# mypy: allow-untyped-defs
import torch
from torch import nan, Tensor
from torch.distributions import constraints
from torch.distributions.transformed_distribution import TransformedDistribution
from torch.distributions.transforms import AffineTransform, PowerTransform
from torch.distributions.uniform import Uniform
from torch.distributions.utils import broadcast_all, euler_constant


__all__ = ["Kumaraswamy"]


def _moments(a, b, n):
    """
    Computes nth moment of Kumaraswamy using using torch.lgamma
    """
    arg1 = 1 + n / a
    log_value = torch.lgamma(arg1) + torch.lgamma(b) - torch.lgamma(arg1 + b)
    return b * torch.exp(log_value)


class Kumaraswamy(TransformedDistribution):
    r"""
    Samples from a Kumaraswamy distribution.

    Example::

        >>> # xdoctest: +IGNORE_WANT("non-deterministic")
        >>> m = Kumaraswamy(torch.tensor([1.0]), torch.tensor([1.0]))
        >>> m.sample()  # sample from a Kumaraswamy distribution with concentration alpha=1 and beta=1
        tensor([ 0.1729])

    Args:
        concentration1 (float or Tensor): 1st concentration parameter of the distribution
            (often referred to as alpha)
        concentration0 (float or Tensor): 2nd concentration parameter of the distribution
            (often referred to as beta)
    """

    arg_constraints = {
        "concentration1": constraints.positive,
        "concentration0": constraints.positive,
    }
    support = constraints.unit_interval
    has_rsample = True

    def __init__(self, concentration1, concentration0, validate_args=None):
        self.concentration1, self.concentration0 = broadcast_all(
            concentration1, concentration0
        )
        base_dist = Uniform(
            torch.full_like(self.concentration0, 0),
            torch.full_like(self.concentration0, 1),
            validate_args=validate_args,
        )
        transforms = [
            PowerTransform(exponent=self.concentration0.reciprocal()),
            AffineTransform(loc=1.0, scale=-1.0),
            PowerTransform(exponent=self.concentration1.reciprocal()),
        ]
        super().__init__(base_dist, transforms, validate_args=validate_args)

    def expand(self, batch_shape, _instance=None):
        new = self._get_checked_instance(Kumaraswamy, _instance)
        new.concentration1 = self.concentration1.expand(batch_shape)
        new.concentration0 = self.concentration0.expand(batch_shape)
        return super().expand(batch_shape, _instance=new)

    @property
    def mean(self) -> Tensor:
        return _moments(self.concentration1, self.concentration0, 1)

    @property
    def mode(self) -> Tensor:
        # Evaluate in log-space for numerical stability.
        log_mode = (
            self.concentration0.reciprocal() * (-self.concentration0).log1p()
            - (-self.concentration0 * self.concentration1).log1p()
        )
        log_mode[(self.concentration0 < 1) | (self.concentration1 < 1)] = nan
        return log_mode.exp()

    @property
    def variance(self) -> Tensor:
        return _moments(self.concentration1, self.concentration0, 2) - torch.pow(
            self.mean, 2
        )

    def entropy(self):
        t1 = 1 - self.concentration1.reciprocal()
        t0 = 1 - self.concentration0.reciprocal()
        H0 = torch.digamma(self.concentration0 + 1) + euler_constant
        return (
            t0
            + t1 * H0
            - torch.log(self.concentration1)
            - torch.log(self.concentration0)
        )