


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   UpperCamelCase			(      snake_case__=2						,  snake_case__=3						,  snake_case__=16						,  snake_case__ = 10						,  snake_case__ = 2):

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

						lowerCAmelCase_	:     List[str]          = get_dataset(__lowerCAmelCase)
						lowerCAmelCase_	:     Optional[int]          = get_dataset(__lowerCAmelCase)
						lowerCAmelCase_	:     str          = DataLoader(__lowerCAmelCase						,  shuffle=__lowerCAmelCase						,  batch_size=__lowerCAmelCase						,  num_workers=4)
						lowerCAmelCase_	:     List[str]          = DataLoader(__lowerCAmelCase						,  shuffle=__lowerCAmelCase						,  batch_size=__lowerCAmelCase						,  num_workers=4)
						return (train_dataloader, valid_dataloader)

def   UpperCamelCase			(      snake_case__						,  snake_case__						,  snake_case__						,  snake_case__						,  snake_case__						,  snake_case__=None):
						lowerCAmelCase_	:     int          = []
						for epoch in range(__lowerCAmelCase):
												# Train quickly
												model.train()
												for batch in dataloader:
																		lowerCAmelCase_    ,					lowerCAmelCase_	:     Optional[int]          = batch
																		lowerCAmelCase_	:     Tuple          = model(__lowerCAmelCase)
																		lowerCAmelCase_	:     int          = 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 __snake_case		( nn.Module ):





		"""simple docstring"""







		def __init__(							self    :   Dict      )      ->							int:




								'''simple docstring'''

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



		def  UpperCAmelCase_							(							self    :   Dict						,lowerCAmelCase__    :   int      )      ->							List[Any]:




								'''simple docstring'''

								return x * self.a + self.b






class __snake_case		( unittest.TestCase ):





		"""simple docstring"""







		def  UpperCAmelCase_							(							self    :   int      )      ->							List[Any]:




								'''simple docstring'''

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

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



		def  UpperCAmelCase_							(							self    :   Optional[int]      )      ->							Dict:




								'''simple docstring'''

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

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

														lowerCAmelCase_	:     Tuple          = train(2						,lowerCAmelCase__						,lowerCAmelCase__						,lowerCAmelCase__						,lowerCAmelCase__      )
														# Save everything
														lowerCAmelCase_	:     str          = os.path.join(lowerCAmelCase__						,"checkpoint"      )
														accelerator.save_state(lowerCAmelCase__      )

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



		def  UpperCAmelCase_							(							self    :   int      )      ->							List[str]:




								'''simple docstring'''

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

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

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

														lowerCAmelCase_	:     str          = train(2						,lowerCAmelCase__						,lowerCAmelCase__						,lowerCAmelCase__						,lowerCAmelCase__      )
														# Save everything
														accelerator.save_state()

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



		def  UpperCAmelCase_							(							self    :   Dict      )      ->							Union[str, Any]:




								'''simple docstring'''

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




								'''simple docstring'''

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

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



		def  UpperCAmelCase_							(							self    :   Optional[Any]      )      ->							Any:




								'''simple docstring'''

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



		@require_cuda
		def  UpperCAmelCase_							(							self    :   Optional[Any]      )      ->							List[Any]:




								'''simple docstring'''

								lowerCAmelCase_	:     Dict          = ["torchrun", f'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__      )]
								execute_subprocess_async(lowerCAmelCase__						,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=0.99)
							_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()




from __future__ import annotations

from collections.abc import Iterator
from typing import Generic, TypeVar

__magic_name__  							=	TypeVar('''T''')




class        _SCREAMING_SNAKE_CASE	(   Generic[T]			):
      def __init__(      self		,						lowerCamelCase			):
          snake_case__    =					data
          snake_case__    =					None



      def __str__(      self			):
          return F"""{self.data}"""





class        _SCREAMING_SNAKE_CASE	(   Generic[T]			):
      def __init__(      self			):
          snake_case__    =					None
      def __iter__(      self			):
          snake_case__    =					self.top
          while node:
              yield node.data
              snake_case__    =					node.next
      def __str__(      self			):
          return "->".join([str(lowerCamelCase			) for item in self]			)
      def __len__(      self			):
          return len(tuple(iter(self			)			)			)
      def A_      (      self			):
          return self.top is None
      def A_      (      self		,						lowerCamelCase			):
          snake_case__    =					Node(lowerCamelCase			)
          if not self.is_empty():
              snake_case__    =					self.top
          snake_case__    =					node
      def A_      (      self			):
          if self.is_empty():
              raise IndexError("pop from empty stack"			)
          assert isinstance(self.top		,						lowerCamelCase			)
          snake_case__    =					self.top
          snake_case__    =					self.top.next
          return pop_node.data
      def A_      (      self			):
          if self.is_empty():
              raise IndexError("peek from empty stack"			)

          assert self.top is not None
          return self.top.data



      def A_      (      self			):
          snake_case__    =					None


if __name__ == "__main__":
     from doctest import testmod

     testmod()



