




import pickle
import unittest

import torch

from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu

@require_cpu
class   __a(					unittest.TestCase       ):






    """simple docstring"""





    def 							a__				(				self  )			->		Tuple:
         UpperCAmelCase_      :							Optional[Any]       =      torch.nn.Linear(10       ,10  )
         UpperCAmelCase_      :							Optional[int]       =      torch.optim.SGD(model.parameters()       ,0.1  )
         UpperCAmelCase_      :							Optional[int]       =      Accelerator()
         UpperCAmelCase_      :							Optional[int]       =      accelerator.prepare(_SCREAMING_SNAKE_CASE  )
         try:
              pickle.loads(pickle.dumps(_SCREAMING_SNAKE_CASE  )  )
         except Exception as e:
              self.fail(f'''Accelerated optimizer pickling failed with {e}'''  )
         AcceleratorState._reset_state()











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
)
__a          =			logging.getLogger(__name__)

if __name__ == "__main__":
     __a          =			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=30_522, type=int)
     __a          =			parser.parse_args()

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

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