mteb/LocBenchRR huggingface dataset:
Add unit tests for traceback formatting of RecursionError
For example, the following snippet:

```python
from loguru import logger

def rec():
    rec()

try:
    rec()
except Exception:
    logger.exception("Oups")
```

Should produce the following logs:

```txt
2024-02-17 12:06:00.577 | ERROR    | __main__:<module>:9 - Oups
Traceback (most recent call last):

> File "/home/delgan/Code/loguru/script.py", line 7, in <module>
    rec()
    └ <function rec at 0x7a13f1314720>

  File "/home/delgan/Code/loguru/script.py", line 4, in rec
    rec()
    └ <function rec at 0x7a13f1314720>

  File "/home/delgan/Code/loguru/script.py", line 4, in rec
    rec()
    └ <function rec at 0x7a13f1314720>

  File "/home/delgan/Code/loguru/script.py", line 4, in rec
    rec()
    └ <function rec at 0x7a13f1314720>
  [Previous line repeated 996 more times]

RecursionError: maximum recursion depth exceeded
```

Note the "[Previous line repeated 996 more times]".
[Reporting tool] Adds the Thunder segmentation and fusion information to the FXReport
There are two immediate next options I can think of:

One option is to make thunder segmentation and fusion information programmatically available. This could be done by having a function like `analyze_with_thunder` that takes an FXGraphReport and creates a ThunderFXGraphReport, which would be a subclass of FXGraphReport. The ThunderFXGraphReport would have additional ThunderSegmentReport classes, and maybe ThunderFusionReport classes, so that the segments and fusions could be iterated over. Then the ability to write reproducers from the segments alone and from the fusions alone could be added to those classes (reusing the existing functionality as much as possible). That would allow for the creation of files that reproduce or benchmark segments and fusions. Each nvFusion could then be compared to executing with torch.compile instead, for example.

I think the second option is to focus on making the timing data programmatically available at the FX level. Basically running the benchmark and returning its data as a Python object. In the future we should let callers specify their own timers, but a first PR can just pick a timer to use. 

Whichever is done first, the second one can probably be done next. The timings don't need to be perfect at first, but it will be exciting to work on the proper timing of fx graphs, segments, and fusions in the near future. @kshitij12345 did some work attempting to compare nvfuser and torch.compile fusions, but the nvFuser team and mixology teams may have some other ideas for timing, too.

Once we have programmatically available timings for fx graphs, segments, and fusions, then I think it's time to revisit running these reports as part of the reports that Mixology produces.

How does that sound, @kiya00?

_Originally posted by @mruberry in https://github.com/Lightning-AI/lightning-thunder/pull/1707#pullrequestreview-2587842277_
            

cc @crcrpar
`finding_uid` is not unique in `iam_rotate_access_key_90_days`
### Steps to Reproduce

python prowler.py aws -s iam


### Expected behavior

The program output CSV file with the same finding_uid like that 
prowler-aws-iam_rotate_access_key_90_days-<AWS ACCOUNT_ID>-us-east-1-<IAM_USER>

This IAM_USER has 2 access keys

### Actual Result with Screenshots or Logs

No log

### How did you install Prowler?

Cloning the repository from github.com (git clone)

### Environment Resource

8. Other (Local PC)

### OS used

2. MacOS 

### Prowler version

Prowler 5.1.0 (You are running the latest version, yay!)

### Pip version

pip 23.3

### Context

The source code of [finding.py ](https://github.com/prowler-cloud/prowler/blob/master/prowler/lib/outputs/finding.py#L238-L241)

```
output_data["uid"] = (
                f"prowler-{provider.type}-{check_output.check_metadata.CheckID}-{output_data['account_uid']}-"
                f"{output_data['region']}-{output_data['resource_name']}"
            )
```

Do we need add UUID to above line to avoid problem?
Avoid unnecessary length checks in `df.squeeze`
It is possible that when `axis=1` in squeeze we still check `len(self.index)`, which is never necessary when `axis=1`. Link to code here: https://github.com/modin-project/modin/blob/eac3c77baf456c7bd7e1e5fde81790a4ed3ebb27/modin/pandas/dataframe.py#L2074-L2084

This is an easy fix, also see https://github.com/snowflakedb/snowpark-python/pull/1767
`array.broadcast_shapes` to return a tuple of `int`, not a tuple of NumPy scalars
<!-- Please do a quick search of existing issues to make sure that this has not been asked before. -->

Before NumPy v2, the `repr` of NumPy scalars returned a plain number. This changed in [NEP 51](https://numpy.org/neps/nep-0051-scalar-representation.html), which changes how the result of `broadcast_shapes()` is printed:

- Before: `(240, 37, 49)`
- After: `(np.int64(240), np.int64(37), np.int64(49))`

There is no functional difference here, but it is much less intuitive to read - especially problematic for documentation/notebooks.

What does everyone think of adding a line into `broadcast_shapes` to ensure that only plain integers are returned? Would this have any unintended consequences? Many thanks.
fix(security): add auth to /project/file get endpoint
fixes https://github.com/Chainlit/chainlit/issues/1101
RequestInfo is documented to be a namedtuple but its actually a dataclass or attrs
https://docs.aiohttp.org/en/stable/client_reference.html#aiohttp.ClientResponse.request_info

It would be about 2x as fast to create if it were a namedtuple though, and although dataclasses are faster to access its rarely accessed so namedtuple probably is a better choice.  frozen dataclasses are also slower to create https://docs.python.org/3.9/library/dataclasses.html#frozen-instances.... and we create them for every request.
> There is a tiny performance penalty when using frozen=True: [__init__()](https://docs.python.org/3.9/reference/datamodel.html#object.__init__) cannot use simple assignment to initialize fields, and must use [object.__setattr__()](https://docs.python.org/3.9/reference/datamodel.html#object.__setattr__).

Additionally the attrs default isn't used on 3.11/3.10 at all

https://github.com/aio-libs/aiohttp/blob/13dc0200d855bdfec1d4c4f3e1c9a1c66f88f1eb/aiohttp/client_reqrep.py#L110

```diff
diff --git a/aiohttp/client_reqrep.py b/aiohttp/client_reqrep.py
index dad7fe78e..1eeaf7068 100644
--- a/aiohttp/client_reqrep.py
+++ b/aiohttp/client_reqrep.py
@@ -105,16 +105,12 @@ class ContentDisposition:
     filename: Optional[str]
 
 
-@attr.s(auto_attribs=True, frozen=True, slots=True)
-class RequestInfo:
+class RequestInfo(NamedTuple):
     url: URL
     method: str
     headers: "CIMultiDictProxy[str]"
-    real_url: URL = attr.ib()
+    real_url: URL
 
-    @real_url.default
-    def real_url_default(self) -> URL:
-        return self.url
 
 
 class Fingerprint:
@@ -401,7 +397,7 @@ class ClientRequest:
     @property
     def request_info(self) -> RequestInfo:
         headers: CIMultiDictProxy[str] = CIMultiDictProxy(self.headers)
-        return RequestInfo(self.url, self.method, headers, self.original_url)
+        return tuple.__new__(RequestInfo, (self.url, self.method, headers, self.original_url))
 
     def update_host(self, url: URL) -> None:
         """Update destination host, port and connection type (ssl)."""
```
sqlmigrate prevents normal colorization of system checks
Description
	 
sqlmigrate forces no_color=True for the sake of not colorizing the sql keywords BEGIN; and COMMIT;, but this has the side effect of preventing system check output from being colorized.
(To reproduce, begin with a project that will emit a system check, and run sqlmigrate.)
Suggesting a small ​PR to preserve the non-colorization of BEGIN; and COMMIT; while still colorizing system checks.
test_get_corsika_telescope_list takes too long
The `test_get_corsika_telescope_list` test takes roughly 20 seconds to run. Profiling it points to excessive time spent initializing the array model, perhaps related to reading from the DB too many times.

```
  ncalls  tottime  percall  cumtime  percall filename:lineno(function)
        1    0.000    0.000   21.427   21.427 /workdir/external/simtools/simtools/model/array_model.py:41(__init__)
        1    0.000    0.000   20.855   20.855 /workdir/external/simtools/simtools/model/array_model.py:69(_initialize)
       60    0.004    0.000   20.773    0.346 /workdir/external/simtools/simtools/model/model_parameter.py:49(__init__)
       58    0.002    0.000   19.823    0.342 /workdir/external/simtools/simtools/model/telescope_model.py:38(__init__)
        1    0.000    0.000    0.994    0.994 /workdir/external/simtools/simtools/model/site_model.py:27(__init__)
       61    0.001    0.000    0.574    0.009 /workdir/external/simtools/simtools/db/db_handler.py:57(__init__)
```

Need to look into it further (in the middle of something else, opening this issue so it is not forgotten).
Bottlenecks writing HFBs LHM
https://github.com/Deltares/imod-python/pull/1157 considerably improves the speed with which the LHM is written: From 34 minutes to 12.5 minutes. This is still slower than expected however. Based on profiling, I've identified 3 main bottlenecks:

- 4.5 minutes: ``xu.DataArray.from_structured`` is called 70 times. This is quite a computationally intensive operation. It can probably be reworked to be called only once.
- 3 minutes are spent in ``DTypeSchema.validate``. This make as call to the ``scalar_None`` function, which is supposed to shortcut if len(da.shape)  != 0, but because I used the ``&`` operator accidentily instead of ``and``, this is not happening.
- 3 minutes: Spent in ``mask_all_models``. This is called because the automatic regridding might introduce inconsistencies in where idomain is located and where data is specified.  I think we can remove this function, because:
     1. in this case no regridding takes place and the masking is unnecessary, besides I see that ``_regrid_like(model: IModel, *args)`` also does masking?
     2. automatic masking can hide issues already present in the original iMOD5 model. 
     3. Regridding might introduce other issues as well: For example, model tops and bottoms might be regridded in such a manner that the bottom exceeds top. Automatically cleaning up all these potentially introduced artifacts is excessive. Probably better is to create cleanup utilities and guide the user to these in the ValidationError messages. 

So tasks:

- [x] Ensure  ``xu.DataArray.from_structured`` is only called once
- [x] Make sure ``scalar_None`` shortcuts appropriately
- [x] Remove call to mask_all_models
[ray metrics launch-prometheus] Incorrect download URL generation for aarch64 architecture
### What happened + What you expected to happen

<img width="802" alt="image" src="https://github.com/user-attachments/assets/e370ab29-db28-432b-b2c5-4c50e8e2dcf6">

- When executing the "ray metrics launch-prometheus" command on an aarch64 architecture system, the download URL is incorrectly generated, leading to a "not found" error. 
- This occurs because the command attempts to download the Prometheus build file from the GitHub releases page (https://github.com/prometheus/prometheus/releases) using "aarch64" in the URL, while Prometheus classifies this architecture as "arm64".

### Versions / Dependencies

- Ray: rayproject/ray:nightly-aarch64

### Reproduction script

1. Run "ray metrics launch-prometheus" on an aarch64 system
2. Observe that the command attempts to download a file with "aarch64" in the URL

### Issue Severity

Low: It annoys or frustrates me.
Perform validation check on updating nested fields
<!--- Provide a general summary of the changes you want in the title above. -->

## Summary
If a nested field is updated via the parent field, a validation on the nested field should be performed, if some validators are provided on the django field.

<!--- This template is entirely optional and can be removed, but is here to help both you and us. -->
<!--- Anything on lines wrapped in comments like these will not show up in the final text. -->

## Feature Request Type

- [ ] Core functionality
- [x] Alteration (enhancement/optimization) of existing feature(s)
- [ ] New behavior

## Description

Assuming we have a Parent model with some nested fields:

```python
class Parent(models.Model);
   foo_field = models.CharField()

class Child(models.Model):
   url = models.URLField(validators=[validators.validate_url])
   parent_field = models.ForeignKey(to=Parent, related_name="children")
```

We create some types and a create mutation:

```python
@strawberry.type(Child, exclude=["parent_field"])
class Child:
    id: auto
    url: auto

@strawberry.input(Child)
class ChildInput(Child):
   pass

@strawberry.type(Parent)
class Parent:
   id: auto
   foo_field: auto
   children: List[Child]

@strawberry.input(Parent)
class ParentInput(Parent):
   pass

@strawberry.type
class Mutation:
    parent: Parent = mutations.create(types.ParentInput)
```

No matter which value is passed to the URL Field of the `Child`, the validator is not executed.

## Solution

Reason is that the `update_m2m` method in **resolvers.py** does not execute a `full_clean` on the respective object.




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[shmap] shard_map collectives don't support axis_index_groups
I am primarily interested in collective matrix multiplication algorithms where `all_to_all` and `all_gather` are useful for overlapping reduce scatter permutes and matrix multiplication.

Collectives like `psum` may be hard to support since the replication rule doesn't make sense, but collectives like `all_gather`, `all_to_all`, and `psum_scatter` should be easy since they are per-device and the output is unreplicated.

Example of code that doesn't work:

```python
from functools import partial

import jax
from jax import numpy as jnp
from jax import sharding
from jax.experimental.shard_map import shard_map

mesh = sharding.Mesh(jax.devices(), ('x',))
x = jax.device_put(
    jnp.arange(64).reshape(8, 8),
    sharding.NamedSharding(mesh, sharding.PartitionSpec(None, 'x')),
)
assert x.addressable_data(0).shape == (8, 1)


@partial(
    shard_map,
    mesh=mesh,
    in_specs=(sharding.PartitionSpec(None, 'x'),),
    out_specs=sharding.PartitionSpec(None, 'x'),
)
def fwd(x):
  return jax.lax.psum_scatter(
      x,
      'x',
      scatter_dimension=0,
      axis_index_groups=((0, 2, 4, 6), (1, 3, 5, 7)),
      tiled=True,
  )


y = fwd(x)

# Expected:
# [[ 12  16  76  80 140 144 204 208]
#  [ 44  48 108 112 172 176 236 240]]
```

See https://github.com/google/jax/blob/bbeddbdcffd7fcd8e52ea9b708c01fdfd4c52bb0/jax/experimental/shard_map.py#L926

cc @mattjj
dhcp.py: decode error  "object has no attribute 'encode'"
### Configuration
  
impacket version:  HEAD
Python version:  3.11
Target OS:  Linux

### Debug Output With Command String  

````
		dhcp = DhcpPacket(buffer)
		print(dhcp)
``````


```
Traceback (most recent call last):
  File "/home/mdt/Source/emdete/honeypot/snooper/main.py", line 14, in learn_dhcp
    dhcp = DHCP(buffer)
           ^^^^^^^^^^^^
  File "/home/mdt/Source/oss/python/impacket/impacket/dhcp.py", line 148, in __init__
    structure.Structure.__init__(self, data, alignment)
  File "/home/mdt/Source/oss/python/impacket/impacket/structure.py", line 87, in __init__
    self.fromString(data)
  File "/home/mdt/Source/oss/python/impacket/impacket/structure.py", line 152, in fromString
    self[field[0]] = self.unpack(field[1], data[:size], dataClassOrCode = dataClassOrCode, field = field[0])
                     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/mdt/Source/oss/python/impacket/impacket/structure.py", line 307, in unpack
    return eval(dataClassOrCode, {}, fields)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "<string>", line 1, in <module>
  File "/home/mdt/Source/oss/python/impacket/impacket/dhcp.py", line 179, in unpackOptions
    value = self.unpack(format, options[i+2:i+2+size])
            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/mdt/Source/oss/python/impacket/impacket/structure.py", line 382, in unpack
    return dataClassOrCode(data)
           ^^^^^^^^^^^^^^^^^^^^^
  File "/usr/lib/python3/dist-packages/six.py", line 644, in b
    return s.encode("latin-1")
           ^^^^^^^^
AttributeError: ("'bytearray' object has no attribute 'encode'", "When unpacking field 'options | _ | b''[:0]'")
```


### PCAP  

```
 b'\x01\x01\x06\x00\xa3Y\xdf\x06\x00\x06\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\xf84A\xdb2.\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00c\x82Sc5\x01\x032\x04\xc0\xa8\x00\x8c6\x04\xc0\xa8\x00\x019\x02\x02@7\x07\x01\x03\x06\x0c\x0f\x1c*<\x0cudhcp 1.36.1=\x07\x01\xf84A\xdb2.\xff\x00\x00\x00\x00\x00\x00\x00\x00'
```

### Additional context
OOM when using VAD
Hi, does somebody else experience issues with memory consumption when transcribing audio files containing a lot of speech (~ 4 hours long)? I am running the latest version of faster-whisper in a Kubernetes pod on a g4dn AWS instance. The server has 4 cores, 1 GPU, and 16GB RAM, but the pod is limited to 2 cores. The base image is `pytorch/pytorch:2.5.1-cuda12.4-cudnn9-runtime` and as per [this pinned issue](https://github.com/SYSTRAN/faster-whisper/issues/1086) the installed versions should be compatible:
- python 3.11
- torch 2.5.1+cu124
- ctranslate2 4.5.0
- cuda 12.4
- cudnn 9.1.0.7

The process gets killed during the transcription phase when VAD is enabled. I tried the solution [described here](https://github.com/snakers4/silero-vad/issues/356), but it doesn't help. See the logs attached. Anyone has any idea what could be the cause of the OOM?

[libraries.txt](https://github.com/user-attachments/files/18039471/libraries.txt)
[logs on sigkill.txt](https://github.com/user-attachments/files/18039459/logs.on.sigkill.txt)
Missconfigured retry_delay_seconds task argument leads to confusing error message
### Bug summary

When the task arg `retry_delay_seconds` is not configured with the correct type, the error message presented is misleading:
```python
from datetime import timedelta
from prefect import get_run_logger, task, flow

@task(retries=1, retry_delay_seconds=timedelta(seconds=1)) #  retry_delay_seconds should be something like an int
def tasky():
    logger = get_run_logger()
    logger.info("hi")

@flow
def flowy():
    tasky()

flowy()
```

The resulting error message is:
```
09:33:08.239 | ERROR   | Flow run 'private-bittern' - Crash detected! Execution was interrupted by an unexpected exception: ValidationError: 2 validation errors for TaskRunPolicy
retry_delay
  value is not a valid integer (type=type_error.integer)
retry_delay
  value is not a valid list (type=type_error.list)
```

Notice is mentions `retry_delay`, not `retry_delay_seconds`

### Version info

```Text
Version:             2.20.13
API version:         0.8.4
Python version:      3.10.13
Git commit:          4d50c394
Built:               Wed, Nov 13, 2024 8:48 AM
OS/Arch:             darwin/arm64
Profile:             default
Server type:         cloud
```
RF02 issue from (x, acc) in aggregate function
### Search before asking

- [X] I searched the [issues](https://github.com/sqlfluff/sqlfluff/issues) and found no similar issues.


### What Happened

This is more of a question, really. I'm not sure if the issue is with sqlfluff, or if the issue is between the keyboard and the chair. I browsed issues and the web and couldn't find anyone with a similar issue, so I very well could be doing something wrong. I'm getting a linting error RF02 (references.qualification) anytime I'm using an aggregate (or reduce) function. Is there a setting somewhere that I am missing so that these parameters are recognized by sqlfluff as part of the function and not a column of the table?
A couple of example queries included.
![image](https://github.com/user-attachments/assets/6fa9fad7-b942-4624-a4bf-2bdebbf74c37)
![image](https://github.com/user-attachments/assets/3b688ab0-454e-472c-9dde-96c841be6d1e)



### Expected Behaviour

No linting errors within the lambda function when using aggregate

### Observed Behaviour

RF02 (references.qualification) error

### How to reproduce

simple select statement using aggregate(some_column, 0, (acc, x) -> acc + x)

### Dialect

databricks

### Version

sqlfluff, version 3.2.5
python version 3.12.7
dbt-core, dbt-databricks 1.8.5

I am, however, using sqlfluff templater jinja rather than dbt

### Configuration

.sqlfluff:
```yml
[sqlfluff]
# Custom rules
max_line_length = 100

# Supported dialects https://docs.sqlfluff.com/en/stable/dialects.html
# Or run 'sqlfluff dialects'
dialect = databricks

# One of [raw|jinja|python|placeholder]
templater = jinja

# Comma separated list of rules to exclude, or None
# See https://docs.sqlfluff.com/en/stable/configuration.html#enabling-and-disabling-rules
# AM04 (ambiguous.column_count) and ST06 (structure.column_order) are
# two of the more controversial rules included to illustrate usage.
exclude_rules = ambiguous.column_count, structure.column_order, aliasing.table

[sqlfluff:templater:jinja] 
load_macros_from_path = ./macros
```

.sqlfluffignore
```yml
/target/
dbt_modules/
dbt_packages/
macros/
```

### Are you willing to work on and submit a PR to address the issue?

- [ ] Yes I am willing to submit a PR!

### Code of Conduct

- [X] I agree to follow this project's [Code of Conduct](https://github.com/sqlfluff/sqlfluff/blob/main/CODE_OF_CONDUCT.md)
[Feature]: Intermediate layer: integrate shapely into Shape2D
### Feature Description

Shapely description: Manipulation and analysis of geometric objects in the Cartesian plane

Might be a basic building block to "easily" develop algorithms that extract useful data from the intermediate layer

### Definition of Done

- [x] Create shapely shape from Shape2D
- [x] Use the shapely.STRtree for accelerating the intersection checks

Work related to the following functions can be found in #599 , #601 :
- Functions for calculating intersection, union, difference between shapes
- Calculate IoU between shapes

### Testability

- [x] Units tests that roughly evaluate the shape creation functions

### Dependencies

- [x] Intermediate layer