from __future__ import unicode_literals
import json
import random
import contextlib
import shutil
import pytest
import tempfile
from pathlib import Path


from ...gold import GoldParse
from ...pipeline import EntityRecognizer
from ...lang.en import English

try:
    unicode
except NameError:
    unicode = str


@pytest.fixture
def train_data():
    return [
            ["hey",[]],
            ["howdy",[]],
            ["hey there",[]],
            ["hello",[]],
            ["hi",[]],
            ["i'm looking for a place to eat",[]],
            ["i'm looking for a place in the north of town",[[31,36,"location"]]],
            ["show me chinese restaurants",[[8,15,"cuisine"]]],
            ["show me chines restaurants",[[8,14,"cuisine"]]],
            ["yes",[]],
            ["yep",[]],
            ["yeah",[]],
            ["show me a mexican place in the centre",[[31,37,"location"], [10,17,"cuisine"]]],
            ["bye",[]],["goodbye",[]],
            ["good bye",[]],
            ["stop",[]],
            ["end",[]],
            ["i am looking for an indian spot",[[20,26,"cuisine"]]],
            ["search for restaurants",[]],
            ["anywhere in the west",[[16,20,"location"]]],
            ["central indian restaurant",[[0,7,"location"],[8,14,"cuisine"]]],
            ["indeed",[]],
            ["that's right",[]],
            ["ok",[]],
            ["great",[]]
    ]

@pytest.fixture
def additional_entity_types():
    return ['cuisine', 'location']


@contextlib.contextmanager
def temp_save_model(model):
    model_dir = Path(tempfile.mkdtemp())
    # store the fine tuned model
    with (model_dir / "config.json").open('w') as file_:
        data = json.dumps(model.cfg)
        if not isinstance(data, unicode):
            data = data.decode('utf8')
        file_.write(data)
    model.model.dump((model_dir / 'model').as_posix())
    yield model_dir
    shutil.rmtree(model_dir.as_posix())



@pytest.mark.models('en')
def test_issue910(EN, train_data, additional_entity_types):
    '''Test that adding entities and resuming training works passably OK.
    There are two issues here:

    1) We have to readd labels. This isn't very nice.
    2) There's no way to set the learning rate for the weight update, so we
        end up out-of-scale, causing it to learn too fast.
    '''
    nlp = EN
    doc = nlp(u"I am looking for a restaurant in Berlin")
    ents_before_train = [(ent.label_, ent.text) for ent in doc.ents]
    # Fine tune the ner model
    for entity_type in additional_entity_types:
        nlp.entity.add_label(entity_type)

    nlp.entity.model.learn_rate = 0.001
    for itn in range(10):
        random.shuffle(train_data)
        for raw_text, entity_offsets in train_data:
            doc = nlp.make_doc(raw_text)
            nlp.tagger(doc)
            gold = GoldParse(doc, entities=entity_offsets)
            loss = nlp.entity.update(doc, gold)

    with temp_save_model(nlp.entity) as model_dir:
        # Load the fine tuned model
        loaded_ner = EntityRecognizer.load(model_dir, nlp.vocab)

    for raw_text, entity_offsets in train_data:
        doc = nlp.make_doc(raw_text)
        nlp.tagger(doc)
        loaded_ner(doc)
        ents = {(ent.start_char, ent.end_char): ent.label_ for ent in doc.ents}
        for start, end, label in entity_offsets:
            if (start, end) not in ents:
                print(ents)
            assert ents[(start, end)] == label