





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):







		def __init__(		self   )				->   Union[str, Any]:
				super().__init__()
				lowerCamelCase   : Union[str, Any]								=nn.Linear(3			,     4   )
				lowerCamelCase   : str								=nn.BatchNormad(4   )
				lowerCamelCase   : str								=nn.Linear(4			,     5   )







		def 			__lowercase   (		self			,     __lowercase   )				->   Any:
				return self.lineara(self.batchnorm(self.lineara(__a   )   )   )

class        snake_case_		(			UpperCamelCase_):







		def 			__lowercase   (		self			,     __lowercase			,     *__lowercase			,     **__lowercase   )				->   Tuple:
				return (args[0] + 1,) + args[1:], kwargs

class        snake_case_		(			UpperCamelCase_):







		def 			__lowercase   (		self			,     __lowercase			,     __lowercase   )				->   int:
				return output + 1

class        snake_case_		(			unittest.TestCase):







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

				lowerCamelCase   : Optional[Any]								=PreForwardHook()
				add_hook_to_module(__a			,     __a   )
				lowerCamelCase   : int								=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   : Dict								=PreForwardHook()
				add_hook_to_module(__a			,     __a   )
				lowerCamelCase   : str								=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   : Dict								=SequentialHook(PreForwardHook()			,     PreForwardHook()   )
				add_hook_to_module(__a			,     __a   )

				lowerCamelCase   : Tuple								=test_model(__a   )
				assert torch.allclose(__a			,     __a			,     atol=1e-5   )







		def 			__lowercase   (		self   )				->   int:
				lowerCamelCase   : Optional[Any]								=ModelForTest()
				lowerCamelCase   : Optional[int]								=torch.randn(2			,     3   )
				lowerCamelCase   : int								=test_model(__a   )

				lowerCamelCase   : Any								=PostForwardHook()
				add_hook_to_module(__a			,     __a   )
				lowerCamelCase   : List[str]								=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   : Tuple								=PostForwardHook()
				add_hook_to_module(__a			,     __a   )
				lowerCamelCase   : Optional[int]								=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   : List[Any]								=SequentialHook(PostForwardHook()			,     PostForwardHook()   )
				add_hook_to_module(__a			,     __a   )

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







		def 			__lowercase   (		self   )				->   List[Any]:
				lowerCamelCase   : List[Any]								=ModelForTest()
				lowerCamelCase   : Dict								=torch.randn(2			,     3   )
				lowerCamelCase   : List[str]								=test_model(__a   )

				lowerCamelCase   : List[str]								=PostForwardHook()
				add_hook_to_module(__a			,     __a   )
				lowerCamelCase   : int								=test_model(__a   )
				self.assertTrue(torch.allclose(__a			,     output + 1   )   )
				self.assertTrue(outputa.requires_grad   )

				lowerCamelCase   : List[str]								=True
				lowerCamelCase   : Any								=test_model(__a   )
				self.assertFalse(outputa.requires_grad   )







		@require_multi_gpu
		def 			__lowercase   (		self   )				->   Union[str, Any]:
				lowerCamelCase   : 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
				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   : Union[str, Any]								=torch.randn(2			,     3   )
				lowerCamelCase   : str								=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   : List[Any]								=torch.randn(2			,     3   ).to(0   )
				lowerCamelCase   : str								=model(__a   )
				self.assertEqual(output.device			,     torch.device(0   )   )







		def 			__lowercase   (		self   )				->   int:
				lowerCamelCase   : Optional[Any]								=ModelForTest()

				# Everything is on CPU
				self.assertEqual(model.lineara.weight.device			,     torch.device('''cpu'''   )   )
				self.assertEqual(model.batchnorm.weight.device			,     torch.device('''cpu'''   )   )
				self.assertEqual(model.lineara.weight.device			,     torch.device('''cpu'''   )   )

				# This will move each submodule on different devices
				lowerCamelCase   : str								={'execution_device': 0 if torch.cuda.is_available() else 'cpu', 'offload': True}

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

				# Parameters have been offloaded, so on the meta device
				self.assertEqual(model.lineara.weight.device			,     torch.device('''meta'''   )   )
				self.assertEqual(model.batchnorm.weight.device			,     torch.device('''meta'''   )   )
				self.assertEqual(model.lineara.weight.device			,     torch.device('''meta'''   )   )
				# Buffers are not included in the offload by default, so are on the execution device
				lowerCamelCase   : Union[str, Any]								=torch.device(hook_kwargs['''execution_device''']   )
				self.assertEqual(model.batchnorm.running_mean.device			,     __a   )

				lowerCamelCase   : List[str]								=torch.randn(2			,     3   )
				lowerCamelCase   : int								=model(__a   )
				self.assertEqual(output.device			,     __a   )

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

				# Now test with buffers included in the offload
				lowerCamelCase   : Optional[Any]								={
				    '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   : Dict								=torch.randn(2			,     3   )
				lowerCamelCase   : Optional[Any]								=model(__a   )
				self.assertEqual(output.device			,     __a   )

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







		def 			__lowercase   (		self   )				->   Union[str, Any]:
				lowerCamelCase   : List[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
				lowerCamelCase   : Any								=0 if torch.cuda.is_available() else 'cpu'
				attach_align_device_hook(__a			,     execution_device=__a			,     offload=__a   )

				# Parameters have been offloaded, so on the meta device
				self.assertEqual(model.lineara.weight.device			,     torch.device('''meta'''   )   )
				self.assertEqual(model.batchnorm.weight.device			,     torch.device('''meta'''   )   )
				self.assertEqual(model.lineara.weight.device			,     torch.device('''meta'''   )   )
				# Buffers are not included in the offload by default, so are on the execution device
				lowerCamelCase   : Dict								=torch.device(__a   )
				self.assertEqual(model.batchnorm.running_mean.device			,     __a   )

				lowerCamelCase   : Dict								=torch.randn(2			,     3   )
				lowerCamelCase   : List[Any]								=model(__a   )
				self.assertEqual(output.device			,     __a   )

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

				# Now test with buffers included in the offload
				attach_align_device_hook(__a			,     execution_device=__a			,     offload=__a			,     offload_buffers=__a   )

				# Parameters have been offloaded, so on the meta device, buffers included
				self.assertEqual(model.lineara.weight.device			,     torch.device('''meta'''   )   )
				self.assertEqual(model.batchnorm.weight.device			,     torch.device('''meta'''   )   )
				self.assertEqual(model.lineara.weight.device			,     torch.device('''meta'''   )   )
				self.assertEqual(model.batchnorm.running_mean.device			,     torch.device('''meta'''   )   )

				lowerCamelCase   : List[str]								=torch.randn(2			,     3   )
				lowerCamelCase   : int								=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 			__lowercase   (		self   )				->   Optional[int]:
				lowerCamelCase   : Any								=ModelForTest()

				# Everything is on CPU
				self.assertEqual(model.lineara.weight.device			,     torch.device('''cpu'''   )   )
				self.assertEqual(model.batchnorm.weight.device			,     torch.device('''cpu'''   )   )
				self.assertEqual(model.lineara.weight.device			,     torch.device('''cpu'''   )   )

				# This will move each submodule on different devices
				lowerCamelCase   : str								=0 if torch.cuda.is_available() else 'cpu'
				attach_align_device_hook(
				    __a			,     execution_device=__a			,     offload=__a			,     weights_map=model.state_dict()   )

				# Parameters have been offloaded, so on the meta device
				self.assertEqual(model.lineara.weight.device			,     torch.device('''meta'''   )   )
				self.assertEqual(model.batchnorm.weight.device			,     torch.device('''meta'''   )   )
				self.assertEqual(model.lineara.weight.device			,     torch.device('''meta'''   )   )
				# Buffers are not included in the offload by default, so are on the execution device
				lowerCamelCase   : Union[str, Any]								=torch.device(__a   )
				self.assertEqual(model.batchnorm.running_mean.device			,     __a   )

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

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

				# Now test with buffers included in the offload
				attach_align_device_hook(
				    __a			,     execution_device=__a			,     offload=__a			,     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   : int								=torch.randn(2			,     3   )
				lowerCamelCase   : Tuple								=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'''   )   )








from __future__ import annotations


def 					A__	(       SCREAMING_SNAKE_CASE_		)   ->     bool:
		if len(SCREAMING_SNAKE_CASE_		) < 2:
				raise ValueError('''Monogons and Digons are not polygons in the Euclidean space'''		)
		if any(i <= 0 for i in nums		):
				raise ValueError('''All values must be greater than 0'''		)
		lowerCamelCase   : List[Any]								=nums.copy()
		copy_nums.sort()
		return copy_nums[-1] < sum(copy_nums[:-1]		)


if __name__ == "__main__":
				import doctest

				doctest.testmod()

