

'''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  _A		(     snake_case		,							snake_case					)      ->     List[Any]:
   _lowercase      :				int      					=       args.log_outputs
   _lowercase      :				Union[str, Any]      					=       '''_'''.join(args.dataset.split("/"					) + [args.config, args.split]					)

   # load metric
   _lowercase      :				Union[str, Any]      					=       load_metric("wer"					)
   _lowercase      :				Tuple      					=       load_metric("cer"					)

   # compute metrics
   _lowercase      :				Union[str, Any]      					=       wer.compute(references=result["target"]		,							predictions=result["prediction"]					)
   _lowercase      :				List[Any]      					=       cer.compute(references=result["target"]		,							predictions=result["prediction"]					)

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

   _lowercase      :				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!
   _lowercase      :				Optional[Any]      					=       ['''\n\n''', '''\n''', '''   ''', '''  ''']

   for t in token_sequences_to_ignore:
      _lowercase      :				int      					=       ''' '''.join(text.split(_snake_case					)					)

   return text







def  _A		(     snake_case					)      ->     Tuple:
   _lowercase      :				Optional[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
   _lowercase      :				Tuple      					=       AutoFeatureExtractor.from_pretrained(args.model_id					)
   _lowercase      :				str      					=       feature_extractor.sampling_rate

   # resample audio
   _lowercase      :				Tuple      					=       dataset.cast_column("audio"		,							Audio(sampling_rate=_snake_case					)					)

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

   # map function to decode audio
   def map_to_pred(snake_case					):
      _lowercase      :				Tuple      					=       asr(
          batch["audio"]["array"]		,							chunk_length_s=args.chunk_length_s		,							stride_length_s=args.stride_length_s					)

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

   # run inference on all examples
   _lowercase      :				Optional[int]      					=       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__":
    _snake_case         =  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.',
    )
    _snake_case         =  parser.parse_args()

    main(args)









'''simple docstring'''
import logging

from transformers import PretrainedConfig


_snake_case         =  logging.getLogger(__name__)


_snake_case         =  {
    'bertabs-finetuned-cnndm': 'https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json',
}


class        a__  (							lowerCamelCase_  ):
   _SCREAMING_SNAKE_CASE   :					List[str]     	=							'bertabs'


   def __init__(			self      ,    _UpperCamelCase=30522      ,    _UpperCamelCase=512      ,    _UpperCamelCase=6      ,    _UpperCamelCase=512      ,    _UpperCamelCase=8      ,    _UpperCamelCase=512      ,    _UpperCamelCase=0.2      ,    _UpperCamelCase=6      ,    _UpperCamelCase=768      ,    _UpperCamelCase=8      ,    _UpperCamelCase=2048      ,    _UpperCamelCase=0.2      ,    **_UpperCamelCase      ,    ):





      """simple docstring"""





      super().__init__(**_UpperCamelCase  )

      _lowercase      :				List[str]      					=       vocab_size
      _lowercase      :				Tuple      					=       max_pos

      _lowercase      :				int      					=       enc_layers
      _lowercase      :				str      					=       enc_hidden_size
      _lowercase      :				Optional[Any]      					=       enc_heads
      _lowercase      :				Union[str, Any]      					=       enc_ff_size
      _lowercase      :				Tuple      					=       enc_dropout

      _lowercase      :				Dict      					=       dec_layers
      _lowercase      :				List[str]      					=       dec_hidden_size
      _lowercase      :				List[str]      					=       dec_heads
      _lowercase      :				str      					=       dec_ff_size
      _lowercase      :				str      					=       dec_dropout






