from typing import Optional, Union

from ..base import ClassificationHead, SegmentationHead, SegmentationModel
from ..encoders import get_encoder
from .decoder import RCNNDecoder


class RCNN(SegmentationModel):

    def __init__(
        self,
        encoder_name: str = "resnet34",
        encoder_depth: int = 5,
        encoder_weights: Optional[str] = "imagenet",
        decoder_pyramid_channels: int = 256,
        decoder_segmentation_channels: int = 128,
        decoder_merge_policy: str = "add",
        decoder_dropout: float = 0.2,
        in_channels: int = 3,
        classes: int = 1,
        activation: Optional[str] = None,
        upsampling: int = 4,
        aux_params: Optional[dict] = None,
        siam_encoder: bool = True,
        fusion_form: str = "concat",
        **kwargs
    ):
        super().__init__()

        self.siam_encoder = siam_encoder

        self.encoder = get_encoder(
            encoder_name,
            in_channels=in_channels,
            depth=encoder_depth,
            weights=encoder_weights,
        )

        if not self.siam_encoder:
            self.encoder_non_siam = get_encoder(
                encoder_name,
                in_channels=in_channels,
                depth=encoder_depth,
                weights=encoder_weights,
            )

        self.decoder = RCNNDecoder(
            encoder_channels=self.encoder.out_channels,
            encoder_depth=encoder_depth,
            pyramid_channels=decoder_pyramid_channels,
            segmentation_channels=decoder_segmentation_channels,
            dropout=decoder_dropout,
            merge_policy=decoder_merge_policy,
            fusion_form=fusion_form,
        )

        self.segmentation_head = SegmentationHead(
            in_channels=self.decoder.out_channels,
            out_channels=classes,
            activation=activation,
            kernel_size=1,
            upsampling=upsampling,
        )

        if aux_params is not None:
            self.classification_head = ClassificationHead(
                in_channels=self.encoder.out_channels[-1], **aux_params
            )
        else:
            self.classification_head = None

        self.name = "rcnn-{}".format(encoder_name)
        self.initialize()