

import os
import re
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


_UpperCamelCase 			=		logging.get_logger(__name__)

_UpperCamelCase 			=		{"vocab_file": "spiece.model"}

_UpperCamelCase 			=		{
    "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"
        ),
    }
}

_UpperCamelCase 			=		{
    "google/bigbird-roberta-base": 4096,
    "google/bigbird-roberta-large": 4096,
    "google/bigbird-base-trivia-itc": 4096,
}







class   __lowercase      (_UpperCAmelCase ):
 _UpperCamelCase			  =  VOCAB_FILES_NAMES
 _UpperCamelCase			  =  PRETRAINED_VOCAB_FILES_MAP
 _UpperCamelCase			  =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _UpperCamelCase			  =  ["""input_ids""", """attention_mask"""]
 _UpperCamelCase			  =  []
 def __init__(							self      ,							A_      ,							A_="<unk>"      ,							A_="<s>"      ,							A_="</s>"      ,							A_="<pad>"      ,							A_="[SEP]"      ,							A_="[MASK]"      ,							A_="[CLS]"      ,							A_ = None      ,							**A_      ,							)					->None:



       '''simple docstring'''





       __lowerCAmelCase					:						Dict					=					AddedToken(A_      ,							lstrip=A_      ,							rstrip=A_					) if isinstance(A_      ,							A_					) else bos_token
       __lowerCAmelCase					:						List[Any]					=					AddedToken(A_      ,							lstrip=A_      ,							rstrip=A_					) if isinstance(A_      ,							A_					) else eos_token
       __lowerCAmelCase					:						Union[str, Any]					=					AddedToken(A_      ,							lstrip=A_      ,							rstrip=A_					) if isinstance(A_      ,							A_					) else unk_token
       __lowerCAmelCase					:						Tuple					=					AddedToken(A_      ,							lstrip=A_      ,							rstrip=A_					) if isinstance(A_      ,							A_					) else pad_token
       __lowerCAmelCase					:						Tuple					=					AddedToken(A_      ,							lstrip=A_      ,							rstrip=A_					) if isinstance(A_      ,							A_					) else cls_token
       __lowerCAmelCase					:						List[Any]					=					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					:						List[Any]					=					AddedToken(A_      ,							lstrip=A_      ,							rstrip=A_					) if isinstance(A_      ,							A_					) else mask_token

       __lowerCAmelCase					:						Any					=					{} if sp_model_kwargs is None else sp_model_kwargs

       super().__init__(
           bos_token=A_      ,							eos_token=A_      ,							unk_token=A_      ,							pad_token=A_      ,							sep_token=A_      ,							mask_token=A_      ,							cls_token=A_      ,							sp_model_kwargs=self.sp_model_kwargs      ,							**A_      ,							)

       __lowerCAmelCase					:						Tuple					=					vocab_file

       __lowerCAmelCase					:						str					=					spm.SentencePieceProcessor(**self.sp_model_kwargs					)
       self.sp_model.Load(A_					)
 @property
 def 	UpperCamelCase__  (							self					)					->str:



       '''simple docstring'''





       return self.sp_model.get_piece_size()
 def 	UpperCamelCase__  (							self					)					->List[str]:



       '''simple docstring'''





       __lowerCAmelCase					:						List[Any]					=					{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					)					->str:



       '''simple docstring'''





       __lowerCAmelCase					:						Optional[int]					=					self.__dict__.copy()
       __lowerCAmelCase					:						Dict					=					None
       return state
 def __setstate__(							self      ,							A_					)					->Optional[Any]:



       '''simple docstring'''





       __lowerCAmelCase					:						str					=					d

       # for backward compatibility
       if not hasattr(self      ,							'''sp_model_kwargs'''					):
             __lowerCAmelCase					:						List[Any]					=					{}

       __lowerCAmelCase					:						Tuple					=					spm.SentencePieceProcessor(**self.sp_model_kwargs					)
       self.sp_model.Load(self.vocab_file					)
 def 	UpperCamelCase__  (							self      ,							A_					)					->List[str]:



       '''simple docstring'''





       return self.sp_model.encode(A_      ,							out_type=A_					)
 def 	UpperCamelCase__  (							self      ,							A_					)					->str:



       '''simple docstring'''





       return self.sp_model.piece_to_id(A_					)
 def 	UpperCamelCase__  (							self      ,							A_					)					->str:



       '''simple docstring'''





       __lowerCAmelCase					:						int					=					self.sp_model.IdToPiece(A_					)
       return token
 def 	UpperCamelCase__  (							self      ,							A_					)					->Tuple:



       '''simple docstring'''





       __lowerCAmelCase					:						Union[str, Any]					=					[]
       __lowerCAmelCase					:						Any					=					''''''
       __lowerCAmelCase					:						Dict					=					False
       for token in tokens:
             # make sure that special tokens are not decoded using sentencepiece model
             if token in self.all_special_tokens:
                   if not prev_is_special:
                         out_string += " "
                   out_string += self.sp_model.decode(A_					) + token
                   __lowerCAmelCase					:						Optional[int]					=					True
                   __lowerCAmelCase					:						List[str]					=					[]
             else:
                   current_sub_tokens.append(A_					)
                   __lowerCAmelCase					:						str					=					False
       out_string += self.sp_model.decode(A_					)
       return out_string.strip()
 def 	UpperCamelCase__  (							self      ,							A_      ,							A_ = False      ,							A_ = None      ,							A_ = True      ,							**A_      ,							)					->str:



       '''simple docstring'''





       __lowerCAmelCase					:						List[Any]					=					kwargs.pop('''use_source_tokenizer'''      ,							A_					)

       __lowerCAmelCase					:						List[str]					=					self.convert_ids_to_tokens(A_      ,							skip_special_tokens=A_					)

       # To avoid mixing byte-level and unicode for byte-level BPT
       # we need to build string separately for added tokens and byte-level tokens
       # cf. https://github.com/huggingface/transformers/issues/1133
       __lowerCAmelCase					:						Tuple					=					[]
       __lowerCAmelCase					:						List[Any]					=					[]
       for token in filtered_tokens:
             if skip_special_tokens and token in self.all_special_ids:
                   continue
             if token in self.added_tokens_encoder:
                   if current_sub_text:
                         sub_texts.append(self.convert_tokens_to_string(A_					)					)
                         __lowerCAmelCase					:						int					=					[]
                   sub_texts.append(A_					)
             else:
                   current_sub_text.append(A_					)
       if current_sub_text:
             sub_texts.append(self.convert_tokens_to_string(A_					)					)

       # Mimic the behavior of the Rust tokenizer:
       # No space before [MASK] and [SEP]
       if spaces_between_special_tokens:
             __lowerCAmelCase					:						int					=					re.sub(R''' (\[(MASK|SEP)\])'''      ,							R'''\1'''      ,							''' '''.join(A_					)					)
       else:
             __lowerCAmelCase					:						int					=					''''''.join(A_					)

       __lowerCAmelCase					:						List[str]					=					(
           clean_up_tokenization_spaces
           if clean_up_tokenization_spaces is not None
           else self.clean_up_tokenization_spaces
       )
       if clean_up_tokenization_spaces:
             __lowerCAmelCase					:						List[Any]					=					self.clean_up_tokenization(A_					)
             return clean_text
       else:
             return text
 def 	UpperCamelCase__  (							self      ,							A_      ,							A_ = None					)					->Tuple[str]:



       '''simple docstring'''





       if not os.path.isdir(A_					):
             logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""					)
             return
       __lowerCAmelCase					:						Optional[Any]					=					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					:						Any					=					self.sp_model.serialized_model_proto()
                   fi.write(A_					)

       return (out_vocab_file,)
 def 	UpperCamelCase__  (							self      ,							A_      ,							A_ = None					)					->List[int]:



       '''simple docstring'''





       if token_ids_a is None:
             return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
       __lowerCAmelCase					:						str					=					[self.cls_token_id]
       __lowerCAmelCase					:						Optional[Any]					=					[self.sep_token_id]
       return cls + token_ids_a + sep + token_ids_a + sep
 def 	UpperCamelCase__  (							self      ,							A_      ,							A_ = None      ,							A_ = False					)					->List[int]:



       '''simple docstring'''





       if already_has_special_tokens:
             return super().get_special_tokens_mask(
                 token_ids_a=A_      ,							token_ids_a=A_      ,							already_has_special_tokens=A_					)

       if token_ids_a is None:
             return [1] + ([0] * len(A_					)) + [1]
       return [1] + ([0] * len(A_					)) + [1] + ([0] * len(A_					)) + [1]





 def 	UpperCamelCase__  (							self      ,							A_      ,							A_ = None					)					->List[int]:



       '''simple docstring'''





       __lowerCAmelCase					:						Union[str, Any]					=					[self.sep_token_id]
       __lowerCAmelCase					:						Optional[Any]					=					[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]





from collections import deque

from .hash_table import HashTable







class   __lowercase      (_UpperCAmelCase ):
 def __init__(							self      ,							*A_      ,							**A_					)					->int:



       '''simple docstring'''





       super().__init__(*A_      ,							**A_					)
 def 	UpperCamelCase__  (							self      ,							A_      ,							A_					)					->Optional[int]:



       '''simple docstring'''





       __lowerCAmelCase					:						Dict					=					deque([]					) if self.values[key] is None else self.values[key]
       self.values[key].appendleft(A_					)
       __lowerCAmelCase					:						int					=					self.values[key]
 def 	UpperCamelCase__  (							self					)					->Tuple:



       '''simple docstring'''





       return (
           sum(self.charge_factor - len(A_					) for slot in self.values					)
           / self.size_table
           * self.charge_factor
       )





 def 	UpperCamelCase__  (							self      ,							A_      ,							A_=None					)					->str:



       '''simple docstring'''





       if not (
           len(self.values[key]					) == self.charge_factor and self.values.count(A_					) == 0
       ):
             return key
       return super()._collision_resolution(A_      ,							A_					)


