'''simple docstring'''

import argparse
import math
import traceback

import dateutil.parser as date_parser
import requests


def __a (	__lowerCamelCase			:						List[str]				)	->							int:
      '''simple docstring'''


      lowercase_     =      {}

      lowercase_     =      job["started_at"]
      lowercase_     =      job["completed_at"]

      lowercase_     =      date_parser.parse(__lowerCamelCase				)
      lowercase_     =      date_parser.parse(__lowerCamelCase				)

      lowercase_     =      round((end_datetime - start_datetime).total_seconds() / 6_0.0				)

      lowercase_     =      start
      lowercase_     =      end
      lowercase_     =      duration_in_min

      return job_info


def __a (	__lowerCamelCase			:						Union[str, Any] ,		__lowerCamelCase			:						int=None				)	->							List[str]:
      '''simple docstring'''


      lowercase_     =      None
      if token is not None:
            lowercase_     =      {"Accept": "application/vnd.github+json", "Authorization": f'Bearer {token}'}

      lowercase_     =      f'https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100'
      lowercase_     =      requests.get(__lowerCamelCase ,		headers=__lowerCamelCase				).json()
      lowercase_     =      {}

      try:
            job_time.update({job["name"]: extract_time_from_single_job(__lowerCamelCase				) for job in result["jobs"]}				)
            lowercase_     =      math.ceil((result["total_count"] - 100) / 100				)

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

       lowerCAmelCase_ : List[Any]						  =      get_job_time(args.workflow_run_id)
       lowerCAmelCase_ : Tuple						  =      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'''

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


lowerCAmelCase_ : str						  =      {
    "configuration_wav2vec2": ["WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Wav2Vec2Config"],
    "feature_extraction_wav2vec2": ["Wav2Vec2FeatureExtractor"],
    "processing_wav2vec2": ["Wav2Vec2Processor"],
    "tokenization_wav2vec2": ["Wav2Vec2CTCTokenizer", "Wav2Vec2Tokenizer"],
}


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase_ : Union[str, Any]						  =      [
           "WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST",
           "Wav2Vec2ForAudioFrameClassification",
           "Wav2Vec2ForCTC",
           "Wav2Vec2ForMaskedLM",
           "Wav2Vec2ForPreTraining",
           "Wav2Vec2ForSequenceClassification",
           "Wav2Vec2ForXVector",
           "Wav2Vec2Model",
           "Wav2Vec2PreTrainedModel",
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase_ : int						  =      [
           "TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TFWav2Vec2ForCTC",
           "TFWav2Vec2Model",
           "TFWav2Vec2PreTrainedModel",
           "TFWav2Vec2ForSequenceClassification",
       ]

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase_ : Dict						  =      [
           "FlaxWav2Vec2ForCTC",
           "FlaxWav2Vec2ForPreTraining",
           "FlaxWav2Vec2Model",
           "FlaxWav2Vec2PreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_wavaveca import WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP, WavaVecaConfig
       from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
       from .processing_wavaveca import WavaVecaProcessor
       from .tokenization_wavaveca import WavaVecaCTCTokenizer, WavaVecaTokenizer

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_wavaveca import (
                  WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
                  WavaVecaForAudioFrameClassification,
                  WavaVecaForCTC,
                  WavaVecaForMaskedLM,
                  WavaVecaForPreTraining,
                  WavaVecaForSequenceClassification,
                  WavaVecaForXVector,
                  WavaVecaModel,
                  WavaVecaPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_wavaveca import (
                  TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFWavaVecaForCTC,
                  TFWavaVecaForSequenceClassification,
                  TFWavaVecaModel,
                  TFWavaVecaPreTrainedModel,
              )

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_wavaveca import (
                  FlaxWavaVecaForCTC,
                  FlaxWavaVecaForPreTraining,
                  FlaxWavaVecaModel,
                  FlaxWavaVecaPreTrainedModel,
              )


else:
       import sys

       lowerCAmelCase_ : Optional[int]						  =      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






