






import argparse

import torch

from transformers import NystromformerConfig, NystromformerForMaskedLM



def      _SCREAMING_SNAKE_CASE  (	__lowercase				:     Any      )  -> Optional[int]:





    """simple docstring"""

    if "model" in orig_key:
        __A             =		orig_key.replace("""model."""     ,			""""""      )
    if "norm1" in orig_key:
        __A             =		orig_key.replace("""norm1"""     ,			"""attention.output.LayerNorm"""      )
    if "norm2" in orig_key:
        __A             =		orig_key.replace("""norm2"""     ,			"""output.LayerNorm"""      )
    if "norm" in orig_key:
        __A             =		orig_key.replace("""norm"""     ,			"""LayerNorm"""      )
    if "transformer" in orig_key:
        __A             =		orig_key.split("""."""      )[0].split("""_"""      )[-1]
        __A             =		orig_key.replace(f"transformer_{layer_num}"     ,			f"encoder.layer.{layer_num}"      )
    if "mha.attn" in orig_key:
        __A             =		orig_key.replace("""mha.attn"""     ,			"""attention.self"""      )
    if "mha" in orig_key:
        __A             =		orig_key.replace("""mha"""     ,			"""attention"""      )
    if "W_q" in orig_key:
        __A             =		orig_key.replace("""W_q"""     ,			"""self.query"""      )
    if "W_k" in orig_key:
        __A             =		orig_key.replace("""W_k"""     ,			"""self.key"""      )
    if "W_v" in orig_key:
        __A             =		orig_key.replace("""W_v"""     ,			"""self.value"""      )
    if "ff1" in orig_key:
        __A             =		orig_key.replace("""ff1"""     ,			"""intermediate.dense"""      )
    if "ff2" in orig_key:
        __A             =		orig_key.replace("""ff2"""     ,			"""output.dense"""      )
    if "ff" in orig_key:
        __A             =		orig_key.replace("""ff"""     ,			"""output.dense"""      )
    if "mlm_class" in orig_key:
        __A             =		orig_key.replace("""mlm.mlm_class"""     ,			"""cls.predictions.decoder"""      )
    if "mlm" in orig_key:
        __A             =		orig_key.replace("""mlm"""     ,			"""cls.predictions.transform"""      )
    if "cls" not in orig_key:
        __A             =		"""nystromformer.""" + orig_key

    return orig_key



def      _SCREAMING_SNAKE_CASE  (	__lowercase				:     Optional[Any]     ,			__lowercase				:     str      )  -> List[str]:





    """simple docstring"""

    for key in orig_state_dict.copy().keys():
        __A             =		orig_state_dict.pop(__lowercase      )

        if ("pooler" in key) or ("sen_class" in key) or ("conv.bias" in key):
            continue
        else:
            __A             =		val

    __A             =		orig_state_dict["""cls.predictions.decoder.bias"""]
    __A             =		(
        torch.arange(config.max_position_embeddings      ).expand((1, -1)      ) + 2
    )

    return orig_state_dict



def      _SCREAMING_SNAKE_CASE  (	__lowercase				:     Union[str, Any]     ,			__lowercase				:     Union[str, Any]     ,			__lowercase				:     Union[str, Any]      )  -> Any:





    """simple docstring"""

    __A             =		torch.load(__lowercase     ,			map_location="""cpu"""      )["""model_state_dict"""]
    __A             =		NystromformerConfig.from_json_file(__lowercase      )
    __A             =		NystromformerForMaskedLM(__lowercase      )

    __A             =		convert_checkpoint_helper(__lowercase     ,			__lowercase      )

    model.load_state_dict(__lowercase      )
    model.eval()
    model.save_pretrained(__lowercase      )

    print(f"Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"      )


if __name__ == "__main__":
  __a :    str								=				argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--pytorch_model_path", default=None, type=str, required=True, help="Path to Nystromformer pytorch checkpoint."
  )
  parser.add_argument(
      "--config_file",
      default=None,
      type=str,
      required=True,
      help="The json file for Nystromformer model config.",
  )
  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_nystromformer_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)












from ..utils import DummyObject, requires_backends


__a :    int								=				None



def      _SCREAMING_SNAKE_CASE  (	*__lowercase				:     Any     ,			**__lowercase				:     Optional[int]      )  -> Tuple:





    """simple docstring"""

    requires_backends(__lowercase     ,			["""sentencepiece""", """tokenizers"""]      )




