




from collections import OrderedDict
from typing import Mapping

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


__a          =			logging.get_logger(__name__)

__a          =			{
    'junnyu/roformer_chinese_small': 'https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json',
    'junnyu/roformer_chinese_base': 'https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json',
    'junnyu/roformer_chinese_char_small': (
        'https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json'
    ),
    'junnyu/roformer_chinese_char_base': (
        'https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json'
    ),
    'junnyu/roformer_small_discriminator': (
        'https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json'
    ),
    'junnyu/roformer_small_generator': (
        'https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json'
    ),
    # See all RoFormer models at https://huggingface.co/models?filter=roformer
}

class   __a(					_a       ):






    """simple docstring"""





    lowerCAmelCase        =				'''roformer'''





    def __init__(				self       ,_SCREAMING_SNAKE_CASE=50_000       ,_SCREAMING_SNAKE_CASE=None       ,_SCREAMING_SNAKE_CASE=768       ,_SCREAMING_SNAKE_CASE=12       ,_SCREAMING_SNAKE_CASE=12       ,_SCREAMING_SNAKE_CASE=3_072       ,_SCREAMING_SNAKE_CASE="gelu"       ,_SCREAMING_SNAKE_CASE=0.1       ,_SCREAMING_SNAKE_CASE=0.1       ,_SCREAMING_SNAKE_CASE=1_536       ,_SCREAMING_SNAKE_CASE=2       ,_SCREAMING_SNAKE_CASE=0.02       ,_SCREAMING_SNAKE_CASE=1e-12       ,_SCREAMING_SNAKE_CASE=0       ,_SCREAMING_SNAKE_CASE=False       ,_SCREAMING_SNAKE_CASE=True       ,**_SCREAMING_SNAKE_CASE       ,)			->		Tuple:
         super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							Tuple       =      vocab_size
         UpperCAmelCase_      :							Any       =      hidden_size if embedding_size is None else embedding_size
         UpperCAmelCase_      :							int       =      hidden_size
         UpperCAmelCase_      :							Union[str, Any]       =      num_hidden_layers
         UpperCAmelCase_      :							int       =      num_attention_heads
         UpperCAmelCase_      :							Optional[int]       =      hidden_act
         UpperCAmelCase_      :							str       =      intermediate_size
         UpperCAmelCase_      :							str       =      hidden_dropout_prob
         UpperCAmelCase_      :							Any       =      attention_probs_dropout_prob
         UpperCAmelCase_      :							Optional[int]       =      max_position_embeddings
         UpperCAmelCase_      :							Tuple       =      type_vocab_size
         UpperCAmelCase_      :							Optional[int]       =      initializer_range
         UpperCAmelCase_      :							List[str]       =      layer_norm_eps
         UpperCAmelCase_      :							Optional[int]       =      rotary_value
         UpperCAmelCase_      :							Dict       =      use_cache


class   __a(					_a       ):






    """simple docstring"""





    @property
    def 							a__				(				self  )			->		Mapping[str, Mapping[int, str]]:
         if self.task == "multiple-choice":
              UpperCAmelCase_      :							int       =      {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
         else:
              UpperCAmelCase_      :							Optional[int]       =      {0: '''batch''', 1: '''sequence'''}
         UpperCAmelCase_      :							Any       =      {0: '''batch''', 1: '''sequence'''}
         return OrderedDict(
             [
                 ('''input_ids''', dynamic_axis),
                 ('''attention_mask''', dynamic_axis),
                 ('''token_type_ids''', dynamic_axis),
             ]  )





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)