'''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    _lowerCamelCase  ()   ->    Optional[Any]:
			raise RuntimeError("CUDA out of memory."     )






class       UpperCamelCase__  (		nn.Module     ):







	def __init__(				self			:   List[Any]		):




				'''simple docstring'''
				super().__init__()
				a__     = nn.Linear(3 , 4		)
				a__     = nn.BatchNormad(4		)
				a__     = nn.Linear(4 , 5		)







	def 						__a   (				self			:   List[str] , lowerCamelCase			:   List[Any]		):




				'''simple docstring'''
				return self.lineara(self.batchnorm(self.lineara(lowerCamelCase		)		)		)






class       UpperCamelCase__  (		unittest.TestCase     ):







	def 						__a   (				self			:   str		):




				'''simple docstring'''
				a__     = []

				@find_executable_batch_size(starting_batch_size=1_2_8		)
				def mock_training_loop_function(lowerCamelCase			:   Optional[Any]		):
							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			:   Any		):




				'''simple docstring'''
				a__     = []

				@find_executable_batch_size(starting_batch_size=1_2_8		)
				def mock_training_loop_function(lowerCamelCase			:   List[Any] , lowerCamelCase			:   List[str]		):
							nonlocal batch_sizes
							batch_sizes.append(lowerCamelCase		)
							if batch_size != 8:
										raise_fake_out_of_memory()
							return batch_size, arga

				a__   ,				a__     = 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			:   List[str]		):




				'''simple docstring'''
				@find_executable_batch_size(starting_batch_size=0		)
				def mock_training_loop_function(lowerCamelCase			:   List[Any]		):
							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			:   List[str]		):




				'''simple docstring'''
				@find_executable_batch_size(starting_batch_size=1_6		)
				def mock_training_loop_function(lowerCamelCase			:   Optional[Any]		):
							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			:   Dict		):




				'''simple docstring'''
				@find_executable_batch_size(starting_batch_size=1_2_8		)
				def mock_training_loop_function(lowerCamelCase			:   Tuple , lowerCamelCase			:   Optional[Any] , lowerCamelCase			:   Optional[Any]		):
							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			:   str		):




				'''simple docstring'''
				@find_executable_batch_size(starting_batch_size=1_6		)
				def mock_training_loop_function(lowerCamelCase			:   int		):
							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			:   Optional[Any]		):




				'''simple docstring'''
				a__     = torch.cuda.memory_allocated()
				a__     = ModelForTest()
				model.cuda()
				self.assertGreater(torch.cuda.memory_allocated() , lowerCamelCase		)
				a__     = release_memory(lowerCamelCase		)
				self.assertEqual(torch.cuda.memory_allocated() , lowerCamelCase		)










'''simple docstring'''


def    _lowerCamelCase  (__lowerCamelCase  :     str     )   ->    bool:
			a__     = 0
			for ch in input_str:
						a__     = ord(__lowerCamelCase     )
						a__     = pow(2					,  __lowerCamelCase     )

						# If we already turned on bit for current character's unicode
						if bitmap >> ch_unicode & 1 == 1:
									return False
						bitmap |= ch_bit_index_on
			return True


if __name__ == "__main__":
		import doctest

		doctest.testmod()



