# 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
from typing import Any, List, Optional, Union

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

from pytorch_lightning import _logger as log
from pytorch_lightning.accelerators.accelerator import Accelerator, ReduceOp
from pytorch_lightning.cluster_environments import ClusterEnvironment
from pytorch_lightning.core.lightning import LightningModule
from pytorch_lightning.distributed.dist import LightningDistributed
from pytorch_lightning.plugins.ddp_plugin import DDPPlugin
from pytorch_lightning.plugins.rpc_plugin import RPCPlugin
from pytorch_lightning.utilities import AMPType
from pytorch_lightning.utilities.distributed import all_gather_ddp_if_available, rank_zero_only, sync_ddp_if_available


class DDPHPCAccelerator(Accelerator):

    def __init__(self,
                 trainer,
                 cluster_environment: Optional[ClusterEnvironment] = None,
                 ddp_plugin: Optional[DDPPlugin] = None):
        """
        Runs training using DDP on an HPC cluster

        Example::

            # default
            trainer = Trainer(accelerator=DDPHPCAccelerator())

        """
        super().__init__(trainer, cluster_environment, ddp_plugin)
        self.task_idx = None
        self._has_spawned_children = False
        self.dist = LightningDistributed()
        self.nickname = 'ddp'

    def setup(self, model):
        self.trainer.model = model
        self.task_idx = self.cluster_environment.local_rank()

    def train(self):
        model = self.trainer.model
        self.ddp_train(process_idx=self.task_idx, model=model)

    def set_world_ranks(self, process_idx):
        self.trainer.local_rank = process_idx
        self.trainer.global_rank = self.trainer.node_rank * self.trainer.num_processes + process_idx
        self.trainer.world_size = self.trainer.num_nodes * self.trainer.num_processes

    def init_device(self, process_idx):
        self.trainer.root_gpu = process_idx
        torch.cuda.set_device(self.trainer.root_gpu)

    def model_to_device(self, model):
        model.cuda(self.trainer.root_gpu)

    def get_device_ids(self):
        device_ids = [self.trainer.root_gpu]
        return device_ids

    def training_step(self, args):
        return self._step(args)

    def validation_step(self, args):
        return self._step(args)

    def test_step(self, args):
        return self._step(args)

    def _step(self, args):
        args = self.ddp_plugin.on_before_forward(self.trainer.get_model(), *args)
        if self.trainer.amp_backend == AMPType.NATIVE:
            with torch.cuda.amp.autocast():
                output = self.trainer.model(*args)
        else:
            output = self.trainer.model(*args)
        return output

    def barrier(self, name: Optional[str] = None):
        if torch_distrib.is_initialized():
            torch_distrib.barrier()

    def early_stopping_should_stop(self, pl_module):
        stop = torch.tensor(int(self.trainer.should_stop), device=pl_module.device)
        dist.all_reduce(stop, op=dist.reduce_op.SUM)
        dist.barrier()
        should_stop = stop == self.trainer.world_size
        return should_stop

    def broadcast(self, obj, src=0):
        return self.dist.broadcast(obj)

    def ddp_train(self, process_idx, model):
        """
        Entry point for ddp

        Args:
            process_idx:
            model:

        Returns:
            Dict with evaluation results

        """
        # determine which process we are and world size
        self.set_world_ranks(process_idx)
        self.init_device(process_idx)

        # toggle prog bar
        if (self.trainer.node_rank != 0 or process_idx != 0) and self.trainer.progress_bar_callback is not None:
            self.trainer.progress_bar_callback.disable()

        # set warning rank
        rank_zero_only.rank = self.trainer.global_rank

        # set up server using proc 0's ip address
        # try to init for 20 times at max in case ports are taken
        # where to store ip_table
        model.trainer = self.trainer
        self.init_ddp_connection(
            self.trainer.global_rank,
            self.trainer.world_size,
            self.trainer.is_slurm_managing_tasks
        )

        if isinstance(self.ddp_plugin, RPCPlugin):
            if not self.ddp_plugin.is_main_rpc_process:
                self.ddp_plugin.on_accelerator_exit_rpc_process(self.trainer)
                self.ddp_plugin.exit_rpc_process()
                if self.ddp_plugin.return_after_exit_rpc_process:
                    return
            else:
                self.ddp_plugin.on_main_rpc_connection(self.trainer)

        # call setup after the ddp process has connected
        self.trainer.call_setup_hook(model)

        # on world_size=0 let everyone know training is starting
        if self.trainer.is_global_zero and not torch.distributed.is_initialized():
            log.info('-' * 100)
            log.info(f'distributed_backend={self.trainer.distributed_backend}')
            log.info(f'All DDP processes registered. Starting ddp with {self.trainer.world_size} processes')
            log.info('-' * 100)

        # call sync_bn before .cuda(), configure_apex and configure_ddp
        if self.trainer.sync_batchnorm:
            model = self.configure_sync_batchnorm(model)

        # move the model to the correct device
        self.model_to_device(model)

        # CHOOSE OPTIMIZER
        # allow for lr schedulers as well
        self.setup_optimizers(model)

        self.ddp_plugin.on_after_setup_optimizers(self.trainer)

        # set model properties before going into wrapper
        self.trainer.model_connector.copy_trainer_model_properties(model)

        # 16-bit
        model = self.trainer.precision_connector.connect(model)

        self.trainer.convert_to_lightning_optimizers()

        # device ids change depending on the DDP setup
        device_ids = self.get_device_ids()

        # allow user to configure ddp
        model = self.configure_ddp(model, device_ids)

        # set up training routine
        self.trainer.train_loop.setup_training(model)

        # train or test
        results = self.train_or_test()

        # clean up memory
        torch.cuda.empty_cache()

        return results

    def configure_ddp(
            self, model: LightningModule, device_ids: List[int]
    ) -> DistributedDataParallel:
        model = self.ddp_plugin.configure_ddp(model, device_ids)
        return model

    def configure_sync_batchnorm(self, model: LightningModule) -> LightningModule:
        """
        Add global batchnorm for a model spread across multiple GPUs and nodes.

        Override to synchronize batchnorm between specific process groups instead
        of the whole world or use a different sync_bn like `apex`'s version.

        Args:
            model: pointer to current :class:`LightningModule`.

        Return:
            LightningModule with batchnorm layers synchronized between process groups
        """
        model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model, process_group=None)

        return model

    def sync_tensor(self,
                    tensor: Union[torch.Tensor],
                    group: Optional[Any] = None,
                    reduce_op: Optional[Union[ReduceOp, str]] = None) -> torch.Tensor:
        return sync_ddp_if_available(tensor, group, reduce_op)

    def all_gather(self, tensor: Union[torch.Tensor], group: Optional[Any] = None, sync_grads: bool = False):
        """
        Function to gather a tensor from several distributed processes

        Args:
            tensor: tensor of shape (batch, ...)
            group: the process group to gather results from. Defaults to all processes (world)
            sync_grads: flag that allows users to synchronize gradients for all_gather op

        Return:
            A tensor of shape (world_size, batch, ...)
        """
        return all_gather_ddp_if_available(tensor, group=group, sync_grads=sync_grads)

    def get_reference_model(self, model) -> LightningModule:
        return self.ddp_plugin.get_model_from_plugin(model)

    @property
    def distributed_sampler_kwargs(self):
        distributed_sampler_kwargs = dict(
            num_replicas=self.trainer.num_nodes * self.trainer.num_processes,
            rank=self.trainer.global_rank
        )
        if self.ddp_plugin is not None:
            distributed_sampler_kwargs = self.ddp_plugin.distributed_sampler_kwargs(distributed_sampler_kwargs)
        return distributed_sampler_kwargs

    @property
    def require_distributed_sampler(self):
        return True