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       A__   (						snake_case_			:		Dataset							,					snake_case_			:		Dict[str, str]   ):
 SCREAMING_SNAKE_CASE__: List[Any]=   args.log_outputs
 SCREAMING_SNAKE_CASE__: Optional[int]=   '''_'''.join(args.dataset.split('''/'''   ) + [args.config, args.split]   )

 # load metric
 SCREAMING_SNAKE_CASE__: Dict=   load_metric('''wer'''   )
 SCREAMING_SNAKE_CASE__: Optional[int]=   load_metric('''cer'''   )

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

 # print & log results
 SCREAMING_SNAKE_CASE__: Tuple=   F'WER: {wer_result}\nCER: {cer_result}'
 print(snake_case_   )

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

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

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

   result.map(snake_case_							,					with_indices=snake_case_   )







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

 SCREAMING_SNAKE_CASE__: Optional[Any]=   re.sub(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!
 SCREAMING_SNAKE_CASE__: Tuple=   ['''\n\n''', '''\n''', '''   ''', '''  ''']

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

 return text







def       A__   (						snake_case_			:		Tuple   ):
 # load dataset
 SCREAMING_SNAKE_CASE__: Union[str, Any]=   load_dataset(args.dataset							,					args.config							,					split=args.split							,					use_auth_token=snake_case_   )

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

 # load processor
 SCREAMING_SNAKE_CASE__: Dict=   AutoFeatureExtractor.from_pretrained(args.model_id   )
 SCREAMING_SNAKE_CASE__: Dict=   feature_extractor.sampling_rate

 # resample audio
 SCREAMING_SNAKE_CASE__: Tuple=   dataset.cast_column('''audio'''							,					Audio(sampling_rate=snake_case_   )   )

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

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

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

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

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


if __name__ == "__main__":
      lowercase_  : int								=					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.',
      )
      lowercase_  : Optional[int]								=					parser.parse_args()

      main(args)




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase_   							=  {
    'configuration_timesformer': ['TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TimesformerConfig'],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_   							=  [
      'TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TimesformerModel',
      'TimesformerForVideoClassification',
      'TimesformerPreTrainedModel',
  ]

if TYPE_CHECKING:
  from .configuration_timesformer import TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, TimesformerConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_timesformer import (
        TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        TimesformerForVideoClassification,
        TimesformerModel,
        TimesformerPreTrainedModel,
    )

else:
  import sys

  lowercase_   							=  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


