import logging
import os
import time

import h5py
import numpy as np
from scipy.sparse import coo_matrix, csr_matrix

from implicit.datasets import _download

log = logging.getLogger("implicit")


URL = "https://github.com/benfred/recommender_data/releases/download/v1.0/lastfm_360k.hdf5"


def get_lastfm():
    """Returns the lastfm360k dataset, downloading locally if necessary.
    Returns a tuple of (artistids, userids, plays) where plays is a CSR matrix"""

    filename = os.path.join(_download.LOCAL_CACHE_DIR, "lastfm_360k.hdf5")
    if not os.path.isfile(filename):
        log.info("Downloading dataset to '%s'", filename)
        _download.download_file(URL, filename)
    else:
        log.info("Using cached dataset at '%s'", filename)

    with h5py.File(filename, "r") as f:
        m = f.get("artist_user_plays")
        plays = csr_matrix((m.get("data"), m.get("indices"), m.get("indptr")))
        return np.array(f["artist"]), np.array(f["user"]), plays


def generate_dataset(filename, outputfilename):
    """Generates a hdf5 lastfm datasetfile from the raw datafiles found at:
    http://ocelma.net/MusicRecommendationDataset/lastfm-360K.html

    You shouldn't have to run this yourself, and can instead just download the
    output using the 'get_lastfm' funciton./

    Note there are some invalid entries in this dataset, running
    this function will clean it up so pandas can read it:
    https://github.com/benfred/bens-blog-code/blob/master/distance-metrics/musicdata.py#L39
    """
    data = _read_dataframe(filename)
    _hfd5_from_dataframe(data, outputfilename)


def _read_dataframe(filename):
    """Reads the original dataset TSV as a pandas dataframe"""
    # delay importing this to avoid another dependency
    import pandas

    # read in triples of user/artist/playcount from the input dataset
    # get a model based off the input params
    start = time.time()
    log.debug("reading data from %s", filename)
    data = pandas.read_table(
        filename, usecols=[0, 2, 3], names=["user", "artist", "plays"], na_filter=False
    )

    # map each artist and user to a unique numeric value
    data["user"] = data["user"].astype("category")
    data["artist"] = data["artist"].astype("category")

    # store as a CSR matrix
    log.debug("read data file in %s", time.time() - start)

    return data


def _hfd5_from_dataframe(data, outputfilename):
    # create a sparse matrix of all the users/plays
    plays = coo_matrix(
        (
            data["plays"].astype(np.float32),
            (data["artist"].cat.codes.copy(), data["user"].cat.codes.copy()),
        )
    ).tocsr()

    with h5py.File(outputfilename, "w") as f:
        g = f.create_group("artist_user_plays")
        g.create_dataset("data", data=plays.data)
        g.create_dataset("indptr", data=plays.indptr)
        g.create_dataset("indices", data=plays.indices)

        dt = h5py.special_dtype(vlen=str)
        artist = list(data["artist"].cat.categories)
        dset = f.create_dataset("artist", (len(artist),), dtype=dt)
        dset[:] = artist

        user = list(data["user"].cat.categories)
        dset = f.create_dataset("user", (len(user),), dtype=dt)
        dset[:] = user
