import torch
import torch.nn as nn
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel


class CLIPVisionTower(nn.Module):
    def __init__(self, vision_tower, args, freeze_vision_tower=False, 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")
        self.freeze_vision_tower = freeze_vision_tower
        if not delay_load:
            self.load_model()
        elif getattr(args, "unfreeze_mm_vision_tower", False):
            self.load_model()
        else:
            self.cfg_only = CLIPVisionConfig.from_pretrained(self.vision_tower_name)

    def load_model(self, device_map=None):
        if self.is_loaded:
            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
        )

        if self.freeze_vision_tower:
            self.vision_tower.requires_grad_(False)

        self.is_loaded = True

    def feature_select(self, image_forward_outs):
        image_features = image_forward_outs.hidden_states[self.select_layer]
        if self.select_feature == "patch":
            image_features = image_features[:, 1:]
        elif self.select_feature == "cls_patch":
            image_features = image_features
        else:
            raise ValueError(f"Unexpected select feature: {self.select_feature}")
        return image_features

    def forward(self, images):
        if type(images) is list:
            image_features = []
            for image in images:
                if self.freeze_vision_tower:
                    with torch.no_grad():
                        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_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:
            if self.freeze_vision_tower:
                with torch.no_grad():
                    image_forward_out = self.vision_tower(
                        images.to(device=self.device, dtype=self.dtype),
                        output_hidden_states=True,
                    )
                    image_features = self.feature_select(image_forward_out).to(
                        images.dtype
                    )
            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):
        return self.config.hidden_size

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

    @property
    def num_patches(self):
        return (self.config.image_size // self.config.patch_size) ** 2