# Copyright 2018 The TensorFlow Probability 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
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# ============================================================================
"""Multivariate Normal distribution classes."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import tensorflow.compat.v2 as tf

from tensorflow_probability.python.bijectors import identity as identity_bijector
from tensorflow_probability.python.bijectors import scale_matvec_linear_operator
from tensorflow_probability.python.bijectors import shift as shift_bijector
from tensorflow_probability.python.distributions import distribution
from tensorflow_probability.python.distributions import kullback_leibler
from tensorflow_probability.python.distributions import normal
from tensorflow_probability.python.distributions import sample
from tensorflow_probability.python.distributions import transformed_distribution
from tensorflow_probability.python.internal import distribution_util
from tensorflow_probability.python.internal import dtype_util
from tensorflow_probability.python.internal import parameter_properties
from tensorflow_probability.python.internal import prefer_static as ps
from tensorflow_probability.python.internal import tensor_util
from tensorflow_probability.python.internal import tensorshape_util


__all__ = [
    'MultivariateNormalLinearOperator',
]


_mvn_sample_note = """
`value` is a batch vector with compatible shape if `value` is a `Tensor` whose
shape can be broadcast up to either:

```python
self.batch_shape + self.event_shape
```

or

```python
[M1, ..., Mm] + self.batch_shape + self.event_shape
```

"""


class MultivariateNormalLinearOperator(
    transformed_distribution.TransformedDistribution,
    distribution.AutoCompositeTensorDistribution):
  """The multivariate normal distribution on `R^k`.

  The Multivariate Normal distribution is defined over `R^k` and parameterized
  by a (batch of) length-`k` `loc` vector (aka "mu") and a (batch of) `k x k`
  `scale` matrix; `covariance = scale @ scale.T`, where `@` denotes
  matrix-multiplication.

  #### Mathematical Details

  The probability density function (pdf) is,

  ```none
  pdf(x; loc, scale) = exp(-0.5 ||y||**2) / Z,
  y = inv(scale) @ (x - loc),
  Z = (2 pi)**(0.5 k) |det(scale)|,
  ```

  where:

  * `loc` is a vector in `R^k`,
  * `scale` is a linear operator in `R^{k x k}`, `cov = scale @ scale.T`,
  * `Z` denotes the normalization constant, and,
  * `||y||**2` denotes the squared Euclidean norm of `y`.

  The MultivariateNormal distribution is a member of the [location-scale
  family](https://en.wikipedia.org/wiki/Location-scale_family), i.e., it can be
  constructed as,

  ```none
  X ~ MultivariateNormal(loc=0, scale=1)   # Identity scale, zero shift.
  Y = scale @ X + loc
  ```

  #### Examples

  ```python
  tfd = tfp.distributions

  # Initialize a single 3-variate Gaussian.
  mu = [1., 2, 3]
  cov = [[ 0.36,  0.12,  0.06],
         [ 0.12,  0.29, -0.13],
         [ 0.06, -0.13,  0.26]]
  scale = tf.linalg.cholesky(cov)
  # ==> [[ 0.6,  0. ,  0. ],
  #      [ 0.2,  0.5,  0. ],
  #      [ 0.1, -0.3,  0.4]])

  mvn = tfd.MultivariateNormalLinearOperator(
      loc=mu,
      scale=tf.linalg.LinearOperatorLowerTriangular(scale))

  # Covariance agrees with cholesky(cov) parameterization.
  mvn.covariance()
  # ==> [[ 0.36,  0.12,  0.06],
  #      [ 0.12,  0.29, -0.13],
  #      [ 0.06, -0.13,  0.26]]

  # Compute the pdf of an`R^3` observation; return a scalar.
  mvn.prob([-1., 0, 1])  # shape: []

  # Initialize a 2-batch of 3-variate Gaussians.
  mu = [[1., 2, 3],
        [11, 22, 33]]              # shape: [2, 3]
  scale_diag = [[1., 2, 3],
                [0.5, 1, 1.5]]     # shape: [2, 3]

  mvn = tfd.MultivariateNormalLinearOperator(
      loc=mu,
      scale=tf.linalg.LinearOperatorDiag(scale_diag))

  # Compute the pdf of two `R^3` observations; return a length-2 vector.
  x = [[-0.9, 0, 0.1],
       [-10, 0, 9]]     # shape: [2, 3]
  mvn.prob(x)    # shape: [2]
  ```

  """

  def __init__(self,
               loc=None,
               scale=None,
               validate_args=False,
               allow_nan_stats=True,
               experimental_use_kahan_sum=False,
               name='MultivariateNormalLinearOperator'):
    """Construct Multivariate Normal distribution on `R^k`.

    The `batch_shape` is the broadcast shape between `loc` and `scale`
    arguments.

    The `event_shape` is given by last dimension of the matrix implied by
    `scale`. The last dimension of `loc` (if provided) must broadcast with this.

    Recall that `covariance = scale @ scale.T`.

    Additional leading dimensions (if any) will index batches.

    Args:
      loc: Floating-point `Tensor`. If this is set to `None`, `loc` is
        implicitly `0`. When specified, may have shape `[B1, ..., Bb, k]` where
        `b >= 0` and `k` is the event size.
      scale: Instance of `LinearOperator` with same `dtype` as `loc` and shape
        `[B1, ..., Bb, k, k]`.
      validate_args: Python `bool`, default `False`. Whether to validate input
        with asserts. If `validate_args` is `False`, and the inputs are
        invalid, correct behavior is not guaranteed.
      allow_nan_stats: Python `bool`, default `True`. If `False`, raise an
        exception if a statistic (e.g. mean/mode/etc...) is undefined for any
        batch member If `True`, batch members with valid parameters leading to
        undefined statistics will return NaN for this statistic.
      experimental_use_kahan_sum: Python `bool`. When `True`, we use Kahan
        summation to aggregate independent underlying log_prob values. For best
        results, Kahan summation should also be applied when computing the
        log-determinant of the `LinearOperator` representing the scale matrix.
        Kahan summation improves against the precision of a naive float32 sum.
        This can be noticeable in particular for large dimensions in float32.
        See CPU caveat on `tfp.math.reduce_kahan_sum`.
      name: The name to give Ops created by the initializer.

    Raises:
      ValueError: if `scale` is unspecified.
      TypeError: if not `scale.dtype.is_floating`
    """
    parameters = dict(locals())
    self._experimental_use_kahan_sum = experimental_use_kahan_sum
    if scale is None:
      raise ValueError('Missing required `scale` parameter.')
    if not dtype_util.is_floating(scale.dtype):
      raise TypeError('`scale` parameter must have floating-point dtype.')

    with tf.name_scope(name) as name:
      dtype = dtype_util.common_dtype([loc, scale], dtype_hint=tf.float32)
      # Since expand_dims doesn't preserve constant-ness, we obtain the
      # non-dynamic value if possible.
      loc = tensor_util.convert_nonref_to_tensor(
          loc, dtype=dtype, name='loc')
      batch_shape, event_shape = distribution_util.shapes_from_loc_and_scale(
          loc, scale)
    self._loc = loc
    self._scale = scale

    bijector = scale_matvec_linear_operator.ScaleMatvecLinearOperator(
        scale, validate_args=validate_args)
    if loc is not None:
      bijector = shift_bijector.Shift(
          shift=loc, validate_args=validate_args)(bijector)
    super(MultivariateNormalLinearOperator, self).__init__(
        # TODO(b/137665504): Use batch-adding meta-distribution to set the batch
        # shape instead of tf.zeros.
        # We use `Sample` instead of `Independent` because `Independent`
        # requires concatenating `batch_shape` and `event_shape`, which loses
        # static `batch_shape` information when `event_shape` is not statically
        # known.
        distribution=sample.Sample(
            normal.Normal(
                loc=tf.zeros(batch_shape, dtype=dtype),
                scale=tf.ones([], dtype=dtype)),
            event_shape,
            experimental_use_kahan_sum=experimental_use_kahan_sum),
        bijector=bijector,
        validate_args=validate_args,
        name=name)
    self._parameters = parameters

  @property
  def loc(self):
    """The `loc` `Tensor` in `Y = scale @ X + loc`."""
    return self._loc

  @property
  def scale(self):
    """The `scale` `LinearOperator` in `Y = scale @ X + loc`."""
    return self._scale

  experimental_is_sharded = False

  @classmethod
  def _parameter_properties(cls, dtype, num_classes=None):
    return dict(
        loc=parameter_properties.ParameterProperties(event_ndims=1),
        scale=parameter_properties.BatchedComponentProperties())

  @distribution_util.AppendDocstring(_mvn_sample_note)
  def _log_prob(self, x):
    return super(MultivariateNormalLinearOperator, self)._log_prob(x)

  @distribution_util.AppendDocstring(_mvn_sample_note)
  def _prob(self, x):
    return super(MultivariateNormalLinearOperator, self)._prob(x)

  def _mean(self):
    shape = ps.concat([
        self.batch_shape_tensor(),
        self.event_shape_tensor(),
    ], axis=0)

    if self.loc is None:
      return tf.zeros(shape, self.dtype)

    return tf.broadcast_to(self.loc, shape)

  def _covariance(self):
    if distribution_util.is_diagonal_scale(self.scale):
      cov = tf.linalg.diag(tf.square(self.scale.diag_part()))
    else:
      cov = self.scale.matmul(self.scale.to_dense(), adjoint_arg=True)
    if self.loc is not None:
      loc_shape = ps.shape(self.loc)
      loc_plus_extra_event_shape = ps.concat([
          loc_shape,
          loc_shape[-1:],
      ], axis=0)
      cov = tf.broadcast_to(
          cov, ps.broadcast_shape(ps.shape(cov), loc_plus_extra_event_shape))
    return cov

  def _variance(self):
    if distribution_util.is_diagonal_scale(self.scale):
      variance = tf.square(self.scale.diag_part())
    elif (isinstance(self.scale, tf.linalg.LinearOperatorLowRankUpdate) and
          self.scale.is_self_adjoint):
      variance = self.scale.matmul(self.scale.adjoint()).diag_part()
    elif isinstance(self.scale, tf.linalg.LinearOperatorKronecker):
      factors_sq_operators = [
          factor.matmul(factor.adjoint()) for factor in self.scale.operators
      ]
      variance = (tf.linalg.LinearOperatorKronecker(factors_sq_operators)
                  .diag_part())
    else:
      variance = self.scale.matmul(self.scale.adjoint()).diag_part()

    if self.loc is not None:
      variance = tf.broadcast_to(
          variance, ps.broadcast_shape(ps.shape(variance), ps.shape(self.loc)))

    return variance

  def _stddev(self):
    if distribution_util.is_diagonal_scale(self.scale):
      stddev = tf.abs(self.scale.diag_part())
    elif (isinstance(self.scale, tf.linalg.LinearOperatorLowRankUpdate) and
          self.scale.is_self_adjoint):
      stddev = tf.sqrt(
          tf.linalg.diag_part(self.scale.matmul(self.scale.to_dense())))
    else:
      stddev = tf.sqrt(
          tf.linalg.diag_part(
              self.scale.matmul(self.scale.to_dense(), adjoint_arg=True)))

    if self.loc is not None:
      stddev = tf.broadcast_to(
          stddev, ps.broadcast_shape(ps.shape(stddev), ps.shape(self.loc)))

    return stddev

  def _mode(self):
    return self._mean()

  def _default_event_space_bijector(self):
    return identity_bijector.Identity(validate_args=self.validate_args)

  def _parameter_control_dependencies(self, is_init):
    # Nothing to do here.
    return []


@kullback_leibler.RegisterKL(MultivariateNormalLinearOperator,
                             MultivariateNormalLinearOperator)
def _kl_brute_force(a, b, name=None):
  """Batched KL divergence `KL(a || b)` for multivariate Normals.

  With `X`, `Y` both multivariate Normals in `R^k` with means `mu_a`, `mu_b` and
  covariance `C_a`, `C_b` respectively,

  ```
  KL(a || b) = 0.5 * ( L - k + T + Q ),
  L := Log[Det(C_b)] - Log[Det(C_a)]
  T := trace(C_b^{-1} C_a),
  Q := (mu_b - mu_a)^T C_b^{-1} (mu_b - mu_a),
  ```

  This `Op` computes the trace by solving `C_b^{-1} C_a`. Although efficient
  methods for solving systems with `C_b` may be available, a dense version of
  (the square root of) `C_a` is used, so performance is `O(B s k**2)` where `B`
  is the batch size, and `s` is the cost of solving `C_b x = y` for vectors `x`
  and `y`.

  Args:
    a: Instance of `MultivariateNormalLinearOperator`.
    b: Instance of `MultivariateNormalLinearOperator`.
    name: (optional) name to use for created ops. Default "kl_mvn".

  Returns:
    Batchwise `KL(a || b)`.
  """

  def squared_frobenius_norm(x):
    """Helper to make KL calculation slightly more readable."""
    # http://mathworld.wolfram.com/FrobeniusNorm.html
    # The gradient of KL[p,q] is not defined when p==q. The culprit is
    # tf.norm, i.e., we cannot use the commented out code.
    # return tf.square(tf.norm(x, ord="fro", axis=[-2, -1]))
    return tf.reduce_sum(tf.square(x), axis=[-2, -1])

  # TODO(b/35041439): See also b/35040945. Remove this function once LinOp
  # supports something like:
  #   A.inverse().solve(B).norm(order='fro', axis=[-1, -2])
  def is_diagonal(x):
    """Helper to identify if `LinearOperator` has only a diagonal component."""
    return (isinstance(x, tf.linalg.LinearOperatorIdentity) or
            isinstance(x, tf.linalg.LinearOperatorScaledIdentity) or
            isinstance(x, tf.linalg.LinearOperatorDiag))

  with tf.name_scope(name or 'kl_mvn'):
    # Calculation is based on:
    # http://stats.stackexchange.com/questions/60680/kl-divergence-between-two-multivariate-gaussians
    # and,
    # https://en.wikipedia.org/wiki/Matrix_norm#Frobenius_norm
    # i.e.,
    #   If Ca = AA', Cb = BB', then
    #   tr[inv(Cb) Ca] = tr[inv(B)' inv(B) A A']
    #                  = tr[inv(B) A A' inv(B)']
    #                  = tr[(inv(B) A) (inv(B) A)']
    #                  = sum_{ij} (inv(B) A)_{ij}**2
    #                  = ||inv(B) A||_F**2
    # where ||.||_F is the Frobenius norm and the second equality follows from
    # the cyclic permutation property.
    if is_diagonal(a.scale) and is_diagonal(b.scale):
      # Using `stddev` because it handles expansion of Identity cases.
      b_inv_a = (a.stddev() / b.stddev())[..., tf.newaxis]
    else:
      b_inv_a = b.scale.solve(a.scale.to_dense())
    kl_div = (
        b.scale.log_abs_determinant() - a.scale.log_abs_determinant() +
        0.5 * (-tf.cast(a.scale.domain_dimension_tensor(), a.dtype) +
               squared_frobenius_norm(b_inv_a) + squared_frobenius_norm(
                   b.scale.solve((b.mean() - a.mean())[..., tf.newaxis]))))
    tensorshape_util.set_shape(
        kl_div, tf.broadcast_static_shape(a.batch_shape, b.batch_shape))
    return kl_div
