




"""simple docstring"""


from datetime import datetime

import matplotlib.pyplot as plt
import torch


def  UpperCAmelCase__	(snake_case__							: Dict   ):
  """simple docstring"""

  for param in module.parameters():
    _snake_case							:		int								=  False


def  UpperCAmelCase__	():
  """simple docstring"""

  _snake_case							:		List[Any]								=  """cuda""" if torch.cuda.is_available() else """cpu"""
  if torch.backends.mps.is_available() and torch.backends.mps.is_built():
    _snake_case							:		str								=  """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  UpperCAmelCase__	(snake_case__							: Union[str, Any]   ):
  """simple docstring"""

  _snake_case							:		str								=  plt.imshow(snake_case__   )
  fig.axes.get_xaxis().set_visible(snake_case__   )
  fig.axes.get_yaxis().set_visible(snake_case__   )
  plt.show()






def  UpperCAmelCase__	():
  """simple docstring"""

  _snake_case							:		Optional[Any]								=  datetime.now()
  _snake_case							:		str								=  current_time.strftime("""%H:%M:%S"""   )
  return timestamp








"""simple docstring"""


def  UpperCAmelCase__	(snake_case__							: int = 1_00_00_00   ):
  """simple docstring"""

  _snake_case							:		Dict								=  [i - 1 for i in range(limit + 1   )]

  for i in range(2		,       limit + 1   ):
    if phi[i] == i - 1:
      for j in range(2 * i		,       limit + 1		,       snake_case__   ):
        phi[j] -= phi[j] // i

  return sum(phi[2 : limit + 1]   )


if __name__ == "__main__":
     print(solution())


