"""Provides the GaussianEmitter."""

from __future__ import annotations

import numbers
from collections.abc import Collection

import numpy as np
from numpy.typing import ArrayLike

from ribs._utils import check_batch_shape, check_shape
from ribs.archives import ArchiveBase
from ribs.emitters._emitter_base import EmitterBase
from ribs.typing import Float, Int


class GaussianEmitter(EmitterBase):
    """Emits solutions by adding Gaussian noise to existing elites.

    If the archive is empty and ``initial_solutions`` is set, a call to :meth:`ask` will
    return ``initial_solutions``. If ``initial_solutions`` is not set, we draw solutions
    from a Gaussian distribution centered at ``x0`` with standard deviation ``sigma``.
    Otherwise, each solution is drawn from a distribution centered at a randomly chosen
    elite with standard deviation ``sigma``.

    Args:
        archive: Archive of solutions, e.g., :class:`ribs.archives.GridArchive`.
        sigma: Standard deviation of the Gaussian distribution. Note we assume the
            Gaussian is diagonal, so if this argument is an array, it must have the same
            dimensionality as the solutions.
        x0: Center of the Gaussian distribution from which to sample solutions when the
            archive is empty. This argument is ignored if ``initial_solutions`` is set.
        initial_solutions: An (n, solution_dim) array of solutions to be used when the
            archive is empty. If this argument is None, then solutions will be sampled
            from a Gaussian distribution centered at ``x0`` with standard deviation
            ``sigma``.
        bounds: Bounds of the solution space. Pass None to indicate there are no bounds.
            Alternatively, pass an array-like to specify the bounds for each dim. Each
            element in this array-like can be None to indicate no bound, or a tuple of
            ``(lower_bound, upper_bound)``, where ``lower_bound`` or ``upper_bound`` may
            be None to indicate no bound. Unbounded upper bounds are set to +inf, and
            unbounded lower bounds are set to -inf.
        lower_bounds: Instead of specifying ``bounds``, ``lower_bounds`` and
            ``upper_bounds`` may be specified. This is useful if, for instance,
            solutions are multi-dimensional. Here, pass None to indicate there are no
            bounds (i.e., bounds are set to -inf), or pass an array specifying the lower
            bounds of the solution space.
        upper_bounds: Upper bounds of the solution space; see ``lower_bounds`` above.
            Pass None to indicate there are no bounds (i.e., bounds are set to inf).
        batch_size: Number of solutions to return in :meth:`ask`.
        seed: Value to seed the random number generator. Set to None to avoid a fixed
            seed.

    Raises:
        ValueError: There is an error in x0 or initial_solutions.
        ValueError: There is an error in the bounds configuration.
    """

    def __init__(
        self,
        archive: ArchiveBase,
        *,
        sigma: Float | ArrayLike,
        x0: ArrayLike | None = None,
        initial_solutions: ArrayLike | None = None,
        bounds: Collection[tuple[None | Float, None | Float]] | None = None,
        lower_bounds: ArrayLike | None = None,
        upper_bounds: ArrayLike | None = None,
        batch_size: Int = 64,
        seed: Int | None = None,
    ) -> None:
        EmitterBase.__init__(
            self,
            archive,
            solution_dim=archive.solution_dim,
            bounds=bounds,
            lower_bounds=lower_bounds,
            upper_bounds=upper_bounds,
        )

        self._rng = np.random.default_rng(seed)
        self._batch_size = batch_size
        self._sigma = np.asarray(sigma, dtype=archive.dtypes["solution"])
        self._x0 = None
        self._initial_solutions = None
        self._noise_shape = (
            (self.batch_size, self.solution_dim)
            if isinstance(self.solution_dim, numbers.Integral)
            else (self.batch_size, *self.solution_dim)
        )

        if x0 is None and initial_solutions is None:
            raise ValueError("Either x0 or initial_solutions must be provided.")
        if x0 is not None and initial_solutions is not None:
            raise ValueError("x0 and initial_solutions cannot both be provided.")

        if x0 is not None:
            self._x0 = np.array(x0, dtype=archive.dtypes["solution"])
            check_shape(self._x0, "x0", archive.solution_dim, "archive.solution_dim")
        elif initial_solutions is not None:
            self._initial_solutions = np.asarray(
                initial_solutions, dtype=archive.dtypes["solution"]
            )
            check_batch_shape(
                self._initial_solutions,
                "initial_solutions",
                archive.solution_dim,
                "archive.solution_dim",
            )

    @property
    def x0(self) -> np.ndarray | None:
        """Initial Gaussian distribution center.

        Solutions are sampled from this distribution when the archive is empty (if
        :attr:`initial_solutions` is not set).
        """
        return self._x0

    @property
    def initial_solutions(self) -> np.ndarray | None:
        """Returned when the archive is empty (if :attr:`x0` is not set)."""
        return self._initial_solutions

    @property
    def sigma(self) -> np.floating | np.ndarray:
        """Standard deviation of the (diagonal) Gaussian distribution."""
        return self._sigma

    @property
    def batch_size(self) -> Int:
        """Number of solutions to return in :meth:`ask`."""
        return self._batch_size

    def _clip(self, solutions: np.ndarray) -> np.ndarray:
        """Clips solutions to the bounds of the solution space."""
        return np.clip(solutions, self.lower_bounds, self.upper_bounds)

    def ask(self) -> np.ndarray:
        """Creates solutions by adding Gaussian noise to elites in the archive.

        If the archive is empty and ``initial_solutions`` is set, a call to :meth:`ask`
        will return ``initial_solutions``. If ``initial_solutions`` is not set, we draw
        solutions from a Gaussian distribution centered at ``x0`` with standard
        deviation ``sigma``. Otherwise, each solution is drawn from a distribution
        centered at a randomly chosen elite with standard deviation ``sigma``.

        Returns:
            If the archive is not empty, ``(batch_size, solution_dim)`` array --
            contains ``batch_size`` new solutions to evaluate. If the archive is empty,
            we return ``initial_solutions``, which might not have ``batch_size``
            solutions.
        """
        if self.archive.empty and self.initial_solutions is not None:
            return self._clip(self.initial_solutions)

        if self.archive.empty:
            parents = np.repeat(self.x0[None], repeats=self.batch_size, axis=0)
        else:
            parents = self.archive.sample_elites(self.batch_size)["solution"]

        noise = self._rng.normal(
            scale=self.sigma,
            size=self._noise_shape,
        ).astype(self.archive.dtypes["solution"])
        return self._clip(parents + noise)
