






'''simple docstring'''




from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType

class    _UpperCAmelCase			(     snake_case_						):




   """simple docstring"""

   snake_case    				=						['''image_processor''', '''tokenizer''']
   snake_case    				=						'''BlipImageProcessor'''
   snake_case    				=						'''AutoTokenizer'''

   def __init__(		self :	Dict      ,      __UpperCAmelCase :	List[str]      ,      __UpperCAmelCase :	Tuple		):





         '''simple docstring'''





         _A			     =	False
         super().__init__(__UpperCAmelCase      ,      __UpperCAmelCase		)
         _A			     =	self.image_processor

   def __call__(		self :	int      ,      __UpperCAmelCase :	ImageInput = None      ,      __UpperCAmelCase :	Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None      ,      __UpperCAmelCase :	bool = True      ,      __UpperCAmelCase :	Union[bool, str, PaddingStrategy] = False      ,      __UpperCAmelCase :	Union[bool, str, TruncationStrategy] = None      ,      __UpperCAmelCase :	Optional[int] = None      ,      __UpperCAmelCase :	int = 0      ,      __UpperCAmelCase :	Optional[int] = None      ,      __UpperCAmelCase :	Optional[bool] = None      ,      __UpperCAmelCase :	bool = False      ,      __UpperCAmelCase :	bool = False      ,      __UpperCAmelCase :	bool = False      ,      __UpperCAmelCase :	bool = False      ,      __UpperCAmelCase :	bool = False      ,      __UpperCAmelCase :	bool = True      ,      __UpperCAmelCase :	Optional[Union[str, TensorType]] = None      ,      **__UpperCAmelCase :	Optional[int]      ,      ):





         '''simple docstring'''





         if images is None and text is None:
               raise ValueError("You have to specify either images or text."		)

         # Get only text
         if images is None:
               _A			     =	self.tokenizer
               _A			     =	self.tokenizer(
                   text=__UpperCAmelCase      ,      add_special_tokens=__UpperCAmelCase      ,      padding=__UpperCAmelCase      ,      truncation=__UpperCAmelCase      ,      max_length=__UpperCAmelCase      ,      stride=__UpperCAmelCase      ,      pad_to_multiple_of=__UpperCAmelCase      ,      return_attention_mask=__UpperCAmelCase      ,      return_overflowing_tokens=__UpperCAmelCase      ,      return_special_tokens_mask=__UpperCAmelCase      ,      return_offsets_mapping=__UpperCAmelCase      ,      return_token_type_ids=__UpperCAmelCase      ,      return_length=__UpperCAmelCase      ,      verbose=__UpperCAmelCase      ,      return_tensors=__UpperCAmelCase      ,      **__UpperCAmelCase      ,      )
               return text_encoding

         # add pixel_values
         _A			     =	self.image_processor(__UpperCAmelCase      ,      return_tensors=__UpperCAmelCase		)

         if text is not None:
               _A			     =	self.tokenizer(
                   text=__UpperCAmelCase      ,      add_special_tokens=__UpperCAmelCase      ,      padding=__UpperCAmelCase      ,      truncation=__UpperCAmelCase      ,      max_length=__UpperCAmelCase      ,      stride=__UpperCAmelCase      ,      pad_to_multiple_of=__UpperCAmelCase      ,      return_attention_mask=__UpperCAmelCase      ,      return_overflowing_tokens=__UpperCAmelCase      ,      return_special_tokens_mask=__UpperCAmelCase      ,      return_offsets_mapping=__UpperCAmelCase      ,      return_token_type_ids=__UpperCAmelCase      ,      return_length=__UpperCAmelCase      ,      verbose=__UpperCAmelCase      ,      return_tensors=__UpperCAmelCase      ,      **__UpperCAmelCase      ,      )
         else:
               _A			     =	None

         if text_encoding is not None:
               encoding_image_processor.update(__UpperCAmelCase		)

         return encoding_image_processor

   def 						lowerCAmelCase (		self :	Dict      ,      *__UpperCAmelCase :	Tuple      ,      **__UpperCAmelCase :	Optional[Any]		):





         '''simple docstring'''





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

   def 						lowerCAmelCase (		self :	Tuple      ,      *__UpperCAmelCase :	List[str]      ,      **__UpperCAmelCase :	Tuple		):





         '''simple docstring'''





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

   @property
   # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
   def 						lowerCAmelCase (		self :	Optional[Any]		):





         '''simple docstring'''





         _A			     =	self.tokenizer.model_input_names
         _A			     =	self.image_processor.model_input_names
         return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names		)		)









'''simple docstring'''



from math import isqrt



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				int							)     ->		bool:
     return all(number % divisor != 0 for divisor in range(2      ,    isqrt(UpperCAmelCase_							) + 1							)							)



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				int = 10**6							)     ->		int:
     __lowerCamelCase   :	Optional[Any]								=		0
     __lowerCamelCase   :	Optional[int]								=		1
     __lowerCamelCase   :	List[str]								=		7
     while prime_candidate < max_prime:
          primes_count += is_prime(UpperCAmelCase_							)

          cube_index += 1
          prime_candidate += 6 * cube_index

     return primes_count


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






