




"""simple docstring"""


import argparse
import json
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


A_      =    16
A_      =    32


def  UpperCAmelCase__	(snake_case__							: Accelerator		,       snake_case__							: int = 16		,       snake_case__							: str = "bert-base-cased"   ):
  """simple docstring"""

  _snake_case							:		Dict								=  AutoTokenizer.from_pretrained(snake_case__   )
  _snake_case							:		Union[str, Any]								=  load_dataset("""glue"""		,       """mrpc"""   )

  def tokenize_function(snake_case__							: Union[str, Any]   ):
    # max_length=None => use the model max length (it's actually the default)
    _snake_case							:		Dict								=  tokenizer(examples["""sentence1"""]		,       examples["""sentence2"""]		,       truncation=snake_case__		,       max_length=snake_case__   )
    return outputs

  # Apply the method we just defined to all the examples in all the splits of the dataset
  _snake_case							:		int								=  datasets.map(
      snake_case__		,       batched=snake_case__		,       remove_columns=["""idx""", """sentence1""", """sentence2"""]		,       load_from_cache_file=snake_case__   )

  # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
  # transformers library
  _snake_case							:		List[Any]								=  tokenized_datasets.rename_column("""label"""		,       """labels"""   )

  def collate_fn(snake_case__							: Optional[int]   ):
    # On TPU it's best to pad everything to the same length or training will be very slow.
    if accelerator.distributed_type == DistributedType.TPU:
      return tokenizer.pad(snake_case__		,       padding="""max_length"""		,       max_length=1_28		,       return_tensors="""pt"""   )
    return tokenizer.pad(snake_case__		,       padding="""longest"""		,       return_tensors="""pt"""   )

  # Instantiate dataloaders.
  _snake_case							:		List[str]								=  DataLoader(
      tokenized_datasets["""train"""]		,       shuffle=snake_case__		,       collate_fn=snake_case__		,       batch_size=snake_case__   )
  _snake_case							:		Dict								=  DataLoader(
      tokenized_datasets["""validation"""]		,       shuffle=snake_case__		,       collate_fn=snake_case__		,       batch_size=snake_case__   )

  return train_dataloader, eval_dataloader


def  UpperCAmelCase__	(snake_case__							: Dict		,       snake_case__							: List[str]		,       snake_case__							: str		,       snake_case__							: Tuple   ):
  """simple docstring"""

  model.eval()
  _snake_case							:		Any								=  0
  for step, batch in enumerate(snake_case__   ):
    # We could avoid this line since we set the accelerator with `device_placement=True`.
    batch.to(accelerator.device   )
    with torch.no_grad():
      _snake_case							:		List[Any]								=  model(**snake_case__   )
    _snake_case							:		int								=  outputs.logits.argmax(dim=-1   )
    # It is slightly faster to call this once, than multiple times
    _snake_case		, _snake_case							:		str								=  accelerator.gather(
        (predictions, batch["""labels"""])   )  # If we are in a multiprocess environment, the last batch has duplicates
    if accelerator.use_distributed:
      if step == len(snake_case__   ) - 1:
        _snake_case							:		Optional[int]								=  predictions[: len(eval_dataloader.dataset   ) - samples_seen]
        _snake_case							:		Dict								=  references[: len(eval_dataloader.dataset   ) - samples_seen]
      else:
        samples_seen += references.shape[0]
    metric.add_batch(
        predictions=snake_case__		,       references=snake_case__		,       )

  _snake_case							:		Union[str, Any]								=  metric.compute()
  return eval_metric["accuracy"]


def  UpperCAmelCase__	(snake_case__							: Dict		,       snake_case__							: List[Any]   ):
  """simple docstring"""

  _snake_case							:		Optional[Any]								=  Accelerator()

  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  _snake_case							:		Optional[Any]								=  config["""lr"""]
  _snake_case							:		List[Any]								=  int(config["""num_epochs"""]   )
  _snake_case							:		Union[str, Any]								=  int(config["""seed"""]   )
  _snake_case							:		str								=  int(config["""batch_size"""]   )
  _snake_case							:		str								=  args.model_name_or_path

  set_seed(snake_case__   )
  _snake_case		, _snake_case							:		List[str]								=  get_dataloaders(snake_case__		,       snake_case__		,       snake_case__   )

  # Instantiate the model (we build the model here so that the seed also control new weights initialization)
  _snake_case							:		str								=  AutoModelForSequenceClassification.from_pretrained(snake_case__		,       return_dict=snake_case__   )

  # Instantiate optimizer
  _snake_case							:		Tuple								=  (
      AdamW
      if accelerator.state.deepspeed_plugin is None
      or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
      else DummyOptim
  )
  _snake_case							:		Any								=  optimizer_cls(params=model.parameters()		,       lr=snake_case__   )

  if accelerator.state.deepspeed_plugin is not None:
    _snake_case							:		List[Any]								=  accelerator.state.deepspeed_plugin.deepspeed_config[
        """gradient_accumulation_steps"""
    ]
  else:
    _snake_case							:		Optional[int]								=  1
  _snake_case							:		Optional[int]								=  (len(snake_case__   ) * num_epochs) // gradient_accumulation_steps

  # Instantiate scheduler
  if (
      accelerator.state.deepspeed_plugin is None
      or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
  ):
    _snake_case							:		Optional[int]								=  get_linear_schedule_with_warmup(
        optimizer=snake_case__		,       num_warmup_steps=0		,       num_training_steps=snake_case__		,       )
  else:
    _snake_case							:		Dict								=  DummyScheduler(snake_case__		,       total_num_steps=snake_case__		,       warmup_num_steps=0   )

  # Prepare everything
  # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
  # prepare method.
  _snake_case		, _snake_case		, _snake_case		, _snake_case		, _snake_case							:		Dict								=  accelerator.prepare(
      snake_case__		,       snake_case__		,       snake_case__		,       snake_case__		,       snake_case__   )

  # We need to keep track of how many total steps we have iterated over
  _snake_case							:		Optional[int]								=  0
  # We also need to keep track of the stating epoch so files are named properly
  _snake_case							:		str								=  0
  _snake_case							:		int								=  evaluate.load("""glue"""		,       """mrpc"""   )
  _snake_case							:		List[str]								=  num_epochs

  if args.partial_train_epoch is not None:
    _snake_case							:		List[str]								=  args.partial_train_epoch

  if args.resume_from_checkpoint:
    accelerator.load_state(args.resume_from_checkpoint   )
    _snake_case							:		str								=  args.resume_from_checkpoint.split("""epoch_"""   )[1]
    _snake_case							:		List[str]								=  """"""
    for char in epoch_string:
      if char.isdigit():
        state_epoch_num += char
      else:
        break
    _snake_case							:		str								=  int(snake_case__   ) + 1
    _snake_case							:		Optional[Any]								=  evaluation_loop(snake_case__		,       snake_case__		,       snake_case__		,       snake_case__   )
    accelerator.print("""resumed checkpoint performance:"""		,       snake_case__   )
    accelerator.print("""resumed checkpoint's scheduler's lr:"""		,       lr_scheduler.get_lr()[0]   )
    accelerator.print("""resumed optimizers's lr:"""		,       optimizer.param_groups[0]["""lr"""]   )
    with open(os.path.join(args.output_dir		,       F"state_{starting_epoch-1}.json"   )		,       """r"""   ) as f:
      _snake_case							:		List[str]								=  json.load(snake_case__   )
      assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
      assert (
          resumed_state["lr"] == lr_scheduler.get_lr()[0]
      ), "Scheduler learning rate mismatch, loading from checkpoint failed"
      assert (
          resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
      ), "Optimizer learning rate mismatch, loading from checkpoint failed"
      assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
      return

    # Now we train the model
  _snake_case							:		Optional[int]								=  {}
  for epoch in range(snake_case__		,       snake_case__   ):
    model.train()
    for step, batch in enumerate(snake_case__   ):
      _snake_case							:		int								=  model(**snake_case__   )
      _snake_case							:		Optional[Any]								=  outputs.loss
      _snake_case							:		Union[str, Any]								=  loss / gradient_accumulation_steps
      accelerator.backward(snake_case__   )
      if step % gradient_accumulation_steps == 0:
        optimizer.step()
        lr_scheduler.step()
        optimizer.zero_grad()

      overall_step += 1
    _snake_case							:		Dict								=  F"epoch_{epoch}"
    _snake_case							:		Dict								=  os.path.join(args.output_dir		,       snake_case__   )
    accelerator.save_state(snake_case__   )
    _snake_case							:		Optional[int]								=  evaluation_loop(snake_case__		,       snake_case__		,       snake_case__		,       snake_case__   )
    _snake_case							:		Tuple								=  accuracy
    _snake_case							:		Union[str, Any]								=  lr_scheduler.get_lr()[0]
    _snake_case							:		Optional[Any]								=  optimizer.param_groups[0]["""lr"""]
    _snake_case							:		List[str]								=  epoch
    _snake_case							:		Any								=  overall_step
    accelerator.print(F"epoch {epoch}:"		,       snake_case__   )

    accelerator.wait_for_everyone()
    if accelerator.is_main_process:
      with open(os.path.join(args.output_dir		,       F"state_{epoch}.json"   )		,       """w"""   ) as f:
        json.dump(snake_case__		,       snake_case__   )


def  UpperCAmelCase__	():
  """simple docstring"""

  _snake_case							:		Optional[Any]								=  argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage."""   )
  parser.add_argument(
      """--model_name_or_path"""		,       type=snake_case__		,       default="""bert-base-cased"""		,       help="""Path to pretrained model or model identifier from huggingface.co/models."""		,       required=snake_case__		,       )
  parser.add_argument(
      """--output_dir"""		,       type=snake_case__		,       default="""."""		,       help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""		,       )
  parser.add_argument(
      """--resume_from_checkpoint"""		,       type=snake_case__		,       default=snake_case__		,       help="""If the training should continue from a checkpoint folder."""		,       )
  parser.add_argument(
      """--partial_train_epoch"""		,       type=snake_case__		,       default=snake_case__		,       help="""If passed, the training will stop after this number of epochs."""		,       )
  parser.add_argument(
      """--num_epochs"""		,       type=snake_case__		,       default=2		,       help="""Number of train epochs."""		,       )
  _snake_case							:		Optional[Any]								=  parser.parse_args()
  _snake_case							:		List[str]								=  {"""lr""": 2e-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}

  training_function(snake_case__		,       snake_case__   )


if __name__ == "__main__":
     main()








"""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  lowercase(					nn.Module			):







 '''simple docstring'''





 def __init__(    self:		Optional[int]		):




   '''simple docstring'''



   super().__init__()
   _snake_case							:		List[str]								=  nn.Linear(3,				4		)
   _snake_case							:		int								=  nn.BatchNormad(4		)
   _snake_case							:		List[str]								=  nn.Linear(4,				5		)


 def 			UpperCamelCase_       (    self:		Any,				a_:		Union[str, Any]		):




   '''simple docstring'''



   return self.lineara(self.batchnorm(self.lineara(a_		)		)		)







class  lowercase(					__a			):







 '''simple docstring'''





 def 			UpperCamelCase_       (    self:		Any,				a_:		int,				*a_:		Dict,				**a_:		Dict		):




   '''simple docstring'''



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







class  lowercase(					__a			):







 '''simple docstring'''





 def 			UpperCamelCase_       (    self:		str,				a_:		Tuple,				a_:		Union[str, Any]		):




   '''simple docstring'''



   return output + 1







class  lowercase(					unittest.TestCase			):







 '''simple docstring'''





 def 			UpperCamelCase_       (    self:		Union[str, Any]		):




   '''simple docstring'''



   _snake_case							:		Tuple								=  ModelForTest()
   _snake_case							:		List[str]								=  ModelHook()

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


 def 			UpperCamelCase_       (    self:		Tuple		):




   '''simple docstring'''



   _snake_case							:		Dict								=  ModelForTest()
   _snake_case							:		List[Any]								=  ModelHook()

   add_hook_to_module(a_,				a_		)
   add_hook_to_module(a_,				a_,				append=a_		)

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


 def 			UpperCamelCase_       (    self:		Tuple		):




   '''simple docstring'''



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

   _snake_case							:		Any								=  PreForwardHook()
   add_hook_to_module(a_,				a_		)
   _snake_case							:		List[Any]								=  test_model(a_		)
   self.assertTrue(torch.allclose(a_,				a_,				atol=1E-5		)		)

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

   # You need to use the sequential hook to chain two or more hooks
   _snake_case							:		str								=  SequentialHook(PreForwardHook(),				PreForwardHook()		)
   add_hook_to_module(a_,				a_		)

   _snake_case							:		str								=  test_model(a_		)
   assert torch.allclose(a_,				a_,				atol=1E-5		)


 def 			UpperCamelCase_       (    self:		Optional[int]		):




   '''simple docstring'''



   _snake_case							:		Optional[int]								=  ModelForTest()
   _snake_case							:		List[Any]								=  torch.randn(2,				3		)
   _snake_case							:		List[str]								=  test_model(a_		)

   _snake_case							:		List[Any]								=  PostForwardHook()
   add_hook_to_module(a_,				a_		)
   _snake_case							:		Union[str, Any]								=  test_model(a_		)
   self.assertTrue(torch.allclose(a_,				output + 1,				atol=1E-5		)		)

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

   # You need to use the sequential hook to chain two or more hooks
   _snake_case							:		Dict								=  SequentialHook(PostForwardHook(),				PostForwardHook()		)
   add_hook_to_module(a_,				a_		)

   _snake_case							:		List[str]								=  test_model(a_		)
   assert torch.allclose(a_,				output + 2,				atol=1E-5		)


 def 			UpperCamelCase_       (    self:		Optional[Any]		):




   '''simple docstring'''



   _snake_case							:		str								=  ModelForTest()
   _snake_case							:		Any								=  torch.randn(2,				3		)
   _snake_case							:		List[str]								=  test_model(a_		)

   _snake_case							:		List[Any]								=  PostForwardHook()
   add_hook_to_module(a_,				a_		)
   _snake_case							:		Dict								=  test_model(a_		)
   self.assertTrue(torch.allclose(a_,				output + 1		)		)
   self.assertTrue(outputa.requires_grad		)

   _snake_case							:		Union[str, Any]								=  True
   _snake_case							:		Dict								=  test_model(a_		)
   self.assertFalse(outputa.requires_grad		)


 @require_multi_gpu
 def 			UpperCamelCase_       (    self:		Tuple		):




   '''simple docstring'''



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


 def 			UpperCamelCase_       (    self:		Union[str, Any]		):




   '''simple docstring'''



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

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

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

   _snake_case							:		str								=  torch.randn(2,				3		)
   _snake_case							:		Optional[int]								=  model(a_		)
   self.assertEqual(output.device,				a_		)

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

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

   # 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"""		)		)

   _snake_case							:		List[Any]								=  torch.randn(2,				3		)
   _snake_case							:		Any								=  model(a_		)
   self.assertEqual(output.device,				a_		)

   # 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:		str		):




   '''simple docstring'''



   _snake_case							:		Any								=  ModelForTest()

   # Everything is on CPU
   self.assertEqual(model.lineara.weight.device,				torch.device("""cpu"""		)		)
   self.assertEqual(model.batchnorm.weight.device,				torch.device("""cpu"""		)		)
   self.assertEqual(model.lineara.weight.device,				torch.device("""cpu"""		)		)

   # This will move each submodule on different devices
   _snake_case							:		Tuple								=  0 if torch.cuda.is_available() else """cpu"""
   attach_align_device_hook(a_,				execution_device=a_,				offload=a_		)

   # 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
   _snake_case							:		Optional[int]								=  torch.device(a_		)
   self.assertEqual(model.batchnorm.running_mean.device,				a_		)

   _snake_case							:		List[Any]								=  torch.randn(2,				3		)
   _snake_case							:		List[Any]								=  model(a_		)
   self.assertEqual(output.device,				a_		)

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

   # 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"""		)		)

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

   # Removing hooks loads back the weights in the model.
   remove_hook_from_submodules(a_		)
   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:		Any		):




   '''simple docstring'''



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

   _snake_case							:		int								=  torch.randn(2,				3		)
   _snake_case							:		str								=  model(a_		)
   self.assertEqual(output.device,				a_		)

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

   # 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"""		)		)

   _snake_case							:		Optional[int]								=  torch.randn(2,				3		)
   _snake_case							:		Any								=  model(a_		)
   self.assertEqual(output.device,				a_		)

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


