#   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
from __future__ import division

import sys
sys.path.append("..")
import unittest
import numpy as np

import paddle.fluid.core as core
from op_test_xpu import XPUOpTest
import paddle.fluid as fluid
from paddle.fluid import Program, program_guard
from test_pool2d_op import adaptive_start_index, adaptive_end_index
import paddle

paddle.enable_static()


def max_pool2D_forward_naive(x,
                             ksize,
                             strides,
                             paddings,
                             global_pool=0,
                             ceil_mode=False,
                             exclusive=True,
                             adaptive=False,
                             data_type=np.float64):
    N, C, H, W = x.shape
    if global_pool == 1:
        ksize = [H, W]
    if adaptive:
        H_out, W_out = ksize
    else:
        H_out = (H - ksize[0] + 2 * paddings[0] + strides[0] - 1
                 ) // strides[0] + 1 if ceil_mode else (
                     H - ksize[0] + 2 * paddings[0]) // strides[0] + 1
        W_out = (W - ksize[1] + 2 * paddings[1] + strides[1] - 1
                 ) // strides[1] + 1 if ceil_mode else (
                     W - ksize[1] + 2 * paddings[1]) // strides[1] + 1
    out = np.zeros((N, C, H_out, W_out))
    for i in range(H_out):
        for j in range(W_out):
            if adaptive:
                r_start = adaptive_start_index(i, H, ksize[0])
                r_end = adaptive_end_index(i, H, ksize[0])
                c_start = adaptive_start_index(j, W, ksize[1])
                c_end = adaptive_end_index(j, W, ksize[1])
            else:
                r_start = np.max((i * strides[0] - paddings[0], 0))
                r_end = np.min((i * strides[0] + ksize[0] - paddings[0], H))
                c_start = np.max((j * strides[1] - paddings[1], 0))
                c_end = np.min((j * strides[1] + ksize[1] - paddings[1], W))
            x_masked = x[:, :, r_start:r_end, c_start:c_end]

            out[:, :, i, j] = np.max(x_masked, axis=(2, 3))
    return out


def avg_pool2D_forward_naive(x,
                             ksize,
                             strides,
                             paddings,
                             global_pool=0,
                             ceil_mode=False,
                             exclusive=True,
                             adaptive=False,
                             data_type=np.float64):
    N, C, H, W = x.shape
    if global_pool == 1:
        ksize = [H, W]
    if adaptive:
        H_out, W_out = ksize
    else:
        H_out = (H - ksize[0] + 2 * paddings[0] + strides[0] - 1
                 ) // strides[0] + 1 if ceil_mode else (
                     H - ksize[0] + 2 * paddings[0]) // strides[0] + 1
        W_out = (W - ksize[1] + 2 * paddings[1] + strides[1] - 1
                 ) // strides[1] + 1 if ceil_mode else (
                     W - ksize[1] + 2 * paddings[1]) // strides[1] + 1
    out = np.zeros((N, C, H_out, W_out))
    for i in range(H_out):
        for j in range(W_out):
            if adaptive:
                r_start = adaptive_start_index(i, H, ksize[0])
                r_end = adaptive_end_index(i, H, ksize[0])
                c_start = adaptive_start_index(j, W, ksize[1])
                c_end = adaptive_end_index(j, W, ksize[1])
            else:
                r_start = i * strides[0] - paddings[0]
                r_end = i * strides[0] + ksize[0] - paddings[0]
                c_start = j * strides[1] - paddings[1]
                c_end = j * strides[1] + ksize[1] - paddings[1]
                field_size = (r_end - r_start) * (c_end - c_start)
                r_start = np.max((r_start, 0))
                r_end = np.min((r_end, H))
                c_start = np.max((c_start, 0))
                c_end = np.min((c_end, W))

            x_masked = x[:, :, r_start:r_end, c_start:c_end]

            if (exclusive or adaptive):
                field_size = (r_end - r_start) * (c_end - c_start)

            if data_type == np.int8 or data_type == np.uint8:
                out[:, :, i, j] = (np.rint(
                    np.sum(x_masked, axis=(2, 3)) /
                    field_size)).astype(data_type)
            else:
                out[:, :, i, j] = (np.sum(x_masked, axis=(2, 3)) /
                                   field_size).astype(data_type)
    return out


def pool2D_forward_naive(x,
                         ksize,
                         strides,
                         paddings,
                         global_pool=0,
                         ceil_mode=False,
                         exclusive=True,
                         adaptive=False,
                         data_format='NCHW',
                         pool_type="max",
                         padding_algorithm="EXPLICIT"):

    # update paddings
    def _get_padding_with_SAME(input_shape, pool_size, pool_stride):
        padding = []
        for input_size, filter_size, stride_size in zip(input_shape, pool_size,
                                                        pool_stride):
            out_size = int((input_size + stride_size - 1) / stride_size)
            pad_sum = np.max((
                (out_size - 1) * stride_size + filter_size - input_size, 0))
            pad_0 = int(pad_sum / 2)
            pad_1 = int(pad_sum - pad_0)
            padding.append(pad_0)
            padding.append(pad_1)
        return padding

    if isinstance(padding_algorithm, str):
        padding_algorithm = padding_algorithm.upper()
        if padding_algorithm not in ["SAME", "VALID", "EXPLICIT"]:
            raise ValueError("Unknown Attr(padding_algorithm): '%s'. "
                             "It can only be 'SAME' or 'VALID'." %
                             str(padding_algorithm))

        if padding_algorithm == "VALID":
            paddings = [0, 0, 0, 0]
            if ceil_mode != False:
                raise ValueError(
                    "When Attr(pool_padding) is \"VALID\", Attr(ceil_mode)"
                    " must be False. "
                    "Received ceil_mode: True.")
        elif padding_algorithm == "SAME":
            input_data_shape = []
            if data_format == "NCHW":
                input_data_shape = x.shape[2:4]
            elif data_format == "NHWC":
                input_data_shape = x.shape[1:3]
            paddings = _get_padding_with_SAME(input_data_shape, ksize, strides)

    assert len(paddings) == 2 or len(paddings) == 4
    is_sys = True if len(paddings) == 2 else False

    N = x.shape[0]
    C, H, W = [x.shape[1], x.shape[2], x.shape[3]] if data_format == 'NCHW' \
        else [x.shape[3], x.shape[1], x.shape[2]]

    if global_pool == 1:
        ksize = [H, W]
        paddings = [0 for _ in range(len(paddings))]

    pad_h_up = paddings[0] if is_sys else paddings[0]
    pad_h_down = paddings[0] if is_sys else paddings[1]
    pad_w_left = paddings[1] if is_sys else paddings[2]
    pad_w_right = paddings[1] if is_sys else paddings[3]

    if adaptive:
        H_out, W_out = ksize
    else:
        H_out = (H - ksize[0] + pad_h_up + pad_h_down + strides[0] - 1) // strides[0] + 1 \
            if ceil_mode else (H - ksize[0] + pad_h_up + pad_h_down) // strides[0] + 1
        W_out = (W - ksize[1] + pad_w_left + pad_w_right + strides[1] - 1) // strides[1] + 1 \
            if ceil_mode else (W - ksize[1] + pad_w_left + pad_w_right) // strides[1] + 1

    out = np.zeros((N, C, H_out, W_out)) if data_format=='NCHW' \
        else np.zeros((N, H_out, W_out, C))
    for i in range(H_out):
        if adaptive:
            in_h_start = adaptive_start_index(i, H, ksize[0])
            in_h_end = adaptive_end_index(i, H, ksize[0])
        else:
            in_h_start = np.max((i * strides[0] - pad_h_up, 0))
            in_h_end = np.min((i * strides[0] + ksize[0] - pad_h_up, H))

        for j in range(W_out):
            if adaptive:
                in_w_start = adaptive_start_index(j, W, ksize[1])
                in_w_end = adaptive_end_index(j, W, ksize[1])
            else:
                in_h_start = i * strides[0] - pad_h_up
                in_w_start = j * strides[1] - pad_w_left
                in_h_end = i * strides[0] + ksize[0] - pad_h_up
                in_w_end = j * strides[1] + ksize[1] - pad_w_left

                field_size = (in_h_end - in_h_start) * (in_w_end - in_w_start)
                in_h_start = np.max((in_h_start, 0))
                in_w_start = np.max((in_w_start, 0))
                in_h_end = np.min((in_h_end, H))
                in_w_end = np.min((in_w_end, W))

            if data_format == 'NCHW':
                x_masked = x[:, :, in_h_start:in_h_end, in_w_start:in_w_end]
                if pool_type == 'avg':
                    if (exclusive or adaptive):
                        field_size = (in_h_end - in_h_start) * (
                            in_w_end - in_w_start)

#                         if (exclusive or adaptive) else (ksize[0] * ksize[1])
                    out[:, :, i, j] = np.sum(x_masked, axis=(2, 3)) / field_size
                elif pool_type == 'max':
                    out[:, :, i, j] = np.max(x_masked, axis=(2, 3))
            elif data_format == 'NHWC':
                x_masked = x[:, in_h_start:in_h_end, in_w_start:in_w_end, :]
                if pool_type == 'avg':
                    if (exclusive or adaptive):
                        field_size = (in_h_end - in_h_start) * (
                            in_w_end - in_w_start)
                    out[:, i, j, :] = np.sum(x_masked, axis=(1, 2)) / field_size
                elif pool_type == 'max':
                    out[:, i, j, :] = np.max(x_masked, axis=(1, 2))
    return out


class TestPool2D_Op(XPUOpTest):
    def setUp(self):
        self.op_type = "pool2d"
        self.use_cudnn = False
        self.init_kernel_type()
        self.use_mkldnn = False
        self.init_data_type()
        self.init_test_case()
        self.padding_algorithm = "EXPLICIT"
        self.init_paddings()
        self.init_global_pool()
        self.init_kernel_type()
        self.init_pool_type()
        self.init_ceil_mode()
        self.init_exclusive()
        self.init_adaptive()
        self.init_data_format()
        self.init_shape()

        input = np.random.random(self.shape).astype(self.dtype)
        output = pool2D_forward_naive(
            input, self.ksize, self.strides, self.paddings, self.global_pool,
            self.ceil_mode, self.exclusive, self.adaptive, self.data_format,
            self.pool_type, self.padding_algorithm).astype(self.dtype)
        self.inputs = {'X': XPUOpTest.np_dtype_to_fluid_dtype(input)}

        self.attrs = {
            'strides': self.strides,
            'paddings': self.paddings,
            'ksize': self.ksize,
            'pooling_type': self.pool_type,
            'global_pooling': self.global_pool,
            'use_cudnn': self.use_cudnn,
            'use_mkldnn': self.use_mkldnn,
            'ceil_mode': self.ceil_mode,
            'data_format': self.data_format,
            'exclusive': self.exclusive,
            'adaptive': self.adaptive,
            "padding_algorithm": self.padding_algorithm,
        }

        self.outputs = {'Out': output}

    def has_xpu(self):
        return core.is_compiled_with_xpu()

    def test_check_output(self):
        if self.has_xpu():
            place = core.XPUPlace(0)
            self.check_output_with_place(place)
        return

    def test_check_grad(self):
        if self.has_xpu():
            place = core.XPUPlace(0)
            self.check_grad_with_place(place, set(['X']), 'Out')
        return

    def init_data_format(self):
        self.data_format = "NCHW"

    def init_shape(self):
        self.shape = [2, 3, 5, 5]

    def init_test_case(self):
        self.ksize = [3, 3]
        self.strides = [1, 1]

    def init_paddings(self):
        self.paddings = [0, 0]
        self.padding_algorithm = "EXPLICIT"

    def init_kernel_type(self):
        self.use_cudnn = False

    def init_data_type(self):
        self.dtype = np.float32

    def init_pool_type(self):
        self.pool_type = "avg"
        self.pool2D_forward_naive = avg_pool2D_forward_naive

    def init_global_pool(self):
        self.global_pool = True

    def init_ceil_mode(self):
        self.ceil_mode = False

    def init_exclusive(self):
        self.exclusive = True

    def init_adaptive(self):
        self.adaptive = False


class TestCase1(TestPool2D_Op):
    def init_test_case(self):
        self.ksize = [3, 3]
        self.strides = [1, 1]

    def init_paddings(self):
        self.paddings = [0, 0]

    def init_pool_type(self):
        self.pool_type = "avg"
        self.pool2D_forward_naive = avg_pool2D_forward_naive

    def init_global_pool(self):
        self.global_pool = False

    def init_shape(self):
        self.shape = [2, 3, 7, 7]


class TestCase2(TestPool2D_Op):
    def init_test_case(self):
        self.ksize = [3, 3]
        self.strides = [1, 1]

    def init_paddings(self):
        self.paddings = [1, 1]

    def init_pool_type(self):
        self.pool_type = "avg"
        self.pool2D_forward_naive = avg_pool2D_forward_naive

    def init_global_pool(self):
        self.global_pool = False

    def init_shape(self):
        self.shape = [2, 3, 7, 7]


class TestCase3(TestPool2D_Op):
    def init_pool_type(self):
        self.pool_type = "max"
        self.pool2D_forward_naive = max_pool2D_forward_naive


class TestCase4(TestCase1):
    def init_pool_type(self):
        self.pool_type = "max"
        self.pool2D_forward_naive = max_pool2D_forward_naive


class TestCase5(TestCase2):
    def init_pool_type(self):
        self.pool_type = "max"
        self.pool2D_forward_naive = max_pool2D_forward_naive


class TestPool2D_AsyPadding(TestPool2D_Op):
    def init_test_case(self):
        self.ksize = [3, 3]
        self.strides = [1, 1]
        self.paddings = [1, 0, 1, 2]

    def init_shape(self):
        self.shape = [2, 3, 5, 5]


class TestCase1_AsyPadding(TestCase1):
    def init_test_case(self):
        self.ksize = [3, 3]
        self.strides = [1, 1]
        self.paddings = [1, 0, 1, 0]

    def init_shape(self):
        self.shape = [2, 3, 7, 7]


class TestCase2_AsyPadding(TestCase2):
    def init_test_case(self):
        self.ksize = [3, 3]
        self.strides = [1, 1]
        self.paddings = [1, 2, 1, 2]

    def init_shape(self):
        self.shape = [2, 3, 7, 7]


class TestCase3_AsyPadding(TestCase3):
    def init_test_case(self):
        self.ksize = [3, 3]
        self.strides = [1, 1]
        self.paddings = [1, 0, 1, 2]

    def init_shape(self):
        self.shape = [2, 3, 5, 5]


class TestCase4_AsyPadding(TestCase4):
    def init_test_case(self):
        self.ksize = [3, 3]
        self.strides = [1, 1]
        self.paddings = [1, 0, 1, 0]

    def init_shape(self):
        self.shape = [2, 3, 7, 7]


class TestCase5_AsyPadding((TestCase5)):
    def init_test_case(self):
        self.ksize = [3, 3]
        self.strides = [1, 1]
        self.paddings = [2, 2, 1, 2]

    def init_shape(self):
        self.shape = [2, 3, 7, 7]


class TestAvgInclude_AsyPadding(TestCase2):
    def init_exclusive(self):
        self.exclusive = False

    def init_test_case(self):
        self.ksize = [3, 3]
        self.strides = [1, 1]
        self.paddings = [1, 2, 1, 2]

    def init_shape(self):
        self.shape = [2, 3, 7, 7]


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