






"""simple docstring"""







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      UpperCamelCase       ( )     ->       List[str]:
				raise RuntimeError("""CUDA out of memory.""" )


class      UpperCamelCase		(nn.Module     ):






						def __init__(		self  :Union[str, Any]				)      ->int:
										super().__init__()
										lowercase    :      int       = nn.Linear(3			, 4				)
										lowercase    :      Optional[Any]       = nn.BatchNormad(4				)
										lowercase    :      List[str]       = nn.Linear(4			, 5				)






						def      __snake_case      (		self  :Any			, __magic_name__  :Dict				)      ->str:
										return self.lineara(self.batchnorm(self.lineara(__magic_name__				)				)				)


class      UpperCamelCase		(unittest.TestCase     ):






						def      __snake_case      (		self  :Any				)      ->str:
										lowercase    :      Tuple       = []

										@find_executable_batch_size(starting_batch_size=128				)
										def mock_training_loop_function(__magic_name__  :List[Any]				):
														nonlocal batch_sizes
														batch_sizes.append(__magic_name__				)
														if batch_size != 8:
																		raise_fake_out_of_memory()

										mock_training_loop_function()
										self.assertListEqual(__magic_name__			, [128, 64, 32, 16, 8]				)






						def      __snake_case      (		self  :Dict				)      ->int:
										lowercase    :      int       = []

										@find_executable_batch_size(starting_batch_size=128				)
										def mock_training_loop_function(__magic_name__  :str			, __magic_name__  :Union[str, Any]				):
														nonlocal batch_sizes
														batch_sizes.append(__magic_name__				)
														if batch_size != 8:
																		raise_fake_out_of_memory()
														return batch_size, arga

										lowercase      ,      lowercase    :      Optional[int]       = mock_training_loop_function("""hello"""				)
										self.assertListEqual(__magic_name__			, [128, 64, 32, 16, 8]				)
										self.assertListEqual([bs, arga]			, [8, """hello"""]				)






						def      __snake_case      (		self  :Any				)      ->Optional[Any]:
										@find_executable_batch_size(starting_batch_size=0				)
										def mock_training_loop_function(__magic_name__  :Optional[Any]				):
														pass

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






						def      __snake_case      (		self  :int				)      ->Optional[Any]:
										@find_executable_batch_size(starting_batch_size=16				)
										def mock_training_loop_function(__magic_name__  :List[Any]				):
														if batch_size > 0:
																		raise_fake_out_of_memory()
														pass

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






						def      __snake_case      (		self  :int				)      ->Dict:
										@find_executable_batch_size(starting_batch_size=128				)
										def mock_training_loop_function(__magic_name__  :List[str]			, __magic_name__  :Optional[Any]			, __magic_name__  :Any				):
														if batch_size != 8:
																		raise raise_fake_out_of_memory()

										with self.assertRaises(__magic_name__				) as cm:
														mock_training_loop_function(128			, """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      __snake_case      (		self  :List[Any]				)      ->Tuple:
										@find_executable_batch_size(starting_batch_size=16				)
										def mock_training_loop_function(__magic_name__  :int				):
														raise ValueError("""Oops, we had an error!"""				)

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






						@require_cuda
						def      __snake_case      (		self  :Dict				)      ->Optional[Any]:
										lowercase    :      Tuple       = torch.cuda.memory_allocated()
										lowercase    :      List[str]       = ModelForTest()
										model.cuda()
										self.assertGreater(torch.cuda.memory_allocated()			, __magic_name__				)
										lowercase    :      Any       = release_memory(__magic_name__				)
										self.assertEqual(torch.cuda.memory_allocated()			, __magic_name__				)








"""simple docstring"""







def      UpperCamelCase       ( _A  ,				_A )     ->       None:
				lowercase    :      List[Any]       = len(_A )
				print("""The following activities are selected:""" )

				# The first activity is always selected
				lowercase    :      Optional[int]       = 0
				print(_A  ,				end=""",""" )

				# Consider rest of the activities
				for j in range(_A ):
								# If this activity has start time greater than
								# or equal to the finish time of previously
								# selected activity, then select it
								if start[j] >= finish[i]:
												print(_A  ,				end=""",""" )
												lowercase    :      str       = j


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				_lowerCAmelCase            =	[1, 3, 0, 5, 8, 5]
				_lowerCAmelCase            =	[2, 4, 6, 7, 9, 9]
				print_max_activities(start, finish)
