




from __future__ import annotations

def lowercase						(     SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) ->       int:


			'''simple docstring'''







			assert n > 1 and a > 0 and greatest_common_divisor(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) == 1
			((SCREAMING_SNAKE_CASE_)	,		(SCREAMING_SNAKE_CASE_)	,		(SCREAMING_SNAKE_CASE_))			=	extended_gcd(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   )  # Implemented below
			SCREAMING_SNAKE_CASE_			=	(b * s) % n
			return x

def lowercase						(     SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) ->       int:


			'''simple docstring'''







			((SCREAMING_SNAKE_CASE_)	,		(SCREAMING_SNAKE_CASE_))			=	extended_euclid(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   )  # Implemented below
			if b < 0:
						SCREAMING_SNAKE_CASE_			=	(b % n + n) % n
			return b

def lowercase						(     SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) ->       int:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_			=	invert_modulo(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   )
			SCREAMING_SNAKE_CASE_			=	(b * s) % n
			return x

def lowercase						(     SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) ->       tuple[int, int, int]:


			'''simple docstring'''







			assert a >= 0 and b >= 0

			if b == 0:
						SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	a, 1, 0
			else:
						((SCREAMING_SNAKE_CASE_)	,		(SCREAMING_SNAKE_CASE_)	,		(SCREAMING_SNAKE_CASE_))			=	extended_gcd(SCREAMING_SNAKE_CASE					,						a % b   )
						SCREAMING_SNAKE_CASE_			=	q
						SCREAMING_SNAKE_CASE_			=	p - q * (a // b)

			assert a % d == 0 and b % d == 0
			assert d == a * x + b * y

			return (d, x, y)

def lowercase						(     SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) ->       tuple[int, int]:


			'''simple docstring'''







			if b == 0:
						return (1, 0)
			((SCREAMING_SNAKE_CASE_)	,		(SCREAMING_SNAKE_CASE_))			=	extended_euclid(SCREAMING_SNAKE_CASE					,						a % b   )
			SCREAMING_SNAKE_CASE_			=	a // b
			return (y, x - k * y)

def lowercase						(     SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) ->       int:


			'''simple docstring'''







			if a < b:
						SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	b, a

			while a % b != 0:
						SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	b, a % b

			return b


if __name__ == "__main__":
				from doctest import testmod

				testmod(name="modular_division", verbose=True)
				testmod(name="modular_division2", verbose=True)
				testmod(name="invert_modulo", verbose=True)
				testmod(name="extended_gcd", verbose=True)
				testmod(name="extended_euclid", verbose=True)
				testmod(name="greatest_common_divisor", verbose=True)






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,
)


SCREAMING_SNAKE_CASE__    :    Optional[Any]								=					namedtuple("ExampleNamedTuple", "a b c")



class 			a_					(    unittest.TestCase    ):





	def 	A_(       self			)     ->				List[str]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			=	torch.randn(5			,    2			)
				SCREAMING_SNAKE_CASE_			=	torch.device('cuda'			) if torch.cuda.is_available() else torch.device('cpu'			)

				SCREAMING_SNAKE_CASE_			=	send_to_device(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)
				self.assertTrue(torch.equal(resulta.cpu()			,    SCREAMING_SNAKE_CASE			)			)

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

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

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

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





	def 	A_(       self			)     ->				int:




				"""simple docstring"""



				with self.assertRaises(SCREAMING_SNAKE_CASE			) as cm:
							SCREAMING_SNAKE_CASE_			=	recursively_apply(torch.tensor			,    (torch.tensor(1			), 1)			,    error_on_other_type=SCREAMING_SNAKE_CASE			)
				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 	A_(       self			)     ->				int:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			=	torch.tensor([1, 2, 3, 4, 5]			)
				self.assertEqual(listify(SCREAMING_SNAKE_CASE			)			,    [1, 2, 3, 4, 5]			)

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

				SCREAMING_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(SCREAMING_SNAKE_CASE			)			,    [[[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]], [[11, 12, 13, 14, 15], [16, 17, 18, 19, 20]]]			)





	def 	A_(       self			)     ->				Tuple:




				"""simple docstring"""



				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 	A_(       self			)     ->				Dict:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			=	RegressionModel()
				SCREAMING_SNAKE_CASE_			=	model.forward
				SCREAMING_SNAKE_CASE_			=	convert_outputs_to_fpaa(model.forward			)
				SCREAMING_SNAKE_CASE_			=	extract_model_from_parallel(SCREAMING_SNAKE_CASE			,    keep_fpaa_wrapper=SCREAMING_SNAKE_CASE			)
				SCREAMING_SNAKE_CASE_			=	pickle.dumps(SCREAMING_SNAKE_CASE			)





	@require_cuda
	def 	A_(       self			)     ->				int:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			=	RegressionModel()
				SCREAMING_SNAKE_CASE_			=	model.forward
				SCREAMING_SNAKE_CASE_			=	torch.cuda.amp.autocast(dtype=torch.floataa			)(model.forward			)
				SCREAMING_SNAKE_CASE_			=	convert_outputs_to_fpaa(model.forward			)
				SCREAMING_SNAKE_CASE_			=	extract_model_from_parallel(SCREAMING_SNAKE_CASE			,    keep_fpaa_wrapper=SCREAMING_SNAKE_CASE			)
				SCREAMING_SNAKE_CASE_			=	pickle.dumps(SCREAMING_SNAKE_CASE			)





	@require_cuda
	@require_torch_min_version(version='2.0'			)
	def 	A_(       self			)     ->				Optional[Any]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			=	RegressionModel()
				SCREAMING_SNAKE_CASE_			=	model.forward
				SCREAMING_SNAKE_CASE_			=	torch.cuda.amp.autocast(dtype=torch.floataa			)(model.forward			)
				SCREAMING_SNAKE_CASE_			=	convert_outputs_to_fpaa(model.forward			)
				SCREAMING_SNAKE_CASE_			=	torch.compile(model.forward			,    backend='inductor'			)
				SCREAMING_SNAKE_CASE_			=	torch.randn(4			,    10			).cuda()
				SCREAMING_SNAKE_CASE_			=	model(SCREAMING_SNAKE_CASE			)





	def 	A_(       self			)     ->				List[str]:




				"""simple docstring"""



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

				self.assertEqual(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)





	@require_torch_min_version(version='2.0'			)
	def 	A_(       self			)     ->				int:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			=	RegressionModel()
				SCREAMING_SNAKE_CASE_			=	torch.compile(SCREAMING_SNAKE_CASE			)

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

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





	def 	A_(       self			)     ->				Optional[Any]:




				"""simple docstring"""



				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']			)			)
