



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 A    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )    ->   Union[str, Any]:
			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 A    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase=True       )    ->   Union[str, Any]:
			model.train()
			A__	  =     model(__UpperCamelCase       )
			A__	  =     F.mse_loss(__UpperCamelCase			,						target.to(output.device       )       )
			if not do_backward:
						loss /= accelerator.gradient_accumulation_steps
						loss.backward()
			else:
						accelerator.backward(__UpperCamelCase       )


def A    (							__UpperCamelCase			,						__UpperCamelCase=False       )    ->   Tuple:
			set_seed(42       )
			A__	  =     RegressionModel()
			A__	  =     deepcopy(__UpperCamelCase       )
			A__	  =     RegressionDataset(length=80       )
			A__	  =     DataLoader(__UpperCamelCase			,						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(__UpperCamelCase			,						lr_lambda=lambda __UpperCamelCase       : epoch**0.65       )
						A__	  =     LambdaLR(__UpperCamelCase			,						lr_lambda=lambda __UpperCamelCase       : epoch**0.65       )
			# Make a copy of `model`
			if sched:
						A__  ,			A__  ,			A__  ,			A__	  =     accelerator.prepare(__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )
			else:
						A__  ,			A__	  =     accelerator.prepare(__UpperCamelCase			,						__UpperCamelCase       )
			if sched:
						return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
			return model, ddp_model, dataloader


def A    (							__UpperCamelCase       )    ->   Union[str, Any]:
			# Test when on a single CPU or GPU that the context manager does nothing
			A__  ,			A__  ,			A__	  =     get_training_setup(__UpperCamelCase       )
			# Use a single batch
			A__  ,			A__	  =     next(iter(__UpperCamelCase       )       ).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(__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(1_337 + iteration       )
						A__	  =     ddp_input[torch.randperm(len(__UpperCamelCase       )       )]


def A    (							__UpperCamelCase       )    ->   Union[str, Any]:
			# Test on distributed setup that context manager behaves properly
			A__  ,			A__  ,			A__	  =     get_training_setup(__UpperCamelCase       )
			# Use a single batch
			A__  ,			A__	  =     next(iter(__UpperCamelCase       )       ).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(__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(1_337 + iteration       )
						A__	  =     ddp_input[torch.randperm(len(__UpperCamelCase       )       )]


def A    (							__UpperCamelCase=False			,						__UpperCamelCase=False       )    ->   Optional[int]:
			A__	  =     Accelerator(
			    split_batches=__UpperCamelCase			,						dispatch_batches=__UpperCamelCase			,						gradient_accumulation_steps=2       )
			# Test that context manager behaves properly
			A__  ,			A__  ,			A__	  =     get_training_setup(__UpperCamelCase       )
			for iteration, batch in enumerate(__UpperCamelCase       ):
						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(__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(1_337 + iteration       )
						A__	  =     ddp_input[torch.randperm(len(__UpperCamelCase       )       )]
			GradientState._reset_state()


def A    (							__UpperCamelCase=False			,						__UpperCamelCase=False       )    ->   Optional[int]:
			A__	  =     Accelerator(
			    split_batches=__UpperCamelCase			,						dispatch_batches=__UpperCamelCase			,						gradient_accumulation_steps=2       )
			# Test that context manager behaves properly
			A__  ,			A__  ,			A__  ,			A__  ,			A__  ,			A__  ,			A__	  =     get_training_setup(__UpperCamelCase			,						__UpperCamelCase       )
			for iteration, batch in enumerate(__UpperCamelCase       ):
						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(__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__	  =     (((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(1_337 + iteration       )
			GradientState._reset_state()


def A    (							)    ->   List[str]:
			A__	  =     Accelerator()

			A__	  =     RegressionDataset(length=80       )
			A__	  =     DataLoader(__UpperCamelCase			,						batch_size=16       )
			A__	  =     RegressionDataset(length=96       )
			A__	  =     DataLoader(__UpperCamelCase			,						batch_size=16       )
			A__  ,			A__	  =     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 A    (							)    ->   Optional[Any]:
			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(__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 A    (							__UpperCamelCase       )    ->   Tuple:
			# For xla_spawn (TPUs)
			main()


if __name__ == "__main__":
							main()









import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel


def A    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )    ->   int:
			A__	  =     OmegaConf.load(__UpperCamelCase       )
			A__	  =     torch.load(__UpperCamelCase			,						map_location='cpu'       )['model']
			A__	  =     list(state_dict.keys()       )

			# extract state_dict for VQVAE
			A__	  =     {}
			A__	  =     'first_stage_model.'
			for key in keys:
						if key.startswith(__UpperCamelCase       ):
									A__	  =     state_dict[key]

    # extract state_dict for UNetLDM
			A__	  =     {}
			A__	  =     'model.diffusion_model.'
			for key in keys:
						if key.startswith(__UpperCamelCase       ):
									A__	  =     state_dict[key]

			A__	  =     config.model.params.first_stage_config.params
			A__	  =     config.model.params.unet_config.params

			A__	  =     VQModel(**__UpperCamelCase       ).eval()
			vqvae.load_state_dict(__UpperCamelCase       )

			A__	  =     UNetLDMModel(**__UpperCamelCase       ).eval()
			unet.load_state_dict(__UpperCamelCase       )

			A__	  =     DDIMScheduler(
			    timesteps=config.model.params.timesteps			,						beta_schedule='scaled_linear'			,						beta_start=config.model.params.linear_start			,						beta_end=config.model.params.linear_end			,						clip_sample=__UpperCamelCase			,						)

			A__	  =     LDMPipeline(__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )
			pipeline.save_pretrained(__UpperCamelCase       )


if __name__ == "__main__":
							SCREAMING_SNAKE_CASE__   			=   argparse.ArgumentParser()
							parser.add_argument('''--checkpoint_path''', type=str, required=True)
							parser.add_argument('''--config_path''', type=str, required=True)
							parser.add_argument('''--output_path''', type=str, required=True)
							SCREAMING_SNAKE_CASE__   			=   parser.parse_args()

							convert_ldm_original(args.checkpoint_path, args.config_path, args.output_path)




