



from __future__ import annotations


def A						(       __UpperCamelCase					)						->      list[int]:
			return [ord(__UpperCamelCase					) - 96 for elem in plain]


def A						(       __UpperCamelCase					)						->      str:
			return "".join(chr(elem + 96					) for elem in encoded					)


def A						(       )						->      None:
			A__       =    encode(input('-> '					).strip().lower()					)
			print('Encoded: ' ,	__UpperCamelCase					)
			print('Decoded:' ,	decode(__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 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)




