from __future__ import annotations

import wave
from pathlib import Path

import pytest
import torch
from torch import nn

from music3lab.editing.audio_prepend import (
    AudioPrependError,
    SuffixProjection,
    SuffixProjector,
    compose_prepend,
    load_wav_window,
    prepend_audio,
)

N, F, C, Z, E = 44032, 86, 2, 128, 2048


def write_wav(path: Path, rate: int, channels: int) -> None:
    with wave.open(str(path), "wb") as handle:
        handle.setnchannels(channels)
        handle.setsampwidth(2)
        handle.setframerate(rate)
        handle.writeframes(torch.arange(rate * channels, dtype=torch.int16).numpy().tobytes())


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

    def __call__(self, suffix: torch.Tensor) -> SuffixProjection:
        self.calls.append(suffix.clone())
        return SuffixProjection(
            torch.full((suffix.shape[0], Z, F), 7.0),
            torch.zeros((suffix.shape[0], F, E)),
        )


class VelocityFixture:
    def __init__(self, value: float = 1.0) -> None:
        self.value = value
        self.calls: list[torch.Tensor] = []

    def __call__(self, latent: torch.Tensor, condition: torch.Tensor, timestep: torch.Tensor) -> torch.Tensor:
        self.calls.append(latent.clone())
        return torch.full_like(latent, self.value)


class DecoderFixture:
    def __call__(self, latent: torch.Tensor, samples: int) -> torch.Tensor:
        return torch.full((latent.shape[0], C, samples), 0.25)


def suffix() -> torch.Tensor:
    return torch.linspace(-0.9, 0.9, N).repeat(C, 1).unsqueeze(0)


def run(**kwargs):
    return prepend_audio(
        suffix_audio=kwargs.pop("suffix_audio", suffix()),
        sample_rate=kwargs.pop("sample_rate", 44100),
        projector=kwargs.pop("projector", ProjectorFixture()),
        flow=kwargs.pop("flow", VelocityFixture()),
        decoder=kwargs.pop("decoder", DecoderFixture()),
        noise=kwargs.pop("noise", torch.zeros(1, Z, F)),
        **kwargs,
    )


def test_wav_mono_stereo_normalization_and_format_rejection(tmp_path: Path) -> None:
    mono, stereo, wrong = (tmp_path / name for name in ("mono.wav", "stereo.wav", "wrong.wav"))
    write_wav(mono, 44100, 1)
    write_wav(stereo, 44100, 2)
    write_wav(wrong, 48000, 2)
    left, right = load_wav_window(mono), load_wav_window(stereo)
    assert left.shape == right.shape == (1, C, N)
    assert torch.equal(left[:, 0], left[:, 1])
    assert not torch.equal(right[:, 0], right[:, 1])
    with pytest.raises(AudioPrependError, match="44.1"):
        load_wav_window(wrong)


def test_suffix_is_the_only_conditioner_call_and_shapes_are_fixed() -> None:
    projector = ProjectorFixture()
    source = suffix()
    result = run(projector=projector, suffix_audio=source)
    assert len(projector.calls) == 1 and torch.equal(projector.calls[0], source)
    assert result.prefix_latent.shape == (1, Z, F)
    assert result.conditioning.shape == (1, F, E)
    assert result.generated_prefix.shape == (1, C, N)


@pytest.mark.parametrize("name", ("hidden_prefix", "captured_c0", "native_tokens", "text", "global_cache"))
def test_forbidden_inputs_are_rejected(name: str) -> None:
    with pytest.raises(AudioPrependError, match="suffix-only"):
        run(**{name: object()})


def test_noise_to_data_sign_and_full_prefix_generation() -> None:
    velocity = VelocityFixture(1.0)
    result = run(flow=velocity, steps=2)
    assert len(velocity.calls) == 2
    assert torch.all(velocity.calls[0] == 0)
    assert torch.all(velocity.calls[1] == 0.5)
    assert torch.all(result.prefix_latent == 1.0)


def test_equal_power_compositor_preserves_source_after_transition() -> None:
    source = suffix()
    generated = torch.full_like(source, 0.25)
    result = compose_prepend(generated, source, transition_samples=1024)
    assert result.shape[-1] == 2 * N - 1024
    assert torch.equal(result[:, :, N:], source[:, :, 1024:])
    assert torch.equal(result[:, :, : N - 1024], generated[:, :, : N - 1024])
    for bad in (0, 1025):
        with pytest.raises(AudioPrependError, match="1024"):
            compose_prepend(generated, source, transition_samples=bad)


class TinyEncoder(nn.Module):
    def __init__(self) -> None:
        super().__init__()
        self.weight = nn.Parameter(torch.ones(()))

    def forward(self, audio: torch.Tensor) -> torch.Tensor:
        return audio.mean(dim=-1, keepdim=True).mean(dim=1, keepdim=True).expand(-1, Z, F) * self.weight


def test_projector_freezes_encoder_and_gradients_stop_at_suffix_latent() -> None:
    encoder = TinyEncoder()
    projector = SuffixProjector(encoder, width=16, layers=1)
    output = projector(suffix()).conditioning
    output.square().mean().backward()
    assert encoder.weight.requires_grad is False and encoder.weight.grad is None
    assert any(parameter.grad is not None for name, parameter in projector.named_parameters() if not name.startswith("encoder."))


def test_deterministic_adjacent_pair_direction_and_baseline_inventory() -> None:
    from music3lab.editing.audio_prepend_runner import BASELINES, make_prepend_pair

    previous = torch.full((C, N), -0.5)
    following = torch.full((C, N), 0.5)
    prefix, suffix_value = make_prepend_pair(previous, following)
    assert torch.equal(prefix, previous) and torch.equal(suffix_value, following)
    assert BASELINES == ("zero_condition", "unrelated_suffix", "repeat_future", "roll_future", "silence")


def test_cpu_focused_batch_keeps_target_out_of_projector() -> None:
    projector = ProjectorFixture()
    hidden = torch.randn(2, C, N)
    following = torch.randn(2, C, N)
    run(projector=projector, suffix_audio=following, noise=torch.randn(2, Z, F))
    assert torch.equal(projector.calls[0], following)
    assert not torch.equal(projector.calls[0], hidden)