text:
SchemaModel inheritance field alias propagation

Describe the bug
A clear and concise description of what the bug is.
In the particular case where a child SchemaModel defines no fields (e.g. the class body consists of pass or a config class only) , field alias names are not inherited from the parent.

[x] I have checked that this issue has not already been reported.
[x] I have confirmed this bug exists on the latest version of pandas.
[ ] (optional) I have confirmed this bug exists on the master branch of pandas.

Code Sample, a copy-pastable example
import pandas as pd
import pandera as pa
from pandera.typing import Series

class Schema(pa.SchemaModel):
    b: Series[str] = pa.Field(alias="test")

    class Config:
        name = "BaseSchema"
        strict = True

class Schema2(Schema):
    c: Series[str]

class Schema3(Schema):
    pass

df = pd.DataFrame({"test": ["2001"]})
df2 = pd.DataFrame({"test": ["2001"],
                    "c": ["2001"]})

Schema.validate(df)
Schema2.validate(df2)
Schema3.validate(df)

Expected behavior
I expect that Schema3.validate(df) should be legal - that is it should inherit the field alias "test" from Schema, in the same way as for Schema2.
The actual behaviour is the following traceback:
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "C:\MyPrograms\Miniconda3\envs\panderaBug\lib\site-packages\pandera\model.py", line 193, in validate
    return cls.to_schema().validate(
  File "C:\MyPrograms\Miniconda3\envs\panderaBug\lib\site-packages\pandera\schemas.py", line 497, in validate
    error_handler.collect_error(
  File "C:\MyPrograms\Miniconda3\envs\panderaBug\lib\site-packages\pandera\error_handlers.py", line 32, in collect_error
    raise schema_error from original_exc
pandera.errors.SchemaError: column 'test' not in DataFrameSchema {'b': <Schema Column(name=b, type=<class 'str'>)>}

Desktop (please complete the following information):

OS: Windows 10
Originally found on Python 3.9.2, pandas 1.2.3 and pandera 0.6.2 (conda install)
Tested the behaviour is the same for Python 3.9.2, pandas 1.2.3 and pandera 0.6.3, this time using pip

Additional context
I originally stumbled across this where I was using my Schema3 equivalent as a temporary definition, before running code to work out what columns were missing when I noticed this behaviour. Perhaps this is obscure enough that this issue isn't really an issue, but the same thing also happens if one defines a Config subclass without adjusting fields, which is more likely to be a realistic issue.
I'd usually try and have a look to work out why a bug is happening, but I'm not familiar with how inheritance works for non-instantiated classes.
[Edit: traceback for pandera 0.6.3]

thanks for submitting this bug @m-richards! #446 should do it, @jeffzi added you as reviewer, feel free to edit if there's a better solution, unit tests should catch this case now.

fixed by #446

authors: cosmicBboy m-richards

repo: pandera-dev/pandera


text:
Remove underscore from file names

1. Are you opening this pull request for bug-fixes, optimizations, or new features?
Fixing the file naming convention based on this.
2. Please describe how these code changes achieve your intention.
There is no code change
(Feel free to reject this PR if you think it's unnecessary)

Not sure how the coverage reduced :P
It's just file names

That was talking about package name, it's no need to change file name like that.

Ok. I thought generally we don't use underscores in files name (except for _test) in go. Or at least that's the convention that I have seen around. We can close the PR if you want. It adds absolutely no value anyway :P

source: gharchive/pull-request

license: MIT

license_type: permissive

license_source: github-api


text:
Debug Audiences not showing up on Pantheon platform

The Audiences admin menu does not show up in Pantheon but does locally.
Should figure out what's up.
If necessary, demo can be run locally with Audiences set up if we can get the ES/Altis Accelerate configuration to work with a local site.

I did try to dig into this. I suspect it's related to ElasticSearch not making the connection/connection not being accepted, which is perhaps disabled locally. For the recording, I used local to show personalization and audiences.

source: gharchive/issue


text:
Update Exploit detection to use re

Background
Updated, more efficient version of this rule
Changes

compile IOC search with re
search event converted to string for compiled re

Testing
Unit tests

I would review the targeted logTypes, this likely does not make sense for VPCFlow flow logs, perhaps others.

authors: bseb rleighton

repo: panther-labs/panther-analysis


text:
Update Posts “whythefaultinourstarsbyjohngreenwillchangethewayyouthink”

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text:
Use human readable time zones in examples

The time zone in the examples all use the complex PST8PDT,M3.2.0,M11.1.0 version for Los Angeles. Nothing wrong with that but the more human readable America/Los_Angeles works as well. In other words: we could simply use the first column in the referenced CSV file instead of the second.

No sorry, it does not. Closing this pull request until I understand why.

repo: paperdink/PaperdInk-Library


text:
ECML/PKDD 2023

Insert ECML/PKDD 2023 deadline.

Thanks for the contribution!

authors: andrewkuanop rmassidda

repo: paperswithcode/ai-deadlines


text:
The output is so bad - total garbage what I am doing wrong? It is also super slow and requires huge amount of RAM

Here my entire command
from transformers import AutoTokenizer, OPTForCausalLM

tokenizer = AutoTokenizer.from_pretrained("facebook/galactica-1.3b")
model = OPTForCausalLM.from_pretrained("facebook/galactica-1.3b", device_map="auto")

input_text = "The benefits of deadlifting\n\n"
input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to("cuda")

outputs = model.generate(input_ids,new_doc=False,top_p=0.7, max_length=1000)
print(tokenizer.decode(outputs[0]))

And the output is total repetition and garbage. I am trying to generate an article based on the topic sentence I provide
Also even 28 GB VRAM is not enough for 6.7b model. I am testing CPU runtime on IPU and it has been more than 2 hours with just 6.7b model.
the output as below

The benefits of deadlifting
The benefits of deadlifting are numerous. It is a simple, inexpensive, and effective method of reducing the risk of injury to the shoulder and elbow. It is also a simple and effective method of reducing the risk of injury to the hand.
Shoulder
The shoulder is the most common site of injury to the upper extremity. The shoulder is the most common site of injury to the upper extremity. The shoulder is the most common site of injury to the upper extremity. The shoulder is the most common site of injury to the upper extremity. The shoulder is the most common site of injury to the upper extremity. The shoulder is the most common site of injury to the upper extremity. The shoulder is the most common site of injury to the upper extremity. The shoulder is the most common site of injury to the upper extremity. The shoulder is the most common site of injury to the upper extremity. The shoulder is the most common site of injury to the upper extremity. The shoulder is the most common site of injury to the upper extremity. The shoulder is the most common site of injury to the upper extremity. The shoulder is the most common site of injury to the upper extremity. The shoulder is the most common site of injury to the upper extremity. The shoulder is the most common site of injury to the upper extremity. The shoulder is the most common site of injury to the upper extremity. The shoulder is the most common site of injury to the upper extremity. The shoulder is the most common site of injury to the upper extremity. The shoulder is the most common site of injury to the upper extremity. The shoulder is the most common site of injury to the upper extremity. The shoulder is the most common site of injury to the upper extremity. The shoulder is the most common site of injury to the upper extremity. The shoulder is the most common site of injury to the upper extremity. The shoulder is the most common site of injury to the upper extremity. The shoulder is the most common site of injury to the upper extremity. The shoulder is the most common site of injury to the upper extremity. The shoulder is the most common site of injury to the upper extremity. The shoulder is the most common site of injury to the upper extremity. The shoulder is the most common site of injury to the upper extremity. The shoulder is the most common site of injury to the upper extremity. The shoulder is the most common site of injury to the upper extremity. The shoulder is the most common site of injury to the upper extremity. The shoulder is the most common site of injury to the upper extremity. The shoulder is the most common site of injury to the upper extremity. The shoulder is the most common site of injury to the upper extremity. The shoulder is the most common site of injury to the upper extremity. The shoulder is the most common site of injury to the upper extremity. The shoulder is the most common site of injury to the upper extremity. The shoulder is the most common site of injury to the upper extremity. The shoulder is the most common site of injury to the upper extremity. The shoulder is the most common site of injury to the upper extremity. The shoulder is the most common site of injury to the upper extremity. The shoulder is the most common site of injury to the upper extremity. The shoulder is the most common site of injury to the upper extremity. The shoulder is the most common site of injury to the upper extremity. The shoulder is the most common site of injury to the upper extremity. The shoulder is the most common site of injury to the upper extremity. The shoulder is the most common site of injury to the upper extremity. The shoulder is the most common site of injury to the upper extremity. The shoulder is the most common site of injury to the upper extremity. The shoulder is the most common site of injury to the upper extremity. The shoulder is the most common site of injury to the upper extremity. The shoulder is the most common site of injury to the upper extremity. The shoulder is the most common site of injury to the upper extremity. The shoulder is the most common site of injury to the upper extremity. The shoulder is the most common site of injury to the upper extremity. The shoulder is the most common site of injury to the upper extremity. The shoulder is the most common site of injury to the upper extremity. The shoulder is the most common site of injury to the upper extremity. The shoulder is the most common site of injury to the upper extremity. The shoulder is the most common site of injury to the upper extremity. The shoulder is the most common site of injury to the upper extremity. The shoulder is the most common site of injury to the upper extremity. The shoulder is the most common site of injury to the upper extremity. The shoulder is the most common site of injury to the upper extremity. The shoulder is the most common site of injury to the upper extremity. The shoulder is the most common site of injury to the upper

Here another example. Why all repetition?

Thanks for reporting, will look into this and get back to you tomorrow

In half precision mode 6.7b fits on a 3090. As for the output quality, you need to tweak generation parameters a little, this blogpost explains quite a bit.
Here's a snippet of how I use it:
import torch, gc
from transformers import AutoTokenizer, OPTForCausalLM

tokenizer = AutoTokenizer.from_pretrained("facebook/galactica-6.7b")
tokenizer.pad_token_id = 1
tokenizer.padding_side = 'left'
tokenizer.model_max_length = 2020

model = OPTForCausalLM.from_pretrained("facebook/galactica-6.7b", device_map="auto", torch_dtype=torch.float16)

input_text = """# Scientific article.
title: Contrastive analysis of models used for DRAM simulation.

# Introduction
"""
input_ids = tokenizer(input_text, return_tensors="pt", padding='max_length').input_ids.to("cuda")

outputs = model.generate(input_ids,
                        max_new_tokens=1000,
                        do_sample=True,
                        temperature=0.7,
                        top_k=25,
                        top_p=0.9,
                        no_repeat_ngram_size=10,
                        early_stopping=True)

print(tokenizer.decode(outputs[0]).lstrip('<pad>'))

gc.collect()
torch.cuda.empty_cache()

padding='max_length' means new_doc=True, set to False to disable

Authors say in the paper that the model was trained on text in markdown format, so giving markdown-ish prompts to the model should probably work best, I guess.
Padding params I took from here https://github.com/paperswithcode/galai/blob/f6d9b0a5b35a0eda53597a5ea7d51963bfc05de1/galai/model.py#L85-L86

@AbstractQbit ty so much for answers
about these hyper parameters, have you tested them or how did you come up with those values?
do_sample=True,
                    temperature=0.7,
                    top_k=25,
                    top_p=0.9,
                    no_repeat_ngram_size=10,
                    early_stopping=True

@FurkanGozukara  I played around with your prompt, and this is what the model came up with.
Title: The benefits of deadlifting

Abstract: The purpose of this study was to determine the effect of deadlifting on the cardiovascular system.
The study consisted of a group of 13 men and 11 women who were randomly assigned to an experimental group (n = 24) or a control group (n = 24,).
Subjects in the experimental group performed deadlifting exercises 2 days per week for 6 weeks, while subjects in the control group did not participate in any exercise program.
The 6-week program consisted of a 3-week progressive phase and a 3-week maintenance phase.
At the end of each phase, a graded exercise test (GXT) was performed on a treadmill to determine peak oxygen consumption (VO2peak), ventilatory threshold (VT), and heart rate (HR) at the VT.
At the end of the 6-week program, VO2peak increased by 15.4% (P < 0.05) in the experimental group compared with a 0.9% increase (P > 0.05) in the control group.
The experimental group demonstrated a 13.9% increase (P < 0.01) in HR at the VT compared with a 2.4% increase (P > 0...</s>

I followed the parameters from @AbstractQbit.
Here is the complete code. I could run it on one Titan RTX (24GB VRAM) but only when using half precision.
from transformers import AutoTokenizer, OPTForCausalLM
import torch

tokenizer = AutoTokenizer.from_pretrained("facebook/galactica-6.7b")
tokenizer.pad_token_id = 1
tokenizer.padding_side = 'left'
tokenizer.model_max_length = 4020

model = OPTForCausalLM.from_pretrained("facebook/galactica-6.7b", device_map="auto", torch_dtype=torch.float16)

#input_text = "The Transformer architecture [START_REF]"
input_text = "Title: The benefits of deadlifting\n\n"

input_ids = tokenizer(input_text, padding='max_length', return_tensors="pt").input_ids.to("cuda")

outputs = model.generate(input_ids, max_new_tokens=1000,
                         do_sample=True,
                         temperature=0.7,
                         top_k=25,
                         top_p=0.9,
                         no_repeat_ngram_size=10,
                         early_stopping=True)

print(tokenizer.decode(outputs[0]).lstrip('<pad>'))

@AbstractQbit your answer has hleped me a lot, many thanks!
Do you (or anyone else) now how to use the new_doc (padding) parameter to continue a document generation after the first prompt? Do I have to use the output of the prompt as input to the next one or it's better to use larger max_new_tokens value?

i'm getting CUDA error with @Legor and @AbstractQbit ' code

details bug report when setting CUDA_LAUNCH_BLOCKING=1

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---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
Cell In [10], line 1
----> 1 outputs_ids = model.generate(
      2 	input_ids, max_new_tokens=1000,
      3 	do_sample=True, temperature=0.7,
      4 	top_k=25, top_p=0.9,
      5 	no_repeat_ngram_size=10,
      6 	early_stopping=True
      7 )
File ███/myconda/condaGA/lib/python3.10/site-packages/torch/autograd/grad_mode.py:27, in _DecoratorContextManager.call..decorate_context(*args, **kwargs)
24 @functools.wraps(func)
25 def decorate_context(*args, **kwargs):
26     with self.clone():
---> 27         return func(*args, **kwargs)
File ███/myconda/condaGA/lib/python3.10/site-packages/transformers/generation_utils.py:1543, in GenerationMixin.generate(self, inputs, max_length, min_length, do_sample, early_stopping, num_beams, temperature, penalty_alpha, top_k, top_p, typical_p, repetition_penalty, bad_words_ids, force_words_ids, bos_token_id, pad_token_id, eos_token_id, length_penalty, no_repeat_ngram_size, encoder_no_repeat_ngram_size, num_return_sequences, max_time, max_new_tokens, decoder_start_token_id, use_cache, num_beam_groups, diversity_penalty, prefix_allowed_tokens_fn, logits_processor, renormalize_logits, stopping_criteria, constraints, output_attentions, output_hidden_states, output_scores, return_dict_in_generate, forced_bos_token_id, forced_eos_token_id, remove_invalid_values, synced_gpus, exponential_decay_length_penalty, suppress_tokens, begin_suppress_tokens, forced_decoder_ids, **model_kwargs)
1535     input_ids, model_kwargs = self._expand_inputs_for_generation(
1536         input_ids,
1537         expand_size=num_return_sequences,
1538         is_encoder_decoder=self.config.is_encoder_decoder,
1539         **model_kwargs,
1540     )
1542     # 12. run sample
-> 1543     return self.sample(
1544         input_ids,
1545         logits_processor=logits_processor,
1546         logits_warper=logits_warper,
1547         stopping_criteria=stopping_criteria,
1548         pad_token_id=pad_token_id,
1549         eos_token_id=eos_token_id,
1550         output_scores=output_scores,
1551         return_dict_in_generate=return_dict_in_generate,
1552         synced_gpus=synced_gpus,
1553         **model_kwargs,
1554     )
1556 elif is_beam_gen_mode:
1557     if num_return_sequences > num_beams:
File ███/myconda/condaGA/lib/python3.10/site-packages/transformers/generation_utils.py:2482, in GenerationMixin.sample(self, input_ids, logits_processor, stopping_criteria, logits_warper, max_length, pad_token_id, eos_token_id, output_attentions, output_hidden_states, output_scores, return_dict_in_generate, synced_gpus, **model_kwargs)
2479 model_inputs = self.prepare_inputs_for_generation(input_ids, **model_kwargs)
2481 # forward pass to get next token
-> 2482 outputs = self(
2483     **model_inputs,
2484     return_dict=True,
2485     output_attentions=output_attentions,
2486     output_hidden_states=output_hidden_states,
2487 )
2489 if synced_gpus and this_peer_finished:
2490     continue  # don't waste resources running the code we don't need
File ███/myconda/condaGA/lib/python3.10/site-packages/torch/nn/modules/module.py:1130, in Module._call_impl(self, *input, **kwargs)
1126 # If we don't have any hooks, we want to skip the rest of the logic in
1127 # this function, and just call forward.
1128 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
1129         or _global_forward_hooks or _global_forward_pre_hooks):
-> 1130     return forward_call(*input, **kwargs)
1131 # Do not call functions when jit is used
1132 full_backward_hooks, non_full_backward_hooks = [], []
File ███/myconda/condaGA/lib/python3.10/site-packages/accelerate/hooks.py:156, in add_hook_to_module..new_forward(*args, **kwargs)
154         output = old_forward(*args, **kwargs)
155 else:
--> 156     output = old_forward(*args, **kwargs)
157 return module._hf_hook.post_forward(module, output)
File ███/myconda/condaGA/lib/python3.10/site-packages/transformers/models/opt/modeling_opt.py:929, in OPTForCausalLM.forward(self, input_ids, attention_mask, head_mask, past_key_values, inputs_embeds, labels, use_cache, output_attentions, output_hidden_states, return_dict)
926 return_dict = return_dict if return_dict is not None else self.config.use_return_dict
928 # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
--> 929 outputs = self.model.decoder(
930     input_ids=input_ids,
931     attention_mask=attention_mask,
932     head_mask=head_mask,
933     past_key_values=past_key_values,
934     inputs_embeds=inputs_embeds,
935     use_cache=use_cache,
936     output_attentions=output_attentions,
937     output_hidden_states=output_hidden_states,
938     return_dict=return_dict,
939 )
941 logits = self.lm_head(outputs[0]).contiguous()
943 loss = None
File ███/myconda/condaGA/lib/python3.10/site-packages/torch/nn/modules/module.py:1130, in Module._call_impl(self, *input, **kwargs)
1126 # If we don't have any hooks, we want to skip the rest of the logic in
1127 # this function, and just call forward.
1128 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
1129         or _global_forward_hooks or _global_forward_pre_hooks):
-> 1130     return forward_call(*input, **kwargs)
1131 # Do not call functions when jit is used
1132 full_backward_hooks, non_full_backward_hooks = [], []
File ███/myconda/condaGA/lib/python3.10/site-packages/accelerate/hooks.py:156, in add_hook_to_module..new_forward(*args, **kwargs)
154         output = old_forward(*args, **kwargs)
155 else:
--> 156     output = old_forward(*args, **kwargs)
157 return module._hf_hook.post_forward(module, output)
File ███/myconda/condaGA/lib/python3.10/site-packages/transformers/models/opt/modeling_opt.py:628, in OPTDecoder.forward(self, input_ids, attention_mask, head_mask, past_key_values, inputs_embeds, use_cache, output_attentions, output_hidden_states, return_dict)
625 past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0
627 if inputs_embeds is None:
--> 628     inputs_embeds = self.embed_tokens(input_ids)
630 # embed positions
631 if attention_mask is None:
File ███/myconda/condaGA/lib/python3.10/site-packages/torch/nn/modules/module.py:1130, in Module._call_impl(self, *input, **kwargs)
1126 # If we don't have any hooks, we want to skip the rest of the logic in
1127 # this function, and just call forward.
1128 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
1129         or _global_forward_hooks or _global_forward_pre_hooks):
-> 1130     return forward_call(*input, **kwargs)
1131 # Do not call functions when jit is used
1132 full_backward_hooks, non_full_backward_hooks = [], []
File ███/myconda/condaGA/lib/python3.10/site-packages/accelerate/hooks.py:156, in add_hook_to_module..new_forward(*args, **kwargs)
154         output = old_forward(*args, **kwargs)
155 else:
--> 156     output = old_forward(*args, **kwargs)
157 return module._hf_hook.post_forward(module, output)
File ███/myconda/condaGA/lib/python3.10/site-packages/torch/nn/modules/sparse.py:158, in Embedding.forward(self, input)
157 def forward(self, input: Tensor) -> Tensor:
--> 158     return F.embedding(
159         input, self.weight, self.padding_idx, self.max_norm,
160         self.norm_type, self.scale_grad_by_freq, self.sparse)
File ███/myconda/condaGA/lib/python3.10/site-packages/torch/nn/functional.py:2199, in embedding(input, weight, padding_idx, max_norm, norm_type, scale_grad_by_freq, sparse)
2193     # Note [embedding_renorm set_grad_enabled]
2194     # XXX: equivalent to
2195     # with torch.no_grad():
2196     #   torch.embedding_renorm_
2197     # remove once script supports set_grad_enabled
2198     no_grad_embedding_renorm(weight, input, max_norm, norm_type)
-> 2199 return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse)
RuntimeError: CUDA error: device-side assert triggered

@Legor ty so much. I wonder they release a model without any proper example as yours.

@AbstractQbit your answer has hleped me a lot, many thanks!
Do you (or anyone else) now how to use the new_doc (padding) parameter to continue a document generation after the first prompt? Do I have to use the output of the prompt as input to the next one or it's better to use larger max_new_tokens value?

unfortunately hugging face doesnt support newdoc i dont know why

authors: AbstractQbit FurkanGozukara Legor RJT1990 iaposto phineas-pta

repo: paperswithcode/galai


text:
Added a question in Javascript Core concepts, fixed a typo.

Please check if the PR fulfills these requirements

[ ] The commit message follows our guidelines
[ ] Tests for the changes have been added (for bug fixes / features)
[ ] Docs have been added / updated (for bug fixes / features)

What kind of change does this PR introduce? (Bug fix, feature, docs update, ...)

What is the current behavior? (You can also link to an open issue here)

What is the new behavior (if this is a feature change)?

Does this PR introduce a breaking change? (What changes might users need to make in their application due to this PR?)

Other information:

@papistan Do we need to add bundle.js as part of our PR?

@hardikyjoshi bundle.js shouldn't be part of any PR. It's the output once the project is compiled. It should be included in the .gitignore

@jllamasi Sure, I'll revert that and commit my necessary changes 👍 Thanks! :)

@jllamasi updated the PR. Thanks!

authors: hardikyjoshi jllamasi

repo: papistan/FrontEndTrivia

license: mit

license_source: bigquery


text:
U256 serialization is still not quantity

Describe the bug
While investigating why ethereum/sync was still showing the following error:
can't query chain head: json: cannot unmarshal hex number with leading zero digits into Go struct field Header.number of type *hexutil.Big

Actions run: https://github.com/paradigmxyz/reth/actions/runs/4267053919/jobs/7428426037
I ran the following:
reth import --chain ~/hive-data/genesis.json --path ~/hive-data/chain.rlp --db ~/hive-data
reth node --chain ~/hive-data/genesis.json --db ~/hive-data --disable-discovery --trusted-only --http
cast block latest

And this was the response:
dan@Dans-MacBook-Pro ~/p/reth (main) [1]> cast block latest
Error:
Deserialization Error: duplicate field `size` at line 1 column 1468. Response: {"hash":"0xc95596f4707fb382554b660b4847c599eb5f8fdcf99be2c5654aaadd4ec97840","parentHash":"0x08b2020a03028e5e786ff92c36b051be8235087e2e0554d38167b29be9f40bd8","sha3Uncles":"0x1dcc4de8dec75d7aab85b567b6ccd41ad312451b948a7413f0a142fd40d49347","author":"0x0000000000000000000000000000000000000000","miner":"0x0000000000000000000000000000000000000000","stateRoot":"0xe3f5a72cc1c86d794fd190473c775a774605059a45775c96e4df01205e40c007","transactionsRoot":"0x56e81f171bcc55a6ff8345e692c0f86e5b48e01b996cadc001622fb5e363b421","receiptsRoot":"0x56e81f171bcc55a6ff8345e692c0f86e5b48e01b996cadc001622fb5e363b421","withdrawalsRoot":null,"number":"0x0bb8","gasUsed":"0x0","gasLimit":"0x80000000","extraData":"0x","logsBloom":"0x00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000","timestamp":"0x080c9f88","difficulty":"0x020000","mixHash":"0x13beb8a6d5bb7540e846a92e4bc88627d76e7a1a61f55806c9e88cb4d81aa0ad","nonce":"0x340f24b19808a115","size":"0x01fd","totalDifficulty":"0x17720000","uncles":[],"transactions":[],"size":"0x0202","withdrawals":null}

There are two issues here:

There is in fact a duplicate size field
The number field is not quantity, because it has a leading zero:

"number":"0x0bb8"

It should instead be:
"number":"0xbb8"

Steps to reproduce
Either running the hive ethereum/sync test suite, or doing the above procedure with a chain.rlp file and genesis.json should reproduce this error
Node logs
No response
Platform(s)
No response
What version/commit are you on?
No response
Code of Conduct

[X] I agree to follow the Code of Conduct

Not only is the size field different, it also has two different values?
Can you own investigating / fixing this?

fixed in #1554

authors: Rjected gakonst

repo: paradigmxyz/reth


text:
feat(h4ck3r-m0d3): adds easter egg CLI option

When running reth node a new hidden flag is now available --h4ck3r-m0d3 that will randomly print some ascii art from an array located in crates/cli/commands/src/ascii_art.rs to stdout every 5 minutes.
Closes #5857

@onbjerg should I add my cargo.lock file changes to the the PR as well?

Hey, thanks for the contribution, however we're currently not looking to include this :)

authors: DaniPopes emnul


text:
Enable clippy's uninlined_format_args linter

This PR enables the uninlined_format_args linter and fixes its findings.

Sorry again, didn't realize it would touch so many files. I wasn't running the fully command (like in the CI workflow) so I didn't notice a lot of the findings. This PR is pretty nit-picky. You're welcome to close without merging if it's too much.

Sorry again, didn't realize it would touch so many files. I wasn't running the full command (like in the CI workflow) so I didn't notice a lot of the findings. This PR is pretty nit-picky. You're welcome to close without merging if it's too much.

love this one, good lint

authors: emhane jtraglia


text:
chore(rpc): expose ethapi in node builder for op customisation

Closes https://github.com/paradigmxyz/reth/issues/8781
Exposes customisable types encompassed by node-add ons.

Adds new AT AddOns: NodeAddOns<N: FullNodeComponents> to Node trait.
Adds new trait  NodeAddOns<N: FullNodeComponents>, that aggregates the customisable types in node add-ons. This is future proof, so we don't have to add a new generic to many types in the node builder, if we make a new add-on type customisable, for example the engine rpc api implementation.
The ATs on NodeAddOns<N> should have the new trait BuilderProvider<N: FullNodeComponents as trait bound, as is done already for AT EthApi. This way we defer instantiating the builder until the entry point to the specific library for this type, i.e. when we leave reth-node-builder scope into the reth-rpc-builder scope. This saves us a lot of hassle of passing around the specific builder instance in reth-node-builder, which comes with the constraint that the type would need to be Send + Sync + Unpin + Default among others to satisfies the existing reth-node-builder code.
Adds new extension trait to BuilderProvider<N> trait, EthApiBuilderProvider<N>, which specifies the exact context type that needed to build the EthApi. This requirement is set by reth-rpc-builder and propagates to reth-node-builder.
Plugs type OpEthApi<Eth> into the node builder.

ye, the whole node builder design could be simplified. in general I prefer

specifying the type we are building from the start, so long as we aren't storing the built type as a trait object later - having the builder output type accessible as AT makes life easier! now we specify the literal type NodeAdapter everywhere, which isn't clearly associated to its builder. we don't really care what the builder type is...it is only relevant on bootstrap...but all other types in the program are constrained by its output type, i.e. the built type....
using all builders as trait objects, these are only called once at start of program, so the extra overhead from dynamic dispatch isn't a problem.
instantiate the builders as late as possible, using trait like new BuilderProvider<N: FullNodeComponents> trait on AT of the type being built. so reversed to first suggestion, let the output type provide its builder as opposed to the builder specifying its output. this way, we don't have to care about the builder being Send + Sync + Clone + Default, just the built type - which probably has those trait bounds imposed on it by how we use that type during the entire execution of the program anyway, i.e. we need to satisfy those anyway for that type.
not having NodeComoponents be a strict subset of FullNodeComponents has been painful. it's impossible to reference the transition from a generic NodeComponents to a generic FullNodeComponents type atm. alt, an intermediary trait that specifies the BlockchainTree type could be added for transit between the NodeComponents and FullNodeComponents traits. see (https://github.com/paradigmxyz/reth/pull/9243).
not having all components returned by getters in FullNodeComponents, defined as AT, has made the code less flexible. thinking in particular of Network and Tasks here, which has now had to be hardcoded to NetworkHandle and TaskExecutor in several places. would make way for sense if those types could be referenced via FullNodeComponents, just like Provider or Pool.

rethink

can't open the link!

can't open the link!

gh added this for some reason lol
all of these points are valid, could you open an issue for this please ?

all of these points are valid, could you open an issue for this please ?

done https://github.com/paradigmxyz/reth/issues/9543 !

authors: emhane mattsse


text:
Identify - UI element (button?)

Add UI element (possibly a button) to display the Identify dialog (if not already displayed) and performs the Identification process on all available ports, showing the results in the dialog.
Split out from #128

I labelled this as a UX issue because we need to know when and how the UI element representing the 'identify' functionality will be presented to the user.

This relates to issue #81 and possibly issue #92.

authors: PropGit phated urbantumbleweed

repo: parallaxinc/Parallax-IDE


text:
🛑 api.parspec.io is down

In c3a6c62, api.parspec.io (https://api.parspec.io/health) was down:

HTTP code: 503
Response time: 226 ms

Resolved: api.parspec.io is back up in 201fbe0 after 9 minutes.

repo: parikshit-parspec/upptime


text:
Propagate error in runtime api call

https://github.com/paritytech/frontier/blob/fa547950d03209bdb50db92ebf1a74dbf8742b18/rpc/primitives/src/lib.rs#L69-L77
It should return Result<(Vec<u8>, U256), DispatchError> so we don't discard valuable information.

Fixed by #126

repo: paritytech/frontier


text:
Nomination Pool Commission

This PR adds commission functionality to Nomination Pools (was #12511).
New structs:
Commission and CommissionChangeRate.
New storage item:
GlobalMaxCommission: Option<Perbill>.
New calls:
set_commission, set_max_commission, and set_commission_change_rate.
polkadot companion: https://github.com/paritytech/polkadot/pull/6264
fixes #11672.

This pull request has been mentioned on Polkadot Forum. There might be relevant details there:
https://forum.polkadot.network/t/the-future-of-polkadot-staking/1848/1

The CI pipeline was cancelled due to failure one of the required jobs.
Job name: cargo-check-each-crate
Logs: https://gitlab.parity.io/parity/mirrors/substrate/-/jobs/2347972

git rebase

The CI pipeline was cancelled due to failure one of the required jobs.
Job name: cargo-check-each-crate
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Are we pursuing this PR as-is, or moving to the new approach?

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This pull request has been mentioned on Polkadot Forum. There might be relevant details there:
https://forum.polkadot.network/t/discussion-on-global-maximum-commission-for-nomination-pools-on-polkadot/2325/1

authors: Polkadot-Forum kianenigma paritytech-cicd-pr rossbulat

repo: paritytech/substrate


text:
Update import benchmark parameters

Should decrease variance

hmm.. this crate in error message builds for me
cargo build --manifest-path=~/.cargo/registry/src/github.com-1ecc6299db9ec823/proc-macro-error-attr-0.4.8/Cargo.toml


text:
Generate storage info for pallet authority_discovery

Fixes part of #8629
Refactor Authority Discovery to use WeakBoundedVec instead of Vec.
Polkadot companion paritytech/polkadot#3517

aura refactoring is also done in this PR https://github.com/paritytech/substrate/pull/9371

aura refactoring is also done in this PR #9371
Maybe this PR can only do authority_discovery

Removed any code change in pallet aura to reflect that fact.

bot merge

authors: KiChjang georgesdib thiolliere


text:
Use an enum for Encoding signedness instead of boolean

Currently we use a boolean to set the signedness of an Encoding:
    public Encoding(final boolean signed) {
        this(signed, DEFAULT_CHARSET, DEFAULT_BYTE_ORDER);
    }
    // ...
    public Encoding(final boolean signed, final Charset charset, final ByteOrder byteOrder) {
        _signed = signed;
        _charset = charset;
        _byteOrder = byteOrder;
    }

Using an enum similar to ByteOrder would be much cleaner, and easier on the eyes.
Proposal: Signedness.SIGNED and Signedness.UNSIGNED.

@jvdb proposes to use Sign instead of Signedness.

repo: parsingdata/metal


text:
Put Featured Cases in Fixed Order

It's fine it we want to display the most recently submitted entries after the Featured Cases on the front page but the Featured Cases should be in a fixed order and should not be re-ordered if one of them is edited. We should also decide if we want to have the most recently submitted or the most recently edited entries to appear after the Featured Cases - currently it's the latter but that's maybe not as useful since editors (like myself) are constantly tweaking old cases and I think visitors would be much more interested in content that's actually new.

Sorting by creation date instead of edited date would solve both of these, correct?

Ideally we'd be able to specify which featured cases come first - I have a pretty good eye for what people are more likely to click on (the "boring" ones - ie. PB, Citizens Juries, etc.) but I'd still like to feature some more 'obscure' cases (global south, informal participation etc.). The click-bait should come first, the unique second

Being able to specify ordering, beyond sorting by date, is a completely new feature that will require changes to the database, the frontend, requires new UI, etc. We can do it, but it has to be worth the cost relative to other features.

Ok, I don't think this is especially important (ie. definitely not P1) but we should have this function down the road - @jesicarson @plscully what do you think? If we want PPedia to be more 'timely' or 'relevant' then it's important to have the ability to feature entries on topical cases/orgs/methods. For example, if there's another sitting of the Irish Citizens' Assembly we would want to have that front and centre which would require being able to specify ordering since it's an old(er) entry

As we specify this feature, don't forget about localization. Right now the ordering is the same regardless of locale, but if we start picking the order, will some locales want to order things differently than others? Will some want different "featured" articles? It can get messy and we need to think that stuff through up front.

Right, good point. I'm going to add this to tomorrow's meeting agenda, let's discuss then

Closing this.
Soon admins will be able to feature and un-feature things.
Decision on last call with @scottofletcher @plscully @dethe and I : Admins will only feature 4 items at one time, so no need to worry about order.

authors: dethe jesicarson scottofletcher

repo: participedia/frontend


text:
CredentialId is no longer mapped

Are we still using CredentialId? It is no longer mapped, and therefore null. Might have been a breaking change from @aseigler ?
https://github.com/passwordless-lib/fido2-net-lib/commit/7b92f4a019a9ed24793eedb8016408b44e9ee858#diff-1aab08f33f616a78e8c75fa260f05ac7726414bab4422b432c6c21c0d661dee4

@dotnet-policy-service agree

Codecov Report
All modified lines are covered by tests :white_check_mark:

Comparison is base (6655af9) 75.54% compared to head (9818444) 75.55%.

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Additional details and impacted files
@@           Coverage Diff           @@
##           master     #428   +/-   ##
=======================================
  Coverage   75.54%   75.55%
=======================================
  Files         100      100
  Lines        2785     2786    +1
  Branches      455      455
=======================================
+ Hits         2104     2105    +1
  Misses        562      562
  Partials      119      119

Files
Coverage Δ

Src/Fido2/AuthenticatorAttestationResponse.cs
79.62% <100.00%> (+0.19%)
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Would be nice to have everything use .CredentialId, would you try and remove the .Id property used in some places in favour of .CredentialId of the base type?
If not I'm ok with that too, in that case this would just be the first step in converging everything to .CredentialId.

@Regenhardt Since I'm embarking on the review of the other #427 - is this not changed in that PR?

@Regenhardt Since I'm embarking on the review of the other #427 - is this not changed in that PR? I imagined it would be.

It is removed in #427 completely. If #427 is approved, we can abandon this PR.

@jonashendrickx Yeah I figured.

As far as I can see, #427 renames CredentialID to CredentialId - the d is small now.
I hoped this might be an attempt at solving 2) from #426, removing the duplicate AttestationVerificationSuccess.Id in favour of the AttestationVerificationResult.CredentialId of its base type.

@Regenhardt #427 Removed the base type from AttestationVerificationSuccess to eliminate the unused CredentialId.
Since the result of AuthenticatorAttestationResponse.VerifyAsync is a newly registered PublicKeyCredential -- keeping the Id property still make sense. We might consider renaming AttestationVerificationSuccess to RegisteredPublicKeyCredential (which infers verification was successful), and also make the Id's meaning clear.
When verifying or making an assertion, we're verifying an existing credential that we reference. The credential is referenced by VerifyAssertionResult -- so the CredentialId property makes sense here.
It we keep the class name (and don't consider this result as an extended PublicKeyCredential), renaming to CredentialId would also be reasonable.
@abergs ^ my thinking on keeping the Id on AttestationVerificationSuccess. Let me know your thoughts, and whether we should also consider renaming this type to RegisteredPublicKeyCredential.

@iamcarbon I agree with your analysis and I asked for changing the name of AttestationVerificationSuccess to RegisteredPublicKeyCredential  in #427.
However - I do think @Regenhardt has a point that we should ponder going forward, that is: Calling it CredentialId throughout is indeed helpful - but it's not clear cut as it gets complicated when we're trying to stay as close to the spec as possible.
Closing this as it is adressed in #427.

authors: Regenhardt abergs codecov-commenter iamcarbon jonashendrickx


text:
Deprecate Autotelic Abstraction

Oleksii called me out hard 😄 for having an interface that will lead to unnecessary breaking changes. While I doubt many, if any, will create their own implementation of the IPasswordlessClient it is technically a breaking change to add a method to a public interface while it is not to add a new method to PasswordlessClient.
While I do like having interfaces for the purposes of mocking, the better more resilient thing to do is to make your own interface that you own in your application that wraps the usage you have of any said library. Bitwarden server follows this practice for our uses of Stripe with StripeAdapter. We should recommend users of our library, who want to mock our service, do the same.
What we can do today:

Change examples and documentation that use IPasswordlessClient to PasswordlessClient
Stop adding new methods to IPasswordlessClient and only add the implementation in PasswordlessClient.

When we do other and only when we do other breaking changes we can but don't have to:

Make IPasswordlessClient have the [EditorBrowsable(Never)] attribute.

We could do this before a breaking change without actually breaking anyone but users who had previous uses of IPasswordlessClient and want to use it elsewhere could be thrown off.

Change the AddHttpClient method use PasswordlessClient as service instead of IPasswordlessClient

This changes logs from looking like: System.Net.Http.HttpClient.IPasswordlessClient.ClientHandler to System.Net.Http.HttpClient.PasswordlessClient.ClientHandler this is breaking because some users could use that category name to tune logs. Rare, but could cause some annoyance to consumers.
This doesn't need to be done other than people being slightly confused why the interface name is showing up in the logs when they don't use the interface at all.
This also changes the name of the client for if people are manually configuring/injecting the HttpClient themselves, this is not a situation we support or document anywhere but it's something that can be done before that would no longer work.

Write a recommended migration path of just removing the I from the type.
(NOT RECOMMENDED) we could also take a more aggressive approach and add [Obsolete] to the type but I don't think this is warranted.

Sorry, I didn't mean to call anyone out specifically 😅
As an interim solution, we can also add a remark to IPasswordlessClient to discourage the end-users using it for mocking/faking in tests and recommend creating a domain-local abstraction instead.

authors: Tyrrrz justindbaur

repo: passwordless/passwordless-dotnet