iOS.NS_DEPRECATED_IOS

本文讨论了如何在iOS应用中处理被NS_DEPRECATED_IOS标记的方法,提供了两种解决方案:一种是检查类是否响应新的选择器,如果不行则使用旧的选择器;另一种方法留作思考。

如何处理被NS_DEPRECATED_IOS标记的selector, 例如:类

AVAudioSession中有:

- (BOOL)setPreferredHardwareSampleRate:(double)sampleRate error:(NSError **)outError NS_DEPRECATED_IOS(3_0, 6_0);

那么App需要支持iOS5到iOS7,那么该如何处理这种case呢?

Solution A:

 AVAudioSession *asession = [AVAudioSession  sharedInstance];

 if ([asession respondsToSelector:@selector(newSelector:)])

{

  [asession newSelector:];

}

else 

{

  [asession oldSelector:];

}

Maybe Solution B...

 

转载于:https://www.cnblogs.com/cwgk/p/3798965.html

标题SpringBoot智能在线预约挂号系统研究AI更换标题第1章引言介绍智能在线预约挂号系统的研究背景、意义、国内外研究现状及论文创新点。1.1研究背景与意义阐述智能在线预约挂号系统对提升医疗服务效率的重要性。1.2国内外研究现状分析国内外智能在线预约挂号系统的研究与应用情况。1.3研究方法及创新点概述本文采用的技术路线、研究方法及主要创新点。第2章相关理论总结智能在线预约挂号系统相关理论,包括系统架构、开发技术等。2.1系统架构设计理论介绍系统架构设计的基本原则和常用方法。2.2SpringBoot开发框架理论阐述SpringBoot框架的特点、优势及其在系统开发中的应用。2.3数据库设计与管理理论介绍数据库设计原则、数据模型及数据库管理系统。2.4网络安全与数据保护理论讨论网络安全威胁、数据保护技术及其在系统中的应用。第3章SpringBoot智能在线预约挂号系统设计详细介绍系统的设计方案,包括功能模块划分、数据库设计等。3.1系统功能模块设计划分系统功能模块,如用户管理、挂号管理、医生排班等。3.2数据库设计与实现设计数据库表结构,确定字段类型、主键及外键关系。3.3用户界面设计设计用户友好的界面,提升用户体验。3.4系统安全设计阐述系统安全策略,包括用户认证、数据加密等。第4章系统实现与测试介绍系统的实现过程,包括编码、测试及优化等。4.1系统编码实现采用SpringBoot框架进行系统编码实现。4.2系统测试方法介绍系统测试的方法、步骤及测试用例设计。4.3系统性能测试与分析对系统进行性能测试,分析测试结果并提出优化建议。4.4系统优化与改进根据测试结果对系统进行优化和改进,提升系统性能。第5章研究结果呈现系统实现后的效果,包括功能实现、性能提升等。5.1系统功能实现效果展示系统各功能模块的实现效果,如挂号成功界面等。5.2系统性能提升效果对比优化前后的系统性能
以上代码出现以下问题,告诉我停在了哪一步,并分析修改:(style_tune) C:\Users\28996\Desktop\AI\persona_contrastive_finetuning>python Contrastive_Training_LM.py INFO:accelerate.utils.modeling:We will use 90% of the memory on device 0 for storing the model, and 10% for the buffer to avoid OOM. You can set `max_memory` in to a higher value to use more memory (at your own risk). trainable params: 1,572,864 || all params: 1,838,401,536 || trainable%: 0.0856 训练集样本示例: {'anchor_input_ids': [56568, 118919, 116122, 11319], 'positive_input_ids': [116122, 20412, 107340, 9370, 100357, 102323, 3837, 109202, 104078, 103975, 100675, 101940, 100912, 105054, 6313], 'negative_input_ids': [100323, 104307, 99245, 9370, 106059, 104060, 3837, 104530, 115604, 99329, 11319]} 验证集样本示例: {'anchor_input_ids': [56568, 118919, 116122, 11319], 'positive_input_ids': [116122, 20412, 107340, 9370, 100357, 102323, 3837, 109202, 104078, 103975, 100675, 101940, 100912, 105054, 6313], 'negative_input_ids': [100323, 104307, 99245, 9370, 106059, 104060, 3837, 104530, 115604, 99329, 11319]} Trainer.tokenizer is now deprecated. You should use `Trainer.processing_class = processing_class` instead. INFO:__main__:GPU内存使用: 已分配 2.93GB, 保留 4.13GB 可训练参数列表: - base_model.model.model.layers.0.self_attn.q_proj.lora_A.default.weight - base_model.model.model.layers.0.self_attn.q_proj.lora_B.default.weight - base_model.model.model.layers.0.self_attn.v_proj.lora_A.default.weight - base_model.model.model.layers.0.self_attn.v_proj.lora_B.default.weight - base_model.model.model.layers.1.self_attn.q_proj.lora_A.default.weight - base_model.model.model.layers.1.self_attn.q_proj.lora_B.default.weight - base_model.model.model.layers.1.self_attn.v_proj.lora_A.default.weight - base_model.model.model.layers.1.self_attn.v_proj.lora_B.default.weight - base_model.model.model.layers.2.self_attn.q_proj.lora_A.default.weight - base_model.model.model.layers.2.self_attn.q_proj.lora_B.default.weight - base_model.model.model.layers.2.self_attn.v_proj.lora_A.default.weight - base_model.model.model.layers.2.self_attn.v_proj.lora_B.default.weight - base_model.model.model.layers.3.self_attn.q_proj.lora_A.default.weight - base_model.model.model.layers.3.self_attn.q_proj.lora_B.default.weight - base_model.model.model.layers.3.self_attn.v_proj.lora_A.default.weight - base_model.model.model.layers.3.self_attn.v_proj.lora_B.default.weight - base_model.model.model.layers.4.self_attn.q_proj.lora_A.default.weight - base_model.model.model.layers.4.self_attn.q_proj.lora_B.default.weight - base_model.model.model.layers.4.self_attn.v_proj.lora_A.default.weight - base_model.model.model.layers.4.self_attn.v_proj.lora_B.default.weight - base_model.model.model.layers.5.self_attn.q_proj.lora_A.default.weight - base_model.model.model.layers.5.self_attn.q_proj.lora_B.default.weight - base_model.model.model.layers.5.self_attn.v_proj.lora_A.default.weight - base_model.model.model.layers.5.self_attn.v_proj.lora_B.default.weight - base_model.model.model.layers.6.self_attn.q_proj.lora_A.default.weight - base_model.model.model.layers.6.self_attn.q_proj.lora_B.default.weight - base_model.model.model.layers.6.self_attn.v_proj.lora_A.default.weight - base_model.model.model.layers.6.self_attn.v_proj.lora_B.default.weight - base_model.model.model.layers.7.self_attn.q_proj.lora_A.default.weight - base_model.model.model.layers.7.self_attn.q_proj.lora_B.default.weight - base_model.model.model.layers.7.self_attn.v_proj.lora_A.default.weight - base_model.model.model.layers.7.self_attn.v_proj.lora_B.default.weight - base_model.model.model.layers.8.self_attn.q_proj.lora_A.default.weight - base_model.model.model.layers.8.self_attn.q_proj.lora_B.default.weight - base_model.model.model.layers.8.self_attn.v_proj.lora_A.default.weight - base_model.model.model.layers.8.self_attn.v_proj.lora_B.default.weight - base_model.model.model.layers.9.self_attn.q_proj.lora_A.default.weight - base_model.model.model.layers.9.self_attn.q_proj.lora_B.default.weight - base_model.model.model.layers.9.self_attn.v_proj.lora_A.default.weight - base_model.model.model.layers.9.self_attn.v_proj.lora_B.default.weight - base_model.model.model.layers.10.self_attn.q_proj.lora_A.default.weight - base_model.model.model.layers.10.self_attn.q_proj.lora_B.default.weight - base_model.model.model.layers.10.self_attn.v_proj.lora_A.default.weight - base_model.model.model.layers.10.self_attn.v_proj.lora_B.default.weight - base_model.model.model.layers.11.self_attn.q_proj.lora_A.default.weight - base_model.model.model.layers.11.self_attn.q_proj.lora_B.default.weight - base_model.model.model.layers.11.self_attn.v_proj.lora_A.default.weight - base_model.model.model.layers.11.self_attn.v_proj.lora_B.default.weight - base_model.model.model.layers.12.self_attn.q_proj.lora_A.default.weight - base_model.model.model.layers.12.self_attn.q_proj.lora_B.default.weight - base_model.model.model.layers.12.self_attn.v_proj.lora_A.default.weight - base_model.model.model.layers.12.self_attn.v_proj.lora_B.default.weight - base_model.model.model.layers.13.self_attn.q_proj.lora_A.default.weight - base_model.model.model.layers.13.self_attn.q_proj.lora_B.default.weight - base_model.model.model.layers.13.self_attn.v_proj.lora_A.default.weight - base_model.model.model.layers.13.self_attn.v_proj.lora_B.default.weight - base_model.model.model.layers.14.self_attn.q_proj.lora_A.default.weight - base_model.model.model.layers.14.self_attn.q_proj.lora_B.default.weight - base_model.model.model.layers.14.self_attn.v_proj.lora_A.default.weight - base_model.model.model.layers.14.self_attn.v_proj.lora_B.default.weight - base_model.model.model.layers.15.self_attn.q_proj.lora_A.default.weight - base_model.model.model.layers.15.self_attn.q_proj.lora_B.default.weight - base_model.model.model.layers.15.self_attn.v_proj.lora_A.default.weight - base_model.model.model.layers.15.self_attn.v_proj.lora_B.default.weight - base_model.model.model.layers.16.self_attn.q_proj.lora_A.default.weight - base_model.model.model.layers.16.self_attn.q_proj.lora_B.default.weight - base_model.model.model.layers.16.self_attn.v_proj.lora_A.default.weight - base_model.model.model.layers.16.self_attn.v_proj.lora_B.default.weight - base_model.model.model.layers.17.self_attn.q_proj.lora_A.default.weight - base_model.model.model.layers.17.self_attn.q_proj.lora_B.default.weight - base_model.model.model.layers.17.self_attn.v_proj.lora_A.default.weight - base_model.model.model.layers.17.self_attn.v_proj.lora_B.default.weight - base_model.model.model.layers.18.self_attn.q_proj.lora_A.default.weight - base_model.model.model.layers.18.self_attn.q_proj.lora_B.default.weight - base_model.model.model.layers.18.self_attn.v_proj.lora_A.default.weight - base_model.model.model.layers.18.self_attn.v_proj.lora_B.default.weight - base_model.model.model.layers.19.self_attn.q_proj.lora_A.default.weight - base_model.model.model.layers.19.self_attn.q_proj.lora_B.default.weight - base_model.model.model.layers.19.self_attn.v_proj.lora_A.default.weight - base_model.model.model.layers.19.self_attn.v_proj.lora_B.default.weight - base_model.model.model.layers.20.self_attn.q_proj.lora_A.default.weight - base_model.model.model.layers.20.self_attn.q_proj.lora_B.default.weight - base_model.model.model.layers.20.self_attn.v_proj.lora_A.default.weight - base_model.model.model.layers.20.self_attn.v_proj.lora_B.default.weight - base_model.model.model.layers.21.self_attn.q_proj.lora_A.default.weight - base_model.model.model.layers.21.self_attn.q_proj.lora_B.default.weight - base_model.model.model.layers.21.self_attn.v_proj.lora_A.default.weight - base_model.model.model.layers.21.self_attn.v_proj.lora_B.default.weight - base_model.model.model.layers.22.self_attn.q_proj.lora_A.default.weight - base_model.model.model.layers.22.self_attn.q_proj.lora_B.default.weight - base_model.model.model.layers.22.self_attn.v_proj.lora_A.default.weight - base_model.model.model.layers.22.self_attn.v_proj.lora_B.default.weight - base_model.model.model.layers.23.self_attn.q_proj.lora_A.default.weight - base_model.model.model.layers.23.self_attn.q_proj.lora_B.default.weight - base_model.model.model.layers.23.self_attn.v_proj.lora_A.default.weight - base_model.model.model.layers.23.self_attn.v_proj.lora_B.default.weight 0%| | 0/3 [00:00<?, ?it/s]You're using a Qwen2TokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding. Trainer.tokenizer is now deprecated. You should use Trainer.processing_class instead. Trainer.tokenizer is now deprecated. You should use Trainer.processing_class instead. INFO:__main__:GPU内存使用: 已分配 4.00GB, 保留 4.21GB Could not estimate the number of tokens of the input, floating-point operations will not be computed Trainer.tokenizer is now deprecated. You should use Trainer.processing_class instead. Trainer.tokenizer is now deprecated. You should use Trainer.processing_class instead. INFO:__main__:GPU内存使用: 已分配 4.02GB, 保留 4.22GB 33%|████████████████████████████ | 1/3 [00:03<00:06, 3.25s/it]Trainer.tokenizer is now deprecated. You should use Trainer.processing_class instead. Trainer.tokenizer is now deprecated. You should use Trainer.processing_class instead. INFO:__main__:GPU内存使用: 已分配 4.01GB, 保留 4.25GB Trainer.tokenizer is now deprecated. You should use Trainer.processing_class instead. Trainer.tokenizer is now deprecated. You should use Trainer.processing_class instead. INFO:__main__:GPU内存使用: 已分配 4.02GB, 保留 4.26GB 67%|████████████████████████████████████████████████████████ | 2/3 [00:06<00:02, 2.98s/it]Trainer.tokenizer is now deprecated. You should use Trainer.processing_class instead. Trainer.tokenizer is now deprecated. You should use Trainer.processing_class instead. INFO:__main__:GPU内存使用: 已分配 4.01GB, 保留 4.25GB Trainer.tokenizer is now deprecated. You should use Trainer.processing_class instead. Trainer.tokenizer is now deprecated. You should use Trainer.processing_class instead. INFO:__main__:GPU内存使用: 已分配 4.02GB, 保留 4.26GB {'train_runtime': 9.034, 'train_samples_per_second': 0.664, 'train_steps_per_second': 0.332, 'train_loss': 1.0772175788879395, 'epoch': 3.0} 100%|████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:09<00:00, 3.01s/it] Traceback (most recent call last): File "C:\Users\28996\Desktop\AI\persona_contrastive_finetuning\Contrastive_Training_LM.py", line 356, in <module> eval_results = trainer.evaluate() File "C:\Users\28996\miniconda3\envs\style_tune\lib\site-packages\transformers\trainer.py", line 4076, in evaluate output = eval_loop( File "C:\Users\28996\miniconda3\envs\style_tune\lib\site-packages\transformers\trainer.py", line 4270, in evaluation_loop losses, logits, labels = self.prediction_step(model, inputs, prediction_loss_only, ignore_keys=ignore_keys) File "C:\Users\28996\miniconda3\envs\style_tune\lib\site-packages\transformers\trainer.py", line 4496, in prediction_step outputs = model(**inputs) File "C:\Users\28996\miniconda3\envs\style_tune\lib\site-packages\torch\nn\modules\module.py", line 1736, in _wrapped_call_impl return self._call_impl(*args, **kwargs) File "C:\Users\28996\miniconda3\envs\style_tune\lib\site-packages\torch\nn\modules\module.py", line 1747, in _call_impl return forward_call(*args, **kwargs) File "C:\Users\28996\miniconda3\envs\style_tune\lib\site-packages\accelerate\utils\operations.py", line 818, in forward return model_forward(*args, **kwargs) File "C:\Users\28996\miniconda3\envs\style_tune\lib\site-packages\accelerate\utils\operations.py", line 806, in __call__ return convert_to_fp32(self.model_forward(*args, **kwargs)) File "C:\Users\28996\miniconda3\envs\style_tune\lib\site-packages\torch\amp\autocast_mode.py", line 44, in decorate_autocast return func(*args, **kwargs) File "C:\Users\28996\miniconda3\envs\style_tune\lib\site-packages\peft\peft_model.py", line 1719, in forward return self.base_model( File "C:\Users\28996\miniconda3\envs\style_tune\lib\site-packages\torch\nn\modules\module.py", line 1736, in _wrapped_call_impl return self._call_impl(*args, **kwargs) File "C:\Users\28996\miniconda3\envs\style_tune\lib\site-packages\torch\nn\modules\module.py", line 1747, in _call_impl return forward_call(*args, **kwargs) File "C:\Users\28996\miniconda3\envs\style_tune\lib\site-packages\peft\tuners\tuners_utils.py", line 197, in forward return self.model.forward(*args, **kwargs) File "C:\Users\28996\miniconda3\envs\style_tune\lib\site-packages\transformers\models\qwen2\modeling_qwen2.py", line 816, in forward outputs = self.model( File "C:\Users\28996\miniconda3\envs\style_tune\lib\site-packages\torch\nn\modules\module.py", line 1736, in _wrapped_call_impl return self._call_impl(*args, **kwargs) File "C:\Users\28996\miniconda3\envs\style_tune\lib\site-packages\torch\nn\modules\module.py", line 1747, in _call_impl return forward_call(*args, **kwargs) File "C:\Users\28996\miniconda3\envs\style_tune\lib\site-packages\transformers\models\qwen2\modeling_qwen2.py", line 521, in forward raise ValueError("You must specify exactly one of input_ids or inputs_embeds") ValueError: You must specify exactly one of input_ids or inputs_embeds
07-21
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