"""Unit tests for data_layer.activity_session_metrics (no DB for most cases)."""

import uuid
from unittest.mock import patch

import pytest

from data_layer.activity_session_metrics import (
    ActivitySessionMetricsError,
    enrich_sessions_with_metrics,
    merge_column_backed_and_eav_metrics,
    merge_parameter_schema_rows,
    resolve_activity_attribute_schema,
    _row_value_tuple,
    _validate_single_value,
)


def _tp_row(
    pid: int,
    key: str,
    *,
    data_type: str = "integer",
    cat_sort: int = 0,
    cat_required: bool = False,
    name_de: str = "X",
    name_en: str = "X",
    param_category: str = "physical",
    unit: str | None = None,
    validation_rules: dict | None = None,
    source_field: str | None = None,
):
    return {
        "training_parameter_id": pid,
        "cat_sort": cat_sort,
        "cat_required": cat_required,
        "cat_ui_group": None,
        "key": key,
        "name_de": name_de,
        "name_en": name_en,
        "param_category": param_category,
        "data_type": data_type,
        "unit": unit,
        "validation_rules": validation_rules or {},
        "source_field": source_field,
    }


def _ttp_row(
    pid: int,
    key: str,
    *,
    typ_sort: int | None = None,
    typ_required: bool | None = None,
    typ_ui_group: str | None = None,
    data_type: str = "integer",
    name_de: str = "X",
    name_en: str = "X",
    param_category: str = "physical",
    unit: str | None = None,
    validation_rules: dict | None = None,
    source_field: str | None = None,
):
    return {
        "training_parameter_id": pid,
        "typ_sort": typ_sort,
        "typ_required": typ_required,
        "typ_ui_group": typ_ui_group,
        "key": key,
        "name_de": name_de,
        "name_en": name_en,
        "param_category": param_category,
        "data_type": data_type,
        "unit": unit,
        "validation_rules": validation_rules or {},
        "source_field": source_field,
    }


def test_merge_category_only_sorted_by_sort_order_then_key():
    cat = [
        _tp_row(2, "zebra", cat_sort=10),
        _tp_row(1, "alpha", cat_sort=5),
    ]
    merged = merge_parameter_schema_rows(cat, [])
    assert [m["key"] for m in merged] == ["alpha", "zebra"]
    assert merged[0]["required"] is False


def test_merge_type_overrides_required_and_sort():
    cat = [_tp_row(1, "rpe", cat_sort=0, cat_required=False)]
    typ = [_ttp_row(1, "rpe", typ_sort=99, typ_required=True)]
    merged = merge_parameter_schema_rows(cat, typ)
    assert len(merged) == 1
    assert merged[0]["sort_order"] == 99
    assert merged[0]["required"] is True


def test_merge_type_adds_parameter_not_in_category():
    typ = [_ttp_row(7, "cadence", typ_sort=1, typ_required=True, data_type="integer")]
    merged = merge_parameter_schema_rows([], typ)
    assert len(merged) == 1
    assert merged[0]["key"] == "cadence"
    assert merged[0]["required"] is True


def test_validate_integer_range():
    _validate_single_value("integer", 5, {"min": 1, "max": 10})
    with pytest.raises(ActivitySessionMetricsError) as ei:
        _validate_single_value("integer", 0, {"min": 1})
    assert ei.value.status_code == 400


def test_validate_float_accepts_int():
    _validate_single_value("float", 3, {"min": 0, "max": 10})


def test_validate_boolean_rejects_int():
    with pytest.raises(ActivitySessionMetricsError):
        _validate_single_value("boolean", 1, {})


def test_row_value_tuple_mapping():
    assert _row_value_tuple("integer", 42) == (None, 42, None, None)
    assert _row_value_tuple("float", 1.5) == (1.5, None, None, None)
    assert _row_value_tuple("string", "hi") == (None, None, "hi", None)
    assert _row_value_tuple("boolean", True) == (None, None, None, True)


class _FakeCursor:
    """Sequences fetchone/fetchall for resolve_activity_attribute_schema."""

    def __init__(self, fetchone_chain, fetchall_chain):
        self._fetchone = list(fetchone_chain)
        self._fetchall = list(fetchall_chain)
        self.executes: list[tuple] = []

    def execute(self, sql, params=None):
        self.executes.append((sql, params))

    def fetchone(self):
        return self._fetchone.pop(0)

    def fetchall(self):
        return self._fetchall.pop(0)


def test_resolve_with_explicit_category_no_type():
    cur = _FakeCursor(
        fetchone_chain=[],
        fetchall_chain=[
            [
                _tp_row(1, "rpe", cat_sort=0),
            ],
        ],
    )
    out = resolve_activity_attribute_schema(cur, "cardio", None)
    assert len(out) == 1
    assert out[0]["key"] == "rpe"
    assert len(cur.executes) == 1


def test_resolve_loads_category_from_training_type_id():
    cur = _FakeCursor(
        fetchone_chain=[{"category": "strength"}],
        fetchall_chain=[
            [_tp_row(1, "rpe", cat_sort=0)],
            [],
        ],
    )
    out = resolve_activity_attribute_schema(cur, None, 42)
    assert len(out) == 1
    assert cur.executes[0][1] == (42,)


@patch("data_layer.activity_session_metrics.resolve_activity_attribute_schema", return_value=[])
def test_enrich_sessions_batch(mock_resolve):
    aid = str(uuid.uuid4())
    bid = str(uuid.uuid4())

    class _Cur:
        def __init__(self):
            self.params = None
            self._fetch_n = 0

        def execute(self, sql, params=None):
            self.sql = sql
            self.params = params

        def fetchall(self):
            self._fetch_n += 1
            if self._fetch_n == 1:
                return [
                    {
                        "id": uuid.UUID(aid),
                        "training_category": None,
                        "training_type_id": None,
                    },
                    {
                        "id": uuid.UUID(bid),
                        "training_category": None,
                        "training_type_id": None,
                    },
                ]
            return [
                {
                    "activity_log_id": uuid.UUID(aid),
                    "training_parameter_id": 3,
                    "key": "rpe",
                    "data_type": "integer",
                    "unit": None,
                    "value_num": None,
                    "value_int": 7,
                    "value_text": None,
                    "value_bool": None,
                },
            ]

    sessions = [{"id": aid}, {"id": bid}]
    enrich_sessions_with_metrics(_Cur(), sessions)
    assert sessions[0]["session_metrics"][0]["value"] == 7
    assert sessions[0]["session_metrics"][0]["key"] == "rpe"
    assert sessions[1]["session_metrics"] == []


def test_merge_column_backed_prefers_column_over_stale_eav():
    schema = [
        {
            "training_parameter_id": 1,
            "key": "hr_avg",
            "data_type": "float",
            "unit": "bpm",
            "validation_rules": {},
            "source_field": "hr_avg",
        }
    ]
    eav = [
        {
            "training_parameter_id": 1,
            "key": "hr_avg",
            "data_type": "float",
            "unit": "bpm",
            "value": 99.0,
        }
    ]
    out = merge_column_backed_and_eav_metrics({"hr_avg": 140.0}, schema, eav)
    assert len(out) == 1
    assert out[0]["value"] == 140.0


def test_merge_falls_back_to_eav_when_column_empty():
    schema = [
        {
            "training_parameter_id": 1,
            "key": "hr_avg",
            "data_type": "float",
            "unit": "bpm",
            "validation_rules": {},
            "source_field": "hr_avg",
        }
    ]
    eav = [
        {
            "training_parameter_id": 1,
            "key": "hr_avg",
            "data_type": "float",
            "unit": "bpm",
            "value": 99.0,
        }
    ]
    out = merge_column_backed_and_eav_metrics({"hr_avg": None}, schema, eav)
    assert len(out) == 1
    assert out[0]["value"] == 99.0


def test_merge_keeps_eav_only_keys():
    schema = []
    eav = [
        {
            "training_parameter_id": 2,
            "key": "custom_param",
            "data_type": "string",
            "unit": None,
            "value": "x",
        }
    ]
    out = merge_column_backed_and_eav_metrics({}, schema, eav)
    assert len(out) == 1
    assert out[0]["key"] == "custom_param"


def test_merge_eav_primary_falls_back_to_legacy_hr_min_column():
    """Kanon: min_hr ohne source_field / ohne EAV — Lesefallback Spalte hr_min."""
    schema = [
        {
            "training_parameter_id": 9,
            "key": "min_hr",
            "data_type": "integer",
            "unit": "bpm",
            "validation_rules": {},
            "source_field": None,
        }
    ]
    out = merge_column_backed_and_eav_metrics({"hr_min": 88}, schema, [])
    assert len(out) == 1
    assert out[0]["key"] == "min_hr"
    assert out[0]["value"] == 88