# Copyright The PyTorch Lightning team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import io
import os
import time
from multiprocessing.queues import SimpleQueue
from typing import Any, Callable, Dict, List, Optional, Union

import torch
import torch.multiprocessing as mp
from torch.nn import Module
from torch.utils.data import DataLoader

import pytorch_lightning as pl
from pytorch_lightning.overrides import LightningDistributedModule
from pytorch_lightning.plugins.io.xla_plugin import XLACheckpointIO
from pytorch_lightning.plugins.precision import PrecisionPlugin
from pytorch_lightning.strategies.ddp_spawn import _FakeQueue, _SpawnOutput, DDPSpawnStrategy
from pytorch_lightning.trainer.connectors.data_connector import DataConnector
from pytorch_lightning.trainer.states import TrainerFn
from pytorch_lightning.utilities import _TPU_AVAILABLE, find_shared_parameters, set_shared_parameters
from pytorch_lightning.utilities.apply_func import move_data_to_device
from pytorch_lightning.utilities.data import has_len
from pytorch_lightning.utilities.distributed import rank_zero_debug, rank_zero_only, ReduceOp
from pytorch_lightning.utilities.exceptions import MisconfigurationException
from pytorch_lightning.utilities.model_helpers import is_overridden
from pytorch_lightning.utilities.seed import reset_seed
from pytorch_lightning.utilities.types import _PATH, STEP_OUTPUT

if _TPU_AVAILABLE:
    import torch_xla.core.xla_env_vars as xenv
    import torch_xla.core.xla_model as xm
    import torch_xla.distributed.xla_multiprocessing as xmp
    from torch_xla.core.xla_model import rendezvous
    from torch_xla.distributed.parallel_loader import MpDeviceLoader
else:
    xm, xmp, MpDeviceLoader, rendezvous = [None] * 4


class TPUSpawnStrategy(DDPSpawnStrategy):
    """Strategy for training multiple TPU devices using the :func:`torch.multiprocessing.spawn` method."""

    def __init__(
        self,
        accelerator: Optional["pl.accelerators.accelerator.Accelerator"] = None,
        parallel_devices: Optional[List[int]] = None,
        checkpoint_io: Optional[XLACheckpointIO] = None,
        precision_plugin: Optional[PrecisionPlugin] = None,
        debug: bool = False,
        **_: Any,
    ) -> None:
        checkpoint_io = checkpoint_io or XLACheckpointIO()
        super().__init__(
            accelerator=accelerator,
            parallel_devices=parallel_devices,
            checkpoint_io=checkpoint_io,
            precision_plugin=precision_plugin,
        )
        self.debug = debug
        self.tpu_local_core_rank = 0
        self.tpu_global_core_rank = 0
        self.start_method = "fork"

    @property
    def global_rank(self) -> int:
        return self.tpu_global_core_rank

    @property
    def local_rank(self) -> int:
        return self.tpu_local_core_rank

    @property
    def world_size(self) -> int:
        return xm.xrt_world_size()

    @property
    def root_device(self) -> torch.device:
        return xm.xla_device()

    @staticmethod
    def _validate_dataloader(dataloaders: Union[List[DataLoader], DataLoader]) -> None:
        if not isinstance(dataloaders, list):
            dataloaders = [dataloaders]

        for dataloader in dataloaders:
            if not has_len(dataloader):
                raise MisconfigurationException(
                    "TPUs do not currently support IterableDataset objects, the dataset must implement `__len__`."
                    " HINT: You can mock the length on your dataset to bypass this MisconfigurationException."
                )

    @staticmethod
    def _validate_patched_dataloaders(model: "pl.LightningModule") -> None:
        """Validate and fail fast if the dataloaders were passed directly to fit."""
        connector: DataConnector = model.trainer._data_connector
        sources = (
            connector._train_dataloader_source,
            connector._val_dataloader_source,
            connector._test_dataloader_source,
            connector._predict_dataloader_source,
        )
        for source in sources:
            if not source.is_module():
                TPUSpawnStrategy._validate_dataloader(source.instance)

    def connect(self, model: "pl.LightningModule") -> None:
        TPUSpawnStrategy._validate_patched_dataloaders(model)
        self.wrapped_model = xmp.MpModelWrapper(LightningDistributedModule(model))
        return super().connect(model)

    def setup(self, trainer: "pl.Trainer") -> None:
        self.start_method = "fork"
        self.accelerator.setup(trainer)
        self.setup_optimizers(trainer)
        self.setup_precision_plugin()
        self._move_optimizer_state()

        if self.debug:
            os.environ["PT_XLA_DEBUG"] = str(1)

        shared_params = find_shared_parameters(self.model)
        self.model_to_device()
        if is_overridden("on_post_move_to_device", self.lightning_module):
            self.model.module.on_post_move_to_device()
        else:
            set_shared_parameters(self.model.module, shared_params)

        self.setup_optimizers(trainer)
        self.precision_plugin.connect(self.model, None, None)

    def _setup_model(self, model: Module) -> Module:
        return model

    @property
    def distributed_sampler_kwargs(self) -> Dict[str, int]:
        return dict(num_replicas=xm.xrt_world_size(), rank=xm.get_ordinal())

    @property
    def is_distributed(self) -> bool:
        # HOST_WORLD_SIZE is None outside the xmp.spawn process
        return os.getenv(xenv.HOST_WORLD_SIZE, None) and self.world_size != 1

    def process_dataloader(self, dataloader: DataLoader) -> MpDeviceLoader:
        TPUSpawnStrategy._validate_dataloader(dataloader)
        dataloader = MpDeviceLoader(dataloader, self.root_device)
        # Mimic interface to torch.utils.data.DataLoader
        dataloader.dataset = dataloader._loader.dataset
        return dataloader

    def configure_ddp(self) -> None:
        pass

    def init_dist_connection(self, global_rank: int, world_size: int) -> None:
        pass

    def set_world_ranks(self, process_idx: int = 0) -> None:
        pass

    def model_to_device(self) -> None:
        self.model = self.wrapped_model.to(self.root_device)

    def barrier(self, name: Optional[str] = None) -> None:
        if self.is_distributed:
            rendezvous(name)

    def _collect_rank_zero_results(self, trainer: "pl.Trainer", results: Any) -> Optional["_SpawnOutput"]:
        rank_zero_debug("Finalizing the TPU spawn environment.")
        checkpoint_callback = trainer.checkpoint_callback
        best_model_path = checkpoint_callback.best_model_path if checkpoint_callback else None

        # requires to compute the state_dict on all processes in case Metrics are present
        state_dict = self.lightning_module.state_dict()

        # save the last weights
        weights_path = None
        if trainer.state.fn == TrainerFn.FITTING:
            weights_path = os.path.join(trainer.default_root_dir, ".temp.ckpt")
            self.checkpoint_io.save_checkpoint(state_dict, weights_path)

        # We use `local_rank` here as separate filesystems are used for each VM for TPU Pod Training
        if self.local_rank != 0:
            return

        # adds the `callback_metrics` to the queue
        extra = _FakeQueue()
        if is_overridden("add_to_queue", self.lightning_module):
            # TODO: Remove the if in v1.7
            self.lightning_module.add_to_queue(extra)
        self.add_to_queue(trainer, extra)

        return _SpawnOutput(best_model_path, weights_path, trainer.state, results, extra)

    def broadcast(self, obj: object, src: int = 0) -> object:
        if not self.is_distributed:
            return obj
        buffer = io.BytesIO()
        torch.save(obj, buffer)
        data = bytearray(buffer.getbuffer())
        data_tensor = torch.tensor(data, device=self.root_device, dtype=torch.float)
        data = xm.all_gather(data_tensor)
        buffer = io.BytesIO(data.cpu().byte().numpy())
        obj = torch.load(buffer)
        return obj

    def reduce_boolean_decision(self, decision: bool) -> bool:
        decision = torch.tensor(int(decision), device=self.lightning_module.device)
        decision = self.reduce(decision, reduce_op="sum")
        decision = bool(decision == self.world_size)
        return decision

    def reduce(self, output, group: Optional[Any] = None, reduce_op: Optional[Union[ReduceOp, str]] = None):
        if not isinstance(output, torch.Tensor):
            output = torch.tensor(output, device=self.lightning_module.device)

        _invalid_reduce_op = isinstance(reduce_op, ReduceOp) and reduce_op != ReduceOp.SUM
        _invalid_reduce_op_str = isinstance(reduce_op, str) and reduce_op.lower() not in ("sum", "mean", "avg")
        if _invalid_reduce_op or _invalid_reduce_op_str:
            raise MisconfigurationException(
                "Currently, TPUSpawn Strategy only support `sum`, `mean`, `avg` reduce operation."
            )

        output = xm.mesh_reduce("reduce", output, sum)

        if isinstance(reduce_op, str) and reduce_op.lower() in ("avg", "mean"):
            output = output / self.world_size

        return output

    def get_mp_spawn_kwargs(self, trainer: Optional["pl.Trainer"] = None) -> Dict[str, Any]:
        return {
            "nprocs": len(self.parallel_devices),
            "start_method": self.start_method,
        }

    def spawn(self, function: Callable, *args: Any, **kwargs: Any) -> Optional[Union[Any, "_SpawnOutput"]]:
        context = mp.get_context(self.start_method or "fork")
        return_queue = context.SimpleQueue()
        xmp.spawn(self._wrapped_function, args=(function, args, kwargs, return_queue), **self.get_mp_spawn_kwargs())
        return return_queue.get()

    def _wrapped_function(
        self, process_idx: int, function: Callable, args: Any, kwargs: Any, return_queue: SimpleQueue
    ) -> None:
        self._worker_setup(process_idx)
        result = function(*args, **kwargs)
        if self.local_rank == 0:
            return_queue.put(move_data_to_device(result, "cpu"))

        # https://github.com/pytorch/xla/issues/1801#issuecomment-602799542
        self.barrier("end-process")

        # Ensure that the rank 0 process is the one exiting last
        # https://github.com/pytorch/xla/issues/2190#issuecomment-641665358
        if self.local_rank == 0:
            time.sleep(2)

    def _worker_setup(self, process_idx: int):
        reset_seed()
        self.tpu_local_core_rank = xm.get_local_ordinal()
        self.tpu_global_core_rank = xm.get_ordinal()
        rank_zero_only.rank = self.global_rank

    def validation_step(self, *args, **kwargs) -> Optional[STEP_OUTPUT]:
        with self.precision_plugin.val_step_context():
            return self.model(*args, **kwargs)

    def test_step(self, *args, **kwargs) -> Optional[STEP_OUTPUT]:
        with self.precision_plugin.test_step_context():
            return self.model(*args, **kwargs)

    def predict_step(self, *args, **kwargs) -> STEP_OUTPUT:
        with self.precision_plugin.predict_step_context():
            return self.model(*args, **kwargs)

    def training_step_end(self, output: STEP_OUTPUT) -> STEP_OUTPUT:
        self._pod_progress_bar_force_stdout()
        return output

    def validation_step_end(self, output: STEP_OUTPUT) -> STEP_OUTPUT:
        self._pod_progress_bar_force_stdout()
        return output

    def test_step_end(self, output: STEP_OUTPUT) -> STEP_OUTPUT:
        self._pod_progress_bar_force_stdout()
        return output

    def _pod_progress_bar_force_stdout(self) -> None:
        # Why is it required? The way `pytorch_xla.distributed` streams logs
        # from different vms to the main worker doesn't work well with tqdm
        # Ref: https://github.com/pytorch/xla/blob/master/torch_xla/distributed/xla_dist.py#L140
        # The print statement seems to force tqdm to flush stdout.
        if self.tpu_global_core_rank == 0 and int(os.getenv(xenv.TPUVM_MODE, 0)) == 1:
            print()

    def save_checkpoint(self, checkpoint: Dict[str, Any], filepath: _PATH) -> None:
        """Save model/training states as a checkpoint file through state-dump and file-write.

        Args:
            checkpoint: dict containing model and trainer state
            filepath: write-target file's path
        """
        # `xla_model.save` needs to be called on all ranks. It internally checks if the local rank is 0
        self.checkpoint_io.save_checkpoint(checkpoint, filepath)

    def remove_checkpoint(self, filepath: _PATH) -> None:
        """Remove checkpoint filepath from the filesystem.

        Args:
            filepath: Path to checkpoint
        """
        if self.local_rank == 0:
            self.checkpoint_io.remove_checkpoint(filepath)

    def all_gather(self, tensor: torch.Tensor, group: Optional[Any] = None, sync_grads: bool = False) -> torch.Tensor:
        """
        Function to gather a tensor from several distributed processes
        Args:
            tensor: tensor of shape (batch, ...)
            group: not available with TPUs
            sync_grads: not available with TPUs
        Return:
            A tensor of shape (world_size, batch, ...)
        """
        if isinstance(tensor, torch.Tensor) and tensor.dim() == 0:
            tensor = tensor.unsqueeze(0)
        return xm.all_gather(tensor)

    def teardown(self) -> None:
        super().teardown()
        os.environ.pop("PT_XLA_DEBUG", None)

    @classmethod
    def register_strategies(cls, strategy_registry: Dict) -> None:
        strategy_registry.register(
            "tpu_spawn_debug", cls, description="TPUSpawn Strategy with `debug` as True", debug=True
        )