import os
import torch
import torch.nn as nn

from transformers import CLIPVisionModel, CLIPImageProcessor, CLIPVisionConfig


class CLIPVisionTower(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:
            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)
        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 feature_select_withcls(self, image_forward_outs):
        image_features = image_forward_outs.hidden_states[self.select_layer]
        image_features = image_features
        return image_features
    @torch.no_grad()
    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_withcls(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_withcls(image_forward_outs).to(images.dtype)

        return image_features
    def forward_select(self, images, token_num):
        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_withcls(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, output_attentions=True)
            attn_weights  = image_forward_outs.attentions[-2]
            hidden_states = image_forward_outs.hidden_states[-2]
            dominant_num =  token_num

            ## Dominant Visual Tokens
            cls_idx = 0
            cls_attention = attn_weights[:, :, cls_idx, cls_idx+1:]  
            cls_attention_sum = cls_attention.sum(dim=1)  
            topk_indices = cls_attention_sum.topk(dominant_num, dim=1).indices 
            
            topk_indices_sorted = torch.sort(topk_indices, dim=1).values

        return topk_indices_sorted

    def forward_select_scope(self, images, token_num, alpha):
        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_withcls(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, output_attentions=True)
            attn_weights  = image_forward_outs.attentions[-2]
            hidden_states = image_forward_outs.hidden_states[-2]
            dominant_num =  token_num

            ## Dominant Visual Tokens
            # cls_idx = 0
            # cls_attention = attn_weights[:, :, cls_idx, cls_idx+1:]  
            # cls_attention_sum = cls_attention.sum(dim=1)  
            # topk_indices = cls_attention_sum.topk(dominant_num, dim=1).indices 
            
            # topk_indices_sorted = torch.sort(topk_indices, dim=1).values

            cls_idx = 0
            cls_attention = attn_weights[:, :, cls_idx, cls_idx+1:]
            cls_attention_sum = cls_attention.sum(dim=1)

            image_features = hidden_states[:, cls_idx + 1:]
            bs = image_features.shape[0]
            dominant_num = int(dominant_num /bs)
            selected_idx, _ = SCOPE(image_features, dominant_num, cls_attention_sum, alpha)
            # selected_idx += 1

            all_indices = selected_idx
            topk_indices_sorted = torch.sort(all_indices, dim=1).values
            # hidden_states_save = dominant_tokens

        return topk_indices_sorted
    @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



class CLIPVisionTowerS2(CLIPVisionTower):
    def __init__(self, vision_tower, args, delay_load=False):
        super().__init__(vision_tower, args, delay_load)

        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]

        try:
            from s2wrapper import forward as multiscale_forward
        except ImportError:
            raise ImportError('Package s2wrapper not found! Please install by running: \npip install git+https://github.com/bfshi/scaling_on_scales.git')
        self.multiscale_forward = multiscale_forward

        # 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:
            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.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

    @torch.no_grad()
    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

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

        return image_features

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


def SCOPE(visual_feature_vectors, num_selected_token, cls_attn=None, alpha=1.0):
    """
    Batched version of SCOPE that processes all batch elements simultaneously.
    Args:
        visual_feature_vectors: [B, N, D] batch of feature vectors
        num_selected_token: Number of tokens to select per batch
        cls_attn: [B, N] batch of attention weights
    Returns:
        selected_idx: [B, K] selected token indices for each batch
        cosine_simi: [B, N, N] batch of cosine similarity matrices
    """
    # Calculate cosine similarity for all batches at once
    norm_vectors = visual_feature_vectors / visual_feature_vectors.norm(dim=-1, keepdim=True)
    cosine_simi = torch.bmm(norm_vectors, norm_vectors.transpose(1, 2))
    
    B, N = visual_feature_vectors.shape[:2]
    device = visual_feature_vectors.device
    dtype = visual_feature_vectors.dtype
    
    # Pre-allocate tensors for all batches
    selected = torch.zeros(B, N, dtype=torch.bool, device=device)
    selected_idx = torch.empty(B, num_selected_token, dtype=torch.long, device=device)
    cur_max = torch.zeros(B, N, dtype=dtype, device=device)
    
    # Precompute cls_attn ** alpha for all batches
    # alpha = float(os.environ.get('ALPHA', '1.0'))
    if cls_attn is not None:
        cls_attn_powered = cls_attn ** alpha
    else:
        cls_attn_powered = torch.ones(B, N, dtype=dtype, device=device)
    
    for i in range(num_selected_token):
        # Calculate gains for all batches simultaneously
        unselected_mask = ~selected
        gains = torch.maximum(
            torch.zeros(1, dtype=dtype, device=device),
            cosine_simi.masked_fill(~unselected_mask.unsqueeze(1), 0) - 
            cur_max.unsqueeze(2)
        ).sum(dim=1)
        
        # Apply attention weights
        combined = os.environ.get('COMBINED', 'multi')
        if combined == 'multi':
            gains = gains * cls_attn_powered
        elif combined == 'add':
            gains = gains + cls_attn_powered
        else:
            raise NotImplementedError
        # Mask out already selected tokens
        gains = gains.masked_fill(~unselected_mask, float('-inf'))
        
        # Find best elements for all batches
        best_idx = gains.argmax(dim=1)
        
        # Update states for all batches
        selected[torch.arange(B, device=device), best_idx] = True
        selected_idx[:, i] = best_idx
        cur_max = torch.maximum(cur_max, cosine_simi[torch.arange(B, device=device), best_idx])
    
    return selected_idx, cosine_simi