




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 						__lowerCAmelCase      (      __magic_name__					):
							UpperCamelCase__   	=    ['''image_processor''', '''tokenizer''']
							UpperCamelCase__   	=    '''BlipImageProcessor'''
							UpperCamelCase__   	=    '''AutoTokenizer'''
							def __init__(		self		:str						, __magic_name__		:Dict						, __magic_name__		:Union[str, Any]							):







								'''simple docstring'''







								a									=				False
								super().__init__(__magic_name__						, __magic_name__							)
								a									=				self.image_processor
							def __call__(		self		:List[str]						, __magic_name__		:ImageInput = None						, __magic_name__		:Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None						, __magic_name__		:bool = True						, __magic_name__		:Union[bool, str, PaddingStrategy] = False						, __magic_name__		:Union[bool, str, TruncationStrategy] = None						, __magic_name__		:Optional[int] = None						, __magic_name__		:int = 0						, __magic_name__		:Optional[int] = None						, __magic_name__		:Optional[bool] = None						, __magic_name__		:bool = False						, __magic_name__		:bool = False						, __magic_name__		:bool = False						, __magic_name__		:bool = False						, __magic_name__		:bool = False						, __magic_name__		:bool = True						, __magic_name__		:Optional[Union[str, TensorType]] = None						, **__magic_name__		: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:
									a									=				self.tokenizer
									a									=				self.tokenizer(
									    text=__magic_name__						, add_special_tokens=__magic_name__						, padding=__magic_name__						, truncation=__magic_name__						, max_length=__magic_name__						, stride=__magic_name__						, pad_to_multiple_of=__magic_name__						, return_attention_mask=__magic_name__						, return_overflowing_tokens=__magic_name__						, return_special_tokens_mask=__magic_name__						, return_offsets_mapping=__magic_name__						, return_token_type_ids=__magic_name__						, return_length=__magic_name__						, verbose=__magic_name__						, return_tensors=__magic_name__						, **__magic_name__						, )
									return text_encoding

								# add pixel_values
								a									=				self.image_processor(__magic_name__						, return_tensors=__magic_name__							)

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

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

								return encoding_image_processor
							def  lowerCamelCase__				(		self		:List[str]						, *__magic_name__		:Union[str, Any]						, **__magic_name__		:Dict							):







								'''simple docstring'''







								return self.tokenizer.batch_decode(*__magic_name__						, **__magic_name__							)
							def  lowerCamelCase__				(		self		:Union[str, Any]						, *__magic_name__		:str						, **__magic_name__		:Dict							):







								'''simple docstring'''







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





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







from bisect import bisect
from itertools import accumulate





def 						__A						(      __lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase       ) ->			Dict:
	a									=				sorted(zip(__lowerCamelCase				,     __lowerCamelCase       )				,     key=lambda __lowerCamelCase       : x[0] / x[1]				,     reverse=__lowerCamelCase       )
	a							,					a									=				[i[0] for i in r], [i[1] for i in r]
	a									=				list(accumulate(__lowerCamelCase       )       )
	a									=				bisect(__lowerCamelCase				,     __lowerCamelCase       )
	return (
	    0
	    if k == 0
	    else sum(vl[:k]       ) + (w - acc[k - 1]) * (vl[k]) / (wt[k])
	    if k != n
	    else sum(vl[:k]       )
	)


if __name__ == "__main__":
		import doctest

		doctest.testmod()

