"""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					(				lowerCAmelCase__   ):
       SCREAMING_SNAKE_CASE_			=      ["image_processor", "tokenizer"]
       SCREAMING_SNAKE_CASE_			=      "BlipImageProcessor"
       SCREAMING_SNAKE_CASE_			=      ("BertTokenizer", "BertTokenizerFast")







       def __init__( self,							lowerCAmelCase__,							lowerCAmelCase__)      ->      List[Any]:
            snake_case_					       =					False
            super().__init__(lowerCAmelCase__,							lowerCAmelCase__)
            snake_case_					       =					self.image_processor







       def __call__( self,							lowerCAmelCase__ = None,							lowerCAmelCase__ = None,							lowerCAmelCase__ = True,							lowerCAmelCase__ = False,							lowerCAmelCase__ = None,							lowerCAmelCase__ = None,							lowerCAmelCase__ = 0,							lowerCAmelCase__ = None,							lowerCAmelCase__ = None,							lowerCAmelCase__ = False,							lowerCAmelCase__ = False,							lowerCAmelCase__ = False,							lowerCAmelCase__ = False,							lowerCAmelCase__ = False,							lowerCAmelCase__ = True,							lowerCAmelCase__ = None,							**lowerCAmelCase__,							)      ->      BatchEncoding:
            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:
                 snake_case_					       =					self.tokenizer
                 snake_case_					       =					self.tokenizer(
                     text=lowerCAmelCase__,							add_special_tokens=lowerCAmelCase__,							padding=lowerCAmelCase__,							truncation=lowerCAmelCase__,							max_length=lowerCAmelCase__,							stride=lowerCAmelCase__,							pad_to_multiple_of=lowerCAmelCase__,							return_attention_mask=lowerCAmelCase__,							return_overflowing_tokens=lowerCAmelCase__,							return_special_tokens_mask=lowerCAmelCase__,							return_offsets_mapping=lowerCAmelCase__,							return_token_type_ids=lowerCAmelCase__,							return_length=lowerCAmelCase__,							verbose=lowerCAmelCase__,							return_tensors=lowerCAmelCase__,							**lowerCAmelCase__,							)
                 return text_encoding

            # add pixel_values
            snake_case_					       =					self.image_processor(lowerCAmelCase__,							return_tensors=lowerCAmelCase__)

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

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

            return encoding_image_processor







       def 		a_						( self,							*lowerCAmelCase__,							**lowerCAmelCase__)      ->      Optional[Any]:
            return self.tokenizer.batch_decode(*lowerCAmelCase__,							**lowerCAmelCase__)







       def 		a_						( self,							*lowerCAmelCase__,							**lowerCAmelCase__)      ->      Union[str, Any]:
            return self.tokenizer.decode(*lowerCAmelCase__,							**lowerCAmelCase__)







       @property
       def 		a_						( self)      ->      List[str]:
            snake_case_					       =					self.tokenizer.model_input_names
            snake_case_					       =					self.image_processor.model_input_names
            return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))






"""simple docstring"""
from math import isqrt, loga

def       _snake_case						(						UpperCamelCase						:		int						):
 UpperCAmelCase	:					Any	=	[True] * max_number
 for i in range(2			,		isqrt(max_number - 1						) + 1						):
  if is_prime[i]:
   for j in range(i**2			,		UpperCamelCase			,		UpperCamelCase						):
    UpperCAmelCase	:					str	=	False

 return [i for i in range(2			,		UpperCamelCase						) if is_prime[i]]

def       _snake_case						(						UpperCamelCase						:		int = 800800			,		UpperCamelCase						:		int = 800800						):
 UpperCAmelCase	:					Union[str, Any]	=	degree * loga(UpperCamelCase						)
 UpperCAmelCase	:					int	=	int(UpperCamelCase						)
 UpperCAmelCase	:					Union[str, Any]	=	calculate_prime_numbers(UpperCamelCase						)

 UpperCAmelCase	:					Dict	=	0
 UpperCAmelCase	:					Optional[int]	=	0
 UpperCAmelCase	:					Dict	=	len(UpperCamelCase						) - 1
 while left < right:
  while (
      prime_numbers[right] * loga(prime_numbers[left]						)
      + prime_numbers[left] * loga(prime_numbers[right]						)
      > upper_bound
  ):
   right -= 1
  hybrid_integers_count += right - left
  left += 1

 return hybrid_integers_count


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