Loading check point

I have tried to load music_audioset_epoch_15_esc_90.14.pt with the example code. Both my machine and colab present the following error.
RuntimeError: Error(s) in loading state_dict for CLAP:
Unexpected key(s) in state_dict: "text_branch.embeddings.position_ids".
It may cause by unmatch audio encoder? Could you tell me which amodel I should choose?
Regards

Hi all, I was tring to fine-tune the model with the trainning scipt. I got error when I use roberta or bert I can't load the checkpoint nieither. I will be glad if you can tell me whcih tmodel I should use.
Regards

Hit the same issue today.

It seems like the transformers  library introduced some changes in version 4.31.0, affecting the loading of the text branch (RoBERTa base model) - resulting in the state_dict error.
As a workaround you can fix the transformers library to version 4.30.2 in your requirements (tested with music_audioset_epoch_15_esc_90.14.pt). @Neptune-S-777 @csteinmetz1

Thanks for the workaround. I've tried it  and it worked for the default model, however for the model you mentioned (which I downloaded from https://huggingface.co/lukewys/laion_clap/resolve/main/music_audioset_epoch_15_esc_90.14.pt) I am getting the following errors (size mismatch and missing keys)
Missing key(s) in state_dict: "audio_branch.patch_embed.mel_conv2d.weight", "audio_branch.patch_embed.mel_conv2d.bias", "audio_branch.patch_embed.fusion_model.local_att.0.weight", "audio_branch.patch_embed.fusion_model.local_att.0.bias", "audio_branch.patch_embed.fusion_model.local_att.1.weight", "audio_branch.patch_embed.fusion_model.local_att.1.bias", "audio_branch.patch_embed.fusion_model.local_att.1.running_mean", "audio_branch.patch_embed.fusion_model.local_att.1.running_var", "audio_branch.patch_embed.fusion_model.local_att.3.weight", "audio_branch.patch_embed.fusion_model.local_att.3.bias", "audio_branch.patch_embed.fusion_model.local_att.4.weight", "audio_branch.patch_embed.fusion_model.local_att.4.bias", "audio_branch.patch_embed.fusion_model.local_att.4.running_mean", "audio_branch.patch_embed.fusion_model.local_att.4.running_var", "audio_branch.patch_embed.fusion_model.global_att.1.weight", "audio_branch.patch_embed.fusion_model.global_att.1.bias", "audio_branch.patch_embed.fusion_model.global_att.2.weight", "audio_branch.patch_embed.fusion_model.global_att.2.bias", "audio_branch.patch_embed.fusion_model.global_att.2.running_mean", "audio_branch.patch_embed.fusion_model.global_att.2.running_var", "audio_branch.patch_embed.fusion_model.global_att.4.weight", "audio_branch.patch_embed.fusion_model.global_att.4.bias", "audio_branch.patch_embed.fusion_model.global_att.5.weight", "audio_branch.patch_embed.fusion_model.global_att.5.bias", "audio_branch.patch_embed.fusion_model.global_att.5.running_mean", "audio_branch.patch_embed.fusion_model.global_att.5.running_var".
        Unexpected key(s) in state_dict: "audio_branch.layers.2.blocks.6.norm1.weight", "audio_branch.layers.2.blocks.6.norm1.bias", "audio_branch.layers.2.blocks.6.attn.relative_position_bias_table", "audio_branch.layers.2.blocks.6.attn.relative_position_index", "audio_branch.layers.2.blocks.6.attn.qkv.weight", "audio_branch.layers.2.blocks.6.attn.qkv.bias", "audio_branch.layers.2.blocks.6.attn.proj.weight", "audio_branch.layers.2.blocks.6.attn.proj.bias", "audio_branch.layers.2.blocks.6.norm2.weight", "audio_branch.layers.2.blocks.6.norm2.bias", "audio_branch.layers.2.blocks.6.mlp.fc1.weight", "audio_branch.layers.2.blocks.6.mlp.fc1.bias", "audio_branch.layers.2.blocks.6.mlp.fc2.weight", "audio_branch.layers.2.blocks.6.mlp.fc2.bias", "audio_branch.layers.2.blocks.7.attn_mask", "audio_branch.layers.2.blocks.7.norm1.weight", "audio_branch.layers.2.blocks.7.norm1.bias", "audio_branch.layers.2.blocks.7.attn.relative_position_bias_table", "audio_branch.layers.2.blocks.7.attn.relative_position_index", "audio_branch.layers.2.blocks.7.attn.qkv.weight", "audio_branch.layers.2.blocks.7.attn.qkv.bias", "audio_branch.layers.2.blocks.7.attn.proj.weight", "audio_branch.layers.2.blocks.7.attn.proj.bias", "audio_branch.layers.2.blocks.7.norm2.weight", "audio_branch.layers.2.blocks.7.norm2.bias", "audio_branch.layers.2.blocks.7.mlp.fc1.weight", "audio_branch.layers.2.blocks.7.mlp.fc1.bias", "audio_branch.layers.2.blocks.7.mlp.fc2.weight", "audio_branch.layers.2.blocks.7.mlp.fc2.bias", "audio_branch.layers.2.blocks.8.norm1.weight", "audio_branch.layers.2.blocks.8.norm1.bias", "audio_branch.layers.2.blocks.8.attn.relative_position_bias_table", "audio_branch.layers.2.blocks.8.attn.relative_position_index", "audio_branch.layers.2.blocks.8.attn.qkv.weight", "audio_branch.layers.2.blocks.8.attn.qkv.bias", "audio_branch.layers.2.blocks.8.attn.proj.weight", "audio_branch.layers.2.blocks.8.attn.proj.bias", "audio_branch.layers.2.blocks.8.norm2.weight", "audio_branch.layers.2.blocks.8.norm2.bias", "audio_branch.layers.2.blocks.8.mlp.fc1.weight", "audio_branch.layers.2.blocks.8.mlp.fc1.bias", "audio_branch.layers.2.blocks.8.mlp.fc2.weight", "audio_branch.layers.2.blocks.8.mlp.fc2.bias", "audio_branch.layers.2.blocks.9.attn_mask", "audio_branch.layers.2.blocks.9.norm1.weight", "audio_branch.layers.2.blocks.9.norm1.bias", "audio_branch.layers.2.blocks.9.attn.relative_position_bias_table", "audio_branch.layers.2.blocks.9.attn.relative_position_index", "audio_branch.layers.2.blocks.9.attn.qkv.weight", "audio_branch.layers.2.blocks.9.attn.qkv.bias", "audio_branch.layers.2.blocks.9.attn.proj.weight", "audio_branch.layers.2.blocks.9.attn.proj.bias", "audio_branch.layers.2.blocks.9.norm2.weight", "audio_branch.layers.2.blocks.9.norm2.bias", "audio_branch.layers.2.blocks.9.mlp.fc1.weight", "audio_branch.layers.2.blocks.9.mlp.fc1.bias", "audio_branch.layers.2.blocks.9.mlp.fc2.weight", "audio_branch.layers.2.blocks.9.mlp.fc2.bias", "audio_branch.layers.2.blocks.10.norm1.weight", "audio_branch.layers.2.blocks.10.norm1.bias", "audio_branch.layers.2.blocks.10.attn.relative_position_bias_table", "audio_branch.layers.2.blocks.10.attn.relative_position_index", "audio_branch.layers.2.blocks.10.attn.qkv.weight", "audio_branch.layers.2.blocks.10.attn.qkv.bias", "audio_branch.layers.2.blocks.10.attn.proj.weight", "audio_branch.layers.2.blocks.10.attn.proj.bias", "audio_branch.layers.2.blocks.10.norm2.weight", "audio_branch.layers.2.blocks.10.norm2.bias", "audio_branch.layers.2.blocks.10.mlp.fc1.weight", "audio_branch.layers.2.blocks.10.mlp.fc1.bias", "audio_branch.layers.2.blocks.10.mlp.fc2.weight", "audio_branch.layers.2.blocks.10.mlp.fc2.bias", "audio_branch.layers.2.blocks.11.attn_mask", "audio_branch.layers.2.blocks.11.norm1.weight", "audio_branch.layers.2.blocks.11.norm1.bias", "audio_branch.layers.2.blocks.11.attn.relative_position_bias_table", "audio_branch.layers.2.blocks.11.attn.relative_position_index", "audio_branch.layers.2.blocks.11.attn.qkv.weight", "audio_branch.layers.2.blocks.11.attn.qkv.bias", "audio_branch.layers.2.blocks.11.attn.proj.weight", "audio_branch.layers.2.blocks.11.attn.proj.bias", "audio_branch.layers.2.blocks.11.norm2.weight", "audio_branch.layers.2.blocks.11.norm2.bias", "audio_branch.layers.2.blocks.11.mlp.fc1.weight", "audio_branch.layers.2.blocks.11.mlp.fc1.bias", "audio_branch.layers.2.blocks.11.mlp.fc2.weight", "audio_branch.layers.2.blocks.11.mlp.fc2.bias".
        size mismatch for audio_branch.patch_embed.proj.weight: copying a param with shape torch.Size([128, 1, 4, 4]) from checkpoint, the shape in current model is torch.Size([96, 1, 4, 4]).
        size mismatch for audio_branch.patch_embed.proj.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([96]).
        size mismatch for audio_branch.patch_embed.norm.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([96]).
        size mismatch for audio_branch.patch_embed.norm.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([96]).
        size mismatch for audio_branch.layers.0.blocks.0.norm1.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([96]).
        size mismatch for audio_branch.layers.0.blocks.0.norm1.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([96]).
        size mismatch for audio_branch.layers.0.blocks.0.attn.qkv.weight: copying a param with shape torch.Size([384, 128]) from checkpoint, the shape in current model is torch.Size([288, 96]).
        size mismatch for audio_branch.layers.0.blocks.0.attn.qkv.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([288]).
        size mismatch for audio_branch.layers.0.blocks.0.attn.proj.weight: copying a param with shape torch.Size([128, 128]) from checkpoint, the shape in current model is torch.Size([96, 96]).
        size mismatch for audio_branch.layers.0.blocks.0.attn.proj.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([96]).
        size mismatch for audio_branch.layers.0.blocks.0.norm2.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([96]).
        size mismatch for audio_branch.layers.0.blocks.0.norm2.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([96]).
        size mismatch for audio_branch.layers.0.blocks.0.mlp.fc1.weight: copying a param with shape torch.Size([512, 128]) from checkpoint, the shape in current model is torch.Size([384, 96]).
        size mismatch for audio_branch.layers.0.blocks.0.mlp.fc1.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([384]).
        size mismatch for audio_branch.layers.0.blocks.0.mlp.fc2.weight: copying a param with shape torch.Size([128, 512]) from checkpoint, the shape in current model is torch.Size([96, 384]).
        size mismatch for audio_branch.layers.0.blocks.0.mlp.fc2.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([96]).
        size mismatch for audio_branch.layers.0.blocks.1.norm1.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([96]).
        size mismatch for audio_branch.layers.0.blocks.1.norm1.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([96]).
        size mismatch for audio_branch.layers.0.blocks.1.attn.qkv.weight: copying a param with shape torch.Size([384, 128]) from checkpoint, the shape in current model is torch.Size([288, 96]).
        size mismatch for audio_branch.layers.0.blocks.1.attn.qkv.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([288]).
        size mismatch for audio_branch.layers.0.blocks.1.attn.proj.weight: copying a param with shape torch.Size([128, 128]) from checkpoint, the shape in current model is torch.Size([96, 96]).
        size mismatch for audio_branch.layers.0.blocks.1.attn.proj.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([96]).
        size mismatch for audio_branch.layers.0.blocks.1.norm2.weight: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([96]).
        size mismatch for audio_branch.layers.0.blocks.1.norm2.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([96]).
        size mismatch for audio_branch.layers.0.blocks.1.mlp.fc1.weight: copying a param with shape torch.Size([512, 128]) from checkpoint, the shape in current model is torch.Size([384, 96]).
        size mismatch for audio_branch.layers.0.blocks.1.mlp.fc1.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([384]).
        size mismatch for audio_branch.layers.0.blocks.1.mlp.fc2.weight: copying a param with shape torch.Size([128, 512]) from checkpoint, the shape in current model is torch.Size([96, 384]).
        size mismatch for audio_branch.layers.0.blocks.1.mlp.fc2.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([96]).
        size mismatch for audio_branch.layers.0.downsample.reduction.weight: copying a param with shape torch.Size([256, 512]) from checkpoint, the shape in current model is torch.Size([192, 384]).
        size mismatch for audio_branch.layers.0.downsample.norm.weight: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([384]).
        size mismatch for audio_branch.layers.0.downsample.norm.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([384]).
        size mismatch for audio_branch.layers.1.blocks.0.norm1.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([192]).
        size mismatch for audio_branch.layers.1.blocks.0.norm1.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([192]).
        size mismatch for audio_branch.layers.1.blocks.0.attn.qkv.weight: copying a param with shape torch.Size([768, 256]) from checkpoint, the shape in current model is torch.Size([576, 192]).
        size mismatch for audio_branch.layers.1.blocks.0.attn.qkv.bias: copying a param with shape torch.Size([768]) from checkpoint, the shape in current model is torch.Size([576]).
        size mismatch for audio_branch.layers.1.blocks.0.attn.proj.weight: copying a param with shape torch.Size([256, 256]) from checkpoint, the shape in current model is torch.Size([192, 192]).
        size mismatch for audio_branch.layers.1.blocks.0.attn.proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([192]).
        size mismatch for audio_branch.layers.1.blocks.0.norm2.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([192]).
        size mismatch for audio_branch.layers.1.blocks.0.norm2.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([192]).
        size mismatch for audio_branch.layers.1.blocks.0.mlp.fc1.weight: copying a param with shape torch.Size([1024, 256]) from checkpoint, the shape in current model is torch.Size([768, 192]).
        size mismatch for audio_branch.layers.1.blocks.0.mlp.fc1.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]).
        size mismatch for audio_branch.layers.1.blocks.0.mlp.fc2.weight: copying a param with shape torch.Size([256, 1024]) from checkpoint, the shape in current model is torch.Size([192, 768]).
        size mismatch for audio_branch.layers.1.blocks.0.mlp.fc2.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([192]).
        size mismatch for audio_branch.layers.1.blocks.1.norm1.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([192]).
        size mismatch for audio_branch.layers.1.blocks.1.norm1.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([192]).
        size mismatch for audio_branch.layers.1.blocks.1.attn.qkv.weight: copying a param with shape torch.Size([768, 256]) from checkpoint, the shape in current model is torch.Size([576, 192]).
        size mismatch for audio_branch.layers.1.blocks.1.attn.qkv.bias: copying a param with shape torch.Size([768]) from checkpoint, the shape in current model is torch.Size([576]).
        size mismatch for audio_branch.layers.1.blocks.1.attn.proj.weight: copying a param with shape torch.Size([256, 256]) from checkpoint, the shape in current model is torch.Size([192, 192]).
        size mismatch for audio_branch.layers.1.blocks.1.attn.proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([192]).
        size mismatch for audio_branch.layers.1.blocks.1.norm2.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([192]).
        size mismatch for audio_branch.layers.1.blocks.1.norm2.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([192]).
        size mismatch for audio_branch.layers.1.blocks.1.mlp.fc1.weight: copying a param with shape torch.Size([1024, 256]) from checkpoint, the shape in current model is torch.Size([768, 192]).
        size mismatch for audio_branch.layers.1.blocks.1.mlp.fc1.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]).
        size mismatch for audio_branch.layers.1.blocks.1.mlp.fc2.weight: copying a param with shape torch.Size([256, 1024]) from checkpoint, the shape in current model is torch.Size([192, 768]).
        size mismatch for audio_branch.layers.1.blocks.1.mlp.fc2.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([192]).
        size mismatch for audio_branch.layers.1.downsample.reduction.weight: copying a param with shape torch.Size([512, 1024]) from checkpoint, the shape in current model is torch.Size([384, 768]).
        size mismatch for audio_branch.layers.1.downsample.norm.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]).
        size mismatch for audio_branch.layers.1.downsample.norm.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]).
        size mismatch for audio_branch.layers.2.blocks.0.norm1.weight: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([384]).
        size mismatch for audio_branch.layers.2.blocks.0.norm1.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([384]).
        size mismatch for audio_branch.layers.2.blocks.0.attn.qkv.weight: copying a param with shape torch.Size([1536, 512]) from checkpoint, the shape in current model is torch.Size([1152, 384]).
        size mismatch for audio_branch.layers.2.blocks.0.attn.qkv.bias: copying a param with shape torch.Size([1536]) from checkpoint, the shape in current model is torch.Size([1152]).
        size mismatch for audio_branch.layers.2.blocks.0.attn.proj.weight: copying a param with shape torch.Size([512, 512]) from checkpoint, the shape in current model is torch.Size([384, 384]).
        size mismatch for audio_branch.layers.2.blocks.0.attn.proj.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([384]).
        size mismatch for audio_branch.layers.2.blocks.0.norm2.weight: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([384]).
        size mismatch for audio_branch.layers.2.blocks.0.norm2.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([384]).
        size mismatch for audio_branch.layers.2.blocks.0.mlp.fc1.weight: copying a param with shape torch.Size([2048, 512]) from checkpoint, the shape in current model is torch.Size([1536, 384]).
        size mismatch for audio_branch.layers.2.blocks.0.mlp.fc1.bias: copying a param with shape torch.Size([2048]) from checkpoint, the shape in current model is torch.Size([1536]).
        size mismatch for audio_branch.layers.2.blocks.0.mlp.fc2.weight: copying a param with shape torch.Size([512, 2048]) from checkpoint, the shape in current model is torch.Size([384, 1536]).
        size mismatch for audio_branch.layers.2.blocks.0.mlp.fc2.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([384]).
        size mismatch for audio_branch.layers.2.blocks.1.norm1.weight: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([384]).
        size mismatch for audio_branch.layers.2.blocks.1.norm1.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([384]).
        size mismatch for audio_branch.layers.2.blocks.1.attn.qkv.weight: copying a param with shape torch.Size([1536, 512]) from checkpoint, the shape in current model is torch.Size([1152, 384]).
        size mismatch for audio_branch.layers.2.blocks.1.attn.qkv.bias: copying a param with shape torch.Size([1536]) from checkpoint, the shape in current model is torch.Size([1152]).
        size mismatch for audio_branch.layers.2.blocks.1.attn.proj.weight: copying a param with shape torch.Size([512, 512]) from checkpoint, the shape in current model is torch.Size([384, 384]).
        size mismatch for audio_branch.layers.2.blocks.1.attn.proj.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([384]).
        size mismatch for audio_branch.layers.2.blocks.1.norm2.weight: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([384]).
        size mismatch for audio_branch.layers.2.blocks.1.norm2.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([384]).
        size mismatch for audio_branch.layers.2.blocks.1.mlp.fc1.weight: copying a param with shape torch.Size([2048, 512]) from checkpoint, the shape in current model is torch.Size([1536, 384]).
        size mismatch for audio_branch.layers.2.blocks.1.mlp.fc1.bias: copying a param with shape torch.Size([2048]) from checkpoint, the shape in current model is torch.Size([1536]).
        size mismatch for audio_branch.layers.2.blocks.1.mlp.fc2.weight: copying a param with shape torch.Size([512, 2048]) from checkpoint, the shape in current model is torch.Size([384, 1536]).
        size mismatch for audio_branch.layers.2.blocks.1.mlp.fc2.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([384]).
        size mismatch for audio_branch.layers.2.blocks.2.norm1.weight: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([384]).
        size mismatch for audio_branch.layers.2.blocks.2.norm1.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([384]).
        size mismatch for audio_branch.layers.2.blocks.2.attn.qkv.weight: copying a param with shape torch.Size([1536, 512]) from checkpoint, the shape in current model is torch.Size([1152, 384]).
        size mismatch for audio_branch.layers.2.blocks.2.attn.qkv.bias: copying a param with shape torch.Size([1536]) from checkpoint, the shape in current model is torch.Size([1152]).
        size mismatch for audio_branch.layers.2.blocks.2.attn.proj.weight: copying a param with shape torch.Size([512, 512]) from checkpoint, the shape in current model is torch.Size([384, 384]).
        size mismatch for audio_branch.layers.2.blocks.2.attn.proj.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([384]).
        size mismatch for audio_branch.layers.2.blocks.2.norm2.weight: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([384]).
        size mismatch for audio_branch.layers.2.blocks.2.norm2.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([384]).
        size mismatch for audio_branch.layers.2.blocks.2.mlp.fc1.weight: copying a param with shape torch.Size([2048, 512]) from checkpoint, the shape in current model is torch.Size([1536, 384]).
        size mismatch for audio_branch.layers.2.blocks.2.mlp.fc1.bias: copying a param with shape torch.Size([2048]) from checkpoint, the shape in current model is torch.Size([1536]).
        size mismatch for audio_branch.layers.2.blocks.2.mlp.fc2.weight: copying a param with shape torch.Size([512, 2048]) from checkpoint, the shape in current model is torch.Size([384, 1536]).
        size mismatch for audio_branch.layers.2.blocks.2.mlp.fc2.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([384]).
        size mismatch for audio_branch.layers.2.blocks.3.norm1.weight: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([384]).
        size mismatch for audio_branch.layers.2.blocks.3.norm1.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([384]).
        size mismatch for audio_branch.layers.2.blocks.3.attn.qkv.weight: copying a param with shape torch.Size([1536, 512]) from checkpoint, the shape in current model is torch.Size([1152, 384]).
        size mismatch for audio_branch.layers.2.blocks.3.attn.qkv.bias: copying a param with shape torch.Size([1536]) from checkpoint, the shape in current model is torch.Size([1152]).
        size mismatch for audio_branch.layers.2.blocks.3.attn.proj.weight: copying a param with shape torch.Size([512, 512]) from checkpoint, the shape in current model is torch.Size([384, 384]).
        size mismatch for audio_branch.layers.2.blocks.3.attn.proj.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([384]).
        size mismatch for audio_branch.layers.2.blocks.3.norm2.weight: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([384]).
        size mismatch for audio_branch.layers.2.blocks.3.norm2.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([384]).
        size mismatch for audio_branch.layers.2.blocks.3.mlp.fc1.weight: copying a param with shape torch.Size([2048, 512]) from checkpoint, the shape in current model is torch.Size([1536, 384]).
        size mismatch for audio_branch.layers.2.blocks.3.mlp.fc1.bias: copying a param with shape torch.Size([2048]) from checkpoint, the shape in current model is torch.Size([1536]).
        size mismatch for audio_branch.layers.2.blocks.3.mlp.fc2.weight: copying a param with shape torch.Size([512, 2048]) from checkpoint, the shape in current model is torch.Size([384, 1536]).
        size mismatch for audio_branch.layers.2.blocks.3.mlp.fc2.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([384]).
        size mismatch for audio_branch.layers.2.blocks.4.norm1.weight: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([384]).
        size mismatch for audio_branch.layers.2.blocks.4.norm1.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([384]).
        size mismatch for audio_branch.layers.2.blocks.4.attn.qkv.weight: copying a param with shape torch.Size([1536, 512]) from checkpoint, the shape in current model is torch.Size([1152, 384]).
        size mismatch for audio_branch.layers.2.blocks.4.attn.qkv.bias: copying a param with shape torch.Size([1536]) from checkpoint, the shape in current model is torch.Size([1152]).
        size mismatch for audio_branch.layers.2.blocks.4.attn.proj.weight: copying a param with shape torch.Size([512, 512]) from checkpoint, the shape in current model is torch.Size([384, 384]).
        size mismatch for audio_branch.layers.2.blocks.4.attn.proj.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([384]).
        size mismatch for audio_branch.layers.2.blocks.4.norm2.weight: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([384]).
        size mismatch for audio_branch.layers.2.blocks.4.norm2.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([384]).
        size mismatch for audio_branch.layers.2.blocks.4.mlp.fc1.weight: copying a param with shape torch.Size([2048, 512]) from checkpoint, the shape in current model is torch.Size([1536, 384]).
        size mismatch for audio_branch.layers.2.blocks.4.mlp.fc1.bias: copying a param with shape torch.Size([2048]) from checkpoint, the shape in current model is torch.Size([1536]).
        size mismatch for audio_branch.layers.2.blocks.4.mlp.fc2.weight: copying a param with shape torch.Size([512, 2048]) from checkpoint, the shape in current model is torch.Size([384, 1536]).
        size mismatch for audio_branch.layers.2.blocks.4.mlp.fc2.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([384]).
        size mismatch for audio_branch.layers.2.blocks.5.norm1.weight: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([384]).
        size mismatch for audio_branch.layers.2.blocks.5.norm1.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([384]).
        size mismatch for audio_branch.layers.2.blocks.5.attn.qkv.weight: copying a param with shape torch.Size([1536, 512]) from checkpoint, the shape in current model is torch.Size([1152, 384]).
        size mismatch for audio_branch.layers.2.blocks.5.attn.qkv.bias: copying a param with shape torch.Size([1536]) from checkpoint, the shape in current model is torch.Size([1152]).
        size mismatch for audio_branch.layers.2.blocks.5.attn.proj.weight: copying a param with shape torch.Size([512, 512]) from checkpoint, the shape in current model is torch.Size([384, 384]).
        size mismatch for audio_branch.layers.2.blocks.5.attn.proj.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([384]).
        size mismatch for audio_branch.layers.2.blocks.5.norm2.weight: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([384]).
        size mismatch for audio_branch.layers.2.blocks.5.norm2.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([384]).
        size mismatch for audio_branch.layers.2.blocks.5.mlp.fc1.weight: copying a param with shape torch.Size([2048, 512]) from checkpoint, the shape in current model is torch.Size([1536, 384]).
        size mismatch for audio_branch.layers.2.blocks.5.mlp.fc1.bias: copying a param with shape torch.Size([2048]) from checkpoint, the shape in current model is torch.Size([1536]).
        size mismatch for audio_branch.layers.2.blocks.5.mlp.fc2.weight: copying a param with shape torch.Size([512, 2048]) from checkpoint, the shape in current model is torch.Size([384, 1536]).
        size mismatch for audio_branch.layers.2.blocks.5.mlp.fc2.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([384]).
        size mismatch for audio_branch.layers.2.downsample.reduction.weight: copying a param with shape torch.Size([1024, 2048]) from checkpoint, the shape in current model is torch.Size([768, 1536]).
        size mismatch for audio_branch.layers.2.downsample.norm.weight: copying a param with shape torch.Size([2048]) from checkpoint, the shape in current model is torch.Size([1536]).
        size mismatch for audio_branch.layers.2.downsample.norm.bias: copying a param with shape torch.Size([2048]) from checkpoint, the shape in current model is torch.Size([1536]).
        size mismatch for audio_branch.layers.3.blocks.0.norm1.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]).
        size mismatch for audio_branch.layers.3.blocks.0.norm1.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]).
        size mismatch for audio_branch.layers.3.blocks.0.attn.qkv.weight: copying a param with shape torch.Size([3072, 1024]) from checkpoint, the shape in current model is torch.Size([2304, 768]).
        size mismatch for audio_branch.layers.3.blocks.0.attn.qkv.bias: copying a param with shape torch.Size([3072]) from checkpoint, the shape in current model is torch.Size([2304]).
        size mismatch for audio_branch.layers.3.blocks.0.attn.proj.weight: copying a param with shape torch.Size([1024, 1024]) from checkpoint, the shape in current model is torch.Size([768, 768]).
        size mismatch for audio_branch.layers.3.blocks.0.attn.proj.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]).
        size mismatch for audio_branch.layers.3.blocks.0.norm2.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]).
        size mismatch for audio_branch.layers.3.blocks.0.norm2.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]).
        size mismatch for audio_branch.layers.3.blocks.0.mlp.fc1.weight: copying a param with shape torch.Size([4096, 1024]) from checkpoint, the shape in current model is torch.Size([3072, 768]).
        size mismatch for audio_branch.layers.3.blocks.0.mlp.fc1.bias: copying a param with shape torch.Size([4096]) from checkpoint, the shape in current model is torch.Size([3072]).
        size mismatch for audio_branch.layers.3.blocks.0.mlp.fc2.weight: copying a param with shape torch.Size([1024, 4096]) from checkpoint, the shape in current model is torch.Size([768, 3072]).
        size mismatch for audio_branch.layers.3.blocks.0.mlp.fc2.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]).
        size mismatch for audio_branch.layers.3.blocks.1.norm1.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]).
        size mismatch for audio_branch.layers.3.blocks.1.norm1.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]).
        size mismatch for audio_branch.layers.3.blocks.1.attn.qkv.weight: copying a param with shape torch.Size([3072, 1024]) from checkpoint, the shape in current model is torch.Size([2304, 768]).
        size mismatch for audio_branch.layers.3.blocks.1.attn.qkv.bias: copying a param with shape torch.Size([3072]) from checkpoint, the shape in current model is torch.Size([2304]).
        size mismatch for audio_branch.layers.3.blocks.1.attn.proj.weight: copying a param with shape torch.Size([1024, 1024]) from checkpoint, the shape in current model is torch.Size([768, 768]).
        size mismatch for audio_branch.layers.3.blocks.1.attn.proj.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]).
        size mismatch for audio_branch.layers.3.blocks.1.norm2.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]).
        size mismatch for audio_branch.layers.3.blocks.1.norm2.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]).
        size mismatch for audio_branch.layers.3.blocks.1.mlp.fc1.weight: copying a param with shape torch.Size([4096, 1024]) from checkpoint, the shape in current model is torch.Size([3072, 768]).
        size mismatch for audio_branch.layers.3.blocks.1.mlp.fc1.bias: copying a param with shape torch.Size([4096]) from checkpoint, the shape in current model is torch.Size([3072]).
        size mismatch for audio_branch.layers.3.blocks.1.mlp.fc2.weight: copying a param with shape torch.Size([1024, 4096]) from checkpoint, the shape in current model is torch.Size([768, 3072]).
        size mismatch for audio_branch.layers.3.blocks.1.mlp.fc2.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]).
        size mismatch for audio_branch.norm.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]).
        size mismatch for audio_branch.norm.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([768]).
        size mismatch for audio_branch.tscam_conv.weight: copying a param with shape torch.Size([527, 1024, 2, 3]) from checkpoint, the shape in current model is torch.Size([527, 768, 2, 3]).
        size mismatch for audio_projection.0.weight: copying a param with shape torch.Size([512, 1024]) from checkpoint, the shape in current model is torch.Size([512, 768]).

My code is simply
from src import laion_clap

model = laion_clap.CLAP_Module(enable_fusion=True)
model.load_ckpt('music_audioset_epoch_15_esc_90.14.pt')

Could you share how you tested @waldleitner?
Thanks

@waldleitner Thanks so much for your answer, the problem is solved.
@PabloPeso Try to define the audio encoder with amodel= 'HTSAT-base'.

Thanks all for the report! I just updated the requirements.txt .

Thanks @Neptune-S-777
Just in case other face the same issue, I had also to modify the option enable_fusion=False, so it looks like this
model = laion_clap.CLAP_Module(enable_fusion=False, amodel= 'HTSAT-base')
model.load_ckpt('music_audioset_epoch_15_esc_90.14.pt')

https://huggingface.co/lukewys/laion_clap/resolve/main/music_audioset_epoch_15_esc_90.14.pt

I'm still getting this error "AssertionError: bert/roberta/bart text encoder does not support pretrained models."
for the same model and transformer version 4.30.0 and 4.30.2 both. Please suggest a workaround.
@waldleitner  @Neptune-S-777 @lukewys


ZAP's matter generation is stateless

This ticket is to make sure that once all the issues under "MatterZapCleanup" label have been resolved then make sure we no longer have any stateful zap code. We should just be using the backend for all our logic.

Closing all MatterZapCleanUp tagged issues since a majority of these have been resolved


ransomeware in README.md

In README.md

ransomeware

is misspelled, should be

ransomware

Thanks for spotting that, we have an update that is in review that we will include this change in.


Bootstrap - 4 framework causing issues in template using ngx-boostrap

Issue type
I'm submitting a (check one):
[x] Bug report
[ ] Feature request
[ ] Regression (something that used to work, but stopped working in a newer version)
[x] Support request
[x] Documentation issue or request
Prerequisites
Before posting, make sure you do the following (check all):
[x] Confirm you are using the latest versions of all necessary packages (or if not, explain why not)
[x] Search GitHub for a similar issue or PR
[x] If submitting a Support request, also search Stack Overflow for similar issues
Note: Please cross-post GitHub support requests to Stack Overflow, and include a link in your GitHub issue to your Stack Overflow question. We do currently respond to support requests on GitHub, but we eventually expect to stop, and will then refer all support questions exclusively to Stack Overflow.
Current behavior
When using the Bootstrap - 4 framework there seems to be some conflict with my Gradient Able template, When typing into any input box the whole screen flickers. I narrowed it down to a conflict between the bootstrap 4 css and the template's ngx-bootrstrap css.
Commenting out the stylesheets from angular2-json-schema-form.es5.js resolved the problem
'bootstrap-4': {                 framework: Bootstrap4FrameworkComponent,                 stylesheets: [                   <!-- removed the styles here -->                 ],                 scripts: [                     '//code.jquery.com/jquery-3.2.1.slim.min.js',                     '//cdnjs.cloudflare.com/ajax/libs/popper.js/1.12.3/umd/popper.min.js',                   ,                 ],             }
I also set load external assets to false
  <json-schema-form       framework="bootstrap-4"        loadExternalAssets="false"         [schema]="formSchema"         [model]="formModel"         [layout]="formLayout"         (onSubmit)="submit($event)"         [(model)]="formData">       </json-schema-form>
How do I create my own custom framework to avoid modifying anything in the node_modules/angular4-json-schema-form folder
Expected behavior

IMPORTANT: How can we reproduce your problem?
You can checkout my repo at https://github.com/neil-coutinho/qdemo.git
You can view my sandbox at http://localhost:4200/schema
Environment
OS name & version:
Browser name & version:
Angular version:
Angular JSON Schema Form version(s):
Other relevant software or packages:

Any other relevant information

Have you read this?:
https://github.com/dschnelldavis/angular2-json-schema-form#changing-or-adding-frameworks

@lucasgranberg yes I did but could not quite get it to work. Do I simply duplicate the Bootstrap4 framework files sans the CSS and do I update it within the node_modules folder itself?

The idea is to never change anything in the node_modules folder. To add your own framework I would start by trying this.
constructor(private frameworkLibrary: FrameworkLibraryService) {
    frameworkLibrary.setFramework({
        framework: YourFrameworkComponent,                                // required
        widgets:     { 'your-widget-name': YourWidgetComponent,   ... },  // optional
        stylesheets: [ '//url-to-framework-external-style-sheet', ... ],  // optional
        scripts:     [ '//url-to-framework-external-script',      ... ]   // optional
    });
}

However as I understand it no assets should be loaded if loadExternalAssets is not set to true. I think your problem might be in how you load your css/scss files on other places in the project (just a guess).

Correct. I do not want to modify anything in the node_modules folder. So looking at the Bootstrap4 framework am i doing the correct thing here
Forgive me, but I'm new to Angular and trying to create custom widgets in my project

@lucasgranberg do you have a plunker or an example of a customFramework? Even when loadExternalAssets = false the cdn files for the bootstrap-4 framework are loaded.
I'm trying to create a duplicate of the bootstrap4 framework without he use of the boostrap 4 css file

No, do you have a plunker where your problem can be reproduced? I only went by the documentation and what I saw in  the code. Maybe something else is loading the bootstrap assets?

@lucasgranberg Sorry I do not have a plunker, but in my repo you can see that I have  loadExternalAssets = false
Also see

@lucasgranberg
Putting square brackets around loadExternalAssets resolved the issue.
<json-schema-form       framework="bootstrap-4"        [loadExternalAssets]="false"        [schema]="formSchema"         (onSubmit)="submit($event)">       </json-schema-form>

@neil-coutinho
HI,
I am using bootstrap4module framework, the form is getting loaded but I am not able to read the form values,
could you please provide any example to read the values and applying css.
below are my fiels for your reference,
login.component.ts
import { Component } from '';
@component({
selector: 'app-login',
templateUrl: './login.component.html',
styleUrls: ['./login.component.css']
})
export class LoginComponent {
title = 'Optum Navigator';
mySchema = {
"properties": {
"msid": {
"type": "string",
"required":true,
"htmlClass": "mystyle",
},
"password": {
"type": "string",
"widget": "password",
"required":true
},
}
};
myform = {
"form": [
"msid",
"password",
{
"type": "submit",
"name": "btnSubmit",
"title": "Login",
}
]
};
public OnSubmitFn(event:any){
console.log("login"+event);
}
}
login.component.html
<json-schema-form
loadExternalAssets="true"
[schema]="mySchema"
[form]="myform "
(onSubmit)="OnSubmitFn($event)"
>

login.component.css
.mystyle {
    border: 2px solid red;
    border-radius: 4px;
}

Thanks in advance.


The parameter loading is incomplete

i use the unireplknet-s model to train my dataset and save a pth, but when i use the reparameterize.py or pytorch2onnx.py all occured the same false patameters
2023-12-02 15:19:52,935 - mmseg - WARNING - unexpected key in source state_dict: norm.weight, norm.bias, head.weight, head.bias
missing keys in source state_dict: stages.2.16.dwconv.bias, stages.2.26.dwconv.bias, stages.2.2.dwconv.bias, stages.2.5.dwconv.bias, stages.2.10.dwconv.bias, stages.2.4.dwconv.bias, stages.2.14.dwconv.bias, stages.2.8.dwconv.bias, stages.2.13.dwconv.bias, stages.2.19.dwconv.bias, stages.0.1.dwconv.bias, stages.2.23.dwconv.bias, stages.2.7.dwconv.bias, stages.2.20.dwconv.bias, stages.2.22.dwconv.bias, stages.0.2.dwconv.bias, stages.2.17.dwconv.bias, stages.0.0.dwconv.bias, stages.2.11.dwconv.bias, stages.2.1.dwconv.bias, stages.2.25.dwconv.bias

Yes it is a bug. Thank you for pointing it out. I have fixed it (see this commit https://github.com/AILab-CVC/UniRepLKNet/commit/5349bcee9a8202c62c8c169220f8cc613914baac).

 The missing keys are due to the bug (the training model should not have dwconv.bias).


USE_LOCAL_PROXY=1 make docker.proxy_debug uses workspace envoy

USE_LOCAL_PROXY=1 make docker.proxy_debug uses workspace envoy

@mandarjog: The following test failed, say /retest to rerun them all:

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Details
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0cf4e16d151e930322e205b4e8976253c4c99cbb
link
/test istio-pilot-multicluster-e2e

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/lgtm
/approve

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Added support for "unmountIfInvisible"

Might be great to be able to UnMount Components as they go out of the viewPort.

this would be killer, any reasons this still not merged?


Added bitcoin.de implementation for market data

excellent addition!
Does the ticker and orderbook require an API key or is it completely public?

It requires an API key unfortunately, and they're not exactly quick about issuing them either.

@bryan4887 do you also have to have an account there or can anyone request an API key?

@timmolter good question.  I registered with a login and everything, but I didn't input any financial info.

@bryan4887 Cool, thanks.


Rewrite CommonJS require.ensure calls to be an IIFE

require.ensure is defined by the CommonJS spec: http://wiki.commonjs.org/wiki/Modules/Async/A. It is implemented by the InjectJS module loader and is also used by WebPack to support async loading of CommonJS modules.
It's a key component to allowing CommonJS modules to work in a browser uncompiled.
After compilation, it serves no use as the modules are all bundled. This removes the calls to require.ensure during module rewriting.

This change is

So this is the CommonJS version of AMD modules?  I hadn't run across it.

@concavelenz Exactly. AMD modules are generally considered over-engineered and difficult to work with. I'm seeing more and more code using CommonJS for browser based projects. Most require rewriting to execute in the browser, but a couple of projects used this signature. I've switched my new projects over from ES6 modules to CommonJS with this signature. The InjectJS module loader works very well and is only needed for uncompiled mode support.

Rebased to fix merge conflicts.


SEME-1983-BE-Rubocop-petal-with-all-rubocop-config-for-new-projects

What's the PR about
SEME-1983-BE-Rubocop-petal-with-all-rubocop-config-for-new-projects
Work done
Unified cops used by monolith to be reused by seme-microservice and new projects.
How to test
-The new project should use this gem rubocop-petal and inherit the base config/default.yml configuration, declaring the heritage in the .rubocop.yml as show below:
require:
- rubocop-rails
- rubocop-performance
- rubocop-rspec
- rubocop-petal
inherit_mode:
  merge:
  - Exclude
inherit_gem:
  rubocop-petal: config/default.yml

If necessary implement any specific bypass or rule, should be declared in the following section like in this example below in the .rubocop.yml file from the project that uses the rubocop-petal gem
# ********************************
# ToDo items
# ********************************
RSpec/EmptyExampleGroup:
  Exclude:
    - 'spec/requests/v1/authentication_api_spec.rb'
    - 'spec/requests/v1/conversations_api_spec.rb'

ps: for now is not possible to apply this new version of rubocop-petal gem in the petalmd.rails project, due many differences of versions from dependencies.

An example I followed: https://github.com/github/rubocop-github#usage
I updated the readme to show how import the config in an other project.

@Bhacaz I've tried to include this new version 0.9.0 in our project, but I think we are missing any process to keep this
/config/base.yml file when bundle the gem.
Please see the output dir list just after install the gem
Installing rubocop-petal 0.9.0
Bundle complete! 18 Gemfile dependencies, 103 gems now installed.
Use `bundle info [gemname]` to see where a bundled gem is installed.
nelalam@petal-0232 secure-messaging % ls -lrt /Users/nelalam/.rbenv/versions/3.1.4/lib/ruby/gems/3.1.0/gems/rubocop-petal-0.9.0/config/*
-rw-r--r--@ 1 nelalam  staff  2412 May 15 14:48 /Users/nelalam/.rbenv/versions/3.1.4/lib/ruby/gems/3.1.0/gems/rubocop-petal-0.9.0/config/default.yml

The result is
nelalam@petal-0232 secure-messaging % rubocop
Configuration file not found: /Users/nelalam/.rbenv/versions/3.1.4/lib/ruby/gems/3.1.0/gems/rubocop-petal-0.9.0/config/base.yml


Revise metaprop attributes

Based on perhaps some of my requests, "certainty" was removed as metaprop attribute, and there is only logic now, while some other things are in metaprop proptype and propvalue which should disappear.
In metaprops, lock (hide) everything apart from:
whole metaprop: logic, certainty (plus elvl is there ontologically speaking, but always 3)
proptype: [nothing - hide all]
propvalue: [nothing - hide all]

@davidzbiral so this is what the model should look like at the end?

@adammertel : yes.


minimal admin template

There are a minimal admin template to start a new application? I don't understand Chinese and also the functional flux

ok, I found!


Integration with Spring-Data-Rest

I am using Spring-Data-Rest to expose my entities as REST endpoints. I like to know if is there any way to integrate with springfox ?
SpringMVC libraries looks for @RequestMapping annotation or @Api annotations to generate swagger documentaion. In SpringDataRest, we don't use the @RequestMapping annotation to expose REST endpoints. SpringDataRest exposes your Entity class as REST endpoints. It looks for a Repository interface for your entities, and if there is one, your entity is exposed as REST endpoint.
I think the Request mapping is generated behind the scenes and you don't have control over it.  Any idea, how springfox can be configured in this case ?

@raranzueque The timing of your question is perfect. We are just about to release v2.0.0 and a big focus of that release has been laying a foundation to support other spring projects like spring-data-rest and spring-integration.
It would be really good to get some community involvement to help drive this.
Including others which may be interested:
@uttrasey
@vivekpandian
@skallio
@jewellcj
@ghorwitchsmith
@jhiemer
@jxc876
The following 2 issues are related:
https://github.com/springfox/springfox/issues/238
https://github.com/springfox/springfox/issues/550

Spring Data Rest is producing Alps metadata, maybe you can use that implementation to get started with swagger metadata generation.
https://spring.io/blog/2014/07/14/spring-data-rest-now-comes-with-alps-metadata

That would be great!
We're starting to use Spring Data Rest, but having a hard time trying to figure out how we are going to have some nice documentation to go with the APIs.
With ALPS, it wouldn't be too hard to create custom documentation by having a JavaScript library point at the ALPS root for the app and build some tables based on the the data available and documentation that's included in a rest-messages.properties file.
However, it would be much nicer to be able to provide docs that people will immediately recognize (such as Swagger docs).
If this feature is still on the table, I definitely vote for it.

After reading some of the comments from @olivergierke (particularly this one https://github.com/springfox/springfox/issues/238#issuecomment-63219435), I'm tending to agree that Swagger-style documentation isn't a fit.  In fact, if you're going to use Spring Data Rest, then you need to do your best to discourage clients from treating your API as a static API.
I'm actually re-thinking my use of Spring Data Rest.  We are planning on plugging our APIs into an API Gateway (WSO2), so the non-static API that Spring Data Rest provides would actually be a terrible fit.
My current thinking is that a happy medium (for me) will be using Spring Data (to get the automatic data layer) and normal RestController controllers to expose a static API.  This will also allow me to use SpringFox out of the box.

@adrianbk Version 2.0 is released now but Spring Data Rest is not supported yet. And now Katharsis is coming into the play... We are at the point to decide whether to go with Swaggerfox or Spring RestDoc. Can you please let me know if there is any plan to support Spring Data Rest? I also need to know how soon it will be because we need to make a decision pretty quick.
Thanks

@xbmono Before getting into when you can expect this to be completed, thought it would be to get a couple of things out of the way, so that you're better informed.

spring-restdocs is inherently a better choice to document spring-data-rest services. Especially if you want to document hypermedia services that has links and relations.
swagger is url centric and the specification does not have a focus on building hypermedia aware applications.
However the BIG difference between swagger and spring-restdocs from the perspective of documentation is that one is machine readable while the latter is not (see :point_down:  5).
There is a great tool build by @RobWin swagger2markup that can leverage your swagger service description to generate documentation just like spring-restdocs
swagger has an amazing amount of tooling and has a strong ecosystem/community, i.e. swagger-code-gen. One would argue that you don't need all this tooling if you're building RESTful services, but thats where the great divide lies.

I would strongly discourage you from making a choice based on availability of not-availability of this feature in time for your project; rather make your choice on how you see the services documentation being consumed. At the moment this feature is scheduled for March 1st, but its very dependent on the amount of bandwidth available for us to work on it. It is an open source project!
Needless to say, anything that you or your team can contribute to this library it will only help getting the long list of feature request accomplished and make this project better. IOW, PRs will be much appreciated! :metal:
/c: @adrianbk

@dilipkrish Thanks so much for the prompt reply. I think we will go with spring-restdocs and perhaps will combine it with swagger using the tool you mentioned. Just one question: I know it's not easy to explain here but in case we want to use ALPs metadata provided by spring and utilize them in swaggerfox, how can we do this customization? Any hint on how to start and what classes/interfaces are involved?

@dilipkrish Thanks so much for the prompt reply. I think we will go with spring-restdocs and perhaps will combine it with swagger using the tool you mentioned. Just one question: I know it's not easy to explain here but in case we want to use ALPs metadata provided by spring and utilize them in swaggerfox, how can we do this customization? Any hint on how to start and what classes/interfaces are involved?

Any updates on a release for this?

+1

+1

+1

+1

+1

+1

Any updates on this?

+1

+1

+1

Initial implementation has landed in master!

Great news! Is it possible to generate swagger API document out of @RepositoryRestResource repositories? (I.e. without RequestMapping annotation)
I would use kongchen/swagger-maven-plugin.

@vzoran absolutely! feedback much appreciated if you use it.

Thanks, and sorry I haven't had time to participate  in this one, other than to state the problem. At the time I worked around it with somewhat a hack adapting the spring source for my specific project overriding  some methods and etc.

I used 2.6.0 version of springfox for swagge2 and swagger-ui. Added a dependency to (as found in one of the doc checkins)

io.springfox
springfox-data-rest
${springfox.version}

I still dont see repositories annotated with @RepositoryRestResource show up in swagger ui.
What else needs to be done to utilize the new spring-data-rest support?

Did you add the configuration as described?

Thanks for the prompt reply.
I have added configuration as described for swagger in general. Created a docket, ApiInfoBuilder.
I am able to see a swagger-ui with the default basic-error-controller. But not able to see the APIs defined below
@RepositoryRestResource(collectionResourceRel = "myapi", path = "myapi") interface MyApiRepository extends PagingAndSortingRepository<MyEntity, Long> {
Is there any other configuration to be added for springfox support for spring-data-rest?

@vramku5 explicitly have you added this configuration?
//For java config
@Import({ ... springfox.documentation.spring.data.rest.configuration.SpringDataRestConfiguration.class, ...})

It works now after adding the above config.
I had interpreted the doc as maven | gradle | java | xml and hence had stopped at adding a maven dependency. However, makes sense as we have to tell somewhere to use that.

@vramku5 What exactly did you add to your @RepositoryRestResource? I still get the basic-error-controller.

+1

@Fredeli/ @MarcoNiemann basic-error-controller is a spring-boot controller. Its got nothing to do with spring-data-rest. If you don't want it to show up, you can specify that in the predicate (apis RequestHandlerSelectors or paths PathSelectors) in your docket to ignore.

@Fredeli
My code looks like this
@SpringBootApplication
@EnableSwagger2
@Import({springfox.documentation.spring.data.rest.configuration.SpringDataRestConfiguration.class})
public class ServletInitializer extends SpringBootServletInitializer {

It worked after I added the above import line. Hope it helps.

@dilipkrish Is there any way to show only exported resources, i.e. those which are not annotated with @RepositoryRestResource(exported = false) ?

Yes you can create a predicate for that in your request handler selection

@dilipkrish But I cannot access Repository class through RequestHandler.. I think there is currently no way to check if the rest resource is exported or not.

You should be able to get to the annotation using this method

Any chance we could get a minimal spring-data-rest + spring boot example?

Attached my working example. Had to click around a little bit in the browser to trigger the refresh
example-springfox-rest-data.zip

@BrunoEberhard thx, works perfectly!

The example cannot be started on tomcat using spring-boot:run

If you have maven installed try
mvn compile exec:java -Dexec.mainClass="org.minimalj.sandbox.springrest.SpringRestApplication"
Or import the project with eclipse as maven project and run the SpringRestApplication .
Then you can access the swagger ui with http://localhost:8080/swagger-ui.html#/
( please thank me by looking at https://github.com/BrunoEberhard/minimal-j and maybe give it a star :) )

Thanks for the prompt reply.
I have been working to resolve this for weeks now. This discussion is a godsend for me! :D
I have maven installed and I managed to run the application although the only operators I get is the basic-error-controller.
By the way I have tried to do my own spring-boot application and I have used spring-data-rest with JpaRepository for my objects instead of CrudRepository. I know this shouldnt be a problem because the JpaRepository extends CrudRepository, but I get the same result as @BrunoEberhard 's example above.

For me JpaRepository works as good as CrudRepository. I really don't know why in your case it doesn't work. Maybe you can attach your example?

@lefkatis Most likely you're not pulling in the spring data rest configuration.

Thanks @dilipkrish and @BrunoEberhard. Much appreciated!
Here is my code if you can help please.
Thanks
// ------   Object Class -------
@Entity
@Table(name = "category")
@NamedQuery(name = "Category.findAll", query = "SELECT c FROM Category c")
public class Category implements Serializable {
	private static final long serialVersionUID = 1L;

	@Id
	@GeneratedValue(strategy = GenerationType.AUTO)
	private int id;

	private boolean isLocked;

	private String name;

	// bi-directional many-to-one association to Question
	@OneToMany(mappedBy = "category")
	private List<Question> questions;

	public Category() {
	}

	public int getId() {
		return this.id;
	}

	public void setId(int id) {
		this.id = id;
	}

	public boolean getIsLocked() {
		return this.isLocked;
	}

	public void setIsLocked(boolean isLocked) {
		this.isLocked = isLocked;
	}

	public String getName() {
		return this.name;
	}

	public void setName(String name) {
		this.name = name;
	}

	public List<Question> getQuestions() {
		return this.questions;
	}

	public void setQuestions(List<Question> questions) {
		this.questions = questions;
	}

	public Question addQuestion(Question question) {
		getQuestions().add(question);
		question.setCategory(this);

		return question;
	}

	public Question removeQuestion(Question question) {
		getQuestions().remove(question);
		question.setCategory(null);

		return question;
	}

	public String toString() {
		return String.format("category=[id=%d, isLocked=%b, name=%s]", id, isLocked, name);
	}
}

// ------   JPARepository Class -------

import com.hellen.apiprojects.domain.Category;
import org.springframework.data.jpa.repository.JpaRepository;
public interface CategoryRepository extends JpaRepository<Category, Integer> {

}

// ------   POM FIle -------

<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
         xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
    <modelVersion>4.0.0</modelVersion>

    <groupId>com.hellen.apiprojects</groupId>
    <artifactId>antavreis-web-api</artifactId>
    <version>0.0.1-SNAPSHOT</version>
    <packaging>war</packaging>

    <name>antavreis-web-api</name>
    <description>Antavreis Game Web REST API</description>

    <parent>
        <groupId>org.springframework.boot</groupId>
        <artifactId>spring-boot-starter-parent</artifactId>
        <version>1.4.2.RELEASE</version>
        <relativePath /> <!-- lookup parent from repository -->
    </parent>

    <dependencies>
        <dependency>
            <groupId>org.springframework.boot</groupId>
            <artifactId>spring-boot-starter-actuator</artifactId>
        </dependency>
        <dependency>
            <groupId>org.springframework.boot</groupId>
            <artifactId>spring-boot-starter-data-jpa</artifactId>
        </dependency>
        <dependency>
            <groupId>org.springframework.boot</groupId>
            <artifactId>spring-boot-starter-data-rest</artifactId>
        </dependency>
        <dependency>
            <groupId>org.springframework.boot</groupId>
            <artifactId>spring-boot-starter-web</artifactId>
        </dependency>
        <dependency>
            <groupId>mysql</groupId>
            <artifactId>mysql-connector-java</artifactId>
            <scope>runtime</scope>
        </dependency>
        <dependency>
            <groupId>org.springframework.boot</groupId>
            <artifactId>spring-boot-starter-tomcat</artifactId>
            <scope>provided</scope>
        </dependency>
        <dependency>
            <groupId>org.springframework.boot</groupId>
            <artifactId>spring-boot-starter-test</artifactId>
            <scope>test</scope>
        </dependency>

        <dependency>
            <groupId>io.springfox</groupId>
            <artifactId>springfox-swagger2</artifactId>
            <version>2.7.0</version>
            <scope>compile</scope>
        </dependency>
        <dependency>
            <groupId>io.springfox</groupId>
            <artifactId>springfox-data-rest</artifactId>
            <version>2.7.0</version>
        </dependency>
        <dependency>
            <groupId>io.springfox</groupId>
            <artifactId>springfox-swagger-ui</artifactId>
            <version>2.7.0</version>
            <scope>compile</scope>
        </dependency>

    </dependencies>

</project>

Works for me. Had to change the parent to
<parent>
    <groupId>org.springframework.boot</groupId>
    <artifactId>spring-boot-starter-parent</artifactId>
    <version>1.5.6.RELEASE</version>
</parent>

And of course add Application and SwaggerConfig class. Took the from my example. Had also to remove the list of question from category as you didn't provide that class.

Thanks very much for the help @BrunoEberhard ! I have copied all my code into your project and it worked! dont know what was my problem though. But at least it works now.

Thank you @BrunoEberhard and @lefkatis  for updating the fixes and in the process documenting it for future visitors.

@BrunoEberhard the example you give is not accessible now. can you help move the example to a github repo? example-springfox-rest-data.zip.


problem when update mysql.data after distribution

version nuget Mysqlbackup 2.3.4
after i update mysql.data from 8.0.23 to 8.0.25
mysql stop working becaus failed load mysql.data 8.0.23

I have just released a new version of nuget package to update the dependency of mysql.data to 8.0.25
You may try again

tks. problem solved


Improvements to Raijin and UXarray websites

The Raijin website has become a bit stale as recent efforts have focused on UXarray. With AGU and AMS talks on Raijin around the corner - and the potential for driving potential new community members to the project - it might behoove us to address some of these issues to try and reduce confusion and improve the user experience. A couple of things to keep in mind: for better or worse we've introduced two brands: "Raijin" and "UXarray". People hearing about the project may only be familiar with one of these. I.e. they may simply google Raijin or UXarray. Moreover, depending on how they search, they might land on the respective websites or on the repos. With that in mind, here are a few observations about the four sites:
Raijin home page

The "Contributing" page is work in progress. It's not clear that we need a detailed contributors guide for raijin itself
The "contact us" page still raises confusion in my mind. There is a really nice flow chart to help guide people, but I still don't know how to delineate a Raijin topic  from a UXarray topic
UXarray in the title bar may have no meaning to most people. Perhaps it should be "Software"?

Raijin repo

The repo has a discussion forum, but it has never been used
Ditto for Project board
People visiting the repo are probably looking for software, not the markdown code for the Raijin home page. Perhaps we should clarify what this repo contains (the source code for the project page), and direct people to UXarray for software packages produced by the Raijin project.

UXarray home page

The new UXarray really looks great! I think it is well organized and super easy to navigate. One additional item that I think would be helpful is a link to a roadmap so that people know what is coming

Suggested actions

Have the Contributor's guide on Raijin site point to the UXarray contributor's guide.
Remove the links to the Raijin issues and discussions from the raijin website "Contact us" page, redo (or remove) the flow chart, and possibly close the raijin repo discussions or project boards
Add a user-friendly roadmap under the "Community" menu on the UXarray website. Perhaps this can just point to a simplified Backlog.
Add clear language in the README for the raijin repo that this site contains only the website source code. If the visitor is looking for our software, go to the UXarray repo.

cc: @brianpm, @cecilehannay, @zarzycki

Making my own comment here for issue tracking
Suggested actions:

[ ] Have the Contributor's guide on Raijin site point to the UXarray contributor's guide.
[ ] Remove the links to the Raijin issues and discussions from the raijin website "Contact us" page, redo (or remove) the flow chart, and possibly close the raijin repo discussions or project boards
[ ] Add a roadmap to the "Community" menu on UXarray website
[ ] Make sure README has clear language about only being website source code and point to UXarray repo for software

[x] Close the raijin repo discussions or project boards


Add jupyterlab to pythia-book-dev environment

Just adds jupyterlab to our environment.yml file.
This is not strictly necessary for building the book from sources, but I would argue that it's an essential ingredient if we're using this environment for developing new content, and/or directing users to this environment for running our example notebooks.

@ktyle since you already thumbed up, can you give a quick review and merge if things are good?


How to generate nuscenes_infos_train_valid_mask_adjusted.pkl ?

Thank you for your wonderful work! I've noticed that nuscenes_infos_train_valid_mask_adjusted.pkl is used in config files, and I would like to ask how to generate it. Looking forward to your reply

Hi @wyz333www33ww ! We have updated the repo and support the OpenPCDet-based MixSup training on nuScenes.


CAMEL-XXX Update documentation for SASL_SSL kafka security protocol - camel-3.x

Description

Target

[ ] I checked that the commit is targeting the correct branch (note that Camel 3 uses camel-3.x, whereas Camel 4 uses the main branch)

Tracking

[ ] If this is a large change, bug fix, or code improvement, I checked there is a JIRA issue filed for the change (usually before you start working on it).

Apache Camel coding standards and style

[ ] I checked that each commit in the pull request has a meaningful subject line and body.

[ ] I formatted the code using mvn -Pformat,fastinstall install && mvn -Psourcecheck

wops, my bad. Gimme 1 minute


Error displaying results in Jupyter notebook workflow

Hello,
I have tried to run the new script for the notebook but I add an error when displaying the results.
As an input, I put an image 1378*2048 pixels and I set the channel to [0,0].
But I've got an error when I want to plot the results using plot.show_segmentation().
The script part is:
from cellpose import plot
print(imgs[1].shape)
nimg = len(imgs)
for idx in range(nimg):
    maski = masks[idx]
    flowi = flows[idx][0]

    fig = plt.figure(figsize=(12,5))
    plot.show_segmentation(fig, imgs[idx], maski, flowi, channels=channels[idx])
    plt.tight_layout()
    plt.show()

And the error that pops up is:

ValueError                                Traceback (most recent call last)
 in
9
10     fig = plt.figure(figsize=(12,5))
---> 11     plot.show_segmentation(fig, imgs[idx], maski, flowi, channels=channels[idx])
12     plt.tight_layout()
13     plt.show()
c:\users\paulinecci\image processing\yuki\cellpose\cellposeenv\lib\site-packages\cellpose\plot.py in show_segmentation(fig, img, maski, flowi, channels, file_name)
59     outX, outY = np.nonzero(outlines)
60     imgout= img0.copy()
---> 61     imgout[outX, outY] = np.array([255,75,75])
62     ax.imshow(imgout)
63     #for o in outpix:
ValueError: shape mismatch: value array of shape (3,) could not be broadcast to indexing result of shape (17932,)

I have tried to investigate the issue put did not find a solution yet.
If you have some solution to help me :)
Thank you !

sorry about this! I fixed this now, please pip install cellpose --upgrade. I'm closing this issue for now please reopen it if there are still problems

Hello,
It works, thanks you very much for your help !


DOC: Mention simulation method of CIR and Heston processes #173

Codecov Report

Merging #174 (334351e) into main (230d090) will not change coverage.
The diff coverage is n/a.

@@            Coverage Diff            @@
##              main      #174   +/-   ##
=========================================
  Coverage   100.00%   100.00%
=========================================
  Files           40        40
  Lines         1157      1157
=========================================
  Hits          1157      1157

Impacted Files
Coverage Δ

pfhedge/stochastic/cir.py
100.00% <ø> (ø)

pfhedge/stochastic/heston.py
100.00% <ø> (ø)

Continue to review full report at Codecov.

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Ensure Fastboot support

See here.

Looked into this briefly; the addon works fine if an initialStep is defined. With no initialStep defined, the isVisible property of the first rendered step is set to false even when it should be true.  Interestingly, isVisible gets its value from isActive which does have the right value at that point (because the content of the slide exists in the response).  So, for some reason, the Fastboot server gets far enough in the render to know that the content should be visible but does not get far enough to un-hide the step.


[CASSANDRA-522] add cassandra.ssl_storage_port config var

fixes #339

Waiting for @andlaz to clarify in #339 before merging.

cc @andlaz
Look good?

Failure is:
dcos.errors.DCOSException: Package file [stub-universe-cassandra/repo/packages/C/cassandra/0/config.json] is not JSON: expected } or , got " (line 318, column 12935)

retest this please (jar fetch flake)

retest this please


Default config first

Show the default config first in the Viewer.
I did this by ordering the configs in dataset-config-names to have the default config in first position
fix https://github.com/huggingface/datasets-server/issues/2541
No need to recompute all the entries, it's fine to apply this to new / updated datasets imo
cc @HugoLaurencon for WebSight

i love this but this also would break the bigcode/the-stack-v2-dedup viewer which we struggled for so long to make it work :crying_cat_face:
the problem is - default config in bigcode/the-stack-v2-dedup is the one that includes all the data and it doesn't work, see https://github.com/huggingface/datasets-server/issues/2552. so @anton-l and I decided to split all the data into many small configs (one per language) so that the first small config is shown in the viewer. and it works now!
so please don't deploy until we find a solution :pray:

this is why i was dreaming about having a dedicated field for the viewer :D https://github.com/huggingface/datasets-server/issues/1953#issuecomment-1983496422


Need the ability to handle "

When the json is from F12 network trace, it contain ". So I need to replace " with " manually.

Basically, you need a command to unescape JSON. Check this online tool https://www.freeformatter.com/json-escape.html and let me know if this is the functionality you are asking for.


Address EnchantedDevice inheritance

Remove the CustomDevice inheritance for the quirks that already extend from EnchantedDevice.
Follow up from #2296

Pull Request Test Coverage Report for Build<PHONE_NUMBER>

9 of 9 (100.0%)  changed or added relevant lines in 1 file are covered.
No unchanged relevant lines lost coverage.
Overall coverage decreased (-0.002%) to 83.959%

Totals

Change from base Build<PHONE_NUMBER>:
-0.002%

Covered Lines:
6914

Relevant Lines:
8235

💛  - Coveralls

Codecov Report
Patch coverage: 100.00% and no project coverage change.

Comparison is base (8e91d98) 83.95% compared to head (a1d2f9c) 83.95%.

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Additional details and impacted files
@@           Coverage Diff           @@
##              dev    #2299   +/-   ##
=======================================
  Coverage   83.95%   83.95%
=======================================
  Files         261      261
  Lines        8231     8235    +4
=======================================
+ Hits         6910     6914    +4
  Misses       1321     1321

Impacted Files
Coverage Δ

zhaquirks/tuya/ts000x.py
100.00% <100.00%> (ø)

... and 6 files with indirect coverage changes
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Bump CUDA version to 12.2.0-devel-ubuntu22.04

Force pushed to fix the version in the commit message.

LGTM

BTW since we always squash merge, you don't have to force push for the purpose of adding new changes.

Looks like our CI cluster needs some update to support CUDA 12.2. I'll try to figure it out shortly.


refactor: update eoa

Pull Request type

Please check the type of change your PR introduces:

[ ] Bugfix
[ ] Feature
[ ] Code style update (formatting, renaming)
[x] Refactoring (no functional changes, no API changes)
[ ] Build-related changes
[ ] Documentation content changes
[ ] Other (please describe):

What is the current behavior?

#207

followed comments


Closes issue #28

I actually already have PR #30 out that fixes the bug you reported (thanks for that, by the way). I didn't bother moving the file to the shared project though. That alarms me: we have many duplicate files now defining the same nested types. I suspect @vbfox's script may have done this. We need to repair that. Let's consider that this PR has two components: the part that I cover in #30 and the part that needs to be much bigger (to solve the whole duplicate file problem).
Let's have a discussion on gitter.im for how to handle this.


fix: remove unused show incoming txs option

Builds ready [2a21fc2]builds: chrome, firefox, opera
bundle viz: background, ui, portal-inpage, portal-contentscript, ui-libs, bg-libs, phishing-detect
dep viz: background
all artifacts

Builds ready [3b26668]builds: chrome, firefox, opera
bundle viz: background, ui, portal-inpage, portal-contentscript, ui-libs, bg-libs, phishing-detect
dep viz: background
all artifacts


Register the community spend proposal correctly

There was a line of boilerplate in codec.go for registering the proposal type that wasn't implemented.

Codecov Report

Merging #396 (9d4b92a) into main (4c09040) will decrease coverage by 0.01%.
The diff coverage is 0.00%.

@@            Coverage Diff             @@
##             main     #396      +/-   ##
==========================================
- Coverage   28.28%   28.26%   -0.02%
==========================================
  Files          47       47
  Lines        6396     6400       +4
==========================================
  Hits         1809     1809
- Misses       4442     4446       +4
  Partials      145      145

Impacted Files
Coverage Δ

module/x/gravity/client/cli/tx.go
0.00% <0.00%> (ø)

module/x/gravity/types/codec.go
5.76% <0.00%> (-0.24%)
:arrow_down:


As a user who needs to regularly work on opps within a set of criteria, I need to easily apply a saved search so that I dont have to re-enter all criteria each time I perform this task

from any grants tab, option to view all saved searches (mine)
view list of saved searches

search title; wrap to show full name
Last used [relative time] ago MM/DD/YYYY

relative breakpoint:

today
days = 1-29
months = 1-11
years = 1+

all criteria; truncate after2 lines
shares or subscribed status

actions on saved searches

share
Rename (mine)
Delete (mine)

grants digest alerts on all saved searches
empty state for saved search sidepanel

Created child tickets

#1450
#1451
#1452
#1453
#1454

The last one in particular seems dependent on #1410. As far as the backend goes, those were originally laid out for #674. I think these should server here too, although we may need a ticket for Update functionality (to support renaming and applying the "last used"). cc @TylerHendrickson

#879
#880
#881
#882


[ACM-7785] Updated hive to latest mce-2.4 commit

Description
Updated config to use latest hive config. For this build we are using the --image-repo and --image-tag-override field to update the build, since by default the script is pulling from the app-sre/hive repo in quay. That repository doesn't have the image built for the commit we are selecting; therefore, we will use the stolostron repo and use the image that was built and made available to us.
Related Issue
https://issues.redhat.com/browse/ACM-7785
Changes Made
Updated scripting to include an image-repo and image-tag-override option for us to enable it in the config.yaml file. This will allow us to pull from our repo, along with the image that was built the latest.
Screenshots (if applicable)
Add screenshots or GIFs that demonstrate the changes visually, if relevant.
Checklist

[x] I have tested the changes locally and they are functioning as expected.
[x] I have updated the documentation (if necessary) to reflect the changes.
[x] I have added/updated relevant unit tests (if applicable).
[x] I have ensured that my code follows the project's coding standards.
[x] I have checked for any potential security issues and addressed them.
[x] I have added necessary comments to the code, especially in complex or unclear sections.
[x] I have rebased my branch on top of the latest main/master branch.

Additional Notes
Add any additional notes, context, or information that might be helpful for reviewers.
Reviewers
Tag the appropriate reviewers who should review this pull request. To add reviewers, please add the following line: /cc @reviewer1 @reviewer2
/cc @cameronmwall @ngraham20
Definition of Done

[ ] Code is reviewed.
[ ] Code is tested.
[ ] Documentation is updated.
[ ] All checks and tests pass.
[ ] Approved by at least one reviewer.
[ ] Merged into the main/master branch.

/lgtm


get rid of .geo column in model output

This will not be fixed in the GEE export step. Will have to be removed when adjusting the summary dataset.

"when adjusting the summary dataset" - is that on my end? Totally OK if so.

Negative! I have an updated tabular file for you shortly!

Amazing!


feat(components): add new component for the stacked cards

can anyone help me with one bug. i can't understand what am i doing wrong here.
i can't make them align vertically.
i have made the code in jsx and its working totally fine. but here it is not.

@ibelick please check.

Closing this PR as the component isn't functioning as expected. Thanks for the effort, feel free to revisit and reopen if you have updates


CBOR support

Hello - and first of all thank you for jless - it's awesome!
I wonder if you would be open to adding CBOR format as input? It is self-descriptive in the same way as JSON is, so I think it would fit nicely into JSON and YAML family.
There are CBOR libraries for Go readily available, e.g. https://pkg.go.dev/github.com/fxamacker/cbor .
Edit: https://github.com/fxamacker/cbor/releases has newer versions as well.
Thank you for considering this.

I don't believe CBOR is relevant enough to merit inclusion in jless at this time. (See Google Trends)
There seems to be at least one CBOR-to-JSON tool that you could use together with jless. You could setup the following function in your .bashrc:
# Usage:
# $ cless data.cbor          # Read a file
# $ cat data.cbor | cless    # Read the output of another command
function cless() {
  if [[ $# -eq 0 ]]; then
    cbor | jless
  else
    cbor "$1" | jless
  fi
}

Fair enough, thank you!


Agent - rotate agent towards object he is interacting with

Fixed in 900322b.
https://youtube.com/clip/UgkxWSOLZpCmklFQ3CRKnP0DQkzERJW9IBCe


add palette: Dracula

PR adding palette for the Dracula color theme (https://draculatheme.com)

Thank you for your contribution to dipc!
I've left some comments on your PR before I can merge the PR.

Hi,
I've updated the PR with the suggested changes. can you take a look now?
:)

Once again thank you for the PR and for updating it with the suggested changes.
Approved!


[Content Fragment] Migrate deprecated sourceUrl fields

Description
Legacy content fragment messages may have a sourceUrl with a private GCS link, only accessible to Dust so non-downloadable. This PR resets those to a null sourceUrl, to enforce the invariant that sourceUrl is a user-accessible link to an original version of the content--which we do not have for old content fragment.
This handle the cases, among others, in which the sourceUrl was  set to textually extracted content (which was against the above invariant)
Risk
None since there was no world in which sourceUrl set to GCS was relevant
Deploy Plan

You're right on the 2 nits, would have been better. Not useful to change here I think, but I will do as you suggest in the future.
:pray: :pray: for the speedy review


Publish API-Doc to gh-pages branch

Coverage remained the same at 100.0% when pulling 259bb4a09aaf24420a7fa3e9a8085c8945e5a810 on akkie:master into 3f5fc234f9a313d0568598d7fd86b0c99ca9645f on mohiva:master.


Unhandled connection and channel state case

A few first observations after trying the relayer commands.

many IDs local to ibc0 are of form ibcone<item> which for me intuitively meant <item> on chain ibc1. Maybe <item>toibc1...like connectiontoibc1,  or connAtoB, etc if this is a connectionID with ibc0/ A scope (see demo json files)

the query connections command doesn't return local connection ID:

$ relayer query connections ibc0
[{"state":"OPEN","client_id":"ibconeclient","counterparty":{"client_id":"ibczeroclient","connection_id":"ibczeroconnection","prefix":{"type":"ibc/commitment/MerklePrefix","value":{"key_prefix":"aWJj"}}},"versions":["1.0.0"]}]

With multiple connections it's going to be hard to figure out the ID to query a given connection on a chain, ibc0 in this case

ditto for channels, query channels doesn't return local channel ID

error numbers are shown on SDK failures, hard to determine what is going on

looking at SDK connection handlers, it seems that both ends can be in INIT state and ConnOpenTry relayed to either end would still progress the handshake (rhis is not the case in the IBC specification). However the relayer does not relay for this case. Same for channel.

[I will collect more here]

Opened https://github.com/cosmos/cosmos-sdk/issues/5829 to address the lack of local identifiers.

Also, was trying to figure out if we can easily demo loopback connection, channel, etc. using relayer commands. It seems that a few commands will need to change to handle same source and dest chains and single client.
Have you looked at that?

@ancazamfir I've substantially refactored the relayer and it should be able to handle the loopback case. Might be a couple of small errors but I don't think it should be an issue. The loopback client just landed in the SDK so would be cool to test it. As for your original issue I think I've addressed pretty much everything there except the unhandled connection and channel handshake cases. I'll leave this issue open to address that.

re: loopback
I am not sure how the loopback connection should be configured. I tried a number of things:

configure a single connectionend with counterparty having the same connectionId as local

rly tx raw client ibc0 ibc0 loopbackclient
rly tx raw conn-init ibc0 ibc0 loopbackclient loopbackclient loopconnsrc loopconnsrc
rly tx raw conn-try ibc0 ibc0 loopbackclient loopbackclient loopconnsrc loopconnsrc
rly tx raw conn-ack ibc0 ibc0 loopbackclient loopbackclient loopconnsrc loopconnsrc

On the conn-ack I see:
{"height":"119","txhash":"50888B8DFB26F4B209F61F1F8D60B579105DCBCD8102C3D41802BE1ABC3081BC","codespace":"connection","code":5,"gas_wanted":"200000","gas_used":"104852"}

I believe this is caused by this in the connection handler:
	// Check connection on ChainA is on correct state: INIT
	if connection.State != exported.INIT {
		return sdkerrors.Wrapf(
			types.ErrInvalidConnectionState,
			"connection state is not INIT (got %s)", connection.State.String(),
		)
	}

We should allow also exported.TRYOPEN
There is an issue for SDK that mentions this, see #4 in https://github.com/cosmos/cosmos-sdk/issues/5908

configured two connection ends,loopconnsrc and loopconndest, and then continue like in the case of separate chains. This one now fails in conn-try

rly tx raw client ibc0 ibc0 loopbackclient
rly tx raw conn-init ibc0 ibc0 loopbackclient loopbackclient loopconnsrc loopconndst
rly tx raw conn-try ibc0 ibc0 loopbackclient loopbackclient loopconndst loopconnsrc

On the conn-try I see:
{"height":"0","txhash":"DF9A6CD07663C5E1F38F971D90D71EF686041112ADE8CDCEF3CADDD879DE70FC","codespace":"commitment","code":1}

It might fail here but not entirely sure:
func (msg MsgConnectionOpenTry) ValidateBasic() error {
...
	if msg.ProofHeight == 0 {
		return sdkerrors.Wrap(ibctypes.ErrInvalidHeight, "proof height must be > 0")
	}
	if msg.ConsensusHeight == 0 {
		return sdkerrors.Wrap(ibctypes.ErrInvalidHeight, "consensus height must be > 0")
	}

Then I tried the same as 2 but init both ends, fails in conn-ack:

rly tx raw client ibc0 ibc0 loopbackclient
rly tx raw conn-init ibc0 ibc0 loopbackclient loopbackclient loopconnsrc loopconndst
rly tx raw conn-init ibc0 ibc0 loopbackclient loopbackclient loopconndst  loopconnsrc

rly tx raw conn-try ibc0 ibc0 loopbackclient loopbackclient loopconndst loopconnsrc
rly tx raw conn-ack ibc0 ibc0 loopbackclient loopbackclient loopconnsrc loopconndst

On the conn-ack I see:
{"height":"661","txhash":"E94E8AEC02BF3B1B4D1B10B8542F5F25CE6B3793B7C21A58CFB37E35C4A3866E","codespace":"client","code":14,"gas_wanted":"200000","gas_used":"113125"}

I believe this fails in the loopback client here:
func (cs ClientState) VerifyConnectionState(
	cdc *codec.Codec,
	_ uint64,
	prefix commitmentexported.Prefix,
	_ commitmentexported.Proof,
	connectionID string,
	connectionEnd connectionexported.ConnectionI,
	_ clientexported.ConsensusState,
) error {
...
	if connectionEnd != prevConnection {
		return sdkerrors.Wrapf(
			clienttypes.ErrFailedConnectionStateVerification,
			"connection end ≠ previous stored connection: \n%v\n≠\n%v", connectionEnd, prevConnection,
		)
	}

...
}

An update on this, the relayer seems to create a tendermint client on rly tx raw client ibc0 ibc0 loopbackclient. Should it create a loopback client if src, dest chains are the same. @jackzampolin?

Closing this issue as addressed! 🎉


Unable to disable CSS validation or linting

Does this issue occur when all extensions are disabled?: No, because the issue occurs in a .vue which is using Vetur extension

VS Code Version: 1.58.2
OS Version: Ubuntu 18.04

Project where this is happening: https://github.com/bensoutendijk/gamut
These settings are present in both my workspace settings and user settings
{
  "css.lint.unknownAtRules": "ignore",
  "scss.lint.unknownAtRules": "ignore",
  "less.lint.unknownAtRules": "ignore",
  "css.validate": false,
  "less.validate": false,
  "scss.validate": false
}

However, I am still getting the error Unknown at rule @apply scss(unknownAtRules) highlighted in my vue template. I thought I had solve the issue with those settings, but upon restarting my machine the linting errors are back despite the settings to disable linting and validation for scss, css, and less.

Need to set "vetur.validation.style": false in settings.json


First pass at Google storage back end

I have tested GDS datastore for blobs and documents, w/ and w/out IDGEN  (also using GDS) and it is working. I also ran a full regression and I encountered no issues.

blob uses GCP Cloud Storage
document uses GCP DataStore

Found this index related bug:
https://incapture.atlassian.net/browse/RAP-4343

I've re-run my tests and using the GCP_ nomenclature in configs and it's working now.

Can you revert your changes to the RaptureIntTests from https://github.com/RapturePlatform/Rapture/commit/31851b9 ?

test this please

I have only be able to test this locally and it appears that the initial OOM is gone, but I do see this after about 20 min of running:
Exception in thread "Thread-6" com.google.api.gax.grpc.ApiException: io.grpc.StatusRuntimeException: DEADLINE_EXCEEDED: The service was unable to fulfill your request. Please try again. [code=8a75]
at com.google.api.gax.grpc.ExceptionTransformingCallable$ExceptionTransformingFuture.onFailure(ExceptionTransformingCallable.java:107)
at com.google.api.gax.grpc.ListenableFutureDelegate$1.onFailure(ListenableFutureDelegate.java:52)
at com.google.common.util.concurrent.Futures$4.run(Futures.java:1123)
at com.google.common.util.concurrent.MoreExecutors$DirectExecutor.execute(MoreExecutors.java:435)
at com.google.common.util.concurrent.AbstractFuture.executeListener(AbstractFuture.java:900)
at com.google.common.util.concurrent.AbstractFuture.complete(AbstractFuture.java:811)
at com.google.common.util.concurrent.AbstractFuture.setException(AbstractFuture.java:675)
at io.grpc.stub.ClientCalls$GrpcFuture.setException(ClientCalls.java:466)
at io.grpc.stub.ClientCalls$UnaryStreamToFuture.onClose(ClientCalls.java:442)
at io.grpc.internal.ClientCallImpl$ClientStreamListenerImpl.close(ClientCallImpl.java:481)
at io.grpc.internal.ClientCallImpl$ClientStreamListenerImpl.access$600(ClientCallImpl.java:398)
at io.grpc.internal.ClientCallImpl$ClientStreamListenerImpl$1StreamClosed.runInContext(ClientCallImpl.java:513)
at io.grpc.internal.ContextRunnable.run(ContextRunnable.java:52)
at io.grpc.internal.SerializingExecutor$TaskRunner.run(SerializingExecutor.java:154)
at java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:511)
at java.util.concurrent.FutureTask.run(FutureTask.java:266)
at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.access$201(ScheduledThreadPoolExecutor.java:180)
at java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.run(ScheduledThreadPoolExecutor.java:293)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:745)
Caused by: io.grpc.StatusRuntimeException: DEADLINE_EXCEEDED: The service was unable to fulfill your request. Please try again. [code=8a75]
at io.grpc.Status.asRuntimeException(Status.java:545)
... 13 more

test this please

test this please

test this please


vscode version is not working on RHEL 6.7

VSCode Version: Latest feb 2019 version
OS Version: Linux RHEL 6.7

While installing using rpm command, it gives following error

libstdc++.so.6(GLIBCXX_3.4.14)(64bit) is needed by code-1.20.1-1518536126.el7.x86_64
libstdc++.so.6(GLIBCXX_3.4.15)(64bit) is needed by code-1.20.1-1518536126.el7.x86_64
libsecret-1.so.0()(64bit) is needed by code-1.20.1-1518536126.el7.x86_64

Reason : what I can see is linux 6.7 version contains below things

GLIBCXX version 3.4.13
GLIBC version 2.12

And as per basic requirement mentioned by vscode is on Linux 7 version it needs

GLIBCXX version 3.4.15 or later
GLIBC version 2.15 or later

so then which version of vscode is suitable to get it installed successfully on RHEL 6.7 platform, as its difficult to get latest RHEL version to get installed

Sorry, we don't support RHEL < 7 https://code.visualstudio.com/docs/supporting/requirements

Thanks for your quick response Tyriar


Allow for customization of path_prefix_server by setting Turbine field.

Currently only a single Perseus app can be served by a single server backend. While Perseus already allows for customization of app base path, this customization is activated by setting PERSEUS_BASE_PATH environment variable, which means that the same path prefix is applied process-wide, so it's not possible to create a non-standard server implementation "mounting" two Turbines on different URL mountpoints.
This means that the only way to have multiple Perseus apps running under the same origin is to use a reverse proxy to forward requests to different Perseus servers.
This PR is meant to allow setting a path_prefix_server on a Turbine, while leaving the default behavior of resolving PERSEUS_BASE_PATH environment variable if a custom value is not set.

Hey, sorry for the late reply, I've been really busy lately, but I am back now! I think this is a good idea in theory, but I don't love the idea of moving this into the code. From my perspective, the base path is very much a configuration value, and implanting that into an app's code isn't amenable to the philosophy of keeping configuration that can change easily (e.g. an app should be able to be hosted wherever you like once the code is ready). Also, to clarify, your use-case for this is running two turbines in the same server? Is there any particular reason you can't run the two as separate processes?

I get your point, however several other "configurations" are currently available for customization in code. I believe this is for good reason: instead of wiring the developer onto a specific way of managing configuration (environment vars), the developer should be free to decide for, say, a configuration file.
That said, my use case is to split the client-side wasm part into sub-apps for different areas of the site, to keep load time low. On the server-side this split would require an unnecessary larger footprint with multiple server processes and some front-end proxying.

Okay, I certainly accept that, and your use-case is far less niche than I was expecting! Let me think about this some more and review your code in greater depth.

@giacomocariello would you mind resolving the conflicts with main? Then this is good to merge.