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





 def __init__(      self	,    lowercase_	,    lowercase_	,    lowercase_ = None	,    lowercase_ = None	,    lowercase_ = None	,    lowercase_ = "longest"	,    lowercase_ = True	,    lowercase_ = 5_12	,    lowercase_ = None	,    lowercase_ = True	,    lowercase_ = True	,    lowercase_ = True	,    **lowercase_	,    ):
     super().__init__()
     if use_fast_bert_tokenizer:
         snake_case_	:      str            =  FastBertTokenizer(
             lowercase_	,    token_out_type=tf.intaa	,    lower_case_nfd_strip_accents=lowercase_	,    **lowercase_)
     else:
         snake_case_	:      Any            =  tf.lookup.StaticVocabularyTable(
             tf.lookup.KeyValueTensorInitializer(
                 keys=lowercase_	,    key_dtype=tf.string	,    values=tf.range(tf.size(lowercase_	,    out_type=tf.intaa)	,    dtype=tf.intaa)	,    value_dtype=tf.intaa	,    )	,    num_oov_buckets=1	,    )
         snake_case_	:      int            =  BertTokenizerLayer(
             lowercase_	,    token_out_type=tf.intaa	,    lower_case=lowercase_	,    **lowercase_)

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





 @classmethod
 def     snake_case__						(      cls	,    lowercase_	,    **lowercase_):  # noqa: F821
     snake_case_	:      List[str]            =  kwargs.pop("do_lower_case"	,    lowercase_)
     snake_case_	:      List[Any]            =  tokenizer.do_lower_case if do_lower_case is None else do_lower_case
     snake_case_	:      Any            =  kwargs.pop("cls_token_id"	,    lowercase_)
     snake_case_	:      int            =  tokenizer.cls_token_id if cls_token_id is None else cls_token_id
     snake_case_	:      Optional[Any]            =  kwargs.pop("sep_token_id"	,    lowercase_)
     snake_case_	:      Union[str, Any]            =  tokenizer.sep_token_id if sep_token_id is None else sep_token_id
     snake_case_	:      Dict            =  kwargs.pop("pad_token_id"	,    lowercase_)
     snake_case_	:      List[str]            =  tokenizer.pad_token_id if pad_token_id is None else pad_token_id

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





 @classmethod
 def     snake_case__						(      cls	,    lowercase_	,    *lowercase_	,    **lowercase_):
     try:
         snake_case_	:      Union[str, Any]            =  BertTokenizer.from_pretrained(lowercase_	,    *lowercase_	,    **lowercase_)
     except:  # noqa: E722
         from .tokenization_bert_fast import BertTokenizerFast

         snake_case_	:      Dict            =  BertTokenizerFast.from_pretrained(lowercase_	,    *lowercase_	,    **lowercase_)
     return cls.from_tokenizer(lowercase_	,    **lowercase_)





 def     snake_case__						(      self	,    lowercase_):
     if self.do_lower_case:
         snake_case_	:      Tuple            =  case_fold_utfa(lowercase_)
     snake_case_	:      Union[str, Any]            =  self.tf_tokenizer.tokenize(lowercase_)
     return tokens.merge_dims(1	,    -1)





 def     snake_case__						(      self	,    lowercase_	,    lowercase_=None	,    lowercase_=None	,    lowercase_=None	,    lowercase_=None	,    lowercase_=None	,    lowercase_=None	,    lowercase_=None	,    ):
     if padding is None:
         snake_case_	:      Union[str, 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:
         snake_case_	:      Optional[int]            =  self.max_length
     if truncation is None:
         snake_case_	:      Tuple            =  self.truncation
     if pad_to_multiple_of is None:
         snake_case_	:      Dict            =  self.pad_to_multiple_of
     if return_token_type_ids is None:
         snake_case_	:      List[str]            =  self.return_token_type_ids
     if return_attention_mask is None:
         snake_case_	:      Union[str, Any]            =  self.return_attention_mask
     if not isinstance(lowercase_	,    tf.Tensor):
         snake_case_	:      Tuple            =  tf.convert_to_tensor(lowercase_)
     if text_pair is not None and not isinstance(lowercase_	,    tf.Tensor):
         snake_case_	:      Any            =  tf.convert_to_tensor(lowercase_)
     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:
         snake_case_	:      Optional[Any]            =  text[:, 0], text[:, 1]
     snake_case_	:      List[str]            =  self.unpaired_tokenize(lowercase_)
     if text_pair is None:  # Unpaired text
         if truncation:
             snake_case_	:      Dict            =  text[:, : max_length - 2]  # Allow room for special tokens
         snake_case_	:      int            =  combine_segments(
             (text,)	,    start_of_sequence_id=self.cls_token_id	,    end_of_segment_id=self.sep_token_id)
     else:  # Paired text
         snake_case_	:      Tuple            =  self.unpaired_tokenize(lowercase_)
         if truncation:
             snake_case_	:      Any            =  self.paired_trimmer.trim([text, text_pair])
         snake_case_	:      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":
         snake_case_	:      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
             snake_case_	:      Dict            =  pad_to_multiple_of * (-tf.math.floordiv(-pad_length	,    lowercase_))
     else:
         snake_case_	:      Union[str, Any]            =  max_length

     snake_case_	:      Optional[int]            =  pad_model_inputs(lowercase_	,    max_seq_length=lowercase_	,    pad_value=self.pad_token_id)
     snake_case_	:      Any            =  {"input_ids": input_ids}
     if return_attention_mask:
         snake_case_	:      Optional[int]            =  attention_mask
     if return_token_type_ids:
         snake_case_	:      Optional[Any]            =  pad_model_inputs(
             lowercase_	,    max_seq_length=lowercase_	,    pad_value=self.pad_token_id)
         snake_case_	:      Tuple            =  token_type_ids
     return output





 def     snake_case__						(      self):
     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'''
import math

def   UpperCamelCase_       (		__SCREAMING_SNAKE_CASE    ):
    """simple docstring"""






    snake_case_	:      Tuple            =  math.log(2,						1_0    ) * number
    snake_case_	:      Dict            =  round((log_number - int(__SCREAMING_SNAKE_CASE    )),						1_5    )

    return difference

def   UpperCamelCase_       (		__SCREAMING_SNAKE_CASE = 6_7_8_9_1_0    ):
    """simple docstring"""






    snake_case_	:      Any            =  9_0
    snake_case_	:      Optional[int]            =  0

    snake_case_	:      Union[str, Any]            =  math.log(1.23,						1_0    )
    snake_case_	:      Dict            =  math.log(1.24,						1_0    )
    snake_case_	:      List[Any]            =  0

    while position < number:
        snake_case_	:      Union[str, Any]            =  log_difference(__SCREAMING_SNAKE_CASE    )

        if difference >= upper_limit:
            power_iterator += 9_3

        elif difference < lower_limit:
            power_iterator += 1_9_6

        else:
            snake_case_	:      Optional[Any]            =  power_iterator
            power_iterator += 1_9_6
            position += 1

    return previous_power


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  print(f"""{solution() = }""")




