'''simple docstring'''
import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel







def 				__UpperCamelCase (					__UpperCAmelCase  :					Dict ,   __UpperCAmelCase  :					Optional[int]			)							->	Optional[int]:
  lowercase				:							Optional[int]									=   UNetaDModel(**__UpperCAmelCase			)
  lowercase				:							Optional[Any]									=   checkpoint['''all_modules.0.W'''].data
  lowercase				:							List[Any]									=   checkpoint['''all_modules.0.W'''].data
  lowercase				:							Tuple									=   checkpoint['''all_modules.1.weight'''].data
  lowercase				:							str									=   checkpoint['''all_modules.1.bias'''].data

  lowercase				:							Union[str, Any]									=   checkpoint['''all_modules.2.weight'''].data
  lowercase				:							Dict									=   checkpoint['''all_modules.2.bias'''].data

  lowercase				:							Union[str, Any]									=   checkpoint['''all_modules.3.weight'''].data
  lowercase				:							List[Any]									=   checkpoint['''all_modules.3.bias'''].data

  lowercase				:							List[str]									=   checkpoint[list(checkpoint.keys()			)[-4]].data
  lowercase				:							List[Any]									=   checkpoint[list(checkpoint.keys()			)[-3]].data
  lowercase				:							List[str]									=   checkpoint[list(checkpoint.keys()			)[-2]].data
  lowercase				:							Any									=   checkpoint[list(checkpoint.keys()			)[-1]].data

  lowercase				:							List[str]									=   4

  def set_attention_weights(__UpperCAmelCase  :					Any ,   __UpperCAmelCase  :					int ,   __UpperCAmelCase  :					Any			):
    lowercase				:							List[Any]									=   old_checkpoint[f"""all_modules.{index}.NIN_0.W"""].data.T
    lowercase				:							str									=   old_checkpoint[f"""all_modules.{index}.NIN_1.W"""].data.T
    lowercase				:							int									=   old_checkpoint[f"""all_modules.{index}.NIN_2.W"""].data.T

    lowercase				:							Tuple									=   old_checkpoint[f"""all_modules.{index}.NIN_0.b"""].data
    lowercase				:							Dict									=   old_checkpoint[f"""all_modules.{index}.NIN_1.b"""].data
    lowercase				:							Any									=   old_checkpoint[f"""all_modules.{index}.NIN_2.b"""].data

    lowercase				:							Dict									=   old_checkpoint[f"""all_modules.{index}.NIN_3.W"""].data.T
    lowercase				:							Any									=   old_checkpoint[f"""all_modules.{index}.NIN_3.b"""].data

    lowercase				:							List[Any]									=   old_checkpoint[f"""all_modules.{index}.GroupNorm_0.weight"""].data
    lowercase				:							Optional[Any]									=   old_checkpoint[f"""all_modules.{index}.GroupNorm_0.bias"""].data

  def set_resnet_weights(__UpperCAmelCase  :					List[str] ,   __UpperCAmelCase  :					Optional[Any] ,   __UpperCAmelCase  :					Tuple			):
    lowercase				:							List[str]									=   old_checkpoint[f"""all_modules.{index}.Conv_0.weight"""].data
    lowercase				:							List[Any]									=   old_checkpoint[f"""all_modules.{index}.Conv_0.bias"""].data
    lowercase				:							Optional[Any]									=   old_checkpoint[f"""all_modules.{index}.GroupNorm_0.weight"""].data
    lowercase				:							str									=   old_checkpoint[f"""all_modules.{index}.GroupNorm_0.bias"""].data

    lowercase				:							Union[str, Any]									=   old_checkpoint[f"""all_modules.{index}.Conv_1.weight"""].data
    lowercase				:							Optional[int]									=   old_checkpoint[f"""all_modules.{index}.Conv_1.bias"""].data
    lowercase				:							List[str]									=   old_checkpoint[f"""all_modules.{index}.GroupNorm_1.weight"""].data
    lowercase				:							List[str]									=   old_checkpoint[f"""all_modules.{index}.GroupNorm_1.bias"""].data

    lowercase				:							Tuple									=   old_checkpoint[f"""all_modules.{index}.Dense_0.weight"""].data
    lowercase				:							int									=   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:
      lowercase				:							Tuple									=   old_checkpoint[f"""all_modules.{index}.Conv_2.weight"""].data
      lowercase				:							Any									=   old_checkpoint[f"""all_modules.{index}.Conv_2.bias"""].data

  for i, block in enumerate(new_model_architecture.downsample_blocks			):
    lowercase				:							List[str]									=   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
      lowercase				:							Tuple									=   checkpoint[f"""all_modules.{module_index}.Conv_0.weight"""].data
      lowercase				:							Tuple									=   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			):
    lowercase				:							int									=   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:
      lowercase				:							Tuple									=   checkpoint[f"""all_modules.{module_index}.weight"""].data
      lowercase				:							Optional[Any]									=   checkpoint[f"""all_modules.{module_index}.bias"""].data
      module_index += 1
      lowercase				:							str									=   checkpoint[f"""all_modules.{module_index}.weight"""].data
      lowercase				:							str									=   checkpoint[f"""all_modules.{module_index}.bias"""].data
      module_index += 1
      set_resnet_weights(block.resnet_up ,   __UpperCAmelCase ,   __UpperCAmelCase			)
      module_index += 1

  lowercase				:							List[str]									=   checkpoint[f"""all_modules.{module_index}.weight"""].data
  lowercase				:							List[str]									=   checkpoint[f"""all_modules.{module_index}.bias"""].data
  module_index += 1
  lowercase				:							Union[str, Any]									=   checkpoint[f"""all_modules.{module_index}.weight"""].data
  lowercase				:							Optional[Any]									=   checkpoint[f"""all_modules.{module_index}.bias"""].data

  return new_model_architecture.state_dict()


if __name__ == "__main__":
 _A		:      List[str]						   =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.''',
 )

 _A		:      Dict						   =parser.parse_args()

 _A		:      str						   =torch.load(args.checkpoint_path, map_location='''cpu''')

 with open(args.config_file) as f:
  _A		:      Optional[int]						   =json.loads(f.read())

 _A		:      Union[str, Any]						   =convert_ncsnpp_checkpoint(
     checkpoint,
     config,
 )

 if "sde" in config:
  del config["sde"]

 _A		:      Any						   =UNetaDModel(**config)
 model.load_state_dict(converted_checkpoint)

 try:
  _A		:      str						   =ScoreSdeVeScheduler.from_config('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))

  _A		:      List[Any]						   =ScoreSdeVePipeline(unet=model, scheduler=scheduler)
  pipe.save_pretrained(args.dump_path)
 except:  # noqa: E722
  model.save_pretrained(args.dump_path)
'''simple docstring'''
def 				__UpperCamelCase (					__UpperCAmelCase  :					float ,   __UpperCAmelCase  :					float ,   __UpperCAmelCase  :					float			)							->	list[int]:
  if hue < 0 or hue > 360:
    raise Exception('''hue should be between 0 and 360'''			)

  if saturation < 0 or saturation > 1:
    raise Exception('''saturation should be between 0 and 1'''			)

  if value < 0 or value > 1:
    raise Exception('''value should be between 0 and 1'''			)

  lowercase				:							List[str]									=   value * saturation
  lowercase				:							Optional[Any]									=   hue / 60
  lowercase				:							Dict									=   chroma * (1 - abs(hue_section % 2 - 1			))
  lowercase				:							List[Any]									=   value - chroma

  if hue_section >= 0 and hue_section <= 1:
    lowercase				:							Optional[int]									=   round(255 * (chroma + match_value)			)
    lowercase				:							List[Any]									=   round(255 * (second_largest_component + match_value)			)
    lowercase				:							int									=   round(255 * (match_value)			)
  elif hue_section > 1 and hue_section <= 2:
    lowercase				:							Optional[Any]									=   round(255 * (second_largest_component + match_value)			)
    lowercase				:							Any									=   round(255 * (chroma + match_value)			)
    lowercase				:							Any									=   round(255 * (match_value)			)
  elif hue_section > 2 and hue_section <= 3:
    lowercase				:							Dict									=   round(255 * (match_value)			)
    lowercase				:							Optional[int]									=   round(255 * (chroma + match_value)			)
    lowercase				:							List[str]									=   round(255 * (second_largest_component + match_value)			)
  elif hue_section > 3 and hue_section <= 4:
    lowercase				:							List[str]									=   round(255 * (match_value)			)
    lowercase				:							List[str]									=   round(255 * (second_largest_component + match_value)			)
    lowercase				:							Tuple									=   round(255 * (chroma + match_value)			)
  elif hue_section > 4 and hue_section <= 5:
    lowercase				:							Optional[int]									=   round(255 * (second_largest_component + match_value)			)
    lowercase				:							Tuple									=   round(255 * (match_value)			)
    lowercase				:							Optional[int]									=   round(255 * (chroma + match_value)			)
  else:
    lowercase				:							int									=   round(255 * (chroma + match_value)			)
    lowercase				:							Optional[Any]									=   round(255 * (match_value)			)
    lowercase				:							Optional[int]									=   round(255 * (second_largest_component + match_value)			)

  return [red, green, blue]







def 				__UpperCamelCase (					__UpperCAmelCase  :					int ,   __UpperCAmelCase  :					int ,   __UpperCAmelCase  :					int			)							->	list[float]:
  if red < 0 or red > 255:
    raise Exception('''red should be between 0 and 255'''			)

  if green < 0 or green > 255:
    raise Exception('''green should be between 0 and 255'''			)

  if blue < 0 or blue > 255:
    raise Exception('''blue should be between 0 and 255'''			)

  lowercase				:							str									=   red / 255
  lowercase				:							Union[str, Any]									=   green / 255
  lowercase				:							Union[str, Any]									=   blue / 255
  lowercase				:							str									=   max(__UpperCAmelCase ,   __UpperCAmelCase ,   __UpperCAmelCase			)
  lowercase				:							int									=   value - min(__UpperCAmelCase ,   __UpperCAmelCase ,   __UpperCAmelCase			)
  lowercase				:							Optional[int]									=   0 if value == 0 else chroma / value

  if chroma == 0:
    lowercase				:							Dict									=   0.0
  elif value == float_red:
    lowercase				:							Tuple									=   60 * (0 + (float_green - float_blue) / chroma)
  elif value == float_green:
    lowercase				:							Optional[int]									=   60 * (2 + (float_blue - float_red) / chroma)
  else:
    lowercase				:							Dict									=   60 * (4 + (float_red - float_green) / chroma)

  lowercase				:							List[str]									=   (hue + 360) % 360

  return [hue, saturation, value]







def 				__UpperCamelCase (					__UpperCAmelCase  :					list[float] ,   __UpperCAmelCase  :					list[float]			)							->	bool:
  lowercase				:							int									=   abs(hsv_a[0] - hsv_a[0]			) < 0.2
  lowercase				:							Optional[int]									=   abs(hsv_a[1] - hsv_a[1]			) < 0.002
  lowercase				:							Dict									=   abs(hsv_a[2] - hsv_a[2]			) < 0.002

  return check_hue and check_saturation and check_value