"""AI-backed sentence explanations and subtitle translation."""

from dataclasses import dataclass
import json
import logging
import re
from urllib.parse import urlsplit

import requests


logger = logging.getLogger(__name__)

_JSON_FENCE_RE = re.compile(r"^```(?:json)?\s*|\s*```$", re.IGNORECASE)
_NUMBERED_LINE_RE = re.compile(r"^\s*(\d+)[\.、:：]\s*(.+?)\s*$")
_TRANSLATE_BATCH_CHARS = 12_000


class AITutorError(RuntimeError):
    """Raised when the configured AI service cannot answer a request."""


@dataclass(frozen=True)
class AITutor:
    endpoint: str
    api_key: str
    model: str
    timeout_seconds: float

    @property
    def available(self) -> bool:
        return bool(self.endpoint and self.model)

    @property
    def request_url(self) -> str:
        """Resolve either a full chat URL or an OpenAI-compatible base URL."""
        parsed = urlsplit(self.endpoint)
        path = parsed.path.rstrip("/")
        if parsed.query or path.endswith("/chat/completions"):
            return self.endpoint
        if path.endswith("/v1") or path.endswith("/v1/openai"):
            return self.endpoint + "/chat/completions"
        if not path:
            return self.endpoint + "/v1/chat/completions"
        return self.endpoint

    def explain(
        self,
        sentence_text: str,
        *,
        course_title: str = "",
        sentence_index: int | None = None,
        language: str = "",
    ) -> str:
        if not self.available:
            raise AITutorError("AI explanation is not configured.")

        context = course_title.strip() or "口语训练课程"
        position = f"第 {sentence_index + 1} 句" if sentence_index is not None else "当前句子"
        language_note = f"句子语言：{language.strip()}。" if language.strip() else ""
        system_prompt = (
            "你是一位耐心的外语口语老师。请用简体中文讲解用户提供的影视/课程句子。"
            "先给出自然的中文翻译，再解释关键词、常用搭配、语法要点，并简要说明这句话适合怎样跟读。"
            "回答使用简明清晰的分段短文，总长度不超过 600 字，不要编造视频里没有的信息。"
        )
        user_prompt = (
            f"课程：{context}\n位置：{position}\n{language_note}句子：{sentence_text}"
        )
        return self._complete(system_prompt, user_prompt, temperature=0.3)

    def translate_sentences(
        self,
        sentences: list[tuple[int, str]],
        *,
        course_title: str = "",
    ) -> dict[int, str]:
        if not self.available:
            raise AITutorError("AI translation is not configured.")
        if not sentences:
            raise AITutorError("No sentences were provided for translation.")

        translations: dict[int, str] = {}
        numbered = [(index, text.strip()) for index, text in sentences if text.strip()]
        if not numbered:
            raise AITutorError("No sentence text was provided for translation.")

        for batch, context_rows in _translation_batches(numbered):
            translations.update(
                self._translate_batch(
                    batch,
                    context_rows=context_rows,
                    course_title=course_title,
                    total_count=len(numbered),
                )
            )
        missing = [index for index, _ in numbered if not translations.get(index, "").strip()]
        if missing:
            raise AITutorError("AI translation did not cover every sentence.")
        return translations

    def _translate_batch(
        self,
        batch: list[tuple[int, str]],
        *,
        context_rows: list[tuple[int, str]],
        course_title: str,
        total_count: int,
    ) -> dict[int, str]:
        context = course_title.strip() or "口语训练课程"
        display_offset = {index: position for position, (index, _) in enumerate(batch, start=1)}
        system_prompt = (
            "你是专业的影视字幕翻译。请结合全文语境，把指定的外语句子翻译成自然、口语化的简体中文。"
            "必须一句对一句，不能合并、拆分、跳过或改写编号。"
            "只输出 JSON 数组，数组长度必须等于待翻译句子数，第 i 项对应第 i 句中文。"
            "不要输出解释、markdown 或编号。"
        )
        lines = [f"{display_offset[index]}. {text}" for index, text in batch]
        user_parts = [
            f"课程：{context}",
            f"全片共 {total_count} 句，本次需要翻译 {len(batch)} 句。",
        ]
        if context_rows:
            user_parts.append("前文（只供理解语境，不要翻译）：")
            user_parts.extend(f"{index + 1}. {text}" for index, text in context_rows)
        user_parts.append("待翻译句子：")
        user_parts.extend(lines)
        content = self._complete(
            system_prompt,
            "\n".join(user_parts),
            temperature=0.2,
            timeout_seconds=max(self.timeout_seconds, 90.0),
        )
        zh_rows = _parse_translation_payload(content, len(batch))
        return {
            index: zh_rows[offset - 1]
            for index, offset in display_offset.items()
        }

    def _complete(
        self,
        system_prompt: str,
        user_prompt: str,
        *,
        temperature: float,
        timeout_seconds: float | None = None,
    ) -> str:
        headers = {"Content-Type": "application/json"}
        if self.api_key:
            headers["Authorization"] = f"Bearer {self.api_key}"
        timeout = self.timeout_seconds if timeout_seconds is None else timeout_seconds
        try:
            response = requests.post(
                self.request_url,
                headers=headers,
                json={
                    "model": self.model,
                    "messages": [
                        {"role": "system", "content": system_prompt},
                        {"role": "user", "content": user_prompt},
                    ],
                    "temperature": temperature,
                    "stream": False,
                },
                timeout=timeout,
            )
        except requests.RequestException as exc:
            logger.exception(
                "AI request failed: endpoint=%s model=%s",
                self.request_url,
                self.model,
            )
            raise AITutorError("AI service request failed.") from exc

        if response.status_code < 200 or response.status_code >= 300:
            logger.warning(
                "AI service returned HTTP %s: endpoint=%s model=%s body=%s",
                response.status_code,
                self.request_url,
                self.model,
                response.text[:1000],
            )
            raise AITutorError(f"AI service returned HTTP {response.status_code}.")
        try:
            body = response.json()
            content = body["choices"][0]["message"]["content"]
        except (KeyError, IndexError, TypeError, ValueError) as exc:
            logger.warning(
                "AI service returned an unexpected body: endpoint=%s body=%s",
                self.request_url,
                response.text[:1000],
            )
            raise AITutorError("AI service returned an unexpected response.") from exc
        if not isinstance(content, str) or not content.strip():
            logger.warning(
                "AI service returned empty content: endpoint=%s body=%s",
                self.request_url,
                response.text[:1000],
            )
            raise AITutorError("AI service returned an empty explanation.")
        return content.strip()


def _translation_batches(
    sentences: list[tuple[int, str]],
) -> list[tuple[list[tuple[int, str]], list[tuple[int, str]]]]:
    batches: list[list[tuple[int, str]]] = []
    current: list[tuple[int, str]] = []
    current_chars = 0
    for item in sentences:
        extra = len(item[1]) + 8
        if current and current_chars + extra > _TRANSLATE_BATCH_CHARS:
            batches.append(current)
            current = []
            current_chars = 0
        current.append(item)
        current_chars += extra
    if current:
        batches.append(current)
    result: list[tuple[list[tuple[int, str]], list[tuple[int, str]]]] = []
    consumed = 0
    for batch in batches:
        context_rows = sentences[max(0, consumed - 8):consumed]
        result.append((batch, context_rows))
        consumed += len(batch)
    return result


def _parse_translation_payload(content: str, expected_count: int) -> list[str]:
    text = _JSON_FENCE_RE.sub("", content.strip()).strip()
    try:
        data = json.loads(text)
        parsed = _translations_from_json(data, expected_count)
        if parsed is not None:
            return parsed
    except (TypeError, ValueError):
        pass
    numbered: dict[int, str] = {}
    for line in text.splitlines():
        match = _NUMBERED_LINE_RE.match(line)
        if match is None:
            continue
        numbered[int(match.group(1))] = match.group(2).strip()
    if len(numbered) == expected_count and all(
        index in numbered and numbered[index] for index in range(1, expected_count + 1)
    ):
        return [numbered[index] for index in range(1, expected_count + 1)]
    raise AITutorError("AI translation returned an unexpected response.")


def _translations_from_json(data: object, expected_count: int) -> list[str] | None:
    if isinstance(data, dict):
        for key in ("translations", "sentences", "zh", "items"):
            if key in data:
                data = data[key]
                break
    if isinstance(data, list) and len(data) == expected_count:
        if all(isinstance(item, str) and item.strip() for item in data):
            return [item.strip() for item in data]
        if all(isinstance(item, dict) for item in data):
            values: list[str] = []
            for item in data:
                zh = item.get("zh_text") or item.get("zh") or item.get("text")
                if not isinstance(zh, str) or not zh.strip():
                    return None
                values.append(zh.strip())
            return values
    return None