import unittest
import numpy as np
from gradient_checker import GradientChecker, create_op
from op_test_util import OpTestMeta
from paddle.v2.framework.op import Operator


class TestMulOp(unittest.TestCase):
    __metaclass__ = OpTestMeta

    def setUp(self):
        self.type = "mul"
        self.inputs = {
            'X': np.random.random((32, 84)).astype("float32"),
            'Y': np.random.random((84, 100)).astype("float32")
        }
        self.outputs = {'Out': np.dot(self.inputs['X'], self.inputs['Y'])}


class TestMulOp2(unittest.TestCase):
    __metaclass__ = OpTestMeta

    def setUp(self):
        self.type = "mul"
        self.inputs = {
            'X': np.random.random((15, 4, 12, 10)).astype("float32"),
            'Y': np.random.random((4, 30, 8, 2, 9)).astype("float32")
        }
        self.attrs = {'x_num_col_dims': 2, 'y_num_col_dims': 2}
        self.outputs = {
            'Out': np.dot(self.inputs['X'].reshape(15 * 4, 12 * 10),
                          self.inputs['Y'].reshape(4 * 30, 8 * 2 * 9))
        }


class TestMulGradOp(GradientChecker):
    def setUp(self):
        self.op = create_op("mul")
        self.inputs = {
            'X': np.random.random((32, 84)).astype("float32"),
            'Y': np.random.random((84, 100)).astype("float32")
        }

    def test_cpu_gpu_compare(self):
        self.compare_grad(self.op, self.inputs)

    def test_normal(self):
        # mul op will enlarge the relative error
        self.check_grad(
            self.op, self.inputs, ["X", "Y"], "Out", max_relative_error=0.5)

    def test_ignore_x(self):
        self.check_grad(
            self.op,
            self.inputs, ["Y"],
            "Out",
            max_relative_error=0.5,
            no_grad_set={"X"})

    def test_ignore_y(self):
        self.check_grad(
            self.op,
            self.inputs, ["X"],
            "Out",
            max_relative_error=0.5,
            no_grad_set={"Y"})


class TestMulGradTest2(GradientChecker):
    def setUp(self):
        self.op = Operator(
            "mul", X="X", Y="Y", Out="Out", x_num_col_dims=2, y_num_col_dims=2)
        self.inputs = {
            "X": np.random.random((15, 4, 12, 10)).astype("float32"),
            "Y": np.random.random((4, 30, 8, 2, 9)).astype("float32")
        }

    def test_cpu_gpu_compare(self):
        self.compare_grad(self.op, self.inputs)

    def test_normal(self):
        self.check_grad(
            self.op, self.inputs, ["X", "Y"], "Out", max_relative_error=0.5)

    def test_ignore_x(self):
        self.check_grad(
            self.op,
            self.inputs, ["Y"],
            "Out",
            max_relative_error=0.5,
            no_grad_set={"X"})

    def test_ignore_y(self):
        self.check_grad(
            self.op,
            self.inputs, ["X"],
            "Out",
            max_relative_error=0.5,
            no_grad_set={"Y"})


# TODO(dzh,qijun) : mulgrad test case need transpose feature of blas library

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