



import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest


class 						lowercase    (							SCREAMING_SNAKE_CASE_):
						'''simple docstring'''


						UpperCAmelCase			:		Optional[Any]		=		(DDPMParallelScheduler,)







						def       lowerCamelCase_				(  self    :				int       ,	**snake_case    :				Optional[Any]	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				{
											    'num_train_timesteps': 1000,
											    'beta_start': 0.0001,
											    'beta_end': 0.02,
											    'beta_schedule': 'linear',
											    'variance_type': 'fixed_small',
											    'clip_sample': True,
											}

											config.update(**snake_case	)
											return config







						def       lowerCamelCase_				(  self    :				Dict	):



											'''simple docstring'''


											for timesteps in [1, 5, 100, 1000]:
																self.check_over_configs(num_train_timesteps=snake_case	)







						def       lowerCamelCase_				(  self    :				Optional[int]	):



											'''simple docstring'''


											for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]       ,	[0.002, 0.02, 0.2, 2]	):
																self.check_over_configs(beta_start=snake_case       ,	beta_end=snake_case	)







						def       lowerCamelCase_				(  self    :				str	):



											'''simple docstring'''


											for schedule in ["linear", "squaredcos_cap_v2"]:
																self.check_over_configs(beta_schedule=snake_case	)







						def       lowerCamelCase_				(  self    :				Dict	):



											'''simple docstring'''


											for variance in ["fixed_small", "fixed_large", "other"]:
																self.check_over_configs(variance_type=snake_case	)







						def       lowerCamelCase_				(  self    :				List[Any]	):



											'''simple docstring'''


											for clip_sample in [True, False]:
																self.check_over_configs(clip_sample=snake_case	)







						def       lowerCamelCase_				(  self    :				List[Any]	):



											'''simple docstring'''


											self.check_over_configs(thresholding=snake_case	)
											for threshold in [0.5, 1.0, 2.0]:
																for prediction_type in ["epsilon", "sample", "v_prediction"]:
																					self.check_over_configs(
																					    thresholding=snake_case       ,	prediction_type=snake_case       ,	sample_max_value=snake_case       ,	)







						def       lowerCamelCase_				(  self    :				str	):



											'''simple docstring'''


											for prediction_type in ["epsilon", "sample", "v_prediction"]:
																self.check_over_configs(prediction_type=snake_case	)







						def       lowerCamelCase_				(  self    :				Tuple	):



											'''simple docstring'''


											for t in [0, 500, 999]:
																self.check_over_forward(time_step=snake_case	)







						def       lowerCamelCase_				(  self    :				Optional[Any]	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Dict						=				self.scheduler_classes[0]
											SCREAMING_SNAKE_CASE							:     List[Any]						=				self.get_scheduler_config()
											SCREAMING_SNAKE_CASE							:     int						=				scheduler_class(**snake_case	)

											assert torch.sum(torch.abs(scheduler._get_variance(0	) - 0.0	)	) < 1E-5
											assert torch.sum(torch.abs(scheduler._get_variance(487	) - 0.00979	)	) < 1E-5
											assert torch.sum(torch.abs(scheduler._get_variance(999	) - 0.02	)	) < 1E-5







						def       lowerCamelCase_				(  self    :				str	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     List[str]						=				self.scheduler_classes[0]
											SCREAMING_SNAKE_CASE							:     Tuple						=				self.get_scheduler_config()
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				scheduler_class(**snake_case	)

											SCREAMING_SNAKE_CASE							:     List[str]						=				len(snake_case	)

											SCREAMING_SNAKE_CASE							:     Optional[int]						=				self.dummy_model()
											SCREAMING_SNAKE_CASE							:     int						=				self.dummy_sample_deter
											SCREAMING_SNAKE_CASE							:     List[str]						=				self.dummy_sample_deter + 0.1
											SCREAMING_SNAKE_CASE							:     Dict						=				self.dummy_sample_deter - 0.1

											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				samplea.shape[0]
											SCREAMING_SNAKE_CASE							:     str						=				torch.stack([samplea, samplea, samplea]       ,	dim=0	)
											SCREAMING_SNAKE_CASE							:     Tuple						=				torch.arange(snake_case	)[0:3, None].repeat(1       ,	snake_case	)

											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				model(samples.flatten(0       ,	1	)       ,	timesteps.flatten(0       ,	1	)	)
											SCREAMING_SNAKE_CASE							:     Dict						=				scheduler.batch_step_no_noise(snake_case       ,	timesteps.flatten(0       ,	1	)       ,	samples.flatten(0       ,	1	)	)

											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				torch.sum(torch.abs(snake_case	)	)
											SCREAMING_SNAKE_CASE							:     str						=				torch.mean(torch.abs(snake_case	)	)

											assert abs(result_sum.item() - 1153.1833	) < 1E-2
											assert abs(result_mean.item() - 0.5005	) < 1E-3







						def       lowerCamelCase_				(  self    :				List[str]	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				self.scheduler_classes[0]
											SCREAMING_SNAKE_CASE							:     List[str]						=				self.get_scheduler_config()
											SCREAMING_SNAKE_CASE							:     Dict						=				scheduler_class(**snake_case	)

											SCREAMING_SNAKE_CASE							:     Tuple						=				len(snake_case	)

											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				self.dummy_model()
											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				self.dummy_sample_deter
											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				torch.manual_seed(0	)

											for t in reversed(range(snake_case	)	):
																# 1. predict noise residual
																SCREAMING_SNAKE_CASE							:     List[str]						=				model(snake_case       ,	snake_case	)

																# 2. predict previous mean of sample x_t-1
																SCREAMING_SNAKE_CASE							:     Any						=				scheduler.step(snake_case       ,	snake_case       ,	snake_case       ,	generator=snake_case	).prev_sample

																SCREAMING_SNAKE_CASE							:     Tuple						=				pred_prev_sample

											SCREAMING_SNAKE_CASE							:     List[str]						=				torch.sum(torch.abs(snake_case	)	)
											SCREAMING_SNAKE_CASE							:     Dict						=				torch.mean(torch.abs(snake_case	)	)

											assert abs(result_sum.item() - 258.9606	) < 1E-2
											assert abs(result_mean.item() - 0.3372	) < 1E-3







						def       lowerCamelCase_				(  self    :				str	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     List[Any]						=				self.scheduler_classes[0]
											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				self.get_scheduler_config(prediction_type='v_prediction'	)
											SCREAMING_SNAKE_CASE							:     Any						=				scheduler_class(**snake_case	)

											SCREAMING_SNAKE_CASE							:     str						=				len(snake_case	)

											SCREAMING_SNAKE_CASE							:     Dict						=				self.dummy_model()
											SCREAMING_SNAKE_CASE							:     List[str]						=				self.dummy_sample_deter
											SCREAMING_SNAKE_CASE							:     str						=				torch.manual_seed(0	)

											for t in reversed(range(snake_case	)	):
																# 1. predict noise residual
																SCREAMING_SNAKE_CASE							:     Any						=				model(snake_case       ,	snake_case	)

																# 2. predict previous mean of sample x_t-1
																SCREAMING_SNAKE_CASE							:     List[Any]						=				scheduler.step(snake_case       ,	snake_case       ,	snake_case       ,	generator=snake_case	).prev_sample

																SCREAMING_SNAKE_CASE							:     Tuple						=				pred_prev_sample

											SCREAMING_SNAKE_CASE							:     List[str]						=				torch.sum(torch.abs(snake_case	)	)
											SCREAMING_SNAKE_CASE							:     List[Any]						=				torch.mean(torch.abs(snake_case	)	)

											assert abs(result_sum.item() - 202.0296	) < 1E-2
											assert abs(result_mean.item() - 0.2631	) < 1E-3







						def       lowerCamelCase_				(  self    :				str	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     List[Any]						=				self.scheduler_classes[0]
											SCREAMING_SNAKE_CASE							:     Any						=				self.get_scheduler_config()
											SCREAMING_SNAKE_CASE							:     Dict						=				scheduler_class(**snake_case	)

											SCREAMING_SNAKE_CASE							:     Any						=				[100, 87, 50, 1, 0]

											scheduler.set_timesteps(timesteps=snake_case	)

											SCREAMING_SNAKE_CASE							:     str						=				scheduler.timesteps

											for i, timestep in enumerate(snake_case	):
																if i == len(snake_case	) - 1:
																					SCREAMING_SNAKE_CASE							:     Optional[int]						=				-1
																else:
																					SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				timesteps[i + 1]

																SCREAMING_SNAKE_CASE							:     Tuple						=				scheduler.previous_timestep(snake_case	)
																SCREAMING_SNAKE_CASE							:     int						=				prev_t.item()

																self.assertEqual(snake_case       ,	snake_case	)







						def       lowerCamelCase_				(  self    :				Optional[int]	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				self.scheduler_classes[0]
											SCREAMING_SNAKE_CASE							:     Tuple						=				self.get_scheduler_config()
											SCREAMING_SNAKE_CASE							:     List[str]						=				scheduler_class(**snake_case	)

											SCREAMING_SNAKE_CASE							:     Any						=				[100, 87, 50, 51, 0]

											with self.assertRaises(snake_case       ,	msg='`custom_timesteps` must be in descending order.'	):
																scheduler.set_timesteps(timesteps=snake_case	)







						def       lowerCamelCase_				(  self    :				List[str]	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     str						=				self.scheduler_classes[0]
											SCREAMING_SNAKE_CASE							:     int						=				self.get_scheduler_config()
											SCREAMING_SNAKE_CASE							:     str						=				scheduler_class(**snake_case	)

											SCREAMING_SNAKE_CASE							:     List[Any]						=				[100, 87, 50, 1, 0]
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				len(snake_case	)

											with self.assertRaises(snake_case       ,	msg='Can only pass one of `num_inference_steps` or `custom_timesteps`.'	):
																scheduler.set_timesteps(num_inference_steps=snake_case       ,	timesteps=snake_case	)







						def       lowerCamelCase_				(  self    :				Union[str, Any]	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     str						=				self.scheduler_classes[0]
											SCREAMING_SNAKE_CASE							:     str						=				self.get_scheduler_config()
											SCREAMING_SNAKE_CASE							:     List[Any]						=				scheduler_class(**snake_case	)

											SCREAMING_SNAKE_CASE							:     List[str]						=				[scheduler.config.num_train_timesteps]

											with self.assertRaises(
											    snake_case       ,	msg='`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'       ,	):
																scheduler.set_timesteps(timesteps=snake_case	)




import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline

def        __a			(   __lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase      ,	__lowerCAmelCase   )     ->     str:
					# load base model
					SCREAMING_SNAKE_CASE							:     Dict						=				StableDiffusionPipeline.from_pretrained(__lowerCAmelCase      ,	torch_dtype=torch.floataa   )

					# load LoRA weight from .safetensors
					SCREAMING_SNAKE_CASE							:     Any						=				load_file(__lowerCAmelCase   )

					SCREAMING_SNAKE_CASE							:     Optional[int]						=				[]

					# directly update weight in diffusers model
					for key in state_dict:
										# it is suggested to print out the key, it usually will be something like below
										# "lora_te_text_model_encoder_layers_0_self_attn_k_proj.lora_down.weight"

										# as we have set the alpha beforehand, so just skip
										if ".alpha" in key or key in visited:
															continue

										if "text" in key:
															SCREAMING_SNAKE_CASE							:     Dict						=				key.split('.'   )[0].split(LORA_PREFIX_TEXT_ENCODER + '_'   )[-1].split('_'   )
															SCREAMING_SNAKE_CASE							:     Tuple						=				pipeline.text_encoder
										else:
															SCREAMING_SNAKE_CASE							:     Any						=				key.split('.'   )[0].split(LORA_PREFIX_UNET + '_'   )[-1].split('_'   )
															SCREAMING_SNAKE_CASE							:     Dict						=				pipeline.unet

										# find the target layer
										SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				layer_infos.pop(0   )
										while len(__lowerCAmelCase   ) > -1:
															try:
																				SCREAMING_SNAKE_CASE							:     List[Any]						=				curr_layer.__getattr__(__lowerCAmelCase   )
																				if len(__lowerCAmelCase   ) > 0:
																									SCREAMING_SNAKE_CASE							:     Optional[int]						=				layer_infos.pop(0   )
																				elif len(__lowerCAmelCase   ) == 0:
																									break
															except Exception:
																				if len(__lowerCAmelCase   ) > 0:
																									temp_name += "_" + layer_infos.pop(0   )
																				else:
																									SCREAMING_SNAKE_CASE							:     List[str]						=				layer_infos.pop(0   )

										SCREAMING_SNAKE_CASE							:     str						=				[]
										if "lora_down" in key:
															pair_keys.append(key.replace('lora_down'      ,	'lora_up'   )   )
															pair_keys.append(__lowerCAmelCase   )
										else:
															pair_keys.append(__lowerCAmelCase   )
															pair_keys.append(key.replace('lora_up'      ,	'lora_down'   )   )

										# update weight
										if len(state_dict[pair_keys[0]].shape   ) == 4:
															SCREAMING_SNAKE_CASE							:     Any						=				state_dict[pair_keys[0]].squeeze(3   ).squeeze(2   ).to(torch.floataa   )
															SCREAMING_SNAKE_CASE							:     str						=				state_dict[pair_keys[1]].squeeze(3   ).squeeze(2   ).to(torch.floataa   )
															curr_layer.weight.data += alpha * torch.mm(__lowerCAmelCase      ,	__lowerCAmelCase   ).unsqueeze(2   ).unsqueeze(3   )
										else:
															SCREAMING_SNAKE_CASE							:     List[Any]						=				state_dict[pair_keys[0]].to(torch.floataa   )
															SCREAMING_SNAKE_CASE							:     Optional[int]						=				state_dict[pair_keys[1]].to(torch.floataa   )
															curr_layer.weight.data += alpha * torch.mm(__lowerCAmelCase      ,	__lowerCAmelCase   )

										# update visited list
										for item in pair_keys:
															visited.append(__lowerCAmelCase   )

					return pipeline


if __name__ == "__main__":
			_lowerCamelCase    :		Tuple		      =    argparse.ArgumentParser()

			parser.add_argument(
			    """--base_model_path""", default=None, type=str, required=True, help="""Path to the base model in diffusers format."""
			)
			parser.add_argument(
			    """--checkpoint_path""", default=None, type=str, required=True, help="""Path to the checkpoint to convert."""
			)
			parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")
			parser.add_argument(
			    """--lora_prefix_unet""", default="""lora_unet""", type=str, help="""The prefix of UNet weight in safetensors"""
			)
			parser.add_argument(
			    """--lora_prefix_text_encoder""",
			    default="""lora_te""",
			    type=str,
			    help="""The prefix of text encoder weight in safetensors""",
			)
			parser.add_argument("""--alpha""", default=0.75, type=float, help="""The merging ratio in W = W0 + alpha * deltaW""")
			parser.add_argument(
			    """--to_safetensors""", action="""store_true""", help="""Whether to store pipeline in safetensors format or not."""
			)
			parser.add_argument("""--device""", type=str, help="""Device to use (e.g. cpu, cuda:0, cuda:1, etc.)""")

			_lowerCamelCase    :		List[Any]		      =    parser.parse_args()

			_lowerCamelCase    :		List[str]		      =    args.base_model_path
			_lowerCamelCase    :		str		      =    args.checkpoint_path
			_lowerCamelCase    :		Dict		      =    args.dump_path
			_lowerCamelCase    :		Any		      =    args.lora_prefix_unet
			_lowerCamelCase    :		List[Any]		      =    args.lora_prefix_text_encoder
			_lowerCamelCase    :		List[Any]		      =    args.alpha

			_lowerCamelCase    :		Optional[Any]		      =    convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)

			_lowerCamelCase    :		Optional[Any]		      =    pipe.to(args.device)
			pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)