from typing import Dict
from torch import nn
from detectron2.config import configurable
from detectron2.layers import ShapeSpec
from detectron2.modeling import SEM_SEG_HEADS_REGISTRY

from ..transformer_decoder.frozenseg_transformer_decoder import build_transformer_decoder
from ..pixel_decoder.msdeformattn import build_pixel_decoder


@SEM_SEG_HEADS_REGISTRY.register()
class FrozenSegHead(nn.Module):

    @configurable
    def __init__(
        self,
        input_shape: Dict[str, ShapeSpec],
        *,
        num_classes: int,
        pixel_decoder: nn.Module,
        loss_weight: float = 1.0,
        ignore_value: int = -1,
        # extra parameters
        transformer_predictor: nn.Module,
        transformer_in_feature: str,
    ):
        """
        NOTE: this interface is experimental.
        Args:
            input_shape: shapes (channels and stride) of the input features
            num_classes: number of classes to predict
            pixel_decoder: the pixel decoder module
            loss_weight: loss weight
            ignore_value: category id to be ignored during training.
            transformer_predictor: the transformer decoder that makes prediction
            transformer_in_feature: input feature name to the transformer_predictor
        """
        super().__init__()
        input_shape = sorted(input_shape.items(), key=lambda x: x[1].stride)
        self.in_features = [k for k, v in input_shape]
        feature_strides = [v.stride for k, v in input_shape]
        feature_channels = [v.channels for k, v in input_shape]

        self.ignore_value = ignore_value
        self.common_stride = 4
        self.loss_weight = loss_weight

        self.pixel_decoder = pixel_decoder
        self.predictor = transformer_predictor
        self.transformer_in_feature = transformer_in_feature

        self.num_classes = num_classes

    @classmethod
    def from_config(cls, cfg, input_shape: Dict[str, ShapeSpec]):
        # figure out in_channels to transformer predictor
        if cfg.MODEL.MASK_FORMER.TRANSFORMER_IN_FEATURE == "multi_scale_pixel_decoder":
            transformer_predictor_in_channels = cfg.MODEL.SEM_SEG_HEAD.CONVS_DIM
        else:
            raise NotImplementedError

        return {
            "input_shape": {
                k: v for k, v in input_shape.items() if k in cfg.MODEL.SEM_SEG_HEAD.IN_FEATURES
            },
            "ignore_value": cfg.MODEL.SEM_SEG_HEAD.IGNORE_VALUE,
            "num_classes": cfg.MODEL.SEM_SEG_HEAD.NUM_CLASSES,
            "pixel_decoder": build_pixel_decoder(cfg, input_shape),
            "loss_weight": cfg.MODEL.SEM_SEG_HEAD.LOSS_WEIGHT,
            "transformer_in_feature": cfg.MODEL.MASK_FORMER.TRANSFORMER_IN_FEATURE,
            "transformer_predictor": build_transformer_decoder(
                cfg,
                transformer_predictor_in_channels,
                mask_classification=True,
            ),
        }

    def forward(self, features, mask=None):
        return self.layers(features, mask)

    def layers(self, features, mask=None):
        mask_features, multi_scale_features, sam_fpn = self.pixel_decoder.forward_features(features)
        if self.transformer_in_feature == "multi_scale_pixel_decoder":
            predictions = self.predictor(multi_scale_features, mask_features, mask,
                                        text_classifier=features["text_classifier"], 
                                        num_templates=features["num_templates"], 
                                        sam_embedding=features['sam_embedding'], 
                                        sam=features["sam"], 
                                        sam_fpn=sam_fpn)
        else:
            raise NotImplementedError
        return predictions