# 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.
# ==============================================================================

"""Adagrad for TensorFlow."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

from tensorflow.python.framework import ops
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import gen_array_ops
from tensorflow.python.ops import init_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.training import optimizer
from tensorflow.python.training import training_ops
from tensorflow.python.util.tf_export import tf_export


@tf_export("train.AdagradOptimizer")
class AdagradOptimizer(optimizer.Optimizer):
  """Optimizer that implements the Adagrad algorithm.

  See this [paper](http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf)
  or this
  [intro](http://cs.stanford.edu/~ppasupat/a9online/uploads/proximal_notes.pdf).
  """

  def __init__(self, learning_rate, initial_accumulator_value=0.1,
               use_locking=False, name="Adagrad"):
    """Construct a new Adagrad optimizer.

    Args:
      learning_rate: A `Tensor` or a floating point value.  The learning rate.
      initial_accumulator_value: A floating point value.
        Starting value for the accumulators, must be positive.
      use_locking: If `True` use locks for update operations.
      name: Optional name prefix for the operations created when applying
        gradients.  Defaults to "Adagrad".

    Raises:
      ValueError: If the `initial_accumulator_value` is invalid.

    @compatibility(eager)
    When eager execution is enabled, `learning_rate` can be a callable that
    takes no arguments and returns the actual value to use. This can be useful
    for changing these values across different invocations of optimizer
    functions.
    @end_compatibility
    """
    if initial_accumulator_value <= 0.0:
      raise ValueError("initial_accumulator_value must be positive: %s" %
                       initial_accumulator_value)
    super(AdagradOptimizer, self).__init__(use_locking, name)
    self._learning_rate = learning_rate
    self._initial_accumulator_value = initial_accumulator_value
    # Created in Initialize.
    self._learning_rate_tensor = None

  def _create_slots(self, var_list):
    for v in var_list:
      with ops.colocate_with(v):
        dtype = v.dtype.base_dtype
        if v.get_shape().is_fully_defined():
          init = init_ops.constant_initializer(self._initial_accumulator_value,
                                               dtype=dtype)
        else:
          # Use a Tensor instead of initializer if variable does not have static
          # shape.
          init_constant = gen_array_ops.fill(array_ops.shape(v),
                                             self._initial_accumulator_value)
          init = math_ops.cast(init_constant, dtype)
      self._get_or_make_slot_with_initializer(v, init, v.get_shape(), dtype,
                                              "accumulator", self._name)

  def _prepare(self):
    learning_rate = self._call_if_callable(self._learning_rate)
    self._learning_rate_tensor = ops.convert_to_tensor(
        learning_rate, name="learning_rate")

  def _apply_dense(self, grad, var):
    acc = self.get_slot(var, "accumulator")
    return training_ops.apply_adagrad(
        var,
        acc,
        math_ops.cast(self._learning_rate_tensor, var.dtype.base_dtype),
        grad,
        use_locking=self._use_locking)

  def _resource_apply_dense(self, grad, var):
    acc = self.get_slot(var, "accumulator")
    return training_ops.resource_apply_adagrad(
        var.handle,
        acc.handle,
        math_ops.cast(self._learning_rate_tensor, grad.dtype.base_dtype),
        grad,
        use_locking=self._use_locking)

  def _apply_sparse(self, grad, var):
    acc = self.get_slot(var, "accumulator")
    return training_ops.sparse_apply_adagrad(
        var,
        acc,
        math_ops.cast(self._learning_rate_tensor, var.dtype.base_dtype),
        grad.values,
        grad.indices,
        use_locking=self._use_locking)

  def _resource_apply_sparse(self, grad, var, indices):
    acc = self.get_slot(var, "accumulator")
    return training_ops.resource_sparse_apply_adagrad(
        var.handle,
        acc.handle,
        math_ops.cast(self._learning_rate_tensor, grad.dtype),
        grad,
        indices,
        use_locking=self._use_locking)