old_contents: # coding: utf8
  from __future__ import unicode_literals
  from thinc.neural import Model
  from mock import Mock
  import pytest
  import numpy
  
  from ..._ml import chain, Tok2Vec, doc2feats
  from ...vocab import Vocab
  from ...pipeline import TokenVectorEncoder
  from ...syntax.arc_eager import ArcEager
  from ...syntax.nn_parser import Parser
  from ...tokens.doc import Doc
  from ...gold import GoldParse
  
  
  @pytest.fixture
  def vocab():
      return Vocab()
  
  
  @pytest.fixture
  def arc_eager(vocab):
      actions = ArcEager.get_actions(left_labels=['L'], right_labels=['R'])
      return ArcEager(vocab.strings, actions)
  
  
  @pytest.fixture
  def tok2vec():
      return Tok2Vec(8, 100, preprocess=doc2feats())
  
  
  @pytest.fixture
  def parser(vocab, arc_eager):
      return Parser(vocab, moves=arc_eager, model=None)
  
  @pytest.fixture
  def model(arc_eager, tok2vec):
      return Parser.Model(arc_eager.n_moves, token_vector_width=tok2vec.nO)[0]
  
  @pytest.fixture
  def doc(vocab):
      return Doc(vocab, words=['a', 'b', 'c'])
  
  @pytest.fixture
  def gold(doc):
      return GoldParse(doc, heads=[1, 1, 1], deps=['L', 'ROOT', 'R'])
  
  
  def test_can_init_nn_parser(parser):
      assert parser.model is None
  
  
  def test_build_model(parser):
      parser.model = Parser.Model(parser.moves.n_moves)[0]
      assert parser.model is not None
  
  
  def test_predict_doc(parser, tok2vec, model, doc):
      doc.tensor = tok2vec([doc])[0]
      parser.model = model
      parser(doc)
  
  
  def test_update_doc(parser, tok2vec, model, doc, gold):
      parser.model = model
      tokvecs, bp_tokvecs = tok2vec.begin_update([doc])
      d_tokvecs = parser.update(([doc], tokvecs), [gold])
      assert d_tokvecs[0].shape == tokvecs[0].shape
      def optimize(weights, gradient, key=None):
          weights -= 0.001 * gradient
      bp_tokvecs(d_tokvecs, sgd=optimize)
      assert d_tokvecs[0].sum() == 0.
  
new_contents: # coding: utf8
  from __future__ import unicode_literals
  from thinc.neural import Model
  from mock import Mock
  import pytest
  import numpy
  
  from ..._ml import chain, Tok2Vec, doc2feats
  from ...vocab import Vocab
  from ...pipeline import TokenVectorEncoder
  from ...syntax.arc_eager import ArcEager
  from ...syntax.nn_parser import Parser
  from ...tokens.doc import Doc
  from ...gold import GoldParse
  
  
  @pytest.fixture
  def vocab():
      return Vocab()
  
  
  @pytest.fixture
  def arc_eager(vocab):
      actions = ArcEager.get_actions(left_labels=['L'], right_labels=['R'])
      return ArcEager(vocab.strings, actions)
  
  
  @pytest.fixture
  def tok2vec():
      return Tok2Vec(8, 100, preprocess=doc2feats())
  
  
  @pytest.fixture
  def parser(vocab, arc_eager):
      return Parser(vocab, moves=arc_eager, model=None)
  
  @pytest.fixture
  def model(arc_eager, tok2vec):
      return Parser.Model(arc_eager.n_moves, token_vector_width=tok2vec.nO)[0]
  
  @pytest.fixture
  def doc(vocab):
      return Doc(vocab, words=['a', 'b', 'c'])
  
  @pytest.fixture
  def gold(doc):
      return GoldParse(doc, heads=[1, 1, 1], deps=['L', 'ROOT', 'R'])
  
  
  def test_can_init_nn_parser(parser):
      assert parser.model is None
  
  
  def test_build_model(parser):
      parser.model = Parser.Model(parser.moves.n_moves)[0]
      assert parser.model is not None
  
  
  def test_predict_doc(parser, tok2vec, model, doc):
      doc.tensor = tok2vec([doc])[0]
      parser.model = model
      parser(doc)
  
  
  def test_update_doc(parser, tok2vec, model, doc, gold):
      parser.model = model
      tokvecs, bp_tokvecs = tok2vec.begin_update([doc])
      d_tokvecs = parser.update(([doc], tokvecs), [gold])
      assert d_tokvecs[0].shape == tokvecs[0].shape
      def optimize(weights, gradient, key=None):
          weights -= 0.001 * gradient
      bp_tokvecs(d_tokvecs, sgd=optimize)
      assert d_tokvecs[0].sum() == 0.
  
  
  def test_predict_doc_beam(parser, tok2vec, model, doc):
      doc.tensor = tok2vec([doc])[0]
      parser.model = model
      parser(doc, beam_width=32, beam_density=0.001)
      for word in doc:
          print(word.text, word.head, word.dep_)
  
subject: Test beam parsing