import zipfile
from util import download_file_if_not_exists, download_file, get_quapy_home
import os
from os.path import join
from data.base import Dataset
from data.reader import from_text, from_sparse
from data.preprocessing import text2tfidf, reduce_columns


REVIEWS_SENTIMENT_DATASETS = ['hp', 'kindle', 'imdb']
TWITTER_SENTIMENT_DATASETS = ['gasp', 'hcr', 'omd', 'sanders', 'semeval13', 'semeval14', 'semeval15', 'semeval16',
                              'sst', 'wa', 'wb']


def fetch_reviews(dataset_name, tfidf=False, min_df=None, data_home=None):
    assert dataset_name in REVIEWS_SENTIMENT_DATASETS, \
        f'Name {dataset_name} does not match any known dataset for sentiment reviews. ' \
        f'Valid ones are {REVIEWS_SENTIMENT_DATASETS}'
    if data_home is None:
        data_home = get_quapy_home()

    URL_TRAIN = f'https://zenodo.org/record/4117827/files/{dataset_name}_train.txt'
    URL_TEST = f'https://zenodo.org/record/4117827/files/{dataset_name}_test.txt'
    os.makedirs(join(data_home, 'reviews'), exist_ok=True)
    train_path = join(data_home, 'reviews', dataset_name, 'train.txt')
    test_path = join(data_home, 'reviews', dataset_name, 'test.txt')
    download_file_if_not_exists(URL_TRAIN, train_path)
    download_file_if_not_exists(URL_TEST, test_path)

    data = Dataset.load(train_path, test_path, from_text)

    if tfidf:
        text2tfidf(data, inplace=True)

    if min_df is not None:
        reduce_columns(data, min_df=min_df, inplace=True)

    return data


def fetch_twitter(dataset_name, model_selection=False, min_df=None, data_home=None):
    assert dataset_name in TWITTER_SENTIMENT_DATASETS, \
        f'Name {dataset_name} does not match any known dataset for sentiment twitter. ' \
        f'Valid ones are {TWITTER_SENTIMENT_DATASETS}'
    if data_home is None:
        data_home = get_quapy_home()

    URL = 'https://zenodo.org/record/4255764/files/tweet_sentiment_quantification_snam.zip'
    unzipped_path = join(data_home, 'tweet_sentiment_quantification_snam')
    if not os.path.exists(unzipped_path):
        downloaded_path = join(data_home, 'tweet_sentiment_quantification_snam.zip')
        download_file(URL, downloaded_path)
        with zipfile.ZipFile(downloaded_path) as file:
            file.extractall(data_home)
        os.remove(downloaded_path)

    if dataset_name in {'semeval13', 'semeval14', 'semeval15'}:
        trainset_name = 'semeval'
        testset_name  = 'semeval' if model_selection else dataset_name
        print(f"the training and development sets for datasets 'semeval13', 'semeval14', 'semeval15' are common "
              f"(called 'semeval'); returning trainin-set='{trainset_name}' and test-set={testset_name}")
    else:
        trainset_name = testset_name = dataset_name

    if model_selection:
        train = join(unzipped_path, 'train', f'{trainset_name}.train.feature.txt')
        test  = join(unzipped_path, 'test', f'{testset_name}.dev.feature.txt')
    else:
        train = join(unzipped_path, 'train', f'{trainset_name}.train+dev.feature.txt')
        if dataset_name == 'semeval16':
            test = join(unzipped_path, 'test', f'{testset_name}.dev-test.feature.txt')
        else:
            test = join(unzipped_path, 'test', f'{testset_name}.test.feature.txt')

    data = Dataset.load(train, test, from_sparse)

    if min_df is not None:
        reduce_columns(data, min_df=min_df, inplace=True)

    return data