from functools import partial
from typing import Any, Callable, List, Optional

import torch
from torch import nn, Tensor


from torchvision.transforms._presets import ImageClassification
from torchvision.utils import _log_api_usage_once
from torchvision.models._api import  Weights, WeightsEnum
from torchvision.models._meta import _IMAGENET_CATEGORIES
from torchvision.models._utils import _make_divisible, _ovewrite_named_param, handle_legacy_interface
import warnings
from typing import Callable, List, Optional, Sequence, Tuple, Union, TypeVar
import collections
from itertools import repeat
M = TypeVar("M", bound=nn.Module)

BUILTIN_MODELS = {}
def register_model(name: Optional[str] = None) -> Callable[[Callable[..., M]], Callable[..., M]]:
    def wrapper(fn: Callable[..., M]) -> Callable[..., M]:
        key = name if name is not None else fn.__name__
        if key in BUILTIN_MODELS:
            raise ValueError(f"An entry is already registered under the name '{key}'.")
        BUILTIN_MODELS[key] = fn
        return fn

    return wrapper

def _make_ntuple(x: Any, n: int) -> Tuple[Any, ...]:
    """
    Make n-tuple from input x. If x is an iterable, then we just convert it to tuple.
    Otherwise, we will make a tuple of length n, all with value of x.
    reference: https://github.com/pytorch/pytorch/blob/master/torch/nn/modules/utils.py#L8

    Args:
        x (Any): input value
        n (int): length of the resulting tuple
    """
    if isinstance(x, collections.abc.Iterable):
        return tuple(x)
    return tuple(repeat(x, n))

class ConvNormActivation(torch.nn.Sequential):
    def __init__(
        self,
        in_channels: int,
        out_channels: int,
        kernel_size: Union[int, Tuple[int, ...]] = 3,
        stride: Union[int, Tuple[int, ...]] = 1,
        padding: Optional[Union[int, Tuple[int, ...], str]] = None,
        groups: int = 1,
        norm_layer: Optional[Callable[..., torch.nn.Module]] = torch.nn.BatchNorm2d,
        activation_layer: Optional[Callable[..., torch.nn.Module]] = torch.nn.ReLU,
        dilation: Union[int, Tuple[int, ...]] = 1,
        inplace: Optional[bool] = True,
        bias: Optional[bool] = None,
        conv_layer: Callable[..., torch.nn.Module] = torch.nn.Conv2d,
    ) -> None:

        if padding is None:
            if isinstance(kernel_size, int) and isinstance(dilation, int):
                padding = (kernel_size - 1) // 2 * dilation
            else:
                _conv_dim = len(kernel_size) if isinstance(kernel_size, Sequence) else len(dilation)
                kernel_size = _make_ntuple(kernel_size, _conv_dim)
                dilation = _make_ntuple(dilation, _conv_dim)
                padding = tuple((kernel_size[i] - 1) // 2 * dilation[i] for i in range(_conv_dim))
        if bias is None:
            bias = norm_layer is None

        layers = [
            conv_layer(
                in_channels,
                out_channels,
                kernel_size,
                stride,
                padding,
                dilation=dilation,
                groups=groups,
                bias=bias,
            )
        ]

        if norm_layer is not None:
            layers.append(norm_layer(out_channels))

        if activation_layer is not None:
            params = {} if inplace is None else {"inplace": inplace}
            layers.append(activation_layer(**params))
        super().__init__(*layers)
        _log_api_usage_once(self)
        self.out_channels = out_channels

        if self.__class__ == ConvNormActivation:
            warnings.warn(
                "Don't use ConvNormActivation directly, please use Conv2dNormActivation and Conv3dNormActivation instead."
            )


class Conv2dNormActivation(ConvNormActivation):
    """
    Configurable block used for Convolution2d-Normalization-Activation blocks.

    Args:
        in_channels (int): Number of channels in the input image
        out_channels (int): Number of channels produced by the Convolution-Normalization-Activation block
        kernel_size: (int, optional): Size of the convolving kernel. Default: 3
        stride (int, optional): Stride of the convolution. Default: 1
        padding (int, tuple or str, optional): Padding added to all four sides of the input. Default: None, in which case it will be calculated as ``padding = (kernel_size - 1) // 2 * dilation``
        groups (int, optional): Number of blocked connections from input channels to output channels. Default: 1
        norm_layer (Callable[..., torch.nn.Module], optional): Norm layer that will be stacked on top of the convolution layer. If ``None`` this layer won't be used. Default: ``torch.nn.BatchNorm2d``
        activation_layer (Callable[..., torch.nn.Module], optional): Activation function which will be stacked on top of the normalization layer (if not None), otherwise on top of the conv layer. If ``None`` this layer won't be used. Default: ``torch.nn.ReLU``
        dilation (int): Spacing between kernel elements. Default: 1
        inplace (bool): Parameter for the activation layer, which can optionally do the operation in-place. Default ``True``
        bias (bool, optional): Whether to use bias in the convolution layer. By default, biases are included if ``norm_layer is None``.

    """

    def __init__(
        self,
        in_channels: int,
        out_channels: int,
        kernel_size: Union[int, Tuple[int, int]] = 3,
        stride: Union[int, Tuple[int, int]] = 1,
        padding: Optional[Union[int, Tuple[int, int], str]] = None,
        groups: int = 1,
        norm_layer: Optional[Callable[..., torch.nn.Module]] = torch.nn.BatchNorm2d,
        activation_layer: Optional[Callable[..., torch.nn.Module]] = torch.nn.ReLU,
        dilation: Union[int, Tuple[int, int]] = 1,
        inplace: Optional[bool] = True,
        bias: Optional[bool] = None,
    ) -> None:

        super().__init__(
            in_channels,
            out_channels,
            kernel_size,
            stride,
            padding,
            groups,
            norm_layer,
            activation_layer,
            dilation,
            inplace,
            bias,
            torch.nn.Conv2d,
        )

__all__ = ["MobileNetV2", "MobileNet_V2_Weights", "mobilenet_v2"]


# necessary for backwards compatibility
class InvertedResidual(nn.Module):
    def __init__(
        self, inp: int, oup: int, stride: int, expand_ratio: int, norm_layer: Optional[Callable[..., nn.Module]] = None
    ) -> None:
        super().__init__()
        self.stride = stride
        if stride not in [1, 2]:
            raise ValueError(f"stride should be 1 or 2 instead of {stride}")

        if norm_layer is None:
            norm_layer = nn.BatchNorm2d

        hidden_dim = int(round(inp * expand_ratio))
        self.use_res_connect = self.stride == 1 and inp == oup

        layers: List[nn.Module] = []
        if expand_ratio != 1:
            # pw
            layers.append(
                Conv2dNormActivation(inp, hidden_dim, kernel_size=1, norm_layer=norm_layer, activation_layer=nn.ReLU6)
            )
        layers.extend(
            [
                # dw
                Conv2dNormActivation(
                    hidden_dim,
                    hidden_dim,
                    stride=stride,
                    groups=hidden_dim,
                    norm_layer=norm_layer,
                    activation_layer=nn.ReLU6,
                ),
                # pw-linear
                nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),
                norm_layer(oup),
            ]
        )
        self.conv = nn.Sequential(*layers)
        self.out_channels = oup
        self._is_cn = stride > 1

    def forward(self, x: Tensor) -> Tensor:
        if self.use_res_connect:
            return x + self.conv(x)
        else:
            return self.conv(x)


class MobileNetV2(nn.Module):
    def __init__(
        self,
        num_classes: int = 1000,
        width_mult: float = 1.0,
        inverted_residual_setting: Optional[List[List[int]]] = None,
        round_nearest: int = 8,
        block: Optional[Callable[..., nn.Module]] = None,
        norm_layer: Optional[Callable[..., nn.Module]] = None,
        dropout: float = 0.2,
    ) -> None:
        """
        MobileNet V2 main class

        Args:
            num_classes (int): Number of classes
            width_mult (float): Width multiplier - adjusts number of channels in each layer by this amount
            inverted_residual_setting: Network structure
            round_nearest (int): Round the number of channels in each layer to be a multiple of this number
            Set to 1 to turn off rounding
            block: Module specifying inverted residual building block for mobilenet
            norm_layer: Module specifying the normalization layer to use
            dropout (float): The droupout probability

        """
        super().__init__()
        _log_api_usage_once(self)

        if block is None:
            block = InvertedResidual

        if norm_layer is None:
            norm_layer = nn.BatchNorm2d

        input_channel = 32
        last_channel = 1280

        if inverted_residual_setting is None:
            inverted_residual_setting = [
                # t, c, n, s
                [1, 16, 1, 1],
                [6, 24, 2, 1],
                [6, 32, 3, 1],
                [6, 64, 4, 2],
                [6, 96, 3, 1],
                [6, 160, 3, 2],
                [6, 320, 1, 1],
            ]

        # only check the first element, assuming user knows t,c,n,s are required
        if len(inverted_residual_setting) == 0 or len(inverted_residual_setting[0]) != 4:
            raise ValueError(
                f"inverted_residual_setting should be non-empty or a 4-element list, got {inverted_residual_setting}"
            )

        # building first layer
        input_channel = _make_divisible(input_channel * width_mult, round_nearest)
        self.last_channel = _make_divisible(last_channel * max(1.0, width_mult), round_nearest)
        features: List[nn.Module] = [
            Conv2dNormActivation(3, input_channel, stride=2, norm_layer=norm_layer, activation_layer=nn.ReLU6)
        ]
        # building inverted residual blocks
        for t, c, n, s in inverted_residual_setting:
            output_channel = _make_divisible(c * width_mult, round_nearest)
            for i in range(n):
                stride = s if i == 0 else 1
                features.append(block(input_channel, output_channel, stride, expand_ratio=t, norm_layer=norm_layer))
                input_channel = output_channel
        # building last several layers
        features.append(
            Conv2dNormActivation(
                input_channel, self.last_channel, kernel_size=1, norm_layer=norm_layer, activation_layer=nn.ReLU6
            )
        )
        # make it nn.Sequential
        self.features = nn.Sequential(*features)
        # self.layer1 = nn.Sequential(*features[:])
        # self.layer2 = features[57:120]
        # self.layer3 = features[120:]

        # building classifier
        self.classifier = nn.Sequential(
            nn.Dropout(p=dropout),
            nn.Linear(self.last_channel, num_classes),
        )

        # weight initialization
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, mode="fan_out")
                if m.bias is not None:
                    nn.init.zeros_(m.bias)
            elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
                nn.init.ones_(m.weight)
                nn.init.zeros_(m.bias)
            elif isinstance(m, nn.Linear):
                nn.init.normal_(m.weight, 0, 0.01)
                nn.init.zeros_(m.bias)

    def _forward_impl(self, x: Tensor) -> Tensor:
        # This exists since TorchScript doesn't support inheritance, so the superclass method
        # (this one) needs to have a name other than `forward` that can be accessed in a subclass
        out_layers = []
        for layer in self.features.named_modules():
            for i, layer1 in enumerate(layer[1]):
                # print(layer1)
                x = layer1(x)
                # print("第{}层，输出大小{}".format(i, x.shape))
                if i in [0, 10, 18]:
                    out_layers.append(x)
            break
        # x = self.features(x)
        # Cannot use "squeeze" as batch-size can be 1
        # x = nn.functional.adaptive_avg_pool2d(x, (1, 1))
        # x = torch.flatten(x, 1)
        # x = self.classifier(x)
        return out_layers

    def forward(self, x: Tensor) -> Tensor:
        return self._forward_impl(x)


_COMMON_META = {
    "num_params": 3504872,
    "min_size": (1, 1),
    "categories": _IMAGENET_CATEGORIES,
}


class MobileNet_V2_Weights(WeightsEnum):
    IMAGENET1K_V1 = Weights(
        url="https://download.pytorch.org/models/mobilenet_v2-b0353104.pth",
        transforms=partial(ImageClassification, crop_size=224),
        meta={
            **_COMMON_META,
            "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#mobilenetv2",
            "_metrics": {
                "ImageNet-1K": {
                    "acc@1": 71.878,
                    "acc@5": 90.286,
                }
            },
            "_ops": 0.301,
            "_file_size": 13.555,
            "_docs": """These weights reproduce closely the results of the paper using a simple training recipe.""",
        },
    )
    IMAGENET1K_V2 = Weights(
        url="https://download.pytorch.org/models/mobilenet_v2-7ebf99e0.pth",
        transforms=partial(ImageClassification, crop_size=224, resize_size=232),
        meta={
            **_COMMON_META,
            "recipe": "https://github.com/pytorch/vision/issues/3995#new-recipe-with-reg-tuning",
            "_metrics": {
                "ImageNet-1K": {
                    "acc@1": 72.154,
                    "acc@5": 90.822,
                }
            },
            "_ops": 0.301,
            "_file_size": 13.598,
            "_docs": """
                These weights improve upon the results of the original paper by using a modified version of TorchVision's
                `new training recipe
                <https://pytorch.org/blog/how-to-train-state-of-the-art-models-using-torchvision-latest-primitives/>`_.
            """,
        },
    )
    DEFAULT = IMAGENET1K_V2


# @register_model()
# @handle_legacy_interface(weights=("pretrained", MobileNet_V2_Weights.IMAGENET1K_V1))
def mobilenet_v2(
    *, weights: Optional[MobileNet_V2_Weights] = MobileNet_V2_Weights.IMAGENET1K_V1, progress: bool = True, **kwargs: Any
) -> MobileNetV2:
    """MobileNetV2 architecture from the `MobileNetV2: Inverted Residuals and Linear
    Bottlenecks <https://arxiv.org/abs/1801.04381>`_ paper.

    Args:
        weights (:class:`~torchvision.models.MobileNet_V2_Weights`, optional): The
            pretrained weights to use. See
            :class:`~torchvision.models.MobileNet_V2_Weights` below for
            more details, and possible values. By default, no pre-trained
            weights are used.
        progress (bool, optional): If True, displays a progress bar of the
            download to stderr. Default is True.
        **kwargs: parameters passed to the ``torchvision.models.mobilenetv2.MobileNetV2``
            base class. Please refer to the `source code
            <https://github.com/pytorch/vision/blob/main/torchvision/models/mobilenetv2.py>`_
            for more details about this class.

    .. autoclass:: torchvision.models.MobileNet_V2_Weights
        :members:
    """
    weights = MobileNet_V2_Weights.verify(weights)

    if weights is not None:
        _ovewrite_named_param(kwargs, "num_classes", len(weights.meta["categories"]))

    model = MobileNetV2(**kwargs)

    if weights is not None:
        model.load_state_dict(weights.get_state_dict(progress=progress))

    return model


def conv1x1(in_planes, out_planes, stride=1):
    """1x1 convolution"""
    return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)

class MobileNetv2Wrapper(nn.Module):
    def __init__(self):
        super(MobileNetv2Wrapper, self).__init__()
        weights = MobileNet_V2_Weights.verify(MobileNet_V2_Weights.IMAGENET1K_V1)

        self.model = MobileNetV2()

        if weights is not None:
            self.model.load_state_dict(weights.get_state_dict(progress=True))
        self.out3 = conv1x1(1280, 128)

    def forward(self, x):
        # print(x.shape)
        out_layers = self.model(x)
        # print(x.shape)

        # out_layers[0] = self.out1(out_layers[0])
        # out_layers[1] = self.out2(out_layers[1])
        out_layers[2] = self.out3(out_layers[2])
        # print(x.shape)
        return out_layers