text: # coding: utf8
  from __future__ import absolute_import, unicode_literals
  
  import random
  import ujson
  import itertools
  import weakref
  import functools
  from collections import OrderedDict
  from contextlib import contextmanager
  from copy import copy
  from thinc.neural import Model
  from thinc.neural.optimizers import Adam
  
  from .tokenizer import Tokenizer
  from .vocab import Vocab
  from .lemmatizer import Lemmatizer
  from .pipeline import DependencyParser, Tensorizer, Tagger, EntityRecognizer
  from .pipeline import SimilarityHook, TextCategorizer, SentenceSegmenter
  from .pipeline import merge_noun_chunks, merge_entities
  from .compat import json_dumps, izip, basestring_
  from .gold import GoldParse
  from .scorer import Scorer
  from ._ml import link_vectors_to_models, create_default_optimizer
  from .attrs import IS_STOP
  from .lang.punctuation import TOKENIZER_PREFIXES, TOKENIZER_SUFFIXES
  from .lang.punctuation import TOKENIZER_INFIXES
  from .lang.tokenizer_exceptions import TOKEN_MATCH
  from .lang.tag_map import TAG_MAP
  from .lang.lex_attrs import LEX_ATTRS, is_stop
  from .errors import Errors
  from . import util
  from . import about
  
  
  class BaseDefaults(object):
      @classmethod
      def create_lemmatizer(cls, nlp=None):
          return Lemmatizer(cls.lemma_index, cls.lemma_exc, cls.lemma_rules,
                            cls.lemma_lookup)
  
      @classmethod
      def create_vocab(cls, nlp=None):
          lemmatizer = cls.create_lemmatizer(nlp)
          lex_attr_getters = dict(cls.lex_attr_getters)
          # This is messy, but it's the minimal working fix to Issue #639.
          lex_attr_getters[IS_STOP] = functools.partial(is_stop,
                                                        stops=cls.stop_words)
          vocab = Vocab(lex_attr_getters=lex_attr_getters, tag_map=cls.tag_map,
                        lemmatizer=lemmatizer)
          for tag_str, exc in cls.morph_rules.items():
              for orth_str, attrs in exc.items():
                  vocab.morphology.add_special_case(tag_str, orth_str, attrs)
          return vocab
  
      @classmethod
      def create_tokenizer(cls, nlp=None):
          rules = cls.tokenizer_exceptions
          token_match = cls.token_match
          prefix_search = (util.compile_prefix_regex(cls.prefixes).search
                           if cls.prefixes else None)
          suffix_search = (util.compile_suffix_regex(cls.suffixes).search
                           if cls.suffixes else None)
          infix_finditer = (util.compile_infix_regex(cls.infixes).finditer
                            if cls.infixes else None)
          vocab = nlp.vocab if nlp is not None else cls.create_vocab(nlp)
          return Tokenizer(vocab, rules=rules,
                           prefix_search=prefix_search,
                           suffix_search=suffix_search,
                           infix_finditer=infix_finditer,
                           token_match=token_match)
  
      pipe_names = ['tagger', 'parser', 'ner']
      token_match = TOKEN_MATCH
      prefixes = tuple(TOKENIZER_PREFIXES)
      suffixes = tuple(TOKENIZER_SUFFIXES)
      infixes = tuple(TOKENIZER_INFIXES)
      tag_map = dict(TAG_MAP)
      tokenizer_exceptions = {}
      stop_words = set()
      lemma_rules = {}
      lemma_exc = {}
      lemma_index = {}
      lemma_lookup = {}
      morph_rules = {}
      lex_attr_getters = LEX_ATTRS
      syntax_iterators = {}
  
  
  class Language(object):
      """A text-processing pipeline. Usually you'll load this once per process,
      and pass the instance around your application.
  
      Defaults (class): Settings, data and factory methods for creating the `nlp`
          object and processing pipeline.
      lang (unicode): Two-letter language ID, i.e. ISO code.
      """
      Defaults = BaseDefaults
      lang = None
  
      factories = {
          'tokenizer': lambda nlp: nlp.Defaults.create_tokenizer(nlp),
          'tensorizer': lambda nlp, **cfg: Tensorizer(nlp.vocab, **cfg),
          'tagger': lambda nlp, **cfg: Tagger(nlp.vocab, **cfg),
          'parser': lambda nlp, **cfg: DependencyParser(nlp.vocab, **cfg),
          'ner': lambda nlp, **cfg: EntityRecognizer(nlp.vocab, **cfg),
          'similarity': lambda nlp, **cfg: SimilarityHook(nlp.vocab, **cfg),
          'textcat': lambda nlp, **cfg: TextCategorizer(nlp.vocab, **cfg),
          'sbd': lambda nlp, **cfg: SentenceSegmenter(nlp.vocab, **cfg),
          'sentencizer': lambda nlp, **cfg: SentenceSegmenter(nlp.vocab, **cfg),
          'merge_noun_chunks': lambda nlp, **cfg: merge_noun_chunks,
          'merge_entities': lambda nlp, **cfg: merge_entities
      }
  
      def __init__(self, vocab=True, make_doc=True, max_length=10**6, meta={}, **kwargs):
          """Initialise a Language object.
  
          vocab (Vocab): A `Vocab` object. If `True`, a vocab is created via
              `Language.Defaults.create_vocab`.
          make_doc (callable): A function that takes text and returns a `Doc`
              object. Usually a `Tokenizer`.
          meta (dict): Custom meta data for the Language class. Is written to by
              models to add model meta data.
          max_length (int) :
              Maximum number of characters in a single text. The current v2 models
              may run out memory on extremely long texts, due to large internal
              allocations. You should segment these texts into meaningful units,
              e.g. paragraphs, subsections etc, before passing them to spaCy.
              Default maximum length is 1,000,000 characters (1mb). As a rule of
              thumb, if all pipeline components are enabled, spaCy's default
              models currently requires roughly 1GB of temporary memory per
              100,000 characters in one text.
          RETURNS (Language): The newly constructed object.
          """
          self._meta = dict(meta)
          self._path = None
          if vocab is True:
              factory = self.Defaults.create_vocab
              vocab = factory(self, **meta.get('vocab', {}))
              if vocab.vectors.name is None:
                  vocab.vectors.name = meta.get('vectors', {}).get('name')
          self.vocab = vocab
          if make_doc is True:
              factory = self.Defaults.create_tokenizer
              make_doc = factory(self, **meta.get('tokenizer', {}))
          self.tokenizer = make_doc
          self.pipeline = []
          self.max_length = max_length
          self._optimizer = None
  
      @property
      def path(self):
          return self._path
  
      @property
      def meta(self):
          self._meta.setdefault('lang', self.vocab.lang)
          self._meta.setdefault('name', 'model')
          self._meta.setdefault('version', '0.0.0')
          self._meta.setdefault('spacy_version', '>={}'.format(about.__version__))
          self._meta.setdefault('description', '')
          self._meta.setdefault('author', '')
          self._meta.setdefault('email', '')
          self._meta.setdefault('url', '')
          self._meta.setdefault('license', '')
          self._meta['vectors'] = {'width': self.vocab.vectors_length,
                                   'vectors': len(self.vocab.vectors),
                                   'keys': self.vocab.vectors.n_keys,
                                   'name': self.vocab.vectors.name}
          self._meta['pipeline'] = self.pipe_names
          return self._meta
  
      @meta.setter
      def meta(self, value):
          self._meta = value
  
      # Conveniences to access pipeline components
      @property
      def tensorizer(self):
          return self.get_pipe('tensorizer')
  
      @property
      def tagger(self):
          return self.get_pipe('tagger')
  
      @property
      def parser(self):
          return self.get_pipe('parser')
  
      @property
      def entity(self):
          return self.get_pipe('ner')
  
      @property
      def matcher(self):
          return self.get_pipe('matcher')
  
      @property
      def pipe_names(self):
          """Get names of available pipeline components.
  
          RETURNS (list): List of component name strings, in order.
          """
          return [pipe_name for pipe_name, _ in self.pipeline]
  
      def get_pipe(self, name):
          """Get a pipeline component for a given component name.
  
          name (unicode): Name of pipeline component to get.
          RETURNS (callable): The pipeline component.
          """
          for pipe_name, component in self.pipeline:
              if pipe_name == name:
                  return component
          raise KeyError(Errors.E001.format(name=name, opts=self.pipe_names))
  
      def create_pipe(self, name, config=dict()):
          """Create a pipeline component from a factory.
  
          name (unicode): Factory name to look up in `Language.factories`.
          config (dict): Configuration parameters to initialise component.
          RETURNS (callable): Pipeline component.
          """
          if name not in self.factories:
              raise KeyError(Errors.E002.format(name=name))
          factory = self.factories[name]
          return factory(self, **config)
  
      def add_pipe(self, component, name=None, before=None, after=None,
                   first=None, last=None):
          """Add a component to the processing pipeline. Valid components are
          callables that take a `Doc` object, modify it and return it. Only one
          of before/after/first/last can be set. Default behaviour is "last".
  
          component (callable): The pipeline component.
          name (unicode): Name of pipeline component. Overwrites existing
              component.name attribute if available. If no name is set and
              the component exposes no name attribute, component.__name__ is
              used. An error is raised if a name already exists in the pipeline.
          before (unicode): Component name to insert component directly before.
          after (unicode): Component name to insert component directly after.
          first (bool): Insert component first / not first in the pipeline.
          last (bool): Insert component last / not last in the pipeline.
  
          EXAMPLE:
              >>> nlp.add_pipe(component, before='ner')
              >>> nlp.add_pipe(component, name='custom_name', last=True)
          """
          if not hasattr(component, '__call__'):
              msg = Errors.E003.format(component=repr(component), name=name)
              if isinstance(component, basestring_) and component in self.factories:
                  msg += Errors.E004.format(component=component)
              raise ValueError(msg)
          if name is None:
              if hasattr(component, 'name'):
                  name = component.name
              elif hasattr(component, '__name__'):
                  name = component.__name__
              elif (hasattr(component, '__class__') and
                    hasattr(component.__class__, '__name__')):
                  name = component.__class__.__name__
              else:
                  name = repr(component)
          if name in self.pipe_names:
              raise ValueError(Errors.E007.format(name=name, opts=self.pipe_names))
          if sum([bool(before), bool(after), bool(first), bool(last)]) >= 2:
              raise ValueError(Errors.E006)
          pipe = (name, component)
          if last or not any([first, before, after]):
              self.pipeline.append(pipe)
          elif first:
              self.pipeline.insert(0, pipe)
          elif before and before in self.pipe_names:
              self.pipeline.insert(self.pipe_names.index(before), pipe)
          elif after and after in self.pipe_names:
              self.pipeline.insert(self.pipe_names.index(after) + 1, pipe)
          else:
              raise ValueError(Errors.E001.format(name=before or after,
                                                  opts=self.pipe_names))
  
      def has_pipe(self, name):
          """Check if a component name is present in the pipeline. Equivalent to
          `name in nlp.pipe_names`.
  
          name (unicode): Name of the component.
          RETURNS (bool): Whether a component of the name exists in the pipeline.
          """
          return name in self.pipe_names
  
      def replace_pipe(self, name, component):
          """Replace a component in the pipeline.
  
          name (unicode): Name of the component to replace.
          component (callable): Pipeline component.
          """
          if name not in self.pipe_names:
              raise ValueError(Errors.E001.format(name=name, opts=self.pipe_names))
          self.pipeline[self.pipe_names.index(name)] = (name, component)
  
      def rename_pipe(self, old_name, new_name):
          """Rename a pipeline component.
  
          old_name (unicode): Name of the component to rename.
          new_name (unicode): New name of the component.
          """
          if old_name not in self.pipe_names:
              raise ValueError(Errors.E001.format(name=old_name, opts=self.pipe_names))
          if new_name in self.pipe_names:
              raise ValueError(Errors.E007.format(name=new_name, opts=self.pipe_names))
          i = self.pipe_names.index(old_name)
          self.pipeline[i] = (new_name, self.pipeline[i][1])
  
      def remove_pipe(self, name):
          """Remove a component from the pipeline.
  
          name (unicode): Name of the component to remove.
          RETURNS (tuple): A `(name, component)` tuple of the removed component.
          """
          if name not in self.pipe_names:
              raise ValueError(Errors.E001.format(name=name, opts=self.pipe_names))
          return self.pipeline.pop(self.pipe_names.index(name))
  
      def __call__(self, text, disable=[]):
          """Apply the pipeline to some text. The text can span multiple sentences,
          and can contain arbtrary whitespace. Alignment into the original string
          is preserved.
  
          text (unicode): The text to be processed.
          disable (list): Names of the pipeline components to disable.
          RETURNS (Doc): A container for accessing the annotations.
  
          EXAMPLE:
              >>> tokens = nlp('An example sentence. Another example sentence.')
              >>> tokens[0].text, tokens[0].head.tag_
              ('An', 'NN')
          """
          if len(text) > self.max_length:
              raise ValueError(Errors.E088.format(length=len(text),
                                                  max_length=self.max_length))
          doc = self.make_doc(text)
          for name, proc in self.pipeline:
              if name in disable:
                  continue
              if not hasattr(proc, '__call__'):
                  raise ValueError(Errors.E003.format(component=type(proc), name=name))
              doc = proc(doc)
              if doc is None:
                  raise ValueError(Errors.E005.format(name=name))
          return doc
  
      def disable_pipes(self, *names):
          """Disable one or more pipeline components. If used as a context
          manager, the pipeline will be restored to the initial state at the end
          of the block. Otherwise, a DisabledPipes object is returned, that has
          a `.restore()` method you can use to undo your changes.
  
          EXAMPLE:
              >>> nlp.add_pipe('parser')
              >>> nlp.add_pipe('tagger')
              >>> with nlp.disable_pipes('parser', 'tagger'):
              >>>     assert not nlp.has_pipe('parser')
              >>> assert nlp.has_pipe('parser')
              >>> disabled = nlp.disable_pipes('parser')
              >>> assert len(disabled) == 1
              >>> assert not nlp.has_pipe('parser')
              >>> disabled.restore()
              >>> assert nlp.has_pipe('parser')
          """
          return DisabledPipes(self, *names)
  
      def make_doc(self, text):
          return self.tokenizer(text)
  
      def update(self, docs, golds, drop=0., sgd=None, losses=None):
          """Update the models in the pipeline.
  
          docs (iterable): A batch of `Doc` objects.
          golds (iterable): A batch of `GoldParse` objects.
          drop (float): The droput rate.
          sgd (callable): An optimizer.
          RETURNS (dict): Results from the update.
  
          EXAMPLE:
              >>> with nlp.begin_training(gold) as (trainer, optimizer):
              >>>    for epoch in trainer.epochs(gold):
              >>>        for docs, golds in epoch:
              >>>            state = nlp.update(docs, golds, sgd=optimizer)
          """
          if len(docs) != len(golds):
              raise IndexError(Errors.E009.format(n_docs=len(docs), n_golds=len(golds)))
          if len(docs) == 0:
              return
          if sgd is None:
              if self._optimizer is None:
                  self._optimizer = create_default_optimizer(Model.ops)
              sgd = self._optimizer
  
          # Allow dict of args to GoldParse, instead of GoldParse objects.
          gold_objs = []
          doc_objs = []
          for doc, gold in zip(docs, golds):
              if isinstance(doc, basestring_):
                  doc = self.make_doc(doc)
              if not isinstance(gold, GoldParse):
                  gold = GoldParse(doc, **gold)
              doc_objs.append(doc)
              gold_objs.append(gold)
          golds = gold_objs
          docs = doc_objs
          grads = {}
  
          def get_grads(W, dW, key=None):
              grads[key] = (W, dW)
  
          pipes = list(self.pipeline)
          random.shuffle(pipes)
          for name, proc in pipes:
              if not hasattr(proc, 'update'):
                  continue
              grads = {}
              proc.update(docs, golds, drop=drop, sgd=get_grads, losses=losses)
              for key, (W, dW) in grads.items():
                  sgd(W, dW, key=key)
  
      def preprocess_gold(self, docs_golds):
          """Can be called before training to pre-process gold data. By default,
          it handles nonprojectivity and adds missing tags to the tag map.
  
          docs_golds (iterable): Tuples of `Doc` and `GoldParse` objects.
          YIELDS (tuple): Tuples of preprocessed `Doc` and `GoldParse` objects.
          """
          for name, proc in self.pipeline:
              if hasattr(proc, 'preprocess_gold'):
                  docs_golds = proc.preprocess_gold(docs_golds)
          for doc, gold in docs_golds:
              yield doc, gold
  
      def begin_training(self, get_gold_tuples=None, sgd=None, **cfg):
          """Allocate models, pre-process training data and acquire a trainer and
          optimizer. Used as a contextmanager.
  
          get_gold_tuples (function): Function returning gold data
          **cfg: Config parameters.
          RETURNS: An optimizer
          """
          if get_gold_tuples is None:
              get_gold_tuples = lambda: []
          # Populate vocab
          else:
              for _, annots_brackets in get_gold_tuples():
                  for annots, _ in annots_brackets:
                      for word in annots[1]:
                          _ = self.vocab[word]
          contexts = []
          if cfg.get('device', -1) >= 0:
              device = util.use_gpu(cfg['device'])
              if self.vocab.vectors.data.shape[1] >= 1:
                  self.vocab.vectors.data = Model.ops.asarray(
                      self.vocab.vectors.data)
          else:
              device = None
          link_vectors_to_models(self.vocab)
          if self.vocab.vectors.data.shape[1]:
              cfg['pretrained_vectors'] = self.vocab.vectors.name
          if sgd is None:
              sgd = create_default_optimizer(Model.ops)
          self._optimizer = sgd
          for name, proc in self.pipeline:
              if hasattr(proc, 'begin_training'):
                  proc.begin_training(get_gold_tuples(),
                                      pipeline=self.pipeline,
                                      sgd=self._optimizer,
                                      **cfg)
          return self._optimizer
  
      def evaluate(self, docs_golds, verbose=False):
          scorer = Scorer()
          docs, golds = zip(*docs_golds)
          docs = list(docs)
          golds = list(golds)
          for name, pipe in self.pipeline:
              if not hasattr(pipe, 'pipe'):
                  docs = (pipe(doc) for doc in docs)
              else:
                  docs = pipe.pipe(docs, batch_size=256)
          for doc, gold in zip(docs, golds):
              if verbose:
                  print(doc)
              scorer.score(doc, gold, verbose=verbose)
          return scorer
  
      @contextmanager
      def use_params(self, params, **cfg):
          """Replace weights of models in the pipeline with those provided in the
          params dictionary. Can be used as a contextmanager, in which case,
          models go back to their original weights after the block.
  
          params (dict): A dictionary of parameters keyed by model ID.
          **cfg: Config parameters.
  
          EXAMPLE:
              >>> with nlp.use_params(optimizer.averages):
              >>>     nlp.to_disk('/tmp/checkpoint')
          """
          contexts = [pipe.use_params(params) for name, pipe
                      in self.pipeline if hasattr(pipe, 'use_params')]
          # TODO: Having trouble with contextlib
          # Workaround: these aren't actually context managers atm.
          for context in contexts:
              try:
                  next(context)
              except StopIteration:
                  pass
          yield
          for context in contexts:
              try:
                  next(context)
              except StopIteration:
                  pass
  
      def pipe(self, texts, as_tuples=False, n_threads=2, batch_size=1000,
               disable=[], cleanup=False):
          """Process texts as a stream, and yield `Doc` objects in order.
  
          texts (iterator): A sequence of texts to process.
          as_tuples (bool):
              If set to True, inputs should be a sequence of
              (text, context) tuples. Output will then be a sequence of
              (doc, context) tuples. Defaults to False.
          n_threads (int): Currently inactive.
          batch_size (int): The number of texts to buffer.
          disable (list): Names of the pipeline components to disable.
          cleanup (bool): If True, unneeded strings are freed,
              to control memory use. Experimental.
          YIELDS (Doc): Documents in the order of the original text.
  
          EXAMPLE:
              >>> texts = [u'One document.', u'...', u'Lots of documents']
              >>>     for doc in nlp.pipe(texts, batch_size=50, n_threads=4):
              >>>         assert doc.is_parsed
          """
          if as_tuples:
              text_context1, text_context2 = itertools.tee(texts)
              texts = (tc[0] for tc in text_context1)
              contexts = (tc[1] for tc in text_context2)
              docs = self.pipe(texts, n_threads=n_threads, batch_size=batch_size,
                               disable=disable)
              for doc, context in izip(docs, contexts):
                  yield (doc, context)
              return
          docs = (self.make_doc(text) for text in texts)
          for name, proc in self.pipeline:
              if name in disable:
                  continue
              if hasattr(proc, 'pipe'):
                  docs = proc.pipe(docs, n_threads=n_threads,
                                   batch_size=batch_size)
              else:
                  # Apply the function, but yield the doc
                  docs = _pipe(proc, docs)
          # Track weakrefs of "recent" documents, so that we can see when they
          # expire from memory. When they do, we know we don't need old strings.
          # This way, we avoid maintaining an unbounded growth in string entries
          # in the string store.
          recent_refs = weakref.WeakSet()
          old_refs = weakref.WeakSet()
          # Keep track of the original string data, so that if we flush old strings,
          # we can recover the original ones. However, we only want to do this if we're
          # really adding strings, to save up-front costs.
          original_strings_data = None
          nr_seen = 0
          for doc in docs:
              yield doc
              if cleanup:
                  recent_refs.add(doc)
                  if nr_seen < 10000:
                      old_refs.add(doc)
                      nr_seen += 1
                  elif len(old_refs) == 0:
                      old_refs, recent_refs = recent_refs, old_refs
                      if original_strings_data is None:
                          original_strings_data = list(self.vocab.strings)
                      else:
                          keys, strings = self.vocab.strings._cleanup_stale_strings(original_strings_data)
                          self.vocab._reset_cache(keys, strings)
                          self.tokenizer._reset_cache(keys)
                      nr_seen = 0
  
      def to_disk(self, path, disable=tuple()):
          """Save the current state to a directory.  If a model is loaded, this
          will include the model.
  
          path (unicode or Path): A path to a directory, which will be created if
              it doesn't exist. Paths may be strings or `Path`-like objects.
          disable (list): Names of pipeline components to disable and prevent
              from being saved.
  
          EXAMPLE:
              >>> nlp.to_disk('/path/to/models')
          """
          path = util.ensure_path(path)
          serializers = OrderedDict((
              ('tokenizer', lambda p: self.tokenizer.to_disk(p, vocab=False)),
              ('meta.json', lambda p: p.open('w').write(json_dumps(self.meta)))
          ))
          for name, proc in self.pipeline:
              if not hasattr(proc, 'name'):
                  continue
              if name in disable:
                  continue
              if not hasattr(proc, 'to_disk'):
                  continue
              serializers[name] = lambda p, proc=proc: proc.to_disk(p, vocab=False)
          serializers['vocab'] = lambda p: self.vocab.to_disk(p)
          util.to_disk(path, serializers, {p: False for p in disable})
  
      def from_disk(self, path, disable=tuple()):
          """Loads state from a directory. Modifies the object in place and
          returns it. If the saved `Language` object contains a model, the
          model will be loaded.
  
          path (unicode or Path): A path to a directory. Paths may be either
              strings or `Path`-like objects.
          disable (list): Names of the pipeline components to disable.
          RETURNS (Language): The modified `Language` object.
  
          EXAMPLE:
              >>> from spacy.language import Language
              >>> nlp = Language().from_disk('/path/to/models')
          """
          path = util.ensure_path(path)
          deserializers = OrderedDict((
              ('meta.json', lambda p: self.meta.update(util.read_json(p))),
              ('vocab', lambda p: (
                  self.vocab.from_disk(p) and _fix_pretrained_vectors_name(self))),
              ('tokenizer', lambda p: self.tokenizer.from_disk(p, vocab=False)),
          ))
          for name, proc in self.pipeline:
              if name in disable:
                  continue
              if not hasattr(proc, 'to_disk'):
                  continue
              deserializers[name] = lambda p, proc=proc: proc.from_disk(p, vocab=False)
          exclude = {p: False for p in disable}
          if not (path / 'vocab').exists():
              exclude['vocab'] = True
          util.from_disk(path, deserializers, exclude)
          self._path = path
          return self
  
      def to_bytes(self, disable=[], **exclude):
          """Serialize the current state to a binary string.
  
          disable (list): Nameds of pipeline components to disable and prevent
              from being serialized.
          RETURNS (bytes): The serialized form of the `Language` object.
          """
          serializers = OrderedDict((
              ('vocab', lambda: self.vocab.to_bytes()),
              ('tokenizer', lambda: self.tokenizer.to_bytes(vocab=False)),
              ('meta', lambda: json_dumps(self.meta))
          ))
          for i, (name, proc) in enumerate(self.pipeline):
              if name in disable:
                  continue
              if not hasattr(proc, 'to_bytes'):
                  continue
              serializers[i] = lambda proc=proc: proc.to_bytes(vocab=False)
          return util.to_bytes(serializers, exclude)
  
      def from_bytes(self, bytes_data, disable=[]):
          """Load state from a binary string.
  
          bytes_data (bytes): The data to load from.
          disable (list): Names of the pipeline components to disable.
          RETURNS (Language): The `Language` object.
          """
          deserializers = OrderedDict((
              ('meta', lambda b: self.meta.update(ujson.loads(b))),
              ('vocab', lambda b: (
                  self.vocab.from_bytes(b) and _fix_pretrained_vectors_name(self))),
              ('tokenizer', lambda b: self.tokenizer.from_bytes(b, vocab=False)),
          ))
          for i, (name, proc) in enumerate(self.pipeline):
              if name in disable:
                  continue
              if not hasattr(proc, 'from_bytes'):
                  continue
              deserializers[i] = lambda b, proc=proc: proc.from_bytes(b, vocab=False)
          msg = util.from_bytes(bytes_data, deserializers, {})
          return self
  
  
  def _fix_pretrained_vectors_name(nlp):
      # TODO: Replace this once we handle vectors consistently as static
      # data
      if 'vectors' in nlp.meta and nlp.meta['vectors'].get('name'):
          nlp.vocab.vectors.name = nlp.meta['vectors']['name']
      elif not nlp.vocab.vectors.size:
          nlp.vocab.vectors.name = None
      elif 'name' in nlp.meta and 'lang' in nlp.meta:
          vectors_name = '%s_%s.vectors' % (nlp.meta['lang'], nlp.meta['name'])
          nlp.vocab.vectors.name = vectors_name
      else:
          raise ValueError(Errors.E092)
      if nlp.vocab.vectors.size != 0:
          link_vectors_to_models(nlp.vocab)
      for name, proc in nlp.pipeline:
          if not hasattr(proc, 'cfg'):
              continue
          proc.cfg.setdefault('deprecation_fixes', {})
          proc.cfg['deprecation_fixes']['vectors_name'] = nlp.vocab.vectors.name
  
  
  class DisabledPipes(list):
      """Manager for temporary pipeline disabling."""
      def __init__(self, nlp, *names):
          self.nlp = nlp
          self.names = names
          # Important! Not deep copy -- we just want the container (but we also
          # want to support people providing arbitrarily typed nlp.pipeline
          # objects.)
          self.original_pipeline = copy(nlp.pipeline)
          list.__init__(self)
          self.extend(nlp.remove_pipe(name) for name in names)
  
      def __enter__(self):
          return self
  
      def __exit__(self, *args):
          self.restore()
  
      def restore(self):
          '''Restore the pipeline to its state when DisabledPipes was created.'''
          current, self.nlp.pipeline = self.nlp.pipeline, self.original_pipeline
          unexpected = [name for name, pipe in current
                        if not self.nlp.has_pipe(name)]
          if unexpected:
              # Don't change the pipeline if we're raising an error.
              self.nlp.pipeline = current
              raise ValueError(Errors.E008.format(names=unexpected))
          self[:] = []
  
  
  def _pipe(func, docs):
      for doc in docs:
          doc = func(doc)
          yield doc
  