



'''simple docstring'''







def 			_lowerCAmelCase(      UpperCAmelCase_      :	Tuple						) ->							Optional[int]:






							if len(a__						) == 1:
														return a[0], a[0]
							else:
														# split A into half.
														lowerCAmelCase__  =   a[: len(a__						) // 2]
														lowerCAmelCase__  =   a[len(a__						) // 2 :]

														# 2 sub problems, 1/2 of original size.
														lowerCAmelCase__  ,lowerCAmelCase__  =   max_difference(a__						)
														lowerCAmelCase__  ,lowerCAmelCase__  =   max_difference(a__						)

														# get min of first and max of second
														# linear time
														lowerCAmelCase__  =   min(a__						)
														lowerCAmelCase__  =   max(a__						)

														# 3 cases, either (small1, big1),
														# (min_first, max_second), (small2, big2)
														# constant comparisons
														if biga - smalla > max_second - min_first and biga - smalla > biga - smalla:
																					return smalla, biga
														elif biga - smalla > max_second - min_first:
																					return smalla, biga
														else:
																					return min_first, max_second


if __name__ == "__main__":
		import doctest

		doctest.testmod()






'''simple docstring'''

import os
import pickle
import unittest
from collections import UserDict, namedtuple

import torch

from accelerate.test_utils.testing import require_cuda, require_torch_min_version
from accelerate.test_utils.training import RegressionModel
from accelerate.utils import (
    convert_outputs_to_fpaa,
    extract_model_from_parallel,
    find_device,
    listify,
    patch_environment,
    recursively_apply,
    send_to_device,
)


lowercase_  		=			namedtuple('''ExampleNamedTuple''', '''a b c''')
class 	SCREAMING_SNAKE_CASE      (    unittest.TestCase):







		"""simple docstring"""






		def 		UpperCamelCase__							( self					)			->  int:
								_lowerCAmelCase					    =torch.randn(5		,    2					)
								_lowerCAmelCase					    =torch.device('cuda'					) if torch.cuda.is_available() else torch.device('cpu'					)

								_lowerCAmelCase					    =send_to_device(__A		,    __A					)
								self.assertTrue(torch.equal(resulta.cpu()		,    __A					)					)

								_lowerCAmelCase					    =send_to_device((tensor, [tensor, tensor], 1)		,    __A					)
								self.assertIsInstance(__A		,    __A					)
								self.assertTrue(torch.equal(resulta[0].cpu()		,    __A					)					)
								self.assertIsInstance(resulta[1]		,    __A					)
								self.assertTrue(torch.equal(resulta[1][0].cpu()		,    __A					)					)
								self.assertTrue(torch.equal(resulta[1][1].cpu()		,    __A					)					)
								self.assertEqual(resulta[2]		,    1					)

								_lowerCAmelCase					    =send_to_device({'a': tensor, 'b': [tensor, tensor], 'c': 1}		,    __A					)
								self.assertIsInstance(__A		,    __A					)
								self.assertTrue(torch.equal(resulta['a'].cpu()		,    __A					)					)
								self.assertIsInstance(resulta['b']		,    __A					)
								self.assertTrue(torch.equal(resulta['b'][0].cpu()		,    __A					)					)
								self.assertTrue(torch.equal(resulta['b'][1].cpu()		,    __A					)					)
								self.assertEqual(resulta['c']		,    1					)

								_lowerCAmelCase					    =send_to_device(ExampleNamedTuple(a=__A		,    b=[tensor, tensor]		,    c=1					)		,    __A					)
								self.assertIsInstance(__A		,    __A					)
								self.assertTrue(torch.equal(resulta.a.cpu()		,    __A					)					)
								self.assertIsInstance(resulta.b		,    __A					)
								self.assertTrue(torch.equal(resulta.b[0].cpu()		,    __A					)					)
								self.assertTrue(torch.equal(resulta.b[1].cpu()		,    __A					)					)
								self.assertEqual(resulta.c		,    1					)

								_lowerCAmelCase					    =send_to_device(UserDict({'a': tensor, 'b': [tensor, tensor], 'c': 1}					)		,    __A					)
								self.assertIsInstance(__A		,    __A					)
								self.assertTrue(torch.equal(resulta['a'].cpu()		,    __A					)					)
								self.assertIsInstance(resulta['b']		,    __A					)
								self.assertTrue(torch.equal(resulta['b'][0].cpu()		,    __A					)					)
								self.assertTrue(torch.equal(resulta['b'][1].cpu()		,    __A					)					)
								self.assertEqual(resulta['c']		,    1					)






		def 		UpperCamelCase__							( self					)			->  Optional[int]:
								with self.assertRaises(__A					) as cm:
														_lowerCAmelCase					    =recursively_apply(torch.tensor		,    (torch.tensor(1					), 1)		,    error_on_other_type=__A					)
								self.assertEqual(
								    str(cm.exception					)		,    'Unsupported types (<class \'int\'>) passed to `tensor`. Only nested list/tuple/dicts of objects that are valid for `is_torch_tensor` should be passed.'		,    )






		def 		UpperCamelCase__							( self					)			->  int:
								_lowerCAmelCase					    =torch.tensor([1, 2, 3, 4, 5]					)
								self.assertEqual(listify(__A					)		,    [1, 2, 3, 4, 5]					)

								_lowerCAmelCase					    =torch.tensor([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]]					)
								self.assertEqual(listify(__A					)		,    [[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]]					)

								_lowerCAmelCase					    =torch.tensor([[[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]], [[11, 12, 13, 14, 15], [16, 17, 18, 19, 20]]]					)
								self.assertEqual(
								    listify(__A					)		,    [[[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]], [[11, 12, 13, 14, 15], [16, 17, 18, 19, 20]]]					)






		def 		UpperCamelCase__							( self					)			->  List[Any]:
								with patch_environment(aa=1		,    BB=2					):
														self.assertEqual(os.environ.get('AA'					)		,    '1'					)
														self.assertEqual(os.environ.get('BB'					)		,    '2'					)

								self.assertNotIn('AA'		,    os.environ					)
								self.assertNotIn('BB'		,    os.environ					)






		def 		UpperCamelCase__							( self					)			->  Union[str, Any]:
								_lowerCAmelCase					    =RegressionModel()
								_lowerCAmelCase					    =model.forward
								_lowerCAmelCase					    =convert_outputs_to_fpaa(model.forward					)
								_lowerCAmelCase					    =extract_model_from_parallel(__A		,    keep_fpaa_wrapper=__A					)
								_lowerCAmelCase					    =pickle.dumps(__A					)






		@require_cuda
		def 		UpperCamelCase__							( self					)			->  Dict:
								_lowerCAmelCase					    =RegressionModel()
								_lowerCAmelCase					    =model.forward
								_lowerCAmelCase					    =torch.cuda.amp.autocast(dtype=torch.floataa					)(model.forward					)
								_lowerCAmelCase					    =convert_outputs_to_fpaa(model.forward					)
								_lowerCAmelCase					    =extract_model_from_parallel(__A		,    keep_fpaa_wrapper=__A					)
								_lowerCAmelCase					    =pickle.dumps(__A					)






		@require_cuda
		@require_torch_min_version(version='2.0'					)
		def 		UpperCamelCase__							( self					)			->  int:
								_lowerCAmelCase					    =RegressionModel()
								_lowerCAmelCase					    =model.forward
								_lowerCAmelCase					    =torch.cuda.amp.autocast(dtype=torch.floataa					)(model.forward					)
								_lowerCAmelCase					    =convert_outputs_to_fpaa(model.forward					)
								_lowerCAmelCase					    =torch.compile(model.forward		,    backend='inductor'					)
								_lowerCAmelCase					    =torch.randn(4		,    10					).cuda()
								_lowerCAmelCase					    =model(__A					)






		def 		UpperCamelCase__							( self					)			->  Optional[int]:
								_lowerCAmelCase					    =RegressionModel()
								# could also do a test with DistributedDataParallel, but difficult to run on CPU or single GPU
								_lowerCAmelCase					    =torch.nn.parallel.DataParallel(__A					)
								_lowerCAmelCase					    =extract_model_from_parallel(__A					)

								self.assertEqual(__A		,    __A					)






		@require_torch_min_version(version='2.0'					)
		def 		UpperCamelCase__							( self					)			->  str:
								_lowerCAmelCase					    =RegressionModel()
								_lowerCAmelCase					    =torch.compile(__A					)

								# could also do a test with DistributedDataParallel, but difficult to run on CPU or single GPU
								_lowerCAmelCase					    =torch.nn.parallel.DataParallel(__A					)
								_lowerCAmelCase					    =torch.compile(__A					)
								_lowerCAmelCase					    =extract_model_from_parallel(__A					)

								self.assertEqual(compiled_model._orig_mod		,    compiled_model_unwrapped._orig_mod					)






		def 		UpperCamelCase__							( self					)			->  Tuple:
								self.assertEqual(find_device([1, 'a', torch.tensor([1, 2, 3]					)]					)		,    torch.device('cpu'					)					)
								self.assertEqual(find_device({'a': 1, 'b': torch.tensor([1, 2, 3]					)}					)		,    torch.device('cpu'					)					)
								self.assertIsNone(find_device([1, 'a']					)					)





