# coding: utf8
from __future__ import unicode_literals

import spacy
from spacy.util import minibatch, compounding


def test_issue3611():
    """ Test whether adding n-grams in the textcat works even when n > token length of some docs """
    unique_classes = ["offensive", "inoffensive"]
    x_train = [
        "This is an offensive text",
        "This is the second offensive text",
        "inoff",
    ]
    y_train = ["offensive", "offensive", "inoffensive"]

    # preparing the data
    pos_cats = list()
    for train_instance in y_train:
        pos_cats.append({label: label == train_instance for label in unique_classes})
    train_data = list(zip(x_train, [{"cats": cats} for cats in pos_cats]))

    # set up the spacy model with a text categorizer component
    nlp = spacy.blank("en")

    textcat = nlp.create_pipe(
        "textcat",
        config={"exclusive_classes": True, "architecture": "bow", "ngram_size": 2},
    )

    for label in unique_classes:
        textcat.add_label(label)
    nlp.add_pipe(textcat, last=True)

    # training the network
    with nlp.disable_pipes([p for p in nlp.pipe_names if p != "textcat"]):
        optimizer = nlp.begin_training()
        for i in range(3):
            losses = {}
            batches = minibatch(train_data, size=compounding(4.0, 32.0, 1.001))

            for batch in batches:
                texts, annotations = zip(*batch)
                nlp.update(
                    docs=texts,
                    golds=annotations,
                    sgd=optimizer,
                    drop=0.1,
                    losses=losses,
                )