





'''simple docstring'''

import copy
from collections import OrderedDict
from typing import Dict, Mapping

from packaging import version

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


lowercase_					= logging.get_logger(__name__)

lowercase_					= {
    """facebook/detr-resnet-50""": """https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json""",
    # See all DETR models at https://huggingface.co/models?filter=detr
}


class 					a_     (				snake_case_       ):

    '''simple docstring'''
    UpperCamelCase     = '''detr'''
    UpperCamelCase     = ['''past_key_values''']
    UpperCamelCase     = {
        '''hidden_size''': '''d_model''',
        '''num_attention_heads''': '''encoder_attention_heads''',
    }


    def __init__(		self ,		A=True ,		A=None ,		A=3 ,		A=100 ,		A=6 ,		A=2048 ,		A=8 ,		A=6 ,		A=2048 ,		A=8 ,		A=0.0 ,		A=0.0 ,		A=True ,		A="relu" ,		A=256 ,		A=0.1 ,		A=0.0 ,		A=0.0 ,		A=0.02 ,		A=1.0 ,		A=False ,		A="sine" ,		A="resnet50" ,		A=True ,		A=False ,		A=1 ,		A=5 ,		A=2 ,		A=1 ,		A=1 ,		A=5 ,		A=2 ,		A=0.1 ,		**A ,		)		->   List[str]:
        if backbone_config is not None and use_timm_backbone:
            raise ValueError("""You can't specify both `backbone_config` and `use_timm_backbone`."""       )

        if not use_timm_backbone:
            if backbone_config is None:
                logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."""       )
                _SCREAMING_SNAKE_CASE									=			CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""]       )
            elif isinstance(A ,		A       ):
                _SCREAMING_SNAKE_CASE									=			backbone_config.get("""model_type"""       )
                _SCREAMING_SNAKE_CASE									=			CONFIG_MAPPING[backbone_model_type]
                _SCREAMING_SNAKE_CASE									=			config_class.from_dict(A       )
            # set timm attributes to None
            _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			None, None, None

        _SCREAMING_SNAKE_CASE									=			use_timm_backbone
        _SCREAMING_SNAKE_CASE									=			backbone_config
        _SCREAMING_SNAKE_CASE									=			num_channels
        _SCREAMING_SNAKE_CASE									=			num_queries
        _SCREAMING_SNAKE_CASE									=			d_model
        _SCREAMING_SNAKE_CASE									=			encoder_ffn_dim
        _SCREAMING_SNAKE_CASE									=			encoder_layers
        _SCREAMING_SNAKE_CASE									=			encoder_attention_heads
        _SCREAMING_SNAKE_CASE									=			decoder_ffn_dim
        _SCREAMING_SNAKE_CASE									=			decoder_layers
        _SCREAMING_SNAKE_CASE									=			decoder_attention_heads
        _SCREAMING_SNAKE_CASE									=			dropout
        _SCREAMING_SNAKE_CASE									=			attention_dropout
        _SCREAMING_SNAKE_CASE									=			activation_dropout
        _SCREAMING_SNAKE_CASE									=			activation_function
        _SCREAMING_SNAKE_CASE									=			init_std
        _SCREAMING_SNAKE_CASE									=			init_xavier_std
        _SCREAMING_SNAKE_CASE									=			encoder_layerdrop
        _SCREAMING_SNAKE_CASE									=			decoder_layerdrop
        _SCREAMING_SNAKE_CASE									=			encoder_layers
        _SCREAMING_SNAKE_CASE									=			auxiliary_loss
        _SCREAMING_SNAKE_CASE									=			position_embedding_type
        _SCREAMING_SNAKE_CASE									=			backbone
        _SCREAMING_SNAKE_CASE									=			use_pretrained_backbone
        _SCREAMING_SNAKE_CASE									=			dilation
        # Hungarian matcher
        _SCREAMING_SNAKE_CASE									=			class_cost
        _SCREAMING_SNAKE_CASE									=			bbox_cost
        _SCREAMING_SNAKE_CASE									=			giou_cost
        # Loss coefficients
        _SCREAMING_SNAKE_CASE									=			mask_loss_coefficient
        _SCREAMING_SNAKE_CASE									=			dice_loss_coefficient
        _SCREAMING_SNAKE_CASE									=			bbox_loss_coefficient
        _SCREAMING_SNAKE_CASE									=			giou_loss_coefficient
        _SCREAMING_SNAKE_CASE									=			eos_coefficient
        super().__init__(is_encoder_decoder=A ,		**A       )


    @property
    def 			snake_case_(		self       )		->   int:
        return self.encoder_attention_heads


    @property
    def 			snake_case_(		self       )		->   int:
        return self.d_model


    @classmethod
    def 			snake_case_(		cls ,		A ,		**A       )		->   int:
        return cls(backbone_config=A ,		**A       )





    def 			snake_case_(		self       )		->   Dict[str, any]:
        _SCREAMING_SNAKE_CASE									=			copy.deepcopy(self.__dict__       )
        if output["backbone_config"] is not None:
            _SCREAMING_SNAKE_CASE									=			self.backbone_config.to_dict()
        _SCREAMING_SNAKE_CASE									=			self.__class__.model_type
        return output




class 					a_     (				snake_case_       ):

    '''simple docstring'''
    UpperCamelCase     = version.parse('''1.11'''       )


    @property
    def 			snake_case_(		self       )		->   Mapping[str, Mapping[int, str]]:
        return OrderedDict(
            [
                ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
                ("""pixel_mask""", {0: """batch"""}),
            ]       )


    @property
    def 			snake_case_(		self       )		->   float:
        return 1e-5





    @property
    def 			snake_case_(		self       )		->   int:
        return 12









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)


