import unittest
import numpy as np
from op_test import OpTest
import math


def find_latest_set(num):
    return 1 + int(math.floor(math.log(num, 2)))


class CodeTable(object):
    def __init__(self, num_classes, code):
        self.c = num_classes + code

    def cal_index(self, bit):
        return (self.c >> (bit + 1)) - 1

    def get_length(self):
        return find_latest_set(self.c) - 1

    def cal_bit(self, bit):
        return self.c & (1 << bit)


def hsigmoid(x, w, ids, bias, num_classes):
    # code length = 
    # initialize pre out with dims={batch_size, code_length}
    batch_size = x.shape[0]
    code_length = find_latest_set(num_classes - 1)
    code_table = [0 for _ in range(code_length)]
    pre_output = np.zeros((batch_size, code_length))
    pre_sum = np.zeros((batch_size, 1))
    out = np.zeros((batch_size, 1)).astype("float32")
    # pre_out += code(bias)
    for i in xrange(batch_size):
        code_table = CodeTable(num_classes, ids[i])
        length = code_table.get_length()
        for j in xrange(length):
            idx = code_table.cal_index(j)
            pre_output[i][j] += bias[0][idx]
    # pre_out += code(w) * x
    for i in xrange(batch_size):
        for j in xrange(batch_size):
            code_table = CodeTable(num_classes, ids[j])
            length = code_table.get_length()
            for k in xrange(length):
                idx = code_table.cal_index(k)
                sum = 0.0
                for l in xrange(x.shape[1]):
                    sum += w[i][idx][l] * x[j][l]
                pre_output[j][k] += sum
    # clip[-40.0, 40.0]
    np.clip(pre_output, -40.0, 40.0)
    # out(i, 0) = \sum_j  bit(i, j) * preout(i, j)
    for i in xrange(batch_size):
        code_table = CodeTable(num_classes, ids[i])
        length = code_table.get_length()
        sum = 0.0
        for j in xrange(length):
            if code_table.cal_bit(j):
                sum += pre_output[i][j]
        out[i] = -1.0 * sum
    # soft relu
    np.clip(pre_output, -40.0, 40.0)
    pre_output = np.log(1 + np.exp(pre_output))
    pre_sum = pre_output.sum(1).reshape((batch_size, 1))
    out += pre_sum
    return out


class TestHSigmoidOp(OpTest):
    def setUp(self):
        self.op_type = "hierarchical_sigmoid"
        num_classes = 6
        embded_size = 10
        batch_size = 5
        x = np.random.random((batch_size, embded_size)).astype("float32")
        w = np.random.random(
            (batch_size, num_classes - 1, embded_size)).astype("float32")
        ids = np.random.randint(0, num_classes, batch_size)
        bias = np.random.random((1, num_classes - 1)).astype("float32")
        self.inputs = {'X': x, 'W': w, 'Ids': ids, 'Bias': bias}
        self.attrs = {'num_classes': num_classes}
        out = hsigmoid(x, w, ids, bias, num_classes)
        self.outputs = {'Out': out}

    def test_check_output(self):
        self.check_output()

    def test_check_grad(self):
        self.check_grad(['X', 'W', 'Bias'], 'Out', no_grad_set=set('Ids'))


if __name__ == '__main__':
    unittest.main()