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 __snake_case				(						lowerCamelCase_		):
   lowerCAmelCase_														=							["image_processor", "tokenizer"]
   lowerCAmelCase_														=							"BlipImageProcessor"
   lowerCAmelCase_														=							"AutoTokenizer"




   def __init__(    self			:				Dict       ,	_lowercase			:				int       ,	_lowercase			:				Optional[int]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      False
     super().__init__(_lowercase       ,	_lowercase      )
     SCREAMING_SNAKE_CASE__										=      self.image_processor




   def __call__(    self			:				int       ,	_lowercase			:				ImageInput = None       ,	_lowercase			:				Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None       ,	_lowercase			:				bool = True       ,	_lowercase			:				Union[bool, str, PaddingStrategy] = False       ,	_lowercase			:				Union[bool, str, TruncationStrategy] = None       ,	_lowercase			:				Optional[int] = None       ,	_lowercase			:				int = 0       ,	_lowercase			:				Optional[int] = None       ,	_lowercase			:				Optional[bool] = None       ,	_lowercase			:				bool = False       ,	_lowercase			:				bool = False       ,	_lowercase			:				bool = False       ,	_lowercase			:				bool = False       ,	_lowercase			:				bool = False       ,	_lowercase			:				bool = True       ,	_lowercase			:				Optional[Union[str, TensorType]] = None       ,	**_lowercase			:				Union[str, Any]       ,	):



     """simple docstring"""






     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:
       SCREAMING_SNAKE_CASE__										=      self.tokenizer
       SCREAMING_SNAKE_CASE__										=      self.tokenizer(
           text=_lowercase       ,	add_special_tokens=_lowercase       ,	padding=_lowercase       ,	truncation=_lowercase       ,	max_length=_lowercase       ,	stride=_lowercase       ,	pad_to_multiple_of=_lowercase       ,	return_attention_mask=_lowercase       ,	return_overflowing_tokens=_lowercase       ,	return_special_tokens_mask=_lowercase       ,	return_offsets_mapping=_lowercase       ,	return_token_type_ids=_lowercase       ,	return_length=_lowercase       ,	verbose=_lowercase       ,	return_tensors=_lowercase       ,	**_lowercase       ,	)
       return text_encoding

     # add pixel_values
     SCREAMING_SNAKE_CASE__										=      self.image_processor(_lowercase       ,	return_tensors=_lowercase      )

     if text is not None:
       SCREAMING_SNAKE_CASE__										=      self.tokenizer(
           text=_lowercase       ,	add_special_tokens=_lowercase       ,	padding=_lowercase       ,	truncation=_lowercase       ,	max_length=_lowercase       ,	stride=_lowercase       ,	pad_to_multiple_of=_lowercase       ,	return_attention_mask=_lowercase       ,	return_overflowing_tokens=_lowercase       ,	return_special_tokens_mask=_lowercase       ,	return_offsets_mapping=_lowercase       ,	return_token_type_ids=_lowercase       ,	return_length=_lowercase       ,	verbose=_lowercase       ,	return_tensors=_lowercase       ,	**_lowercase       ,	)
     else:
       SCREAMING_SNAKE_CASE__										=      None

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

     return encoding_image_processor




   def 	__a						(    self			:				List[str]       ,	*_lowercase			:				Optional[int]       ,	**_lowercase			:				str      ):



     """simple docstring"""






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




   def 	__a						(    self			:				Union[str, Any]       ,	*_lowercase			:				Optional[Any]       ,	**_lowercase			:				Optional[int]      ):



     """simple docstring"""






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




   @property
   # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
   def 	__a						(    self			:				int      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      self.tokenizer.model_input_names
     SCREAMING_SNAKE_CASE__										=      self.image_processor.model_input_names
     return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names      )      )

def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       int ,     __UpperCamelCase    :       int	)							->					str:







  """simple docstring"""





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

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

  SCREAMING_SNAKE_CASE__										=      max(len(__UpperCamelCase	) ,     len(__UpperCamelCase	)	)

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


if __name__ == "__main__":
   import doctest

   doctest.testmod()
