from dataclasses import dataclass
import io
import logging
import os
from datetime import timedelta
from typing import Dict, Optional

import ray
from ray.train.backend import BackendConfig, Backend, EncodedData
from ray.train._internal.worker_group import WorkerGroup
from ray.train._internal.utils import get_address_and_port
from ray.util import PublicAPI

import torch
import torch.distributed as dist
from torch.nn.parallel import DistributedDataParallel

try:
    from torch.profiler import profile
except ImportError:
    profile = None

logger = logging.getLogger(__name__)


@PublicAPI(stability="beta")
@dataclass
class TorchConfig(BackendConfig):
    """Configuration for torch process group setup.

    See https://pytorch.org/docs/stable/distributed.html for more info.

    Args:
        backend: The backend to use for training.
            See ``torch.distributed.init_process_group`` for more info and
            valid values.
            If set to None, nccl will be used if GPUs are requested, else gloo
            will be used.
        init_method: The initialization method to use. Either "env"
            for environment variable initialization or "tcp" for TCP
            initialization. Defaults to "env".
        timeout_s: Seconds for process group operations to timeout.
    """

    backend: Optional[str] = None
    init_method: str = "env"
    timeout_s: int = 1800

    @property
    def backend_cls(self):
        return _TorchBackend


def _setup_torch_process_group(
    backend: str,
    world_rank: int,
    world_size: int,
    init_method: str,
    timeout_s: int = 1800,
):
    """Connects the distributed PyTorch backend.

    Args:
        backend: The backend (nccl, gloo, etc.) to use for training.
        world_rank: Rank of the current worker.
        world_size: Number of workers participating in the job.
        init_method: URL specifying how to initialize the process group.
        timeout_s: Seconds for process group operations to timeout.
    """
    if world_rank == 0:
        logger.info(
            f"Setting up process group for: {init_method} [rank={world_rank}, "
            f"world_size={world_size}]"
        )
    else:
        logger.debug(
            f"Setting up process group for: {init_method} [rank={world_rank}, "
            f"world_size={world_size}]"
        )
    logger.debug(f"using {backend}")

    if backend == "nccl" and "NCCL_BLOCKING_WAIT" not in os.environ:
        logger.debug(
            "Setting NCCL_BLOCKING_WAIT for detecting node failure. "
            "To override this behavior, you can set NCCL_BLOCKING_WAIT=0."
        )
        os.environ["NCCL_BLOCKING_WAIT"] = "1"

    dist.init_process_group(
        backend=backend,
        init_method=init_method,
        rank=world_rank,
        world_size=world_size,
        timeout=timedelta(seconds=timeout_s),
    )


def _shutdown_torch(destroy_process_group=False):
    if destroy_process_group:
        dist.destroy_process_group()
    if torch.cuda.is_available():
        torch.cuda.empty_cache()


class _TorchBackend(Backend):
    share_cuda_visible_devices: bool = True

    def on_start(self, worker_group: WorkerGroup, backend_config: TorchConfig):
        if dist.is_available():
            # Set the appropriate training backend.
            if backend_config.backend is None:
                if worker_group.num_gpus_per_worker > 0:
                    backend = "nccl"
                else:
                    backend = "gloo"
            else:
                backend = backend_config.backend

            master_addr, master_port = worker_group.execute_single(
                0, get_address_and_port
            )
            if backend_config.init_method == "env":

                def set_env_vars(addr, port):
                    os.environ["MASTER_ADDR"] = addr
                    os.environ["MASTER_PORT"] = str(port)

                worker_group.execute(set_env_vars, addr=master_addr, port=master_port)
                url = "env://"
            elif backend_config.init_method == "tcp":
                url = f"tcp://{master_addr}:{master_port}"
            else:
                raise ValueError(
                    f"The provided init_method ("
                    f"{backend_config.init_method}) is not supported. Must "
                    f"be either 'env' or 'tcp'."
                )

            setup_futures = []
            for i in range(len(worker_group)):
                setup_futures.append(
                    worker_group.execute_single_async(
                        i,
                        _setup_torch_process_group,
                        backend=backend,
                        world_rank=i,
                        world_size=len(worker_group),
                        init_method=url,
                        timeout_s=backend_config.timeout_s,
                    )
                )
            ray.get(setup_futures)
        else:
            raise RuntimeError("Distributed torch is not available.")

    def on_shutdown(self, worker_group: WorkerGroup, backend_config: TorchConfig):

        worker_group.execute(
            _shutdown_torch, destroy_process_group=len(worker_group) > 1
        )

    @staticmethod
    def encode_data(data_dict: Dict) -> EncodedData:
        """Special handling for moving model from worker to driver."""

        # If model is being checkpointed and is wrapped in DDP, then extract
        # out the underlying module. If not, then deserialization will fail
        # since the torch process group is not initialized on the driver.

        for k, v in data_dict.items():
            if isinstance(v, DistributedDataParallel) and hasattr(v, "module"):
                data_dict[k] = v.module

        # Convert the checkpoint dict to bytes, so that any GPU tensors that
        # are in the checkpoint dict can be properly deserialized on the
        # driver side, even if the driver does not have access to a GPU device.
        _buffer = io.BytesIO()
        torch.save(data_dict, _buffer)
        return _buffer.getvalue()

    @staticmethod
    def decode_data(encoded_data: EncodedData) -> Dict:
        # When decoding the bytes on the driver side, always map to CPU.
        _buffer = io.BytesIO(encoded_data)
        checkpoint_dict = torch.load(_buffer, map_location="cpu")
        return checkpoint_dict