"""Tests for the highlight scoring engine.

Verifies heuristic scoring produces sensible orderings and handles
edge cases gracefully.
"""

from __future__ import annotations

import pytest

from backend.pipeline.highlight_scorer import (
    _content_type_weight,
    _duration_fitness,
    _pause_density,
    _plugin_richness,
    _source_quality_weight,
    _speaking_pace_fitness,
    _specificity_density,
    _speech_rate_variance,
    _transcript_energy,
    _video_type_weight,
    extract_word_timings,
    score_moment,
)


# ── Fixture helpers ──────────────────────────────────────────────────────────

def _ideal_moment() -> dict:
    """45s technique moment, 3 plugins, specific summary, structured source."""
    return dict(
        start_time=10.0,
        end_time=55.0,  # 45s duration
        content_type="technique",
        summary=(
            "Set the compressor threshold to -18 dB with a 4:1 ratio, "
            "then boost the high shelf at 12 kHz by 3.5 dB using FabFilter Pro-Q 3."
        ),
        plugins=["FabFilter Pro-Q 3", "SSL G-Bus Compressor", "Valhalla Room"],
        raw_transcript=(
            "The trick is to set the threshold low enough. Notice how "
            "the compressor grabs the transients. Because we want to preserve "
            "the dynamics, I always back off the ratio. The key is finding "
            "that sweet spot where it's controlling but not squashing."
        ),
        source_quality="structured",
        video_content_type="tutorial",
    )


def _mediocre_moment() -> dict:
    """90s settings moment, 1 plugin, decent summary, mixed source."""
    return dict(
        start_time=120.0,
        end_time=210.0,  # 90s duration
        content_type="settings",
        summary="Adjust the EQ settings for the vocal track to get a clearer sound.",
        plugins=["FabFilter Pro-Q 3"],
        raw_transcript=(
            "So here we're just going to adjust this. I think it sounds "
            "better when we cut some of the low end. Let me show you what "
            "I mean. Yeah, that's better."
        ),
        source_quality="mixed",
        video_content_type="breakdown",
    )


def _poor_moment() -> dict:
    """300s reasoning moment, 0 plugins, vague summary, unstructured source."""
    return dict(
        start_time=0.0,
        end_time=300.0,  # 300s duration → zero for duration_fitness
        content_type="reasoning",
        summary="General discussion about mixing philosophy and approach.",
        plugins=[],
        raw_transcript=(
            "I think mixing is really about taste. Everyone has their own "
            "approach. Some people like it loud, some people like it quiet. "
            "There's no right or wrong way to do it really."
        ),
        source_quality="unstructured",
        video_content_type="livestream",
    )


def _make_word_timings(
    start: float = 0.0,
    count: int = 40,
    wps: float = 4.0,
    pause_every: int | None = None,
    pause_duration: float = 0.8,
) -> list[dict]:
    """Generate synthetic word-timing dicts for testing.

    Parameters
    ----------
    start : float
        Start time in seconds.
    count : int
        Number of words to generate.
    wps : float
        Words per second (base rate).
    pause_every : int | None
        Insert a pause every N words. None = no pauses.
    pause_duration : float
        Duration of each pause in seconds.
    """
    timings = []
    t = start
    word_dur = 1.0 / wps * 0.7  # 70% speaking, 30% normal gap
    gap = 1.0 / wps * 0.3

    for i in range(count):
        timings.append({"word": f"word{i}", "start": t, "end": t + word_dur})
        t += word_dur + gap
        if pause_every and (i + 1) % pause_every == 0:
            t += pause_duration
    return timings


def _make_transcript_segments(word_timings: list[dict], words_per_segment: int = 10) -> list[dict]:
    """Group word timings into transcript segments for extract_word_timings tests."""
    segments = []
    for i in range(0, len(word_timings), words_per_segment):
        chunk = word_timings[i : i + words_per_segment]
        segments.append({"words": chunk})
    return segments


# ── Tests ────────────────────────────────────────────────────────────────────

class TestScoreMoment:
    def test_ideal_moment_scores_high(self):
        result = score_moment(**_ideal_moment())
        assert result["score"] > 0.7, f"Ideal moment scored {result['score']}, expected > 0.7"

    def test_poor_moment_scores_low(self):
        result = score_moment(**_poor_moment())
        assert result["score"] < 0.4, f"Poor moment scored {result['score']}, expected < 0.4"

    def test_ordering_is_sensible(self):
        ideal = score_moment(**_ideal_moment())
        mediocre = score_moment(**_mediocre_moment())
        poor = score_moment(**_poor_moment())

        assert ideal["score"] > mediocre["score"] > poor["score"], (
            f"Expected ideal ({ideal['score']:.3f}) > "
            f"mediocre ({mediocre['score']:.3f}) > "
            f"poor ({poor['score']:.3f})"
        )

    def test_score_bounds(self):
        """All scores in [0.0, 1.0] for edge cases."""
        edge_cases = [
            dict(start_time=0, end_time=0, summary="", plugins=None, raw_transcript=None),
            dict(start_time=0, end_time=500, summary=None, plugins=[], raw_transcript=""),
            dict(start_time=0, end_time=45, summary="x" * 10000, plugins=["a"] * 100),
            dict(start_time=100, end_time=100),  # zero duration
        ]
        for kwargs in edge_cases:
            result = score_moment(**kwargs)
            assert 0.0 <= result["score"] <= 1.0, f"Score {result['score']} out of bounds for {kwargs}"
            for dim, val in result["score_breakdown"].items():
                assert 0.0 <= val <= 1.0, f"{dim}={val} out of bounds for {kwargs}"

    def test_missing_optional_fields(self):
        """None raw_transcript and None plugins don't crash."""
        result = score_moment(
            start_time=10.0,
            end_time=55.0,
            content_type="technique",
            summary="A summary.",
            plugins=None,
            raw_transcript=None,
            source_quality=None,
            video_content_type=None,
        )
        assert 0.0 <= result["score"] <= 1.0
        assert result["duration_secs"] == 45.0
        assert len(result["score_breakdown"]) == 10

    def test_returns_duration_secs(self):
        result = score_moment(start_time=10.0, end_time=55.0)
        assert result["duration_secs"] == 45.0

    def test_breakdown_has_ten_dimensions(self):
        result = score_moment(**_ideal_moment())
        assert len(result["score_breakdown"]) == 10
        expected_keys = {
            "duration_score", "content_density_score", "technique_relevance_score",
            "plugin_diversity_score", "engagement_proxy_score", "position_score",
            "uniqueness_score", "speech_rate_variance_score", "pause_density_score",
            "speaking_pace_score",
        }
        assert set(result["score_breakdown"].keys()) == expected_keys

    def test_without_word_timings_audio_dims_are_neutral(self):
        """When word_timings is None, audio proxy dimensions score 0.5."""
        result = score_moment(start_time=10.0, end_time=55.0)
        bd = result["score_breakdown"]
        assert bd["speech_rate_variance_score"] == 0.5
        assert bd["pause_density_score"] == 0.5
        assert bd["speaking_pace_score"] == 0.5

    def test_with_word_timings_changes_score(self):
        """Providing word_timings should shift the composite score vs without."""
        base = _ideal_moment()
        without = score_moment(**base)
        # Add word timings at a good teaching pace (~4 WPS) with some pauses
        timings = _make_word_timings(start=10.0, count=120, wps=4.0, pause_every=15)
        with_timings = score_moment(**base, word_timings=timings)
        # Scores should differ since audio dims are no longer neutral
        assert with_timings["score"] != without["score"]


class TestDurationFitness:
    def test_bell_curve_peak(self):
        """45s scores higher than 10s, 10s scores higher than 400s."""
        assert _duration_fitness(45) > _duration_fitness(10)
        assert _duration_fitness(10) > _duration_fitness(400)

    def test_sweet_spot(self):
        assert _duration_fitness(30) == 1.0
        assert _duration_fitness(45) == 1.0
        assert _duration_fitness(60) == 1.0

    def test_zero_at_extremes(self):
        assert _duration_fitness(0) == 0.0
        assert _duration_fitness(300) == 0.0
        assert _duration_fitness(500) == 0.0

    def test_negative_duration(self):
        assert _duration_fitness(-10) == 0.0


class TestContentTypeWeight:
    def test_technique_highest(self):
        assert _content_type_weight("technique") == 1.0

    def test_reasoning_lowest_known(self):
        assert _content_type_weight("reasoning") == 0.4

    def test_unknown_gets_default(self):
        assert _content_type_weight("unknown") == 0.5
        assert _content_type_weight(None) == 0.5


class TestSpecificityDensity:
    def test_specific_summary_scores_high(self):
        summary = "Set threshold to -18 dB with 4:1 ratio, boost 12 kHz by 3.5 dB"
        score = _specificity_density(summary)
        assert score > 0.5

    def test_vague_summary_scores_low(self):
        score = _specificity_density("General discussion about mixing philosophy.")
        assert score < 0.3

    def test_empty_returns_zero(self):
        assert _specificity_density("") == 0.0
        assert _specificity_density(None) == 0.0


class TestPluginRichness:
    def test_three_plugins_maxes_out(self):
        assert _plugin_richness(["a", "b", "c"]) == 1.0

    def test_more_than_three_capped(self):
        assert _plugin_richness(["a", "b", "c", "d"]) == 1.0

    def test_empty(self):
        assert _plugin_richness([]) == 0.0
        assert _plugin_richness(None) == 0.0


class TestTranscriptEnergy:
    def test_teaching_phrases_score_high(self):
        transcript = (
            "The trick is to notice how the compressor behaves. "
            "Because we want dynamics, I always set it gently. The key is balance."
        )
        score = _transcript_energy(transcript)
        assert score > 0.5

    def test_bland_transcript_scores_low(self):
        transcript = "And then we adjust this slider here. Okay that sounds fine."
        score = _transcript_energy(transcript)
        assert score < 0.3

    def test_empty(self):
        assert _transcript_energy("") == 0.0
        assert _transcript_energy(None) == 0.0


class TestSourceQualityWeight:
    def test_structured_highest(self):
        assert _source_quality_weight("structured") == 1.0

    def test_none_default(self):
        assert _source_quality_weight(None) == 0.5


class TestVideoTypeWeight:
    def test_tutorial_highest(self):
        assert _video_type_weight("tutorial") == 1.0

    def test_short_form_lowest(self):
        assert _video_type_weight("short_form") == 0.3

    def test_none_default(self):
        assert _video_type_weight(None) == 0.5


# ── Audio proxy function tests ───────────────────────────────────────────────


class TestExtractWordTimings:
    def test_filters_by_time_window(self):
        words = _make_word_timings(start=0.0, count=40, wps=4.0)
        segments = _make_transcript_segments(words)
        # Extract window 2.0–5.0s
        result = extract_word_timings(segments, start_time=2.0, end_time=5.0)
        for w in result:
            assert 2.0 <= w["start"] <= 5.0

    def test_returns_all_when_window_covers_entire_range(self):
        words = _make_word_timings(start=0.0, count=20, wps=4.0)
        segments = _make_transcript_segments(words)
        result = extract_word_timings(segments, start_time=0.0, end_time=100.0)
        assert len(result) == 20

    def test_empty_transcript_data(self):
        assert extract_word_timings([], start_time=0.0, end_time=10.0) == []

    def test_no_words_in_window(self):
        words = _make_word_timings(start=0.0, count=10, wps=4.0)
        segments = _make_transcript_segments(words)
        # Window far beyond the word timings
        result = extract_word_timings(segments, start_time=100.0, end_time=200.0)
        assert result == []

    def test_segments_without_words_key(self):
        """Segments missing 'words' are skipped gracefully."""
        segments = [{"text": "hello"}, {"words": [{"start": 1.0, "end": 1.2, "word": "a"}]}]
        result = extract_word_timings(segments, start_time=0.0, end_time=10.0)
        assert len(result) == 1

    def test_words_without_start_are_skipped(self):
        segments = [{"words": [{"end": 1.2, "word": "a"}, {"start": 2.0, "end": 2.2, "word": "b"}]}]
        result = extract_word_timings(segments, start_time=0.0, end_time=10.0)
        assert len(result) == 1
        assert result[0]["word"] == "b"


class TestSpeechRateVariance:
    def test_none_returns_neutral(self):
        assert _speech_rate_variance(None) == 0.5

    def test_too_few_words_returns_neutral(self):
        timings = _make_word_timings(count=3, wps=4.0)
        assert _speech_rate_variance(timings) == 0.5

    def test_short_span_returns_neutral(self):
        """Words spanning <5s should return neutral."""
        timings = _make_word_timings(count=10, wps=4.0, start=0.0)
        # 10 words at 4 WPS = 2.5s span → too short
        assert _speech_rate_variance(timings) == 0.5

    def test_uniform_pace_scores_low(self):
        """Steady 4 WPS for 30s → low variance."""
        timings = _make_word_timings(start=0.0, count=120, wps=4.0)
        score = _speech_rate_variance(timings)
        assert score < 0.4, f"Uniform pace scored {score}, expected < 0.4"

    def test_varied_pace_scores_higher(self):
        """Alternating fast/slow sections → higher variance."""
        timings = []
        t = 0.0
        # Fast section: 6 WPS for 10s
        for i in range(60):
            dur = 0.12
            timings.append({"word": f"w{i}", "start": t, "end": t + dur})
            t += 1.0 / 6.0
        # Slow section: 2 WPS for 10s
        for i in range(20):
            dur = 0.3
            timings.append({"word": f"w{60+i}", "start": t, "end": t + dur})
            t += 0.5
        score = _speech_rate_variance(timings)
        uniform_score = _speech_rate_variance(
            _make_word_timings(start=0.0, count=80, wps=4.0)
        )
        assert score > uniform_score, (
            f"Varied pace ({score:.3f}) should be > uniform ({uniform_score:.3f})"
        )

    def test_score_bounded(self):
        timings = _make_word_timings(start=0.0, count=200, wps=4.0)
        score = _speech_rate_variance(timings)
        assert 0.0 <= score <= 1.0


class TestPauseDensity:
    def test_none_returns_neutral(self):
        assert _pause_density(None) == 0.5

    def test_single_word_returns_neutral(self):
        assert _pause_density([{"start": 0.0, "end": 0.2}]) == 0.5

    def test_no_pauses_scores_zero(self):
        """Continuous speech with no gaps >0.5s → 0."""
        timings = _make_word_timings(start=0.0, count=60, wps=4.0)
        score = _pause_density(timings)
        assert score == 0.0

    def test_frequent_pauses_scores_high(self):
        """Pauses every 5 words → high density."""
        timings = _make_word_timings(start=0.0, count=60, wps=4.0, pause_every=5, pause_duration=0.8)
        score = _pause_density(timings)
        assert score > 0.5, f"Frequent pauses scored {score}, expected > 0.5"

    def test_long_pauses_weighted_more(self):
        """One 1.5s pause should score higher than one 0.6s pause in a longer segment."""
        # Build timings with one long pause at midpoint — 60 words for longer duration
        long_pause = []
        t = 0.0
        for i in range(60):
            long_pause.append({"word": f"w{i}", "start": t, "end": t + 0.15})
            t += 0.25
            if i == 29:
                t += 1.5  # long pause >1.0s
        # Build timings with one short pause — same word count
        short_pause = []
        t = 0.0
        for i in range(60):
            short_pause.append({"word": f"w{i}", "start": t, "end": t + 0.15})
            t += 0.25
            if i == 29:
                t += 0.6  # short pause >0.5s but <1.0s
        assert _pause_density(long_pause) > _pause_density(short_pause)

    def test_score_bounded(self):
        timings = _make_word_timings(start=0.0, count=60, wps=4.0, pause_every=3, pause_duration=1.5)
        score = _pause_density(timings)
        assert 0.0 <= score <= 1.0


class TestSpeakingPaceFitness:
    def test_none_returns_neutral(self):
        assert _speaking_pace_fitness(None) == 0.5

    def test_single_word_returns_neutral(self):
        assert _speaking_pace_fitness([{"start": 0.0, "end": 0.2}]) == 0.5

    def test_optimal_pace_scores_high(self):
        """4 WPS (optimal teaching pace) → 1.0."""
        timings = _make_word_timings(start=0.0, count=40, wps=4.0)
        score = _speaking_pace_fitness(timings)
        assert score == 1.0, f"4 WPS scored {score}, expected 1.0"

    def test_three_wps_is_sweet_spot_edge(self):
        timings = _make_word_timings(start=0.0, count=30, wps=3.0)
        score = _speaking_pace_fitness(timings)
        assert score == 1.0

    def test_five_wps_is_sweet_spot_edge(self):
        timings = _make_word_timings(start=0.0, count=50, wps=5.0)
        score = _speaking_pace_fitness(timings)
        assert score > 0.95, f"5 WPS scored {score}, expected near 1.0"

    def test_too_slow_scores_lower(self):
        """1.5 WPS → below sweet spot."""
        timings = _make_word_timings(start=0.0, count=15, wps=1.5)
        score = _speaking_pace_fitness(timings)
        assert 0.4 < score < 0.6, f"1.5 WPS scored {score}, expected ~0.5"

    def test_too_fast_scores_lower(self):
        """8 WPS → above sweet spot."""
        timings = _make_word_timings(start=0.0, count=80, wps=8.0)
        score = _speaking_pace_fitness(timings)
        assert 0.0 < score < 1.0

    def test_very_fast_scores_zero(self):
        """10+ WPS → 0."""
        timings = _make_word_timings(start=0.0, count=110, wps=11.0)
        score = _speaking_pace_fitness(timings)
        assert score == 0.0

    def test_zero_wps_scores_zero(self):
        """Very short duration → neutral."""
        timings = [{"start": 0.0, "end": 0.01}, {"start": 0.005, "end": 0.015}]
        score = _speaking_pace_fitness(timings)
        # Duration ~0.015s → too short → 0.5 (neutral)
        assert score == 0.5

    def test_score_bounded(self):
        for wps in [0.5, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 8.0, 10.0]:
            timings = _make_word_timings(start=0.0, count=max(10, int(wps * 10)), wps=wps)
            score = _speaking_pace_fitness(timings)
            assert 0.0 <= score <= 1.0, f"WPS {wps} scored {score} out of bounds"


class TestBackwardCompatibility:
    """Ensure the weight rebalancing doesn't break existing relative orderings."""

    def test_ideal_still_beats_poor(self):
        ideal = score_moment(**_ideal_moment())
        poor = score_moment(**_poor_moment())
        assert ideal["score"] > poor["score"]

    def test_ideal_still_above_threshold(self):
        result = score_moment(**_ideal_moment())
        assert result["score"] > 0.6, f"Ideal scored {result['score']}, expected > 0.6"

    def test_poor_still_below_threshold(self):
        result = score_moment(**_poor_moment())
        assert result["score"] < 0.45, f"Poor scored {result['score']}, expected < 0.45"

    def test_weights_sum_to_one(self):
        from backend.pipeline.highlight_scorer import _WEIGHTS
        assert abs(sum(_WEIGHTS.values()) - 1.0) < 1e-9