import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel


def 							lowerCAmelCase__    (       a__  ,			a__      )	->str:




  '''simple docstring'''

  _UpperCamelCase				=  UNetaDModel(**a__      )
  _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(a__  ,			a__  ,			a__      ):
    _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(a__  ,			a__  ,			a__      ):
    _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(a__  ,			"attentions"      )
    for j in range(len(block.resnets      )      ):
      set_resnet_weights(block.resnets[j]  ,			a__  ,			a__      )
      module_index += 1
      if has_attentions:
        set_attention_weights(block.attentions[j]  ,			a__  ,			a__      )
        module_index += 1

    if hasattr(a__  ,			"downsamplers"      ) and block.downsamplers is not None:
      set_resnet_weights(block.resnet_down  ,			a__  ,			a__      )
      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]  ,			a__  ,			a__      )
  module_index += 1
  set_attention_weights(new_model_architecture.mid_block.attentions[0]  ,			a__  ,			a__      )
  module_index += 1
  set_resnet_weights(new_model_architecture.mid_block.resnets[1]  ,			a__  ,			a__      )
  module_index += 1

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

    if hasattr(a__  ,			"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  ,			a__  ,			a__      )
      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__":
   lowerCamelCase__   = 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.''',
   )

   lowerCamelCase__   = parser.parse_args()

   lowerCamelCase__   = torch.load(args.checkpoint_path, map_location='''cpu''')

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

   lowerCamelCase__   = convert_ncsnpp_checkpoint(
       checkpoint,
       config,
   )

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

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

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

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











"""simple docstring"""


def SCREAMING_SNAKE_CASE_   (       )->		Tuple:
       _lowerCamelCase    	=					1

       while True:
              if (
                  sorted(str(snake_case		)		)
                  == sorted(str(2 * i		)		)
                  == sorted(str(3 * i		)		)
                  == sorted(str(4 * i		)		)
                  == sorted(str(5 * i		)		)
                  == sorted(str(6 * i		)		)
              ):
                     return i

              i += 1


if __name__ == "__main__":
      print(solution())






