


'''simple docstring'''





import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel


def 					a__    (				UpperCamelCase_	:			Dict,      UpperCamelCase_	:			List[Any]	):
   UpperCAmelCase__  :List[str]            =			UNetaDModel(**UpperCamelCase_	)
   UpperCAmelCase__  :Optional[int]            =			checkpoint['''all_modules.0.W'''].data
   UpperCAmelCase__  :Optional[int]            =			checkpoint['''all_modules.0.W'''].data
   UpperCAmelCase__  :List[str]            =			checkpoint['''all_modules.1.weight'''].data
   UpperCAmelCase__  :Optional[int]            =			checkpoint['''all_modules.1.bias'''].data

   UpperCAmelCase__  :Any            =			checkpoint['''all_modules.2.weight'''].data
   UpperCAmelCase__  :int            =			checkpoint['''all_modules.2.bias'''].data

   UpperCAmelCase__  :str            =			checkpoint['''all_modules.3.weight'''].data
   UpperCAmelCase__  :Dict            =			checkpoint['''all_modules.3.bias'''].data

   UpperCAmelCase__  :Tuple            =			checkpoint[list(checkpoint.keys()	)[-4]].data
   UpperCAmelCase__  :Dict            =			checkpoint[list(checkpoint.keys()	)[-3]].data
   UpperCAmelCase__  :Union[str, Any]            =			checkpoint[list(checkpoint.keys()	)[-2]].data
   UpperCAmelCase__  :Optional[int]            =			checkpoint[list(checkpoint.keys()	)[-1]].data

   UpperCAmelCase__  :Any            =			4

   def set_attention_weights(UpperCamelCase_	:			Dict,      UpperCamelCase_	:			Optional[Any],      UpperCamelCase_	:			Optional[Any]	):
      UpperCAmelCase__  :Optional[int]            =			old_checkpoint[F'''all_modules.{index}.NIN_0.W'''].data.T
      UpperCAmelCase__  :Tuple            =			old_checkpoint[F'''all_modules.{index}.NIN_1.W'''].data.T
      UpperCAmelCase__  :str            =			old_checkpoint[F'''all_modules.{index}.NIN_2.W'''].data.T

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

      UpperCAmelCase__  :Any            =			old_checkpoint[F'''all_modules.{index}.NIN_3.W'''].data.T
      UpperCAmelCase__  :Tuple            =			old_checkpoint[F'''all_modules.{index}.NIN_3.b'''].data

      UpperCAmelCase__  :List[str]            =			old_checkpoint[F'''all_modules.{index}.GroupNorm_0.weight'''].data
      UpperCAmelCase__  :Dict            =			old_checkpoint[F'''all_modules.{index}.GroupNorm_0.bias'''].data

   def set_resnet_weights(UpperCamelCase_	:			int,      UpperCamelCase_	:			Optional[int],      UpperCamelCase_	:			str	):
      UpperCAmelCase__  :Tuple            =			old_checkpoint[F'''all_modules.{index}.Conv_0.weight'''].data
      UpperCAmelCase__  :int            =			old_checkpoint[F'''all_modules.{index}.Conv_0.bias'''].data
      UpperCAmelCase__  :Optional[int]            =			old_checkpoint[F'''all_modules.{index}.GroupNorm_0.weight'''].data
      UpperCAmelCase__  :Union[str, Any]            =			old_checkpoint[F'''all_modules.{index}.GroupNorm_0.bias'''].data

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

      UpperCAmelCase__  :Optional[int]            =			old_checkpoint[F'''all_modules.{index}.Dense_0.weight'''].data
      UpperCAmelCase__  :List[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:
         UpperCAmelCase__  :Optional[int]            =			old_checkpoint[F'''all_modules.{index}.Conv_2.weight'''].data
         UpperCAmelCase__  :int            =			old_checkpoint[F'''all_modules.{index}.Conv_2.bias'''].data

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

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

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

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

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

   UpperCAmelCase__  :Tuple            =			checkpoint[F'''all_modules.{module_index}.weight'''].data
   UpperCAmelCase__  :List[Any]            =			checkpoint[F'''all_modules.{module_index}.bias'''].data
   module_index += 1
   UpperCAmelCase__  :List[Any]            =			checkpoint[F'''all_modules.{module_index}.weight'''].data
   UpperCAmelCase__  :Dict            =			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'''





from scipy.constants import g


def 					a__    (				UpperCamelCase_	:			float,      UpperCamelCase_	:			float	):
   if mass < 0:
      # handling of negative values of mass
      raise ValueError('''The mass of a body cannot be negative'''	)
   if height < 0:
      # handling of negative values of height
      raise ValueError('''The height above the ground cannot be negative'''	)
   return mass * g * height


if __name__ == "__main__":
    from doctest import testmod

    testmod(name='''potential_energy''')



