



import itertools
from dataclasses import dataclass
from typing import Optional

import pandas as pd
import pyarrow as pa

import datasets
from datasets.table import table_cast

@dataclass
class 	lowerCamelCase_    ( datasets.BuilderConfig						):






						'''simple docstring'''
						a__   =  None







class 	lowerCamelCase_    ( datasets.ArrowBasedBuilder						):






						'''simple docstring'''
						a__   =  PandasConfig







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Dict )      ->				Union[str, Any]:
									return datasets.DatasetInfo(features=self.config.features )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Any	,	__lowerCamelCase      :			Any )      ->				Union[str, Any]:
									if not self.config.data_files:
												raise ValueError(F"""At least one data file must be specified, but got data_files={self.config.data_files}""" )
									A							:					Union[str, Any]              =						dl_manager.download_and_extract(self.config.data_files )
									if isinstance(__lowerCamelCase	,	(str, list, tuple) ):
												A							:					Union[str, Any]              =						data_files
												if isinstance(__lowerCamelCase	,	__lowerCamelCase ):
															A							:					Any              =						[files]
												# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
												A							:					Optional[Any]              =						[dl_manager.iter_files(__lowerCamelCase ) for file in files]
												return [datasets.SplitGenerator(name=datasets.Split.TRAIN	,	gen_kwargs={"files": files} )]
									A							:					int              =						[]
									for split_name, files in data_files.items():
												if isinstance(__lowerCamelCase	,	__lowerCamelCase ):
															A							:					int              =						[files]
												# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
												A							:					Optional[int]              =						[dl_manager.iter_files(__lowerCamelCase ) for file in files]
												splits.append(datasets.SplitGenerator(name=__lowerCamelCase	,	gen_kwargs={"files": files} ) )
									return splits







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Union[str, Any]	,	__lowerCamelCase      :			pa.Table )      ->				pa.Table:
									if self.config.features is not None:
												# more expensive cast to support nested features with keys in a different order
												# allows str <-> int/float or str to Audio for example
												A							:					List[Any]              =						table_cast(__lowerCamelCase	,	self.config.features.arrow_schema )
									return pa_table







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Dict	,	__lowerCamelCase      :			Tuple )      ->				Union[str, Any]:
									for i, file in enumerate(itertools.chain.from_iterable(__lowerCamelCase ) ):
												with open(__lowerCamelCase	,	"rb" ) as f:
															A							:					str              =						pa.Table.from_pandas(pd.read_pickle(__lowerCamelCase ) )
															yield i, self._cast_table(__lowerCamelCase )




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           =  logging.get_logger(__name__)




def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase    ):
			A							:					Optional[Any]              =						RobertaPreLayerNormConfig.from_pretrained(
			    _lowerCamelCase ,    architectures=["RobertaPreLayerNormForMaskedLM"]    )

			# convert state_dict
			A							:					List[Any]              =						torch.load(hf_hub_download(repo_id=_lowerCamelCase ,    filename="pytorch_model.bin"    )    )
			A							:					Union[str, 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							:					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

						A							:					Any              =						tensor_value

			A							:					Optional[int]              =						RobertaPreLayerNormForMaskedLM.from_pretrained(
			    pretrained_model_name_or_path=_lowerCamelCase ,    config=_lowerCamelCase ,    state_dict=_lowerCamelCase    )
			model.save_pretrained(_lowerCamelCase    )

			# convert tokenizer
			A							:					Optional[Any]              =						AutoTokenizer.from_pretrained(_lowerCamelCase    )
			tokenizer.save_pretrained(_lowerCamelCase    )


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