


'''simple docstring'''






import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


__A					:  Union[str, Any]											=      logging.get_logger(__name__)

__A					:  List[Any]											=      "▁"

__A					:  Union[str, Any]											=      {"vocab_file": "sentencepiece.bpe.model", "monolingual_vocab_file": "dict.txt"}

__A					:  int											=      {
    "vocab_file": {
        "vinai/bartpho-syllable": "https://huggingface.co/vinai/bartpho-syllable/resolve/main/sentencepiece.bpe.model",
    },
    "monolingual_vocab_file": {
        "vinai/bartpho-syllable": "https://huggingface.co/vinai/bartpho-syllable/resolve/main/dict.txt",
    },
}

__A					:  Union[str, Any]											=      {"vinai/bartpho-syllable": 1024}






class    __snake_case       (   _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    lowercase    =					VOCAB_FILES_NAMES
    lowercase    =					PRETRAINED_VOCAB_FILES_MAP
    lowercase    =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowercase    =					['input_ids', 'attention_mask']

    def __init__(   self    :  List[str]    ,						lowerCamelCase    :  str    ,						lowerCamelCase    :  str    ,						lowerCamelCase    :  Union[str, Any]="<s>"    ,						lowerCamelCase    :  Any="</s>"    ,						lowerCamelCase    :  List[Any]="</s>"    ,						lowerCamelCase    :  Dict="<s>"    ,						lowerCamelCase    :  int="<unk>"    ,						lowerCamelCase    :  Optional[int]="<pad>"    ,						lowerCamelCase    :  Dict="<mask>"    ,						lowerCamelCase    :  Optional[Dict[str, Any]] = None    ,						**lowerCamelCase    :  Optional[Any]    ,						)   ->  None:
      # Mask token behave like a normal word, i.e. include the space before it
      lowerCAmelCase_			:      List[str]			=   AddedToken(lowerCamelCase    ,						lstrip=lowerCamelCase    ,						rstrip=lowerCamelCase		) if isinstance(lowerCamelCase    ,						lowerCamelCase		) else mask_token

      lowerCAmelCase_			:      Optional[int]			=   {} if sp_model_kwargs is None else sp_model_kwargs

      super().__init__(
          bos_token=lowerCamelCase    ,						eos_token=lowerCamelCase    ,						unk_token=lowerCamelCase    ,						sep_token=lowerCamelCase    ,						cls_token=lowerCamelCase    ,						pad_token=lowerCamelCase    ,						mask_token=lowerCamelCase    ,						sp_model_kwargs=self.sp_model_kwargs    ,						**lowerCamelCase    ,						)

      lowerCAmelCase_			:      str			=   vocab_file
      lowerCAmelCase_			:      int			=   monolingual_vocab_file
      lowerCAmelCase_			:      Any			=   spm.SentencePieceProcessor(**self.sp_model_kwargs		)
      self.sp_model.Load(str(lowerCamelCase		)		)

      # Load the reduced vocab

      # Keep order of special tokens for backward compatibility
      lowerCAmelCase_			:      Optional[int]			=   {}
      lowerCAmelCase_			:      List[Any]			=   0
      for token in [bos_token, pad_token, eos_token, unk_token, sep_token, cls_token]:
        if str(lowerCamelCase		) not in self.fairseq_tokens_to_ids:
          lowerCAmelCase_			:      Tuple			=   cnt
          cnt += 1
      with open(lowerCamelCase    ,						"""r"""    ,						encoding="""utf-8"""		) as f:
        for line in f.readlines():
          lowerCAmelCase_			:      Optional[int]			=   line.strip().split()[0]
          lowerCAmelCase_			:      int			=   len(self.fairseq_tokens_to_ids		)
      if str(lowerCamelCase		) not in self.fairseq_tokens_to_ids:
        lowerCAmelCase_			:      str			=   len(self.fairseq_tokens_to_ids		)

      lowerCAmelCase_			:      Optional[Any]			=   {v: k for k, v in self.fairseq_tokens_to_ids.items()}

    def __getstate__(   self    :  Optional[int]		)   ->  List[str]:
      lowerCAmelCase_			:      str			=   self.__dict__.copy()
      lowerCAmelCase_			:      str			=   None
      lowerCAmelCase_			:      List[Any]			=   self.sp_model.serialized_model_proto()
      return state

    def __setstate__(   self    :  Optional[int]    ,						lowerCamelCase    :  Optional[int]		)   ->  Optional[Any]:
      lowerCAmelCase_			:      int			=   d

      # for backward compatibility
      if not hasattr(self    ,						"""sp_model_kwargs"""		):
        lowerCAmelCase_			:      Optional[int]			=   {}

      lowerCAmelCase_			:      Union[str, Any]			=   spm.SentencePieceProcessor(**self.sp_model_kwargs		)
      self.sp_model.LoadFromSerializedProto(self.sp_model_proto		)

    def __lowercase   (   self    :  Union[str, Any]    ,						lowerCamelCase    :  List[int]    ,						lowerCamelCase    :  Optional[List[int]] = None		)   ->  List[int]:

      if token_ids_a is None:
        return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
      lowerCAmelCase_			:      List[str]			=   [self.cls_token_id]
      lowerCAmelCase_			:      Union[str, Any]			=   [self.sep_token_id]
      return cls + token_ids_a + sep + sep + token_ids_a + sep

    def __lowercase   (   self    :  List[str]    ,						lowerCamelCase    :  List[int]    ,						lowerCamelCase    :  Optional[List[int]] = None    ,						lowerCamelCase    :  bool = False		)   ->  List[int]:

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

      if token_ids_a is None:
        return [1] + ([0] * len(lowerCamelCase		)) + [1]
      return [1] + ([0] * len(lowerCamelCase		)) + [1, 1] + ([0] * len(lowerCamelCase		)) + [1]

    def __lowercase   (   self    :  Optional[Any]    ,						lowerCamelCase    :  List[int]    ,						lowerCamelCase    :  Optional[List[int]] = None		)   ->  List[int]:
      lowerCAmelCase_			:      Dict			=   [self.sep_token_id]
      lowerCAmelCase_			:      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]

    @property
    def __lowercase   (   self    :  str		)   ->  Dict:
      return len(self.fairseq_ids_to_tokens		)

    def __lowercase   (   self    :  Dict		)   ->  Dict:
      lowerCAmelCase_			:      Dict			=   {self.convert_ids_to_tokens(lowerCamelCase		): i for i in range(self.vocab_size		)}
      vocab.update(self.added_tokens_encoder		)
      return vocab

    def __lowercase   (   self    :  Tuple    ,						lowerCamelCase    :  str		)   ->  List[str]:
      return self.sp_model.encode(lowerCamelCase    ,						out_type=lowerCamelCase		)

    def __lowercase   (   self    :  Union[str, Any]    ,						lowerCamelCase    :  Any		)   ->  List[Any]:
      if token in self.fairseq_tokens_to_ids:
        return self.fairseq_tokens_to_ids[token]
      else:
        return self.unk_token_id

    def __lowercase   (   self    :  Optional[int]    ,						lowerCamelCase    :  Any		)   ->  Tuple:
      return self.fairseq_ids_to_tokens[index]

    def __lowercase   (   self    :  Dict    ,						lowerCamelCase    :  Optional[Any]		)   ->  Dict:
      lowerCAmelCase_			:      Dict			=   """""".join(lowerCamelCase		).replace(lowerCamelCase    ,						""" """		).strip()
      return out_string





    def __lowercase   (   self    :  Union[str, Any]    ,						lowerCamelCase    :  str    ,						lowerCamelCase    :  Optional[str] = None		)   ->  Tuple[str]:
      if not os.path.isdir(lowerCamelCase		):
        logger.error(F'Vocabulary path ({save_directory}) should be a directory'		)
        return
      lowerCAmelCase_			:      Tuple			=   os.path.join(
          lowerCamelCase    ,						(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]		)
      lowerCAmelCase_			:      List[Any]			=   os.path.join(
          lowerCamelCase    ,						(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""monolingual_vocab_file"""]    ,						)

      if os.path.abspath(self.vocab_file		) != os.path.abspath(lowerCamelCase		) and os.path.isfile(self.vocab_file		):
        copyfile(self.vocab_file    ,						lowerCamelCase		)
      elif not os.path.isfile(self.vocab_file		):
        with open(lowerCamelCase    ,						"""wb"""		) as fi:
          lowerCAmelCase_			:      int			=   self.sp_model.serialized_model_proto()
          fi.write(lowerCamelCase		)

      if os.path.abspath(self.monolingual_vocab_file		) != os.path.abspath(
          lowerCamelCase		) and os.path.isfile(self.monolingual_vocab_file		):
        copyfile(self.monolingual_vocab_file    ,						lowerCamelCase		)
      elif not os.path.isfile(self.monolingual_vocab_file		):
        with open(lowerCamelCase    ,						"""w"""    ,						encoding="""utf-8"""		) as fp:
          for token in self.fairseq_tokens_to_ids:
            if token not in self.all_special_tokens:
              fp.write(F'{str(lowerCamelCase		)} \n'		)

      return out_vocab_file, out_monolingual_vocab_file







'''simple docstring'''






from statistics import mean, stdev







def 				UpperCamelCase_			(    A__					:   list							,    A__					:   int = 3					):
  '''simple docstring'''





  lowerCAmelCase_			:      List[str]			=   min(A__					)
  lowerCAmelCase_			:      Optional[int]			=   max(A__					)
  # normalize data
  return [round((x - x_min) / (x_max - x_min)							,    A__					) for x in data]







def 				UpperCamelCase_			(    A__					:   list							,    A__					:   int = 3					):
  '''simple docstring'''





  lowerCAmelCase_			:      str			=   mean(A__					)
  lowerCAmelCase_			:      List[Any]			=   stdev(A__					)
  # standardize data
  return [round((x - mu) / (sigma)							,    A__					) for x in data]



