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)


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)

