"""This script is experimental.

Try pre-training the CNN component of the text categorizer using a cheap
language modelling-like objective. Specifically, we load pre-trained vectors
(from something like word2vec, GloVe, FastText etc), and use the CNN to
predict the tokens' pre-trained vectors. This isn't as easy as it sounds:
we're not merely doing compression here, because heavy dropout is applied,
including over the input words. This means the model must often (50% of the time)
use the context in order to predict the word.

To evaluate the technique, we're pre-training with the 50k texts from the IMDB
corpus, and then training with only 100 labels. Note that it's a bit dirty to
pre-train with the development data, but also not *so* terrible: we're not using
the development labels, after all --- only the unlabelled text.
"""
import plac
import random
import spacy
import thinc.extra.datasets
from spacy.util import minibatch, use_gpu, compounding
from spacy._ml import Tok2Vec
from spacy.pipeline import TextCategorizer
import numpy


def load_texts(limit=0):
    train, dev = thinc.extra.datasets.imdb()
    train_texts, train_labels = zip(*train)
    dev_texts, dev_labels = zip(*train)
    train_texts = list(train_texts)
    dev_texts = list(dev_texts)
    random.shuffle(train_texts)
    random.shuffle(dev_texts)
    if limit >= 1:
        return train_texts[:limit]
    else:
        return list(train_texts) + list(dev_texts)


def load_textcat_data(limit=0):
    """Load data from the IMDB dataset."""
    # Partition off part of the train data for evaluation
    train_data, eval_data = thinc.extra.datasets.imdb()
    random.shuffle(train_data)
    train_data = train_data[-limit:]
    texts, labels = zip(*train_data)
    eval_texts, eval_labels = zip(*eval_data)
    cats = [{"POSITIVE": bool(y), "NEGATIVE": not bool(y)} for y in labels]
    eval_cats = [{"POSITIVE": bool(y), "NEGATIVE": not bool(y)} for y in eval_labels]
    return (texts, cats), (eval_texts, eval_cats)


def prefer_gpu():
    used = spacy.util.use_gpu(0)
    if used is None:
        return False
    else:
        import cupy.random

        cupy.random.seed(0)
        return True


def build_textcat_model(tok2vec, nr_class, width):
    from thinc.v2v import Model, Softmax, Maxout
    from thinc.api import flatten_add_lengths, chain
    from thinc.t2v import Pooling, sum_pool, mean_pool, max_pool
    from thinc.misc import Residual, LayerNorm
    from spacy._ml import logistic, zero_init

    with Model.define_operators({">>": chain}):
        model = (
            tok2vec
            >> flatten_add_lengths
            >> Pooling(mean_pool)
            >> Softmax(nr_class, width)
        )
    model.tok2vec = tok2vec
    return model


def block_gradients(model):
    from thinc.api import wrap

    def forward(X, drop=0.0):
        Y, _ = model.begin_update(X, drop=drop)
        return Y, None

    return wrap(forward, model)


def create_pipeline(width, embed_size, vectors_model):
    print("Load vectors")
    nlp = spacy.load(vectors_model)
    print("Start training")
    textcat = TextCategorizer(
        nlp.vocab,
        labels=["POSITIVE", "NEGATIVE"],
        model=build_textcat_model(
            Tok2Vec(width=width, embed_size=embed_size), 2, width
        ),
    )

    nlp.add_pipe(textcat)
    return nlp


def train_tensorizer(nlp, texts, dropout, n_iter):
    # temp fix to avoid import issues cf https://github.com/explosion/spaCy/issues/4200
    import tqdm

    tensorizer = nlp.create_pipe("tensorizer")
    nlp.add_pipe(tensorizer)
    optimizer = nlp.begin_training()
    for i in range(n_iter):
        losses = {}
        for i, batch in enumerate(minibatch(tqdm.tqdm(texts))):
            docs = [nlp.make_doc(text) for text in batch]
            tensorizer.update(docs, None, losses=losses, sgd=optimizer, drop=dropout)
        print(losses)
    return optimizer


def train_textcat(nlp, n_texts, n_iter=10):
    # temp fix to avoid import issues cf https://github.com/explosion/spaCy/issues/4200
    import tqdm

    textcat = nlp.get_pipe("textcat")
    tok2vec_weights = textcat.model.tok2vec.to_bytes()
    (train_texts, train_cats), (dev_texts, dev_cats) = load_textcat_data(limit=n_texts)
    print(
        "Using {} examples ({} training, {} evaluation)".format(
            n_texts, len(train_texts), len(dev_texts)
        )
    )
    train_data = list(zip(train_texts, [{"cats": cats} for cats in train_cats]))

    # get names of other pipes to disable them during training
    other_pipes = [pipe for pipe in nlp.pipe_names if pipe != "textcat"]
    with nlp.disable_pipes(*other_pipes):  # only train textcat
        optimizer = nlp.begin_training()
        textcat.model.tok2vec.from_bytes(tok2vec_weights)
        print("Training the model...")
        print("{:^5}\t{:^5}\t{:^5}\t{:^5}".format("LOSS", "P", "R", "F"))
        for i in range(n_iter):
            losses = {"textcat": 0.0}
            # batch up the examples using spaCy's minibatch
            batches = minibatch(tqdm.tqdm(train_data), size=2)
            for batch in batches:
                texts, annotations = zip(*batch)
                nlp.update(texts, annotations, sgd=optimizer, drop=0.2, losses=losses)
            with textcat.model.use_params(optimizer.averages):
                # evaluate on the dev data split off in load_data()
                scores = evaluate_textcat(nlp.tokenizer, textcat, dev_texts, dev_cats)
            print(
                "{0:.3f}\t{1:.3f}\t{2:.3f}\t{3:.3f}".format(  # print a simple table
                    losses["textcat"],
                    scores["textcat_p"],
                    scores["textcat_r"],
                    scores["textcat_f"],
                )
            )


def evaluate_textcat(tokenizer, textcat, texts, cats):
    docs = (tokenizer(text) for text in texts)
    tp = 1e-8
    fp = 1e-8
    tn = 1e-8
    fn = 1e-8
    for i, doc in enumerate(textcat.pipe(docs)):
        gold = cats[i]
        for label, score in doc.cats.items():
            if label not in gold:
                continue
            if score >= 0.5 and gold[label] >= 0.5:
                tp += 1.0
            elif score >= 0.5 and gold[label] < 0.5:
                fp += 1.0
            elif score < 0.5 and gold[label] < 0.5:
                tn += 1
            elif score < 0.5 and gold[label] >= 0.5:
                fn += 1
    precision = tp / (tp + fp)
    recall = tp / (tp + fn)
    f_score = 2 * (precision * recall) / (precision + recall)
    return {"textcat_p": precision, "textcat_r": recall, "textcat_f": f_score}


@plac.annotations(
    width=("Width of CNN layers", "positional", None, int),
    embed_size=("Embedding rows", "positional", None, int),
    pretrain_iters=("Number of iterations to pretrain", "option", "pn", int),
    train_iters=("Number of iterations to pretrain", "option", "tn", int),
    train_examples=("Number of labelled examples", "option", "eg", int),
    vectors_model=("Name or path to vectors model to learn from"),
)
def main(
    width,
    embed_size,
    vectors_model,
    pretrain_iters=30,
    train_iters=30,
    train_examples=1000,
):
    random.seed(0)
    numpy.random.seed(0)
    use_gpu = prefer_gpu()
    print("Using GPU?", use_gpu)

    nlp = create_pipeline(width, embed_size, vectors_model)
    print("Load data")
    texts = load_texts(limit=0)
    print("Train tensorizer")
    optimizer = train_tensorizer(nlp, texts, dropout=0.2, n_iter=pretrain_iters)
    print("Train textcat")
    train_textcat(nlp, train_examples, n_iter=train_iters)


if __name__ == "__main__":
    plac.call(main)