"""
Fast Path Optimizer — identifies and executes optimized execution paths.

Analyzes goals to determine if they can be handled via a "fast path"
that bypasses the full DAG mesh. Fast paths are pre-compiled execution
strategies for common goal types.

Ultra-lightweight: pure Python pattern matching, no ML overhead.
"""
import os
from typing import Optional, Callable, Awaitable


class FastPathOptimizer:
    """
    Identifies and executes optimized fast paths for common goal types.

    Fast paths bypass the full DAG-based execution mesh for simple
    goals, reducing latency from seconds to milliseconds.

    Pre-compiled fast paths:
    - direct_reply: Simple Q&A, no tool use required
    - quick_calc: Mathematical calculations
    - code_snippet: Generate a code snippet
    - definition: Provide a definition or explanation
    """

    def __init__(self, llm_call_fn=None):
        self._llm = llm_call_fn
        self._fast_paths = {
            "direct_reply": self._fast_direct,
            "quick_calc": self._fast_calc,
        }

    def detect_fast_path(self, goal: str) -> Optional[str]:
        """
        Detect if a goal can be handled via a fast path.
        Returns the fast path name or None.
        """
        if not goal:
            return None

        goal_lower = goal.strip().lower()

        # Very short queries (under 40 chars, no special chars)
        if len(goal_lower) < 40 and not any(c in goal for c in ["\n", "{", "}", "[", "]"]):
            return "direct_reply"

        # Simple calculations
        if any(op in goal_lower for op in ["+", "-", "*", "/", "calculate", "compute"]):
            if len(goal_lower) < 80:
                return "quick_calc"

        # Definition/explanation requests
        if goal_lower.startswith(("what is", "define", "what does", "explain")):
            if len(goal_lower) < 60:
                return "direct_reply"

        return None

    async def execute_fast_path(self, path_name: str, goal: str) -> Optional[str]:
        """
        Execute a fast path and return the result.
        Returns None if the fast path fails.
        """
        handler = self._fast_paths.get(path_name)
        if not handler:
            return None

        try:
            return await handler(goal)
        except Exception:
            return None

    async def _fast_direct(self, goal: str) -> str:
        """Fast path for direct Q&A."""
        if self._llm:
            return await self._llm(goal, model_hint="fast", max_tokens=500)
        return f"Response to: {goal}"

    async def _fast_calc(self, goal: str) -> str:
        """Fast path for mathematical calculations."""
        # Try to extract and compute
        import re
        # Remove words, keep math expression
        expr = re.sub(r'[^0-9+\-*/().%\s]', '', goal).strip()
        if expr:
            try:
                result = eval(expr, {"__builtins__": {}}, {})
                return f"Result: {result}"
            except Exception:
                pass
        # Fallback to LLM
        if self._llm:
            return await self._llm(f"Calculate: {goal}", model_hint="fast", max_tokens=100)
        return f"Cannot calculate: {goal}"