

import argparse

import torch

from transformers import NystromformerConfig, NystromformerForMaskedLM





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

    return orig_key





def 							__a       (					__A ,       __A	):
    for key in orig_state_dict.copy().keys():
        SCREAMING_SNAKE_CASE__					     =						orig_state_dict.pop(__A	)

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

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

    return orig_state_dict





def 							__a       (					__A ,       __A ,       __A	):
    SCREAMING_SNAKE_CASE__					     =						torch.load(__A ,       map_location="cpu"	)["model_state_dict"]
    SCREAMING_SNAKE_CASE__					     =						NystromformerConfig.from_json_file(__A	)
    SCREAMING_SNAKE_CASE__					     =						NystromformerForMaskedLM(__A	)

    SCREAMING_SNAKE_CASE__					     =						convert_checkpoint_helper(__A ,       __A	)

    model.load_state_dict(__A	)
    model.eval()
    model.save_pretrained(__A	)

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


if __name__ == "__main__":
  A__        =			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__        =			parser.parse_args()
  convert_nystromformer_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)





from collections import namedtuple

A__        =			namedtuple("from_to", "from_ to")

A__        =			{
    "atm": from_to(1, 1),
    "pascal": from_to(0.0_000_098, 10_1325),
    "bar": from_to(0.986_923, 1.01_325),
    "kilopascal": from_to(0.00_986_923, 101.325),
    "megapascal": from_to(9.86_923, 0.101_325),
    "psi": from_to(0.068_046, 14.6_959),
    "inHg": from_to(0.0_334_211, 29.9_213),
    "torr": from_to(0.00_131_579, 760),
}





def 							__a       (					__A ,       __A ,       __A	):
    if from_type not in PRESSURE_CONVERSION:
        raise ValueError(
            f"Invalid 'from_type' value: {from_type!r}  Supported values are:\n"
            + ", ".join(__A	)	)
    if to_type not in PRESSURE_CONVERSION:
        raise ValueError(
            f"Invalid 'to_type' value: {to_type!r}.  Supported values are:\n"
            + ", ".join(__A	)	)
    return (
        value * PRESSURE_CONVERSION[from_type].from_ * PRESSURE_CONVERSION[to_type].to
    )


if __name__ == "__main__":
  import doctest

  doctest.testmod()


