




"""simple docstring"""


import warnings

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






class  lowercase(					__lowercase			):







  '''simple docstring'''





  lowercase__			 = ['''image_processor''', '''tokenizer''']
  lowercase__			 = '''CLIPImageProcessor'''
  lowercase__			 = ('''CLIPTokenizer''', '''CLIPTokenizerFast''')


  def __init__(    self:		Tuple,				a_:		List[str]=None,				a_:		Optional[int]=None,				**a_:		List[str]		):




    '''simple docstring'''



    _snake_case							:		Dict								=  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__,				)
      _snake_case							:		Dict								=  kwargs.pop("""feature_extractor"""		)

    _snake_case							:		Union[str, Any]								=  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:		Any,				a_:		Optional[Any]=None,				a_:		Any=None,				a_:		Optional[Any]=None,				**a_:		List[str]		):




    '''simple docstring'''



    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:
      _snake_case							:		List[Any]								=  self.tokenizer(UpperCAmelCase__,				return_tensors=UpperCAmelCase__,				**UpperCAmelCase__		)

    if images is not None:
      _snake_case							:		str								=  self.image_processor(UpperCAmelCase__,				return_tensors=UpperCAmelCase__,				**UpperCAmelCase__		)

    if text is not None and images is not None:
      _snake_case							:		Tuple								=  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:		List[Any],				*a_:		Union[str, Any],				**a_:		Optional[int]		):




    '''simple docstring'''



    return self.tokenizer.batch_decode(*UpperCAmelCase__,				**UpperCAmelCase__		)


  def 			UpperCamelCase_       (    self:		Union[str, Any],				*a_:		Dict,				**a_:		Dict		):




    '''simple docstring'''



    return self.tokenizer.decode(*UpperCAmelCase__,				**UpperCAmelCase__		)


  @property
  def 			UpperCamelCase_       (    self:		Optional[Any]		):




    '''simple docstring'''



    _snake_case							:		int								=  self.tokenizer.model_input_names
    _snake_case							:		Union[str, Any]								=  self.image_processor.model_input_names
    return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names		)		)


  @property
  def 			UpperCamelCase_       (    self:		Any		):




    '''simple docstring'''



    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:		int		):




    '''simple docstring'''



    warnings.warn(
        """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.""",				UpperCAmelCase__,				)
    return self.image_processor








"""simple docstring"""


def  UpperCAmelCase__	(snake_case__							: int = 10_00   ):
  """simple docstring"""

  _snake_case		, _snake_case							:		List[Any]								=  1, 1
  _snake_case							:		str								=  []
  for i in range(1		,       n + 1   ):
    _snake_case							:		Any								=  prev_numerator + 2 * prev_denominator
    _snake_case							:		Optional[Any]								=  prev_numerator + prev_denominator
    if len(str(snake_case__   )   ) > len(str(snake_case__   )   ):
      result.append(snake_case__   )
    _snake_case							:		int								=  numerator
    _snake_case							:		Any								=  denominator

  return len(snake_case__   )


if __name__ == "__main__":
     print(F'''{solution() = }''')


