




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


lowerCAmelCase_						=			logging.getLogger(__name__)






def  snake_case(    __magic_name__=2			,		__magic_name__=3			,		__magic_name__=16			,		__magic_name__ = 10			,		__magic_name__ = 2   )			->							Dict:





     '''simple docstring'''


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

     lowercase :       Dict       =			get_dataset(__magic_name__   )
     lowercase :       int       =			get_dataset(__magic_name__   )
     lowercase :       List[str]       =			DataLoader(__magic_name__			,		shuffle=__magic_name__			,		batch_size=__magic_name__			,		num_workers=4   )
     lowercase :       Dict       =			DataLoader(__magic_name__			,		shuffle=__magic_name__			,		batch_size=__magic_name__			,		num_workers=4   )
     return (train_dataloader, valid_dataloader)






def  snake_case(    __magic_name__			,		__magic_name__			,		__magic_name__			,		__magic_name__			,		__magic_name__			,		__magic_name__=None   )			->							Tuple:





     '''simple docstring'''


     lowercase :       List[Any]       =			[]
     for epoch in range(__magic_name__   ):
          # Train quickly
          model.train()
          for batch in dataloader:
               lowercase :       Dict       =			batch
               lowercase :       List[str]       =			model(__magic_name__   )
               lowercase :       Optional[Any]       =			torch.nn.functional.mse_loss(__magic_name__			,		__magic_name__   )
               accelerator.backward(__magic_name__   )
               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		:     int				)		->   Optional[Any]:






       """simple docstring"""







       super().__init__()
       lowercase :       Dict       =			nn.Parameter(torch.randn(1				)				)
       lowercase :       str       =			nn.Parameter(torch.randn(1				)				)






  def  __a      (  self		:     Any				,						_A		:     Union[str, Any]				)		->   Any:






       """simple docstring"""







       return x * self.a + self.b


class 						_A (  unittest.TestCase				):






  def  __a      (  self		:     Dict				)		->   Any:






       """simple docstring"""







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






       """simple docstring"""







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

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

            lowercase :       List[str]       =			train(2				,						_A				,						_A				,						_A				,						_A				)
            # Save everything
            lowercase :       Tuple       =			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				)
            (lowercase) :       Optional[Any]       =			model.a.item(), model.b.item()
            lowercase :       Optional[int]       =			optimizer.state_dict()
            self.assertEqual(_A				,						_A				)
            self.assertEqual(_A				,						_A				)
            self.assertEqual(_A				,						_A				)
            self.assertEqual(_A				,						_A				)






  def  __a      (  self		:     Tuple				)		->   Any:






       """simple docstring"""







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

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

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

            lowercase :       Union[str, Any]       =			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				)
            (lowercase) :       Any       =			model.a.item(), model.b.item()
            lowercase :       Optional[int]       =			optimizer.state_dict()
            self.assertEqual(_A				,						_A				)
            self.assertEqual(_A				,						_A				)
            self.assertEqual(_A				,						_A				)
            self.assertEqual(_A				,						_A				)






  def  __a      (  self		:     Dict				)		->   str:






       """simple docstring"""







       lowercase :       List[Any]       =			torch.tensor([1, 2, 3]				)
       lowercase :       List[Any]       =			torch.tensor([2, 3, 4]				)
       lowercase :       Optional[Any]       =			DummyModel()
       lowercase :       Optional[Any]       =			torch.optim.Adam(net.parameters()				)
       lowercase :       Any       =			Accelerator()
       with self.assertRaises(_A				) as ve:
            accelerator.register_for_checkpointing(_A				,						_A				,						_A				,						_A				)
       lowercase :       List[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  __a      (  self		:     List[Any]				)		->   Any:






       """simple docstring"""







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






       """simple docstring"""







       with tempfile.TemporaryDirectory() as tmpdir:
            set_seed(42				)
            lowercase :       List[str]       =			DummyModel()
            lowercase :       str       =			ProjectConfiguration(automatic_checkpoint_naming=_A				,						total_limit=2				)
            # Train baseline
            lowercase :       Dict       =			Accelerator(project_dir=_A				,						project_config=_A				)
            lowercase :       str       =			accelerator.prepare(_A				)
            # Save 3 states:
            for _ in range(11				):
                 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  __a      (  self		:     Any				)		->   Optional[Any]:






       """simple docstring"""







       lowercase :       Tuple       =			['''torchrun''', f"""--nproc_per_node={torch.cuda.device_count()}""", inspect.getfile(self.__class__				)]
       execute_subprocess_async(_A				,						env=os.environ.copy()				)


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

lowerCAmelCase_						=			{char: i for i, char in enumerate(ascii_uppercase)}
lowerCAmelCase_						=			dict(enumerate(ascii_uppercase))






def  snake_case(    __magic_name__			,		__magic_name__   )			->							str:





     '''simple docstring'''


     lowercase :       Optional[Any]       =			len(__magic_name__   )
     lowercase :       Any       =			0
     while True:
          if x == i:
               lowercase :       Any       =			0
          if len(__magic_name__   ) == len(__magic_name__   ):
               break
          key += key[i]
          i += 1
     return key






def  snake_case(    __magic_name__			,		__magic_name__   )			->							str:





     '''simple docstring'''


     lowercase :       str       =			''''''
     lowercase :       Dict       =			0
     for letter in message:
          if letter == " ":
               cipher_text += " "
          else:
               lowercase :       Dict       =			(dicta[letter] - dicta[key_new[i]]) % 26
               i += 1
               cipher_text += dicta[x]
     return cipher_text






def  snake_case(    __magic_name__			,		__magic_name__   )			->							str:





     '''simple docstring'''


     lowercase :       Any       =			''''''
     lowercase :       str       =			0
     for letter in cipher_text:
          if letter == " ":
               or_txt += " "
          else:
               lowercase :       Any       =			(dicta[letter] + dicta[key_new[i]] + 26) % 26
               i += 1
               or_txt += dicta[x]
     return or_txt






def  snake_case(    )			->							None:





     '''simple docstring'''


     lowercase :       Dict       =			'''THE GERMAN ATTACK'''
     lowercase :       Dict       =			'''SECRET'''
     lowercase :       Union[str, Any]       =			generate_key(__magic_name__			,		__magic_name__   )
     lowercase :       List[str]       =			cipher_text(__magic_name__			,		__magic_name__   )
     print(F"""Encrypted Text = {s}"""   )
     print(F"""Original Text = {original_text(__magic_name__			,		__magic_name__   )}"""   )


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 main()