


'''simple docstring'''
import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class        lowerCAmelCase_  (     UpperCAmelCase	):
       __UpperCAmelCase					=['image_processor', 'tokenizer']
       __UpperCAmelCase					='CLIPImageProcessor'
       __UpperCAmelCase					=('CLIPTokenizer', 'CLIPTokenizerFast')






       def __init__( self			,     _UpperCamelCase=None			,     _UpperCamelCase=None			,     **_UpperCamelCase   )->      Tuple:
            _A				=							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			,     )
                 _A				=							kwargs.pop('feature_extractor'   )

            _A				=							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   )






       def __call__( self			,     _UpperCamelCase=None			,     _UpperCamelCase=None			,     _UpperCamelCase=None			,     **_UpperCamelCase   )->      List[Any]:

            if text is None and images is None:
                 raise ValueError('You have to specify either text or images. Both cannot be none.'   )

            if text is not None:
                 _A				=							self.tokenizer(_UpperCamelCase			,     return_tensors=_UpperCamelCase			,     **_UpperCamelCase   )

            if images is not None:
                 _A				=							self.image_processor(_UpperCamelCase			,     return_tensors=_UpperCamelCase			,     **_UpperCamelCase   )

            if text is not None and images is not None:
                 _A				=							image_features.pixel_values
                 return encoding
            elif text is not None:
                 return encoding
            else:
                 return BatchEncoding(data=dict(**_UpperCamelCase   )			,     tensor_type=_UpperCamelCase   )






       def   UpperCamelCase						( self			,     *_UpperCamelCase			,     **_UpperCamelCase   )->      Tuple:
            return self.tokenizer.batch_decode(*_UpperCamelCase			,     **_UpperCamelCase   )






       def   UpperCamelCase						( self			,     *_UpperCamelCase			,     **_UpperCamelCase   )->      List[str]:
            return self.tokenizer.decode(*_UpperCamelCase			,     **_UpperCamelCase   )






       @property
       def   UpperCamelCase						( self   )->      Optional[Any]:
            _A				=							self.tokenizer.model_input_names
            _A				=							self.image_processor.model_input_names
            return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names   )   )






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









'''simple docstring'''
from __future__ import annotations

def 					lowerCamelCase_ ( __UpperCamelCase					:       dict							,    __UpperCamelCase					:       str		)					->	set[str]:







     """simple docstring"""
     _A		,				_A				=							set(__UpperCamelCase		), [start]

     while stack:
          _A				=							stack.pop()
          explored.add(__UpperCamelCase		)
          # Differences from BFS:
          # 1) pop last element instead of first one
          # 2) add adjacent elements to stack without exploring them
          for adj in reversed(graph[v]		):
               if adj not in explored:
                    stack.append(__UpperCamelCase		)
     return explored


lowerCAmelCase						=					{
    """A""": ["""B""", """C""", """D"""],
    """B""": ["""A""", """D""", """E"""],
    """C""": ["""A""", """F"""],
    """D""": ["""B""", """D"""],
    """E""": ["""B""", """F"""],
    """F""": ["""C""", """E""", """G"""],
    """G""": ["""F"""],
}

if __name__ == "__main__":
    import doctest

    doctest.testmod()
    print(depth_first_search(G, """A"""))





