"""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,
)


a							=namedtuple('ExampleNamedTuple', 'a b c')






class    __UpperCAmelCase						(	unittest.TestCase	):

				def 		_a	(      self      ):
										lowerCamelCase__					=torch.randn(5	, 2      )
										lowerCamelCase__					=torch.device("cuda"      ) if torch.cuda.is_available() else torch.device("cpu"      )

										lowerCamelCase__					=send_to_device(_lowerCamelCase	, _lowerCamelCase      )
										self.assertTrue(torch.equal(resulta.cpu()	, _lowerCamelCase      )      )

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

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

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

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

				def 		_a	(      self      ):
										with self.assertRaises(_lowerCamelCase      ) as cm:
																lowerCamelCase__					=recursively_apply(torch.tensor	, (torch.tensor(1      ), 1)	, error_on_other_type=_lowerCamelCase      )
										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      ):
										lowerCamelCase__					=torch.tensor([1, 2, 3, 4, 5]      )
										self.assertEqual(listify(_lowerCamelCase      )	, [1, 2, 3, 4, 5]      )

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

				def 		_a	(      self      ):
										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      ):
										lowerCamelCase__					=RegressionModel()
										lowerCamelCase__					=model.forward
										lowerCamelCase__					=convert_outputs_to_fpaa(model.forward      )
										lowerCamelCase__					=extract_model_from_parallel(_lowerCamelCase	, keep_fpaa_wrapper=_lowerCamelCase      )
										lowerCamelCase__					=pickle.dumps(_lowerCamelCase      )

				@require_cuda
				def 		_a	(      self      ):
										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(_lowerCamelCase	, keep_fpaa_wrapper=_lowerCamelCase      )
										lowerCamelCase__					=pickle.dumps(_lowerCamelCase      )

				@require_cuda
				@require_torch_min_version(version="2.0"      )
				def 		_a	(      self      ):
										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(_lowerCamelCase      )

				def 		_a	(      self      ):
										lowerCamelCase__					=RegressionModel()
										# could also do a test with DistributedDataParallel, but difficult to run on CPU or single GPU
										lowerCamelCase__					=torch.nn.parallel.DataParallel(_lowerCamelCase      )
										lowerCamelCase__					=extract_model_from_parallel(_lowerCamelCase      )

										self.assertEqual(_lowerCamelCase	, _lowerCamelCase      )

				@require_torch_min_version(version="2.0"      )
				def 		_a	(      self      ):
										lowerCamelCase__					=RegressionModel()
										lowerCamelCase__					=torch.compile(_lowerCamelCase      )

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

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






				def 		_a	(      self      ):
										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"]      )      )



"""simple docstring"""


def 			lowerCamelCase_ (					__lowerCAmelCase				)							->					str:




						'''simple docstring'''



						lowerCamelCase__					=1
						lowerCamelCase__					=0
						lowerCamelCase__					=1
						lowerCamelCase__					=1
						lowerCamelCase__					=3
						lowerCamelCase__					=3
						lowerCamelCase__					=limit
						lowerCamelCase__					=0

						lowerCamelCase__					=""
						while counter != decimal + 1:
												if 4 * q + r - t < n * t:
																		result += str(__lowerCAmelCase				)
																		if counter == 0:
																								result += "."

																		if decimal == counter:
																								break

																		counter += 1
																		lowerCamelCase__					=10 * (r - n * t)
																		lowerCamelCase__					=((10 * (3 * q + r)) // t) - 10 * n
																		q *= 10
																		lowerCamelCase__					=nr
												else:
																		lowerCamelCase__					=(2 * q + r) * l
																		lowerCamelCase__					=(q * (7 * k) + 2 + (r * l)) // (t * l)
																		q *= k
																		t *= l
																		l += 2
																		k += 1
																		lowerCamelCase__					=nn
																		lowerCamelCase__					=nr
						return result





def 			lowerCamelCase_ (					)							->					None:




						'''simple docstring'''



						print(F'''{calculate_pi(50				) = }'''				)
						import doctest

						doctest.testmod()


if __name__ == "__main__":
	main()


