# 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
# limitations under the License.
# ============================================================================
"""A `Bijector` that computes `b(x) = 1. / x`."""

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

from tensorflow_probability.python.internal.backend.jax.compat import v2 as tf

from tensorflow_probability.substrates.jax.bijectors import bijector
from tensorflow_probability.substrates.jax.internal import assert_util
from tensorflow_probability.substrates.jax.internal import dtype_util

__all__ = ["Reciprocal"]


class Reciprocal(bijector.Bijector):
  """A `Bijector` that computes the reciprocal `b(x) = 1. / x` entrywise.

  This bijector accepts any non-zero values for both `forward` and `inverse`.

  #### Examples

  ```python
  bijector.Reciprocal().forward(x=[[1., 2.], [4., 5.]])
  # Result: [[1., .5], [.25, .2]], i.e., 1 / x

  bijector.Reciprocal().forward(x=[[0., 2.], [4., 5.]])
  # Result: AssertionError, doesn't accept zero.

  bijector.Square().inverse(y=[[1., 2.], [4., 5.]])
  # Result: [[1., .5], [.25, .2]], i.e. 1 / x

  ```
  """

  def __init__(self, validate_args=False, name="reciprocal"):
    """Instantiates the `Reciprocal`.

    Args:
      validate_args: Python `bool` indicating whether arguments should be
        checked for correctness.
      name: Python `str` name given to ops managed by this object.
    """
    parameters = dict(locals())
    with tf.name_scope(name) as name:
      super(Reciprocal, self).__init__(
          forward_min_event_ndims=0,
          validate_args=validate_args,
          parameters=parameters,
          name=name)

  @classmethod
  def _is_increasing(cls):
    return False

  def _forward(self, x):
    with tf.control_dependencies(self._assertions(x)):
      return 1. / x

  _inverse = _forward

  def _forward_log_det_jacobian(self, x):
    with tf.control_dependencies(self._assertions(x)):
      return -2. * tf.math.log(tf.math.abs(x))

  _inverse_log_det_jacobian = _forward_log_det_jacobian

  def _assertions(self, t):
    if not self.validate_args:
      return []
    return [assert_util.assert_none_equal(
        t, dtype_util.as_numpy_dtype(t.dtype)(0.),
        message="All elements must be non-zero.")]

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# This file is auto-generated by substrates/meta/rewrite.py
# It will be surfaced by the build system as a symlink at:
#   `tensorflow_probability/substrates/jax/bijectors/reciprocal.py`
# For more info, see substrate_runfiles_symlinks in build_defs.bzl
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