
def        UpperCamelCase    (							lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__		):
					'''simple docstring'''
					global f  # a global dp table for knapsack
					if f[i][j] < 0:
										if j < wt[i - 1]:
															lowercase							=						mf_knapsack(i - 1 ,			lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__		)
										else:
															lowercase							=						max(
															    mf_knapsack(i - 1 ,			lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__		) ,			mf_knapsack(i - 1 ,			lowerCAmelCase__ ,			lowerCAmelCase__ ,			j - wt[i - 1]		) + val[i - 1] ,			)
										lowercase							=						val
					return f[i][j]



def        UpperCamelCase    (							lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__		):
					'''simple docstring'''
					lowercase							=						[[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_:
																				lowercase							=						max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]] ,			dp[i - 1][w_]		)
															else:
																				lowercase							=						dp[i - 1][w_]

					return dp[n][w_], dp



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

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

					lowercase	,						lowercase							=						knapsack(lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__		)
					lowercase							=						set()
					_construct_solution(lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__		)

					return optimal_val, example_optional_set



def        UpperCamelCase    (							lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__		):
					'''simple docstring'''
					# 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(lowerCAmelCase__ ,			lowerCAmelCase__ ,			i - 1 ,			lowerCAmelCase__ ,			lowerCAmelCase__		)
										else:
															optimal_set.add(lowerCAmelCase__		)
															_construct_solution(lowerCAmelCase__ ,			lowerCAmelCase__ ,			i - 1 ,			j - wt[i - 1] ,			lowerCAmelCase__		)


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






from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
  from PIL import Image

  from ..image_utils import load_image

if is_tf_available():
  import tensorflow as tf

  from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
  from ..tf_utils import stable_softmax

if is_torch_available():
  from ..models.auto.modeling_auto import MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING

lowercase  :					Any						    =							logging.get_logger(__name__)

@add_end_docstrings(lowerCAmelCase     )
class __snake_case		(      lowerCAmelCase     ):
    def __init__(  self		,*snake_case		,**snake_case      ):



           '''simple docstring'''







           super().__init__(*snake_case		,**snake_case      )
           requires_backends(self		,"""vision"""      )
           self.check_model_type(
               TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
               if self.framework == """tf"""
               else MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case=None      ):



           '''simple docstring'''







           lowercase							:     List[Any]       	=      {}
           if top_k is not None:
                  lowercase							:     int       	=      top_k
           return {}, {}, postprocess_params
    def __call__(  self		,snake_case		,**snake_case      ):



           '''simple docstring'''







           return super().__call__(snake_case		,**snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case      ):



           '''simple docstring'''







           lowercase							:     Any       	=      load_image(snake_case      )
           lowercase							:     List[Any]       	=      self.image_processor(images=snake_case		,return_tensors=self.framework      )
           return model_inputs
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case      ):



           '''simple docstring'''







           lowercase							:     int       	=      self.model(**snake_case      )
           return model_outputs
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case=5      ):



           '''simple docstring'''







           if top_k > self.model.config.num_labels:
                  lowercase							:     Tuple       	=      self.model.config.num_labels

           if self.framework == "pt":
                  lowercase							:     str       	=      model_outputs.logits.softmax(-1      )[0]
                  lowercase	,			lowercase							:     Dict       	=      probs.topk(snake_case      )
           elif self.framework == "tf":
                  lowercase							:     Optional[int]       	=      stable_softmax(model_outputs.logits		,axis=-1      )[0]
                  lowercase							:     Union[str, Any]       	=      tf.math.top_k(snake_case		,k=snake_case      )
                  lowercase	,			lowercase							:     List[str]       	=      topk.values.numpy(), topk.indices.numpy()
           else:
                  raise ValueError(f"Unsupported framework: {self.framework}"      )

           lowercase							:     Tuple       	=      scores.tolist()
           lowercase							:     Dict       	=      ids.tolist()
           return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(snake_case		,snake_case      )]
