"""simple docstring"""




import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline







def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__     ):





		"""simple docstring"""




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

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

		lowerCAmelCase__        =   []

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

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

				lowerCAmelCase__        =   []
				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:
						lowerCAmelCase__        =   state_dict[pair_keys[0]].squeeze(3     ).squeeze(2     ).to(torch.floataa     )
						lowerCAmelCase__        =   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:
						lowerCAmelCase__        =   state_dict[pair_keys[0]].to(torch.floataa     )
						lowerCAmelCase__        =   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    :							List[Any]													=						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    :							List[str]													=						parser.parse_args()

			__lowerCAmelCase    :							Tuple													=						args.base_model_path
			__lowerCAmelCase    :							Dict													=						args.checkpoint_path
			__lowerCAmelCase    :							Union[str, Any]													=						args.dump_path
			__lowerCAmelCase    :							Dict													=						args.lora_prefix_unet
			__lowerCAmelCase    :							Tuple													=						args.lora_prefix_text_encoder
			__lowerCAmelCase    :							List[str]													=						args.alpha

			__lowerCAmelCase    :							Dict													=						convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)

			__lowerCAmelCase    :							str													=						pipe.to(args.device)
			pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)






"""simple docstring"""




import copy
import os

import cva
import numpy as np
from matplotlib import pyplot as plt

class       a_      :




					def __init__(	self						:     Optional[int]     ):
							lowerCAmelCase__        =   """"""
							lowerCAmelCase__        =   """"""
							lowerCAmelCase__        =   []
							lowerCAmelCase__        =   0
							lowerCAmelCase__        =   256
							lowerCAmelCase__        =   0
							lowerCAmelCase__        =   0
							lowerCAmelCase__        =   0
							lowerCAmelCase__        =   0




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[Any]  ,						snake_case__						:     Union[str, Any]     ):
							lowerCAmelCase__        =   cva.imread(snake_case__  ,						0     )
							lowerCAmelCase__        =   copy.deepcopy(self.img     )
							lowerCAmelCase__   ,					lowerCAmelCase__   ,					lowerCAmelCase__        =   plt.hist(self.img.ravel()  ,						256  ,						[0, 256]  ,						label="""x"""     )
							lowerCAmelCase__        =   np.sum(snake_case__     )
							for i in range(len(snake_case__     )     ):
									lowerCAmelCase__        =   x[i] / self.k
									self.sk += prk
									lowerCAmelCase__        =   (self.L - 1) * self.sk
									if self.rem != 0:
											lowerCAmelCase__        =   int(last % last     )
									lowerCAmelCase__        =   int(last + 1 if self.rem >= 0.5 else last     )
									self.last_list.append(snake_case__     )
									lowerCAmelCase__        =   int(np.ma.count(self.img     ) / self.img[1].size     )
									lowerCAmelCase__        =   self.img[1].size
							for i in range(self.number_of_cols     ):
									for j in range(self.number_of_rows     ):
											lowerCAmelCase__        =   self.img[j][i]
											if num != self.last_list[num]:
													lowerCAmelCase__        =   self.last_list[num]
							cva.imwrite("""output_data/output.jpg"""  ,						self.img     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]     ):
							plt.hist(self.img.ravel()  ,						256  ,						[0, 256]     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]     ):
							cva.imshow("""Output-Image"""  ,						self.img     )
							cva.imshow("""Input-Image"""  ,						self.original_image     )
							cva.waitKey(5000     )
							cva.destroyAllWindows()


if __name__ == "__main__":
			__lowerCAmelCase    :							Dict													=						os.path.join(os.path.basename(__file__), "image_data/input.jpg")
			__lowerCAmelCase    :							Optional[int]													=						ConstantStretch()
			stretcher.stretch(file_path)
			stretcher.plot_histogram()
			stretcher.show_image()




