





"""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()
lowerCamelCase_          =	logging.get_logger(__name__)



def   __lowerCamelCase     (							)    ->   Union[str, Any]:
      __SCREAMING_SNAKE_CASE     :Any	       =						DetaConfig(
          num_queries=9_00				,     encoder_ffn_dim=20_48				,     decoder_ffn_dim=20_48				,     num_feature_levels=5				,     assign_first_stage=a_				,     with_box_refine=a_				,     two_stage=a_				,     )

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

      return config



def   __lowerCamelCase     (							a_  :	List[str]							)    ->   Optional[Any]:
      __SCREAMING_SNAKE_CASE     :Dict	       =						[]

      # 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     (							a_  :	Optional[int]				,     a_  :	Any				,     a_  :	Optional[Any]							)    ->   Tuple:
      __SCREAMING_SNAKE_CASE     :int	       =						dct.pop(a_							)
      __SCREAMING_SNAKE_CASE     :str	       =						val



def   __lowerCamelCase     (							a_  :	Optional[Any]				,     a_  :	List[Any]							)    ->   Optional[int]:
      # transformer decoder self-attention layers
      __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[int]	       =						state_dict.pop(f'''transformer.decoder.layers.{i}.self_attn.in_proj_weight'''							)
            __SCREAMING_SNAKE_CASE     :Optional[int]	       =						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     :List[Any]	       =						in_proj_weight[:hidden_size, :]
            __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						in_proj_bias[:hidden_size]
            __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						in_proj_weight[
                hidden_size : hidden_size * 2, :
            ]
            __SCREAMING_SNAKE_CASE     :Optional[int]	       =						in_proj_bias[hidden_size : hidden_size * 2]
            __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						in_proj_weight[-hidden_size:, :]
            __SCREAMING_SNAKE_CASE     :Optional[int]	       =						in_proj_bias[-hidden_size:]



def   __lowerCamelCase     (							)    ->   List[Any]:
      __SCREAMING_SNAKE_CASE     :Dict	       =						'''http://images.cocodataset.org/val2017/000000039769.jpg'''
      __SCREAMING_SNAKE_CASE     :Dict	       =						Image.open(requests.get(a_				,     stream=a_							).raw							)

      return im



@torch.no_grad()
def   __lowerCamelCase     (							a_  :	str				,     a_  :	int				,     a_  :	int							)    ->   int:
      __SCREAMING_SNAKE_CASE     :Dict	       =						get_deta_config()

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

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

    # finally, create HuggingFace model and load state dict
      __SCREAMING_SNAKE_CASE     :Optional[int]	       =						DetaForObjectDetection(a_							)
      model.load_state_dict(a_							)
      model.eval()

      __SCREAMING_SNAKE_CASE     :str	       =						'''cuda''' if torch.cuda.is_available() else '''cpu'''
      model.to(a_							)

      # load image processor
      __SCREAMING_SNAKE_CASE     :List[str]	       =						DetaImageProcessor(format='''coco_detection'''							)

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

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










"""simple docstring"""
# Lint as: python3

import hashlib
from typing import Union



def   __lowerCamelCase     (							a_  :	Union[str, int, bytes]							)    ->   bytes:
      if isinstance(a_				,     a_							):
            # Data already in bytes, returns as it as
            return hash_data
      elif isinstance(a_				,     a_							):
            # We keep the data as it as for it ot be later encoded to UTF-8
            # However replace `\\` with `/` for Windows compatibility
            __SCREAMING_SNAKE_CASE     :str	       =						hash_data.replace('''\\'''				,     '''/'''							)
      elif isinstance(a_				,     a_							):
            __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						str(a_							)
      else:
            # If data is not of the required type, raise error
            raise InvalidKeyError(a_							)

      return hash_data.encode('''utf-8'''							)






class       _SCREAMING_SNAKE_CASE(     A						):




       def __init__(		self	,SCREAMING_SNAKE_CASE__							)    ->   Optional[int]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :Tuple	       =						'''\nFAILURE TO GENERATE DATASET: Invalid key type detected'''
             __SCREAMING_SNAKE_CASE     :int	       =						f'''\nFound Key {hash_data} of type {type(SCREAMING_SNAKE_CASE__							)}'''
             __SCREAMING_SNAKE_CASE     :Optional[int]	       =						'''\nKeys should be either str, int or bytes type'''
             super().__init__(f'''{self.prefix}{self.err_msg}{self.suffix}'''							)






class       _SCREAMING_SNAKE_CASE(     A						):




       def __init__(		self	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__=""							)    ->   List[str]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :str	       =						key
             __SCREAMING_SNAKE_CASE     :str	       =						duplicate_key_indices
             __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						fix_msg
             __SCREAMING_SNAKE_CASE     :int	       =						'''Found multiple examples generated with the same key'''
             if len(SCREAMING_SNAKE_CASE__							) <= 20:
                   __SCREAMING_SNAKE_CASE     :Dict	       =						f'''\nThe examples at index {", ".join(SCREAMING_SNAKE_CASE__							)} have the key {key}'''
             else:
                   __SCREAMING_SNAKE_CASE     :List[str]	       =						f'''\nThe examples at index {", ".join(duplicate_key_indices[:20]							)}... ({len(SCREAMING_SNAKE_CASE__							) - 20} more) have the key {key}'''
             __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						'''\n''' + fix_msg if fix_msg else ''''''
             super().__init__(f'''{self.prefix}{self.err_msg}{self.suffix}'''							)






class       _SCREAMING_SNAKE_CASE:




       def __init__(		self	,SCREAMING_SNAKE_CASE__							)    ->   Union[str, Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :Dict	       =						hashlib.mda(_as_bytes(SCREAMING_SNAKE_CASE__							)							)




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__							)    ->   int:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :Tuple	       =						self._split_mda.copy()
             __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						_as_bytes(SCREAMING_SNAKE_CASE__							)
             mda.update(SCREAMING_SNAKE_CASE__							)
             # Convert to integer with hexadecimal conversion
             return int(mda.hexdigest()	,16							)