

import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel






def    __lowercase (       _A  ,      _A     )       ->  List[str]:
      SCREAMING_SNAKE_CASE	: Optional[Any]					       =				UNetaDModel(**_A     )
      SCREAMING_SNAKE_CASE	: Tuple					       =				checkpoint["""all_modules.0.W"""].data
      SCREAMING_SNAKE_CASE	: Optional[int]					       =				checkpoint["""all_modules.0.W"""].data
      SCREAMING_SNAKE_CASE	: str					       =				checkpoint["""all_modules.1.weight"""].data
      SCREAMING_SNAKE_CASE	: str					       =				checkpoint["""all_modules.1.bias"""].data

      SCREAMING_SNAKE_CASE	: int					       =				checkpoint["""all_modules.2.weight"""].data
      SCREAMING_SNAKE_CASE	: List[Any]					       =				checkpoint["""all_modules.2.bias"""].data

      SCREAMING_SNAKE_CASE	: List[str]					       =				checkpoint["""all_modules.3.weight"""].data
      SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				checkpoint["""all_modules.3.bias"""].data

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

      SCREAMING_SNAKE_CASE	: Tuple					       =				4

      def set_attention_weights(_A  ,      _A  ,      _A     ):
            SCREAMING_SNAKE_CASE	: Dict					       =				old_checkpoint[F"all_modules.{index}.NIN_0.W"].data.T
            SCREAMING_SNAKE_CASE	: Any					       =				old_checkpoint[F"all_modules.{index}.NIN_1.W"].data.T
            SCREAMING_SNAKE_CASE	: Tuple					       =				old_checkpoint[F"all_modules.{index}.NIN_2.W"].data.T

            SCREAMING_SNAKE_CASE	: List[str]					       =				old_checkpoint[F"all_modules.{index}.NIN_0.b"].data
            SCREAMING_SNAKE_CASE	: Tuple					       =				old_checkpoint[F"all_modules.{index}.NIN_1.b"].data
            SCREAMING_SNAKE_CASE	: List[str]					       =				old_checkpoint[F"all_modules.{index}.NIN_2.b"].data

            SCREAMING_SNAKE_CASE	: Any					       =				old_checkpoint[F"all_modules.{index}.NIN_3.W"].data.T
            SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				old_checkpoint[F"all_modules.{index}.NIN_3.b"].data

            SCREAMING_SNAKE_CASE	: List[str]					       =				old_checkpoint[F"all_modules.{index}.GroupNorm_0.weight"].data
            SCREAMING_SNAKE_CASE	: Any					       =				old_checkpoint[F"all_modules.{index}.GroupNorm_0.bias"].data

      def set_resnet_weights(_A  ,      _A  ,      _A     ):
            SCREAMING_SNAKE_CASE	: Optional[int]					       =				old_checkpoint[F"all_modules.{index}.Conv_0.weight"].data
            SCREAMING_SNAKE_CASE	: Optional[int]					       =				old_checkpoint[F"all_modules.{index}.Conv_0.bias"].data
            SCREAMING_SNAKE_CASE	: Tuple					       =				old_checkpoint[F"all_modules.{index}.GroupNorm_0.weight"].data
            SCREAMING_SNAKE_CASE	: Dict					       =				old_checkpoint[F"all_modules.{index}.GroupNorm_0.bias"].data

            SCREAMING_SNAKE_CASE	: Dict					       =				old_checkpoint[F"all_modules.{index}.Conv_1.weight"].data
            SCREAMING_SNAKE_CASE	: Optional[Any]					       =				old_checkpoint[F"all_modules.{index}.Conv_1.bias"].data
            SCREAMING_SNAKE_CASE	: Optional[int]					       =				old_checkpoint[F"all_modules.{index}.GroupNorm_1.weight"].data
            SCREAMING_SNAKE_CASE	: List[str]					       =				old_checkpoint[F"all_modules.{index}.GroupNorm_1.bias"].data

            SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				old_checkpoint[F"all_modules.{index}.Dense_0.weight"].data
            SCREAMING_SNAKE_CASE	: 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:
                  SCREAMING_SNAKE_CASE	: Optional[int]					       =				old_checkpoint[F"all_modules.{index}.Conv_2.weight"].data
                  SCREAMING_SNAKE_CASE	: Optional[Any]					       =				old_checkpoint[F"all_modules.{index}.Conv_2.bias"].data

      for i, block in enumerate(new_model_architecture.downsample_blocks     ):
            SCREAMING_SNAKE_CASE	: str					       =				hasattr(_A  ,      """attentions"""     )
            for j in range(len(block.resnets     )     ):
                  set_resnet_weights(block.resnets[j]  ,      _A  ,      _A     )
                  module_index += 1
                  if has_attentions:
                        set_attention_weights(block.attentions[j]  ,      _A  ,      _A     )
                        module_index += 1

            if hasattr(_A  ,      """downsamplers"""     ) and block.downsamplers is not None:
                  set_resnet_weights(block.resnet_down  ,      _A  ,      _A     )
                  module_index += 1
                  SCREAMING_SNAKE_CASE	: Optional[int]					       =				checkpoint[F"all_modules.{module_index}.Conv_0.weight"].data
                  SCREAMING_SNAKE_CASE	: List[str]					       =				checkpoint[F"all_modules.{module_index}.Conv_0.bias"].data
                  module_index += 1

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

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

            if hasattr(_A  ,      """resnet_up"""     ) and block.resnet_up is not None:
                  SCREAMING_SNAKE_CASE	: int					       =				checkpoint[F"all_modules.{module_index}.weight"].data
                  SCREAMING_SNAKE_CASE	: Tuple					       =				checkpoint[F"all_modules.{module_index}.bias"].data
                  module_index += 1
                  SCREAMING_SNAKE_CASE	: str					       =				checkpoint[F"all_modules.{module_index}.weight"].data
                  SCREAMING_SNAKE_CASE	: Optional[Any]					       =				checkpoint[F"all_modules.{module_index}.bias"].data
                  module_index += 1
                  set_resnet_weights(block.resnet_up  ,      _A  ,      _A     )
                  module_index += 1

      SCREAMING_SNAKE_CASE	: Tuple					       =				checkpoint[F"all_modules.{module_index}.weight"].data
      SCREAMING_SNAKE_CASE	: Optional[Any]					       =				checkpoint[F"all_modules.{module_index}.bias"].data
      module_index += 1
      SCREAMING_SNAKE_CASE	: Tuple					       =				checkpoint[F"all_modules.{module_index}.weight"].data
      SCREAMING_SNAKE_CASE	: Any					       =				checkpoint[F"all_modules.{module_index}.bias"].data

      return new_model_architecture.state_dict()


if __name__ == "__main__":
  UpperCAmelCase__	:       List[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__	:       Dict			      =      parser.parse_args()

  UpperCAmelCase__	:       List[Any]			      =      torch.load(args.checkpoint_path, map_location="""cpu""")

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

  UpperCAmelCase__	:       str			      =      convert_ncsnpp_checkpoint(
      checkpoint,
      config,
  )

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

  UpperCAmelCase__	:       str			      =      UNetaDModel(**config)
  model.load_state_dict(converted_checkpoint)

  try:
    UpperCAmelCase__	:       Union[str, Any]			      =      ScoreSdeVeScheduler.from_config("""/""".join(args.checkpoint_path.split("""/""")[:-1]))

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







import inspect
import os
import unittest

import torch

import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
    execute_subprocess_async,
    require_cpu,
    require_multi_gpu,
    require_single_gpu,
    test_sync,
)
from accelerate.utils import patch_environment







class     a__    (				unittest.TestCase    ):







  """simple docstring"""




  def 	_lowercase (     self      :	Optional[int]						)   ->Tuple:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: Optional[Any]					       =				inspect.getfile(accelerate.test_utils						)
        SCREAMING_SNAKE_CASE	: int					       =				os.path.sep.join(mod_file.split(os.path.sep						)[:-1] + ["""scripts""", """test_sync.py"""]						)


  @require_cpu
  def 	_lowercase (     self      :	Optional[int]						)   ->str:
        """simple docstring"""







        debug_launcher(test_sync.main			,			num_processes=1						)


  @require_cpu
  def 	_lowercase (     self      :	Optional[int]						)   ->int:
        """simple docstring"""







        debug_launcher(test_sync.main						)


  @require_single_gpu
  def 	_lowercase (     self      :	str						)   ->Any:
        """simple docstring"""







        test_sync.main()




  @require_multi_gpu
  def 	_lowercase (     self      :	Union[str, Any]						)   ->List[Any]:
        """simple docstring"""







        print(f"Found {torch.cuda.device_count()} devices."						)
        SCREAMING_SNAKE_CASE	: List[str]					       =				["""torchrun""", f"--nproc_per_node={torch.cuda.device_count()}", self.test_file_path]
        with patch_environment(omp_num_threads=1						):
              execute_subprocess_async(UpperCAmelCase__			,			env=os.environ.copy()						)




