


import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel






def 			__lowerCAmelCase       (SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )->    Optional[Any]:




      """simple docstring"""

      snake_case_			= UNetaDModel(**SCREAMING_SNAKE_CASE   )
      snake_case_			= checkpoint['''all_modules.0.W'''].data
      snake_case_			= checkpoint['''all_modules.0.W'''].data
      snake_case_			= checkpoint['''all_modules.1.weight'''].data
      snake_case_			= checkpoint['''all_modules.1.bias'''].data

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

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

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

      snake_case_			= 4

      def set_attention_weights(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ):
            snake_case_			= old_checkpoint[f'''all_modules.{index}.NIN_0.W'''].data.T
            snake_case_			= old_checkpoint[f'''all_modules.{index}.NIN_1.W'''].data.T
            snake_case_			= old_checkpoint[f'''all_modules.{index}.NIN_2.W'''].data.T

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

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

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

      def set_resnet_weights(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ):
            snake_case_			= old_checkpoint[f'''all_modules.{index}.Conv_0.weight'''].data
            snake_case_			= old_checkpoint[f'''all_modules.{index}.Conv_0.bias'''].data
            snake_case_			= old_checkpoint[f'''all_modules.{index}.GroupNorm_0.weight'''].data
            snake_case_			= old_checkpoint[f'''all_modules.{index}.GroupNorm_0.bias'''].data

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

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

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

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

      return new_model_architecture.state_dict()


if __name__ == "__main__":
   UpperCAmelCase     =       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     =       parser.parse_args()

   UpperCAmelCase     =       torch.load(args.checkpoint_path, map_location="""cpu""")

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

   UpperCAmelCase     =       convert_ncsnpp_checkpoint(
       checkpoint,
       config,
   )

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

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

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

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



def 			__lowerCAmelCase       (SCREAMING_SNAKE_CASE   )->    list:




      """simple docstring"""

      snake_case_			= [True for i in range(n + 1   )]
      snake_case_			= [i - 1 for i in range(n + 1   )]
      snake_case_			= []
      for i in range(2   ,   n + 1   ):
            if is_prime[i]:
                  primes.append(SCREAMING_SNAKE_CASE   )
            for j in range(0   ,   len(SCREAMING_SNAKE_CASE   )   ):
                  if i * primes[j] >= n:
                        break
                  snake_case_			= False

                  if i % primes[j] == 0:
                        snake_case_			= totients[i] * primes[j]
                        break

                  snake_case_			= totients[i] * (primes[j] - 1)

      return totients


if __name__ == "__main__":
   import doctest

   doctest.testmod()