


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

import numpy as np
class    lowercase	:

		"""simple docstring"""

		def __init__(		self							):

							'''simple docstring'''


							UpperCamelCase__   :int												= [2, 1, 2, -1]
							UpperCamelCase__   :Tuple												= [1, 2, 3, 4]




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :Any												= len(self.first_signal							)
							UpperCamelCase__   :Tuple												= len(self.second_signal							)

							UpperCamelCase__   :Optional[int]												= max(UpperCamelCase_    ,   UpperCamelCase_							)

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

							# 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(UpperCamelCase_							):
												UpperCamelCase__   :Any												= deque(self.second_signal							)
												rotated_signal.rotate(UpperCamelCase_							)
												for j, item in enumerate(UpperCamelCase_							):
																	matrix[i][j] += item

        # multiply the matrix with the first signal
							UpperCamelCase__   :str												= np.matmul(np.transpose(UpperCamelCase_							)    ,   np.transpose(self.first_signal							)							)

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


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



'''simple docstring'''
from datetime import datetime

import matplotlib.pyplot as plt
import torch


def 			a      (			__a   )  ->	int:



					'''simple docstring'''






					for param in module.parameters():
										UpperCamelCase__   :Dict												= False


def 			a      (			)  ->	Union[str, Any]:



					'''simple docstring'''






					UpperCamelCase__   :List[Any]												= '''cuda''' if torch.cuda.is_available() else '''cpu'''
					if torch.backends.mps.is_available() and torch.backends.mps.is_built():
										UpperCamelCase__   :Optional[int]												= '''mps'''
					if device == "mps":
										print(
										    '''WARNING: MPS currently doesn\'t seem to work, and messes up backpropagation without any visible torch'''
										    ''' errors. I recommend using CUDA on a colab notebook or CPU instead if you\'re facing inexplicable issues'''
										    ''' with generations.'''   )
					return device


def 			a      (			__a   )  ->	Any:



					'''simple docstring'''






					UpperCamelCase__   :Dict												= plt.imshow(__a   )
					fig.axes.get_xaxis().set_visible(__a   )
					fig.axes.get_yaxis().set_visible(__a   )
					plt.show()







def 			a      (			)  ->	str:



					'''simple docstring'''






					UpperCamelCase__   :int												= datetime.now()
					UpperCamelCase__   :str												= current_time.strftime('''%H:%M:%S'''   )
					return timestamp