


import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
def 							UpperCamelCase_  (				__snake_case			:						List[Any]	,			__snake_case			:						Union[str, Any]     )->      Tuple:




      '''simple docstring'''
      __magic_name__ 		=			UNetaDModel(**_UpperCAmelCase     )
      __magic_name__ 		=			checkpoint['all_modules.0.W'].data
      __magic_name__ 		=			checkpoint['all_modules.0.W'].data
      __magic_name__ 		=			checkpoint['all_modules.1.weight'].data
      __magic_name__ 		=			checkpoint['all_modules.1.bias'].data

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

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

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

      __magic_name__ 		=			4

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

            __magic_name__ 		=			old_checkpoint[F'''all_modules.{index}.NIN_0.b'''].data
            __magic_name__ 		=			old_checkpoint[F'''all_modules.{index}.NIN_1.b'''].data
            __magic_name__ 		=			old_checkpoint[F'''all_modules.{index}.NIN_2.b'''].data

            __magic_name__ 		=			old_checkpoint[F'''all_modules.{index}.NIN_3.W'''].data.T
            __magic_name__ 		=			old_checkpoint[F'''all_modules.{index}.NIN_3.b'''].data

            __magic_name__ 		=			old_checkpoint[F'''all_modules.{index}.GroupNorm_0.weight'''].data
            __magic_name__ 		=			old_checkpoint[F'''all_modules.{index}.GroupNorm_0.bias'''].data

      def set_resnet_weights(__snake_case			:						Optional[int]	,			__snake_case			:						Any	,			__snake_case			:						List[str]     ):
            __magic_name__ 		=			old_checkpoint[F'''all_modules.{index}.Conv_0.weight'''].data
            __magic_name__ 		=			old_checkpoint[F'''all_modules.{index}.Conv_0.bias'''].data
            __magic_name__ 		=			old_checkpoint[F'''all_modules.{index}.GroupNorm_0.weight'''].data
            __magic_name__ 		=			old_checkpoint[F'''all_modules.{index}.GroupNorm_0.bias'''].data

            __magic_name__ 		=			old_checkpoint[F'''all_modules.{index}.Conv_1.weight'''].data
            __magic_name__ 		=			old_checkpoint[F'''all_modules.{index}.Conv_1.bias'''].data
            __magic_name__ 		=			old_checkpoint[F'''all_modules.{index}.GroupNorm_1.weight'''].data
            __magic_name__ 		=			old_checkpoint[F'''all_modules.{index}.GroupNorm_1.bias'''].data

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

      for i, block in enumerate(new_model_architecture.downsample_blocks     ):
            __magic_name__ 		=			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
                  __magic_name__ 		=			checkpoint[F'''all_modules.{module_index}.Conv_0.weight'''].data
                  __magic_name__ 		=			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     ):
            __magic_name__ 		=			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:
                  __magic_name__ 		=			checkpoint[F'''all_modules.{module_index}.weight'''].data
                  __magic_name__ 		=			checkpoint[F'''all_modules.{module_index}.bias'''].data
                  module_index += 1
                  __magic_name__ 		=			checkpoint[F'''all_modules.{module_index}.weight'''].data
                  __magic_name__ 		=			checkpoint[F'''all_modules.{module_index}.bias'''].data
                  module_index += 1
                  set_resnet_weights(block.resnet_up	,			_UpperCAmelCase	,			_UpperCAmelCase     )
                  module_index += 1

      __magic_name__ 		=			checkpoint[F'''all_modules.{module_index}.weight'''].data
      __magic_name__ 		=			checkpoint[F'''all_modules.{module_index}.bias'''].data
      module_index += 1
      __magic_name__ 		=			checkpoint[F'''all_modules.{module_index}.weight'''].data
      __magic_name__ 		=			checkpoint[F'''all_modules.{module_index}.bias'''].data

      return new_model_architecture.state_dict()


if __name__ == "__main__":
     UpperCamelCase :				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 :				int						    =       parser.parse_args()

     UpperCamelCase :				Optional[int]						    =       torch.load(args.checkpoint_path, map_location='''cpu''')

     with open(args.config_file) as f:
          UpperCamelCase :				Union[str, Any]						    =       json.loads(f.read())

     UpperCamelCase :				List[Any]						    =       convert_ncsnpp_checkpoint(
         checkpoint,
         config,
     )

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

     UpperCamelCase :				int						    =       UNetaDModel(**config)
     model.load_state_dict(converted_checkpoint)

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

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





def 							UpperCamelCase_  (				__snake_case			:						list[int]     )->      tuple[int, int]:




						'''simple docstring'''
						# base case
						if len(__snake_case     ) == 1:
												return a[0], a[0]
						else:
												# split A into half.
												__magic_name__ 		=			a[: len(__snake_case     ) // 2]
												__magic_name__ 		=			a[len(__snake_case     ) // 2 :]

												# 2 sub problems, 1/2 of original size.
												__magic_name__		,			__magic_name__ 		=			max_difference(__snake_case     )
												__magic_name__		,			__magic_name__ 		=			max_difference(__snake_case     )

												# get min of first and max of second
												# linear time
												__magic_name__ 		=			min(__snake_case     )
												__magic_name__ 		=			max(__snake_case     )

												# 3 cases, either (small1, big1),
												# (min_first, max_second), (small2, big2)
												# constant comparisons
												if biga - smalla > max_second - min_first and biga - smalla > biga - smalla:
																		return smalla, biga
												elif biga - smalla > max_second - min_first:
																		return smalla, biga
												else:
																		return min_first, max_second


if __name__ == "__main__":
					import doctest

					doctest.testmod()

