'''Test that the parser respects preset sentence boundaries.'''
from __future__ import unicode_literals
import pytest
from thinc.neural.optimizers import Adam
from thinc.neural.ops import NumpyOps

from ...attrs import NORM
from ...gold import GoldParse
from ...vocab import Vocab
from ...tokens import Doc
from ...pipeline import DependencyParser

@pytest.fixture
def vocab():
    return Vocab(lex_attr_getters={NORM: lambda s: s})

@pytest.fixture
def parser(vocab):
    parser = DependencyParser(vocab)
    parser.cfg['token_vector_width'] = 4
    parser.cfg['hidden_width'] = 32
    #parser.add_label('right')
    parser.add_label('left')
    parser.begin_training([], **parser.cfg)
    sgd = Adam(NumpyOps(), 0.001)

    for i in range(10):
        losses = {}
        doc = Doc(vocab, words=['a', 'b', 'c', 'd'])
        gold = GoldParse(doc, heads=[1, 1, 3, 3],
                deps=['left', 'ROOT', 'left', 'ROOT'])
        parser.update([doc], [gold], sgd=sgd, losses=losses)
    return parser

def test_no_sentences(parser):
    doc = Doc(parser.vocab, words=['a', 'b', 'c', 'd'])
    doc = parser(doc)
    assert len(list(doc.sents)) >= 1


def test_sents_1(parser):
    doc = Doc(parser.vocab, words=['a', 'b', 'c', 'd'])
    doc[2].sent_start = True
    doc = parser(doc)
    assert len(list(doc.sents)) >= 2
    doc = Doc(parser.vocab, words=['a', 'b', 'c', 'd'])
    doc[1].sent_start = False
    doc[2].sent_start = True
    doc[3].sent_start = False
    doc = parser(doc)
    assert len(list(doc.sents)) == 2


def test_sents_1_2(parser):
    doc = Doc(parser.vocab, words=['a', 'b', 'c', 'd'])
    doc[1].sent_start = True
    doc[2].sent_start = True
    doc = parser(doc)
    assert len(list(doc.sents)) == 3


def test_sents_1_3(parser):
    doc = Doc(parser.vocab, words=['a', 'b', 'c', 'd'])
    doc[1].sent_start = True
    doc[3].sent_start = True
    doc = parser(doc)
    assert len(list(doc.sents)) >= 3
    doc = Doc(parser.vocab, words=['a', 'b', 'c', 'd'])
    doc[1].sent_start = True
    doc[2].sent_start = False
    doc[3].sent_start = True
    doc = parser(doc)
    assert len(list(doc.sents)) == 3

'''Test that the parser respects preset sentence boundaries.'''
from __future__ import unicode_literals
import pytest
from thinc.neural.optimizers import Adam
from thinc.neural.ops import NumpyOps

from ...attrs import NORM
from ...gold import GoldParse
from ...vocab import Vocab
from ...tokens import Doc
from ...pipeline import DependencyParser

@pytest.fixture
def vocab():
    return Vocab(lex_attr_getters={NORM: lambda s: s})

@pytest.fixture
def parser(vocab):
    parser = DependencyParser(vocab)
    parser.cfg['token_vector_width'] = 4
    parser.cfg['hidden_width'] = 32
    #parser.add_label('right')
    parser.add_label('left')
    parser.begin_training([], **parser.cfg)
    sgd = Adam(NumpyOps(), 0.001)

    for i in range(10):
        losses = {}
        doc = Doc(vocab, words=['a', 'b', 'c', 'd'])
        gold = GoldParse(doc, heads=[1, 1, 3, 3],
                deps=['left', 'ROOT', 'left', 'ROOT'])
        parser.update([doc], [gold], sgd=sgd, losses=losses)
    return parser

def test_no_sentences(parser):
    doc = Doc(parser.vocab, words=['a', 'b', 'c', 'd'])
    doc = parser(doc)
    assert len(list(doc.sents)) >= 1


def test_sents_1(parser):
    doc = Doc(parser.vocab, words=['a', 'b', 'c', 'd'])
    doc[2].sent_start = True
    doc = parser(doc)
    assert len(list(doc.sents)) >= 2
    doc = Doc(parser.vocab, words=['a', 'b', 'c', 'd'])
    doc[1].sent_start = False
    doc[2].sent_start = True
    doc[3].sent_start = False
    doc = parser(doc)
    assert len(list(doc.sents)) == 2


def test_sents_1_2(parser):
    doc = Doc(parser.vocab, words=['a', 'b', 'c', 'd'])
    doc[1].sent_start = True
    doc[2].sent_start = True
    doc = parser(doc)
    assert len(list(doc.sents)) >= 3


def test_sents_1_3(parser):
    doc = Doc(parser.vocab, words=['a', 'b', 'c', 'd'])
    doc[1].sent_start = True
    doc[3].sent_start = True
    doc = parser(doc)
    assert len(list(doc.sents)) >= 3
    doc = Doc(parser.vocab, words=['a', 'b', 'c', 'd'])
    doc[1].sent_start = True
    doc[2].sent_start = False
    doc[3].sent_start = True
    doc = parser(doc)
    assert len(list(doc.sents)) == 3

Make test less flakey