


'''simple docstring'''

import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel






def __UpperCamelCase (      __lowerCamelCase		:			List[str]   ,	__lowerCamelCase		:			str					)   ->  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		:			str   ,	__lowerCamelCase		:			Any   ,	__lowerCamelCase		:			int					):
          _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		:			str   ,	__lowerCamelCase		:			Dict   ,	__lowerCamelCase		:			Tuple					):
          _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__":
   lowercase__					  =		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.",
   )

   lowercase__					  =		parser.parse_args()

   lowercase__					  =		torch.load(args.checkpoint_path, map_location="cpu")

   with open(args.config_file) as f:
      lowercase__					  =		json.loads(f.read())

   lowercase__					  =		convert_ncsnpp_checkpoint(
       checkpoint,
       config,
   )

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

   lowercase__					  =		UNetaDModel(**config)
   model.load_state_dict(converted_checkpoint)

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

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






import math
from decimal import Decimal, getcontext




def 						UpperCAmelCase (    _lowerCamelCase = 400_0000     ):

			try:
						A				:       Any														=    int(_lowerCamelCase     )
			except (TypeError, ValueError):
						raise TypeError("Parameter n must be int or castable to int."     )
			if n <= 0:
						raise ValueError("Parameter n must be greater than or equal to one."     )
			A				:       int														=    100
			A				:       Tuple														=    (Decimal(5     ) ** Decimal(0.5     ) + 1) / Decimal(2     )

			A				:       str														=    (math.floor(math.log(n * (phi + 2)			,     _lowerCamelCase     ) - 1     ) // 3) * 3 + 2
			A				:       Optional[int]														=    Decimal(round(phi ** Decimal(index + 1     )     )     ) / (phi + 2)
			A				:       str														=    num // 2
			return int(_lowerCamelCase     )


if __name__ == "__main__":
							print(F"""{solution() = }""")