





"""simple docstring"""






from collections import defaultdict




class  __lowercase	:







					'''simple docstring'''




					def __init__(  self		,	_UpperCAmelCase		,	_UpperCAmelCase   ):
									__a       :  Optional[Any]					=     total  # total no of tasks (N)

									# DP table will have a dimension of (2^M)*N
									# initially all values are set to -1
									__a       :  Any					=     [
									    [-1 for i in range(total + 1   )] for j in range(2 ** len(a__   )   )
									]

									__a       :  Dict					=     defaultdict(a__   )  # stores the list of persons for each task

									# final_mask is used to check if all persons are included by setting all bits
									# to 1
									__a       :  Union[str, Any]					=     (1 << len(a__   )) - 1







					def        _lowerCamelCase						(  self		,	_UpperCAmelCase		,	_UpperCAmelCase   ):
									# if mask == self.finalmask all persons are distributed tasks, return 1
									if mask == self.final_mask:
													return 1

									# if not everyone gets the task and no more tasks are available, return 0
									if task_no > self.total_tasks:
													return 0

									# if case already considered
									if self.dp[mask][task_no] != -1:
													return self.dp[mask][task_no]

									# Number of ways when we don't this task in the arrangement
									__a       :  Any					=     self.count_ways_until(a__		,	task_no + 1   )

									# now assign the tasks one by one to all possible persons and recursively
									# assign for the remaining tasks.
									if task_no in self.task:
													for p in self.task[task_no]:
																	# if p is already given a task
																	if mask & (1 << p):
																					continue

																	# assign this task to p and change the mask value. And recursively
																	# assign tasks with the new mask value.
																	total_ways_util += self.count_ways_until(mask | (1 << p)		,	task_no + 1   )

        # save the value.
									__a       :  List[str]					=     total_ways_util

									return self.dp[mask][task_no]







					def        _lowerCamelCase						(  self		,	_UpperCAmelCase   ):
									# Store the list of persons for each task
									for i in range(len(a__   )   ):
													for j in task_performed[i]:
																	self.task[j].append(a__   )

        # call the function to fill the DP table, final answer is stored in dp[0][1]
									return self.count_ways_until(0		,	1   )


if __name__ == "__main__":
		A       	=     5  # total no of tasks (the value of N)

		# the list of tasks that can be done by M persons.
		A       	=     [[1, 3, 4], [1, 2, 5], [3, 4]]
		print(
		    AssignmentUsingBitmask(task_performed, total_tasks).count_no_of_ways(
		        task_performed
		    )
		)
"""simple docstring"""




from itertools import product

from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Optional[Any]			,_lowerCamelCase						:       int	)       ->      List[str]:
  _lowerCAmelCase						:       Tuple  			=     k_size // 2
  _lowerCAmelCase			,	_lowerCAmelCase						:       List[str]  			=     mgrid[0 - center : k_size - center, 0 - center : k_size - center]
  _lowerCAmelCase						:       Union[str, Any]  			=     1 / (2 * pi * sigma) * exp(-(square(_lowerCamelCase	) + square(_lowerCamelCase	)) / (2 * square(_lowerCamelCase	))	)
  return g





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Dict			,_lowerCamelCase						:       int			,_lowerCamelCase						:       int	)       ->      Dict:
  _lowerCAmelCase			,	_lowerCAmelCase						:       str  			=     image.shape[0], image.shape[1]
  # dst image height and width
  _lowerCAmelCase						:       Optional[int]  			=     height - k_size + 1
  _lowerCAmelCase						:       Dict  			=     width - k_size + 1

  # im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
  _lowerCAmelCase						:       Tuple  			=     zeros((dst_height * dst_width, k_size * k_size)	)
  _lowerCAmelCase						:       int  			=     0
  for i, j in product(range(_lowerCamelCase	)			,range(_lowerCamelCase	)	):
    _lowerCAmelCase						:       Any  			=     ravel(image[i : i + k_size, j : j + k_size]	)
    _lowerCAmelCase						:       Union[str, Any]  			=     window
    row += 1

  #  turn the kernel into shape(k*k, 1)
  _lowerCAmelCase						:       List[Any]  			=     gen_gaussian_kernel(_lowerCamelCase			,_lowerCamelCase	)
  _lowerCAmelCase						:       str  			=     ravel(_lowerCamelCase	)

  # reshape and get the dst image
  _lowerCAmelCase						:       int  			=     dot(_lowerCamelCase			,_lowerCamelCase	).reshape(_lowerCamelCase			,_lowerCamelCase	).astype(_lowerCamelCase	)

  return dst


if __name__ == "__main__":
     # read original image
     _a      :		Optional[Any]           =   imread(r'../image_data/lena.jpg')
     # turn image in gray scale value
     _a      :		Dict           =   cvtColor(img, COLOR_BGR2GRAY)

     # get values with two different mask size
     _a      :		Union[str, Any]           =   gaussian_filter(gray, 3, sigma=1)
     _a      :		List[Any]           =   gaussian_filter(gray, 5, sigma=0.8)

     # show result images
     imshow('gaussian filter with 3x3 mask', gaussianaxa)
     imshow('gaussian filter with 5x5 mask', gaussianaxa)
     waitKey()




