"""Semantic Segmenter Module

将字幕分段按语义合并为段落。基于简单启发式规则：
1. 时间间隔超过阈值时分段
2. 段落长度限制

不使用 LLM 进行分段判断，保持简单和快速。
"""

from __future__ import annotations

from typing import List

from kb_workflow.srt.parser import Segment


class SemanticSegmenter:
    """语义分段器

    将字幕分段转换为语义段落。基于时间和长度规则：
    - 长时间停顿（>gap_threshold 秒）作为分段点
    - 段落长度限制（max_paragraph_chars 字符）

    Attributes:
        gap_threshold: 时间间隔阈值（秒），默认 3.0
        max_paragraph_chars: 单段落最大字符数，默认 500
    """

    def __init__(
        self,
        gap_threshold: float = 3.0,
        max_paragraph_chars: int = 500
    ):
        """初始化分段器

        Args:
            gap_threshold: 时间间隔阈值（秒），超过此值时分段
            max_paragraph_chars: 单段落最大字符数
        """
        self.gap_threshold = gap_threshold
        self.max_paragraph_chars = max_paragraph_chars

    def segment(self, segments: List[Segment]) -> List[str]:
        """将字幕分段转换为语义段落

        Args:
            segments: 字幕分段列表

        Returns:
            段落文本列表（已去除时间戳）

        Example:
            >>> segmenter = SemanticSegmenter()
            >>> segments = [Segment(1, 0, 5, "第一句"), Segment(2, 5, 10, "第二句")]
            >>> paragraphs = segmenter.segment(segments)
            >>> len(paragraphs)
            1
        """
        if not segments:
            return []

        paragraphs = []
        current_paragraph = []
        current_length = 0
        last_end_time = None

        for seg in segments:
            # 检查是否需要分段
            should_start_new = False

            # 规则1: 时间间隔超过阈值
            if last_end_time is not None:
                gap = seg.start_time - last_end_time
                if gap > self.gap_threshold:
                    should_start_new = True

            # 规则2: 段落长度超过限制
            if current_length + len(seg.text) > self.max_paragraph_chars:
                if current_paragraph:  # 只有当前段落有内容时才分段
                    should_start_new = True

            # 执行分段
            if should_start_new and current_paragraph:
                paragraphs.append(' '.join(current_paragraph))
                current_paragraph = []
                current_length = 0

            # 添加当前分段
            current_paragraph.append(seg.text)
            current_length += len(seg.text)
            last_end_time = seg.end_time

        # 添加最后一个段落
        if current_paragraph:
            paragraphs.append(' '.join(current_paragraph))

        return paragraphs