



"""simple docstring"""





from typing import Optional

import pyspark

from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader




class 			lowercase__			(      A    ):
							'''simple docstring'''







							def __init__(  self	,       snake_case	,       snake_case = None	,       snake_case = None	,       snake_case = True	,       snake_case = None	,       snake_case = False	,       snake_case = None	,       snake_case = True	,       snake_case = "arrow"	,       **snake_case	,       )			->							Optional[int]:
									super().__init__(
									    split=snake_case	,       features=snake_case	,       cache_dir=snake_case	,       keep_in_memory=snake_case	,       streaming=snake_case	,       **snake_case	,       )
									_UpperCAmelCase							 =		load_from_cache_file
									_UpperCAmelCase							 =		file_format
									_UpperCAmelCase							 =		Spark(
									    df=snake_case	,       features=snake_case	,       cache_dir=snake_case	,       working_dir=snake_case	,       **snake_case	,       )






							def        lowerCamelCase_   (  self						)			->							Optional[Any]:
									if self.streaming:
											return self.builder.as_streaming_dataset(split=self.split						)
									_UpperCAmelCase							 =		None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
									self.builder.download_and_prepare(
									    download_mode=snake_case	,       file_format=self._file_format	,       )
									return self.builder.as_dataset(split=self.split						)







"""simple docstring"""





import argparse
import logging
import pickle
from collections import Counter


logging.basicConfig(
    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
lowercase  							=					logging.getLogger(__name__)

if __name__ == "__main__":
					lowercase  							=					argparse.ArgumentParser(
					    description='''Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)'''
					)
					parser.add_argument(
					    '''--data_file''', type=str, default='''data/dump.bert-base-uncased.pickle''', help='''The binarized dataset.'''
					)
					parser.add_argument(
					    '''--token_counts_dump''', type=str, default='''data/token_counts.bert-base-uncased.pickle''', help='''The dump file.'''
					)
					parser.add_argument('''--vocab_size''', default=3_05_22, type=int)
					lowercase  							=					parser.parse_args()

					logger.info(F'''Loading data from {args.data_file}''')
					with open(args.data_file, '''rb''') as fp:
										lowercase  							=					pickle.load(fp)

					logger.info('''Counting occurrences for MLM.''')
					lowercase  							=					Counter()
					for tk_ids in data:
										counter.update(tk_ids)
					lowercase  							=					[0] * args.vocab_size
					for k, v in counter.items():
										lowercase  							=					v

					logger.info(F'''Dump to {args.token_counts_dump}''')
					with open(args.token_counts_dump, '''wb''') as handle:
										pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)


