#   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 sys
import numpy as np

sys.path.append("..")
from op_test import OpTest
import paddle.fluid as fluid
from paddle.fluid import Program, program_guard
import paddle

paddle.enable_static()
np.random.seed(10)


# CANN Op Support X: float16, float32, int32, int8 ,uint8
# Situation 1: shape is a list(without tensor)
class TestExpandV2NPUOpRank1(OpTest):

    def setUp(self):
        self.set_npu()
        self.place = paddle.NPUPlace(0)
        self.op_type = "expand_v2"
        self.dtype = np.float32
        self.init_data()

        self.inputs = {'X': np.random.random(self.ori_shape).astype(self.dtype)}
        self.attrs = {'shape': self.shape}
        output = np.tile(self.inputs['X'], self.expand_times)
        self.outputs = {'Out': output}

    def set_npu(self):
        self.__class__.use_npu = True

    def init_data(self):
        self.ori_shape = [100]
        self.shape = [100]
        self.expand_times = [1]

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

    def test_check_grad(self):
        self.check_grad_with_place(self.place, ['X'], 'Out')


class TestExpandV2OpRank2_DimExpanding(TestExpandV2NPUOpRank1):

    def init_data(self):
        self.ori_shape = [120]
        self.shape = [2, 120]
        self.expand_times = [2, 1]


class TestExpandV2OpRank2(TestExpandV2NPUOpRank1):

    def init_data(self):
        self.ori_shape = [1, 140]
        self.shape = [12, 140]
        self.expand_times = [12, 1]


class TestExpandV2OpRank3_Corner(TestExpandV2NPUOpRank1):

    def init_data(self):
        self.ori_shape = (2, 10, 5)
        self.shape = (2, 10, 5)
        self.expand_times = (1, 1, 1)


class TestExpandV2OpRank4(TestExpandV2NPUOpRank1):

    def init_data(self):
        self.ori_shape = (2, 4, 5, 7)
        self.shape = (-1, -1, -1, -1)
        self.expand_times = (1, 1, 1, 1)


class TestExpandV2OpRank5(TestExpandV2NPUOpRank1):

    def init_data(self):
        self.ori_shape = (2, 4, 1, 15)
        self.shape = (2, -1, 4, -1)
        self.expand_times = (1, 1, 4, 1)


class TestExpandV2OpRank6(TestExpandV2NPUOpRank1):

    def init_data(self):
        self.ori_shape = (4, 1, 30)
        self.shape = (2, -1, 4, 30)
        self.expand_times = (2, 1, 4, 1)


# Situation 2: shape is a list(with tensor)
class TestExpandV2OpNPURank1_tensor_attr(OpTest):

    def setUp(self):
        self.set_npu()
        self.place = paddle.NPUPlace(0)
        self.op_type = "expand_v2"
        self.init_data()
        self.dtype = np.float32
        expand_shapes_tensor = []
        for index, ele in enumerate(self.expand_shape):
            expand_shapes_tensor.append(("x" + str(index), np.ones(
                (1)).astype('int32') * ele))

        self.inputs = {
            'X': np.random.random(self.ori_shape).astype(self.dtype),
            'expand_shapes_tensor': expand_shapes_tensor,
        }
        self.attrs = {"shape": self.infer_expand_shape}
        output = np.tile(self.inputs['X'], self.expand_times)
        self.outputs = {'Out': output}

    def set_npu(self):
        self.__class__.use_npu = True

    def init_data(self):
        self.ori_shape = [100]
        self.expand_times = [1]
        self.expand_shape = [100]
        self.infer_expand_shape = [-1]

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

    def test_check_grad(self):
        self.check_grad_with_place(self.place, ['X'], 'Out')


class TestExpandV2OpRank2_Corner_tensor_attr(TestExpandV2OpNPURank1_tensor_attr
                                             ):

    def init_data(self):
        self.ori_shape = [12, 14]
        self.expand_times = [1, 1]
        self.expand_shape = [12, 14]
        self.infer_expand_shape = [12, -1]


# Situation 3: shape is a tensor
class TestExpandV2NPUOpRank1_tensor(OpTest):

    def setUp(self):
        self.set_npu()
        self.place = paddle.NPUPlace(0)
        self.op_type = "expand_v2"
        self.init_data()
        self.dtype = np.float32

        self.inputs = {
            'X': np.random.random(self.ori_shape).astype(self.dtype),
            'Shape': np.array(self.expand_shape).astype("int32"),
        }
        self.attrs = {}
        output = np.tile(self.inputs['X'], self.expand_times)
        self.outputs = {'Out': output}

    def set_npu(self):
        self.__class__.use_npu = True

    def init_data(self):
        self.ori_shape = [100]
        self.expand_times = [2, 1]
        self.expand_shape = [2, 100]

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

    def test_check_grad(self):
        self.check_grad_with_place(self.place, ['X'], 'Out')


# Situation 4: input x is float16
# skip grad check for float16
class TestExpandV2OpFloat(OpTest):

    def setUp(self):
        self.set_npu()
        self.place = paddle.NPUPlace(0)
        self.op_type = "expand_v2"
        self.dtype = np.float16
        self.ori_shape = (2, 4, 20)
        self.inputs = {'X': np.random.random(self.ori_shape).astype(self.dtype)}
        self.attrs = {'shape': [2, 4, 20]}
        output = np.tile(self.inputs['X'], (1, 1, 1))
        self.outputs = {'Out': output}

    def set_npu(self):
        self.__class__.use_npu = True
        self.__class__.no_need_check_grad = True

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


# Situation 5: input x is int32
# skip grad check for int32
class TestExpandV2OpInteger(OpTest):

    def init_dtype(self):
        self.dtype = 'int32'

    def setUp(self):
        self.set_npu()
        self.place = paddle.NPUPlace(0)
        self.op_type = "expand_v2"
        self.inputs = {
            'X': np.random.randint(10, size=(2, 4, 20)).astype(self.dtype)
        }
        self.attrs = {'shape': [2, 4, 20]}
        output = np.tile(self.inputs['X'], (1, 1, 1))
        self.outputs = {'Out': output}

    def set_npu(self):
        self.__class__.use_npu = True
        self.__class__.no_need_check_grad = True

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


class TesstExpandV2OpInt64(TestExpandV2OpInteger):

    def init_dtype(self):
        self.dtype = 'int64'


class TesstExpandV2OpBool(TestExpandV2OpInteger):

    def init_dtype(self):
        self.dtype = 'bool'

    def setUp(self):
        self.set_npu()
        self.place = paddle.NPUPlace(0)
        self.op_type = "expand_v2"
        self.inputs = {'X': np.random.randint(10, size=(2, 4, 20)) > 5}
        self.attrs = {'shape': [2, 4, 20]}
        output = np.tile(self.inputs['X'], (1, 1, 1))
        self.outputs = {'Out': output}


class TestExpandV2Error(unittest.TestCase):

    def test_errors(self):
        with program_guard(Program(), Program()):
            x1 = fluid.create_lod_tensor(np.array([[-1]]), [[1]],
                                         paddle.NPUPlace(0))
            shape = [2, 2]
            self.assertRaises(TypeError, paddle.tensor.expand, x1, shape)
            x2 = fluid.layers.data(name='x2', shape=[2], dtype="uint8")
            self.assertRaises(TypeError, paddle.tensor.expand, x2, shape)
            x3 = fluid.layers.data(name='x3', shape=[2], dtype="bool")
            x3.stop_gradient = False
            self.assertRaises(ValueError, paddle.tensor.expand, x3, shape)


# Test python API
class TestExpandV2API(unittest.TestCase):

    def test_static(self):
        with fluid.program_guard(fluid.Program(), fluid.Program()):
            input = np.random.random([12, 14]).astype("float32")
            x = fluid.layers.data(name='x',
                                  shape=[12, 14],
                                  append_batch_size=False,
                                  dtype="float32")

            positive_2 = fluid.layers.fill_constant([1], "int32", 12)
            expand_shape = fluid.layers.data(name="expand_shape",
                                             shape=[2],
                                             append_batch_size=False,
                                             dtype="int32")

            out_1 = paddle.expand(x, shape=[12, 14])
            out_2 = paddle.expand(x, shape=[positive_2, 14])
            out_3 = paddle.expand(x, shape=expand_shape)

            g0 = fluid.backward.calc_gradient(out_2, x)

            exe = fluid.Executor(place=paddle.NPUPlace(0))
            res_1, res_2, res_3 = exe.run(fluid.default_main_program(),
                                          feed={
                                              "x":
                                              input,
                                              "expand_shape":
                                              np.array([12, 14]).astype("int32")
                                          },
                                          fetch_list=[out_1, out_2, out_3])

            assert np.array_equal(res_1, np.tile(input, (1, 1)))
            assert np.array_equal(res_2, np.tile(input, (1, 1)))
            assert np.array_equal(res_3, np.tile(input, (1, 1)))


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