from langchain_core.messages import AIMessage, HumanMessage
from langgraph.graph import START, END, StateGraph

from typing import Any, Dict, List, Literal, Optional, TypedDict
import logging
from pathlib import Path

from args import Args


class State(TypedDict):
    """State class for the agent graph."""
    initial_query: str
    messages: List[Dict[str, Any]]
    nr_interactions: int
    final_response: Optional[str]


class Nodes:
    """
    Collection of node functions for the agent graph.
    """
    def manager_node(self, state: State) -> State:
        """
        Orchestrates the workflow by delegating tasks to specialized nodes and integrating their outputs
        """
        # TODO: To implement...
        pass

    def final_answer_node(self, state: State) -> State:
        """
        Formats and delivers the final response to the user
        """
        # TODO: To implement...
        pass

    def auditor_node(self, state: State) -> State:
        """
        Reviews manager's outputs for accuracy, safety, and quality
        """
        # TODO: To implement...
        pass

    def solver_node(self, state: State) -> State:
        """
        Central problem-solving node that coordinates with specialized experts based on task requirements
        """
        # TODO: To implement...
        pass

    def researcher_node(self, state: State) -> State:
        """
        Retrieves and synthesizes information from various sources to answer knowledge-based questions
        """
        # TODO: To implement...
        pass

    def encryption_expert_node(self, state: State) -> State:
        """
        Handles encryption/decryption tasks and encoding/decoding operations
        """
        # TODO: To implement...
        pass

    def encryption_advisor_node(self, state: State) -> State:
        """
        Provides specialized guidance on complex encryption problems to the encryption expert
        """
        # TODO: To implement...
        pass

    def math_expert_node(self, state: State) -> State:
        """
        Performs mathematical calculations and solves numerical problems
        """
        # TODO: To implement...
        pass

    def math_advisor_node(self, state: State) -> State:
        """
        Provides specialized guidance on complex mathematical problems to the math expert
        """
        # TODO: To implement...
        pass

    def reasoner_node(self, state: State) -> State:
        """
        Performs logical reasoning, inference, and step-by-step problem-solving
        """
        # TODO: To implement...
        pass

    def image_handler_node(self, state: State) -> State:
        """
        Processes, analyzes, and generates information related to images
        """
        # TODO: To implement...
        pass

    def video_handler_node(self, state: State) -> State:
        """
        Processes, analyzes, and generates information related to videos
        """
        # TODO: To implement...
        pass


class Edges:
    """
    Collection of conditional edge functions for the agent graph.
    """
    def manager_edge(self, state: State) -> Literal["solver", "auditor", "final_answer"]:
        """
        Conditional edge for manager node.
        Returns one of: "solver", "auditor", "final_answer"
        """
        # TODO: To implement...
        pass

    def solver_edge(self, state: State) -> Literal["manager", "researcher", "encryption_expert", "math_expert", "reasoner", "image_handler", "video_handler"]:
        """
        Conditional edge for solver node.
        Returns one of: "manager", "researcher", "encryption_expert", "math_expert", "reasoner", "image_handler", "video_handler"
        """
        # TODO: To implement...
        pass

    def encryption_expert_edge(self, state: State) -> Literal["solver", "encryption_advisor"]:
        """
        Conditional edge for encryption_expert node.
        Returns one of: "solver", "encryption_advisor"
        """
        # TODO: To implement...
        pass

    def math_expert_edge(self, state: State) -> Literal["solver", "math_advisor"]:
        """
        Conditional edge for math_expert node.
        Returns one of: "solver", "math_advisor"
        """
        # TODO: To implement...
        pass