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_      =      logging.get_logger(__name__)

A_      =      {"vocab_file": "sentencepiece.model"}

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

A_      =      {
    "google/rembert": 256,
}






class      __lowercase					(    _A    ):
						lowercase					 =     VOCAB_FILES_NAMES
						lowercase					 =     PRETRAINED_VOCAB_FILES_MAP
						lowercase					 =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES

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


									'''simple docstring'''

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

									lowercase							=		do_lower_case
									lowercase							=		remove_space
									lowercase							=		keep_accents
									lowercase							=		vocab_file

									lowercase							=		spm.SentencePieceProcessor()
									self.sp_model.Load(__lowerCamelCase   )

						@property
						def        __a		(	self				:      List[str]   )							->		List[Any]:


									'''simple docstring'''

									return len(self.sp_model   )

						def        __a		(	self				:      int   )							->		List[Any]:


									'''simple docstring'''

									lowercase							=		{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				:      Any   )							->		Dict:


									'''simple docstring'''

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

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


									'''simple docstring'''

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

						def        __a		(	self				:      str							,							__lowerCamelCase				:      Optional[int]							,							__lowerCamelCase				:      Tuple=False   )							->		Optional[int]:


									'''simple docstring'''

									lowercase							=		self.sp_model.EncodeAsPieces(__lowerCamelCase   )
									return pieces

						def        __a		(	self				:      Any							,							__lowerCamelCase				:      str   )							->		Optional[Any]:


									'''simple docstring'''

									return self.sp_model.PieceToId(__lowerCamelCase   )

						def        __a		(	self				:      int							,							__lowerCamelCase				:      Optional[int]   )							->		int:


									'''simple docstring'''

									return self.sp_model.IdToPiece(__lowerCamelCase   )

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


									'''simple docstring'''

									lowercase							=		self.sp_model.decode_pieces(__lowerCamelCase   )
									return out_string

						def        __a		(	self				:      Optional[int]							,							__lowerCamelCase				:      List[int]							,							__lowerCamelCase				:      Optional[List[int]] = None   )							->		List[int]:


									'''simple docstring'''

									lowercase							=		[self.sep_token_id]
									lowercase							=		[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				:      Optional[Any]							,							__lowerCamelCase				:      List[int]							,							__lowerCamelCase				:      Optional[List[int]] = None							,							__lowerCamelCase				:      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(__lowerCamelCase   )) + [1] + ([0] * len(__lowerCamelCase   )) + [1]
									return [1] + ([0] * len(__lowerCamelCase   )) + [1]

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


									'''simple docstring'''

									lowercase							=		[self.sep_token_id]
									lowercase							=		[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				:      Dict							,							__lowerCamelCase				:      str							,							__lowerCamelCase				:      Optional[str] = None   )							->		Tuple[str]:


									'''simple docstring'''

									if not os.path.isdir(__lowerCamelCase   ):
												logger.error('''Vocabulary path ({}) should be a directory'''.format(__lowerCamelCase   )   )
												return
									lowercase							=		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   ):
												copyfile(self.vocab_file							,							__lowerCamelCase   )

									return (out_vocab_file,)






import logging

from transformers import PretrainedConfig


A_      =      logging.getLogger(__name__)


A_      =      {
    "bertabs-finetuned-cnndm": "https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json",
}






class      __lowercase					(    _A    ):
						lowercase					 =     'bertabs'

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


									'''simple docstring'''

									super().__init__(**__lowerCamelCase   )

									lowercase							=		vocab_size
									lowercase							=		max_pos

									lowercase							=		enc_layers
									lowercase							=		enc_hidden_size
									lowercase							=		enc_heads
									lowercase							=		enc_ff_size
									lowercase							=		enc_dropout

									lowercase							=		dec_layers
									lowercase							=		dec_hidden_size
									lowercase							=		dec_heads
									lowercase							=		dec_ff_size
									lowercase							=		dec_dropout





