"""
helper class that supports empty tensors on some nn functions.

Ideally, add support directly in PyTorch to empty tensors in
those functions.

This can be removed once https://github.com/pytorch/pytorch/issues/12013
is implemented
"""

import warnings
from typing import Callable, List, Optional

import torch
from torch import Tensor


class Conv2d(torch.nn.Conv2d):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        warnings.warn(
            "torchvision.ops.misc.Conv2d is deprecated and will be "
            "removed in future versions, use torch.nn.Conv2d instead.",
            FutureWarning,
        )


class ConvTranspose2d(torch.nn.ConvTranspose2d):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        warnings.warn(
            "torchvision.ops.misc.ConvTranspose2d is deprecated and will be "
            "removed in future versions, use torch.nn.ConvTranspose2d instead.",
            FutureWarning,
        )


class BatchNorm2d(torch.nn.BatchNorm2d):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        warnings.warn(
            "torchvision.ops.misc.BatchNorm2d is deprecated and will be "
            "removed in future versions, use torch.nn.BatchNorm2d instead.",
            FutureWarning,
        )


interpolate = torch.nn.functional.interpolate


# This is not in nn
class FrozenBatchNorm2d(torch.nn.Module):
    """
    BatchNorm2d where the batch statistics and the affine parameters
    are fixed
    """

    def __init__(
        self,
        num_features: int,
        eps: float = 1e-5,
        n: Optional[int] = None,
    ):
        # n=None for backward-compatibility
        if n is not None:
            warnings.warn("`n` argument is deprecated and has been renamed `num_features`", DeprecationWarning)
            num_features = n
        super(FrozenBatchNorm2d, self).__init__()
        self.eps = eps
        self.register_buffer("weight", torch.ones(num_features))
        self.register_buffer("bias", torch.zeros(num_features))
        self.register_buffer("running_mean", torch.zeros(num_features))
        self.register_buffer("running_var", torch.ones(num_features))

    def _load_from_state_dict(
        self,
        state_dict: dict,
        prefix: str,
        local_metadata: dict,
        strict: bool,
        missing_keys: List[str],
        unexpected_keys: List[str],
        error_msgs: List[str],
    ):
        num_batches_tracked_key = prefix + "num_batches_tracked"
        if num_batches_tracked_key in state_dict:
            del state_dict[num_batches_tracked_key]

        super(FrozenBatchNorm2d, self)._load_from_state_dict(
            state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
        )

    def forward(self, x: Tensor) -> Tensor:
        # move reshapes to the beginning
        # to make it fuser-friendly
        w = self.weight.reshape(1, -1, 1, 1)
        b = self.bias.reshape(1, -1, 1, 1)
        rv = self.running_var.reshape(1, -1, 1, 1)
        rm = self.running_mean.reshape(1, -1, 1, 1)
        scale = w * (rv + self.eps).rsqrt()
        bias = b - rm * scale
        return x * scale + bias

    def __repr__(self) -> str:
        return f"{self.__class__.__name__}({self.weight.shape[0]}, eps={self.eps})"


class ConvNormActivation(torch.nn.Sequential):
    def __init__(
        self,
        in_channels: int,
        out_channels: int,
        kernel_size: int = 3,
        stride: int = 1,
        padding: Optional[int] = 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: int = 1,
        inplace: bool = True,
    ) -> None:
        if padding is None:
            padding = (kernel_size - 1) // 2 * dilation
        layers = [
            torch.nn.Conv2d(
                in_channels,
                out_channels,
                kernel_size,
                stride,
                padding,
                dilation=dilation,
                groups=groups,
                bias=norm_layer is None,
            )
        ]
        if norm_layer is not None:
            layers.append(norm_layer(out_channels))
        if activation_layer is not None:
            layers.append(activation_layer(inplace=inplace))
        super().__init__(*layers)
        self.out_channels = out_channels


class SqueezeExcitation(torch.nn.Module):
    def __init__(
        self,
        input_channels: int,
        squeeze_channels: int,
        activation: Callable[..., torch.nn.Module] = torch.nn.ReLU,
        scale_activation: Callable[..., torch.nn.Module] = torch.nn.Sigmoid,
    ) -> None:
        super().__init__()
        self.avgpool = torch.nn.AdaptiveAvgPool2d(1)
        self.fc1 = torch.nn.Conv2d(input_channels, squeeze_channels, 1)
        self.fc2 = torch.nn.Conv2d(squeeze_channels, input_channels, 1)
        self.activation = activation()
        self.scale_activation = scale_activation()

    def _scale(self, input: Tensor) -> Tensor:
        scale = self.avgpool(input)
        scale = self.fc1(scale)
        scale = self.activation(scale)
        scale = self.fc2(scale)
        return self.scale_activation(scale)

    def forward(self, input: Tensor) -> Tensor:
        scale = self._scale(input)
        return scale * input
