import os
import argparse

import torch
from torch import nn
import torch.multiprocessing as mp
import torch.distributed.rpc as rpc
from torch.optim import SGD, Adam, RMSprop, Optimizer
from colorama import Back, Style


def color_debug(text, prefix=' ', color='blue'):
    color = color.upper()
    print(getattr(Back, color), prefix, Style.RESET_ALL, text)


class RpcTestModel(nn.Module):

    def __init__(self, stage_id, actual_stage_num, feat_num, h) -> None:
        super().__init__()
        self.rank = stage_id
        self.is_last_rank = stage_id == actual_stage_num - 1
        self.linear_name = f'linear_{stage_id}'
        if stage_id == 0:
            setattr(self, self.linear_name, nn.Linear(feat_num, h))
        elif stage_id == actual_stage_num - 1:
            setattr(self, self.linear_name, nn.Linear(h, 1))
        else:
            setattr(self, self.linear_name, nn.Linear(h, h))

    def forward(self, x) -> torch.Tensor:
        linear: nn.Module = getattr(self, self.linear_name)
        out: torch.Tensor = linear(x)

        if self.is_last_rank:
            out = out.sum()
        return out


def parse_args():
    parser = argparse.ArgumentParser()
    parser.add_argument('--epoch', type=int, default=1)
    parser.add_argument('--world_size', type=int, default=2)
    parser.add_argument('--num_microbatches', type=int, default=2)
    parser.add_argument('--chunk', type=int, default=1)
    parser.add_argument('--use_checkpoint', action='store_true')
    parser.add_argument('--optimizer', type=str, choices=['SGD', 'Adam', 'RMSprop'], default='SGD')
    parser.add_argument('--device', type=str, choices=['cpu', 'cuda'], default='cuda')
    parser.add_argument('--master_addr', type=str, default='localhost')
    parser.add_argument('--master_port', type=str, default='29020')
    parser.add_argument('--num_worker_threads', type=str, default=128)
    return parser.parse_args()


def run_worker(rank, args, master_func):
    os.environ['MASTER_ADDR'] = args.master_addr
    os.environ['MASTER_PORT'] = args.master_port

    # config rpc
    # if cuda is used, set_device_map is a must is configured
    # for cuda is not supported in torch rpc by default
    options = rpc.TensorPipeRpcBackendOptions(num_worker_threads=args.num_worker_threads)

    world_size = args.world_size
    for rank_idx in range(world_size):
        options.set_device_map(f'work{rank_idx}', {rank: rank_idx})

    rpc.init_rpc(name=f'work{rank}', rank=rank, world_size=world_size, rpc_backend_options=options)

    # in rpc mode, only rank 0 is needed to be coded
    if rank == 0:
        master_func(args)
    # barrier here
    rpc.shutdown()


def rpc_run(args, master_func):
    world_size = args.world_size
    assert args.num_microbatches >= args.world_size, "num_microbatches cannot be fewer than world_size!"
    mp.spawn(run_worker, args=(args, master_func), nprocs=world_size)