import numpy as np
import pytest
from packaging.version import parse as version_parse

import aesara.tensor as at
from aesara.configdefaults import config
from aesara.graph.fg import FunctionGraph
from aesara.scan.basic import scan
from aesara.tensor.math import gammaln, log
from aesara.tensor.type import ivector, lscalar, scalar
from tests.link.jax.test_basic import compare_jax_and_py


jax = pytest.importorskip("jax")


@pytest.mark.xfail(
    version_parse(jax.__version__) >= version_parse("0.2.12"),
    reason="Omnistaging cannot be disabled",
)
def test_jax_scan_multiple_output():
    """Test a scan implementation of a SEIR model.

    SEIR model definition:
    S[t+1] = S[t] - B[t]
    E[t+1] = E[t] +B[t] - C[t]
    I[t+1] = I[t+1] + C[t] - D[t]

    B[t] ~ Binom(S[t], beta)
    C[t] ~ Binom(E[t], gamma)
    D[t] ~ Binom(I[t], delta)
    """

    def binomln(n, k):
        return gammaln(n + 1) - gammaln(k + 1) - gammaln(n - k + 1)

    def binom_log_prob(n, p, value):
        return binomln(n, value) + value * log(p) + (n - value) * log(1 - p)

    # sequences
    at_C = ivector("C_t")
    at_D = ivector("D_t")
    # outputs_info (initial conditions)
    st0 = lscalar("s_t0")
    et0 = lscalar("e_t0")
    it0 = lscalar("i_t0")
    logp_c = scalar("logp_c")
    logp_d = scalar("logp_d")
    # non_sequences
    beta = scalar("beta")
    gamma = scalar("gamma")
    delta = scalar("delta")

    # TODO: Use random streams when their JAX conversions are implemented.
    # trng = aesara.tensor.random.RandomStream(1234)

    def seir_one_step(ct0, dt0, st0, et0, it0, logp_c, logp_d, beta, gamma, delta):
        # bt0 = trng.binomial(n=st0, p=beta)
        bt0 = st0 * beta
        bt0 = bt0.astype(st0.dtype)

        logp_c1 = binom_log_prob(et0, gamma, ct0).astype(logp_c.dtype)
        logp_d1 = binom_log_prob(it0, delta, dt0).astype(logp_d.dtype)

        st1 = st0 - bt0
        et1 = et0 + bt0 - ct0
        it1 = it0 + ct0 - dt0
        return st1, et1, it1, logp_c1, logp_d1

    (st, et, it, logp_c_all, logp_d_all), _ = scan(
        fn=seir_one_step,
        sequences=[at_C, at_D],
        outputs_info=[st0, et0, it0, logp_c, logp_d],
        non_sequences=[beta, gamma, delta],
    )
    st.name = "S_t"
    et.name = "E_t"
    it.name = "I_t"
    logp_c_all.name = "C_t_logp"
    logp_d_all.name = "D_t_logp"

    out_fg = FunctionGraph(
        [at_C, at_D, st0, et0, it0, logp_c, logp_d, beta, gamma, delta],
        [st, et, it, logp_c_all, logp_d_all],
    )

    s0, e0, i0 = 100, 50, 25
    logp_c0 = np.array(0.0, dtype=config.floatX)
    logp_d0 = np.array(0.0, dtype=config.floatX)
    beta_val, gamma_val, delta_val = [
        np.array(val, dtype=config.floatX) for val in [0.277792, 0.135330, 0.108753]
    ]
    C = np.array([3, 5, 8, 13, 21, 26, 10, 3], dtype=np.int32)
    D = np.array([1, 2, 3, 7, 9, 11, 5, 1], dtype=np.int32)

    test_input_vals = [
        C,
        D,
        s0,
        e0,
        i0,
        logp_c0,
        logp_d0,
        beta_val,
        gamma_val,
        delta_val,
    ]
    compare_jax_and_py(out_fg, test_input_vals)


@pytest.mark.xfail(
    version_parse(jax.__version__) >= version_parse("0.2.12"),
    reason="Omnistaging cannot be disabled",
)
def test_jax_scan_tap_output():

    a_at = scalar("a")

    def input_step_fn(y_tm1, y_tm3, a):
        y_tm1.name = "y_tm1"
        y_tm3.name = "y_tm3"
        res = (y_tm1 + y_tm3) * a
        res.name = "y_t"
        return res

    y_scan_at, _ = scan(
        fn=input_step_fn,
        outputs_info=[
            {
                "initial": at.as_tensor_variable(
                    np.r_[-1.0, 1.3, 0.0].astype(config.floatX)
                ),
                "taps": [-1, -3],
            },
        ],
        non_sequences=[a_at],
        n_steps=10,
        name="y_scan",
    )
    y_scan_at.name = "y"
    y_scan_at.owner.inputs[0].name = "y_all"

    out_fg = FunctionGraph([a_at], [y_scan_at])

    test_input_vals = [np.array(10.0).astype(config.floatX)]
    compare_jax_and_py(out_fg, test_input_vals)
