import os
from pathlib import Path
from typing import Optional, Union

import pandas as pd

from ray.air.result import Result
from ray.cloudpickle import cloudpickle
from ray.exceptions import RayTaskError
from ray.tune.analysis import ExperimentAnalysis
from ray.tune.error import TuneError
from ray.tune.experiment import Trial
from ray.util import PublicAPI


@PublicAPI(stability="beta")
class ResultGrid:
    """A set of ``Result`` objects returned from a call to ``tuner.fit()``.

    You can use it to inspect the trials run as well as obtaining the best result.

    The constructor is a private API.

    Usage pattern:

    .. code-block:: python

        result_grid = tuner.fit()
        for i in range(len(result_grid)):
            result = result_grid[i]
            if not result.error:
                print(f"Trial finishes successfully with metric {result.metric}.")
            else:
                print(f"Trial errors out with {result.error}.")
        best_result = result_grid.get_best_result()
        best_checkpoint = best_result.checkpoint
        best_metric = best_result.metric

    Note that trials of all statuses are included in the final result grid.
    If a trial is not in terminated state, its latest result and checkpoint as
    seen by Tune will be provided.
    """

    def __init__(
        self,
        experiment_analysis: ExperimentAnalysis,
    ):
        self._experiment_analysis = experiment_analysis

    def get_best_result(
        self,
        metric: Optional[str] = None,
        mode: Optional[str] = None,
        scope: str = "last",
        filter_nan_and_inf: bool = True,
    ) -> Result:
        """Get the best result from all the trials run.

        Args:
            metric: Key for trial info to order on. Defaults to
                the metric specified in your Tuner's ``TuneConfig``.
            mode: One of [min, max]. Defaults to the mode specified
                in your Tuner's ``TuneConfig``.
            scope: One of [all, last, avg, last-5-avg, last-10-avg].
                If `scope=last`, only look at each trial's final step for
                `metric`, and compare across trials based on `mode=[min,max]`.
                If `scope=avg`, consider the simple average over all steps
                for `metric` and compare across trials based on
                `mode=[min,max]`. If `scope=last-5-avg` or `scope=last-10-avg`,
                consider the simple average over the last 5 or 10 steps for
                `metric` and compare across trials based on `mode=[min,max]`.
                If `scope=all`, find each trial's min/max score for `metric`
                based on `mode`, and compare trials based on `mode=[min,max]`.
            filter_nan_and_inf: If True (default), NaN or infinite
                values are disregarded and these trials are never selected as
                the best trial.
        """
        if len(self._experiment_analysis.trials) == 1:
            return self._trial_to_result(self._experiment_analysis.trials[0])
        if not metric and not self._experiment_analysis.default_metric:
            raise ValueError(
                "No metric is provided. Either pass in a `metric` arg to "
                "`get_best_result` or specify a metric in the "
                "`TuneConfig` of your `Tuner`."
            )
        if not mode and not self._experiment_analysis.default_mode:
            raise ValueError(
                "No mode is provided. Either pass in a `mode` arg to "
                "`get_best_result` or specify a mode in the "
                "`TuneConfig` of your `Tuner`."
            )

        best_trial = self._experiment_analysis.get_best_trial(
            metric=metric,
            mode=mode,
            scope=scope,
            filter_nan_and_inf=filter_nan_and_inf,
        )
        if not best_trial:
            error_msg = (
                "No best trial found for the given metric: "
                f"{metric or self._experiment_analysis.default_metric}. "
                "This means that no trial has reported this metric"
            )
            error_msg += (
                ", or all values reported for this metric are NaN. To not ignore NaN "
                "values, you can set the `filter_nan_and_inf` arg to False."
                if filter_nan_and_inf
                else "."
            )
            raise RuntimeError(error_msg)

        return self._trial_to_result(best_trial)

    def get_dataframe(
        self,
        filter_metric: Optional[str] = None,
        filter_mode: Optional[str] = None,
    ) -> pd.DataFrame:
        """Return dataframe of all trials with their configs and reported results.

        Per default, this returns the last reported results for each trial.

        If ``filter_metric`` and ``filter_mode`` are set, the results from each
        trial are filtered for this metric and mode. For example, if
        ``filter_metric="some_metric"`` and ``filter_mode="max"``, for each trial,
        every received result is checked, and the one where ``some_metric`` is
        maximal is returned.


        Example:

            .. code-block:: python

                result_grid = Tuner.fit(...)

                # Get last reported results per trial
                df = result_grid.get_dataframe()

                # Get best ever reported accuracy per trial
                df = result_grid.get_dataframe(metric="accuracy", mode="max")

        Args:
            filter_metric: Metric to filter best result for.
            filter_mode: If ``filter_metric`` is given, one of ``["min", "max"]``
                to specify if we should find the minimum or maximum result.

        Returns:
            Pandas DataFrame with each trial as a row and their results as columns.
        """
        return self._experiment_analysis.dataframe(
            metric=filter_metric, mode=filter_mode
        )

    def __len__(self) -> int:
        return len(self._experiment_analysis.trials)

    def __getitem__(self, i: int) -> Result:
        """Returns the i'th result in the grid."""
        return self._trial_to_result(
            self._experiment_analysis.trials[i],
        )

    @property
    def errors(self):
        """Returns the exceptions of errored trials."""
        return [result.error for result in self if result.error]

    @property
    def num_errors(self):
        """Returns the number of errored trials."""
        return len(
            [t for t in self._experiment_analysis.trials if t.status == Trial.ERROR]
        )

    @property
    def num_terminated(self):
        """Returns the number of terminated (but not errored) trials."""
        return len(
            [
                t
                for t in self._experiment_analysis.trials
                if t.status == Trial.TERMINATED
            ]
        )

    @staticmethod
    def _populate_exception(trial: Trial) -> Optional[Union[TuneError, RayTaskError]]:
        if trial.pickled_error_file and os.path.exists(trial.pickled_error_file):
            with open(trial.pickled_error_file, "rb") as f:
                e = cloudpickle.load(f)
                return e
        elif trial.error_file and os.path.exists(trial.error_file):
            with open(trial.error_file, "r") as f:
                return TuneError(f.read())
        return None

    def _trial_to_result(self, trial: Trial) -> Result:
        checkpoint = trial.checkpoint.to_air_checkpoint()
        best_checkpoints = [
            (checkpoint.to_air_checkpoint(), checkpoint.metrics)
            for checkpoint in trial.get_trial_checkpoints()
        ]

        result = Result(
            checkpoint=checkpoint,
            metrics=trial.last_result.copy(),
            error=self._populate_exception(trial),
            log_dir=Path(trial.logdir) if trial.logdir else None,
            metrics_dataframe=self._experiment_analysis.trial_dataframes.get(
                trial.logdir
            )
            if self._experiment_analysis
            else None,
            best_checkpoints=best_checkpoints,
        )
        return result