





"""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_      (   SCREAMING_SNAKE_CASE							:				Dataset     ,    SCREAMING_SNAKE_CASE							:				Dict[str, str] ):


							'''simple docstring'''







							__lowerCamelCase   :   Tuple       =args.log_outputs
							__lowerCamelCase   :   Optional[int]       ='''_'''.join(args.dataset.split('''/''' ) + [args.config, args.split] )

							# load metric
							__lowerCamelCase   :   List[str]       =load_metric('''wer''' )
							__lowerCamelCase   :   List[str]       =load_metric('''cer''' )

							# compute metrics
							__lowerCamelCase   :   List[Any]       =wer.compute(references=result['''target''']     ,    predictions=result['''prediction'''] )
							__lowerCamelCase   :   Tuple       =cer.compute(references=result['''target''']     ,    predictions=result['''prediction'''] )

							# print & log results
							__lowerCamelCase   :   List[str]       =F'WER: {wer_result}\nCER: {cer_result}'
							print(SCREAMING_SNAKE_CASE )

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

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

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

																					result.map(SCREAMING_SNAKE_CASE     ,    with_indices=SCREAMING_SNAKE_CASE )



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				str ):


							'''simple docstring'''







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

							__lowerCamelCase   :   Any       =re.sub(SCREAMING_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!
							__lowerCamelCase   :   str       =['''\n\n''', '''\n''', '''   ''', '''  ''']

							for t in token_sequences_to_ignore:
														__lowerCamelCase   :   Any       =''' '''.join(text.split(SCREAMING_SNAKE_CASE ) )

							return text



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				Any ):


							'''simple docstring'''







							__lowerCamelCase   :   List[str]       =load_dataset(args.dataset     ,    args.config     ,    split=args.split     ,    use_auth_token=SCREAMING_SNAKE_CASE )

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

							# load processor
							__lowerCamelCase   :   List[str]       =AutoFeatureExtractor.from_pretrained(args.model_id )
							__lowerCamelCase   :   List[str]       =feature_extractor.sampling_rate

							# resample audio
							__lowerCamelCase   :   List[str]       =dataset.cast_column('''audio'''     ,    Audio(sampling_rate=SCREAMING_SNAKE_CASE ) )

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

							# map function to decode audio
							def map_to_pred(SCREAMING_SNAKE_CASE							:				int ):
														__lowerCamelCase   :   Union[str, Any]       =asr(
														    batch['''audio''']['''array''']     ,    chunk_length_s=args.chunk_length_s     ,    stride_length_s=args.stride_length_s )

														__lowerCamelCase   :   Union[str, Any]       =prediction['''text''']
														__lowerCamelCase   :   List[Any]       =normalize_text(batch['''sentence'''] )
														return batch

							# run inference on all examples
							__lowerCamelCase   :   Optional[Any]       =dataset.map(SCREAMING_SNAKE_CASE     ,    remove_columns=dataset.column_names )

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


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

							main(args)











"""simple docstring"""





import numpy as np



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				np.array ):


							'''simple docstring'''







							return 1 / (1 + np.exp(-vector ))


if __name__ == "__main__":
							import doctest

							doctest.testmod()




