
def   __SCREAMING_SNAKE_CASE (		SCREAMING_SNAKE_CASE )		-> str:






		if isinstance(SCREAMING_SNAKE_CASE_ ,    SCREAMING_SNAKE_CASE_ ):
				raise TypeError('\'float\' object cannot be interpreted as an integer' )
		if isinstance(SCREAMING_SNAKE_CASE_ ,    SCREAMING_SNAKE_CASE_ ):
				raise TypeError('\'str\' object cannot be interpreted as an integer' )

		if num == 0:
				return "0b0"

		SCREAMING_SNAKE_CASE_					:	Union[str, Any]		  =			False

		if num < 0:
				SCREAMING_SNAKE_CASE_					:	int		  =			True
				SCREAMING_SNAKE_CASE_					:	Union[str, Any]		  =			-num

		SCREAMING_SNAKE_CASE_					:	List[Any]		  =			[]
		while num > 0:
				binary.insert(0 ,    num % 2 )
				num >>= 1

		if negative:
				return "-0b" + "".join(str(SCREAMING_SNAKE_CASE_ ) for e in binary )

		return "0b" + "".join(str(SCREAMING_SNAKE_CASE_ ) for e in binary )


if __name__ == "__main__":
		import doctest

		doctest.testmod()









from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType






class 						__magic_name__					(							snake_case				):
     UpperCamelCase_	:Dict										=      ["""image_processor""", """tokenizer"""]
     UpperCamelCase_	:str										=      """BridgeTowerImageProcessor"""
     UpperCamelCase_	:Tuple										=      ("""RobertaTokenizer""", """RobertaTokenizerFast""")






     def __init__(				self		,						_lowercase		,						_lowercase	)->     Any:
         super().__init__(_lowercase		,						_lowercase	)






     def __call__(				self		,						_lowercase		,						_lowercase = None		,						_lowercase = True		,						_lowercase = False		,						_lowercase = None		,						_lowercase = None		,						_lowercase = 0		,						_lowercase = None		,						_lowercase = None		,						_lowercase = None		,						_lowercase = False		,						_lowercase = False		,						_lowercase = False		,						_lowercase = False		,						_lowercase = True		,						_lowercase = None		,						**_lowercase		,						)->     BatchEncoding:
         UpperCamelCase_			  =						self.tokenizer(
             text=_lowercase		,						add_special_tokens=_lowercase		,						padding=_lowercase		,						truncation=_lowercase		,						max_length=_lowercase		,						stride=_lowercase		,						pad_to_multiple_of=_lowercase		,						return_token_type_ids=_lowercase		,						return_attention_mask=_lowercase		,						return_overflowing_tokens=_lowercase		,						return_special_tokens_mask=_lowercase		,						return_offsets_mapping=_lowercase		,						return_length=_lowercase		,						verbose=_lowercase		,						return_tensors=_lowercase		,						**_lowercase		,						)
         # add pixel_values + pixel_mask
         UpperCamelCase_			  =						self.image_processor(
             _lowercase		,						return_tensors=_lowercase		,						do_normalize=_lowercase		,						do_center_crop=_lowercase		,						**_lowercase	)
         encoding.update(_lowercase	)

         return encoding






     def 	UpperCAmelCase_				(				self		,						*_lowercase		,						**_lowercase	)->     Union[str, Any]:
         return self.tokenizer.batch_decode(*_lowercase		,						**_lowercase	)






     def 	UpperCAmelCase_				(				self		,						*_lowercase		,						**_lowercase	)->     List[Any]:
         return self.tokenizer.decode(*_lowercase		,						**_lowercase	)






     @property
     def 	UpperCAmelCase_				(				self	)->     Any:
         UpperCamelCase_			  =						self.tokenizer.model_input_names
         UpperCamelCase_			  =						self.image_processor.model_input_names
         return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names	)	)
