





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"""







						UpperCAmelCase__						=      ["""image_processor""", """tokenizer"""]
						UpperCAmelCase__						=      """BlipImageProcessor"""
						UpperCAmelCase__						=      """AutoTokenizer"""


						def __init__(		self      ,							a__      ,							a__):



													"""simple docstring"""





													_lowerCamelCase							:     Optional[Any]	=		False
													super().__init__(__UpperCamelCase      ,							__UpperCamelCase)
													_lowerCamelCase							:     Optional[Any]	=		self.image_processor


						def __call__(		self      ,							a__ = None      ,							a__ = None      ,							a__ = True      ,							a__ = False      ,							a__ = None      ,							a__ = None      ,							a__ = 0      ,							a__ = None      ,							a__ = None      ,							a__ = False      ,							a__ = False      ,							a__ = False      ,							a__ = False      ,							a__ = False      ,							a__ = True      ,							a__ = None      ,							**a__      ,							):



													"""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:
																				_lowerCamelCase							:     Dict	=		self.tokenizer
																				_lowerCamelCase							:     Optional[int]	=		self.tokenizer(
																				    text=__UpperCamelCase      ,							add_special_tokens=__UpperCamelCase      ,							padding=__UpperCamelCase      ,							truncation=__UpperCamelCase      ,							max_length=__UpperCamelCase      ,							stride=__UpperCamelCase      ,							pad_to_multiple_of=__UpperCamelCase      ,							return_attention_mask=__UpperCamelCase      ,							return_overflowing_tokens=__UpperCamelCase      ,							return_special_tokens_mask=__UpperCamelCase      ,							return_offsets_mapping=__UpperCamelCase      ,							return_token_type_ids=__UpperCamelCase      ,							return_length=__UpperCamelCase      ,							verbose=__UpperCamelCase      ,							return_tensors=__UpperCamelCase      ,							**__UpperCamelCase      ,							)
																				return text_encoding

													# add pixel_values
													_lowerCamelCase							:     Tuple	=		self.image_processor(__UpperCamelCase      ,							return_tensors=__UpperCamelCase)

													if text is not None:
																				_lowerCamelCase							:     Optional[Any]	=		self.tokenizer(
																				    text=__UpperCamelCase      ,							add_special_tokens=__UpperCamelCase      ,							padding=__UpperCamelCase      ,							truncation=__UpperCamelCase      ,							max_length=__UpperCamelCase      ,							stride=__UpperCamelCase      ,							pad_to_multiple_of=__UpperCamelCase      ,							return_attention_mask=__UpperCamelCase      ,							return_overflowing_tokens=__UpperCamelCase      ,							return_special_tokens_mask=__UpperCamelCase      ,							return_offsets_mapping=__UpperCamelCase      ,							return_token_type_ids=__UpperCamelCase      ,							return_length=__UpperCamelCase      ,							verbose=__UpperCamelCase      ,							return_tensors=__UpperCamelCase      ,							**__UpperCamelCase      ,							)
													else:
																				_lowerCamelCase							:     Dict	=		None

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

													return encoding_image_processor


						def 				__snake_case			(		self      ,							*a__      ,							**a__):



													"""simple docstring"""





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


						def 				__snake_case			(		self      ,							*a__      ,							**a__):



													"""simple docstring"""





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



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



													"""simple docstring"""





													_lowerCamelCase							:     Optional[int]	=		self.tokenizer.model_input_names
													_lowerCamelCase							:     Tuple	=		self.image_processor.model_input_names
													return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))








import unittest

from knapsack import knapsack as k







class 			__A       (      unittest.TestCase  ):




      """simple docstring"""







      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Dict	=		0
             _lowerCamelCase							:     int	=		[0]
             _lowerCamelCase							:     Optional[int]	=		[0]
             _lowerCamelCase							:     Dict	=		len(a__)
             self.assertEqual(k.knapsack(a__      ,							a__      ,							a__      ,							a__)      ,							0)

             _lowerCamelCase							:     str	=		[60]
             _lowerCamelCase							:     List[str]	=		[10]
             _lowerCamelCase							:     List[Any]	=		len(a__)
             self.assertEqual(k.knapsack(a__      ,							a__      ,							a__      ,							a__)      ,							0)


      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Union[str, Any]	=		3
             _lowerCamelCase							:     List[Any]	=		[1, 2, 3]
             _lowerCamelCase							:     Dict	=		[3, 2, 1]
             _lowerCamelCase							:     Any	=		len(a__)
             self.assertEqual(k.knapsack(a__      ,							a__      ,							a__      ,							a__)      ,							5)



      def 				__snake_case			(		self):



             """simple docstring"""





             _lowerCamelCase							:     Optional[Any]	=		50
             _lowerCamelCase							:     Dict	=		[60, 100, 120]
             _lowerCamelCase							:     Dict	=		[10, 20, 30]
             _lowerCamelCase							:     Union[str, Any]	=		len(a__)
             self.assertEqual(k.knapsack(a__      ,							a__      ,							a__      ,							a__)      ,							220)


if __name__ == "__main__":
       unittest.main()

