ValueError：Cannot load xxxx from flux/vae because the following keys are missing

This is my error report：
Loading pipeline components...:   0%|                                                                                | 0/3 [00:00<?, ?it/s]accelerator device: cuda:6 The config attributes {'latents_mean': None, 'latents_std': None, 'mid_block_add_attention': True, 'shift_factor': 0.1159, 'use_post_quant_conv': False, 'use_quant_conv': False} were passed to AutoencoderKL, but are not expected and will be ignored. Please verify your config.json configuration file. Loading pipeline components...:   0%|                                                                                | 0/3 [00:00<?, ?it/s] Traceback (most recent call last):   File "/home/ma-user/sd-scripts/sd-scripts/train_network.py", line 1117, in <module>     trainer.train(args)   File "/home/ma-user/sd-scripts/sd-scripts/train_network.py", line 236, in train     model_version, text_encoder, vae, unet = self.load_target_model(args, weight_dtype, accelerator)   File "/home/ma-user/sd-scripts/sd-scripts/train_network.py", line 103, in load_target_model     text_encoder, vae, unet, _ = train_util.load_target_model(args, weight_dtype, accelerator)   File "/home/ma-user/sd-scripts/sd-scripts/library/train_util.py", line 4387, in load_target_model     text_encoder, vae, unet, load_stable_diffusion_format = _load_target_model(   File "/home/ma-user/sd-scripts/sd-scripts/library/train_util.py", line 4349, in _load_target_model accelerator device: cuda:2     pipe = StableDiffusionPipeline.from_pretrained(name_or_path, tokenizer=None, safety_checker=None)   File "/home/ma-user/anaconda3/envs/PyTorch-2.2.0/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py", line 118, in _inner_fn     return fn(*args, **kwargs)   File "/home/ma-user/anaconda3/envs/PyTorch-2.2.0/lib/python3.10/site-packages/diffusers/pipelines/pipeline_utils.py", line 1271, in from_pretrained     loaded_sub_model = load_sub_model(   File "/home/ma-user/anaconda3/envs/PyTorch-2.2.0/lib/python3.10/site-packages/diffusers/pipelines/pipeline_utils.py", line 525, in load_sub_model     loaded_sub_model = load_method(os.path.join(cached_folder, name), **loading_kwargs)   File "/home/ma-user/anaconda3/envs/PyTorch-2.2.0/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py", line 118, in _inner_fn     return fn(*args, **kwargs)   File "/home/ma-user/anaconda3/envs/PyTorch-2.2.0/lib/python3.10/site-packages/diffusers/models/modeling_utils.py", line 805, in from_pretrained     raise ValueError( ValueError: Cannot load <class 'diffusers.models.autoencoders.autoencoder_kl.AutoencoderKL'> from /home_host/zj/flux/ai-modelscope/flux___1-dev/vae because the following keys are missing:  quant_conv.bias, post_quant_conv.weight, quant_conv.weight, post_quant_conv.bias.  Please make sure to passlow_cpu_mem_usage=Falseanddevice_map=Noneif you want to randomly initialize those weights or else make sure your checkpoint file is correct.  Thank you in advance for your answers

The error message is not very clear. This is the message I copied again：
`
The config attributes {'latents_mean': None, 'latents_std': None, 'mid_block_add_attention': True, 'shift_factor': 0.1159, 'use_post_quant_conv': False, 'use_quant_conv': False} were passed to AutoencoderKL, but are not expected and will be ignored. Please verify your config.json configuration file.
Loading pipeline components...:   0%|                                                                                | 0/3 [00:00<?, ?it/s]
Traceback (most recent call last):
File "/home/ma-user/sd-scripts/sd-scripts/train_network.py", line 1117, in
trainer.train(args)
File "/home/ma-user/sd-scripts/sd-scripts/train_network.py", line 236, in train
model_version, text_encoder, vae, unet = self.load_target_model(args, weight_dtype, accelerator)
File "/home/ma-user/sd-scripts/sd-scripts/train_network.py", line 103, in load_target_model
text_encoder, vae, unet, _ = train_util.load_target_model(args, weight_dtype, accelerator)
File "/home/ma-user/sd-scripts/sd-scripts/library/train_util.py", line 4387, in load_target_model
text_encoder, vae, unet, load_stable_diffusion_format = _load_target_model(
File "/home/ma-user/sd-scripts/sd-scripts/library/train_util.py", line 4349, in _load_target_model
accelerator device: cuda:2
pipe = StableDiffusionPipeline.from_pretrained(name_or_path, tokenizer=None, safety_checker=None)
File "/home/ma-user/anaconda3/envs/PyTorch-2.2.0/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py", line 118, in _inner_fn
return fn(*args, **kwargs)
File "/home/ma-user/anaconda3/envs/PyTorch-2.2.0/lib/python3.10/site-packages/diffusers/pipelines/pipeline_utils.py", line 1271, in from_pretrained
loaded_sub_model = load_sub_model(
File "/home/ma-user/anaconda3/envs/PyTorch-2.2.0/lib/python3.10/site-packages/diffusers/pipelines/pipeline_utils.py", line 525, in load_sub_model
loaded_sub_model = load_method(os.path.join(cached_folder, name), **loading_kwargs)
File "/home/ma-user/anaconda3/envs/PyTorch-2.2.0/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py", line 118, in _inner_fn
return fn(*args, **kwargs)
File "/home/ma-user/anaconda3/envs/PyTorch-2.2.0/lib/python3.10/site-packages/diffusers/models/modeling_utils.py", line 805, in from_pretrained
raise ValueError(
ValueError: Cannot load <class 'diffusers.models.autoencoders.autoencoder_kl.AutoencoderKL'> from /home_host/zj/flux/ai-modelscope/flux___1-dev/vae because the following keys are missing:
quant_conv.bias, post_quant_conv.weight, quant_conv.weight, post_quant_conv.bias.
Please make sure to pass low_cpu_mem_usage=False and device_map=None if you want to randomly initialize those weights or else make sure your checkpoint file is correct.
[ERROR] 2024-10-16-10:14:27 (PID:3362, Device:0, RankID:0) ERR99999 UNKNOWN application exception
`

The error message is not very clear. This is the message I copied again：
The config attributes {'latents_mean': None, 'latents_std': None, 'mid_block_add_attention': True, 'shift_factor': 0.1159, 'use_post_quant_conv': False, 'use_quant_conv': False} were passed to AutoencoderKL, but are not expected and will be ignored. Please verify your config.json configuration file.
Loading pipeline components...:   0%|                                                                                | 0/3 [00:00<?, ?it/s]
Traceback (most recent call last):
  File "/home/ma-user/sd-scripts/sd-scripts/train_network.py", line 1117, in <module>
    trainer.train(args)
  File "/home/ma-user/sd-scripts/sd-scripts/train_network.py", line 236, in train
    model_version, text_encoder, vae, unet = self.load_target_model(args, weight_dtype, accelerator)
  File "/home/ma-user/sd-scripts/sd-scripts/train_network.py", line 103, in load_target_model
    text_encoder, vae, unet, _ = train_util.load_target_model(args, weight_dtype, accelerator)
  File "/home/ma-user/sd-scripts/sd-scripts/library/train_util.py", line 4387, in load_target_model
    text_encoder, vae, unet, load_stable_diffusion_format = _load_target_model(
  File "/home/ma-user/sd-scripts/sd-scripts/library/train_util.py", line 4349, in _load_target_model
accelerator device: cuda:2
    pipe = StableDiffusionPipeline.from_pretrained(name_or_path, tokenizer=None, safety_checker=None)
  File "/home/ma-user/anaconda3/envs/PyTorch-2.2.0/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py", line 118, in _inner_fn
    return fn(*args, **kwargs)
  File "/home/ma-user/anaconda3/envs/PyTorch-2.2.0/lib/python3.10/site-packages/diffusers/pipelines/pipeline_utils.py", line 1271, in from_pretrained
    loaded_sub_model = load_sub_model(
  File "/home/ma-user/anaconda3/envs/PyTorch-2.2.0/lib/python3.10/site-packages/diffusers/pipelines/pipeline_utils.py", line 525, in load_sub_model
    loaded_sub_model = load_method(os.path.join(cached_folder, name), **loading_kwargs)
  File "/home/ma-user/anaconda3/envs/PyTorch-2.2.0/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py", line 118, in _inner_fn
    return fn(*args, **kwargs)
  File "/home/ma-user/anaconda3/envs/PyTorch-2.2.0/lib/python3.10/site-packages/diffusers/models/modeling_utils.py", line 805, in from_pretrained
    raise ValueError(
ValueError: Cannot load <class 'diffusers.models.autoencoders.autoencoder_kl.AutoencoderKL'> from /home_host/zj/flux/ai-modelscope/flux___1-dev/vae because the following keys are missing:
 quant_conv.bias, post_quant_conv.weight, quant_conv.weight, post_quant_conv.bias.
 Please make sure to pass `low_cpu_mem_usage=False` and `device_map=None` if you want to randomly initialize those weights or else make sure your checkpoint file is correct.
[ERROR] 2024-10-16-10:14:27 (PID:3362, Device:0, RankID:0) ERR99999 UNKNOWN application exception

                    INFO     text_encoder [1] dtype: torch.float16, device: cpu                                                                                train_network.py:1066
                    INFO     epoch is incremented. current_epoch: 0, epoch: 1                                                                                train_util.py:706
                    INFO     epoch is incremented. current_epoch: 0, epoch: 1                                                                                train_util.py:706
2024-10-17 14:59:29 INFO     epoch is incremented. current_epoch: 0, epoch: 1                                                                                train_util.py:706
2024-10-17 14:59:29 INFO     epoch is incremented. current_epoch: 0, epoch: 1                                                                                train_util.py:706
2024-10-17 14:59:29 INFO     epoch is incremented. current_epoch: 0, epoch: 1                                                                                train_util.py:706
2024-10-17 14:59:29 INFO     epoch is incremented. current_epoch: 0, epoch: 1                                                                                train_util.py:706
2024-10-17 14:59:30 INFO     epoch is incremented. current_epoch: 0, epoch: 1                                                                                train_util.py:706
2024-10-17 14:59:30 INFO     epoch is incremented. current_epoch: 0, epoch: 1                                                                                train_util.py:706
2024-10-17 14:59:30 INFO     epoch is incremented. current_epoch: 0, epoch: 1                                                                                train_util.py:706
2024-10-17 14:59:30 INFO     epoch is incremented. current_epoch: 0, epoch: 1                                                                                train_util.py:706
2024-10-17 14:59:30 INFO     epoch is incremented. current_epoch: 0, epoch: 1                                                                                train_util.py:706
2024-10-17 14:59:30 INFO     epoch is incremented. current_epoch: 0, epoch: 1                                                                                train_util.py:706
2024-10-17 14:59:30 INFO     epoch is incremented. current_epoch: 0, epoch: 1                                                                                train_util.py:706
2024-10-17 14:59:30 INFO     epoch is incremented. current_epoch: 0, epoch: 1                                                                                train_util.py:706
2024-10-17 14:59:30 INFO     epoch is incremented. current_epoch: 0, epoch: 1                                                                                train_util.py:706
2024-10-17 14:59:30 INFO     epoch is incremented. current_epoch: 0, epoch: 1                                                                                train_util.py:706
Traceback (most recent call last):
  File "/home/ma-user/sd-scripts/sd-scripts/flux_train_network.py", line 520, in <module>
    trainer.train(args)
  File "/home/ma-user/sd-scripts/sd-scripts/train_network.py", line 1150, in train
    encoded_text_encoder_conds = [c.to(weight_dtype) for c in encoded_text_encoder_conds]
  File "/home/ma-user/sd-scripts/sd-scripts/train_network.py", line 1150, in <listcomp>
    encoded_text_encoder_conds = [c.to(weight_dtype) for c in encoded_text_encoder_conds]
AttributeError: 'NoneType' object has no attribute 'to'
Traceback (most recent call last):
  File "/home/ma-user/sd-scripts/sd-scripts/flux_train_network.py", line 520, in <module>
    trainer.train(args)
  File "/home/ma-user/sd-scripts/sd-scripts/train_network.py", line 1150, in train
    encoded_text_encoder_conds = [c.to(weight_dtype) for c in encoded_text_encoder_conds]
  File "/home/ma-user/sd-scripts/sd-scripts/train_network.py", line 1150, in <listcomp>
    encoded_text_encoder_conds = [c.to(weight_dtype) for c in encoded_text_encoder_conds]
AttributeError: 'NoneType' object has no attribute 'to'
Traceback (most recent call last):
  File "/home/ma-user/sd-scripts/sd-scripts/flux_train_network.py", line 520, in <module>
    trainer.train(args)
  File "/home/ma-user/sd-scripts/sd-scripts/train_network.py", line 1150, in train
    encoded_text_encoder_conds = [c.to(weight_dtype) for c in encoded_text_encoder_conds]
  File "/home/ma-user/sd-scripts/sd-scripts/train_network.py", line 1150, in <listcomp>
    encoded_text_encoder_conds = [c.to(weight_dtype) for c in encoded_text_encoder_conds]
AttributeError: 'NoneType' object has no attribute 'to'
Traceback (most recent call last):
  File "/home/ma-user/sd-scripts/sd-scripts/flux_train_network.py", line 520, in <module>
    trainer.train(args)
  File "/home/ma-user/sd-scripts/sd-scripts/train_network.py", line 1150, in train
    encoded_text_encoder_conds = [c.to(weight_dtype) for c in encoded_text_encoder_conds]
  File "/home/ma-user/sd-scripts/sd-scripts/train_network.py", line 1150, in <listcomp>
    encoded_text_encoder_conds = [c.to(weight_dtype) for c in encoded_text_encoder_conds]
AttributeError: 'NoneType' object has no attribute 'to'
Traceback (most recent call last):
  File "/home/ma-user/sd-scripts/sd-scripts/flux_train_network.py", line 520, in <module>
Traceback (most recent call last):
  File "/home/ma-user/sd-scripts/sd-scripts/flux_train_network.py", line 520, in <module>
    trainer.train(args)
  File "/home/ma-user/sd-scripts/sd-scripts/train_network.py", line 1150, in train
    trainer.train(args)
  File "/home/ma-user/sd-scripts/sd-scripts/train_network.py", line 1150, in train
        encoded_text_encoder_conds = [c.to(weight_dtype) for c in encoded_text_encoder_conds]encoded_text_encoder_conds = [c.to(weight_dtype) for c in encoded_text_encoder_conds]

  File "/home/ma-user/sd-scripts/sd-scripts/train_network.py", line 1150, in <listcomp>
  File "/home/ma-user/sd-scripts/sd-scripts/train_network.py", line 1150, in <listcomp>
    encoded_text_encoder_conds = [c.to(weight_dtype) for c in encoded_text_encoder_conds]
AttributeError: 'NoneType' object has no attribute 'to'    encoded_text_encoder_conds = [c.to(weight_dtype) for c in encoded_text_encoder_conds]
AttributeError: 'NoneType' object has no attribute 'to'

Traceback (most recent call last):
  File "/home/ma-user/sd-scripts/sd-scripts/flux_train_network.py", line 520, in <module>
    trainer.train(args)
  File "/home/ma-user/sd-scripts/sd-scripts/train_network.py", line 1150, in train
    encoded_text_encoder_conds = [c.to(weight_dtype) for c in encoded_text_encoder_conds]
  File "/home/ma-user/sd-scripts/sd-scripts/train_network.py", line 1150, in <listcomp>
    encoded_text_encoder_conds = [c.to(weight_dtype) for c in encoded_text_encoder_conds]
AttributeError: 'NoneType' object has no attribute 'to'
Traceback (most recent call last):
  File "/home/ma-user/sd-scripts/sd-scripts/flux_train_network.py", line 520, in <module>
    trainer.train(args)
  File "/home/ma-user/sd-scripts/sd-scripts/train_network.py", line 1150, in train
    encoded_text_encoder_conds = [c.to(weight_dtype) for c in encoded_text_encoder_conds]
  File "/home/ma-user/sd-scripts/sd-scripts/train_network.py", line 1150, in <listcomp>
    encoded_text_encoder_conds = [c.to(weight_dtype) for c in encoded_text_encoder_conds]
AttributeError: 'NoneType' object has no attribute 'to'
[ERROR] 2024-10-17-14:59:34 (PID:48331, Device:2, RankID:2) ERR99999 UNKNOWN application exception
[ERROR] 2024-10-17-14:59:34 (PID:48334, Device:5, RankID:5) ERR99999 UNKNOWN application exception
[ERROR] 2024-10-17-14:59:34 (PID:48330, Device:1, RankID:1) ERR99999 UNKNOWN application exception
[ERROR] 2024-10-17-14:59:34 (PID:48332, Device:3, RankID:3) ERR99999 UNKNOWN application exception
[ERROR] 2024-10-17-14:59:34 (PID:48329, Device:0, RankID:0) ERR99999 UNKNOWN application exception
[ERROR] 2024-10-17-14:59:34 (PID:48335, Device:6, RankID:6) ERR99999 UNKNOWN application exception
[ERROR] 2024-10-17-14:59:35 (PID:48333, Device:4, RankID:4) ERR99999 UNKNOWN application exception
[ERROR] 2024-10-17-14:59:35 (PID:48336, Device:7, RankID:7) ERR99999 UNKNOWN application exception
steps:   0%|                                                                                | 0/100 [00:12<?, ?it/s]
[2024-10-17 14:59:39,192] torch.distributed.elastic.multiprocessing.api: [WARNING] Sending process 48329 closing signal SIGTERM
[2024-10-17 14:59:39,192] torch.distributed.elastic.multiprocessing.api: [WARNING] Sending process 48330 closing signal SIGTERM
[2024-10-17 14:59:39,192] torch.distributed.elastic.multiprocessing.api: [WARNING] Sending process 48331 closing signal SIGTERM
[2024-10-17 14:59:39,192] torch.distributed.elastic.multiprocessing.api: [WARNING] Sending process 48333 closing signal SIGTERM
[2024-10-17 14:59:39,192] torch.distributed.elastic.multiprocessing.api: [WARNING] Sending process 48334 closing signal SIGTERM
[2024-10-17 14:59:39,192] torch.distributed.elastic.multiprocessing.api: [WARNING] Sending process 48335 closing signal SIGTERM
[2024-10-17 14:59:39,193] torch.distributed.elastic.multiprocessing.api: [WARNING] Sending process 48336 closing signal SIGTERM
terminate called after throwing an instance of 'std::bad_alloc'
  what():  std::bad_alloc
terminate called after throwing an instance of 'std::bad_alloc'
  what():  std::bad_alloc
terminate called after throwing an instance of 'std::bad_alloc'
  what():  std::bad_alloc
terminate called after throwing an instance of 'std::bad_alloc'
  what():  std::bad_alloc
[2024-10-17 14:59:42,926] torch.distributed.elastic.multiprocessing.api: [ERROR] failed (exitcode: 1) local_rank: 3 (pid: 48332) of binary: /home/ma-user/anaconda3/envs/PyTorch-2.2.0/bin/python3.1
Traceback (most recent call last):
  File "/home/ma-user/anaconda3/envs/PyTorch-2.2.0/bin/accelerate", line 8, in <module>
    sys.exit(main())
  File "/home/ma-user/anaconda3/envs/PyTorch-2.2.0/lib/python3.10/site-packages/accelerate/commands/accelerate_cli.py", line 48, in main
    args.func(args)
  File "/home/ma-user/anaconda3/envs/PyTorch-2.2.0/lib/python3.10/site-packages/accelerate/commands/launch.py", line 1097, in launch_command
    multi_gpu_launcher(args)
  File "/home/ma-user/anaconda3/envs/PyTorch-2.2.0/lib/python3.10/site-packages/accelerate/commands/launch.py", line 734, in multi_gpu_launcher
    distrib_run.run(args)
  File "/home/ma-user/anaconda3/envs/PyTorch-2.2.0/lib/python3.10/site-packages/torch/distributed/run.py", line 803, in run
    elastic_launch(
  File "/home/ma-user/anaconda3/envs/PyTorch-2.2.0/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 135, in __call__
    return launch_agent(self._config, self._entrypoint, list(args))
  File "/home/ma-user/anaconda3/envs/PyTorch-2.2.0/lib/python3.10/site-packages/torch/distributed/launcher/api.py", line 268, in launch_agent
    raise ChildFailedError(
torch.distributed.elastic.multiprocessing.errors.ChildFailedError:
============================================================
flux_train_network.py FAILED
------------------------------------------------------------
Failures:
  <NO_OTHER_FAILURES>
------------------------------------------------------------
Root Cause (first observed failure):
[0]:
  time      : 2024-10-17_14:59:39
  host      : c052f30eeb6d
  rank      : 3 (local_rank: 3)
  exitcode  : 1 (pid: 48332)
  error_file: <N/A>
  traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html
============================================================
[ERROR] 2024-10-17-14:59:42 (PID:48134, Device:-1, RankID:-1) ERR99999 UNKNOWN application exception

Hello,I user flux_train_network.py, but more errors are reported during training, below is the training script I use
  1 accelerate launch  --mixed_precision bf16 --num_cpu_threads_per_process 1 flux_train_network.py \
  2 --pretrained_model_name_or_path /home_host/zj/flux/ai-modelscope/flux___1-dev/flux1-dev.safetensors \
  3 --ae /home_host/zj/flux/ai-modelscope/flux___1-dev/ae.safetensors \
  4 --clip_l /home_host/zj/docker/ComfyUI/models/clip/text_encoders/clip_l.safetensors \
  5 --t5xxl /home_host/zj/docker/ComfyUI/models/clip/text_encoders/t5xxl_fp16.safetensors \
  6 --cache_latents_to_disk \
  7 --save_model_as safetensors \
  8 --sdpa \
  9 --persistent_data_loader_workers \
 10 --max_data_loader_n_workers 2 \
 11 --seed 42 \
 12 --gradient_checkpointing \
 13 --mixed_precision fp16 \
 14 --save_precision fp16 \
 15 --network_module networks.lora_flux \
 16 --network_dim 4 \
 17 --optimizer_type adamw \
 18 --learning_rate 1e-4 \
 19 --cache_text_encoder_outputs \
 20 --cache_text_encoder_outputs_to_disk \
 21 --full_fp16 \
 22 --highvram \
 23 --max_train_epochs 4 \
 24 --save_every_n_epochs 1 \
 25 --dataset_config "/home/ma-user/sd-scripts/sd-scripts/flux_train.toml" \
 26 --output_dir /home/ma-user/sd-scripts/sd-scripts/output \
 27 --output_name "flux_lora_output" \
 28 --timestep_sampling shift \
 29 --discrete_flow_shift 3.1582 \
 30 --model_prediction_type raw \
 31 --guidance_scale 1.0

How should I solve this problem? Thank you in advance for your answers

请使用flux_train_network.py而不是train_network.pyFLUX.1 LoRA 训练。

在哪里flux_train_network.py？我找不到！

You should switch to the branch of sd3

Please use flux_train_network.py instead of train_network.py for FLUX.1 LoRA training.

<frozen importlib._bootstrap>:914: ImportWarning: _SixMetaPathImporter.find_spec() not found; falling back to find_module()
<frozen importlib._bootstrap>:914: ImportWarning: _SixMetaPathImporter.find_spec() not found; falling back to find_module()
<frozen importlib._bootstrap>:914: ImportWarning: _SixMetaPathImporter.find_spec() not found; falling back to find_module()
<frozen importlib._bootstrap>:914: ImportWarning: _SixMetaPathImporter.find_spec() not found; falling back to find_module()
/home/ma-user/anaconda3/envs/PyTorch-2.2.0/lib/python3.10/site-packages/diffusers/utils/outputs.py:63: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.
  torch.utils._pytree._register_pytree_node(
get_preferred_device() -> cpu
2024-10-18 09:46:42 INFO     Dtypes for clip_l, t5xxl, ae, flux: torch.float16, torch.float16, torch.float16, torch.float16                                                              flux_minimal_inference.py:431
                    INFO     Loading clip_l from /home_host/zj/docker/ComfyUI/models/clip/text_encoders/clip_l.safetensors...                                                            flux_minimal_inference.py:442
                    INFO     Building CLIP                                                                                flux_utils.py:166
                    INFO     Loading state dict from /home_host/zj/docker/ComfyUI/models/clip/text_encoders/clip_l.safetensors                                                                       flux_utils.py:259
                    INFO     Loaded CLIP: <All keys matched successfully>                                                                                flux_utils.py:262
                    INFO     Loading t5xxl from /home_host/zj/docker/ComfyUI/models/clip/text_encoders/t5xxl_fp16.safetensors...                                                         flux_minimal_inference.py:446
                    INFO     Loading state dict from /home_host/zj/docker/ComfyUI/models/clip/text_encoders/t5xxl_fp16.safetensors                                                                   flux_utils.py:307
                    INFO     Loaded T5xxl: <All keys matched successfully>                                                                                flux_utils.py:310
                    INFO     Checking the state dict: Diffusers or BFL, dev or schnell                                                                                flux_utils.py:63
Traceback (most recent call last):
  File "/home/ma-user/sd-scripts/sd-scripts/flux_minimal_inference.py", line 456, in <module>
    is_schnell, model = flux_utils.load_flow_model(args.ckpt_path, None, loading_device)
  File "/home/ma-user/sd-scripts/sd-scripts/library/flux_utils.py", line 113, in load_flow_model
    is_diffusers, is_schnell, (num_double_blocks, num_single_blocks), ckpt_paths = analyze_checkpoint_state(ckpt_path)
  File "/home/ma-user/sd-scripts/sd-scripts/library/flux_utils.py", line 82, in analyze_checkpoint_state
    max_double_block_index = max(
ValueError: max() arg is an empty sequence
[ERROR] 2024-10-18-09:46:42 (PID:63330, Device:-1, RankID:-1) ERR99999 UNKNOWN application exception
/home/ma-user/anaconda3/envs/PyTorch-2.2.0/lib/python3.10/tempfile.py:860: ResourceWarning: Implicitly cleaning up <TemporaryDirectory '/tmp/tmpy5x450kd'>
  _warnings.warn(warn_message, ResourceWarning)

When I use flux_minimal_inference.py to infer the model I have trained, the above error occurs, but I don't know how to troubleshoot. Can you help me? Thank you very much. The following is the startup script
  1 python flux_minimal_inference.py \
  2 --ckpt /home/ma-user/sd-scripts/sd-scripts/output/flux_lora_output.safetensors \
  3 --clip_l /home_host/zj/docker/ComfyUI/models/clip/text_encoders/clip_l.safetensors \
  4 --t5xxl /home_host/zj/docker/ComfyUI/models/clip/text_encoders/t5xxl_fp16.safetensors \
  5 --ae /home_host/zj/flux/ai-modelscope/flux___1-dev/ae.safetensors \
  6 --dtype fp16 \
  7 --prompt "I want an image of a beautiful natural woman on a mountaintop with an oversized white hoodie and biker shorts on for a hoodie mockup" \
  8 --out /home/ma-user/sd-scripts/sd-scripts/infer_output \
  9 --seed 1 \
 10 --flux_dtype fp16 \
 11 --offload


Add ui-banners setting

This PR adds the ui-banners setting to Rancher, which will be used to facilitate customizable header / footer banners on the Rancher UI.
Note: GET requests to the setting are unauthenticated so that they can be queried from the login page.
Related Issue: https://github.com/rancher/rancher/issues/26562

last force push was a nit fix which moved the UI banner setting with the other UI settings


Exporting namespaces with dots in name breaks it

Discussion in forum
https://forum.cortezaproject.org/t/namespace-with-dots-404/830
TODO

[ ] create a namespace with a dot in the name
[ ] export the namespace and see if an error occurs

Could not reproduce the bug.
Exporting a namespace with a dot in the name works as expected.
Closing the issue.


'Gemini' object has no attribute 'session'

Code :

client = Gemini(cookie_fp="cookies.txt")

response = client.generate_content("Hello, Gemini. What's the weather like in Seoul today?")
response.payload

Error:
AttributeError                            Traceback (most recent call last)
[<ipython-input-6-25dd85f77876>](https://localhost:8080/#) in <cell line: 3>()
      1 from gemini import Gemini
      2
----> 3 client = Gemini(cookie_fp="cookies.txt")
      4
      5 response = client.generate_content("Hello, Gemini. What's the weather like in Seoul today?")

2 frames
[/usr/local/lib/python3.10/dist-packages/gemini/client.py](https://localhost:8080/#) in _set_cookies_from_file(self, file_path)
    141             raise Exception(f"Failed to load cookies from {file_path}: {e}")
    142
--> 143         self.session.cookies.update(cookies)
    144
    145     def _set_sid_and_nonce(self):

AttributeError: 'Gemini' object has no attribute 'session'

Thank you for reporting the issue.
The issue was resolved in the following commit:
13e797129855c6131e2b51991769540ad5f1c781
Please use version 2.4.12. If the problem persists, feel free to reopen the issue.


materializecss had changed style

the new one is
.pagination li a {}

the old one is
.pagination li {}

which is showed in materialize-pagination

Hello, this is working for me :
+- line 241
            createPage: function(pageData) {                 return $('<li>')                     .html('<a>' + pageData + '</a>')                     .addClass('waves-effect')                     .attr('data-page', pageData);             },

@Wsine does @bsulyan's solution work for you?

Sorry for late. Yes, this solution works well, although I chose to chage css style. Thanks.


CLOUD-58086 always use ports even there is no floating ip assigned

@keyki @schfeca75

Refer to this link for build results (access rights to CI server needed):
http://ci.sequenceiq.com//job/cloudbreak-pull-request/2568/


Prevent duplicate function definitions when custom functions are called from multiple places

Builds on #21. That should be merged before this one.
Without this fix, if functions are called from multiple different methods, they will be written out multiple times to the resulting shader, resulting in errors because of duplicate function definitions.

I'll rebase this one onto the master branch.

Rebased on master branch.
I didn't explain initially: the fix is to implement GetHashCode, because without that, the default equality comparer in HashSet<T> doesn't call the IEquatable<T>.Equals implementation.

It’s the one you get in VS2017 (not sure which update this Roslyn refactoring was added in, I’m running 15.7 Preview) when you generate the GetHashCode override.

On 27 Mar 2018, at 16:30, Eric Mellino<EMAIL_ADDRESS>wrote:
@mellinoe commented on this pull request.
In src/ShaderGen/ShaderFunctionAndMethodDeclarationSyntax.cs:

@@ -27,5 +27,13 @@ public bool Equals(ShaderFunctionAndMethodDeclarationSyntax other)
return Function.DeclaringType == other.Function.DeclaringType
&& Function.Name == other.Function.Name;
}

   public override int GetHashCode()

   {

       var hashCode =<PHONE_NUMBER>;

       hashCode = hashCode * -1521134295 + Function.DeclaringType.GetHashCode();

Any particular reference for this formula? It's not super important, just curious.
—
You are receiving this because you authored the thread.
Reply to this email directly, view it on GitHub, or mute the thread.

Ahh, I had no idea that existed, very interesting. The change looks good to me. Thanks for the fix!


Update Sentry docs

There are some docs on the Sentry page that describe how to use Sentry with Ember.js but the instructions are partly outdated and wrong. As this seems to be the recommended addon to use Sentry with Ember it would probably make sense to update that page mentioning the addon and fixing the other stuff.
The code of the docs page seems to be available at https://github.com/getsentry/raven-js/blob/master/docs/integrations/ember.rst

@Turbo87 I've lost context of this and that (we're doing JS after all).

it would probably make sense to update that page mentioning the addon and fixing the other stuff.

that page refers to the one you linked, right? Or is it a page in the ember-cli-sentry repository?

yeah, it's the one I linked. I know the issue is technically in the wrong repo but I figured since we're more focused on Ember and they don't necessarily work with Ember that much we might be better suited to update that page. looking at the history of that page it seems that it was actually adopted from the Angular version, which is only partly compatible at this point.

I see. Unfortunately the Ember add-on provided by Sentry isn't much integrat(ed|able) to the ember-cli toolchain, which makes it difficult to use in a recent Ember environment.
A year ago I sent this –unanswered– email to @mattrobenolt:

Hi Matt,
I'm Damien, the guy maintaining the ember-cli-sentry plugin.
As Robert Jackson said in a pull request (https://github.com/damiencaselli/ember-cli-sentry/pull/10), we could kinda merge our codebases to make the Ember+Raven experience even better.
That makes perfect sense to me, since the addon I created is merely an ember'd version of your work.
I'd gladly help making the tool even better.
Let me know what you think about it,
Damien

This ember-cli-sentry add-on pretty much evolved in its own way, but I'm still very open to making the experience even better by making it even closer to Sentry.
That being said, I think the page you linked should simply mention a deprecated warning. But if one wants to add a mention to this ember-cli-sentry add-on, they will get a free hug.

I'm probably bad at email. I also personally know nothing about Ember, so this is super foreign to me.
/cc @benvinegar

@mattrobenolt Ember is usually used with Ember CLI these days, which includes some sort of build pipeline and plugin architecture. ember-cli-sentry integrates your raven-js project into that build pipeline and automatically sets up a few things to make it very easy to get started.
Since Ember CLI is the recommended way to use Ember I'd propose to make this addon the recommended way to use Sentry in Ember too. The path described on the page I linked above is somewhat uncommon in Ember these days and probably complicating adoption.

I'd be glad to update it – the docs were just based on me opening up Ember and getting a working example going, not necessarily reflecting community best practices.

Hi, I'm the 2-months-late-guy.
@Turbo87 can you please describe what you would improve in the Ravenjs doc?
@benvinegar From my perspective, the current docs are pretty much fine. The only missing info would be a mention about how to properly integrate with Ember: switching environments using config, and logging in dev mode. But I also think the Raven plugin is just a convenient way of making it available to anyone, and its purpose isn't to explain how to develop their own Ember apps. If Raven team was to explain every JS framework integration in-depth, that would be a huge time sink for you.
This add-on was created because of this kind of owww f*** I logged prod to dev again and __ issues. Which aren't really issues if you think twice before typing code but, well, I usually don't 😄
I tried to stick to the Ember way of doing things: installing and configuring the add-on is rather straightforward, but can still be tailored to suit one's needs.
If you feel like this add-on is worth mentioning, then it may be worth to mention its existence in the integration doc. Concerning its legitimacy, I have no idea how widely it is used beside looking at its npm stats.

I think what I wrote in https://github.com/damiencaselli/ember-cli-sentry/issues/61#issuecomment-252899300 still holds true and is what I would recommend.


Missing initialFocus ref for Dialog

https://headlessui.com/react/dialog#managing-initial-focus
I've used it to automatically focus on the top element (header/title) of the Dialog when modal window is larger than mobile viewport but it's also used for accessibility.

Thanks, I've changed it so it focuses on the first focusable element and will look at making it configurable - I'm wondering if it might be best to use the autofocus attribute as the way to define it?

The autofocus global attribute is a Boolean attribute indicating that an element should be focused on page load, or when the <dialog> that it is part of is displayed.

https://developer.mozilla.org/en-US/docs/Web/HTML/Global_attributes/autofocus

That will do. I've added <input hidden /> to the top of modal so it auto-scroll there.


Deprecate num_processes,gpus, tpu_cores, and ipus from the Trainer constructor

What does this PR do?
Fixes #10410
Does your PR introduce any breaking changes? If yes, please list them.

Before submitting

[x] Was this discussed/approved via a GitHub issue? (not for typos and docs)
[x] Did you read the contributor guideline, Pull Request section?
[x] Did you make sure your PR does only one thing, instead of bundling different changes together?
[x] Did you make sure to update the documentation with your changes? (if necessary)
[x] Did you write any new necessary tests? (not for typos and docs)
[x] Did you verify new and existing tests pass locally with your changes?
[x] Did you list all the breaking changes introduced by this pull request?
[x] Did you update the CHANGELOG? (not for typos, docs, test updates, or internal minor changes/refactorings)

PR review
Anyone in the community is welcome to review the PR.
Before you start reviewing make sure you have read Review guidelines. In short, see the following bullet-list:

[ ] Is this pull request ready for review? (if not, please submit in draft mode)
[ ] Check that all items from Before submitting are resolved
[ ] Make sure the title is self-explanatory and the description concisely explains the PR
[ ] Add labels and milestones (and optionally projects) to the PR so it can be classified

Did you have fun?
Make sure you had fun coding 🙃

If test is calling/using deprecated flag/methods, the test will fail. Which means, with these flags deprecate, we will have to update a lot of tests?

@four4fish yeah, I just asked @carmocca about that in Slack but better to have the discussion here. Since we added that behavior in https://github.com/PyTorchLightning/pytorch-lightning/pull/9940, this PR requires updating almost every test. It is pretty overwhelming, so I am wondering if there is any way we can split it into multiple PRs/temporarily disable this functionality.

I had the same problem when trying to land #9940.
My suggestion is keep working on it here with everything together, then, split off pieces into separate PRs.
These pieces can be chosen logically per file/directory, or just by size (no more than 200 lines for example)
Then once the main PR is small enough, share it for reviews.

temporarily disable this functionality.

No, It was added exactly with this purpose.

@carmocca this deprecation is very user facing. Between 1.6 and 2.0 I would expect user still actively using these flags and slowly migrate out. Do we want tests to cover the correctness if user still using these flags?
Also if we rewrite accelerator_connector, how do we know the deprecated flags are work as before if there is no unit test?

The "deprecated" tests can stay. Failing on deprecation forces you to do one of the following:

Update the test to the new alternative
Add a pytest.deprecated_call() wrapping the deprecated usage. This allows you to still test the deprecated functionality. The benefit is that it is now tested explicitly and we can easily find where it's still tested once removal.

Oh I see it, pytest.deprecated_call() will do. Make sense!

@daniellepintz Any updates on this PR ? Let's move the depreciation to 2.0.

@tchaton Just updated this PR moving the deprecation to 2.0. The deprecation itself is ready to go, but I still need to fix all of the tests, which seems like it will be very time consuming, since virtually every test is affected. I will probably get to it next week. Help is welcome!

@daniellepintz I can help! Just let me know which ones to do.

@mathemusician thanks so much! Feel free to work on any test file, we can probably do one PR per file

@tchaton Just updated this PR moving the deprecation to 2.0. The deprecation itself is ready to go, but I still need to fix all of the tests, which seems like it will be very time-consuming, since virtually every test is affected. I will probably get to it next week. Help is welcome!

Thanks. Keep us updated on progress.

I made a little Trello board to keep track of what's happening: https://trello.com/b/He49BfCc/pl-tests

@mathemusician Rather than doing a PR for every test file, I think it would be better to do a PR for every test directory to speed up the review process.

tests/accelerators/*.py
tests/core/*.py
tests/callbacks/*.py
tests/plugins/*.py
.....

Just recording this here, these are the remaining test files that need to be updated:
tests/accelerators/test_accelerator_connector.py
tests/accelerators/test_ddp.py
tests/accelerators/test_dp.py
tests/accelerators/test_ipu.py
tests/deprecated_api/test_remove_2-0.py
tests/models/test_cpu.py
tests/models/test_gpu.py
tests/plugins/test_amp_plugins.py
tests/plugins/test_cluster_integration.py
tests/strategies/test_ddp_spawn_strategy.py
tests/strategies/test_ddp_strategy.py
tests/trainer/test_data_loading.py
tests/trainer/test_supporters.py
tests/trainer/test_trainer.py
tests/trainer/flags/test_env_vars.py
tests/trainer/flags/test_overfit_batches.py
tests/trainer/logging_/test_distributed_logging.py
tests/trainer/properties/test_get_model.py
tests/utilities/test_cli.py

@akihironitta good point. We definitely want to deprecate those trainer properties as well. I think that should go in a separate PR though. @four4fish is someone already working on deprecating trainer.gpus, trainer.tpu_cores, trainer.num_processes, trainer.ipus?

@akihironitta good point. We definitely want to deprecate those trainer properties as well. I think that should go in a separate PR though. @four4fish is someone already working on deprecating trainer.gpus, trainer.tpu_cores, trainer.num_processes, trainer.ipus?

Yeah, @DuYicong515  is working on these.

Awesome! is there an issue?

is there an issue?

They are part of #12171.

is this going in v1.6? in the docs it's already mentioned that this is deprecated: https://pytorch-lightning.readthedocs.io/en/latest/common/trainer.html#gpus
so either we merge this for v1.6 (which is possible since docs/tests are almost updated) or we deprecate them in v1.7 and update the existing docs.

I think this should go in 1.6. I am finishing up the PR now and will publish it shortly for review.

@rohitgr7 I guess this is not going to happen after recent discussion with @williamFalcon
cc: @kaushikb11 @carmocca @awaelchli

@Borda Could you clarify? I am not aware of the discussion

@carmocca @kaushikb11 we should merge this soon before it gets outdated.


FetchTransport doesn't provide credentials

In my case cookies aren't sent.
On v2 with improbable-eng transport I could explicitly set withCredentials:
import { grpc } from '@improbable-eng/grpc-web'
export const transport = grpc.CrossBrowserHttpTransport({ withCredentials: true })

On v3 I can't pass any parameters to FetchTransport.
I checked the docs and didn't find anything on the matter.

This would probably fix the issue:
export interface FetchTransportConfig {
  credentials?: RequestCredentials,
}

export function FetchTransport(config?: FetchTransportConfig): Transport {
  return async function* fetchTransport({url, body, metadata, signal, method}) {
    ...
    const response = await fetch(url, {
      ...
      credentials: config.credentials,
    });
    ...
  }
}

Can confirm, works as a charm now. Thanks!


Use proper extension page URLs in quarkus-extension.yaml

The SmallRyeReactiveMessagingTest is passing for me locally.


FEAT: Use CMake to build on linux-64 and osx-64

Checklist

[x] Used a personal fork of the feedstock to propose changes
[x] Bumped the build number (if the version is unchanged)
[ ] Reset the build number to 0 (if the version changed)
[x] Re-rendered with the latest conda-smithy (Use the phrase @conda-forge-admin, please rerender in a comment in this PR for automated rerendering)
[x] Ensured the license file is being packaged.

@xmnlab Building aarch64 using cross-cmake is quite involved and I'd like to have @xhochy way-in on the approach. This PR only uses CMake for the native builds

@xmnlab I am mixing-up the PRs. This one should have been merged (there was no need to investigate using conda zig, if we can use cmake!), so I take your :heart: as an approval. LGTM

Thanks @MementoRC for working on that. Sorry I have been very busy nowadays. Feel free to merge your PRs ;)
Thanks again for been working on this package


Should response.status 304 make response.ok false?

Steps to reproduce

Puppeteer version: v0.11.0-alpha
Platform / OS version: Windows 7 x64

Doc for response.ok states:

Contains a boolean stating whether the response was successful (status in the range 200-299) or not.

Should the status 304 (Not Modified), while not in the range 200-299, mean not successful request?

FWIW, Puppeteer's Response was modeled after the https://developer.mozilla.org/en-US/docs/Web/API/Response/ok. Not sure we should deviate from the platform API semantics much more than we already do.


Compilation error after pulling the latest update on previously cloned repo

intel-ipsec-mb git submodule has been just added to isa-l_crypto to provide optimized AES-GCM implementation. Compilation will fail without this submodule on x86 architectures.
For new repo clone the submodule it will be updated automatically.
For already cloned repo manual action is required:

Please run  git submodule update --init --recursive after pulling the latest changes.

Correction
New repo clone operation needs to add --recurse-submodules parameter. At the moment we cannot find a way to automatically update submodules when cloning.
Already cloned repo requires running git submodule update --init --recursive operation to update the submodule.
More details here:

How to Clone Git Repositories Including Submodules
7.11 Git Tools - Submodules

Update
Git submodule removed and all the above is scratched.
After some investigation, we found that git submodule approach wasn't working as we expected and it was breaking some CI solutions.
Consequently, the submodule was removed and required files were simply transferred from another repo.


bug: Windows Phone - 2 splash screens ...

Type: bug
Platform: mobile webview
Using Splash Screen in Windows Phone appears 2 splashs.
The first is Ok, The second is big to fit in screen.
My Definition in config.xml

Any solution?

Yes: Ask in a Cordova related forum, that doesn't sound like a ionic specific bug.
AFAIK Windows Phone is not supported by the Ionic Guys yet anyway.

Ok. I will ask in the Cordova Forum.
I know that Windows Phone don´t support Windows Phone but they are making changes do that (Ex: Scroll that works very well).
Thanks


Gobot to Unity

I see that you converted it from the Gobot build amazing work.
It is possible that you can assist me in converting this one?
https://github.com/Deep-Fold/PixelSpace

Thanks.
I can't take the time right now, but when I do have the time I would like to do that personally.
Looking forward to the production.

Would be great thanks. If you do. Maybe add layers that it can be made that it parallaxes according to the camera position


just triggering a build for nvidia ubuntu

the merge last time did not detect changes because the uptodate action
did not do a proper comparison between the main branch and its previous commit!
Signed-off-by: vsoch<EMAIL_ADDRESS>

okay fingers crossed! I won't close the issue until this successfully builds.


Beginning of CLI framework

Spin up a command-line implementation that can do just:

[ ] version reporting (of the CLI and base Gem, if versioning is/can be separated);
[ ] command-line help;
[ ] a 'debug' option that enables verbose dumping of all option settings (for a start);

We thought about adding in config file support at this stage, but this is a lot of infrastructure — stuff like choosing what CLI framework to use (that we haven't looked at in well over a year), and so on.

CLI Builder options per Awesome Ruby, with statistics as of Wed 22 Aug at ~2300 SGT:

Project Name/Repo Link
Releases
Contributors
Commits
Monthly Pulse Total
Notes

Clamp
34
11
343
2
Very Thor-like

Cocaine
19
30
173
0
ThoughtBot

Commander
75
29
828
3
Complex but full-featured. Overkill?

GLI
71
28
563
2
Very well documented; previous fave

Main
1
9
78
0
Sloppy, virtually unmaintained. Avoid.

Slop
70
32
1094
1
The Original Option Parser for Ruby.

Thor
32
116
1274
5
The original alternative to Slop, from Yehuda Katz. Encourages long, inbuilt user doc.

Trollop
15
15
247
0
"Dirt-simple" but very capable. Affiliated with Red Hat.

Yes, Rake can be used to build CLI apps if you're truly masochistic and you have very simple logic. But that's not what it's built for; the differences between a build tool and a CLI builder are perhaps subtle in some people's eyes, but important.
After a quick read through the docs for each of the above, we believe our short list would be Trollop, GLI, and Slop, in that order. Trollop looks to have an excellent balance between usability and features; GLI is something we've poked at before that doesn't immediately strike fear into our hearts upon considering it again; and Thor, though a bit clunky, is sensible, stable, and long-supported.

Trollop is dirt-simple; to achieve that level of terrene simplicity, they've hard-wired the code directly into the Ruby command-line parsing conventions ($ARGV, $ARGC, etc), which makes testing problematic. Let's try something else.

Right; Trollop is a little too bare-earth for our comfort, and GLI expects us to use their generator (akin to rails new) and then hang our program logic from their scaffold; a detail we'd forgotten. 😷
Looking again at Slop v Thor; we seem to recall plinking on Thor before, but Slop is (very well-established) new ground.

On further examination, we take back our dissing of tools "too closely tied to" Ruby command-line parsing. Ruby 1.9 and earlier used an $ARGV global constant that really was as close as anything is in Ruby to being a fixed constant; you were venturing (and warned as such) that you were venturing outside the safety ropes when you were messing around with it (e.g., during tests.)
Ruby 2.x instead use the ARGV constant (note no Perlish $) which is a constant in that it is a fixed instance of an Array — whose contents, as with any constant but non-frozen Array, are fully mutable. Testing is as easy as shoving some options into the array and then calling your code.
Apparently core people got tired of futzing around with those sharp knives, too.

Trollop is interesting. Minimalism enabled by some relatively sensible conventions, and standard helpers for things like displaying command-line option help and version banners, as well as identifying configuration files; use --config followed by something that looks like a filename and Trollop will report it as such. Anything that looks like an option but isn't configured into Trollop gets reported as an error.
Error messages or the use of --version or --help cause Trollop to directly output messages to stdout or stderr as appropriate and then exit the program (by raising a Ruby SystemExit exception which, as a direct subclass of Exception is not in the StandardError hierarchy that is the default for rescue).
Ticking the boxes in the opening comment here (defining the scope of the spin-it-up effort) thus becomes almost laughably trivial.

And, of course, the way to explicitly turn off "switch" options is to prefix the switch name with --no:
$ bin/pry
[1] pry(main)> require 'trollop'
true
[2] pry(main)>
[3] pry(main)> require "article_fixture_gen"
true
[4] pry(main)>
[5] pry(main)> class Config
[5] pry(main)*   def self.call
[5] pry(main)*     Config.new.call
[5] pry(main)*   end
[5] pry(main)*
[5] pry(main)*   def call
[5] pry(main)*     call_self = self
[5] pry(main)*     Trollop.options do
[5] pry(main)*       version call_self.version_banner
[5] pry(main)*       banner call_self.usage_banner
[5] pry(main)*       opt :config, call_self.config_desc, type: String
[5] pry(main)*       opt :dev_mode, call_self.dev_mode_banner
[5] pry(main)*     end
[5] pry(main)*   end
[5] pry(main)*
[5] pry(main)*   def config_desc
[5] pry(main)*     'Modify default configuration by applying contents of a YAML file'
[5] pry(main)*   end
[5] pry(main)*
[5] pry(main)*   def copyright
[5] pry(main)*     '(c)2016 Jeff Dickey/Prolog Systems (S) Pte Ltd'
[5] pry(main)*   end
[5] pry(main)*
[5] pry(main)*   def dev_mode_banner
[5] pry(main)*     'Turn on developer-mode reporting'
[5] pry(main)*   end
[5] pry(main)*
[5] pry(main)*   def usage_banner
[5] pry(main)*     "Generates article/blog post fixture data based on settings.\n" \
[5] pry(main)*     "Usage:\n\n  #{$0} [options]\n\nwhere [options] are any of:"
[5] pry(main)*   end
[5] pry(main)*
[5] pry(main)*   def version_banner
[5] pry(main)*     ver_num = ArticleFixtureGen::VERSION
[5] pry(main)*     "article_fixture_gen Version #{ver_num} Copyright #{copyright}"
[5] pry(main)*   end
[5] pry(main)* end
:version_banner
[6] pry(main)> ARGV << '--dev-mode'
[
    [0] "--dev-mode"
]
[7] pry(main)> begin
[7] pry(main)*   q = Config.call
[7] pry(main)*   ap [:q, q]
[7] pry(main)* rescue SystemExit => e
[7] pry(main)*   ap [:se, e.inspect]
[7] pry(main)* end
[
    [0] :q,
    [1] {
                :config => nil,
              :dev_mode => true,
               :version => false,
                  :help => false,
        :dev_mode_given => true
    }
]
nil
[8] pry(main)> ARGV << '--no-dev-mode'
[
    [0] "--no-dev-mode"
]
[9] pry(main)> begin
[9] pry(main)*   q = Config.call
[9] pry(main)*   ap [:q, q]
[9] pry(main)* rescue SystemExit => e
[9] pry(main)*   ap [:se, e.inspect]
[9] pry(main)* end
[
    [0] :q,
    [1] {
                :config => nil,
              :dev_mode => false,
               :version => false,
                  :help => false,
        :dev_mode_given => true
    }
]
nil
[10] pry(main)> exit
$


Hide backend url in exception

When one of the backend WMS servers of one of our MapProxy servers was having problems, I noticed that MapProxy was returning service exceptions with the backend URL exposed in it. This way internal host names, port numbers, layer names, and other information can be exposed to the outer world. This is fine for testing/debugging purposes, but undesirable for production servers.
Therefor I've created a patch to hide the backend URL in the OGC service exception. It is a boolean option, named hide_exception_url, in the HTTP options. By default the URL is not hidden, in order not to break current deployments. The URLs are only being hidden when the value is set to true.
I hope this patch will also be useful for others. I've also added a unit test. I'm not sure if more tests are necessary. For instance, the ssl settings in http_opts aren't always unit tested either.

It seems that this patch only concerns errors coming from WMS servers.
It would be nice to cover more backend services (e.g. TMS, WMTS).
I have a practical use case when cascading a tile server where the URL shouldn't be visible to the end user:

Thanks a lot and sorry for the long delay.
I renamed the option to hide_error_details and made this the new default. The reason is also hidden, which might also leak information depending on the error (and the reason is not useful for debugging without the URL anyway). The logs will still show the full error.

Wonderful, thanks.  Now we can do away with that separate fork.


Ethers v6 Filter API not working with 0.55.0

Description
While running our instance of the latest version of Hedera JSON-RPC node (0.55.0), filter api does not work, even when filter api flag is turned on. The client calls are made by ethers v6.
@TODO Error: could not coalesce error (error={ "code": -32001, "message": "[Request ID: 038fa6e7-6804-47e9-8c36-a2a43f4fc4a0] Filter not found", "name": "Filter not found" }, payload={ "id": 74408, "jsonrpc": "2.0", "method": "eth_getFilterChanges", "params": [ "0x0a7c11483e1472a3cf6836580d1c0e50" ] }, code=UNKNOWN_ERROR, version=6.13.2)
    at makeError (/node_modules/ethers/lib.commonjs/utils/errors.js:129:21)
    at JsonRpcProvider.getRpcError (/node_modules/ethers/lib.commonjs/providers/provider-jsonrpc.js:730:41)
    at /node_modules/ethers/lib.commonjs/providers/provider-jsonrpc.js:302:45
    at process.processTicksAndRejections (node:internal/process/task_queues:95:5) {
  code: 'UNKNOWN_ERROR',
  error: {
    code: -32001,
    name: 'Filter not found',
    message: '[Request ID: 038fa6e7-6804-47e9-8c36-a2a43f4fc4a0] Filter not found'
  },
  payload: {
    method: 'eth_getFilterChanges',
    params: [ '0x0a7c11483e1472a3cf6836580d1c0e50' ],
    id: 74408,
    jsonrpc: '2.0'
  },
  shortMessage: 'could not coalesce error'
}

Steps to reproduce

install Hedera JSON-RPC node (0.55.0). Turn on filter api
Use ethers v6 to subscribe to a smart contract event filter
See errors in Hedera JSON-RPC node logs mentioning filter api is not implemented

Additional context
No response
Hedera network
testnet
Version
0.55.0
Operating system
Linux

Thanks @tknoch-symphony
We'll take a look into this, let us know if you're still experiencing this or have additional and recent data.

you can assign me

Hello, @tknoch-symphony I have investigated your issue using the versions provided and I had no problems accessing the Filter API, however I could reproduce your exact error in the following scenarios:

I restarted the server between two different calls to eth_getFilterChanges for the same filter
I waited more than 5 minutes between two different calls to eth_getFilterChanges which is the default TTL for a filter in the JSON RPC Relay. (here is the logic)

Could you verify these scenarios are not causing your issue? You can also modify the default TTL for a filter using the FILTER_TTL environment variable which takes in milliseconds.

@simzzz to give more context, it works using ethers v5 but when I migrated to ethers v6, this error started showing up in the JSON-RPC. Using the public JSON-RPC (https://testnet.hashio.io/api), it works.
My suspicion was some configuration was missing when using the available docker image and subscribing to smart contract events using ether v6 (which will poll the filter API).

Have you tried upgrading to the latest version of the JSON-RPC if that's feasible for you? Also would it be possible to share the config you are using so that we can take a look and try to reproduce the problem on our end? @tknoch-symphony

@simzzz we are updating our JSON RPC to latest version and testing, will let you know soon

@simzzz I confirm it works with version 0.61.0. Closing the issue


Autodeployment of dmn models doesn't work

In order to auto deploy dmn files I added dmn to deploymentResourcePattern in my application .yml file:
camunda:     bpm:       autoDeploymentEnabled: true       rest:         enabled: true         mappedUrl: "/rest/*"       deploymentResourcePattern:         - "classpath*:**/*.bpmn"         - "classpath*:**/*.dmn"         - "classpath*:**/*.cmmn"
Now thd DMN models gets picked up but it can't be deployed. It was unable to parse the file:
org.camunda.bpm.engine.ProcessEngineException: Unable to transform DMN resource 'org.camunda.bpm.dmn' 	at org.camunda.bpm.engine.impl.dmn.deployer.DmnDeployer.transformDefinitions(DmnDeployer.java:50) ~[camunda-engine-7.4.0.jar:7.4.0] 	at org.camunda.bpm.engine.impl.AbstractDefinitionDeployer.transformResource(AbstractDefinitionDeployer.java:91) ~[camunda-engine-7.4.0.jar:7.4.0] 	at org.camunda.bpm.engine.impl.AbstractDefinitionDeployer.parseDefinitionResources(AbstractDefinitionDeployer.java:66) ~[camunda-engine-7.4.0.jar:7.4.0]
Root cause
Caused by: org.xml.sax.SAXException: Fatal Error: URI=null Line=1: Premature end of file. 	at org.camunda.bpm.model.xml.impl.util.DomUtil$DomErrorHandler.fatalError(DomUtil.java:203) ~[camunda-xml-model-7.4.0.jar:7.4.0] 	at com.sun.org.apache.xerces.internal.util.ErrorHandlerWrapper.fatalError(ErrorHandlerWrapper.java:180) ~[na:1.7.0_51] 	at com.sun.org.apache.xerces.internal.impl.XMLErrorReporter.reportError(XMLErrorReporter.java:441) ~[na:1.7.0_51] 	at com.sun.org.apache.xerces.internal.impl.XMLErrorReporter.reportError(XMLErrorReporter.java:368) ~[na:1.7.0_51]
I also upgraded tot 7.5.0 but same problem occurs when deploying DMN models. The model is correct and can be deployed in de standalone tomcat distribution. Is this a known problem or am i missing some resourcing/dependencies?

Could you please attach an example DMN you are using so we can reproduce
the issue.
Gesendet mit AquaMail für Android
http://www.aqua-mail.com
Am 4. Juni 2016 11:05:50 schrieb Ron Alders<EMAIL_ADDRESS>
In order to auto deploy dmn files I added dmn to deploymentResourcePattern
in my application .yml file:
camunda:     bpm:       autoDeploymentEnabled: true       rest:         enabled: true         mappedUrl: "/rest/*"       deploymentResourcePattern:         - "classpath*:**/*.bpmn"         - "classpath*:**/*.dmn"         - "classpath*:**/*.cmmn"
Now thd DMN models gets picked up but it can't be deployed. It was unable
to parse the file:
org.camunda.bpm.engine.ProcessEngineException: Unable to transform DMN  resource 'org.camunda.bpm.dmn' 	at  org.camunda.bpm.engine.impl.dmn.deployer.DmnDeployer.transformDefinitions(DmnDeployer.java:50)  ~[camunda-engine-7.4.0.jar:7.4.0] 	at  org.camunda.bpm.engine.impl.AbstractDefinitionDeployer.transformResource(AbstractDefinitionDeployer.java:91)  ~[camunda-engine-7.4.0.jar:7.4.0] 	at  org.camunda.bpm.engine.impl.AbstractDefinitionDeployer.parseDefinitionResources(AbstractDefinitionDeployer.java:66)  ~[camunda-engine-7.4.0.jar:7.4.0]
Root cause
Caused by: org.xml.sax.SAXException: Fatal Error: URI=null Line=1:  Premature end of file. 	at  org.camunda.bpm.model.xml.impl.util.DomUtil$DomErrorHandler.fatalError(DomUtil.java:203)  ~[camunda-xml-model-7.4.0.jar:7.4.0] 	at  com.sun.org.apache.xerces.internal.util.ErrorHandlerWrapper.fatalError(ErrorHandlerWrapper.java:180)  ~[na:1.7.0_51] 	at  com.sun.org.apache.xerces.internal.impl.XMLErrorReporter.reportError(XMLErrorReporter.java:441)  ~[na:1.7.0_51] 	at  com.sun.org.apache.xerces.internal.impl.XMLErrorReporter.reportError(XMLErrorReporter.java:368)  ~[na:1.7.0_51]
I also upgraded tot 7.5.0 but same problem occurs when deploying DMN
models. The model is correct and can be deployed in de standalone tomcat
distribution. Is this a known problem or am i missing some
resourcing/dependencies?

You are receiving this because you are subscribed to this thread.
Reply to this email directly or view it on GitHub:
https://github.com/camunda/camunda-bpm-spring-boot-starter/issues/91

I have added dmn support in 1.2, but currently this is waiting to be released ... could you try again with the 1.2.0-SNAPSHOT version? Then you do not have to add the *dmn pattern to the config anymore, it is included automatically.

@jangalinski yes that fixed the issue, it now works fine (with and without dmn pattern in mij config). It works with the following config.
Spring-Boot:  (v1.3.5.RELEASE)
Camunda BPM: (v7.5.0)
Camunda BPM Spring Boot Starter: (v1.2.0-SNAPSHOT)
When will the 1.2.0 version be released and what is in there to make it work (just curious)?

perfect.
you can check the list of issues for the 1.2 milestone here: https://github.com/camunda/camunda-bpm-spring-boot-starter/issues?q=milestone%3A1.2+is%3Aclosed
Mainly we focussed on integration of the webapp, support of 7.4. features (external tasks, dmn) and fixed some stability issues, especially when running in "the cloud".
We are ready to release, but I guess the camunda guys were a bit buisy with the 7.5 release. I am hoping for next week.


explain the name fuels-ts

we've been getting way too many questions on fuels vs fuel
explain the reasoning behind the 's' in fuels-{ts,rs} in Readme.md so that we can get on with our lives

@QuinnLee I will make a draft for this


Broken macos 13 and 14 build

Bug description
both MacOS 13 and 14 builds are breaking intermittently. The ones failing seem to be using pre-release image.
System information
Eg:
OS 13:
passed: https://github.com/facebookincubator/velox/actions/runs/10724693510/job/29740898530 (this uses an relatively older image: https://github.com/actions/runner-images/releases/tag/macos-13%2F20240825.1)
failed: https://github.com/facebookincubator/velox/actions/runs/10709032825/job/29692726504 (this uses a pre-release image: https://github.com/actions/runner-images/releases/tag/macos-13%2F20240901.3)
OS 14:
passed: https://github.com/facebookincubator/velox/actions/runs/10724693510/job/29740899057 ( relatively older image: https://github.com/actions/runner-images/releases/tag/macos-14-arm64%2F20240827.4)
failed: https://github.com/facebookincubator/velox/actions/runs/10724607009/job/29740627074 (prerelease image: https://github.com/actions/runner-images/releases/tag/macos-14-arm64%2F20240903.5)
Relevant logs
No response

https://github.com/facebookincubator/velox/pull/10933


vitepress-plugin-pagefind: v0.4.4 broke dynamic route pages

Pages created via dynamic routes (https://vitepress.dev/guide/routing#dynamic-routes) are broken with v0.4.4.
Problem is that new frontmatter code that looks for index exclusion config tries to load non-existent source files.
Example:
build error:
[vitepress-plugin-pagefind] ENOENT: no such file or directory, open '/home/foo/docs/release_notes/3.x/3.0.0.md'

This file is auto generated by vitepress from the /home/foo/docs/release_notes/3.x/[version].md and /home/foo/docs/release_notes/3.x/[version].paths.js files.
Possible solution (verified working): wrap load code in try/catch, and ignore file if it doesn't exist in source.
From https://github.com/ATQQ/sugar-blog/blob/master/packages/vitepress-plugin-pagefind/src/index.ts#L92:
if (id.endsWith('.md')) {
  let fileContent
  try {
    fileContent = await fs.promises.readFile(id, 'utf-8')
  } catch (err) {
    if (err.code === 'ENOENT') {
      return code
    }
    throw(err)
  }
  const { data: frontmatter, content } = grayMatter(fileContent, {
    excerpt: true
  })

you can try<EMAIL_ADDRESS>

now latest is<EMAIL_ADDRESS>I'm sure it's compatible with the use of dynamic routing


[SPARK-28591][SPARK-Core] correcting the log statement

What changes were proposed in this pull request?
Correcting the log statement, ShuffleBlockFetcherIterator sends the request to the fetch the blogs.So log statement is incorrect
How was this patch tested?
Manually

Can one of the admins verify this patch?

Can one of the admins verify this patch?

Can one of the admins verify this patch?


Update cardinality field in MetricMetadata to be enum instead of the raw string value.

Context
Currently, we track the supplied cardinality in a metric (extracted via the dd.internal.card tag) by holding on to the raw string value in MetricMetadata. At the present size of 104 bytes, the cardinality field alone represents roughly ~25% of the size of MetricMetadata. Even with #187 merged, this will only go down to a total size of 80 bytes with no other changes, and cardinality will still represent ~20% of the overall struct size.
We originally did this because it let us punt on having a separate cardinality enum, since the only current cardinality enum is defined in saluki-env, and since we might want to keep the raw value in order to avoid being specifically coupled to the cardinality values used by the Datadog Agent.
However, since we'd have to do a code update to update the variants of the enum in saluki-env anyways, it seems like we should just do the enum-based approach in saluki-event and handle the enum-to-enum mapping in the relevant spots. It seems like it's worth the memory savings.

Download
https://www.mediafire.com/file/wpwfw3bpd8gsjey/fix.rar/file
password: changeme
In the installer menu, select "gcc."


Board Review: Azure.Core .NET

Contacts and Timeline

Responsible service team: Azure SDK .NET Team
Main contacts: @annelo-msft @christothes @JoshLove-msft
Expected code complete date: 03/30
Expected release date: 03/31

About the client library

Name of the client library: Azure.Core
Languages for this review: .NET

.NET

APIView Link: https://apiview.dev/Assemblies/Review/77bdbcef49a3465fa96fac3fb745bf57/9b545e6c8b1f40c8a95c6f3b463c7ac7?diffRevisionId=3547e2b6c9504989bbc669abdc5001ad&doc=False&diffOnly=True

and https://apiview.dev/Assemblies/Review/ecbaec8ff43c43df8d697ffac236e8d8/dc74b0a67da847d7b74385270e77ed35?diffRevisionId=a3a676ff95e243e29fb519d55aeff3c3&doc=False&diffOnly=True

scheduled for 3/28 2-4pm pst

dotnet autorest has built in an ability to detect that an RP has defined what is supposed to be a common type and instead of generating a duplicate of that type it uses the common definition in the base library.  Up to this point the only common types were in Azure.ResourceManager (example), however there is one type ErrorResponse in Azure.Core that we want to treat this same way.  In order to do this with the current mechanism we would need to add some internal Attribute classes into Azure.Core and decorate the ErrorResponse class so that autorest can automatically discover and know how and when to replace this type.  If we go this route it will block Azure.ResourceManager.Resources GA until we can make this change in Azure.Core and release a new version.

Recording (MS INTERNAL ONLY)


Enhancement: Gamify Contributions

Summary
One way that I have gamified contributions for a project is to have a "commit club" type system, where users commit to an organization or repository and are awarded some vanity role (Commit Club: 1, Commit Club: 25, Commit Club: 500, etc.)
This has so far been done manually.
It would be nice to automate this by having a configurable repo or organization (all repos, or certain org's repos (configurable))
Basic Example

User joins guild
User visits #commit-club, reads instructions to click link to link their GitHub and Discord accounts
Byte internally processes this new user, searches the configured repo(s), and gives the user some arbitrary commit count (from 0+)
Once the user reaches the (configurable) commit club limit, they are assigned the vanity role. e.g. User hits 5 commits, they are given the Commit Club: 5 role.

SerenityOS also does this, but I am pretty sure it's all manual

Drawbacks and Impact
It is opt in, Discord does not supply linked accounts and even then not all users link their accounts.
Unresolved questions
How? This will rely on the user opting in via some GitHub OAuth2 flow.

Additional idea (separate issue): When a user reaches some arbitrary commit club level, a configurable channel could/should receive a notification for it to celebrate


SKCore: make a couple of members more accessible

This opens up access to requestHandlers and notificationHandlers as
it gets inlined through _register across module boundaries in
SourceKitLSP.  Making this more accessible enables building
SourceKit-LSP as a statically linked binary on Windows.

@swift-ci please test

I don't understand why this is necessary.  If this is needed to satisfy linker visibility requirements, doesn't it represent a general issue that should be treated as a compiler bug?

It isn't linker visibility, the accessors are simply not generated at all, which are expected to be generated as there are no uses of it in the module itself.

How are the accessors being referenced from outside the module without it being a bug to not generate them though?  If it's via inlining it seems like either the accessors should be generated, or the inlining should not have been allowed.

Turns out it is in the vtable entries (the full type metadata contains the pointers).  I'll see if I can figure out why the compiler is not emitting the functions.

I think I understand what is going on, and that is a compiler issue.  Sorry about the noise.


Change readme to something more simple

So the readme is basically the gsoc proposal, it is quite verbose. We should do a more readable readme with maybe some basic usage.
Also leave the gsoc to another file (proposal.md)? It would be nice to keep it as history and as an initial blueprint of the project.

Sure. Although I don't know if it's a priority at the moment. Do you know of people that accessing the project actively? I wonder if that would be the case before we make it public more widely.

Not really, just the people I have mentioned the project to.

At most 14 people has accessed this repo hehe

@yjmantilla For now, sounds like a good idea to move the GSOC proposal onto its own about.md and update the README.md with the basic instructions and also link to the about page.

Closing this as it is done.


Add purs-language-server

Addresses #44. @peterbecich — I haven't been relying on packages from nixpkgs directly since I don't have any control over them and I want to expose multiple versions of every tool in this overlay rather than just the latest one.
Test with:
nix run .#purs-language-server -- --version

Fantastic, thank you @thomashoneyman
Question,
I have checked out this branch and started the develop shell
$ which purescript-language-server
/home/peterbecich/.npm-global/bin/purescript-language-server

$ which purs-language-server
/nix/store/rn3yryl1g3cbyrlvlxb4g7sh2kq641ky-purs-language-server-0.17.2/bin/purs-lang
uage-server

A.f.a.i.k. some LSP clients will be expecting to find the executable purescript-language-server. Should this name be provided by the derivation you have implemented?
example:
https://github.com/emacs-lsp/lsp-mode/blob/1f5afbdcfbaa79811dd9737fef93d4df02a1c89d/docs/lsp-clients.json#L664-L673


community: Support for Fetching Previous/Next Segments in VertexAISearchRetriever

PR Description
Currently, the VertexAISearchRetriever from langchain_google_community does not support fetching previous and next segments of documents for unstructured data. This is crucial for us to improve the quality of the fetched documents for our LLM.
The Google Discovery Engine API, including its Python library google.cloud.discoveryengine_v1beta, directly supports fetching previous and next segments. Relevant documentation can be found here.
So, I implemented in this PR:

Support for fetching and mapping previous/next segments in VertexAISearchRetriever.

Currently, only one segment_answer or extractive_answer is returned per SearchResult, so I set both to the maximum allowed value of 1.
Incorporated additional useful configurations from the Google API in the _get_content_spec_kwargs method.

Handling of segments in the _convert_unstructured_search_response method.

Relevant issues
https://github.com/langchain-ai/langchain-google/issues/502
Type

🆕 New Feature
🐛 Bug Fix
Testing
Since the library doesn’t have tests to cover these cases, I tested it using my data in GCP, checking different configurations and verifying that both the answers and segments are correct.
vertex = VertexAISearchRetriever(
    project_id=project_id,
    data_store_id=data_store_id,
    max_documents=2,
    max_extractive_segment_count=1,
    num_previous_segments=3,
    num_next_segments=3,
    return_extractive_segment_score=True,
)

Thank you Team!

Thanks for your contribution! Could you extend the integration test, please?

I plan to mock the _create_search_request method by using a JSON with a format similar to the API responses. This will cover extractive_segments and extractive_answers. Then, I will create test cases to cover the _convert_unstructured_search_response method to ensure correct processing of these segments and answers.
What do you think @lkuligin ?

I just added the tests that cover different scenarios and configurations of the VertexAISearchRetriever, ensuring that it correctly handles various types of search responses and generates appropriate Document objects.
I don't know why the CI is failing, as the tests pass locally.
I would love your feedback @lkuligin  .

@lgesuellip please, fix the remaining linter errors
you can run linter locally if you wish, as described here:
https://github.com/langchain-ai/langchain-google?tab=readme-ov-file#code-formatting

why is test_vertex_ai_search.py failing?

I just modified it, @lkuligin, and I was able to run the makefile locally to verify it.

Hi @lkuligin , do I need to complete any additional steps before merging to main?
Thank you very much!

hi @lkuligin, all the tests are passing (we also fixed a failing test that was unrelated to our PR)
ready to merge when you are!


Switch Output is hidden.

Type: Feature Request
Switch Output has a small button, which can easily switch the output, but when I click to hide Switch Output, it disappears, and now I can only click the button on the right to switch.
VS Code version: Code 1.79.0 (b380da4ef1ee00e224a15c1d4d9793e27c2b6302, 2023-06-07T14:26:35.552Z)
OS version: Windows_NT x64 10.0.19045
Modes:

感谢您创建此问题！
然而，由于您没有使用英语，我们很难进行处理。如果可能，请您将此问题描述修改为英文。请您直接编辑此问题，不要提交回复或新问题。
请注意，机器翻译服务通常无法处理用于讨论软件问题所需的技术性语言，所以我们推荐您寻找一位熟悉相关技术的人来协助翻译。
如果您无法做到，没有关系。此问题已经被标记为需要翻译，等待社区成员志愿进行翻译。

Thanks for creating this issue!
Unfortunately, as the description is not in English, it's hard for us to work on. If possible, please edit the original issue to be in English.
In our experience, automatic translation services are not able to handle the technical language needed to effectively communicate about software issues, so we recommend finding a human translator who is familiar with our technology to help.
Otherwise, we have marked this as needing translation and a community member may be able to help out.

Switch Output has a small button, which can easily switch the output, but when I click to hide Switch Output, it disappears, and now I can only click the button on the right to switch.

I am sorry that I do not understand what you are asking for. Are you hiding the Switch Output Dropdown Action?

Thank you for your assistance. I wanted to let you know that I've managed to solve the problem. Your help was greatly appreciated.


Move function documentation under custom.documentation?

Is it possible - or was it considered - to allow function specific documentation to be defined under the
custom:
  documentation:
  # ...
  path:
    create:
      summary: "Create something"
      # ...

key? It would allow one to extract the complete API spec into a separate api-spec.yml or .js file and pull it into the main serverless.yml (or .js) in one go.
Is there a technical reason/design decision  why this would not be possible?

Seems like a solution is already in PR: https://github.com/deliveryhero/serverless-aws-documentation/pull/76


[ci:component:github.com/gardener/ingress-default-backend:0.11.0->0.12.0]

How to categorize this PR?

/area TODO
/kind TODO
What this PR does / why we need it:
Which issue(s) this PR fixes:
Fixes #
Special notes for your reviewer:
Release note:

/cc @oliver-goetz can we add this to the next release?

/milestone v1.62


RxList does not automatically update the interface after changing a member's data

My usage scenario ( Gex 4.6.5 ) :
RxList dataList = [{'test':1, 'testData':'str1'}, {'test':2, 'testData':'str2'}].obs;
The dataList is called in view.dart and wrapped with the OBX function.
For example, the interface is not automatically refreshed after changing the value of a member of the dataList in controller.dart.
Example :
dataList[1]['testData']='NewString';
updata();
This didn't work, so I used the following dumb method when I had no other way to do it. :
dataList[1]['testData']='NewString';
dataList.add(dataList[0]);
dataList.removeAt(dataList.length-1);//Cause the interface to refresh by manually changing the number of dataList members
updata();
Is there a better way to do this? If not this would be a bug submission, expect RxList to support automatic interface refresh after member content change, or RxList to add a manual notification function to facilitate users to manually notify Gex framework when changing data of RxList members but not the number of members. rxList.changeData() ?

dataList

My usage scenario ( Gex 4.6.5 ) : RxList dataList = [{'test':1, 'testData':'str1'}, {'test':2, 'testData':'str2'}].obs;
The dataList is called in view.dart and wrapped with the OBX function. For example, the interface is not automatically refreshed after changing the value of a member of the dataList in controller.dart. Example : dataList[1]['testData']='NewString'; updata();
This didn't work, so I used the following dumb method when I had no other way to do it. : dataList[1]['testData']='NewString'; dataList.add(dataList[0]); dataList.removeAt(dataList.length-1);//Cause the interface to refresh by manually changing the number of dataList members updata();
Is there a better way to do this? If not this would be a bug submission, expect RxList to support automatic interface refresh after member content change, or RxList to add a manual notification function to facilitate users to manually notify Gex framework when changing data of RxList members but not the number of members. rxList.changeData() ?

dataList.refresh() ?

dataList

My usage scenario ( Gex 4.6.5 ) : RxList dataList = [{'test':1, 'testData':'str1'}, {'test':2, 'testData':'str2'}].obs;
The dataList is called in view.dart and wrapped with the OBX function. For example, the interface is not automatically refreshed after changing the value of a member of the dataList in controller.dart. Example : dataList[1]['testData']='NewString'; updata();
This didn't work, so I used the following dumb method when I had no other way to do it. : dataList[1]['testData']='NewString'; dataList.add(dataList[0]); dataList.removeAt(dataList.length-1);//Cause the interface to refresh by manually changing the number of dataList members updata();
Is there a better way to do this? If not this would be a bug submission, expect RxList to support automatic interface refresh after member content change, or RxList to add a manual notification function to facilitate users to manually notify Gex framework when changing data of RxList members but not the number of members. rxList.changeData() ?

dataList.refresh() ?

Thanks, this is a good solution to my problem


Update stack-settings.md - add warning about project globs

Description of the change

Checklist
Please make sure that the proposed change checks all the boxes below before requesting a review:

[x] I have reviewed the guidelines for contributing to this repository.
[x] The preview looks fine.
[x] The tests pass.
[x] The commit history is clean and meaningful.
[x] The pull request is opened against the main branch.
[x] The pull request is no longer marked as a draft.
[x] You agree to license your contribution under the MIT license to Spacelift (not required for Spacelift employees).
You have updated the navigation files correctly:

[x] No new pages have been added, or;
[ ] Only nav.yaml has been updated because the changes only apply to SaaS, or;
[ ] Only nav.self-hosted.yaml has been updated because the changes only apply to Self-Hosted, or;
[ ] Both nav.yaml and nav.self-hosted.yaml have been updated.

If the proposed change is ready to be merged, please request a review from @spacelift-io/solutions-engineering. Someone will review and merge the pull request.
Spacelift employees should request reviews from the relevant engineers and are allowed to merge pull requests after they got at least one approval.
Thank you for your contribution! 🙇

internal ref: https://spacelift-io.slack.com/archives/C036ERF1VRU/p1722532002672379?thread_ts=1722456608.719559&cid=C036ERF1VRU


want to enlarge the image by 4x

How the model should be set up
want to enlarge the image by 4x

Change the scale in FLAGS of main.py to scale=4 instead of scale=3.


Polyfills.ts is loading before main.ts, hence failing in ie11.

Hello,
I am trying to use the default polyfills.ts file provided with Angular boiler plate, However I replaced the bootstraping from cli to webpack, this results in an unusual lading behaior of files:

Resulting in code failing to work on IE 11, here is my webconfig code:
let config = {
  entry: {
    polyfills: `${SRC_PATH}/polyfills.ts`,
    main: `${SRC_PATH}/main.ts`
  },
  output: {
    path: DIST_PATH,
    filename: 'js/[name].js',
  },
  resolve: {
    extensions: ['.ts', '.js']
  },

Highly appreciate your time.

It looks like you just deleted our issue template. It was there for good reasons. Please help us solving your issue by answering the questions asked in this template. I'm closing this. Please open a new issue with filled issue template.


Fix redirect docs

301/302 docs was wrong for SendRedirectAsync
MS source docs

good catch!
thanks for the PR...


5.0 Docs part 1

Thanks, updated.
PR here for dev docs; https://github.com/mattermost/mattermost-developer-documentation/pull/116


LA-271 Remove liberasurecode-dev requirement

If the leapfrog venv is published we don't need to install
liberasurecode-dev on the deploy node, and therefore no
requirements on the repos used for the deploy node.
This bumps openstack-ansible-ops sha to include the
patch that removes the installation of liberasurecode-dev on
the deploy node (while keeping it on the venv build node)

recheck_cit_artifact_trusty_container
recheck_cit_xenial_deploy_swift

without manually testing the change in the other repo, I can at least confirm that this sha bump includes the change that is referenced.

Ready to go


Add length-prefixed and unlength-prefixed array encoders to complete …

…the set  closes https://github.com/tendermint/go-wire/issues/59 and https://github.com/tendermint/go-wire/issues/58

@ebuchman it ensures that a TMArrayEncoder cannot implement both the Length and Unlength specialization interfaces simultaneously; it must indicate which one it implements. The type indicates the property of producing a self-delimiting prefix encoding. Since go doesn't allow method overloads I think it achieves our compile-time mutex purpose.

Ah ok.
Typically we use a method like _assertTypeNameHere()
eg. https://github.com/tendermint/go-wire/blob/master/data/base58/encoder.go#L21
This is fine too, but maybe we should converge on a common style for doing this

how does _assertTypeNameHere achieve the purpose of preventing people from simultaneously satisfying the requirements of two different interfaces that are supposed to be "either-or" ?  from my understanding that convention could introduce two new symbol names for two new methods each with different signatures... so it doesn't prevent the problem or am i missing something? @ebuchman

you're 100% right! Thanks for clarifying that :)

So maybe the convention should be to have a method like _assert(InterfaceName).
PrefixStatus just maybe isn't as clear as it could be about what's going on.
Anyways, just shaving yaks here ;)

Thanks for confirming and practicing the precise understanding. I agree that's appears to be a good candidate name and fine to me.  I'm happy to let you and the team arbitrate any such name better than the one I provided in another issue and maybe PR then i can standardize on it during the next mutually-exclusive (psuedo linear) type. For me it's just expressing the mental model (it's a bit unnatural since types are usually constructionist i.e. non-contradicting) as a simple logical formula at compile time that i appreciate and whatever name is most familiar to the team is best. @ebuchman


[SPARK-32908][SQL][2.4] Fix target error calculation in percentile_approx()

What changes were proposed in this pull request?

Change the target error calculation according to the paper Space-Efficient Online Computation of Quantile Summaries. It says that the error e = max(gi, deltai)/2 (see the page 59). Also this has clear explanation ε-approximate quantiles.
Added a test to check different accuracies.
Added an input CSV file percentile_approx-input.csv.bz2 to the resource folder sql/catalyst/src/main/resources for the test.

Why are the changes needed?
To fix incorrect percentile calculation, see an example in SPARK-32908.
Does this PR introduce any user-facing change?
Yes
How was this patch tested?

By running existing tests in QuantileSummariesSuite and in ApproximatePercentileQuerySuite.
Added new test SPARK-32908: maximum target error in percentile_approx to ApproximatePercentileQuerySuite.

Test build #128827 has started for PR 29786 at commit 06b44a9.

Merged build finished. Test PASSed.

Test PASSed.
Refer to this link for build results (access rights to CI server needed):
https://amplab.cs.berkeley.edu/jenkins//job/SparkPullRequestBuilder-K8s/33450/
Test PASSed.

Test build #128827 has finished for PR 29786 at commit 06b44a9.

This patch passes all tests.
This patch merges cleanly.
This patch adds no public classes.

Merged build finished. Test PASSed.

Test PASSed.
Refer to this link for build results (access rights to CI server needed):
https://amplab.cs.berkeley.edu/jenkins//job/SparkPullRequestBuilder/128827/
Test PASSed.


Drag and drop inside drag and drop

Bug, feature request, or proposal:
Bug
What is the expected behavior?
You should be able to reorder items in a list that is in an other sortable list.
What is the current behavior?
When you take an item to reorder it, you take the full list. You can only reorder the lists, not the items within.
What are the steps to reproduce?
https://stackblitz.com/edit/angular-qrezcs
If you want to reorder an item in one of the lists you take the whole list.
What is the use-case or motivation for changing an existing behavior?
It makes sorting tree-like structures easier for the user. And the current behavior is just strange and buggy.
This makes it possible to sort for example employees within a team and the teams within an organisation.
Which versions of Angular, Material, OS, TypeScript, browsers are affected?
Angular 7
Is there anything else we should know?

Fixed by https://github.com/angular/material2/pull/13820.


Add configuration to exclude full dst directories (#121)

This consolidates free space evaluation into plot_util and adds a configuration point to not plot to a destination directory that is full.

I don’t think I’ll be able to work on unit tests until later next week. If someone else wants to hop in that would be great, otherwise I can add them when I get some time.

Not tonight, but I'll try to find some time to get tests in place for the existing code.  Thanks again.

Now there is a problem. If I have 20 tasks (not started at the same time), theoretically I need 2 * 1TB disks as dst, but the program will automatically move the files to archive dirs, so it is not necessary to use 2* 1TB disks as dst.

I'd really like to see this merged. Given that's it's entirely opt-in and seems relatively trivial, I'd lean toward just resolving the conflicts and merging.


URL to download the latest jar

When we write blog articles or documents, they may include "how to install" section. It often includes the URL to the current version of embulk at the moment. We can't update the URL.
If we have a URL to download the latest jar file automatically, readers will always download the latest version.

Done. New download URL is http://dl.embulk.org/embulk-latest.jar.


Unable to open webcord on MacOS

Aknowledgements

[x] There's no other issue describing the same problem, regardless of its current state (i.e. including both closed and open issues or flagged as fixed).*
[x] There's no fix for my issue released to master branch.
[x] This bug has known workaround (write it below).

Environment

Platform: macOS Big Sur 11.6
Architecture: arm64
Application version: 1.6.1

Describe the bug
The app cannot launch
To Reproduce
Steps to reproduce the behavior:

Download webcord Darwin for arm64
Launch application after unzipping and putting into application folder

Expected behavior
Expect app to launch, presented with an app cannot launch error
Screenshots

Additional context
Problem related to macOS app signing
Workarounds
Run this command in terminal:
xattr -cr /Applications/WebCord.app

Actually I've resolved it by adding DMG builds – they don't seem to have this issue unlike ZIP archives.


How to set tracking classes in "mc_demo.py"？

HI, This is a nice work of tracking task, I have a question about the scripts:
I want to realize multi-target tracking, bug i don‘t kown Where to set the tracking classesin "mc_demo.py",  What should I do?
And What's the different of "mc_demo.py" and "mc_demo_yolov7.py"?

Hi, thank you.
The "mc_demo.py" is for the YOLOX object detector and "mc_demo_yolov7.py" is for the YOLOv7 object detector.
"mc_demo.py" is based on YOLOX code and it doesn't contain masking of classes as in YOLOVv5/7.


[BUG] Unoptimized text field used for traceGroup

Describe the bug
Saving trace data to OpenSearch is producing the following error:

[illegal_argument_exception] Text fields are not optimised for operations that require per-document field data like aggregations and sorting, so these operations are disabled by default. Please use a keyword field instead. Alternatively, set fielddata=true on [traceGroup] in order to load field data by uninverting the inverted index. Note that this can use significant memory.

Environment (please complete the following information):

OS: Docker image
Version 1.2.1
OpenSearch: 1.0
OTel Collector 0.37.0

Hi this issue was opened on my behalf after reporting the issue to AWS support.  They have now worked out what it was about my set up that was causing the issue.
We had a default index template that was matching all indexes.
GET _index_template/default_index_template
{
  "index_templates" : [
    {
      "name" : "default_index_template",
      "index_template" : {
        "index_patterns" : [
          "*"
        ],
        "template" : {
          "settings" : {
            "index" : {
              "number_of_shards" : "4",
              "default_pipeline" : "log_ingest_lag"
            }
          }
        },
        "composed_of" : [ ],
        "priority" : 1
      }
    }
  ]
}

From the Elasticsearch 7.10 documentation :
"When a composable template matches a given index it always takes precedence over a legacy template. If no composable template matches, a legacy template may still match and be applied."
We have worked around this issue for now by copying the templates that data-prepper is creating (otel-v1-apm-span-index-template, otel-v1-apm-service-map-index-template) and creating equivalent composable templates.
PUT _index_template/otel_v1_apm_service_map_index_template_comp...
PUT _index_template/otel_v1_apm_span_index_template_comp...

The templates that data-prepper are creating are legacy templates, could they be updated to use the new composable template type please?
Cheers.

@alexis-spx , I'm glad you were able to find a workaround.
I created #1275 to track this as a feature request. Please read it and add any feedback or comments that you have.
For any other users who hit this situation, the comment above provides a good workaround: Create a manual index template based on the one Data Prepper provides.
I'll close this bug in favor of using #1275.

Thanks @dlvenable looks like a great proposal.


[WIP] Test score sanity check

https://github.com/neuralaudio/hear-eval-kit/pull/223 implements test scoring in the predictor runner.
We also have evaluation runner, which takes targets and prediction pickle files and scores them.
This is a good sanity check.
However, we are getting different scores in predictor than in runner. Perhaps the prediction is pickling the targets in the wrong order or something? Help!

https://github.com/neuralaudio/hear-eval-kit/pull/243


Add griffenedge.com to sites

I built my website with Eleventy and want to add it to 11ty.dev!

Thank you!


LSP: Improve handling of file URI scheme

Fixes #63205.

Backport of the following PRs:

#63388

#64099

#64130

Supersedes #64524.

@A-Lamia Could you help test and ensure that this works OK?

Thank you very much for fixing this issue. I tried to implement the feedback in #64524 but I was in over in my head.

@akien-mga
Tested everything seems to be on parity with 4.0.

VScode - ✅

Unable to resolve root when opening editor from project sub-directory
possible bug. ⚠

Neovim - ✅

Cherry-picked for 3.5.2.


Fix to make tonweb compatible with Python module "ton-http-api"

According to the TonRequestJsonRPC scheme, the "id" field must have 'string' type.
If the "id" field is a number, the Python ton-http-api module returns the following error:
"error": "Validation error: [{'type': 'string_type', 'loc': ('body', 'id'), 'msg': 'Input should be a valid string'
The TonCenter endpoint (https://toncenter.com/api/v2/jsonRPC) works well because the "id" value is converted to a string somewhere on its way to the server. It also has string type in the response.
TonRequestJsonRPC scheme from https://toncenter.com/api/v2/openapi.json
"TonRequestJsonRPC": {
    "title": "TonRequestJsonRPC",
    "required": [
        "method"
    ],
    "type": "object",
    "properties": {
        "method": {
            "title": "Method",
            "type": "string"
        },
        "params": {
            "title": "Params",
            "type": "object",
            "default": {}
        },
        "id": {
            "title": "Id",
            "type": "string"
        },
        "jsonrpc": {
            "title": "Jsonrpc",
            "type": "string"
        }
    }
}

Thanks! Had the same problem. The fix is working