# Copyright 2018 The JAX Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import scipy.stats as osp_stats

from jax import lax
import jax.numpy as jnp
from jax._src.lax.lax import _const as _lax_const
from jax._src.numpy.util import _wraps, promote_args_inexact
from jax._src.typing import Array, ArrayLike
from jax.scipy.special import betaln, betainc, xlogy, xlog1py


@_wraps(osp_stats.beta.logpdf, update_doc=False)
def logpdf(x: ArrayLike, a: ArrayLike, b: ArrayLike,
           loc: ArrayLike = 0, scale: ArrayLike = 1) -> Array:
  x, a, b, loc, scale = promote_args_inexact("beta.logpdf", x, a, b, loc, scale)
  one = _lax_const(x, 1)
  shape_term = lax.neg(betaln(a, b))
  y = lax.div(lax.sub(x, loc), scale)
  log_linear_term = lax.add(xlogy(lax.sub(a, one), y),
                            xlog1py(lax.sub(b, one), lax.neg(y)))
  log_probs = lax.sub(lax.add(shape_term, log_linear_term), lax.log(scale))
  return jnp.where(jnp.logical_or(lax.gt(x, lax.add(loc, scale)),
                                  lax.lt(x, loc)), -jnp.inf, log_probs)


@_wraps(osp_stats.beta.pdf, update_doc=False)
def pdf(x: ArrayLike, a: ArrayLike, b: ArrayLike,
        loc: ArrayLike = 0, scale: ArrayLike = 1) -> Array:
  return lax.exp(logpdf(x, a, b, loc, scale))


@_wraps(osp_stats.beta.cdf, update_doc=False)
def cdf(x: ArrayLike, a: ArrayLike, b: ArrayLike,
        loc: ArrayLike = 0, scale: ArrayLike = 1) -> Array:
  x, a, b, loc, scale = promote_args_inexact("beta.cdf", x, a, b, loc, scale)
  return betainc(
    a,
    b,
    lax.clamp(
      _lax_const(x, 0),
      lax.div(lax.sub(x, loc), scale),
      _lax_const(x, 1),
    )
  )


@_wraps(osp_stats.beta.logcdf, update_doc=False)
def logcdf(x: ArrayLike, a: ArrayLike, b: ArrayLike,
        loc: ArrayLike = 0, scale: ArrayLike = 1) -> Array:
  return lax.log(cdf(x, a, b, loc, scale))


@_wraps(osp_stats.beta.sf, update_doc=False)
def sf(x: ArrayLike, a: ArrayLike, b: ArrayLike,
        loc: ArrayLike = 0, scale: ArrayLike = 1) -> Array:
  cdf_result = cdf(x, a, b, loc, scale)
  return lax.sub(_lax_const(cdf_result, 1), cdf_result)
