# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# 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

__dir__ = os.path.dirname(os.path.abspath(__file__))
sys.path.append(__dir__)
sys.path.insert(0, os.path.abspath(os.path.join(__dir__, "../..")))

os.environ["FLAGS_allocator_strategy"] = "auto_growth"

import cv2
import json
import numpy as np
import time

import tools.infer.utility as utility
from ppocr.data import create_operators, transform
from ppocr.postprocess import build_post_process
from ppocr.utils.logging import get_logger
from ppocr.utils.visual import draw_ser_results
from ppocr.utils.utility import get_image_file_list, check_and_read
from ppstructure.utility import parse_args

from paddleocr import PaddleOCR

logger = get_logger()


class SerPredictor(object):
    def __init__(self, args):
        self.args = args
        self.ocr_engine = PaddleOCR(
            use_angle_cls=args.use_angle_cls,
            det_model_dir=args.det_model_dir,
            rec_model_dir=args.rec_model_dir,
            show_log=False,
            use_gpu=args.use_gpu,
        )

        pre_process_list = [
            {
                "VQATokenLabelEncode": {
                    "algorithm": args.kie_algorithm,
                    "class_path": args.ser_dict_path,
                    "contains_re": False,
                    "ocr_engine": self.ocr_engine,
                    "order_method": args.ocr_order_method,
                }
            },
            {"VQATokenPad": {"max_seq_len": 512, "return_attention_mask": True}},
            {"VQASerTokenChunk": {"max_seq_len": 512, "return_attention_mask": True}},
            {"Resize": {"size": [224, 224]}},
            {
                "NormalizeImage": {
                    "std": [58.395, 57.12, 57.375],
                    "mean": [123.675, 116.28, 103.53],
                    "scale": "1",
                    "order": "hwc",
                }
            },
            {"ToCHWImage": None},
            {
                "KeepKeys": {
                    "keep_keys": [
                        "input_ids",
                        "bbox",
                        "attention_mask",
                        "token_type_ids",
                        "image",
                        "labels",
                        "segment_offset_id",
                        "ocr_info",
                        "entities",
                    ]
                }
            },
        ]
        postprocess_params = {
            "name": "VQASerTokenLayoutLMPostProcess",
            "class_path": args.ser_dict_path,
        }

        self.preprocess_op = create_operators(pre_process_list, {"infer_mode": True})
        self.postprocess_op = build_post_process(postprocess_params)
        (
            self.predictor,
            self.input_tensor,
            self.output_tensors,
            self.config,
        ) = utility.create_predictor(args, "ser", logger)

    def __call__(self, img):
        ori_im = img.copy()
        data = {"image": img}
        data = transform(data, self.preprocess_op)
        if data[0] is None:
            return None, 0
        starttime = time.time()

        for idx in range(len(data)):
            if isinstance(data[idx], np.ndarray):
                data[idx] = np.expand_dims(data[idx], axis=0)
            else:
                data[idx] = [data[idx]]
        if self.args.use_onnx:
            input_tensor = {
                name: data[idx] for idx, name in enumerate(self.input_tensor)
            }
            self.output_tensors = self.predictor.run(None, input_tensor)
        else:
            for idx in range(len(self.input_tensor)):
                self.input_tensor[idx].copy_from_cpu(data[idx])

            self.predictor.run()

        outputs = []
        for output_tensor in self.output_tensors:
            output = (
                output_tensor if self.args.use_onnx else output_tensor.copy_to_cpu()
            )
            outputs.append(output)
        preds = outputs[0]

        post_result = self.postprocess_op(
            preds, segment_offset_ids=data[6], ocr_infos=data[7]
        )
        elapse = time.time() - starttime
        return post_result, data, elapse


def main(args):
    image_file_list = get_image_file_list(args.image_dir)
    ser_predictor = SerPredictor(args)
    count = 0
    total_time = 0

    os.makedirs(args.output, exist_ok=True)
    with open(
        os.path.join(args.output, "infer.txt"), mode="w", encoding="utf-8"
    ) as f_w:
        for image_file in image_file_list:
            img, flag, _ = check_and_read(image_file)
            if not flag:
                img = cv2.imread(image_file)
                img = img[:, :, ::-1]
            if img is None:
                logger.info("error in loading image:{}".format(image_file))
                continue
            ser_res, _, elapse = ser_predictor(img)
            ser_res = ser_res[0]

            res_str = "{}\t{}\n".format(
                image_file,
                json.dumps(
                    {
                        "ocr_info": ser_res,
                    },
                    ensure_ascii=False,
                ),
            )
            f_w.write(res_str)

            img_res = draw_ser_results(
                image_file,
                ser_res,
                font_path=args.vis_font_path,
            )

            img_save_path = os.path.join(args.output, os.path.basename(image_file))
            cv2.imwrite(img_save_path, img_res)
            logger.info("save vis result to {}".format(img_save_path))
            if count > 0:
                total_time += elapse
            count += 1
            logger.info("Predict time of {}: {}".format(image_file, elapse))


if __name__ == "__main__":
    main(parse_args())