


"""simple docstring"""






import copy
from typing import Dict, List, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


__snake_case				=				{
    """facebook/mask2former-swin-small-coco-instance""": (
        """https://huggingface.co/facebook/mask2former-swin-small-coco-instance/blob/main/config.json"""
    )
    # See all Mask2Former models at https://huggingface.co/models?filter=mask2former
}

__snake_case				=				logging.get_logger(__name__)


class 			__lowerCamelCase     (    UpperCAmelCase__ ):

     '''simple docstring'''
     A_				:		int			=						'mask2former'
     A_				:		Any			=						['swin']
     A_				:		Optional[int]			=						{'hidden_size': 'hidden_dim'}


     def __init__(					self			, __UpperCAmelCase = None			, __UpperCAmelCase = 256			, __UpperCAmelCase = 256			, __UpperCAmelCase = 256			, __UpperCAmelCase = 1024			, __UpperCAmelCase = "relu"			, __UpperCAmelCase = 6			, __UpperCAmelCase = 10			, __UpperCAmelCase = 8			, __UpperCAmelCase = 0.0			, __UpperCAmelCase = 2048			, __UpperCAmelCase = False			, __UpperCAmelCase = False			, __UpperCAmelCase = 4			, __UpperCAmelCase = 255			, __UpperCAmelCase = 100			, __UpperCAmelCase = 0.1			, __UpperCAmelCase = 2.0			, __UpperCAmelCase = 5.0			, __UpperCAmelCase = 5.0			, __UpperCAmelCase = 12544			, __UpperCAmelCase = 3.0			, __UpperCAmelCase = 0.75			, __UpperCAmelCase = 0.02			, __UpperCAmelCase = 1.0			, __UpperCAmelCase = True			, __UpperCAmelCase = [4, 8, 16, 32]			, __UpperCAmelCase = None			, **__UpperCAmelCase			, )				->							Optional[Any]:

          if backbone_config is None:
               logger.info('''`backbone_config` is `None`. Initializing the config with the default `Swin` backbone.'''							)
               _a					= CONFIG_MAPPING['''swin'''](
                   image_size=224			, in_channels=3			, patch_size=4			, embed_dim=96			, depths=[2, 2, 18, 2]			, num_heads=[3, 6, 12, 24]			, window_size=7			, drop_path_rate=0.3			, use_absolute_embeddings=UpperCAmelCase__			, out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']			, )

          if isinstance(UpperCAmelCase__			, UpperCAmelCase__							):
               _a					= backbone_config.pop('''model_type'''							)
               _a					= CONFIG_MAPPING[backbone_model_type]
               _a					= config_class.from_dict(UpperCAmelCase__							)

          # verify that the backbone is supported
          if backbone_config.model_type not in self.backbones_supported:
               logger.warning_once(
                   F'Backbone {backbone_config.model_type} is not a supported model and may not be compatible with Mask2Former. '
                   F'Supported model types: {",".join(self.backbones_supported							)}'							)

          _a					= backbone_config
          _a					= feature_size
          _a					= mask_feature_size
          _a					= hidden_dim
          _a					= encoder_feedforward_dim
          _a					= activation_function
          _a					= encoder_layers
          _a					= decoder_layers
          _a					= num_attention_heads
          _a					= dropout
          _a					= dim_feedforward
          _a					= pre_norm
          _a					= enforce_input_projection
          _a					= common_stride
          _a					= ignore_value
          _a					= num_queries
          _a					= no_object_weight
          _a					= class_weight
          _a					= mask_weight
          _a					= dice_weight
          _a					= train_num_points
          _a					= oversample_ratio
          _a					= importance_sample_ratio
          _a					= init_std
          _a					= init_xavier_std
          _a					= use_auxiliary_loss
          _a					= feature_strides
          _a					= output_auxiliary_logits
          _a					= decoder_layers

          super().__init__(**UpperCAmelCase__							)


     @classmethod
     def 							_UpperCAmelCase					(					cls			, __UpperCAmelCase			, **__UpperCAmelCase							)				->							str:
          return cls(
              backbone_config=UpperCAmelCase__			, **UpperCAmelCase__			, )





     def 							_UpperCAmelCase					(					self							)				->							Dict[str, any]:
          _a					= copy.deepcopy(self.__dict__							)
          _a					= self.backbone_config.to_dict()
          _a					= self.__class__.model_type
          return output





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)


