# Copyright (c) OpenMMLab. All rights reserved.
import math
from collections import OrderedDict

import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import build_activation_layer
from mmcv.cnn.utils.weight_init import trunc_normal_
from mmcv.runner import Sequential

from ..builder import HEADS
from .cls_head import ClsHead


@HEADS.register_module()
class VisionTransformerClsHead(ClsHead):
    """Vision Transformer classifier head.

    Args:
        num_classes (int): Number of categories excluding the background
            category.
        in_channels (int): Number of channels in the input feature map.
        hidden_dim (int): Number of the dimensions for hidden layer. Only
            available during pre-training. Default None.
        act_cfg (dict): The activation config. Only available during
            pre-training. Defaults to Tanh.
    """

    def __init__(self,
                 num_classes,
                 in_channels,
                 hidden_dim=None,
                 act_cfg=dict(type='Tanh'),
                 init_cfg=dict(type='Constant', layer='Linear', val=0),
                 *args,
                 **kwargs):
        super(VisionTransformerClsHead, self).__init__(
            init_cfg=init_cfg, *args, **kwargs)
        self.in_channels = in_channels
        self.num_classes = num_classes
        self.hidden_dim = hidden_dim
        self.act_cfg = act_cfg

        if self.num_classes <= 0:
            raise ValueError(
                f'num_classes={num_classes} must be a positive integer')

        self._init_layers()

    def _init_layers(self):
        if self.hidden_dim is None:
            layers = [('head', nn.Linear(self.in_channels, self.num_classes))]
        else:
            layers = [
                ('pre_logits', nn.Linear(self.in_channels, self.hidden_dim)),
                ('act', build_activation_layer(self.act_cfg)),
                ('head', nn.Linear(self.hidden_dim, self.num_classes)),
            ]
        self.layers = Sequential(OrderedDict(layers))

    def init_weights(self):
        super(VisionTransformerClsHead, self).init_weights()
        # Modified from ClassyVision
        if hasattr(self.layers, 'pre_logits'):
            # Lecun norm
            trunc_normal_(
                self.layers.pre_logits.weight,
                std=math.sqrt(1 / self.layers.pre_logits.in_features))
            nn.init.zeros_(self.layers.pre_logits.bias)

    def pre_logits(self, x):
        if isinstance(x, tuple):
            x = x[-1]
        _, cls_token = x
        if self.hidden_dim is None:
            return cls_token
        else:
            x = self.layers.pre_logits(cls_token)
            return self.layers.act(x)

    def simple_test(self, x, softmax=True, post_process=True):
        """Inference without augmentation.

        Args:
            x (tuple[tuple[tensor, tensor]]): The input features.
                Multi-stage inputs are acceptable but only the last stage will
                be used to classify. Every item should be a tuple which
                includes patch token and cls token. The cls token will be used
                to classify and the shape of it should be
                ``(num_samples, in_channels)``.
            softmax (bool): Whether to softmax the classification score.
            post_process (bool): Whether to do post processing the
                inference results. It will convert the output to a list.

        Returns:
            Tensor | list: The inference results.

                - If no post processing, the output is a tensor with shape
                  ``(num_samples, num_classes)``.
                - If post processing, the output is a multi-dimentional list of
                  float and the dimensions are ``(num_samples, num_classes)``.
        """
        x = self.pre_logits(x)
        cls_score = self.layers.head(x)

        if softmax:
            pred = (
                F.softmax(cls_score, dim=1) if cls_score is not None else None)
        else:
            pred = cls_score

        if post_process:
            return self.post_process(pred)
        else:
            return pred

    def forward_train(self, x, gt_label, **kwargs):
        x = self.pre_logits(x)
        cls_score = self.layers.head(x)
        losses = self.loss(cls_score, gt_label, **kwargs)
        return losses