"""
Base Tool Manager

This module provides a general framework for managing different types of tools
in the agentic pipeline. It can be extended for specific tool types.
"""

import os
import sys
from abc import ABC, abstractmethod
from typing import Dict, List, Any, Optional, Type, Callable
from pathlib import Path
import json
import tempfile
from dataclasses import dataclass
from enum import Enum

from langchain_core.tools import BaseTool, tool
from pydantic import BaseModel, Field

# Add parent directory to path for imports
sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))

# Define base configuration classes here since they might not exist in new_agent
@dataclass
class ToolConfig:
    """Configuration for a tool."""
    name: str
    tool_type: str
    description: str
    fallback_enabled: bool = True

class ToolStatus(Enum):
    """Status of a tool."""
    NOT_AVAILABLE = "not_available"
    AVAILABLE = "available"
    FALLBACK = "fallback"
    ERROR = "error"

@dataclass
class ToolConfigExtended(ToolConfig):
    """Extended configuration for a tool."""
    input_schema: Type[BaseModel] = None
    cache_results: bool = True


class BaseToolManager(ABC):
    """
    Abstract base class for tool managers.
    Provides common functionality for tool initialization and management.
    """
    
    def __init__(self, config: ToolConfigExtended):
        """
        Initialize the tool manager.
        
        Args:
            config: Tool configuration
        """
        self.config = config
        self.status = ToolStatus.NOT_AVAILABLE
        self.tool = None
        self.temp_dir = Path(tempfile.mkdtemp())
        self.temp_dir.mkdir(parents=True, exist_ok=True)
        
        # Initialize tool
        self._initialize_tool()
    
    @abstractmethod
    def _initialize_tool(self):
        """Initialize the specific tool. Must be implemented by subclasses."""
        pass
    
    @abstractmethod
    def _create_tool(self) -> BaseTool:
        """Create the tool instance. Must be implemented by subclasses."""
        pass
    
    @abstractmethod
    def _create_fallback_tool(self) -> BaseTool:
        """Create fallback tool. Must be implemented by subclasses."""
        pass
    
    def is_available(self) -> bool:
        """Check if the tool is available."""
        return self.status in [ToolStatus.AVAILABLE, ToolStatus.FALLBACK]
    
    def get_status(self) -> ToolStatus:
        """Get current tool status."""
        return self.status
    
    def get_tool(self) -> Optional[BaseTool]:
        """Get the tool instance."""
        return self.tool
    
    def get_tool_info(self) -> Dict[str, Any]:
        """Get information about the tool."""
        return {
            "name": self.config.name,
            "type": self.config.tool_type,
            "status": self.status.value,
            "description": self.config.description,
            "temp_dir": str(self.temp_dir)
        }
    
    def _set_status(self, status: ToolStatus):
        """Set the tool status."""
        self.status = status
    
    def _handle_error(self, error: Exception, fallback: bool = True):
        """Handle errors during tool initialization."""
        print(f"Error in {self.config.name}: {error}")
        self._set_status(ToolStatus.ERROR)
        
        if fallback and self.config.fallback_enabled:
            self._initialize_fallback()
    
    def _initialize_fallback(self):
        """Initialize fallback tool when main tool fails."""
        print(f"Initializing fallback for {self.config.name}")
        self._set_status(ToolStatus.FALLBACK)
        self.tool = self._create_fallback_tool()
    
    def cleanup(self):
        """Clean up resources."""
        self.tool = None
        self._set_status(ToolStatus.NOT_AVAILABLE)


class ToolRegistry:
    """
    Registry for managing multiple tool managers.
    Provides a centralized way to access different tools.
    """
    
    def __init__(self):
        self._managers: Dict[str, BaseToolManager] = {}
        self._configs: Dict[str, ToolConfigExtended] = {}
        self._tools: Dict[str, BaseTool] = {}
    
    def register_tool(self, name: str, manager: BaseToolManager, config: ToolConfigExtended):
        """Register a tool manager."""
        self._managers[name] = manager
        self._configs[name] = config
        self._tools[name] = manager.get_tool()
        print(f"Registered tool: {name}")
    
    def get_tool(self, name: str) -> Optional[BaseTool]:
        """Get a tool by name."""
        return self._tools.get(name)
    
    def get_manager(self, name: str) -> Optional[BaseToolManager]:
        """Get a tool manager by name."""
        return self._managers.get(name)
    
    def get_available_tools(self) -> List[str]:
        """Get list of available tool names."""
        return [name for name, manager in self._managers.items() if manager.is_available()]
    
    def get_tool_info(self, name: str) -> Optional[Dict[str, Any]]:
        """Get information about a specific tool."""
        manager = self._managers.get(name)
        if manager:
            return manager.get_tool_info()
        return None
    
    def get_all_tools(self) -> Dict[str, BaseTool]:
        """Get all available tools."""
        return {name: tool for name, tool in self._tools.items() if tool is not None}
    
    def cleanup_all(self):
        """Clean up all registered tools."""
        for manager in self._managers.values():
            manager.cleanup()
        self._managers.clear()
        self._configs.clear()
        self._tools.clear()


# Global tool registry
tool_registry = ToolRegistry()


def create_tool_wrapper(tool_name: str, description: str, input_schema: Type[BaseModel]) -> Callable:
    """
    Create a tool wrapper function for LangChain integration.
    
    Args:
        tool_name: Name of the tool
        description: Description of the tool
        input_schema: Input schema for the tool
        
    Returns:
        Tool wrapper function
    """
    def tool_wrapper(**kwargs) -> Dict[str, Any]:
        """Tool wrapper function."""
        tool = tool_registry.get_tool(tool_name)
        if tool is None:
            return {"error": f"Tool {tool_name} not available"}
        
        try:
            # Convert kwargs to input schema
            input_data = input_schema(**kwargs)
            result = tool.run(input_data.dict())
            return result
        except Exception as e:
            return {"error": f"Tool {tool_name} failed: {str(e)}"}
    
    # Set function attributes for LangChain
    tool_wrapper.__name__ = tool_name
    tool_wrapper.__doc__ = description
    
    return tool_wrapper