




'''simple docstring'''





def 				UpperCamelCase	(  snake_case	,     snake_case   ):




   '''simple docstring'''

   SCREAMING_SNAKE_CASE__			:				str       						= 1
   while b > 0:
      if b & 1:
         res *= a

      a *= a
      b >>= 1

   return res


def 				UpperCamelCase	(  snake_case	,     snake_case	,     snake_case   ):




   '''simple docstring'''

   SCREAMING_SNAKE_CASE__			:				List[Any]       						= 1
   while b > 0:
      if b & 1:
         SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= ((res % c) * (a % c)) % c

      a *= a
      b >>= 1

   return res







'''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
from transformers.utils import logging


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


def 				UpperCamelCase	(  ):




   '''simple docstring'''

   SCREAMING_SNAKE_CASE__			:				Tuple       						= DetaConfig(
       num_queries=9_00	,     encoder_ffn_dim=20_48	,     decoder_ffn_dim=20_48	,     num_feature_levels=5	,     assign_first_stage=snake_case	,     with_box_refine=snake_case	,     two_stage=snake_case	,     )

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

   return config


def 				UpperCamelCase	(  snake_case   ):




   '''simple docstring'''

   SCREAMING_SNAKE_CASE__			:				Optional[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 				UpperCamelCase	(  snake_case	,     snake_case	,     snake_case   ):




   '''simple docstring'''

   SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= dct.pop(snake_case   )
   SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= val


def 				UpperCamelCase	(  snake_case	,     snake_case   ):




   '''simple docstring'''

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


def 				UpperCamelCase	(  ):




   '''simple docstring'''

   SCREAMING_SNAKE_CASE__			:				Dict       						= '''http://images.cocodataset.org/val2017/000000039769.jpg'''
   SCREAMING_SNAKE_CASE__			:				List[Any]       						= Image.open(requests.get(snake_case	,     stream=snake_case   ).raw   )

   return im


@torch.no_grad()
def 				UpperCamelCase	(  snake_case	,     snake_case	,     snake_case   ):




   '''simple docstring'''

   SCREAMING_SNAKE_CASE__			:				List[Any]       						= get_deta_config()

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

   # rename keys
   SCREAMING_SNAKE_CASE__			:				Dict       						= 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:
         SCREAMING_SNAKE_CASE__			:				Optional[int]       						= state_dict.pop(snake_case   )
         SCREAMING_SNAKE_CASE__			:				Dict       						= val
      if "input_proj" in key:
         SCREAMING_SNAKE_CASE__			:				Any       						= state_dict.pop(snake_case   )
         SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= val
      if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
         SCREAMING_SNAKE_CASE__			:				List[str]       						= state_dict.pop(snake_case   )
         SCREAMING_SNAKE_CASE__			:				Optional[int]       						= val

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

   SCREAMING_SNAKE_CASE__			:				Tuple       						= '''cuda''' if torch.cuda.is_available() else '''cpu'''
   model.to(snake_case   )

   # load image processor
   SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= DetaImageProcessor(format='''coco_detection'''   )

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

   # verify logits
   if model_name == "deta-resnet-50":
      SCREAMING_SNAKE_CASE__			:				str       						= torch.tensor(
          [[-7.3978, -2.5406, -4.1668], [-8.2684, -3.9933, -3.8096], [-7.0515, -3.7973, -5.8516]]   )
      SCREAMING_SNAKE_CASE__			:				List[Any]       						= 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":
      SCREAMING_SNAKE_CASE__			:				List[str]       						= torch.tensor(
          [[-7.1688, -2.4857, -4.8669], [-7.8630, -3.8154, -4.2674], [-7.2730, -4.1865, -5.5323]]   )
      SCREAMING_SNAKE_CASE__			:				Tuple       						= 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__":
       lowercase__			=	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.'''
       )
       lowercase__			=	parser.parse_args()
       convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)

