# 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 annotations

from typing import (
    TYPE_CHECKING,
    TypedDict,
)

from typing_extensions import NotRequired, Unpack

import paddle
import paddle.nn.functional as F
from paddle import nn
from paddle.base.param_attr import ParamAttr
from paddle.nn import (
    AdaptiveAvgPool2D,
    AvgPool2D,
    Conv2D,
    Dropout,
    Linear,
    MaxPool2D,
)
from paddle.nn.initializer import Uniform
from paddle.utils.download import get_weights_path_from_url

if TYPE_CHECKING:
    from paddle import Tensor
    from paddle._typing import Size2

    class _GoogLeNetOptions(TypedDict):
        num_classes: NotRequired[int]
        with_pool: NotRequired[bool]


__all__ = []

model_urls = {
    "googlenet": (
        "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/GoogLeNet_pretrained.pdparams",
        "80c06f038e905c53ab32c40eca6e26ae",
    )
}


def xavier(channels: int, filter_size: int) -> ParamAttr:
    stdv = (3.0 / (filter_size**2 * channels)) ** 0.5
    param_attr = ParamAttr(initializer=Uniform(-stdv, stdv))
    return param_attr


class ConvLayer(nn.Layer):
    def __init__(
        self,
        num_channels: int,
        num_filters: int,
        filter_size: int,
        stride: Size2 = 1,
        groups: int = 1,
    ):
        super().__init__()

        self._conv = Conv2D(
            in_channels=num_channels,
            out_channels=num_filters,
            kernel_size=filter_size,
            stride=stride,
            padding=(filter_size - 1) // 2,
            groups=groups,
            bias_attr=False,
        )

    def forward(self, inputs: Tensor) -> Tensor:
        y = self._conv(inputs)
        return y


class Inception(nn.Layer):
    def __init__(
        self,
        input_channels: int,
        output_channels: int,
        filter1: int,
        filter3R: int,
        filter3: int,
        filter5R: int,
        filter5: int,
        proj: int,
    ):
        super().__init__()

        self._conv1 = ConvLayer(input_channels, filter1, 1)
        self._conv3r = ConvLayer(input_channels, filter3R, 1)
        self._conv3 = ConvLayer(filter3R, filter3, 3)
        self._conv5r = ConvLayer(input_channels, filter5R, 1)
        self._conv5 = ConvLayer(filter5R, filter5, 5)
        self._pool = MaxPool2D(kernel_size=3, stride=1, padding=1)

        self._convprj = ConvLayer(input_channels, proj, 1)

    def forward(self, inputs: Tensor) -> Tensor:
        conv1 = self._conv1(inputs)

        conv3r = self._conv3r(inputs)
        conv3 = self._conv3(conv3r)

        conv5r = self._conv5r(inputs)
        conv5 = self._conv5(conv5r)

        pool = self._pool(inputs)
        convprj = self._convprj(pool)

        cat = paddle.concat([conv1, conv3, conv5, convprj], axis=1)
        cat = F.relu(cat)
        return cat


class GoogLeNet(nn.Layer):
    """GoogLeNet (Inception v1) model architecture from
    `"Going Deeper with Convolutions" <https://arxiv.org/pdf/1409.4842.pdf>`_.

    Args:
        num_classes (int, optional): Output dim of last fc layer. If num_classes <= 0, last fc layer
            will not be defined. Default: 1000.
        with_pool (bool, optional): Use pool before the last fc layer or not. Default: True.

    Returns:
        :ref:`api_paddle_nn_Layer`. An instance of GoogLeNet (Inception v1) model.

    Examples:
        .. code-block:: pycon

            >>> import paddle
            >>> from paddle.vision.models import GoogLeNet

            >>> # Build model
            >>> model = GoogLeNet()

            >>> x = paddle.rand([1, 3, 224, 224])
            >>> out, out1, out2 = model(x)

            >>> print(out.shape, out1.shape, out2.shape)
            paddle.Size([1, 1000]) paddle.Size([1, 1000]) paddle.Size([1, 1000])
    """

    num_classes: int
    with_pool: bool

    def __init__(self, num_classes: int = 1000, with_pool: bool = True) -> None:
        super().__init__()
        self.num_classes = num_classes
        self.with_pool = with_pool

        self._conv = ConvLayer(3, 64, 7, 2)
        self._pool = MaxPool2D(kernel_size=3, stride=2)
        self._conv_1 = ConvLayer(64, 64, 1)
        self._conv_2 = ConvLayer(64, 192, 3)

        self._ince3a = Inception(192, 192, 64, 96, 128, 16, 32, 32)
        self._ince3b = Inception(256, 256, 128, 128, 192, 32, 96, 64)

        self._ince4a = Inception(480, 480, 192, 96, 208, 16, 48, 64)
        self._ince4b = Inception(512, 512, 160, 112, 224, 24, 64, 64)
        self._ince4c = Inception(512, 512, 128, 128, 256, 24, 64, 64)
        self._ince4d = Inception(512, 512, 112, 144, 288, 32, 64, 64)
        self._ince4e = Inception(528, 528, 256, 160, 320, 32, 128, 128)

        self._ince5a = Inception(832, 832, 256, 160, 320, 32, 128, 128)
        self._ince5b = Inception(832, 832, 384, 192, 384, 48, 128, 128)

        if with_pool:
            # out
            self._pool_5 = AdaptiveAvgPool2D(1)
            # out1
            self._pool_o1 = AvgPool2D(kernel_size=5, stride=3)
            # out2
            self._pool_o2 = AvgPool2D(kernel_size=5, stride=3)

        if num_classes > 0:
            # out
            self._drop = Dropout(p=0.4, mode="downscale_in_infer")
            self._fc_out = Linear(
                1024, num_classes, weight_attr=xavier(1024, 1)
            )

            # out1
            self._conv_o1 = ConvLayer(512, 128, 1)
            self._fc_o1 = Linear(1152, 1024, weight_attr=xavier(2048, 1))
            self._drop_o1 = Dropout(p=0.7, mode="downscale_in_infer")
            self._out1 = Linear(1024, num_classes, weight_attr=xavier(1024, 1))

            # out2
            self._conv_o2 = ConvLayer(528, 128, 1)
            self._fc_o2 = Linear(1152, 1024, weight_attr=xavier(2048, 1))
            self._drop_o2 = Dropout(p=0.7, mode="downscale_in_infer")
            self._out2 = Linear(1024, num_classes, weight_attr=xavier(1024, 1))

    def forward(self, inputs: Tensor) -> tuple[Tensor, Tensor, Tensor]:
        x = self._conv(inputs)
        x = self._pool(x)
        x = self._conv_1(x)
        x = self._conv_2(x)
        x = self._pool(x)

        x = self._ince3a(x)
        x = self._ince3b(x)
        x = self._pool(x)

        ince4a = self._ince4a(x)
        x = self._ince4b(ince4a)
        x = self._ince4c(x)
        ince4d = self._ince4d(x)
        x = self._ince4e(ince4d)
        x = self._pool(x)

        x = self._ince5a(x)
        ince5b = self._ince5b(x)

        out, out1, out2 = ince5b, ince4a, ince4d

        if self.with_pool:
            out = self._pool_5(out)
            out1 = self._pool_o1(out1)
            out2 = self._pool_o2(out2)

        if self.num_classes > 0:
            out = self._drop(out)
            out = paddle.squeeze(out, axis=[2, 3])
            out = self._fc_out(out)

            out1 = self._conv_o1(out1)
            out1 = paddle.flatten(out1, start_axis=1, stop_axis=-1)
            out1 = self._fc_o1(out1)
            out1 = F.relu(out1)
            out1 = self._drop_o1(out1)
            out1 = self._out1(out1)

            out2 = self._conv_o2(out2)
            out2 = paddle.flatten(out2, start_axis=1, stop_axis=-1)
            out2 = self._fc_o2(out2)
            out2 = self._drop_o2(out2)
            out2 = self._out2(out2)

        return out, out1, out2


def googlenet(
    pretrained: bool = False, **kwargs: Unpack[_GoogLeNetOptions]
) -> GoogLeNet:
    """GoogLeNet (Inception v1) model architecture from
    `"Going Deeper with Convolutions" <https://arxiv.org/pdf/1409.4842.pdf>`_.

    Args:
        pretrained (bool, optional): Whether to load pre-trained weights. If True, returns a model pre-trained
            on ImageNet. Default: False.
        **kwargs (optional): Additional keyword arguments. For details, please refer to :ref:`GoogLeNet <api_paddle_vision_models_GoogLeNet>`.

    Returns:
        :ref:`api_paddle_nn_Layer`. An instance of GoogLeNet (Inception v1) model.

    Examples:
        .. code-block:: pycon

            >>> import paddle
            >>> from paddle.vision.models import googlenet

            >>> # Build model
            >>> model = googlenet()

            >>> # Build model and load imagenet pretrained weight
            >>> # model = googlenet(pretrained=True)

            >>> x = paddle.rand([1, 3, 224, 224])
            >>> out, out1, out2 = model(x)

            >>> print(out.shape, out1.shape, out2.shape)
            paddle.Size([1, 1000]) paddle.Size([1, 1000]) paddle.Size([1, 1000])
    """
    model = GoogLeNet(**kwargs)
    arch = "googlenet"
    if pretrained:
        assert arch in model_urls, (
            f"{arch} model do not have a pretrained model now, you should set pretrained=False"
        )
        weight_path = get_weights_path_from_url(
            model_urls[arch][0], model_urls[arch][1]
        )

        param = paddle.load(weight_path)
        model.set_dict(param)
    return model