
"""simple docstring"""




import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()


def   __magic_name__     (				__snake_case    :	Optional[Any]					,			__snake_case    :	Dict					,			__snake_case    :	Union[str, Any]  )			->    Tuple:
       # Initialise PyTorch model
       lowercase   :    str			=							TaConfig.from_json_file(__snake_case  )
       print(f"""Building PyTorch model from configuration: {config}"""  )
       lowercase   :    Optional[int]			=							TaForConditionalGeneration(__snake_case  )

       # Load weights from tf checkpoint
       load_tf_weights_in_ta(__snake_case					,			__snake_case					,			__snake_case  )

       # Save pytorch-model
       print(f"""Save PyTorch model to {pytorch_dump_path}"""  )
       model.save_pretrained(__snake_case  )


if __name__ == "__main__":
  _A      :	Union[str, Any]       		=	argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
  )
  parser.add_argument(
      """--config_file""",
      default=None,
      type=str,
      required=True,
      help=(
          """The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture."""
      ),
  )
  parser.add_argument(
      """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
  )
  _A      :	Optional[int]       		=	parser.parse_args()
  convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)




"""simple docstring"""




import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel


def   __magic_name__     (				__snake_case    :	str					,			__snake_case    :	Any  )			->    Union[str, Any]:
       lowercase   :    List[str]			=							UNetaDModel(**__snake_case  )
       lowercase   :    str			=							checkpoint["all_modules.0.W"].data
       lowercase   :    Union[str, Any]			=							checkpoint["all_modules.0.W"].data
       lowercase   :    Optional[Any]			=							checkpoint["all_modules.1.weight"].data
       lowercase   :    List[str]			=							checkpoint["all_modules.1.bias"].data

       lowercase   :    List[Any]			=							checkpoint["all_modules.2.weight"].data
       lowercase   :    Union[str, Any]			=							checkpoint["all_modules.2.bias"].data

       lowercase   :    str			=							checkpoint["all_modules.3.weight"].data
       lowercase   :    str			=							checkpoint["all_modules.3.bias"].data

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

       lowercase   :    Tuple			=							4

       def set_attention_weights(__snake_case    :	Optional[int]					,			__snake_case    :	List[str]					,			__snake_case    :	List[str]  ):
              lowercase   :    Union[str, Any]			=							old_checkpoint[f"""all_modules.{index}.NIN_0.W"""].data.T
              lowercase   :    Tuple			=							old_checkpoint[f"""all_modules.{index}.NIN_1.W"""].data.T
              lowercase   :    Optional[Any]			=							old_checkpoint[f"""all_modules.{index}.NIN_2.W"""].data.T

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

              lowercase   :    List[str]			=							old_checkpoint[f"""all_modules.{index}.NIN_3.W"""].data.T
              lowercase   :    Optional[int]			=							old_checkpoint[f"""all_modules.{index}.NIN_3.b"""].data

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

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

              lowercase   :    int			=							old_checkpoint[f"""all_modules.{index}.Conv_1.weight"""].data
              lowercase   :    Union[str, Any]			=							old_checkpoint[f"""all_modules.{index}.Conv_1.bias"""].data
              lowercase   :    Any			=							old_checkpoint[f"""all_modules.{index}.GroupNorm_1.weight"""].data
              lowercase   :    Optional[int]			=							old_checkpoint[f"""all_modules.{index}.GroupNorm_1.bias"""].data

              lowercase   :    Optional[Any]			=							old_checkpoint[f"""all_modules.{index}.Dense_0.weight"""].data
              lowercase   :    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:
                     lowercase   :    str			=							old_checkpoint[f"""all_modules.{index}.Conv_2.weight"""].data
                     lowercase   :    Optional[Any]			=							old_checkpoint[f"""all_modules.{index}.Conv_2.bias"""].data

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

              if hasattr(__snake_case					,			"downsamplers"  ) and block.downsamplers is not None:
                     set_resnet_weights(block.resnet_down					,			__snake_case					,			__snake_case  )
                     module_index += 1
                     lowercase   :    Dict			=							checkpoint[f"""all_modules.{module_index}.Conv_0.weight"""].data
                     lowercase   :    Dict			=							checkpoint[f"""all_modules.{module_index}.Conv_0.bias"""].data
                     module_index += 1

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

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

              if hasattr(__snake_case					,			"resnet_up"  ) and block.resnet_up is not None:
                     lowercase   :    Any			=							checkpoint[f"""all_modules.{module_index}.weight"""].data
                     lowercase   :    Union[str, Any]			=							checkpoint[f"""all_modules.{module_index}.bias"""].data
                     module_index += 1
                     lowercase   :    Any			=							checkpoint[f"""all_modules.{module_index}.weight"""].data
                     lowercase   :    str			=							checkpoint[f"""all_modules.{module_index}.bias"""].data
                     module_index += 1
                     set_resnet_weights(block.resnet_up					,			__snake_case					,			__snake_case  )
                     module_index += 1

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

       return new_model_architecture.state_dict()


if __name__ == "__main__":
   _A      :	Any       		=	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      :	Union[str, Any]       		=	parser.parse_args()

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

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

   _A      :	Any       		=	convert_ncsnpp_checkpoint(
       checkpoint,
       config,
   )

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

   _A      :	Any       		=	UNetaDModel(**config)
   model.load_state_dict(converted_checkpoint)

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

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


