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   )


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)

