import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel



def 						__snake_case (       lowerCAmelCase       :     Tuple      ,  lowerCAmelCase       :     Optional[int]			):
 __UpperCAmelCase         =					UNetaDModel(**lowercase_			)
 __UpperCAmelCase         =					checkpoint['all_modules.0.W'].data
 __UpperCAmelCase         =					checkpoint['all_modules.0.W'].data
 __UpperCAmelCase         =					checkpoint['all_modules.1.weight'].data
 __UpperCAmelCase         =					checkpoint['all_modules.1.bias'].data

 __UpperCAmelCase         =					checkpoint['all_modules.2.weight'].data
 __UpperCAmelCase         =					checkpoint['all_modules.2.bias'].data

 __UpperCAmelCase         =					checkpoint['all_modules.3.weight'].data
 __UpperCAmelCase         =					checkpoint['all_modules.3.bias'].data

 __UpperCAmelCase         =					checkpoint[list(checkpoint.keys()			)[-4]].data
 __UpperCAmelCase         =					checkpoint[list(checkpoint.keys()			)[-3]].data
 __UpperCAmelCase         =					checkpoint[list(checkpoint.keys()			)[-2]].data
 __UpperCAmelCase         =					checkpoint[list(checkpoint.keys()			)[-1]].data

 __UpperCAmelCase         =					4

 def set_attention_weights(lowerCAmelCase       :     Dict      ,  lowerCAmelCase       :     List[Any]      ,  lowerCAmelCase       :     Optional[Any]			):
  __UpperCAmelCase         =					old_checkpoint[F"""all_modules.{index}.NIN_0.W"""].data.T
  __UpperCAmelCase         =					old_checkpoint[F"""all_modules.{index}.NIN_1.W"""].data.T
  __UpperCAmelCase         =					old_checkpoint[F"""all_modules.{index}.NIN_2.W"""].data.T

  __UpperCAmelCase         =					old_checkpoint[F"""all_modules.{index}.NIN_0.b"""].data
  __UpperCAmelCase         =					old_checkpoint[F"""all_modules.{index}.NIN_1.b"""].data
  __UpperCAmelCase         =					old_checkpoint[F"""all_modules.{index}.NIN_2.b"""].data

  __UpperCAmelCase         =					old_checkpoint[F"""all_modules.{index}.NIN_3.W"""].data.T
  __UpperCAmelCase         =					old_checkpoint[F"""all_modules.{index}.NIN_3.b"""].data

  __UpperCAmelCase         =					old_checkpoint[F"""all_modules.{index}.GroupNorm_0.weight"""].data
  __UpperCAmelCase         =					old_checkpoint[F"""all_modules.{index}.GroupNorm_0.bias"""].data

 def set_resnet_weights(lowerCAmelCase       :     Optional[Any]      ,  lowerCAmelCase       :     int      ,  lowerCAmelCase       :     Tuple			):
  __UpperCAmelCase         =					old_checkpoint[F"""all_modules.{index}.Conv_0.weight"""].data
  __UpperCAmelCase         =					old_checkpoint[F"""all_modules.{index}.Conv_0.bias"""].data
  __UpperCAmelCase         =					old_checkpoint[F"""all_modules.{index}.GroupNorm_0.weight"""].data
  __UpperCAmelCase         =					old_checkpoint[F"""all_modules.{index}.GroupNorm_0.bias"""].data

  __UpperCAmelCase         =					old_checkpoint[F"""all_modules.{index}.Conv_1.weight"""].data
  __UpperCAmelCase         =					old_checkpoint[F"""all_modules.{index}.Conv_1.bias"""].data
  __UpperCAmelCase         =					old_checkpoint[F"""all_modules.{index}.GroupNorm_1.weight"""].data
  __UpperCAmelCase         =					old_checkpoint[F"""all_modules.{index}.GroupNorm_1.bias"""].data

  __UpperCAmelCase         =					old_checkpoint[F"""all_modules.{index}.Dense_0.weight"""].data
  __UpperCAmelCase         =					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:
   __UpperCAmelCase         =					old_checkpoint[F"""all_modules.{index}.Conv_2.weight"""].data
   __UpperCAmelCase         =					old_checkpoint[F"""all_modules.{index}.Conv_2.bias"""].data

 for i, block in enumerate(new_model_architecture.downsample_blocks			):
  __UpperCAmelCase         =					hasattr(lowercase_      ,  'attentions'			)
  for j in range(len(block.resnets			)			):
   set_resnet_weights(block.resnets[j]      ,  lowercase_      ,  lowercase_			)
   module_index += 1
   if has_attentions:
    set_attention_weights(block.attentions[j]      ,  lowercase_      ,  lowercase_			)
    module_index += 1

  if hasattr(lowercase_      ,  'downsamplers'			) and block.downsamplers is not None:
   set_resnet_weights(block.resnet_down      ,  lowercase_      ,  lowercase_			)
   module_index += 1
   __UpperCAmelCase         =					checkpoint[F"""all_modules.{module_index}.Conv_0.weight"""].data
   __UpperCAmelCase         =					checkpoint[F"""all_modules.{module_index}.Conv_0.bias"""].data
   module_index += 1

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

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

  if hasattr(lowercase_      ,  'resnet_up'			) and block.resnet_up is not None:
   __UpperCAmelCase         =					checkpoint[F"""all_modules.{module_index}.weight"""].data
   __UpperCAmelCase         =					checkpoint[F"""all_modules.{module_index}.bias"""].data
   module_index += 1
   __UpperCAmelCase         =					checkpoint[F"""all_modules.{module_index}.weight"""].data
   __UpperCAmelCase         =					checkpoint[F"""all_modules.{module_index}.bias"""].data
   module_index += 1
   set_resnet_weights(block.resnet_up      ,  lowercase_      ,  lowercase_			)
   module_index += 1

 __UpperCAmelCase         =					checkpoint[F"""all_modules.{module_index}.weight"""].data
 __UpperCAmelCase         =					checkpoint[F"""all_modules.{module_index}.bias"""].data
 module_index += 1
 __UpperCAmelCase         =					checkpoint[F"""all_modules.{module_index}.weight"""].data
 __UpperCAmelCase         =					checkpoint[F"""all_modules.{module_index}.bias"""].data

 return new_model_architecture.state_dict()


if __name__ == "__main__":
     _UpperCamelCase				:		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.',
     )

     _UpperCamelCase				:		Optional[Any]       			=		parser.parse_args()

     _UpperCamelCase				:		int       			=		torch.load(args.checkpoint_path, map_location='cpu')

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

     _UpperCamelCase				:		Tuple       			=		convert_ncsnpp_checkpoint(
         checkpoint,
         config,
     )

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

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

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

          _UpperCamelCase				:		Optional[Any]       			=		ScoreSdeVePipeline(unet=model, scheduler=scheduler)
          pipe.save_pretrained(args.dump_path)
     except:  # noqa: E722
          model.save_pretrained(args.dump_path)




'''simple docstring'''




def 						__snake_case (       lowerCAmelCase       :     str			):
 __UpperCAmelCase         =					5381
 for x in s:
  __UpperCAmelCase         =					((hash_value << 5) + hash_value) + ord(lowerCAmelCase			)
 return hash_value & 0Xffff_ffff



