import logging
import os
import random
import types
import collections
from packaging.version import Version

from typing import Any, Dict, List, Optional, Callable, Union

from ray.train._internal import session
from ray.train._internal.accelerator import Accelerator
from torch.optim import Optimizer
from ray.train._internal.session import get_accelerator, set_accelerator
from ray.util.annotations import PublicAPI, Deprecated

import numpy as np

import torch
from torch.cuda.amp import autocast, GradScaler
from torch.nn.parallel import DistributedDataParallel

if Version(torch.__version__) < Version("1.11.0"):
    FullyShardedDataParallel = None
else:
    from torch.distributed.fsdp import FullyShardedDataParallel
from torch.utils.data import (
    DistributedSampler,
    DataLoader,
    IterableDataset,
    SequentialSampler,
    RandomSampler,
)

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

logger = logging.getLogger(__name__)


@PublicAPI(stability="beta")
def get_device() -> Union[torch.device, List[torch.device]]:
    """Gets the correct torch device configured for this process.

    Returns a list of devices if more than 1 GPU per worker
    is requested.

    Assumes that `CUDA_VISIBLE_DEVICES` is set and is a
    superset of the `ray.get_gpu_ids()`.

    Example:
        >>> # os.environ["CUDA_VISIBLE_DEVICES"] = "3,4"
        >>> # ray.get_gpu_ids() == [3]
        >>> # torch.cuda.is_available() == True
        >>> # get_device() == torch.device("cuda:0")

        >>> # os.environ["CUDA_VISIBLE_DEVICES"] = "0,1,2,3,4"
        >>> # ray.get_gpu_ids() == [4]
        >>> # torch.cuda.is_available() == True
        >>> # get_device() == torch.device("cuda:4")

        >>> # os.environ["CUDA_VISIBLE_DEVICES"] = "0,1,2,3,4,5"
        >>> # ray.get_gpu_ids() == [4,5]
        >>> # torch.cuda.is_available() == True
        >>> # get_device() == torch.device("cuda:4")
    """
    from ray.air._internal import torch_utils

    return torch_utils.get_device()


# TODO: Deprecation: Hard-deprecate args in Ray 2.2.
@PublicAPI(stability="beta")
def prepare_model(
    model: torch.nn.Module,
    move_to_device: Union[bool, torch.device] = True,
    parallel_strategy: Optional[str] = "ddp",
    parallel_strategy_kwargs: Optional[Dict[str, Any]] = None,
) -> torch.nn.Module:
    """Prepares the model for distributed execution.

    This allows you to use the same exact code regardless of number of
    workers or the device type being used (CPU, GPU).

    Args:
        model (torch.nn.Module): A torch model to prepare.
        move_to_device: Either a boolean indiciating whether to move
            the model to the correct device or an actual device to
            move the model to. If set to False, the model needs
            to manually be moved to the correct device.
        parallel_strategy ("ddp", "fsdp", or None): Whether to wrap models
            in ``DistributedDataParallel``, ``FullyShardedDataParallel``,
            or neither.
        parallel_strategy_kwargs (Dict[str, Any]): Args to pass into
            ``DistributedDataParallel`` or ``FullyShardedDataParallel``
            initialization if ``parallel_strategy`` is set to "ddp"
            or "fsdp", respectively.
    """

    if parallel_strategy == "fsdp" and FullyShardedDataParallel is None:
        raise ImportError(
            "FullyShardedDataParallel requires torch>=1.11.0. "
            "Run `pip install 'torch>=1.11.0'` to use FullyShardedDataParallel."
        )

    return get_accelerator(_TorchAccelerator).prepare_model(
        model,
        move_to_device=move_to_device,
        parallel_strategy=parallel_strategy,
        parallel_strategy_kwargs=parallel_strategy_kwargs,
    )


@PublicAPI(stability="beta")
def prepare_data_loader(
    data_loader: torch.utils.data.DataLoader,
    add_dist_sampler: bool = True,
    move_to_device: bool = True,
    auto_transfer: bool = True,
) -> torch.utils.data.DataLoader:
    """Prepares DataLoader for distributed execution.

    This allows you to use the same exact code regardless of number of
    workers or the device type being used (CPU, GPU).

    Args:
        data_loader (torch.utils.data.DataLoader): The DataLoader to
            prepare.
        add_dist_sampler: Whether to add a DistributedSampler to
            the provided DataLoader.
        move_to_device: If set, automatically move the data
            returned by the data loader to the correct device.
        auto_transfer: If set and device is GPU, another CUDA stream
            is created to automatically copy data from host (CPU) memory
            to device (GPU) memory (the default CUDA stream still runs the
            training procedure). If device is CPU, it will be disabled
            regardless of the setting. This configuration will be ignored
            if ``move_to_device`` is False.
    """
    return get_accelerator(_TorchAccelerator).prepare_data_loader(
        data_loader,
        add_dist_sampler=add_dist_sampler,
        move_to_device=move_to_device,
        auto_transfer=auto_transfer,
    )


@PublicAPI(stability="beta")
def accelerate(amp: bool = False) -> None:
    """Enables training optimizations.

    Arguments:
        amp: If true, perform training with automatic mixed precision.
            Otherwise, use full precision.

    .. warning:: ``train.torch.accelerate`` cannot be called more than once, and it
       must be called before any other ``train.torch`` utility function.
    """
    try:
        set_accelerator(_TorchAccelerator(amp=amp))
    except RuntimeError:
        raise RuntimeError(
            "An accelerator has already been set. Make sure "
            "`train.torch.accelerate()` is not called multiple times, and is called "
            "before any of the prepare methods."
        )


@PublicAPI(stability="beta")
def prepare_optimizer(optimizer: torch.optim.Optimizer) -> torch.optim.Optimizer:
    """Wraps optimizer to support automatic mixed precision.

    Args:
        optimizer (torch.optim.Optimizer): The DataLoader to prepare.

    Returns:
        A wrapped optimizer.
    """
    return get_accelerator(_TorchAccelerator).prepare_optimizer(optimizer)


@PublicAPI(stability="beta")
def backward(tensor: torch.Tensor) -> None:
    """Computes the gradient of the specified tensor w.r.t. graph leaves.

    Args:
        tensor (torch.Tensor): Tensor of which the derivative will be computed.
    """
    get_accelerator(_TorchAccelerator).backward(tensor)


@PublicAPI(stability="beta")
def enable_reproducibility(seed: int = 0) -> None:
    """Limits sources of nondeterministic behavior.

    This function:

        * Seeds PyTorch, Python, and NumPy.
        * Disables CUDA convolution benchmarking.
        * Configures PyTorch to use determinstic algorithms.
        * Seeds workers spawned for multi-process data loading.

    Args:
        seed: The number to seed libraries and data workers with.

    .. warning:: ``train.torch.enable_reproducibility()`` can't guarantee
        completely reproducible results across executions. To learn more, read
        the `PyTorch notes on randomness
        <https://pytorch.org/docs/stable/notes/randomness.html>`_.
    """
    get_accelerator(_TorchAccelerator).enable_reproducibility(seed)


@Deprecated
class TorchWorkerProfiler:
    """Utility class for running PyTorch Profiler on a Train worker.

    Args:
        trace_dir (Optional[str]): The directory to store traces on the
           worker node. If ``None``, this will use a default temporary dir.
    """

    WORKER_TRACE_DIR_NAME = "pytorch_profiler_worker_traces"

    def __init__(self, trace_dir: Optional[str] = None):
        raise DeprecationWarning(
            "The `ray.train.torch.TorchWorkerProfiler` API is deprecated in Ray 2.0.",
        )


class _TorchAccelerator(Accelerator):
    """A utility that implements methods to accelerate PyTorch training.

    Arguments:
        amp: If true, perform training with automatic mixed precision.
            Otherwise, use full precision.
    """

    def __init__(self, amp: bool = False):
        self.amp_is_enabled = amp
        self.scaler = GradScaler() if amp else None
        self._seed = None

    def prepare_model(
        self,
        model: torch.nn.Module,
        move_to_device: bool = True,
        parallel_strategy: Optional[str] = "ddp",
        parallel_strategy_kwargs: Optional[Dict[str, Any]] = None,
    ) -> torch.nn.Module:
        """Prepares the model for distributed execution.

        This allows you to use the same exact code regardless of number of
        workers or the device type being used (CPU, GPU).

        Args:
            model (torch.nn.Module): A torch model to prepare.
            move_to_device: Whether to move the model to the correct
                device. If set to False, the model needs to manually be moved
                to the correct device.
            parallel_strategy ("ddp", "fsdp", or None): Whether to wrap models
                in ``DistributedDataParallel``, ``FullyShardedDataParallel`` (
                Experimental), or neither.
            parallel_strategy_kwargs (Dict[str, Any]): Args to pass into
                ``DistributedDataParallel`` or ``FullyShardedDataParallel``
                initialization if ``parallel_strategy`` is set to "ddp"
                or "fsdp", respectively.
        """
        parallel_strategy_kwargs = parallel_strategy_kwargs or {}

        rank = session.get_local_rank()

        if isinstance(move_to_device, torch.device):
            device = move_to_device
        else:
            device = get_device()
            if isinstance(device, list):
                device = device[0]

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

        if move_to_device:
            if rank == 0:
                logger.info(f"Moving model to device: {device}")
            else:
                logger.debug(f"Moving model to device: {device}")
            model = model.to(device)

        def model_get_state(self):
            # `__getstate__` is an special method that informs pickle which attributes
            # to serialize. This custom implementation ensures that the wrapped forward
            # method and custom `__getstate__` method aren't serialized.
            if hasattr(self, "_original_get_state"):
                state = self._original_get_state()
                state["__getstate__"] = state["_original_get_state"]
                del state["_original_get_state"]
            else:
                # If model does not have a `__getstate__` already defined, use default
                # implementation.
                state = self.__dict__.copy()
                del state["__getstate__"]
            state["forward"] = state["_unwrapped_forward"]
            del state["_unwrapped_forward"]

            return state

        if self.amp_is_enabled:
            # Pickle cannot serialize the wrapped forward method. As a workaround,
            # define a custom `__getstate__` method that unwraps the forward method.
            model._unwrapped_forward = model.forward
            model.forward = autocast()(model.forward)

            # TODO(amogkam): Replace below logic with a generic "unpack model" method.
            # Replacing the `model.forward` method makes the model no longer
            # serializable. When serializing the model, we have to override the
            # `__getstate__` method to set back the original forward method.
            if hasattr(model, "__getstate__"):
                model._original_get_state = model.__getstate__
            # `__getstate__` must be a bound method rather than an callable attribute.
            # See https://stackoverflow.com/questions/972/adding-a-method-to-an-existing-object-instance.  # noqa: E501
            model.__getstate__ = types.MethodType(model_get_state, model)

        world_size = session.get_world_size()

        if parallel_strategy and world_size > 1:
            if parallel_strategy == "ddp":
                DataParallel = DistributedDataParallel
                if torch.cuda.is_available():
                    parallel_strategy_kwargs = {
                        "device_ids": [device],
                        "output_device": device,
                        **parallel_strategy_kwargs,
                    }
            else:
                if not torch.cuda.is_available():
                    raise RuntimeError(
                        "FSDP is only available with GPU-enabled "
                        "training. Set "
                        "`use_gpu=True` in your Trainer to train with "
                        "GPUs."
                    )
                DataParallel = FullyShardedDataParallel
            if rank == 0:
                logger.info(f"Wrapping provided model in {DataParallel.__name__}.")
            else:
                logger.debug(f"Wrapping provided model in {DataParallel.__name__}.")
            model = DataParallel(model, **parallel_strategy_kwargs)

        return model

    def prepare_data_loader(
        self,
        data_loader: torch.utils.data.DataLoader,
        add_dist_sampler: bool = True,
        move_to_device: bool = True,
        auto_transfer: bool = False,
    ) -> torch.utils.data.DataLoader:
        """Prepares DataLoader for distributed execution.

        This allows you to use the same exact code regardless of number of
        workers or the device type being used (CPU, GPU).

        Args:
            data_loader (torch.utils.data.DataLoader): The DataLoader to
                prepare.
            add_dist_sampler: Whether to add a DistributedSampler to
                the provided DataLoader.
            move_to_device: If set, automatically move the data
                returned by the data loader to the correct device.
            auto_transfer: (Experimental) If set and device is GPU, another CUDA stream
                is created to automatically copy data from host (CPU) memory
                to device (GPU) memory (the default CUDA stream still runs the
                training procedure). If device is CPU, it will be disabled
                regardless of the setting. This configuration will be ignored
                if ``move_to_device`` is False.
        """

        world_size = session.get_world_size()
        world_rank = session.get_world_rank()

        # Only add Distributed Sampler if the following conditions hold:
        # 1. More than one training worker is being used.
        # 2. A DistributedSampler has not already been added by the user.
        # 3. The dataset is not an IterableDataset. Samplers do not worker with
        # IterableDatasets.
        if (
            world_size > 1
            and not isinstance(data_loader.sampler, DistributedSampler)
            and not (
                hasattr(data_loader, "dataset")
                and isinstance(data_loader.dataset, IterableDataset)
            )
            and add_dist_sampler
        ):

            def with_sampler(loader):
                # Automatically set the DistributedSampler

                # If you're using a sampler, the DataLoader shuffle flag must be set to
                # False. Shuffling is instead determined by the shuffle argument passed
                # to the DistributedSampler constructor.

                # If no sampler is passed to the DataLoader constructor, Torch
                # constructs a default sampler. The default sampler is a RandomSampler
                # if shuffling is enabled and a SequentialSampler otherwise. DataLoader
                # does not have a shuffle attribute, so we instead identify whether
                # shuffling is enabled by checking the default sampler type.
                shuffle = not isinstance(loader.sampler, SequentialSampler)

                def seeded_worker_init_fn(
                    worker_init_fn: Optional[Callable[[int], None]]
                ):
                    def wrapper(worker_id: int):
                        worker_seed = torch.initial_seed() % 2**32
                        np.random.seed(worker_seed)
                        random.seed(worker_seed)
                        if worker_init_fn:
                            worker_init_fn(worker_id)

                    return wrapper

                worker_init_fn: Optional[Callable[[int], None]] = loader.worker_init_fn
                generator: Optional[torch.Generator] = loader.generator
                if self._seed is not None:
                    worker_init_fn = seeded_worker_init_fn(worker_init_fn)
                    generator = torch.Generator()
                    generator.manual_seed(self._seed)

                using_default_sampler = isinstance(
                    loader.sampler, (SequentialSampler, RandomSampler)
                )
                if not using_default_sampler and world_rank == 0:
                    logger.warn(
                        f"The {loader.sampler.__class__.__name__} will be overwritten "
                        "with a DistributedSampler. You can disable this by setting "
                        "`with_sampler` to False in `prepare_data_loader`."
                    )

                data_loader_args = {
                    "dataset": loader.dataset,
                    "batch_size": loader.batch_size,
                    "shuffle": False,
                    "num_workers": loader.num_workers,
                    "collate_fn": loader.collate_fn,
                    "pin_memory": loader.pin_memory,
                    "drop_last": loader.drop_last,
                    "timeout": loader.timeout,
                    "worker_init_fn": worker_init_fn,
                    "generator": generator,
                    "sampler": DistributedSampler(loader.dataset, shuffle=shuffle),
                }
                return DataLoader(**data_loader_args)

            data_loader = with_sampler(data_loader)

        if move_to_device:
            device = get_device()
            data_loader = _WrappedDataLoader(data_loader, device, auto_transfer)

        return data_loader

    def prepare_optimizer(self, optimizer: Optimizer) -> Optimizer:
        """Wraps optimizer to support automatic mixed precision.

        Args:
            optimizer (torch.optim.Optimizer): The DataLoader to prepare.

        Returns:
            A wrapped optimizer.
        """
        return _WrappedOptimizer(optimizer, scaler=self.scaler)

    def backward(self, tensor: torch.Tensor) -> None:
        """Computes the gradient of the specified tensor w.r.t. graph leaves.

        Args:
            tensor (torch.Tensor): Tensor of which the derivative will be computed.
        """
        if self.amp_is_enabled:
            self.scaler.scale(tensor).backward()
        else:
            tensor.backward()

    def enable_reproducibility(self, seed: int = 0) -> None:
        """Limits sources of nondeterministic behavior."""
        self._seed = seed

        torch.manual_seed(seed)
        random.seed(seed)
        np.random.seed(seed)

        torch.use_deterministic_algorithms(True)
        torch.backends.cudnn.benchmark = False

        # If you want to use deterministic algorithms with CUDA, then you need to set
        # the CUBLAS_WORKSPACE_CONFIG environment variable; otherwise, Torch errors.
        # See https://docs.nvidia.com/cuda/cublas/index.html#cublasApi_reproducibility.
        os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":4096:8"


class _WrappedDataLoader(DataLoader):
    def __init__(
        self, base_dataloader: DataLoader, device: torch.device, auto_transfer: bool
    ):

        self.__dict__.update(getattr(base_dataloader, "__dict__", {}))
        self._dataloader = base_dataloader
        self.dataloader_iter = None
        self.device = device
        # disable auto transfer (host->device) if cpu is used
        self._auto_transfer = auto_transfer if device.type == "cuda" else False
        # create a new CUDA stream to move data from host to device concurrently
        self._memcpy_stream = (
            torch.cuda.Stream(device)
            if device.type == "cuda" and self._auto_transfer
            else None
        )
        self.next_batch = None

    def _move_to_device(self, item):
        if item is None:
            return None

        def try_move_device(i):
            try:
                i = i.to(self.device, non_blocking=self._auto_transfer)
            except AttributeError:
                logger.debug(f"Item {i} cannot be moved to device " f"{self.device}.")
            return i

        with torch.cuda.stream(self._memcpy_stream):
            if isinstance(item, collections.abc.Mapping):
                item_on_device = {k: self._move_to_device(v) for k, v in item.items()}
            elif isinstance(item, tuple):
                item_on_device = tuple(self._move_to_device(i) for i in item)
            elif isinstance(item, list):
                item_on_device = [self._move_to_device(i) for i in item]
            elif isinstance(item, torch.Tensor):
                item_on_device = try_move_device(item)
            else:
                logger.debug(
                    f"Data type {type(item)} doesn't support being moved to device."
                )
                item_on_device = item

            return item_on_device

    def _wait_for_batch(self, item):
        if self._memcpy_stream is None:
            return
        # Reference:
        # https://pytorch.org/docs/stable/generated/torch.Tensor.record_stream.html
        # The training stream (current) needs to wait until
        # the memory copy stream finishes.
        curr_stream = torch.cuda.current_stream()
        curr_stream.wait_stream(self._memcpy_stream)
        # When a tensor is used by CUDA streams different from
        # its original allocator, we need to call ``record_stream``
        # to inform the allocator of all these streams. Otherwise,
        # the tensor might be freed once it is no longer used by
        # the creator stream.
        for i in item:
            # The Pytorch DataLoader has no restrictions on what is outputted for
            # each batch. We should only ``record_stream`` if the item has the
            # ability to do so.
            try:
                i.record_stream(curr_stream)
            except AttributeError:
                pass

    def __len__(self):
        return len(self._dataloader)

    def _prefetch_next_batch(self):
        next_batch = next(self.dataloader_iter, None)
        self.next_batch = self._move_to_device(next_batch)

    def __iter__(self):
        self.dataloader_iter = iter(self._dataloader)
        self._prefetch_next_batch()
        return self

    def __next__(self):
        next_batch = self.next_batch
        if next_batch is None:
            raise StopIteration
        self._wait_for_batch(next_batch)
        self._prefetch_next_batch()
        return next_batch


class _WrappedOptimizer(Optimizer):
    def __init__(self, optimizer: Optimizer, scaler: Optional[GradScaler] = None):
        self.optimizer = optimizer
        self.scaler = scaler

    @property
    def state(self):
        return self.optimizer.state

    @state.setter
    def state(self, state):
        self.optimizer.state = state

    @property
    def param_groups(self):
        return self.optimizer.param_groups

    @param_groups.setter
    def param_groups(self, param_groups):
        self.optimizer.param_groups = param_groups

    @property
    def defaults(self):
        return self.optimizer.defaults

    @defaults.setter
    def defaults(self, defaults):
        self.optimizer.defaults = defaults

    def add_param_group(self, param_group):
        self.optimizer.add_param_group(param_group)

    def load_state_dict(self, state_dict):
        self.optimizer.load_state_dict(state_dict)

    def state_dict(self):
        return self.optimizer.state_dict()

    def zero_grad(self):
        self.optimizer.zero_grad()

    def step(self, closure=None):
        if self.scaler is not None:
            self.scaler.step(self.optimizer, closure)
            self.scaler.update()
        else:
            self.optimizer.step(closure)
