import os
join = os.path.join

import torch
import torch.nn as nn

from segment_anything.modeling import Sam


class MedSAM(nn.Module):
    """MedSAM implementation
    freeze image encoder and prompt encoder, tune mask decoder

    Args:
        sam: segment anything model, see 'segment_anything' dir
    """

    def __init__(self, sam: Sam):
        super().__init__()

        # freeze SAM image and prompt encoder
        for param in sam.image_encoder.parameters():
            param.requires_grad = False
        for param in sam.prompt_encoder.parameters():
            param.requires_grad = False

        self.sam = sam

    def save_parameters(self) -> dict:
        r"""save both image encoder and mask decoder parameters.
        """
        if isinstance(self.sam, torch.nn.DataParallel) or isinstance(self.sam, torch.nn.parallel.DistributedDataParallel):
            state_dict = self.sam.module.state_dict()
        else:
            state_dict = self.sam.state_dict()

        # save image encoder parameters
        image_encoder_tensors = {}
        # for key, value in state_dict.items():
        #     if 'image_encoder' in key:
        #         image_encoder_tensors[key] = value

        # save prompt encoder parameters
        prompt_encoder_tensors = {}
        # for key, value in state_dict.items():
        #     if 'prompt_encoder' in key:
        #         prompt_encoder_tensors[key] = value

        # save mask decoder parameters
        mask_decoder_tensors = {}
        for key, value in state_dict.items():
            if 'mask_decoder' in key:
                mask_decoder_tensors[key] = value

        merged_dict = {**image_encoder_tensors, **prompt_encoder_tensors, **mask_decoder_tensors}
        return merged_dict

    def load_parameters(self, state_dict) -> None:
        r"""load both image encoder and mask decoder parameters.
        """

        sam_dict = self.sam.state_dict()
        sam_keys = sam_dict.keys()

        # load image encoder parameters
        # image_encoder_keys = [k for k in sam_keys if 'image_encoder' in k]
        # image_encoder_values = [state_dict[k] for k in image_encoder_keys]
        # image_encoder_new_state_dict = {k: v for k, v in zip(image_encoder_keys, image_encoder_values)}
        # sam_dict.update(image_encoder_new_state_dict)

        # load prompt encoder parameters
        # prompt_encoder_keys = [k for k in sam_keys if 'prompt_encoder' in k]
        # prompt_encoder_values = [state_dict[k] for k in prompt_encoder_keys]
        # prompt_encoder_new_state_dict = {k: v for k, v in zip(prompt_encoder_keys, prompt_encoder_values)}
        # sam_dict.update(prompt_encoder_new_state_dict)

        # load mask decoder parameters
        mask_decoder_keys = [k for k in sam_keys if 'mask_decoder' in k]
        mask_decoder_values = [state_dict[k] for k in mask_decoder_keys]
        mask_decoder_new_state_dict = {k: v for k, v in zip(mask_decoder_keys, mask_decoder_values)}
        sam_dict.update(mask_decoder_new_state_dict)

        self.sam.load_state_dict(sam_dict)

    def forward(self, data):
        img, box = data['img'], data['box']

        # prompt encoder
        if len(box.shape) == 2:
            box = box[:, None, :]  # (B, 1, 4)

        sparse_embeddings, dense_embeddings = self.sam.prompt_encoder(
            points=None,
            boxes=box,
            masks=None,
        )

        # image encoder
        input_image = self.sam.preprocess(img) # (B, 3, 1024, 1024)
        image_embedding = self.sam.image_encoder(input_image) # (B, 256, 64, 64)

        # predicted masks
        mask_predictions, _ = self.sam.mask_decoder(
            image_embeddings=image_embedding, # (B, 256, 64, 64)
            image_pe=self.sam.prompt_encoder.get_dense_pe(), # (B, 256, 64, 64)
            sparse_prompt_embeddings=sparse_embeddings, # (B, 2, 256)
            dense_prompt_embeddings=dense_embeddings, # (B, 256, 64, 64)
            multimask_output=True,
          )

        return mask_predictions