"""Unit + integration tests for the LLM-fallback CSV parser."""
from __future__ import annotations

import pytest


def test_csv_format_template_model_columns():
    """Model exposes every column the spec requires, with correct types."""
    from sqlalchemy import inspect

    from app.models import CsvFormatTemplate

    cols = {c.name: c for c in inspect(CsvFormatTemplate).columns}
    assert "fingerprint" in cols
    assert "headers" in cols
    assert "sample_row" in cols
    assert "mapping" in cols
    assert "preamble_rows" in cols
    assert "delimiter" in cols
    assert "broker_label" in cols
    assert "first_seen_at" in cols
    assert "use_count" in cols
    assert "last_used_at" in cols
    assert "llm_model" in cols
    assert "llm_cost_usd" in cols
    # Crucially, no user attribution.
    assert "user_id" not in cols
    assert "first_seen_user_id" not in cols
    # Fingerprint is the cache key.
    assert cols["fingerprint"].unique is True
    assert cols["fingerprint"].nullable is False


def test_fingerprint_stable_across_case_and_whitespace():
    from app.services.llm_csv_parser import _fingerprint

    a = _fingerprint(["Symbol", "Quantity", "Avg Price"])
    b = _fingerprint(["symbol", "quantity", "avg price"])
    c = _fingerprint(["  SYMBOL ", "Quantity", " AVG PRICE"])
    assert a == b == c


def test_fingerprint_differs_for_different_columns():
    from app.services.llm_csv_parser import _fingerprint

    a = _fingerprint(["Symbol", "Quantity"])
    b = _fingerprint(["Symbol", "Quantity", "Avg Price"])
    assert a != b


def test_fingerprint_is_sha256_hex_64_chars():
    from app.services.llm_csv_parser import _fingerprint

    f = _fingerprint(["Symbol", "Quantity"])
    assert len(f) == 64
    assert all(c in "0123456789abcdef" for c in f)


def test_detect_dialect_no_preamble_comma():
    from app.services.llm_csv_parser import _detect_dialect

    raw = b"Symbol,Quantity,Avg Price\nAAPL,100,150.25\nMSFT,50,310.00\n"
    delimiter, preamble = _detect_dialect(raw)
    assert delimiter == ","
    assert preamble == 0


def test_detect_dialect_with_preamble():
    from app.services.llm_csv_parser import _detect_dialect

    raw = (
        b"Statement,Header,Field Name,Field Value\n"
        b"Statement,Data,BrokerName,Interactive Brokers LLC\n"
        b"Statement,Data,Title,Activity Statement\n"
        b"Statement,Data,Period,\"January 1, 2026 - January 31, 2026\"\n"
        b"Symbol,Quantity,Avg Price,Currency,Description\n"
        b"AAPL,100,150.25,USD,Apple Inc\n"
    )
    delimiter, preamble = _detect_dialect(raw)
    assert delimiter == ","
    # The data-row header line is the FIFTH line (index 4); preamble = 4.
    assert preamble == 4


def test_detect_dialect_tab_delimited():
    from app.services.llm_csv_parser import _detect_dialect

    raw = b"Symbol\tQuantity\tAvg Price\nAAPL\t100\t150.25\n"
    delimiter, preamble = _detect_dialect(raw)
    assert delimiter == "\t"
    assert preamble == 0


def test_detect_dialect_empty_raises():
    from app.services.llm_csv_parser import LLMParseError, _detect_dialect

    with pytest.raises(LLMParseError):
        _detect_dialect(b"")


def test_validate_mapping_accepts_well_formed():
    from app.services.llm_csv_parser import _validate_mapping

    headers = ["Symbol", "Quantity", "Avg Price", "Currency"]
    first_row = ["AAPL", "100", "150.25", "USD"]
    mapping = {
        "ticker_col": "Symbol",
        "qty_col": "Quantity",
        "cost_col": "Avg Price",
        "currency_col": "Currency",
        "name_col": None,
    }
    _validate_mapping(mapping, headers, first_row)  # no raise


def test_validate_mapping_missing_ticker_raises():
    from app.services.llm_csv_parser import LLMParseError, _validate_mapping

    headers = ["Symbol", "Quantity"]
    first_row = ["AAPL", "100"]
    mapping = {"ticker_col": None, "qty_col": "Quantity"}
    with pytest.raises(LLMParseError, match="ticker"):
        _validate_mapping(mapping, headers, first_row)


def test_validate_mapping_missing_qty_raises():
    from app.services.llm_csv_parser import LLMParseError, _validate_mapping

    headers = ["Symbol", "Quantity"]
    first_row = ["AAPL", "100"]
    mapping = {"ticker_col": "Symbol", "qty_col": None}
    with pytest.raises(LLMParseError, match="qty"):
        _validate_mapping(mapping, headers, first_row)


def test_validate_mapping_unknown_column_raises():
    from app.services.llm_csv_parser import LLMParseError, _validate_mapping

    headers = ["Symbol", "Quantity"]
    first_row = ["AAPL", "100"]
    mapping = {"ticker_col": "Symbol", "qty_col": "NotARealColumn"}
    with pytest.raises(LLMParseError, match="NotARealColumn"):
        _validate_mapping(mapping, headers, first_row)


def test_validate_mapping_non_numeric_qty_raises():
    from app.services.llm_csv_parser import LLMParseError, _validate_mapping

    headers = ["Symbol", "Description"]
    first_row = ["AAPL", "Apple Inc"]
    # Mapping says qty is "Description", but "Apple Inc" can't parse as a number.
    mapping = {"ticker_col": "Symbol", "qty_col": "Description"}
    with pytest.raises(LLMParseError, match="numeric"):
        _validate_mapping(mapping, headers, first_row)


def test_apply_mapping_builds_parsed_pie():
    from app.services.csv_import import ParsedPie, ParsedPosition
    from app.services.llm_csv_parser import _apply_mapping

    headers = ["Symbol", "Quantity", "Avg Price", "Currency", "Description"]
    data_rows = [
        ["AAPL", "100", "150.25", "USD", "Apple Inc"],
        ["MSFT", "50", "310.00", "USD", "Microsoft Corp"],
    ]
    mapping = {
        "ticker_col": "Symbol",
        "qty_col": "Quantity",
        "cost_col": "Avg Price",
        "currency_col": "Currency",
        "name_col": "Description",
    }

    pie = _apply_mapping(headers, data_rows, mapping)

    assert isinstance(pie, ParsedPie)
    assert len(pie.positions) == 2
    p0 = pie.positions[0]
    assert isinstance(p0, ParsedPosition)
    assert p0.slice == "AAPL"
    assert p0.name == "Apple Inc"
    assert p0.quantity == 100.0
    assert p0.invested_value == pytest.approx(15025.0)
    # invested = qty * avg_cost = 100 * 150.25 = 15025
    assert pie.invested == pytest.approx(15025.0 + 50 * 310.00)


def test_apply_mapping_handles_missing_optional_columns():
    from app.services.llm_csv_parser import _apply_mapping

    headers = ["Symbol", "Quantity"]
    data_rows = [["AAPL", "100"]]
    mapping = {
        "ticker_col": "Symbol",
        "qty_col": "Quantity",
        "cost_col": None,
        "currency_col": None,
        "name_col": None,
    }

    pie = _apply_mapping(headers, data_rows, mapping)
    p = pie.positions[0]
    assert p.slice == "AAPL"
    assert p.quantity == 100.0
    assert p.invested_value is None
    assert p.name == "AAPL"  # falls back to ticker when name_col absent


def test_apply_mapping_skips_blank_and_unparseable_rows():
    from app.services.llm_csv_parser import _apply_mapping

    headers = ["Symbol", "Quantity"]
    data_rows = [
        ["AAPL", "100"],
        ["", ""],                   # blank
        ["MSFT", "not-a-number"],   # bad qty
        ["NVDA", "40"],
    ]
    mapping = {"ticker_col": "Symbol", "qty_col": "Quantity"}

    pie = _apply_mapping(headers, data_rows, mapping)
    assert [p.slice for p in pie.positions] == ["AAPL", "NVDA"]


@pytest.mark.asyncio
async def test_extract_mapping_via_llm_parses_valid_json():
    from unittest.mock import AsyncMock, MagicMock
    from app.services.llm_csv_parser import _extract_mapping_via_llm
    from app.services.openrouter import LogResult

    fake_result = LogResult(
        content='{"ticker_col": "Symbol", "qty_col": "Quantity", '
                '"cost_col": "Avg Price", "currency_col": "Currency", '
                '"name_col": null, "broker_label": "IBKR Activity Statement"}',
        model="deepseek/deepseek-v4-flash",
        prompt_tokens=100,
        completion_tokens=50,
        cost_usd=0.0001,
    )
    fake_client = MagicMock()
    fake_call_llm = AsyncMock(return_value=fake_result)

    import app.services.llm_csv_parser as mod
    mod.call_llm = fake_call_llm  # monkeypatch

    headers = ["Symbol", "Quantity", "Avg Price", "Currency"]
    samples = [["AAPL", "100", "150.25", "USD"]]
    mapping, log = await _extract_mapping_via_llm(fake_client, headers, samples)

    assert mapping["ticker_col"] == "Symbol"
    assert mapping["qty_col"] == "Quantity"
    assert mapping["broker_label"] == "IBKR Activity Statement"
    assert log.model == "deepseek/deepseek-v4-flash"
    fake_call_llm.assert_awaited_once()


@pytest.mark.asyncio
async def test_extract_mapping_via_llm_malformed_json_raises():
    from unittest.mock import AsyncMock, MagicMock
    from app.services.llm_csv_parser import LLMParseError, _extract_mapping_via_llm
    from app.services.openrouter import LogResult

    fake_result = LogResult(
        content="Sure thing — here is the mapping!  ticker=Symbol",
        model="deepseek/deepseek-v4-flash",
        prompt_tokens=10,
        completion_tokens=20,
        cost_usd=0.00005,
    )
    fake_client = MagicMock()
    fake_call_llm = AsyncMock(return_value=fake_result)

    import app.services.llm_csv_parser as mod
    mod.call_llm = fake_call_llm

    with pytest.raises(LLMParseError, match="JSON"):
        await _extract_mapping_via_llm(fake_client, ["Symbol"], [["AAPL"]])


@pytest.mark.asyncio
async def test_extract_mapping_via_llm_provider_failure_wraps():
    from unittest.mock import AsyncMock, MagicMock
    from app.services.llm_csv_parser import LLMParseError, _extract_mapping_via_llm

    fake_client = MagicMock()
    fake_call_llm = AsyncMock(side_effect=RuntimeError("provider down"))

    import app.services.llm_csv_parser as mod
    mod.call_llm = fake_call_llm

    with pytest.raises(LLMParseError, match="provider"):
        await _extract_mapping_via_llm(fake_client, ["Symbol"], [["AAPL"]])