






import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline






def 							_lowerCAmelCase (  _a   :					int					,   _a   :					Dict					,   _a   :					Optional[Any]					,   _a   :					str					,   _a   :					Any			)	->			str:
     # load base model
     lowerCAmelCase_					:       List[str]    =		StableDiffusionPipeline.from_pretrained(UpperCAmelCase__					,   torch_dtype=torch.floataa			)

     # load LoRA weight from .safetensors
     lowerCAmelCase_					:       Optional[int]    =		load_file(UpperCAmelCase__			)

     lowerCAmelCase_					:       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:
               lowerCAmelCase_					:       List[str]    =		key.split("""."""			)[0].split(LORA_PREFIX_TEXT_ENCODER + """_"""			)[-1].split("""_"""			)
               lowerCAmelCase_					:       int    =		pipeline.text_encoder
          else:
               lowerCAmelCase_					:       str    =		key.split("""."""			)[0].split(LORA_PREFIX_UNET + """_"""			)[-1].split("""_"""			)
               lowerCAmelCase_					:       Optional[int]    =		pipeline.unet

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

          lowerCAmelCase_					:       int    =		[]
          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:
               lowerCAmelCase_					:       str    =		state_dict[pair_keys[0]].squeeze(3			).squeeze(2			).to(torch.floataa			)
               lowerCAmelCase_					:       List[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:
               lowerCAmelCase_					:       Dict    =		state_dict[pair_keys[0]].to(torch.floataa			)
               lowerCAmelCase_					:       int    =		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__":
      UpperCAmelCase_					:   List[Any] 							=						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.)""")

      UpperCAmelCase_					:   List[str] 							=						parser.parse_args()

      UpperCAmelCase_					:   Tuple 							=						args.base_model_path
      UpperCAmelCase_					:   Dict 							=						args.checkpoint_path
      UpperCAmelCase_					:   Union[str, Any] 							=						args.dump_path
      UpperCAmelCase_					:   Dict 							=						args.lora_prefix_unet
      UpperCAmelCase_					:   Tuple 							=						args.lora_prefix_text_encoder
      UpperCAmelCase_					:   List[str] 							=						args.alpha

      UpperCAmelCase_					:   Dict 							=						convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)

      UpperCAmelCase_					:   str 							=						pipe.to(args.device)
      pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


UpperCAmelCase_					:   Dict 							=						{
    """configuration_poolformer""": [
        """POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """PoolFormerConfig""",
        """PoolFormerOnnxConfig""",
    ]
}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCAmelCase_					:   Union[str, Any] 							=						["""PoolFormerFeatureExtractor"""]
      UpperCAmelCase_					:   str 							=						["""PoolFormerImageProcessor"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCAmelCase_					:   List[str] 							=						[
          """POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """PoolFormerForImageClassification""",
          """PoolFormerModel""",
          """PoolFormerPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_poolformer import (
          POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
          PoolFormerConfig,
          PoolFormerOnnxConfig,
      )

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_poolformer import PoolFormerFeatureExtractor
            from .image_processing_poolformer import PoolFormerImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_poolformer import (
                POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                PoolFormerForImageClassification,
                PoolFormerModel,
                PoolFormerPreTrainedModel,
            )


else:
      import sys

      UpperCAmelCase_					:   Optional[Any] 							=						_LazyModule(__name__, globals()["""__file__"""], _import_structure)
