#!/usr/bin/env python3
"""End-to-end experiment: train → evaluate → spectral analysis → attack simulation."""

import os
import json
import random
import time
import yaml
import argparse

import numpy as np
import torch

from multi_manifold_retrieval.models.encoders import DualEncoder
from multi_manifold_retrieval.models.cross_manifold_operator import CrossManifoldOperator
from multi_manifold_retrieval.models.baseline import BaselineOperator
from multi_manifold_retrieval.training.train import train
from multi_manifold_retrieval.training.data import MSMARCOEvalDataset
from multi_manifold_retrieval.evaluation.spectral_analysis import run_spectral_analysis
from multi_manifold_retrieval.evaluation.retrieval_metrics import compute_retrieval_metrics
from multi_manifold_retrieval.evaluation.attack_simulation import (
    run_attack_simulation,
    select_domain_documents,
)


def set_seed(seed: int):
    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    if torch.cuda.is_available():
        torch.cuda.manual_seed_all(seed)


def main():
    parser = argparse.ArgumentParser(description="Multi-Manifold Retrieval PoC Experiment")
    parser.add_argument("--config", type=str, default="configs/default.yaml")
    parser.add_argument("--skip-train", action="store_true", help="Skip training, load from checkpoint")
    parser.add_argument("--checkpoint", type=str, default="checkpoints/best_operator.pt")
    parser.add_argument("--output", type=str, default="results.json")
    args = parser.parse_args()

    with open(args.config) as f:
        config = yaml.safe_load(f)

    set_seed(config["seed"])
    device = "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu"
    print(f"Using device: {device}")

    results = {"config": config, "device": device}

    # =========================================================================
    # Phase 1: Training
    # =========================================================================
    print("\n" + "=" * 60)
    print("PHASE 1: TRAINING")
    print("=" * 60)

    if args.skip_train and os.path.exists(args.checkpoint):
        print(f"Loading encoder and operator from checkpoint: {args.checkpoint}")
        encoder = DualEncoder(
            model_name=config["encoder"]["model_name"],
            max_seq_length=config["training"]["max_seq_length"],
            freeze=config["encoder"]["freeze"],
        )
        cm_config = config["cross_manifold"]
        operator = CrossManifoldOperator(
            embedding_dim=encoder.embedding_dim,
            num_heads=cm_config["num_heads"],
            value_hidden_dim=cm_config["value_mlp_hidden"],
            value_num_layers=cm_config["value_mlp_layers"],
            dropout=cm_config["dropout"],
        )
        operator.load_state_dict(torch.load(args.checkpoint, map_location=device, weights_only=True))
        operator.to(device)
    else:
        encoder, operator = train(config_path=args.config, device=device)

    encoder.model.to(device)
    operator.to(device)
    baseline_operator = BaselineOperator().to(device)

    # =========================================================================
    # Phase 2: Evaluation Data Preparation
    # =========================================================================
    print("\n" + "=" * 60)
    print("PHASE 2: EVALUATION DATA PREPARATION")
    print("=" * 60)

    eval_data = MSMARCOEvalDataset(
        tokenizer=encoder.tokenizer,
        max_queries=config["evaluation"]["max_eval_queries"],
        max_seq_length=config["training"]["max_seq_length"],
        seed=config["seed"],
    )

    # Encode all evaluation passages
    print(f"Encoding {len(eval_data.all_passages)} passages...")
    passage_embeddings = encoder.encode_documents(
        eval_data.all_passages, batch_size=128, show_progress=True,
    )

    # Encode evaluation queries
    print(f"Encoding {len(eval_data.queries)} queries...")
    query_embeddings = encoder.encode_queries(
        eval_data.queries, batch_size=128, show_progress=True,
    )

    # =========================================================================
    # Phase 3: Retrieval Quality Evaluation
    # =========================================================================
    print("\n" + "=" * 60)
    print("PHASE 3: RETRIEVAL QUALITY")
    print("=" * 60)

    print("\n--- Multi-Manifold Model ---")
    metrics_mm = compute_retrieval_metrics(
        query_embeddings=query_embeddings,
        doc_embeddings=passage_embeddings,
        operator=operator,
        query_texts=eval_data.queries,
        positive_passages=eval_data.positives,
        all_passages=eval_data.all_passages,
        passage_embeddings=passage_embeddings,
        device=device,
    )
    results["retrieval_multi_manifold"] = metrics_mm

    print("\n--- Baseline (Cosine Similarity) ---")
    metrics_base = compute_retrieval_metrics(
        query_embeddings=query_embeddings,
        doc_embeddings=passage_embeddings,
        operator=baseline_operator,
        query_texts=eval_data.queries,
        positive_passages=eval_data.positives,
        all_passages=eval_data.all_passages,
        passage_embeddings=passage_embeddings,
        device=device,
    )
    results["retrieval_baseline"] = metrics_base

    # Check: multi-manifold within 80% of baseline
    if metrics_base["mrr@10"] > 0:
        ratio = metrics_mm["mrr@10"] / metrics_base["mrr@10"]
        print(f"\nMRR@10 ratio (mm/baseline): {ratio:.4f} "
              f"({'PASS' if ratio >= 0.8 else 'BELOW TARGET'}, target >= 0.8)")
        results["mrr_ratio"] = ratio

    # =========================================================================
    # Phase 4: Spectral Analysis
    # =========================================================================
    print("\n" + "=" * 60)
    print("PHASE 4: SPECTRAL ANALYSIS")
    print("=" * 60)

    # Sample documents for spectral analysis
    num_spectral_docs = min(config["spectral"]["num_documents"], len(eval_data.all_passages))
    num_spectral_queries = min(config["spectral"]["num_queries"], len(eval_data.queries))

    spectral_doc_indices = np.random.choice(
        len(eval_data.all_passages), num_spectral_docs, replace=False
    )
    spectral_query_indices = np.random.choice(
        len(eval_data.queries), num_spectral_queries, replace=False
    )

    spectral_doc_emb_np = passage_embeddings[spectral_doc_indices].cpu().numpy()
    spectral_doc_emb_torch = passage_embeddings[spectral_doc_indices]
    spectral_query_emb_torch = query_embeddings[spectral_query_indices]

    spectral_results = run_spectral_analysis(
        doc_embeddings_np=spectral_doc_emb_np,
        doc_embeddings_torch=spectral_doc_emb_torch,
        query_embeddings_torch=spectral_query_emb_torch,
        operator=operator,
        baseline_operator=baseline_operator,
        device=device,
    )

    results["spectral"] = {
        "multi_manifold": spectral_results["multi_manifold"],
        "baseline": spectral_results["baseline"],
        "num_documents": num_spectral_docs,
        "num_queries": num_spectral_queries,
    }

    # =========================================================================
    # Phase 5: Attack Simulation
    # =========================================================================
    print("\n" + "=" * 60)
    print("PHASE 5: ATTACK SIMULATION")
    print("=" * 60)

    attack_config = config["attack"]

    # Select target queries (medical domain)
    target_queries = []
    for q in eval_data.queries:
        q_lower = q.lower()
        if any(kw in q_lower for kw in attack_config["medical_keywords"]):
            target_queries.append(q)
        if len(target_queries) >= attack_config["num_target_queries"]:
            break

    if len(target_queries) < 10:
        # Fall back: use random queries if not enough medical ones
        print(f"Only found {len(target_queries)} medical queries; "
              f"using random queries to reach {attack_config['num_target_queries']}.")
        remaining = attack_config["num_target_queries"] - len(target_queries)
        other_queries = [q for q in eval_data.queries if q not in target_queries]
        target_queries.extend(random.sample(other_queries, min(remaining, len(other_queries))))

    print(f"Using {len(target_queries)} target queries for attack simulation.")

    attack_results = run_attack_simulation(
        encoder=encoder,
        operator=operator,
        baseline_operator=baseline_operator,
        passages=eval_data.all_passages,
        passage_embeddings_torch=passage_embeddings,
        target_query_texts=target_queries,
        medical_keywords=attack_config["medical_keywords"],
        top_k=attack_config["top_k"],
        device=device,
    )
    results["attack"] = attack_results

    # =========================================================================
    # Summary
    # =========================================================================
    print("\n" + "=" * 60)
    print("EXPERIMENT SUMMARY")
    print("=" * 60)

    print(f"\n1. Retrieval Quality:")
    print(f"   Baseline MRR@10:        {metrics_base['mrr@10']:.4f}")
    print(f"   Multi-Manifold MRR@10:  {metrics_mm['mrr@10']:.4f}")
    if metrics_base["mrr@10"] > 0:
        print(f"   Ratio:                  {metrics_mm['mrr@10']/metrics_base['mrr@10']:.4f}")

    print(f"\n2. Spectral Analysis:")
    print(f"   Baseline δ:             {spectral_results['baseline']['spectral_discrepancy']:.4f}")
    print(f"   Multi-Manifold δ:       {spectral_results['multi_manifold']['spectral_discrepancy']:.4f}")
    print(f"   Baseline cos(θ):        {spectral_results['baseline']['fiedler_alignment']:.4f}")
    print(f"   Multi-Manifold cos(θ):  {spectral_results['multi_manifold']['fiedler_alignment']:.4f}")

    if "error" not in attack_results:
        print(f"\n3. Attack Simulation:")
        print(f"   Baseline ASR@{attack_config['top_k']}:        {attack_results['baseline_asr']:.4f}")
        print(f"   Multi-Manifold ASR@{attack_config['top_k']}:  {attack_results['multi_manifold_asr']:.4f}")

    # Save results (exclude numpy arrays)
    save_results = {k: v for k, v in results.items()
                    if k not in ("L_D", "L_R_mm", "L_R_base")}
    with open(args.output, "w") as f:
        json.dump(save_results, f, indent=2, default=str)
    print(f"\nResults saved to {args.output}")


if __name__ == "__main__":
    main()