# SPDX-License-Identifier: AGPL-3.0-only
# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0

"""Unit tests for the pre-warmed torch.compile cache (``diffusion_compile_cache.py``).

The Mega-cache API (``torch.compiler.save_cache_artifacts`` / ``load_cache_artifacts``)
is monkeypatched with deterministic in-memory fakes so the fingerprint / exact-match /
integrity / fallback / lifecycle logic is exercised without a real compile. The
fingerprint helpers run against the real torch on this box.
"""

from __future__ import annotations

import json
import types

import pytest

from core.inference import diffusion_compile_cache as cc


def _transformer(blocks = ("FluxTransformerBlock", "FluxSingleTransformerBlock")):
    return types.SimpleNamespace(_repeated_blocks = list(blocks))


_BEGIN_KW = dict(
    family = "flux.1",
    dtype = "torch.bfloat16",
    quant = None,
    attention_backend = "_native_cudnn",
    compile_kwargs = {"fullgraph": True, "dynamic": True},
    shape_bucket = "1024x1024",
)


# --------------------------------------------------------------------------- fingerprint
def test_environment_fingerprint_has_hard_dimensions():
    fp = cc.environment_fingerprint()
    for k in ("torch", "torch_cuda", "triton", "diffusers", "gpu_name", "gpu_capability"):
        assert k in fp


def test_cache_key_stable_across_kwarg_order():
    efp = cc.environment_fingerprint()
    t = _transformer()
    a = cc.model_fingerprint(
        family = "flux.1",
        transformer = t,
        dtype = "bf16",
        quant = None,
        attention_backend = "x",
        compile_kwargs = {"fullgraph": True, "dynamic": True},
    )
    b = cc.model_fingerprint(
        family = "flux.1",
        transformer = t,
        dtype = "bf16",
        quant = None,
        attention_backend = "x",
        compile_kwargs = {"dynamic": True, "fullgraph": True},
    )
    assert cc.cache_key(efp, a) == cc.cache_key(efp, b)


@pytest.mark.parametrize(
    "field,value",
    [
        ("family", "qwen-image"),
        ("dtype", "torch.float16"),
        ("quant", "int8"),
        ("attention_backend", "native"),
        ("shape_bucket", "512x512"),
    ],
)
def test_cache_key_sensitive_to_model_dims(field, value):
    efp = cc.environment_fingerprint()
    t = _transformer()
    base = dict(
        family = "flux.1",
        transformer = t,
        dtype = "bf16",
        quant = None,
        attention_backend = "x",
        compile_kwargs = {"fullgraph": True},
        shape_bucket = "1024x1024",
    )
    k0 = cc.cache_key(efp, cc.model_fingerprint(**base))
    base[field] = value
    assert cc.cache_key(efp, cc.model_fingerprint(**base)) != k0


def test_repeated_blocks_change_key():
    efp = cc.environment_fingerprint()
    k1 = cc.cache_key(
        efp,
        cc.model_fingerprint(
            family = "f",
            transformer = _transformer(("A",)),
            dtype = "bf16",
            quant = None,
            attention_backend = "x",
            compile_kwargs = {},
        ),
    )
    k2 = cc.cache_key(
        efp,
        cc.model_fingerprint(
            family = "f",
            transformer = _transformer(("B",)),
            dtype = "bf16",
            quant = None,
            attention_backend = "x",
            compile_kwargs = {},
        ),
    )
    assert k1 != k2


# ----------------------------------------------------------------------------- env knobs
@pytest.mark.parametrize(
    "raw,expected",
    [
        ("0", "off"),
        ("off", "off"),
        ("1", "on"),
        ("on", "on"),
        ("auto", "auto"),
        ("", "auto"),
        ("garbage", "auto"),
    ],
)
def test_cache_mode(monkeypatch, raw, expected):
    monkeypatch.setenv(cc._ENV_MODE, raw)
    assert cc.cache_mode() == expected


def test_cache_mode_default_auto(monkeypatch):
    monkeypatch.delenv(cc._ENV_MODE, raising = False)
    assert cc.cache_mode() == "auto"


# ------------------------------------------------------------------------------ disabled
def test_begin_returns_none_when_disabled(monkeypatch):
    monkeypatch.setenv(cc._ENV_MODE, "0")
    assert cc.begin(transformer = _transformer(), **_BEGIN_KW) is None


def test_begin_returns_none_without_megacache_api(monkeypatch):
    monkeypatch.setenv(cc._ENV_MODE, "auto")
    fake_torch = types.ModuleType("torch")
    fake_torch.compiler = types.SimpleNamespace()  # no save/load attrs
    monkeypatch.setitem(__import__("sys").modules, "torch", fake_torch)
    assert cc.begin(transformer = _transformer(), **_BEGIN_KW) is None


# ----------------------------------------------------------------- megacache fake + flow
@pytest.fixture
def fake_megacache(monkeypatch):
    """Patch torch.compiler save/load with deterministic in-memory behaviour."""
    import torch

    state = {"saved": None, "loaded_with": None}

    def fake_save():
        return (b"ARTIFACT-BYTES", None)

    def fake_load(data: bytes):
        state["loaded_with"] = data
        return object() if data == b"ARTIFACT-BYTES" else None

    monkeypatch.setattr(torch.compiler, "save_cache_artifacts", fake_save, raising = False)
    monkeypatch.setattr(torch.compiler, "load_cache_artifacts", fake_load, raising = False)
    return state


def test_save_then_load_roundtrip(monkeypatch, tmp_path, fake_megacache):
    monkeypatch.setenv(cc._ENV_MODE, "on")  # load + save
    monkeypatch.setenv(cc._ENV_DIR, str(tmp_path))

    # First load: cold (no bundle yet).
    ctx = cc.begin(transformer = _transformer(), **_BEGIN_KW)
    assert ctx is not None and ctx.hit is False
    assert cc.save(ctx) is True
    assert ctx.bundle.exists() and ctx.manifest_path.exists()

    # Second load with the SAME fingerprint: warm hit.
    ctx2 = cc.begin(transformer = _transformer(), **_BEGIN_KW)
    assert ctx2 is not None and ctx2.hit is True
    assert fake_megacache["loaded_with"] == b"ARTIFACT-BYTES"
    assert ctx2.key == ctx.key


def test_no_save_in_auto_mode(monkeypatch, tmp_path, fake_megacache):
    monkeypatch.setenv(cc._ENV_MODE, "auto")
    monkeypatch.delenv(cc._ENV_SAVE, raising = False)
    monkeypatch.setenv(cc._ENV_DIR, str(tmp_path))
    ctx = cc.begin(transformer = _transformer(), **_BEGIN_KW)
    assert cc.save(ctx) is False  # auto without SAVE opt-in does not write
    assert not ctx.bundle.exists()


def test_fingerprint_mismatch_falls_back(monkeypatch, tmp_path, fake_megacache):
    monkeypatch.setenv(cc._ENV_MODE, "on")
    monkeypatch.setenv(cc._ENV_DIR, str(tmp_path))
    ctx = cc.begin(transformer = _transformer(), **_BEGIN_KW)
    cc.save(ctx)

    # Tamper the manifest's env fingerprint -> exact-match guard must reject the bundle.
    manifest = json.loads(ctx.manifest_path.read_text())
    manifest["env"]["torch"] = "0.0.0-other"
    ctx.manifest_path.write_text(json.dumps(manifest))

    ctx2 = cc.begin(transformer = _transformer(), **_BEGIN_KW)
    assert ctx2.hit is False  # mismatch -> local compile, non-fatal


def test_corrupt_bundle_rejected(monkeypatch, tmp_path, fake_megacache):
    monkeypatch.setenv(cc._ENV_MODE, "on")
    monkeypatch.setenv(cc._ENV_DIR, str(tmp_path))
    ctx = cc.begin(transformer = _transformer(), **_BEGIN_KW)
    cc.save(ctx)
    ctx.bundle.write_bytes(b"CORRUPTED")  # manifest sha256 no longer matches

    ctx2 = cc.begin(transformer = _transformer(), **_BEGIN_KW)
    assert ctx2.hit is False


# ------------------------------------------------------------------------------- restore
def test_restore_inductor_dir(monkeypatch, tmp_path, fake_megacache):
    import os

    monkeypatch.setenv(cc._ENV_MODE, "auto")
    monkeypatch.setenv(cc._ENV_DIR, str(tmp_path))
    monkeypatch.setenv("TORCHINDUCTOR_CACHE_DIR", "/tmp/prior-inductor")
    ctx = cc.begin(transformer = _transformer(), **_BEGIN_KW)
    assert os.environ["TORCHINDUCTOR_CACHE_DIR"] != "/tmp/prior-inductor"  # redirected
    cc.restore(ctx)
    assert os.environ["TORCHINDUCTOR_CACHE_DIR"] == "/tmp/prior-inductor"  # restored