"""Mock supply chain graph and traversal helpers for news correlation."""

from collections import deque

SUPPLY_CHAIN_GRAPH = {
    "nodes": [
        {"id": "lithium", "name": "Lithium", "type": "Commodity"},
        {"id": "nickel", "name": "Nickel", "type": "Commodity"},
        {"id": "neon-gas", "name": "Neon Gas", "type": "Commodity"},
        {"id": "sqm", "name": "SQM", "type": "Supplier"},
        {"id": "albemarle", "name": "Albemarle", "type": "Supplier"},
        {"id": "tsmc", "name": "TSMC", "type": "Supplier"},
        {"id": "foxconn", "name": "Foxconn", "type": "Supplier"},
        {"id": "catl", "name": "CATL", "type": "Supplier"},
        {"id": "tesla", "name": "Tesla", "type": "Company"},
        {"id": "apple", "name": "Apple", "type": "Company"},
        {"id": "nvidia", "name": "NVIDIA", "type": "Company"},
    ],
    "edges": [
        {"from": "lithium", "to": "sqm", "type": "ImpactedBy"},
        {"from": "lithium", "to": "albemarle", "type": "ImpactedBy"},
        {"from": "nickel", "to": "catl", "type": "ImpactedBy"},
        {"from": "neon-gas", "to": "tsmc", "type": "ImpactedBy"},
        {"from": "sqm", "to": "catl", "type": "Supplies"},
        {"from": "albemarle", "to": "catl", "type": "Supplies"},
        {"from": "catl", "to": "tesla", "type": "Supplies"},
        {"from": "tsmc", "to": "apple", "type": "Supplies"},
        {"from": "tsmc", "to": "nvidia", "type": "Supplies"},
        {"from": "foxconn", "to": "apple", "type": "Supplies"},
    ],
}

NEWS_TRIGGER_KEYWORDS = {
    "lithium": "lithium",
    "nickel": "nickel",
    "neon": "neon-gas",
    "taiwan": "tsmc",
    "chile": "lithium",
    "semiconductor": "tsmc",
}


def _nodes_by_id():
    return {node["id"]: node for node in SUPPLY_CHAIN_GRAPH["nodes"]}


def _outgoing_edges():
    adjacency = {}
    for edge in SUPPLY_CHAIN_GRAPH["edges"]:
        adjacency.setdefault(edge["from"], []).append(edge)
    return adjacency


def resolve_start_nodes(headline: str, extracted_entities: list[str]) -> list[str]:
    headline_lc = (headline or "").lower()
    start_nodes = set()

    for keyword, node_id in NEWS_TRIGGER_KEYWORDS.items():
        if keyword in headline_lc:
            start_nodes.add(node_id)

    node_map = _nodes_by_id()
    extracted_entities_lc = {entity.lower() for entity in extracted_entities}
    for node in node_map.values():
        if node["name"].lower() in extracted_entities_lc:
            start_nodes.add(node["id"])

    if not start_nodes:
        start_nodes.add("lithium")

    return list(start_nodes)


def traverse_impacts(headline: str, extracted_entities: list[str]) -> list[dict]:
    """Run BFS from resolved start nodes and return downstream impact flow."""
    node_map = _nodes_by_id()
    adjacency = _outgoing_edges()
    queue = deque((start_id, 1) for start_id in resolve_start_nodes(headline, extracted_entities))
    visited = set()
    impact_flow = []

    while queue:
        node_id, depth = queue.popleft()
        if node_id in visited:
            continue
        visited.add(node_id)
        node = node_map.get(node_id)
        if node is None:
            continue

        severity = "High" if depth == 1 else "Medium" if depth == 2 else "Low"
        probability = 0.78 if depth == 1 else 0.61 if depth == 2 else 0.44
        timeframe = "0-30d" if depth == 1 else "1-2q" if depth == 2 else "2-4q"

        impact_flow.append(
            {
                "step": depth,
                "nodeName": node["name"],
                "description": f"{node['name']} is exposed through the supply chain propagation path.",
                "financialImpact": severity,
                "probability": probability,
                "timeframe": timeframe,
                "nodeType": node["type"],
            }
        )

        for edge in adjacency.get(node_id, []):
            queue.append((edge["to"], depth + 1))

    return sorted(impact_flow, key=lambda item: (item["step"], item["nodeName"]))