



'''simple docstring'''





import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline




def lowerCAmelCase						(__A						,			__A						,			__A						,			__A						,			__A):



     """simple docstring"""



     _a											=						StableDiffusionPipeline.from_pretrained(__A						,			torch_dtype=torch.floataa)

     # load LoRA weight from .safetensors
     _a											=						load_file(__A)

     _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(__A) > -1:
               try:
                    _a											=						curr_layer.__getattr__(__A)
                    if len(__A) > 0:
                         _a											=						layer_infos.pop(0)
                    elif len(__A) == 0:
                         break
               except Exception:
                    if len(__A) > 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(__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:
               _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(__A						,			__A).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(__A						,			__A)

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

     return pipeline


if __name__ == "__main__":
  lowercase_	 =						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.)")

  lowercase_	 =						parser.parse_args()

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

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

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








'''simple docstring'''





import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError

from transformers import AutoImageProcessor, ViTImageProcessor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test


sys.path.append(str(Path(__file__).parent.parent / "utils"))

from test_module.custom_image_processing import CustomImageProcessor  # noqa E402


lowercase_	 =						get_tests_dir("fixtures")






class __A    (						unittest.TestCase		):



 '''simple docstring'''







 def       a__						(self )					->  List[Any]:


      """simple docstring"""



      _a											=						mock.Mock()
      _a											=						500
      _a											=						{}
      _a											=						HTTPError
      _a											=						{}

      # Download this model to make sure it's in the cache.
      _a											=						ViTImageProcessor.from_pretrained('''hf-internal-testing/tiny-random-vit''' )
      # Under the mock environment we get a 500 error when trying to reach the model.
      with mock.patch('''requests.Session.request'''				,     return_value=A ) as mock_head:
           _a											=						ViTImageProcessor.from_pretrained('''hf-internal-testing/tiny-random-vit''' )
           # This check we did call the fake head request
           mock_head.assert_called()



 def       a__						(self )					->  str:


      """simple docstring"""



      _a											=						ViTImageProcessor.from_pretrained(
          '''https://huggingface.co/hf-internal-testing/tiny-random-vit/resolve/main/preprocessor_config.json''' )



 def       a__						(self )					->  Dict:


      """simple docstring"""



      with self.assertRaises(A ):
           # config is in subfolder, the following should not work without specifying the subfolder
           _a											=						AutoImageProcessor.from_pretrained('''hf-internal-testing/stable-diffusion-all-variants''' )

      _a											=						AutoImageProcessor.from_pretrained(
          '''hf-internal-testing/stable-diffusion-all-variants'''				,     subfolder='''feature_extractor''' )

      self.assertIsNotNone(A )






@is_staging_test
class __A    (						unittest.TestCase		):



 '''simple docstring'''







 @classmethod
 def       a__						(cls )					->  Any:


      """simple docstring"""



      _a											=						TOKEN
      HfFolder.save_token(A )



 @classmethod
 def       a__						(cls )					->  Union[str, Any]:


      """simple docstring"""



      try:
           delete_repo(token=cls._token				,     repo_id='''test-image-processor''' )
      except HTTPError:
           pass

      try:
           delete_repo(token=cls._token				,     repo_id='''valid_org/test-image-processor-org''' )
      except HTTPError:
           pass

      try:
           delete_repo(token=cls._token				,     repo_id='''test-dynamic-image-processor''' )
      except HTTPError:
           pass



 def       a__						(self )					->  str:


      """simple docstring"""



      _a											=						ViTImageProcessor.from_pretrained(A )
      image_processor.push_to_hub('''test-image-processor'''				,     use_auth_token=self._token )

      _a											=						ViTImageProcessor.from_pretrained(f'''{USER}/test-image-processor''' )
      for k, v in image_processor.__dict__.items():
           self.assertEqual(A				,     getattr(A				,     A ) )

      # Reset repo
      delete_repo(token=self._token				,     repo_id='''test-image-processor''' )

      # Push to hub via save_pretrained
      with tempfile.TemporaryDirectory() as tmp_dir:
           image_processor.save_pretrained(
               A				,     repo_id='''test-image-processor'''				,     push_to_hub=A				,     use_auth_token=self._token )

      _a											=						ViTImageProcessor.from_pretrained(f'''{USER}/test-image-processor''' )
      for k, v in image_processor.__dict__.items():
           self.assertEqual(A				,     getattr(A				,     A ) )



 def       a__						(self )					->  List[Any]:


      """simple docstring"""



      _a											=						ViTImageProcessor.from_pretrained(A )
      image_processor.push_to_hub('''valid_org/test-image-processor'''				,     use_auth_token=self._token )

      _a											=						ViTImageProcessor.from_pretrained('''valid_org/test-image-processor''' )
      for k, v in image_processor.__dict__.items():
           self.assertEqual(A				,     getattr(A				,     A ) )

      # Reset repo
      delete_repo(token=self._token				,     repo_id='''valid_org/test-image-processor''' )

      # Push to hub via save_pretrained
      with tempfile.TemporaryDirectory() as tmp_dir:
           image_processor.save_pretrained(
               A				,     repo_id='''valid_org/test-image-processor-org'''				,     push_to_hub=A				,     use_auth_token=self._token )

      _a											=						ViTImageProcessor.from_pretrained('''valid_org/test-image-processor-org''' )
      for k, v in image_processor.__dict__.items():
           self.assertEqual(A				,     getattr(A				,     A ) )



 def       a__						(self )					->  Union[str, Any]:


      """simple docstring"""



      CustomImageProcessor.register_for_auto_class()
      _a											=						CustomImageProcessor.from_pretrained(A )

      image_processor.push_to_hub('''test-dynamic-image-processor'''				,     use_auth_token=self._token )

      # This has added the proper auto_map field to the config
      self.assertDictEqual(
          image_processor.auto_map				,     {'''AutoImageProcessor''': '''custom_image_processing.CustomImageProcessor'''}				,     )

      _a											=						AutoImageProcessor.from_pretrained(
          f'''{USER}/test-dynamic-image-processor'''				,     trust_remote_code=A )
      # Can't make an isinstance check because the new_image_processor is from the CustomImageProcessor class of a dynamic module
      self.assertEqual(new_image_processor.__class__.__name__				,     '''CustomImageProcessor''' )



