# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.


import unittest
from collections import defaultdict
from dataclasses import dataclass

from pytorch3d.implicitron.dataset.dataset_base import DatasetBase
from pytorch3d.implicitron.dataset.scene_batch_sampler import SceneBatchSampler


@dataclass
class MockFrameAnnotation:
    frame_number: int
    frame_timestamp: float = 0.0


class MockDataset(DatasetBase):
    def __init__(self, num_seq, max_frame_gap=1):
        """
        Makes a gap of max_frame_gap frame numbers in the middle of each sequence
        """
        self.seq_annots = {f"seq_{i}": None for i in range(num_seq)}
        self._seq_to_idx = {
            f"seq_{i}": list(range(i * 10, i * 10 + 10)) for i in range(num_seq)
        }

        # frame numbers within sequence: [0, ..., 4, n, ..., n+4]
        # where n - 4 == max_frame_gap
        frame_nos = list(range(5)) + list(range(4 + max_frame_gap, 9 + max_frame_gap))
        self.frame_annots = [
            {"frame_annotation": MockFrameAnnotation(no)} for no in frame_nos * num_seq
        ]

    def get_frame_numbers_and_timestamps(self, idxs):
        out = []
        for idx in idxs:
            frame_annotation = self.frame_annots[idx]["frame_annotation"]
            out.append(
                (frame_annotation.frame_number, frame_annotation.frame_timestamp)
            )
        return out


class TestSceneBatchSampler(unittest.TestCase):
    def setUp(self):
        self.dataset_overfit = MockDataset(1)

    def test_overfit(self):
        num_batches = 3
        batch_size = 10
        sampler = SceneBatchSampler(
            self.dataset_overfit,
            batch_size=batch_size,
            num_batches=num_batches,
            images_per_seq_options=[10],  # will try to sample batch_size anyway
        )

        self.assertEqual(len(sampler), num_batches)

        it = iter(sampler)
        for _ in range(num_batches):
            batch = next(it)
            self.assertIsNotNone(batch)
            self.assertEqual(len(batch), batch_size)  # true for our examples
            self.assertTrue(all(idx // 10 == 0 for idx in batch))

        with self.assertRaises(StopIteration):
            batch = next(it)

    def test_multiseq(self):
        for ips_options in [[10], [2], [3], [2, 3, 4]]:
            for sample_consecutive_frames in [True, False]:
                for consecutive_frames_max_gap in [0, 1, 3]:
                    self._test_multiseq_flavour(
                        ips_options,
                        sample_consecutive_frames,
                        consecutive_frames_max_gap,
                    )

    def test_multiseq_gaps(self):
        num_batches = 16
        batch_size = 10
        dataset_multiseq = MockDataset(5, max_frame_gap=3)
        for ips_options in [[10], [2], [3], [2, 3, 4]]:
            debug_info = f" Images per sequence: {ips_options}."

            sampler = SceneBatchSampler(
                dataset_multiseq,
                batch_size=batch_size,
                num_batches=num_batches,
                images_per_seq_options=ips_options,
                sample_consecutive_frames=True,
                consecutive_frames_max_gap=1,
            )

            self.assertEqual(len(sampler), num_batches, msg=debug_info)

            it = iter(sampler)
            for _ in range(num_batches):
                batch = next(it)
                self.assertIsNotNone(batch, "batch is None in" + debug_info)
                if max(ips_options) > 5:
                    # true for our examples
                    self.assertEqual(len(batch), 5, msg=debug_info)
                else:
                    # true for our examples
                    self.assertEqual(len(batch), batch_size, msg=debug_info)

                self._check_frames_are_consecutive(
                    batch, dataset_multiseq.frame_annots, debug_info
                )

    def _test_multiseq_flavour(
        self,
        ips_options,
        sample_consecutive_frames,
        consecutive_frames_max_gap,
        num_batches=16,
        batch_size=10,
    ):
        debug_info = (
            f" Images per sequence: {ips_options}, "
            f"sample_consecutive_frames: {sample_consecutive_frames}, "
            f"consecutive_frames_max_gap: {consecutive_frames_max_gap}, "
        )
        # in this test, either consecutive_frames_max_gap == max_frame_gap,
        # or consecutive_frames_max_gap == 0, so segments consist of full sequences
        frame_gap = consecutive_frames_max_gap if consecutive_frames_max_gap > 0 else 3
        dataset_multiseq = MockDataset(5, max_frame_gap=frame_gap)
        sampler = SceneBatchSampler(
            dataset_multiseq,
            batch_size=batch_size,
            num_batches=num_batches,
            images_per_seq_options=ips_options,
            sample_consecutive_frames=sample_consecutive_frames,
            consecutive_frames_max_gap=consecutive_frames_max_gap,
        )

        self.assertEqual(len(sampler), num_batches, msg=debug_info)

        it = iter(sampler)
        typical_counts = set()
        for _ in range(num_batches):
            batch = next(it)
            self.assertIsNotNone(batch, "batch is None in" + debug_info)
            # true for our examples
            self.assertEqual(len(batch), batch_size, msg=debug_info)
            # find distribution over sequences
            counts = _count_by_quotient(batch, 10)
            freqs = _count_by_quotient(counts.values(), 1)
            self.assertLessEqual(
                len(freqs),
                2,
                msg="We should have maximum of 2 different "
                "frequences of sequences in the batch." + debug_info,
            )
            if len(freqs) == 2:
                most_seq_count = max(*freqs.keys())
                last_seq = min(*freqs.keys())
                self.assertEqual(
                    freqs[last_seq],
                    1,
                    msg="Only one odd sequence allowed." + debug_info,
                )
            else:
                self.assertEqual(len(freqs), 1)
                most_seq_count = next(iter(freqs))

            self.assertIn(most_seq_count, ips_options)
            typical_counts.add(most_seq_count)

            if sample_consecutive_frames:
                self._check_frames_are_consecutive(
                    batch,
                    dataset_multiseq.frame_annots,
                    debug_info,
                    max_gap=consecutive_frames_max_gap,
                )

        self.assertTrue(
            all(i in typical_counts for i in ips_options),
            "Some of the frequency options did not occur among "
            f"the {num_batches} batches (could be just bad luck)." + debug_info,
        )

        with self.assertRaises(StopIteration):
            batch = next(it)

    def _check_frames_are_consecutive(self, batch, annots, debug_info, max_gap=1):
        # make sure that sampled frames are consecutive
        for i in range(len(batch) - 1):
            curr_idx, next_idx = batch[i : i + 2]
            if curr_idx // 10 == next_idx // 10:  # same sequence
                if max_gap > 0:
                    curr_idx, next_idx = [
                        annots[idx]["frame_annotation"].frame_number
                        for idx in (curr_idx, next_idx)
                    ]
                    gap = max_gap
                else:
                    gap = 1  # we'll check that raw dataset indices are consecutive

                self.assertLessEqual(next_idx - curr_idx, gap, msg=debug_info)


def _count_by_quotient(indices, divisor):
    counter = defaultdict(int)
    for i in indices:
        counter[i // divisor] += 1

    return counter
