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

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
						from .tokenization_rembert import RemBertTokenizer
else:
						A_      =      None

A_      =      logging.get_logger(__name__)
A_      =      {"vocab_file": "sentencepiece.model", "tokenizer_file": "tokenizer.json"}

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

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

A_      =      "▁"






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

						def __init__(	self				:      Optional[Any]							,							__lowerCamelCase				:      Any=None							,							__lowerCamelCase				:      Dict=None							,							__lowerCamelCase				:      int=True							,							__lowerCamelCase				:      str=True							,							__lowerCamelCase				:      Optional[int]=False							,							__lowerCamelCase				:      Optional[Any]="[CLS]"							,							__lowerCamelCase				:      List[Any]="[SEP]"							,							__lowerCamelCase				:      str="<unk>"							,							__lowerCamelCase				:      Dict="[SEP]"							,							__lowerCamelCase				:      Any="<pad>"							,							__lowerCamelCase				:      str="[CLS]"							,							__lowerCamelCase				:      str="[MASK]"							,							**__lowerCamelCase				:      str							,							)							->		Tuple:


									'''simple docstring'''

									lowercase							=		AddedToken(__lowerCamelCase							,							lstrip=__lowerCamelCase							,							rstrip=__lowerCamelCase   ) if isinstance(__lowerCamelCase							,							__lowerCamelCase   ) else mask_token

									super().__init__(
									    __lowerCamelCase							,							tokenizer_file=__lowerCamelCase							,							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							=		False if not self.vocab_file else True

						def        __a		(	self				:      List[str]							,							__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				:      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				:      Dict							,							__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				:      int							,							__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





