



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


lowercase_	       =			logging.getLogger(__name__)






def 					__lowerCAmelCase		(  __lowerCamelCase	:    str=2				,   __lowerCamelCase	:    List[str]=3				,   __lowerCamelCase	:    Tuple=16				,   __lowerCamelCase	:    int = 10				,   __lowerCamelCase	:    int = 2	)  ->						List[Any]:

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

					__lowerCAmelCase	   =get_dataset(__lowerCamelCase	)
					__lowerCAmelCase	   =get_dataset(__lowerCamelCase	)
					__lowerCAmelCase	   =DataLoader(__lowerCamelCase				,   shuffle=__lowerCamelCase				,   batch_size=__lowerCamelCase				,   num_workers=4	)
					__lowerCAmelCase	   =DataLoader(__lowerCamelCase				,   shuffle=__lowerCamelCase				,   batch_size=__lowerCamelCase				,   num_workers=4	)
					return (train_dataloader, valid_dataloader)






def 					__lowerCAmelCase		(  __lowerCamelCase	:    Union[str, Any]				,   __lowerCamelCase	:    int				,   __lowerCamelCase	:    List[Any]				,   __lowerCamelCase	:    Union[str, Any]				,   __lowerCamelCase	:    Tuple				,   __lowerCamelCase	:    Any=None	)  ->						int:
					__lowerCAmelCase	   =[]
					for epoch in range(__lowerCamelCase	):
										# Train quickly
										model.train()
										for batch in dataloader:
															__lowerCAmelCase   ,	__lowerCAmelCase	   =batch
															__lowerCAmelCase	   =model(__lowerCamelCase	)
															__lowerCAmelCase	   =torch.nn.functional.mse_loss(__lowerCamelCase				,   __lowerCamelCase	)
															accelerator.backward(__lowerCamelCase	)
															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							:    List[str])->							int:
						super().__init__()
						__lowerCAmelCase	   =nn.Parameter(torch.randn(1))
						__lowerCAmelCase	   =nn.Parameter(torch.randn(1))



	def 		UpperCamelCase							(						self							:    str				,	snake_case_							:    Optional[Any])->							str:
						return x * self.a + self.b






class 						__a (     unittest.TestCase				):



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

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



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

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

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

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



	def 		UpperCamelCase							(						self							:    List[str])->							Tuple:
						with tempfile.TemporaryDirectory() as tmpdir:
											set_seed(42)
											__lowerCAmelCase	   =DummyModel()
											__lowerCAmelCase	   =torch.optim.Adam(params=model.parameters()				,	lr=1e-3)
											__lowerCAmelCase   ,	__lowerCAmelCase	   =dummy_dataloaders()
											__lowerCAmelCase	   =ProjectConfiguration(automatic_checkpoint_naming=snake_case_)

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

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

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

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



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

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



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



	@require_cuda
	def 		UpperCamelCase							(						self							:    List[str])->							Union[str, Any]:
						__lowerCAmelCase	   =["""torchrun""", F"""--nproc_per_node={torch.cuda.device_count()}""", inspect.getfile(self.__class__)]
						execute_subprocess_async(snake_case_				,	env=os.environ.copy())


if __name__ == "__main__":
				lowercase_	       =			'''/tmp/accelerate/state_checkpointing'''
				lowercase_	       =			DummyModel()
				lowercase_	       =			torch.optim.Adam(params=model.parameters(), lr=1E-3)
				lowercase_	       =			torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.9_9)
				lowercase_       ,       lowercase_	       =			dummy_dataloaders()
				lowercase_	       =			ProjectConfiguration(automatic_checkpoint_naming=True)
				# Train baseline
				lowercase_	       =			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)
				lowercase_       ,       lowercase_       ,       lowercase_       ,       lowercase_       ,       lowercase_	       =			accelerator.prepare(
				    model, optimizer, train_dataloader, valid_dataloader, scheduler
				)
				lowercase_       ,       lowercase_	       =			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:
								lowercase_	       =			group['''params'''][0].device
								break
				assert param_device.type == accelerator.device.type
				lowercase_	       =			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:
								lowercase_	       =			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:
								lowercase_	       =			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 warnings

from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor


lowercase_	       =			logging.get_logger(__name__)






class 						__a (     SCREAMING_SNAKE_CASE				):



	def __init__(						self							:    Optional[Any]				,	*snake_case_							:    List[str]				,	**snake_case_							:    Union[str, Any])->							None:
						warnings.warn(
						    """The class PoolFormerFeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
						    """ Please use PoolFormerImageProcessor instead."""				,	snake_case_				,	)
						super().__init__(*snake_case_				,	**snake_case_)




