# minimal_graph_interpreter.py
# A tiny, in-memory, non-cyclic node graph + interpreter.
# Nodes have: Type, ID, attrs (all strings). Connections are '@NodeID.Attr'.

from typing import Dict, Any, List, Callable, Optional, Tuple

Node = Dict[str, Any]  # {"ID": str, "Type": str, "attrs": {str:str}, "state": {str:Any}}

def is_ref(value: Any) -> bool:
    return isinstance(value, str) and value.startswith("@") and "." in value[1:]

def parse_ref(ref: str) -> Tuple[str, str]:
    # "@NodeID.Attr" -> ("NodeID", "Attr")
    target = ref[1:]
    node_id, attr = target.split(".", 1)
    return node_id, attr

def to_number(s: Any) -> Optional[float]:
    if isinstance(s, (int, float)):
        return float(s)
    if not isinstance(s, str):
        return None
    try:
        return float(int(s))
    except ValueError:
        try:
            return float(s)
        except ValueError:
            return None

class Interpreter:
    def __init__(self, nodes: List[Node]):
        # normalize nodes and build index
        self.nodes: List[Node] = []
        self.by_id: Dict[str, Node] = {}
        for n in nodes:
            node = {"ID": n["ID"], "Type": n["Type"], "attrs": dict(n.get("attrs", {})), "state": {}}
            self.nodes.append(node)
            self.by_id[node["ID"]] = node

        # map Type -> evaluator
        self.ops: Dict[str, Callable[[Node], bool]] = {
            "DefineNumber": self.op_define_number,
            "AddNumbers": self.op_add_numbers,
            "Print": self.op_print,
        }

    # ---- helpers ----
    def get_attr_value(self, node_id: str, attr: str) -> Any:
        """Return the most 'evaluated' value for an attribute (state overrides attrs)."""
        node = self.by_id.get(node_id)
        if not node:
            return None
        if attr in node["state"]:
            return node["state"][attr]
        return node["attrs"].get(attr)

    def resolve(self, raw: Any) -> Any:
        """Dereference '@Node.Attr' chains once (graph is acyclic so one hop is enough)."""
        if is_ref(raw):
            nid, a = parse_ref(raw)
            return self.get_attr_value(nid, a)
        return raw

    def all_resolved(self, values: List[Any]) -> bool:
        return all(not is_ref(v) and v is not None for v in values)

    # ---- operators ----
    def op_define_number(self, node: Node) -> bool:
        # Input: attrs["Value"] (string number). Output: state["Value"] (numeric)
        if "Value" in node["state"]:
            return False  # already done
        raw = node["attrs"].get("Value")
        val = self.resolve(raw)
        num = to_number(val)
        if num is None:
            return False  # can't parse yet
        node["state"]["Value"] = num
        return True

    def op_add_numbers(self, node: Node) -> bool:
        # Inputs: attrs["Value1"], attrs["Value2"] (can be @ refs). Output: state["Result"]
        if "Result" in node["state"]:
            return False
        v1 = to_number(self.resolve(node["attrs"].get("Value1")))
        v2 = to_number(self.resolve(node["attrs"].get("Value2")))
        if v1 is None or v2 is None:
            return False
        node["state"]["Result"] = v1 + v2
        return True

    def op_print(self, node: Node) -> bool:
        # Input: attrs["Result"] (@ ref). Side effect: print once. Also store state["Printed"]=True
        if node["state"].get("Printed"):
            return False
        r = self.resolve(node["attrs"].get("Result"))
        # Allow printing numbers or strings once the reference resolves
        if r is None or is_ref(r):
            return False
        print(r)
        node["state"]["Printed"] = True
        return True

    # ---- execution ----
    def step(self) -> bool:
        """Try to make progress by evaluating any node whose inputs are ready."""
        progressed = False
        for node in self.nodes:
            op = self.ops.get(node["Type"])
            if not op:
                # Unknown node type: ignore
                continue
            progressed = op(node) or progressed
        return progressed

    def run(self, max_iters: int = 100):
        """Iteratively evaluate until no changes (DAG assumed, so this stabilizes quickly)."""
        for _ in range(max_iters):
            if not self.step():
                return
        raise RuntimeError("Exceeded max iterations (graph might be cyclic or ill-formed).")


if __name__ == "__main__":
    # --- Example in-memory graph ---
    graph = [
        {"ID": "Node1", "Type": "DefineNumber", "attrs": {"Value": "3"}},
        {"ID": "Node2", "Type": "DefineNumber", "attrs": {"Value": "5"}},
        {
            "ID": "Adder",
            "Type": "AddNumbers",
            "attrs": {"Value1": "@Node1.Value", "Value2": "@Node2.Value"},
        },
        {"ID": "Printer", "Type": "Print", "attrs": {"Result": "@Adder.Result"}},
    ]

    interp = Interpreter(graph)
    interp.run()   # Should print: 8.0