





"""simple docstring"""


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

import sentencepiece as spm

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


lowerCamelCase__				  =   logging.get_logger(__name__)


lowerCamelCase__				  =   "▁"

lowerCamelCase__				  =   {"vocab_file": "spiece.model"}

lowerCamelCase__				  =   {
    "vocab_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"
        )
    }
}

lowerCamelCase__				  =   {
    "google/reformer-crime-and-punishment": 52_4288,
}



class      __SCREAMING_SNAKE_CASE    (						__UpperCAmelCase   ):
  SCREAMING_SNAKE_CASE__							:str	     =					VOCAB_FILES_NAMES
  SCREAMING_SNAKE_CASE__							:int	     =					PRETRAINED_VOCAB_FILES_MAP
  SCREAMING_SNAKE_CASE__							:List[Any]	     =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  SCREAMING_SNAKE_CASE__							:Optional[int]	     =					["input_ids", "attention_mask"]

  def __init__(		self   :       int					,   __a   :       Any					,   __a   :       Dict="</s>"					,   __a   :       Dict="<unk>"					,   __a   :       Dict=[]					,   __a   :       Optional[Dict[str, Any]] = None					,   **__a   :       List[str]					,   )		->				Optional[int]:
        _UpperCamelCase      :	str						=       {} if sp_model_kwargs is None else sp_model_kwargs

        super().__init__(
            eos_token=lowerCAmelCase_					,   unk_token=lowerCAmelCase_					,   additional_special_tokens=lowerCAmelCase_					,   sp_model_kwargs=self.sp_model_kwargs					,   **lowerCAmelCase_					,   )

        _UpperCamelCase      :	Optional[int]						=       vocab_file
        _UpperCamelCase      :	Tuple						=       spm.SentencePieceProcessor(**self.sp_model_kwargs	)
        self.sp_model.Load(lowerCAmelCase_	)

  @property
  def    __SCREAMING_SNAKE_CASE				(		self   :       List[Any]	)		->				str:
        return self.sp_model.get_piece_size()

  def    __SCREAMING_SNAKE_CASE				(		self   :       Dict	)		->				Optional[int]:
        _UpperCamelCase      :	List[str]						=       {self.convert_ids_to_tokens(lowerCAmelCase_	): i for i in range(self.vocab_size	)}
        vocab.update(self.added_tokens_encoder	)
        return vocab

  def __getstate__(		self   :       Union[str, Any]	)		->				int:
        _UpperCamelCase      :	Tuple						=       self.__dict__.copy()
        _UpperCamelCase      :	Optional[Any]						=       None
        return state

  def __setstate__(		self   :       Optional[Any]					,   __a   :       int	)		->				int:
        _UpperCamelCase      :	List[Any]						=       d

        # for backward compatibility
        if not hasattr(self					,   "sp_model_kwargs"	):
              _UpperCamelCase      :	str						=       {}

        _UpperCamelCase      :	Tuple						=       spm.SentencePieceProcessor(**self.sp_model_kwargs	)
        self.sp_model.Load(self.vocab_file	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       Union[str, Any]					,   __a   :       str	)		->				Optional[Any]:
        return self.sp_model.encode(lowerCAmelCase_					,   out_type=lowerCAmelCase_	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       Tuple					,   __a   :       List[str]	)		->				Union[str, Any]:
        return self.sp_model.piece_to_id(lowerCAmelCase_	)

  def    __SCREAMING_SNAKE_CASE				(		self   :       str					,   __a   :       Any	)		->				List[Any]:







        if index < self.sp_model.get_piece_size():
              _UpperCamelCase      :	List[str]						=       self.sp_model.IdToPiece(lowerCAmelCase_	)
        return token

  def    __SCREAMING_SNAKE_CASE				(		self   :       int					,   __a   :       Any	)		->				Optional[int]:
        _UpperCamelCase      :	Tuple						=       []
        _UpperCamelCase      :	Union[str, Any]						=       ""
        for token in tokens:
              # make sure that special tokens are not decoded using sentencepiece model
              if token in self.all_special_tokens:
                    out_string += self.sp_model.decode(lowerCAmelCase_	) + token
                    _UpperCamelCase      :	int						=       []
              else:
                    current_sub_tokens.append(lowerCAmelCase_	)
        out_string += self.sp_model.decode(lowerCAmelCase_	)
        return out_string.strip()


  def    __SCREAMING_SNAKE_CASE				(		self   :       Optional[int]					,   __a   :       str					,   __a   :       Optional[str] = None	)		->				int:







        if not os.path.isdir(lowerCAmelCase_	):
              logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''	)
              return
        _UpperCamelCase      :	Union[str, Any]						=       os.path.join(
            lowerCAmelCase_					,   (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["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:
                    _UpperCamelCase      :	Union[str, Any]						=       self.sp_model.serialized_model_proto()
                    fi.write(lowerCAmelCase_	)

        return (out_vocab_file,)







"""simple docstring"""


from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Value
from .base import TaskTemplate



@dataclass(frozen=_UpperCamelCase   )
class      __SCREAMING_SNAKE_CASE    (						_UpperCamelCase   ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__							:str	     =					field(default="summarization"		,  metadata={"include_in_asdict_even_if_is_default": True}   )
  SCREAMING_SNAKE_CASE__							:ClassVar[Features]	     =					Features({"text": Value("string"   )}   )
  SCREAMING_SNAKE_CASE__							:ClassVar[Features]	     =					Features({"summary": Value("string"   )}   )
  SCREAMING_SNAKE_CASE__							:str	     =					"text"
  SCREAMING_SNAKE_CASE__							:str	     =					"summary"

  @property
  def    __SCREAMING_SNAKE_CASE				(		self   :       List[Any]	)		->				Dict[str, str]:
        return {self.text_column: "text", self.summary_column: "summary"}
