





"""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()
lowerCamelCase__						= logging.get_logger(__name__)




def        lowercase__     (       lowercase_      )       ->		str:




      """simple docstring"""

      _UpperCamelCase   :						Optional[int]    =							SwinConfig(
          embed_dim=192		,depths=(2, 2, 18, 2)		,num_heads=(6, 12, 24, 48)		,window_size=12		,out_features=["stage2", "stage3", "stage4"]		,)

      _UpperCamelCase   :						int    =							DetaConfig(
          backbone_config=lowercase_		,num_queries=900		,encoder_ffn_dim=2_048		,decoder_ffn_dim=2_048		,num_feature_levels=5		,assign_first_stage=lowercase_		,with_box_refine=lowercase_		,two_stage=lowercase_		,)

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

      _UpperCamelCase   :						Any    =							num_labels
      _UpperCamelCase   :						Optional[int]    =							json.load(open(cached_download(hf_hub_url(lowercase_		,lowercase_		,repo_type="dataset"      )      )		,"r"      )      )
      _UpperCamelCase   :						Tuple    =							{int(lowercase_      ): v for k, v in idalabel.items()}
      _UpperCamelCase   :						Optional[Any]    =							idalabel
      _UpperCamelCase   :						str    =							{v: k for k, v in idalabel.items()}

      return config




def        lowercase__     (       lowercase_      )       ->		Tuple:




      """simple docstring"""

      _UpperCamelCase   :						Any    =							[]

      # 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        lowercase__     (       lowercase_		,lowercase_		,lowercase_      )       ->		List[str]:




      """simple docstring"""

      _UpperCamelCase   :						Tuple    =							dct.pop(lowercase_      )
      _UpperCamelCase   :						str    =							val




def        lowercase__     (       lowercase_		,lowercase_      )       ->		Optional[int]:




      """simple docstring"""

      _UpperCamelCase   :						Optional[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   :						List[str]    =							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   :						Tuple    =							state_dict.pop(F'''backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight'''      )
                  _UpperCamelCase   :						List[str]    =							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   :						Any    =							in_proj_weight[:dim, :]
                  _UpperCamelCase   :						List[str]    =							in_proj_bias[: dim]
                  _UpperCamelCase   :						Dict    =							in_proj_weight[
                      dim : dim * 2, :
                  ]
                  _UpperCamelCase   :						List[Any]    =							in_proj_bias[
                      dim : dim * 2
                  ]
                  _UpperCamelCase   :						Tuple    =							in_proj_weight[
                      -dim :, :
                  ]
                  _UpperCamelCase   :						List[Any]    =							in_proj_bias[-dim :]
                  # fmt: on




def        lowercase__     (       lowercase_		,lowercase_      )       ->		str:




      """simple docstring"""

      _UpperCamelCase   :						Any    =							config.d_model
      for i in range(config.decoder_layers      ):
            # read in weights + bias of input projection layer of self-attention
            _UpperCamelCase   :						List[str]    =							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   :						Union[str, Any]    =							in_proj_weight[:hidden_size, :]
            _UpperCamelCase   :						Optional[int]    =							in_proj_bias[:hidden_size]
            _UpperCamelCase   :						Union[str, Any]    =							in_proj_weight[
                hidden_size : hidden_size * 2, :
            ]
            _UpperCamelCase   :						List[Any]    =							in_proj_bias[hidden_size : hidden_size * 2]
            _UpperCamelCase   :						Optional[Any]    =							in_proj_weight[-hidden_size:, :]
            _UpperCamelCase   :						int    =							in_proj_bias[-hidden_size:]




def        lowercase__     (       )       ->		Union[str, Any]:




      """simple docstring"""

      _UpperCamelCase   :						List[str]    =							"http://images.cocodataset.org/val2017/000000039769.jpg"
      _UpperCamelCase   :						Optional[Any]    =							Image.open(requests.get(lowercase_		,stream=lowercase_      ).raw      )

      return im




@torch.no_grad()
def        lowercase__     (       lowercase_		,lowercase_		,lowercase_      )       ->		List[str]:




      """simple docstring"""

      _UpperCamelCase   :						Tuple    =							get_deta_config(lowercase_      )

      # 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   :						Tuple    =							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   :						List[str]    =							torch.load(lowercase_		,map_location="cpu"      )["model"]

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

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

      # fix some prefixes
      for key in state_dict.copy().keys():
            if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
                  _UpperCamelCase   :						Dict    =							state_dict.pop(lowercase_      )
                  _UpperCamelCase   :						int    =							val
            if "input_proj" in key:
                  _UpperCamelCase   :						Tuple    =							state_dict.pop(lowercase_      )
                  _UpperCamelCase   :						int    =							val
            if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
                  _UpperCamelCase   :						Optional[Any]    =							state_dict.pop(lowercase_      )
                  _UpperCamelCase   :						Any    =							val

    # finally, create HuggingFace model and load state dict
      _UpperCamelCase   :						Any    =							DetaForObjectDetection(lowercase_      )
      model.load_state_dict(lowercase_      )
      model.eval()

      _UpperCamelCase   :						Tuple    =							"cuda" if torch.cuda.is_available() else "cpu"
      model.to(lowercase_      )

      # load image processor
      _UpperCamelCase   :						Dict    =							DetaImageProcessor(format="coco_detection"      )

      # verify our conversion on image
      _UpperCamelCase   :						str    =							prepare_img()
      _UpperCamelCase   :						Optional[int]    =							processor(images=lowercase_		,return_tensors="pt"      )
      _UpperCamelCase   :						Optional[int]    =							encoding["pixel_values"]
      _UpperCamelCase   :						List[Any]    =							model(pixel_values.to(lowercase_      )      )

      # verify logits
      print("Logits:"		,outputs.logits[0, :3, :3]      )
      print("Boxes:"		,outputs.pred_boxes[0, :3, :3]      )
      if model_name == "deta-swin-large":
            _UpperCamelCase   :						str    =							torch.tensor(
                [[-7.6308, -2.8485, -5.3737], [-7.2037, -4.5505, -4.8027], [-7.2943, -4.2611, -4.6617]]      )
            _UpperCamelCase   :						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   :						List[Any]    =							torch.tensor(
                [[-8.0122, -3.5720, -4.9717], [-8.1547, -3.6886, -4.6389], [-7.6610, -3.6194, -5.0134]]      )
            _UpperCamelCase   :						int    =							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(lowercase_      )		,atol=1e-4      )
      assert torch.allclose(outputs.pred_boxes[0, :3, :3]		,expected_boxes.to(lowercase_      )		,atol=1e-4      )
      print("Everything ok!"      )

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

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


if __name__ == "__main__":
  lowerCamelCase__						= 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."
  )
  lowerCamelCase__						= parser.parse_args()
  convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)







"""simple docstring"""


import warnings


warnings.warn(
    "memory_utils has been reorganized to utils.memory. Import `find_executable_batchsize` from the main `__init__`: "
    "`from accelerate import find_executable_batch_size` to avoid this warning.",
    FutureWarning,
)
