
'''simple docstring'''
import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    snake_case_ :    str     			=						StableDiffusionPipeline.from_pretrained(__SCREAMING_SNAKE_CASE,		torch_dtype=torch.floataa       )

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

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

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

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

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

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

    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 ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class    UpperCAmelCase_				(	snake_case__					):
 UpperCAmelCase_		    =						"""ClapFeatureExtractor"""
 UpperCAmelCase_		    =						("""RobertaTokenizer""", """RobertaTokenizerFast""")





 def __init__(    self ,       lowercase_ ,       lowercase_):
     super().__init__(lowercase_ ,       lowercase_)





 def __call__(    self ,       lowercase_=None ,       lowercase_=None ,       lowercase_=None ,       **lowercase_):
     snake_case_ :    Any     			=						kwargs.pop("sampling_rate" ,       lowercase_)

     if text is None and audios is None:
         raise ValueError("You have to specify either text or audios. Both cannot be none.")

     if text is not None:
         snake_case_ :    Optional[int]     			=						self.tokenizer(lowercase_ ,       return_tensors=lowercase_ ,       **lowercase_)

     if audios is not None:
         snake_case_ :    Any     			=						self.feature_extractor(
             lowercase_ ,       sampling_rate=lowercase_ ,       return_tensors=lowercase_ ,       **lowercase_)

     if text is not None and audios is not None:
         snake_case_ :    Dict     			=						audio_features.input_features
         return encoding
     elif text is not None:
         return encoding
     else:
         return BatchEncoding(data=dict(**lowercase_) ,       tensor_type=lowercase_)





 def        snake_case__		(    self ,       *lowercase_ ,       **lowercase_):
     return self.tokenizer.batch_decode(*lowercase_ ,       **lowercase_)





 def        snake_case__		(    self ,       *lowercase_ ,       **lowercase_):
     return self.tokenizer.decode(*lowercase_ ,       **lowercase_)





 @property
 def        snake_case__		(    self):
     snake_case_ :    Union[str, Any]     			=						self.tokenizer.model_input_names
     snake_case_ :    int     			=						self.feature_extractor.model_input_names
     return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names))




