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  __UpperCamelCase    (						A ,							A							):
     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(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:
          UpperCamelCase__										=      f"log_{dataset_id}_predictions.txt"
          UpperCamelCase__										=      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 ,							A							):
                    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  __UpperCamelCase    (						A							):
     UpperCamelCase__										=      '''[,?.!\-\;\:"“%‘”�—’…–]'''  # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training

     UpperCamelCase__										=      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!
     UpperCamelCase__										=      ['''\n\n''', '''\n''', '''   ''', '''  ''']

     for t in token_sequences_to_ignore:
          UpperCamelCase__										=      ''' '''.join(text.split(A							)							)

     return text







def  __UpperCamelCase    (						A							):
     # load dataset
     UpperCamelCase__										=      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
     UpperCamelCase__										=      AutoFeatureExtractor.from_pretrained(args.model_id							)
     UpperCamelCase__										=      feature_extractor.sampling_rate

     # resample audio
     UpperCamelCase__										=      dataset.cast_column('''audio''' ,							Audio(sampling_rate=A							)							)

     # 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(A							):
          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(A ,							remove_columns=dataset.column_names							)

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


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

     main(args)




import warnings

from ...utils import logging
from .image_processing_beit import BeitImageProcessor


__magic_name__								=logging.get_logger(__name__)



class     _A   (    __UpperCamelCase							):






  def __init__(self   ,				*SCREAMING_SNAKE_CASE_   ,				**SCREAMING_SNAKE_CASE_	)		->      None:





       '''simple docstring'''





       warnings.warn(
           '''The class BeitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
           ''' use BeitImageProcessor instead.'''   ,				SCREAMING_SNAKE_CASE_   ,				)
       super().__init__(*SCREAMING_SNAKE_CASE_   ,				**SCREAMING_SNAKE_CASE_	)



