# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.

import collections
import unittest

import numpy as np

from fairseq.data import ListDataset, ResamplingDataset


class TestResamplingDataset(unittest.TestCase):
    def setUp(self):
        self.strings = ["ab", "c", "def", "ghij"]
        self.weights = [4.0, 2.0, 7.0, 1.5]
        self.size_ratio = 2
        self.dataset = ListDataset(
            self.strings, np.array([len(s) for s in self.strings])
        )

    def _test_common(self, resampling_dataset, iters):
        assert len(self.dataset) == len(self.strings) == len(self.weights)
        assert len(resampling_dataset) == self.size_ratio * len(self.strings)

        results = {"ordered_by_size": True, "max_distribution_diff": 0.0}

        totalfreqs = 0
        freqs = collections.defaultdict(int)

        for epoch_num in range(iters):
            resampling_dataset.set_epoch(epoch_num)

            indices = resampling_dataset.ordered_indices()
            assert len(indices) == len(resampling_dataset)

            prev_size = -1

            for i in indices:
                cur_size = resampling_dataset.size(i)
                # Make sure indices map to same sequences within an epoch
                assert resampling_dataset[i] == resampling_dataset[i]

                # Make sure length of sequence is correct
                assert cur_size == len(resampling_dataset[i])

                freqs[resampling_dataset[i]] += 1
                totalfreqs += 1

                if prev_size > cur_size:
                    results["ordered_by_size"] = False

                prev_size = cur_size

        assert set(freqs.keys()) == set(self.strings)
        for s, weight in zip(self.strings, self.weights):
            freq = freqs[s] / totalfreqs
            expected_freq = weight / sum(self.weights)
            results["max_distribution_diff"] = max(
                results["max_distribution_diff"], abs(expected_freq - freq)
            )

        return results

    def test_resampling_dataset_batch_by_size_false(self):
        resampling_dataset = ResamplingDataset(
            self.dataset,
            self.weights,
            size_ratio=self.size_ratio,
            batch_by_size=False,
            seed=0,
        )

        results = self._test_common(resampling_dataset, iters=1000)

        # For batch_by_size = False, the batches should be returned in
        # arbitrary order of size.
        assert not results["ordered_by_size"]

        # Allow tolerance in distribution error of 2%.
        assert results["max_distribution_diff"] < 0.02

    def test_resampling_dataset_batch_by_size_true(self):
        resampling_dataset = ResamplingDataset(
            self.dataset,
            self.weights,
            size_ratio=self.size_ratio,
            batch_by_size=True,
            seed=0,
        )

        results = self._test_common(resampling_dataset, iters=1000)

        # For batch_by_size = True, the batches should be returned in
        # increasing order of size.
        assert results["ordered_by_size"]

        # Allow tolerance in distribution error of 2%.
        assert results["max_distribution_diff"] < 0.02


if __name__ == "__main__":
    unittest.main()
