





'''simple docstring'''







import re
import warnings
from contextlib import contextmanager

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







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

      snake_case				=			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__(A__    ,   A__    )
      snake_case				=			self.image_processor
      snake_case				=			False







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

      snake_case				=			kwargs.pop('''images'''    ,   A__    )
      snake_case				=			kwargs.pop('''text'''    ,   A__    )
      if len(A__    ) > 0:
       snake_case				=			args[0]
       snake_case				=			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:
       snake_case				=			self.image_processor(A__    ,   *A__    ,   **A__    )
      if text is not None:
       snake_case				=			self.tokenizer(A__    ,   **A__    )

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







     def     UpperCamelCase			( self    ,   *A__    ,   **A__    ):
      return self.tokenizer.batch_decode(*A__    ,   **A__    )







     def     UpperCamelCase			( self    ,   *A__    ,   **A__    ):
      return self.tokenizer.decode(*A__    ,   **A__    )







     @contextmanager
     def     UpperCamelCase			( 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.'''    )
      snake_case				=			True
      snake_case				=			self.tokenizer
      yield
      snake_case				=			self.image_processor
      snake_case				=			False







     def     UpperCamelCase			( self    ,   A__    ,   A__=False    ,   A__=None    ):
      if added_vocab is None:
       snake_case				=			self.tokenizer.get_added_vocab()

      snake_case				=			{}

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

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

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







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







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








'''simple docstring'''







_lowercase       						=			{
    'Pillow': 'Pillow',
    'accelerate': 'accelerate>=0.11.0',
    'compel': 'compel==0.1.8',
    'black': 'black~=23.1',
    'datasets': 'datasets',
    'filelock': 'filelock',
    'flax': 'flax>=0.4.1',
    'hf-doc-builder': 'hf-doc-builder>=0.3.0',
    'huggingface-hub': 'huggingface-hub>=0.13.2',
    'requests-mock': 'requests-mock==1.10.0',
    'importlib_metadata': 'importlib_metadata',
    'invisible-watermark': 'invisible-watermark',
    'isort': 'isort>=5.5.4',
    'jax': 'jax>=0.2.8,!=0.3.2',
    'jaxlib': 'jaxlib>=0.1.65',
    'Jinja2': 'Jinja2',
    'k-diffusion': 'k-diffusion>=0.0.12',
    'torchsde': 'torchsde',
    'note_seq': 'note_seq',
    'librosa': 'librosa',
    'numpy': 'numpy',
    'omegaconf': 'omegaconf',
    'parameterized': 'parameterized',
    'protobuf': 'protobuf>=3.20.3,<4',
    'pytest': 'pytest',
    'pytest-timeout': 'pytest-timeout',
    'pytest-xdist': 'pytest-xdist',
    'ruff': 'ruff>=0.0.241',
    'safetensors': 'safetensors',
    'sentencepiece': 'sentencepiece>=0.1.91,!=0.1.92',
    'scipy': 'scipy',
    'onnx': 'onnx',
    'regex': 'regex!=2019.12.17',
    'requests': 'requests',
    'tensorboard': 'tensorboard',
    'torch': 'torch>=1.4',
    'torchvision': 'torchvision',
    'transformers': 'transformers>=4.25.1',
    'urllib3': 'urllib3<=2.0.0',
}

