from __future__ import annotations

from typing import Any

import pandas as pd

from models.batter_zone_model import classify_zone_bucket, normalize_pitch_family


def build_pitcher_zone_feature_row(
    statcast_df: pd.DataFrame,
    pitcher_name: str,
) -> dict[str, Any]:
    if statcast_df is None or statcast_df.empty:
        return {"pitcher_name": pitcher_name}

    if "player_name" not in statcast_df.columns:
        return {"pitcher_name": pitcher_name}

    df = statcast_df[
        statcast_df["player_name"].astype(str) == str(pitcher_name)
    ].copy()

    if df.empty:
        return {"pitcher_name": pitcher_name}

    if "plate_x" not in df.columns or "plate_z" not in df.columns:
        return {"pitcher_name": pitcher_name}

    if "pitch_name" in df.columns:
        pitch_name_series = df["pitch_name"]
    elif "pitch_type" in df.columns:
        pitch_name_series = df["pitch_type"]
    else:
        pitch_name_series = pd.Series(["unknown"] * len(df), index=df.index)

    df = df.copy()
    df["pitch_family"] = pitch_name_series.apply(normalize_pitch_family)
    df["zone_bucket"] = df.apply(
        lambda row: classify_zone_bucket(row.get("plate_x"), row.get("plate_z")),
        axis=1,
    )

    row: dict[str, Any] = {
        "pitcher_name": pitcher_name,
        "zone_sample_size": int(len(df)),
    }

    pitch_families = ["fastball", "breaking", "offspeed"]
    zones = ["heart", "shadow", "chase", "waste"]

    total_count = float(len(df)) if len(df) > 0 else 0.0

    for family in pitch_families:
        family_df = df[df["pitch_family"] == family].copy()
        family_count = int(len(family_df))

        row[f"{family}_usage_rate"] = float(family_count / total_count) if total_count > 0 else None
        row[f"sample_size_{family}"] = family_count

        for zone in zones:
            overall_mask = (
                (df["pitch_family"] == family)
                & (df["zone_bucket"] == zone)
            )
            overall_sample_size = int(overall_mask.sum())
            overall_rate = float(overall_sample_size / total_count) if total_count > 0 else None

            row[f"{family}_{zone}_rate"] = overall_rate
            row[f"sample_size_{family}_{zone}"] = overall_sample_size

            if family_count > 0:
                conditional_mask = family_df["zone_bucket"] == zone
                conditional_sample_size = int(conditional_mask.sum())
                conditional_rate = float(conditional_sample_size / family_count)
            else:
                conditional_sample_size = 0
                conditional_rate = None

            row[f"{family}_{zone}_cond_rate"] = conditional_rate
            row[f"sample_size_{family}_{zone}_cond"] = conditional_sample_size

    return row