





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    ):
 UpperCAmelCase__									=	["""image_processor""", """tokenizer"""]
 UpperCAmelCase__									=	"""BlipImageProcessor"""
 UpperCAmelCase__									=	("""BertTokenizer""", """BertTokenizerFast""")
 def __init__(   self						:   Tuple    ,  UpperCAmelCase						:   Dict    ,  UpperCAmelCase						:   int			)    ->			List[Any]:
     lowerCamelCase__					:   Optional[int]      				=	False
     super().__init__(UpperCAmelCase    ,  UpperCAmelCase			)
     lowerCamelCase__					:   int      				=	self.image_processor
 def __call__(   self						:   Dict    ,  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						:   Union[str, Any]    ,  )    ->			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__					:   Any      				=	self.tokenizer
         lowerCamelCase__					:   Any      				=	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
     lowerCamelCase__					:   Union[str, Any]      				=	self.image_processor(UpperCAmelCase    ,  return_tensors=UpperCAmelCase			)

     if text is not None:
         lowerCamelCase__					:   str      				=	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:
         lowerCamelCase__					:   List[Any]      				=	None

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

     return encoding_image_processor
 def   A_			(   self						:   int    ,  *UpperCAmelCase						:   Dict    ,  **UpperCAmelCase						:   str			)    ->			Optional[int]:
     return self.tokenizer.batch_decode(*UpperCAmelCase    ,  **UpperCAmelCase			)
 def   A_			(   self						:   Dict    ,  *UpperCAmelCase						:   int    ,  **UpperCAmelCase						:   List[str]			)    ->			Tuple:
     return self.tokenizer.decode(*UpperCAmelCase    ,  **UpperCAmelCase			)







 @property
 def   A_			(   self						:   Dict			)    ->			Optional[Any]:
     lowerCamelCase__					:   List[str]      				=	self.tokenizer.model_input_names
     lowerCamelCase__					:   Optional[Any]      				=	self.image_processor.model_input_names
     return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names			)			)












from __future__ import annotations

from collections.abc import Generator


def  SCREAMING_SNAKE_CASE   ( )    ->  Generator[int, None, None]:
    lowerCamelCase__					:   dict[int, int]      				=	{}
    lowerCamelCase__					:   Union[str, Any]      				=	2
    while True:
        lowerCamelCase__					:   Optional[int]      				=	factor_map.pop(_UpperCAmelCase		,			_UpperCAmelCase				)
        if factor:
            lowerCamelCase__					:   Optional[Any]      				=	factor + prime
            while x in factor_map:
                x += factor
            lowerCamelCase__					:   Optional[Any]      				=	factor
        else:
            lowerCamelCase__					:   Union[str, Any]      				=	prime
            yield prime
        prime += 1


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase = 1e10				)    ->  int:
    lowerCamelCase__					:   Tuple      				=	sieve()

    lowerCamelCase__					:   Dict      				=	1
    while True:
        lowerCamelCase__					:   List[Any]      				=	next(_UpperCAmelCase				)
        if (2 * prime * n) > limit:
            return n
        # Ignore the next prime as the reminder will be 2.
        next(_UpperCAmelCase				)
        n += 2


if __name__ == "__main__":
    print(solution())





