





from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


UpperCAmelCase	:	Dict		     =       logging.get_logger(__name__)







class 				__lowerCAmelCase       (    UpperCamelCase__):
      _lowercase				:					Tuple						=			["""pixel_values"""]







      def __init__(    self		,      lowerCAmelCase__ = True		,      lowerCAmelCase__ = 1 / 2_5_5		,      lowerCAmelCase__ = True		,      lowerCAmelCase__ = 8		,      **lowerCAmelCase__		,      )   ->						None:



        '''simple docstring'''




        super().__init__(**lowerCAmelCase__							)

        a__	:					Optional[Any]										=do_rescale
        a__	:					Dict										=rescale_factor
        a__	:					Optional[Any]										=do_pad
        a__	:					Tuple										=pad_size







      def    _lowercase  (    self		,      lowerCAmelCase__		,      lowerCAmelCase__		,      lowerCAmelCase__ = None		,      **lowerCAmelCase__							)   ->						np.ndarray:



        '''simple docstring'''




        return rescale(lowerCAmelCase__		,      scale=lowerCAmelCase__		,      data_format=lowerCAmelCase__		,      **lowerCAmelCase__							)







      def    _lowercase  (    self		,      lowerCAmelCase__		,      lowerCAmelCase__		,      lowerCAmelCase__ = None							)   ->						str:



        '''simple docstring'''




        a__					,							a__	:					Tuple										=get_image_size(lowerCAmelCase__							)
        a__	:					int										=(old_height // size + 1) * size - old_height
        a__	:					str										=(old_width // size + 1) * size - old_width

        return pad(lowerCAmelCase__		,      ((0, pad_height), (0, pad_width))		,      mode="symmetric"		,      data_format=lowerCAmelCase__							)







      def    _lowercase  (    self		,      lowerCAmelCase__		,      lowerCAmelCase__ = None		,      lowerCAmelCase__ = None		,      lowerCAmelCase__ = None		,      lowerCAmelCase__ = None		,      lowerCAmelCase__ = None		,      lowerCAmelCase__ = ChannelDimension.FIRST		,      **lowerCAmelCase__		,      )   ->						Dict:



        '''simple docstring'''




        a__	:					List[Any]										=do_rescale if do_rescale is not None else self.do_rescale
        a__	:					Dict										=rescale_factor if rescale_factor is not None else self.rescale_factor
        a__	:					Tuple										=do_pad if do_pad is not None else self.do_pad
        a__	:					Tuple										=pad_size if pad_size is not None else self.pad_size

        a__	:					List[Any]										=make_list_of_images(lowerCAmelCase__							)

        if not valid_images(lowerCAmelCase__							):
          raise ValueError(
              "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
              "torch.Tensor, tf.Tensor or jax.ndarray."							)

        if do_rescale and rescale_factor is None:
          raise ValueError("Rescale factor must be specified if do_rescale is True."							)

        # All transformations expect numpy arrays.
        a__	:					List[str]										=[to_numpy_array(lowerCAmelCase__							) for image in images]

        if do_rescale:
          a__	:					Tuple										=[self.rescale(image=lowerCAmelCase__		,      scale=lowerCAmelCase__							) for image in images]

        if do_pad:
          a__	:					List[Any]										=[self.pad(lowerCAmelCase__		,      size=lowerCAmelCase__							) for image in images]

        a__	:					int										=[to_channel_dimension_format(lowerCAmelCase__		,      lowerCAmelCase__							) for image in images]

        a__	:					Union[str, Any]										={"pixel_values": images}
        return BatchFeature(data=lowerCAmelCase__		,      tensor_type=lowerCAmelCase__							)













import numpy

# List of input, output pairs
UpperCAmelCase	:	str		     =       (
    ((5, 2, 3), 15),
    ((6, 5, 9), 25),
    ((11, 12, 13), 41),
    ((1, 1, 1), 8),
    ((11, 12, 13), 41),
)
UpperCAmelCase	:	Optional[int]		     =       (((515, 22, 13), 555), ((61, 35, 49), 150))
UpperCAmelCase	:	str		     =       [2, 4, 1, 5]
UpperCAmelCase	:	List[str]		     =       len(train_data)
UpperCAmelCase	:	Dict		     =       0.0_0_9
def      _A       (		SCREAMING_SNAKE_CASE     :   Tuple       ,       SCREAMING_SNAKE_CASE     :   Tuple="train"						):





  """simple docstring"""



  return calculate_hypothesis_value(SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE						) - output(
      SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE						)
def      _A       (		SCREAMING_SNAKE_CASE     :   Tuple						):





  """simple docstring"""



  a__	:					Tuple										=0
  for i in range(len(SCREAMING_SNAKE_CASE						) - 1						):
    hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
  hyp_val += parameter_vector[0]
  return hyp_val
def      _A       (		SCREAMING_SNAKE_CASE     :   Dict       ,       SCREAMING_SNAKE_CASE     :   Dict						):





  """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      _A       (		SCREAMING_SNAKE_CASE     :   Optional[Any]       ,       SCREAMING_SNAKE_CASE     :   Tuple						):





  """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      _A       (		SCREAMING_SNAKE_CASE     :   Union[str, Any]       ,       SCREAMING_SNAKE_CASE     :   int=m						):





  """simple docstring"""



  a__	:					Any										=0
  for i in range(SCREAMING_SNAKE_CASE						):
    if index == -1:
      summation_value += _error(SCREAMING_SNAKE_CASE						)
    else:
      summation_value += _error(SCREAMING_SNAKE_CASE						) * train_data[i][0][index]
  return summation_value
def      _A       (		SCREAMING_SNAKE_CASE     :   int						):





  """simple docstring"""



  a__	:					Any										=summation_of_cost_derivative(SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE						) / m
  return cost_derivative_value
def      _A       (		):





  """simple docstring"""



  global parameter_vector
  # Tune these values to set a tolerance value for predicted output
  a__	:					Dict										=0.0_0_0_0_0_2
  a__	:					Union[str, Any]										=0
  a__	:					Any										=0
  while True:
    j += 1
    a__	:					Any										=[0, 0, 0, 0]
    for i in range(0       ,       len(SCREAMING_SNAKE_CASE						)						):
      a__	:					Tuple										=get_cost_derivative(i - 1						)
      a__	:					List[Any]										=(
          parameter_vector[i] - LEARNING_RATE * cost_derivative
      )
    if numpy.allclose(
        SCREAMING_SNAKE_CASE       ,       SCREAMING_SNAKE_CASE       ,       atol=SCREAMING_SNAKE_CASE       ,       rtol=SCREAMING_SNAKE_CASE       ,       ):
      break
    a__	:					Optional[Any]										=temp_parameter_vector
  print(("Number of iterations:", j)						)
def      _A       (		):





  """simple docstring"""



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


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






