





'''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


a        =						logging.getLogger(__name__)

def 						__magic_name__       (							__UpperCAmelCase=2							,	__UpperCAmelCase=3							,	__UpperCAmelCase=16							,	__UpperCAmelCase = 10							,	__UpperCAmelCase = 2      )						->					int:

   '''simple docstring'''







   def get_dataset(__UpperCAmelCase      ):
      __SCREAMING_SNAKE_CASE      							=       torch.randn(batch_size * n_batches							,	1      )
      return TensorDataset(lowerCAmelCase__							,	a * x + b + 0.1 * torch.randn(batch_size * n_batches							,	1      )      )

   __SCREAMING_SNAKE_CASE      							=       get_dataset(lowerCAmelCase__      )
   __SCREAMING_SNAKE_CASE      							=       get_dataset(lowerCAmelCase__      )
   __SCREAMING_SNAKE_CASE      							=       DataLoader(lowerCAmelCase__							,	shuffle=lowerCAmelCase__							,	batch_size=lowerCAmelCase__							,	num_workers=4      )
   __SCREAMING_SNAKE_CASE      							=       DataLoader(lowerCAmelCase__							,	shuffle=lowerCAmelCase__							,	batch_size=lowerCAmelCase__							,	num_workers=4      )
   return (train_dataloader, valid_dataloader)

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase=None      )						->					List[str]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       []
   for epoch in range(lowerCAmelCase__      ):
      # Train quickly
      model.train()
      for batch in dataloader:
         __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE      							=       batch
         __SCREAMING_SNAKE_CASE      							=       model(lowerCAmelCase__      )
         __SCREAMING_SNAKE_CASE      							=       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 __a (   nn.Module			):

   '''simple docstring'''





   def __init__(					self				:			Any    ):

      '''simple docstring'''

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





   def      UpperCAmelCase__     (					self				:			List[Any]				,lowerCamelCase				:			Any    ):

      '''simple docstring'''

      return x * self.a + self.b







class __a (   unittest.TestCase			):

   '''simple docstring'''





   def      UpperCAmelCase__     (					self				:			int    ):

      '''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=snake_case__				,automatic_checkpoint_naming=snake_case__    )
         # Train baseline
         __SCREAMING_SNAKE_CASE      							=       Accelerator(project_config=snake_case__    )
         __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE      							=       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      UpperCAmelCase__     (					self				:			List[str]    ):

      '''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(
             snake_case__				,snake_case__				,snake_case__				,snake_case__    )
         # Save initial
         __SCREAMING_SNAKE_CASE      							=       os.path.join(snake_case__				,"""initial"""    )
         accelerator.save_state(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				,snake_case__				,snake_case__				,snake_case__				,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(
             snake_case__				,snake_case__				,snake_case__				,snake_case__    )
         accelerator.load_state(snake_case__    )
         ((__SCREAMING_SNAKE_CASE)							,     (__SCREAMING_SNAKE_CASE))      							=       model.a.item(), model.b.item()
         __SCREAMING_SNAKE_CASE      							=       optimizer.state_dict()
         self.assertEqual(snake_case__				,snake_case__    )
         self.assertEqual(snake_case__				,snake_case__    )
         self.assertEqual(snake_case__				,snake_case__    )

         __SCREAMING_SNAKE_CASE      							=       train(2				,snake_case__				,snake_case__				,snake_case__				,snake_case__    )
         # Save everything
         __SCREAMING_SNAKE_CASE      							=       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__    )
         ((__SCREAMING_SNAKE_CASE)							,     (__SCREAMING_SNAKE_CASE))      							=       model.a.item(), model.b.item()
         __SCREAMING_SNAKE_CASE      							=       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      UpperCAmelCase__     (					self				:			List[str]    ):

      '''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=snake_case__    )

         # Train baseline
         __SCREAMING_SNAKE_CASE      							=       Accelerator(project_dir=snake_case__				,project_config=snake_case__    )
         __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE      							=       accelerator.prepare(
             snake_case__				,snake_case__				,snake_case__				,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				,snake_case__				,snake_case__				,snake_case__				,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=snake_case__    )
         __SCREAMING_SNAKE_CASE      							=       Accelerator(project_dir=snake_case__				,project_config=snake_case__    )
         __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE      							=       accelerator.prepare(
             snake_case__				,snake_case__				,snake_case__				,snake_case__    )
         accelerator.load_state(os.path.join(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(snake_case__				,snake_case__    )
         self.assertEqual(snake_case__				,snake_case__    )
         self.assertEqual(snake_case__				,snake_case__    )

         __SCREAMING_SNAKE_CASE      							=       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__    )
         ((__SCREAMING_SNAKE_CASE)							,     (__SCREAMING_SNAKE_CASE))      							=       model.a.item(), model.b.item()
         __SCREAMING_SNAKE_CASE      							=       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      UpperCAmelCase__     (					self				:			Any    ):

      '''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(snake_case__    ) as ve:
         accelerator.register_for_checkpointing(snake_case__				,snake_case__				,snake_case__				,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      UpperCAmelCase__     (					self				:			Union[str, Any]    ):

      '''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(snake_case__				,step_size=1				,gamma=0.99    )
         __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE      							=       dummy_dataloaders()
         __SCREAMING_SNAKE_CASE      							=       ProjectConfiguration(automatic_checkpoint_naming=snake_case__    )
         # Train baseline
         __SCREAMING_SNAKE_CASE      							=       Accelerator(project_dir=snake_case__				,project_config=snake_case__    )
         __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE      							=       accelerator.prepare(
             snake_case__				,snake_case__				,snake_case__				,snake_case__				,snake_case__    )
         # Save initial
         accelerator.save_state()
         __SCREAMING_SNAKE_CASE      							=       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      UpperCAmelCase__     (					self				:			List[str]    ):

      '''simple docstring'''

      with tempfile.TemporaryDirectory() as tmpdir:
         set_seed(42    )
         __SCREAMING_SNAKE_CASE      							=       DummyModel()
         __SCREAMING_SNAKE_CASE      							=       ProjectConfiguration(automatic_checkpoint_naming=snake_case__				,total_limit=2    )
         # Train baseline
         __SCREAMING_SNAKE_CASE      							=       Accelerator(project_dir=snake_case__				,project_config=snake_case__    )
         __SCREAMING_SNAKE_CASE      							=       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      UpperCAmelCase__     (					self				:			int    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       ["""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__":
    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=0.99)
    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        =						accelerator.prepare(
        model, optimizer, train_dataloader, valid_dataloader, scheduler
    )
    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'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a        =						{"configuration_sew": ["SEW_PRETRAINED_CONFIG_ARCHIVE_MAP", "SEWConfig"]}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    a        =						[
        "SEW_PRETRAINED_MODEL_ARCHIVE_LIST",
        "SEWForCTC",
        "SEWForSequenceClassification",
        "SEWModel",
        "SEWPreTrainedModel",
    ]

if TYPE_CHECKING:
    from .configuration_sew import SEW_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_sew import (
            SEW_PRETRAINED_MODEL_ARCHIVE_LIST,
            SEWForCTC,
            SEWForSequenceClassification,
            SEWModel,
            SEWPreTrainedModel,
        )


else:
    import sys

    a        =						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





