"""
完整测试 DashScope ASR 批量识别
按照官方文档要求测试
"""
import os
import sys
import time
import logging

# 添加 lover 目录到路径
sys.path.insert(0, os.path.join(os.path.dirname(__file__), 'lover'))

from lover.config import settings
from lover.oss_utils import upload_audio_file, delete_audio_file
import dashscope
from dashscope.audio.asr import Transcription

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

def create_test_audio():
    """创建一个简单的测试音频（PCM 格式）"""
    # 生成 1 秒的 16kHz 单声道 PCM 数据
    # 简单的正弦波
    import struct
    import math
    
    sample_rate = 16000
    duration = 2  # 2 秒
    frequency = 440  # A4 音符
    
    samples = []
    for i in range(sample_rate * duration):
        # 生成正弦波
        value = int(32767 * 0.3 * math.sin(2 * math.pi * frequency * i / sample_rate))
        samples.append(struct.pack('<h', value))
    
    return b''.join(samples)

def test_dashscope_asr():
    """测试 DashScope ASR 完整流程"""
    print("=" * 60)
    print("测试 DashScope ASR 批量识别")
    print("=" * 60)
    
    # 检查配置
    print(f"\n📋 检查配置:")
    if not settings.DASHSCOPE_API_KEY:
        print("❌ 未配置 DASHSCOPE_API_KEY")
        return False
    
    print(f"  API Key: {settings.DASHSCOPE_API_KEY[:10]}***")
    print(f"  Bucket: {settings.ALIYUN_OSS_BUCKET_NAME}")
    
    # 设置 API Key
    dashscope.api_key = settings.DASHSCOPE_API_KEY
    
    # 创建测试音频
    print(f"\n🎵 创建测试音频...")
    audio_data = create_test_audio()
    print(f"  音频大小: {len(audio_data)} 字节")
    print(f"  预期时长: 2 秒")
    
    # 上传到 OSS
    print(f"\n📤 上传音频到 OSS...")
    try:
        # 转换为 WAV 格式
        import wave
        import tempfile
        
        with tempfile.NamedTemporaryFile(suffix='.wav', delete=False) as temp_file:
            with wave.open(temp_file.name, 'wb') as wav_file:
                wav_file.setnchannels(1)  # 单声道
                wav_file.setsampwidth(2)  # 16-bit
                wav_file.setframerate(16000)  # 16kHz
                wav_file.writeframes(audio_data)
            
            temp_file_path = temp_file.name
        
        # 读取 WAV 文件
        with open(temp_file_path, 'rb') as f:
            wav_data = f.read()
        
        # 清理临时文件
        os.unlink(temp_file_path)
        
        file_url = upload_audio_file(wav_data, "wav")
        print(f"✅ 上传成功")
        print(f"  URL: {file_url}")
        
        # 验证 URL 格式
        if not (file_url.startswith('https://') or file_url.startswith('http://')):
            print(f"❌ URL 格式错误: {file_url}")
            return False
        
    except Exception as e:
        print(f"❌ 上传失败: {e}")
        import traceback
        traceback.print_exc()
        return False
    
    # 调用 ASR
    print(f"\n🎤 调用 DashScope ASR...")
    try:
        print(f"  模型: paraformer-v2")
        print(f"  文件: {file_url}")
        
        # 创建任务
        task_response = Transcription.async_call(
            model='paraformer-v2',
            file_urls=[file_url],
            parameters={
                'format': 'wav',
                'sample_rate': 16000,
                'enable_words': False
            }
        )
        
        print(f"\n📋 任务响应:")
        print(f"  状态码: {task_response.status_code}")
        
        if task_response.status_code != 200:
            error_msg = getattr(task_response, 'message', 'Unknown error')
            print(f"❌ 任务创建失败: {error_msg}")
            
            # 打印详细错误信息
            if hasattr(task_response, 'output'):
                print(f"  错误详情: {task_response.output}")
            
            return False
        
        task_id = task_response.output.task_id
        print(f"✅ 任务创建成功")
        print(f"  任务 ID: {task_id}")
        
        # 等待结果
        print(f"\n⏳ 等待识别完成...")
        max_wait = 30
        start_time = time.time()
        
        while time.time() - start_time < max_wait:
            result = Transcription.wait(task=task_id)
            
            if result.status_code == 200:
                task_status = result.output.task_status
                print(f"  任务状态: {task_status}")
                
                if task_status == "SUCCEEDED":
                    print(f"\n✅ 识别成功")
                    
                    # 解析结果
                    if hasattr(result.output, 'results') and result.output.results:
                        print(f"\n📝 识别结果:")
                        
                        for i, item in enumerate(result.output.results):
                            if isinstance(item, dict) and 'transcription_url' in item:
                                transcription_url = item['transcription_url']
                                print(f"  转录 URL: {transcription_url}")
                                
                                # 下载转录结果
                                import requests
                                resp = requests.get(transcription_url, timeout=10)
                                if resp.status_code == 200:
                                    transcription_data = resp.json()
                                    print(f"  转录数据: {transcription_data}")
                                    
                                    if 'transcripts' in transcription_data:
                                        for transcript in transcription_data['transcripts']:
                                            if 'text' in transcript:
                                                print(f"  识别文本: {transcript['text']}")
                    else:
                        print(f"⚠️  未找到识别结果")
                        print(f"  输出: {result.output}")
                    
                    break
                    
                elif task_status == "FAILED":
                    error_code = getattr(result.output, 'code', 'Unknown')
                    error_message = getattr(result.output, 'message', 'Unknown error')
                    print(f"\n❌ 识别失败")
                    print(f"  错误码: {error_code}")
                    print(f"  错误信息: {error_message}")
                    
                    # 分析常见错误
                    if error_code == "SUCCESS_WITH_NO_VALID_FRAGMENT":
                        print(f"\n💡 原因分析:")
                        print(f"  - 音频中未检测到有效语音")
                        print(f"  - 这是正常的，因为我们使用的是测试音频（正弦波）")
                        print(f"  - 使用真实语音录音应该可以正常识别")
                    elif error_code == "FILE_DOWNLOAD_FAILED":
                        print(f"\n💡 原因分析:")
                        print(f"  - DashScope 无法下载 OSS 文件")
                        print(f"  - 检查 OSS Bucket 权限设置")
                        print(f"  - 确保文件 URL 可公开访问")
                    
                    break
                else:
                    # 继续等待
                    time.sleep(2)
            else:
                print(f"❌ 查询失败: {result.status_code}")
                break
        
        if time.time() - start_time >= max_wait:
            print(f"\n⏰ 等待超时（{max_wait}秒）")
            return False
        
    except Exception as e:
        print(f"❌ ASR 调用失败: {e}")
        import traceback
        traceback.print_exc()
        return False
    
    finally:
        # 清理 OSS 文件
        print(f"\n🗑️  清理 OSS 文件...")
        try:
            delete_audio_file(file_url)
            print(f"✅ 文件已删除")
        except Exception as e:
            print(f"⚠️  删除失败: {e}")
    
    print(f"\n" + "=" * 60)
    print("🎉 DashScope ASR 测试完成！")
    print("=" * 60)
    
    print(f"\n📚 官方文档要求总结:")
    print(f"  1. ✅ 文件必须通过 HTTPS URL 访问")
    print(f"  2. ✅ 支持的格式: WAV, MP3, PCM 等")
    print(f"  3. ✅ 推荐采样率: 16kHz")
    print(f"  4. ✅ 推荐声道: 单声道")
    print(f"  5. ⚠️  音频必须包含有效语音内容")
    
    return True

if __name__ == "__main__":
    success = test_dashscope_asr()
    sys.exit(0 if success else 1)