




"""simple docstring"""

import doctest
from collections import deque

import numpy as np





class 							lowercase_			:



 '''simple docstring'''

 def __init__(					self			:			Dict						):
       _A     =  [2, 1, 2, -1]
       _A     =  [1, 2, 3, 4]



 def      lowerCAmelCase_				(					self			:			Dict						):
       _A     =  len(self.first_signal						)
       _A     =  len(self.second_signal						)

       _A     =  max(_UpperCAmelCase       ,				_UpperCAmelCase						)

       # create a zero matrix of max_length x max_length
       _A     =  [[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						):
             _A     =  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
       _A     =  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 		_snake_case	(   _snake_case				:   Dict				)       ->   Optional[Any]:






      '''simple docstring'''


      for param in module.parameters():
            _A     =  False






def 		_snake_case	(   )       ->   Tuple:






      '''simple docstring'''


      _A     =  'cuda' if torch.cuda.is_available() else 'cpu'
      if torch.backends.mps.is_available() and torch.backends.mps.is_built():
            _A     =  '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 		_snake_case	(   _snake_case				:   Dict				)       ->   Optional[Any]:






      '''simple docstring'''


      _A     =  plt.imshow(_snake_case				)
      fig.axes.get_xaxis().set_visible(_snake_case				)
      fig.axes.get_yaxis().set_visible(_snake_case				)
      plt.show()







def 		_snake_case	(   )       ->   Optional[Any]:






      '''simple docstring'''


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

