# Copyright 2019 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
#
# 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.
# ==============================================================================
"""Common TF utilities."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import math
import six
import tensorflow.compat.v2 as tf


def pack_inputs(inputs):
  """Pack a list of `inputs` tensors to a tuple.

  Args:
    inputs: a list of tensors.

  Returns:
    a tuple of tensors. if any input is None, replace it with a special constant
    tensor.
  """
  inputs = tf.nest.flatten(inputs)
  outputs = []
  for x in inputs:
    if x is None:
      outputs.append(tf.constant(0, shape=[], dtype=tf.int32))
    else:
      outputs.append(x)
  return tuple(outputs)


def unpack_inputs(inputs):
  """unpack a tuple of `inputs` tensors to a tuple.

  Args:
    inputs: a list of tensors.

  Returns:
    a tuple of tensors. if any input is a special constant tensor, replace it
    with None.
  """
  inputs = tf.nest.flatten(inputs)
  outputs = []
  for x in inputs:
    if is_special_none_tensor(x):
      outputs.append(None)
    else:
      outputs.append(x)
  x = tuple(outputs)

  # To trick the very pointless 'unbalanced-tuple-unpacking' pylint check
  # from triggering.
  if len(x) == 1:
    return x[0]
  return tuple(outputs)


def is_special_none_tensor(tensor):
  """Checks if a tensor is a special None Tensor."""
  return tensor.shape.ndims == 0 and tensor.dtype == tf.int32


def gelu(x):
  """Gaussian Error Linear Unit.

  This is a smoother version of the RELU.
  Original paper: https://arxiv.org/abs/1606.08415
  Args:
    x: float Tensor to perform activation.

  Returns:
    `x` with the GELU activation applied.
  """
  cdf = 0.5 * (1.0 + tf.tanh(
      (math.sqrt(2 / math.pi) * (x + 0.044715 * tf.pow(x, 3)))))
  return x * cdf


def get_activation(identifier):
  """Maps a string to a Python function, e.g., "relu" => `tf.nn.relu`.

  Args:
    identifier: String name of the activation function.

  Returns:
    A Python function corresponding to the activation function. If
    `identifier` is None, empty, or "linear", this will return None.
    If `identifier` is not a string, it will return `identifier`.

  Raises:
    ValueError: The `identifier` does not correspond to a known
      activation.
  """
  if identifier is None:
    return None
  elif isinstance(identifier, six.string_types):
    name_to_fn = {
        "linear": None,
        "relu": tf.nn.relu,
        "gelu": gelu,
        "tanh": tf.nn.tanh,
    }
    identifier = str(identifier).lower()
    if identifier not in name_to_fn:
      raise ValueError("Unsupported activation function: %s" % (identifier))
    return name_to_fn[identifier]
  elif callable(identifier):
    return identifier
  else:
    raise ValueError("Could not interpret activation "
                     "function identifier: %s" % (identifier))


def get_shape_list(tensor, expected_rank=None, name=None):
  """Returns a list of the shape of tensor, preferring static dimensions.

  Args:
    tensor: A tf.Tensor object to find the shape of.
    expected_rank: (optional) int. The expected rank of `tensor`. If this is
      specified and the `tensor` has a different rank, and exception will be
      thrown.
    name: Optional name of the tensor for the error message.

  Returns:
    A list of dimensions of the shape of tensor. All static dimensions will
    be returned as python integers, and dynamic dimensions will be returned
    as tf.Tensor scalars.
  """
  if expected_rank is not None:
    assert_rank(tensor, expected_rank, name)

  shape = tensor.shape.as_list()

  non_static_indexes = []
  for (index, dim) in enumerate(shape):
    if dim is None:
      non_static_indexes.append(index)

  if not non_static_indexes:
    return shape

  dyn_shape = tf.shape(tensor)
  for index in non_static_indexes:
    shape[index] = dyn_shape[index]
  return shape


def assert_rank(tensor, expected_rank, name=None):
  """Raises an exception if the tensor rank is not of the expected rank.

  Args:
    tensor: A tf.Tensor to check the rank of.
    expected_rank: Python integer or list of integers, expected rank.
    name: Optional name of the tensor for the error message.

  Raises:
    ValueError: If the expected shape doesn't match the actual shape.
  """
  expected_rank_dict = {}
  if isinstance(expected_rank, six.integer_types):
    expected_rank_dict[expected_rank] = True
  else:
    for x in expected_rank:
      expected_rank_dict[x] = True

  actual_rank = tensor.shape.ndims
  if actual_rank not in expected_rank_dict:
    raise ValueError(
        "For the tensor `%s`, the actual tensor rank `%d` (shape = %s) is not "
        "equal to the expected tensor rank `%s`" %
        (name, actual_rank, str(tensor.shape), str(expected_rank)))