

"""simple docstring"""




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 			A						(							lowerCamelCase__			):







    """simple docstring"""





    lowerCamelCase		     =							['image_processor', 'tokenizer']
    lowerCamelCase		     =							'BlipImageProcessor'
    lowerCamelCase		     =							('BertTokenizer', 'BertTokenizerFast')


    def __init__(				self			:	Union[str, Any],lowercase_			:	Any,lowercase_			:	Optional[Any]		)->   Union[str, Any]:





        '''simple docstring'''







        A__   				=       False
        super().__init__(__A,__A		)
        A__   				=       self.image_processor


    def __call__(				self			:	List[str],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_			:	int,)->   str:





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

        # add pixel_values
        A__   				=       self.image_processor(__A,return_tensors=__A		)

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

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

        return encoding_image_processor


    def 						snake_case__					(				self			:	Union[str, Any],*lowercase_			:	str,**lowercase_			:	List[Any]		)->   Optional[Any]:





        '''simple docstring'''







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


    def 						snake_case__					(				self			:	Tuple,*lowercase_			:	int,**lowercase_			:	Dict		)->   List[str]:





        '''simple docstring'''







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


    @property
    def 						snake_case__					(				self			:	int		)->   str:





        '''simple docstring'''







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




def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     int = 1000				)							->				int:





    '''simple docstring'''






    A__   				=       3
    A__   				=       0
    while a < n:
        if a % 3 == 0 or a % 5 == 0:
            result += a
        elif a % 15 == 0:
            result -= a
        a += 1
    return result


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

