import pytest
from fastai import *
from fastai.text import *

pytestmark = pytest.mark.integration

import fastai_contrib.data as contrib_data

from fastai_contrib.learner import bilm_learner

def read_file(fname):
    texts = []
    with open(fname, 'r') as f:
        texts = f.readlines()
    labels = [0] * len(texts)
    df = pd.DataFrame({'labels':labels, 'texts':texts}, columns = ['labels', 'texts'])
    return df

def prep_human_numbers():
    path = untar_data(URLs.HUMAN_NUMBERS)
    df_trn = read_file(path/'train.txt')
    df_val = read_file(path/'valid.txt')
    return path, df_trn, df_val

def manual_seed(seed=42):
    torch.manual_seed(seed)
    np.random.seed(seed)
    if torch.cuda.is_available():
        torch.cuda.manual_seed_all(seed)
        torch.backends.cudnn.deterministic = True
        torch.backends.cudnn.benchmark = False

@pytest.fixture(scope="module")
def learn():
    path, df_trn, df_val = prep_human_numbers()
    data = TextLMDataBunch.from_df(path, df_trn, df_val, tokenizer=Tokenizer(BaseTokenizer))
    learn = language_model_learner(data, emb_sz=100, nl=1, drop_mult=0.1)
    learn.fit_one_cycle(4, 5e-3)
    return learn

###################### NEW CODE

def test_val_loss(learn):
    assert learn.validate()[1] > 0.5

def test_bilm_lstm_can_be_trained():
    manual_seed()
    path, df_trn, df_val = prep_human_numbers()
    data = TextLMDataBunch.from_df(path, df_trn, df_val, tokenizer=Tokenizer(BaseTokenizer),
                                   lm_type = contrib_data.LanguageModelType.BiLM)

    learn = bilm_learner(data, emb_sz=100, nl=1, drop_mult=0.1, qrnn=False)
    learn.metrics = []
    learn.fit_one_cycle(4, 5e-3)
    assert learn.validate()[0] < 2 #TODO Change to accuracy once it is fixed

def test_bwdlm_lstm_can_be_trained():
    manual_seed()
    path, df_trn, df_val = prep_human_numbers()
    data = TextLMDataBunch.from_df(path, df_trn, df_val, tokenizer=Tokenizer(BaseTokenizer),
                                   lm_type = contrib_data.LanguageModelType.BwdLM)

    learn = language_model_learner(data, emb_sz=100, nl=1, drop_mult=0.1, qrnn=False)
    learn.fit_one_cycle(4, 5e-3)
    assert learn.validate()[1] > 0.5