import logging
import multiprocessing
import os
import pickle
import queue
import subprocess
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional

from parsl.multiprocessing import SpawnContext

logger = logging.getLogger(__name__)


class Scheduler(Enum):
    Unknown = 0
    Slurm = 1
    PBS = 2


def get_slurm_hosts_list() -> List[str]:
    """Get list of slurm hosts from scontrol"""
    cmd = "scontrol show hostname $SLURM_NODELIST"
    b_output = subprocess.check_output(
        cmd, stderr=subprocess.STDOUT, shell=True
    )  # bytes
    output = b_output.decode().strip().split()
    return output


def get_pbs_hosts_list() -> List[str]:
    """Get list of PBS hosts from envvar: PBS_NODEFILE"""
    nodefile_name = os.environ["PBS_NODEFILE"]
    with open(nodefile_name) as f:
        return [line.strip() for line in f.readlines()]


def get_nodes_in_batchjob(scheduler: Scheduler) -> List[str]:
    """Get nodelist from all supported schedulers"""
    nodelist = []
    if scheduler == Scheduler.Slurm:
        nodelist = get_slurm_hosts_list()
    elif scheduler == Scheduler.PBS:
        nodelist = get_pbs_hosts_list()
    else:
        raise RuntimeError(f"mpi_mode does not support scheduler:{scheduler}")
    return nodelist


def identify_scheduler() -> Scheduler:
    """Use envvars to determine batch scheduler"""
    if os.environ.get("SLURM_NODELIST"):
        return Scheduler.Slurm
    elif os.environ.get("PBS_NODEFILE"):
        return Scheduler.PBS
    else:
        return Scheduler.Unknown


class MPINodesUnavailable(Exception):
    """Raised if there are no free nodes available for an MPI request"""

    def __init__(self, requested: int, available: int):
        self.requested = requested
        self.available = available

    def __str__(self):
        return f"MPINodesUnavailable(requested={self.requested} available={self.available})"


@dataclass(order=True)
class PrioritizedTask:
    # Comparing dict will fail since they are unhashable
    # This dataclass limits comparison to the priority field
    priority: int
    task: Dict = field(compare=False)


class TaskScheduler:
    """Default TaskScheduler that does no taskscheduling

    This class simply acts as an abstraction over the task_q and result_q
    that can be extended to implement more complex task scheduling logic
    """
    def __init__(
        self,
        pending_task_q: multiprocessing.Queue,
        pending_result_q: multiprocessing.Queue,
    ):
        self.pending_task_q = pending_task_q
        self.pending_result_q = pending_result_q

    def put_task(self, task) -> None:
        return self.pending_task_q.put(task)

    def get_result(self, block: bool = True, timeout: Optional[float] = None):
        return self.pending_result_q.get(block, timeout)


class MPITaskScheduler(TaskScheduler):
    """Extends TaskScheduler to schedule MPI functions over provisioned nodes
    The MPITaskScheduler runs on a Manager on the lead node of a batch job, as
    such it is expected to control task placement over this single batch job.

    The MPITaskScheduler adds the following functionality:
    1) Determine list of nodes attached to current batch job
    2) put_task for execution onto workers:
        a) if resources are available attach resource list
        b) if unavailable place tasks into backlog
    3) get_result will fetch a result and relinquish nodes,
       and attempt to schedule tasks in backlog if any.
    """
    def __init__(
        self,
        pending_task_q: multiprocessing.Queue,
        pending_result_q: multiprocessing.Queue,
    ):
        super().__init__(pending_task_q, pending_result_q)
        self.scheduler = identify_scheduler()
        # PriorityQueue is threadsafe
        self._backlog_queue: queue.PriorityQueue[PrioritizedTask] = queue.PriorityQueue()
        self._map_tasks_to_nodes: Dict[str, List[str]] = {}
        self.available_nodes = get_nodes_in_batchjob(self.scheduler)
        self._free_node_counter = SpawnContext.Value("i", len(self.available_nodes))
        # mp.Value has issues with mypy
        # issue https://github.com/python/typeshed/issues/8799
        # from mypy 0.981 onwards
        self.nodes_q: queue.Queue = queue.Queue()
        for node in self.available_nodes:
            self.nodes_q.put(node)

        logger.info(
            f"Starting MPITaskScheduler with {len(self.available_nodes)}"
        )

    def _get_nodes(self, num_nodes: int) -> List[str]:
        """Thread safe method to acquire num_nodes from free resources

        Raises: MPINodesUnavailable if there aren't enough resources
        Returns: List of nodenames:str
        """
        logger.debug(
            f"Requesting : {num_nodes=} we have {self._free_node_counter}"
        )
        acquired_nodes = []
        with self._free_node_counter.get_lock():
            if num_nodes <= self._free_node_counter.value:
                self._free_node_counter.value -= num_nodes
            else:
                raise MPINodesUnavailable(
                    requested=num_nodes, available=self._free_node_counter.value
                )

            for i in range(num_nodes):
                node = self.nodes_q.get()
                acquired_nodes.append(node)
        return acquired_nodes

    def _return_nodes(self, nodes: List[str]) -> None:
        """Threadsafe method to return a list of nodes"""
        for node in nodes:
            self.nodes_q.put(node)
        with self._free_node_counter.get_lock():
            self._free_node_counter.value += len(nodes)

    def put_task(self, task_package: dict):
        """Schedule task if resources are available otherwise backlog the task"""
        resource_spec = task_package.get("context", {}).get("resource_spec", {})

        if nodes_needed := resource_spec.get("num_nodes"):
            tid = task_package["task_id"]
            try:
                allocated_nodes = self._get_nodes(nodes_needed)
            except MPINodesUnavailable:
                logger.info(f"Not enough resources, placing task {tid} into backlog")
                self._backlog_queue.put(PrioritizedTask(nodes_needed, task_package))
                return
            else:
                resource_spec["MPI_NODELIST"] = ",".join(allocated_nodes)
                self._map_tasks_to_nodes[tid] = allocated_nodes

        self.pending_task_q.put(task_package)

    def _schedule_backlog_tasks(self):
        """Attempt to schedule backlogged tasks"""
        try:
            prioritized_task = self._backlog_queue.get(block=False)
            self.put_task(prioritized_task.task)
        except queue.Empty:
            return
        else:
            # Keep attempting to schedule tasks till we are out of resources
            self._schedule_backlog_tasks()

    def get_result(self, block: bool = True, timeout: Optional[float] = None):
        """Return result and relinquish provisioned nodes"""
        result_pkl = self.pending_result_q.get(block, timeout)
        if result_pkl is None:
            return None
        result_dict = pickle.loads(result_pkl)
        # TODO (wardlt): If the task did not request nodes, it won't be in `self._map_tasks_to_nodes`.
        #  Causes Parsl to hang. See Issue #3427
        if result_dict["type"] == "result":
            task_id = result_dict["task_id"]
            assert task_id in self._map_tasks_to_nodes, "You are about to experience issue #3427"
            nodes_to_reallocate = self._map_tasks_to_nodes[task_id]
            self._return_nodes(nodes_to_reallocate)
            self._schedule_backlog_tasks()

        return result_pkl
