




import comet  # From: unbabel-comet
import torch

import datasets


UpperCamelCase								=					datasets.logging.get_logger(__name__)

UpperCamelCase								=					'\\n@inproceedings{rei-EtAl:2020:WMT,\n   author    = {Rei, Ricardo  and  Stewart, Craig  and  Farinha, Ana C  and  Lavie, Alon},\n   title     = {Unbabel\'s Participation in the WMT20 Metrics Shared Task},\n   booktitle      = {Proceedings of the Fifth Conference on Machine Translation},\n   month          = {November},\n   year           = {2020},\n   address        = {Online},\n   publisher      = {Association for Computational Linguistics},\n   pages     = {909--918},\n}\n@inproceedings{rei-etal-2020-comet,\n   title = "{COMET}: A Neural Framework for {MT} Evaluation",\n   author = "Rei, Ricardo  and\n      Stewart, Craig  and\n      Farinha, Ana C  and\n      Lavie, Alon",\n   booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",\n   month = nov,\n   year = "2020",\n   address = "Online",\n   publisher = "Association for Computational Linguistics",\n   url = "https://www.aclweb.org/anthology/2020.emnlp-main.213",\n   pages = "2685--2702",\n}\n'

UpperCamelCase								=					'\\nCrosslingual Optimized Metric for Evaluation of Translation (COMET) is an open-source framework used to train Machine Translation metrics that achieve high levels of correlation with different types of human judgments (HTER, DA\'s or MQM).\nWith the release of the framework the authors also released fully trained models that were used to compete in the WMT20 Metrics Shared Task achieving SOTA in that years competition.\n\nSee the [README.md] file at https://unbabel.github.io/COMET/html/models.html for more information.\n'

UpperCamelCase								=					'\nCOMET score.\n\nArgs:\n\n`sources` (list of str): Source sentences\n`predictions` (list of str): candidate translations\n`references` (list of str): reference translations\n`cuda` (bool): If set to True, runs COMET using GPU\n`show_progress` (bool): Shows progress\n`model`: COMET model to be used. Will default to `wmt-large-da-estimator-1719` if None.\n\nReturns:\n    `samples`: List of dictionaries with `src`, `mt`, `ref` and `score`.\n    `scores`: List of scores.\n\nExamples:\n\n    >>> comet_metric = datasets.load_metric(\'comet\')\n    >>> # comet_metric = load_metric(\'comet\', \'wmt20-comet-da\')  # you can also choose which model to use\n    >>> source = ["Dem Feuer konnte Einhalt geboten werden", "Schulen und Kindergärten wurden eröffnet."]\n    >>> hypothesis = ["The fire could be stopped", "Schools and kindergartens were open"]\n    >>> reference = ["They were able to control the fire.", "Schools and kindergartens opened"]\n    >>> results = comet_metric.compute(predictions=hypothesis, references=reference, sources=source)\n    >>> print([round(v, 2) for v in results["scores"]])\n    [0.19, 0.92]\n'





@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION     ,    _KWARGS_DESCRIPTION   )
class 			__lowerCamelCase	(   datasets.Metric   ):




    """simple docstring"""

    def        a				(	self					:					str       )				->	str:
         return datasets.MetricInfo(
             description=_DESCRIPTION       ,     citation=_CITATION       ,     homepage="https://unbabel.github.io/COMET/html/index.html"       ,     inputs_description=_KWARGS_DESCRIPTION       ,     features=datasets.Features(
                 {
                     "sources": datasets.Value("string"       ,     id="sequence"       ),
                     "predictions": datasets.Value("string"       ,     id="sequence"       ),
                     "references": datasets.Value("string"       ,     id="sequence"       ),
                 }       )       ,     codebase_urls=["https://github.com/Unbabel/COMET"]       ,     reference_urls=[
                 "https://github.com/Unbabel/COMET",
                 "https://www.aclweb.org/anthology/2020.emnlp-main.213/",
                 "http://www.statmt.org/wmt20/pdf/2020.wmt-1.101.pdf6",
             ]       ,     )


    def        a				(	self					:					Any       ,     SCREAMING_SNAKE_CASE__					:					Dict       )				->	int:
         if self.config_name == "default":
              lowerCAmelCase__          =			comet.load_from_checkpoint(comet.download_model("wmt20-comet-da"       )       )
         else:
              lowerCAmelCase__          =			comet.load_from_checkpoint(comet.download_model(self.config_name       )       )


    def        a				(	self					:					str       ,     SCREAMING_SNAKE_CASE__					:					Union[str, Any]       ,     SCREAMING_SNAKE_CASE__					:					str       ,     SCREAMING_SNAKE_CASE__					:					Dict       ,     SCREAMING_SNAKE_CASE__					:					List[str]=None       ,     SCREAMING_SNAKE_CASE__					:					int=False       )				->	Optional[Any]:
         if gpus is None:
              lowerCAmelCase__          =			1 if torch.cuda.is_available() else 0
         lowerCAmelCase__          =			{"src": sources, "mt": predictions, "ref": references}
         lowerCAmelCase__          =			[dict(zip(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       )       ) for t in zip(*data.values()       )]
         lowerCAmelCase__     ,		lowerCAmelCase__          =			self.scorer.predict(SCREAMING_SNAKE_CASE__       ,     gpus=SCREAMING_SNAKE_CASE__       ,     progress_bar=SCREAMING_SNAKE_CASE__       )
         return {"mean_score": mean_score, "scores": scores}






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 			__lowerCamelCase	(   nn.Module   ):




    """simple docstring"""

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


    def        a				(	self					:					Union[str, Any]       ,     SCREAMING_SNAKE_CASE__					:					int       )				->	List[Any]:
         return self.lineara(self.batchnorm(self.lineara(SCREAMING_SNAKE_CASE__       )       )       )






class 			__lowerCamelCase	(   UpperCamelCase__   ):




    """simple docstring"""

    def        a				(	self					:					Any       ,     SCREAMING_SNAKE_CASE__					:					str       ,     *SCREAMING_SNAKE_CASE__					:					Tuple       ,     **SCREAMING_SNAKE_CASE__					:					Any       )				->	Union[str, Any]:
         return (args[0] + 1,) + args[1:], kwargs






class 			__lowerCamelCase	(   UpperCamelCase__   ):




    """simple docstring"""

    def        a				(	self					:					List[Any]       ,     SCREAMING_SNAKE_CASE__					:					Tuple       ,     SCREAMING_SNAKE_CASE__					:					str       )				->	Dict:
         return output + 1






class 			__lowerCamelCase	(   unittest.TestCase   ):




    """simple docstring"""

    def        a				(	self					:					List[str]       )				->	Tuple:
         lowerCAmelCase__          =			ModelForTest()
         lowerCAmelCase__          =			ModelHook()

         add_hook_to_module(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       )
         self.assertEqual(test_model._hf_hook       ,     SCREAMING_SNAKE_CASE__       )
         self.assertTrue(hasattr(SCREAMING_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(SCREAMING_SNAKE_CASE__       )
         self.assertFalse(hasattr(SCREAMING_SNAKE_CASE__       ,     "_hf_hook"       )       )
         self.assertFalse(hasattr(SCREAMING_SNAKE_CASE__       ,     "_old_forward"       )       )


    def        a				(	self					:					Union[str, Any]       )				->	int:
         lowerCAmelCase__          =			ModelForTest()
         lowerCAmelCase__          =			ModelHook()

         add_hook_to_module(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       )
         add_hook_to_module(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     append=SCREAMING_SNAKE_CASE__       )

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


    def        a				(	self					:					List[str]       )				->	Any:
         lowerCAmelCase__          =			ModelForTest()
         lowerCAmelCase__          =			torch.randn(2       ,     3       )
         lowerCAmelCase__          =			test_model(x + 1       )
         lowerCAmelCase__          =			test_model(x + 2       )

         lowerCAmelCase__          =			PreForwardHook()
         add_hook_to_module(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			test_model(SCREAMING_SNAKE_CASE__       )
         self.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     atol=1e-5       )       )

         # Attaching a hook to a model when it already has one replaces, does not chain
         lowerCAmelCase__          =			PreForwardHook()
         add_hook_to_module(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			test_model(SCREAMING_SNAKE_CASE__       )
         self.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     atol=1e-5       )       )

         # You need to use the sequential hook to chain two or more hooks
         lowerCAmelCase__          =			SequentialHook(PreForwardHook()       ,     PreForwardHook()       )
         add_hook_to_module(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       )

         lowerCAmelCase__          =			test_model(SCREAMING_SNAKE_CASE__       )
         assert torch.allclose(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     atol=1e-5       )


    def        a				(	self					:					Any       )				->	Union[str, Any]:
         lowerCAmelCase__          =			ModelForTest()
         lowerCAmelCase__          =			torch.randn(2       ,     3       )
         lowerCAmelCase__          =			test_model(SCREAMING_SNAKE_CASE__       )

         lowerCAmelCase__          =			PostForwardHook()
         add_hook_to_module(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			test_model(SCREAMING_SNAKE_CASE__       )
         self.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE__       ,     output + 1       ,     atol=1e-5       )       )

         # Attaching a hook to a model when it already has one replaces, does not chain
         lowerCAmelCase__          =			PostForwardHook()
         add_hook_to_module(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			test_model(SCREAMING_SNAKE_CASE__       )
         self.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE__       ,     output + 1       ,     atol=1e-5       )       )

         # You need to use the sequential hook to chain two or more hooks
         lowerCAmelCase__          =			SequentialHook(PostForwardHook()       ,     PostForwardHook()       )
         add_hook_to_module(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       )

         lowerCAmelCase__          =			test_model(SCREAMING_SNAKE_CASE__       )
         assert torch.allclose(SCREAMING_SNAKE_CASE__       ,     output + 2       ,     atol=1e-5       )


    def        a				(	self					:					Optional[int]       )				->	int:
         lowerCAmelCase__          =			ModelForTest()
         lowerCAmelCase__          =			torch.randn(2       ,     3       )
         lowerCAmelCase__          =			test_model(SCREAMING_SNAKE_CASE__       )

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

         lowerCAmelCase__          =			True
         lowerCAmelCase__          =			test_model(SCREAMING_SNAKE_CASE__       )
         self.assertFalse(outputa.requires_grad       )


    @require_multi_gpu
    def        a				(	self					:					Optional[Any]       )				->	List[str]:
         lowerCAmelCase__          =			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__          =			torch.randn(2       ,     3       )
         lowerCAmelCase__          =			model(SCREAMING_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(SCREAMING_SNAKE_CASE__       ,     AlignDevicesHook(io_same_device=SCREAMING_SNAKE_CASE__       )       )
         lowerCAmelCase__          =			torch.randn(2       ,     3       ).to(0       )
         lowerCAmelCase__          =			model(SCREAMING_SNAKE_CASE__       )
         self.assertEqual(output.device       ,     torch.device(0       )       )


    def        a				(	self					:					List[str]       )				->	List[str]:
         lowerCAmelCase__          =			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__          =			{"execution_device": 0 if torch.cuda.is_available() else "cpu", "offload": True}

         add_hook_to_module(model.lineara       ,     AlignDevicesHook(**SCREAMING_SNAKE_CASE__       )       )
         add_hook_to_module(model.batchnorm       ,     AlignDevicesHook(**SCREAMING_SNAKE_CASE__       )       )
         add_hook_to_module(model.lineara       ,     AlignDevicesHook(**SCREAMING_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
         lowerCAmelCase__          =			torch.device(hook_kwargs["execution_device"]       )
         self.assertEqual(model.batchnorm.running_mean.device       ,     SCREAMING_SNAKE_CASE__       )

         lowerCAmelCase__          =			torch.randn(2       ,     3       )
         lowerCAmelCase__          =			model(SCREAMING_SNAKE_CASE__       )
         self.assertEqual(output.device       ,     SCREAMING_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
         lowerCAmelCase__          =			{
             "execution_device": 0 if torch.cuda.is_available() else "cpu",
             "offload": True,
             "offload_buffers": True,
         }

         add_hook_to_module(model.lineara       ,     AlignDevicesHook(**SCREAMING_SNAKE_CASE__       )       )
         add_hook_to_module(model.batchnorm       ,     AlignDevicesHook(**SCREAMING_SNAKE_CASE__       )       )
         add_hook_to_module(model.lineara       ,     AlignDevicesHook(**SCREAMING_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"       )       )

         lowerCAmelCase__          =			torch.randn(2       ,     3       )
         lowerCAmelCase__          =			model(SCREAMING_SNAKE_CASE__       )
         self.assertEqual(output.device       ,     SCREAMING_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					:					Optional[int]       )				->	Union[str, Any]:
         lowerCAmelCase__          =			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__          =			0 if torch.cuda.is_available() else "cpu"
         attach_align_device_hook(SCREAMING_SNAKE_CASE__       ,     execution_device=SCREAMING_SNAKE_CASE__       ,     offload=SCREAMING_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
         lowerCAmelCase__          =			torch.device(SCREAMING_SNAKE_CASE__       )
         self.assertEqual(model.batchnorm.running_mean.device       ,     SCREAMING_SNAKE_CASE__       )

         lowerCAmelCase__          =			torch.randn(2       ,     3       )
         lowerCAmelCase__          =			model(SCREAMING_SNAKE_CASE__       )
         self.assertEqual(output.device       ,     SCREAMING_SNAKE_CASE__       )

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

         lowerCAmelCase__          =			torch.randn(2       ,     3       )
         lowerCAmelCase__          =			model(SCREAMING_SNAKE_CASE__       )
         self.assertEqual(output.device       ,     SCREAMING_SNAKE_CASE__       )

         # Removing hooks loads back the weights in the model.
         remove_hook_from_submodules(SCREAMING_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					:					Optional[Any]       )				->	str:
         lowerCAmelCase__          =			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__          =			0 if torch.cuda.is_available() else "cpu"
         attach_align_device_hook(
             SCREAMING_SNAKE_CASE__       ,     execution_device=SCREAMING_SNAKE_CASE__       ,     offload=SCREAMING_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
         lowerCAmelCase__          =			torch.device(SCREAMING_SNAKE_CASE__       )
         self.assertEqual(model.batchnorm.running_mean.device       ,     SCREAMING_SNAKE_CASE__       )

         lowerCAmelCase__          =			torch.randn(2       ,     3       )
         lowerCAmelCase__          =			model(SCREAMING_SNAKE_CASE__       )
         self.assertEqual(output.device       ,     SCREAMING_SNAKE_CASE__       )

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

         lowerCAmelCase__          =			torch.randn(2       ,     3       )
         lowerCAmelCase__          =			model(SCREAMING_SNAKE_CASE__       )
         self.assertEqual(output.device       ,     SCREAMING_SNAKE_CASE__       )

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