from copy import copy

import pandas
from openstack.connection import Connection
from watcher_visio.settings import PROMETHEUS_METRICS, WATCHER_ENDPOINT_NAME, WATCHER_INTERFACE_NAME

from dashboard.prometheus_utils.query import query_prometheus


def convert_cpu_data(data: list):
    metrics = []

    if not data:
        return pandas.DataFrame(columns=["host", "cpu_usage"])

    for entry in data:
        for t, val in entry["values"]:
            metrics.append(
                {
                    "timestamp": int(t),
                    "host": entry["metric"]["host"],
                    "cpu_usage": float(val),
                    "instance": entry["metric"]["instanceName"],
                }
            )

    df_cpu = pandas.DataFrame(metrics)
    df_cpu["timestamp"] = pandas.to_datetime(df_cpu["timestamp"], unit="s")

    # Aggregate CPU usage per host
    return (
        df_cpu.groupby(["host", "timestamp"])["cpu_usage"]
        .sum()
        .groupby("host")
        .mean()
        .reset_index()
    )


def get_current_cluster_cpu(connection: Connection) -> dict:
    """Return current per-host CPU state for the cluster (no Watcher dependency)."""
    cpu_data = query_prometheus(PROMETHEUS_METRICS["cpu_usage"])
    cpu_metrics = convert_cpu_data(data=cpu_data)
    if cpu_metrics.empty:
        return {"host_labels": [], "cpu_current": []}
    return {
        "host_labels": cpu_metrics["host"].to_list(),
        "cpu_current": cpu_metrics["cpu_usage"].to_list(),
    }


def get_audits(connection: Connection) -> list[dict] | None:
    session = connection.session

    watcher_endpoint = connection.endpoint_for(
        service_type=WATCHER_ENDPOINT_NAME, interface=WATCHER_INTERFACE_NAME
    )

    # Collect instances prometheus metrics
    cpu_data = query_prometheus(PROMETHEUS_METRICS["cpu_usage"])

    cpu_metrics = convert_cpu_data(data=cpu_data)

    # Fetch audit list
    audits_resp = session.get(f"{watcher_endpoint}/v1/audits")
    audits_resp.raise_for_status()
    audits_resp = audits_resp.json().get("audits") or []

    # Fetch action plan list
    actionplans_resp = session.get(f"{watcher_endpoint}/v1/action_plans")
    actionplans_resp.raise_for_status()
    actionplans_resp = actionplans_resp.json().get("action_plans") or []

    # Filtering audits by PENDING state
    pending_audits = [plan for plan in actionplans_resp if plan["state"] == "RECOMMENDED"]

    result = []
    for item in pending_audits:
        projected_cpu_data = copy(cpu_data)

        audit_resp = session.get(f"{watcher_endpoint}/v1/audits/{item['audit_uuid']}")
        audit_resp.raise_for_status()
        audit_resp = audit_resp.json()

        actionplan = next(
            filter(lambda x: x.get("audit_uuid") == audit_resp["uuid"], actionplans_resp), None
        )
        if actionplan is None:
            continue

        actions_resp = session.get(
            f"{watcher_endpoint}/v1/actions/?action_plan_uuid={actionplan['uuid']}"
        )
        actions_resp.raise_for_status()
        actions_resp = actions_resp.json().get("actions") or []

        migrations = []
        mapping = {}
        for action in actions_resp:
            action_resp = session.get(f"{watcher_endpoint}/v1/actions/{action['uuid']}")
            action_resp.raise_for_status()
            action_resp = action_resp.json()

            server = connection.get_server_by_id(action_resp["input_parameters"]["resource_id"])
            params = action_resp["input_parameters"]
            mapping[params["resource_name"]] = params["destination_node"]

            migrations.append(
                {
                    "instanceName": action_resp["input_parameters"]["resource_name"],
                    "source": action_resp["input_parameters"]["source_node"],
                    "destination": action_resp["input_parameters"]["destination_node"],
                    "flavor": server.flavor.name,
                    "impact": "Low",
                }
            )

        for entry in projected_cpu_data:
            if (instance := entry["metric"]["instanceName"]) in mapping:
                entry["metric"]["host"] = mapping[instance]

        projected_cpu_metrics = convert_cpu_data(projected_cpu_data)

        result.append(
            {
                "id": audit_resp["uuid"],
                "name": audit_resp["name"],
                "created_at": audit_resp["created_at"],
                "strategy": audit_resp["strategy_name"],
                "goal": audit_resp["goal_name"],
                "type": audit_resp["audit_type"],
                "scope": audit_resp["scope"],
                "cpu_weight": audit_resp["parameters"]
                .get("weights", {})
                .get("instance_cpu_usage_weight", "none"),
                "ram_weight": audit_resp["parameters"]
                .get("weights", {})
                .get("instance_ram_usage_weight", "none"),
                "migrations": migrations,
                "host_labels": cpu_metrics["host"].to_list(),
                "cpu_current": cpu_metrics["cpu_usage"].to_list(),
                "cpu_projected": projected_cpu_metrics["cpu_usage"].to_list(),
            }
        )

    return result