import operator
from collections.abc import Sequence
from typing import Annotated

import pytest
from langchain_core.messages import AIMessage, HumanMessage, RemoveMessage
from langgraph.checkpoint.memory import InMemorySaver
from langgraph.checkpoint.serde.types import _DeltaSnapshot
from typing_extensions import NotRequired, TypedDict

from langgraph._internal._typing import MISSING
from langgraph.channels.binop import BinaryOperatorAggregate
from langgraph.channels.delta import DeltaChannel
from langgraph.channels.last_value import LastValue
from langgraph.channels.topic import Topic
from langgraph.channels.untracked_value import UntrackedValue
from langgraph.errors import EmptyChannelError, InvalidUpdateError
from langgraph.graph import START, StateGraph
from langgraph.graph.message import _messages_delta_reducer
from langgraph.graph.state import _get_channel
from langgraph.types import Overwrite

pytestmark = pytest.mark.anyio


# ---------------------------------------------------------------------------
# Core channel primitives
# ---------------------------------------------------------------------------


def test_last_value() -> None:
    channel = LastValue(int).from_checkpoint(MISSING)
    assert channel.ValueType is int
    assert channel.UpdateType is int

    with pytest.raises(EmptyChannelError):
        channel.get()
    with pytest.raises(InvalidUpdateError):
        channel.update([5, 6])

    channel.update([3])
    assert channel.get() == 3
    channel.update([4])
    assert channel.get() == 4
    checkpoint = channel.checkpoint()
    channel = LastValue(int).from_checkpoint(checkpoint)
    assert channel.get() == 4


def test_topic() -> None:
    channel = Topic(str).from_checkpoint(MISSING)
    assert channel.ValueType == Sequence[str]
    assert channel.UpdateType == str | list[str]

    assert channel.update(["a", "b"])
    assert channel.get() == ["a", "b"]
    assert channel.update([["c", "d"], "d"])
    assert channel.get() == ["c", "d", "d"]
    assert channel.update([])
    with pytest.raises(EmptyChannelError):
        channel.get()
    assert not channel.update([]), "channel already empty"
    assert channel.update(["e"])
    assert channel.get() == ["e"]
    checkpoint = channel.checkpoint()
    channel = Topic(str).from_checkpoint(checkpoint)
    assert channel.get() == ["e"]
    channel_copy = Topic(str).from_checkpoint(checkpoint)
    channel_copy.update(["f"])
    assert channel_copy.get() == ["f"]
    assert channel.get() == ["e"]


def test_topic_accumulate() -> None:
    channel = Topic(str, accumulate=True).from_checkpoint(MISSING)
    assert channel.ValueType == Sequence[str]
    assert channel.UpdateType == str | list[str]

    assert channel.update(["a", "b"])
    assert channel.get() == ["a", "b"]
    assert channel.update(["b", ["c", "d"], "d"])
    assert channel.get() == ["a", "b", "b", "c", "d", "d"]
    assert not channel.update([])
    assert channel.get() == ["a", "b", "b", "c", "d", "d"]
    checkpoint = channel.checkpoint()
    channel = Topic(str, accumulate=True).from_checkpoint(checkpoint)
    assert channel.get() == ["a", "b", "b", "c", "d", "d"]
    assert channel.update(["e"])
    assert channel.get() == ["a", "b", "b", "c", "d", "d", "e"]


def test_binop() -> None:
    channel = BinaryOperatorAggregate(int, operator.add).from_checkpoint(MISSING)
    assert channel.ValueType is int
    assert channel.UpdateType is int

    assert channel.get() == 0

    channel.update([1, 2, 3])
    assert channel.get() == 6
    channel.update([4])
    assert channel.get() == 10
    checkpoint = channel.checkpoint()
    channel = BinaryOperatorAggregate(int, operator.add).from_checkpoint(checkpoint)
    assert channel.get() == 10


def test_untracked_value() -> None:
    channel = UntrackedValue(dict).from_checkpoint(MISSING)
    assert channel.ValueType is dict
    assert channel.UpdateType is dict

    with pytest.raises(EmptyChannelError):
        channel.get()

    test_data = {"session": "test", "temp": "dir"}
    channel.update([test_data])
    assert channel.get() == test_data

    new_data = {"session": "updated", "temp": "newdir"}
    channel.update([new_data])
    assert channel.get() == new_data

    checkpoint = channel.checkpoint()
    assert checkpoint is MISSING

    new_channel = UntrackedValue(dict).from_checkpoint(checkpoint)
    with pytest.raises(EmptyChannelError):
        new_channel.get()


# ---------------------------------------------------------------------------
# DeltaChannel — message reducer
# ---------------------------------------------------------------------------


def test_delta_channel_basic_two_steps() -> None:
    ch = DeltaChannel(_messages_delta_reducer, list).from_checkpoint(MISSING)

    ch.update([HumanMessage(content="hi", id="h1")])
    d1 = ch.checkpoint()
    assert d1 is MISSING

    ch.update([AIMessage(content="hello", id="a1")])
    d2 = ch.checkpoint()
    assert d2 is MISSING

    assert len(ch.get()) == 2
    assert ch.get()[0].content == "hi"
    assert ch.get()[1].content == "hello"


def test_delta_channel_from_checkpoint_writes_list() -> None:
    """replay_writes on a fresh channel replays through the operator."""
    spec = DeltaChannel(_messages_delta_reducer, list)
    ch = spec.from_checkpoint(MISSING)
    ch.replay_writes(
        [
            ("t0", "messages", HumanMessage(content="hi", id="h1")),
            ("t1", "messages", AIMessage(content="hello", id="a1")),
            ("t2", "messages", HumanMessage(content="bye", id="h2")),
        ]
    )
    msgs = ch.get()
    assert len(msgs) == 3
    assert msgs[0].content == "hi"
    assert msgs[1].content == "hello"
    assert msgs[2].content == "bye"


def test_delta_channel_from_checkpoint_backwards_compat() -> None:
    spec = DeltaChannel(_messages_delta_reducer, list)
    old_value = [HumanMessage(content="old", id="h1")]
    ch = spec.from_checkpoint(old_value)
    assert ch.get() == old_value


def test_delta_channel_overwrite() -> None:
    ch = DeltaChannel(_messages_delta_reducer, list).from_checkpoint(MISSING)
    ch.update([HumanMessage(content="old", id="h1")])

    ch.update([Overwrite([HumanMessage(content="new", id="h2")])])
    d = ch.checkpoint()
    assert d is MISSING
    assert len(ch.get()) == 1
    assert ch.get()[0].content == "new"


def test_overwrite_dataclass_form_survives_json_roundtrip() -> None:
    """`Overwrite` serialised with `orjson` collapses to a plain dict but
    must still be recognised as an overwrite by the channel reducer.

    Without the `type` discriminator the dataclass-erased shape (`{"value":
    ...}`) is indistinguishable from a literal channel value, and downstream
    reducers raise `MESSAGE_COERCION_FAILURE` (or similar) on read.
    """
    import orjson

    from langgraph._internal._constants import OVERWRITE
    from langgraph.channels.binop import _get_overwrite

    ow = Overwrite(value=[HumanMessage(content="new", id="h2")])
    erased = orjson.loads(orjson.dumps(ow, default=lambda o: o.model_dump()))

    assert erased["type"] == OVERWRITE
    is_overwrite, value = _get_overwrite(erased)
    assert is_overwrite
    assert isinstance(value, list)
    assert value[0]["content"] == "new"


def test_overwrite_sentinel_dict_still_recognised() -> None:
    """The pre-existing `{"__overwrite__": value}` dict form continues to be
    recognised. This is the canonical sentinel emitted by producers that do
    not have an `Overwrite` dataclass available."""
    from langgraph._internal._constants import OVERWRITE
    from langgraph.channels.binop import _get_overwrite

    is_overwrite, value = _get_overwrite({OVERWRITE: ["b"]})
    assert is_overwrite
    assert value == ["b"]


def test_overwrite_non_matching_dict_not_recognised() -> None:
    """Dicts that resemble the erased shape but do not carry the
    `__overwrite__` discriminator must not be misclassified as overwrites."""
    from langgraph.channels.binop import _get_overwrite

    assert _get_overwrite({"value": ["b"]}) == (False, None)
    assert _get_overwrite({"type": "human", "value": "hi"}) == (False, None)


def test_delta_channel_remove_message_and_replay() -> None:
    """RemoveMessage must round-trip correctly when writes are replayed."""
    spec = DeltaChannel(_messages_delta_reducer, list)
    ch = spec.from_checkpoint(MISSING)

    ch.update([HumanMessage(content="hi", id="h1")])
    ch.update([AIMessage(content="hello", id="a1")])
    assert ch.get() == [
        HumanMessage(content="hi", id="h1"),
        AIMessage(content="hello", id="a1"),
    ]

    ch.update([RemoveMessage(id="a1")])
    assert ch.get() == [HumanMessage(content="hi", id="h1")]

    ch2 = spec.from_checkpoint(MISSING)
    ch2.replay_writes(
        [
            ("t0", "messages", HumanMessage(content="hi", id="h1")),
            ("t1", "messages", AIMessage(content="hello", id="a1")),
            ("t2", "messages", RemoveMessage(id="a1")),
        ]
    )
    assert ch2.get() == [HumanMessage(content="hi", id="h1")]


def test_delta_channel_update_by_id_and_replay() -> None:
    """Updating a message by ID must round-trip correctly through writes replay."""
    spec = DeltaChannel(_messages_delta_reducer, list)
    ch = spec.from_checkpoint(MISSING)

    ch.update([HumanMessage(content="original", id="h1")])
    ch.update([HumanMessage(content="updated", id="h1")])
    assert ch.get() == [HumanMessage(content="updated", id="h1")]

    ch2 = spec.from_checkpoint(MISSING)
    ch2.replay_writes(
        [
            ("t0", "messages", HumanMessage(content="original", id="h1")),
            ("t1", "messages", HumanMessage(content="updated", id="h1")),
        ]
    )
    assert len(ch2.get()) == 1
    assert ch2.get()[0].content == "updated"


def test_delta_channel_dict_coercion() -> None:
    """_messages_delta_reducer coerces dict writes to BaseMessage objects.

    HTTP-driven input always arrives as JSON dicts.  The reducer must coerce
    them (same contract as add_messages) so graphs work without a separate
    coercion step.
    """
    ch = DeltaChannel(_messages_delta_reducer, list).from_checkpoint(MISSING)

    # dict input — simulates what arrives from the HTTP API
    ch.update([{"role": "human", "content": "hello", "id": "h1"}])
    assert len(ch.get()) == 1
    assert isinstance(ch.get()[0], HumanMessage)
    assert ch.get()[0].content == "hello"
    assert ch.get()[0].id == "h1"

    # update by ID via dict
    ch.update([{"role": "ai", "content": "world", "id": "h1"}])
    assert len(ch.get()) == 1
    assert ch.get()[0].content == "world"

    # remove via RemoveMessage instance (same contract as add_messages)
    ch.update([RemoveMessage(id="h1")])
    assert ch.get() == []


def test_messages_delta_reducer_coerces_state() -> None:
    """State (left side) is coerced when raw — supports raw initial input
    and deserialized blobs. The steady-state path (state already typed)
    short-circuits and skips coercion.
    """
    state = [{"role": "human", "content": "hello", "id": "h1"}]
    writes = [[{"role": "ai", "content": "world", "id": "h1"}]]
    result = _messages_delta_reducer(state, writes)  # type: ignore[arg-type]
    assert len(result) == 1
    assert isinstance(result[0], AIMessage)
    assert result[0].content == "world"
    assert result[0].id == "h1"


def test_messages_delta_reducer_tuple_write_is_one_message() -> None:
    """A top-level tuple write is one message-like, not a sequence to flatten.

    `("user", "hi")` is a valid `MessageLikeRepresentation`; flattening it
    would produce two HumanMessages ("user", "hi") instead of one.
    """
    result = _messages_delta_reducer([], [("user", "hi")])  # type: ignore[arg-type]
    assert len(result) == 1
    assert isinstance(result[0], HumanMessage)
    assert result[0].content == "hi"


def test_delta_channel_checkpoint_returns_missing() -> None:
    """checkpoint() always returns MISSING regardless of state.

    Pregel writes `_DeltaSnapshot(ch.get())` directly into `channel_values`
    on snapshot steps; the channel itself never participates in snapshot
    serialization, so its `checkpoint()` is always the absence sentinel.
    """
    ch = DeltaChannel(_messages_delta_reducer, list).from_checkpoint(MISSING)
    assert ch.checkpoint() is MISSING

    ch.update([HumanMessage(content="hi", id="h1")])
    assert ch.checkpoint() is MISSING


# ---------------------------------------------------------------------------
# DeltaChannel — snapshot frequency
# ---------------------------------------------------------------------------


def test_delta_channel_snapshot_version_based() -> None:
    """Snapshots fire when a channel accumulates `snapshot_frequency` updates.

    Under the version-delta cadence, every time the channel's
    `current_version - last_snapshot_version >= snapshot_frequency` a
    `_DeltaSnapshot` blob is written. Bounds the ancestor walk to at most
    `snapshot_frequency` steps on any read for that channel.
    """

    class State(TypedDict):
        messages: Annotated[
            list, DeltaChannel(_messages_delta_reducer, snapshot_frequency=5)
        ]
        other: str

    def node_a(state: State) -> dict:
        i = len(state["messages"]) // 2
        return {"messages": [AIMessage(content=f"a{i}", id=f"a{i}")]}

    def node_b(state: State) -> dict:
        return {"other": "y"}

    g = StateGraph(State)
    g.add_node("a", node_a)
    g.add_node("b", node_b)
    g.add_edge(START, "a")
    g.add_edge("a", "b")
    saver = InMemorySaver()
    graph = g.compile(checkpointer=saver)

    config = {"configurable": {"thread_id": "t1"}}
    for i in range(6):
        graph.invoke(
            {"messages": [HumanMessage(content=f"h{i}", id=f"h{i}")], "other": ""},
            config,
        )

    msg_blob_values = [
        saver.serde.loads_typed((type_tag, blob))
        for k, (type_tag, blob) in saver.blobs.items()
        if k[2] == "messages" and type_tag == "msgpack" and blob
    ]
    snapshots = [v for v in msg_blob_values if isinstance(v, _DeltaSnapshot)]
    assert snapshots, "expected at least one _DeltaSnapshot blob for messages"

    state = graph.get_state(config)
    assert len(state.values["messages"]) == 12  # 6 human + 6 AI


# TODO(delta-channel-cadence): the previous "snapshot fires even when channel
# was not written" test asserted eager step-based snapshotting; under the new
# version-delta cadence (`should_snapshot` triggers on per-channel update
# count, not superstep count), no snapshot fires for an unwritten channel.
# Replace with a test that exercises the version-delta trigger plus the
# durability="exit" force-snapshot branch — see
# `docs/superpowers/specs/2026-05-04-delta-channel-batched-reads-design.md`
# section "Snapshot cadence".


# ---------------------------------------------------------------------------
# DeltaChannel — end-to-end (InMemorySaver)
# ---------------------------------------------------------------------------


def test_delta_channel_inmemory_saver_assembles_writes() -> None:
    """InMemorySaver assembles writes from checkpoint_writes inside get_tuple."""

    class State(TypedDict):
        messages: Annotated[list, DeltaChannel(_messages_delta_reducer, list)]

    n = {"v": 0}

    def respond(state: State) -> dict:
        n["v"] += 1
        return {"messages": [AIMessage(content=f"ok{n['v']}", id=f"ai{n['v']}")]}

    builder = StateGraph(State)
    builder.add_node("respond", respond)
    builder.add_edge(START, "respond")
    saver = InMemorySaver()
    graph = builder.compile(checkpointer=saver)
    config = {"configurable": {"thread_id": "t1"}}

    graph.invoke({"messages": [HumanMessage(content="hi", id="h1")]}, config)
    graph.invoke({"messages": [HumanMessage(content="bye", id="h2")]}, config)

    saved = saver.get_tuple(config)
    assert saved is not None
    assert "messages" not in saved.checkpoint["channel_values"]

    state = graph.get_state(config)
    assert len(state.values["messages"]) == 4  # 2 human + 2 AI


def test_delta_channel_overwrite_superstep_snapshots() -> None:
    def reducer(state: list[str], writes: Sequence[list[str]]) -> list[str]:
        result = list(state)
        for write in writes:
            result.extend(write)
        return result

    class State(TypedDict):
        items: Annotated[
            list[str], DeltaChannel(reducer, list, snapshot_frequency=1000)
        ]

    def node_a(state: State) -> dict:
        return {"items": ["a"]}

    def node_b(state: State) -> dict:
        return {"items": Overwrite(["b"])}

    def node_c(state: State) -> dict:
        return {"items": ["c"]}

    builder = StateGraph(State)
    builder.add_node("node_a", node_a)
    builder.add_node("node_b", node_b)
    builder.add_node("node_c", node_c)
    builder.add_edge(START, "node_a")
    builder.add_edge("node_a", "node_b")
    builder.add_edge("node_a", "node_c")

    saver = InMemorySaver()
    graph = builder.compile(checkpointer=saver)
    config = {"configurable": {"thread_id": "overwrite-snapshot"}}

    result = graph.invoke({"items": ["START"]}, config)
    assert result == {"items": ["b"]}

    saved = saver.get_tuple(config)
    assert saved is not None
    snapshot = saved.checkpoint["channel_values"].get("items")
    assert isinstance(snapshot, _DeltaSnapshot)
    assert snapshot.value == ["b"]
    assert saved.metadata.get("counters_since_delta_snapshot", {}).get("items") is None


def test_delta_channel_replay_after_overwrite_snapshot() -> None:
    def reducer(state: list[str], writes: Sequence[list[str]]) -> list[str]:
        result = list(state)
        for write in writes:
            result.extend(write)
        return result

    class State(TypedDict):
        items: Annotated[
            list[str], DeltaChannel(reducer, list, snapshot_frequency=1000)
        ]

    calls = 0

    def node(state: State) -> dict:
        nonlocal calls
        calls += 1
        if calls == 1:
            return {"items": Overwrite(["reset"])}
        return {"items": ["after"]}

    builder = StateGraph(State)
    builder.add_node("node", node)
    builder.add_edge(START, "node")

    saver = InMemorySaver()
    graph = builder.compile(checkpointer=saver)
    config = {"configurable": {"thread_id": "overwrite-replay"}}

    assert graph.invoke({"items": ["before"]}, config) == {"items": ["reset"]}
    first_saved = saver.get_tuple(config)
    assert first_saved is not None
    assert isinstance(
        first_saved.checkpoint["channel_values"].get("items"), _DeltaSnapshot
    )

    assert graph.invoke({"items": []}, config) == {"items": ["reset", "after"]}
    second_saved = saver.get_tuple(config)
    assert second_saved is not None
    assert "items" not in second_saved.checkpoint["channel_values"]
    assert graph.get_state(config).values == {"items": ["reset", "after"]}


# ---------------------------------------------------------------------------
# DeltaChannel — dict reducer
# ---------------------------------------------------------------------------


def _delta_channel_with_type(op, typ):
    """Build a DeltaChannel with an explicit type via the Annotated injection path."""
    return _get_channel("_test", Annotated[typ, DeltaChannel(op)])


def test_delta_channel_dict_reducer_fresh_channel() -> None:
    """DeltaChannel with a dict reducer starts as empty dict on MISSING checkpoint."""

    def merge_dicts(state: dict, writes: list) -> dict:
        result = dict(state)
        for w in writes:
            result.update(w)
        return result

    ch = _delta_channel_with_type(merge_dicts, dict).from_checkpoint(MISSING)
    assert ch.is_available()
    assert ch.get() == {}


def test_delta_channel_dict_reducer_basic_updates() -> None:
    """DeltaChannel with a dict reducer accumulates key/value pairs across steps."""

    def merge_dicts(state: dict, writes: list) -> dict:
        result = dict(state)
        for w in writes:
            result.update(w)
        return result

    ch = _delta_channel_with_type(merge_dicts, dict).from_checkpoint(MISSING)

    ch.update([{"a": 1}])
    d1 = ch.checkpoint()
    assert d1 is MISSING

    ch.update([{"b": 2}])
    d2 = ch.checkpoint()
    assert d2 is MISSING

    assert ch.get() == {"a": 1, "b": 2}


def test_delta_channel_dict_reducer_writes_reconstruction() -> None:
    """replay_writes on a fresh channel replays through a dict merge reducer."""

    def merge_dicts(state: dict, writes: list) -> dict:
        result = dict(state)
        for w in writes:
            result.update(w)
        return result

    spec = _delta_channel_with_type(merge_dicts, dict)
    ch = spec.from_checkpoint(MISSING)
    ch.replay_writes(
        [
            ("t0", "files", {"a": 1}),
            ("t1", "files", {"b": 2}),
            ("t2", "files", {"c": 3}),
        ]
    )
    assert ch.get() == {"a": 1, "b": 2, "c": 3}


def test_delta_channel_dict_reducer_with_deletions() -> None:
    """Dict reducer that treats None values as deletions works end-to-end."""

    def merge_files(state: dict, writes: list) -> dict:
        result = dict(state)
        for w in writes:
            for k, v in w.items():
                if v is None:
                    result.pop(k, None)
                else:
                    result[k] = v
        return result

    ch = _delta_channel_with_type(merge_files, dict).from_checkpoint(MISSING)
    ch.update([{"file1.py": "content1", "file2.py": "content2"}])
    ch.update([{"file1.py": None, "file3.py": "content3"}])
    assert ch.get() == {"file2.py": "content2", "file3.py": "content3"}

    spec = _delta_channel_with_type(merge_files, dict)
    ch2 = spec.from_checkpoint(MISSING)
    ch2.replay_writes(
        [
            ("t0", "files", {"file1.py": "content1", "file2.py": "content2"}),
            ("t1", "files", {"file1.py": None, "file3.py": "content3"}),
        ]
    )
    assert ch2.get() == {"file2.py": "content2", "file3.py": "content3"}


def test_delta_channel_dict_reducer_overwrite_in_update() -> None:
    """Overwrite(dict) in update() must preserve dict shape, not coerce to list."""

    def merge_dicts(state: dict, writes: list) -> dict:
        result = dict(state)
        for w in writes:
            result.update(w)
        return result

    ch = _delta_channel_with_type(merge_dicts, dict).from_checkpoint(MISSING)
    ch.update([{"a": 1}])
    ch.update([Overwrite({"b": 2, "c": 3})])
    assert ch.get() == {"b": 2, "c": 3}


def test_delta_channel_dict_reducer_overwrite_in_writes_replay() -> None:
    """Overwrite(dict) embedded in replayed writes must reconstruct as dict."""

    def merge_dicts(state: dict, writes: list) -> dict:
        result = dict(state)
        for w in writes:
            result.update(w)
        return result

    spec = _delta_channel_with_type(merge_dicts, dict)
    ch = spec.from_checkpoint(MISSING)
    ch.replay_writes(
        [
            ("t0", "files", {"a": 1}),
            ("t1", "files", Overwrite({"x": 10, "y": 20})),
            ("t2", "files", {"z": 30}),
        ]
    )
    assert ch.get() == {"x": 10, "y": 20, "z": 30}


def test_delta_channel_dict_reducer_with_notrequired_annotation() -> None:
    """DeltaChannel infers dict type through `Annotated[NotRequired[dict[...]], ch]`."""

    def merge_dicts(state: dict, writes: list) -> dict:
        result = dict(state)
        for w in writes:
            result.update(w)
        return result

    annotation = Annotated[NotRequired[dict[str, int]], DeltaChannel(merge_dicts)]
    ch = _get_channel("files", annotation).from_checkpoint(MISSING)
    assert ch.get() == {}
    ch.update([{"a": 1}])
    ch.update([{"b": 2}])
    assert ch.get() == {"a": 1, "b": 2}


def test_delta_channel_dict_reducer_end_to_end_filesystem() -> None:
    """End-to-end: graph with dict-reducer (filesystem-style) channel wrapped in DeltaChannel."""

    def merge_files(state: dict, writes: list) -> dict:
        result = dict(state)
        for w in writes:
            for k, v in w.items():
                if v is None:
                    result.pop(k, None)
                else:
                    result[k] = v
        return result

    class State(TypedDict):
        files: Annotated[dict[str, str], DeltaChannel(merge_files)]

    turn = {"v": 0}

    def write_file(state: State) -> dict:
        turn["v"] += 1
        n = turn["v"]
        return {"files": {f"/doc_{n}.txt": f"content for turn {n}"}}

    builder = StateGraph(State)
    builder.add_node("write_file", write_file)
    builder.add_edge(START, "write_file")
    saver = InMemorySaver()
    graph = builder.compile(checkpointer=saver)
    config = {"configurable": {"thread_id": "fs"}}

    for _ in range(3):
        graph.invoke({"files": {}}, config)

    saved = saver.get_tuple(config)
    assert saved is not None
    assert "files" not in saved.checkpoint["channel_values"]
    state = graph.get_state(config)
    assert state.values["files"] == {
        "/doc_1.txt": "content for turn 1",
        "/doc_2.txt": "content for turn 2",
        "/doc_3.txt": "content for turn 3",
    }

    def delete_file(state: State) -> dict:
        return {"files": {"/doc_1.txt": None}}

    builder2 = StateGraph(State)
    builder2.add_node("write_file", write_file)
    builder2.add_node("delete_file", delete_file)
    builder2.add_edge(START, "write_file")
    builder2.add_edge("write_file", "delete_file")
    turn["v"] = 0
    saver2 = InMemorySaver()
    graph2 = builder2.compile(checkpointer=saver2)
    config2 = {"configurable": {"thread_id": "fs2"}}
    graph2.invoke({"files": {}}, config2)
    state2 = graph2.get_state(config2)
    assert state2.values["files"] == {}


def test_delta_channel_dict_reducer_backwards_compat() -> None:
    """A pre-DeltaChannel dict checkpoint must load as a dict, not be listified."""

    def merge_dicts(state: dict, writes: list) -> dict:
        result = dict(state)
        for w in writes:
            result.update(w)
        return result

    spec = _delta_channel_with_type(merge_dicts, dict)
    old_value = {"a": 1, "b": 2}
    ch = spec.from_checkpoint(old_value)
    assert ch.get() == {"a": 1, "b": 2}


# ---------------------------------------------------------------------------
# DeltaChannel — seed / pre-delta migration
# ---------------------------------------------------------------------------


def test_delta_channel_from_checkpoint_honors_seed() -> None:
    """A non-sentinel value to from_checkpoint is used as the pre-delta seed.

    Guards the pre-delta migration path: when the saver's ancestor walk hits
    a pre-DeltaChannel blob it passes it as `seed` so replay reconstructs
    the post-migration state correctly rather than replaying from empty.
    """
    spec = DeltaChannel(_messages_delta_reducer, list)
    seed = [HumanMessage(content="pre-delta", id="p1")]
    ch = spec.from_checkpoint(seed)
    ch.replay_writes(
        [
            ("t0", "messages", AIMessage(content="delta-1", id="d1")),
            ("t1", "messages", HumanMessage(content="delta-2", id="d2")),
        ]
    )
    msgs = ch.get()
    assert [m.content for m in msgs] == ["pre-delta", "delta-1", "delta-2"]


def test_delta_channel_from_checkpoint_seed_without_writes() -> None:
    """Reconstruction at a pre-delta ancestor with no newer deltas returns
    just the seed — the saver's terminator fired immediately."""
    spec = DeltaChannel(_messages_delta_reducer, list)
    seed = [HumanMessage(content="only-snap", id="s1")]
    ch = spec.from_checkpoint(seed)
    ch.replay_writes([])
    assert ch.get() == seed


def test_delta_channel_from_checkpoint_seed_none_is_distinct_from_sentinel() -> None:
    """`seed=None` must start replay from None, not from an empty channel.

    The `MISSING` absence sentinel means 'no seed'; passing `None`
    explicitly should feed None to the reducer as the left operand.
    """

    def replace(state, writes):
        return writes[-1] if writes else state

    spec = DeltaChannel(replace, list)
    ch = spec.from_checkpoint(None)
    ch.replay_writes([("t0", "x", "after")])
    assert ch.get() == "after"
