import os

os.system('cd TimeSformer;'
          'pip install .; cd ..')

os.system('ls -l')
os.system('pwd')

import os, sys
sys.path.append("/home/user/app/TimeSformer/")

import timesformer


import torch
from torchvision import transforms


from transformers import AutoTokenizer


from PIL import Image
import json 
import os

from torchvision import transforms

from models.epalm import ePALM

import os

from transformers import AutoTokenizer

# import ruamel_yaml as yaml
from ruamel.yaml import YAML

import torch
import gradio as gr

import torchaudio 

yaml=YAML(typ='safe')



use_cuda = torch.cuda.is_available()
device = torch.device('cuda') if use_cuda else torch.device('cpu')
device_type = 'cuda' if use_cuda else 'cpu'

## Load model

### Captioning 
config = 'configs/image/ePALM_caption.yaml'
# config = yaml.load(open(config, 'r'), Loader=yaml.Loader)
config = yaml.load(open(config, 'r'))

text_model = 'facebook/opt-2.7b' 
vision_model_name = 'vit_base_patch16_224'

# text_model = 'facebook/opt-6.7b' 
# vision_model_name = 'vit_large_patch16_224'

start_layer_idx = 19
end_layer_idx = 31
low_cpu = True 
MODEL = ePALM(opt_model_name=text_model, 
               vision_model_name=vision_model_name, 
               use_vis_prefix=True, 
               start_layer_idx=start_layer_idx, 
               end_layer_idx=end_layer_idx, 
               return_hidden_state_vision=True, 
               config=config,
               low_cpu=low_cpu
)
print("Model Built")
MODEL.to(device)

checkpoint_path = 'checkpoints/float32/ePALM_caption/checkpoint_best.pth'
# checkpoint_path = '/data/mshukor/logs/eplam/models/accelerate/ePALM_pt_L_acc_caption/checkpoint_best.pth'
checkpoint = torch.load(checkpoint_path, map_location='cpu') 
state_dict = checkpoint['model']
msg = MODEL.load_state_dict(state_dict,strict=False)  

MODEL.bfloat16()




checkpoint_path = 'checkpoints/float32/ePALM_vqa/checkpoint_best.pth'
checkpoint = torch.load(checkpoint_path, map_location='cpu') 
state_dict_vqa = checkpoint['model']






## Load tokenizer
tokenizer = AutoTokenizer.from_pretrained(text_model, use_fast=False)
eos_token = tokenizer.eos_token
pad_token = tokenizer.pad_token

special_answer_token = '</a>'

special_tokens_dict = {'additional_special_tokens': [special_answer_token]}
tokenizer.add_special_tokens(special_tokens_dict)


image_size = 224
normalize = transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711))

transform = transforms.Compose([
            transforms.Resize((image_size,image_size),interpolation=Image.BICUBIC),
            transforms.ToTensor(),
            normalize,
            ])  


do_sample=False
num_beams=5
max_length=30





def inference(image, task_type, instruction):

    if task_type == 'Image Captioning':
        text = ['']  
        text_input = tokenizer(text, padding='longest', return_tensors="pt").to(device) 
        model = MODEL
    elif task_type == 'Visual Question Answering':
        question = instruction+'?'+special_answer_token
        text_input = tokenizer(question, padding='longest', return_tensors="pt").to(device) 
        msg = MODEL.load_state_dict(state_dict_vqa,strict=False)  
        model = MODEL
        print(msg)
    else:
        raise NotImplemented
    
    image = transform(image)
    image = image.to(device,non_blocking=True).unsqueeze(0)




    with torch.autocast(device_type=device_type, dtype=torch.bfloat16, enabled=True):

        out = model(image=image, text=text_input, mode='generate', return_dict=True, max_length=max_length, 
                    do_sample=do_sample, num_beams=num_beams)


    if 'Captioning' in task_type:
        for i, o in enumerate(out):
            res = tokenizer.decode(o)
            response = res.split('</s>')[1].replace(pad_token, '').replace('</s>', '').replace(eos_token, '') # skip_special_tokens=True
    else:
        for o in out:
            o_list = o.tolist()
            response = tokenizer.decode(o_list).split(special_answer_token)[1].replace(pad_token, '').replace('</s>', '').replace(eos_token, '') # skip_special_tokens=True

    return response


inputs = [gr.inputs.Image(type='pil'), gr.inputs.Radio(choices=['Image Captioning',  "Visual Question Answering",], type="value", default="Image Captioning", label="Task"), gr.inputs.Textbox(lines=1, label="Instruction")]
outputs = ['text']
examples = [
    ['examples/images/soccer.jpg',  'Image Captioning', None],
    ['examples/images/ski.jpg',  'Visual Question Answering', 'what does the woman do?'],
    ['examples/images/banana.jpg', 'Image Captioning', None],
    ['examples/images/skateboard.jpg',  'Visual Question Answering', 'what is the colour of the bird?'],
    ['examples/images/baseball.jpg', 'Image Captioning', None],
]

title = "eP-ALM"
description = "Gradio Demo for eP-ALM. For this demo, we use 2.7B OPT. As the model runs on CPUs and float16 mixed precision is not supported on CPUs, the generation can take up to 2 mins."
article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2303.11403' target='_blank'>Paper</a> | <a href='https://github.com/mshukor/eP-ALM' target='_blank'>Github Repo</a></p>"

io = gr.Interface(fn=inference, inputs=inputs, outputs=outputs,
                  title=title, description=description, article=article, examples=examples, cache_examples=False)
io.launch()