"""Implementation of a space that represents closed boxes in euclidean space."""

from __future__ import annotations

from collections.abc import Iterable, Mapping, Sequence
from typing import Any, SupportsFloat

import numpy as np
from numpy.typing import NDArray

import gymnasium as gym
from gymnasium.spaces.space import Space


def array_short_repr(arr: NDArray[Any]) -> str:
    """Create a shortened string representation of a numpy array.

    If arr is a multiple of the all-ones vector, return a string representation of the multiplier.
    Otherwise, return a string representation of the entire array.

    Args:
        arr: The array to represent

    Returns:
        A short representation of the array
    """
    if arr.size != 0 and np.min(arr) == np.max(arr):
        return str(np.min(arr))
    return str(arr)


def is_float_integer(var: Any) -> bool:
    """Checks if a scalar variable is an integer or float (does not include bool)."""
    return np.issubdtype(type(var), np.integer) or np.issubdtype(type(var), np.floating)


class Box(Space[NDArray[Any]]):
    r"""A (possibly unbounded) box in :math:`\mathbb{R}^n`.

    Specifically, a Box represents the Cartesian product of n closed intervals.
    Each interval has the form of one of :math:`[a, b]`, :math:`(-\infty, b]`,
    :math:`[a, \infty)`, or :math:`(-\infty, \infty)`.

    There are two common use cases:

    * Identical bound for each dimension::

        >>> Box(low=-1.0, high=2.0, shape=(3, 4), dtype=np.float32)
        Box(-1.0, 2.0, (3, 4), float32)

    * Independent bound for each dimension::

        >>> Box(low=np.array([-1.0, -2.0]), high=np.array([2.0, 4.0]), dtype=np.float32)
        Box([-1. -2.], [2. 4.], (2,), float32)
    """

    def __init__(
        self,
        low: SupportsFloat | NDArray[Any],
        high: SupportsFloat | NDArray[Any],
        shape: Sequence[int] | None = None,
        dtype: type[np.floating[Any]] | type[np.integer[Any]] = np.float32,
        seed: int | np.random.Generator | None = None,
    ):
        r"""Constructor of :class:`Box`.

        The argument ``low`` specifies the lower bound of each dimension and ``high`` specifies the upper bounds.
        I.e., the space that is constructed will be the product of the intervals :math:`[\text{low}[i], \text{high}[i]]`.

        If ``low`` (or ``high``) is a scalar, the lower bound (or upper bound, respectively) will be assumed to be
        this value across all dimensions.

        Args:
            low (SupportsFloat | np.ndarray): Lower bounds of the intervals. If integer, must be at least ``-2**63``.
            high (SupportsFloat | np.ndarray]): Upper bounds of the intervals. If integer, must be at most ``2**63 - 2``.
            shape (Optional[Sequence[int]]): The shape is inferred from the shape of `low` or `high` `np.ndarray`s with
                `low` and `high` scalars defaulting to a shape of (1,)
            dtype: The dtype of the elements of the space. If this is an integer type, the :class:`Box` is essentially a discrete space.
            seed: Optionally, you can use this argument to seed the RNG that is used to sample from the space.

        Raises:
            ValueError: If no shape information is provided (shape is None, low is None and high is None) then a
                value error is raised.
        """
        # determine dtype
        if dtype is None:
            raise ValueError("Box dtype must be explicitly provided, cannot be None.")
        self.dtype = np.dtype(dtype)

        #  * check that dtype is an accepted dtype
        if not (
            np.issubdtype(self.dtype, np.integer)
            or np.issubdtype(self.dtype, np.floating)
            or self.dtype == np.bool_
        ):
            raise ValueError(
                f"Invalid Box dtype ({self.dtype}), must be an integer, floating, or bool dtype"
            )

        # determine shape
        if shape is not None:
            if not isinstance(shape, Iterable):
                raise TypeError(
                    f"Expected Box shape to be an iterable, actual type={type(shape)}"
                )
            elif not all(np.issubdtype(type(dim), np.integer) for dim in shape):
                raise TypeError(
                    f"Expected all Box shape elements to be integer, actual type={tuple(type(dim) for dim in shape)}"
                )

            # Casts the `shape` argument to tuple[int, ...] (otherwise dim can `np.int64`)
            shape = tuple(int(dim) for dim in shape)
        elif isinstance(low, np.ndarray) and isinstance(high, np.ndarray):
            if low.shape != high.shape:
                raise ValueError(
                    f"Box low.shape and high.shape don't match, low.shape={low.shape}, high.shape={high.shape}"
                )
            shape = low.shape
        elif isinstance(low, np.ndarray):
            shape = low.shape
        elif isinstance(high, np.ndarray):
            shape = high.shape
        elif is_float_integer(low) and is_float_integer(high):
            shape = (1,)  # low and high are scalars
        else:
            raise ValueError(
                "Box shape is not specified, therefore inferred from low and high. Expected low and high to be np.ndarray, integer, or float."
                f"Actual types low={type(low)}, high={type(high)}"
            )
        self._shape: tuple[int, ...] = shape

        # Cast scalar values to `np.ndarray` and capture the boundedness information
        # disallowed cases
        #  * out of range - this must be done before casting to low and high otherwise, the value is within dtype and cannot be out of range
        #  * nan - must be done beforehand as int dtype can cast `nan` to another value
        #  * unsign int inf and -inf - special case that is disallowed

        if self.dtype == np.bool_:
            dtype_min, dtype_max = 0, 1
        elif np.issubdtype(self.dtype, np.floating):
            dtype_min = float(np.finfo(self.dtype).min)
            dtype_max = float(np.finfo(self.dtype).max)
        else:
            dtype_min = int(np.iinfo(self.dtype).min)
            dtype_max = int(np.iinfo(self.dtype).max)

        # Cast `low` and `high` to ndarray for the dtype min and max for out of range tests
        self.low, self.bounded_below = self._cast_low(low, dtype_min)
        self.high, self.bounded_above = self._cast_high(high, dtype_max)

        # recheck shape for case where shape and (low or high) are provided
        if self.low.shape != shape:
            raise ValueError(
                f"Box low.shape doesn't match provided shape, low.shape={self.low.shape}, shape={self.shape}"
            )
        if self.high.shape != shape:
            raise ValueError(
                f"Box high.shape doesn't match provided shape, high.shape={self.high.shape}, shape={self.shape}"
            )

        # check that low <= high
        if np.any(self.low > self.high):
            raise ValueError(
                f"Box all low values must be less than or equal to high (some values break this), low={self.low}, high={self.high}"
            )

        self.low_repr = array_short_repr(self.low)
        self.high_repr = array_short_repr(self.high)

        super().__init__(self.shape, self.dtype, seed)

    def _cast_low(self, low, dtype_min) -> tuple[np.ndarray, np.ndarray]:
        """Casts the input Box low value to ndarray with provided dtype.

        Args:
            low: The input box low value
            dtype_min: The dtype's minimum value

        Returns:
            The updated low value and for what values the input is bounded (below)
        """
        if is_float_integer(low):
            bounded_below = -np.inf < np.full(self.shape, low, dtype=float)

            if np.isnan(low):
                raise ValueError(f"No low value can be equal to `np.nan`, low={low}")
            elif np.isneginf(low):
                if self.dtype.kind == "i":  # signed int
                    low = dtype_min
                elif self.dtype.kind in {"u", "b"}:  # unsigned int and bool
                    raise ValueError(
                        f"Box unsigned int dtype don't support `-np.inf`, low={low}"
                    )
            elif low < dtype_min:
                raise ValueError(
                    f"Box low is out of bounds of the dtype range, low={low}, min dtype={dtype_min}"
                )

            low = np.full(self.shape, low, dtype=self.dtype)
            return low, bounded_below
        else:  # cast for low - array
            if not isinstance(low, np.ndarray):
                raise ValueError(
                    f"Box low must be a np.ndarray, integer, or float, actual type={type(low)}"
                )
            elif not (
                np.issubdtype(low.dtype, np.floating)
                or np.issubdtype(low.dtype, np.integer)
                or low.dtype == np.bool_
            ):
                raise ValueError(
                    f"Box low must be a floating, integer, or bool dtype, actual dtype={low.dtype}"
                )
            elif np.any(np.isnan(low)):
                raise ValueError(f"No low value can be equal to `np.nan`, low={low}")

            bounded_below = -np.inf < low

            if np.any(np.isneginf(low)):
                if self.dtype.kind == "i":  # signed int
                    low[np.isneginf(low)] = dtype_min
                elif self.dtype.kind in {"u", "b"}:  # unsigned int and bool
                    raise ValueError(
                        f"Box unsigned int dtype don't support `-np.inf`, low={low}"
                    )
            elif low.dtype != self.dtype and np.any(low < dtype_min):
                raise ValueError(
                    f"Box low is out of bounds of the dtype range, low={low}, min dtype={dtype_min}"
                )

            if (
                np.issubdtype(low.dtype, np.floating)
                and np.issubdtype(self.dtype, np.floating)
                and np.finfo(self.dtype).precision < np.finfo(low.dtype).precision
            ):
                gym.logger.warn(
                    f"Box low's precision lowered by casting to {self.dtype}, current low.dtype={low.dtype}"
                )
            return low.astype(self.dtype), bounded_below

    def _cast_high(self, high, dtype_max) -> tuple[np.ndarray, np.ndarray]:
        """Casts the input Box high value to ndarray with provided dtype.

        Args:
            high: The input box high value
            dtype_max: The dtype's maximum value

        Returns:
            The updated high value and for what values the input is bounded (above)
        """
        if is_float_integer(high):
            bounded_above = np.full(self.shape, high, dtype=float) < np.inf

            if np.isnan(high):
                raise ValueError(f"No high value can be equal to `np.nan`, high={high}")
            elif np.isposinf(high):
                if self.dtype.kind == "i":  # signed int
                    high = dtype_max
                elif self.dtype.kind in {"u", "b"}:  # unsigned int
                    raise ValueError(
                        f"Box unsigned int dtype don't support `np.inf`, high={high}"
                    )
            elif high > dtype_max:
                raise ValueError(
                    f"Box high is out of bounds of the dtype range, high={high}, max dtype={dtype_max}"
                )

            high = np.full(self.shape, high, dtype=self.dtype)
            return high, bounded_above
        else:
            if not isinstance(high, np.ndarray):
                raise ValueError(
                    f"Box high must be a np.ndarray, integer, or float, actual type={type(high)}"
                )
            elif not (
                np.issubdtype(high.dtype, np.floating)
                or np.issubdtype(high.dtype, np.integer)
                or high.dtype == np.bool_
            ):
                raise ValueError(
                    f"Box high must be a floating or integer dtype, actual dtype={high.dtype}"
                )
            elif np.any(np.isnan(high)):
                raise ValueError(f"No high value can be equal to `np.nan`, high={high}")

            bounded_above = high < np.inf

            posinf = np.isposinf(high)
            if np.any(posinf):
                if self.dtype.kind == "i":  # signed int
                    high[posinf] = dtype_max
                elif self.dtype.kind in {"u", "b"}:  # unsigned int
                    raise ValueError(
                        f"Box unsigned int dtype don't support `np.inf`, high={high}"
                    )
            elif high.dtype != self.dtype and np.any(dtype_max < high):
                raise ValueError(
                    f"Box high is out of bounds of the dtype range, high={high}, max dtype={dtype_max}"
                )

            if (
                np.issubdtype(high.dtype, np.floating)
                and np.issubdtype(self.dtype, np.floating)
                and np.finfo(self.dtype).precision < np.finfo(high.dtype).precision
            ):
                gym.logger.warn(
                    f"Box high's precision lowered by casting to {self.dtype}, current high.dtype={high.dtype}"
                )
            return high.astype(self.dtype), bounded_above

    @property
    def shape(self) -> tuple[int, ...]:
        """Has stricter type than gym.Space - never None."""
        return self._shape

    @property
    def is_np_flattenable(self):
        """Checks whether this space can be flattened to a :class:`spaces.Box`."""
        return True

    def is_bounded(self, manner: str = "both") -> bool:
        """Checks whether the box is bounded in some sense.

        Args:
            manner (str): One of ``"both"``, ``"below"``, ``"above"``.

        Returns:
            If the space is bounded

        Raises:
            ValueError: If `manner` is neither ``"both"`` nor ``"below"`` or ``"above"``
        """
        below = bool(np.all(self.bounded_below))
        above = bool(np.all(self.bounded_above))
        if manner == "both":
            return below and above
        elif manner == "below":
            return below
        elif manner == "above":
            return above
        else:
            raise ValueError(
                f"manner is not in {{'below', 'above', 'both'}}, actual value: {manner}"
            )

    def sample(self, mask: None = None, probability: None = None) -> NDArray[Any]:
        r"""Generates a single random sample inside the Box.

        In creating a sample of the box, each coordinate is sampled (independently) from a distribution
        that is chosen according to the form of the interval:

        * :math:`[a, b]` : uniform distribution
        * :math:`[a, \infty)` : shifted exponential distribution
        * :math:`(-\infty, b]` : shifted negative exponential distribution
        * :math:`(-\infty, \infty)` : normal distribution

        Args:
            mask: A mask for sampling values from the Box space, currently unsupported.
            probability: A probability mask for sampling values from the Box space, currently unsupported.

        Returns:
            A sampled value from the Box
        """
        if mask is not None:
            raise gym.error.Error(
                f"Box.sample cannot be provided a mask, actual value: {mask}"
            )
        elif probability is not None:
            raise gym.error.Error(
                f"Box.sample cannot be provided a probability mask, actual value: {probability}"
            )

        high = self.high if self.dtype.kind == "f" else self.high.astype("int64") + 1
        sample = np.empty(self.shape)

        # Masking arrays which classify the coordinates according to interval type
        unbounded = ~self.bounded_below & ~self.bounded_above
        upp_bounded = ~self.bounded_below & self.bounded_above
        low_bounded = self.bounded_below & ~self.bounded_above
        bounded = self.bounded_below & self.bounded_above

        # Vectorized sampling by interval type
        sample[unbounded] = self.np_random.normal(size=unbounded[unbounded].shape)

        sample[low_bounded] = (
            self.np_random.exponential(size=low_bounded[low_bounded].shape)
            + self.low[low_bounded]
        )

        sample[upp_bounded] = (
            -self.np_random.exponential(size=upp_bounded[upp_bounded].shape)
            + high[upp_bounded]
        )

        sample[bounded] = self.np_random.uniform(
            low=self.low[bounded], high=high[bounded], size=bounded[bounded].shape
        )

        if self.dtype.kind in ["i", "u", "b"]:
            sample = np.floor(sample)

        # clip values that would underflow/overflow
        if np.issubdtype(self.dtype, np.signedinteger):
            dtype_min = np.iinfo(self.dtype).min + 2
            dtype_max = np.iinfo(self.dtype).max - 2
            sample = sample.clip(min=dtype_min, max=dtype_max)
        elif np.issubdtype(self.dtype, np.unsignedinteger):
            dtype_min = np.iinfo(self.dtype).min
            dtype_max = np.iinfo(self.dtype).max
            sample = sample.clip(min=dtype_min, max=dtype_max)

        sample = sample.astype(self.dtype)

        # float64 values have lower than integer precision near int64 min/max, so clip
        # again in case something has been cast to an out-of-bounds value
        if self.dtype == np.int64:
            sample = sample.clip(min=self.low, max=self.high)

        return sample

    def contains(self, x: Any) -> bool:
        """Return boolean specifying if x is a valid member of this space."""
        if not isinstance(x, np.ndarray):
            gym.logger.warn("Casting input x to numpy array.")
            try:
                x = np.asarray(x, dtype=self.dtype)
            except (ValueError, TypeError):
                return False

        return bool(
            np.can_cast(x.dtype, self.dtype)
            and x.shape == self.shape
            and np.all(x >= self.low)
            and np.all(x <= self.high)
        )

    def to_jsonable(self, sample_n: Sequence[NDArray[Any]]) -> list[list]:
        """Convert a batch of samples from this space to a JSONable data type."""
        return [sample.tolist() for sample in sample_n]

    def from_jsonable(self, sample_n: Sequence[float | int]) -> list[NDArray[Any]]:
        """Convert a JSONable data type to a batch of samples from this space."""
        return [np.asarray(sample, dtype=self.dtype) for sample in sample_n]

    def __repr__(self) -> str:
        """A string representation of this space.

        The representation will include bounds, shape and dtype.
        If a bound is uniform, only the corresponding scalar will be given to avoid redundant and ugly strings.

        Returns:
            A representation of the space
        """
        return f"Box({self.low_repr}, {self.high_repr}, {self.shape}, {self.dtype})"

    def __eq__(self, other: Any) -> bool:
        """Check whether `other` is equivalent to this instance. Doesn't check dtype equivalence."""
        return (
            isinstance(other, Box)
            and (self.shape == other.shape)
            and (self.dtype == other.dtype)
            and np.allclose(self.low, other.low)
            and np.allclose(self.high, other.high)
        )

    def __setstate__(self, state: Iterable[tuple[str, Any]] | Mapping[str, Any]):
        """Sets the state of the box for unpickling a box with legacy support."""
        super().__setstate__(state)

        # legacy support through re-adding "low_repr" and "high_repr" if missing from pickled state
        if not hasattr(self, "low_repr"):
            self.low_repr = array_short_repr(self.low)

        if not hasattr(self, "high_repr"):
            self.high_repr = array_short_repr(self.high)