


"""simple docstring"""



import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mvp import MvpTokenizer


__snake_case             =   logging.get_logger(__name__)


__snake_case             =   {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

# See all MVP models at https://huggingface.co/models?filter=mvp
__snake_case             =   {
    """vocab_file""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json""",
    },
    """added_tokens.json""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json""",
    },
    """merges_file""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/tokenizer.json""",
    },
}

__snake_case             =   {
    """RUCAIBox/mvp""": 1024,
}







class _lowerCAmelCase    (				a__     ):
   __UpperCAmelCase			:	int							  =  VOCAB_FILES_NAMES
   __UpperCAmelCase			:	Any							  =  PRETRAINED_VOCAB_FILES_MAP
   __UpperCAmelCase			:	Tuple							  =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __UpperCAmelCase			:	Optional[Any]							  =  ['''input_ids''', '''attention_mask''']
   __UpperCAmelCase			:	Any							  =  MvpTokenizer







   def __init__(     self						,     UpperCamelCase__=None						,     UpperCamelCase__=None						,     UpperCamelCase__=None						,     UpperCamelCase__="replace"						,     UpperCamelCase__="<s>"						,     UpperCamelCase__="</s>"						,     UpperCamelCase__="</s>"						,     UpperCamelCase__="<s>"						,     UpperCamelCase__="<unk>"						,     UpperCamelCase__="<pad>"						,     UpperCamelCase__="<mask>"						,     UpperCamelCase__=False						,     UpperCamelCase__=True						,     **UpperCamelCase__						,     )     ->		Union[str, Any]:

          '''simple docstring'''




          super().__init__(
              UpperCamelCase__						,     UpperCamelCase__						,     tokenizer_file=UpperCamelCase__						,     errors=UpperCamelCase__						,     bos_token=UpperCamelCase__						,     eos_token=UpperCamelCase__						,     sep_token=UpperCamelCase__						,     cls_token=UpperCamelCase__						,     unk_token=UpperCamelCase__						,     pad_token=UpperCamelCase__						,     mask_token=UpperCamelCase__						,     add_prefix_space=UpperCamelCase__						,     trim_offsets=UpperCamelCase__						,     **UpperCamelCase__						,     )

          snake_case   :				str				 =   json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()						)
          if pre_tok_state.get("add_prefix_space"						,     UpperCamelCase__						) != add_prefix_space:
                 snake_case   :				List[str]				 =   getattr(UpperCamelCase__						,     pre_tok_state.pop("type"						)						)
                 snake_case   :				Dict				 =   add_prefix_space
                 snake_case   :				str				 =   pre_tok_class(**UpperCamelCase__						)

          snake_case   :				Union[str, Any]				 =   add_prefix_space

          # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
          snake_case   :				int				 =   "post_processor"
          snake_case   :				List[str]				 =   getattr(self.backend_tokenizer						,     UpperCamelCase__						,     UpperCamelCase__						)
          if tokenizer_component_instance:
                 snake_case   :				Union[str, Any]				 =   json.loads(tokenizer_component_instance.__getstate__()						)

                 # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
                 if "sep" in state:
                        snake_case   :				List[str]				 =   tuple(state["sep"]						)
                 if "cls" in state:
                        snake_case   :				Dict				 =   tuple(state["cls"]						)

                 snake_case   :				str				 =   False

                 if state.get("add_prefix_space"						,     UpperCamelCase__						) != add_prefix_space:
                        snake_case   :				List[str]				 =   add_prefix_space
                        snake_case   :				Optional[Any]				 =   True

                 if state.get("trim_offsets"						,     UpperCamelCase__						) != trim_offsets:
                        snake_case   :				Dict				 =   trim_offsets
                        snake_case   :				Tuple				 =   True

                 if changes_to_apply:
                        snake_case   :				List[Any]				 =   getattr(UpperCamelCase__						,     state.pop("type"						)						)
                        snake_case   :				Union[str, Any]				 =   component_class(**UpperCamelCase__						)
                        setattr(self.backend_tokenizer						,     UpperCamelCase__						,     UpperCamelCase__						)







   @property
   def 			lowerCamelCase		(     self						)     ->		Optional[Any]:

          '''simple docstring'''




          if self._mask_token is None:
                 if self.verbose:
                        logger.error("Using mask_token, but it is not set yet."						)
                 return None
          return str(self._mask_token						)







   @mask_token.setter
   def 			lowerCamelCase		(     self						,     UpperCamelCase__						)     ->		List[Any]:

          '''simple docstring'''




          snake_case   :				Dict				 =   AddedToken(UpperCamelCase__						,     lstrip=UpperCamelCase__						,     rstrip=UpperCamelCase__						) if isinstance(UpperCamelCase__						,     UpperCamelCase__						) else value
          snake_case   :				List[str]				 =   value







   def 			lowerCamelCase		(     self						,     *UpperCamelCase__						,     **UpperCamelCase__						)     ->		Union[str, Any]:

          '''simple docstring'''




          snake_case   :				List[str]				 =   kwargs.get("is_split_into_words"						,     UpperCamelCase__						)

          if is_split_into_words and not self.add_prefix_space:
                 raise ValueError(
                     F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
                     "to use it with pretokenized inputs."						)

          return super()._batch_encode_plus(*UpperCamelCase__						,     **UpperCamelCase__						)







   def 			lowerCamelCase		(     self						,     *UpperCamelCase__						,     **UpperCamelCase__						)     ->		Tuple:

          '''simple docstring'''




          snake_case   :				Optional[Any]				 =   kwargs.get("is_split_into_words"						,     UpperCamelCase__						)

          if is_split_into_words and not self.add_prefix_space:
                 raise ValueError(
                     F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
                     "to use it with pretokenized inputs."						)

          return super()._encode_plus(*UpperCamelCase__						,     **UpperCamelCase__						)







   def 			lowerCamelCase		(     self						,     UpperCamelCase__						,     UpperCamelCase__ = None						)     ->		Any:

          '''simple docstring'''




          snake_case   :				Tuple				 =   self._tokenizer.model.save(UpperCamelCase__						,     name=UpperCamelCase__						)
          return tuple(UpperCamelCase__						)







   def 			lowerCamelCase		(     self						,     UpperCamelCase__						,     UpperCamelCase__=None						)     ->		Any:

          '''simple docstring'''




          snake_case   :				Dict				 =   [self.bos_token_id] + token_ids_a + [self.eos_token_id]
          if token_ids_a is None:
                 return output

          return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]







   def 			lowerCamelCase		(     self						,     UpperCamelCase__						,     UpperCamelCase__ = None						)     ->		int:

          '''simple docstring'''




          snake_case   :				Union[str, Any]				 =   [self.sep_token_id]
          snake_case   :				str				 =   [self.cls_token_id]

          if token_ids_a is None:
                 return len(cls + token_ids_a + sep						) * [0]
          return len(cls + token_ids_a + sep + sep + token_ids_a + sep						) * [0]









"""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
)
__snake_case             =   logging.getLogger(__name__)

if __name__ == "__main__":
  __snake_case             =   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=30522, type=int)
  __snake_case             =   parser.parse_args()

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

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





