code: 
  
  """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						=     logging.get_logger(__name__)
  
  
  
  
  def 				a__	(	)  ->   Tuple:
        UpperCAmelCase__ :       Any						=						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
        UpperCAmelCase__ :       Tuple						=						91
        UpperCAmelCase__ :       Optional[int]						=						"""huggingface/label-files"""
        UpperCAmelCase__ :       str						=						"""coco-detection-id2label.json"""
        UpperCAmelCase__ :       Optional[int]						=						json.load(open(cached_download(hf_hub_url(lowerCAmelCase ,  lowerCAmelCase ,  repo_type="""dataset"""						)						) ,  """r"""						)						)
        UpperCAmelCase__ :       Optional[Any]						=						{int(lowerCAmelCase						): v for k, v in idalabel.items()}
        UpperCAmelCase__ :       str						=						idalabel
        UpperCAmelCase__ :       List[str]						=						{v: k for k, v in idalabel.items()}
  
        return config
  
  
  
  
  def 				a__	(	lowerCAmelCase						)  ->   List[str]:
        UpperCAmelCase__ :       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 				a__	(	lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase						)  ->   Any:
        UpperCAmelCase__ :       Optional[Any]						=						dct.pop(lowerCAmelCase						)
        UpperCAmelCase__ :       Any						=						val
  
  
  
  
  def 				a__	(	lowerCAmelCase ,  lowerCAmelCase						)  ->   int:
        # transformer decoder self-attention layers
        UpperCAmelCase__ :       str						=						config.d_model
        for i in range(config.decoder_layers						):
              # read in weights + bias of input projection layer of self-attention
              UpperCAmelCase__ :       Any						=						state_dict.pop(F"""transformer.decoder.layers.{i}.self_attn.in_proj_weight"""						)
              UpperCAmelCase__ :       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
              UpperCAmelCase__ :       int						=						in_proj_weight[:hidden_size, :]
              UpperCAmelCase__ :       str						=						in_proj_bias[:hidden_size]
              UpperCAmelCase__ :       Dict						=						in_proj_weight[
                  hidden_size : hidden_size * 2, :
              ]
              UpperCAmelCase__ :       List[Any]						=						in_proj_bias[hidden_size : hidden_size * 2]
              UpperCAmelCase__ :       List[str]						=						in_proj_weight[-hidden_size:, :]
              UpperCAmelCase__ :       List[Any]						=						in_proj_bias[-hidden_size:]
  
  
  
  
  def 				a__	(	)  ->   List[str]:
        UpperCAmelCase__ :       Union[str, Any]						=						"""http://images.cocodataset.org/val2017/000000039769.jpg"""
        UpperCAmelCase__ :       List[str]						=						Image.open(requests.get(lowerCAmelCase ,  stream=lowerCAmelCase						).raw						)
  
        return im
  
  
  
  
  @torch.no_grad()
  def 				a__	(	lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase						)  ->   Tuple:
        UpperCAmelCase__ :       List[str]						=						get_deta_config()
  
        # load original state dict
        if model_name == "deta-resnet-50":
              UpperCAmelCase__ :       Optional[int]						=						"""adet_checkpoint0011.pth"""
        elif model_name == "deta-resnet-50-24-epochs":
              UpperCAmelCase__ :       List[str]						=						"""adet_2x_checkpoint0023.pth"""
        else:
              raise ValueError(F"""Model name {model_name} not supported"""						)
        UpperCAmelCase__ :       Union[str, Any]						=						hf_hub_download(repo_id="""nielsr/deta-checkpoints""" ,  filename=lowerCAmelCase						)
        UpperCAmelCase__ :       List[Any]						=						torch.load(lowerCAmelCase ,  map_location="""cpu"""						)["""model"""]
  
        # rename keys
        UpperCAmelCase__ :       List[Any]						=						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:
                    UpperCAmelCase__ :       str						=						state_dict.pop(lowerCAmelCase						)
                    UpperCAmelCase__ :       Union[str, Any]						=						val
              if "input_proj" in key:
                    UpperCAmelCase__ :       Optional[int]						=						state_dict.pop(lowerCAmelCase						)
                    UpperCAmelCase__ :       List[Any]						=						val
              if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
                    UpperCAmelCase__ :       Optional[Any]						=						state_dict.pop(lowerCAmelCase						)
                    UpperCAmelCase__ :       Dict						=						val
  
      # finally, create HuggingFace model and load state dict
        UpperCAmelCase__ :       List[str]						=						DetaForObjectDetection(lowerCAmelCase						)
        model.load_state_dict(lowerCAmelCase						)
        model.eval()
  
        UpperCAmelCase__ :       List[Any]						=						"""cuda""" if torch.cuda.is_available() else """cpu"""
        model.to(lowerCAmelCase						)
  
        # load image processor
        UpperCAmelCase__ :       str						=						DetaImageProcessor(format="""coco_detection"""						)
  
        # verify our conversion on image
        UpperCAmelCase__ :       str						=						prepare_img()
        UpperCAmelCase__ :       Any						=						processor(images=lowerCAmelCase ,  return_tensors="""pt"""						)
        UpperCAmelCase__ :       str						=						encoding["""pixel_values"""]
        UpperCAmelCase__ :       int						=						model(pixel_values.to(lowerCAmelCase						)						)
  
        # verify logits
        if model_name == "deta-resnet-50":
              UpperCAmelCase__ :       int						=						torch.tensor(
                  [[-7.3978, -2.5406, -4.1668], [-8.2684, -3.9933, -3.8096], [-7.0515, -3.7973, -5.8516]]						)
              UpperCAmelCase__ :       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":
              UpperCAmelCase__ :       str						=						torch.tensor(
                  [[-7.1688, -2.4857, -4.8669], [-7.8630, -3.8154, -4.2674], [-7.2730, -4.1865, -5.5323]]						)
              UpperCAmelCase__ :       Optional[int]						=						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__":
       _A						=     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						=     parser.parse_args()
       convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
  
  
style_context: 
  
  """simple docstring"""
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_flax_available,
      is_keras_nlp_available,
      is_tensorflow_text_available,
      is_tf_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  _A						=     {
      """configuration_gpt2""": ["""GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPT2Config""", """GPT2OnnxConfig"""],
      """tokenization_gpt2""": ["""GPT2Tokenizer"""],
  }
  
  try:
      if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      _A						=     ["""GPT2TokenizerFast"""]
  
  try:
      if not is_torch_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      _A						=     [
          """GPT2_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """GPT2DoubleHeadsModel""",
          """GPT2ForQuestionAnswering""",
          """GPT2ForSequenceClassification""",
          """GPT2ForTokenClassification""",
          """GPT2LMHeadModel""",
          """GPT2Model""",
          """GPT2PreTrainedModel""",
          """load_tf_weights_in_gpt2""",
      ]
  
  try:
      if not is_tf_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      _A						=     [
          """TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """TFGPT2DoubleHeadsModel""",
          """TFGPT2ForSequenceClassification""",
          """TFGPT2LMHeadModel""",
          """TFGPT2MainLayer""",
          """TFGPT2Model""",
          """TFGPT2PreTrainedModel""",
      ]
  
  try:
      if not is_keras_nlp_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      _A						=     ["""TFGPT2Tokenizer"""]
  
  try:
      if not is_flax_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      _A						=     ["""FlaxGPT2LMHeadModel""", """FlaxGPT2Model""", """FlaxGPT2PreTrainedModel"""]
  
  if TYPE_CHECKING:
      from .configuration_gpta import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTaConfig, GPTaOnnxConfig
      from .tokenization_gpta import GPTaTokenizer
  
      try:
          if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .tokenization_gpta_fast import GPTaTokenizerFast
  
      try:
          if not is_torch_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .modeling_gpta import (
              GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
              GPTaDoubleHeadsModel,
              GPTaForQuestionAnswering,
              GPTaForSequenceClassification,
              GPTaForTokenClassification,
              GPTaLMHeadModel,
              GPTaModel,
              GPTaPreTrainedModel,
              load_tf_weights_in_gpta,
          )
  
      try:
          if not is_tf_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .modeling_tf_gpta import (
              TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFGPTaDoubleHeadsModel,
              TFGPTaForSequenceClassification,
              TFGPTaLMHeadModel,
              TFGPTaMainLayer,
              TFGPTaModel,
              TFGPTaPreTrainedModel,
          )
  
      try:
          if not is_keras_nlp_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .tokenization_gpta_tf import TFGPTaTokenizer
  
      try:
          if not is_flax_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel, FlaxGPTaPreTrainedModel
  
  else:
      import sys
  
      _A						=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
  
  