'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE_	   =			{'configuration_sew': ['SEW_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SEWConfig']}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    SCREAMING_SNAKE_CASE_	   =			[
        'SEW_PRETRAINED_MODEL_ARCHIVE_LIST',
        'SEWForCTC',
        'SEWForSequenceClassification',
        'SEWModel',
        'SEWPreTrainedModel',
    ]

if TYPE_CHECKING:
    from .configuration_sew import SEW_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_sew import (
            SEW_PRETRAINED_MODEL_ARCHIVE_LIST,
            SEWForCTC,
            SEWForSequenceClassification,
            SEWModel,
            SEWPreTrainedModel,
        )


else:
    import sys

    SCREAMING_SNAKE_CASE_	   =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
'''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()