
import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline



def 		lowercase_    (     A__				,	A__				,	A__				,	A__				,	A__	)							->							List[Any]:
    """simple docstring"""

    snake_case				  =      StableDiffusionPipeline.from_pretrained(A__				,	torch_dtype=torch.floataa	)

    # load LoRA weight from .safetensors
    snake_case				  =      load_file(A__	)

    snake_case				  =      []

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

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

        snake_case				  =      []
        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:
            snake_case				  =      state_dict[pair_keys[0]].squeeze(3	).squeeze(2	).to(torch.floataa	)
            snake_case				  =      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:
            snake_case				  =      state_dict[pair_keys[0]].to(torch.floataa	)
            snake_case				  =      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__":
   _A						      =				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						      =				parser.parse_args()

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

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

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








import os

# Precomputes a list of the 100 first triangular numbers
_A						      =				[int(0.5 * n * (n + 1)) for n in range(1, 1_01)]



def 		lowercase_    (     )							->							Tuple:
    """simple docstring"""

    snake_case				  =      os.path.dirname(os.path.realpath(A__	)	)
    snake_case				  =      os.path.join(A__				,	"words.txt"	)

    snake_case				  =      ""
    with open(A__	) as f:
        snake_case				  =      f.readline()

    snake_case				  =      [word.strip("\""	) for word in words.strip("\r\n"	).split(","	)]
    snake_case				  =      [
        word
        for word in [sum(ord(A__	) - 64 for x in word	) for word in words]
        if word in TRIANGULAR_NUMBERS
    ]
    return len(A__	)


if __name__ == "__main__":
   print(solution())

