'''Test the ability to add a label to a (potentially trained) parsing model.'''
from __future__ import unicode_literals
import pytest
import numpy.random
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 NeuralDependencyParser

numpy.random.seed(0)


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


@pytest.fixture
def parser(vocab):
    parser = NeuralDependencyParser(vocab)
    parser.cfg['token_vector_width'] = 4
    parser.cfg['hidden_width'] = 6
    parser.cfg['hist_size'] = 0
    parser.add_label('left')
    parser.begin_training([], **parser.cfg)
    sgd = Adam(NumpyOps(), 0.001)

    for i in range(30):
        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_add_label(parser):
    doc = Doc(parser.vocab, words=['a', 'b', 'c', 'd'])
    doc = parser(doc)
    assert doc[0].head.i == 1
    assert doc[0].dep_ == 'left'
    assert doc[1].head.i == 1
    assert doc[2].head.i == 3
    assert doc[2].head.i == 3
    parser.add_label('right')
    doc = Doc(parser.vocab, words=['a', 'b', 'c', 'd'])
    doc = parser(doc)
    assert doc[0].head.i == 1
    assert doc[0].dep_ == 'left'
    assert doc[1].head.i == 1
    assert doc[2].head.i == 3
    assert doc[2].head.i == 3
    sgd = Adam(NumpyOps(), 0.001)
    for i in range(10):
        losses = {}
        doc = Doc(parser.vocab, words=['a', 'b', 'c', 'd'])
        gold = GoldParse(doc, heads=[1, 1, 3, 3],
                deps=['right', 'ROOT', 'left', 'ROOT'])
        parser.update([doc], [gold], sgd=sgd, losses=losses)
    doc = Doc(parser.vocab, words=['a', 'b', 'c', 'd'])
    doc = parser(doc)
    assert doc[0].dep_ == 'right'
    assert doc[2].dep_ == 'left'