import json
from argparse import ArgumentParser
import torch
import os
import json
from tqdm import tqdm
from PIL import Image
import math
import multiprocessing
from multiprocessing import Pool, Queue, Manager

from transformers import TextStreamer
from mplug_owl2.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN
from mplug_owl2.conversation import conv_templates, SeparatorStyle
from mplug_owl2.model.builder import load_pretrained_model
from mplug_owl2.mm_utils import process_images, tokenizer_image_token, get_model_name_from_path, KeywordsStoppingCriteria

# https://github.com/X-PLUG/mPLUG-Owl/tree/main/mPLUG-Owl2
def split_list(lst, n):
    length = len(lst)
    avg = length // n  # 每份的大小
    result = []  # 存储分割后的子列表
    for i in range(n - 1):
        result.append(lst[i*avg:(i+1)*avg])
    result.append(lst[(n-1)*avg:])
    return result

def save_json(json_list,save_path):
    with open(save_path, 'w') as file:
        json.dump(json_list, file,indent=4)

def _get_args():
    parser = ArgumentParser()
    parser.add_argument("--image_folder", type=str, default="./data")
    parser.add_argument("--output_folder", type=str, default="./results")
    parser.add_argument("--OCRBench_file", type=str, default="./OCRBench/OCRBench.json")
    parser.add_argument("--model_path", type=str, default="./model_weights/mplug-owl2")
    parser.add_argument("--save_name", type=str, default="mplug-owl2")
    parser.add_argument("--conv_mode", type=str, default="mplug_owl2")
    parser.add_argument("--num_workers", type=int, default=8)
    parser.add_argument("--temperature", type=float, default=0.0)
    args = parser.parse_args()
    return args

OCRBench_score = {"Regular Text Recognition":0,"Irregular Text Recognition":0,"Artistic Text Recognition":0,"Handwriting Recognition":0,
"Digit String Recognition":0,"Non-Semantic Text Recognition":0,"Scene Text-centric VQA":0,"Doc-oriented VQA":0,"Doc-oriented VQA":0,
"Key Information Extraction":0,"Handwritten Mathematical Expression Recognition":0}
AllDataset_score = {"IIIT5K":0,"svt":0,"IC13_857":0,"IC15_1811":0,"svtp":0,"ct80":0,"cocotext":0,"ctw":0,"totaltext":0,"HOST":0,"WOST":0,"WordArt":0,"IAM":0,"ReCTS":0,"ORAND":0,"NonSemanticText":0,"SemanticText":0,
"STVQA":0,"textVQA":0,"ocrVQA":0,"ESTVQA":0,"ESTVQA_cn":0,"docVQA":0,"infographicVQA":0,"ChartQA":0,"ChartQA_Human":0,"FUNSD":0,"SROIE":0,"POIE":0,"HME100k":0}
num_all = {"IIIT5K":0,"svt":0,"IC13_857":0,"IC15_1811":0,"svtp":0,"ct80":0,"cocotext":0,"ctw":0,"totaltext":0,"HOST":0,"WOST":0,"WordArt":0,"IAM":0,"ReCTS":0,"ORAND":0,"NonSemanticText":0,"SemanticText":0,
"STVQA":0,"textVQA":0,"ocrVQA":0,"ESTVQA":0,"ESTVQA_cn":0,"docVQA":0,"infographicVQA":0,"ChartQA":0,"ChartQA_Human":0,"FUNSD":0,"SROIE":0,"POIE":0,"HME100k":0}

def eval_worker(args, data, eval_id, output_queue):
    print(f"Process {eval_id} start.")
    model_name = get_model_name_from_path(args.model_path)
    tokenizer, model, image_processor, context_len = load_pretrained_model(args.model_path, None, model_name, load_8bit=False, load_4bit=False, device=f"cuda:{eval_id}")
    for i in tqdm(range(len(data))):
        img_path = os.path.join(args.image_folder, data[i]['image_path'])
        qs = data[i]['question']
        if data[i].get("predict", 0)!=0:
            print(f"{img_path} predict exist, continue.")
            continue
        conv = conv_templates[args.conv_mode].copy()
        roles = conv.roles
        image = Image.open(img_path).convert('RGB')
        max_edge = max(image.size) # We recommand you to resize to squared image for BEST performance.
        image = image.resize((max_edge, max_edge))
        image_tensor = process_images([image], image_processor)
        image_tensor = image_tensor.to(model.device, dtype=torch.float16)

        inp = DEFAULT_IMAGE_TOKEN + qs
        conv.append_message(conv.roles[0], inp)
        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).to(model.device)
        stop_str = conv.sep2
        keywords = [stop_str]
        stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids)
        streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
        with torch.inference_mode():
            output_ids = model.generate(
                input_ids,
                images=image_tensor,
                do_sample=False,
                temperature=args.temperature,
                max_new_tokens=100,
                streamer=streamer,
                use_cache=True,
                stopping_criteria=[stopping_criteria])
        outputs = tokenizer.decode(output_ids[0, input_ids.shape[1]:]).strip()    
        data[i]['predict'] = outputs
    output_queue.put({eval_id: data})
    print(f"Process {eval_id} has completed.")

if __name__=="__main__":
    multiprocessing.set_start_method('spawn')
    args = _get_args()
    
    if os.path.exists(os.path.join(args.output_folder,f"{args.save_name}.json")):
        data_path = os.path.join(args.output_folder,f"{args.save_name}.json")
        print(f"output_path:{data_path} exist! Only generate the results that were not generated in {data_path}.")
    else:
        data_path = args.OCRBench_file

    with open(data_path, "r") as f:
        data = json.load(f)
    
    data_list = split_list(data, args.num_workers)
    output_queue = Manager().Queue()

    pool = Pool(processes=args.num_workers)
    for i in range(len(data_list)):
        pool.apply_async(eval_worker, args=(args, data_list[i], i, output_queue))
    pool.close()
    pool.join()

    results = {}
    while not output_queue.empty():
        result = output_queue.get()
        results.update(result)
    data = []
    for i in range(len(data_list)):
        data.extend(results[i])

    for i in range(len(data)):
        data_type = data[i]["type"]
        dataset_name = data[i]["dataset_name"]
        answers = data[i]["answers"]
        if data[i].get('predict',0)==0:
            continue
        predict = data[i]['predict']
        data[i]['result'] = 0
        if dataset_name == "HME100k":
            if type(answers)==list:
                for j in range(len(answers)):
                    answer = answers[j].strip().replace("\n"," ").replace(" ","")
                    predict = predict.strip().replace("\n"," ").replace(" ","")
                    if answer in predict:
                        data[i]['result'] = 1
            else:
                answers = answers.strip().replace("\n"," ").replace(" ","")
                predict = predict.strip().replace("\n"," ").replace(" ","")
                if answers in predict:
                    data[i]['result'] = 1
        else:
            if type(answers)==list:
                for j in range(len(answers)):
                    answer = answers[j].lower().strip().replace("\n"," ")
                    predict = predict.lower().strip().replace("\n"," ")
                    if answer in predict:
                        data[i]['result'] = 1
            else:
                answers = answers.lower().strip().replace("\n"," ")
                predict = predict.lower().strip().replace("\n"," ")
                if answers in predict:
                    data[i]['result'] = 1
    save_json(data, os.path.join(args.output_folder,f"{args.save_name}.json"))
    if len(data)==1000:
        for i in range(len(data)):
            if data[i].get("result",100)==100:
                continue
            OCRBench_score[data[i]['type']] += data[i]['result']
        recognition_score = OCRBench_score['Regular Text Recognition']+OCRBench_score['Irregular Text Recognition']+OCRBench_score['Artistic Text Recognition']+OCRBench_score['Handwriting Recognition']+OCRBench_score['Digit String Recognition']+OCRBench_score['Non-Semantic Text Recognition']
        Final_score = recognition_score+OCRBench_score['Scene Text-centric VQA']+OCRBench_score['Doc-oriented VQA']+OCRBench_score['Key Information Extraction']+OCRBench_score['Handwritten Mathematical Expression Recognition']
        print("###########################OCRBench##############################")
        print(f"Text Recognition(Total 300):{recognition_score}")
        print("------------------Details of Recognition Score-------------------")
        print(f"Regular Text Recognition(Total 50): {OCRBench_score['Regular Text Recognition']}")
        print(f"Irregular Text Recognition(Total 50): {OCRBench_score['Irregular Text Recognition']}")
        print(f"Artistic Text Recognition(Total 50): {OCRBench_score['Artistic Text Recognition']}")
        print(f"Handwriting Recognition(Total 50): {OCRBench_score['Handwriting Recognition']}")
        print(f"Digit String Recognition(Total 50): {OCRBench_score['Digit String Recognition']}")
        print(f"Non-Semantic Text Recognition(Total 50): {OCRBench_score['Non-Semantic Text Recognition']}")
        print("----------------------------------------------------------------")
        print(f"Scene Text-centric VQA(Total 200): {OCRBench_score['Scene Text-centric VQA']}")
        print("----------------------------------------------------------------")
        print(f"Doc-oriented VQA(Total 200): {OCRBench_score['Doc-oriented VQA']}")
        print("----------------------------------------------------------------")
        print(f"Key Information Extraction(Total 200): {OCRBench_score['Key Information Extraction']}")
        print("----------------------------------------------------------------")
        print(f"Handwritten Mathematical Expression Recognition(Total 100): {OCRBench_score['Handwritten Mathematical Expression Recognition']}")
        print("----------------------Final Score-------------------------------")
        print(f"Final Score(Total 1000): {Final_score}")
    else:
        for i in range(len(data)):
            num_all[data[i]['dataset_name']] += 1
            if data[i].get("result",100)==100:
                continue
            AllDataset_score[data[i]['dataset_name']] += data[i]['result']
        for key in AllDataset_score.keys():
            print(f"{key}: {AllDataset_score[key]/float(num_all[key])}")