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			:							Union[str, Any]     =logging.get_logger(__name__)







def 						A__  (					__A   , __A	):
    _lowerCamelCase     :		Dict 						=						RobertaPreLayerNormConfig.from_pretrained(
        __A   , architectures=["""RobertaPreLayerNormForMaskedLM"""]	)

    # convert state_dict
    _lowerCamelCase     :		Dict 						=						torch.load(hf_hub_download(repo_id=__A   , filename="""pytorch_model.bin"""	)	)
    _lowerCamelCase     :		Union[str, Any] 						=						{}
    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     :		Optional[Any] 						=						'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     :		int 						=						tensor_value

    _lowerCamelCase     :		str 						=						RobertaPreLayerNormForMaskedLM.from_pretrained(
        pretrained_model_name_or_path=__A   , config=__A   , state_dict=__A	)
    model.save_pretrained(__A	)

    # convert tokenizer
    _lowerCamelCase     :		List[Any] 						=						AutoTokenizer.from_pretrained(__A	)
    tokenizer.save_pretrained(__A	)


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





def 						A__  (					__A	):







    '''simple docstring'''
    _lowerCamelCase     :		Tuple 						=						0
    for ch in input_str:
        _lowerCamelCase     :		Optional[Any] 						=						ord(__A	)
        _lowerCamelCase     :		List[str] 						=						pow(2   , __A	)

        # If we already turned on bit for current character's unicode
        if bitmap >> ch_unicode & 1 == 1:
            return False
        bitmap |= ch_bit_index_on
    return True


if __name__ == "__main__":
    import doctest

    doctest.testmod()




