





"""simple docstring"""


import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin



class    __SCREAMING_SNAKE_CASE			(  _UpperCamelCase      ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__							:List[str]		   =    ["image_processor", "tokenizer"]
  SCREAMING_SNAKE_CASE__							:List[Any]		   =    "AutoImageProcessor"
  SCREAMING_SNAKE_CASE__							:Union[str, Any]		   =    "AutoTokenizer"

  def __init__(						self      :				List[str]    ,      __a      :				Optional[int]=None    ,      __a      :				Dict=None    ,      **__a      :				Union[str, Any]		)						-> Optional[Any]:
        _UpperCamelCase					:		Optional[Any]  =				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    ,      )
              _UpperCamelCase					:		List[Any]  =				kwargs.pop("feature_extractor"		)

        _UpperCamelCase					:		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		)
        _UpperCamelCase					:		Union[str, Any]  =				self.image_processor
        _UpperCamelCase					:		Union[str, Any]  =				False

  def __call__(						self      :				str    ,      *__a      :				List[Any]    ,      **__a      :				Optional[Any]		)						-> str:
        # For backward compatibility
        if self._in_target_context_manager:
              return self.current_processor(*__a    ,      **__a		)

        _UpperCamelCase					:		List[Any]  =				kwargs.pop("images"    ,      __a		)
        _UpperCamelCase					:		Optional[Any]  =				kwargs.pop("text"    ,      __a		)
        if len(__a		) > 0:
              _UpperCamelCase					:		Optional[Any]  =				args[0]
              _UpperCamelCase					:		Union[str, 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:
              _UpperCamelCase					:		Optional[Any]  =				self.image_processor(__a    ,      *__a    ,      **__a		)
        if text is not None:
              _UpperCamelCase					:		Tuple  =				self.tokenizer(__a    ,      **__a		)

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

  def       __SCREAMING_SNAKE_CASE (						self      :				str    ,      *__a      :				str    ,      **__a      :				Dict		)						-> Tuple:
        return self.tokenizer.batch_decode(*__a    ,      **__a		)

  def       __SCREAMING_SNAKE_CASE (						self      :				Union[str, Any]    ,      *__a      :				List[str]    ,      **__a      :				List[Any]		)						-> Union[str, Any]:
        return self.tokenizer.decode(*__a    ,      **__a		)

  @contextmanager
  def       __SCREAMING_SNAKE_CASE (						self      :				Optional[Any]		)						-> Tuple:
        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."		)
        _UpperCamelCase					:		Tuple  =				True
        _UpperCamelCase					:		Optional[int]  =				self.tokenizer
        yield
        _UpperCamelCase					:		Optional[Any]  =				self.image_processor
        _UpperCamelCase					:		Optional[Any]  =				False

  @property
  def       __SCREAMING_SNAKE_CASE (						self      :				Tuple		)						-> Optional[Any]:
        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       __SCREAMING_SNAKE_CASE (						self      :				Any		)						-> List[Any]:
        warnings.warn(
            "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."    ,      __a    ,      )
        return self.image_processor







"""simple docstring"""


def     lowercase__			(    lowercase_	)    ->						str:




      """simple docstring"""

      return " ".join(sorted(set(sentence.split()	)	)	)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
