





'''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()
_SCREAMING_SNAKE_CASE	 =							logging.get_logger(__name__)


def 	__a(SCREAMING_SNAKE_CASE_  :					str		,       SCREAMING_SNAKE_CASE_  :					str    ):



    '''simple docstring'''





    _lowerCAmelCase				=      RobertaPreLayerNormConfig.from_pretrained(
        SCREAMING_SNAKE_CASE_		,       architectures=["RobertaPreLayerNormForMaskedLM"]    )

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

        _lowerCAmelCase				=      tensor_value

    _lowerCAmelCase				=      RobertaPreLayerNormForMaskedLM.from_pretrained(
        pretrained_model_name_or_path=SCREAMING_SNAKE_CASE_		,       config=SCREAMING_SNAKE_CASE_		,       state_dict=SCREAMING_SNAKE_CASE_    )
    model.save_pretrained(SCREAMING_SNAKE_CASE_    )

    # convert tokenizer
    _lowerCAmelCase				=      AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_    )
    tokenizer.save_pretrained(SCREAMING_SNAKE_CASE_    )


if __name__ == "__main__":
       _SCREAMING_SNAKE_CASE	 =							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."
       )
       _SCREAMING_SNAKE_CASE	 =							parser.parse_args()
       convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)









'''simple docstring'''


import math


def 	__a(SCREAMING_SNAKE_CASE_  :					int = 100    ):



    '''simple docstring'''





    _lowerCAmelCase				=      sum(i * i for i in range(1		,       n + 1    )    )
    _lowerCAmelCase				=      int(math.pow(sum(range(1		,       n + 1    )    )		,       2    )    )
    return square_of_sum - sum_of_squares


if __name__ == "__main__":
       print(f'''{solution() = }''')


