"""simple docstring"""




def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       List[Any]			,_lowerCamelCase						:       Tuple			,_lowerCamelCase						:       int			,_lowerCamelCase						:       Optional[int]	)       ->      str:
  global f  # a global dp table for knapsack
  if f[i][j] < 0:
    if j < wt[i - 1]:
      _lowerCAmelCase						:       Optional[int]  			=     mf_knapsack(i - 1			,_lowerCamelCase			,_lowerCamelCase			,_lowerCamelCase	)
    else:
      _lowerCAmelCase						:       List[str]  			=     max(
          mf_knapsack(i - 1			,_lowerCamelCase			,_lowerCamelCase			,_lowerCamelCase	)			,mf_knapsack(i - 1			,_lowerCamelCase			,_lowerCamelCase			,j - wt[i - 1]	) + val[i - 1]			,)
    _lowerCAmelCase						:       Optional[Any]  			=     val
  return f[i][j]





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Any			,_lowerCamelCase						:       int			,_lowerCamelCase						:       Tuple			,_lowerCamelCase						:       Any	)       ->      Any:
  _lowerCAmelCase						:       List[str]  			=     [[0] * (w + 1) for _ in range(n + 1	)]

  for i in range(1			,n + 1	):
    for w_ in range(1			,w + 1	):
      if wt[i - 1] <= w_:
        _lowerCAmelCase						:       List[Any]  			=     max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]]			,dp[i - 1][w_]	)
      else:
        _lowerCAmelCase						:       Dict  			=     dp[i - 1][w_]

  return dp[n][w_], dp





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       int			,_lowerCamelCase						:       list			,_lowerCamelCase						:       list	)       ->      Optional[int]:
  if not (isinstance(_lowerCamelCase			,(list, tuple)	) and isinstance(_lowerCamelCase			,(list, tuple)	)):
    raise ValueError(
        """Both the weights and values vectors must be either lists or tuples"""	)

  _lowerCAmelCase						:       Optional[int]  			=     len(_lowerCamelCase	)
  if num_items != len(_lowerCamelCase	):
    _lowerCAmelCase						:       Optional[Any]  			=     (
        """The number of weights must be the same as the number of values.\n"""
        f"But got {num_items} weights and {len(_lowerCamelCase	)} values"
    )
    raise ValueError(_lowerCamelCase	)
  for i in range(_lowerCamelCase	):
    if not isinstance(wt[i]			,_lowerCamelCase	):
      _lowerCAmelCase						:       str  			=     (
          """All weights must be integers but got weight of """
          f"type {type(wt[i]	)} at index {i}"
      )
      raise TypeError(_lowerCamelCase	)

  _lowerCAmelCase			,	_lowerCAmelCase						:       Tuple  			=     knapsack(_lowerCamelCase			,_lowerCamelCase			,_lowerCamelCase			,_lowerCamelCase	)
  _lowerCAmelCase						:       set  			=     set()
  _construct_solution(_lowerCamelCase			,_lowerCamelCase			,_lowerCamelCase			,_lowerCamelCase			,_lowerCamelCase	)

  return optimal_val, example_optional_set





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       list			,_lowerCamelCase						:       list			,_lowerCamelCase						:       int			,_lowerCamelCase						:       int			,_lowerCamelCase						:       set	)       ->      Optional[int]:
  # for the current item i at a maximum weight j to be part of an optimal subset,
  # the optimal value at (i, j) must be greater than the optimal value at (i-1, j).
  # where i - 1 means considering only the previous items at the given maximum weight
  if i > 0 and j > 0:
    if dp[i - 1][j] == dp[i][j]:
      _construct_solution(_lowerCamelCase			,_lowerCamelCase			,i - 1			,_lowerCamelCase			,_lowerCamelCase	)
    else:
      optimal_set.add(_lowerCamelCase	)
      _construct_solution(_lowerCamelCase			,_lowerCamelCase			,i - 1			,j - wt[i - 1]			,_lowerCamelCase	)


if __name__ == "__main__":
     _a      :		str           =   [3, 2, 4, 4]
     _a      :		List[Any]           =   [4, 3, 2, 3]
     _a      :		List[Any]           =   4
     _a      :		Union[str, Any]           =   6
     _a      :		Union[str, Any]           =   [[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
     _a    ,   _a      :		Optional[Any]           =   knapsack(w, wt, val, n)
     print(optimal_solution)
     print(mf_knapsack(n, wt, val, w))  # switched the n and w

     # testing the dynamic programming problem with example
     # the optimal subset for the above example are items 3 and 4
     _a    ,   _a      :		List[str]           =   knapsack_with_example_solution(w, wt, val)
     assert optimal_solution == 8
     assert optimal_subset == {3, 4}
     print('optimal_value = ', optimal_solution)
     print('An optimal subset corresponding to the optimal value', optimal_subset)







import argparse
import math
import traceback

import dateutil.parser as date_parser
import requests
def      a      (			A__	:   Dict     )    ->      str:





      """simple docstring"""



      _lowercase	       ={}

      _lowercase	       =job['started_at']
      _lowercase	       =job['completed_at']

      _lowercase	       =date_parser.parse(A__     )
      _lowercase	       =date_parser.parse(A__     )

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

      _lowercase	       =start
      _lowercase	       =end
      _lowercase	       =duration_in_min

      return job_info
def      a      (			A__	:   Dict      ,   A__	:   str=None     )    ->      Tuple:





      """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(A__      ,   headers=A__     ).json()
      _lowercase	       ={}

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

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

  lowercase_									=   get_job_time(args.workflow_run_id)
  lowercase_									=   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']}")


