


import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


_snake_case	:						List[str]         =							logging.get_logger(__name__)







def 							lowerCAmelCase_	(   __lowerCamelCase      ):
							__snake_case						:     List[Any]									=  R"\w+[.]\d+"
							__snake_case						:     Dict									=  re.findall(__lowerCamelCase				,      __lowerCamelCase      )
							for pat in pats:
														__snake_case						:     Optional[Any]									=  key.replace(__lowerCamelCase				,      "_".join(pat.split("."      )      )      )
							return key







def 							lowerCAmelCase_	(   __lowerCamelCase				,      __lowerCamelCase				,      __lowerCamelCase      ):
							__snake_case						:     Union[str, Any]									=  pt_tuple_key[:-1] + ("scale",)
							if (
							    any("norm" in str_ for str_ in pt_tuple_key      )
							    and (pt_tuple_key[-1] == "bias")
							    and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
							    and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
							):
														__snake_case						:     List[str]									=  pt_tuple_key[:-1] + ("scale",)
														return renamed_pt_tuple_key, pt_tensor
							elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
														__snake_case						:     Optional[int]									=  pt_tuple_key[:-1] + ("scale",)
														return renamed_pt_tuple_key, pt_tensor

							# embedding
							if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
														__snake_case						:     Dict									=  pt_tuple_key[:-1] + ("embedding",)
														return renamed_pt_tuple_key, pt_tensor

							# conv layer
							__snake_case						:     Any									=  pt_tuple_key[:-1] + ("kernel",)
							if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
														__snake_case						:     List[str]									=  pt_tensor.transpose(2				,      3				,      1				,      0      )
														return renamed_pt_tuple_key, pt_tensor

							# linear layer
							__snake_case						:     Tuple									=  pt_tuple_key[:-1] + ("kernel",)
							if pt_tuple_key[-1] == "weight":
														__snake_case						:     Dict									=  pt_tensor.T
														return renamed_pt_tuple_key, pt_tensor

							# old PyTorch layer norm weight
							__snake_case						:     Tuple									=  pt_tuple_key[:-1] + ("weight",)
							if pt_tuple_key[-1] == "gamma":
														return renamed_pt_tuple_key, pt_tensor

							# old PyTorch layer norm bias
							__snake_case						:     Optional[Any]									=  pt_tuple_key[:-1] + ("bias",)
							if pt_tuple_key[-1] == "beta":
														return renamed_pt_tuple_key, pt_tensor

							return pt_tuple_key, pt_tensor







def 							lowerCAmelCase_	(   __lowerCamelCase				,      __lowerCamelCase				,      __lowerCamelCase=4_2      ):
							# Step 1: Convert pytorch tensor to numpy
							__snake_case						:     str									=  {k: v.numpy() for k, v in pt_state_dict.items()}

							# Step 2: Since the model is stateless, get random Flax params
							__snake_case						:     str									=  flax_model.init_weights(PRNGKey(__lowerCamelCase      )      )

							__snake_case						:     Union[str, Any]									=  flatten_dict(__lowerCamelCase      )
							__snake_case						:     List[Any]									=  {}

							# Need to change some parameters name to match Flax names
							for pt_key, pt_tensor in pt_state_dict.items():
														__snake_case						:     List[str]									=  rename_key(__lowerCamelCase      )
														__snake_case						:     Optional[int]									=  tuple(renamed_pt_key.split("."      )      )

														# Correctly rename weight parameters
														__snake_case					,     __snake_case						:     List[str]									=  rename_key_and_reshape_tensor(__lowerCamelCase				,      __lowerCamelCase				,      __lowerCamelCase      )

														if flax_key in random_flax_state_dict:
																					if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
																												raise ValueError(
																												    F'PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '
																												    F'{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.'      )

        # also add unexpected weight so that warning is thrown
														__snake_case						:     Dict									=  jnp.asarray(__lowerCamelCase      )

							return unflatten_dict(__lowerCamelCase      )






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)