# 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 unittest

import tests.utils as test_utils
import torch
from fairseq.data import TokenBlockDataset


class TestTokenBlockDataset(unittest.TestCase):
    def _build_dataset(self, data, **kwargs):
        sizes = [len(x) for x in data]
        underlying_ds = test_utils.TestDataset(data)
        return TokenBlockDataset(underlying_ds, sizes, **kwargs)

    def test_eos_break_mode(self):
        data = [
            torch.tensor([5, 4, 3, 2, 1], dtype=torch.long),
            torch.tensor([1], dtype=torch.long),
            torch.tensor([8, 7, 6, 1], dtype=torch.long),
        ]
        ds = self._build_dataset(data, block_size=None, pad=0, eos=1, break_mode="eos")
        self.assertEqual(ds[0].tolist(), [5, 4, 3, 2, 1])
        self.assertEqual(ds[1].tolist(), [1])
        self.assertEqual(ds[2].tolist(), [8, 7, 6, 1])

        data = [
            torch.tensor([5, 4, 3, 2, 1], dtype=torch.long),
            torch.tensor([8, 7, 6, 1], dtype=torch.long),
            torch.tensor([1], dtype=torch.long),
        ]
        ds = self._build_dataset(data, block_size=None, pad=0, eos=1, break_mode="eos")
        self.assertEqual(ds[0].tolist(), [5, 4, 3, 2, 1])
        self.assertEqual(ds[1].tolist(), [8, 7, 6, 1])
        self.assertEqual(ds[2].tolist(), [1])

    def test_block_break_mode(self):
        data = [
            torch.tensor([5, 4, 3, 2, 1], dtype=torch.long),
            torch.tensor([8, 7, 6, 1], dtype=torch.long),
            torch.tensor([9, 1], dtype=torch.long),
        ]
        ds = self._build_dataset(data, block_size=3, pad=0, eos=1, break_mode="none")
        self.assertEqual(ds[0].tolist(), [5, 4, 3])
        self.assertEqual(ds[1].tolist(), [2, 1, 8])
        self.assertEqual(ds[2].tolist(), [7, 6, 1])
        self.assertEqual(ds[3].tolist(), [9, 1])

    def test_complete_break_mode(self):
        data = [
            torch.tensor([5, 4, 3, 2, 1], dtype=torch.long),
            torch.tensor([8, 7, 6, 1], dtype=torch.long),
            torch.tensor([9, 1], dtype=torch.long),
        ]
        ds = self._build_dataset(
            data, block_size=6, pad=0, eos=1, break_mode="complete"
        )
        self.assertEqual(ds[0].tolist(), [5, 4, 3, 2, 1])
        self.assertEqual(ds[1].tolist(), [8, 7, 6, 1, 9, 1])

        data = [
            torch.tensor([4, 3, 2, 1], dtype=torch.long),
            torch.tensor([5, 1], dtype=torch.long),
            torch.tensor([1], dtype=torch.long),
            torch.tensor([6, 1], dtype=torch.long),
        ]
        ds = self._build_dataset(
            data, block_size=3, pad=0, eos=1, break_mode="complete"
        )
        self.assertEqual(ds[0].tolist(), [4, 3, 2, 1])
        self.assertEqual(ds[1].tolist(), [5, 1, 1])
        self.assertEqual(ds[2].tolist(), [6, 1])

    def test_4billion_tokens(self):
        """Regression test for numpy type promotion issue https://github.com/numpy/numpy/issues/5745"""
        data = [torch.tensor(list(range(10000)), dtype=torch.long)] * 430000
        ds = self._build_dataset(
            data, block_size=6, pad=0, eos=1, break_mode="complete"
        )
        ds[-1]  # __getitem__ works
        start, end = ds.slice_indices[-1]
        assert end > 4294967295  # data must be sufficiently large to overflow uint32
        assert not isinstance(
            end + 1, float
        )  # this would also raise, since np.uint64(1) + 1 => 2.0


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