from sklearn.cluster import DBSCAN
import numpy as np
from loguru import logger

from magic_pdf.libs.boxbase import _is_in


LINE_STOP_FLAG = ['.', '!', '?', '。', '！', '？',"：", ":", ")", "）", ";"]
INLINE_EQUATION = 'inline_equation'
INTER_EQUATION = "displayed_equation"
TEXT = "text"

def __add_line_period(blocks, layout_bboxes):
    """
    为每行添加句号
    如果这个行
    1. 以行内公式结尾，但没有任何标点符号,此时加个句号，认为他就是段落结尾。
    """
    for block in blocks:
        for line in block['lines']:
            last_span = line['spans'][-1]
            span_type = last_span['type']
            if span_type in [TEXT, INLINE_EQUATION]:
                span_content = last_span['content'].strip()
                if span_type==INLINE_EQUATION and span_content[-1] not in LINE_STOP_FLAG:
                    if span_type in [INLINE_EQUATION, INTER_EQUATION]:
                        last_span['content'] = span_content + '.'



def __valign_lines(blocks, layout_bboxes):
    """
    对齐行的左侧和右侧。
    扫描行的左侧和右侧，如果x0, x1差距不超过3就强行对齐到所处layout的左右两侧（和layout有一段距离）。
    3是个经验值，TODO，计算得来
    
    """
    
    min_distance = 3
    min_sample = 2
    
    for layout_box in layout_bboxes:
        blocks_in_layoutbox = [b for b in blocks if _is_in(b['bbox'], layout_box['layout_bbox'])]
        if len(blocks_in_layoutbox)==0:
            continue
        
        x0_lst = np.array([[line['bbox'][0], 0] for block in blocks_in_layoutbox for line in block['lines']])
        x1_lst = np.array([[line['bbox'][2], 0] for block in blocks_in_layoutbox for line in block['lines']])
        x0_clusters = DBSCAN(eps=min_distance, min_samples=min_sample).fit(x0_lst)
        x1_clusters = DBSCAN(eps=min_distance, min_samples=min_sample).fit(x1_lst)
        x0_uniq_label = np.unique(x0_clusters.labels_)
        x1_uniq_label = np.unique(x1_clusters.labels_)
        
        x0_2_new_val = {} # 存储旧值对应的新值映射
        x1_2_new_val = {}
        for label in x0_uniq_label:
            if label==-1:
                continue
            x0_index_of_label = np.where(x0_clusters.labels_==label)
            x0_raw_val = x0_lst[x0_index_of_label][:,0]
            x0_new_val = np.min(x0_lst[x0_index_of_label][:,0])
            x0_2_new_val.update({idx: x0_new_val for idx in x0_raw_val})
        for label in x1_uniq_label:
            if label==-1:
                continue
            x1_index_of_label = np.where(x1_clusters.labels_==label)
            x1_raw_val = x1_lst[x1_index_of_label][:,0]
            x1_new_val = np.max(x1_lst[x1_index_of_label][:,0])
            x1_2_new_val.update({idx: x1_new_val for idx in x1_raw_val})
        
        for block in blocks_in_layoutbox:
            for line in block['lines']:
                x0, x1 = line['bbox'][0], line['bbox'][2]
                if x0 in x0_2_new_val:
                    line['bbox'][0] = int(x0_2_new_val[x0])

                if x1 in x1_2_new_val:
                    line['bbox'][2] = int(x1_2_new_val[x1])
            # 其余对不齐的保持不动
            
        # 由于修改了block里的line长度，现在需要重新计算block的bbox
        for block in blocks_in_layoutbox:
            block['bbox'] = [min([line['bbox'][0] for line in block['lines']]), 
                            min([line['bbox'][1] for line in block['lines']]), 
                            max([line['bbox'][2] for line in block['lines']]), 
                            max([line['bbox'][3] for line in block['lines']])]


def __common_pre_proc(blocks, layout_bboxes):
    """
    不分语言的，对文本进行预处理
    """
    __add_line_period(blocks, layout_bboxes)
    __valign_lines(blocks, layout_bboxes)
    

def __pre_proc_zh_blocks(blocks, layout_bboxes):
    """
    对中文文本进行分段预处理
    """
    pass


def __pre_proc_en_blocks(blocks, layout_bboxes):
    """
    对英文文本进行分段预处理
    """
    pass


def __group_line_by_layout(blocks, layout_bboxes, lang="en"):
    """
    每个layout内的行进行聚合
    """
    # 因为只是一个block一行目前, 一个block就是一个段落
    lines_group = []
    
    for lyout in layout_bboxes:
        lines = [line for block in blocks if _is_in(block['bbox'], lyout['layout_bbox']) for line in block['lines']]
        lines_group.append(lines)

    return lines_group
    

def __split_para_in_layoutbox(lines_group, layout_bboxes, lang="en", char_avg_len=10):
    """
    lines_group 进行行分段——layout内部进行分段。
    1. 先计算每个group的左右边界。
    2. 然后根据行末尾特征进行分段。
        末尾特征：以句号等结束符结尾。并且距离右侧边界有一定距离。
    
    """
    def get_span_text(span):
        c = span.get('content', '')
        if len(c)==0:
            c = span.get('image-path', '')
            
        return c
    
    paras = []
    right_tail_distance = 1.5 * char_avg_len
    for lines in lines_group:
        if len(lines)==0:
            continue
        layout_right = max([line['bbox'][2] for line in lines])
        para = [] # 元素是line
        for line in lines:
            line_text = ''.join([get_span_text(span) for span in line['spans']])
            #logger.info(line_text)
            last_span_type = line['spans'][-1]['type']
            if last_span_type in [TEXT, INLINE_EQUATION]:
                last_char = line['spans'][-1]['content'][-1]
                if last_char in LINE_STOP_FLAG or line['bbox'][2] < layout_right - right_tail_distance:
                    para.append(line)
                    paras.append(para)
                    # para_text = ''.join([span['content'] for line in para for span in line['spans']])
                    # logger.info(para_text)
                    para = []
                else: 
                    para.append(line)
            else: # 其他，图片、表格、行间公式，各自占一段
                para.append(line)
                paras.append(para)
                # para_text = ''.join([get_span_text(span) for line in para for span in line['spans']])
                # logger.info(para_text)
                para = []
        if len(para)>0:
            paras.append(para)
            # para_text = ''.join([get_span_text(span) for line in para for span in line['spans']])
            # logger.info(para_text)
            para = []
                    
    return paras
            

def __do_split(blocks, layout_bboxes, lang="en"):
    """
    根据line和layout情况进行分段
    先实现一个根据行末尾特征分段的简单方法。
    """
    """
    算法思路：
    1. 扫描layout里每一行，找出来行尾距离layout有边界有一定距离的行。
    2. 从上述行中找到末尾是句号等可作为断行标志的行。
    3. 参照上述行尾特征进行分段。
    4. 图、表，目前独占一行，不考虑分段。
    """
    lines_group = __group_line_by_layout(blocks, layout_bboxes, lang) # block内分段
    layout_paras = __split_para_in_layoutbox(lines_group, layout_bboxes, lang) # block间连接分段
    
    return layout_paras
    
    
def para_split(blocks, layout_bboxes, lang="en"):
    """
    根据line和layout情况进行分段
    """
    __common_pre_proc(blocks, layout_bboxes)
    if lang=='en':
        __do_split(blocks, layout_bboxes, lang)
    elif lang=='zh':
        __do_split(blocks, layout_bboxes, lang)
    
    splited_blocks = __do_split(blocks, layout_bboxes, lang)
    
    return splited_blocks
