# Copyright 2015 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.
# =============================================================================

"""Contains layer utilies for input validation and format conversion.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

from tensorflow.python.ops import variables
from tensorflow.python.ops import control_flow_ops
from tensorflow.python.framework import smart_cond as smart_module
from tensorflow.python.util import nest


def convert_data_format(data_format, ndim):
  if data_format == 'channels_last':
    if ndim == 3:
      return 'NWC'
    elif ndim == 4:
      return 'NHWC'
    elif ndim == 5:
      return 'NDHWC'
    else:
      raise ValueError('Input rank not supported:', ndim)
  elif data_format == 'channels_first':
    if ndim == 3:
      return 'NCW'
    elif ndim == 4:
      return 'NCHW'
    elif ndim == 5:
      return 'NCDHW'
    else:
      raise ValueError('Input rank not supported:', ndim)
  else:
    raise ValueError('Invalid data_format:', data_format)


def normalize_tuple(value, n, name):
  """Transforms a single integer or iterable of integers into an integer tuple.

  Arguments:
    value: The value to validate and convert. Could an int, or any iterable
      of ints.
    n: The size of the tuple to be returned.
    name: The name of the argument being validated, e.g. "strides" or
      "kernel_size". This is only used to format error messages.

  Returns:
    A tuple of n integers.

  Raises:
    ValueError: If something else than an int/long or iterable thereof was
      passed.
  """
  if isinstance(value, int):
    return (value,) * n
  else:
    try:
      value_tuple = tuple(value)
    except TypeError:
      raise ValueError('The `' + name + '` argument must be a tuple of ' +
                       str(n) + ' integers. Received: ' + str(value))
    if len(value_tuple) != n:
      raise ValueError('The `' + name + '` argument must be a tuple of ' +
                       str(n) + ' integers. Received: ' + str(value))
    for single_value in value_tuple:
      try:
        int(single_value)
      except (ValueError, TypeError):
        raise ValueError('The `' + name + '` argument must be a tuple of ' +
                         str(n) + ' integers. Received: ' + str(value) + ' '
                         'including element ' + str(single_value) + ' of type' +
                         ' ' + str(type(single_value)))
    return value_tuple


def normalize_data_format(value):
  data_format = value.lower()
  if data_format not in {'channels_first', 'channels_last'}:
    raise ValueError('The `data_format` argument must be one of '
                     '"channels_first", "channels_last". Received: ' +
                     str(value))
  return data_format


def normalize_padding(value):
  padding = value.lower()
  if padding not in {'valid', 'same'}:
    raise ValueError('The `padding` argument must be one of "valid", "same". '
                     'Received: ' + str(padding))
  return padding


def conv_output_length(input_length, filter_size, padding, stride, dilation=1):
  """Determines output length of a convolution given input length.

  Arguments:
      input_length: integer.
      filter_size: integer.
      padding: one of "same", "valid", "full".
      stride: integer.
      dilation: dilation rate, integer.

  Returns:
      The output length (integer).
  """
  if input_length is None:
    return None
  assert padding in {'same', 'valid', 'full'}
  dilated_filter_size = filter_size + (filter_size - 1) * (dilation - 1)
  if padding == 'same':
    output_length = input_length
  elif padding == 'valid':
    output_length = input_length - dilated_filter_size + 1
  elif padding == 'full':
    output_length = input_length + dilated_filter_size - 1
  return (output_length + stride - 1) // stride


def conv_input_length(output_length, filter_size, padding, stride):
  """Determines input length of a convolution given output length.

  Arguments:
      output_length: integer.
      filter_size: integer.
      padding: one of "same", "valid", "full".
      stride: integer.

  Returns:
      The input length (integer).
  """
  if output_length is None:
    return None
  assert padding in {'same', 'valid', 'full'}
  if padding == 'same':
    pad = filter_size // 2
  elif padding == 'valid':
    pad = 0
  elif padding == 'full':
    pad = filter_size - 1
  return (output_length - 1) * stride - 2 * pad + filter_size


def deconv_output_length(input_length, filter_size, padding, stride):
  """Determines output length of a transposed convolution given input length.

  Arguments:
      input_length: integer.
      filter_size: integer.
      padding: one of "same", "valid", "full".
      stride: integer.

  Returns:
      The output length (integer).
  """
  if input_length is None:
    return None
  input_length *= stride
  if padding == 'valid':
    input_length += max(filter_size - stride, 0)
  elif padding == 'full':
    input_length -= (stride + filter_size - 2)
  return input_length


def smart_cond(pred, true_fn=None, false_fn=None, name=None):
  """Return either `true_fn()` if predicate `pred` is true else `false_fn()`.

  If `pred` is a bool or has a constant value, we return either `true_fn()`
  or `false_fn()`, otherwise we use `tf.cond` to dynamically route to both.

  Arguments:
    pred: A scalar determining whether to return the result of `true_fn` or
      `false_fn`.
    true_fn: The callable to be performed if pred is true.
    false_fn: The callable to be performed if pred is false.
    name: Optional name prefix when using `tf.cond`.

  Returns:
    Tensors returned by the call to either `true_fn` or `false_fn`.

  Raises:
    TypeError: If `true_fn` or `false_fn` is not callable.
  """
  if isinstance(pred, variables.Variable):
    return control_flow_ops.cond(
        pred, true_fn=true_fn, false_fn=false_fn, name=name)
  return smart_module.smart_cond(
      pred, true_fn=true_fn, false_fn=false_fn, name=name)


def constant_value(pred):
  """Return the bool value for `pred`, or None if `pred` had a dynamic value.

    Arguments:
      pred: A scalar, either a Python bool or a TensorFlow boolean variable
        or tensor, or the Python integer 1 or 0.

    Returns:
      True or False if `pred` has a constant boolean value, None otherwise.

    Raises:
      TypeError: If `pred` is not a Variable, Tensor or bool, or Python
        interger 1 or 0.
    """
  # Allow integer booleans.
  if isinstance(pred, int):
    if pred == 1:
      pred = True
    elif pred == 0:
      pred = False

  if isinstance(pred, variables.Variable):
    return None
  return smart_module.smart_constant_value(pred)


def object_list_uid(object_list):
  """Creates a single string from object ids."""
  object_list = nest.flatten(object_list)
  return ', '.join([str(abs(id(x))) for x in object_list])


def static_shape(x):
  """Get the static shape of a Tensor, or None if it is unavailable."""
  if x is None:
    return None
  try:
    return tuple(x.get_shape().as_list())
  except ValueError:
    return None


def get_reachable_from_inputs(inputs, targets=None):
  """Returns the set of tensors reachable from `inputs`.

  Stops if all targets have been found (target is optional).

  Only valid in Symbolic mode, not Eager mode.

  Args:
    inputs: List of tensors.
    targets: List of tensors.

  Returns:
    A set of tensors reachable from the inputs (includes the inputs themselves).
  """
  reachable = set(inputs)
  if targets:
    targets = set(targets)
  queue = inputs[:]

  while queue:
    x = queue.pop()
    outputs = []
    try:
      consumers = x.consumers()
    except AttributeError:
      # Case where x is a variable type
      consumers = [x.op]
    for z in consumers:
      consumer_outputs = z.outputs
      if consumer_outputs:  # May be None
        outputs += consumer_outputs

    for y in outputs:
      if y not in reachable:
        reachable.add(y)
        queue.insert(0, y)

    if targets and targets.issubset(reachable):
      return reachable
  return reachable