#  Copyright 2023 LanceDB Developers
#
#  Licensed under the Apache License, Version 2.0 (the "License");
#  you may not use this file except in compliance with the License.
#  You may obtain a copy of the License at
#      http://www.apache.org/licenses/LICENSE-2.0
#
#  Unless required by applicable law or agreed to in writing, software
#  distributed under the License is distributed on an "AS IS" BASIS,
#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
#  See the License for the specific language governing permissions and
#  limitations under the License.

import unittest.mock as mock
from datetime import timedelta
from typing import Optional

import lance
import lancedb
import numpy as np
import pandas.testing as tm
import pyarrow as pa
import pytest
import pytest_asyncio
from lancedb.db import LanceDBConnection
from lancedb.pydantic import LanceModel, Vector
from lancedb.query import AsyncQueryBase, LanceVectorQueryBuilder, Query
from lancedb.table import AsyncTable, LanceTable


class MockTable:
    def __init__(self, tmp_path):
        self.uri = tmp_path
        self._conn = LanceDBConnection(self.uri)

    def to_lance(self):
        return lance.dataset(self.uri)

    def _execute_query(self, query, batch_size: Optional[int] = None):
        ds = self.to_lance()
        return ds.scanner(
            columns=query.columns,
            filter=query.filter,
            prefilter=query.prefilter,
            nearest={
                "column": query.vector_column,
                "q": query.vector,
                "k": query.k,
                "metric": query.metric,
                "nprobes": query.nprobes,
                "refine_factor": query.refine_factor,
            },
            batch_size=batch_size,
        ).to_reader()


@pytest.fixture
def table(tmp_path) -> MockTable:
    df = pa.table(
        {
            "vector": pa.array(
                [[1, 2], [3, 4]], type=pa.list_(pa.float32(), list_size=2)
            ),
            "id": pa.array([1, 2]),
            "str_field": pa.array(["a", "b"]),
            "float_field": pa.array([1.0, 2.0]),
        }
    )
    lance.write_dataset(df, tmp_path)
    return MockTable(tmp_path)


@pytest_asyncio.fixture
async def table_async(tmp_path) -> AsyncTable:
    conn = await lancedb.connect_async(
        tmp_path, read_consistency_interval=timedelta(seconds=0)
    )
    data = pa.table(
        {
            "vector": pa.array(
                [[1, 2], [3, 4]], type=pa.list_(pa.float32(), list_size=2)
            ),
            "id": pa.array([1, 2]),
            "str_field": pa.array(["a", "b"]),
            "float_field": pa.array([1.0, 2.0]),
        }
    )
    return await conn.create_table("test", data)


def test_cast(table):
    class TestModel(LanceModel):
        vector: Vector(2)
        id: int
        str_field: str
        float_field: float

    q = LanceVectorQueryBuilder(table, [0, 0], "vector").limit(1)
    results = q.to_pydantic(TestModel)
    assert len(results) == 1
    r0 = results[0]
    assert isinstance(r0, TestModel)
    assert r0.id == 1
    assert r0.vector == [1, 2]
    assert r0.str_field == "a"
    assert r0.float_field == 1.0


def test_query_builder(table):
    rs = (
        LanceVectorQueryBuilder(table, [0, 0], "vector")
        .limit(1)
        .select(["id", "vector"])
        .to_list()
    )
    assert rs[0]["id"] == 1
    assert all(np.array(rs[0]["vector"]) == [1, 2])


def test_query_builder_batches(table):
    rs = (
        LanceVectorQueryBuilder(table, [0, 0], "vector")
        .limit(2)
        .select(["id", "vector"])
        .to_batches(1)
    )
    rs_list = []
    for item in rs:
        rs_list.append(item)
        assert isinstance(item, pa.RecordBatch)
    assert len(rs_list) == 1
    assert len(rs_list[0]["id"]) == 2
    assert all(rs_list[0].to_pandas()["vector"][0] == [1.0, 2.0])
    assert rs_list[0].to_pandas()["id"][0] == 1
    assert all(rs_list[0].to_pandas()["vector"][1] == [3.0, 4.0])
    assert rs_list[0].to_pandas()["id"][1] == 2


def test_dynamic_projection(table):
    rs = (
        LanceVectorQueryBuilder(table, [0, 0], "vector")
        .limit(1)
        .select({"id": "id", "id2": "id * 2"})
        .to_list()
    )
    assert rs[0]["id"] == 1
    assert rs[0]["id2"] == 2


def test_query_builder_with_filter(table):
    rs = LanceVectorQueryBuilder(table, [0, 0], "vector").where("id = 2").to_list()
    assert rs[0]["id"] == 2
    assert all(np.array(rs[0]["vector"]) == [3, 4])


def test_query_builder_with_prefilter(table):
    df = (
        LanceVectorQueryBuilder(table, [0, 0], "vector")
        .where("id = 2")
        .limit(1)
        .to_pandas()
    )
    assert len(df) == 0

    df = (
        LanceVectorQueryBuilder(table, [0, 0], "vector")
        .where("id = 2", prefilter=True)
        .limit(1)
        .to_pandas()
    )
    assert df["id"].values[0] == 2
    assert all(df["vector"].values[0] == [3, 4])


def test_query_builder_with_metric(table):
    query = [4, 8]
    vector_column_name = "vector"
    df_default = LanceVectorQueryBuilder(table, query, vector_column_name).to_pandas()
    df_l2 = (
        LanceVectorQueryBuilder(table, query, vector_column_name)
        .metric("L2")
        .to_pandas()
    )
    tm.assert_frame_equal(df_default, df_l2)

    df_cosine = (
        LanceVectorQueryBuilder(table, query, vector_column_name)
        .metric("cosine")
        .limit(1)
        .to_pandas()
    )
    assert df_cosine._distance[0] == pytest.approx(
        cosine_distance(query, df_cosine.vector[0]),
        abs=1e-6,
    )
    assert 0 <= df_cosine._distance[0] <= 1


def test_query_builder_with_different_vector_column():
    table = mock.MagicMock(spec=LanceTable)
    query = [4, 8]
    vector_column_name = "foo_vector"
    builder = (
        LanceVectorQueryBuilder(table, query, vector_column_name)
        .metric("cosine")
        .where("b < 10")
        .select(["b"])
        .limit(2)
    )
    ds = mock.Mock()
    table.to_lance.return_value = ds
    builder.to_arrow()
    table._execute_query.assert_called_once_with(
        Query(
            vector=query,
            filter="b < 10",
            k=2,
            metric="cosine",
            columns=["b"],
            nprobes=20,
            refine_factor=None,
            vector_column="foo_vector",
        ),
        None,
    )


def cosine_distance(vec1, vec2):
    return 1 - np.dot(vec1, vec2) / (np.linalg.norm(vec1) * np.linalg.norm(vec2))


async def check_query(
    query: AsyncQueryBase, *, expected_num_rows=None, expected_columns=None
):
    num_rows = 0
    results = await query.to_batches()
    async for batch in results:
        if expected_columns is not None:
            assert batch.schema.names == expected_columns
        num_rows += batch.num_rows
    if expected_num_rows is not None:
        assert num_rows == expected_num_rows


@pytest.mark.asyncio
async def test_query_async(table_async: AsyncTable):
    await check_query(
        table_async.query(),
        expected_num_rows=2,
        expected_columns=["vector", "id", "str_field", "float_field"],
    )
    await check_query(table_async.query().where("id = 2"), expected_num_rows=1)
    await check_query(
        table_async.query().select(["id", "vector"]), expected_columns=["id", "vector"]
    )
    await check_query(
        table_async.query().select({"foo": "id", "bar": "id + 1"}),
        expected_columns=["foo", "bar"],
    )
    await check_query(table_async.query().limit(1), expected_num_rows=1)
    await check_query(
        table_async.query().nearest_to(pa.array([1, 2])), expected_num_rows=2
    )
    # Support different types of inputs for the vector query
    for vector_query in [
        [1, 2],
        [1.0, 2.0],
        np.array([1, 2]),
        (1, 2),
    ]:
        await check_query(
            table_async.query().nearest_to(vector_query), expected_num_rows=2
        )

    # No easy way to check these vector query parameters are doing what they say.  We
    # just check that they don't raise exceptions and assume this is tested at a lower
    # level.
    await check_query(
        table_async.query().where("id = 2").nearest_to(pa.array([1, 2])).postfilter(),
        expected_num_rows=1,
    )
    await check_query(
        table_async.query().nearest_to(pa.array([1, 2])).refine_factor(1),
        expected_num_rows=2,
    )
    await check_query(
        table_async.query().nearest_to(pa.array([1, 2])).nprobes(10),
        expected_num_rows=2,
    )
    await check_query(
        table_async.query().nearest_to(pa.array([1, 2])).bypass_vector_index(),
        expected_num_rows=2,
    )
    await check_query(
        table_async.query().nearest_to(pa.array([1, 2])).distance_type("dot"),
        expected_num_rows=2,
    )
    await check_query(
        table_async.query().nearest_to(pa.array([1, 2])).distance_type("DoT"),
        expected_num_rows=2,
    )

    # Make sure we can use a vector query as a base query (e.g. call limit on it)
    # Also make sure `vector_search` works
    await check_query(table_async.vector_search([1, 2]).limit(1), expected_num_rows=1)

    # Also check an empty query
    await check_query(table_async.query().where("id < 0"), expected_num_rows=0)


@pytest.mark.asyncio
async def test_query_to_arrow_async(table_async: AsyncTable):
    table = await table_async.to_arrow()
    assert table.num_rows == 2
    assert table.num_columns == 4

    table = await table_async.query().to_arrow()
    assert table.num_rows == 2
    assert table.num_columns == 4

    table = await table_async.query().where("id < 0").to_arrow()
    assert table.num_rows == 0
    assert table.num_columns == 4


@pytest.mark.asyncio
async def test_query_to_pandas_async(table_async: AsyncTable):
    df = await table_async.to_pandas()
    assert df.shape == (2, 4)

    df = await table_async.query().to_pandas()
    assert df.shape == (2, 4)

    df = await table_async.query().where("id < 0").to_pandas()
    assert df.shape == (0, 4)