

'''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      a__						(							tf.keras.layers.Layer						):


   def __init__(				self    ,       _UpperCamelCase    ,       _UpperCamelCase    ,       _UpperCamelCase = None    ,       _UpperCamelCase = None    ,       _UpperCamelCase = None    ,       _UpperCamelCase = "longest"    ,       _UpperCamelCase = True    ,       _UpperCamelCase = 512    ,       _UpperCamelCase = None    ,       _UpperCamelCase = True    ,       _UpperCamelCase = True    ,       _UpperCamelCase = True    ,       **_UpperCamelCase    ,       ):





      """simple docstring"""





      super().__init__()
      if use_fast_bert_tokenizer:
         _lowercase    :   int      =     FastBertTokenizer(
             _UpperCamelCase    ,       token_out_type=tf.intaa    ,       lower_case_nfd_strip_accents=_UpperCamelCase    ,       **_UpperCamelCase						)
      else:
         _lowercase    :   List[str]      =     tf.lookup.StaticVocabularyTable(
             tf.lookup.KeyValueTensorInitializer(
                 keys=_UpperCamelCase    ,       key_dtype=tf.string    ,       values=tf.range(tf.size(_UpperCamelCase    ,       out_type=tf.intaa						)    ,       dtype=tf.intaa						)    ,       value_dtype=tf.intaa    ,       )    ,       num_oov_buckets=1    ,       )
         _lowercase    :   List[str]      =     BertTokenizerLayer(
             _UpperCamelCase    ,       token_out_type=tf.intaa    ,       lower_case=_UpperCamelCase    ,       **_UpperCamelCase						)

      _lowercase    :   Union[str, Any]      =     vocab_list
      _lowercase    :   List[Any]      =     do_lower_case
      _lowercase    :   Optional[int]      =     cls_token_id or vocab_list.index("[CLS]"						)
      _lowercase    :   str      =     sep_token_id or vocab_list.index("[SEP]"						)
      _lowercase    :   str      =     pad_token_id or vocab_list.index("[PAD]"						)
      _lowercase    :   int      =     ShrinkLongestTrimmer(max_length - 3    ,       axis=1						)  # Allow room for special tokens
      _lowercase    :   Union[str, Any]      =     max_length
      _lowercase    :   Any      =     padding
      _lowercase    :   List[Any]      =     truncation
      _lowercase    :   int      =     pad_to_multiple_of
      _lowercase    :   Optional[Any]      =     return_token_type_ids
      _lowercase    :   Any      =     return_attention_mask


   @classmethod
   def 		_lowerCamelCase     (				cls    ,       _UpperCamelCase    ,       **_UpperCamelCase						):  # noqa: F821





      """simple docstring"""





      _lowercase    :   List[Any]      =     kwargs.pop("do_lower_case"    ,       _UpperCamelCase						)
      _lowercase    :   int      =     tokenizer.do_lower_case if do_lower_case is None else do_lower_case
      _lowercase    :   List[Any]      =     kwargs.pop("cls_token_id"    ,       _UpperCamelCase						)
      _lowercase    :   Optional[Any]      =     tokenizer.cls_token_id if cls_token_id is None else cls_token_id
      _lowercase    :   Optional[Any]      =     kwargs.pop("sep_token_id"    ,       _UpperCamelCase						)
      _lowercase    :   Dict      =     tokenizer.sep_token_id if sep_token_id is None else sep_token_id
      _lowercase    :   Union[str, Any]      =     kwargs.pop("pad_token_id"    ,       _UpperCamelCase						)
      _lowercase    :   Tuple      =     tokenizer.pad_token_id if pad_token_id is None else pad_token_id

      _lowercase    :   Optional[Any]      =     tokenizer.get_vocab()
      _lowercase    :   List[Any]      =     sorted([(wordpiece, idx) for wordpiece, idx in vocab.items()]    ,       key=lambda _UpperCamelCase						: x[1]						)
      _lowercase    :   Optional[int]      =     [entry[0] for entry in vocab]
      return cls(
          vocab_list=_UpperCamelCase    ,       do_lower_case=_UpperCamelCase    ,       cls_token_id=_UpperCamelCase    ,       sep_token_id=_UpperCamelCase    ,       pad_token_id=_UpperCamelCase    ,       **_UpperCamelCase    ,       )


   @classmethod
   def 		_lowerCamelCase     (				cls    ,       _UpperCamelCase    ,       *_UpperCamelCase    ,       **_UpperCamelCase						):





      """simple docstring"""





      try:
         _lowercase    :   int      =     BertTokenizer.from_pretrained(_UpperCamelCase    ,       *_UpperCamelCase    ,       **_UpperCamelCase						)
      except:  # noqa: E722
         from .tokenization_bert_fast import BertTokenizerFast

         _lowercase    :   List[Any]      =     BertTokenizerFast.from_pretrained(_UpperCamelCase    ,       *_UpperCamelCase    ,       **_UpperCamelCase						)
      return cls.from_tokenizer(_UpperCamelCase    ,       **_UpperCamelCase						)


   def 		_lowerCamelCase     (				self    ,       _UpperCamelCase						):





      """simple docstring"""





      if self.do_lower_case:
         _lowercase    :   Tuple      =     case_fold_utfa(_UpperCamelCase						)
      _lowercase    :   Dict      =     self.tf_tokenizer.tokenize(_UpperCamelCase						)
      return tokens.merge_dims(1    ,       -1						)


   def 		_lowerCamelCase     (				self    ,       _UpperCamelCase    ,       _UpperCamelCase=None    ,       _UpperCamelCase=None    ,       _UpperCamelCase=None    ,       _UpperCamelCase=None    ,       _UpperCamelCase=None    ,       _UpperCamelCase=None    ,       _UpperCamelCase=None    ,       ):





      """simple docstring"""





      if padding is None:
         _lowercase    :   Optional[Any]      =     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:
         _lowercase    :   Tuple      =     self.max_length
      if truncation is None:
         _lowercase    :   Union[str, Any]      =     self.truncation
      if pad_to_multiple_of is None:
         _lowercase    :   int      =     self.pad_to_multiple_of
      if return_token_type_ids is None:
         _lowercase    :   Optional[int]      =     self.return_token_type_ids
      if return_attention_mask is None:
         _lowercase    :   List[str]      =     self.return_attention_mask
      if not isinstance(_UpperCamelCase    ,       tf.Tensor						):
         _lowercase    :   Tuple      =     tf.convert_to_tensor(_UpperCamelCase						)
      if text_pair is not None and not isinstance(_UpperCamelCase    ,       tf.Tensor						):
         _lowercase    :   List[Any]      =     tf.convert_to_tensor(_UpperCamelCase						)
      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:
         _lowercase  ,   _lowercase    :   Optional[int]      =     text[:, 0], text[:, 1]
      _lowercase    :   Optional[int]      =     self.unpaired_tokenize(_UpperCamelCase						)
      if text_pair is None:  # Unpaired text
         if truncation:
            _lowercase    :   str      =     text[:, : max_length - 2]  # Allow room for special tokens
         _lowercase  ,   _lowercase    :   List[Any]      =     combine_segments(
             (text,)    ,       start_of_sequence_id=self.cls_token_id    ,       end_of_segment_id=self.sep_token_id						)
      else:  # Paired text
         _lowercase    :   List[Any]      =     self.unpaired_tokenize(_UpperCamelCase						)
         if truncation:
            _lowercase  ,   _lowercase    :   Union[str, Any]      =     self.paired_trimmer.trim([text, text_pair]						)
         _lowercase  ,   _lowercase    :   Union[str, Any]      =     combine_segments(
             (text, text_pair)    ,       start_of_sequence_id=self.cls_token_id    ,       end_of_segment_id=self.sep_token_id						)
      if padding == "longest":
         _lowercase    :   Dict      =     input_ids.bounding_shape(axis=1						)
         if pad_to_multiple_of is not None:
            # No ceiling division in tensorflow, so we negate floordiv instead
            _lowercase    :   List[str]      =     pad_to_multiple_of * (-tf.math.floordiv(-pad_length    ,       _UpperCamelCase						))
      else:
         _lowercase    :   Dict      =     max_length

      _lowercase  ,   _lowercase    :   Optional[Any]      =     pad_model_inputs(_UpperCamelCase    ,       max_seq_length=_UpperCamelCase    ,       pad_value=self.pad_token_id						)
      _lowercase    :   Dict      =     {"input_ids": input_ids}
      if return_attention_mask:
         _lowercase    :   Tuple      =     attention_mask
      if return_token_type_ids:
         _lowercase  ,   _lowercase    :   Tuple      =     pad_model_inputs(
             _UpperCamelCase    ,       max_seq_length=_UpperCamelCase    ,       pad_value=self.pad_token_id						)
         _lowercase    :   str      =     token_type_ids
      return output






   def 		_lowerCamelCase     (				self						):





      """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,
      }









'''simple docstring'''
def       _A	(			snake_case  )    ->			int:

   # validate:
   if not isinstance(snake_case	,							snake_case  ) or not all(
       isinstance(snake_case	,							snake_case  ) and item >= 0 for item in citations  ):
      raise ValueError("The citations should be a list of non negative integers."  )

   citations.sort()
   _lowercase    :   Tuple      =     len(snake_case  )

   for i in range(snake_case  ):
      if citations[len_citations - 1 - i] <= i:
         return i

   return len_citations


if __name__ == "__main__":
    import doctest

    doctest.testmod()






