

'''simple docstring'''







import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline
def       UpperCAmelCase__    (      UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__,       UpperCAmelCase__				)			->			Optional[int]:
       # load base model
       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)



'''simple docstring'''







from __future__ import annotations

import math
def       UpperCAmelCase__    (      UpperCAmelCase__				)			->			bool:

       if 1 < number < 4:
              # 2 and 3 are primes
              return True
       elif number < 2 or number % 2 == 0 or number % 3 == 0:
              # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
              return False

       # All primes number are in format of 6k +/- 1
       for i in range(5,       int(math.sqrt(UpperCAmelCase__				) + 1				),       6				):
              if number % i == 0 or number % (i + 2) == 0:
                     return False
       return True


__lowerCamelCase		       =    [num for num in range(3, 10_0001, 2) if not is_prime(num)]
def       UpperCAmelCase__    (      UpperCAmelCase__				)			->			list[int]:
       if not isinstance(UpperCAmelCase__,       UpperCAmelCase__				):
              raise ValueError("""n must be an integer"""				)
       if n <= 0:
              raise ValueError("""n must be >= 0"""				)

       A_						=      []
       for num in range(len(UpperCAmelCase__				)				):
              A_						=      0
              while 2 * i * i <= odd_composites[num]:
                     A_						=      odd_composites[num] - 2 * i * i
                     if is_prime(UpperCAmelCase__				):
                            break
                     i += 1
              else:
                     list_nums.append(odd_composites[num]				)
                     if len(UpperCAmelCase__				) == n:
                            return list_nums

       return []
def       UpperCAmelCase__    (      )			->			int:
       return compute_nums(1				)[0]


if __name__ == "__main__":
      print(f"""{solution() = }""")
