"""simple docstring"""
from datetime import datetime

import matplotlib.pyplot as plt
import torch





def 				lowerCAmelCase__      (     _UpperCamelCase    :			Optional[int]   )   ->			Optional[int]:





						"""simple docstring"""






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





def 				lowerCAmelCase__      (     )   ->			str:





						"""simple docstring"""






						snake_case        =     'cuda' if torch.cuda.is_available() else 'cpu'
						if torch.backends.mps.is_available() and torch.backends.mps.is_built():
												snake_case        =     '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__      (     _UpperCamelCase    :			Any   )   ->			List[str]:





						"""simple docstring"""






						snake_case        =     plt.imshow(lowerCAmelCase__   )
						fig.axes.get_xaxis().set_visible(lowerCAmelCase__   )
						fig.axes.get_yaxis().set_visible(lowerCAmelCase__   )
						plt.show()





def 				lowerCAmelCase__      (     )   ->			int:





						"""simple docstring"""






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



"""simple docstring"""
import pickle
import unittest

import torch

from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu



@require_cpu
class 					lowerCAmelCase_      (     unittest.TestCase   ):



				"""simple docstring"""

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     torch.nn.Linear(10						,      10      )
										snake_case        =     torch.optim.SGD(model.parameters()						,      0.1      )
										snake_case        =     Accelerator()
										snake_case        =     accelerator.prepare(lowerCAmelCase      )
										try:
																pickle.loads(pickle.dumps(lowerCAmelCase      )      )
										except Exception as e:
																self.fail(F"""Accelerated optimizer pickling failed with {e}"""      )
										AcceleratorState._reset_state()


