# type: ignore
from __future__ import annotations

import asyncio
import itertools
import sys
import uuid
from collections.abc import AsyncIterator
from concurrent.futures import ThreadPoolExecutor
from contextlib import asynccontextmanager
from typing import Any

import pytest
from langchain_core.embeddings import Embeddings
from psycopg import AsyncConnection

from langgraph.store.base import (
    GetOp,
    Item,
    ListNamespacesOp,
    PutOp,
    SearchOp,
)
from langgraph.store.postgres import AsyncPostgresStore
from tests.conftest import (
    DEFAULT_URI,
    VECTOR_TYPES,
    CharacterEmbeddings,
)

TTL_SECONDS = 6
TTL_MINUTES = TTL_SECONDS / 60


@pytest.fixture(scope="function", params=["default", "pipe", "pool"])
async def store(request) -> AsyncIterator[AsyncPostgresStore]:
    if sys.version_info < (3, 10):
        pytest.skip("Async Postgres tests require Python 3.10+")

    database = f"test_{uuid.uuid4().hex[:16]}"
    uri_parts = DEFAULT_URI.split("/")
    uri_base = "/".join(uri_parts[:-1])
    query_params = ""
    if "?" in uri_parts[-1]:
        db_name, query_params = uri_parts[-1].split("?", 1)
        query_params = "?" + query_params

    conn_string = f"{uri_base}/{database}{query_params}"
    admin_conn_string = DEFAULT_URI
    ttl_config = {
        "default_ttl": TTL_MINUTES,
        "refresh_on_read": True,
        "sweep_interval_minutes": TTL_MINUTES / 2,
    }
    async with await AsyncConnection.connect(
        admin_conn_string, autocommit=True
    ) as conn:
        await conn.execute(f"CREATE DATABASE {database}")
    try:
        async with AsyncPostgresStore.from_conn_string(
            conn_string, ttl=ttl_config
        ) as store:
            store.MIGRATIONS = [
                (
                    mig.replace("ttl_minutes INT;", "ttl_minutes FLOAT;")
                    if isinstance(mig, str)
                    else mig
                )
                for mig in store.MIGRATIONS
            ]
            await store.setup()
            async with store._cursor() as cur:
                # drop the migration index
                await cur.execute("DROP TABLE IF EXISTS store_migrations")
            await store.setup()  # Will fail if migrations aren't idempotent

        if request.param == "pipe":
            async with AsyncPostgresStore.from_conn_string(
                conn_string, pipeline=True, ttl=ttl_config
            ) as store:
                await store.start_ttl_sweeper()
                yield store
                await store.stop_ttl_sweeper()
        elif request.param == "pool":
            async with AsyncPostgresStore.from_conn_string(
                conn_string, pool_config={"min_size": 1, "max_size": 10}, ttl=ttl_config
            ) as store:
                await store.start_ttl_sweeper()
                yield store
                await store.stop_ttl_sweeper()
        else:  # default
            async with AsyncPostgresStore.from_conn_string(
                conn_string, ttl=ttl_config
            ) as store:
                await store.start_ttl_sweeper()
                yield store
                await store.stop_ttl_sweeper()
    finally:
        async with await AsyncConnection.connect(
            admin_conn_string, autocommit=True
        ) as conn:
            await conn.execute(f"DROP DATABASE {database}")


async def test_no_running_loop(store: AsyncPostgresStore) -> None:
    with pytest.raises(asyncio.InvalidStateError):
        store.put(("foo", "bar"), "baz", {"val": "baz"})
    with pytest.raises(asyncio.InvalidStateError):
        store.get(("foo", "bar"), "baz")
    with pytest.raises(asyncio.InvalidStateError):
        store.delete(("foo", "bar"), "baz")
    with pytest.raises(asyncio.InvalidStateError):
        store.search(("foo", "bar"))
    with pytest.raises(asyncio.InvalidStateError):
        store.list_namespaces(prefix=("foo",))
    with pytest.raises(asyncio.InvalidStateError):
        store.batch([PutOp(namespace=("foo", "bar"), key="baz", value={"val": "baz"})])
    with ThreadPoolExecutor(max_workers=1) as executor:
        future = executor.submit(store.put, ("foo", "bar"), "baz", {"val": "baz"})
        result = await asyncio.wrap_future(future)
        assert result is None
        future = executor.submit(store.get, ("foo", "bar"), "baz")
        result = await asyncio.wrap_future(future)
        assert result.value == {"val": "baz"}
        result = await asyncio.wrap_future(
            executor.submit(store.list_namespaces, prefix=("foo",))
        )


async def test_large_batches(request: Any, store: AsyncPostgresStore) -> None:
    N = 100  # less important that we are performant here
    M = 10

    with ThreadPoolExecutor(max_workers=10) as executor:
        futures = []
        for m in range(M):
            for i in range(N):
                futures += [
                    executor.submit(
                        store.put,
                        ("test", "foo", "bar", "baz", str(m % 2)),
                        f"key{i}",
                        value={"foo": "bar" + str(i)},
                    ),
                    executor.submit(
                        store.get,
                        ("test", "foo", "bar", "baz", str(m % 2)),
                        f"key{i}",
                    ),
                    executor.submit(
                        store.list_namespaces,
                        prefix=None,
                        max_depth=m + 1,
                    ),
                    executor.submit(
                        store.search,
                        ("test",),
                    ),
                    executor.submit(
                        store.put,
                        ("test", "foo", "bar", "baz", str(m % 2)),
                        f"key{i}",
                        value={"foo": "bar" + str(i)},
                    ),
                    executor.submit(
                        store.put,
                        ("test", "foo", "bar", "baz", str(m % 2)),
                        f"key{i}",
                        None,
                    ),
                ]

        results = await asyncio.gather(
            *(asyncio.wrap_future(future) for future in futures)
        )
    assert len(results) == M * N * 6


async def test_large_batches_async(store: AsyncPostgresStore) -> None:
    N = 1000
    M = 10
    coros = []
    for m in range(M):
        for i in range(N):
            coros.append(
                store.aput(
                    ("test", "foo", "bar", "baz", str(m % 2)),
                    f"key{i}",
                    value={"foo": "bar" + str(i)},
                )
            )
            coros.append(
                store.aget(
                    ("test", "foo", "bar", "baz", str(m % 2)),
                    f"key{i}",
                )
            )
            coros.append(
                store.alist_namespaces(
                    prefix=None,
                    max_depth=m + 1,
                )
            )
            coros.append(
                store.asearch(
                    ("test",),
                )
            )
            coros.append(
                store.aput(
                    ("test", "foo", "bar", "baz", str(m % 2)),
                    f"key{i}",
                    value={"foo": "bar" + str(i)},
                )
            )
            coros.append(
                store.adelete(
                    ("test", "foo", "bar", "baz", str(m % 2)),
                    f"key{i}",
                )
            )

    results = await asyncio.gather(*coros)
    assert len(results) == M * N * 6


async def test_abatch_order(store: AsyncPostgresStore) -> None:
    # Setup test data
    await store.aput(("test", "foo"), "key1", {"data": "value1"})
    await store.aput(("test", "bar"), "key2", {"data": "value2"})

    ops = [
        GetOp(namespace=("test", "foo"), key="key1"),
        PutOp(namespace=("test", "bar"), key="key2", value={"data": "value2"}),
        SearchOp(
            namespace_prefix=("test",), filter={"data": "value1"}, limit=10, offset=0
        ),
        ListNamespacesOp(match_conditions=None, max_depth=None, limit=10, offset=0),
        GetOp(namespace=("test",), key="key3"),
    ]

    results = await store.abatch(ops)
    assert len(results) == 5
    assert isinstance(results[0], Item)
    assert isinstance(results[0].value, dict)
    assert results[0].value == {"data": "value1"}
    assert results[0].key == "key1"
    assert results[1] is None
    assert isinstance(results[2], list)
    assert len(results[2]) == 1
    assert isinstance(results[3], list)
    assert ("test", "foo") in results[3] and ("test", "bar") in results[3]
    assert results[4] is None

    ops_reordered = [
        SearchOp(namespace_prefix=("test",), filter=None, limit=5, offset=0),
        GetOp(namespace=("test", "bar"), key="key2"),
        ListNamespacesOp(match_conditions=None, max_depth=None, limit=5, offset=0),
        PutOp(namespace=("test",), key="key3", value={"data": "value3"}),
        GetOp(namespace=("test", "foo"), key="key1"),
    ]

    results_reordered = await store.abatch(ops_reordered)
    assert len(results_reordered) == 5
    assert isinstance(results_reordered[0], list)
    assert len(results_reordered[0]) == 2
    assert isinstance(results_reordered[1], Item)
    assert results_reordered[1].value == {"data": "value2"}
    assert results_reordered[1].key == "key2"
    assert isinstance(results_reordered[2], list)
    assert ("test", "foo") in results_reordered[2] and (
        "test",
        "bar",
    ) in results_reordered[2]
    assert results_reordered[3] is None
    assert isinstance(results_reordered[4], Item)
    assert results_reordered[4].value == {"data": "value1"}
    assert results_reordered[4].key == "key1"


async def test_batch_get_ops(store: AsyncPostgresStore) -> None:
    # Setup test data
    await store.aput(("test",), "key1", {"data": "value1"})
    await store.aput(("test",), "key2", {"data": "value2"})

    ops = [
        GetOp(namespace=("test",), key="key1"),
        GetOp(namespace=("test",), key="key2"),
        GetOp(namespace=("test",), key="key3"),
    ]

    results = await store.abatch(ops)

    assert len(results) == 3
    assert results[0] is not None
    assert results[1] is not None
    assert results[2] is None
    assert results[0].key == "key1"
    assert results[1].key == "key2"


async def test_batch_put_ops(store: AsyncPostgresStore) -> None:
    ops = [
        PutOp(namespace=("test",), key="key1", value={"data": "value1"}),
        PutOp(namespace=("test",), key="key2", value={"data": "value2"}),
        PutOp(namespace=("test",), key="key3", value=None),
    ]

    results = await store.abatch(ops)

    assert len(results) == 3
    assert all(result is None for result in results)

    # Verify the puts worked
    items = await store.asearch(["test"], limit=10)
    assert len(items) == 2  # key3 had None value so wasn't stored


async def test_batch_search_ops(store: AsyncPostgresStore) -> None:
    # Setup test data
    await store.aput(("test", "foo"), "key1", {"data": "value1"})
    await store.aput(("test", "bar"), "key2", {"data": "value2"})

    ops = [
        SearchOp(
            namespace_prefix=("test",), filter={"data": "value1"}, limit=10, offset=0
        ),
        SearchOp(namespace_prefix=("test",), filter=None, limit=5, offset=0),
    ]

    results = await store.abatch(ops)

    assert len(results) == 2
    assert len(results[0]) == 1  # Filtered results
    assert len(results[1]) == 2  # All results


async def test_batch_list_namespaces_ops(store: AsyncPostgresStore) -> None:
    # Setup test data
    await store.aput(("test", "namespace1"), "key1", {"data": "value1"})
    await store.aput(("test", "namespace2"), "key2", {"data": "value2"})

    ops = [ListNamespacesOp(match_conditions=None, max_depth=None, limit=10, offset=0)]

    results = await store.abatch(ops)

    assert len(results) == 1
    assert len(results[0]) == 2
    assert ("test", "namespace1") in results[0]
    assert ("test", "namespace2") in results[0]


@asynccontextmanager
async def _create_vector_store(
    vector_type: str,
    distance_type: str,
    fake_embeddings: CharacterEmbeddings,
    text_fields: list[str] | None = None,
) -> AsyncIterator[AsyncPostgresStore]:
    """Create a store with vector search enabled."""
    if sys.version_info < (3, 10):
        pytest.skip("Async Postgres tests require Python 3.10+")

    database = f"test_{uuid.uuid4().hex[:16]}"
    uri_parts = DEFAULT_URI.split("/")
    uri_base = "/".join(uri_parts[:-1])
    query_params = ""
    if "?" in uri_parts[-1]:
        db_name, query_params = uri_parts[-1].split("?", 1)
        query_params = "?" + query_params

    conn_string = f"{uri_base}/{database}{query_params}"
    admin_conn_string = DEFAULT_URI

    index_config = {
        "dims": fake_embeddings.dims,
        "embed": fake_embeddings,
        "ann_index_config": {
            "vector_type": vector_type,
        },
        "distance_type": distance_type,
        "fields": text_fields,
    }

    async with await AsyncConnection.connect(
        admin_conn_string, autocommit=True
    ) as conn:
        await conn.execute(f"CREATE DATABASE {database}")
    try:
        async with AsyncPostgresStore.from_conn_string(
            conn_string,
            index=index_config,
        ) as store:
            await store.setup()
            yield store
    finally:
        async with await AsyncConnection.connect(
            admin_conn_string, autocommit=True
        ) as conn:
            await conn.execute(f"DROP DATABASE {database}")


@pytest.fixture(
    scope="function",
    params=[
        (vector_type, distance_type)
        for vector_type in VECTOR_TYPES
        for distance_type in (
            ["hamming"] if vector_type == "bit" else ["l2", "inner_product", "cosine"]
        )
    ],
    ids=lambda p: f"{p[0]}_{p[1]}",
)
async def vector_store(
    request,
    fake_embeddings: CharacterEmbeddings,
) -> AsyncIterator[AsyncPostgresStore]:
    """Create a store with vector search enabled."""
    vector_type, distance_type = request.param
    async with _create_vector_store(
        vector_type, distance_type, fake_embeddings
    ) as store:
        yield store


async def test_vector_store_initialization(
    vector_store: AsyncPostgresStore, fake_embeddings: CharacterEmbeddings
) -> None:
    """Test store initialization with embedding config."""
    assert vector_store.index_config is not None
    assert vector_store.index_config["dims"] == fake_embeddings.dims
    if isinstance(vector_store.index_config["embed"], Embeddings):
        assert vector_store.index_config["embed"] == fake_embeddings


async def test_vector_insert_with_auto_embedding(
    vector_store: AsyncPostgresStore,
) -> None:
    """Test inserting items that get auto-embedded."""
    docs = [
        ("doc1", {"text": "short text"}),
        ("doc2", {"text": "longer text document"}),
        ("doc3", {"text": "longest text document here"}),
        ("doc4", {"description": "text in description field"}),
        ("doc5", {"content": "text in content field"}),
        ("doc6", {"body": "text in body field"}),
    ]

    for key, value in docs:
        await vector_store.aput(("test",), key, value)

    results = await vector_store.asearch(("test",), query="long text")
    assert len(results) > 0

    doc_order = [r.key for r in results]
    assert "doc2" in doc_order
    assert "doc3" in doc_order


async def test_vector_update_with_embedding(vector_store: AsyncPostgresStore) -> None:
    """Test that updating items properly updates their embeddings."""
    await vector_store.aput(("test",), "doc1", {"text": "zany zebra Xerxes"})
    await vector_store.aput(("test",), "doc2", {"text": "something about dogs"})
    await vector_store.aput(("test",), "doc3", {"text": "text about birds"})

    results_initial = await vector_store.asearch(("test",), query="Zany Xerxes")
    assert len(results_initial) > 0
    assert results_initial[0].key == "doc1"
    initial_score = results_initial[0].score

    await vector_store.aput(("test",), "doc1", {"text": "new text about dogs"})

    results_after = await vector_store.asearch(("test",), query="Zany Xerxes")
    after_score = next((r.score for r in results_after if r.key == "doc1"), 0.0)
    assert after_score < initial_score

    results_new = await vector_store.asearch(("test",), query="new text about dogs")
    for r in results_new:
        if r.key == "doc1":
            assert r.score > after_score

    # Don't index this one
    await vector_store.aput(
        ("test",), "doc4", {"text": "new text about dogs"}, index=False
    )
    results_new = await vector_store.asearch(
        ("test",), query="new text about dogs", limit=3
    )
    assert not any(r.key == "doc4" for r in results_new)


async def test_vector_search_with_filters(vector_store: AsyncPostgresStore) -> None:
    """Test combining vector search with filters."""
    docs = [
        ("doc1", {"text": "red apple", "color": "red", "score": 4.5}),
        ("doc2", {"text": "red car", "color": "red", "score": 3.0}),
        ("doc3", {"text": "green apple", "color": "green", "score": 4.0}),
        ("doc4", {"text": "blue car", "color": "blue", "score": 3.5}),
    ]

    for key, value in docs:
        await vector_store.aput(("test",), key, value)

    results = await vector_store.asearch(
        ("test",), query="apple", filter={"color": "red"}
    )
    assert len(results) == 2
    assert results[0].key == "doc1"

    results = await vector_store.asearch(
        ("test",), query="car", filter={"color": "red"}
    )
    assert len(results) == 2
    assert results[0].key == "doc2"

    results = await vector_store.asearch(
        ("test",), query="bbbbluuu", filter={"score": {"$gt": 3.2}}
    )
    assert len(results) == 3
    assert results[0].key == "doc4"

    results = await vector_store.asearch(
        ("test",), query="apple", filter={"score": {"$gte": 4.0}, "color": "green"}
    )
    assert len(results) == 1
    assert results[0].key == "doc3"


async def test_vector_search_pagination(vector_store: AsyncPostgresStore) -> None:
    """Test pagination with vector search."""
    for i in range(5):
        await vector_store.aput(
            ("test",), f"doc{i}", {"text": f"test document number {i}"}
        )

    results_page1 = await vector_store.asearch(("test",), query="test", limit=2)
    results_page2 = await vector_store.asearch(
        ("test",), query="test", limit=2, offset=2
    )

    assert len(results_page1) == 2
    assert len(results_page2) == 2
    assert results_page1[0].key != results_page2[0].key

    all_results = await vector_store.asearch(("test",), query="test", limit=10)
    assert len(all_results) == 5


async def test_vector_search_edge_cases(vector_store: AsyncPostgresStore) -> None:
    """Test edge cases in vector search."""
    await vector_store.aput(("test",), "doc1", {"text": "test document"})

    perfect_match = await vector_store.asearch(("test",), query="text test document")
    perfect_score = perfect_match[0].score

    results = await vector_store.asearch(("test",), query="")
    assert len(results) == 1
    assert results[0].score is None

    results = await vector_store.asearch(("test",), query=None)
    assert len(results) == 1
    assert results[0].score is None

    long_query = "foo " * 100
    results = await vector_store.asearch(("test",), query=long_query)
    assert len(results) == 1
    assert results[0].score < perfect_score

    special_query = "test!@#$%^&*()"
    results = await vector_store.asearch(("test",), query=special_query)
    assert len(results) == 1
    assert results[0].score < perfect_score


@pytest.mark.parametrize(
    "vector_type,distance_type",
    [
        *itertools.product(["vector", "halfvec"], ["cosine", "inner_product", "l2"]),
    ],
)
async def test_embed_with_path(
    request: Any,
    fake_embeddings: CharacterEmbeddings,
    vector_type: str,
    distance_type: str,
) -> None:
    """Test vector search with specific text fields in Postgres store."""
    async with _create_vector_store(
        vector_type,
        distance_type,
        fake_embeddings,
        text_fields=["key0", "key1", "key3"],
    ) as store:
        # This will have 2 vectors representing it
        doc1 = {
            # Omit key0 - check it doesn't raise an error
            "key1": "xxx",
            "key2": "yyy",
            "key3": "zzz",
        }
        # This will have 3 vectors representing it
        doc2 = {
            "key0": "uuu",
            "key1": "vvv",
            "key2": "www",
            "key3": "xxx",
        }
        await store.aput(("test",), "doc1", doc1)
        await store.aput(("test",), "doc2", doc2)

        # doc2.key3 and doc1.key1 both would have the highest score
        results = await store.asearch(("test",), query="xxx")
        assert len(results) == 2
        assert results[0].key != results[1].key
        ascore = results[0].score
        bscore = results[1].score
        assert ascore == pytest.approx(bscore, abs=1e-3)

        results = await store.asearch(("test",), query="uuu")
        assert len(results) == 2
        assert results[0].key != results[1].key
        assert results[0].key == "doc2"
        assert results[0].score > results[1].score
        assert ascore == pytest.approx(results[0].score, abs=1e-3)

        # Un-indexed - will have low results for both. Not zero (because we're projecting)
        # but less than the above.
        results = await store.asearch(("test",), query="www")
        assert len(results) == 2
        assert results[0].score < ascore
        assert results[1].score < ascore


@pytest.mark.parametrize(
    "vector_type,distance_type",
    [
        *itertools.product(["vector", "halfvec"], ["cosine", "inner_product", "l2"]),
    ],
)
async def test_search_sorting(
    request: Any,
    fake_embeddings: CharacterEmbeddings,
    vector_type: str,
    distance_type: str,
) -> None:
    """Test operation-level field configuration for vector search."""
    async with _create_vector_store(
        vector_type,
        distance_type,
        fake_embeddings,
        text_fields=["key1"],  # Default fields that won't match our test data
    ) as store:
        amatch = {
            "key1": "mmm",
        }

        await store.aput(("test", "M"), "M", amatch)
        N = 100
        for i in range(N):
            await store.aput(("test", "A"), f"A{i}", {"key1": "no"})
        for i in range(N):
            await store.aput(("test", "Z"), f"Z{i}", {"key1": "no"})

        results = await store.asearch(("test",), query="mmm", limit=10)
        assert len(results) == 10
        assert len(set(r.key for r in results)) == 10
        assert results[0].key == "M"
        assert results[0].score > results[1].score


async def test_store_ttl(store):
    # Assumes a TTL of 1 minute = 60 seconds
    ns = ("foo",)
    await store.start_ttl_sweeper()
    await store.aput(
        ns,
        key="item1",
        value={"foo": "bar"},
        ttl=TTL_MINUTES,  # type: ignore
    )
    await asyncio.sleep(TTL_SECONDS - 2)
    res = await store.aget(ns, key="item1", refresh_ttl=True)
    assert res is not None
    await asyncio.sleep(TTL_SECONDS - 2)
    results = await store.asearch(ns, query="foo", refresh_ttl=True)
    assert len(results) == 1
    await asyncio.sleep(TTL_SECONDS - 2)
    res = await store.aget(ns, key="item1", refresh_ttl=False)
    assert res is not None
    await asyncio.sleep(TTL_SECONDS - 1)
    # Now has been (TTL_SECONDS-2)*2 > TTL_SECONDS + TTL_SECONDS/2
    results = await store.asearch(ns, query="bar", refresh_ttl=False)
    assert len(results) == 0