from datetime import datetime

import matplotlib.pyplot as plt
import torch






def _lowerCamelCase  (		snake_case  ):
							for param in module.parameters():
														_lowerCAmelCase							=							False






def _lowerCamelCase  (		):
							_lowerCAmelCase							=							'cuda' if torch.cuda.is_available() else 'cpu'
							if torch.backends.mps.is_available() and torch.backends.mps.is_built():
														_lowerCAmelCase							=							'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 _lowerCamelCase  (		snake_case  ):
							_lowerCAmelCase							=							plt.imshow(snake_case  )
							fig.axes.get_xaxis().set_visible(snake_case  )
							fig.axes.get_yaxis().set_visible(snake_case  )
							plt.show()






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







def _lowerCamelCase  (		snake_case  ):
							assert (
							    isinstance(snake_case     , snake_case  ) and number_of_steps > 0
							), F'number_of_steps needs to be positive integer, your input {number_of_steps}'
							if number_of_steps == 1:
														return 1
							_lowerCAmelCase			,		_lowerCAmelCase							=							1, 1
							for _ in range(number_of_steps - 1  ):
														_lowerCAmelCase			,		_lowerCAmelCase							=							current + previous, current
							return current


if __name__ == "__main__":
				import doctest

				doctest.testmod()






