






'''simple docstring'''



import inspect
import unittest

import numpy as np

from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
 import jax

 from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel
class 							__SCREAMING_SNAKE_CASE       (							unittest.TestCase     ):




      def __init__(  self ,   lowerCamelCase ,   lowerCamelCase=13 ,   lowerCamelCase=30 ,   lowerCamelCase=2 ,   lowerCamelCase=3 ,   lowerCamelCase=True ,   lowerCamelCase=True ,   lowerCamelCase=32 ,   lowerCamelCase=5 ,   lowerCamelCase=4 ,   lowerCamelCase=37 ,   lowerCamelCase="gelu" ,   lowerCamelCase=0.1 ,   lowerCamelCase=0.1 ,   lowerCamelCase=10 ,   lowerCamelCase=0.02 ,   )							->Dict:



         '''simple docstring'''
         __a				     =    parent
         __a				     =    batch_size
         __a				     =    image_size
         __a				     =    patch_size
         __a				     =    num_channels
         __a				     =    is_training
         __a				     =    use_labels
         __a				     =    hidden_size
         __a				     =    num_hidden_layers
         __a				     =    num_attention_heads
         __a				     =    intermediate_size
         __a				     =    hidden_act
         __a				     =    hidden_dropout_prob
         __a				     =    attention_probs_dropout_prob
         __a				     =    type_sequence_label_size
         __a				     =    initializer_range

         # in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
         __a				     =    (image_size // patch_size) ** 2
         __a				     =    num_patches + 1




      def  __UpperCamelCase							(  self      )							->Optional[Any]:



         '''simple docstring'''
         __a				     =    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

         __a				     =    ViTConfig(
             image_size=self.image_size ,   patch_size=self.patch_size ,   num_channels=self.num_channels ,   hidden_size=self.hidden_size ,   num_hidden_layers=self.num_hidden_layers ,   num_attention_heads=self.num_attention_heads ,   intermediate_size=self.intermediate_size ,   hidden_act=self.hidden_act ,   hidden_dropout_prob=self.hidden_dropout_prob ,   attention_probs_dropout_prob=self.attention_probs_dropout_prob ,   is_decoder=lowerCamelCase ,   initializer_range=self.initializer_range ,   )

         return config, pixel_values




      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase      )							->Any:



         '''simple docstring'''
         __a				     =    FlaxViTModel(config=lowerCamelCase      )
         __a				     =    model(lowerCamelCase      )
         # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
         __a				     =    (self.image_size, self.image_size)
         __a				     =    (self.patch_size, self.patch_size)
         __a				     =    (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
         self.parent.assertEqual(result.last_hidden_state.shape ,   (self.batch_size, num_patches + 1, self.hidden_size)      )




      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase      )							->str:



         '''simple docstring'''
         __a				     =    self.type_sequence_label_size
         __a				     =    FlaxViTForImageClassification(config=lowerCamelCase      )
         __a				     =    model(lowerCamelCase      )
         self.parent.assertEqual(result.logits.shape ,   (self.batch_size, self.type_sequence_label_size)      )

         # test greyscale images
         __a				     =    1
         __a				     =    FlaxViTForImageClassification(lowerCamelCase      )

         __a				     =    floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
         __a				     =    model(lowerCamelCase      )






      def  __UpperCamelCase							(  self      )							->List[str]:



         '''simple docstring'''
         __a				     =    self.prepare_config_and_inputs()
         (
             (
             __a
         )     ,			(
             __a
         )     ,			
         )				     =    config_and_inputs
         __a				     =    {'pixel_values': pixel_values}
         return config, inputs_dict







@require_flax
class 							__SCREAMING_SNAKE_CASE       (							_lowerCAmelCase      ,      unittest.TestCase     ):
      __a  					=(FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()




      def  __UpperCamelCase							(  self      )							->None:



         '''simple docstring'''
         __a				     =    FlaxViTModelTester(self      )
         __a				     =    ConfigTester(self ,   config_class=lowerCamelCase ,   has_text_modality=lowerCamelCase ,   hidden_size=37      )




      def  __UpperCamelCase							(  self      )							->Dict:



         '''simple docstring'''
         self.config_tester.run_common_tests()




      def  __UpperCamelCase							(  self      )							->Optional[int]:



         '''simple docstring'''
         __a				     =    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*lowerCamelCase      )




      def  __UpperCamelCase							(  self      )							->Optional[int]:



         '''simple docstring'''
         __a				     =    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*lowerCamelCase      )




      def  __UpperCamelCase							(  self      )							->Union[str, Any]:



         '''simple docstring'''
         __a     ,			__a				     =    self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
            __a				     =    model_class(lowerCamelCase      )
            __a				     =    inspect.signature(model.__call__      )
            # signature.parameters is an OrderedDict => so arg_names order is deterministic
            __a				     =    [*signature.parameters.keys()]

            __a				     =    ['pixel_values']
            self.assertListEqual(arg_names[:1] ,   lowerCamelCase      )




      def  __UpperCamelCase							(  self      )							->List[str]:



         '''simple docstring'''
         __a     ,			__a				     =    self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
            with self.subTest(model_class.__name__      ):
               __a				     =    self._prepare_for_class(lowerCamelCase ,   lowerCamelCase      )
               __a				     =    model_class(lowerCamelCase      )

               @jax.jit
               def model_jitted(lowerCamelCase ,   **lowerCamelCase      ):
                  return model(pixel_values=lowerCamelCase ,   **lowerCamelCase      )

               with self.subTest('JIT Enabled'      ):
                  __a				     =    model_jitted(**lowerCamelCase      ).to_tuple()

               with self.subTest('JIT Disabled'      ):
                  with jax.disable_jit():
                     __a				     =    model_jitted(**lowerCamelCase      ).to_tuple()

               self.assertEqual(len(lowerCamelCase      ) ,   len(lowerCamelCase      )      )
               for jitted_output, output in zip(lowerCamelCase ,   lowerCamelCase      ):
                  self.assertEqual(jitted_output.shape ,   output.shape      )






      @slow
      def  __UpperCamelCase							(  self      )							->List[Any]:



         '''simple docstring'''
         for model_class_name in self.all_model_classes:
            __a				     =    model_class_name.from_pretrained('google/vit-base-patch16-224'      )
            __a				     =    model(np.ones((1, 3, 224, 224)      )      )
            self.assertIsNotNone(lowerCamelCase      )







'''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 							__SCREAMING_SNAKE_CASE       (							nn.Module     ):




      def __init__(  self      )							->Dict:



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






      def  __UpperCamelCase							(  self ,   lowerCamelCase      )							->str:



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







class 							__SCREAMING_SNAKE_CASE       (							_lowerCAmelCase     ):




      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   *lowerCamelCase ,   **lowerCamelCase      )							->str:



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







class 							__SCREAMING_SNAKE_CASE       (							_lowerCAmelCase     ):




      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase      )							->int:



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







class 							__SCREAMING_SNAKE_CASE       (							unittest.TestCase     ):




      def  __UpperCamelCase							(  self      )							->Union[str, Any]:



         '''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      )							->Tuple:



         '''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      )							->Tuple:



         '''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      )							->str:



         '''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      )							->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
         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      )							->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      )							->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      )

         # 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      )							->int:



         '''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'      )      )