






from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


SCREAMING_SNAKE_CASE__      :			List[Any]							=		logging.get_logger(__name__)
class 						lowerCamelCase_				(      lowerCamelCase ):
				a__					       =  ['''pixel_values''']







				def __init__(						self       ,					__lowerCAmelCase = True       ,					__lowerCAmelCase = 1 / 2_5_5       ,					__lowerCAmelCase = True       ,					__lowerCAmelCase = 8       ,					**__lowerCAmelCase       ,					):
								"""simple docstring"""
								super().__init__(**__lowerCAmelCase     )

								__magic_name__		:Any        =      do_rescale
								__magic_name__		:List[str]        =      rescale_factor
								__magic_name__		:int        =      do_pad
								__magic_name__		:Union[str, Any]        =      pad_size







				def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase = None       ,					**__lowerCAmelCase     ):
								"""simple docstring"""
								return rescale(__lowerCAmelCase       ,					scale=__lowerCAmelCase       ,					data_format=__lowerCAmelCase       ,					**__lowerCAmelCase     )







				def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase = None     ):
								"""simple docstring"""
								__magic_name__     ,				__magic_name__		:List[Any]        =      get_image_size(__lowerCAmelCase     )
								__magic_name__		:int        =      (old_height // size + 1) * size - old_height
								__magic_name__		:Any        =      (old_width // size + 1) * size - old_width

								return pad(__lowerCAmelCase       ,					((0, pad_height), (0, pad_width))       ,					mode='''symmetric'''       ,					data_format=__lowerCAmelCase     )







				def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase = None       ,					__lowerCAmelCase = None       ,					__lowerCAmelCase = None       ,					__lowerCAmelCase = None       ,					__lowerCAmelCase = None       ,					__lowerCAmelCase = ChannelDimension.FIRST       ,					**__lowerCAmelCase       ,					):
								"""simple docstring"""
								__magic_name__		:List[str]        =      do_rescale if do_rescale is not None else self.do_rescale
								__magic_name__		:List[str]        =      rescale_factor if rescale_factor is not None else self.rescale_factor
								__magic_name__		:str        =      do_pad if do_pad is not None else self.do_pad
								__magic_name__		:Optional[int]        =      pad_size if pad_size is not None else self.pad_size

								__magic_name__		:Union[str, Any]        =      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_rescale and rescale_factor is None:
												raise ValueError('''Rescale factor must be specified if do_rescale is True.'''     )

								# All transformations expect numpy arrays.
								__magic_name__		:List[str]        =      [to_numpy_array(__lowerCAmelCase     ) for image in images]

								if do_rescale:
												__magic_name__		:Dict        =      [self.rescale(image=__lowerCAmelCase       ,					scale=__lowerCAmelCase     ) for image in images]

								if do_pad:
												__magic_name__		:int        =      [self.pad(__lowerCAmelCase       ,					size=__lowerCAmelCase     ) for image in images]

								__magic_name__		:Optional[int]        =      [to_channel_dimension_format(__lowerCAmelCase       ,					__lowerCAmelCase     ) for image in images]

								__magic_name__		:int        =      {'''pixel_values''': images}
								return BatchFeature(data=__lowerCAmelCase       ,					tensor_type=__lowerCAmelCase     )














def      __lowercase					( snake_case,  snake_case,  snake_case,  snake_case ):







				"""simple docstring"""






				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,  snake_case,  snake_case,  snake_case )
								else:
												__magic_name__		:Optional[Any]        =      max(
												    mf_knapsack(i - 1,  snake_case,  snake_case,  snake_case ),  mf_knapsack(i - 1,  snake_case,  snake_case,  j - wt[i - 1] ) + val[i - 1],  )
								__magic_name__		:List[Any]        =      val
				return f[i][j]

def      __lowercase					( snake_case,  snake_case,  snake_case,  snake_case ):







				"""simple docstring"""






				__magic_name__		:str        =      [[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__		:List[str]        =      max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]],  dp[i - 1][w_] )
												else:
																__magic_name__		:Optional[int]        =      dp[i - 1][w_]

				return dp[n][w_], dp

def      __lowercase					( snake_case,  snake_case,  snake_case ):







				"""simple docstring"""






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

				__magic_name__		:Dict        =      len(snake_case )
				if num_items != len(snake_case ):
								__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(snake_case )} values'''
								)
								raise ValueError(snake_case )
				for i in range(snake_case ):
								if not isinstance(wt[i],  snake_case ):
												__magic_name__		:str        =      (
												    '''All weights must be integers but got weight of '''
												    f'''type {type(wt[i] )} at index {i}'''
												)
												raise TypeError(snake_case )

				__magic_name__     ,				__magic_name__		:Tuple        =      knapsack(snake_case,  snake_case,  snake_case,  snake_case )
				__magic_name__		:set        =      set()
				_construct_solution(snake_case,  snake_case,  snake_case,  snake_case,  snake_case )

				return optimal_val, example_optional_set

def      __lowercase					( snake_case,  snake_case,  snake_case,  snake_case,  snake_case ):







				"""simple docstring"""






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


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






