
'''simple docstring'''

import numpy

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


def        UpperCAmelCase_ (  lowerCamelCase_      ,			lowerCamelCase_="train"			):







 """simple docstring"""


 return calculate_hypothesis_value(lowerCamelCase_      ,			lowerCamelCase_			) - output(
     lowerCamelCase_      ,			lowerCamelCase_			)


def        UpperCAmelCase_ (  lowerCamelCase_			):







 """simple docstring"""


 lowerCAmelCase__     :   List[Any]     		=	0
 for i in range(len(lowerCamelCase_			) - 1			):
  hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
 hyp_val += parameter_vector[0]
 return hyp_val


def        UpperCAmelCase_ (  lowerCamelCase_      ,			lowerCamelCase_			):







 """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        UpperCAmelCase_ (  lowerCamelCase_      ,			lowerCamelCase_			):







 """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        UpperCAmelCase_ (  lowerCamelCase_      ,			lowerCamelCase_=m			):







 """simple docstring"""


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


def        UpperCAmelCase_ (  lowerCamelCase_			):







 """simple docstring"""


 lowerCAmelCase__     :   List[str]     		=	summation_of_cost_derivative(lowerCamelCase_      ,			lowerCamelCase_			) / m
 return cost_derivative_value


def        UpperCAmelCase_ (  ):







 """simple docstring"""


 global parameter_vector
 # Tune these values to set a tolerance value for predicted output
 lowerCAmelCase__     :   str     		=	0.00_0002
 lowerCAmelCase__     :   List[Any]     		=	0
 lowerCAmelCase__     :   Tuple     		=	0
 while True:
  j += 1
  lowerCAmelCase__     :   Tuple     		=	[0, 0, 0, 0]
  for i in range(0      ,			len(lowerCamelCase_			)			):
   lowerCAmelCase__     :   Optional[int]     		=	get_cost_derivative(i - 1			)
   lowerCAmelCase__     :   Dict     		=	(
       parameter_vector[i] - LEARNING_RATE * cost_derivative
   )
  if numpy.allclose(
      lowerCamelCase_      ,			lowerCamelCase_      ,			atol=lowerCamelCase_      ,			rtol=lowerCamelCase_      ,			):
   break
  lowerCAmelCase__     :   Optional[int]     		=	temp_parameter_vector
 print(("Number of iterations:", j)			)


def        UpperCAmelCase_ (  ):







 """simple docstring"""


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


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











'''simple docstring'''


from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging


if is_torch_available():
				import torch


_snake_case : Tuple  				=						logging.get_logger(__name__)

class      A       (						_a       ):
							lowercase_							     =							['pixel_values']







							def __init__(    self			:    str				,     lowerCAmelCase_			:    bool = True				,     lowerCAmelCase_			:    Optional[Dict[str, int]] = None				,     lowerCAmelCase_			:    PILImageResampling = PILImageResampling.BILINEAR				,     lowerCAmelCase_			:    bool = True				,     lowerCAmelCase_			:    Dict[str, int] = None				,     lowerCAmelCase_			:    bool = True				,     lowerCAmelCase_			:    Union[int, float] = 1 / 2_55				,     lowerCAmelCase_			:    bool = True				,     lowerCAmelCase_			:    Optional[Union[float, List[float]]] = None				,     lowerCAmelCase_			:    Optional[Union[float, List[float]]] = None				,     **lowerCAmelCase_			:    Any				,     )     ->				None:


														"""simple docstring"""


														super().__init__(**lowerCAmelCase_   )
														_a      	=							size if size is not None else {'''shortest_edge''': 2_56}
														_a      	=							get_size_dict(lowerCAmelCase_				,     default_to_square=lowerCAmelCase_   )
														_a      	=							crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
														_a      	=							get_size_dict(lowerCAmelCase_				,     param_name='''crop_size'''   )
														_a      	=							do_resize
														_a      	=							size
														_a      	=							resample
														_a      	=							do_center_crop
														_a      	=							crop_size
														_a      	=							do_rescale
														_a      	=							rescale_factor
														_a      	=							do_normalize
														_a      	=							image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
														_a      	=							image_std if image_std is not None else IMAGENET_STANDARD_STD







							def   __lowerCAmelCase     (    self			:    Union[str, Any]				,     lowerCAmelCase_			:    np.ndarray				,     lowerCAmelCase_			:    Dict[str, int]				,     lowerCAmelCase_			:    PILImageResampling = PILImageResampling.BICUBIC				,     lowerCAmelCase_			:    Optional[Union[str, ChannelDimension]] = None				,     **lowerCAmelCase_			:    int				,     )     ->				np.ndarray:


														"""simple docstring"""


														_a      	=							get_size_dict(lowerCAmelCase_				,     default_to_square=lowerCAmelCase_   )
														if "shortest_edge" not in size:
																					raise ValueError(F'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'   )
														_a      	=							get_resize_output_image_size(lowerCAmelCase_				,     size=size['''shortest_edge''']				,     default_to_square=lowerCAmelCase_   )
														return resize(lowerCAmelCase_				,     size=lowerCAmelCase_				,     resample=lowerCAmelCase_				,     data_format=lowerCAmelCase_				,     **lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Union[str, Any]				,     lowerCAmelCase_			:    np.ndarray				,     lowerCAmelCase_			:    Dict[str, int]				,     lowerCAmelCase_			:    Optional[Union[str, ChannelDimension]] = None				,     **lowerCAmelCase_			:    List[Any]				,     )     ->				np.ndarray:


														"""simple docstring"""


														_a      	=							get_size_dict(lowerCAmelCase_   )
														if "height" not in size or "width" not in size:
																					raise ValueError(F'The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}'   )
														return center_crop(lowerCAmelCase_				,     size=(size['''height'''], size['''width'''])				,     data_format=lowerCAmelCase_				,     **lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Optional[Any]				,     lowerCAmelCase_			:    np.ndarray				,     lowerCAmelCase_			:    float				,     lowerCAmelCase_			:    Optional[Union[str, ChannelDimension]] = None				,     **lowerCAmelCase_			:    Tuple   )     ->				np.ndarray:


														"""simple docstring"""


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







							def   __lowerCAmelCase     (    self			:    List[Any]				,     lowerCAmelCase_			:    np.ndarray				,     lowerCAmelCase_			:    Union[float, List[float]]				,     lowerCAmelCase_			:    Union[float, List[float]]				,     lowerCAmelCase_			:    Optional[Union[str, ChannelDimension]] = None				,     **lowerCAmelCase_			:    int				,     )     ->				np.ndarray:


														"""simple docstring"""


														return normalize(lowerCAmelCase_				,     mean=lowerCAmelCase_				,     std=lowerCAmelCase_				,     data_format=lowerCAmelCase_				,     **lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Tuple				,     lowerCAmelCase_			:    ImageInput				,     lowerCAmelCase_			:    Optional[bool] = None				,     lowerCAmelCase_			:    Dict[str, int] = None				,     lowerCAmelCase_			:    PILImageResampling = None				,     lowerCAmelCase_			:    bool = None				,     lowerCAmelCase_			:    Dict[str, int] = None				,     lowerCAmelCase_			:    Optional[bool] = None				,     lowerCAmelCase_			:    Optional[float] = None				,     lowerCAmelCase_			:    Optional[bool] = None				,     lowerCAmelCase_			:    Optional[Union[float, List[float]]] = None				,     lowerCAmelCase_			:    Optional[Union[float, List[float]]] = None				,     lowerCAmelCase_			:    Optional[Union[str, TensorType]] = None				,     lowerCAmelCase_			:    Union[str, ChannelDimension] = ChannelDimension.FIRST				,     **lowerCAmelCase_			:    Union[str, Any]				,     )     ->				Union[str, Any]:


														"""simple docstring"""


														_a      	=							do_resize if do_resize is not None else self.do_resize
														_a      	=							size if size is not None else self.size
														_a      	=							get_size_dict(lowerCAmelCase_				,     default_to_square=lowerCAmelCase_   )
														_a      	=							resample if resample is not None else self.resample
														_a      	=							do_center_crop if do_center_crop is not None else self.do_center_crop
														_a      	=							crop_size if crop_size is not None else self.crop_size
														_a      	=							get_size_dict(lowerCAmelCase_				,     param_name='''crop_size'''   )
														_a      	=							do_rescale if do_rescale is not None else self.do_rescale
														_a      	=							rescale_factor if rescale_factor is not None else self.rescale_factor
														_a      	=							do_normalize if do_normalize is not None else self.do_normalize
														_a      	=							image_mean if image_mean is not None else self.image_mean
														_a      	=							image_std if image_std is not None else self.image_std

														_a      	=							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_resize and size is None:
																					raise ValueError('''Size must be specified if do_resize is True.'''   )

														if do_center_crop and crop_size is None:
																					raise ValueError('''Crop size must be specified if do_center_crop is True.'''   )

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

														if do_normalize and (image_mean is None or image_std is None):
																					raise ValueError('''Image mean and std must be specified if do_normalize is True.'''   )

														# All transformations expect numpy arrays.
														_a      	=							[to_numpy_array(lowerCAmelCase_   ) for image in images]

														if do_resize:
																					_a      	=							[self.resize(image=lowerCAmelCase_				,     size=lowerCAmelCase_				,     resample=lowerCAmelCase_   ) for image in images]

														if do_center_crop:
																					_a      	=							[self.center_crop(image=lowerCAmelCase_				,     size=lowerCAmelCase_   ) for image in images]

														if do_rescale:
																					_a      	=							[self.rescale(image=lowerCAmelCase_				,     scale=lowerCAmelCase_   ) for image in images]

														if do_normalize:
																					_a      	=							[self.normalize(image=lowerCAmelCase_				,     mean=lowerCAmelCase_				,     std=lowerCAmelCase_   ) for image in images]

														_a      	=							[to_channel_dimension_format(lowerCAmelCase_				,     lowerCAmelCase_   ) for image in images]

														_a      	=							{'''pixel_values''': images}
														return BatchFeature(data=lowerCAmelCase_				,     tensor_type=lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Union[str, Any]				,     lowerCAmelCase_			:    Optional[int]				,     lowerCAmelCase_			:    List[Tuple] = None   )     ->				Any:


														"""simple docstring"""


														_a      	=							outputs.logits

														# Resize logits and compute semantic segmentation maps
														if target_sizes is not None:
																					if len(lowerCAmelCase_   ) != len(lowerCAmelCase_   ):
																												raise ValueError(
																												    '''Make sure that you pass in as many target sizes as the batch dimension of the logits'''   )

																					if is_torch_tensor(lowerCAmelCase_   ):
																												_a      	=							target_sizes.numpy()

																					_a      	=							[]

																					for idx in range(len(lowerCAmelCase_   )   ):
																												_a      	=							torch.nn.functional.interpolate(
																												    logits[idx].unsqueeze(dim=0   )				,     size=target_sizes[idx]				,     mode='''bilinear'''				,     align_corners=lowerCAmelCase_   )
																												_a      	=							resized_logits[0].argmax(dim=0   )
																												semantic_segmentation.append(lowerCAmelCase_   )
														else:
																					_a      	=							logits.argmax(dim=1   )
																					_a      	=							[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]   )]

														return semantic_segmentation

