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		:     Optional[int]         =	logging.get_logger(__name__)






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				str		, _SCREAMING_SNAKE_CASE     :				str			):
						UpperCamelCase_		:							Union[str, Any]  =		RobertaPreLayerNormConfig.from_pretrained(
						    SCREAMING_SNAKE_CASE__		, architectures=["""RobertaPreLayerNormForMaskedLM"""]			)

						# convert state_dict
						UpperCamelCase_		:							str  =		torch.load(hf_hub_download(repo_id=SCREAMING_SNAKE_CASE__		, filename="""pytorch_model.bin"""			)			)
						UpperCamelCase_		:							int  =		{}
						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_		:							Optional[int]  =		'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_		:							Optional[int]  =		tensor_value

						UpperCamelCase_		:							int  =		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
						UpperCamelCase_		:							Union[str, Any]  =		AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__			)
						tokenizer.save_pretrained(SCREAMING_SNAKE_CASE__			)


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










import warnings

from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor


lowerCamelCase		   =			logging.get_logger(__name__)




class A    (  UpperCamelCase_	):






      def __init__(   self		:	Optional[Any]     ,  *lowercase_		:	str     ,  **lowercase_		:	List[Any]					)   ->		None:
             """simple docstring"""




             warnings.warn(
                 'The class MobileViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
                 ' Please use MobileViTImageProcessor instead.'     ,  lowercase_     ,  )
             super().__init__(*lowercase_     ,  **lowercase_					)


