






'''simple docstring'''



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 UpperCAmelCase_					(    __lowerCAmelCase				):




      """simple docstring"""




      UpperCamelCase_								=			['''image_processor''', '''tokenizer''']
      UpperCamelCase_								=			'''BlipImageProcessor'''
      UpperCamelCase_								=			'''AutoTokenizer'''






      def __init__( self			:		Optional[Any]     ,      UpperCAmelCase			:		Optional[Any]     ,      UpperCAmelCase			:		Optional[Any]     ,      UpperCAmelCase			:		Tuple		)  ->  str:


       '''simple docstring'''

       super().__init__(_UpperCamelCase     ,      _UpperCamelCase		)

       # add QFormer tokenizer
       lowercase					:   Dict				  =qformer_tokenizer






      def __call__( self			:		Union[str, 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			:		Dict     ,      )  ->  BatchFeature:


       '''simple docstring'''

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

       lowercase					:   Tuple				  =BatchFeature()

       if text is not None:
        lowercase					:   Union[str, 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     ,      )
        encoding.update(_UpperCamelCase		)
        lowercase					:   List[str]				  =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     ,      )
        lowercase					:   str				  =qformer_text_encoding.pop('''input_ids'''		)
        lowercase					:   Any				  =qformer_text_encoding.pop('''attention_mask'''		)

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

       return encoding






      def 				A__      ( self			:		int     ,      *UpperCAmelCase			:		List[Any]     ,      **UpperCAmelCase			:		Optional[Any]		)  ->  List[str]:


       '''simple docstring'''

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






      def 				A__      ( self			:		Tuple     ,      *UpperCAmelCase			:		List[str]     ,      **UpperCAmelCase			:		Any		)  ->  List[Any]:


       '''simple docstring'''

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






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


       '''simple docstring'''

       lowercase					:   int				  =self.tokenizer.model_input_names
       lowercase					:   Union[str, Any]				  =self.image_processor.model_input_names
       return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names		)		)






      def 				A__      ( self			:		int     ,      UpperCAmelCase			:		str     ,      **UpperCAmelCase			:		Union[str, Any]		)  ->  str:


       '''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		)
       lowercase					:   str				  =os.path.join(_UpperCamelCase     ,      '''qformer_tokenizer'''		)
       self.qformer_tokenizer.save_pretrained(_UpperCamelCase		)
       return super().save_pretrained(_UpperCamelCase     ,      **_UpperCamelCase		)






      @classmethod
      def 				A__      ( cls			:		int     ,      UpperCAmelCase			:		str     ,      **UpperCAmelCase			:		int		)  ->  List[str]:


       '''simple docstring'''

       lowercase					:   Dict				  =AutoTokenizer.from_pretrained(_UpperCamelCase     ,      subfolder='''qformer_tokenizer'''		)
       lowercase					:   str				  =cls._get_arguments_from_pretrained(_UpperCamelCase     ,      **_UpperCamelCase		)
       args.append(_UpperCamelCase		)
       return cls(*_UpperCamelCase		)











'''simple docstring'''



def 							lowercase_				(    __A :       int					,      __A :       int			)       ->      str:



 """simple docstring"""




 if number < 0 or shift_amount < 0:
  raise ValueError('''both inputs must be positive integers'''			)

 lowercase					:   List[Any]				  =str(bin(__A			)			)
 binary_number += "0" * shift_amount
 return binary_number


def 							lowercase_				(    __A :       int					,      __A :       int			)       ->      str:



 """simple docstring"""




 if number < 0 or shift_amount < 0:
  raise ValueError('''both inputs must be positive integers'''			)

 lowercase					:   Union[str, Any]				  =str(bin(__A			)			)[2:]
 if shift_amount >= len(__A			):
  return "0b0"
 lowercase					:   Any				  =binary_number[: len(__A			) - shift_amount]
 return "0b" + shifted_binary_number


def 							lowercase_				(    __A :       int					,      __A :       int			)       ->      str:



 """simple docstring"""




 if number >= 0:  # Get binary representation of positive number
  lowercase					:   str				  ='''0''' + str(bin(__A			)			).strip('''-'''			)[2:]
 else:  # Get binary (2's complement) representation of negative number
  lowercase					:   Dict				  =len(bin(__A			)[3:]			)  # Find 2's complement of number
  lowercase					:   Optional[Any]				  =bin(abs(__A			) - (1 << binary_number_length)			)[3:]
  lowercase					:   int				  =(
      '''1''' + '''0''' * (binary_number_length - len(__A			)) + binary_number
  )

 if shift_amount >= len(__A			):
  return "0b" + binary_number[0] * len(__A			)
 return (
     "0b"
     + binary_number[0] * shift_amount
     + binary_number[: len(__A			) - shift_amount]
 )


if __name__ == "__main__":
      import doctest

      doctest.testmod()



