"""simple docstring"""
import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel







def 		__a  (      _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )      ->List[str]:
  a__:	Any     				=      UNetaDModel(**_SCREAMING_SNAKE_CASE  )
  a__:	Union[str, Any]     				=      checkpoint['all_modules.0.W'].data
  a__:	Optional[Any]     				=      checkpoint['all_modules.0.W'].data
  a__:	int     				=      checkpoint['all_modules.1.weight'].data
  a__:	Dict     				=      checkpoint['all_modules.1.bias'].data

  a__:	Any     				=      checkpoint['all_modules.2.weight'].data
  a__:	List[Any]     				=      checkpoint['all_modules.2.bias'].data

  a__:	Tuple     				=      checkpoint['all_modules.3.weight'].data
  a__:	Optional[int]     				=      checkpoint['all_modules.3.bias'].data

  a__:	int     				=      checkpoint[list(checkpoint.keys()  )[-4]].data
  a__:	Optional[Any]     				=      checkpoint[list(checkpoint.keys()  )[-3]].data
  a__:	Any     				=      checkpoint[list(checkpoint.keys()  )[-2]].data
  a__:	int     				=      checkpoint[list(checkpoint.keys()  )[-1]].data

  a__:	Tuple     				=      4

  def set_attention_weights(_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  ):
    a__:	Dict     				=      old_checkpoint[F'all_modules.{index}.NIN_0.W'].data.T
    a__:	Dict     				=      old_checkpoint[F'all_modules.{index}.NIN_1.W'].data.T
    a__:	str     				=      old_checkpoint[F'all_modules.{index}.NIN_2.W'].data.T

    a__:	List[str]     				=      old_checkpoint[F'all_modules.{index}.NIN_0.b'].data
    a__:	Union[str, Any]     				=      old_checkpoint[F'all_modules.{index}.NIN_1.b'].data
    a__:	str     				=      old_checkpoint[F'all_modules.{index}.NIN_2.b'].data

    a__:	Union[str, Any]     				=      old_checkpoint[F'all_modules.{index}.NIN_3.W'].data.T
    a__:	Optional[int]     				=      old_checkpoint[F'all_modules.{index}.NIN_3.b'].data

    a__:	Optional[Any]     				=      old_checkpoint[F'all_modules.{index}.GroupNorm_0.weight'].data
    a__:	Dict     				=      old_checkpoint[F'all_modules.{index}.GroupNorm_0.bias'].data

  def set_resnet_weights(_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  ):
    a__:	int     				=      old_checkpoint[F'all_modules.{index}.Conv_0.weight'].data
    a__:	Dict     				=      old_checkpoint[F'all_modules.{index}.Conv_0.bias'].data
    a__:	str     				=      old_checkpoint[F'all_modules.{index}.GroupNorm_0.weight'].data
    a__:	Any     				=      old_checkpoint[F'all_modules.{index}.GroupNorm_0.bias'].data

    a__:	Any     				=      old_checkpoint[F'all_modules.{index}.Conv_1.weight'].data
    a__:	Union[str, Any]     				=      old_checkpoint[F'all_modules.{index}.Conv_1.bias'].data
    a__:	int     				=      old_checkpoint[F'all_modules.{index}.GroupNorm_1.weight'].data
    a__:	Optional[int]     				=      old_checkpoint[F'all_modules.{index}.GroupNorm_1.bias'].data

    a__:	str     				=      old_checkpoint[F'all_modules.{index}.Dense_0.weight'].data
    a__:	Union[str, 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:
      a__:	Union[str, Any]     				=      old_checkpoint[F'all_modules.{index}.Conv_2.weight'].data
      a__:	Any     				=      old_checkpoint[F'all_modules.{index}.Conv_2.bias'].data

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

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

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

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

    if hasattr(_SCREAMING_SNAKE_CASE						,	'resnet_up'  ) and block.resnet_up is not None:
      a__:	List[Any]     				=      checkpoint[F'all_modules.{module_index}.weight'].data
      a__:	Dict     				=      checkpoint[F'all_modules.{module_index}.bias'].data
      module_index += 1
      a__:	Dict     				=      checkpoint[F'all_modules.{module_index}.weight'].data
      a__:	Optional[int]     				=      checkpoint[F'all_modules.{module_index}.bias'].data
      module_index += 1
      set_resnet_weights(block.resnet_up						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )
      module_index += 1

  a__:	str     				=      checkpoint[F'all_modules.{module_index}.weight'].data
  a__:	str     				=      checkpoint[F'all_modules.{module_index}.bias'].data
  module_index += 1
  a__:	Union[str, Any]     				=      checkpoint[F'all_modules.{module_index}.weight'].data
  a__:	Tuple     				=      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)



from hashlib import mda, shaaaa

__snake_case         =     (shaaaa, mda)







class       __snake_case :
 def __init__(	self		, snake_case__ = 8    )						->				None:
       '''simple docstring'''





       UpperCAmelCase     :			str     	=0b0
       UpperCAmelCase     :			str     	=size
 def 						UpperCAmelCase__							(	self		, snake_case__    )						->				None:
       '''simple docstring'''





       UpperCAmelCase     :			Union[str, Any]     	=self.hash_(snake_case__    )
       self.bitarray |= h
 def 						UpperCAmelCase__							(	self		, snake_case__    )						->				bool:
       '''simple docstring'''





       UpperCAmelCase     :			List[Any]     	=self.hash_(snake_case__    )
       return (h & self.bitarray) == h
 def __contains__(	self		, snake_case__    )						->				bool:
       '''simple docstring'''





       return self.exists(snake_case__    )
 def 						UpperCAmelCase__							(	self		, snake_case__    )						->				str:
       '''simple docstring'''





       UpperCAmelCase     :			Dict     	=bin(snake_case__    )[2:]
       return res.zfill(self.size    )
 @property
 def 						UpperCAmelCase__							(	self    )						->				str:
       '''simple docstring'''





       return self.format_bin(self.bitarray    )
 def 						UpperCAmelCase__							(	self		, snake_case__    )						->				int:
       '''simple docstring'''





       UpperCAmelCase     :			Union[str, Any]     	=0b0
       for func in HASH_FUNCTIONS:
             UpperCAmelCase     :			Optional[Any]     	=(
                 int.from_bytes(func(value.encode()    ).digest()		, '''little'''    ) % self.size
             )
             res |= 2**position
       return res
 def 						UpperCAmelCase__							(	self		, snake_case__    )						->				str:
       '''simple docstring'''





       return self.format_bin(self.hash_(snake_case__    )    )






 @property
 def 						UpperCAmelCase__							(	self    )						->				float:
       '''simple docstring'''





       UpperCAmelCase     :			int     	=bin(self.bitarray    ).count('''1'''    )
       return (n_ones / self.size) ** len(snake_case__    )
