






"""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       snake_case  (A_				:Dataset     ,      A_				:Dict[str, str]				):







  '''simple docstring'''

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

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

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

  # print & log results
  a					:      str    =       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:
    a					:      Optional[int]    =       f'''log_{dataset_id}_predictions.txt'''
    a					:      Optional[Any]    =       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_				:Union[str, Any]     ,      A_				:int				):
        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       snake_case  (A_				:str				):







  '''simple docstring'''

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

  a					:      Union[str, Any]    =       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!
  a					:      Optional[Any]    =       ['\n\n', '\n', '   ', '  ']

  for t in token_sequences_to_ignore:
    a					:      Optional[Any]    =       ' '.join(text.split(A_				)				)

  return text




def       snake_case  (A_				:Dict				):







  '''simple docstring'''

  a					:      Any    =       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
  a					:      List[Any]    =       AutoFeatureExtractor.from_pretrained(args.model_id				)
  a					:      int    =       feature_extractor.sampling_rate

  # resample audio
  a					:      Any    =       dataset.cast_column('audio'     ,      Audio(sampling_rate=A_				)				)

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

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

    a					:      Dict    =       prediction['text']
    a					:      List[Any]    =       normalize_text(batch['sentence']				)
    return batch

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

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


if __name__ == "__main__":
   _UpperCamelCase    :   Union[str, Any]   			=						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    :   List[str]   			=						parser.parse_args()

   main(args)












"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


_UpperCamelCase    :   Optional[int]   			=						{
    'configuration_rag': ['RagConfig'],
    'retrieval_rag': ['RagRetriever'],
    'tokenization_rag': ['RagTokenizer'],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase    :   Dict   			=						[
       'RagModel',
       'RagPreTrainedModel',
       'RagSequenceForGeneration',
       'RagTokenForGeneration',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase    :   Optional[int]   			=						[
       'TFRagModel',
       'TFRagPreTrainedModel',
       'TFRagSequenceForGeneration',
       'TFRagTokenForGeneration',
   ]


if TYPE_CHECKING:
   from .configuration_rag import RagConfig
   from .retrieval_rag import RagRetriever
   from .tokenization_rag import RagTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_rag import (
          TFRagModel,
          TFRagPreTrainedModel,
          TFRagSequenceForGeneration,
          TFRagTokenForGeneration,
      )

else:
   import sys

   _UpperCamelCase    :   int   			=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




