
"""simple docstring"""




import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors

import transformers
from transformers import (
    AutoConfig,
    AutoModelForMultipleChoice,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


lowerCAmelCase__							=				logging.getLogger(__name__)



def        _lowerCamelCase ( __a,							__a    ):
  return (preds == labels).mean()





@dataclass
class  snake_case     :
       UpperCAmelCase__						     =      field(
           metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}   )
       UpperCAmelCase__						     =      field(
           default=__lowercase       ,			metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}   )
       UpperCAmelCase__						     =      field(
           default=__lowercase       ,			metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}   )
       UpperCAmelCase__						     =      field(
           default=__lowercase       ,			metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}       ,			)







@dataclass
class  snake_case     :
       UpperCAmelCase__						     =      field(metadata={'''help''': '''The name of the task to train on: ''' + ''', '''.join(processors.keys()   )}   )
       UpperCAmelCase__						     =      field(metadata={'''help''': '''Should contain the data files for the task.'''}   )
       UpperCAmelCase__						     =      field(
           default=128       ,			metadata={
               '''help''': (
                   '''The maximum total input sequence length after tokenization. Sequences longer '''
                   '''than this will be truncated, sequences shorter will be padded.'''
               )
           }       ,			)
       UpperCAmelCase__						     =      field(
           default=__lowercase       ,			metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}   )







def        _lowerCamelCase ( ):
  # See all possible arguments in src/transformers/training_args.py
  # or by passing the --help flag to this script.
  # We now keep distinct sets of args, for a cleaner separation of concerns.

  SCREAMING_SNAKE_CASE_						 = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)    )
  SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = parser.parse_args_into_dataclasses()

  if (
      os.path.exists(training_args.output_dir    )
      and os.listdir(training_args.output_dir    )
      and training_args.do_train
      and not training_args.overwrite_output_dir
  ):
    raise ValueError(
        F'Output directory ({training_args.output_dir}) already exists and is not empty. Use'
        ''' --overwrite_output_dir to overcome.'''    )

  # Setup logging
  logging.basicConfig(
      format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''',							datefmt='''%m/%d/%Y %H:%M:%S''',							level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN,							)
  logger.warning(
      '''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s''',							training_args.local_rank,							training_args.device,							training_args.n_gpu,							bool(training_args.local_rank != -1    ),							training_args.fpaa,							)
  # Set the verbosity to info of the Transformers logger (on main process only):
  if is_main_process(training_args.local_rank    ):
    transformers.utils.logging.set_verbosity_info()
    transformers.utils.logging.enable_default_handler()
    transformers.utils.logging.enable_explicit_format()
  logger.info('''Training/evaluation parameters %s''',							__a    )

  # Set seed
  set_seed(training_args.seed    )

  try:
    SCREAMING_SNAKE_CASE_						 = processors[data_args.task_name]()
    SCREAMING_SNAKE_CASE_						 = processor.get_labels()
    SCREAMING_SNAKE_CASE_						 = len(__a    )
  except KeyError:
    raise ValueError('''Task not found: %s''' % (data_args.task_name)    )

  # Load pretrained model and tokenizer
  #
  # Distributed training:
  # The .from_pretrained methods guarantee that only one local process can concurrently
  # download model & vocab.

  SCREAMING_SNAKE_CASE_						 = AutoConfig.from_pretrained(
      model_args.config_name if model_args.config_name else model_args.model_name_or_path,							num_labels=__a,							finetuning_task=data_args.task_name,							cache_dir=model_args.cache_dir,							)
  SCREAMING_SNAKE_CASE_						 = AutoTokenizer.from_pretrained(
      model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,							cache_dir=model_args.cache_dir,							)
  SCREAMING_SNAKE_CASE_						 = AutoModelForMultipleChoice.from_pretrained(
      model_args.model_name_or_path,							from_tf=bool('''.ckpt''' in model_args.model_name_or_path    ),							config=__a,							cache_dir=model_args.cache_dir,							)

  # Get datasets
  SCREAMING_SNAKE_CASE_						 = (
      MultipleChoiceDataset(
          data_dir=data_args.data_dir,							tokenizer=__a,							task=data_args.task_name,							max_seq_length=data_args.max_seq_length,							overwrite_cache=data_args.overwrite_cache,							mode=Split.train,							)
      if training_args.do_train
      else None
  )
  SCREAMING_SNAKE_CASE_						 = (
      MultipleChoiceDataset(
          data_dir=data_args.data_dir,							tokenizer=__a,							task=data_args.task_name,							max_seq_length=data_args.max_seq_length,							overwrite_cache=data_args.overwrite_cache,							mode=Split.dev,							)
      if training_args.do_eval
      else None
  )

  def compute_metrics(__a    ) -> Dict:
    SCREAMING_SNAKE_CASE_						 = np.argmax(p.predictions,							axis=1    )
    return {"acc": simple_accuracy(__a,							p.label_ids    )}

  # Data collator
  SCREAMING_SNAKE_CASE_						 = DataCollatorWithPadding(__a,							pad_to_multiple_of=8    ) if training_args.fpaa else None

  # Initialize our Trainer
  SCREAMING_SNAKE_CASE_						 = Trainer(
      model=__a,							args=__a,							train_dataset=__a,							eval_dataset=__a,							compute_metrics=__a,							data_collator=__a,							)

  # Training
  if training_args.do_train:
    trainer.train(
        model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path    ) else None    )
    trainer.save_model()
    # For convenience, we also re-save the tokenizer to the same directory,
    # so that you can share your model easily on huggingface.co/models =)
    if trainer.is_world_master():
      tokenizer.save_pretrained(training_args.output_dir    )

    # Evaluation
  SCREAMING_SNAKE_CASE_						 = {}
  if training_args.do_eval:
    logger.info('''*** Evaluate ***'''    )

    SCREAMING_SNAKE_CASE_						 = trainer.evaluate()

    SCREAMING_SNAKE_CASE_						 = os.path.join(training_args.output_dir,							'''eval_results.txt'''    )
    if trainer.is_world_master():
      with open(__a,							'''w'''    ) as writer:
        logger.info('''***** Eval results *****'''    )
        for key, value in result.items():
          logger.info('''  %s = %s''',							__a,							__a    )
          writer.write('''%s = %s\n''' % (key, value)    )

        results.update(__a    )

  return results



def        _lowerCamelCase ( __a    ):
  # For xla_spawn (TPUs)
  main()


if __name__ == "__main__":
      main()

"""simple docstring"""




import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest

import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset

from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_cuda
from accelerate.utils import ProjectConfiguration, set_seed


lowerCAmelCase__							=				logging.getLogger(__name__)



def        _lowerCamelCase ( __a=2,							__a=3,							__a=16,							__a = 10,							__a = 2    ):

		def get_dataset(__a    ):
				SCREAMING_SNAKE_CASE_						 = torch.randn(batch_size * n_batches,							1    )
				return TensorDataset(__a,							a * x + b + 0.1 * torch.randn(batch_size * n_batches,							1    )    )

		SCREAMING_SNAKE_CASE_						 = get_dataset(__a    )
		SCREAMING_SNAKE_CASE_						 = get_dataset(__a    )
		SCREAMING_SNAKE_CASE_						 = DataLoader(__a,							shuffle=__a,							batch_size=__a,							num_workers=4    )
		SCREAMING_SNAKE_CASE_						 = DataLoader(__a,							shuffle=__a,							batch_size=__a,							num_workers=4    )
		return (train_dataloader, valid_dataloader)



def        _lowerCamelCase ( __a,							__a,							__a,							__a,							__a,							__a=None    ):
		SCREAMING_SNAKE_CASE_						 = []
		for epoch in range(__a    ):
				# Train quickly
				model.train()
				for batch in dataloader:
						SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = batch
						SCREAMING_SNAKE_CASE_						 = model(__a    )
						SCREAMING_SNAKE_CASE_						 = torch.nn.functional.mse_loss(__a,							__a    )
						accelerator.backward(__a    )
						optimizer.step()
						optimizer.zero_grad()
				rands.append(random.random()    )  # Introduce some randomness
				if scheduler is not None:
						scheduler.step()
		return rands





class  snake_case     (							nn.Module   ):

							def __init__(self    ):

									"""simple docstring"""







									super().__init__()
									SCREAMING_SNAKE_CASE_						 = nn.Parameter(torch.randn(1    )    )
									SCREAMING_SNAKE_CASE_						 = nn.Parameter(torch.randn(1    )    )







							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									return x * self.a + self.b







class  snake_case     (							unittest.TestCase   ):

							def _lowercase					(self    ):

									"""simple docstring"""







									with tempfile.TemporaryDirectory() as tmpdir:
											set_seed(42    )
											SCREAMING_SNAKE_CASE_						 = DummyModel()
											SCREAMING_SNAKE_CASE_						 = torch.optim.Adam(params=model.parameters()       ,       lr=1e-3    )
											SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = dummy_dataloaders()
											SCREAMING_SNAKE_CASE_						 = ProjectConfiguration(total_limit=1       ,       project_dir=SCREAMING_SNAKE_CASE_       ,       automatic_checkpoint_naming=SCREAMING_SNAKE_CASE_    )
											# Train baseline
											SCREAMING_SNAKE_CASE_						 = Accelerator(project_config=SCREAMING_SNAKE_CASE_    )
											SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = accelerator.prepare(
											    SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
											# Save initial
											accelerator.save_state()

											# Save second state
											accelerator.save_state()
											self.assertEqual(len(os.listdir(accelerator.project_dir    )    )       ,       1    )

							def _lowercase					(self    ):

									"""simple docstring"""







									with tempfile.TemporaryDirectory() as tmpdir:
											set_seed(42    )
											SCREAMING_SNAKE_CASE_						 = DummyModel()
											SCREAMING_SNAKE_CASE_						 = torch.optim.Adam(params=model.parameters()       ,       lr=1e-3    )
											SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = dummy_dataloaders()
											# Train baseline
											SCREAMING_SNAKE_CASE_						 = Accelerator()
											SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = accelerator.prepare(
											    SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
											# Save initial
											SCREAMING_SNAKE_CASE_						 = os.path.join(SCREAMING_SNAKE_CASE_       ,       '''initial'''    )
											accelerator.save_state(SCREAMING_SNAKE_CASE_    )
											((SCREAMING_SNAKE_CASE_) ,(SCREAMING_SNAKE_CASE_))						 = model.a.item(), model.b.item()
											SCREAMING_SNAKE_CASE_						 = optimizer.state_dict()
											SCREAMING_SNAKE_CASE_						 = train(3       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
											((SCREAMING_SNAKE_CASE_) ,(SCREAMING_SNAKE_CASE_))						 = model.a.item(), model.b.item()
											SCREAMING_SNAKE_CASE_						 = optimizer.state_dict()

											# Train partially
											set_seed(42    )
											SCREAMING_SNAKE_CASE_						 = DummyModel()
											SCREAMING_SNAKE_CASE_						 = torch.optim.Adam(params=model.parameters()       ,       lr=1e-3    )
											SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = dummy_dataloaders()
											SCREAMING_SNAKE_CASE_						 = Accelerator()
											SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = accelerator.prepare(
											    SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
											accelerator.load_state(SCREAMING_SNAKE_CASE_    )
											((SCREAMING_SNAKE_CASE_) ,(SCREAMING_SNAKE_CASE_))						 = model.a.item(), model.b.item()
											SCREAMING_SNAKE_CASE_						 = optimizer.state_dict()
											self.assertEqual(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
											self.assertEqual(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
											self.assertEqual(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )

											SCREAMING_SNAKE_CASE_						 = train(2       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
											# Save everything
											SCREAMING_SNAKE_CASE_						 = os.path.join(SCREAMING_SNAKE_CASE_       ,       '''checkpoint'''    )
											accelerator.save_state(SCREAMING_SNAKE_CASE_    )

											# Load everything back in and make sure all states work
											accelerator.load_state(SCREAMING_SNAKE_CASE_    )
											test_rands += train(1       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
											((SCREAMING_SNAKE_CASE_) ,(SCREAMING_SNAKE_CASE_))						 = model.a.item(), model.b.item()
											SCREAMING_SNAKE_CASE_						 = optimizer.state_dict()
											self.assertEqual(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
											self.assertEqual(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
											self.assertEqual(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
											self.assertEqual(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self    ):

									"""simple docstring"""







									with tempfile.TemporaryDirectory() as tmpdir:
											set_seed(42    )
											SCREAMING_SNAKE_CASE_						 = DummyModel()
											SCREAMING_SNAKE_CASE_						 = torch.optim.Adam(params=model.parameters()       ,       lr=1e-3    )
											SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = dummy_dataloaders()
											SCREAMING_SNAKE_CASE_						 = ProjectConfiguration(automatic_checkpoint_naming=SCREAMING_SNAKE_CASE_    )

											# Train baseline
											SCREAMING_SNAKE_CASE_						 = Accelerator(project_dir=SCREAMING_SNAKE_CASE_       ,       project_config=SCREAMING_SNAKE_CASE_    )
											SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = accelerator.prepare(
											    SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
											# Save initial
											accelerator.save_state()
											((SCREAMING_SNAKE_CASE_) ,(SCREAMING_SNAKE_CASE_))						 = model.a.item(), model.b.item()
											SCREAMING_SNAKE_CASE_						 = optimizer.state_dict()
											SCREAMING_SNAKE_CASE_						 = train(3       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
											((SCREAMING_SNAKE_CASE_) ,(SCREAMING_SNAKE_CASE_))						 = model.a.item(), model.b.item()
											SCREAMING_SNAKE_CASE_						 = optimizer.state_dict()

											# Train partially
											set_seed(42    )
											SCREAMING_SNAKE_CASE_						 = DummyModel()
											SCREAMING_SNAKE_CASE_						 = torch.optim.Adam(params=model.parameters()       ,       lr=1e-3    )
											SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = dummy_dataloaders()
											SCREAMING_SNAKE_CASE_						 = ProjectConfiguration(iteration=1       ,       automatic_checkpoint_naming=SCREAMING_SNAKE_CASE_    )
											SCREAMING_SNAKE_CASE_						 = Accelerator(project_dir=SCREAMING_SNAKE_CASE_       ,       project_config=SCREAMING_SNAKE_CASE_    )
											SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = accelerator.prepare(
											    SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
											accelerator.load_state(os.path.join(SCREAMING_SNAKE_CASE_       ,       '''checkpoints'''       ,       '''checkpoint_0'''    )    )
											((SCREAMING_SNAKE_CASE_) ,(SCREAMING_SNAKE_CASE_))						 = model.a.item(), model.b.item()
											SCREAMING_SNAKE_CASE_						 = optimizer.state_dict()
											self.assertEqual(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
											self.assertEqual(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
											self.assertEqual(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )

											SCREAMING_SNAKE_CASE_						 = train(2       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
											# Save everything
											accelerator.save_state()

											# Load everything back in and make sure all states work
											accelerator.load_state(os.path.join(SCREAMING_SNAKE_CASE_       ,       '''checkpoints'''       ,       '''checkpoint_1'''    )    )
											test_rands += train(1       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
											((SCREAMING_SNAKE_CASE_) ,(SCREAMING_SNAKE_CASE_))						 = model.a.item(), model.b.item()
											SCREAMING_SNAKE_CASE_						 = optimizer.state_dict()
											self.assertEqual(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
											self.assertEqual(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
											self.assertEqual(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
											self.assertEqual(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = torch.tensor([1, 2, 3]    )
									SCREAMING_SNAKE_CASE_						 = torch.tensor([2, 3, 4]    )
									SCREAMING_SNAKE_CASE_						 = DummyModel()
									SCREAMING_SNAKE_CASE_						 = torch.optim.Adam(net.parameters()    )
									SCREAMING_SNAKE_CASE_						 = Accelerator()
									with self.assertRaises(SCREAMING_SNAKE_CASE_    ) as ve:
											accelerator.register_for_checkpointing(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = str(ve.exception    )
									self.assertTrue('''Item at index 0''' in message    )
									self.assertTrue('''Item at index 1''' in message    )
									self.assertFalse('''Item at index 2''' in message    )
									self.assertFalse('''Item at index 3''' in message    )

							def _lowercase					(self    ):

									"""simple docstring"""







									with tempfile.TemporaryDirectory() as tmpdir:
											set_seed(42    )
											SCREAMING_SNAKE_CASE_						 = DummyModel()
											SCREAMING_SNAKE_CASE_						 = torch.optim.Adam(params=model.parameters()       ,       lr=1e-3    )
											SCREAMING_SNAKE_CASE_						 = torch.optim.lr_scheduler.StepLR(SCREAMING_SNAKE_CASE_       ,       step_size=1       ,       gamma=0.99    )
											SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = dummy_dataloaders()
											SCREAMING_SNAKE_CASE_						 = ProjectConfiguration(automatic_checkpoint_naming=SCREAMING_SNAKE_CASE_    )
											# Train baseline
											SCREAMING_SNAKE_CASE_						 = Accelerator(project_dir=SCREAMING_SNAKE_CASE_       ,       project_config=SCREAMING_SNAKE_CASE_    )
											SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = accelerator.prepare(
											    SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
											# Save initial
											accelerator.save_state()
											SCREAMING_SNAKE_CASE_						 = scheduler.state_dict()
											train(3       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )
											self.assertNotEqual(SCREAMING_SNAKE_CASE_       ,       scheduler.state_dict()    )

											# Load everything back in and make sure all states work
											accelerator.load_state(os.path.join(SCREAMING_SNAKE_CASE_       ,       '''checkpoints'''       ,       '''checkpoint_0'''    )    )
											self.assertEqual(SCREAMING_SNAKE_CASE_       ,       scheduler.state_dict()    )

							def _lowercase					(self    ):

									"""simple docstring"""







									with tempfile.TemporaryDirectory() as tmpdir:
											set_seed(42    )
											SCREAMING_SNAKE_CASE_						 = DummyModel()
											SCREAMING_SNAKE_CASE_						 = ProjectConfiguration(automatic_checkpoint_naming=SCREAMING_SNAKE_CASE_       ,       total_limit=2    )
											# Train baseline
											SCREAMING_SNAKE_CASE_						 = Accelerator(project_dir=SCREAMING_SNAKE_CASE_       ,       project_config=SCREAMING_SNAKE_CASE_    )
											SCREAMING_SNAKE_CASE_						 = accelerator.prepare(SCREAMING_SNAKE_CASE_    )
											# Save 3 states:
											for _ in range(11    ):
													accelerator.save_state()
											self.assertTrue(not os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_       ,       '''checkpoints'''       ,       '''checkpoint_0'''    )    )    )
											self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_       ,       '''checkpoints'''       ,       '''checkpoint_9'''    )    )    )
											self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_       ,       '''checkpoints'''       ,       '''checkpoint_10'''    )    )    )







							@require_cuda
							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = ['''torchrun''', f'--nproc_per_node={torch.cuda.device_count()}', inspect.getfile(self.__class__    )]
									execute_subprocess_async(SCREAMING_SNAKE_CASE_       ,       env=os.environ.copy()    )


if __name__ == "__main__":
						lowerCAmelCase__							=				'/tmp/accelerate/state_checkpointing'
						lowerCAmelCase__							=				DummyModel()
						lowerCAmelCase__							=				torch.optim.Adam(params=model.parameters(), lr=1e-3)
						lowerCAmelCase__							=				torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.99)
						lowerCAmelCase__,  lowerCAmelCase__							=				dummy_dataloaders()
						lowerCAmelCase__							=				ProjectConfiguration(automatic_checkpoint_naming=True)
						# Train baseline
						lowerCAmelCase__							=				Accelerator(project_dir=savedir, project_config=project_config, mixed_precision='no')
						if accelerator.process_index == 0:
												if os.path.exists(savedir):
																		shutil.rmtree(savedir)
												os.makedirs(savedir)
						lowerCAmelCase__,  lowerCAmelCase__,  lowerCAmelCase__,  lowerCAmelCase__,  lowerCAmelCase__							=				accelerator.prepare(
						    model, optimizer, train_dataloader, valid_dataloader, scheduler
						)
						lowerCAmelCase__,  lowerCAmelCase__							=				accelerator.prepare(model, optimizer)
						train(3, model, train_dataloader, optimizer, accelerator, scheduler)
						# Check that the intial optimizer is loaded on the GPU
						for group in optimizer.param_groups:
												lowerCAmelCase__							=				group['params'][0].device
												break
						assert param_device.type == accelerator.device.type
						lowerCAmelCase__							=				model.cpu()
						accelerator.wait_for_everyone()
						accelerator.save_state()
						accelerator.wait_for_everyone()

						# Check CPU state
						accelerator.load_state(os.path.join(savedir, 'checkpoints', 'checkpoint_0'), map_location='cpu')
						for group in optimizer.param_groups:
												lowerCAmelCase__							=				group['params'][0].device
												break
						assert (
						    param_device.type == torch.device('cpu').type
						), f"Loaded optimizer states did not match, expected to be loaded on the CPU but got {param_device}"

						# Check device state
						model.to(accelerator.device)
						accelerator.load_state(os.path.join(savedir, 'checkpoints', 'checkpoint_0'), map_location='on_device')
						for group in optimizer.param_groups:
												lowerCAmelCase__							=				group['params'][0].device
												break
						assert (
						    param_device.type == accelerator.device.type
						), f"Loaded optimizer states did not match, expected to be loaded on {accelerator.device} but got {param_device}"

						# Check error
						with pytest.raises(TypeError, match='Unsupported optimizer map location passed'):
												accelerator.load_state(os.path.join(savedir, 'checkpoints', 'checkpoint_0'), map_location='invalid')
						accelerator.wait_for_everyone()
						if accelerator.process_index == 0:
												shutil.rmtree(savedir)
						accelerator.wait_for_everyone()