import csv
import json
from pathlib import Path

import pytest

from ingestion.embed.benchmark_local import (
    BenchmarkCase,
    candidate_chunks,
    load_cases,
    run_benchmark,
)
from ingestion.embed.local_bge_m3 import BgeM3Local


def _write_csv(path: Path, rows: list[dict]) -> None:
    with path.open("w", encoding="utf-8", newline="") as handle:
        writer = csv.DictWriter(handle, fieldnames=list(rows[0]))
        writer.writeheader()
        writer.writerows(rows)


def test_load_cases_uses_verified_aliases_and_skips_policy_and_multidrug(tmp_path):
    entities = tmp_path / "entities.json"
    entities.write_text(
        json.dumps(
            {
                "entities": [
                    {"drug_id": "paracetamol", "aliases": ["Paracetamol"]}
                ]
            }
        ),
        encoding="utf-8",
    )
    golden = tmp_path / "golden.csv"
    _write_csv(
        golden,
        [
            {
                "id": "1",
                "cau_hoi": "Liều Paracetamol?",
                "thuoc_ky_vong": "Paracetamol",
                "thuoc_tinh_ky_vong": "lieu_dung",
            },
            {
                "id": "2",
                "cau_hoi": "Uống gì?",
                "thuoc_ky_vong": "",
                "thuoc_tinh_ky_vong": "",
            },
            {
                "id": "3",
                "cau_hoi": "So sánh",
                "thuoc_ky_vong": "Paracetamol; Ibuprofen",
                "thuoc_tinh_ky_vong": "lieu_dung",
            },
        ],
    )

    assert load_cases(golden, entities) == [
        BenchmarkCase(
            case_id="1",
            query="Liều Paracetamol?",
            drug_id="paracetamol",
            expected_section="lieu_luong_va_cach_dung",
        )
    ]


def test_candidate_chunks_fails_when_ground_truth_is_missing(tmp_path):
    path = tmp_path / "chunks.jsonl"
    path.write_text(
        json.dumps(
            {
                "drug_id": "drug",
                "section_key": "chi_dinh",
                "chunk_kind": "prose",
                "text": "text",
            }
        )
        + "\n",
        encoding="utf-8",
    )
    cases = [BenchmarkCase("1", "dose", "drug", "lieu_luong_va_cach_dung")]

    with pytest.raises(ValueError, match="no canonical target"):
        candidate_chunks(path, cases)


def test_load_cases_rejects_an_alias_ambiguous_in_the_verified_catalog(tmp_path):
    entities = tmp_path / "entities.json"
    entities.write_text(
        json.dumps(
            {
                "entities": [
                    {"drug_id": "drug_a", "aliases": ["Shared"]},
                    {"drug_id": "drug_b", "aliases": ["Shared"]},
                ]
            }
        ),
        encoding="utf-8",
    )
    golden = tmp_path / "golden.csv"
    _write_csv(
        golden,
        [
            {
                "id": "1",
                "cau_hoi": "Liều Shared?",
                "thuoc_ky_vong": "Shared",
                "thuoc_tinh_ky_vong": "lieu_dung",
            }
        ],
    )

    with pytest.raises(ValueError, match="is ambiguous"):
        load_cases(golden, entities)


def test_benchmark_filters_by_drug_and_reports_first_relevant_rank():
    vectors = {
        "query": [1.0, 0.0],
        "wrong section": [0.9, 0.1],
        "correct section": [0.8, 0.2],
        "other drug": [1.0, 0.0],
    }
    provider = BgeM3Local(encoder=lambda texts: [vectors[text] for text in texts])
    provider._check_dimensions = lambda _values: None
    cases = [BenchmarkCase("1", "query", "drug", "target")]
    chunks = [
        {
            "chunk_id": "wrong",
            "drug_id": "drug",
            "section_key": "other",
            "text": "wrong section",
        },
        {
            "chunk_id": "right",
            "drug_id": "drug",
            "section_key": "target",
            "text": "correct section",
        },
        {
            "chunk_id": "leak",
            "drug_id": "other",
            "section_key": "target",
            "text": "other drug",
        },
    ]

    report = run_benchmark(provider, cases, chunks)

    assert report.case_count == 1
    assert report.cases[0].first_relevant_rank == 2
    assert report.hit_at_1 == 0.0
    assert report.hit_at_3 == 1.0
    assert report.mrr == 0.5