



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   (						):
			raise RuntimeError("CUDA out of memory."    )

class 	lowerCamelCase_    ( nn.Module						):






						'''simple docstring'''







						def __init__(	self      :			Tuple )      ->				Tuple:
									super().__init__()
									A							:					Dict              =						nn.Linear(3	,	4 )
									A							:					Dict              =						nn.BatchNormad(4 )
									A							:					Optional[Any]              =						nn.Linear(4	,	5 )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[int]	,	__lowerCamelCase      :			Optional[Any] )      ->				str:
									return self.lineara(self.batchnorm(self.lineara(__lowerCamelCase ) ) )







class 	lowerCamelCase_    ( unittest.TestCase						):






						'''simple docstring'''







						def 			SCREAMING_SNAKE_CASE__		(	self      :			int )      ->				Optional[Any]:
									A							:					Union[str, Any]              =						[]

									@find_executable_batch_size(starting_batch_size=1_28 )
									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_28, 64, 32, 16, 8] )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Dict )      ->				Optional[Any]:
									A							:					Optional[int]              =						[]

									@find_executable_batch_size(starting_batch_size=1_28 )
									def mock_training_loop_function(__lowerCamelCase      :			Tuple	,	__lowerCamelCase      :			int ):
												nonlocal batch_sizes
												batch_sizes.append(__lowerCamelCase )
												if batch_size != 8:
															raise_fake_out_of_memory()
												return batch_size, arga

									A       , A							:					str              =						mock_training_loop_function("hello" )
									self.assertListEqual(__lowerCamelCase	,	[1_28, 64, 32, 16, 8] )
									self.assertListEqual([bs, arga]	,	[8, "hello"] )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			str )      ->				str:
									@find_executable_batch_size(starting_batch_size=0 )
									def mock_training_loop_function(__lowerCamelCase      :			Union[str, 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 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[int] )      ->				Dict:
									@find_executable_batch_size(starting_batch_size=16 )
									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 			SCREAMING_SNAKE_CASE__		(	self      :			Tuple )      ->				Optional[Any]:
									@find_executable_batch_size(starting_batch_size=1_28 )
									def mock_training_loop_function(__lowerCamelCase      :			int	,	__lowerCamelCase      :			Optional[int]	,	__lowerCamelCase      :			Dict ):
												if batch_size != 8:
															raise raise_fake_out_of_memory()

									with self.assertRaises(__lowerCamelCase ) as cm:
												mock_training_loop_function(1_28	,	"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 			SCREAMING_SNAKE_CASE__		(	self      :			Dict )      ->				Any:
									@find_executable_batch_size(starting_batch_size=16 )
									def mock_training_loop_function(__lowerCamelCase      :			Optional[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 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[Any] )      ->				Optional[int]:
									A							:					Optional[int]              =						torch.cuda.memory_allocated()
									A							:					Optional[int]              =						ModelForTest()
									model.cuda()
									self.assertGreater(torch.cuda.memory_allocated()	,	__lowerCamelCase )
									A							:					Optional[Any]              =						release_memory(__lowerCamelCase )
									self.assertEqual(torch.cuda.memory_allocated()	,	__lowerCamelCase )




from collections.abc import Sequence




def 	UpperCAmelCase   (						_lowerCamelCase = None    ):
			if nums is None or not nums:
						raise ValueError("Input sequence should not be empty"    )

			A							:					Dict              =						nums[0]
			for i in range(1 ,    len(_lowerCamelCase    )    ):
						A							:					Tuple              =						nums[i]
						A							:					List[Any]              =						max(_lowerCamelCase ,    ans + num ,    _lowerCamelCase    )

			return ans


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							# Try on a sample input from the user
							__SCREAMING_SNAKE_CASE           =  int(input("""Enter number of elements : """).strip())
							__SCREAMING_SNAKE_CASE           =  list(map(int, input("""\nEnter the numbers : """).strip().split()))[:n]
							print(max_subsequence_sum(array))