from models.med import BertConfig
from models.nlvr_encoder import BertModel
from models.vit import interpolate_pos_embed
from models.blip import create_vit, init_tokenizer, is_url
import os
from timm.models.hub import download_cached_file
import ipdb
import torch
from torch import nn
import torch.nn.functional as F
from transformers import BertTokenizer
import numpy as np

class BLIP_NLVR(nn.Module):
    def __init__(self,                 
                 med_config = 'configs/med_config.json',  
                 image_size = 480,
                 vit = 'base',
                 vit_grad_ckpt = False,
                 vit_ckpt_layer = 0,                   
                 ):
        """
        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, drop_path_rate=0.1)
        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) 
                    
        self.cls_head = nn.Sequential(
                  nn.Linear(self.text_encoder.config.hidden_size, self.text_encoder.config.hidden_size),
                  nn.ReLU(),
                  nn.Linear(self.text_encoder.config.hidden_size, 2)
                )  

    def forward(self, image, text, targets, train=True):
        
        image_embeds = self.visual_encoder(image) 
        image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(image.device)        
        image0_embeds, image1_embeds = torch.split(image_embeds,targets.size(0))     
        text = self.tokenizer(text, padding='longest', return_tensors="pt").to(image.device) 
        text.input_ids[:,0] = self.tokenizer.enc_token_id        
        output = self.text_encoder(text.input_ids, 
                                   attention_mask = text.attention_mask, 
                                   encoder_hidden_states = [image0_embeds,image1_embeds],
                                   encoder_attention_mask = [image_atts[:image0_embeds.size(0)],
                                                             image_atts[image0_embeds.size(0):]],        
                                   return_dict = True,
                                  )  
        hidden_state = output.last_hidden_state[:,0,:]        
        prediction = self.cls_head(hidden_state)

        if train:            
            loss = F.cross_entropy(prediction, targets)   
            return loss
        else:
            return prediction
    
def blip_nlvr(pretrained='',**kwargs):
    model = BLIP_NLVR(**kwargs)
    if pretrained:
        model,msg = load_checkpoint(model,pretrained)
        print("missing keys:")
        print(msg.missing_keys)
    return model  

        
def load_checkpoint(model,url_or_filename):
    if is_url(url_or_filename):
        cached_file = download_cached_file(url_or_filename, check_hash=False, progress=True)
        checkpoint = torch.load(cached_file, map_location='cpu') 
    elif os.path.isfile(url_or_filename):        
        checkpoint = torch.load(url_or_filename, map_location='cpu') 
    else:
        raise RuntimeError('checkpoint url or path is invalid')
    state_dict = checkpoint['model']
    
    state_dict['visual_encoder.pos_embed'] = interpolate_pos_embed(state_dict['visual_encoder.pos_embed'],model.visual_encoder) 
    
    for key in list(state_dict.keys()):
        if 'crossattention.self.' in key:
            new_key0 = key.replace('self','self0')
            new_key1 = key.replace('self','self1')
            state_dict[new_key0] = state_dict[key]
            state_dict[new_key1] = state_dict[key]
        elif 'crossattention.output.dense.' in key:
            new_key0 = key.replace('dense','dense0')
            new_key1 = key.replace('dense','dense1')
            state_dict[new_key0] = state_dict[key]
            state_dict[new_key1] = state_dict[key]  
                
    msg = model.load_state_dict(state_dict,strict=False)
    print('load checkpoint from %s'%url_or_filename)  
    return model,msg
            