import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin







class 			SCREAMING_SNAKE_CASE__ (   __UpperCamelCase  ):
       A				:     List[Any]					    =     ["image_processor", "tokenizer"]
       A				:     str					    =     "AutoImageProcessor"
       A				:     List[str]					    =     "AutoTokenizer"

       def __init__(				self :			Union[str, Any]       ,       _lowerCAmelCase :			str=None       ,       _lowerCAmelCase :			Tuple=None       ,       **_lowerCAmelCase :			str       ):
           __snake_case    :							List[str]             =  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    :							int             =  kwargs.pop("""feature_extractor"""       )

           __snake_case    :							str             =  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    :							int             =  self.image_processor
           __snake_case    :							int             =  False

       def __call__(				self :			str       ,       *_lowerCAmelCase :			List[Any]       ,       **_lowerCAmelCase :			int       ):


           if self._in_target_context_manager:
               return self.current_processor(*A_       ,       **A_       )

           __snake_case    :							Dict             =  kwargs.pop("""images"""       ,       A_       )
           __snake_case    :							Any             =  kwargs.pop("""text"""       ,       A_       )
           if len(A_       ) > 0:
               __snake_case    :							List[Any]             =  args[0]
               __snake_case    :							Any             =  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    :							Any             =  self.image_processor(A_       ,       *A_       ,       **A_       )
           if text is not None:
               __snake_case    :							int             =  self.tokenizer(A_       ,       **A_       )

           if text is None:
               return inputs
           elif images is None:
               return encodings
           else:
               __snake_case    :							Optional[Any]             =  encodings["input_ids"]
               return inputs

       def        snake_case__  (				self :			Tuple       ,       *_lowerCAmelCase :			List[Any]       ,       **_lowerCAmelCase :			Optional[Any]       ):
           return self.tokenizer.batch_decode(*A_       ,       **A_       )

       def        snake_case__  (				self :			List[Any]       ,       *_lowerCAmelCase :			Union[str, Any]       ,       **_lowerCAmelCase :			Optional[Any]       ):
           return self.tokenizer.decode(*A_       ,       **A_       )

       @contextmanager
       def        snake_case__  (				self :			str       ):
           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    :							Union[str, Any]             =  True
           __snake_case    :							int             =  self.tokenizer
           yield
           __snake_case    :							Optional[int]             =  self.image_processor
           __snake_case    :							Any             =  False

       @property
       def        snake_case__  (				self :			Dict       ):
           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        snake_case__  (				self :			Dict       ):
           warnings.warn(
               """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""       ,       A_       ,       )
           return self.image_processor






from hashlib import mda, shaaaa

SCREAMING_SNAKE_CASE_						=					(shaaaa, mda)

class  a     :


						def __init__(				self  ,    A_ = 8		):







										'''simple docstring'''


										_UpperCAmelCase				:						Any       						=			0B0
										_UpperCAmelCase				:						Optional[Any]       						=			size


						def 				_UpperCAmelCase (				self  ,    A_		):







										'''simple docstring'''


										_UpperCAmelCase				:						Optional[Any]       						=			self.hash_(A_		)
										self.bitarray |= h


						def 				_UpperCAmelCase (				self  ,    A_		):







										'''simple docstring'''


										_UpperCAmelCase				:						int       						=			self.hash_(A_		)
										return (h & self.bitarray) == h


						def __contains__(				self  ,    A_		):







										'''simple docstring'''


										return self.exists(A_		)


						def 				_UpperCAmelCase (				self  ,    A_		):







										'''simple docstring'''


										_UpperCAmelCase				:						Optional[int]       						=			bin(A_		)[2:]
										return res.zfill(self.size		)


						@property
						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										return self.format_bin(self.bitarray		)


						def 				_UpperCAmelCase (				self  ,    A_		):







										'''simple docstring'''


										_UpperCAmelCase				:						Union[str, Any]       						=			0B0
										for func in HASH_FUNCTIONS:
														_UpperCAmelCase				:						str       						=			(
														    int.from_bytes(func(value.encode()		).digest()  ,    "little"		) % self.size
														)
														res |= 2**position
										return res


						def 				_UpperCAmelCase (				self  ,    A_		):







										'''simple docstring'''


										return self.format_bin(self.hash_(A_		)		)





						@property
						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										_UpperCAmelCase				:						Union[str, Any]       						=			bin(self.bitarray		).count("1"		)
										return (n_ones / self.size) ** len(A_		)


