# Copyright The Lightning AI 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.
r"""Timer ^^^^^"""
import logging
import time
from datetime import timedelta
from typing import Any, Dict, Optional, Union

import lightning.pytorch as pl
from lightning.pytorch.callbacks.callback import Callback
from lightning.pytorch.trainer.states import RunningStage
from lightning.pytorch.utilities import LightningEnum
from lightning.pytorch.utilities.exceptions import MisconfigurationException
from lightning.pytorch.utilities.rank_zero import rank_zero_info

log = logging.getLogger(__name__)


class Interval(LightningEnum):
    step = "step"
    epoch = "epoch"


class Timer(Callback):
    """The Timer callback tracks the time spent in the training, validation, and test loops and interrupts the
    Trainer if the given time limit for the training loop is reached.

    Args:
        duration: A string in the format DD:HH:MM:SS (days, hours, minutes seconds), or a :class:`datetime.timedelta`,
            or a dict containing key-value compatible with :class:`~datetime.timedelta`.
        interval: Determines if the interruption happens on epoch level or mid-epoch.
            Can be either ``"epoch"`` or ``"step"``.
        verbose: Set this to ``False`` to suppress logging messages.

    Raises:
        MisconfigurationException:
            If ``interval`` is not one of the supported choices.

    Example::

        from lightning.pytorch import Trainer
        from lightning.pytorch.callbacks import Timer

        # stop training after 12 hours
        timer = Timer(duration="00:12:00:00")

        # or provide a datetime.timedelta
        from datetime import timedelta
        timer = Timer(duration=timedelta(weeks=1))

        # or provide a dictionary
        timer = Timer(duration=dict(weeks=4, days=2))

        # force training to stop after given time limit
        trainer = Trainer(callbacks=[timer])

        # query training/validation/test time (in seconds)
        timer.time_elapsed("train")
        timer.start_time("validate")
        timer.end_time("test")
    """

    def __init__(
        self,
        duration: Optional[Union[str, timedelta, Dict[str, int]]] = None,
        interval: str = Interval.step,
        verbose: bool = True,
    ) -> None:
        super().__init__()
        if isinstance(duration, str):
            dhms = duration.strip().split(":")
            dhms = [int(i) for i in dhms]
            duration = timedelta(days=dhms[0], hours=dhms[1], minutes=dhms[2], seconds=dhms[3])
        if isinstance(duration, dict):
            duration = timedelta(**duration)
        if interval not in set(Interval):
            raise MisconfigurationException(
                f"Unsupported parameter value `Timer(interval={interval})`. Possible choices are:"
                f" {', '.join(set(Interval))}"
            )
        self._duration = duration.total_seconds() if duration is not None else None
        self._interval = interval
        self._verbose = verbose
        self._start_time: Dict[RunningStage, Optional[float]] = {stage: None for stage in RunningStage}
        self._end_time: Dict[RunningStage, Optional[float]] = {stage: None for stage in RunningStage}
        self._offset = 0

    def start_time(self, stage: str = RunningStage.TRAINING) -> Optional[float]:
        """Return the start time of a particular stage (in seconds)"""
        stage = RunningStage(stage)
        return self._start_time[stage]

    def end_time(self, stage: str = RunningStage.TRAINING) -> Optional[float]:
        """Return the end time of a particular stage (in seconds)"""
        stage = RunningStage(stage)
        return self._end_time[stage]

    def time_elapsed(self, stage: str = RunningStage.TRAINING) -> float:
        """Return the time elapsed for a particular stage (in seconds)"""
        start = self.start_time(stage)
        end = self.end_time(stage)
        offset = self._offset if stage == RunningStage.TRAINING else 0
        if start is None:
            return offset
        if end is None:
            return time.monotonic() - start + offset
        return end - start + offset

    def time_remaining(self, stage: str = RunningStage.TRAINING) -> Optional[float]:
        """Return the time remaining for a particular stage (in seconds)"""
        if self._duration is not None:
            return self._duration - self.time_elapsed(stage)
        return None

    def on_train_start(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
        self._start_time[RunningStage.TRAINING] = time.monotonic()

    def on_train_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
        self._end_time[RunningStage.TRAINING] = time.monotonic()

    def on_validation_start(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
        self._start_time[RunningStage.VALIDATING] = time.monotonic()

    def on_validation_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
        self._end_time[RunningStage.VALIDATING] = time.monotonic()

    def on_test_start(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
        self._start_time[RunningStage.TESTING] = time.monotonic()

    def on_test_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
        self._end_time[RunningStage.TESTING] = time.monotonic()

    def on_fit_start(self, trainer: "pl.Trainer", *args: Any, **kwargs: Any) -> None:
        # this checks the time after the state is reloaded, regardless of the interval.
        # this is necessary in case we load a state whose timer is already depleted
        if self._duration is None:
            return
        self._check_time_remaining(trainer)

    def on_train_batch_end(self, trainer: "pl.Trainer", *args: Any, **kwargs: Any) -> None:
        if self._interval != Interval.step or self._duration is None:
            return
        self._check_time_remaining(trainer)

    def on_train_epoch_end(self, trainer: "pl.Trainer", *args: Any, **kwargs: Any) -> None:
        if self._interval != Interval.epoch or self._duration is None:
            return
        self._check_time_remaining(trainer)

    def state_dict(self) -> Dict[str, Any]:
        return {"time_elapsed": {stage.value: self.time_elapsed(stage) for stage in RunningStage}}

    def load_state_dict(self, state_dict: Dict[str, Any]) -> None:
        time_elapsed = state_dict.get("time_elapsed", {})
        self._offset = time_elapsed.get(RunningStage.TRAINING.value, 0)

    def _check_time_remaining(self, trainer: "pl.Trainer") -> None:
        assert self._duration is not None
        should_stop = self.time_elapsed() >= self._duration
        should_stop = trainer.strategy.broadcast(should_stop)
        trainer.should_stop = trainer.should_stop or should_stop
        if should_stop and self._verbose:
            elapsed = timedelta(seconds=int(self.time_elapsed(RunningStage.TRAINING)))
            rank_zero_info(f"Time limit reached. Elapsed time is {elapsed}. Signaling Trainer to stop.")
