






import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			str				,     _SCREAMING_SNAKE_CASE       :			Dict				,     _SCREAMING_SNAKE_CASE       :			List[Any]				,     _SCREAMING_SNAKE_CASE       :			Union[str, Any]				,     _SCREAMING_SNAKE_CASE       :			Tuple    ):
				"""simple docstring"""







				__a       =		StableDiffusionPipeline.from_pretrained(_SCREAMING_SNAKE_CASE				,     torch_dtype=torch.floataa    )

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

				__a       =		[]

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

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

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

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

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

				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)





from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase__			= logging.get_logger(__name__)

lowerCamelCase__			= {
    """alibaba-damo/mgp-str-base""": """https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json""",
}






class    SCREAMING_SNAKE_CASE			(							lowerCamelCase__					):
							__lowerCamelCase	:	Dict    ='mgp-str'





							def __init__(   self		:		Optional[Any]      ,				__lowercase		:		Dict=[32, 128]      ,				__lowercase		:		Tuple=4      ,				__lowercase		:		Optional[Any]=3      ,				__lowercase		:		str=27      ,				__lowercase		:		Any=38      ,				__lowercase		:		str=50257      ,				__lowercase		:		List[str]=30522      ,				__lowercase		:		Tuple=768      ,				__lowercase		:		str=12      ,				__lowercase		:		Optional[Any]=12      ,				__lowercase		:		str=4.0      ,				__lowercase		:		Union[str, Any]=True      ,				__lowercase		:		List[Any]=False      ,				__lowercase		:		Union[str, Any]=1E-5      ,				__lowercase		:		Optional[int]=0.0      ,				__lowercase		:		Dict=0.0      ,				__lowercase		:		Union[str, Any]=0.0      ,				__lowercase		:		List[str]=False      ,				__lowercase		:		List[Any]=0.02      ,				**__lowercase		:		List[Any]      ,				):

											'''simple docstring'''
											super().__init__(**__lowercase	)

											__a       =		image_size
											__a       =		patch_size
											__a       =		num_channels
											__a       =		max_token_length
											__a       =		num_character_labels
											__a       =		num_bpe_labels
											__a       =		num_wordpiece_labels
											__a       =		hidden_size
											__a       =		num_hidden_layers
											__a       =		num_attention_heads
											__a       =		mlp_ratio
											__a       =		distilled
											__a       =		layer_norm_eps
											__a       =		drop_rate
											__a       =		qkv_bias
											__a       =		attn_drop_rate
											__a       =		drop_path_rate
											__a       =		output_aa_attentions
											__a       =		initializer_range
