# Copyright 2016 Google Inc. 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.

"""Contains model definitions."""
import math

import models
import tensorflow as tf
import utils

from tensorflow import flags
import tensorflow.contrib.slim as slim

FLAGS = flags.FLAGS
# flags.DEFINE_integer(
#     "moe_num_mixtures", 2,
#     "The number of mixtures (excluding the dummy 'expert') used for MoeModel.")
flags.DEFINE_float(
    "moe_l2", 1e-8,
    "L2 penalty for MoeModel.")
flags.DEFINE_integer(
    "moe_low_rank_gating", -1,
    "Low rank gating for MoeModel.")
flags.DEFINE_bool(
    "moe_prob_gating", True,
    "Prob gating for MoeModel.")
flags.DEFINE_string(
    "moe_prob_gating_input", "prob",
    "input Prob gating for MoeModel.")


FLAGS = flags.FLAGS
flags.DEFINE_integer(
    "moe_num_mixtures", 2,
    "The number of mixtures (excluding the dummy 'expert') used for MoeModel.")

flags.DEFINE_integer(
    "moe_num_hiddens", 512,
    "The number of hidden neural used for MoeModel.")

flags.DEFINE_integer(
    "num_maxout", 4,
    "The number of maxout neural used for maxoutMoeModel.")

flags.DEFINE_integer(
    "num_layers", 4,
    "The number of layers used for maxoutMoeModel.")

flags.DEFINE_float(
    "dropout_rate", 0.0,
    "Dropout rate.")


class LogisticModel(models.BaseModel):
  """Logistic model with L2 regularization."""

  def create_model(self, model_input, vocab_size, l2_penalty=1e-8, **unused_params):
    """Creates a logistic model.

    Args:
      model_input: 'batch' x 'num_features' matrix of input features.
      vocab_size: The number of classes in the dataset.

    Returns:
      A dictionary with a tensor containing the probability predictions of the
      model in the 'predictions' key. The dimensions of the tensor are
      batch_size x num_classes."""
    output = slim.fully_connected(
        model_input, vocab_size, activation_fn=tf.nn.sigmoid,
        weights_regularizer=slim.l2_regularizer(l2_penalty))
    return {"predictions": output}


class MoeModel(models.BaseModel):
  """A softmax over a mixture of logistic models (with L2 regularization)."""

  def create_model(self,
                   model_input,
                   vocab_size,
                   num_mixtures=None,
                   l2_penalty=1e-8,
                   **unused_params):
    """Creates a Mixture of (Logistic) Experts model.

     The model consists of a per-class softmax distribution over a
     configurable number of logistic classifiers. One of the classifiers in the
     mixture is not trained, and always predicts 0.

    Args:
      model_input: 'batch_size' x 'num_features' matrix of input features.
      vocab_size: The number of classes in the dataset.
      num_mixtures: The number of mixtures (excluding a dummy 'expert' that
        always predicts the non-existence of an entity).
      l2_penalty: How much to penalize the squared magnitudes of parameter
        values.
    Returns:
      A dictionary with a tensor containing the probability predictions of the
      model in the 'predictions' key. The dimensions of the tensor are
      batch_size x num_classes.
    """
    num_mixtures = 2

    gate_activations = slim.fully_connected(
        model_input,
        vocab_size * (num_mixtures + 1),
        activation_fn=None,
        biases_initializer=None,
        weights_regularizer=slim.l2_regularizer(l2_penalty),
        scope="gates")
    expert_activations = slim.fully_connected(
        model_input,
        vocab_size * num_mixtures,
        activation_fn=None,
        weights_regularizer=slim.l2_regularizer(l2_penalty),
        scope="experts")

    gating_distribution = tf.nn.softmax(tf.reshape(
        gate_activations,
        [-1, num_mixtures + 1]))  # (Batch * #Labels) x (num_mixtures + 1)
    expert_distribution = tf.nn.sigmoid(tf.reshape(
        expert_activations,
        [-1, num_mixtures]))  # (Batch * #Labels) x num_mixtures

    final_probabilities_by_class_and_batch = tf.reduce_sum(
        gating_distribution[:, :num_mixtures] * expert_distribution, 1)
    final_probabilities = tf.reshape(final_probabilities_by_class_and_batch,
                                     [-1, vocab_size])
  
    return {"predictions": final_probabilities}


class willow_MoeModel(models.BaseModel):
  """A softmax over a mixture of logistic models (with L2 regularization)."""

  def create_model(self,
                   model_input,
                   vocab_size,
                   is_training,
                   num_mixtures=None,
                   l2_penalty=1e-8,
                   **unused_params):
    """Creates a Mixture of (Logistic) Experts model.
     It also includes the possibility of gating the probabilities
     The model consists of a per-class softmax distribution over a
     configurable number of logistic classifiers. One of the classifiers in the
     mixture is not trained, and always predicts 0.
    Args:
      model_input: 'batch_size' x 'num_features' matrix of input features.
      vocab_size: The number of classes in the dataset.
      is_training: Is this the training phase ?
      num_mixtures: The number of mixtures (excluding a dummy 'expert' that
        always predicts the non-existence of an entity).
      l2_penalty: How much to penalize the squared magnitudes of parameter
        values.
    Returns:
      A dictionary with a tensor containing the probability predictions of the
      model in the 'predictions' key. The dimensions of the tensor are
      batch_size x num_classes.
    """
    num_mixtures = 8
    low_rank_gating = FLAGS.moe_low_rank_gating
    l2_penalty = FLAGS.moe_l2
    gating_probabilities = FLAGS.moe_prob_gating
    gating_input = FLAGS.moe_prob_gating_input

    input_size = model_input.get_shape().as_list()[1]
    remove_diag = False

    if low_rank_gating == -1:
      gate_activations = slim.fully_connected(
          model_input,
          vocab_size * (num_mixtures + 1),
          activation_fn=None,
          biases_initializer=None,
          weights_regularizer=slim.l2_regularizer(l2_penalty),
          scope="gates")
    else:
      gate_activations1 = slim.fully_connected(
          model_input,
          low_rank_gating,
          activation_fn=None,
          biases_initializer=None,
          weights_regularizer=slim.l2_regularizer(l2_penalty),
          scope="gates1")
      gate_activations = slim.fully_connected(
          gate_activations1,
          vocab_size * (num_mixtures + 1),
          activation_fn=None,
          biases_initializer=None,
          weights_regularizer=slim.l2_regularizer(l2_penalty),
          scope="gates2")

    expert_activations = slim.fully_connected(
        model_input,
        vocab_size * num_mixtures,
        activation_fn=None,
        weights_regularizer=slim.l2_regularizer(l2_penalty),
        scope="experts")

    gating_distribution = tf.nn.softmax(tf.reshape(
        gate_activations,
        [-1, num_mixtures + 1]))  # (Batch * #Labels) x (num_mixtures + 1)
    expert_distribution = tf.nn.sigmoid(tf.reshape(
        expert_activations,
        [-1, num_mixtures]))  # (Batch * #Labels) x num_mixtures

    probabilities_by_class_and_batch = tf.reduce_sum(
        gating_distribution[:, :num_mixtures] * expert_distribution, 1)
    probabilities = tf.reshape(probabilities_by_class_and_batch,
                               [-1, vocab_size])

    if gating_probabilities:
      if gating_input == 'prob':
        gating_weights = tf.get_variable("gating_prob_weights",
                                         [vocab_size, vocab_size],
                                         initializer=tf.random_normal_initializer(stddev=1 / math.sqrt(vocab_size)))
        gates = tf.matmul(probabilities, gating_weights)
      else:
        gating_weights = tf.get_variable("gating_prob_weights",
                                         [input_size, vocab_size],
                                         initializer=tf.random_normal_initializer(stddev=1 / math.sqrt(vocab_size)))

        gates = tf.matmul(model_input, gating_weights)

      if remove_diag:
        # removes diagonals coefficients
        diagonals = tf.matrix_diag_part(gating_weights)
        gates = gates - tf.multiply(diagonals, probabilities)

      gates = slim.batch_norm(
          gates,
          center=True,
          scale=True,
          is_training=is_training,
          scope="gating_prob_bn")

      gates = tf.sigmoid(gates)

      probabilities = tf.multiply(probabilities, gates)

    return {"predictions": probabilities}