



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				:     Any          =	logging.get_logger(__name__)






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






   """simple docstring"""


   _lowerCAmelCase					     =		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
   _lowerCAmelCase					     =		91
   _lowerCAmelCase					     =		"""huggingface/label-files"""
   _lowerCAmelCase					     =		"""coco-detection-id2label.json"""
   _lowerCAmelCase					     =		json.load(open(cached_download(hf_hub_url(snake_case_   ,	snake_case_   ,	repo_type="""dataset"""   )   )   ,	"""r"""   )   )
   _lowerCAmelCase					     =		{int(snake_case_   ): v for k, v in idalabel.items()}
   _lowerCAmelCase					     =		idalabel
   _lowerCAmelCase					     =		{v: k for k, v in idalabel.items()}

   return config






def 	__UpperCAmelCase					(       snake_case_  :  List[Any]   )							->	Tuple:






   """simple docstring"""


   _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 	__UpperCAmelCase					(       snake_case_  :  Optional[Any]   ,	snake_case_  :  Optional[Any]   ,	snake_case_  :  List[str]   )							->	Optional[int]:






   """simple docstring"""


   _lowerCAmelCase					     =		dct.pop(snake_case_   )
   _lowerCAmelCase					     =		val






def 	__UpperCAmelCase					(       snake_case_  :  Any   ,	snake_case_  :  Union[str, Any]   )							->	List[Any]:






   """simple docstring"""


   _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 	__UpperCAmelCase					(       )							->	Union[str, Any]:






   """simple docstring"""


   _lowerCAmelCase					     =		"""http://images.cocodataset.org/val2017/000000039769.jpg"""
   _lowerCAmelCase					     =		Image.open(requests.get(snake_case_   ,	stream=snake_case_   ).raw   )

   return im






@torch.no_grad()
def 	__UpperCAmelCase					(       snake_case_  :  List[str]   ,	snake_case_  :  List[Any]   ,	snake_case_  :  Union[str, Any]   )							->	Optional[int]:






   """simple docstring"""


   _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=snake_case_   )
   _lowerCAmelCase					     =		torch.load(snake_case_   ,	map_location="""cpu"""   )["""model"""]

   # rename keys
   _lowerCAmelCase					     =		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:
         _lowerCAmelCase					     =		state_dict.pop(snake_case_   )
         _lowerCAmelCase					     =		val
      if "input_proj" in key:
         _lowerCAmelCase					     =		state_dict.pop(snake_case_   )
         _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(snake_case_   )
         _lowerCAmelCase					     =		val

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

   _lowerCAmelCase					     =		"""cuda""" if torch.cuda.is_available() else """cpu"""
   model.to(snake_case_   )

   # load image processor
   _lowerCAmelCase					     =		DetaImageProcessor(format="""coco_detection"""   )

   # verify our conversion on image
   _lowerCAmelCase					     =		prepare_img()
   _lowerCAmelCase					     =		processor(images=snake_case_   ,	return_tensors="""pt"""   )
   _lowerCAmelCase					     =		encoding["""pixel_values"""]
   _lowerCAmelCase					     =		model(pixel_values.to(snake_case_   )   )

   # verify logits
   if model_name == "deta-resnet-50":
      _lowerCAmelCase					     =		torch.tensor(
          [[-7.3_9_7_8, -2.5_4_0_6, -4.1_6_6_8], [-8.2_6_8_4, -3.9_9_3_3, -3.8_0_9_6], [-7.0_5_1_5, -3.7_9_7_3, -5.8_5_1_6]]   )
      _lowerCAmelCase					     =		torch.tensor([[0.5_0_4_3, 0.4_9_7_3, 0.9_9_9_8], [0.2_5_4_2, 0.5_4_8_9, 0.4_7_4_8], [0.5_4_9_0, 0.2_7_6_5, 0.0_5_7_0]]   )
   elif model_name == "deta-resnet-50-24-epochs":
      _lowerCAmelCase					     =		torch.tensor(
          [[-7.1_6_8_8, -2.4_8_5_7, -4.8_6_6_9], [-7.8_6_3_0, -3.8_1_5_4, -4.2_6_7_4], [-7.2_7_3_0, -4.1_8_6_5, -5.5_3_2_3]]   )
      _lowerCAmelCase					     =		torch.tensor([[0.5_0_2_1, 0.4_9_7_1, 0.9_9_9_4], [0.2_5_4_6, 0.5_4_8_6, 0.4_7_3_1], [0.1_6_8_6, 0.1_9_8_6, 0.2_1_4_2]]   )

   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__":
  SCREAMING_SNAKE_CASE				:     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.'''
  )
  SCREAMING_SNAKE_CASE				:     int          =	parser.parse_args()
  convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)




"""simple docstring"""





def 	__UpperCAmelCase					(       snake_case_  :  float   )							->	int:






   """simple docstring"""


   return int(snake_case_   ) if x - int(snake_case_   ) <= 0 else int(snake_case_   ) + 1


if __name__ == "__main__":
  import doctest

  doctest.testmod()