#   Copyright (c) 2021 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 sys
import unittest
import numpy as np
sys.path.append("..")

import paddle
import paddle.fluid as fluid
import paddle.fluid.core as core
from op_test import OpTest
from test_pool2d_op import pool2D_forward_naive, avg_pool2D_forward_naive, max_pool2D_forward_naive, adaptive_start_index, adaptive_end_index
from paddle.nn.functional import avg_pool2d, max_pool2d

paddle.enable_static()


def create_test_padding_SAME_class(parent):
    class TestPaddingSMAECase(parent):
        def init_paddings(self):
            self.paddings = [0, 0]
            self.padding_algorithm = "SAME"

    cls_name = "{0}_{1}".format(parent.__name__, "PaddingSAMEOp")
    TestPaddingSMAECase.__name__ = cls_name
    globals()[cls_name] = TestPaddingSMAECase


def create_test_use_ceil_class(parent):
    class TestPool2DUseCeilCase(parent):
        def init_ceil_mode(self):
            self.ceil_mode = True

    cls_name = "{0}_{1}".format(parent.__name__, "CeilModeCast")
    TestPool2DUseCeilCase.__name__ = cls_name
    globals()[cls_name] = TestPool2DUseCeilCase


def create_test_padding_VALID_class(parent):
    class TestPaddingVALIDCase(parent):
        def init_paddings(self):
            self.paddings = [1, 1]
            self.padding_algorithm = "VALID"

    cls_name = "{0}_{1}".format(parent.__name__, "PaddingVALIDOp")
    TestPaddingVALIDCase.__name__ = cls_name
    globals()[cls_name] = TestPaddingVALIDCase


def create_test_fp16_class(parent):
    class TestFp16Case(parent):
        def init_kernel_type(self):
            self.use_cudnn = False
            self.dtype = np.float16

        def test_check_grad(self):
            return

    cls_name = "{0}_{1}".format(parent.__name__, "Fp16Op")
    TestFp16Case.__name__ = cls_name
    globals()[cls_name] = TestFp16Case


def pool2d_backward_navie(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

    if data_format == "NHWC":
        x = x.transpose([0, 3, 1, 2])

    N, C, H, W = x.shape

    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

    x_grad = np.zeros_like(x)
    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 pool_type == 'avg':
                if (exclusive or adaptive):
                    field_size = (in_h_end - in_h_start) * (
                        in_w_end - in_w_start)
                x_grad[:, :, in_h_start:in_h_end, in_w_start:
                       in_w_end] += 1 / field_size
            elif pool_type == 'max':
                for n in range(N):
                    for c in range(C):
                        idx = np.argmax(x[n, c, in_h_start:in_h_end, in_w_start:
                                          in_w_end].flatten())
                        idx_h = idx // (in_w_end - in_w_start)
                        idx_w = idx % (in_w_end - in_w_start)
                        x_grad[n, c, in_h_start + idx_h, in_w_start +
                               idx_w] += 1

    if data_format == "NHWC":
        x_grad = x_grad.transpose([0, 2, 3, 1])
    return x_grad


class TestPool2D_Op(OpTest):
    def setUp(self):
        self.set_npu()
        self.op_type = "pool2d"
        self.init_kernel_type()
        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)
        if self.pool_type == "max":
            input = np.array([x for x in range(np.prod(self.shape))]).reshape(
                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': OpTest.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': False,
            'use_mkldnn': False,
            '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 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

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

    def test_check_output(self):
        self.check_output_with_place(fluid.NPUPlace(0), atol=1e-3)

    def test_check_grad(self):
        x_grad = pool2d_backward_navie(
            self.inputs["X"],
            ksize=self.ksize,
            strides=self.strides,
            paddings=self.paddings,
            global_pool=self.global_pool,
            ceil_mode=False,
            exclusive=self.exclusive,
            adaptive=self.adaptive,
            data_format=self.data_format,
            pool_type=self.pool_type,
            padding_algorithm=self.padding_algorithm)
        x_grad = x_grad / np.prod(self.outputs['Out'].shape)
        self.check_grad_with_place(
            fluid.NPUPlace(0),
            set(['X']),
            'Out',
            max_relative_error=0.06,
            user_defined_grads=[x_grad])


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 TestAvgInclude(TestCase2):
    def init_exclusive(self):
        self.exclusive = False


class TestAvgPoolAdaptive(TestCase1):
    def init_adaptive(self):
        self.adaptive = True

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

    def init_test_case(self):
        self.ksize = [7, 7]
        self.strides = [7, 7]
        self.paddings = [0, 0, 0, 0]


class TestAvgPoolAdaptiveAsyOutSize(TestCase1):
    def init_adaptive(self):
        self.adaptive = True

    def init_shape(self):
        self.shape = [2, 3, 8, 8]

    def init_test_case(self):
        self.ksize = [2, 4]
        # fixme: CANN AvgPoolGradV3 dose not support asymmetric strides
        # self.strides = [2, 4]
        self.strides = [4, 4]
        self.paddings = [0, 0, 0, 0]


#-------test pool2d with asymmetric padding-----
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]


class TestAvgPoolAdaptive_AsyPadding(TestCase1):
    def init_adaptive(self):
        self.adaptive = True

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

    def init_shape(self):
        self.shape = [2, 3, 8, 8]


#----------- test channel_last --------------
class TestPool2D_channel_last(TestPool2D_Op):
    def init_data_format(self):
        self.data_format = "NHWC"

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


class TestCase1_channel_last(TestCase1):
    def init_data_format(self):
        self.data_format = "NHWC"

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


class TestCase2_channel_last(TestCase2):
    def init_data_format(self):
        self.data_format = "NHWC"

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


class TestCase3_channel_last(TestCase3):
    def init_data_format(self):
        self.data_format = "NHWC"

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


class TestCase4_channel_last(TestCase4):
    def init_data_format(self):
        self.data_format = "NHWC"

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


class TestCase5_channel_last(TestCase5):
    def init_data_format(self):
        self.data_format = "NHWC"

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


class TestCase5_Max(TestCase2):
    def init_pool_type(self):
        self.pool_type = "max"


class TestCase5_channel_last_Max(TestCase5_Max):
    def init_data_format(self):
        self.data_format = "NHWC"

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


class TestAvgInclude_channel_last(TestCase2_channel_last):
    def init_exclusive(self):
        self.exclusive = False


class TestAvgPoolAdaptive_channel_last(TestCase1_channel_last):
    def init_adaptive(self):
        self.adaptive = True

    def init_shape(self):
        self.shape = [2, 8, 8, 3]

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


class TestPool2D_AsyPadding_channel_last(TestPool2D_AsyPadding):
    def init_data_format(self):
        self.data_format = "NHWC"

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


class TestCase1_AsyPadding_channel_last(TestCase1_AsyPadding):
    def init_data_format(self):
        self.data_format = "NHWC"

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


class TestCase2_AsyPadding_channel_last(TestCase2_AsyPadding):
    def init_data_format(self):
        self.data_format = "NHWC"

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


class TestCase3_AsyPadding_channel_last(TestCase3_AsyPadding):
    def init_data_format(self):
        self.data_format = "NHWC"

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


class TestCase4_AsyPadding_channel_last(TestCase4_AsyPadding):
    def init_data_format(self):
        self.data_format = "NHWC"

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


class TestCase5_AsyPadding_channel_last(TestCase5_AsyPadding):
    def init_data_format(self):
        self.data_format = "NHWC"

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


class TestAvgInclude_AsyPadding_channel_last(TestAvgInclude_AsyPadding):
    def init_data_format(self):
        self.data_format = "NHWC"

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


class TestAvgPoolAdaptive_AsyPadding_channel_last(
        TestAvgPoolAdaptive_AsyPadding):
    def init_data_format(self):
        self.data_format = "NHWC"

    def init_shape(self):
        self.shape = [2, 8, 8, 3]


class TestCase1_strides(TestCase1):
    def init_test_case(self):
        self.ksize = [3, 3]
        # fixme: CANN AvgPoolGradV3 dose not support asymmetric strides
        # self.strides = [1, 2]
        self.strides = [2, 2]

    def init_shape(self):
        self.shape = [2, 3, 4, 5]


create_test_padding_SAME_class(TestPool2D_Op)
create_test_padding_SAME_class(TestCase1)
create_test_padding_SAME_class(TestCase2)
create_test_padding_SAME_class(TestCase3)
create_test_padding_SAME_class(TestCase4)
create_test_padding_SAME_class(TestCase5)
create_test_padding_SAME_class(TestPool2D_channel_last)
create_test_padding_SAME_class(TestCase1_channel_last)
create_test_padding_SAME_class(TestCase2_channel_last)
create_test_padding_SAME_class(TestCase3_channel_last)
create_test_padding_SAME_class(TestCase4_channel_last)
create_test_padding_SAME_class(TestCase5_channel_last)
create_test_padding_SAME_class(TestCase1_strides)

create_test_padding_VALID_class(TestPool2D_Op)
create_test_padding_VALID_class(TestCase1)
create_test_padding_VALID_class(TestCase2)
create_test_padding_VALID_class(TestCase3)
create_test_padding_VALID_class(TestCase4)
create_test_padding_VALID_class(TestCase5)
create_test_padding_VALID_class(TestPool2D_channel_last)
create_test_padding_VALID_class(TestCase1_channel_last)
create_test_padding_VALID_class(TestCase2_channel_last)
create_test_padding_VALID_class(TestCase3_channel_last)
create_test_padding_VALID_class(TestCase4_channel_last)
create_test_padding_VALID_class(TestCase5_channel_last)

create_test_use_ceil_class(TestCase1)
create_test_use_ceil_class(TestCase2)
create_test_use_ceil_class(TestCase1_AsyPadding)
create_test_use_ceil_class(TestCase2_AsyPadding)
create_test_use_ceil_class(TestCase1_channel_last)
create_test_use_ceil_class(TestCase2_channel_last)
create_test_use_ceil_class(TestCase1_AsyPadding_channel_last)
create_test_use_ceil_class(TestCase2_AsyPadding_channel_last)

create_test_fp16_class(TestPool2D_Op)
create_test_fp16_class(TestCase1)
create_test_fp16_class(TestCase2)
create_test_fp16_class(TestCase3)
create_test_fp16_class(TestCase4)
create_test_fp16_class(TestCase5)
create_test_fp16_class(TestPool2D_channel_last)
create_test_fp16_class(TestCase1_channel_last)
create_test_fp16_class(TestCase2_channel_last)
create_test_fp16_class(TestCase3_channel_last)
create_test_fp16_class(TestCase4_channel_last)
create_test_fp16_class(TestCase5_channel_last)

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