text: from __future__ import annotations
  
  import logging
  import sys
  import warnings
  import weakref
  from json import dumps
  
  import dask
  import dask.config
  
  from .spec import ProcessInterface, SpecCluster
  
  logger = logging.getLogger(__name__)
  
  
  class Process(ProcessInterface):
      """A superclass for SSH Workers and Nannies
  
      See Also
      --------
      Worker
      Scheduler
      """
  
      def __init__(self, **kwargs):
          self.connection = None
          self.proc = None
          super().__init__(**kwargs)
  
      async def start(self):
          assert self.connection
          weakref.finalize(
              self, self.proc.kill
          )  # https://github.com/ronf/asyncssh/issues/112
          await super().start()
  
      async def close(self):
          self.proc.kill()  # https://github.com/ronf/asyncssh/issues/112
          self.connection.close()
          await super().close()
  
  
  class Worker(Process):
      """A Remote Dask Worker controled by SSH
  
      Parameters
      ----------
      scheduler: str
          The address of the scheduler
      address: str
          The hostname where we should run this worker
      worker_class: str
          The python class to use to create the worker.
      connect_options: dict
          kwargs to be passed to asyncssh connections
      remote_python: str
          Path to Python on remote node to run this worker.
      kwargs: dict
          These will be passed through the dask-worker CLI to the
          dask.distributed.Worker class
      """
  
      def __init__(
          self,
          scheduler: str,
          address: str,
          connect_options: dict,
          kwargs: dict,
          worker_module="deprecated",
          worker_class="distributed.Nanny",
          remote_python=None,
          loop=None,
          name=None,
      ):
          super().__init__()
  
          if worker_module != "deprecated":
              raise ValueError(
                  "worker_module has been deprecated in favor of worker_class. "
                  "Please specify a Python class rather than a CLI module."
              )
  
          self.address = address
          self.scheduler = scheduler
          self.worker_class = worker_class
          self.connect_options = connect_options
          self.kwargs = kwargs
          self.name = name
          self.remote_python = remote_python
          self.nprocs = self.kwargs.pop("nprocs", 1)
  
      async def start(self):
          try:
              import asyncssh  # import now to avoid adding to module startup time
          except ImportError:
              raise ImportError(
                  "Dask's SSHCluster requires the `asyncssh` package to be installed. "
                  "Please install it using pip or conda."
              )
  
          self.connection = await asyncssh.connect(self.address, **self.connect_options)
  
          result = await self.connection.run("uname")
          if result.exit_status == 0:
              set_env = 'env DASK_INTERNAL_INHERIT_CONFIG="{}"'.format(
                  dask.config.serialize(dask.config.global_config)
              )
          else:
              result = await self.connection.run("cmd /c ver")
              if result.exit_status == 0:
                  set_env = "set DASK_INTERNAL_INHERIT_CONFIG={} &&".format(
                      dask.config.serialize(dask.config.global_config)
                  )
              else:
                  raise Exception(
                      "Worker failed to set DASK_INTERNAL_INHERIT_CONFIG variable "
                  )
  
          if not self.remote_python:
              self.remote_python = sys.executable
  
          cmd = " ".join(
              [
                  set_env,
                  self.remote_python,
                  "-m",
                  "distributed.cli.dask_spec",
                  self.scheduler,
                  "--spec",
                  "'%s'"
                  % dumps(
                      {
                          i: {
                              "cls": self.worker_class,
                              "opts": {
                                  **self.kwargs,
                                  "name": self.name,
                              },
                          }
                          for i in range(self.nprocs)
                      }
                  ),
              ]
          )
  
          self.proc = await self.connection.create_process(cmd)
  
          # We watch stderr in order to get the address, then we return
          started_workers = 0
          while started_workers < self.nprocs:
              line = await self.proc.stderr.readline()
              if not line.strip():
                  raise Exception("Worker failed to start")
              logger.info(line.strip())
              if "worker at" in line:
                  started_workers += 1
          logger.debug("%s", line)
          await super().start()
  
  
  class Scheduler(Process):
      """A Remote Dask Scheduler controlled by SSH
  
      Parameters
      ----------
      address: str
          The hostname where we should run this worker
      connect_options: dict
          kwargs to be passed to asyncssh connections
      remote_python: str
          Path to Python on remote node to run this scheduler.
      kwargs: dict
          These will be passed through the dask-scheduler CLI to the
          dask.distributed.Scheduler class
      """
  
      def __init__(
          self, address: str, connect_options: dict, kwargs: dict, remote_python=None
      ):
          super().__init__()
  
          self.address = address
          self.kwargs = kwargs
          self.connect_options = connect_options
          self.remote_python = remote_python
  
      async def start(self):
          try:
              import asyncssh  # import now to avoid adding to module startup time
          except ImportError:
              raise ImportError(
                  "Dask's SSHCluster requires the `asyncssh` package to be installed. "
                  "Please install it using pip or conda."
              )
  
          logger.debug("Created Scheduler Connection")
  
          self.connection = await asyncssh.connect(self.address, **self.connect_options)
  
          result = await self.connection.run("uname")
          if result.exit_status == 0:
              set_env = 'env DASK_INTERNAL_INHERIT_CONFIG="{}"'.format(
                  dask.config.serialize(dask.config.global_config)
              )
          else:
              result = await self.connection.run("cmd /c ver")
              if result.exit_status == 0:
                  set_env = "set DASK_INTERNAL_INHERIT_CONFIG={} &&".format(
                      dask.config.serialize(dask.config.global_config)
                  )
              else:
                  raise Exception(
                      "Scheduler failed to set DASK_INTERNAL_INHERIT_CONFIG variable "
                  )
  
          if not self.remote_python:
              self.remote_python = sys.executable
  
          cmd = " ".join(
              [
                  set_env,
                  self.remote_python,
                  "-m",
                  "distributed.cli.dask_spec",
                  "--spec",
                  "'%s'" % dumps({"cls": "distributed.Scheduler", "opts": self.kwargs}),
              ]
          )
          self.proc = await self.connection.create_process(cmd)
  
          # We watch stderr in order to get the address, then we return
          while True:
              line = await self.proc.stderr.readline()
              if not line.strip():
                  raise Exception("Worker failed to start")
              logger.info(line.strip())
              if "Scheduler at" in line:
                  self.address = line.split("Scheduler at:")[1].strip()
                  break
          logger.debug("%s", line)
          await super().start()
  
  
  old_cluster_kwargs = {
      "scheduler_addr",
      "scheduler_port",
      "worker_addrs",
      "nthreads",
      "nprocs",
      "ssh_username",
      "ssh_port",
      "ssh_private_key",
      "nohost",
      "logdir",
      "remote_python",
      "memory_limit",
      "worker_port",
      "nanny_port",
      "remote_dask_worker",
  }
  
  
  def SSHCluster(
      hosts: list[str] | None = None,
      connect_options: dict | list[dict] = {},
      worker_options: dict = {},
      scheduler_options: dict = {},
      worker_module: str = "deprecated",
      worker_class: str = "distributed.Nanny",
      remote_python: str | list[str] | None = None,
      **kwargs,
  ):
      """Deploy a Dask cluster using SSH
  
      The SSHCluster function deploys a Dask Scheduler and Workers for you on a
      set of machine addresses that you provide.  The first address will be used
      for the scheduler while the rest will be used for the workers (feel free to
      repeat the first hostname if you want to have the scheduler and worker
      co-habitate one machine.)
  
      You may configure the scheduler and workers by passing
      ``scheduler_options`` and ``worker_options`` dictionary keywords.  See the
      ``dask.distributed.Scheduler`` and ``dask.distributed.Worker`` classes for
      details on the available options, but the defaults should work in most
      situations.
  
      You may configure your use of SSH itself using the ``connect_options``
      keyword, which passes values to the ``asyncssh.connect`` function.  For
      more information on these see the documentation for the ``asyncssh``
      library https://asyncssh.readthedocs.io .
  
      Parameters
      ----------
      hosts : list[str]
          List of hostnames or addresses on which to launch our cluster.
          The first will be used for the scheduler and the rest for workers.
      connect_options : dict or list of dict, optional
          Keywords to pass through to :func:`asyncssh.connect`.
          This could include things such as ``port``, ``username``, ``password``
          or ``known_hosts``. See docs for :func:`asyncssh.connect` and
          :class:`asyncssh.SSHClientConnectionOptions` for full information.
          If a list it must have the same length as ``hosts``.
      worker_options : dict, optional
          Keywords to pass on to workers.
      scheduler_options : dict, optional
          Keywords to pass on to scheduler.
      worker_class: str
          The python class to use to create the worker(s).
      remote_python : str or list of str, optional
          Path to Python on remote nodes.
  
      Examples
      --------
      Create a cluster with one worker:
  
      >>> from dask.distributed import Client, SSHCluster
      >>> cluster = SSHCluster(["localhost", "localhost"])
      >>> client = Client(cluster)
  
      Create a cluster with three workers, each with two threads
      and host the dashdoard on port 8797:
  
      >>> from dask.distributed import Client, SSHCluster
      >>> cluster = SSHCluster(
      ...     ["localhost", "localhost", "localhost", "localhost"],
      ...     connect_options={"known_hosts": None},
      ...     worker_options={"nthreads": 2},
      ...     scheduler_options={"port": 0, "dashboard_address": ":8797"}
      ... )
      >>> client = Client(cluster)
  
      Create a cluster with two workers on each host:
  
      >>> from dask.distributed import Client, SSHCluster
      >>> cluster = SSHCluster(
      ...     ["localhost", "localhost", "localhost", "localhost"],
      ...     connect_options={"known_hosts": None},
      ...     worker_options={"nthreads": 2, "nprocs": 2},
      ...     scheduler_options={"port": 0, "dashboard_address": ":8797"}
      ... )
      >>> client = Client(cluster)
  
      An example using a different worker class, in particular the
      ``CUDAWorker`` from the ``dask-cuda`` project:
  
      >>> from dask.distributed import Client, SSHCluster
      >>> cluster = SSHCluster(
      ...     ["localhost", "hostwithgpus", "anothergpuhost"],
      ...     connect_options={"known_hosts": None},
      ...     scheduler_options={"port": 0, "dashboard_address": ":8797"},
      ...     worker_class="dask_cuda.CUDAWorker")
      >>> client = Client(cluster)
  
      See Also
      --------
      dask.distributed.Scheduler
      dask.distributed.Worker
      asyncssh.connect
      """
      if worker_module != "deprecated":
          raise ValueError(
              "worker_module has been deprecated in favor of worker_class. "
              "Please specify a Python class rather than a CLI module."
          )
  
      if set(kwargs) & old_cluster_kwargs:
          from .old_ssh import SSHCluster as OldSSHCluster
  
          warnings.warn(
              "Note that the SSHCluster API has been replaced.  "
              "We're routing you to the older implementation.  "
              "This will be removed in the future"
          )
          kwargs.setdefault("worker_addrs", hosts)
          return OldSSHCluster(**kwargs)
  
      if not hosts:
          raise ValueError(
              f"`hosts` must be a non empty list, value {repr(hosts)!r} found."
          )
      if isinstance(connect_options, list) and len(connect_options) != len(hosts):
          raise RuntimeError(
              "When specifying a list of connect_options you must provide a "
              "dictionary for each address."
          )
  
      if isinstance(remote_python, list) and len(remote_python) != len(hosts):
          raise RuntimeError(
              "When specifying a list of remote_python you must provide a "
              "path for each address."
          )
  
      scheduler = {
          "cls": Scheduler,
          "options": {
              "address": hosts[0],
              "connect_options": connect_options
              if isinstance(connect_options, dict)
              else connect_options[0],
              "kwargs": scheduler_options,
              "remote_python": remote_python[0]
              if isinstance(remote_python, list)
              else remote_python,
          },
      }
      workers = {
          i: {
              "cls": Worker,
              "options": {
                  "address": host,
                  "connect_options": connect_options
                  if isinstance(connect_options, dict)
                  else connect_options[i + 1],
                  "kwargs": worker_options,
                  "worker_class": worker_class,
                  "remote_python": remote_python[i + 1]
                  if isinstance(remote_python, list)
                  else remote_python,
              },
          }
          for i, host in enumerate(hosts[1:])
      }
      return SpecCluster(workers, scheduler, name="SSHCluster", **kwargs)
  