






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()
snake_case__   :						Tuple													=   logging.get_logger(__name__)






def      _snake_case     (__lowercase):
   UpperCamelCase_						=  SwinConfig(
       embed_dim=192     ,   depths=(2, 2, 18, 2)     ,   num_heads=(6, 12, 24, 48)     ,   window_size=12     ,   out_features=['stage2', 'stage3', 'stage4']     ,   )

   UpperCamelCase_						=  DetaConfig(
       backbone_config=A__     ,   num_queries=900     ,   encoder_ffn_dim=2048     ,   decoder_ffn_dim=2048     ,   num_feature_levels=5     ,   assign_first_stage=A__     ,   with_box_refine=A__     ,   two_stage=A__     ,   )

   # set labels
   UpperCamelCase_						=  'huggingface/label-files'
   if "o365" in model_name:
      UpperCamelCase_						=  366
      UpperCamelCase_						=  'object365-id2label.json'
   else:
      UpperCamelCase_						=  91
      UpperCamelCase_						=  'coco-detection-id2label.json'

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

   return config






def      _snake_case     (__lowercase):
   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      _snake_case     (__lowercase     ,   __lowercase     ,   __lowercase):
   UpperCamelCase_						=  dct.pop(A__)
   UpperCamelCase_						=  val






def      _snake_case     (__lowercase     ,   __lowercase):
   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      _snake_case     (__lowercase     ,   __lowercase):
   # 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      _snake_case     ():
   UpperCamelCase_						=  'http://images.cocodataset.org/val2017/000000039769.jpg'
   UpperCamelCase_						=  Image.open(requests.get(A__     ,   stream=A__).raw)

   return im






@torch.no_grad()
def      _snake_case     (__lowercase     ,   __lowercase     ,   __lowercase):
   UpperCamelCase_						=  get_deta_config(A__)

   # 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(A__     ,   map_location='cpu')['model']

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

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

   # 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(A__)
         UpperCamelCase_						=  val
      if "input_proj" in key:
         UpperCamelCase_						=  state_dict.pop(A__)
         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(A__)
         UpperCamelCase_						=  val

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

   UpperCamelCase_						=  'cuda' if torch.cuda.is_available() else 'cpu'
   model.to(A__)

   # load image processor
   UpperCamelCase_						=  DetaImageProcessor(format='coco_detection')

   # verify our conversion on image
   UpperCamelCase_						=  prepare_img()
   UpperCamelCase_						=  processor(images=A__     ,   return_tensors='pt')
   UpperCamelCase_						=  encoding['pixel_values']
   UpperCamelCase_						=  model(pixel_values.to(A__))

   # 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(
          [[-7.6_308, -2.8_485, -5.3_737], [-7.2_037, -4.5_505, -4.8_027], [-7.2_943, -4.2_611, -4.6_617]])
      UpperCamelCase_						=  torch.tensor([[0.4_987, 0.4_969, 0.9_999], [0.2_549, 0.5_498, 0.4_805], [0.5_498, 0.2_757, 0.0_569]])
   elif model_name == "deta-swin-large-o365":
      UpperCamelCase_						=  torch.tensor(
          [[-8.0_122, -3.5_720, -4.9_717], [-8.1_547, -3.6_886, -4.6_389], [-7.6_610, -3.6_194, -5.0_134]])
      UpperCamelCase_						=  torch.tensor([[0.2_523, 0.5_549, 0.4_881], [0.7_715, 0.4_149, 0.4_601], [0.5_503, 0.2_753, 0.0_575]])
   assert torch.allclose(outputs.logits[0, :3, :3]     ,   expected_logits.to(A__)     ,   atol=1e-4)
   assert torch.allclose(outputs.pred_boxes[0, :3, :3]     ,   expected_boxes.to(A__)     ,   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(A__).mkdir(exist_ok=A__)
      model.save_pretrained(A__)
      processor.save_pretrained(A__)

   # 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__":
 snake_case__   :						str													=   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."""
 )
 snake_case__   :						str													=   parser.parse_args()
 convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)








import argparse
import json
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    ConditionalDetrConfig,
    ConditionalDetrForObjectDetection,
    ConditionalDetrForSegmentation,
    ConditionalDetrImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
__A			:					Union[str, Any]						=			logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
__A			:					List[Any]						=			[]
for i in range(6):
    # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
    rename_keys.append(
        (f'transformer.encoder.layers.{i}.self_attn.out_proj.weight', f'encoder.layers.{i}.self_attn.out_proj.weight')
    )
    rename_keys.append(
        (f'transformer.encoder.layers.{i}.self_attn.out_proj.bias', f'encoder.layers.{i}.self_attn.out_proj.bias')
    )
    rename_keys.append((f'transformer.encoder.layers.{i}.linear1.weight', f'encoder.layers.{i}.fc1.weight'))
    rename_keys.append((f'transformer.encoder.layers.{i}.linear1.bias', f'encoder.layers.{i}.fc1.bias'))
    rename_keys.append((f'transformer.encoder.layers.{i}.linear2.weight', f'encoder.layers.{i}.fc2.weight'))
    rename_keys.append((f'transformer.encoder.layers.{i}.linear2.bias', f'encoder.layers.{i}.fc2.bias'))
    rename_keys.append(
        (f'transformer.encoder.layers.{i}.norm1.weight', f'encoder.layers.{i}.self_attn_layer_norm.weight')
    )
    rename_keys.append((f'transformer.encoder.layers.{i}.norm1.bias', f'encoder.layers.{i}.self_attn_layer_norm.bias'))
    rename_keys.append((f'transformer.encoder.layers.{i}.norm2.weight', f'encoder.layers.{i}.final_layer_norm.weight'))
    rename_keys.append((f'transformer.encoder.layers.{i}.norm2.bias', f'encoder.layers.{i}.final_layer_norm.bias'))
    # decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
    rename_keys.append(
        (f'transformer.decoder.layers.{i}.self_attn.out_proj.weight', f'decoder.layers.{i}.self_attn.out_proj.weight')
    )
    rename_keys.append(
        (f'transformer.decoder.layers.{i}.self_attn.out_proj.bias', f'decoder.layers.{i}.self_attn.out_proj.bias')
    )
    rename_keys.append(
        (
            f'transformer.decoder.layers.{i}.cross_attn.out_proj.weight',
            f'decoder.layers.{i}.encoder_attn.out_proj.weight',
        )
    )
    rename_keys.append(
        (
            f'transformer.decoder.layers.{i}.cross_attn.out_proj.bias',
            f'decoder.layers.{i}.encoder_attn.out_proj.bias',
        )
    )
    rename_keys.append((f'transformer.decoder.layers.{i}.linear1.weight', f'decoder.layers.{i}.fc1.weight'))
    rename_keys.append((f'transformer.decoder.layers.{i}.linear1.bias', f'decoder.layers.{i}.fc1.bias'))
    rename_keys.append((f'transformer.decoder.layers.{i}.linear2.weight', f'decoder.layers.{i}.fc2.weight'))
    rename_keys.append((f'transformer.decoder.layers.{i}.linear2.bias', f'decoder.layers.{i}.fc2.bias'))
    rename_keys.append(
        (f'transformer.decoder.layers.{i}.norm1.weight', f'decoder.layers.{i}.self_attn_layer_norm.weight')
    )
    rename_keys.append((f'transformer.decoder.layers.{i}.norm1.bias', f'decoder.layers.{i}.self_attn_layer_norm.bias'))
    rename_keys.append(
        (f'transformer.decoder.layers.{i}.norm2.weight', f'decoder.layers.{i}.encoder_attn_layer_norm.weight')
    )
    rename_keys.append(
        (f'transformer.decoder.layers.{i}.norm2.bias', f'decoder.layers.{i}.encoder_attn_layer_norm.bias')
    )
    rename_keys.append((f'transformer.decoder.layers.{i}.norm3.weight', f'decoder.layers.{i}.final_layer_norm.weight'))
    rename_keys.append((f'transformer.decoder.layers.{i}.norm3.bias', f'decoder.layers.{i}.final_layer_norm.bias'))

    # q, k, v projections in self/cross-attention in decoder for conditional DETR
    rename_keys.append(
        (f'transformer.decoder.layers.{i}.sa_qcontent_proj.weight', f'decoder.layers.{i}.sa_qcontent_proj.weight')
    )
    rename_keys.append(
        (f'transformer.decoder.layers.{i}.sa_kcontent_proj.weight', f'decoder.layers.{i}.sa_kcontent_proj.weight')
    )
    rename_keys.append(
        (f'transformer.decoder.layers.{i}.sa_qpos_proj.weight', f'decoder.layers.{i}.sa_qpos_proj.weight')
    )
    rename_keys.append(
        (f'transformer.decoder.layers.{i}.sa_kpos_proj.weight', f'decoder.layers.{i}.sa_kpos_proj.weight')
    )
    rename_keys.append((f'transformer.decoder.layers.{i}.sa_v_proj.weight', f'decoder.layers.{i}.sa_v_proj.weight'))
    rename_keys.append(
        (f'transformer.decoder.layers.{i}.ca_qcontent_proj.weight', f'decoder.layers.{i}.ca_qcontent_proj.weight')
    )
    # rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.weight", f"decoder.layers.{i}.ca_qpos_proj.weight"))
    rename_keys.append(
        (f'transformer.decoder.layers.{i}.ca_kcontent_proj.weight', f'decoder.layers.{i}.ca_kcontent_proj.weight')
    )
    rename_keys.append(
        (f'transformer.decoder.layers.{i}.ca_kpos_proj.weight', f'decoder.layers.{i}.ca_kpos_proj.weight')
    )
    rename_keys.append((f'transformer.decoder.layers.{i}.ca_v_proj.weight', f'decoder.layers.{i}.ca_v_proj.weight'))
    rename_keys.append(
        (f'transformer.decoder.layers.{i}.ca_qpos_sine_proj.weight', f'decoder.layers.{i}.ca_qpos_sine_proj.weight')
    )

    rename_keys.append(
        (f'transformer.decoder.layers.{i}.sa_qcontent_proj.bias', f'decoder.layers.{i}.sa_qcontent_proj.bias')
    )
    rename_keys.append(
        (f'transformer.decoder.layers.{i}.sa_kcontent_proj.bias', f'decoder.layers.{i}.sa_kcontent_proj.bias')
    )
    rename_keys.append((f'transformer.decoder.layers.{i}.sa_qpos_proj.bias', f'decoder.layers.{i}.sa_qpos_proj.bias'))
    rename_keys.append((f'transformer.decoder.layers.{i}.sa_kpos_proj.bias', f'decoder.layers.{i}.sa_kpos_proj.bias'))
    rename_keys.append((f'transformer.decoder.layers.{i}.sa_v_proj.bias', f'decoder.layers.{i}.sa_v_proj.bias'))
    rename_keys.append(
        (f'transformer.decoder.layers.{i}.ca_qcontent_proj.bias', f'decoder.layers.{i}.ca_qcontent_proj.bias')
    )
    # rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.bias", f"decoder.layers.{i}.ca_qpos_proj.bias"))
    rename_keys.append(
        (f'transformer.decoder.layers.{i}.ca_kcontent_proj.bias', f'decoder.layers.{i}.ca_kcontent_proj.bias')
    )
    rename_keys.append((f'transformer.decoder.layers.{i}.ca_kpos_proj.bias', f'decoder.layers.{i}.ca_kpos_proj.bias'))
    rename_keys.append((f'transformer.decoder.layers.{i}.ca_v_proj.bias', f'decoder.layers.{i}.ca_v_proj.bias'))
    rename_keys.append(
        (f'transformer.decoder.layers.{i}.ca_qpos_sine_proj.bias', f'decoder.layers.{i}.ca_qpos_sine_proj.bias')
    )

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
# for conditional DETR, also convert reference point head and query scale MLP
rename_keys.extend(
    [
        ('input_proj.weight', 'input_projection.weight'),
        ('input_proj.bias', 'input_projection.bias'),
        ('query_embed.weight', 'query_position_embeddings.weight'),
        ('transformer.decoder.norm.weight', 'decoder.layernorm.weight'),
        ('transformer.decoder.norm.bias', 'decoder.layernorm.bias'),
        ('class_embed.weight', 'class_labels_classifier.weight'),
        ('class_embed.bias', 'class_labels_classifier.bias'),
        ('bbox_embed.layers.0.weight', 'bbox_predictor.layers.0.weight'),
        ('bbox_embed.layers.0.bias', 'bbox_predictor.layers.0.bias'),
        ('bbox_embed.layers.1.weight', 'bbox_predictor.layers.1.weight'),
        ('bbox_embed.layers.1.bias', 'bbox_predictor.layers.1.bias'),
        ('bbox_embed.layers.2.weight', 'bbox_predictor.layers.2.weight'),
        ('bbox_embed.layers.2.bias', 'bbox_predictor.layers.2.bias'),
        ('transformer.decoder.ref_point_head.layers.0.weight', 'decoder.ref_point_head.layers.0.weight'),
        ('transformer.decoder.ref_point_head.layers.0.bias', 'decoder.ref_point_head.layers.0.bias'),
        ('transformer.decoder.ref_point_head.layers.1.weight', 'decoder.ref_point_head.layers.1.weight'),
        ('transformer.decoder.ref_point_head.layers.1.bias', 'decoder.ref_point_head.layers.1.bias'),
        ('transformer.decoder.query_scale.layers.0.weight', 'decoder.query_scale.layers.0.weight'),
        ('transformer.decoder.query_scale.layers.0.bias', 'decoder.query_scale.layers.0.bias'),
        ('transformer.decoder.query_scale.layers.1.weight', 'decoder.query_scale.layers.1.weight'),
        ('transformer.decoder.query_scale.layers.1.bias', 'decoder.query_scale.layers.1.bias'),
        ('transformer.decoder.layers.0.ca_qpos_proj.weight', 'decoder.layers.0.ca_qpos_proj.weight'),
        ('transformer.decoder.layers.0.ca_qpos_proj.bias', 'decoder.layers.0.ca_qpos_proj.bias'),
    ]
)





def 	__a     (      A__			:  Dict			,  A__			:  Dict			,  A__			:  Any       ):
    SCREAMING_SNAKE_CASE										=   state_dict.pop(A__       )
    SCREAMING_SNAKE_CASE										=   val





def 	__a     (      A__			:  Optional[int]       ):
    SCREAMING_SNAKE_CASE										=   OrderedDict()
    for key, value in state_dict.items():
        if "backbone.0.body" in key:
            SCREAMING_SNAKE_CASE										=   key.replace("backbone.0.body"			,  "backbone.conv_encoder.model"       )
            SCREAMING_SNAKE_CASE										=   value
        else:
            SCREAMING_SNAKE_CASE										=   value

    return new_state_dict





def 	__a     (      A__			:  Optional[Any]			,  A__			:  Tuple=False       ):
    SCREAMING_SNAKE_CASE										=   ""
    if is_panoptic:
        SCREAMING_SNAKE_CASE										=   "conditional_detr."

    # first: transformer encoder
    for i in range(6       ):
        # read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
        SCREAMING_SNAKE_CASE										=   state_dict.pop(F"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"       )
        SCREAMING_SNAKE_CASE										=   state_dict.pop(F"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias"       )
        # next, add query, keys and values (in that order) to the state dict
        SCREAMING_SNAKE_CASE										=   in_proj_weight[:256, :]
        SCREAMING_SNAKE_CASE										=   in_proj_bias[:256]
        SCREAMING_SNAKE_CASE										=   in_proj_weight[256:512, :]
        SCREAMING_SNAKE_CASE										=   in_proj_bias[256:512]
        SCREAMING_SNAKE_CASE										=   in_proj_weight[-256:, :]
        SCREAMING_SNAKE_CASE										=   in_proj_bias[-256:]





def 	__a     (      ):
    SCREAMING_SNAKE_CASE										=   "http://images.cocodataset.org/val2017/000000039769.jpg"
    SCREAMING_SNAKE_CASE										=   Image.open(requests.get(A__			,  stream=A__       ).raw       )

    return im





@torch.no_grad()
def 	__a     (      A__			:  List[str]			,  A__			:  Union[str, Any]       ):
    SCREAMING_SNAKE_CASE										=   ConditionalDetrConfig()
    # set backbone and dilation attributes
    if "resnet101" in model_name:
        SCREAMING_SNAKE_CASE										=   "resnet101"
    if "dc5" in model_name:
        SCREAMING_SNAKE_CASE										=   True
    SCREAMING_SNAKE_CASE										=   "panoptic" in model_name
    if is_panoptic:
        SCREAMING_SNAKE_CASE										=   250
    else:
        SCREAMING_SNAKE_CASE										=   91
        SCREAMING_SNAKE_CASE										=   "huggingface/label-files"
        SCREAMING_SNAKE_CASE										=   "coco-detection-id2label.json"
        SCREAMING_SNAKE_CASE										=   json.load(open(hf_hub_download(A__			,  A__			,  repo_type="dataset"       )			,  "r"       )       )
        SCREAMING_SNAKE_CASE										=   {int(A__       ): v for k, v in idalabel.items()}
        SCREAMING_SNAKE_CASE										=   idalabel
        SCREAMING_SNAKE_CASE										=   {v: k for k, v in idalabel.items()}

    # load image processor
    SCREAMING_SNAKE_CASE										=   "coco_panoptic" if is_panoptic else "coco_detection"
    SCREAMING_SNAKE_CASE										=   ConditionalDetrImageProcessor(format=A__       )

    # prepare image
    SCREAMING_SNAKE_CASE										=   prepare_img()
    SCREAMING_SNAKE_CASE										=   image_processor(images=A__			,  return_tensors="pt"       )
    SCREAMING_SNAKE_CASE										=   encoding["pixel_values"]

    logger.info(F"Converting model {model_name}..."       )

    # load original model from torch hub
    SCREAMING_SNAKE_CASE										=   torch.hub.load("DeppMeng/ConditionalDETR"			,  A__			,  pretrained=A__       ).eval()
    SCREAMING_SNAKE_CASE										=   conditional_detr.state_dict()
    # rename keys
    for src, dest in rename_keys:
        if is_panoptic:
            SCREAMING_SNAKE_CASE										=   "conditional_detr." + src
        rename_key(A__			,  A__			,  A__       )
    SCREAMING_SNAKE_CASE										=   rename_backbone_keys(A__       )
    # query, key and value matrices need special treatment
    read_in_q_k_v(A__			,  is_panoptic=A__       )
    # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
    SCREAMING_SNAKE_CASE										=   "conditional_detr.model." if is_panoptic else "model."
    for key in state_dict.copy().keys():
        if is_panoptic:
            if (
                key.startswith("conditional_detr"       )
                and not key.startswith("class_labels_classifier"       )
                and not key.startswith("bbox_predictor"       )
            ):
                SCREAMING_SNAKE_CASE										=   state_dict.pop(A__       )
                SCREAMING_SNAKE_CASE										=   val
            elif "class_labels_classifier" in key or "bbox_predictor" in key:
                SCREAMING_SNAKE_CASE										=   state_dict.pop(A__       )
                SCREAMING_SNAKE_CASE										=   val
            elif key.startswith("bbox_attention"       ) or key.startswith("mask_head"       ):
                continue
            else:
                SCREAMING_SNAKE_CASE										=   state_dict.pop(A__       )
                SCREAMING_SNAKE_CASE										=   val
        else:
            if not key.startswith("class_labels_classifier"       ) and not key.startswith("bbox_predictor"       ):
                SCREAMING_SNAKE_CASE										=   state_dict.pop(A__       )
                SCREAMING_SNAKE_CASE										=   val
    # finally, create HuggingFace model and load state dict
    SCREAMING_SNAKE_CASE										=   ConditionalDetrForSegmentation(A__       ) if is_panoptic else ConditionalDetrForObjectDetection(A__       )
    model.load_state_dict(A__       )
    model.eval()
    model.push_to_hub(repo_id=A__			,  organization="DepuMeng"			,  commit_message="Add model"       )
    # verify our conversion
    SCREAMING_SNAKE_CASE										=   conditional_detr(A__       )
    SCREAMING_SNAKE_CASE										=   model(A__       )
    assert torch.allclose(outputs.logits			,  original_outputs["pred_logits"]			,  atol=1E-4       )
    assert torch.allclose(outputs.pred_boxes			,  original_outputs["pred_boxes"]			,  atol=1E-4       )
    if is_panoptic:
        assert torch.allclose(outputs.pred_masks			,  original_outputs["pred_masks"]			,  atol=1E-4       )

    # Save model and image processor
    logger.info(F"Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."       )
    Path(A__       ).mkdir(exist_ok=A__       )
    model.save_pretrained(A__       )
    image_processor.save_pretrained(A__       )


if __name__ == "__main__":
    __A			:					str						=			argparse.ArgumentParser()

    parser.add_argument(
        '--model_name',
        default='conditional_detr_resnet50',
        type=str,
        help='Name of the CONDITIONAL_DETR 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.'
    )
    __A			:					int						=			parser.parse_args()
    convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)