
"""simple docstring"""




def        _lowerCamelCase ( __a = 100    ):
  SCREAMING_SNAKE_CASE_						 = n * (n + 1) * (2 * n + 1) / 6
  SCREAMING_SNAKE_CASE_						 = (n * (n + 1) / 2) ** 2
  return int(square_of_sum - sum_of_squares    )


if __name__ == "__main__":
      print(f'''{solution() = }''')

"""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()