


import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline




def        snake_case_					(  snake_case      ,							snake_case      ,							snake_case      ,							snake_case      ,							snake_case					)  ->    List[Any]:
       # load base model
       lowercase__:       Dict        =       StableDiffusionPipeline.from_pretrained(snake_case      ,							torch_dtype=torch.floataa					)

       # load LoRA weight from .safetensors
       lowercase__:       int        =       load_file(snake_case					)

       lowercase__:       str        =       []

       # 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:
                     lowercase__:       Dict        =       key.split('.'					)[0].split(LORA_PREFIX_TEXT_ENCODER + '_'					)[-1].split('_'					)
                     lowercase__:       Optional[int]        =       pipeline.text_encoder
              else:
                     lowercase__:       Union[str, Any]        =       key.split('.'					)[0].split(LORA_PREFIX_UNET + '_'					)[-1].split('_'					)
                     lowercase__:       Optional[int]        =       pipeline.unet

              # find the target layer
              lowercase__:       Tuple        =       layer_infos.pop(0					)
              while len(snake_case					) > -1:
                     try:
                            lowercase__:       Dict        =       curr_layer.__getattr__(snake_case					)
                            if len(snake_case					) > 0:
                                   lowercase__:       Union[str, Any]        =       layer_infos.pop(0					)
                            elif len(snake_case					) == 0:
                                   break
                     except Exception:
                            if len(snake_case					) > 0:
                                   temp_name += "_" + layer_infos.pop(0					)
                            else:
                                   lowercase__:       Optional[Any]        =       layer_infos.pop(0					)

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

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

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

       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)






from __future__ import annotations

from numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray




def        snake_case_					(  snake_case      ,							snake_case      ,							snake_case = False					)  ->    list[float]:
       if radian_mode:
              return [magnitude * cos(snake_case					), magnitude * sin(snake_case					)]
       return [magnitude * cos(radians(snake_case					)					), magnitude * sin(radians(snake_case					)					)]




def        snake_case_					(  snake_case      ,							snake_case      ,							snake_case = 10**-1					)  ->    bool:
       lowercase__:       NDArray[floataa]        =       cross(snake_case      ,							snake_case					)
       lowercase__:       float        =       sum(snake_case					)
       return abs(snake_case					) < eps


if __name__ == "__main__":
 # Test to check if it works
 __lowerCAmelCase			=							array(
     [
         polar_force(718.4, 1_80 - 30),
         polar_force(879.54, 45),
         polar_force(1_00, -90),
     ]
 )

 __lowerCAmelCase			=							array([[0, 0], [0, 0], [0, 0]])

 assert in_static_equilibrium(forces, location)

 # Problem 1 in image_data/2D_problems.jpg
 __lowerCAmelCase			=							array(
     [
         polar_force(30 * 9.81, 15),
         polar_force(2_15, 1_80 - 45),
         polar_force(2_64, 90 - 30),
     ]
 )

 __lowerCAmelCase			=							array([[0, 0], [0, 0], [0, 0]])

 assert in_static_equilibrium(forces, location)

 # Problem in image_data/2D_problems_1.jpg
 __lowerCAmelCase			=							array([[0, -20_00], [0, -12_00], [0, 1_56_00], [0, -1_24_00]])

 __lowerCAmelCase			=							array([[0, 0], [6, 0], [10, 0], [12, 0]])

 assert in_static_equilibrium(forces, location)

 import doctest

 doctest.testmod()


