from vllm import LLM, SamplingParams
from vllm.distributed import initialize_distributed_environment
import torch
from typing import List, Dict, Optional
import asyncio

class DistributedInferenceEngine:
    def __init__(self, model_name: str, tensor_parallel_size: int = 2,
                 pipeline_parallel_size: int = 1):
        self.model_name = model_name
        self.tensor_parallel_size = tensor_parallel_size
        self.pipeline_parallel_size = pipeline_parallel_size
        self.setup_engine()

    def setup_engine(self):
        """Initialize vLLM with distributed settings"""
        # Configure for distributed inference
        self.llm = LLM(
            model=self.model_name,
            tensor_parallel_size=self.tensor_parallel_size,
            pipeline_parallel_size=self.pipeline_parallel_size,
            trust_remote_code=True,
            dtype="float16",  # or "bfloat16" for newer models
            max_model_len=32768,  # Adjust based on model
            gpu_memory_utilization=0.95,  # Maximize GPU usage
            swap_space=4,  # GB of CPU swap space
            enforce_eager=False,  # Use CUDA graphs for speed
            enable_prefix_caching=True,  # Cache common prefixes
            enable_chunked_prefill=True,  # Better handling of long contexts
            max_num_batched_tokens=8192,
            max_num_seqs=256  # Maximum concurrent sequences
        )

        # Default sampling parameters
        self.default_sampling = SamplingParams(
            temperature=0.7,
            top_p=0.95,
            max_tokens=2048,
            stop=["</s>", "\n\n"]
        )

    def generate(self, prompts: List[str],
                sampling_params: Optional[SamplingParams] = None) -> List[str]:
        """Generate responses for batch of prompts"""
        if sampling_params is None:
            sampling_params = self.default_sampling

        outputs = self.llm.generate(prompts, sampling_params)

        responses = []
        for output in outputs:
            response = output.outputs[0].text
            responses.append(response)

        return responses

    async def async_generate(self, prompts: List[str]) -> List[str]:
        """Asynchronous generation for better concurrency"""
        loop = asyncio.get_event_loop()
        return await loop.run_in_executor(None, self.generate, prompts)

    def profile_performance(self, test_prompts: List[str]) -> Dict:
        """Profile inference performance"""
        import time
        import numpy as np

        metrics = {
            "throughput": [],
            "latency": [],
            "tokens_per_second": []
        }

        for batch_size in [1, 8, 16, 32, 64]:
            batch = test_prompts[:batch_size]

            start_time = time.time()
            outputs = self.llm.generate(batch, self.default_sampling)
            end_time = time.time()

            total_time = end_time - start_time
            total_tokens = sum(len(o.outputs[0].token_ids) for o in outputs)

            metrics["throughput"].append(batch_size / total_time)
            metrics["latency"].append(total_time / batch_size)
            metrics["tokens_per_second"].append(total_tokens / total_time)

        return {
            "avg_throughput": np.mean(metrics["throughput"]),
            "avg_latency": np.mean(metrics["latency"]),
            "avg_tokens_per_second": np.mean(metrics["tokens_per_second"]),
            "details": metrics
        }

import ray
from ray import serve
from ray.serve.handle import RayServeHandle
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from typing import List, Dict
import asyncio

# Initialize Ray
ray.init(address="auto", ignore_reinit_error=True)
serve.start(detached=True)

@serve.deployment(
    num_replicas=4,  # Number of model replicas
    ray_actor_options={
        "num_cpus": 2,
        "num_gpus": 1,
        "memory": 16 * 1024 * 1024 * 1024  # 16GB per replica
    },
    autoscaling_config={
        "min_replicas": 2,
        "max_replicas": 10,
        "target_num_ongoing_requests_per_replica": 5,
        "upscale_delay_s": 10,
        "downscale_delay_s": 60
    },
    health_check_period_s=10,
    health_check_timeout_s=30
)
class ModelServer:
    def __init__(self, model_name: str):
        self.device = "cuda" if torch.cuda.is_available() else "cpu"
        self.model = AutoModelForCausalLM.from_pretrained(
            model_name,
            torch_dtype=torch.float16,
            device_map="auto"
        )
        self.tokenizer = AutoTokenizer.from_pretrained(model_name)
        self.tokenizer.pad_token = self.tokenizer.eos_token

    async def __call__(self, request: Dict) -> Dict:
        """Handle inference request"""
        prompt = request["prompt"]
        params = request.get("params", {})

        # Tokenize input
        inputs = self.tokenizer(
            prompt,
            return_tensors="pt",
            padding=True,
            truncation=True,
            max_length=2048
        ).to(self.device)

        # Generate
        with torch.no_grad():
            outputs = self.model.generate(
                **inputs,
                max_new_tokens=params.get("max_tokens", 256),
                temperature=params.get("temperature", 0.7),
                top_p=params.get("top_p", 0.95),
                do_sample=True
            )

        # Decode
        response = self.tokenizer.decode(outputs[0], skip_special_tokens=True)

        return {
            "response": response,
            "model": self.model.config.name_or_path,
            "tokens_generated": len(outputs[0]) - len(inputs["input_ids"][0])
        }

# Deploy the model
deployment = ModelServer.bind(model_name="meta-llama/Llama-2-7b-chat-hf")
serve.run(deployment, name="llm_service", route_prefix="/generate")

class LoadBalancer:
    def __init__(self, endpoints: List[str]):
        self.endpoints = endpoints
        self.current_index = 0
        self.health_status = {ep: True for ep in endpoints}
        self.request_counts = {ep: 0 for ep in endpoints}

    async def route_request(self, request: Dict) -> Dict:
        """Route request to healthy endpoint with least load"""
        # Get healthy endpoints sorted by load
        healthy_endpoints = [
            ep for ep in self.endpoints
            if self.health_status[ep]
        ]

        if not healthy_endpoints:
            raise Exception("No healthy endpoints available")

        # Select endpoint with least requests
        selected = min(healthy_endpoints, key=lambda x: self.request_counts[x])
        self.request_counts[selected] += 1

        try:
            # Make request to selected endpoint
            response = await self.make_request(selected, request)
            return response
        except Exception as e:
            # Mark endpoint as unhealthy
            self.health_status[selected] = False
            # Retry with different endpoint
            return await self.route_request(request)
        finally:
            self.request_counts[selected] -= 1

    async def health_check(self):
        """Periodic health check for all endpoints"""
        while True:
            for endpoint in self.endpoints:
                try:
                    # Simple health check
                    await self.make_request(endpoint, {"prompt": "test", "params": {"max_tokens": 1}})
                    self.health_status[endpoint] = True
                except:
                    self.health_status[endpoint] = False

            await asyncio.sleep(30)  # Check every 30 seconds

import heapq
from dataclasses import dataclass, field
from typing import List, Optional, Dict, Any
import time
import threading
from queue import PriorityQueue

@dataclass(order=True)
class InferenceRequest:
    priority: int
    request_id: str = field(compare=False)
    prompt: str = field(compare=False)
    params: Dict = field(compare=False)
    timestamp: float = field(compare=False)
    callback: Any = field(compare=False)

class DynamicBatcher:
    def __init__(self, model, max_batch_size: int = 32,
                 max_wait_time: float = 0.1):
        self.model = model
        self.max_batch_size = max_batch_size
        self.max_wait_time = max_wait_time
        self.request_queue = PriorityQueue()
        self.processing = False
        self.start_processing()

    def add_request(self, request: InferenceRequest):
        """Add request to batching queue"""
        request.timestamp = time.time()
        self.request_queue.put(request)

    def start_processing(self):
        """Start batch processing thread"""
        thread = threading.Thread(target=self._process_batches, daemon=True)
        thread.start()

    def _process_batches(self):
        """Process batches continuously"""
        while True:
            batch = self._collect_batch()
            if batch:
                self._process_batch(batch)
            else:
                time.sleep(0.01)  # Small sleep if no requests

    def _collect_batch(self) -> List[InferenceRequest]:
        """Collect requests into a batch"""
        batch = []
        deadline = time.time() + self.max_wait_time

        while len(batch) < self.max_batch_size and time.time() < deadline:
            if not self.request_queue.empty():
                try:
                    request = self.request_queue.get_nowait()
                    batch.append(request)
                except:
                    break
            else:
                # Wait a bit for more requests
                time.sleep(0.001)

        return batch

    def _process_batch(self, batch: List[InferenceRequest]):
        """Process a batch of requests"""
        # Sort by sequence length for efficient padding
        batch.sort(key=lambda x: len(x.prompt))

        # Extract prompts and parameters
        prompts = [r.prompt for r in batch]

        # Batch inference
        responses = self.model.generate_batch(prompts)

        # Return results via callbacks
        for request, response in zip(batch, responses):
            if request.callback:
                request.callback(response)

class ContinuousBatching:
    """
    Implement continuous batching for optimal throughput
    """
    def __init__(self, model, max_batch_size: int = 128):
        self.model = model
        self.max_batch_size = max_batch_size
        self.active_sequences = {}
        self.kv_cache = {}

    def add_sequence(self, seq_id: str, prompt: str):
        """Add new sequence to active batch"""
        self.active_sequences[seq_id] = {
            "prompt": prompt,
            "generated": [],
            "done": False,
            "position": 0
        }

    def step(self) -> Dict[str, str]:
        """Perform one generation step for all active sequences"""
        if not self.active_sequences:
            return {}

        # Prepare batch
        batch_inputs = []
        seq_ids = []

        for seq_id, seq_data in self.active_sequences.items():
            if not seq_data["done"]:
                batch_inputs.append(seq_data)
                seq_ids.append(seq_id)

        # Generate next tokens
        next_tokens = self.model.generate_next_tokens(
            batch_inputs,
            kv_cache=self.kv_cache
        )

        # Update sequences
        results = {}
        for seq_id, next_token in zip(seq_ids, next_tokens):
            self.active_sequences[seq_id]["generated"].append(next_token)

            # Check if sequence is done
            if next_token in ["</s>", "<|endoftext|>"]:
                self.active_sequences[seq_id]["done"] = True
                results[seq_id] = "".join(self.active_sequences[seq_id]["generated"])

        # Remove completed sequences
        for seq_id in results:
            del self.active_sequences[seq_id]
            if seq_id in self.kv_cache:
                del self.kv_cache[seq_id]

        return results

class ContextWindowManager:
    def __init__(self, max_context_length: int = 32768):
        self.max_context_length = max_context_length
        self.compression_strategies = {
            "summarization": self.compress_by_summarization,
            "selective": self.selective_context_retention,
            "hierarchical": self.hierarchical_compression,
            "sliding": self.sliding_window_compression
        }

    def manage_context(self, context: str, new_input: str,
                      strategy: str = "hierarchical") -> str:
        """Manage context to fit within window"""
        total_length = len(context) + len(new_input)

        if total_length <= self.max_context_length:
            return context + "\n" + new_input

        # Apply compression strategy
        compression_func = self.compression_strategies[strategy]
        compressed_context = compression_func(context, new_input)

        return compressed_context

    def compress_by_summarization(self, context: str, new_input: str) -> str:
        """Compress context by summarizing older parts"""
        # Split context into chunks
        chunks = self.split_into_chunks(context, chunk_size=1000)

        # Keep recent chunks as-is, summarize older ones
        cutoff = len(chunks) // 3
        summarized = []

        for i, chunk in enumerate(chunks):
            if i < cutoff:
                # Summarize older chunks more aggressively
                summary = self.summarize_chunk(chunk, max_length=100)
                summarized.append(f"[Summarized]: {summary}")
            else:
                # Keep recent chunks
                summarized.append(chunk)

        compressed = "\n".join(summarized) + "\n" + new_input
        return self.truncate_to_fit(compressed)

    def selective_context_retention(self, context: str, new_input: str) -> str:
        """Keep only relevant parts of context"""
        # Extract key information
        entities = self.extract_entities(context)
        facts = self.extract_facts(context)
        recent_exchanges = self.get_recent_exchanges(context, n=3)

        # Reconstruct compressed context
        compressed_parts = [
            f"Key entities: {', '.join(entities)}",
            f"Important facts: {' '.join(facts)}",
            "Recent context:",
            "\n".join(recent_exchanges)
        ]

        compressed = "\n".join(compressed_parts) + "\n" + new_input
        return self.truncate_to_fit(compressed)

    def hierarchical_compression(self, context: str, new_input: str) -> str:
        """Multi-level compression based on importance"""
        # Parse context into segments with importance scores
        segments = self.segment_and_score(context)

        # Sort by importance
        segments.sort(key=lambda x: x["importance"], reverse=True)

        # Build compressed context within limit
        compressed_parts = []
        current_length = len(new_input)

        for segment in segments:
            segment_text = segment["text"]

            # Apply compression based on importance
            if segment["importance"] > 0.8:
                # Keep as-is
                compressed_text = segment_text
            elif segment["importance"] > 0.5:
                # Light compression
                compressed_text = self.light_compress(segment_text)
            else:
                # Heavy compression
                compressed_text = self.heavy_compress(segment_text)

            if current_length + len(compressed_text) < self.max_context_length:
                compressed_parts.append(compressed_text)
                current_length += len(compressed_text)
            else:
                break

        return "\n".join(compressed_parts) + "\n" + new_input

    def sliding_window_compression(self, context: str, new_input: str) -> str:
        """Maintain sliding window with decay"""
        window_size = self.max_context_length - len(new_input) - 100  # Buffer

        if len(context) <= window_size:
            return context + "\n" + new_input

        # Keep most recent content
        return context[-window_size:] + "\n" + new_input

    def segment_and_score(self, context: str) -> List[Dict]:
        """Segment context and assign importance scores"""
        segments = []

        # Simple heuristic-based scoring
        lines = context.split("\n")
        for line in lines:
            importance = 0.5  # Base score

            # Adjust based on content
            if any(keyword in line.lower() for keyword in ["important", "key", "critical"]):
                importance += 0.3
            if any(keyword in line.lower() for keyword in ["user:", "assistant:"]):
                importance += 0.2
            if len(line) > 200:  # Longer content might be more detailed
                importance += 0.1

            segments.append({
                "text": line,
                "importance": min(1.0, importance)
            })

        return segments

class MemoryEfficientLoader:
    def __init__(self):
        self.loaded_models = {}
        self.memory_monitor = MemoryMonitor()

    def load_model_sharded(self, model_name: str, num_shards: int = 4):
        """Load model in shards across multiple GPUs"""
        from accelerate import init_empty_weights, load_checkpoint_and_dispatch

        # Initialize model with empty weights
        with init_empty_weights():
            model = AutoModelForCausalLM.from_config(
                AutoConfig.from_pretrained(model_name)
            )

        # Load and dispatch across devices
        model = load_checkpoint_and_dispatch(
            model,
            model_name,
            device_map="auto",
            max_memory={
                0: "10GiB",
                1: "10GiB",
                2: "10GiB",
                3: "10GiB",
                "cpu": "30GiB"
            },
            no_split_module_classes=["Block"],
            dtype=torch.float16
        )

        return model

    def quantize_model(self, model, quantization_config: Dict):
        """Apply quantization for memory reduction"""
        from transformers import BitsAndBytesConfig
        import bitsandbytes as bnb

        if quantization_config["method"] == "int8":
            # 8-bit quantization
            model = bnb.nn.Linear8bitLt(model)
        elif quantization_config["method"] == "int4":
            # 4-bit quantization
            config = BitsAndBytesConfig(
                load_in_4bit=True,
                bnb_4bit_compute_dtype=torch.float16,
                bnb_4bit_use_double_quant=True,
                bnb_4bit_quant_type="nf4"
            )
            model = model.quantize(config)
        elif quantization_config["method"] == "fp8":
            # FP8 quantization (for newest GPUs)
            model = self.apply_fp8_quantization(model)

        return model

    def offload_to_cpu(self, model, offload_config: Dict):
        """Offload model layers to CPU when not in use"""
        from accelerate import cpu_offload_with_hook

        # Offload specific layers
        offload_layers = offload_config.get("layers", [])
        hooks = []

        for layer_name in offload_layers:
            layer = getattr(model, layer_name)
            hook = cpu_offload_with_hook(layer, execution_device="cuda:0")
            hooks.append(hook)

        return model, hooks

    def implement_gradient_checkpointing(self, model):
        """Enable gradient checkpointing to save memory during training"""
        model.gradient_checkpointing_enable()

        # Custom checkpointing for specific layers
        def checkpoint_forward(module, *args, **kwargs):
            return torch.utils.checkpoint.checkpoint(
                module._forward,
                *args,
                use_reentrant=False,
                **kwargs
            )

        # Apply to transformer layers
        for layer in model.transformer.h:
            layer.forward = lambda *args, **kwargs: checkpoint_forward(layer, *args, **kwargs)

        return model

class MemoryMonitor:
    def __init__(self):
        self.gpu_memory_reserved = {}
        self.gpu_memory_allocated = {}

    def get_memory_stats(self) -> Dict:
        """Get current memory statistics"""
        stats = {}

        if torch.cuda.is_available():
            for i in range(torch.cuda.device_count()):
                stats[f"gpu_{i}"] = {
                    "allocated": torch.cuda.memory_allocated(i) / 1024**3,  # GB
                    "reserved": torch.cuda.memory_reserved(i) / 1024**3,    # GB
                    "free": (torch.cuda.get_device_properties(i).total_memory -
                            torch.cuda.memory_allocated(i)) / 1024**3       # GB
                }

        # CPU memory
        import psutil
        mem = psutil.virtual_memory()
        stats["cpu"] = {
            "used": mem.used / 1024**3,
            "available": mem.available / 1024**3,
            "percent": mem.percent
        }

        return stats

    def optimize_memory(self):
        """Optimize memory usage"""
        # Clear cache
        if torch.cuda.is_available():
            torch.cuda.empty_cache()

        # Garbage collection
        import gc
        gc.collect()

        # Clear unused variables
        for obj in gc.get_objects():
            if torch.is_tensor(obj):
                if obj.device.type == 'cuda' and not obj.is_pinned():
                    del obj

class CostOptimizer:
    def __init__(self):
        self.model_registry = self.initialize_model_registry()
        self.cost_tracker = CostTracker()
        self.performance_monitor = PerformanceMonitor()

    def initialize_model_registry(self) -> Dict:
        """Registry of available models with costs and capabilities"""
        return {
            "gpt-4o": {
                "cost_per_1k_input": 0.005,
                "cost_per_1k_output": 0.015,
                "quality_score": 0.95,
                "latency": 2.5,
                "capabilities": ["complex_reasoning", "creative", "analysis"]
            },
            "gpt-4o-mini": {
                "cost_per_1k_input": 0.00015,
                "cost_per_1k_output": 0.0006,
                "quality_score": 0.80,
                "latency": 1.0,
                "capabilities": ["general", "simple_tasks"]
            },
            "claude-3-5-sonnet": {
                "cost_per_1k_input": 0.003,
                "cost_per_1k_output": 0.015,
                "quality_score": 0.93,
                "latency": 2.0,
                "capabilities": ["analysis", "coding", "long_context"]
            },
            "llama-3-70b": {
                "cost_per_1k_input": 0.0008,
                "cost_per_1k_output": 0.0008,
                "quality_score": 0.85,
                "latency": 1.5,
                "capabilities": ["general", "multilingual"]
            },
            "mixtral-8x7b": {
                "cost_per_1k_input": 0.0005,
                "cost_per_1k_output": 0.0005,
                "quality_score": 0.78,
                "latency": 1.2,
                "capabilities": ["general", "fast"]
            }
        }

    def select_optimal_model(self, task: Dict, constraints: Dict) -> str:
        """Select most cost-effective model for task"""
        required_quality = constraints.get("min_quality", 0.7)
        max_latency = constraints.get("max_latency", 5.0)
        max_cost = constraints.get("max_cost", float('inf'))
        required_capabilities = set(task.get("capabilities", []))

        candidates = []

        for model_name, model_info in self.model_registry.items():
            # Check constraints
            if model_info["quality_score"] < required_quality:
                continue
            if model_info["latency"] > max_latency:
                continue

            # Check capabilities
            model_capabilities = set(model_info["capabilities"])
            if required_capabilities and not required_capabilities.issubset(model_capabilities):
                continue

            # Estimate cost for this task
            estimated_cost = self.estimate_task_cost(task, model_info)
            if estimated_cost > max_cost:
                continue

            # Calculate efficiency score
            efficiency = model_info["quality_score"] / estimated_cost
            candidates.append({
                "model": model_name,
                "cost": estimated_cost,
                "quality": model_info["quality_score"],
                "efficiency": efficiency
            })

        if not candidates:
            # Fallback to cheapest model if no candidates meet criteria
            return "mixtral-8x7b"

        # Select model with best efficiency
        best = max(candidates, key=lambda x: x["efficiency"])
        return best["model"]

    def estimate_task_cost(self, task: Dict, model_info: Dict) -> float:
        """Estimate cost for specific task"""
        # Estimate token counts
        input_tokens = task.get("estimated_input_tokens", 500)
        output_tokens = task.get("estimated_output_tokens", 500)

        input_cost = (input_tokens / 1000) * model_info["cost_per_1k_input"]
        output_cost = (output_tokens / 1000) * model_info["cost_per_1k_output"]

        return input_cost + output_cost

    def implement_cascading_strategy(self, task: str) -> Dict:
        """Implement cost-efficient cascading strategy"""
        # Start with cheapest model
        models_tried = []
        total_cost = 0

        for model_name in sorted(self.model_registry.keys(),
                                key=lambda x: self.model_registry[x]["cost_per_1k_input"]):
            model_info = self.model_registry[model_name]

            # Try current model
            result = self.try_model(model_name, task)
            cost = self.calculate_actual_cost(result, model_info)
            total_cost += cost

            models_tried.append({
                "model": model_name,
                "cost": cost,
                "quality": result.get("quality_score", 0)
            })

            # Check if quality is sufficient
            if result.get("quality_score", 0) >= 0.8:
                return {
                    "final_model": model_name,
                    "result": result,
                    "total_cost": total_cost,
                    "models_tried": models_tried
                }

        # Return best result if quality threshold not met
        best_result = max(models_tried, key=lambda x: x["quality"])
        return {
            "final_model": best_result["model"],
            "total_cost": total_cost,
            "models_tried": models_tried,
            "quality_warning": "Quality threshold not met"
        }

class CostTracker:
    def __init__(self):
        self.usage_history = []
        self.cost_by_model = {}
        self.cost_by_user = {}
        self.daily_budget = 1000  # $1000 per day

    def track_usage(self, model: str, input_tokens: int,
                   output_tokens: int, user_id: str):
        """Track API usage and costs"""
        timestamp = time.time()

        # Calculate cost
        model_costs = {
            "gpt-4o": {"input": 0.005, "output": 0.015},
            "gpt-4o-mini": {"input": 0.00015, "output": 0.0006},
            # Add more models
        }

        costs = model_costs.get(model, {"input": 0.001, "output": 0.001})
        total_cost = (input_tokens / 1000) * costs["input"] + \
                    (output_tokens / 1000) * costs["output"]

        # Record usage
        usage = {
            "timestamp": timestamp,
            "model": model,
            "input_tokens": input_tokens,
            "output_tokens": output_tokens,
            "cost": total_cost,
            "user_id": user_id
        }

        self.usage_history.append(usage)

        # Update aggregates
        self.cost_by_model[model] = self.cost_by_model.get(model, 0) + total_cost
        self.cost_by_user[user_id] = self.cost_by_user.get(user_id, 0) + total_cost

        # Check budget
        self.check_budget_alerts()

    def check_budget_alerts(self):
        """Monitor and alert on budget usage"""
        # Calculate today's spending
        today_start = time.time() - (time.time() % 86400)
        today_cost = sum(
            u["cost"] for u in self.usage_history
            if u["timestamp"] >= today_start
        )

        if today_cost > self.daily_budget * 0.8:
            self.send_alert(f"Daily budget 80% consumed: ${today_cost:.2f}")

        if today_cost > self.daily_budget:
            self.send_alert(f"Daily budget exceeded: ${today_cost:.2f}")
            self.implement_throttling()

    def generate_cost_report(self, period: str = "daily") -> Dict:
        """Generate cost analysis report"""
        report = {
            "period": period,
            "total_cost": sum(u["cost"] for u in self.usage_history),
            "by_model": self.cost_by_model,
            "by_user": self.cost_by_user,
            "top_users": sorted(
                self.cost_by_user.items(),
                key=lambda x: x[1],
                reverse=True
            )[:10],
            "optimization_opportunities": self.identify_optimizations()
        }

        return report

    def identify_optimizations(self) -> List[Dict]:
        """Identify cost optimization opportunities"""
        optimizations = []

        # Analyze model usage patterns
        model_usage = {}
        for usage in self.usage_history:
            model = usage["model"]
            if model not in model_usage:
                model_usage[model] = {"count": 0, "total_cost": 0}
            model_usage[model]["count"] += 1
            model_usage[model]["total_cost"] += usage["cost"]

        # Suggest model downgrades for simple tasks
        for model, stats in model_usage.items():
            if model == "gpt-4o" and stats["count"] > 100:
                avg_tokens = sum(u["output_tokens"] for u in self.usage_history
                               if u["model"] == model) / stats["count"]
                if avg_tokens < 200:
                    optimizations.append({
                        "type": "model_downgrade",
                        "current_model": model,
                        "suggested_model": "gpt-4o-mini",
                        "potential_savings": stats["total_cost"] * 0.8
                    })

        return optimizations

class PrivacyPreservingAI:
    def __init__(self):
        self.setup_privacy_tools()
        self.compliance_standards = ["GDPR", "HIPAA", "SOC2", "CCPA"]

    def setup_privacy_tools(self):
        """Initialize privacy protection tools"""
        self.pii_detector = PIIDetector()
        self.data_anonymizer = DataAnonymizer()
        self.differential_privacy = DifferentialPrivacy()
        self.audit_logger = AuditLogger()

    def process_with_privacy(self, data: str, user_consent: Dict) -> str:
        """Process data with privacy protection"""
        # Log access
        self.audit_logger.log_access(data, user_consent)

        # Detect and handle PII
        pii_scan = self.pii_detector.scan(data)
        if pii_scan["contains_pii"]:
            if not user_consent.get("allow_pii_processing", False):
                # Anonymize PII
                data = self.data_anonymizer.anonymize(data, pii_scan["entities"])

        # Apply differential privacy for aggregations
        if user_consent.get("require_differential_privacy", False):
            data = self.differential_privacy.add_noise(data)

        return data

class PIIDetector:
    def __init__(self):
        import spacy
        self.nlp = spacy.load("en_core_web_sm")
        self.patterns = {
            "ssn": r'\b\d{3}-\d{2}-\d{4}\b',
            "credit_card": r'\b\d{4}[\s-]?\d{4}[\s-]?\d{4}[\s-]?\d{4}\b',
            "email": r'\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b',
            "phone": r'\b(?:\+?1[-.]?)?\(?[0-9]{3}\)?[-.]?[0-9]{3}[-.]?[0-9]{4}\b',
            "ip_address": r'\b(?:[0-9]{1,3}\.){3}[0-9]{1,3}\b',
            "medical_record": r'\b[A-Z]{2}\d{6,8}\b',
            "passport": r'\b[A-Z][0-9]{8}\b'
        }

    def scan(self, text: str) -> Dict:
        """Scan text for PII"""
        results = {
            "contains_pii": False,
            "entities": [],
            "risk_level": "low"
        }

        # Named entity recognition
        doc = self.nlp(text)
        for ent in doc.ents:
            if ent.label_ in ["PERSON", "ORG", "GPE", "DATE"]:
                results["entities"].append({
                    "text": ent.text,
                    "type": ent.label_,
                    "start": ent.start_char,
                    "end": ent.end_char
                })

        # Pattern matching
        import re
        for pii_type, pattern in self.patterns.items():
            matches = re.finditer(pattern, text)
            for match in matches:
                results["entities"].append({
                    "text": match.group(),
                    "type": pii_type,
                    "start": match.start(),
                    "end": match.end()
                })

        if results["entities"]:
            results["contains_pii"] = True
            results["risk_level"] = self.assess_risk_level(results["entities"])

        return results

    def assess_risk_level(self, entities: List[Dict]) -> str:
        """Assess privacy risk level"""
        high_risk_types = ["ssn", "credit_card", "medical_record", "passport"]
        medium_risk_types = ["email", "phone", "ip_address"]

        for entity in entities:
            if entity["type"] in high_risk_types:
                return "high"

        for entity in entities:
            if entity["type"] in medium_risk_types:
                return "medium"

        return "low"

class DataAnonymizer:
    def __init__(self):
        self.replacement_strategies = {
            "PERSON": self.anonymize_person,
            "email": self.anonymize_email,
            "phone": self.anonymize_phone,
            "ssn": lambda x: "[SSN_REDACTED]",
            "credit_card": lambda x: "[CC_REDACTED]"
        }

    def anonymize(self, text: str, entities: List[Dict]) -> str:
        """Anonymize PII in text"""
        # Sort entities by position (reverse order for replacement)
        entities.sort(key=lambda x: x["start"], reverse=True)

        anonymized_text = text
        for entity in entities:
            entity_type = entity["type"]

            if entity_type in self.replacement_strategies:
                replacement = self.replacement_strategies[entity_type](entity["text"])
            else:
                replacement = f"[{entity_type}_REDACTED]"

            anonymized_text = (
                anonymized_text[:entity["start"]] +
                replacement +
                anonymized_text[entity["end"]:]
            )

        return anonymized_text

    def anonymize_person(self, name: str) -> str:
        """Generate consistent anonymous name"""
        import hashlib

        # Generate consistent hash for name
        name_hash = hashlib.md5(name.encode()).hexdigest()

        # Use hash to select from anonymous names
        first_names = ["Alex", "Jordan", "Casey", "Morgan", "Riley"]
        last_names = ["Smith", "Johnson", "Williams", "Brown", "Jones"]

        first_idx = int(name_hash[:8], 16) % len(first_names)
        last_idx = int(name_hash[8:16], 16) % len(last_names)

        return f"{first_names[first_idx]} {last_names[last_idx]}"

    def anonymize_email(self, email: str) -> str:
        """Anonymize email while preserving domain"""
        parts = email.split("@")
        if len(parts) == 2:
            return f"user_xxxx@{parts[1]}"
        return "[EMAIL_REDACTED]"

    def anonymize_phone(self, phone: str) -> str:
        """Anonymize phone number keeping area code"""
        import re

        # Extract area code if present
        match = re.match(r'(\+?\d{1,3}[-.]?)?\(?(\d{3})\)?', phone)
        if match:
            area_code = match.group(2)
            return f"({area_code}) XXX-XXXX"
        return "[PHONE_REDACTED]"

class ComplianceManager:
    def __init__(self):
        self.compliance_checks = {
            "GDPR": self.check_gdpr_compliance,
            "HIPAA": self.check_hipaa_compliance,
            "SOC2": self.check_soc2_compliance,
            "CCPA": self.check_ccpa_compliance
        }
        self.data_retention_policies = self.load_retention_policies()

    def check_compliance(self, data_processing: Dict) -> Dict:
        """Check compliance with regulations"""
        results = {
            "compliant": True,
            "violations": [],
            "warnings": [],
            "recommendations": []
        }

        for regulation, check_func in self.compliance_checks.items():
            check_result = check_func(data_processing)

            if not check_result["compliant"]:
                results["compliant"] = False
                results["violations"].extend(check_result.get("violations", []))

            results["warnings"].extend(check_result.get("warnings", []))
            results["recommendations"].extend(check_result.get("recommendations", []))

        return results

    def check_gdpr_compliance(self, data_processing: Dict) -> Dict:
        """Check GDPR compliance"""
        result = {"compliant": True, "violations": [], "warnings": []}

        # Check for lawful basis
        if not data_processing.get("lawful_basis"):
            result["violations"].append("No lawful basis for processing")
            result["compliant"] = False

        # Check for user consent
        if data_processing.get("personal_data") and not data_processing.get("user_consent"):
            result["violations"].append("Processing personal data without consent")
            result["compliant"] = False

        # Check data minimization
        if data_processing.get("data_collected_unnecessarily"):
            result["warnings"].append("Potential violation of data minimization principle")

        # Check right to erasure
        if not data_processing.get("deletion_mechanism"):
            result["warnings"].append("No mechanism for right to erasure")

        return result

    def implement_data_retention(self, data: Dict, policy: str):
        """Implement data retention policies"""
        retention_period = self.data_retention_policies.get(policy, 90)  # days

        # Schedule deletion
        deletion_date = time.time() + (retention_period * 86400)

        return {
            "data_id": data.get("id"),
            "retention_period": retention_period,
            "deletion_scheduled": deletion_date,
            "policy": policy
        }

class AuditLogger:
    def __init__(self, log_path: str = "/var/log/ai_audit.log"):
        self.log_path = log_path
        self.setup_logger()

    def setup_logger(self):
        """Setup secure audit logging"""
        import logging
        from logging.handlers import RotatingFileHandler

        self.logger = logging.getLogger("ai_audit")
        self.logger.setLevel(logging.INFO)

        # Rotating file handler with encryption
        handler = RotatingFileHandler(
            self.log_path,
            maxBytes=100 * 1024 * 1024,  # 100MB
            backupCount=10
        )

        formatter = logging.Formatter(
            '%(asctime)s - %(levelname)s - %(message)s',
            datefmt='%Y-%m-%d %H:%M:%S'
        )
        handler.setFormatter(formatter)
        self.logger.addHandler(handler)

    def log_access(self, data: Any, user_context: Dict):
        """Log data access for audit trail"""
        import json
        import hashlib

        log_entry = {
            "timestamp": time.time(),
            "user_id": user_context.get("user_id"),
            "action": user_context.get("action", "access"),
            "data_hash": hashlib.sha256(str(data).encode()).hexdigest(),
            "ip_address": user_context.get("ip_address"),
            "session_id": user_context.get("session_id"),
            "compliance_flags": user_context.get("compliance_flags", [])
        }

        self.logger.info(json.dumps(log_entry))

    def generate_audit_report(self, start_date: str, end_date: str) -> Dict:
        """Generate audit report for compliance"""
        # Parse logs and generate report
        report = {
            "period": f"{start_date} to {end_date}",
            "total_accesses": 0,
            "unique_users": set(),
            "actions": {},
            "compliance_violations": []
        }

        # Read and parse logs
        with open(self.log_path, 'r') as f:
            for line in f:
                try:
                    # Parse log entry
                    entry = json.loads(line.split(" - ")[-1])

                    # Update statistics
                    report["total_accesses"] += 1
                    report["unique_users"].add(entry.get("user_id"))

                    action = entry.get("action")
                    report["actions"][action] = report["actions"].get(action, 0) + 1

                    # Check for violations
                    if entry.get("compliance_flags"):
                        report["compliance_violations"].append(entry)

                except Exception as e:
                    continue

        report["unique_users"] = len(report["unique_users"])
        return report