



'''simple docstring'''

import inspect
import unittest

import torch
import torch.nn as nn

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

class __snake_case(				nn.Module ):


						'''simple docstring'''



						def __init__( self				)     ->     Any:
							super().__init__()
							lowerCAmelCase   =     nn.Linear(3     ,	4				)
							lowerCAmelCase   =     nn.BatchNormad(4				)
							lowerCAmelCase   =     nn.Linear(4     ,	5				)




						def     __snake_case   ( self     ,	A_				)     ->     Union[str, Any]:
							return self.lineara(self.batchnorm(self.lineara(A_				)				)				)







class __snake_case(				_lowerCAmelCase ):


						'''simple docstring'''



						def     __snake_case   ( self     ,	A_     ,	*A_     ,	**A_				)     ->     Any:
							return (args[0] + 1,) + args[1:], kwargs







class __snake_case(				_lowerCAmelCase ):


						'''simple docstring'''



						def     __snake_case   ( self     ,	A_     ,	A_				)     ->     Tuple:
							return output + 1







class __snake_case(				unittest.TestCase ):


						'''simple docstring'''



						def     __snake_case   ( self				)     ->     Dict:
							lowerCAmelCase   =     ModelForTest()
							lowerCAmelCase   =     ModelHook()

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

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

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


						def     __snake_case   ( self				)     ->     Tuple:
							lowerCAmelCase   =     ModelForTest()
							lowerCAmelCase   =     ModelHook()

							add_hook_to_module(A_     ,	A_				)
							add_hook_to_module(A_     ,	A_     ,	append=A_				)

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

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

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


						def     __snake_case   ( self				)     ->     Optional[Any]:
							lowerCAmelCase   =     ModelForTest()
							lowerCAmelCase   =     torch.randn(2     ,	3				)
							lowerCAmelCase   =     test_model(x + 1				)
							lowerCAmelCase   =     test_model(x + 2				)

							lowerCAmelCase   =     PreForwardHook()
							add_hook_to_module(A_     ,	A_				)
							lowerCAmelCase   =     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   =     PreForwardHook()
							add_hook_to_module(A_     ,	A_				)
							lowerCAmelCase   =     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   =     SequentialHook(PreForwardHook()     ,	PreForwardHook()				)
							add_hook_to_module(A_     ,	A_				)

							lowerCAmelCase   =     test_model(A_				)
							assert torch.allclose(A_     ,	A_     ,	atol=1e-5				)


						def     __snake_case   ( self				)     ->     Optional[Any]:
							lowerCAmelCase   =     ModelForTest()
							lowerCAmelCase   =     torch.randn(2     ,	3				)
							lowerCAmelCase   =     test_model(A_				)

							lowerCAmelCase   =     PostForwardHook()
							add_hook_to_module(A_     ,	A_				)
							lowerCAmelCase   =     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   =     PostForwardHook()
							add_hook_to_module(A_     ,	A_				)
							lowerCAmelCase   =     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   =     SequentialHook(PostForwardHook()     ,	PostForwardHook()				)
							add_hook_to_module(A_     ,	A_				)

							lowerCAmelCase   =     test_model(A_				)
							assert torch.allclose(A_     ,	output + 2     ,	atol=1e-5				)


						def     __snake_case   ( self				)     ->     List[str]:
							lowerCAmelCase   =     ModelForTest()
							lowerCAmelCase   =     torch.randn(2     ,	3				)
							lowerCAmelCase   =     test_model(A_				)

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

							lowerCAmelCase   =     True
							lowerCAmelCase   =     test_model(A_				)
							self.assertFalse(outputa.requires_grad				)


						@require_multi_gpu
						def     __snake_case   ( self				)     ->     Tuple:
							lowerCAmelCase   =     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   =     torch.randn(2     ,	3				)
							lowerCAmelCase   =     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   =     torch.randn(2     ,	3				).to(0				)
							lowerCAmelCase   =     model(A_				)
							self.assertEqual(output.device     ,	torch.device(0				)				)


						def     __snake_case   ( self				)     ->     Union[str, Any]:
							lowerCAmelCase   =     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   =     {"""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   =     torch.device(hook_kwargs["""execution_device"""]				)
							self.assertEqual(model.batchnorm.running_mean.device     ,	A_				)

							lowerCAmelCase   =     torch.randn(2     ,	3				)
							lowerCAmelCase   =     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   =     {
							    """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   =     torch.randn(2     ,	3				)
							lowerCAmelCase   =     model(A_				)
							self.assertEqual(output.device     ,	A_				)

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


						def     __snake_case   ( self				)     ->     int:
							lowerCAmelCase   =     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   =     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   =     torch.device(A_				)
							self.assertEqual(model.batchnorm.running_mean.device     ,	A_				)

							lowerCAmelCase   =     torch.randn(2     ,	3				)
							lowerCAmelCase   =     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   =     torch.randn(2     ,	3				)
							lowerCAmelCase   =     model(A_				)
							self.assertEqual(output.device     ,	A_				)

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




						def     __snake_case   ( self				)     ->     str:
							lowerCAmelCase   =     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   =     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   =     torch.device(A_				)
							self.assertEqual(model.batchnorm.running_mean.device     ,	A_				)

							lowerCAmelCase   =     torch.randn(2     ,	3				)
							lowerCAmelCase   =     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   =     torch.randn(2     ,	3				)
							lowerCAmelCase   =     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'''

class __snake_case(				_lowerCAmelCase ):


						'''simple docstring'''



						pass







class __snake_case(				_lowerCAmelCase ):


						'''simple docstring'''



						pass







class __snake_case:


						'''simple docstring'''



						def __init__( self				)     ->     int:
							lowerCAmelCase   =     [
							    [],
							    [],
							    [],
							]


						def     __snake_case   ( self     ,	A_     ,	A_				)     ->     None:
							try:
								if len(self.queues[priority]				) >= 100:
									raise OverflowError("""Maximum queue size is 100"""				)
								self.queues[priority].append(A_				)
							except IndexError:
								raise ValueError("""Valid priorities are 0, 1, and 2"""				)


						def     __snake_case   ( self				)     ->     int:
							for queue in self.queues:
								if queue:
									return queue.pop(0				)
							raise UnderFlowError("""All queues are empty"""				)




						def __str__( self				)     ->     str:
							return "\n".join(f'Priority {i}: {q}' for i, q in enumerate(self.queues				)				)







class __snake_case:


						'''simple docstring'''



						def __init__( self				)     ->     Dict:
							lowerCAmelCase   =     []


						def     __snake_case   ( self     ,	A_				)     ->     None:
							if len(self.queue				) == 100:
								raise OverFlowError("""Maximum queue size is 100"""				)
							self.queue.append(A_				)


						def     __snake_case   ( self				)     ->     int:
							if not self.queue:
								raise UnderFlowError("""The queue is empty"""				)
							else:
								lowerCAmelCase   =     min(self.queue				)
								self.queue.remove(A_				)
								return data




						def __str__( self				)     ->     str:
							return str(self.queue				)







def 		_snake_case		( )      ->						Tuple:







	"""simple docstring"""
	lowerCAmelCase   =     FixedPriorityQueue()
	fpq.enqueue(0							,		10							)
	fpq.enqueue(1							,		70							)
	fpq.enqueue(0							,		100							)
	fpq.enqueue(2							,		1							)
	fpq.enqueue(2							,		5							)
	fpq.enqueue(1							,		7							)
	fpq.enqueue(2							,		4							)
	fpq.enqueue(1							,		64							)
	fpq.enqueue(0							,		128							)
	print(_SCREAMING_SNAKE_CASE							)
	print(fpq.dequeue()							)
	print(fpq.dequeue()							)
	print(fpq.dequeue()							)
	print(fpq.dequeue()							)
	print(fpq.dequeue()							)
	print(_SCREAMING_SNAKE_CASE							)
	print(fpq.dequeue()							)
	print(fpq.dequeue()							)
	print(fpq.dequeue()							)
	print(fpq.dequeue()							)
	print(fpq.dequeue()							)






def 		_snake_case		( )      ->						Optional[Any]:







	"""simple docstring"""
	lowerCAmelCase   =     ElementPriorityQueue()
	epq.enqueue(10							)
	epq.enqueue(70							)
	epq.enqueue(100							)
	epq.enqueue(1							)
	epq.enqueue(5							)
	epq.enqueue(7							)
	epq.enqueue(4							)
	epq.enqueue(64							)
	epq.enqueue(128							)
	print(_SCREAMING_SNAKE_CASE							)
	print(epq.dequeue()							)
	print(epq.dequeue()							)
	print(epq.dequeue()							)
	print(epq.dequeue()							)
	print(epq.dequeue()							)
	print(_SCREAMING_SNAKE_CASE							)
	print(epq.dequeue()							)
	print(epq.dequeue()							)
	print(epq.dequeue()							)
	print(epq.dequeue()							)
	print(epq.dequeue()							)


if __name__ == "__main__":
				fixed_priority_queue()
				element_priority_queue()