# Copyright 2015 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.
# ==============================================================================
"""Built-in loss functions.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import abc

import six

from tensorflow.python.distribute import distribution_strategy_context
from tensorflow.python.framework import ops
from tensorflow.python.framework import smart_cond
from tensorflow.python.framework import tensor_util
from tensorflow.python.keras import backend as K
from tensorflow.python.keras.utils import losses_utils
from tensorflow.python.keras.utils import tf_utils
from tensorflow.python.keras.utils.generic_utils import deserialize_keras_object
from tensorflow.python.keras.utils.generic_utils import serialize_keras_object
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import nn
from tensorflow.python.ops.losses import losses_impl
from tensorflow.python.ops.losses import util as tf_losses_util
from tensorflow.python.util.tf_export import keras_export
from tensorflow.tools.docs import doc_controls


@keras_export('keras.losses.Loss')
class Loss(object):
  """Loss base class.

  To be implemented by subclasses:
  * `call()`: Contains the logic for loss calculation using `y_true`, `y_pred`.

  Example subclass implementation:
  ```
  class MeanSquaredError(Loss):
    def call(self, y_true, y_pred):
      y_pred = ops.convert_to_tensor(y_pred)
      y_true = math_ops.cast(y_true, y_pred.dtype)
      return K.mean(math_ops.square(y_pred - y_true), axis=-1)
  ```

  When used with `tf.distribute.Strategy`, outside of built-in training loops
  such as `tf.keras` `compile` and `fit`, please use 'SUM' or 'NONE' reduction
  types, and reduce losses explicitly in your training loop. Using 'AUTO' or
  'SUM_OVER_BATCH_SIZE' will raise an error.

  Please see
  https://www.tensorflow.org/tutorials/distribute/custom_training for more
  details on this.

  You can implement 'SUM_OVER_BATCH_SIZE' using global batch size like:
  ```
  with strategy.scope():
    loss_obj = tf.keras.losses.CategoricalCrossentropy(
        reduction=tf.keras.losses.Reduction.NONE)
    ....
    loss = (tf.reduce_sum(loss_obj(labels, predictions)) *
            (1. / global_batch_size))
  ```

  Args:
    reduction: (Optional) Type of `tf.keras.losses.Reduction` to apply to loss.
      Default value is `AUTO`. `AUTO` indicates that the reduction option will
      be determined by the usage context. For almost all cases this defaults to
      `SUM_OVER_BATCH_SIZE`.
      When used with `tf.distribute.Strategy`, outside of built-in training
      loops such as `tf.keras` `compile` and `fit`, using `AUTO` or
      `SUM_OVER_BATCH_SIZE` will raise an error. Please see
      https://www.tensorflow.org/tutorials/distribute/custom_training
      for more details on this.
    name: Optional name for the op.
  """

  def __init__(self, reduction=losses_utils.ReductionV2.AUTO, name=None):
    losses_utils.ReductionV2.validate(reduction)
    self.reduction = reduction
    self.name = name

  def __call__(self, y_true, y_pred, sample_weight=None):
    """Invokes the `Loss` instance.

    Args:
      y_true: Ground truth values. shape = `[batch_size, d0, .. dN]`
      y_pred: The predicted values. shape = `[batch_size, d0, .. dN]`
      sample_weight: Optional `sample_weight` acts as a
        coefficient for the loss. If a scalar is provided, then the loss is
        simply scaled by the given value. If `sample_weight` is a tensor of size
        `[batch_size]`, then the total loss for each sample of the batch is
        rescaled by the corresponding element in the `sample_weight` vector. If
        the shape of `sample_weight` is `[batch_size, d0, .. dN-1]` (or can be
        broadcasted to this shape), then each loss element of `y_pred` is scaled
        by the corresponding value of `sample_weight`. (Note on`dN-1`: all loss
        functions reduce by 1 dimension, usually axis=-1.)

    Returns:
      Weighted loss float `Tensor`. If `reduction` is `NONE`, this has
        shape `[batch_size, d0, .. dN-1]`; otherwise, it is scalar. (Note `dN-1`
        because all loss functions reduce by 1 dimension, usually axis=-1.)

    Raises:
      ValueError: If the shape of `sample_weight` is invalid.
    """
    # If we are wrapping a lambda function strip '<>' from the name as it is not
    # accepted in scope name.
    scope_name = 'lambda' if self.name == '<lambda>' else self.name
    graph_ctx = tf_utils.graph_context_for_symbolic_tensors(
        y_true, y_pred, sample_weight)
    with K.name_scope(scope_name or self.__class__.__name__), graph_ctx:
      losses = self.call(y_true, y_pred)
      return losses_utils.compute_weighted_loss(
          losses, sample_weight, reduction=self._get_reduction())

  @classmethod
  def from_config(cls, config):
    """Instantiates a `Loss` from its config (output of `get_config()`).

    Args:
        config: Output of `get_config()`.

    Returns:
        A `Loss` instance.
    """
    return cls(**config)

  def get_config(self):
    return {'reduction': self.reduction, 'name': self.name}

  @abc.abstractmethod
  @doc_controls.for_subclass_implementers
  def call(self, y_true, y_pred):
    """Invokes the `Loss` instance.

    Args:
      y_true: Ground truth values, with the same shape as 'y_pred'.
      y_pred: The predicted values.
    """
    NotImplementedError('Must be implemented in subclasses.')

  def _get_reduction(self):
    """Handles `AUTO` reduction cases and returns the reduction value."""
    if distribution_strategy_context.has_strategy() and (
        self.reduction == losses_utils.ReductionV2.AUTO or
        self.reduction == losses_utils.ReductionV2.SUM_OVER_BATCH_SIZE):
      raise ValueError(
          'Please use `tf.keras.losses.Reduction.SUM` or '
          '`tf.keras.losses.Reduction.NONE` for loss reduction when losses are '
          'used with `tf.distribute.Strategy` outside of the built-in training '
          'loops. You can implement '
          '`tf.keras.losses.Reduction.SUM_OVER_BATCH_SIZE` using global batch '
          'size like:\n```\nwith strategy.scope():\n'
          '    loss_obj = tf.keras.losses.CategoricalCrossentropy('
          'reduction=tf.keras.losses.Reduction.NONE)\n....\n'
          '    loss = tf.reduce_sum(loss_obj(labels, predictions)) * '
          '(1. / global_batch_size)\n```\nPlease see '
          'https://www.tensorflow.org/tutorials/distribute/custom_training'
          ' for more details.')

    if self.reduction == losses_utils.ReductionV2.AUTO:
      return losses_utils.ReductionV2.SUM_OVER_BATCH_SIZE
    return self.reduction


class LossFunctionWrapper(Loss):
  """Wraps a loss function in the `Loss` class.

  Args:
    fn: The loss function to wrap, with signature `fn(y_true, y_pred,
      **kwargs)`.
    reduction: (Optional) Type of `tf.keras.losses.Reduction` to apply to loss.
      Default value is `AUTO`. `AUTO` indicates that the reduction option will
      be determined by the usage context. For almost all cases this defaults to
      `SUM_OVER_BATCH_SIZE`.
      When used with `tf.distribute.Strategy`, outside of built-in training
      loops such as `tf.keras` `compile` and `fit`, using `AUTO` or
      `SUM_OVER_BATCH_SIZE` will raise an error. Please see
      https://www.tensorflow.org/tutorials/distribute/custom_training
      for more details on this.
    name: (Optional) name for the loss.
    **kwargs: The keyword arguments that are passed on to `fn`.
  """

  def __init__(self,
               fn,
               reduction=losses_utils.ReductionV2.AUTO,
               name=None,
               **kwargs):
    super(LossFunctionWrapper, self).__init__(reduction=reduction, name=name)
    self.fn = fn
    self._fn_kwargs = kwargs

  def call(self, y_true, y_pred):
    """Invokes the `LossFunctionWrapper` instance.

    Args:
      y_true: Ground truth values.
      y_pred: The predicted values.

    Returns:
      Loss values per sample.
    """
    if tensor_util.is_tensor(y_pred) and tensor_util.is_tensor(y_true):
      y_pred, y_true = tf_losses_util.squeeze_or_expand_dimensions(
          y_pred, y_true)
    return self.fn(y_true, y_pred, **self._fn_kwargs)

  def get_config(self):
    config = {}
    for k, v in six.iteritems(self._fn_kwargs):
      config[k] = K.eval(v) if tf_utils.is_tensor_or_variable(v) else v
    base_config = super(LossFunctionWrapper, self).get_config()
    return dict(list(base_config.items()) + list(config.items()))


@keras_export('keras.losses.MeanSquaredError')
class MeanSquaredError(LossFunctionWrapper):
  """Computes the mean of squares of errors between labels and predictions.

  `loss = square(y_true - y_pred)`

  Usage:

  ```python
  mse = tf.keras.losses.MeanSquaredError()
  loss = mse([0., 0., 1., 1.], [1., 1., 1., 0.])
  print('Loss: ', loss.numpy())  # Loss: 0.75
  ```

  Usage with the `compile` API:

  ```python
  model = tf.keras.Model(inputs, outputs)
  model.compile('sgd', loss=tf.keras.losses.MeanSquaredError())
  ```
  """

  def __init__(self,
               reduction=losses_utils.ReductionV2.AUTO,
               name='mean_squared_error'):
    super(MeanSquaredError, self).__init__(
        mean_squared_error, name=name, reduction=reduction)


@keras_export('keras.losses.MeanAbsoluteError')
class MeanAbsoluteError(LossFunctionWrapper):
  """Computes the mean of absolute difference between labels and predictions.

  `loss = abs(y_true - y_pred)`

  Usage:

  ```python
  mae = tf.keras.losses.MeanAbsoluteError()
  loss = mae([0., 0., 1., 1.], [1., 1., 1., 0.])
  print('Loss: ', loss.numpy())  # Loss: 0.75
  ```

  Usage with the `compile` API:

  ```python
  model = tf.keras.Model(inputs, outputs)
  model.compile('sgd', loss=tf.keras.losses.MeanAbsoluteError())
  ```
  """

  def __init__(self,
               reduction=losses_utils.ReductionV2.AUTO,
               name='mean_absolute_error'):
    super(MeanAbsoluteError, self).__init__(
        mean_absolute_error, name=name, reduction=reduction)


@keras_export('keras.losses.MeanAbsolutePercentageError')
class MeanAbsolutePercentageError(LossFunctionWrapper):
  """Computes the mean absolute percentage error between `y_true` and `y_pred`.

  `loss = 100 * abs(y_true - y_pred) / y_true`

  Usage:

  ```python
  mape = tf.keras.losses.MeanAbsolutePercentageError()
  loss = mape([0., 0., 1., 1.], [1., 1., 1., 0.])
  print('Loss: ', loss.numpy())  # Loss: 5e+08
  ```

  Usage with the `compile` API:

  ```python
  model = tf.keras.Model(inputs, outputs)
  model.compile('sgd', loss=tf.keras.losses.MeanAbsolutePercentageError())
  ```
  """

  def __init__(self,
               reduction=losses_utils.ReductionV2.AUTO,
               name='mean_absolute_percentage_error'):
    super(MeanAbsolutePercentageError, self).__init__(
        mean_absolute_percentage_error, name=name, reduction=reduction)


@keras_export('keras.losses.MeanSquaredLogarithmicError')
class MeanSquaredLogarithmicError(LossFunctionWrapper):
  """Computes the mean squared logarithmic error between `y_true` and `y_pred`.

  `loss = square(log(y_true) - log(y_pred))`

  Usage:

  ```python
  msle = tf.keras.losses.MeanSquaredLogarithmicError()
  loss = msle([0., 0., 1., 1.], [1., 1., 1., 0.])
  print('Loss: ', loss.numpy())  # Loss: 0.36034
  ```

  Usage with the `compile` API:

  ```python
  model = tf.keras.Model(inputs, outputs)
  model.compile('sgd', loss=tf.keras.losses.MeanSquaredLogarithmicError())
  ```
  """

  def __init__(self,
               reduction=losses_utils.ReductionV2.AUTO,
               name='mean_squared_logarithmic_error'):
    super(MeanSquaredLogarithmicError, self).__init__(
        mean_squared_logarithmic_error, name=name, reduction=reduction)


@keras_export('keras.losses.BinaryCrossentropy')
class BinaryCrossentropy(LossFunctionWrapper):
  """Computes the cross-entropy loss between true labels and predicted labels.

  Use this cross-entropy loss when there are only two label classes (assumed to
  be 0 and 1). For each example, there should be a single floating-point value
  per prediction.

  In the snippet below, each of the four examples has only a single
  floating-pointing value, and both `y_pred` and `y_true` have the shape
  `[batch_size]`.

  Usage:

  ```python
  bce = tf.keras.losses.BinaryCrossentropy()
  loss = bce([0., 0., 1., 1.], [1., 1., 1., 0.])
  print('Loss: ', loss.numpy())  # Loss: 11.522857
  ```

  Usage with the `tf.keras` API:

  ```python
  model = tf.keras.Model(inputs, outputs)
  model.compile('sgd', loss=tf.keras.losses.BinaryCrossentropy())
  ```

  Args:
    from_logits: Whether to interpret `y_pred` as a tensor of
      [logit](https://en.wikipedia.org/wiki/Logit) values. By default, we assume
        that `y_pred` contains probabilities (i.e., values in [0, 1]).
      Note: Using from_logits=True may be more numerically stable.
    label_smoothing: Float in [0, 1]. When 0, no smoothing occurs. When > 0, we
      compute the loss between the predicted labels and a smoothed version of
      the true labels, where the smoothing squeezes the labels towards 0.5.
      Larger values of `label_smoothing` correspond to heavier smoothing.
    reduction: (Optional) Type of `tf.keras.losses.Reduction` to apply to loss.
      Default value is `AUTO`. `AUTO` indicates that the reduction option will
      be determined by the usage context. For almost all cases this defaults to
      `SUM_OVER_BATCH_SIZE`.
      When used with `tf.distribute.Strategy`, outside of built-in training
      loops such as `tf.keras` `compile` and `fit`, using `AUTO` or
      `SUM_OVER_BATCH_SIZE` will raise an error. Please see
      https://www.tensorflow.org/tutorials/distribute/custom_training
      for more details on this.
    name: (Optional) Name for the op.
  """

  def __init__(self,
               from_logits=False,
               label_smoothing=0,
               reduction=losses_utils.ReductionV2.AUTO,
               name='binary_crossentropy'):
    super(BinaryCrossentropy, self).__init__(
        binary_crossentropy,
        name=name,
        reduction=reduction,
        from_logits=from_logits,
        label_smoothing=label_smoothing)
    self.from_logits = from_logits


@keras_export('keras.losses.CategoricalCrossentropy')
class CategoricalCrossentropy(LossFunctionWrapper):
  """Computes the crossentropy loss between the labels and predictions.

  Use this crossentropy loss function when there are two or more label classes.
  We expect labels to be provided in a `one_hot` representation. If you want to
  provide labels as integers, please use `SparseCategoricalCrossentropy` loss.
  There should be `# classes` floating point values per feature.

  In the snippet below, there is `# classes` floating pointing values per
  example. The shape of both `y_pred` and `y_true` are
  `[batch_size, num_classes]`.

  Usage:

  ```python
  cce = tf.keras.losses.CategoricalCrossentropy()
  loss = cce(
    [[1., 0., 0.], [0., 1., 0.], [0., 0., 1.]],
    [[.9, .05, .05], [.05, .89, .06], [.05, .01, .94]])
  print('Loss: ', loss.numpy())  # Loss: 0.0945
  ```

  Usage with the `compile` API:

  ```python
  model = tf.keras.Model(inputs, outputs)
  model.compile('sgd', loss=tf.keras.losses.CategoricalCrossentropy())
  ```

  Args:
    from_logits: Whether `y_pred` is expected to be a logits tensor. By default,
      we assume that `y_pred` encodes a probability distribution.
      Note: Using from_logits=True may be more numerically stable.
    label_smoothing: Float in [0, 1]. When > 0, label values are smoothed,
      meaning the confidence on label values are relaxed. e.g.
      `label_smoothing=0.2` means that we will use a value of `0.1` for label
      `0` and `0.9` for label `1`"
    reduction: (Optional) Type of `tf.keras.losses.Reduction` to apply to loss.
      Default value is `AUTO`. `AUTO` indicates that the reduction option will
      be determined by the usage context. For almost all cases this defaults to
      `SUM_OVER_BATCH_SIZE`.
      When used with `tf.distribute.Strategy`, outside of built-in training
      loops such as `tf.keras` `compile` and `fit`, using `AUTO` or
      `SUM_OVER_BATCH_SIZE` will raise an error. Please see
      https://www.tensorflow.org/tutorials/distribute/custom_training
      for more details on this.
    name: Optional name for the op.
  """

  def __init__(self,
               from_logits=False,
               label_smoothing=0,
               reduction=losses_utils.ReductionV2.AUTO,
               name='categorical_crossentropy'):
    super(CategoricalCrossentropy, self).__init__(
        categorical_crossentropy,
        name=name,
        reduction=reduction,
        from_logits=from_logits,
        label_smoothing=label_smoothing)


@keras_export('keras.losses.SparseCategoricalCrossentropy')
class SparseCategoricalCrossentropy(LossFunctionWrapper):
  """Computes the crossentropy loss between the labels and predictions.

  Use this crossentropy loss function when there are two or more label classes.
  We expect labels to be provided as integers. If you want to provide labels
  using `one-hot` representation, please use `CategoricalCrossentropy` loss.
  There should be `# classes` floating point values per feature for `y_pred`
  and a single floating point value per feature for `y_true`.

  In the snippet below, there is a single floating point value per example for
  `y_true` and `# classes` floating pointing values per example for `y_pred`.
  The shape of `y_true` is `[batch_size]` and the shape of `y_pred` is
  `[batch_size, num_classes]`.

  Usage:

  ```python
  cce = tf.keras.losses.SparseCategoricalCrossentropy()
  loss = cce(
    tf.convert_to_tensor([0, 1, 2]),
    tf.convert_to_tensor([[.9, .05, .05], [.5, .89, .6], [.05, .01, .94]]))
  print('Loss: ', loss.numpy())  # Loss: 0.3239
  ```

  Usage with the `compile` API:

  ```python
  model = tf.keras.Model(inputs, outputs)
  model.compile('sgd', loss=tf.keras.losses.SparseCategoricalCrossentropy())
  ```

  Args:
    from_logits: Whether `y_pred` is expected to be a logits tensor. By default,
      we assume that `y_pred` encodes a probability distribution.
      Note: Using from_logits=True may be more numerically stable.
    reduction: (Optional) Type of `tf.keras.losses.Reduction` to apply to loss.
      Default value is `AUTO`. `AUTO` indicates that the reduction option will
      be determined by the usage context. For almost all cases this defaults to
      `SUM_OVER_BATCH_SIZE`.
      When used with `tf.distribute.Strategy`, outside of built-in training
      loops such as `tf.keras` `compile` and `fit`, using `AUTO` or
      `SUM_OVER_BATCH_SIZE` will raise an error. Please see
      https://www.tensorflow.org/tutorials/distribute/custom_training
      for more details on this.
    name: Optional name for the op.
  """