





import warnings

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



class   _SCREAMING_SNAKE_CASE		(      __SCREAMING_SNAKE_CASE       ):







 '''simple docstring'''
 lowercase_						=     ["""image_processor""", """tokenizer"""]
 lowercase_						=     """ViTImageProcessor"""
 lowercase_						=     ("""CLIPTokenizer""", """CLIPTokenizerFast""")

 def __init__(self							:  Any			,					UpperCAmelCase_							:  List[Any]=None			,					UpperCAmelCase_							:  Union[str, Any]=None			,					**UpperCAmelCase_							:  List[str])			->str:



   '''simple docstring'''



   lowerCamelCase__: 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."			,					a_			,					)
     lowerCamelCase__: int         =kwargs.pop("feature_extractor")

   lowerCamelCase__: 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__(a_			,					a_)

 def __call__(self							:  Dict			,					UpperCAmelCase_							:  List[str]=None			,					UpperCAmelCase_							:  int=None			,					UpperCAmelCase_							:  Tuple=None			,					UpperCAmelCase_							:  Optional[Any]=None			,					**UpperCAmelCase_							:  List[Any])			->Dict:



   '''simple docstring'''



   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:
     lowerCamelCase__: List[Any]         =self.tokenizer(a_			,					return_tensors=a_			,					**a_)

   if visual_prompt is not None:
     lowerCamelCase__: Union[str, Any]         =self.image_processor(a_			,					return_tensors=a_			,					**a_)

   if images is not None:
     lowerCamelCase__: Any         =self.image_processor(a_			,					return_tensors=a_			,					**a_)

   if visual_prompt is not None and images is not None:
     lowerCamelCase__: List[Any]         ={
         '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:
     lowerCamelCase__: List[str]         =image_features.pixel_values
     return encoding
   elif text is not None:
     return encoding
   elif visual_prompt is not None:
     lowerCamelCase__: Tuple         ={
         'conditional_pixel_values': prompt_features.pixel_values,
     }
     return encoding
   else:
     return BatchEncoding(data=dict(**a_)			,					tensor_type=a_)

 def 				SCREAMING_SNAKE_CASE_   (self							:  Optional[Any]			,					*UpperCAmelCase_							:  List[Any]			,					**UpperCAmelCase_							:  List[Any])			->Union[str, Any]:



   '''simple docstring'''



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

 def 				SCREAMING_SNAKE_CASE_   (self							:  Any			,					*UpperCAmelCase_							:  str			,					**UpperCAmelCase_							:  Optional[int])			->int:



   '''simple docstring'''



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

 @property
 def 				SCREAMING_SNAKE_CASE_   (self							:  Dict)			->Optional[Any]:



   '''simple docstring'''



   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							:  str)			->str:



   '''simple docstring'''



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






'''simple docstring'''

from __future__ import annotations







def a_     (    __UpperCAmelCase					)				->					list[int]:





			"""simple docstring"""







			snake_case:    Tuple      							=[True] * limit
			snake_case:    Optional[int]      							=False
			snake_case:    Union[str, Any]      							=False
			snake_case:    List[Any]      							=True

			for i in range(3				,							int(limit**0.5 + 1					)				,							2					):
						snake_case:    str      							=i * 2
						while index < limit:
									snake_case:    List[Any]      							=False
									snake_case:    List[str]      							=index + i

			snake_case:    Union[str, Any]      							=[2]

			for i in range(3				,							__UpperCAmelCase				,							2					):
						if is_prime[i]:
									primes.append(__UpperCAmelCase					)

			return primes







def a_     (    __UpperCAmelCase = 1_00_00_00					)				->					int:





			"""simple docstring"""







			snake_case:    str      							=prime_sieve(__UpperCAmelCase					)
			snake_case:    str      							=0
			snake_case:    str      							=0

			for i in range(len(__UpperCAmelCase					)					):
						for j in range(i + length				,							len(__UpperCAmelCase					)					):
									snake_case:    Tuple      							=sum(primes[i:j]					)
									if sol >= ceiling:
												break

									if sol in primes:
												snake_case:    List[str]      							=j - i
												snake_case:    Optional[int]      							=sol

			return largest


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




