# Copyright 2023 The JAXampler 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

#     http://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.

from functools import partial

from jax import jit
from jax import numpy as jnp
from jax.scipy.special import erf
from jax.typing import ArrayLike

from ...utils import jx_cast
from .crvs import ContinuousRV


class Boltzmann(ContinuousRV):

    def __init__(self, a: ArrayLike, name: str = None) -> None:
        self._a, = jx_cast(a)
        self.check_params()
        super().__init__(name)

    def check_params(self) -> None:
        assert jnp.all(self._a > 0.0), "a must be positive"

    @partial(jit, static_argnums=(0,))
    def logpdf(self, x: ArrayLike) -> ArrayLike:
        logpdf_val = 2 * jnp.log(x) - 0.5 * jnp.power(x / self._a, 2)
        logpdf_val -= 0.5 * jnp.log(jnp.pi / 2) + 3 * jnp.log(self._a)
        logpdf_val = jnp.where(x > 0.0, logpdf_val, jnp.nan)
        return logpdf_val

    @partial(jit, static_argnums=(0,))
    def cdf(self, x: ArrayLike) -> ArrayLike:
        cdf_val = jnp.log(x) - 0.5 * jnp.power(x / self._a, 2)
        cdf_val -= 0.5 * jnp.log(jnp.pi / 2) + jnp.log(self._a)
        cdf_val = jnp.exp(cdf_val)
        cdf_val = erf(x / (jnp.sqrt(2) * self._a)) - cdf_val
        return cdf_val

    def __repr__(self) -> str:
        string = f"Boltzmann(a={self._a}"
        if self._name is not None:
            string += f", name={self._name}"
        string += ")"
        return string
