# Copyright (c) OpenMMLab. All rights reserved.
from mmcv.utils import TORCH_VERSION
from ...dist_utils import master_only
from ..hook import HOOKS
from .base import LoggerHook


@HOOKS.register_module()
class MlflowLoggerHook(LoggerHook):
    """Class to log metrics and (optionally) a trained model to MLflow.

    It requires `MLflow`_ to be installed.

    Args:
        exp_name (str, optional): Name of the experiment to be used.
            Default None. If not None, set the active experiment.
            If experiment does not exist, an experiment with provided name
            will be created.
        tags (Dict[str], optional): Tags for the current run.
            Default None. If not None, set tags for the current run.
        log_model (bool, optional): Whether to log an MLflow artifact.
            Default True. If True, log runner.model as an MLflow artifact
            for the current run.
        interval (int): Logging interval (every k iterations). Default: 10.
        ignore_last (bool): Ignore the log of last iterations in each epoch
            if less than `interval`. Default: True.
        reset_flag (bool): Whether to clear the output buffer after logging.
            Default: False.
        by_epoch (bool): Whether EpochBasedRunner is used. Default: True.

    .. _MLflow:
        https://www.mlflow.org/docs/latest/index.html
    """

    def __init__(self,
                 exp_name=None,
                 tags=None,
                 log_model=True,
                 interval=10,
                 ignore_last=True,
                 reset_flag=False,
                 by_epoch=True):
        super().__init__(interval, ignore_last, reset_flag, by_epoch)
        self.import_mlflow()
        self.exp_name = exp_name
        self.tags = tags
        self.log_model = log_model

    def import_mlflow(self):
        try:
            import mlflow
            import mlflow.pytorch as mlflow_pytorch
        except ImportError:
            raise ImportError(
                'Please run "pip install mlflow" to install mlflow')
        self.mlflow = mlflow
        self.mlflow_pytorch = mlflow_pytorch

    @master_only
    def before_run(self, runner):
        super().before_run(runner)
        if self.exp_name is not None:
            self.mlflow.set_experiment(self.exp_name)
        if self.tags is not None:
            self.mlflow.set_tags(self.tags)

    @master_only
    def log(self, runner):
        tags = self.get_loggable_tags(runner)
        if tags:
            self.mlflow.log_metrics(tags, step=self.get_iter(runner))

    @master_only
    def after_run(self, runner):
        if self.log_model:
            self.mlflow_pytorch.log_model(
                runner.model,
                'models',
                pip_requirements=[f'torch=={TORCH_VERSION}'])
