





import argparse
import math
import traceback

import dateutil.parser as date_parser
import requests






def 							_lowerCAmelCase (  _a   :					Dict			)	->			Union[str, Any]:
     lowerCAmelCase_					:       int    =		{}

     lowerCAmelCase_					:       List[Any]    =		job["""started_at"""]
     lowerCAmelCase_					:       Optional[int]    =		job["""completed_at"""]

     lowerCAmelCase_					:       Optional[int]    =		date_parser.parse(_a			)
     lowerCAmelCase_					:       Optional[Any]    =		date_parser.parse(_a			)

     lowerCAmelCase_					:       Any    =		round((end_datetime - start_datetime).total_seconds() / 60.0			)

     lowerCAmelCase_					:       List[Any]    =		start
     lowerCAmelCase_					:       int    =		end
     lowerCAmelCase_					:       Optional[int]    =		duration_in_min

     return job_info






def 							_lowerCAmelCase (  _a   :					Tuple					,   _a   :					Optional[Any]=None			)	->			int:
     lowerCAmelCase_					:       Optional[Any]    =		None
     if token is not None:
          lowerCAmelCase_					:       Tuple    =		{"""Accept""": """application/vnd.github+json""", """Authorization""": F'Bearer {token}'}

     lowerCAmelCase_					:       Dict    =		F'https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100'
     lowerCAmelCase_					:       Tuple    =		requests.get(_a					,   headers=_a			).json()
     lowerCAmelCase_					:       Any    =		{}

     try:
          job_time.update({job["""name"""]: extract_time_from_single_job(_a			) for job in result["""jobs"""]}			)
          lowerCAmelCase_					:       Any    =		math.ceil((result["""total_count"""] - 1_00) / 1_00			)

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

      UpperCAmelCase_					:   Any 							=						get_job_time(args.workflow_run_id)
      UpperCAmelCase_					:   Union[str, Any] 							=						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_torch_available, is_vision_available


UpperCAmelCase_					:   Dict 							=						{
    """configuration_poolformer""": [
        """POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """PoolFormerConfig""",
        """PoolFormerOnnxConfig""",
    ]
}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCAmelCase_					:   Union[str, Any] 							=						["""PoolFormerFeatureExtractor"""]
      UpperCAmelCase_					:   str 							=						["""PoolFormerImageProcessor"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCAmelCase_					:   List[str] 							=						[
          """POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """PoolFormerForImageClassification""",
          """PoolFormerModel""",
          """PoolFormerPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_poolformer import (
          POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
          PoolFormerConfig,
          PoolFormerOnnxConfig,
      )

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_poolformer import PoolFormerFeatureExtractor
            from .image_processing_poolformer import PoolFormerImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_poolformer import (
                POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                PoolFormerForImageClassification,
                PoolFormerModel,
                PoolFormerPreTrainedModel,
            )


else:
      import sys

      UpperCAmelCase_					:   Optional[Any] 							=						_LazyModule(__name__, globals()["""__file__"""], _import_structure)
