import os
import time

import torch
import torch.distributed as dist
from torch.multiprocessing import Process

import pytest

# Worker timeout *after* the first worker has completed.
DEEPSPEED_UNIT_WORKER_TIMEOUT = 120


def distributed_test(world_size=2, backend='nccl'):
    """A decorator for executing a function (e.g., a unit test) in a distributed manner.
    This decorator manages the spawning and joining of processes, initialization of
    torch.distributed, and catching of errors.

    Usage example:
        @distributed_test(worker_size=[2,3])
        def my_test():
            rank = dist.get_rank()
            world_size = dist.get_world_size()
            assert(rank < world_size)

    Arguments:
        world_size (int or list): number of ranks to spawn. Can be a list to spawn
        multiple tests.
    """
    def dist_wrap(run_func):
        """Second-level decorator for dist_test. This actually wraps the function. """
        def dist_init(local_rank, num_procs, *func_args, **func_kwargs):
            """Initialize torch.distributed and execute the user function. """
            os.environ['MASTER_ADDR'] = '127.0.0.1'
            os.environ['MASTER_PORT'] = '29503'
            dist.init_process_group(backend=backend,
                                    init_method='env://',
                                    rank=local_rank,
                                    world_size=num_procs)

            if torch.cuda.is_available():
                torch.cuda.set_device(local_rank)

            if 'args' in func_kwargs:
                func_kwargs['args'].local_rank = local_rank
            run_func(*func_args, **func_kwargs)

        def dist_launcher(num_procs, *func_args, **func_kwargs):
            """Launch processes and gracefully handle failures. """

            # Spawn all workers on subprocesses.
            processes = []
            for local_rank in range(num_procs):
                p = Process(target=dist_init,
                            args=(local_rank,
                                  num_procs,
                                  *func_args),
                            kwargs=func_kwargs)
                p.start()
                processes.append(p)

            # Now loop and wait for a test to complete. The spin-wait here isn't a big
            # deal because the number of processes will be O(#GPUs) << O(#CPUs).
            any_done = False
            while not any_done:
                for p in processes:
                    if not p.is_alive():
                        any_done = True
                        break

            # Wait for all other processes to complete
            for p in processes:
                p.join(DEEPSPEED_UNIT_WORKER_TIMEOUT)

            failed = [(rank, p) for rank, p in enumerate(processes) if p.exitcode != 0]
            for rank, p in failed:
                # If it still hasn't terminated, kill it because it hung.
                if p.exitcode is None:
                    p.terminate()
                    pytest.fail(f'Worker {rank} hung.', pytrace=False)
                if p.exitcode < 0:
                    pytest.fail(f'Worker {rank} killed by signal {-p.exitcode}',
                                pytrace=False)
                if p.exitcode > 0:
                    pytest.fail(f'Worker {rank} exited with code {p.exitcode}',
                                pytrace=False)

        def run_func_decorator(*func_args, **func_kwargs):
            """Entry point for @distributed_test(). """

            if isinstance(world_size, int):
                dist_launcher(world_size, *func_args, **func_kwargs)
            elif isinstance(world_size, list):
                for procs in world_size:
                    dist_launcher(procs, *func_args, **func_kwargs)
                    time.sleep(0.5)
            else:
                raise TypeError(f'world_size must be an integer or a list of integers.')

        return run_func_decorator

    return dist_wrap
