code: 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   						=    logging.get_logger(__name__)
  
  
  
  
  
  def 				__lowercase	(						)				->Optional[Any]:
  
    '''simple docstring'''
  
  
  
  
    a__     		=     DetaConfig(
        num_queries=9_00 ,				encoder_ffn_dim=20_48 ,				decoder_ffn_dim=20_48 ,				num_feature_levels=5 ,				assign_first_stage=lowercase__ ,				with_box_refine=lowercase__ ,				two_stage=lowercase__ ,				)
  
    # set labels
    a__     		=     91
    a__     		=     "huggingface/label-files"
    a__     		=     "coco-detection-id2label.json"
    a__     		=     json.load(open(cached_download(hf_hub_url(lowercase__ ,				lowercase__ ,				repo_type="dataset"					)					) ,				"r"					)					)
    a__     		=     {int(lowercase__					): v for k, v in idalabel.items()}
    a__     		=     idalabel
    a__     		=     {v: k for k, v in idalabel.items()}
  
    return config
  
  
  
  
  
  def 				__lowercase	(						lowerCamelCase__					)				->Optional[int]:
  
    '''simple docstring'''
  
  
  
  
    a__     		=     []
  
    # 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 				__lowercase	(						lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__					)				->List[Any]:
  
    '''simple docstring'''
  
  
  
  
    a__     		=     dct.pop(lowercase__					)
    a__     		=     val
  
  
  
  
  
  def 				__lowercase	(						lowerCamelCase__ ,				lowerCamelCase__					)				->str:
  
    '''simple docstring'''
  
  
  
  
    a__     		=     config.d_model
    for i in range(config.decoder_layers					):
      # read in weights + bias of input projection layer of self-attention
      a__     		=     state_dict.pop(f"transformer.decoder.layers.{i}.self_attn.in_proj_weight"					)
      a__     		=     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
      a__     		=     in_proj_weight[:hidden_size, :]
      a__     		=     in_proj_bias[:hidden_size]
      a__     		=     in_proj_weight[
          hidden_size : hidden_size * 2, :
      ]
      a__     		=     in_proj_bias[hidden_size : hidden_size * 2]
      a__     		=     in_proj_weight[-hidden_size:, :]
      a__     		=     in_proj_bias[-hidden_size:]
  
  
  
  
  
  def 				__lowercase	(						)				->Dict:
  
    '''simple docstring'''
  
  
  
  
    a__     		=     "http://images.cocodataset.org/val2017/000000039769.jpg"
    a__     		=     Image.open(requests.get(lowercase__ ,				stream=lowercase__					).raw					)
  
    return im
  
  
  
  
  
  @torch.no_grad()
  def 				__lowercase	(						lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__					)				->Tuple:
  
    '''simple docstring'''
  
  
  
  
    a__     		=     get_deta_config()
  
    # load original state dict
    if model_name == "deta-resnet-50":
      a__     		=     "adet_checkpoint0011.pth"
    elif model_name == "deta-resnet-50-24-epochs":
      a__     		=     "adet_2x_checkpoint0023.pth"
    else:
      raise ValueError(f"Model name {model_name} not supported"					)
    a__     		=     hf_hub_download(repo_id="nielsr/deta-checkpoints" ,				filename=lowercase__					)
    a__     		=     torch.load(lowercase__ ,				map_location="cpu"					)["model"]
  
    # rename keys
    a__     		=     create_rename_keys(lowercase__					)
    for src, dest in rename_keys:
      rename_key(lowercase__ ,				lowercase__ ,				lowercase__					)
    read_in_decoder_q_k_v(lowercase__ ,				lowercase__					)
  
    # fix some prefixes
    for key in state_dict.copy().keys():
      if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
        a__     		=     state_dict.pop(lowercase__					)
        a__     		=     val
      if "input_proj" in key:
        a__     		=     state_dict.pop(lowercase__					)
        a__     		=     val
      if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
        a__     		=     state_dict.pop(lowercase__					)
        a__     		=     val
  
      # finally, create HuggingFace model and load state dict
    a__     		=     DetaForObjectDetection(lowercase__					)
    model.load_state_dict(lowercase__					)
    model.eval()
  
    a__     		=     "cuda" if torch.cuda.is_available() else "cpu"
    model.to(lowercase__					)
  
    # load image processor
    a__     		=     DetaImageProcessor(format="coco_detection"					)
  
    # verify our conversion on image
    a__     		=     prepare_img()
    a__     		=     processor(images=lowercase__ ,				return_tensors="pt"					)
    a__     		=     encoding["pixel_values"]
    a__     		=     model(pixel_values.to(lowercase__					)					)
  
    # verify logits
    if model_name == "deta-resnet-50":
      a__     		=     torch.tensor(
          [[-7.3978, -2.5406, -4.1668], [-8.2684, -3.9933, -3.8096], [-7.0515, -3.7973, -5.8516]]					)
      a__     		=     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":
      a__     		=     torch.tensor(
          [[-7.1688, -2.4857, -4.8669], [-7.8630, -3.8154, -4.2674], [-7.2730, -4.1865, -5.5323]]					)
      a__     		=     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(lowercase__					) ,				atol=1E-4					)
    assert torch.allclose(outputs.pred_boxes[0, :3, :3] ,				expected_boxes.to(lowercase__					) ,				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(lowercase__					).mkdir(exist_ok=lowercase__					)
      model.save_pretrained(lowercase__					)
      processor.save_pretrained(lowercase__					)
  
    # 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   						=    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   						=    parser.parse_args()
       convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
  
  
style_context: import argparse
  import json
  
  import torch
  
  from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
  def 				__lowercase	(						lowerCamelCase__ ,				lowerCamelCase__					)				->List[str]:
  
    '''simple docstring'''
  
  
  
  
    a__     		=     UNetaDModel(**lowerCamelCase__					)
    a__     		=     checkpoint["all_modules.0.W"].data
    a__     		=     checkpoint["all_modules.0.W"].data
    a__     		=     checkpoint["all_modules.1.weight"].data
    a__     		=     checkpoint["all_modules.1.bias"].data
  
    a__     		=     checkpoint["all_modules.2.weight"].data
    a__     		=     checkpoint["all_modules.2.bias"].data
  
    a__     		=     checkpoint["all_modules.3.weight"].data
    a__     		=     checkpoint["all_modules.3.bias"].data
  
    a__     		=     checkpoint[list(checkpoint.keys()					)[-4]].data
    a__     		=     checkpoint[list(checkpoint.keys()					)[-3]].data
    a__     		=     checkpoint[list(checkpoint.keys()					)[-2]].data
    a__     		=     checkpoint[list(checkpoint.keys()					)[-1]].data
  
    a__     		=     4
  
    def set_attention_weights(lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__					):
      a__     		=     old_checkpoint[f"all_modules.{index}.NIN_0.W"].data.T
      a__     		=     old_checkpoint[f"all_modules.{index}.NIN_1.W"].data.T
      a__     		=     old_checkpoint[f"all_modules.{index}.NIN_2.W"].data.T
  
      a__     		=     old_checkpoint[f"all_modules.{index}.NIN_0.b"].data
      a__     		=     old_checkpoint[f"all_modules.{index}.NIN_1.b"].data
      a__     		=     old_checkpoint[f"all_modules.{index}.NIN_2.b"].data
  
      a__     		=     old_checkpoint[f"all_modules.{index}.NIN_3.W"].data.T
      a__     		=     old_checkpoint[f"all_modules.{index}.NIN_3.b"].data
  
      a__     		=     old_checkpoint[f"all_modules.{index}.GroupNorm_0.weight"].data
      a__     		=     old_checkpoint[f"all_modules.{index}.GroupNorm_0.bias"].data
  
    def set_resnet_weights(lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__					):
      a__     		=     old_checkpoint[f"all_modules.{index}.Conv_0.weight"].data
      a__     		=     old_checkpoint[f"all_modules.{index}.Conv_0.bias"].data
      a__     		=     old_checkpoint[f"all_modules.{index}.GroupNorm_0.weight"].data
      a__     		=     old_checkpoint[f"all_modules.{index}.GroupNorm_0.bias"].data
  
      a__     		=     old_checkpoint[f"all_modules.{index}.Conv_1.weight"].data
      a__     		=     old_checkpoint[f"all_modules.{index}.Conv_1.bias"].data
      a__     		=     old_checkpoint[f"all_modules.{index}.GroupNorm_1.weight"].data
      a__     		=     old_checkpoint[f"all_modules.{index}.GroupNorm_1.bias"].data
  
      a__     		=     old_checkpoint[f"all_modules.{index}.Dense_0.weight"].data
      a__     		=     old_checkpoint[f"all_modules.{index}.Dense_0.bias"].data
  
      if new_layer.in_channels != new_layer.out_channels or new_layer.up or new_layer.down:
        a__     		=     old_checkpoint[f"all_modules.{index}.Conv_2.weight"].data
        a__     		=     old_checkpoint[f"all_modules.{index}.Conv_2.bias"].data
  
    for i, block in enumerate(new_model_architecture.downsample_blocks					):
      a__     		=     hasattr(lowerCamelCase__ ,				"attentions"					)
      for j in range(len(block.resnets					)					):
        set_resnet_weights(block.resnets[j] ,				lowerCamelCase__ ,				lowerCamelCase__					)
        module_index += 1
        if has_attentions:
          set_attention_weights(block.attentions[j] ,				lowerCamelCase__ ,				lowerCamelCase__					)
          module_index += 1
  
      if hasattr(lowerCamelCase__ ,				"downsamplers"					) and block.downsamplers is not None:
        set_resnet_weights(block.resnet_down ,				lowerCamelCase__ ,				lowerCamelCase__					)
        module_index += 1
        a__     		=     checkpoint[f"all_modules.{module_index}.Conv_0.weight"].data
        a__     		=     checkpoint[f"all_modules.{module_index}.Conv_0.bias"].data
        module_index += 1
  
    set_resnet_weights(new_model_architecture.mid_block.resnets[0] ,				lowerCamelCase__ ,				lowerCamelCase__					)
    module_index += 1
    set_attention_weights(new_model_architecture.mid_block.attentions[0] ,				lowerCamelCase__ ,				lowerCamelCase__					)
    module_index += 1
    set_resnet_weights(new_model_architecture.mid_block.resnets[1] ,				lowerCamelCase__ ,				lowerCamelCase__					)
    module_index += 1
  
    for i, block in enumerate(new_model_architecture.up_blocks					):
      a__     		=     hasattr(lowerCamelCase__ ,				"attentions"					)
      for j in range(len(block.resnets					)					):
        set_resnet_weights(block.resnets[j] ,				lowerCamelCase__ ,				lowerCamelCase__					)
        module_index += 1
      if has_attentions:
        set_attention_weights(
            block.attentions[0] ,				lowerCamelCase__ ,				lowerCamelCase__					)  # why can there only be a single attention layer for up?
        module_index += 1
  
      if hasattr(lowerCamelCase__ ,				"resnet_up"					) and block.resnet_up is not None:
        a__     		=     checkpoint[f"all_modules.{module_index}.weight"].data
        a__     		=     checkpoint[f"all_modules.{module_index}.bias"].data
        module_index += 1
        a__     		=     checkpoint[f"all_modules.{module_index}.weight"].data
        a__     		=     checkpoint[f"all_modules.{module_index}.bias"].data
        module_index += 1
        set_resnet_weights(block.resnet_up ,				lowerCamelCase__ ,				lowerCamelCase__					)
        module_index += 1
  
    a__     		=     checkpoint[f"all_modules.{module_index}.weight"].data
    a__     		=     checkpoint[f"all_modules.{module_index}.bias"].data
    module_index += 1
    a__     		=     checkpoint[f"all_modules.{module_index}.weight"].data
    a__     		=     checkpoint[f"all_modules.{module_index}.bias"].data
  
    return new_model_architecture.state_dict()
  
  
  if __name__ == "__main__":
       _SCREAMING_SNAKE_CASE   						=    argparse.ArgumentParser()
  
       parser.add_argument(
           """--checkpoint_path""",
           default="""/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_pytorch_model.bin""",
           type=str,
           required=False,
           help="""Path to the checkpoint to convert.""",
       )
  
       parser.add_argument(
           """--config_file""",
           default="""/Users/arthurzucker/Work/diffusers/ArthurZ/config.json""",
           type=str,
           required=False,
           help="""The config json file corresponding to the architecture.""",
       )
  
       parser.add_argument(
           """--dump_path""",
           default="""/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_model_new.pt""",
           type=str,
           required=False,
           help="""Path to the output model.""",
       )
  
       _SCREAMING_SNAKE_CASE   						=    parser.parse_args()
  
       _SCREAMING_SNAKE_CASE   						=    torch.load(args.checkpoint_path, map_location="""cpu""")
  
       with open(args.config_file) as f:
            _SCREAMING_SNAKE_CASE   						=    json.loads(f.read())
  
       _SCREAMING_SNAKE_CASE   						=    convert_ncsnpp_checkpoint(
           checkpoint,
           config,
       )
  
       if "sde" in config:
            del config["sde"]
  
       _SCREAMING_SNAKE_CASE   						=    UNetaDModel(**config)
       model.load_state_dict(converted_checkpoint)
  
       try:
            _SCREAMING_SNAKE_CASE   						=    ScoreSdeVeScheduler.from_config("""/""".join(args.checkpoint_path.split("""/""")[:-1]))
  
            _SCREAMING_SNAKE_CASE   						=    ScoreSdeVePipeline(unet=model, scheduler=scheduler)
            pipe.save_pretrained(args.dump_path)
       except:  # noqa: E722
            model.save_pretrained(args.dump_path)
  
  