import re
from typing import Any, List, Optional

from langchain_text_splitters import RecursiveCharacterTextSplitter
from typing import Any

# CHUNK_SIZE是指在处理大型数据集时，将数据分成多个小块（chunk）时，每个小块的大小。这样做可以有效地管理内存使用，避免一次性加载过多数据导致内存溢出。在这里应根据大模型API请求的上下文 tokens 大小进行设置。
# CHUNK_OVERLAP是指在处理文本数据时，将文本分成多个小块（chunk）时，相邻块之间重叠的部分。这样做可以确保在分块处理时不会丢失重要信息，特别是在进行文本分类、实体识别等任务时，有助于提高模型的准确性和连贯性。
DEFAULT_CHUNK_SIZE = 2500  # tokens
DEFAULT_CHUNK_OVERLAP = 300  # tokens


def chunking_by_chinese_splitter(
    content: str,
    overlap_token_size=128,  # 重叠部分的token数量
    max_token_size=1024,     # 每个chunk的最大token数量
    tiktoken_model="gpt-4o"  # 使用的tokenizer模型名称
):
    """将长文本按照token数量切分成多个重叠的chunk

    Args:
        content (str): 需要切分的原始文本内容
        overlap_token_size (int, optional): 相邻chunk之间的重叠token数. Defaults to 128.
        max_token_size (int, optional): 每个chunk的最大token数. Defaults to 1024.
        tiktoken_model (str, optional): 使用的tokenizer模型. Defaults to "gpt-4o".

    Returns:
        list[dict]: 包含切分后的chunk列表,每个chunk包含以下字段:
            - tokens: chunk实际包含的token数
            - content: chunk的文本内容
            - chunk_order_index: chunk的序号
    """
    text_splitter = ChineseRecursiveTextSplitter(keep_separator=True, is_separator_regex=True, chunk_size=max_token_size, chunk_overlap=overlap_token_size)
    # 切分文本
    chunks = text_splitter.split_text(content)
    # 构建返回结果
    result = []
    for i, chunk in enumerate(chunks):
        result.append({
            "tokens": len(chunk),
            "content": chunk,
            "chunk_order_index": i
        })

    return result


def _split_text_with_regex_from_end(
        text: str, separator: str, keep_separator: bool
) -> List[str]:
    if separator:
        if keep_separator:
            # 模式中的括号会保留结果中的分隔符。
            _splits = re.split(f"({separator})", text)
            splits = ["".join(i) for i in zip(_splits[0::2], _splits[1::2])]
            if len(_splits) % 2 == 1:
                splits += _splits[-1:]
        else:
            splits = re.split(separator, text)
    else:
        splits = list(text)
    return [s for s in splits if s != ""]


class ChineseRecursiveTextSplitter(RecursiveCharacterTextSplitter):
    def __init__(
            self,
            separators: Optional[List[str]] = None,
            keep_separator: bool = True,
            is_separator_regex: bool = True,
            **kwargs: Any,
    ) -> None:
        super().__init__(keep_separator=keep_separator, **kwargs)
        self._separators = separators or [
            r"\n\n",
            r"\n",
            r"。|！|？",
            r"\.\s|\!\s|\?\s",
            r"；|;\s",
            r"，|,\s",
        ]
        self._is_separator_regex = is_separator_regex

    def _split_text(self, text: str, separators: List[str]) -> List[str]:
        """拆分传入的文本并返回处理后的块。"""
        final_chunks = []
        # 获取适当的分隔符以使用
        separator = separators[-1]
        new_separators = []
        for i, _s in enumerate(separators):
            _separator = _s if self._is_separator_regex else re.escape(_s)
            if _s == "":
                separator = _s
                break
            if re.search(_separator, text):
                separator = _s
                new_separators = separators[i + 1:]
                break

        _separator = separator if self._is_separator_regex else re.escape(separator)
        splits = _split_text_with_regex_from_end(text, _separator, self._keep_separator)

        _good_splits = []
        _separator = "" if self._keep_separator else separator
        for s in splits:
            if self._length_function(s) < self._chunk_size:
                _good_splits.append(s)
            else:
                if _good_splits:
                    merged_text = self._merge_splits(_good_splits, _separator)
                    final_chunks.extend(merged_text)
                    _good_splits = []
                if not new_separators:
                    final_chunks.append(s)
                else:
                    other_info = self._split_text(s, new_separators)
                    final_chunks.extend(other_info)
        if _good_splits:
            merged_text = self._merge_splits(_good_splits, _separator)
            final_chunks.extend(merged_text)
        return [
            re.sub(r"\n{2,}", "\n", chunk.strip())
            for chunk in final_chunks
            if chunk.strip() != ""
        ]