



from datetime import datetime

import matplotlib.pyplot as plt
import torch




def      __UpperCamelCase     (  _lowerCAmelCase		)	->  str:



    """simple docstring"""






    for param in module.parameters():
        A			:	List[str]										=       False




def      __UpperCamelCase     (  )	->  Tuple:



    """simple docstring"""






    A			:	Dict										=       """cuda""" if torch.cuda.is_available() else """cpu"""
    if torch.backends.mps.is_available() and torch.backends.mps.is_built():
        A			:	Tuple										=       """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     (  _lowerCAmelCase		)	->  str:



    """simple docstring"""






    A			:	str										=       plt.imshow(_lowerCAmelCase		)
    fig.axes.get_xaxis().set_visible(_lowerCAmelCase		)
    fig.axes.get_yaxis().set_visible(_lowerCAmelCase		)
    plt.show()




def      __UpperCamelCase     (  )	->  List[Any]:



    """simple docstring"""






    A			:	Tuple										=       datetime.now()
    A			:	List[Any]										=       current_time.strftime("""%H:%M:%S"""		)
    return timestamp







from __future__ import annotations




def      __UpperCamelCase     (  _lowerCAmelCase		)	->  list[int]:



    """simple docstring"""






    A			:	Tuple										=       2
    A			:	List[Any]										=       []
    while i * i <= n:
        if n % i:
            i += 1
        else:
            n //= i
            factors.append(_lowerCAmelCase		)
    if n > 1:
        factors.append(_lowerCAmelCase		)
    return factors


if __name__ == "__main__":
     import doctest

     doctest.testmod()


