




'''simple docstring'''

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


a       :						Optional[Any]								=		logging.get_logger(__name__)

a       :						Dict								=		{'''vocab_file''': '''sentencepiece.model'''}

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

a       :						str								=		{
    '''google/rembert''': 256,
}



class 		SCREAMING_SNAKE_CASE__  (			_UpperCamelCase	):
			__SCREAMING_SNAKE_CASE    =			VOCAB_FILES_NAMES
			__SCREAMING_SNAKE_CASE    =			PRETRAINED_VOCAB_FILES_MAP
			__SCREAMING_SNAKE_CASE    =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES





			def __init__(      self						:    Optional[Any]      ,		a_						:    int      ,		a_						:    Any=False      ,		a_						:    List[Any]=True      ,		a_						:    List[Any]=True      ,		a_						:    List[Any]="[CLS]"      ,		a_						:    List[Any]="[SEP]"      ,		a_						:    List[Any]="[UNK]"      ,		a_						:    str="[SEP]"      ,		a_						:    List[str]="[PAD]"      ,		a_						:    Optional[int]="[CLS]"      ,		a_						:    List[str]="[MASK]"      ,		**a_						:    str      ,		):



						"""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_      ,		)

						__snake_case			=		do_lower_case
						__snake_case			=		remove_space
						__snake_case			=		keep_accents
						__snake_case			=		vocab_file

						__snake_case			=		spm.SentencePieceProcessor()
						self.sp_model.Load(a_							)





			@property
			def 							A (      self						:    Optional[Any]							):



						"""simple docstring"""




						return len(self.sp_model							)





			def 							A (      self						:    Optional[Any]							):



						"""simple docstring"""




						__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						:    Dict							):



						"""simple docstring"""




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





			def __setstate__(      self						:    str      ,		a_						:    Optional[int]							):



						"""simple docstring"""




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





			def 							A (      self						:    Tuple      ,		a_						:    Optional[int]      ,		a_						:    int=False							):



						"""simple docstring"""




						__snake_case			=		self.sp_model.EncodeAsPieces(a_							)
						return pieces





			def 							A (      self						:    Any      ,		a_						:    Optional[Any]							):



						"""simple docstring"""




						return self.sp_model.PieceToId(a_							)





			def 							A (      self						:    Optional[Any]      ,		a_						:    List[str]							):



						"""simple docstring"""




						return self.sp_model.IdToPiece(a_							)





			def 							A (      self						:    Optional[Any]      ,		a_						:    int							):



						"""simple docstring"""




						__snake_case			=		self.sp_model.decode_pieces(a_							)
						return out_string





			def 							A (      self						:    Union[str, Any]      ,		a_						:    List[int]      ,		a_						:    Optional[List[int]] = None							):



						"""simple docstring"""




						__snake_case			=		[self.sep_token_id]
						__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 							A (      self						:    List[str]      ,		a_						:    List[int]      ,		a_						:    Optional[List[int]] = None      ,		a_						:    bool = False							):



						"""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 							A (      self						:    Tuple      ,		a_						:    List[int]      ,		a_						:    Optional[List[int]] = None							):



						"""simple docstring"""




						__snake_case			=		[self.sep_token_id]
						__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 							A (      self						:    List[Any]      ,		a_						:    str      ,		a_						:    Optional[str] = None							):



						"""simple docstring"""




						if not os.path.isdir(a_							):
									logger.error("Vocabulary path ({}) should be a directory".format(a_							)							)
									return
						__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 logging

from transformers import PretrainedConfig


_snake_case      = logging.getLogger(__name__)


_snake_case      = {
    '''bertabs-finetuned-cnndm''': '''https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json''',
}
class       _lowerCAmelCase  (		__magic_name__							):

 """simple docstring"""
 SCREAMING_SNAKE_CASE_				:					int       						="bertabs"

 def __init__(						self			:			Optional[int]					, SCREAMING_SNAKE_CASE__			:			Union[str, Any]=3_05_22					, SCREAMING_SNAKE_CASE__			:			Union[str, Any]=5_12					, SCREAMING_SNAKE_CASE__			:			List[Any]=6					, SCREAMING_SNAKE_CASE__			:			List[str]=5_12					, SCREAMING_SNAKE_CASE__			:			Union[str, Any]=8					, SCREAMING_SNAKE_CASE__			:			List[str]=5_12					, SCREAMING_SNAKE_CASE__			:			List[Any]=0.2					, SCREAMING_SNAKE_CASE__			:			Tuple=6					, SCREAMING_SNAKE_CASE__			:			Any=7_68					, SCREAMING_SNAKE_CASE__			:			str=8					, SCREAMING_SNAKE_CASE__			:			Dict=20_48					, SCREAMING_SNAKE_CASE__			:			List[str]=0.2					, **SCREAMING_SNAKE_CASE__			:			List[str]					, ):






      """simple docstring"""



      super().__init__(**SCREAMING_SNAKE_CASE__   )

      UpperCamelCase						    =					vocab_size
      UpperCamelCase						    =					max_pos

      UpperCamelCase						    =					enc_layers
      UpperCamelCase						    =					enc_hidden_size
      UpperCamelCase						    =					enc_heads
      UpperCamelCase						    =					enc_ff_size
      UpperCamelCase						    =					enc_dropout

      UpperCamelCase						    =					dec_layers
      UpperCamelCase						    =					dec_hidden_size
      UpperCamelCase						    =					dec_heads
      UpperCamelCase						    =					dec_ff_size
      UpperCamelCase						    =					dec_dropout


