




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	:       List[Any]     =	logging.get_logger(__name__)




def     __magic_name__       (				A	:      str,						A	:      str     ):






			'''simple docstring'''







			a					=    RobertaPreLayerNormConfig.from_pretrained(
			    A,						architectures=["RobertaPreLayerNormForMaskedLM"]     )

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

						a					=    tensor_value

			a					=    RobertaPreLayerNormForMaskedLM.from_pretrained(
			    pretrained_model_name_or_path=A,						config=A,						state_dict=A     )
			model.save_pretrained(A     )

			# convert tokenizer
			a					=    AutoTokenizer.from_pretrained(A     )
			tokenizer.save_pretrained(A     )


if __name__ == "__main__":
		__lowerCAmelCase	:       Any     =	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     __magic_name__       (				A	:      int,						A	:      int,						A	:      int     ):






			'''simple docstring'''







			if exponent == 1:
						return base
			if exponent % 2 == 0:
						a					=    _modexpt(A,						exponent // 2,						A     ) % modulo_value
						return (x * x) % modulo_value
			else:
						return (base * _modexpt(A,						exponent - 1,						A     )) % modulo_value




def     __magic_name__       (				A	:      int = 1777,						A	:      int = 1855,						A	:      int = 8     ):






			'''simple docstring'''







			a					=    base
			for _ in range(1,						A     ):
						a					=    _modexpt(A,						A,						10**digits     )

			return result


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






