






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   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__  )	->    List[str]:
  UpperCamelCase_: List[str]   				=    args.log_outputs
  UpperCamelCase_: List[Any]   				=    '_'.join(args.dataset.split('/'  ) + [args.config, args.split]  )

  # load metric
  UpperCamelCase_: List[Any]   				=    load_metric('wer'  )
  UpperCamelCase_: Any   				=    load_metric('cer'  )

  # compute metrics
  UpperCamelCase_: str   				=    wer.compute(references=result['target']     ,   predictions=result['prediction']  )
  UpperCamelCase_: Optional[Any]   				=    cer.compute(references=result['target']     ,   predictions=result['prediction']  )

  # print & log results
  UpperCamelCase_: Optional[Any]   				=    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:
    UpperCamelCase_: str   				=    F'''log_{dataset_id}_predictions.txt'''
    UpperCamelCase_: Tuple   				=    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   snake_case (UpperCAmelCase__  )	->    str:
  UpperCamelCase_: List[str]   				=    '[,?.!\-\;\:"“%‘”�—’…–]'  # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training

  UpperCamelCase_: Dict   				=    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!
  UpperCamelCase_: str   				=    ['\n\n', '\n', '   ', '  ']

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

  return text

def   snake_case (UpperCAmelCase__  )	->    Optional[int]:
  # load dataset
  UpperCamelCase_: Optional[Any]   				=    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
  UpperCamelCase_: int   				=    AutoFeatureExtractor.from_pretrained(args.model_id  )
  UpperCamelCase_: Optional[int]   				=    feature_extractor.sampling_rate

  # resample audio
  UpperCamelCase_: List[Any]   				=    dataset.cast_column('audio'     ,   Audio(sampling_rate=UpperCAmelCase__  )  )

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

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

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

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

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


if __name__ == "__main__":
   A_		:		List[str]		     =			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_		:		List[str]		     =			parser.parse_args()

   main(args)







import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
   from transformers import AutoModelForSeqaSeqLM, AutoTokenizer




@require_torch
@require_sentencepiece
@require_tokenizers
class     _lowerCAmelCase(	unittest.TestCase ):





       """simple docstring"""







       @slow
       def       _a   ( self							):
         UpperCamelCase_: Optional[int]   				=    AutoModelForSeqaSeqLM.from_pretrained('google/mt5-small'  ,      return_dict=_lowerCamelCase							).to(_lowerCamelCase							)
         UpperCamelCase_: Dict   				=    AutoTokenizer.from_pretrained('google/mt5-small'							)

         UpperCamelCase_: Dict   				=    tokenizer('Hello there'  ,      return_tensors='pt'							).input_ids
         UpperCamelCase_: Optional[Any]   				=    tokenizer('Hi I am'  ,      return_tensors='pt'							).input_ids

         UpperCamelCase_: int   				=    model(input_ids.to(_lowerCamelCase							)  ,      labels=labels.to(_lowerCamelCase							)							).loss
         UpperCamelCase_: Tuple   				=    -(labels.shape[-1] * loss.item())

         UpperCamelCase_: Any   				=    -8_4.9_1_2_7
         self.assertTrue(abs(mtf_score - EXPECTED_SCORE							) < 1e-4							)