
"""simple docstring"""




import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline



def 							_lowerCamelCase			(			__a,				__a,				__a,				__a,				__a     ):
		# load base model
		SCREAMING_SNAKE_CASE_    			=			StableDiffusionPipeline.from_pretrained(__a,				torch_dtype=torch.floataa     )

		# load LoRA weight from .safetensors
		SCREAMING_SNAKE_CASE_    			=			load_file(__a     )

		SCREAMING_SNAKE_CASE_    			=			[]

		# 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_    			=			key.split('''.'''     )[0].split(LORA_PREFIX_TEXT_ENCODER + '''_'''     )[-1].split('''_'''     )
						SCREAMING_SNAKE_CASE_    			=			pipeline.text_encoder
				else:
						SCREAMING_SNAKE_CASE_    			=			key.split('''.'''     )[0].split(LORA_PREFIX_UNET + '''_'''     )[-1].split('''_'''     )
						SCREAMING_SNAKE_CASE_    			=			pipeline.unet

				# find the target layer
				SCREAMING_SNAKE_CASE_    			=			layer_infos.pop(0     )
				while len(__a     ) > -1:
						try:
								SCREAMING_SNAKE_CASE_    			=			curr_layer.__getattr__(__a     )
								if len(__a     ) > 0:
										SCREAMING_SNAKE_CASE_    			=			layer_infos.pop(0     )
								elif len(__a     ) == 0:
										break
						except Exception:
								if len(__a     ) > 0:
										temp_name += "_" + layer_infos.pop(0     )
								else:
										SCREAMING_SNAKE_CASE_    			=			layer_infos.pop(0     )

				SCREAMING_SNAKE_CASE_    			=			[]
				if "lora_down" in key:
						pair_keys.append(key.replace('''lora_down''',				'''lora_up'''     )     )
						pair_keys.append(__a     )
				else:
						pair_keys.append(__a     )
						pair_keys.append(key.replace('''lora_up''',				'''lora_down'''     )     )

				# update weight
				if len(state_dict[pair_keys[0]].shape     ) == 4:
						SCREAMING_SNAKE_CASE_    			=			state_dict[pair_keys[0]].squeeze(3     ).squeeze(2     ).to(torch.floataa     )
						SCREAMING_SNAKE_CASE_    			=			state_dict[pair_keys[1]].squeeze(3     ).squeeze(2     ).to(torch.floataa     )
						curr_layer.weight.data += alpha * torch.mm(__a,				__a     ).unsqueeze(2     ).unsqueeze(3     )
				else:
						SCREAMING_SNAKE_CASE_    			=			state_dict[pair_keys[0]].to(torch.floataa     )
						SCREAMING_SNAKE_CASE_    			=			state_dict[pair_keys[1]].to(torch.floataa     )
						curr_layer.weight.data += alpha * torch.mm(__a,				__a     )

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

		return pipeline


if __name__ == "__main__":
						lowerCAmelCase__				=					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__				=					parser.parse_args()

						lowerCAmelCase__				=					args.base_model_path
						lowerCAmelCase__				=					args.checkpoint_path
						lowerCAmelCase__				=					args.dump_path
						lowerCAmelCase__				=					args.lora_prefix_unet
						lowerCAmelCase__				=					args.lora_prefix_text_encoder
						lowerCAmelCase__				=					args.alpha

						lowerCAmelCase__				=					convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)

						lowerCAmelCase__				=					pipe.to(args.device)
						pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)

"""simple docstring"""




import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest





class 			snake_case  (				__lowercase ):
							UpperCAmelCase__      =   (UnCLIPScheduler,)

							def _lowercase       (self							,							**SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			{
									    '''num_train_timesteps''': 10_00,
									    '''variance_type''': '''fixed_small_log''',
									    '''clip_sample''': True,
									    '''clip_sample_range''': 1.0,
									    '''prediction_type''': '''epsilon''',
									}

									config.update(**SCREAMING_SNAKE_CASE_     )
									return config

							def _lowercase       (self     ):

									"""simple docstring"""







									for timesteps in [1, 5, 1_00, 10_00]:
											self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE_     )

							def _lowercase       (self     ):

									"""simple docstring"""







									for variance in ["fixed_small_log", "learned_range"]:
											self.check_over_configs(variance_type=SCREAMING_SNAKE_CASE_     )

							def _lowercase       (self     ):

									"""simple docstring"""







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

							def _lowercase       (self     ):

									"""simple docstring"""







									for clip_sample_range in [1, 5, 10, 20]:
											self.check_over_configs(clip_sample_range=SCREAMING_SNAKE_CASE_     )

							def _lowercase       (self     ):

									"""simple docstring"""







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

							def _lowercase       (self     ):

									"""simple docstring"""







									for time_step in [0, 5_00, 9_99]:
											for prev_timestep in [None, 5, 1_00, 2_50, 5_00, 7_50]:
													if prev_timestep is not None and prev_timestep >= time_step:
															continue

													self.check_over_forward(time_step=SCREAMING_SNAKE_CASE_							,							prev_timestep=SCREAMING_SNAKE_CASE_     )

							def _lowercase       (self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE_    			=			self.get_scheduler_config(variance_type='''fixed_small_log'''     )
									SCREAMING_SNAKE_CASE_    			=			scheduler_class(**SCREAMING_SNAKE_CASE_     )

									assert torch.sum(torch.abs(scheduler._get_variance(0     ) - 1.0_0_0_0e-1_0     )     ) < 1e-5
									assert torch.sum(torch.abs(scheduler._get_variance(4_87     ) - 0.0_54_96_25     )     ) < 1e-5
									assert torch.sum(torch.abs(scheduler._get_variance(9_99     ) - 0.9_99_49_87     )     ) < 1e-5

							def _lowercase       (self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE_    			=			self.get_scheduler_config(variance_type='''learned_range'''     )
									SCREAMING_SNAKE_CASE_    			=			scheduler_class(**SCREAMING_SNAKE_CASE_     )

									SCREAMING_SNAKE_CASE_    			=			0.5

									assert scheduler._get_variance(1							,							predicted_variance=SCREAMING_SNAKE_CASE_     ) - -10.1_71_27_90 < 1e-5
									assert scheduler._get_variance(4_87							,							predicted_variance=SCREAMING_SNAKE_CASE_     ) - -5.7_99_80_52 < 1e-5
									assert scheduler._get_variance(9_99							,							predicted_variance=SCREAMING_SNAKE_CASE_     ) - -0.0_01_00_11 < 1e-5

							def _lowercase       (self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE_    			=			self.get_scheduler_config()
									SCREAMING_SNAKE_CASE_    			=			scheduler_class(**SCREAMING_SNAKE_CASE_     )

									SCREAMING_SNAKE_CASE_    			=			scheduler.timesteps

									SCREAMING_SNAKE_CASE_    			=			self.dummy_model()
									SCREAMING_SNAKE_CASE_    			=			self.dummy_sample_deter
									SCREAMING_SNAKE_CASE_    			=			torch.manual_seed(0     )

									for i, t in enumerate(SCREAMING_SNAKE_CASE_     ):
											# 1. predict noise residual
											SCREAMING_SNAKE_CASE_    			=			model(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     )

											# 2. predict previous mean of sample x_t-1
											SCREAMING_SNAKE_CASE_    			=			scheduler.step(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							generator=SCREAMING_SNAKE_CASE_     ).prev_sample

											SCREAMING_SNAKE_CASE_    			=			pred_prev_sample

									SCREAMING_SNAKE_CASE_    			=			torch.sum(torch.abs(SCREAMING_SNAKE_CASE_     )     )
									SCREAMING_SNAKE_CASE_    			=			torch.mean(torch.abs(SCREAMING_SNAKE_CASE_     )     )

									assert abs(result_sum.item() - 2_52.2_68_24_95     ) < 1e-2
									assert abs(result_mean.item() - 0.3_28_47_43     ) < 1e-3

							def _lowercase       (self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE_    			=			self.get_scheduler_config()
									SCREAMING_SNAKE_CASE_    			=			scheduler_class(**SCREAMING_SNAKE_CASE_     )

									scheduler.set_timesteps(25     )

									SCREAMING_SNAKE_CASE_    			=			scheduler.timesteps

									SCREAMING_SNAKE_CASE_    			=			self.dummy_model()
									SCREAMING_SNAKE_CASE_    			=			self.dummy_sample_deter
									SCREAMING_SNAKE_CASE_    			=			torch.manual_seed(0     )

									for i, t in enumerate(SCREAMING_SNAKE_CASE_     ):
											# 1. predict noise residual
											SCREAMING_SNAKE_CASE_    			=			model(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     )

											if i + 1 == timesteps.shape[0]:
													SCREAMING_SNAKE_CASE_    			=			None
											else:
													SCREAMING_SNAKE_CASE_    			=			timesteps[i + 1]

											# 2. predict previous mean of sample x_t-1
											SCREAMING_SNAKE_CASE_    			=			scheduler.step(
											    SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_							,							prev_timestep=SCREAMING_SNAKE_CASE_							,							generator=SCREAMING_SNAKE_CASE_     ).prev_sample

											SCREAMING_SNAKE_CASE_    			=			pred_prev_sample

									SCREAMING_SNAKE_CASE_    			=			torch.sum(torch.abs(SCREAMING_SNAKE_CASE_     )     )
									SCREAMING_SNAKE_CASE_    			=			torch.mean(torch.abs(SCREAMING_SNAKE_CASE_     )     )

									assert abs(result_sum.item() - 2_58.2_04_49_83     ) < 1e-2
									assert abs(result_mean.item() - 0.3_36_20_38     ) < 1e-3

							def _lowercase       (self     ):

									"""simple docstring"""







									pass







							def _lowercase       (self     ):

									"""simple docstring"""







									pass