"""simple docstring"""





import os
from typing import List, Optional, Union

from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer


class      UpperCAmelCase_	(    _a):
      lowerCamelCase__		:    Dict   =  ["image_processor", "tokenizer"]
      lowerCamelCase__		:    Dict   =  "BlipImageProcessor"
      lowerCamelCase__		:    Union[str, Any]   =  "AutoTokenizer"


      def __init__(   self		,			a		,			a		,			a							)       ->					Optional[int]:
             super().__init__(a		,			a							)

             # add QFormer tokenizer
             lowercase__			:		Dict       =	qformer_tokenizer


      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		,			)       ->					BatchFeature:
             if images is None and text is None:
                    raise ValueError('You have to specify at least images or text.'							)

             lowercase__			:		List[Any]       =	BatchFeature()

             if text is not None:
                    lowercase__			:		Optional[int]       =	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		,			)
                    encoding.update(a							)
                    lowercase__			:		Optional[int]       =	self.qformer_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		,			)
                    lowercase__			:		List[str]       =	qformer_text_encoding.pop('input_ids'							)
                    lowercase__			:		Any       =	qformer_text_encoding.pop('attention_mask'							)

             if images is not None:
                    lowercase__			:		List[Any]       =	self.image_processor(a		,			return_tensors=a							)
                    encoding.update(a							)

             return encoding


      def 		_UpperCAmelCase       (   self		,			*a		,			**a							)       ->					List[str]:
             return self.tokenizer.batch_decode(*a		,			**a							)


      def 		_UpperCAmelCase       (   self		,			*a		,			**a							)       ->					Tuple:
             return self.tokenizer.decode(*a		,			**a							)


      @property
      # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
      def 		_UpperCAmelCase       (   self							)       ->					Union[str, Any]:
             lowercase__			:		str       =	self.tokenizer.model_input_names
             lowercase__			:		List[Any]       =	self.image_processor.model_input_names
             return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)


      def 		_UpperCAmelCase       (   self		,			a		,			**a							)       ->					Optional[int]:
             if os.path.isfile(a							):
                    raise ValueError(f"""Provided path ({save_directory}) should be a directory, not a file"""							)
             os.makedirs(a		,			exist_ok=a							)
             lowercase__			:		int       =	os.path.join(a		,			'qformer_tokenizer'							)
             self.qformer_tokenizer.save_pretrained(a							)
             return super().save_pretrained(a		,			**a							)


      @classmethod
      def 		_UpperCAmelCase       (   cls		,			a		,			**a							)       ->					str:
             lowercase__			:		str       =	AutoTokenizer.from_pretrained(a		,			subfolder='qformer_tokenizer'							)
             lowercase__			:		int       =	cls._get_arguments_from_pretrained(a		,			**a							)
             args.append(a							)
             return cls(*a							)






def        lowercase_							(A : Union[str, Any]		,					A : List[str]		,					A : int		,					A : Optional[int]      ):
       global f  # a global dp table for knapsack
       if f[i][j] < 0:
              if j < wt[i - 1]:
                     snake_case__							:	Union[str, Any]   	=	mf_knapsack(i - 1		,					A		,					A		,					A      )
              else:
                     snake_case__							:	Any   	=	max(
                         mf_knapsack(i - 1		,					A		,					A		,					A      )		,					mf_knapsack(i - 1		,					A		,					A		,					j - wt[i - 1]      ) + val[i - 1]		,					)
              snake_case__							:	Optional[int]   	=	val
       return f[i][j]
def        lowercase_							(A : Optional[int]		,					A : Union[str, Any]		,					A : str		,					A : Dict      ):
       snake_case__							:	int   	=	[[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_:
                            snake_case__							:	Union[str, Any]   	=	max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]]		,					dp[i - 1][w_]      )
                     else:
                            snake_case__							:	str   	=	dp[i - 1][w_]

       return dp[n][w_], dp
def        lowercase_							(A : int		,					A : list		,					A : list      ):
       if not (isinstance(A		,					(list, tuple)      ) and isinstance(A		,					(list, tuple)      )):
              raise ValueError(
                  'Both the weights and values vectors must be either lists or tuples'      )

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

       snake_case__	,				snake_case__							:	Optional[int]   	=	knapsack(A		,					A		,					A		,					A      )
       snake_case__							:	set   	=	set()
       _construct_solution(A		,					A		,					A		,					A		,					A      )

       return optimal_val, example_optional_set
def        lowercase_							(A : list		,					A : list		,					A : int		,					A : int		,					A : set      ):
       # for the current item i at a maximum weight j to be part of an optimal subset,
       # the optimal value at (i, j) must be greater than the optimal value at (i-1, j).
       # where i - 1 means considering only the previous items at the given maximum weight
       if i > 0 and j > 0:
              if dp[i - 1][j] == dp[i][j]:
                     _construct_solution(A		,					A		,					i - 1		,					A		,					A      )
              else:
                     optimal_set.add(A      )
                     _construct_solution(A		,					A		,					i - 1		,					j - wt[i - 1]		,					A      )


if __name__ == "__main__":
 a_   :Any        =      [3, 2, 4, 4]
 a_   :List[Any]        =      [4, 3, 2, 3]
 a_   :Union[str, Any]        =      4
 a_   :List[str]        =      6
 a_   :Union[str, Any]        =      [[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
 a_  ,						a_   :List[Any]        =      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
 a_  ,						a_   :Any        =      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)






