





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

import sentencepiece as spm

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


_lowerCamelCase 					=      logging.get_logger(__name__)

_lowerCamelCase 					=      {'vocab_file': 'sentencepiece.model'}

_lowerCamelCase 					=      {
    'vocab_file': {
        'google/rembert': 'https://huggingface.co/google/rembert/resolve/main/sentencepiece.model',
    },
}

_lowerCamelCase 					=      {
    'google/rembert': 256,
}




class 	UpperCamelCase_							(  UpperCamelCase__     ):
							lowerCamelCase_							     =       VOCAB_FILES_NAMES
							lowerCamelCase_							     =       PRETRAINED_VOCAB_FILES_MAP
							lowerCamelCase_							     =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES


							def __init__(				self     :Optional[int]				,					__A     :Any				,					__A     :int=False				,					__A     :Any=True				,					__A     :List[Any]=True				,					__A     :int="[CLS]"				,					__A     :Optional[Any]="[SEP]"				,					__A     :Union[str, Any]="[UNK]"				,					__A     :List[Any]="[SEP]"				,					__A     :Union[str, Any]="[PAD]"				,					__A     :List[str]="[CLS]"				,					__A     :Dict="[MASK]"				,					**__A     :List[str]				,					) -> Tuple:



									"""simple docstring"""



									super().__init__(
									    do_lower_case=__A				,					remove_space=__A				,					keep_accents=__A				,					bos_token=__A				,					eos_token=__A				,					unk_token=__A				,					sep_token=__A				,					pad_token=__A				,					cls_token=__A				,					mask_token=__A				,					**__A				,					)

									SCREAMING_SNAKE_CASE__								=   do_lower_case
									SCREAMING_SNAKE_CASE__								=   remove_space
									SCREAMING_SNAKE_CASE__								=   keep_accents
									SCREAMING_SNAKE_CASE__								=   vocab_file

									SCREAMING_SNAKE_CASE__								=   spm.SentencePieceProcessor()
									self.sp_model.Load(__A		)


							@property
							def   _snake_case	(				self     :Optional[int]		) -> Optional[int]:



									"""simple docstring"""



									return len(self.sp_model		)


							def   _snake_case	(				self     :Dict		) -> str:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   {self.convert_ids_to_tokens(__A		): i for i in range(self.vocab_size		)}
									vocab.update(self.added_tokens_encoder		)
									return vocab


							def __getstate__(				self     :Optional[Any]		) -> str:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   self.__dict__.copy()
									SCREAMING_SNAKE_CASE__								=   None
									return state


							def __setstate__(				self     :Dict				,					__A     :Any		) -> Optional[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   d
									SCREAMING_SNAKE_CASE__								=   spm.SentencePieceProcessor()
									self.sp_model.Load(self.vocab_file		)


							def   _snake_case	(				self     :Union[str, Any]				,					__A     :Dict				,					__A     :Optional[Any]=False		) -> Tuple:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   self.sp_model.EncodeAsPieces(__A		)
									return pieces


							def   _snake_case	(				self     :str				,					__A     :int		) -> int:



									"""simple docstring"""



									return self.sp_model.PieceToId(__A		)


							def   _snake_case	(				self     :Optional[Any]				,					__A     :List[str]		) -> int:



									"""simple docstring"""



									return self.sp_model.IdToPiece(__A		)


							def   _snake_case	(				self     :Optional[int]				,					__A     :Optional[Any]		) -> Tuple:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   self.sp_model.decode_pieces(__A		)
									return out_string


							def   _snake_case	(				self     :int				,					__A     :List[int]				,					__A     :Optional[List[int]] = None		) -> List[int]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   [self.sep_token_id]
									SCREAMING_SNAKE_CASE__								=   [self.cls_token_id]
									if token_ids_a is None:
											return cls + token_ids_a + sep
									return cls + token_ids_a + sep + token_ids_a + sep


							def   _snake_case	(				self     :List[Any]				,					__A     :List[int]				,					__A     :Optional[List[int]] = None				,					__A     :bool = False		) -> List[int]:



									"""simple docstring"""



									if already_has_special_tokens:
											if token_ids_a is not None:
													raise ValueError(
													    """You should not supply a second sequence if the provided sequence of """
													    """ids is already formatted with special tokens for the model."""		)
											return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

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


							def   _snake_case	(				self     :Optional[Any]				,					__A     :List[int]				,					__A     :Optional[List[int]] = None		) -> List[int]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   [self.sep_token_id]
									SCREAMING_SNAKE_CASE__								=   [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		) * [0] + len(token_ids_a + sep		) * [1]


							def   _snake_case	(				self     :Optional[Any]				,					__A     :str				,					__A     :Optional[str] = None		) -> Tuple[str]:



									"""simple docstring"""



									if not os.path.isdir(__A		):
											logger.error("""Vocabulary path ({}) should be a directory""".format(__A		)		)
											return
									SCREAMING_SNAKE_CASE__								=   os.path.join(
									    __A				,					(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]		)

									if os.path.abspath(self.vocab_file		) != os.path.abspath(__A		):
											copyfile(self.vocab_file				,					__A		)

									return (out_vocab_file,)






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

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




@dataclass(frozen=UpperCamelCase__     )
class 	UpperCamelCase_							(  UpperCamelCase__     ):
							# `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization
							lowerCamelCase_							     =       field(default="text-classification"					,	metadata={"include_in_asdict_even_if_is_default": True}     )
							lowerCamelCase_							     =       Features({"text": Value("string"     )}     )
							lowerCamelCase_							     =       Features({"labels": ClassLabel}     )
							lowerCamelCase_							     =       "text"
							lowerCamelCase_							     =       "labels"


							def   _snake_case	(				self     :Any				,					__A     :Dict		) -> Optional[Any]:



									"""simple docstring"""



									if self.label_column not in features:
											raise ValueError(f'''Column {self.label_column} is not present in features.'''		)
									if not isinstance(features[self.label_column]				,					__A		):
											raise ValueError(f'''Column {self.label_column} is not a ClassLabel.'''		)
									SCREAMING_SNAKE_CASE__								=   copy.deepcopy(self		)
									SCREAMING_SNAKE_CASE__								=   self.label_schema.copy()
									SCREAMING_SNAKE_CASE__								=   features[self.label_column]
									SCREAMING_SNAKE_CASE__								=   label_schema
									return task_template


							@property
							def   _snake_case	(				self     :str		) -> Dict[str, str]:



									"""simple docstring"""



									return {
									    self.text_column: "text",
									    self.label_column: "labels",
									}