sharjeelyunus/github-issues-dataset huggingface dataset:
Repository: bitcoin
Issue: LevelDB error: Corruption: block checksum mismatch didn't trigger reindex.
Labels: Bug,Data corruption

### Is there an existing issue for this?

- [X] I have searched the existing issues

### Current behaviour

For the past 29 hours, it's been stuck trying to sync:
>**Number of blocks left** Unknown. Syncing Headers (843771, 100.0%)…

and

>Connecting to peers…

I'm also pruning (`prune=550`).

### Expected behaviour

It should sync the full chain and reindex if needed, not try to sync what it can't (due to possibly corrupt chainstate DB).

### Steps to reproduce

Open up `bitcoin-qt`. It doesn't say I need to reindex, but everything else works. It connects to many peers, but very little date (<25 KB) is transferred between each of them.

### Relevant log output

```
2024-05-16T22:38:54Z Writing 0 unbroadcast transactions to disk.
2024-05-16T22:38:54Z Dumped mempool: 0.055s to copy, 2.286s to dump
2024-05-16T22:38:54Z Flushed fee estimates to fee_estimates.dat.
2024-05-16T22:38:54Z Fatal LevelDB error: Corruption: block checksum mismatch: /home/geremia/.bitcoin/chainstate/5753777.ldb
2024-05-16T22:38:54Z You can use -debug=leveldb to get more complete diagnostic messages
2024-05-16T22:38:54Z *** System error while flushing: Fatal LevelDB error: Corruption: block checksum mismatch: /home/geremia/.bitcoin/chainstate/5753777.ldb
2024-05-16T22:38:54Z Error: A fatal internal error occurred, see debug.log for details
2024-05-16T22:38:54Z ForceFlushStateToDisk: failed to flush state (System error while flushing: Fatal LevelDB error: Corruption: block checksum mismatch: /home/geremia/.bitcoin/chainstate/5753777.ldb)
2024-05-16T22:38:54Z Fatal LevelDB error: Corruption: block checksum mismatch: /home/geremia/.bitcoin/chainstate/5753777.ldb
2024-05-16T22:38:54Z You can use -debug=leveldb to get more complete diagnostic messages
2024-05-16T22:38:54Z *** System error while flushing: Fatal LevelDB error: Corruption: block checksum mismatch: /home/geremia/.bitcoin/chainstate/5753777.ldb
2024-05-16T22:38:54Z Error: A fatal internal error occurred, see debug.log for details
2024-05-16T22:38:54Z ForceFlushStateToDisk: failed to flush state (System error while flushing: Fatal LevelDB error: Corruption: block checksum mismatch: /home/geremia/.bitcoin/chainstate/5753777.ldb)
2024-05-16T22:38:54Z [default wallet] Releasing wallet
2024-05-16T22:38:54Z Shutdown: done
```

### How did you obtain Bitcoin Core

Compiled from source

### What version of Bitcoin Core are you using?

v27.0

### Operating system and version

slackware64-current

### Machine specifications

_No response_
Repository: neovim
Issue: Inbuilt comment feature not working properly for JSX files
Labels: enhancement,treesitter,comment

### Problem

when i type 'gcc; this is how it gets commented in a JSX file
![image](https://github.com/neovim/neovim/assets/99483158/308e9531-44b2-4588-a302-8c9d2580f73d)


### Steps to reproduce

![image](https://github.com/neovim/neovim/assets/99483158/45cd06f3-6c75-4555-a879-25176dc43470)


### Expected behavior

this is how it should be for JSX when commented.
![image](https://github.com/neovim/neovim/assets/99483158/96e60b1b-ab7d-4d87-b1b8-6ec7684666e6)


### Neovim version (nvim -v)

0.10

### Vim (not Nvim) behaves the same?

no

### Operating system/version

ubuntu 22.04

### Terminal name/version

 GNOME Terminal 3.44.0 using VTE 0.68.0 +BIDI +GNUTLS +ICU +SYSTEMD

### $TERM environment variable

xterm-256color

### Installation

appimage
Repository: tensorflow
Issue: Memory leak from using tf.constant in loop in TF 2.16
Labels: stat:awaiting tensorflower,comp:ops,type:performance,TF 2.16

### Issue type

Bug

### Have you reproduced the bug with TensorFlow Nightly?

No

### Source

binary from pip

### TensorFlow version

2.16.1

### Custom code

Yes

### OS platform and distribution

Linux Ubuntu 24.04

### Mobile device

_No response_

### Python version

3.12.3

### Bazel version

_No response_

### GCC/compiler version

_No response_

### CUDA/cuDNN version

_No response_

### GPU model and memory

_No response_

### Current behavior?

I believe I tracked down a memory leak in my code to a `tf.constant` creation in a short lived object of a class. I can reproduce it in an even simpler way by just creating the constant in a loop. No `@tf.function` decoration or model training necessary to cause it. It seems to happen if I replace the `tf.constant` with a `tf.random.uniform()`. I tried suggestions I've seen elsewhere, like trying to use `del` on the variable followed by `gc.collect()`.

Some similar looking bug reports:
- https://github.com/tensorflow/tensorflow/issues/57982 
- https://github.com/tensorflow/tensorflow/issues/50765
- https://discuss.tensorflow.org/t/tensorflow-memory-leak-during-inference-in-loop/23044

It looks like upgrading to 2.15 solved it for some people, but it appears to be back in 2.16?

is this expected and I'm doing something wrong?

### Standalone code to reproduce the issue

```shell
import time
import psutil
import tensorflow as tf


def loop_copy():
    for _ in range(2000):
        y = tf.constant(5.0)  # still leaks with this


start_mem = psutil.Process().memory_info().rss

for _ in range(500):
    time.sleep(0.1)
    print((psutil.Process().memory_info().rss - start_mem) / 10**6)
    loop_copy()
```
```


### Relevant log output

```shell
0.0
6.029312
6.815744
7.471104
8.126464
8.781824
9.437184
10.092544
10.747904
11.534336
12.189696
12.845056
13.500416
14.155776
14.811136
15.597568
16.252928
...
```
Repository: PowerToys
Issue: PowerRename Clear List Button
Labels: Idea-Enhancement,Product-PowerRename,Needs-Triage

### Description of the new feature / enhancement

Add a button on PowerRename to clear the list of files after completing a batch.

### Scenario when this would be used?

I'd love to be able to keep PowerRename open while I do different batches of renaming, so I'd like to be able to clear the list so I can drag in new files to rename without the clutter of the old files I'm done with. 

### Supporting information

_No response_
Repository: yt-dlp
Issue: Add options to prevent download if sponsorblock entry not found
Labels: enhancement

### DO NOT REMOVE OR SKIP THE ISSUE TEMPLATE

- [X] I understand that I will be **blocked** if I *intentionally* remove or skip any mandatory\* field

### Checklist

- [X] I'm requesting a feature unrelated to a specific site
- [X] I've looked through the [README](https://github.com/yt-dlp/yt-dlp#readme)
- [X] I've verified that I have **updated yt-dlp to nightly or master** ([update instructions](https://github.com/yt-dlp/yt-dlp#update-channels))
- [X] I've searched [known issues](https://github.com/yt-dlp/yt-dlp/issues/3766) and the [bugtracker](https://github.com/yt-dlp/yt-dlp/issues?q=) for similar issues **including closed ones**. DO NOT post duplicates
- [X] I've read the [guidelines for opening an issue](https://github.com/yt-dlp/yt-dlp/blob/master/CONTRIBUTING.md#opening-an-issue)

### Provide a description that is worded well enough to be understood

Hi,

A great feature to add would be the ability to prevent a download if sponsorblock info is not found. In my setup, I check a channels a few times a day ( and don’t download videos younger than a day old) and the sponsor block info isn’t updated in time.

Given at the moment it seems sponsorblock is checked after download, if the sponsorblock check finds nothing it could delete the file. This obviously isn’t the most efficient so the check could come before the download. Not familiar with the codebase so don’t know how hard this would be.

### Provide verbose output that clearly demonstrates the problem

- [X] Run **your** yt-dlp command with **-vU** flag added (`yt-dlp -vU <your command line>`)
- [X] If using API, add `'verbose': True` to `YoutubeDL` params instead
- [X] Copy the WHOLE output (starting with `[debug] Command-line config`) and insert it below

### Complete Verbose Output

N/A

_No response_
Repository: tauri
Issue: [bug] "tauri"//localhost" keep showing in MacOS Activity Monitor
Labels: type: bug,status: needs triage

### Describe the bug

on MacOS, process named "tauri://localhost" keep showing in Activity Monitor even on release build. Is there a way to customize the name here. I belive name like this makes it suspicious for user to be not authentic. Or, maybe I'm missing something here. 

![image](https://github.com/tauri-apps/tauri/assets/51436631/d954051e-d76e-43e5-968b-7f86e517e3bd)

Device: Macbook Air M2


### Reproduction

_No response_

### Expected behavior

_No response_

### Full `tauri info` output

```text
This is on release build so no idea how to show this.
```


### Stack trace

_No response_

### Additional context

_No response_
Repository: pytorch
Issue: [inductor] `aten.index_put_`  runtime shape mismatch on H100 but not on A100
Labels: triaged,oncall: pt2,module: inductor

### 🐛 Describe the bug


Recently compiling https://github.com/pytorch/pytorch/issues/121504 (already analyzed there by @williamwen42) is working on A100/L4 but on H100, with the same function compiled, I got this error.



### Error logs

```python
  aten.index_put_(buf6, [reinterpret_tensor(buf0, (1, 1, 4624, 82, 82), (0, 0, 6784, 82, 1), 0)], reinterpret_tensor(buf7, (1040400, ), (1, ), 0), False)
  File "/opt/conda/lib/python3.11/site-packages/torch/_ops.py", line 1060, in __call__
    return self_._op(*args, **(kwargs or {}))
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
RuntimeError: shape mismatch: value tensor of shape [1040400] cannot be broadcast to indexing result of shape [1033097]
```

### Minified repro

_No response_

### Versions

nightly

cc @ezyang @msaroufim @bdhirsh @anijain2305 @chauhang @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @peterbell10 @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @ColinPeppler @amjames @desertfire
Repository: rust
Issue: Vec<T> implements PartialEq<[T; N]> but the other way round doesn't work
Labels: T-libs-api,C-bug

Hello,

it seems that there is an oversight (or possibly it's wanted behaviour, but it felt odd to me) in how vectors and arrays can be compared, based on `PartialEq` implementation.


I tried this code:

```rust
fn main() {
    let a = vec![1, 2, 3];
    let b = [1, 2, 3];
    
    assert_eq!(a, b);
    assert_eq!(b, a);
}
```
https://play.rust-lang.org/?version=stable&mode=debug&edition=2021&gist=3c95d140909d5ac62d769b8f663bcd47

I expected to see this happen: No errors because `assert_eq` should consider these two equal (or shouldn't it?)

Instead, this happened: 

```
   Compiling playground v0.0.1 (/playground)
error[E0277]: can't compare `[{integer}; 3]` with `Vec<{integer}>`
 --> src/main.rs:6:5
  |
6 |     assert_eq!(b, a);
  |     ^^^^^^^^^^^^^^^^ no implementation for `[{integer}; 3] == Vec<{integer}>`
  |
  = help: the trait `PartialEq<Vec<{integer}>>` is not implemented for `[{integer}; 3]`
  = note: this error originates in the macro `assert_eq` (in Nightly builds, run with -Z macro-backtrace for more info)
help: consider dereferencing here
 --> /playground/.rustup/toolchains/stable-x86_64-unknown-linux-gnu/lib/rustlib/src/rust/library/core/src/macros/mod.rs:40:35
  |
40|                 if !(*left_val == **right_val) {
  |                                   +

For more information about this error, try `rustc --explain E0277`.
error: could not compile `playground` (bin "playground") due to 1 previous error
```

### Meta
I also checked beta and nightly (on the playground, not on my own machine; please tell me if I should repeat it here), and the results are the same over all three of these.

`rustc --version --verbose`:
```
rustc 1.77.2 (25ef9e3d8 2024-04-09)
binary: rustc
commit-hash: 25ef9e3d85d934b27d9dada2f9dd52b1dc63bb04
commit-date: 2024-04-09
host: x86_64-unknown-linux-gnu
release: 1.77.2
LLVM version: 17.0.6

```

<!--
Include a backtrace in the code block by setting `RUST_BACKTRACE=1` in your
environment. E.g. `RUST_BACKTRACE=1 cargo build`.
-->
<details><summary>Backtrace</summary>
<p>
(I did activate RUST_BACKTRACE=1 but the output was the same as before with the other runs)
</p>
</details>
Repository: ollama
Issue: Agent retrieval is not peristant
Labels: bug

### What is the issue?

I use an agent to scrape a website using  the "webscraper" agent. The @ agent icon disappears and it seems as if the interface is still talking to the agent. However, this is not so. When the question is asked about the retrieved resultant web page, the LLM answers (as seen by icon and the answers) and does not consider the retrieved website nor information. You have to say /exit before the @ icon is shown again.
Basically what has to be done is formulate everything in the prompt to the agent before running the agent and after that you loose control over the results.. It is not used in any context again.
What I expect to happen is that the agent remains in agent mode, alternative agents are called when necessary and the retrieved data is considered by the LLM as a context. That is because you want to retrieve a webpage and process it further, is necessary
You might argue that the other options do cover this, but that would not be very user friendly nor practically. You want to have a chat with the agent AND LLM, especially for information retrieved from the net or files.
If you propose the later RAG method then there is no need for agents in the Chat window except to query the database. It just confuses the issue.
Maybe it is as simple as feeding the retrieved information of the agent into the context window of the LLM and parse the next question if an agent is required or the LLM, such as automatically initialising a storage agent if the next question says "save the result for late processing", etc.

Just to say...
Thanks for the great program. It is really exceptional. Keep the good work going!


### OS

Linux

### GPU

Nvidia

### CPU

_No response_

### Ollama version

latest
Repository: ant-design
Issue: Refactor Collapse to <details />
Labels: help wanted,Inactive,⌨️ Accessibility

Suggested by https://x.com/yisibl/status/1791813544104849708

<img width="666" alt="图片" src="https://github.com/ant-design/ant-design/assets/507615/dff1752b-b558-46ad-bd8e-d6fd212f2216">


https://developer.mozilla.org/en-US/docs/Web/HTML/Element/details#browser_compatibility
Repository: tensorflow
Issue: Strange finding: When the global seed and @tf.function decorator are used, the random sampling values of the two adjacent periods are equal
Labels: stat:awaiting tensorflower,type:bug,comp:apis,comp:tf.function,TF 2.16

### Issue type

Bug

### Have you reproduced the bug with TensorFlow Nightly?

Yes

### Source

source

### TensorFlow version

tf 2.16.1

### Custom code

Yes

### OS platform and distribution

Windows 10

### Mobile device

_No response_

### Python version

_No response_

### Bazel version

_No response_

### GCC/compiler version

_No response_

### CUDA/cuDNN version

_No response_

### GPU model and memory

_No response_

### Current behavior?

When I set the global seed, applied the @tf.function decorator, and performed gradient updates every two periods, I observed an unexpected phenomenon: the error values were randomly sampled from two consecutive periods with identical values. However, my expectation was that the error values should differ when sampled in each period instead of being equal in adjacent periods. Furthermore, I noticed that removing the @tf.function decorator from the model function prevented this issue of having identical error values in consecutive periods. What could be causing this phenomenon? How should one handle this situation when using @tf.function?"

### Standalone code to reproduce the issue

```shell
import os
import random
import numpy as np
import tensorflow as tf

# set seeds
SEED = 0
random.seed(SEED)
np.random.seed(SEED)
tf.random.set_seed(SEED)
os.environ['PYTHONHASHSEED'] = str(SEED)
tf.keras.utils.set_random_seed(SEED)
tf.config.experimental.enable_op_determinism()

@tf.function
def model(x):
    err = tf.random.uniform(shape=(1,))
    loss = x + err
    return err, loss

optimizer = tf.keras.optimizers.Adam(learning_rate=1e-3)
training_periods = 10

# initialize x
x = tf.Variable(tf.random.uniform(shape=(1,)), trainable=True)

for tt in range(training_periods):
    if tt % 2 == 0:
        with tf.GradientTape() as tape:
            err, loss = model(x)
        
        gradients = tape.gradient(loss, [x])  # suppose x need to be optimized
        optimizer.apply_gradients(zip(gradients, [x]))  # update x
        
        print(f"Period: {tt}, err (trained): {err.numpy()}")
        
    else:
        err, loss = model(x)
        
        print(f"Period: {tt}, err (not trained): {err.numpy()}")

# outcomes
Period: 0, err (trained): [0.01975703]
Period: 1, err (not trained): [0.01975703]
Period: 2, err (trained): [0.5400312]
Period: 3, err (not trained): [0.5400312]
Period: 4, err (trained): [0.51667833]
Period: 5, err (not trained): [0.51667833]
Period: 6, err (trained): [0.4683528]
Period: 7, err (not trained): [0.4683528]
Period: 8, err (trained): [0.14856052]
Period: 9, err (not trained): [0.14856052]
```


### Relevant log output

_No response_
Repository: rust
Issue: Suboptimal interaction between lifetime oriented and value oriented type system in the presence of closures.
Labels: A-lifetimes,T-lang,C-discussion,S-has-mcve,T-types

This code compiles with the comments and does not compile without the comments.

```rust
fn main() {
    fn subparsing_lifetime<'s, 'r>(args: &'s Vec<&'s str>) -> (&'r ()/*, &'s ()*/) { unreachable!() }
    let closure = |args| subparsing_lifetime(&args);
}
```

It runs in the compiler error "cannot return value referencing function parameter".

This highlights, to me, suboptimal interaction between the lifetime oriented type system and the value oriented type system.

The typing guarantees that the function parameter is not referenced in the values.

Moreover, there are use cases, bumpalo arenas, for instance, where it is perfectly legitimate to have lifetimes shared between arguments and returned values, with no dependencies between one and the other.

So this code should compile if uncommented.

One of the interest I have in rust is precisely the ability to share lifetimes between independent references.
Repository: pytorch
Issue: OnnxExporterError using dynamo_export
Labels: module: onnx,triaged

### 🐛 Describe the bug

I got an ```OnnxExporterError``` trying to export a PyTorch model using ```dynamo_export```. See below:
```
Traceback (most recent call last):
  File "/Users/albasaco/Desktop/work/createsafe/demucs_browser/demucs_onnx_export.py", line 15, in <module>
    onnx_program = torch.onnx.dynamo_export(model, padded_mix)
                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/albasaco/Desktop/work/createsafe/demucs_browser/env/lib/python3.11/site-packages/torch/onnx/_internal/exporter.py", line 1206, in dynamo_export
    raise OnnxExporterError(
torch.onnx.OnnxExporterError: Failed to export the model to ONNX. Generating SARIF report at 'report_dynamo_export.sarif'. SARIF is a standard format for the output of static analysis tools. SARIF logs can be loaded in VS Code SARIF viewer extension, or SARIF web viewer (https://microsoft.github.io/sarif-web-component/). Please report a bug on PyTorch Github: https://github.com/pytorch/pytorch/issues
```
As requested in the error message, I'm raising an issue and pasting the contents of the SARIF report below (I'm unable to attach it as a file). Please let me know if there is other information I can provide and I would love to stay up to date on efforts to improve this functionality.
Thank you.

```
{
 "runs":[
  {
   "tool":{
    "driver":{
     "name":"torch.onnx.dynamo_export",
     "contents":[
      "localizedData",
      "nonLocalizedData"
     ],
     "language":"en-US",
     "rules":[],
     "version":"2.1.2"
    }
   },
   "language":"en-US",
   "newlineSequences":[
    "\r\n",
    "\n"
   ],
   "results":[]
  }
 ],
 "version":"2.1.0",
 "schemaUri":"https://docs.oasis-open.org/sarif/sarif/v2.1.0/cs01/schemas/sarif-schema-2.1.0.json"
}
```

### Versions

```
Collecting environment information...
PyTorch version: 2.1.2
Is debug build: False
CUDA used to build PyTorch: None
ROCM used to build PyTorch: N/A

OS: macOS 14.3 (arm64)
GCC version: Could not collect
Clang version: 15.0.0 (clang-1500.3.9.4)
CMake version: Could not collect
Libc version: N/A

Python version: 3.11.6 (v3.11.6:8b6ee5ba3b, Oct  2 2023, 11:18:21) [Clang 13.0.0 (clang-1300.0.29.30)] (64-bit runtime)
Python platform: macOS-14.3-arm64-arm-64bit
Is CUDA available: False
CUDA runtime version: No CUDA
CUDA_MODULE_LOADING set to: N/A
GPU models and configuration: No CUDA
Nvidia driver version: No CUDA
cuDNN version: No CUDA
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

CPU:
Apple M3 Pro

Versions of relevant libraries:
[pip3] numpy==1.26.4
[pip3] onnx==1.16.0
[pip3] onnxscript==0.1.0.dev20240518
[pip3] torch==2.1.2
[pip3] torchaudio==2.1.2
[conda] Could not collect
```
Repository: godot
Issue: Moving directory reimports image assets and recalculates all metadata
Labels: topic:editor,needs testing,topic:import,performance

### Tested versions

4.2.2 - reproducable

### System information

Windows 7

### Issue description

Moving a directory of already imported assets (jpgs in this case) re-imports them at great computational expense.

I don't know what the import process entails, but given the insane temporary resource usage (12GB of RAM for this use-case), I expect part of it is analysing the images themselves to gather metadata, which makes sense. However, when moving a directory, Godot should only need to change relative file locations in the metadata; not do anything computationally expensive like recalculate all of the imagery metadata.

### Steps to reproduce

1) Create a new project
2) add a directory of several hundred MB of pictures to the project. Ideally some of them should be 30+MB in size.
3) Create a subfolder.
3) In godot, drag the directory of images into the subfolder

### Minimal reproduction project (MRP)

N/A