



import numpy

# List of input, output pairs
a_						:     Optional[Any]	     =		(
    ((5, 2, 3), 1_5),
    ((6, 5, 9), 2_5),
    ((1_1, 1_2, 1_3), 4_1),
    ((1, 1, 1), 8),
    ((1_1, 1_2, 1_3), 4_1),
)
a_						:     int	     =		(((5_1_5, 2_2, 1_3), 5_5_5), ((6_1, 3_5, 4_9), 1_5_0))
a_						:     Union[str, Any]	     =		[2, 4, 1, 5]
a_						:     Optional[Any]	     =		len(train_data)
a_						:     Optional[int]	     =		0.009

def     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__="train" ):


      """simple docstring"""



      return calculate_hypothesis_value(UpperCAmelCase__   ,   UpperCAmelCase__ ) - output(
          UpperCAmelCase__   ,   UpperCAmelCase__ )

def     __lowercase(	UpperCAmelCase__ ):


      """simple docstring"""



      lowerCamelCase								=		0
      for i in range(len(UpperCAmelCase__ ) - 1 ):
            hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
      hyp_val += parameter_vector[0]
      return hyp_val

def     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__ ):


      """simple docstring"""



      if data_set == "train":
            return train_data[example_no][1]
      elif data_set == "test":
            return test_data[example_no][1]
      return None

def     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__ ):


      """simple docstring"""



      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     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__=m ):


      """simple docstring"""



      lowerCamelCase								=		0
      for i in range(UpperCAmelCase__ ):
            if index == -1:
                  summation_value += _error(UpperCAmelCase__ )
            else:
                  summation_value += _error(UpperCAmelCase__ ) * train_data[i][0][index]
      return summation_value

def     __lowercase(	UpperCAmelCase__ ):


      """simple docstring"""



      lowerCamelCase								=		summation_of_cost_derivative(UpperCAmelCase__   ,   UpperCAmelCase__ ) / m
      return cost_derivative_value

def     __lowercase(	):


      """simple docstring"""



      global parameter_vector
      # Tune these values to set a tolerance value for predicted output
      lowerCamelCase								=		0.0_0_0_0_0_2
      lowerCamelCase								=		0
      lowerCamelCase								=		0
      while True:
            j += 1
            lowerCamelCase								=		[0, 0, 0, 0]
            for i in range(0   ,   len(UpperCAmelCase__ ) ):
                  lowerCamelCase								=		get_cost_derivative(i - 1 )
                  lowerCamelCase								=		(
                      parameter_vector[i] - LEARNING_RATE * cost_derivative
                  )
            if numpy.allclose(
                UpperCAmelCase__   ,   UpperCAmelCase__   ,   atol=UpperCAmelCase__   ,   rtol=UpperCAmelCase__   ,   ):
                  break
            lowerCamelCase								=		temp_parameter_vector
      print(("Number of iterations:", j) )

def     __lowercase(	):


      """simple docstring"""



      for i in range(len(UpperCAmelCase__ ) ):
            print(("Actual output value:", output(UpperCAmelCase__   ,   "test" )) )
            print(("Hypothesis output:", calculate_hypothesis_value(UpperCAmelCase__   ,   "test" )) )


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




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a_						:     List[str]	     =		{
    'configuration_clipseg': [
        'CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'CLIPSegConfig',
        'CLIPSegTextConfig',
        'CLIPSegVisionConfig',
    ],
    'processing_clipseg': ['CLIPSegProcessor'],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     a_						:     Any	     =		[
         'CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST',
         'CLIPSegModel',
         'CLIPSegPreTrainedModel',
         'CLIPSegTextModel',
         'CLIPSegVisionModel',
         'CLIPSegForImageSegmentation',
     ]

if TYPE_CHECKING:
     from .configuration_clipseg import (
         CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP,
         CLIPSegConfig,
         CLIPSegTextConfig,
         CLIPSegVisionConfig,
     )
     from .processing_clipseg import CLIPSegProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_clipseg import (
              CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST,
              CLIPSegForImageSegmentation,
              CLIPSegModel,
              CLIPSegPreTrainedModel,
              CLIPSegTextModel,
              CLIPSegVisionModel,
          )

else:
     import sys

     a_						:     Dict	     =		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)