"""
Text Splitter
"""
import re
from typing import List, Dict, Optional
from langchain_text_splitters import RecursiveCharacterTextSplitter


class TextSplitter:
    """Base text splitter"""

    def __init__(self, chunk_size: int = 500, overlap: int = 50):
        self.chunk_size = chunk_size
        self.overlap = overlap

    def split(self, text: str) -> List[Dict]:
        """Split text into chunks"""
        raise NotImplementedError


class RecursiveTextSplitter(TextSplitter):
    """Recursive character text splitter using langchain"""

    def __init__(self, chunk_size: int = 500, overlap: int = 50, separators: List[str] = None):
        super().__init__(chunk_size, overlap)
        self.splitter = RecursiveCharacterTextSplitter(
            chunk_size=chunk_size,
            chunk_overlap=overlap,
            separators=separators or [
                "\n\n", "\n", ". ", " ", ",", ""
            ]
        )

    def split(self, text: str) -> List[Dict]:
        """Split text recursively"""
        chunks = self.splitter.split_text(text)
        result = []
        for i, chunk in enumerate(chunks):
            result.append({
                "index": i,
                "content": chunk.strip(),
                "word_count": len(chunk.split())
            })
        return result


class MarkdownStructureSplitter(TextSplitter):
    """Split text based on Markdown structure (headings)"""

    def __init__(self, chunk_size: int = 2000, overlap: int = 100):
        super().__init__(chunk_size, overlap)

    def split(self, text: str) -> List[Dict]:
        """Split text by Markdown headings"""
        # Find all heading patterns
        heading_pattern = r'^(#{1,6})\s+(.+)$'
        lines = text.split('\n')

        chunks = []
        current_chunk = ""
        current_heading = "文档开头"
        chunk_index = 0

        for line in lines:
            heading_match = re.match(heading_pattern, line.strip())

            if heading_match:
                # Save previous chunk if exists
                if current_chunk.strip():
                    chunks.append({
                        "index": chunk_index,
                        "name": current_heading,
                        "content": current_chunk.strip(),
                        "word_count": len(current_chunk.split())
                    })
                    chunk_index += 1

                current_heading = heading_match.group(2).strip()
                current_chunk = line + "\n"
            else:
                # Check chunk size
                if len(current_chunk) > self.chunk_size:
                    chunks.append({
                        "index": chunk_index,
                        "name": current_heading,
                        "content": current_chunk.strip(),
                        "word_count": len(current_chunk.split())
                    })
                    chunk_index += 1

                    # Handle overlap
                    if self.overlap > 0:
                        overlap_lines = current_chunk.split('\n')[-self.overlap:]
                        current_chunk = '\n'.join(overlap_lines) + '\n'
                    else:
                        current_chunk = ""

                current_chunk += line + "\n"

        # Add last chunk
        if current_chunk.strip():
            chunks.append({
                "index": chunk_index,
                "name": current_heading,
                "content": current_chunk.strip(),
                "word_count": len(current_chunk.split())
            })

        return chunks


class TokenSplitter(TextSplitter):
    """Split text by token count"""

    def __init__(self, chunk_size: int = 500, overlap: int = 50):
        super().__init__(chunk_size, overlap)

    def split(self, text: str) -> List[Dict]:
        """Split text by approximate token count"""
        words = text.split()
        chunks = []
        chunk_index = 0

        for i in range(0, len(words), self.chunk_size - self.overlap):
            chunk_words = words[i:i + self.chunk_size]
            chunk_text = " ".join(chunk_words)

            chunks.append({
                "index": chunk_index,
                "content": chunk_text,
                "word_count": len(chunk_words),
                "token_estimate": len(chunk_words) * 1.3  # rough token estimate
            })
            chunk_index += 1

        return chunks


class CodeSplitter(TextSplitter):
    """Split text with code awareness"""

    def __init__(self, chunk_size: int = 500, overlap: int = 50):
        super().__init__(chunk_size, overlap)

    def split(self, text: str) -> List[Dict]:
        """Split text preserving code blocks"""
        # Split by code blocks first
        code_pattern = r'```[\s\S]*?```'
        parts = re.split(code_pattern, text)

        chunks = []
        chunk_index = 0
        current_chunk = ""

        for part in parts:
            if len(current_chunk) + len(part) > self.chunk_size:
                if current_chunk.strip():
                    chunks.append({
                        "index": chunk_index,
                        "content": current_chunk.strip(),
                        "word_count": len(current_chunk.split())
                    })
                    chunk_index += 1
                current_chunk = part
            else:
                current_chunk += part

        if current_chunk.strip():
            chunks.append({
                "index": chunk_index,
                "content": current_chunk.strip(),
                "word_count": len(current_chunk.split())
            })

        return chunks


class CustomSplitter(TextSplitter):
    """Custom separator splitter"""

    def __init__(self, separator: str = "\n\n", chunk_size: int = 500):
        super().__init__(chunk_size, 0)
        self.separator = separator

    def split(self, text: str) -> List[Dict]:
        """Split by custom separator"""
        parts = text.split(self.separator)
        chunks = []

        current_chunk = ""
        chunk_index = 0

        for part in parts:
            if len(current_chunk) + len(part) > self.chunk_size:
                if current_chunk.strip():
                    chunks.append({
                        "index": chunk_index,
                        "content": current_chunk.strip(),
                        "word_count": len(current_chunk.split())
                    })
                    chunk_index += 1
                current_chunk = part
            else:
                current_chunk += self.separator + part if current_chunk else part

        if current_chunk.strip():
            chunks.append({
                "index": chunk_index,
                "content": current_chunk.strip(),
                "word_count": len(current_chunk.split())
            })

        return chunks


def get_splitter(method: str, **kwargs) -> TextSplitter:
    """Get text splitter by method name"""
    # 导入 embedding 分割器
    from .semantic_embedding import (
        SemanticEmbeddingSplitter,
        create_embedding_provider
    )

    splitters = {
        "recursive": RecursiveTextSplitter,
        "markdown_structure": MarkdownStructureSplitter,
        "token": TokenSplitter,
        "code": CodeSplitter,
        "custom": CustomSplitter,
        "semantic": SemanticSentenceSplitter,     # 语义分割（按段落+句子）
        "semantic_embedding": None,              # 需要特殊处理
        "sentence": SentenceSplitter,              # 严格按单句分割
        "paragraph": ParagraphSplitter,           # 按段落分割
    }

    # 特殊处理 embedding 分割器
    if method == "semantic_embedding":
        # 提取 embedding 相关参数
        embedding_provider = kwargs.pop('embedding_provider', None)
        if embedding_provider is None:
            # 如果没有提供 provider，使用默认配置
            # 从 kwargs 中获取模型配置
            provider = kwargs.pop('embedding_provider_type', 'openai')
            api_key = kwargs.pop('embedding_api_key', '')
            base_url = kwargs.pop('embedding_base_url', 'https://api.minimax.chat/v1')
            model = kwargs.pop('embedding_model', 'text-embedding-3-small')

            if api_key:
                embedding_provider = create_embedding_provider(
                    provider, api_key, base_url, model
                )

        # 创建分割器
        if embedding_provider:
            return SemanticEmbeddingSplitter(
                embedding_provider=embedding_provider,
                **kwargs
            )
        else:
            # 没有 embedding provider，降级到 semantic
            method = "semantic"

    splitter_class = splitters.get(method, RecursiveTextSplitter)
    return splitter_class(**kwargs)


class SemanticSentenceSplitter(TextSplitter):
    """语义分割器 - 按段落优先，其次按句子"""

    def __init__(self, chunk_size: int = 500, overlap: int = 50):
        super().__init__(chunk_size, overlap)
        self.splitter = RecursiveCharacterTextSplitter(
            chunk_size=chunk_size,
            chunk_overlap=overlap,
            separators=[
                "\n\n",  # 段落分隔优先
                "。",    # 中文句号
                "！",    # 中文感叹号
                "？",    # 中文问号
                ". ",    # 英文句号
                "! ",    # 英文感叹号
                "? ",    # 英文问号
                "\n",    # 换行
                " ",     # 空格
            ],
            length_function=self._count_chars
        )

    def _count_chars(self, text: str) -> int:
        chinese_chars = len(re.findall(r'[\u4e00-\u9fff]', text))
        other_chars = len(re.sub(r'[\u4e00-\u9fff]', '', text))
        return chinese_chars + int(other_chars * 1.5)

    def split(self, text: str) -> List[Dict]:
        chunks = self.splitter.split_text(text)
        result = []
        for i, chunk in enumerate(chunks):
            result.append({
                "index": i,
                "content": chunk.strip(),
                "word_count": len(chunk.split()),
                "char_count": len(chunk)
            })
        return result


class SentenceSplitter(TextSplitter):
    """严格按单句分割 - 每个chunk就是一句话"""

    def __init__(self, chunk_size: int = 200, overlap: int = 0):
        super().__init__(chunk_size, overlap)
        # 只按句子结束符分割，不合并
        self.splitter = RecursiveCharacterTextSplitter(
            chunk_size=chunk_size,
            chunk_overlap=overlap,
            separators=[
                "。",    # 中文句号
                "！",    # 中文感叹号
                "？",    # 中文问号
                ". ",    # 英文句号
                "! ",    # 英文感叹号
                "? ",    # 英文问号
                "\n",    # 换行
                " ",     # 空格
            ],
            length_function=lambda x: len(x)
        )

    def split(self, text: str) -> List[Dict]:
        chunks = self.splitter.split_text(text)
        result = []
        for i, chunk in enumerate(chunks):
            chunk = chunk.strip()
            if chunk:  # 跳过空chunk
                result.append({
                    "index": i,
                    "content": chunk,
                    "word_count": len(chunk.split()),
                    "char_count": len(chunk)
                })
        return result


class ParagraphSplitter(TextSplitter):
    """按段落分割 - 以空行分隔"""

    def __init__(self, chunk_size: int = 2000, overlap: int = 100):
        overlap = min(overlap, chunk_size // 2)  # overlap 不能超过 chunk_size
        super().__init__(chunk_size, overlap)

    def split(self, text: str) -> List[Dict]:
        # 按空行分割段落
        paragraphs = re.split(r'\n\s*\n', text)
        result = []
        current_chunk = ""
        chunk_index = 0

        for para in paragraphs:
            para = para.strip()
            if not para:
                continue

            # 如果单个段落超过chunk_size，递归分割
            if len(para) > self.chunk_size:
                if current_chunk:
                    result.append({
                        "index": chunk_index,
                        "content": current_chunk.strip(),
                        "word_count": len(current_chunk.split()),
                        "char_count": len(current_chunk)
                    })
                    chunk_index += 1
                    current_chunk = ""

                # 递归处理大段落
                sub_splitter = RecursiveCharacterTextSplitter(
                    chunk_size=self.chunk_size,
                    chunk_overlap=self.overlap,
                    separators=["\n", "。", "！", "？", ". ", "! ", "? "]
                )
                sub_chunks = sub_splitter.split_text(para)
                for sub in sub_chunks:
                    result.append({
                        "index": chunk_index,
                        "content": sub.strip(),
                        "word_count": len(sub.split()),
                        "char_count": len(sub)
                    })
                    chunk_index += 1
            else:
                if len(current_chunk) + len(para) > self.chunk_size:
                    if current_chunk:
                        result.append({
                            "index": chunk_index,
                            "content": current_chunk.strip(),
                            "word_count": len(current_chunk.split()),
                            "char_count": len(current_chunk)
                        })
                        chunk_index += 1
                        current_chunk = ""

                current_chunk += para + "\n\n"

        # 添加最后一个chunk
        if current_chunk.strip():
            result.append({
                "index": chunk_index,
                "content": current_chunk.strip(),
                "word_count": len(current_chunk.split()),
                "char_count": len(current_chunk)
            })

        return result