






"""simple docstring"""





def   a    (     __UpperCAmelCase      :    Union[str, Any]      ,   __UpperCAmelCase      :    int      ,   __UpperCAmelCase      :    Tuple      ,   __UpperCAmelCase      :    List[Any]  )    ->							Dict:
						global f  # a global dp table for knapsack
						if f[i][j] < 0:
												if j < wt[i - 1]:
																		__magic_name__:	Dict							=       mf_knapsack(i - 1      ,   __UpperCAmelCase      ,   __UpperCAmelCase      ,   __UpperCAmelCase  )
												else:
																		__magic_name__:	List[str]							=       max(
																		    mf_knapsack(i - 1      ,   __UpperCAmelCase      ,   __UpperCAmelCase      ,   __UpperCAmelCase  )      ,   mf_knapsack(i - 1      ,   __UpperCAmelCase      ,   __UpperCAmelCase      ,   j - wt[i - 1]  ) + val[i - 1]      ,   )
												__magic_name__:	List[str]							=       val
						return f[i][j]



def   a    (     __UpperCAmelCase      :    Tuple      ,   __UpperCAmelCase      :    int      ,   __UpperCAmelCase      :    List[Any]      ,   __UpperCAmelCase      :    List[str]  )    ->							int:
						__magic_name__:	Optional[Any]							=       [[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_:
																								__magic_name__:	Union[str, Any]							=       max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]]      ,   dp[i - 1][w_]  )
																		else:
																								__magic_name__:	Optional[Any]							=       dp[i - 1][w_]

						return dp[n][w_], dp



def   a    (     __UpperCAmelCase      :    int      ,   __UpperCAmelCase      :    list      ,   __UpperCAmelCase      :    list  )    ->							str:
						if not (isinstance(__UpperCAmelCase      ,   (list, tuple)  ) and isinstance(__UpperCAmelCase      ,   (list, tuple)  )):
												raise ValueError(
												    """Both the weights and values vectors must be either lists or tuples"""  )

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

						__magic_name__,			__magic_name__:	str							=       knapsack(__UpperCAmelCase      ,   __UpperCAmelCase      ,   __UpperCAmelCase      ,   __UpperCAmelCase  )
						__magic_name__:	set							=       set()
						_construct_solution(__UpperCAmelCase      ,   __UpperCAmelCase      ,   __UpperCAmelCase      ,   __UpperCAmelCase      ,   __UpperCAmelCase  )

						return optimal_val, example_optional_set



def   a    (     __UpperCAmelCase      :    list      ,   __UpperCAmelCase      :    list      ,   __UpperCAmelCase      :    int      ,   __UpperCAmelCase      :    int      ,   __UpperCAmelCase      :    set  )    ->							Dict:
						# 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(__UpperCAmelCase      ,   __UpperCAmelCase      ,   i - 1      ,   __UpperCAmelCase      ,   __UpperCAmelCase  )
												else:
																		optimal_set.add(__UpperCAmelCase  )
																		_construct_solution(__UpperCAmelCase      ,   __UpperCAmelCase      ,   i - 1      ,   j - wt[i - 1]      ,   __UpperCAmelCase  )


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




'''simple docstring'''




from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
						import PIL


__lowercase        =    logging.get_logger(__name__)






def 							snake_case__				(     _A:   Dict ,  _A:   Any					)       ->      Union[str, Any]:

		'''simple docstring'''


		lowerCAmelCase     =    b.T
		lowerCAmelCase     =    np.sum(np.square(_A					) ,  axis=1					)
		lowerCAmelCase     =    np.sum(np.square(_A					) ,  axis=0					)
		lowerCAmelCase     =    np.matmul(_A ,  _A					)
		lowerCAmelCase     =    aa[:, None] - 2 * ab + ba[None, :]
		return d






def 							snake_case__				(     _A:   Dict ,  _A:   Dict					)       ->      Optional[Any]:

		'''simple docstring'''


		lowerCAmelCase     =    x.reshape(-1 ,  3					)
		lowerCAmelCase     =    squared_euclidean_distance(_A ,  _A					)
		return np.argmin(_A ,  axis=1					)






class 							a__(   lowerCAmelCase__      ):


			'''simple docstring'''






			UpperCAmelCase_     :	List[Any]						=			['''pixel_values''']



			def __init__(  self			,       __lowerCAmelCase = None			,       __lowerCAmelCase = True			,       __lowerCAmelCase = None			,       __lowerCAmelCase = PILImageResampling.BILINEAR			,       __lowerCAmelCase = True			,       __lowerCAmelCase = True			,       **__lowerCAmelCase			,       ):




					"""simple docstring"""


					super().__init__(**__lowerCAmelCase)
					lowerCAmelCase     =    size if size is not None else {"""height""": 256, """width""": 256}
					lowerCAmelCase     =    get_size_dict(__lowerCAmelCase)
					lowerCAmelCase     =    np.array(__lowerCAmelCase) if clusters is not None else None
					lowerCAmelCase     =    do_resize
					lowerCAmelCase     =    size
					lowerCAmelCase     =    resample
					lowerCAmelCase     =    do_normalize
					lowerCAmelCase     =    do_color_quantize



			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase = PILImageResampling.BILINEAR			,       __lowerCAmelCase = None			,       **__lowerCAmelCase			,       ):




					"""simple docstring"""


					lowerCAmelCase     =    get_size_dict(__lowerCAmelCase)
					if "height" not in size or "width" not in size:
							raise ValueError(f"Size dictionary must contain both height and width keys. Got {size.keys()}")
					return resize(
					    __lowerCAmelCase			,       size=(size["""height"""], size["""width"""])			,       resample=__lowerCAmelCase			,       data_format=__lowerCAmelCase			,       **__lowerCAmelCase)



			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase = None			,       ):




					"""simple docstring"""


					lowerCAmelCase     =    rescale(image=__lowerCAmelCase			,       scale=1 / 127.5			,       data_format=__lowerCAmelCase)
					lowerCAmelCase     =    image - 1
					return image







			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = None			,       __lowerCAmelCase = ChannelDimension.FIRST			,       **__lowerCAmelCase			,       ):




					"""simple docstring"""


					lowerCAmelCase     =    do_resize if do_resize is not None else self.do_resize
					lowerCAmelCase     =    size if size is not None else self.size
					lowerCAmelCase     =    get_size_dict(__lowerCAmelCase)
					lowerCAmelCase     =    resample if resample is not None else self.resample
					lowerCAmelCase     =    do_normalize if do_normalize is not None else self.do_normalize
					lowerCAmelCase     =    do_color_quantize if do_color_quantize is not None else self.do_color_quantize
					lowerCAmelCase     =    clusters if clusters is not None else self.clusters
					lowerCAmelCase     =    np.array(__lowerCAmelCase)

					lowerCAmelCase     =    make_list_of_images(__lowerCAmelCase)

					if not valid_images(__lowerCAmelCase):
							raise ValueError(
							    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
							    """torch.Tensor, tf.Tensor or jax.ndarray.""")

					if do_resize and size is None or resample is None:
							raise ValueError("""Size and resample must be specified if do_resize is True.""")

					if do_color_quantize and clusters is None:
							raise ValueError("""Clusters must be specified if do_color_quantize is True.""")

					# All transformations expect numpy arrays.
					lowerCAmelCase     =    [to_numpy_array(__lowerCAmelCase) for image in images]

					if do_resize:
							lowerCAmelCase     =    [self.resize(image=__lowerCAmelCase			,       size=__lowerCAmelCase			,       resample=__lowerCAmelCase) for image in images]

					if do_normalize:
							lowerCAmelCase     =    [self.normalize(image=__lowerCAmelCase) for image in images]

					if do_color_quantize:
							lowerCAmelCase     =    [to_channel_dimension_format(__lowerCAmelCase			,       ChannelDimension.LAST) for image in images]
							# color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
							lowerCAmelCase     =    np.array(__lowerCAmelCase)
							lowerCAmelCase     =    color_quantize(__lowerCAmelCase			,       __lowerCAmelCase).reshape(images.shape[:-1])

							# flatten to (batch_size, height*width)
							lowerCAmelCase     =    images.shape[0]
							lowerCAmelCase     =    images.reshape(__lowerCAmelCase			,       -1)

							# We need to convert back to a list of images to keep consistent behaviour across processors.
							lowerCAmelCase     =    list(__lowerCAmelCase)
					else:
							lowerCAmelCase     =    [to_channel_dimension_format(__lowerCAmelCase			,       __lowerCAmelCase) for image in images]

					lowerCAmelCase     =    {"""input_ids""": images}
					return BatchFeature(data=__lowerCAmelCase			,       tensor_type=__lowerCAmelCase)




