#   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.

from __future__ import print_function

import unittest
from paddle.fluid.framework import default_main_program, Program, convert_np_dtype_to_dtype_
import paddle.fluid as fluid
import paddle.fluid.core as core
import numpy as np


class TestVariable(unittest.TestCase):
    def test_np_dtype_convert(self):
        DT = core.VarDesc.VarType
        convert = convert_np_dtype_to_dtype_
        self.assertEqual(DT.FP32, convert(np.float32))
        self.assertEqual(DT.FP16, convert("float16"))
        self.assertEqual(DT.FP64, convert("float64"))
        self.assertEqual(DT.INT32, convert("int32"))
        self.assertEqual(DT.INT16, convert("int16"))
        self.assertEqual(DT.INT64, convert("int64"))
        self.assertEqual(DT.BOOL, convert("bool"))
        self.assertEqual(DT.INT8, convert("int8"))
        self.assertEqual(DT.UINT8, convert("uint8"))

    def test_var(self):
        b = default_main_program().current_block()
        w = b.create_var(
            dtype="float64", shape=[784, 100], lod_level=0, name="fc.w")
        self.assertNotEqual(str(w), "")
        self.assertEqual(core.VarDesc.VarType.FP64, w.dtype)
        self.assertEqual((784, 100), w.shape)
        self.assertEqual("fc.w", w.name)
        self.assertEqual(0, w.lod_level)

        w = b.create_var(name='fc.w')
        self.assertEqual(core.VarDesc.VarType.FP64, w.dtype)
        self.assertEqual((784, 100), w.shape)
        self.assertEqual("fc.w", w.name)
        self.assertEqual(0, w.lod_level)

        self.assertRaises(ValueError,
                          lambda: b.create_var(name="fc.w", shape=(24, 100)))

    def test_step_scopes(self):
        prog = Program()
        b = prog.current_block()
        var = b.create_var(
            name='step_scopes', type=core.VarDesc.VarType.STEP_SCOPES)
        self.assertEqual(core.VarDesc.VarType.STEP_SCOPES, var.type)

    def _test_slice(self, place):
        b = default_main_program().current_block()
        w = b.create_var(dtype="float64", shape=[784, 100, 100], lod_level=0)

        for i in range(3):
            nw = w[i]
            self.assertEqual((1, 100, 100), nw.shape)

        nw = w[:]
        self.assertEqual((784, 100, 100), nw.shape)

        nw = w[:, :, ...]
        self.assertEqual((784, 100, 100), nw.shape)

        nw = w[::2, ::2, :]
        self.assertEqual((392, 50, 100), nw.shape)

        nw = w[::-2, ::-2, :]
        self.assertEqual((392, 50, 100), nw.shape)

        self.assertEqual(0, nw.lod_level)

        main = fluid.Program()
        with fluid.program_guard(main):
            exe = fluid.Executor(place)
            tensor_array = np.array(
                [[[1, 2, 3], [4, 5, 6], [7, 8, 9]],
                 [[10, 11, 12], [13, 14, 15], [16, 17, 18]],
                 [[19, 20, 21], [22, 23, 24], [25, 26, 27]]]).astype('float32')
            var = fluid.layers.assign(tensor_array)
            var1 = var[0, 1, 1]
            var2 = var[1:]
            var3 = var[0:1]
            var4 = var[..., ]
            var5 = var[2::-2]
            var6 = var[1, 1:, 1:]
            var7 = var[1, ..., 1:]
            var8 = var[1, ...]
            var_reshape = fluid.layers.reshape(var, [3, -1, 3])
            var9 = var_reshape[1, ..., 2]
            var10 = var_reshape[:, :, -1]

            x = fluid.layers.data(name='x', shape=[13], dtype='float32')
            y = fluid.layers.fc(input=x, size=1, act=None)
            var11 = y[:, 0]
            feeder = fluid.DataFeeder(place=place, feed_list=[x])
            data = []
            data.append((np.random.randint(10, size=[13]).astype('float32')))
            exe.run(fluid.default_startup_program())

            local_out = exe.run(main,
                                feed=feeder.feed([data]),
                                fetch_list=[
                                    var, var1, var2, var3, var4, var5, var6,
                                    var7, var8, var9, var10, var11
                                ])

            self.assertTrue((np.array(local_out[1]) == np.array(tensor_array[
                0, 1, 1])).all())
            self.assertTrue((np.array(local_out[2]) == np.array(tensor_array[
                1:])).all())
            self.assertTrue((np.array(local_out[3]) == np.array(tensor_array[
                0:1])).all())
            self.assertTrue((np.array(local_out[4]) == np.array(
                tensor_array[..., ])).all())
            self.assertTrue((np.array(local_out[5]) == np.array(tensor_array[
                2::-2])).all())
            self.assertTrue((np.array(local_out[6]) == np.array(tensor_array[
                1, 1:, 1:])).all())
            self.assertTrue((np.array(local_out[7]) == np.array(tensor_array[
                1, ..., 1:])).all())
            self.assertTrue((np.array(local_out[8]) == np.array(tensor_array[
                1, ...])).all())
            self.assertEqual(local_out[9].shape, (1, 3, 1))
            self.assertEqual(local_out[10].shape, (3, 3, 1))
            self.assertEqual(local_out[11].shape, (1, 1))

    def test_slice(self):
        place = fluid.CPUPlace()
        self._test_slice(place)

        if core.is_compiled_with_cuda():
            self._test_slice(core.CUDAPlace(0))


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