



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_big_bird import BigBirdTokenizer
else:
     a_						:     Union[str, Any]	     =		None

a_						:     Tuple	     =		logging.get_logger(__name__)
a_						:     List[Any]	     =		{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

a_						:     Union[str, Any]	     =		{
    'vocab_file': {
        'google/bigbird-roberta-base': 'https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model',
        'google/bigbird-roberta-large': (
            'https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model'
        ),
        'google/bigbird-base-trivia-itc': (
            'https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model'
        ),
    },
    'tokenizer_file': {
        'google/bigbird-roberta-base': (
            'https://huggingface.co/google/bigbird-roberta-base/resolve/main/tokenizer.json'
        ),
        'google/bigbird-roberta-large': (
            'https://huggingface.co/google/bigbird-roberta-large/resolve/main/tokenizer.json'
        ),
        'google/bigbird-base-trivia-itc': (
            'https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/tokenizer.json'
        ),
    },
}

a_						:     Dict	     =		{
    'google/bigbird-roberta-base': 4_0_9_6,
    'google/bigbird-roberta-large': 4_0_9_6,
    'google/bigbird-base-trivia-itc': 4_0_9_6,
}


a_						:     str	     =		'▁'





class    lowerCamelCase__  (						__SCREAMING_SNAKE_CASE):
     """simple docstring"""



     _A       =			VOCAB_FILES_NAMES
     _A       =			PRETRAINED_VOCAB_FILES_MAP
     _A       =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _A       =			BigBirdTokenizer
     _A       =			['input_ids', 'attention_mask']
     _A       =			[]







     def __init__(self				,			__a=None				,			__a=None				,			__a="<unk>"				,			__a="<s>"				,			__a="</s>"				,			__a="<pad>"				,			__a="[SEP]"				,			__a="[MASK]"				,			__a="[CLS]"				,			**__a				,			):






           '''simple docstring'''



           lowerCamelCase								=		AddedToken(_a				,			lstrip=_a				,			rstrip=_a    ) if isinstance(_a				,			_a    ) else bos_token
           lowerCamelCase								=		AddedToken(_a				,			lstrip=_a				,			rstrip=_a    ) if isinstance(_a				,			_a    ) else eos_token
           lowerCamelCase								=		AddedToken(_a				,			lstrip=_a				,			rstrip=_a    ) if isinstance(_a				,			_a    ) else unk_token
           lowerCamelCase								=		AddedToken(_a				,			lstrip=_a				,			rstrip=_a    ) if isinstance(_a				,			_a    ) else pad_token
           lowerCamelCase								=		AddedToken(_a				,			lstrip=_a				,			rstrip=_a    ) if isinstance(_a				,			_a    ) else cls_token
           lowerCamelCase								=		AddedToken(_a				,			lstrip=_a				,			rstrip=_a    ) if isinstance(_a				,			_a    ) else sep_token

           # Mask token behave like a normal word, i.e. include the space before it
           lowerCamelCase								=		AddedToken(_a				,			lstrip=_a				,			rstrip=_a    ) if isinstance(_a				,			_a    ) else mask_token

           super().__init__(
               _a				,			tokenizer_file=_a				,			bos_token=_a				,			eos_token=_a				,			unk_token=_a				,			sep_token=_a				,			pad_token=_a				,			cls_token=_a				,			mask_token=_a				,			**_a				,			)

           lowerCamelCase								=		vocab_file
           lowerCamelCase								=		False if not self.vocab_file else True







     def 						_a      (self				,			__a				,			__a = None    ):






           '''simple docstring'''



           lowerCamelCase								=		[self.sep_token_id]
           lowerCamelCase								=		[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				,			__a				,			__a = None				,			__a = 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 None:
                 return [1] + ([0] * len(_a    )) + [1]
           return [1] + ([0] * len(_a    )) + [1] + ([0] * len(_a    )) + [1]







     def 						_a      (self				,			__a				,			__a = None    ):






           '''simple docstring'''



           lowerCamelCase								=		[self.sep_token_id]
           lowerCamelCase								=		[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				,			__a				,			__a = None    ):






           '''simple docstring'''



           if not self.can_save_slow_tokenizer:
                 raise ValueError(
                     "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
                     "tokenizer."    )

           if not os.path.isdir(_a    ):
                 logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""    )
                 return
           lowerCamelCase								=		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 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


a_						:     Optional[int]	     =		logging.get_logger(__name__)

a_						:     Any	     =		{'vocab_file': 'spiece.model'}

a_						:     List[Any]	     =		{
    'vocab_file': {
        'bert_for_seq_generation': (
            'https://huggingface.co/google/bert_for_seq_generation_L-24_bbc_encoder/resolve/main/spiece.model'
        ),
    }
}

a_						:     List[Any]	     =		{'bert_for_seq_generation': 5_1_2}





class    lowerCamelCase__  (						UpperCAmelCase_):
     """simple docstring"""



     _A       =			VOCAB_FILES_NAMES
     _A       =			PRETRAINED_VOCAB_FILES_MAP
     _A       =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _A       =			[]
     _A       =			['input_ids', 'attention_mask']







     def __init__(self				,			__a				,			__a="<s>"				,			__a="</s>"				,			__a="<unk>"				,			__a="<pad>"				,			__a="<::::>"				,			__a = None				,			**__a				,			):






           '''simple docstring'''



           lowerCamelCase								=		{} if sp_model_kwargs is None else sp_model_kwargs

           # Add extra_ids to the special token list
           super().__init__(
               bos_token=__a				,			eos_token=__a				,			unk_token=__a				,			pad_token=__a				,			sep_token=__a				,			sp_model_kwargs=self.sp_model_kwargs				,			**__a				,			)

           lowerCamelCase								=		vocab_file

           lowerCamelCase								=		spm.SentencePieceProcessor(**self.sp_model_kwargs    )
           self.sp_model.Load(__a    )







     @property
     def 						_a      (self    ):






           '''simple docstring'''



           return self.sp_model.get_piece_size()







     def 						_a      (self    ):






           '''simple docstring'''



           lowerCamelCase								=		{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    ):






           '''simple docstring'''



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







     def __setstate__(self				,			__a    ):






           '''simple docstring'''



           lowerCamelCase								=		d

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

           lowerCamelCase								=		spm.SentencePieceProcessor(**self.sp_model_kwargs    )
           self.sp_model.Load(self.vocab_file    )







     def 						_a      (self				,			__a    ):






           '''simple docstring'''



           return self.sp_model.encode(__a				,			out_type=__a    )







     def 						_a      (self				,			__a    ):






           '''simple docstring'''



           return self.sp_model.piece_to_id(__a    )







     def 						_a      (self				,			__a    ):






           '''simple docstring'''



           lowerCamelCase								=		self.sp_model.IdToPiece(__a    )
           return token







     def 						_a      (self				,			__a    ):






           '''simple docstring'''



           lowerCamelCase								=		[]
           lowerCamelCase								=		""
           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(__a    ) + token
                       lowerCamelCase								=		[]
                 else:
                       current_sub_tokens.append(__a    )
           out_string += self.sp_model.decode(__a    )
           return out_string.strip()







     def 						_a      (self				,			__a				,			__a = None    ):






           '''simple docstring'''



           if not os.path.isdir(__a    ):
                 logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""    )
                 return
           lowerCamelCase								=		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    ) and os.path.isfile(self.vocab_file    ):
                 copyfile(self.vocab_file				,			__a    )
           elif not os.path.isfile(self.vocab_file    ):
                 with open(__a				,			"wb"    ) as fi:
                       lowerCamelCase								=		self.sp_model.serialized_model_proto()
                       fi.write(__a    )

           return (out_vocab_file,)