



'''simple docstring'''

from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType

class __snake_case(				_lowerCAmelCase ):


						'''simple docstring'''



						UpperCAmelCase							:   Union[str, Any]									=		["image_processor", "tokenizer"]
						UpperCAmelCase							:   Tuple									=		"BlipImageProcessor"
						UpperCAmelCase							:   Union[str, Any]									=		("BertTokenizer", "BertTokenizerFast")


						def __init__( self     ,	A_     ,	A_				)     ->     Dict:
							lowerCAmelCase   =     False
							super().__init__(A_     ,	A_				)
							lowerCAmelCase   =     self.image_processor


						def __call__( self     ,	A_ = None     ,	A_ = None     ,	A_ = True     ,	A_ = False     ,	A_ = None     ,	A_ = None     ,	A_ = 0     ,	A_ = None     ,	A_ = None     ,	A_ = False     ,	A_ = False     ,	A_ = False     ,	A_ = False     ,	A_ = False     ,	A_ = True     ,	A_ = None     ,	**A_     ,	)     ->     BatchEncoding:
							if images is None and text is None:
								raise ValueError("""You have to specify either images or text."""				)

							# Get only text
							if images is None:
								lowerCAmelCase   =     self.tokenizer
								lowerCAmelCase   =     self.tokenizer(
								    text=A_     ,	add_special_tokens=A_     ,	padding=A_     ,	truncation=A_     ,	max_length=A_     ,	stride=A_     ,	pad_to_multiple_of=A_     ,	return_attention_mask=A_     ,	return_overflowing_tokens=A_     ,	return_special_tokens_mask=A_     ,	return_offsets_mapping=A_     ,	return_token_type_ids=A_     ,	return_length=A_     ,	verbose=A_     ,	return_tensors=A_     ,	**A_     ,	)
								return text_encoding

							# add pixel_values
							lowerCAmelCase   =     self.image_processor(A_     ,	return_tensors=A_				)

							if text is not None:
								lowerCAmelCase   =     self.tokenizer(
								    text=A_     ,	add_special_tokens=A_     ,	padding=A_     ,	truncation=A_     ,	max_length=A_     ,	stride=A_     ,	pad_to_multiple_of=A_     ,	return_attention_mask=A_     ,	return_overflowing_tokens=A_     ,	return_special_tokens_mask=A_     ,	return_offsets_mapping=A_     ,	return_token_type_ids=A_     ,	return_length=A_     ,	verbose=A_     ,	return_tensors=A_     ,	**A_     ,	)
							else:
								lowerCAmelCase   =     None

							if text_encoding is not None:
								encoding_image_processor.update(A_				)

							return encoding_image_processor


						def     __snake_case   ( self     ,	*A_     ,	**A_				)     ->     Union[str, Any]:
							return self.tokenizer.batch_decode(*A_     ,	**A_				)


						def     __snake_case   ( self     ,	*A_     ,	**A_				)     ->     Tuple:
							return self.tokenizer.decode(*A_     ,	**A_				)




						@property
						def     __snake_case   ( self				)     ->     str:
							lowerCAmelCase   =     self.tokenizer.model_input_names
							lowerCAmelCase   =     self.image_processor.model_input_names
							return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)




'''simple docstring'''

import numpy

class __snake_case:


						'''simple docstring'''



						def __init__( self     ,	A_     ,	A_				)     ->     None:
							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     __snake_case   ( self				)     ->     numpy.ndarray:
							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     __snake_case   ( self				)     ->     None:
							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     __snake_case   ( self     ,	A_     ,	A_     ,	A_				)     ->     None:
							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     __snake_case   ( self     ,	A_				)     ->     int:
							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 		_snake_case		( _SCREAMING_SNAKE_CASE					:						numpy.ndarray							)      ->						numpy.ndarray:







	"""simple docstring"""
	return 1 / (1 + numpy.exp(-value							))






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						numpy.ndarray							)      ->						numpy.ndarray:







	"""simple docstring"""
	return (value) * (1 - (value))






def 		_snake_case		( )      ->						int:







	"""simple docstring"""
	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=10							,		give_loss=_SCREAMING_SNAKE_CASE							)

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


if __name__ == "__main__":
				example()