




'''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()
__SCREAMING_SNAKE_CASE    :   List[str]       =logging.get_logger(__name__)

def 			lowerCAmelCase_			(  )					->			List[Any]:


      '''simple docstring'''




      lowerCamelCase__        =				DetaConfig(
          num_queries=9_00     ,				encoder_ffn_dim=20_48     ,				decoder_ffn_dim=20_48     ,				num_feature_levels=5     ,				assign_first_stage=lowerCamelCase     ,				with_box_refine=lowerCamelCase     ,				two_stage=lowerCamelCase     ,				)

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

      return config

def 			lowerCAmelCase_			(  lowerCamelCase:       List[str]						)					->			Optional[Any]:


      '''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 			lowerCAmelCase_			(  lowerCamelCase:       Optional[Any]     ,				lowerCamelCase:       List[Any]     ,				lowerCamelCase:       Tuple						)					->			List[Any]:


      '''simple docstring'''




      lowerCamelCase__        =				dct.pop(lowerCamelCase						)
      lowerCamelCase__        =				val

def 			lowerCAmelCase_			(  lowerCamelCase:       Optional[int]     ,				lowerCamelCase:       Optional[Any]						)					->			List[str]:


      '''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 			lowerCAmelCase_			(  )					->			List[str]:


      '''simple docstring'''




      lowerCamelCase__        =				'http://images.cocodataset.org/val2017/000000039769.jpg'
      lowerCamelCase__        =				Image.open(requests.get(lowerCamelCase     ,				stream=lowerCamelCase						).raw						)

      return im

@torch.no_grad()
def 			lowerCAmelCase_			(  lowerCamelCase:       Dict     ,				lowerCamelCase:       Tuple     ,				lowerCamelCase:       List[Any]						)					->			str:


      '''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=lowerCamelCase						)
      lowerCamelCase__        =				torch.load(lowerCamelCase     ,				map_location='cpu'						)['model']

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

      # 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(lowerCamelCase						)
                  lowerCamelCase__        =				val
            if "input_proj" in key:
                  lowerCamelCase__        =				state_dict.pop(lowerCamelCase						)
                  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(lowerCamelCase						)
                  lowerCamelCase__        =				val

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

      lowerCamelCase__        =				'cuda' if torch.cuda.is_available() else 'cpu'
      model.to(lowerCamelCase						)

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

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

      # verify logits
      if model_name == "deta-resnet-50":
            lowerCamelCase__        =				torch.tensor(
                [[-7.3978, -2.5406, -4.1668], [-8.2684, -3.9933, -3.8096], [-7.0515, -3.7973, -5.8516]]						)
            lowerCamelCase__        =				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":
            lowerCamelCase__        =				torch.tensor(
                [[-7.1688, -2.4857, -4.8669], [-7.8630, -3.8154, -4.2674], [-7.2730, -4.1865, -5.5323]]						)
            lowerCamelCase__        =				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(lowerCamelCase						)     ,				atol=1e-4						)
      assert torch.allclose(outputs.pred_boxes[0, :3, :3]     ,				expected_boxes.to(lowerCamelCase						)     ,				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(lowerCamelCase						).mkdir(exist_ok=lowerCamelCase						)
            model.save_pretrained(lowerCamelCase						)
            processor.save_pretrained(lowerCamelCase						)

      # 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    :   Dict       =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    :   Optional[int]       =parser.parse_args()
      convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





from ..utils import DummyObject, requires_backends






class 			A			(		metaclass=_lowerCAmelCase		):
     """simple docstring"""

     _snake_case:	Optional[Any]     =["torch", "transformers", "invisible_watermark"]




     def __init__(    self  :   Dict	,  *A__  :   Optional[int]	,  **A__  :   Dict			)				->       Union[str, Any]:
           requires_backends(self	,  ['torch', 'transformers', 'invisible_watermark']			)




     @classmethod
     def  __snake_case   (    cls  :   Optional[int]	,  *A__  :   Dict	,  **A__  :   List[str]			)				->       Union[str, Any]:
           requires_backends(cls	,  ['torch', 'transformers', 'invisible_watermark']			)




     @classmethod
     def  __snake_case   (    cls  :   Optional[Any]	,  *A__  :   Dict	,  **A__  :   List[Any]			)				->       Dict:
           requires_backends(cls	,  ['torch', 'transformers', 'invisible_watermark']			)






class 			A			(		metaclass=_lowerCAmelCase		):
     """simple docstring"""

     _snake_case:	Dict     =["torch", "transformers", "invisible_watermark"]




     def __init__(    self  :   List[str]	,  *A__  :   str	,  **A__  :   int			)				->       List[Any]:
           requires_backends(self	,  ['torch', 'transformers', 'invisible_watermark']			)




     @classmethod
     def  __snake_case   (    cls  :   str	,  *A__  :   Any	,  **A__  :   Dict			)				->       Optional[Any]:
           requires_backends(cls	,  ['torch', 'transformers', 'invisible_watermark']			)




     @classmethod
     def  __snake_case   (    cls  :   List[str]	,  *A__  :   Optional[Any]	,  **A__  :   int			)				->       Dict:
           requires_backends(cls	,  ['torch', 'transformers', 'invisible_watermark']			)