



import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures


UpperCAmelCase_							:      Dict      =  logging.get_logger(__name__)






@dataclass
class      UpperCamelCase    :
	lowerCAmelCase	:   str				=	field(metadata={"""help""": """The name of the task to train on: """ + """, """.join(glue_processors.keys()     )}     )
	lowerCAmelCase	:   str				=	field(
	    metadata={"""help""": """The input data dir. Should contain the .tsv files (or other data files) for the task."""}     )
	lowerCAmelCase	:   int				=	field(
	    default=128       ,    metadata={
	        """help""": (
	            """The maximum total input sequence length after tokenization. Sequences longer """
	            """than this will be truncated, sequences shorter will be padded."""
	        )
	    }       ,    )
	lowerCAmelCase	:   bool				=	field(
	    default=__lowercase       ,    metadata={"""help""": """Overwrite the cached training and evaluation sets"""}     )



	def 			__A							(       self		):
							A__							=       self.task_name.lower()






class      UpperCamelCase    (						__lowercase     ):
	lowerCAmelCase	:   str				=	"train"
	lowerCAmelCase	:   Optional[Any]				=	"dev"
	lowerCAmelCase	:   int				=	"test"






class      UpperCamelCase    (						__lowercase     ):
	lowerCAmelCase	:   GlueDataTrainingArguments
	lowerCAmelCase	:   str
	lowerCAmelCase	:   List[InputFeatures]



	def __init__(       self			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__ = None			,	UpperCAmelCase__ = Split.train			,	UpperCAmelCase__ = None			,	):
							warnings.warn(
							    "This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets "
							    "library. You can have a look at this example script for pointers: "
							    "https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py"			,	_a			,	)
							A__							=       args
							A__							=       glue_processors[args.task_name]()
							A__							=       glue_output_modes[args.task_name]
							if isinstance(_a			,	_a		):
													try:
																			A__							=       Split[mode]
													except KeyError:
																			raise KeyError("mode is not a valid split name"		)
        # Load data features from cache or dataset file
							A__							=       os.path.join(
							    cache_dir if cache_dir is not None else args.data_dir			,	F"""cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}"""			,	)
							A__							=       self.processor.get_labels()
							if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
							    "RobertaTokenizer",
							    "RobertaTokenizerFast",
							    "XLMRobertaTokenizer",
							    "BartTokenizer",
							    "BartTokenizerFast",
							):
													# HACK(label indices are swapped in RoBERTa pretrained model)
													A__							=       label_list[2], label_list[1]
							A__							=       label_list

							# Make sure only the first process in distributed training processes the dataset,
							# and the others will use the cache.
							A__							=       cached_features_file + '''.lock'''
							with FileLock(_a		):
													if os.path.exists(_a		) and not args.overwrite_cache:
																			A__							=       time.time()
																			A__							=       torch.load(_a		)
																			logger.info(
																			    F"""Loading features from cached file {cached_features_file} [took %.3f s]"""			,	time.time() - start		)
													else:
																			logger.info(F"""Creating features from dataset file at {args.data_dir}"""		)

																			if mode == Split.dev:
																									A__							=       self.processor.get_dev_examples(args.data_dir		)
																			elif mode == Split.test:
																									A__							=       self.processor.get_test_examples(args.data_dir		)
																			else:
																									A__							=       self.processor.get_train_examples(args.data_dir		)
																			if limit_length is not None:
																									A__							=       examples[:limit_length]
																			A__							=       glue_convert_examples_to_features(
																			    _a			,	_a			,	max_length=args.max_seq_length			,	label_list=_a			,	output_mode=self.output_mode			,	)
																			A__							=       time.time()
																			torch.save(self.features			,	_a		)
																			# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
																			logger.info(
																			    F"""Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]"""		)



	def __len__(       self		):
							return len(self.features		)



	def __getitem__(       self			,	UpperCAmelCase__		):
							return self.features[i]



	def 			__A							(       self		):
							return self.label_list










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  UpperCamelCase						(       _A			:				str			,							_A			:				str    )->       Any:





						"""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__":
						UpperCAmelCase_							:      str      =  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_							:      List[Any]      =  parser.parse_args()
						convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)





