





'''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 				lowercase__   (   __lowercase						:  Dataset				,  __lowercase						:  Dict[str, str]     ) ->      Tuple:



       """simple docstring"""






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

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

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

       # print & log results
       __UpperCamelCase       =  F'''WER: {wer_result}\nCER: {cer_result}'''
       print(__lowercase     )

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

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

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

                     result.map(__lowercase				,  with_indices=__lowercase     )
def 				lowercase__   (   __lowercase						:  str     ) ->      str:



       """simple docstring"""






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

       __UpperCamelCase       =  re.sub(__lowercase				,  ''				,  text.lower()     )

       # In addition, we can normalize the target text, e.g. removing new lines characters etc...
       # note that order is important here!
       __UpperCamelCase       =  ['\n\n', '\n', '   ', '  ']

       for t in token_sequences_to_ignore:
              __UpperCamelCase       =  ' '.join(text.split(__lowercase     )     )

       return text
def 				lowercase__   (   __lowercase						:  Any     ) ->      Union[str, Any]:



       """simple docstring"""






       __UpperCamelCase       =  load_dataset(args.dataset				,  args.config				,  split=args.split				,  use_auth_token=__lowercase     )

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

       # load processor
       __UpperCamelCase       =  AutoFeatureExtractor.from_pretrained(args.model_id     )
       __UpperCamelCase       =  feature_extractor.sampling_rate

       # resample audio
       __UpperCamelCase       =  dataset.cast_column('audio'				,  Audio(sampling_rate=__lowercase     )     )

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

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

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

       # run inference on all examples
       __UpperCamelCase       =  dataset.map(__lowercase				,  remove_columns=dataset.column_names     )

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


if __name__ == "__main__":
    a__							:							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.''',
    )
    a__							:							Any =parser.parse_args()

    main(args)







'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a__							:							List[str] ={
    '''configuration_bigbird_pegasus''': [
        '''BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''BigBirdPegasusConfig''',
        '''BigBirdPegasusOnnxConfig''',
    ],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    a__							:							Any =[
        '''BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''BigBirdPegasusForCausalLM''',
        '''BigBirdPegasusForConditionalGeneration''',
        '''BigBirdPegasusForQuestionAnswering''',
        '''BigBirdPegasusForSequenceClassification''',
        '''BigBirdPegasusModel''',
        '''BigBirdPegasusPreTrainedModel''',
    ]


if TYPE_CHECKING:
    from .configuration_bigbird_pegasus import (
        BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP,
        BigBirdPegasusConfig,
        BigBirdPegasusOnnxConfig,
    )

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_bigbird_pegasus import (
            BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
            BigBirdPegasusForCausalLM,
            BigBirdPegasusForConditionalGeneration,
            BigBirdPegasusForQuestionAnswering,
            BigBirdPegasusForSequenceClassification,
            BigBirdPegasusModel,
            BigBirdPegasusPreTrainedModel,
        )


else:
    import sys

    a__							:							str =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
