






"""simple docstring"""



import argparse
import math
import traceback

import dateutil.parser as date_parser
import requests




def        _lowerCAmelCase						(							UpperCAmelCase__    :   List[str] )						->str:
						A__  :					str											=					{}

						A__  :					List[Any]											=					job["""started_at"""]
						A__  :					List[Any]											=					job["""completed_at"""]

						A__  :					Union[str, Any]											=					date_parser.parse(UpperCAmelCase__ )
						A__  :					Optional[int]											=					date_parser.parse(UpperCAmelCase__ )

						A__  :					List[Any]											=					round((end_datetime - start_datetime).total_seconds() / 60.0 )

						A__  :					List[Any]											=					start
						A__  :					Optional[Any]											=					end
						A__  :					Tuple											=					duration_in_min

						return job_info




def        _lowerCAmelCase						(							UpperCAmelCase__    :   Tuple,  UpperCAmelCase__    :   List[str]=None )						->List[Any]:
						A__  :					Any											=					None
						if token is not None:
												A__  :					int											=					{"""Accept""": """application/vnd.github+json""", """Authorization""": f'Bearer {token}'}

						A__  :					Optional[Any]											=					f'https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100'
						A__  :					Optional[int]											=					requests.get(UpperCAmelCase__,  headers=UpperCAmelCase__ ).json()
						A__  :					Optional[int]											=					{}

						try:
												job_time.update({job["""name"""]: extract_time_from_single_job(UpperCAmelCase__ ) for job in result["""jobs"""]} )
												A__  :					Tuple											=					math.ceil((result["""total_count"""] - 1_0_0) / 1_0_0 )

												for i in range(UpperCAmelCase__ ):
																		A__  :					Tuple											=					requests.get(url + f'&page={i + 2}',  headers=UpperCAmelCase__ ).json()
																		job_time.update({job["""name"""]: extract_time_from_single_job(UpperCAmelCase__ ) for job in result["""jobs"""]} )

												return job_time
						except Exception:
												print(f'Unknown error, could not fetch links:\n{traceback.format_exc()}' )

						return {}


if __name__ == "__main__":
						A_      =							argparse.ArgumentParser()
						# Required parameters
						parser.add_argument('''--workflow_run_id''', type=str, required=True, help='''A GitHub Actions workflow run id.''')
						A_      =							parser.parse_args()

						A_      =							get_job_time(args.workflow_run_id)
						A_      =							dict(sorted(job_time.items(), key=lambda item: item[1]["duration"], reverse=True))

						for k, v in job_time.items():
												print(F'{k}: {v["duration"]}')














"""simple docstring"""



import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging


A_      =							logging.get_logger(__name__)

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

A_      =							{
    '''vocab_file''': {
        '''xlnet-base-cased''': '''https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model''',
        '''xlnet-large-cased''': '''https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model''',
    }
}

A_      =							{
    '''xlnet-base-cased''': None,
    '''xlnet-large-cased''': None,
}

# Segments (not really needed)
A_      =							0
A_      =							1
A_      =							2
A_      =							3
A_      =							4






class 							__SCREAMING_SNAKE_CASE  (			UpperCamelCase						):
							snake_case_       =							VOCAB_FILES_NAMES
							snake_case_       =							PRETRAINED_VOCAB_FILES_MAP
							snake_case_       =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							snake_case_       =							'left'


							def __init__(     self :  Dict     ,      snake_case :  int     ,      snake_case :  List[Any]=False     ,      snake_case :  List[str]=True     ,      snake_case :  Dict=False     ,      snake_case :  Optional[Any]="<s>"     ,      snake_case :  List[str]="</s>"     ,      snake_case :  Tuple="<unk>"     ,      snake_case :  Tuple="<sep>"     ,      snake_case :  Union[str, Any]="<pad>"     ,      snake_case :  Dict="<cls>"     ,      snake_case :  Optional[Any]="<mask>"     ,      snake_case :  Optional[int]=["<eop>", "<eod>"]     ,      snake_case :  Optional[Dict[str, Any]] = None     ,      **snake_case :  Dict     ,      ):
													'''simple docstring'''





													A__  :					Optional[int]											=					AddedToken(snake_case     ,      lstrip=snake_case     ,      rstrip=snake_case							) if isinstance(snake_case     ,      snake_case							) else mask_token

													A__  :					Union[str, Any]											=					{} if sp_model_kwargs is None else sp_model_kwargs

													super().__init__(
													    do_lower_case=snake_case     ,      remove_space=snake_case     ,      keep_accents=snake_case     ,      bos_token=snake_case     ,      eos_token=snake_case     ,      unk_token=snake_case     ,      sep_token=snake_case     ,      pad_token=snake_case     ,      cls_token=snake_case     ,      mask_token=snake_case     ,      additional_special_tokens=snake_case     ,      sp_model_kwargs=self.sp_model_kwargs     ,      **snake_case     ,      )

													A__  :					str											=					3

													A__  :					str											=					do_lower_case
													A__  :					Optional[Any]											=					remove_space
													A__  :					List[Any]											=					keep_accents
													A__  :					Union[str, Any]											=					vocab_file

													A__  :					Dict											=					spm.SentencePieceProcessor(**self.sp_model_kwargs							)
													self.sp_model.Load(snake_case							)


							@property
							def   _UpperCamelCase    (     self :  Optional[int]							):
													'''simple docstring'''





													return len(self.sp_model							)


							def   _UpperCamelCase    (     self :  List[Any]							):
													'''simple docstring'''





													A__  :					int											=					{self.convert_ids_to_tokens(snake_case							): i for i in range(self.vocab_size							)}
													vocab.update(self.added_tokens_encoder							)
													return vocab


							def __getstate__(     self :  str							):
													'''simple docstring'''





													A__  :					int											=					self.__dict__.copy()
													A__  :					int											=					None
													return state


							def __setstate__(     self :  Tuple     ,      snake_case :  Union[str, Any]							):
													'''simple docstring'''





													A__  :					int											=					d

													# for backward compatibility
													if not hasattr(self     ,      """sp_model_kwargs"""							):
																			A__  :					Optional[int]											=					{}

													A__  :					Optional[int]											=					spm.SentencePieceProcessor(**self.sp_model_kwargs							)
													self.sp_model.Load(self.vocab_file							)


							def   _UpperCamelCase    (     self :  List[str]     ,      snake_case :  Optional[Any]							):
													'''simple docstring'''





													if self.remove_space:
																			A__  :					Optional[Any]											=					""" """.join(inputs.strip().split()							)
													else:
																			A__  :					Dict											=					inputs
													A__  :					str											=					outputs.replace("""``"""     ,      """\""""							).replace("""''"""     ,      """\""""							)

													if not self.keep_accents:
																			A__  :					Any											=					unicodedata.normalize("""NFKD"""     ,      snake_case							)
																			A__  :					Optional[int]											=					"""""".join([c for c in outputs if not unicodedata.combining(snake_case							)]							)
													if self.do_lower_case:
																			A__  :					Any											=					outputs.lower()

													return outputs


							def   _UpperCamelCase    (     self :  Union[str, Any]     ,      snake_case :  str							):
													'''simple docstring'''





													A__  :					Dict											=					self.preprocess_text(snake_case							)
													A__  :					Dict											=					self.sp_model.encode(snake_case     ,      out_type=snake_case							)
													A__  :					Optional[int]											=					[]
													for piece in pieces:
																			if len(snake_case							) > 1 and piece[-1] == str(""","""							) and piece[-2].isdigit():
																									A__  :					Optional[Any]											=					self.sp_model.EncodeAsPieces(piece[:-1].replace(snake_case     ,      """"""							)							)
																									if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
																															if len(cur_pieces[0]							) == 1:
																																					A__  :					int											=					cur_pieces[1:]
																															else:
																																					A__  :					Any											=					cur_pieces[0][1:]
																									cur_pieces.append(piece[-1]							)
																									new_pieces.extend(snake_case							)
																			else:
																									new_pieces.append(snake_case							)

													return new_pieces


							def   _UpperCamelCase    (     self :  List[str]     ,      snake_case :  Tuple							):
													'''simple docstring'''





													return self.sp_model.PieceToId(snake_case							)


							def   _UpperCamelCase    (     self :  List[str]     ,      snake_case :  Any							):
													'''simple docstring'''





													return self.sp_model.IdToPiece(snake_case							)


							def   _UpperCamelCase    (     self :  Optional[int]     ,      snake_case :  Any							):
													'''simple docstring'''





													A__  :					Union[str, Any]											=					"""""".join(snake_case							).replace(snake_case     ,      """ """							).strip()
													return out_string


							def   _UpperCamelCase    (     self :  int     ,      snake_case :  List[int]     ,      snake_case :  bool = False     ,      snake_case :  bool = None     ,      snake_case :  bool = True     ,      **snake_case :  Union[str, Any]     ,      ):
													'''simple docstring'''





													A__  :					List[str]											=					kwargs.pop("""use_source_tokenizer"""     ,      snake_case							)

													A__  :					Any											=					self.convert_ids_to_tokens(snake_case     ,      skip_special_tokens=snake_case							)

													# To avoid mixing byte-level and unicode for byte-level BPT
													# we need to build string separately for added tokens and byte-level tokens
													# cf. https://github.com/huggingface/transformers/issues/1133
													A__  :					Any											=					[]
													A__  :					Any											=					[]
													for token in filtered_tokens:
																			if skip_special_tokens and token in self.all_special_ids:
																									continue
																			if token in self.added_tokens_encoder:
																									if current_sub_text:
																															sub_texts.append(self.convert_tokens_to_string(snake_case							)							)
																															A__  :					str											=					[]
																									sub_texts.append(snake_case							)
																			else:
																									current_sub_text.append(snake_case							)
													if current_sub_text:
																			sub_texts.append(self.convert_tokens_to_string(snake_case							)							)

													# Mimic the behavior of the Rust tokenizer:
													# By default, there are no spaces between special tokens
													A__  :					Dict											=					"""""".join(snake_case							)

													A__  :					int											=					(
													    clean_up_tokenization_spaces
													    if clean_up_tokenization_spaces is not None
													    else self.clean_up_tokenization_spaces
													)
													if clean_up_tokenization_spaces:
																			A__  :					Tuple											=					self.clean_up_tokenization(snake_case							)
																			return clean_text
													else:
																			return text


							def   _UpperCamelCase    (     self :  str     ,      snake_case :  List[int]     ,      snake_case :  Optional[List[int]] = None							):
													'''simple docstring'''





													A__  :					Tuple											=					[self.sep_token_id]
													A__  :					Dict											=					[self.cls_token_id]
													if token_ids_a is None:
																			return token_ids_a + sep + cls
													return token_ids_a + sep + token_ids_a + sep + cls


							def   _UpperCamelCase    (     self :  Dict     ,      snake_case :  List[int]     ,      snake_case :  Optional[List[int]] = None     ,      snake_case :  bool = False							):
													'''simple docstring'''





													if already_has_special_tokens:
																			return super().get_special_tokens_mask(
																			    token_ids_a=snake_case     ,      token_ids_a=snake_case     ,      already_has_special_tokens=snake_case							)

													if token_ids_a is not None:
																			return ([0] * len(snake_case							)) + [1] + ([0] * len(snake_case							)) + [1, 1]
													return ([0] * len(snake_case							)) + [1, 1]


							def   _UpperCamelCase    (     self :  str     ,      snake_case :  List[int]     ,      snake_case :  Optional[List[int]] = None							):
													'''simple docstring'''





													A__  :					Any											=					[self.sep_token_id]
													A__  :					int											=					[2]

													if token_ids_a is None:
																			return len(token_ids_a + sep							) * [0] + cls_segment_id
													return len(token_ids_a + sep							) * [0] + len(token_ids_a + sep							) * [1] + cls_segment_id


							def   _UpperCamelCase    (     self :  Optional[Any]     ,      snake_case :  str     ,      snake_case :  Optional[str] = None							):
													'''simple docstring'''





													if not os.path.isdir(snake_case							):
																			logger.error(F'Vocabulary path ({save_directory}) should be a directory'							)
																			return
													A__  :					List[Any]											=					os.path.join(
													    snake_case     ,      (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]							)

													if os.path.abspath(self.vocab_file							) != os.path.abspath(snake_case							) and os.path.isfile(self.vocab_file							):
																			copyfile(self.vocab_file     ,      snake_case							)
													elif not os.path.isfile(self.vocab_file							):
																			with open(snake_case     ,      """wb"""							) as fi:
																									A__  :					Optional[Any]											=					self.sp_model.serialized_model_proto()
																									fi.write(snake_case							)

													return (out_vocab_file,)






