






"""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[Any]:
										super().__init__()
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    nn.Linear(3   ,				4							)
										_SCREAMING_SNAKE_CASE   :		Any       		=    nn.BatchNormad(4							)
										_SCREAMING_SNAKE_CASE   :		Any       		=    nn.Linear(4   ,				5							)







						def 			A      (    self   ,				lowerCAmelCase_							)		->							List[Any]:
										return self.lineara(self.batchnorm(self.lineara(lowerCAmelCase_							)							)							)







class _lowerCAmelCase						(    __UpperCAmelCase       ):







						def 			A      (    self   ,				lowerCAmelCase_   ,				*lowerCAmelCase_   ,				**lowerCAmelCase_							)		->							int:
										return (args[0] + 1,) + args[1:], kwargs







class _lowerCAmelCase						(    __UpperCAmelCase       ):







						def 			A      (    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)		->							Optional[Any]:
										return output + 1







class _lowerCAmelCase						(    unittest.TestCase       ):







						def 			A      (    self							)		->							Optional[Any]:
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    ModelForTest()
										_SCREAMING_SNAKE_CASE   :		Any       		=    ModelHook()

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







						def 			A      (    self							)		->							Tuple:
										_SCREAMING_SNAKE_CASE   :		Dict       		=    ModelForTest()
										_SCREAMING_SNAKE_CASE   :		Any       		=    ModelHook()

										add_hook_to_module(lowerCAmelCase_   ,				lowerCAmelCase_							)
										add_hook_to_module(lowerCAmelCase_   ,				lowerCAmelCase_   ,				append=lowerCAmelCase_							)

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







						def 			A      (    self							)		->							Dict:
										_SCREAMING_SNAKE_CASE   :		int       		=    ModelForTest()
										_SCREAMING_SNAKE_CASE   :		int       		=    torch.randn(2   ,				3							)
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    test_model(x + 1							)
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    test_model(x + 2							)

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

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

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

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







						def 			A      (    self							)		->							List[str]:
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    ModelForTest()
										_SCREAMING_SNAKE_CASE   :		int       		=    torch.randn(2   ,				3							)
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    test_model(lowerCAmelCase_							)

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

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

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

										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    test_model(lowerCAmelCase_							)
										assert torch.allclose(lowerCAmelCase_   ,				output + 2   ,				atol=1e-5							)







						def 			A      (    self							)		->							Tuple:
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    ModelForTest()
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    torch.randn(2   ,				3							)
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    test_model(lowerCAmelCase_							)

										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    PostForwardHook()
										add_hook_to_module(lowerCAmelCase_   ,				lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		int       		=    test_model(lowerCAmelCase_							)
										self.assertTrue(torch.allclose(lowerCAmelCase_   ,				output + 1							)							)
										self.assertTrue(outputa.requires_grad							)

										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    True
										_SCREAMING_SNAKE_CASE   :		int       		=    test_model(lowerCAmelCase_							)
										self.assertFalse(outputa.requires_grad							)







						@require_multi_gpu
						def 			A      (    self							)		->							Dict:
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    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
										_SCREAMING_SNAKE_CASE   :		int       		=    torch.randn(2   ,				3							)
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    model(lowerCAmelCase_							)
										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(lowerCAmelCase_   ,				AlignDevicesHook(io_same_device=lowerCAmelCase_							)							)
										_SCREAMING_SNAKE_CASE   :		Dict       		=    torch.randn(2   ,				3							).to(0							)
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    model(lowerCAmelCase_							)
										self.assertEqual(output.device   ,				torch.device(0							)							)







						def 			A      (    self							)		->							Tuple:
										_SCREAMING_SNAKE_CASE   :		List[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
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    {'execution_device': 0 if torch.cuda.is_available() else 'cpu', 'offload': True}

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

										# 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
										_SCREAMING_SNAKE_CASE   :		Any       		=    torch.device(hook_kwargs['execution_device']							)
										self.assertEqual(model.batchnorm.running_mean.device   ,				lowerCAmelCase_							)

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

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

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

										# 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'							)							)

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

										# 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							)		->							str:
										_SCREAMING_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
										_SCREAMING_SNAKE_CASE   :		str       		=    0 if torch.cuda.is_available() else 'cpu'
										attach_align_device_hook(lowerCAmelCase_   ,				execution_device=lowerCAmelCase_   ,				offload=lowerCAmelCase_							)

										# 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
										_SCREAMING_SNAKE_CASE   :		int       		=    torch.device(lowerCAmelCase_							)
										self.assertEqual(model.batchnorm.running_mean.device   ,				lowerCAmelCase_							)

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

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

										# 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'							)							)

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

										# Removing hooks loads back the weights in the model.
										remove_hook_from_submodules(lowerCAmelCase_							)
										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							)		->							int:
										_SCREAMING_SNAKE_CASE   :		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
										_SCREAMING_SNAKE_CASE   :		int       		=    0 if torch.cuda.is_available() else 'cpu'
										attach_align_device_hook(
										    lowerCAmelCase_   ,				execution_device=lowerCAmelCase_   ,				offload=lowerCAmelCase_   ,				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
										_SCREAMING_SNAKE_CASE   :		Dict       		=    torch.device(lowerCAmelCase_							)
										self.assertEqual(model.batchnorm.running_mean.device   ,				lowerCAmelCase_							)

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

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

										# 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'							)							)

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

										# Removing hooks loads back the weights in the model.
										remove_hook_from_submodules(lowerCAmelCase_							)
										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"""
class _lowerCAmelCase						:







						def __init__(    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_=None   ,				lowerCAmelCase_=None							)		->							int:
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    data
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    previous
										_SCREAMING_SNAKE_CASE   :		Any       		=    next_node







						def __str__(    self							)		->							str:
										return F"""{self.data}"""







						def 			A      (    self							)		->							int:
										return self.data







						def 			A      (    self							)		->							Dict:
										return self.next







						def 			A      (    self							)		->							Union[str, Any]:
										return self.previous







class _lowerCAmelCase						:







						def __init__(    self   ,				lowerCAmelCase_							)		->							List[str]:
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    head







						def __iter__(    self							)		->							Any:
										return self







						def 			A      (    self							)		->							Dict:
										if not self.current:
														raise StopIteration
										else:
														_SCREAMING_SNAKE_CASE   :		Any       		=    self.current.get_data()
														_SCREAMING_SNAKE_CASE   :		Dict       		=    self.current.get_next()
														return value







class _lowerCAmelCase						:







						def __init__(    self							)		->							Dict:
										_SCREAMING_SNAKE_CASE   :		Dict       		=    None  # First node in list
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    None  # Last node in list







						def __str__(    self							)		->							Any:
										_SCREAMING_SNAKE_CASE   :		str       		=    self.head
										_SCREAMING_SNAKE_CASE   :		int       		=    []
										while current is not None:
														nodes.append(current.get_data()							)
														_SCREAMING_SNAKE_CASE   :		List[Any]       		=    current.get_next()
										return " ".join(str(lowerCAmelCase_							) for node in nodes							)







						def __contains__(    self   ,				lowerCAmelCase_							)		->							Any:
										_SCREAMING_SNAKE_CASE   :		Any       		=    self.head
										while current:
														if current.get_data() == value:
																		return True
														_SCREAMING_SNAKE_CASE   :		int       		=    current.get_next()
										return False







						def __iter__(    self							)		->							str:
										return LinkedListIterator(self.head							)







						def 			A      (    self							)		->							Dict:
										if self.head:
														return self.head.get_data()
										return None







						def 			A      (    self							)		->							List[Any]:
										if self.tail:
														return self.tail.get_data()
										return None







						def 			A      (    self   ,				lowerCAmelCase_							)		->							None:
										if self.head is None:
														_SCREAMING_SNAKE_CASE   :		List[Any]       		=    node
														_SCREAMING_SNAKE_CASE   :		Dict       		=    node
										else:
														self.insert_before_node(self.head   ,				lowerCAmelCase_							)







						def 			A      (    self   ,				lowerCAmelCase_							)		->							None:
										if self.head is None:
														self.set_head(lowerCAmelCase_							)
										else:
														self.insert_after_node(self.tail   ,				lowerCAmelCase_							)







						def 			A      (    self   ,				lowerCAmelCase_							)		->							None:
										_SCREAMING_SNAKE_CASE   :		Any       		=    Node(lowerCAmelCase_							)
										if self.head is None:
														self.set_head(lowerCAmelCase_							)
										else:
														self.set_tail(lowerCAmelCase_							)







						def 			A      (    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)		->							None:
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    node
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    node.previous

										if node.get_previous() is None:
														_SCREAMING_SNAKE_CASE   :		List[Any]       		=    node_to_insert
										else:
														_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    node_to_insert

										_SCREAMING_SNAKE_CASE   :		Any       		=    node_to_insert







						def 			A      (    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)		->							None:
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    node
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    node.next

										if node.get_next() is None:
														_SCREAMING_SNAKE_CASE   :		Tuple       		=    node_to_insert
										else:
														_SCREAMING_SNAKE_CASE   :		Any       		=    node_to_insert

										_SCREAMING_SNAKE_CASE   :		List[str]       		=    node_to_insert







						def 			A      (    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)		->							None:
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    1
										_SCREAMING_SNAKE_CASE   :		str       		=    Node(lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    self.head
										while node:
														if current_position == position:
																		self.insert_before_node(lowerCAmelCase_   ,				lowerCAmelCase_							)
																		return
														current_position += 1
														_SCREAMING_SNAKE_CASE   :		List[str]       		=    node.next
										self.insert_after_node(self.tail   ,				lowerCAmelCase_							)







						def 			A      (    self   ,				lowerCAmelCase_							)		->							Node:
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    self.head
										while node:
														if node.get_data() == item:
																		return node
														_SCREAMING_SNAKE_CASE   :		Dict       		=    node.get_next()
										raise Exception('Node not found'							)







						def 			A      (    self   ,				lowerCAmelCase_							)		->							int:
										if (node := self.get_node(lowerCAmelCase_							)) is not None:
														if node == self.head:
																		_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    self.head.get_next()

														if node == self.tail:
																		_SCREAMING_SNAKE_CASE   :		int       		=    self.tail.get_previous()

														self.remove_node_pointers(lowerCAmelCase_							)







						@staticmethod
						def 			A      (    lowerCAmelCase_							)		->							None:
										if node.get_next():
														_SCREAMING_SNAKE_CASE   :		List[Any]       		=    node.previous

										if node.get_previous():
														_SCREAMING_SNAKE_CASE   :		str       		=    node.next

										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    None
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    None







						def 			A      (    self							)		->							List[str]:
										return self.head is None







def 				lowercase__		(					):
				pass


if __name__ == "__main__":
			import doctest

			doctest.testmod()

