


from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


snake_case				:    Tuple       				=     logging.get_logger(__name__)

snake_case				:    Optional[Any]       				=     {
    '''facebook/data2vec-text-base''': '''https://huggingface.co/data2vec/resolve/main/config.json''',
}







class 		_snake_case  (      snake_case_							):
 SCREAMING_SNAKE_CASE__      =       'data2vec-text'




 def __init__(		self					,  _lowerCamelCase=3_0522					,  _lowerCamelCase=768					,  _lowerCamelCase=12					,  _lowerCamelCase=12					,  _lowerCamelCase=3072					,  _lowerCamelCase="gelu"					,  _lowerCamelCase=0.1					,  _lowerCamelCase=0.1					,  _lowerCamelCase=512					,  _lowerCamelCase=2					,  _lowerCamelCase=0.02					,  _lowerCamelCase=1e-12					,  _lowerCamelCase=1					,  _lowerCamelCase=0					,  _lowerCamelCase=2					,  _lowerCamelCase="absolute"					,  _lowerCamelCase=True					,  _lowerCamelCase=None					,  **_lowerCamelCase					,  ):
       super().__init__(pad_token_id=_lowerCamelCase					,  bos_token_id=_lowerCamelCase					,  eos_token_id=_lowerCamelCase					,  **_lowerCamelCase    )

       a						:int 							=       vocab_size
       a						:Dict 							=       hidden_size
       a						:Optional[int] 							=       num_hidden_layers
       a						:Dict 							=       num_attention_heads
       a						:int 							=       hidden_act
       a						:Optional[int] 							=       intermediate_size
       a						:Optional[Any] 							=       hidden_dropout_prob
       a						:int 							=       attention_probs_dropout_prob
       a						:List[Any] 							=       max_position_embeddings
       a						:Union[str, Any] 							=       type_vocab_size
       a						:Tuple 							=       initializer_range
       a						:Optional[Any] 							=       layer_norm_eps
       a						:Optional[int] 							=       position_embedding_type
       a						:Optional[int] 							=       use_cache
       a						:Dict 							=       classifier_dropout







class 		_snake_case  (      snake_case_							):




 @property
 def        SCREAMING_SNAKE_CASE__				(		self    ):
       if self.task == "multiple-choice":
             a						:Dict 							=       {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
       else:
             a						:List[Any] 							=       {0: '''batch''', 1: '''sequence'''}
       return OrderedDict(
           [
               ('''input_ids''', dynamic_axis),
               ('''attention_mask''', dynamic_axis),
           ]    )












'''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=30_522, 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)



