



"""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


UpperCAmelCase						:		List[str]			= logging.getLogger(__name__)


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase=2 ,  __lowerCAmelCase=3 ,  __lowerCAmelCase=16 ,  __lowerCAmelCase = 10 ,  __lowerCAmelCase = 2							) ->						int:







  '''simple docstring'''


  def get_dataset(__lowerCAmelCase							):
    lowercase_								= torch.randn(batch_size * n_batches ,  1							)
    return TensorDataset(_lowerCamelCase ,  a * x + b + 0.1 * torch.randn(batch_size * n_batches ,  1							)							)

  lowercase_								= get_dataset(_lowerCamelCase							)
  lowercase_								= get_dataset(_lowerCamelCase							)
  lowercase_								= DataLoader(_lowerCamelCase ,  shuffle=_lowerCamelCase ,  batch_size=_lowerCamelCase ,  num_workers=4							)
  lowercase_								= DataLoader(_lowerCamelCase ,  shuffle=_lowerCamelCase ,  batch_size=_lowerCamelCase ,  num_workers=4							)
  return (train_dataloader, valid_dataloader)


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase=None							) ->						Dict:







  '''simple docstring'''


  lowercase_								= []
  for epoch in range(_lowerCamelCase							):
    # Train quickly
    model.train()
    for batch in dataloader:
      lowercase_								= batch
      lowercase_								= model(_lowerCamelCase							)
      lowercase_								= 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    SCREAMING_SNAKE_CASE__ (							nn.Module   ):

    def __init__(				self			:				Tuple):







      """simple docstring"""







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




    def 					_UpperCAmelCase	(				self			:				Union[str, Any] ,  lowerCAmelCase_			:				Union[str, Any]):







      """simple docstring"""







      return x * self.a + self.b







class    SCREAMING_SNAKE_CASE__ (							unittest.TestCase   ):

    def 					_UpperCAmelCase	(				self			:				Optional[Any]):







      """simple docstring"""







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

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

    def 					_UpperCAmelCase	(				self			:				Optional[int]):







      """simple docstring"""







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

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

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

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

    def 					_UpperCAmelCase	(				self			:				str):







      """simple docstring"""







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

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

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

        lowercase_								= train(2 ,  _lowercase ,  _lowercase ,  _lowercase ,  _lowercase)
        # Save everything
        accelerator.save_state()

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

    def 					_UpperCAmelCase	(				self			:				Optional[int]):







      """simple docstring"""







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







      """simple docstring"""







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

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

    def 					_UpperCAmelCase	(				self			:				Any):







      """simple docstring"""







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




    @require_cuda
    def 					_UpperCAmelCase	(				self			:				str):







      """simple docstring"""







      lowercase_								= ["""torchrun""", F'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__)]
      execute_subprocess_async(_lowercase ,  env=os.environ.copy())


if __name__ == "__main__":
       UpperCAmelCase						:		str			= "/tmp/accelerate/state_checkpointing"
       UpperCAmelCase						:		Tuple			= DummyModel()
       UpperCAmelCase						:		List[Any]			= torch.optim.Adam(params=model.parameters(), lr=1E-3)
       UpperCAmelCase						:		Dict			= torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.99)
       UpperCAmelCase			,				UpperCAmelCase						:		List[Any]			= dummy_dataloaders()
       UpperCAmelCase						:		Tuple			= ProjectConfiguration(automatic_checkpoint_naming=True)
       # Train baseline
       UpperCAmelCase						:		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)
       UpperCAmelCase			,				UpperCAmelCase			,				UpperCAmelCase			,				UpperCAmelCase			,				UpperCAmelCase						:		Any			= accelerator.prepare(
           model, optimizer, train_dataloader, valid_dataloader, scheduler
       )
       UpperCAmelCase			,				UpperCAmelCase						:		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:
              UpperCAmelCase						:		Optional[Any]			= 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						:		Optional[int]			= 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 dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin


@flax.struct.dataclass
class    SCREAMING_SNAKE_CASE__ :
    # setable values
    lowercase__      =   None
    lowercase__      =   None
    lowercase__      =   None  # sigma(t_i)

    @classmethod
    def 					_UpperCAmelCase	(				cls			:				Any):







      """simple docstring"""







      return cls()







@dataclass
class    SCREAMING_SNAKE_CASE__ (							__UpperCAmelCase   ):
    lowercase__      =   42
    lowercase__      =   42
    lowercase__      =   42







class    SCREAMING_SNAKE_CASE__ (							__UpperCAmelCase      ,  __UpperCAmelCase   ):

    @property
    def 					_UpperCAmelCase	(				self			:				int):







      """simple docstring"""







      return True

    @register_to_config
    def __init__(				self			:				Union[str, Any] ,  lowerCAmelCase_			:				float = 0.02 ,  lowerCAmelCase_			:				float = 1_0_0 ,  lowerCAmelCase_			:				float = 1.007 ,  lowerCAmelCase_			:				float = 8_0 ,  lowerCAmelCase_			:				float = 0.05 ,  lowerCAmelCase_			:				float = 5_0 ,  ):







      """simple docstring"""







      pass

    def 					_UpperCAmelCase	(				self			:				Optional[int]):







      """simple docstring"""







      return KarrasVeSchedulerState.create()

    def 					_UpperCAmelCase	(				self			:				Union[str, Any] ,  lowerCAmelCase_			:				KarrasVeSchedulerState ,  lowerCAmelCase_			:				int ,  lowerCAmelCase_			:				Tuple = ()):







      """simple docstring"""







      lowercase_								= jnp.arange(0 ,  lowerCAmelCase_)[::-1].copy()
      lowercase_								= [
          (
              self.config.sigma_max**2
              * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
          )
          for i in timesteps
      ]

      return state.replace(
          num_inference_steps=lowerCAmelCase_ ,  schedule=jnp.array(lowerCAmelCase_ ,  dtype=jnp.floataa) ,  timesteps=lowerCAmelCase_ ,  )

    def 					_UpperCAmelCase	(				self			:				Optional[int] ,  lowerCAmelCase_			:				KarrasVeSchedulerState ,  lowerCAmelCase_			:				jnp.ndarray ,  lowerCAmelCase_			:				float ,  lowerCAmelCase_			:				random.KeyArray ,  ):







      """simple docstring"""







      if self.config.s_min <= sigma <= self.config.s_max:
        lowercase_								= min(self.config.s_churn / state.num_inference_steps ,  2**0.5 - 1)
      else:
        lowercase_								= 0

      # sample eps ~ N(0, S_noise^2 * I)
      lowercase_								= random.split(lowerCAmelCase_ ,  num=1)
      lowercase_								= self.config.s_noise * random.normal(key=lowerCAmelCase_ ,  shape=sample.shape)
      lowercase_								= sigma + gamma * sigma
      lowercase_								= sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

      return sample_hat, sigma_hat

    def 					_UpperCAmelCase	(				self			:				Tuple ,  lowerCAmelCase_			:				KarrasVeSchedulerState ,  lowerCAmelCase_			:				jnp.ndarray ,  lowerCAmelCase_			:				float ,  lowerCAmelCase_			:				float ,  lowerCAmelCase_			:				jnp.ndarray ,  lowerCAmelCase_			:				bool = True ,  ):







      """simple docstring"""







      lowercase_								= sample_hat + sigma_hat * model_output
      lowercase_								= (sample_hat - pred_original_sample) / sigma_hat
      lowercase_								= sample_hat + (sigma_prev - sigma_hat) * derivative

      if not return_dict:
        return (sample_prev, derivative, state)

      return FlaxKarrasVeOutput(prev_sample=lowerCAmelCase_ ,  derivative=lowerCAmelCase_ ,  state=lowerCAmelCase_)

    def 					_UpperCAmelCase	(				self			:				Dict ,  lowerCAmelCase_			:				KarrasVeSchedulerState ,  lowerCAmelCase_			:				jnp.ndarray ,  lowerCAmelCase_			:				float ,  lowerCAmelCase_			:				float ,  lowerCAmelCase_			:				jnp.ndarray ,  lowerCAmelCase_			:				jnp.ndarray ,  lowerCAmelCase_			:				jnp.ndarray ,  lowerCAmelCase_			:				bool = True ,  ):







      """simple docstring"""







      lowercase_								= sample_prev + sigma_prev * model_output
      lowercase_								= (sample_prev - pred_original_sample) / sigma_prev
      lowercase_								= sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

      if not return_dict:
        return (sample_prev, derivative, state)

      return FlaxKarrasVeOutput(prev_sample=lowerCAmelCase_ ,  derivative=lowerCAmelCase_ ,  state=lowerCAmelCase_)




    def 					_UpperCAmelCase	(				self			:				List[Any] ,  lowerCAmelCase_			:				KarrasVeSchedulerState ,  lowerCAmelCase_			:				int ,  lowerCAmelCase_			:				Optional[int] ,  lowerCAmelCase_			:				List[Any]):







      """simple docstring"""







      raise NotImplementedError()

