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()
  lowerCAmelCase__   	=				logging.get_logger(__name__)
  def 							_UpperCAmelCase       ():
         _A   : Dict       					=				DetaConfig(
             num_queries=900       ,							encoder_ffn_dim=2048       ,							decoder_ffn_dim=2048       ,							num_feature_levels=5       ,							assign_first_stage=UpperCamelCase__       ,							with_box_refine=UpperCamelCase__       ,							two_stage=UpperCamelCase__       ,							)
  
         # set labels
         _A   : Any       					=				91
         _A   : List[Any]       					=				"huggingface/label-files"
         _A   : Optional[Any]       					=				"coco-detection-id2label.json"
         _A   : Tuple       					=				json.load(open(cached_download(hf_hub_url(UpperCamelCase__       ,							UpperCamelCase__       ,							repo_type="dataset"		)		)       ,							"r"		)		)
         _A   : List[Any]       					=				{int(UpperCamelCase__		): v for k, v in idalabel.items()}
         _A   : int       					=				idalabel
         _A   : Union[str, Any]       					=				{v: k for k, v in idalabel.items()}
  
         return config
  def 							_UpperCAmelCase       (UpperCamelCase__  :						Optional[Any]		):
         _A   : Dict       					=				[]
  
         # 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       (UpperCamelCase__  :						str       ,							UpperCamelCase__  :						Optional[Any]       ,							UpperCamelCase__  :						Optional[int]		):
         _A   : Optional[Any]       					=				dct.pop(UpperCamelCase__		)
         _A   : List[Any]       					=				val
  def 							_UpperCAmelCase       (UpperCamelCase__  :						List[Any]       ,							UpperCamelCase__  :						Optional[Any]		):
         _A   : Any       					=				config.d_model
         for i in range(config.decoder_layers		):
                # read in weights + bias of input projection layer of self-attention
                _A   : Optional[Any]       					=				state_dict.pop(f"transformer.decoder.layers.{i}.self_attn.in_proj_weight"		)
                _A   : 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
                _A   : Tuple       					=				in_proj_weight[:hidden_size, :]
                _A   : List[str]       					=				in_proj_bias[:hidden_size]
                _A   : Any       					=				in_proj_weight[
                    hidden_size : hidden_size * 2, :
                ]
                _A   : List[Any]       					=				in_proj_bias[hidden_size : hidden_size * 2]
                _A   : Any       					=				in_proj_weight[-hidden_size:, :]
                _A   : Any       					=				in_proj_bias[-hidden_size:]
  def 							_UpperCAmelCase       ():
         _A   : Union[str, Any]       					=				"http://images.cocodataset.org/val2017/000000039769.jpg"
         _A   : int       					=				Image.open(requests.get(UpperCamelCase__       ,							stream=UpperCamelCase__		).raw		)
  
         return im
  @torch.no_grad()
  def 							_UpperCAmelCase       (UpperCamelCase__  :						Any       ,							UpperCamelCase__  :						Optional[Any]       ,							UpperCamelCase__  :						List[Any]		):
         _A   : int       					=				get_deta_config()
  
         # load original state dict
         if model_name == "deta-resnet-50":
                _A   : Optional[int]       					=				"adet_checkpoint0011.pth"
         elif model_name == "deta-resnet-50-24-epochs":
                _A   : Dict       					=				"adet_2x_checkpoint0023.pth"
         else:
                raise ValueError(f"Model name {model_name} not supported"		)
         _A   : Union[str, Any]       					=				hf_hub_download(repo_id="nielsr/deta-checkpoints"       ,							filename=UpperCamelCase__		)
         _A   : Tuple       					=				torch.load(UpperCamelCase__       ,							map_location="cpu"		)["model"]
  
         # rename keys
         _A   : Union[str, Any]       					=				create_rename_keys(UpperCamelCase__		)
         for src, dest in rename_keys:
                rename_key(UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__		)
         read_in_decoder_q_k_v(UpperCamelCase__       ,							UpperCamelCase__		)
  
         # 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   : Union[str, Any]       					=				state_dict.pop(UpperCamelCase__		)
                       _A   : Any       					=				val
                if "input_proj" in key:
                       _A   : Union[str, Any]       					=				state_dict.pop(UpperCamelCase__		)
                       _A   : int       					=				val
                if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
                       _A   : Tuple       					=				state_dict.pop(UpperCamelCase__		)
                       _A   : Optional[Any]       					=				val
  
      # finally, create HuggingFace model and load state dict
         _A   : str       					=				DetaForObjectDetection(UpperCamelCase__		)
         model.load_state_dict(UpperCamelCase__		)
         model.eval()
  
         _A   : List[Any]       					=				"cuda" if torch.cuda.is_available() else "cpu"
         model.to(UpperCamelCase__		)
  
         # load image processor
         _A   : int       					=				DetaImageProcessor(format="coco_detection"		)
  
         # verify our conversion on image
         _A   : int       					=				prepare_img()
         _A   : int       					=				processor(images=UpperCamelCase__       ,							return_tensors="pt"		)
         _A   : List[Any]       					=				encoding["pixel_values"]
         _A   : Optional[Any]       					=				model(pixel_values.to(UpperCamelCase__		)		)
  
         # verify logits
         if model_name == "deta-resnet-50":
                _A   : List[str]       					=				torch.tensor(
                    [[-7.39_78, -2.54_06, -4.16_68], [-8.26_84, -3.99_33, -3.80_96], [-7.05_15, -3.79_73, -5.85_16]]		)
                _A   : List[str]       					=				torch.tensor([[0.50_43, 0.49_73, 0.99_98], [0.25_42, 0.54_89, 0.47_48], [0.54_90, 0.27_65, 0.05_70]]		)
         elif model_name == "deta-resnet-50-24-epochs":
                _A   : Any       					=				torch.tensor(
                    [[-7.16_88, -2.48_57, -4.86_69], [-7.86_30, -3.81_54, -4.26_74], [-7.27_30, -4.18_65, -5.53_23]]		)
                _A   : str       					=				torch.tensor([[0.50_21, 0.49_71, 0.99_94], [0.25_46, 0.54_86, 0.47_31], [0.16_86, 0.19_86, 0.21_42]]		)
  
         assert torch.allclose(outputs.logits[0, :3, :3]       ,							expected_logits.to(UpperCamelCase__		)       ,							atol=1E-4		)
         assert torch.allclose(outputs.pred_boxes[0, :3, :3]       ,							expected_boxes.to(UpperCamelCase__		)       ,							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(UpperCamelCase__		).mkdir(exist_ok=UpperCamelCase__		)
                model.save_pretrained(UpperCamelCase__		)
                processor.save_pretrained(UpperCamelCase__		)
  
         # 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__":
     lowerCAmelCase__   	=				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.'
     )
     lowerCAmelCase__   	=				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 							_UpperCAmelCase       (UpperCamelCase__  :						Union[str, Any]       ,							UpperCamelCase__  :						Dict		):
         _A   : Dict       					=				UNetaDModel(**UpperCamelCase__		)
         _A   : Any       					=				checkpoint["all_modules.0.W"].data
         _A   : Any       					=				checkpoint["all_modules.0.W"].data
         _A   : Union[str, Any]       					=				checkpoint["all_modules.1.weight"].data
         _A   : Optional[Any]       					=				checkpoint["all_modules.1.bias"].data
  
         _A   : Dict       					=				checkpoint["all_modules.2.weight"].data
         _A   : List[str]       					=				checkpoint["all_modules.2.bias"].data
  
         _A   : Dict       					=				checkpoint["all_modules.3.weight"].data
         _A   : Union[str, Any]       					=				checkpoint["all_modules.3.bias"].data
  
         _A   : List[str]       					=				checkpoint[list(checkpoint.keys()		)[-4]].data
         _A   : Any       					=				checkpoint[list(checkpoint.keys()		)[-3]].data
         _A   : List[str]       					=				checkpoint[list(checkpoint.keys()		)[-2]].data
         _A   : Optional[int]       					=				checkpoint[list(checkpoint.keys()		)[-1]].data
  
         _A   : str       					=				4
  
         def set_attention_weights(UpperCamelCase__  :						Optional[Any]       ,							UpperCamelCase__  :						Optional[Any]       ,							UpperCamelCase__  :						Tuple		):
                _A   : Any       					=				old_checkpoint[f"all_modules.{index}.NIN_0.W"].data.T
                _A   : int       					=				old_checkpoint[f"all_modules.{index}.NIN_1.W"].data.T
                _A   : int       					=				old_checkpoint[f"all_modules.{index}.NIN_2.W"].data.T
  
                _A   : Any       					=				old_checkpoint[f"all_modules.{index}.NIN_0.b"].data
                _A   : Any       					=				old_checkpoint[f"all_modules.{index}.NIN_1.b"].data
                _A   : List[str]       					=				old_checkpoint[f"all_modules.{index}.NIN_2.b"].data
  
                _A   : Optional[Any]       					=				old_checkpoint[f"all_modules.{index}.NIN_3.W"].data.T
                _A   : Dict       					=				old_checkpoint[f"all_modules.{index}.NIN_3.b"].data
  
                _A   : Union[str, Any]       					=				old_checkpoint[f"all_modules.{index}.GroupNorm_0.weight"].data
                _A   : int       					=				old_checkpoint[f"all_modules.{index}.GroupNorm_0.bias"].data
  
         def set_resnet_weights(UpperCamelCase__  :						str       ,							UpperCamelCase__  :						Any       ,							UpperCamelCase__  :						Dict		):
                _A   : Optional[int]       					=				old_checkpoint[f"all_modules.{index}.Conv_0.weight"].data
                _A   : Optional[int]       					=				old_checkpoint[f"all_modules.{index}.Conv_0.bias"].data
                _A   : Union[str, Any]       					=				old_checkpoint[f"all_modules.{index}.GroupNorm_0.weight"].data
                _A   : int       					=				old_checkpoint[f"all_modules.{index}.GroupNorm_0.bias"].data
  
                _A   : Union[str, Any]       					=				old_checkpoint[f"all_modules.{index}.Conv_1.weight"].data
                _A   : Optional[int]       					=				old_checkpoint[f"all_modules.{index}.Conv_1.bias"].data
                _A   : Tuple       					=				old_checkpoint[f"all_modules.{index}.GroupNorm_1.weight"].data
                _A   : str       					=				old_checkpoint[f"all_modules.{index}.GroupNorm_1.bias"].data
  
                _A   : Tuple       					=				old_checkpoint[f"all_modules.{index}.Dense_0.weight"].data
                _A   : Union[str, Any]       					=				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   : Tuple       					=				old_checkpoint[f"all_modules.{index}.Conv_2.weight"].data
                       _A   : Optional[Any]       					=				old_checkpoint[f"all_modules.{index}.Conv_2.bias"].data
  
         for i, block in enumerate(new_model_architecture.downsample_blocks		):
                _A   : Any       					=				hasattr(UpperCamelCase__       ,							"attentions"		)
                for j in range(len(block.resnets		)		):
                       set_resnet_weights(block.resnets[j]       ,							UpperCamelCase__       ,							UpperCamelCase__		)
                       module_index += 1
                       if has_attentions:
                              set_attention_weights(block.attentions[j]       ,							UpperCamelCase__       ,							UpperCamelCase__		)
                              module_index += 1
  
                if hasattr(UpperCamelCase__       ,							"downsamplers"		) and block.downsamplers is not None:
                       set_resnet_weights(block.resnet_down       ,							UpperCamelCase__       ,							UpperCamelCase__		)
                       module_index += 1
                       _A   : List[str]       					=				checkpoint[f"all_modules.{module_index}.Conv_0.weight"].data
                       _A   : Optional[int]       					=				checkpoint[f"all_modules.{module_index}.Conv_0.bias"].data
                       module_index += 1
  
         set_resnet_weights(new_model_architecture.mid_block.resnets[0]       ,							UpperCamelCase__       ,							UpperCamelCase__		)
         module_index += 1
         set_attention_weights(new_model_architecture.mid_block.attentions[0]       ,							UpperCamelCase__       ,							UpperCamelCase__		)
         module_index += 1
         set_resnet_weights(new_model_architecture.mid_block.resnets[1]       ,							UpperCamelCase__       ,							UpperCamelCase__		)
         module_index += 1
  
         for i, block in enumerate(new_model_architecture.up_blocks		):
                _A   : Dict       					=				hasattr(UpperCamelCase__       ,							"attentions"		)
                for j in range(len(block.resnets		)		):
                       set_resnet_weights(block.resnets[j]       ,							UpperCamelCase__       ,							UpperCamelCase__		)
                       module_index += 1
                if has_attentions:
                       set_attention_weights(
                           block.attentions[0]       ,							UpperCamelCase__       ,							UpperCamelCase__		)  # why can there only be a single attention layer for up?
                       module_index += 1
  
                if hasattr(UpperCamelCase__       ,							"resnet_up"		) and block.resnet_up is not None:
                       _A   : Dict       					=				checkpoint[f"all_modules.{module_index}.weight"].data
                       _A   : List[Any]       					=				checkpoint[f"all_modules.{module_index}.bias"].data
                       module_index += 1
                       _A   : int       					=				checkpoint[f"all_modules.{module_index}.weight"].data
                       _A   : List[Any]       					=				checkpoint[f"all_modules.{module_index}.bias"].data
                       module_index += 1
                       set_resnet_weights(block.resnet_up       ,							UpperCamelCase__       ,							UpperCamelCase__		)
                       module_index += 1
  
         _A   : str       					=				checkpoint[f"all_modules.{module_index}.weight"].data
         _A   : str       					=				checkpoint[f"all_modules.{module_index}.bias"].data
         module_index += 1
         _A   : Tuple       					=				checkpoint[f"all_modules.{module_index}.weight"].data
         _A   : str       					=				checkpoint[f"all_modules.{module_index}.bias"].data
  
         return new_model_architecture.state_dict()
  
  
  if __name__ == "__main__":
      lowerCAmelCase__   	=				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.',
      )
  
      lowerCAmelCase__   	=				parser.parse_args()
  
      lowerCAmelCase__   	=				torch.load(args.checkpoint_path, map_location='cpu')
  
      with open(args.config_file) as f:
          lowerCAmelCase__   	=				json.loads(f.read())
  
      lowerCAmelCase__   	=				convert_ncsnpp_checkpoint(
          checkpoint,
          config,
      )
  
      if "sde" in config:
          del config["sde"]
  
      lowerCAmelCase__   	=				UNetaDModel(**config)
      model.load_state_dict(converted_checkpoint)
  
      try:
          lowerCAmelCase__   	=				ScoreSdeVeScheduler.from_config('/'.join(args.checkpoint_path.split('/')[:-1]))
  
          lowerCAmelCase__   	=				ScoreSdeVePipeline(unet=model, scheduler=scheduler)
          pipe.save_pretrained(args.dump_path)
      except:  # noqa: E722
          model.save_pretrained(args.dump_path)
  