
'''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


lowercase     :							str       						=     logging.getLogger(__name__)







def 						lowerCAmelCase_							(  snake_case__=2						,							snake_case__=3						,							snake_case__=16						,							snake_case__ = 10						,							snake_case__ = 2		):




    '''simple docstring'''



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

    A			:  Any   =		get_dataset(snake_case__		)
    A			:  Union[str, Any]   =		get_dataset(snake_case__		)
    A			:  Union[str, Any]   =		DataLoader(snake_case__						,							shuffle=snake_case__						,							batch_size=snake_case__						,							num_workers=4		)
    A			:  List[str]   =		DataLoader(snake_case__						,							shuffle=snake_case__						,							batch_size=snake_case__						,							num_workers=4		)
    return (train_dataloader, valid_dataloader)







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__						,							snake_case__						,							snake_case__						,							snake_case__						,							snake_case__=None		):




    '''simple docstring'''



    A			:  List[str]   =		[]
    for epoch in range(snake_case__		):
        # Train quickly
        model.train()
        for batch in dataloader:
            A,    A			:  Union[str, Any]   =		batch
            A			:  Union[str, Any]   =		model(snake_case__		)
            A			:  Any   =		torch.nn.functional.mse_loss(snake_case__						,							snake_case__		)
            accelerator.backward(snake_case__		)
            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      )					->							Tuple:






         """simple docstring"""






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





     def 							__lowerCAmelCase				(				self		,     SCREAMING_SNAKE_CASE      )					->							Dict:






         """simple docstring"""






         return x * self.a + self.b





class 					A							(				unittest.TestCase		):





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






         """simple docstring"""






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






         """simple docstring"""






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

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

             A			:  Tuple   =		train(2		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )
             # Save everything
             A			:  List[Any]   =		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      )
             ((A),    (A))			:  Optional[int]   =		model.a.item(), model.b.item()
             A			:  Tuple   =		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 							__lowerCAmelCase				(				self      )					->							int:






         """simple docstring"""






         with tempfile.TemporaryDirectory() as tmpdir:
             set_seed(42      )
             A			:  Optional[int]   =		DummyModel()
             A			:  Optional[int]   =		torch.optim.Adam(params=model.parameters()		,     lr=1e-3      )
             A,    A			:  Tuple   =		dummy_dataloaders()
             A			:  List[str]   =		ProjectConfiguration(automatic_checkpoint_naming=SCREAMING_SNAKE_CASE      )

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

             # Train partially
             set_seed(42      )
             A			:  Union[str, Any]   =		DummyModel()
             A			:  Any   =		torch.optim.Adam(params=model.parameters()		,     lr=1e-3      )
             A,    A			:  Dict   =		dummy_dataloaders()
             A			:  int   =		ProjectConfiguration(iteration=1		,     automatic_checkpoint_naming=SCREAMING_SNAKE_CASE      )
             A			:  Any   =		Accelerator(project_dir=SCREAMING_SNAKE_CASE		,     project_config=SCREAMING_SNAKE_CASE      )
             A,    A,    A,    A			:  List[Any]   =		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'''      )      )
             ((A),    (A))			:  List[Any]   =		model.a.item(), model.b.item()
             A			:  str   =		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      )

             A			:  Union[str, Any]   =		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      )
             ((A),    (A))			:  Optional[int]   =		model.a.item(), model.b.item()
             A			:  Optional[Any]   =		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 							__lowerCAmelCase				(				self      )					->							List[str]:






         """simple docstring"""






         A			:  str   =		torch.tensor([1, 2, 3]      )
         A			:  Union[str, Any]   =		torch.tensor([2, 3, 4]      )
         A			:  Optional[int]   =		DummyModel()
         A			:  Any   =		torch.optim.Adam(net.parameters()      )
         A			:  int   =		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      )
         A			:  List[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 							__lowerCAmelCase				(				self      )					->							int:






         """simple docstring"""






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






         """simple docstring"""






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






         """simple docstring"""






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






'''simple docstring'''




import random







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__		):




    '''simple docstring'''



    A,    A,    A			:  Any   =		[], [], []
    for element in data:
        if element < pivot:
            less.append(snake_case__		)
        elif element > pivot:
            greater.append(snake_case__		)
        else:
            equal.append(snake_case__		)
    return less, equal, greater







def 						lowerCAmelCase_							(  snake_case__						,							snake_case__		):




    '''simple docstring'''



    if index >= len(snake_case__		) or index < 0:
        return None

    A			:  List[Any]   =		items[random.randint(0						,							len(snake_case__		) - 1		)]
    A			:  Optional[int]   =		0
    A,    A,    A			:  Dict   =		_partition(snake_case__						,							snake_case__		)
    A			:  int   =		len(snake_case__		)
    A			:  List[Any]   =		len(snake_case__		)

    # index is the pivot
    if m <= index < m + count:
        return pivot
    # must be in smaller
    elif m > index:
        return quick_select(snake_case__						,							snake_case__		)
    # must be in larger
    else:
        return quick_select(snake_case__						,							index - (m + count)		)




