



import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline

def      lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE				:	List[Any] ,   _SCREAMING_SNAKE_CASE				:	Optional[Any] ,   _SCREAMING_SNAKE_CASE				:	Union[str, Any] ,   _SCREAMING_SNAKE_CASE				:	Any ,   _SCREAMING_SNAKE_CASE				:	Tuple			):
				"""simple docstring"""







				__a        =						StableDiffusionPipeline.from_pretrained(_UpperCamelCase ,   torch_dtype=torch.floataa			)

				# load LoRA weight from .safetensors
				__a        =						load_file(_UpperCamelCase			)

				__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(_UpperCamelCase			) > -1:
												try:
																__a        =						curr_layer.__getattr__(_UpperCamelCase			)
																if len(_UpperCamelCase			) > 0:
																				__a        =						layer_infos.pop(0			)
																elif len(_UpperCamelCase			) == 0:
																				break
												except Exception:
																if len(_UpperCamelCase			) > 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(_UpperCamelCase			)
								else:
												pair_keys.append(_UpperCamelCase			)
												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(_UpperCamelCase ,   _UpperCamelCase			).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(_UpperCamelCase ,   _UpperCamelCase			)

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

				return pipeline


if __name__ == "__main__":
						lowerCamelCase__	=       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__	=       parser.parse_args()

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

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

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







import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
	import torch

if is_vision_available():
	from PIL import Image

	from transformers import GLPNImageProcessor



class 					__lowerCAmelCase       (			unittest.TestCase     ):






			def __init__(self					,							__magic_name__					,							__magic_name__=7					,							__magic_name__=3					,							__magic_name__=18					,							__magic_name__=30					,							__magic_name__=400					,							__magic_name__=True					,							__magic_name__=32					,							__magic_name__=True					,							)     ->   Dict:


							'''simple docstring'''



							snake_case_						:    Tuple				=  parent
							snake_case_						:    Union[str, Any]				=  batch_size
							snake_case_						:    Union[str, Any]				=  num_channels
							snake_case_						:    Optional[Any]				=  image_size
							snake_case_						:    int				=  min_resolution
							snake_case_						:    Any				=  max_resolution
							snake_case_						:    Tuple				=  do_resize
							snake_case_						:    str				=  size_divisor
							snake_case_						:    Optional[Any]				=  do_rescale






			def 			lowerCamelCase  (self    )     ->   Dict:


							'''simple docstring'''



							return {
							    "do_resize": self.do_resize,
							    "size_divisor": self.size_divisor,
							    "do_rescale": self.do_rescale,
							}




@require_torch
@require_vision
class 					__lowerCAmelCase       (			_a,     unittest.TestCase     ):
			lowerCamelCase_     :     Optional[Any]       			=      GLPNImageProcessor if is_vision_available() else None






			def 			lowerCamelCase  (self    )     ->   Optional[Any]:


							'''simple docstring'''



							snake_case_						:    str				=  GLPNImageProcessingTester(self    )






			@property
			def 			lowerCamelCase  (self    )     ->   Union[str, Any]:


							'''simple docstring'''



							return self.image_processor_tester.prepare_image_processor_dict()






			def 			lowerCamelCase  (self    )     ->   List[str]:


							'''simple docstring'''



							snake_case_						:    Optional[int]				=  self.image_processing_class(**self.image_processor_dict    )
							self.assertTrue(hasattr(__magic_name__					,							'''do_resize'''    )    )
							self.assertTrue(hasattr(__magic_name__					,							'''size_divisor'''    )    )
							self.assertTrue(hasattr(__magic_name__					,							'''resample'''    )    )
							self.assertTrue(hasattr(__magic_name__					,							'''do_rescale'''    )    )






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


							'''simple docstring'''



							pass






			def 			lowerCamelCase  (self    )     ->   int:


							'''simple docstring'''



							snake_case_						:    Optional[Any]				=  self.image_processing_class(**self.image_processor_dict    )
							# create random PIL images
							snake_case_						:    Union[str, Any]				=  prepare_image_inputs(self.image_processor_tester					,							equal_resolution=__magic_name__    )
							for image in image_inputs:
											self.assertIsInstance(__magic_name__					,							Image.Image    )

							# Test not batched input (GLPNImageProcessor doesn't support batching)
							snake_case_						:    Optional[int]				=  image_processing(image_inputs[0]					,							return_tensors='''pt'''    ).pixel_values
							self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0    )
							self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0    )






			def 			lowerCamelCase  (self    )     ->   Dict:


							'''simple docstring'''



							snake_case_						:    str				=  self.image_processing_class(**self.image_processor_dict    )
							# create random numpy tensors
							snake_case_						:    List[str]				=  prepare_image_inputs(self.image_processor_tester					,							equal_resolution=__magic_name__					,							numpify=__magic_name__    )
							for image in image_inputs:
											self.assertIsInstance(__magic_name__					,							np.ndarray    )

							# Test not batched input (GLPNImageProcessor doesn't support batching)
							snake_case_						:    Any				=  image_processing(image_inputs[0]					,							return_tensors='''pt'''    ).pixel_values
							self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0    )
							self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0    )






			def 			lowerCamelCase  (self    )     ->   Optional[Any]:


							'''simple docstring'''



							snake_case_						:    Dict				=  self.image_processing_class(**self.image_processor_dict    )
							# create random PyTorch tensors
							snake_case_						:    Optional[Any]				=  prepare_image_inputs(self.image_processor_tester					,							equal_resolution=__magic_name__					,							torchify=__magic_name__    )
							for image in image_inputs:
											self.assertIsInstance(__magic_name__					,							torch.Tensor    )

							# Test not batched input (GLPNImageProcessor doesn't support batching)
							snake_case_						:    Dict				=  image_processing(image_inputs[0]					,							return_tensors='''pt'''    ).pixel_values
							self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0    )
							self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0    )
