




import collections
import os
from typing import List, Optional, Tuple

from transformers.utils import is_jieba_available, requires_backends


if is_jieba_available():
     import jieba

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__a          =			logging.get_logger(__name__)

__a          =			{'vocab_file': 'vocab.txt'}

__a          =			{
    'vocab_file': {
        'openbmb/cpm-ant-10b': 'https://huggingface.co/openbmb/cpm-ant-10b/blob/main/vocab.txt',
    },
}

__a          =			{
    'openbmb/cpm-ant-10b': 1_024,
}







def lowerCamelCase__       (      _lowercase				):

     '''simple docstring'''


     UpperCAmelCase_      :							Dict       =      collections.OrderedDict()
     with open(_lowercase			,						'''r'''			,						encoding='''utf-8'''				) as reader:
          UpperCAmelCase_      :							str       =      reader.readlines()
     for index, token in enumerate(_lowercase				):
          UpperCAmelCase_      :							Optional[int]       =      token.rstrip('''\n'''				)
          UpperCAmelCase_      :							Optional[int]       =      index
     return vocab

class   __a(					_a       ):






    """simple docstring"""





    def __init__(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE="<unk>"       ,_SCREAMING_SNAKE_CASE=200  )			->		Optional[int]:
         UpperCAmelCase_      :							Optional[Any]       =      vocab
         UpperCAmelCase_      :							List[Any]       =      unk_token
         UpperCAmelCase_      :							int       =      max_input_chars_per_word





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE  )			->		Dict:
         UpperCAmelCase_      :							Any       =      list(_SCREAMING_SNAKE_CASE  )
         if len(_SCREAMING_SNAKE_CASE  ) > self.max_input_chars_per_word:
              return [self.unk_token]

         UpperCAmelCase_      :							List[Any]       =      0
         UpperCAmelCase_      :							int       =      []
         while start < len(_SCREAMING_SNAKE_CASE  ):
              UpperCAmelCase_      :							List[Any]       =      len(_SCREAMING_SNAKE_CASE  )
              UpperCAmelCase_      :							Tuple       =      None
              while start < end:
                   UpperCAmelCase_      :							Optional[Any]       =      ''''''.join(chars[start:end]  )
                   if substr in self.vocab:
                        UpperCAmelCase_      :							Optional[int]       =      substr
                        break
                   end -= 1
              if cur_substr is None:
                   sub_tokens.append(self.unk_token  )
                   start += 1
              else:
                   sub_tokens.append(_SCREAMING_SNAKE_CASE  )
                   UpperCAmelCase_      :							List[Any]       =      end

         return sub_tokens


class   __a(					_a       ):






    """simple docstring"""





    lowerCAmelCase        =				VOCAB_FILES_NAMES
    lowerCAmelCase        =				PRETRAINED_VOCAB_FILES_MAP
    lowerCAmelCase        =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowerCAmelCase        =				['''input_ids''', '''attention_mask''']
    lowerCAmelCase        =				False





    def __init__(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE="<d>"       ,_SCREAMING_SNAKE_CASE="</d>"       ,_SCREAMING_SNAKE_CASE="<s>"       ,_SCREAMING_SNAKE_CASE="</s>"       ,_SCREAMING_SNAKE_CASE="<pad>"       ,_SCREAMING_SNAKE_CASE="<unk>"       ,_SCREAMING_SNAKE_CASE="</n>"       ,_SCREAMING_SNAKE_CASE="</_>"       ,_SCREAMING_SNAKE_CASE="left"       ,**_SCREAMING_SNAKE_CASE       ,)			->		Optional[int]:
         requires_backends(self       ,['''jieba''']  )
         super().__init__(
             bod_token=_SCREAMING_SNAKE_CASE       ,eod_token=_SCREAMING_SNAKE_CASE       ,bos_token=_SCREAMING_SNAKE_CASE       ,eos_token=_SCREAMING_SNAKE_CASE       ,pad_token=_SCREAMING_SNAKE_CASE       ,unk_token=_SCREAMING_SNAKE_CASE       ,line_token=_SCREAMING_SNAKE_CASE       ,space_token=_SCREAMING_SNAKE_CASE       ,padding_side=_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE       ,)
         UpperCAmelCase_      :							str       =      bod_token
         UpperCAmelCase_      :							Tuple       =      eod_token
         UpperCAmelCase_      :							int       =      load_vocab(_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							Any       =      self.encoder[space_token]
         UpperCAmelCase_      :							List[str]       =      self.encoder[line_token]

         del self.encoder[space_token]
         del self.encoder[line_token]

         UpperCAmelCase_      :							List[str]       =      collections.OrderedDict(sorted(self.encoder.items()       ,key=lambda _SCREAMING_SNAKE_CASE  : x[1]  )  )
         UpperCAmelCase_      :							Dict       =      {v: k for k, v in self.encoder.items()}

         UpperCAmelCase_      :							str       =      WordpieceTokenizer(vocab=self.encoder       ,unk_token=self.unk_token  )





    @property
    def 							a__				(				self  )			->		List[Any]:
         return self.encoder[self.bod_token]





    @property
    def 							a__				(				self  )			->		Optional[int]:
         return self.encoder[self.eod_token]





    @property
    def 							a__				(				self  )			->		int:
         return self.encoder["\n"]





    @property
    def 							a__				(				self  )			->		int:
         return len(self.encoder  )





    def 							a__				(				self  )			->		Any:
         return dict(self.encoder       ,**self.added_tokens_encoder  )





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE  )			->		Tuple:
         UpperCAmelCase_      :							Union[str, Any]       =      []
         for x in jieba.cut(_SCREAMING_SNAKE_CASE       ,cut_all=_SCREAMING_SNAKE_CASE  ):
              output_tokens.extend(self.wordpiece_tokenizer.tokenize(_SCREAMING_SNAKE_CASE  )  )
         return output_tokens





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  )			->		Tuple:
         UpperCAmelCase_      :							List[Any]       =      [i for i in token_ids if i >= 0]
         UpperCAmelCase_      :							Optional[Any]       =      [
             x for x in token_ids if x != self.pad_token_id and x != self.eos_token_id and x != self.bos_token_id
         ]
         return super()._decode(_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE  )			->		int:
         return token in self.encoder





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE  )			->		str:
         return "".join(_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE  )			->		int:
         return self.encoder.get(_SCREAMING_SNAKE_CASE       ,self.encoder.get(self.unk_token  )  )





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE  )			->		Dict:
         return self.decoder.get(_SCREAMING_SNAKE_CASE       ,self.unk_token  )





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE = None  )			->		Tuple[str]:
         if os.path.isdir(_SCREAMING_SNAKE_CASE  ):
              UpperCAmelCase_      :							Union[str, Any]       =      os.path.join(
                  _SCREAMING_SNAKE_CASE       ,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']  )
         else:
              UpperCAmelCase_      :							List[str]       =      (filename_prefix + '''-''' if filename_prefix else '''''') + save_directory
         UpperCAmelCase_      :							List[Any]       =      0
         if " " in self.encoder:
              UpperCAmelCase_      :							Optional[Any]       =      self.encoder[''' ''']
              del self.encoder[" "]
         if "\n" in self.encoder:
              UpperCAmelCase_      :							List[Any]       =      self.encoder['''\n''']
              del self.encoder["\n"]
         UpperCAmelCase_      :							List[Any]       =      collections.OrderedDict(sorted(self.encoder.items()       ,key=lambda _SCREAMING_SNAKE_CASE  : x[1]  )  )
         with open(_SCREAMING_SNAKE_CASE       ,'''w'''       ,encoding='''utf-8'''  ) as writer:
              for token, token_index in self.encoder.items():
                   if index != token_index:
                        logger.warning(
                            f'''Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive.'''
                            ''' Please check that the vocabulary is not corrupted!'''  )
                        UpperCAmelCase_      :							List[str]       =      token_index
                   writer.write(token + '''\n'''  )
                   index += 1
         return (vocab_file,)





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE = None  )			->		List[int]:
         if token_ids_a is None:
              return [self.bos_token_id] + token_ids_a
         return [self.bos_token_id] + token_ids_a + [self.bos_token_id] + token_ids_a





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = False  )			->		List[int]:

         if already_has_special_tokens:
              return super().get_special_tokens_mask(
                  token_ids_a=_SCREAMING_SNAKE_CASE       ,token_ids_a=_SCREAMING_SNAKE_CASE       ,already_has_special_tokens=_SCREAMING_SNAKE_CASE  )

         if token_ids_a is not None:
              return [1] + ([0] * len(_SCREAMING_SNAKE_CASE  )) + [1] + ([0] * len(_SCREAMING_SNAKE_CASE  ))
         return [1] + ([0] * len(_SCREAMING_SNAKE_CASE  ))





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)