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 PIL import Image
from transformers import AutoModel, CLIPImageProcessor
from transformers import AutoTokenizer
import torchvision.transforms as T
from torchvision.transforms.functional import InterpolationMode

#https://github.com/Yuliang-Liu/Monkey/tree/main/project/mini_monkey

IMAGENET_MEAN = (0.485, 0.456, 0.406)
IMAGENET_STD = (0.229, 0.224, 0.225)


def build_transform(input_size):
    MEAN, STD = IMAGENET_MEAN, IMAGENET_STD
    transform = T.Compose([
        T.Lambda(lambda img: img.convert('RGB') if img.mode != 'RGB' else img),
        T.Resize((input_size, input_size), interpolation=InterpolationMode.BICUBIC),
        T.ToTensor(),
        T.Normalize(mean=MEAN, std=STD)
    ])
    return transform


def find_closest_aspect_ratio(aspect_ratio, target_ratios, width, height, image_size):
    best_ratio_diff = float('inf')
    best_ratio = (1, 1)
    area = width * height
    for ratio in target_ratios:
        target_aspect_ratio = ratio[0] / ratio[1]
        ratio_diff = abs(aspect_ratio - target_aspect_ratio)
        if ratio_diff < best_ratio_diff:
            best_ratio_diff = ratio_diff
            best_ratio = ratio
        elif ratio_diff == best_ratio_diff:
            if area > 0.5 * image_size * image_size * ratio[0] * ratio[1]:
                best_ratio = ratio
    return best_ratio


def dynamic_preprocess(image, min_num=5, max_num=6, image_size=448, use_thumbnail=False):
    orig_width, orig_height = image.size
    aspect_ratio = orig_width / orig_height

    # calculate the existing image aspect ratio
    target_ratios = set(
        (i, j) for n in range(min_num, max_num + 1) for i in range(1, n + 1) for j in range(1, n + 1) if
        i * j <= max_num and i * j >= min_num)
    target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1])

    # find the closest aspect ratio to the target
    target_aspect_ratio = find_closest_aspect_ratio(
        aspect_ratio, target_ratios, orig_width, orig_height, image_size)

    # calculate the target width and height
    target_width = image_size * target_aspect_ratio[0]
    target_height = image_size * target_aspect_ratio[1]
    blocks = target_aspect_ratio[0] * target_aspect_ratio[1]

    # resize the image
    resized_img = image.resize((target_width, target_height))
    processed_images = []
    for i in range(blocks):
        box = (
            (i % (target_width // image_size)) * image_size,
            (i // (target_width // image_size)) * image_size,
            ((i % (target_width // image_size)) + 1) * image_size,
            ((i // (target_width // image_size)) + 1) * image_size
        )
        # split the image
        split_img = resized_img.crop(box)
        processed_images.append(split_img)
    assert len(processed_images) == blocks
    if use_thumbnail and len(processed_images) != 1:
        thumbnail_img = image.resize((image_size, image_size))
        processed_images.append(thumbnail_img)
    return processed_images, target_aspect_ratio

def dynamic_preprocess2(image, min_num=1, max_num=6, image_size=448, use_thumbnail=False, prior_aspect_ratio=None):
    orig_width, orig_height = image.size
    aspect_ratio = orig_width / orig_height

    # calculate the existing image aspect ratio
    target_ratios = set(
        (i, j) for n in range(min_num, max_num + 1) for i in range(1, n + 1) for j in range(1, n + 1) if
        i * j <= max_num and i * j >= min_num)
    target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1])

    new_target_ratios = []
    if prior_aspect_ratio is not None:
        for i in target_ratios:
            if prior_aspect_ratio[0]%i[0] !=0 or prior_aspect_ratio[1]%i[1] !=0:
                new_target_ratios.append(i)
            else:
                continue
    # find the closest aspect ratio to the target
    target_aspect_ratio = find_closest_aspect_ratio(
        aspect_ratio, new_target_ratios, orig_width, orig_height, image_size)

    # calculate the target width and height
    target_width = image_size * target_aspect_ratio[0]
    target_height = image_size * target_aspect_ratio[1]
    blocks = target_aspect_ratio[0] * target_aspect_ratio[1]

    # resize the image
    resized_img = image.resize((target_width, target_height))
    processed_images = []
    for i in range(blocks):
        box = (
            (i % (target_width // image_size)) * image_size,
            (i // (target_width // image_size)) * image_size,
            ((i % (target_width // image_size)) + 1) * image_size,
            ((i // (target_width // image_size)) + 1) * image_size
        )
        # split the image
        split_img = resized_img.crop(box)
        processed_images.append(split_img)
    assert len(processed_images) == blocks
    if use_thumbnail and len(processed_images) != 1:
        thumbnail_img = image.resize((image_size, image_size))
        processed_images.append(thumbnail_img)
    return processed_images

def load_image(image_file, input_size=448, min_num=1, max_num=6):
    image = Image.open(image_file).convert('RGB')
    transform = build_transform(input_size=input_size)
    images, target_aspect_ratio = dynamic_preprocess(image, image_size=input_size, use_thumbnail=True, min_num=min_num, max_num=max_num)
    pixel_values = [transform(image) for image in images]
    pixel_values = torch.stack(pixel_values)
    return pixel_values, target_aspect_ratio

def load_image2(image_file, input_size=448, target_aspect_ratio=(1,1), min_num=1, max_num=6):
    image = Image.open(image_file).convert('RGB')
    transform = build_transform(input_size=input_size)
    images = dynamic_preprocess2(image, image_size=input_size, prior_aspect_ratio=target_aspect_ratio, use_thumbnail=True, min_num=min_num, max_num=max_num)
    pixel_values = [transform(image) for image in images]
    pixel_values = torch.stack(pixel_values)
    return pixel_values

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="./OCRBench_Images")
    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='mx262/MiniMokney')#TODO Set the address of your model's weights
    parser.add_argument("--save_name", type=str, default="MiniMokney") #TODO Set the name of the JSON file you save in the output_folder.
    parser.add_argument("--num_workers", type=int, default=1)
    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.")
    checkpoint = args.model_path
    model = AutoModel.from_pretrained(
            checkpoint,
            torch_dtype=torch.bfloat16,
            low_cpu_mem_usage=True,
            trust_remote_code=True).eval().to(f'cuda:{eval_id}')

    tokenizer = AutoTokenizer.from_pretrained(checkpoint, trust_remote_code=True)
    
    for i in tqdm(range(len(data))):
        dataset_name = data[i]["dataset_name"]

        image_path = os.path.join(args.image_folder, data[i]['image_path'])
        qs = data[i]['question']

        pixel_values, target_aspect_ratio = load_image(image_path, min_num=12, max_num=24)
        pixel_values = pixel_values.to(f'cuda:{eval_id}').to(torch.bfloat16)
        pixel_values2 = load_image2(image_path, target_aspect_ratio=target_aspect_ratio, min_num=3, max_num=11)
        pixel_values2 = pixel_values2.to(f'cuda:{eval_id}').to(torch.bfloat16)
        pixel_values = torch.cat((pixel_values[:-1],  pixel_values2[:-1], pixel_values[-1:]), 0)

        generation_config = dict(
            num_beams=1,
            max_new_tokens=512,
            do_sample=False,
            )
        question = '<image>\n'+qs+ '\nAnswer the question using a single word or phrase.'
        response = model.chat(tokenizer, pixel_values, target_aspect_ratio, question, generation_config)
        data[i]['predict'] = response
    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])}")