#   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 unittest
import numpy as np
from op_test import OpTest
import paddle
import paddle.fluid as fluid
import paddle.fluid.core as core
from paddle.fluid.framework import Program, program_guard


class TestOneHotOp(OpTest):
    def setUp(self):
        self.op_type = 'one_hot'
        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]), 1])

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

        for i in range(np.product(x.shape)):
            out[i, 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'
        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]), 1])

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

        for i in range(np.product(x.shape)):
            out[i, 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 TestOneHotOp_out_of_range(OpTest):
    def setUp(self):
        self.op_type = 'one_hot'
        depth = 10
        x_lod = [[4, 1, 3, 3]]
        x = [np.random.choice([-1, depth]) 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]), depth)).astype(
            'float32'
        )

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

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


class TestOneHotOp_exception(unittest.TestCase):
    def setUp(self):
        self.op_type = 'one_hot'
        self.depth = 10
        self.place = core.CPUPlace()
        self.dimension = 12
        self.x = core.LoDTensor()
        x_lod = [[4, 1, 3, 3]]
        data = [np.random.randint(11, 20) for i in range(sum(x_lod[0]))]
        data = np.array(data).astype('int').reshape([sum(x_lod[0]), 1])
        self.x.set(data, self.place)
        self.x.set_recursive_sequence_lengths(x_lod)

    def test_check_output(self):
        program = Program()
        with program_guard(program):
            x = fluid.layers.data(
                name='x', shape=[self.dimension], dtype='float32', lod_level=1
            )
            block = program.current_block()
            one_hot_out = block.create_var(
                name="one_hot_out",
                type=core.VarDesc.VarType.LOD_TENSOR,
                dtype='float32',
            )
            block.append_op(
                type='one_hot',
                inputs={'X': x},
                attrs={'depth': self.depth},
                outputs={'Out': one_hot_out},
            )
            exe = fluid.Executor(self.place)

            def run():
                exe.run(
                    feed={'x': self.x},
                    fetch_list=[one_hot_out],
                    return_numpy=False,
                )

            self.assertRaises(ValueError, run)


class TestOneHotOpError(unittest.TestCase):
    def test_errors(self):
        with program_guard(Program(), Program()):
            # the input must be Variable
            in_w = np.random.random((4, 1)).astype("int32")
            self.assertRaises(TypeError, fluid.layers.one_hot, in_w)
            # the input must be int32 or int 64
            in_w2 = fluid.layers.data(
                name="in_w2",
                shape=[4, 1],
                append_batch_size=False,
                dtype="float32",
            )
            self.assertRaises(TypeError, fluid.layers.one_hot, in_w2)
            # the depth must be int, long or Variable
            in_r = fluid.layers.data(
                name="in_r",
                shape=[4, 1],
                append_batch_size=False,
                dtype="int32",
            )
            depth_w = np.array([4])
            self.assertRaises(TypeError, fluid.layers.one_hot, in_r, 4.1)
            self.assertRaises(TypeError, fluid.layers.one_hot, in_r, depth_w)


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