'''simple docstring'''






import os
from datetime import datetime as dt

from github import Github


SCREAMING_SNAKE_CASE_	   =			[
    'good first issue',
    'feature request',
    'wip',
]






def  UpperCamelCase__					(					)							->	str:
  __UpperCAmelCase:					Optional[int]		= Github(os.environ["""GITHUB_TOKEN"""]     )
  __UpperCAmelCase:					Optional[Any]		= g.get_repo("""huggingface/accelerate"""     )
  __UpperCAmelCase:					Any		= repo.get_issues(state="""open"""     )

  for issue in open_issues:
    __UpperCAmelCase:					Optional[int]		= sorted([comment for comment in issue.get_comments()]   ,    key=lambda _lowercase     : i.created_at   ,    reverse=_lowercase     )
    __UpperCAmelCase:					Any		= comments[0] if len(_lowercase     ) > 0 else None
    __UpperCAmelCase:					int		= dt.utcnow()
    __UpperCAmelCase:					List[Any]		= (current_time - issue.updated_at).days
    __UpperCAmelCase:					Tuple		= (current_time - issue.created_at).days
    if (
        last_comment is not None
        and last_comment.user.login == "github-actions[bot]"
        and days_since_updated > 7
        and days_since_creation >= 3_0
        and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()     )
    ):
      # Close issue since it has been 7 days of inactivity since bot mention.
      issue.edit(state="""closed"""     )
    elif (
        days_since_updated > 2_3
        and days_since_creation >= 3_0
        and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()     )
    ):
      # Add stale comment
      issue.create_comment(
          """This issue has been automatically marked as stale because it has not had """
          """recent activity. If you think this still needs to be addressed """
          """please comment on this thread.\n\nPlease note that issues that do not follow the """
          """[contributing guidelines](https://github.com/huggingface/accelerate/blob/main/CONTRIBUTING.md) """
          """are likely to be ignored."""     )


if __name__ == "__main__":
    main()
'''simple docstring'''






import numpy

# List of input, output pairs
SCREAMING_SNAKE_CASE_	   =			(
    ((5, 2, 3), 15),
    ((6, 5, 9), 25),
    ((11, 12, 13), 41),
    ((1, 1, 1), 8),
    ((11, 12, 13), 41),
)
SCREAMING_SNAKE_CASE_	   =			(((5_15, 22, 13), 5_55), ((61, 35, 49), 1_50))
SCREAMING_SNAKE_CASE_	   =			[2, 4, 1, 5]
SCREAMING_SNAKE_CASE_	   =			len(train_data)
SCREAMING_SNAKE_CASE_	   =			0.009






def  UpperCamelCase__					(					_lowercase    :				Union[str, Any]   ,    _lowercase    :				List[Any]="train"     )							->	int:
  return calculate_hypothesis_value(_lowercase   ,    _lowercase     ) - output(
      _lowercase   ,    _lowercase     )






def  UpperCamelCase__					(					_lowercase    :				Dict     )							->	Optional[Any]:
  __UpperCAmelCase:					List[Any]		= 0
  for i in range(len(_lowercase     ) - 1     ):
    hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
  hyp_val += parameter_vector[0]
  return hyp_val






def  UpperCamelCase__					(					_lowercase    :				List[str]   ,    _lowercase    :				List[str]     )							->	Optional[Any]:
  if data_set == "train":
    return train_data[example_no][1]
  elif data_set == "test":
    return test_data[example_no][1]
  return None






def  UpperCamelCase__					(					_lowercase    :				Dict   ,    _lowercase    :				str     )							->	Dict:
  if data_set == "train":
    return _hypothesis_value(train_data[example_no][0]     )
  elif data_set == "test":
    return _hypothesis_value(test_data[example_no][0]     )
  return None






def  UpperCamelCase__					(					_lowercase    :				Optional[int]   ,    _lowercase    :				Union[str, Any]=m     )							->	Optional[Any]:
  __UpperCAmelCase:					int		= 0
  for i in range(_lowercase     ):
    if index == -1:
      summation_value += _error(_lowercase     )
    else:
      summation_value += _error(_lowercase     ) * train_data[i][0][index]
  return summation_value






def  UpperCamelCase__					(					_lowercase    :				Tuple     )							->	Optional[int]:
  __UpperCAmelCase:					Any		= summation_of_cost_derivative(_lowercase   ,    _lowercase     ) / m
  return cost_derivative_value






def  UpperCamelCase__					(					)							->	Tuple:
  global parameter_vector
  # Tune these values to set a tolerance value for predicted output
  __UpperCAmelCase:					str		= 0.00_00_02
  __UpperCAmelCase:					List[Any]		= 0
  __UpperCAmelCase:					Optional[Any]		= 0
  while True:
    j += 1
    __UpperCAmelCase:					Any		= [0, 0, 0, 0]
    for i in range(0   ,    len(_lowercase     )     ):
      __UpperCAmelCase:					str		= get_cost_derivative(i - 1     )
      __UpperCAmelCase:					List[str]		= (
          parameter_vector[i] - LEARNING_RATE * cost_derivative
      )
    if numpy.allclose(
        _lowercase   ,    _lowercase   ,    atol=_lowercase   ,    rtol=_lowercase   ,    ):
      break
    __UpperCAmelCase:					List[Any]		= temp_parameter_vector
  print(("""Number of iterations:""", j)     )






def  UpperCamelCase__					(					)							->	Dict:
  for i in range(len(_lowercase     )     ):
    print(("""Actual output value:""", output(_lowercase   ,    """test"""     ))     )
    print(("""Hypothesis output:""", calculate_hypothesis_value(_lowercase   ,    """test"""     ))     )


if __name__ == "__main__":
    run_gradient_descent()
    print('\nTesting gradient descent for a linear hypothesis function.\n')
    test_gradient_descent()