



"""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:     Dict										=	logging.getLogger(__name__)

def    _snake_case		(		UpperCamelCase		:	Any=2    ,	UpperCamelCase		:	Union[str, Any]=3    ,	UpperCamelCase		:	Union[str, Any]=16    ,	UpperCamelCase		:	int = 10    ,	UpperCamelCase		:	int = 2		):

 def get_dataset(UpperCamelCase		:	Dict		):
  UpperCAmelCase			:      Tuple			=			torch.randn(batch_size * n_batches    ,	1		)
  return TensorDataset(UpperCamelCase    ,	a * x + b + 0.1 * torch.randn(batch_size * n_batches    ,	1		)		)

 UpperCAmelCase			:      str			=			get_dataset(UpperCamelCase		)
 UpperCAmelCase			:      Optional[Any]			=			get_dataset(UpperCamelCase		)
 UpperCAmelCase			:      Union[str, Any]			=			DataLoader(UpperCamelCase    ,	shuffle=UpperCamelCase    ,	batch_size=UpperCamelCase    ,	num_workers=4		)
 UpperCAmelCase			:      Dict			=			DataLoader(UpperCamelCase    ,	shuffle=UpperCamelCase    ,	batch_size=UpperCamelCase    ,	num_workers=4		)
 return (train_dataloader, valid_dataloader)

def    _snake_case		(		UpperCamelCase		:	Optional[int]    ,	UpperCamelCase		:	Union[str, Any]    ,	UpperCamelCase		:	Any    ,	UpperCamelCase		:	Any    ,	UpperCamelCase		:	Optional[Any]    ,	UpperCamelCase		:	List[str]=None		):
 UpperCAmelCase			:      str			=			[]
 for epoch in range(UpperCamelCase		):
  # Train quickly
  model.train()
  for batch in dataloader:
   UpperCAmelCase			,	UpperCAmelCase			:      Any			=			batch
   UpperCAmelCase			:      int			=			model(UpperCamelCase		)
   UpperCAmelCase			:      Tuple			=			torch.nn.functional.mse_loss(UpperCamelCase    ,	UpperCamelCase		)
   accelerator.backward(UpperCamelCase		)
   optimizer.step()
   optimizer.zero_grad()
  rands.append(random.random()		)  # Introduce some randomness
  if scheduler is not None:
   scheduler.step()
 return rands



class    SCREAMING_SNAKE_CASE__						(      nn.Module				):

   def __init__(     self		)						->   Any:





    '''simple docstring'''




    super().__init__()
    UpperCAmelCase			:      Dict			=			nn.Parameter(torch.randn(1		)		)
    UpperCAmelCase			:      int			=			nn.Parameter(torch.randn(1		)		)




   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE		)						->   Tuple:





    '''simple docstring'''




    return x * self.a + self.b



class    SCREAMING_SNAKE_CASE__						(      unittest.TestCase				):

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Any:





    '''simple docstring'''




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





    '''simple docstring'''




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

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

     UpperCAmelCase			:      str			=			train(2   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)
     # Save everything
     UpperCAmelCase			:      Dict			=			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		)
     ((UpperCAmelCase)			,	(UpperCAmelCase))			:      str			=			model.a.item(), model.b.item()
     UpperCAmelCase			:      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		)
     self.assertEqual(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Tuple:





    '''simple docstring'''




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

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

     # Train partially
     set_seed(42		)
     UpperCAmelCase			:      Dict			=			DummyModel()
     UpperCAmelCase			:      str			=			torch.optim.Adam(params=model.parameters()   ,  lr=1E-3		)
     UpperCAmelCase			,	UpperCAmelCase			:      Tuple			=			dummy_dataloaders()
     UpperCAmelCase			:      List[str]			=			ProjectConfiguration(iteration=1   ,  automatic_checkpoint_naming=_SCREAMING_SNAKE_CASE		)
     UpperCAmelCase			:      Dict			=			Accelerator(project_dir=_SCREAMING_SNAKE_CASE   ,  project_config=_SCREAMING_SNAKE_CASE		)
     UpperCAmelCase			,	UpperCAmelCase			,	UpperCAmelCase			,	UpperCAmelCase			:      str			=			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"""		)		)
     ((UpperCAmelCase)			,	(UpperCAmelCase))			:      Union[str, Any]			=			model.a.item(), model.b.item()
     UpperCAmelCase			:      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		)

     UpperCAmelCase			:      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		)
     ((UpperCAmelCase)			,	(UpperCAmelCase))			:      Optional[Any]			=			model.a.item(), model.b.item()
     UpperCAmelCase			:      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		)
     self.assertEqual(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Tuple:





    '''simple docstring'''




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





    '''simple docstring'''




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





    '''simple docstring'''




    with tempfile.TemporaryDirectory() as tmpdir:
     set_seed(42		)
     UpperCAmelCase			:      str			=			DummyModel()
     UpperCAmelCase			:      str			=			ProjectConfiguration(automatic_checkpoint_naming=_SCREAMING_SNAKE_CASE   ,  total_limit=2		)
     # Train baseline
     UpperCAmelCase			:      List[str]			=			Accelerator(project_dir=_SCREAMING_SNAKE_CASE   ,  project_config=_SCREAMING_SNAKE_CASE		)
     UpperCAmelCase			:      Any			=			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 			SCREAMING_SNAKE_CASE  (     self		)						->   List[Any]:





    '''simple docstring'''




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

from collections.abc import Generator

def    _snake_case		(		):
 UpperCAmelCase			:      dict[int, int]			=			{}
 UpperCAmelCase			:      str			=			2
 while True:
  UpperCAmelCase			:      List[str]			=			factor_map.pop(UpperCamelCase    ,	UpperCamelCase		)
  if factor:
   UpperCAmelCase			:      Dict			=			factor + prime
   while x in factor_map:
    x += factor
   UpperCAmelCase			:      Tuple			=			factor
  else:
   UpperCAmelCase			:      Optional[int]			=			prime
   yield prime
  prime += 1

def    _snake_case		(		UpperCamelCase		:	float = 1e1_0		):
 UpperCAmelCase			:      Tuple			=			sieve()

 UpperCAmelCase			:      Optional[Any]			=			1
 while True:
  UpperCAmelCase			:      List[str]			=			next(UpperCamelCase		)
  if (2 * prime * n) > limit:
   return n
  # Ignore the next prime as the reminder will be 2.
  next(UpperCamelCase		)
  n += 2


if __name__ == "__main__":
      print(solution())
