


'''simple docstring'''

import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer





class   snake_case					(					lowercase    ):





  """simple docstring"""




  _lowerCamelCase						=						"Wav2Vec2FeatureExtractor"
  _lowerCamelCase						=						"AutoTokenizer"






  def __init__(					self    ,						UpperCamelCase    ,						UpperCamelCase  ):







   """simple docstring"""
   super().__init__(UpperCamelCase    ,						UpperCamelCase  )
   lowerCamelCase_  				=						self.feature_extractor
   lowerCamelCase_  				=						False






  @classmethod
  def 						snake_case	(					cls    ,						UpperCamelCase    ,						**UpperCamelCase  ):







   """simple docstring"""
   try:
    return super().from_pretrained(UpperCamelCase    ,						**UpperCamelCase  )
   except OSError:
    warnings.warn(
        f'''Loading a tokenizer inside {cls.__name__} from a config that does not'''
        " include a `tokenizer_class` attribute is deprecated and will be "
        "removed in v5. Please add `'tokenizer_class': 'Wav2Vec2CTCTokenizer'`"
        " attribute to either your `config.json` or `tokenizer_config.json` "
        "file to suppress this warning: "    ,						UpperCamelCase    ,						)

    lowerCamelCase_  				=						WavaVecaFeatureExtractor.from_pretrained(UpperCamelCase    ,						**UpperCamelCase  )
    lowerCamelCase_  				=						WavaVecaCTCTokenizer.from_pretrained(UpperCamelCase    ,						**UpperCamelCase  )

    return cls(feature_extractor=UpperCamelCase    ,						tokenizer=UpperCamelCase  )






  def __call__(					self    ,						*UpperCamelCase    ,						**UpperCamelCase  ):







   """simple docstring"""
   # For backward compatibility
   if self._in_target_context_manager:
    return self.current_processor(*UpperCamelCase    ,						**UpperCamelCase  )

   if "raw_speech" in kwargs:
    warnings.warn("Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead."  )
    lowerCamelCase_  				=						kwargs.pop("raw_speech"  )
   else:
    lowerCamelCase_  				=						kwargs.pop("audio"    ,						UpperCamelCase  )
   lowerCamelCase_  				=						kwargs.pop("sampling_rate"    ,						UpperCamelCase  )
   lowerCamelCase_  				=						kwargs.pop("text"    ,						UpperCamelCase  )
   if len(UpperCamelCase  ) > 0:
    lowerCamelCase_  				=						args[0]
    lowerCamelCase_  				=						args[1:]

   if audio is None and text is None:
    raise ValueError("You need to specify either an `audio` or `text` input to process."  )

   if audio is not None:
    lowerCamelCase_  				=						self.feature_extractor(UpperCamelCase    ,						*UpperCamelCase    ,						sampling_rate=UpperCamelCase    ,						**UpperCamelCase  )
   if text is not None:
    lowerCamelCase_  				=						self.tokenizer(UpperCamelCase    ,						**UpperCamelCase  )

   if text is None:
    return inputs
   elif audio is None:
    return encodings
   else:
    lowerCamelCase_  				=						encodings["input_ids"]
    return inputs






  def 						snake_case	(					self    ,						*UpperCamelCase    ,						**UpperCamelCase  ):







   """simple docstring"""
   # For backward compatibility
   if self._in_target_context_manager:
    return self.current_processor.pad(*UpperCamelCase    ,						**UpperCamelCase  )

   lowerCamelCase_  				=						kwargs.pop("input_features"    ,						UpperCamelCase  )
   lowerCamelCase_  				=						kwargs.pop("labels"    ,						UpperCamelCase  )
   if len(UpperCamelCase  ) > 0:
    lowerCamelCase_  				=						args[0]
    lowerCamelCase_  				=						args[1:]

   if input_features is not None:
    lowerCamelCase_  				=						self.feature_extractor.pad(UpperCamelCase    ,						*UpperCamelCase    ,						**UpperCamelCase  )
   if labels is not None:
    lowerCamelCase_  				=						self.tokenizer.pad(UpperCamelCase    ,						**UpperCamelCase  )

   if labels is None:
    return input_features
   elif input_features is None:
    return labels
   else:
    lowerCamelCase_  				=						labels["input_ids"]
    return input_features






  def 						snake_case	(					self    ,						*UpperCamelCase    ,						**UpperCamelCase  ):







   """simple docstring"""
   return self.tokenizer.batch_decode(*UpperCamelCase    ,						**UpperCamelCase  )






  def 						snake_case	(					self    ,						*UpperCamelCase    ,						**UpperCamelCase  ):







   """simple docstring"""
   return self.tokenizer.decode(*UpperCamelCase    ,						**UpperCamelCase  )







  @contextmanager
  def 						snake_case	(					self  ):







   """simple docstring"""
   warnings.warn(
       "`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your "
       "labels by using the argument `text` of the regular `__call__` method (either in the same call as "
       "your audio inputs, or in a separate call."  )
   lowerCamelCase_  				=						True
   lowerCamelCase_  				=						self.tokenizer
   yield
   lowerCamelCase_  				=						self.feature_extractor
   lowerCamelCase_  				=						False







'''simple docstring'''

import fire
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save





def __snake_case      (   UpperCAmelCase_		:		List[str]      ,       UpperCAmelCase_		:		Optional[int]      ,       UpperCAmelCase_		:		Optional[int]=1024      ,       UpperCAmelCase_		:		int=1024      ,       UpperCAmelCase_		:		Optional[Any]=False      ,       **UpperCAmelCase_		:		Optional[Any]	):
 lowerCamelCase_  				=						AutoTokenizer.from_pretrained(UpperCAmelCase_	)
 lowerCamelCase_  				=						SeqaSeqDataset(UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       type_path="train"      ,       **UpperCAmelCase_	)
 lowerCamelCase_  				=						tok.pad_token_id

 def get_lens(UpperCAmelCase_		:		str	):
  lowerCamelCase_  				=						tqdm(
      DataLoader(UpperCAmelCase_      ,       batch_size=512      ,       num_workers=8      ,       shuffle=UpperCAmelCase_      ,       collate_fn=ds.collate_fn	)      ,       desc=str(ds.len_file	)      ,       )
  lowerCamelCase_  				=						[]
  for batch in dl:
   lowerCamelCase_  				=						batch["input_ids"].ne(UpperCAmelCase_	).sum(1	).tolist()
   lowerCamelCase_  				=						batch["labels"].ne(UpperCAmelCase_	).sum(1	).tolist()
   if consider_target:
    for src, tgt in zip(UpperCAmelCase_      ,       UpperCAmelCase_	):
     max_lens.append(max(UpperCAmelCase_      ,       UpperCAmelCase_	)	)
   else:
    max_lens.extend(UpperCAmelCase_	)
  return max_lens

 lowerCamelCase_  				=						get_lens(UpperCAmelCase_	)
 lowerCamelCase_  				=						SeqaSeqDataset(UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       type_path="val"      ,       **UpperCAmelCase_	)
 lowerCamelCase_  				=						get_lens(UpperCAmelCase_	)
 pickle_save(UpperCAmelCase_      ,       train_ds.len_file	)
 pickle_save(UpperCAmelCase_      ,       val_ds.len_file	)


if __name__ == "__main__":
     fire.Fire(save_len_file)



