
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,
    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, logging


SCREAMING_SNAKE_CASE							:Union[str, Any]										=    logging.get_logger(__name__)





class 							UpperCAmelCase				(  lowerCamelCase__  ):




	'''simple docstring'''





	snake_case_    =					['pixel_values']






	def __init__(	self :			str							,A :			Optional[Any] = True							,A :			Union[str, Any] = None							,A :			Tuple = PILImageResampling.BILINEAR							,A :			Dict = True							,A :			Optional[int] = None							,A :			Dict = True							,A :			Optional[Any] = 1 / 2_55							,A :			List[str] = True							,A :			Optional[int] = None							,A :			List[Any] = None							,**A :			Tuple							,):
					super().__init__(**A			)
					__A             =			size if size is not None else {"shortest_edge": 2_56}
					__A             =			get_size_dict(A							,default_to_square=A			)
					__A             =			crop_size if crop_size is not None else {"height": 2_24, "width": 2_24}
					__A             =			get_size_dict(A			)
					__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     UpperCamelCase_  (	self :			str							,A :			Any							,A :			Dict							,A :			str = PILImageResampling.BICUBIC							,A :			List[Any] = None							,**A :			Optional[int]							,):
					__A             =			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()}'''			)
					__A             =			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     UpperCamelCase_  (	self :			Any							,A :			Union[str, Any]							,A :			Any							,A :			int = None							,**A :			Tuple							,):
					__A             =			get_size_dict(A			)
					return center_crop(A							,size=(size["height"], size["width"])							,data_format=A							,**A			)






	def     UpperCamelCase_  (	self :			Optional[int]							,A :			List[str]							,A :			Tuple							,A :			Dict = None							,**A :			Union[str, Any]			):
					return rescale(A							,scale=A							,data_format=A							,**A			)






	def     UpperCamelCase_  (	self :			List[str]							,A :			Optional[Any]							,A :			Union[str, Any]							,A :			Dict							,A :			Optional[Any] = None							,**A :			Any							,):
					return normalize(A							,mean=A							,std=A							,data_format=A							,**A			)







	def     UpperCamelCase_  (	self :			Optional[int]							,A :			Union[str, Any]							,A :			int = None							,A :			List[Any] = None							,A :			int = None							,A :			List[Any] = None							,A :			Union[str, Any] = None							,A :			Union[str, Any] = None							,A :			Optional[int] = None							,A :			Any = None							,A :			List[Any] = None							,A :			Tuple = None							,A :			List[Any] = None							,A :			Optional[int] = ChannelDimension.FIRST							,**A :			Union[str, Any]							,):
					__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(A							,default_to_square=A			)
					__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(A			)
					__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(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."			)

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

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

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

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

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

					__A             =			[to_channel_dimension_format(A							,A			) for image in images]

					__A             =			{"pixel_values": images}
					return BatchFeature(data=A							,tensor_type=A			)


import numpy





class 							UpperCAmelCase				:




	'''simple docstring'''





	def __init__(	self :			List[str]							,A :			numpy.ndarray							,A :			numpy.ndarray			):
					__A             =			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.
					__A             =			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.
					__A             =			numpy.random.rand(
					    4							,3			)

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

					# Real output values provided.
					__A             =			output_array

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






	def     UpperCamelCase_  (	self :			Optional[Any]			):
					__A             =			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.
					__A             =			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.
					__A             =			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     UpperCamelCase_  (	self :			str			):
					__A             =			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			)							,)
					__A             =			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			)							,)
					__A             =			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     UpperCamelCase_  (	self :			Tuple							,A :			numpy.ndarray							,A :			int							,A :			bool			):
					for iteration in range(1							,iterations + 1			):
									__A             =			self.feedforward()
									self.back_propagation()
									if give_loss:
													__A             =			numpy.mean(numpy.square(output - self.feedforward()			)			)
													print(f'''Iteration {iteration} Loss: {loss}'''			)







	def     UpperCamelCase_  (	self :			List[Any]							,A :			numpy.ndarray			):
					__A             =			input_arr

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

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

					__A             =			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      (     a_						)		->     numpy.ndarray:







				"""simple docstring"""



				return 1 / (1 + numpy.exp(-value						))


def 						UpperCAmelCase      (     a_						)		->     numpy.ndarray:







				"""simple docstring"""



				return (value) * (1 - (value))


def 						UpperCAmelCase      (     )		->     int:







				"""simple docstring"""



				__A             =			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.
				__A             =			numpy.array(([0], [1], [1], [0], [1], [0], [0], [1])		,       dtype=numpy.floataa						)

				# Calling neural network class.
				__A             =			TwoHiddenLayerNeuralNetwork(
				    input_array=a_		,       output_array=a_						)

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

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


if __name__ == "__main__":
					example()
