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    SCREAMING_SNAKE_CASE			(							nn.Module					):





							def __init__(   self		:		Any	):

											'''simple docstring'''
											super().__init__()
											__a       =		nn.Linear(3      ,				4	)
											__a       =		nn.BatchNormad(4	)
											__a       =		nn.Linear(4      ,				5	)







							def 						UpperCamelCase_					(   self		:		Optional[int]      ,				__lowercase		:		List[Any]	):

											'''simple docstring'''
											return self.lineara(self.batchnorm(self.lineara(__lowerCamelCase	)	)	)






class    SCREAMING_SNAKE_CASE			(							_A					):





							def 						UpperCamelCase_					(   self		:		Tuple      ,				__lowercase		:		List[str]      ,				*__lowercase		:		str      ,				**__lowercase		:		Optional[Any]	):

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






class    SCREAMING_SNAKE_CASE			(							_A					):





							def 						UpperCamelCase_					(   self		:		Optional[Any]      ,				__lowercase		:		Any      ,				__lowercase		:		Tuple	):

											'''simple docstring'''
											return output + 1






class    SCREAMING_SNAKE_CASE			(							unittest.TestCase					):





							def 						UpperCamelCase_					(   self		:		List[str]	):

											'''simple docstring'''
											__a       =		ModelForTest()
											__a       =		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 						UpperCamelCase_					(   self		:		Optional[Any]	):

											'''simple docstring'''
											__a       =		ModelForTest()
											__a       =		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 						UpperCamelCase_					(   self		:		int	):

											'''simple docstring'''
											__a       =		ModelForTest()
											__a       =		torch.randn(2      ,				3	)
											__a       =		test_model(x + 1	)
											__a       =		test_model(x + 2	)

											__a       =		PreForwardHook()
											add_hook_to_module(__lowerCamelCase      ,				__lowerCamelCase	)
											__a       =		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
											__a       =		PreForwardHook()
											add_hook_to_module(__lowerCamelCase      ,				__lowerCamelCase	)
											__a       =		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
											__a       =		SequentialHook(PreForwardHook()      ,				PreForwardHook()	)
											add_hook_to_module(__lowerCamelCase      ,				__lowerCamelCase	)

											__a       =		test_model(__lowerCamelCase	)
											assert torch.allclose(__lowerCamelCase      ,				__lowerCamelCase      ,				atol=1E-5	)





							def 						UpperCamelCase_					(   self		:		Dict	):

											'''simple docstring'''
											__a       =		ModelForTest()
											__a       =		torch.randn(2      ,				3	)
											__a       =		test_model(__lowerCamelCase	)

											__a       =		PostForwardHook()
											add_hook_to_module(__lowerCamelCase      ,				__lowerCamelCase	)
											__a       =		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
											__a       =		PostForwardHook()
											add_hook_to_module(__lowerCamelCase      ,				__lowerCamelCase	)
											__a       =		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
											__a       =		SequentialHook(PostForwardHook()      ,				PostForwardHook()	)
											add_hook_to_module(__lowerCamelCase      ,				__lowerCamelCase	)

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





							def 						UpperCamelCase_					(   self		:		Tuple	):

											'''simple docstring'''
											__a       =		ModelForTest()
											__a       =		torch.randn(2      ,				3	)
											__a       =		test_model(__lowerCamelCase	)

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

											__a       =		True
											__a       =		test_model(__lowerCamelCase	)
											self.assertFalse(outputa.requires_grad	)





							@require_multi_gpu
							def 						UpperCamelCase_					(   self		:		List[str]	):

											'''simple docstring'''
											__a       =		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
											__a       =		torch.randn(2      ,				3	)
											__a       =		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	)	)
											__a       =		torch.randn(2      ,				3	).to(0	)
											__a       =		model(__lowerCamelCase	)
											self.assertEqual(output.device      ,				torch.device(0	)	)





							def 						UpperCamelCase_					(   self		:		List[Any]	):

											'''simple docstring'''
											__a       =		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
											__a       =		{"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
											__a       =		torch.device(hook_kwargs["""execution_device"""]	)
											self.assertEqual(model.batchnorm.running_mean.device      ,				__lowerCamelCase	)

											__a       =		torch.randn(2      ,				3	)
											__a       =		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
											__a       =		{
											    "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"""	)	)

											__a       =		torch.randn(2      ,				3	)
											__a       =		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 						UpperCamelCase_					(   self		:		Union[str, Any]	):

											'''simple docstring'''
											__a       =		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
											__a       =		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
											__a       =		torch.device(__lowerCamelCase	)
											self.assertEqual(model.batchnorm.running_mean.device      ,				__lowerCamelCase	)

											__a       =		torch.randn(2      ,				3	)
											__a       =		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"""	)	)

											__a       =		torch.randn(2      ,				3	)
											__a       =		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 						UpperCamelCase_					(   self		:		List[Any]	):

											'''simple docstring'''
											__a       =		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
											__a       =		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
											__a       =		torch.device(__lowerCamelCase	)
											self.assertEqual(model.batchnorm.running_mean.device      ,				__lowerCamelCase	)

											__a       =		torch.randn(2      ,				3	)
											__a       =		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"""	)	)

											__a       =		torch.randn(2      ,				3	)
											__a       =		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   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			Tuple				,     _SCREAMING_SNAKE_CASE       :			Tuple				,     _SCREAMING_SNAKE_CASE       :			Tuple=False    ):
				"""simple docstring"""







				if isinstance(_SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE    ) and isinstance(_SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE    ):
								__a       =		len(set_a.intersection(_SCREAMING_SNAKE_CASE    )    )

								if alternative_union:
												__a       =		len(_SCREAMING_SNAKE_CASE    ) + len(_SCREAMING_SNAKE_CASE    )
								else:
												__a       =		len(set_a.union(_SCREAMING_SNAKE_CASE    )    )

								return intersection / union

				if isinstance(_SCREAMING_SNAKE_CASE				,     (list, tuple)    ) and isinstance(_SCREAMING_SNAKE_CASE				,     (list, tuple)    ):
								__a       =		[element for element in set_a if element in set_b]

								if alternative_union:
												__a       =		len(_SCREAMING_SNAKE_CASE    ) + len(_SCREAMING_SNAKE_CASE    )
												return len(_SCREAMING_SNAKE_CASE    ) / union
								else:
												__a       =		set_a + [element for element in set_b if element not in set_a]
												return len(_SCREAMING_SNAKE_CASE    ) / len(_SCREAMING_SNAKE_CASE    )

								return len(_SCREAMING_SNAKE_CASE    ) / len(_SCREAMING_SNAKE_CASE    )
				return None


if __name__ == "__main__":
						lowerCamelCase__			= {"""a""", """b""", """c""", """d""", """e"""}
						lowerCamelCase__			= {"""c""", """d""", """e""", """f""", """h""", """i"""}
						print(jaccard_similarity(set_a, set_b))
