
import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline

def 		UpperCamelCase						(   lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_ )		->						List[Any]:


  '''simple docstring'''






  lowercase__	: Dict     		=   StableDiffusionPipeline.from_pretrained(lowercase_			,			torch_dtype=torch.floataa )

  # load LoRA weight from .safetensors
  lowercase__	: str     		=   load_file(lowercase_ )

  lowercase__	: List[Any]     		=   []

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

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

    lowercase__	: Optional[Any]     		=   []
    if "lora_down" in key:
      pair_keys.append(key.replace("""lora_down"""			,			"""lora_up""" ) )
      pair_keys.append(lowercase_ )
    else:
      pair_keys.append(lowercase_ )
      pair_keys.append(key.replace("""lora_up"""			,			"""lora_down""" ) )

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

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

  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__     :		Dict        = parser.parse_args()

     lowerCamelCase__     :		int        = args.base_model_path
     lowerCamelCase__     :		Any        = args.checkpoint_path
     lowerCamelCase__     :		Union[str, Any]        = args.dump_path
     lowerCamelCase__     :		str        = args.lora_prefix_unet
     lowerCamelCase__     :		Optional[int]        = args.lora_prefix_text_encoder
     lowerCamelCase__     :		Dict        = args.alpha

     lowerCamelCase__     :		str        = convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)

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








# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase__     :		Tuple        = {
    """configuration_mgp_str""": ["""MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MgpstrConfig"""],
    """processing_mgp_str""": ["""MgpstrProcessor"""],
    """tokenization_mgp_str""": ["""MgpstrTokenizer"""],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCamelCase__     :		Optional[int]        = [
         """MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """MgpstrModel""",
         """MgpstrPreTrainedModel""",
         """MgpstrForSceneTextRecognition""",
     ]

if TYPE_CHECKING:
     from .configuration_mgp_str import MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP, MgpstrConfig
     from .processing_mgp_str import MgpstrProcessor
     from .tokenization_mgp_str import MgpstrTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_mgp_str import (
              MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST,
              MgpstrForSceneTextRecognition,
              MgpstrModel,
              MgpstrPreTrainedModel,
          )
else:
     import sys

     lowerCamelCase__     :		List[Any]        = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






