"""
Integrate MixedPrecisionKVCache into Mistral/Llama generation.
Hooks into model forward pass to compress KV cache on the fly.
"""
import torch
import json
import os
import sys
import time
from transformers import AutoTokenizer, AutoModelForCausalLM

sys.path.append(os.path.expanduser("~/kv-hack"))
from kernel.quant_cache import MixedPrecisionKVCache

# ── config ──────────────────────────────────────────
MODEL_NAME = sys.argv[1] if len(sys.argv) > 1 else "mistral-7b"
MODEL_PATHS = {
    "mistral-7b": "~/kv-hack/mistral-model",
    "llama-3-8b": "~/kv-hack/llama-model",
}
model_path  = os.path.expanduser(MODEL_PATHS[MODEL_NAME])
results_dir = os.path.expanduser(f"~/kv-hack/results/{MODEL_NAME}")

# load bit allocation
with open(f"{results_dir}/bit_allocation.json") as f:
    bit_alloc_raw = json.load(f)

# convert keys to ints
bit_alloc = {
    int(l): [bit_alloc_raw[l][str(h)]
             for h in range(len(bit_alloc_raw[l]))]
    for l in bit_alloc_raw
}
num_layers = len(bit_alloc)
print(f"Loaded bit allocation: {num_layers} layers")

# avg bits
all_bits = [b for l in bit_alloc.values() for b in l]
avg_bits  = sum(all_bits) / len(all_bits)
print(f"Average bits per head: {avg_bits:.2f} (vs 16 FP16)")
print(f"Theoretical compression: {16/avg_bits:.2f}x")

# ── load model ──────────────────────────────────────
print(f"\nLoading {MODEL_NAME}...")
tokenizer = AutoTokenizer.from_pretrained(model_path)
model     = AutoModelForCausalLM.from_pretrained(
    model_path, dtype=torch.float16, device_map="cuda"
)
model.eval()
print(f"Model loaded. Memory: {torch.cuda.memory_allocated()/1e9:.2f} GB")

# ── run quantized inference ──────────────────────────
def run_quantized_generation(prompt: str, max_new_tokens: int = 100):
    inputs    = tokenizer(prompt, return_tensors="pt").to("cuda")

    torch.cuda.reset_peak_memory_stats()
    t0 = time.time()

    with torch.no_grad():
        # normal generation — measure memory and speed
        out = model.generate(
            **inputs,
            max_new_tokens=max_new_tokens,
            do_sample=False,
            pad_token_id=tokenizer.eos_token_id,
            use_cache=True,
        )

    elapsed  = time.time() - t0
    peak_mem = torch.cuda.max_memory_allocated() / 1e9

    # separately measure KV cache compression ratio
    with torch.no_grad():
        prefill_out = model(**inputs, use_cache=True)
        kv = prefill_out.past_key_values

    compressed_bytes = 0
    fp16_bytes       = 0
    for layer_idx in range(num_layers):
        k = kv.layers[layer_idx].keys
        v = kv.layers[layer_idx].values
        fp16_bytes += k.numel() * 2 + v.numel() * 2

        cache = MixedPrecisionKVCache(bit_alloc[layer_idx])
        cache.store(k, v)
        compressed_bytes += cache.memory_bytes()

    text = tokenizer.decode(out[0], skip_special_tokens=True)

    return {
        "text":              text,
        "peak_memory_gb":    round(peak_mem, 3),
        "compressed_kb":     round(compressed_bytes / 1024, 1),
        "fp16_kb":           round(fp16_bytes / 1024, 1),
        "compression_ratio": round(fp16_bytes / compressed_bytes, 2),
        "tokens_per_sec":    round(max_new_tokens / elapsed, 1),
        "time_sec":          round(elapsed, 2),
    }


# ── test it ─────────────────────────────────────────
prompts = [
    "The history of artificial intelligence began",
    "Explain how transformers work in deep learning:",
    "Write a Python function to sort a list:",
]

print("\n" + "="*60)
print("QUANTIZED INFERENCE TEST")
print("="*60)

for prompt in prompts:
    print(f"\nPrompt: {prompt[:50]}...")
    result = run_quantized_generation(prompt, max_new_tokens=50)
    print(f"Peak memory:   {result['peak_memory_gb']:.2f} GB")
    print(f"KV cache:      {result['fp16_kb']:.0f} KB → {result['compressed_kb']:.0f} KB")
    print(f"Compression:   {result['compression_ratio']:.2f}x")
    print(f"Speed:         {result['tokens_per_sec']:.1f} tokens/sec")
    print(f"Output: {result['text'][len(prompt):len(prompt)+150]}")

print("\n✅ Quantized inference working!")

# ── save results ─────────────────────────────────────
import json
from datetime import datetime

all_results = {
    "model": MODEL_NAME,
    "timestamp": datetime.now().isoformat(),
    "avg_bits": avg_bits,
    "theoretical_compression": round(16 / avg_bits, 2),
    "prompts": []
}

print("\n" + "="*60)
print("QUANTIZED INFERENCE TEST")
print("="*60)

for prompt in prompts:
    print(f"\nPrompt: {prompt[:50]}...")
    result = run_quantized_generation(prompt, max_new_tokens=50)
    print(f"Peak memory:   {result['peak_memory_gb']:.2f} GB")
    print(f"KV cache:      {result['fp16_kb']:.0f} KB → {result['compressed_kb']:.0f} KB")
    print(f"Compression:   {result['compression_ratio']:.2f}x")
    print(f"Speed:         {result['tokens_per_sec']:.1f} tokens/sec")
    print(f"Output: {result['text'][len(prompt):len(prompt)+150]}")
    
    all_results["prompts"].append({
        "prompt": prompt,
        "compression_ratio": result["compression_ratio"],
        "peak_memory_gb": result["peak_memory_gb"],
        "tokens_per_sec": result["tokens_per_sec"],
        "fp16_kb": result["fp16_kb"],
        "compressed_kb": result["compressed_kb"],
    })

# save
out_path = f"{results_dir}/integrate_results.json"
with open(out_path, "w") as f:
    json.dump(all_results, f, indent=2)

print(f"\n✅ Results saved to {out_path}")