#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import os
from functools import partial
from pathlib import Path
from typing import Dict, Iterable

import datasets
from datasets import DatasetDict, DownloadManager, load_dataset
import pandas as pd


AVAILABLE_DATASETS = {
    'small': 'https://files.grouplens.org/datasets/movielens/ml-latest-small.zip',
    'full': 'https://files.grouplens.org/datasets/movielens/ml-latest.zip',
}


VERSION = datasets.Version("0.0.1")


class MovielensDataset(datasets.GeneratorBasedBuilder):
    """MovielensDataset dataset."""

    BUILDER_CONFIGS = [
        datasets.BuilderConfig(
            name=data_name, version=VERSION, description=f"{data_name} movielens dataset"
        )
        for data_name in AVAILABLE_DATASETS
    ]

    def _info(self) -> datasets.DatasetInfo:
        return datasets.DatasetInfo(
            description="",
            features=datasets.Features(
                {
                    "movieId": datasets.Value("string"),
                    "title": datasets.Value("string"),
                    "genres": datasets.Sequence(datasets.Value("string")),
                    "tag": datasets.Sequence(datasets.Value("string")),
                }
            ),
            supervised_keys=None,
            homepage="https://grouplens.org/datasets/movielens/latest/",
            citation="",
        )

    def _split_generators(
        self, dl_manager: DownloadManager
    ) -> Iterable[datasets.SplitGenerator]:
        downloader = partial(
            lambda split: dl_manager.download_and_extract(
                AVAILABLE_DATASETS[self.config.name]
            )
        )

        folder = os.path.splitext(os.path.basename(AVAILABLE_DATASETS[self.config.name]))[
            0
        ]

        # There is no predefined train/val/test split for this dataset.
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "root_path": downloader("train"),
                    "split": "train",
                    "folder": folder,
                },
            ),
        ]

    def _generate_examples(
        self, root_path: str, split: str, folder: str
    ) -> Iterable[Dict]:
        split_path = Path(root_path) / folder
        movies_file = split_path / "movies.csv"
        movies = pd.read_csv(movies_file, sep=',', encoding='utf-8')
        movies['genres'] = movies['genres'].str.split('|')
        tags_file = split_path / "tags.csv"
        tags = pd.read_csv(tags_file, sep=',', encoding='utf-8')
        tags = tags.groupby('movieId').agg({'tag': list}).reset_index()
        movies = movies.merge(tags, on='movieId')
        for idx, row in movies.iterrows():
            yield idx, {
                'movieId': row['movieId'],
                'title': row['title'],
                'genres': row['genres'],
                'tag': row['tag'],
            }