code: from typing import Dict, List, Optional, Union
  
  import numpy as np
  
  from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
  from ...image_transforms import (
      center_crop,
      convert_to_rgb,
      get_resize_output_image_size,
      normalize,
      rescale,
      resize,
      to_channel_dimension_format,
  )
  from ...image_utils import (
      OPENAI_CLIP_MEAN,
      OPENAI_CLIP_STD,
      ChannelDimension,
      ImageInput,
      PILImageResampling,
      make_list_of_images,
      to_numpy_array,
      valid_images,
  )
  from ...utils import TensorType, is_vision_available, logging
  
  
  _SCREAMING_SNAKE_CASE   						=    logging.get_logger(__name__)
  
  
  if is_vision_available():
       import PIL
  
  
  
  
  
  
  
  class 					UpperCAmelCase_				(							_a    ):
      UpperCAmelCase_			:       List[Any]						 =							['pixel_values']
  
  
  
  
      def __init__(					self  :   str      ,      lowerCAmelCase  :   bool = True      ,      lowerCAmelCase  :   Dict[str, int] = None      ,      lowerCAmelCase  :   PILImageResampling = PILImageResampling.BICUBIC      ,      lowerCAmelCase  :   bool = True      ,      lowerCAmelCase  :   Dict[str, int] = None      ,      lowerCAmelCase  :   bool = True      ,      lowerCAmelCase  :   Union[int, float] = 1 / 255      ,      lowerCAmelCase  :   bool = True      ,      lowerCAmelCase  :   Optional[Union[float, List[float]]] = None      ,      lowerCAmelCase  :   Optional[Union[float, List[float]]] = None      ,      lowerCAmelCase  :   bool = True      ,      **lowerCAmelCase  :   Any      ,      )	-> None:
        super().__init__(**lowerCAmelCase   )
        a__     		=     size if size is not None else {"shortest_edge": 224}
        a__     		=     get_size_dict(lowerCAmelCase      ,      default_to_square=lowerCAmelCase   )
        a__     		=     crop_size if crop_size is not None else {"height": 224, "width": 224}
        a__     		=     get_size_dict(lowerCAmelCase   )
  
        a__     		=     do_resize
        a__     		=     size
        a__     		=     resample
        a__     		=     do_center_crop
        a__     		=     crop_size
        a__     		=     do_rescale
        a__     		=     rescale_factor
        a__     		=     do_normalize
        a__     		=     image_mean if image_mean is not None else OPENAI_CLIP_MEAN
        a__     		=     image_std if image_std is not None else OPENAI_CLIP_STD
        a__     		=     do_convert_rgb
  
  
  
  
      def 						__lowerCamelCase (					self  :   Optional[int]      ,      lowerCAmelCase  :   np.ndarray      ,      lowerCAmelCase  :   Dict[str, int]      ,      lowerCAmelCase  :   PILImageResampling = PILImageResampling.BICUBIC      ,      lowerCAmelCase  :   Optional[Union[str, ChannelDimension]] = None      ,      **lowerCAmelCase  :   Any      ,      )	-> np.ndarray:
        a__     		=     get_size_dict(lowerCAmelCase      ,      default_to_square=lowerCAmelCase   )
        a__     		=     get_resize_output_image_size(
            lowerCAmelCase      ,      size=(size["height"], size["width"])      ,      default_to_square=lowerCAmelCase   )
        return resize(lowerCAmelCase      ,      size=lowerCAmelCase      ,      resample=lowerCAmelCase      ,      data_format=lowerCAmelCase      ,      **lowerCAmelCase   )
  
  
  
  
      def 						__lowerCamelCase (					self  :   List[Any]      ,      lowerCAmelCase  :   np.ndarray      ,      lowerCAmelCase  :   Dict[str, int]      ,      lowerCAmelCase  :   Optional[Union[str, ChannelDimension]] = None      ,      **lowerCAmelCase  :   int      ,      )	-> np.ndarray:
        a__     		=     get_size_dict(lowerCAmelCase   )
        return center_crop(lowerCAmelCase      ,      size=(size["height"], size["width"])      ,      data_format=lowerCAmelCase      ,      **lowerCAmelCase   )
  
  
  
  
      def 						__lowerCamelCase (					self  :   int      ,      lowerCAmelCase  :   np.ndarray      ,      lowerCAmelCase  :   Union[int, float]      ,      lowerCAmelCase  :   Optional[Union[str, ChannelDimension]] = None      ,      **lowerCAmelCase  :   Tuple      ,      )	-> Dict:
        return rescale(lowerCAmelCase      ,      scale=lowerCAmelCase      ,      data_format=lowerCAmelCase      ,      **lowerCAmelCase   )
  
  
  
  
      def 						__lowerCamelCase (					self  :   int      ,      lowerCAmelCase  :   np.ndarray      ,      lowerCAmelCase  :   Union[float, List[float]]      ,      lowerCAmelCase  :   Union[float, List[float]]      ,      lowerCAmelCase  :   Optional[Union[str, ChannelDimension]] = None      ,      **lowerCAmelCase  :   List[str]      ,      )	-> np.ndarray:
        return normalize(lowerCAmelCase      ,      mean=lowerCAmelCase      ,      std=lowerCAmelCase      ,      data_format=lowerCAmelCase      ,      **lowerCAmelCase   )
  
  
  
  
      def 						__lowerCamelCase (					self  :   Dict      ,      lowerCAmelCase  :   ImageInput      ,      lowerCAmelCase  :   bool = None      ,      lowerCAmelCase  :   Dict[str, int] = None      ,      lowerCAmelCase  :   PILImageResampling = None      ,      lowerCAmelCase  :   bool = None      ,      lowerCAmelCase  :   int = None      ,      lowerCAmelCase  :   bool = None      ,      lowerCAmelCase  :   float = None      ,      lowerCAmelCase  :   bool = None      ,      lowerCAmelCase  :   Optional[Union[float, List[float]]] = None      ,      lowerCAmelCase  :   Optional[Union[float, List[float]]] = None      ,      lowerCAmelCase  :   bool = None      ,      lowerCAmelCase  :   Optional[Union[str, TensorType]] = None      ,      lowerCAmelCase  :   Optional[ChannelDimension] = ChannelDimension.FIRST      ,      **lowerCAmelCase  :   List[str]      ,      )	-> PIL.Image.Image:
        a__     		=     do_resize if do_resize is not None else self.do_resize
        a__     		=     size if size is not None else self.size
        a__     		=     get_size_dict(lowerCAmelCase      ,      default_to_square=lowerCAmelCase   )
        a__     		=     resample if resample is not None else self.resample
        a__     		=     do_center_crop if do_center_crop is not None else self.do_center_crop
        a__     		=     crop_size if crop_size is not None else self.crop_size
        a__     		=     get_size_dict(lowerCAmelCase   )
        a__     		=     do_rescale if do_rescale is not None else self.do_rescale
        a__     		=     rescale_factor if rescale_factor is not None else self.rescale_factor
        a__     		=     do_normalize if do_normalize is not None else self.do_normalize
        a__     		=     image_mean if image_mean is not None else self.image_mean
        a__     		=     image_std if image_std is not None else self.image_std
        a__     		=     do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
  
        a__     		=     make_list_of_images(lowerCAmelCase   )
  
        if not valid_images(lowerCAmelCase   ):
          raise ValueError(
              "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
              "torch.Tensor, tf.Tensor or jax.ndarray."   )
  
        if do_resize and size is None:
          raise ValueError("Size must be specified if do_resize is True."   )
  
        if do_center_crop and crop_size is None:
          raise ValueError("Crop size must be specified if do_center_crop is True."   )
  
        if do_rescale and rescale_factor is None:
          raise ValueError("Rescale factor must be specified if do_rescale is True."   )
  
        if do_normalize and (image_mean is None or image_std is None):
          raise ValueError("Image mean and std must be specified if do_normalize is True."   )
  
        # PIL RGBA images are converted to RGB
        if do_convert_rgb:
          a__     		=     [convert_to_rgb(lowerCAmelCase   ) for image in images]
  
        # All transformations expect numpy arrays.
        a__     		=     [to_numpy_array(lowerCAmelCase   ) for image in images]
  
        if do_resize:
          a__     		=     [self.resize(image=lowerCAmelCase      ,      size=lowerCAmelCase      ,      resample=lowerCAmelCase   ) for image in images]
  
        if do_center_crop:
          a__     		=     [self.center_crop(image=lowerCAmelCase      ,      size=lowerCAmelCase   ) for image in images]
  
        if do_rescale:
          a__     		=     [self.rescale(image=lowerCAmelCase      ,      scale=lowerCAmelCase   ) for image in images]
  
        if do_normalize:
          a__     		=     [self.normalize(image=lowerCAmelCase      ,      mean=lowerCAmelCase      ,      std=lowerCAmelCase   ) for image in images]
  
        a__     		=     [to_channel_dimension_format(lowerCAmelCase      ,      lowerCAmelCase   ) for image in images]
  
        a__     		=     {"pixel_values": images}
        return BatchFeature(data=lowerCAmelCase      ,      tensor_type=lowerCAmelCase   )
  
  
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)
  
  