

"""simple docstring"""





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 					_lowerCAmelCase  (	unittest.TestCase   ):







 """simple docstring"""







 @property
 def 					_lowercase (			self							:			Dict	):
    torch.manual_seed(0	)
    __lowercase								=						UNetaDModel(
        block_out_channels=(3_2, 6_4),					layers_per_block=2,					sample_size=3_2,					in_channels=3,					out_channels=3,					down_block_types=("DownBlock2D", "AttnDownBlock2D"),					up_block_types=("AttnUpBlock2D", "UpBlock2D"),					)
    return model



 def 					_lowercase (			self							:			List[str]	):
    __lowercase								=						self.dummy_uncond_unet
    __lowercase								=						KarrasVeScheduler()

    __lowercase								=						KarrasVePipeline(unet=__lowerCamelCase,					scheduler=__lowerCamelCase	)
    pipe.to(__lowerCamelCase	)
    pipe.set_progress_bar_config(disable=__lowerCamelCase	)

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

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

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

    assert image.shape == (1, 3_2, 3_2, 3)
    __lowercase								=						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 					_lowerCAmelCase  (	unittest.TestCase   ):







 """simple docstring"""







 def 					_lowercase (			self							:			Optional[Any]	):
    __lowercase								=						"google/ncsnpp-celebahq-256"
    __lowercase								=						UNetaDModel.from_pretrained(__lowerCamelCase	)
    __lowercase								=						KarrasVeScheduler()

    __lowercase								=						KarrasVePipeline(unet=__lowerCamelCase,					scheduler=__lowerCamelCase	)
    pipe.to(__lowerCamelCase	)
    pipe.set_progress_bar_config(disable=__lowerCamelCase	)

    __lowercase								=						torch.manual_seed(0	)
    __lowercase								=						pipe(num_inference_steps=2_0,					generator=__lowerCamelCase,					output_type="numpy"	).images

    __lowercase								=						image[0, -3:, -3:, -1]
    assert image.shape == (1, 2_5_6, 2_5_6, 3)
    __lowercase								=						np.array([0.578, 0.5_811, 0.5_924, 0.5_809, 0.587, 0.5_886, 0.5_861, 0.5_802, 0.586]	)

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










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 			__magic_name__				( A		:					Tuple,  A		:					List[Any],  A		:					List[Any],  A		:					Dict   ):






			'''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 			__magic_name__				( A		:					List[Any],  A		:					int,  A		:					Optional[Any],  A		:					Optional[int],  A		:					Any=True   ):






			'''simple docstring'''







			model.train()
			a    =			model(A   )
			a    =			F.mse_loss(A,  target.to(output.device   )   )
			if not do_backward:
						loss /= accelerator.gradient_accumulation_steps
						loss.backward()
			else:
						accelerator.backward(A   )




def 			__magic_name__				( A		:					Any,  A		:					Any=False   ):






			'''simple docstring'''







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




def 			__magic_name__				( A		:					List[Any]   ):






			'''simple docstring'''







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

						# Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
						check_model_parameters(A,  A,  A,  A   )
						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    =			ddp_input[torch.randperm(len(A   )   )]




def 			__magic_name__				( A		:					Optional[int]   ):






			'''simple docstring'''







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

						# 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    =			ddp_input[torch.randperm(len(A   )   )]




def 			__magic_name__				( A		:					List[Any]=False,  A		:					List[Any]=False   ):






			'''simple docstring'''







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

						# 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(A   ) - 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    =			ddp_input[torch.randperm(len(A   )   )]
			GradientState._reset_state()




def 			__magic_name__				( A		:					List[Any]=False,  A		:					Any=False   ):






			'''simple docstring'''







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

						if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(A   )):
									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(A   ):
									step_model(A,  A,  A,  A   )
									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    =			(((iteration + 1) % 2) == 0) or ((iteration + 1) == len(A   ))
						if accelerator.num_processes > 1:
									check_model_parameters(A,  A,  A,  A   )
						# Shuffle ddp_input on each iteration
						torch.manual_seed(1337 + iteration   )
			GradientState._reset_state()




def 			__magic_name__				( ):






			'''simple docstring'''







			a    =			Accelerator()

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






			'''simple docstring'''







			a    =			Accelerator()
			a    =			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(A   )
			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(A   )
			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(A,  A   )

    # 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(A,  A   )




def 			__magic_name__				( A		:					Optional[int]   ):






			'''simple docstring'''







			main()


if __name__ == "__main__":
		main()






