import torch as th

from stable_baselines3.common.distributions import Distribution as SB3_Distribution


def get_mean_and_chol(p, expand=False):
    if isinstance(p, th.distributions.Normal):
        if expand:
            return p.mean, th.diag_embed(p.stddev)
        else:
            return p.mean, p.stddev
    elif isinstance(p, th.distributions.MultivariateNormal):
        return p.mean, p.scale_tril
    elif isinstance(p, SB3_Distribution):
        return get_mean_and_chol(p.distribution, expand=expand)
    else:
        raise Exception('Dist-Type not implemented')


def get_mean_and_sqrt(p):
    raise Exception('Not yet implemented...')
    if isinstance(p, th.distributions.Normal):
        return p.mean, p.stddev
    elif isinstance(p, th.distributions.MultivariateNormal):
        return p.mean, p.scale_tril
    elif isinstance(p, SB3_Distribution):
        return get_mean_and_chol(p.distribution)
    else:
        raise Exception('Dist-Type not implemented')


def get_cov(p):
    if isinstance(p, th.distributions.Normal):
        return th.diag_embed(p.variance)
    elif isinstance(p, th.distributions.MultivariateNormal):
        return p.covariance_matrix
    elif isinstance(p, SB3_Distribution):
        return get_cov(p.distribution)
    else:
        raise Exception('Dist-Type not implemented')


def has_diag_cov(p, numerical_check=True):
    if isinstance(p, SB3_Distribution):
        return has_diag_cov(p.distribution, numerical_check=numerical_check)
    if isinstance(p, th.distributions.Normal):
        return True
    if not numerical_check:
        return False
    # Check if matrix is diag
    cov = get_cov(p)
    return th.equal(cov - th.diag_embed(th.diagonal(cov, dim1=-2, dim2=-1), th.zeros_like(cov)))


def get_diag_cov_vec(p, check_diag=True, numerical_check=True):
    if check_diag and not has_diag_cov(p):
        raise Exception('Cannot reduce cov-mat to diag-vec: Is not diagonal')
    return th.diagonal(get_cov(p), dim1=-2, dim2=-1)


def new_dist_like(orig_p, mean, chol):
    if isinstance(orig_p, th.distributions.Normal):
        if orig_p.stddev.shape != chol.shape:
            chol = th.diagonal(chol, dim1=1, dim2=2)
        return th.distributions.Normal(mean, chol)
    elif isinstance(orig_p, th.distributions.MultivariateNormal):
        return th.distributions.MultivariateNormal(mean, scale_tril=chol)
    elif isinstance(orig_p, SB3_Distribution):
        p = orig_p.distribution
        if isinstance(p, th.distributions.Normal):
            p_out = orig_p.__class__(orig_p.action_dim)
            p_out.distribution = th.distributions.Normal(mean, chol)
        elif isinstance(p, th.distributions.MultivariateNormal):
            p_out = orig_p.__class__(orig_p.action_dim)
            p_out.distribution = th.distributions.MultivariateNormal(
                mean, scale_tril=chol)
        else:
            raise Exception('Dist-Type not implemented (of sb3 dist)')
        return p_out
    else:
        raise Exception('Dist-Type not implemented')