



import numpy

class        UpperCAmelCase_  :

			'''simple docstring'''




			def __init__(   self				,							_A				,							_A		):







										'''simple docstring'''

										__SCREAMING_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.
										__SCREAMING_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.
										__SCREAMING_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.
										__SCREAMING_SNAKE_CASE									= numpy.random.rand(3				,							1		)

										# Real output values provided.
										__SCREAMING_SNAKE_CASE									= output_array

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






			def 	_A     (   self		):







										'''simple docstring'''

										__SCREAMING_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.
										__SCREAMING_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.
										__SCREAMING_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 	_A     (   self		):







										'''simple docstring'''

										__SCREAMING_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		)				,							)
										__SCREAMING_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		)				,							)
										__SCREAMING_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 	_A     (   self				,							_A				,							_A				,							_A		):







										'''simple docstring'''

										for iteration in range(1				,							iterations + 1		):
																	__SCREAMING_SNAKE_CASE									= self.feedforward()
																	self.back_propagation()
																	if give_loss:
																								__SCREAMING_SNAKE_CASE									= numpy.mean(numpy.square(output - self.feedforward()		)		)
																								print(f"""Iteration {iteration} Loss: {loss}"""		)






			def 	_A     (   self				,							_A		):







										'''simple docstring'''

										__SCREAMING_SNAKE_CASE									= input_arr

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

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

										__SCREAMING_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      __lowercase       (			a__       )    ->			numpy.ndarray:
							return 1 / (1 + numpy.exp(-value       ))






def      __lowercase       (			a__       )    ->			numpy.ndarray:
							return (value) * (1 - (value))






def      __lowercase       (			)    ->			int:
							__SCREAMING_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.
							__SCREAMING_SNAKE_CASE									= numpy.array(([0], [1], [1], [0], [1], [0], [0], [1])							,       dtype=numpy.floataa       )

							# Calling neural network class.
							__SCREAMING_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()











import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase__		:					Dict							=logging.get_logger(__name__)

lowerCAmelCase__		:					Optional[int]							={
    '''asapp/sew-tiny-100k''': '''https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json''',
    # See all SEW models at https://huggingface.co/models?filter=sew
}

class        UpperCAmelCase_  (       UpperCamelCase_	):

						'''simple docstring'''




						UpperCamelCase__      :			Optional[Any]      =  '''sew'''






						def __init__(   self				,							_A=32				,							_A=768				,							_A=12				,							_A=12				,							_A=3_072				,							_A=2				,							_A="gelu"				,							_A=0.1				,							_A=0.1				,							_A=0.1				,							_A=0.0				,							_A=0.1				,							_A=0.1				,							_A=0.0_2				,							_A=1e-5				,							_A="group"				,							_A="gelu"				,							_A=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)				,							_A=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)				,							_A=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)				,							_A=False				,							_A=128				,							_A=16				,							_A=True				,							_A=0.0_5				,							_A=10				,							_A=2				,							_A=0.0				,							_A=10				,							_A=0				,							_A="mean"				,							_A=False				,							_A=False				,							_A=256				,							_A=0				,							_A=1				,							_A=2				,							**_A				,							):







													'''simple docstring'''

													super().__init__(**_A				,							pad_token_id=_A				,							bos_token_id=_A				,							eos_token_id=_A		)
													__SCREAMING_SNAKE_CASE									= hidden_size
													__SCREAMING_SNAKE_CASE									= feat_extract_norm
													__SCREAMING_SNAKE_CASE									= feat_extract_activation
													__SCREAMING_SNAKE_CASE									= list(_A		)
													__SCREAMING_SNAKE_CASE									= list(_A		)
													__SCREAMING_SNAKE_CASE									= list(_A		)
													__SCREAMING_SNAKE_CASE									= conv_bias
													__SCREAMING_SNAKE_CASE									= num_conv_pos_embeddings
													__SCREAMING_SNAKE_CASE									= num_conv_pos_embedding_groups
													__SCREAMING_SNAKE_CASE									= len(self.conv_dim		)
													__SCREAMING_SNAKE_CASE									= num_hidden_layers
													__SCREAMING_SNAKE_CASE									= intermediate_size
													__SCREAMING_SNAKE_CASE									= squeeze_factor
													__SCREAMING_SNAKE_CASE									= hidden_act
													__SCREAMING_SNAKE_CASE									= num_attention_heads
													__SCREAMING_SNAKE_CASE									= hidden_dropout
													__SCREAMING_SNAKE_CASE									= attention_dropout
													__SCREAMING_SNAKE_CASE									= activation_dropout
													__SCREAMING_SNAKE_CASE									= feat_proj_dropout
													__SCREAMING_SNAKE_CASE									= final_dropout
													__SCREAMING_SNAKE_CASE									= layerdrop
													__SCREAMING_SNAKE_CASE									= layer_norm_eps
													__SCREAMING_SNAKE_CASE									= initializer_range
													__SCREAMING_SNAKE_CASE									= vocab_size

													if (
													    (len(self.conv_stride		) != self.num_feat_extract_layers)
													    or (len(self.conv_kernel		) != self.num_feat_extract_layers)
													    or (len(self.conv_dim		) != self.num_feat_extract_layers)
													):
																				raise ValueError(
																				    'Configuration for convolutional layers is incorrect.'
																				    'It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,'
																				    f"""but is `len(config.conv_dim) = {len(self.conv_dim		)}`, `len(config.conv_stride)"""
																				    f"""= {len(self.conv_stride		)}`, `len(config.conv_kernel) = {len(self.conv_kernel		)}`."""		)

													# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
													__SCREAMING_SNAKE_CASE									= apply_spec_augment
													__SCREAMING_SNAKE_CASE									= mask_time_prob
													__SCREAMING_SNAKE_CASE									= mask_time_length
													__SCREAMING_SNAKE_CASE									= mask_time_min_masks
													__SCREAMING_SNAKE_CASE									= mask_feature_prob
													__SCREAMING_SNAKE_CASE									= mask_feature_length
													__SCREAMING_SNAKE_CASE									= mask_feature_min_masks

													# ctc loss
													__SCREAMING_SNAKE_CASE									= ctc_loss_reduction
													__SCREAMING_SNAKE_CASE									= ctc_zero_infinity

													# sequence classification
													__SCREAMING_SNAKE_CASE									= use_weighted_layer_sum
													__SCREAMING_SNAKE_CASE									= classifier_proj_size






						@property
						def 	_A     (   self		):







													'''simple docstring'''

													return functools.reduce(operator.mul				,							self.conv_stride				,							1		)






