"""
文本语料加载器接口

功能:
- 从预存文件加载文本语料
- 支持按领域（domain）组织语料
- 将语料保存到文件

数据格式约定:
- 语料文件：.jsonl 格式，每行 {"text": "...", "metadata": {...}}
- 领域目录：database/corpus/{domain}/texts.jsonl

目录结构:
    database/
    ├── corpus/           # 文本语料
    │   ├── news_en/
    │   │   └── texts.jsonl
    │   ├── news_zh/
    │   ├── academic/
    │   ├── code/
    │   ├── dialogue/
    │   └── literature/
    └── splits/           # 数据集划分（可选）
        └── train_val_test.json
"""

import json
import os
from pathlib import Path
from typing import Dict, List, Optional, Tuple, Iterator
from dataclasses import dataclass, field


@dataclass
class CorpusMeta:
    """语料库元数据"""
    domain: str
    language: str
    num_texts: int
    avg_length: float
    source: str = ""
    extra: Dict = field(default_factory=dict)


class CorpusLoader:
    """
    从预存文件加载文本语料
    """

    def __init__(self, corpus_dir: str = "database/corpus"):
        self.base_dir = Path(corpus_dir)
        self.base_dir.mkdir(parents=True, exist_ok=True)

    def get_domain_path(self, domain: str) -> Path:
        """获取领域目录路径"""
        return self.base_dir / domain

    def get_texts_path(self, domain: str) -> Path:
        """获取语料文件路径"""
        return self.get_domain_path(domain) / "texts.jsonl"

    def load_texts(
        self,
        domain: str,
        limit: Optional[int] = None
    ) -> List[str]:
        """
        加载某个领域的文本列表

        Args:
            domain: 领域标识（如 "news_en", "academic"）
            limit: 最多加载多少条（用于快速测试）

        Returns:
            texts: 文本列表
        """
        path = self.get_texts_path(domain)

        if not path.exists():
            raise FileNotFoundError(f"Corpus not found: {path}")

        texts = []
        with open(path, "r", encoding="utf-8") as f:
            for i, line in enumerate(f):
                if limit and i >= limit:
                    break
                record = json.loads(line.strip())
                texts.append(record.get("text", ""))

        return texts

    def load_texts_with_metadata(
        self,
        domain: str,
        limit: Optional[int] = None
    ) -> List[Tuple[str, Dict]]:
        """
        加载文本及其元数据

        Returns:
            [(text, metadata), ...]
        """
        path = self.get_texts_path(domain)

        if not path.exists():
            raise FileNotFoundError(f"Corpus not found: {path}")

        result = []
        with open(path, "r", encoding="utf-8") as f:
            for i, line in enumerate(f):
                if limit and i >= limit:
                    break
                record = json.loads(line.strip())
                result.append((
                    record.get("text", ""),
                    record.get("metadata", {})
                ))

        return result

    def load_all_domains(
        self,
        domains: Optional[List[str]] = None,
        limit_per_domain: Optional[int] = None
    ) -> Dict[str, List[str]]:
        """
        加载多个领域的文本

        Args:
            domains: 领域列表，None 表示加载所有
            limit_per_domain: 每个领域最多加载多少条

        Returns:
            {domain: [texts], ...}
        """
        if domains is None:
            domains = self.list_available_domains()

        result = {}
        for domain in domains:
            result[domain] = self.load_texts(domain, limit_per_domain)
        return result

    def list_available_domains(self) -> List[str]:
        """列出所有可用的领域"""
        domains = []
        for path in self.base_dir.iterdir():
            if path.is_dir() and self.get_texts_path(path.name).exists():
                domains.append(path.name)
        return sorted(domains)

    def save_texts(
        self,
        texts: List[str],
        domain: str,
        metadata_list: Optional[List[Dict]] = None,
        source: str = ""
    ) -> None:
        """
        保存文本到语料文件

        Args:
            texts: 文本列表
            domain: 领域标识
            metadata_list: 可选的元数据列表
            source: 数据来源说明
        """
        path = self.get_texts_path(domain)
        path.parent.mkdir(parents=True, exist_ok=True)

        with open(path, "w", encoding="utf-8") as f:
            for i, text in enumerate(texts):
                record = {
                    "text": text,
                    "metadata": {
                        "index": i,
                        "source": source,
                        **(metadata_list[i] if metadata_list and i < len(metadata_list) else {})
                    }
                }
                f.write(json.dumps(record, ensure_ascii=False) + "\n")

    def get_corpus_stats(self, domain: str) -> Dict:
        """获取语料库统计信息"""
        texts = self.load_texts(domain)

        lengths = [len(t) for t in texts]
        char_lengths = [len(t) for t in texts]

        return {
            "domain": domain,
            "num_texts": len(texts),
            "total_chars": sum(char_lengths),
            "avg_chars": sum(char_lengths) / len(texts) if texts else 0,
            "min_chars": min(char_lengths) if texts else 0,
            "max_chars": max(char_lengths) if texts else 0,
        }

    def compute_corpus_statistics(self) -> Dict[str, Dict]:
        """计算所有领域的统计信息"""
        result = {}
        for domain in self.list_available_domains():
            result[domain] = self.get_corpus_stats(domain)
        return result


class CorpusBuilder:
    """
    语料构建器 - 用于从原始数据构建标准格式语料
    （当需要预处理原始数据时使用）
    """

    def __init__(self, output_dir: str = "database/corpus"):
        self.output_dir = Path(output_dir)

    def from_text_files(
        self,
        input_dir: str,
        domain: str,
        pattern: str = "*.txt",
        encoding: str = "utf-8"
    ) -> int:
        """
        从目录中的文本文件构建语料

        Args:
            input_dir: 输入目录
            domain: 领域标识
            pattern: 文件匹配模式
            encoding: 文件编码

        Returns:
            加载的文本数量
        """
        input_path = Path(input_dir)
        texts = []

        for file_path in input_path.glob(pattern):
            with open(file_path, "r", encoding=encoding) as f:
                content = f.read().strip()
                if content:
                    texts.append(content)

        loader = CorpusLoader(self.output_dir)
        loader.save_texts(texts, domain, source=f"from_text_files:{input_dir}")
        return len(texts)

    def from_jsonl(
        self,
        input_path: str,
        domain: str,
        text_field: str = "text"
    ) -> int:
        """
        从 JSONL 文件复制/转换语料

        Args:
            input_path: 输入 JSONL 路径
            domain: 领域标识
            text_field: 文本字段名

        Returns:
            加载的文本数量
        """
        texts = []
        with open(input_path, "r", encoding="utf-8") as f:
            for line in f:
                record = json.loads(line.strip())
                texts.append(record.get(text_field, ""))

        loader = CorpusLoader(self.output_dir)
        loader.save_texts(texts, domain, source=f"from_jsonl:{input_path}")
        return len(texts)


# ============= 便捷函数 =============

def load_texts(
    domain: str,
    corpus_dir: str = "database/corpus",
    limit: Optional[int] = None
) -> List[str]:
    """便捷函数：加载单个领域的文本"""
    loader = CorpusLoader(corpus_dir)
    return loader.load_texts(domain, limit)


def load_all_texts(
    domains: Optional[List[str]] = None,
    corpus_dir: str = "database/corpus",
    limit_per_domain: Optional[int] = None
) -> Dict[str, List[str]]:
    """便捷函数：加载多个领域的文本"""
    loader = CorpusLoader(corpus_dir)
    return loader.load_all_domains(domains, limit_per_domain)


def save_texts(
    texts: List[str],
    domain: str,
    corpus_dir: str = "database/corpus",
    source: str = ""
) -> None:
    """便捷函数：保存文本语料"""
    loader = CorpusLoader(corpus_dir)
    loader.save_texts(texts, domain, source=source)


def list_domains(corpus_dir: str = "database/corpus") -> List[str]:
    """便捷函数：列出所有可用领域"""
    loader = CorpusLoader(corpus_dir)
    return loader.list_available_domains()