


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 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		List[Any]       ,  SCREAMING_SNAKE_CASE_   :		Tuple       ,  SCREAMING_SNAKE_CASE_   :		Tuple       ,  SCREAMING_SNAKE_CASE_   :		Tuple	):





			"""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 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		Optional[Any]       ,  SCREAMING_SNAKE_CASE_   :		Optional[int]       ,  SCREAMING_SNAKE_CASE_   :		int       ,  SCREAMING_SNAKE_CASE_   :		Tuple       ,  SCREAMING_SNAKE_CASE_   :		List[str]=True	):





			"""simple docstring"""




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






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		Any       ,  SCREAMING_SNAKE_CASE_   :		Dict=False	):





			"""simple docstring"""




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






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		Optional[int]	):





			"""simple docstring"""




			UpperCamelCase  ,  UpperCamelCase  ,  UpperCamelCase         =					get_training_setup(__A	)
			# Use a single batch
			UpperCamelCase  ,  UpperCamelCase         =					next(iter(__A	)	).values()
			for iteration in range(3	):
						# Gather the distributed inputs and targs for the base model
						UpperCamelCase  ,  UpperCamelCase         =					accelerator.gather((ddp_input, ddp_target)	)
						UpperCamelCase  ,  UpperCamelCase         =					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(1_337 + iteration	)
						UpperCamelCase         =					ddp_input[torch.randperm(len(__A	)	)]






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		List[str]	):





			"""simple docstring"""




			UpperCamelCase  ,  UpperCamelCase  ,  UpperCamelCase         =					get_training_setup(__A	)
			# Use a single batch
			UpperCamelCase  ,  UpperCamelCase         =					next(iter(__A	)	).values()
			for iteration in range(3	):
						# Gather the distributed inputs and targs for the base model
						UpperCamelCase  ,  UpperCamelCase         =					accelerator.gather((ddp_input, ddp_target)	)
						UpperCamelCase  ,  UpperCamelCase         =					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(1_337 + iteration	)
						UpperCamelCase         =					ddp_input[torch.randperm(len(__A	)	)]






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		Any=False       ,  SCREAMING_SNAKE_CASE_   :		Optional[Any]=False	):





			"""simple docstring"""




			UpperCamelCase         =					Accelerator(
			    split_batches=__A       ,  dispatch_batches=__A       ,  gradient_accumulation_steps=2	)
			# Test that context manager behaves properly
			UpperCamelCase  ,  UpperCamelCase  ,  UpperCamelCase         =					get_training_setup(__A	)
			for iteration, batch in enumerate(__A	):
						UpperCamelCase  ,  UpperCamelCase         =					batch.values()
						# Gather the distributed inputs and targs for the base model
						UpperCamelCase  ,  UpperCamelCase         =					accelerator.gather((ddp_input, ddp_target)	)
						UpperCamelCase  ,  UpperCamelCase         =					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(1_337 + iteration	)
						UpperCamelCase         =					ddp_input[torch.randperm(len(__A	)	)]
			GradientState._reset_state()






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		List[Any]=False       ,  SCREAMING_SNAKE_CASE_   :		Union[str, Any]=False	):





			"""simple docstring"""




			UpperCamelCase         =					Accelerator(
			    split_batches=__A       ,  dispatch_batches=__A       ,  gradient_accumulation_steps=2	)
			# Test that context manager behaves properly
			UpperCamelCase  ,  UpperCamelCase  ,  UpperCamelCase  ,  UpperCamelCase  ,  UpperCamelCase  ,  UpperCamelCase  ,  UpperCamelCase         =					get_training_setup(__A       ,  __A	)
			for iteration, batch in enumerate(__A	):
						UpperCamelCase  ,  UpperCamelCase         =					batch.values()
						# Gather the distributed inputs and targs for the base model
						UpperCamelCase  ,  UpperCamelCase         =					accelerator.gather((ddp_input, ddp_target)	)
						UpperCamelCase  ,  UpperCamelCase         =					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'
						UpperCamelCase         =					(((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(1_337 + iteration	)
			GradientState._reset_state()






def 							_lowercase    (		):





			"""simple docstring"""




			UpperCamelCase         =					Accelerator()

			UpperCamelCase         =					RegressionDataset(length=80	)
			UpperCamelCase         =					DataLoader(__A       ,  batch_size=16	)
			UpperCamelCase         =					RegressionDataset(length=96	)
			UpperCamelCase         =					DataLoader(__A       ,  batch_size=16	)
			UpperCamelCase  ,  UpperCamelCase         =					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 							_lowercase    (		):





			"""simple docstring"""




			UpperCamelCase         =					Accelerator()
			UpperCamelCase         =					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 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		str	):





			"""simple docstring"""




			main()


if __name__ == "__main__":
			main()








from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
			from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline




