






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  							=					logging.get_logger(__name__)





def 						__UpperCAmelCase			(     a_							,     a_):
							snake_case_				=							RobertaPreLayerNormConfig.from_pretrained(
							    a_							,     architectures=['RobertaPreLayerNormForMaskedLM'])

							# convert state_dict
							snake_case_				=							torch.load(hf_hub_download(repo_id=a_							,     filename='pytorch_model.bin'))
							snake_case_				=							{}
							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.'):
																					snake_case_				=							'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

														snake_case_				=							tensor_value

							snake_case_				=							RobertaPreLayerNormForMaskedLM.from_pretrained(
							    pretrained_model_name_or_path=a_							,     config=a_							,     state_dict=a_)
							model.save_pretrained(a_)

							# convert tokenizer
							snake_case_				=							AutoTokenizer.from_pretrained(a_)
							tokenizer.save_pretrained(a_)


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













from typing import TYPE_CHECKING

from ...utils import _LazyModule


lowercase  							=					{"processing_wav2vec2_with_lm": ["Wav2Vec2ProcessorWithLM"]}


if TYPE_CHECKING:
	from .processing_wavaveca_with_lm import WavaVecaProcessorWithLM
else:
	import sys

	lowercase  							=					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





