code: 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)
  
  
style_context: from unittest.mock import patch
  
  import datasets
  from datasets import Dataset
  def 				__lowercase	(						)				->Union[str, Any]:
  
    '''simple docstring'''
  
  
  
  
    a__     		=     Dataset.from_dict({"col_1": [3, 2, 0, 1]}					)
  
    with patch("tqdm.auto.tqdm"					) as mock_tqdm:
      datasets.disable_progress_bar()
      dset.map(lambda lowerCamelCase__					: {"col_2": x["col_1"] + 1}					)
      mock_tqdm.assert_not_called()
  
      mock_tqdm.reset_mock()
  
      datasets.enable_progress_bar()
      dset.map(lambda lowerCamelCase__					: {"col_2": x["col_1"] + 1}					)
      mock_tqdm.assert_called()
  
  