





'''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    lowerCAmelCase_		(		UpperCAmelCase__		):
 __lowerCamelCase						:							Any         =					["image_processor", "tokenizer"]
 __lowerCamelCase						:							List[Any]         =					"BlipImageProcessor"
 __lowerCamelCase						:							Union[str, Any]         =					"AutoTokenizer"




 def __init__(	self			,     _lowerCAmelCase			,     _lowerCAmelCase		)    ->							Optional[int]:
     _lowerCAmelCase				=      False
     super().__init__(lowerCamelCase__			,     lowerCamelCase__		)
     _lowerCAmelCase				=      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:
         _lowerCAmelCase				=      self.tokenizer
         _lowerCAmelCase				=      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
     _lowerCAmelCase				=      self.image_processor(lowerCamelCase__			,     return_tensors=lowerCamelCase__		)

     if text is not None:
         _lowerCAmelCase				=      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:
         _lowerCAmelCase				=      None

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

     return encoding_image_processor




 def 							_snake_case   (	self			,     *_lowerCAmelCase			,     **_lowerCAmelCase		)    ->							Optional[Any]:
     return self.tokenizer.batch_decode(*lowerCamelCase__			,     **lowerCamelCase__		)




 def 							_snake_case   (	self			,     *_lowerCAmelCase			,     **_lowerCAmelCase		)    ->							Optional[int]:
     return self.tokenizer.decode(*lowerCamelCase__			,     **lowerCamelCase__		)





 @property
 # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
 def 							_snake_case   (	self		)    ->							Optional[int]:
     _lowerCAmelCase				=      self.tokenizer.model_input_names
     _lowerCAmelCase				=      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 	__a(SCREAMING_SNAKE_CASE_  :					int    ):



    '''simple docstring'''





    _lowerCAmelCase				=      [True] * max_number
    for i in range(2		,       isqrt(max_number - 1    ) + 1    ):
        if is_prime[i]:
            for j in range(i**2		,       SCREAMING_SNAKE_CASE_		,       SCREAMING_SNAKE_CASE_    ):
                _lowerCAmelCase				=      False

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


def 	__a(SCREAMING_SNAKE_CASE_  :					int = 800800		,       SCREAMING_SNAKE_CASE_  :					int = 800800    ):



    '''simple docstring'''





    _lowerCAmelCase				=      degree * loga(SCREAMING_SNAKE_CASE_    )
    _lowerCAmelCase				=      int(SCREAMING_SNAKE_CASE_    )
    _lowerCAmelCase				=      calculate_prime_numbers(SCREAMING_SNAKE_CASE_    )

    _lowerCAmelCase				=      0
    _lowerCAmelCase				=      0
    _lowerCAmelCase				=      len(SCREAMING_SNAKE_CASE_    ) - 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() = }''')


