




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


_UpperCAmelCase		:  Optional[int]	=   logging.getLogger(__name__)



def 					UpperCAmelCase__     (   lowerCamelCase=2,	lowerCamelCase=3,	lowerCamelCase=16,	lowerCamelCase = 10,	lowerCamelCase = 2      ):


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

  lowercase			:Any							=  get_dataset(_SCREAMING_SNAKE_CASE      )
  lowercase			:Any							=  get_dataset(_SCREAMING_SNAKE_CASE      )
  lowercase			:List[Any]							=  DataLoader(_SCREAMING_SNAKE_CASE,	shuffle=_SCREAMING_SNAKE_CASE,	batch_size=_SCREAMING_SNAKE_CASE,	num_workers=4      )
  lowercase			:Optional[int]							=  DataLoader(_SCREAMING_SNAKE_CASE,	shuffle=_SCREAMING_SNAKE_CASE,	batch_size=_SCREAMING_SNAKE_CASE,	num_workers=4      )
  return (train_dataloader, valid_dataloader)



def 					UpperCAmelCase__     (   lowerCamelCase,	lowerCamelCase,	lowerCamelCase,	lowerCamelCase,	lowerCamelCase,	lowerCamelCase=None      ):
  lowercase			:Any							=  []
  for epoch in range(_SCREAMING_SNAKE_CASE      ):
    # Train quickly
    model.train()
    for batch in dataloader:
      lowercase		,			lowercase			:Optional[int]							=  batch
      lowercase			:int							=  model(_SCREAMING_SNAKE_CASE      )
      lowercase			:Dict							=  torch.nn.functional.mse_loss(_SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE      )
      accelerator.backward(_SCREAMING_SNAKE_CASE      )
      optimizer.step()
      optimizer.zero_grad()
    rands.append(random.random()      )  # Introduce some randomness
    if scheduler is not None:
      scheduler.step()
  return rands




class       __lowerCAmelCase				(							nn.Module):






       def __init__(      self:    int ):
         super().__init__()
         lowercase			:Optional[Any]							=  nn.Parameter(torch.randn(1 ) )
         lowercase			:Tuple							=  nn.Parameter(torch.randn(1 ) )






       def     SCREAMING_SNAKE_CASE			(      self:    int				,    _lowerCAmelCase:    Optional[Any] ):
         return x * self.a + self.b







class       __lowerCAmelCase				(							unittest.TestCase):






       def     SCREAMING_SNAKE_CASE			(      self:    List[str] ):


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


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

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

           lowercase			:List[str]							=  train(2				,    lowerCamelCase__				,    lowerCamelCase__				,    lowerCamelCase__				,    lowerCamelCase__ )
           # Save everything
           lowercase			:Any							=  os.path.join(lowerCamelCase__				,    "checkpoint" )
           accelerator.save_state(lowerCamelCase__ )

           # Load everything back in and make sure all states work
           accelerator.load_state(lowerCamelCase__ )
           test_rands += train(1				,    lowerCamelCase__				,    lowerCamelCase__				,    lowerCamelCase__				,    lowerCamelCase__ )
           ((lowercase)		,			(lowercase))			:List[Any]							=  model.a.item(), model.b.item()
           lowercase			:str							=  optimizer.state_dict()
           self.assertEqual(lowerCamelCase__				,    lowerCamelCase__ )
           self.assertEqual(lowerCamelCase__				,    lowerCamelCase__ )
           self.assertEqual(lowerCamelCase__				,    lowerCamelCase__ )
           self.assertEqual(lowerCamelCase__				,    lowerCamelCase__ )






       def     SCREAMING_SNAKE_CASE			(      self:    List[Any] ):


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

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

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

           lowercase			:Union[str, Any]							=  train(2				,    lowerCamelCase__				,    lowerCamelCase__				,    lowerCamelCase__				,    lowerCamelCase__ )
           # Save everything
           accelerator.save_state()

           # Load everything back in and make sure all states work
           accelerator.load_state(os.path.join(lowerCamelCase__				,    "checkpoints"				,    "checkpoint_1" ) )
           test_rands += train(1				,    lowerCamelCase__				,    lowerCamelCase__				,    lowerCamelCase__				,    lowerCamelCase__ )
           ((lowercase)		,			(lowercase))			:Optional[Any]							=  model.a.item(), model.b.item()
           lowercase			:Union[str, Any]							=  optimizer.state_dict()
           self.assertEqual(lowerCamelCase__				,    lowerCamelCase__ )
           self.assertEqual(lowerCamelCase__				,    lowerCamelCase__ )
           self.assertEqual(lowerCamelCase__				,    lowerCamelCase__ )
           self.assertEqual(lowerCamelCase__				,    lowerCamelCase__ )






       def     SCREAMING_SNAKE_CASE			(      self:    Union[str, Any] ):
         lowercase			:Optional[Any]							=  torch.tensor([1, 2, 3] )
         lowercase			:Union[str, Any]							=  torch.tensor([2, 3, 4] )
         lowercase			:List[str]							=  DummyModel()
         lowercase			:Dict							=  torch.optim.Adam(net.parameters() )
         lowercase			:str							=  Accelerator()
         with self.assertRaises(lowerCamelCase__ ) as ve:
           accelerator.register_for_checkpointing(lowerCamelCase__				,    lowerCamelCase__				,    lowerCamelCase__				,    lowerCamelCase__ )
         lowercase			: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     SCREAMING_SNAKE_CASE			(      self:    Optional[int] ):


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

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






       def     SCREAMING_SNAKE_CASE			(      self:    Dict ):


         with tempfile.TemporaryDirectory() as tmpdir:
           set_seed(42 )
           lowercase			:Optional[Any]							=  DummyModel()
           lowercase			:List[str]							=  ProjectConfiguration(automatic_checkpoint_naming=lowerCamelCase__				,    total_limit=2 )
           # Train baseline
           lowercase			:Any							=  Accelerator(project_dir=lowerCamelCase__				,    project_config=lowerCamelCase__ )
           lowercase			:str							=  accelerator.prepare(lowerCamelCase__ )
           # Save 3 states:
           for _ in range(11 ):
             accelerator.save_state()
           self.assertTrue(not os.path.exists(os.path.join(lowerCamelCase__				,    "checkpoints"				,    "checkpoint_0" ) ) )
           self.assertTrue(os.path.exists(os.path.join(lowerCamelCase__				,    "checkpoints"				,    "checkpoint_9" ) ) )
           self.assertTrue(os.path.exists(os.path.join(lowerCamelCase__				,    "checkpoints"				,    "checkpoint_10" ) ) )






       @require_cuda
       def     SCREAMING_SNAKE_CASE			(      self:    Tuple ):
         lowercase			:Optional[int]							=  ["torchrun", F"--nproc_per_node={torch.cuda.device_count()}", inspect.getfile(self.__class__ )]
         execute_subprocess_async(lowerCamelCase__				,    env=os.environ.copy() )


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










import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()

class 		UpperCamelCase__ (      unittest.TestCase   ):



     '''simple docstring'''

     @property
     def 				SCREAMING_SNAKE_CASE__  (							self						:    str       )     ->		Optional[int]:




           '''simple docstring'''


           torch.manual_seed(0       )
           SCREAMING_SNAKE_CASE		=						UNetaDModel(
               block_out_channels=(32, 64)						,layers_per_block=2						,sample_size=32						,in_channels=3						,out_channels=3						,down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")						,up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")						,)
           return model






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Tuple       )     ->		Union[str, Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.dummy_uncond_unet
           SCREAMING_SNAKE_CASE		=						KarrasVeScheduler()

           SCREAMING_SNAKE_CASE		=						KarrasVePipeline(unet=lowerCamelCase__						,scheduler=lowerCamelCase__       )
           pipe.to(lowerCamelCase__       )
           pipe.set_progress_bar_config(disable=lowerCamelCase__       )

           SCREAMING_SNAKE_CASE		=						torch.manual_seed(0       )
           SCREAMING_SNAKE_CASE		=						pipe(num_inference_steps=2						,generator=lowerCamelCase__						,output_type="""numpy"""       ).images

           SCREAMING_SNAKE_CASE		=						torch.manual_seed(0       )
           SCREAMING_SNAKE_CASE		=						pipe(num_inference_steps=2						,generator=lowerCamelCase__						,output_type="""numpy"""						,return_dict=lowerCamelCase__       )[0]

           SCREAMING_SNAKE_CASE		=						image[0, -3:, -3:, -1]
           SCREAMING_SNAKE_CASE		=						image_from_tuple[0, -3:, -3:, -1]

           assert image.shape == (1, 32, 32, 3)
           SCREAMING_SNAKE_CASE		=						np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]       )

           assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-2
           assert np.abs(image_from_tuple_slice.flatten() - expected_slice       ).max() < 1e-2

@slow
@require_torch
class 		UpperCamelCase__ (      unittest.TestCase   ):



     '''simple docstring'''

     def 				SCREAMING_SNAKE_CASE__  (							self						:    Union[str, Any]       )     ->		Optional[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						"""google/ncsnpp-celebahq-256"""
           SCREAMING_SNAKE_CASE		=						UNetaDModel.from_pretrained(lowerCamelCase__       )
           SCREAMING_SNAKE_CASE		=						KarrasVeScheduler()

           SCREAMING_SNAKE_CASE		=						KarrasVePipeline(unet=lowerCamelCase__						,scheduler=lowerCamelCase__       )
           pipe.to(lowerCamelCase__       )
           pipe.set_progress_bar_config(disable=lowerCamelCase__       )

           SCREAMING_SNAKE_CASE		=						torch.manual_seed(0       )
           SCREAMING_SNAKE_CASE		=						pipe(num_inference_steps=20						,generator=lowerCamelCase__						,output_type="""numpy"""       ).images

           SCREAMING_SNAKE_CASE		=						image[0, -3:, -3:, -1]
           assert image.shape == (1, 256, 256, 3)
           SCREAMING_SNAKE_CASE		=						np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586]       )

           assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-2


