text: """Encoders for text data.
  
  * TextEncoder: base class
  * ByteTextEncoder: for ascii text
  * TokenTextEncoder: with user-supplied vocabulary file
  * SubwordTextEncoder: invertible
  """
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import collections
  from itertools import chain
  import re
  import time
  import numpy as np
  import six
  from six.moves import range  # pylint: disable=redefined-builtin
  
  import tensorflow as tf
  from data_generators import tokenizer
  
  # Reserved tokens for things like padding and EOS symbols.
  PAD = "<pad>"
  EOS = "<EOS>"
  RESERVED_TOKENS = [PAD, EOS]
  NUM_RESERVED_TOKENS = len(RESERVED_TOKENS)
  PAD_ID = RESERVED_TOKENS.index(PAD)  # Normally 0
  EOS_ID = RESERVED_TOKENS.index(EOS)  # Normally 1
  
  if six.PY2:
    RESERVED_TOKENS_BYTES = RESERVED_TOKENS
  else:
    RESERVED_TOKENS_BYTES = [bytes(PAD, "ascii"), bytes(EOS, "ascii")]
  
  # Regular expression for unescaping token strings.
  # '\u' is converted to '_'
  # '\\' is converted to '\'
  # '\213;' is converted to unichr(213)
  _UNESCAPE_REGEX = re.compile(r"\\u|\\\\|\\([0-9]+);")
  _ESCAPE_CHARS = set(u"\\_u;0123456789")
  
  
  # Unicode utility functions that work with Python 2 and 3
  def native_to_unicode(s):
    if is_unicode(s):
      return s
    try:
      return to_unicode(s)
    except UnicodeDecodeError:
      res = to_unicode(s, ignore_errors=True)
      tf.logging.info("Ignoring Unicode error, outputting: %s" % res)
      return res
  
  
  def unicode_to_native(s):
    if six.PY2:
      return s.encode("utf-8") if is_unicode(s) else s
    else:
      return s
  
  
  def is_unicode(s):
    if six.PY2:
      if isinstance(s, unicode):
        return True
    else:
      if isinstance(s, str):
        return True
    return False
  
  
  def to_unicode(s, ignore_errors=False):
    if is_unicode(s):
      return s
    error_mode = "ignore" if ignore_errors else "strict"
    return s.decode("utf-8", errors=error_mode)
  
  
  def to_unicode_ignore_errors(s):
    return to_unicode(s, ignore_errors=True)
  
  
  def strip_ids(ids, ids_to_strip):
    """Strip ids_to_strip from the end ids."""
    ids = list(ids)
    while ids and ids[-1] in ids_to_strip:
      ids.pop()
    return ids
  
  
  class TextEncoder(object):
    """Base class for converting from ints to/from human readable strings."""
  
    def __init__(self, num_reserved_ids=NUM_RESERVED_TOKENS):
      self._num_reserved_ids = num_reserved_ids
  
    @property
    def num_reserved_ids(self):
      return self._num_reserved_ids
  
    def encode(self, s):
      """Transform a human-readable string into a sequence of int ids.
  
      The ids should be in the range [num_reserved_ids, vocab_size). Ids [0,
      num_reserved_ids) are reserved.
  
      EOS is not appended.
  
      Args:
        s: human-readable string to be converted.
  
      Returns:
        ids: list of integers
      """
      return [int(w) + self._num_reserved_ids for w in s.split()]
  
    def decode(self, ids, strip_extraneous=False):
      """Transform a sequence of int ids into a human-readable string.
  
      EOS is not expected in ids.
  
      Args:
        ids: list of integers to be converted.
        strip_extraneous: bool, whether to strip off extraneous tokens
          (EOS and PAD).
  
      Returns:
        s: human-readable string.
      """
      if strip_extraneous:
        ids = strip_ids(ids, list(range(self._num_reserved_ids or 0)))
      return " ".join(self.decode_list(ids))
  
    def decode_list(self, ids):
      """Transform a sequence of int ids into a their string versions.
  
      This method supports transforming individual input/output ids to their
      string versions so that sequence to/from text conversions can be visualized
      in a human readable format.
  
      Args:
        ids: list of integers to be converted.
  
      Returns:
        strs: list of human-readable string.
      """
      decoded_ids = []
      for id_ in ids:
        if 0 <= id_ < self._num_reserved_ids:
          decoded_ids.append(RESERVED_TOKENS[int(id_)])
        else:
          decoded_ids.append(id_ - self._num_reserved_ids)
      return [str(d) for d in decoded_ids]
  
    @property
    def vocab_size(self):
      raise NotImplementedError()
  
  
  class ByteTextEncoder(TextEncoder):
    """Encodes each byte to an id. For 8-bit strings only."""
  
    def encode(self, s):
      numres = self._num_reserved_ids
      if six.PY2:
        if isinstance(s, unicode):
          s = s.encode("utf-8")
        return [ord(c) + numres for c in s]
      # Python3: explicitly convert to UTF-8
      return [c + numres for c in s.encode("utf-8")]
  
    def decode(self, ids, strip_extraneous=False):
      if strip_extraneous:
        ids = strip_ids(ids, list(range(self._num_reserved_ids or 0)))
      numres = self._num_reserved_ids
      decoded_ids = []
      int2byte = six.int2byte
      for id_ in ids:
        if 0 <= id_ < numres:
          decoded_ids.append(RESERVED_TOKENS_BYTES[int(id_)])
        else:
          decoded_ids.append(int2byte(id_ - numres))
      if six.PY2:
        return "".join(decoded_ids)
      # Python3: join byte arrays and then decode string
      return b"".join(decoded_ids).decode("utf-8", "replace")
  
    def decode_list(self, ids):
      numres = self._num_reserved_ids
      decoded_ids = []
      int2byte = six.int2byte
      for id_ in ids:
        if 0 <= id_ < numres:
          decoded_ids.append(RESERVED_TOKENS_BYTES[int(id_)])
        else:
          decoded_ids.append(int2byte(id_ - numres))
      # Python3: join byte arrays and then decode string
      return decoded_ids
  
    @property
    def vocab_size(self):
      return 2**8 + self._num_reserved_ids
  
  
  class ClassLabelEncoder(TextEncoder):
    """Encoder for class labels."""
  
    def __init__(self, class_labels=None, class_labels_fname=None):
      super(ClassLabelEncoder, self).__init__(num_reserved_ids=0)
  
      if class_labels_fname:
        with tf.gfile.Open(class_labels_fname) as f:
          class_labels = [label.strip() for label in f.readlines()]
  
      assert class_labels
      self._class_labels = class_labels
  
    def encode(self, s):
      label_str = s
      return self._class_labels.index(label_str)
  
    def decode(self, ids, strip_extraneous=False):
      del strip_extraneous
      label_id = ids
      if isinstance(label_id, list):
        assert len(label_id) == 1
        label_id, = label_id
      if isinstance(label_id, np.ndarray):
        label_id = np.squeeze(label_id)
      return self._class_labels[label_id]
  
    def decode_list(self, ids):
      return [self._class_labels[i] for i in ids]
  
    @property
    def vocab_size(self):
      return len(self._class_labels)
  
  
  class OneHotClassLabelEncoder(ClassLabelEncoder):
    """One-hot encoder for class labels."""
  
    def encode(self, label_str, on_value=1, off_value=0):  # pylint: disable=arguments-differ
      e = np.full(self.vocab_size, off_value, dtype=np.int32)
      e[self._class_labels.index(label_str)] = on_value
      return e.tolist()
  
    def decode(self, ids, strip_extraneous=False):
      del strip_extraneous
      label_id = ids
      if isinstance(label_id, np.ndarray):
        label_id = np.squeeze(label_id).astype(np.int8).tolist()
      assert isinstance(label_id, list)
      assert len(label_id) == self.vocab_size
      return self._class_labels[label_id.index(1)]
  
    @property
    def vocab_size(self):
      return len(self._class_labels)
  
  
  class TokenTextEncoder(TextEncoder):
    """Encoder based on a user-supplied vocabulary (file or list)."""
  
    def __init__(self,
                 vocab_filename,
                 reverse=False,
                 vocab_list=None,
                 replace_oov=None,
                 num_reserved_ids=NUM_RESERVED_TOKENS):
      """Initialize from a file or list, one token per line.
  
      Handling of reserved tokens works as follows:
      - When initializing from a list, we add reserved tokens to the vocab.
      - When initializing from a file, we do not add reserved tokens to the vocab.
      - When saving vocab files, we save reserved tokens to the file.
  
      Args:
        vocab_filename: If not None, the full filename to read vocab from. If this
           is not None, then vocab_list should be None.
        reverse: Boolean indicating if tokens should be reversed during encoding
           and decoding.
        vocab_list: If not None, a list of elements of the vocabulary. If this is
           not None, then vocab_filename should be None.
        replace_oov: If not None, every out-of-vocabulary token seen when
           encoding will be replaced by this string (which must be in vocab).
        num_reserved_ids: Number of IDs to save for reserved tokens like <EOS>.
      """
      super(TokenTextEncoder, self).__init__(num_reserved_ids=num_reserved_ids)
      self._reverse = reverse
      self._replace_oov = replace_oov
      if vocab_filename:
        self._init_vocab_from_file(vocab_filename)
      else:
        assert vocab_list is not None
        self._init_vocab_from_list(vocab_list)
  
    def encode(self, s):
      """Converts a space-separated string of tokens to a list of ids."""
      sentence = s
      tokens = sentence.strip().split()
      if self._replace_oov is not None:
        tokens = [t if t in self._token_to_id else self._replace_oov
                  for t in tokens]
      ret = [self._token_to_id[tok] for tok in tokens]
      return ret[::-1] if self._reverse else ret
  
    def decode(self, ids, strip_extraneous=False):
      return " ".join(self.decode_list(ids))
  
    def decode_list(self, ids):
      seq = reversed(ids) if self._reverse else ids
      return [self._safe_id_to_token(i) for i in seq]
  
    @property
    def vocab_size(self):
      return len(self._id_to_token)
  
    def _safe_id_to_token(self, idx):
      return self._id_to_token.get(idx, "ID_%d" % idx)
  
    def _init_vocab_from_file(self, filename):
      """Load vocab from a file.
  
      Args:
        filename: The file to load vocabulary from.
      """
      with tf.gfile.Open(filename) as f:
        tokens = [token.strip() for token in f.readlines()]
  
      def token_gen():
        for token in tokens:
          yield token
  
      self._init_vocab(token_gen(), add_reserved_tokens=False)
  
    def _init_vocab_from_list(self, vocab_list):
      """Initialize tokens from a list of tokens.
  
      It is ok if reserved tokens appear in the vocab list. They will be
      removed. The set of tokens in vocab_list should be unique.
  
      Args:
        vocab_list: A list of tokens.
      """
      def token_gen():
        for token in vocab_list:
          if token not in RESERVED_TOKENS:
            yield token
  
      self._init_vocab(token_gen())
  
    def _init_vocab(self, token_generator, add_reserved_tokens=True):
      """Initialize vocabulary with tokens from token_generator."""
  
      self._id_to_token = {}
      non_reserved_start_index = 0
  
      if add_reserved_tokens:
        self._id_to_token.update(enumerate(RESERVED_TOKENS))
        non_reserved_start_index = len(RESERVED_TOKENS)
  
      self._id_to_token.update(
          enumerate(token_generator, start=non_reserved_start_index))
  
      # _token_to_id is the reverse of _id_to_token
      self._token_to_id = dict((v, k)
                               for k, v in six.iteritems(self._id_to_token))
  
    def store_to_file(self, filename):
      """Write vocab file to disk.
  
      Vocab files have one token per line. The file ends in a newline. Reserved
      tokens are written to the vocab file as well.
  
      Args:
        filename: Full path of the file to store the vocab to.
      """
      with tf.gfile.Open(filename, "w") as f:
        for i in range(len(self._id_to_token)):
          f.write(self._id_to_token[i] + "\n")
  
  
  def _escape_token(token, alphabet):
    """Escape away underscores and OOV characters and append '_'.
  
    This allows the token to be expressed as the concatenation of a list
    of subtokens from the vocabulary. The underscore acts as a sentinel
    which allows us to invertibly concatenate multiple such lists.
  
    Args:
      token: A unicode string to be escaped.
      alphabet: A set of all characters in the vocabulary's alphabet.
  
    Returns:
      escaped_token: An escaped unicode string.
  
    Raises:
      ValueError: If the provided token is not unicode.
    """
    if not isinstance(token, six.text_type):
      raise ValueError("Expected string type for token, got %s" % type(token))
  
    token = token.replace(u"\\", u"\\\\").replace(u"_", u"\\u")
    ret = [c if c in alphabet and c != u"\n" else r"\%d;" % ord(c) for c in token]
    return u"".join(ret) + "_"
  
  
  def _unescape_token(escaped_token):
    """Inverse of _escape_token().
  
    Args:
      escaped_token: a unicode string
  
    Returns:
      token: a unicode string
    """
  
    def match(m):
      if m.group(1) is None:
        return u"_" if m.group(0) == u"\\u" else u"\\"
  
      try:
        return six.unichr(int(m.group(1)))
      except (ValueError, OverflowError) as _:
        return u"\u3013"  # Unicode for undefined character.
  
    trimmed = escaped_token[:-1] if escaped_token.endswith("_") else escaped_token
    return _UNESCAPE_REGEX.sub(match, trimmed)
  
  
  class SubwordTextEncoder(TextEncoder):
    """Class for invertibly encoding text using a limited vocabulary.
  
    Invertibly encodes a native string as a sequence of subtokens from a limited
    vocabulary.
  
    A SubwordTextEncoder is built from a corpus (so it is tailored to the text in
    the corpus), and stored to a file. See text_encoder_build_subword.py.
  
    It can then be loaded and used to encode/decode any text.
  
    Encoding has four phases:
  
    1. Tokenize into a list of tokens.  Each token is a unicode string of either
       all alphanumeric characters or all non-alphanumeric characters.  We drop
       tokens consisting of a single space that are between two alphanumeric
       tokens.
  
    2. Escape each token.  This escapes away special and out-of-vocabulary
       characters, and makes sure that each token ends with an underscore, and
       has no other underscores.
  
    3. Represent each escaped token as a the concatenation of a list of subtokens
       from the limited vocabulary.  Subtoken selection is done greedily from
       beginning to end.  That is, we construct the list in order, always picking
       the longest subtoken in our vocabulary that matches a prefix of the
       remaining portion of the encoded token.
  
    4. Concatenate these lists.  This concatenation is invertible due to the
       fact that the trailing underscores indicate when one list is finished.
  
    """
  
    def __init__(self, filename=None):
      """Initialize and read from a file, if provided.
  
      Args:
        filename: filename from which to read vocab. If None, do not load a
          vocab
      """
      self._alphabet = set()
      self.filename = filename
      if filename is not None:
        self._load_from_file(filename)
      super(SubwordTextEncoder, self).__init__()
  
    def encode(self, s):
      """Converts a native string to a list of subtoken ids.
  
      Args:
        s: a native string.
      Returns:
        a list of integers in the range [0, vocab_size)
      """
      return self._tokens_to_subtoken_ids(
          tokenizer.encode(native_to_unicode(s)))
  
    def encode_without_tokenizing(self, token_text):
      """Converts string to list of subtoken ids without calling tokenizer.
  
      This treats `token_text` as a single token and directly converts it
      to subtoken ids. This may be useful when the default tokenizer doesn't
      do what we want (e.g., when encoding text with tokens composed of lots of
      nonalphanumeric characters). It is then up to the caller to make sure that
      raw text is consistently converted into tokens. Only use this if you are
      sure that `encode` doesn't suit your needs.
  
      Args:
        token_text: A native string representation of a single token.
      Returns:
        A list of subword token ids; i.e., integers in the range [0, vocab_size).
      """
      return self._tokens_to_subtoken_ids([native_to_unicode(token_text)])
  
    def decode(self, ids, strip_extraneous=False):
      """Converts a sequence of subtoken ids to a native string.
  
      Args:
        ids: a list of integers in the range [0, vocab_size)
        strip_extraneous: bool, whether to strip off extraneous tokens
          (EOS and PAD).
  
      Returns:
        a native string
      """
      if strip_extraneous:
        ids = strip_ids(ids, list(range(self._num_reserved_ids or 0)))
      return unicode_to_native(
          tokenizer.decode(self._subtoken_ids_to_tokens(ids)))
  
    def decode_list(self, ids):
      return [self._subtoken_id_to_subtoken_string(s) for s in ids]
  
    @property
    def vocab_size(self):
      """The subtoken vocabulary size."""
      return len(self._all_subtoken_strings)
  
    def _tokens_to_subtoken_ids(self, tokens):
      """Converts a list of tokens to a list of subtoken ids.
  
      Args:
        tokens: a list of strings.
      Returns:
        a list of integers in the range [0, vocab_size)
      """
      ret = []
      for token in tokens:
        ret.extend(self._token_to_subtoken_ids(token))
      return ret
  
    def _token_to_subtoken_ids(self, token):
      """Converts token to a list of subtoken ids.
  
      Args:
        token: a string.
      Returns:
        a list of integers in the range [0, vocab_size)
      """
      cache_location = hash(token) % self._cache_size
      cache_key, cache_value = self._cache[cache_location]
      if cache_key == token:
        return cache_value
      ret = self._escaped_token_to_subtoken_ids(
          _escape_token(token, self._alphabet))
      self._cache[cache_location] = (token, ret)
      return ret
  
    def _subtoken_ids_to_tokens(self, subtokens):
      """Converts a list of subtoken ids to a list of tokens.
  
      Args:
        subtokens: a list of integers in the range [0, vocab_size)
      Returns:
        a list of strings.
      """
      concatenated = "".join(
          [self._subtoken_id_to_subtoken_string(s) for s in subtokens])
      split = concatenated.split("_")
      ret = []
      for t in split:
        if t:
          unescaped = _unescape_token(t + "_")
          if unescaped:
            ret.append(unescaped)
      return ret
  
    def _subtoken_id_to_subtoken_string(self, subtoken):
      """Converts a subtoken integer ID to a subtoken string."""
      if 0 <= subtoken < self.vocab_size:
        return self._all_subtoken_strings[subtoken]
      return u""
  
    def _escaped_token_to_subtoken_strings(self, escaped_token):
      """Converts an escaped token string to a list of subtoken strings.
  
      Args:
        escaped_token: An escaped token as a unicode string.
      Returns:
        A list of subtokens as unicode strings.
      """
      # NOTE: This algorithm is greedy; it won't necessarily produce the "best"
      # list of subtokens.
      ret = []
      start = 0
      token_len = len(escaped_token)
      while start < token_len:
        for end in range(
            min(token_len, start + self._max_subtoken_len), start, -1):
          subtoken = escaped_token[start:end]
          if subtoken in self._subtoken_string_to_id:
            ret.append(subtoken)
            start = end
            break
  
        else:  # Did not break
          # If there is no possible encoding of the escaped token then one of the
          # characters in the token is not in the alphabet. This should be
          # impossible and would be indicative of a bug.
          assert False, "Token substring not found in subtoken vocabulary."
  
      return ret
  
    def _escaped_token_to_subtoken_ids(self, escaped_token):
      """Converts an escaped token string to a list of subtoken IDs.
  
      Args:
        escaped_token: An escaped token as a unicode string.
      Returns:
        A list of subtoken IDs as integers.
      """
      return [
          self._subtoken_string_to_id[subtoken]
          for subtoken in self._escaped_token_to_subtoken_strings(escaped_token)
      ]
  
    @classmethod
    def build_from_generator(cls,
                             generator,
                             target_size,
                             max_subtoken_length=None,
                             reserved_tokens=None):
      """Builds a SubwordTextEncoder from the generated text.
  
      Args:
        generator: yields text.
        target_size: int, approximate vocabulary size to create.
        max_subtoken_length: Maximum length of a subtoken. If this is not set,
          then the runtime and memory use of creating the vocab is quadratic in
          the length of the longest token. If this is set, then it is instead
          O(max_subtoken_length * length of longest token).
        reserved_tokens: List of reserved tokens. The global variable
          `RESERVED_TOKENS` must be a prefix of `reserved_tokens`. If this
          argument is `None`, it will use `RESERVED_TOKENS`.
  
      Returns:
        SubwordTextEncoder with `vocab_size` approximately `target_size`.
      """
      token_counts = collections.defaultdict(int)
      for item in generator:
        for tok in tokenizer.encode(native_to_unicode(item)):
          token_counts[tok] += 1
      encoder = cls.build_to_target_size(
          target_size, token_counts, 1, 1e3,
          max_subtoken_length=max_subtoken_length,
          reserved_tokens=reserved_tokens)
      return encoder
  
    @classmethod
    def build_to_target_size(cls,
                             target_size,
                             token_counts,
                             min_val,
                             max_val,
                             max_subtoken_length=None,
                             reserved_tokens=None,
                             num_iterations=4):
      """Builds a SubwordTextEncoder that has `vocab_size` near `target_size`.
  
      Uses simple recursive binary search to find a minimum token count that most
      closely matches the `target_size`.
  
      Args:
        target_size: Desired vocab_size to approximate.
        token_counts: A dictionary of token counts, mapping string to int.
        min_val: An integer; lower bound for the minimum token count.
        max_val: An integer; upper bound for the minimum token count.
        max_subtoken_length: Maximum length of a subtoken. If this is not set,
          then the runtime and memory use of creating the vocab is quadratic in
          the length of the longest token. If this is set, then it is instead
          O(max_subtoken_length * length of longest token).
        reserved_tokens: List of reserved tokens. The global variable
          `RESERVED_TOKENS` must be a prefix of `reserved_tokens`. If this
          argument is `None`, it will use `RESERVED_TOKENS`.
        num_iterations: An integer; how many iterations of refinement.
  
      Returns:
        A SubwordTextEncoder instance.
  
      Raises:
        ValueError: If `min_val` is greater than `max_val`.
      """
      if min_val > max_val:
        raise ValueError("Lower bound for the minimum token count "
                         "is greater than the upper bound.")
      if target_size < 1:
        raise ValueError("Target size must be positive.")
  
      if reserved_tokens is None:
        reserved_tokens = RESERVED_TOKENS
  
      def bisect(min_val, max_val):
        """Bisection to find the right size."""
        present_count = (max_val + min_val) // 2
        tf.logging.info("Trying min_count %d" % present_count)
        subtokenizer = cls()
        subtokenizer.build_from_token_counts(
            token_counts, present_count, num_iterations,
            max_subtoken_length=max_subtoken_length,
            reserved_tokens=reserved_tokens)
  
        # Being within 1% of the target size is ok.
        is_ok = abs(subtokenizer.vocab_size - target_size) * 100 < target_size
        # If min_val == max_val, we can't do any better than this.
        if is_ok or min_val >= max_val or present_count < 2:
          return subtokenizer
  
        if subtokenizer.vocab_size > target_size:
          other_subtokenizer = bisect(present_count + 1, max_val)
        else:
          other_subtokenizer = bisect(min_val, present_count - 1)
  
        if other_subtokenizer is None:
          return subtokenizer
  
        if (abs(other_subtokenizer.vocab_size - target_size) <
            abs(subtokenizer.vocab_size - target_size)):
          return other_subtokenizer
        return subtokenizer
  
      return bisect(min_val, max_val)
  
    def build_from_token_counts(self,
                                token_counts,
                                min_count,
                                num_iterations=4,
                                reserved_tokens=None,
                                max_subtoken_length=None):
      """Train a SubwordTextEncoder based on a dictionary of word counts.
  
      Args:
        token_counts: a dictionary of Unicode strings to int.
        min_count: an integer - discard subtokens with lower counts.
        num_iterations: an integer.  how many iterations of refinement.
        reserved_tokens: List of reserved tokens. The global variable
          `RESERVED_TOKENS` must be a prefix of `reserved_tokens`. If this
          argument is `None`, it will use `RESERVED_TOKENS`.
        max_subtoken_length: Maximum length of a subtoken. If this is not set,
          then the runtime and memory use of creating the vocab is quadratic in
          the length of the longest token. If this is set, then it is instead
          O(max_subtoken_length * length of longest token).
  
      Raises:
        ValueError: if reserved is not 0 or len(RESERVED_TOKENS). In this case, it
          is not clear what the space is being reserved for, or when it will be
          filled in.
      """
      if reserved_tokens is None:
        reserved_tokens = RESERVED_TOKENS
      else:
        # There is not complete freedom in replacing RESERVED_TOKENS.
        for default, proposed in zip(RESERVED_TOKENS, reserved_tokens):
          if default != proposed:
            raise ValueError("RESERVED_TOKENS must be a prefix of "
                             "reserved_tokens.")
  
      # Initialize the alphabet. Note, this must include reserved tokens or it can
      # result in encoding failures.
      alphabet_tokens = chain(six.iterkeys(token_counts),
                              [native_to_unicode(t) for t in reserved_tokens])
  
      self._init_alphabet_from_tokens(alphabet_tokens)
  
      # Bootstrap the initial list of subtokens with the characters from the
      # alphabet plus the escaping characters.
      self._init_subtokens_from_list(list(self._alphabet),
                                     reserved_tokens=reserved_tokens)
  
      # We build iteratively.  On each iteration, we segment all the words,
      # then count the resulting potential subtokens, keeping the ones
      # with high enough counts for our new vocabulary.
      if min_count < 1:
        min_count = 1
      for i in range(num_iterations):
        tf.logging.info("Iteration {0}".format(i))
  
        # Collect all substrings of the encoded token that break along current
        # subtoken boundaries.
        subtoken_counts = collections.defaultdict(int)
        for token, count in six.iteritems(token_counts):
          iter_start_time = time.time()
          escaped_token = _escape_token(token, self._alphabet)
          subtokens = self._escaped_token_to_subtoken_strings(escaped_token)
          start = 0
          for subtoken in subtokens:
            last_position = len(escaped_token) + 1
            if max_subtoken_length is not None:
              last_position = min(last_position, start + max_subtoken_length)
  
            for end in range(start + 1, last_position):
              new_subtoken = escaped_token[start:end]
              subtoken_counts[new_subtoken] += count
            start += len(subtoken)
          iter_time_secs = time.time() - iter_start_time
          if iter_time_secs > 0.1:
            tf.logging.info(u"Processing token [{0}] took {1} seconds, consider "
                            "setting Text2TextProblem.max_subtoken_length to a "
                            "smaller value.".format(token, iter_time_secs))
  
        # Array of sets of candidate subtoken strings, by length.
        len_to_subtoken_strings = []
        for subtoken_string, count in six.iteritems(subtoken_counts):
          lsub = len(subtoken_string)
          if count >= min_count:
            while len(len_to_subtoken_strings) <= lsub:
              len_to_subtoken_strings.append(set())
            len_to_subtoken_strings[lsub].add(subtoken_string)
  
        # Consider the candidates longest to shortest, so that if we accept
        # a longer subtoken string, we can decrement the counts of its prefixes.
        new_subtoken_strings = []
        for lsub in range(len(len_to_subtoken_strings) - 1, 0, -1):
          subtoken_strings = len_to_subtoken_strings[lsub]
          for subtoken_string in subtoken_strings:
            count = subtoken_counts[subtoken_string]
            if count >= min_count:
              # Exclude alphabet tokens here, as they must be included later,
              # explicitly, regardless of count.
              if subtoken_string not in self._alphabet:
                new_subtoken_strings.append((count, subtoken_string))
              for l in range(1, lsub):
                subtoken_counts[subtoken_string[:l]] -= count
  
        # Include the alphabet explicitly to guarantee all strings are encodable.
        new_subtoken_strings.extend((subtoken_counts.get(a, 0), a)
                                    for a in self._alphabet)
        new_subtoken_strings.sort(reverse=True)
  
        # Reinitialize to the candidate vocabulary.
        new_subtoken_strings = [subtoken for _, subtoken in new_subtoken_strings]
        if reserved_tokens:
          escaped_reserved_tokens = [
              _escape_token(native_to_unicode(t), self._alphabet)
              for t in reserved_tokens
          ]
          new_subtoken_strings = escaped_reserved_tokens + new_subtoken_strings
  
        self._init_subtokens_from_list(new_subtoken_strings)
        tf.logging.info("vocab_size = %d" % self.vocab_size)
  
    @property
    def all_subtoken_strings(self):
      return tuple(self._all_subtoken_strings)
  
    def dump(self):
      """Debugging dump of the current subtoken vocabulary."""
      subtoken_strings = [(i, s)
                          for s, i in six.iteritems(self._subtoken_string_to_id)]
      print(u", ".join(u"{0} : '{1}'".format(i, s)
                       for i, s in sorted(subtoken_strings)))
  
    def _init_subtokens_from_list(self, subtoken_strings, reserved_tokens=None):
      """Initialize token information from a list of subtoken strings.
  
      Args:
        subtoken_strings: a list of subtokens
        reserved_tokens: List of reserved tokens. We must have `reserved_tokens`
          as None or the empty list, or else the global variable `RESERVED_TOKENS`
          must be a prefix of `reserved_tokens`.
  
      Raises:
        ValueError: if reserved is not 0 or len(RESERVED_TOKENS). In this case, it
          is not clear what the space is being reserved for, or when it will be
          filled in.
      """
      if reserved_tokens is None:
        reserved_tokens = []
  
      if reserved_tokens:
        self._all_subtoken_strings = reserved_tokens + subtoken_strings
      else:
        self._all_subtoken_strings = subtoken_strings
  
      # we remember the maximum length of any subtoken to avoid having to
      # check arbitrarily long strings.
      self._max_subtoken_len = max([len(s) for s in subtoken_strings])
      self._subtoken_string_to_id = {
          s: i + len(reserved_tokens)
          for i, s in enumerate(subtoken_strings) if s
      }
      # Initialize the cache to empty.
      self._cache_size = 2 ** 20
      self._cache = [(None, None)] * self._cache_size
  
    def _init_alphabet_from_tokens(self, tokens):
      """Initialize alphabet from an iterable of token or subtoken strings."""
      # Include all characters from all tokens in the alphabet to guarantee that
      # any token can be encoded. Additionally, include all escaping characters.
      self._alphabet = {c for token in tokens for c in token}
      self._alphabet |= _ESCAPE_CHARS
  
    def _load_from_file_object(self, f):
      """Load from a file object.
  
      Args:
        f: File object to load vocabulary from
      """
      subtoken_strings = []
      for line in f:
        s = line.strip()
        # Some vocab files wrap words in single quotes, but others don't
        if ((s.startswith("'") and s.endswith("'")) or
            (s.startswith("\"") and s.endswith("\""))):
          s = s[1:-1]
        subtoken_strings.append(native_to_unicode(s))
      self._init_subtokens_from_list(subtoken_strings)
      self._init_alphabet_from_tokens(subtoken_strings)
  
    def _load_from_file(self, filename):
      """Load from a vocab file."""
      if not tf.gfile.Exists(filename):
        raise ValueError("File %s not found" % filename)
      with tf.gfile.Open(filename) as f:
        self._load_from_file_object(f)
  
    def store_to_file(self, filename, add_single_quotes=True):
      with tf.gfile.Open(filename, "w") as f:
        for subtoken_string in self._all_subtoken_strings:
          if add_single_quotes:
            f.write("'" + unicode_to_native(subtoken_string) + "'\n")
          else:
            f.write(unicode_to_native(subtoken_string) + "\n")
  