"""simple docstring"""


import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image

from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase__			=    logging.get_logger(__name__)






def      __UpperCAmelCase       (      lowercase		):







  """simple docstring"""
  _UpperCAmelCase    			=   SwinConfig(
      embed_dim=1_92 ,depths=(2, 2, 18, 2) ,num_heads=(6, 12, 24, 48) ,window_size=12 ,out_features=["""stage2""", """stage3""", """stage4"""] ,)

  _UpperCAmelCase    			=   DetaConfig(
      backbone_config=lowercase ,num_queries=9_00 ,encoder_ffn_dim=20_48 ,decoder_ffn_dim=20_48 ,num_feature_levels=5 ,assign_first_stage=lowercase ,with_box_refine=lowercase ,two_stage=lowercase ,)

  # set labels
  _UpperCAmelCase    			=   """huggingface/label-files"""
  if "o365" in model_name:
    _UpperCAmelCase    			=   3_66
    _UpperCAmelCase    			=   """object365-id2label.json"""
  else:
    _UpperCAmelCase    			=   91
    _UpperCAmelCase    			=   """coco-detection-id2label.json"""

  _UpperCAmelCase    			=   num_labels
  _UpperCAmelCase    			=   json.load(open(cached_download(hf_hub_url(lowercase ,lowercase ,repo_type="""dataset"""		)		) ,"""r"""		)		)
  _UpperCAmelCase    			=   {int(lowercase		): v for k, v in idalabel.items()}
  _UpperCAmelCase    			=   idalabel
  _UpperCAmelCase    			=   {v: k for k, v in idalabel.items()}

  return config






def      __UpperCAmelCase       (      lowercase		):







  """simple docstring"""
  _UpperCAmelCase    			=   []

  # stem
  # fmt: off
  rename_keys.append(("""backbone.0.body.patch_embed.proj.weight""", """model.backbone.model.embeddings.patch_embeddings.projection.weight""")		)
  rename_keys.append(("""backbone.0.body.patch_embed.proj.bias""", """model.backbone.model.embeddings.patch_embeddings.projection.bias""")		)
  rename_keys.append(("""backbone.0.body.patch_embed.norm.weight""", """model.backbone.model.embeddings.norm.weight""")		)
  rename_keys.append(("""backbone.0.body.patch_embed.norm.bias""", """model.backbone.model.embeddings.norm.bias""")		)
  # stages
  for i in range(len(config.backbone_config.depths		)		):
    for j in range(config.backbone_config.depths[i]		):
      rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.norm1.weight''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight''')		)
      rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.norm1.bias''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias''')		)
      rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_bias_table''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table''')		)
      rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_index''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index''')		)
      rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.attn.proj.weight''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight''')		)
      rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.attn.proj.bias''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias''')		)
      rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.norm2.weight''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight''')		)
      rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.norm2.bias''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias''')		)
      rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.weight''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight''')		)
      rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.bias''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias''')		)
      rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.weight''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.weight''')		)
      rename_keys.append((f'''backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.bias''', f'''model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.bias''')		)

    if i < 3:
      rename_keys.append((f'''backbone.0.body.layers.{i}.downsample.reduction.weight''', f'''model.backbone.model.encoder.layers.{i}.downsample.reduction.weight''')		)
      rename_keys.append((f'''backbone.0.body.layers.{i}.downsample.norm.weight''', f'''model.backbone.model.encoder.layers.{i}.downsample.norm.weight''')		)
      rename_keys.append((f'''backbone.0.body.layers.{i}.downsample.norm.bias''', f'''model.backbone.model.encoder.layers.{i}.downsample.norm.bias''')		)

  rename_keys.append(("""backbone.0.body.norm1.weight""", """model.backbone.model.hidden_states_norms.stage2.weight""")		)
  rename_keys.append(("""backbone.0.body.norm1.bias""", """model.backbone.model.hidden_states_norms.stage2.bias""")		)
  rename_keys.append(("""backbone.0.body.norm2.weight""", """model.backbone.model.hidden_states_norms.stage3.weight""")		)
  rename_keys.append(("""backbone.0.body.norm2.bias""", """model.backbone.model.hidden_states_norms.stage3.bias""")		)
  rename_keys.append(("""backbone.0.body.norm3.weight""", """model.backbone.model.hidden_states_norms.stage4.weight""")		)
  rename_keys.append(("""backbone.0.body.norm3.bias""", """model.backbone.model.hidden_states_norms.stage4.bias""")		)

  # transformer encoder
  for i in range(config.encoder_layers		):
    rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.sampling_offsets.weight''', f'''model.encoder.layers.{i}.self_attn.sampling_offsets.weight''')		)
    rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.sampling_offsets.bias''', f'''model.encoder.layers.{i}.self_attn.sampling_offsets.bias''')		)
    rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.attention_weights.weight''', f'''model.encoder.layers.{i}.self_attn.attention_weights.weight''')		)
    rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.attention_weights.bias''', f'''model.encoder.layers.{i}.self_attn.attention_weights.bias''')		)
    rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.value_proj.weight''', f'''model.encoder.layers.{i}.self_attn.value_proj.weight''')		)
    rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.value_proj.bias''', f'''model.encoder.layers.{i}.self_attn.value_proj.bias''')		)
    rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.output_proj.weight''', f'''model.encoder.layers.{i}.self_attn.output_proj.weight''')		)
    rename_keys.append((f'''transformer.encoder.layers.{i}.self_attn.output_proj.bias''', f'''model.encoder.layers.{i}.self_attn.output_proj.bias''')		)
    rename_keys.append((f'''transformer.encoder.layers.{i}.norm1.weight''', f'''model.encoder.layers.{i}.self_attn_layer_norm.weight''')		)
    rename_keys.append((f'''transformer.encoder.layers.{i}.norm1.bias''', f'''model.encoder.layers.{i}.self_attn_layer_norm.bias''')		)
    rename_keys.append((f'''transformer.encoder.layers.{i}.linear1.weight''', f'''model.encoder.layers.{i}.fc1.weight''')		)
    rename_keys.append((f'''transformer.encoder.layers.{i}.linear1.bias''', f'''model.encoder.layers.{i}.fc1.bias''')		)
    rename_keys.append((f'''transformer.encoder.layers.{i}.linear2.weight''', f'''model.encoder.layers.{i}.fc2.weight''')		)
    rename_keys.append((f'''transformer.encoder.layers.{i}.linear2.bias''', f'''model.encoder.layers.{i}.fc2.bias''')		)
    rename_keys.append((f'''transformer.encoder.layers.{i}.norm2.weight''', f'''model.encoder.layers.{i}.final_layer_norm.weight''')		)
    rename_keys.append((f'''transformer.encoder.layers.{i}.norm2.bias''', f'''model.encoder.layers.{i}.final_layer_norm.bias''')		)

  # transformer decoder
  for i in range(config.decoder_layers		):
    rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.sampling_offsets.weight''', f'''model.decoder.layers.{i}.encoder_attn.sampling_offsets.weight''')		)
    rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.sampling_offsets.bias''', f'''model.decoder.layers.{i}.encoder_attn.sampling_offsets.bias''')		)
    rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.attention_weights.weight''', f'''model.decoder.layers.{i}.encoder_attn.attention_weights.weight''')		)
    rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.attention_weights.bias''', f'''model.decoder.layers.{i}.encoder_attn.attention_weights.bias''')		)
    rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.value_proj.weight''', f'''model.decoder.layers.{i}.encoder_attn.value_proj.weight''')		)
    rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.value_proj.bias''', f'''model.decoder.layers.{i}.encoder_attn.value_proj.bias''')		)
    rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.output_proj.weight''', f'''model.decoder.layers.{i}.encoder_attn.output_proj.weight''')		)
    rename_keys.append((f'''transformer.decoder.layers.{i}.cross_attn.output_proj.bias''', f'''model.decoder.layers.{i}.encoder_attn.output_proj.bias''')		)
    rename_keys.append((f'''transformer.decoder.layers.{i}.norm1.weight''', f'''model.decoder.layers.{i}.encoder_attn_layer_norm.weight''')		)
    rename_keys.append((f'''transformer.decoder.layers.{i}.norm1.bias''', f'''model.decoder.layers.{i}.encoder_attn_layer_norm.bias''')		)
    rename_keys.append((f'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''', f'''model.decoder.layers.{i}.self_attn.out_proj.weight''')		)
    rename_keys.append((f'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', f'''model.decoder.layers.{i}.self_attn.out_proj.bias''')		)
    rename_keys.append((f'''transformer.decoder.layers.{i}.norm2.weight''', f'''model.decoder.layers.{i}.self_attn_layer_norm.weight''')		)
    rename_keys.append((f'''transformer.decoder.layers.{i}.norm2.bias''', f'''model.decoder.layers.{i}.self_attn_layer_norm.bias''')		)
    rename_keys.append((f'''transformer.decoder.layers.{i}.linear1.weight''', f'''model.decoder.layers.{i}.fc1.weight''')		)
    rename_keys.append((f'''transformer.decoder.layers.{i}.linear1.bias''', f'''model.decoder.layers.{i}.fc1.bias''')		)
    rename_keys.append((f'''transformer.decoder.layers.{i}.linear2.weight''', f'''model.decoder.layers.{i}.fc2.weight''')		)
    rename_keys.append((f'''transformer.decoder.layers.{i}.linear2.bias''', f'''model.decoder.layers.{i}.fc2.bias''')		)
    rename_keys.append((f'''transformer.decoder.layers.{i}.norm3.weight''', f'''model.decoder.layers.{i}.final_layer_norm.weight''')		)
    rename_keys.append((f'''transformer.decoder.layers.{i}.norm3.bias''', f'''model.decoder.layers.{i}.final_layer_norm.bias''')		)

  # fmt: on

  return rename_keys






def      __UpperCAmelCase       (      lowercase ,lowercase ,lowercase		):







  """simple docstring"""
  _UpperCAmelCase    			=   dct.pop(lowercase		)
  _UpperCAmelCase    			=   val






def      __UpperCAmelCase       (      lowercase ,lowercase		):







  """simple docstring"""
  _UpperCAmelCase    			=   [int(backbone_config.embed_dim * 2**i		) for i in range(len(backbone_config.depths		)		)]
  for i in range(len(backbone_config.depths		)		):
    _UpperCAmelCase    			=   num_features[i]
    for j in range(backbone_config.depths[i]		):
      # fmt: off
      # read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
      _UpperCAmelCase    			=   state_dict.pop(f'''backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight'''		)
      _UpperCAmelCase    			=   state_dict.pop(f'''backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.bias'''		)
      # next, add query, keys and values (in that order) to the state dict
      _UpperCAmelCase    			=   in_proj_weight[:dim, :]
      _UpperCAmelCase    			=   in_proj_bias[: dim]
      _UpperCAmelCase    			=   in_proj_weight[
          dim : dim * 2, :
      ]
      _UpperCAmelCase    			=   in_proj_bias[
          dim : dim * 2
      ]
      _UpperCAmelCase    			=   in_proj_weight[
          -dim :, :
      ]
      _UpperCAmelCase    			=   in_proj_bias[-dim :]
      # fmt: on






def      __UpperCAmelCase       (      lowercase ,lowercase		):







  """simple docstring"""
  # transformer decoder self-attention layers
  _UpperCAmelCase    			=   config.d_model
  for i in range(config.decoder_layers		):
    # read in weights + bias of input projection layer of self-attention
    _UpperCAmelCase    			=   state_dict.pop(f'''transformer.decoder.layers.{i}.self_attn.in_proj_weight'''		)
    _UpperCAmelCase    			=   state_dict.pop(f'''transformer.decoder.layers.{i}.self_attn.in_proj_bias'''		)
    # next, add query, keys and values (in that order) to the state dict
    _UpperCAmelCase    			=   in_proj_weight[:hidden_size, :]
    _UpperCAmelCase    			=   in_proj_bias[:hidden_size]
    _UpperCAmelCase    			=   in_proj_weight[
        hidden_size : hidden_size * 2, :
    ]
    _UpperCAmelCase    			=   in_proj_bias[hidden_size : hidden_size * 2]
    _UpperCAmelCase    			=   in_proj_weight[-hidden_size:, :]
    _UpperCAmelCase    			=   in_proj_bias[-hidden_size:]






def      __UpperCAmelCase       (      ):







  """simple docstring"""
  _UpperCAmelCase    			=   """http://images.cocodataset.org/val2017/000000039769.jpg"""
  _UpperCAmelCase    			=   Image.open(requests.get(lowercase ,stream=lowercase		).raw		)

  return im






@torch.no_grad()
def      __UpperCAmelCase       (      lowercase ,lowercase ,lowercase		):







  """simple docstring"""
  _UpperCAmelCase    			=   get_deta_config(lowercase		)

  # load original state dict
  if model_name == "deta-swin-large":
    _UpperCAmelCase    			=   hf_hub_download(repo_id="""nielsr/deta-checkpoints""" ,filename="""adet_swin_ft.pth"""		)
  elif model_name == "deta-swin-large-o365":
    _UpperCAmelCase    			=   hf_hub_download(repo_id="""jozhang97/deta-swin-l-o365""" ,filename="""deta_swin_pt_o365.pth"""		)
  else:
    raise ValueError(f'''Model name {model_name} not supported'''		)

  _UpperCAmelCase    			=   torch.load(lowercase ,map_location="""cpu"""		)["""model"""]

  # original state dict
  for name, param in state_dict.items():
    print(lowercase ,param.shape		)

  # rename keys
  _UpperCAmelCase    			=   create_rename_keys(lowercase		)
  for src, dest in rename_keys:
    rename_key(lowercase ,lowercase ,lowercase		)
  read_in_swin_q_k_v(lowercase ,config.backbone_config		)
  read_in_decoder_q_k_v(lowercase ,lowercase		)

  # fix some prefixes
  for key in state_dict.copy().keys():
    if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
      _UpperCAmelCase    			=   state_dict.pop(lowercase		)
      _UpperCAmelCase    			=   val
    if "input_proj" in key:
      _UpperCAmelCase    			=   state_dict.pop(lowercase		)
      _UpperCAmelCase    			=   val
    if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
      _UpperCAmelCase    			=   state_dict.pop(lowercase		)
      _UpperCAmelCase    			=   val

    # finally, create HuggingFace model and load state dict
  _UpperCAmelCase    			=   DetaForObjectDetection(lowercase		)
  model.load_state_dict(lowercase		)
  model.eval()

  _UpperCAmelCase    			=   """cuda""" if torch.cuda.is_available() else """cpu"""
  model.to(lowercase		)

  # load image processor
  _UpperCAmelCase    			=   DetaImageProcessor(format="""coco_detection"""		)

  # verify our conversion on image
  _UpperCAmelCase    			=   prepare_img()
  _UpperCAmelCase    			=   processor(images=lowercase ,return_tensors="""pt"""		)
  _UpperCAmelCase    			=   encoding["""pixel_values"""]
  _UpperCAmelCase    			=   model(pixel_values.to(lowercase		)		)

  # verify logits
  print("""Logits:""" ,outputs.logits[0, :3, :3]		)
  print("""Boxes:""" ,outputs.pred_boxes[0, :3, :3]		)
  if model_name == "deta-swin-large":
    _UpperCAmelCase    			=   torch.tensor(
        [[-7.63_08, -2.84_85, -5.37_37], [-7.20_37, -4.55_05, -4.80_27], [-7.29_43, -4.26_11, -4.66_17]]		)
    _UpperCAmelCase    			=   torch.tensor([[0.49_87, 0.49_69, 0.99_99], [0.25_49, 0.54_98, 0.48_05], [0.54_98, 0.27_57, 0.05_69]]		)
  elif model_name == "deta-swin-large-o365":
    _UpperCAmelCase    			=   torch.tensor(
        [[-8.01_22, -3.57_20, -4.97_17], [-8.15_47, -3.68_86, -4.63_89], [-7.66_10, -3.61_94, -5.01_34]]		)
    _UpperCAmelCase    			=   torch.tensor([[0.25_23, 0.55_49, 0.48_81], [0.77_15, 0.41_49, 0.46_01], [0.55_03, 0.27_53, 0.05_75]]		)
  assert torch.allclose(outputs.logits[0, :3, :3] ,expected_logits.to(lowercase		) ,atol=1E-4		)
  assert torch.allclose(outputs.pred_boxes[0, :3, :3] ,expected_boxes.to(lowercase		) ,atol=1E-4		)
  print("""Everything ok!"""		)

  if pytorch_dump_folder_path:
    # Save model and processor
    logger.info(f'''Saving PyTorch model and processor to {pytorch_dump_folder_path}...'''		)
    Path(lowercase		).mkdir(exist_ok=lowercase		)
    model.save_pretrained(lowercase		)
    processor.save_pretrained(lowercase		)

  # Push to hub
  if push_to_hub:
    print("""Pushing model and processor to hub..."""		)
    model.push_to_hub(f'''jozhang97/{model_name}'''		)
    processor.push_to_hub(f'''jozhang97/{model_name}'''		)


if __name__ == "__main__":
    UpperCAmelCase__			=    argparse.ArgumentParser()

    parser.add_argument(
        """--model_name""",
        type=str,
        default="""deta-swin-large""",
        choices=["""deta-swin-large""", """deta-swin-large-o365"""],
        help="""Name of the model you'd like to convert.""",
    )
    parser.add_argument(
        """--pytorch_dump_folder_path""",
        default=None,
        type=str,
        help="""Path to the folder to output PyTorch model.""",
    )
    parser.add_argument(
        """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
    )
    UpperCAmelCase__			=    parser.parse_args()
    convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)

"""simple docstring"""


import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig


UpperCAmelCase__			=    logging.get_logger(__name__)

UpperCAmelCase__			=    {
    """Intel/dpt-large""": """https://huggingface.co/Intel/dpt-large/resolve/main/config.json""",
    # See all DPT models at https://huggingface.co/models?filter=dpt
}


class 							a					(		lowerCAmelCase_ ):
    _snake_case  :	int  				=				'dpt'


    def __init__(       self						:	Any ,					__lowerCAmelCase						:	Optional[Any]=768 ,					__lowerCAmelCase						:	str=12 ,					__lowerCAmelCase						:	Optional[int]=12 ,					__lowerCAmelCase						:	Dict=3072 ,					__lowerCAmelCase						:	List[Any]="gelu" ,					__lowerCAmelCase						:	Tuple=0.0 ,					__lowerCAmelCase						:	str=0.0 ,					__lowerCAmelCase						:	List[Any]=0.02 ,					__lowerCAmelCase						:	Optional[Any]=1e-1_2 ,					__lowerCAmelCase						:	List[str]=384 ,					__lowerCAmelCase						:	Tuple=16 ,					__lowerCAmelCase						:	List[str]=3 ,					__lowerCAmelCase						:	Optional[Any]=False ,					__lowerCAmelCase						:	Optional[Any]=True ,					__lowerCAmelCase						:	Optional[Any]=[2, 5, 8, 11] ,					__lowerCAmelCase						:	Any="project" ,					__lowerCAmelCase						:	Dict=[4, 2, 1, 0.5] ,					__lowerCAmelCase						:	int=[96, 192, 384, 768] ,					__lowerCAmelCase						:	Dict=256 ,					__lowerCAmelCase						:	Optional[Any]=-1 ,					__lowerCAmelCase						:	Optional[Any]=False ,					__lowerCAmelCase						:	Union[str, Any]=True ,					__lowerCAmelCase						:	List[Any]=0.4 ,					__lowerCAmelCase						:	List[str]=255 ,					__lowerCAmelCase						:	List[Any]=0.1 ,					__lowerCAmelCase						:	Optional[int]=[1, 1024, 24, 24] ,					__lowerCAmelCase						:	List[Any]=[0, 1] ,					__lowerCAmelCase						:	Dict=None ,					**__lowerCAmelCase						:	str ,					):
      super().__init__(**__lowerCAmelCase       )

      _UpperCAmelCase    			=   hidden_size
      _UpperCAmelCase    			=   is_hybrid

      if self.is_hybrid:
        if backbone_config is None:
          logger.info("""Initializing the config with a `BiT` backbone."""       )
          _UpperCAmelCase    			=   {
              """global_padding""": """same""",
              """layer_type""": """bottleneck""",
              """depths""": [3, 4, 9],
              """out_features""": ["""stage1""", """stage2""", """stage3"""],
              """embedding_dynamic_padding""": True,
          }
          _UpperCAmelCase    			=   BitConfig(**__lowerCAmelCase       )
        elif isinstance(__lowerCAmelCase ,					__lowerCAmelCase       ):
          logger.info("""Initializing the config with a `BiT` backbone."""       )
          _UpperCAmelCase    			=   BitConfig(**__lowerCAmelCase       )
        elif isinstance(__lowerCAmelCase ,					__lowerCAmelCase       ):
          _UpperCAmelCase    			=   backbone_config
        else:
          raise ValueError(
              f'''backbone_config must be a dictionary or a `PretrainedConfig`, got {backbone_config.__class__}.'''       )

        _UpperCAmelCase    			=   backbone_featmap_shape
        _UpperCAmelCase    			=   neck_ignore_stages

        if readout_type != "project":
          raise ValueError("""Readout type must be 'project' when using `DPT-hybrid` mode."""       )
      else:
        _UpperCAmelCase    			=   None
        _UpperCAmelCase    			=   None
        _UpperCAmelCase    			=   []

      _UpperCAmelCase    			=   num_hidden_layers
      _UpperCAmelCase    			=   num_attention_heads
      _UpperCAmelCase    			=   intermediate_size
      _UpperCAmelCase    			=   hidden_act
      _UpperCAmelCase    			=   hidden_dropout_prob
      _UpperCAmelCase    			=   attention_probs_dropout_prob
      _UpperCAmelCase    			=   initializer_range
      _UpperCAmelCase    			=   layer_norm_eps
      _UpperCAmelCase    			=   image_size
      _UpperCAmelCase    			=   patch_size
      _UpperCAmelCase    			=   num_channels
      _UpperCAmelCase    			=   qkv_bias
      _UpperCAmelCase    			=   backbone_out_indices
      if readout_type not in ["ignore", "add", "project"]:
        raise ValueError("""Readout_type must be one of ['ignore', 'add', 'project']"""       )
      _UpperCAmelCase    			=   readout_type
      _UpperCAmelCase    			=   reassemble_factors
      _UpperCAmelCase    			=   neck_hidden_sizes
      _UpperCAmelCase    			=   fusion_hidden_size
      _UpperCAmelCase    			=   head_in_index
      _UpperCAmelCase    			=   use_batch_norm_in_fusion_residual
      # auxiliary head attributes (semantic segmentation)
      _UpperCAmelCase    			=   use_auxiliary_head
      _UpperCAmelCase    			=   auxiliary_loss_weight
      _UpperCAmelCase    			=   semantic_loss_ignore_index
      _UpperCAmelCase    			=   semantic_classifier_dropout


    def   lowerCAmelCase_	(       self						:	List[str]       ):
      _UpperCAmelCase    			=   copy.deepcopy(self.__dict__       )

      if output["backbone_config"] is not None:
        _UpperCAmelCase    			=   self.backbone_config.to_dict()

      _UpperCAmelCase    			=   self.__class__.model_type
      return output
