

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_      (	_lowercase	):
	_lowercase		:   Dict     					=				['''image_processor''', '''tokenizer''']
	_lowercase		:   Any     					=				'''BlipImageProcessor'''
	_lowercase		:   List[str]     					=				('''BertTokenizer''', '''BertTokenizerFast''')




	def __init__(     self						:     Tuple					,      __A						:     Tuple					,      __A						:     Dict							):
		__A :			Optional[int]		    =			False
		super().__init__(__A					,      __A							)
		__A :			Union[str, Any]		    =			self.image_processor




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

		if text is not None:
			__A :			Dict		    =			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 :			Optional[Any]		    =			None

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

		return encoding_image_processor




	def        lowerCAmelCase_       (     self						:     str					,      *__A						:     Optional[int]					,      **__A						:     List[str]							):
		return self.tokenizer.batch_decode(*__A					,      **__A							)




	def        lowerCAmelCase_       (     self						:     List[Any]					,      *__A						:     str					,      **__A						:     List[str]							):
		return self.tokenizer.decode(*__A					,      **__A							)




	@property
	def        lowerCAmelCase_       (     self						:     List[Any]							):
		__A :			Dict		    =			self.tokenizer.model_input_names
		__A :			Dict		    =			self.image_processor.model_input_names
		return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)








class      lowerCamelCase_      :




	def __init__(     self						:     Dict					,      __A						:     Tuple					,      __A						:     Optional[int]					,      __A						:     int							):
		__A :			List[str]		    =			name
		__A :			Optional[int]		    =			value
		__A :			Optional[Any]		    =			weight




	def __repr__(     self						:     Any							):
		return F"""{self.__class__.__name__}({self.name}, {self.value}, {self.weight})"""




	def        lowerCAmelCase_       (     self						:     Union[str, Any]							):
		return self.value




	def        lowerCAmelCase_       (     self						:     str							):
		return self.name




	def        lowerCAmelCase_       (     self						:     str							):
		return self.weight




	def        lowerCAmelCase_       (     self						:     Dict							):
		return self.value / self.weight






def 							__SCREAMING_SNAKE_CASE     (   a__    :   str			,a__    :   Optional[int]			,a__    :   Union[str, Any]       )					->				int:
	__A :			Tuple		    =			[]
	for i in range(len(a__       )       ):
		menu.append(Things(name[i]			,value[i]			,weight[i]       )       )
	return menu
def 							__SCREAMING_SNAKE_CASE     (   a__    :   Tuple			,a__    :   Any			,a__    :   Optional[int]       )					->				Tuple:
	__A :			Optional[int]		    =			sorted(a__			,key=a__			,reverse=a__       )
	__A :			Optional[Any]		    =			[]
	__A    ,				__A :			Tuple		    =			0.0, 0.0
	for i in range(len(a__       )       ):
		if (total_cost + items_copy[i].get_weight()) <= max_cost:
			result.append(items_copy[i]       )
			total_cost += items_copy[i].get_weight()
			total_value += items_copy[i].get_value()
	return (result, total_value)
def 							__SCREAMING_SNAKE_CASE     (   )					->				List[Any]:
	pass


if __name__ == "__main__":
			import doctest

			doctest.testmod()





