# Copyright 2017 Paul Balanca. 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.
# ==============================================================================
"""TF Extended: additional tensors operations.
"""
import tensorflow as tf

from tensorflow.contrib.framework.python.ops import variables as contrib_variables
from tensorflow.contrib.metrics.python.ops import set_ops
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.framework import sparse_tensor
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import check_ops
from tensorflow.python.ops import control_flow_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import nn
from tensorflow.python.ops import state_ops
from tensorflow.python.ops import variable_scope
from tensorflow.python.ops import variables


def get_shape(x, rank=None):
    """Returns the dimensions of a Tensor as list of integers or scale tensors.

    Args:
      x: N-d Tensor;
      rank: Rank of the Tensor. If None, will try to guess it.
    Returns:
      A list of `[d1, d2, ..., dN]` corresponding to the dimensions of the
        input tensor.  Dimensions that are statically known are python integers,
        otherwise they are integer scalar tensors.
    """
    if x.get_shape().is_fully_defined():
        return x.get_shape().as_list()
    else:
        static_shape = x.get_shape()
        if rank is None:
            static_shape = static_shape.as_list()
            rank = len(static_shape)
        else:
            static_shape = x.get_shape().with_rank(rank).as_list()
        dynamic_shape = tf.unstack(tf.shape(x), rank)
        return [s if s is not None else d
                for s, d in zip(static_shape, dynamic_shape)]


def pad_axis(x, offset, size, axis=0, name=None):
    """Pad a tensor on an axis, with a given offset and output size.
    The tensor is padded with zero (i.e. CONSTANT mode). Note that the if the
    `size` is smaller than existing size + `offset`, the output tensor
    was the latter dimension.

    Args:
      x: Tensor to pad;
      offset: Offset to add on the dimension chosen;
      size: Final size of the dimension.
    Return:
      Padded tensor whose dimension on `axis` is `size`, or greater if
      the input vector was larger.
    """
    with tf.name_scope(name, 'pad_axis'):
        shape = get_shape(x)
        rank = len(shape)
        # Padding description.
        new_size = tf.maximum(size-offset-shape[axis], 0)
        pad1 = tf.stack([0]*axis + [offset] + [0]*(rank-axis-1))
        pad2 = tf.stack([0]*axis + [new_size] + [0]*(rank-axis-1))
        paddings = tf.stack([pad1, pad2], axis=1)
        x = tf.pad(x, paddings, mode='CONSTANT')
        # Reshape, to get fully defined shape if possible.
        # TODO: fix with tf.slice
        shape[axis] = size
        x = tf.reshape(x, tf.stack(shape))
        return x


# def select_at_index(idx, val, t):
#     """Return a tensor.
#     """
#     idx = tf.expand_dims(tf.expand_dims(idx, 0), 0)
#     val = tf.expand_dims(val, 0)
#     t = t + tf.scatter_nd(idx, val, tf.shape(t))
#     return t