


'''simple docstring'''



import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline







def       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__   ):
			# load base model
			UpperCAmelCase__					:   str       				=       StableDiffusionPipeline.from_pretrained(UpperCamelCase__	, torch_dtype=torch.floataa   )

			# load LoRA weight from .safetensors
			UpperCAmelCase__					:   int       				=       load_file(UpperCamelCase__   )

			UpperCAmelCase__					:   str       				=       []

			# 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:
									UpperCAmelCase__					:   str       				=       key.split("""."""   )[0].split(LORA_PREFIX_TEXT_ENCODER + """_"""   )[-1].split("""_"""   )
									UpperCAmelCase__					:   Dict       				=       pipeline.text_encoder
						else:
									UpperCAmelCase__					:   str       				=       key.split("""."""   )[0].split(LORA_PREFIX_UNET + """_"""   )[-1].split("""_"""   )
									UpperCAmelCase__					:   Dict       				=       pipeline.unet

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

						UpperCAmelCase__					:   List[Any]       				=       []
						if "lora_down" in key:
									pair_keys.append(key.replace("""lora_down"""	, """lora_up"""   )   )
									pair_keys.append(UpperCamelCase__   )
						else:
									pair_keys.append(UpperCamelCase__   )
									pair_keys.append(key.replace("""lora_up"""	, """lora_down"""   )   )

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

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

			return pipeline


if __name__ == "__main__":
		__A		=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=0.7_5, 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.)')

		__A		=parser.parse_args()

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

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

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



'''simple docstring'''



from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer


@dataclass
class 						_snake_case   (       a__		):
					lowerCAmelCase :torch.FloatTensor


class 						_snake_case   (       a__    ,				a__		):




					@register_to_config
					def __init__( self							,							_lowerCamelCase = 3							,							_lowerCamelCase = 3							,							_lowerCamelCase = ("DownEncoderBlock2D",)							,							_lowerCamelCase = ("UpDecoderBlock2D",)							,							_lowerCamelCase = (64,)							,							_lowerCamelCase = 1							,							_lowerCamelCase = "silu"							,							_lowerCamelCase = 3							,							_lowerCamelCase = 32							,							_lowerCamelCase = 256							,							_lowerCamelCase = 32							,							_lowerCamelCase = None							,							_lowerCamelCase = 0.18215							,							_lowerCamelCase = "group"							,							):
								super().__init__()

								# pass init params to Encoder
								UpperCAmelCase__					:   str       				=       Encoder(
								    in_channels=_lowerCamelCase							,							out_channels=_lowerCamelCase							,							down_block_types=_lowerCamelCase							,							block_out_channels=_lowerCamelCase							,							layers_per_block=_lowerCamelCase							,							act_fn=_lowerCamelCase							,							norm_num_groups=_lowerCamelCase							,							double_z=_lowerCamelCase							,							)

								UpperCAmelCase__					:   Optional[Any]       				=       vq_embed_dim if vq_embed_dim is not None else latent_channels

								UpperCAmelCase__					:   Any       				=       nn.Convad(_lowerCamelCase							,							_lowerCamelCase							,							1)
								UpperCAmelCase__					:   Optional[int]       				=       VectorQuantizer(_lowerCamelCase							,							_lowerCamelCase							,							beta=0.25							,							remap=_lowerCamelCase							,							sane_index_shape=_lowerCamelCase)
								UpperCAmelCase__					:   Optional[int]       				=       nn.Convad(_lowerCamelCase							,							_lowerCamelCase							,							1)

								# pass init params to Decoder
								UpperCAmelCase__					:   str       				=       Decoder(
								    in_channels=_lowerCamelCase							,							out_channels=_lowerCamelCase							,							up_block_types=_lowerCamelCase							,							block_out_channels=_lowerCamelCase							,							layers_per_block=_lowerCamelCase							,							act_fn=_lowerCamelCase							,							norm_num_groups=_lowerCamelCase							,							norm_type=_lowerCamelCase							,							)




					@apply_forward_hook
					def 			snake_case__  ( self							,							_lowerCamelCase							,							_lowerCamelCase = True):
								UpperCAmelCase__					:   Union[str, Any]       				=       self.encoder(_lowerCamelCase)
								UpperCAmelCase__					:   str       				=       self.quant_conv(_lowerCamelCase)

								if not return_dict:
											return (h,)

								return VQEncoderOutput(latents=_lowerCamelCase)




					@apply_forward_hook
					def 			snake_case__  ( self							,							_lowerCamelCase							,							_lowerCamelCase = False							,							_lowerCamelCase = True):
								# also go through quantization layer
								if not force_not_quantize:
											UpperCAmelCase__				,    UpperCAmelCase__				,    UpperCAmelCase__					:   Optional[Any]       				=       self.quantize(_lowerCamelCase)
								else:
											UpperCAmelCase__					:   Union[str, Any]       				=       h
								UpperCAmelCase__					:   Any       				=       self.post_quant_conv(_lowerCamelCase)
								UpperCAmelCase__					:   Any       				=       self.decoder(_lowerCamelCase							,							quant if self.config.norm_type == """spatial""" else None)

								if not return_dict:
											return (dec,)

								return DecoderOutput(sample=_lowerCamelCase)




					def 			snake_case__  ( self							,							_lowerCamelCase							,							_lowerCamelCase = True):
								UpperCAmelCase__					:   Dict       				=       sample
								UpperCAmelCase__					:   Dict       				=       self.encode(_lowerCamelCase).latents
								UpperCAmelCase__					:   List[str]       				=       self.decode(_lowerCamelCase).sample

								if not return_dict:
											return (dec,)

								return DecoderOutput(sample=_lowerCamelCase)