# Copyright (c) 2023-2024 DeepSeek.
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from typing import Dict, List, Literal, Optional, Tuple, Union

import torch
import torch.nn as nn
import torchvision.transforms
from einops import rearrange

from loguru import logger

from vlmeval.vlm.deepsek_vl.models.sam import create_sam_vit
from vlmeval.vlm.deepsek_vl.models.siglip_vit import create_siglip_vit


class CLIPVisionTower(nn.Module):
    def __init__(
        self,
        model_name: str = "siglip_large_patch16_384",
        image_size: Union[Tuple[int, int], int] = 336,
        select_feature: str = "patch",
        select_layer: int = -2,
        select_layers: list = None,
        ckpt_path: str = "",
        pixel_mean: Optional[List[float]] = None,
        pixel_std: Optional[List[float]] = None,
        **kwargs,
    ):
        super().__init__()

        self.model_name = model_name
        self.select_feature = select_feature
        self.select_layer = select_layer
        self.select_layers = select_layers

        vision_tower_params = {
            "model_name": model_name,
            "image_size": image_size,
            "ckpt_path": ckpt_path,
            "select_layer": select_layer,
        }
        vision_tower_params.update(kwargs)
        self.vision_tower, self.forward_kwargs = self.build_vision_tower(
            vision_tower_params
        )

        if pixel_mean is not None and pixel_std is not None:
            image_norm = torchvision.transforms.Normalize(
                mean=pixel_mean, std=pixel_std
            )
        else:
            image_norm = None

        self.image_norm = image_norm

    def build_vision_tower(self, vision_tower_params):
        if self.model_name.startswith("siglip"):
            self.select_feature = "same"
            vision_tower = create_siglip_vit(**vision_tower_params)
            forward_kwargs = dict()

        elif self.model_name.startswith("sam"):
            vision_tower = create_sam_vit(**vision_tower_params)
            forward_kwargs = dict()

        else:  # huggingface
            from transformers import CLIPVisionModel

            vision_tower = CLIPVisionModel.from_pretrained(**vision_tower_params)
            forward_kwargs = dict(output_hidden_states=True)

        return vision_tower, forward_kwargs

    def feature_select(self, image_forward_outs):
        if isinstance(image_forward_outs, torch.Tensor):
            # the output has been the self.select_layer"s features
            image_features = image_forward_outs
        else:
            image_features = image_forward_outs.hidden_states[self.select_layer]

        if self.select_feature == "patch":
            # if the output has cls_token
            image_features = image_features[:, 1:]
        elif self.select_feature == "cls_patch":
            image_features = image_features
        elif self.select_feature == "same":
            image_features = image_features

        else:
            raise ValueError(f"Unexpected select feature: {self.select_feature}")
        return image_features

    def forward(self, images):
        """

        Args:
            images (torch.Tensor): [b, 3, H, W]

        Returns:
            image_features (torch.Tensor): [b, n_patch, d]
        """

        if self.image_norm is not None:
            images = self.image_norm(images)

        image_forward_outs = self.vision_tower(images, **self.forward_kwargs)
        image_features = self.feature_select(image_forward_outs)
        return image_features


class HybridVisionTower(nn.Module):
    def __init__(
        self,
        high_res_cfg: Dict,
        low_res_cfg: Dict,
        freeze_high: bool = False,
        freeze_low: bool = False,
        concat_type: Literal["feature", "sequence", "add", "tuple"] = "tuple",
        **ignore_kwargs,
    ):
        super().__init__()

        self.vision_tower_high = CLIPVisionTower(**high_res_cfg)
        self.vision_tower_low = CLIPVisionTower(**low_res_cfg)
        self.low_res_size = low_res_cfg["image_size"]
        self.concat_type = concat_type

        self.high_layer_norm = nn.LayerNorm(high_res_cfg.get("output_dim", 1024))
        self.low_layer_norm = nn.LayerNorm(low_res_cfg.get("output_dim", 1024))

        if freeze_high:
            for p_name, p in self.vision_tower_high.named_parameters():
                p.requires_grad = False
            self.vision_tower_high = self.vision_tower_high.eval()
        else:
            # train donwsamples and neck
            for p_name, p in self.vision_tower_high.named_parameters():
                if "downsamples" in p_name or "neck" in p_name:
                    p.requires_grad = True
                else:
                    p.requires_grad = False

        if freeze_low:
            for p in self.vision_tower_low.parameters():
                p.requires_grad = False
            self.vision_tower_low = self.vision_tower_low.eval()

        self.resize = torchvision.transforms.Resize(self.low_res_size, antialias=True)

        self.mask_index = ["low"]
        logger.info(f"mask_index: {self.mask_index}")

    def forward(self, images: torch.Tensor):
        """

        Args:
            images (torch.Tensor): [bs, 3, H, W]

        Returns:
            res (torch.Tensor): [bs, t, c]
        """

        # [bs, c, h, w]
        high_images = images

        # [bs, c, h_low, w_low]
        low_images = self.resize(images)

        # separately run two vision towers
        # run high_res vision tower
        high_res = self.vision_tower_high(high_images)

        if "high" in self.mask_index:
            high_res = torch.zeros_like(high_res)

        # [bs, c, h, w] -> [bs, h*w, c]
        high_res = rearrange(high_res, "b c h w -> b (h w) c")
        # run low_res vision tower
        low_res = self.vision_tower_low(low_images)

        if "low" in self.mask_index:
            low_res = torch.zeros_like(low_res)

        if self.concat_type == "feature":
            images_features = torch.cat([high_res, low_res], dim=-1)
        elif self.concat_type == "sequence":
            images_features = torch.cat([high_res, low_res], dim=1)
        elif self.concat_type == "add":
            images_features = high_res + low_res
        elif self.concat_type == "tuple":
            images_features = (high_res, low_res)

        else:
            raise ValueError(
                "Currently only support `feature`, `sequence`, `add` and `tuple` concat type."
            )

        return images_features


if __name__ == "__main__":
    image_size = 1024
    x = torch.zeros(2, 3, image_size, image_size).bfloat16().cuda()

    high_res_cfg = dict(
        model_name="sam_b_downsample",
        select_feature="same",
        image_size=image_size,
        pixel_mean=(0.48145466, 0.4578275, 0.40821073),
        pixel_std=(0.26862954, 0.26130258, 0.27577711),
        select_layer=-1,
        ckpt_path="",
    )

    low_res_cfg = dict(
        model_name="siglip_large_patch16_384",
        select_feature="same",
        image_size=384,
        pixel_mean=(0.5, 0.5, 0.5),
        pixel_std=(0.5, 0.5, 0.5),
        select_layer=-1,
        ckpt_path="",
    )

    net = (
        HybridVisionTower(
            high_res_cfg=high_res_cfg,
            low_res_cfg=low_res_cfg,
            freeze_high=True,
            freeze_low=True,
            concat_type="tuple",
        )
        .bfloat16()
        .cuda()
    )
    high_x, low_x = net(x)
    print(x.shape, high_x.shape, low_x.shape)
