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"""Transformer model helper methods."""

import math

import numpy as np
import tensorflow as tf, tf_keras

# Very low numbers to represent -infinity. We do not actually use -Inf, since we
# want to be able to multiply these values by zero to get zero. (-Inf * 0 = NaN)
_NEG_INF_FP32 = -1e9
_NEG_INF_FP16 = np.finfo(np.float16).min


def get_position_encoding(length,
                          hidden_size,
                          min_timescale=1.0,
                          max_timescale=1.0e4):
  """Return positional encoding.

  Calculates the position encoding as a mix of sine and cosine functions with
  geometrically increasing wavelengths.
  Defined and formulized in Attention is All You Need, section 3.5.

  Args:
    length: Sequence length.
    hidden_size: Size of the
    min_timescale: Minimum scale that will be applied at each position
    max_timescale: Maximum scale that will be applied at each position

  Returns:
    Tensor with shape [length, hidden_size]
  """
  # We compute the positional encoding in float32 even if the model uses
  # float16, as many of the ops used, like log and exp, are numerically unstable
  # in float16.
  position = tf.cast(tf.range(length), tf.float32)
  num_timescales = hidden_size // 2
  log_timescale_increment = (
      math.log(float(max_timescale) / float(min_timescale)) /
      (tf.cast(num_timescales, tf.float32) - 1))
  inv_timescales = min_timescale * tf.exp(
      tf.cast(tf.range(num_timescales), tf.float32) * -log_timescale_increment)
  scaled_time = tf.expand_dims(position, 1) * tf.expand_dims(inv_timescales, 0)
  signal = tf.concat([tf.sin(scaled_time), tf.cos(scaled_time)], axis=1)
  return signal


def get_decoder_self_attention_bias(length, dtype=tf.float32):
  """Calculate bias for decoder that maintains model's autoregressive property.

  Creates a tensor that masks out locations that correspond to illegal
  connections, so prediction at position i cannot draw information from future
  positions.

  Args:
    length: int length of sequences in batch.
    dtype: The dtype of the return value.

  Returns:
    float tensor of shape [1, 1, length, length]
  """
  neg_inf = _NEG_INF_FP16 if dtype == tf.float16 else _NEG_INF_FP32
  with tf.name_scope("decoder_self_attention_bias"):
    valid_locs = tf.linalg.band_part(
        tf.ones([length, length], dtype=dtype), -1, 0)
    valid_locs = tf.reshape(valid_locs, [1, 1, length, length])
    decoder_bias = neg_inf * (1.0 - valid_locs)
  return decoder_bias


def get_padding(x, padding_value=0, dtype=tf.float32):
  """Return float tensor representing the padding values in x.

  Args:
    x: int tensor with any shape
    padding_value: int which represents padded values in input
    dtype: The dtype of the return value.

  Returns:
    float tensor with same shape as x containing values 0 or 1.
      0 -> non-padding, 1 -> padding
  """
  with tf.name_scope("padding"):
    return tf.cast(tf.equal(x, padding_value), dtype)


def get_padding_bias(x, padding_value=0, dtype=tf.float32):
  """Calculate bias tensor from padding values in tensor.

  Bias tensor that is added to the pre-softmax multi-headed attention logits,
  which has shape [batch_size, num_heads, length, length]. The tensor is zero at
  non-padding locations, and -1e9 (negative infinity) at padding locations.

  Args:
    x: int tensor with shape [batch_size, length]
    padding_value: int which represents padded values in input
    dtype: The dtype of the return value

  Returns:
    Attention bias tensor of shape [batch_size, 1, 1, length].
  """
  with tf.name_scope("attention_bias"):
    padding = get_padding(x, padding_value, dtype)
    attention_bias = padding * _NEG_INF_FP32
    attention_bias = tf.expand_dims(
        tf.expand_dims(attention_bias, axis=1), axis=1)
  return attention_bias