from __future__ import annotations

from datetime import datetime, timezone
from uuid import uuid4

from fastapi.testclient import TestClient

from app.main import app
from app.models import Metric, QualityCheck
from app.schemas.qa import QualityCheckRead
from app.services.quality_check_service import QualityCheckService


class FakeQuery:
    def __init__(self, rows):
        self.rows = rows

    def filter(self, *_args):
        return self

    def order_by(self, *_args):
        return self

    def count(self):
        return len(self.rows)

    def offset(self, _offset):
        return self

    def limit(self, _limit):
        return self

    def all(self):
        return self.rows


class FakeSession:
    def __init__(self, quality_checks, metrics):
        self.quality_checks = quality_checks
        self.metrics = metrics

    def query(self, model):
        if model is QualityCheck:
            return FakeQuery(self.quality_checks)
        if model is Metric:
            return FakeQuery(self.metrics)
        return FakeQuery([])


def test_quality_check_service_lists_checks_with_metrics() -> None:
    project_id = uuid4()
    quality_check_id = uuid4()
    reference_dataset_id = uuid4()
    candidate_dataset_id = uuid4()
    created_at = datetime.now(timezone.utc)
    quality_check = QualityCheck(
        id=quality_check_id,
        project_id=project_id,
        candidate_dataset_id=candidate_dataset_id,
        reference_dataset_id=reference_dataset_id,
        check_type="demo_candidate_vs_reference",
        status="ok",
        score=0.5,
        parameters_json={"iou_threshold": 0.5},
        findings_json={"matches": 1},
        created_at=created_at,
        completed_at=created_at,
    )
    metric = Metric(
        id=uuid4(),
        quality_check_id=quality_check_id,
        metric_key="precision",
        metric_value=0.5,
        metadata_json={},
        created_at=created_at,
    )

    items, total = QualityCheckService.list_quality_checks(
        FakeSession([quality_check], [metric]),
        project_id=project_id,
    )

    assert total == 1
    assert len(items) == 1
    assert items[0].id == quality_check_id
    assert items[0].metrics[0].metric_key == "precision"
    assert items[0].metrics[0].metric_value == 0.5


def test_quality_checks_endpoint_returns_canonical_envelope(monkeypatch) -> None:
    project_id = uuid4()
    quality_check_id = uuid4()
    reference_dataset_id = uuid4()

    monkeypatch.setattr(
        QualityCheckService,
        "list_quality_checks",
        lambda *_args, **_kwargs: (
            [
                QualityCheckRead(
                    id=quality_check_id,
                    project_id=project_id,
                    reference_dataset_id=reference_dataset_id,
                    check_type="demo_candidate_vs_reference",
                    status="ok",
                    score=0.5,
                    metrics=[],
                )
            ],
            1,
        ),
    )

    response = TestClient(app).get(f"/api/v1/projects/{project_id}/quality-checks")

    assert response.status_code == 200
    payload = response.json()
    assert set(payload) == {"data"}
    assert payload["data"]["total"] == 1
    assert payload["data"]["items"][0]["id"] == str(quality_check_id)
    assert payload["data"]["items"][0]["check_type"] == "demo_candidate_vs_reference"