"""Polyaxon logger and its helper handlers."""

from typing import Any, Callable, List, Optional, Union

from torch.optim import Optimizer

from ignite.engine import Engine, Events

from ignite.handlers.base_logger import BaseLogger, BaseOptimizerParamsHandler, BaseOutputHandler
from ignite.handlers.utils import global_step_from_engine  # noqa

__all__ = ["PolyaxonLogger", "OutputHandler", "OptimizerParamsHandler", "global_step_from_engine"]


class PolyaxonLogger(BaseLogger):
    """
    `Polyaxon tracking client <https://polyaxon.com/>`_ handler to log parameters and metrics during the training
    and validation.

    This class requires `polyaxon <https://github.com/polyaxon/polyaxon/>`_ package to be installed:

    .. code-block:: bash

        pip install polyaxon

        // If you are using polyaxon v0.x

        pip install polyaxon-client

    Args:
        args: Positional arguments accepted from
            `Experiment <https://polyaxon.com/docs/experimentation/tracking/client/>`_.
        kwargs: Keyword arguments accepted from
            `Experiment <https://polyaxon.com/docs/experimentation/tracking/client/>`_.

    Examples:
        .. code-block:: python

            from ignite.handlers.polyaxon_logger import *

            # Create a logger
            plx_logger = PolyaxonLogger()

            # Log experiment parameters:
            plx_logger.log_inputs(**{
                "seed": seed,
                "batch_size": batch_size,
                "model": model.__class__.__name__,

                "pytorch version": torch.__version__,
                "ignite version": ignite.__version__,
                "cuda version": torch.version.cuda,
                "device name": torch.cuda.get_device_name(0)
            })

            # Attach the logger to the trainer to log training loss at each iteration
            plx_logger.attach_output_handler(
                trainer,
                event_name=Events.ITERATION_COMPLETED,
                tag="training",
                output_transform=lambda loss: {"loss": loss}
            )

            # Attach the logger to the evaluator on the training dataset and log NLL, Accuracy metrics after each epoch
            # We setup `global_step_transform=global_step_from_engine(trainer)` to take the epoch
            # of the `trainer` instead of `train_evaluator`.
            plx_logger.attach_output_handler(
                train_evaluator,
                event_name=Events.EPOCH_COMPLETED,
                tag="training",
                metric_names=["nll", "accuracy"],
                global_step_transform=global_step_from_engine(trainer),
            )

            # Attach the logger to the evaluator on the validation dataset and log NLL, Accuracy metrics after
            # each epoch. We setup `global_step_transform=global_step_from_engine(trainer)` to take the epoch of the
            # `trainer` instead of `evaluator`.
            plx_logger.attach_output_handler(
                evaluator,
                event_name=Events.EPOCH_COMPLETED,
                tag="validation",
                metric_names=["nll", "accuracy"],
                global_step_transform=global_step_from_engine(trainer)),
            )

            # Attach the logger to the trainer to log optimizer's parameters, e.g. learning rate at each iteration
            plx_logger.attach_opt_params_handler(
                trainer,
                event_name=Events.ITERATION_STARTED,
                optimizer=optimizer,
                param_name='lr'  # optional
            )
            # to manually end a run
            plx_logger.close()
    """

    def __init__(self, *args: Any, **kwargs: Any):
        try:
            from polyaxon.tracking import Run

            self.experiment = Run(*args, **kwargs)

        except ImportError:
            try:
                from polyaxon_client.tracking import Experiment

                self.experiment = Experiment(*args, **kwargs)
            except ImportError:
                raise ModuleNotFoundError(
                    "This contrib module requires polyaxon to be installed.\n"
                    "For Polyaxon v1.x please install it with command: \n pip install polyaxon\n"
                    "For Polyaxon v0.x please install it with command: \n pip install polyaxon-client"
                )

    def close(self) -> None:
        try:
            self.experiment.end()
        except:
            pass

    def __getattr__(self, attr: Any) -> Any:
        return getattr(self.experiment, attr)

    def _create_output_handler(self, *args: Any, **kwargs: Any) -> "OutputHandler":
        return OutputHandler(*args, **kwargs)

    def _create_opt_params_handler(self, *args: Any, **kwargs: Any) -> "OptimizerParamsHandler":
        return OptimizerParamsHandler(*args, **kwargs)


class OutputHandler(BaseOutputHandler):
    """Helper handler to log engine's output and/or metrics.

    Args:
        tag: common title for all produced plots. For example, "training"
        metric_names: list of metric names to plot or a string "all" to plot all available
            metrics.
        output_transform: output transform function to prepare `engine.state.output` as a number.
            For example, `output_transform = lambda output: output`
            This function can also return a dictionary, e.g `{"loss": loss1, "another_loss": loss2}` to label the plot
            with corresponding keys.
        global_step_transform: global step transform function to output a desired global step.
            Input of the function is `(engine, event_name)`. Output of function should be an integer.
            Default is None, global_step based on attached engine. If provided,
            uses function output as global_step. To setup global step from another engine, please use
            :meth:`~ignite.handlers.polyaxon_logger.global_step_from_engine`.
        state_attributes: list of attributes of the ``trainer.state`` to plot.

    Examples:
        .. code-block:: python

            from ignite.handlers.polyaxon_logger import *

            # Create a logger
            plx_logger = PolyaxonLogger()

            # Attach the logger to the evaluator on the validation dataset and log NLL, Accuracy metrics after
            # each epoch. We setup `global_step_transform=global_step_from_engine(trainer)` to take the epoch
            # of the `trainer`:
            plx_logger.attach(
                evaluator,
                log_handler=OutputHandler(
                    tag="validation",
                    metric_names=["nll", "accuracy"],
                    global_step_transform=global_step_from_engine(trainer)
                ),
                event_name=Events.EPOCH_COMPLETED
            )
            # or equivalently
            plx_logger.attach_output_handler(
                evaluator,
                event_name=Events.EPOCH_COMPLETED,
                tag="validation",
                metric_names=["nll", "accuracy"],
                global_step_transform=global_step_from_engine(trainer)
            )

        Another example, where model is evaluated every 500 iterations:

        .. code-block:: python

            from ignite.handlers.polyaxon_logger import *

            @trainer.on(Events.ITERATION_COMPLETED(every=500))
            def evaluate(engine):
                evaluator.run(validation_set, max_epochs=1)

            plx_logger = PolyaxonLogger()

            def global_step_transform(*args, **kwargs):
                return trainer.state.iteration

            # Attach the logger to the evaluator on the validation dataset and log NLL, Accuracy metrics after
            # every 500 iterations. Since evaluator engine does not have access to the training iteration, we
            # provide a global_step_transform to return the trainer.state.iteration for the global_step, each time
            # evaluator metrics are plotted on Polyaxon.

            plx_logger.attach_output_handler(
                evaluator,
                event_name=Events.EPOCH_COMPLETED,
                tag="validation",
                metrics=["nll", "accuracy"],
                global_step_transform=global_step_transform
            )

        Another example where the State Attributes ``trainer.state.alpha`` and ``trainer.state.beta``
        are also logged along with the NLL and Accuracy after each iteration:

        .. code-block:: python

            plx_logger.attach_output_handler(
                trainer,
                event_name=Events.ITERATION_COMPLETED,
                tag="training",
                metrics=["nll", "accuracy"],
                state_attributes=["alpha", "beta"],
            )

        Example of `global_step_transform`:

        .. code-block:: python

            def global_step_transform(engine, event_name):
                return engine.state.get_event_attrib_value(event_name)

    .. versionchanged:: 0.4.7
        accepts an optional list of `state_attributes`
    """

    def __init__(
        self,
        tag: str,
        metric_names: Optional[List[str]] = None,
        output_transform: Optional[Callable] = None,
        global_step_transform: Optional[Callable[[Engine, Union[str, Events]], int]] = None,
        state_attributes: Optional[List[str]] = None,
    ):
        super(OutputHandler, self).__init__(
            tag, metric_names, output_transform, global_step_transform, state_attributes
        )

    def __call__(self, engine: Engine, logger: PolyaxonLogger, event_name: Union[str, Events]) -> None:
        if not isinstance(logger, PolyaxonLogger):
            raise RuntimeError("Handler 'OutputHandler' works only with PolyaxonLogger")

        metrics = self._setup_output_metrics_state_attrs(engine, key_tuple=False)

        global_step = self.global_step_transform(engine, event_name)

        if not isinstance(global_step, int):
            raise TypeError(
                f"global_step must be int, got {type(global_step)}."
                " Please check the output of global_step_transform."
            )

        metrics.update({"step": global_step})

        logger.log_metrics(**metrics)


class OptimizerParamsHandler(BaseOptimizerParamsHandler):
    """Helper handler to log optimizer parameters

    Args:
        optimizer: torch optimizer or any object with attribute ``param_groups``
            as a sequence.
        param_name: parameter name
        tag: common title for all produced plots. For example, "generator"

    Examples:
        .. code-block:: python

            from ignite.handlers.polyaxon_logger import *

            # Create a logger
            plx_logger = PolyaxonLogger()

            # Attach the logger to the trainer to log optimizer's parameters, e.g. learning rate at each iteration
            plx_logger.attach(
                trainer,
                log_handler=OptimizerParamsHandler(optimizer),
                event_name=Events.ITERATION_STARTED
            )
            # or equivalently
            plx_logger.attach_opt_params_handler(
                trainer,
                event_name=Events.ITERATION_STARTED,
                optimizer=optimizer
            )
    """

    def __init__(self, optimizer: Optimizer, param_name: str = "lr", tag: Optional[str] = None):
        super(OptimizerParamsHandler, self).__init__(optimizer, param_name, tag)

    def __call__(self, engine: Engine, logger: PolyaxonLogger, event_name: Union[str, Events]) -> None:
        if not isinstance(logger, PolyaxonLogger):
            raise RuntimeError("Handler OptimizerParamsHandler works only with PolyaxonLogger")

        global_step = engine.state.get_event_attrib_value(event_name)
        tag_prefix = f"{self.tag}/" if self.tag else ""
        params = {
            f"{tag_prefix}{self.param_name}/group_{i}": float(param_group[self.param_name])
            for i, param_group in enumerate(self.optimizer.param_groups)
        }
        params["step"] = global_step
        logger.log_metrics(**params)