"""Expected-red CPU contract for prompt-free arbitrary-WAV continuation.

This deliberately imports the future continuous-latent implementation.  It is
not a native AR/RVQ continuation test and it makes no long-song claim.
"""
from __future__ import annotations

import wave
from pathlib import Path

import pytest
import torch

from music3lab.editing.audio_continuation import (  # expected red until implemented
    AudioContinuationError,
    AudioContinuationProjection,
    continue_audio,
    load_audio_tail,
)

SAMPLE_RATE = 44_100
SAMPLES = 44_032
OVERLAP = 1_024
CAPABILITY = "continuous-latent local continuation; not native AR/token continuation or long-song generalization"


def _source(samples: int = SAMPLES) -> torch.Tensor:
    time = torch.arange(samples, dtype=torch.float32) / SAMPLE_RATE
    return torch.stack((0.25 * torch.sin(2 * torch.pi * 220 * time), 0.2 * torch.cos(2 * torch.pi * 330 * time))).unsqueeze(0)


def _write_pcm(path: Path, audio: torch.Tensor, sample_rate: int) -> None:
    channels = audio.shape[0]
    pcm = (audio.clamp(-1, 1).transpose(0, 1) * 32767).to(torch.int16).numpy().tobytes()
    with wave.open(str(path), "wb") as output:
        output.setnchannels(channels)
        output.setsampwidth(2)
        output.setframerate(sample_rate)
        output.writeframes(pcm)


class _Projector:
    capability = CAPABILITY

    def __init__(self) -> None:
        self.seen: list[torch.Tensor] = []

    def __call__(self, source_audio_tail: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
        self.seen.append(source_audio_tail.clone())
        return torch.ones(1, 128, 86), torch.ones(1, 86, 2048)


class _Renderer:
    def __init__(self, audio: torch.Tensor) -> None:
        self.audio, self.calls = audio, 0

    def __call__(self, encoded_context: torch.Tensor, conditioning: torch.Tensor) -> torch.Tensor:
        self.calls += 1
        assert encoded_context.shape == (1, 128, 86)
        assert conditioning.shape == (1, 86, 2048)
        return self.audio.clone()


def test_load_audio_tail_normalizes_recent_mono_and_stereo_wav_to_music3_shape(tmp_path: Path) -> None:
    mono = _source(SAMPLES // 2)[0, :1]
    stereo = _source(SAMPLES)[0]
    mono_path, stereo_path = tmp_path / "mono.wav", tmp_path / "stereo.wav"
    _write_pcm(mono_path, mono, SAMPLE_RATE // 2)
    _write_pcm(stereo_path, stereo, SAMPLE_RATE)
    for path in (mono_path, stereo_path):
        tail = load_audio_tail(path)
        assert tail.shape == (1, 2, SAMPLES)
        assert tail.dtype == torch.float32 and bool(torch.isfinite(tail).all())
        assert tail.abs().max() > 0


def test_projection_receives_only_audio_tail_and_exposes_continuous_geometry() -> None:
    projector, source = _Projector(), _source()
    projection = AudioContinuationProjection(projector)(source)
    assert len(projector.seen) == 1 and torch.equal(projector.seen[0], source)
    assert projection.encoded_context.shape == (1, 128, 86)
    assert projection.conditioning.shape == (1, 86, 2048)
    assert projection.capability == CAPABILITY


def test_continuation_rejects_text_captured_tokens_and_future_audio_inputs() -> None:
    source, projector, renderer = _source(), _Projector(), _Renderer(_source())
    forbidden = {
        "text": "continue this song",
        "captured_c0": torch.zeros(1, 86, 2048),
        "native_tokens": torch.zeros(1, 8, 86, dtype=torch.long),
        "right_audio": _source(),
    }
    for name, value in forbidden.items():
        with pytest.raises(AudioContinuationError, match=name):
            continue_audio(source, projector=projector, renderer=renderer, **{name: value})


def test_continuation_renders_one_section_and_preserves_pre_overlap_source_bit_exactly() -> None:
    source, projector = _source(), _Projector()
    renderer = _Renderer(torch.flip(_source(), dims=(-1,)))
    result = continue_audio(source, projector=projector, renderer=renderer, overlap_samples=OVERLAP)
    assert renderer.calls == 1
    assert result.shape == (1, 2, SAMPLES + SAMPLES - OVERLAP)
    assert torch.equal(result[:, :, : SAMPLES - OVERLAP], source[:, :, : SAMPLES - OVERLAP])
    assert result[:, :, -SAMPLES:].shape == (1, 2, SAMPLES)


def test_continuation_rejects_silent_nonfinite_or_source_copy_renderer_output() -> None:
    source, projector = _source(), _Projector()
    invalid = (torch.zeros_like(source), torch.full_like(source, float("nan")), source)
    for rendered in invalid:
        with pytest.raises(AudioContinuationError, match="finite|silent|copy"):
            continue_audio(source, projector=projector, renderer=_Renderer(rendered), overlap_samples=OVERLAP)


def test_both_audio_conditioning_beats_zero_and_unrelated_injected_metric_baselines() -> None:
    source, projector, rendered = _source(), _Projector(), torch.flip(_source(), dims=(-1,))
    scores = {"both": 0.91, "zero": 0.40, "unrelated": 0.31}
    result = continue_audio(
        source,
        projector=projector,
        renderer=_Renderer(rendered),
        overlap_samples=OVERLAP,
        baseline_conditions={"zero": torch.zeros(1, 86, 2048), "unrelated": -torch.ones(1, 86, 2048)},
        metric=lambda name, _: scores[name],
    )
    assert result.metrics == scores
    assert result.metrics["both"] > result.metrics["zero"] > result.metrics["unrelated"]
    assert result.capability == CAPABILITY