
"""simple docstring"""




import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline



def 							a__ (				lowerCAmelCase	:       Union[str, Any]       ,    lowerCAmelCase	:       int       ,    lowerCAmelCase	:       str       ,    lowerCAmelCase	:       Optional[Any]       ,    lowerCAmelCase	:       Union[str, Any]		):






     '''simple docstring'''
     # load base model
     UpperCAmelCase__							: Optional[int] 							=						StableDiffusionPipeline.from_pretrained(lowerCAmelCase       ,    torch_dtype=torch.floataa		)

     # load LoRA weight from .safetensors
     UpperCAmelCase__							: int 							=						load_file(lowerCAmelCase		)

     UpperCAmelCase__							: Optional[Any] 							=						[]

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

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

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

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

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

     return pipeline


if __name__ == "__main__":
    A__      :	int          =    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__      :	Union[str, Any]          =    parser.parse_args()

    A__      :	int          =    args.base_model_path
    A__      :	str          =    args.checkpoint_path
    A__      :	int          =    args.dump_path
    A__      :	Optional[int]          =    args.lora_prefix_unet
    A__      :	Optional[int]          =    args.lora_prefix_text_encoder
    A__      :	Optional[int]          =    args.alpha

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

    A__      :	List[str]          =    pipe.to(args.device)
    pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)





"""simple docstring"""




import unittest

import numpy as np

from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
    import onnxruntime as ort







class   _lowercase						(       lowerCAmelCase_	,   unittest.TestCase				):


     '''simple docstring'''





     pass







@nightly
@require_onnxruntime
@require_torch_gpu
class   _lowercase						(       unittest.TestCase				):


     '''simple docstring'''





     @property
     def  lowerCAmelCase__       (      self  )->					int:
          return (
              "CUDAExecutionProvider",
              {
                  "gpu_mem_limit": "15000000000",  # 15GB
                  "arena_extend_strategy": "kSameAsRequested",
              },
          )



     @property
     def  lowerCAmelCase__       (      self  )->					Any:
          UpperCAmelCase__							: Tuple 							=						ort.SessionOptions()
          UpperCAmelCase__							: List[str] 							=						False
          return options



     def  lowerCAmelCase__       (      self  )->					List[str]:
          UpperCAmelCase__							: Union[str, Any] 							=						load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
              "/in_paint/overture-creations-5sI6fQgYIuo.png"  )
          UpperCAmelCase__							: int 							=						load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
              "/in_paint/overture-creations-5sI6fQgYIuo_mask.png"  )
          UpperCAmelCase__							: str 							=						OnnxStableDiffusionInpaintPipeline.from_pretrained(
              "runwayml/stable-diffusion-inpainting" ,			revision="onnx" ,			safety_checker=__UpperCamelCase ,			feature_extractor=__UpperCamelCase ,			provider=self.gpu_provider ,			sess_options=self.gpu_options ,			)
          pipe.set_progress_bar_config(disable=__UpperCamelCase  )

          UpperCAmelCase__							: int 							=						"A red cat sitting on a park bench"

          UpperCAmelCase__							: Tuple 							=						np.random.RandomState(0  )
          UpperCAmelCase__							: Any 							=						pipe(
              prompt=__UpperCamelCase ,			image=__UpperCamelCase ,			mask_image=__UpperCamelCase ,			guidance_scale=7.5 ,			num_inference_steps=10 ,			generator=__UpperCamelCase ,			output_type="np" ,			)
          UpperCAmelCase__							: Tuple 							=						output.images
          UpperCAmelCase__							: Dict 							=						images[0, 2_55:2_58, 2_55:2_58, -1]

          assert images.shape == (1, 5_12, 5_12, 3)
          UpperCAmelCase__							: Union[str, Any] 							=						np.array([0.2514, 0.3007, 0.3517, 0.1790, 0.2382, 0.3167, 0.1944, 0.2273, 0.2464]  )

          assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1E-3



     def  lowerCAmelCase__       (      self  )->					Tuple:
          UpperCAmelCase__							: Tuple 							=						load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
              "/in_paint/overture-creations-5sI6fQgYIuo.png"  )
          UpperCAmelCase__							: Tuple 							=						load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
              "/in_paint/overture-creations-5sI6fQgYIuo_mask.png"  )
          UpperCAmelCase__							: Optional[Any] 							=						LMSDiscreteScheduler.from_pretrained(
              "runwayml/stable-diffusion-inpainting" ,			subfolder="scheduler" ,			revision="onnx"  )
          UpperCAmelCase__							: Optional[Any] 							=						OnnxStableDiffusionInpaintPipeline.from_pretrained(
              "runwayml/stable-diffusion-inpainting" ,			revision="onnx" ,			scheduler=__UpperCamelCase ,			safety_checker=__UpperCamelCase ,			feature_extractor=__UpperCamelCase ,			provider=self.gpu_provider ,			sess_options=self.gpu_options ,			)
          pipe.set_progress_bar_config(disable=__UpperCamelCase  )

          UpperCAmelCase__							: int 							=						"A red cat sitting on a park bench"

          UpperCAmelCase__							: List[str] 							=						np.random.RandomState(0  )
          UpperCAmelCase__							: str 							=						pipe(
              prompt=__UpperCamelCase ,			image=__UpperCamelCase ,			mask_image=__UpperCamelCase ,			guidance_scale=7.5 ,			num_inference_steps=20 ,			generator=__UpperCamelCase ,			output_type="np" ,			)
          UpperCAmelCase__							: List[str] 							=						output.images
          UpperCAmelCase__							: List[Any] 							=						images[0, 2_55:2_58, 2_55:2_58, -1]

          assert images.shape == (1, 5_12, 5_12, 3)
          UpperCAmelCase__							: int 							=						np.array([0.0086, 0.0077, 0.0083, 0.0093, 0.0107, 0.0139, 0.0094, 0.0097, 0.0125]  )

          assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1E-3



