAmelCase__) if (p.endswith('.zip') or from_gh)]
				for p in paths:
								selected_warnings.update(extract_warnings_from_single_artifact(UpperCAmelCase__       ,							UpperCAmelCase__))

				return selected_warnings


if __name__ == "__main__":





					def    UpperCAmelCase							(  UpperCAmelCase__							:							Optional[int]):
									return values.split(',')

					A			      =      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.',
					)

					A			      =      parser.parse_args()

					A			      =      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
										A			      =      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
					A			      =      extract_warnings(args.output_dir, args.targets)
					A			      =      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)


'''simple docstring'''






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


A			      =      logging.get_logger(__name__)


A			      =      '▁'

A			      =      {'vocab_file': 'spiece.model'}

A			      =      {
    'vocab_file': {
        'google/reformer-crime-and-punishment': (
            'https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model'
        )
    }
}

A			      =      {
    'google/reformer-crime-and-punishment': 52_4288,
}




class     __snake_case		(   a__):
					_lowerCAmelCase			     =	VOCAB_FILES_NAMES
					_lowerCAmelCase			     =	PRETRAINED_VOCAB_FILES_MAP
					_lowerCAmelCase			     =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					_lowerCAmelCase			     =	['''input_ids''', '''attention_mask''']
					def __init__(     self,    A,    A="</s>",    A="<unk>",    A=[],    A = None,    **A,    ):

									"""simple docstring"""


									lowerCamelCase					:			str									= {} if sp_model_kwargs is None else sp_model_kwargs

									super().__init__(
									    eos_token=A,    unk_token=A,    additional_special_tokens=A,    sp_model_kwargs=self.sp_model_kwargs,    **A,    )

									lowerCamelCase					:			Any									= vocab_file
									lowerCamelCase					:			List[Any]									= spm.SentencePieceProcessor(**self.sp_model_kwargs      )
									self.sp_model.Load(A      )
					@property
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									return self.sp_model.get_piece_size()
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			int									= {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      ):

									"""simple docstring"""


									lowerCamelCase					:			Union[str, Any]									= self.__dict__.copy()
									lowerCamelCase					:			List[Any]									= None
									return state
					def __setstate__(     self,    A      ):

									"""simple docstring"""


									lowerCamelCase					:			int									= d

									# for backward compatibility
									if not hasattr(self,    'sp_model_kwargs'      ):
													lowerCamelCase					:			List[str]									= {}

									lowerCamelCase					:			Optional[int]									= spm.SentencePieceProcessor(**self.sp_model_kwargs      )
									self.sp_model.Load(self.vocab_file      )
					def     UpperCAmelCase_							(     self,    A      ):

									"""simple docstring"""


									return self.sp_model.encode(A,    out_type=A      )
					def     UpperCAmelCase_							(     self,    A      ):

									"""simple docstring"""


									return self.sp_model.piece_to_id(A      )
					def     UpperCAmelCase_							(     self,    A      ):

									"""simple docstring"""


									if index < self.sp_model.get_piece_size():
													lowerCamelCase					:			Union[str, Any]									= self.sp_model.IdToPiece(A      )
									return token
					def     UpperCAmelCase_							(     self,    A      ):

									"""simple docstring"""


									lowerCamelCase					:			Union[str, Any]									= []
									lowerCamelCase					:			Optional[int]									= ''
									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
																	lowerCamelCase					:			Any									= []
													else:
																	current_sub_tokens.append(A      )
									out_string += self.sp_model.decode(A      )
									return out_string.strip()




					def     UpperCAmelCase_							(     self,    A,    A = None      ):

									"""simple docstring"""


									if not os.path.isdir(A      ):
													logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''      )
													return
									lowerCamelCase					:			str									= 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:
																	lowerCamelCase					:			Optional[Any]									= self.sp_model.serialized_model_proto()
																	fi.write(A      )

									return (out_vocab_file,)
