# 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.
# ==============================================================================
"""Optimizer ops for use in layers and tf.learn."""

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

import six

from tensorflow.contrib import framework as contrib_framework
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import clip_ops
from tensorflow.python.ops import control_flow_ops
from tensorflow.python.ops import init_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import random_ops
from tensorflow.python.ops import variable_scope as vs
from tensorflow.python.ops import variables as vars_
from tensorflow.python.summary import summary
from tensorflow.python.training import moving_averages
from tensorflow.python.training import optimizer as optimizer_
from tensorflow.python.training import training as train

OPTIMIZER_CLS_NAMES = {
    "Adagrad": train.AdagradOptimizer,
    "Adam": train.AdamOptimizer,
    "Ftrl": train.FtrlOptimizer,
    "Momentum": train.MomentumOptimizer,
    "RMSProp": train.RMSPropOptimizer,
    "SGD": train.GradientDescentOptimizer,
}

OPTIMIZER_SUMMARIES = [
    "learning_rate",
    "loss",
    "gradients",
    "gradient_norm",
]


def optimize_loss(loss,
                  global_step,
                  learning_rate,
                  optimizer,
                  gradient_noise_scale=None,
                  gradient_multipliers=None,
                  clip_gradients=None,
                  learning_rate_decay_fn=None,
                  update_ops=None,
                  variables=None,
                  name=None,
                  summaries=None,
                  colocate_gradients_with_ops=False,
                  increment_global_step=True):
  """Given loss and parameters for optimizer, returns a training op.

  Various ways of passing optimizers, include:

  - string, name of the optimizer like 'SGD', 'Adam', see OPTIMIZER_CLS_NAMES
      for full list. E.g. `optimize_loss(..., optimizer='Adam')`.
  - function, takes learning rate `Tensor` as argument and must return
      `Optimizer` instance. E.g. `optimize_loss(...,
      optimizer=lambda lr: tf.train.MomentumOptimizer(lr, momentum=0.5))`.
    Alternatively, if `learning_rate` is `None`, the function takes no
    arguments. E.g. `optimize_loss(..., learning_rate=None,
      optimizer=lambda: tf.train.MomentumOptimizer(0.5, momentum=0.5))`.
  - class, subclass of `Optimizer` that takes only one required argument -
      learning rate, such as AdamOptimizer, AdagradOptimizer.
      E.g. `optimize_loss(..., optimizer=tf.train.AdagradOptimizer)`.
  - object, instance of subclass of `Optimizer`.
      E.g., `optimizer_loss(..., optimizer=tf.train.AdagradOptimizer(0.5))`.

  Args:
    loss: Scalar `Tensor`.
    global_step: Scalar int `Tensor`, step counter to update on each step
                 unless `increment_global_step` is `False`. If not supplied,
                 it will be fetched from the default graph (see
                 `tf.train.get_global_step` for details). If it's
                 not been created, no step will be incremented with each weight
                 update. `learning_rate_decay_fn` requires `global_step`.
    learning_rate: float or `Tensor`, magnitude of update per each training
                   step. Can be `None`.
    optimizer: string, class or optimizer instance, used as trainer.
               string should be name of optimizer, like 'SGD',
                 'Adam', 'Adagrad'. Full list in OPTIMIZER_CLS_NAMES constant.
               class should be sub-class of `tf.Optimizer` that implements
                 `compute_gradients` and `apply_gradients` functions.
               optimizer instance should be instantiation of `tf.Optimizer`
                 sub-class and have `compute_gradients` and `apply_gradients`
                 functions.
    gradient_noise_scale: float or None, adds 0-mean normal noise scaled by this
                          value.
    gradient_multipliers: dict of variables or variable names to floats.
                          If present, gradients for specified
                          variables will be multiplied by given constant.
    clip_gradients: float, callable or `None`. If float, is provided, a global
      clipping is applied to prevent the norm of the gradient to exceed this
      value. Alternatively, a callable can be provided e.g.: adaptive_clipping.
      This callable takes a `list` of `(gradients, variables)` `tuple`s and
      returns the same thing with the gradients modified.
    learning_rate_decay_fn: function, takes `learning_rate` and `global_step`
                            `Tensor`s, returns `Tensor`.
                            Can be used to implement any learning rate decay
                            functions.
                            For example: `tf.train.exponential_decay`.
                            Ignored if `learning_rate` is not supplied.
    update_ops: list of update `Operation`s to execute at each step. If `None`,
                uses elements of UPDATE_OPS collection. The order of execution
                between `update_ops` and `loss` is non-deterministic.
    variables: list of variables to optimize or
               `None` to use all trainable variables.
    name: The name for this operation is used to scope operations and summaries.
    summaries: List of internal quantities to visualize on tensorboard. If not
               set only the loss and the learning rate will be reported. The
               complete list is in OPTIMIZER_SUMMARIES.
    colocate_gradients_with_ops: If True, try colocating gradients with the
                                 corresponding op.
    increment_global_step: Whether to increment `global_step`. If your model
      calls `optimize_loss` multiple times per training step (e.g. to optimize
      different parts of the model), use this arg to avoid incrementing
      `global_step` more times than necessary.

  Returns:
    Training op.

  Raises:
    ValueError: if:
        * `loss` is an invalid type or shape.
        * `global_step` is an invalid type or shape.
        * `learning_rate` is an invalid type or value.
        * `optimizer` is wrong type.
        * `clip_gradients` is not float or callable.
        * `learning_rate` and `learning_rate_decay_fn` are supplied, but no
          `global_step` is available.
        * `gradients` is empty
  """
  loss = ops.convert_to_tensor(loss)
  contrib_framework.assert_scalar(loss)
  if global_step is None:
    global_step = contrib_framework.get_global_step()
  else:
    contrib_framework.assert_global_step(global_step)
  with vs.variable_scope(name, "OptimizeLoss", [loss, global_step]):
    # Update ops take UPDATE_OPS collection if not provided.
    if update_ops is None:
      update_ops = set(ops.get_collection(ops.GraphKeys.UPDATE_OPS))
    # Make sure update ops are ran before computing loss.
    if update_ops:
      loss = control_flow_ops.with_dependencies(list(update_ops), loss)

    # Learning rate variable, with possible decay.
    lr = None
    if learning_rate is not None:
      if (isinstance(learning_rate, ops.Tensor) and
          learning_rate.get_shape().ndims == 0):
        lr = learning_rate
      elif isinstance(learning_rate, float):
        if learning_rate < 0.0:
          raise ValueError("Invalid learning_rate %s.", learning_rate)
        lr = vs.get_variable(
            "learning_rate", [],
            trainable=False,
            initializer=init_ops.constant_initializer(learning_rate))
      else:
        raise ValueError("Learning rate should be 0d Tensor or float. "
                         "Got %s of type %s" % (str(learning_rate),
                                                str(type(learning_rate))))
    if summaries is None:
      summaries = ["loss", "learning_rate"]
    else:
      for summ in summaries:
        if summ not in OPTIMIZER_SUMMARIES:
          raise ValueError("Summaries should be one of [%s], you provided %s." %
                           (", ".join(OPTIMIZER_SUMMARIES), summ))
    if learning_rate is not None and learning_rate_decay_fn is not None:
      if global_step is None:
        raise ValueError("global_step is required for learning_rate_decay_fn.")
      lr = learning_rate_decay_fn(lr, global_step)
      if "learning_rate" in summaries:
        summary.scalar("learning_rate", lr)

    # Create optimizer, given specified parameters.
    if isinstance(optimizer, six.string_types):
      if lr is None:
        raise ValueError("Learning rate is None, but should be specified if "
                         "optimizer is string (%s)." % optimizer)
      if optimizer not in OPTIMIZER_CLS_NAMES:
        raise ValueError(
            "Optimizer name should be one of [%s], you provided %s." %
            (", ".join(OPTIMIZER_CLS_NAMES), optimizer))
      opt = OPTIMIZER_CLS_NAMES[optimizer](learning_rate=lr)
    elif (isinstance(optimizer, type) and
          issubclass(optimizer, optimizer_.Optimizer)):
      if lr is None:
        raise ValueError("Learning rate is None, but should be specified if "
                         "optimizer is class (%s)." % optimizer)
      opt = optimizer(learning_rate=lr)
    elif isinstance(optimizer, optimizer_.Optimizer):
      opt = optimizer
    elif callable(optimizer):
      if learning_rate is not None:
        opt = optimizer(lr)
      else:
        opt = optimizer()
      if not isinstance(opt, optimizer_.Optimizer):
        raise ValueError("Unrecognized optimizer: function should return "
                         "subclass of Optimizer. Got %s." % str(opt))
    else:
      raise ValueError("Unrecognized optimizer: should be string, "
                       "subclass of Optimizer, instance of "
                       "subclass of Optimizer or function with one argument. "
                       "Got %s." % str(optimizer))

    # All trainable variables, if specific variables are not specified.
    if variables is None:
      variables = vars_.trainable_variables()

    # Compute gradients.
    gradients = opt.compute_gradients(
        loss,
        variables,
        colocate_gradients_with_ops=colocate_gradients_with_ops)

    # Optionally add gradient noise.
    if gradient_noise_scale is not None:
      gradients = _add_scaled_noise_to_gradients(gradients,
                                                 gradient_noise_scale)

    # Multiply some gradients.
    if gradient_multipliers is not None:
      gradients = _multiply_gradients(gradients, gradient_multipliers)
      if not gradients:
        raise ValueError(
            "Empty list of (gradient, var) pairs encountered. This is most "
            "likely to be caused by an improper value of gradient_multipliers.")

    if "gradient_norm" in summaries:
      summary.scalar("global_norm/gradient_norm",
                     clip_ops.global_norm(list(zip(*gradients))[0]))

    # Optionally clip gradients by global norm.
    if isinstance(clip_gradients, float):
      gradients = _clip_gradients_by_norm(gradients, clip_gradients)
    elif callable(clip_gradients):
      gradients = clip_gradients(gradients)
    elif clip_gradients is not None:
      raise ValueError(
          "Unknown type %s for clip_gradients" % type(clip_gradients))

    # Add scalar summary for loss.
    if "loss" in summaries:
      summary.scalar("loss", loss)

    # Add histograms for variables, gradients and gradient norms.
    for gradient, variable in gradients:
      if isinstance(gradient, ops.IndexedSlices):
        grad_values = gradient.values
      else:
        grad_values = gradient

      if grad_values is not None:
        var_name = variable.name.replace(":", "_")
        if "gradients" in summaries:
          summary.histogram("gradients/%s" % var_name, grad_values)
        if "gradient_norm" in summaries:
          summary.scalar("gradient_norm/%s" % var_name,
                         clip_ops.global_norm([grad_values]))

    if clip_gradients is not None and "gradient_norm" in summaries:
      summary.scalar("global_norm/clipped_gradient_norm",
                     clip_ops.global_norm(list(zip(*gradients))[0]))

    # Create gradient updates.
    grad_updates = opt.apply_gradients(
        gradients,
        global_step=global_step if increment_global_step else None,
        name="train")

    # Ensure the train_tensor computes grad_updates.
    train_tensor = control_flow_ops.with_dependencies([grad_updates], loss)

    return train_tensor


def _clip_gradients_by_norm(grads_and_vars, clip_gradients):
  """Clips gradients by global norm."""
  gradients, variables = zip(*grads_and_vars)
  clipped_gradients, _ = clip_ops.clip_by_global_norm(gradients, clip_gradients)
  return list(zip(clipped_gradients, variables))


def _adaptive_max_norm(norm, std_factor, decay, global_step, epsilon, name):
  """Find max_norm given norm and previous average."""
  with vs.variable_scope(name, "AdaptiveMaxNorm", [norm]):
    log_norm = math_ops.log(norm + epsilon)

    def moving_average(name, value, decay):
      moving_average_variable = vs.get_variable(
          name,
          shape=value.get_shape(),
          dtype=value.dtype,
          initializer=init_ops.zeros_initializer(),
          trainable=False)
      return moving_averages.assign_moving_average(
          moving_average_variable, value, decay, zero_debias=False)

    # quicker adaptation at the beginning
    if global_step is not None:
      n = math_ops.to_float(global_step)
      decay = math_ops.minimum(decay, n / (n + 1.))

    # update averages
    mean = moving_average("mean", log_norm, decay)
    sq_mean = moving_average("sq_mean", math_ops.square(log_norm), decay)

    variance = sq_mean - math_ops.square(mean)
    std = math_ops.sqrt(math_ops.maximum(epsilon, variance))
    max_norms = math_ops.exp(mean + std_factor * std)
    return max_norms, mean


def adaptive_clipping_fn(std_factor=2.,
                         decay=0.95,
                         static_max_norm=None,
                         global_step=None,
                         report_summary=False,
                         epsilon=1e-8,
                         name=None):
  """Adapt the clipping value using statistics on the norms.

  Implement adaptive gradient as presented in section 3.2.1 of
  https://arxiv.org/abs/1412.1602.

  Keeps a moving average of the mean and std of the log(norm) of the gradient.
  if the norm exceeds `exp(mean + std_factor*std)`, all gradients are rescaled
  such that the global norm becomes `exp(mean)`.

  Args:
    std_factor: Python scaler (or tensor).
      `max_norm = exp(mean + std_factor*std)`
    decay: The smoothing factor of the moving averages.
    static_max_norm: If provided, will threshold the norm to this value as an
      extra safety.
    global_step: Optional global_step. If provided, `decay = decay*n/(n+1)`.
      This provides a quicker adaptation of the mean for the first steps.
    report_summary: If `True`, will add histogram summaries of the `max_norm`.
    epsilon: Small value chosen to avoid zero variance.
    name: The name for this operation is used to scope operations and summaries.

  Returns:
    A function for applying gradient clipping.
  """

  def gradient_clipping(grads_and_vars):
    """Internal function for adaptive clipping."""
    grads, variables = zip(*grads_and_vars)

    norm = clip_ops.global_norm(grads)

    max_norm, log_mean = _adaptive_max_norm(norm, std_factor, decay,
                                            global_step, epsilon, name)

    # reports the max gradient norm for debugging
    if report_summary:
      summary.scalar("global_norm/adaptive_max_gradient_norm", max_norm)

    # factor will be 1. if norm is smaller than max_norm
    factor = array_ops.where(norm < max_norm,
                             array_ops.ones_like(norm),
                             math_ops.exp(log_mean) / norm)

    if static_max_norm is not None:
      factor = math_ops.minimum(static_max_norm / norm, factor)

    # apply factor
    clipped_grads = []
    for grad in grads:
      if grad is None:
        clipped_grads.append(None)
      elif isinstance(grad, ops.IndexedSlices):
        clipped_grads.append(
            ops.IndexedSlices(grad.values * factor, grad.indices,
                              grad.dense_shape))
      else:
        clipped_grads.append(grad * factor)

    return list(zip(clipped_grads, variables))

  return gradient_clipping


def _add_scaled_noise_to_gradients(grads_and_vars, gradient_noise_scale):
  """Adds scaled noise from a 0-mean normal distribution to gradients."""
  gradients, variables = zip(*grads_and_vars)
  noisy_gradients = []
  for gradient in gradients:
    if gradient is None:
      noisy_gradients.append(None)
      continue
    if isinstance(gradient, ops.IndexedSlices):
      gradient_shape = gradient.dense_shape
    else:
      gradient_shape = gradient.get_shape()
    noise = random_ops.truncated_normal(gradient_shape) * gradient_noise_scale
    noisy_gradients.append(gradient + noise)
  return list(zip(noisy_gradients, variables))


def _multiply_gradients(grads_and_vars, gradient_multipliers):
  """Multiply specified gradients."""
  multiplied_grads_and_vars = []
  for grad, var in grads_and_vars:
    if (grad is not None and
        (var in gradient_multipliers or var.name in gradient_multipliers)):
      key = var if var in gradient_multipliers else var.name
      multiplier = constant_op.constant(
          gradient_multipliers[key], dtype=dtypes.float32)
      if isinstance(grad, ops.IndexedSlices):
        grad_values = grad.values * multiplier
        grad = ops.IndexedSlices(grad_values, grad.indices, grad.dense_shape)
      else:
        grad *= multiplier
    multiplied_grads_and_vars.append((grad, var))
  return multiplied_grads_and_vars
