
'''simple docstring'''
from collections import OrderedDict
from typing import List, Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


A__   :		List[Any] 		=logging.get_logger(__name__)

A__   :		Optional[Any] 		={
    'google/efficientnet-b7': 'https://huggingface.co/google/efficientnet-b7/resolve/main/config.json',
}







class  __A				( _SCREAMING_SNAKE_CASE			):
 lowerCamelCase				       ='''efficientnet'''
 def __init__(   self			:    Tuple				,   lowerCamelCase			:    Optional[Any] = 3				,   lowerCamelCase			:    Union[str, Any] = 6_00				,   lowerCamelCase			:    Optional[Any] = 2.0				,   lowerCamelCase			:    Optional[Any] = 3.1				,   lowerCamelCase			:    str = 8				,   lowerCamelCase			:    List[Any] = [3, 3, 5, 3, 5, 5, 3]				,   lowerCamelCase			:    Tuple = [32, 16, 24, 40, 80, 1_12, 1_92]				,   lowerCamelCase			:    Optional[Any] = [16, 24, 40, 80, 1_12, 1_92, 3_20]				,   lowerCamelCase			:    Union[str, Any] = []				,   lowerCamelCase			:    Dict = [1, 2, 2, 2, 1, 2, 1]				,   lowerCamelCase			:    List[str] = [1, 2, 2, 3, 3, 4, 1]				,   lowerCamelCase			:    Any = [1, 6, 6, 6, 6, 6, 6]				,   lowerCamelCase			:    Any = 0.25				,   lowerCamelCase			:    int = "swish"				,   lowerCamelCase			:    List[Any] = 25_60				,   lowerCamelCase			:    Any = "mean"				,   lowerCamelCase			:    Optional[Any] = 0.02				,   lowerCamelCase			:    Dict = 0.001				,   lowerCamelCase			:    List[str] = 0.99				,   lowerCamelCase			:    Tuple = 0.5				,   lowerCamelCase			:    List[str] = 0.2				,   **lowerCamelCase			:    List[Any]				,   ):



   """simple docstring"""





   super().__init__(**__a       )

   __A		:     Optional[Any]												=		num_channels
   __A		:     Dict												=		image_size
   __A		:     Optional[Any]												=		width_coefficient
   __A		:     List[Any]												=		depth_coefficient
   __A		:     Dict												=		depth_divisor
   __A		:     Tuple												=		kernel_sizes
   __A		:     Optional[Any]												=		in_channels
   __A		:     Any												=		out_channels
   __A		:     Dict												=		depthwise_padding
   __A		:     Any												=		strides
   __A		:     Optional[int]												=		num_block_repeats
   __A		:     int												=		expand_ratios
   __A		:     Optional[int]												=		squeeze_expansion_ratio
   __A		:     str												=		hidden_act
   __A		:     Dict												=		hidden_dim
   __A		:     Optional[int]												=		pooling_type
   __A		:     List[Any]												=		initializer_range
   __A		:     Union[str, Any]												=		batch_norm_eps
   __A		:     Dict												=		batch_norm_momentum
   __A		:     int												=		dropout_rate
   __A		:     Optional[int]												=		drop_connect_rate
   __A		:     Dict												=		sum(__a       ) * 4







class  __A				( _SCREAMING_SNAKE_CASE			):
 lowerCamelCase				       =version.parse('''1.11'''			)
 @property
 def 		lowercase_(   self			:    Optional[int]       ):



   """simple docstring"""





   return OrderedDict(
       [
           ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
       ]       )






 @property
 def 		lowercase_(   self			:    Tuple       ):



   """simple docstring"""





   return 1e-5





'''simple docstring'''
import argparse
import math
import traceback

import dateutil.parser as date_parser
import requests







def        A_     (						__SCREAMING_SNAKE_CASE						:  Dict )				->       Tuple:





  """simple docstring"""

  __A		:     Dict												=		{}

  __A		:     Optional[Any]												=		job["""started_at"""]
  __A		:     Tuple												=		job["""completed_at"""]

  __A		:     Optional[Any]												=		date_parser.parse(__SCREAMING_SNAKE_CASE )
  __A		:     List[Any]												=		date_parser.parse(__SCREAMING_SNAKE_CASE )

  __A		:     Union[str, Any]												=		round((end_datetime - start_datetime).total_seconds() / 60.0 )

  __A		:     List[Any]												=		start
  __A		:     Tuple												=		end
  __A		:     int												=		duration_in_min

  return job_info







def        A_     (						__SCREAMING_SNAKE_CASE						:  Union[str, Any]			,     __SCREAMING_SNAKE_CASE						:  int=None )				->       Dict:





  """simple docstring"""

  __A		:     List[Any]												=		None
  if token is not None:
    __A		:     Dict												=		{"""Accept""": """application/vnd.github+json""", """Authorization""": F"Bearer {token}"}

  __A		:     Tuple												=		F"https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100"
  __A		:     List[str]												=		requests.get(__SCREAMING_SNAKE_CASE			,     headers=__SCREAMING_SNAKE_CASE ).json()
  __A		:     List[str]												=		{}

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

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

     A__   :		Any 		=get_job_time(args.workflow_run_id)
     A__   :		Dict 		=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']}")



