"""
OpenAI Router - Handles OpenAI format API requests via Antigravity
通过Antigravity处理OpenAI格式请求的路由模块
"""

import sys
from pathlib import Path

# 添加项目根目录到Python路径
project_root = Path(__file__).resolve().parent.parent.parent.parent
if str(project_root) not in sys.path:
    sys.path.insert(0, str(project_root))

# 标准库
import asyncio
import json

# 第三方库
from fastapi import APIRouter, Depends, HTTPException
from fastapi.responses import JSONResponse, StreamingResponse

# 本地模块 - 配置和日志
from config import get_anti_truncation_max_attempts
from log import log

# 本地模块 - 工具和认证
from src.utils import (
    get_base_model_from_feature_model,
    is_anti_truncation_model,
    is_fake_streaming_model,
    authenticate_bearer,
)

# 本地模块 - 转换器（假流式需要）
from src.converter.fake_stream import (
    parse_response_for_fake_stream,
    build_openai_fake_stream_chunks,
    create_openai_heartbeat_chunk,
)

# 本地模块 - 基础路由工具
from src.router.hi_check import is_health_check_request, create_health_check_response
from src.router.stream_passthrough import (
    build_streaming_response_or_error,
    prepend_async_item,
    read_first_async_item,
)

# 本地模块 - 数据模型
from src.models import OpenAIChatCompletionRequest, model_to_dict

# 本地模块 - 任务管理
from src.task_manager import create_managed_task


# ==================== 路由器初始化 ====================

router = APIRouter()


# ==================== API 路由 ====================

@router.post("/antigravity/v1/chat/completions")
async def chat_completions(
    openai_request: OpenAIChatCompletionRequest,
    token: str = Depends(authenticate_bearer)
):
    """
    处理OpenAI格式的聊天完成请求（流式和非流式）

    Args:
        openai_request: OpenAI格式的请求体
        token: Bearer认证令牌
    """
    log.debug(f"[ANTIGRAVITY-OPENAI] Request for model: {openai_request.model}")

    # 转换为字典
    normalized_dict = model_to_dict(openai_request)

    # 健康检查
    if is_health_check_request(normalized_dict, format="openai"):
        response = create_health_check_response(format="openai")
        return JSONResponse(content=response)

    # 处理模型名称和功能检测
    use_fake_streaming = is_fake_streaming_model(openai_request.model)
    use_anti_truncation = is_anti_truncation_model(openai_request.model)
    real_model = get_base_model_from_feature_model(openai_request.model)

    # 获取流式标志
    is_streaming = openai_request.stream

    # 对于抗截断模型的非流式请求，给出警告
    if use_anti_truncation and not is_streaming:
        log.warning("抗截断功能仅在流式传输时有效，非流式请求将忽略此设置")

    # 更新模型名为真实模型名
    normalized_dict["model"] = real_model

    # 转换为 Gemini 格式 (使用 converter)
    from src.converter.openai2gemini import convert_openai_to_gemini_request
    gemini_dict = await convert_openai_to_gemini_request(normalized_dict)

    # convert_openai_to_gemini_request 不包含 model 字段，需要手动添加
    gemini_dict["model"] = real_model

    # 规范化 Gemini 请求 (使用 antigravity 模式)
    from src.converter.gemini_fix import normalize_gemini_request
    gemini_dict = await normalize_gemini_request(gemini_dict, mode="antigravity")

    # 准备API请求格式 - 提取model并将其他字段放入request中
    api_request = {
        "model": gemini_dict.pop("model"),
        "request": gemini_dict
    }

    # ========== 非流式请求 ==========
    if not is_streaming:
        # 调用 API 层的非流式请求
        from src.api.antigravity import non_stream_request
        response = await non_stream_request(body=api_request)

        # 检查响应状态码
        status_code = getattr(response, "status_code", 200)

        # 提取响应体
        if hasattr(response, "body"):
            response_body = response.body.decode() if isinstance(response.body, bytes) else response.body
        elif hasattr(response, "content"):
            response_body = response.content.decode() if isinstance(response.content, bytes) else response.content
        else:
            response_body = str(response)

        try:
            gemini_response = json.loads(response_body)
        except Exception as e:
            log.error(f"Failed to parse Gemini response: {e}")
            raise HTTPException(status_code=500, detail="Response parsing failed")

        # 转换为 OpenAI 格式
        from src.converter.openai2gemini import convert_gemini_to_openai_response
        openai_response = convert_gemini_to_openai_response(
            gemini_response,
            real_model,
            status_code
        )

        return JSONResponse(content=openai_response, status_code=status_code)

    # ========== 流式请求 ==========

    # ========== 假流式生成器 ==========
    async def fake_stream_generator():
        from src.api.antigravity import non_stream_request

        response = await non_stream_request(body=api_request)

        # 检查响应状态码
        if hasattr(response, "status_code") and response.status_code != 200:
            log.error(f"Fake streaming got error response: status={response.status_code}")
            yield response
            return

        # 处理成功响应 - 提取响应内容
        if hasattr(response, "body"):
            response_body = response.body.decode() if isinstance(response.body, bytes) else response.body
        elif hasattr(response, "content"):
            response_body = response.content.decode() if isinstance(response.content, bytes) else response.content
        else:
            response_body = str(response)

        try:
            gemini_response = json.loads(response_body)
            log.debug(f"OpenAI fake stream Gemini response: {gemini_response}")

            # 检查是否是错误响应（有些错误可能status_code是200但包含error字段）
            if "error" in gemini_response:
                log.error(f"Fake streaming got error in response body: {gemini_response['error']}")
                # 转换错误为 OpenAI 格式
                from src.converter.openai2gemini import convert_gemini_to_openai_response
                openai_error = convert_gemini_to_openai_response(
                    gemini_response,
                    real_model,
                    200
                )
                yield f"data: {json.dumps(openai_error)}\n\n".encode()
                yield "data: [DONE]\n\n".encode()
                return

            # 使用统一的解析函数
            content, reasoning_content, finish_reason, images = parse_response_for_fake_stream(gemini_response)

            log.debug(f"OpenAI extracted content: {content}")
            log.debug(f"OpenAI extracted reasoning: {reasoning_content[:100] if reasoning_content else 'None'}...")
            log.debug(f"OpenAI extracted images count: {len(images)}")

            # 构建响应块
            chunks = build_openai_fake_stream_chunks(content, reasoning_content, finish_reason, real_model, images)
            for idx, chunk in enumerate(chunks):
                chunk_json = json.dumps(chunk)
                log.debug(f"[FAKE_STREAM] Yielding chunk #{idx+1}: {chunk_json[:200]}")
                yield f"data: {chunk_json}\n\n".encode()

        except Exception as e:
            log.error(f"Response parsing failed: {e}, directly yield error")
            # 构建错误响应
            error_chunk = {
                "id": "error",
                "object": "chat.completion.chunk",
                "created": int(asyncio.get_event_loop().time()),
                "model": real_model,
                "choices": [{
                    "index": 0,
                    "delta": {"content": f"Error: {str(e)}"},
                    "finish_reason": "error"
                }]
            }
            yield f"data: {json.dumps(error_chunk)}\n\n".encode()

        yield "data: [DONE]\n\n".encode()

    # ========== 流式抗截断生成器 ==========
    async def anti_truncation_generator():
        from src.converter.anti_truncation import AntiTruncationStreamProcessor
        from src.api.antigravity import stream_request
        from src.converter.anti_truncation import apply_anti_truncation
        from fastapi import Response

        max_attempts = await get_anti_truncation_max_attempts()

        # 首先对payload应用反截断指令
        anti_truncation_payload = apply_anti_truncation(api_request)

        first_attempt_stream = stream_request(body=anti_truncation_payload, native=False)
        try:
            first_chunk = await read_first_async_item(first_attempt_stream)
        except StopAsyncIteration:
            return

        if isinstance(first_chunk, Response):
            yield first_chunk
            return

        first_attempt_pending = True

        async def stream_request_wrapper(payload):
            nonlocal first_attempt_pending

            if first_attempt_pending:
                first_attempt_pending = False
                stream_gen = prepend_async_item(first_chunk, first_attempt_stream)
            else:
                stream_gen = stream_request(body=payload, native=False)

            return StreamingResponse(stream_gen, media_type="text/event-stream")

        # 创建反截断处理器
        processor = AntiTruncationStreamProcessor(
            stream_request_wrapper,
            anti_truncation_payload,
            max_attempts,
            enable_prefill_mode=("claude" not in str(api_request.get("model", "")).lower()),
        )

        # 转换为 OpenAI 格式
        import uuid
        response_id = str(uuid.uuid4())

        # 直接迭代 process_stream() 生成器，并转换为 OpenAI 格式
        async for chunk in processor.process_stream():
            if not chunk:
                continue

            # 解析 Gemini SSE 格式
            chunk_str = chunk.decode('utf-8') if isinstance(chunk, bytes) else chunk

            # 跳过空行
            if not chunk_str.strip():
                continue

            # 处理 [DONE] 标记
            if chunk_str.strip() == "data: [DONE]":
                yield "data: [DONE]\n\n".encode('utf-8')
                return

            # 解析 "data: {...}" 格式
            if chunk_str.startswith("data: "):
                try:
                    # 转换为 OpenAI 格式
                    from src.converter.openai2gemini import convert_gemini_to_openai_stream
                    openai_chunk_str = convert_gemini_to_openai_stream(
                        chunk_str,
                        real_model,
                        response_id
                    )

                    if openai_chunk_str:
                        yield openai_chunk_str.encode('utf-8')

                except Exception as e:
                    log.error(f"Failed to convert chunk: {e}")
                    continue

        # 发送结束标记
        yield "data: [DONE]\n\n".encode('utf-8')

    # ========== 普通流式生成器 ==========
    async def normal_stream_generator():
        from src.api.antigravity import stream_request
        from fastapi import Response
        import uuid

        # 调用 API 层的流式请求（不使用 native 模式）
        stream_gen = stream_request(body=api_request, native=False)
        try:
            first_chunk = await read_first_async_item(stream_gen)
        except StopAsyncIteration:
            return

        if isinstance(first_chunk, Response):
            yield first_chunk
            return

        response_id = str(uuid.uuid4())

        # yield所有数据,处理可能的错误Response
        async for chunk in prepend_async_item(first_chunk, stream_gen):
            # 检查是否是Response对象（错误情况）
            if isinstance(chunk, Response):
                # 将Response转换为SSE格式的错误消息
                try:
                    error_content = chunk.body if isinstance(chunk.body, bytes) else (chunk.body or b'').encode('utf-8')
                    gemini_error = json.loads(error_content.decode('utf-8'))
                    # 转换为 OpenAI 格式错误
                    from src.converter.openai2gemini import convert_gemini_to_openai_response
                    openai_error = convert_gemini_to_openai_response(
                        gemini_error,
                        real_model,
                        chunk.status_code
                    )
                    yield f"data: {json.dumps(openai_error)}\n\n".encode('utf-8')
                except Exception:
                    yield f"data: {json.dumps({'error': 'Stream error'})}\n\n".encode('utf-8')
                yield b"data: [DONE]\n\n"
                return
            else:
                # 正常的bytes数据，转换为 OpenAI 格式
                chunk_str = chunk.decode('utf-8') if isinstance(chunk, bytes) else chunk

                # 跳过空行
                if not chunk_str.strip():
                    continue

                # 处理 [DONE] 标记
                if chunk_str.strip() == "data: [DONE]":
                    yield "data: [DONE]\n\n".encode('utf-8')
                    return

                # 解析并转换 Gemini chunk 为 OpenAI 格式
                if chunk_str.startswith("data: "):
                    try:
                        # 转换为 OpenAI 格式
                        from src.converter.openai2gemini import convert_gemini_to_openai_stream
                        openai_chunk_str = convert_gemini_to_openai_stream(
                            chunk_str,
                            real_model,
                            response_id
                        )

                        if openai_chunk_str:
                            yield openai_chunk_str.encode('utf-8')

                    except Exception as e:
                        log.error(f"Failed to convert chunk: {e}")
                        continue

        # 发送结束标记
        yield "data: [DONE]\n\n".encode('utf-8')

    # ========== 根据模式选择生成器 ==========
    if use_fake_streaming:
        return await build_streaming_response_or_error(fake_stream_generator())
    elif use_anti_truncation:
        log.info("启用流式抗截断功能")
        return await build_streaming_response_or_error(anti_truncation_generator())
    else:
        return await build_streaming_response_or_error(normal_stream_generator())


# ==================== 测试代码 ====================

if __name__ == "__main__":
    """
    测试代码：演示OpenAI路由的流式和非流式响应
    运行方式: python src/router/antigravity/openai.py
    """

    from fastapi.testclient import TestClient
    from fastapi import FastAPI

    print("=" * 80)
    print("OpenAI Router 测试")
    print("=" * 80)

    # 创建测试应用
    app = FastAPI()
    app.include_router(router)

    # 测试客户端
    client = TestClient(app)

    # 测试请求体 (OpenAI格式)
    test_request_body = {
        "model": "gemini-2.5-flash",
        "messages": [
            {"role": "user", "content": "Hello, tell me a joke in one sentence."}
        ]
    }

    # 测试Bearer令牌（模拟）
    test_token = "Bearer pwd"

    def test_non_stream_request():
        """测试非流式请求"""
        print("\n" + "=" * 80)
        print("【测试1】非流式请求 (POST /antigravity/v1/chat/completions)")
        print("=" * 80)
        print(f"请求体: {json.dumps(test_request_body, indent=2, ensure_ascii=False)}\n")

        response = client.post(
            "/antigravity/v1/chat/completions",
            json=test_request_body,
            headers={"Authorization": test_token}
        )

        print("非流式响应数据:")
        print("-" * 80)
        print(f"状态码: {response.status_code}")
        print(f"Content-Type: {response.headers.get('content-type', 'N/A')}")

        try:
            content = response.text
            print(f"\n响应内容 (原始):\n{content}\n")

            # 尝试解析JSON
            try:
                json_data = response.json()
                print(f"响应内容 (格式化JSON):")
                print(json.dumps(json_data, indent=2, ensure_ascii=False))
            except json.JSONDecodeError:
                print("(非JSON格式)")
        except Exception as e:
            print(f"内容解析失败: {e}")

    def test_stream_request():
        """测试流式请求"""
        print("\n" + "=" * 80)
        print("【测试2】流式请求 (POST /antigravity/v1/chat/completions)")
        print("=" * 80)

        stream_request_body = test_request_body.copy()
        stream_request_body["stream"] = True

        print(f"请求体: {json.dumps(stream_request_body, indent=2, ensure_ascii=False)}\n")

        print("流式响应数据 (每个chunk):")
        print("-" * 80)

        with client.stream(
            "POST",
            "/antigravity/v1/chat/completions",
            json=stream_request_body,
            headers={"Authorization": test_token}
        ) as response:
            print(f"状态码: {response.status_code}")
            print(f"Content-Type: {response.headers.get('content-type', 'N/A')}\n")

            chunk_count = 0
            for chunk in response.iter_bytes():
                if chunk:
                    chunk_count += 1
                    print(f"\nChunk #{chunk_count}:")
                    print(f"  类型: {type(chunk).__name__}")
                    print(f"  长度: {len(chunk)}")

                    # 解码chunk
                    try:
                        chunk_str = chunk.decode('utf-8')
                        print(f"  内容预览: {repr(chunk_str[:200] if len(chunk_str) > 200 else chunk_str)}")

                        # 如果是SSE格式，尝试解析每一行
                        if chunk_str.startswith("data: "):
                            # 按行分割，处理每个SSE事件
                            for line in chunk_str.strip().split('\n'):
                                line = line.strip()
                                if not line:
                                    continue

                                if line == "data: [DONE]":
                                    print(f"  => 流结束标记")
                                elif line.startswith("data: "):
                                    try:
                                        json_str = line[6:]  # 去掉 "data: " 前缀
                                        json_data = json.loads(json_str)
                                        print(f"  解析后的JSON: {json.dumps(json_data, indent=4, ensure_ascii=False)}")
                                    except Exception as e:
                                        print(f"  SSE解析失败: {e}")
                    except Exception as e:
                        print(f"  解码失败: {e}")

            print(f"\n总共收到 {chunk_count} 个chunk")

    def test_fake_stream_request():
        """测试假流式请求"""
        print("\n" + "=" * 80)
        print("【测试3】假流式请求 (POST /antigravity/v1/chat/completions with 假流式 prefix)")
        print("=" * 80)

        fake_stream_request_body = test_request_body.copy()
        fake_stream_request_body["model"] = "假流式/gemini-2.5-flash"
        fake_stream_request_body["stream"] = True

        print(f"请求体: {json.dumps(fake_stream_request_body, indent=2, ensure_ascii=False)}\n")

        print("假流式响应数据 (每个chunk):")
        print("-" * 80)

        with client.stream(
            "POST",
            "/antigravity/v1/chat/completions",
            json=fake_stream_request_body,
            headers={"Authorization": test_token}
        ) as response:
            print(f"状态码: {response.status_code}")
            print(f"Content-Type: {response.headers.get('content-type', 'N/A')}\n")

            chunk_count = 0
            for chunk in response.iter_bytes():
                if chunk:
                    chunk_count += 1
                    chunk_str = chunk.decode('utf-8')

                    print(f"\nChunk #{chunk_count}:")
                    print(f"  长度: {len(chunk_str)} 字节")

                    # 解析chunk中的所有SSE事件
                    events = []
                    for line in chunk_str.split('\n'):
                        line = line.strip()
                        if line.startswith("data: "):
                            events.append(line)

                    print(f"  包含 {len(events)} 个SSE事件")

                    # 显示每个事件
                    for event_idx, event_line in enumerate(events, 1):
                        if event_line == "data: [DONE]":
                            print(f"  事件 #{event_idx}: [DONE]")
                        else:
                            try:
                                json_str = event_line[6:]  # 去掉 "data: " 前缀
                                json_data = json.loads(json_str)
                                # 提取content内容
                                content = json_data.get("choices", [{}])[0].get("delta", {}).get("content", "")
                                finish_reason = json_data.get("choices", [{}])[0].get("finish_reason")
                                print(f"  事件 #{event_idx}: content={repr(content[:50])}{'...' if len(content) > 50 else ''}, finish_reason={finish_reason}")
                            except Exception as e:
                                print(f"  事件 #{event_idx}: 解析失败 - {e}")

            print(f"\n总共收到 {chunk_count} 个HTTP chunk")

    # 运行测试
    try:
        # 测试非流式请求
        test_non_stream_request()

        # 测试流式请求
        test_stream_request()

        # 测试假流式请求
        test_fake_stream_request()

        print("\n" + "=" * 80)
        print("测试完成")
        print("=" * 80)

    except Exception as e:
        print(f"\n❌ 测试过程中出现异常: {e}")
        import traceback
        traceback.print_exc()