
"""
用户输入：
    model数组，每个元素代表一个页面
    pdf在s3的路径
    截图保存的s3位置

然后：
    1）根据s3路径，调用spark集群的api,拿到ak,sk,endpoint，构造出s3PDFReader
    2）根据用户输入的s3地址，调用spark集群的api,拿到ak,sk,endpoint，构造出s3ImageWriter

其余部分至于构造s3cli, 获取ak,sk都在code-clean里写代码完成。不要反向依赖！！！

"""
import re

from loguru import logger

from magic_pdf.rw import AbsReaderWriter
from magic_pdf.pdf_parse_by_ocr_v2 import parse_pdf_by_ocr
from magic_pdf.pdf_parse_by_txt_v2 import parse_pdf_by_txt


PARSE_TYPE_TXT = "txt"
PARSE_TYPE_OCR = "ocr"

def parse_txt_pdf(pdf_bytes: bytes, pdf_models: list, imageWriter: AbsReaderWriter, is_debug=False, start_page=0, *args,
                  **kwargs):
    """
    解析文本类pdf
    """
    pdf_info_dict = parse_pdf_by_txt(
        pdf_bytes,
        pdf_models,
        imageWriter,
        start_page_id=start_page,
        debug_mode=is_debug,
    )

    pdf_info_dict["_parse_type"] = PARSE_TYPE_TXT

    return pdf_info_dict


def parse_ocr_pdf(pdf_bytes: bytes, pdf_models: list, imageWriter: AbsReaderWriter, is_debug=False, start_page=0, *args,
                  **kwargs):
    """
    解析ocr类pdf
    """
    pdf_info_dict = parse_pdf_by_ocr(
        pdf_bytes,
        pdf_models,
        imageWriter,
        start_page_id=start_page,
        debug_mode=is_debug,
    )

    pdf_info_dict["_parse_type"] = PARSE_TYPE_OCR

    return pdf_info_dict


def parse_union_pdf(pdf_bytes: bytes, pdf_models: list, imageWriter: AbsReaderWriter, is_debug=False, start_page=0,
                    *args, **kwargs):
    """
    ocr和文本混合的pdf，全部解析出来
    """

    def parse_pdf(method):
        try:
            return method(
                pdf_bytes,
                pdf_models,
                imageWriter,
                start_page_id=start_page,
                debug_mode=is_debug,
            )
        except Exception as e:
            logger.exception(e)
            return None

    pdf_info_dict = parse_pdf(parse_pdf_by_txt)
    text_all = ""
    for page_dict in pdf_info_dict['pdf_info']:
        for para_block in page_dict['para_blocks']:
            if para_block['type'] in ['title', 'text']:
                for line in para_block['lines']:
                    for span in line['spans']:
                        text_all += span['content']

    def calculate_not_common_character_rate(text):
        garbage_regex = re.compile(r'[^\u4e00-\u9fa5\u0030-\u0039\u0041-\u005a\u0061-\u007a\u3000-\u303f\uff00-\uffef]')
        # 计算乱码字符的数量
        garbage_count = len(garbage_regex.findall(text))
        total = len(text)
        if total == 0:
            return 0  # 避免除以零的错误
        return garbage_count / total

    def calculate_not_printable_rate(text):
        printable = sum(1 for c in text if c.isprintable())
        total = len(text)
        if total == 0:
            return 0  # 避免除以零的错误
        return (total - printable) / total

    # not_common_character_rate = calculate_not_common_character_rate(text_all)
    not_printable_rate = calculate_not_printable_rate(text_all)
    # 测试乱码pdf，not_common_character_rate > 0.9, not_printable_rate > 0.1
    # not_common_character_rate对小语种可能会有误伤，not_printable_rate对小语种较为友好
    if pdf_info_dict is None or pdf_info_dict.get("_need_drop", False) or not_printable_rate > 0.1:
        logger.warning(f"parse_pdf_by_txt drop or error or garbled_rate too large, switch to parse_pdf_by_ocr")
        pdf_info_dict = parse_pdf(parse_pdf_by_ocr)
        if pdf_info_dict is None:
            raise Exception("Both parse_pdf_by_txt and parse_pdf_by_ocr failed.")
        else:
            pdf_info_dict["_parse_type"] = PARSE_TYPE_OCR
    else:
        pdf_info_dict["_parse_type"] = PARSE_TYPE_TXT

    return pdf_info_dict
