code: 
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
  
  
  lowercase__			=	{"configuration_yoso": ["YOSO_PRETRAINED_CONFIG_ARCHIVE_MAP", "YosoConfig"]}
  
  try:
         if not is_torch_available():
                raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
         pass
  else:
         lowercase__			=	[
             "YOSO_PRETRAINED_MODEL_ARCHIVE_LIST",
             "YosoForMaskedLM",
             "YosoForMultipleChoice",
             "YosoForQuestionAnswering",
             "YosoForSequenceClassification",
             "YosoForTokenClassification",
             "YosoLayer",
             "YosoModel",
             "YosoPreTrainedModel",
         ]
  
  
  if TYPE_CHECKING:
         from .configuration_yoso import YOSO_PRETRAINED_CONFIG_ARCHIVE_MAP, YosoConfig
  
         try:
                if not is_torch_available():
                       raise OptionalDependencyNotAvailable()
         except OptionalDependencyNotAvailable:
                pass
         else:
                from .modeling_yoso import (
                    YOSO_PRETRAINED_MODEL_ARCHIVE_LIST,
                    YosoForMaskedLM,
                    YosoForMultipleChoice,
                    YosoForQuestionAnswering,
                    YosoForSequenceClassification,
                    YosoForTokenClassification,
                    YosoLayer,
                    YosoModel,
                    YosoPreTrainedModel,
                )
  
  
  else:
         import sys
  
         lowercase__			=	_LazyModule(__name__, globals()['''__file__'''], _import_structure)
  
  
style_context: 
  
  
  
  
  '''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)
  
  