

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       ):





     '''simple docstring'''




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

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

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

     # print & log results
     SCREAMING_SNAKE_CASE__					=					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:
          SCREAMING_SNAKE_CASE__					=					F'''log_{dataset_id}_predictions.txt'''
          SCREAMING_SNAKE_CASE__					=					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       ):





     '''simple docstring'''




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

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

     for t in token_sequences_to_ignore:
          SCREAMING_SNAKE_CASE__					=					''' '''.join(text.split(_A       )       )

     return text
def        UpperCAmelCase_     (		_A       ):





     '''simple docstring'''




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

     # resample audio
     SCREAMING_SNAKE_CASE__					=					dataset.cast_column('''audio'''   ,	Audio(sampling_rate=_A       )       )

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

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

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

     # run inference on all examples
     SCREAMING_SNAKE_CASE__					=					dataset.map(_A   ,	remove_columns=dataset.column_names       )

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


if __name__ == "__main__":
       _SCREAMING_SNAKE_CASE  :						Dict         =    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  :						List[Any]         =    parser.parse_args()

       main(args)





from collections.abc import Callable

import numpy as np
def        UpperCAmelCase_     (		_A   ,	_A   ,	_A   ,	_A   ,	_A       ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					int(np.ceil((x_end - xa) / step_size       )       )
     SCREAMING_SNAKE_CASE__					=					np.zeros((n + 1,)       )
     SCREAMING_SNAKE_CASE__					=					ya
     SCREAMING_SNAKE_CASE__					=					xa

     for k in range(_A       ):
          SCREAMING_SNAKE_CASE__					=					y[k] + step_size * ode_func(_A   ,	y[k]       )
          SCREAMING_SNAKE_CASE__					=					y[k] + (
              (step_size / 2) * (ode_func(_A   ,	y[k]       ) + ode_func(x + step_size   ,	_A       ))
          )
          x += step_size

     return y


if __name__ == "__main__":
       import doctest

       doctest.testmod()


