#!/usr/bin/env python3
"""
Anomaly multiplicity per class: how many anomaly types share an image, broken
down by the class present in it. Same visual format as plot_multimodal_aupro.py:
seaborn whitegrid, viridis grouped bars, red dotted class means.

    x axis   the anchor classes
    hue      multiplicity 1..7, i.e. the total number of distinct anomaly
             types present in the image
"""

import argparse
import sys
from pathlib import Path

import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
from matplotlib.patches import Patch
import numpy as np
import pandas as pd
import seaborn as sns

from plot_difficulty import load_annotations, load_scores

DEFAULT_CLASSES = ["residual_content", "nuisance_sticker_inside",
                   "nuisance_gunk", "nuisance_paper"]


def coco_files(entries, pattern):
    found = []
    for item in entries:
        path = Path(item)
        if path.is_dir():
            found.extend(path.rglob(pattern))
        elif path.is_file():
            found.append(path)
    return sorted(set(str(path) for path in found))


def type_label(k):
    return f"{k} type" if k == 1 else f"{k} types"


def count_table(memberships, classes, max_k):
    rows = []
    for types in memberships.values():
        if not types:
            continue
        k = len(types)
        if k < 1 or k > max_k:
            continue
        for cls in classes:
            if cls in types:
                rows.append({"Class": cls, "Multiplicity": k})
    if not rows:
        return None, None
    df = pd.DataFrame(rows)
    agg = (df.groupby(["Class", "Multiplicity"]).size()
             .reset_index(name="Value"))
    agg["n"] = agg["Value"]
    means = agg.groupby("Class")["Value"].mean()
    return agg, means


def score_table(memberships, scores, classes, max_k):
    rows = []
    for key, types in memberships.items():
        if not types or key not in scores:
            continue
        k = len(types)
        if k < 1 or k > max_k:
            continue
        for cls in classes:
            if cls in types:
                rows.append({"Class": cls, "Multiplicity": k,
                             "Value": scores[key]})
    if not rows:
        return None, None
    df = pd.DataFrame(rows)
    agg = (df.groupby(["Class", "Multiplicity"])["Value"]
             .agg(["mean", "count"]).reset_index()
             .rename(columns={"mean": "Value", "count": "n"}))
    means = df.groupby("Class")["Value"].mean()
    return agg, means


def add_class_means(classes, class_means, ylim, fmt="{:.2f}"):
    for i, cls in enumerate(classes):
        if cls not in class_means:
            continue
        mean_val = float(class_means[cls])
        plt.hlines(y=mean_val, xmin=i - 0.4, xmax=i + 0.4,
                   colors="red", linestyles=":", linewidth=3,
                   label="Class Mean" if i == 0 else "")
        plt.text(i + 0.3, mean_val + 0.018 * (ylim[1] - ylim[0]),
                 fmt.format(mean_val), color="red", ha="left", va="center",
                 fontsize=9, fontweight="bold")


def draw(df_agg, class_means, classes, hue_col, hue_order, ylim, ylabel,
         stem, annotate_n=False, integer_y=False, mean_fmt="{:.2f}",
         legend_labels=None):
    sns.set_theme(style="whitegrid", font_scale=1.1)
    plt.figure(figsize=(14, 5))

    chart = sns.barplot(
        data=df_agg,
        x="Class",
        y="Value",
        order=classes,
        hue=hue_col,
        hue_order=hue_order,
        palette="viridis",
    )

    add_class_means(classes, class_means, ylim, fmt=mean_fmt)

    if annotate_n and "n" in df_agg.columns:
        for p in chart.patches:
            h = p.get_height()
            if not np.isfinite(h) or h <= 0:
                continue
            chart.annotate(f"{int(round(h))}",
                           (p.get_x() + p.get_width() / 2, h),
                           ha="center", va="bottom", fontsize=7, color="0.25",
                           xytext=(0, 2), textcoords="offset points")

    plt.xticks(rotation=15, ha="right")
    plt.xlabel("")
    plt.ylabel(ylabel)
    plt.ylim(ylim)
    if integer_y:
        plt.gca().yaxis.set_major_locator(
            matplotlib.ticker.MaxNLocator(integer=True)
        )

    handles, labels = plt.gca().get_legend_handles_labels()
    if legend_labels:
        labels = [legend_labels.get(l, l) for l in labels]
    plt.legend(
        handles, labels,
        loc="upper left",
        ncol=4,
        frameon=True,
        framealpha=0.2,
        facecolor="white",
        edgecolor="black",
    )

    plt.tight_layout()
    plt.savefig(f"{stem}.pdf", bbox_inches="tight")
    plt.savefig(f"{stem}.png", dpi=300, bbox_inches="tight")
    plt.close()


def report(label, agg, classes, ks, value_name):
    print(f"\n{label}")
    pivot = (agg.pivot(index="Class", columns="Multiplicity", values="Value")
                .reindex(index=classes, columns=ks))
    if value_name == "images":
        print("  images per group")
        print(pivot.fillna(0).astype(int).to_string().replace("\n", "\n  "))
        print(f"  total images (class occurrences): {int(agg['Value'].sum())}")
    else:
        pivot_n = (agg.pivot(index="Class", columns="Multiplicity", values="n")
                      .reindex(index=classes, columns=ks))
        print("  images per group")
        print(pivot_n.fillna(0).astype(int).to_string().replace("\n", "\n  "))
        print("  mean AUPRO per group")
        print(pivot.round(3).to_string().replace("\n", "\n  "))


def default_coco_search():
    candidates = [
        Path("test"),
        Path("../test"),
        Path(__file__).resolve().parent.parent / "test",
        Path("train"),
        Path("../train"),
        Path(__file__).resolve().parent.parent / "train",
        Path("."),
        Path(".."),
        Path(__file__).resolve().parent.parent,
    ]
    for c in candidates:
        if c.is_dir() and list(c.rglob("annotations*.coco.json")):
            return [str(c)]
    return ["."]


def find_files(names):
    resolved = []
    for name in names:
        candidates = [
            Path(f"checkpoint_data/{name}"),
            Path(f"../checkpoint_data/{name}"),
            Path(__file__).resolve().parent.parent / "checkpoint_data" / name,
            Path(__file__).resolve().parent / name,
            Path(name),
        ]
        found = next((str(c) for c in candidates if c.is_file()), None)
        if found:
            resolved.append(found)
    return resolved


def default_out_dir():
    if Path("checkpoint_data").is_dir():
        return Path("checkpoint_data/multiplicity_figures")
    if (Path(__file__).resolve().parent.parent / "checkpoint_data").is_dir():
        return Path(__file__).resolve().parent.parent / "checkpoint_data/multiplicity_figures"
    return Path("./multiplicity_figures")


def main():
    parser = argparse.ArgumentParser(description="Anomaly multiplicity per class")
    parser.add_argument("--coco", nargs="+", default=None,
                        help="COCO annotation files or root directory (auto-detected if omitted)")
    parser.add_argument("--coco-pattern", default="annotations*.coco.json")
    parser.add_argument("--scores", action="append", default=[],
                        metavar="NAME=CSV[,CSV]",
                        help="repeatable: per-image AUPRO CSVs for one method. "
                             "Auto-detected from checkpoint_data/ if omitted.")
    parser.add_argument("--metric", default="aupro_30")
    parser.add_argument("--classes", nargs="+", default=DEFAULT_CLASSES,
                        help="anchor classes, in the order they appear on x")
    parser.add_argument("--max-k", type=int, default=7,
                        help="highest multiplicity shown (default 7)")
    parser.add_argument("--min-n", type=int, default=1,
                        help="groups with fewer images are left empty")
    parser.add_argument("--annotate-n", action="store_true",
                        help="print the image count above each bar")
    parser.add_argument("--no-shared-y", action="store_true",
                        help="let each method figure scale its own y axis")
    parser.add_argument("--out", default=None,
                        help="Output directory (default: checkpoint_data/multiplicity_figures or ./multiplicity_figures)")
    args = parser.parse_args()

    coco_entries = args.coco if args.coco is not None else default_coco_search()
    annotations = coco_files(coco_entries, args.coco_pattern)
    if not annotations:
        raise SystemExit(f"No COCO annotation files found in {coco_entries}")
    memberships = load_annotations(annotations)
    annotated = {key for key, types in memberships.items() if types}

    ks = list(range(1, args.max_k + 1))
    out = Path(args.out) if args.out is not None else default_out_dir()
    out.mkdir(parents=True, exist_ok=True)

    count_agg, count_means = count_table(memberships, args.classes, args.max_k)
    if count_agg is None:
        raise SystemExit("none of the requested classes occur in the data")

    classes = [c for c in args.classes if c in set(count_agg["Class"])]
    missing = [c for c in args.classes if c not in classes]
    if missing:
        print(f"! not present in the data: {', '.join(missing)}", file=sys.stderr)

    scores_specs = args.scores
    if not scores_specs:
        m3dm_csvs = find_files(["per_image_aupro_zivid.csv", "per_image_aupro_realsense.csv"])
        stfpm_csvs = find_files(["per_image_aupro_stfpm.csv"])
        if m3dm_csvs:
            scores_specs.append(f"M3DM={','.join(m3dm_csvs)}")
        if stfpm_csvs:
            scores_specs.append(f"STFPM={','.join(stfpm_csvs)}")

    per_method = {}
    for spec in scores_specs:
        if "=" not in spec:
            raise SystemExit(f"--scores needs NAME=CSV, got {spec!r}")
        name, paths = spec.split("=", 1)
        files = [p.strip() for p in paths.split(",") if p.strip()]
        per_method[name.strip()] = load_scores(files, args.metric, name.strip())

    if per_method:
        print()
        for name, scores in per_method.items():
            print(f"  {name:<20} {len(annotated & set(scores))} images matched to types")

    method_tables = {}
    for index, (name, scores) in enumerate(per_method.items(), start=2):
        agg, means = score_table(memberships, scores, classes, args.max_k)
        if agg is None:
            print(f"! {name}: no matching images, skipped", file=sys.stderr)
            continue
        thin = agg[agg["n"] < args.min_n]
        if len(thin):
            print(f"  {name}: {len(thin)} group(s) below --min-n {args.min_n}, left empty")
            agg = agg[agg["n"] >= args.min_n]
        slug = name.lower().replace(" ", "_").replace("/", "_")
        method_tables[f"{index:02d}_multiplicity_{slug}"] = (name, agg, means)

    method_ylim = None
    if method_tables and not args.no_shared_y:
        top = max(max(float(agg["Value"].max()), float(means.max()))
                  for _, agg, means in method_tables.values())
        method_ylim = (0, top * 1.42)

    thin_counts = count_agg[count_agg["n"] < args.min_n]
    if len(thin_counts):
        count_agg = count_agg[count_agg["n"] >= args.min_n]

    report("dataset-wide (composition)", count_agg, classes, ks, "images")
    count_top = max(float(count_agg["Value"].max()), float(count_means.max()))
    type_names = {k: type_label(k) for k in ks}
    draw(count_agg, count_means, classes, "Multiplicity", ks,
         (0, count_top * 1.42), "Number of Images",
         str(out / "01_multiplicity_dataset_wide"),
         annotate_n=False, integer_y=True, mean_fmt="{:.0f}",
         legend_labels={str(k): v for k, v in type_names.items()})
    count_agg.sort_values(["Class", "Multiplicity"]).to_csv(
        out / "01_multiplicity_dataset_wide_values.csv", index=False)

    for stem, (label, agg, means) in method_tables.items():
        report(label, agg, classes, ks, "aupro")
        ylim = method_ylim
        if ylim is None:
            top = max(float(agg["Value"].max()), float(means.max()))
            ylim = (0, top * 1.42)
        draw(agg, means, classes, "Multiplicity", ks, ylim,
             "Mean Pixel AUPRO Score", str(out / stem),
             annotate_n=args.annotate_n,
             legend_labels={str(k): v for k, v in type_names.items()})
        agg.sort_values(["Class", "Multiplicity"]).to_csv(
            out / f"{stem}_values.csv", index=False)

    print(f"\nWrote figures and CSVs to {out}")


if __name__ == "__main__":
    main()