






import unittest

import torch
from torch import nn

from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory






def    __magic_name__    (	)     ->    Any:
							raise RuntimeError('CUDA out of memory.'				)

class 				lowerCAmelCase_	(		nn.Module			):


				def __init__(					self      ):
											super().__init__()
											_lowercase							:   Tuple 							=   nn.Linear(3 ,						4      )
											_lowercase							:   List[str] 							=   nn.BatchNormad(4      )
											_lowercase							:   Optional[Any] 							=   nn.Linear(4 ,						5      )


				def 						__a					(					self ,						_lowerCAmelCase      ):
											return self.lineara(self.batchnorm(self.lineara(_lowerCAmelCase      )      )      )




class 				lowerCAmelCase_	(		unittest.TestCase			):


				def 						__a					(					self      ):
											_lowercase							:   Tuple 							=   []

											@find_executable_batch_size(starting_batch_size=1_2_8      )
											def mock_training_loop_function(_lowerCAmelCase      ):
																		nonlocal batch_sizes
																		batch_sizes.append(_lowerCAmelCase      )
																		if batch_size != 8:
																									raise_fake_out_of_memory()

											mock_training_loop_function()
											self.assertListEqual(_lowerCAmelCase ,						[1_2_8, 6_4, 3_2, 1_6, 8]      )


				def 						__a					(					self      ):
											_lowercase							:   Optional[Any] 							=   []

											@find_executable_batch_size(starting_batch_size=1_2_8      )
											def mock_training_loop_function(_lowerCAmelCase ,						_lowerCAmelCase      ):
																		nonlocal batch_sizes
																		batch_sizes.append(_lowerCAmelCase      )
																		if batch_size != 8:
																									raise_fake_out_of_memory()
																		return batch_size, arga

											_lowercase							,							_lowercase							:   List[str] 							=   mock_training_loop_function('hello'      )
											self.assertListEqual(_lowerCAmelCase ,						[1_2_8, 6_4, 3_2, 1_6, 8]      )
											self.assertListEqual([bs, arga] ,						[8, 'hello']      )


				def 						__a					(					self      ):






											@find_executable_batch_size(starting_batch_size=0      )
											def mock_training_loop_function(_lowerCAmelCase      ):
																		pass

											with self.assertRaises(_lowerCAmelCase      ) as cm:
																		mock_training_loop_function()
																		self.assertIn('No executable batch size found, reached zero.' ,						cm.exception.args[0]      )


				def 						__a					(					self      ):






											@find_executable_batch_size(starting_batch_size=1_6      )
											def mock_training_loop_function(_lowerCAmelCase      ):
																		if batch_size > 0:
																									raise_fake_out_of_memory()
																		pass

											with self.assertRaises(_lowerCAmelCase      ) as cm:
																		mock_training_loop_function()
																		self.assertIn('No executable batch size found, reached zero.' ,						cm.exception.args[0]      )


				def 						__a					(					self      ):






											@find_executable_batch_size(starting_batch_size=1_2_8      )
											def mock_training_loop_function(_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase      ):
																		if batch_size != 8:
																									raise raise_fake_out_of_memory()

											with self.assertRaises(_lowerCAmelCase      ) as cm:
																		mock_training_loop_function(1_2_8 ,						'hello' ,						'world'      )
																		self.assertIn('Batch size was passed into `f`' ,						cm.exception.args[0]      )
																		self.assertIn('`f(arg1=\'hello\', arg2=\'world\')' ,						cm.exception.args[0]      )


				def 						__a					(					self      ):






											@find_executable_batch_size(starting_batch_size=1_6      )
											def mock_training_loop_function(_lowerCAmelCase      ):
																		raise ValueError('Oops, we had an error!'      )

											with self.assertRaises(_lowerCAmelCase      ) as cm:
																		mock_training_loop_function()
																		self.assertIn('Oops, we had an error!' ,						cm.exception.args[0]      )


				@require_cuda
				def 						__a					(					self      ):
											_lowercase							:   int 							=   torch.cuda.memory_allocated()
											_lowercase							:   Union[str, Any] 							=   ModelForTest()
											model.cuda()
											self.assertGreater(torch.cuda.memory_allocated() ,						_lowerCAmelCase      )
											_lowercase							:   Dict 							=   release_memory(_lowerCAmelCase      )
											self.assertEqual(torch.cuda.memory_allocated() ,						_lowerCAmelCase      )






from PIL import Image


def    a      ( a	)     ->Image:




			'''simple docstring'''


			SCREAMING_SNAKE_CASE						,							SCREAMING_SNAKE_CASE     						= image.size
			SCREAMING_SNAKE_CASE     						= 0
			SCREAMING_SNAKE_CASE     						= image.load()
			for i in range(a	):
						for j in range(a	):
									SCREAMING_SNAKE_CASE     						= pixels[j, i]
									mean += pixel
			mean //= width * height

			for j in range(a	):
						for i in range(a	):
									SCREAMING_SNAKE_CASE     						= 255 if pixels[i, j] > mean else 0
			return image


if __name__ == "__main__":
							__lowerCAmelCase					   =       mean_threshold(Image.open('path_to_image').convert('L'))
							image.save('output_image_path')