text: """
  A Vocabulary maps strings to integers, allowing for strings to be mapped to an
  out-of-vocabulary token.
  """
  
  from collections import defaultdict
  from typing import Any, Callable, Dict, Union, Sequence, Set, Optional, Iterable
  import codecs
  import logging
  import os
  import gzip
  
  from allennlp.common.util import namespace_match
  from allennlp.common.params import Params
  from allennlp.common.checks import ConfigurationError
  from allennlp.common.file_utils import cached_path
  from allennlp.common.tqdm import Tqdm
  from allennlp.data import instance as adi  # pylint: disable=unused-import
  
  logger = logging.getLogger(__name__)  # pylint: disable=invalid-name
  
  DEFAULT_NON_PADDED_NAMESPACES = ("*tags", "*labels")
  DEFAULT_PADDING_TOKEN = "@@PADDING@@"
  DEFAULT_OOV_TOKEN = "@@UNKNOWN@@"
  NAMESPACE_PADDING_FILE = 'non_padded_namespaces.txt'
  
  
  class _NamespaceDependentDefaultDict(defaultdict):
      """
      This is a `defaultdict
      <https://docs.python.org/2/library/collections.html#collections.defaultdict>`_ where the
      default value is dependent on the key that is passed.
  
      We use "namespaces" in the :class:`Vocabulary` object to keep track of several different
      mappings from strings to integers, so that we have a consistent API for mapping words, tags,
      labels, characters, or whatever else you want, into integers.  The issue is that some of those
      namespaces (words and characters) should have integers reserved for padding and
      out-of-vocabulary tokens, while others (labels and tags) shouldn't.  This class allows you to
      specify filters on the namespace (the key used in the ``defaultdict``), and use different
      default values depending on whether the namespace passes the filter.
  
      To do filtering, we take a sequence of ``non_padded_namespaces``.  This is a list or tuple of
      strings that are either matched exactly against the keys, or treated as suffixes, if the
      string starts with ``*``.  In other words, if ``*tags`` is in ``non_padded_namespaces`` then
      ``passage_tags``, ``question_tags``, etc. (anything that ends with ``tags``) will have the
      ``non_padded`` default value.
  
      Parameters
      ----------
      non_padded_namespaces : ``Sequence[str]``
          A list or tuple of strings describing which namespaces are not padded.  If a namespace
          (key) is missing from this dictionary, we will use :func:`namespace_match` to see whether
          the namespace should be padded.  If the given namespace matches any of the strings in this
          list, we will use ``non_padded_function`` to initialize the value for that namespace, and
          we will use ``padded_function`` otherwise.
      padded_function : ``Callable[[], Any]``
          A zero-argument function to call to initialize a value for a namespace that `should` be
          padded.
      non_padded_function : ``Callable[[], Any]``
          A zero-argument function to call to initialize a value for a namespace that should `not` be
          padded.
      """
      def __init__(self,
                   non_padded_namespaces: Sequence[str],
                   padded_function: Callable[[], Any],
                   non_padded_function: Callable[[], Any]) -> None:
          self._non_padded_namespaces = non_padded_namespaces
          self._padded_function = padded_function
          self._non_padded_function = non_padded_function
          super(_NamespaceDependentDefaultDict, self).__init__()
  
      def __missing__(self, key: str):
          if any(namespace_match(pattern, key) for pattern in self._non_padded_namespaces):
              value = self._non_padded_function()
          else:
              value = self._padded_function()
          dict.__setitem__(self, key, value)
          return value
  
  
  class _TokenToIndexDefaultDict(_NamespaceDependentDefaultDict):
      def __init__(self, non_padded_namespaces: Sequence[str], padding_token: str, oov_token: str) -> None:
          super(_TokenToIndexDefaultDict, self).__init__(non_padded_namespaces,
                                                         lambda: {padding_token: 0, oov_token: 1},
                                                         lambda: {})
  
  
  class _IndexToTokenDefaultDict(_NamespaceDependentDefaultDict):
      def __init__(self, non_padded_namespaces: Sequence[str], padding_token: str, oov_token: str) -> None:
          super(_IndexToTokenDefaultDict, self).__init__(non_padded_namespaces,
                                                         lambda: {0: padding_token, 1: oov_token},
                                                         lambda: {})
  
  def _read_pretrained_words(embeddings_filename: str)-> Set[str]:
      words = set()
      with gzip.open(cached_path(embeddings_filename), 'rb') as embeddings_file:
          for line in embeddings_file:
              fields = line.decode('utf-8').strip().split(' ')
              word = fields[0]
              words.add(word)
      return words
  
  class Vocabulary:
      """
      A Vocabulary maps strings to integers, allowing for strings to be mapped to an
      out-of-vocabulary token.
  
      Vocabularies are fit to a particular dataset, which we use to decide which tokens are
      in-vocabulary.
  
      Vocabularies also allow for several different namespaces, so you can have separate indices for
      'a' as a word, and 'a' as a character, for instance, and so we can use this object to also map
      tag and label strings to indices, for a unified :class:`~.fields.field.Field` API.  Most of the
      methods on this class allow you to pass in a namespace; by default we use the 'tokens'
      namespace, and you can omit the namespace argument everywhere and just use the default.
  
      Parameters
      ----------
      counter : ``Dict[str, Dict[str, int]]``, optional (default=``None``)
          A collection of counts from which to initialize this vocabulary.  We will examine the
          counts and, together with the other parameters to this class, use them to decide which
          words are in-vocabulary.  If this is ``None``, we just won't initialize the vocabulary with
          anything.
      min_count : ``Dict[str, int]``, optional (default=None)
          When initializing the vocab from a counter, you can specify a minimum count, and every
          token with a count less than this will not be added to the dictionary.  These minimum
          counts are `namespace-specific`, so you can specify different minimums for labels versus
          words tokens, for example.  If a namespace does not have a key in the given dictionary, we
          will add all seen tokens to that namespace.
      max_vocab_size : ``Union[int, Dict[str, int]]``, optional (default=``None``)
          If you want to cap the number of tokens in your vocabulary, you can do so with this
          parameter.  If you specify a single integer, every namespace will have its vocabulary fixed
          to be no larger than this.  If you specify a dictionary, then each namespace in the
          ``counter`` can have a separate maximum vocabulary size.  Any missing key will have a value
          of ``None``, which means no cap on the vocabulary size.
      non_padded_namespaces : ``Sequence[str]``, optional
          By default, we assume you are mapping word / character tokens to integers, and so you want
          to reserve word indices for padding and out-of-vocabulary tokens.  However, if you are
          mapping NER or SRL tags, or class labels, to integers, you probably do not want to reserve
          indices for padding and out-of-vocabulary tokens.  Use this field to specify which
          namespaces should `not` have padding and OOV tokens added.
  
          The format of each element of this is either a string, which must match field names
          exactly,  or ``*`` followed by a string, which we match as a suffix against field names.
  
          We try to make the default here reasonable, so that you don't have to think about this.
          The default is ``("*tags", "*labels")``, so as long as your namespace ends in "tags" or
          "labels" (which is true by default for all tag and label fields in this code), you don't
          have to specify anything here.
      pretrained_files : ``Dict[str, str]``, optional
          If provided, this map specifies the path to optional pretrained embedding files for each
          namespace. This can be used to either restrict the vocabulary to only words which appear
          in this file, or to ensure that any words in this file are included in the vocabulary
          regardless of their count, depending on the value of ``only_include_pretrained_words``.
          Words which appear in the pretrained embedding file but not in the data are NOT included
          in the Vocabulary.
      only_include_pretrained_words : bool, optional (default = False)
          This defines the stategy for using any pretrained embedding files which may have been
          specified in ``pretrained_files``. If False, an inclusive stategy is used: and words
          which are in the ``counter`` and in the pretrained file are added to the ``Vocabulary``,
          regardless of whether their count exceeds ``min_count`` or not. If True, we use an
          exclusive strategy: words are only included in the Vocabulary if they are in the pretrained
          embedding file (their count must still be at least ``min_count``).
      """
      def __init__(self,
                   counter: Dict[str, Dict[str, int]] = None,
                   min_count: Dict[str, int] = None,
                   max_vocab_size: Union[int, Dict[str, int]] = None,
                   non_padded_namespaces: Sequence[str] = DEFAULT_NON_PADDED_NAMESPACES,
                   pretrained_files: Optional[Dict[str, str]] = None,
                   only_include_pretrained_words: bool = False) -> None:
          self._padding_token = DEFAULT_PADDING_TOKEN
          self._oov_token = DEFAULT_OOV_TOKEN
          if not isinstance(max_vocab_size, dict):
              int_max_vocab_size = max_vocab_size
              max_vocab_size = defaultdict(lambda: int_max_vocab_size)  # type: ignore
          self._non_padded_namespaces = non_padded_namespaces
          self._token_to_index = _TokenToIndexDefaultDict(non_padded_namespaces,
                                                          self._padding_token,
                                                          self._oov_token)
          self._index_to_token = _IndexToTokenDefaultDict(non_padded_namespaces,
                                                          self._padding_token,
                                                          self._oov_token)
          min_count = min_count or {}
          pretrained_files = pretrained_files or {}
          if counter is not None:
              for namespace in counter:
                  if namespace in pretrained_files:
                      pretrained_list = _read_pretrained_words(pretrained_files[namespace])
                  else:
                      pretrained_list = None
                  token_counts = list(counter[namespace].items())
                  token_counts.sort(key=lambda x: x[1], reverse=True)
                  max_vocab = max_vocab_size[namespace]
                  if max_vocab:
                      token_counts = token_counts[:max_vocab]
                  for token, count in token_counts:
                      if pretrained_list is not None:
                          if only_include_pretrained_words:
                              if token in pretrained_list and count >= min_count.get(namespace, 1):
                                  self.add_token_to_namespace(token, namespace)
                          elif token in pretrained_list or count >= min_count.get(namespace, 1):
                              self.add_token_to_namespace(token, namespace)
                      elif count >= min_count.get(namespace, 1):
                          self.add_token_to_namespace(token, namespace)
  
      def save_to_files(self, directory: str) -> None:
          """
          Persist this Vocabulary to files so it can be reloaded later.
          Each namespace corresponds to one file.
  
          Parameters
          ----------
          directory : ``str``
              The directory where we save the serialized vocabulary.
          """
          os.makedirs(directory, exist_ok=True)
          if os.listdir(directory):
              logging.warning("vocabulary serialization directory %s is not empty", directory)
  
          with codecs.open(os.path.join(directory, NAMESPACE_PADDING_FILE), 'w', 'utf-8') as namespace_file:
              for namespace_str in self._non_padded_namespaces:
                  print(namespace_str, file=namespace_file)
  
          for namespace, mapping in self._index_to_token.items():
              # Each namespace gets written to its own file, in index order.
              with codecs.open(os.path.join(directory, namespace + '.txt'), 'w', 'utf-8') as token_file:
                  num_tokens = len(mapping)
                  start_index = 1 if mapping[0] == self._padding_token else 0
                  for i in range(start_index, num_tokens):
                      print(mapping[i].replace('\n', '@@NEWLINE@@'), file=token_file)
  
      @classmethod
      def from_files(cls, directory: str) -> 'Vocabulary':
          """
          Loads a ``Vocabulary`` that was serialized using ``save_to_files``.
  
          Parameters
          ----------
          directory : ``str``
              The directory containing the serialized vocabulary.
          """
          logger.info("Loading token dictionary from %s.", directory)
          with codecs.open(os.path.join(directory, NAMESPACE_PADDING_FILE), 'r', 'utf-8') as namespace_file:
              non_padded_namespaces = [namespace_str.strip() for namespace_str in namespace_file]
  
          vocab = Vocabulary(non_padded_namespaces=non_padded_namespaces)
  
          # Check every file in the directory.
          for namespace_filename in os.listdir(directory):
              if namespace_filename == NAMESPACE_PADDING_FILE:
                  continue
              namespace = namespace_filename.replace('.txt', '')
              if any(namespace_match(pattern, namespace) for pattern in non_padded_namespaces):
                  is_padded = False
              else:
                  is_padded = True
              filename = os.path.join(directory, namespace_filename)
              vocab.set_from_file(filename, is_padded, namespace=namespace)
  
          return vocab
  
      def set_from_file(self,
                        filename: str,
                        is_padded: bool = True,
                        oov_token: str = DEFAULT_OOV_TOKEN,
                        namespace: str = "tokens"):
          """
          If you already have a vocabulary file for a trained model somewhere, and you really want to
          use that vocabulary file instead of just setting the vocabulary from a dataset, for
          whatever reason, you can do that with this method.  You must specify the namespace to use,
          and we assume that you want to use padding and OOV tokens for this.
  
          Parameters
          ----------
          filename : ``str``
              The file containing the vocabulary to load.  It should be formatted as one token per
              line, with nothing else in the line.  The index we assign to the token is the line
              number in the file (1-indexed if ``is_padded``, 0-indexed otherwise).  Note that this
              file should contain the OOV token string!
          is_padded : ``bool``, optional (default=True)
              Is this vocabulary padded?  For token / word / character vocabularies, this should be
              ``True``; while for tag or label vocabularies, this should typically be ``False``.  If
              ``True``, we add a padding token with index 0, and we enforce that the ``oov_token`` is
              present in the file.
          oov_token : ``str``, optional (default=DEFAULT_OOV_TOKEN)
              What token does this vocabulary use to represent out-of-vocabulary characters?  This
              must show up as a line in the vocabulary file.  When we find it, we replace
              ``oov_token`` with ``self._oov_token``, because we only use one OOV token across
              namespaces.
          namespace : ``str``, optional (default="tokens")
              What namespace should we overwrite with this vocab file?
          """
          if is_padded:
              self._token_to_index[namespace] = {self._padding_token: 0}
              self._index_to_token[namespace] = {0: self._padding_token}
          else:
              self._token_to_index[namespace] = {}
              self._index_to_token[namespace] = {}
          with codecs.open(filename, 'r', 'utf-8') as input_file:
              lines = input_file.read().split('\n')
              # Be flexible about having final newline or not
              if lines and lines[-1] == '':
                  lines = lines[:-1]
              for i, line in enumerate(lines):
                  index = i + 1 if is_padded else i
                  token = line.replace('@@NEWLINE@@', '\n')
                  if token == oov_token:
                      token = self._oov_token
                  self._token_to_index[namespace][token] = index
                  self._index_to_token[namespace][index] = token
          if is_padded:
              assert self._oov_token in self._token_to_index[namespace], "OOV token not found!"
  
      @classmethod
      def from_instances(cls,
                         instances: Iterable['adi.Instance'],
                         min_count: Dict[str, int] = None,
                         max_vocab_size: Union[int, Dict[str, int]] = None,
                         non_padded_namespaces: Sequence[str] = DEFAULT_NON_PADDED_NAMESPACES,
                         pretrained_files: Optional[Dict[str, str]] = None,
                         only_include_pretrained_words: bool = False) -> 'Vocabulary':
          """
          Constructs a vocabulary given a collection of `Instances` and some parameters.
          We count all of the vocabulary items in the instances, then pass those counts
          and the other parameters, to :func:`__init__`.  See that method for a description
          of what the other parameters do.
          """
          logger.info("Fitting token dictionary from dataset.")
          namespace_token_counts: Dict[str, Dict[str, int]] = defaultdict(lambda: defaultdict(int))
          for instance in Tqdm.tqdm(instances):
              instance.count_vocab_items(namespace_token_counts)
  
          return Vocabulary(counter=namespace_token_counts,
                            min_count=min_count,
                            max_vocab_size=max_vocab_size,
                            non_padded_namespaces=non_padded_namespaces,
                            pretrained_files=pretrained_files,
                            only_include_pretrained_words=only_include_pretrained_words)
  
      @classmethod
      def from_params(cls, params: Params, instances: Iterable['adi.Instance'] = None):
          """
          There are two possible ways to build a vocabulary; from a
          collection of instances, using :func:`Vocabulary.from_instances`, or
          from a pre-saved vocabulary, using :func:`Vocabulary.from_files`.
          This method wraps both of these options, allowing their specification
          from a ``Params`` object, generated from a JSON configuration file.
  
          Parameters
          ----------
          params: Params, required.
          dataset: Dataset, optional.
              If ``params`` doesn't contain a ``vocabulary_directory`` key,
              the ``Vocabulary`` can be built directly from a ``Dataset``.
  
          Returns
          -------
          A ``Vocabulary``.
          """
          vocabulary_directory = params.pop("directory_path", None)
          if not vocabulary_directory and not instances:
              raise ConfigurationError("You must provide either a Params object containing a "
                                       "vocab_directory key or a Dataset to build a vocabulary from.")
          if vocabulary_directory and instances:
              logger.info("Loading Vocab from files instead of dataset.")
  
          if vocabulary_directory:
              params.assert_empty("Vocabulary - from files")
              return Vocabulary.from_files(vocabulary_directory)
  
          min_count = params.pop("min_count", None)
          max_vocab_size = params.pop_int("max_vocab_size", None)
          non_padded_namespaces = params.pop("non_padded_namespaces", DEFAULT_NON_PADDED_NAMESPACES)
          pretrained_files = params.pop("pretrained_files", {})
          only_include_pretrained_words = params.pop_bool("only_include_pretrained_words", False)
          params.assert_empty("Vocabulary - from dataset")
          return Vocabulary.from_instances(instances=instances,
                                           min_count=min_count,
                                           max_vocab_size=max_vocab_size,
                                           non_padded_namespaces=non_padded_namespaces,
                                           pretrained_files=pretrained_files,
                                           only_include_pretrained_words=only_include_pretrained_words)
  
      def add_token_to_namespace(self, token: str, namespace: str = 'tokens') -> int:
          """
          Adds ``token`` to the index, if it is not already present.  Either way, we return the index of
          the token.
          """
          if not isinstance(token, str):
              raise ValueError("Vocabulary tokens must be strings, or saving and loading will break."
                               "  Got %s (with type %s)" % (repr(token), type(token)))
          if token not in self._token_to_index[namespace]:
              index = len(self._token_to_index[namespace])
              self._token_to_index[namespace][token] = index
              self._index_to_token[namespace][index] = token
              return index
          else:
              return self._token_to_index[namespace][token]
  
      def get_index_to_token_vocabulary(self, namespace: str = 'tokens') -> Dict[int, str]:
          return self._index_to_token[namespace]
  
      def get_token_index(self, token: str, namespace: str = 'tokens') -> int:
          if token in self._token_to_index[namespace]:
              return self._token_to_index[namespace][token]
          else:
              try:
                  return self._token_to_index[namespace][self._oov_token]
              except KeyError:
                  logger.error('Namespace: %s', namespace)
                  logger.error('Token: %s', token)
                  raise
  
      def get_token_from_index(self, index: int, namespace: str = 'tokens') -> str:
          return self._index_to_token[namespace][index]
  
      def get_vocab_size(self, namespace: str = 'tokens') -> int:
          return len(self._token_to_index[namespace])
  