#  Copyright (c) 2021 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 unittest

import numpy as np
from get_test_cover_info import (
    XPUOpTestWrapper,
    create_test_class,
    get_xpu_op_support_types,
)
from op_test_xpu import XPUOpTest

import paddle
from paddle import fluid

paddle.enable_static()
np.random.seed(10)


class XPUTestExpandAsV2Op(XPUOpTestWrapper):
    def __init__(self):
        self.op_name = 'expand_as_v2'
        self.use_dynamic_create_class = False

    class TestExpandAsV2XPUOp(XPUOpTest):
        def setUp(self):
            self.init_dtype()
            self.set_xpu()
            self.op_type = "expand_as_v2"
            self.place = paddle.XPUPlace(0)
            self.set_inputs()
            self.set_output()

        def init_dtype(self):
            self.dtype = self.in_type

        def set_inputs(self):
            x = np.random.rand(100).astype(self.dtype)
            self.inputs = {'X': x}
            target_tensor = np.random.rand(2, 100).astype(self.dtype)
            self.attrs = {'target_shape': target_tensor.shape}

        def set_output(self):
            bcast_dims = [2, 1]
            output = np.tile(self.inputs['X'], bcast_dims)
            self.outputs = {'Out': output}

        def set_xpu(self):
            self.__class__.use_xpu = True
            self.__class__.no_need_check_grad = True
            self.__class__.op_type = self.in_type

        def test_check_output(self):
            self.check_output_with_place(self.place)

    class TestExpandAsOpRank2(TestExpandAsV2XPUOp):
        def set_inputs(self):
            x = np.random.rand(10, 12).astype(self.dtype)
            self.inputs = {'X': x}
            target_tensor = np.random.rand(10, 12).astype(self.dtype)
            self.attrs = {'target_shape': target_tensor.shape}

        def set_output(self):
            bcast_dims = [1, 1]
            output = np.tile(self.inputs['X'], bcast_dims)
            self.outputs = {'Out': output}

    class TestExpandAsOpRank3(TestExpandAsV2XPUOp):
        def set_inputs(self):
            x = np.random.rand(2, 3, 20).astype(self.dtype)
            self.inputs = {'X': x}
            target_tensor = np.random.rand(2, 3, 20).astype(self.dtype)
            self.attrs = {'target_shape': target_tensor.shape}

        def set_output(self):
            bcast_dims = [1, 1, 1]
            output = np.tile(self.inputs['X'], bcast_dims)
            self.outputs = {'Out': output}

    class TestExpandAsOpRank4(TestExpandAsV2XPUOp):
        def set_inputs(self):
            x = np.random.rand(1, 1, 7, 16).astype(self.dtype)
            self.inputs = {'X': x}
            target_tensor = np.random.rand(4, 6, 7, 16).astype(self.dtype)
            self.attrs = {'target_shape': target_tensor.shape}

        def set_output(self):
            bcast_dims = [4, 6, 1, 1]
            output = np.tile(self.inputs['X'], bcast_dims)
            self.outputs = {'Out': output}

    class TestExpandAsOpRank5(TestExpandAsV2XPUOp):
        def set_inputs(self):
            x = np.random.rand(1, 1, 7, 16, 1).astype(self.dtype)
            self.inputs = {'X': x}
            target_tensor = np.random.rand(4, 6, 7, 16, 1).astype(self.dtype)
            self.attrs = {'target_shape': target_tensor.shape}

        def set_output(self):
            bcast_dims = [4, 6, 1, 1, 1]
            output = np.tile(self.inputs['X'], bcast_dims)
            self.outputs = {'Out': output}

    class TestExpandAsOpRank6(TestExpandAsV2XPUOp):
        def set_inputs(self):
            x = np.random.rand(1, 1, 7, 16, 1, 1).astype(self.dtype)
            self.inputs = {'X': x}
            target_tensor = np.random.rand(4, 6, 7, 16, 1, 1).astype(self.dtype)
            self.attrs = {'target_shape': target_tensor.shape}

        def set_output(self):
            bcast_dims = [4, 6, 1, 1, 1, 1]
            output = np.tile(self.inputs['X'], bcast_dims)
            self.outputs = {'Out': output}


# Test python API
class TestExpandAsV2API(unittest.TestCase):
    def test_api(self):
        x_np = np.random.random([12, 14]).astype("float32")
        y_np = np.random.random([2, 12, 14]).astype("float32")
        x = paddle.static.data(name='x', shape=[12, 14], dtype="float32")

        y = paddle.static.data(
            name='target_tensor',
            shape=[2, 12, 14],
            dtype="float32",
        )

        out_1 = paddle.expand_as(x, y=y)

        exe = fluid.Executor(place=fluid.XPUPlace(0))
        res_1 = exe.run(
            fluid.default_main_program(),
            feed={"x": x_np, "target_tensor": y_np},
            fetch_list=[out_1],
        )
        assert np.array_equal(res_1[0], np.tile(x_np, (2, 1, 1)))


support_types = get_xpu_op_support_types('expand_as_v2')
for stype in support_types:
    create_test_class(globals(), XPUTestExpandAsV2Op, stype)

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