
'''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 		_UpperCAmelCase (     _lowerCamelCase  :				Dataset     ,					_lowerCamelCase  :				Dict[str, str]						)			->  Any:
  _lowerCAmelCase				:     int       =    args.log_outputs
  _lowerCAmelCase				:     Tuple       =    """_""".join(args.dataset.split("""/"""						) + [args.config, args.split]						)

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

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

  # print & log results
  _lowerCAmelCase				:     Union[str, Any]       =    f'WER: {wer_result}\nCER: {cer_result}'
  print(UpperCamelCase__						)

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

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

    with open(UpperCamelCase__     ,					"""w"""						) as p, open(UpperCamelCase__     ,					"""w"""						) as t:
      # mapping function to write output
      def write_to_file(_lowerCamelCase  :				Any     ,					_lowerCamelCase  :				Union[str, Any]						):
        p.write(f'{i}' + """\n"""						)
        p.write(batch["""prediction"""] + """\n"""						)
        t.write(f'{i}' + """\n"""						)
        t.write(batch["""target"""] + """\n"""						)

      result.map(UpperCamelCase__     ,					with_indices=UpperCamelCase__						)


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

  _lowerCAmelCase				:     Tuple       =    re.sub(UpperCamelCase__     ,					""""""     ,					text.lower()						)

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

  for t in token_sequences_to_ignore:
    _lowerCAmelCase				:     Tuple       =    """ """.join(text.split(UpperCamelCase__						)						)

  return text


def 		_UpperCAmelCase (     _lowerCamelCase  :				Dict						)			->  Dict:
  # load dataset
  _lowerCAmelCase				:     List[str]       =    load_dataset(args.dataset     ,					args.config     ,					split=args.split     ,					use_auth_token=UpperCamelCase__						)

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

  # load processor
  _lowerCAmelCase				:     Tuple       =    AutoFeatureExtractor.from_pretrained(args.model_id						)
  _lowerCAmelCase				:     Optional[int]       =    feature_extractor.sampling_rate

  # resample audio
  _lowerCAmelCase				:     Optional[Any]       =    dataset.cast_column("""audio"""     ,					Audio(sampling_rate=UpperCamelCase__						)						)

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

  # map function to decode audio
  def map_to_pred(_lowerCamelCase  :				Dict						):
    _lowerCAmelCase				:     int       =    asr(
        batch["""audio"""]["""array"""]     ,					chunk_length_s=args.chunk_length_s     ,					stride_length_s=args.stride_length_s						)

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

  # run inference on all examples
  _lowerCAmelCase				:     str       =    dataset.map(UpperCamelCase__     ,					remove_columns=dataset.column_names						)

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


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

     main(args)









import os
import re
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


a__:      Union[str, Any]									=		logging.get_logger(__name__)

a__:      Union[str, Any]									=		{'vocab_file': 'spiece.model'}

a__:      Tuple									=		{
    'vocab_file': {
        'google/bigbird-roberta-base': 'https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model',
        'google/bigbird-roberta-large': (
            'https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model'
        ),
        'google/bigbird-base-trivia-itc': (
            'https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model'
        ),
    }
}

a__:      Any									=		{
    'google/bigbird-roberta-base': 4_096,
    'google/bigbird-roberta-large': 4_096,
    'google/bigbird-base-trivia-itc': 4_096,
}




class     SCREAMING_SNAKE_CASE__ (						UpperCamelCase__      ):
 __SCREAMING_SNAKE_CASE							       =	VOCAB_FILES_NAMES
 __SCREAMING_SNAKE_CASE							       =	PRETRAINED_VOCAB_FILES_MAP
 __SCREAMING_SNAKE_CASE							       =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __SCREAMING_SNAKE_CASE							       =	['''input_ids''', '''attention_mask''']
 __SCREAMING_SNAKE_CASE							       =	[]



 def __init__(							self,__lowerCamelCase,__lowerCamelCase="<unk>",__lowerCamelCase="<s>",__lowerCamelCase="</s>",__lowerCamelCase="<pad>",__lowerCamelCase="[SEP]",__lowerCamelCase="[MASK]",__lowerCamelCase="[CLS]",__lowerCamelCase = None,**__lowerCamelCase,):
        A__											=						AddedToken(__lowerCamelCase,lstrip=__lowerCamelCase,rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase,__lowerCamelCase ) else bos_token
        A__											=						AddedToken(__lowerCamelCase,lstrip=__lowerCamelCase,rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase,__lowerCamelCase ) else eos_token
        A__											=						AddedToken(__lowerCamelCase,lstrip=__lowerCamelCase,rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase,__lowerCamelCase ) else unk_token
        A__											=						AddedToken(__lowerCamelCase,lstrip=__lowerCamelCase,rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase,__lowerCamelCase ) else pad_token
        A__											=						AddedToken(__lowerCamelCase,lstrip=__lowerCamelCase,rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase,__lowerCamelCase ) else cls_token
        A__											=						AddedToken(__lowerCamelCase,lstrip=__lowerCamelCase,rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase,__lowerCamelCase ) else sep_token

        # Mask token behave like a normal word, i.e. include the space before it
        A__											=						AddedToken(__lowerCamelCase,lstrip=__lowerCamelCase,rstrip=__lowerCamelCase ) if isinstance(__lowerCamelCase,__lowerCamelCase ) else mask_token

        A__											=						{} if sp_model_kwargs is None else sp_model_kwargs

        super().__init__(
            bos_token=__lowerCamelCase,eos_token=__lowerCamelCase,unk_token=__lowerCamelCase,pad_token=__lowerCamelCase,sep_token=__lowerCamelCase,mask_token=__lowerCamelCase,cls_token=__lowerCamelCase,sp_model_kwargs=self.sp_model_kwargs,**__lowerCamelCase,)

        A__											=						vocab_file

        A__											=						spm.SentencePieceProcessor(**self.sp_model_kwargs )
        self.sp_model.Load(__lowerCamelCase )



 @property
 def 				UpperCamelCase			(							self ):
        return self.sp_model.get_piece_size()



 def 				UpperCamelCase			(							self ):
        A__											=						{self.convert_ids_to_tokens(__lowerCamelCase ): i for i in range(self.vocab_size )}
        vocab.update(self.added_tokens_encoder )
        return vocab



 def __getstate__(							self ):
        A__											=						self.__dict__.copy()
        A__											=						None
        return state



 def __setstate__(							self,__lowerCamelCase ):
        A__											=						d

        # for backward compatibility
        if not hasattr(self,'''sp_model_kwargs''' ):
               A__											=						{}

        A__											=						spm.SentencePieceProcessor(**self.sp_model_kwargs )
        self.sp_model.Load(self.vocab_file )



 def 				UpperCamelCase			(							self,__lowerCamelCase ):
        return self.sp_model.encode(__lowerCamelCase,out_type=__lowerCamelCase )



 def 				UpperCamelCase			(							self,__lowerCamelCase ):
        return self.sp_model.piece_to_id(__lowerCamelCase )



 def 				UpperCamelCase			(							self,__lowerCamelCase ):
        A__											=						self.sp_model.IdToPiece(__lowerCamelCase )
        return token



 def 				UpperCamelCase			(							self,__lowerCamelCase ):
        A__											=						[]
        A__											=						''''''
        A__											=						False
        for token in tokens:
               # make sure that special tokens are not decoded using sentencepiece model
               if token in self.all_special_tokens:
                      if not prev_is_special:
                             out_string += " "
                      out_string += self.sp_model.decode(__lowerCamelCase ) + token
                      A__											=						True
                      A__											=						[]
               else:
                      current_sub_tokens.append(__lowerCamelCase )
                      A__											=						False
        out_string += self.sp_model.decode(__lowerCamelCase )
        return out_string.strip()



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase = False,__lowerCamelCase = None,__lowerCamelCase = True,**__lowerCamelCase,):
        A__											=						kwargs.pop('''use_source_tokenizer''',__lowerCamelCase )

        A__											=						self.convert_ids_to_tokens(__lowerCamelCase,skip_special_tokens=__lowerCamelCase )

        # To avoid mixing byte-level and unicode for byte-level BPT
        # we need to build string separately for added tokens and byte-level tokens
        # cf. https://github.com/huggingface/transformers/issues/1133
        A__											=						[]
        A__											=						[]
        for token in filtered_tokens:
               if skip_special_tokens and token in self.all_special_ids:
                      continue
               if token in self.added_tokens_encoder:
                      if current_sub_text:
                             sub_texts.append(self.convert_tokens_to_string(__lowerCamelCase ) )
                             A__											=						[]
                      sub_texts.append(__lowerCamelCase )
               else:
                      current_sub_text.append(__lowerCamelCase )
        if current_sub_text:
               sub_texts.append(self.convert_tokens_to_string(__lowerCamelCase ) )

        # Mimic the behavior of the Rust tokenizer:
        # No space before [MASK] and [SEP]
        if spaces_between_special_tokens:
               A__											=						re.sub(r''' (\[(MASK|SEP)\])''',r'''\1''',''' '''.join(__lowerCamelCase ) )
        else:
               A__											=						''''''.join(__lowerCamelCase )

        A__											=						(
            clean_up_tokenization_spaces
            if clean_up_tokenization_spaces is not None
            else self.clean_up_tokenization_spaces
        )
        if clean_up_tokenization_spaces:
               A__											=						self.clean_up_tokenization(__lowerCamelCase )
               return clean_text
        else:
               return text



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase = None ):
        if not os.path.isdir(__lowerCamelCase ):
               logger.error(f"Vocabulary path ({save_directory}) should be a directory" )
               return
        A__											=						os.path.join(
            __lowerCamelCase,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )

        if os.path.abspath(self.vocab_file ) != os.path.abspath(__lowerCamelCase ) and os.path.isfile(self.vocab_file ):
               copyfile(self.vocab_file,__lowerCamelCase )
        elif not os.path.isfile(self.vocab_file ):
               with open(__lowerCamelCase,'''wb''' ) as fi:
                      A__											=						self.sp_model.serialized_model_proto()
                      fi.write(__lowerCamelCase )

        return (out_vocab_file,)



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase = None ):
        if token_ids_a is None:
               return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
        A__											=						[self.cls_token_id]
        A__											=						[self.sep_token_id]
        return cls + token_ids_a + sep + token_ids_a + sep



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase = None,__lowerCamelCase = False ):
        if already_has_special_tokens:
               return super().get_special_tokens_mask(
                   token_ids_a=__lowerCamelCase,token_ids_a=__lowerCamelCase,already_has_special_tokens=__lowerCamelCase )

        if token_ids_a is None:
               return [1] + ([0] * len(__lowerCamelCase )) + [1]
        return [1] + ([0] * len(__lowerCamelCase )) + [1] + ([0] * len(__lowerCamelCase )) + [1]




 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase = None ):
        A__											=						[self.sep_token_id]
        A__											=						[self.cls_token_id]
        if token_ids_a is None:
               return len(cls + token_ids_a + sep ) * [0]
        return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]





