#   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
import numpy as np
import paddle
import paddle.fluid as fluid
from paddle.fluid import Program, program_guard
from op_test import OpTest
from paddle.fluid.framework import _test_eager_guard


class TestClipOp(OpTest):

    def setUp(self):
        self.max_relative_error = 0.006
        self.python_api = paddle.clip

        self.inputs = {}
        self.initTestCase()

        self.op_type = "clip"
        self.attrs = {}
        self.attrs['min'] = self.min
        self.attrs['max'] = self.max
        if 'Min' in self.inputs:
            min_v = self.inputs['Min']
        else:
            min_v = self.attrs['min']

        if 'Max' in self.inputs:
            max_v = self.inputs['Max']
        else:
            max_v = self.attrs['max']

        input = np.random.random(self.shape).astype(self.dtype)
        input[np.abs(input - min_v) < self.max_relative_error] = 0.5
        input[np.abs(input - max_v) < self.max_relative_error] = 0.5
        self.inputs['X'] = input
        self.outputs = {'Out': np.clip(self.inputs['X'], min_v, max_v)}

    def test_check_output(self):
        paddle.enable_static()
        self.check_output(check_eager=True)
        paddle.disable_static()

    def test_check_grad_normal(self):
        paddle.enable_static()
        self.check_grad(['X'], 'Out', check_eager=True)
        paddle.disable_static()

    def initTestCase(self):
        self.dtype = np.float32
        self.shape = (4, 10, 10)
        self.max = 0.8
        self.min = 0.3
        self.inputs['Max'] = np.array([0.8]).astype(self.dtype)
        self.inputs['Min'] = np.array([0.1]).astype(self.dtype)


class TestCase1(TestClipOp):

    def initTestCase(self):
        self.dtype = np.float32
        self.shape = (8, 16, 8)
        self.max = 0.7
        self.min = 0.0


class TestCase2(TestClipOp):

    def initTestCase(self):
        self.dtype = np.float32
        self.shape = (8, 16)
        self.max = 1.0
        self.min = 0.0


class TestCase3(TestClipOp):

    def initTestCase(self):
        self.dtype = np.float32
        self.shape = (4, 8, 16)
        self.max = 0.7
        self.min = 0.2


class TestCase4(TestClipOp):

    def initTestCase(self):
        self.dtype = np.float32
        self.shape = (4, 8, 8)
        self.max = 0.7
        self.min = 0.2
        self.inputs['Max'] = np.array([0.8]).astype(self.dtype)
        self.inputs['Min'] = np.array([0.3]).astype(self.dtype)


class TestCase5(TestClipOp):

    def initTestCase(self):
        self.dtype = np.float32
        self.shape = (4, 8, 16)
        self.max = 0.5
        self.min = 0.5


class TestCase6(TestClipOp):

    def initTestCase(self):
        self.dtype == np.float16
        self.shape = (4, 8, 8)
        self.max = 0.7
        self.min = 0.2
        self.inputs['Max'] = np.array([0.8]).astype(self.dtype)
        self.inputs['Min'] = np.array([0.3]).astype(self.dtype)


class TestClipOpError(unittest.TestCase):

    def test_errors(self):
        paddle.enable_static()
        with program_guard(Program(), Program()):
            input_data = np.random.random((2, 4)).astype("float32")

            def test_Variable():
                fluid.layers.clip(x=input_data, min=-1.0, max=1.0)

            self.assertRaises(TypeError, test_Variable)

            def test_dtype():
                x2 = fluid.layers.data(name='x2', shape=[1], dtype='int32')
                fluid.layers.clip(x=x2, min=-1.0, max=1.0)

            self.assertRaises(TypeError, test_dtype)
        paddle.disable_static()


class TestClipAPI(unittest.TestCase):

    def _executed_api(self, x, min=None, max=None):
        return paddle.clip(x, min, max)

    def test_clip(self):
        paddle.enable_static()
        data_shape = [1, 9, 9, 4]
        data = np.random.random(data_shape).astype('float32')
        images = fluid.data(name='image', shape=data_shape, dtype='float32')
        min = fluid.data(name='min', shape=[1], dtype='float32')
        max = fluid.data(name='max', shape=[1], dtype='float32')

        place = fluid.CUDAPlace(
            0) if fluid.core.is_compiled_with_cuda() else fluid.CPUPlace()
        exe = fluid.Executor(place)

        out_1 = self._executed_api(images, min=min, max=max)
        out_2 = self._executed_api(images, min=0.2, max=0.9)
        out_3 = self._executed_api(images, min=0.3)
        out_4 = self._executed_api(images, max=0.7)
        out_5 = self._executed_api(images, min=min)
        out_6 = self._executed_api(images, max=max)
        out_7 = self._executed_api(images, max=-1.)
        out_8 = self._executed_api(images)
        out_9 = self._executed_api(paddle.cast(images, 'float64'),
                                   min=0.2,
                                   max=0.9)
        out_10 = self._executed_api(paddle.cast(images * 10, 'int32'),
                                    min=2,
                                    max=8)
        out_11 = self._executed_api(paddle.cast(images * 10, 'int64'),
                                    min=2,
                                    max=8)

        res1, res2, res3, res4, res5, res6, res7, res8, res9, res10, res11 = exe.run(
            fluid.default_main_program(),
            feed={
                "image": data,
                "min": np.array([0.2]).astype('float32'),
                "max": np.array([0.8]).astype('float32')
            },
            fetch_list=[
                out_1, out_2, out_3, out_4, out_5, out_6, out_7, out_8, out_9,
                out_10, out_11
            ])

        self.assertTrue(np.allclose(res1, data.clip(0.2, 0.8)))
        self.assertTrue(np.allclose(res2, data.clip(0.2, 0.9)))
        self.assertTrue(np.allclose(res3, data.clip(min=0.3)))
        self.assertTrue(np.allclose(res4, data.clip(max=0.7)))
        self.assertTrue(np.allclose(res5, data.clip(min=0.2)))
        self.assertTrue(np.allclose(res6, data.clip(max=0.8)))
        self.assertTrue(np.allclose(res7, data.clip(max=-1)))
        self.assertTrue(np.allclose(res8, data))
        self.assertTrue(
            np.allclose(res9,
                        data.astype(np.float64).clip(0.2, 0.9)))
        self.assertTrue(
            np.allclose(res10, (data * 10).astype(np.int32).clip(2, 8)))
        self.assertTrue(
            np.allclose(res11, (data * 10).astype(np.int64).clip(2, 8)))
        paddle.disable_static()

    def func_clip_dygraph(self):
        paddle.disable_static()
        place = fluid.CUDAPlace(
            0) if fluid.core.is_compiled_with_cuda() else fluid.CPUPlace()
        paddle.disable_static(place)
        data_shape = [1, 9, 9, 4]
        data = np.random.random(data_shape).astype('float32')
        images = paddle.to_tensor(data, dtype='float32')
        v_min = paddle.to_tensor(np.array([0.2], dtype=np.float32))
        v_max = paddle.to_tensor(np.array([0.8], dtype=np.float32))

        out_1 = self._executed_api(images, min=0.2, max=0.8)
        images = paddle.to_tensor(data, dtype='float32')
        out_2 = self._executed_api(images, min=0.2, max=0.9)
        images = paddle.to_tensor(data, dtype='float32')
        out_3 = self._executed_api(images, min=v_min, max=v_max)

        out_4 = self._executed_api(paddle.cast(images * 10, 'int32'),
                                   min=2,
                                   max=8)
        out_5 = self._executed_api(paddle.cast(images * 10, 'int64'),
                                   min=2,
                                   max=8)
        # test with numpy.generic
        out_6 = self._executed_api(images, min=np.abs(0.2), max=np.abs(0.8))

        self.assertTrue(np.allclose(out_1.numpy(), data.clip(0.2, 0.8)))
        self.assertTrue(np.allclose(out_2.numpy(), data.clip(0.2, 0.9)))
        self.assertTrue(np.allclose(out_3.numpy(), data.clip(0.2, 0.8)))
        self.assertTrue(
            np.allclose(out_4.numpy(), (data * 10).astype(np.int32).clip(2, 8)))
        self.assertTrue(
            np.allclose(out_5.numpy(), (data * 10).astype(np.int64).clip(2, 8)))
        self.assertTrue(np.allclose(out_6.numpy(), data.clip(0.2, 0.8)))

    def test_clip_dygraph(self):
        with _test_eager_guard():
            self.func_clip_dygraph()
        self.func_clip_dygraph()

    def test_clip_dygraph_default_max(self):
        paddle.disable_static()
        with _test_eager_guard():
            x_int32 = paddle.to_tensor([1, 2, 3], dtype="int32")
            x_int64 = paddle.to_tensor([1, 2, 3], dtype="int64")
            x_f32 = paddle.to_tensor([1, 2, 3], dtype="float32")
            egr_out1 = paddle.clip(x_int32, min=1)
            egr_out2 = paddle.clip(x_int64, min=1)
            egr_out3 = paddle.clip(x_f32, min=1)
        x_int32 = paddle.to_tensor([1, 2, 3], dtype="int32")
        x_int64 = paddle.to_tensor([1, 2, 3], dtype="int64")
        x_f32 = paddle.to_tensor([1, 2, 3], dtype="float32")
        out1 = paddle.clip(x_int32, min=1)
        out2 = paddle.clip(x_int64, min=1)
        out3 = paddle.clip(x_f32, min=1)
        self.assertTrue(np.allclose(out1.numpy(), egr_out1.numpy()))
        self.assertTrue(np.allclose(out2.numpy(), egr_out2.numpy()))
        self.assertTrue(np.allclose(out3.numpy(), egr_out3.numpy()))

    def test_errors(self):
        paddle.enable_static()
        x1 = fluid.data(name='x1', shape=[1], dtype="int16")
        x2 = fluid.data(name='x2', shape=[1], dtype="int8")
        self.assertRaises(TypeError, paddle.clip, x=x1, min=0.2, max=0.8)
        self.assertRaises(TypeError, paddle.clip, x=x2, min=0.2, max=0.8)
        paddle.disable_static()


class TestInplaceClipAPI(TestClipAPI):

    def _executed_api(self, x, min=None, max=None):
        return x.clip_(min, max)


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