




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)








import os
import sys
import unittest


_lowerCamelCase	:		Optional[Any]							       =   os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))

import get_test_info  # noqa: E402
from get_test_info import (  # noqa: E402
    get_model_to_test_mapping,
    get_model_to_tester_mapping,
    get_test_to_tester_mapping,
)


_lowerCamelCase	:		Any							       =   os.path.join("""tests""", """models""", """bert""", """test_modeling_bert.py""")
_lowerCamelCase	:		str							       =   os.path.join("""tests""", """models""", """blip""", """test_modeling_blip.py""")


class 				UpperCamelCase_    ( unittest.TestCase     ):

    '''simple docstring'''






    def        SCREAMING_SNAKE_CASE							(			self      :	Tuple)						->Tuple:





           '''simple docstring'''

           A__       =    get_test_to_tester_mapping(UpperCAmelCase__)
           A__       =    get_test_to_tester_mapping(UpperCAmelCase__)

           A__       =    {'''BertModelTest''': '''BertModelTester'''}

           A__       =    {
               '''BlipModelTest''': '''BlipModelTester''',
               '''BlipTextImageModelTest''': '''BlipTextImageModelsModelTester''',
               '''BlipTextModelTest''': '''BlipTextModelTester''',
               '''BlipTextRetrievalModelTest''': '''BlipTextRetrievalModelTester''',
               '''BlipVQAModelTest''': '''BlipVQAModelTester''',
               '''BlipVisionModelTest''': '''BlipVisionModelTester''',
           }

           self.assertEqual(get_test_info.to_json(UpperCAmelCase__)					, UpperCAmelCase__)
           self.assertEqual(get_test_info.to_json(UpperCAmelCase__)					, UpperCAmelCase__)


    def        SCREAMING_SNAKE_CASE							(			self      :	Tuple)						->List[Any]:





           '''simple docstring'''

           A__       =    get_model_to_test_mapping(UpperCAmelCase__)
           A__       =    get_model_to_test_mapping(UpperCAmelCase__)

           A__       =    {
               '''BertForMaskedLM''': ['''BertModelTest'''],
               '''BertForMultipleChoice''': ['''BertModelTest'''],
               '''BertForNextSentencePrediction''': ['''BertModelTest'''],
               '''BertForPreTraining''': ['''BertModelTest'''],
               '''BertForQuestionAnswering''': ['''BertModelTest'''],
               '''BertForSequenceClassification''': ['''BertModelTest'''],
               '''BertForTokenClassification''': ['''BertModelTest'''],
               '''BertLMHeadModel''': ['''BertModelTest'''],
               '''BertModel''': ['''BertModelTest'''],
           }

           A__       =    {
               '''BlipForConditionalGeneration''': ['''BlipTextImageModelTest'''],
               '''BlipForImageTextRetrieval''': ['''BlipTextRetrievalModelTest'''],
               '''BlipForQuestionAnswering''': ['''BlipVQAModelTest'''],
               '''BlipModel''': ['''BlipModelTest'''],
               '''BlipTextModel''': ['''BlipTextModelTest'''],
               '''BlipVisionModel''': ['''BlipVisionModelTest'''],
           }

           self.assertEqual(get_test_info.to_json(UpperCAmelCase__)					, UpperCAmelCase__)
           self.assertEqual(get_test_info.to_json(UpperCAmelCase__)					, UpperCAmelCase__)





    def        SCREAMING_SNAKE_CASE							(			self      :	Optional[Any])						->str:





           '''simple docstring'''

           A__       =    get_model_to_tester_mapping(UpperCAmelCase__)
           A__       =    get_model_to_tester_mapping(UpperCAmelCase__)

           A__       =    {
               '''BertForMaskedLM''': ['''BertModelTester'''],
               '''BertForMultipleChoice''': ['''BertModelTester'''],
               '''BertForNextSentencePrediction''': ['''BertModelTester'''],
               '''BertForPreTraining''': ['''BertModelTester'''],
               '''BertForQuestionAnswering''': ['''BertModelTester'''],
               '''BertForSequenceClassification''': ['''BertModelTester'''],
               '''BertForTokenClassification''': ['''BertModelTester'''],
               '''BertLMHeadModel''': ['''BertModelTester'''],
               '''BertModel''': ['''BertModelTester'''],
           }

           A__       =    {
               '''BlipForConditionalGeneration''': ['''BlipTextImageModelsModelTester'''],
               '''BlipForImageTextRetrieval''': ['''BlipTextRetrievalModelTester'''],
               '''BlipForQuestionAnswering''': ['''BlipVQAModelTester'''],
               '''BlipModel''': ['''BlipModelTester'''],
               '''BlipTextModel''': ['''BlipTextModelTester'''],
               '''BlipVisionModel''': ['''BlipVisionModelTester'''],
           }

           self.assertEqual(get_test_info.to_json(UpperCAmelCase__)					, UpperCAmelCase__)
           self.assertEqual(get_test_info.to_json(UpperCAmelCase__)					, UpperCAmelCase__)


