"""
Post-Merge Validation — run after EVERY merge step.

Tests:
1. Canary recall (did knowledge transfer?)
2. Perplexity check (did we break the model?)
3. Thinking mode (do <think> tags still work?)
4. Quick reasoning test (can it still think?)

Kill criteria: >10% performance drop on any test → abort merge.
Findings: #11, #22, #25
"""

import sys
import time
import torch
import math
from transformers import AutoModelForCausalLM, AutoTokenizer

from .canary import test_all_canaries
from .config import MergeConfig


def validate_merged_model(
    model: AutoModelForCausalLM,
    tokenizer: AutoTokenizer,
    merged_sources: list[str],
    cfg: MergeConfig,
    baseline_perplexity: float = None,
) -> dict:
    """
    Run full validation suite on a merged model.

    Args:
        model: The merged model to validate
        tokenizer: The tokenizer
        merged_sources: List of source models merged so far
        cfg: Merge configuration
        baseline_perplexity: Perplexity of the target model before merging

    Returns:
        Dict with test results and overall pass/fail
    """
    val_start = time.time()
    print("\n" + "=" * 60)
    print(f"VALIDATION — After merging: {', '.join(merged_sources)}")
    print(f"Started at: {time.strftime('%H:%M:%S')}")
    print("=" * 60)
    sys.stdout.flush()

    results = {
        "canary": None,
        "perplexity": None,
        "thinking_mode": None,
        "reasoning": None,
        "overall": False,
    }

    # --- Test 1: Canary recall ---
    print("[validate] Test 1/4: Canary recall..."); sys.stdout.flush()
    canary_results = test_all_canaries(model, tokenizer, merged_sources)
    passed_canaries = sum(1 for v in canary_results.values() if v)
    total_canaries = len(canary_results)
    results["canary"] = {
        "passed": passed_canaries,
        "total": total_canaries,
        "ok": passed_canaries >= min(cfg.canary_pass_threshold, total_canaries),
        "details": canary_results,
    }

    # --- Test 2: Perplexity ---
    print("[validate] Test 2/4: Perplexity..."); sys.stdout.flush()
    perplexity = compute_perplexity(model, tokenizer)
    ppl_ok = True
    if baseline_perplexity is not None:
        ratio = perplexity / baseline_perplexity
        ppl_ok = ratio < cfg.perplexity_threshold
        print(f"\n[validate] Perplexity: {perplexity:.2f} (baseline: {baseline_perplexity:.2f}, ratio: {ratio:.2f})")
        if not ppl_ok:
            print(f"[validate] ⚠ Perplexity ratio {ratio:.2f} exceeds threshold {cfg.perplexity_threshold}")
    else:
        print(f"\n[validate] Perplexity: {perplexity:.2f} (no baseline to compare)")
    ppl_ratio = ratio if baseline_perplexity is not None else 1.0
    results["perplexity"] = {"value": perplexity, "ok": ppl_ok, "ratio": ppl_ratio}

    # --- Test 3: Thinking mode ---
    print("[validate] Test 3/4: Thinking mode..."); sys.stdout.flush()
    think_ok = test_thinking_mode(model, tokenizer)
    results["thinking_mode"] = {"ok": think_ok}

    # --- Test 4: Quick reasoning ---
    print("[validate] Test 4/4: Quick reasoning..."); sys.stdout.flush()
    reason_ok = test_reasoning(model, tokenizer)
    results["reasoning"] = {"ok": reason_ok}

    # --- Overall verdict ---
    all_ok = (
        results["canary"]["ok"]
        and results["perplexity"]["ok"]
        and results["thinking_mode"]["ok"]
        and results["reasoning"]["ok"]
    )
    results["overall"] = all_ok

    # Summary
    print("\n" + "-" * 60)
    print("VALIDATION SUMMARY")
    print("-" * 60)
    print(f"  Canary recall:   {'✓' if results['canary']['ok'] else '✗'} ({passed_canaries}/{total_canaries})")
    print(f"  Perplexity:      {'✓' if ppl_ok else '✗'} ({perplexity:.2f})")
    print(f"  Thinking mode:   {'✓' if think_ok else '✗'}")
    print(f"  Reasoning:       {'✓' if reason_ok else '✗'}")
    print(f"  OVERALL:         {'PASS' if all_ok else 'FAIL -- consider aborting'}")
    print(f"  Validation time: {(time.time()-val_start)/60:.1f} min")
    print("-" * 60)
    sys.stdout.flush()

    return results


def compute_perplexity(
    model: AutoModelForCausalLM,
    tokenizer: AutoTokenizer,
    test_texts: list[str] = None,
) -> float:
    """
    Compute perplexity on a small test set.

    Lower perplexity = model is more confident about predicting text.
    A big spike after merging means the model was damaged.
    """
    if test_texts is None:
        test_texts = [
            "The quick brown fox jumps over the lazy dog.",
            "In mathematics, a prime number is a natural number greater than 1.",
            "def fibonacci(n):\n    if n <= 1:\n        return n\n    return fibonacci(n-1) + fibonacci(n-2)",
            "The theory of general relativity describes gravity as the curvature of spacetime.",
            "To solve 3x + 7 = 22, subtract 7 from both sides to get 3x = 15, then divide by 3.",
        ]

    model.eval()
    total_loss = 0.0
    total_tokens = 0

    for text in test_texts:
        inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
        inputs = {k: v.to(model.device) for k, v in inputs.items()}

        with torch.no_grad():
            outputs = model(**inputs, labels=inputs["input_ids"])
            total_loss += outputs.loss.item() * inputs["input_ids"].shape[1]
            total_tokens += inputs["input_ids"].shape[1]

    avg_loss = total_loss / total_tokens
    perplexity = math.exp(avg_loss)
    return perplexity


def _format_chat_prompt(tokenizer, user_message: str, enable_thinking: bool = True) -> dict:
    """
    Format a prompt using Qwen3's chat template.

    Qwen3 models expect messages in chat format — without it, the model
    just autocompletes the text instead of answering.

    Args:
        tokenizer: The tokenizer (or processor.tokenizer for VL models)
        user_message: The user's question
        enable_thinking: If True, allow <think> tags. If False, add /no_think.

    Returns:
        Dict with input_ids ready for model.generate()
    """
    messages = [{"role": "user", "content": user_message}]

    # Try using the chat template (Qwen3 has one built in)
    try:
        text = tokenizer.apply_chat_template(
            messages,
            tokenize=False,
            add_generation_prompt=True,
            enable_thinking=enable_thinking,
        )
        # Verify the template actually produced thinking tokens
        if enable_thinking and "<think>" not in text:
            # Template didn't add thinking trigger — use manual format
            raise ValueError("Template missing think trigger")
        inputs = tokenizer(text, return_tensors="pt")
        return inputs
    except Exception:
        pass

    # Fallback: manual Qwen3 chat format
    if enable_thinking:
        # Qwen3 thinking mode: start assistant turn with <think> to trigger CoT
        text = f"<|im_start|>user\n{user_message}<|im_end|>\n<|im_start|>assistant\n<think>\n"
    else:
        text = f"<|im_start|>user\n{user_message}<|im_end|>\n<|im_start|>assistant\n/no_think\n"
    inputs = tokenizer(text, return_tensors="pt")
    return inputs


def test_thinking_mode(
    model: AutoModelForCausalLM,
    tokenizer: AutoTokenizer,
) -> bool:
    """
    Test if the model still uses <think> tags for reasoning.

    The thinking mode is Qwen3's special feature — if it's gone,
    the merge damaged something critical.
    """
    prompt = "Solve step by step: What is 15 × 13?"

    inputs = _format_chat_prompt(tokenizer, prompt, enable_thinking=True)
    inputs = {k: v.to(model.device) for k, v in inputs.items()}

    with torch.no_grad():
        outputs = model.generate(
            **inputs,
            max_new_tokens=800,
            do_sample=False,
        )

    # Decode only the NEW tokens (skip the prompt)
    new_tokens = outputs[0][inputs["input_ids"].shape[1]:]
    response = tokenizer.decode(new_tokens, skip_special_tokens=False)

    # Check for thinking tags (we may have prefilled <think> in the prompt,
    # so check for </think> which the model must produce to end thinking)
    has_think_close = "</think>" in response
    # If template handled it, <think> appears in new tokens too
    has_think_open = "<think>" in response
    # Pass if model produced </think> (thinking happened, whether <think> was prefilled or not)
    passed = has_think_close

    print(f"\n[validate] Thinking mode test:")
    print(f"  Prompt:    {prompt}")
    print(f"  Response:  {response[:300]}...")
    print(f"  <think>:   {'✓ found' if has_think_open else '(prefilled in prompt)'}")
    print(f"  </think>:  {'✓ found' if has_think_close else '✗ missing'}")
    print(f"  Status:    {'✓ PASS' if passed else '✗ FAIL'}")

    return passed


def test_reasoning(
    model: AutoModelForCausalLM,
    tokenizer: AutoTokenizer,
) -> bool:
    """
    Quick reasoning sanity check — can the model still do basic math?

    This catches catastrophic failures where the merge produced gibberish.
    Uses /no_think mode so the model answers directly without chain-of-thought.
    """
    prompt = "What is 7 + 8?"
    expected_answer = "15"

    inputs = _format_chat_prompt(tokenizer, prompt, enable_thinking=False)
    inputs = {k: v.to(model.device) for k, v in inputs.items()}

    with torch.no_grad():
        outputs = model.generate(
            **inputs,
            max_new_tokens=50,
            do_sample=False,
        )

    # Decode only the NEW tokens (skip the prompt)
    new_tokens = outputs[0][inputs["input_ids"].shape[1]:]
    response = tokenizer.decode(new_tokens, skip_special_tokens=True)
    passed = expected_answer in response

    print(f"\n[validate] Quick reasoning test:")
    print(f"  Prompt:   {prompt}")
    print(f"  Expected: {expected_answer}")
    print(f"  Got:      {response[:200]}")
    print(f"  Status:   {'✓ PASS' if passed else '✗ FAIL'}")

    return passed