from typing import Callable, List, Optional

import torch
from torch import Tensor

from .vision_transformer_utils import _log_api_usage_once


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

    Args:
        num_features (int): Number of features ``C`` from an expected input of size ``(N, C, H, W)``
        eps (float): a value added to the denominator for numerical stability. Default: 1e-5
    """

    def __init__(
        self,
        num_features: int,
        eps: float = 1e-5,
    ):
        super().__init__()
        _log_api_usage_once(self)
        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()._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):
    """
    Configurable block used for Convolution-Normalzation-Activation blocks.

    Args:
        in_channels (int): Number of channels in the input image
        out_channels (int): Number of channels produced by the Convolution-Normalzation-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 wich case it will 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 convolutiuon layer. If ``None`` this layer wont be used. Default: ``torch.nn.BatchNorm2d``
        activation_layer (Callable[..., torch.nn.Module], optinal): 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 wont 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: 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: Optional[bool] = True,
        bias: Optional[bool] = None,
    ) -> None:
        if padding is None:
            padding = (kernel_size - 1) // 2 * dilation
        if bias is None:
            bias = norm_layer is None
        layers = [
            torch.nn.Conv2d(
                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


class SqueezeExcitation(torch.nn.Module):
    """
    This block implements the Squeeze-and-Excitation block from https://arxiv.org/abs/1709.01507 (see Fig. 1).
    Parameters ``activation``, and ``scale_activation`` correspond to ``delta`` and ``sigma`` in in eq. 3.

    Args:
        input_channels (int): Number of channels in the input image
        squeeze_channels (int): Number of squeeze channels
        activation (Callable[..., torch.nn.Module], optional): ``delta`` activation. Default: ``torch.nn.ReLU``
        scale_activation (Callable[..., torch.nn.Module]): ``sigma`` activation. Default: ``torch.nn.Sigmoid``
    """

    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__()
        _log_api_usage_once(self)
        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