# Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import os
import sys
import time
from tqdm import tqdm
from glob import glob
import argparse
import math
import random
import numpy as np
from PIL import Image

import torch
import torch.distributed as dist

from src.flux.generate import seed_everything
from src.utils.data_utils import get_train_config, get_rank_and_worldsize
from src.utils.data_utils import pad_to_square, pad_to_target, json_dump, json_load, split_grid, image_grid
import shutil
from eval.tools.dpg_score import DPGScore


def parse_args():
    parser = argparse.ArgumentParser()
    parser.add_argument("--input_dir", type=str, default="../examples")
    parser.add_argument("--test_list_name", type=str, default="base_test_list_200")
    args = parser.parse_args()
    
    return args

def main():
    args = parse_args()
    print(args)
    
    local_rank, global_rank, world_size = get_rank_and_worldsize()
    print(f"local_rank={local_rank}, global_rank={global_rank}, world_size={world_size}")
    is_local_main_process = local_rank == 0
    is_main_process = global_rank == 0
    torch.cuda.set_device(local_rank)
    
    dtype = torch.bfloat16
    device = "cuda"
    
    run_name = time.strftime("%m%d_$H")
    dpg_score_model = DPGScore(f"cuda:{local_rank}")
    
    test_list = json_load(f"eval/tools/{args.test_list_name}.json", 'utf-8')
    dsg_list = json_load(f"eval/tools/{args.test_list_name}_DSG.json", 'utf-8')
    images = list(glob(f"{args.input_dir}/*.png"))
    print(args.input_dir)
    print(len(test_list), len(dsg_list), len(images))
    assert len(test_list) == len(dsg_list)
    
    num_samples = min(len(test_list), len(images))
    num_ranks = world_size
    assert local_rank == global_rank
    if world_size > 1:
        num_per_rank = math.ceil(num_samples / num_ranks)
        test_list_indices = list(range(num_samples))
        random.seed(0)
        random.shuffle(test_list_indices)
        local_test_list_indices = test_list_indices[local_rank*num_per_rank:(local_rank+1)*num_per_rank]
        os.environ['CUDA_VISIBLE_DEVICES'] = str(local_rank % 8)
        print(f"[worker {local_rank}] got {len(local_test_list_indices)} local samples")

    run_name = time.strftime("%Y%m%d-%H")
    temp_dir = os.path.join(args.input_dir, f"eval_temp_{run_name}")

    if is_main_process:
        if os.path.exists(temp_dir):
            shutil.rmtree(temp_dir)
        os.makedirs(temp_dir)

    rank_json = {}
    with torch.no_grad():
        for i in tqdm(local_test_list_indices):
            test_sample = test_list[i]
            q_dict = dsg_list[i]
            assert q_dict["prompt"] == test_sample["prompt"]
            image_path = list(filter(lambda x: x.split("/")[-1].split("_")[0] == str(i), images))[0]
            
            rank_json[i] = []
            for j, img in enumerate(split_grid(Image.open(image_path))):
                rank_json[i].append({})
                result = dpg_score_model(img, q_dict)
                for q_id, question in result["qid2question"].items():
                    answer = result["qid2answer"][q_id]
                    rank_json[i][j][question] = answer
                rank_json[i][j]['average_score_with_dependency'] = result['average_score_with_dependency']
                rank_json[i][j]['average_score_without_dependency'] = result['average_score_without_dependency']

    json_dump(rank_json, f"{temp_dir}/scores_{global_rank}.json", "utf-8")

    if is_main_process:
        while len(glob(f"{temp_dir}/scores_*.json")) < world_size:
            time.sleep(5)
        time.sleep(5) # wait for the file writting to be finished
        merged_json = {}
        prompt_scores = {}
        scores = []
        for rank_path in glob(f"{temp_dir}/scores_*.json"):
            rank_json = json_load(rank_path, "utf-8")
            merged_json.update(rank_json)
            for i in rank_json:
                score_list = [x['average_score_with_dependency'] for x in rank_json[i]]
                prompt_scores[i] = np.mean(score_list)
                scores += score_list

        json_dump(merged_json, f"{args.input_dir}/dpg_scores_{run_name}.json", "utf-8")
        dpg_score = np.mean(scores)
        lines_to_write = [
            f"DPG Score: {dpg_score:.2f}\n"
        ]
        print(lines_to_write[0])
        for i, score in prompt_scores.items():
            lines_to_write.append(f"{i}: {score:.2f}\n")

        with open(f"{args.input_dir}/dpg_scores_{run_name}.txt", "w") as f:
            f.writelines(lines_to_write)

        shutil.rmtree(temp_dir)

if __name__ == "__main__":
    main()