



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      = logging.get_logger(__name__)
def 	UpperCamelCase			(     )			->  List[str]:
   UpperCamelCase					:						Optional[Any]					=			DetaConfig(
       num_queries=900 ,  encoder_ffn_dim=2048 ,  decoder_ffn_dim=2048 ,  num_feature_levels=5 ,  assign_first_stage=snake_case__ ,  with_box_refine=snake_case__ ,  two_stage=snake_case__ ,  )

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

   return config
def 	UpperCamelCase			(     snake_case__ :					Dict			)			->  Dict:
   UpperCamelCase					:						str					=			[]

   # 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 	UpperCamelCase			(     snake_case__ :					Any ,  snake_case__ :					List[Any] ,  snake_case__ :					Optional[int]			)			->  List[Any]:
   UpperCamelCase					:						int					=			dct.pop(snake_case__			)
   UpperCamelCase					:						List[Any]					=			val
def 	UpperCamelCase			(     snake_case__ :					Dict ,  snake_case__ :					Tuple			)			->  Any:
   # transformer decoder self-attention layers
   UpperCamelCase					:						Union[str, 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					:						Tuple					=			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[Any]					=			in_proj_weight[:hidden_size, :]
      UpperCamelCase					:						int					=			in_proj_bias[:hidden_size]
      UpperCamelCase					:						Optional[Any]					=			in_proj_weight[
          hidden_size : hidden_size * 2, :
      ]
      UpperCamelCase					:						str					=			in_proj_bias[hidden_size : hidden_size * 2]
      UpperCamelCase					:						Optional[int]					=			in_proj_weight[-hidden_size:, :]
      UpperCamelCase					:						Union[str, Any]					=			in_proj_bias[-hidden_size:]
def 	UpperCamelCase			(     )			->  List[Any]:
   UpperCamelCase					:						Optional[int]					=			'http://images.cocodataset.org/val2017/000000039769.jpg'
   UpperCamelCase					:						Union[str, Any]					=			Image.open(requests.get(snake_case__ ,  stream=snake_case__			).raw			)

   return im
@torch.no_grad()
def 	UpperCamelCase			(     snake_case__ :					List[Any] ,  snake_case__ :					Union[str, Any] ,  snake_case__ :					List[Any]			)			->  Tuple:
   UpperCamelCase					:						Union[str, Any]					=			get_deta_config()

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

   # rename keys
   UpperCamelCase					:						Optional[int]					=			create_rename_keys(snake_case__			)
   for src, dest in rename_keys:
      rename_key(snake_case__ ,  snake_case__ ,  snake_case__			)
   read_in_decoder_q_k_v(snake_case__ ,  snake_case__			)

   # 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					:						List[str]					=			state_dict.pop(snake_case__			)
         UpperCamelCase					:						Optional[Any]					=			val
      if "input_proj" in key:
         UpperCamelCase					:						str					=			state_dict.pop(snake_case__			)
         UpperCamelCase					:						Tuple					=			val
      if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
         UpperCamelCase					:						List[Any]					=			state_dict.pop(snake_case__			)
         UpperCamelCase					:						Dict					=			val

    # finally, create HuggingFace model and load state dict
   UpperCamelCase					:						List[Any]					=			DetaForObjectDetection(snake_case__			)
   model.load_state_dict(snake_case__			)
   model.eval()

   UpperCamelCase					:						int					=			'cuda' if torch.cuda.is_available() else 'cpu'
   model.to(snake_case__			)

   # load image processor
   UpperCamelCase					:						Optional[Any]					=			DetaImageProcessor(format='coco_detection'			)

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

   # verify logits
   if model_name == "deta-resnet-50":
      UpperCamelCase					:						Dict					=			torch.tensor(
          [[-7.3978, -2.5406, -4.1668], [-8.2684, -3.9933, -3.8096], [-7.0515, -3.7973, -5.8516]]			)
      UpperCamelCase					:						Tuple					=			torch.tensor([[0.5043, 0.4973, 0.9998], [0.2542, 0.5489, 0.4748], [0.5490, 0.2765, 0.0570]]			)
   elif model_name == "deta-resnet-50-24-epochs":
      UpperCamelCase					:						Any					=			torch.tensor(
          [[-7.1688, -2.4857, -4.8669], [-7.8630, -3.8154, -4.2674], [-7.2730, -4.1865, -5.5323]]			)
      UpperCamelCase					:						List[Any]					=			torch.tensor([[0.5021, 0.4971, 0.9994], [0.2546, 0.5486, 0.4731], [0.1686, 0.1986, 0.2142]]			)

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

   # 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-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      = parser.parse_args()
    convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)








from __future__ import annotations

import sys
def 	UpperCamelCase			(     snake_case__ :					int ,  snake_case__ :					int			)			->  tuple[int, int]:
   # base cases
   if abs(snake_case__			) == 1:
      return a, 0
   elif abs(snake_case__			) == 1:
      return 0, b

   UpperCamelCase			,  UpperCamelCase					:						Dict					=			a, b
   UpperCamelCase			,  UpperCamelCase					:						int					=			1, 0
   UpperCamelCase			,  UpperCamelCase					:						Union[str, Any]					=			0, 1

   while remainder != 0:
      UpperCamelCase					:						str					=			old_remainder // remainder
      UpperCamelCase			,  UpperCamelCase					:						Union[str, Any]					=			remainder, old_remainder - quotient * remainder
      UpperCamelCase			,  UpperCamelCase					:						List[Any]					=			coeff_a, old_coeff_a - quotient * coeff_a
      UpperCamelCase			,  UpperCamelCase					:						Optional[Any]					=			coeff_b, old_coeff_b - quotient * coeff_b

   # sign correction for negative numbers
   if a < 0:
      UpperCamelCase					:						Dict					=			-old_coeff_a
   if b < 0:
      UpperCamelCase					:						Any					=			-old_coeff_b

   return old_coeff_a, old_coeff_b
def 	UpperCamelCase			(     )			->  Tuple:
   if len(sys.argv			) < 3:
      print('2 integer arguments required'			)
      return 1
   UpperCamelCase					:						Union[str, Any]					=			int(sys.argv[1]			)
   UpperCamelCase					:						Optional[int]					=			int(sys.argv[2]			)
   print(extended_euclidean_algorithm(snake_case__ ,  snake_case__			)			)
   return 0


if __name__ == "__main__":
    raise SystemExit(main())



