code: # Taken from https://raw.githubusercontent.com/tensorflow/text/v2.5.0/tensorflow_text/tools/wordpiece_vocab/wordpiece_tokenizer_learner_lib.py
  #
  # coding=utf-8
  # Copyright 2021 TF.Text Authors.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  
  """Algorithm for learning wordpiece vocabulary."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import collections
  import numpy as np
  from typing import List, Optional
  
  Params = collections.namedtuple('Params', [
      'upper_thresh', 'lower_thresh', 'num_iterations', 'max_input_tokens',
      'max_token_length', 'max_unique_chars', 'vocab_size', 'slack_ratio',
      'include_joiner_token', 'joiner', 'reserved_tokens'
  ])
  
  
  def extract_char_tokens(word_counts):
      """Extracts all single-character tokens from word_counts.
  
      Args:
        word_counts: list of (string, int) tuples
  
      Returns:
        set of single-character strings contained within word_counts
      """
  
      seen_chars = set()
      for word, _ in word_counts:
          for char in word:
              seen_chars.add(char)
      return seen_chars
  
  
  def ensure_all_tokens_exist(input_tokens, output_tokens, include_joiner_token,
                              joiner):
      """Adds all tokens in input_tokens to output_tokens if not already present.
  
      Args:
        input_tokens: set of strings (tokens) we want to include
        output_tokens: string to int dictionary mapping token to count
        include_joiner_token: bool whether to include joiner token
        joiner: string used to indicate suffixes
  
      Returns:
        string to int dictionary with all tokens in input_tokens included
      """
  
      for token in input_tokens:
          if token not in output_tokens:
              output_tokens[token] = 1
  
          if include_joiner_token:
              joined_token = joiner + token
              if joined_token not in output_tokens:
                  output_tokens[joined_token] = 1
  
      return output_tokens
  
  
  def get_split_indices(word, curr_tokens, include_joiner_token, joiner):
      """Gets indices for valid substrings of word, for iterations > 0.
  
      For iterations > 0, rather than considering every possible substring, we only
      want to consider starting points corresponding to the start of wordpieces in
      the current vocabulary.
  
      Args:
        word: string we want to split into substrings
        curr_tokens: string to int dict of tokens in vocab (from previous iteration)
        include_joiner_token: bool whether to include joiner token
        joiner: string used to indicate suffixes
  
      Returns:
        list of ints containing valid starting indices for word
      """
  
      indices = []
      start = 0
      while start < len(word):
          end = len(word)
          while end > start:
              subtoken = word[start:end]
              # Subtoken includes the joiner token.
              if include_joiner_token and start > 0:
                  subtoken = joiner + subtoken
              # If subtoken is part of vocab, 'end' is a valid start index.
              if subtoken in curr_tokens:
                  indices.append(end)
                  break
              end -= 1
  
          if end == start:
              return None
          start = end
  
      return indices
  
  
  def get_search_threshs(word_counts, upper_thresh, lower_thresh):
      """Clips the thresholds for binary search based on current word counts.
  
      The upper threshold parameter typically has a large default value that can
      result in many iterations of unnecessary search. Thus we clip the upper and
      lower bounds of search to the maximum and the minimum wordcount values.
  
      Args:
        word_counts: list of (string, int) tuples
        upper_thresh: int, upper threshold for binary search
        lower_thresh: int, lower threshold for binary search
  
      Returns:
        upper_search: int, clipped upper threshold for binary search
        lower_search: int, clipped lower threshold for binary search
      """
  
      counts = [count for _, count in word_counts]
      max_count = max(counts)
      min_count = min(counts)
  
      if upper_thresh is None:
          upper_search = max_count
      else:
          upper_search = max_count if max_count < upper_thresh else upper_thresh
  
      if lower_thresh is None:
          lower_search = min_count
      else:
          lower_search = min_count if min_count > lower_thresh else lower_thresh
  
      return upper_search, lower_search
  
  
  def get_input_words(word_counts, reserved_tokens, max_token_length):
      """Filters out words that are longer than max_token_length or are reserved.
  
      Args:
        word_counts: list of (string, int) tuples
        reserved_tokens: list of strings
        max_token_length: int, maximum length of a token
  
      Returns:
        list of (string, int) tuples of filtered wordcounts
      """
  
      all_counts = []
  
      for word, count in word_counts:
          if len(word) > max_token_length or word in reserved_tokens:
              continue
          all_counts.append((word, count))
  
      return all_counts
  
  
  def get_allowed_chars(all_counts, max_unique_chars):
      """Get the top max_unique_chars characters within our wordcounts.
  
      We want each character to be in the vocabulary so that we can keep splitting
      down to the character level if necessary. However, in order not to inflate
      our vocabulary with rare characters, we only keep the top max_unique_chars
      characters.
  
      Args:
        all_counts: list of (string, int) tuples
        max_unique_chars: int, maximum number of unique single-character tokens
  
      Returns:
        set of strings containing top max_unique_chars characters in all_counts
      """
  
      char_counts = collections.defaultdict(int)
  
      for word, count in all_counts:
          for char in word:
              char_counts[char] += count
  
      # Sort by count, then alphabetically.
      sorted_counts = sorted(sorted(char_counts.items(), key=lambda x: x[0]),
                             key=lambda x: x[1], reverse=True)
  
      allowed_chars = set()
      for i in range(min(len(sorted_counts), max_unique_chars)):
          allowed_chars.add(sorted_counts[i][0])
      return allowed_chars
  
  
  def filter_input_words(all_counts, allowed_chars, max_input_tokens):
      """Filters out words with unallowed chars and limits words to max_input_tokens.
  
      Args:
        all_counts: list of (string, int) tuples
        allowed_chars: list of single-character strings
        max_input_tokens: int, maximum number of tokens accepted as input
  
      Returns:
        list of (string, int) tuples of filtered wordcounts
      """
      # Ensure that the input is sorted so that if `max_input_tokens` is reached
      # the least common tokens are dropped.
      all_counts = sorted(
          all_counts, key=lambda word_and_count: word_and_count[1], reverse=True)
      filtered_counts = []
      for word, count in all_counts:
          if (max_input_tokens != -1 and
                  len(filtered_counts) >= max_input_tokens):
              break
          has_unallowed_chars = False
          for char in word:
              if char not in allowed_chars:
                  has_unallowed_chars = True
                  break
          if has_unallowed_chars:
              continue
          filtered_counts.append((word, count))
  
      return filtered_counts
  
  
  def generate_final_vocabulary(reserved_tokens, char_tokens, curr_tokens):
      """Generates final vocab given reserved, single-character, and current tokens.
  
      Args:
        reserved_tokens: list of strings (tokens) that must be included in vocab
        char_tokens: set of single-character strings
        curr_tokens: string to int dict mapping token to count
  
      Returns:
        list of strings representing final vocabulary
      """
  
      sorted_char_tokens = sorted(list(char_tokens))
      vocab_char_arrays = []
      vocab_char_arrays.extend(reserved_tokens)
      vocab_char_arrays.extend(sorted_char_tokens)
  
      # Sort by count, then alphabetically.
      sorted_tokens = sorted(sorted(curr_tokens.items(), key=lambda x: x[0]),
                             key=lambda x: x[1], reverse=True)
      for token, _ in sorted_tokens:
          vocab_char_arrays.append(token)
  
      seen_tokens = set()
      # Adding unique tokens to list to maintain sorted order.
      vocab_words = []
      for word in vocab_char_arrays:
          if word in seen_tokens:
              continue
          seen_tokens.add(word)
          vocab_words.append(word)
  
      return vocab_words
  
  
  def learn_with_thresh(word_counts, thresh, params):
      """Wordpiece learning algorithm to produce a vocab given frequency threshold.
  
      Args:
        word_counts: list of (string, int) tuples
        thresh: int, frequency threshold for a token to be included in the vocab
        params: Params namedtuple, parameters for learning
  
      Returns:
        list of strings, vocabulary generated for the given thresh
      """
  
      # Set of single-character tokens.
      char_tokens = extract_char_tokens(word_counts)
      curr_tokens = ensure_all_tokens_exist(char_tokens, {},
                                            params.include_joiner_token,
                                            params.joiner)
  
      for iteration in range(params.num_iterations):
          subtokens = [dict() for _ in range(params.max_token_length + 1)]
          # Populate array with counts of each subtoken.
          for word, count in word_counts:
              if iteration == 0:
                  split_indices = range(1, len(word) + 1)
              else:
                  split_indices = get_split_indices(word, curr_tokens,
                                                    params.include_joiner_token,
                                                    params.joiner)
                  if not split_indices:
                      continue
  
              start = 0
              for index in split_indices:
                  for end in range(start + 1, len(word) + 1):
                      subtoken = word[start:end]
                      length = len(subtoken)
                      if params.include_joiner_token and start > 0:
                          subtoken = params.joiner + subtoken
                      if subtoken in subtokens[length]:
                          # Subtoken exists, increment count.
                          subtokens[length][subtoken] += count
                      else:
                          # New subtoken, add to dict.
                          subtokens[length][subtoken] = count
                  start = index
  
          next_tokens = {}
          # Get all tokens that have a count above the threshold.
          for length in range(params.max_token_length, 0, -1):
              for token, count in subtokens[length].items():
                  if count >= thresh:
                      next_tokens[token] = count
                  # Decrement the count of all prefixes.
                  if len(token) > length:  # This token includes the joiner.
                      joiner_len = len(params.joiner)
                      for i in range(1 + joiner_len, length + joiner_len):
                          prefix = token[0:i]
                          if prefix in subtokens[i - joiner_len]:
                              subtokens[i - joiner_len][prefix] -= count
                  else:
                      for i in range(1, length):
                          prefix = token[0:i]
                          if prefix in subtokens[i]:
                              subtokens[i][prefix] -= count
  
          # Add back single-character tokens.
          curr_tokens = ensure_all_tokens_exist(char_tokens, next_tokens,
                                                params.include_joiner_token,
                                                params.joiner)
  
      vocab_words = generate_final_vocabulary(params.reserved_tokens, char_tokens,
                                              curr_tokens)
  
      return vocab_words
  
  
  def learn_binary_search(word_counts, lower, upper, params):
      """Performs binary search to find wordcount frequency threshold.
  
      Given upper and lower bounds and a list of (word, count) tuples, performs
      binary search to find the threshold closest to producing a vocabulary
      of size vocab_size.
  
      Args:
        word_counts: list of (string, int) tuples
        lower: int, lower bound for binary search
        upper: int, upper bound for binary search
        params: Params namedtuple, parameters for learning
  
      Returns:
        list of strings, vocab that is closest to target vocab_size
      """
      thresh = (upper + lower) // 2
      current_vocab = learn_with_thresh(word_counts, thresh, params)
      current_vocab_size = len(current_vocab)
  
      # Allow count to be within k% of the target count, where k is slack ratio.
      slack_count = params.slack_ratio * params.vocab_size
      if slack_count < 0:
          slack_count = 0
  
      is_within_slack = (current_vocab_size <= params.vocab_size) and (
              params.vocab_size - current_vocab_size <= slack_count)
  
      # We've created a vocab within our goal range (or, ran out of search space).
      if is_within_slack or lower >= upper or thresh <= 1:
          return current_vocab
  
      current_vocab = None
  
      if current_vocab_size > params.vocab_size:
          return learn_binary_search(word_counts, thresh + 1, upper, params)
  
      else:
          return learn_binary_search(word_counts, lower, thresh - 1, params)
  
  
  def count_words(iterable) -> collections.Counter:
      """Converts a iterable of arrays of words into a `Counter` of word counts."""
      counts = collections.Counter()
      for words in iterable:
          # Convert a RaggedTensor to a flat/dense Tensor.
          words = getattr(words, 'flat_values', words)
          # Flatten any dense tensor
          words = np.reshape(words, [-1])
          counts.update(words)
  
      # Decode the words if necessary.
      example_word = next(iter(counts.keys()))
      if isinstance(example_word, bytes):
          counts = collections.Counter(
              {word.decode('utf-8'): count for word, count in counts.items()})
  
      return counts
  
  
  def learn(word_counts,
            vocab_size: int,
            reserved_tokens: List[str],
            upper_thresh: Optional[int] = int(1e7),
            lower_thresh: Optional[int] = 10,
            num_iterations: int = 4,
            max_input_tokens: Optional[int] = int(5e6),
            max_token_length: int = 50,
            max_unique_chars: int = 1000,
            slack_ratio: float = 0.05,
            include_joiner_token: bool = True,
            joiner: str = '##') -> List[str]:
      """Takes in wordcounts and returns wordpiece vocabulary.
  
      Args:
        word_counts: (word, count) pairs as a dictionary, or list of tuples.
        vocab_size: The target vocabulary size. This is the maximum size.
        reserved_tokens: A list of tokens that must be included in the vocabulary.
        upper_thresh: Initial upper bound on the token frequency threshold.
        lower_thresh: Initial lower bound on the token frequency threchold.
        num_iterations: Number of iterations to run.
        max_input_tokens: The maximum number of words in the initial vocabulary. The
          words with the lowest counts are discarded. Use `None` or `-1` for "no
          maximum".
        max_token_length: The maximum token length. Counts for longer words are
          discarded.
        max_unique_chars: The maximum alphabet size. This prevents rare characters
          from inflating the vocabulary. Counts for words containing characters
          ouside of the selected alphabet are discarded.
        slack_ratio: The maximum deviation acceptable from `vocab_size` for an
          acceptable vocabulary. The acceptable range of vocabulary sizes is from
          `vocab_size*(1-slack_ratio)` to `vocab_size`.
        include_joiner_token: If true, include the `joiner` token in the output
          vocabulary.
        joiner: The prefix to include on suffix tokens in the output vocabulary.
          Usually "##". For example 'places' could be tokenized as `['place',
          '##s']`.
  
      Returns:
        string, final vocabulary with each word separated by newline
      """
      if isinstance(word_counts, dict):
          word_counts = word_counts.items()
  
      params = Params(upper_thresh, lower_thresh, num_iterations, max_input_tokens,
                      max_token_length, max_unique_chars, vocab_size, slack_ratio,
                      include_joiner_token, joiner, reserved_tokens)
  
      upper_search, lower_search = get_search_threshs(word_counts,
                                                      params.upper_thresh,
                                                      params.lower_thresh)
      all_counts = get_input_words(word_counts, params.reserved_tokens,
                                   params.max_token_length)
      allowed_chars = get_allowed_chars(all_counts, params.max_unique_chars)
  
      filtered_counts = filter_input_words(all_counts, allowed_chars,
                                           params.max_input_tokens)
  
      vocab = learn_binary_search(filtered_counts, lower_search, upper_search,
                                  params)
  
      return vocab
  
apis: numpy.reshape