# Copyright 2020 The TensorFlow Probability Authors. All Rights Reserved.
# @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
# THIS FILE IS AUTO-GENERATED BY `gen_linear_operators.py`.
# DO NOT MODIFY DIRECTLY.
# @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
# pylint: disable=g-import-not-at-top
# pylint: disable=g-direct-tensorflow-import
# pylint: disable=g-bad-import-order
# pylint: disable=unused-import
# pylint: disable=line-too-long
# pylint: disable=reimported
# pylint: disable=g-bool-id-comparison
# pylint: disable=g-statement-before-imports
# pylint: disable=bad-continuation
# pylint: disable=useless-import-alias
# pylint: disable=property-with-parameters
# pylint: disable=trailing-whitespace

# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# 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
# limitations under the License.
# ==============================================================================
"""Registrations for LinearOperator.matmul."""

from __future__ import absolute_import
from __future__ import division
# [internal] enable type annotations
from __future__ import print_function

from tensorflow_probability.python.internal.backend.jax.gen import linear_operator
from tensorflow_probability.python.internal.backend.jax.gen import linear_operator_algebra
from tensorflow_probability.python.internal.backend.jax.gen import linear_operator_circulant
from tensorflow_probability.python.internal.backend.jax.gen import linear_operator_composition
from tensorflow_probability.python.internal.backend.jax.gen import linear_operator_diag
from tensorflow_probability.python.internal.backend.jax.gen import linear_operator_identity
from tensorflow_probability.python.internal.backend.jax.gen import linear_operator_lower_triangular
from tensorflow_probability.python.internal.backend.jax.gen import linear_operator_zeros
from tensorflow_probability.python.internal.backend.jax.gen import registrations_util


# By default, use a LinearOperatorComposition to delay the computation.
@linear_operator_algebra.RegisterMatmul(
    linear_operator.LinearOperator, linear_operator.LinearOperator)
def _matmul_linear_operator(linop_a, linop_b):
  """Generic matmul of two `LinearOperator`s."""
  is_square = registrations_util.is_square(linop_a, linop_b)
  is_non_singular = None
  is_self_adjoint = None
  is_positive_definite = None

  if is_square:
    is_non_singular = registrations_util.combined_non_singular_hint(
        linop_a, linop_b)
  elif is_square is False:  # pylint:disable=g-bool-id-comparison
    is_non_singular = False
    is_self_adjoint = False
    is_positive_definite = False

  return linear_operator_composition.LinearOperatorComposition(
      operators=[linop_a, linop_b],
      is_non_singular=is_non_singular,
      is_self_adjoint=is_self_adjoint,
      is_positive_definite=is_positive_definite,
      is_square=is_square,
  )

# Identity


@linear_operator_algebra.RegisterMatmul(
    linear_operator_identity.LinearOperatorIdentity,
    linear_operator.LinearOperator)
def _matmul_linear_operator_identity_left(identity, linop):
  del identity
  return linop


@linear_operator_algebra.RegisterMatmul(
    linear_operator.LinearOperator,
    linear_operator_identity.LinearOperatorIdentity)
def _matmul_linear_operator_identity_right(linop, identity):
  del identity
  return linop


@linear_operator_algebra.RegisterMatmul(
    linear_operator_identity.LinearOperatorScaledIdentity,
    linear_operator_identity.LinearOperatorScaledIdentity)
def _matmul_linear_operator_scaled_identity(linop_a, linop_b):
  """Matmul of two ScaledIdentity `LinearOperators`."""
  return linear_operator_identity.LinearOperatorScaledIdentity(
      num_rows=linop_a.domain_dimension_tensor(),
      multiplier=linop_a.multiplier * linop_b.multiplier,
      is_non_singular=registrations_util.combined_non_singular_hint(
          linop_a, linop_b),
      is_self_adjoint=registrations_util.combined_commuting_self_adjoint_hint(
          linop_a, linop_b),
      is_positive_definite=(
          registrations_util.combined_commuting_positive_definite_hint(
              linop_a, linop_b)),
      is_square=True)


# Zeros


@linear_operator_algebra.RegisterMatmul(
    linear_operator.LinearOperator,
    linear_operator_zeros.LinearOperatorZeros)
def _matmul_linear_operator_zeros_right(linop, zeros):
  if not zeros.is_square or not linop.is_square:
    raise ValueError("Matmul with non-square `LinearOperator`s or non-square "
                     "`LinearOperatorZeros` not supported at this time.")
  return zeros


@linear_operator_algebra.RegisterMatmul(
    linear_operator_zeros.LinearOperatorZeros,
    linear_operator.LinearOperator)
def _matmul_linear_operator_zeros_left(zeros, linop):
  if not zeros.is_square or not linop.is_square:
    raise ValueError("Matmul with non-square `LinearOperator`s or non-square "
                     "`LinearOperatorZeros` not supported at this time.")
  return zeros


# Diag.


@linear_operator_algebra.RegisterMatmul(
    linear_operator_diag.LinearOperatorDiag,
    linear_operator_diag.LinearOperatorDiag)
def _matmul_linear_operator_diag(linop_a, linop_b):
  return linear_operator_diag.LinearOperatorDiag(
      diag=linop_a.diag * linop_b.diag,
      is_non_singular=registrations_util.combined_non_singular_hint(
          linop_a, linop_b),
      is_self_adjoint=registrations_util.combined_commuting_self_adjoint_hint(
          linop_a, linop_b),
      is_positive_definite=(
          registrations_util.combined_commuting_positive_definite_hint(
              linop_a, linop_b)),
      is_square=True)


@linear_operator_algebra.RegisterMatmul(
    linear_operator_diag.LinearOperatorDiag,
    linear_operator_identity.LinearOperatorScaledIdentity)
def _matmul_linear_operator_diag_scaled_identity_right(
    linop_diag, linop_scaled_identity):
  return linear_operator_diag.LinearOperatorDiag(
      diag=linop_diag.diag * linop_scaled_identity.multiplier,
      is_non_singular=registrations_util.combined_non_singular_hint(
          linop_diag, linop_scaled_identity),
      is_self_adjoint=registrations_util.combined_commuting_self_adjoint_hint(
          linop_diag, linop_scaled_identity),
      is_positive_definite=(
          registrations_util.combined_commuting_positive_definite_hint(
              linop_diag, linop_scaled_identity)),
      is_square=True)


@linear_operator_algebra.RegisterMatmul(
    linear_operator_identity.LinearOperatorScaledIdentity,
    linear_operator_diag.LinearOperatorDiag)
def _matmul_linear_operator_diag_scaled_identity_left(
    linop_scaled_identity, linop_diag):
  return linear_operator_diag.LinearOperatorDiag(
      diag=linop_diag.diag * linop_scaled_identity.multiplier,
      is_non_singular=registrations_util.combined_non_singular_hint(
          linop_diag, linop_scaled_identity),
      is_self_adjoint=registrations_util.combined_commuting_self_adjoint_hint(
          linop_diag, linop_scaled_identity),
      is_positive_definite=(
          registrations_util.combined_commuting_positive_definite_hint(
              linop_diag, linop_scaled_identity)),
      is_square=True)


@linear_operator_algebra.RegisterMatmul(
    linear_operator_diag.LinearOperatorDiag,
    linear_operator_lower_triangular.LinearOperatorLowerTriangular)
def _matmul_linear_operator_diag_tril(linop_diag, linop_triangular):
  return linear_operator_lower_triangular.LinearOperatorLowerTriangular(
      tril=linop_diag.diag[..., None] * linop_triangular.to_dense(),
      is_non_singular=registrations_util.combined_non_singular_hint(
          linop_diag, linop_triangular),
      # This is safe to do since the Triangular matrix is only self-adjoint
      # when it is a diagonal matrix, and hence commutes.
      is_self_adjoint=registrations_util.combined_commuting_self_adjoint_hint(
          linop_diag, linop_triangular),
      is_positive_definite=None,
      is_square=True)


@linear_operator_algebra.RegisterMatmul(
    linear_operator_lower_triangular.LinearOperatorLowerTriangular,
    linear_operator_diag.LinearOperatorDiag)
def _matmul_linear_operator_tril_diag(linop_triangular, linop_diag):
  return linear_operator_lower_triangular.LinearOperatorLowerTriangular(
      tril=linop_triangular.to_dense() * linop_diag.diag,
      is_non_singular=registrations_util.combined_non_singular_hint(
          linop_diag, linop_triangular),
      # This is safe to do since the Triangular matrix is only self-adjoint
      # when it is a diagonal matrix, and hence commutes.
      is_self_adjoint=registrations_util.combined_commuting_self_adjoint_hint(
          linop_diag, linop_triangular),
      is_positive_definite=None,
      is_square=True)

# Circulant.


@linear_operator_algebra.RegisterMatmul(
    linear_operator_circulant.LinearOperatorCirculant,
    linear_operator_circulant.LinearOperatorCirculant)
def _matmul_linear_operator_circulant_circulant(linop_a, linop_b):
  return linear_operator_circulant.LinearOperatorCirculant(
      spectrum=linop_a.spectrum * linop_b.spectrum,
      is_non_singular=registrations_util.combined_non_singular_hint(
          linop_a, linop_b),
      is_self_adjoint=registrations_util.combined_commuting_self_adjoint_hint(
          linop_a, linop_b),
      is_positive_definite=(
          registrations_util.combined_commuting_positive_definite_hint(
              linop_a, linop_b)),
      is_square=True)

import numpy as np; onp = np
from tensorflow_probability.python.internal.backend.jax import linalg_impl as _linalg
from tensorflow_probability.python.internal.backend.jax import ops as _ops
from tensorflow_probability.python.internal.backend.jax.gen import tensor_shape

from tensorflow_probability.python.internal.backend.jax import private
distribution_util = private.LazyLoader(
    "distribution_util", globals(),
    "tensorflow_probability.substrates.numpy.internal.distribution_util")
tensorshape_util = private.LazyLoader(
    "tensorshape_util", globals(),
    "tensorflow_probability.substrates.numpy.internal.tensorshape_util")


