hamori/tests/test_generate.py

"""Tests for src/generate.py — prefix encoding and position tracking."""

import pytest
import torch
import torch.nn as nn

from src.generate import _encode_prefix, _EOS, _HOLD, _NC, _ROOT_START, _BASS_START, _BASS_END, generate_period
from src.tokenizer import TOKEN_TO_ID, VOCAB


# ---------------------------------------------------------------------------
# Mock model that outputs uniform logits (EOS suppressed so generation runs
# until the bar-count limit or max_tokens).
# ---------------------------------------------------------------------------

class _UniformModel(nn.Module):
    """Always returns zero logits except EOS=-1000, forcing non-EOS sampling."""
    def __init__(self, vocab_size: int = len(VOCAB), max_seq_len: int = 512):
        super().__init__()
        self.max_seq_len = max_seq_len
        self._vocab_size = vocab_size
        self._dummy = nn.Parameter(torch.zeros(1))  # gives .parameters() something

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        b, s = x.shape
        logits = torch.zeros(b, s, self._vocab_size, device=x.device)
        logits[:, :, _EOS] = -1000.0
        return logits


class _EosHungryModel(nn.Module):
    """Strongly prefers EOS at every step — simulates a model that wants to stop early."""
    def __init__(self, vocab_size: int = len(VOCAB), max_seq_len: int = 512):
        super().__init__()
        self.max_seq_len = max_seq_len
        self._vocab_size = vocab_size
        self._dummy = nn.Parameter(torch.zeros(1))

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        b, s = x.shape
        logits = torch.zeros(b, s, self._vocab_size, device=x.device)
        logits[:, :, _EOS] = 1000.0   # model desperately wants to emit EOS
        return logits


def test_encode_prefix_chord_only():
    # "Cmaj7" in C major (shift=0) → ROOT_C QUAL_maj7 EXT_none BASS_root
    ids, n_pos = _encode_prefix(["Cmaj7"], shift=0)
    assert len(ids) == 4
    assert n_pos == 1
    assert ids[0] == TOKEN_TO_ID["ROOT_C"]
    assert ids[3] == TOKEN_TO_ID["BASS_root"]


def test_encode_prefix_hold():
    ids, n_pos = _encode_prefix(["."], shift=0)
    assert ids == [_HOLD]
    assert n_pos == 1


def test_encode_prefix_nc():
    ids, n_pos = _encode_prefix(["NC"], shift=0)
    assert ids == [_NC]
    assert n_pos == 1


def test_encode_prefix_mixed():
    # "Abmaj7 . Fm7 . Db . Bbm ." — what the user tried and got a parse error on
    symbols = ["Abmaj7", ".", "Fm7", ".", "Db", ".", "Bbm", "."]
    # Ab major → shift to canonical C major = (0 - 8) % 12 = 4
    shift = (0 - 8) % 12
    ids, n_pos = _encode_prefix(symbols, shift=shift)
    assert n_pos == 8                     # 8 bar positions
    # Dots become single HOLD tokens; chords become 4-token groups
    # Total tokens: 4 chords × 4 + 4 holds × 1 = 20
    assert len(ids) == 20
    # Every dot position must be a HOLD
    hold_positions = [1, 3, 5, 7]        # indices in the symbol list
    # Reconstruct expected token positions: chord at 0→tokens 0-3, dot→token 4, etc.
    token_idx = 0
    for sym in symbols:
        if sym == ".":
            assert ids[token_idx] == _HOLD
            token_idx += 1
        else:
            assert _ROOT_START <= ids[token_idx] <= TOKEN_TO_ID["ROOT_B"]
            assert _BASS_START <= ids[token_idx + 3] <= _BASS_END
            token_idx += 4


def test_encode_prefix_position_count_with_holds():
    # Mixed prefix: 2 chords and 2 holds = 4 positions
    ids, n_pos = _encode_prefix(["Am", ".", "G", "."], shift=0)
    assert n_pos == 4
    assert len(ids) == 2 * 4 + 2 * 1   # 10 tokens


# ---------------------------------------------------------------------------
# n_bars tests
# ---------------------------------------------------------------------------

def test_generate_exact_bars():
    model = _UniformModel()
    period = generate_period(
        model=model, mode="major", time="4/4", subdivision=4,
        style="H1K0", function="verse", key="C_major",
        n_bars=4, seed=0,
    )
    assert len(period.bars) == 4


def test_generate_exact_bars_various():
    model = _UniformModel()
    for n in (1, 2, 8, 16):
        period = generate_period(
            model=model, mode="major", time="4/4", subdivision=4,
            style="H1K0", function="verse", key="C_major",
            n_bars=n, seed=0,
        )
        assert len(period.bars) == n, f"expected {n} bars, got {len(period.bars)}"


def test_generate_bars_with_prefix():
    # 4-position prefix = 1 bar; n_bars=4 → 3 more bars generated → 4 total
    model = _UniformModel()
    period = generate_period(
        model=model, mode="major", time="4/4", subdivision=4,
        style="H1K0", function="verse", key="C_major",
        prefix=["C", ".", ".", "."],
        n_bars=4, seed=0,
    )
    assert len(period.bars) == 4


def test_generate_bars_overrides_early_eos():
    # Model desperately wants EOS — n_bars must prevent it from stopping early
    model = _EosHungryModel()
    period = generate_period(
        model=model, mode="major", time="4/4", subdivision=4,
        style="H1K0", function="verse", key="C_major",
        n_bars=4, seed=0,
    )
    assert len(period.bars) == 4


def test_generate_no_bars_arg_still_works():
    # Without n_bars the model generates until EOS or max_tokens
    model = _UniformModel()
    period = generate_period(
        model=model, mode="major", time="4/4", subdivision=4,
        style="H1K0", function="verse", key="C_major",
        max_tokens=64, seed=0,
    )
    assert len(period.bars) >= 1