"""模型切换 API"""
import gc
import os
from typing import Optional

import torch
from model_paths import MODEL_PATHS
from backend.model_manager import project_registry
from backend.app_context import get_app_context
from backend.api.utils import require_admin


def get_available_models():
    """获取所有可用的模型列表"""
    return {
        'success': True,
        'models': list(MODEL_PATHS.keys())
    }, 200


def _get_device_type() -> str:
    """获取当前设备类型"""
    if torch.cuda.is_available():
        return "cuda"
    elif hasattr(torch.backends, 'mps') and torch.backends.mps.is_available():
        return "mps"
    else:
        return "cpu"


def _restore_env_vars(old_force_int8: Optional[str], old_force_bfloat16: Optional[str]) -> None:
    """恢复环境变量配置"""
    if old_force_int8 is not None:
        os.environ['FORCE_INT8'] = old_force_int8
    else:
        os.environ.pop('FORCE_INT8', None)
    
    if old_force_bfloat16 is not None:
        os.environ['CPU_FORCE_BFLOAT16'] = old_force_bfloat16
    else:
        os.environ.pop('CPU_FORCE_BFLOAT16', None)


def get_current_model():
    """获取当前使用的模型及量化配置"""
    # 使用模块级上下文以获取持久化的模型状态
    context = get_app_context(prefer_module_context=True)
    device_type = _get_device_type()
    
    return {
        'success': True,
        'model': context.model_name,
        'loading': context.model_loading,
        'device_type': device_type,
        'use_int8': os.environ.get('FORCE_INT8') == '1',
        'use_bfloat16': os.environ.get('CPU_FORCE_BFLOAT16') == '1'
    }, 200


@require_admin
def switch_model(switch_request):
    """
    切换模型（需要管理员权限）
    
    Args:
        switch_request: 切换请求字典，包含：
            - model: 目标模型名称
            - use_int8: 是否使用 INT8 量化（可选）
            - use_bfloat16: 是否使用 bfloat16（可选，仅CPU）
    
    Returns:
        (响应字典, 状态码) 元组
    """
    target_model = switch_request.get('model')
    use_int8 = switch_request.get('use_int8', False)
    use_bfloat16 = switch_request.get('use_bfloat16', False)
    
    # 验证请求
    if not target_model:
        return {
            'success': False,
            'message': 'Missing model parameter'
        }, 400
    
    # 检查模型是否可用
    if target_model not in MODEL_PATHS:
        available_models = list(MODEL_PATHS.keys())
        return {
            'success': False,
            'message': f'Model {target_model} does not exist. Available models: {", ".join(available_models)}'
        }, 404
    
    # 获取设备类型
    device_type = _get_device_type()
    
    # 验证量化参数与设备兼容性
    if use_int8 and device_type == "mps":
        return {
            'success': False,
            'message': 'INT8 quantization is not supported on MPS device'
        }, 400
    
    if use_bfloat16 and device_type != "cpu":
        return {
            'success': False,
            'message': 'bfloat16 quantization is only supported on CPU device'
        }, 400
    
    if use_int8 and use_bfloat16:
        return {
            'success': False,
            'message': 'Cannot enable both INT8 and bfloat16 quantization'
        }, 400
    
    # 使用模块级上下文以确保状态修改持久化（不会被后续请求重置）
    context = get_app_context(prefer_module_context=True)
    current_model = context.model_name
    
    # 保存当前环境变量配置（用于回滚）
    old_force_int8 = os.environ.get('FORCE_INT8')
    old_force_bfloat16 = os.environ.get('CPU_FORCE_BFLOAT16')
    
    # 检查是否已经是目标模型且量化配置相同
    current_int8 = os.environ.get('FORCE_INT8') == '1'
    current_bfloat16 = os.environ.get('CPU_FORCE_BFLOAT16') == '1'
    
    if (current_model == target_model and 
        current_int8 == use_int8 and 
        current_bfloat16 == use_bfloat16):
        return {
            'success': True,
            'message': f'Already using model {target_model} (same quantization configuration)',
            'model': target_model
        }, 200
    
    # 检查模型是否正在加载中（初始加载或切换）
    if context.model_loading:
        return {
            'success': False,
            'message': 'Model is currently loading, please try again later'
        }, 503
    
    try:
        # 标记开始加载
        context.set_model_loading(True)
        print(f"🔄 开始切换模型: {current_model} -> {target_model}")
        
        # 设置新的量化环境变量
        if use_int8:
            os.environ['FORCE_INT8'] = '1'
            print(f"   设置量化: INT8")
        else:
            os.environ.pop('FORCE_INT8', None)
        
        if use_bfloat16:
            os.environ['CPU_FORCE_BFLOAT16'] = '1'
            print(f"   设置量化: bfloat16")
        else:
            os.environ.pop('CPU_FORCE_BFLOAT16', None)
        
        # 卸载旧模型
        if current_model and current_model in project_registry:
            print(f"   卸载旧模型: {current_model}")
            project_registry.unload(current_model)
            gc.collect()
            if device_type == "cuda":
                torch.cuda.empty_cache()
            elif device_type == "mps":
                torch.mps.empty_cache()
        
        # 加载新模型
        print(f"   加载新模型: {target_model}")
        project_registry.ensure_loaded(target_model)
        
        # 更新当前模型
        context.set_current_model(target_model)
        
        print(f"✅ 模型切换成功: {target_model}")
        
        return {
            'success': True,
            'message': f'Model switched to {target_model}',
            'model': target_model
        }, 200
        
    except KeyError as e:
        # 模型不存在（虽然前面已经检查过，但以防万一）
        print(f"❌ 模型切换失败: 模型 {target_model} 未注册")
        # 回滚：恢复旧模型名称和环境变量
        context.set_current_model(current_model)
        _restore_env_vars(old_force_int8, old_force_bfloat16)
        return {
            'success': False,
            'message': f'Model {target_model} is not registered'
        }, 404
        
    except Exception as e:
        # 加载失败，尝试回滚
        print(f"❌ 模型切换失败: {e}")
        print(f"   尝试回滚到旧模型: {current_model}")
        
        try:
            # 回滚：恢复环境变量和重新加载旧模型
            _restore_env_vars(old_force_int8, old_force_bfloat16)
            if current_model:
                project_registry.ensure_loaded(current_model)
                context.set_current_model(current_model)
                print(f"✅ 已回滚到旧模型: {current_model}")
        except Exception as rollback_error:
            print(f"⚠️  回滚失败: {rollback_error}")
        
        return {
            'success': False,
            'message': f'Model switch failed: {str(e)}'
        }, 500
        
    finally:
        # 无论成功还是失败，都要清除加载标志
        context.set_model_loading(False)
        gc.collect()