"""simple docstring"""
from typing import Dict, List, Optional, Tuple

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


lowercase__   			=      logging.get_logger(__name__)




class   __snake_case			(	__lowerCAmelCase							):
      a__								=						["""input_ids""", """attention_mask"""]



      def __init__(		self       ,     lowercase="[PAD]"       ,     lowercase="[BOS]"       ,     lowercase="[EOS]"       ,     lowercase="[MASK]"       ,     lowercase="[CLS]"       ,     lowercase="[SEP]"       ,     lowercase=20_48       ,     **lowercase       ,     )      ->     None:





        '''simple docstring'''





        a__:	Any     				=      AddedToken(lowercase       ,     lstrip=lowercase       ,     rstrip=lowercase) if isinstance(lowercase       ,     lowercase) else pad_token
        a__:	Tuple     				=      AddedToken(lowercase       ,     lstrip=lowercase       ,     rstrip=lowercase) if isinstance(lowercase       ,     lowercase) else bos_token
        a__:	Union[str, Any]     				=      AddedToken(lowercase       ,     lstrip=lowercase       ,     rstrip=lowercase) if isinstance(lowercase       ,     lowercase) else eos_token
        a__:	Tuple     				=      AddedToken(lowercase       ,     lstrip=lowercase       ,     rstrip=lowercase) if isinstance(lowercase       ,     lowercase) else mask_token
        a__:	int     				=      AddedToken(lowercase       ,     lstrip=lowercase       ,     rstrip=lowercase) if isinstance(lowercase       ,     lowercase) else cls_token
        a__:	Any     				=      AddedToken(lowercase       ,     lstrip=lowercase       ,     rstrip=lowercase) if isinstance(lowercase       ,     lowercase) else sep_token

        super().__init__(
            pad_token=lowercase       ,     bos_token=lowercase       ,     eos_token=lowercase       ,     mask_token=lowercase       ,     cls_token=lowercase       ,     sep_token=lowercase       ,     model_max_length=lowercase       ,     **lowercase       ,     )

        a__:	Optional[Any]     				=      2**8  # utf is 8 bits

        # define special tokens dict
        a__:	Dict[str, int]     				=      {
            self.pad_token: 0,
            self.bos_token: 1,
            self.eos_token: 2,
            self.mask_token: 3,
            self.cls_token: 4,
            self.sep_token: 5,
        }
        a__:	Union[str, Any]     				=      len(self.special_tokens_encoder)
        a__:	Dict[int, str]     				=      {v: k for k, v in self.special_tokens_encoder.items()}



      def     lowerCamelCase_  (		self)      ->     Dict[str, int]:





        '''simple docstring'''





        a__:	Optional[int]     				=      self.special_tokens_encoder.copy()
        vocab.update(self.added_tokens_encoder)
        for i in range(self._utf_vocab_size):
          a__:	Optional[int]     				=      chr(lowercase)
          a__:	str     				=      i + len(self.special_tokens_encoder)
        return vocab



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





        '''simple docstring'''





        return self._utf_vocab_size + self._num_special_tokens



      def     lowerCamelCase_  (		self       ,     lowercase       ,     lowercase = None       ,     lowercase = False)      ->     List[int]:





        '''simple docstring'''





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

        # normal case: some special tokens
        if token_ids_a is None:
          return [1] + [0] * len(lowercase) + [1]
        return [1] + ([0] * len(lowercase)) + [1] + ([0] * len(lowercase)) + [1]



      def     lowerCamelCase_  (		self       ,     lowercase       ,     lowercase = None)      ->     List[int]:





        '''simple docstring'''





        if token_ids_a is None:
          return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
        else:
          return [self.cls_token_id] + token_ids_a + [self.sep_token_id] + token_ids_a + [self.sep_token_id]



      def     lowerCamelCase_  (		self       ,     lowercase)      ->     List[str]:





        '''simple docstring'''





        a__:	Any     				=      [chr(lowercase) for i in text.encode('utf-8')]
        return tokens



      def     lowerCamelCase_  (		self       ,     lowercase)      ->     Union[str, Any]:





        '''simple docstring'''





        if token in self.special_tokens_encoder:
          a__:	Optional[int]     				=      self.special_tokens_encoder[token]
        elif token in self.added_tokens_encoder:
          a__:	Optional[int]     				=      self.added_tokens_encoder[token]
        elif len(lowercase) != 1:
          a__:	Dict     				=      self.unk_token_id
        else:
          a__:	Optional[Any]     				=      ord(lowercase) + self._num_special_tokens
        return token_id



      def     lowerCamelCase_  (		self       ,     lowercase)      ->     Tuple:





        '''simple docstring'''





        if index in self.special_tokens_decoder:
          a__:	List[Any]     				=      self.special_tokens_decoder[index]
        elif index in self.added_tokens_decoder:
          a__:	Tuple     				=      self.added_tokens_decoder[index]
        else:
          a__:	List[str]     				=      chr(index - self._num_special_tokens)
        return token



      def     lowerCamelCase_  (		self       ,     lowercase)      ->     Optional[int]:





        '''simple docstring'''





        a__:	Any     				=      B''
        for token in tokens:
          if token in self.special_tokens_decoder:
            a__:	str     				=      self.special_tokens_decoder[token].encode('utf-8')
          elif token in self.added_tokens_decoder:
            a__:	Optional[Any]     				=      self.special_tokens_decoder[token].encode('utf-8')
          elif token in self.special_tokens_encoder:
            a__:	Optional[Any]     				=      token.encode('utf-8')
          elif token in self.added_tokens_encoder:
            a__:	str     				=      token.encode('utf-8')
          else:
            a__:	Tuple     				=      bytes([ord(lowercase)])
          bstring += tok_string
        a__:	List[str]     				=      bstring.decode('utf-8'       ,     errors='replace')
        return string



      def     lowerCamelCase_  (		self       ,     lowercase       ,     lowercase = None)      ->     Tuple[str]:





        '''simple docstring'''





        return ()










"""simple docstring"""


import os
from typing import List, Union

import tensorflow as tf
from tensorflow_text import BertTokenizer as BertTokenizerLayer
from tensorflow_text import FastBertTokenizer, ShrinkLongestTrimmer, case_fold_utfa, combine_segments, pad_model_inputs

from .tokenization_bert import BertTokenizer


class    snake_case						(				tf.keras.layers.Layer		):


   '''simple docstring'''

   def __init__(      self					:	List[str],			_lowerCamelCase					:	List,			_lowerCamelCase					:	bool,			_lowerCamelCase					:	int = None,			_lowerCamelCase					:	int = None,			_lowerCamelCase					:	int = None,			_lowerCamelCase					:	str = "longest",			_lowerCamelCase					:	bool = True,			_lowerCamelCase					:	int = 5_12,			_lowerCamelCase					:	int = None,			_lowerCamelCase					:	bool = True,			_lowerCamelCase					:	bool = True,			_lowerCamelCase					:	bool = True,			**_lowerCamelCase					:	int,			):

     '''simple docstring'''
     super().__init__()
     if use_fast_bert_tokenizer:
       __A		     =					FastBertTokenizer(
           _lowerCamelCase,			token_out_type=tf.intaa,			lower_case_nfd_strip_accents=_lowerCamelCase,			**_lowerCamelCase    )
     else:
       __A		     =					tf.lookup.StaticVocabularyTable(
           tf.lookup.KeyValueTensorInitializer(
               keys=_lowerCamelCase,			key_dtype=tf.string,			values=tf.range(tf.size(_lowerCamelCase,			out_type=tf.intaa    ),			dtype=tf.intaa    ),			value_dtype=tf.intaa,			),			num_oov_buckets=1,			)
       __A		     =					BertTokenizerLayer(
           _lowerCamelCase,			token_out_type=tf.intaa,			lower_case=_lowerCamelCase,			**_lowerCamelCase    )

     __A		     =					vocab_list
     __A		     =					do_lower_case
     __A		     =					cls_token_id or vocab_list.index('''[CLS]'''    )
     __A		     =					sep_token_id or vocab_list.index('''[SEP]'''    )
     __A		     =					pad_token_id or vocab_list.index('''[PAD]'''    )
     __A		     =					ShrinkLongestTrimmer(max_length - 3,			axis=1    )  # Allow room for special tokens
     __A		     =					max_length
     __A		     =					padding
     __A		     =					truncation
     __A		     =					pad_to_multiple_of
     __A		     =					return_token_type_ids
     __A		     =					return_attention_mask


   @classmethod
   def 							_SCREAMING_SNAKE_CASE							(      cls					:	int,			_lowerCamelCase					:	"PreTrainedTokenizerBase",			**_lowerCamelCase					:	List[Any]    ):  # noqa: F821

     '''simple docstring'''
     __A		     =					kwargs.pop('''do_lower_case''',			_lowerCamelCase    )
     __A		     =					tokenizer.do_lower_case if do_lower_case is None else do_lower_case
     __A		     =					kwargs.pop('''cls_token_id''',			_lowerCamelCase    )
     __A		     =					tokenizer.cls_token_id if cls_token_id is None else cls_token_id
     __A		     =					kwargs.pop('''sep_token_id''',			_lowerCamelCase    )
     __A		     =					tokenizer.sep_token_id if sep_token_id is None else sep_token_id
     __A		     =					kwargs.pop('''pad_token_id''',			_lowerCamelCase    )
     __A		     =					tokenizer.pad_token_id if pad_token_id is None else pad_token_id

     __A		     =					tokenizer.get_vocab()
     __A		     =					sorted([(wordpiece, idx) for wordpiece, idx in vocab.items()],			key=lambda _lowerCamelCase    : x[1]    )
     __A		     =					[entry[0] for entry in vocab]
     return cls(
         vocab_list=_lowerCamelCase,			do_lower_case=_lowerCamelCase,			cls_token_id=_lowerCamelCase,			sep_token_id=_lowerCamelCase,			pad_token_id=_lowerCamelCase,			**_lowerCamelCase,			)


   @classmethod
   def 							_SCREAMING_SNAKE_CASE							(      cls					:	Optional[Any],			_lowerCamelCase					:	Union[str, os.PathLike],			*_lowerCamelCase					:	Any,			**_lowerCamelCase					:	List[str]    ):

     '''simple docstring'''
     try:
       __A		     =					BertTokenizer.from_pretrained(_lowerCamelCase,			*_lowerCamelCase,			**_lowerCamelCase    )
     except:  # noqa: E722
       from .tokenization_bert_fast import BertTokenizerFast

       __A		     =					BertTokenizerFast.from_pretrained(_lowerCamelCase,			*_lowerCamelCase,			**_lowerCamelCase    )
     return cls.from_tokenizer(_lowerCamelCase,			**_lowerCamelCase    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	List[Any],			_lowerCamelCase					:	Tuple    ):

     '''simple docstring'''
     if self.do_lower_case:
       __A		     =					case_fold_utfa(_lowerCamelCase    )
     __A		     =					self.tf_tokenizer.tokenize(_lowerCamelCase    )
     return tokens.merge_dims(1,			-1    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Dict,			_lowerCamelCase					:	Any,			_lowerCamelCase					:	Dict=None,			_lowerCamelCase					:	Optional[int]=None,			_lowerCamelCase					:	Union[str, Any]=None,			_lowerCamelCase					:	Any=None,			_lowerCamelCase					:	List[Any]=None,			_lowerCamelCase					:	str=None,			_lowerCamelCase					:	Dict=None,			):

     '''simple docstring'''
     if padding is None:
       __A		     =					self.padding
     if padding not in ("longest", "max_length"):
       raise ValueError('''Padding must be either \'longest\' or \'max_length\'!'''    )
     if max_length is not None and text_pair is not None:
       # Because we have to instantiate a Trimmer to do it properly
       raise ValueError('''max_length cannot be overridden at call time when truncating paired texts!'''    )
     if max_length is None:
       __A		     =					self.max_length
     if truncation is None:
       __A		     =					self.truncation
     if pad_to_multiple_of is None:
       __A		     =					self.pad_to_multiple_of
     if return_token_type_ids is None:
       __A		     =					self.return_token_type_ids
     if return_attention_mask is None:
       __A		     =					self.return_attention_mask
     if not isinstance(_lowerCamelCase,			tf.Tensor    ):
       __A		     =					tf.convert_to_tensor(_lowerCamelCase    )
     if text_pair is not None and not isinstance(_lowerCamelCase,			tf.Tensor    ):
       __A		     =					tf.convert_to_tensor(_lowerCamelCase    )
     if text_pair is not None:
       if text.shape.rank > 1:
         raise ValueError('''text argument should not be multidimensional when a text pair is supplied!'''    )
       if text_pair.shape.rank > 1:
         raise ValueError('''text_pair should not be multidimensional!'''    )
     if text.shape.rank == 2:
       __A     ,       __A		     =					text[:, 0], text[:, 1]
     __A		     =					self.unpaired_tokenize(_lowerCamelCase    )
     if text_pair is None:  # Unpaired text
       if truncation:
         __A		     =					text[:, : max_length - 2]  # Allow room for special tokens
       __A     ,       __A		     =					combine_segments(
           (text,),			start_of_sequence_id=self.cls_token_id,			end_of_segment_id=self.sep_token_id    )
     else:  # Paired text
       __A		     =					self.unpaired_tokenize(_lowerCamelCase    )
       if truncation:
         __A     ,       __A		     =					self.paired_trimmer.trim([text, text_pair]    )
       __A     ,       __A		     =					combine_segments(
           (text, text_pair),			start_of_sequence_id=self.cls_token_id,			end_of_segment_id=self.sep_token_id    )
     if padding == "longest":
       __A		     =					input_ids.bounding_shape(axis=1    )
       if pad_to_multiple_of is not None:
         # No ceiling division in tensorflow, so we negate floordiv instead
         __A		     =					pad_to_multiple_of * (-tf.math.floordiv(-pad_length,			_lowerCamelCase    ))
     else:
       __A		     =					max_length

     __A     ,       __A		     =					pad_model_inputs(_lowerCamelCase,			max_seq_length=_lowerCamelCase,			pad_value=self.pad_token_id    )
     __A		     =					{'''input_ids''': input_ids}
     if return_attention_mask:
       __A		     =					attention_mask
     if return_token_type_ids:
       __A     ,       __A		     =					pad_model_inputs(
           _lowerCamelCase,			max_seq_length=_lowerCamelCase,			pad_value=self.pad_token_id    )
       __A		     =					token_type_ids
     return output






   def 							_SCREAMING_SNAKE_CASE							(      self					:	Optional[Any]    ):

     '''simple docstring'''
     return {
         "vocab_list": self.vocab_list,
         "do_lower_case": self.do_lower_case,
         "cls_token_id": self.cls_token_id,
         "sep_token_id": self.sep_token_id,
         "pad_token_id": self.pad_token_id,
     }




