



from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


UpperCAmelCase_									=							logging.get_logger(__name__)


if is_vision_available():
       import PIL





class 		UpperCAmelCase						(						UpperCamelCase__				):
   SCREAMING_SNAKE_CASE__							=     ['''pixel_values''']





   def __init__(	self       ,     _lowerCAmelCase = True       ,     _lowerCAmelCase = None       ,     _lowerCAmelCase = PILImageResampling.BICUBIC       ,     _lowerCAmelCase = True       ,     _lowerCAmelCase = None       ,     _lowerCAmelCase = True       ,     _lowerCAmelCase = 1 / 255       ,     _lowerCAmelCase = True       ,     _lowerCAmelCase = None       ,     _lowerCAmelCase = None       ,     _lowerCAmelCase = True       ,     **_lowerCAmelCase       ,     ):
         super().__init__(**_a			)
         _lowerCAmelCase 			=  size if size is not None else {"""shortest_edge""": 224}
         _lowerCAmelCase 			=  get_size_dict(_a       ,     default_to_square=_a			)
         _lowerCAmelCase 			=  crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
         _lowerCAmelCase 			=  get_size_dict(_a       ,     default_to_square=_a       ,     param_name='''crop_size'''			)

         _lowerCAmelCase 			=  do_resize
         _lowerCAmelCase 			=  size
         _lowerCAmelCase 			=  resample
         _lowerCAmelCase 			=  do_center_crop
         _lowerCAmelCase 			=  crop_size
         _lowerCAmelCase 			=  do_rescale
         _lowerCAmelCase 			=  rescale_factor
         _lowerCAmelCase 			=  do_normalize
         _lowerCAmelCase 			=  image_mean if image_mean is not None else OPENAI_CLIP_MEAN
         _lowerCAmelCase 			=  image_std if image_std is not None else OPENAI_CLIP_STD
         _lowerCAmelCase 			=  do_convert_rgb





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase = PILImageResampling.BICUBIC       ,     _lowerCAmelCase = None       ,     **_lowerCAmelCase       ,     ):
         _lowerCAmelCase 			=  get_size_dict(_a       ,     default_to_square=_a			)
         if "shortest_edge" not in size:
               raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''			)
         _lowerCAmelCase 			=  get_resize_output_image_size(_a       ,     size=size['''shortest_edge''']       ,     default_to_square=_a			)
         return resize(_a       ,     size=_a       ,     resample=_a       ,     data_format=_a       ,     **_a			)





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase = None       ,     **_lowerCAmelCase       ,     ):
         _lowerCAmelCase 			=  get_size_dict(_a			)
         if "height" not in size or "width" not in size:
               raise ValueError(F'''The `size` parameter must contain the keys (height, width). Got {size.keys()}'''			)
         return center_crop(_a       ,     size=(size['''height'''], size['''width'''])       ,     data_format=_a       ,     **_a			)





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase = None       ,     **_lowerCAmelCase       ,     ):
         return rescale(_a       ,     scale=_a       ,     data_format=_a       ,     **_a			)





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase = None       ,     **_lowerCAmelCase       ,     ):
         return normalize(_a       ,     mean=_a       ,     std=_a       ,     data_format=_a       ,     **_a			)





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase = None       ,     _lowerCAmelCase = None       ,     _lowerCAmelCase = None       ,     _lowerCAmelCase = None       ,     _lowerCAmelCase = None       ,     _lowerCAmelCase = None       ,     _lowerCAmelCase = None       ,     _lowerCAmelCase = None       ,     _lowerCAmelCase = None       ,     _lowerCAmelCase = None       ,     _lowerCAmelCase = None       ,     _lowerCAmelCase = None       ,     _lowerCAmelCase = ChannelDimension.FIRST       ,     **_lowerCAmelCase       ,     ):
         _lowerCAmelCase 			=  do_resize if do_resize is not None else self.do_resize
         _lowerCAmelCase 			=  size if size is not None else self.size
         _lowerCAmelCase 			=  get_size_dict(_a       ,     param_name='''size'''       ,     default_to_square=_a			)
         _lowerCAmelCase 			=  resample if resample is not None else self.resample
         _lowerCAmelCase 			=  do_center_crop if do_center_crop is not None else self.do_center_crop
         _lowerCAmelCase 			=  crop_size if crop_size is not None else self.crop_size
         _lowerCAmelCase 			=  get_size_dict(_a       ,     param_name='''crop_size'''       ,     default_to_square=_a			)
         _lowerCAmelCase 			=  do_rescale if do_rescale is not None else self.do_rescale
         _lowerCAmelCase 			=  rescale_factor if rescale_factor is not None else self.rescale_factor
         _lowerCAmelCase 			=  do_normalize if do_normalize is not None else self.do_normalize
         _lowerCAmelCase 			=  image_mean if image_mean is not None else self.image_mean
         _lowerCAmelCase 			=  image_std if image_std is not None else self.image_std
         _lowerCAmelCase 			=  do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

         _lowerCAmelCase 			=  make_list_of_images(_a			)

         if not valid_images(_a			):
               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.'''			)

         # PIL RGBA images are converted to RGB
         if do_convert_rgb:
               _lowerCAmelCase 			=  [convert_to_rgb(_a			) for image in images]

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

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

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

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

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

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

         _lowerCAmelCase 			=  {"""pixel_values""": images}
         return BatchFeature(data=_a       ,     tensor_type=_a			)




import numpy





class 		UpperCAmelCase						:





   def __init__(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase			):
         _lowerCAmelCase 			=  input_array

         # Random initial weights are assigned where first argument is the
         # number of nodes in previous layer and second argument is the
         # number of nodes in the next layer.

         # Random initial weights are assigned.
         # self.input_array.shape[1] is used to represent number of nodes in input layer.
         # First hidden layer consists of 4 nodes.
         _lowerCAmelCase 			=  numpy.random.rand(
             self.input_array.shape[1]       ,     4			)

         # Random initial values for the first hidden layer.
         # First hidden layer has 4 nodes.
         # Second hidden layer has 3 nodes.
         _lowerCAmelCase 			=  numpy.random.rand(
             4       ,     3			)

         # Random initial values for the second hidden layer.
         # Second hidden layer has 3 nodes.
         # Output layer has 1 node.
         _lowerCAmelCase 			=  numpy.random.rand(3       ,     1			)

         # Real output values provided.
         _lowerCAmelCase 			=  output_array

         # Predicted output values by the neural network.
         # Predicted_output array initially consists of zeroes.
         _lowerCAmelCase 			=  numpy.zeros(output_array.shape			)





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  sigmoid(
             numpy.dot(self.input_array       ,     self.input_layer_and_first_hidden_layer_weights			)			)

         # layer_between_first_hidden_layer_and_second_hidden_layer is the layer
         # connecting the first hidden set of nodes with the second hidden set of nodes.
         _lowerCAmelCase 			=  sigmoid(
             numpy.dot(
                 self.layer_between_input_and_first_hidden_layer       ,     self.first_hidden_layer_and_second_hidden_layer_weights       ,     )			)

         # layer_between_second_hidden_layer_and_output is the layer connecting
         # second hidden layer with the output node.
         _lowerCAmelCase 			=  sigmoid(
             numpy.dot(
                 self.layer_between_first_hidden_layer_and_second_hidden_layer       ,     self.second_hidden_layer_and_output_layer_weights       ,     )			)

         return self.layer_between_second_hidden_layer_and_output





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  numpy.dot(
             self.layer_between_first_hidden_layer_and_second_hidden_layer.T       ,     2
             * (self.output_array - self.predicted_output)
             * sigmoid_derivative(self.predicted_output			)       ,     )
         _lowerCAmelCase 			=  numpy.dot(
             self.layer_between_input_and_first_hidden_layer.T       ,     numpy.dot(
                 2
                 * (self.output_array - self.predicted_output)
                 * sigmoid_derivative(self.predicted_output			)       ,     self.second_hidden_layer_and_output_layer_weights.T       ,     )
             * sigmoid_derivative(
                 self.layer_between_first_hidden_layer_and_second_hidden_layer			)       ,     )
         _lowerCAmelCase 			=  numpy.dot(
             self.input_array.T       ,     numpy.dot(
                 numpy.dot(
                     2
                     * (self.output_array - self.predicted_output)
                     * sigmoid_derivative(self.predicted_output			)       ,     self.second_hidden_layer_and_output_layer_weights.T       ,     )
                 * sigmoid_derivative(
                     self.layer_between_first_hidden_layer_and_second_hidden_layer			)       ,     self.first_hidden_layer_and_second_hidden_layer_weights.T       ,     )
             * sigmoid_derivative(self.layer_between_input_and_first_hidden_layer			)       ,     )

         self.input_layer_and_first_hidden_layer_weights += (
             updated_input_layer_and_first_hidden_layer_weights
         )
         self.first_hidden_layer_and_second_hidden_layer_weights += (
             updated_first_hidden_layer_and_second_hidden_layer_weights
         )
         self.second_hidden_layer_and_output_layer_weights += (
             updated_second_hidden_layer_and_output_layer_weights
         )





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase       ,     _lowerCAmelCase			):
         for iteration in range(1       ,     iterations + 1			):
               _lowerCAmelCase 			=  self.feedforward()
               self.back_propagation()
               if give_loss:
                     _lowerCAmelCase 			=  numpy.mean(numpy.square(output - self.feedforward()			)			)
                     print(F'''Iteration {iteration} Loss: {loss}'''			)





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase			):
         _lowerCAmelCase 			=  input_arr

         _lowerCAmelCase 			=  sigmoid(
             numpy.dot(self.array       ,     self.input_layer_and_first_hidden_layer_weights			)			)

         _lowerCAmelCase 			=  sigmoid(
             numpy.dot(
                 self.layer_between_input_and_first_hidden_layer       ,     self.first_hidden_layer_and_second_hidden_layer_weights       ,     )			)

         _lowerCAmelCase 			=  sigmoid(
             numpy.dot(
                 self.layer_between_first_hidden_layer_and_second_hidden_layer       ,     self.second_hidden_layer_and_output_layer_weights       ,     )			)

         return int(self.layer_between_second_hidden_layer_and_output > 0.6			)






def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	numpy.ndarray				)->numpy.ndarray:
      return 1 / (1 + numpy.exp(-value				))
def 							UpperCAmelCase__		(				_SCREAMING_SNAKE_CASE   :	numpy.ndarray				)->numpy.ndarray:
      return (value) * (1 - (value))
def 							UpperCAmelCase__		(				)->int:
      _lowerCAmelCase 			=  numpy.array(
          (
              [0, 0, 0],
              [0, 0, 1],
              [0, 1, 0],
              [0, 1, 1],
              [1, 0, 0],
              [1, 0, 1],
              [1, 1, 0],
              [1, 1, 1],
          )     ,				dtype=numpy.floataa     ,				)

      # True output values for the given input values.
      _lowerCAmelCase 			=  numpy.array(([0], [1], [1], [0], [1], [0], [0], [1])     ,				dtype=numpy.floataa				)

      # Calling neural network class.
      _lowerCAmelCase 			=  TwoHiddenLayerNeuralNetwork(
          input_array=_SCREAMING_SNAKE_CASE     ,				output_array=_SCREAMING_SNAKE_CASE				)

      # Calling training function.
      # Set give_loss to True if you want to see loss in every iteration.
      neural_network.train(output=_SCREAMING_SNAKE_CASE     ,				iterations=1_0     ,				give_loss=_SCREAMING_SNAKE_CASE				)

      return neural_network.predict(numpy.array(([1, 1, 1])     ,				dtype=numpy.floataa				)				)


if __name__ == "__main__":
    example()