
import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel


def     UpperCamelCase      (					__lowerCamelCase  :				str       ,      __lowerCamelCase  :				str					):
       snake_case :     List[str]           =   UNetaDModel(**__a					)
       snake_case :     Optional[int]           =   checkpoint['''all_modules.0.W'''].data
       snake_case :     Dict           =   checkpoint['''all_modules.0.W'''].data
       snake_case :     Optional[int]           =   checkpoint['''all_modules.1.weight'''].data
       snake_case :     Optional[Any]           =   checkpoint['''all_modules.1.bias'''].data

       snake_case :     Optional[int]           =   checkpoint['''all_modules.2.weight'''].data
       snake_case :     Union[str, Any]           =   checkpoint['''all_modules.2.bias'''].data

       snake_case :     List[Any]           =   checkpoint['''all_modules.3.weight'''].data
       snake_case :     int           =   checkpoint['''all_modules.3.bias'''].data

       snake_case :     int           =   checkpoint[list(checkpoint.keys()					)[-4]].data
       snake_case :     int           =   checkpoint[list(checkpoint.keys()					)[-3]].data
       snake_case :     Tuple           =   checkpoint[list(checkpoint.keys()					)[-2]].data
       snake_case :     int           =   checkpoint[list(checkpoint.keys()					)[-1]].data

       snake_case :     Optional[Any]           =   4

       def set_attention_weights(__lowerCamelCase  :				str       ,      __lowerCamelCase  :				Union[str, Any]       ,      __lowerCamelCase  :				int					):
              snake_case :     int           =   old_checkpoint[f"""all_modules.{index}.NIN_0.W"""].data.T
              snake_case :     Any           =   old_checkpoint[f"""all_modules.{index}.NIN_1.W"""].data.T
              snake_case :     Union[str, Any]           =   old_checkpoint[f"""all_modules.{index}.NIN_2.W"""].data.T

              snake_case :     Union[str, Any]           =   old_checkpoint[f"""all_modules.{index}.NIN_0.b"""].data
              snake_case :     int           =   old_checkpoint[f"""all_modules.{index}.NIN_1.b"""].data
              snake_case :     Dict           =   old_checkpoint[f"""all_modules.{index}.NIN_2.b"""].data

              snake_case :     Optional[Any]           =   old_checkpoint[f"""all_modules.{index}.NIN_3.W"""].data.T
              snake_case :     int           =   old_checkpoint[f"""all_modules.{index}.NIN_3.b"""].data

              snake_case :     Optional[Any]           =   old_checkpoint[f"""all_modules.{index}.GroupNorm_0.weight"""].data
              snake_case :     Optional[int]           =   old_checkpoint[f"""all_modules.{index}.GroupNorm_0.bias"""].data

       def set_resnet_weights(__lowerCamelCase  :				List[Any]       ,      __lowerCamelCase  :				Tuple       ,      __lowerCamelCase  :				Optional[int]					):
              snake_case :     str           =   old_checkpoint[f"""all_modules.{index}.Conv_0.weight"""].data
              snake_case :     Any           =   old_checkpoint[f"""all_modules.{index}.Conv_0.bias"""].data
              snake_case :     Union[str, Any]           =   old_checkpoint[f"""all_modules.{index}.GroupNorm_0.weight"""].data
              snake_case :     Optional[int]           =   old_checkpoint[f"""all_modules.{index}.GroupNorm_0.bias"""].data

              snake_case :     Tuple           =   old_checkpoint[f"""all_modules.{index}.Conv_1.weight"""].data
              snake_case :     Any           =   old_checkpoint[f"""all_modules.{index}.Conv_1.bias"""].data
              snake_case :     Any           =   old_checkpoint[f"""all_modules.{index}.GroupNorm_1.weight"""].data
              snake_case :     List[Any]           =   old_checkpoint[f"""all_modules.{index}.GroupNorm_1.bias"""].data

              snake_case :     Any           =   old_checkpoint[f"""all_modules.{index}.Dense_0.weight"""].data
              snake_case :     Optional[int]           =   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 :     List[Any]           =   old_checkpoint[f"""all_modules.{index}.Conv_2.weight"""].data
                     snake_case :     List[Any]           =   old_checkpoint[f"""all_modules.{index}.Conv_2.bias"""].data

       for i, block in enumerate(new_model_architecture.downsample_blocks					):
              snake_case :     Tuple           =   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
                     snake_case :     Union[str, Any]           =   checkpoint[f"""all_modules.{module_index}.Conv_0.weight"""].data
                     snake_case :     Tuple           =   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					):
              snake_case :     Dict           =   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:
                     snake_case :     str           =   checkpoint[f"""all_modules.{module_index}.weight"""].data
                     snake_case :     int           =   checkpoint[f"""all_modules.{module_index}.bias"""].data
                     module_index += 1
                     snake_case :     Dict           =   checkpoint[f"""all_modules.{module_index}.weight"""].data
                     snake_case :     str           =   checkpoint[f"""all_modules.{module_index}.bias"""].data
                     module_index += 1
                     set_resnet_weights(block.resnet_up       ,      __a       ,      __a					)
                     module_index += 1

       snake_case :     int           =   checkpoint[f"""all_modules.{module_index}.weight"""].data
       snake_case :     Optional[Any]           =   checkpoint[f"""all_modules.{module_index}.bias"""].data
       module_index += 1
       snake_case :     List[str]           =   checkpoint[f"""all_modules.{module_index}.weight"""].data
       snake_case :     Optional[int]           =   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)







import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from unittest.mock import Mock, patch

import diffusers.utils.hub_utils






class 						UpperCAmelCase	(		unittest.TestCase				):



   @patch("diffusers.utils.hub_utils.get_full_repo_name"				)
   def        _SCREAMING_SNAKE_CASE     (self	:      Any					,		snake_case__	:      Mock				)	->			None:


          '''simple docstring'''


          snake_case :     str           =   "full_repo_name"
          with TemporaryDirectory() as tmpdir:
                 # Dummy args values
                 snake_case :     List[str]           =   Mock()
                 snake_case :     List[str]           =   tmpdir
                 snake_case :     Optional[int]           =   0
                 snake_case :     int           =   "hub_token"
                 snake_case :     Union[str, Any]           =   "dataset_name"
                 snake_case :     Tuple           =   0.01
                 snake_case :     int           =   10_00_00
                 snake_case :     Optional[Any]           =   1_00_00
                 snake_case :     Optional[Any]           =   0.01
                 snake_case :     Dict           =   0.02
                 snake_case :     List[Any]           =   0.03
                 snake_case :     str           =   0.0005
                 snake_case :     List[str]           =   0.000001
                 snake_case :     Tuple           =   1
                 snake_case :     Optional[int]           =   10
                 snake_case :     List[str]           =   0.001
                 snake_case :     List[Any]           =   0.1
                 snake_case :     List[str]           =   0.2
                 snake_case :     Tuple           =   True

                 # Model card mush be rendered and saved
                 diffusers.utils.hub_utils.create_model_card(snake_case__					,		model_name="model_name"				)
                 self.assertTrue((Path(snake_case__				) / "README.md").is_file()				)





