





import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline






def 				_UpperCAmelCase			(    A				,							A				,							A				,							A				,							A     ):




   '''simple docstring'''

   UpperCAmelCase__						=StableDiffusionPipeline.from_pretrained(A				,							torch_dtype=torch.floataa     )

   # load LoRA weight from .safetensors
   UpperCAmelCase__						=load_file(A     )

   UpperCAmelCase__						=[]

   # 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__						=key.split("."     )[0].split(LORA_PREFIX_TEXT_ENCODER + "_"     )[-1].split("_"     )
         UpperCAmelCase__						=pipeline.text_encoder
      else:
         UpperCAmelCase__						=key.split("."     )[0].split(LORA_PREFIX_UNET + "_"     )[-1].split("_"     )
         UpperCAmelCase__						=pipeline.unet

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

      UpperCAmelCase__						=[]
      if "lora_down" in key:
         pair_keys.append(key.replace("lora_down"				,							"lora_up"     )     )
         pair_keys.append(A     )
      else:
         pair_keys.append(A     )
         pair_keys.append(key.replace("lora_up"				,							"lora_down"     )     )

      # update weight
      if len(state_dict[pair_keys[0]].shape     ) == 4:
         UpperCAmelCase__						=state_dict[pair_keys[0]].squeeze(3     ).squeeze(2     ).to(torch.floataa     )
         UpperCAmelCase__						=state_dict[pair_keys[1]].squeeze(3     ).squeeze(2     ).to(torch.floataa     )
         curr_layer.weight.data += alpha * torch.mm(A				,							A     ).unsqueeze(2     ).unsqueeze(3     )
      else:
         UpperCAmelCase__						=state_dict[pair_keys[0]].to(torch.floataa     )
         UpperCAmelCase__						=state_dict[pair_keys[1]].to(torch.floataa     )
         curr_layer.weight.data += alpha * torch.mm(A				,							A     )

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

   return pipeline


if __name__ == "__main__":
    UpperCamelCase_												=	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=0.7_5, 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_												=	parser.parse_args()

    UpperCamelCase_												=	args.base_model_path
    UpperCamelCase_												=	args.checkpoint_path
    UpperCamelCase_												=	args.dump_path
    UpperCamelCase_												=	args.lora_prefix_unet
    UpperCamelCase_												=	args.lora_prefix_text_encoder
    UpperCamelCase_												=	args.alpha

    UpperCamelCase_												=	convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)

    UpperCamelCase_												=	pipe.to(args.device)
    pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)










import os
from typing import List, Optional, Union

from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer

class     snake_case_		(   a							):






 '''simple docstring'''





 __UpperCamelCase       =      ['image_processor', 'tokenizer']
 __UpperCamelCase       =      'BlipImageProcessor'
 __UpperCamelCase       =      'AutoTokenizer'

 def __init__(  self,		A_,		A_,		A_		)	->       str:
    super().__init__(A_,		A_		)

    # add QFormer tokenizer
    UpperCAmelCase__						=qformer_tokenizer

 def __call__(  self,		A_ = None,		A_ = None,		A_ = True,		A_ = False,		A_ = None,		A_ = None,		A_ = 0,		A_ = None,		A_ = None,		A_ = False,		A_ = False,		A_ = False,		A_ = False,		A_ = False,		A_ = True,		A_ = None,		**A_,		)	->       BatchFeature:
    if images is None and text is None:
       raise ValueError("You have to specify at least images or text."		)

    UpperCAmelCase__						=BatchFeature()

    if text is not None:
       UpperCAmelCase__						=self.tokenizer(
           text=A_,		add_special_tokens=A_,		padding=A_,		truncation=A_,		max_length=A_,		stride=A_,		pad_to_multiple_of=A_,		return_attention_mask=A_,		return_overflowing_tokens=A_,		return_special_tokens_mask=A_,		return_offsets_mapping=A_,		return_token_type_ids=A_,		return_length=A_,		verbose=A_,		return_tensors=A_,		**A_,		)
       encoding.update(A_		)
       UpperCAmelCase__						=self.qformer_tokenizer(
           text=A_,		add_special_tokens=A_,		padding=A_,		truncation=A_,		max_length=A_,		stride=A_,		pad_to_multiple_of=A_,		return_attention_mask=A_,		return_overflowing_tokens=A_,		return_special_tokens_mask=A_,		return_offsets_mapping=A_,		return_token_type_ids=A_,		return_length=A_,		verbose=A_,		return_tensors=A_,		**A_,		)
       UpperCAmelCase__						=qformer_text_encoding.pop("input_ids"		)
       UpperCAmelCase__						=qformer_text_encoding.pop("attention_mask"		)

    if images is not None:
       UpperCAmelCase__						=self.image_processor(A_,		return_tensors=A_		)
       encoding.update(A_		)

    return encoding

 def  __UpperCAmelCase						(  self,		*A_,		**A_		)	->       List[Any]:
    return self.tokenizer.batch_decode(*A_,		**A_		)

 def  __UpperCAmelCase						(  self,		*A_,		**A_		)	->       int:
    return self.tokenizer.decode(*A_,		**A_		)

 @property
 # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
 def  __UpperCAmelCase						(  self		)	->       List[str]:
    UpperCAmelCase__						=self.tokenizer.model_input_names
    UpperCAmelCase__						=self.image_processor.model_input_names
    return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names		)		)

 def  __UpperCAmelCase						(  self,		A_,		**A_		)	->       str:
    if os.path.isfile(A_		):
       raise ValueError(f"""Provided path ({save_directory}) should be a directory, not a file"""		)
    os.makedirs(A_,		exist_ok=A_		)
    UpperCAmelCase__						=os.path.join(A_,		"qformer_tokenizer"		)
    self.qformer_tokenizer.save_pretrained(A_		)
    return super().save_pretrained(A_,		**A_		)






 @classmethod
 def  __UpperCAmelCase						(  cls,		A_,		**A_		)	->       int:
    UpperCAmelCase__						=AutoTokenizer.from_pretrained(A_,		subfolder="qformer_tokenizer"		)
    UpperCAmelCase__						=cls._get_arguments_from_pretrained(A_,		**A_		)
    args.append(A_		)
    return cls(*A_		)



