

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





def       lowercase__						(       snake_case_   :str			,		snake_case_   :Tuple    ):
	__UpperCAmelCase      		= RobertaPreLayerNormConfig.from_pretrained(
	    lowerCAmelCase_			,		architectures=['''RobertaPreLayerNormForMaskedLM''']    )

	# convert state_dict
	__UpperCAmelCase      		= torch.load(hf_hub_download(repo_id=lowerCAmelCase_			,		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=lowerCAmelCase_			,		config=lowerCAmelCase_			,		state_dict=lowerCAmelCase_    )
	model.save_pretrained(lowerCAmelCase_    )

	# convert tokenizer
	__UpperCAmelCase      		= AutoTokenizer.from_pretrained(lowerCAmelCase_    )
	tokenizer.save_pretrained(lowerCAmelCase_    )


if __name__ == "__main__":
				_lowercase   :	Dict												=						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.'
				)
				_lowercase   :	Union[str, Any]												=						parser.parse_args()
				convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)





'''simple docstring'''

from abc import ABC, abstractmethod
from argparse import ArgumentParser

class       __magic_name__		(       _UpperCamelCase  ):







							@staticmethod
							@abstractmethod
							def 				__lowercase     (				_UpperCAmelCase   :			ArgumentParser						):
									raise NotImplementedError()







							@abstractmethod
							def 				__lowercase     (				self   :			str						):
									raise NotImplementedError()




