
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


A		: Optional[Any]								=     logging.getLogger(__name__)




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	Optional[Any]=2		,   SCREAMING_SNAKE_CASE_	:	Dict=3		,   SCREAMING_SNAKE_CASE_	:	Optional[Any]=16		,   SCREAMING_SNAKE_CASE_	:	int = 10		,   SCREAMING_SNAKE_CASE_	:	int = 2       )  ->     List[Any]:

						def get_dataset(SCREAMING_SNAKE_CASE_	:	Optional[Any]       ):
												_lowercase					       =					torch.randn(batch_size * n_batches		,   1       )
												return TensorDataset(SCREAMING_SNAKE_CASE_		,   a * x + b + 0.1 * torch.randn(batch_size * n_batches		,   1       )       )

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




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	List[str]		,   SCREAMING_SNAKE_CASE_	:	Optional[int]		,   SCREAMING_SNAKE_CASE_	:	List[Any]		,   SCREAMING_SNAKE_CASE_	:	List[Any]		,   SCREAMING_SNAKE_CASE_	:	Optional[Any]		,   SCREAMING_SNAKE_CASE_	:	List[str]=None       )  ->     Any:
						_lowercase					       =					[]
						for epoch in range(SCREAMING_SNAKE_CASE_       ):
												# Train quickly
												model.train()
												for batch in dataloader:
																		_lowercase       ,						_lowercase					       =					batch
																		_lowercase					       =					model(SCREAMING_SNAKE_CASE_       )
																		_lowercase					       =					torch.nn.functional.mse_loss(SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_       )
																		accelerator.backward(SCREAMING_SNAKE_CASE_       )
																		optimizer.step()
																		optimizer.zero_grad()
												rands.append(random.random()       )  # Introduce some randomness
												if scheduler is not None:
																		scheduler.step()
						return rands



class    a_ (			nn.Module    ):






			def __init__(      self  ):
									super().__init__()
									_lowercase					       =					nn.Parameter(torch.randn(1  )  )
									_lowercase					       =					nn.Parameter(torch.randn(1  )  )






			def    UpperCamelCase_				(      self				,			__UpperCamelCase  ):
									return x * self.a + self.b







class    a_ (			unittest.TestCase    ):






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

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






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

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

															_lowercase					       =					train(2				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase  )
															# Save everything
															_lowercase					       =					os.path.join(__UpperCamelCase				,			"""checkpoint"""  )
															accelerator.save_state(__UpperCamelCase  )

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






			def    UpperCamelCase_				(      self  ):
									with tempfile.TemporaryDirectory() as tmpdir:
															set_seed(42  )
															_lowercase					       =					DummyModel()
															_lowercase					       =					torch.optim.Adam(params=model.parameters()				,			lr=1E-3  )
															_lowercase       ,						_lowercase					       =					dummy_dataloaders()
															_lowercase					       =					ProjectConfiguration(automatic_checkpoint_naming=__UpperCamelCase  )

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

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

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

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






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					torch.tensor([1, 2, 3]  )
									_lowercase					       =					torch.tensor([2, 3, 4]  )
									_lowercase					       =					DummyModel()
									_lowercase					       =					torch.optim.Adam(net.parameters()  )
									_lowercase					       =					Accelerator()
									with self.assertRaises(__UpperCamelCase  ) as ve:
															accelerator.register_for_checkpointing(__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase  )
									_lowercase					       =					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    UpperCamelCase_				(      self  ):
									with tempfile.TemporaryDirectory() as tmpdir:
															set_seed(42  )
															_lowercase					       =					DummyModel()
															_lowercase					       =					torch.optim.Adam(params=model.parameters()				,			lr=1E-3  )
															_lowercase					       =					torch.optim.lr_scheduler.StepLR(__UpperCamelCase				,			step_size=1				,			gamma=0.99  )
															_lowercase       ,						_lowercase					       =					dummy_dataloaders()
															_lowercase					       =					ProjectConfiguration(automatic_checkpoint_naming=__UpperCamelCase  )
															# Train baseline
															_lowercase					       =					Accelerator(project_dir=__UpperCamelCase				,			project_config=__UpperCamelCase  )
															_lowercase       ,						_lowercase       ,						_lowercase       ,						_lowercase       ,						_lowercase					       =					accelerator.prepare(
															    __UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase  )
															# Save initial
															accelerator.save_state()
															_lowercase					       =					scheduler.state_dict()
															train(3				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase  )
															self.assertNotEqual(__UpperCamelCase				,			scheduler.state_dict()  )

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






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






			@require_cuda
			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					["""torchrun""", f"""--nproc_per_node={torch.cuda.device_count()}""", inspect.getfile(self.__class__  )]
									execute_subprocess_async(__UpperCamelCase				,			env=os.environ.copy()  )


if __name__ == "__main__":
		A		: List[str]								=     '''/tmp/accelerate/state_checkpointing'''
		A		: Optional[int]								=     DummyModel()
		A		: List[str]								=     torch.optim.Adam(params=model.parameters(), lr=1e-3)
		A		: Dict								=     torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.99)
		A							,      A		: Optional[int]								=     dummy_dataloaders()
		A		: Optional[Any]								=     ProjectConfiguration(automatic_checkpoint_naming=True)
		# Train baseline
		A		: int								=     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)
		A							,      A							,      A							,      A							,      A		: Optional[Any]								=     accelerator.prepare(
		    model, optimizer, train_dataloader, valid_dataloader, scheduler
		)
		A							,      A		: Optional[int]								=     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:
				A		: int								=     group['''params'''][0].device
				break
		assert param_device.type == accelerator.device.type
		A		: str								=     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:
				A		: str								=     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:
				A		: Dict								=     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()

import datasets


A		: Optional[int]								=     '''\
@InProceedings{conneau2018xnli,
  author = "Conneau, Alexis
                 and Rinott, Ruty
                 and Lample, Guillaume
                 and Williams, Adina
                 and Bowman, Samuel R.
                 and Schwenk, Holger
                 and Stoyanov, Veselin",
  title = "XNLI: Evaluating Cross-lingual Sentence Representations",
  booktitle = "Proceedings of the 2018 Conference on Empirical Methods
               in Natural Language Processing",
  year = "2018",
  publisher = "Association for Computational Linguistics",
  location = "Brussels, Belgium",
}
'''

A		: Optional[int]								=     '''\
XNLI is a subset of a few thousand examples from MNLI which has been translated
into a 14 different languages (some low-ish resource). As with MNLI, the goal is
to predict textual entailment (does sentence A imply/contradict/neither sentence
B) and is a classification task (given two sentences, predict one of three
labels).
'''

A		: str								=     '''
Computes XNLI score which is just simple accuracy.
Args:
    predictions: Predicted labels.
    references: Ground truth labels.
Returns:
    \'accuracy\': accuracy
Examples:

    >>> predictions = [0, 1]
    >>> references = [0, 1]
    >>> xnli_metric = datasets.load_metric("xnli")
    >>> results = xnli_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'accuracy\': 1.0}
'''




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	List[str]		,   SCREAMING_SNAKE_CASE_	:	List[Any]       )  ->     str:
						return (preds == labels).mean()



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






			def    UpperCamelCase_				(      self  ):
									return datasets.MetricInfo(
									    description=_DESCRIPTION				,			citation=_CITATION				,			inputs_description=_KWARGS_DESCRIPTION				,			features=datasets.Features(
									        {
									            """predictions""": datasets.Value("""int64""" if self.config_name != """sts-b""" else """float32"""  ),
									            """references""": datasets.Value("""int64""" if self.config_name != """sts-b""" else """float32"""  ),
									        }  )				,			codebase_urls=[]				,			reference_urls=[]				,			format="""numpy"""				,			)






			def    UpperCamelCase_				(      self				,			__UpperCamelCase				,			__UpperCamelCase  ):
									return {"accuracy": simple_accuracy(__UpperCamelCase				,			__UpperCamelCase  )}