





'''simple docstring'''






from __future__ import annotations

import math
import random
from typing import Any







class 			lowerCamelCase					:


					def __init__(					self	:  List[str]       ):





											'''simple docstring'''





											_snake_case:   list[Any]  					=		[]
											_snake_case:   int  					=		0
											_snake_case:   int  					=		0


					def    SCREAMING_SNAKE_CASE_		(					self	:  int       ):





											'''simple docstring'''





											return self.head == self.tail


					def    SCREAMING_SNAKE_CASE_		(					self	:  List[Any]       ,     __snake_case	:  Any       ):





											'''simple docstring'''





											self.data.append(__snake_case       )
											_snake_case:   Tuple  					=		self.tail + 1


					def    SCREAMING_SNAKE_CASE_		(					self	:  Union[str, Any]       ):





											'''simple docstring'''





											_snake_case:   List[str]  					=		self.data[self.head]
											_snake_case:   Tuple  					=		self.head + 1
											return ret


					def    SCREAMING_SNAKE_CASE_		(					self	:  Dict       ):





											'''simple docstring'''





											return self.tail - self.head




					def    SCREAMING_SNAKE_CASE_		(					self	:  List[str]       ):





											'''simple docstring'''





											print(self.data       )
											print('**************'       )
											print(self.data[self.head : self.tail]       )







class 			lowerCamelCase					:


					def __init__(					self	:  Optional[Any]       ,     __snake_case	:  Any       ):





											'''simple docstring'''





											_snake_case:   List[str]  					=		data
											_snake_case:   MyNode | None  					=		None
											_snake_case:   MyNode | None  					=		None
											_snake_case:   int  					=		1


					def    SCREAMING_SNAKE_CASE_		(					self	:  List[str]       ):





											'''simple docstring'''





											return self.data


					def    SCREAMING_SNAKE_CASE_		(					self	:  Tuple       ):





											'''simple docstring'''





											return self.left


					def    SCREAMING_SNAKE_CASE_		(					self	:  Optional[Any]       ):





											'''simple docstring'''





											return self.right


					def    SCREAMING_SNAKE_CASE_		(					self	:  str       ):





											'''simple docstring'''





											return self.height


					def    SCREAMING_SNAKE_CASE_		(					self	:  Dict       ,     __snake_case	:  Any       ):





											'''simple docstring'''





											_snake_case:   Union[str, Any]  					=		data


					def    SCREAMING_SNAKE_CASE_		(					self	:  Any       ,     __snake_case	:  MyNode | None       ):





											'''simple docstring'''





											_snake_case:   Optional[int]  					=		node


					def    SCREAMING_SNAKE_CASE_		(					self	:  List[str]       ,     __snake_case	:  MyNode | None       ):





											'''simple docstring'''





											_snake_case:   Union[str, Any]  					=		node




					def    SCREAMING_SNAKE_CASE_		(					self	:  Union[str, Any]       ,     __snake_case	:  int       ):





											'''simple docstring'''





											_snake_case:   List[str]  					=		height





def       lowercase_      (		lowercase__						)   ->int:
						if node is None:
												return 0
						return node.get_height()





def       lowercase_      (		lowercase__				,  lowercase__						)   ->int:
						if a > b:
												return a
						return b





def       lowercase_      (		lowercase__						)   ->MyNode:
						print('left rotation node:'				,  node.get_data()						)
						_snake_case:   str  					=		node.get_left()
						assert ret is not None
						node.set_left(ret.get_right()						)
						ret.set_right(lowercase__						)
						_snake_case:   Tuple  					=		my_max(get_height(node.get_right()						)				,  get_height(node.get_left()						)						) + 1
						node.set_height(lowercase__						)
						_snake_case:   List[str]  					=		my_max(get_height(ret.get_right()						)				,  get_height(ret.get_left()						)						) + 1
						ret.set_height(lowercase__						)
						return ret





def       lowercase_      (		lowercase__						)   ->MyNode:
						print('right rotation node:'				,  node.get_data()						)
						_snake_case:   Any  					=		node.get_right()
						assert ret is not None
						node.set_right(ret.get_left()						)
						ret.set_left(lowercase__						)
						_snake_case:   Optional[int]  					=		my_max(get_height(node.get_right()						)				,  get_height(node.get_left()						)						) + 1
						node.set_height(lowercase__						)
						_snake_case:   Optional[int]  					=		my_max(get_height(ret.get_right()						)				,  get_height(ret.get_left()						)						) + 1
						ret.set_height(lowercase__						)
						return ret





def       lowercase_      (		lowercase__						)   ->MyNode:
						_snake_case:   List[str]  					=		node.get_left()
						assert left_child is not None
						node.set_left(left_rotation(lowercase__						)						)
						return right_rotation(lowercase__						)





def       lowercase_      (		lowercase__						)   ->MyNode:
						_snake_case:   Tuple  					=		node.get_right()
						assert right_child is not None
						node.set_right(right_rotation(lowercase__						)						)
						return left_rotation(lowercase__						)





def       lowercase_      (		lowercase__				,  lowercase__						)   ->MyNode | None:
						if node is None:
												return MyNode(lowercase__						)
						if data < node.get_data():
												node.set_left(insert_node(node.get_left()				,  lowercase__						)						)
												if (
												    get_height(node.get_left()						) - get_height(node.get_right()						) == 2
												):  # an unbalance detected
																		_snake_case:   int  					=		node.get_left()
																		assert left_child is not None
																		if (
																		    data < left_child.get_data()
																		):  # new node is the left child of the left child
																								_snake_case:   int  					=		right_rotation(lowercase__						)
																		else:
																								_snake_case:   Union[str, Any]  					=		lr_rotation(lowercase__						)
						else:
												node.set_right(insert_node(node.get_right()				,  lowercase__						)						)
												if get_height(node.get_right()						) - get_height(node.get_left()						) == 2:
																		_snake_case:   Tuple  					=		node.get_right()
																		assert right_child is not None
																		if data < right_child.get_data():
																								_snake_case:   List[str]  					=		rl_rotation(lowercase__						)
																		else:
																								_snake_case:   Any  					=		left_rotation(lowercase__						)
						_snake_case:   int  					=		my_max(get_height(node.get_right()						)				,  get_height(node.get_left()						)						) + 1
						node.set_height(lowercase__						)
						return node





def       lowercase_      (		lowercase__						)   ->Any:
						while True:
												_snake_case:   Dict  					=		root.get_right()
												if right_child is None:
																		break
												_snake_case:   Optional[Any]  					=		right_child
						return root.get_data()





def       lowercase_      (		lowercase__						)   ->Any:
						while True:
												_snake_case:   Optional[int]  					=		root.get_left()
												if left_child is None:
																		break
												_snake_case:   Any  					=		left_child
						return root.get_data()





def       lowercase_      (		lowercase__				,  lowercase__						)   ->MyNode | None:
						_snake_case:   Tuple  					=		root.get_left()
						_snake_case:   List[str]  					=		root.get_right()
						if root.get_data() == data:
												if left_child is not None and right_child is not None:
																		_snake_case:   List[Any]  					=		get_left_most(lowercase__						)
																		root.set_data(lowercase__						)
																		root.set_right(del_node(lowercase__				,  lowercase__						)						)
												elif left_child is not None:
																		_snake_case:   int  					=		left_child
												elif right_child is not None:
																		_snake_case:   Any  					=		right_child
												else:
																		return None
						elif root.get_data() > data:
												if left_child is None:
																		print('No such data'						)
																		return root
												else:
																		root.set_left(del_node(lowercase__				,  lowercase__						)						)
						else:  # root.get_data() < data
												if right_child is None:
																		return root
												else:
																		root.set_right(del_node(lowercase__				,  lowercase__						)						)

						if get_height(lowercase__						) - get_height(lowercase__						) == 2:
												assert right_child is not None
												if get_height(right_child.get_right()						) > get_height(right_child.get_left()						):
																		_snake_case:   Optional[Any]  					=		left_rotation(lowercase__						)
												else:
																		_snake_case:   Optional[int]  					=		rl_rotation(lowercase__						)
						elif get_height(lowercase__						) - get_height(lowercase__						) == -2:
												assert left_child is not None
												if get_height(left_child.get_left()						) > get_height(left_child.get_right()						):
																		_snake_case:   Optional[int]  					=		right_rotation(lowercase__						)
												else:
																		_snake_case:   Tuple  					=		lr_rotation(lowercase__						)
						_snake_case:   Union[str, Any]  					=		my_max(get_height(root.get_right()						)				,  get_height(root.get_left()						)						) + 1
						root.set_height(lowercase__						)
						return root







class 			lowerCamelCase					:


					def __init__(					self	:  Optional[Any]       ):





											'''simple docstring'''





											_snake_case:   MyNode | None  					=		None


					def    SCREAMING_SNAKE_CASE_		(					self	:  Union[str, Any]       ):





											'''simple docstring'''





											return get_height(self.root       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  Dict       ,     __snake_case	:  Any       ):





											'''simple docstring'''





											print('insert:' + str(__snake_case       )       )
											_snake_case:   Optional[int]  					=		insert_node(self.root       ,     __snake_case       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  Any       ,     __snake_case	:  Any       ):





											'''simple docstring'''





											print('delete:' + str(__snake_case       )       )
											if self.root is None:
																	print('Tree is empty!'       )
																	return
											_snake_case:   int  					=		del_node(self.root       ,     __snake_case       )




					def __str__(					self	:  Optional[Any]       ,     ):  # a level traversale, gives a more intuitive look on the tree





											'''simple docstring'''





											_snake_case:   List[str]  					=		''
											_snake_case:   Optional[Any]  					=		MyQueue()
											q.push(self.root       )
											_snake_case:   List[Any]  					=		self.get_height()
											if layer == 0:
																	return output
											_snake_case:   Any  					=		0
											while not q.is_empty():
																	_snake_case:   List[Any]  					=		q.pop()
																	_snake_case:   Dict  					=		' ' * int(math.pow(2       ,     layer - 1       )       )
																	output += space
																	if node is None:
																							output += "*"
																							q.push(__snake_case       )
																							q.push(__snake_case       )
																	else:
																							output += str(node.get_data()       )
																							q.push(node.get_left()       )
																							q.push(node.get_right()       )
																	output += space
																	_snake_case:   str  					=		cnt + 1
																	for i in range(1_00       ):
																							if cnt == math.pow(2       ,     __snake_case       ) - 1:
																													_snake_case:   List[Any]  					=		layer - 1
																													if layer == 0:
																																			output += "\n*************************************"
																																			return output
																													output += "\n"
																													break
											output += "\n*************************************"
											return output





def       lowercase_      (		)   ->None:
						import doctest

						doctest.testmod()


if __name__ == "__main__":
		_test()
		A   : Dict             =			AVLtree()
		A   : str             =			list(range(10))
		random.shuffle(lst)
		for i in lst:
				t.insert(i)
				print(str(t))
		random.shuffle(lst)
		for i in lst:
				t.del_node(i)
				print(str(t))












'''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 			lowerCamelCase					(						nn.Module  ):


					def __init__(					self	:  Optional[int]       ):





											'''simple docstring'''





											super().__init__()
											_snake_case:   int  					=		nn.Linear(3       ,     4       )
											_snake_case:   Any  					=		nn.BatchNormad(4       )
											_snake_case:   Union[str, Any]  					=		nn.Linear(4       ,     5       )




					def    SCREAMING_SNAKE_CASE_		(					self	:  Tuple       ,     __snake_case	:  List[Any]       ):





											'''simple docstring'''





											return self.lineara(self.batchnorm(self.lineara(__snake_case       )       )       )







class 			lowerCamelCase					(						__UpperCAmelCase  ):


					def    SCREAMING_SNAKE_CASE_		(					self	:  Tuple       ,     __snake_case	:  Optional[Any]       ,     *__snake_case	:  Optional[Any]       ,     **__snake_case	:  List[Any]       ):





											'''simple docstring'''





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







class 			lowerCamelCase					(						__UpperCAmelCase  ):


					def    SCREAMING_SNAKE_CASE_		(					self	:  Dict       ,     __snake_case	:  Union[str, Any]       ,     __snake_case	:  Tuple       ):





											'''simple docstring'''





											return output + 1







class 			lowerCamelCase					(						unittest.TestCase  ):


					def    SCREAMING_SNAKE_CASE_		(					self	:  Union[str, Any]       ):





											'''simple docstring'''





											_snake_case:   str  					=		ModelForTest()
											_snake_case:   List[str]  					=		ModelHook()

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


					def    SCREAMING_SNAKE_CASE_		(					self	:  List[Any]       ):





											'''simple docstring'''





											_snake_case:   Optional[Any]  					=		ModelForTest()
											_snake_case:   Union[str, Any]  					=		ModelHook()

											add_hook_to_module(__snake_case       ,     __snake_case       )
											add_hook_to_module(__snake_case       ,     __snake_case       ,     append=__snake_case       )

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


					def    SCREAMING_SNAKE_CASE_		(					self	:  Optional[Any]       ):





											'''simple docstring'''





											_snake_case:   Dict  					=		ModelForTest()
											_snake_case:   Tuple  					=		torch.randn(2       ,     3       )
											_snake_case:   List[Any]  					=		test_model(x + 1       )
											_snake_case:   int  					=		test_model(x + 2       )

											_snake_case:   List[str]  					=		PreForwardHook()
											add_hook_to_module(__snake_case       ,     __snake_case       )
											_snake_case:   List[Any]  					=		test_model(__snake_case       )
											self.assertTrue(torch.allclose(__snake_case       ,     __snake_case       ,     atol=1e-5       )       )

											# Attaching a hook to a model when it already has one replaces, does not chain
											_snake_case:   Any  					=		PreForwardHook()
											add_hook_to_module(__snake_case       ,     __snake_case       )
											_snake_case:   Dict  					=		test_model(__snake_case       )
											self.assertTrue(torch.allclose(__snake_case       ,     __snake_case       ,     atol=1e-5       )       )

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

											_snake_case:   int  					=		test_model(__snake_case       )
											assert torch.allclose(__snake_case       ,     __snake_case       ,     atol=1e-5       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  List[str]       ):





											'''simple docstring'''





											_snake_case:   int  					=		ModelForTest()
											_snake_case:   str  					=		torch.randn(2       ,     3       )
											_snake_case:   int  					=		test_model(__snake_case       )

											_snake_case:   Union[str, Any]  					=		PostForwardHook()
											add_hook_to_module(__snake_case       ,     __snake_case       )
											_snake_case:   int  					=		test_model(__snake_case       )
											self.assertTrue(torch.allclose(__snake_case       ,     output + 1       ,     atol=1e-5       )       )

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

											# You need to use the sequential hook to chain two or more hooks
											_snake_case:   List[str]  					=		SequentialHook(PostForwardHook()       ,     PostForwardHook()       )
											add_hook_to_module(__snake_case       ,     __snake_case       )

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


					def    SCREAMING_SNAKE_CASE_		(					self	:  Optional[Any]       ):





											'''simple docstring'''





											_snake_case:   Optional[Any]  					=		ModelForTest()
											_snake_case:   Dict  					=		torch.randn(2       ,     3       )
											_snake_case:   Tuple  					=		test_model(__snake_case       )

											_snake_case:   Tuple  					=		PostForwardHook()
											add_hook_to_module(__snake_case       ,     __snake_case       )
											_snake_case:   List[str]  					=		test_model(__snake_case       )
											self.assertTrue(torch.allclose(__snake_case       ,     output + 1       )       )
											self.assertTrue(outputa.requires_grad       )

											_snake_case:   Any  					=		True
											_snake_case:   str  					=		test_model(__snake_case       )
											self.assertFalse(outputa.requires_grad       )


					@require_multi_gpu
					def    SCREAMING_SNAKE_CASE_		(					self	:  Tuple       ):





											'''simple docstring'''





											_snake_case:   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
											_snake_case:   int  					=		torch.randn(2       ,     3       )
											_snake_case:   Tuple  					=		model(__snake_case       )
											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(__snake_case       ,     AlignDevicesHook(io_same_device=__snake_case       )       )
											_snake_case:   Optional[Any]  					=		torch.randn(2       ,     3       ).to(0       )
											_snake_case:   Any  					=		model(__snake_case       )
											self.assertEqual(output.device       ,     torch.device(0       )       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  Any       ):





											'''simple docstring'''





											_snake_case:   Union[str, 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
											_snake_case:   Optional[int]  					=		{'execution_device': 0 if torch.cuda.is_available() else 'cpu', 'offload': True}

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

											# 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
											_snake_case:   Optional[int]  					=		torch.device(hook_kwargs['execution_device']       )
											self.assertEqual(model.batchnorm.running_mean.device       ,     __snake_case       )

											_snake_case:   Tuple  					=		torch.randn(2       ,     3       )
											_snake_case:   Union[str, Any]  					=		model(__snake_case       )
											self.assertEqual(output.device       ,     __snake_case       )

											# 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
											_snake_case:   Optional[Any]  					=		{
											    'execution_device': 0 if torch.cuda.is_available() else 'cpu',
											    'offload': True,
											    'offload_buffers': True,
											}

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

											# 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'       )       )

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

											# 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    SCREAMING_SNAKE_CASE_		(					self	:  Any       ):





											'''simple docstring'''





											_snake_case:   Union[str, 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
											_snake_case:   Optional[Any]  					=		0 if torch.cuda.is_available() else 'cpu'
											attach_align_device_hook(__snake_case       ,     execution_device=__snake_case       ,     offload=__snake_case       )

											# 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
											_snake_case:   List[str]  					=		torch.device(__snake_case       )
											self.assertEqual(model.batchnorm.running_mean.device       ,     __snake_case       )

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

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

											# 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'       )       )

											_snake_case:   Any  					=		torch.randn(2       ,     3       )
											_snake_case:   List[Any]  					=		model(__snake_case       )
											self.assertEqual(output.device       ,     __snake_case       )

											# Removing hooks loads back the weights in the model.
											remove_hook_from_submodules(__snake_case       )
											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    SCREAMING_SNAKE_CASE_		(					self	:  Any       ):





											'''simple docstring'''





											_snake_case:   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
											_snake_case:   Tuple  					=		0 if torch.cuda.is_available() else 'cpu'
											attach_align_device_hook(
											    __snake_case       ,     execution_device=__snake_case       ,     offload=__snake_case       ,     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
											_snake_case:   str  					=		torch.device(__snake_case       )
											self.assertEqual(model.batchnorm.running_mean.device       ,     __snake_case       )

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

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

											# 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'       )       )

											_snake_case:   List[Any]  					=		torch.randn(2       ,     3       )
											_snake_case:   List[str]  					=		model(__snake_case       )
											self.assertEqual(output.device       ,     __snake_case       )

											# Removing hooks loads back the weights in the model.
											remove_hook_from_submodules(__snake_case       )
											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'       )       )





