


import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase__							:						List[Any]							     =   logging.get_logger()
@dataclass
class 		__magic_name__						:





     '''simple docstring'''



     __lowercase				:							nn.Module
     __lowercase				:							List[nn.Module]	   =       field(default_factory=snake_case_				)
     __lowercase				:							list	   =       field(default_factory=snake_case_				)

     def 	SCREAMING_SNAKE_CASE__						(					self:Dict     ,		_a:str     ,		_a:Tensor     ,		_a:Tensor   ):
       snake_case__						= len(list(m.modules()   )   ) == 1 or isinstance(_a     ,		nn.Convad   ) or isinstance(_a     ,		nn.BatchNormad   )
       if has_not_submodules:
         self.traced.append(_a   )

     def __call__(					self:str     ,		_a:Tensor   ):
       for m in self.module.modules():
         self.handles.append(m.register_forward_hook(self._forward_hook   )   )
       self.module(_a   )
       [x.remove() for x in self.handles]
       return self







     @property
     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[int]   ):
       # check the len of the state_dict keys to see if we have learnable params
       return list(filter(lambda _a   : len(list(x.state_dict().keys()   )   ) > 0     ,		self.traced   )   )




@dataclass
class 		__magic_name__						:





     '''simple docstring'''



     __lowercase				:							nn.Module
     __lowercase				:							nn.Module
     __lowercase				:							int	   =       0
     __lowercase				:							List	   =       field(default_factory=snake_case_				)
     __lowercase				:							List	   =       field(default_factory=snake_case_				)

     def __call__(					self:Tuple     ,		_a:Tensor   ):
       snake_case__						= Tracker(self.dest   )(_a   ).parametrized
       snake_case__						= Tracker(self.src   )(_a   ).parametrized

       snake_case__						= list(filter(lambda _a   : type(_a   ) not in self.src_skip     ,		_a   )   )
       snake_case__						= list(filter(lambda _a   : type(_a   ) not in self.dest_skip     ,		_a   )   )

       if len(_a   ) != len(_a   ):
         raise Exception(
             F"""Numbers of operations are different. Source module has {len(_a   )} operations while"""
             F""" destination module has {len(_a   )}."""   )

       for dest_m, src_m in zip(_a     ,		_a   ):
         dest_m.load_state_dict(src_m.state_dict()   )
         if self.verbose == 1:
           print(F"""Transfered from={src_m} to={dest_m}"""   )







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase = True				)    ->				int:
  print(F"""Converting {name}..."""				)
  with torch.no_grad():
    snake_case__						= timm.create_model(__lowerCAmelCase							,   pretrained=__lowerCAmelCase				).eval()
    snake_case__						= ResNetForImageClassification(__lowerCAmelCase				).eval()
    snake_case__						= ModuleTransfer(src=__lowerCAmelCase							,   dest=__lowerCAmelCase				)
    snake_case__						= torch.randn((1, 3, 224, 224)				)
    module_transfer(__lowerCAmelCase				)

  assert torch.allclose(from_model(__lowerCAmelCase				)							,   our_model(__lowerCAmelCase				).logits				), "The model logits don't match the original one."

  snake_case__						= F"""resnet{'-'.join(name.split('resnet'				)				)}"""
  print(__lowerCAmelCase				)

  if push_to_hub:
    our_model.push_to_hub(
        repo_path_or_name=save_directory / checkpoint_name							,   commit_message='''Add model'''							,   use_temp_dir=__lowerCAmelCase							,   )

    # we can use the convnext one
    snake_case__						= AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k'''				)
    image_processor.push_to_hub(
        repo_path_or_name=save_directory / checkpoint_name							,   commit_message='''Add image processor'''							,   use_temp_dir=__lowerCAmelCase							,   )

    print(F"""Pushed {checkpoint_name}"""				)







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase							,   __lowerCAmelCase = None							,   __lowerCAmelCase = True				)    ->				List[Any]:
  snake_case__						= '''imagenet-1k-id2label.json'''
  snake_case__						= 1000
  snake_case__						= (1, num_labels)

  snake_case__						= '''huggingface/label-files'''
  snake_case__						= num_labels
  snake_case__						= json.load(open(hf_hub_download(__lowerCAmelCase							,   __lowerCAmelCase							,   repo_type='''dataset'''				)							,   '''r'''				)				)
  snake_case__						= {int(__lowerCAmelCase				): v for k, v in idalabel.items()}

  snake_case__						= idalabel
  snake_case__						= {v: k for k, v in idalabel.items()}

  snake_case__						= partial(__lowerCAmelCase							,   num_labels=__lowerCAmelCase							,   idalabel=__lowerCAmelCase							,   labelaid=__lowerCAmelCase				)

  snake_case__						= {
      '''resnet18''': ImageNetPreTrainedConfig(
          depths=[2, 2, 2, 2]							,   hidden_sizes=[64, 128, 256, 512]							,   layer_type='''basic'''				),
      '''resnet26''': ImageNetPreTrainedConfig(
          depths=[2, 2, 2, 2]							,   hidden_sizes=[256, 512, 1024, 2048]							,   layer_type='''bottleneck'''				),
      '''resnet34''': ImageNetPreTrainedConfig(
          depths=[3, 4, 6, 3]							,   hidden_sizes=[64, 128, 256, 512]							,   layer_type='''basic'''				),
      '''resnet50''': ImageNetPreTrainedConfig(
          depths=[3, 4, 6, 3]							,   hidden_sizes=[256, 512, 1024, 2048]							,   layer_type='''bottleneck'''				),
      '''resnet101''': ImageNetPreTrainedConfig(
          depths=[3, 4, 23, 3]							,   hidden_sizes=[256, 512, 1024, 2048]							,   layer_type='''bottleneck'''				),
      '''resnet152''': ImageNetPreTrainedConfig(
          depths=[3, 8, 36, 3]							,   hidden_sizes=[256, 512, 1024, 2048]							,   layer_type='''bottleneck'''				),
  }

  if model_name:
    convert_weight_and_push(__lowerCAmelCase							,   names_to_config[model_name]							,   __lowerCAmelCase							,   __lowerCAmelCase				)
  else:
    for model_name, config in names_to_config.items():
      convert_weight_and_push(__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase				)
  return config, expected_shape


if __name__ == "__main__":
   lowerCamelCase__							:						List[str]							     =   argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       """--model_name""",
       default=None,
       type=str,
       help=(
           """The name of the model you wish to convert, it must be one of the supported resnet* architecture,"""
           """ currently: resnet18,26,34,50,101,152. If `None`, all of them will the converted."""
       ),
   )
   parser.add_argument(
       """--pytorch_dump_folder_path""",
       default=None,
       type=Path,
       required=True,
       help="""Path to the output PyTorch model directory.""",
   )
   parser.add_argument(
       """--push_to_hub""",
       default=True,
       type=bool,
       required=False,
       help="""If True, push model and image processor to the hub.""",
   )

   lowerCamelCase__							:						Tuple							     =   parser.parse_args()
   lowerCamelCase__							:						Path							     =   args.pytorch_dump_folder_path
   pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
   convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)







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 		__A   (      nn.Module      ):

						def __init__(		self :List[Any]			):
										'''simple docstring'''

										super().__init__()
										__magic_name__						:						Tuple										=nn.Linear(3     ,			4			)
										__magic_name__						:						Union[str, Any]										=nn.BatchNormad(4			)
										__magic_name__						:						List[str]										=nn.Linear(4     ,			5			)


						def      A__    (		self :Dict     ,			__snake_case :Tuple			):
										'''simple docstring'''

										return self.lineara(self.batchnorm(self.lineara(__snake_case			)			)			)




class 		__A   (      UpperCamelCase__      ):

						def      A__    (		self :Any     ,			__snake_case :Optional[Any]     ,			*__snake_case :List[Any]     ,			**__snake_case :Any			):
										'''simple docstring'''

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




class 		__A   (      UpperCamelCase__      ):

						def      A__    (		self :List[str]     ,			__snake_case :Tuple     ,			__snake_case :Union[str, Any]			):
										'''simple docstring'''

										return output + 1




class 		__A   (      unittest.TestCase      ):

						def      A__    (		self :List[str]			):
										'''simple docstring'''

										__magic_name__						:						int										=ModelForTest()
										__magic_name__						:						Tuple										=ModelHook()

										add_hook_to_module(__snake_case     ,			__snake_case			)
										self.assertEqual(test_model._hf_hook     ,			__snake_case			)
										self.assertTrue(hasattr(__snake_case     ,			"""_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(__snake_case			)
										self.assertFalse(hasattr(__snake_case     ,			"""_hf_hook"""			)			)
										self.assertFalse(hasattr(__snake_case     ,			"""_old_forward"""			)			)

						def      A__    (		self :Tuple			):
										'''simple docstring'''

										__magic_name__						:						int										=ModelForTest()
										__magic_name__						:						List[str]										=ModelHook()

										add_hook_to_module(__snake_case     ,			__snake_case			)
										add_hook_to_module(__snake_case     ,			__snake_case     ,			append=__snake_case			)

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

						def      A__    (		self :List[str]			):
										'''simple docstring'''

										__magic_name__						:						Any										=ModelForTest()
										__magic_name__						:						Any										=torch.randn(2     ,			3			)
										__magic_name__						:						Any										=test_model(x + 1			)
										__magic_name__						:						Optional[Any]										=test_model(x + 2			)

										__magic_name__						:						int										=PreForwardHook()
										add_hook_to_module(__snake_case     ,			__snake_case			)
										__magic_name__						:						int										=test_model(__snake_case			)
										self.assertTrue(torch.allclose(__snake_case     ,			__snake_case     ,			atol=1E-5			)			)

										# Attaching a hook to a model when it already has one replaces, does not chain
										__magic_name__						:						str										=PreForwardHook()
										add_hook_to_module(__snake_case     ,			__snake_case			)
										__magic_name__						:						List[str]										=test_model(__snake_case			)
										self.assertTrue(torch.allclose(__snake_case     ,			__snake_case     ,			atol=1E-5			)			)

										# You need to use the sequential hook to chain two or more hooks
										__magic_name__						:						Optional[Any]										=SequentialHook(PreForwardHook()     ,			PreForwardHook()			)
										add_hook_to_module(__snake_case     ,			__snake_case			)

										__magic_name__						:						Any										=test_model(__snake_case			)
										assert torch.allclose(__snake_case     ,			__snake_case     ,			atol=1E-5			)

						def      A__    (		self :Any			):
										'''simple docstring'''

										__magic_name__						:						Optional[Any]										=ModelForTest()
										__magic_name__						:						Dict										=torch.randn(2     ,			3			)
										__magic_name__						:						Any										=test_model(__snake_case			)

										__magic_name__						:						Dict										=PostForwardHook()
										add_hook_to_module(__snake_case     ,			__snake_case			)
										__magic_name__						:						Any										=test_model(__snake_case			)
										self.assertTrue(torch.allclose(__snake_case     ,			output + 1     ,			atol=1E-5			)			)

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

										# You need to use the sequential hook to chain two or more hooks
										__magic_name__						:						Union[str, Any]										=SequentialHook(PostForwardHook()     ,			PostForwardHook()			)
										add_hook_to_module(__snake_case     ,			__snake_case			)

										__magic_name__						:						Union[str, Any]										=test_model(__snake_case			)
										assert torch.allclose(__snake_case     ,			output + 2     ,			atol=1E-5			)

						def      A__    (		self :Tuple			):
										'''simple docstring'''

										__magic_name__						:						Tuple										=ModelForTest()
										__magic_name__						:						int										=torch.randn(2     ,			3			)
										__magic_name__						:						Union[str, Any]										=test_model(__snake_case			)

										__magic_name__						:						Union[str, Any]										=PostForwardHook()
										add_hook_to_module(__snake_case     ,			__snake_case			)
										__magic_name__						:						Dict										=test_model(__snake_case			)
										self.assertTrue(torch.allclose(__snake_case     ,			output + 1			)			)
										self.assertTrue(outputa.requires_grad			)

										__magic_name__						:						Any										=True
										__magic_name__						:						Any										=test_model(__snake_case			)
										self.assertFalse(outputa.requires_grad			)

						@require_multi_gpu
						def      A__    (		self :List[str]			):
										'''simple docstring'''

										__magic_name__						:						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
										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
										__magic_name__						:						List[Any]										=torch.randn(2     ,			3			)
										__magic_name__						:						Optional[Any]										=model(__snake_case			)
										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(__snake_case     ,			AlignDevicesHook(io_same_device=__snake_case			)			)
										__magic_name__						:						int										=torch.randn(2     ,			3			).to(0			)
										__magic_name__						:						Optional[int]										=model(__snake_case			)
										self.assertEqual(output.device     ,			torch.device(0			)			)

						def      A__    (		self :List[Any]			):
										'''simple docstring'''

										__magic_name__						:						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
										__magic_name__						:						int										={"""execution_device""": 0 if torch.cuda.is_available() else """cpu""", """offload""": True}

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

										# 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
										__magic_name__						:						Optional[int]										=torch.device(hook_kwargs["""execution_device"""]			)
										self.assertEqual(model.batchnorm.running_mean.device     ,			__snake_case			)

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

										# 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
										__magic_name__						:						Tuple										={
										    """execution_device""": 0 if torch.cuda.is_available() else """cpu""",
										    """offload""": True,
										    """offload_buffers""": True,
										}

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

										# 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"""			)			)

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

										# 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 :List[Any]			):
										'''simple docstring'''

										__magic_name__						:						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
										__magic_name__						:						str										=0 if torch.cuda.is_available() else """cpu"""
										attach_align_device_hook(__snake_case     ,			execution_device=__snake_case     ,			offload=__snake_case			)

										# 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
										__magic_name__						:						Optional[Any]										=torch.device(__snake_case			)
										self.assertEqual(model.batchnorm.running_mean.device     ,			__snake_case			)

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

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

										# 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"""			)			)

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

										# Removing hooks loads back the weights in the model.
										remove_hook_from_submodules(__snake_case			)
										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 :List[str]			):
										'''simple docstring'''

										__magic_name__						:						Dict										=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
										__magic_name__						:						List[str]										=0 if torch.cuda.is_available() else """cpu"""
										attach_align_device_hook(
										    __snake_case     ,			execution_device=__snake_case     ,			offload=__snake_case     ,			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
										__magic_name__						:						Optional[Any]										=torch.device(__snake_case			)
										self.assertEqual(model.batchnorm.running_mean.device     ,			__snake_case			)

										__magic_name__						:						int										=torch.randn(2     ,			3			)
										__magic_name__						:						Any										=model(__snake_case			)
										self.assertEqual(output.device     ,			__snake_case			)

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

										# 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"""			)			)

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

										# Removing hooks loads back the weights in the model.
										remove_hook_from_submodules(__snake_case			)
										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"""			)			)




