# Copyright (c) 2023, NVIDIA CORPORATION.  All rights reserved.
#
# 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 contextlib
import signal
import sys
from typing import Optional

import torch
from lightning.pytorch.callbacks import Callback
from lightning.pytorch.trainer.trainer import Trainer

from nemo.lightning.io.mixin import IOMixin
from nemo.utils import logging
from nemo.utils.callbacks.dist_ckpt_io import AsyncFinalizableCheckpointIO


class PreemptionCallback(Callback, IOMixin):
    """
    PreemptionCallback checks for preemption during training at the end of every step.
    Upon preemption, it signals the trainer to stop gracefully.

    Args:
        sig (int, optional): The signal to listen for. Defaults to signal.SIGTERM.

    Example:
        >>> from nemo.lightning.pytorch.callbacks import PreemptionCallback
        >>> callback = PreemptionCallback()
        >>> trainer = Trainer(callbacks=[callback])
    """

    def __init__(self, sig: Optional[int] = None):
        self.sig = sig if sig is not None else signal.SIGTERM
        self._interrupted = False
        self._handler_context = None
        self._preemption_supported = None

    def on_train_start(self, trainer: Trainer, pl_module) -> None:
        if self.preemption_supported:
            self._handler_context = self._preemption_handler()
            self._handler_context.__enter__()

    def on_train_batch_start(self, trainer: Trainer, pl_module, batch, batch_idx: int) -> None:
        if not self.preemption_supported:
            self._preemption_supported = self._check_preemption_support()
            if self.preemption_supported:
                self._handler_context = self._preemption_handler()
                self._handler_context.__enter__()

    def on_train_end(self, trainer: Trainer, pl_module) -> None:
        if self._handler_context:
            self._handler_context.__exit__(None, None, None)

    def on_train_batch_end(self, trainer: Trainer, pl_module, outputs, batch, batch_idx: int) -> None:
        if self.interrupted:
            logging.info("Preemption detected, saving checkpoint and exiting")
            trainer.should_stop = True
            if trainer.checkpoint_callback:
                monitor_candidates = trainer.checkpoint_callback._monitor_candidates(trainer)
                trainer.checkpoint_callback._save_last_checkpoint(trainer, monitor_candidates)
                if isinstance(trainer.strategy.checkpoint_io, AsyncFinalizableCheckpointIO):
                    logging.info("Async checkpointing detected, waiting for it to complete")
                    trainer.strategy.checkpoint_io.maybe_finalize_save_checkpoint(blocking=True)
                sys.exit(0)

    @contextlib.contextmanager
    def _preemption_handler(self):
        if not self.preemption_supported:
            logging.warning("Preemption requires torch distributed to be initialized, preemption may be disabled")
            yield
            return

        original_handler = signal.getsignal(self.sig)

        def master_handler(signum, frame):
            logging.info(f"Received signal {signum}, initiating graceful stop")
            self._interrupted = True

        def ignoring_handler(signum, frame):
            logging.debug(f"Received signal {signum} on non-master rank, ignoring")

        try:
            private_rank = torch.distributed.get_rank()
            signal.signal(self.sig, master_handler if private_rank == 0 else ignoring_handler)
            yield
        finally:
            signal.signal(self.sig, original_handler)

    @property
    def preemption_supported(self) -> bool:
        if self._preemption_supported is None:
            self._preemption_supported = self._check_preemption_support()
        return self._preemption_supported

    def _check_preemption_support(self) -> bool:
        return torch.distributed.is_available() and torch.distributed.is_initialized()

    @property
    def interrupted(self) -> bool:
        if not self.preemption_supported:
            return False
        interrupted = torch.tensor(self._interrupted, device=torch.cuda.current_device(), dtype=torch.int32)
        torch.distributed.broadcast(interrupted, 0)
        return bool(interrupted.item())