
"""simple docstring"""





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 				lowerCamelCase__		(      __snake_case,					__snake_case     )							->		Optional[int]:


					"""simple docstring"""





					_UpperCamelCase										=  args.log_outputs
					_UpperCamelCase										=  '''_'''.join(args.dataset.split('''/'''     ) + [args.config, args.split]     )

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

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

					# print & log results
					_UpperCamelCase										=  F'''WER: {wer_result}\nCER: {cer_result}'''
					print(__snake_case     )

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

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

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

															result.map(__snake_case,					with_indices=__snake_case     )



def 				lowerCamelCase__		(      __snake_case     )							->		str:


					"""simple docstring"""





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

					_UpperCamelCase										=  re.sub(__snake_case,					'''''',					text.lower()     )

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

					for t in token_sequences_to_ignore:
										_UpperCamelCase										=  ''' '''.join(text.split(__snake_case     )     )

					return text



def 				lowerCamelCase__		(      __snake_case     )							->		Tuple:


					"""simple docstring"""





					_UpperCamelCase										=  load_dataset(args.dataset,					args.config,					split=args.split,					use_auth_token=__snake_case     )

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

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

					# resample audio
					_UpperCamelCase										=  dataset.cast_column('''audio''',					Audio(sampling_rate=__snake_case     )     )

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

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

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

					# run inference on all examples
					_UpperCamelCase										=  dataset.map(__snake_case,					remove_columns=dataset.column_names     )

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


if __name__ == "__main__":
	_a														=		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.""",
	)
	_a														=		parser.parse_args()

	main(args)








"""simple docstring"""





import numpy






class    _UpperCAmelCase:



		def __init__( self    ,		__a    ,		__a)	->	None:






							'''simple docstring'''
							_UpperCamelCase										=  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.
							_UpperCamelCase										=  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.
							_UpperCamelCase										=  numpy.random.rand(
							    4    ,		3)

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

							# Real output values provided.
							_UpperCamelCase										=  output_array

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



		def 						UpperCAmelCase							( self)	->	numpy.ndarray:






							'''simple docstring'''
							_UpperCamelCase										=  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.
							_UpperCamelCase										=  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.
							_UpperCamelCase										=  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 						UpperCAmelCase							( self)	->	None:






							'''simple docstring'''
							_UpperCamelCase										=  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)    ,		)
							_UpperCamelCase										=  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)    ,		)
							_UpperCamelCase										=  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 						UpperCAmelCase							( self    ,		__a    ,		__a    ,		__a)	->	None:






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




		def 						UpperCAmelCase							( self    ,		__a)	->	int:






							'''simple docstring'''
							_UpperCamelCase										=  input_arr

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

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

							_UpperCamelCase										=  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__		(      __snake_case     )							->		numpy.ndarray:


					"""simple docstring"""





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



def 				lowerCamelCase__		(      __snake_case     )							->		numpy.ndarray:


					"""simple docstring"""





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



def 				lowerCamelCase__		(      )							->		int:


					"""simple docstring"""





					_UpperCamelCase										=  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.
					_UpperCamelCase										=  numpy.array(([0], [1], [1], [0], [1], [0], [0], [1]),					dtype=numpy.floataa     )

					# Calling neural network class.
					_UpperCamelCase										=  TwoHiddenLayerNeuralNetwork(
					    input_array=__snake_case,					output_array=__snake_case     )

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

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


if __name__ == "__main__":
				example()






