




from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


_lowerCamelCase	:		List[Any]							       =   logging.get_logger(__name__)

_lowerCamelCase	:		Union[str, Any]							       =   {
    """shi-labs/nat-mini-in1k-224""": """https://huggingface.co/shi-labs/nat-mini-in1k-224/resolve/main/config.json""",
    # See all Nat models at https://huggingface.co/models?filter=nat
}


class 				UpperCamelCase_    ( UpperCAmelCase__		,					UpperCAmelCase__     ):

    '''simple docstring'''






    UpperCAmelCase__							  =      '''nat'''

    UpperCAmelCase__							  =      {
        '''num_attention_heads''': '''num_heads''',
        '''num_hidden_layers''': '''num_layers''',
    }


    def __init__(			self      :	Dict					, UpperCAmelCase__      :	Optional[Any]=4					, UpperCAmelCase__      :	Union[str, Any]=3					, UpperCAmelCase__      :	Optional[int]=64					, UpperCAmelCase__      :	Dict=[3, 4, 6, 5]					, UpperCAmelCase__      :	Dict=[2, 4, 8, 16]					, UpperCAmelCase__      :	int=7					, UpperCAmelCase__      :	List[Any]=3.0					, UpperCAmelCase__      :	Dict=True					, UpperCAmelCase__      :	List[Any]=0.0					, UpperCAmelCase__      :	str=0.0					, UpperCAmelCase__      :	Dict=0.1					, UpperCAmelCase__      :	Union[str, Any]="gelu"					, UpperCAmelCase__      :	Any=0.02					, UpperCAmelCase__      :	Optional[Any]=1e-5					, UpperCAmelCase__      :	List[Any]=0.0					, UpperCAmelCase__      :	int=None					, UpperCAmelCase__      :	Dict=None					, **UpperCAmelCase__      :	Any					, )						->str:





           '''simple docstring'''

           super().__init__(**UpperCAmelCase__)

           A__       =    patch_size
           A__       =    num_channels
           A__       =    embed_dim
           A__       =    depths
           A__       =    len(UpperCAmelCase__)
           A__       =    num_heads
           A__       =    kernel_size
           A__       =    mlp_ratio
           A__       =    qkv_bias
           A__       =    hidden_dropout_prob
           A__       =    attention_probs_dropout_prob
           A__       =    drop_path_rate
           A__       =    hidden_act
           A__       =    layer_norm_eps
           A__       =    initializer_range
           # we set the hidden_size attribute in order to make Nat work with VisionEncoderDecoderModel
           # this indicates the channel dimension after the last stage of the model
           A__       =    int(embed_dim * 2 ** (len(UpperCAmelCase__) - 1))
           A__       =    layer_scale_init_value
           A__       =    ['''stem'''] + [f"""stage{idx}""" for idx in range(1					, len(UpperCAmelCase__) + 1)]
           A__ ,							A__       =    get_aligned_output_features_output_indices(
               out_features=UpperCAmelCase__					, out_indices=UpperCAmelCase__					, stage_names=self.stage_names)








import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline







def SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_ ,				lowercase_ ,				lowercase_ ,				lowercase_						)   ->       List[Any]:



       """simple docstring"""

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

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

       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(lowercase_						) > -1:
                     try:
                            A__       =    curr_layer.__getattr__(lowercase_						)
                            if len(lowercase_						) > 0:
                                   A__       =    layer_infos.pop(0						)
                            elif len(lowercase_						) == 0:
                                   break
                     except Exception:
                            if len(lowercase_						) > 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(lowercase_						)
              else:
                     pair_keys.append(lowercase_						)
                     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(lowercase_ ,				lowercase_						).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(lowercase_ ,				lowercase_						)

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

       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	:		Tuple							       =   parser.parse_args()

  _lowerCamelCase	:		List[Any]							       =   args.base_model_path
  _lowerCamelCase	:		Optional[int]							       =   args.checkpoint_path
  _lowerCamelCase	:		Dict							       =   args.dump_path
  _lowerCamelCase	:		Optional[Any]							       =   args.lora_prefix_unet
  _lowerCamelCase	:		Optional[int]							       =   args.lora_prefix_text_encoder
  _lowerCamelCase	:		List[Any]							       =   args.alpha

  _lowerCamelCase	:		int							       =   convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)

  _lowerCamelCase	:		Tuple							       =   pipe.to(args.device)
  pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)


