"""Tests para summarize_table_duckdb."""

import duckdb
import pytest

from .summarize_table_duckdb import summarize_table_duckdb


@pytest.fixture
def db(tmp_path):
    """Crea una DuckDB temporal con numerica + categorica + nulls + id unico."""
    path = str(tmp_path / "eda_test.duckdb")
    con = duckdb.connect(path)
    con.execute(
        "CREATE TABLE ventas ("
        "  id INTEGER,"          # unico, sin nulls -> possible_id
        "  region VARCHAR,"      # categorica baja cardinalidad
        "  total DOUBLE,"        # numerica con un null
        "  pais VARCHAR"         # constante
        ")"
    )
    con.execute(
        "INSERT INTO ventas VALUES "
        "(1, 'norte', 120.5, 'ES'), "
        "(2, 'sur',   80.0,  'ES'), "
        "(3, 'norte', NULL,  'ES'), "
        "(4, 'este',  45.25, 'ES')"
    )
    con.close()
    return path


def test_shape_y_metadatos_tabla(db):
    res = summarize_table_duckdb(db, "ventas")
    assert res["status"] == "ok"
    profile = res["profile"]

    # Claves del TableProfile presentes.
    for key in (
        "table", "source", "profiled_at", "n_rows", "n_cols", "size_bytes",
        "duplicate_rows", "duplicate_pct", "constant_cols", "all_null_cols",
        "null_cell_pct", "type_breakdown", "columns", "correlations",
        "key_candidates", "quality_score", "llm", "models",
    ):
        assert key in profile, f"falta clave {key} en TableProfile"

    assert profile["table"] == "ventas"
    assert profile["source"] == "duckdb"
    assert profile["n_rows"] == 4
    assert profile["n_cols"] == 4
    assert len(profile["columns"]) == 4
    assert profile["key_candidates"] == []
    assert profile["quality_score"] is None
    assert profile["correlations"] is None


def test_column_profile_shape(db):
    profile = summarize_table_duckdb(db, "ventas")["profile"]
    by_name = {c["name"]: c for c in profile["columns"]}

    for col in profile["columns"]:
        for key in (
            "name", "physical_type", "inferred_type", "semantic_type", "count",
            "n_rows", "null_count", "null_pct", "empty_count", "empty_pct",
            "distinct_count", "unique_pct", "flags", "quality_score",
            "numeric", "categorical", "datetime",
        ):
            assert key in col, f"falta clave {key} en ColumnProfile {col['name']}"

    # id: numerica, sin nulls, unica.
    assert by_name["id"]["inferred_type"] == "numeric"
    assert by_name["id"]["null_count"] == 0
    assert by_name["id"]["count"] == 4
    assert by_name["id"]["distinct_count"] == 4
    assert "possible_id" in by_name["id"]["flags"]

    # region: categorica baja cardinalidad.
    assert by_name["region"]["inferred_type"] == "categorical"
    assert by_name["region"]["distinct_count"] == 3

    # total: numerica con un null. count no-nulo = 3.
    total = by_name["total"]
    assert total["inferred_type"] == "numeric"
    assert total["null_count"] == 1
    assert total["count"] == 3
    assert total["numeric"] is not None
    # SUMMARIZE rellena min/max/mean/std/p25/p50/p75; el resto queda en None.
    assert total["numeric"]["min"] == pytest.approx(45.25)
    assert total["numeric"]["max"] == pytest.approx(120.5)
    assert total["numeric"]["mean"] is not None
    assert total["numeric"]["skew"] is None
    assert total["numeric"]["histogram"] is None
    assert total["numeric"]["p99"] is None

    # pais: constante -> flag constant + aparece en constant_cols.
    assert "constant" in by_name["pais"]["flags"]
    assert "pais" in profile["constant_cols"]


def test_distinct_no_excede_filas(db):
    """distinct_count exacto: nunca supera n_rows ni unique_pct pasa de 1.0.

    Regresion: SUMMARIZE.approx_unique (HyperLogLog) sobreestimaba y reportaba
    mas distintos que filas en tablas pequenas, inflando unique_pct > 1.0 y
    disparando flags possible_id falsos.
    """
    profile = summarize_table_duckdb(db, "ventas")["profile"]
    n_rows = profile["n_rows"]
    for col in profile["columns"]:
        assert col["distinct_count"] <= n_rows, (
            f"{col['name']}: distinct_count {col['distinct_count']} > n_rows {n_rows}"
        )
        assert col["unique_pct"] <= 1.0, (
            f"{col['name']}: unique_pct {col['unique_pct']} > 1.0"
        )


def test_columna_unica_da_possible_id(db):
    """Una columna con todos los valores unicos -> unique_pct == 1.0 + possible_id."""
    profile = summarize_table_duckdb(db, "ventas")["profile"]
    by_name = {c["name"]: c for c in profile["columns"]}

    # id: 4 valores distintos sobre 4 filas, sin nulls.
    idc = by_name["id"]
    assert idc["distinct_count"] == 4
    assert idc["unique_pct"] == 1.0
    assert "possible_id" in idc["flags"]


def test_type_breakdown(db):
    profile = summarize_table_duckdb(db, "ventas")["profile"]
    tb = profile["type_breakdown"]
    assert set(tb.keys()) == {
        "numeric", "categorical", "datetime", "text", "boolean"
    }
    assert tb["numeric"] == 2          # id, total
    assert tb["categorical"] == 2      # region, pais
    assert tb["datetime"] == 0
    assert tb["boolean"] == 0


def test_tabla_invalida_devuelve_error(db):
    res = summarize_table_duckdb(db, "ventas; DROP TABLE ventas")
    assert res["status"] == "error"
    assert "invalido" in res["error"]


def test_tabla_inexistente_devuelve_error(db):
    res = summarize_table_duckdb(db, "no_existe")
    assert res["status"] == "error"