

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     __snake_case    (				snake_case__  ):
       """simple docstring"""







       __SCREAMING_SNAKE_CASE				    =							["image_processor", "tokenizer"]
       __SCREAMING_SNAKE_CASE				    =							"BlipImageProcessor"
       __SCREAMING_SNAKE_CASE				    =							"AutoTokenizer"


       def __init__(		self      , _UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase   )      ->							List[Any]:






              """simple docstring"""


              super().__init__(_UpperCamelCase      , _UpperCamelCase   )

              # add QFormer tokenizer
              __snake_case       	=							qformer_tokenizer


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






              """simple docstring"""


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

              __snake_case       	=							BatchFeature()

              if text is not None:
                     __snake_case       	=							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      , )
                     encoding.update(_UpperCamelCase   )
                     __snake_case       	=							self.qformer_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      , )
                     __snake_case       	=							qformer_text_encoding.pop("""input_ids"""   )
                     __snake_case       	=							qformer_text_encoding.pop("""attention_mask"""   )

              if images is not None:
                     __snake_case       	=							self.image_processor(_UpperCamelCase      , return_tensors=_UpperCamelCase   )
                     encoding.update(_UpperCamelCase   )

              return encoding


       def 			a		(		self      , *_UpperCamelCase      , **_UpperCamelCase   )      ->							Optional[int]:






              """simple docstring"""


              return self.tokenizer.batch_decode(*_UpperCamelCase      , **_UpperCamelCase   )


       def 			a		(		self      , *_UpperCamelCase      , **_UpperCamelCase   )      ->							Dict:






              """simple docstring"""


              return self.tokenizer.decode(*_UpperCamelCase      , **_UpperCamelCase   )


       @property
       # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
       def 			a		(		self   )      ->							Optional[Any]:






              """simple docstring"""


              __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   )   )


       def 			a		(		self      , _UpperCamelCase      , **_UpperCamelCase   )      ->							int:






              """simple docstring"""


              if os.path.isfile(_UpperCamelCase   ):
                     raise ValueError(F'Provided path ({save_directory}) should be a directory, not a file'   )
              os.makedirs(_UpperCamelCase      , exist_ok=_UpperCamelCase   )
              __snake_case       	=							os.path.join(_UpperCamelCase      , """qformer_tokenizer"""   )
              self.qformer_tokenizer.save_pretrained(_UpperCamelCase   )
              return super().save_pretrained(_UpperCamelCase      , **_UpperCamelCase   )






       @classmethod
       def 			a		(		cls      , _UpperCamelCase      , **_UpperCamelCase   )      ->							Tuple:






              """simple docstring"""


              __snake_case       	=							AutoTokenizer.from_pretrained(_UpperCamelCase      , subfolder="""qformer_tokenizer"""   )
              __snake_case       	=							cls._get_arguments_from_pretrained(_UpperCamelCase      , **_UpperCamelCase   )
              args.append(_UpperCamelCase   )
              return cls(*_UpperCamelCase   )




def 						lowerCamelCase__      ( __A    :int	,__A    :int		):


       """simple docstring"""




       if a < 0 or b < 0:
              raise ValueError("""the value of both inputs must be positive"""		)

       __snake_case       	=							str(bin(__A		)		)[2:]  # remove the leading "0b"
       __snake_case       	=							str(bin(__A		)		)[2:]  # remove the leading "0b"

       __snake_case       	=							max(len(__A		)	,len(__A		)		)

       return "0b" + "".join(
           str(int(char_a == """1""" and char_b == """1"""		)		)
           for char_a, char_b in zip(a_binary.zfill(__A		)	,b_binary.zfill(__A		)		)		)


if __name__ == "__main__":
    import doctest

    doctest.testmod()

