




'''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  (	__a ):
       __a				:     List[str]   				=					["""image_processor""", """tokenizer"""]
       __a				:     List[str]   				=					"""BlipImageProcessor"""
       __a				:     Union[str, Any]   				=					("""BertTokenizer""", """BertTokenizerFast""")
       def __init__(	self						:	Any   ,				lowercase						:	List[str]   ,				lowercase						:	Union[str, Any]				):



           '''simple docstring'''

           UpperCAmelCase			       =		False
           super().__init__(lowercase   ,				lowercase				)
           UpperCAmelCase			       =		self.image_processor
       def __call__(	self						:	Optional[Any]   ,				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						:	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:
               UpperCAmelCase			       =		self.tokenizer
               UpperCAmelCase			       =		self.tokenizer(
                   text=lowercase   ,				add_special_tokens=lowercase   ,				padding=lowercase   ,				truncation=lowercase   ,				max_length=lowercase   ,				stride=lowercase   ,				pad_to_multiple_of=lowercase   ,				return_attention_mask=lowercase   ,				return_overflowing_tokens=lowercase   ,				return_special_tokens_mask=lowercase   ,				return_offsets_mapping=lowercase   ,				return_token_type_ids=lowercase   ,				return_length=lowercase   ,				verbose=lowercase   ,				return_tensors=lowercase   ,				**lowercase   ,				)
               return text_encoding

           # add pixel_values
           UpperCAmelCase			       =		self.image_processor(lowercase   ,				return_tensors=lowercase				)

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

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

           return encoding_image_processor
       def     A      (	self						:	Union[str, Any]   ,				*lowercase						:	Union[str, Any]   ,				**lowercase						:	Optional[Any]				):



           '''simple docstring'''

           return self.tokenizer.batch_decode(*lowercase   ,				**lowercase				)
       def     A      (	self						:	Dict   ,				*lowercase						:	List[str]   ,				**lowercase						:	Optional[Any]				):



           '''simple docstring'''

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







       @property
       def     A      (	self						:	str				):



           '''simple docstring'''

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







def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Union[str, Any]    ,			SCREAMING_SNAKE_CASE__			:	Tuple    ,			SCREAMING_SNAKE_CASE__			:	Union[str, Any]    ,			SCREAMING_SNAKE_CASE__			:	Any      )							->      Optional[int]:





    '''simple docstring'''






    global f  # a global dp table for knapsack
    if f[i][j] < 0:
        if j < wt[i - 1]:
            A__          =   mf_knapsack(i - 1    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )
        else:
            A__          =   max(
                mf_knapsack(i - 1    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )    ,			mf_knapsack(i - 1    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			j - wt[i - 1]      ) + val[i - 1]    ,			)
        A__          =   val
    return f[i][j]
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Tuple    ,			SCREAMING_SNAKE_CASE__			:	Optional[int]    ,			SCREAMING_SNAKE_CASE__			:	Optional[int]    ,			SCREAMING_SNAKE_CASE__			:	Optional[int]      )							->      Tuple:





    '''simple docstring'''






    A__          =   [[0] * (w + 1) for _ in range(n + 1      )]

    for i in range(1    ,			n + 1      ):
        for w_ in range(1    ,			w + 1      ):
            if wt[i - 1] <= w_:
                A__          =   max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]]    ,			dp[i - 1][w_]      )
            else:
                A__          =   dp[i - 1][w_]

    return dp[n][w_], dp
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	int    ,			SCREAMING_SNAKE_CASE__			:	list    ,			SCREAMING_SNAKE_CASE__			:	list      )							->      Union[str, Any]:





    '''simple docstring'''






    if not (isinstance(SCREAMING_SNAKE_CASE__    ,			(list, tuple)      ) and isinstance(SCREAMING_SNAKE_CASE__    ,			(list, tuple)      )):
        raise ValueError(
            'Both the weights and values vectors must be either lists or tuples'      )

    A__          =   len(SCREAMING_SNAKE_CASE__      )
    if num_items != len(SCREAMING_SNAKE_CASE__      ):
        A__          =   (
            'The number of weights must be the same as the number of values.\n'
            f'But got {num_items} weights and {len(SCREAMING_SNAKE_CASE__      )} values'
        )
        raise ValueError(SCREAMING_SNAKE_CASE__      )
    for i in range(SCREAMING_SNAKE_CASE__      ):
        if not isinstance(wt[i]    ,			SCREAMING_SNAKE_CASE__      ):
            A__          =   (
                'All weights must be integers but got weight of '
                f'type {type(wt[i]      )} at index {i}'
            )
            raise TypeError(SCREAMING_SNAKE_CASE__      )

    A__      ,						A__          =   knapsack(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )
    A__          =   set()
    _construct_solution(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )

    return optimal_val, example_optional_set
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	list    ,			SCREAMING_SNAKE_CASE__			:	list    ,			SCREAMING_SNAKE_CASE__			:	int    ,			SCREAMING_SNAKE_CASE__			:	int    ,			SCREAMING_SNAKE_CASE__			:	set      )							->      Optional[int]:





    '''simple docstring'''






    if i > 0 and j > 0:
        if dp[i - 1][j] == dp[i][j]:
            _construct_solution(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			i - 1    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )
        else:
            optimal_set.add(SCREAMING_SNAKE_CASE__      )
            _construct_solution(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			i - 1    ,			j - wt[i - 1]    ,			SCREAMING_SNAKE_CASE__      )


if __name__ == "__main__":
     lowercase_       		=  [3, 2, 4, 4]
     lowercase_       		=  [4, 3, 2, 3]
     lowercase_       		=  4
     lowercase_       		=  6
     lowercase_       		=  [[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
     lowercase_		,			lowercase_       		=  knapsack(w, wt, val, n)
     print(optimal_solution)
     print(mf_knapsack(n, wt, val, w))  # switched the n and w

     # testing the dynamic programming problem with example
     # the optimal subset for the above example are items 3 and 4
     lowercase_		,			lowercase_       		=  knapsack_with_example_solution(w, wt, val)
     assert optimal_solution == 8
     assert optimal_subset == {3, 4}
     print("optimal_value = ", optimal_solution)
     print("An optimal subset corresponding to the optimal value", optimal_subset)

