




from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


_lowerCamelCase	:		Union[str, Any]							       =   transforms.Compose(
    [
        transforms.Resize((256, 256)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)







def SCREAMING_SNAKE_CASE				(       lowercase_						)   ->       Tuple:



       """simple docstring"""

       if isinstance(lowercase_ ,				torch.Tensor						):
              return image
       elif isinstance(lowercase_ ,				PIL.Image.Image						):
              A__       =    [image]

       A__       =    [trans(img.convert('''RGB'''						)						) for img in image]
       A__       =    torch.stack(lowercase_						)
       return image


class 				UpperCamelCase_    ( UpperCAmelCase__     ):

    '''simple docstring'''






    def __init__(			self      :	List[str]					, UpperCAmelCase__      :	Union[str, Any]					, UpperCAmelCase__      :	Dict)						->Optional[Any]:





           '''simple docstring'''

           super().__init__()

           # make sure scheduler can always be converted to DDIM
           A__       =    DDIMScheduler.from_config(scheduler.config)

           self.register_modules(unet=UpperCAmelCase__					, scheduler=UpperCAmelCase__)


    def        SCREAMING_SNAKE_CASE							(			self      :	Optional[Any]					, UpperCAmelCase__      :	str)						->Dict:





           '''simple docstring'''

           if strength < 0 or strength > 1:
                  raise ValueError(f"""The value of strength should in [0.0, 1.0] but is {strength}""")


    def        SCREAMING_SNAKE_CASE							(			self      :	Union[str, Any]					, UpperCAmelCase__      :	Dict					, UpperCAmelCase__      :	List[Any]					, UpperCAmelCase__      :	List[Any])						->int:





           '''simple docstring'''

           A__       =    min(int(num_inference_steps * strength)					, UpperCAmelCase__)

           A__       =    max(num_inference_steps - init_timestep					, 0)
           A__       =    self.scheduler.timesteps[t_start:]

           return timesteps, num_inference_steps - t_start


    def        SCREAMING_SNAKE_CASE							(			self      :	Optional[Any]					, UpperCAmelCase__      :	Any					, UpperCAmelCase__      :	Dict					, UpperCAmelCase__      :	Dict					, UpperCAmelCase__      :	str					, UpperCAmelCase__      :	Any					, UpperCAmelCase__      :	Tuple=None)						->Any:





           '''simple docstring'''

           if not isinstance(UpperCAmelCase__					, (torch.Tensor, PIL.Image.Image, list)):
                  raise ValueError(
                      f"""`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(UpperCAmelCase__)}""")

           A__       =    image.to(device=UpperCAmelCase__					, dtype=UpperCAmelCase__)

           if isinstance(UpperCAmelCase__					, UpperCAmelCase__) and len(UpperCAmelCase__) != batch_size:
                  raise ValueError(
                      f"""You have passed a list of generators of length {len(UpperCAmelCase__)}, but requested an effective batch"""
                      f""" size of {batch_size}. Make sure the batch size matches the length of the generators.""")

           A__       =    init_latents.shape
           A__       =    randn_tensor(UpperCAmelCase__					, generator=UpperCAmelCase__					, device=UpperCAmelCase__					, dtype=UpperCAmelCase__)

           # get latents
           print('''add noise to latents at timestep'''					, UpperCAmelCase__)
           A__       =    self.scheduler.add_noise(UpperCAmelCase__					, UpperCAmelCase__					, UpperCAmelCase__)
           A__       =    init_latents

           return latents





    @torch.no_grad()
    def __call__(			self      :	Tuple					, UpperCAmelCase__      :	Union[torch.FloatTensor, PIL.Image.Image] = None					, UpperCAmelCase__      :	float = 0.8					, UpperCAmelCase__      :	int = 1					, UpperCAmelCase__      :	Optional[Union[torch.Generator, List[torch.Generator]]] = None					, UpperCAmelCase__      :	float = 0.0					, UpperCAmelCase__      :	int = 50					, UpperCAmelCase__      :	Optional[bool] = None					, UpperCAmelCase__      :	Optional[str] = "pil"					, UpperCAmelCase__      :	bool = True					, )						->Union[ImagePipelineOutput, Tuple]:





           '''simple docstring'''

           self.check_inputs(UpperCAmelCase__)

           # 2. Preprocess image
           A__       =    preprocess(UpperCAmelCase__)

           # 3. set timesteps
           self.scheduler.set_timesteps(UpperCAmelCase__					, device=self.device)
           A__ ,							A__       =    self.get_timesteps(UpperCAmelCase__					, UpperCAmelCase__					, self.device)
           A__       =    timesteps[:1].repeat(UpperCAmelCase__)

           # 4. Prepare latent variables
           A__       =    self.prepare_latents(UpperCAmelCase__					, UpperCAmelCase__					, UpperCAmelCase__					, self.unet.dtype					, self.device					, UpperCAmelCase__)
           A__       =    latents

           # 5. Denoising loop
           for t in self.progress_bar(UpperCAmelCase__):
                  # 1. predict noise model_output
                  A__       =    self.unet(UpperCAmelCase__					, UpperCAmelCase__).sample

                  # 2. predict previous mean of image x_t-1 and add variance depending on eta
                  # eta corresponds to η in paper and should be between [0, 1]
                  # do x_t -> x_t-1
                  A__       =    self.scheduler.step(
                      UpperCAmelCase__					, UpperCAmelCase__					, UpperCAmelCase__					, eta=UpperCAmelCase__					, use_clipped_model_output=UpperCAmelCase__					, generator=UpperCAmelCase__					, ).prev_sample

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

           if not return_dict:
                  return (image, latent_timestep.item())

           return ImagePipelineOutput(images=UpperCAmelCase__)






from copy import deepcopy

import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader

from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import DistributedType, is_torch_version, set_seed
def 				__lowerCamelCase						( UpperCAmelCase_						:		Any ,  UpperCAmelCase_						:		List[Any] ,  UpperCAmelCase_						:		int ,  UpperCAmelCase_						:		Union[str, Any]						):




      """simple docstring"""





      for param, grad_param in zip(model_a.parameters() ,  model_b.parameters()						):
            if not param.requires_grad:
                  continue
            if not did_step:
                  # Grads should not be in sync
                  assert (
                      torch.allclose(param.grad ,  grad_param.grad						) is False
                  ), F'''Gradients in sync when they should not be at iteration {iteration}:\nmodel_a grad ({param.grad}) == model_b grad ({grad_param.grad})'''
            else:
                  # Grads should be in sync
                  assert (
                      torch.allclose(param.grad ,  grad_param.grad						) is True
                  ), F'''Gradients not in sync when they should be at iteration {iteration}:\nmodel_a grad ({param.grad}) != model_b grad ({grad_param.grad})'''
def 				__lowerCamelCase						( UpperCAmelCase_						:		List[str] ,  UpperCAmelCase_						:		List[str] ,  UpperCAmelCase_						:		str ,  UpperCAmelCase_						:		Optional[Any] ,  UpperCAmelCase_						:		Optional[Any]=True						):




      """simple docstring"""





      model.train()
      a						:str 							=       model(UpperCAmelCase_						)
      a						:List[str] 							=       F.mse_loss(UpperCAmelCase_ ,  target.to(output.device						)						)
      if not do_backward:
            loss /= accelerator.gradient_accumulation_steps
            loss.backward()
      else:
            accelerator.backward(UpperCAmelCase_						)
def 				__lowerCamelCase						( UpperCAmelCase_						:		str ,  UpperCAmelCase_						:		int=False						):




      """simple docstring"""





      set_seed(42						)
      a						:List[Any] 							=       RegressionModel()
      a						:Any 							=       deepcopy(UpperCAmelCase_						)
      a						:Tuple 							=       RegressionDataset(length=80						)
      a						:Tuple 							=       DataLoader(UpperCAmelCase_ ,  batch_size=16						)
      model.to(accelerator.device						)
      if sched:
            a						:str 							=       AdamW(params=model.parameters() ,  lr=1E-3						)
            a						:str 							=       AdamW(params=ddp_model.parameters() ,  lr=1E-3						)
            a						:List[str] 							=       LambdaLR(UpperCAmelCase_ ,  lr_lambda=lambda UpperCAmelCase_						: epoch**0.65						)
            a						:List[str] 							=       LambdaLR(UpperCAmelCase_ ,  lr_lambda=lambda UpperCAmelCase_						: epoch**0.65						)
      # Make a copy of `model`
      if sched:
            a							,				a							,				a							,				a						:List[Any] 							=       accelerator.prepare(UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_						)
      else:
            a							,				a						:str 							=       accelerator.prepare(UpperCAmelCase_ ,  UpperCAmelCase_						)
      if sched:
            return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
      return model, ddp_model, dataloader
def 				__lowerCamelCase						( UpperCAmelCase_						:		Union[str, Any]						):




      """simple docstring"""





      a							,				a							,				a						:str 							=       get_training_setup(UpperCAmelCase_						)
      # Use a single batch
      a							,				a						:Dict 							=       next(iter(UpperCAmelCase_						)						).values()
      for iteration in range(3						):
            # Gather the distributed inputs and targs for the base model
            a							,				a						:int 							=       accelerator.gather((ddp_input, ddp_target)						)
            a							,				a						:Union[str, Any] 							=       input.to(accelerator.device						), target.to(accelerator.device						)
            # Perform our initial ground truth step in non "DDP"
            step_model(UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_						)
            # Do "gradient accumulation" (noop)
            if iteration % 2 == 0:
                  # Accumulate grads locally
                  with accelerator.no_sync(UpperCAmelCase_						):
                        step_model(UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_						)
            else:
                  # Sync grads
                  step_model(UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_						)

            # Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
            check_model_parameters(UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_						)
            for param, ddp_param in zip(model.parameters() ,  ddp_model.parameters()						):
                  if not param.requires_grad:
                        continue
                  assert torch.allclose(
                      param.grad ,  ddp_param.grad						), F'''Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'''

            # Shuffle ddp_input on each iteration
            torch.manual_seed(1337 + iteration						)
            a						:Union[str, Any] 							=       ddp_input[torch.randperm(len(UpperCAmelCase_						)						)]
def 				__lowerCamelCase						( UpperCAmelCase_						:		Union[str, Any]						):




      """simple docstring"""





      a							,				a							,				a						:List[str] 							=       get_training_setup(UpperCAmelCase_						)
      # Use a single batch
      a							,				a						:List[str] 							=       next(iter(UpperCAmelCase_						)						).values()
      for iteration in range(3						):
            # Gather the distributed inputs and targs for the base model
            a							,				a						:List[Any] 							=       accelerator.gather((ddp_input, ddp_target)						)
            a							,				a						:Union[str, Any] 							=       input.to(accelerator.device						), target.to(accelerator.device						)
            # Perform our initial ground truth step in non "DDP"
            step_model(UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_						)
            # Do "gradient accumulation" (noop)
            if iteration % 2 == 0:
                  # Accumulate grads locally
                  with accelerator.no_sync(UpperCAmelCase_						):
                        step_model(UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_						)
            else:
                  # Sync grads
                  step_model(UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_						)

            # DDP model and model should only be in sync when not (iteration % 2 == 0)
            for param, ddp_param in zip(model.parameters() ,  ddp_model.parameters()						):
                  if not param.requires_grad:
                        continue
                  if iteration % 2 == 0:
                        # Grads should not be in sync
                        assert (
                            torch.allclose(param.grad ,  ddp_param.grad						) is False
                        ), F'''Gradients in sync when they should not be:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})'''
                  else:
                        # Grads should be in sync
                        assert (
                            torch.allclose(param.grad ,  ddp_param.grad						) is True
                        ), F'''Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'''

        # Shuffle ddp_input on each iteration
            torch.manual_seed(1337 + iteration						)
            a						:Any 							=       ddp_input[torch.randperm(len(UpperCAmelCase_						)						)]
def 				__lowerCamelCase						( UpperCAmelCase_						:		Union[str, Any]=False ,  UpperCAmelCase_						:		int=False						):




      """simple docstring"""





      a						:Optional[int] 							=       Accelerator(
          split_batches=UpperCAmelCase_ ,  dispatch_batches=UpperCAmelCase_ ,  gradient_accumulation_steps=2						)
      # Test that context manager behaves properly
      a							,				a							,				a						:List[str] 							=       get_training_setup(UpperCAmelCase_						)
      for iteration, batch in enumerate(UpperCAmelCase_						):
            a							,				a						:List[Any] 							=       batch.values()
            # Gather the distributed inputs and targs for the base model
            a							,				a						:List[str] 							=       accelerator.gather((ddp_input, ddp_target)						)
            a							,				a						:List[str] 							=       input.to(accelerator.device						), target.to(accelerator.device						)
            # Perform our initial ground truth step in non "DDP"
            step_model(UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_						)
            # Do "gradient accumulation" (noop)
            with accelerator.accumulate(UpperCAmelCase_						):
                  step_model(UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_						)

            # DDP model and model should only be in sync when not (iteration % 2 == 0)
            for param, ddp_param in zip(model.parameters() ,  ddp_model.parameters()						):
                  if not param.requires_grad:
                        continue
                  if ((iteration + 1) % 2 == 0) or (iteration == len(UpperCAmelCase_						) - 1):
                        # Grads should be in sync
                        assert (
                            torch.allclose(param.grad ,  ddp_param.grad						) is True
                        ), F'''Gradients not in sync when they should be at iteration {iteration}:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'''
                  else:
                        # Grads should not be in sync
                        assert (
                            torch.allclose(param.grad ,  ddp_param.grad						) is False
                        ), F'''Gradients in sync when they should not be at iteration {iteration}:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})'''

        # Shuffle ddp_input on each iteration
            torch.manual_seed(1337 + iteration						)
            a						:List[str] 							=       ddp_input[torch.randperm(len(UpperCAmelCase_						)						)]
      GradientState._reset_state()
def 				__lowerCamelCase						( UpperCAmelCase_						:		Any=False ,  UpperCAmelCase_						:		Optional[int]=False						):




      """simple docstring"""





      a						:Optional[Any] 							=       Accelerator(
          split_batches=UpperCAmelCase_ ,  dispatch_batches=UpperCAmelCase_ ,  gradient_accumulation_steps=2						)
      # Test that context manager behaves properly
      a							,				a							,				a							,				a							,				a							,				a							,				a						:Optional[Any] 							=       get_training_setup(UpperCAmelCase_ ,  UpperCAmelCase_						)
      for iteration, batch in enumerate(UpperCAmelCase_						):
            a							,				a						:int 							=       batch.values()
            # Gather the distributed inputs and targs for the base model
            a							,				a						:List[str] 							=       accelerator.gather((ddp_input, ddp_target)						)
            a							,				a						:str 							=       input.to(accelerator.device						), target.to(accelerator.device						)
            # Perform our initial ground truth step in non "DDP"
            model.train()
            ddp_model.train()
            step_model(UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_						)
            opt.step()

            if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(UpperCAmelCase_						)):
                  if split_batches:
                        sched.step()
                  else:
                        for _ in range(accelerator.num_processes						):
                              sched.step()
            opt.zero_grad()
            # Perform gradient accumulation under wrapper
            with accelerator.accumulate(UpperCAmelCase_						):
                  step_model(UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_						)
                  ddp_opt.step()
                  ddp_sched.step()
                  ddp_opt.zero_grad()

            # Learning rates should be the same
            assert (
                opt.param_groups[0]["lr"] == ddp_opt.param_groups[0]["lr"]
            ), F'''Learning rates found in each optimizer did not align\nopt: {opt.param_groups[0]['lr']}\nDDP opt: {ddp_opt.param_groups[0]['lr']}\n'''
            a						:Tuple 							=       (((iteration + 1) % 2) == 0) or ((iteration + 1) == len(UpperCAmelCase_						))
            if accelerator.num_processes > 1:
                  check_model_parameters(UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_ ,  UpperCAmelCase_						)
            # Shuffle ddp_input on each iteration
            torch.manual_seed(1337 + iteration						)
      GradientState._reset_state()
def 				__lowerCamelCase						( ):




      """simple docstring"""





      a						:Optional[Any] 							=       Accelerator()

      a						:int 							=       RegressionDataset(length=80						)
      a						:List[str] 							=       DataLoader(UpperCAmelCase_ ,  batch_size=16						)
      a						:List[Any] 							=       RegressionDataset(length=96						)
      a						:Any 							=       DataLoader(UpperCAmelCase_ ,  batch_size=16						)
      a							,				a						:Optional[int] 							=       accelerator.prepare(UpperCAmelCase_ ,  UpperCAmelCase_						)
      assert accelerator.gradient_state.active_dataloader is None
      for iteration, _ in enumerate(UpperCAmelCase_						):
            assert id(accelerator.gradient_state.active_dataloader						) == id(UpperCAmelCase_						)
            if iteration < len(UpperCAmelCase_						) - 1:
                  assert not accelerator.gradient_state.end_of_dataloader
                  if iteration == 1:
                        for batch_num, _ in enumerate(UpperCAmelCase_						):
                              assert id(accelerator.gradient_state.active_dataloader						) == id(UpperCAmelCase_						)
                              if batch_num < len(UpperCAmelCase_						) - 1:
                                    assert not accelerator.gradient_state.end_of_dataloader
                              else:
                                    assert accelerator.gradient_state.end_of_dataloader
            else:
                  assert accelerator.gradient_state.end_of_dataloader
      assert accelerator.gradient_state.active_dataloader is None
def 				__lowerCamelCase						( ):




      """simple docstring"""





      a						:Optional[int] 							=       Accelerator()
      a						:Optional[int] 							=       accelerator.state
      if state.local_process_index == 0:
            print('''**Test `accumulate` gradient accumulation with dataloader break**'''						)
      test_dataloader_break()
      if state.distributed_type == DistributedType.NO:
            if state.local_process_index == 0:
                  print('''**Test NOOP `no_sync` context manager**'''						)
            test_noop_sync(UpperCAmelCase_						)
      if state.distributed_type in (DistributedType.MULTI_GPU, DistributedType.MULTI_CPU):
            if state.local_process_index == 0:
                  print('''**Test Distributed `no_sync` context manager**'''						)
            test_distributed_sync(UpperCAmelCase_						)
      if state.distributed_type == DistributedType.MULTI_GPU:
            for split_batch in [True, False]:
                  for dispatch_batches in [True, False]:
                        if state.local_process_index == 0:
                              print(
                                  '''**Test `accumulate` gradient accumulation, ''' ,  F'''`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**''' ,  )
                        test_gradient_accumulation(UpperCAmelCase_ ,  UpperCAmelCase_						)

    # Currently will break on torch 2.0 +, need to investigate why
      if is_torch_version('''<''' ,  '''2.0'''						) or state.distributed_type == DistributedType.NO:
            if state.local_process_index == 0:
                  print(
                      '''**Test `accumulate` gradient accumulation with optimizer and scheduler, ''' ,  '''`split_batches=False`, `dispatch_batches=False`**''' ,  )
            test_gradient_accumulation_with_opt_and_scheduler()
            if state.distributed_type == DistributedType.MULTI_GPU:
                  for split_batch in [True, False]:
                        for dispatch_batches in [True, False]:
                              if not split_batch and not dispatch_batches:
                                    continue
                              if state.local_process_index == 0:
                                    print(
                                        '''**Test `accumulate` gradient accumulation with optimizer and scheduler, ''' ,  F'''`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**''' ,  )
                              test_gradient_accumulation_with_opt_and_scheduler(UpperCAmelCase_ ,  UpperCAmelCase_						)
def 				__lowerCamelCase						( UpperCAmelCase_						:		Tuple						):




      """simple docstring"""





      main()


if __name__ == "__main__":
     main()





