


"""simple docstring"""






# Usage:
# ./gen-card-facebook-wmt19.py

import os
from pathlib import Path
def      lowerCamelCase       (     _UpperCamelCase		:      List[Any]       ,			_UpperCamelCase		:      Dict       ,			_UpperCamelCase		:      Dict							)		->       Tuple:

							'''simple docstring'''



							__UpperCAmelCase   :						Dict      							=					{
							    """en""": """Machine learning is great, isn't it?""",
							    """ru""": """Машинное обучение - это здорово, не так ли?""",
							    """de""": """Maschinelles Lernen ist großartig, oder?""",
							}

							# BLUE scores as follows:
							# "pair": [fairseq, transformers]
							__UpperCAmelCase   :						str      							=					{
							    """ru-en""": ["""[41.3](http://matrix.statmt.org/matrix/output/1907?run_id=6937)""", """39.20"""],
							    """en-ru""": ["""[36.4](http://matrix.statmt.org/matrix/output/1914?run_id=6724)""", """33.47"""],
							    """en-de""": ["""[43.1](http://matrix.statmt.org/matrix/output/1909?run_id=6862)""", """42.83"""],
							    """de-en""": ["""[42.3](http://matrix.statmt.org/matrix/output/1902?run_id=6750)""", """41.35"""],
							}
							__UpperCAmelCase   :						int      							=					f'''{src_lang}-{tgt_lang}'''

							__UpperCAmelCase   :						Optional[int]      							=					f'''
---
language: 
- {src_lang}
- {tgt_lang}
thumbnail:
tags:
- translation
- wmt19
- facebook
license: apache-2.0
datasets:
- wmt19
metrics:
- bleu
---

# FSMT

## Model description

This is a ported version of [fairseq wmt19 transformer](https://github.com/pytorch/fairseq/blob/master/examples/wmt19/README.md) for {src_lang}-{tgt_lang}.

For more details, please see, [Facebook FAIR\'s WMT19 News Translation Task Submission](https://arxiv.org/abs/1907.06616).

The abbreviation FSMT stands for FairSeqMachineTranslation

All four models are available:

* [wmt19-en-ru](https://huggingface.co/facebook/wmt19-en-ru)
* [wmt19-ru-en](https://huggingface.co/facebook/wmt19-ru-en)
* [wmt19-en-de](https://huggingface.co/facebook/wmt19-en-de)
* [wmt19-de-en](https://huggingface.co/facebook/wmt19-de-en)

## Intended uses & limitations

#### How to use

```python
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
mname = "facebook/wmt19-{src_lang}-{tgt_lang}"
tokenizer = FSMTTokenizer.from_pretrained(mname)
model = FSMTForConditionalGeneration.from_pretrained(mname)

input = "{texts[src_lang]}"
input_ids = tokenizer.encode(input, return_tensors="pt")
outputs = model.generate(input_ids)
decoded = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(decoded) # {texts[tgt_lang]}

```

#### Limitations and bias

- The original (and this ported model) doesn\'t seem to handle well inputs with repeated sub-phrases, [content gets truncated](https://discuss.huggingface.co/t/issues-with-translating-inputs-containing-repeated-phrases/981)

## Training data

Pretrained weights were left identical to the original model released by fairseq. For more details, please, see the [paper](https://arxiv.org/abs/1907.06616).

## Eval results

pair   | fairseq | transformers
-------|---------|----------
{pair}  | {scores[pair][0]} | {scores[pair][1]}

The score is slightly below the score reported by `fairseq`, since `transformers`` currently doesn\'t support:
- model ensemble, therefore the best performing checkpoint was ported (``model4.pt``).
- re-ranking

The score was calculated using this code:

```bash
git clone https://github.com/huggingface/transformers
cd transformers
export PAIR={pair}
export DATA_DIR=data/$PAIR
export SAVE_DIR=data/$PAIR
export BS=8
export NUM_BEAMS=15
mkdir -p $DATA_DIR
sacrebleu -t wmt19 -l $PAIR --echo src > $DATA_DIR/val.source
sacrebleu -t wmt19 -l $PAIR --echo ref > $DATA_DIR/val.target
echo $PAIR
PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval.py facebook/wmt19-$PAIR $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation --num_beams $NUM_BEAMS
```
note: fairseq reports using a beam of 50, so you should get a slightly higher score if re-run with `--num_beams 50`.

## Data Sources

- [training, etc.](http://www.statmt.org/wmt19/)
- [test set](http://matrix.statmt.org/test_sets/newstest2019.tgz?1556572561)


### BibTeX entry and citation info

```bibtex
@inproceedings{{...,
  year={{2020}},
  title={{Facebook FAIR\'s WMT19 News Translation Task Submission}},
  author={{Ng, Nathan and Yee, Kyra and Baevski, Alexei and Ott, Myle and Auli, Michael and Edunov, Sergey}},
  booktitle={{Proc. of WMT}},
}}
```


## TODO

- port model ensemble (fairseq uses 4 model checkpoints)

'''
							os.makedirs(_UpperCamelCase       ,			exist_ok=_UpperCamelCase							)
							__UpperCAmelCase   :						Optional[Any]      							=					os.path.join(_UpperCamelCase       ,			"""README.md"""							)
							print(f'''Generating {path}'''							)
							with open(_UpperCamelCase       ,			"""w"""       ,			encoding="""utf-8"""							) as f:
														f.write(_UpperCamelCase							)


# make sure we are under the root of the project
UpperCAmelCase    :     Dict   						=		Path(__file__).resolve().parent.parent.parent
UpperCAmelCase    :     Any   						=		repo_dir / 'model_cards'

for model_name in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
							UpperCAmelCase						, UpperCAmelCase						, UpperCAmelCase    :     int   						=		model_name.split('-')
							UpperCAmelCase    :     Any   						=		model_cards_dir / 'facebook' / model_name
							write_model_card(model_card_dir, src_lang=src_lang, tgt_lang=tgt_lang)




"""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      lowerCamelCase__   (     nn.Module				):







					"""simple docstring"""


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






												super().__init__()
												__UpperCAmelCase   :						Optional[Any]      							=					nn.Linear(3 ,       4  )
												__UpperCAmelCase   :						Optional[int]      							=					nn.BatchNormad(4  )
												__UpperCAmelCase   :						int      							=					nn.Linear(4 ,       5  )






					def lowerCamelCase__							(       self    : List[str] ,       UpperCamelCase    : Dict  ):
												'''simple docstring'''






												return self.lineara(self.batchnorm(self.lineara(UpperCamelCase  )  )  )






class      lowerCamelCase__   (     A				):







					"""simple docstring"""


					def lowerCamelCase__							(       self    : str ,       UpperCamelCase    : Any ,       *UpperCamelCase    : List[Any] ,       **UpperCamelCase    : Dict  ):
												'''simple docstring'''






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






class      lowerCamelCase__   (     A				):







					"""simple docstring"""


					def lowerCamelCase__							(       self    : Any ,       UpperCamelCase    : Dict ,       UpperCamelCase    : List[Any]  ):
												'''simple docstring'''






												return output + 1






class      lowerCamelCase__   (     unittest.TestCase				):







					"""simple docstring"""


					def lowerCamelCase__							(       self    : str  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Dict      							=					ModelForTest()
												__UpperCAmelCase   :						List[str]      							=					ModelHook()

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






					def lowerCamelCase__							(       self    : str  ):
												'''simple docstring'''






												__UpperCAmelCase   :						int      							=					ModelForTest()
												__UpperCAmelCase   :						Optional[Any]      							=					ModelHook()

												add_hook_to_module(UpperCamelCase ,       UpperCamelCase  )
												add_hook_to_module(UpperCamelCase ,       UpperCamelCase ,       append=UpperCamelCase  )

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






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






												__UpperCAmelCase   :						Union[str, Any]      							=					ModelForTest()
												__UpperCAmelCase   :						str      							=					torch.randn(2 ,       3  )
												__UpperCAmelCase   :						List[str]      							=					test_model(x + 1  )
												__UpperCAmelCase   :						Optional[int]      							=					test_model(x + 2  )

												__UpperCAmelCase   :						Optional[Any]      							=					PreForwardHook()
												add_hook_to_module(UpperCamelCase ,       UpperCamelCase  )
												__UpperCAmelCase   :						Optional[Any]      							=					test_model(UpperCamelCase  )
												self.assertTrue(torch.allclose(UpperCamelCase ,       UpperCamelCase ,       atol=1e-5  )  )

												# Attaching a hook to a model when it already has one replaces, does not chain
												__UpperCAmelCase   :						Optional[Any]      							=					PreForwardHook()
												add_hook_to_module(UpperCamelCase ,       UpperCamelCase  )
												__UpperCAmelCase   :						Optional[int]      							=					test_model(UpperCamelCase  )
												self.assertTrue(torch.allclose(UpperCamelCase ,       UpperCamelCase ,       atol=1e-5  )  )

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

												__UpperCAmelCase   :						List[str]      							=					test_model(UpperCamelCase  )
												assert torch.allclose(UpperCamelCase ,       UpperCamelCase ,       atol=1e-5  )






					def lowerCamelCase__							(       self    : Dict  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Optional[int]      							=					ModelForTest()
												__UpperCAmelCase   :						str      							=					torch.randn(2 ,       3  )
												__UpperCAmelCase   :						int      							=					test_model(UpperCamelCase  )

												__UpperCAmelCase   :						Dict      							=					PostForwardHook()
												add_hook_to_module(UpperCamelCase ,       UpperCamelCase  )
												__UpperCAmelCase   :						Any      							=					test_model(UpperCamelCase  )
												self.assertTrue(torch.allclose(UpperCamelCase ,       output + 1 ,       atol=1e-5  )  )

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

												# You need to use the sequential hook to chain two or more hooks
												__UpperCAmelCase   :						Optional[int]      							=					SequentialHook(PostForwardHook() ,       PostForwardHook()  )
												add_hook_to_module(UpperCamelCase ,       UpperCamelCase  )

												__UpperCAmelCase   :						int      							=					test_model(UpperCamelCase  )
												assert torch.allclose(UpperCamelCase ,       output + 2 ,       atol=1e-5  )






					def lowerCamelCase__							(       self    : Optional[int]  ):
												'''simple docstring'''






												__UpperCAmelCase   :						int      							=					ModelForTest()
												__UpperCAmelCase   :						str      							=					torch.randn(2 ,       3  )
												__UpperCAmelCase   :						Optional[Any]      							=					test_model(UpperCamelCase  )

												__UpperCAmelCase   :						Union[str, Any]      							=					PostForwardHook()
												add_hook_to_module(UpperCamelCase ,       UpperCamelCase  )
												__UpperCAmelCase   :						Optional[int]      							=					test_model(UpperCamelCase  )
												self.assertTrue(torch.allclose(UpperCamelCase ,       output + 1  )  )
												self.assertTrue(outputa.requires_grad  )

												__UpperCAmelCase   :						List[str]      							=					True
												__UpperCAmelCase   :						Optional[int]      							=					test_model(UpperCamelCase  )
												self.assertFalse(outputa.requires_grad  )






					@require_multi_gpu
					def lowerCamelCase__							(       self    : Optional[Any]  ):
												'''simple docstring'''






												__UpperCAmelCase   :						str      							=					ModelForTest()
												# Everything is on CPU
												self.assertEqual(model.lineara.weight.device ,       torch.device("""cpu"""  )  )
												self.assertEqual(model.batchnorm.weight.device ,       torch.device("""cpu"""  )  )
												self.assertEqual(model.lineara.weight.device ,       torch.device("""cpu"""  )  )

												# This will move each submodule on different devices
												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   :						Any      							=					model(UpperCamelCase  )
												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(UpperCamelCase ,       AlignDevicesHook(io_same_device=UpperCamelCase  )  )
												__UpperCAmelCase   :						int      							=					torch.randn(2 ,       3  ).to(0  )
												__UpperCAmelCase   :						Any      							=					model(UpperCamelCase  )
												self.assertEqual(output.device ,       torch.device(0  )  )






					def lowerCamelCase__							(       self    : Optional[int]  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Tuple      							=					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   :						Union[str, Any]      							=					{"""execution_device""": 0 if torch.cuda.is_available() else """cpu""", """offload""": True}

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

												# 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   :						Any      							=					torch.device(hook_kwargs["""execution_device"""]  )
												self.assertEqual(model.batchnorm.running_mean.device ,       UpperCamelCase  )

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

												# 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   :						Union[str, Any]      							=					{
												    """execution_device""": 0 if torch.cuda.is_available() else """cpu""",
												    """offload""": True,
												    """offload_buffers""": True,
												}

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

												# 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   :						Dict      							=					model(UpperCamelCase  )
												self.assertEqual(output.device ,       UpperCamelCase  )

												# 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 lowerCamelCase__							(       self    : Union[str, Any]  ):
												'''simple docstring'''






												__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   :						Optional[int]      							=					0 if torch.cuda.is_available() else """cpu"""
												attach_align_device_hook(UpperCamelCase ,       execution_device=UpperCamelCase ,       offload=UpperCamelCase  )

												# 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   :						Any      							=					torch.device(UpperCamelCase  )
												self.assertEqual(model.batchnorm.running_mean.device ,       UpperCamelCase  )

												__UpperCAmelCase   :						str      							=					torch.randn(2 ,       3  )
												__UpperCAmelCase   :						Any      							=					model(UpperCamelCase  )
												self.assertEqual(output.device ,       UpperCamelCase  )

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

												# 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[str]      							=					torch.randn(2 ,       3  )
												__UpperCAmelCase   :						Any      							=					model(UpperCamelCase  )
												self.assertEqual(output.device ,       UpperCamelCase  )

												# Removing hooks loads back the weights in the model.
												remove_hook_from_submodules(UpperCamelCase  )
												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 lowerCamelCase__							(       self    : Any  ):
												'''simple docstring'''






												__UpperCAmelCase   :						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
												__UpperCAmelCase   :						Any      							=					0 if torch.cuda.is_available() else """cpu"""
												attach_align_device_hook(
												    UpperCamelCase ,       execution_device=UpperCamelCase ,       offload=UpperCamelCase ,       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   :						str      							=					torch.device(UpperCamelCase  )
												self.assertEqual(model.batchnorm.running_mean.device ,       UpperCamelCase  )

												__UpperCAmelCase   :						Dict      							=					torch.randn(2 ,       3  )
												__UpperCAmelCase   :						Optional[Any]      							=					model(UpperCamelCase  )
												self.assertEqual(output.device ,       UpperCamelCase  )

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

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

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