






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




def       snake_case  (A_				:str     ,      A_				:str				):







		'''simple docstring'''

		a					:      Any    =       RobertaPreLayerNormConfig.from_pretrained(
		    A_     ,      architectures=['RobertaPreLayerNormForMaskedLM']				)

		# convert state_dict
		a					:      Optional[int]    =       torch.load(hf_hub_download(repo_id=A_     ,      filename='pytorch_model.bin'				)				)
		a					:      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.'				):
						a					:      List[str]    =       '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					:      str    =       tensor_value

		a					:      Union[str, Any]    =       RobertaPreLayerNormForMaskedLM.from_pretrained(
		    pretrained_model_name_or_path=A_     ,      config=A_     ,      state_dict=A_				)
		model.save_pretrained(A_				)

		# convert tokenizer
		a					:      str    =       AutoTokenizer.from_pretrained(A_				)
		tokenizer.save_pretrained(A_				)


if __name__ == "__main__":
			_UpperCamelCase    :   Tuple   			=						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.'
			)
			_UpperCamelCase    :   Optional[int]   			=						parser.parse_args()
			convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)












"""simple docstring"""


from .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, InputFeatures, SingleSentenceClassificationProcessor
from .xnli import xnli_output_modes, xnli_processors, xnli_tasks_num_labels




