'''simple docstring'''

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_ : Optional[Any]						  =      logging.get_logger(__name__)


def __a (	__lowerCamelCase			:						str ,		__lowerCamelCase			:						str				)	->							str:
      '''simple docstring'''


      lowercase_     =      RobertaPreLayerNormConfig.from_pretrained(
          __lowerCamelCase ,		architectures=["RobertaPreLayerNormForMaskedLM"]				)

      # convert state_dict
      lowercase_     =      torch.load(hf_hub_download(repo_id=__lowerCamelCase ,		filename="pytorch_model.bin"				)				)
      lowercase_     =      {}
      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."				):
                  lowercase_     =      "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

            lowercase_     =      tensor_value

      lowercase_     =      RobertaPreLayerNormForMaskedLM.from_pretrained(
          pretrained_model_name_or_path=__lowerCamelCase ,		config=__lowerCamelCase ,		state_dict=__lowerCamelCase				)
      model.save_pretrained(__lowerCamelCase				)

      # convert tokenizer
      lowercase_     =      AutoTokenizer.from_pretrained(__lowerCamelCase				)
      tokenizer.save_pretrained(__lowerCamelCase				)


if __name__ == "__main__":
       lowerCAmelCase_ : Tuple						  =      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_ : int						  =      parser.parse_args()
       convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)







'''simple docstring'''

def __a (	__lowerCamelCase			:						int = 200				)	->							int:
      '''simple docstring'''


      lowercase_     =      [1, 2, 5, 10, 20, 50, 100, 200]
      lowercase_     =      [0] * (pence + 1)
      lowercase_     =      1  # base case: 1 way to make 0 pence

      for coin in coins:
            for i in range(__lowerCamelCase ,		pence + 1 ,		1				):
                  number_of_ways[i] += number_of_ways[i - coin]
      return number_of_ways[pence]


if __name__ == "__main__":
       assert solution(200) == 73_682






