# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM-Omni project
"""Unit tests for the AR-Diffusion KV cache helpers (Phase 1, PR-2).

Covers the request adapter, the chunk-window spec/manager (registration + the
eviction policy), and the pool builder — exercised against the installed vLLM
KV stack on CPU (block bookkeeping only, no GPU tensors).
"""

import pytest
import torch
from vllm.v1.kv_cache_interface import KVCacheSpecKind, get_kv_cache_spec_kind
from vllm.v1.kv_cache_spec_registry import KVCacheSpecRegistry
from vllm.v1.request import RequestStatus

from vllm_omni.experimental.ar_diffusion.capability import ARDiffusionKVBranchSpec
from vllm_omni.experimental.ar_diffusion.kv_cache import (
    ARDiffusionKVCache,
    ARDiffusionKVConfig,
    ARDiffusionRequestAdapter,
    ChunkWindowManager,
    ChunkWindowSpec,
    allocate_kv_pool_with_views,
    build_kv_manager,
    compute_num_blocks,
)
from vllm_omni.experimental.ar_diffusion.kv_cache.paged import chunk_window_skipped_tokens

BLOCK = 16

pytestmark = [pytest.mark.core_model, pytest.mark.diffusion, pytest.mark.cpu]


def make_spec(*, chunk_size=BLOCK, window_chunks=2, sink_chunks=0, reset_at_boundary=False):
    return ChunkWindowSpec(
        block_size=BLOCK,
        num_kv_heads=4,
        head_size=64,
        dtype=torch.float16,
        sliding_window=window_chunks * chunk_size,
        chunk_size=chunk_size,
        window_chunks=window_chunks,
        sink_chunks=sink_chunks,
        reset_at_boundary=reset_at_boundary,
    )


# --- ChunkWindowSpec registration -------------------------------------------


def test_spec_registration_resolves_to_chunk_window_manager():
    # Without explicit registration the MRO walk would fall back to the parent
    # SlidingWindowManager; assert the subclass manager wins.
    spec = make_spec()
    assert KVCacheSpecRegistry.get_manager_class(spec) is ChunkWindowManager


def test_spec_kind_is_sliding_window():
    assert get_kv_cache_spec_kind(make_spec()) == KVCacheSpecKind.SLIDING_WINDOW


def test_spec_rejects_inconsistent_window():
    with pytest.raises(ValueError):
        ChunkWindowSpec(
            block_size=BLOCK,
            num_kv_heads=4,
            head_size=64,
            dtype=torch.float16,
            sliding_window=99,  # != window_chunks * chunk_size
            chunk_size=BLOCK,
            window_chunks=2,
        )


# --- eviction policy (pure) -------------------------------------------------


def test_sliding_replace_keeps_window():
    # window = 2 chunks * 16 = 32. Base sliding formula keeps `window` tokens;
    # the snap is to chunk boundaries.
    def skip(n):
        return chunk_window_skipped_tokens(n, chunk_size=16, sliding_window=32, sink_chunks=0, reset_at_boundary=False)

    assert skip(32) == 0  # nothing past the window yet
    assert skip(48) == 16  # one chunk fell out of the window
    assert skip(64) == 32


def test_sliding_replace_snaps_to_chunk_boundary():
    # A non-chunk-aligned overflow must snap down so a chunk is never half-evicted.
    skip = chunk_window_skipped_tokens(50, chunk_size=16, sliding_window=32, sink_chunks=0, reset_at_boundary=False)
    assert skip % 16 == 0 and skip == 16


def test_sink_chunks_protected():
    # sink = 1 chunk (16 tokens) is never skipped.
    skip = chunk_window_skipped_tokens(80, chunk_size=16, sliding_window=32, sink_chunks=1, reset_at_boundary=False)
    assert skip == 32


def test_reset_at_boundary_drops_completed_past_sink():
    skip = chunk_window_skipped_tokens(48, chunk_size=16, sliding_window=32, sink_chunks=1, reset_at_boundary=True)
    # completed = 48; sink = 16 -> drop 32
    assert skip == 32


# --- ARDiffusionKVConfig ------------------------------------------------------------


def test_kv_config_sliding_window_property():
    assert ARDiffusionKVConfig(chunk_size=16, window_chunks=3).sliding_window == 48
    assert ARDiffusionKVConfig(chunk_size=16, window_chunks=None).sliding_window is None


# --- ARDiffusionRequestAdapter ------------------------------------------------------


def test_adapter_advances_per_chunk_not_per_step():
    a = ARDiffusionRequestAdapter("r0", chunk_size=16)
    assert a.num_computed_tokens == 0
    assert a.num_tokens == 16  # in-flight chunk
    a.on_chunk_committed()
    assert a.num_computed_tokens == 16
    assert a.num_tokens == 32


def test_adapter_accounts_for_prefill_prefix():
    a = ARDiffusionRequestAdapter("r0", chunk_size=16, prefill_prefix_tokens=4)
    assert a.num_computed_tokens == 4
    assert a.num_prompt_tokens == 4
    assert a.num_tokens == 20


def test_adapter_status_is_vllm_enum():
    assert isinstance(ARDiffusionRequestAdapter("r0", chunk_size=16).status, RequestStatus)


# --- pool / manager ---------------------------------------------------------


def test_compute_num_blocks():
    # 1 MiB budget at fraction 0.5 with 16 KiB pages -> 32 blocks.
    assert compute_num_blocks(1 << 20, 0.5, 16 << 10) == 32


def test_paged_pool_layout_exposes_flat_slot_views():
    kv_pools, k_pools, v_pools = allocate_kv_pool_with_views(
        num_blocks=4,
        block_size=BLOCK,
        num_layers=1,
        num_kv_heads=4,
        head_dim=64,
        dtype=torch.float32,
        device=torch.device("cpu"),
    )
    key_cache, value_cache = kv_pools[0]
    assert key_cache.shape == value_cache.shape == (4, BLOCK, 4, 64)
    assert k_pools[0].shape == (4 * BLOCK, 4, 64)
    assert v_pools[0].shape == (4 * BLOCK, 4, 64)

    # The flat views must alias their block-shaped cache, so a slot write is
    # visible through the layout the attention kernel reads.
    k_pools[0][BLOCK + 3].fill_(7)
    v_pools[0][2 * BLOCK + 5].fill_(11)
    assert torch.equal(key_cache[1, 3], k_pools[0][BLOCK + 3])
    assert torch.equal(value_cache[2, 5], v_pools[0][2 * BLOCK + 5])


def test_key_and_value_caches_do_not_share_storage():
    """K and V must be separate allocations, not halves of one tensor.

    The paged-write custom op declares both as mutated. When they alias one
    storage, inductor's reinplace pass can re-inplace only the first of the
    two, and auto_functionalized_v2's clone of the entire second pool survives
    into the compiled graph -- one full pool copied per compiled region per
    denoising step.
    """
    kv_pools, k_pools, v_pools = allocate_kv_pool_with_views(
        num_blocks=4,
        block_size=BLOCK,
        num_layers=2,
        num_kv_heads=4,
        head_dim=64,
        dtype=torch.float32,
        device=torch.device("cpu"),
    )
    seen = set()
    for layer, (key_cache, value_cache) in enumerate(kv_pools):
        k_storage = key_cache.untyped_storage().data_ptr()
        v_storage = value_cache.untyped_storage().data_ptr()
        assert k_storage != v_storage, f"layer {layer}: K and V share one allocation"
        assert k_pools[layer].untyped_storage().data_ptr() == k_storage
        assert v_pools[layer].untyped_storage().data_ptr() == v_storage
        seen.update((k_storage, v_storage))
    # Every layer's K and V are distinct allocations as well.
    assert len(seen) == 2 * len(kv_pools)

    # Negative control: writing V must not disturb K.
    k_pools[0].fill_(0)
    v_pools[0].fill_(5)
    assert torch.equal(k_pools[0], torch.zeros_like(k_pools[0]))


def _pool_sized_allocations(code: str, pool_numel: int) -> list[int]:
    """Sizes of generated allocations at least as large as one pool."""
    import re

    sizes = []
    for shape in re.findall(r"empty_strided_\w+\(\(([\d, ]*?)\)", code):
        dims = [int(part) for part in shape.replace(" ", "").strip(",").split(",") if part]
        numel = 1
        for dim_size in dims:
            numel *= dim_size
        if dims and numel >= pool_numel:
            sizes.append(numel)
    return sizes


def _generated_code_for_pools(key_cache, value_cache):
    """Compile a paged-write-shaped op over these pools, return the generated code."""
    from torch._inductor import config as inductor_config
    from torch._inductor.utils import run_and_get_code

    class _Holder(torch.nn.Module):
        def __init__(self):
            super().__init__()
            self.register_buffer("key_cache", key_cache)
            self.register_buffer("value_cache", value_cache)

        def forward(self, value):
            return torch.ops.test_kv_pool_clone.write(self.key_cache, self.value_cache, value)

    torch._dynamo.reset()
    # Inductor caches compiled code across ``torch._dynamo.reset()``, so the
    # second call in a process would otherwise return the first one's code and
    # both branches of this test would describe the same graph.
    with inductor_config.patch(force_disable_caches=True):
        _, codes = run_and_get_code(torch.compile(_Holder(), backend="inductor", fullgraph=True), torch.ones(1))
    return "\n".join(codes)


def test_compiled_paged_write_does_not_clone_the_pool():
    """The compiled graph must write the pools in place, not through a clone.

    ``test_key_and_value_caches_do_not_share_storage`` pins the mechanism this
    change introduces -- two allocations rather than one. This pins the effect
    that mechanism buys, which is the reason the change exists: with K and V as
    halves of one tensor, inductor's reinplace pass re-inplaces only the first,
    and ``auto_functionalized_v2``'s clone of the pool survives into the
    generated code as a pool-sized allocation plus a copy, once per compiled
    region per denoising step.

    The shared layout is compiled here as a positive control. Without it, a
    codegen change that stops emitting ``empty_strided_*`` would leave the
    detector matching nothing and this test passing for the wrong reason.
    """
    if "write" not in dir(getattr(torch.ops, "test_kv_pool_clone", object())):

        @torch.library.custom_op("test_kv_pool_clone::write", mutates_args=("key_pool", "value_pool"))
        def _write(key_pool: torch.Tensor, value_pool: torch.Tensor, value: torch.Tensor) -> torch.Tensor:
            key_pool[0, 0, 0, 0] = value[0]
            value_pool[0, 0, 0, 0] = value[0]
            return value * 2

        @_write.register_fake
        def _(key_pool, value_pool, value):
            return torch.empty_like(value)

    # The stand-in models the real op's contract; that declaration is what
    # drives the reinplace pass, so a drift here would silently test nothing.
    real_schema = torch.ops.vllm_omni.ar_diffusion_paged_write_attn.default._schema
    real_mutated = [arg.name for arg in real_schema.arguments if arg.alias_info and arg.alias_info.is_write]
    # The staging buffers are mutated too when reuse_history_staging is on, but they are separate
    # allocations and not what this test is about; the pools are what must not be cloned.
    assert real_mutated[:2] == ["key_pool", "value_pool"], (
        f"the real paged-write op now mutates {real_mutated}; this test still models two pools"
    )
    assert set(real_mutated) - {"key_pool", "value_pool"} <= {"stage_key", "stage_value"}, (
        f"unexpected mutated argument in the real paged-write op: {real_mutated}"
    )

    num_blocks, heads, dim = 8, 4, 16
    cache_shape = (num_blocks, BLOCK, heads, dim)
    pool_numel = num_blocks * BLOCK * heads * dim

    # Positive control: one allocation, K and V aliasing it.
    shared = torch.empty(2, *cache_shape)
    shared_allocations = _pool_sized_allocations(_generated_code_for_pools(shared[0], shared[1]), pool_numel)
    assert shared_allocations, (
        "the shared layout produced no pool-sized allocation, so this test can no "
        "longer tell the two layouts apart -- inductor's codegen or the detector changed"
    )

    # The allocator under test.
    kv_pools, _, _ = allocate_kv_pool_with_views(
        num_blocks=num_blocks,
        block_size=BLOCK,
        num_layers=1,
        num_kv_heads=heads,
        head_dim=dim,
        dtype=torch.float32,
        device=torch.device("cpu"),
    )
    key_cache, value_cache = kv_pools[0]
    separate_allocations = _pool_sized_allocations(_generated_code_for_pools(key_cache, value_cache), pool_numel)
    assert not separate_allocations, (
        "the compiled graph allocates a pool-sized buffer, so the pool is being cloned "
        f"rather than written in place: {separate_allocations} (control saw {shared_allocations})"
    )


def test_build_manager_allocate_free_roundtrip():
    """End-to-end: a ARDiffusionRequestAdapter drives a real KVCacheManager.

    This is the adapter conformance check — if the adapter were missing an
    attribute the manager reads, allocate_slots/free would raise here.
    """
    spec = make_spec()
    mgr = build_kv_manager(spec, ["layer0"], num_blocks=16, max_model_len=1024)
    free_before = mgr.block_pool.get_num_free_blocks()

    adapter = ARDiffusionRequestAdapter("req-0", chunk_size=BLOCK)
    blocks = mgr.allocate_slots(adapter, num_new_tokens=BLOCK, full_sequence_must_fit=True)
    assert blocks is not None
    assert mgr.block_pool.get_num_free_blocks() < free_before

    mgr.free(adapter)
    assert mgr.block_pool.get_num_free_blocks() == free_before


def test_cross_attn_pool_deducted_from_self_attn_budget():
    """The cross-attn pool is allocated directly; its bytes are subtracted from
    the self-attn paged-pool budget so the two together stay within the free
    memory budget (review: zwhzzz0821)."""
    L = 512
    avail = 1 << 30  # 1 GiB
    kv = ARDiffusionKVCache(
        ARDiffusionKVConfig(enable=True, chunk_size=BLOCK, window_chunks=2, gpu_memory_fraction=0.5),
        num_layers=2,
        num_kv_heads=4,
        head_size=64,
        dtype=torch.float16,
        block_size=BLOCK,
        max_model_len=4096,
        available_bytes=avail,
        kv_branches=(ARDiffusionKVBranchSpec("positive", 0), ARDiffusionKVBranchSpec("negative", 0)),
        session_capacity=1,
        cross_attention_lengths={"text": L},
    )
    cross_bytes = 2 * 2 * L * 4 * 64 * torch.float16.itemsize * 2  # K+V, pos+neg, layers
    page_bytes = kv.spec.page_size_bytes * 2
    scratch_bytes = kv.scratch_num_blocks * page_bytes
    expected = (int(avail * 0.5) - cross_bytes - scratch_bytes) // page_bytes
    assert kv.num_blocks == expected
    assert kv.cross_attention_reserved_bytes == cross_bytes
    assert (kv.num_blocks_total * page_bytes) + cross_bytes <= int(avail * 0.5)
    assert expected > 1 * (2 + 1) + 2  # above the local-slot minimum, so the cross deduction is what's tested


def _make_tiny_capacity_kv(
    *,
    requested_capacity: int,
    available_bytes: int,
    gpu_memory_fraction: float = 1.0,
    model_owned_state_bytes_per_session: int = 0,
    reuse_history_staging: bool = False,
) -> ARDiffusionKVCache:
    return ARDiffusionKVCache(
        ARDiffusionKVConfig(
            enable=True,
            chunk_size=1,
            window_chunks=3,
            sink_chunks=3,
            gpu_memory_fraction=gpu_memory_fraction,
            reuse_history_staging=reuse_history_staging,
        ),
        num_layers=1,
        num_kv_heads=1,
        head_size=1,
        dtype=torch.float32,
        block_size=1,
        max_model_len=128,
        available_bytes=available_bytes,
        kv_branches=(ARDiffusionKVBranchSpec("main", 0),),
        session_capacity=requested_capacity,
        cross_attention_lengths={"text": 2},
        frames_per_block=3,
        model_owned_state_bytes_per_session=model_owned_state_bytes_per_session,
        device=torch.device("cpu"),
    )


def test_history_staging_is_reserved_in_the_kv_budget(monkeypatch):
    """The per-layer staging pair is worker-wide memory allocated after admission; the budget must hold it."""
    monkeypatch.setenv("VLLM_OMNI_AR_DIFFUSION_KV_GATHER", "1")
    # Same geometry as the capacity-two test: 192 bytes fits two sessions exactly without staging.
    plain = _make_tiny_capacity_kv(requested_capacity=2, available_bytes=192)
    assert plain.session_capacity == 2 and plain.history_staging_reserved_bytes == 0

    staged = _make_tiny_capacity_kv(requested_capacity=2, available_bytes=192, reuse_history_staging=True)
    # 2 (K, V) x 1 layer x (sink 3 + window 3 + one action-capacity block) tokens x 1 head x 1 dim x 4 bytes.
    assert staged.history_staging_reserved_bytes == 2 * 1 * 7 * 1 * 1 * 4
    # It comes out of the same budget, so the second session no longer fits.
    assert staged.session_capacity == 1
    assert staged.num_blocks_total * 8 + staged.history_staging_reserved_bytes <= 192

    # Boundary: one session fits at 128 bytes without staging; with staging the same budget is rejected
    # up front instead of failing on the first forward.
    _make_tiny_capacity_kv(requested_capacity=1, available_bytes=128)
    with pytest.raises(ValueError, match="cannot fit one session"):
        _make_tiny_capacity_kv(requested_capacity=1, available_bytes=128, reuse_history_staging=True)


def test_history_staging_is_not_reserved_when_the_gather_path_is_off(monkeypatch):
    monkeypatch.delenv("VLLM_OMNI_AR_DIFFUSION_KV_GATHER", raising=False)
    staged = _make_tiny_capacity_kv(requested_capacity=2, available_bytes=192, reuse_history_staging=True)
    assert staged.history_staging_reserved_bytes == 0 and staged.session_capacity == 2


def test_capacity_two_retains_both_windows_and_allocates_next_block():
    # LingBot-like single-branch geometry: page=8 bytes; capacity=2 requires
    # 17 managed + 3 scratch blocks, plus two 16-byte cross-attention
    # reservations: 192 bytes exactly.
    kv = _make_tiny_capacity_kv(requested_capacity=2, available_bytes=192)
    assert kv.requested_session_capacity == 2
    assert kv.session_capacity == 2
    assert kv.managed_num_blocks == 17

    adapters = [kv.begin_request(session_id) for session_id in ("first", "second")]
    for adapter in adapters:
        for _ in range(6):  # three sink + three window blocks
            kv.allocate_token_slots(adapter, 1)
            kv.commit_chunk(adapter)
        assert len(kv.window_block_ids(adapter)) == 6

    # Only one request is in flight, so its three-frame block is counted once.
    kv.allocate_token_slots(adapters[0], 3)


def test_requested_capacity_is_capped_and_cross_reservation_uses_effective_capacity():
    kv = _make_tiny_capacity_kv(requested_capacity=64, available_bytes=192)

    assert kv.requested_session_capacity == 64
    assert kv.session_capacity == 2
    assert kv.cross_attention_bytes_per_session == 16
    assert kv.cross_attention_reserved_bytes == 32
    assert kv.num_blocks_total * 8 + kv.cross_attention_reserved_bytes == kv.memory_budget_bytes


def test_capacity_rejects_budget_that_cannot_fit_one_session():
    # One session needs 11 managed + 3 scratch blocks and 16 cross bytes:
    # 14 * 8 + 16 = 128 bytes.
    with pytest.raises(ValueError, match="cannot fit one session.*available=127 bytes.*required=128 bytes"):
        _make_tiny_capacity_kv(requested_capacity=1, available_bytes=127)


def test_fraction_is_soft_floor_when_one_session_fits_actual_memory():
    kv = _make_tiny_capacity_kv(
        requested_capacity=2,
        available_bytes=1280,
        gpu_memory_fraction=0.05,
    )

    assert kv.configured_memory_budget_bytes == 64
    assert kv.memory_budget_bytes == 128
    assert kv.session_capacity == 1


def test_model_owned_state_reduces_effective_session_capacity():
    kv = _make_tiny_capacity_kv(
        requested_capacity=2,
        available_bytes=200,
        model_owned_state_bytes_per_session=16,
    )

    assert kv.session_capacity == 1
    assert kv.model_owned_state_reserved_bytes == 16
    assert (
        kv.num_blocks_total * 8 + kv.cross_attention_reserved_bytes + kv.model_owned_state_reserved_bytes
        <= kv.memory_budget_bytes
    )


def _make_shipped_lingbot_geometry(*, gpu_memory_fraction: float) -> ARDiffusionKVCache:
    return ARDiffusionKVCache(
        ARDiffusionKVConfig(
            enable=True,
            chunk_size=1560,
            window_chunks=9,
            sink_chunks=9,
            gpu_memory_fraction=gpu_memory_fraction,
        ),
        num_layers=40,
        num_kv_heads=40,
        head_size=128,
        dtype=torch.bfloat16,
        block_size=1560,
        max_model_len=1 << 20,
        available_bytes=100 * (1 << 30),
        kv_branches=(ARDiffusionKVBranchSpec("main", 0),),
        session_capacity=2,
        cross_attention_lengths={"text": 512},
        frames_per_block=3,
        model_owned_state_bytes_per_session=7_488_000,
    )


def test_shipped_lingbot_geometry_default_fraction_admits_one_session():
    kv = _make_shipped_lingbot_geometry(gpu_memory_fraction=0.1)

    assert kv.configured_memory_budget_bytes == 10 * (1 << 30)
    assert kv.memory_budget_bytes == 33_653_670_400
    assert kv.session_capacity == 1


def test_shipped_lingbot_geometry_tuned_fraction_admits_two_sessions():
    kv = _make_shipped_lingbot_geometry(gpu_memory_fraction=0.6)

    assert kv.memory_budget_bytes == 60 * (1 << 30)
    assert kv.session_capacity == 2


def _make_kv(
    *,
    local_branches,
    num_frame_per_block=2,
    window_chunks=9,
    max_scratch_tokens_per_branch=0,
):
    kv_branches = (
        (ARDiffusionKVBranchSpec("positive", 0), ARDiffusionKVBranchSpec("negative", 0))
        if local_branches == 1
        else (ARDiffusionKVBranchSpec("positive", 0), ARDiffusionKVBranchSpec("negative", 1))
    )
    page_bytes = 2 * BLOCK * 4 * 64 * torch.float32.itemsize
    declared_scratch_blocks = (max_scratch_tokens_per_branch + BLOCK - 1) // BLOCK
    scratch_blocks = local_branches * (num_frame_per_block + declared_scratch_blocks)
    managed_blocks = local_branches * (window_chunks + num_frame_per_block) + 2
    return ARDiffusionKVCache(
        ARDiffusionKVConfig(
            enable=True,
            chunk_size=BLOCK,
            window_chunks=window_chunks,
            gpu_memory_fraction=1.0,
        ),
        num_layers=1,
        num_kv_heads=4,
        head_size=64,
        dtype=torch.float32,
        block_size=BLOCK,
        max_model_len=4096,
        available_bytes=(managed_blocks + scratch_blocks) * page_bytes,
        device=torch.device("cpu"),
        kv_branches=kv_branches,
        session_capacity=1,
        frames_per_block=num_frame_per_block,
        max_scratch_tokens_per_branch=max_scratch_tokens_per_branch,
    )


def test_pool_floor_is_branch_aware():
    """CFG-parallel rank (one local kv_branch) sizes for one window + in-flight chunk;
    a single-process run (both kv_branches) sizes for two. Scratch scales the same way."""
    one = _make_kv(local_branches=1)
    two = _make_kv(local_branches=2)

    assert one.managed_num_blocks == 1 * (9 + 2) + 2  # 13
    assert two.managed_num_blocks == 2 * (9 + 2) + 2  # 24
    assert one.scratch_num_blocks == one.scratch_blocks_per_kv_branch
    assert two.scratch_num_blocks == 2 * two.scratch_blocks_per_kv_branch
    assert one.scratch_blocks_per_kv_branch == 2
    assert one.num_blocks_total == 13 + one.scratch_blocks_per_kv_branch


def test_scratch_capacity_is_derived_from_declared_geometry(monkeypatch):
    monkeypatch.delenv("AR_DIFFUSION_KV_SCRATCH_BLOCKS_PER_BRANCH", raising=False)
    kv = _make_kv(
        local_branches=1,
        num_frame_per_block=6,
        max_scratch_tokens_per_branch=BLOCK + 1,
    )
    assert kv.scratch_blocks_per_kv_branch == 8  # six video blocks + two auxiliary blocks


def test_scratch_env_override_cannot_reduce_declared_minimum(monkeypatch):
    monkeypatch.setenv("AR_DIFFUSION_KV_SCRATCH_BLOCKS_PER_BRANCH", "1")
    kv = _make_kv(
        local_branches=1,
        num_frame_per_block=6,
        max_scratch_tokens_per_branch=BLOCK + 1,
    )
    assert kv.scratch_blocks_per_kv_branch == 8


def test_scratch_maps_to_slot_zero_with_one_local_branch():
    """A CFG-parallel rank runs exactly one kv_branch: whichever CFG side it is,
    its scratch lands in the rank's single slot (no dead second slot)."""
    one = _make_kv(local_branches=1)
    assert one.scratch_block_ids("positive", 0, 2) == one.scratch_block_ids("negative", 0, 2)

    two = _make_kv(local_branches=2)
    assert two.scratch_block_ids("positive", 0, 2) != two.scratch_block_ids("negative", 0, 2)


def test_scratch_exhaustion_still_raises():
    one = _make_kv(local_branches=1)
    cap = one.scratch_blocks_per_kv_branch
    with pytest.raises(RuntimeError, match="scratch blocks exhausted"):
        one.scratch_block_ids("positive", 0, cap + 1)


def test_non_contiguous_branch_indices_rejected():
    with pytest.raises(ValueError, match="contiguous"):
        ARDiffusionKVCache(
            ARDiffusionKVConfig(enable=True, chunk_size=BLOCK, window_chunks=2),
            num_layers=1,
            num_kv_heads=4,
            head_size=64,
            dtype=torch.float32,
            block_size=BLOCK,
            max_model_len=4096,
            available_bytes=1 << 16,
            kv_branches=(ARDiffusionKVBranchSpec("main", 1),),
            session_capacity=1,
        )


def test_staged_window_reuse_matches_a_full_gather():
    """Reusing the staged history must be byte-identical to re-gathering the whole window.

    This is the property the optimisation rests on: the leading blocks were staged by an earlier forward of
    the same AR block, so skipping them can only be correct if what is already there equals what a fresh
    gather would write. The test stages once, mutates only the current chunk's blocks in the pool (what a
    later probe of the same block does), restages the tail alone, and compares against a full gather.
    """
    from vllm_omni.experimental.ar_diffusion.kv_cache.paged_attention import _stage_window

    torch.manual_seed(0)
    num_blocks, block_size, heads, dim = 6, 4, 2, 8
    current_blocks = 2
    key_cache = torch.randn(num_blocks, block_size, heads, dim)
    value_cache = torch.randn(num_blocks, block_size, heads, dim)
    block_ids = torch.tensor([5, 4, 3, 2, 1, 0])
    stage_key = torch.zeros(num_blocks * block_size, heads, dim)
    stage_value = torch.zeros(num_blocks * block_size, heads, dim)

    _stage_window(stage_key, stage_value, key_cache, value_cache, block_ids, num_blocks, block_size, first_block=0)

    # A later probe rewrites only the current chunk's blocks in the pool.
    for slot in block_ids[num_blocks - current_blocks :]:
        key_cache[slot] = torch.randn(block_size, heads, dim)
        value_cache[slot] = torch.randn(block_size, heads, dim)

    _stage_window(
        stage_key,
        stage_value,
        key_cache,
        value_cache,
        block_ids,
        num_blocks,
        block_size,
        first_block=num_blocks - current_blocks,
    )

    full_key = torch.zeros_like(stage_key)
    full_value = torch.zeros_like(stage_value)
    _stage_window(full_key, full_value, key_cache, value_cache, block_ids, num_blocks, block_size, first_block=0)

    torch.testing.assert_close(stage_key, full_key, rtol=0, atol=0)
    torch.testing.assert_close(stage_value, full_value, rtol=0, atol=0)


def test_staged_window_tail_refresh_leaves_history_untouched():
    """The tail restage must not write the history rows -- that is what makes it cheaper."""
    from vllm_omni.experimental.ar_diffusion.kv_cache.paged_attention import _stage_window

    torch.manual_seed(1)
    num_blocks, block_size, heads, dim = 5, 4, 1, 4
    key_cache = torch.randn(num_blocks, block_size, heads, dim)
    value_cache = torch.randn(num_blocks, block_size, heads, dim)
    block_ids = torch.arange(num_blocks)
    stage_key = torch.full((num_blocks * block_size, heads, dim), -1.0)
    stage_value = torch.full((num_blocks * block_size, heads, dim), -1.0)

    _stage_window(
        stage_key, stage_value, key_cache, value_cache, block_ids, num_blocks, block_size, first_block=num_blocks - 1
    )

    history_rows = (num_blocks - 1) * block_size
    assert (stage_key[:history_rows] == -1.0).all(), "history rows were rewritten by a tail restage"
    assert (stage_value[:history_rows] == -1.0).all()
    torch.testing.assert_close(stage_key[history_rows:], key_cache[-1].reshape(block_size, heads, dim), rtol=0, atol=0)