
"""simple docstring"""


import argparse
import math
import traceback

import dateutil.parser as date_parser
import requests



def 	lowerCamelCase__			(							UpperCAmelCase_				)->							str:







       """simple docstring"""
       UpperCamelCase					 =		{}

       UpperCamelCase					 =		job["started_at"]
       UpperCamelCase					 =		job["completed_at"]

       UpperCamelCase					 =		date_parser.parse(UpperCAmelCase_				)
       UpperCamelCase					 =		date_parser.parse(UpperCAmelCase_				)

       UpperCamelCase					 =		round((end_datetime - start_datetime).total_seconds() / 60.0				)

       UpperCamelCase					 =		start
       UpperCamelCase					 =		end
       UpperCamelCase					 =		duration_in_min

       return job_info



def 	lowerCamelCase__			(							UpperCAmelCase_		,      UpperCAmelCase_=None				)->							Dict:







       """simple docstring"""
       UpperCamelCase					 =		None
       if token is not None:
              UpperCamelCase					 =		{"Accept": "application/vnd.github+json", "Authorization": F"Bearer {token}"}

       UpperCamelCase					 =		F"https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100"
       UpperCamelCase					 =		requests.get(UpperCAmelCase_		,      headers=UpperCAmelCase_				).json()
       UpperCamelCase					 =		{}

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

              for i in range(UpperCAmelCase_				):
                     UpperCamelCase					 =		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__":
      SCREAMING_SNAKE_CASE      =     argparse.ArgumentParser()
      # Required parameters
      parser.add_argument("""--workflow_run_id""", type=str, required=True, help="""A GitHub Actions workflow run id.""")
      SCREAMING_SNAKE_CASE      =     parser.parse_args()

      SCREAMING_SNAKE_CASE      =     get_job_time(args.workflow_run_id)
      SCREAMING_SNAKE_CASE      =     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_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE      =     {"""configuration_mbart""": ["""MBART_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MBartConfig""", """MBartOnnxConfig"""]}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE      =     ["""MBartTokenizer"""]

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE      =     ["""MBartTokenizerFast"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE      =     [
          """MBART_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """MBartForCausalLM""",
          """MBartForConditionalGeneration""",
          """MBartForQuestionAnswering""",
          """MBartForSequenceClassification""",
          """MBartModel""",
          """MBartPreTrainedModel""",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE      =     [
          """TFMBartForConditionalGeneration""",
          """TFMBartModel""",
          """TFMBartPreTrainedModel""",
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE      =     [
          """FlaxMBartForConditionalGeneration""",
          """FlaxMBartForQuestionAnswering""",
          """FlaxMBartForSequenceClassification""",
          """FlaxMBartModel""",
          """FlaxMBartPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_mbart import MBART_PRETRAINED_CONFIG_ARCHIVE_MAP, MBartConfig, MBartOnnxConfig

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_mbart import MBartTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_mbart_fast import MBartTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_mbart import (
                MBART_PRETRAINED_MODEL_ARCHIVE_LIST,
                MBartForCausalLM,
                MBartForConditionalGeneration,
                MBartForQuestionAnswering,
                MBartForSequenceClassification,
                MBartModel,
                MBartPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_mbart import TFMBartForConditionalGeneration, TFMBartModel, TFMBartPreTrainedModel

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_mbart import (
                FlaxMBartForConditionalGeneration,
                FlaxMBartForQuestionAnswering,
                FlaxMBartForSequenceClassification,
                FlaxMBartModel,
                FlaxMBartPreTrainedModel,
            )

else:
      import sys

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





