






"""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     UpperCamelCase_ (nn.Module  ):





	def __init__(   self      :				str		)		->    Dict:
				super().__init__()
				UpperCAmelCase_  :				Optional[int]      =      nn.Linear(3			,			4		)
				UpperCAmelCase_  :				List[Any]      =      nn.BatchNormad(4		)
				UpperCAmelCase_  :				Any      =      nn.Linear(4			,			5		)





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







class     UpperCamelCase_ (__A  ):





	def   _SCREAMING_SNAKE_CASE   (   self      :				List[str]			,			lowerCAmelCase_      :				Union[str, Any]			,			*lowerCAmelCase_      :				str			,			**lowerCAmelCase_      :				int		)		->    Optional[int]:
				return (args[0] + 1,) + args[1:], kwargs







class     UpperCamelCase_ (__A  ):





	def   _SCREAMING_SNAKE_CASE   (   self      :				Union[str, Any]			,			lowerCAmelCase_      :				Any			,			lowerCAmelCase_      :				Optional[Any]		)		->    Dict:
				return output + 1







class     UpperCamelCase_ (unittest.TestCase  ):





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

				UpperCAmelCase_  :				List[Any]      =      PreForwardHook()
				add_hook_to_module(lowerCAmelCase_			,			lowerCAmelCase_		)
				UpperCAmelCase_  :				Optional[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
				UpperCAmelCase_  :				Optional[int]      =      PreForwardHook()
				add_hook_to_module(lowerCAmelCase_			,			lowerCAmelCase_		)
				UpperCAmelCase_  :				Any      =      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
				UpperCAmelCase_  :				Tuple      =      SequentialHook(PreForwardHook()			,			PreForwardHook()		)
				add_hook_to_module(lowerCAmelCase_			,			lowerCAmelCase_		)

				UpperCAmelCase_  :				Optional[Any]      =      test_model(lowerCAmelCase_		)
				assert torch.allclose(lowerCAmelCase_			,			lowerCAmelCase_			,			atol=1e-5		)





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

				UpperCAmelCase_  :				Dict      =      PostForwardHook()
				add_hook_to_module(lowerCAmelCase_			,			lowerCAmelCase_		)
				UpperCAmelCase_  :				Union[str, 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
				UpperCAmelCase_  :				Any      =      PostForwardHook()
				add_hook_to_module(lowerCAmelCase_			,			lowerCAmelCase_		)
				UpperCAmelCase_  :				Optional[Any]      =      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
				UpperCAmelCase_  :				Any      =      SequentialHook(PostForwardHook()			,			PostForwardHook()		)
				add_hook_to_module(lowerCAmelCase_			,			lowerCAmelCase_		)

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





	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[Any]		)		->    Dict:
				UpperCAmelCase_  :				Optional[Any]      =      ModelForTest()
				UpperCAmelCase_  :				int      =      torch.randn(2			,			3		)
				UpperCAmelCase_  :				Tuple      =      test_model(lowerCAmelCase_		)

				UpperCAmelCase_  :				str      =      PostForwardHook()
				add_hook_to_module(lowerCAmelCase_			,			lowerCAmelCase_		)
				UpperCAmelCase_  :				Any      =      test_model(lowerCAmelCase_		)
				self.assertTrue(torch.allclose(lowerCAmelCase_			,			output + 1		)		)
				self.assertTrue(outputa.requires_grad		)

				UpperCAmelCase_  :				Dict      =      True
				UpperCAmelCase_  :				Optional[Any]      =      test_model(lowerCAmelCase_		)
				self.assertFalse(outputa.requires_grad		)





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





	def   _SCREAMING_SNAKE_CASE   (   self      :				Dict		)		->    Dict:
				UpperCAmelCase_  :				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
				UpperCAmelCase_  :				List[str]      =      {"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
				UpperCAmelCase_  :				Union[str, Any]      =      torch.device(hook_kwargs["execution_device"]		)
				self.assertEqual(model.batchnorm.running_mean.device			,			lowerCAmelCase_		)

				UpperCAmelCase_  :				Dict      =      torch.randn(2			,			3		)
				UpperCAmelCase_  :				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"		)		)

				# Now test with buffers included in the offload
				UpperCAmelCase_  :				Optional[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"		)		)

				UpperCAmelCase_  :				Any      =      torch.randn(2			,			3		)
				UpperCAmelCase_  :				Union[str, Any]      =      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   _SCREAMING_SNAKE_CASE   (   self      :				Tuple		)		->    Optional[Any]:
				UpperCAmelCase_  :				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
				UpperCAmelCase_  :				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
				UpperCAmelCase_  :				Tuple      =      torch.device(lowerCAmelCase_		)
				self.assertEqual(model.batchnorm.running_mean.device			,			lowerCAmelCase_		)

				UpperCAmelCase_  :				str      =      torch.randn(2			,			3		)
				UpperCAmelCase_  :				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_			,			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"		)		)

				UpperCAmelCase_  :				str      =      torch.randn(2			,			3		)
				UpperCAmelCase_  :				Union[str, 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"		)		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				List[str]		)		->    Tuple:
				UpperCAmelCase_  :				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
				UpperCAmelCase_  :				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
				UpperCAmelCase_  :				int      =      torch.device(lowerCAmelCase_		)
				self.assertEqual(model.batchnorm.running_mean.device			,			lowerCAmelCase_		)

				UpperCAmelCase_  :				Optional[int]      =      torch.randn(2			,			3		)
				UpperCAmelCase_  :				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_			,			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"		)		)

				UpperCAmelCase_  :				List[Any]      =      torch.randn(2			,			3		)
				UpperCAmelCase_  :				Tuple      =      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"""
lowerCamelCase_       			=   {
    '''a''': '''AAAAA''',
    '''b''': '''AAAAB''',
    '''c''': '''AAABA''',
    '''d''': '''AAABB''',
    '''e''': '''AABAA''',
    '''f''': '''AABAB''',
    '''g''': '''AABBA''',
    '''h''': '''AABBB''',
    '''i''': '''ABAAA''',
    '''j''': '''BBBAA''',
    '''k''': '''ABAAB''',
    '''l''': '''ABABA''',
    '''m''': '''ABABB''',
    '''n''': '''ABBAA''',
    '''o''': '''ABBAB''',
    '''p''': '''ABBBA''',
    '''q''': '''ABBBB''',
    '''r''': '''BAAAA''',
    '''s''': '''BAAAB''',
    '''t''': '''BAABA''',
    '''u''': '''BAABB''',
    '''v''': '''BBBAB''',
    '''w''': '''BABAA''',
    '''x''': '''BABAB''',
    '''y''': '''BABBA''',
    '''z''': '''BABBB''',
    ''' ''': ''' ''',
}


lowerCamelCase_       			=   {value: key for key, value in encode_dict.items()}

def        snake_case   (     A__		):
			UpperCAmelCase_  :				Union[str, Any]      =      ""
			for letter in word.lower():
						if letter.isalpha() or letter == " ":
									encoded += encode_dict[letter]
						else:
									raise Exception("encode() accepts only letters of the alphabet and spaces"		)
			return encoded

def        snake_case   (     A__		):
			if set(A__		) - {"A", "B", " "} != set():
						raise Exception("decode() accepts only 'A', 'B' and spaces"		)
			UpperCAmelCase_  :				Dict      =      ""
			for word in coded.split():
						while len(A__		) != 0:
									decoded += decode_dict[word[:5]]
									UpperCAmelCase_  :				str      =      word[5:]
						decoded += " "
			return decoded.strip()


if __name__ == "__main__":
			from doctest import testmod

			testmod()



