





import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer


UpperCAmelCase	:	Any		     =       logging.get_logger(__name__)


UpperCAmelCase	:	List[str]		     =       {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

# See all BART models at https://huggingface.co/models?filter=bart
UpperCAmelCase	:	Any		     =       {
    """vocab_file""": {
        """facebook/bart-base""": """https://huggingface.co/facebook/bart-base/resolve/main/vocab.json""",
        """facebook/bart-large""": """https://huggingface.co/facebook/bart-large/resolve/main/vocab.json""",
        """facebook/bart-large-mnli""": """https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json""",
        """facebook/bart-large-cnn""": """https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json""",
        """facebook/bart-large-xsum""": """https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json""",
        """yjernite/bart_eli5""": """https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """facebook/bart-base""": """https://huggingface.co/facebook/bart-base/resolve/main/merges.txt""",
        """facebook/bart-large""": """https://huggingface.co/facebook/bart-large/resolve/main/merges.txt""",
        """facebook/bart-large-mnli""": """https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt""",
        """facebook/bart-large-cnn""": """https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt""",
        """facebook/bart-large-xsum""": """https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt""",
        """yjernite/bart_eli5""": """https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """facebook/bart-base""": """https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json""",
        """facebook/bart-large""": """https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json""",
        """facebook/bart-large-mnli""": """https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json""",
        """facebook/bart-large-cnn""": """https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json""",
        """facebook/bart-large-xsum""": """https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json""",
        """yjernite/bart_eli5""": """https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json""",
    },
}

UpperCAmelCase	:	Any		     =       {
    """facebook/bart-base""": 1024,
    """facebook/bart-large""": 1024,
    """facebook/bart-large-mnli""": 1024,
    """facebook/bart-large-cnn""": 1024,
    """facebook/bart-large-xsum""": 1024,
    """yjernite/bart_eli5""": 1024,
}







class 				__lowerCAmelCase       (    a_):
      _lowercase				:					List[str]						=			VOCAB_FILES_NAMES
      _lowercase				:					Optional[Any]						=			PRETRAINED_VOCAB_FILES_MAP
      _lowercase				:					List[Any]						=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _lowercase				:					Optional[Any]						=			["""input_ids""", """attention_mask"""]
      _lowercase				:					List[Any]						=			BartTokenizer







      def __init__(    self		,      lowerCAmelCase__=None		,      lowerCAmelCase__=None		,      lowerCAmelCase__=None		,      lowerCAmelCase__="replace"		,      lowerCAmelCase__="<s>"		,      lowerCAmelCase__="</s>"		,      lowerCAmelCase__="</s>"		,      lowerCAmelCase__="<s>"		,      lowerCAmelCase__="<unk>"		,      lowerCAmelCase__="<pad>"		,      lowerCAmelCase__="<mask>"		,      lowerCAmelCase__=False		,      lowerCAmelCase__=True		,      **lowerCAmelCase__		,      )   ->						Dict:



        '''simple docstring'''




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

        a__	:					Any										=json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()							)
        if pre_tok_state.get("add_prefix_space"		,      _a							) != add_prefix_space:
          a__	:					Union[str, Any]										=getattr(_a		,      pre_tok_state.pop("type"							)							)
          a__	:					Optional[int]										=add_prefix_space
          a__	:					int										=pre_tok_class(**_a							)

        a__	:					Optional[Any]										=add_prefix_space

        # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
        a__	:					Optional[int]										="post_processor"
        a__	:					List[str]										=getattr(self.backend_tokenizer		,      _a		,      _a							)
        if tokenizer_component_instance:
          a__	:					Optional[Any]										=json.loads(tokenizer_component_instance.__getstate__()							)

          # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
          if "sep" in state:
            a__	:					Optional[int]										=tuple(state["sep"]							)
          if "cls" in state:
            a__	:					int										=tuple(state["cls"]							)

          a__	:					int										=False

          if state.get("add_prefix_space"		,      _a							) != add_prefix_space:
            a__	:					int										=add_prefix_space
            a__	:					List[str]										=True

          if state.get("trim_offsets"		,      _a							) != trim_offsets:
            a__	:					Dict										=trim_offsets
            a__	:					List[str]										=True

          if changes_to_apply:
            a__	:					List[Any]										=getattr(_a		,      state.pop("type"							)							)
            a__	:					Union[str, Any]										=component_class(**_a							)
            setattr(self.backend_tokenizer		,      _a		,      _a							)







      @property
      def    _lowercase  (    self							)   ->						Optional[int]:



        '''simple docstring'''




        if self._mask_token is None:
          if self.verbose:
            logger.error("Using mask_token, but it is not set yet."							)
          return None
        return str(self._mask_token							)







      @mask_token.setter
      def    _lowercase  (    self		,      lowerCAmelCase__							)   ->						Optional[int]:



        '''simple docstring'''




        a__	:					List[str]										=AddedToken(_a		,      lstrip=_a		,      rstrip=_a							) if isinstance(_a		,      _a							) else value
        a__	:					List[Any]										=value







      def    _lowercase  (    self		,      *lowerCAmelCase__		,      **lowerCAmelCase__							)   ->						List[Any]:



        '''simple docstring'''




        a__	:					Dict										=kwargs.get("is_split_into_words"		,      _a							)

        if is_split_into_words and not self.add_prefix_space:
          raise ValueError(
              F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
              "to use it with pretokenized inputs."							)

        return super()._batch_encode_plus(*_a		,      **_a							)







      def    _lowercase  (    self		,      *lowerCAmelCase__		,      **lowerCAmelCase__							)   ->						Dict:



        '''simple docstring'''




        a__	:					Tuple										=kwargs.get("is_split_into_words"		,      _a							)

        if is_split_into_words and not self.add_prefix_space:
          raise ValueError(
              F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
              "to use it with pretokenized inputs."							)

        return super()._encode_plus(*_a		,      **_a							)







      def    _lowercase  (    self		,      lowerCAmelCase__		,      lowerCAmelCase__ = None							)   ->						str:



        '''simple docstring'''




        a__	:					str										=self._tokenizer.model.save(_a		,      name=_a							)
        return tuple(_a							)







      def    _lowercase  (    self		,      lowerCAmelCase__		,      lowerCAmelCase__=None							)   ->						List[str]:



        '''simple docstring'''




        a__	:					Tuple										=[self.bos_token_id] + token_ids_a + [self.eos_token_id]
        if token_ids_a is None:
          return output

        return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]







      def    _lowercase  (    self		,      lowerCAmelCase__		,      lowerCAmelCase__ = None							)   ->						str:



        '''simple docstring'''




        a__	:					Union[str, Any]										=[self.sep_token_id]
        a__	:					Dict										=[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 + sep + token_ids_a + sep							) * [0]








"""simple docstring"""




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


_A			:       List[str]   		=       logging.get_logger(__name__)

_A			:       List[str]   		=       {"""vocab_file""": """spiece.model"""}

_A			:       List[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"""
        ),
    }
}

_A			:       str   		=       {
    """google/bigbird-roberta-base""": 40_96,
    """google/bigbird-roberta-large""": 40_96,
    """google/bigbird-base-trivia-itc""": 40_96,
}

class 		a__						(      a_						):
   __lowerCAmelCase            =				VOCAB_FILES_NAMES
   __lowerCAmelCase            =				PRETRAINED_VOCAB_FILES_MAP
   __lowerCAmelCase            =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __lowerCAmelCase            =				["""input_ids""", """attention_mask"""]
   __lowerCAmelCase            =				[]
   def __init__(			self	,    _a	,    _a="<unk>"	,    _a="<s>"	,    _a="</s>"	,    _a="<pad>"	,    _a="[SEP]"	,    _a="[MASK]"	,    _a="[CLS]"	,    _a = None	,    **_a	,    ):
          lowercase    :				Dict       		=							AddedToken(_a	,    lstrip=_a	,    rstrip=_a				) if isinstance(_a	,    _a				) else bos_token
          lowercase    :				Any       		=							AddedToken(_a	,    lstrip=_a	,    rstrip=_a				) if isinstance(_a	,    _a				) else eos_token
          lowercase    :				Any       		=							AddedToken(_a	,    lstrip=_a	,    rstrip=_a				) if isinstance(_a	,    _a				) else unk_token
          lowercase    :				List[Any]       		=							AddedToken(_a	,    lstrip=_a	,    rstrip=_a				) if isinstance(_a	,    _a				) else pad_token
          lowercase    :				Any       		=							AddedToken(_a	,    lstrip=_a	,    rstrip=_a				) if isinstance(_a	,    _a				) else cls_token
          lowercase    :				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
          lowercase    :				List[Any]       		=							AddedToken(_a	,    lstrip=_a	,    rstrip=_a				) if isinstance(_a	,    _a				) else mask_token

          lowercase    :				Optional[int]       		=							{} 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	,    )

          lowercase    :				Optional[Any]       		=							vocab_file

          lowercase    :				List[str]       		=							spm.SentencePieceProcessor(**self.sp_model_kwargs				)
          self.sp_model.Load(_a				)
   @property
   def 			__magic_name__			(			self				):
          return self.sp_model.get_piece_size()
   def 			__magic_name__			(			self				):
          lowercase    :				str       		=							{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				):
          lowercase    :				Union[str, Any]       		=							self.__dict__.copy()
          lowercase    :				Dict       		=							None
          return state
   def __setstate__(			self	,    _a				):
          lowercase    :				str       		=							d

          # for backward compatibility
          if not hasattr(self	,    "sp_model_kwargs"				):
                 lowercase    :				List[str]       		=							{}

          lowercase    :				Any       		=							spm.SentencePieceProcessor(**self.sp_model_kwargs				)
          self.sp_model.Load(self.vocab_file				)
   def 			__magic_name__			(			self	,    _a				):
          return self.sp_model.encode(_a	,    out_type=_a				)
   def 			__magic_name__			(			self	,    _a				):
          return self.sp_model.piece_to_id(_a				)
   def 			__magic_name__			(			self	,    _a				):
          lowercase    :				Union[str, Any]       		=							self.sp_model.IdToPiece(_a				)
          return token
   def 			__magic_name__			(			self	,    _a				):
          lowercase    :				List[Any]       		=							[]
          lowercase    :				List[Any]       		=							""
          lowercase    :				List[Any]       		=							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
                        lowercase    :				int       		=							True
                        lowercase    :				Union[str, Any]       		=							[]
                 else:
                        current_sub_tokens.append(_a				)
                        lowercase    :				int       		=							False
          out_string += self.sp_model.decode(_a				)
          return out_string.strip()
   def 			__magic_name__			(			self	,    _a	,    _a = False	,    _a = None	,    _a = True	,    **_a	,    ):
          lowercase    :				int       		=							kwargs.pop("use_source_tokenizer"	,    _a				)

          lowercase    :				Union[str, Any]       		=							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
          lowercase    :				Any       		=							[]
          lowercase    :				Dict       		=							[]
          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				)				)
                               lowercase    :				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:
                 lowercase    :				Dict       		=							re.sub(R" (\[(MASK|SEP)\])"	,    R"\1"	,    " ".join(_a				)				)
          else:
                 lowercase    :				Union[str, Any]       		=							"".join(_a				)

          lowercase    :				int       		=							(
              clean_up_tokenization_spaces
              if clean_up_tokenization_spaces is not None
              else self.clean_up_tokenization_spaces
          )
          if clean_up_tokenization_spaces:
                 lowercase    :				Union[str, Any]       		=							self.clean_up_tokenization(_a				)
                 return clean_text
          else:
                 return text
   def 			__magic_name__			(			self	,    _a	,    _a = None				):
          if not os.path.isdir(_a				):
                 logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""				)
                 return
          lowercase    :				List[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:
                        lowercase    :				int       		=							self.sp_model.serialized_model_proto()
                        fi.write(_a				)

          return (out_vocab_file,)
   def 			__magic_name__			(			self	,    _a	,    _a = None				):
          if token_ids_a is None:
                 return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
          lowercase    :				List[str]       		=							[self.cls_token_id]
          lowercase    :				Tuple       		=							[self.sep_token_id]
          return cls + token_ids_a + sep + token_ids_a + sep
   def 			__magic_name__			(			self	,    _a	,    _a = None	,    _a = False				):
          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 			__magic_name__			(			self	,    _a	,    _a = None				):
          lowercase    :				Tuple       		=							[self.sep_token_id]
          lowercase    :				Tuple       		=							[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]


