


import argparse
import math
import traceback

import dateutil.parser as date_parser
import requests


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE       ):
  A_      :				Optional[Any]		     =					{}

  A_      :				str		     =					job['''started_at''']
  A_      :				Optional[Any]		     =					job['''completed_at''']

  A_      :				List[str]		     =					date_parser.parse(SCREAMING_SNAKE_CASE       )
  A_      :				Tuple		     =					date_parser.parse(SCREAMING_SNAKE_CASE       )

  A_      :				str		     =					round((end_datetime - start_datetime).total_seconds() / 6_0.0       )

  A_      :				Any		     =					start
  A_      :				str		     =					end
  A_      :				Dict		     =					duration_in_min

  return job_info


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE=None       ):
  A_      :				int		     =					None
  if token is not None:
    A_      :				List[Any]		     =					{'''Accept''': '''application/vnd.github+json''', '''Authorization''': f'''Bearer {token}'''}

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

  try:
    job_time.update({job['''name''']: extract_time_from_single_job(SCREAMING_SNAKE_CASE       ) for job in result['''jobs''']}       )
    A_      :				List[Any]		     =					math.ceil((result['''total_count'''] - 100) / 100       )

    for i in range(SCREAMING_SNAKE_CASE       ):
      A_      :				Optional[int]		     =					requests.get(url + f'''&page={i + 2}''' ,			headers=SCREAMING_SNAKE_CASE       ).json()
      job_time.update({job['''name''']: extract_time_from_single_job(SCREAMING_SNAKE_CASE       ) 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__":
     UpperCamelCase								=	argparse.ArgumentParser()
     # Required parameters
     parser.add_argument("""--workflow_run_id""", type=str, required=True, help="""A GitHub Actions workflow run id.""")
     UpperCamelCase								=	parser.parse_args()

     UpperCamelCase								=	get_job_time(args.workflow_run_id)
     UpperCamelCase								=	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']}''')








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


UpperCamelCase								=	{
    """configuration_nezha""": ["""NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """NezhaConfig"""],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase								=	[
         """NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """NezhaForNextSentencePrediction""",
         """NezhaForMaskedLM""",
         """NezhaForPreTraining""",
         """NezhaForMultipleChoice""",
         """NezhaForQuestionAnswering""",
         """NezhaForSequenceClassification""",
         """NezhaForTokenClassification""",
         """NezhaModel""",
         """NezhaPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_nezha import NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP, NezhaConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_nezha import (
              NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST,
              NezhaForMaskedLM,
              NezhaForMultipleChoice,
              NezhaForNextSentencePrediction,
              NezhaForPreTraining,
              NezhaForQuestionAnswering,
              NezhaForSequenceClassification,
              NezhaForTokenClassification,
              NezhaModel,
              NezhaPreTrainedModel,
          )


else:
     import sys

     UpperCamelCase								=	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




