# 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 os
import sys
import zlib

from absl.testing import absltest
from absl.testing import parameterized

import numpy as np

from jax import lax
from jax import random
import jax.numpy as jnp

sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from examples import kernel_lsq
sys.path.pop()

from jax import config
config.parse_flags_with_absl()


def _CheckShapeAgreement(test_case, init_fun, apply_fun, input_shape):
  jax_rng = random.PRNGKey(0)
  result_shape, params = init_fun(jax_rng, input_shape)
  result = apply_fun(params, test_case.rng.normal(size=input_shape).astype("float32"))
  test_case.assertEqual(result.shape, result_shape)


class ExamplesTest(parameterized.TestCase):

  def setUp(self):
    self.rng = np.random.default_rng(zlib.adler32(self.__class__.__name__.encode()))

  def testKernelRegressionGram(self):
    n, d = 100, 20
    xs = self.rng.normal(size=(n, d))
    kernel = lambda x, y: jnp.dot(x, y)
    np.testing.assert_allclose(kernel_lsq.gram(kernel, xs), jnp.dot(xs, xs.T), atol=1E-5)

  def testKernelRegressionTrainAndPredict(self):
    n, d = 100, 20
    truth = self.rng.normal(size=d)
    xs = self.rng.normal(size=(n, d))
    ys = jnp.dot(xs, truth)
    kernel = lambda x, y: jnp.dot(x, y, precision=lax.Precision.HIGH)
    predict = kernel_lsq.train(kernel, xs, ys)
    np.testing.assert_allclose(predict(xs), ys, atol=1e-3, rtol=1e-3)


if __name__ == "__main__":
  absltest.main()