



import argparse

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase__       = logging.get_logger(__name__)
def      _lowerCAmelCase(	__A   ,       __A			):
  UpperCAmelCase					      =  RobertaPreLayerNormConfig.from_pretrained(
      __A   ,       architectures=["RobertaPreLayerNormForMaskedLM"]			)

  # convert state_dict
  UpperCAmelCase					      =  torch.load(hf_hub_download(repo_id=__A   ,       filename="pytorch_model.bin"			)			)
  UpperCAmelCase					      =  {}
  for tensor_key, tensor_value in original_state_dict.items():
    # The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
    if tensor_key.startswith("roberta."			):
      UpperCAmelCase					      =  '''roberta_prelayernorm.''' + tensor_key[len("roberta."			) :]

    # The original implementation contains weights which are not used, remove them from the state_dict
    if tensor_key.endswith(".self.LayerNorm.weight"			) or tensor_key.endswith(".self.LayerNorm.bias"			):
      continue

    UpperCAmelCase					      =  tensor_value

  UpperCAmelCase					      =  RobertaPreLayerNormForMaskedLM.from_pretrained(
      pretrained_model_name_or_path=__A   ,       config=__A   ,       state_dict=__A			)
  model.save_pretrained(__A			)

  # convert tokenizer
  UpperCAmelCase					      =  AutoTokenizer.from_pretrained(__A			)
  tokenizer.save_pretrained(__A			)


if __name__ == "__main__":
      lowerCAmelCase__       = argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          "--checkpoint-repo",
          default=None,
          type=str,
          required=True,
          help="Path the official PyTorch dump, e.g. \'andreasmadsen/efficient_mlm_m0.40\'.",
      )
      parser.add_argument(
          "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
      )
      lowerCAmelCase__       = parser.parse_args()
      convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)






def      _lowerCAmelCase(	__A   ,       __A   ,       __A			):
  if n == 0:
    return 1

  elif n % 2 == 1:
    return (binary_exponentiation(__A   ,       n - 1   ,       __A			) * a) % mod

  else:
    UpperCAmelCase					      =  binary_exponentiation(__A   ,       n / 2   ,       __A			)
    return (b * b) % mod


# a prime number
lowerCAmelCase__       = 701

lowerCAmelCase__       = 1000000000
lowerCAmelCase__       = 10

# using binary exponentiation function, O(log(p)):
print((a / b) % p == (a * binary_exponentiation(b, p - 2, p)) % p)





print((a / b) % p == (a * b ** (p - 2)) % p)



