
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


_A		     =    logging.getLogger(__name__)




def  lowerCamelCase__ (  a__     :     Optional[int]=2		,					a__     :     Optional[int]=3		,					a__     :     int=16		,					a__     :     int = 10		,					a__     :     int = 2   )						->		Union[str, Any]:

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

       UpperCamelCase_											=				get_dataset(__a   )
       UpperCamelCase_											=				get_dataset(__a   )
       UpperCamelCase_											=				DataLoader(__a		,					shuffle=__a		,					batch_size=__a		,					num_workers=4   )
       UpperCamelCase_											=				DataLoader(__a		,					shuffle=__a		,					batch_size=__a		,					num_workers=4   )
       return (train_dataloader, valid_dataloader)




def  lowerCamelCase__ (  a__     :     List[str]		,					a__     :     int		,					a__     :     str		,					a__     :     Union[str, Any]		,					a__     :     Any		,					a__     :     Optional[Any]=None   )						->		str:
       UpperCamelCase_											=				[]
       for epoch in range(__a   ):
              # Train quickly
              model.train()
              for batch in dataloader:
                     UpperCamelCase_											=				batch
                     UpperCamelCase_											=				model(__a   )
                     UpperCamelCase_											=				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 						lowercase_				(				nn.Module ):

 def __init__(					self   ):




        """simple docstring"""


        super().__init__()
        UpperCamelCase_											=				nn.Parameter(torch.randn(1   )   )
        UpperCamelCase_											=				nn.Parameter(torch.randn(1   )   )

 def lowerCamelCase_ (					self   ,		__UpperCamelCase   ):




        """simple docstring"""


        return x * self.a + self.b



class 						lowercase_				(				unittest.TestCase ):

 def lowerCamelCase_ (					self   ):




        """simple docstring"""


        with tempfile.TemporaryDirectory() as tmpdir:
               set_seed(4_2   )
               UpperCamelCase_											=				DummyModel()
               UpperCamelCase_											=				torch.optim.Adam(params=model.parameters()   ,		lr=1e-3   )
               UpperCamelCase_											=				dummy_dataloaders()
               UpperCamelCase_											=				ProjectConfiguration(total_limit=1   ,		project_dir=_a   ,		automatic_checkpoint_naming=_a   )
               # Train baseline
               UpperCamelCase_											=				Accelerator(project_config=_a   )
               UpperCamelCase_											=				accelerator.prepare(
                   _a   ,		_a   ,		_a   ,		_a   )
               # Save initial
               accelerator.save_state()

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

 def lowerCamelCase_ (					self   ):




        """simple docstring"""


        with tempfile.TemporaryDirectory() as tmpdir:
               set_seed(4_2   )
               UpperCamelCase_											=				DummyModel()
               UpperCamelCase_											=				torch.optim.Adam(params=model.parameters()   ,		lr=1e-3   )
               UpperCamelCase_											=				dummy_dataloaders()
               # Train baseline
               UpperCamelCase_											=				Accelerator()
               UpperCamelCase_											=				accelerator.prepare(
                   _a   ,		_a   ,		_a   ,		_a   )
               # Save initial
               UpperCamelCase_											=				os.path.join(_a   ,		"""initial"""   )
               accelerator.save_state(_a   )
               (UpperCamelCase_)											=				model.a.item(), model.b.item()
               UpperCamelCase_											=				optimizer.state_dict()
               UpperCamelCase_											=				train(3   ,		_a   ,		_a   ,		_a   ,		_a   )
               (UpperCamelCase_)											=				model.a.item(), model.b.item()
               UpperCamelCase_											=				optimizer.state_dict()

               # Train partially
               set_seed(4_2   )
               UpperCamelCase_											=				DummyModel()
               UpperCamelCase_											=				torch.optim.Adam(params=model.parameters()   ,		lr=1e-3   )
               UpperCamelCase_											=				dummy_dataloaders()
               UpperCamelCase_											=				Accelerator()
               UpperCamelCase_											=				accelerator.prepare(
                   _a   ,		_a   ,		_a   ,		_a   )
               accelerator.load_state(_a   )
               (UpperCamelCase_)											=				model.a.item(), model.b.item()
               UpperCamelCase_											=				optimizer.state_dict()
               self.assertEqual(_a   ,		_a   )
               self.assertEqual(_a   ,		_a   )
               self.assertEqual(_a   ,		_a   )

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

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

 def lowerCamelCase_ (					self   ):




        """simple docstring"""


        with tempfile.TemporaryDirectory() as tmpdir:
               set_seed(4_2   )
               UpperCamelCase_											=				DummyModel()
               UpperCamelCase_											=				torch.optim.Adam(params=model.parameters()   ,		lr=1e-3   )
               UpperCamelCase_											=				dummy_dataloaders()
               UpperCamelCase_											=				ProjectConfiguration(automatic_checkpoint_naming=_a   )

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

               # Train partially
               set_seed(4_2   )
               UpperCamelCase_											=				DummyModel()
               UpperCamelCase_											=				torch.optim.Adam(params=model.parameters()   ,		lr=1e-3   )
               UpperCamelCase_											=				dummy_dataloaders()
               UpperCamelCase_											=				ProjectConfiguration(iteration=1   ,		automatic_checkpoint_naming=_a   )
               UpperCamelCase_											=				Accelerator(project_dir=_a   ,		project_config=_a   )
               UpperCamelCase_											=				accelerator.prepare(
                   _a   ,		_a   ,		_a   ,		_a   )
               accelerator.load_state(os.path.join(_a   ,		"""checkpoints"""   ,		"""checkpoint_0"""   )   )
               (UpperCamelCase_)											=				model.a.item(), model.b.item()
               UpperCamelCase_											=				optimizer.state_dict()
               self.assertEqual(_a   ,		_a   )
               self.assertEqual(_a   ,		_a   )
               self.assertEqual(_a   ,		_a   )

               UpperCamelCase_											=				train(2   ,		_a   ,		_a   ,		_a   ,		_a   )
               # Save everything
               accelerator.save_state()

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

 def lowerCamelCase_ (					self   ):




        """simple docstring"""


        UpperCamelCase_											=				torch.tensor([1, 2, 3]   )
        UpperCamelCase_											=				torch.tensor([2, 3, 4]   )
        UpperCamelCase_											=				DummyModel()
        UpperCamelCase_											=				torch.optim.Adam(net.parameters()   )
        UpperCamelCase_											=				Accelerator()
        with self.assertRaises(_a   ) as ve:
               accelerator.register_for_checkpointing(_a   ,		_a   ,		_a   ,		_a   )
        UpperCamelCase_											=				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   ):




        """simple docstring"""


        with tempfile.TemporaryDirectory() as tmpdir:
               set_seed(4_2   )
               UpperCamelCase_											=				DummyModel()
               UpperCamelCase_											=				torch.optim.Adam(params=model.parameters()   ,		lr=1e-3   )
               UpperCamelCase_											=				torch.optim.lr_scheduler.StepLR(_a   ,		step_size=1   ,		gamma=0.99   )
               UpperCamelCase_											=				dummy_dataloaders()
               UpperCamelCase_											=				ProjectConfiguration(automatic_checkpoint_naming=_a   )
               # Train baseline
               UpperCamelCase_											=				Accelerator(project_dir=_a   ,		project_config=_a   )
               UpperCamelCase_											=				accelerator.prepare(
                   _a   ,		_a   ,		_a   ,		_a   ,		_a   )
               # Save initial
               accelerator.save_state()
               UpperCamelCase_											=				scheduler.state_dict()
               train(3   ,		_a   ,		_a   ,		_a   ,		_a   ,		_a   )
               self.assertNotEqual(_a   ,		scheduler.state_dict()   )

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

 def lowerCamelCase_ (					self   ):




        """simple docstring"""


        with tempfile.TemporaryDirectory() as tmpdir:
               set_seed(4_2   )
               UpperCamelCase_											=				DummyModel()
               UpperCamelCase_											=				ProjectConfiguration(automatic_checkpoint_naming=_a   ,		total_limit=2   )
               # Train baseline
               UpperCamelCase_											=				Accelerator(project_dir=_a   ,		project_config=_a   )
               UpperCamelCase_											=				accelerator.prepare(_a   )
               # Save 3 states:
               for _ in range(1_1   ):
                      accelerator.save_state()
               self.assertTrue(not os.path.exists(os.path.join(_a   ,		"""checkpoints"""   ,		"""checkpoint_0"""   )   )   )
               self.assertTrue(os.path.exists(os.path.join(_a   ,		"""checkpoints"""   ,		"""checkpoint_9"""   )   )   )
               self.assertTrue(os.path.exists(os.path.join(_a   ,		"""checkpoints"""   ,		"""checkpoint_10"""   )   )   )

 @require_cuda
 def lowerCamelCase_ (					self   ):




        """simple docstring"""


        UpperCamelCase_											=				['torchrun', f'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__   )]
        execute_subprocess_async(_a   ,		env=os.environ.copy()   )


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

from ...utils import logging
from .image_processing_videomae import VideoMAEImageProcessor


__lowerCAmelCase         =    logging.get_logger(__name__)







class UpperCAmelCase__	( lowercase__	):





  """simple docstring"""

  def __init__(		self      :     Tuple							,*_a      :     List[str]							,**_a      :     Any       ):


     '''simple docstring'''






     warnings.warn(
         'The class VideoMAEFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
         ' Please use VideoMAEImageProcessor instead.'							,_a							,)
     super().__init__(*_a							,**_a       )



