import torch
import torch.nn as nn
from llava.utils import rank0_print
# from transformers import CLIPVisionModel, CLIPImageProcessor, CLIPVisionConfig
from transformers import CLIPImageProcessor
from .mlcd.vit_rope2d_hf import MLCDVisionModel, MLCDVisionConfig

try:
    from s2wrapper import forward as multiscale_forward
except:
    pass


# class CLIPVisionTower(nn.Module):
class MLCDVisionTower(nn.Module):
    def __init__(self, vision_tower, args, delay_load=False):
        super().__init__()

        self.is_loaded = False

        self.vision_tower_name = vision_tower
        self.select_layer = args.mm_vision_select_layer
        self.select_feature = getattr(args, "mm_vision_select_feature", "patch")

        if not delay_load:
            rank0_print(f"Loading vision tower: {vision_tower}")
            self.load_model()
        elif getattr(args, "unfreeze_mm_vision_tower", False):
            # TODO: better detector is needed.
            rank0_print(f"The checkpoint seems to contain `vision_tower` weights: `unfreeze_mm_vision_tower`: True.")
            self.load_model()
        elif hasattr(args, "mm_tunable_parts") and "mm_vision_tower" in args.mm_tunable_parts:
            rank0_print(f"The checkpoint seems to contain `vision_tower` weights: `mm_tunable_parts` contains `mm_vision_tower`.")
            self.load_model()
        else:
            # self.cfg_only = CLIPVisionConfig.from_pretrained(self.vision_tower_name)
            self.cfg_only = MLCDVisionConfig.from_pretrained(self.vision_tower_name)

    def load_model(self, device_map=None):
        if self.is_loaded:
            rank0_print("{} is already loaded, `load_model` called again, skipping.".format(self.vision_tower_name))
            return

        self.image_processor = CLIPImageProcessor.from_pretrained(self.vision_tower_name)
        # self.vision_tower = CLIPVisionModel.from_pretrained(self.vision_tower_name, device_map=device_map)
        self.vision_tower = MLCDVisionModel.from_pretrained(self.vision_tower_name, device_map=device_map)
        self.vision_tower.requires_grad_(False)

        self.is_loaded = True

    def feature_select(self, image_forward_outs):
        select_feature_type = self.select_feature

        if self.select_feature in ["slicefour_patch", "slicefour_cls_patch"]:
            select_every_k_layer = len(image_forward_outs.hidden_states) // 4
            image_features = torch.cat([image_forward_outs.hidden_states[i] for i in range(select_every_k_layer + self.select_layer, len(image_forward_outs.hidden_states), select_every_k_layer)], dim=-1)
            select_feature_type = select_feature_type.replace("slicefour_", "")
        elif self.select_feature in ["slice_m25811_f6_patch", "slice_m25811_f6_cls_patch"]:
            select_layers = [-2, -5, -8, -11, 6]
            image_features = torch.cat([image_forward_outs.hidden_states[i] for i in select_layers], dim=-1)
            select_feature_type = select_feature_type.replace("slice_m25811_f6_", "")
        else:
            image_features = image_forward_outs.hidden_states[self.select_layer]

        if select_feature_type == "patch":
            image_features = image_features[:, 1:]
        elif select_feature_type == "cls_patch":
            image_features = image_features
        else:
            raise ValueError(f"Unexpected select feature: {select_feature_type}")
        return image_features

    def forward(self, images):
        if type(images) is list:
            image_features = []
            for image in images:
                image_forward_out = self.vision_tower(image.to(device=self.device, dtype=self.dtype).unsqueeze(0), output_hidden_states=True)
                image_feature = self.feature_select(image_forward_out).to(image.dtype)
                image_features.append(image_feature)
        else:
            image_forward_outs = self.vision_tower(images.to(device=self.device, dtype=self.dtype), output_hidden_states=True)
            image_features = self.feature_select(image_forward_outs).to(images.dtype)

        return image_features

    @property
    def dummy_feature(self):
        return torch.zeros(1, self.hidden_size, device=self.device, dtype=self.dtype)

    @property
    def dtype(self):
        return self.vision_tower.dtype

    @property
    def device(self):
        return self.vision_tower.device

    @property
    def config(self):
        if self.is_loaded:
            return self.vision_tower.config
        else:
            return self.cfg_only

    @property
    def hidden_size(self):
        _hidden_size = self.config.hidden_size
        if "slicefour" in self.select_feature:
            _hidden_size *= 4
        if "slice_m25811_f6" in self.select_feature:
            _hidden_size *= 5
        return _hidden_size

    @property
    def num_patches_per_side(self):
        return self.config.image_size // self.config.patch_size

    @property
    def num_patches(self):
        _num_patches = (self.config.image_size // self.config.patch_size) ** 2
        if "cls_patch" in self.select_feature:
            _num_patches += 1
        return _num_patches

    @property
    def image_size(self):
        return self.config.image_size


# class CLIPVisionTowerS2(CLIPVisionTower):
class MLCDVisionTowerS2(MLCDVisionTower):
    def __init__(self, vision_tower, args, delay_load=False):

        self.s2_scales = getattr(args, "s2_scales", "336,672,1008")
        self.s2_scales = list(map(int, self.s2_scales.split(",")))
        self.s2_scales.sort()
        self.s2_split_size = self.s2_scales[0]
        self.s2_image_size = self.s2_scales[-1]

        super().__init__(vision_tower, args, delay_load)

        # change resize/crop size in preprocessing to the largest image size in s2_scale
        if not delay_load or getattr(args, "unfreeze_mm_vision_tower", False):
            self.image_processor.size["shortest_edge"] = self.s2_image_size
            self.image_processor.crop_size["height"] = self.image_processor.crop_size["width"] = self.s2_image_size

    def load_model(self, device_map=None):
        if self.is_loaded:
            rank0_print("{} is already loaded, `load_model` called again, skipping.".format(self.vision_tower_name))
            return

        self.image_processor = CLIPImageProcessor.from_pretrained(self.vision_tower_name)
        self.vision_tower = MLCDVisionModel.from_pretrained(self.vision_tower_name, device_map=device_map)
        self.vision_tower.requires_grad_(False)

        self.image_processor.size["shortest_edge"] = self.s2_image_size
        self.image_processor.crop_size["height"] = self.image_processor.crop_size["width"] = self.s2_image_size

        self.is_loaded = True

    def forward_feature(self, images):
        image_forward_outs = self.vision_tower(images.to(device=self.device, dtype=self.dtype), output_hidden_states=True)
        image_features = self.feature_select(image_forward_outs).to(images.dtype)
        return image_features

    def forward(self, images):
        if type(images) is list:
            image_features = []
            for image in images:
                image_feature = multiscale_forward(self.forward_feature, image.unsqueeze(0), img_sizes=self.s2_scales, max_split_size=self.s2_split_size, split_forward=True)
                image_features.append(image_feature)
        else:
            image_features = multiscale_forward(self.forward_feature, images, img_sizes=self.s2_scales, max_split_size=self.s2_split_size, split_forward=True)

        return image_features

    @property
    def hidden_size(self):
        return self.config.hidden_size * len(self.s2_scales)
