import spaces

import argparse
import torch
import os
import json
from tqdm import tqdm
import shortuuid

from llava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN
from llava.conversation import conv_templates, SeparatorStyle
from llava.model.builder import load_pretrained_model
from llava.utils import disable_torch_init
from llava.mm_utils import tokenizer_image_token, get_model_name_from_path, KeywordsStoppingCriteria

from PIL import Image
import math


model_path = 'kaist-ai/prometheus-vision-13b-v1.0'
model_name = 'llava-v1.5'

def split_list(lst, n):
    """Split a list into n (roughly) equal-sized chunks"""
    chunk_size = math.ceil(len(lst) / n)  # integer division
    return [lst[i:i+chunk_size] for i in range(0, len(lst), chunk_size)]


def get_chunk(lst, n, k):
    chunks = split_list(lst, n)
    return chunks[k]

@spaces.GPU
def eval_model(args, model_name = model_name, model_path = model_path):
    disable_torch_init()
    tokenizer, model, image_processor, context_len = load_pretrained_model(model_path, args.model_base, model_name)

    questions = [json.loads(q) for q in open(os.path.expanduser(args.question_file), "r")]
    questions = get_chunk(questions, args.num_chunks, args.chunk_idx)
    answers_file = os.path.expanduser(args.answers_file)
    os.makedirs(os.path.dirname(answers_file), exist_ok=True)
    ans_file = open(answers_file, "w")
    for line in tqdm(questions):
        idx = line["question_id"]
        image_file = line["image"]
        qs = line["text"]
        cur_prompt = qs
        if model.config.mm_use_im_start_end:
            qs = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN + '\n' + qs
        else:
            qs = DEFAULT_IMAGE_TOKEN + '\n' + qs

        conv = conv_templates[args.conv_mode].copy()
        conv.append_message(conv.roles[0], qs)
        conv.append_message(conv.roles[1], None)
        prompt = conv.get_prompt()

        input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).cuda()

        image = Image.open(os.path.join(args.image_folder, image_file))
        image_tensor = image_processor.preprocess(image, return_tensors='pt')['pixel_values'][0]

        stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2
        keywords = [stop_str]
        stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids)

        with torch.inference_mode():
            output_ids = model.generate(
                input_ids,
                images=image_tensor.unsqueeze(0).half().cuda(),
                do_sample=True if args.temperature > 0 else False,
                temperature=args.temperature,
                top_p=args.top_p,
                num_beams=args.num_beams,
                # no_repeat_ngram_size=3,
                max_new_tokens=1024,
                use_cache=True)

        input_token_len = input_ids.shape[1]
        n_diff_input_output = (input_ids != output_ids[:, :input_token_len]).sum().item()
        if n_diff_input_output > 0:
            print(f'[Warning] {n_diff_input_output} output_ids are not the same as the input_ids')
        outputs = tokenizer.batch_decode(output_ids[:, input_token_len:], skip_special_tokens=True)[0]
        outputs = outputs.strip()
        if outputs.endswith(stop_str):
            outputs = outputs[:-len(stop_str)]
        outputs = outputs.strip()

        ans_id = shortuuid.uuid()
        ans_file.write(json.dumps({"question_id": idx,
                                   "prompt": cur_prompt,
                                   "text": outputs,
                                   "answer_id": ans_id,
                                   "model_id": model_name,
                                   "metadata": {}}) + "\n")
        ans_file.flush()
    ans_file.close()

def gradio_wrapper( image_folder, question_file, answers_file, conv_mode, num_chunks, chunk_idx, temperature, top_p, num_beams, model_path = model_path , model_name = model_name):
    
    question_file_path = os.path.join(tempfile.mkdtemp(), "question.jsonl")
    with open(question_file_path, "w") as f:
        for question in question_file:
            f.write(json.dumps(question) + "\n")
    
    temp_image_folder = tempfile.mkdtemp()
    for image_file in image_folder:
        image_path = os.path.join(temp_image_folder, image_file.name)
        image_file.save(image_path)
    
    args = argparse.Namespace(
        model_path=model_path,
        model_base=model_base,
        image_folder=temp_image_folder,
        question_file=question_file_path,
        answers_file=answers_file,
        conv_mode=conv_mode,
        num_chunks=num_chunks,
        chunk_idx=chunk_idx,
        temperature=temperature,
        top_p=top_p,
        num_beams=num_beams
    )
    
    eval_model(args)
    
    with open(answers_file, "r") as f:
        answers = f.readlines()
        
    return answers

iface = gr.Interface(
    fn=gradio_wrapper,
    inputs=[
        gr.File(label="Image Folder", type="file", multiple=True),
        gr.JSON(label="Question File"),
        gr.Textbox(label="Answers File"),
        gr.Dropdown(label="Conversation Mode", choices=["llava_v1"]),
        gr.Slider(label="Number of Chunks", min_value=1, max_value=10, step=1, value=1),
        gr.Slider(label="Chunk Index", min_value=0, max_value=9, step=1, value=0),
        gr.Slider(label="Temperature", min_value=0.0, max_value=1.0, step=0.01, value=0.2),
        gr.Textbox(label="Top P", value=None),
        gr.Slider(label="Number of Beams", min_value=1, max_value=10, step=1, value=1)
    ],
    outputs=[
        gr.Textbox(label="Answers")
    ],
    title="Model Evaluation Interface",
    description="A Gradio interface for evaluating models."
)

if __name__ == "__main__":
    iface.launch()