code: 
  
  
  
  
  """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 							lowerCamelCase		( tf.keras.layers.Layer				):
  
  
  
  
  
  
  
     def __init__(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ = None    ,      SCREAMING_SNAKE_CASE_ = None    ,      SCREAMING_SNAKE_CASE_ = None    ,      SCREAMING_SNAKE_CASE_ = "longest"    ,      SCREAMING_SNAKE_CASE_ = True    ,      SCREAMING_SNAKE_CASE_ = 512    ,      SCREAMING_SNAKE_CASE_ = None    ,      SCREAMING_SNAKE_CASE_ = True    ,      SCREAMING_SNAKE_CASE_ = True    ,      SCREAMING_SNAKE_CASE_ = True    ,      **SCREAMING_SNAKE_CASE_    ,      ):
         super().__init__()
         if use_fast_bert_tokenizer:
             UpperCamelCase				:   Union[str, Any]          =							FastBertTokenizer(
                 SCREAMING_SNAKE_CASE_    ,      token_out_type=tf.intaa    ,      lower_case_nfd_strip_accents=SCREAMING_SNAKE_CASE_    ,      **SCREAMING_SNAKE_CASE_ )
         else:
             UpperCamelCase				:   Any          =							tf.lookup.StaticVocabularyTable(
                 tf.lookup.KeyValueTensorInitializer(
                     keys=SCREAMING_SNAKE_CASE_    ,      key_dtype=tf.string    ,      values=tf.range(tf.size(SCREAMING_SNAKE_CASE_    ,      out_type=tf.intaa )    ,      dtype=tf.intaa )    ,      value_dtype=tf.intaa    ,      )    ,      num_oov_buckets=1    ,      )
             UpperCamelCase				:   Union[str, Any]          =							BertTokenizerLayer(
                 SCREAMING_SNAKE_CASE_    ,      token_out_type=tf.intaa    ,      lower_case=SCREAMING_SNAKE_CASE_    ,      **SCREAMING_SNAKE_CASE_ )
  
         UpperCamelCase				:   str          =							vocab_list
         UpperCamelCase				:   Optional[int]          =							do_lower_case
         UpperCamelCase				:   Union[str, Any]          =							cls_token_id or vocab_list.index("""[CLS]""" )
         UpperCamelCase				:   Optional[int]          =							sep_token_id or vocab_list.index("""[SEP]""" )
         UpperCamelCase				:   Optional[int]          =							pad_token_id or vocab_list.index("""[PAD]""" )
         UpperCamelCase				:   Union[str, Any]          =							ShrinkLongestTrimmer(max_length - 3    ,      axis=1 )  # Allow room for special tokens
         UpperCamelCase				:   Any          =							max_length
         UpperCamelCase				:   str          =							padding
         UpperCamelCase				:   Any          =							truncation
         UpperCamelCase				:   str          =							pad_to_multiple_of
         UpperCamelCase				:   Dict          =							return_token_type_ids
         UpperCamelCase				:   Union[str, Any]          =							return_attention_mask
  
  
  
  
  
  
  
     @classmethod
     def       a_							(							cls    ,      SCREAMING_SNAKE_CASE_    ,      **SCREAMING_SNAKE_CASE_ ):  # noqa: F821
         UpperCamelCase				:   Optional[Any]          =							kwargs.pop("""do_lower_case"""    ,      SCREAMING_SNAKE_CASE_ )
         UpperCamelCase				:   Dict          =							tokenizer.do_lower_case if do_lower_case is None else do_lower_case
         UpperCamelCase				:   List[Any]          =							kwargs.pop("""cls_token_id"""    ,      SCREAMING_SNAKE_CASE_ )
         UpperCamelCase				:   int          =							tokenizer.cls_token_id if cls_token_id is None else cls_token_id
         UpperCamelCase				:   Union[str, Any]          =							kwargs.pop("""sep_token_id"""    ,      SCREAMING_SNAKE_CASE_ )
         UpperCamelCase				:   int          =							tokenizer.sep_token_id if sep_token_id is None else sep_token_id
         UpperCamelCase				:   int          =							kwargs.pop("""pad_token_id"""    ,      SCREAMING_SNAKE_CASE_ )
         UpperCamelCase				:   List[Any]          =							tokenizer.pad_token_id if pad_token_id is None else pad_token_id
  
         UpperCamelCase				:   Tuple          =							tokenizer.get_vocab()
         UpperCamelCase				:   Tuple          =							sorted([(wordpiece, idx) for wordpiece, idx in vocab.items()]    ,      key=lambda SCREAMING_SNAKE_CASE_ : x[1] )
         UpperCamelCase				:   Dict          =							[entry[0] for entry in vocab]
         return cls(
             vocab_list=SCREAMING_SNAKE_CASE_    ,      do_lower_case=SCREAMING_SNAKE_CASE_    ,      cls_token_id=SCREAMING_SNAKE_CASE_    ,      sep_token_id=SCREAMING_SNAKE_CASE_    ,      pad_token_id=SCREAMING_SNAKE_CASE_    ,      **SCREAMING_SNAKE_CASE_    ,      )
  
  
  
  
  
  
  
     @classmethod
     def       a_							(							cls    ,      SCREAMING_SNAKE_CASE_    ,      *SCREAMING_SNAKE_CASE_    ,      **SCREAMING_SNAKE_CASE_ ):
         try:
             UpperCamelCase				:   Tuple          =							BertTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_    ,      *SCREAMING_SNAKE_CASE_    ,      **SCREAMING_SNAKE_CASE_ )
         except:  # noqa: E722
             from .tokenization_bert_fast import BertTokenizerFast
  
             UpperCamelCase				:   Tuple          =							BertTokenizerFast.from_pretrained(SCREAMING_SNAKE_CASE_    ,      *SCREAMING_SNAKE_CASE_    ,      **SCREAMING_SNAKE_CASE_ )
         return cls.from_tokenizer(SCREAMING_SNAKE_CASE_    ,      **SCREAMING_SNAKE_CASE_ )
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_ ):
         if self.do_lower_case:
             UpperCamelCase				:   List[str]          =							case_fold_utfa(SCREAMING_SNAKE_CASE_ )
         UpperCamelCase				:   Optional[Any]          =							self.tf_tokenizer.tokenize(SCREAMING_SNAKE_CASE_ )
         return tokens.merge_dims(1    ,      -1 )
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_=None    ,      SCREAMING_SNAKE_CASE_=None    ,      SCREAMING_SNAKE_CASE_=None    ,      SCREAMING_SNAKE_CASE_=None    ,      SCREAMING_SNAKE_CASE_=None    ,      SCREAMING_SNAKE_CASE_=None    ,      SCREAMING_SNAKE_CASE_=None    ,      ):
         if padding is None:
             UpperCamelCase				:   Optional[int]          =							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:
             UpperCamelCase				:   List[Any]          =							self.max_length
         if truncation is None:
             UpperCamelCase				:   str          =							self.truncation
         if pad_to_multiple_of is None:
             UpperCamelCase				:   Optional[int]          =							self.pad_to_multiple_of
         if return_token_type_ids is None:
             UpperCamelCase				:   Any          =							self.return_token_type_ids
         if return_attention_mask is None:
             UpperCamelCase				:   Optional[Any]          =							self.return_attention_mask
         if not isinstance(SCREAMING_SNAKE_CASE_    ,      tf.Tensor ):
             UpperCamelCase				:   Tuple          =							tf.convert_to_tensor(SCREAMING_SNAKE_CASE_ )
         if text_pair is not None and not isinstance(SCREAMING_SNAKE_CASE_    ,      tf.Tensor ):
             UpperCamelCase				:   Optional[Any]          =							tf.convert_to_tensor(SCREAMING_SNAKE_CASE_ )
         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:
             UpperCamelCase       ,							UpperCamelCase				:   Tuple          =							text[:, 0], text[:, 1]
         UpperCamelCase				:   List[str]          =							self.unpaired_tokenize(SCREAMING_SNAKE_CASE_ )
         if text_pair is None:  # Unpaired text
             if truncation:
                 UpperCamelCase				:   Dict          =							text[:, : max_length - 2]  # Allow room for special tokens
             UpperCamelCase       ,							UpperCamelCase				:   Dict          =							combine_segments(
                 (text,)    ,      start_of_sequence_id=self.cls_token_id    ,      end_of_segment_id=self.sep_token_id )
         else:  # Paired text
             UpperCamelCase				:   List[str]          =							self.unpaired_tokenize(SCREAMING_SNAKE_CASE_ )
             if truncation:
                 UpperCamelCase       ,							UpperCamelCase				:   List[str]          =							self.paired_trimmer.trim([text, text_pair] )
             UpperCamelCase       ,							UpperCamelCase				:   List[str]          =							combine_segments(
                 (text, text_pair)    ,      start_of_sequence_id=self.cls_token_id    ,      end_of_segment_id=self.sep_token_id )
         if padding == "longest":
             UpperCamelCase				:   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
                 UpperCamelCase				:   Union[str, Any]          =							pad_to_multiple_of * (-tf.math.floordiv(-pad_length    ,      SCREAMING_SNAKE_CASE_ ))
         else:
             UpperCamelCase				:   Dict          =							max_length
  
         UpperCamelCase       ,							UpperCamelCase				:   Any          =							pad_model_inputs(SCREAMING_SNAKE_CASE_    ,      max_seq_length=SCREAMING_SNAKE_CASE_    ,      pad_value=self.pad_token_id )
         UpperCamelCase				:   Any          =							{"""input_ids""": input_ids}
         if return_attention_mask:
             UpperCamelCase				:   Optional[Any]          =							attention_mask
         if return_token_type_ids:
             UpperCamelCase       ,							UpperCamelCase				:   int          =							pad_model_inputs(
                 SCREAMING_SNAKE_CASE_    ,      max_seq_length=SCREAMING_SNAKE_CASE_    ,      pad_value=self.pad_token_id )
             UpperCamelCase				:   List[Any]          =							token_type_ids
         return output
  
  
  
  
  
  
  
     def       a_							(							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,
         }
  
  
  
  
  
style_context: 
  
  
  
  
  """simple docstring"""
  
  
  
  
  class 							lowerCamelCase		:
  
  
  
  
  
  
  
     def __init__(							self    ,      SCREAMING_SNAKE_CASE_=None ):
         UpperCamelCase				:   Optional[Any]          =							data
         UpperCamelCase				:   str          =							None
  
  
  
  
  
  
  
     def __repr__(							self ):
         UpperCamelCase				:   str          =							""""""
         UpperCamelCase				:   Any          =							self
         while temp:
             string_rep += f'<{temp.data}> ---> '
             UpperCamelCase				:   Optional[int]          =							temp.next
         string_rep += "<END>"
         return string_rep
  
  
  
  
  
  
  
  def 					A_			(		snake_case_     :       Tuple    ):
  
  
  
  
  
  
  
      '''simple docstring'''
  
      # if elements_list is empty
      if not elements_list:
          raise Exception("""The Elements List is empty"""    )
  
      # Set first element as Head
      UpperCamelCase				:   List[Any]          =							Node(elements_list[0]    )
      UpperCamelCase				:   str          =							head
      # Loop through elements from position 1
      for data in elements_list[1:]:
          UpperCamelCase				:   Optional[int]          =							Node(snake_case_    )
          UpperCamelCase				:   Optional[Any]          =							current.next
      return head
  
  
  __A :		List[Any]      							=    [1, 3, 5, 32, 44, 12, 43]
  print(F'''List: {list_data}''')
  print('''Creating Linked List from List.''')
  __A :		int      							=    make_linked_list(list_data)
  print('''Linked List:''')
  
  
  print(linked_list)
  
  
  
  
  