





import os
from typing import List, Optional, Union

from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer






class     lowercase__       (	__lowerCamelCase				):

   '''simple docstring'''


   a						:							int   		=		["image_processor", "tokenizer"]
   a						:							int   		=		"BlipImageProcessor"
   a						:							Optional[int]   		=		"AutoTokenizer"




   def __init__(    self,		__magic_name__,		__magic_name__,		__magic_name__							) ->      Dict:






       """simple docstring"""
       super().__init__(__magic_name__,		__magic_name__							)

       # add QFormer tokenizer
       UpperCamelCase__							:				Any          =      qformer_tokenizer




   def __call__(    self,		__magic_name__ = None,		__magic_name__ = None,		__magic_name__ = True,		__magic_name__ = False,		__magic_name__ = None,		__magic_name__ = None,		__magic_name__ = 0,		__magic_name__ = None,		__magic_name__ = None,		__magic_name__ = False,		__magic_name__ = False,		__magic_name__ = False,		__magic_name__ = False,		__magic_name__ = False,		__magic_name__ = True,		__magic_name__ = None,		**__magic_name__,		) ->      BatchFeature:






       """simple docstring"""
       if images is None and text is None:
           raise ValueError('''You have to specify at least images or text.'''							)

       UpperCamelCase__							:				Any          =      BatchFeature()

       if text is not None:
           UpperCamelCase__							:				List[str]          =      self.tokenizer(
               text=__magic_name__,		add_special_tokens=__magic_name__,		padding=__magic_name__,		truncation=__magic_name__,		max_length=__magic_name__,		stride=__magic_name__,		pad_to_multiple_of=__magic_name__,		return_attention_mask=__magic_name__,		return_overflowing_tokens=__magic_name__,		return_special_tokens_mask=__magic_name__,		return_offsets_mapping=__magic_name__,		return_token_type_ids=__magic_name__,		return_length=__magic_name__,		verbose=__magic_name__,		return_tensors=__magic_name__,		**__magic_name__,		)
           encoding.update(__magic_name__							)
           UpperCamelCase__							:				int          =      self.qformer_tokenizer(
               text=__magic_name__,		add_special_tokens=__magic_name__,		padding=__magic_name__,		truncation=__magic_name__,		max_length=__magic_name__,		stride=__magic_name__,		pad_to_multiple_of=__magic_name__,		return_attention_mask=__magic_name__,		return_overflowing_tokens=__magic_name__,		return_special_tokens_mask=__magic_name__,		return_offsets_mapping=__magic_name__,		return_token_type_ids=__magic_name__,		return_length=__magic_name__,		verbose=__magic_name__,		return_tensors=__magic_name__,		**__magic_name__,		)
           UpperCamelCase__							:				Optional[int]          =      qformer_text_encoding.pop('''input_ids'''							)
           UpperCamelCase__							:				Dict          =      qformer_text_encoding.pop('''attention_mask'''							)

       if images is not None:
           UpperCamelCase__							:				List[Any]          =      self.image_processor(__magic_name__,		return_tensors=__magic_name__							)
           encoding.update(__magic_name__							)

       return encoding




   def       UpperCamelCase__  (    self,		*__magic_name__,		**__magic_name__							) ->      List[Any]:






       """simple docstring"""
       return self.tokenizer.batch_decode(*__magic_name__,		**__magic_name__							)




   def       UpperCamelCase__  (    self,		*__magic_name__,		**__magic_name__							) ->      List[str]:






       """simple docstring"""
       return self.tokenizer.decode(*__magic_name__,		**__magic_name__							)




   @property
   # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
   def       UpperCamelCase__  (    self							) ->      List[str]:






       """simple docstring"""
       UpperCamelCase__							:				Optional[int]          =      self.tokenizer.model_input_names
       UpperCamelCase__							:				List[str]          =      self.image_processor.model_input_names
       return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)




   def       UpperCamelCase__  (    self,		__magic_name__,		**__magic_name__							) ->      str:






       """simple docstring"""
       if os.path.isfile(__magic_name__							):
           raise ValueError(f"Provided path ({save_directory}) should be a directory, not a file"							)
       os.makedirs(__magic_name__,		exist_ok=__magic_name__							)
       UpperCamelCase__							:				List[Any]          =      os.path.join(__magic_name__,		'''qformer_tokenizer'''							)
       self.qformer_tokenizer.save_pretrained(__magic_name__							)
       return super().save_pretrained(__magic_name__,		**__magic_name__							)




   @classmethod
   def       UpperCamelCase__  (    cls,		__magic_name__,		**__magic_name__							) ->      Optional[int]:






       """simple docstring"""
       UpperCamelCase__							:				str          =      AutoTokenizer.from_pretrained(__magic_name__,		subfolder='''qformer_tokenizer'''							)
       UpperCamelCase__							:				Union[str, Any]          =      cls._get_arguments_from_pretrained(__magic_name__,		**__magic_name__							)
       args.append(__magic_name__							)
       return cls(*__magic_name__							)








def 		lowerCAmelCase_    (  __UpperCAmelCase:			int      )      ->	bool:
    return str(__UpperCAmelCase      ) == str(__UpperCAmelCase      )[::-1]




def 		lowerCAmelCase_    (  __UpperCAmelCase:			int      )      ->	int:
    return int(__UpperCAmelCase      ) + int(str(__UpperCAmelCase      )[::-1]      )




def 		lowerCAmelCase_    (  __UpperCAmelCase:			int = 1_0000      )      ->	int:
    UpperCamelCase__							:				Optional[Any]          =      []
    for num in range(1		,   __UpperCAmelCase      ):
        UpperCamelCase__							:				str          =      0
        UpperCamelCase__							:				Any          =      num
        while iterations < 50:
            UpperCamelCase__							:				List[Any]          =      sum_reverse(__UpperCAmelCase      )
            iterations += 1
            if is_palindrome(__UpperCAmelCase      ):
                break
        else:
            lychrel_nums.append(__UpperCAmelCase      )
    return len(__UpperCAmelCase      )


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

