from __future__ import annotations

import argparse
import pickle
from pathlib import Path
import sys

import matplotlib.pyplot as plt
import pandas as pd

PROJECT_ROOT = Path(__file__).resolve().parents[1]
if str(PROJECT_ROOT) not in sys.path:
    sys.path.insert(0, str(PROJECT_ROOT))

from scripts.dataset import build_datasets, report_to_text

try:
    from .config import FEATURE_NAMES, evaluation_dir, make_experiment_config
except ImportError:
    from config import FEATURE_NAMES, evaluation_dir, make_experiment_config


def parse_args() -> argparse.Namespace:
    parser = argparse.ArgumentParser(description="Evaluate tree model on train/val/test splits.")
    parser.add_argument("--split", choices=("train", "val", "test"), default="val")
    parser.add_argument("--sample-index", type=int, default=0)
    parser.add_argument("--all-samples", action="store_true")
    parser.add_argument("--checkpoint", type=str, default=None)
    return parser.parse_args()


def resolve_checkpoint_path(project_root: Path, checkpoint_arg: str | None) -> Path:
    if checkpoint_arg:
        return Path(checkpoint_arg).resolve()
    return project_root / "checkpoints_tree" / "task1_tr_tree" / "best_tr_tree.pkl"


def relative_percent_error(true_value: float, pred_value: float) -> float:
    return abs(pred_value - true_value) / max(abs(true_value), 1e-12) * 100.0


def evaluate_record(model, record) -> dict[str, float | str]:
    feature_matrix = [record.features.tolist()]
    pred_tr = max(float(model.predict(feature_matrix)[0]), 1e-6)
    x_rms = float(record.x_rms.item())
    y_rms = float(record.y_rms.item())
    pred_rms = pred_tr * x_rms
    row = {
        "file_name": record.file_path.name,
        "frequency_hz": float(record.frequency_hz),
        "true_rms": y_rms,
        "pred_rms": pred_rms,
        "relative_error_percent": relative_percent_error(y_rms, pred_rms),
        "x_rms": x_rms,
        "pred_tr": pred_tr,
        "true_tr": float(record.target_y_rms.item()),
    }
    for name, value in zip(FEATURE_NAMES, record.features.tolist()):
        row[name] = float(value)
    row["sampling_rate"] = float(record.sampling_rate)
    return row


def save_all_samples_plot(result_df: pd.DataFrame, split: str, save_dir: Path) -> Path | None:
    if result_df["frequency_hz"].isna().any():
        return None
    plot_df = result_df.sort_values("frequency_hz").reset_index(drop=True)
    fig, axes = plt.subplots(2, 1, figsize=(12, 8))
    fig.suptitle(f"Task1 TR Tree Evaluation | {split}")

    axes[0].plot(plot_df["frequency_hz"], plot_df["true_rms"], marker="o", label="True RMS")
    axes[0].plot(plot_df["frequency_hz"], plot_df["pred_rms"], marker="o", label="Pred RMS")
    axes[0].set_xlabel("Frequency (Hz)")
    axes[0].set_ylabel("RMS")
    axes[0].grid(True, alpha=0.3)
    axes[0].legend()

    axes[1].bar(plot_df["frequency_hz"].astype(str), plot_df["relative_error_percent"], color="tab:orange")
    axes[1].set_xlabel("Frequency (Hz)")
    axes[1].set_ylabel("Relative Error (%)")
    axes[1].grid(True, axis="y", alpha=0.3)
    axes[1].tick_params(axis="x", labelrotation=45)

    plt.tight_layout()
    figure_path = save_dir / f"evaluation_{split}_curve.png"
    plt.savefig(figure_path, dpi=180, bbox_inches="tight")
    plt.close(fig)
    return figure_path


def save_single_sample_plot(record, result: dict[str, float | str], split: str, save_dir: Path, sample_index: int) -> Path:
    time_middle = record.time_middle.detach().cpu().numpy().reshape(-1)
    x_middle = record.x_middle.detach().cpu().numpy().reshape(-1)
    y_middle = record.y_middle.detach().cpu().numpy().reshape(-1)
    frequency_hz = float(result["frequency_hz"])
    pred_rms = float(result["pred_rms"])
    true_rms = float(result["true_rms"])

    fig, axes = plt.subplots(3, 1, figsize=(12, 10))
    fig.suptitle(f"Task1 TR Tree | {split} | {result['file_name']}")

    axes[0].plot(time_middle, x_middle, color="tab:blue")
    axes[0].set_title("Input Base Excitation (Middle Segment)")
    axes[0].set_xlabel("Time")
    axes[0].set_ylabel("Acceleration")
    axes[0].grid(True, alpha=0.3)

    axes[1].plot(time_middle, y_middle, color="tab:green")
    axes[1].set_title("True Top Response (Middle Segment)")
    axes[1].set_xlabel("Time")
    axes[1].set_ylabel("Acceleration")
    axes[1].grid(True, alpha=0.3)

    axes[2].bar(["True RMS", "Pred RMS"], [true_rms, pred_rms], color=["tab:green", "tab:orange"])
    axes[2].set_title(
        f"Freq: {frequency_hz:.4f} Hz | True RMS: {true_rms:.6f} | Pred RMS: {pred_rms:.6f} | "
        f"Error: {float(result['relative_error_percent']):.2f}%"
    )
    axes[2].set_ylabel("RMS")
    axes[2].grid(True, axis="y", alpha=0.3)

    plt.tight_layout()
    figure_path = save_dir / f"evaluation_{split}_s{sample_index}.png"
    plt.savefig(figure_path, dpi=180, bbox_inches="tight")
    plt.close(fig)
    return figure_path


def main() -> None:
    args = parse_args()
    config = make_experiment_config()
    ckpt_path = resolve_checkpoint_path(config.data.project_root, args.checkpoint)
    with ckpt_path.open("rb") as handle:
        bundle = pickle.load(handle)
    model = bundle["model"]

    _, raw_records, reports = build_datasets(config.data)
    print(report_to_text(reports))
    records = raw_records[args.split]
    save_dir = evaluation_dir(config)

    if args.all_samples:
        rows = [evaluate_record(model, record) for record in records]
        result_df = pd.DataFrame(rows).sort_values(["relative_error_percent", "file_name"]).reset_index(drop=True)
        csv_path = save_dir / f"evaluation_{args.split}_all_samples.csv"
        result_df.to_csv(csv_path, index=False)
        fig_path = save_all_samples_plot(result_df, args.split, save_dir)
        summary = {
            "count": len(result_df),
            "mean_error_percent": float(result_df["relative_error_percent"].mean()),
            "median_error_percent": float(result_df["relative_error_percent"].median()),
            "max_error_percent": float(result_df["relative_error_percent"].max()),
            "min_error_percent": float(result_df["relative_error_percent"].min()),
        }
        print(f"Checkpoint: {ckpt_path}")
        print(f"Model: {bundle['model_name']}")
        print(f"Summary: {summary}")
        print(f"CSV saved to: {csv_path}")
        if fig_path is not None:
            print(f"Figure saved to: {fig_path}")
        print(result_df.to_string(index=False))
        return

    record = records[args.sample_index]
    result = evaluate_record(model, record)
    fig_path = save_single_sample_plot(record, result, args.split, save_dir, args.sample_index)
    print(f"Checkpoint: {ckpt_path}")
    print(f"Model: {bundle['model_name']}")
    print(f"Sample file: {result['file_name']}")
    print(f"True RMS: {float(result['true_rms']):.6f}")
    print(f"Pred RMS: {float(result['pred_rms']):.6f}")
    print(f"Relative RMS Error (%): {float(result['relative_error_percent']):.4f}")
    print(f"Figure saved to: {fig_path}")


if __name__ == "__main__":
    main()