


'''simple docstring'''







from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


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


if is_vision_available():
  import PIL
class 					lowercase_     (    a__   ):
    __UpperCAmelCase   			=	['pixel_values']


    def __init__(     self  ,					a = True  ,					a = None  ,					a = PILImageResampling.BICUBIC  ,					a = True  ,					a = None  ,					a = True  ,					a = 1 / 2_55  ,					a = True  ,					a = None  ,					a = None  ,					a = True  ,					**a  ,					):
          super().__init__(**a    )
          UpperCamelCase__										=				size if size is not None else {"shortest_edge": 2_24}
          UpperCamelCase__										=				get_size_dict(a  ,					default_to_square=a    )
          UpperCamelCase__										=				crop_size if crop_size is not None else {"height": 2_24, "width": 2_24}
          UpperCamelCase__										=				get_size_dict(a  ,					default_to_square=a  ,					param_name="crop_size"    )

          UpperCamelCase__										=				do_resize
          UpperCamelCase__										=				size
          UpperCamelCase__										=				resample
          UpperCamelCase__										=				do_center_crop
          UpperCamelCase__										=				crop_size
          UpperCamelCase__										=				do_rescale
          UpperCamelCase__										=				rescale_factor
          UpperCamelCase__										=				do_normalize
          UpperCamelCase__										=				image_mean if image_mean is not None else OPENAI_CLIP_MEAN
          UpperCamelCase__										=				image_std if image_std is not None else OPENAI_CLIP_STD
          UpperCamelCase__										=				do_convert_rgb


    def    __a							(     self  ,					a  ,					a  ,					a = PILImageResampling.BICUBIC  ,					a = None  ,					**a  ,					):
          UpperCamelCase__										=				get_size_dict(a  ,					default_to_square=a    )
          if "shortest_edge" not in size:
                raise ValueError(f'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''    )
          UpperCamelCase__										=				get_resize_output_image_size(a  ,					size=size["shortest_edge"]  ,					default_to_square=a    )
          return resize(a  ,					size=a  ,					resample=a  ,					data_format=a  ,					**a    )


    def    __a							(     self  ,					a  ,					a  ,					a = None  ,					**a  ,					):
          UpperCamelCase__										=				get_size_dict(a    )
          if "height" not in size or "width" not in size:
                raise ValueError(f'''The `size` parameter must contain the keys (height, width). Got {size.keys()}'''    )
          return center_crop(a  ,					size=(size["height"], size["width"])  ,					data_format=a  ,					**a    )


    def    __a							(     self  ,					a  ,					a  ,					a = None  ,					**a  ,					):
          return rescale(a  ,					scale=a  ,					data_format=a  ,					**a    )


    def    __a							(     self  ,					a  ,					a  ,					a  ,					a = None  ,					**a  ,					):
          return normalize(a  ,					mean=a  ,					std=a  ,					data_format=a  ,					**a    )


    def    __a							(     self  ,					a  ,					a = None  ,					a = None  ,					a = None  ,					a = None  ,					a = None  ,					a = None  ,					a = None  ,					a = None  ,					a = None  ,					a = None  ,					a = None  ,					a = None  ,					a = ChannelDimension.FIRST  ,					**a  ,					):
          UpperCamelCase__										=				do_resize if do_resize is not None else self.do_resize
          UpperCamelCase__										=				size if size is not None else self.size
          UpperCamelCase__										=				get_size_dict(a  ,					param_name="size"  ,					default_to_square=a    )
          UpperCamelCase__										=				resample if resample is not None else self.resample
          UpperCamelCase__										=				do_center_crop if do_center_crop is not None else self.do_center_crop
          UpperCamelCase__										=				crop_size if crop_size is not None else self.crop_size
          UpperCamelCase__										=				get_size_dict(a  ,					param_name="crop_size"  ,					default_to_square=a    )
          UpperCamelCase__										=				do_rescale if do_rescale is not None else self.do_rescale
          UpperCamelCase__										=				rescale_factor if rescale_factor is not None else self.rescale_factor
          UpperCamelCase__										=				do_normalize if do_normalize is not None else self.do_normalize
          UpperCamelCase__										=				image_mean if image_mean is not None else self.image_mean
          UpperCamelCase__										=				image_std if image_std is not None else self.image_std
          UpperCamelCase__										=				do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

          UpperCamelCase__										=				make_list_of_images(a    )

          if not valid_images(a    ):
                raise ValueError(
                    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
                    "torch.Tensor, tf.Tensor or jax.ndarray."    )

          if do_resize and size is None:
                raise ValueError("Size must be specified if do_resize is True."    )

          if do_center_crop and crop_size is None:
                raise ValueError("Crop size must be specified if do_center_crop is True."    )

          if do_rescale and rescale_factor is None:
                raise ValueError("Rescale factor must be specified if do_rescale is True."    )

          if do_normalize and (image_mean is None or image_std is None):
                raise ValueError("Image mean and std must be specified if do_normalize is True."    )

          # PIL RGBA images are converted to RGB
          if do_convert_rgb:
                UpperCamelCase__										=				[convert_to_rgb(a    ) for image in images]

          # All transformations expect numpy arrays.
          UpperCamelCase__										=				[to_numpy_array(a    ) for image in images]

          if do_resize:
                UpperCamelCase__										=				[self.resize(image=a  ,					size=a  ,					resample=a    ) for image in images]

          if do_center_crop:
                UpperCamelCase__										=				[self.center_crop(image=a  ,					size=a    ) for image in images]

          if do_rescale:
                UpperCamelCase__										=				[self.rescale(image=a  ,					scale=a    ) for image in images]

          if do_normalize:
                UpperCamelCase__										=				[self.normalize(image=a  ,					mean=a  ,					std=a    ) for image in images]

          UpperCamelCase__										=				[to_channel_dimension_format(a  ,					a    ) for image in images]

          UpperCamelCase__										=				{"pixel_values": images}
          return BatchFeature(data=a  ,					tensor_type=a    )











import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline







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



       """simple docstring"""

       A__       =    StableDiffusionPipeline.from_pretrained(lowercase_ ,				torch_dtype=torch.floataa						)

       # load LoRA weight from .safetensors
       A__       =    load_file(lowercase_						)

       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(lowercase_						) > -1:
                     try:
                            A__       =    curr_layer.__getattr__(lowercase_						)
                            if len(lowercase_						) > 0:
                                   A__       =    layer_infos.pop(0						)
                            elif len(lowercase_						) == 0:
                                   break
                     except Exception:
                            if len(lowercase_						) > 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(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:
                     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(lowercase_ ,				lowercase_						).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(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	:		Tuple							       =   parser.parse_args()

  _lowerCamelCase	:		List[Any]							       =   args.base_model_path
  _lowerCamelCase	:		Optional[int]							       =   args.checkpoint_path
  _lowerCamelCase	:		Dict							       =   args.dump_path
  _lowerCamelCase	:		Optional[Any]							       =   args.lora_prefix_unet
  _lowerCamelCase	:		Optional[int]							       =   args.lora_prefix_text_encoder
  _lowerCamelCase	:		List[Any]							       =   args.alpha

  _lowerCamelCase	:		int							       =   convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)

  _lowerCamelCase	:		Tuple							       =   pipe.to(args.device)
  pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)


