

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					(lowercase_: Tuple=2    ,      lowercase_: Any=3    ,      lowercase_: List[str]=16    ,      lowercase_: int = 10    ,      lowercase_: int = 2					)     ->      Optional[int]:

	def get_dataset(lowercase_: int					):
		A__							:    List[Any]        =		torch.randn(batch_size * n_batches    ,      1					)
		return TensorDataset(lowercase_    ,      a * x + b + 0.1 * torch.randn(batch_size * n_batches    ,      1					)					)

	A__							:    str        =		get_dataset(lowercase_					)
	A__							:    Union[str, Any]        =		get_dataset(lowercase_					)
	A__							:    Any        =		DataLoader(lowercase_    ,      shuffle=lowercase_    ,      batch_size=lowercase_    ,      num_workers=4					)
	A__							:    Optional[int]        =		DataLoader(lowercase_    ,      shuffle=lowercase_    ,      batch_size=lowercase_    ,      num_workers=4					)
	return (train_dataloader, valid_dataloader)

def 						UpperCamelCase					(lowercase_: Union[str, Any]    ,      lowercase_: List[str]    ,      lowercase_: str    ,      lowercase_: Dict    ,      lowercase_: int    ,      lowercase_: Union[str, Any]=None					)     ->      Optional[int]:
	A__							:    int        =		[]
	for epoch in range(lowercase_					):
		# Train quickly
		model.train()
		for batch in dataloader:
			A__   ,   A__							:    Optional[int]        =		batch
			A__							:    List[str]        =		model(lowercase_					)
			A__							:    List[str]        =		torch.nn.functional.mse_loss(lowercase_    ,      lowercase_					)
			accelerator.backward(lowercase_					)
			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				):


					'''simple docstring'''






					def __init__( self			):
						super().__init__()
						A__							:    int        =		nn.Parameter(torch.randn(1			)			)
						A__							:    Tuple        =		nn.Parameter(torch.randn(1			)			)






					def   __A			( self    ,       A__			):
						return x * self.a + self.b



class     _a						(unittest.TestCase				):


					'''simple docstring'''






					def   __A			( self			):
						with tempfile.TemporaryDirectory() as tmpdir:
							set_seed(42			)
							A__							:    Dict        =		DummyModel()
							A__							:    str        =		torch.optim.Adam(params=model.parameters()    ,       lr=1e-3			)
							A__   ,   A__							:    str        =		dummy_dataloaders()
							A__							:    List[str]        =		ProjectConfiguration(total_limit=1    ,       project_dir=A__    ,       automatic_checkpoint_naming=A__			)
							# Train baseline
							A__							:    List[Any]        =		Accelerator(project_config=A__			)
							A__   ,   A__   ,   A__   ,   A__							:    List[str]        =		accelerator.prepare(
							    A__    ,       A__    ,       A__    ,       A__			)
							# Save initial
							accelerator.save_state()

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






					def   __A			( self			):
						with tempfile.TemporaryDirectory() as tmpdir:
							set_seed(42			)
							A__							:    Any        =		DummyModel()
							A__							:    List[str]        =		torch.optim.Adam(params=model.parameters()    ,       lr=1e-3			)
							A__   ,   A__							:    Any        =		dummy_dataloaders()
							# Train baseline
							A__							:    str        =		Accelerator()
							A__   ,   A__   ,   A__   ,   A__							:    Dict        =		accelerator.prepare(
							    A__    ,       A__    ,       A__    ,       A__			)
							# Save initial
							A__							:    Union[str, Any]        =		os.path.join(A__    ,       """initial"""			)
							accelerator.save_state(A__			)
							((A__)   ,   (A__))							:    Dict        =		model.a.item(), model.b.item()
							A__							:    Tuple        =		optimizer.state_dict()
							A__							:    List[Any]        =		train(3    ,       A__    ,       A__    ,       A__    ,       A__			)
							((A__)   ,   (A__))							:    Dict        =		model.a.item(), model.b.item()
							A__							:    str        =		optimizer.state_dict()

							# Train partially
							set_seed(42			)
							A__							:    Tuple        =		DummyModel()
							A__							:    List[Any]        =		torch.optim.Adam(params=model.parameters()    ,       lr=1e-3			)
							A__   ,   A__							:    Union[str, Any]        =		dummy_dataloaders()
							A__							:    Tuple        =		Accelerator()
							A__   ,   A__   ,   A__   ,   A__							:    List[str]        =		accelerator.prepare(
							    A__    ,       A__    ,       A__    ,       A__			)
							accelerator.load_state(A__			)
							((A__)   ,   (A__))							:    int        =		model.a.item(), model.b.item()
							A__							:    str        =		optimizer.state_dict()
							self.assertEqual(A__    ,       A__			)
							self.assertEqual(A__    ,       A__			)
							self.assertEqual(A__    ,       A__			)

							A__							:    List[Any]        =		train(2    ,       A__    ,       A__    ,       A__    ,       A__			)
							# Save everything
							A__							:    int        =		os.path.join(A__    ,       """checkpoint"""			)
							accelerator.save_state(A__			)

							# Load everything back in and make sure all states work
							accelerator.load_state(A__			)
							test_rands += train(1    ,       A__    ,       A__    ,       A__    ,       A__			)
							((A__)   ,   (A__))							:    Union[str, Any]        =		model.a.item(), model.b.item()
							A__							:    Union[str, Any]        =		optimizer.state_dict()
							self.assertEqual(A__    ,       A__			)
							self.assertEqual(A__    ,       A__			)
							self.assertEqual(A__    ,       A__			)
							self.assertEqual(A__    ,       A__			)






					def   __A			( self			):
						with tempfile.TemporaryDirectory() as tmpdir:
							set_seed(42			)
							A__							:    Union[str, Any]        =		DummyModel()
							A__							:    str        =		torch.optim.Adam(params=model.parameters()    ,       lr=1e-3			)
							A__   ,   A__							:    str        =		dummy_dataloaders()
							A__							:    Any        =		ProjectConfiguration(automatic_checkpoint_naming=A__			)

							# Train baseline
							A__							:    List[Any]        =		Accelerator(project_dir=A__    ,       project_config=A__			)
							A__   ,   A__   ,   A__   ,   A__							:    Optional[int]        =		accelerator.prepare(
							    A__    ,       A__    ,       A__    ,       A__			)
							# Save initial
							accelerator.save_state()
							((A__)   ,   (A__))							:    Optional[int]        =		model.a.item(), model.b.item()
							A__							:    Union[str, Any]        =		optimizer.state_dict()
							A__							:    str        =		train(3    ,       A__    ,       A__    ,       A__    ,       A__			)
							((A__)   ,   (A__))							:    Tuple        =		model.a.item(), model.b.item()
							A__							:    Any        =		optimizer.state_dict()

							# Train partially
							set_seed(42			)
							A__							:    Union[str, Any]        =		DummyModel()
							A__							:    Optional[Any]        =		torch.optim.Adam(params=model.parameters()    ,       lr=1e-3			)
							A__   ,   A__							:    Optional[Any]        =		dummy_dataloaders()
							A__							:    Union[str, Any]        =		ProjectConfiguration(iteration=1    ,       automatic_checkpoint_naming=A__			)
							A__							:    int        =		Accelerator(project_dir=A__    ,       project_config=A__			)
							A__   ,   A__   ,   A__   ,   A__							:    int        =		accelerator.prepare(
							    A__    ,       A__    ,       A__    ,       A__			)
							accelerator.load_state(os.path.join(A__    ,       """checkpoints"""    ,       """checkpoint_0"""			)			)
							((A__)   ,   (A__))							:    Dict        =		model.a.item(), model.b.item()
							A__							:    Any        =		optimizer.state_dict()
							self.assertEqual(A__    ,       A__			)
							self.assertEqual(A__    ,       A__			)
							self.assertEqual(A__    ,       A__			)

							A__							:    Tuple        =		train(2    ,       A__    ,       A__    ,       A__    ,       A__			)
							# Save everything
							accelerator.save_state()

							# Load everything back in and make sure all states work
							accelerator.load_state(os.path.join(A__    ,       """checkpoints"""    ,       """checkpoint_1"""			)			)
							test_rands += train(1    ,       A__    ,       A__    ,       A__    ,       A__			)
							((A__)   ,   (A__))							:    List[str]        =		model.a.item(), model.b.item()
							A__							:    Optional[int]        =		optimizer.state_dict()
							self.assertEqual(A__    ,       A__			)
							self.assertEqual(A__    ,       A__			)
							self.assertEqual(A__    ,       A__			)
							self.assertEqual(A__    ,       A__			)






					def   __A			( self			):
						A__							:    Optional[int]        =		torch.tensor([1, 2, 3]			)
						A__							:    Optional[int]        =		torch.tensor([2, 3, 4]			)
						A__							:    str        =		DummyModel()
						A__							:    List[str]        =		torch.optim.Adam(net.parameters()			)
						A__							:    Optional[Any]        =		Accelerator()
						with self.assertRaises(A__			) as ve:
							accelerator.register_for_checkpointing(A__    ,       A__    ,       A__    ,       A__			)
						A__							:    str        =		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   __A			( self			):
						with tempfile.TemporaryDirectory() as tmpdir:
							set_seed(42			)
							A__							:    List[str]        =		DummyModel()
							A__							:    int        =		torch.optim.Adam(params=model.parameters()    ,       lr=1e-3			)
							A__							:    List[str]        =		torch.optim.lr_scheduler.StepLR(A__    ,       step_size=1    ,       gamma=0.9_9			)
							A__   ,   A__							:    List[str]        =		dummy_dataloaders()
							A__							:    Any        =		ProjectConfiguration(automatic_checkpoint_naming=A__			)
							# Train baseline
							A__							:    Tuple        =		Accelerator(project_dir=A__    ,       project_config=A__			)
							A__   ,   A__   ,   A__   ,   A__   ,   A__							:    Dict        =		accelerator.prepare(
							    A__    ,       A__    ,       A__    ,       A__    ,       A__			)
							# Save initial
							accelerator.save_state()
							A__							:    Optional[Any]        =		scheduler.state_dict()
							train(3    ,       A__    ,       A__    ,       A__    ,       A__    ,       A__			)
							self.assertNotEqual(A__    ,       scheduler.state_dict()			)

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






					def   __A			( self			):
						with tempfile.TemporaryDirectory() as tmpdir:
							set_seed(42			)
							A__							:    Optional[int]        =		DummyModel()
							A__							:    Union[str, Any]        =		ProjectConfiguration(automatic_checkpoint_naming=A__    ,       total_limit=2			)
							# Train baseline
							A__							:    List[Any]        =		Accelerator(project_dir=A__    ,       project_config=A__			)
							A__							:    Any        =		accelerator.prepare(A__			)
							# Save 3 states:
							for _ in range(11			):
								accelerator.save_state()
							self.assertTrue(not os.path.exists(os.path.join(A__    ,       """checkpoints"""    ,       """checkpoint_0"""			)			)			)
							self.assertTrue(os.path.exists(os.path.join(A__    ,       """checkpoints"""    ,       """checkpoint_9"""			)			)			)
							self.assertTrue(os.path.exists(os.path.join(A__    ,       """checkpoints"""    ,       """checkpoint_10"""			)			)			)






					@require_cuda
					def   __A			( self			):
						A__							:    Optional[Any]        =		["""torchrun""", F"""--nproc_per_node={torch.cuda.device_count()}""", inspect.getfile(self.__class__			)]
						execute_subprocess_async(A__    ,       env=os.environ.copy()			)


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

import pytest

from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict


A_       : Any         =			namedtuple(
    '_TestCommandArgs',
    [
        'dataset',
        'name',
        'cache_dir',
        'data_dir',
        'all_configs',
        'save_infos',
        'ignore_verifications',
        'force_redownload',
        'clear_cache',
    ],
    defaults=[None, None, None, False, False, False, False, False],
)

def 						UpperCamelCase					(lowercase_: Any    ,      lowercase_: List[str]					)     ->      Optional[int]:
	return (abs(source - target					) / target) < 0.01


@pytest.mark.integration
def 						UpperCamelCase					(lowercase_: str					)     ->      str:
	A__							:    List[str]        =		_TestCommandArgs(dataset=lowercase_    ,      all_configs=lowercase_    ,      save_infos=lowercase_					)
	A__							:    int        =		TestCommand(*lowercase_					)
	test_command.run()
	A__							:    Optional[Any]        =		os.path.join(lowercase_    ,      """README.md"""					)
	assert os.path.exists(lowercase_					)
	A__							:    Dict        =		DatasetInfosDict.from_directory(lowercase_					)
	A__							:    str        =		DatasetInfosDict(
	    {
	        """default""": DatasetInfo(
	            features=Features(
	                {
	                    """tokens""": Sequence(Value("""string"""					)					),
	                    """ner_tags""": Sequence(
	                        ClassLabel(names=["""O""", """B-PER""", """I-PER""", """B-ORG""", """I-ORG""", """B-LOC""", """I-LOC"""]					)					),
	                    """langs""": Sequence(Value("""string"""					)					),
	                    """spans""": Sequence(Value("""string"""					)					),
	                }					)    ,      splits=[
	                {
	                    """name""": """train""",
	                    """num_bytes""": 2351563,
	                    """num_examples""": 10000,
	                },
	                {
	                    """name""": """validation""",
	                    """num_bytes""": 238418,
	                    """num_examples""": 1000,
	                },
	            ]    ,      download_size=3940680    ,      dataset_size=2589981    ,      )
	    }					)
	assert dataset_infos.keys() == expected_dataset_infos.keys()
	for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
		A__   ,   A__							:    Optional[Any]        =		getattr(dataset_infos["""default"""]    ,      lowercase_					), getattr(expected_dataset_infos["""default"""]    ,      lowercase_					)
		if key == "num_bytes":
			assert is_apercent_close(lowercase_    ,      lowercase_					)
		elif key == "splits":
			assert list(lowercase_					) == list(lowercase_					)
			for split in result:
				assert result[split].name == expected[split].name
				assert result[split].num_examples == expected[split].num_examples
				assert is_apercent_close(result[split].num_bytes    ,      expected[split].num_bytes					)
		else:
			result == expected



