

"""simple docstring"""






import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
class        _lowercase					(     __UpperCAmelCase     ):
     _lowerCamelCase     =       ['''image_processor''', '''tokenizer''']
     _lowerCamelCase     =       '''AutoImageProcessor'''
     _lowerCamelCase     =       '''AutoTokenizer'''







     def __init__(      self ,						UpperCamelCase_=None ,						UpperCamelCase_=None ,						**UpperCamelCase_       ):
      __magic_name__     =      None
      if "feature_extractor" in kwargs:
       warnings.warn(
           '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
           ''' instead.''' ,						UpperCamelCase_ ,						)
       __magic_name__     =      kwargs.pop('''feature_extractor'''       )

      __magic_name__     =      image_processor if image_processor is not None else feature_extractor
      if image_processor is None:
       raise ValueError('''You need to specify an `image_processor`.'''       )
      if tokenizer is None:
       raise ValueError('''You need to specify a `tokenizer`.'''       )

      super().__init__(UpperCamelCase_ ,						UpperCamelCase_       )
      __magic_name__     =      self.image_processor
      __magic_name__     =      False







     def __call__(      self ,						*UpperCamelCase_ ,						**UpperCamelCase_       ):
      # For backward compatibility
      if self._in_target_context_manager:
       return self.current_processor(*UpperCamelCase_ ,						**UpperCamelCase_       )

      __magic_name__     =      kwargs.pop('''images''' ,						UpperCamelCase_       )
      __magic_name__     =      kwargs.pop('''text''' ,						UpperCamelCase_       )
      if len(UpperCamelCase_       ) > 0:
       __magic_name__     =      args[0]
       __magic_name__     =      args[1:]

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

      if images is not None:
       __magic_name__     =      self.image_processor(UpperCamelCase_ ,						*UpperCamelCase_ ,						**UpperCamelCase_       )
      if text is not None:
       __magic_name__     =      self.tokenizer(UpperCamelCase_ ,						**UpperCamelCase_       )

      if text is None:
       return inputs
      elif images is None:
       return encodings
      else:
       __magic_name__     =      encodings['''input_ids''']
       return inputs







     def 	lowerCAmelCase__    (      self ,						*UpperCamelCase_ ,						**UpperCamelCase_       ):
      return self.tokenizer.batch_decode(*UpperCamelCase_ ,						**UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self ,						*UpperCamelCase_ ,						**UpperCamelCase_       ):
      return self.tokenizer.decode(*UpperCamelCase_ ,						**UpperCamelCase_       )







     @contextmanager
     def 	lowerCAmelCase__    (      self       ):
      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 images inputs, or in a separate call.'''       )
      __magic_name__     =      True
      __magic_name__     =      self.tokenizer
      yield
      __magic_name__     =      self.image_processor
      __magic_name__     =      False







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_=False ,						UpperCamelCase_=None       ):
      if added_vocab is None:
       __magic_name__     =      self.tokenizer.get_added_vocab()

      __magic_name__     =      {}

      while tokens:
       __magic_name__     =      re.search(R'''<s_(.*?)>''' ,						UpperCamelCase_ ,						re.IGNORECASE       )
       if start_token is None:
        break
       __magic_name__     =      start_token.group(1       )
       __magic_name__     =      re.search(Rf'''</s_{key}>''' ,						UpperCamelCase_ ,						re.IGNORECASE       )
       __magic_name__     =      start_token.group()
       if end_token is None:
        __magic_name__     =      tokens.replace(UpperCamelCase_ ,						''''''       )
       else:
        __magic_name__     =      end_token.group()
        __magic_name__     =      re.escape(UpperCamelCase_       )
        __magic_name__     =      re.escape(UpperCamelCase_       )
        __magic_name__     =      re.search(f'''{start_token_escaped}(.*?){end_token_escaped}''' ,						UpperCamelCase_ ,						re.IGNORECASE       )
        if content is not None:
         __magic_name__     =      content.group(1       ).strip()
         if r"<s_" in content and r"</s_" in content:  # non-leaf node
          __magic_name__     =      self.tokenajson(UpperCamelCase_ ,						is_inner_value=UpperCamelCase_ ,						added_vocab=UpperCamelCase_       )
          if value:
           if len(UpperCamelCase_       ) == 1:
            __magic_name__     =      value[0]
           __magic_name__     =      value
         else:  # leaf nodes
          __magic_name__     =      []
          for leaf in content.split(R'''<sep/>'''       ):
           __magic_name__     =      leaf.strip()
           if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
            __magic_name__     =      leaf[1:-2]  # for categorical special tokens
           output[key].append(UpperCamelCase_       )
          if len(output[key]       ) == 1:
           __magic_name__     =      output[key][0]

        __magic_name__     =      tokens[tokens.find(UpperCamelCase_       ) + len(UpperCamelCase_       ) :].strip()
        if tokens[:6] == r"<sep/>":  # non-leaf nodes
         return [output] + self.tokenajson(tokens[6:] ,						is_inner_value=UpperCamelCase_ ,						added_vocab=UpperCamelCase_       )

      if len(UpperCamelCase_       ):
       return [output] if is_inner_value else output
      else:
       return [] if is_inner_value else {"text_sequence": tokens}







     @property
     def 	lowerCAmelCase__    (      self       ):
      warnings.warn(
          '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.''' ,						UpperCamelCase_ ,						)
      return self.image_processor_class







     @property
     def 	lowerCAmelCase__    (      self       ):
      warnings.warn(
          '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.''' ,						UpperCamelCase_ ,						)
      return self.image_processor




"""simple docstring"""






from pathlib import Path

import fire





def 	lowercase      (   __UpperCamelCase      ,   __UpperCamelCase      ,   __UpperCamelCase						) ->      Optional[int]:
 __magic_name__     =      Path(__UpperCamelCase						)
 __magic_name__     =      Path(__UpperCamelCase						)
 dest_dir.mkdir(exist_ok=__UpperCamelCase						)
 for path in src_dir.iterdir():
  __magic_name__     =      [x.rstrip() for x in list(path.open().readlines()						)][:n]
  __magic_name__     =      dest_dir.joinpath(path.name						)
  print(__UpperCamelCase						)
  dest_path.open('''w'''						).write('''\n'''.join(__UpperCamelCase						)						)


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

