





'''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()
A__       :     int          =logging.get_logger(__name__)
def      UpperCamelCase__   (	):





      """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=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__   (	lowerCAmelCase						):





      """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__   (	lowerCAmelCase							,			lowerCAmelCase							,			lowerCAmelCase						):





      """simple docstring"""


      _lowerCAmelCase							  =							dct.pop(snake_case__						)
      _lowerCAmelCase							  =							val
def      UpperCamelCase__   (	lowerCAmelCase							,			lowerCAmelCase						):





      """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__   (	):





      """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__   (	lowerCAmelCase							,			lowerCAmelCase							,			lowerCAmelCase						):





      """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_978, -2.5_406, -4.1_668], [-8.2_684, -3.9_933, -3.8_096], [-7.0_515, -3.7_973, -5.8_516]]						)
            _lowerCAmelCase							  =							torch.tensor([[0.5_043, 0.4_973, 0.9_998], [0.2_542, 0.5_489, 0.4_748], [0.5_490, 0.2_765, 0.0_570]]						)
      elif model_name == "deta-resnet-50-24-epochs":
            _lowerCAmelCase							  =							torch.tensor(
                [[-7.1_688, -2.4_857, -4.8_669], [-7.8_630, -3.8_154, -4.2_674], [-7.2_730, -4.1_865, -5.5_323]]						)
            _lowerCAmelCase							  =							torch.tensor([[0.5_021, 0.4_971, 0.9_994], [0.2_546, 0.5_486, 0.4_731], [0.1_686, 0.1_986, 0.2_142]]						)

      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__":
      A__       :     List[str]          =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.'''
      )
      A__       :     Union[str, Any]          =parser.parse_args()
      convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






'''simple docstring'''






import torch

from transformers import PreTrainedModel

from .custom_configuration import CustomConfig, NoSuperInitConfig




class 				UpperCAmelCase  (    snake_case_     ):
      _lowercase:    str		  =     CustomConfig




      def __init__(    self    :				Optional[int]    ,    __snake_case    :				Tuple						)       ->   Optional[Any]:
            super().__init__(__snake_case						)
            _lowerCAmelCase							  =							torch.nn.Linear(config.hidden_size    ,    config.hidden_size						)




      def        lowercase__					(    self    :				Dict    ,    __snake_case    :				List[Any]						)       ->   Dict:
            return self.linear(__snake_case						)




      def        lowercase__					(    self    :				List[Any]    ,    __snake_case    :				int						)       ->   Any:
            pass




class 				UpperCAmelCase  (    snake_case_     ):
      _lowercase:    List[str]		  =     NoSuperInitConfig




      def __init__(    self    :				List[str]    ,    __snake_case    :				List[str]						)       ->   int:
            super().__init__(__snake_case						)
            _lowerCAmelCase							  =							torch.nn.Linear(config.attribute    ,    config.attribute						)




      def        lowercase__					(    self    :				List[str]    ,    __snake_case    :				Dict						)       ->   Any:
            return self.linear(__snake_case						)




      def        lowercase__					(    self    :				Optional[int]    ,    __snake_case    :				List[Any]						)       ->   Dict:
            pass






