from langchain_core.messages import AnyMessage, BaseMessage, AIMessage, HumanMessage
from langgraph.graph import START, END, StateGraph
from langgraph.graph.message import add_messages
from langgraph.prebuilt import ToolNode, tools_condition

from typing import Annotated, 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]]
    messages: Annotated[list[AnyMessage], add_messages]
    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 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", "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