"""simple docstring"""
from __future__ import annotations







def 						__a       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE = None		,					_SCREAMING_SNAKE_CASE = None		,					_SCREAMING_SNAKE_CASE = False		,					)     ->Dict:
  a__:					Optional[int]      							=     cipher_alphabet or [chr(_lowercase       ) for i in range(97		,					123       )]

  # If the argument is None or the user provided an empty dictionary
  if not frequencies_dict:
    # Frequencies of letters in the english language (how much they show up)
    a__:					str      							=     {
        'a': 0.08_497,
        'b': 0.01_492,
        'c': 0.02_202,
        'd': 0.04_253,
        'e': 0.11_162,
        'f': 0.02_228,
        'g': 0.02_015,
        'h': 0.06_094,
        'i': 0.07_546,
        'j': 0.00_153,
        'k': 0.01_292,
        'l': 0.04_025,
        'm': 0.02_406,
        'n': 0.06_749,
        'o': 0.07_507,
        'p': 0.01_929,
        'q': 0.00_095,
        'r': 0.07_587,
        's': 0.06_327,
        't': 0.09_356,
        'u': 0.02_758,
        'v': 0.00_978,
        'w': 0.02_560,
        'x': 0.00_150,
        'y': 0.01_994,
        'z': 0.00_077,
    }
  else:
    # Custom frequencies dictionary
    a__:					Dict      							=     frequencies_dict

  if not case_sensitive:
    a__:					List[Any]      							=     ciphertext.lower()

  # Chi squared statistic values
  a__:					Any      							=     {}

  # cycle through all of the shifts
  for shift in range(len(_lowercase       )       ):
    a__:					Union[str, Any]      							=     ''

    # decrypt the message with the shift
    for letter in ciphertext:
      try:
        # Try to index the letter in the alphabet
        a__:					List[str]      							=     (alphabet_letters.index(letter.lower()       ) - shift) % len(
            _lowercase       )
        decrypted_with_shift += (
            alphabet_letters[new_key].upper()
            if case_sensitive and letter.isupper()
            else alphabet_letters[new_key]
        )
      except ValueError:
        # Append the character if it isn't in the alphabet
        decrypted_with_shift += letter

    a__:					List[Any]      							=     0.0

    # Loop through each letter in the decoded message with the shift
    for letter in decrypted_with_shift:
      if case_sensitive:
        a__:					List[Any]      							=     letter.lower()
        if letter in frequencies:
          # Get the amount of times the letter occurs in the message
          a__:					Optional[int]      							=     decrypted_with_shift.lower().count(_lowercase       )

          # Get the excepcted amount of times the letter should appear based
          # on letter frequencies
          a__:					List[str]      							=     frequencies[letter] * occurrences

          # Complete the chi squared statistic formula
          a__:					Optional[int]      							=     ((occurrences - expected) ** 2) / expected

          # Add the margin of error to the total chi squared statistic
          chi_squared_statistic += chi_letter_value
      else:
        if letter.lower() in frequencies:
          # Get the amount of times the letter occurs in the message
          a__:					str      							=     decrypted_with_shift.count(_lowercase       )

          # Get the excepcted amount of times the letter should appear based
          # on letter frequencies
          a__:					Tuple      							=     frequencies[letter] * occurrences

          # Complete the chi squared statistic formula
          a__:					Dict      							=     ((occurrences - expected) ** 2) / expected

          # Add the margin of error to the total chi squared statistic
          chi_squared_statistic += chi_letter_value

        # Add the data to the chi_squared_statistic_values dictionary
    a__:					str      							=     (
        chi_squared_statistic,
        decrypted_with_shift,
    )

  # Get the most likely cipher by finding the cipher with the smallest chi squared
  # statistic
  def chi_squared_statistic_values_sorting_key(_SCREAMING_SNAKE_CASE       ) -> tuple[float, str]:
    return chi_squared_statistic_values[key]

  a__:					str      							=     min(
      _lowercase		,					key=_lowercase		,					)

  # Get all the data from the most likely cipher (key, decoded message)
  (
      (
      a__
  )							,   (
      a__
  )							,   
  ):					List[str]      							=     chi_squared_statistic_values[most_likely_cipher]

  # Return the data on the most likely shift
  return (
      most_likely_cipher,
      most_likely_cipher_chi_squared_value,
      decoded_most_likely_cipher,
  )



"""simple docstring"""
import argparse
import math
import traceback

import dateutil.parser as date_parser
import requests







def 						__a       (     _SCREAMING_SNAKE_CASE       )     ->Tuple:
  a__:					Tuple      							=     {}

  a__:					Tuple      							=     job['started_at']
  a__:					int      							=     job['completed_at']

  a__:					Any      							=     date_parser.parse(_SCREAMING_SNAKE_CASE       )
  a__:					Tuple      							=     date_parser.parse(_SCREAMING_SNAKE_CASE       )

  a__:					str      							=     round((end_datetime - start_datetime).total_seconds() / 60.0       )

  a__:					Any      							=     start
  a__:					Dict      							=     end
  a__:					Optional[int]      							=     duration_in_min

  return job_info







def 						__a       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE=None       )     ->Optional[int]:
  a__:					Tuple      							=     None
  if token is not None:
    a__:					List[str]      							=     {'Accept': 'application/vnd.github+json', 'Authorization': F'Bearer {token}'}

  a__:					int      							=     F'https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100'
  a__:					Union[str, Any]      							=     requests.get(_SCREAMING_SNAKE_CASE		,					headers=_SCREAMING_SNAKE_CASE       ).json()
  a__:					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__:					str      							=     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__":
 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']}")


