



'''simple docstring'''






import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline
def    lowerCamelCase__ (		_A				,    _A				,    _A				,    _A				,    _A       ):
  # load base model
  a		:      List[Any]       =  StableDiffusionPipeline.from_pretrained(_A				,    torch_dtype=torch.floataa       )

  # load LoRA weight from .safetensors
  a		:      Optional[int]       =  load_file(_A       )

  a		:      int       =  []

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

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

    a		:      str       =  []
    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:
      a		:      Optional[Any]       =  state_dict[pair_keys[0]].squeeze(3       ).squeeze(2       ).to(torch.floataa       )
      a		:      str       =  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:
      a		:      str       =  state_dict[pair_keys[0]].to(torch.floataa       )
      a		:      str       =  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__":
     lowerCAmelCase:      Tuple		 =		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:      List[str]		 =		args.base_model_path
     lowerCAmelCase:      List[str]		 =		args.checkpoint_path
     lowerCAmelCase:      List[str]		 =		args.dump_path
     lowerCAmelCase:      List[Any]		 =		args.lora_prefix_unet
     lowerCAmelCase:      Optional[int]		 =		args.lora_prefix_text_encoder
     lowerCAmelCase:      Optional[Any]		 =		args.alpha

     lowerCAmelCase:      Optional[Any]		 =		convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)

     lowerCAmelCase:      Union[str, Any]		 =		pipe.to(args.device)
     pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)




'''simple docstring'''






import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand
def    lowerCamelCase__ (		_A       ):
  return EnvironmentCommand()

class 							a__(      lowerCamelCase__			):







    @staticmethod
    def     lowercase_ (   __snake_case				:						ArgumentParser			):
      a		:      Tuple       =  parser.add_parser('env'			)
      download_parser.set_defaults(func=__snake_case			)







    def     lowercase_ (   self				:						List[str]			):
      a		:      str       =  huggingface_hub.__version__

      a		:      List[str]       =  'not installed'
      a		:      List[str]       =  'NA'
      if is_torch_available():
        import torch

        a		:      Optional[Any]       =  torch.__version__
        a		:      int       =  torch.cuda.is_available()

      a		:      Optional[int]       =  'not installed'
      if is_transformers_available():
        import transformers

        a		:      Tuple       =  transformers.__version__

      a		:      Dict       =  'not installed'
      if is_accelerate_available():
        import accelerate

        a		:      int       =  accelerate.__version__

      a		:      Any       =  'not installed'
      if is_xformers_available():
        import xformers

        a		:      Optional[int]       =  xformers.__version__

      a		:      List[str]       =  {
          '`diffusers` version': version,
          'Platform': platform.platform(),
          'Python version': platform.python_version(),
          'PyTorch version (GPU?)': F"""{pt_version} ({pt_cuda_available})""",
          'Huggingface_hub version': hub_version,
          'Transformers version': transformers_version,
          'Accelerate version': accelerate_version,
          'xFormers version': xformers_version,
          'Using GPU in script?': '<fill in>',
          'Using distributed or parallel set-up in script?': '<fill in>',
      }

      print('\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n'			)
      print(self.format_dict(__snake_case			)			)

      return info







    @staticmethod
    def     lowercase_ (   __snake_case				:						Union[str, Any]			):
      return "\n".join([F"""- {prop}: {val}""" for prop, val in d.items()]			) + "\n"