# Copyright (c) 2020 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, convert_float_to_uint16

import paddle
from paddle import fluid, tensor
from paddle.fluid import core


class TestTraceOp(OpTest):
    def setUp(self):
        self.op_type = "trace"
        self.python_api = paddle.trace
        self.init_config()
        self.outputs = {'Out': self.target}

    def test_check_output(self):
        self.check_output()

    def test_check_grad(self):
        self.check_grad(['Input'], 'Out')

    def init_config(self):
        self.case = np.random.randn(20, 6).astype('float64')
        self.inputs = {'Input': self.case}
        self.attrs = {'offset': 0, 'axis1': 0, 'axis2': 1}
        self.target = np.trace(self.inputs['Input'])


class TestTraceOpCase1(TestTraceOp):
    def init_config(self):
        self.case = np.random.randn(2, 20, 2, 3).astype('float32')
        self.inputs = {'Input': self.case}
        self.attrs = {'offset': 1, 'axis1': 0, 'axis2': 2}
        self.target = np.trace(
            self.inputs['Input'],
            offset=self.attrs['offset'],
            axis1=self.attrs['axis1'],
            axis2=self.attrs['axis2'],
        )


class TestTraceOpCase2(TestTraceOp):
    def init_config(self):
        self.case = np.random.randn(2, 20, 2, 3).astype('float32')
        self.inputs = {'Input': self.case}
        self.attrs = {'offset': -5, 'axis1': 1, 'axis2': -1}
        self.target = np.trace(
            self.inputs['Input'],
            offset=self.attrs['offset'],
            axis1=self.attrs['axis1'],
            axis2=self.attrs['axis2'],
        )


class TestTraceFP16Op1(TestTraceOp):
    def init_config(self):
        self.dtype = np.float16
        self.case = np.random.randn(20, 6).astype(self.dtype)
        self.inputs = {'Input': self.case}
        self.attrs = {'offset': 0, 'axis1': 0, 'axis2': 1}
        self.target = np.trace(self.inputs['Input'])


class TestTraceFP16Op2(TestTraceOp):
    def init_config(self):
        self.dtype = np.float16
        self.case = np.random.randn(2, 20, 2, 3).astype(self.dtype)
        self.inputs = {'Input': self.case}
        self.attrs = {'offset': -5, 'axis1': 1, 'axis2': -1}
        self.target = np.trace(
            self.inputs['Input'],
            offset=self.attrs['offset'],
            axis1=self.attrs['axis1'],
            axis2=self.attrs['axis2'],
        )


@unittest.skipIf(
    not core.is_compiled_with_cuda()
    or not core.is_bfloat16_supported(core.CUDAPlace(0)),
    "core is not compiled with CUDA or not support bfloat16",
)
class TestTraceBF16Op1(OpTest):
    def setUp(self):
        self.op_type = "trace"
        self.python_api = paddle.trace
        self.init_config()
        self.outputs = {'Out': self.target}

        self.inputs['Input'] = convert_float_to_uint16(self.inputs['Input'])
        self.outputs['Out'] = convert_float_to_uint16(self.outputs['Out'])
        self.place = core.CUDAPlace(0)

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

    def test_check_grad(self):
        self.check_grad_with_place(
            self.place, ['Input'], 'Out', numeric_grad_delta=0.02
        )

    def init_config(self):
        self.dtype = np.uint16
        self.np_dtype = np.float32
        self.case = np.random.randn(20, 6).astype(self.np_dtype)
        self.inputs = {'Input': self.case}
        self.attrs = {'offset': 0, 'axis1': 0, 'axis2': 1}
        self.target = np.trace(self.inputs['Input'])


@unittest.skipIf(
    not core.is_compiled_with_cuda()
    or not core.is_bfloat16_supported(core.CUDAPlace(0)),
    "core is not compiled with CUDA or not support bfloat16",
)
class TestTraceBF16Op2(TestTraceBF16Op1):
    def init_config(self):
        self.dtype = np.uint16
        self.np_dtype = np.float32
        self.case = np.random.randn(2, 20, 2, 3).astype(self.np_dtype)
        self.inputs = {'Input': self.case}
        self.attrs = {'offset': -5, 'axis1': 1, 'axis2': -1}
        self.target = np.trace(
            self.inputs['Input'],
            offset=self.attrs['offset'],
            axis1=self.attrs['axis1'],
            axis2=self.attrs['axis2'],
        )


class TestTraceAPICase(unittest.TestCase):
    def test_case1(self):
        case = np.random.randn(2, 20, 2, 3).astype('float32')
        data1 = paddle.static.data(
            name='data1', shape=[2, 20, 2, 3], dtype='float32'
        )
        out1 = tensor.trace(data1)
        out2 = tensor.trace(data1, offset=-5, axis1=1, axis2=-1)

        place = core.CPUPlace()
        exe = fluid.Executor(place)
        results = exe.run(
            fluid.default_main_program(),
            feed={"data1": case},
            fetch_list=[out1, out2],
            return_numpy=True,
        )
        target1 = np.trace(case)
        target2 = np.trace(case, offset=-5, axis1=1, axis2=-1)
        np.testing.assert_allclose(results[0], target1, rtol=1e-05)
        np.testing.assert_allclose(results[1], target2, rtol=1e-05)


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