





from typing import List, Optional, Union

import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


__lowercase       :List[Any]       =  logging.get_logger(__name__)



class    _a     (				lowercase__							):






							"""simple docstring"""

							snake_case_							 =						["input_features", "attention_mask"]

							def __init__(      self				:     Optional[int]							,	a				:     Tuple=80							,	a				:     Union[str, Any]=1_60_00							,	a				:     Optional[Any]=80							,	a				:     Union[str, Any]=0.0							,	a				:     Optional[Any]=True							,	a				:     int=True							,	a				:     int=True							,	**a				:     Optional[Any]							,	)	->Tuple:
													super().__init__(feature_size=a							,	sampling_rate=a							,	padding_value=a							,	**a    )
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					num_mel_bins
													SCREAMING_SNAKE_CASE__     :				Dict   =					do_ceptral_normalize
													SCREAMING_SNAKE_CASE__     :				List[str]   =					normalize_means
													SCREAMING_SNAKE_CASE__     :				List[str]   =					normalize_vars
													SCREAMING_SNAKE_CASE__     :				Any   =					True

							def      A_  (      self				:     Optional[Any]							,	a				:     np.ndarray							,	)	->np.ndarray:
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					waveform * (2**15)  # Kaldi compliance: 16-bit signed integers
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					torch.from_numpy(a    ).unsqueeze(0    )
													SCREAMING_SNAKE_CASE__     :				int   =					ta_kaldi.fbank(a							,	num_mel_bins=self.num_mel_bins							,	sample_frequency=self.sampling_rate    )
													return features.numpy()

							@staticmethod
							def      A_  (      a				:     np.ndarray							,	a				:     int							,	a				:     Optional[bool] = True							,	a				:     Optional[bool] = True							,	a				:     float = 0.0							,	)	->np.ndarray:
													# make sure we normalize float32 arrays
													if normalize_means:
																			SCREAMING_SNAKE_CASE__     :				List[str]   =					x[:input_length].mean(axis=0    )
																			SCREAMING_SNAKE_CASE__     :				Tuple   =					np.subtract(a							,	a    )
													if normalize_vars:
																			SCREAMING_SNAKE_CASE__     :				Dict   =					x[:input_length].std(axis=0    )
																			SCREAMING_SNAKE_CASE__     :				List[str]   =					np.divide(a							,	a    )

													if input_length < x.shape[0]:
																			SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					padding_value

													# make sure array is in float32
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					x.astype(np.floataa    )

													return x

							def      A_  (      self				:     List[str]							,	a				:     List[np.ndarray]							,	a				:     Optional[np.ndarray] = None    )	->List[np.ndarray]:
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					attention_mask.sum(-1    ) if attention_mask is not None else [x.shape[0] for x in input_features]
													return [
													    self.utterance_cmvn(a							,	a							,	self.normalize_means							,	self.normalize_vars							,	self.padding_value    )
													    for x, n in zip(a							,	a    )
													]






							def __call__(      self				:     Optional[int]							,	a				:     Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]							,	a				:     Union[bool, str, PaddingStrategy] = False							,	a				:     Optional[int] = None							,	a				:     bool = False							,	a				:     Optional[int] = None							,	a				:     Optional[Union[str, TensorType]] = None							,	a				:     Optional[int] = None							,	a				:     Optional[bool] = None							,	**a				:     List[Any]							,	)	->BatchFeature:

													if sampling_rate is not None:
																			if sampling_rate != self.sampling_rate:
																									raise ValueError(
																									    f"""The model corresponding to this feature extractor: {self} was trained using a sampling rate of"""
																									    f""" {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with"""
																									    f""" {self.sampling_rate} and not {sampling_rate}."""    )
													else:
																			logger.warning(
																			    "It is strongly recommended to pass the `sampling_rate` argument to this function. "
																			    "Failing to do so can result in silent errors that might be hard to debug."    )

													SCREAMING_SNAKE_CASE__     :				List[str]   =					isinstance(a							,	np.ndarray    ) and len(raw_speech.shape    ) > 1
													if is_batched_numpy and len(raw_speech.shape    ) > 2:
																			raise ValueError(f"""Only mono-channel audio is supported for input to {self}"""    )
													SCREAMING_SNAKE_CASE__     :				Any   =					is_batched_numpy or (
													    isinstance(a							,	(list, tuple)    ) and (isinstance(raw_speech[0]							,	(np.ndarray, tuple, list)    ))
													)

													if is_batched:
																			SCREAMING_SNAKE_CASE__     :				Any   =					[np.asarray(a							,	dtype=np.floataa    ) for speech in raw_speech]
													elif not is_batched and not isinstance(a							,	np.ndarray    ):
																			SCREAMING_SNAKE_CASE__     :				Dict   =					np.asarray(a							,	dtype=np.floataa    )
													elif isinstance(a							,	np.ndarray    ) and raw_speech.dtype is np.dtype(np.floataa    ):
																			SCREAMING_SNAKE_CASE__     :				List[Any]   =					raw_speech.astype(np.floataa    )

													# always return batch
													if not is_batched:
																			SCREAMING_SNAKE_CASE__     :				Any   =					[raw_speech]

													# extract fbank features
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					[self._extract_fbank_features(a    ) for waveform in raw_speech]

													# convert into correct format for padding
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					BatchFeature({"input_features": features}    )

													SCREAMING_SNAKE_CASE__     :				Any   =					self.pad(
													    a							,	padding=a							,	max_length=a							,	truncation=a							,	pad_to_multiple_of=a							,	return_attention_mask=a							,	**a							,	)

													# make sure list is in array format
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					padded_inputs.get("input_features"    )
													if isinstance(input_features[0]							,	a    ):
																			SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					[np.asarray(a							,	dtype=np.floataa    ) for feature in input_features]

													SCREAMING_SNAKE_CASE__     :				List[Any]   =					padded_inputs.get("attention_mask"    )
													if attention_mask is not None:
																			SCREAMING_SNAKE_CASE__     :				List[Any]   =					[np.asarray(a							,	dtype=np.intaa    ) for array in attention_mask]

													# Utterance-level cepstral mean and variance normalization
													if self.do_ceptral_normalize:
																			SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					(
																			    np.array(a							,	dtype=np.intaa    )
																			    if self._get_padding_strategies(a							,	max_length=a    ) is not PaddingStrategy.DO_NOT_PAD
																			    else None
																			)
																			SCREAMING_SNAKE_CASE__     :				Any   =					self.normalize(
																			    padded_inputs["input_features"]							,	attention_mask=a    )

													if return_tensors is not None:
																			SCREAMING_SNAKE_CASE__     :				List[str]   =					padded_inputs.convert_to_tensors(a    )

													return padded_inputs






import numpy



class    _a     :






							"""simple docstring"""

							def __init__(      self				:     Optional[int]							,	a				:     numpy.ndarray							,	a				:     numpy.ndarray    )	->None:
													SCREAMING_SNAKE_CASE__     :				Any   =					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__     :				int   =					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__     :				Dict   =					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__     :				List[Any]   =					numpy.random.rand(3							,	1    )

													# Real output values provided.
													SCREAMING_SNAKE_CASE__     :				str   =					output_array

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

							def      A_  (      self				:     Union[str, Any]    )	->numpy.ndarray:
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					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__     :				Optional[int]   =					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__     :				int   =					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				:     int    )	->None:
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					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__     :				Union[str, Any]   =					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__     :				int   =					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				:     int							,	a				:     numpy.ndarray							,	a				:     int							,	a				:     bool    )	->None:
													for iteration in range(1							,	iterations + 1    ):
																			SCREAMING_SNAKE_CASE__     :				Dict   =					self.feedforward()
																			self.back_propagation()
																			if give_loss:
																									SCREAMING_SNAKE_CASE__     :				int   =					numpy.mean(numpy.square(output - self.feedforward()    )    )
																									print(f"""Iteration {iteration} Loss: {loss}"""    )






							def      A_  (      self				:     Tuple							,	a				:     numpy.ndarray    )	->int:
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					input_arr

													SCREAMING_SNAKE_CASE__     :				Dict   =					sigmoid(
													    numpy.dot(self.array							,	self.input_layer_and_first_hidden_layer_weights    )    )

													SCREAMING_SNAKE_CASE__     :				Any   =					sigmoid(
													    numpy.dot(
													        self.layer_between_input_and_first_hidden_layer							,	self.first_hidden_layer_and_second_hidden_layer_weights							,	)    )

													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					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							(     _lowerCamelCase      :  numpy.ndarray     ):





						'''simple docstring'''


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







def       UpperCAmelCase							(     _lowerCamelCase      :  numpy.ndarray     ):





						'''simple docstring'''


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







def       UpperCAmelCase							(     ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				str   =					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__     :				Any   =					numpy.array(([0], [1], [1], [0], [1], [0], [0], [1])			,   dtype=numpy.floataa     )

						# Calling neural network class.
						SCREAMING_SNAKE_CASE__     :				List[Any]   =					TwoHiddenLayerNeuralNetwork(
						    input_array=_lowerCamelCase			,   output_array=_lowerCamelCase     )

						# Calling training function.
						# Set give_loss to True if you want to see loss in every iteration.
						neural_network.train(output=_lowerCamelCase			,   iterations=10			,   give_loss=_lowerCamelCase     )

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


if __name__ == "__main__":
				example()