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UXARRAY/uxarray
UXARRAY__uxarray-1117
fe4cae1311db7fb21187b505e06018334a015c48
diff --git a/uxarray/grid/connectivity.py b/uxarray/grid/connectivity.py index 78e936117..54bd1017e 100644 --- a/uxarray/grid/connectivity.py +++ b/uxarray/grid/connectivity.py @@ -146,13 +146,14 @@ def _build_n_nodes_per_face(face_nodes, n_face, n_max_face_nodes): """Constructs ``n_nodes_per_face``, which contain...
Optimize Face Centroid Calculations If `Grid.face_lon` does not exist, `_populate_face_centroids()`, actually `_construct_face_centroids()` in it, takes extremely long for large datasets. For instance, the benchmark/profiling below is for a ~4GB SCREAM dataset, around 5 mins: @rajeeja FYI: I'm already working on this ...
1,734,798,627,000
[ "run-benchmark" ]
Performance Issue
[ "uxarray/grid/connectivity.py:_build_n_nodes_per_face", "uxarray/grid/coordinates.py:_construct_face_centroids" ]
[]
ultralytics/ultralytics
ultralytics__ultralytics-17810
d8c43874ae830a36d2adeac4a44a8ce5697e972c
diff --git a/ultralytics/utils/ops.py b/ultralytics/utils/ops.py index 25e83c61c3a..ac53546ed1b 100644 --- a/ultralytics/utils/ops.py +++ b/ultralytics/utils/ops.py @@ -75,9 +75,8 @@ def segment2box(segment, width=640, height=640): (np.ndarray): the minimum and maximum x and y values of the segment. """ ...
Training labels not applied properly to training data ### Search before asking - [X] I have searched the Ultralytics YOLO [issues](https://github.com/ultralytics/ultralytics/issues) and found no similar bug report. ### Ultralytics YOLO Component Train ### Bug # Bug Labels are not included in the gener...
πŸ‘‹ Hello @TheOfficialOzone, thank you for bringing this to our attention πŸš€! We understand that you're encountering an issue with labels not being applied correctly during the training of a segmentation model on the Ultralytics repository. For us to assist you effectively, please ensure that you've provided a [minimum...
1,732,632,265,000
[ "enhancement", "segment" ]
Bug Report
[ "ultralytics/utils/ops.py:segment2box" ]
[]
Chainlit/chainlit
Chainlit__chainlit-1575
8b2d4bacfd4fa2c8af72e2d140d527d20125b07b
diff --git a/backend/chainlit/config.py b/backend/chainlit/config.py index b90f162f07..18ee6be8db 100644 --- a/backend/chainlit/config.py +++ b/backend/chainlit/config.py @@ -311,6 +311,8 @@ class CodeSettings: @dataclass() class ProjectSettings(DataClassJsonMixin): allow_origins: List[str] = Field(default_facto...
diff --git a/cypress/e2e/copilot/.chainlit/config.toml b/cypress/e2e/copilot/.chainlit/config.toml index e2a93af08f..9c42755715 100644 --- a/cypress/e2e/copilot/.chainlit/config.toml +++ b/cypress/e2e/copilot/.chainlit/config.toml @@ -13,7 +13,7 @@ session_timeout = 3600 cache = false # Authorized origins -allow_or...
Security: allowed origins should not be * by default CORS headers should be restricted to the current domain at least, by default.
@dosu Where do we have to look in the settings/code to set this to a sensible/safe default value? <!-- Answer --> To set the allowed origins for CORS headers to a sensible/safe default value, you need to look at the `allow_origins` setting in the `config.toml` file. ```toml # Authorized origins allow_origins = ["*"] `...
1,733,733,602,000
[ "size:M" ]
Security Vulnerability
[ "backend/chainlit/config.py:ProjectSettings", "backend/chainlit/server.py:get_html_template", "backend/chainlit/socket.py:build_anon_user_identifier", "backend/chainlit/socket.py:connect", "backend/chainlit/socket.py:connection_successful", "backend/chainlit/socket.py:window_message" ]
[]
huggingface/transformers
huggingface__transformers-22496
41d47db90fbe9937c0941f2f9cdb2ddd83e49a2e
diff --git a/src/transformers/models/whisper/modeling_whisper.py b/src/transformers/models/whisper/modeling_whisper.py index 91de6810b17e..96f91a0a43dd 100644 --- a/src/transformers/models/whisper/modeling_whisper.py +++ b/src/transformers/models/whisper/modeling_whisper.py @@ -34,7 +34,12 @@ SequenceClassifierOut...
diff --git a/tests/models/whisper/test_modeling_whisper.py b/tests/models/whisper/test_modeling_whisper.py index 883a2021b9bb..98bbbb3214a7 100644 --- a/tests/models/whisper/test_modeling_whisper.py +++ b/tests/models/whisper/test_modeling_whisper.py @@ -1013,6 +1013,48 @@ def test_mask_time_prob(self): en...
Whisper Prompting ### Feature request Add prompting for the Whisper model to control the style/formatting of the generated text. ### Motivation During training, Whisper can be fed a "previous context window" to condition on longer passages of text. The original OpenAI Whisper implementation provides the use...
cc @hollance Hello, I'd like to pick up this issue!
1,680,278,096,000
[]
Feature Request
[ "src/transformers/models/whisper/modeling_whisper.py:WhisperForConditionalGeneration.generate", "src/transformers/models/whisper/tokenization_whisper.py:WhisperTokenizer._decode", "src/transformers/models/whisper/tokenization_whisper.py:WhisperTokenizer", "src/transformers/models/whisper/tokenization_whisper_...
[ "src/transformers/models/whisper/tokenization_whisper.py:WhisperTokenizer.get_prompt_ids", "src/transformers/models/whisper/tokenization_whisper.py:WhisperTokenizer._strip_prompt", "src/transformers/models/whisper/tokenization_whisper_fast.py:WhisperTokenizerFast.get_prompt_ids", "src/transformers/models/whis...
scikit-learn/scikit-learn
scikit-learn__scikit-learn-24145
55af30d981ea2f72346ff93602f0b3b740cfe8d6
diff --git a/doc/whats_new/v1.3.rst b/doc/whats_new/v1.3.rst index 9cab0db995c5d..ec1301844b877 100644 --- a/doc/whats_new/v1.3.rst +++ b/doc/whats_new/v1.3.rst @@ -487,6 +487,11 @@ Changelog categorical encoding based on target mean conditioned on the value of the category. :pr:`25334` by `Thomas Fan`_. +- |En...
diff --git a/sklearn/preprocessing/tests/test_polynomial.py b/sklearn/preprocessing/tests/test_polynomial.py index 727b31b793b1d..1062a3da820e7 100644 --- a/sklearn/preprocessing/tests/test_polynomial.py +++ b/sklearn/preprocessing/tests/test_polynomial.py @@ -35,6 +35,22 @@ def is_c_contiguous(a): assert np.isfor...
Add sparse matrix output to SplineTransformer ### Describe the workflow you want to enable As B-splines naturally have a sparse structure, I'd like to have the option that `SplineTransformer` returns a sparse matrix instead of always an ndarray. ```python import numpy as np from sklearn.preprocessing import SplineT...
1,659,969,522,000
[ "module:preprocessing" ]
Feature Request
[ "sklearn/preprocessing/_polynomial.py:SplineTransformer", "sklearn/preprocessing/_polynomial.py:SplineTransformer.__init__", "sklearn/preprocessing/_polynomial.py:SplineTransformer.fit", "sklearn/preprocessing/_polynomial.py:SplineTransformer.transform" ]
[]
avantifellows/quiz-backend
avantifellows__quiz-backend-84
f970b54634a9a9ba000aaf76d05338a5d77b0d60
diff --git a/app/models.py b/app/models.py index cfb9644b..80f94b94 100644 --- a/app/models.py +++ b/app/models.py @@ -365,6 +365,11 @@ class Config: schema_extra = {"example": {"answer": [0, 1, 2], "visited": True}} +""" +Note : The below model is not being used currently anywhere +""" + + class SessionA...
diff --git a/app/tests/test_session_answers.py b/app/tests/test_session_answers.py index a2c04a1e..2d05a9be 100644 --- a/app/tests/test_session_answers.py +++ b/app/tests/test_session_answers.py @@ -7,12 +7,13 @@ class SessionAnswerTestCase(SessionsBaseTestCase): def setUp(self): super().setUp() ...
At some places we're updating just one key of an object or one element of an array but we send the whole object to MongoDB to update which is inefficient.
1,680,002,295,000
[]
Performance Issue
[ "app/routers/session_answers.py:update_session_answer", "app/routers/session_answers.py:get_session_answer", "app/routers/sessions.py:create_session", "app/routers/sessions.py:update_session" ]
[ "app/routers/session_answers.py:update_session_answer_in_a_session", "app/routers/session_answers.py:get_session_answer_from_a_session" ]
internetarchive/openlibrary
internetarchive__openlibrary-7929
dc49fddb78a3cb25138922790ddd6a5dd2b5741c
diff --git a/openlibrary/core/lending.py b/openlibrary/core/lending.py index 6162ed5b081..d7e2a1949cb 100644 --- a/openlibrary/core/lending.py +++ b/openlibrary/core/lending.py @@ -511,13 +511,53 @@ def _get_ia_loan(identifier, userid): def get_loans_of_user(user_key): """TODO: Remove inclusion of local data; s...
Cache Patron's Active Loans <!-- A clear and concise description of what the problem is. Ex. I'm always frustrated when [...] --> On several pages (e.g. LoanStatus) we fetch the patron's active loans (which can be expensive) to see if they've borrowed a book (e.g. on the book page). Ideally, we'd cache this every...
1,685,797,378,000
[ "Priority: 1", "Needs: Patch Deploy" ]
Performance Issue
[ "openlibrary/core/lending.py:get_loans_of_user", "openlibrary/core/models.py:User.get_loan_for", "openlibrary/core/models.py:User.get_waiting_loan_for", "openlibrary/plugins/upstream/borrow.py:borrow.POST" ]
[ "openlibrary/core/lending.py:get_user_waiting_loans", "openlibrary/core/models.py:User.get_user_waiting_loans" ]
rwth-i6/sisyphus
rwth-i6__sisyphus-191
a5ddfaa5257beafb5fdce28d96e6ae1e574ee9fe
diff --git a/sisyphus/aws_batch_engine.py b/sisyphus/aws_batch_engine.py index 4b0173f..80f454e 100644 --- a/sisyphus/aws_batch_engine.py +++ b/sisyphus/aws_batch_engine.py @@ -1,4 +1,4 @@ -""" This is an experimental implementation for the aws batch engine. +"""This is an experimental implementation for the aws batch ...
Too many open file descriptors Hi, I was using sisyphus today for a big recipe and I got an error in my worker which claimed `too many open files`: ``` OSError: [Errno 24] Unable to synchronously open file (unable to open file: name = <filename>, errno = 24, error message = 'Too many open files', flags = 0, o_flags...
I had this happen again. It was with a relatively big setup, but I'm not sure what causes the issue yet since my manager shouldn't be opening many files, if any. Please find attached the corresponding stack trace from the manager [here](https://github.com/user-attachments/files/15509563/manager_too_many_open_files_gi...
1,717,145,069,000
[]
Performance Issue
[ "sisyphus/aws_batch_engine.py:AWSBatchEngine.system_call", "sisyphus/load_sharing_facility_engine.py:LoadSharingFacilityEngine.system_call", "sisyphus/simple_linux_utility_for_resource_management_engine.py:SimpleLinuxUtilityForResourceManagementEngine.system_call", "sisyphus/son_of_grid_engine.py:SonOfGridEng...
[]
vllm-project/vllm
vllm-project__vllm-4109
ec784b2526219cd96159a52074ab8cd4e684410a
diff --git a/requirements-common.txt b/requirements-common.txt index f41873570aa67..bf9987e3af014 100644 --- a/requirements-common.txt +++ b/requirements-common.txt @@ -17,6 +17,6 @@ prometheus_client >= 0.18.0 prometheus-fastapi-instrumentator >= 7.0.0 tiktoken >= 0.6.0 # Required for DBRX tokenizer lm-format-enfo...
diff --git a/tests/entrypoints/test_guided_processors.py b/tests/entrypoints/test_guided_processors.py index 5d4163e96fd87..fb32a9d155bc0 100644 --- a/tests/entrypoints/test_guided_processors.py +++ b/tests/entrypoints/test_guided_processors.py @@ -63,7 +63,6 @@ def test_guided_logits_processors(): ...
[Feature]: Update Outlines Integration from `FSM` to `Guide` ### πŸš€ The feature, motivation and pitch Recently outlines updated their interface from FSM to Guide to support "acceleration"/"fast-forward" which will output next sets of tokens if they are directly available. For JSON schema, the cases are the keys, the `...
@simon-mo do you think this is an issue that could be driven by a community contribution? I'd be interested in working on it, but would most probably need a more knowledgeable counterpart to guide me through it. Yes. Contributions welcomed. Great. Here a first conceptual sketch: 1. introduce a new abstraction (or ...
1,713,254,764,000
[]
Feature Request
[ "vllm/model_executor/guided_decoding/outlines_decoding.py:get_outlines_guided_decoding_logits_processor", "vllm/model_executor/guided_decoding/outlines_decoding.py:_get_cached_logits_processor", "vllm/model_executor/guided_decoding/outlines_logits_processors.py:BaseLogitsProcessor.__init__", "vllm/model_execu...
[ "vllm/model_executor/guided_decoding/outlines_decoding.py:_get_logits_processor", "vllm/model_executor/guided_decoding/outlines_logits_processors.py:RegexLogitsProcessor._get_guide", "vllm/model_executor/guided_decoding/outlines_logits_processors.py:RegexLogitsProcessor", "vllm/model_executor/guided_decoding/...
sympy/sympy
sympy__sympy-27223
d293133e81194adc11177729af91c970f092a6e7
diff --git a/sympy/utilities/lambdify.py b/sympy/utilities/lambdify.py index a84d1a1c26c1..518f5cb67bf5 100644 --- a/sympy/utilities/lambdify.py +++ b/sympy/utilities/lambdify.py @@ -11,6 +11,7 @@ import keyword import textwrap import linecache +import weakref # Required despite static analysis claiming it is not...
diff --git a/sympy/utilities/tests/test_lambdify.py b/sympy/utilities/tests/test_lambdify.py index 4a82290569ea..428cbaed92b6 100644 --- a/sympy/utilities/tests/test_lambdify.py +++ b/sympy/utilities/tests/test_lambdify.py @@ -1,6 +1,8 @@ from itertools import product import math import inspect +import linecache +im...
Memory Leak in `sympy.lambdify` Hi there, I'm working with an [algorithm](https://github.com/SymposiumOrganization/NeuralSymbolicRegressionThatScales) that relies on calling `sympy.lambdify` hundreds of millions of times (~200M) and noticed the memory usage of the process steadily creeping up and eventually crashing t...
> The memory usage increases by about +390KB per lambdified equation I assume you mean per 10000 lambdified equations so it is about 400 bytes per lambdified equation. My guess is that each call to lambdify creates a Dummy and then something creates a polynomial ring with that dummy and the polynomial ring never gets ...
1,730,837,688,000
[ "utilities.lambdify" ]
Bug Report
[ "sympy/utilities/lambdify.py:lambdify" ]
[]
modin-project/modin
modin-project__modin-6836
097ea527c8e3f099e1f252b067a1d5eb055ad0b5
diff --git a/modin/core/dataframe/algebra/binary.py b/modin/core/dataframe/algebra/binary.py index f19040cc104..af0c6ee7e8e 100644 --- a/modin/core/dataframe/algebra/binary.py +++ b/modin/core/dataframe/algebra/binary.py @@ -415,7 +415,9 @@ def caller( ): shape_hint = "colu...
FEAT: Do not put binary functions to the Ray storage multiple times. Currently, the binary operations are wrapped into lambdas which are put into the Ray storage on each operation.
1,703,167,333,000
[]
Feature Request
[ "modin/core/dataframe/algebra/binary.py:Binary.register", "modin/core/dataframe/pandas/dataframe/dataframe.py:PandasDataframe.map", "modin/core/dataframe/pandas/partitioning/partition_manager.py:PandasDataframePartitionManager.map_partitions", "modin/core/dataframe/pandas/partitioning/partition_manager.py:Pan...
[]
Open-MSS/MSS
Open-MSS__MSS-1967
56e9528b552a9d8f2e267661473b8f0e724fd093
diff --git a/.github/workflows/python-flake8.yml b/.github/workflows/python-flake8.yml index b578708e4..0e9003135 100644 --- a/.github/workflows/python-flake8.yml +++ b/.github/workflows/python-flake8.yml @@ -19,10 +19,10 @@ jobs: timeout-minutes: 10 steps: - uses: actions/checkout@v3 - - name: Set up...
diff --git a/conftest.py b/conftest.py index be546d782..83f33ca85 100644 --- a/conftest.py +++ b/conftest.py @@ -211,9 +211,8 @@ def _load_module(module_name, path): @pytest.fixture(autouse=True) -def close_open_windows(): - """ - Closes all windows after every test +def fail_if_open_message_boxes_left(): + ...
What to do with "UserWarning: An unhandled message box popped up during your test!"? There are many of these warnings in the CI logs basically spamming the output and drowning out other more interesting warnings. These warnings are originating from https://github.com/Open-MSS/MSS/blob/1327ede1dbe3f4eb26bf3889934fa76...
see here, the warning comes from the fixture https://github.com/Open-MSS/MSS/blob/develop/conftest.py#L214 tests better should fail instead of hiding one cause, some of the tests showing that have to do a second turn. Sometimes functionality gets added but not the test improved e.g. ``` call(<mslib.msui.msc...
1,693,392,061,000
[]
Performance Issue
[ "mslib/msui/mscolab.py:MSUIMscolab.add_operation", "mslib/msui/mscolab.py:MSUIMscolab.change_category_handler", "mslib/msui/mscolab.py:MSUIMscolab.change_description_handler", "mslib/msui/mscolab.py:MSUIMscolab.rename_operation_handler", "mslib/msui/mscolab.py:MSUIMscolab.logout", "mslib/msui/socket_contr...
[]
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LOC-BENCH: A Benchmark for Code Localization

LOC-BENCH is a dataset specifically designed for evaluating code localization methods in software repositories. LOC-BENCH provides a diverse set of issues, including bug reports, feature requests, security vulnerabilities, and performance optimizations.

Please refer to the official version Loc-Bench_V1 for evaluating code localization methods and for easy comparison with our approach.

Code: https://github.com/gersteinlab/LocAgent

πŸ“Š Details

This is the dataset that was used in the early version of our paper. We later released a refined version, czlll/Loc-Bench_V1, with improved data quality by filtering out examples that do not modify any functions. We recommend using the refined dataset to evaluate code localization performance.

The table below shows the distribution of categories in the dataset Loc-Bench_V0.1.

category count
Bug Report 282
Feature Request 203
Performance Issue 144
Security Vulnerability 31

πŸ”§ How to Use

You can easily load LOC-BENCH using Hugging Face's datasets library:

from datasets import load_dataset

dataset = load_dataset("czlll/Loc-Bench_V0.1", split='test')

πŸ“„ Citation

If you use LOC-BENCH in your research, please cite our paper:

@article{chen2025locagent,
title={LocAgent: Graph-Guided LLM Agents for Code Localization},
author={Chen, Zhaoling and Tang,Xiangru and Deng,Gangda and Wu,Fang and Wu,Jialong and Jiang,Zhiwei and Prasanna,Viktor and Cohan,Arman and Wang,Xingyao},
journal={arXiv preprint arXiv:2503.09089},
year={2025}
}
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Paper for czlll/Loc-Bench_V0.1