app.py

import os
import re
import json
import pickle
import threading
import traceback
import requests
import numpy as np
from typing import *
from datetime import datetime

# Web 服务与 HF Hub 依赖
from fastapi import FastAPI
import uvicorn
from huggingface_hub import HfApi, hf_hub_download

# ==========================================
# [新增] 导入我们刚刚制作的单文件下载器
# ==========================================
from tensoul_2 import download_paipu

# 底层特征引擎 (Teacher)
from libriichi3p.mjai import Bot as RiichiBot
from libriichi3p.consts import ACTION_SPACE

# 底层特征引擎 (Student)
try:
    from libriichiSanma import state as sanma_state
except ImportError:
    import libriichi as sanma_state

# ==========================================
# [配置与环境变量]
# ==========================================
HF_TOKEN = os.environ.get("HF_TOKEN", "")
DATASET_REPO = os.environ.get("DATASET_REPO", "AstraNASA/tenhou-scc") 
URL_LIST_FILE = os.environ.get("URL_LIST_FILE", "shangfenliu_logs.txt")

MASK_3P = [
    "1m", "2m", "3m", "4m", "5m", "6m", "7m", "8m", "9m",
    "1p", "2p", "3p", "4p", "5p", "6p", "7p", "8p", "9p",
    "1s", "2s", "3s", "4s", "5s", "6s", "7s", "8s", "9s",
    "E",  "S",  "W",  "N",  "P",  "F",  "C",
    '5mr', '5pr', '5sr', 
    'reach', 'pon', 'kan', 'nukidora', 'hora', 'ryukyoku', 'none'
]

NONE_CODE = MASK_3P.index('none')
KAN_CODE = MASK_3P.index('kan')
_thread_local = threading.local()

worker_status = {
    "status": "Starting up...",
    "urls_processed": 0,
    "total_chunks_uploaded": 0,
    "total_records_extracted": 0,
    "current_target": "",
    "errors": 0
}

# ==========================================
# [解析器] 保持不变
# ==========================================
class TenhouParser:
    @staticmethod
    def tile_name(x):
        if x in (51, 52, 53): return ['5mr', '5pr', '5sr'][x - 51]
        num, suit = x % 10, x // 10
        if suit in (1, 2, 3): return str(num) + 'mps'[suit - 1]
        if suit == 4: return 'ESWNPFC'[num - 1]
        return '?'

    @classmethod
    def get_meld_tiles(cls, actor, s):
        i, player = 0, 0
        result = {'pai': [], 'consumed': [], 'actor': actor}
        while i < len(s):
            player += 1
            tile_type = 'consumed'
            if s[i] in 'cpmakf':
                tile_type = 'pai'
                result['type'] = ['chi', 'pon', 'daiminkan', 'ankan', 'kakan', 'nukidora']['cpmakf'.index(s[i])]
                if s[i] in 'cpm':
                    result['target'] = (4 - player + actor) % 4
                i += 1
            result[tile_type].append(cls.tile_name(int(s[i:i+2])))
            i += 2
        result['pai'] = result['pai'][0]
        if result.get('type') == 'ankan': result['consumed'].append(result['pai'])
        return result

    @classmethod
    def parse_events(cls, actor, income, outcome):
        incoming, outcoming = [], []
        for i, event in enumerate(income):
            if type(event) is str: incoming.append(cls.get_meld_tiles(actor, event))
            else: incoming.append({'type': 'tsumo', 'pai': cls.tile_name(event), 'actor': actor})
        for i, event in enumerate(outcome):
            if type(event) is str and event[0] != 'r':
                outcoming.append(cls.get_meld_tiles(actor, event))
            else:
                if event == 0:
                    outcoming.append({'type': 'empty'})
                    continue
                reach = False
                if type(event) is str and event[0] == 'r':
                    reach, event = True, int(event[1:])
                    outcoming.append({'type': 'reach', 'actor': actor})
                outcoming.append({'type': 'dahai', 'pai': cls.tile_name(event if event != 60 else income[i]), 'actor': actor, 'tsumogiri': event == 60})
                if reach: outcoming.append({'type': 'reach_accepted', 'actor': actor})
        return incoming, outcoming

    @classmethod
    def merge_events(cls, oya, events, dora_markers):
        current, result = oya, []
        def finished(x): return all(len(i[0]) == 0 and len(i[1]) == 0 for i in x)
        while not finished(events):
            income, outcome = events[current]
            nuki = False
            if len(income):
                result.append(income.pop(0))
                if result[-1]['type'] == 'daiminkan':
                    if len(dora_markers) > 0: result.append({'type': 'dora', 'dora_marker': cls.tile_name(dora_markers.pop(0))})
                    outcome.pop(0)
                    continue
            if len(outcome):
                result.append(outcome.pop(0))
                pai, t = result[-1].get('pai'), result[-1]['type']
                if t == 'reach':
                    result.append(outcome.pop(0))
                    pai = result[-1].get('pai')
                    result.append(outcome.pop(0))
                nuki = False
                for actor, x in enumerate(events):
                    if actor == current or len(x[1]) == 0: continue
                    if x[0][0]['type'] != 'tsumo' and x[0][0].get('pai') == pai and not (x[0][0]['type'] == 'chi' and not (x[0][0]['actor'] + 3) % 4 == actor):
                        nuki, current = True, actor
                        break
                if t in ('ankan', 'kakan', 'nukidora'):
                    if t != 'nukidora' and len(dora_markers) > 0: result.append({'type': 'dora', 'dora_marker': cls.tile_name(dora_markers.pop(0))})
                    nuki = True
            if not nuki: current = (current + 1) % 4
        return result

    @classmethod
    def parse_single_round(cls, data):
        round_info, scores, dora_markers, uradora, result_info = data[0], data[1], data[2], data[3], data[-1]
        oya = round_info[0] % 4
        patch = lambda arr: arr if len(arr) >= 13 else [0] * 13
        events = [{
            'type': 'start_kyoku', 'bakaze': 'ESWN'[round_info[0] // 4], 'kyoku': oya + 1,
            'honba': round_info[1], 'kyotaku': round_info[2], 'oya': oya,
            'dora_marker': cls.tile_name(dora_markers.pop(0)), 'scores': scores,
            # 这里的 data[13] 在三麻时虽然是结果数据，但 patch() 会把它变成 13 个 '?'
            'tehais': [[cls.tile_name(i) for i in patch(data[k])] for k in [4, 7, 10, 13]]
        }]
        
        # 还原原版的做法：分别解析，只在四麻时解析第四个玩家
        e1 = cls.parse_events(0, data[5], data[6])
        e2 = cls.parse_events(1, data[8], data[9])
        e3 = cls.parse_events(2, data[11], data[12])
        e4 = ([], [])
        if len(data) >= 15:
            e4 = cls.parse_events(3, data[14], data[15])
            
        e_list = [e1, e2, e3, e4]
        events += cls.merge_events(oya, e_list, dora_markers)
        
        last_type = events[-1]['type']
        if last_type == 'tsumo' and result_info[0] == '和了': 
            events.append({'type': 'hora', 'actor': events[-1]['actor'], 'target': events[-1]['actor']})
        elif result_info[0] == '和了':
            actor = next(i for i, x in enumerate(result_info[1]) if x > 0)
            events.append({'type': 'hora', 'actor': actor, 'target': actor}) 
        elif last_type == 'tsumo' or '九牌' in result_info[0]: 
            events.append({'type': 'ryukyoku', 'actor': events[-1]['actor']})
            
        return events
        
    @classmethod
    def parse_log(cls, log):
        scores = log.get('sc', [])
        weights = [1.0, 1.0, 1.0] 
        seat = log['name'].index('私') if '私' in log['name'] else -1
        parsed_rounds = []
        for i in log['log'][:]:
            round_events = [{"type": "start_game", "id": seat, "weight": weights}] + cls.parse_single_round(i)
            parsed_rounds.append(round_events)
        return parsed_rounds

# ==========================================
# [特征拦截假引擎 (Teacher)]
# ==========================================
class DummyFeatureEngine:
    def __init__(self):
        self.engine_type = 'mortal'
        self.name = 'DataMiner'
        self.version = 4
        self.is_oracle = False
        self.enable_quick_eval = True
        self.enable_rule_based_agari_guard = True

    def react_batch(self, obs, masks, invisible_obs):
        _thread_local.interception = (obs, masks, invisible_obs)
        batch_size = len(obs)
        actions, q_outs, pure_masks = [], [], []
        
        for m in masks:
            m_list = m.tolist() if hasattr(m, 'tolist') else list(m)
            pure_masks.append(m_list)
            try: valid_action = m_list.index(True)
            except ValueError: valid_action = 0
            actions.append(valid_action)
            q_outs.append([0.0] * len(m_list))
        return actions, q_outs, pure_masks, [True] * batch_size

# ==========================================
# [双重特征打包架构 (Distillation)]
# ==========================================
class FeatureEncoder:
    def __init__(self, chunk_size=2048, pool_size=8): 
        self.chunk_size = chunk_size
        self.pool_size = pool_size
        self.inputs, self.outputs, self.weights = [], [], []
        self.chunk_count = 0
        self.hf_api = HfApi(token=HF_TOKEN) if HF_TOKEN else None
        
        self.local_pool_dir = "local_chunks_pool"
        os.makedirs(self.local_pool_dir, exist_ok=True)

    @staticmethod
    def action_to_mask(who, action):
        if action is None: return NONE_CODE
        if type(action) is str: action = json.loads(action)
        if action.get('actor') != who or action.get('type') == 'tsumo': return NONE_CODE
        if action['type'] == 'dahai': return MASK_3P.index(action['pai'])
        if action['type'] in ('daiminkan', 'ankan', 'kakan'): return KAN_CODE
        if action['type'] in MASK_3P: return MASK_3P.index(action['type'])
        raise Exception(f"Unknown action map: {action}")

    def save_and_check_upload(self):
        if not self.inputs: return
        
        filename = f"chunk_distill_{datetime.now().strftime('%Y%m%d_%H%M%S')}_{self.chunk_count}.pkl"
        filepath = os.path.join(self.local_pool_dir, filename)
        
        with open(filepath, 'wb') as f:
            pickle.dump({'inputs': self.inputs, 'outputs': self.outputs, 'weights': self.weights}, f)
            
        print(f"📦 已生成蒸馏缓存: {filename} ({len(self.inputs)} records).")
        
        self.chunk_count += 1
        self.inputs.clear()
        self.outputs.clear()
        self.weights.clear()

        current_files = os.listdir(self.local_pool_dir)
        if len(current_files) >= self.pool_size:
            self.upload_pool()

    def upload_pool(self):
        current_files = os.listdir(self.local_pool_dir)
        if not current_files or not self.hf_api or not DATASET_REPO: return

        import time
        print(f"🚀 本地池满，正在批量上传 {len(current_files)} 个文件...")
        
        for attempt in range(6):
            try:
                self.hf_api.upload_folder(
                    folder_path=self.local_pool_dir,
                    path_in_repo="distill_better_3",
                    repo_id=DATASET_REPO,
                    repo_type="dataset"
                )
                print(f"✅ 上传成功 (Attempt {attempt + 1}).")
                worker_status["total_chunks_uploaded"] += len(current_files)
                for f in current_files: os.remove(os.path.join(self.local_pool_dir, f))
                break
            except Exception as e:
                wait_time = 5 * (2 ** attempt) 
                print(f"⚠️ Upload failed: {e}. Waiting {wait_time}s...")
                time.sleep(wait_time)

    def process_game(self, events):
        who = -1
        current_weight = 1.0 
        
        ps_student = None
        bot_teacher = None
        
        for i, event in enumerate(events):
            if event.get('type') == 'start_game': 
                who = event['id']
                weights_list = event.get('weight', [1.0, 1.0, 1.0])
                current_weight = weights_list[who]
                
                # 初始化双模型状态机
                ps_student = sanma_state.PlayerState(who)
                bot_teacher = RiichiBot(DummyFeatureEngine(), who)

            if ps_student is None or bot_teacher is None:
                continue

            if event.get('type') == 'end_game':
                continue
                
            next_event = None
            for j in range(i + 1, len(events)):
                if events[j].get('type') not in ('dora', 'reach_accepted'):
                    next_event = events[j]; break
            
            event_str = json.dumps(event, separators=(",", ":"))
            
            # --- 1. Teacher 更新与拦截 ---
            _thread_local.interception = None 
            bot_teacher.react(event_str)
            intercepted = getattr(_thread_local, 'interception', None)
            
            # --- 2. Student 更新与特征生成 ---
            cans = ps_student.update(event_str)
            
            if intercepted is None or not cans.can_act: 
                continue
                
            obs_t, masks_t, _ = intercepted
            obs_s, mask_s = ps_student.encode_obs(4, False)
            
            valid_actions_count = int(np.count_nonzero(masks_t[0]))
            if valid_actions_count <= 1: 
                continue
            
            try:
                output_code = self.action_to_mask(who, next_event)
                
                # 存入字典，解耦新老数据格式
                self.inputs.append({
                    "obs_student": obs_s,
                    "mask_student": mask_s,
                    "obs_teacher": obs_t[0],     # 去除 batch 维度
                    "mask_teacher": masks_t[0]
                })
                self.outputs.append(output_code)
                self.weights.append(current_weight) 
                
                worker_status["total_records_extracted"] += 1
            except Exception: pass
            
            if len(self.inputs) >= self.chunk_size:
                self.save_and_check_upload()

# ==========================================
# [数据挖掘总管线]
# ==========================================
def worker_pipeline():
    if not HF_TOKEN or not DATASET_REPO:
        worker_status["status"] = "Error: HF_TOKEN or DATASET_REPO missing!"
        return

    worker_status["status"] = "Fetching target URL list..."
    try:
        url_file_path = hf_hub_download(repo_id=DATASET_REPO, filename=URL_LIST_FILE, repo_type="dataset", token=HF_TOKEN)
        with open(url_file_path, 'r') as f: target_urls = [line.strip() for line in f if line.strip()]
    except Exception as e:
        worker_status["status"] = f"Failed to fetch {URL_LIST_FILE}: {e}"
        return

    encoder = FeatureEncoder(chunk_size=2048, pool_size=8)
    worker_status["status"] = "Mining..."

    for url in target_urls:
        worker_status["current_target"] = url
        
        try:
            # 1. 使用统一下载接口（支持天凤/雀魂自动识别）
            paipu_json_str = download_paipu(url)
            paipu_data = json.loads(paipu_json_str)
            
            # 2. 从解析后的数据中提取目标玩家座位 (0, 1, 2)
            target_seat = paipu_data.get('pov', -1)
            
            # 3. 将统一的 JSON 数据喂给解析器
            parsed_games = TenhouParser.parse_log(paipu_data)
            
            for game in parsed_games:
                # 默认是三麻，遍历3个座位
                for j in range(3):
                    # 如果能解析出固定视角(target_seat != -1)，就只处理那个视角的特征，丢弃其他视角的冗余计算
                    if target_seat != -1 and j != target_seat: 
                        continue
                    
                    # 动态注入当前处理的视角ID
                    game[0]['id'] = j
                    encoder.process_game(game)
                    
            worker_status["urls_processed"] += 1
        except Exception as e:
            print(f"Error processing {url}: {e}")
            traceback.print_exc()
            worker_status["errors"] += 1

    encoder.save_and_check_upload()
    encoder.upload_pool() 
    worker_status["status"] = "Finished! All URLs processed."
    worker_status["current_target"] = "Idle"

app = FastAPI()
@app.get("/")
def read_status(): return worker_status

if __name__ == '__main__':
    thread = threading.Thread(target=worker_pipeline, daemon=True)
    thread.start()
    uvicorn.run(app, host="0.0.0.0", port=7860)