import math

import torch
from torch import inf, nan
from torch.distributions import Chi2, constraints
from torch.distributions.distribution import Distribution
from torch.distributions.utils import _standard_normal, broadcast_all

__all__ = ['StudentT']

class StudentT(Distribution):
    r"""
    Creates a Student's t-distribution parameterized by degree of
    freedom :attr:`df`, mean :attr:`loc` and scale :attr:`scale`.

    Example::

        >>> # xdoctest: +IGNORE_WANT("non-deterinistic")
        >>> m = StudentT(torch.tensor([2.0]))
        >>> m.sample()  # Student's t-distributed with degrees of freedom=2
        tensor([ 0.1046])

    Args:
        df (float or Tensor): degrees of freedom
        loc (float or Tensor): mean of the distribution
        scale (float or Tensor): scale of the distribution
    """
    arg_constraints = {'df': constraints.positive, 'loc': constraints.real, 'scale': constraints.positive}
    support = constraints.real
    has_rsample = True

    @property
    def mean(self):
        m = self.loc.clone(memory_format=torch.contiguous_format)
        m[self.df <= 1] = nan
        return m

    @property
    def mode(self):
        return self.loc

    @property
    def variance(self):
        m = self.df.clone(memory_format=torch.contiguous_format)
        m[self.df > 2] = self.scale[self.df > 2].pow(2) * self.df[self.df > 2] / (self.df[self.df > 2] - 2)
        m[(self.df <= 2) & (self.df > 1)] = inf
        m[self.df <= 1] = nan
        return m

    def __init__(self, df, loc=0., scale=1., validate_args=None):
        self.df, self.loc, self.scale = broadcast_all(df, loc, scale)
        self._chi2 = Chi2(self.df)
        batch_shape = self.df.size()
        super().__init__(batch_shape, validate_args=validate_args)

    def expand(self, batch_shape, _instance=None):
        new = self._get_checked_instance(StudentT, _instance)
        batch_shape = torch.Size(batch_shape)
        new.df = self.df.expand(batch_shape)
        new.loc = self.loc.expand(batch_shape)
        new.scale = self.scale.expand(batch_shape)
        new._chi2 = self._chi2.expand(batch_shape)
        super(StudentT, new).__init__(batch_shape, validate_args=False)
        new._validate_args = self._validate_args
        return new

    def rsample(self, sample_shape=torch.Size()):
        # NOTE: This does not agree with scipy implementation as much as other distributions.
        # (see https://github.com/fritzo/notebooks/blob/master/debug-student-t.ipynb). Using DoubleTensor
        # parameters seems to help.

        #   X ~ Normal(0, 1)
        #   Z ~ Chi2(df)
        #   Y = X / sqrt(Z / df) ~ StudentT(df)
        shape = self._extended_shape(sample_shape)
        X = _standard_normal(shape, dtype=self.df.dtype, device=self.df.device)
        Z = self._chi2.rsample(sample_shape)
        Y = X * torch.rsqrt(Z / self.df)
        return self.loc + self.scale * Y

    def log_prob(self, value):
        if self._validate_args:
            self._validate_sample(value)
        y = (value - self.loc) / self.scale
        Z = (self.scale.log() +
             0.5 * self.df.log() +
             0.5 * math.log(math.pi) +
             torch.lgamma(0.5 * self.df) -
             torch.lgamma(0.5 * (self.df + 1.)))
        return -0.5 * (self.df + 1.) * torch.log1p(y**2. / self.df) - Z

    def entropy(self):
        lbeta = torch.lgamma(0.5 * self.df) + math.lgamma(0.5) - torch.lgamma(0.5 * (self.df + 1))
        return (self.scale.log() +
                0.5 * (self.df + 1) *
                (torch.digamma(0.5 * (self.df + 1)) - torch.digamma(0.5 * self.df)) +
                0.5 * self.df.log() + lbeta)