from __future__ import annotations

from dataclasses import dataclass

import pytest


@dataclass(frozen=True)
class Case:
    id: str
    raw: str
    expected_deterministic: str
    needs_llm: bool


# Regression table: 17 real-shape GAIA answer fixtures.
# CI runs assertions against the deterministic layer + gating layer only.
# No network / LLM calls — the LLM path is exercised with a mock further down.
CASES: list[Case] = [
    # ---- Already clean, short — must pass through untouched and skip LLM ----
    Case("bare_number",      "42",                  "42",                  False),
    Case("bare_word",        "Paris",               "Paris",               False),
    Case("year",             "1969",                "1969",                False),
    Case("short_phrase",     "Mount Everest",       "Mount Everest",       False),
    Case("abbrev_period",    "Mr.",                 "Mr.",                 False),
    Case("abbrev_dots",      "U.S.",                "U.S.",                False),
    Case("scene_descriptor", "INT. OFFICE - DAY",   "INT. OFFICE - DAY",   False),
    Case("large_number",     "3000",                "3000",                False),

    # ---- Clean after safe wrapper-strip — must skip LLM ----
    Case("markdown_bold",    "**42**",              "42",                  False),
    Case("double_quoted",    '"Paris"',             "Paris",               False),
    Case("backticked",       "`code`",              "code",                False),
    Case("final_answer",     "Final Answer: 42",    "42",                  False),
    Case("answer_colon",     "Answer: Paris",       "Paris",               False),
    Case("bold_and_prefix",  "**Final Answer: 42**","42",                  False),

    # ---- Verbose — deterministic leaves alone; gate must route to LLM ----
    Case("verbose_filler",   "The answer is 42",
         "The answer is 42", True),
    Case("verbose_approx",
         "Based on my calculations, the answer is approximately 1500",
         "Based on my calculations, the answer is approximately 1500",
         True),
    Case("long_sentence",
         "I found that the total number of papers published was 127 in 2019",
         "I found that the total number of papers published was 127 in 2019",
         True),

    # ---- Final-Answer tail extraction (the real GAIA regression pattern) ----
    # When a model produces a verbose preamble followed by `Final Answer: X`,
    # take X and drop the preamble. This is what the LLM formatter USED to do
    # unreliably — the deterministic layer now handles it safely.
    Case("tail_simple",
         "Some reasoning about it.\n\nFinal Answer: 142",
         "142", False),
    Case("tail_with_markdown",
         "**The object** is *Tritia gibbosula*, dated to 142 thousand years.\n\nFinal Answer: 142",
         "142", False),
    Case("tail_multi_word",
         "Based on the spreadsheet, the oldest title is shown below.\n\nFinal Answer: Time-Parking 2: Parallel Universe",
         "Time-Parking 2: Parallel Universe", False),
    Case("tail_case_insensitive",
         "Long reasoning paragraph.\n\nfinal answer: egalitarian",
         "egalitarian", False),
    Case("tail_last_wins",
         "Preliminary guess — Final Answer: 41.\nOn re-check it's actually 42.\nFinal Answer: 42",
         "42", False),
    Case("tail_bold_wrapper",
         "Reasoning...\n\nFinal Answer: **Paris**",
         "Paris", False),
    Case("tail_trailing_period",
         "Reasoning concluded.\n\nFinal Answer: dot.",
         "dot.", False),
]

_CASE_IDS = [c.id for c in CASES]


@pytest.mark.parametrize("case", CASES, ids=_CASE_IDS)
def test_deterministic_format_table(case: Case):
    from lilith_agent.runner import _deterministic_format

    assert _deterministic_format(case.raw) == case.expected_deterministic


@pytest.mark.parametrize("case", CASES, ids=_CASE_IDS)
def test_gating_table(case: Case):
    from lilith_agent.runner import _needs_llm_formatter

    assert _needs_llm_formatter(case.expected_deterministic) is case.needs_llm


class _RaiseIfCalled:
    def __init__(self):
        self.called = False

    def invoke(self, _messages):  # pragma: no cover - should never run
        self.called = True
        raise AssertionError("LLM formatter was called when it shouldn't have been")


class _FakeModel:
    def __init__(self, response: str):
        self.response = response
        self.called = False

    def invoke(self, _messages):
        self.called = True

        class _Resp:
            pass

        r = _Resp()
        r.content = self.response
        return r


def test_short_clean_answer_bypasses_llm():
    from lilith_agent.runner import _final_formatting_cleanup

    model = _RaiseIfCalled()
    out = _final_formatting_cleanup(model, "What is 6*7?", "42")
    assert out == "42"
    assert model.called is False


def test_verbose_answer_invokes_llm_by_default():
    from lilith_agent.runner import _final_formatting_cleanup

    model = _FakeModel(response="42")
    out = _final_formatting_cleanup(
        model,
        "What is 6*7?",
        "Based on my calculations, the answer is approximately 42",
    )
    assert model.called is True
    assert out == "42"


def test_llm_formatter_expands_word_internal_substring_to_full_token():
    from lilith_agent.runner import _final_formatting_cleanup

    raw = "The answer is foobarbaz."
    model = _FakeModel(response="baz")

    out = _final_formatting_cleanup(model, "Which token?", raw)

    assert model.called is True
    assert out == "foobarbaz"


def test_llm_formatter_accepts_whole_phrase_extraction():
    from lilith_agent.runner import _final_formatting_cleanup

    model = _FakeModel(response="Mount Everest")

    out = _final_formatting_cleanup(
        model,
        "Which mountain?",
        "Based on the evidence, the answer is Mount Everest.",
    )

    assert model.called is True
    assert out == "Mount Everest"


def test_llm_formatter_disabled_returns_deterministic_only():
    from lilith_agent.runner import _final_formatting_cleanup

    model = _RaiseIfCalled()
    out = _final_formatting_cleanup(
        model,
        "What is 6*7?",
        "Based on my calculations, the answer is approximately 42",
        llm_formatter_enabled=False,
    )
    assert model.called is False
    # deterministic is a no-op on verbose text — raw is returned unchanged
    assert out == "Based on my calculations, the answer is approximately 42"


def test_llm_formatter_disabled_still_applies_deterministic_strip():
    from lilith_agent.runner import _final_formatting_cleanup

    model = _RaiseIfCalled()
    out = _final_formatting_cleanup(
        model,
        "Q",
        "**Final Answer: 42**",
        llm_formatter_enabled=False,
    )
    assert model.called is False
    assert out == "42"


@pytest.mark.parametrize(
    ("answer", "expected"),
    [
        ("** 47", "47"),
        ("** 8", "8"),
        ("x = 563.9", "563.9"),
        ("answer = 42", "42"),
        ("result: 85", "85"),
        ("56,000", "56000"),
        ("3.1.3.1;1.11.1.7", "3.1.3.1; 1.11.1.7"),
        ("Final Answer: **47**", "47"),
        ("Right.", "Right"),
        ("Paris!", "Paris"),
        ("Tokyo.", "Tokyo"),
        ("Final Answer: Right.", "Right"),
    ],
)
def test_gaia_submission_normalizer_fixes_safe_formatting_artifacts(answer: str, expected: str):
    from lilith_agent.runner import _normalize_gaia_submission

    assert _normalize_gaia_submission("Q", answer) == expected


@pytest.mark.parametrize(
    "answer",
    [
        "U.S.",
        "Mr.",
        "Mrs.",
        "Dr.",
        "St.",
        "Inc.",
        "Ltd.",
        "Etc.",
        "Paris, France",
        "1,234.56",
        "C**H",
        "number of citations",
    ],
)
def test_gaia_submission_normalizer_leaves_risky_answers_unchanged(answer: str):
    from lilith_agent.runner import _normalize_gaia_submission

    assert _normalize_gaia_submission("Q", answer) == answer


@pytest.mark.parametrize(
    ("question", "answer", "expected"),
    [
        # rounds excess decimal places down
        ("Give answer to 3 decimal places.", "1.4560", "1.456"),
        # pads too-short answer to required precision
        ("Round to 3 decimal places.", "17.06", "17.060"),
        # nearest-tenth keyword
        ("Express to the nearest tenth.", "1.46", "1.5"),
        # nearest-hundredth keyword
        ("Round to the nearest hundredth.", "0.2690", "0.27"),
        # nearest-thousandth keyword
        ("Round to the nearest thousandth.", "0.2690", "0.269"),
        # no precision requirement → untouched
        ("What is the answer?", "1.456", "1.456"),
    ],
)
def test_decimal_precision_normalization(question: str, answer: str, expected: str):
    from lilith_agent.runner import _normalize_gaia_submission

    assert _normalize_gaia_submission(question, answer) == expected


@pytest.mark.parametrize(
    ("question", "answer"),
    [
        # non-numeric answer — precision check skipped
        ("Give answer to 3 decimal places.", "Paris"),
        # dotted identifier — not a bare scalar, precision check skipped
        ("Round to 3 decimal places.", "3.1.3.1"),
    ],
)
def test_decimal_precision_skipped_for_non_numeric(question: str, answer: str):
    from lilith_agent.runner import _normalize_gaia_submission

    assert _normalize_gaia_submission(question, answer) == answer


def test_config_llm_formatter_enabled_defaults_on_and_is_env_overridable(monkeypatch):
    from lilith_agent.config import Config

    monkeypatch.delenv("GAIA_LLM_FORMATTER_ENABLED", raising=False)
    assert Config.from_env().llm_formatter_enabled is True

    monkeypatch.setenv("GAIA_LLM_FORMATTER_ENABLED", "false")
    assert Config.from_env().llm_formatter_enabled is False