#!/usr/bin/env python3
"""
Minimal high-level example: evaluate a pretrained timm model on CraterBench-R
using one global descriptor per image and cosine retrieval.
"""

from __future__ import annotations

import argparse
import json
from pathlib import Path
from typing import Any

import numpy as np
import timm
import torch
import torch.nn.functional as F
from PIL import Image
from timm.data import create_transform, resolve_model_data_config
from torch.utils.data import DataLoader, Dataset


class ImageDataset(Dataset):
    def __init__(self, entries: list[dict[str, Any]], root: Path, transform):
        self.entries = entries
        self.root = root
        self.transform = transform

    def __len__(self) -> int:
        return len(self.entries)

    def __getitem__(self, idx: int):
        entry = self.entries[idx]
        path = self.root / entry["path"]
        image = Image.open(path).convert("RGB")
        return self.transform(image), entry


def collate_with_entries(batch):
    images = torch.stack([item[0] for item in batch])
    entries = [item[1] for item in batch]
    return images, entries


def unwrap_features(features: Any) -> torch.Tensor:
    if isinstance(features, torch.Tensor):
        return features
    if isinstance(features, (list, tuple)):
        return unwrap_features(features[0])
    if isinstance(features, dict):
        for key in ("x", "features", "last_hidden_state"):
            if key in features:
                return unwrap_features(features[key])
        for value in features.values():
            if isinstance(value, torch.Tensor):
                return value
    raise TypeError(f"Unsupported feature output type: {type(features)!r}")


def pool_features(features: torch.Tensor, pool: str) -> torch.Tensor:
    if features.ndim == 2:
        return features
    if features.ndim == 3:
        if pool == "cls":
            return features[:, 0]
        if pool == "mean":
            return features.mean(dim=1)
        if pool == "max":
            return features.max(dim=1).values
    if features.ndim == 4:
        if pool == "mean":
            return features.mean(dim=(2, 3))
        if pool == "max":
            return features.amax(dim=(2, 3))
    raise ValueError(f"Unsupported feature shape {tuple(features.shape)} for pool={pool}")


def extract_embeddings(
    model,
    loader: DataLoader,
    device: torch.device,
    pool: str,
) -> tuple[np.ndarray, list[dict[str, Any]]]:
    all_embeddings = []
    all_entries: list[dict[str, Any]] = []

    model.eval()
    with torch.no_grad():
        for images, entries in loader:
            images = images.to(device)
            features = unwrap_features(model.forward_features(images))
            pooled = pool_features(features, pool)
            pooled = F.normalize(pooled, dim=1)
            all_embeddings.append(pooled.cpu().numpy().astype(np.float32))
            all_entries.extend(entries)

    return np.concatenate(all_embeddings, axis=0), all_entries


def compute_metrics(
    ranking: np.ndarray,
    query_ids: list[str],
    gallery_ids: list[str],
    ground_truth: dict[str, list[str]],
    k_values: list[int],
) -> dict[str, float]:
    metrics: dict[str, float] = {}
    max_k = ranking.shape[1]

    for k in k_values:
        recalls = []
        for row, query_id in enumerate(query_ids):
            acceptable = set(ground_truth[query_id])
            retrieved = [gallery_ids[idx] for idx in ranking[row, :k]]
            unique_correct = set(retrieved) & acceptable
            recalls.append(min(len(unique_correct) / len(acceptable), 1.0))
        metrics[f"recall@{k}"] = float(np.mean(recalls))

    aps = []
    reciprocal_ranks = []
    for row, query_id in enumerate(query_ids):
        acceptable = set(ground_truth[query_id])
        retrieved = [gallery_ids[idx] for idx in ranking[row, :max_k]]

        seen = set()
        precision_at_k = []
        rr = 0.0
        for rank, crater_id in enumerate(retrieved, start=1):
            if crater_id in acceptable and crater_id not in seen:
                seen.add(crater_id)
                precision_at_k.append(len(seen) / rank)
                if rr == 0.0:
                    rr = 1.0 / rank
        aps.append(float(np.mean(precision_at_k)) if precision_at_k else 0.0)
        reciprocal_ranks.append(rr)

    metrics["map"] = float(np.mean(aps))
    metrics["mrr"] = float(np.mean(reciprocal_ranks))
    return metrics


def search_topk(
    query_embeddings: np.ndarray,
    gallery_embeddings: np.ndarray,
    topk: int,
    device: torch.device,
    query_chunk_size: int,
) -> np.ndarray:
    gallery = torch.from_numpy(gallery_embeddings).to(device)
    results = []

    for start in range(0, len(query_embeddings), query_chunk_size):
        end = min(start + query_chunk_size, len(query_embeddings))
        query = torch.from_numpy(query_embeddings[start:end]).to(device)
        scores = query @ gallery.T
        indices = torch.topk(scores, k=topk, dim=1).indices.cpu().numpy()
        results.append(indices)

    return np.concatenate(results, axis=0)


def main() -> None:
    parser = argparse.ArgumentParser()
    parser.add_argument("--data-root", type=Path, default=Path("."))
    parser.add_argument("--split", type=str, default="test")
    parser.add_argument("--model", type=str, required=True)
    parser.add_argument("--pool", type=str, default="mean", choices=["cls", "mean", "max"])
    parser.add_argument("--batch-size", type=int, default=64)
    parser.add_argument("--query-chunk-size", type=int, default=256)
    parser.add_argument("--device", type=str, default="cuda" if torch.cuda.is_available() else "cpu")
    args = parser.parse_args()

    split_path = args.data_root / "splits" / f"{args.split}.json"
    with split_path.open("r") as handle:
        split = json.load(handle)

    model = timm.create_model(args.model, pretrained=True, num_classes=0)
    model.to(args.device)

    data_config = resolve_model_data_config(model)
    transform = create_transform(**data_config, is_training=False)

    gallery_loader = DataLoader(
        ImageDataset(split["gallery_images"], args.data_root, transform),
        batch_size=args.batch_size,
        shuffle=False,
        num_workers=4,
        pin_memory=True,
        collate_fn=collate_with_entries,
    )
    query_loader = DataLoader(
        ImageDataset(split["query_images"], args.data_root, transform),
        batch_size=args.batch_size,
        shuffle=False,
        num_workers=4,
        pin_memory=True,
        collate_fn=collate_with_entries,
    )

    gallery_embeddings, gallery_entries = extract_embeddings(
        model, gallery_loader, torch.device(args.device), args.pool
    )
    query_embeddings, query_entries = extract_embeddings(
        model, query_loader, torch.device(args.device), args.pool
    )

    ranking = search_topk(
        query_embeddings,
        gallery_embeddings,
        topk=10,
        device=torch.device(args.device),
        query_chunk_size=args.query_chunk_size,
    )

    gallery_ids = [entry["crater_id"] for entry in gallery_entries]
    query_ids = [entry["crater_id"] for entry in query_entries]
    metrics = compute_metrics(ranking, query_ids, gallery_ids, split["ground_truth"], [1, 5, 10])

    print("Model:", args.model)
    print("Pool:", args.pool)
    for key, value in metrics.items():
        print(f"{key}: {value:.4f}")


if __name__ == "__main__":
    main()