


"""simple docstring"""







import warnings

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






class       __A				(       _SCREAMING_SNAKE_CASE			):






 """simple docstring"""




 __lowerCAmelCase 					=  ["image_processor", "tokenizer"]
 __lowerCAmelCase 					=  "ViTImageProcessor"
 __lowerCAmelCase 					=  ("CLIPTokenizer", "CLIPTokenizerFast")


 def __init__(		self ,    __A=None ,    __A=None ,    **__A						)					->     List[Any]:
     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.''' ,    __A ,    )
         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__(__A ,    __A						)


 def __call__(		self ,    __A=None ,    __A=None ,    __A=None ,    __A=None ,    **__A						)					->     Any:
     if text is None and visual_prompt is None and images is None:
         raise ValueError('''You have to specify either text, visual prompt or images.'''						)

     if text is not None and visual_prompt is not None:
         raise ValueError('''You have to specify exactly one type of prompt. Either text or visual prompt.'''						)

     if text is not None:
         a 		=self.tokenizer(__A ,    return_tensors=__A ,    **__A						)

     if visual_prompt is not None:
         a 		=self.image_processor(__A ,    return_tensors=__A ,    **__A						)

     if images is not None:
         a 		=self.image_processor(__A ,    return_tensors=__A ,    **__A						)

     if visual_prompt is not None and images is not None:
         a 		={
             '''pixel_values''': image_features.pixel_values,
             '''conditional_pixel_values''': prompt_features.pixel_values,
         }
         return encoding
     elif text is not None and images is not None:
         a 		=image_features.pixel_values
         return encoding
     elif text is not None:
         return encoding
     elif visual_prompt is not None:
         a 		={
             '''conditional_pixel_values''': prompt_features.pixel_values,
         }
         return encoding
     else:
         return BatchEncoding(data=dict(**__A						) ,    tensor_type=__A						)


 def       SCREAMING_SNAKE_CASE							(		self ,    *__A ,    **__A						)					->     str:
     return self.tokenizer.batch_decode(*__A ,    **__A						)


 def       SCREAMING_SNAKE_CASE							(		self ,    *__A ,    **__A						)					->     Tuple:
     return self.tokenizer.decode(*__A ,    **__A						)


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


import math
import sys







def 			A_      (    _UpperCAmelCase    ):
						if number != int(_UpperCAmelCase    ):
												raise ValueError("the value of input must be a natural number"    )
						if number < 0:
												raise ValueError("the value of input must not be a negative number"    )
						if number == 0:
												return 1
						SCREAMING_SNAKE_CASE_: List[str]  		=       [-1] * (number + 1)
						SCREAMING_SNAKE_CASE_: str  		=       0
						for i in range(1				,	number + 1    ):
												SCREAMING_SNAKE_CASE_: str  		=       sys.maxsize
												SCREAMING_SNAKE_CASE_: List[Any]  		=       int(math.sqrt(_UpperCAmelCase    )    )
												for j in range(1				,	root + 1    ):
																		SCREAMING_SNAKE_CASE_: List[str]  		=       1 + answers[i - (j**2)]
																		SCREAMING_SNAKE_CASE_: Optional[Any]  		=       min(_UpperCAmelCase				,	_UpperCAmelCase    )
												SCREAMING_SNAKE_CASE_: Dict  		=       answer
						return answers[number]


if __name__ == "__main__":
						import doctest

						doctest.testmod()






