from __future__ import absolute_import

import numpy as np
import tensorflow as tf
from scipy.sparse import coo_matrix


def cuda():
    return '/gpu:0'


def is_cuda_available():
    return tf.test.is_gpu_available(cuda_only=True)


def array_equal(a, b):
    return np.array_equal(a.numpy(), b.numpy())


def allclose(a, b, rtol=1e-4, atol=1e-4):
    return np.allclose(tf.convert_to_tensor(a).numpy(),
                       tf.convert_to_tensor(b).numpy(), rtol=rtol, atol=atol)


def randn(shape):
    return tf.random.normal(shape)


def full(shape, fill_value, dtype, ctx):
    with tf.device(ctx):
        t = tf.constant(fill_value, shape=shape, dtype=dtype)
    return t


def narrow_row_set(x, start, stop, new):
    # x[start:stop] = new
    raise NotImplementedError("TF doesn't support inplace update")


def sparse_to_numpy(x):
    # tf.sparse.to_dense assume sorted indices, need to turn off validate_indices in our cases
    return tf.sparse.to_dense(x, validate_indices=False).numpy()


def clone(x):
    return tf.identity(x)


def reduce_sum(x):
    return tf.reduce_sum(x)


def softmax(x, dim):
    return tf.math.softmax(x, axis=dim)


def spmm(x, y):
    return tf.sparse.sparse_dense_matmul(x, y)


def add(a, b):
    return a + b


def sub(a, b):
    return a - b


def mul(a, b):
    return a * b


def div(a, b):
    return a / b


def sum(x, dim, keepdims=False):
    return tf.reduce_sum(x, axis=dim, keepdims=keepdims)


def max(x, dim):
    return tf.reduce_max(x, axis=dim)


def min(x, dim):
    return tf.reduce_min(x, axis=dim)


def prod(x, dim):
    return tf.reduce_prod(x, axis=dim)


def matmul(a, b):
    return tf.linalg.matmul(a, b)


def dot(a, b):
    return sum(mul(a, b), dim=-1)
