
'''simple docstring'''
from __future__ import annotations

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    snake_case_ :    list[list[int]]     			=						[]
    snake_case_ :    list[int]     			=						[]
    snake_case_ :    Dict     			=						0
    snake_case_ :    List[str]     			=						sum(__SCREAMING_SNAKE_CASE       )
    create_state_space_tree(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )
    return result

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		):
    """simple docstring"""






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


print(*result)






'''simple docstring'''
import doctest
from collections import deque

import numpy as np






class    UpperCAmelCase_				:





 def __init__(    self):
     snake_case_ :    List[Any]     			=						[2, 1, 2, -1]
     snake_case_ :    int     			=						[1, 2, 3, 4]





 def        snake_case__		(    self):
     snake_case_ :    str     			=						len(self.first_signal)
     snake_case_ :    Any     			=						len(self.second_signal)

     snake_case_ :    List[Any]     			=						max(lowercase_ ,       lowercase_)

     # create a zero matrix of max_length x max_length
     snake_case_ :    Dict     			=						[[0] * max_length for i in range(lowercase_)]

     # fills the smaller signal with zeros to make both signals of same length
     if length_first_signal < length_second_signal:
         self.first_signal += [0] * (max_length - length_first_signal)
     elif length_first_signal > length_second_signal:
         self.second_signal += [0] * (max_length - length_second_signal)
     for i in range(lowercase_):
         snake_case_ :    List[str]     			=						deque(self.second_signal)
         rotated_signal.rotate(lowercase_)
         for j, item in enumerate(lowercase_):
             matrix[i][j] += item

        # multiply the matrix with the first signal
     snake_case_ :    Any     			=						np.matmul(np.transpose(lowercase_) ,       np.transpose(self.first_signal))

     # rounding-off to two decimal places
     return [round(lowercase_ ,       2) for i in final_signal]


if __name__ == "__main__":
  doctest.testmod()




