





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 A_					(			_lowerCamelCase   ):
   lowerCAmelCase__        = ["""image_processor""", """tokenizer"""]
   lowerCAmelCase__        = """BlipImageProcessor"""
   lowerCAmelCase__        = """AutoTokenizer"""




   def __init__(self	:List[str]    ,   _UpperCamelCase	:Union[str, Any]    ,   _UpperCamelCase	:Any )->					Optional[Any]:
         __A          =				False
         super().__init__(_UpperCamelCase    ,   _UpperCamelCase )
         __A          =				self.image_processor




   def __call__(self	:Any    ,   _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	:Tuple    ,   )->					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:
               __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	:Optional[int]    ,   *_UpperCamelCase	:int    ,   **_UpperCamelCase	:Dict )->					Any:
         return self.tokenizer.batch_decode(*_UpperCamelCase    ,   **_UpperCamelCase )




   def _lowerCAmelCase      (self	:Dict    ,   *_UpperCamelCase	:Dict    ,   **_UpperCamelCase	:List[str] )->					Union[str, Any]:
         return self.tokenizer.decode(*_UpperCamelCase    ,   **_UpperCamelCase )






   @property
   # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
   def _lowerCAmelCase      (self	:Any )->					int:
         __A          =				self.tokenizer.model_input_names
         __A          =				self.image_processor.model_input_names
         return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )












from itertools import permutations



def _a				(	lowerCamelCase:   tuple )	->						bool:







      '''simple docstring'''






      if num[3] % 2 != 0:
            return False

      if (num[2] + num[3] + num[4]) % 3 != 0:
            return False

      if num[5] % 5 != 0:
            return False

      __A          =				[7, 11, 13, 17]
      for i, test in enumerate(lowerCamelCase ):
            if (num[i + 4] * 1_00 + num[i + 5] * 10 + num[i + 6]) % test != 0:
                  return False
      return True



def _a				(	lowerCamelCase:   int = 10 )	->						int:







      '''simple docstring'''






      return sum(
          int(''''''.join(map(lowerCamelCase					,				lowerCamelCase ) ) )
          for num in permutations(range(lowerCamelCase ) )
          if is_substring_divisible(lowerCamelCase ) )


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





