# Copyright 2021 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
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# Unless required by applicable law or agreed to in writing, software
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"""Contains common building blocks for neural networks."""
from typing import Any, Callable, Dict, List, Mapping, Optional, Tuple, Union

from absl import logging
import tensorflow as tf
import tensorflow_addons as tfa

from official.modeling import tf_utils
from official.vision.beta.ops import spatial_transform_ops


# Type annotations.
States = Dict[str, tf.Tensor]
Activation = Union[str, Callable]


def make_divisible(value: float,
                   divisor: int,
                   min_value: Optional[float] = None
                   ) -> int:
  """This is to ensure that all layers have channels that are divisible by 8.

  Args:
    value: A `float` of original value.
    divisor: An `int` of the divisor that need to be checked upon.
    min_value: A `float` of  minimum value threshold.

  Returns:
    The adjusted value in `int` that is divisible against divisor.
  """
  if min_value is None:
    min_value = divisor
  new_value = max(min_value, int(value + divisor / 2) // divisor * divisor)
  # Make sure that round down does not go down by more than 10%.
  if new_value < 0.9 * value:
    new_value += divisor
  return int(new_value)


def round_filters(filters: int,
                  multiplier: float,
                  divisor: int = 8,
                  min_depth: Optional[int] = None,
                  skip: bool = False):
  """Rounds number of filters based on width multiplier."""
  orig_f = filters
  if skip or not multiplier:
    return filters

  new_filters = make_divisible(value=filters * multiplier,
                               divisor=divisor,
                               min_value=min_depth)

  logging.info('round_filter input=%s output=%s', orig_f, new_filters)
  return int(new_filters)


def get_padding_for_kernel_size(kernel_size):
  """Compute padding size given kernel size."""
  if kernel_size == 7:
    return (3, 3)
  elif kernel_size == 3:
    return (1, 1)
  else:
    raise ValueError('Padding for kernel size {} not known.'.format(
        kernel_size))


def hard_swish(x: tf.Tensor) -> tf.Tensor:
  """A Swish6/H-Swish activation function.

  Reference: Section 5.2 of Howard et al. "Searching for MobileNet V3."
  https://arxiv.org/pdf/1905.02244.pdf

  Args:
    x: the input tensor.

  Returns:
    The activation output.
  """
  return x * tf.nn.relu6(x + 3.) * (1. / 6.)

tf.keras.utils.get_custom_objects().update({'hard_swish': hard_swish})


def simple_swish(x: tf.Tensor) -> tf.Tensor:
  """A swish/silu activation function without custom gradients.

  Useful for exporting to SavedModel to avoid custom gradient warnings.

  Args:
    x: the input tensor.

  Returns:
    The activation output.
  """
  return x * tf.math.sigmoid(x)

tf.keras.utils.get_custom_objects().update({'simple_swish': simple_swish})


@tf.keras.utils.register_keras_serializable(package='Vision')
class SqueezeExcitation(tf.keras.layers.Layer):
  """Creates a squeeze and excitation layer."""

  def __init__(self,
               in_filters,
               out_filters,
               se_ratio,
               divisible_by=1,
               use_3d_input=False,
               kernel_initializer='VarianceScaling',
               kernel_regularizer=None,
               bias_regularizer=None,
               activation='relu',
               gating_activation='sigmoid',
               **kwargs):
    """Initializes a squeeze and excitation layer.

    Args:
      in_filters: An `int` number of filters of the input tensor.
      out_filters: An `int` number of filters of the output tensor.
      se_ratio: A `float` or None. If not None, se ratio for the squeeze and
        excitation layer.
      divisible_by: An `int` that ensures all inner dimensions are divisible by
        this number.
      use_3d_input: A `bool` of whether input is 2D or 3D image.
      kernel_initializer: A `str` of kernel_initializer for convolutional
        layers.
      kernel_regularizer: A `tf.keras.regularizers.Regularizer` object for
        Conv2D. Default to None.
      bias_regularizer: A `tf.keras.regularizers.Regularizer` object for Conv2d.
        Default to None.
      activation: A `str` name of the activation function.
      gating_activation: A `str` name of the activation function for final
        gating function.
      **kwargs: Additional keyword arguments to be passed.
    """
    super(SqueezeExcitation, self).__init__(**kwargs)

    self._in_filters = in_filters
    self._out_filters = out_filters
    self._se_ratio = se_ratio
    self._divisible_by = divisible_by
    self._use_3d_input = use_3d_input
    self._activation = activation
    self._gating_activation = gating_activation
    self._kernel_initializer = kernel_initializer
    self._kernel_regularizer = kernel_regularizer
    self._bias_regularizer = bias_regularizer
    if tf.keras.backend.image_data_format() == 'channels_last':
      if not use_3d_input:
        self._spatial_axis = [1, 2]
      else:
        self._spatial_axis = [1, 2, 3]
    else:
      if not use_3d_input:
        self._spatial_axis = [2, 3]
      else:
        self._spatial_axis = [2, 3, 4]
    self._activation_fn = tf_utils.get_activation(activation)
    self._gating_activation_fn = tf_utils.get_activation(gating_activation)

  def build(self, input_shape):
    num_reduced_filters = make_divisible(
        max(1, int(self._in_filters * self._se_ratio)),
        divisor=self._divisible_by)

    self._se_reduce = tf.keras.layers.Conv2D(
        filters=num_reduced_filters,
        kernel_size=1,
        strides=1,
        padding='same',
        use_bias=True,
        kernel_initializer=self._kernel_initializer,
        kernel_regularizer=self._kernel_regularizer,
        bias_regularizer=self._bias_regularizer)

    self._se_expand = tf.keras.layers.Conv2D(
        filters=self._out_filters,
        kernel_size=1,
        strides=1,
        padding='same',
        use_bias=True,
        kernel_initializer=self._kernel_initializer,
        kernel_regularizer=self._kernel_regularizer,
        bias_regularizer=self._bias_regularizer)

    super(SqueezeExcitation, self).build(input_shape)

  def get_config(self):
    config = {
        'in_filters': self._in_filters,
        'out_filters': self._out_filters,
        'se_ratio': self._se_ratio,
        'divisible_by': self._divisible_by,
        'use_3d_input': self._use_3d_input,
        'kernel_initializer': self._kernel_initializer,
        'kernel_regularizer': self._kernel_regularizer,
        'bias_regularizer': self._bias_regularizer,
        'activation': self._activation,
        'gating_activation': self._gating_activation,
    }
    base_config = super(SqueezeExcitation, self).get_config()
    return dict(list(base_config.items()) + list(config.items()))

  def call(self, inputs):
    x = tf.reduce_mean(inputs, self._spatial_axis, keepdims=True)
    x = self._activation_fn(self._se_reduce(x))
    x = self._gating_activation_fn(self._se_expand(x))
    return x * inputs


def get_stochastic_depth_rate(init_rate, i, n):
  """Get drop connect rate for the ith block.

  Args:
    init_rate: A `float` of initial drop rate.
    i: An `int` of order of the current block.
    n: An `int` total number of blocks.

  Returns:
    Drop rate of the ith block.
  """
  if init_rate is not None:
    if init_rate < 0 or init_rate > 1:
      raise ValueError('Initial drop rate must be within 0 and 1.')
    rate = init_rate * float(i) / n
  else:
    rate = None
  return rate


@tf.keras.utils.register_keras_serializable(package='Vision')
class StochasticDepth(tf.keras.layers.Layer):
  """Creates a stochastic depth layer."""

  def __init__(self, stochastic_depth_drop_rate, **kwargs):
    """Initializes a stochastic depth layer.

    Args:
      stochastic_depth_drop_rate: A `float` of drop rate.
      **kwargs: Additional keyword arguments to be passed.

    Returns:
      A output `tf.Tensor` of which should have the same shape as input.
    """
    super(StochasticDepth, self).__init__(**kwargs)
    self._drop_rate = stochastic_depth_drop_rate

  def get_config(self):
    config = {'drop_rate': self._drop_rate}
    base_config = super(StochasticDepth, self).get_config()
    return dict(list(base_config.items()) + list(config.items()))

  def call(self, inputs, training=None):
    if training is None:
      training = tf.keras.backend.learning_phase()
    if not training or self._drop_rate is None or self._drop_rate == 0:
      return inputs

    keep_prob = 1.0 - self._drop_rate
    batch_size = tf.shape(inputs)[0]
    random_tensor = keep_prob
    random_tensor += tf.random.uniform(
        [batch_size] + [1] * (inputs.shape.rank - 1), dtype=inputs.dtype)
    binary_tensor = tf.floor(random_tensor)
    output = tf.math.divide(inputs, keep_prob) * binary_tensor
    return output


@tf.keras.utils.register_keras_serializable(package='Vision')
def pyramid_feature_fusion(inputs, target_level):
  """Fuses all feature maps in the feature pyramid at the target level.

  Args:
    inputs: A dictionary containing the feature pyramid. The size of the input
      tensor needs to be fixed.
    target_level: An `int` of the target feature level for feature fusion.

  Returns:
    A `float` `tf.Tensor` of shape [batch_size, feature_height, feature_width,
      feature_channel].
  """
  # Convert keys to int.
  pyramid_feats = {int(k): v for k, v in inputs.items()}
  min_level = min(pyramid_feats.keys())
  max_level = max(pyramid_feats.keys())
  resampled_feats = []

  for l in range(min_level, max_level + 1):
    if l == target_level:
      resampled_feats.append(pyramid_feats[l])
    else:
      feat = pyramid_feats[l]
      target_size = list(feat.shape[1:3])
      target_size[0] *= 2**(l - target_level)
      target_size[1] *= 2**(l - target_level)
      # Casts feat to float32 so the resize op can be run on TPU.
      feat = tf.cast(feat, tf.float32)
      feat = tf.image.resize(
          feat, size=target_size, method=tf.image.ResizeMethod.BILINEAR)
      # Casts it back to be compatible with the rest opetations.
      feat = tf.cast(feat, pyramid_feats[l].dtype)
      resampled_feats.append(feat)

  return tf.math.add_n(resampled_feats)


class PanopticFPNFusion(tf.keras.Model):
  """Creates a Panoptic FPN feature Fusion layer.

  This implements feature fusion for semantic segmentation head from the paper:
  Alexander Kirillov, Ross Girshick, Kaiming He and Piotr Dollar.
  Panoptic Feature Pyramid Networks.
  (https://arxiv.org/pdf/1901.02446.pdf)
  """

  def __init__(
      self,
      min_level: int = 2,
      max_level: int = 5,
      target_level: int = 2,
      num_filters: int = 128,
      num_fpn_filters: int = 256,
      activation: str = 'relu',
      kernel_regularizer: Optional[tf.keras.regularizers.Regularizer] = None,
      bias_regularizer: Optional[tf.keras.regularizers.Regularizer] = None,
      **kwargs):

    """Initializes panoptic FPN feature fusion layer.

    Args:
      min_level: An `int` of minimum level to use in feature fusion.
      max_level: An `int` of maximum level to use in feature fusion.
      target_level: An `int` of the target feature level for feature fusion.
      num_filters: An `int` number of filters in conv2d layers.
      num_fpn_filters: An `int` number of filters in the FPN outputs
      activation: A `str` name of the activation function.
      kernel_regularizer: A `tf.keras.regularizers.Regularizer` object for
        Conv2D. Default is None.
      bias_regularizer: A `tf.keras.regularizers.Regularizer` object for Conv2D.
      **kwargs: Additional keyword arguments to be passed.
    Returns:
      A `float` `tf.Tensor` of shape [batch_size, feature_height, feature_width,
        feature_channel].
    """
    if target_level > max_level:
      raise ValueError('target_level should be less than max_level')

    self._config_dict = {
        'min_level': min_level,
        'max_level': max_level,
        'target_level': target_level,
        'num_filters': num_filters,
        'num_fpn_filters': num_fpn_filters,
        'activation': activation,
        'kernel_regularizer': kernel_regularizer,
        'bias_regularizer': bias_regularizer,
    }
    norm = tfa.layers.GroupNormalization
    conv2d = tf.keras.layers.Conv2D
    activation_fn = tf_utils.get_activation(activation)
    if tf.keras.backend.image_data_format() == 'channels_last':
      norm_axis = -1
    else:
      norm_axis = 1
    inputs = self._build_inputs(num_fpn_filters, min_level, max_level)

    upscaled_features = []
    for level in range(min_level, max_level + 1):
      num_conv_layers = max(1, level - target_level)
      x = inputs[str(level)]
      for i in range(num_conv_layers):
        x = conv2d(
            filters=num_filters,
            kernel_size=3,
            padding='same',
            kernel_initializer=tf.keras.initializers.VarianceScaling(),
            kernel_regularizer=kernel_regularizer,
            bias_regularizer=bias_regularizer)(x)
        x = norm(groups=32, axis=norm_axis)(x)
        x = activation_fn(x)
        if level != target_level:
          x = spatial_transform_ops.nearest_upsampling(x, scale=2)
      upscaled_features.append(x)

    fused_features = tf.math.add_n(upscaled_features)
    self._output_specs = {str(target_level): fused_features.get_shape()}

    super(PanopticFPNFusion, self).__init__(
        inputs=inputs, outputs=fused_features, **kwargs)

  def _build_inputs(self, num_filters: int,
                    min_level: int, max_level: int):
    inputs = {}
    for level in range(min_level, max_level + 1):
      inputs[str(level)] = tf.keras.Input(shape=[None, None, num_filters])
    return inputs

  def get_config(self) -> Mapping[str, Any]:
    return self._config_dict

  @classmethod
  def from_config(cls, config, custom_objects=None):
    return cls(**config)

  @property
  def output_specs(self) -> Mapping[str, tf.TensorShape]:
    """A dict of {level: TensorShape} pairs for the model output."""
    return self._output_specs


@tf.keras.utils.register_keras_serializable(package='Vision')
class Scale(tf.keras.layers.Layer):
  """Scales the input by a trainable scalar weight.

  This is useful for applying ReZero to layers, which improves convergence
  speed. This implements the paper:
  ReZero is All You Need: Fast Convergence at Large Depth.
  (https://arxiv.org/pdf/2003.04887.pdf).
  """

  def __init__(
      self,
      initializer: tf.keras.initializers.Initializer = 'ones',
      regularizer: Optional[tf.keras.regularizers.Regularizer] = None,
      **kwargs):
    """Initializes a scale layer.

    Args:
      initializer: A `str` of initializer for the scalar weight.
      regularizer: A `tf.keras.regularizers.Regularizer` for the scalar weight.
      **kwargs: Additional keyword arguments to be passed to this layer.

    Returns:
      An `tf.Tensor` of which should have the same shape as input.
    """
    super(Scale, self).__init__(**kwargs)

    self._initializer = initializer
    self._regularizer = regularizer

    self._scale = self.add_weight(
        name='scale',
        shape=[],
        dtype=self.dtype,
        initializer=self._initializer,
        regularizer=self._regularizer,
        trainable=True)

  def get_config(self):
    """Returns a dictionary containing the config used for initialization."""
    config = {
        'initializer': self._initializer,
        'regularizer': self._regularizer,
    }
    base_config = super(Scale, self).get_config()
    return dict(list(base_config.items()) + list(config.items()))

  def call(self, inputs):
    """Calls the layer with the given inputs."""
    scale = tf.cast(self._scale, inputs.dtype)
    return scale * inputs


@tf.keras.utils.register_keras_serializable(package='Vision')
class TemporalSoftmaxPool(tf.keras.layers.Layer):
  """Creates a network layer corresponding to temporal softmax pooling.

  This is useful for multi-class logits (used in e.g., Charades). Modified from
  AssembleNet Charades evaluation from:

  Michael S. Ryoo, AJ Piergiovanni, Mingxing Tan, Anelia Angelova.
  AssembleNet: Searching for Multi-Stream Neural Connectivity in Video
  Architectures.
  (https://arxiv.org/pdf/1905.13209.pdf).
  """

  def call(self, inputs):
    """Calls the layer with the given inputs."""
    assert inputs.shape.rank in (3, 4, 5)
    frames = tf.shape(inputs)[1]
    pre_logits = inputs / tf.sqrt(tf.cast(frames, inputs.dtype))
    activations = tf.nn.softmax(pre_logits, axis=1)
    outputs = inputs * activations
    return outputs


@tf.keras.utils.register_keras_serializable(package='Vision')
class PositionalEncoding(tf.keras.layers.Layer):
  """Creates a network layer that adds a sinusoidal positional encoding.

  Positional encoding is incremented across frames, and is added to the input.
  The positional encoding is first weighted at 0 so that the network can choose
  to ignore it. This implements:

  Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones,
  Aidan N. Gomez, Lukasz Kaiser, Illia Polosukhin.
  Attention Is All You Need.
  (https://arxiv.org/pdf/1706.03762.pdf).
  """

  def __init__(self,
               initializer: tf.keras.initializers.Initializer = 'zeros',
               cache_encoding: bool = False,
               state_prefix: Optional[str] = None,
               **kwargs):
    """Initializes positional encoding.

    Args:
      initializer: A `str` of initializer for weighting the positional encoding.
      cache_encoding: A `bool`. If True, cache the positional encoding tensor
        after calling build. Otherwise, rebuild the tensor for every call.
        Setting this to False can be useful when we want to input a variable
        number of frames, so the positional encoding tensor can change shape.
      state_prefix: a prefix string to identify states.
      **kwargs: Additional keyword arguments to be passed to this layer.

    Returns:
      A `tf.Tensor` of which should have the same shape as input.
    """
    super(PositionalEncoding, self).__init__(**kwargs)
    self._initializer = initializer
    self._cache_encoding = cache_encoding
    self._pos_encoding = None
    self._rezero = Scale(initializer=initializer, name='rezero')
    state_prefix = state_prefix if state_prefix is not None else ''
    self._state_prefix = state_prefix
    self._frame_count_name = f'{state_prefix}_pos_enc_frame_count'

  def get_config(self):
    """Returns a dictionary containing the config used for initialization."""
    config = {
        'initializer': self._initializer,
        'cache_encoding': self._cache_encoding,
        'state_prefix': self._state_prefix,
    }
    base_config = super(PositionalEncoding, self).get_config()
    return dict(list(base_config.items()) + list(config.items()))

  def _positional_encoding(self,
                           num_positions: Union[int, tf.Tensor],
                           hidden_size: Union[int, tf.Tensor],
                           start_position: Union[int, tf.Tensor] = 0,
                           dtype: str = 'float32') -> tf.Tensor:
    """Creates a sequence of sinusoidal positional encoding vectors.

    Args:
      num_positions: the total number of positions (frames).
      hidden_size: the number of channels used for the hidden vectors.
      start_position: the start position.
      dtype: the dtype of the output tensor.

    Returns:
      The positional encoding tensor with shape [num_positions, hidden_size].
    """
    if isinstance(start_position, tf.Tensor) and start_position.shape.rank == 1:
      start_position = start_position[0]

    # Calling `tf.range` with `dtype=tf.bfloat16` results in an error,
    # so we cast afterward.
    positions = tf.range(start_position, start_position + num_positions)
    positions = tf.cast(positions, dtype)[:, tf.newaxis]
    idx = tf.range(hidden_size)[tf.newaxis, :]

    power = tf.cast(2 * (idx // 2), dtype)
    power /= tf.cast(hidden_size, dtype)
    angles = 1. / tf.math.pow(10_000., power)
    radians = positions * angles

    sin = tf.math.sin(radians[:, 0::2])
    cos = tf.math.cos(radians[:, 1::2])
    pos_encoding = tf.concat([sin, cos], axis=-1)

    return pos_encoding

  def _get_pos_encoding(self,
                        input_shape: tf.Tensor,
                        frame_count: int = 0) -> tf.Tensor:
    """Calculates the positional encoding from the input shape.

    Args:
      input_shape: the shape of the input.
      frame_count: a count of frames that indicates the index of the first
        frame.

    Returns:
      The positional encoding tensor with shape [num_positions, hidden_size].

    """
    frames = input_shape[1]
    channels = input_shape[-1]
    pos_encoding = self._positional_encoding(
        frames, channels, start_position=frame_count, dtype=self.dtype)
    pos_encoding = tf.reshape(pos_encoding, [1, frames, 1, 1, channels])
    return pos_encoding

  def build(self, input_shape):
    """Builds the layer with the given input shape.

    Args:
      input_shape: The input shape.

    Raises:
      ValueError: If using 'channels_first' data format.
    """
    if tf.keras.backend.image_data_format() == 'channels_first':
      raise ValueError('"channels_first" mode is unsupported.')

    if self._cache_encoding:
      self._pos_encoding = self._get_pos_encoding(input_shape)

    super(PositionalEncoding, self).build(input_shape)

  def call(
      self,
      inputs: tf.Tensor,
      states: Optional[States] = None,
      output_states: bool = True,
  ) -> Union[tf.Tensor, Tuple[tf.Tensor, States]]:
    """Calls the layer with the given inputs.

    Args:
      inputs: An input `tf.Tensor`.
      states: A `dict` of states such that, if any of the keys match for this
        layer, will overwrite the contents of the buffer(s). Expected keys
        include `state_prefix + '_pos_enc_frame_count'`.
      output_states: A `bool`. If True, returns the output tensor and output
        states. Returns just the output tensor otherwise.

    Returns:
      An output `tf.Tensor` (and optionally the states if `output_states=True`).

    Raises:
      ValueError: If using 'channels_first' data format.
    """
    states = dict(states) if states is not None else {}

    # Keep a count of frames encountered across input iterations in
    # num_frames to be able to accurately update the positional encoding.
    num_frames = tf.shape(inputs)[1]
    frame_count = tf.cast(states.get(self._frame_count_name, [0]), tf.int32)
    states[self._frame_count_name] = frame_count + num_frames

    if self._cache_encoding:
      pos_encoding = self._pos_encoding
    else:
      pos_encoding = self._get_pos_encoding(
          tf.shape(inputs), frame_count=frame_count)
    pos_encoding = tf.cast(pos_encoding, inputs.dtype)
    pos_encoding = self._rezero(pos_encoding)
    outputs = inputs + pos_encoding

    return (outputs, states) if output_states else outputs


@tf.keras.utils.register_keras_serializable(package='Vision')
class GlobalAveragePool3D(tf.keras.layers.Layer):
  """Creates a global average pooling layer with causal mode.

  Implements causal mode, which runs a cumulative sum (with `tf.cumsum`) across
  frames in the time dimension, allowing the use of a stream buffer. Sums any
  valid input state with the current input to allow state to accumulate over
  several iterations.
  """

  def __init__(self,
               keepdims: bool = False,
               causal: bool = False,
               state_prefix: Optional[str] = None,
               **kwargs):
    """Initializes a global average pool layer.

    Args:
      keepdims: A `bool`. If True, keep the averaged dimensions.
      causal: A `bool` of whether to run in causal mode with a cumulative sum
        across frames.
      state_prefix: a prefix string to identify states.
      **kwargs: Additional keyword arguments to be passed to this layer.

    Returns:
      An output `tf.Tensor`.
    """
    super(GlobalAveragePool3D, self).__init__(**kwargs)

    self._keepdims = keepdims
    self._causal = causal
    state_prefix = state_prefix if state_prefix is not None else ''
    self._state_prefix = state_prefix

    self._state_name = f'{state_prefix}_pool_buffer'
    self._frame_count_name = f'{state_prefix}_pool_frame_count'

  def get_config(self):
    """Returns a dictionary containing the config used for initialization."""
    config = {
        'keepdims': self._keepdims,
        'causal': self._causal,
        'state_prefix': self._state_prefix,
    }
    base_config = super(GlobalAveragePool3D, self).get_config()
    return dict(list(base_config.items()) + list(config.items()))

  def call(self,
           inputs: tf.Tensor,
           states: Optional[States] = None,
           output_states: bool = True
           ) -> Union[tf.Tensor, Tuple[tf.Tensor, States]]:
    """Calls the layer with the given inputs.

    Args:
      inputs: An input `tf.Tensor`.
      states: A `dict` of states such that, if any of the keys match for this
        layer, will overwrite the contents of the buffer(s).
        Expected keys include `state_prefix + '__pool_buffer'` and
        `state_prefix + '__pool_frame_count'`.
      output_states: A `bool`. If True, returns the output tensor and output
        states. Returns just the output tensor otherwise.

    Returns:
      An output `tf.Tensor` (and optionally the states if `output_states=True`).
      If `causal=True`, the output tensor will have shape
      `[batch_size, num_frames, 1, 1, channels]` if `keepdims=True`. We keep
      the frame dimension in this case to simulate a cumulative global average
      as if we are inputting one frame at a time. If `causal=False`, the output
      is equivalent to `tf.keras.layers.GlobalAveragePooling3D` with shape
      `[batch_size, 1, 1, 1, channels]` if `keepdims=True` (plus the optional
      buffer stored in `states`).

    Raises:
      ValueError: If using 'channels_first' data format.
    """
    states = dict(states) if states is not None else {}

    if tf.keras.backend.image_data_format() == 'channels_first':
      raise ValueError('"channels_first" mode is unsupported.')

    # Shape: [batch_size, 1, 1, 1, channels]
    buffer = states.get(self._state_name, None)
    if buffer is None:
      buffer = tf.zeros_like(inputs[:, :1, :1, :1], dtype=inputs.dtype)
      states[self._state_name] = buffer

    # Keep a count of frames encountered across input iterations in
    # num_frames to be able to accurately take a cumulative average across
    # all frames when running in streaming mode
    num_frames = tf.shape(inputs)[1]
    frame_count = states.get(self._frame_count_name, tf.constant([0]))
    frame_count = tf.cast(frame_count, tf.int32)
    states[self._frame_count_name] = frame_count + num_frames

    if self._causal:
      # Take a mean of spatial dimensions to make computation more efficient.
      x = tf.reduce_mean(inputs, axis=[2, 3], keepdims=True)
      x = tf.cumsum(x, axis=1)
      x = x + buffer

      # The last frame will be the value of the next state
      # Shape: [batch_size, 1, 1, 1, channels]
      states[self._state_name] = x[:, -1:]

      # In causal mode, the divisor increments by 1 for every frame to
      # calculate cumulative averages instead of one global average
      mean_divisors = tf.range(num_frames) + frame_count + 1
      mean_divisors = tf.reshape(mean_divisors, [1, num_frames, 1, 1, 1])
      mean_divisors = tf.cast(mean_divisors, x.dtype)

      # Shape: [batch_size, num_frames, 1, 1, channels]
      x = x / mean_divisors
    else:
      # In non-causal mode, we (optionally) sum across frames to take a
      # cumulative average across input iterations rather than individual
      # frames. If no buffer state is passed, this essentially becomes
      # regular global average pooling.
      # Shape: [batch_size, 1, 1, 1, channels]
      x = tf.reduce_sum(inputs, axis=(1, 2, 3), keepdims=True)
      x = x / tf.cast(tf.shape(inputs)[2] * tf.shape(inputs)[3], x.dtype)
      x = x + buffer

      # Shape: [batch_size, 1, 1, 1, channels]
      states[self._state_name] = x

      x = x / tf.cast(frame_count + num_frames, x.dtype)

    if not self._keepdims:
      x = tf.squeeze(x, axis=(1, 2, 3))

    return (x, states) if output_states else x


@tf.keras.utils.register_keras_serializable(package='Vision')
class SpatialAveragePool3D(tf.keras.layers.Layer):
  """Creates a global average pooling layer pooling across spatial dimentions."""

  def __init__(self, keepdims: bool = False, **kwargs):
    """Initializes a global average pool layer.

    Args:
      keepdims: A `bool`. If True, keep the averaged dimensions.
      **kwargs: Additional keyword arguments to be passed to this layer.

    Returns:
      An output `tf.Tensor`.
    """
    super(SpatialAveragePool3D, self).__init__(**kwargs)
    self._keepdims = keepdims

  def get_config(self):
    """Returns a dictionary containing the config used for initialization."""
    config = {
        'keepdims': self._keepdims,
    }
    base_config = super(SpatialAveragePool3D, self).get_config()
    return dict(list(base_config.items()) + list(config.items()))

  def build(self, input_shape):
    """Builds the layer with the given input shape."""
    if tf.keras.backend.image_data_format() == 'channels_first':
      raise ValueError('"channels_first" mode is unsupported.')

    super(SpatialAveragePool3D, self).build(input_shape)

  def call(self, inputs):
    """Calls the layer with the given inputs."""
    if inputs.shape.rank != 5:
      raise ValueError(
          'Input should have rank {}, got {}'.format(5, inputs.shape.rank))

    return tf.reduce_mean(inputs, axis=(2, 3), keepdims=self._keepdims)


class CausalConvMixin:
  """Mixin class to implement CausalConv for `tf.keras.layers.Conv` layers."""

  @property
  def use_buffered_input(self) -> bool:
    return self._use_buffered_input

  @use_buffered_input.setter
  def use_buffered_input(self, variable: bool):
    self._use_buffered_input = variable

  def _compute_buffered_causal_padding(self,
                                       inputs: tf.Tensor,
                                       use_buffered_input: bool = False,
                                       time_axis: int = 1,
                                       ) -> List[List[int]]:
    """Calculates padding for 'causal' option for conv layers.

    Args:
      inputs: An optional input `tf.Tensor` to be padded.
      use_buffered_input: A `bool`. If True, use 'valid' padding along the time
        dimension. This should be set when applying the stream buffer.
      time_axis: An `int` of the axis of the time dimension.

    Returns:
      A list of paddings for `tf.pad`.
    """
    input_shape = tf.shape(inputs)[1:-1]

    if tf.keras.backend.image_data_format() == 'channels_first':
      raise ValueError('"channels_first" mode is unsupported.')

    kernel_size_effective = [
        (self.kernel_size[i] +
         (self.kernel_size[i] - 1) * (self.dilation_rate[i] - 1))
        for i in range(self.rank)
    ]
    pad_total = [kernel_size_effective[0] - 1]
    for i in range(1, self.rank):
      overlap = (input_shape[i] - 1) % self.strides[i] + 1
      pad_total.append(tf.maximum(kernel_size_effective[i] - overlap, 0))
    pad_beg = [pad_total[i] // 2 for i in range(self.rank)]
    pad_end = [pad_total[i] - pad_beg[i] for i in range(self.rank)]
    padding = [[pad_beg[i], pad_end[i]] for i in range(self.rank)]
    padding = [[0, 0]] + padding + [[0, 0]]

    if use_buffered_input:
      padding[time_axis] = [0, 0]
    else:
      padding[time_axis] = [padding[time_axis][0] + padding[time_axis][1], 0]
    return padding

  def _causal_validate_init(self):
    """Validates the Conv layer initial configuration."""
    # Overriding this method is meant to circumvent unnecessary errors when
    # using causal padding.
    if (self.filters is not None
        and self.filters % self.groups != 0):
      raise ValueError(
          'The number of filters must be evenly divisible by the number of '
          'groups. Received: groups={}, filters={}'.format(
              self.groups, self.filters))

    if not all(self.kernel_size):
      raise ValueError('The argument `kernel_size` cannot contain 0(s). '
                       'Received: %s' % (self.kernel_size,))

  def _buffered_spatial_output_shape(self, spatial_output_shape: List[int]):
    """Computes the spatial output shape from the input shape."""
    # When buffer padding, use 'valid' padding across time. The output shape
    # across time should be the input shape minus any padding, assuming
    # the stride across time is 1.
    if self._use_buffered_input and spatial_output_shape[0] is not None:
      padding = self._compute_buffered_causal_padding(
          tf.zeros([1] + spatial_output_shape + [1]), use_buffered_input=False)
      spatial_output_shape[0] -= sum(padding[1])
    return spatial_output_shape


@tf.keras.utils.register_keras_serializable(package='Vision')
class Conv2D(tf.keras.layers.Conv2D, CausalConvMixin):
  """Conv2D layer supporting CausalConv.

  Supports `padding='causal'` option (like in `tf.keras.layers.Conv1D`),
  which applies causal padding to the temporal dimension, and same padding in
  the spatial dimensions.
  """

  def __init__(self, *args, use_buffered_input=False, **kwargs):
    """Initializes conv2d.

    Args:
      *args: Arguments to be passed.
      use_buffered_input: A `bool`. If True, the input is expected to be padded
        beforehand. In effect, calling this layer will use 'valid' padding on
        the temporal dimension to simulate 'causal' padding.
      **kwargs: Additional keyword arguments to be passed.

    Returns:
      An output `tf.Tensor` of the Conv2D operation.
    """
    super(Conv2D, self).__init__(*args, **kwargs)
    self._use_buffered_input = use_buffered_input

  def get_config(self):
    """Returns a dictionary containing the config used for initialization."""
    config = {
        'use_buffered_input': self._use_buffered_input,
    }
    base_config = super(Conv2D, self).get_config()
    return dict(list(base_config.items()) + list(config.items()))

  def _compute_causal_padding(self, inputs):
    """Computes causal padding dimensions for the given inputs."""
    return self._compute_buffered_causal_padding(
        inputs, use_buffered_input=self._use_buffered_input)

  def _validate_init(self):
    """Validates the Conv layer initial configuration."""
    self._causal_validate_init()

  def _spatial_output_shape(self, spatial_input_shape: List[int]):
    """Computes the spatial output shape from the input shape."""
    shape = super(Conv2D, self)._spatial_output_shape(spatial_input_shape)
    return self._buffered_spatial_output_shape(shape)


@tf.keras.utils.register_keras_serializable(package='Vision')
class DepthwiseConv2D(tf.keras.layers.DepthwiseConv2D, CausalConvMixin):
  """DepthwiseConv2D layer supporting CausalConv.

  Supports `padding='causal'` option (like in `tf.keras.layers.Conv1D`),
  which applies causal padding to the temporal dimension, and same padding in
  the spatial dimensions.
  """

  def __init__(self, *args, use_buffered_input=False, **kwargs):
    """Initializes depthwise conv2d.

    Args:
      *args: Arguments to be passed.
      use_buffered_input: A `bool`. If True, the input is expected to be padded
        beforehand. In effect, calling this layer will use 'valid' padding on
        the temporal dimension to simulate 'causal' padding.
      **kwargs: Additional keyword arguments to be passed.

    Returns:
      An output `tf.Tensor` of the DepthwiseConv2D operation.
    """
    super(DepthwiseConv2D, self).__init__(*args, **kwargs)
    self._use_buffered_input = use_buffered_input

    # Causal padding is unsupported by default for DepthwiseConv2D,
    # so we resort to valid padding internally. However, we handle
    # causal padding as a special case with `self._is_causal`, which is
    # defined by the super class.
    if self.padding == 'causal':
      self.padding = 'valid'

  def get_config(self):
    """Returns a dictionary containing the config used for initialization."""
    config = {
        'use_buffered_input': self._use_buffered_input,
    }
    base_config = super(DepthwiseConv2D, self).get_config()
    return dict(list(base_config.items()) + list(config.items()))

  def call(self, inputs):
    """Calls the layer with the given inputs."""
    if self._is_causal:
      inputs = tf.pad(inputs, self._compute_causal_padding(inputs))
    return super(DepthwiseConv2D, self).call(inputs)

  def _compute_causal_padding(self, inputs):
    """Computes causal padding dimensions for the given inputs."""
    return self._compute_buffered_causal_padding(
        inputs, use_buffered_input=self._use_buffered_input)

  def _validate_init(self):
    """Validates the Conv layer initial configuration."""
    self._causal_validate_init()

  def _spatial_output_shape(self, spatial_input_shape: List[int]):
    """Computes the spatial output shape from the input shape."""
    shape = super(DepthwiseConv2D, self)._spatial_output_shape(
        spatial_input_shape)
    return self._buffered_spatial_output_shape(shape)


@tf.keras.utils.register_keras_serializable(package='Vision')
class Conv3D(tf.keras.layers.Conv3D, CausalConvMixin):
  """Conv3D layer supporting CausalConv.

  Supports `padding='causal'` option (like in `tf.keras.layers.Conv1D`),
  which applies causal padding to the temporal dimension, and same padding in
  the spatial dimensions.
  """

  def __init__(self, *args, use_buffered_input=False, **kwargs):
    """Initializes conv3d.

    Args:
      *args: Arguments to be passed.
      use_buffered_input: A `bool`. If True, the input is expected to be padded
        beforehand. In effect, calling this layer will use 'valid' padding on
        the temporal dimension to simulate 'causal' padding.
      **kwargs: Additional keyword arguments to be passed.

    Returns:
      An output `tf.Tensor` of the Conv3D operation.
    """
    super(Conv3D, self).__init__(*args, **kwargs)
    self._use_buffered_input = use_buffered_input

  def get_config(self):
    """Returns a dictionary containing the config used for initialization."""
    config = {
        'use_buffered_input': self._use_buffered_input,
    }
    base_config = super(Conv3D, self).get_config()
    return dict(list(base_config.items()) + list(config.items()))

  def call(self, inputs):
    """Call the layer with the given inputs."""
    # Note: tf.nn.conv3d with depthwise kernels on CPU is currently only
    # supported when compiling with TF graph (XLA) using tf.function, so it
    # is compiled by default here (b/186463870).
    conv_fn = tf.function(super(Conv3D, self).call, jit_compile=True)
    return conv_fn(inputs)

  def _compute_causal_padding(self, inputs):
    """Computes causal padding dimensions for the given inputs."""
    return self._compute_buffered_causal_padding(
        inputs, use_buffered_input=self._use_buffered_input)

  def _validate_init(self):
    """Validates the Conv layer initial configuration."""
    self._causal_validate_init()

  def _spatial_output_shape(self, spatial_input_shape: List[int]):
    """Computes the spatial output shape from the input shape."""
    shape = super(Conv3D, self)._spatial_output_shape(spatial_input_shape)
    return self._buffered_spatial_output_shape(shape)


@tf.keras.utils.register_keras_serializable(package='Vision')
class SpatialPyramidPooling(tf.keras.layers.Layer):
  """Implements the Atrous Spatial Pyramid Pooling.

  References:
    [Rethinking Atrous Convolution for Semantic Image Segmentation](
      https://arxiv.org/pdf/1706.05587.pdf)
    [Encoder-Decoder with Atrous Separable Convolution for Semantic Image
    Segmentation](https://arxiv.org/pdf/1802.02611.pdf)
  """

  def __init__(
      self,
      output_channels: int,
      dilation_rates: List[int],
      pool_kernel_size: Optional[List[int]] = None,
      use_sync_bn: bool = False,
      batchnorm_momentum: float = 0.99,
      batchnorm_epsilon: float = 0.001,
      activation: str = 'relu',
      dropout: float = 0.5,
      kernel_initializer: str = 'GlorotUniform',
      kernel_regularizer: Optional[tf.keras.regularizers.Regularizer] = None,
      interpolation: str = 'bilinear',
      use_depthwise_convolution: bool = False,
      **kwargs):
    """Initializes `SpatialPyramidPooling`.

    Args:
      output_channels: Number of channels produced by SpatialPyramidPooling.
      dilation_rates: A list of integers for parallel dilated conv.
      pool_kernel_size: A list of integers or None. If None, global average
        pooling is applied, otherwise an average pooling of pool_kernel_size is
        applied.
      use_sync_bn: A bool, whether or not to use sync batch normalization.
      batchnorm_momentum: A float for the momentum in BatchNorm. Defaults to
        0.99.
      batchnorm_epsilon: A float for the epsilon value in BatchNorm. Defaults to
        0.001.
      activation: A `str` for type of activation to be used. Defaults to 'relu'.
      dropout: A float for the dropout rate before output. Defaults to 0.5.
      kernel_initializer: Kernel initializer for conv layers. Defaults to
        `glorot_uniform`.
      kernel_regularizer: Kernel regularizer for conv layers. Defaults to None.
      interpolation: The interpolation method for upsampling. Defaults to
        `bilinear`.
      use_depthwise_convolution: Allows spatial pooling to be separable
        depthwise convolusions. [Encoder-Decoder with Atrous Separable
        Convolution for Semantic Image Segmentation](
         https://arxiv.org/pdf/1802.02611.pdf)
      **kwargs: Other keyword arguments for the layer.
    """
    super().__init__(**kwargs)

    self._output_channels = output_channels
    self._dilation_rates = dilation_rates
    self._use_sync_bn = use_sync_bn
    self._batchnorm_momentum = batchnorm_momentum
    self._batchnorm_epsilon = batchnorm_epsilon
    self._activation = activation
    self._dropout = dropout
    self._kernel_initializer = kernel_initializer
    self._kernel_regularizer = kernel_regularizer
    self._interpolation = interpolation
    self._pool_kernel_size = pool_kernel_size
    self._use_depthwise_convolution = use_depthwise_convolution
    self._activation_fn = tf_utils.get_activation(activation)
    if self._use_sync_bn:
      self._bn_op = tf.keras.layers.experimental.SyncBatchNormalization
    else:
      self._bn_op = tf.keras.layers.BatchNormalization

    if tf.keras.backend.image_data_format() == 'channels_last':
      self._bn_axis = -1
    else:
      self._bn_axis = 1

  def build(self, input_shape):
    height = input_shape[1]
    width = input_shape[2]
    channels = input_shape[3]

    self.aspp_layers = []

    conv1 = tf.keras.layers.Conv2D(
        filters=self._output_channels,
        kernel_size=(1, 1),
        kernel_initializer=self._kernel_initializer,
        kernel_regularizer=self._kernel_regularizer,
        use_bias=False)
    norm1 = self._bn_op(
        axis=self._bn_axis,
        momentum=self._batchnorm_momentum,
        epsilon=self._batchnorm_epsilon)

    self.aspp_layers.append([conv1, norm1])

    for dilation_rate in self._dilation_rates:
      leading_layers = []
      kernel_size = (3, 3)
      if self._use_depthwise_convolution:
        leading_layers += [
            tf.keras.layers.DepthwiseConv2D(
                depth_multiplier=1,
                kernel_size=kernel_size,
                padding='same',
                depthwise_regularizer=self._kernel_regularizer,
                depthwise_initializer=self._kernel_initializer,
                dilation_rate=dilation_rate,
                use_bias=False)
        ]
        kernel_size = (1, 1)
      conv_dilation = leading_layers + [
          tf.keras.layers.Conv2D(
              filters=self._output_channels,
              kernel_size=kernel_size,
              padding='same',
              kernel_regularizer=self._kernel_regularizer,
              kernel_initializer=self._kernel_initializer,
              dilation_rate=dilation_rate,
              use_bias=False)
      ]
      norm_dilation = self._bn_op(
          axis=self._bn_axis,
          momentum=self._batchnorm_momentum,
          epsilon=self._batchnorm_epsilon)

      self.aspp_layers.append(conv_dilation + [norm_dilation])

    if self._pool_kernel_size is None:
      pooling = [
          tf.keras.layers.GlobalAveragePooling2D(),
          tf.keras.layers.Reshape((1, 1, channels))
      ]
    else:
      pooling = [tf.keras.layers.AveragePooling2D(self._pool_kernel_size)]

    conv2 = tf.keras.layers.Conv2D(
        filters=self._output_channels,
        kernel_size=(1, 1),
        kernel_initializer=self._kernel_initializer,
        kernel_regularizer=self._kernel_regularizer,
        use_bias=False)
    norm2 = self._bn_op(
        axis=self._bn_axis,
        momentum=self._batchnorm_momentum,
        epsilon=self._batchnorm_epsilon)

    self.aspp_layers.append(pooling + [conv2, norm2])

    self._resize_layer = tf.keras.layers.Resizing(
        height, width, interpolation=self._interpolation, dtype=tf.float32)

    self._projection = [
        tf.keras.layers.Conv2D(
            filters=self._output_channels,
            kernel_size=(1, 1),
            kernel_initializer=self._kernel_initializer,
            kernel_regularizer=self._kernel_regularizer,
            use_bias=False),
        self._bn_op(
            axis=self._bn_axis,
            momentum=self._batchnorm_momentum,
            epsilon=self._batchnorm_epsilon)
    ]
    self._dropout_layer = tf.keras.layers.Dropout(rate=self._dropout)
    self._concat_layer = tf.keras.layers.Concatenate(axis=-1)

  def call(self,
           inputs: tf.Tensor,
           training: Optional[bool] = None) -> tf.Tensor:
    if training is None:
      training = tf.keras.backend.learning_phase()
    result = []
    for i, layers in enumerate(self.aspp_layers):
      x = inputs
      for layer in layers:
        # Apply layers sequentially.
        x = layer(x, training=training)
      x = self._activation_fn(x)

      # Apply resize layer to the end of the last set of layers.
      if i == len(self.aspp_layers) - 1:
        x = self._resize_layer(x)

      result.append(tf.cast(x, inputs.dtype))
    x = self._concat_layer(result)
    for layer in self._projection:
      x = layer(x, training=training)
    x = self._activation_fn(x)
    return self._dropout_layer(x)

  def get_config(self):
    config = {
        'output_channels': self._output_channels,
        'dilation_rates': self._dilation_rates,
        'pool_kernel_size': self._pool_kernel_size,
        'use_sync_bn': self._use_sync_bn,
        'batchnorm_momentum': self._batchnorm_momentum,
        'batchnorm_epsilon': self._batchnorm_epsilon,
        'activation': self._activation,
        'dropout': self._dropout,
        'kernel_initializer': self._kernel_initializer,
        'kernel_regularizer': self._kernel_regularizer,
        'interpolation': self._interpolation,
    }
    base_config = super().get_config()
    return dict(list(base_config.items()) + list(config.items()))
