from typing import Optional

import torch.nn as nn

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


class DVCA(SegmentationModel):
   

    def __init__(
            self,
            encoder_name: str = "resnet34",
            encoder_depth: int = 5,
            encoder_weights: Optional[str] = "imagenet",
            decoder_channels: int = 256,
            in_channels: int = 3,
            classes: int = 1,
            activation: Optional[str] = None,
            upsampling: int = 8,
            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,
            output_stride=8,
        )

        if not self.siam_encoder:
            self.encoder_non_siam = get_encoder(
                encoder_name,
                in_channels=in_channels,
                depth=encoder_depth,
                weights=encoder_weights,
                output_stride=8,
            )

        self.decoder = DVCADecoder(
            in_channels=self.encoder.out_channels[-1],
            out_channels=decoder_channels,
            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