""" Normalization + Activation Layers

Provides Norm+Act fns for standard PyTorch norm layers such as
* BatchNorm
* GroupNorm
* LayerNorm

This allows swapping with alternative layers that are natively both norm + act such as
* EvoNorm (evo_norm.py)
* FilterResponseNorm (filter_response_norm.py)
* InplaceABN (inplace_abn.py)

Hacked together by / Copyright 2022 Ross Wightman
"""
from typing import Union, List, Optional, Any

import torch
from torch import nn as nn
from torch.nn import functional as F
from torchvision.ops.misc import FrozenBatchNorm2d

from .create_act import get_act_layer
from .fast_norm import is_fast_norm, fast_group_norm, fast_layer_norm
from .trace_utils import _assert


class BatchNormAct2d(nn.BatchNorm2d):
    """BatchNorm + Activation

    This module performs BatchNorm + Activation in a manner that will remain backwards
    compatible with weights trained with separate bn, act. This is why we inherit from BN
    instead of composing it as a .bn member.
    """
    def __init__(
            self,
            num_features,
            eps=1e-5,
            momentum=0.1,
            affine=True,
            track_running_stats=True,
            apply_act=True,
            act_layer=nn.ReLU,
            inplace=True,
            drop_layer=None,
            device=None,
            dtype=None
    ):
        try:
            factory_kwargs = {'device': device, 'dtype': dtype}
            super(BatchNormAct2d, self).__init__(
                num_features, eps=eps, momentum=momentum, affine=affine, track_running_stats=track_running_stats,
                **factory_kwargs
            )
        except TypeError:
            # NOTE for backwards compat with old PyTorch w/o factory device/dtype support
            super(BatchNormAct2d, self).__init__(
                num_features, eps=eps, momentum=momentum, affine=affine, track_running_stats=track_running_stats)
        self.drop = drop_layer() if drop_layer is not None else nn.Identity()
        act_layer = get_act_layer(act_layer)  # string -> nn.Module
        if act_layer is not None and apply_act:
            act_args = dict(inplace=True) if inplace else {}
            self.act = act_layer(**act_args)
        else:
            self.act = nn.Identity()

    def forward(self, x):
        # cut & paste of torch.nn.BatchNorm2d.forward impl to avoid issues with torchscript and tracing
        _assert(x.ndim == 4, f'expected 4D input (got {x.ndim}D input)')

        # exponential_average_factor is set to self.momentum
        # (when it is available) only so that it gets updated
        # in ONNX graph when this node is exported to ONNX.
        if self.momentum is None:
            exponential_average_factor = 0.0
        else:
            exponential_average_factor = self.momentum

        if self.training and self.track_running_stats:
            # TODO: if statement only here to tell the jit to skip emitting this when it is None
            if self.num_batches_tracked is not None:  # type: ignore[has-type]
                self.num_batches_tracked.add_(1)  # type: ignore[has-type]
                if self.momentum is None:  # use cumulative moving average
                    exponential_average_factor = 1.0 / float(self.num_batches_tracked)
                else:  # use exponential moving average
                    exponential_average_factor = self.momentum

        r"""
        Decide whether the mini-batch stats should be used for normalization rather than the buffers.
        Mini-batch stats are used in training mode, and in eval mode when buffers are None.
        """
        if self.training:
            bn_training = True
        else:
            bn_training = (self.running_mean is None) and (self.running_var is None)

        r"""
        Buffers are only updated if they are to be tracked and we are in training mode. Thus they only need to be
        passed when the update should occur (i.e. in training mode when they are tracked), or when buffer stats are
        used for normalization (i.e. in eval mode when buffers are not None).
        """
        x = F.batch_norm(
            x,
            # If buffers are not to be tracked, ensure that they won't be updated
            self.running_mean if not self.training or self.track_running_stats else None,
            self.running_var if not self.training or self.track_running_stats else None,
            self.weight,
            self.bias,
            bn_training,
            exponential_average_factor,
            self.eps,
        )
        x = self.drop(x)
        x = self.act(x)
        return x


class SyncBatchNormAct(nn.SyncBatchNorm):
    # Thanks to Selim Seferbekov (https://github.com/rwightman/pytorch-image-models/issues/1254)
    # This is a quick workaround to support SyncBatchNorm for timm BatchNormAct2d layers
    # but ONLY when used in conjunction with the timm conversion function below.
    # Do not create this module directly or use the PyTorch conversion function.
    def forward(self, x: torch.Tensor) -> torch.Tensor:
        x = super().forward(x)  # SyncBN doesn't work with torchscript anyways, so this is fine
        if hasattr(self, "drop"):
            x = self.drop(x)
        if hasattr(self, "act"):
            x = self.act(x)
        return x


def convert_sync_batchnorm(module, process_group=None):
    # convert both BatchNorm and BatchNormAct layers to Synchronized variants
    module_output = module
    if isinstance(module, torch.nn.modules.batchnorm._BatchNorm):
        if isinstance(module, BatchNormAct2d):
            # convert timm norm + act layer
            module_output = SyncBatchNormAct(
                module.num_features,
                module.eps,
                module.momentum,
                module.affine,
                module.track_running_stats,
                process_group=process_group,
            )
            # set act and drop attr from the original module
            module_output.act = module.act
            module_output.drop = module.drop
        else:
            # convert standard BatchNorm layers
            module_output = torch.nn.SyncBatchNorm(
                module.num_features,
                module.eps,
                module.momentum,
                module.affine,
                module.track_running_stats,
                process_group,
            )
        if module.affine:
            with torch.no_grad():
                module_output.weight = module.weight
                module_output.bias = module.bias
        module_output.running_mean = module.running_mean
        module_output.running_var = module.running_var
        module_output.num_batches_tracked = module.num_batches_tracked
        if hasattr(module, "qconfig"):
            module_output.qconfig = module.qconfig
    for name, child in module.named_children():
        module_output.add_module(name, convert_sync_batchnorm(child, process_group))
    del module
    return module_output


class FrozenBatchNormAct2d(torch.nn.Module):
    """
    BatchNormAct2d 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,
        apply_act=True,
        act_layer=nn.ReLU,
        inplace=True,
        drop_layer=None,
    ):
        super().__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))

        self.drop = drop_layer() if drop_layer is not None else nn.Identity()
        act_layer = get_act_layer(act_layer)  # string -> nn.Module
        if act_layer is not None and apply_act:
            act_args = dict(inplace=True) if inplace else {}
            self.act = act_layer(**act_args)
        else:
            self.act = nn.Identity()

    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: torch.Tensor) -> torch.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
        x = x * scale + bias
        x = self.act(self.drop(x))
        return x

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


def freeze_batch_norm_2d(module):
    """
    Converts all `BatchNorm2d` and `SyncBatchNorm` or `BatchNormAct2d` and `SyncBatchNormAct2d` layers
    of provided module into `FrozenBatchNorm2d` or `FrozenBatchNormAct2d` respectively.

    Args:
        module (torch.nn.Module): Any PyTorch module.

    Returns:
        torch.nn.Module: Resulting module

    Inspired by https://github.com/pytorch/pytorch/blob/a5895f85be0f10212791145bfedc0261d364f103/torch/nn/modules/batchnorm.py#L762
    """
    res = module
    if isinstance(module, (BatchNormAct2d, SyncBatchNormAct)):
        res = FrozenBatchNormAct2d(module.num_features)
        res.num_features = module.num_features
        res.affine = module.affine
        if module.affine:
            res.weight.data = module.weight.data.clone().detach()
            res.bias.data = module.bias.data.clone().detach()
        res.running_mean.data = module.running_mean.data
        res.running_var.data = module.running_var.data
        res.eps = module.eps
        res.drop = module.drop
        res.act = module.act
    elif isinstance(module, (torch.nn.modules.batchnorm.BatchNorm2d, torch.nn.modules.batchnorm.SyncBatchNorm)):
        res = FrozenBatchNorm2d(module.num_features)
        res.num_features = module.num_features
        res.affine = module.affine
        if module.affine:
            res.weight.data = module.weight.data.clone().detach()
            res.bias.data = module.bias.data.clone().detach()
        res.running_mean.data = module.running_mean.data
        res.running_var.data = module.running_var.data
        res.eps = module.eps
    else:
        for name, child in module.named_children():
            new_child = freeze_batch_norm_2d(child)
            if new_child is not child:
                res.add_module(name, new_child)
    return res


def unfreeze_batch_norm_2d(module):
    """
    Converts all `FrozenBatchNorm2d` layers of provided module into `BatchNorm2d`. If `module` is itself and instance
    of `FrozenBatchNorm2d`, it is converted into `BatchNorm2d` and returned. Otherwise, the module is walked
    recursively and submodules are converted in place.

    Args:
        module (torch.nn.Module): Any PyTorch module.

    Returns:
        torch.nn.Module: Resulting module

    Inspired by https://github.com/pytorch/pytorch/blob/a5895f85be0f10212791145bfedc0261d364f103/torch/nn/modules/batchnorm.py#L762
    """
    res = module
    if isinstance(module, FrozenBatchNormAct2d):
        res = BatchNormAct2d(module.num_features)
        if module.affine:
            res.weight.data = module.weight.data.clone().detach()
            res.bias.data = module.bias.data.clone().detach()
        res.running_mean.data = module.running_mean.data
        res.running_var.data = module.running_var.data
        res.eps = module.eps
        res.drop = module.drop
        res.act = module.act
    elif isinstance(module, FrozenBatchNorm2d):
        res = torch.nn.BatchNorm2d(module.num_features)
        if module.affine:
            res.weight.data = module.weight.data.clone().detach()
            res.bias.data = module.bias.data.clone().detach()
        res.running_mean.data = module.running_mean.data
        res.running_var.data = module.running_var.data
        res.eps = module.eps
    else:
        for name, child in module.named_children():
            new_child = unfreeze_batch_norm_2d(child)
            if new_child is not child:
                res.add_module(name, new_child)
    return res


def _num_groups(num_channels, num_groups, group_size):
    if group_size:
        assert num_channels % group_size == 0
        return num_channels // group_size
    return num_groups


class GroupNormAct(nn.GroupNorm):
    # NOTE num_channel and num_groups order flipped for easier layer swaps / binding of fixed args
    def __init__(
            self,
            num_channels,
            num_groups=32,
            eps=1e-5,
            affine=True,
            group_size=None,
            apply_act=True,
            act_layer=nn.ReLU,
            inplace=True,
            drop_layer=None,
    ):
        super(GroupNormAct, self).__init__(
            _num_groups(num_channels, num_groups, group_size),
            num_channels,
            eps=eps,
            affine=affine,
        )
        self.drop = drop_layer() if drop_layer is not None else nn.Identity()
        act_layer = get_act_layer(act_layer)  # string -> nn.Module
        if act_layer is not None and apply_act:
            act_args = dict(inplace=True) if inplace else {}
            self.act = act_layer(**act_args)
        else:
            self.act = nn.Identity()
        self._fast_norm = is_fast_norm()

    def forward(self, x):
        if self._fast_norm:
            x = fast_group_norm(x, self.num_groups, self.weight, self.bias, self.eps)
        else:
            x = F.group_norm(x, self.num_groups, self.weight, self.bias, self.eps)
        x = self.drop(x)
        x = self.act(x)
        return x


class GroupNorm1Act(nn.GroupNorm):
    def __init__(
            self,
            num_channels,
            eps=1e-5,
            affine=True,
            apply_act=True,
            act_layer=nn.ReLU,
            inplace=True,
            drop_layer=None,
    ):
        super(GroupNorm1Act, self).__init__(1, num_channels, eps=eps, affine=affine)
        self.drop = drop_layer() if drop_layer is not None else nn.Identity()
        act_layer = get_act_layer(act_layer)  # string -> nn.Module
        if act_layer is not None and apply_act:
            act_args = dict(inplace=True) if inplace else {}
            self.act = act_layer(**act_args)
        else:
            self.act = nn.Identity()
        self._fast_norm = is_fast_norm()

    def forward(self, x):
        if self._fast_norm:
            x = fast_group_norm(x, self.num_groups, self.weight, self.bias, self.eps)
        else:
            x = F.group_norm(x, self.num_groups, self.weight, self.bias, self.eps)
        x = self.drop(x)
        x = self.act(x)
        return x


class LayerNormAct(nn.LayerNorm):
    def __init__(
            self,
            normalization_shape: Union[int, List[int], torch.Size],
            eps=1e-5,
            affine=True,
            apply_act=True,
            act_layer=nn.ReLU,
            inplace=True,
            drop_layer=None,
    ):
        super(LayerNormAct, self).__init__(normalization_shape, eps=eps, elementwise_affine=affine)
        self.drop = drop_layer() if drop_layer is not None else nn.Identity()
        act_layer = get_act_layer(act_layer)  # string -> nn.Module
        if act_layer is not None and apply_act:
            act_args = dict(inplace=True) if inplace else {}
            self.act = act_layer(**act_args)
        else:
            self.act = nn.Identity()
        self._fast_norm = is_fast_norm()

    def forward(self, x):
        if self._fast_norm:
            x = fast_layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps)
        else:
            x = F.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps)
        x = self.drop(x)
        x = self.act(x)
        return x


class LayerNormAct2d(nn.LayerNorm):
    def __init__(
            self,
            num_channels,
            eps=1e-5,
            affine=True,
            apply_act=True,
            act_layer=nn.ReLU,
            inplace=True,
            drop_layer=None,
    ):
        super(LayerNormAct2d, self).__init__(num_channels, eps=eps, elementwise_affine=affine)
        self.drop = drop_layer() if drop_layer is not None else nn.Identity()
        act_layer = get_act_layer(act_layer)  # string -> nn.Module
        if act_layer is not None and apply_act:
            act_args = dict(inplace=True) if inplace else {}
            self.act = act_layer(**act_args)
        else:
            self.act = nn.Identity()
        self._fast_norm = is_fast_norm()

    def forward(self, x):
        x = x.permute(0, 2, 3, 1)
        if self._fast_norm:
            x = fast_layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps)
        else:
            x = F.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps)
        x = x.permute(0, 3, 1, 2)
        x = self.drop(x)
        x = self.act(x)
        return x