#   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.

from __future__ import print_function

import sys
import unittest
import numpy as np

sys.path.append("..")

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

paddle.enable_static()


class TestOneHotOp(OpTest):

    def set_npu(self):
        self.__class__.use_npu = True

    def setUp(self):
        self.set_npu()
        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_with_place(paddle.NPUPlace(0), check_dygraph=False)


class TestOneHotOp_attr(OpTest):

    def set_npu(self):
        self.__class__.use_npu = True

    def setUp(self):
        self.set_npu()
        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_with_place(paddle.NPUPlace(0), check_dygraph=False)


class TestOneHotOp_default_dtype(OpTest):

    def set_npu(self):
        self.__class__.use_npu = True

    def setUp(self):
        self.set_npu()
        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_with_place(paddle.NPUPlace(0), check_dygraph=False)


class TestOneHotOp_default_dtype_attr(OpTest):

    def set_npu(self):
        self.__class__.use_npu = True

    def setUp(self):
        self.set_npu()
        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_with_place(paddle.NPUPlace(0), check_dygraph=False)


class TestOneHotOp_out_of_range(OpTest):

    def set_npu(self):
        self.__class__.use_npu = True

    def setUp(self):
        self.set_npu()
        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_with_place(paddle.NPUPlace(0), check_dygraph=False)


class TestOneHotOp_dtype_int64(OpTest):

    def set_npu(self):
        self.__class__.use_npu = True

    def setUp(self):
        self.set_npu()
        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('int64').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_with_place(paddle.NPUPlace(0), check_dygraph=False)


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