from dataclasses import dataclass, field
from collections import defaultdict
import time
import threading

@dataclass
class TokenUsage:
    prompt_tokens: int = 0
    completion_tokens: int = 0
    total_tokens: int = 0
    
    def __add__(self, other: "TokenUsage"):
        return TokenUsage(
            prompt_tokens=self.prompt_tokens + other.prompt_tokens,
            completion_tokens=self.completion_tokens + other.completion_tokens,
            total_tokens=self.total_tokens + other.total_tokens
        )

class TokenCounter:
    """Tracks token usage across all agents with attribution."""
    
    def __init__(self):
        self.usage: dict[str, dict[str, TokenUsage]] = defaultdict(
            lambda: defaultdict(TokenUsage)
        )
        self._lock = threading.Lock()
    
    def record(self, agent_name: str, task_id: str, 
               prompt_tokens: int, completion_tokens: int):
        """Record token usage for an agent-task pair."""
        with self._lock:
            usage = TokenUsage(
                prompt_tokens=prompt_tokens,
                completion_tokens=completion_tokens,
                total_tokens=prompt_tokens + completion_tokens
            )
            self.usage[agent_name][task_id] = usage
    
    def agent_total(self, agent_name: str) -> TokenUsage:
        """Get total tokens for an agent."""
        with self._lock:
            total = TokenUsage()
            for task_usage in self.usage[agent_name].values():
                total += task_usage
            return total
    
    def task_cost(self, agent_name: str, task_id: str,
                  input_rate: float, output_rate: float) -> float:
        """Calculate monetary cost for a specific task."""
        usage = self.usage[agent_name].get(task_id)
        if not usage:
            return 0.0
        return (
            usage.prompt_tokens * input_rate / 1_000_000 +
            usage.completion_tokens * output_rate / 1_000_000
        )
    
    def top_agents(self, n: int = 10) -> list[tuple[str, TokenUsage]]:
        """Get the n highest-consuming agents."""
        with self._lock:
            totals = [
                (agent, self.agent_total(agent))
                for agent in self.usage
            ]
            totals.sort(key=lambda x: x[1].total_tokens, reverse=True)
            return totals[:n]

class TokenBudget:
    """Enforce per-agent and global token budgets."""
    
    def __init__(self, counter: TokenCounter):
        self.counter = counter
        self.agent_limits: dict[str, int] = {}  # agent -> max tokens
        self.period_limits: dict[str, int] = {}  # period -> max tokens
        
        # Current period tracking
        self.period_start = time.time()
        self.period_usage: dict[str, int] = defaultdict(int)
    
    def set_agent_limit(self, agent_name: str, max_tokens: int):
        """Set a per-agent token budget."""
        self.agent_limits[agent_name] = max_tokens
    
    def set_period_limit(self, period_name: str, max_tokens: int):
        """Set a global period budget (e.g., daily, weekly)."""
        self.period_limits[period_name] = max_tokens
    
    async def check_and_apply_budget(self, agent_name: str, 
                                       estimated_tokens: int) -> bool:
        """Check if this request would exceed any budget. Returns True if allowed."""
        
        # 1. Check per-agent limit
        agent_limit = self.agent_limits.get(agent_name)
        if agent_limit:
            current = self.counter.agent_total(agent_name).total_tokens
            if current + estimated_tokens > agent_limit:
                return False  # Agent budget exceeded
        
        # 2. Check period limits
        current_period = self._current_period()
        for period, limit in self.period_limits.items():
            if self.period_usage[period] + estimated_tokens > limit:
                return False  # Global period budget exceeded
        
        # 3. Reserve tokens
        self.period_usage[current_period] += estimated_tokens
        return True
    
    def _current_period(self) -> str:
        """Get the current period label (e.g., 'daily:2024-01-15')."""
        now = datetime.now()
        return f"daily:{now.strftime('%Y-%m-%d')}"
    
    def reset_period(self):
        """Reset period tracking (call daily, weekly)."""
        self.period_start = time.time()
        self.period_usage.clear()

class TokenOptimizer:
    """Apply optimization strategies to reduce token consumption."""
    
    def __init__(self, llm):
        self.llm = llm
    
    async def compress_context(self, messages: list[dict], 
                                max_tokens: int) -> list[dict]:
        """Compress conversation history to fit within token budget."""
        total = self._count_tokens(messages)
        
        if total <= max_tokens:
            return messages  # No compression needed
        
        # Strategy 1: Remove low-signal turns
        messages = self._remove_greetings(messages)
        
        # Strategy 2: Summarize older messages
        if self._count_tokens(messages) > max_tokens:
            messages = await self._summarize_older(messages)
        
        # Strategy 3: Truncate long messages
        if self._count_tokens(messages) > max_tokens:
            messages = self._truncate_longest(messages, max_tokens)
        
        return messages
    
    def _remove_greetings(self, messages: list[dict]) -> list[dict]:
        """Remove low-value greetings and acknowledgments."""
        greetings = {"hi", "hello", "thanks", "okay", "sure", "got it"}
        return [
            m for m in messages
            if not (m["role"] == "assistant" and 
                    m["content"].strip().lower() in greetings)
        ]
    
    async def _summarize_older(self, messages: list[dict]) -> list[dict]:
        """Summarize messages beyond a threshold."""
        keep_recent = messages[-4:]  # Keep last 4 exchanges verbatim
        to_summarize = messages[:-4]
        
        if not to_summarize:
            return messages
        
        text = "\n".join(
            f"[{m['role']}]: {m['content'][:500]}"
            for m in to_summarize
        )
        
        summary = await self.llm.generate(
            f"Summarize this conversation history concisely while preserving "
            f"all key facts, decisions, and user preferences:\n\n{text}",
            max_tokens=200
        )
        
        return [
            {"role": "system", "content": f"[Summarized Context]: {summary}"}
        ] + keep_recent
    
    def _truncate_longest(self, messages: list[dict], 
                           max_tokens: int) -> list[dict]:
        """Truncate the longest messages to fit budget."""
        while self._count_tokens(messages) > max_tokens:
            longest = max(
                messages, 
                key=lambda m: len(m["content"].split())
            )
            # Truncate by half
            words = longest["content"].split()
            longest["content"] = " ".join(words[:len(words)//2])
        
        return messages
    
    def _count_tokens(self, messages: list[dict]) -> int:
        """Rough token estimation (4 chars ≈ 1 token)."""
        total = sum(len(m["content"]) for m in messages)
        return total // 4

class BudgetDashboard:
    """Real-time token budget monitoring."""
    
    def __init__(self, counter: TokenCounter, budgets: TokenBudget):
        self.counter = counter
        self.budgets = budgets
    
    def current_status(self) -> dict:
        """Get current budget status for all agents."""
        agents = {}
        for agent_name in self.counter.usage:
            usage = self.counter.agent_total(agent_name)
            limit = self.budgets.agent_limits.get(agent_name, float('inf'))
            agents[agent_name] = {
                "total_tokens": usage.total_tokens,
                "prompt_tokens": usage.prompt_tokens,
                "completion_tokens": usage.completion_tokens,
                "budget_limit": limit,
                "percent_used": (usage.total_tokens / limit * 100) if limit != float('inf') else 0,
                "estimated_cost": self._estimate_cost(usage),
            }
        
        return {
            "agents": agents,
            "global": {
                "total_tokens": sum(a["total_tokens"] for a in agents.values()),
                "total_cost": sum(a["estimated_cost"] for a in agents.values()),
            },
            "period_usage": dict(self.budgets.period_usage)
        }
    
    def _estimate_cost(self, usage: TokenUsage) -> float:
        """Estimate cost at blended rate ($0.003/1K tokens)."""
        return usage.total_tokens * 0.003 / 1000
    
    def budget_alert(self, threshold: float = 0.8) -> list[str]:
        """Get alerts for agents approaching budget limits."""
        alerts = []
        for agent_name, info in self.current_status()["agents"].items():
            if info["percent_used"] > threshold * 100:
                alerts.append(
                    f"{agent_name}: {info['percent_used']:.0f}% of budget used "
                    f"({info['total_tokens']:,} tokens)"
                )
        return alerts

class CostController:
    """Proactive cost optimization controls."""
    
    def __init__(self, optimizer: TokenOptimizer, budgets: TokenBudget):
        self.optimizer = optimizer
        self.budgets = budgets
    
    async def optimize_request(self, agent_name: str, 
                                 messages: list[dict]) -> list[dict]:
        """Optimize a request before sending to LLM."""
        
        # Apply optimizations based on agent's budget status
        agent_usage = self.budgets.counter.agent_total(agent_name)
        agent_limit = self.budgets.agent_limits.get(agent_name, float('inf'))
        
        usage_ratio = agent_usage.total_tokens / agent_limit if agent_limit else 0
        
        if usage_ratio > 0.9:
            # Critical: aggressive compression
            return await self.optimizer.compress_context(
                messages, max_tokens=2000
            )
        elif usage_ratio > 0.7:
            # Warning: moderate compression
            return await self.optimizer.compress_context(
                messages, max_tokens=4000
            )
        
        return messages  # Within budget, no optimization needed
    
    def model_selection(self, task_difficulty: str, 
                         available_budget: float) -> str:
        """Select the most cost-effective model for the task."""
        models = {
            "easy": {"model": "gpt-4o-mini", "cost_per_1k": 0.00015},
            "medium": {"model": "gpt-4o", "cost_per_1k": 0.0025},
            "hard": {"model": "gpt-4o", "cost_per_1k": 0.0025},
        }
        
        recommended = models.get(task_difficulty, models["medium"])
        
        # If budget is very tight, downgrade
        if available_budget < recommended["cost_per_1k"] * 100:
            return models["easy"]["model"]
        
        return recommended["model"]

# budget-alerts.yaml
budget_rules:
  - name: daily_budget_exceeded
    condition: daily_tokens > daily_limit
    severity: P1
    action: pause_all_noncritical_agents
    message: "Daily token budget exceeded: {used}/{limit}"

  - name: agent_spike
    condition: agent_hourly_tokens > agent_hourly_limit * 2
    severity: P2
    action: investigate_agent
    message: "Agent {name} token usage spiked: {hourly_used}/hr"

  - name: runaway_detected
    condition: agent_tokens_per_task > task_limit * 3
    severity: P1
    action: kill_agent_task
    message: "Agent {name} may be looping: {tokens_per_task} tokens/task"

  - name: budget_forecast
    condition: projected_daily_tokens > daily_limit * 0.9
    severity: P3
    action: notify_team
    message: "On track to exceed daily budget by {overshoot}%"