





'''simple docstring'''






import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import (
    BitConfig,
    ViTHybridConfig,
    ViTHybridForImageClassification,
    ViTHybridImageProcessor,
    ViTHybridModel,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
A_		:  Optional[int]		 =						logging.get_logger(__name__)



def        UpperCamelCase__		(						__magic_name__  :   Optional[Any]		,       __magic_name__  :   str=False      )				->			Tuple:
 '''simple docstring'''






 snake_case__     :					int    =  []

 # fmt: off
 # stem:
 rename_keys.append(("""cls_token""", """vit.embeddings.cls_token""")      )
 rename_keys.append(("""pos_embed""", """vit.embeddings.position_embeddings""")      )

 rename_keys.append(("""patch_embed.proj.weight""", """vit.embeddings.patch_embeddings.projection.weight""")      )
 rename_keys.append(("""patch_embed.proj.bias""", """vit.embeddings.patch_embeddings.projection.bias""")      )

 # backbone
 rename_keys.append(("""patch_embed.backbone.stem.conv.weight""", """vit.embeddings.patch_embeddings.backbone.bit.embedder.convolution.weight""")      )
 rename_keys.append(("""patch_embed.backbone.stem.norm.weight""", """vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.weight""")      )
 rename_keys.append(("""patch_embed.backbone.stem.norm.bias""", """vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.bias""")      )

 for stage_idx in range(len(config.backbone_config.depths      )      ):
  for layer_idx in range(config.backbone_config.depths[stage_idx]      ):
   rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv1.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv1.weight")      )
   rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.weight")      )
   rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.bias", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.bias")      )
   rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv2.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv2.weight")      )
   rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.weight")      )
   rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.bias", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.bias")      )
   rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv3.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv3.weight")      )
   rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.weight")      )
   rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.bias", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.bias")      )

  rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.conv.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.conv.weight")      )
  rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.weight")      )
  rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.bias", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.bias")      )

 # transformer encoder
 for i in range(config.num_hidden_layers      ):
  # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
  rename_keys.append((f"blocks.{i}.norm1.weight", f"vit.encoder.layer.{i}.layernorm_before.weight")      )
  rename_keys.append((f"blocks.{i}.norm1.bias", f"vit.encoder.layer.{i}.layernorm_before.bias")      )
  rename_keys.append((f"blocks.{i}.attn.proj.weight", f"vit.encoder.layer.{i}.attention.output.dense.weight")      )
  rename_keys.append((f"blocks.{i}.attn.proj.bias", f"vit.encoder.layer.{i}.attention.output.dense.bias")      )
  rename_keys.append((f"blocks.{i}.norm2.weight", f"vit.encoder.layer.{i}.layernorm_after.weight")      )
  rename_keys.append((f"blocks.{i}.norm2.bias", f"vit.encoder.layer.{i}.layernorm_after.bias")      )
  rename_keys.append((f"blocks.{i}.mlp.fc1.weight", f"vit.encoder.layer.{i}.intermediate.dense.weight")      )
  rename_keys.append((f"blocks.{i}.mlp.fc1.bias", f"vit.encoder.layer.{i}.intermediate.dense.bias")      )
  rename_keys.append((f"blocks.{i}.mlp.fc2.weight", f"vit.encoder.layer.{i}.output.dense.weight")      )
  rename_keys.append((f"blocks.{i}.mlp.fc2.bias", f"vit.encoder.layer.{i}.output.dense.bias")      )

 if base_model:
  # layernorm + pooler
  rename_keys.extend(
      [
          ("""norm.weight""", """layernorm.weight"""),
          ("""norm.bias""", """layernorm.bias"""),
          ("""pre_logits.fc.weight""", """pooler.dense.weight"""),
          ("""pre_logits.fc.bias""", """pooler.dense.bias"""),
      ]      )

  # if just the base model, we should remove "vit" from all keys that start with "vit"
  snake_case__     :					List[Any]    =  [(pair[0], pair[1][4:]) if pair[1].startswith("""vit"""      ) else pair for pair in rename_keys]
 else:
  # layernorm + classification head
  rename_keys.extend(
      [
          ("""norm.weight""", """vit.layernorm.weight"""),
          ("""norm.bias""", """vit.layernorm.bias"""),
          ("""head.weight""", """classifier.weight"""),
          ("""head.bias""", """classifier.bias"""),
      ]      )
 # fmt: on

 return rename_keys



def        UpperCamelCase__		(						__magic_name__  :   Tuple		,       __magic_name__  :   int		,       __magic_name__  :   Tuple=False      )				->			str:
 '''simple docstring'''






 for i in range(config.num_hidden_layers      ):
  if base_model:
   snake_case__     :					int    =  """"""
  else:
   snake_case__     :					Dict    =  """vit."""
  # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
  snake_case__     :					int    =  state_dict.pop(f"blocks.{i}.attn.qkv.weight"      )
  snake_case__     :					Union[str, Any]    =  state_dict.pop(f"blocks.{i}.attn.qkv.bias"      )
  # next, add query, keys and values (in that order) to the state dict
  snake_case__     :					Optional[int]    =  in_proj_weight[
      : config.hidden_size, :
  ]
  snake_case__     :					Optional[Any]    =  in_proj_bias[: config.hidden_size]
  snake_case__     :					List[Any]    =  in_proj_weight[
      config.hidden_size : config.hidden_size * 2, :
  ]
  snake_case__     :					List[Any]    =  in_proj_bias[
      config.hidden_size : config.hidden_size * 2
  ]
  snake_case__     :					List[Any]    =  in_proj_weight[
      -config.hidden_size :, :
  ]
  snake_case__     :					Optional[int]    =  in_proj_bias[-config.hidden_size :]



def        UpperCamelCase__		(						__magic_name__  :   Optional[Any]      )				->			List[str]:
 '''simple docstring'''






 snake_case__     :					str    =  ["""head.weight""", """head.bias"""]
 for k in ignore_keys:
  state_dict.pop(__magic_name__		,       __magic_name__      )



def        UpperCamelCase__		(						__magic_name__  :   List[str]		,       __magic_name__  :   Union[str, Any]		,       __magic_name__  :   str      )				->			Union[str, Any]:
 '''simple docstring'''






 snake_case__     :					List[str]    =  dct.pop(__magic_name__      )
 snake_case__     :					Dict    =  val



def        UpperCamelCase__		(						)				->			str:
 '''simple docstring'''






 snake_case__     :					Optional[int]    =  """http://images.cocodataset.org/val2017/000000039769.jpg"""
 snake_case__     :					Optional[int]    =  Image.open(requests.get(__magic_name__		,       stream=__magic_name__      ).raw      )
 return im



@torch.no_grad()
def        UpperCamelCase__		(						__magic_name__  :   List[Any]		,       __magic_name__  :   Union[str, Any]		,       __magic_name__  :   int=False      )				->			Optional[int]:
 '''simple docstring'''






 snake_case__     :					int    =  BitConfig(
     global_padding="""same"""		,       layer_type="""bottleneck"""		,       depths=(3, 4, 9)		,       out_features=["""stage3"""]		,       embedding_dynamic_padding=__magic_name__		,       )
 snake_case__     :					Optional[int]    =  ViTHybridConfig(backbone_config=__magic_name__		,       image_size=3_84		,       num_labels=10_00      )
 snake_case__     :					Union[str, Any]    =  False

 # load original model from timm
 snake_case__     :					List[Any]    =  timm.create_model(__magic_name__		,       pretrained=__magic_name__      )
 timm_model.eval()

 # load state_dict of original model, remove and rename some keys
 snake_case__     :					Optional[int]    =  timm_model.state_dict()
 if base_model:
  remove_classification_head_(__magic_name__      )
 snake_case__     :					int    =  create_rename_keys(__magic_name__		,       __magic_name__      )
 for src, dest in rename_keys:
  rename_key(__magic_name__		,       __magic_name__		,       __magic_name__      )
 read_in_q_k_v(__magic_name__		,       __magic_name__		,       __magic_name__      )

 snake_case__     :					str    =  """huggingface/label-files"""
 snake_case__     :					Union[str, Any]    =  """imagenet-1k-id2label.json"""
 snake_case__     :					Dict    =  json.load(open(hf_hub_download(__magic_name__		,       __magic_name__		,       repo_type="""dataset"""      )		,       """r"""      )      )
 snake_case__     :					List[Any]    =  {int(__magic_name__      ): v for k, v in idalabel.items()}
 snake_case__     :					int    =  idalabel
 snake_case__     :					str    =  {v: k for k, v in idalabel.items()}

 # load HuggingFace model
 if vit_name[-5:] == "in21k":
  snake_case__     :					str    =  ViTHybridModel(__magic_name__      ).eval()
 else:
  snake_case__     :					Union[str, Any]    =  ViTHybridForImageClassification(__magic_name__      ).eval()
 model.load_state_dict(__magic_name__      )

 # create image processor
 snake_case__     :					Optional[Any]    =  create_transform(**resolve_data_config({}		,       model=__magic_name__      )      )
 snake_case__     :					Union[str, Any]    =  transform.transforms

 snake_case__     :					Tuple    =  {
     """bilinear""": PILImageResampling.BILINEAR,
     """bicubic""": PILImageResampling.BICUBIC,
     """nearest""": PILImageResampling.NEAREST,
 }

 snake_case__     :					Any    =  ViTHybridImageProcessor(
     do_resize=__magic_name__		,       size={"""shortest_edge""": timm_transforms[0].size}		,       resample=pillow_resamplings[timm_transforms[0].interpolation.value]		,       do_center_crop=__magic_name__		,       crop_size={"""height""": timm_transforms[1].size[0], """width""": timm_transforms[1].size[1]}		,       do_normalize=__magic_name__		,       image_mean=timm_transforms[-1].mean.tolist()		,       image_std=timm_transforms[-1].std.tolist()		,       )

 snake_case__     :					Any    =  prepare_img()
 snake_case__     :					int    =  transform(__magic_name__      ).unsqueeze(0      )
 snake_case__     :					List[str]    =  processor(__magic_name__		,       return_tensors="""pt"""      ).pixel_values

 # verify pixel values
 assert torch.allclose(__magic_name__		,       __magic_name__      )

 # verify logits
 with torch.no_grad():
  snake_case__     :					Optional[Any]    =  model(__magic_name__      )
  snake_case__     :					Union[str, Any]    =  outputs.logits

 print("""Predicted class:"""		,       logits.argmax(-1      ).item()      )
 if base_model:
  snake_case__     :					Dict    =  timm_model.forward_features(__magic_name__      )
  assert timm_pooled_output.shape == outputs.pooler_output.shape
  assert torch.allclose(__magic_name__		,       outputs.pooler_output		,       atol=1E-3      )
 else:
  snake_case__     :					int    =  timm_model(__magic_name__      )
  assert timm_logits.shape == outputs.logits.shape
  assert torch.allclose(__magic_name__		,       outputs.logits		,       atol=1E-3      )
 print("""Looks ok!"""      )

 if pytorch_dump_folder_path is not None:
  Path(__magic_name__      ).mkdir(exist_ok=__magic_name__      )
  print(f"Saving model {vit_name} to {pytorch_dump_folder_path}"      )
  model.save_pretrained(__magic_name__      )
  print(f"Saving processor to {pytorch_dump_folder_path}"      )
  processor.save_pretrained(__magic_name__      )

 if push_to_hub:
  print(f"Pushing model and processor to the hub {vit_name}"      )
  model.push_to_hub(f"ybelkada/{vit_name}"      )
  processor.push_to_hub(f"ybelkada/{vit_name}"      )


if __name__ == "__main__":
    A_		:  List[str]		 =						argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        "--vit_name",
        default="vit_base_r50_s16_384",
        type=str,
        help="Name of the hybrid ViT timm model you'd like to convert.",
    )
    parser.add_argument(
        "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
    )
    parser.add_argument(
        "--push_to_hub", action="store_true", help="Whether to upload the model to the HuggingFace hub."
    )

    A_		:  Union[str, Any]		 =						parser.parse_args()
    convert_vit_checkpoint(args.vit_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()
_SCREAMING_SNAKE_CASE   =  logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
_SCREAMING_SNAKE_CASE   =  []
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        _snake_case   (_snake_case				:       Optional[int]      ,    _snake_case				:       Dict      ,    _snake_case				:       Dict)					->	str:
   _lowercase              =state_dict.pop(_snake_case)
   _lowercase              =val





def        _snake_case   (_snake_case				:       Any)					->	Union[str, Any]:
   _lowercase              =OrderedDict()
   for key, value in state_dict.items():
      if "backbone.0.body" in key:
         _lowercase              =key.replace('backbone.0.body'      ,    'backbone.conv_encoder.model')
         _lowercase              =value
      else:
         _lowercase              =value

   return new_state_dict





def        _snake_case   (_snake_case				:       Tuple      ,    _snake_case				:       Tuple=False)					->	str:
   _lowercase              =''
   if is_panoptic:
      _lowercase              ='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)
      _lowercase              =state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight''')
      _lowercase              =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
      _lowercase              =in_proj_weight[:256, :]
      _lowercase              =in_proj_bias[:256]
      _lowercase              =in_proj_weight[256:512, :]
      _lowercase              =in_proj_bias[256:512]
      _lowercase              =in_proj_weight[-256:, :]
      _lowercase              =in_proj_bias[-256:]





def        _snake_case   ()					->	List[Any]:
   _lowercase              ='http://images.cocodataset.org/val2017/000000039769.jpg'
   _lowercase              =Image.open(requests.get(_snake_case      ,    stream=_snake_case).raw)

   return im





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

   # load image processor
   _lowercase              ='coco_panoptic' if is_panoptic else 'coco_detection'
   _lowercase              =ConditionalDetrImageProcessor(format=_snake_case)

   # prepare image
   _lowercase              =prepare_img()
   _lowercase              =image_processor(images=_snake_case      ,    return_tensors='pt')
   _lowercase              =encoding['pixel_values']

   logger.info(f'''Converting model {model_name}...''')

   # load original model from torch hub
   _lowercase              =torch.hub.load('DeppMeng/ConditionalDETR'      ,    _snake_case      ,    pretrained=_snake_case).eval()
   _lowercase              =conditional_detr.state_dict()
   # rename keys
   for src, dest in rename_keys:
      if is_panoptic:
         _lowercase              ='conditional_detr.' + src
      rename_key(_snake_case      ,    _snake_case      ,    _snake_case)
   _lowercase              =rename_backbone_keys(_snake_case)
   # query, key and value matrices need special treatment
   read_in_q_k_v(_snake_case      ,    is_panoptic=_snake_case)
   # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
   _lowercase              ='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')
         ):
            _lowercase              =state_dict.pop(_snake_case)
            _lowercase              =val
         elif "class_labels_classifier" in key or "bbox_predictor" in key:
            _lowercase              =state_dict.pop(_snake_case)
            _lowercase              =val
         elif key.startswith('bbox_attention') or key.startswith('mask_head'):
            continue
         else:
            _lowercase              =state_dict.pop(_snake_case)
            _lowercase              =val
      else:
         if not key.startswith('class_labels_classifier') and not key.startswith('bbox_predictor'):
            _lowercase              =state_dict.pop(_snake_case)
            _lowercase              =val
    # finally, create HuggingFace model and load state dict
   _lowercase              =ConditionalDetrForSegmentation(_snake_case) if is_panoptic else ConditionalDetrForObjectDetection(_snake_case)
   model.load_state_dict(_snake_case)
   model.eval()
   model.push_to_hub(repo_id=_snake_case      ,    organization='DepuMeng'      ,    commit_message='Add model')
   # verify our conversion
   _lowercase              =conditional_detr(_snake_case)
   _lowercase              =model(_snake_case)
   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(_snake_case).mkdir(exist_ok=_snake_case)
   model.save_pretrained(_snake_case)
   image_processor.save_pretrained(_snake_case)


if __name__ == "__main__":
    _SCREAMING_SNAKE_CASE   =  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."
    )
    _SCREAMING_SNAKE_CASE   =  parser.parse_args()
    convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)


