

from __future__ import annotations





class     snake_case_			:


							"""simple docstring"""





							def __init__( self					,						_A=None							):
												__lowerCAmelCase					=							data
												__lowerCAmelCase					=							None




							def __repr__( self							):
												__lowerCAmelCase					=							[]
												__lowerCAmelCase					=							self
												while temp:
																	string_rep.append(F"""{temp.data}"""							)
																	__lowerCAmelCase					=							temp.next
												return "->".join(_A							)


def        __lowercase    (  UpperCAmelCase__      ):

					"""simple docstring"""






					if not elements_list:
										raise Exception('The Elements List is empty'      )

					__lowerCAmelCase					=							__lowerCAmelCase					=							Node(elements_list[0]      )
					for i in range(1		,					len(UpperCAmelCase__      )      ):
										__lowerCAmelCase					=							Node(elements_list[i]      )
										__lowerCAmelCase					=							current.next
					return head


def        __lowercase    (  UpperCAmelCase__      ):

					"""simple docstring"""






					if head_node is not None and isinstance(UpperCAmelCase__		,					UpperCAmelCase__      ):
										print_reverse(head_node.next      )
										print(head_node.data      )


def        __lowercase    (  ):

					"""simple docstring"""






					from doctest import testmod

					testmod()

					__lowerCAmelCase					=							make_linked_list([14, 52, 14, 12, 43]      )
					print('Linked List:'      )
					print(UpperCAmelCase__      )
					print('Elements in Reverse:'      )
					print_reverse(UpperCAmelCase__      )


if __name__ == "__main__":
		main()









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        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






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

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

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

					# print & log results
					__lowerCAmelCase					=							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:
										__lowerCAmelCase					=							F"""log_{dataset_id}_predictions.txt"""
										__lowerCAmelCase					=							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        __lowercase    (  UpperCAmelCase__      ):

					"""simple docstring"""






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

					__lowerCAmelCase					=							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!
					__lowerCAmelCase					=							['\n\n', '\n', '   ', '  ']

					for t in token_sequences_to_ignore:
										__lowerCAmelCase					=							' '.join(text.split(UpperCAmelCase__      )      )

					return text


def        __lowercase    (  UpperCAmelCase__      ):

					"""simple docstring"""






					__lowerCAmelCase					=							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
					__lowerCAmelCase					=							AutoFeatureExtractor.from_pretrained(args.model_id      )
					__lowerCAmelCase					=							feature_extractor.sampling_rate

					# resample audio
					__lowerCAmelCase					=							dataset.cast_column('audio'		,					Audio(sampling_rate=UpperCAmelCase__      )      )

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

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

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

					# run inference on all examples
					__lowerCAmelCase					=							dataset.map(UpperCAmelCase__		,					remove_columns=dataset.column_names      )

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


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

		main(args)






