#   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

import paddle
from paddle import fluid
from paddle.fluid import Program, program_guard


# Test python API
class TestFullAPI(unittest.TestCase):
    def test_api(self):
        positive_2_int32 = paddle.tensor.fill_constant([1], "int32", 2)

        positive_2_int64 = paddle.tensor.fill_constant([1], "int64", 2)
        shape_tensor_int32 = paddle.static.data(
            name="shape_tensor_int32", shape=[2], dtype="int32"
        )

        shape_tensor_int64 = paddle.static.data(
            name="shape_tensor_int64", shape=[2], dtype="int64"
        )

        out_1 = paddle.full(shape=[1, 2], dtype="float32", fill_value=1.1)

        out_2 = paddle.full(
            shape=[1, positive_2_int32], dtype="float32", fill_value=1.1
        )

        out_3 = paddle.full(
            shape=[1, positive_2_int64], dtype="float32", fill_value=1.1
        )

        out_4 = paddle.full(
            shape=shape_tensor_int32, dtype="float32", fill_value=1.2
        )

        out_5 = paddle.full(
            shape=shape_tensor_int64, dtype="float32", fill_value=1.1
        )

        out_6 = paddle.full(
            shape=shape_tensor_int64, dtype=np.float32, fill_value=1.1
        )

        val = paddle.tensor.fill_constant(
            shape=[1], dtype=np.float32, value=1.1
        )
        out_7 = paddle.full(
            shape=shape_tensor_int64, dtype=np.float32, fill_value=val
        )

        exe = fluid.Executor(place=fluid.CPUPlace())
        res_1, res_2, res_3, res_4, res_5, res_6, res_7 = exe.run(
            fluid.default_main_program(),
            feed={
                "shape_tensor_int32": np.array([1, 2]).astype("int32"),
                "shape_tensor_int64": np.array([1, 2]).astype("int64"),
            },
            fetch_list=[out_1, out_2, out_3, out_4, out_5, out_6, out_7],
        )

        assert np.array_equal(res_1, np.full([1, 2], 1.1, dtype="float32"))
        assert np.array_equal(res_2, np.full([1, 2], 1.1, dtype="float32"))
        assert np.array_equal(res_3, np.full([1, 2], 1.1, dtype="float32"))
        assert np.array_equal(res_4, np.full([1, 2], 1.2, dtype="float32"))
        assert np.array_equal(res_5, np.full([1, 2], 1.1, dtype="float32"))
        assert np.array_equal(res_6, np.full([1, 2], 1.1, dtype="float32"))
        assert np.array_equal(res_7, np.full([1, 2], 1.1, dtype="float32"))

    def test_api_eager(self):
        with fluid.dygraph.base.guard():
            positive_2_int32 = paddle.tensor.fill_constant([1], "int32", 2)
            positive_2_int64 = paddle.tensor.fill_constant([1], "int64", 2)
            positive_4_int64 = paddle.tensor.fill_constant(
                [1], "int64", 4, True
            )

            out_1 = paddle.full(shape=[1, 2], dtype="float32", fill_value=1.1)

            out_2 = paddle.full(
                shape=[1, positive_2_int32.item()],
                dtype="float32",
                fill_value=1.1,
            )

            out_3 = paddle.full(
                shape=[1, positive_2_int64.item()],
                dtype="float32",
                fill_value=1.1,
            )

            out_4 = paddle.full(shape=[1, 2], dtype="float32", fill_value=1.2)

            out_5 = paddle.full(shape=[1, 2], dtype="float32", fill_value=1.1)

            out_6 = paddle.full(shape=[1, 2], dtype=np.float32, fill_value=1.1)

            val = paddle.tensor.fill_constant(
                shape=[1], dtype=np.float32, value=1.1
            )
            out_7 = paddle.full(shape=[1, 2], dtype=np.float32, fill_value=val)

            out_8 = paddle.full(
                shape=positive_2_int32, dtype="float32", fill_value=1.1
            )

            out_9 = paddle.full(
                shape=[
                    positive_2_int32,
                    positive_2_int64,
                    positive_4_int64,
                ],
                dtype="float32",
                fill_value=1.1,
            )

            # test for numpy.float64 as fill_value
            out_10 = paddle.full_like(
                out_7, dtype=np.float32, fill_value=np.abs(1.1)
            )

            assert np.array_equal(out_1, np.full([1, 2], 1.1, dtype="float32"))
            assert np.array_equal(out_2, np.full([1, 2], 1.1, dtype="float32"))
            assert np.array_equal(out_3, np.full([1, 2], 1.1, dtype="float32"))
            assert np.array_equal(out_4, np.full([1, 2], 1.2, dtype="float32"))
            assert np.array_equal(out_5, np.full([1, 2], 1.1, dtype="float32"))
            assert np.array_equal(out_6, np.full([1, 2], 1.1, dtype="float32"))
            assert np.array_equal(out_7, np.full([1, 2], 1.1, dtype="float32"))
            assert np.array_equal(out_8, np.full([2], 1.1, dtype="float32"))
            assert np.array_equal(
                out_9, np.full([2, 2, 4], 1.1, dtype="float32")
            )
            assert np.array_equal(out_10, np.full([1, 2], 1.1, dtype="float32"))


class TestFullOpError(unittest.TestCase):
    def test_errors(self):
        with program_guard(Program(), Program()):
            # for ci coverage
            self.assertRaises(
                TypeError, paddle.full, shape=[1], fill_value=5, dtype='uint4'
            )

            # The argument dtype of full must be one of bool, float16,
            # float32, float64, uint8, int16, int32 or int64

            # The argument shape's type of full_op  must be list, tuple or Variable.
            def test_shape_type():
                paddle.full(shape=1, dtype="float32", fill_value=1)

            self.assertRaises(TypeError, test_shape_type)

            # The shape dtype of full op must be int32 or int64.
            def test_shape_tensor_dtype():
                shape = paddle.static.data(
                    name="shape_tensor", shape=[2], dtype="float32"
                )
                paddle.full(shape=shape, dtype="float32", fill_value=1)

            self.assertRaises(TypeError, test_shape_tensor_dtype)

            def test_shape_tensor_list_dtype():
                shape = paddle.static.data(
                    name="shape_tensor_list", shape=[1], dtype="bool"
                )
                paddle.full(shape=[shape, 2], dtype="float32", fill_value=1)

            self.assertRaises(TypeError, test_shape_tensor_list_dtype)


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