from .med import BertConfig, BertModel
from transformers import BertTokenizer

import torch
from torch import nn
import torch.nn.functional as F

from .blip import create_vit, init_tokenizer, load_checkpoint

class BLIP_ITM(nn.Module):
    def __init__(self,                 
                 med_config = 'configs/med_config.json',  
                 image_size = 384,
                 vit = 'base',
                 vit_grad_ckpt = False,
                 vit_ckpt_layer = 0,                      
                 embed_dim = 256,     
                 ):
        """
        Args:
            med_config (str): path for the mixture of encoder-decoder model's configuration file
            image_size (int): input image size
            vit (str): model size of vision transformer
        """               
        super().__init__()
        
        self.visual_encoder, vision_width = create_vit(vit,image_size, vit_grad_ckpt, vit_ckpt_layer)
        self.tokenizer = init_tokenizer()   
        med_config = BertConfig.from_json_file(med_config)
        med_config.encoder_width = vision_width
        self.text_encoder = BertModel(config=med_config, add_pooling_layer=False)          

        text_width = self.text_encoder.config.hidden_size
        
        self.vision_proj = nn.Linear(vision_width, embed_dim)
        self.text_proj = nn.Linear(text_width, embed_dim)

        self.itm_head = nn.Linear(text_width, 2) 
        
        
    def forward(self, image, caption, match_head='itm'):

        image_embeds = self.visual_encoder(image) 
        image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(image.device)        
      
        text = self.tokenizer(caption, padding='max_length', truncation=True, max_length=35, 
                              return_tensors="pt").to(image.device) 

                 
        if match_head=='itm':
            output = self.text_encoder(text.input_ids,
                                       attention_mask = text.attention_mask,
                                       encoder_hidden_states = image_embeds,
                                       encoder_attention_mask = image_atts,      
                                       return_dict = True,
                                      )
            itm_output = self.itm_head(output.last_hidden_state[:,0,:])     
            return itm_output
            
        elif match_head=='itc':
            text_output = self.text_encoder(text.input_ids, attention_mask = text.attention_mask,                      
                                            return_dict = True, mode = 'text')                     
            image_feat = F.normalize(self.vision_proj(image_embeds[:,0,:]),dim=-1)   
            text_feat = F.normalize(self.text_proj(text_output.last_hidden_state[:,0,:]),dim=-1)    
            
            sim = image_feat @ text_feat.t()
            return sim
        
        
def blip_itm(pretrained='',**kwargs):
    model = BLIP_ITM(**kwargs)
    if pretrained:
        model,msg = load_checkpoint(model,pretrained)
        assert(len(msg.missing_keys)==0)
    return model         
            