



"""simple docstring"""

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__    (   _lowerCamelCase				: Dict  ,       _lowerCamelCase				: Any  ,       _lowerCamelCase				: str  ,       _lowerCamelCase				: Optional[int]					):
							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__    (   _lowerCamelCase				: Optional[int]  ,       _lowerCamelCase				: str  ,       _lowerCamelCase				: Dict  ,       _lowerCamelCase				: Union[str, Any]  ,       _lowerCamelCase				: Dict=True					):
							model.train()
							__a		:		Optional[int]									=       model(_lowerCamelCase					)
							__a		:		Union[str, Any]									=       F.mse_loss(_lowerCamelCase  ,       target.to(output.device					)					)
							if not do_backward:
														loss /= accelerator.gradient_accumulation_steps
														loss.backward()
							else:
														accelerator.backward(_lowerCamelCase					)





def       __magic_name__    (   _lowerCamelCase				: List[str]  ,       _lowerCamelCase				: Any=False					):
							set_seed(4_2					)
							__a		:		Dict									=       RegressionModel()
							__a		:		Optional[Any]									=       deepcopy(_lowerCamelCase					)
							__a		:		str									=       RegressionDataset(length=8_0					)
							__a		:		int									=       DataLoader(_lowerCamelCase  ,       batch_size=1_6					)
							model.to(accelerator.device					)
							if sched:
														__a		:		List[str]									=       AdamW(params=model.parameters()  ,       lr=1E-3					)
														__a		:		Dict									=       AdamW(params=ddp_model.parameters()  ,       lr=1E-3					)
														__a		:		Tuple									=       LambdaLR(_lowerCamelCase  ,       lr_lambda=lambda _lowerCamelCase					: epoch**0.65					)
														__a		:		Union[str, Any]									=       LambdaLR(_lowerCamelCase  ,       lr_lambda=lambda _lowerCamelCase					: epoch**0.65					)
							# Make a copy of `model`
							if sched:
														__a							,     __a							,     __a							,     __a		:		List[Any]									=       accelerator.prepare(_lowerCamelCase  ,       _lowerCamelCase  ,       _lowerCamelCase  ,       _lowerCamelCase					)
							else:
														__a							,     __a		:		Any									=       accelerator.prepare(_lowerCamelCase  ,       _lowerCamelCase					)
							if sched:
														return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
							return model, ddp_model, dataloader





def       __magic_name__    (   _lowerCamelCase				: List[Any]					):
							# Test when on a single CPU or GPU that the context manager does nothing
							__a							,     __a							,     __a		:		str									=       get_training_setup(_lowerCamelCase					)
							# Use a single batch
							__a							,     __a		:		List[str]									=       next(iter(_lowerCamelCase					)					).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		:		str									=       input.to(accelerator.device					), target.to(accelerator.device					)
														# Perform our initial ground truth step in non "DDP"
														step_model(_lowerCamelCase  ,       _lowerCamelCase  ,       _lowerCamelCase  ,       _lowerCamelCase					)
														# Do "gradient accumulation" (noop)
														if iteration % 2 == 0:
																					# Accumulate grads locally
																					with accelerator.no_sync(_lowerCamelCase					):
																												step_model(_lowerCamelCase  ,       _lowerCamelCase  ,       _lowerCamelCase  ,       _lowerCamelCase					)
														else:
																					# Sync grads
																					step_model(_lowerCamelCase  ,       _lowerCamelCase  ,       _lowerCamelCase  ,       _lowerCamelCase					)

														# Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
														check_model_parameters(_lowerCamelCase  ,       _lowerCamelCase  ,       _lowerCamelCase  ,       _lowerCamelCase					)
														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_3_3_7 + iteration					)
														__a		:		List[Any]									=       ddp_input[torch.randperm(len(_lowerCamelCase					)					)]





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

														# 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_3_3_7 + iteration					)
														__a		:		Optional[Any]									=       ddp_input[torch.randperm(len(_lowerCamelCase					)					)]





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

														# 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(_lowerCamelCase					) - 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_3_3_7 + iteration					)
														__a		:		int									=       ddp_input[torch.randperm(len(_lowerCamelCase					)					)]
							GradientState._reset_state()





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

														if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(_lowerCamelCase					)):
																					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(_lowerCamelCase					):
																					step_model(_lowerCamelCase  ,       _lowerCamelCase  ,       _lowerCamelCase  ,       _lowerCamelCase					)
																					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		:		Optional[int]									=       (((iteration + 1) % 2) == 0) or ((iteration + 1) == len(_lowerCamelCase					))
														if accelerator.num_processes > 1:
																					check_model_parameters(_lowerCamelCase  ,       _lowerCamelCase  ,       _lowerCamelCase  ,       _lowerCamelCase					)
														# Shuffle ddp_input on each iteration
														torch.manual_seed(1_3_3_7 + iteration					)
							GradientState._reset_state()





def       __magic_name__    (   ):
							__a		:		List[Any]									=       Accelerator()

							__a		:		str									=       RegressionDataset(length=8_0					)
							__a		:		Dict									=       DataLoader(_lowerCamelCase  ,       batch_size=1_6					)
							__a		:		int									=       RegressionDataset(length=9_6					)
							__a		:		List[Any]									=       DataLoader(_lowerCamelCase  ,       batch_size=1_6					)
							__a							,     __a		:		List[str]									=       accelerator.prepare(_lowerCamelCase  ,       _lowerCamelCase					)
							assert accelerator.gradient_state.active_dataloader is None
							for iteration, _ in enumerate(_lowerCamelCase					):
														assert id(accelerator.gradient_state.active_dataloader					) == id(_lowerCamelCase					)
														if iteration < len(_lowerCamelCase					) - 1:
																					assert not accelerator.gradient_state.end_of_dataloader
																					if iteration == 1:
																												for batch_num, _ in enumerate(_lowerCamelCase					):
																																			assert id(accelerator.gradient_state.active_dataloader					) == id(_lowerCamelCase					)
																																			if batch_num < len(_lowerCamelCase					) - 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__    (   ):
							__a		:		int									=       Accelerator()
							__a		:		str									=       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(_lowerCamelCase					)
							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(_lowerCamelCase					)
							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(_lowerCamelCase  ,       _lowerCamelCase					)

    # 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(_lowerCamelCase  ,       _lowerCamelCase					)





def       __magic_name__    (   _lowerCamelCase				: Union[str, Any]					):
							# For xla_spawn (TPUs)
							main()


if __name__ == "__main__":
	main()






"""simple docstring"""

import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
	lowercase__			   =  argparse.ArgumentParser()

	parser.add_argument(
	    "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
	)
	parser.add_argument(
	    "--original_config_file",
	    type=str,
	    required=True,
	    help="The YAML config file corresponding to the original architecture.",
	)
	parser.add_argument(
	    "--num_in_channels",
	    default=None,
	    type=int,
	    help="The number of input channels. If `None` number of input channels will be automatically inferred.",
	)
	parser.add_argument(
	    "--image_size",
	    default=512,
	    type=int,
	    help=(
	        "The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2"
	        " Base. Use 768 for Stable Diffusion v2."
	    ),
	)
	parser.add_argument(
	    "--extract_ema",
	    action="store_true",
	    help=(
	        "Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights"
	        " or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield"
	        " higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning."
	    ),
	)
	parser.add_argument(
	    "--upcast_attention",
	    action="store_true",
	    help=(
	        "Whether the attention computation should always be upcasted. This is necessary when running stable"
	        " diffusion 2.1."
	    ),
	)
	parser.add_argument(
	    "--from_safetensors",
	    action="store_true",
	    help="If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.",
	)
	parser.add_argument(
	    "--to_safetensors",
	    action="store_true",
	    help="Whether to store pipeline in safetensors format or not.",
	)
	parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
	parser.add_argument("--device", type=str, help="Device to use (e.g. cpu, cuda:0, cuda:1, etc.)")





	def       __magic_name__    (   _lowerCamelCase				: Optional[Any]					):
								if string == "True":
															return True
								elif string == "False":
															return False
								else:
															raise ValueError(F'''could not parse string as bool {string}'''					)

	parser.add_argument(
	    "--use_linear_projection", help="Override for use linear projection", required=False, type=parse_bool
	)

	parser.add_argument("--cross_attention_dim", help="Override for cross attention_dim", required=False, type=int)

	lowercase__			   =  parser.parse_args()

	lowercase__			   =  download_controlnet_from_original_ckpt(
	    checkpoint_path=args.checkpoint_path,
	    original_config_file=args.original_config_file,
	    image_size=args.image_size,
	    extract_ema=args.extract_ema,
	    num_in_channels=args.num_in_channels,
	    upcast_attention=args.upcast_attention,
	    from_safetensors=args.from_safetensors,
	    device=args.device,
	    use_linear_projection=args.use_linear_projection,
	    cross_attention_dim=args.cross_attention_dim,
	)







	controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)

