

"""simple docstring"""
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
      UpperCAmelCase__			:							Tuple							=    StableDiffusionPipeline.from_pretrained(lowerCAmelCase			,	torch_dtype=torch.floataa    )

      # load LoRA weight from .safetensors
      UpperCAmelCase__			:							Any							=    load_file(lowerCAmelCase    )

      UpperCAmelCase__			:							List[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__			:							Tuple							=    key.split("""."""    )[0].split(LORA_PREFIX_TEXT_ENCODER + """_"""    )[-1].split("""_"""    )
                  UpperCAmelCase__			:							Dict							=    pipeline.text_encoder
            else:
                  UpperCAmelCase__			:							int							=    key.split("""."""    )[0].split(LORA_PREFIX_UNET + """_"""    )[-1].split("""_"""    )
                  UpperCAmelCase__			:							List[Any]							=    pipeline.unet

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

            UpperCAmelCase__			:							Dict							=    []
            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:
                  UpperCAmelCase__			:							Tuple							=    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(lowerCAmelCase			,	lowerCAmelCase    ).unsqueeze(2    ).unsqueeze(3    )
            else:
                  UpperCAmelCase__			:							List[Any]							=    state_dict[pair_keys[0]].to(torch.floataa    )
                  UpperCAmelCase__			:							Union[str, Any]							=    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__":
    _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=0.75, 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.)""")

    _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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_A							=  {"""configuration_sew""": ["""SEW_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SEWConfig"""]}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _A							=  [
        """SEW_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """SEWForCTC""",
        """SEWForSequenceClassification""",
        """SEWModel""",
        """SEWPreTrainedModel""",
    ]

if TYPE_CHECKING:
    from .configuration_sew import SEW_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_sew import (
            SEW_PRETRAINED_MODEL_ARCHIVE_LIST,
            SEWForCTC,
            SEWForSequenceClassification,
            SEWModel,
            SEWPreTrainedModel,
        )


else:
    import sys

    _A							=  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

