



'''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


__snake_case       :	Union[str, Any]            =logging.getLogger(__name__)






def 			lowerCAmelCase__	(					lowerCamelCase_	:	Tuple=2 ,lowerCamelCase_	:	Optional[int]=3 ,lowerCamelCase_	:	Tuple=16 ,lowerCamelCase_	:	int = 10 ,lowerCamelCase_	:	int = 2):





				'''simple docstring'''

				def get_dataset(lowerCamelCase_	:	List[str]):
								lowerCAmelCase__  :       int				=  torch.randn(batch_size * n_batches ,1)
								return TensorDataset(a_ ,a * x + b + 0.1 * torch.randn(batch_size * n_batches ,1))

				lowerCAmelCase__  :       List[Any]				=  get_dataset(a_)
				lowerCAmelCase__  :       Dict				=  get_dataset(a_)
				lowerCAmelCase__  :       Optional[Any]				=  DataLoader(a_ ,shuffle=a_ ,batch_size=a_ ,num_workers=4)
				lowerCAmelCase__  :       int				=  DataLoader(a_ ,shuffle=a_ ,batch_size=a_ ,num_workers=4)
				return (train_dataloader, valid_dataloader)






def 			lowerCAmelCase__	(					lowerCamelCase_	:	Any ,lowerCamelCase_	:	Dict ,lowerCamelCase_	:	str ,lowerCamelCase_	:	List[str] ,lowerCamelCase_	:	Optional[int] ,lowerCamelCase_	:	Union[str, Any]=None):





				'''simple docstring'''

				lowerCAmelCase__  :       int				=  []
				for epoch in range(a_):
								# Train quickly
								model.train()
								for batch in dataloader:
												lowerCAmelCase__  :       List[Any]				=  batch
												lowerCAmelCase__  :       List[Any]				=  model(a_)
												lowerCAmelCase__  :       Dict				=  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  lowerCamelCase__    (							nn.Module):




		'''simple docstring'''



		def __init__(self				)  ->    Dict:

						"""simple docstring"""






						super().__init__()
						lowerCAmelCase__  :       Optional[int]				=  nn.Parameter(torch.randn(1				)				)
						lowerCAmelCase__  :       List[Any]				=  nn.Parameter(torch.randn(1				)				)



		def 	lowerCAmelCase__   (self	,__lowerCamelCase				)  ->    int:

						"""simple docstring"""






						return x * self.a + self.b



class  lowerCamelCase__    (							unittest.TestCase):




		'''simple docstring'''



		def 	lowerCAmelCase__   (self				)  ->    str:

						"""simple docstring"""






						with tempfile.TemporaryDirectory() as tmpdir:
										set_seed(42				)
										lowerCAmelCase__  :       Optional[Any]				=  DummyModel()
										lowerCAmelCase__  :       Optional[Any]				=  torch.optim.Adam(params=model.parameters()	,lr=1e-3				)
										lowerCAmelCase__  :       str				=  dummy_dataloaders()
										lowerCAmelCase__  :       List[Any]				=  ProjectConfiguration(total_limit=1	,project_dir=__snake_case	,automatic_checkpoint_naming=__snake_case				)
										# Train baseline
										lowerCAmelCase__  :       List[str]				=  Accelerator(project_config=__snake_case				)
										lowerCAmelCase__  :       Dict				=  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 	lowerCAmelCase__   (self				)  ->    Any:

						"""simple docstring"""






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

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

										lowerCAmelCase__  :       List[Any]				=  train(2	,__snake_case	,__snake_case	,__snake_case	,__snake_case				)
										# Save everything
										lowerCAmelCase__  :       int				=  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__)  :       Union[str, Any]				=  model.a.item(), model.b.item()
										lowerCAmelCase__  :       str				=  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 	lowerCAmelCase__   (self				)  ->    List[Any]:

						"""simple docstring"""






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

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

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

										lowerCAmelCase__  :       List[str]				=  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__)  :       Any				=  model.a.item(), model.b.item()
										lowerCAmelCase__  :       int				=  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 	lowerCAmelCase__   (self				)  ->    Tuple:

						"""simple docstring"""






						lowerCAmelCase__  :       str				=  torch.tensor([1, 2, 3]				)
						lowerCAmelCase__  :       int				=  torch.tensor([2, 3, 4]				)
						lowerCAmelCase__  :       Union[str, Any]				=  DummyModel()
						lowerCAmelCase__  :       Any				=  torch.optim.Adam(net.parameters()				)
						lowerCAmelCase__  :       Union[str, Any]				=  Accelerator()
						with self.assertRaises(__snake_case				) as ve:
										accelerator.register_for_checkpointing(__snake_case	,__snake_case	,__snake_case	,__snake_case				)
						lowerCAmelCase__  :       Union[str, Any]				=  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 	lowerCAmelCase__   (self				)  ->    Any:

						"""simple docstring"""






						with tempfile.TemporaryDirectory() as tmpdir:
										set_seed(42				)
										lowerCAmelCase__  :       List[str]				=  DummyModel()
										lowerCAmelCase__  :       Optional[Any]				=  torch.optim.Adam(params=model.parameters()	,lr=1e-3				)
										lowerCAmelCase__  :       Optional[int]				=  torch.optim.lr_scheduler.StepLR(__snake_case	,step_size=1	,gamma=0.99				)
										lowerCAmelCase__  :       List[Any]				=  dummy_dataloaders()
										lowerCAmelCase__  :       Tuple				=  ProjectConfiguration(automatic_checkpoint_naming=__snake_case				)
										# Train baseline
										lowerCAmelCase__  :       List[Any]				=  Accelerator(project_dir=__snake_case	,project_config=__snake_case				)
										lowerCAmelCase__  :       str				=  accelerator.prepare(
										    __snake_case	,__snake_case	,__snake_case	,__snake_case	,__snake_case				)
										# Save initial
										accelerator.save_state()
										lowerCAmelCase__  :       Optional[Any]				=  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 	lowerCAmelCase__   (self				)  ->    List[str]:

						"""simple docstring"""






						with tempfile.TemporaryDirectory() as tmpdir:
										set_seed(42				)
										lowerCAmelCase__  :       int				=  DummyModel()
										lowerCAmelCase__  :       Optional[Any]				=  ProjectConfiguration(automatic_checkpoint_naming=__snake_case	,total_limit=2				)
										# Train baseline
										lowerCAmelCase__  :       Dict				=  Accelerator(project_dir=__snake_case	,project_config=__snake_case				)
										lowerCAmelCase__  :       Tuple				=  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 	lowerCAmelCase__   (self				)  ->    Dict:

						"""simple docstring"""






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






from bisect import bisect
from itertools import accumulate






def 			lowerCAmelCase__	(					lowerCamelCase_	:	List[str] ,lowerCamelCase_	:	Optional[Any] ,lowerCamelCase_	:	List[str] ,lowerCamelCase_	:	Optional[int]):





				'''simple docstring'''

				lowerCAmelCase__  :       Any				=  sorted(zip(lowerCamelCase_ ,lowerCamelCase_) ,key=lambda lowerCamelCase_: x[0] / x[1] ,reverse=lowerCamelCase_)
				lowerCAmelCase__		,	lowerCAmelCase__  :       List[str]				=  [i[0] for i in r], [i[1] for i in r]
				lowerCAmelCase__  :       Tuple				=  list(accumulate(lowerCamelCase_))
				lowerCAmelCase__  :       str				=  bisect(lowerCamelCase_ ,lowerCamelCase_)
				return (
				    0
				    if k == 0
				    else sum(vl[:k]) + (w - acc[k - 1]) * (vl[k]) / (wt[k])
				    if k != n
				    else sum(vl[:k])
				)


if __name__ == "__main__":
							import doctest

							doctest.testmod()

