from __future__ import annotations

import base64
import json
import mimetypes
import os
import re
import time
from collections.abc import Iterator
from pathlib import Path
from typing import Any

from src.character_registry import build_tavern_system_prompt, get_character
from src.persona_skills import select_skill
from src.stream_protocol import normalize_event, parse_sse_lines
from src.tts_engine import synthesize_sentence


DEFAULT_VLLM_BASE_URL = "https://veronicaulises0--virtual-characters-vllm-gemma-serve.modal.run"
STAGE_TAG_RE = re.compile(
    r"<stage\s+expression=\"(?P<expression>[a-z_]+)\"\s+motion=\"(?P<motion>[a-z_]+)\"\s+intensity=\"(?P<intensity>[0-9.]+)\"\s*>"
)


def stream_reply(
    user_text: str,
    history: list[dict],
    state: dict,
    media_inputs: dict[str, list[dict[str, Any]]] | None = None,
    voice_state: dict[str, Any] | None = None,
) -> Iterator[dict]:
    media_inputs = media_inputs or {"images": []}
    if os.environ.get("VC_USE_MOCK") == "1":
        yield from _stream_mock_reply(user_text, state, voice_state)
        return

    modal_url = os.environ.get("VC_MODAL_LLM_URL")
    if modal_url:
        yield from _stream_modal_reply(modal_url, user_text, history, state, media_inputs, voice_state)
        return

    vllm_url = os.environ.get("VC_MODAL_VLLM_URL") or DEFAULT_VLLM_BASE_URL
    if vllm_url:
        yield from _stream_vllm_reply(vllm_url, user_text, history, state, media_inputs, voice_state)
        return

    yield from _stream_mock_reply(user_text, state, voice_state)


def _stream_modal_reply(
    url: str,
    user_text: str,
    history: list[dict],
    state: dict,
    media_inputs: dict[str, list[dict[str, Any]]],
    voice_state: dict[str, Any] | None,
) -> Iterator[dict]:
    import httpx

    character = state.get("character") or get_character(state.get("character_id", "star_knight"))
    payload = {
        "text": user_text,
        "history": history[-8:],
        "character": character,
        "vision_note": state.get("last_vision_note"),
        "image_urls": [_data_url(item["path"]) for item in media_inputs.get("images", []) if item.get("path")],
        "max_new_tokens": 180,
    }
    last_stage = state.get("stage", {})
    with httpx.stream("POST", url, json=payload, timeout=180) as response:
        response.raise_for_status()
        for event in parse_sse_lines(response.iter_lines()):
            if event.get("type") == "stage":
                last_stage = {**last_stage, **event}
            yield event
            if event.get("type") == "sentence_end" and _voice_enabled(voice_state):
                yield from _synthesize_audio_event(event.get("text", ""), character, voice_state or state.get("voice", {}))
    settle = _settled_stage(last_stage)
    if settle:
        yield settle


def _stream_vllm_reply(
    base_url: str,
    user_text: str,
    history: list[dict],
    state: dict,
    media_inputs: dict[str, list[dict[str, Any]]] | None,
    voice_state: dict[str, Any] | None,
) -> Iterator[dict]:
    import httpx

    media_inputs = media_inputs or {"images": []}
    character = state.get("character") or get_character(state.get("character_id", "star_knight"))
    skill = select_skill(user_text, character)
    voice = {**character.get("voice", {}), **(voice_state or {})}
    reply_text = ""
    last_stage = {"type": "stage", "expression": "thinking", "motion": "look_at_user", "intensity": 0.55}

    yield normalize_event({"type": "skill", "name": skill})
    yield normalize_event(last_stage)
    yield normalize_event({"type": "voice", "style": voice.get("style", "soft"), "speed": voice.get("speed", 0.96), "energy": voice.get("energy", 0.5)})
    yield {"type": "debug", "message": f"Modal vLLM endpoint: {base_url.rstrip('/')}"}

    payload = {
        "model": os.environ.get("VC_VLLM_SERVED_MODEL", "llm"),
        "messages": _build_openai_messages(user_text, history, character, skill, state.get("last_vision_note"), media_inputs),
        "max_tokens": int(os.environ.get("VC_VLLM_MAX_TOKENS", "220")),
        "temperature": float(os.environ.get("VC_VLLM_TEMPERATURE", "0.75")),
        "stream": True,
        "chat_template_kwargs": {"enable_thinking": False},
    }
    timeout = httpx.Timeout(connect=20, read=float(os.environ.get("VC_VLLM_READ_TIMEOUT", "600")), write=20, pool=20)
    parser_state = {"pending_stage": True, "buffer": "", "started_text": False}
    url = base_url.rstrip("/") + "/v1/chat/completions"

    max_attempts = int(os.environ.get("VC_VLLM_RETRIES", "2"))
    for attempt in range(1, max_attempts + 1):
        try:
            with httpx.Client(timeout=timeout, trust_env=False) as client:
                with client.stream("POST", url, json=payload) as response:
                    response.raise_for_status()
                    for raw_line in response.iter_lines():
                        if not raw_line:
                            continue
                        line = raw_line.strip()
                        if line.startswith("data:"):
                            line = line[5:].strip()
                        if line == "[DONE]":
                            break
                        try:
                            chunk = json.loads(line)
                        except json.JSONDecodeError:
                            yield {"type": "debug", "message": "invalid vLLM stream chunk", "raw": line[:200]}
                            continue

                        for text in _extract_delta_text(chunk):
                            for event in _events_from_vllm_delta(text, parser_state):
                                if event["type"] == "text_delta":
                                    reply_text += event["text"]
                                elif event["type"] == "stage":
                                    last_stage = {**last_stage, **event}
                                yield event
            break
        except Exception as exc:
            if attempt < max_attempts and not reply_text:
                parser_state = {"pending_stage": True, "buffer": "", "started_text": False}
                yield {"type": "debug", "message": f"Modal vLLM retry {attempt}/{max_attempts}: {exc}"}
                time.sleep(2 * attempt)
                continue
            worried = normalize_event({"type": "stage", "expression": "worried", "motion": "gentle_blink", "intensity": 0.8})
            yield worried
            yield {"type": "error", "message": f"Modal vLLM 调用失败：{exc}"}
            yield from _stream_mock_reply(user_text, state, voice_state)
            return

    for event in _flush_vllm_parser(parser_state):
        if event["type"] == "text_delta":
            reply_text += event["text"]
        elif event["type"] == "stage":
            last_stage = {**last_stage, **event}
        yield event

    clean_reply = reply_text.strip()
    if clean_reply:
        yield normalize_event({"type": "sentence_end", "text": clean_reply})
        if _voice_enabled(voice_state):
            yield from _synthesize_audio_event(clean_reply, character, voice_state or state.get("voice", {}))
    settle = _settled_stage(last_stage)
    if settle:
        yield settle
    yield {"type": "done"}


def _build_openai_messages(
    user_text: str,
    history: list[dict],
    character: dict,
    skill: str,
    vision_note: str | None,
    media_inputs: dict[str, list[dict[str, Any]]] | None = None,
) -> list[dict[str, Any]]:
    system_prompt = build_tavern_system_prompt(
        character,
        skill=skill,
        vision_note=vision_note,
        include_examples=len(history) < 8,
    )

    messages: list[dict[str, Any]] = [{"role": "system", "content": system_prompt}]
    for item in history[-10:]:
        role = item.get("role")
        content = item.get("content")
        if role not in {"user", "assistant"}:
            continue
        if isinstance(content, list):
            continue
        content_text = str(content or "").strip()
        if content_text:
            messages.append({"role": role, "content": content_text})

    user_content = _build_user_content(user_text, media_inputs or {"images": []})
    messages.append({"role": "user", "content": user_content})
    return messages


def _build_user_content(user_text: str, media_inputs: dict[str, list[dict[str, Any]]]) -> str | list[dict[str, Any]]:
    content: list[dict[str, Any]] = []
    if user_text.strip():
        content.append({"type": "text", "text": user_text.strip()})
    for item in media_inputs.get("images", []):
        path = item.get("path")
        if path:
            content.append({"type": "image_url", "image_url": {"url": _data_url(path)}})
    if not content:
        return user_text
    return content


def _extract_delta_text(chunk: dict) -> Iterator[str]:
    for choice in chunk.get("choices") or []:
        delta = choice.get("delta") or {}
        text = delta.get("content") or delta.get("reasoning") or delta.get("reasoning_content")
        if text:
            yield str(text)


def _events_from_vllm_delta(text: str, parser_state: dict) -> Iterator[dict]:
    if not parser_state.get("pending_stage"):
        if not parser_state.get("started_text"):
            text = text.lstrip()
            if not text:
                return
            parser_state["started_text"] = True
        yield normalize_event({"type": "text_delta", "text": text})
        return

    parser_state["buffer"] += text
    buffered = parser_state["buffer"]
    stripped = buffered.lstrip()
    if not (stripped.startswith("<stage") or "<stage".startswith(stripped)):
        parser_state["pending_stage"] = False
        yield normalize_event({"type": "stage", "expression": "smile", "motion": "talk", "intensity": 0.55})
        text = buffered.lstrip()
        if text:
            parser_state["started_text"] = True
            yield normalize_event({"type": "text_delta", "text": text})
        parser_state["buffer"] = ""
        return

    if ">" not in stripped:
        if len(stripped) > 180:
            parser_state["pending_stage"] = False
            yield normalize_event({"type": "stage", "expression": "smile", "motion": "talk", "intensity": 0.55})
            text = buffered.lstrip()
            if text:
                parser_state["started_text"] = True
                yield normalize_event({"type": "text_delta", "text": text})
            parser_state["buffer"] = ""
        return

    tag, rest = stripped.split(">", 1)
    tag = tag + ">"
    parser_state["pending_stage"] = False
    parser_state["buffer"] = ""
    yield _parse_stage_tag(tag)
    text = rest.lstrip()
    if text:
        parser_state["started_text"] = True
        yield normalize_event({"type": "text_delta", "text": text})


def _flush_vllm_parser(parser_state: dict) -> Iterator[dict]:
    if parser_state.get("pending_stage") and parser_state.get("buffer"):
        parser_state["pending_stage"] = False
        yield normalize_event({"type": "stage", "expression": "smile", "motion": "talk", "intensity": 0.55})
        text = parser_state["buffer"].lstrip()
        if text:
            parser_state["started_text"] = True
            yield normalize_event({"type": "text_delta", "text": text})
        parser_state["buffer"] = ""


def _parse_stage_tag(tag: str) -> dict:
    match = STAGE_TAG_RE.fullmatch(tag.strip())
    if not match:
        return normalize_event({"type": "stage", "expression": "smile", "motion": "talk", "intensity": 0.55})
    return normalize_event(
        {
            "type": "stage",
            "expression": match.group("expression"),
            "motion": match.group("motion"),
            "intensity": match.group("intensity"),
        }
    )


def _stream_mock_reply(user_text: str, state: dict, voice_state: dict[str, Any] | None = None) -> Iterator[dict]:
    character = state.get("character") or get_character(state.get("character_id", "star_knight"))
    skill = select_skill(user_text, character)
    voice = {**character.get("voice", {}), **(voice_state or {})}
    yield normalize_event({"type": "skill", "name": skill})
    yield normalize_event({"type": "stage", "expression": "thinking", "motion": "look_at_user", "intensity": 0.45})
    yield normalize_event({"type": "voice", "style": voice.get("style", "soft"), "speed": voice.get("speed", 0.96), "energy": voice.get("energy", 0.5)})

    if skill == "emotional_support":
        reply = "我在听。你不用马上把自己整理好，先把这口气慢慢呼出来。"
        last_stage = normalize_event({"type": "stage", "expression": "worried", "motion": "gentle_blink", "intensity": 0.7})
        yield last_stage
    elif skill == "battle_focus":
        reply = "收到。先确认边界，再判断风险。你站在我身后就好。"
        last_stage = normalize_event({"type": "stage", "expression": "thinking", "motion": "focus", "intensity": 0.8})
        yield last_stage
    elif skill == "playful_reframe":
        reply = "这听起来像一个支线任务。目标很小，但奖励可能意外地不错。"
        last_stage = normalize_event({"type": "stage", "expression": "smile", "motion": "soft_sway", "intensity": 0.7})
        yield last_stage
    else:
        catchphrase = (character.get("dialogue_style", {}).get("catchphrases") or ["我在。"])[0]
        reply = f"{catchphrase} 你刚才说的我记下了，我们可以从最容易的一步开始。"
        last_stage = normalize_event({"type": "stage", "expression": "smile", "motion": "gentle_blink", "intensity": 0.5})
        yield last_stage

    for part in _chunk_text(reply):
        yield normalize_event({"type": "text_delta", "text": part})
    yield normalize_event({"type": "sentence_end", "text": reply})
    if _voice_enabled(voice_state):
        yield from _synthesize_audio_event(reply, character, voice_state or state.get("voice", {}))
    settle = _settled_stage(last_stage)
    if settle:
        yield settle
    yield {"type": "done"}


def _chunk_text(text: str, size: int = 4) -> Iterator[str]:
    for index in range(0, len(text), size):
        yield text[index : index + size]


def _settled_stage(stage: dict[str, Any] | None) -> dict[str, Any] | None:
    if not stage:
        return None
    if stage.get("motion") != "talk":
        return None
    return normalize_event(
        {
            "type": "stage",
            "expression": stage.get("expression", "smile"),
            "motion": "gentle_blink",
            "intensity": min(float(stage.get("intensity", 0.55)), 0.6),
        }
    )


def _voice_enabled(voice_state: dict[str, Any] | None) -> bool:
    if not voice_state:
        return True
    return bool(voice_state.get("enabled", True))


def _synthesize_audio_event(text: str, character: dict, voice_state: dict[str, Any]) -> Iterator[dict]:
    try:
        audio = synthesize_sentence(text, character, voice_state)
    except Exception as exc:
        yield {"type": "error", "message": f"TTS 调用失败：{exc}"}
        return
    if audio:
        yield {"type": "audio", "path": audio}


def _data_url(path_value: str | Path) -> str:
    path = Path(path_value)
    mime_type = mimetypes.guess_type(path.name)[0] or "application/octet-stream"
    return f"data:{mime_type};base64,{_base64_file(path)}"


def _base64_file(path_value: str | Path) -> str:
    return base64.b64encode(Path(path_value).read_bytes()).decode("ascii")