#   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.fluid import core
from paddle.static import Program, program_guard


def arange_wrapper(start, end, step, dtype="float32"):
    return paddle.arange(start, end, step, dtype)


class TestArangeOp(OpTest):
    def setUp(self):
        self.op_type = "range"
        self.init_config()
        self.inputs = {
            'Start': np.array([self.case[0]]).astype(self.dtype),
            'End': np.array([self.case[1]]).astype(self.dtype),
            'Step': np.array([self.case[2]]).astype(self.dtype),
        }

        self.outputs = {
            'Out': np.arange(self.case[0], self.case[1], self.case[2]).astype(
                self.dtype
            )
        }

    def init_config(self):
        self.dtype = np.float32
        self.python_api = arange_wrapper
        self.case = (0, 1, 0.2)

    def test_check_output(self):
        self.check_output()


class TestFloatArangeOp(TestArangeOp):
    def init_config(self):
        self.dtype = np.float32
        self.python_api = paddle.arange
        self.case = (0, 5, 1)


class TestFloa16ArangeOp(TestArangeOp):
    def init_config(self):
        self.dtype = np.float16
        self.python_api = paddle.arange
        self.case = (0, 5, 1)

    def test_check_output(self):
        self.check_output()


@unittest.skipIf(
    not core.is_compiled_with_cuda()
    or not core.is_bfloat16_supported(core.CUDAPlace(0)),
    "core is not complied with CUDA and not support the bfloat16",
)
class TestBFloat16ArangeOp(OpTest):
    def setUp(self):
        self.op_type = "range"
        self.init_config()
        self.inputs = {
            'Start': convert_float_to_uint16(self.start),
            'End': convert_float_to_uint16(self.end),
            'Step': convert_float_to_uint16(self.step),
        }

        self.outputs = {
            'Out': convert_float_to_uint16(
                np.arange(self.start, self.end, self.step)
            )
        }

    def init_config(self):
        self.dtype = np.uint16
        self.python_api = arange_wrapper
        self.case = (0, 5, 1)
        self.start = np.array([self.case[0]]).astype(np.float32)
        self.end = np.array([self.case[1]]).astype(np.float32)
        self.step = np.array([self.case[2]]).astype(np.float32)

    def test_check_output(self):
        place = core.CUDAPlace(0)
        self.check_output_with_place(place)


class TestInt32ArangeOp(TestArangeOp):
    def init_config(self):
        self.dtype = np.int32
        self.python_api = paddle.arange
        self.case = (0, 5, 2)


class TestFloat64ArangeOp(TestArangeOp):
    def init_config(self):
        self.dtype = np.float64
        self.python_api = paddle.arange
        self.case = (10, 1, -2)


class TestInt64ArangeOp(TestArangeOp):
    def init_config(self):
        self.dtype = np.int64
        self.python_api = paddle.arange
        self.case = (-1, -10, -2)


class TestZeroSizeArangeOp(TestArangeOp):
    def init_config(self):
        self.dtype = np.int32
        self.python_api = paddle.arange
        self.case = (0, 0, 1)


class TestArangeOpError(unittest.TestCase):
    def test_errors(self):
        with program_guard(Program(), Program()):
            self.assertRaises(TypeError, paddle.arange, 10, dtype='int8')


class TestArangeAPI(unittest.TestCase):
    def test_out(self):
        with program_guard(Program(), Program()):
            x1 = paddle.arange(0, 5, 1, 'float32')

            place = (
                paddle.CUDAPlace(0)
                if core.is_compiled_with_cuda()
                else paddle.CPUPlace()
            )
            exe = paddle.static.Executor(place)
            out = exe.run(fetch_list=[x1])

        expected_data = np.arange(0, 5, 1).astype(np.float32)
        self.assertEqual((out == expected_data).all(), True)
        self.assertListEqual(list(x1.shape), [5])


class TestArangeImperative(unittest.TestCase):
    def test_out(self):
        place = (
            paddle.CUDAPlace(0)
            if core.is_compiled_with_cuda()
            else paddle.CPUPlace()
        )
        paddle.disable_static(place)
        x1 = paddle.arange(0, 5, 1)
        x2 = paddle.tensor.arange(5)
        x3 = paddle.tensor.creation.arange(5)

        start = paddle.to_tensor(np.array([0], 'float32'))
        end = paddle.to_tensor(np.array([5], 'float32'))
        step = paddle.to_tensor(np.array([1], 'float32'))
        x4 = paddle.arange(start, end, step, 'int64')

        expected_data = np.arange(0, 5, 1).astype(np.int64)
        for i in [x1, x2, x3, x4]:
            self.assertEqual((i.numpy() == expected_data).all(), True)

        start_float = paddle.to_tensor(np.array([0.5], 'float32'))
        end_float = paddle.to_tensor(np.array([1.5], 'float32'))
        step_float = paddle.to_tensor(np.array([0.5], 'float32'))
        # all [start, end, step] is float
        x5 = paddle.arange(start_float, end_float, step_float)
        x5_expected_data = np.arange(0.5, 1.5, 0.5).astype(np.float32)
        self.assertEqual((x5.numpy() == x5_expected_data).all(), True)

        # [start, end] is float , [step] is int
        x6 = paddle.arange(start_float, end_float, 1)
        x6_expected_data = np.arange(0.5, 1.5, 1).astype(np.float32)
        self.assertEqual((x6.numpy() == x6_expected_data).all(), True)

        # [start] is float , [end] is int
        x7 = paddle.arange(start_float, 1)
        x7_expected_data = np.arange(0.5, 1).astype(np.float32)
        self.assertEqual((x7.numpy() == x7_expected_data).all(), True)

        # [start] is float
        x8 = paddle.arange(start_float)
        x8_expected_data = np.arange(0.5).astype(np.float32)
        self.assertEqual((x8.numpy() == x8_expected_data).all(), True)

        # [start] is int
        x9 = paddle.arange(1)
        x9_expected_data = np.arange(1).astype(np.int64)
        self.assertEqual((x9.numpy() == x9_expected_data).all(), True)

        # [start] is float
        x10 = paddle.arange(1.0)
        x10_expected_data = np.arange(1).astype(np.float32)
        self.assertEqual((x10.numpy() == x10_expected_data).all(), True)

        paddle.enable_static()


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