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     _A   (    nn.Module							):






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





       '''simple docstring'''





       super().__init__()
       UpperCamelCase__										=      nn.Linear(3   ,				4	)
       UpperCamelCase__										=      nn.BatchNormad(4	)
       UpperCamelCase__										=      nn.Linear(4   ,				5	)






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_	)		->      List[Any]:





       '''simple docstring'''





       return self.lineara(self.batchnorm(self.lineara(_UpperCAmelCase	)	)	)



class     _A   (    _UpperCamelCase							):






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				*SCREAMING_SNAKE_CASE_   ,				**SCREAMING_SNAKE_CASE_	)		->      str:





       '''simple docstring'''





       return (args[0] + 1,) + args[1:], kwargs



class     _A   (    _UpperCamelCase							):






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)		->      str:





       '''simple docstring'''





       return output + 1



class     _A   (    unittest.TestCase							):






  def 		_a					(self	)		->      int:





       '''simple docstring'''





       UpperCamelCase__										=      ModelForTest()
       UpperCamelCase__										=      ModelHook()

       add_hook_to_module(_UpperCAmelCase   ,				_UpperCAmelCase	)
       self.assertEqual(test_model._hf_hook   ,				_UpperCAmelCase	)
       self.assertTrue(hasattr(_UpperCAmelCase   ,				'''_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(_UpperCAmelCase	)
       self.assertFalse(hasattr(_UpperCAmelCase   ,				'''_hf_hook'''	)	)
       self.assertFalse(hasattr(_UpperCAmelCase   ,				'''_old_forward'''	)	)






  def 		_a					(self	)		->      Tuple:





       '''simple docstring'''





       UpperCamelCase__										=      ModelForTest()
       UpperCamelCase__										=      ModelHook()

       add_hook_to_module(_UpperCAmelCase   ,				_UpperCAmelCase	)
       add_hook_to_module(_UpperCAmelCase   ,				_UpperCAmelCase   ,				append=_UpperCAmelCase	)

       self.assertEqual(isinstance(test_model._hf_hook   ,				_UpperCAmelCase	)   ,				_UpperCAmelCase	)
       self.assertEqual(len(test_model._hf_hook.hooks	)   ,				2	)
       self.assertTrue(hasattr(_UpperCAmelCase   ,				'''_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(_UpperCAmelCase	)
       self.assertFalse(hasattr(_UpperCAmelCase   ,				'''_hf_hook'''	)	)
       self.assertFalse(hasattr(_UpperCAmelCase   ,				'''_old_forward'''	)	)






  def 		_a					(self	)		->      str:





       '''simple docstring'''





       UpperCamelCase__										=      ModelForTest()
       UpperCamelCase__										=      torch.randn(2   ,				3	)
       UpperCamelCase__										=      test_model(x + 1	)
       UpperCamelCase__										=      test_model(x + 2	)

       UpperCamelCase__										=      PreForwardHook()
       add_hook_to_module(_UpperCAmelCase   ,				_UpperCAmelCase	)
       UpperCamelCase__										=      test_model(_UpperCAmelCase	)
       self.assertTrue(torch.allclose(_UpperCAmelCase   ,				_UpperCAmelCase   ,				atol=1E-5	)	)

       # Attaching a hook to a model when it already has one replaces, does not chain
       UpperCamelCase__										=      PreForwardHook()
       add_hook_to_module(_UpperCAmelCase   ,				_UpperCAmelCase	)
       UpperCamelCase__										=      test_model(_UpperCAmelCase	)
       self.assertTrue(torch.allclose(_UpperCAmelCase   ,				_UpperCAmelCase   ,				atol=1E-5	)	)

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

       UpperCamelCase__										=      test_model(_UpperCAmelCase	)
       assert torch.allclose(_UpperCAmelCase   ,				_UpperCAmelCase   ,				atol=1E-5	)






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





       '''simple docstring'''





       UpperCamelCase__										=      ModelForTest()
       UpperCamelCase__										=      torch.randn(2   ,				3	)
       UpperCamelCase__										=      test_model(_UpperCAmelCase	)

       UpperCamelCase__										=      PostForwardHook()
       add_hook_to_module(_UpperCAmelCase   ,				_UpperCAmelCase	)
       UpperCamelCase__										=      test_model(_UpperCAmelCase	)
       self.assertTrue(torch.allclose(_UpperCAmelCase   ,				output + 1   ,				atol=1E-5	)	)

       # Attaching a hook to a model when it already has one replaces, does not chain
       UpperCamelCase__										=      PostForwardHook()
       add_hook_to_module(_UpperCAmelCase   ,				_UpperCAmelCase	)
       UpperCamelCase__										=      test_model(_UpperCAmelCase	)
       self.assertTrue(torch.allclose(_UpperCAmelCase   ,				output + 1   ,				atol=1E-5	)	)

       # You need to use the sequential hook to chain two or more hooks
       UpperCamelCase__										=      SequentialHook(PostForwardHook()   ,				PostForwardHook()	)
       add_hook_to_module(_UpperCAmelCase   ,				_UpperCAmelCase	)

       UpperCamelCase__										=      test_model(_UpperCAmelCase	)
       assert torch.allclose(_UpperCAmelCase   ,				output + 2   ,				atol=1E-5	)






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





       '''simple docstring'''





       UpperCamelCase__										=      ModelForTest()
       UpperCamelCase__										=      torch.randn(2   ,				3	)
       UpperCamelCase__										=      test_model(_UpperCAmelCase	)

       UpperCamelCase__										=      PostForwardHook()
       add_hook_to_module(_UpperCAmelCase   ,				_UpperCAmelCase	)
       UpperCamelCase__										=      test_model(_UpperCAmelCase	)
       self.assertTrue(torch.allclose(_UpperCAmelCase   ,				output + 1	)	)
       self.assertTrue(outputa.requires_grad	)

       UpperCamelCase__										=      True
       UpperCamelCase__										=      test_model(_UpperCAmelCase	)
       self.assertFalse(outputa.requires_grad	)






  @require_multi_gpu
  def 		_a					(self	)		->      str:





       '''simple docstring'''





       UpperCamelCase__										=      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
       UpperCamelCase__										=      torch.randn(2   ,				3	)
       UpperCamelCase__										=      model(_UpperCAmelCase	)
       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(_UpperCAmelCase   ,				AlignDevicesHook(io_same_device=_UpperCAmelCase	)	)
       UpperCamelCase__										=      torch.randn(2   ,				3	).to(0	)
       UpperCamelCase__										=      model(_UpperCAmelCase	)
       self.assertEqual(output.device   ,				torch.device(0	)	)






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





       '''simple docstring'''





       UpperCamelCase__										=      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
       UpperCamelCase__										=      {'''execution_device''': 0 if torch.cuda.is_available() else '''cpu''', '''offload''': True}

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

       # 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
       UpperCamelCase__										=      torch.device(hook_kwargs['''execution_device''']	)
       self.assertEqual(model.batchnorm.running_mean.device   ,				_UpperCAmelCase	)

       UpperCamelCase__										=      torch.randn(2   ,				3	)
       UpperCamelCase__										=      model(_UpperCAmelCase	)
       self.assertEqual(output.device   ,				_UpperCAmelCase	)

       # 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
       UpperCamelCase__										=      {
           '''execution_device''': 0 if torch.cuda.is_available() else '''cpu''',
           '''offload''': True,
           '''offload_buffers''': True,
       }

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

       # 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'''	)	)

       UpperCamelCase__										=      torch.randn(2   ,				3	)
       UpperCamelCase__										=      model(_UpperCAmelCase	)
       self.assertEqual(output.device   ,				_UpperCAmelCase	)

       # 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 		_a					(self	)		->      Tuple:





       '''simple docstring'''





       UpperCamelCase__										=      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
       UpperCamelCase__										=      0 if torch.cuda.is_available() else '''cpu'''
       attach_align_device_hook(_UpperCAmelCase   ,				execution_device=_UpperCAmelCase   ,				offload=_UpperCAmelCase	)

       # 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
       UpperCamelCase__										=      torch.device(_UpperCAmelCase	)
       self.assertEqual(model.batchnorm.running_mean.device   ,				_UpperCAmelCase	)

       UpperCamelCase__										=      torch.randn(2   ,				3	)
       UpperCamelCase__										=      model(_UpperCAmelCase	)
       self.assertEqual(output.device   ,				_UpperCAmelCase	)

       # Removing hooks loads back the weights in the model.
       remove_hook_from_submodules(_UpperCAmelCase	)
       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(_UpperCAmelCase   ,				execution_device=_UpperCAmelCase   ,				offload=_UpperCAmelCase   ,				offload_buffers=_UpperCAmelCase	)

       # 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'''	)	)

       UpperCamelCase__										=      torch.randn(2   ,				3	)
       UpperCamelCase__										=      model(_UpperCAmelCase	)
       self.assertEqual(output.device   ,				_UpperCAmelCase	)

       # Removing hooks loads back the weights in the model.
       remove_hook_from_submodules(_UpperCAmelCase	)
       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 		_a					(self	)		->      Optional[int]:





       '''simple docstring'''





       UpperCamelCase__										=      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
       UpperCamelCase__										=      0 if torch.cuda.is_available() else '''cpu'''
       attach_align_device_hook(
           _UpperCAmelCase   ,				execution_device=_UpperCAmelCase   ,				offload=_UpperCAmelCase   ,				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
       UpperCamelCase__										=      torch.device(_UpperCAmelCase	)
       self.assertEqual(model.batchnorm.running_mean.device   ,				_UpperCAmelCase	)

       UpperCamelCase__										=      torch.randn(2   ,				3	)
       UpperCamelCase__										=      model(_UpperCAmelCase	)
       self.assertEqual(output.device   ,				_UpperCAmelCase	)

       # Removing hooks loads back the weights in the model.
       remove_hook_from_submodules(_UpperCAmelCase	)
       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(
           _UpperCAmelCase   ,				execution_device=_UpperCAmelCase   ,				offload=_UpperCAmelCase   ,				weights_map=model.state_dict()   ,				offload_buffers=_UpperCAmelCase   ,				)

       # 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'''	)	)

       UpperCamelCase__										=      torch.randn(2   ,				3	)
       UpperCamelCase__										=      model(_UpperCAmelCase	)
       self.assertEqual(output.device   ,				_UpperCAmelCase	)

       # Removing hooks loads back the weights in the model.
       remove_hook_from_submodules(_UpperCAmelCase	)
       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  __UpperCamelCase    (						A = 600851475143							):

     try:
          UpperCamelCase__										=      int(A							)
     except (TypeError, ValueError):
          raise TypeError('''Parameter n must be int or castable to int.'''							)
     if n <= 0:
          raise ValueError('''Parameter n must be greater than or equal to one.'''							)
     UpperCamelCase__										=      1
     UpperCamelCase__										=      2
     while i * i <= n:
          while n % i == 0:
               UpperCamelCase__										=      i
               n //= i
          i += 1
     if n > 1:
          UpperCamelCase__										=      n
     return int(A							)


if __name__ == "__main__":
     print(f"""{solution() = }""")



