




'''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 						a_				(			UpperCAmelCase__						):
	lowercase_       :     Optional[Any]			  =      ['''image_processor''', '''tokenizer''']
	lowercase_       :     Tuple			  =      '''BlipImageProcessor'''
	lowercase_       :     Optional[int]			  =      '''AutoTokenizer'''




	def __init__(       self     :  Optional[Any]       ,		__lowerCAmelCase     :  Optional[Any]       ,		__lowerCAmelCase     :  List[Any]      ):
							__snake_case						 =   False
							super().__init__(__lowerCAmelCase       ,		__lowerCAmelCase      )
							__snake_case						 =   self.image_processor




	def __call__(       self     :  Optional[Any]       ,		__lowerCAmelCase     :  ImageInput = None       ,		__lowerCAmelCase     :  Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None       ,		__lowerCAmelCase     :  bool = True       ,		__lowerCAmelCase     :  Union[bool, str, PaddingStrategy] = False       ,		__lowerCAmelCase     :  Union[bool, str, TruncationStrategy] = None       ,		__lowerCAmelCase     :  Optional[int] = None       ,		__lowerCAmelCase     :  int = 0       ,		__lowerCAmelCase     :  Optional[int] = None       ,		__lowerCAmelCase     :  Optional[bool] = None       ,		__lowerCAmelCase     :  bool = False       ,		__lowerCAmelCase     :  bool = False       ,		__lowerCAmelCase     :  bool = False       ,		__lowerCAmelCase     :  bool = False       ,		__lowerCAmelCase     :  bool = False       ,		__lowerCAmelCase     :  bool = True       ,		__lowerCAmelCase     :  Optional[Union[str, TensorType]] = None       ,		**__lowerCAmelCase     :  Tuple       ,		):
							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:
													__snake_case						 =   self.tokenizer
													__snake_case						 =   self.tokenizer(
													    text=__lowerCAmelCase       ,		add_special_tokens=__lowerCAmelCase       ,		padding=__lowerCAmelCase       ,		truncation=__lowerCAmelCase       ,		max_length=__lowerCAmelCase       ,		stride=__lowerCAmelCase       ,		pad_to_multiple_of=__lowerCAmelCase       ,		return_attention_mask=__lowerCAmelCase       ,		return_overflowing_tokens=__lowerCAmelCase       ,		return_special_tokens_mask=__lowerCAmelCase       ,		return_offsets_mapping=__lowerCAmelCase       ,		return_token_type_ids=__lowerCAmelCase       ,		return_length=__lowerCAmelCase       ,		verbose=__lowerCAmelCase       ,		return_tensors=__lowerCAmelCase       ,		**__lowerCAmelCase       ,		)
													return text_encoding

							# add pixel_values
							__snake_case						 =   self.image_processor(__lowerCAmelCase       ,		return_tensors=__lowerCAmelCase      )

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

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

							return encoding_image_processor




	def   lowercase__   (       self     :  Optional[Any]       ,		*__lowerCAmelCase     :  Union[str, Any]       ,		**__lowerCAmelCase     :  str      ):
							return self.tokenizer.batch_decode(*__lowerCAmelCase       ,		**__lowerCAmelCase      )




	def   lowercase__   (       self     :  List[Any]       ,		*__lowerCAmelCase     :  Any       ,		**__lowerCAmelCase     :  Optional[int]      ):
							return self.tokenizer.decode(*__lowerCAmelCase       ,		**__lowerCAmelCase      )




	@property
	# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
	def   lowercase__   (       self     :  List[Any]      ):
							__snake_case						 =   self.tokenizer.model_input_names
							__snake_case						 =   self.image_processor.model_input_names
							return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names      )      )







'''simple docstring'''


import numpy
class 						a_				:




	def __init__(       self     :  Any       ,		__lowerCAmelCase     :  numpy.ndarray       ,		__lowerCAmelCase     :  numpy.ndarray      ):
							__snake_case						 =   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.
							__snake_case						 =   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.
							__snake_case						 =   numpy.random.rand(
							    4       ,		3      )

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

							# Real output values provided.
							__snake_case						 =   output_array

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




	def   lowercase__   (       self     :  Optional[Any]      ):
							__snake_case						 =   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.
							__snake_case						 =   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.
							__snake_case						 =   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   lowercase__   (       self     :  List[str]      ):
							__snake_case						 =   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      )       ,		)
							__snake_case						 =   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      )       ,		)
							__snake_case						 =   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   lowercase__   (       self     :  Optional[Any]       ,		__lowerCAmelCase     :  numpy.ndarray       ,		__lowerCAmelCase     :  int       ,		__lowerCAmelCase     :  bool      ):
							for iteration in range(1       ,		iterations + 1      ):
													__snake_case						 =   self.feedforward()
													self.back_propagation()
													if give_loss:
																			__snake_case						 =   numpy.mean(numpy.square(output - self.feedforward()      )      )
																			print(F'Iteration {iteration} Loss: {loss}'      )




	def   lowercase__   (       self     :  Optional[Any]       ,		__lowerCAmelCase     :  numpy.ndarray      ):
							__snake_case						 =   input_arr

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

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

							__snake_case						 =   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 						lowerCamelCase__   (				a							):
						return 1 / (1 + numpy.exp(-value							))
def 						lowerCamelCase__   (				a							):
						return (value) * (1 - (value))
def 						lowerCamelCase__   (				):
						__snake_case						 =   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.
						__snake_case						 =   numpy.array(([0], [1], [1], [0], [1], [0], [0], [1])    ,	dtype=numpy.floataa							)

						# Calling neural network class.
						__snake_case						 =   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=10    ,	give_loss=a							)

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


if __name__ == "__main__":
	example()

