# -*- coding: utf-8 -*-
"""Reuters topic classification dataset.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

from ..utils.data_utils import get_file
from ..preprocessing.sequence import _remove_long_seq
import numpy as np
import json
import warnings


def load_data(path='reuters.npz', num_words=None, skip_top=0,
              maxlen=None, test_split=0.2, seed=113,
              start_char=1, oov_char=2, index_from=3, **kwargs):
    """Loads the Reuters newswire classification dataset.

    # Arguments
        path: where to cache the data (relative to `~/.keras/dataset`).
        num_words: max number of words to include. Words are ranked
            by how often they occur (in the training set) and only
            the most frequent words are kept
        skip_top: skip the top N most frequently occurring words
            (which may not be informative).
        maxlen: truncate sequences after this length.
        test_split: Fraction of the dataset to be used as test data.
        seed: random seed for sample shuffling.
        start_char: The start of a sequence will be marked with this character.
            Set to 1 because 0 is usually the padding character.
        oov_char: words that were cut out because of the `num_words`
            or `skip_top` limit will be replaced with this character.
        index_from: index actual words with this index and higher.

    # Returns
        Tuple of Numpy arrays: `(x_train, y_train), (x_test, y_test)`.

    Note that the 'out of vocabulary' character is only used for
    words that were present in the training set but are not included
    because they're not making the `num_words` cut here.
    Words that were not seen in the training set but are in the test set
    have simply been skipped.
    """
    # Legacy support
    if 'nb_words' in kwargs:
        warnings.warn('The `nb_words` argument in `load_data` '
                      'has been renamed `num_words`.')
        num_words = kwargs.pop('nb_words')
    if kwargs:
        raise TypeError('Unrecognized keyword arguments: ' + str(kwargs))

    path = get_file(path,
                    origin='https://s3.amazonaws.com/text-datasets/reuters.npz',
                    file_hash='87aedbeb0cb229e378797a632c1997b6')
    with np.load(path) as f:
        xs, labels = f['x'], f['y']

    np.random.seed(seed)
    indices = np.arange(len(xs))
    np.random.shuffle(indices)
    xs = xs[indices]
    labels = labels[indices]

    if start_char is not None:
        xs = [[start_char] + [w + index_from for w in x] for x in xs]
    elif index_from:
        xs = [[w + index_from for w in x] for x in xs]

    if maxlen:
        xs, labels = _remove_long_seq(maxlen, xs, labels)

    if not num_words:
        num_words = max([max(x) for x in xs])

    # by convention, use 2 as OOV word
    # reserve 'index_from' (=3 by default) characters:
    # 0 (padding), 1 (start), 2 (OOV)
    if oov_char is not None:
        xs = [[w if skip_top <= w < num_words else oov_char for w in x] for x in xs]
    else:
        xs = [[w for w in x if skip_top <= w < num_words] for x in xs]

    idx = int(len(xs) * (1 - test_split))
    x_train, y_train = np.array(xs[:idx]), np.array(labels[:idx])
    x_test, y_test = np.array(xs[idx:]), np.array(labels[idx:])

    return (x_train, y_train), (x_test, y_test)


def get_word_index(path='reuters_word_index.json'):
    """Retrieves the dictionary mapping word indices back to words.

    # Arguments
        path: where to cache the data (relative to `~/.keras/dataset`).

    # Returns
        The word index dictionary.
    """
    path = get_file(path,
                    origin='https://s3.amazonaws.com/text-datasets/reuters_word_index.json',
                    file_hash='4d44cc38712099c9e383dc6e5f11a921')
    f = open(path)
    data = json.load(f)
    f.close()
    return data