"""
DS-STAR Graph: Connects all agents into a workflow.

This module builds the LangGraph StateGraph that orchestrates the multi-agent system.
"""

from typing import Literal

from langgraph.checkpoint.memory import MemorySaver
from langgraph.graph import END, StateGraph

from .agents.analyzer_agent import analyzer_node
from .agents.coder_agent import coder_node
from .agents.finalyzer_agent import finalyzer_node
from .agents.planner_agent import planner_node
from .agents.router_agent import backtrack_node, router_node
from .agents.verifier_agent import verifier_node
from .utils.state import DSStarState

# ==================== CONDITIONAL ROUTING FUNCTIONS ====================


def route_after_analyzer(state: DSStarState) -> Literal["planner", "__end__"]:
    """
    Route after analyzer based on success.

    If analyzer found errors, end workflow.
    Otherwise, proceed to planner.
    """
    if "error" in state.get("data_descriptions", {}):
        return "__end__"
    return state.get("next", "planner")


def route_after_planner(state: DSStarState) -> Literal["coder", "__end__"]:
    """Route after planner to coder."""
    return state.get("next", "coder")


def route_after_coder(state: DSStarState) -> Literal["verifier", "__end__"]:
    """Route after coder to verifier."""
    return state.get("next", "verifier")


def route_after_verifier(
    state: DSStarState,
) -> Literal["router", "finalyzer", "__end__"]:
    """
    Route after verifier based on sufficiency and iteration count.

    If max iterations reached, go to finalyzer.
    If sufficient, go to finalyzer.
    Otherwise, go to router to decide next action.
    """
    # Check max iterations
    if state["iteration"] >= state["max_iterations"]:
        print(f"\n⚠ Max iterations ({state['max_iterations']}) reached, finalizing...")
        return "finalyzer"

    return state.get("next", "router")


def route_after_router(state: DSStarState) -> Literal["planner", "backtrack"]:
    """
    Route after router based on decision.

    If router says "Add Step", go to planner.
    If router says "Step N", go to backtrack.
    """
    return state.get("next", "planner")


# ==================== GRAPH BUILDER ====================


def build_ds_star_graph(llm, max_iterations: int = 20):
    """
    Constructs the LangGraph workflow for DS-STAR.

    The workflow follows this pattern:
    1. Analyzer: Analyze data files (runs once)
    2. Planner: Generate next plan step
    3. Coder: Implement plan as code
    4. Verifier: Check if sufficient
    5. If insufficient:
       a. Router: Decide to add step or backtrack
       b. Backtrack (optional): Remove wrong steps
       c. Go back to Planner
    6. If sufficient:
       Finalyzer: Create polished final solution

    Args:
        llm: LLM instance (e.g., ChatOpenAI, ChatGoogleGenerativeAI)
        max_iterations: Maximum refinement iterations (default: 20)

    Returns:
        Compiled LangGraph application with checkpointing
    """
    # Initialize graph with state schema
    workflow = StateGraph(DSStarState)

    # Add all agent nodes
    workflow.add_node("analyzer", analyzer_node)
    workflow.add_node("planner", planner_node)
    workflow.add_node("coder", coder_node)
    workflow.add_node("verifier", verifier_node)
    workflow.add_node("router", router_node)
    workflow.add_node("backtrack", backtrack_node)
    workflow.add_node("finalyzer", finalyzer_node)

    # Set entry point
    workflow.set_entry_point("analyzer")

    # Add conditional edges with proper routing
    workflow.add_conditional_edges(
        "analyzer", route_after_analyzer, {"planner": "planner", "__end__": END}
    )

    workflow.add_conditional_edges(
        "planner", route_after_planner, {"coder": "coder", "__end__": END}
    )

    workflow.add_conditional_edges(
        "coder", route_after_coder, {"verifier": "verifier", "__end__": END}
    )

    workflow.add_conditional_edges(
        "verifier",
        route_after_verifier,
        {"router": "router", "finalyzer": "finalyzer", "__end__": END},
    )

    workflow.add_conditional_edges(
        "router", route_after_router, {"planner": "planner", "backtrack": "backtrack"}
    )

    workflow.add_edge("backtrack", "planner")
    workflow.add_edge("finalyzer", END)

    # Add memory/checkpointing
    memory = MemorySaver()

    # Compile graph
    app = workflow.compile(checkpointer=memory)

    return app


def create_initial_state(query: str, llm, max_iterations: int = 20) -> DSStarState:
    """
    Create initial state for the DS-STAR workflow.

    Args:
        query: User's question to answer
        llm: LLM instance
        max_iterations: Maximum refinement iterations

    Returns:
        Initial DSStarState dictionary
    """
    return {
        "query": query,
        "data_descriptions": {},
        "plan": [],
        "current_code": "",
        "execution_result": "",
        "is_sufficient": False,
        "router_decision": "",
        "iteration": 0,
        "max_iterations": max_iterations,
        "messages": [],
        "next": "analyzer",
        "llm": llm,
    }


def run_ds_star(
    query: str, llm, max_iterations: int = 20, thread_id: str = "ds-star-1"
):
    """
    Run the complete DS-STAR workflow.

    Args:
        query: User's question to answer
        llm: LLM instance
        max_iterations: Maximum refinement iterations
        thread_id: Unique thread ID for checkpointing

    Returns:
        Final state after workflow completion
    """
    print("=" * 60)
    print("DS-STAR MULTI-AGENT SYSTEM")
    print("=" * 60)
    print(f"Query: {query}")
    print(f"Max Iterations: {max_iterations}")
    print("=" * 60)

    # Build graph
    app = build_ds_star_graph(llm, max_iterations)

    # Create initial state
    initial_state = create_initial_state(query, llm, max_iterations)

    # Run with checkpointing
    config = {"configurable": {"thread_id": thread_id}}

    try:
        # Execute the workflow
        final_state = app.invoke(initial_state, config)

        # Display results
        print("\n" + "=" * 60)
        print("FINAL SOLUTION")
        print("=" * 60)
        print("\nGenerated Code:")
        print("-" * 60)
        print(final_state["current_code"])
        print("\n" + "-" * 60)
        print("Execution Result:")
        print("-" * 60)
        print(final_state["execution_result"])
        print("=" * 60)

        return final_state

    except Exception as e:
        print(f"\n✗ Error during execution: {str(e)}")
        import traceback

        traceback.print_exc()
        return None