# Copyright 2018 Google LLC
#
# 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 unittest
import zlib

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

import numpy as np

import jax
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
from examples import resnet50
sys.path.pop()

from jax.config 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()))

  @parameterized.named_parameters(
      {"testcase_name": "_input_shape={}".format(input_shape),
       "input_shape": input_shape}
      for input_shape in [(2, 20, 25, 2)])
  @unittest.skipIf(config.x64_enabled, "skip in x64 mode")
  def testIdentityBlockShape(self, input_shape):
    init_fun, apply_fun = resnet50.IdentityBlock(2, (4, 3))
    _CheckShapeAgreement(self, init_fun, apply_fun, input_shape)

  @parameterized.named_parameters(
      {"testcase_name": "_input_shape={}".format(input_shape),
       "input_shape": input_shape}
      for input_shape in [(2, 20, 25, 3)])
  @unittest.skipIf(config.x64_enabled, "skip in x64 mode")
  def testConvBlockShape(self, input_shape):
    init_fun, apply_fun = resnet50.ConvBlock(3, (2, 3, 4))
    _CheckShapeAgreement(self, init_fun, apply_fun, input_shape)

  @parameterized.named_parameters(
      {"testcase_name": "_num_classes={}_input_shape={}"
                        .format(num_classes, input_shape),
       "num_classes": num_classes, "input_shape": input_shape}
      for num_classes in [5, 10]
      for input_shape in [(224, 224, 3, 2)])
  @unittest.skipIf(config.x64_enabled, "skip in x64 mode")
  @jax.numpy_rank_promotion("allow")  # Uses stax, which exercises implicit rank promotion.
  def testResNet50Shape(self, num_classes, input_shape):
    init_fun, apply_fun = resnet50.ResNet50(num_classes)
    _CheckShapeAgreement(self, init_fun, apply_fun, input_shape)

  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()