# Copyright (c) OpenMMLab. All rights reserved.
import warnings
from abc import ABCMeta, abstractmethod
from collections import OrderedDict

import torch
import torch.distributed as dist
import torch.nn as nn
import torch.nn.functional as F
from mmcv.runner import auto_fp16

from .. import builder


class BaseRecognizer(nn.Module, metaclass=ABCMeta):
    """Base class for recognizers.

    All recognizers should subclass it.
    All subclass should overwrite:

    - Methods:``forward_train``, supporting to forward when training.
    - Methods:``forward_test``, supporting to forward when testing.

    Args:
        backbone (dict): Backbone modules to extract feature.
        cls_head (dict | None): Classification head to process feature.
            Default: None.
        neck (dict | None): Neck for feature fusion. Default: None.
        train_cfg (dict | None): Config for training. Default: None.
        test_cfg (dict | None): Config for testing. Default: None.
    """

    def __init__(self,
                 backbone,
                 cls_head=None,
                 neck=None,
                 train_cfg=None,
                 test_cfg=None):
        super().__init__()
        # record the source of the backbone
        self.backbone_from = 'mmaction2'

        if backbone['type'].startswith('mmcls.'):
            try:
                import mmcls.models.builder as mmcls_builder
            except (ImportError, ModuleNotFoundError):
                raise ImportError('Please install mmcls to use this backbone.')
            backbone['type'] = backbone['type'][6:]
            self.backbone = mmcls_builder.build_backbone(backbone)
            self.backbone_from = 'mmcls'
        elif backbone['type'].startswith('torchvision.'):
            try:
                import torchvision.models
            except (ImportError, ModuleNotFoundError):
                raise ImportError('Please install torchvision to use this '
                                  'backbone.')
            backbone_type = backbone.pop('type')[12:]
            self.backbone = torchvision.models.__dict__[backbone_type](
                **backbone)
            # disable the classifier
            self.backbone.classifier = nn.Identity()
            self.backbone.fc = nn.Identity()
            self.backbone_from = 'torchvision'
        elif backbone['type'].startswith('timm.'):
            try:
                import timm
            except (ImportError, ModuleNotFoundError):
                raise ImportError('Please install timm to use this '
                                  'backbone.')
            backbone_type = backbone.pop('type')[5:]
            # disable the classifier
            backbone['num_classes'] = 0
            self.backbone = timm.create_model(backbone_type, **backbone)
            self.backbone_from = 'timm'
        else:
            self.backbone = builder.build_backbone(backbone)

        if neck is not None:
            self.neck = builder.build_neck(neck)

        self.cls_head = builder.build_head(cls_head) if cls_head else None

        self.train_cfg = train_cfg
        self.test_cfg = test_cfg

        # aux_info is the list of tensor names beyond 'imgs' and 'label' which
        # will be used in train_step and val_step, data_batch should contain
        # these tensors
        self.aux_info = []
        if train_cfg is not None and 'aux_info' in train_cfg:
            self.aux_info = train_cfg['aux_info']
        # max_testing_views should be int
        self.max_testing_views = None
        if test_cfg is not None and 'max_testing_views' in test_cfg:
            self.max_testing_views = test_cfg['max_testing_views']
            assert isinstance(self.max_testing_views, int)

        if test_cfg is not None and 'feature_extraction' in test_cfg:
            self.feature_extraction = test_cfg['feature_extraction']
        else:
            self.feature_extraction = False

        # mini-batch blending, e.g. mixup, cutmix, etc.
        self.blending = None
        if train_cfg is not None and 'blending' in train_cfg:
            from mmcv.utils import build_from_cfg

            from mmaction.datasets.builder import BLENDINGS
            self.blending = build_from_cfg(train_cfg['blending'], BLENDINGS)

        self.init_weights()

        self.fp16_enabled = False

    @property
    def with_neck(self):
        """bool: whether the recognizer has a neck"""
        return hasattr(self, 'neck') and self.neck is not None

    @property
    def with_cls_head(self):
        """bool: whether the recognizer has a cls_head"""
        return hasattr(self, 'cls_head') and self.cls_head is not None

    def init_weights(self):
        """Initialize the model network weights."""
        if self.backbone_from in ['mmcls', 'mmaction2']:
            self.backbone.init_weights()
        elif self.backbone_from in ['torchvision', 'timm']:
            warnings.warn('We do not initialize weights for backbones in '
                          f'{self.backbone_from}, since the weights for '
                          f'backbones in {self.backbone_from} are initialized'
                          'in their __init__ functions.')
        else:
            raise NotImplementedError('Unsupported backbone source '
                                      f'{self.backbone_from}!')

        if self.with_cls_head:
            self.cls_head.init_weights()
        if self.with_neck:
            self.neck.init_weights()

    @auto_fp16()
    def extract_feat(self, imgs):
        """Extract features through a backbone.

        Args:
            imgs (torch.Tensor): The input images.

        Returns:
            torch.tensor: The extracted features.
        """
        if (hasattr(self.backbone, 'features')
                and self.backbone_from == 'torchvision'):
            x = self.backbone.features(imgs)
        elif self.backbone_from == 'timm':
            x = self.backbone.forward_features(imgs)
        elif self.backbone_from == 'mmcls':
            x = self.backbone(imgs)
            if isinstance(x, tuple):
                assert len(x) == 1
                x = x[0]
        else:
            x = self.backbone(imgs)
        return x

    def average_clip(self, cls_score, num_segs=1):
        """Averaging class score over multiple clips.

        Using different averaging types ('score' or 'prob' or None,
        which defined in test_cfg) to computed the final averaged
        class score. Only called in test mode.

        Args:
            cls_score (torch.Tensor): Class score to be averaged.
            num_segs (int): Number of clips for each input sample.

        Returns:
            torch.Tensor: Averaged class score.
        """
        if 'average_clips' not in self.test_cfg.keys():
            raise KeyError('"average_clips" must defined in test_cfg\'s keys')

        average_clips = self.test_cfg['average_clips']
        if average_clips not in ['score', 'prob', None]:
            raise ValueError(f'{average_clips} is not supported. '
                             f'Currently supported ones are '
                             f'["score", "prob", None]')

        if average_clips is None:
            return cls_score

        batch_size = cls_score.shape[0]
        cls_score = cls_score.view(batch_size // num_segs, num_segs, -1)

        if average_clips == 'prob':
            cls_score = F.softmax(cls_score, dim=2).mean(dim=1)
        elif average_clips == 'score':
            cls_score = cls_score.mean(dim=1)

        return cls_score

    @abstractmethod
    def forward_train(self, imgs, labels, **kwargs):
        """Defines the computation performed at every call when training."""

    @abstractmethod
    def forward_test(self, imgs):
        """Defines the computation performed at every call when evaluation and
        testing."""

    @abstractmethod
    def forward_gradcam(self, imgs):
        """Defines the computation performed at every all when using gradcam
        utils."""

    @staticmethod
    def _parse_losses(losses):
        """Parse the raw outputs (losses) of the network.

        Args:
            losses (dict): Raw output of the network, which usually contain
                losses and other necessary information.

        Returns:
            tuple[Tensor, dict]: (loss, log_vars), loss is the loss tensor
                which may be a weighted sum of all losses, log_vars contains
                all the variables to be sent to the logger.
        """
        log_vars = OrderedDict()
        for loss_name, loss_value in losses.items():
            if isinstance(loss_value, torch.Tensor):
                log_vars[loss_name] = loss_value.mean()
            elif isinstance(loss_value, list):
                log_vars[loss_name] = sum(_loss.mean() for _loss in loss_value)
            else:
                raise TypeError(
                    f'{loss_name} is not a tensor or list of tensors')

        loss = sum(_value for _key, _value in log_vars.items()
                   if 'loss' in _key)

        log_vars['loss'] = loss
        for loss_name, loss_value in log_vars.items():
            # reduce loss when distributed training
            if dist.is_available() and dist.is_initialized():
                loss_value = loss_value.data.clone()
                dist.all_reduce(loss_value.div_(dist.get_world_size()))
            log_vars[loss_name] = loss_value.item()

        return loss, log_vars

    def forward(self, imgs, label=None, return_loss=True, **kwargs):
        """Define the computation performed at every call."""
        if kwargs.get('gradcam', False):
            del kwargs['gradcam']
            return self.forward_gradcam(imgs, **kwargs)
        if return_loss:
            if label is None:
                raise ValueError('Label should not be None.')
            if self.blending is not None:
                imgs, label = self.blending(imgs, label)
            return self.forward_train(imgs, label, **kwargs)

        return self.forward_test(imgs, **kwargs)

    def train_step(self, data_batch, optimizer, **kwargs):
        """The iteration step during training.

        This method defines an iteration step during training, except for the
        back propagation and optimizer updating, which are done in an optimizer
        hook. Note that in some complicated cases or models, the whole process
        including back propagation and optimizer updating is also defined in
        this method, such as GAN.

        Args:
            data_batch (dict): The output of dataloader.
            optimizer (:obj:`torch.optim.Optimizer` | dict): The optimizer of
                runner is passed to ``train_step()``. This argument is unused
                and reserved.

        Returns:
            dict: It should contain at least 3 keys: ``loss``, ``log_vars``,
                ``num_samples``.
                ``loss`` is a tensor for back propagation, which can be a
                weighted sum of multiple losses.
                ``log_vars`` contains all the variables to be sent to the
                logger.
                ``num_samples`` indicates the batch size (when the model is
                DDP, it means the batch size on each GPU), which is used for
                averaging the logs.
        """
        imgs = data_batch['imgs']
        label = data_batch['label']

        aux_info = {}
        for item in self.aux_info:
            assert item in data_batch
            aux_info[item] = data_batch[item]

        losses = self(imgs, label, return_loss=True, **aux_info)

        loss, log_vars = self._parse_losses(losses)

        outputs = dict(
            loss=loss,
            log_vars=log_vars,
            num_samples=len(next(iter(data_batch.values()))))

        return outputs

    def val_step(self, data_batch, optimizer, **kwargs):
        """The iteration step during validation.

        This method shares the same signature as :func:`train_step`, but used
        during val epochs. Note that the evaluation after training epochs is
        not implemented with this method, but an evaluation hook.
        """
        imgs = data_batch['imgs']
        label = data_batch['label']

        aux_info = {}
        for item in self.aux_info:
            aux_info[item] = data_batch[item]

        losses = self(imgs, label, return_loss=True, **aux_info)

        loss, log_vars = self._parse_losses(losses)

        outputs = dict(
            loss=loss,
            log_vars=log_vars,
            num_samples=len(next(iter(data_batch.values()))))

        return outputs
