#   Copyright (c) 2018 PaddlePaddle 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.

from __future__ import print_function

import unittest
import paddle
import paddle.fluid as fluid
import numpy as np
import six
import inspect


class TestMathOpPatchesVarBase(unittest.TestCase):
    def setUp(self):
        self.shape = [10, 1024]
        self.dtype = np.float32

    def test_add(self):
        a_np = np.random.random(self.shape).astype(self.dtype)
        b_np = np.random.random(self.shape).astype(self.dtype)
        with fluid.dygraph.guard():
            a = fluid.dygraph.to_variable(a_np)
            b = fluid.dygraph.to_variable(b_np)
            res = a + b
            self.assertTrue(np.array_equal(res.numpy(), a_np + b_np))

    def test_sub(self):
        a_np = np.random.random(self.shape).astype(self.dtype)
        b_np = np.random.random(self.shape).astype(self.dtype)
        with fluid.dygraph.guard():
            a = fluid.dygraph.to_variable(a_np)
            b = fluid.dygraph.to_variable(b_np)
            res = a - b
            self.assertTrue(np.array_equal(res.numpy(), a_np - b_np))

    def test_mul(self):
        a_np = np.random.random(self.shape).astype(self.dtype)
        b_np = np.random.random(self.shape).astype(self.dtype)
        with fluid.dygraph.guard():
            a = fluid.dygraph.to_variable(a_np)
            b = fluid.dygraph.to_variable(b_np)
            res = a * b
            self.assertTrue(np.array_equal(res.numpy(), a_np * b_np))

    def test_div(self):
        a_np = np.random.random(self.shape).astype(self.dtype)
        b_np = np.random.random(self.shape).astype(self.dtype)
        with fluid.dygraph.guard():
            a = fluid.dygraph.to_variable(a_np)
            b = fluid.dygraph.to_variable(b_np)
            res = a / b
            #NOTE: Not sure why array_equal fails on windows, allclose is acceptable
            self.assertTrue(np.allclose(res.numpy(), a_np / b_np))

    def test_add_scalar(self):
        a_np = np.random.random(self.shape).astype(self.dtype)
        with fluid.dygraph.guard():
            a = fluid.dygraph.to_variable(a_np)
            b = 0.1
            res = a + b
            self.assertTrue(np.array_equal(res.numpy(), a_np + b))

    def test_add_scalar_reverse(self):
        a_np = np.random.random(self.shape).astype(self.dtype)
        with fluid.dygraph.guard():
            a = fluid.dygraph.to_variable(a_np)
            b = 0.1
            res = b + a
            self.assertTrue(np.array_equal(res.numpy(), b + a_np))

    def test_sub_scalar(self):
        a_np = np.random.random(self.shape).astype(self.dtype)
        with fluid.dygraph.guard():
            a = fluid.dygraph.to_variable(a_np)
            b = 0.1
            res = a - b
            self.assertTrue(np.array_equal(res.numpy(), a_np - b))

    def test_sub_scalar_reverse(self):
        a_np = np.random.random(self.shape).astype(self.dtype)
        with fluid.dygraph.guard():
            a = fluid.dygraph.to_variable(a_np)
            b = 0.1
            res = b - a
            self.assertTrue(np.array_equal(res.numpy(), b - a_np))

    def test_mul_scalar(self):
        a_np = np.random.random(self.shape).astype(self.dtype)
        with fluid.dygraph.guard():
            a = fluid.dygraph.to_variable(a_np)
            b = 0.1
            res = a * b
            self.assertTrue(np.array_equal(res.numpy(), a_np * b))

    # div_scalar, not equal
    def test_div_scalar(self):
        a_np = np.random.random(self.shape).astype(self.dtype)
        with fluid.dygraph.guard():
            a = fluid.dygraph.to_variable(a_np)
            b = 0.1
            res = a / b
            self.assertTrue(np.allclose(res.numpy(), a_np / b))

    # pow of float type, not equal
    def test_pow(self):
        a_np = np.random.random(self.shape).astype(self.dtype)
        b_np = np.random.random(self.shape).astype(self.dtype)
        with fluid.dygraph.guard():
            a = fluid.dygraph.to_variable(a_np)
            b = fluid.dygraph.to_variable(b_np)
            res = a**b
            self.assertTrue(np.allclose(res.numpy(), a_np**b_np))

    def test_floor_div(self):
        a_np = np.random.randint(1, 100, size=self.shape)
        b_np = np.random.randint(1, 100, size=self.shape)
        with fluid.dygraph.guard():
            a = fluid.dygraph.to_variable(a_np)
            b = fluid.dygraph.to_variable(b_np)
            res = a // b
            self.assertTrue(np.array_equal(res.numpy(), a_np // b_np))

    def test_mod(self):
        a_np = np.random.randint(1, 100, size=self.shape)
        b_np = np.random.randint(1, 100, size=self.shape)
        with fluid.dygraph.guard():
            a = fluid.dygraph.to_variable(a_np)
            b = fluid.dygraph.to_variable(b_np)
            res = a % b
            self.assertTrue(np.array_equal(res.numpy(), a_np % b_np))

    # for logical compare
    def test_equal(self):
        a_np = np.asarray([1, 2, 3, 4, 5])
        b_np = np.asarray([1, 2, 3, 4, 5])
        c_np = np.asarray([1, 2, 2, 4, 5])
        with fluid.dygraph.guard():
            a = fluid.dygraph.to_variable(a_np)
            b = fluid.dygraph.to_variable(b_np)
            c = fluid.dygraph.to_variable(c_np)
            res1 = (a == b)
            res2 = (a == c)
            self.assertTrue(np.array_equal(res1.numpy(), a_np == b_np))
            self.assertTrue(np.array_equal(res2.numpy(), a_np == c_np))

    def test_not_equal(self):
        a_np = np.asarray([1, 2, 3, 4, 5])
        b_np = np.asarray([1, 2, 3, 4, 5])
        c_np = np.asarray([1, 2, 2, 4, 5])
        with fluid.dygraph.guard():
            a = fluid.dygraph.to_variable(a_np)
            b = fluid.dygraph.to_variable(b_np)
            c = fluid.dygraph.to_variable(c_np)
            res1 = (a != b)
            res2 = (a != c)
            self.assertTrue(np.array_equal(res1.numpy(), a_np != b_np))
            self.assertTrue(np.array_equal(res2.numpy(), a_np != c_np))

    def test_less_than(self):
        a_np = np.random.random(self.shape).astype(self.dtype)
        b_np = np.random.random(self.shape).astype(self.dtype)
        with fluid.dygraph.guard():
            a = fluid.dygraph.to_variable(a_np)
            b = fluid.dygraph.to_variable(b_np)
            res = (a < b)
            self.assertTrue(np.array_equal(res.numpy(), a_np < b_np))

    def test_less_equal(self):
        a_np = np.random.random(self.shape).astype(self.dtype)
        b_np = np.random.random(self.shape).astype(self.dtype)
        with fluid.dygraph.guard():
            a = fluid.dygraph.to_variable(a_np)
            b = fluid.dygraph.to_variable(b_np)
            res = (a <= b)
            self.assertTrue(np.array_equal(res.numpy(), a_np <= b_np))

    def test_greater_than(self):
        a_np = np.random.random(self.shape).astype(self.dtype)
        b_np = np.random.random(self.shape).astype(self.dtype)
        with fluid.dygraph.guard():
            a = fluid.dygraph.to_variable(a_np)
            b = fluid.dygraph.to_variable(b_np)
            res = (a > b)
            self.assertTrue(np.array_equal(res.numpy(), a_np > b_np))

    def test_greater_equal(self):
        a_np = np.random.random(self.shape).astype(self.dtype)
        b_np = np.random.random(self.shape).astype(self.dtype)
        with fluid.dygraph.guard():
            a = fluid.dygraph.to_variable(a_np)
            b = fluid.dygraph.to_variable(b_np)
            res = (a >= b)
            self.assertTrue(np.array_equal(res.numpy(), a_np >= b_np))

    def test_neg(self):
        a_np = np.random.uniform(-1, 1, self.shape).astype(self.dtype)
        with fluid.dygraph.guard():
            a = fluid.dygraph.to_variable(a_np)
            res = -a
            self.assertTrue(np.array_equal(res.numpy(), -a_np))

    def test_float_int_long(self):
        with fluid.dygraph.guard():
            a = fluid.dygraph.to_variable(np.array([100.1]))
            self.assertTrue(float(a) == 100.1)
            self.assertTrue(int(a) == 100)
            if six.PY2:
                self.assertTrue(long(a) == 100)
            else:
                self.assertTrue(int(a) == 100)

    def test_len(self):
        a_np = np.random.uniform(-1, 1, self.shape).astype(self.dtype)
        with fluid.dygraph.guard():
            a = fluid.dygraph.to_variable(a_np)
            self.assertTrue(len(a) == 10)

    def test_index(self):
        with fluid.dygraph.guard():
            var1 = fluid.dygraph.to_variable(np.array([2]))
            i_tmp = 0
            for i in range(var1):
                self.assertTrue(i == i_tmp)
                i_tmp = i_tmp + 1
            list1 = [1, 2, 3, 4, 5]
            self.assertTrue(list1[var1] == 3)
            str1 = "just test"
            self.assertTrue(str1[var1] == 's')

    def test_np_left_mul(self):
        with fluid.dygraph.guard():
            t = np.sqrt(2.0 * np.pi)
            x = fluid.layers.ones((2, 2), dtype="float32")
            y = t * x

            self.assertTrue(
                np.allclose(
                    y.numpy(),
                    t * np.ones(
                        (2, 2), dtype="float32"),
                    rtol=1e-05,
                    atol=0.0))

    def test_add_different_dtype(self):
        a_np = np.random.random(self.shape).astype(np.float32)
        b_np = np.random.random(self.shape).astype(np.float16)
        with fluid.dygraph.guard():
            a = fluid.dygraph.to_variable(a_np)
            b = fluid.dygraph.to_variable(b_np)
            res = a + b
            self.assertTrue(np.array_equal(res.numpy(), a_np + b_np))

    def test_floordiv_different_dtype(self):
        a_np = np.full(self.shape, 10, np.int64)
        b_np = np.full(self.shape, 2, np.int32)
        with fluid.dygraph.guard():
            a = paddle.to_tensor(a_np)
            b = paddle.to_tensor(b_np)
            res = a // b
            self.assertTrue(np.array_equal(res.numpy(), a_np // b_np))

    def test_astype(self):
        a_np = np.random.uniform(-1, 1, self.shape).astype(self.dtype)
        with fluid.dygraph.guard():
            a = fluid.dygraph.to_variable(a_np)
            res1 = a.astype(np.float16)
            res2 = a.astype('float16')
            res3 = a.astype(fluid.core.VarDesc.VarType.FP16)

            self.assertEqual(res1.dtype, res2.dtype)
            self.assertEqual(res1.dtype, res3.dtype)

            self.assertTrue(np.array_equal(res1.numpy(), res2.numpy()))
            self.assertTrue(np.array_equal(res1.numpy(), res3.numpy()))

    def test_conpare_op_broadcast(self):
        a_np = np.random.uniform(-1, 1, [10, 1, 10]).astype(self.dtype)
        b_np = np.random.uniform(-1, 1, [1, 1, 10]).astype(self.dtype)
        with fluid.dygraph.guard():
            a = fluid.dygraph.to_variable(a_np)
            b = fluid.dygraph.to_variable(b_np)

            self.assertEqual((a != b).dtype, fluid.core.VarDesc.VarType.BOOL)
            self.assertTrue(np.array_equal((a != b).numpy(), a_np != b_np))

    def test_tensor_patch_method(self):
        paddle.disable_static()
        x_np = np.random.uniform(-1, 1, [2, 3]).astype(self.dtype)
        y_np = np.random.uniform(-1, 1, [2, 3]).astype(self.dtype)
        z_np = np.random.uniform(-1, 1, [6, 9]).astype(self.dtype)

        x = paddle.to_tensor(x_np)
        y = paddle.to_tensor(y_np)
        z = paddle.to_tensor(z_np)

        a = paddle.to_tensor([[1, 1], [2, 2], [3, 3]])
        b = paddle.to_tensor([[1, 1], [2, 2], [3, 3]])

        # 1. Unary operation for Tensor
        self.assertEqual(x.dim(), 2)
        self.assertEqual(x.ndimension(), 2)
        self.assertEqual(x.ndim, 2)
        self.assertEqual(x.size, 6)
        self.assertEqual(x.numel(), 6)
        self.assertTrue(np.array_equal(x.exp().numpy(), paddle.exp(x).numpy()))
        self.assertTrue(
            np.array_equal(x.tanh().numpy(), paddle.tanh(x).numpy()))
        self.assertTrue(
            np.array_equal(x.atan().numpy(), paddle.atan(x).numpy()))
        self.assertTrue(np.array_equal(x.abs().numpy(), paddle.abs(x).numpy()))
        m = x.abs()
        self.assertTrue(
            np.array_equal(m.sqrt().numpy(), paddle.sqrt(m).numpy()))
        self.assertTrue(
            np.array_equal(m.rsqrt().numpy(), paddle.rsqrt(m).numpy()))
        self.assertTrue(
            np.array_equal(x.ceil().numpy(), paddle.ceil(x).numpy()))
        self.assertTrue(
            np.array_equal(x.floor().numpy(), paddle.floor(x).numpy()))
        self.assertTrue(np.array_equal(x.cos().numpy(), paddle.cos(x).numpy()))
        self.assertTrue(
            np.array_equal(x.acos().numpy(), paddle.acos(x).numpy()))
        self.assertTrue(
            np.array_equal(x.asin().numpy(), paddle.asin(x).numpy()))
        self.assertTrue(np.array_equal(x.sin().numpy(), paddle.sin(x).numpy()))
        self.assertTrue(
            np.array_equal(x.sinh().numpy(), paddle.sinh(x).numpy()))
        self.assertTrue(
            np.array_equal(x.cosh().numpy(), paddle.cosh(x).numpy()))
        self.assertTrue(
            np.array_equal(x.round().numpy(), paddle.round(x).numpy()))
        self.assertTrue(
            np.array_equal(x.reciprocal().numpy(), paddle.reciprocal(x).numpy(
            )))
        self.assertTrue(
            np.array_equal(x.square().numpy(), paddle.square(x).numpy()))
        self.assertTrue(
            np.array_equal(x.rank().numpy(), paddle.rank(x).numpy()))
        self.assertTrue(
            np.array_equal(x[0].t().numpy(), paddle.t(x[0]).numpy()))
        d = paddle.to_tensor([[1.2285208, 1.3491015, 1.4899898],
                              [1.30058, 1.0688717, 1.4928783],
                              [1.0958099, 1.3724753, 1.8926544]])
        d = d.matmul(d.t())
        # ROCM not support cholesky
        if not fluid.core.is_compiled_with_rocm():
            self.assertTrue(
                np.array_equal(d.cholesky().numpy(), paddle.cholesky(d).numpy(
                )))

        self.assertTrue(
            np.array_equal(x.is_empty().numpy(), paddle.is_empty(x).numpy()))
        self.assertTrue(
            np.array_equal(x.isfinite().numpy(), paddle.isfinite(x).numpy()))
        self.assertTrue(
            np.array_equal(
                x.cast('int32').numpy(), paddle.cast(x, 'int32').numpy()))
        self.assertTrue(
            np.array_equal(
                x.expand([3, 2, 3]).numpy(),
                paddle.expand(x, [3, 2, 3]).numpy()))
        self.assertTrue(
            np.array_equal(
                x.tile([2, 2]).numpy(), paddle.tile(x, [2, 2]).numpy()))
        self.assertTrue(
            np.array_equal(x.flatten().numpy(), paddle.flatten(x).numpy()))
        index = paddle.to_tensor([0, 1])
        self.assertTrue(
            np.array_equal(
                x.gather(index).numpy(), paddle.gather(x, index).numpy()))
        index = paddle.to_tensor([[0, 1], [1, 2]])
        self.assertTrue(
            np.array_equal(
                x.gather_nd(index).numpy(), paddle.gather_nd(x, index).numpy()))
        self.assertTrue(
            np.array_equal(
                x.reverse([0, 1]).numpy(), paddle.reverse(x, [0, 1]).numpy()))
        self.assertTrue(
            np.array_equal(
                a.reshape([3, 2]).numpy(), paddle.reshape(a, [3, 2]).numpy()))
        self.assertTrue(
            np.array_equal(
                x.slice([0, 1], [0, 0], [1, 2]).numpy(),
                paddle.slice(x, [0, 1], [0, 0], [1, 2]).numpy()))
        self.assertTrue(
            np.array_equal(
                x.split(2)[0].numpy(), paddle.split(x, 2)[0].numpy()))
        m = paddle.to_tensor(
            np.random.uniform(-1, 1, [1, 6, 1, 1]).astype(self.dtype))
        self.assertTrue(
            np.array_equal(
                m.squeeze([]).numpy(), paddle.squeeze(m, []).numpy()))
        self.assertTrue(
            np.array_equal(
                m.squeeze([1, 2]).numpy(), paddle.squeeze(m, [1, 2]).numpy()))
        m = paddle.to_tensor([2, 3, 3, 1, 5, 3], 'float32')
        self.assertTrue(
            np.array_equal(m.unique()[0].numpy(), paddle.unique(m)[0].numpy()))
        self.assertTrue(
            np.array_equal(
                m.unique(return_counts=True)[1],
                paddle.unique(
                    m, return_counts=True)[1]))
        self.assertTrue(np.array_equal(x.flip([0]), paddle.flip(x, [0])))
        self.assertTrue(np.array_equal(x.unbind(0), paddle.unbind(x, 0)))
        self.assertTrue(np.array_equal(x.roll(1), paddle.roll(x, 1)))
        self.assertTrue(np.array_equal(x.cumsum(1), paddle.cumsum(x, 1)))
        m = paddle.to_tensor(1)
        self.assertTrue(np.array_equal(m.increment(), paddle.increment(m)))
        m = x.abs()
        self.assertTrue(np.array_equal(m.log(), paddle.log(m)))
        self.assertTrue(np.array_equal(x.pow(2), paddle.pow(x, 2)))
        self.assertTrue(np.array_equal(x.reciprocal(), paddle.reciprocal(x)))

        # 2. Binary operation
        self.assertTrue(
            np.array_equal(x.divide(y).numpy(), paddle.divide(x, y).numpy()))
        self.assertTrue(
            np.array_equal(
                x.matmul(y, True, False).numpy(),
                paddle.matmul(x, y, True, False).numpy()))
        self.assertTrue(
            np.array_equal(
                x.norm(
                    p='fro', axis=[0, 1]).numpy(),
                paddle.norm(
                    x, p='fro', axis=[0, 1]).numpy()))
        self.assertTrue(
            np.array_equal(x.dist(y).numpy(), paddle.dist(x, y).numpy()))
        self.assertTrue(
            np.array_equal(x.cross(y).numpy(), paddle.cross(x, y).numpy()))
        m = x.expand([2, 2, 3])
        n = y.expand([2, 2, 3]).transpose([0, 2, 1])
        self.assertTrue(
            np.array_equal(m.bmm(n).numpy(), paddle.bmm(m, n).numpy()))
        self.assertTrue(
            np.array_equal(
                x.histogram(5, -1, 1).numpy(),
                paddle.histogram(x, 5, -1, 1).numpy()))
        self.assertTrue(
            np.array_equal(x.equal(y).numpy(), paddle.equal(x, y).numpy()))
        self.assertTrue(
            np.array_equal(
                x.greater_equal(y).numpy(), paddle.greater_equal(x, y).numpy()))
        self.assertTrue(
            np.array_equal(
                x.greater_than(y).numpy(), paddle.greater_than(x, y).numpy()))
        self.assertTrue(
            np.array_equal(
                x.less_equal(y).numpy(), paddle.less_equal(x, y).numpy()))
        self.assertTrue(
            np.array_equal(
                x.less_than(y).numpy(), paddle.less_than(x, y).numpy()))
        self.assertTrue(
            np.array_equal(
                x.not_equal(y).numpy(), paddle.not_equal(x, y).numpy()))
        self.assertTrue(
            np.array_equal(
                x.equal_all(y).numpy(), paddle.equal_all(x, y).numpy()))
        self.assertTrue(
            np.array_equal(
                x.allclose(y).numpy(), paddle.allclose(x, y).numpy()))
        m = x.expand([2, 2, 3])
        self.assertTrue(
            np.array_equal(
                x.expand_as(m).numpy(), paddle.expand_as(x, m).numpy()))
        index = paddle.to_tensor([2, 1, 0])
        self.assertTrue(
            np.array_equal(
                a.scatter(index, b).numpy(),
                paddle.scatter(a, index, b).numpy()))

        # 3. Bool tensor operation
        x = paddle.to_tensor([[True, False], [True, False]])
        y = paddle.to_tensor([[False, False], [False, True]])
        self.assertTrue(
            np.array_equal(
                x.logical_and(y).numpy(), paddle.logical_and(x, y).numpy()))
        self.assertTrue(
            np.array_equal(
                x.logical_not(y).numpy(), paddle.logical_not(x, y).numpy()))
        self.assertTrue(
            np.array_equal(
                x.logical_or(y).numpy(), paddle.logical_or(x, y).numpy()))
        self.assertTrue(
            np.array_equal(
                x.logical_xor(y).numpy(), paddle.logical_xor(x, y).numpy()))
        self.assertTrue(
            np.array_equal(
                x.logical_and(y).numpy(), paddle.logical_and(x, y).numpy()))
        a = paddle.to_tensor([[1, 2], [3, 4]])
        b = paddle.to_tensor([[4, 3], [2, 1]])
        self.assertTrue(
            np.array_equal(
                x.where(a, b).numpy(), paddle.where(x, a, b).numpy()))

        self.assertTrue(inspect.ismethod(a.dot))
        self.assertTrue(inspect.ismethod(a.logsumexp))
        self.assertTrue(inspect.ismethod(a.multiplex))
        self.assertTrue(inspect.ismethod(a.prod))
        self.assertTrue(inspect.ismethod(a.scale))
        self.assertTrue(inspect.ismethod(a.stanh))
        self.assertTrue(inspect.ismethod(a.add_n))
        self.assertTrue(inspect.ismethod(a.max))
        self.assertTrue(inspect.ismethod(a.maximum))
        self.assertTrue(inspect.ismethod(a.min))
        self.assertTrue(inspect.ismethod(a.minimum))
        self.assertTrue(inspect.ismethod(a.floor_divide))
        self.assertTrue(inspect.ismethod(a.remainder))
        self.assertTrue(inspect.ismethod(a.floor_mod))
        self.assertTrue(inspect.ismethod(a.multiply))
        self.assertTrue(inspect.ismethod(a.logsumexp))
        self.assertTrue(inspect.ismethod(a.inverse))
        self.assertTrue(inspect.ismethod(a.log1p))
        self.assertTrue(inspect.ismethod(a.erf))
        self.assertTrue(inspect.ismethod(a.addmm))
        self.assertTrue(inspect.ismethod(a.clip))
        self.assertTrue(inspect.ismethod(a.trace))
        self.assertTrue(inspect.ismethod(a.kron))
        self.assertTrue(inspect.ismethod(a.isinf))
        self.assertTrue(inspect.ismethod(a.isnan))
        self.assertTrue(inspect.ismethod(a.concat))
        self.assertTrue(inspect.ismethod(a.broadcast_to))
        self.assertTrue(inspect.ismethod(a.scatter_nd_add))
        self.assertTrue(inspect.ismethod(a.scatter_nd))
        self.assertTrue(inspect.ismethod(a.shard_index))
        self.assertTrue(inspect.ismethod(a.chunk))
        self.assertTrue(inspect.ismethod(a.stack))
        self.assertTrue(inspect.ismethod(a.strided_slice))
        self.assertTrue(inspect.ismethod(a.unsqueeze))
        self.assertTrue(inspect.ismethod(a.unstack))
        self.assertTrue(inspect.ismethod(a.argmax))
        self.assertTrue(inspect.ismethod(a.argmin))
        self.assertTrue(inspect.ismethod(a.argsort))
        self.assertTrue(inspect.ismethod(a.masked_select))
        self.assertTrue(inspect.ismethod(a.topk))
        self.assertTrue(inspect.ismethod(a.index_select))
        self.assertTrue(inspect.ismethod(a.nonzero))
        self.assertTrue(inspect.ismethod(a.sort))
        self.assertTrue(inspect.ismethod(a.index_sample))
        self.assertTrue(inspect.ismethod(a.mean))
        self.assertTrue(inspect.ismethod(a.std))
        self.assertTrue(inspect.ismethod(a.numel))


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