# 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,
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"""Contains a collection of models which operate on variable-length sequences."""
import math

import model_utils as utils
import models
import tensorflow as tf
from tensorflow import flags
import tensorflow.contrib.slim as slim
import video_level_models

FLAGS = flags.FLAGS
flags.DEFINE_integer("iterations", 30, "Number of frames per batch for DBoF.")
flags.DEFINE_bool("dbof_add_batch_norm", True,
                  "Adds batch normalization to the DBoF model.")
flags.DEFINE_bool(
    "sample_random_frames", True,
    "If true samples random frames (for frame level models). If false, a random"
    "sequence of frames is sampled instead.")
flags.DEFINE_integer("dbof_cluster_size", 8192,
                     "Number of units in the DBoF cluster layer.")
flags.DEFINE_integer("dbof_hidden_size", 1024,
                     "Number of units in the DBoF hidden layer.")
flags.DEFINE_string(
    "dbof_pooling_method", "max",
    "The pooling method used in the DBoF cluster layer. "
    "Choices are 'average' and 'max'.")
flags.DEFINE_string(
    "dbof_activation", "sigmoid",
    "The nonlinear activation method for cluster and hidden dense layer, e.g., "
    "sigmoid, relu6, etc.")
flags.DEFINE_string(
    "video_level_classifier_model", "MoeModel",
    "Some Frame-Level models can be decomposed into a "
    "generalized pooling operation followed by a "
    "classifier layer")
flags.DEFINE_integer("lstm_cells", 512, "Number of LSTM cells.")
flags.DEFINE_integer("lstm_layers", 2, "Number of LSTM layers.")


class DbofModel(models.BaseModel):
  """Creates a Deep Bag of Frames model.

  The model projects the features for each frame into a higher dimensional
  'clustering' space, pools across frames in that space, and then
  uses a configurable video-level model to classify the now aggregated features.

  The model will randomly sample either frames or sequences of frames during
  training to speed up convergence.
  """

  ACT_FN_MAP = {
      "sigmoid": tf.nn.sigmoid,
      "relu6": tf.nn.relu6,
  }

  def create_model(self,
                   model_input,
                   vocab_size,
                   num_frames,
                   iterations=None,
                   add_batch_norm=None,
                   sample_random_frames=None,
                   cluster_size=None,
                   hidden_size=None,
                   is_training=True,
                   **unused_params):
    """See base class.

    Args:
      model_input: A 'batch_size' x 'max_frames' x 'num_features' matrix of
        input features.
      vocab_size: The number of classes in the dataset.
      num_frames: A vector of length 'batch' which indicates the number of
        frames for each video (before padding).
      iterations: the number of frames to be sampled.
      add_batch_norm: whether to add batch norm during training.
      sample_random_frames: whether to sample random frames or random sequences.
      cluster_size: the output neuron number of the cluster layer.
      hidden_size: the output neuron number of the hidden layer.
      is_training: whether to build the graph in training mode.

    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'.
    """
    iterations = iterations or FLAGS.iterations
    add_batch_norm = add_batch_norm or FLAGS.dbof_add_batch_norm
    random_frames = sample_random_frames or FLAGS.sample_random_frames
    cluster_size = cluster_size or FLAGS.dbof_cluster_size
    hidden1_size = hidden_size or FLAGS.dbof_hidden_size
    act_fn = self.ACT_FN_MAP.get(FLAGS.dbof_activation)
    assert act_fn is not None, ("dbof_activation is not valid: %s." %
                                FLAGS.dbof_activation)

    num_frames = tf.cast(tf.expand_dims(num_frames, 1), tf.float32)
    if random_frames:
      model_input = utils.SampleRandomFrames(model_input, num_frames,
                                             iterations)
    else:
      model_input = utils.SampleRandomSequence(model_input, num_frames,
                                               iterations)
    max_frames = model_input.get_shape().as_list()[1]
    feature_size = model_input.get_shape().as_list()[2]
    reshaped_input = tf.reshape(model_input, [-1, feature_size])
    tf.compat.v1.summary.histogram("input_hist", reshaped_input)

    if add_batch_norm:
      reshaped_input = slim.batch_norm(reshaped_input,
                                       center=True,
                                       scale=True,
                                       is_training=is_training,
                                       scope="input_bn")

    cluster_weights = tf.compat.v1.get_variable(
        "cluster_weights", [feature_size, cluster_size],
        initializer=tf.random_normal_initializer(stddev=1 /
                                                 math.sqrt(feature_size)))
    tf.compat.v1.summary.histogram("cluster_weights", cluster_weights)
    activation = tf.matmul(reshaped_input, cluster_weights)
    if add_batch_norm:
      activation = slim.batch_norm(activation,
                                   center=True,
                                   scale=True,
                                   is_training=is_training,
                                   scope="cluster_bn")
    else:
      cluster_biases = tf.compat.v1.get_variable(
          "cluster_biases", [cluster_size],
          initializer=tf.random_normal_initializer(stddev=1 /
                                                   math.sqrt(feature_size)))
      tf.compat.v1.summary.histogram("cluster_biases", cluster_biases)
      activation += cluster_biases
    activation = act_fn(activation)
    tf.compat.v1.summary.histogram("cluster_output", activation)

    activation = tf.reshape(activation, [-1, max_frames, cluster_size])
    activation = utils.FramePooling(activation, FLAGS.dbof_pooling_method)

    hidden1_weights = tf.compat.v1.get_variable(
        "hidden1_weights", [cluster_size, hidden1_size],
        initializer=tf.random_normal_initializer(stddev=1 /
                                                 math.sqrt(cluster_size)))
    tf.compat.v1.summary.histogram("hidden1_weights", hidden1_weights)
    activation = tf.matmul(activation, hidden1_weights)
    if add_batch_norm:
      activation = slim.batch_norm(activation,
                                   center=True,
                                   scale=True,
                                   is_training=is_training,
                                   scope="hidden1_bn")
    else:
      hidden1_biases = tf.compat.v1.get_variable(
          "hidden1_biases", [hidden1_size],
          initializer=tf.random_normal_initializer(stddev=0.01))
      tf.compat.v1.summary.histogram("hidden1_biases", hidden1_biases)
      activation += hidden1_biases
    activation = act_fn(activation)
    tf.compat.v1.summary.histogram("hidden1_output", activation)

    aggregated_model = getattr(video_level_models,
                               FLAGS.video_level_classifier_model)
    return aggregated_model().create_model(model_input=activation,
                                           vocab_size=vocab_size,
                                           **unused_params)


class LstmModel(models.BaseModel):
  """Creates a model which uses a stack of LSTMs to represent the video."""

  def create_model(self, model_input, vocab_size, num_frames, **unused_params):
    """See base class.

    Args:
      model_input: A 'batch_size' x 'max_frames' x 'num_features' matrix of
        input features.
      vocab_size: The number of classes in the dataset.
      num_frames: A vector of length 'batch' which indicates the number of
        frames for each video (before padding).

    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'.
    """
    lstm_size = FLAGS.lstm_cells
    number_of_layers = FLAGS.lstm_layers

    stacked_lstm = tf.contrib.rnn.MultiRNNCell([
        tf.contrib.rnn.BasicLSTMCell(lstm_size, forget_bias=1.0)
        for _ in range(number_of_layers)
    ])
    _, state = tf.nn.dynamic_rnn(stacked_lstm,
                                 model_input,
                                 sequence_length=num_frames,
                                 dtype=tf.float32)

    aggregated_model = getattr(video_level_models,
                               FLAGS.video_level_classifier_model)

    return aggregated_model().create_model(model_input=state[-1].h,
                                           vocab_size=vocab_size,
                                           **unused_params)

class GruModel(models.BaseModel):    
  def create_model(self, model_input, vocab_size, num_frames, is_training=True, **unused_params):
    """Creates a model which uses a stack of GRUs to represent the video.
    Args:
      model_input: A 'batch_size' x 'max_frames' x 'num_features' matrix of
                   input features.
      vocab_size: The number of classes in the dataset.
      num_frames: A vector of length 'batch' which indicates the number of
           frames for each video (before padding).
    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'.
    """
    gru_size = FLAGS.lstm_cells
    number_of_layers = FLAGS.lstm_layers
    backward = False
    random_frames = False
    iterations = 30

    if random_frames:
      num_frames_2 = tf.cast(tf.expand_dims(num_frames, 1), tf.float32)
      model_input = utils.SampleRandomFrames(model_input, num_frames_2,
                                             iterations)

    if backward:
        model_input = tf.reverse_sequence(model_input, num_frames, seq_axis=1)

    stacked_GRU = tf.contrib.rnn.MultiRNNCell(
            [
                tf.contrib.rnn.GRUCell(gru_size)
                for _ in range(number_of_layers)
                ], state_is_tuple=False)
    
    loss = 0.0
    with tf.variable_scope("RNN"):
      outputs, state = tf.nn.dynamic_rnn(stacked_GRU, model_input,
                                         sequence_length=num_frames,
                                         dtype=tf.float32)

    aggregated_model = getattr(video_level_models,
                               'MoeModel')
    return aggregated_model().create_model(
        model_input=state,
        vocab_size=vocab_size,
        is_training=is_training,
        **unused_params)
    
class FrameLevelLogisticModel(models.BaseModel):
  """Creates a logistic classifier over the aggregated frame-level features."""
  def create_model(self, model_input, vocab_size, num_frames, **unused_params):
    """See base class.

    This class is intended to be an example for implementors of frame level
    models. If you want to train a model over averaged features it is more
    efficient to average them beforehand rather than on the fly.

    Args:
      model_input: A 'batch_size' x 'max_frames' x 'num_features' matrix of
        input features.
      vocab_size: The number of classes in the dataset.
      num_frames: A vector of length 'batch' which indicates the number of
        frames for each video (before padding).

    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_frames = tf.cast(tf.expand_dims(num_frames, 1), tf.float32)
    feature_size = model_input.get_shape().as_list()[2]
  
    denominators = tf.reshape(tf.tile(num_frames, [1, feature_size]),
                              [-1, feature_size])
    avg_pooled = tf.reduce_sum(model_input, axis=[1]) / denominators

    output = slim.fully_connected(avg_pooled,
                                  vocab_size,
                                  activation_fn=tf.nn.sigmoid,
                                  weights_regularizer=slim.l2_regularizer(1e-8))
    return {"predictions": output}


class NetVLAD_NonLocal_types():
  def __init__(self, feature_size,max_frames,cluster_size, add_batch_norm, is_training):
    self.feature_size = feature_size
    self.max_frames = max_frames
    self.is_training = is_training
    self.add_batch_norm = add_batch_norm
    self.cluster_size = cluster_size

  def forward(self,reshaped_input):

    cluster_weights = tf.get_variable("cluster_weights",
              [self.feature_size, self.cluster_size],
              initializer = tf.random_normal_initializer(stddev=1 / math.sqrt(self.feature_size)))

    tf.summary.histogram("cluster_weights", cluster_weights)
    activation = tf.matmul(reshaped_input, cluster_weights)

    if self.add_batch_norm:
      activation = slim.batch_norm(activation, center=True, scale=True, is_training=self.is_training, scope="cluster_bn")
    else:
      cluster_biases = tf.get_variable("cluster_biases",
            [cluster_size],
            initializer = tf.random_normal_initializer(stddev=1 / math.sqrt(self.feature_size)))
      tf.summary.histogram("cluster_biases", cluster_biases)
      activation += cluster_biases

    activation = tf.nn.softmax(activation)
    tf.summary.histogram("cluster_output", activation)

    activation = tf.reshape(activation, [-1, self.max_frames, self.cluster_size])

    a_sum = tf.reduce_sum(activation,-2,keep_dims=True)

    cluster_weights2 = tf.get_variable("cluster_weights2",
            [1,self.feature_size, self.cluster_size],
            initializer = tf.random_normal_initializer(stddev=1 / math.sqrt(self.feature_size)))

    a = tf.multiply(a_sum,cluster_weights2)

    activation = tf.transpose(activation,perm=[0,2,1])

    reshaped_input = tf.reshape(reshaped_input,[-1,self.max_frames,self.feature_size])
    vlad = tf.matmul(activation,reshaped_input)
    vlad = tf.transpose(vlad,perm=[0,2,1])
    vlad = tf.subtract(vlad,a)

    vlad = tf.transpose(vlad,perm=[0,2,1])
    vlad = tf.reshape(vlad, [-1, self.feature_size])

    vlad_softmax = self.embedgaussian_relation(vlad, 1/float(64))


    nonlocal_g = tf.get_variable("nonlocal_g",
              [self.feature_size, self.cluster_size],
              initializer = tf.random_normal_initializer(stddev=1 / math.sqrt(self.feature_size)))
    nonlocal_out = tf.get_variable("nonlocal_out",
              [self.cluster_size, self.feature_size],
              initializer = tf.random_normal_initializer(stddev=1 / math.sqrt(self.cluster_size)))

    vlad_g = tf.matmul(vlad, nonlocal_g)
    vlad_g = tf.reshape(vlad_g, [-1, self.cluster_size, self.cluster_size])
    vlad_g = tf.matmul(vlad_softmax, vlad_g)
    vlad_g = tf.reshape(vlad_g, [-1, self.cluster_size])

    vlad_g = tf.matmul(vlad_g, nonlocal_out)
    vlad_g = tf.reshape(vlad_g, [-1, self.cluster_size, self.feature_size])
    vlad = tf.reshape(vlad, [-1, self.cluster_size, self.feature_size])
    vlad = vlad + vlad_g

    vlad = tf.transpose(vlad,perm=[0,2,1])
    vlad = tf.nn.l2_normalize(vlad,1) # [b,f,c]

    vlad = tf.reshape(vlad,[-1,self.cluster_size*self.feature_size])
    vlad = tf.nn.l2_normalize(vlad,1)

    return vlad

  def embedgaussian_relation(self, input_, temp=1/float(32)):
    nonlocal_theta = tf.get_variable("nonlocal_theta",
            [self.feature_size, self.cluster_size],
            initializer = tf.random_normal_initializer(stddev=1 / math.sqrt(self.feature_size)))
    nonlocal_phi = tf.get_variable("nonlocal_phi",
            [self.feature_size, self.cluster_size],
            initializer = tf.random_normal_initializer(stddev=1 / math.sqrt(self.feature_size)))

    vlad_theta = tf.matmul(input_, nonlocal_theta)
    vlad_phi = tf.matmul(input_, nonlocal_phi)
    vlad_theta = tf.reshape(vlad_theta, [-1, self.cluster_size, self.cluster_size])
    vlad_phi = tf.reshape(vlad_phi, [-1, self.cluster_size, self.cluster_size])
    vlad_softmax = tf.nn.softmax(temp * tf.matmul(vlad_theta, tf.transpose(vlad_phi,perm=[0,2,1])))
    return vlad_softmax

class NetVLADModelLF(models.BaseModel):
  """Creates a NetVLAD based model.
  Args:
    model_input: A 'batch_size' x 'max_frames' x 'num_features' matrix of
                 input features.
    vocab_size: The number of classes in the dataset.
    num_frames: A vector of length 'batch' which indicates the number of
         frames for each video (before padding).
  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'.
  """


  def create_model(self,model_input,vocab_size,num_frames,iterations=None,add_batch_norm=None,sample_random_frames=None,cluster_size=None,hidden_size=None,is_training=True,**unused_params):
    iterations = 300
    add_batch_norm = True
    random_frames = True
    cluster_size = 64
    hidden1_size = 1024
    relu = True
    dimred = -1
    gating = True
    remove_diag = False

    num_frames = tf.cast(tf.expand_dims(num_frames, 1), tf.float32)
    if random_frames:
      model_input = utils.SampleRandomFrames(model_input, num_frames,
                                             iterations)
    else:
      model_input = utils.SampleRandomSequence(model_input, num_frames,
                                               iterations)


    max_frames = model_input.get_shape().as_list()[1]
    feature_size = model_input.get_shape().as_list()[2]
    reshaped_input = tf.reshape(model_input, [-1, feature_size])


    video_NetVLAD = NetVLAD_NonLocal_types(1024,int(max_frames),int(cluster_size), add_batch_norm, is_training)
    audio_NetVLAD = NetVLAD_NonLocal_types(128,int(max_frames),int(cluster_size/2), add_batch_norm, is_training)


    if add_batch_norm:# and not lightvlad:
      reshaped_input = slim.batch_norm(
          reshaped_input,
          center=True,
          scale=True,
          is_training=is_training,
          scope="input_bn")

    with tf.variable_scope("video_VLAD"):
        vlad_video = video_NetVLAD.forward(reshaped_input[:,0:1024])

    with tf.variable_scope("audio_VLAD"):
        vlad_audio = audio_NetVLAD.forward(reshaped_input[:,1024:])

    vlad = tf.concat([vlad_video, vlad_audio],1)

    vlad_dim = vlad.get_shape().as_list()[1]
    hidden1_weights = tf.get_variable("hidden1_weights",
      [vlad_dim, hidden1_size],
      initializer=tf.random_normal_initializer(stddev=1 / math.sqrt(cluster_size)))

    activation = tf.matmul(vlad, hidden1_weights)

    if add_batch_norm and relu:
      activation = slim.batch_norm(
          activation,
          center=True,
          scale=True,
          is_training=is_training,
          scope="hidden1_bn")

    else:
      hidden1_biases = tf.get_variable("hidden1_biases",
        [hidden1_size],
        initializer = tf.random_normal_initializer(stddev=0.01))
      tf.summary.histogram("hidden1_biases", hidden1_biases)
      activation += hidden1_biases

    if relu:
      activation = tf.nn.relu6(activation)


    if gating:
        gating_weights = tf.get_variable("gating_weights_2",
          [hidden1_size, hidden1_size],
          initializer = tf.random_normal_initializer(stddev=1 / math.sqrt(hidden1_size)))

        gates = tf.matmul(activation, gating_weights)

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


        if add_batch_norm:
          gates = slim.batch_norm(
              gates,
              center=True,
              scale=True,
              is_training=is_training,
              scope="gating_bn")
        else:
          gating_biases = tf.get_variable("gating_biases",
            [cluster_size],
            initializer = tf.random_normal(stddev=1 / math.sqrt(feature_size)))
          gates += gating_biases

        gates = tf.sigmoid(gates)

        activation = tf.multiply(activation,gates)

    aggregated_model = getattr(video_level_models,
                               'willow_MoeModel')


    return aggregated_model().create_model(
        model_input=activation,
        vocab_size=vocab_size,
        is_training=is_training,
        **unused_params)