



"""simple docstring"""
import inspect
import unittest

import torch
import torch.nn as nn

from accelerate.hooks import (
    AlignDevicesHook,
    ModelHook,
    SequentialHook,
    add_hook_to_module,
    attach_align_device_hook,
    remove_hook_from_module,
    remove_hook_from_submodules,
)
from accelerate.test_utils import require_multi_gpu




class 			snake_case_						(						nn.Module				):






							def __init__(   self      ):
									super().__init__()
									a_     :      Optional[int] 						=     nn.Linear(3			,     4      )
									a_     :      List[str] 						=     nn.BatchNormad(4      )
									a_     :      Tuple 						=     nn.Linear(4			,     5      )






							def snake_case_  (   self			,     a_      ):
									return self.lineara(self.batchnorm(self.lineara(a_      )      )      )




class 			snake_case_						(						a_				):






							def snake_case_  (   self			,     a_			,     *a_			,     **a_      ):
									return (args[0] + 1,) + args[1:], kwargs




class 			snake_case_						(						a_				):






							def snake_case_  (   self			,     a_			,     a_      ):
									return output + 1




class 			snake_case_						(						unittest.TestCase				):






							def snake_case_  (   self      ):
									a_     :      Optional[Any] 						=     ModelForTest()
									a_     :      Tuple 						=     ModelHook()

									add_hook_to_module(a_			,     a_      )
									self.assertEqual(test_model._hf_hook			,     a_      )
									self.assertTrue(hasattr(a_			,     "_old_forward"      )      )

									# Check adding the hook did not change the name or the signature
									self.assertEqual(test_model.forward.__name__			,     "forward"      )
									self.assertListEqual(list(inspect.signature(test_model.forward      ).parameters      )			,     ["x"]      )

									remove_hook_from_module(a_      )
									self.assertFalse(hasattr(a_			,     "_hf_hook"      )      )
									self.assertFalse(hasattr(a_			,     "_old_forward"      )      )






							def snake_case_  (   self      ):
									a_     :      Optional[int] 						=     ModelForTest()
									a_     :      Dict 						=     ModelHook()

									add_hook_to_module(a_			,     a_      )
									add_hook_to_module(a_			,     a_			,     append=a_      )

									self.assertEqual(isinstance(test_model._hf_hook			,     a_      )			,     a_      )
									self.assertEqual(len(test_model._hf_hook.hooks      )			,     2      )
									self.assertTrue(hasattr(a_			,     "_old_forward"      )      )

									# Check adding the hook did not change the name or the signature
									self.assertEqual(test_model.forward.__name__			,     "forward"      )
									self.assertListEqual(list(inspect.signature(test_model.forward      ).parameters      )			,     ["x"]      )

									remove_hook_from_module(a_      )
									self.assertFalse(hasattr(a_			,     "_hf_hook"      )      )
									self.assertFalse(hasattr(a_			,     "_old_forward"      )      )






							def snake_case_  (   self      ):
									a_     :      Optional[int] 						=     ModelForTest()
									a_     :      Union[str, Any] 						=     torch.randn(2			,     3      )
									a_     :      List[str] 						=     test_model(x + 1      )
									a_     :      Dict 						=     test_model(x + 2      )

									a_     :      Optional[int] 						=     PreForwardHook()
									add_hook_to_module(a_			,     a_      )
									a_     :      Optional[int] 						=     test_model(a_      )
									self.assertTrue(torch.allclose(a_			,     a_			,     atol=1e-5      )      )

									# Attaching a hook to a model when it already has one replaces, does not chain
									a_     :      List[str] 						=     PreForwardHook()
									add_hook_to_module(a_			,     a_      )
									a_     :      Optional[Any] 						=     test_model(a_      )
									self.assertTrue(torch.allclose(a_			,     a_			,     atol=1e-5      )      )

									# You need to use the sequential hook to chain two or more hooks
									a_     :      str 						=     SequentialHook(PreForwardHook()			,     PreForwardHook()      )
									add_hook_to_module(a_			,     a_      )

									a_     :      List[Any] 						=     test_model(a_      )
									assert torch.allclose(a_			,     a_			,     atol=1e-5      )






							def snake_case_  (   self      ):
									a_     :      Optional[Any] 						=     ModelForTest()
									a_     :      str 						=     torch.randn(2			,     3      )
									a_     :      int 						=     test_model(a_      )

									a_     :      Optional[Any] 						=     PostForwardHook()
									add_hook_to_module(a_			,     a_      )
									a_     :      List[str] 						=     test_model(a_      )
									self.assertTrue(torch.allclose(a_			,     output + 1			,     atol=1e-5      )      )

									# Attaching a hook to a model when it already has one replaces, does not chain
									a_     :      str 						=     PostForwardHook()
									add_hook_to_module(a_			,     a_      )
									a_     :      Optional[Any] 						=     test_model(a_      )
									self.assertTrue(torch.allclose(a_			,     output + 1			,     atol=1e-5      )      )

									# You need to use the sequential hook to chain two or more hooks
									a_     :      Optional[Any] 						=     SequentialHook(PostForwardHook()			,     PostForwardHook()      )
									add_hook_to_module(a_			,     a_      )

									a_     :      List[str] 						=     test_model(a_      )
									assert torch.allclose(a_			,     output + 2			,     atol=1e-5      )






							def snake_case_  (   self      ):
									a_     :      str 						=     ModelForTest()
									a_     :      Dict 						=     torch.randn(2			,     3      )
									a_     :      Dict 						=     test_model(a_      )

									a_     :      Optional[int] 						=     PostForwardHook()
									add_hook_to_module(a_			,     a_      )
									a_     :      Optional[Any] 						=     test_model(a_      )
									self.assertTrue(torch.allclose(a_			,     output + 1      )      )
									self.assertTrue(outputa.requires_grad      )

									a_     :      Optional[Any] 						=     True
									a_     :      Any 						=     test_model(a_      )
									self.assertFalse(outputa.requires_grad      )






							@require_multi_gpu
							def snake_case_  (   self      ):
									a_     :      str 						=     ModelForTest()
									# Everything is on CPU
									self.assertEqual(model.lineara.weight.device			,     torch.device("cpu"      )      )
									self.assertEqual(model.batchnorm.weight.device			,     torch.device("cpu"      )      )
									self.assertEqual(model.lineara.weight.device			,     torch.device("cpu"      )      )

									# This will move each submodule on different devices
									add_hook_to_module(model.lineara			,     AlignDevicesHook(execution_device=0      )      )
									add_hook_to_module(model.batchnorm			,     AlignDevicesHook(execution_device=0      )      )
									add_hook_to_module(model.lineara			,     AlignDevicesHook(execution_device=1      )      )

									self.assertEqual(model.lineara.weight.device			,     torch.device(0      )      )
									self.assertEqual(model.batchnorm.weight.device			,     torch.device(0      )      )
									self.assertEqual(model.batchnorm.running_mean.device			,     torch.device(0      )      )
									self.assertEqual(model.lineara.weight.device			,     torch.device(1      )      )

									# We can still make a forward pass. The input does not need to be on any particular device
									a_     :      List[Any] 						=     torch.randn(2			,     3      )
									a_     :      int 						=     model(a_      )
									self.assertEqual(output.device			,     torch.device(1      )      )

									# We can add a general hook to put back output on same device as input.
									add_hook_to_module(a_			,     AlignDevicesHook(io_same_device=a_      )      )
									a_     :      List[str] 						=     torch.randn(2			,     3      ).to(0      )
									a_     :      Optional[Any] 						=     model(a_      )
									self.assertEqual(output.device			,     torch.device(0      )      )






							def snake_case_  (   self      ):
									a_     :      List[str] 						=     ModelForTest()

									# Everything is on CPU
									self.assertEqual(model.lineara.weight.device			,     torch.device("cpu"      )      )
									self.assertEqual(model.batchnorm.weight.device			,     torch.device("cpu"      )      )
									self.assertEqual(model.lineara.weight.device			,     torch.device("cpu"      )      )

									# This will move each submodule on different devices
									a_     :      int 						=     {"execution_device": 0 if torch.cuda.is_available() else "cpu", "offload": True}

									add_hook_to_module(model.lineara			,     AlignDevicesHook(**a_      )      )
									add_hook_to_module(model.batchnorm			,     AlignDevicesHook(**a_      )      )
									add_hook_to_module(model.lineara			,     AlignDevicesHook(**a_      )      )

									# Parameters have been offloaded, so on the meta device
									self.assertEqual(model.lineara.weight.device			,     torch.device("meta"      )      )
									self.assertEqual(model.batchnorm.weight.device			,     torch.device("meta"      )      )
									self.assertEqual(model.lineara.weight.device			,     torch.device("meta"      )      )
									# Buffers are not included in the offload by default, so are on the execution device
									a_     :      Tuple 						=     torch.device(hook_kwargs["execution_device"]      )
									self.assertEqual(model.batchnorm.running_mean.device			,     a_      )

									a_     :      Any 						=     torch.randn(2			,     3      )
									a_     :      Any 						=     model(a_      )
									self.assertEqual(output.device			,     a_      )

									# Removing hooks loads back the weights in the model.
									remove_hook_from_module(model.lineara      )
									remove_hook_from_module(model.batchnorm      )
									remove_hook_from_module(model.lineara      )
									self.assertEqual(model.lineara.weight.device			,     torch.device("cpu"      )      )
									self.assertEqual(model.batchnorm.weight.device			,     torch.device("cpu"      )      )
									self.assertEqual(model.lineara.weight.device			,     torch.device("cpu"      )      )

									# Now test with buffers included in the offload
									a_     :      List[Any] 						=     {
									    "execution_device": 0 if torch.cuda.is_available() else "cpu",
									    "offload": True,
									    "offload_buffers": True,
									}

									add_hook_to_module(model.lineara			,     AlignDevicesHook(**a_      )      )
									add_hook_to_module(model.batchnorm			,     AlignDevicesHook(**a_      )      )
									add_hook_to_module(model.lineara			,     AlignDevicesHook(**a_      )      )

									# Parameters have been offloaded, so on the meta device, buffers included
									self.assertEqual(model.lineara.weight.device			,     torch.device("meta"      )      )
									self.assertEqual(model.batchnorm.weight.device			,     torch.device("meta"      )      )
									self.assertEqual(model.lineara.weight.device			,     torch.device("meta"      )      )
									self.assertEqual(model.batchnorm.running_mean.device			,     torch.device("meta"      )      )

									a_     :      Dict 						=     torch.randn(2			,     3      )
									a_     :      Tuple 						=     model(a_      )
									self.assertEqual(output.device			,     a_      )

									# Removing hooks loads back the weights in the model.
									remove_hook_from_module(model.lineara      )
									remove_hook_from_module(model.batchnorm      )
									remove_hook_from_module(model.lineara      )
									self.assertEqual(model.lineara.weight.device			,     torch.device("cpu"      )      )
									self.assertEqual(model.batchnorm.weight.device			,     torch.device("cpu"      )      )
									self.assertEqual(model.lineara.weight.device			,     torch.device("cpu"      )      )






							def snake_case_  (   self      ):
									a_     :      str 						=     ModelForTest()

									# Everything is on CPU
									self.assertEqual(model.lineara.weight.device			,     torch.device("cpu"      )      )
									self.assertEqual(model.batchnorm.weight.device			,     torch.device("cpu"      )      )
									self.assertEqual(model.lineara.weight.device			,     torch.device("cpu"      )      )

									# This will move each submodule on different devices
									a_     :      int 						=     0 if torch.cuda.is_available() else "cpu"
									attach_align_device_hook(a_			,     execution_device=a_			,     offload=a_      )

									# Parameters have been offloaded, so on the meta device
									self.assertEqual(model.lineara.weight.device			,     torch.device("meta"      )      )
									self.assertEqual(model.batchnorm.weight.device			,     torch.device("meta"      )      )
									self.assertEqual(model.lineara.weight.device			,     torch.device("meta"      )      )
									# Buffers are not included in the offload by default, so are on the execution device
									a_     :      Tuple 						=     torch.device(a_      )
									self.assertEqual(model.batchnorm.running_mean.device			,     a_      )

									a_     :      int 						=     torch.randn(2			,     3      )
									a_     :      int 						=     model(a_      )
									self.assertEqual(output.device			,     a_      )

									# Removing hooks loads back the weights in the model.
									remove_hook_from_submodules(a_      )
									self.assertEqual(model.lineara.weight.device			,     torch.device("cpu"      )      )
									self.assertEqual(model.batchnorm.weight.device			,     torch.device("cpu"      )      )
									self.assertEqual(model.lineara.weight.device			,     torch.device("cpu"      )      )

									# Now test with buffers included in the offload
									attach_align_device_hook(a_			,     execution_device=a_			,     offload=a_			,     offload_buffers=a_      )

									# Parameters have been offloaded, so on the meta device, buffers included
									self.assertEqual(model.lineara.weight.device			,     torch.device("meta"      )      )
									self.assertEqual(model.batchnorm.weight.device			,     torch.device("meta"      )      )
									self.assertEqual(model.lineara.weight.device			,     torch.device("meta"      )      )
									self.assertEqual(model.batchnorm.running_mean.device			,     torch.device("meta"      )      )

									a_     :      Optional[Any] 						=     torch.randn(2			,     3      )
									a_     :      str 						=     model(a_      )
									self.assertEqual(output.device			,     a_      )

									# Removing hooks loads back the weights in the model.
									remove_hook_from_submodules(a_      )
									self.assertEqual(model.lineara.weight.device			,     torch.device("cpu"      )      )
									self.assertEqual(model.batchnorm.weight.device			,     torch.device("cpu"      )      )
									self.assertEqual(model.lineara.weight.device			,     torch.device("cpu"      )      )






							def snake_case_  (   self      ):
									a_     :      List[str] 						=     ModelForTest()

									# Everything is on CPU
									self.assertEqual(model.lineara.weight.device			,     torch.device("cpu"      )      )
									self.assertEqual(model.batchnorm.weight.device			,     torch.device("cpu"      )      )
									self.assertEqual(model.lineara.weight.device			,     torch.device("cpu"      )      )

									# This will move each submodule on different devices
									a_     :      Optional[Any] 						=     0 if torch.cuda.is_available() else "cpu"
									attach_align_device_hook(
									    a_			,     execution_device=a_			,     offload=a_			,     weights_map=model.state_dict()      )

									# Parameters have been offloaded, so on the meta device
									self.assertEqual(model.lineara.weight.device			,     torch.device("meta"      )      )
									self.assertEqual(model.batchnorm.weight.device			,     torch.device("meta"      )      )
									self.assertEqual(model.lineara.weight.device			,     torch.device("meta"      )      )
									# Buffers are not included in the offload by default, so are on the execution device
									a_     :      Optional[int] 						=     torch.device(a_      )
									self.assertEqual(model.batchnorm.running_mean.device			,     a_      )

									a_     :      Optional[int] 						=     torch.randn(2			,     3      )
									a_     :      Dict 						=     model(a_      )
									self.assertEqual(output.device			,     a_      )

									# Removing hooks loads back the weights in the model.
									remove_hook_from_submodules(a_      )
									self.assertEqual(model.lineara.weight.device			,     torch.device("cpu"      )      )
									self.assertEqual(model.batchnorm.weight.device			,     torch.device("cpu"      )      )
									self.assertEqual(model.lineara.weight.device			,     torch.device("cpu"      )      )

									# Now test with buffers included in the offload
									attach_align_device_hook(
									    a_			,     execution_device=a_			,     offload=a_			,     weights_map=model.state_dict()			,     offload_buffers=a_			,     )

									# Parameters have been offloaded, so on the meta device, buffers included
									self.assertEqual(model.lineara.weight.device			,     torch.device("meta"      )      )
									self.assertEqual(model.batchnorm.weight.device			,     torch.device("meta"      )      )
									self.assertEqual(model.lineara.weight.device			,     torch.device("meta"      )      )
									self.assertEqual(model.batchnorm.running_mean.device			,     torch.device("meta"      )      )

									a_     :      Dict 						=     torch.randn(2			,     3      )
									a_     :      List[str] 						=     model(a_      )
									self.assertEqual(output.device			,     a_      )

									# Removing hooks loads back the weights in the model.
									remove_hook_from_submodules(a_      )
									self.assertEqual(model.lineara.weight.device			,     torch.device("cpu"      )      )
									self.assertEqual(model.batchnorm.weight.device			,     torch.device("cpu"      )      )
									self.assertEqual(model.lineara.weight.device			,     torch.device("cpu"      )      )




"""simple docstring"""
def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__ = False	)			->      bool:
		if n == 2:
				return True
		if not n % 2 or n < 2:
				return False
		if n > 5 and n % 10 not in (1, 3, 7, 9):  # can quickly check last digit
				return False
		if n > 3_317_044_064_679_887_385_961_981 and not allow_probable:
				raise ValueError(
				    "Warning: upper bound of deterministic test is exceeded. "
				    "Pass allow_probable=True to allow probabilistic test. "
				    "A return value of True indicates a probable prime."	)
		# array bounds provided by analysis
		a_     :      Optional[Any] 						=     [
		    2_047,
		    1_373_653,
		    25_326_001,
		    3_215_031_751,
		    2_152_302_898_747,
		    3_474_749_660_383,
		    341_550_071_728_321,
		    1,
		    3_825_123_056_546_413_051,
		    1,
		    1,
		    318_665_857_834_031_151_167_461,
		    3_317_044_064_679_887_385_961_981,
		]

		a_     :      Optional[int] 						=     [2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41]
		for idx, _p in enumerate(SCREAMING_SNAKE_CASE__, 1	):
				if n < _p:
						# then we have our last prime to check
						a_     :      List[Any] 						=     primes[:idx]
						break
		a_						,				a_     :      Tuple 						=     n - 1, 0
		# break up n -1 into a power of 2 (s) and
		# remaining odd component
		# essentially, solve for d * 2 ** s == n - 1
		while d % 2 == 0:
				d //= 2
				s += 1
		for prime in plist:
				a_     :      str 						=     False
				for r in range(SCREAMING_SNAKE_CASE__	):
						a_     :      List[str] 						=     pow(SCREAMING_SNAKE_CASE__, d * 2**r, SCREAMING_SNAKE_CASE__	)
						# see article for analysis explanation for m
						if (r == 0 and m == 1) or ((m + 1) % n == 0):
								a_     :      List[Any] 						=     True
								# this loop will not determine compositeness
								break
				if pr:
						continue
				# if pr is False, then the above loop never evaluated to true,
				# and the n MUST be composite
				return False
		return True



def        lowerCAmelCase_						(							)			->      None:
		assert not miller_rabin(561	)
		assert miller_rabin(563	)
		# 2047

		assert not miller_rabin(838_201	)
		assert miller_rabin(838_207	)
		# 1_373_653

		assert not miller_rabin(17_316_001	)
		assert miller_rabin(17_316_017	)
		# 25_326_001

		assert not miller_rabin(3_078_386_641	)
		assert miller_rabin(3_078_386_653	)
		# 3_215_031_751

		assert not miller_rabin(1_713_045_574_801	)
		assert miller_rabin(1_713_045_574_819	)
		# 2_152_302_898_747

		assert not miller_rabin(2_779_799_728_307	)
		assert miller_rabin(2_779_799_728_327	)
		# 3_474_749_660_383

		assert not miller_rabin(113_850_023_909_441	)
		assert miller_rabin(113_850_023_909_527	)
		# 341_550_071_728_321

		assert not miller_rabin(1_275_041_018_848_804_351	)
		assert miller_rabin(1_275_041_018_848_804_391	)
		# 3_825_123_056_546_413_051

		assert not miller_rabin(79_666_464_458_507_787_791_867	)
		assert miller_rabin(79_666_464_458_507_787_791_951	)
		# 318_665_857_834_031_151_167_461

		assert not miller_rabin(552_840_677_446_647_897_660_333	)
		assert miller_rabin(552_840_677_446_647_897_660_359	)
		# 3_317_044_064_679_887_385_961_981
		# upper limit for probabilistic test


if __name__ == "__main__":
					test_miller_rabin()