





'''simple docstring'''





import argparse
import json
from pathlib import Path

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

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


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




def 	lowerCAmelCase_					(						__UpperCAmelCase:    List[str]   )   ->			Optional[Any]:
    UpperCamelCase__							:       Optional[Any]      =   SwinConfig(
        embed_dim=192	,     depths=(2, 2, 18, 2)	,     num_heads=(6, 12, 24, 48)	,     window_size=12	,     out_features=['''stage2''', '''stage3''', '''stage4''']	,     )

    UpperCamelCase__							:       List[Any]      =   DetaConfig(
        backbone_config=__UpperCAmelCase	,     num_queries=900	,     encoder_ffn_dim=2048	,     decoder_ffn_dim=2048	,     num_feature_levels=5	,     assign_first_stage=__UpperCAmelCase	,     with_box_refine=__UpperCAmelCase	,     two_stage=__UpperCAmelCase	,     )

    # set labels
    UpperCamelCase__							:       str      =   '''huggingface/label-files'''
    if "o365" in model_name:
        UpperCamelCase__							:       str      =   366
        UpperCamelCase__							:       List[Any]      =   '''object365-id2label.json'''
    else:
        UpperCamelCase__							:       Dict      =   91
        UpperCamelCase__							:       Optional[int]      =   '''coco-detection-id2label.json'''

    UpperCamelCase__							:       Dict      =   num_labels
    UpperCamelCase__							:       List[str]      =   json.load(open(cached_download(hf_hub_url(__UpperCAmelCase	,     __UpperCAmelCase	,     repo_type='''dataset'''   )   )	,     '''r'''   )   )
    UpperCamelCase__							:       Optional[Any]      =   {int(__UpperCAmelCase   ): v for k, v in idalabel.items()}
    UpperCamelCase__							:       Optional[Any]      =   idalabel
    UpperCamelCase__							:       Optional[int]      =   {v: k for k, v in idalabel.items()}

    return config




def 	lowerCAmelCase_					(						__UpperCAmelCase:    List[Any]   )   ->			Optional[int]:
    UpperCamelCase__							:       List[str]      =   []

    # 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 	lowerCAmelCase_					(						__UpperCAmelCase:    Union[str, Any]	,     __UpperCAmelCase:    int	,     __UpperCAmelCase:    List[Any]   )   ->			str:
    UpperCamelCase__							:       Optional[Any]      =   dct.pop(__UpperCAmelCase   )
    UpperCamelCase__							:       Optional[int]      =   val




def 	lowerCAmelCase_					(						__UpperCAmelCase:    Tuple	,     __UpperCAmelCase:    Optional[int]   )   ->			int:
    UpperCamelCase__							:       Any      =   [int(backbone_config.embed_dim * 2**i   ) for i in range(len(backbone_config.depths   )   )]
    for i in range(len(backbone_config.depths   )   ):
        UpperCamelCase__							:       int      =   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__							:       Optional[Any]      =   state_dict.pop(f"backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight"   )
            UpperCamelCase__							:       Dict      =   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__							:       List[str]      =   in_proj_weight[:dim, :]
            UpperCamelCase__							:       Optional[Any]      =   in_proj_bias[: dim]
            UpperCamelCase__							:       str      =   in_proj_weight[
                dim : dim * 2, :
            ]
            UpperCamelCase__							:       Dict      =   in_proj_bias[
                dim : dim * 2
            ]
            UpperCamelCase__							:       Optional[Any]      =   in_proj_weight[
                -dim :, :
            ]
            UpperCamelCase__							:       Optional[Any]      =   in_proj_bias[-dim :]
            # fmt: on




def 	lowerCAmelCase_					(						__UpperCAmelCase:    int	,     __UpperCAmelCase:    Optional[int]   )   ->			Optional[Any]:
    # transformer decoder self-attention layers
    UpperCamelCase__							:       List[str]      =   config.d_model
    for i in range(config.decoder_layers   ):
        # read in weights + bias of input projection layer of self-attention
        UpperCamelCase__							:       Tuple      =   state_dict.pop(f"transformer.decoder.layers.{i}.self_attn.in_proj_weight"   )
        UpperCamelCase__							:       Any      =   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__							:       Optional[int]      =   in_proj_weight[:hidden_size, :]
        UpperCamelCase__							:       List[str]      =   in_proj_bias[:hidden_size]
        UpperCamelCase__							:       Optional[int]      =   in_proj_weight[
            hidden_size : hidden_size * 2, :
        ]
        UpperCamelCase__							:       Any      =   in_proj_bias[hidden_size : hidden_size * 2]
        UpperCamelCase__							:       Optional[int]      =   in_proj_weight[-hidden_size:, :]
        UpperCamelCase__							:       List[Any]      =   in_proj_bias[-hidden_size:]




def 	lowerCAmelCase_					(						)   ->			Union[str, Any]:
    UpperCamelCase__							:       str      =   '''http://images.cocodataset.org/val2017/000000039769.jpg'''
    UpperCamelCase__							:       int      =   Image.open(requests.get(__UpperCAmelCase	,     stream=__UpperCAmelCase   ).raw   )

    return im




@torch.no_grad()
def 	lowerCAmelCase_					(						__UpperCAmelCase:    Union[str, Any]	,     __UpperCAmelCase:    Dict	,     __UpperCAmelCase:    Any   )   ->			Union[str, Any]:
    UpperCamelCase__							:       str      =   get_deta_config(__UpperCAmelCase   )

    # load original state dict
    if model_name == "deta-swin-large":
        UpperCamelCase__							:       Optional[int]      =   hf_hub_download(repo_id='''nielsr/deta-checkpoints'''	,     filename='''adet_swin_ft.pth'''   )
    elif model_name == "deta-swin-large-o365":
        UpperCamelCase__							:       Optional[Any]      =   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__							:       Union[str, Any]      =   torch.load(__UpperCAmelCase	,     map_location='''cpu'''   )['''model''']

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

    # rename keys
    UpperCamelCase__							:       List[str]      =   create_rename_keys(__UpperCAmelCase   )
    for src, dest in rename_keys:
        rename_key(__UpperCAmelCase	,     __UpperCAmelCase	,     __UpperCAmelCase   )
    read_in_swin_q_k_v(__UpperCAmelCase	,     config.backbone_config   )
    read_in_decoder_q_k_v(__UpperCAmelCase	,     __UpperCAmelCase   )

    # 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__							:       Tuple      =   state_dict.pop(__UpperCAmelCase   )
            UpperCamelCase__							:       Dict      =   val
        if "input_proj" in key:
            UpperCamelCase__							:       Optional[int]      =   state_dict.pop(__UpperCAmelCase   )
            UpperCamelCase__							:       Optional[int]      =   val
        if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
            UpperCamelCase__							:       Optional[int]      =   state_dict.pop(__UpperCAmelCase   )
            UpperCamelCase__							:       Union[str, Any]      =   val

    # finally, create HuggingFace model and load state dict
    UpperCamelCase__							:       Optional[int]      =   DetaForObjectDetection(__UpperCAmelCase   )
    model.load_state_dict(__UpperCAmelCase   )
    model.eval()

    UpperCamelCase__							:       List[str]      =   '''cuda''' if torch.cuda.is_available() else '''cpu'''
    model.to(__UpperCAmelCase   )

    # load image processor
    UpperCamelCase__							:       Any      =   DetaImageProcessor(format='''coco_detection'''   )

    # verify our conversion on image
    UpperCamelCase__							:       Optional[Any]      =   prepare_img()
    UpperCamelCase__							:       str      =   processor(images=__UpperCAmelCase	,     return_tensors='''pt'''   )
    UpperCamelCase__							:       Union[str, Any]      =   encoding['''pixel_values''']
    UpperCamelCase__							:       str      =   model(pixel_values.to(__UpperCAmelCase   )   )

    # verify logits
    print('''Logits:'''	,     outputs.logits[0, :3, :3]   )
    print('''Boxes:'''	,     outputs.pred_boxes[0, :3, :3]   )
    if model_name == "deta-swin-large":
        UpperCamelCase__							:       Any      =   torch.tensor(
            [[-7.6308, -2.8485, -5.3737], [-7.2037, -4.5505, -4.8027], [-7.2943, -4.2611, -4.6617]]   )
        UpperCamelCase__							:       List[str]      =   torch.tensor([[0.4987, 0.4969, 0.9999], [0.2549, 0.5498, 0.4805], [0.5498, 0.2757, 0.0569]]   )
    elif model_name == "deta-swin-large-o365":
        UpperCamelCase__							:       Any      =   torch.tensor(
            [[-8.0122, -3.5720, -4.9717], [-8.1547, -3.6886, -4.6389], [-7.6610, -3.6194, -5.0134]]   )
        UpperCamelCase__							:       Tuple      =   torch.tensor([[0.2523, 0.5549, 0.4881], [0.7715, 0.4149, 0.4601], [0.5503, 0.2753, 0.0575]]   )
    assert torch.allclose(outputs.logits[0, :3, :3]	,     expected_logits.to(__UpperCAmelCase   )	,     atol=1e-4   )
    assert torch.allclose(outputs.pred_boxes[0, :3, :3]	,     expected_boxes.to(__UpperCAmelCase   )	,     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(__UpperCAmelCase   ).mkdir(exist_ok=__UpperCAmelCase   )
        model.save_pretrained(__UpperCAmelCase   )
        processor.save_pretrained(__UpperCAmelCase   )

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


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

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








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()
UpperCAmelCase_  							=				logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
UpperCAmelCase_  							=				[]
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 	lowerCAmelCase_					(						__UpperCAmelCase:    Tuple	,     __UpperCAmelCase:    List[Any]	,     __UpperCAmelCase:    Dict   )   ->			Dict:
    UpperCamelCase__							:       List[str]      =   state_dict.pop(__UpperCAmelCase   )
    UpperCamelCase__							:       Union[str, Any]      =   val




def 	lowerCAmelCase_					(						__UpperCAmelCase:    Dict   )   ->			List[str]:
    UpperCamelCase__							:       List[str]      =   OrderedDict()
    for key, value in state_dict.items():
        if "backbone.0.body" in key:
            UpperCamelCase__							:       Optional[Any]      =   key.replace('''backbone.0.body'''	,     '''backbone.conv_encoder.model'''   )
            UpperCamelCase__							:       List[str]      =   value
        else:
            UpperCamelCase__							:       Optional[int]      =   value

    return new_state_dict




def 	lowerCAmelCase_					(						__UpperCAmelCase:    Optional[int]	,     __UpperCAmelCase:    Optional[int]=False   )   ->			Optional[int]:
    UpperCamelCase__							:       List[str]      =   ''''''
    if is_panoptic:
        UpperCamelCase__							:       List[Any]      =   '''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__							:       List[Any]      =   state_dict.pop(f"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"   )
        UpperCamelCase__							:       int      =   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__							:       Any      =   in_proj_weight[:256, :]
        UpperCamelCase__							:       List[str]      =   in_proj_bias[:256]
        UpperCamelCase__							:       Optional[int]      =   in_proj_weight[256:512, :]
        UpperCamelCase__							:       List[str]      =   in_proj_bias[256:512]
        UpperCamelCase__							:       Tuple      =   in_proj_weight[-256:, :]
        UpperCamelCase__							:       List[Any]      =   in_proj_bias[-256:]




def 	lowerCAmelCase_					(						)   ->			int:
    UpperCamelCase__							:       Any      =   '''http://images.cocodataset.org/val2017/000000039769.jpg'''
    UpperCamelCase__							:       Optional[int]      =   Image.open(requests.get(__UpperCAmelCase	,     stream=__UpperCAmelCase   ).raw   )

    return im




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

    # load image processor
    UpperCamelCase__							:       int      =   '''coco_panoptic''' if is_panoptic else '''coco_detection'''
    UpperCamelCase__							:       str      =   ConditionalDetrImageProcessor(format=__UpperCAmelCase   )

    # prepare image
    UpperCamelCase__							:       Tuple      =   prepare_img()
    UpperCamelCase__							:       List[str]      =   image_processor(images=__UpperCAmelCase	,     return_tensors='''pt'''   )
    UpperCamelCase__							:       Union[str, Any]      =   encoding['''pixel_values''']

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

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


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

