"""Provides the GeneticAlgorithmEmitter."""

from __future__ import annotations

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.emitters.operators import _get_op
from ribs.typing import Float, Int


class GeneticAlgorithmEmitter(EmitterBase):
    """Creates solutions with a genetic algorithm.

    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 pass ``x0``
    through the operator.

    Args:
        archive: Archive of solutions, e.g., :class:`ribs.archives.GridArchive`.
        operator: Internal operator for mutating solutions. See
            :mod:`ribs.emitters.operators` for the list of allowed names.
        operator_kwargs: Additional arguments to pass to the operator. See
            :mod:`ribs.emitters.operators` for the arguments allowed by each operator.
        x0: Initial solution.
        initial_solutions: An (n, solution_dim) array of solutions to be used when the
            archive is empty, in lieu of ``x0``.
        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,
        *,
        operator: str,
        operator_kwargs: dict | None = None,
        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._batch_size = batch_size
        self._x0 = x0
        self._initial_solutions = None

        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",
            )

        operator_class = _get_op(operator)
        self._operator = operator_class(
            **(operator_kwargs if operator_kwargs is not None else {}),
            # We assume the class takes in a seed, but this is technically not part of
            # the API.
            seed=seed,  # ty: ignore[unknown-argument]
        )

    @property
    def x0(self) -> np.ndarray | None:
        """Initial solution (if ``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 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 with the provided operator.

        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 pass
        ``x0`` through the operator. Otherwise, we sample parents from the archive to be
        passed to the operator.

        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.

        Raises:
            ValueError: The ``parent_type`` of the operator is unknown.
        """
        if self.archive.empty and self.initial_solutions is not None:
            return self._clip(self.initial_solutions)

        if self._operator.parent_type == 1:
            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"]
            return self._clip(self._operator.ask(parents))

        elif self._operator.parent_type == 2:
            if self.archive.empty:
                parents = np.repeat(self.x0[None], repeats=2 * self.batch_size, axis=0)
            else:
                parents = self.archive.sample_elites(2 * self.batch_size)["solution"]
            return self._clip(
                self._operator.ask(parents.reshape(2, self.batch_size, -1))
            )

        else:
            raise ValueError(
                f"Unknown operator `parent_type` {self._operator.parent_type}"
            )
