





from __future__ import annotations






def 	a_		(	__lowercase       :     list[int]	, __lowercase       :     int			)			->  list[list[int]]:
			_snake_case          =      []
			_snake_case          =      []
			_snake_case          =      0
			_snake_case          =      sum(lowerCAmelCase__			)
			create_state_space_tree(lowerCAmelCase__	, lowerCAmelCase__	, lowerCAmelCase__	, lowerCAmelCase__	, lowerCAmelCase__	, lowerCAmelCase__			)
			return result






def 	a_		(	__lowercase       :     list[int]	, __lowercase       :     int	, __lowercase       :     int	, __lowercase       :     list[int]	, __lowercase       :     list[list[int]]	, __lowercase       :     int	, )			->  None:


			if sum(lowerCAmelCase__			) > max_sum or (remaining_nums_sum + sum(lowerCAmelCase__			)) < max_sum:
						return
			if sum(lowerCAmelCase__			) == max_sum:
						result.append(lowerCAmelCase__			)
						return
			for index in range(lowerCAmelCase__	, len(lowerCAmelCase__			)			):
						create_state_space_tree(
						    lowerCAmelCase__	, lowerCAmelCase__	, index + 1	, [*path, nums[index]]	, lowerCAmelCase__	, remaining_nums_sum - nums[index]	, )
_lowerCamelCase	:    Dict          =      [3, 34, 4, 12, 5, 2]
_lowerCamelCase	:    str          =      9
_lowerCamelCase	:    Optional[Any]          =      generate_sum_of_subsets_soln(nums, max_sum)

print(*result)

from pathlib import Path

import cva
import numpy as np
from matplotlib import pyplot as plt






def 	a_		(	__lowercase       :     np.ndarray	, __lowercase       :     np.ndarray	, __lowercase       :     np.ndarray	, __lowercase       :     int	, __lowercase       :     int			)			->  np.ndarray:
			_snake_case          =      cva.getAffineTransform(__lowercase	, __lowercase			)
			return cva.warpAffine(__lowercase	, __lowercase	, (rows, cols)			)


if __name__ == "__main__":
			# read original image
			_lowerCamelCase	:    Optional[Any]          =      cva.imread(
			    str(Path(__file__).resolve().parent.parent / '''image_data''' / '''lena.jpg''')
			)
			# turn image in gray scale value
			_lowerCamelCase	:    List[str]          =      cva.cvtColor(image, cva.COLOR_BGR2GRAY)
			# get image shape
			_lowerCamelCase   ,					_lowerCamelCase	:    List[Any]          =      gray_img.shape

			# set different points to rotate image
			_lowerCamelCase	:    str          =      np.array([[50, 50], [200, 50], [50, 200]], np.floataa)
			_lowerCamelCase	:    Optional[Any]          =      np.array([[10, 100], [200, 50], [100, 250]], np.floataa)
			_lowerCamelCase	:    List[str]          =      np.array([[50, 50], [150, 50], [120, 200]], np.floataa)
			_lowerCamelCase	:    Dict          =      np.array([[10, 100], [80, 50], [180, 250]], np.floataa)

			# add all rotated images in a list
			_lowerCamelCase	:    int          =      [
			    gray_img,
			    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
			    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
			    get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
			]

			# plot different image rotations
			_lowerCamelCase	:    Any          =      plt.figure(1)
			_lowerCamelCase	:    List[Any]          =      ['''Original''', '''Rotation 1''', '''Rotation 2''', '''Rotation 3''']
			for i, image in enumerate(images):
						plt.subplot(2, 2, i + 1), plt.imshow(image, '''gray''')
						plt.title(titles[i])
						plt.axis('''off''')
						plt.subplots_adjust(left=0.0, bottom=0.0_5, right=1.0, top=0.9_5)
			plt.show()