




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__a          =			{
    'configuration_xlm_roberta_xl': [
        'XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'XLMRobertaXLConfig',
        'XLMRobertaXLOnnxConfig',
    ],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     __a          =			[
         'XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
         'XLMRobertaXLForCausalLM',
         'XLMRobertaXLForMaskedLM',
         'XLMRobertaXLForMultipleChoice',
         'XLMRobertaXLForQuestionAnswering',
         'XLMRobertaXLForSequenceClassification',
         'XLMRobertaXLForTokenClassification',
         'XLMRobertaXLModel',
         'XLMRobertaXLPreTrainedModel',
     ]

if TYPE_CHECKING:
     from .configuration_xlm_roberta_xl import (
         XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
         XLMRobertaXLConfig,
         XLMRobertaXLOnnxConfig,
     )

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_xlm_roberta_xl import (
              XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
              XLMRobertaXLForCausalLM,
              XLMRobertaXLForMaskedLM,
              XLMRobertaXLForMultipleChoice,
              XLMRobertaXLForQuestionAnswering,
              XLMRobertaXLForSequenceClassification,
              XLMRobertaXLForTokenClassification,
              XLMRobertaXLModel,
              XLMRobertaXLPreTrainedModel,
          )

else:
     import sys

     __a          =			_LazyModule(__name__, globals()['__file__'], _import_structure)











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)