"""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__( __SCREAMING_SNAKE_CASE							:  Dataset  ,    __SCREAMING_SNAKE_CASE							:  Dict[str, str]   ):
     lowercase_			:       Optional[int]									=			args.log_outputs
     lowercase_			:       Any									=			'_'.join(args.dataset.split('/'   ) + [args.config, args.split]   )

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

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

     # print & log results
     lowercase_			:       Dict									=			F'''WER: {wer_result}\nCER: {cer_result}'''
     print(__SCREAMING_SNAKE_CASE   )

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

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

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

               result.map(__SCREAMING_SNAKE_CASE  ,    with_indices=__SCREAMING_SNAKE_CASE   )



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

     lowercase_			:       List[str]									=			re.sub(__SCREAMING_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_			:       Union[str, Any]									=			['\n\n', '\n', '   ', '  ']

     for t in token_sequences_to_ignore:
          lowercase_			:       str									=			' '.join(text.split(__SCREAMING_SNAKE_CASE   )   )

     return text



def     lowercase__( __SCREAMING_SNAKE_CASE							:  Tuple   ):
     # load dataset
     lowercase_			:       Optional[Any]									=			load_dataset(args.dataset  ,    args.config  ,    split=args.split  ,    use_auth_token=__SCREAMING_SNAKE_CASE   )

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

     # load processor
     lowercase_			:       int									=			AutoFeatureExtractor.from_pretrained(args.model_id   )
     lowercase_			:       str									=			feature_extractor.sampling_rate

     # resample audio
     lowercase_			:       Optional[Any]									=			dataset.cast_column('audio'  ,    Audio(sampling_rate=__SCREAMING_SNAKE_CASE   )   )

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

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

          lowercase_			:       Optional[Any]									=			prediction['text']
          lowercase_			:       int									=			normalize_text(batch['sentence']   )
          return batch

     # run inference on all examples
     lowercase_			:       Optional[int]									=			dataset.map(__SCREAMING_SNAKE_CASE  ,    remove_columns=dataset.column_names   )

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


if __name__ == "__main__":
       __SCREAMING_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.",
       )
       __SCREAMING_SNAKE_CASE								=parser.parse_args()

       main(args)


"""simple docstring"""







import numpy as np



def     lowercase__( __SCREAMING_SNAKE_CASE							:  np.array   ):
     return 1 / (1 + np.exp(-vector   ))


if __name__ == "__main__":
       import doctest

       doctest.testmod()

