


"""simple docstring"""






import argparse
import json
from pathlib import Path

import torch
import torchaudio
from datasets import load_dataset
from huggingface_hub import hf_hub_download

from transformers import ASTConfig, ASTFeatureExtractor, ASTForAudioClassification
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase    :     List[Any]   						=		logging.get_logger(__name__)
def      lowerCamelCase       (     _UpperCamelCase		:      Dict							)		->       List[Any]:

							'''simple docstring'''



							__UpperCAmelCase   :						str      							=					ASTConfig()

							if "10-10" in model_name:
														pass
							elif "speech-commands" in model_name:
														__UpperCAmelCase   :						List[Any]      							=					1_2_8
							elif "12-12" in model_name:
														__UpperCAmelCase   :						Optional[Any]      							=					1_2
														__UpperCAmelCase   :						Dict      							=					1_2
							elif "14-14" in model_name:
														__UpperCAmelCase   :						str      							=					1_4
														__UpperCAmelCase   :						Any      							=					1_4
							elif "16-16" in model_name:
														__UpperCAmelCase   :						Optional[Any]      							=					1_6
														__UpperCAmelCase   :						Union[str, Any]      							=					1_6
							else:
														raise ValueError("""Model not supported"""							)

							__UpperCAmelCase   :						Any      							=					"""huggingface/label-files"""
							if "speech-commands" in model_name:
														__UpperCAmelCase   :						Dict      							=					3_5
														__UpperCAmelCase   :						Union[str, Any]      							=					"""speech-commands-v2-id2label.json"""
							else:
														__UpperCAmelCase   :						Optional[int]      							=					5_2_7
														__UpperCAmelCase   :						List[Any]      							=					"""audioset-id2label.json"""

							__UpperCAmelCase   :						List[str]      							=					json.load(open(hf_hub_download(_UpperCamelCase       ,			_UpperCamelCase       ,			repo_type="""dataset"""							)       ,			"""r"""							)							)
							__UpperCAmelCase   :						List[str]      							=					{int(_UpperCamelCase							): v for k, v in idalabel.items()}
							__UpperCAmelCase   :						List[Any]      							=					idalabel
							__UpperCAmelCase   :						Optional[Any]      							=					{v: k for k, v in idalabel.items()}

							return config
def      lowerCamelCase       (     _UpperCamelCase		:      Optional[Any]							)		->       Optional[Any]:

							'''simple docstring'''



							if "module.v" in name:
														__UpperCAmelCase   :						Optional[int]      							=					name.replace("""module.v"""       ,			"""audio_spectrogram_transformer"""							)
							if "cls_token" in name:
														__UpperCAmelCase   :						Tuple      							=					name.replace("""cls_token"""       ,			"""embeddings.cls_token"""							)
							if "dist_token" in name:
														__UpperCAmelCase   :						Optional[int]      							=					name.replace("""dist_token"""       ,			"""embeddings.distillation_token"""							)
							if "pos_embed" in name:
														__UpperCAmelCase   :						Union[str, Any]      							=					name.replace("""pos_embed"""       ,			"""embeddings.position_embeddings"""							)
							if "patch_embed.proj" in name:
														__UpperCAmelCase   :						str      							=					name.replace("""patch_embed.proj"""       ,			"""embeddings.patch_embeddings.projection"""							)
							# transformer blocks
							if "blocks" in name:
														__UpperCAmelCase   :						str      							=					name.replace("""blocks"""       ,			"""encoder.layer"""							)
							if "attn.proj" in name:
														__UpperCAmelCase   :						Optional[int]      							=					name.replace("""attn.proj"""       ,			"""attention.output.dense"""							)
							if "attn" in name:
														__UpperCAmelCase   :						List[str]      							=					name.replace("""attn"""       ,			"""attention.self"""							)
							if "norm1" in name:
														__UpperCAmelCase   :						Optional[Any]      							=					name.replace("""norm1"""       ,			"""layernorm_before"""							)
							if "norm2" in name:
														__UpperCAmelCase   :						Union[str, Any]      							=					name.replace("""norm2"""       ,			"""layernorm_after"""							)
							if "mlp.fc1" in name:
														__UpperCAmelCase   :						Any      							=					name.replace("""mlp.fc1"""       ,			"""intermediate.dense"""							)
							if "mlp.fc2" in name:
														__UpperCAmelCase   :						Dict      							=					name.replace("""mlp.fc2"""       ,			"""output.dense"""							)
							# final layernorm
							if "audio_spectrogram_transformer.norm" in name:
														__UpperCAmelCase   :						List[Any]      							=					name.replace("""audio_spectrogram_transformer.norm"""       ,			"""audio_spectrogram_transformer.layernorm"""							)
							# classifier head
							if "module.mlp_head.0" in name:
														__UpperCAmelCase   :						Union[str, Any]      							=					name.replace("""module.mlp_head.0"""       ,			"""classifier.layernorm"""							)
							if "module.mlp_head.1" in name:
														__UpperCAmelCase   :						Union[str, Any]      							=					name.replace("""module.mlp_head.1"""       ,			"""classifier.dense"""							)

							return name
def      lowerCamelCase       (     _UpperCamelCase		:      Any       ,			_UpperCamelCase		:      Dict							)		->       Any:

							'''simple docstring'''



							for key in orig_state_dict.copy().keys():
														__UpperCAmelCase   :						Any      							=					orig_state_dict.pop(_UpperCamelCase							)

														if "qkv" in key:
																					__UpperCAmelCase   :						int      							=					key.split("""."""							)
																					__UpperCAmelCase   :						List[str]      							=					int(key_split[3]							)
																					__UpperCAmelCase   :						Any      							=					config.hidden_size
																					if "weight" in key:
																												__UpperCAmelCase   :						List[Any]      							=					val[:dim, :]
																												__UpperCAmelCase   :						Optional[int]      							=					val[dim : dim * 2, :]
																												__UpperCAmelCase   :						Dict      							=					val[-dim:, :]
																					else:
																												__UpperCAmelCase   :						Optional[Any]      							=					val[:dim]
																												__UpperCAmelCase   :						Any      							=					val[dim : dim * 2]
																												__UpperCAmelCase   :						Union[str, Any]      							=					val[-dim:]
														else:
																					__UpperCAmelCase   :						Optional[Any]      							=					val

							return orig_state_dict
def      lowerCamelCase       (     _UpperCamelCase		:      Optional[Any]							)		->       Any:

							'''simple docstring'''



							__UpperCAmelCase   :						Dict      							=					[
							    """module.v.head.weight""",
							    """module.v.head.bias""",
							    """module.v.head_dist.weight""",
							    """module.v.head_dist.bias""",
							]
							for k in ignore_keys:
														state_dict.pop(_UpperCamelCase       ,			_UpperCamelCase							)
@torch.no_grad()
def      lowerCamelCase       (     _UpperCamelCase		:      Dict       ,			_UpperCamelCase		:      List[Any]       ,			_UpperCamelCase		:      Any=False							)		->       Optional[Any]:

							'''simple docstring'''



							__UpperCAmelCase   :						Optional[int]      							=					get_audio_spectrogram_transformer_config(_UpperCamelCase							)

							__UpperCAmelCase   :						Any      							=					{
							    """ast-finetuned-audioset-10-10-0.4593""": (
							        """https://www.dropbox.com/s/ca0b1v2nlxzyeb4/audioset_10_10_0.4593.pth?dl=1"""
							    ),
							    """ast-finetuned-audioset-10-10-0.450""": (
							        """https://www.dropbox.com/s/1tv0hovue1bxupk/audioset_10_10_0.4495.pth?dl=1"""
							    ),
							    """ast-finetuned-audioset-10-10-0.448""": (
							        """https://www.dropbox.com/s/6u5sikl4b9wo4u5/audioset_10_10_0.4483.pth?dl=1"""
							    ),
							    """ast-finetuned-audioset-10-10-0.448-v2""": (
							        """https://www.dropbox.com/s/kt6i0v9fvfm1mbq/audioset_10_10_0.4475.pth?dl=1"""
							    ),
							    """ast-finetuned-audioset-12-12-0.447""": (
							        """https://www.dropbox.com/s/snfhx3tizr4nuc8/audioset_12_12_0.4467.pth?dl=1"""
							    ),
							    """ast-finetuned-audioset-14-14-0.443""": (
							        """https://www.dropbox.com/s/z18s6pemtnxm4k7/audioset_14_14_0.4431.pth?dl=1"""
							    ),
							    """ast-finetuned-audioset-16-16-0.442""": (
							        """https://www.dropbox.com/s/mdsa4t1xmcimia6/audioset_16_16_0.4422.pth?dl=1"""
							    ),
							    """ast-finetuned-speech-commands-v2""": (
							        """https://www.dropbox.com/s/q0tbqpwv44pquwy/speechcommands_10_10_0.9812.pth?dl=1"""
							    ),
							}

							# load original state_dict
							__UpperCAmelCase   :						Dict      							=					model_name_to_url[model_name]
							__UpperCAmelCase   :						Any      							=					torch.hub.load_state_dict_from_url(_UpperCamelCase       ,			map_location="""cpu"""							)
							# remove some keys
							remove_keys(_UpperCamelCase							)
							# rename some keys
							__UpperCAmelCase   :						Union[str, Any]      							=					convert_state_dict(_UpperCamelCase       ,			_UpperCamelCase							)

							# load 🤗 model
							__UpperCAmelCase   :						Tuple      							=					ASTForAudioClassification(_UpperCamelCase							)
							model.eval()

							model.load_state_dict(_UpperCamelCase							)

							# verify outputs on dummy input
							# source: https://github.com/YuanGongND/ast/blob/79e873b8a54d0a3b330dd522584ff2b9926cd581/src/run.py#L62
							__UpperCAmelCase   :						Optional[Any]      							=					-4.2_677_393 if """speech-commands""" not in model_name else -6.845_978
							__UpperCAmelCase   :						List[Any]      							=					4.5_689_974 if """speech-commands""" not in model_name else 5.5_654_526
							__UpperCAmelCase   :						List[Any]      							=					1_0_2_4 if """speech-commands""" not in model_name else 1_2_8
							__UpperCAmelCase   :						str      							=					ASTFeatureExtractor(mean=_UpperCamelCase       ,			std=_UpperCamelCase       ,			max_length=_UpperCamelCase							)

							if "speech-commands" in model_name:
														__UpperCAmelCase   :						Tuple      							=					load_dataset("""speech_commands"""       ,			"""v0.02"""       ,			split="""validation"""							)
														__UpperCAmelCase   :						Optional[int]      							=					dataset[0]["""audio"""]["""array"""]
							else:
														__UpperCAmelCase   :						Any      							=					hf_hub_download(
														    repo_id="""nielsr/audio-spectogram-transformer-checkpoint"""       ,			filename="""sample_audio.flac"""       ,			repo_type="""dataset"""       ,			)

														__UpperCAmelCase							,__UpperCAmelCase   :						Tuple      							=					torchaudio.load(_UpperCamelCase							)
														__UpperCAmelCase   :						Optional[int]      							=					waveform.squeeze().numpy()

							__UpperCAmelCase   :						Optional[Any]      							=					feature_extractor(_UpperCamelCase       ,			sampling_rate=1_6_0_0_0       ,			return_tensors="""pt"""							)

							# forward pass
							__UpperCAmelCase   :						int      							=					model(**_UpperCamelCase							)
							__UpperCAmelCase   :						Any      							=					outputs.logits

							if model_name == "ast-finetuned-audioset-10-10-0.4593":
														__UpperCAmelCase   :						int      							=					torch.tensor([-0.8_760, -7.0_042, -8.6_602]							)
							elif model_name == "ast-finetuned-audioset-10-10-0.450":
														__UpperCAmelCase   :						Tuple      							=					torch.tensor([-1.1_986, -7.0_903, -8.2_718]							)
							elif model_name == "ast-finetuned-audioset-10-10-0.448":
														__UpperCAmelCase   :						Any      							=					torch.tensor([-2.6_128, -8.0_080, -9.4_344]							)
							elif model_name == "ast-finetuned-audioset-10-10-0.448-v2":
														__UpperCAmelCase   :						List[Any]      							=					torch.tensor([-1.5_080, -7.4_534, -8.8_917]							)
							elif model_name == "ast-finetuned-audioset-12-12-0.447":
														__UpperCAmelCase   :						str      							=					torch.tensor([-0.5_050, -6.5_833, -8.0_843]							)
							elif model_name == "ast-finetuned-audioset-14-14-0.443":
														__UpperCAmelCase   :						List[str]      							=					torch.tensor([-0.3_826, -7.0_336, -8.2_413]							)
							elif model_name == "ast-finetuned-audioset-16-16-0.442":
														__UpperCAmelCase   :						int      							=					torch.tensor([-1.2_113, -6.9_101, -8.3_470]							)
							elif model_name == "ast-finetuned-speech-commands-v2":
														__UpperCAmelCase   :						Any      							=					torch.tensor([6.1_589, -8.0_566, -8.7_984]							)
							else:
														raise ValueError("""Unknown model name"""							)
							if not torch.allclose(logits[0, :3]       ,			_UpperCamelCase       ,			atol=1E-4							):
														raise ValueError("""Logits don't match"""							)
							print("""Looks ok!"""							)

							if pytorch_dump_folder_path is not None:
														Path(_UpperCamelCase							).mkdir(exist_ok=_UpperCamelCase							)
														print(f'''Saving model {model_name} to {pytorch_dump_folder_path}'''							)
														model.save_pretrained(_UpperCamelCase							)
														print(f'''Saving feature extractor to {pytorch_dump_folder_path}'''							)
														feature_extractor.save_pretrained(_UpperCamelCase							)

							if push_to_hub:
														print("""Pushing model and feature extractor to the hub..."""							)
														model.push_to_hub(f'''MIT/{model_name}'''							)
														feature_extractor.push_to_hub(f'''MIT/{model_name}'''							)


if __name__ == "__main__":
							UpperCAmelCase    :     str   						=		argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '--model_name',
							    default='ast-finetuned-audioset-10-10-0.4593',
							    type=str,
							    help='Name of the Audio Spectrogram Transformer model you\'d like to convert.',
							)
							parser.add_argument(
							    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
							)
							parser.add_argument(
							    '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
							)

							UpperCAmelCase    :     Dict   						=		parser.parse_args()
							convert_audio_spectrogram_transformer_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)




"""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"""  )  )
