






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()
_SCREAMING_SNAKE_CASE :    Union[str, Any]    =						logging.get_logger(__name__)







def 					lowerCamelCase__	(							)				->    Optional[int]:
  lowerCamelCase_   					=						DetaConfig(
      num_queries=900		,    encoder_ffn_dim=2048		,    decoder_ffn_dim=2048		,    num_feature_levels=5		,    assign_first_stage=_A		,    with_box_refine=_A		,    two_stage=_A		,    )

  # set labels
  lowerCamelCase_   					=						91
  lowerCamelCase_   					=						'huggingface/label-files'
  lowerCamelCase_   					=						'coco-detection-id2label.json'
  lowerCamelCase_   					=						json.load(open(cached_download(hf_hub_url(_A		,    _A		,    repo_type='dataset'			)			)		,    'r'			)			)
  lowerCamelCase_   					=						{int(_A			): v for k, v in idalabel.items()}
  lowerCamelCase_   					=						idalabel
  lowerCamelCase_   					=						{v: k for k, v in idalabel.items()}

  return config







def 					lowerCamelCase__	(							_lowerCamelCase	:		Tuple			)				->    Dict:
  lowerCamelCase_   					=						[]

  # 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__	(							_lowerCamelCase	:		Optional[Any]		,    _lowerCamelCase	:		Tuple		,    _lowerCamelCase	:		List[Any]			)				->    int:
  lowerCamelCase_   					=						dct.pop(_A			)
  lowerCamelCase_   					=						val







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







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

  return im







@torch.no_grad()
def 					lowerCamelCase__	(							_lowerCamelCase	:		Tuple		,    _lowerCamelCase	:		Any		,    _lowerCamelCase	:		Optional[Any]			)				->    Union[str, Any]:
  lowerCamelCase_   					=						get_deta_config()

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

  # rename keys
  lowerCamelCase_   					=						create_rename_keys(_A			)
  for src, dest in rename_keys:
    rename_key(_A		,    _A		,    _A			)
  read_in_decoder_q_k_v(_A		,    _A			)

  # fix some prefixes
  for key in state_dict.copy().keys():
    if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
      lowerCamelCase_   					=						state_dict.pop(_A			)
      lowerCamelCase_   					=						val
    if "input_proj" in key:
      lowerCamelCase_   					=						state_dict.pop(_A			)
      lowerCamelCase_   					=						val
    if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
      lowerCamelCase_   					=						state_dict.pop(_A			)
      lowerCamelCase_   					=						val

    # finally, create HuggingFace model and load state dict
  lowerCamelCase_   					=						DetaForObjectDetection(_A			)
  model.load_state_dict(_A			)
  model.eval()

  lowerCamelCase_   					=						'cuda' if torch.cuda.is_available() else 'cpu'
  model.to(_A			)

  # load image processor
  lowerCamelCase_   					=						DetaImageProcessor(format='coco_detection'			)

  # verify our conversion on image
  lowerCamelCase_   					=						prepare_img()
  lowerCamelCase_   					=						processor(images=_A		,    return_tensors='pt'			)
  lowerCamelCase_   					=						encoding['pixel_values']
  lowerCamelCase_   					=						model(pixel_values.to(_A			)			)

  # verify logits
  if model_name == "deta-resnet-50":
    lowerCamelCase_   					=						torch.tensor(
        [[-7.39_78, -2.54_06, -4.16_68], [-8.26_84, -3.99_33, -3.80_96], [-7.05_15, -3.79_73, -5.85_16]]			)
    lowerCamelCase_   					=						torch.tensor([[0.50_43, 0.49_73, 0.99_98], [0.25_42, 0.54_89, 0.47_48], [0.54_90, 0.27_65, 0.05_70]]			)
  elif model_name == "deta-resnet-50-24-epochs":
    lowerCamelCase_   					=						torch.tensor(
        [[-7.16_88, -2.48_57, -4.86_69], [-7.86_30, -3.81_54, -4.26_74], [-7.27_30, -4.18_65, -5.53_23]]			)
    lowerCamelCase_   					=						torch.tensor([[0.50_21, 0.49_71, 0.99_94], [0.25_46, 0.54_86, 0.47_31], [0.16_86, 0.19_86, 0.21_42]]			)

  assert torch.allclose(outputs.logits[0, :3, :3]		,    expected_logits.to(_A			)		,    atol=1e-4			)
  assert torch.allclose(outputs.pred_boxes[0, :3, :3]		,    expected_boxes.to(_A			)		,    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(_A			).mkdir(exist_ok=_A			)
    model.save_pretrained(_A			)
    processor.save_pretrained(_A			)

  # 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__":
  _SCREAMING_SNAKE_CASE :    str    =						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.'''
  )
  _SCREAMING_SNAKE_CASE :    Dict    =						parser.parse_args()
  convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)












"""simple docstring"""





def 					lowerCamelCase__	(							_lowerCamelCase	:		int = 1000			)				->    int:
  lowerCamelCase_   					=						0
  lowerCamelCase_   					=						(n - 1) // 3
  total += ((terms) * (6 + (terms - 1) * 3)) // 2  # total of an A.P.
  lowerCamelCase_   					=						(n - 1) // 5
  total += ((terms) * (10 + (terms - 1) * 5)) // 2
  lowerCamelCase_   					=						(n - 1) // 15
  total -= ((terms) * (30 + (terms - 1) * 15)) // 2
  return total


if __name__ == "__main__":
  print(F'''{solution() = }''')




