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,
)


__UpperCamelCase   :						Union[str, Any]							=  namedtuple("""ExampleNamedTuple""", """a b c""")


class 					__SCREAMING_SNAKE_CASE(		unittest.TestCase		):
							def lowerCAmelCase_	(       self: Optional[Any]  )						->						List[Any]:
											snake_case__													=       torch.randn(5    ,		2  )
											snake_case__													=       torch.device('cuda'  ) if torch.cuda.is_available() else torch.device('cpu'  )

											snake_case__													=       send_to_device(UpperCamelCase    ,		UpperCamelCase  )
											self.assertTrue(torch.equal(resulta.cpu()    ,		UpperCamelCase  )  )

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

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

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

											snake_case__													=       send_to_device(UserDict({'a': tensor, 'b': [tensor, tensor], 'c': 1}  )    ,		UpperCamelCase  )
											self.assertIsInstance(UpperCamelCase    ,		UpperCamelCase  )
											self.assertTrue(torch.equal(resulta['a'].cpu()    ,		UpperCamelCase  )  )
											self.assertIsInstance(resulta['b']    ,		UpperCamelCase  )
											self.assertTrue(torch.equal(resulta['b'][0].cpu()    ,		UpperCamelCase  )  )
											self.assertTrue(torch.equal(resulta['b'][1].cpu()    ,		UpperCamelCase  )  )
											self.assertEqual(resulta['c']    ,		1  )
							def lowerCAmelCase_	(       self: int  )						->						int:



											with self.assertRaises(UpperCamelCase  ) as cm:
															snake_case__													=       recursively_apply(torch.tensor    ,		(torch.tensor(1  ), 1)    ,		error_on_other_type=UpperCamelCase  )
											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 lowerCAmelCase_	(       self: int  )						->						Optional[int]:
											snake_case__													=       torch.tensor([1, 2, 3, 4, 5]  )
											self.assertEqual(listify(UpperCamelCase  )    ,		[1, 2, 3, 4, 5]  )

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

											snake_case__													=       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(UpperCamelCase  )    ,		[[[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]], [[11, 12, 13, 14, 15], [16, 17, 18, 19, 20]]]  )
							def lowerCAmelCase_	(       self: int  )						->						Optional[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 lowerCAmelCase_	(       self: int  )						->						Union[str, Any]:
											snake_case__													=       RegressionModel()
											snake_case__													=       model.forward
											snake_case__													=       convert_outputs_to_fpaa(model.forward  )
											snake_case__													=       extract_model_from_parallel(UpperCamelCase    ,		keep_fpaa_wrapper=UpperCamelCase  )
											snake_case__													=       pickle.dumps(UpperCamelCase  )
							@require_cuda
							def lowerCAmelCase_	(       self: Optional[int]  )						->						List[str]:
											snake_case__													=       RegressionModel()
											snake_case__													=       model.forward
											snake_case__													=       torch.cuda.amp.autocast(dtype=torch.floataa  )(model.forward  )
											snake_case__													=       convert_outputs_to_fpaa(model.forward  )
											snake_case__													=       extract_model_from_parallel(UpperCamelCase    ,		keep_fpaa_wrapper=UpperCamelCase  )
											snake_case__													=       pickle.dumps(UpperCamelCase  )
							@require_cuda
							@require_torch_min_version(version='2.0'  )
							def lowerCAmelCase_	(       self: List[Any]  )						->						Dict:
											snake_case__													=       RegressionModel()
											snake_case__													=       model.forward
											snake_case__													=       torch.cuda.amp.autocast(dtype=torch.floataa  )(model.forward  )
											snake_case__													=       convert_outputs_to_fpaa(model.forward  )
											snake_case__													=       torch.compile(model.forward    ,		backend='inductor'  )
											snake_case__													=       torch.randn(4    ,		10  ).cuda()
											snake_case__													=       model(UpperCamelCase  )
							def lowerCAmelCase_	(       self: Tuple  )						->						List[str]:
											snake_case__													=       RegressionModel()
											# could also do a test with DistributedDataParallel, but difficult to run on CPU or single GPU
											snake_case__													=       torch.nn.parallel.DataParallel(UpperCamelCase  )
											snake_case__													=       extract_model_from_parallel(UpperCamelCase  )

											self.assertEqual(UpperCamelCase    ,		UpperCamelCase  )
							@require_torch_min_version(version='2.0'  )
							def lowerCAmelCase_	(       self: Dict  )						->						Tuple:
											snake_case__													=       RegressionModel()
											snake_case__													=       torch.compile(UpperCamelCase  )

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

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


							def lowerCAmelCase_	(       self: List[str]  )						->						int:
											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']  )  )












def        a_	( _A ,				_A					)     ->     Union[str, Any]:







				"""simple docstring"""
				snake_case__													=       1
				while b > 0:
								if b & 1:
												res *= a

								a *= a
								b >>= 1

				return res
def        a_	( _A ,				_A ,				_A					)     ->     Dict:







				"""simple docstring"""
				snake_case__													=       1
				while b > 0:
								if b & 1:
												snake_case__													=       ((res % c) * (a % c)) % c

								a *= a
								b >>= 1

				return res




