#   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.

from __future__ import print_function

import unittest
import numpy as np
from op_test import OpTest
import paddle.fluid as fluid
import paddle.tensor as tensor


class TrilTriuOpDefaultTest(OpTest):
    """ the base class of other op testcases
    """

    def setUp(self):
        self.initTestCase()
        self.real_np_op = getattr(np, self.real_op_type)

        self.op_type = "tril_triu"
        self.inputs = {'X': self.X}
        self.attrs = {
            'diagonal': self.diagonal,
            'lower': True if self.real_op_type == 'tril' else False,
        }
        self.outputs = {
            'Out': self.real_np_op(self.X, self.diagonal)
            if self.diagonal else self.real_np_op(self.X)
        }

    def test_check_output(self):
        self.check_output()

    def test_check_grad_normal(self):
        self.check_grad(['X'], 'Out')

    def initTestCase(self):
        self.real_op_type = np.random.choice(['triu', 'tril'])
        self.diagonal = None
        self.X = np.arange(1, 101, dtype="float64").reshape([10, -1])


def case_generator(op_type, Xshape, diagonal, expected):
    """
    Generate testcases with the params shape of X, diagonal and op_type.
    If arg`expercted` is 'success', it will register an Optest case and expect to pass.
    Otherwise, it will register an API case and check the expect failure.
    """
    cls_name = "{0}_{1}_shape_{2}_diag_{3}".format(expected, op_type, Xshape,
                                                   diagonal)
    errmsg = {
        "diagonal: TypeError":
        "diagonal in {} must be a python Int".format(op_type),
        "input: ValueError":
        "x shape in {} must be at least 2-D".format(op_type),
    }

    class FailureCase(unittest.TestCase):
        def test_failure(self):
            data = fluid.data(shape=Xshape, dtype='float64', name=cls_name)
            with self.assertRaisesRegexp(
                    eval(expected.split(':')[-1]), errmsg[expected]):
                getattr(tensor, op_type)(x=data, diagonal=diagonal)

    class SuccessCase(TrilTriuOpDefaultTest):
        def initTestCase(self):
            self.real_op_type = op_type
            self.diagonal = diagonal
            self.X = np.random.random(Xshape).astype("float64")

    CLASS = locals()['SuccessCase' if expected == "success" else 'FailureCase']
    CLASS.__name__ = cls_name
    globals()[cls_name] = CLASS


### NOTE: meaningful diagonal is [1 - min(H, W), max(H, W) -1]
### test the diagonal just at the border, upper/lower the border, 
###     negative/positive integer within range and a zero
cases = {
    'success': {
        (2, 2, 3, 4, 5): [-100, -3, -1, 0, 2, 4, 100],  # normal shape
        (10, 10, 1, 1): [-100, -1, 0, 1, 100],  # small size of matrix
    },
    'diagonal: TypeError': {
        (20, 20): [
            '2020',
            [20],
            {
                20: 20
            },
            (20, 20),
            20.20,
        ],  # str, list, dict, tuple, float
    },
    'input: ValueError': {
        (2020, ): [None],
    },
}
for _op_type in ['tril', 'triu']:
    for _expected, _params in cases.items():
        for _Xshape, _diaglist in _params.items():
            list(
                map(lambda _diagonal: case_generator(_op_type, _Xshape, _diagonal, _expected),
                    _diaglist))


class TestTrilTriuOpAPI(unittest.TestCase):
    """ test case by using API and has -1 dimension 
    """

    def test_api(self):
        data = np.random.random([1, 9, 9, 4]).astype('float32')
        x = fluid.data(shape=[1, 9, -1, 4], dtype='float32', name='x')
        tril_out, triu_out = tensor.tril(x), tensor.triu(x)

        place = fluid.CUDAPlace(0) if fluid.core.is_compiled_with_cuda(
        ) else fluid.CPUPlace()
        exe = fluid.Executor(place)
        tril_out, triu_out = exe.run(
            fluid.default_main_program(),
            feed={"x": data},
            fetch_list=[tril_out, triu_out], )
        self.assertTrue(np.allclose(tril_out, np.tril(data)))
        self.assertTrue(np.allclose(triu_out, np.triu(data)))

    def test_api_with_dygraph(self):
        with fluid.dygraph.guard():
            data = np.random.random([1, 9, 9, 4]).astype('float32')
            x = fluid.dygraph.to_variable(data)
            tril_out, triu_out = tensor.tril(x).numpy(), tensor.triu(x).numpy()
            self.assertTrue(np.allclose(tril_out, np.tril(data)))
            self.assertTrue(np.allclose(triu_out, np.triu(data)))

    def test_fluid_api(self):
        data = np.random.random([1, 9, 9, 4]).astype('float32')
        x = fluid.data(shape=[1, 9, -1, 4], dtype='float32', name='x')
        triu_out = fluid.layers.triu(x)

        place = fluid.CUDAPlace(0) if fluid.core.is_compiled_with_cuda(
        ) else fluid.CPUPlace()
        exe = fluid.Executor(place)
        triu_out = exe.run(fluid.default_main_program(),
                           feed={"x": data},
                           fetch_list=[triu_out])


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