#   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.

import sys

sys.path.append("..")
import unittest
import numpy as np
from op_test_xpu import XPUOpTest
import paddle
import paddle.fluid as fluid
from paddle.fluid import Program, program_guard

from xpu.get_test_cover_info import create_test_class, get_xpu_op_support_types, XPUOpTestWrapper


def reference_matmul(X, Y, transpose_X=False, transpose_Y=False):
    """Reference forward implementation using np.matmul."""
    # np.matmul does not support the transpose flags, so we manually
    # transpose X and Y appropriately.
    if transpose_X:
        if X.ndim == 1:
            X = X.reshape((X.size, 1))
        elif X.ndim == 2:
            X = X.T
        else:
            dim = [i for i in range(len(X.shape))]
            dim[-1], dim[len(X.shape) - 2] = dim[len(X.shape) - 2], dim[-1]
            X = np.transpose(X, tuple(dim))
    if transpose_Y:
        if Y.ndim == 1:
            Y = Y.reshape((1, Y.size))
        elif Y.ndim == 2:
            Y = Y.T
        else:
            dim = [i for i in range(len(Y.shape))]
            dim[-1], dim[len(Y.shape) - 2] = dim[len(Y.shape) - 2], dim[-1]
            Y = np.transpose(Y, tuple(dim))

    if X.ndim == 3 and Y.ndim == 2:
        x_dims = X.shape
        X = X.reshape((x_dims[0] * x_dims[1], x_dims[2]))
    if Y.ndim == 3 and X.ndim == 2:
        y_dims = Y.shape
        Y = Y.reshape((y_dims[0] * y_dims[1], y_dims[2]))
    Out = np.matmul(X, Y)
    if not Out.shape:
        # We do not support 0-dimensional Tensors (scalars). So where
        # np.matmul outputs a scalar, we must convert to a Tensor of
        # shape (1, ) instead.
        # Everywhere else, we are compatible with np.matmul.
        Out = np.array([Out], dtype="float32")
    return Out


def generate_compatible_shapes(dim_X, dim_Y, transpose_X, transpose_Y,
                               batch_size):
    BATCH_SIZE = 2
    if batch_size != None:
        BATCH_SIZE = batch_size

    M = 3
    N = 4
    K = 5
    if (dim_X == 1 and transpose_X) or (dim_Y == 1 and transpose_Y):
        K = 1
    if dim_X == 1:
        if transpose_X:
            shape_X = [M]
        else:
            shape_X = [K]
    if dim_Y == 1:
        if transpose_Y:
            shape_Y = [N]
        else:
            shape_Y = [K]
    if dim_X >= 2:
        if transpose_X:
            shape_X = [K, M]
        else:
            shape_X = [M, K]
    if dim_X == 3:
        shape_X = [BATCH_SIZE] + shape_X
    if dim_Y >= 2:
        if transpose_Y:
            shape_Y = [N, K]
        else:
            shape_Y = [K, N]
    if dim_Y == 3:
        shape_Y = [BATCH_SIZE] + shape_Y

    if dim_Y == 3 and dim_X == 2:
        if transpose_X == False:
            shape_X[1] = shape_X[1] * BATCH_SIZE
        else:
            shape_X[0] = shape_X[0] * BATCH_SIZE

    return shape_X, shape_Y


def generate_compatible_shapes_2(dim, transpose_X, transpose_Y):
    M = 2
    N = 4
    K = 3
    shape_X = [2 for _ in range(dim - 2)]
    shape_Y = [2 for _ in range(dim - 2)]

    if transpose_X:
        shape_X += [K, M]
    else:
        shape_X += [M, K]

    if transpose_Y:
        shape_Y += [N, K]
    else:
        shape_Y += [K, N]

    return shape_X, shape_Y


def generate_negative_dims(in_shape):
    from itertools import combinations
    size = len(in_shape)
    indexs = list()
    shapes = list()
    for i in range(size):
        indexs.extend(list(combinations([j for j in range(size)], i + 1)))
    for idx in indexs:
        shapes.append(
            [in_shape[i] if i not in idx else -1 for i in range(size)])
    return shapes


def test_negative_dims_program(obj):
    for shape_x in generate_negative_dims(obj.shape_X):
        for shape_y in generate_negative_dims(obj.shape_Y):
            X = np.random.random(obj.shape_X).astype(obj.in_type)
            Y = np.random.random(obj.shape_Y).astype(obj.in_type)
            Ref = reference_matmul(X, Y, obj.transpose_X, obj.transpose_Y)
            with program_guard(Program(), Program()):
                x = fluid.data(name='x', shape=shape_x, dtype=obj.in_type_str)
                y = fluid.data(name='y', shape=shape_y, dtype=obj.in_type_str)
                output = fluid.layers.matmul(x, y, obj.transpose_X,
                                             obj.transpose_Y)
                obj.assertEqual(len(Ref.shape), len(output.shape))
                for idx in range(len(Ref.shape)):
                    if output.shape[idx] != -1:
                        obj.assertEqual(Ref.shape[idx], output.shape[idx])
                exe = fluid.Executor(fluid.XPUPlace(0))
                res, = exe.run(fluid.default_main_program(),
                               feed={
                                   'x': X,
                                   'y': Y
                               },
                               fetch_list=[output])
                np.allclose(res, Ref, atol=1e-3)


class XPUTestMatmulOpErr(XPUOpTestWrapper):

    def __init__(self):
        self.op_name = "matmul"
        self.use_dynamic_create_class = False

    class TestMatmulOpError(unittest.TestCase):

        def test_errors(self):
            with program_guard(Program(), Program()):
                # The inputs type of matmul_op must be Variable.
                input1 = 12
                self.assertRaises(TypeError, fluid.layers.matmul, input1,
                                  input1)
                # The inputs dtype of matmul_op must be float32, float16
                input2 = fluid.layers.data(name='input2',
                                           shape=[10, 10],
                                           dtype="int32")
                self.assertRaises(TypeError, fluid.layers.matmul, input2,
                                  input2)
                input3 = fluid.layers.data(name='input3',
                                           shape=[2, 2],
                                           dtype="float16")
                fluid.layers.matmul(input3, input3)

    class API_TestMm(unittest.TestCase):

        def test_out(self):
            with fluid.program_guard(fluid.Program()):
                x = fluid.data(name="x", shape=[2], dtype=self.in_type)
                y = fluid.data(name='y', shape=[2], dtype=self.in_type)
                res = fluid.data(name="output", shape=[1], dtype=self.in_type)
                result = paddle.mm(x, y)
                exe = fluid.Executor(fluid.XPUPlace(0))
                data1 = np.random.rand(2).astype(self.in_type)
                data2 = np.random.rand(2).astype(self.in_type)
                np_res = exe.run(feed={
                    'x': data1,
                    'y': data2
                },
                                 fetch_list=[result])
                expected_result = np.matmul(data1.reshape(1, 2),
                                            data2.reshape(2, 1))

                np.testing.assert_allclose(np_res, expected_result, atol=1e-3)

        def test_dygraph_without_out(self):
            device = fluid.XPUPlace(0)
            with fluid.dygraph.guard(device):
                input_array1 = np.random.rand(3, 4).astype(self.in_type)
                input_array2 = np.random.rand(4, 3).astype(self.in_type)
                data1 = fluid.dygraph.to_variable(input_array1)
                data2 = fluid.dygraph.to_variable(input_array2)
                out = paddle.mm(data1, data2)
                expected_result = np.matmul(input_array1, input_array2)
                np.testing.assert_allclose(expected_result,
                                           out.numpy(),
                                           atol=1e-3)

    class Test_API_Matmul(unittest.TestCase):

        def test_dygraph_without_out(self):
            device = fluid.XPUPlace(0)
            with fluid.dygraph.guard(device):
                input_array1 = np.random.rand(3, 4).astype(self.in_type)
                input_array2 = np.random.rand(4, 3).astype(self.in_type)
                data1 = fluid.dygraph.to_variable(input_array1).astype(
                    self.in_type)
                data2 = fluid.dygraph.to_variable(input_array2).astype(
                    self.in_type)
                out = paddle.matmul(data1, data2)
                expected_result = np.matmul(input_array1, input_array2)
                np.testing.assert_allclose(expected_result,
                                           out.numpy(),
                                           atol=1e-3)

    class API_TestMmError(unittest.TestCase):

        def test_errors(self):

            def test_error1():
                with fluid.program_guard(fluid.Program(), fluid.Program()):
                    data1 = fluid.data(name="data1",
                                       shape=[10, 2],
                                       dtype="float32")
                    data2 = fluid.data(name="data2",
                                       shape=[3, 10],
                                       dtype="float32")
                    paddle.mm(data1, data2)

            self.assertRaises(ValueError, test_error1)

            def test_error2():
                with fluid.program_guard(fluid.Program(), fluid.Program()):
                    data1 = fluid.data(name="data1",
                                       shape=[-1, 10, 2],
                                       dtype="float32")
                    data2 = fluid.data(name="data2",
                                       shape=[-1, 2, 10],
                                       dtype="float32")
                    paddle.mm(data1, data2)

            test_error2()

            def test_error3():
                with fluid.program_guard(fluid.Program(), fluid.Program()):
                    data1 = fluid.data(name="data1",
                                       shape=[10, 10, 2],
                                       dtype="float32")
                    data2 = fluid.data(name="data2",
                                       shape=[3, 2, 10],
                                       dtype="float32")
                    paddle.mm(data1, data2)

            self.assertRaises(ValueError, test_error3)


class TestMatmulBaseGenerator(XPUOpTest):

    def setUp(self):
        self.op_type = "matmul"
        self.dtype = np.float32 if not hasattr(self,
                                               'in_type') else self.in_type

        self.__class__.no_need_check_grad = False if not hasattr(
            self, 'no_need_check_grad') else self.no_need_check_grad

        shape_X = [4, 5] if not hasattr(self, 'shape_X') else self.shape_X
        shape_Y = [5, 6] if not hasattr(self, 'shape_Y') else self.shape_Y
        transpose_X = False if not hasattr(self,
                                           'transpose_X') else self.transpose_X
        transpose_Y = False if not hasattr(self,
                                           'transpose_Y') else self.transpose_Y

        X = np.random.random(shape_X).astype(self.dtype)
        Y = np.random.random(shape_Y).astype(self.dtype)
        Out = reference_matmul(X, Y, transpose_X,
                               transpose_Y).astype(self.dtype)
        self.inputs = {'X': X, 'Y': Y}
        self.attrs = {'transpose_X': transpose_X, 'transpose_Y': transpose_Y}
        self.outputs = {'Out': Out}

    def test_check_output(self):
        place = paddle.XPUPlace(0)
        self.check_output_with_place(place, atol=1e-3)

    def test_check_grad_normal(self):
        if hasattr(self.__class__, "no_need_check_grad"
                   ) and self.__class__.no_need_check_grad == True:
            return

        place = paddle.XPUPlace(0)
        self.check_grad_with_place(place, ['X', 'Y'],
                                   'Out',
                                   max_relative_error=5e-2)

    def test_check_grad_ignore_x(self):
        if hasattr(self.__class__, "no_need_check_grad"
                   ) and self.__class__.no_need_check_grad == True:
            return

        place = paddle.XPUPlace(0)
        self.check_grad_with_place(place, ['Y'],
                                   'Out',
                                   max_relative_error=5e-2,
                                   no_grad_set=set("X"))

    def test_check_grad_ignore_y(self):
        if hasattr(self.__class__, "no_need_check_grad"
                   ) and self.__class__.no_need_check_grad == True:
            return

        place = paddle.XPUPlace(0)
        self.check_grad_with_place(place, ['X'],
                                   'Out',
                                   max_relative_error=5e-2,
                                   no_grad_set=set('Y'))


class XPUTestMatmulOp1(XPUOpTestWrapper):

    def __init__(self):
        self.op_name = "matmul"
        self.use_dynamic_create_class = True

    def dynamic_create_class(self):
        base_class = TestMatmulBaseGenerator
        classes = []
        xpu_support_dims_list = [[1, 1], [2, 2], [3, 3]]
        batch_size = [2, 4, 5, 10, 50, 100, 300]
        for dims in xpu_support_dims_list:
            dim_X = dims[0]
            dim_Y = dims[1]
            for transose_x in [True, False]:
                for transose_y in [True, False]:
                    for batch in batch_size:
                        no_need_check_grad = False
                        if batch >= 5:
                            no_need_check_grad = True
                        class_name = (
                            'TestMatMulOp_dimX_{}_dim_Y_{}_transX_{}_transY_{}_batch_{}'
                            .format(dim_X, dim_Y, transose_x, transose_y,
                                    batch))
                        shape_x, shape_y = generate_compatible_shapes(
                            dim_X, dim_Y, transose_x, transose_y, batch)
                        attr_dict = {
                            'shape_X': shape_x,
                            'shape_Y': shape_y,
                            'transpose_X': transose_x,
                            'transpose_Y': transose_y,
                            'no_need_check_grad': no_need_check_grad,
                            'op_type': "matmul"
                        }
                        classes.append([class_name, attr_dict])

        return base_class, classes


class XPUTestMatmulOp2(XPUOpTestWrapper):

    def __init__(self):
        self.op_name = "matmul"
        self.use_dynamic_create_class = True

    def dynamic_create_class(self):
        base_class = unittest.TestCase
        classes = []
        xpu_support_dims_list = [[1, 1], [2, 2], [3, 3]]
        batch_size = [2, 4, 5, 10, 50, 100, 300]
        for dims in xpu_support_dims_list:
            dim_X = dims[0]
            dim_Y = dims[1]
            for transose_x in [True, False]:
                for transose_y in [True, False]:
                    for batch in batch_size:
                        class_name = (
                            'TestMatMulAPI_dimX_{}_dim_Y_{}_transX_{}_transY_{}_batch_{}'
                            .format(dim_X, dim_Y, transose_x, transose_y,
                                    batch))
                        shape_x, shape_y = generate_compatible_shapes(
                            dim_X, dim_Y, transose_x, transose_y, batch)
                        attr_dict = {
                            'shape_X': shape_x,
                            'shape_Y': shape_y,
                            'transpose_X': transose_x,
                            'transpose_Y': transose_y,
                            'test_propram': test_negative_dims_program,
                        }
                        classes.append([class_name, attr_dict])
        return base_class, classes


class XPUTestMatmulOp3(XPUOpTestWrapper):

    def __init__(self):
        self.op_name = "matmul"
        self.use_dynamic_create_class = True

    def dynamic_create_class(self):
        base_class = TestMatmulBaseGenerator
        classes = []
        for dim in [4]:
            for transpose_X in [False, True]:
                for transpose_Y in [False, True]:
                    class_name = (
                        'TestMatMulOp2_dimX_{}_dim_Y_{}_transX_{}_transY_{}'.
                        format(dim, dim, transpose_X, transpose_Y))
                    shape_X, shape_Y = generate_compatible_shapes_2(
                        dim, transpose_X, transpose_Y)
                    attr_dict = {
                        'shape_X': shape_X,
                        'shape_Y': shape_Y,
                        'transpose_X': transpose_X,
                        'transpose_Y': transpose_Y,
                        'op_type': "matmul"
                    }
                    classes.append([class_name, attr_dict])
        return base_class, classes


support_types = get_xpu_op_support_types('matmul')
for stype in support_types:
    create_test_class(globals(), XPUTestMatmulOpErr, stype)
    create_test_class(globals(), XPUTestMatmulOp1, stype)
    create_test_class(globals(), XPUTestMatmulOp2, stype)
    create_test_class(globals(), XPUTestMatmulOp3, stype)

if __name__ == "__main__":
    paddle.enable_static()
    unittest.main()
