
"""simple docstring"""




import math


def   __magic_name__     (				__snake_case    :	int  )			->    str:
       if number == 0:
              lowercase   :    Optional[int]			=							"Zero"
              return words
       else:
              lowercase   :    Dict			=							math.logaa(__snake_case  )
              lowercase   :    Optional[int]			=							digits + 1
              lowercase   :    str			=							{}
              lowercase   :    Optional[Any]			=							""
              lowercase   :    List[Any]			=							"One"
              lowercase   :    int			=							"Two"
              lowercase   :    int			=							"Three"
              lowercase   :    List[str]			=							"Four"
              lowercase   :    Union[str, Any]			=							"Five"
              lowercase   :    str			=							"Six"
              lowercase   :    Optional[int]			=							"Seven"
              lowercase   :    Dict			=							"Eight"
              lowercase   :    List[Any]			=							"Nine"

              lowercase   :    str			=							{}
              lowercase   :    Union[str, Any]			=							""
              lowercase   :    Optional[Any]			=							"Twenty"
              lowercase   :    str			=							"Thirty"
              lowercase   :    Optional[Any]			=							"Forty"
              lowercase   :    str			=							"Fifty"
              lowercase   :    Any			=							"Sixty"
              lowercase   :    List[str]			=							"Seventy"
              lowercase   :    List[str]			=							"Eighty"
              lowercase   :    List[str]			=							"Ninety"

              lowercase   :    Tuple			=							{}
              lowercase   :    Dict			=							"Ten"
              lowercase   :    str			=							"Eleven"
              lowercase   :    List[Any]			=							"Twelve"
              lowercase   :    int			=							"Thirteen"
              lowercase   :    Tuple			=							"Fourteen"
              lowercase   :    int			=							"Fifteen"
              lowercase   :    Optional[Any]			=							"Sixteen"
              lowercase   :    Dict			=							"Seventeen"
              lowercase   :    Optional[int]			=							"Eighteen"
              lowercase   :    List[str]			=							"Nineteen"

              lowercase   :    Optional[int]			=							{}
              lowercase   :    List[Any]			=							"Hundred,"
              lowercase   :    Tuple			=							"Thousand,"
              lowercase   :    Optional[int]			=							"Lakh,"
              lowercase   :    Tuple			=							"Crore,"

              lowercase   :    Optional[Any]			=							number
              lowercase   :    Tuple			=							""
              lowercase   :    Any			=							0
              lowercase   :    str			=							int(__snake_case  )
              while counter < digits:
                     lowercase   :    Tuple			=							temp_num % 10
                     if counter % 2 == 0:
                            lowercase   :    Optional[int]			=							""
                            if counter in placevalue and current != 0:
                                   lowercase   :    Tuple			=							placevalue[counter]
                            if counter == 2:
                                   lowercase   :    List[str]			=							singles[current] + addition + words
                            elif counter == 0:
                                   if ((temp_num % 100) // 10) == 1:
                                          lowercase   :    List[str]			=							teens[current] + addition + words
                                          lowercase   :    Dict			=							temp_num // 10
                                          counter += 1
                                   else:
                                          lowercase   :    Optional[Any]			=							singles[current] + addition + words

                            else:
                                   lowercase   :    Dict			=							doubles[current] + addition + words

                     else:
                            if counter == 1:
                                   if current == 1:
                                          lowercase   :    Optional[Any]			=							teens[number % 10] + words
                                   else:
                                          lowercase   :    int			=							""
                                          if counter in placevalue:
                                                 lowercase   :    Tuple			=							placevalue[counter]
                                          lowercase   :    Optional[Any]			=							doubles[current] + addition + words
                            else:
                                   lowercase   :    Any			=							""
                                   if counter in placevalue:
                                          if current == 0 and ((temp_num % 100) // 10) == 0:
                                                 lowercase   :    List[str]			=							""
                                          else:
                                                 lowercase   :    Union[str, Any]			=							placevalue[counter]
                                   if ((temp_num % 100) // 10) == 1:
                                          lowercase   :    Optional[Any]			=							teens[current] + addition + words
                                          lowercase   :    List[Any]			=							temp_num // 10
                                          counter += 1
                                   else:
                                          lowercase   :    Tuple			=							singles[current] + addition + words
                     counter += 1
                     lowercase   :    int			=							temp_num // 10
       return words


if __name__ == "__main__":
  import doctest

  doctest.testmod()




"""simple docstring"""




import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel


def   __magic_name__     (				__snake_case    :	str					,			__snake_case    :	Any  )			->    Union[str, Any]:
       lowercase   :    List[str]			=							UNetaDModel(**__snake_case  )
       lowercase   :    str			=							checkpoint["all_modules.0.W"].data
       lowercase   :    Union[str, Any]			=							checkpoint["all_modules.0.W"].data
       lowercase   :    Optional[Any]			=							checkpoint["all_modules.1.weight"].data
       lowercase   :    List[str]			=							checkpoint["all_modules.1.bias"].data

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

       lowercase   :    str			=							checkpoint["all_modules.3.weight"].data
       lowercase   :    str			=							checkpoint["all_modules.3.bias"].data

       lowercase   :    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   :    int			=							checkpoint[list(checkpoint.keys()  )[-1]].data

       lowercase   :    Tuple			=							4

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

              lowercase   :    Union[str, Any]			=							old_checkpoint[f"""all_modules.{index}.NIN_0.b"""].data
              lowercase   :    int			=							old_checkpoint[f"""all_modules.{index}.NIN_1.b"""].data
              lowercase   :    List[Any]			=							old_checkpoint[f"""all_modules.{index}.NIN_2.b"""].data

              lowercase   :    List[str]			=							old_checkpoint[f"""all_modules.{index}.NIN_3.W"""].data.T
              lowercase   :    Optional[int]			=							old_checkpoint[f"""all_modules.{index}.NIN_3.b"""].data

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

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

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

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

       for i, block in enumerate(new_model_architecture.downsample_blocks  ):
              lowercase   :    List[Any]			=							hasattr(__snake_case					,			"attentions"  )
              for j in range(len(block.resnets  )  ):
                     set_resnet_weights(block.resnets[j]					,			__snake_case					,			__snake_case  )
                     module_index += 1
                     if has_attentions:
                            set_attention_weights(block.attentions[j]					,			__snake_case					,			__snake_case  )
                            module_index += 1

              if hasattr(__snake_case					,			"downsamplers"  ) and block.downsamplers is not None:
                     set_resnet_weights(block.resnet_down					,			__snake_case					,			__snake_case  )
                     module_index += 1
                     lowercase   :    Dict			=							checkpoint[f"""all_modules.{module_index}.Conv_0.weight"""].data
                     lowercase   :    Dict			=							checkpoint[f"""all_modules.{module_index}.Conv_0.bias"""].data
                     module_index += 1

       set_resnet_weights(new_model_architecture.mid_block.resnets[0]					,			__snake_case					,			__snake_case  )
       module_index += 1
       set_attention_weights(new_model_architecture.mid_block.attentions[0]					,			__snake_case					,			__snake_case  )
       module_index += 1
       set_resnet_weights(new_model_architecture.mid_block.resnets[1]					,			__snake_case					,			__snake_case  )
       module_index += 1

       for i, block in enumerate(new_model_architecture.up_blocks  ):
              lowercase   :    Optional[int]			=							hasattr(__snake_case					,			"attentions"  )
              for j in range(len(block.resnets  )  ):
                     set_resnet_weights(block.resnets[j]					,			__snake_case					,			__snake_case  )
                     module_index += 1
              if has_attentions:
                     set_attention_weights(
                         block.attentions[0]					,			__snake_case					,			__snake_case  )  # why can there only be a single attention layer for up?
                     module_index += 1

              if hasattr(__snake_case					,			"resnet_up"  ) and block.resnet_up is not None:
                     lowercase   :    Any			=							checkpoint[f"""all_modules.{module_index}.weight"""].data
                     lowercase   :    Union[str, Any]			=							checkpoint[f"""all_modules.{module_index}.bias"""].data
                     module_index += 1
                     lowercase   :    Any			=							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					,			__snake_case					,			__snake_case  )
                     module_index += 1

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

       return new_model_architecture.state_dict()


if __name__ == "__main__":
   _A      :	Any       		=	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      :	Union[str, Any]       		=	parser.parse_args()

   _A      :	List[str]       		=	torch.load(args.checkpoint_path, map_location="""cpu""")

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

   _A      :	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      :	Tuple       		=	ScoreSdeVeScheduler.from_config("""/""".join(args.checkpoint_path.split("""/""")[:-1]))

      _A      :	int       		=	ScoreSdeVePipeline(unet=model, scheduler=scheduler)
      pipe.save_pretrained(args.dump_path)
   except:  # noqa: E722
      model.save_pretrained(args.dump_path)


