#!/usr/bin/env python

# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import logging

import pytest
import torch

pytest.importorskip("datasets", reason="datasets is required (install lerobot[dataset])")

from datasets import Dataset  # noqa: E402

from lerobot.datasets.io_utils import (
    hf_transform_to_torch,
)
from lerobot.datasets.sampler import EpisodeAwareSampler, WeightedEpisodeAwareSampler


def calculate_episode_data_index(hf_dataset: Dataset) -> dict[str, torch.Tensor]:
    """Calculate episode data index for testing. Returns {"from": Tensor, "to": Tensor}."""
    episode_data_index: dict[str, list[int]] = {"from": [], "to": []}
    current_episode = None
    if len(hf_dataset) == 0:
        return {"from": torch.tensor([]), "to": torch.tensor([])}
    for idx, episode_idx in enumerate(hf_dataset["episode_index"]):
        if episode_idx != current_episode:
            episode_data_index["from"].append(idx)
            if current_episode is not None:
                episode_data_index["to"].append(idx)
            current_episode = episode_idx
    episode_data_index["to"].append(idx + 1)
    return {k: torch.tensor(v) for k, v in episode_data_index.items()}


def test_drop_n_first_frames():
    dataset = Dataset.from_dict(
        {
            "timestamp": [0.1, 0.2, 0.3, 0.4, 0.5, 0.6],
            "index": [0, 1, 2, 3, 4, 5],
            "episode_index": [0, 0, 1, 2, 2, 2],
        },
    )
    dataset.set_transform(hf_transform_to_torch)
    episode_data_index = calculate_episode_data_index(dataset)
    sampler = EpisodeAwareSampler(episode_data_index["from"], episode_data_index["to"], drop_n_first_frames=1)
    assert sampler.indices == [1, 4, 5]
    assert len(sampler) == 3
    assert list(sampler) == [1, 4, 5]


def test_drop_n_last_frames():
    dataset = Dataset.from_dict(
        {
            "timestamp": [0.1, 0.2, 0.3, 0.4, 0.5, 0.6],
            "index": [0, 1, 2, 3, 4, 5],
            "episode_index": [0, 0, 1, 2, 2, 2],
        },
    )
    dataset.set_transform(hf_transform_to_torch)
    episode_data_index = calculate_episode_data_index(dataset)
    sampler = EpisodeAwareSampler(episode_data_index["from"], episode_data_index["to"], drop_n_last_frames=1)
    assert sampler.indices == [0, 3, 4]
    assert len(sampler) == 3
    assert list(sampler) == [0, 3, 4]


def test_episode_indices_to_use():
    dataset = Dataset.from_dict(
        {
            "timestamp": [0.1, 0.2, 0.3, 0.4, 0.5, 0.6],
            "index": [0, 1, 2, 3, 4, 5],
            "episode_index": [0, 0, 1, 2, 2, 2],
        },
    )
    dataset.set_transform(hf_transform_to_torch)
    episode_data_index = calculate_episode_data_index(dataset)
    sampler = EpisodeAwareSampler(
        episode_data_index["from"], episode_data_index["to"], episode_indices_to_use=[0, 2]
    )
    assert sampler.indices == [0, 1, 3, 4, 5]
    assert len(sampler) == 5
    assert list(sampler) == [0, 1, 3, 4, 5]


def test_shuffle():
    dataset = Dataset.from_dict(
        {
            "timestamp": [0.1, 0.2, 0.3, 0.4, 0.5, 0.6],
            "index": [0, 1, 2, 3, 4, 5],
            "episode_index": [0, 0, 1, 2, 2, 2],
        },
    )
    dataset.set_transform(hf_transform_to_torch)
    episode_data_index = calculate_episode_data_index(dataset)
    sampler = EpisodeAwareSampler(episode_data_index["from"], episode_data_index["to"], shuffle=False)
    assert sampler.indices == [0, 1, 2, 3, 4, 5]
    assert len(sampler) == 6
    assert list(sampler) == [0, 1, 2, 3, 4, 5]
    sampler = EpisodeAwareSampler(episode_data_index["from"], episode_data_index["to"], shuffle=True)
    assert sampler.indices == [0, 1, 2, 3, 4, 5]
    assert len(sampler) == 6
    assert set(sampler) == {0, 1, 2, 3, 4, 5}


def test_negative_drop_first_frames_raises():
    with pytest.raises(ValueError, match="drop_n_first_frames must be >= 0"):
        EpisodeAwareSampler([0], [10], drop_n_first_frames=-1)


def test_negative_drop_last_frames_raises():
    with pytest.raises(ValueError, match="drop_n_last_frames must be >= 0"):
        EpisodeAwareSampler([0], [10], drop_n_last_frames=-1)


def test_all_episodes_dropped_raises():
    # All episodes have 1 frame, drop_n_first_frames=1 removes all
    with pytest.raises(ValueError, match="No valid frames remain"):
        EpisodeAwareSampler([0, 1, 2], [1, 2, 3], drop_n_first_frames=1)


def test_partial_episode_drop_warns(caplog):
    # Episode 0: 1 frame (dropped), Episode 1: 5 frames (kept)
    with caplog.at_level(logging.WARNING, logger="lerobot.datasets.sampler"):
        sampler = EpisodeAwareSampler([0, 1], [1, 6], drop_n_first_frames=1)
    # Episode 0 is skipped (1 frame, drop 1), Episode 1 keeps frames 2-5
    assert sampler.indices == [2, 3, 4, 5]
    assert "Episode 0" in caplog.text


# --- WeightedEpisodeAwareSampler --------------------------------------------


def test_weighted_sampler_respects_episode_drop_and_length():
    """The episode-boundary frame filtering is applied before weighting,
    and one epoch still yields ``len(indices)`` samples."""
    # One episode, 10 frames; drop the last 2.
    sampler = WeightedEpisodeAwareSampler([0], [10], frame_weights=torch.ones(10), drop_n_last_frames=2)
    assert sampler.indices == list(range(8))
    assert len(sampler) == 8
    draws = list(sampler)
    assert len(draws) == 8
    # Dropped frames 8 and 9 must never be sampled.
    assert all(d in set(range(8)) for d in draws)


def test_weighted_sampler_oversamples_high_weight_frames():
    """A heavily-weighted frame dominates the draws."""
    torch.manual_seed(0)
    # 100 frames, frame 7 is weighted 1000x.
    weights = torch.ones(100)
    weights[7] = 1000.0
    sampler = WeightedEpisodeAwareSampler([0], [100], frame_weights=weights)
    counts = {}
    for _ in range(20):  # 20 epochs
        for d in sampler:
            counts[d] = counts.get(d, 0) + 1
    total = sum(counts.values())
    # Frame 7 should be the overwhelming majority of the 2000 draws.
    assert counts.get(7, 0) / total > 0.9


def test_weighted_sampler_zero_weights_fall_back_to_uniform():
    """If every surviving frame has zero weight, sampling is uniform
    rather than crashing."""
    sampler = WeightedEpisodeAwareSampler([0], [6], frame_weights=torch.zeros(6))
    draws = set(sampler)
    assert draws.issubset(set(range(6)))
    assert len(list(sampler)) == 6


def test_weighted_sampler_rejects_short_weight_vector():
    with pytest.raises(ValueError, match="frame_weights"):
        WeightedEpisodeAwareSampler([0], [10], frame_weights=torch.ones(5))