

import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel

def 			__snake_case	(   __UpperCamelCase  :   List[Any]							,__UpperCamelCase  :   Any						):



      """simple docstring"""

      A_	     =							UNetaDModel(**__UpperCamelCase						)
      A_	     =							checkpoint["all_modules.0.W"].data
      A_	     =							checkpoint["all_modules.0.W"].data
      A_	     =							checkpoint["all_modules.1.weight"].data
      A_	     =							checkpoint["all_modules.1.bias"].data

      A_	     =							checkpoint["all_modules.2.weight"].data
      A_	     =							checkpoint["all_modules.2.bias"].data

      A_	     =							checkpoint["all_modules.3.weight"].data
      A_	     =							checkpoint["all_modules.3.bias"].data

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

      A_	     =							4

      def set_attention_weights(__UpperCamelCase  :   str							,__UpperCamelCase  :   Any							,__UpperCamelCase  :   Optional[Any]						):
            A_	     =							old_checkpoint[f'''all_modules.{index}.NIN_0.W'''].data.T
            A_	     =							old_checkpoint[f'''all_modules.{index}.NIN_1.W'''].data.T
            A_	     =							old_checkpoint[f'''all_modules.{index}.NIN_2.W'''].data.T

            A_	     =							old_checkpoint[f'''all_modules.{index}.NIN_0.b'''].data
            A_	     =							old_checkpoint[f'''all_modules.{index}.NIN_1.b'''].data
            A_	     =							old_checkpoint[f'''all_modules.{index}.NIN_2.b'''].data

            A_	     =							old_checkpoint[f'''all_modules.{index}.NIN_3.W'''].data.T
            A_	     =							old_checkpoint[f'''all_modules.{index}.NIN_3.b'''].data

            A_	     =							old_checkpoint[f'''all_modules.{index}.GroupNorm_0.weight'''].data
            A_	     =							old_checkpoint[f'''all_modules.{index}.GroupNorm_0.bias'''].data

      def set_resnet_weights(__UpperCamelCase  :   Optional[Any]							,__UpperCamelCase  :   List[str]							,__UpperCamelCase  :   Union[str, Any]						):
            A_	     =							old_checkpoint[f'''all_modules.{index}.Conv_0.weight'''].data
            A_	     =							old_checkpoint[f'''all_modules.{index}.Conv_0.bias'''].data
            A_	     =							old_checkpoint[f'''all_modules.{index}.GroupNorm_0.weight'''].data
            A_	     =							old_checkpoint[f'''all_modules.{index}.GroupNorm_0.bias'''].data

            A_	     =							old_checkpoint[f'''all_modules.{index}.Conv_1.weight'''].data
            A_	     =							old_checkpoint[f'''all_modules.{index}.Conv_1.bias'''].data
            A_	     =							old_checkpoint[f'''all_modules.{index}.GroupNorm_1.weight'''].data
            A_	     =							old_checkpoint[f'''all_modules.{index}.GroupNorm_1.bias'''].data

            A_	     =							old_checkpoint[f'''all_modules.{index}.Dense_0.weight'''].data
            A_	     =							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_	     =							old_checkpoint[f'''all_modules.{index}.Conv_2.weight'''].data
                  A_	     =							old_checkpoint[f'''all_modules.{index}.Conv_2.bias'''].data

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

      A_	     =							checkpoint[f'''all_modules.{module_index}.weight'''].data
      A_	     =							checkpoint[f'''all_modules.{module_index}.bias'''].data
      module_index += 1
      A_	     =							checkpoint[f'''all_modules.{module_index}.weight'''].data
      A_	     =							checkpoint[f'''all_modules.{module_index}.bias'''].data

      return new_model_architecture.state_dict()


if __name__ == "__main__":
      __a						:Optional[int]            = 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.',
      )

      __a						:Dict            = parser.parse_args()

      __a						:Any            = torch.load(args.checkpoint_path, map_location='cpu')

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

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

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

      __a						:Union[str, Any]            = UNetaDModel(**config)
      model.load_state_dict(converted_checkpoint)

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

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









__a						:Dict            = {hex(i)[2:]: i for i in range(16)}  # Use [:2] to strip off the leading '0x'

def 			__snake_case	(   __UpperCamelCase  :   str						):



      """simple docstring"""

      A_	     =							hex_string.strip().lower()
      if not hex_string:
            raise ValueError("Empty string was passed to the function"						)
      A_	     =							hex_string[0] == "-"
      if is_negative:
            A_	     =							hex_string[1:]
      if not all(char in hex_table for char in hex_string						):
            raise ValueError("Non-hexadecimal value was passed to the function"						)
      A_	     =							0
      for char in hex_string:
            A_	     =							16 * decimal_number + hex_table[char]
      return -decimal_number if is_negative else decimal_number


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

      testmod()