

import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class 					a					(				lowercase__     ):



      """simple docstring"""


      a						:				Optional[Any]							 =			['image_processor', 'tokenizer']
      a						:				Union[str, Any]							 =			'AutoImageProcessor'
      a						:				Union[str, Any]							 =			'AutoTokenizer'
      def __init__(   self      :    Optional[int]	,   __lowercase      :    Any=None	,   __lowercase      :    Any=None	,   **__lowercase      :    List[str]      )							->   Dict:
        __UpperCAmelCase							:			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."""	,   __lowercase	,   )
          __UpperCAmelCase							:			List[str]     =						kwargs.pop("""feature_extractor"""      )

        __UpperCAmelCase							:			Union[str, Any]     =						image_processor if image_processor is not None else feature_extractor
        if image_processor is None:
          raise ValueError("""You have to specify an image_processor."""      )
        if tokenizer is None:
          raise ValueError("""You have to specify a tokenizer."""      )

        super().__init__(__lowercase	,   __lowercase      )
        __UpperCAmelCase							:			str     =						self.image_processor
      def __call__(   self      :    int	,   __lowercase      :    List[str]=None	,   __lowercase      :    Optional[Any]=None	,   __lowercase      :    Optional[Any]=None	,   **__lowercase      :    Tuple      )							->   List[str]:

        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:
          __UpperCAmelCase							:			List[Any]     =						self.tokenizer(__lowercase	,   return_tensors=__lowercase	,   **__lowercase      )

        if images is not None:
          __UpperCAmelCase							:			Tuple     =						self.image_processor(__lowercase	,   return_tensors=__lowercase	,   **__lowercase      )

        if text is not None and images is not None:
          __UpperCAmelCase							:			Tuple     =						image_features.pixel_values
          return encoding
        elif text is not None:
          return encoding
        else:
          return BatchEncoding(data=dict(**__lowercase      )	,   tensor_type=__lowercase      )
      def 			UpperCAmelCase   (   self      :    List[str]	,   *__lowercase      :    Optional[Any]	,   **__lowercase      :    str      )							->   Any:
        return self.tokenizer.batch_decode(*__lowercase	,   **__lowercase      )
      def 			UpperCAmelCase   (   self      :    Dict	,   *__lowercase      :    List[str]	,   **__lowercase      :    str      )							->   Union[str, Any]:
        return self.tokenizer.decode(*__lowercase	,   **__lowercase      )
      @property
      def 			UpperCAmelCase   (   self      :    Union[str, Any]      )							->   str:
        __UpperCAmelCase							:			Dict     =						self.tokenizer.model_input_names
        __UpperCAmelCase							:			Tuple     =						self.image_processor.model_input_names
        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names      )      )
      @property
      def 			UpperCAmelCase   (   self      :    int      )							->   Optional[int]:
        warnings.warn(
            """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""	,   __lowercase	,   )
        return self.image_processor_class




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










import decimal






def        lowerCamelCase__   (					):
  __UpperCAmelCase							:			str     =						0
  __UpperCAmelCase							:			Tuple     =						decimal.Context(prec=105	)
  for i in range(2						,	100	):
    __UpperCAmelCase							:			Optional[int]     =						decimal.Decimal(__lowerCamelCase	)
    __UpperCAmelCase							:			Any     =						number.sqrt(__lowerCamelCase	)
    if len(str(__lowerCamelCase	)	) > 1:
      answer += int(str(__lowerCamelCase	)[0]	)
      __UpperCAmelCase							:			Optional[Any]     =						str(__lowerCamelCase	)[2:101]
      answer += sum(int(__lowerCamelCase	) for x in sqrt_number_str	)
  return answer


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       print(f"""{solution() = }""")


