'''simple docstring'''




import numpy






class 				a__:


			'''simple docstring'''






			def __init__(   self       ,    __lowerCAmelCase       ,    __lowerCAmelCase):




					"""simple docstring"""


					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 							a_       (   self):




					"""simple docstring"""


					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 							a_       (   self):




					"""simple docstring"""


					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 							a_       (   self       ,    __lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase):




					"""simple docstring"""


					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 							a_       (   self       ,    __lowerCAmelCase):




					"""simple docstring"""


					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__							(			_A:					numpy.ndarray )    ->	numpy.ndarray:

		'''simple docstring'''


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






def 				snake_case__							(			_A:					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=_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()





'''simple docstring'''




from typing import List, Optional

import numpy as np

from ...processing_utils import ProcessorMixin
from ...utils import to_numpy






class 				a__(					lowerCAmelCase__	):


			'''simple docstring'''






			UpperCAmelCase_			:     str      =       '''EncodecFeatureExtractor'''
			UpperCAmelCase_			:     Dict      =       ('''T5Tokenizer''', '''T5TokenizerFast''')



			def __init__(   self       ,    __lowerCAmelCase       ,    __lowerCAmelCase):




					"""simple docstring"""


					super().__init__(__lowerCAmelCase       ,    __lowerCAmelCase)
					lowerCAmelCase   					=  self.feature_extractor
					lowerCAmelCase   					=  False



			def 							a_       (   self       ,    __lowerCAmelCase=None       ,    __lowerCAmelCase=None       ,    __lowerCAmelCase=True):




					"""simple docstring"""


					return self.tokenizer.get_decoder_prompt_ids(task=__lowerCAmelCase       ,    language=__lowerCAmelCase       ,    no_timestamps=__lowerCAmelCase)



			def __call__(   self       ,    *__lowerCAmelCase       ,    **__lowerCAmelCase):




					"""simple docstring"""


					if self._in_target_context_manager:
							return self.current_processor(*__lowerCAmelCase       ,    **__lowerCAmelCase)

					lowerCAmelCase   					=  kwargs.pop("""audio"""       ,    __lowerCAmelCase)
					lowerCAmelCase   					=  kwargs.pop("""sampling_rate"""       ,    __lowerCAmelCase)
					lowerCAmelCase   					=  kwargs.pop("""text"""       ,    __lowerCAmelCase)
					if len(__lowerCAmelCase) > 0:
							lowerCAmelCase   					=  args[0]
							lowerCAmelCase   					=  args[1:]

					if audio is None and text is None:
							raise ValueError("""You need to specify either an `audio` or `text` input to process.""")

					if text is not None:
							lowerCAmelCase   					=  self.tokenizer(__lowerCAmelCase       ,    **__lowerCAmelCase)

					if audio is not None:
							lowerCAmelCase   					=  self.feature_extractor(__lowerCAmelCase       ,    *__lowerCAmelCase       ,    sampling_rate=__lowerCAmelCase       ,    **__lowerCAmelCase)

					if audio is None:
							return inputs

					elif text is None:
							return audio_inputs

					else:
							lowerCAmelCase   					=  audio_inputs["""input_values"""]
							if "padding_mask" in audio_inputs:
									lowerCAmelCase   					=  audio_inputs["""padding_mask"""]
							return inputs



			def 							a_       (   self       ,    *__lowerCAmelCase       ,    **__lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase   					=  kwargs.pop("""audio"""       ,    __lowerCAmelCase)
					lowerCAmelCase   					=  kwargs.pop("""padding_mask"""       ,    __lowerCAmelCase)

					if len(__lowerCAmelCase) > 0:
							lowerCAmelCase   					=  args[0]
							lowerCAmelCase   					=  args[1:]

					if audio_values is not None:
							return self._decode_audio(__lowerCAmelCase       ,    padding_mask=__lowerCAmelCase)
					else:
							return self.tokenizer.batch_decode(*__lowerCAmelCase       ,    **__lowerCAmelCase)



			def 							a_       (   self       ,    *__lowerCAmelCase       ,    **__lowerCAmelCase):




					"""simple docstring"""


					return self.tokenizer.decode(*__lowerCAmelCase       ,    **__lowerCAmelCase)







			def 							a_       (   self       ,    __lowerCAmelCase       ,    __lowerCAmelCase = None):




					"""simple docstring"""


					lowerCAmelCase   					=  to_numpy(__lowerCAmelCase)
					lowerCAmelCase					,     lowerCAmelCase					,     lowerCAmelCase   					=  audio_values.shape

					if padding_mask is None:
							return list(__lowerCAmelCase)

					lowerCAmelCase   					=  to_numpy(__lowerCAmelCase)

					# match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
					# token (so that the generated audio values are **not** treated as padded tokens)
					lowerCAmelCase   					=  seq_len - padding_mask.shape[-1]
					lowerCAmelCase   					=  1 - self.feature_extractor.padding_value
					lowerCAmelCase   					=  np.pad(__lowerCAmelCase       ,    ((0, 0), (0, difference))       ,    """constant"""       ,    constant_values=__lowerCAmelCase)

					lowerCAmelCase   					=  audio_values.tolist()
					for i in range(__lowerCAmelCase):
							lowerCAmelCase   					=  np.asarray(audio_values[i])[
							    padding_mask[i][None, :] != self.feature_extractor.padding_value
							]
							lowerCAmelCase   					=  sliced_audio.reshape(__lowerCAmelCase       ,    -1)

					return audio_values




