




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
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase__						:       int							=    logging.get_logger(__name__)


def 		_lowerCamelCase				(					)			->		int:





  """simple docstring"""





  _lowercase: Optional[Any]						     =    DetaConfig(
      num_queries=900					,     encoder_ffn_dim=2_048					,     decoder_ffn_dim=2_048					,     num_feature_levels=5					,     assign_first_stage=UpperCamelCase					,     with_box_refine=UpperCamelCase					,     two_stage=UpperCamelCase					,     )

  # set labels
  _lowercase: Union[str, Any]						     =    91
  _lowercase: List[Any]						     =    'huggingface/label-files'
  _lowercase: Dict						     =    'coco-detection-id2label.json'
  _lowercase: int						     =    json.load(open(cached_download(hf_hub_url(UpperCamelCase					,     UpperCamelCase					,     repo_type='dataset'							)							)					,     'r'							)							)
  _lowercase: Union[str, Any]						     =    {int(UpperCamelCase							): v for k, v in idalabel.items()}
  _lowercase: Tuple						     =    idalabel
  _lowercase: List[Any]						     =    {v: k for k, v in idalabel.items()}

  return config


def 		_lowerCamelCase				(					UpperCamelCase							)			->		Optional[Any]:





  """simple docstring"""





  _lowercase: Any						     =    []

  # stem
  # fmt: off
  rename_keys.append(('backbone.0.body.conv1.weight', 'model.backbone.model.embedder.embedder.convolution.weight')							)
  rename_keys.append(('backbone.0.body.bn1.weight', 'model.backbone.model.embedder.embedder.normalization.weight')							)
  rename_keys.append(('backbone.0.body.bn1.bias', 'model.backbone.model.embedder.embedder.normalization.bias')							)
  rename_keys.append(('backbone.0.body.bn1.running_mean', 'model.backbone.model.embedder.embedder.normalization.running_mean')							)
  rename_keys.append(('backbone.0.body.bn1.running_var', 'model.backbone.model.embedder.embedder.normalization.running_var')							)
  # stages
  for stage_idx in range(len(config.backbone_config.depths							)							):
    for layer_idx in range(config.backbone_config.depths[stage_idx]							):
      # shortcut
      if layer_idx == 0:
        rename_keys.append(
            (
                F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight',
                F'model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight',
            )							)
        rename_keys.append(
            (
                F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight',
                F'model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight',
            )							)
        rename_keys.append(
            (
                F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias',
                F'model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias',
            )							)
        rename_keys.append(
            (
                F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean',
                F'model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean',
            )							)
        rename_keys.append(
            (
                F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var',
                F'model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var',
            )							)
      # 3 convs
      for i in range(3							):
        rename_keys.append(
            (
                F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight',
                F'model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight',
            )							)
        rename_keys.append(
            (
                F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight',
                F'model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight',
            )							)
        rename_keys.append(
            (
                F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias',
                F'model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias',
            )							)
        rename_keys.append(
            (
                F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean',
                F'model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean',
            )							)
        rename_keys.append(
            (
                F'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var',
                F'model.backbone.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var',
            )							)
    # 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					,     UpperCamelCase					,     UpperCamelCase							)			->		Union[str, Any]:





  """simple docstring"""





  _lowercase: Union[str, Any]						     =    dct.pop(UpperCamelCase							)
  _lowercase: Optional[int]						     =    val


def 		_lowerCamelCase				(					UpperCamelCase					,     UpperCamelCase							)			->		Optional[Any]:





  """simple docstring"""





  _lowercase: Any						     =    config.d_model
  for i in range(config.decoder_layers							):
    # read in weights + bias of input projection layer of self-attention
    _lowercase: Any						     =    state_dict.pop(F'transformer.decoder.layers.{i}.self_attn.in_proj_weight'							)
    _lowercase: List[str]						     =    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
    _lowercase: Optional[int]						     =    in_proj_weight[:hidden_size, :]
    _lowercase: Dict						     =    in_proj_bias[:hidden_size]
    _lowercase: int						     =    in_proj_weight[
        hidden_size : hidden_size * 2, :
    ]
    _lowercase: Optional[Any]						     =    in_proj_bias[hidden_size : hidden_size * 2]
    _lowercase: Optional[int]						     =    in_proj_weight[-hidden_size:, :]
    _lowercase: List[str]						     =    in_proj_bias[-hidden_size:]


def 		_lowerCamelCase				(					)			->		Any:





  """simple docstring"""





  _lowercase: Union[str, Any]						     =    'http://images.cocodataset.org/val2017/000000039769.jpg'
  _lowercase: Optional[Any]						     =    Image.open(requests.get(UpperCamelCase					,     stream=UpperCamelCase							).raw							)

  return im


@torch.no_grad()
def 		_lowerCamelCase				(					UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase							)			->		int:





  """simple docstring"""





  _lowercase: Union[str, Any]						     =    get_deta_config()

  # load original state dict
  if model_name == "deta-resnet-50":
    _lowercase: List[Any]						     =    'adet_checkpoint0011.pth'
  elif model_name == "deta-resnet-50-24-epochs":
    _lowercase: Any						     =    'adet_2x_checkpoint0023.pth'
  else:
    raise ValueError(F'Model name {model_name} not supported'							)
  _lowercase: List[Any]						     =    hf_hub_download(repo_id='nielsr/deta-checkpoints'					,     filename=UpperCamelCase							)
  _lowercase: Optional[Any]						     =    torch.load(UpperCamelCase					,     map_location='cpu'							)['model']

  # rename keys
  _lowercase: List[Any]						     =    create_rename_keys(UpperCamelCase							)
  for src, dest in rename_keys:
    rename_key(UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase							)
  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:
      _lowercase: Optional[int]						     =    state_dict.pop(UpperCamelCase							)
      _lowercase: Optional[int]						     =    val
    if "input_proj" in key:
      _lowercase: Dict						     =    state_dict.pop(UpperCamelCase							)
      _lowercase: int						     =    val
    if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
      _lowercase: List[str]						     =    state_dict.pop(UpperCamelCase							)
      _lowercase: Dict						     =    val

    # finally, create HuggingFace model and load state dict
  _lowercase: Tuple						     =    DetaForObjectDetection(UpperCamelCase							)
  model.load_state_dict(UpperCamelCase							)
  model.eval()

  _lowercase: Any						     =    'cuda' if torch.cuda.is_available() else 'cpu'
  model.to(UpperCamelCase							)

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

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

  # verify logits
  if model_name == "deta-resnet-50":
    _lowercase: Any						     =    torch.tensor(
        [[-7.3_978, -2.5_406, -4.1_668], [-8.2_684, -3.9_933, -3.8_096], [-7.0_515, -3.7_973, -5.8_516]]							)
    _lowercase: int						     =    torch.tensor([[0.5_043, 0.4_973, 0.9_998], [0.2_542, 0.5_489, 0.4_748], [0.5_490, 0.2_765, 0.0_570]]							)
  elif model_name == "deta-resnet-50-24-epochs":
    _lowercase: Optional[int]						     =    torch.tensor(
        [[-7.1_688, -2.4_857, -4.8_669], [-7.8_630, -3.8_154, -4.2_674], [-7.2_730, -4.1_865, -5.5_323]]							)
    _lowercase: Dict						     =    torch.tensor([[0.5_021, 0.4_971, 0.9_994], [0.2_546, 0.5_486, 0.4_731], [0.1_686, 0.1_986, 0.2_142]]							)

  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__						:       Optional[Any]							=    argparse.ArgumentParser()

   parser.add_argument(
       '--model_name',
       type=str,
       default='deta-resnet-50',
       choices=['deta-resnet-50', 'deta-resnet-50-24-epochs'],
       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__						:       Optional[int]							=    parser.parse_args()
   convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






from cva import destroyAllWindows, imread, imshow, waitKey


def 		_lowerCamelCase				(					UpperCamelCase					,     UpperCamelCase							)			->		List[str]:





  """simple docstring"""





  _lowercase     ,					_lowercase: Tuple						     =    img.shape[0], img.shape[1]

  def to_grayscale(UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase							):
    return 0.2_126 * red + 0.587 * green + 0.114 * blue

  def normalize(UpperCamelCase							):
    return min(UpperCamelCase					,     255							)

  for i in range(UpperCamelCase							):
    for j in range(UpperCamelCase							):
      _lowercase: Any						     =    int(to_grayscale(*img[i][j]							)							)
      _lowercase: str						     =    [
          normalize(UpperCamelCase							),
          normalize(greyscale + factor							),
          normalize(greyscale + 2 * factor							),
      ]

  return img


if __name__ == "__main__":
   # read original image
   UpperCAmelCase__						:       Optional[Any]							=    {
       percentage: imread('image_data/lena.jpg', 1) for percentage in (10, 20, 30, 40)
   }

   for percentage, img in images.items():
      make_sepia(img, percentage)

   for percentage, img in images.items():
      imshow(f'''Original image with sepia (factor: {percentage})''', img)

   waitKey(0)
   destroyAllWindows()
