"""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


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


def      UpperCAmelCase__			(							lowerCAmelCase__	:List[str]=2   ,						lowerCAmelCase__	:Optional[Any]=3   ,						lowerCAmelCase__	:Union[str, Any]=1_6   ,						lowerCAmelCase__	:int = 1_0   ,						lowerCAmelCase__	:int = 2						)					->       Any:
      '''simple docstring'''


      def get_dataset(lowerCAmelCase__	:Tuple						):
            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      UpperCAmelCase__			(							lowerCAmelCase__	:Any   ,						lowerCAmelCase__	:List[Any]   ,						lowerCAmelCase__	:Optional[Any]   ,						lowerCAmelCase__	:int   ,						lowerCAmelCase__	:Any   ,						lowerCAmelCase__	:Dict=None						)					->       int:
      '''simple docstring'''


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

      def __init__(		self		):







            """simple docstring"""



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







      def 							A__			(		self						,			__lowerCAmelCase		):







            """simple docstring"""



            return x * self.a + self.b






class 					_A		(			unittest.TestCase      ):

      def 							A__			(		self		):







            """simple docstring"""



            with tempfile.TemporaryDirectory() as tmpdir:
                  set_seed(42		)
                  lowercase								=      DummyModel()
                  lowercase								=      torch.optim.Adam(params=model.parameters()						,			lr=1E-3		)
                  lowercase				,				lowercase								=      dummy_dataloaders()
                  lowercase								=      ProjectConfiguration(total_limit=1						,			project_dir=__lowerCAmelCase						,			automatic_checkpoint_naming=__lowerCAmelCase		)
                  # Train baseline
                  lowercase								=      Accelerator(project_config=__lowerCAmelCase		)
                  lowercase				,				lowercase				,				lowercase				,				lowercase								=      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 							A__			(		self		):







            """simple docstring"""



            with tempfile.TemporaryDirectory() as tmpdir:
                  set_seed(42		)
                  lowercase								=      DummyModel()
                  lowercase								=      torch.optim.Adam(params=model.parameters()						,			lr=1E-3		)
                  lowercase				,				lowercase								=      dummy_dataloaders()
                  # Train baseline
                  lowercase								=      Accelerator()
                  lowercase				,				lowercase				,				lowercase				,				lowercase								=      accelerator.prepare(
                      __lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase		)
                  # Save initial
                  lowercase								=      os.path.join(__lowerCAmelCase						,			"""initial"""		)
                  accelerator.save_state(__lowerCAmelCase		)
                  ((lowercase)				,				(lowercase))								=      model.a.item(), model.b.item()
                  lowercase								=      optimizer.state_dict()
                  lowercase								=      train(3						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase		)
                  ((lowercase)				,				(lowercase))								=      model.a.item(), model.b.item()
                  lowercase								=      optimizer.state_dict()

                  # Train partially
                  set_seed(42		)
                  lowercase								=      DummyModel()
                  lowercase								=      torch.optim.Adam(params=model.parameters()						,			lr=1E-3		)
                  lowercase				,				lowercase								=      dummy_dataloaders()
                  lowercase								=      Accelerator()
                  lowercase				,				lowercase				,				lowercase				,				lowercase								=      accelerator.prepare(
                      __lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase		)
                  accelerator.load_state(__lowerCAmelCase		)
                  ((lowercase)				,				(lowercase))								=      model.a.item(), model.b.item()
                  lowercase								=      optimizer.state_dict()
                  self.assertEqual(__lowerCAmelCase						,			__lowerCAmelCase		)
                  self.assertEqual(__lowerCAmelCase						,			__lowerCAmelCase		)
                  self.assertEqual(__lowerCAmelCase						,			__lowerCAmelCase		)

                  lowercase								=      train(2						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase		)
                  # Save everything
                  lowercase								=      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))								=      model.a.item(), model.b.item()
                  lowercase								=      optimizer.state_dict()
                  self.assertEqual(__lowerCAmelCase						,			__lowerCAmelCase		)
                  self.assertEqual(__lowerCAmelCase						,			__lowerCAmelCase		)
                  self.assertEqual(__lowerCAmelCase						,			__lowerCAmelCase		)
                  self.assertEqual(__lowerCAmelCase						,			__lowerCAmelCase		)

      def 							A__			(		self		):







            """simple docstring"""



            with tempfile.TemporaryDirectory() as tmpdir:
                  set_seed(42		)
                  lowercase								=      DummyModel()
                  lowercase								=      torch.optim.Adam(params=model.parameters()						,			lr=1E-3		)
                  lowercase				,				lowercase								=      dummy_dataloaders()
                  lowercase								=      ProjectConfiguration(automatic_checkpoint_naming=__lowerCAmelCase		)

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

                  # Train partially
                  set_seed(42		)
                  lowercase								=      DummyModel()
                  lowercase								=      torch.optim.Adam(params=model.parameters()						,			lr=1E-3		)
                  lowercase				,				lowercase								=      dummy_dataloaders()
                  lowercase								=      ProjectConfiguration(iteration=1						,			automatic_checkpoint_naming=__lowerCAmelCase		)
                  lowercase								=      Accelerator(project_dir=__lowerCAmelCase						,			project_config=__lowerCAmelCase		)
                  lowercase				,				lowercase				,				lowercase				,				lowercase								=      accelerator.prepare(
                      __lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase		)
                  accelerator.load_state(os.path.join(__lowerCAmelCase						,			"""checkpoints"""						,			"""checkpoint_0"""		)		)
                  ((lowercase)				,				(lowercase))								=      model.a.item(), model.b.item()
                  lowercase								=      optimizer.state_dict()
                  self.assertEqual(__lowerCAmelCase						,			__lowerCAmelCase		)
                  self.assertEqual(__lowerCAmelCase						,			__lowerCAmelCase		)
                  self.assertEqual(__lowerCAmelCase						,			__lowerCAmelCase		)

                  lowercase								=      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))								=      model.a.item(), model.b.item()
                  lowercase								=      optimizer.state_dict()
                  self.assertEqual(__lowerCAmelCase						,			__lowerCAmelCase		)
                  self.assertEqual(__lowerCAmelCase						,			__lowerCAmelCase		)
                  self.assertEqual(__lowerCAmelCase						,			__lowerCAmelCase		)
                  self.assertEqual(__lowerCAmelCase						,			__lowerCAmelCase		)

      def 							A__			(		self		):







            """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(__lowerCAmelCase		) as ve:
                  accelerator.register_for_checkpointing(__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase		)
            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 							A__			(		self		):







            """simple docstring"""



            with tempfile.TemporaryDirectory() as tmpdir:
                  set_seed(42		)
                  lowercase								=      DummyModel()
                  lowercase								=      torch.optim.Adam(params=model.parameters()						,			lr=1E-3		)
                  lowercase								=      torch.optim.lr_scheduler.StepLR(__lowerCAmelCase						,			step_size=1						,			gamma=0.9_9		)
                  lowercase				,				lowercase								=      dummy_dataloaders()
                  lowercase								=      ProjectConfiguration(automatic_checkpoint_naming=__lowerCAmelCase		)
                  # Train baseline
                  lowercase								=      Accelerator(project_dir=__lowerCAmelCase						,			project_config=__lowerCAmelCase		)
                  lowercase				,				lowercase				,				lowercase				,				lowercase				,				lowercase								=      accelerator.prepare(
                      __lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase		)
                  # Save initial
                  accelerator.save_state()
                  lowercase								=      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 							A__			(		self		):







            """simple docstring"""



            with tempfile.TemporaryDirectory() as tmpdir:
                  set_seed(42		)
                  lowercase								=      DummyModel()
                  lowercase								=      ProjectConfiguration(automatic_checkpoint_naming=__lowerCAmelCase						,			total_limit=2		)
                  # Train baseline
                  lowercase								=      Accelerator(project_dir=__lowerCAmelCase						,			project_config=__lowerCAmelCase		)
                  lowercase								=      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 							A__			(		self		):







            """simple docstring"""



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


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






import warnings
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch

from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


__lowerCAmelCase       :			Optional[Any]   	=logging.get_logger(__name__)  # pylint: disable=invalid-name


def      UpperCAmelCase__			(							lowerCAmelCase__	:Union[List, PIL.Image.Image, torch.Tensor]						)					->       Optional[Any]:
      '''simple docstring'''


      warnings.warn(
          """The preprocess method is deprecated and will be removed in a future version. Please"""
          """ use VaeImageProcessor.preprocess instead"""   ,						lowerCAmelCase__   ,						)
      if isinstance(lowerCAmelCase__   ,						torch.Tensor						):
            return image
      elif isinstance(lowerCAmelCase__   ,						PIL.Image.Image						):
            lowercase								=      [image]

      if isinstance(image[0]   ,						PIL.Image.Image						):
            lowercase				,				lowercase								=      image[0].size
            lowercase				,				lowercase								=      (x - x % 8 for x in (w, h))  # resize to integer multiple of 8

            lowercase								=      [np.array(i.resize((w, h)   ,						resample=PIL_INTERPOLATION["""lanczos"""]						)						)[None, :] for i in image]
            lowercase								=      np.concatenate(lowerCAmelCase__   ,						axis=0						)
            lowercase								=      np.array(lowerCAmelCase__						).astype(np.floataa						) / 255.0
            lowercase								=      image.transpose(0   ,						3   ,						1   ,						2						)
            lowercase								=      2.0 * image - 1.0
            lowercase								=      torch.from_numpy(lowerCAmelCase__						)
      elif isinstance(image[0]   ,						torch.Tensor						):
            lowercase								=      torch.cat(lowerCAmelCase__   ,						dim=0						)
      return image


def      UpperCAmelCase__			(							lowerCAmelCase__	:Union[List, PIL.Image.Image, torch.Tensor]						)					->       Dict:
      '''simple docstring'''


      if isinstance(lowerCAmelCase__   ,						torch.Tensor						):
            return mask
      elif isinstance(lowerCAmelCase__   ,						PIL.Image.Image						):
            lowercase								=      [mask]

      if isinstance(mask[0]   ,						PIL.Image.Image						):
            lowercase				,				lowercase								=      mask[0].size
            lowercase				,				lowercase								=      (x - x % 3_2 for x in (w, h))  # resize to integer multiple of 32
            lowercase								=      [np.array(m.convert("""L"""						).resize((w, h)   ,						resample=PIL_INTERPOLATION["""nearest"""]						)						)[None, :] for m in mask]
            lowercase								=      np.concatenate(lowerCAmelCase__   ,						axis=0						)
            lowercase								=      mask.astype(np.floataa						) / 255.0
            lowercase								=      0
            lowercase								=      1
            lowercase								=      torch.from_numpy(lowerCAmelCase__						)
      elif isinstance(mask[0]   ,						torch.Tensor						):
            lowercase								=      torch.cat(lowerCAmelCase__   ,						dim=0						)
      return mask






class 					_A		(			lowerCAmelCase      ):
      snake_case__     :  UNetaDModel
      snake_case__     :  RePaintScheduler

      def __init__(		self						,			__lowerCAmelCase						,			__lowerCAmelCase		):







            """simple docstring"""



            super().__init__()
            self.register_modules(unet=__lowerCAmelCase						,			scheduler=__lowerCAmelCase		)







      @torch.no_grad()
      def __call__(		self						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase = 250						,			__lowerCAmelCase = 0.0						,			__lowerCAmelCase = 10						,			__lowerCAmelCase = 10						,			__lowerCAmelCase = None						,			__lowerCAmelCase = "pil"						,			__lowerCAmelCase = True						,			):







            """simple docstring"""



            lowercase								=      image

            lowercase								=      _preprocess_image(__lowerCAmelCase		)
            lowercase								=      original_image.to(device=self.device						,			dtype=self.unet.dtype		)
            lowercase								=      _preprocess_mask(__lowerCAmelCase		)
            lowercase								=      mask_image.to(device=self.device						,			dtype=self.unet.dtype		)

            lowercase								=      original_image.shape[0]

            # sample gaussian noise to begin the loop
            if isinstance(__lowerCAmelCase						,			__lowerCAmelCase		) and len(__lowerCAmelCase		) != batch_size:
                  raise ValueError(
                      f'You have passed a list of generators of length {len(__lowerCAmelCase		)}, but requested an effective batch'
                      f' size of {batch_size}. Make sure the batch size matches the length of the generators.'		)

            lowercase								=      original_image.shape
            lowercase								=      randn_tensor(__lowerCAmelCase						,			generator=__lowerCAmelCase						,			device=self.device						,			dtype=self.unet.dtype		)

            # set step values
            self.scheduler.set_timesteps(__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			self.device		)
            lowercase								=      eta

            lowercase								=      self.scheduler.timesteps[0] + 1
            lowercase								=      generator[0] if isinstance(__lowerCAmelCase						,			__lowerCAmelCase		) else generator
            for i, t in enumerate(self.progress_bar(self.scheduler.timesteps		)		):
                  if t < t_last:
                        # predict the noise residual
                        lowercase								=      self.unet(__lowerCAmelCase						,			__lowerCAmelCase		).sample
                        # compute previous image: x_t -> x_t-1
                        lowercase								=      self.scheduler.step(__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase		).prev_sample

                  else:
                        # compute the reverse: x_t-1 -> x_t
                        lowercase								=      self.scheduler.undo_step(__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase		)
                  lowercase								=      t

            lowercase								=      (image / 2 + 0.5).clamp(0						,			1		)
            lowercase								=      image.cpu().permute(0						,			2						,			3						,			1		).numpy()
            if output_type == "pil":
                  lowercase								=      self.numpy_to_pil(__lowerCAmelCase		)

            if not return_dict:
                  return (image,)

            return ImagePipelineOutput(images=__lowerCAmelCase		)



