from __future__ import annotations

from tools.graphrag.evaluation.graphrag_evaluator import (
    aggregate_rows,
    collect_actual_relationships,
    evaluate_result,
    normalize_text,
    property_present,
)


def test_normalize_text() -> None:
    assert normalize_text(
        "Dárius Lindvai"
    ) == "darius lindvai"


def test_relationships_can_be_derived_from_fact() -> None:
    result = {
        "paths": [],
        "facts": [
            {
                "fact_type": (
                    "person_keyword"
                ),
                "person": (
                    "Jakub Maruniak"
                ),
                "keyword": "NLP",
            }
        ],
    }

    relationships = (
        collect_actual_relationships(
            result
        )
    )

    assert relationships == {
        "HAS_DOCUMENT",
        "HAS_KEYWORD",
    }


def test_thesis_properties_from_fact() -> None:
    result = {
        "resolved_entities": [],
        "paths": [],
        "facts": [
            {
                "fact_type": (
                    "person_thesis"
                ),
                "person": (
                    "Dárius Lindvai"
                ),
                "title": (
                    "Obnovenie interpunkcie"
                ),
                "year": 2021,
                "thesis_type": (
                    "diploma_thesis"
                ),
            }
        ],
    }

    assert property_present(
        "Thesis.title",
        result,
    )

    assert property_present(
        "Thesis.year",
        result,
    )

    assert property_present(
        "Thesis.year|Work.year",
        result,
    )


def test_strict_thesis_result() -> None:
    question = {
        "id": "g-test",
        "split": "dev",
        "category": (
            "person_title_year"
        ),
        "difficulty": "medium",
        "question": (
            "Aký názov a rok práce?"
        ),
        "expected_answer_contains": [
            "Dárius Lindvai",
            (
                "Obnovenie interpunkcie "
                "pomocou hlbokých neurónových sietí"
            ),
            "2021",
        ],
        "expected_documents": [
            (
                "pages/students/2016/"
                "darius_lindvai/README.md"
            )
        ],
        "expected_source_urls": [
            (
                "https://zp.kemt.fei.tuke.sk/"
                "students/2016/darius_lindvai"
            )
        ],
        "graph": {
            "schema_version": 2,
            "expected_entities": [
                {
                    "type": (
                        "thesis_title"
                    ),
                    "value": (
                        "Obnovenie interpunkcie "
                        "pomocou hlbokých neurónových sietí"
                    ),
                },
                {
                    "type": "year",
                    "value": "2021",
                },
            ],
            "expected_relations": [
                "AUTHORED",
            ],
            "expected_properties": [
                "Thesis.title",
                "Thesis.year",
            ],
        },
    }

    result = {
        "strategies": [
            "entity_resolution",
            "thesis_constraints",
        ],
        "resolved_entities": [],
        "paths": [],
        "facts": [
            {
                "fact_type": (
                    "person_thesis"
                ),
                "person": (
                    "Dárius Lindvai"
                ),
                "title": (
                    "Obnovenie interpunkcie "
                    "pomocou hlbokých neurónových sietí"
                ),
                "year": 2021,
                "thesis_type": (
                    "diploma_thesis"
                ),
                "document_path": (
                    "pages/students/2016/"
                    "darius_lindvai/README.md"
                ),
                "source_url": (
                    "https://zp.kemt.fei.tuke.sk/"
                    "students/2016/darius_lindvai"
                ),
            }
        ],
        "documents": [
            {
                "document_path": (
                    "pages/students/2016/"
                    "darius_lindvai/README.md"
                ),
                "source_url": (
                    "https://zp.kemt.fei.tuke.sk/"
                    "students/2016/darius_lindvai"
                ),
            }
        ],
    }

    row = evaluate_result(
        question,
        result,
        latency_seconds=0.01,
    )

    assert row[
        "strict"
    ] is True

    assert row[
        "value_score"
    ] == 1.0

    assert row[
        "document_recall"
    ] == 1.0


def test_failed_result_is_not_strict() -> None:
    question = {
        "id": "g-test",
        "split": "dev",
        "category": (
            "shared_keyword_pair"
        ),
        "difficulty": "hard",
        "question": "Otázka",
        "expected_answer_contains": [
            "NLP",
        ],
        "expected_documents": [
            "a.md",
        ],
        "expected_source_urls": [],
        "graph": {
            "schema_version": 2,
            "expected_entities": [
                {
                    "type": "keyword",
                    "value": "NLP",
                }
            ],
            "expected_relations": [
                "HAS_KEYWORD",
            ],
            "expected_properties": [],
        },
    }

    result = {
        "strategies": [],
        "resolved_entities": [],
        "paths": [],
        "facts": [],
        "documents": [],
    }

    row = evaluate_result(
        question,
        result,
        latency_seconds=0.01,
    )

    assert row[
        "strict"
    ] is False


def test_aggregate_rows() -> None:
    rows = [
        {
            "strict": True,
            "error": None,
            "value_score": 1.0,
            "entity_score": 1.0,
            "relation_score": 1.0,
            "property_score": 1.0,
            "relation_choice_score": 1.0,
            "document_recall": 1.0,
            "source_recall": 1.0,
            "latency_seconds": 0.1,
        },
        {
            "strict": False,
            "error": None,
            "value_score": 0.5,
            "entity_score": 0.5,
            "relation_score": 0.5,
            "property_score": 0.5,
            "relation_choice_score": 0.5,
            "document_recall": 0.5,
            "source_recall": 0.5,
            "latency_seconds": 0.2,
        },
    ]

    summary = aggregate_rows(
        rows
    )

    assert summary[
        "questions"
    ] == 2

    assert summary[
        "strict_count"
    ] == 1

    assert summary[
        "strict_accuracy"
    ] == 0.5

    assert summary[
        "latency_mean_seconds"
    ] == 0.15