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

"""VideoBackend lifecycle on a faked torch/diffusers runtime (CPU-only, offline).
Mirrors test_diffusion_backend's fake_runtime pattern: explicit fake signatures so
the signature-gated kwargs actually exercise, sys.modules stubs so no real ML
stack loads."""

import contextlib
import sys
import types

import pytest

from core.inference.video import (
    VideoBackend,
    _detect_load_family,
    get_video_backend,
    resolve_video_model_kind,
)
from core.inference.video_families import VIDEO_CANCELLED_MSG, VIDEO_NOT_LOADED_MSG


class _FakeDtype:
    def __init__(self, name: str) -> None:
        self._name = name

    def __repr__(self) -> str:
        return f"torch.{self._name}"

    __str__ = __repr__


class _FakeGenerator:
    def __init__(self, device = None) -> None:
        self.device = device
        self.manual = None

    def seed(self) -> int:
        return 4242

    def manual_seed(self, value: int):
        self.manual = value
        return self


class _FakeVae:
    def __init__(self) -> None:
        self.tiled = False

    def enable_tiling(self) -> None:
        self.tiled = True


class _FakePipe:
    def __init__(self) -> None:
        self.moved_to = None
        self.vae = _FakeVae()
        self.last_kwargs = None
        self._interrupt = False

    def to(self, device):
        self.moved_to = device
        return self

    def enable_vae_tiling(self) -> None:
        self.vae.tiled = True

    # Explicit signature so generate()'s signature-gated kwargs (negative_prompt,
    # frame_rate, callback) actually engage; **kwargs would defeat the gates.
    def __call__(
        self,
        *,
        prompt = None,
        negative_prompt = None,
        num_inference_steps = None,
        guidance_scale = None,
        width = None,
        height = None,
        num_frames = None,
        frame_rate = None,
        generator = None,
        sigmas = None,
        callback_on_step_end = None,
        **kwargs,
    ):
        self.last_kwargs = {
            "prompt": prompt,
            "negative_prompt": negative_prompt,
            "num_inference_steps": num_inference_steps,
            "guidance_scale": guidance_scale,
            "width": width,
            "height": height,
            "num_frames": num_frames,
            "frame_rate": frame_rate,
            "sigmas": sigmas,
            **kwargs,
        }
        if callback_on_step_end is not None:
            for step in range(int(num_inference_steps or 1)):
                callback_on_step_end(self, step, 0, {})
                if self._interrupt:
                    break
        frames = [[object() for _ in range(int(num_frames or 1))]]
        return types.SimpleNamespace(frames = frames, audio = None)


class _FakePipeline:
    last: dict = {}

    @classmethod
    def from_pretrained(cls, base, **kwargs):
        _FakePipeline.last = {"base": base, **kwargs}
        return _FakePipe()


class _FakeTransformer:
    last: dict = {}

    @classmethod
    def from_single_file(cls, path, **kwargs):
        _FakeTransformer.last = {"path": path, **kwargs}
        return object()


# ── Wan2.2 fakes: a per-DiT trackable transformer so the dual-DiT tests can assert speed /
# cache / attention on BOTH experts, plus single-DiT (TI2V-5B) and dual-DiT MoE (A14B) pipeline
# fakes. The MoE __call__ carries guidance_scale_2 so the cfg2 signature-gate exercises; the
# single-DiT omits it so the gate proves it is NOT threaded there.


class _FakeWanDiT:
    """One Wan denoiser. Records which optimisation helpers touched it (the loader
    applies each once per expert on an MoE load), so a test can prove BOTH experts
    were covered. compile_repeated_blocks / enable_cache / set_attention_backend are
    exactly the attribute names the imported helpers look for."""

    def __init__(self) -> None:
        self.compiled = False
        self.cache_config = None
        self.attention = None

    def compile_repeated_blocks(self, **kwargs) -> None:
        self.compiled = True

    def enable_cache(self, config) -> None:
        self.cache_config = config

    def disable_cache(self) -> None:
        self.cache_config = None

    def set_attention_backend(self, backend) -> None:
        self.attention = backend

    @contextlib.contextmanager
    def cache_context(self, name):
        # Real Wan / HV15 / LTX pipelines open a cache_context around the denoise loop; the
        # First-Block-Cache hook needs it, so the fake transformer provides it too.
        yield


class _FakeWanVae:
    def __init__(self) -> None:
        self.tiled = False

    def enable_tiling(self) -> None:
        self.tiled = True

    def to(self, *args, **kwargs):
        return self


class _FakeWanPipeBase:
    """Shared Wan pipeline state. Subclasses provide the __call__ with the right
    explicit signature (with/without guidance_scale_2) so the generate() cfg2 and
    frame_rate signature-gates actually exercise -- ``**kwargs`` alone would hide the
    parameter names inspect.signature reads."""

    moe: bool = False

    def __init__(self) -> None:
        self.vae = _FakeWanVae()
        self.transformer = _FakeWanDiT()
        self.transformer_2 = _FakeWanDiT() if self.moe else None
        self.components = {"transformer": self.transformer, "vae": self.vae}
        if self.moe:
            self.components["transformer_2"] = self.transformer_2
        self.moved_to = None
        self.last_kwargs = None
        self._interrupt = False

    def to(self, device):
        self.moved_to = device
        return self

    def enable_vae_tiling(self) -> None:
        self.vae.tiled = True

    def _finish(self, num_inference_steps, num_frames, callback_on_step_end):
        if callback_on_step_end is not None:
            for step in range(int(num_inference_steps or 1)):
                callback_on_step_end(self, step, 0, {})
                if self._interrupt:
                    break
        frames = [[object() for _ in range(int(num_frames or 1))]]
        return types.SimpleNamespace(frames = frames, audio = None)


class _FakeWanPipeSingle(_FakeWanPipeBase):
    """Single-DiT Wan pipeline (TI2V-5B): NO guidance_scale_2 in the signature, so the
    cfg2 gate must not thread it."""

    moe = False

    def __call__(
        self,
        *,
        prompt = None,
        negative_prompt = None,
        num_inference_steps = None,
        guidance_scale = None,
        width = None,
        height = None,
        num_frames = None,
        generator = None,
        callback_on_step_end = None,
        **kwargs,
    ):
        self.last_kwargs = {
            "prompt": prompt,
            "negative_prompt": negative_prompt,
            "num_inference_steps": num_inference_steps,
            "guidance_scale": guidance_scale,
            "width": width,
            "height": height,
            "num_frames": num_frames,
            **kwargs,
        }
        with self.transformer.cache_context("cond"):  # real Wan pipeline wraps the denoise loop
            pass
        return self._finish(num_inference_steps, num_frames, callback_on_step_end)


class _FakeWanPipeMoE(_FakeWanPipeBase):
    """Dual-DiT MoE Wan pipeline (A14B): guidance_scale_2 IS in the signature, matching
    WanPipeline.__call__ in diffusers 0.39, so the cfg2 gate threads it."""

    moe = True

    def __call__(
        self,
        *,
        prompt = None,
        negative_prompt = None,
        num_inference_steps = None,
        guidance_scale = None,
        guidance_scale_2 = None,
        width = None,
        height = None,
        num_frames = None,
        generator = None,
        callback_on_step_end = None,
        **kwargs,
    ):
        self.last_kwargs = {
            "prompt": prompt,
            "negative_prompt": negative_prompt,
            "num_inference_steps": num_inference_steps,
            "guidance_scale": guidance_scale,
            "guidance_scale_2": guidance_scale_2,
            "width": width,
            "height": height,
            "num_frames": num_frames,
            **kwargs,
        }
        with self.transformer.cache_context("cond"):  # real Wan pipeline wraps the denoise loop
            pass
        return self._finish(num_inference_steps, num_frames, callback_on_step_end)


class _FakeWanI2VPipe(_FakeWanPipeBase):
    """Dual-DiT image-to-video Wan pipeline (I2V-A14B): ``image`` IS in the signature
    (WanImageToVideoPipeline.__call__ in diffusers 0.39) alongside guidance_scale_2."""

    moe = True

    def __call__(
        self,
        *,
        image = None,
        prompt = None,
        negative_prompt = None,
        num_inference_steps = None,
        guidance_scale = None,
        guidance_scale_2 = None,
        width = None,
        height = None,
        num_frames = None,
        generator = None,
        callback_on_step_end = None,
        **kwargs,
    ):
        self.last_kwargs = {
            "image": image,
            "prompt": prompt,
            "negative_prompt": negative_prompt,
            "num_inference_steps": num_inference_steps,
            "guidance_scale": guidance_scale,
            "guidance_scale_2": guidance_scale_2,
            "width": width,
            "height": height,
            "num_frames": num_frames,
            **kwargs,
        }
        with self.transformer.cache_context("cond"):  # real Wan pipeline wraps the denoise loop
            pass
        return self._finish(num_inference_steps, num_frames, callback_on_step_end)


class _FakeWanImageToVideoPipeline:
    """WanImageToVideoPipeline fake (from_pretrained) for the I2V-A14B family."""

    last: dict = {}

    @classmethod
    def from_pretrained(cls, repo, **kwargs):
        _FakeWanImageToVideoPipeline.last = {"repo": repo, **kwargs}
        return _FakeWanI2VPipe()


class _FakeWanPipelineSingle:
    """WanPipeline fake (from_pretrained). One class serves both families and picks the
    single-DiT / dual-DiT pipe by the repo id, exactly as diffusers dispatches on the
    repo's model_index.json (A14B lists transformer_2, TI2V-5B does not)."""

    last: dict = {}

    @classmethod
    def from_pretrained(cls, repo, **kwargs):
        _FakeWanPipelineSingle.last = {"repo": repo, **kwargs}
        moe = "a14b" in str(repo).lower()
        return _FakeWanPipeMoE() if moe else _FakeWanPipeSingle()


# ── HunyuanVideo-1.5 fakes: __call__ has NO guidance kwarg and NO callback_on_step_end (matching
# diffusers 0.39), a guider carries the CFG scale, and the loop drives scheduler.step -- so the
# guider write and the scheduler-wrap progress/cancel paths exercise.


class _FakeHV15Scheduler:
    def __init__(self) -> None:
        self.calls = 0
        # Test hook fired from the ORIGINAL step (i.e. inside the wrapped call),
        # letting a test cancel mid-denoise exactly as a user request would land.
        self.on_step = None

    def step(self, *args, **kwargs):
        self.calls += 1
        if self.on_step is not None:
            self.on_step(self.calls)
        return object()


class _FakeHV15Pipe:
    def __init__(self) -> None:
        self.vae = _FakeWanVae()
        self.transformer = _FakeWanDiT()
        self.scheduler = _FakeHV15Scheduler()
        self.guider = types.SimpleNamespace(guidance_scale = 6.0)
        self.components = {"transformer": self.transformer, "vae": self.vae}
        self.moved_to = None
        self.last_kwargs = None
        self.hooks_freed = 0

    def maybe_free_model_hooks(self):
        self.hooks_freed += 1

    def to(self, device):
        self.moved_to = device
        return self

    def enable_vae_tiling(self) -> None:
        self.vae.tiled = True

    def __call__(
        self,
        *,
        prompt = None,
        negative_prompt = None,
        height = None,
        width = None,
        num_frames = None,
        num_inference_steps = None,
        generator = None,
        **kwargs,
    ):
        self.last_kwargs = {
            "prompt": prompt,
            "negative_prompt": negative_prompt,
            "num_inference_steps": num_inference_steps,
            "width": width,
            "height": height,
            "num_frames": num_frames,
            **kwargs,
        }
        with self.transformer.cache_context("cond"):  # real HV15 pipeline wraps the denoise loop
            pass
        for _ in range(int(num_inference_steps or 1)):
            self.scheduler.step()
        frames = [[object() for _ in range(int(num_frames or 1))]]
        return types.SimpleNamespace(frames = frames, audio = None)


class _FakeHV15Pipeline:
    last: dict = {}
    instance = None

    @classmethod
    def from_pretrained(cls, repo, **kwargs):
        _FakeHV15Pipeline.last = {"repo": repo, **kwargs}
        _FakeHV15Pipeline.instance = _FakeHV15Pipe()
        return _FakeHV15Pipeline.instance


@pytest.fixture
def fake_runtime(monkeypatch):
    torch = types.ModuleType("torch")
    torch.bfloat16 = _FakeDtype("bfloat16")
    torch.float16 = _FakeDtype("float16")
    torch.float32 = _FakeDtype("float32")
    torch.Generator = _FakeGenerator
    torch.cuda = types.SimpleNamespace(is_available = lambda: False)
    torch.backends = types.SimpleNamespace(mps = None)
    torch.inference_mode = lambda: contextlib.nullcontext()

    diffusers = types.ModuleType("diffusers")
    diffusers.GGUFQuantizationConfig = lambda compute_dtype = None: ("quant", compute_dtype)
    diffusers.LTX2Pipeline = _FakePipeline
    diffusers.LTX2VideoTransformer3DModel = _FakeTransformer
    # Wan2.2: one pipeline class serves both families (it dispatches on the repo id).
    diffusers.WanPipeline = _FakeWanPipelineSingle
    diffusers.WanImageToVideoPipeline = _FakeWanImageToVideoPipeline
    diffusers.WanTransformer3DModel = _FakeTransformer
    diffusers.HunyuanVideo15Pipeline = _FakeHV15Pipeline
    diffusers.HunyuanVideo15Transformer3DModel = _FakeTransformer
    diffusers.FirstBlockCacheConfig = lambda threshold = None: ("fbcache", threshold)
    # diffusers.hooks.MagCacheConfig: the auto cache mode for the HunyuanVideo-1.5 and
    # Wan2.2-TI2V-5B families (calibrated curves); the fake records its kwargs so the
    # cache tests can assert the engaged mode + step count.
    diffusers_hooks = types.ModuleType("diffusers.hooks")
    diffusers_hooks.MagCacheConfig = lambda **kwargs: ("magcache", kwargs)
    diffusers.hooks = diffusers_hooks

    monkeypatch.setitem(sys.modules, "torch", torch)
    monkeypatch.setitem(sys.modules, "diffusers", diffusers)
    monkeypatch.setitem(sys.modules, "diffusers.hooks", diffusers_hooks)
    monkeypatch.setattr("core.inference.video.clear_gpu_cache", lambda: None)
    # MP4 encode needs real frames + PyAV; the backend contract under test is the
    # byte handoff, so stub the encoder.
    monkeypatch.setattr(
        VideoBackend, "_encode_mp4", staticmethod(lambda frames, fps, audio, pipe: b"MP4")
    )
    _FakePipeline.last = {}
    _FakeTransformer.last = {}
    yield


def _load_gguf(backend, tmp_path):
    (tmp_path / "model.gguf").write_bytes(b"weights")
    return backend.load_pipeline(
        str(tmp_path),
        gguf_filename = "model.gguf",
        base_repo = "Lightricks/LTX-2",
        family_override = "ltx-2",
    )


def test_resolve_kind():
    assert resolve_video_model_kind("x.gguf", None) == "gguf"
    assert resolve_video_model_kind("x.safetensors", None) == "single_file"
    assert resolve_video_model_kind(None, None) == "pipeline"
    with pytest.raises(ValueError):
        resolve_video_model_kind(None, "bogus")


def test_validate_rejects_unknown_and_untrusted():
    backend = VideoBackend()
    with pytest.raises(ValueError, match = "not a supported"):
        backend.validate_load_request("someorg/some-image-model")
    # A known family but an untrusted repo id must not open from_pretrained.
    with pytest.raises(ValueError, match = "limited to"):
        backend.validate_load_request("evil/ltx-2-repack")
    # GGUF loads stay open to any repo (single-file read, no pickle).
    fam = backend.validate_load_request(
        "anyorg/ltx-2-GGUF", gguf_filename = "x.gguf", model_kind = "gguf"
    )
    assert fam.name == "ltx-2"
    with pytest.raises(ValueError, match = "filename"):
        backend.validate_load_request("unsloth/LTX-2.3-GGUF", model_kind = "gguf")


def test_validate_gates_base_repo_and_local_paths(tmp_path):
    backend = VideoBackend()
    # An arbitrary remote base_repo must not reach from_pretrained via a GGUF pick.
    with pytest.raises(ValueError, match = "base_repo"):
        backend.validate_load_request(
            "unsloth/LTX-2.3-GGUF",
            gguf_filename = "x.gguf",
            model_kind = "gguf",
            base_repo = "evil/companions",
        )
    # The family base and local dirs stay allowed.
    fam = backend.validate_load_request(
        "unsloth/LTX-2.3-GGUF",
        gguf_filename = "x.gguf",
        model_kind = "gguf",
        base_repo = "Lightricks/LTX-2",
    )
    assert fam.name == "ltx-2"
    # A local dir without the picked checkpoint fails BEFORE the GPU handoff.
    with pytest.raises(ValueError):
        backend.validate_load_request(
            str(tmp_path), gguf_filename = "missing.gguf", family_override = "ltx-2"
        )
    # A path-shaped repo id that does not exist fails validation too.
    with pytest.raises(ValueError, match = "does not exist"):
        backend.validate_load_request(
            str(tmp_path / "nope" / "model.gguf"),
            gguf_filename = "model.gguf",
            family_override = "ltx-2",
        )


def test_validate_rejects_kind_extension_mismatch(tmp_path):
    backend = VideoBackend()
    # single_file with a .gguf file, or gguf with a non-.gguf file, must be rejected BEFORE the
    # GPU handoff, not fail in the wrong loader after the route evicted the resident model.
    with pytest.raises(ValueError, match = "needs model_kind 'gguf'"):
        backend.validate_load_request(
            "unsloth/LTX-2.3-GGUF",
            gguf_filename = "x.gguf",
            model_kind = "single_file",
            family_override = "ltx-2",
        )
    with pytest.raises(ValueError, match = "requires a .gguf"):
        backend.validate_load_request(
            "unsloth/LTX-2.3",
            gguf_filename = "x.safetensors",
            model_kind = "gguf",
            family_override = "ltx-2",
        )
    with pytest.raises(ValueError, match = "not a loadable single-file checkpoint"):
        backend.validate_load_request(
            "unsloth/LTX-2.3",
            gguf_filename = "readme.md",
            model_kind = "single_file",
            family_override = "ltx-2",
        )


def test_validate_rejects_local_file_suffix_kind_mismatch(tmp_path):
    backend = VideoBackend()
    # A local FILE goes straight to the gguf/single_file loader (_resolve_checkpoint_path ignores
    # gguf_filename), so the file's OWN suffix must match the kind. A mismatched suffix slips past
    # the gguf_filename checks, so reject it HERE before the route evicts the resident GPU owner.
    gguf_file = tmp_path / "ltx.gguf"
    gguf_file.write_bytes(b"weights")
    safetensors_file = tmp_path / "ltx.safetensors"
    safetensors_file.write_bytes(b"weights")
    with pytest.raises(ValueError, match = "not a .safetensors file"):
        backend.validate_load_request(
            str(gguf_file),
            gguf_filename = "ltx.safetensors",
            model_kind = "single_file",
            family_override = "ltx-2",
        )
    with pytest.raises(ValueError, match = "not a .gguf file"):
        backend.validate_load_request(
            str(safetensors_file),
            gguf_filename = "ltx.gguf",
            model_kind = "gguf",
            family_override = "ltx-2",
        )
    # Matching pairs still validate: the local file's suffix agrees with the resolved kind.
    assert (
        backend.validate_load_request(
            str(gguf_file),
            gguf_filename = "ltx.gguf",
            model_kind = "gguf",
            family_override = "ltx-2",
        ).name
        == "ltx-2"
    )
    assert (
        backend.validate_load_request(
            str(safetensors_file),
            gguf_filename = "ltx.safetensors",
            model_kind = "single_file",
            family_override = "ltx-2",
        ).name
        == "ltx-2"
    )


def test_validate_rejects_windows_shaped_missing_checkpoint(tmp_path):
    backend = VideoBackend()
    # A missing Windows-shaped local pick (backslash / C:/ path) must fail HERE, not be treated as
    # a Hub repo and fail after the route evicts the resident owner. Mirrors the image loader.
    with pytest.raises(ValueError, match = "does not exist"):
        backend.validate_load_request(
            "C:\\models\\ltx.gguf",
            gguf_filename = "ltx.gguf",
            family_override = "ltx-2",
        )
    # A bare "org/name" Hub id (no path shape) is still left for the background load to resolve.
    fam = backend.validate_load_request(
        "unsloth/LTX-2.3-GGUF",
        gguf_filename = "ltx.gguf",
        family_override = "ltx-2",
    )
    assert fam.name == "ltx-2"


def test_validate_rejects_local_pipeline_without_model_index(tmp_path):
    backend = VideoBackend()
    d = tmp_path / "ltx-local"
    (d / "transformer").mkdir(parents = True)
    (d / "transformer" / "diffusion_pytorch_model.safetensors").write_bytes(b"x")
    # A local dir missing model_index.json is not a loadable diffusers pipeline; it must fail
    # preflight BEFORE the route evicts the resident model, mirroring the image loader.
    with pytest.raises(ValueError, match = "model_index.json"):
        backend.validate_load_request(str(d), family_override = "ltx-2")
    # With a model_index.json it is a valid local pipeline pick and passes preflight.
    (d / "model_index.json").write_text("{}")
    fam = backend.validate_load_request(str(d), family_override = "ltx-2")
    assert fam.name == "ltx-2"


def test_validate_rejects_local_file_picked_as_pipeline(tmp_path):
    backend = VideoBackend()
    # A local FILE sent as a pipeline is not a diffusers directory, so from_pretrained would only
    # fail deep in the background load. Reject it HERE -- the check gates on .exists() (not
    # .is_dir()), mirroring the image loader, so it catches files too.
    f = tmp_path / "ltx-2.safetensors"
    f.write_bytes(b"x")
    with pytest.raises(ValueError, match = "model_index.json"):
        backend.validate_load_request(str(f), model_kind = "pipeline", family_override = "ltx-2")


def test_validate_rejects_local_base_repo_without_model_index(tmp_path):
    backend = VideoBackend()
    # A local base_repo dir with no model_index.json passes the existing-path trust check but
    # the base loads via from_pretrained (needs model_index), so reject it HERE. The pipeline-kind
    # check covers only repo_id, and base_repo only matters for a gguf/single_file load.
    bad_base = tmp_path / "bare-base"
    bad_base.mkdir()
    with pytest.raises(ValueError, match = "model_index.json"):
        backend.validate_load_request(
            "unsloth/LTX-2.3-GGUF",
            gguf_filename = "x.gguf",
            model_kind = "gguf",
            base_repo = str(bad_base),
        )
    # A local base_repo that IS a real pipeline dir passes the gate.
    (bad_base / "model_index.json").write_text("{}")
    fam = backend.validate_load_request(
        "unsloth/LTX-2.3-GGUF",
        gguf_filename = "x.gguf",
        model_kind = "gguf",
        base_repo = str(bad_base),
    )
    assert fam.name == "ltx-2"


def test_validate_rejects_gguf_repo_as_pipeline():
    backend = VideoBackend()
    # A -GGUF repo with no quant filename resolves to the pipeline kind and would
    # only fail minutes later in from_pretrained, AFTER evicting the GPU owner.
    with pytest.raises(ValueError, match = "pick one of its .gguf files"):
        backend.validate_load_request("unsloth/LTX-2.3-GGUF")
    with pytest.raises(ValueError, match = "pick one of its .gguf files"):
        backend.validate_load_request("unsloth/Wan2.2-TI2V-5B-GGUF/")


def test_detect_load_family_filename_fallback():
    # Repo id alone carries the family.
    fam = _detect_load_family("Lightricks/LTX-2", None, None)
    assert fam is not None and fam.name == "ltx-2"
    # Repo id is opaque but the picked filename carries it: fall back to the
    # combined path so validate and _run_load agree on the family.
    fam = _detect_load_family("someorg/quants", "ltx-2-19b-Q4_K_M.gguf", None)
    assert fam is not None and fam.name == "ltx-2"
    # No filename and no recognisable repo id: no family.
    assert _detect_load_family("someorg/quants", None, None) is None
    # An explicit override resolves by name/alias and skips the filename fallback:
    # a bogus override stays None even when the filename would have matched.
    fam = _detect_load_family("someorg/quants", "ltx-2-19b-Q4_K_M.gguf", "ltxv")
    assert fam is not None and fam.name == "ltx-2"
    assert _detect_load_family("someorg/quants", "ltx-2-19b-Q4_K_M.gguf", "bogus") is None


def test_detect_load_family_cached_hub_arch_fallback(monkeypatch):
    # A CACHED HUB GGUF is admitted to the picker by its general.architecture, but an opaque repo
    # id + renamed file carry no family token so name detection misses, and the local-file arch
    # read misses too. Without a cache fallback the loader 400s a SUPPORTED checkpoint.
    import huggingface_hub

    import utils.models.gguf_metadata as gguf_meta

    # No local file at Path(repo_id)/filename; resolve the arch from the cached blob instead.
    monkeypatch.setattr(
        huggingface_hub,
        "try_to_load_from_cache",
        lambda repo_id, filename, **kw: "/fake/cache/blobs/model.gguf",
    )
    monkeypatch.setattr(
        gguf_meta, "read_gguf_general_metadata", lambda path: {"general.architecture": "ltxv"}
    )
    fam = _detect_load_family("someorg/opaque-quants", "model.gguf", None)
    assert fam is not None and fam.name == "ltx-2"

    # A cache MISS (blob not present -> None) still yields None (400 exactly as before).
    monkeypatch.setattr(huggingface_hub, "try_to_load_from_cache", lambda *a, **k: None)
    assert _detect_load_family("someorg/opaque-quants", "model.gguf", None) is None

    # A recognised-but-unsupported video arch (wan has no backend family in this build) stays None,
    # so an unsupported cached pick 400s just like the local-dir case.
    monkeypatch.setattr(
        huggingface_hub, "try_to_load_from_cache", lambda *a, **k: "/fake/cache/blobs/model.gguf"
    )
    monkeypatch.setattr(
        gguf_meta, "read_gguf_general_metadata", lambda path: {"general.architecture": "wan"}
    )
    assert _detect_load_family("someorg/opaque-quants", "model.gguf", None) is None

    # The blob lives in a NON-active cache root (legacy / default): the active probe (no cache_dir)
    # misses, but the per-root probe finds it, so a GGUF the picker offered from any root resolves.
    import hub.utils.paths as hub_paths

    monkeypatch.setattr(hub_paths, "legacy_hf_cache_dir", lambda: "/fake/legacy")
    monkeypatch.setattr(hub_paths, "hf_default_cache_dir", lambda: "/fake/default")
    monkeypatch.setattr(
        gguf_meta, "read_gguf_general_metadata", lambda path: {"general.architecture": "ltxv"}
    )
    monkeypatch.setattr(
        huggingface_hub,
        "try_to_load_from_cache",
        # Active root (cache_dir absent) misses; only the legacy/default roots have the blob.
        lambda repo_id, filename, cache_dir = None: (
            "/fake/legacy/blobs/model.gguf" if cache_dir else None
        ),
    )
    fam = _detect_load_family("someorg/opaque-quants", "model.gguf", None)
    assert fam is not None and fam.name == "ltx-2"


def test_loading_repo_ids_guards_in_flight_delete():
    # During a background load status()["loaded"] is still False but the target repo is
    # downloading, so the delete-cached guard needs loading_repo_ids to refuse deletion and not
    # yank blobs from under the in-flight download.
    from core.inference.video import _VideoLoadingState

    backend = VideoBackend()
    assert backend.loading_repo_ids() == ()  # idle: nothing to guard
    backend._loading = _VideoLoadingState(repo_id = "org/ckpt", base_repo = "Lightricks/LTX-2")
    assert set(backend.loading_repo_ids()) == {"org/ckpt", "Lightricks/LTX-2"}
    # An errored load is no longer in flight -> the files are safe to delete.
    backend._loading = _VideoLoadingState(
        repo_id = "org/ckpt", base_repo = "Lightricks/LTX-2", error = "boom"
    )
    assert backend.loading_repo_ids() == ()
    # A load whose base equals the repo (or is empty) yields just the one id.
    backend._loading = _VideoLoadingState(repo_id = "org/ckpt", base_repo = "")
    assert backend.loading_repo_ids() == ("org/ckpt",)


def test_load_generate_unload_gguf(fake_runtime, tmp_path):
    backend = VideoBackend()
    status = _load_gguf(backend, tmp_path)
    assert status["loaded"] is True and status["family"] == "ltx-2"
    assert status["model_kind"] == "gguf"
    assert status["has_audio"] is True
    # The GGUF transformer is dequant-configured and assembled onto the base repo.
    assert _FakeTransformer.last["path"].endswith("model.gguf")
    assert _FakeTransformer.last["quantization_config"][0] == "quant"
    assert _FakePipeline.last["base"] == "Lightricks/LTX-2"
    assert "transformer" in _FakePipeline.last
    # Video decode is the memory peak: tiling is always on.
    assert status["vae_tiling"] is True
    assert status["defaults"]["frame_step"] == 8

    result = backend.generate(
        prompt = "a sloth surfing", width = 1000, height = 700, num_frames = 120, fps = 24
    )
    call = backend._state.pipe.last_kwargs
    # Shape snapping happened BEFORE the pipe call: /32 sizes, 8k+1 frames.
    assert (call["width"], call["height"]) == (992, 672)
    assert call["num_frames"] == 113
    assert call["frame_rate"] == 24.0
    assert result["mp4_bytes"] == b"MP4"
    assert result["num_frames"] == 113 and result["fps"] == 24
    assert result["has_audio"] is False  # fake pipe returned no audio track
    assert 0 <= result["seed"] < 2**53

    status = backend.unload()
    assert status["loaded"] is False


def test_load_holds_generate_lock_across_placement(fake_runtime, tmp_path, monkeypatch):
    # The load must hold _generate_lock across GPU placement so an unload / eviction (which
    # barriers on _generate_lock before freeing) can't hand the GPU away mid-placement. Verify
    # unload() blocks until placement releases the lock, and the superseded load aborts.
    import threading

    from core.inference import video as video_mod

    backend = VideoBackend()
    placement_started = threading.Event()
    release_placement = threading.Event()
    real_apply = video_mod.apply_memory_plan

    def blocking_apply(pipe, plan, **kw):
        placement_started.set()
        assert release_placement.wait(timeout = 5), "test placement barrier never released"
        return real_apply(pipe, plan, **kw)

    monkeypatch.setattr(video_mod, "apply_memory_plan", blocking_apply)

    load_exc = []

    def do_load():
        try:
            _load_gguf(backend, tmp_path)
        except Exception as e:  # noqa: BLE001 -- the concurrent unload supersedes this load
            load_exc.append(e)

    load_thread = threading.Thread(target = do_load)
    load_thread.start()
    assert placement_started.wait(timeout = 5), "load never reached placement"

    # Placement is in flight, holding _generate_lock. unload() must block on its barrier.
    unload_done = []

    def do_unload():
        backend.unload()
        unload_done.append(True)

    unload_thread = threading.Thread(target = do_unload)
    unload_thread.start()
    unload_thread.join(timeout = 0.5)
    assert not unload_done, "unload() returned while placement still held _generate_lock (the race)"

    # Release placement; unload()'s barrier then passes and its teardown runs strictly AFTER
    # the load's placement+commit -- never concurrently -- so no two pipelines are ever resident.
    release_placement.set()
    unload_thread.join(timeout = 5)
    load_thread.join(timeout = 5)
    assert unload_done, "unload() did not complete after placement released _generate_lock"
    assert not load_thread.is_alive() and not load_exc
    assert backend._state is None  # unload's teardown ran after the load, leaving nothing resident


def test_load_records_engaged_speed_optims(fake_runtime, tmp_path, monkeypatch):
    # Regression: the load tail once re-ran the already-filtered speed_optims tuple through
    # ``.items()``, so every real-GPU load crashed with 'tuple' has no attribute 'items'. The
    # fake runtime forces every optim False, so this only reproduces when one engages.
    from core.inference import video as video_mod

    monkeypatch.setattr(
        video_mod,
        "apply_speed_optims",
        lambda *a, **k: {"channels_last": True, "cudnn_benchmark": False},
    )
    backend = VideoBackend()
    status = _load_gguf(backend, tmp_path)
    assert status["loaded"] is True
    assert status["speed_optims"] == ["channels_last"]


def test_generate_defaults_from_variant(fake_runtime, tmp_path):
    # A distilled GGUF pick defaults to the few-step no-CFG schedule.
    (tmp_path / "ltx-2.3-22b-distilled-1.1-Q4_K_M.gguf").write_bytes(b"w")
    backend = VideoBackend()
    backend.load_pipeline(
        str(tmp_path),
        gguf_filename = "ltx-2.3-22b-distilled-1.1-Q4_K_M.gguf",
        base_repo = "Lightricks/LTX-2",
        family_override = "ltx-2",
    )
    backend.generate(prompt = "a sloth")
    call = backend._state.pipe.last_kwargs
    assert call["num_inference_steps"] == 8
    assert call["guidance_scale"] == 1.0
    # At the distilled default step count the calibrated ltx_core curve is passed verbatim
    # (the DiT was trained against it; the scheduler's own 8-step spacing lands far off).
    from core.inference.video_ltx2 import LTX23_DISTILLED_SIGMAS

    assert call["sigmas"] == list(LTX23_DISTILLED_SIGMAS)


def test_generate_distilled_custom_steps_keep_scheduler_spacing(fake_runtime, tmp_path):
    # A non-default step count on the distilled DiT has no calibrated list; the scheduler's
    # spacing applies and no sigmas kwarg is injected.
    (tmp_path / "ltx-2.3-22b-distilled-1.1-Q4_K_M.gguf").write_bytes(b"w")
    backend = VideoBackend()
    backend.load_pipeline(
        str(tmp_path),
        gguf_filename = "ltx-2.3-22b-distilled-1.1-Q4_K_M.gguf",
        base_repo = "Lightricks/LTX-2",
        family_override = "ltx-2",
    )
    backend.generate(prompt = "a sloth", steps = 12)
    call = backend._state.pipe.last_kwargs
    assert call["num_inference_steps"] == 12
    assert call["sigmas"] is None


def test_generate_dev_base_never_gets_distilled_sigmas(fake_runtime, tmp_path):
    # The dev/base DiT uses the resolution-shifted scheduler spacing even at 8 steps: the
    # calibrated list is distilled-only.
    (tmp_path / "ltx-2.3-22b-dev-Q4_K_M.gguf").write_bytes(b"w")
    backend = VideoBackend()
    backend.load_pipeline(
        str(tmp_path),
        gguf_filename = "ltx-2.3-22b-dev-Q4_K_M.gguf",
        base_repo = "Lightricks/LTX-2",
        family_override = "ltx-2",
    )
    backend.generate(prompt = "a sloth", steps = 8)
    call = backend._state.pipe.last_kwargs
    assert call["num_inference_steps"] == 8
    assert call["sigmas"] is None


def test_ltx23_verbatim_sigmas_restores_scheduler_config():
    # The context manager must neutralise exactly the transforms that distort explicit
    # sigmas and put the original values back afterwards, even on error.
    from core.inference.video_ltx2 import ltx23_verbatim_sigmas

    class _Cfg(dict):
        pass

    class _Sched:
        def __init__(self):
            self.config = _Cfg(
                use_dynamic_shifting = True, shift = 1.0, shift_terminal = 0.1
            )

        def register_to_config(self, **kw):
            self.config.update(kw)

    pipe = types.SimpleNamespace(scheduler = _Sched())
    with ltx23_verbatim_sigmas(pipe):
        assert pipe.scheduler.config["use_dynamic_shifting"] is False
        assert pipe.scheduler.config["shift_terminal"] is None
    assert pipe.scheduler.config["use_dynamic_shifting"] is True
    assert pipe.scheduler.config["shift_terminal"] == 0.1
    with pytest.raises(RuntimeError):
        with ltx23_verbatim_sigmas(pipe):
            raise RuntimeError("boom")
    assert pipe.scheduler.config["use_dynamic_shifting"] is True
    # A pipe without a scheduler is a no-op, not a crash.
    with ltx23_verbatim_sigmas(types.SimpleNamespace()):
        pass


def test_generate_resets_step_cache_only_when_engaged(fake_runtime, tmp_path):
    # FBCache residuals live on the long-lived DiT(s) and survive a generation, so the next clip
    # at a new resolution would crash on stale state. generate must reset them when a cache is
    # engaged (via _reset_stateful_cache) and not touch an uncached load. transformer_2 (the Wan
    # dual expert) resets too when present.
    import dataclasses

    (tmp_path / "ltx-2.3-22b-distilled-1.1-Q4_K_M.gguf").write_bytes(b"w")
    backend = VideoBackend()
    backend.load_pipeline(
        str(tmp_path),
        gguf_filename = "ltx-2.3-22b-distilled-1.1-Q4_K_M.gguf",
        base_repo = "Lightricks/LTX-2",
        family_override = "ltx-2",
    )
    resets = []
    backend._state.pipe.transformer = types.SimpleNamespace(
        _reset_stateful_cache = lambda: resets.append("transformer")
    )
    backend._state.pipe.transformer_2 = types.SimpleNamespace(
        _reset_stateful_cache = lambda: resets.append("transformer_2")
    )
    # No cache engaged -> no reset.
    backend.generate(prompt = "a sloth")
    assert resets == []
    # Cache engaged -> both resident DiTs reset before the pipe call.
    backend._state = dataclasses.replace(backend._state, transformer_cache = "fbcache")
    backend.generate(prompt = "a sloth")
    assert resets == ["transformer", "transformer_2"]


def test_is_ltx23_checkpoint_gguf(monkeypatch, tmp_path):
    # diffusers maps every LTX-2 single file to the 2.0 config; a 2.3 checkpoint (9-row
    # modulation tables in the header) must be detected so the loader routes to the 2.3 assembly.
    # A 2.0 header must not, and an unreadable header falls back to False, never raises.
    from core.inference.video_ltx2 import is_ltx23_checkpoint

    def _reader_for(shapes):
        tensors = [types.SimpleNamespace(name = n, shape = s) for n, s in shapes.items()]
        return lambda path: types.SimpleNamespace(tensors = tensors)

    gguf = types.ModuleType("gguf")
    # GGUF headers store dims in GGML (reversed) order.
    gguf.GGUFReader = _reader_for(
        {
            "model.diffusion_model.transformer_blocks.0.scale_shift_table": (4096, 9),
        }
    )
    monkeypatch.setitem(sys.modules, "gguf", gguf)
    path = tmp_path / "ltx23.gguf"
    path.write_bytes(b"x")
    assert is_ltx23_checkpoint(path) is True

    gguf.GGUFReader = _reader_for(
        {
            "model.diffusion_model.transformer_blocks.0.scale_shift_table": (4096, 6),
        }
    )
    assert is_ltx23_checkpoint(path) is False

    def _boom(path):
        raise RuntimeError("bad magic")

    gguf.GGUFReader = _boom
    assert is_ltx23_checkpoint(path) is False


def test_is_ltx23_checkpoint_safetensors(monkeypatch, tmp_path):
    from core.inference.video_ltx2 import is_ltx23_checkpoint

    class _FakeSlice:
        def __init__(self, shape):
            self._shape = shape

        def get_shape(self):
            return self._shape

    class _FakeSafe:
        def __init__(self, shapes):
            self._shapes = shapes

        def __enter__(self):
            return self

        def __exit__(self, *args):
            return False

        def keys(self):
            return list(self._shapes)

        def get_slice(self, name):
            return _FakeSlice(self._shapes[name])

    shapes = {
        "model.diffusion_model.transformer_blocks.0.scale_shift_table": (9, 4096),
    }
    safetensors = types.ModuleType("safetensors")
    safetensors.safe_open = lambda path, framework = None: _FakeSafe(shapes)
    monkeypatch.setitem(sys.modules, "safetensors", safetensors)
    path = tmp_path / "ltx23.safetensors"
    path.write_bytes(b"x")
    assert is_ltx23_checkpoint(path) is True


def test_ltx23_split_and_variant(tmp_path):
    # Pure functions: combined-checkpoint partitioning and companion-set choice.
    from core.inference.video_ltx2 import _split_checkpoint, checkpoint_variant

    state = {
        "model.diffusion_model.transformer_blocks.0.attn1.to_q.weight": 1,
        "model.diffusion_model.video_embeddings_connector.learnable_registers": 2,
        "model.diffusion_model.prompt_adaln_single.linear.weight": 3,
        "text_embedding_projection.video_aggregate_embed.weight": 4,
        "vae.decoder.conv_in.weight": 5,
        "audio_vae.encoder.conv_in.weight": 6,
        "vocoder.bwe_generator.conv_pre.weight": 7,
    }
    groups = _split_checkpoint(state)
    assert set(groups["dit"]) == {
        "transformer_blocks.0.attn1.to_q.weight",
        "prompt_adaln_single.linear.weight",
    }
    assert set(groups["connectors"]) == {
        "video_embeddings_connector.learnable_registers",
        "text_embedding_projection.video_aggregate_embed.weight",
    }
    assert groups["vae"] == {"decoder.conv_in.weight": 5}
    assert groups["audio_vae"] == {"encoder.conv_in.weight": 6}
    assert groups["vocoder"] == {"bwe_generator.conv_pre.weight": 7}

    assert checkpoint_variant("x/ltx-2.3-22b-distilled-1.1-Q4_K_M.gguf") == "distilled"
    assert checkpoint_variant("x/ltx-2.3-22b-dev-Q8_0.gguf") == "dev"


def test_ltx23_scaled_fp8_refused(monkeypatch, tmp_path):
    # The Lightricks fp8 files carry .weight_scale/.input_scale companions; a plain dtype cast
    # would corrupt them, so the loader must refuse with a pointer to the supported GGUF path.
    from core.inference import video_ltx2

    # Stub the module tree so this also runs under the CI sim, which blocks the
    # real diffusers import.
    diffusers = types.ModuleType("diffusers")
    diffusers.LTX2Pipeline = object
    loaders = types.ModuleType("diffusers.loaders")
    sfu = types.ModuleType("diffusers.loaders.single_file_utils")
    sfu.load_single_file_checkpoint = lambda path: {
        "model.diffusion_model.transformer_blocks.0.attn1.to_q.weight": object(),
        "model.diffusion_model.transformer_blocks.0.attn1.to_q.weight_scale": object(),
    }
    diffusers.loaders = loaders
    loaders.single_file_utils = sfu
    monkeypatch.setitem(sys.modules, "diffusers", diffusers)
    monkeypatch.setitem(sys.modules, "diffusers.loaders", loaders)
    monkeypatch.setitem(sys.modules, "diffusers.loaders.single_file_utils", sfu)
    monkeypatch.setitem(sys.modules, "transformers", types.ModuleType("transformers"))

    path = tmp_path / "ltx-2.3-22b-distilled-fp8.safetensors"
    path.write_bytes(b"x")
    with pytest.raises(ValueError, match = "scaled fp8"):
        video_ltx2.load_ltx23_pipeline(
            path, base_repo = "Lightricks/LTX-2", torch_dtype = None, is_gguf = False
        )


def test_generate_without_load_raises(fake_runtime):
    backend = VideoBackend()
    with pytest.raises(RuntimeError, match = VIDEO_NOT_LOADED_MSG):
        backend.generate(prompt = "x")


def test_generate_progress_and_cancel_idle(fake_runtime):
    backend = VideoBackend()
    # Idle shape carries the image-endpoint-compatible aliases (total_steps / fraction)
    # so one poller works against both generate-progress APIs.
    assert backend.generate_progress() == {
        "active": False,
        "total_steps": 0,
        "fraction": 0.0,
    }
    assert backend.cancel_generate() is False


def test_generate_progress_derives_total_steps_and_fraction(fake_runtime):
    # A mid-denoise poll must report fraction = step / total under BOTH field names:
    # a client polling the image API's shape against video used to read
    # total_steps=null / fraction=0 while step advanced.
    backend = VideoBackend()
    backend._gen = {"active": True, "phase": "denoise", "step": 5, "total": 20}
    gen = backend.generate_progress()
    assert gen["total"] == 20 and gen["total_steps"] == 20
    assert gen["step"] == 5 and gen["fraction"] == 0.25


def test_cache_bytes_counts_incomplete_blobs(fake_runtime, tmp_path, monkeypatch):
    # scan_cache_dir skips in-flight *.incomplete blobs, so the old counter froze at the
    # last completed blob for the whole multi-GB shard pull. The walk must count both,
    # without double-counting snapshot symlinks.
    import huggingface_hub.constants as hub_constants

    repo_dir = tmp_path / "models--Wan-AI--Wan2.2-TI2V-5B-Diffusers"
    blobs = repo_dir / "blobs"
    blobs.mkdir(parents = True)
    (blobs / "aa11").write_bytes(b"x" * 1000)  # completed blob
    (blobs / "bb22.incomplete").write_bytes(b"y" * 500)  # in-flight shard
    snap = repo_dir / "snapshots" / "deadbeef"
    snap.mkdir(parents = True)
    (snap / "model_index.json").symlink_to(blobs / "aa11")  # must not double-count
    monkeypatch.setattr(hub_constants, "HF_HUB_CACHE", str(tmp_path))

    backend = VideoBackend()
    assert backend._cache_bytes("Wan-AI/Wan2.2-TI2V-5B-Diffusers") == 1500
    assert backend._cache_bytes("Wan-AI/absent-repo") == 0
    assert backend._cache_bytes(None) == 0


def test_hv15_guider_and_scheduler_progress(fake_runtime):
    # HunyuanVideo-1.5: no guidance kwarg (CFG set on the guider), no step
    # callback (progress via the scheduler.step wrapper, restored afterwards).
    backend = VideoBackend()
    status = backend.load_pipeline(
        "hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_t2v",
        model_kind = "pipeline",
    )
    assert status["family"] == "hunyuanvideo-1.5"
    assert status["has_audio"] is False
    assert status["defaults"]["frame_step"] == 4

    pipe = _FakeHV15Pipeline.instance
    result = backend.generate(
        prompt = "a fox in the snow", steps = 4, guidance = 3.5, num_frames = 9, fps = 24
    )
    assert "guidance_scale" not in pipe.last_kwargs
    assert "callback_on_step_end" not in pipe.last_kwargs
    assert pipe.guider.guidance_scale == 3.5
    # One wrapped tick per denoise step, then the original method back in place.
    assert pipe.scheduler.calls == 4
    assert pipe.scheduler.step.__func__ is _FakeHV15Scheduler.step
    assert result["num_frames"] == 9 and result["has_audio"] is False


def test_hv15_cancel_unwinds_scheduler_loop(fake_runtime):
    backend = VideoBackend()
    backend.load_pipeline(
        "hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_t2v",
        model_kind = "pipeline",
    )
    pipe = _FakeHV15Pipeline.instance
    # Cancel lands during the FIRST real step; the next wrapped call must raise out
    # of the denoise loop and generate() must surface the cancelled sentinel.
    pipe.scheduler.on_step = lambda n: backend.cancel_generate() if n == 1 else None
    with pytest.raises(RuntimeError, match = VIDEO_CANCELLED_MSG):
        backend.generate(prompt = "a fox", steps = 4)
    assert pipe.scheduler.calls == 1
    # The wrapper must restore scheduler.step even on the exception path.
    assert pipe.scheduler.step.__func__ is _FakeHV15Scheduler.step
    # The exception unwound pipe.__call__ before its own end-of-call cleanup, so generate() must
    # have freed the offload hooks itself (VRAM would otherwise stay onloaded until the next
    # request).
    assert pipe.hooks_freed == 1


def test_cancel_during_export_discards_clip(fake_runtime, monkeypatch):
    # A cancel landing during the (blocking, uncancellable) export/mux must still discard the
    # clip: cancel_generate() already reported success, so generate() must raise the cancelled
    # sentinel rather than return the clip to be persisted to the gallery.
    backend = VideoBackend()
    backend.load_pipeline(
        "hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_t2v",
        model_kind = "pipeline",
    )

    def _encode_and_cancel(frames, fps, audio, pipe):
        backend.cancel_generate()  # cancel arrives mid-mux, after the last denoise-step check
        return b"MP4"

    monkeypatch.setattr(VideoBackend, "_encode_mp4", staticmethod(_encode_and_cancel))
    with pytest.raises(RuntimeError, match = VIDEO_CANCELLED_MSG):
        backend.generate(prompt = "a fox", steps = 4)


def test_singleton():
    assert get_video_backend() is get_video_backend()


# ── Wan2.2 ─────────────────────────────────────────────────────────────────────


def test_load_wan_ti2v_5b_pipeline(fake_runtime):
    # A full-pipeline load of the single-DiT TI2V-5B repo: WanPipeline.from_pretrained,
    # no audio, tiling forced on, and the 4k+1 frame lattice surfaced.
    backend = VideoBackend()
    status = backend.load_pipeline("Wan-AI/Wan2.2-TI2V-5B-Diffusers", model_kind = "pipeline")
    assert status["loaded"] is True
    assert status["family"] == "wan2.2-ti2v-5b"
    assert status["model_kind"] == "pipeline"
    assert status["has_audio"] is False
    assert status["vae_tiling"] is True
    assert status["defaults"]["frame_step"] == 4
    assert status["transformer_quant"] is None
    assert _FakeWanPipelineSingle.last["repo"] == "Wan-AI/Wan2.2-TI2V-5B-Diffusers"


def test_video_dense_speed_defaults_to_compile_profile(fake_runtime):
    # A clip denoise amortises the one-time compile within a single run, so an UNSET speed on a
    # dense (pipeline) load resolves to `default` -- never `max`, never `off`. Explicit "off" is
    # still honored verbatim.
    backend = VideoBackend()
    status = backend.load_pipeline("Wan-AI/Wan2.2-TI2V-5B-Diffusers", model_kind = "pipeline")
    assert status["speed_mode"] == "default"
    assert status["resolved"]["speed_mode"]["source"] == "auto"
    backend.unload()
    status_off = backend.load_pipeline(
        "Wan-AI/Wan2.2-TI2V-5B-Diffusers", model_kind = "pipeline", speed_mode = "off"
    )
    assert status_off["speed_mode"] == "off"
    assert status_off["resolved"]["speed_mode"]["source"] == "explicit"


def test_video_speed_off_suppresses_auto_dtype_quant(fake_runtime, monkeypatch):
    # An explicit Speed="off" (bit-exact) load with Precision at auto must NOT promote the unset
    # precision to auto-quant (that would quantize and force speed back to default, breaking the
    # request). On a dense-capable GPU (stubbed) quantize_transformer must not run.
    import core.inference.video as video_mod

    monkeypatch.setattr(video_mod, "dense_transformer_supported", lambda target: True)
    calls: list = []
    monkeypatch.setattr(
        video_mod, "quantize_transformer", lambda view, target, **kw: calls.append(True) or "int8"
    )

    backend = VideoBackend()
    # speed=off + precision auto (unset): no auto-quant, speed stays off (bit-exact).
    status = backend.load_pipeline(
        "Wan-AI/Wan2.2-TI2V-5B-Diffusers", model_kind = "pipeline", speed_mode = "off"
    )
    assert calls == []  # quantize_transformer never ran
    assert status["transformer_quant"] is None
    assert status["speed_mode"] == "off"
    backend.unload()

    # Control: with speed NOT off, the auto precision promotion still engages the dense quant, so
    # the suppression above is specific to speed=off (not a blanket disable).
    backend.load_pipeline("Wan-AI/Wan2.2-TI2V-5B-Diffusers", model_kind = "pipeline")
    assert calls == [True]


def test_video_speed_off_suppresses_auto_companion_quant(fake_runtime, monkeypatch):
    # Like the DiT suppression above: an explicit Speed="off" load with TE/VAE at auto must NOT
    # auto-quant them (would fp8/int8 the encoder + VAE and break the request). Unset speed still
    # auto-quantises.
    import core.inference.video as video_mod

    te_modes: list = []
    vae_modes: list = []
    monkeypatch.setattr(
        video_mod,
        "quantize_text_encoders",
        lambda pipe, target, *, mode, **kw: te_modes.append(mode),
    )
    monkeypatch.setattr(
        video_mod, "quantize_vae", lambda pipe, target, *, mode, **kw: vae_modes.append(mode)
    )
    backend = VideoBackend()
    backend.load_pipeline(
        "Wan-AI/Wan2.2-TI2V-5B-Diffusers", model_kind = "pipeline", speed_mode = "off"
    )
    assert te_modes == ["off"] and vae_modes == ["off"]  # dense, not auto
    backend.unload()
    backend.load_pipeline("Wan-AI/Wan2.2-TI2V-5B-Diffusers", model_kind = "pipeline")
    assert te_modes[-1] == "auto" and vae_modes[-1] == "auto"  # promoted when speed is not off


def test_video_speed_off_suppresses_explicit_auto_companion_quant(fake_runtime, monkeypatch):
    # auto is backend-owned: an EXPLICIT text_encoder_quant/vae_quant="auto" must also go dense
    # under Speed="off", not only an unset default, else auto + off would fp8/int8 the companions.
    # A concrete scheme still forces it.
    import core.inference.video as video_mod

    te_modes: list = []
    vae_modes: list = []
    monkeypatch.setattr(
        video_mod,
        "quantize_text_encoders",
        lambda pipe, target, *, mode, **kw: te_modes.append(mode),
    )
    monkeypatch.setattr(
        video_mod, "quantize_vae", lambda pipe, target, *, mode, **kw: vae_modes.append(mode)
    )
    backend = VideoBackend()
    backend.load_pipeline(
        "Wan-AI/Wan2.2-TI2V-5B-Diffusers",
        model_kind = "pipeline",
        speed_mode = "off",
        text_encoder_quant = "auto",
        vae_quant = "auto",
    )
    assert te_modes == ["off"] and vae_modes == ["off"]  # explicit auto suppressed under off
    backend.unload()
    # A concrete scheme is still honoured under off (only auto is backend-owned).
    backend.load_pipeline(
        "Wan-AI/Wan2.2-TI2V-5B-Diffusers",
        model_kind = "pipeline",
        speed_mode = "off",
        text_encoder_quant = "fp8",
    )
    assert te_modes[-1] == "fp8"


def test_video_speed_off_skips_hunyuan_trim(fake_runtime, monkeypatch):
    # The trim is a speed lever (swaps to the fused SDPA kernel), so Speed="off" keeps the stock
    # dense-mask attention, like the attention backend below. Unset/active speed installs it.
    import core.inference.video as video_mod

    trim_calls: list = []
    monkeypatch.setattr(
        video_mod,
        "install_hunyuan_attention_trim",
        lambda view, family, **kw: trim_calls.append(True) or False,
    )
    backend = VideoBackend()
    backend.load_pipeline(
        "hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_t2v",
        model_kind = "pipeline",
        speed_mode = "off",
    )
    assert trim_calls == []  # not installed on the bit-exact path
    backend.unload()
    backend.load_pipeline(
        "hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_t2v", model_kind = "pipeline"
    )
    assert trim_calls == [True]  # installed once (single DiT) when speed is active


def test_video_step_cache_auto_from_default_schedule(fake_runtime, tmp_path):
    # Unset step cache is AUTO, decided from the default schedule: Wan TI2V-5B's 50-step default
    # engages MagCache at load; the LTX distilled 8-step default keeps it off. Both re-checked
    # per generation.
    backend = VideoBackend()
    status = backend.load_pipeline("Wan-AI/Wan2.2-TI2V-5B-Diffusers", model_kind = "pipeline")
    assert status["transformer_cache"] == "magcache"
    assert status["resolved"]["transformer_cache"]["source"] == "auto"
    backend.unload()

    (tmp_path / "ltx-2.3-22b-distilled-1.1-Q4_K_M.gguf").write_bytes(b"w")
    status2 = backend.load_pipeline(
        str(tmp_path),
        gguf_filename = "ltx-2.3-22b-distilled-1.1-Q4_K_M.gguf",
        base_repo = "Lightricks/LTX-2",
        family_override = "ltx-2",
    )
    assert status2["transformer_cache"] is None
    assert status2["resolved"]["transformer_cache"]["source"] == "auto"
    backend.unload()


def test_video_step_cache_auto_toggles_on_actual_steps(fake_runtime):
    # The AUTO decision follows the ACTUAL step count of each generation: a few-step request drops
    # the load-time cache, a many-step request restores it. An explicit "off" never toggles.
    backend = VideoBackend()
    backend.load_pipeline("Wan-AI/Wan2.2-TI2V-5B-Diffusers", model_kind = "pipeline")
    assert backend.status()["transformer_cache"] == "magcache"
    backend.generate(prompt = "a sloth", steps = 8)
    assert backend.status()["transformer_cache"] is None
    backend.generate(prompt = "a sloth", steps = 30)
    assert backend.status()["transformer_cache"] == "magcache"
    # The re-engage interpolated the calibrated curve over the ACTUAL step count.
    cfg = backend._state.pipe.transformer.cache_config
    assert cfg[0] == "magcache" and cfg[1]["num_inference_steps"] == 30
    backend.unload()

    backend.load_pipeline(
        "Wan-AI/Wan2.2-TI2V-5B-Diffusers", model_kind = "pipeline", transformer_cache = "off"
    )
    assert backend.status()["transformer_cache"] is None
    backend.generate(prompt = "a sloth", steps = 30)
    assert backend.status()["transformer_cache"] is None
    backend.unload()


def test_explicit_magcache_reinterpolates_on_step_change(fake_runtime):
    # An EXPLICIT magcache load never toggles off, but its curve/retention/skip budget interpolate
    # over the CONFIGURED step count, so a clip at a different step count must re-engage to keep
    # skips aligned (auto does this via maybe_toggle_step_cache).
    backend = VideoBackend()
    backend.load_pipeline(
        "Wan-AI/Wan2.2-TI2V-5B-Diffusers",
        model_kind = "pipeline",
        transformer_cache = "magcache",
    )
    assert backend.status()["transformer_cache"] == "magcache"
    cfg = backend._state.pipe.transformer.cache_config
    assert cfg[0] == "magcache" and cfg[1]["num_inference_steps"] == 50  # load default
    backend.generate(prompt = "a sloth", steps = 30)
    cfg = backend._state.pipe.transformer.cache_config
    assert cfg[0] == "magcache" and cfg[1]["num_inference_steps"] == 30
    # Explicit stays ON even below the auto min-steps bar: only the sizing changes.
    backend.generate(prompt = "a sloth", steps = 8)
    cfg = backend._state.pipe.transformer.cache_config
    assert cfg[1]["num_inference_steps"] == 8
    assert backend.status()["transformer_cache"] == "magcache"
    backend.unload()


def test_rollback_precommit_cfg_parallel_is_token_scoped(fake_runtime, monkeypatch):
    # A load that installed the CFG-parallel proxy then died before committing _VideoLoadState has
    # nothing for _teardown_state to reach: _run_load's error handler tears the stash down -- but
    # only for its own token, so a superseded worker can't clobber a newer load's proxy.
    from core.inference import video as video_mod

    calls = []
    monkeypatch.setattr(
        video_mod,
        "teardown_cfg_parallel",
        lambda pipe, proxy, logger = None: calls.append((pipe, proxy)),
    )
    backend = VideoBackend()
    pipe, proxy = object(), object()
    backend._precommit_cfg_parallel = (7, pipe, proxy)
    backend._rollback_precommit_cfg_parallel(8)  # stale worker: leave the stash alone
    assert calls == [] and backend._precommit_cfg_parallel is not None
    backend._rollback_precommit_cfg_parallel(7)  # owning worker: torn down + cleared
    assert calls == [(pipe, proxy)] and backend._precommit_cfg_parallel is None
    backend._rollback_precommit_cfg_parallel(7)  # idempotent
    assert len(calls) == 1


def test_wan_frame_snapping_4k_plus_1(fake_runtime):
    # Wan snaps num_frames to 4k+1 (temporal factor 4), unlike LTX-2's 8k+1.
    backend = VideoBackend()
    backend.load_pipeline("Wan-AI/Wan2.2-TI2V-5B-Diffusers", model_kind = "pipeline")
    backend.generate(prompt = "a sloth", width = 1000, height = 700, num_frames = 120)
    call = backend._state.pipe.last_kwargs
    assert call["num_frames"] == 117  # 4*29 + 1
    # /32 spatial snap for TI2V-5B: its VAE is 16x spatial * patch 2 = 32 (WanPipeline floors
    # H/W to 32), so 1000x700 -> 992x672 (not the /16 992x688).
    assert (call["width"], call["height"]) == (992, 672)


def test_wan_ti2v_defaults_applied(fake_runtime):
    # No steps/guidance passed -> the Wan pipeline defaults (50 / 5.0).
    backend = VideoBackend()
    backend.load_pipeline("Wan-AI/Wan2.2-TI2V-5B-Diffusers", model_kind = "pipeline")
    backend.generate(prompt = "a sloth")
    call = backend._state.pipe.last_kwargs
    assert call["num_inference_steps"] == 50
    assert call["guidance_scale"] == 5.0


def test_wan_ti2v_does_not_thread_cfg2(fake_runtime):
    # The single-DiT TI2V pipeline has no guidance_scale_2 in its signature, so a request value
    # must NOT be threaded (WanPipeline raises on it when boundary_ratio is None), even if the
    # caller passes guidance_2.
    backend = VideoBackend()
    backend.load_pipeline("Wan-AI/Wan2.2-TI2V-5B-Diffusers", model_kind = "pipeline")
    backend.generate(prompt = "a sloth", guidance_2 = 3.5)
    call = backend._state.pipe.last_kwargs
    assert "guidance_scale_2" not in call


def test_wan_a14b_dual_dit_pipeline_loads(fake_runtime):
    # The A14B repo builds a dual-DiT MoE pipeline (transformer + transformer_2).
    backend = VideoBackend()
    status = backend.load_pipeline("Wan-AI/Wan2.2-T2V-A14B-Diffusers", model_kind = "pipeline")
    assert status["loaded"] is True and status["family"] == "wan2.2-t2v-a14b"
    pipe = backend._state.pipe
    assert pipe.transformer is not None and pipe.transformer_2 is not None


def test_wan_a14b_cfg2_threaded_when_signature_has_it(fake_runtime):
    # The MoE pipeline's __call__ carries guidance_scale_2, so an explicit guidance_2
    # is threaded through as that kwarg (the cfg2_kwarg the family declares).
    backend = VideoBackend()
    backend.load_pipeline("Wan-AI/Wan2.2-T2V-A14B-Diffusers", model_kind = "pipeline")
    backend.generate(prompt = "a sloth", guidance = 5.0, guidance_2 = 3.0)
    call = backend._state.pipe.last_kwargs
    assert call["guidance_scale"] == 5.0
    assert call["guidance_scale_2"] == 3.0

    # A None guidance_2 must NOT be threaded, so the pipeline defaults it itself.
    backend.generate(prompt = "a sloth", guidance = 5.0)
    call2 = backend._state.pipe.last_kwargs
    assert call2["guidance_scale_2"] is None


def _tiny_png_data_url(width = 8, height = 8):
    import base64
    import io

    from PIL import Image

    buf = io.BytesIO()
    Image.new("RGB", (width, height), (128, 64, 32)).save(buf, format = "PNG")
    return "data:image/png;base64," + base64.b64encode(buf.getvalue()).decode()


def test_wan_i2v_repo_is_trusted_and_detected():
    # The official -Diffusers repo loads as a full pipeline: it must be on the video
    # trust allowlist and resolve to the image-conditioned dual-expert family.
    backend = VideoBackend()
    fam = backend.validate_load_request(
        "Wan-AI/Wan2.2-I2V-A14B-Diffusers", model_kind = "pipeline"
    )
    assert fam.name == "wan2.2-i2v-a14b"
    assert fam.image_conditioned is True


def test_wan_i2v_requires_image_and_threads_it(fake_runtime):
    # Loading the I2V family builds the dual-DiT image pipeline; generate() without a
    # source image is client input error, with one the decoded PIL image (resized to the
    # snapped output size) is threaded as the pipeline's ``image`` kwarg.
    backend = VideoBackend()
    status = backend.load_pipeline("Wan-AI/Wan2.2-I2V-A14B-Diffusers", model_kind = "pipeline")
    assert status["loaded"] is True and status["family"] == "wan2.2-i2v-a14b"
    assert status["image_input"] is True
    pipe = backend._state.pipe
    assert pipe.transformer is not None and pipe.transformer_2 is not None

    with pytest.raises(ValueError, match = "image-to-video"):
        backend.generate(prompt = "a sloth")
    # begin_generate 400s synchronously too (no failed background job for a missing image),
    # and a raise must not leave the busy flag set.
    with pytest.raises(ValueError, match = "image-to-video"):
        backend.begin_generate(prompt = "a sloth")
    assert backend._generate_job_active is False

    backend.generate(
        prompt = "a sloth", width = 832, height = 480, init_image = _tiny_png_data_url()
    )
    sent = pipe.last_kwargs["image"]
    assert sent is not None and sent.size == (832, 480)
    # The I2V defaults (40 steps / CFG 3.5, the card recipe) beat the generic wan 50/5.0.
    assert pipe.last_kwargs["num_inference_steps"] == 40
    assert pipe.last_kwargs["guidance_scale"] == 3.5


def test_wan_t2v_rejects_source_image(fake_runtime):
    # A text-only family given an init_image must 400-fail loudly, not silently ignore it.
    backend = VideoBackend()
    status = backend.load_pipeline("Wan-AI/Wan2.2-T2V-A14B-Diffusers", model_kind = "pipeline")
    assert status["image_input"] is False
    with pytest.raises(ValueError, match = "does not take a source image"):
        backend.generate(prompt = "a sloth", init_image = _tiny_png_data_url())


def test_wan_a14b_step_cache_applies_to_both_dits(fake_runtime):
    # A dual-DiT MoE load must engage the step cache on BOTH experts, not just the
    # first: transformer_2 handles the low-noise steps and would otherwise run uncached.
    backend = VideoBackend()
    status = backend.load_pipeline(
        "Wan-AI/Wan2.2-T2V-A14B-Diffusers",
        model_kind = "pipeline",
        transformer_cache = "fbcache",
    )
    pipe = backend._state.pipe
    assert pipe.transformer.cache_config is not None
    assert pipe.transformer_2.cache_config is not None
    assert status["transformer_cache"] == "fbcache"


def test_wan_a14b_attention_applies_to_both_dits(fake_runtime, monkeypatch):
    # An explicit attention backend must be set on both experts. The fake runtime is a CPU target,
    # where the NVIDIA gate drops explicit kernels; pin the gate open so the explicit-set path is
    # what this test exercises.
    from core.inference import diffusion_attention as attn_mod

    monkeypatch.setattr(attn_mod, "_is_cuda_nvidia", lambda target: True)
    backend = VideoBackend()
    backend.load_pipeline(
        "Wan-AI/Wan2.2-T2V-A14B-Diffusers",
        model_kind = "pipeline",
        attention_backend = "cudnn",
    )
    pipe = backend._state.pipe
    assert pipe.transformer.attention is not None
    assert pipe.transformer_2.attention is not None
    # Both experts got the SAME kernel.
    assert pipe.transformer.attention == pipe.transformer_2.attention


def test_wan_ti2v_single_dit_only_touches_one(fake_runtime):
    # A single-DiT load must not fabricate a second expert or try to optimise one.
    backend = VideoBackend()
    backend.load_pipeline(
        "Wan-AI/Wan2.2-TI2V-5B-Diffusers",
        model_kind = "pipeline",
        transformer_cache = "fbcache",
    )
    pipe = backend._state.pipe
    assert pipe.transformer_2 is None
    assert pipe.transformer.cache_config is not None


def test_wan_a14b_dense_quant_applies_to_both_dits(fake_runtime, monkeypatch):
    # transformer_quant on a pipeline load quantises the dense DiT(s). On CPU dense is
    # unsupported, so stub the quant seams to record which view each helper saw: BOTH experts
    # must be quantised (via _SecondDiTView), and status reports the engaged scheme.
    import core.inference.video as video_mod

    monkeypatch.setattr(video_mod, "dense_transformer_supported", lambda target: True)
    quantised = []

    def _fake_quant(
        view,
        target,
        *,
        mode,
        family,
        logger = None,
    ):
        # The helper reads view.transformer; record the object it would quantise so the
        # test proves the second expert was reached through the proxy.
        quantised.append(view.transformer)
        return "int8"

    monkeypatch.setattr(video_mod, "quantize_transformer", _fake_quant)

    backend = VideoBackend()
    status = backend.load_pipeline(
        "Wan-AI/Wan2.2-T2V-A14B-Diffusers",
        model_kind = "pipeline",
        transformer_quant = "int8",
    )
    pipe = backend._state.pipe
    # Both experts were passed to quantize_transformer, in that order.
    assert quantised == [pipe.transformer, pipe.transformer_2]
    assert status["transformer_quant"] == "int8"


def test_dense_quant_skipped_under_offload(fake_runtime, monkeypatch):
    # Offload hooks move modules with Module.to(), which torchao tensors reject (a hard crash on
    # the A14B run). When the plan resolves to any offload policy, quant must be SKIPPED: the load
    # succeeds dense and the resolved record explains why.
    import core.inference.video as video_mod

    monkeypatch.setattr(video_mod, "dense_transformer_supported", lambda target: True)
    quantised = []

    def _fake_quant(
        view,
        target,
        *,
        mode,
        family,
        logger = None,
    ):
        quantised.append(view.transformer)
        return "int8"

    monkeypatch.setattr(video_mod, "quantize_transformer", _fake_quant)
    # The CPU fake target never plans an offload, so force one at the plan seam (frozen dataclass
    # -> dataclasses.replace) and stub the apply step, which would else call offload hooks the fake
    # pipe lacks.
    import dataclasses

    real_plan = video_mod.plan_diffusion_memory
    monkeypatch.setattr(
        video_mod,
        "plan_diffusion_memory",
        lambda **kwargs: dataclasses.replace(real_plan(**kwargs), offload_policy = "model"),
    )
    monkeypatch.setattr(
        video_mod,
        "apply_memory_plan",
        lambda pipe, plan, device = None, logger = None: ("model", True),
    )

    backend = VideoBackend()
    status = backend.load_pipeline(
        "Wan-AI/Wan2.2-T2V-A14B-Diffusers",
        model_kind = "pipeline",
        transformer_quant = "int8",
    )
    assert status["offload_policy"] == "model"
    assert quantised == []
    assert status["transformer_quant"] is None
    assert "offload moves the DiT" in status["resolved"]["transformer_quant"]["reason"]


def test_wan_a14b_partial_quant_fails_the_load(fake_runtime, monkeypatch):
    # If the first expert quantises but the second doesn't, the pipe is left at mismatched
    # precision with no way back, so the load must fail cleanly rather than run mixed.
    import core.inference.video as video_mod

    monkeypatch.setattr(video_mod, "dense_transformer_supported", lambda target: True)
    outcomes = iter(["int8", None])
    monkeypatch.setattr(
        video_mod,
        "quantize_transformer",
        lambda view, target, *, mode, family, logger = None: next(outcomes),
    )

    backend = VideoBackend()
    with pytest.raises(RuntimeError, match = "1/2 experts"):
        backend.load_pipeline(
            "Wan-AI/Wan2.2-T2V-A14B-Diffusers",
            model_kind = "pipeline",
            transformer_quant = "int8",
        )
    assert backend.status()["loaded"] is False


def test_wan_ti2v_dense_quant_applies_to_single_dit(fake_runtime, monkeypatch):
    # A single-DiT pipeline load quantises exactly one transformer.
    import core.inference.video as video_mod

    monkeypatch.setattr(video_mod, "dense_transformer_supported", lambda target: True)
    quantised = []

    def _fake_quant(
        view,
        target,
        *,
        mode,
        family,
        logger = None,
    ):
        quantised.append(view.transformer)
        return "fp8"

    monkeypatch.setattr(video_mod, "quantize_transformer", _fake_quant)

    backend = VideoBackend()
    status = backend.load_pipeline(
        "Wan-AI/Wan2.2-TI2V-5B-Diffusers",
        model_kind = "pipeline",
        transformer_quant = "fp8",
    )
    assert quantised == [backend._state.pipe.transformer]
    assert status["transformer_quant"] == "fp8"


def test_wan_validate_trusted_repos(fake_runtime):
    # The two Wan base repos are trusted for non-GGUF (pipeline) loads; an unrelated
    # repo carrying the family name is not.
    backend = VideoBackend()
    fam = backend.validate_load_request("Wan-AI/Wan2.2-TI2V-5B-Diffusers", model_kind = "pipeline")
    assert fam.name == "wan2.2-ti2v-5b"
    fam2 = backend.validate_load_request("Wan-AI/Wan2.2-T2V-A14B-Diffusers", model_kind = "pipeline")
    assert fam2.name == "wan2.2-t2v-a14b"
    with pytest.raises(ValueError, match = "limited to"):
        backend.validate_load_request("evil/wan2.2-ti2v-5b-repack", model_kind = "pipeline")
    # A bad transformer_quant scheme is rejected cheaply at validate time.
    with pytest.raises(ValueError, match = "transformer_quant"):
        backend.validate_load_request(
            "Wan-AI/Wan2.2-TI2V-5B-Diffusers",
            model_kind = "pipeline",
            transformer_quant = "bogus",
        )


def test_wan_a14b_refuses_single_file_loads(fake_runtime):
    # A single gguf/safetensors checkpoint carries only one of the A14B's two experts; the
    # pipeline would pull the other dense bf16 from the base repo outside the memory plan, so
    # validate refuses it up front (before any download).
    backend = VideoBackend()
    with pytest.raises(ValueError, match = "dual-expert"):
        backend.validate_load_request(
            "QuantStack/Wan2.2-T2V-A14B-GGUF",
            gguf_filename = "HighNoise/Wan2.2-T2V-A14B-HighNoise-Q4_K_M.gguf",
        )
    # The single-DiT 5B family still accepts GGUF.
    fam = backend.validate_load_request(
        "QuantStack/Wan2.2-TI2V-5B-GGUF",
        gguf_filename = "Wan2.2-TI2V-5B-Q4_K_M.gguf",
    )
    assert fam.name == "wan2.2-ti2v-5b"


def test_second_dit_view_write_through():
    # Attribute writes on the proxy must land on the real pipe (a helper's side effect would else
    # vanish with the temporary view); a ``transformer`` write mirrors the read property onto the
    # second expert.
    from core.inference.video import _SecondDiTView

    pipe = types.SimpleNamespace(transformer = "t1", transformer_2 = "t2", flag = None)
    view = _SecondDiTView(pipe)
    assert view.transformer == "t2"
    view.transformer = "t2-compiled"
    assert pipe.transformer_2 == "t2-compiled" and pipe.transformer == "t1"
    view.flag = "set"
    assert pipe.flag == "set"


# ── scoped base-repo download ─────────────────────────────────────────────────


def _sibling(name, size):
    return types.SimpleNamespace(rfilename = name, size = size)


_LTX2_SIBLINGS = [
    _sibling("model_index.json", 10),
    _sibling("ltx-2-19b-packaged-fp8.safetensors", 170),
    _sibling("transformer/config.json", 1),
    _sibling("transformer/diffusion_pytorch_model-00001-of-00002.safetensors", 20),
    _sibling("transformer/diffusion_pytorch_model-00002-of-00002.safetensors", 18),
    _sibling("text_encoder/model-00001-of-00002.safetensors", 25),
    _sibling("text_encoder/model-00002-of-00002.safetensors", 25),
    _sibling("text_encoder/diffusion_pytorch_model-00001-of-00002.safetensors", 25),
    _sibling("text_encoder/diffusion_pytorch_model-00002-of-00002.safetensors", 25),
    _sibling("vae/diffusion_pytorch_model.safetensors", 3),
    _sibling("tokenizer/tokenizer.model", 1),
    _sibling("tokenizer/chat_template.jinja", 1),
    _sibling("assets/example.mp4", 500),
]


def test_base_download_files_scopes_pipeline_pull():
    # A pipeline load skips the packaged root checkpoint, the duplicate
    # text-encoder shard naming, and non-weight assets -- and keeps everything else.
    info = types.SimpleNamespace(siblings = _LTX2_SIBLINGS)
    files = dict(VideoBackend._base_download_files(info, "pipeline"))
    assert "ltx-2-19b-packaged-fp8.safetensors" not in files
    assert "text_encoder/diffusion_pytorch_model-00001-of-00002.safetensors" not in files
    assert "assets/example.mp4" not in files
    assert files["text_encoder/model-00001-of-00002.safetensors"] == 25
    assert files["transformer/diffusion_pytorch_model-00001-of-00002.safetensors"] == 20
    # The standalone chat template must survive the whitelist: apply_chat_template
    # reads it at generation time and it is not embedded in tokenizer_config.json.
    assert "tokenizer/chat_template.jinja" in files
    assert sum(files.values()) == 10 + 1 + 20 + 18 + 25 + 25 + 3 + 1 + 1


def test_base_download_files_gguf_drops_transformer():
    # A GGUF/single-file checkpoint replaces the DiT: the base transformer never pulls.
    info = types.SimpleNamespace(siblings = _LTX2_SIBLINGS)
    names = [n for n, _ in VideoBackend._base_download_files(info, "gguf")]
    assert not any(n.startswith("transformer/") for n in names)
    assert "text_encoder/model-00001-of-00002.safetensors" in names


def test_load_progress_clamps_overshoot(fake_runtime, monkeypatch):
    # The cache scan counts blobs a broader previous pull left behind; the reported
    # counter must never exceed the scoped estimate (no "282 GB of 263 GB").
    backend = VideoBackend()
    backend._loading = types.SimpleNamespace(
        repo_id = "Lightricks/LTX-2", base_repo = None, expected_bytes = 100, error = None
    )
    monkeypatch.setattr(VideoBackend, "_cache_bytes", lambda self, repo: 150)
    progress = backend.load_progress()
    assert progress["phase"] == "finalizing"
    assert progress["downloaded_bytes"] == 100
    assert progress["expected_bytes"] == 100


def test_pipeline_load_uses_predownloaded_dir(fake_runtime, tmp_path):
    # When the scoped pre-download produced a local snapshot, from_pretrained must
    # receive that dir (keeping diffusers' own broader snapshot sweep off the hub).
    backend = VideoBackend()
    backend.load_pipeline(
        "Lightricks/LTX-2",
        model_kind = "pipeline",
        _base_local_dir = str(tmp_path),
    )
    assert _FakePipeline.last["base"] == str(tmp_path)
    backend.unload()


def test_base_download_files_ltx23_keeps_only_shared_components():
    # A 2.3 checkpoint supplies the DiT, connectors, both VAEs and the vocoder, so
    # the base pull shrinks to scheduler + text encoder + tokenizer (+ root manifest).
    siblings = _LTX2_SIBLINGS + [
        _sibling("scheduler/scheduler_config.json", 1),
        _sibling("connectors/diffusion_pytorch_model.safetensors", 3),
        _sibling("latent_upsampler/diffusion_pytorch_model.safetensors", 1),
    ]
    info = types.SimpleNamespace(siblings = siblings)
    names = [n for n, _ in VideoBackend._base_download_files(info, "gguf", ltx23 = True)]
    assert "model_index.json" in names
    assert "scheduler/scheduler_config.json" in names
    assert "text_encoder/model-00001-of-00002.safetensors" in names
    assert "tokenizer/tokenizer.model" in names
    assert not any(
        n.startswith(("vae/", "connectors/", "latent_upsampler/", "transformer/")) for n in names
    )


def test_hv15_720p_repo_gets_720p_family_defaults():
    # The 720p repack is trusted, but it must resolve its OWN family entry: the
    # generic hunyuanvideo-1.5 entry would default generation to 832x480.
    from core.inference.video_families import detect_video_family

    fam = detect_video_family("hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-720p_t2v")
    assert fam is not None and fam.name == "hunyuanvideo-1.5-720p"
    assert fam.resolution_presets[0] == (1280, 720)
    assert fam.base_repo.endswith("720p_t2v")
    # The 480p repo keeps the original entry.
    fam480 = detect_video_family("hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_t2v")
    assert fam480 is not None and fam480.name == "hunyuanvideo-1.5"
    assert fam480.resolution_presets[0] == (832, 480)


def test_predownload_base_honors_cancel_between_files(monkeypatch):
    # A warm-cache sweep returns each file instantly without consulting the event,
    # so the loop must check it explicitly or an unload mid-predownload is ignored.
    backend = VideoBackend()
    backend._cancel_event.set()
    calls: list = []
    monkeypatch.setattr(
        "utils.hf_xet_fallback.hf_hub_download_with_xet_fallback",
        lambda repo, fn, tok, **kw: (calls.append(fn), f"/cache/{fn}")[1],
    )

    class _Api:
        def __init__(self, token = None):
            pass

        def model_info(
            self,
            repo,
            files_metadata = True,
        ):
            return types.SimpleNamespace(
                siblings = [
                    _sibling("model_index.json", 1),
                    _sibling("vae/diffusion_pytorch_model.safetensors", 2),
                ]
            )

    import huggingface_hub

    monkeypatch.setattr(huggingface_hub, "HfApi", _Api)
    with pytest.raises(RuntimeError, match = "cancelled"):
        backend._predownload_base("base/repo", None, "pipeline")
    assert calls == []


def test_detect_load_family_arch_fallback_for_local_gguf(tmp_path, monkeypatch):
    # A local GGUF is admitted to the Video picker by its general.architecture, but its path name
    # may carry no whole-segment family token (a renamed "model.gguf"). The loader must resolve the
    # same family the picker offered by reading the arch, not only the name.
    from core.inference import video as vid
    from core.inference.video_families import detect_video_family

    d = tmp_path / "my-videos"
    d.mkdir()
    (d / "model.gguf").write_bytes(b"GGUF")  # exists; content irrelevant (reader is patched)

    # Name-only detection misses it (no "ltx" token in the path or filename).
    assert detect_video_family(str(d)) is None
    assert detect_video_family(f"{d}/model.gguf") is None

    # ltxv arch resolves to the ltx-2 family via the arch fallback.
    monkeypatch.setattr(
        "utils.models.gguf_metadata.read_gguf_general_metadata",
        lambda p: {"general.architecture": "ltxv"},
    )
    fam = vid._detect_load_family(str(d), "model.gguf", None)
    assert fam is not None and fam.name == "ltx-2"

    # A video arch with no backend family (wan) stays None -> the loader 400s as before.
    monkeypatch.setattr(
        "utils.models.gguf_metadata.read_gguf_general_metadata",
        lambda p: {"general.architecture": "wan"},
    )
    assert vid._detect_load_family(str(d), "model.gguf", None) is None

    # An explicit family_override skips the arch read entirely (worker parity).
    monkeypatch.setattr(
        "utils.models.gguf_metadata.read_gguf_general_metadata",
        lambda p: {"general.architecture": "ltxv"},
    )
    assert vid._detect_load_family(str(d), "model.gguf", "ltx-2").name == "ltx-2"


# ── pre-warmed torch.compile cache (Mega-cache) wiring ───────────────────────────


def _stub_compile_cache(monkeypatch, ctx = None):
    """Record begin/save/restore calls on the compile-cache module video.py imports."""
    from core.inference import video as video_mod

    calls = {"begin": [], "save": [], "restore": []}
    monkeypatch.setattr(
        video_mod.compile_cache,
        "begin",
        lambda **kwargs: calls["begin"].append(kwargs) or ctx,
    )
    monkeypatch.setattr(
        video_mod.compile_cache,
        "save",
        lambda c, logger = None: calls["save"].append(c) or False,
    )
    monkeypatch.setattr(video_mod.compile_cache, "restore", lambda c: calls["restore"].append(c))
    return calls


def test_video_compile_cache_begin_save_restore_lifecycle(fake_runtime, monkeypatch):
    # A compiled-tier load must run compile_cache.begin BEFORE the speed profile, commit the
    # context to _VideoLoadState, persist the bundle after the first generation, and restore
    # TORCHINDUCTOR_CACHE_DIR on unload -- mirroring the image backend.
    from core.inference import video as video_mod

    monkeypatch.setattr(video_mod, "compile_eligible", lambda *a, **k: True)
    ctx = object()
    calls = _stub_compile_cache(monkeypatch, ctx = ctx)
    backend = VideoBackend()
    backend.load_pipeline("Wan-AI/Wan2.2-TI2V-5B-Diffusers", model_kind = "pipeline")
    assert len(calls["begin"]) == 1
    kwargs = calls["begin"][0]
    assert kwargs["family"] == "wan2.2-ti2v-5b"
    # The wan5b auto step cache engages (or may toggle) on the default 50-step
    # schedule, so the cached bundle must be keyed fullgraph=False like the compile.
    assert kwargs["compile_kwargs"]["fullgraph"] is False
    assert kwargs["compile_kwargs"]["dynamic"] is True
    assert backend._state.compile_cache_ctx is ctx

    backend.generate(prompt = "a sloth")
    assert calls["save"] == [ctx]

    backend.unload()
    assert calls["restore"] == [ctx]


def test_video_register_shape_records_static_generation_dims(fake_runtime, monkeypatch):
    # A speed=max video load compiles dynamic=False (per-(width, height, frames) artifacts),
    # so the save path must register the ACTUAL generation shape before saving -- otherwise a
    # post-bundle-hit ctx.saved stays true and a later resolution/frame count never re-dirties
    # the bundle, so warm restarts silently recompile it. Mirrors the image backend.
    from core.inference import video as video_mod

    monkeypatch.setattr(video_mod, "compile_eligible", lambda *a, **k: True)
    monkeypatch.setattr(video_mod, "apply_speed_optims", lambda *a, **k: {"compiled": True})
    registered: list = []
    monkeypatch.setattr(
        video_mod.compile_cache,
        "register_shape",
        lambda ctx, shape, *, static: registered.append((tuple(shape), static)),
    )
    monkeypatch.setattr(video_mod.compile_cache, "save", lambda c, logger = None: True)

    backend = VideoBackend()
    backend.load_pipeline(
        "Wan-AI/Wan2.2-TI2V-5B-Diffusers", model_kind = "pipeline", speed_mode = "max"
    )
    # Two distinct shapes must each register (static coverage grows per shape), tagged static.
    r1 = backend.generate(prompt = "a sloth", width = 1000, height = 700, num_frames = 120)
    assert registered[-1] == ((r1["width"], r1["height"], r1["num_frames"]), True)
    r2 = backend.generate(prompt = "a sloth", width = 512, height = 512, num_frames = 41)
    assert registered[-1] == ((r2["width"], r2["height"], r2["num_frames"]), True)
    assert registered[-1][0] != registered[-2][0]
    backend.unload()


def test_video_register_shape_not_static_on_dynamic_default_tier(fake_runtime, monkeypatch):
    # The default video tier compiles dynamic=True (one artifact across shapes), so the DiT
    # never dirties per shape: register_shape must be called with static=False so the bundle
    # is not needlessly rewritten on every new resolution.
    from core.inference import video as video_mod

    # Kill the background prewarm so only the explicit generation registers a shape.
    monkeypatch.setenv("UNSLOTH_DIFFUSION_COMPILE_PREWARM", "0")
    monkeypatch.setattr(video_mod, "compile_eligible", lambda *a, **k: True)
    monkeypatch.setattr(video_mod, "apply_speed_optims", lambda *a, **k: {"compiled": True})
    registered: list = []
    monkeypatch.setattr(
        video_mod.compile_cache,
        "register_shape",
        lambda ctx, shape, *, static: registered.append((tuple(shape), static)),
    )
    monkeypatch.setattr(video_mod.compile_cache, "save", lambda c, logger = None: True)

    backend = VideoBackend()
    backend.load_pipeline("Wan-AI/Wan2.2-TI2V-5B-Diffusers", model_kind = "pipeline")
    backend.generate(prompt = "a sloth")
    assert registered and registered[-1][1] is False
    backend.unload()


def test_video_compile_cache_skipped_on_speed_off_and_ineligible(fake_runtime, monkeypatch):
    # Speed=off (bit-exact reference) or a compile-ineligible target must never touch
    # the compile cache: no begin, no context, no restore side effects to leak.
    from core.inference import video as video_mod

    monkeypatch.setattr(video_mod, "compile_eligible", lambda *a, **k: True)
    calls = _stub_compile_cache(monkeypatch, ctx = object())
    backend = VideoBackend()
    backend.load_pipeline(
        "Wan-AI/Wan2.2-TI2V-5B-Diffusers", model_kind = "pipeline", speed_mode = "off"
    )
    assert calls["begin"] == []
    assert backend._state.compile_cache_ctx is None
    backend.unload()

    monkeypatch.setattr(video_mod, "compile_eligible", lambda *a, **k: False)
    backend.load_pipeline("Wan-AI/Wan2.2-TI2V-5B-Diffusers", model_kind = "pipeline")
    assert calls["begin"] == []
    assert backend._state.compile_cache_ctx is None


def test_rollback_precommit_compile_cache_is_token_scoped(fake_runtime, monkeypatch):
    # A load that ran compile_cache.begin then died before committing must restore
    # TORCHINDUCTOR_CACHE_DIR itself -- but only for its own token, so a superseded worker can't
    # clobber a newer load's redirect. Mirrors _rollback_precommit_cfg_parallel.
    from core.inference import video as video_mod

    calls = []
    monkeypatch.setattr(video_mod.compile_cache, "restore", lambda ctx: calls.append(ctx))
    backend = VideoBackend()
    ctx = object()
    backend._precommit_compile_cache = (7, ctx)
    backend._rollback_precommit_compile_cache(8)  # stale worker: leave the stash alone
    assert calls == [] and backend._precommit_compile_cache is not None
    backend._rollback_precommit_compile_cache(7)  # owning worker: restored + cleared
    assert calls == [ctx] and backend._precommit_compile_cache is None
    backend._rollback_precommit_compile_cache(7)  # idempotent
    assert len(calls) == 1


def test_compile_prewarm_decision_gates(monkeypatch):
    # The pure gate: on only for a compiled DEFAULT-tier resident load on a family
    # that allows it, with the env kill switch and the cfg-parallel/offload/max
    # exclusions each carrying their own resolved reason.
    import dataclasses

    from core.inference import video as video_mod
    from core.inference.video_families import detect_video_family

    fam = detect_video_family("Wan-AI/Wan2.2-TI2V-5B-Diffusers")
    base = dict(
        speed_mode = "default",
        speed_optims = ("compiled",),
        offload_policy = "none",
        cfg_parallel_active = False,
    )

    on, reason = video_mod.compile_prewarm_decision(fam, **base)
    assert on is True and "absorbs" in reason

    monkeypatch.setenv("UNSLOTH_DIFFUSION_COMPILE_PREWARM", "0")
    on, reason = video_mod.compile_prewarm_decision(fam, **base)
    assert on is False and "UNSLOTH_DIFFUSION_COMPILE_PREWARM" in reason
    monkeypatch.delenv("UNSLOTH_DIFFUSION_COMPILE_PREWARM")

    on, reason = video_mod.compile_prewarm_decision(
        fam, **{**base, "speed_optims": ("cudnn_benchmark",)}
    )
    assert on is False and "no regional compile" in reason

    on, reason = video_mod.compile_prewarm_decision(fam, **{**base, "speed_mode": "max"})
    assert on is False and "static per-shape" in reason

    opted_out = dataclasses.replace(fam, supports_compile_prewarm = False)
    on, reason = video_mod.compile_prewarm_decision(opted_out, **base)
    assert on is False and "family opted out" in reason

    on, reason = video_mod.compile_prewarm_decision(fam, **{**base, "offload_policy": "group"})
    assert on is False and "offload" in reason

    on, reason = video_mod.compile_prewarm_decision(fam, **{**base, "cfg_parallel_active": True})
    assert on is False and "CFG parallel" in reason


def test_compile_prewarm_runs_after_compiled_load(fake_runtime, monkeypatch):
    # A compiled default-tier load must spawn the background prewarm: one tiny throwaway
    # generation (192x128, 4k+1 frames, 2 steps), no user-visible progress, cancel slot cleared
    # afterwards, and the resolved record says why it ran.
    from core.inference import video as video_mod

    monkeypatch.setattr(video_mod, "apply_speed_optims", lambda *a, **k: {"compiled": True})
    backend = VideoBackend()
    status = backend.load_pipeline("Wan-AI/Wan2.2-TI2V-5B-Diffusers", model_kind = "pipeline")
    assert status["resolved"]["compile_prewarm"]["value"] == "on"

    thread = backend._prewarm_thread
    assert thread is not None
    thread.join(timeout = 5)
    assert not thread.is_alive()

    call = backend._state.pipe.last_kwargs
    assert call is not None, "prewarm never reached the pipe"
    assert call["prompt"] == "warmup"
    assert call["num_inference_steps"] == 2
    assert (call["width"], call["height"]) == (192, 128)
    assert call["num_frames"] == 9  # 4k+1 lattice for Wan's frame_step=4
    # The warmup is invisible: no generation progress, no leaked cancel event.
    assert backend._gen.get("active") is False
    assert backend._active_generate_cancel is None
    backend.unload()


def test_compile_prewarm_skipped_without_compile(fake_runtime):
    # The fake runtime engages no speed optims, so the load has nothing to warm:
    # no thread, no pipe call, and the resolved record carries the reason.
    backend = VideoBackend()
    status = backend.load_pipeline("Wan-AI/Wan2.2-TI2V-5B-Diffusers", model_kind = "pipeline")
    assert status["resolved"]["compile_prewarm"]["value"] == "off"
    assert "no regional compile" in status["resolved"]["compile_prewarm"]["reason"]
    assert backend._prewarm_thread is None
    assert backend._state.pipe.last_kwargs is None


def test_compile_prewarm_yields_to_generations_and_stale_tokens(fake_runtime):
    # The worker must abort without touching the pipe when a real generation got
    # in first (it absorbs the warmup itself) or when its load was superseded.
    backend = VideoBackend()
    backend.load_pipeline("Wan-AI/Wan2.2-TI2V-5B-Diffusers", model_kind = "pipeline")

    backend._compile_prewarm(backend._load_token + 1)  # superseded load
    assert backend._state.pipe.last_kwargs is None

    backend._generate_job_active = True
    backend._compile_prewarm(backend._load_token)  # a request beat the warmup
    assert backend._state.pipe.last_kwargs is None
    backend._generate_job_active = False


def test_begin_generate_preempts_running_prewarm(fake_runtime, monkeypatch):
    # A real generation arriving while the prewarm holds _generate_lock must signal the prewarm's
    # dedicated cancel handle (so the warmup aborts at its next step boundary, not in front of the
    # user job), and the prewarm must clear that handle when it exits.
    import inspect
    import threading as _threading
    import time

    backend = VideoBackend()
    backend.load_pipeline("Wan-AI/Wan2.2-TI2V-5B-Diffusers", model_kind = "pipeline")

    prewarm_entered = _threading.Event()
    release_prewarm = _threading.Event()
    pipe = backend._state.pipe
    real_call = pipe.__class__.__call__

    def _blocking_call(self, **kwargs):
        if kwargs.get("prompt") == "warmup":
            prewarm_entered.set()
            release_prewarm.wait(timeout = 5)
        return real_call(self, **kwargs)

    # Keep the real signature visible: _compile_prewarm picks its cancel plumbing
    # by inspecting pipe.__call__ for callback_on_step_end.
    _blocking_call.__signature__ = inspect.signature(real_call)
    monkeypatch.setattr(pipe.__class__, "__call__", _blocking_call)

    prewarm = _threading.Thread(
        target = backend._compile_prewarm,
        args = (backend._load_token,),
        daemon = True,
    )
    prewarm.start()
    assert prewarm_entered.wait(timeout = 5), "prewarm never reached the pipe"
    prewarm_cancel = backend._prewarm_cancel
    assert prewarm_cancel is not None and not prewarm_cancel.is_set()

    # The user job lands mid-warmup: it must fire the prewarm's cancel handle
    # and own the active-cancel slot for the run that follows.
    backend.begin_generate(prompt = "real request")
    assert prewarm_cancel.is_set()
    assert backend._active_generate_cancel is not prewarm_cancel

    release_prewarm.set()
    prewarm.join(timeout = 5)
    assert not prewarm.is_alive()
    assert backend._prewarm_cancel is None

    deadline = time.monotonic() + 5
    while backend._generate_job_active and time.monotonic() < deadline:
        time.sleep(0.02)
    assert backend._generate_job_active is False
    backend.unload()


# ── all-or-none step cache across MoE experts (_step_cache_all_or_none) ────────────
def _moe_pipe_and_fam():
    fam = types.SimpleNamespace(is_moe = True, name = "wan2.2-t2v-a14b")
    t1 = types.SimpleNamespace(tag = "expert1")
    t2 = types.SimpleNamespace(tag = "expert2")
    pipe = types.SimpleNamespace(transformer = t1, transformer_2 = t2)
    return pipe, fam, t1, t2


def test_step_cache_all_or_none_rolls_back_second_expert_failure(monkeypatch):
    # apply_step_cache never raises -- a second-expert failure returns None while the FIRST stays
    # cached. The helper must disengage the engaged expert and report off (all-or-none).
    import core.inference.video as video

    pipe, fam, t1, _t2 = _moe_pipe_and_fam()
    disengaged: list = []
    monkeypatch.setattr(
        video,
        "_disengage_step_cache",
        lambda transformer, *, reason, logger = None: disengaged.append((transformer, reason))
        or True,
    )
    calls: list = []

    def engage(view, expert_name):
        calls.append(expert_name)
        return "fbcache" if expert_name == "transformer" else None

    mode, reason = video._step_cache_all_or_none(pipe, fam, engage, logger = None)
    assert calls == ["transformer", "transformer_2"]
    assert mode is None
    assert reason is not None and "1/2" in reason and "transformer_2" in reason
    assert len(disengaged) == 1 and disengaged[0][0] is t1


def test_step_cache_all_or_none_rolls_back_first_expert_failure(monkeypatch):
    # Mirror image: only the SECOND expert engaged -> it is the one disengaged.
    import core.inference.video as video

    pipe, fam, _t1, t2 = _moe_pipe_and_fam()
    disengaged: list = []
    monkeypatch.setattr(
        video,
        "_disengage_step_cache",
        lambda transformer, *, reason, logger = None: disengaged.append(transformer) or True,
    )
    mode, reason = video._step_cache_all_or_none(
        pipe,
        fam,
        lambda view, expert_name: "magcache" if expert_name == "transformer_2" else None,
        logger = None,
    )
    assert mode is None and reason is not None
    assert disengaged == [t2]


def test_step_cache_all_or_none_rolls_back_when_later_expert_raises(monkeypatch):
    # A later expert RAISING mid-loop (not returning None) must not leave earlier experts
    # engaged: the helper disengages any expert that got a cache marker, then re-raises.
    import core.inference.video as video

    pipe, fam, t1, _t2 = _moe_pipe_and_fam()
    disengaged: list = []
    monkeypatch.setattr(
        video,
        "_disengage_step_cache",
        lambda transformer, *, reason, logger = None: disengaged.append(transformer) or True,
    )

    def engage(view, expert_name):
        if expert_name == "transformer":
            view.transformer._unsloth_step_cache = "magcache@0.1#s30"
            return "magcache"
        raise RuntimeError("expert 2 boom")

    with pytest.raises(RuntimeError, match = "expert 2 boom"):
        video._step_cache_all_or_none(pipe, fam, engage, logger = None)
    assert disengaged == [t1]


def test_step_cache_all_or_none_uniform_outcomes(monkeypatch):
    # Both experts engaged -> the mode is reported with no rollback; neither engaged
    # -> plain uncached with no failure reason (the pre-existing best-effort path).
    import core.inference.video as video

    pipe, fam, _t1, _t2 = _moe_pipe_and_fam()
    monkeypatch.setattr(
        video,
        "_disengage_step_cache",
        lambda transformer, *, reason, logger = None: pytest.fail("no rollback on uniform outcome"),
    )
    assert video._step_cache_all_or_none(
        pipe, fam, lambda view, expert_name: "fbcache", logger = None
    ) == ("fbcache", None)
    assert video._step_cache_all_or_none(
        pipe, fam, lambda view, expert_name: None, logger = None
    ) == (None, None)


def test_step_cache_all_or_none_single_dit(monkeypatch):
    # A single-DiT family runs the engage exactly once and can never see a mixed
    # outcome -- behaviour identical to the pre-helper loop.
    import core.inference.video as video

    fam = types.SimpleNamespace(is_moe = False, name = "wan2.2-ti2v-5b")
    pipe = types.SimpleNamespace(transformer = types.SimpleNamespace(), transformer_2 = None)
    calls: list = []

    def engage(view, expert_name):
        calls.append((view, expert_name))
        return "magcache"

    assert video._step_cache_all_or_none(pipe, fam, engage, logger = None) == ("magcache", None)
    assert calls == [(pipe, "transformer")]


def test_step_cache_all_or_none_raises_when_rollback_fails(monkeypatch):
    # Partial engagement AND a failed rollback of the engaged expert leaves it cached while state
    # would report the pipeline uncached -- a silent inconsistency, so raise a hard reload-required
    # error instead of a false "uncached".
    import core.inference.video as video

    pipe, fam, _t1, _t2 = _moe_pipe_and_fam()
    monkeypatch.setattr(
        video,
        "_disengage_step_cache",
        lambda transformer, *, reason, logger = None: False,  # rollback fails
    )
    with pytest.raises(RuntimeError, match = "rollback failed"):
        video._step_cache_all_or_none(
            pipe,
            fam,
            lambda view, expert_name: "fbcache" if expert_name == "transformer" else None,
            logger = None,
        )


def test_explicit_magcache_hard_errors_when_disable_fails(fake_runtime, monkeypatch):
    # An explicit MagCache resize must be transactional: if the existing cache cannot be disabled,
    # refuse to stack a fresh cache over it (which would double-hook) and hard-error instead of
    # silently re-applying while status still reports MagCache.
    import core.inference.video as video

    backend = VideoBackend()
    backend.load_pipeline(
        "Wan-AI/Wan2.2-TI2V-5B-Diffusers",
        model_kind = "pipeline",
        transformer_cache = "magcache",
    )
    reapplied: list = []
    monkeypatch.setattr(video, "_disengage_step_cache", lambda *a, **k: False)
    monkeypatch.setattr(
        video,
        "apply_step_cache",
        lambda *a, **k: reapplied.append(k.get("steps")) or "magcache",
    )
    with pytest.raises(RuntimeError, match = "reload the video model"):
        backend.generate(prompt = "a sloth", steps = 30)
    assert reapplied == []  # never stacked a new cache over the un-removable one
    backend.unload()


def test_prequant_shortcut_loads_all_experts_and_skips_inplace_quant(fake_runtime, monkeypatch):
    # With hosted checkpoints resolving for BOTH A14B experts, the load must ride the
    # prequant modules into from_pretrained (component overrides), never call the in-place
    # quantize helper, and still report the engaged scheme.
    import core.inference.video as video_mod

    monkeypatch.setattr(video_mod, "dense_transformer_supported", lambda target: True)
    # The real selector runs a CUDA quantise smoke test; pin the scheme on CPU.
    monkeypatch.setattr(video_mod, "select_transformer_quant_scheme", lambda t, req, family = None: "int8")
    loaded, quantised = [], []

    def _fake_prequant_load(cls, base, source, **kw):
        loaded.append((source.filename, kw.get("subfolder")))
        return object()

    monkeypatch.setattr(video_mod, "load_prequantized_transformer", _fake_prequant_load)
    monkeypatch.setattr(
        video_mod, "quantize_transformer", lambda *a, **k: quantised.append(a) or "int8"
    )

    backend = VideoBackend()
    status = backend.load_pipeline(
        "Wan-AI/Wan2.2-T2V-A14B-Diffusers",
        model_kind = "pipeline",
        transformer_quant = "int8",
    )
    assert loaded == [
        ("Wan2.2-T2V-A14B-INT8.pt", "transformer"),
        ("Wan2.2-T2V-A14B-INT8-2.pt", "transformer_2"),
    ]
    assert quantised == []
    assert status["transformer_quant"] == "int8"


def test_prequant_shortcut_partial_pair_falls_back_dense(fake_runtime, monkeypatch):
    # All-or-none: when the second expert's checkpoint fails to load, NOTHING prequant ships
    # (mixed-precision experts would corrupt the boundary handoff) and the dense in-place
    # quant path runs for both experts instead.
    import core.inference.video as video_mod

    monkeypatch.setattr(video_mod, "dense_transformer_supported", lambda target: True)
    # The real selector runs a CUDA quantise smoke test; pin the scheme on CPU.
    monkeypatch.setattr(video_mod, "select_transformer_quant_scheme", lambda t, req, family = None: "int8")
    calls = []

    def _fake_prequant_load(cls, base, source, **kw):
        calls.append(kw.get("subfolder"))
        return object() if kw.get("subfolder") == "transformer" else None

    monkeypatch.setattr(video_mod, "load_prequantized_transformer", _fake_prequant_load)
    quantised = []
    monkeypatch.setattr(
        video_mod, "quantize_transformer", lambda view, *a, **k: quantised.append(view) or "int8"
    )

    backend = VideoBackend()
    status = backend.load_pipeline(
        "Wan-AI/Wan2.2-T2V-A14B-Diffusers",
        model_kind = "pipeline",
        transformer_quant = "int8",
    )
    assert calls == ["transformer", "transformer_2"]
    assert len(quantised) == 2  # dense fallback quantised both experts in place
    assert status["transformer_quant"] == "int8"


def test_prequant_shortcut_not_taken_for_unwired_family(fake_runtime, monkeypatch):
    # LTX-2 (no prequant_repos) must never consult the prequant loader.
    import core.inference.video as video_mod

    monkeypatch.setattr(video_mod, "dense_transformer_supported", lambda target: True)
    monkeypatch.setattr(
        video_mod,
        "load_prequantized_transformer",
        lambda *a, **k: (_ for _ in ()).throw(AssertionError("prequant consulted for LTX")),
    )
    monkeypatch.setattr(video_mod, "quantize_transformer", lambda *a, **k: "int8")

    backend = VideoBackend()
    status = backend.load_pipeline(
        "Lightricks/LTX-2", model_kind = "pipeline", transformer_quant = "int8"
    )
    assert status["loaded"] is True


def test_predownload_may_skip_denoiser_gating():
    # Only an explicit wired scheme skips the DiT shards; auto or an unwired scheme keeps them.
    from core.inference.video import _predownload_may_skip_denoiser
    from core.inference.video_families import detect_video_family

    a14b = detect_video_family("Wan-AI/Wan2.2-T2V-A14B-Diffusers")
    hv = detect_video_family("hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_t2v")
    assert _predownload_may_skip_denoiser(a14b, "pipeline", "int8") is True
    assert _predownload_may_skip_denoiser(a14b, "pipeline", "fp8") is True
    assert _predownload_may_skip_denoiser(a14b, "pipeline", "auto") is False
    assert _predownload_may_skip_denoiser(a14b, "pipeline", None) is False
    assert _predownload_may_skip_denoiser(a14b, "gguf", "int8") is False
    assert _predownload_may_skip_denoiser(hv, "pipeline", "fp8") is False  # fp8 unwired
    assert _predownload_may_skip_denoiser(hv, "pipeline", "int8") is True
    assert _predownload_may_skip_denoiser(None, "pipeline", "int8") is False