# Copyright 2019 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.

"""Tests for nn module."""

import collections
from functools import partial
import itertools

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

import scipy.stats

from jax import core
from jax._src import test_util as jtu
from jax.test_util import check_grads
from jax import nn
from jax import random
import jax
import jax.numpy as jnp

from jax.config import config
config.parse_flags_with_absl()


class NNFunctionsTest(jtu.JaxTestCase):
  @jtu.skip_on_flag("jax_skip_slow_tests", True)
  def testSoftplusGrad(self):
    check_grads(nn.softplus, (1e-8,), order=4,
                rtol=1e-2 if jtu.device_under_test() == "tpu" else None)

  def testSoftplusGradZero(self):
    check_grads(nn.softplus, (0.,), order=1,
                rtol=1e-2 if jtu.device_under_test() == "tpu" else None)

  def testSoftplusGradInf(self):
    self.assertAllClose(
        1., jax.grad(nn.softplus)(float('inf')))

  def testSoftplusGradNegInf(self):
    check_grads(nn.softplus, (-float('inf'),), order=1,
                rtol=1e-2 if jtu.device_under_test() == "tpu" else None)

  def testSoftplusGradNan(self):
    check_grads(nn.softplus, (float('nan'),), order=1,
                rtol=1e-2 if jtu.device_under_test() == "tpu" else None)

  @parameterized.parameters([int, float] + jtu.dtypes.floating + jtu.dtypes.integer)
  def testSoftplusZero(self, dtype):
    self.assertEqual(jnp.log(dtype(2)), nn.softplus(dtype(0)))

  def testReluGrad(self):
    rtol = 1e-2 if jtu.device_under_test() == "tpu" else None
    check_grads(nn.relu, (1.,), order=3, rtol=rtol)
    check_grads(nn.relu, (-1.,), order=3, rtol=rtol)
    jaxpr = jax.make_jaxpr(jax.grad(nn.relu))(0.)
    self.assertGreaterEqual(len(jaxpr.jaxpr.eqns), 2)

  def testSoftplusValue(self):
    val = nn.softplus(89.)
    self.assertAllClose(val, 89., check_dtypes=False)

  @jtu.skip_on_flag("jax_skip_slow_tests", True)
  def testEluGrad(self):
    check_grads(nn.elu, (1e4,), order=4, eps=1.)

  def testEluValue(self):
    val = nn.elu(1e4)
    self.assertAllClose(val, 1e4, check_dtypes=False)

  def testGluValue(self):
    val = nn.glu(jnp.array([1.0, 0.0]), axis=0)
    self.assertAllClose(val, jnp.array([0.5]))

  @parameterized.parameters(False, True)
  def testGeluIntType(self, approximate):
    val_float = nn.gelu(jnp.array(-1.0), approximate=approximate)
    val_int = nn.gelu(jnp.array(-1), approximate=approximate)
    self.assertAllClose(val_float, val_int)

  @parameterized.parameters(False, True)
  def testGelu(self, approximate):
    def gelu_reference(x):
      return x * scipy.stats.norm.cdf(x)
    rng = jtu.rand_default(self.rng())
    args_maker = lambda: [rng((4, 5, 6), jnp.float32)]
    self._CheckAgainstNumpy(
      gelu_reference, partial(nn.gelu, approximate=approximate), args_maker,
      check_dtypes=False, tol=1e-3 if approximate else None)

  @parameterized.parameters(*itertools.product(
      (jnp.float32, jnp.bfloat16, jnp.float16),
      (partial(nn.gelu, approximate=False),
       partial(nn.gelu, approximate=True),
       nn.relu, nn.softplus, nn.sigmoid)))
  def testDtypeMatchesInput(self, dtype, fn):
    x = jnp.zeros((), dtype=dtype)
    out = fn(x)
    self.assertEqual(out.dtype, dtype)

  def testEluMemory(self):
    # see https://github.com/google/jax/pull/1640
    with jax.enable_checks(False):  # With checks we materialize the array
      jax.make_jaxpr(lambda: nn.elu(jnp.ones((10 ** 12,))))  # don't oom

  def testHardTanhMemory(self):
    # see https://github.com/google/jax/pull/1640
    with jax.enable_checks(False):  # With checks we materialize the array
      jax.make_jaxpr(lambda: nn.hard_tanh(jnp.ones((10 ** 12,))))  # don't oom

  @parameterized.parameters([nn.softmax, nn.log_softmax])
  def testSoftmaxWhereMask(self, fn):
    x = jnp.array([5.5, 1.3, -4.2, 0.9])
    m = jnp.array([True, False, True, True])
    x_filtered = jnp.take(x, jnp.array([0, 2, 3]))

    out_masked = jnp.take(
        fn(x, where=m, initial=-jnp.inf), jnp.array([0, 2, 3]))
    out_filtered = fn(x_filtered)

    self.assertAllClose(out_masked, out_filtered)

  def testStandardizeWhereMask(self):
    x = jnp.array([5.5, 1.3, -4.2, 0.9])
    m = jnp.array([True, False, True, True])
    x_filtered = jnp.take(x, jnp.array([0, 2, 3]))

    out_masked = jnp.take(nn.standardize(x, where=m), jnp.array([0, 2, 3]))
    out_filtered = nn.standardize(x_filtered)

    self.assertAllClose(out_masked, out_filtered)

  def testOneHot(self):
    actual = nn.one_hot(jnp.array([0, 1, 2]), 3)
    expected = jnp.array([[1., 0., 0.],
                          [0., 1., 0.],
                          [0., 0., 1.]])
    self.assertAllClose(actual, expected, check_dtypes=False)

    actual = nn.one_hot(jnp.array([1, 2, 0]), 3)
    expected = jnp.array([[0., 1., 0.],
                          [0., 0., 1.],
                          [1., 0., 0.]])
    self.assertAllClose(actual, expected, check_dtypes=False)

  def testOneHotOutOfBound(self):
    actual = nn.one_hot(jnp.array([-1, 3]), 3)
    expected = jnp.array([[0., 0., 0.],
                          [0., 0., 0.]])
    self.assertAllClose(actual, expected, check_dtypes=False)

  def testOneHotNonArrayInput(self):
    actual = nn.one_hot([0, 1, 2], 3)
    expected = jnp.array([[1., 0., 0.],
                          [0., 1., 0.],
                          [0., 0., 1.]])
    self.assertAllClose(actual, expected, check_dtypes=False)

  def testOneHotCustomDtype(self):
    actual = nn.one_hot(jnp.array([0, 1, 2]), 3, dtype=jnp.bool_)
    expected = jnp.array([[True, False, False],
                          [False, True, False],
                          [False, False, True]])
    self.assertAllClose(actual, expected)

  def testOneHotConcretizationError(self):
    # https://github.com/google/jax/issues/3654
    msg = r"in jax.nn.one_hot argument `num_classes`"
    with self.assertRaisesRegex(core.ConcretizationTypeError, msg):
      jax.jit(nn.one_hot)(3, 5)

  def testOneHotAxis(self):
    expected = jnp.array([[0., 1., 0.],
                          [0., 0., 1.],
                          [1., 0., 0.]]).T

    actual = nn.one_hot(jnp.array([1, 2, 0]), 3, axis=0)
    self.assertAllClose(actual, expected, check_dtypes=False)

    actual = nn.one_hot(jnp.array([1, 2, 0]), 3, axis=-2)
    self.assertAllClose(actual, expected, check_dtypes=False)

  def testTanhExists(self):
    nn.tanh  # doesn't crash

  def testCustomJVPLeak(self):
    # https://github.com/google/jax/issues/8171
    @jax.jit
    def fwd():
      a = jnp.array(1.)

      def f(hx, _):
        hx = jax.nn.sigmoid(hx + a)
        return hx, None

      hx = jnp.array(0.)
      jax.lax.scan(f, hx, None, length=2)

    with jax.checking_leaks():
      fwd()  # doesn't crash

  def testCustomJVPLeak2(self):
    # https://github.com/google/jax/issues/8171
    # The above test uses jax.nn.sigmoid, as in the original #8171, but that
    # function no longer actually has a custom_jvp! So we inline the old def.

    @jax.custom_jvp
    def sigmoid(x):
      one = jnp.float32(1)
      return jax.lax.div(one, jax.lax.add(one, jax.lax.exp(jax.lax.neg(x))))
    sigmoid.defjvps(lambda g, ans, x: g * ans * (jnp.float32(1) - ans))

    @jax.jit
    def fwd():
      a = jnp.array(1., 'float32')

      def f(hx, _):
        hx = sigmoid(hx + a)
        return hx, None

      hx = jnp.array(0., 'float32')
      jax.lax.scan(f, hx, None, length=2)

    with jax.checking_leaks():
      fwd()  # doesn't crash


InitializerRecord = collections.namedtuple(
  "InitializerRecord",
  ["name", "initializer", "shapes", "dtypes"])

ALL_SHAPES = [(2,), (2, 2), (2, 3), (3, 2), (2, 3, 4), (4, 3, 2), (2, 3, 4, 5)]

def initializer_record(name, initializer, dtypes, min_dims=2, max_dims=4):
  shapes = [shape for shape in ALL_SHAPES
            if min_dims <= len(shape) <= max_dims]
  return InitializerRecord(name, initializer, shapes, dtypes)

INITIALIZER_RECS = [
    initializer_record("uniform", nn.initializers.uniform, jtu.dtypes.floating, 1),
    initializer_record("normal", nn.initializers.normal, jtu.dtypes.inexact, 1),
    initializer_record("he_normal", nn.initializers.he_normal, jtu.dtypes.inexact),
    initializer_record("he_uniform", nn.initializers.he_uniform, jtu.dtypes.inexact),
    initializer_record("glorot_normal", nn.initializers.glorot_normal, jtu.dtypes.inexact),
    initializer_record("glorot_uniform", nn.initializers.glorot_uniform, jtu.dtypes.inexact),
    initializer_record("lecun_normal", nn.initializers.lecun_normal, jtu.dtypes.inexact),
    initializer_record("lecun_uniform", nn.initializers.lecun_uniform, jtu.dtypes.inexact),
    initializer_record("orthogonal", nn.initializers.orthogonal, jtu.dtypes.floating, 2, 2),
    initializer_record("delta_orthogonal", nn.initializers.delta_orthogonal, jtu.dtypes.floating, 4, 4)
]


class NNInitializersTest(jtu.JaxTestCase):
  @parameterized.parameters(itertools.chain.from_iterable(
    jtu.sample_product_testcases(
      [dict(initializer=rec.initializer())],
      shape=rec.shapes,
      dtype=rec.dtypes
    )
    for rec in INITIALIZER_RECS
  ))
  def testInitializer(self, initializer, shape, dtype):
    rng = random.PRNGKey(0)
    val = initializer(rng, shape, dtype)

    self.assertEqual(shape, jnp.shape(val))
    self.assertEqual(jax.dtypes.canonicalize_dtype(dtype), jnp.dtype(val))

  @parameterized.parameters(itertools.chain.from_iterable(
    jtu.sample_product_testcases(
      [dict(initializer_provider=rec.initializer)],
      shape=rec.shapes,
      dtype=rec.dtypes
    )
    for rec in INITIALIZER_RECS
  ))
  def testInitializerProvider(self, initializer_provider, shape, dtype):
    rng = random.PRNGKey(0)
    initializer = initializer_provider(dtype=dtype)
    val = initializer(rng, shape)

    self.assertEqual(shape, jnp.shape(val))
    self.assertEqual(jax.dtypes.canonicalize_dtype(dtype), jnp.dtype(val))

  def testVarianceScalingMultiAxis(self):
    rng = random.PRNGKey(0)
    shape = (2, 3, 4, 5)
    initializer = nn.initializers.variance_scaling(
      scale=1.0, mode='fan_avg', distribution='truncated_normal',
      in_axis=(0, 1), out_axis=(-2, -1))
    val = initializer(rng, shape)

    self.assertEqual(shape, jnp.shape(val))

  def testVarianceScalingBatchAxis(self):
    rng = random.PRNGKey(0)
    shape = (2, 3, 4, 5)
    initializer = nn.initializers.variance_scaling(
      scale=1.0, mode='fan_avg', distribution='truncated_normal',
      in_axis=0, out_axis=(2, 3), batch_axis=1)
    val = initializer(rng, shape)

    self.assertEqual(shape, jnp.shape(val))


if __name__ == "__main__":
  absltest.main(testLoader=jtu.JaxTestLoader())