"""simple docstring"""






def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase )  ->  Tuple:
   lowercase__:   Any					=      len(_A )
   lowercase__:   Tuple					=      len(_A )
   lowercase__:   List[Any]					=      (
       first_str_length if first_str_length > second_str_length else second_str_length
   )
   lowercase__:   int					=      []
   for char_count in range(_A ):
      if char_count < first_str_length:
         output_list.append(first_str[char_count] )
      if char_count < second_str_length:
         output_list.append(second_str[char_count] )
   return "".join(_A )


if __name__ == "__main__":
       print(alternative_string_arrange("AB", "XYZ"), end=" ")






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 __UpperCamelCase			(       _A ,  _A  ):
						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(_A  )

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

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

																		result.map(_A ,  with_indices=_A  )





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

						lowerCAmelCase_      			=  re.sub(_A ,  '''''' ,  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(_A  )  )

						return text





def __UpperCamelCase			(       _A  ):
						# load dataset
						lowerCAmelCase_      			=  load_dataset(args.dataset ,  args.config ,  split=args.split ,  use_auth_token=_A  )

						# 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=_A  )  )

						# 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(_A  ):
												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(_A ,  remove_columns=dataset.column_names  )

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


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)




