from pdfminer.high_level import extract_pages
from pdfminer.layout import LTTextBoxHorizontal
from pdfminer.pdfinterp import resolve1
from pdfminer.pdfdocument import PDFDocument
from pdfminer.pdfparser import PDFParser
from matcher import Matcher
from get_info import PdfExtractAttr, is_title
from typing import Callable, Union, List, Tuple, Dict, Optional
from tqdm import tqdm
import pandas as pd


def pagination_texts(contents: List[dict], start: int, end: int = None) -> Tuple[Dict, List[str]]:
    if end is None:
        end = start + 1
    results = {}
    texts = []
    pages = set(range(start, end))
    for page in contents:
        if page['page_number'] in pages:
            results.get(int(page['page_number']), {}).update(
                {
                    page['index']: {
                        'page_number': page['page_number'],
                        'index': page['index'],
                        'text': page['text'],
                        'lines': page['lines'],
                        'is_table_name': page['is_table_name']
                    }
                })
            texts.append(page['text'])
    return results, texts


def similarity_filter(data: List[dict], expect_similarity: float = None):
    def f(x: dict):
        return x['相似度'] > (expect_similarity if isinstance(expect_similarity, float) else 0.5)

    return filter(f, data)


def extract_from_texts(text: List[str], extractor: Union[Callable[[str, float], List[str]], Callable[[str], List[str]]],
                       instances: List[str], similarity: float = None) -> Tuple[List[str], List[int]]:
    texts = '，'.join(filter(lambda x: x != '',
                            ''.join([''.join(filter(lambda x: x != ' ', list(i.strip()))) for i in text]).split(
                                '。'))).split('，')
    sims = similar_match([{'text': i} for i in texts], instances, 'text')
    s_texts = [i['text'] for i in sims]
    similarities = [i['相似度'] for i in sims]
    if similarity is None:
        return list(filter(lambda x: x != [], [extractor(i) for i in s_texts])), similarities
    else:
        return list(filter(lambda x: x != [], [extractor(i, similarity) for i in s_texts])), similarities


def similar_match(data: List[dict], instances: List[str], key: str) -> {}:
    matcher = Matcher()
    df = pd.DataFrame(data)
    keyword_embeddings = matcher.get_embeddings(instances)
    tqdm.pandas(desc='标题相似度匹配')
    result = df[key].apply(lambda x: matcher.TopK1(x, instances, matcher.get_embedding(x), keyword_embeddings))
    result.columns = ['因素', '相似度']

    df['因素'] = result['因素']
    df['相似度'] = result['相似度']

    max_sim_idx = df.groupby('因素')['相似度'].idxmax()
    max_sim_rows = df.loc[max_sim_idx]
    return max_sim_rows.to_dict(orient='records')


def get_instance(title_instances: List[str],
                 content_instances: List[str],
                 extractor: Union[Callable[[str, float], List[str]], Callable[[str], List[str]]],
                 titles_list: Optional[list] = None,
                 texts_list: Optional[list] = None,
                 pdf_path: Optional[str] = None,
                 page_bias: int = 1,
                 similarity: float = None,
                ):
    """
    Args:
        title_instances
        content_instances
        file_path
        extractor
        page_bias
        similarity

    Returns:
        results
    """
    if titles_list:
        titles = titles_list
    if texts_list:
        texts = texts_list
    # file = PdfExtractAttr(file_path=pdf_path)
    # titles = file.parse_title()
    # texts = file.parse_text()

    title_sims = similarity_filter(
        similar_match(
            titles,
            title_instances,
            key='title'
        ),
        similarity
    )

    results = []

    for i in title_sims:
        current_page = i['page_number']
        _, text = pagination_texts(texts, current_page, current_page + page_bias)
        results.extend(extract_from_texts(text, extractor, content_instances))

    return results


if __name__ == '__main__':
    pdf_path = './2022年度工程类-公招采购资料/三峡左岸及地下电站地坪整治/投标文件/湖北建新建设工程有限公司_T221100130348%2F01整本文件/MainPdfFile/投标文件-修改版9-5-1-1.pdf'
    file = PdfExtractAttr(file_path=pdf_path)
    titles = file.parse_title()
    texts = file.parse_text()

    price_zhs = get_instance(
        title_instances=['投标函', '开标一览表'],
        content_instances=['人民币投标总报价'],
        titles_list=titles,
        texts_list=texts,
        extractor=match_price_zhs
    )
    price_num = get_instance(
        title_instances=['投标函', '开标一览表'],
        content_instances=['人民币投标总报价'],
        titles_list=titles,
        texts_list=texts,
        extractor=match_price_num
    )
    duration = get_instance(
        title_instances=['投标函', '开标一览表'], 
        content_instances=['工期日历天'],
        titles_list=titles,
        texts_list=texts,
        extractor=match_duration
    )
    quality = get_instance(
        title_instances=['投标函', '开标一览表'], 
        content_instances=['工程质量'],
        titles_list=titles,
        texts_list=texts,
        extractor=match_quality
    )
    valid = rmb_to_digit(price_zhs[0][0][0]) == price_num[0][0][0][1:]
    test = rmb_to_digit('壹仟肆佰贰拾万捌仟玖佰陆拾柒元叁角陆分元')
    valid = (rmb_to_digit('壹仟肆佰贰拾万捌仟玖佰陆拾柒元叁角陆分元')) == '14208967.36'
    print(price_zhs)
    pass