# Copyright 2016 James Hensman, alexggmatthews
#
# 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.


"""
A collection of wrappers and extensions for tensorflow.
"""

import tensorflow as tf
import numpy as np


def vec_to_tri( vectors, N ):
	#Takes a D x M tensor `vectors'
	#and maps it to a D x matrix_size X matrix_sizetensor
	#where the where the lower
	#triangle of each matrix_size x matrix_size matrix
	#is constructed by unpacking each M-vector.
	#Native TensorFlow version of Custom Op by Mark van der Wilk.
	#def int_shape(x):
	#	return list(map(int, x.get_shape()))

	#D, M = int_shape(vectors)
	#N = int( np.floor( 0.5 * np.sqrt( M * 8. + 1. ) - 0.5 ) )
	#assert( (matri*(N+1)) == (2 * M ) ) #check M is a valid triangle number.
	indices = list(zip(*np.tril_indices(N)))
	indices = tf.constant([ list(i) for i in indices], dtype=tf.int64)

	def vec_to_tri_vector(vector):
		return tf.scatter_nd(indices=indices, shape=[N, N], updates=vector)

	return tf.map_fn( vec_to_tri_vector, vectors )