"""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 		__A							(				nn.Module	):






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






     def 					__A						(	self       ,  a__						):
       return self.lineara(self.batchnorm(self.lineara(a__						)						)						)







class 		__A							(				SCREAMING_SNAKE_CASE_	):






     def 					__A						(	self       ,  a__       ,  *a__       ,  **a__						):
       return (args[0] + 1,) + args[1:], kwargs







class 		__A							(				SCREAMING_SNAKE_CASE_	):






     def 					__A						(	self       ,  a__       ,  a__						):
       return output + 1







class 		__A							(				unittest.TestCase	):






     def 					__A						(	self						):
       _lowerCAmelCase		: str						    =  ModelForTest()
       _lowerCAmelCase		: Optional[Any]						    =  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 					__A						(	self						):
       _lowerCAmelCase		: Optional[int]						    =  ModelForTest()
       _lowerCAmelCase		: int						    =  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 					__A						(	self						):
       _lowerCAmelCase		: Tuple						    =  ModelForTest()
       _lowerCAmelCase		: Optional[int]						    =  torch.randn(2       ,  3						)
       _lowerCAmelCase		: Optional[Any]						    =  test_model(x + 1						)
       _lowerCAmelCase		: Optional[int]						    =  test_model(x + 2						)

       _lowerCAmelCase		: Tuple						    =  PreForwardHook()
       add_hook_to_module(a__       ,  a__						)
       _lowerCAmelCase		: List[str]						    =  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
       _lowerCAmelCase		: int						    =  PreForwardHook()
       add_hook_to_module(a__       ,  a__						)
       _lowerCAmelCase		: Union[str, 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
       _lowerCAmelCase		: Tuple						    =  SequentialHook(PreForwardHook()       ,  PreForwardHook()						)
       add_hook_to_module(a__       ,  a__						)

       _lowerCAmelCase		: Union[str, Any]						    =  test_model(a__						)
       assert torch.allclose(a__       ,  a__       ,  atol=1e-5						)






     def 					__A						(	self						):
       _lowerCAmelCase		: List[str]						    =  ModelForTest()
       _lowerCAmelCase		: int						    =  torch.randn(2       ,  3						)
       _lowerCAmelCase		: List[Any]						    =  test_model(a__						)

       _lowerCAmelCase		: Any						    =  PostForwardHook()
       add_hook_to_module(a__       ,  a__						)
       _lowerCAmelCase		: Optional[int]						    =  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
       _lowerCAmelCase		: int						    =  PostForwardHook()
       add_hook_to_module(a__       ,  a__						)
       _lowerCAmelCase		: str						    =  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
       _lowerCAmelCase		: Optional[Any]						    =  SequentialHook(PostForwardHook()       ,  PostForwardHook()						)
       add_hook_to_module(a__       ,  a__						)

       _lowerCAmelCase		: Union[str, Any]						    =  test_model(a__						)
       assert torch.allclose(a__       ,  output + 2       ,  atol=1e-5						)






     def 					__A						(	self						):
       _lowerCAmelCase		: Union[str, Any]						    =  ModelForTest()
       _lowerCAmelCase		: Optional[Any]						    =  torch.randn(2       ,  3						)
       _lowerCAmelCase		: Any						    =  test_model(a__						)

       _lowerCAmelCase		: Tuple						    =  PostForwardHook()
       add_hook_to_module(a__       ,  a__						)
       _lowerCAmelCase		: str						    =  test_model(a__						)
       self.assertTrue(torch.allclose(a__       ,  output + 1						)						)
       self.assertTrue(outputa.requires_grad						)

       _lowerCAmelCase		: List[str]						    =  True
       _lowerCAmelCase		: Tuple						    =  test_model(a__						)
       self.assertFalse(outputa.requires_grad						)






     @require_multi_gpu
     def 					__A						(	self						):
       _lowerCAmelCase		: Optional[Any]						    =  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
       _lowerCAmelCase		: List[Any]						    =  torch.randn(2       ,  3						)
       _lowerCAmelCase		: Optional[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__						)						)
       _lowerCAmelCase		: Union[str, Any]						    =  torch.randn(2       ,  3						).to(0						)
       _lowerCAmelCase		: List[Any]						    =  model(a__						)
       self.assertEqual(output.device       ,  torch.device(0						)						)






     def 					__A						(	self						):
       _lowerCAmelCase		: Any						    =  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
       _lowerCAmelCase		: List[str]						    =  {"""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
       _lowerCAmelCase		: Optional[Any]						    =  torch.device(hook_kwargs["""execution_device"""]						)
       self.assertEqual(model.batchnorm.running_mean.device       ,  a__						)

       _lowerCAmelCase		: Dict						    =  torch.randn(2       ,  3						)
       _lowerCAmelCase		: Union[str, 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
       _lowerCAmelCase		: Optional[int]						    =  {
           """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"""						)						)

       _lowerCAmelCase		: Union[str, Any]						    =  torch.randn(2       ,  3						)
       _lowerCAmelCase		: 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"""						)						)






     def 					__A						(	self						):
       _lowerCAmelCase		: Tuple						    =  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
       _lowerCAmelCase		: Tuple						    =  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
       _lowerCAmelCase		: Dict						    =  torch.device(a__						)
       self.assertEqual(model.batchnorm.running_mean.device       ,  a__						)

       _lowerCAmelCase		: Any						    =  torch.randn(2       ,  3						)
       _lowerCAmelCase		: 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__       ,  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"""						)						)

       _lowerCAmelCase		: Union[str, Any]						    =  torch.randn(2       ,  3						)
       _lowerCAmelCase		: Union[str, Any]						    =  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 					__A						(	self						):
       _lowerCAmelCase		: Any						    =  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
       _lowerCAmelCase		: str						    =  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
       _lowerCAmelCase		: int						    =  torch.device(a__						)
       self.assertEqual(model.batchnorm.running_mean.device       ,  a__						)

       _lowerCAmelCase		: Dict						    =  torch.randn(2       ,  3						)
       _lowerCAmelCase		: Any						    =  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"""						)						)

       _lowerCAmelCase		: List[str]						    =  torch.randn(2       ,  3						)
       _lowerCAmelCase		: Any						    =  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 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: int = 1000000			) ->   int:
  _lowerCAmelCase		: List[str]						    =  [i - 1 for i in range(limit + 1			)]

  for i in range(2    ,limit + 1			):
    if phi[i] == i - 1:
      for j in range(2 * i    ,limit + 1    ,_lowerCamelCase			):
        phi[j] -= phi[j] // i

  return sum(phi[2 : limit + 1]			)


if __name__ == "__main__":
     print(solution())




