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#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#
#     http://www.apache.org/licenses/LICENSE-2.0
#
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"""Common modeling utilities."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from typing import Optional, Text
import numpy as np
import tensorflow as tf, tf_keras
import tensorflow.compat.v1 as tf1
from tensorflow.python.tpu import tpu_function


@tf_keras.utils.register_keras_serializable(package='Vision')
class TpuBatchNormalization(tf_keras.layers.BatchNormalization):
  """Cross replica batch normalization."""

  def __init__(self, fused: Optional[bool] = False, **kwargs):
    if fused in (True, None):
      raise ValueError('TpuBatchNormalization does not support fused=True.')
    super(TpuBatchNormalization, self).__init__(fused=fused, **kwargs)

  def _cross_replica_average(self, t: tf.Tensor, num_shards_per_group: int):
    """Calculates the average value of input tensor across TPU replicas."""
    num_shards = tpu_function.get_tpu_context().number_of_shards
    group_assignment = None
    if num_shards_per_group > 1:
      if num_shards % num_shards_per_group != 0:
        raise ValueError(
            'num_shards: %d mod shards_per_group: %d, should be 0' %
            (num_shards, num_shards_per_group))
      num_groups = num_shards // num_shards_per_group
      group_assignment = [[
          x for x in range(num_shards) if x // num_shards_per_group == y
      ] for y in range(num_groups)]
    return tf1.tpu.cross_replica_sum(t, group_assignment) / tf.cast(
        num_shards_per_group, t.dtype)

  def _moments(self, inputs: tf.Tensor, reduction_axes: int, keep_dims: int):
    """Compute the mean and variance: it overrides the original _moments."""
    shard_mean, shard_variance = super(TpuBatchNormalization, self)._moments(
        inputs, reduction_axes, keep_dims=keep_dims)

    num_shards = tpu_function.get_tpu_context().number_of_shards or 1
    if num_shards <= 8:  # Skip cross_replica for 2x2 or smaller slices.
      num_shards_per_group = 1
    else:
      num_shards_per_group = max(8, num_shards // 8)
    if num_shards_per_group > 1:
      # Compute variance using: Var[X]= E[X^2] - E[X]^2.
      shard_square_of_mean = tf.math.square(shard_mean)
      shard_mean_of_square = shard_variance + shard_square_of_mean
      group_mean = self._cross_replica_average(shard_mean, num_shards_per_group)
      group_mean_of_square = self._cross_replica_average(
          shard_mean_of_square, num_shards_per_group)
      group_variance = group_mean_of_square - tf.math.square(group_mean)
      return (group_mean, group_variance)
    else:
      return (shard_mean, shard_variance)


def get_batch_norm(batch_norm_type: Text) -> tf_keras.layers.BatchNormalization:
  """A helper to create a batch normalization getter.

  Args:
    batch_norm_type: The type of batch normalization layer implementation. `tpu`
      will use `TpuBatchNormalization`.

  Returns:
    An instance of `tf_keras.layers.BatchNormalization`.
  """
  if batch_norm_type == 'tpu':
    return TpuBatchNormalization

  return tf_keras.layers.BatchNormalization  # pytype: disable=bad-return-type  # typed-keras


def count_params(model, trainable_only=True):
  """Returns the count of all model parameters, or just trainable ones."""
  if not trainable_only:
    return model.count_params()
  else:
    return int(
        np.sum([
            tf_keras.backend.count_params(p) for p in model.trainable_weights
        ]))


def load_weights(model: tf_keras.Model,
                 model_weights_path: Text,
                 weights_format: Text = 'saved_model'):
  """Load model weights from the given file path.

  Args:
    model: the model to load weights into
    model_weights_path: the path of the model weights
    weights_format: the model weights format. One of 'saved_model', 'h5', or
      'checkpoint'.
  """
  if weights_format == 'saved_model':
    loaded_model = tf_keras.models.load_model(model_weights_path)
    model.set_weights(loaded_model.get_weights())
  else:
    model.load_weights(model_weights_path)
