#   Copyright (c) 2022 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 numpy as np
import sys
sys.path.append('..')
from op_test import OpTest
import paddle
import paddle.fluid.core as core


def numpy_topk(x, k=1, axis=-1, largest=True):
    if axis < 0:
        axis = len(x.shape) + axis
    if largest:
        indices = np.argsort(-x, axis=axis)
    else:
        indices = np.argsort(x, axis=axis)
    if largest:
        value = -np.sort(-x, axis=axis)
    else:
        value = np.sort(x, axis=axis)
    indices = indices.take(indices=range(0, k), axis=axis)
    value = value.take(indices=range(0, k), axis=axis)
    return value, indices


class TestTopkOp(OpTest):
    def init_args(self):
        self.k = 3
        self.axis = 1
        self.largest = True

    def setUp(self):
        self.op_type = "top_k_v2"
        self.place = paddle.device.MLUPlace(0)
        self.__class__.use_mlu = True
        self.__class__.no_need_check_grad = True
        self.dtype = np.float32
        self.input_data = np.random.rand(10, 20).astype(self.dtype)
        self.init_args()
        self.inputs = {'X': self.input_data}
        self.attrs = {'k': self.k, 'axis': self.axis, 'largest': self.largest}
        output, indices = numpy_topk(
            self.input_data, axis=self.axis, k=self.k, largest=self.largest)
        self.outputs = {'Out': output, 'Indices': indices}

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


class TestTopkOp1(TestTopkOp):
    def init_args(self):
        self.k = 3
        self.axis = 0
        self.largest = False


class TestTopkOp2(TestTopkOp):
    def init_args(self):
        self.k = 4
        self.axis = 0
        self.largest = False


class TestTopkOp3(OpTest):
    def init_args(self):
        self.k = 6
        self.axis = 1
        self.largest = True

    def setUp(self):
        self.op_type = "top_k_v2"
        self.dtype = np.float32
        self.input_data = np.random.rand(16, 100).astype(self.dtype)
        self.init_args()
        self.inputs = {'X': self.input_data}
        self.attrs = {'k': self.k, 'axis': self.axis, 'largest': self.largest}
        output, indices = numpy_topk(
            self.input_data, axis=self.axis, k=self.k, largest=self.largest)
        self.outputs = {'Out': output, 'Indices': indices}


class TestTopkOp4(TestTopkOp):
    def init_args(self):
        self.k = 3
        self.axis = 1
        self.largest = True

    def setUp(self):
        self.op_type = "top_k_v2"
        self.place = paddle.device.MLUPlace(0)
        self.__class__.use_mlu = True
        self.dtype = np.float32
        self.input_data = np.random.rand(10, 10, 5).astype(self.dtype)
        self.init_args()
        self.inputs = {'X': self.input_data}
        self.attrs = {'k': self.k, 'axis': self.axis, 'largest': self.largest}
        output, indices = numpy_topk(
            self.input_data, axis=self.axis, k=self.k, largest=self.largest)
        self.outputs = {'Out': output, 'Indices': indices}


class TestTopkOp5(TestTopkOp):
    def init_args(self):
        self.k = 3
        self.axis = 1
        self.largest = True

    def setUp(self):
        self.op_type = "top_k_v2"
        self.place = paddle.device.MLUPlace(0)
        self.__class__.use_mlu = True
        self.dtype = np.float32
        self.input_data = np.random.rand(10, 10, 5).astype(self.dtype)
        self.init_args()
        self.inputs = {'X': self.input_data}
        self.attrs = {'k': self.k, 'axis': self.axis, 'largest': self.largest}
        output, indices = numpy_topk(
            self.input_data, axis=self.axis, k=self.k, largest=self.largest)
        self.outputs = {'Out': output, 'Indices': indices}


class TestTopkOp6(OpTest):
    def init_args(self):
        self.k = 100
        self.axis = 1
        self.largest = True

    def setUp(self):
        self.op_type = "top_k_v2"
        self.place = paddle.device.MLUPlace(0)
        self.__class__.use_mlu = True
        self.dtype = np.float32
        self.input_data = np.random.rand(80, 16384).astype(self.dtype)
        self.init_args()
        self.inputs = {'X': self.input_data}
        self.attrs = {'k': self.k, 'axis': self.axis, 'largest': self.largest}
        output, indices = numpy_topk(
            self.input_data, axis=self.axis, k=self.k, largest=self.largest)
        self.outputs = {'Out': output, 'Indices': indices}


class TestTopKAPI(unittest.TestCase):
    def setUp(self):
        np.random.seed(123)
        self.dtype = np.float32
        self.input_data = np.random.rand(6, 7, 8).astype(self.dtype)
        self.large_input_data = np.random.rand(2, 1030).astype(self.dtype)

    def run_dygraph(self, place):
        paddle.disable_static(place)
        input_tensor = paddle.to_tensor(self.input_data)
        large_input_tensor = paddle.to_tensor(self.large_input_data)
        # test case for basic test case 1
        paddle_result = paddle.topk(input_tensor, k=2)
        numpy_result = numpy_topk(self.input_data, k=2)
        self.assertTrue(np.allclose(paddle_result[0].numpy(), numpy_result[0]))
        self.assertTrue(np.allclose(paddle_result[1].numpy(), numpy_result[1]))
        # test case for basic test case 2 with axis
        paddle_result = paddle.topk(input_tensor, k=2, axis=1)
        numpy_result = numpy_topk(self.input_data, k=2, axis=1)
        self.assertTrue(np.allclose(paddle_result[0].numpy(), numpy_result[0]))
        self.assertTrue(np.allclose(paddle_result[1].numpy(), numpy_result[1]))
        # test case for basic test case 3 with tensor K
        k_tensor = paddle.to_tensor(np.array([2]))
        paddle_result = paddle.topk(input_tensor, k=k_tensor, axis=1)
        numpy_result = numpy_topk(self.input_data, k=2, axis=1)
        self.assertTrue(np.allclose(paddle_result[0].numpy(), numpy_result[0]))
        self.assertTrue(np.allclose(paddle_result[1].numpy(), numpy_result[1]))
        # test case for basic test case 4 with tensor largest
        k_tensor = paddle.to_tensor(np.array([2]))
        paddle_result = paddle.topk(input_tensor, k=2, axis=1, largest=False)
        numpy_result = numpy_topk(self.input_data, k=2, axis=1, largest=False)
        self.assertTrue(np.allclose(paddle_result[0].numpy(), numpy_result[0]))
        self.assertTrue(np.allclose(paddle_result[1].numpy(), numpy_result[1]))
        # test case for basic test case 5 with axis -1
        k_tensor = paddle.to_tensor(np.array([2]))
        paddle_result = paddle.topk(input_tensor, k=2, axis=-1, largest=False)
        numpy_result = numpy_topk(self.input_data, k=2, axis=-1, largest=False)
        self.assertTrue(np.allclose(paddle_result[0].numpy(), numpy_result[0]))
        self.assertTrue(np.allclose(paddle_result[1].numpy(), numpy_result[1]))
        # test case for basic test case 6 for the partial sort 
        paddle_result = paddle.topk(large_input_tensor, k=1, axis=-1)
        numpy_result = numpy_topk(self.large_input_data, k=1, axis=-1)
        self.assertTrue(np.allclose(paddle_result[0].numpy(), numpy_result[0]))
        self.assertTrue(np.allclose(paddle_result[1].numpy(), numpy_result[1]))
        # test case for basic test case 7 for the unsorted 
        paddle_result = paddle.topk(input_tensor, k=2, axis=1, sorted=False)
        sort_paddle = numpy_topk(
            np.array(paddle_result[0].numpy()), axis=1, k=2)
        numpy_result = numpy_topk(self.input_data, k=2, axis=1)
        self.assertTrue(np.allclose(sort_paddle[0], numpy_result[0]))

    def run_static(self, place):
        paddle.enable_static()
        with paddle.static.program_guard(paddle.static.Program(),
                                         paddle.static.Program()):
            input_tensor = paddle.static.data(
                name="x", shape=[6, 7, 8], dtype="float32")
            large_input_tensor = paddle.static.data(
                name="large_x", shape=[2, 1030], dtype="float32")
            k_tensor = paddle.static.data(name="k", shape=[1], dtype="int32")
            result1 = paddle.topk(input_tensor, k=2)
            result2 = paddle.topk(input_tensor, k=2, axis=-1)
            result3 = paddle.topk(input_tensor, k=k_tensor, axis=1)
            self.assertEqual(result3[0].shape, (6, -1, 8))
            self.assertEqual(result3[1].shape, (6, -1, 8))
            result4 = paddle.topk(input_tensor, k=2, axis=1, largest=False)
            result5 = paddle.topk(input_tensor, k=2, axis=-1, largest=False)
            result6 = paddle.topk(large_input_tensor, k=1, axis=-1)
            result7 = paddle.topk(input_tensor, k=2, axis=1, sorted=False)
            exe = paddle.static.Executor(place)
            input_data = np.random.rand(10, 20).astype("float32")
            large_input_data = np.random.rand(2, 100).astype("float32")
            paddle_result = exe.run(
                feed={
                    "x": self.input_data,
                    "large_x": self.large_input_data,
                    "k": np.array([2]).astype("int32")
                },
                fetch_list=[
                    result1[0], result1[1], result2[0], result2[1], result3[0],
                    result3[1], result4[0], result4[1], result5[0], result5[1],
                    result6[0], result6[1], result7[0], result7[1]
                ])
            numpy_result = numpy_topk(self.input_data, k=2)
            self.assertTrue(np.allclose(paddle_result[0], numpy_result[0]))
            self.assertTrue(np.allclose(paddle_result[1], numpy_result[1]))
            numpy_result = numpy_topk(self.input_data, k=2, axis=-1)
            self.assertTrue(np.allclose(paddle_result[2], numpy_result[0]))
            self.assertTrue(np.allclose(paddle_result[3], numpy_result[1]))
            numpy_result = numpy_topk(self.input_data, k=2, axis=1)
            self.assertTrue(np.allclose(paddle_result[4], numpy_result[0]))
            self.assertTrue(np.allclose(paddle_result[5], numpy_result[1]))
            numpy_result = numpy_topk(
                self.input_data, k=2, axis=1, largest=False)
            self.assertTrue(np.allclose(paddle_result[6], numpy_result[0]))
            self.assertTrue(np.allclose(paddle_result[7], numpy_result[1]))
            numpy_result = numpy_topk(
                self.input_data, k=2, axis=-1, largest=False)
            self.assertTrue(np.allclose(paddle_result[8], numpy_result[0]))
            self.assertTrue(np.allclose(paddle_result[9], numpy_result[1]))
            numpy_result = numpy_topk(self.large_input_data, k=1, axis=-1)
            self.assertTrue(np.allclose(paddle_result[10], numpy_result[0]))
            self.assertTrue(np.allclose(paddle_result[11], numpy_result[1]))
            sort_paddle = numpy_topk(paddle_result[12], axis=1, k=2)
            numpy_result = numpy_topk(self.input_data, k=2, axis=1)
            self.assertTrue(np.allclose(sort_paddle[0], numpy_result[0]))

    def test_cases(self):
        places = [core.CPUPlace()]
        if core.is_compiled_with_mlu():
            places.append(core.MLUPlace(0))
        for place in places:
            self.run_dygraph(place)
            self.run_static(place)

    def test_errors(self):
        paddle.disable_static()
        x = paddle.to_tensor([1, 2, 3], dtype="float32")
        with self.assertRaises(BaseException):
            paddle.topk(x, k=-1)

        with self.assertRaises(BaseException):
            paddle.topk(x, k=0)


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