"""
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
import torch
from torch import Tensor, Size
from torch.jit.annotations import List, Optional, Tuple


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 = 0.,
        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})"