
def 				a						(							snake_case__:					list[int]						,   snake_case__:					str					):


				'''simple docstring'''
				lowercase_       =					int(snake_case__					)

				# Initialize Result
				lowercase_       =					[]

				# Traverse through all denomination
				for denomination in reversed(snake_case__					):
								# Find denominations
								while int(snake_case__					) >= int(snake_case__					):
												total_value -= int(snake_case__					)
												answer.append(snake_case__					)  # Append the "answers" array

				return answer


# Driver Code
if __name__ == "__main__":
						__a  			=					[]
						__a  			=					'0'

						if (
						    input('Do you want to enter your denominations ? (yY/n): ').strip().lower()
						    == "y"
						):
												__a  			=					int(input('Enter the number of denominations you want to add: ').strip())

												for i in range(0, n):
																		denominations.append(int(input(f"Denomination {i}: ").strip()))
												__a  			=					input('Enter the change you want to make in Indian Currency: ').strip()
						else:
												# All denominations of Indian Currency if user does not enter
												__a  			=					[1, 2, 5, 1_0, 2_0, 5_0, 1_0_0, 5_0_0, 2_0_0_0]
												__a  			=					input('Enter the change you want to make: ').strip()

						if int(value) == 0 or int(value) < 0:
												print('The total value cannot be zero or negative.')

						else:
												print(f"Following is minimal change for {value}: ")
												__a  			=					find_minimum_change(denominations, value)
												# Print result
												for i in range(len(answer)):
																		print(answer[i], end=' ')








"""simple docstring"""


import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image

from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processing.dithering import burkes as bs
from digital_image_processing.edge_detection import canny
from digital_image_processing.filters import convolve as conv
from digital_image_processing.filters import gaussian_filter as gg
from digital_image_processing.filters import local_binary_pattern as lbp
from digital_image_processing.filters import median_filter as med
from digital_image_processing.filters import sobel_filter as sob
from digital_image_processing.resize import resize as rs

A     :   Union[str, Any]			    =			imread(R"digital_image_processing/image_data/lena_small.jpg")
A     :   Optional[Any]			    =			cvtColor(img, COLOR_BGR2GRAY)


def _lowerCamelCase	(		):

			'''simple docstring'''




			__lowerCAmelCase				    =			cn.convert_to_negative(_UpperCamelCase      )
			# assert negative_img array for at least one True
			assert negative_img.any()


def _lowerCamelCase	(		):

			'''simple docstring'''




			with Image.open("digital_image_processing/image_data/lena_small.jpg"      ) as img:
						# Work around assertion for response
						assert str(cc.change_contrast(_UpperCamelCase    , 110      )      ).startswith(
						    "<PIL.Image.Image image mode=RGB size=100x100 at"      )


def _lowerCamelCase	(		):

			'''simple docstring'''




			__lowerCAmelCase				    =			canny.gen_gaussian_kernel(9    , sigma=1.4      )
			# Assert ambiguous array
			assert resp.all()


def _lowerCamelCase	(		):

			'''simple docstring'''




			__lowerCAmelCase				    =			imread("digital_image_processing/image_data/lena_small.jpg"    , 0      )
			# assert ambiguous array for all == True
			assert canny_img.all()
			__lowerCAmelCase				    =			canny.canny(_UpperCamelCase      )
			# assert canny array for at least one True
			assert canny_array.any()


def _lowerCamelCase	(		):

			'''simple docstring'''




			assert gg.gaussian_filter(_UpperCamelCase    , 5    , sigma=0.9      ).all()


def _lowerCamelCase	(		):

			'''simple docstring'''




			__lowerCAmelCase				    =			array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]]      )
			__lowerCAmelCase				    =			conv.img_convolve(_UpperCamelCase    , _UpperCamelCase      ).astype(_UpperCamelCase      )
			assert res.any()


def _lowerCamelCase	(		):

			'''simple docstring'''




			assert med.median_filter(_UpperCamelCase    , 3      ).any()


def _lowerCamelCase	(		):

			'''simple docstring'''




			__lowerCAmelCase    ,	__lowerCAmelCase				    =			sob.sobel_filter(_UpperCamelCase      )
			assert grad.any() and theta.any()


def _lowerCamelCase	(		):

			'''simple docstring'''




			__lowerCAmelCase				    =			sp.make_sepia(_UpperCamelCase    , 20      )
			assert sepia.all()


def _lowerCamelCase	(		_UpperCamelCase = "digital_image_processing/image_data/lena_small.jpg"      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			bs.Burkes(imread(_UpperCamelCase    , 1      )    , 120      )
			burkes.process()
			assert burkes.output_img.any()


def _lowerCamelCase	(		_UpperCamelCase = "digital_image_processing/image_data/lena_small.jpg"    , ):

			'''simple docstring'''




			__lowerCAmelCase				    =			rs.NearestNeighbour(imread(_UpperCamelCase    , 1      )    , 400    , 200      )
			nn.process()
			assert nn.output.any()


def _lowerCamelCase	(		):

			'''simple docstring'''




			__lowerCAmelCase				    =			"digital_image_processing/image_data/lena.jpg"

			# Reading the image and converting it to grayscale.
			__lowerCAmelCase				    =			imread(_UpperCamelCase    , 0      )

			# Test for get_neighbors_pixel function() return not None
			__lowerCAmelCase				    =			0
			__lowerCAmelCase				    =			0
			__lowerCAmelCase				    =			image[x_coordinate][y_coordinate]

			__lowerCAmelCase				    =			lbp.get_neighbors_pixel(
			    _UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase      )

			assert neighbors_pixels is not None

			# Test for local_binary_pattern function()
			# Create a numpy array as the same height and width of read image
			__lowerCAmelCase				    =			np.zeros((image.shape[0], image.shape[1])      )

			# Iterating through the image and calculating the local binary pattern value
			# for each pixel.
			for i in range(0    , image.shape[0]      ):
						for j in range(0    , image.shape[1]      ):
									__lowerCAmelCase				    =			lbp.local_binary_value(_UpperCamelCase    , _UpperCamelCase    , _UpperCamelCase      )

			assert lbp_image.any()




