# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
#
# 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 absolute_import, division, print_function

from paddle import ParamAttr
import paddle.nn as nn
from paddle.nn import Conv2D, BatchNorm, Linear, ReLU, Flatten
from paddle.nn import AdaptiveAvgPool2D
from paddle.nn.initializer import KaimingNormal

from ppcls.arch.backbone.base.theseus_layer import TheseusLayer
from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url

MODEL_URLS = {
    "MobileNetV1_x0_25":
    "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV1_x0_25_pretrained.pdparams",
    "MobileNetV1_x0_5":
    "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV1_x0_5_pretrained.pdparams",
    "MobileNetV1_x0_75":
    "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV1_x0_75_pretrained.pdparams",
    "MobileNetV1":
    "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV1_pretrained.pdparams"
}

MODEL_STAGES_PATTERN = {
    "MobileNetV1": ["blocks[0]", "blocks[2]", "blocks[4]", "blocks[10]"]
}

__all__ = MODEL_URLS.keys()


class ConvBNLayer(TheseusLayer):
    def __init__(self,
                 num_channels,
                 filter_size,
                 num_filters,
                 stride,
                 padding,
                 num_groups=1):
        super().__init__()

        self.conv = Conv2D(
            in_channels=num_channels,
            out_channels=num_filters,
            kernel_size=filter_size,
            stride=stride,
            padding=padding,
            groups=num_groups,
            weight_attr=ParamAttr(initializer=KaimingNormal()),
            bias_attr=False)
        self.bn = BatchNorm(num_filters)
        self.relu = ReLU()

    def forward(self, x):
        x = self.conv(x)
        x = self.bn(x)
        x = self.relu(x)
        return x


class DepthwiseSeparable(TheseusLayer):
    def __init__(self, num_channels, num_filters1, num_filters2, num_groups,
                 stride, scale):
        super().__init__()

        self.depthwise_conv = ConvBNLayer(
            num_channels=num_channels,
            num_filters=int(num_filters1 * scale),
            filter_size=3,
            stride=stride,
            padding=1,
            num_groups=int(num_groups * scale))

        self.pointwise_conv = ConvBNLayer(
            num_channels=int(num_filters1 * scale),
            filter_size=1,
            num_filters=int(num_filters2 * scale),
            stride=1,
            padding=0)

    def forward(self, x):
        x = self.depthwise_conv(x)
        x = self.pointwise_conv(x)
        return x


class MobileNet(TheseusLayer):
    """
    MobileNet
    Args:
        scale: float=1.0. The coefficient that controls the size of network parameters. 
        class_num: int=1000. The number of classes.
    Returns:
        model: nn.Layer. Specific MobileNet model depends on args.
    """

    def __init__(self,
                 stages_pattern,
                 scale=1.0,
                 class_num=1000,
                 return_patterns=None,
                 return_stages=None):
        super().__init__()
        self.scale = scale

        self.conv = ConvBNLayer(
            num_channels=3,
            filter_size=3,
            num_filters=int(32 * scale),
            stride=2,
            padding=1)

        #num_channels, num_filters1, num_filters2, num_groups, stride
        self.cfg = [[int(32 * scale), 32, 64, 32, 1],
                    [int(64 * scale), 64, 128, 64, 2],
                    [int(128 * scale), 128, 128, 128, 1],
                    [int(128 * scale), 128, 256, 128, 2],
                    [int(256 * scale), 256, 256, 256, 1],
                    [int(256 * scale), 256, 512, 256, 2],
                    [int(512 * scale), 512, 512, 512, 1],
                    [int(512 * scale), 512, 512, 512, 1],
                    [int(512 * scale), 512, 512, 512, 1],
                    [int(512 * scale), 512, 512, 512, 1],
                    [int(512 * scale), 512, 512, 512, 1],
                    [int(512 * scale), 512, 1024, 512, 2],
                    [int(1024 * scale), 1024, 1024, 1024, 1]]

        self.blocks = nn.Sequential(*[
            DepthwiseSeparable(
                num_channels=params[0],
                num_filters1=params[1],
                num_filters2=params[2],
                num_groups=params[3],
                stride=params[4],
                scale=scale) for params in self.cfg
        ])

        self.avg_pool = AdaptiveAvgPool2D(1)
        self.flatten = Flatten(start_axis=1, stop_axis=-1)

        self.fc = Linear(
            int(1024 * scale),
            class_num,
            weight_attr=ParamAttr(initializer=KaimingNormal()))

        super().init_res(
            stages_pattern,
            return_patterns=return_patterns,
            return_stages=return_stages)

    def forward(self, x):
        x = self.conv(x)
        x = self.blocks(x)
        x = self.avg_pool(x)
        x = self.flatten(x)
        x = self.fc(x)
        return x


def _load_pretrained(pretrained, model, model_url, use_ssld):
    if pretrained is False:
        pass
    elif pretrained is True:
        load_dygraph_pretrain_from_url(model, model_url, use_ssld=use_ssld)
    elif isinstance(pretrained, str):
        load_dygraph_pretrain(model, pretrained)
    else:
        raise RuntimeError(
            "pretrained type is not available. Please use `string` or `boolean` type."
        )


def MobileNetV1_x0_25(pretrained=False, use_ssld=False, **kwargs):
    """
    MobileNetV1_x0_25
    Args:
        pretrained: bool=False or str. If `True` load pretrained parameters, `False` otherwise.
                    If str, means the path of the pretrained model.
        use_ssld: bool=False. Whether using distillation pretrained model when pretrained=True.
    Returns:
        model: nn.Layer. Specific `MobileNetV1_x0_25` model depends on args.
    """
    model = MobileNet(
        scale=0.25,
        stages_pattern=MODEL_STAGES_PATTERN["MobileNetV1"],
        **kwargs)
    _load_pretrained(pretrained, model, MODEL_URLS["MobileNetV1_x0_25"],
                     use_ssld)
    return model


def MobileNetV1_x0_5(pretrained=False, use_ssld=False, **kwargs):
    """
    MobileNetV1_x0_5
    Args:
        pretrained: bool=False or str. If `True` load pretrained parameters, `False` otherwise.
                    If str, means the path of the pretrained model.
        use_ssld: bool=False. Whether using distillation pretrained model when pretrained=True.
    Returns:
        model: nn.Layer. Specific `MobileNetV1_x0_5` model depends on args.
    """
    model = MobileNet(
        scale=0.5, stages_pattern=MODEL_STAGES_PATTERN["MobileNetV1"], **kwargs)
    _load_pretrained(pretrained, model, MODEL_URLS["MobileNetV1_x0_5"],
                     use_ssld)
    return model


def MobileNetV1_x0_75(pretrained=False, use_ssld=False, **kwargs):
    """
    MobileNetV1_x0_75
    Args:
        pretrained: bool=False or str. If `True` load pretrained parameters, `False` otherwise.
                    If str, means the path of the pretrained model.
        use_ssld: bool=False. Whether using distillation pretrained model when pretrained=True.
    Returns:
        model: nn.Layer. Specific `MobileNetV1_x0_75` model depends on args.
    """
    model = MobileNet(
        scale=0.75,
        stages_pattern=MODEL_STAGES_PATTERN["MobileNetV1"],
        **kwargs)
    _load_pretrained(pretrained, model, MODEL_URLS["MobileNetV1_x0_75"],
                     use_ssld)
    return model


def MobileNetV1(pretrained=False, use_ssld=False, **kwargs):
    """
    MobileNetV1
    Args:
        pretrained: bool=False or str. If `True` load pretrained parameters, `False` otherwise.
                    If str, means the path of the pretrained model.
        use_ssld: bool=False. Whether using distillation pretrained model when pretrained=True.
    Returns:
        model: nn.Layer. Specific `MobileNetV1` model depends on args.
    """
    model = MobileNet(
        scale=1.0, stages_pattern=MODEL_STAGES_PATTERN["MobileNetV1"], **kwargs)
    _load_pretrained(pretrained, model, MODEL_URLS["MobileNetV1"], use_ssld)
    return model