



import numpy


class      __lowerCAmelCase     :
					"""simple docstring"""






					def __init__(     self					:       Optional[Any]				,					_snake_case					:       List[str]				,					_snake_case					:       Tuple					):






								"""simple docstring"""


								A__       =    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.
								A__       =    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.
								A__       =    numpy.random.rand(
								    4				,					3					)

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

								# Real output values provided.
								A__       =    output_array

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



					def     _a							(     self					:       str					):






								"""simple docstring"""


								A__       =    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.
								A__       =    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.
								A__       =    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					:       Union[str, Any]					):






								"""simple docstring"""


								A__       =    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					)				,					)
								A__       =    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					)				,					)
								A__       =    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					:       Tuple				,					_snake_case					:       Optional[Any]				,					_snake_case					:       List[Any]				,					_snake_case					:       Tuple					):






								"""simple docstring"""


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



					def     _a							(     self					:       Any				,					_snake_case					:       Any					):






								"""simple docstring"""


								A__       =    input_arr

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

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

								A__       =    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 A						(       __UpperCamelCase					)						->      str:
			return 1 / (1 + numpy.exp(-value					))


def A						(       __UpperCamelCase					)						->      Tuple:
			return (value) * (1 - (value))


def A						(       )						->      Union[str, Any]:
			A__       =    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.
			A__       =    numpy.array(([0], [1], [1], [0], [1], [0], [0], [1]) ,	dtype=numpy.floataa					)

			# Calling neural network class.
			A__       =    TwoHiddenLayerNeuralNetwork(
			    input_array=UpperCAmelCase__ ,	output_array=UpperCAmelCase__					)

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

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


if __name__ == "__main__":
							example()




import argparse
import re
from typing import Dict

import torch
from datasets import Audio, Dataset, load_dataset, load_metric

from transformers import AutoFeatureExtractor, pipeline


def A						(       __UpperCamelCase ,	__UpperCamelCase					)						->      Tuple:
			A__       =    args.log_outputs
			A__       =    '_'.join(args.dataset.split('/'					) + [args.config, args.split]					)

			# load metric
			A__       =    load_metric('wer'					)
			A__       =    load_metric('cer'					)

			# compute metrics
			A__       =    wer.compute(references=result['target'] ,	predictions=result['prediction']					)
			A__       =    cer.compute(references=result['target'] ,	predictions=result['prediction']					)

			# print & log results
			A__       =    f'''WER: {wer_result}\nCER: {cer_result}'''
			print(__UpperCamelCase					)

			with open(f'''{dataset_id}_eval_results.txt''' ,	'w'					) as f:
						f.write(__UpperCamelCase					)

			# log all results in text file. Possibly interesting for analysis
			if log_outputs is not None:
						A__       =    f'''log_{dataset_id}_predictions.txt'''
						A__       =    f'''log_{dataset_id}_targets.txt'''

						with open(__UpperCamelCase ,	'w'					) as p, open(__UpperCamelCase ,	'w'					) as t:
									# mapping function to write output
									def write_to_file(__UpperCamelCase ,	__UpperCamelCase					):
												p.write(f'''{i}''' + '\n'					)
												p.write(batch['prediction'] + '\n'					)
												t.write(f'''{i}''' + '\n'					)
												t.write(batch['target'] + '\n'					)

									result.map(__UpperCamelCase ,	with_indices=__UpperCamelCase					)


def A						(       __UpperCamelCase					)						->      str:
			A__       =    '[,?.!\-\;\:"“%‘”�—’…–]'  # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training

			A__       =    re.sub(__UpperCamelCase ,	'' ,	text.lower()					)

			# In addition, we can normalize the target text, e.g. removing new lines characters etc...
			# note that order is important here!
			A__       =    ['\n\n', '\n', '   ', '  ']

			for t in token_sequences_to_ignore:
						A__       =    ' '.join(text.split(__UpperCamelCase					)					)

			return text


def A						(       __UpperCamelCase					)						->      Union[str, Any]:
			# load dataset
			A__       =    load_dataset(args.dataset ,	args.config ,	split=args.split ,	use_auth_token=__UpperCamelCase					)

			# for testing: only process the first two examples as a test
			# dataset = dataset.select(range(10))

			# load processor
			A__       =    AutoFeatureExtractor.from_pretrained(args.model_id					)
			A__       =    feature_extractor.sampling_rate

			# resample audio
			A__       =    dataset.cast_column('audio' ,	Audio(sampling_rate=__UpperCamelCase					)					)

			# load eval pipeline
			if args.device is None:
						A__       =    0 if torch.cuda.is_available() else -1
			A__       =    pipeline('automatic-speech-recognition' ,	model=args.model_id ,	device=args.device					)

			# map function to decode audio
			def map_to_pred(__UpperCamelCase					):
						A__       =    asr(
						    batch['audio']['array'] ,	chunk_length_s=args.chunk_length_s ,	stride_length_s=args.stride_length_s					)

						A__       =    prediction['text']
						A__       =    normalize_text(batch['sentence']					)
						return batch

			# run inference on all examples
			A__       =    dataset.map(__UpperCamelCase ,	remove_columns=dataset.column_names					)

			# compute and log_results
			# do not change function below
			log_results(__UpperCamelCase ,	__UpperCamelCase					)


if __name__ == "__main__":
							SCREAMING_SNAKE_CASE__       =      argparse.ArgumentParser()

							parser.add_argument(
							    '''--model_id''', type=str, required=True, help='''Model identifier. Should be loadable with 🤗 Transformers'''
							)
							parser.add_argument(
							    '''--dataset''',
							    type=str,
							    required=True,
							    help='''Dataset name to evaluate the `model_id`. Should be loadable with 🤗 Datasets''',
							)
							parser.add_argument(
							    '''--config''', type=str, required=True, help='''Config of the dataset. *E.g.* `\'en\'`  for Common Voice'''
							)
							parser.add_argument('''--split''', type=str, required=True, help='''Split of the dataset. *E.g.* `\'test\'`''')
							parser.add_argument(
							    '''--chunk_length_s''', type=float, default=None, help='''Chunk length in seconds. Defaults to 5 seconds.'''
							)
							parser.add_argument(
							    '''--stride_length_s''', type=float, default=None, help='''Stride of the audio chunks. Defaults to 1 second.'''
							)
							parser.add_argument(
							    '''--log_outputs''', action='''store_true''', help='''If defined, write outputs to log file for analysis.'''
							)
							parser.add_argument(
							    '''--device''',
							    type=int,
							    default=None,
							    help='''The device to run the pipeline on. -1 for CPU (default), 0 for the first GPU and so on.''',
							)
							SCREAMING_SNAKE_CASE__       =      parser.parse_args()

							main(args)




