import pandas as pd
from app.services.results_mapping import map_oeb_results_to_contents

def _request():
    return {
        "model_name": "BIOT-frozen",
        "organization": "braindecode",
        "model_url": "https://huggingface.co/braindecode/biot",
        "paper_url": "",
        "submitted_time": "2026-06-15T12:00:00Z",
        "benchmark_kwargs": {"hub_repo": "braindecode/biot", "model_cls": "braindecode.models.BIOT"},
    }

def _df():
    # two datasets x one strategy x two seeds, all completed
    return pd.DataFrame([
        {"backbone": "BIOT", "dataset": "braindecode/bcic2a", "finetuning": "frozen",
         "head": "linear_head", "seed": 0, "status": "completed",
         "test_balanced_accuracy": 0.40, "trainable_params": 3_190_000},
        {"backbone": "BIOT", "dataset": "braindecode/bcic2a", "finetuning": "frozen",
         "head": "linear_head", "seed": 1, "status": "completed",
         "test_balanced_accuracy": 0.60, "trainable_params": 3_190_000},
        {"backbone": "BIOT", "dataset": "braindecode/physionet", "finetuning": "frozen",
         "head": "linear_head", "seed": 0, "status": "completed",
         "test_balanced_accuracy": 0.50, "trainable_params": 3_190_000},
    ])

def test_one_row_per_strategy():
    rows = map_oeb_results_to_contents(_df(), _request())
    assert len(rows) == 1
    row = rows[0]
    assert row["fullname"] == "BIOT-frozen"
    assert row["adapter"] == "probe"  # oeb "frozen" linear probing -> arena "probe"
    assert row["Architecture"] == "BIOT"
    # mean over seeds: bcic2a = (0.40+0.60)/2 = 0.50 (fraction)
    assert abs(row["bcic2a_accuracy"] - 0.50) < 1e-9
    assert abs(row["physionet_accuracy"] - 0.50) < 1e-9
    # Average over available datasets * 100 = 50.0
    assert abs(row["Average ⬆️"] - 50.0) < 1e-9
    assert abs(row["#Params (M)"] - 3.19) < 1e-6
    assert row["Available on the hub"] is True

def test_failed_rows_ignored():
    df = _df()
    df.loc[len(df)] = {"backbone": "BIOT", "dataset": "braindecode/tuab", "finetuning": "frozen",
                       "head": "linear_head", "seed": 0, "status": "failed",
                       "exception": "boom", "test_balanced_accuracy": None, "trainable_params": None}
    rows = map_oeb_results_to_contents(df, _request())
    assert rows[0].get("tuab_accuracy", 0) == 0  # failed dataset not scored

def test_multiple_strategies_multiple_rows():
    df = _df()
    extra = df.copy(); extra["finetuning"] = "lora"
    rows = map_oeb_results_to_contents(pd.concat([df, extra], ignore_index=True), _request())
    assert {r["adapter"] for r in rows} == {"probe", "lora"}  # frozen aliased to probe; lora unchanged

def test_all_failed_run_has_no_metric_column():
    # When every experiment fails, oeb's DataFrame has NO test_balanced_accuracy
    # column at all. Mapping must return [] (not KeyError), so the worker reports
    # the clean "no completed results" failure. (Regression: live worker test.)
    df = pd.DataFrame([
        {"backbone": "BIOT", "dataset": "braindecode/seed_v", "finetuning": "frozen",
         "head": "linear_head", "seed": 0, "status": "failed", "exception": "could not load weights"},
    ])
    assert map_oeb_results_to_contents(df, _request()) == []

def test_metric_column_all_nan():
    df = _df()
    df["test_balanced_accuracy"] = None
    assert map_oeb_results_to_contents(df, _request()) == []