"""Quantization & Compression Analysis for CAAE vLLM Optimization"""

import torch
import json
from typing import Dict
from dataclasses import dataclass


@dataclass
class QuantizationMetrics:
    compression_ratio: float
    memory_savings_mb: float
    latency_impact_ms: float
    accuracy_degradation: float


class QuantizationAnalyzer:
    """Minimal quantization analysis for vLLM optimization"""

    def __init__(self):
        self.supported_dtypes = [torch.float16, torch.bfloat16, torch.int8, torch.int4]

    def analyze_model_compression(
        self, model_size_gb: float, target_dtype: str
    ) -> QuantizationMetrics:
        """Analyze compression impact for given model size and target dtype"""
        dtype_ratios = {
            "fp16": 0.5,  # 32->16 bit
            "bf16": 0.5,  # 32->16 bit
            "int8": 0.25,  # 32->8 bit
            "int4": 0.125,  # 32->4 bit
        }

        ratio = dtype_ratios.get(target_dtype, 1.0)
        memory_savings = model_size_gb * 1024 * (1 - ratio)  # MB saved

        # Empirical latency impact (ms per GB)
        latency_impacts = {"fp16": 0.1, "bf16": 0.05, "int8": 0.3, "int4": 0.8}
        latency_impact = model_size_gb * latency_impacts.get(target_dtype, 0)

        # Accuracy degradation estimates
        accuracy_loss = {"fp16": 0.001, "bf16": 0.0005, "int8": 0.02, "int4": 0.08}

        return QuantizationMetrics(
            compression_ratio=1 / ratio,
            memory_savings_mb=memory_savings,
            latency_impact_ms=latency_impact,
            accuracy_degradation=accuracy_loss.get(target_dtype, 0),
        )

    def recommend_quantization(
        self, model_size_gb: float, memory_budget_gb: float
    ) -> str:
        """Recommend optimal quantization strategy"""
        if model_size_gb <= memory_budget_gb:
            return "fp16"  # No aggressive quantization needed

        compression_needed = model_size_gb / memory_budget_gb

        if compression_needed <= 2:
            return "fp16"
        elif compression_needed <= 4:
            return "int8"
        else:
            return "int4"

    def calculate_kv_cache_compression(
        self, batch_size: int, seq_len: int, hidden_size: int, num_layers: int
    ) -> Dict:
        """Calculate KV cache compression benefits"""
        # KV cache size calculation (K + V tensors)
        kv_size_fp16 = 2 * batch_size * seq_len * hidden_size * num_layers * 2  # bytes
        kv_size_int8 = kv_size_fp16 // 2

        return {
            "fp16_size_mb": kv_size_fp16 / (1024**2),
            "int8_size_mb": kv_size_int8 / (1024**2),
            "compression_ratio": 2.0,
            "memory_saved_mb": (kv_size_fp16 - kv_size_int8) / (1024**2),
        }


def run_quantization_analysis(model_configs: Dict) -> Dict:
    """Run comprehensive quantization analysis"""
    analyzer = QuantizationAnalyzer()
    results = {}

    for model_name, config in model_configs.items():
        model_size = config["size_gb"]
        memory_budget = config.get("memory_budget_gb", model_size * 0.8)

        # Analyze different quantization options
        quantization_results = {}
        for dtype in ["fp16", "bf16", "int8", "int4"]:
            metrics = analyzer.analyze_model_compression(model_size, dtype)
            quantization_results[dtype] = {
                "compression_ratio": metrics.compression_ratio,
                "memory_savings_mb": metrics.memory_savings_mb,
                "latency_impact_ms": metrics.latency_impact_ms,
                "accuracy_degradation": metrics.accuracy_degradation,
            }

        # Get recommendation
        recommendation = analyzer.recommend_quantization(model_size, memory_budget)

        # KV cache analysis
        kv_analysis = analyzer.calculate_kv_cache_compression(
            batch_size=config.get("batch_size", 32),
            seq_len=config.get("seq_len", 2048),
            hidden_size=config.get("hidden_size", 4096),
            num_layers=config.get("num_layers", 32),
        )

        results[model_name] = {
            "quantization_options": quantization_results,
            "recommendation": recommendation,
            "kv_cache_compression": kv_analysis,
        }

    return results


if __name__ == "__main__":
    # Example analysis for common models
    model_configs = {
        "mistral-7b": {
            "size_gb": 14,
            "memory_budget_gb": 24,
            "batch_size": 32,
            "seq_len": 2048,
            "hidden_size": 4096,
            "num_layers": 32,
        },
        "llama2-13b": {
            "size_gb": 26,
            "memory_budget_gb": 40,
            "batch_size": 16,
            "seq_len": 4096,
            "hidden_size": 5120,
            "num_layers": 40,
        },
    }

    results = run_quantization_analysis(model_configs)
    print(json.dumps(results, indent=2))
