




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_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
			'''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_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE=True   ):
			'''simple docstring'''
			model.train()
			SCREAMING_SNAKE_CASE           =					model(SCREAMING_SNAKE_CASE   )
			SCREAMING_SNAKE_CASE           =					F.mse_loss(SCREAMING_SNAKE_CASE					,	target.to(output.device   )   )
			if not do_backward:
						loss /= accelerator.gradient_accumulation_steps
						loss.backward()
			else:
						accelerator.backward(SCREAMING_SNAKE_CASE   )
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE=False   ):
			'''simple docstring'''
			set_seed(42   )
			SCREAMING_SNAKE_CASE           =					RegressionModel()
			SCREAMING_SNAKE_CASE           =					deepcopy(SCREAMING_SNAKE_CASE   )
			SCREAMING_SNAKE_CASE           =					RegressionDataset(length=80   )
			SCREAMING_SNAKE_CASE           =					DataLoader(SCREAMING_SNAKE_CASE					,	batch_size=16   )
			model.to(accelerator.device   )
			if sched:
						SCREAMING_SNAKE_CASE           =					AdamW(params=model.parameters()					,	lr=1E-3   )
						SCREAMING_SNAKE_CASE           =					AdamW(params=ddp_model.parameters()					,	lr=1E-3   )
						SCREAMING_SNAKE_CASE           =					LambdaLR(SCREAMING_SNAKE_CASE					,	lr_lambda=lambda SCREAMING_SNAKE_CASE   : epoch**0.65   )
						SCREAMING_SNAKE_CASE           =					LambdaLR(SCREAMING_SNAKE_CASE					,	lr_lambda=lambda SCREAMING_SNAKE_CASE   : epoch**0.65   )
			# Make a copy of `model`
			if sched:
						SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					accelerator.prepare(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   )
			else:
						SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					accelerator.prepare(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   )
			if sched:
						return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
			return model, ddp_model, dataloader
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					get_training_setup(SCREAMING_SNAKE_CASE   )
			# Use a single batch
			SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					next(iter(SCREAMING_SNAKE_CASE   )   ).values()
			for iteration in range(3   ):
						# Gather the distributed inputs and targs for the base model
						SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					accelerator.gather((ddp_input, ddp_target)   )
						SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					input.to(accelerator.device   ), target.to(accelerator.device   )
						# Perform our initial ground truth step in non "DDP"
						step_model(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   )
						# Do "gradient accumulation" (noop)
						if iteration % 2 == 0:
									# Accumulate grads locally
									with accelerator.no_sync(SCREAMING_SNAKE_CASE   ):
												step_model(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   )
						else:
									# Sync grads
									step_model(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   )

						# Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
						check_model_parameters(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   )
						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(13_37 + iteration   )
						SCREAMING_SNAKE_CASE           =					ddp_input[torch.randperm(len(SCREAMING_SNAKE_CASE   )   )]
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					get_training_setup(SCREAMING_SNAKE_CASE   )
			# Use a single batch
			SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					next(iter(SCREAMING_SNAKE_CASE   )   ).values()
			for iteration in range(3   ):
						# Gather the distributed inputs and targs for the base model
						SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					accelerator.gather((ddp_input, ddp_target)   )
						SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					input.to(accelerator.device   ), target.to(accelerator.device   )
						# Perform our initial ground truth step in non "DDP"
						step_model(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   )
						# Do "gradient accumulation" (noop)
						if iteration % 2 == 0:
									# Accumulate grads locally
									with accelerator.no_sync(SCREAMING_SNAKE_CASE   ):
												step_model(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   )
						else:
									# Sync grads
									step_model(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   )

						# 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(13_37 + iteration   )
						SCREAMING_SNAKE_CASE           =					ddp_input[torch.randperm(len(SCREAMING_SNAKE_CASE   )   )]
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE=False					,	SCREAMING_SNAKE_CASE=False   ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					Accelerator(
			    split_batches=SCREAMING_SNAKE_CASE					,	dispatch_batches=SCREAMING_SNAKE_CASE					,	gradient_accumulation_steps=2   )
			# Test that context manager behaves properly
			SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					get_training_setup(SCREAMING_SNAKE_CASE   )
			for iteration, batch in enumerate(SCREAMING_SNAKE_CASE   ):
						SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					batch.values()
						# Gather the distributed inputs and targs for the base model
						SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					accelerator.gather((ddp_input, ddp_target)   )
						SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					input.to(accelerator.device   ), target.to(accelerator.device   )
						# Perform our initial ground truth step in non "DDP"
						step_model(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   )
						# Do "gradient accumulation" (noop)
						with accelerator.accumulate(SCREAMING_SNAKE_CASE   ):
									step_model(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   )

						# 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(SCREAMING_SNAKE_CASE   ) - 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(13_37 + iteration   )
						SCREAMING_SNAKE_CASE           =					ddp_input[torch.randperm(len(SCREAMING_SNAKE_CASE   )   )]
			GradientState._reset_state()
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE=False					,	SCREAMING_SNAKE_CASE=False   ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					Accelerator(
			    split_batches=SCREAMING_SNAKE_CASE					,	dispatch_batches=SCREAMING_SNAKE_CASE					,	gradient_accumulation_steps=2   )
			# Test that context manager behaves properly
			SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					get_training_setup(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   )
			for iteration, batch in enumerate(SCREAMING_SNAKE_CASE   ):
						SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					batch.values()
						# Gather the distributed inputs and targs for the base model
						SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					accelerator.gather((ddp_input, ddp_target)   )
						SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					input.to(accelerator.device   ), target.to(accelerator.device   )
						# Perform our initial ground truth step in non "DDP"
						model.train()
						ddp_model.train()
						step_model(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   )
						opt.step()

						if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(SCREAMING_SNAKE_CASE   )):
									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(SCREAMING_SNAKE_CASE   ):
									step_model(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   )
									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"""
						SCREAMING_SNAKE_CASE           =					(((iteration + 1) % 2) == 0) or ((iteration + 1) == len(SCREAMING_SNAKE_CASE   ))
						if accelerator.num_processes > 1:
									check_model_parameters(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   )
						# Shuffle ddp_input on each iteration
						torch.manual_seed(13_37 + iteration   )
			GradientState._reset_state()
def 					lowerCamelCase_    (    ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					Accelerator()

			SCREAMING_SNAKE_CASE           =					RegressionDataset(length=80   )
			SCREAMING_SNAKE_CASE           =					DataLoader(SCREAMING_SNAKE_CASE					,	batch_size=16   )
			SCREAMING_SNAKE_CASE           =					RegressionDataset(length=96   )
			SCREAMING_SNAKE_CASE           =					DataLoader(SCREAMING_SNAKE_CASE					,	batch_size=16   )
			SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					accelerator.prepare(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   )
			assert accelerator.gradient_state.active_dataloader is None
			for iteration, _ in enumerate(SCREAMING_SNAKE_CASE   ):
						assert id(accelerator.gradient_state.active_dataloader   ) == id(SCREAMING_SNAKE_CASE   )
						if iteration < len(SCREAMING_SNAKE_CASE   ) - 1:
									assert not accelerator.gradient_state.end_of_dataloader
									if iteration == 1:
												for batch_num, _ in enumerate(SCREAMING_SNAKE_CASE   ):
															assert id(accelerator.gradient_state.active_dataloader   ) == id(SCREAMING_SNAKE_CASE   )
															if batch_num < len(SCREAMING_SNAKE_CASE   ) - 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'''
			SCREAMING_SNAKE_CASE           =					Accelerator()
			SCREAMING_SNAKE_CASE           =					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(SCREAMING_SNAKE_CASE   )
			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(SCREAMING_SNAKE_CASE   )
			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(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   )

    # 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(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   )
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			main()


if __name__ == "__main__":
							main()












from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




class 			UpperCAmelCase_      (   A       ):




							'''simple docstring'''



							a__						  =     42
							a__						  =     42





							def __init__(					self       :	Any       ,     a       :	UNetaDModel       ,     a       :	ScoreSdeVeScheduler			)			->					str:
										super().__init__()
										self.register_modules(unet=a       ,     scheduler=a			)







							@torch.no_grad()
							def __call__(					self       :	List[Any]       ,     a       :	int = 1       ,     a       :	int = 2_000       ,     a       :	Optional[Union[torch.Generator, List[torch.Generator]]] = None       ,     a       :	Optional[str] = "pil"       ,     a       :	bool = True       ,     **a       :	Optional[int]       ,     )			->					Union[ImagePipelineOutput, Tuple]:
										SCREAMING_SNAKE_CASE           =					self.unet.config.sample_size
										SCREAMING_SNAKE_CASE           =					(batch_size, 3, img_size, img_size)

										SCREAMING_SNAKE_CASE           =					self.unet

										SCREAMING_SNAKE_CASE           =					randn_tensor(a       ,     generator=a			) * self.scheduler.init_noise_sigma
										SCREAMING_SNAKE_CASE           =					sample.to(self.device			)

										self.scheduler.set_timesteps(a			)
										self.scheduler.set_sigmas(a			)

										for i, t in enumerate(self.progress_bar(self.scheduler.timesteps			)			):
													SCREAMING_SNAKE_CASE           =					self.scheduler.sigmas[i] * torch.ones(shape[0]       ,     device=self.device			)

													# correction step
													for _ in range(self.scheduler.config.correct_steps			):
																SCREAMING_SNAKE_CASE           =					self.unet(a       ,     a			).sample
																SCREAMING_SNAKE_CASE           =					self.scheduler.step_correct(a       ,     a       ,     generator=a			).prev_sample

													# prediction step
													SCREAMING_SNAKE_CASE           =					model(a       ,     a			).sample
													SCREAMING_SNAKE_CASE           =					self.scheduler.step_pred(a       ,     a       ,     a       ,     generator=a			)

													SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					output.prev_sample, output.prev_sample_mean

										SCREAMING_SNAKE_CASE           =					sample_mean.clamp(0       ,     1			)
										SCREAMING_SNAKE_CASE           =					sample.cpu().permute(0       ,     2       ,     3       ,     1			).numpy()
										if output_type == "pil":
													SCREAMING_SNAKE_CASE           =					self.numpy_to_pil(a			)

										if not return_dict:
													return (sample,)

										return ImagePipelineOutput(images=a			)






