





import argparse
import json
from pathlib import Path

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

from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging


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




def     a__ (					lowercase__  ):






   '''simple docstring'''






   UpperCAmelCase_											=DPTConfig(embedding_type="hybrid"  )

   if "large" in checkpoint_url:
      UpperCAmelCase_											=1_0_2_4
      UpperCAmelCase_											=4_0_9_6
      UpperCAmelCase_											=2_4
      UpperCAmelCase_											=1_6
      UpperCAmelCase_											=[5, 1_1, 1_7, 2_3]
      UpperCAmelCase_											=[2_5_6, 5_1_2, 1_0_2_4, 1_0_2_4]
      UpperCAmelCase_											=(1, 3_8_4, 3_8_4)

   if "nyu" or "midas" in checkpoint_url:
      UpperCAmelCase_											=7_6_8
      UpperCAmelCase_											=[1, 1, 1, 0.5]
      UpperCAmelCase_											=[2_5_6, 5_1_2, 7_6_8, 7_6_8]
      UpperCAmelCase_											=1_5_0
      UpperCAmelCase_											=1_6
      UpperCAmelCase_											=(1, 3_8_4, 3_8_4)
      UpperCAmelCase_											=False
      UpperCAmelCase_											="project"

   if "ade" in checkpoint_url:
      UpperCAmelCase_											=True
      UpperCAmelCase_											=7_6_8
      UpperCAmelCase_											=[1, 1, 1, 0.5]
      UpperCAmelCase_											=1_5_0
      UpperCAmelCase_											=1_6
      UpperCAmelCase_											="huggingface/label-files"
      UpperCAmelCase_											="ade20k-id2label.json"
      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()}
      UpperCAmelCase_											=[1, 1_5_0, 4_8_0, 4_8_0]

   return config, expected_shape




def     a__ (					lowercase__  ):






   '''simple docstring'''






   UpperCAmelCase_											=["pretrained.model.head.weight", "pretrained.model.head.bias"]
   for k in ignore_keys:
      state_dict.pop(lowercase__  ,    lowercase__  )




def     a__ (					lowercase__  ):






   '''simple docstring'''






   if (
       "pretrained.model" in name
       and "cls_token" not in name
       and "pos_embed" not in name
       and "patch_embed" not in name
   ):
      UpperCAmelCase_											=name.replace("pretrained.model"  ,    "dpt.encoder"  )
   if "pretrained.model" in name:
      UpperCAmelCase_											=name.replace("pretrained.model"  ,    "dpt.embeddings"  )
   if "patch_embed" in name:
      UpperCAmelCase_											=name.replace("patch_embed"  ,    ""  )
   if "pos_embed" in name:
      UpperCAmelCase_											=name.replace("pos_embed"  ,    "position_embeddings"  )
   if "attn.proj" in name:
      UpperCAmelCase_											=name.replace("attn.proj"  ,    "attention.output.dense"  )
   if "proj" in name and "project" not in name:
      UpperCAmelCase_											=name.replace("proj"  ,    "projection"  )
   if "blocks" in name:
      UpperCAmelCase_											=name.replace("blocks"  ,    "layer"  )
   if "mlp.fc1" in name:
      UpperCAmelCase_											=name.replace("mlp.fc1"  ,    "intermediate.dense"  )
   if "mlp.fc2" in name:
      UpperCAmelCase_											=name.replace("mlp.fc2"  ,    "output.dense"  )
   if "norm1" in name and "backbone" not in name:
      UpperCAmelCase_											=name.replace("norm1"  ,    "layernorm_before"  )
   if "norm2" in name and "backbone" not in name:
      UpperCAmelCase_											=name.replace("norm2"  ,    "layernorm_after"  )
   if "scratch.output_conv" in name:
      UpperCAmelCase_											=name.replace("scratch.output_conv"  ,    "head"  )
   if "scratch" in name:
      UpperCAmelCase_											=name.replace("scratch"  ,    "neck"  )
   if "layer1_rn" in name:
      UpperCAmelCase_											=name.replace("layer1_rn"  ,    "convs.0"  )
   if "layer2_rn" in name:
      UpperCAmelCase_											=name.replace("layer2_rn"  ,    "convs.1"  )
   if "layer3_rn" in name:
      UpperCAmelCase_											=name.replace("layer3_rn"  ,    "convs.2"  )
   if "layer4_rn" in name:
      UpperCAmelCase_											=name.replace("layer4_rn"  ,    "convs.3"  )
   if "refinenet" in name:
      UpperCAmelCase_											=int(name[len("neck.refinenet"  ) : len("neck.refinenet"  ) + 1]  )
      # tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
      UpperCAmelCase_											=name.replace(F'refinenet{layer_idx}'  ,    F'fusion_stage.layers.{abs(layer_idx-4  )}'  )
   if "out_conv" in name:
      UpperCAmelCase_											=name.replace("out_conv"  ,    "projection"  )
   if "resConfUnit1" in name:
      UpperCAmelCase_											=name.replace("resConfUnit1"  ,    "residual_layer1"  )
   if "resConfUnit2" in name:
      UpperCAmelCase_											=name.replace("resConfUnit2"  ,    "residual_layer2"  )
   if "conv1" in name:
      UpperCAmelCase_											=name.replace("conv1"  ,    "convolution1"  )
   if "conv2" in name:
      UpperCAmelCase_											=name.replace("conv2"  ,    "convolution2"  )
   # readout blocks
   if "pretrained.act_postprocess1.0.project.0" in name:
      UpperCAmelCase_											=name.replace("pretrained.act_postprocess1.0.project.0"  ,    "neck.reassemble_stage.readout_projects.0.0"  )
   if "pretrained.act_postprocess2.0.project.0" in name:
      UpperCAmelCase_											=name.replace("pretrained.act_postprocess2.0.project.0"  ,    "neck.reassemble_stage.readout_projects.1.0"  )
   if "pretrained.act_postprocess3.0.project.0" in name:
      UpperCAmelCase_											=name.replace("pretrained.act_postprocess3.0.project.0"  ,    "neck.reassemble_stage.readout_projects.2.0"  )
   if "pretrained.act_postprocess4.0.project.0" in name:
      UpperCAmelCase_											=name.replace("pretrained.act_postprocess4.0.project.0"  ,    "neck.reassemble_stage.readout_projects.3.0"  )

   # resize blocks
   if "pretrained.act_postprocess1.3" in name:
      UpperCAmelCase_											=name.replace("pretrained.act_postprocess1.3"  ,    "neck.reassemble_stage.layers.0.projection"  )
   if "pretrained.act_postprocess1.4" in name:
      UpperCAmelCase_											=name.replace("pretrained.act_postprocess1.4"  ,    "neck.reassemble_stage.layers.0.resize"  )
   if "pretrained.act_postprocess2.3" in name:
      UpperCAmelCase_											=name.replace("pretrained.act_postprocess2.3"  ,    "neck.reassemble_stage.layers.1.projection"  )
   if "pretrained.act_postprocess2.4" in name:
      UpperCAmelCase_											=name.replace("pretrained.act_postprocess2.4"  ,    "neck.reassemble_stage.layers.1.resize"  )
   if "pretrained.act_postprocess3.3" in name:
      UpperCAmelCase_											=name.replace("pretrained.act_postprocess3.3"  ,    "neck.reassemble_stage.layers.2.projection"  )
   if "pretrained.act_postprocess4.3" in name:
      UpperCAmelCase_											=name.replace("pretrained.act_postprocess4.3"  ,    "neck.reassemble_stage.layers.3.projection"  )
   if "pretrained.act_postprocess4.4" in name:
      UpperCAmelCase_											=name.replace("pretrained.act_postprocess4.4"  ,    "neck.reassemble_stage.layers.3.resize"  )
   if "pretrained" in name:
      UpperCAmelCase_											=name.replace("pretrained"  ,    "dpt"  )
   if "bn" in name:
      UpperCAmelCase_											=name.replace("bn"  ,    "batch_norm"  )
   if "head" in name:
      UpperCAmelCase_											=name.replace("head"  ,    "head.head"  )
   if "encoder.norm" in name:
      UpperCAmelCase_											=name.replace("encoder.norm"  ,    "layernorm"  )
   if "auxlayer" in name:
      UpperCAmelCase_											=name.replace("auxlayer"  ,    "auxiliary_head.head"  )
   if "backbone" in name:
      UpperCAmelCase_											=name.replace("backbone"  ,    "backbone.bit.encoder"  )

   if ".." in name:
      UpperCAmelCase_											=name.replace(".."  ,    "."  )

   if "stem.conv" in name:
      UpperCAmelCase_											=name.replace("stem.conv"  ,    "bit.embedder.convolution"  )
   if "blocks" in name:
      UpperCAmelCase_											=name.replace("blocks"  ,    "layers"  )
   if "convolution" in name and "backbone" in name:
      UpperCAmelCase_											=name.replace("convolution"  ,    "conv"  )
   if "layer" in name and "backbone" in name:
      UpperCAmelCase_											=name.replace("layer"  ,    "layers"  )
   if "backbone.bit.encoder.bit" in name:
      UpperCAmelCase_											=name.replace("backbone.bit.encoder.bit"  ,    "backbone.bit"  )
   if "embedder.conv" in name:
      UpperCAmelCase_											=name.replace("embedder.conv"  ,    "embedder.convolution"  )
   if "backbone.bit.encoder.stem.norm" in name:
      UpperCAmelCase_											=name.replace("backbone.bit.encoder.stem.norm"  ,    "backbone.bit.embedder.norm"  )
   return name




def     a__ (					lowercase__  ,    lowercase__  ):






   '''simple docstring'''






   for i in range(config.num_hidden_layers  ):
      # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
      UpperCAmelCase_											=state_dict.pop(F'dpt.encoder.layer.{i}.attn.qkv.weight'  )
      UpperCAmelCase_											=state_dict.pop(F'dpt.encoder.layer.{i}.attn.qkv.bias'  )
      # next, add query, keys and values (in that order) to the state dict
      UpperCAmelCase_											=in_proj_weight[: config.hidden_size, :]
      UpperCAmelCase_											=in_proj_bias[: config.hidden_size]
      UpperCAmelCase_											=in_proj_weight[
          config.hidden_size : config.hidden_size * 2, :
      ]
      UpperCAmelCase_											=in_proj_bias[
          config.hidden_size : config.hidden_size * 2
      ]
      UpperCAmelCase_											=in_proj_weight[
          -config.hidden_size :, :
      ]
      UpperCAmelCase_											=in_proj_bias[-config.hidden_size :]




def     a__ (					):






   '''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     a__ (					lowercase__  ,    lowercase__  ,    lowercase__  ,    lowercase__  ,    lowercase__  ):






   '''simple docstring'''






   UpperCAmelCase_						,       UpperCAmelCase_											=get_dpt_config(lowercase__  )
   # load original state_dict from URL
   # state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
   UpperCAmelCase_											=torch.load(lowercase__  ,    map_location="cpu"  )
   # remove certain keys
   remove_ignore_keys_(lowercase__  )
   # rename keys
   for key in state_dict.copy().keys():
      UpperCAmelCase_											=state_dict.pop(lowercase__  )
      UpperCAmelCase_											=val
   # read in qkv matrices
   read_in_q_k_v(lowercase__  ,    lowercase__  )

   # load HuggingFace model
   UpperCAmelCase_											=DPTForSemanticSegmentation(lowercase__  ) if "ade" in checkpoint_url else DPTForDepthEstimation(lowercase__  )
   model.load_state_dict(lowercase__  )
   model.eval()

   # Check outputs on an image
   UpperCAmelCase_											=4_8_0 if "ade" in checkpoint_url else 3_8_4
   UpperCAmelCase_											=DPTImageProcessor(size=lowercase__  )

   UpperCAmelCase_											=prepare_img()
   UpperCAmelCase_											=image_processor(lowercase__  ,    return_tensors="pt"  )

   # forward pass
   UpperCAmelCase_											=model(**lowercase__  ).logits if "ade" in checkpoint_url else model(**lowercase__  ).predicted_depth

   if show_prediction:
      UpperCAmelCase_											=(
          torch.nn.functional.interpolate(
              outputs.unsqueeze(1  )  ,    size=(image.size[1], image.size[0])  ,    mode="bicubic"  ,    align_corners=lowercase__  ,    )
          .squeeze()
          .cpu()
          .numpy()
      )

      Image.fromarray((prediction / prediction.max()) * 2_5_5  ).show()

   if pytorch_dump_folder_path is not None:
      Path(lowercase__  ).mkdir(exist_ok=lowercase__  )
      print(F'Saving model to {pytorch_dump_folder_path}'  )
      model.save_pretrained(lowercase__  )
      print(F'Saving image processor to {pytorch_dump_folder_path}'  )
      image_processor.save_pretrained(lowercase__  )

   if push_to_hub:
      model.push_to_hub("ybelkada/dpt-hybrid-midas"  )
      image_processor.push_to_hub("ybelkada/dpt-hybrid-midas"  )


if __name__ == "__main__":
     __lowercase  :   Tuple						      =argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         """--checkpoint_url""",
         default="""https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt""",
         type=str,
         help="""URL of the original DPT checkpoint you'd like to convert.""",
     )
     parser.add_argument(
         """--pytorch_dump_folder_path""",
         default=None,
         type=str,
         required=False,
         help="""Path to the output PyTorch model directory.""",
     )
     parser.add_argument(
         """--push_to_hub""",
         action="""store_true""",
     )
     parser.add_argument(
         """--model_name""",
         default="""dpt-large""",
         type=str,
         help="""Name of the model, in case you're pushing to the hub.""",
     )
     parser.add_argument(
         """--show_prediction""",
         action="""store_true""",
     )

     __lowercase  :   str						      =parser.parse_args()
     convert_dpt_checkpoint(
         args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction
     )






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()
__lowercase  :   Optional[Any]						      =logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
__lowercase  :   str						      =[]
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__ (					lowercase__  ,    lowercase__  ,    lowercase__  ):






   '''simple docstring'''






   UpperCAmelCase_											=state_dict.pop(lowercase__  )
   UpperCAmelCase_											=val




def     a__ (					lowercase__  ):






   '''simple docstring'''






   UpperCAmelCase_											=OrderedDict()
   for key, value in state_dict.items():
      if "backbone.0.body" in key:
         UpperCAmelCase_											=key.replace("backbone.0.body"  ,    "backbone.conv_encoder.model"  )
         UpperCAmelCase_											=value
      else:
         UpperCAmelCase_											=value

   return new_state_dict




def     a__ (					lowercase__  ,    lowercase__=False  ):






   '''simple docstring'''






   UpperCAmelCase_											=""
   if is_panoptic:
      UpperCAmelCase_											="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)
      UpperCAmelCase_											=state_dict.pop(F'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'  )
      UpperCAmelCase_											=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
      UpperCAmelCase_											=in_proj_weight[:2_5_6, :]
      UpperCAmelCase_											=in_proj_bias[:2_5_6]
      UpperCAmelCase_											=in_proj_weight[2_5_6:5_1_2, :]
      UpperCAmelCase_											=in_proj_bias[2_5_6:5_1_2]
      UpperCAmelCase_											=in_proj_weight[-2_5_6:, :]
      UpperCAmelCase_											=in_proj_bias[-2_5_6:]




def     a__ (					):






   '''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     a__ (					lowercase__  ,    lowercase__  ):






   '''simple docstring'''






   UpperCAmelCase_											=ConditionalDetrConfig()
   # set backbone and dilation attributes
   if "resnet101" in model_name:
      UpperCAmelCase_											="resnet101"
   if "dc5" in model_name:
      UpperCAmelCase_											=True
   UpperCAmelCase_											="panoptic" in model_name
   if is_panoptic:
      UpperCAmelCase_											=2_5_0
   else:
      UpperCAmelCase_											=9_1
      UpperCAmelCase_											="huggingface/label-files"
      UpperCAmelCase_											="coco-detection-id2label.json"
      UpperCAmelCase_											=json.load(open(hf_hub_download(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()}

   # load image processor
   UpperCAmelCase_											="coco_panoptic" if is_panoptic else "coco_detection"
   UpperCAmelCase_											=ConditionalDetrImageProcessor(format=lowercase__  )

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

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

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


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




