#   Copyright (c) 2019 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 eager_op_test import OpTest

import paddle
from paddle import fluid
from paddle.fluid import core
from paddle.nn import functional


class TestOneHotOp(OpTest):
    def setUp(self):
        self.op_type = 'one_hot_v2'
        depth = 10
        depth_np = np.array(10).astype('int32')
        dimension = 12
        x_lod = [[4, 1, 3, 3]]
        x = [np.random.randint(0, depth - 1) for i in range(sum(x_lod[0]))]
        x = np.array(x).astype('int32').reshape([sum(x_lod[0])])

        out = np.zeros(shape=(np.product(x.shape), depth)).astype('float32')

        for i in range(np.product(x.shape)):
            out[i, x[i]] = 1.0

        self.inputs = {'X': (x, x_lod), 'depth_tensor': depth_np}
        self.attrs = {'dtype': int(core.VarDesc.VarType.FP32)}
        self.outputs = {'Out': (out, x_lod)}

    def test_check_output(self):
        self.check_output(check_dygraph=False)


class TestOneHotOp_attr(OpTest):
    def setUp(self):
        self.op_type = 'one_hot_v2'
        depth = 10
        dimension = 12
        x_lod = [[4, 1, 3, 3]]
        x = [np.random.randint(0, depth - 1) for i in range(sum(x_lod[0]))]
        x = np.array(x).astype('int32').reshape([sum(x_lod[0]), 1])

        out = np.zeros(shape=(np.product(x.shape[:-1]), 1, depth)).astype(
            'float32'
        )

        for i in range(np.product(x.shape)):
            out[i, 0, x[i]] = 1.0

        self.inputs = {'X': (x, x_lod)}
        self.attrs = {'dtype': int(core.VarDesc.VarType.FP32), 'depth': depth}
        self.outputs = {'Out': (out, x_lod)}

    def test_check_output(self):
        self.check_output(check_dygraph=False)


class TestOneHotOp_default_dtype(OpTest):
    def setUp(self):
        self.op_type = 'one_hot_v2'
        depth = 10
        depth_np = np.array(10).astype('int32')
        dimension = 12
        x_lod = [[4, 1, 3, 3]]
        x = [np.random.randint(0, depth - 1) for i in range(sum(x_lod[0]))]
        x = np.array(x).astype('int32').reshape([sum(x_lod[0])])

        out = np.zeros(shape=(np.product(x.shape), depth)).astype('float32')

        for i in range(np.product(x.shape)):
            out[i, x[i]] = 1.0

        self.inputs = {'X': (x, x_lod), 'depth_tensor': depth_np}
        self.attrs = {}
        self.outputs = {'Out': (out, x_lod)}

    def test_check_output(self):
        self.check_output(check_dygraph=False)


class TestOneHotOp_default_dtype_attr(OpTest):
    def setUp(self):
        self.op_type = 'one_hot_v2'
        depth = 10
        dimension = 12
        x_lod = [[4, 1, 3, 3]]
        x = [np.random.randint(0, depth - 1) for i in range(sum(x_lod[0]))]
        x = np.array(x).astype('int32').reshape([sum(x_lod[0]), 1])

        out = np.zeros(shape=(np.product(x.shape[:-1]), 1, depth)).astype(
            'float32'
        )

        for i in range(np.product(x.shape)):
            out[i, 0, x[i]] = 1.0

        self.inputs = {'X': (x, x_lod)}
        self.attrs = {'depth': depth}
        self.outputs = {'Out': (out, x_lod)}

    def test_check_output(self):
        self.check_output(check_dygraph=False)


class TestOneHotOpApi(unittest.TestCase):
    def test_api(self):
        num_classes = 10
        self._run(num_classes)

    def test_api_with_depthTensor(self):
        num_classes = paddle.assign(np.array([10], dtype=np.int32))
        self._run(num_classes)

    def test_api_with_dygraph(self):
        num_classes = 10
        label = np.array(
            [np.random.randint(0, num_classes - 1) for i in range(6)]
        ).reshape([6, 1])
        with fluid.dygraph.guard():
            one_hot_label = functional.one_hot(
                x=fluid.dygraph.to_variable(label), num_classes=num_classes
            )

    def _run(self, num_classes):
        label = paddle.static.data(name="label", shape=[-1, 1], dtype="int64")
        label.desc.set_need_check_feed(False)
        one_hot_label = functional.one_hot(x=label, num_classes=num_classes)

        place = fluid.CPUPlace()
        label_data = np.array(
            [np.random.randint(0, 10 - 1) for i in range(6)]
        ).reshape([6, 1])

        exe = fluid.Executor(place)
        exe.run(fluid.default_startup_program())
        ret = exe.run(
            feed={
                'label': label_data,
            },
            fetch_list=[one_hot_label],
            return_numpy=False,
        )


class BadInputTestOnehotV2(unittest.TestCase):
    def test_error(self):
        with fluid.program_guard(fluid.Program()):

            def test_bad_x():
                label = paddle.static.data(
                    name="label",
                    shape=[4],
                    dtype="float32",
                )
                label.desc.set_need_check_feed(False)
                one_hot_label = functional.one_hot(x=label, num_classes=4)

            self.assertRaises(TypeError, test_bad_x)


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