sample_id string | instruction string | completion string | category string | label int64 | metadata string |
|---|---|---|---|---|---|
ray-project/ray:release/train_tests/benchmark/image_classification/s3_url/imagenet.py:_list_s3_image_files_cached | # Context:
from functools import lru_cache
from typing import Callable, Dict, List, Optional, Tuple
def _get_class_labels(bucket: str, prefix: str) -> List[str]: ...
def _list_files_for_label(bucket: str, prefix: str, label: str) -> List[Tuple[str, str]]: ...
def list_s3_image_files(data_dir: str) -> List[Dict[str, st... | def _list_s3_image_files_cached(data_dir: str) -> Tuple[Tuple[str, str], ...]:
"""Cached implementation of S3 file listing using Ray tasks for parallelism.
Returns a tuple of tuples for hashability (required by lru_cache).
"""
logger.info(f"Listing JPEG files from {data_dir}...")
# Parse S3 URL: s... | function_simple | 0 | {"cognitive_complexity": 3, "loc": 34, "code_loc": 18, "docstring_loc": 4, "function_name": "_list_s3_image_files_cached", "class_name": null, "qualname": "_list_s3_image_files_cached", "file_path": "release/train_tests/benchmark/image_classification/s3_url/imagenet.py", "repo_id": "ray-project/ray", "has_docstring": t... |
ray-project/ray:rllib/utils/metrics/stats/mean.py:MeanStats.push | # Context:
from typing import Any, Union
class MeanStats(SeriesStats):
stats_cls_identifier = "mean"
def _np_reduce_fn(self, values: np.ndarray) -> float: ...
def _torch_reduce_fn(self, values: 'torch.Tensor'): ...
def reduce(self, compile: bool) -> Union[Any, 'MeanStats']: ...
def __repr__(self) -... | def push(self, value: Any) -> None:
"""Pushes a value into this Stats object.
Args:
value: The value to be pushed. Can be of any type.
PyTorch GPU tensors are kept on GPU until reduce() or peek().
TensorFlow tensors are moved to CPU immediately.
"""
... | function_simple | 0 | {"cognitive_complexity": 2, "loc": 13, "code_loc": 3, "docstring_loc": 7, "function_name": "push", "class_name": "MeanStats", "qualname": "MeanStats.push", "file_path": "rllib/utils/metrics/stats/mean.py", "repo_id": "ray-project/ray", "has_docstring": true, "runnable_level": "file_runnable"} |
ocrmypdf/OCRmyPDF:src/ocrmypdf/font/font_provider.py:BuiltinFontProvider._load_fonts | # Context:
from ocrmypdf.font.font_manager import FontManager
class FontProvider(Protocol): ...
class ChainedFontProvider: ...
class BuiltinFontProvider:
FONT_FILES = {
def __init__(self, font_dir: Path | None = None):
"""Initialize builtin font provider.
Args:
font_dir: Directory... | def _load_fonts(self) -> None:
"""Load available fonts, logging warnings for missing ones."""
for font_name, font_file in self.FONT_FILES.items():
font_path = self.font_dir / font_file
if not font_path.exists():
if font_name == 'Occulta':
raise... | function_complex | 1 | {"cognitive_complexity": 10, "loc": 29, "code_loc": 26, "docstring_loc": 1, "function_name": "_load_fonts", "class_name": "BuiltinFontProvider", "qualname": "BuiltinFontProvider._load_fonts", "file_path": "src/ocrmypdf/font/font_provider.py", "repo_id": "ocrmypdf/OCRmyPDF", "has_docstring": true, "runnable_level": "pro... |
ray-project/ray:rllib/algorithms/tqc/tests/test_tqc.py:module_doc | Write a module-level docstring for the Python module `test_tqc` which contains class `SimpleEnv`, class `TestTQC`. | Tests for the TQC (Truncated Quantile Critics) algorithm. | documentation | 0 | {"doc_type": "module", "module_name": "test_tqc", "file_path": "rllib/algorithms/tqc/tests/test_tqc.py", "repo_id": "ray-project/ray", "char_length": 57} |
huggingface/transformers:src/transformers/models/gemma3n/modular_gemma3n.py:Gemma3nModel.forward | # Context:
import torch
from ...cache_utils import Cache, DynamicCache
from ...processing_utils import Unpack
from ...utils import TransformersKwargs, auto_docstring, can_return_tuple, logging, torch_compilable_check
class Gemma3nTextConfig(Gemma2Config, PreTrainedConfig): ...
class Gemma3nAudioConfig(PreTrainedConfig... | def forward(
self,
input_ids: torch.LongTensor | None = None, # text inputs
pixel_values: torch.FloatTensor | None = None, # vision inputs
input_features: torch.FloatTensor | None = None, # audio inputs
attention_mask: torch.Tensor | None = None,
input_features_mask: t... | function_complex | 0 | {"cognitive_complexity": 8, "loc": 144, "code_loc": 74, "docstring_loc": 30, "function_name": "forward", "class_name": "Gemma3nModel", "qualname": "Gemma3nModel.forward", "file_path": "src/transformers/models/gemma3n/modular_gemma3n.py", "repo_id": "huggingface/transformers", "has_docstring": true, "runnable_level": "p... |
docling-project/docling:docs/examples/post_process_ocr_with_vlm.py:no_long_repeats | # Context:
import re
def is_empty_fast_with_lines_pil(pil_img: Image.Image, downscale_max_side: int, grad_threshold: float, min_line_coverage: float, max_allowed_lines: int, edge_fraction_threshold: float): ...
def remove_break_lines(text: str) -> str: ...
def safe_crop(img: Image.Image, bbox): ...
class PostOcrEnrich... | def no_long_repeats(s: str, threshold: int) -> bool:
"""
Returns False if the string `s` contains more than `threshold`
identical characters in a row, otherwise True.
"""
pattern = r"(.)\1{" + str(threshold) + ",}"
return re.search(pattern, s) is None | function_simple | 1 | {"cognitive_complexity": 0, "loc": 7, "code_loc": 2, "docstring_loc": 4, "function_name": "no_long_repeats", "class_name": null, "qualname": "no_long_repeats", "file_path": "docs/examples/post_process_ocr_with_vlm.py", "repo_id": "docling-project/docling", "has_docstring": true, "runnable_level": "slib_runnable"} |
crewAIInc/crewAI:lib/crewai/src/crewai/a2a/utils/agent_card.py:inject_a2a_server_methods | # Context:
from types import MethodType
from a2a.types import AgentCapabilities, AgentCard, AgentSkill
from crewai.agent import Agent
def _get_tls_verify(auth: ClientAuthScheme | None) -> ssl.SSLContext | bool | str: ...
async def _prepare_auth_headers(auth: ClientAuthScheme | None, timeout: int) -> tuple[MutableMappi... | def inject_a2a_server_methods(agent: Agent) -> None:
"""Inject A2A server methods onto an Agent instance.
Adds a `to_agent_card(url: str) -> AgentCard` method to the agent
that generates an A2A-compliant AgentCard.
Only injects if the agent has an A2AServerConfig.
Args:
agent: The Agent i... | function_simple | 0 | {"cognitive_complexity": 1, "loc": 18, "code_loc": 5, "docstring_loc": 10, "function_name": "inject_a2a_server_methods", "class_name": null, "qualname": "inject_a2a_server_methods", "file_path": "lib/crewai/src/crewai/a2a/utils/agent_card.py", "repo_id": "crewAIInc/crewAI", "has_docstring": true, "runnable_level": "pro... |
infiniflow/ragflow:test/testcases/test_sdk_api/test_chunk_management_within_dataset/test_delete_chunks.py:TestChunksDeletion.test_delete_1k | # Context:
import pytest
from common import batch_add_chunks
from time import sleep
class TestChunksDeletion:
def test_delete_partial_invalid_id(self, add_chunks_func, payload): ...
def test_repeated_deletion(self, add_chunks_func): ...
def test_duplicate_deletion(self, add_chunks_func): ...
def test_c... | def test_delete_1k(self, add_document):
count = 1_000
_, document = add_document
chunks = batch_add_chunks(document, count)
chunk_ids = [chunk.id for chunk in chunks]
from time import sleep
sleep(1)
document.delete_chunks(ids=chunk_ids)
remaining_chunks... | test | 1 | {"function_name": "test_delete_1k", "class_name": "TestChunksDeletion", "qualname": "TestChunksDeletion.test_delete_1k", "file_path": "test/testcases/test_sdk_api/test_chunk_management_within_dataset/test_delete_chunks.py", "repo_id": "infiniflow/ragflow", "loc": 13, "tested_modules": ["concurrent.futures", "common", "... |
crewAIInc/crewAI:lib/crewai/tests/llms/test_multimodal_integration.py:TestLiteLLMMultimodalIntegration.test_describe_image_claude | # Context:
import pytest
from crewai.llm import LLM
from crewai_files import (
AudioFile,
File,
ImageFile,
PDFFile,
TextFile,
VideoFile,
format_multimodal_content,
)
def test_image_bytes() -> bytes: ...
def test_text_bytes() -> bytes: ...
def test_video_bytes() -> bytes: ...
def test_audio_... | def test_describe_image_claude(self, test_image_bytes: bytes) -> None:
"""Test LiteLLM with Claude can describe an image."""
llm = LLM(model="anthropic/claude-3-5-haiku-20241022", is_litellm=True)
files = {"image": ImageFile(source=test_image_bytes)}
messages = _build_multimodal_message... | test | 0 | {"function_name": "test_describe_image_claude", "class_name": "TestLiteLLMMultimodalIntegration", "qualname": "TestLiteLLMMultimodalIntegration.test_describe_image_claude", "file_path": "lib/crewai/tests/llms/test_multimodal_integration.py", "repo_id": "crewAIInc/crewAI", "loc": 16, "tested_modules": ["pathlib", "crewa... |
ray-project/ray:python/ray/serve/tests/test_direct_ingress.py:TestDirectIngressBackpressure.test_requests_are_not_running_serially | # Context:
import asyncio
from concurrent.futures import ThreadPoolExecutor
import httpx
from ray import serve
from ray._common.test_utils import Semaphore, SignalActor, wait_for_condition
from ray.serve._private.test_utils import (
check_deployment_status,
check_num_replicas_gte,
check_num_replicas_lte,
... | def test_requests_are_not_running_serially(
self, _skip_if_ff_not_enabled, serve_instance
):
"""Test that requests are processed concurrently, not serially"""
@serve.deployment(
max_ongoing_requests=20,
)
class A:
async def __call__(self):
... | test | 0 | {"function_name": "test_requests_are_not_running_serially", "class_name": "TestDirectIngressBackpressure", "qualname": "TestDirectIngressBackpressure.test_requests_are_not_running_serially", "file_path": "python/ray/serve/tests/test_direct_ingress.py", "repo_id": "ray-project/ray", "loc": 30, "tested_modules": ["concur... |
ray-project/ray:python/ray/llm/_internal/common/utils/cloud_filesystem/pyarrow_filesystem.py:PyArrowFileSystem._create_azure_filesystem | # Context:
from typing import List, Optional, Tuple, Union
from urllib.parse import urlparse
import pyarrow.fs as pa_fs
import adlfs
from azure.identity import DefaultAzureCredential
class PyArrowFileSystem(BaseCloudFileSystem):
def get_fs_and_path(object_uri: str) -> Tuple[pa_fs.FileSystem, str]: ...
def _cre... | def _create_azure_filesystem(object_uri: str) -> Tuple[pa_fs.FileSystem, str]:
"""Create an Azure filesystem for Azure Blob Storage or ABFSS.
Args:
object_uri: Azure URI (azure://container@account.blob.core.windows.net/path or
abfss://container@account.dfs.core.window... | function_complex | 0 | {"cognitive_complexity": 10, "loc": 78, "code_loc": 45, "docstring_loc": 13, "function_name": "_create_azure_filesystem", "class_name": "PyArrowFileSystem", "qualname": "PyArrowFileSystem._create_azure_filesystem", "file_path": "python/ray/llm/_internal/common/utils/cloud_filesystem/pyarrow_filesystem.py", "repo_id": "... |
ray-project/ray:python/ray/data/tests/test_limit_operator.py:test_per_block_limit_fn | # Context:
import pytest
from ray.data._internal.execution.interfaces.task_context import TaskContext
from ray.data._internal.execution.operators.map_operator import _per_block_limit_fn
import pandas as pd
def test_limit_operator(ray_start_regular_shared): ...
def test_limit_operator_memory_leak_fix(ray_start_regular_... | def test_per_block_limit_fn(blocks_data, per_block_limit, expected_output):
"""Test the _per_block_limit_fn function with various inputs."""
import pandas as pd
# Convert test data to pandas blocks
blocks = [pd.DataFrame({"value": data}) for data in blocks_data]
# Create a mock TaskContext
ctx... | test | 0 | {"function_name": "test_per_block_limit_fn", "class_name": null, "qualname": "test_per_block_limit_fn", "file_path": "python/ray/data/tests/test_limit_operator.py", "repo_id": "ray-project/ray", "loc": 20, "tested_modules": ["ray.data._internal.execution.interfaces.task_context", "ray.data._internal.execution.operators... |
langflow-ai/langflow:src/backend/tests/unit/components/bundles/agentics/test_semantic_map.py:TestSemanticMapComponent.test_should_have_schema_with_table_schema | # Context:
from lfx.components.agentics.semantic_map import SemanticMap
class TestSemanticMapComponent:
def test_should_have_correct_display_name(self): ...
def test_should_have_correct_icon(self): ...
def test_should_have_required_inputs(self): ...
def test_should_have_dataframe_output(self): ...
... | def test_should_have_schema_with_table_schema(self):
"""Test that schema input has table_schema defined."""
schema_input = next((i for i in SemanticMap.inputs if i.name == "schema"), None)
assert schema_input is not None
assert schema_input.table_schema is not None
assert len(sch... | test | 1 | {"function_name": "test_should_have_schema_with_table_schema", "class_name": "TestSemanticMapComponent", "qualname": "TestSemanticMapComponent.test_should_have_schema_with_table_schema", "file_path": "src/backend/tests/unit/components/bundles/agentics/test_semantic_map.py", "repo_id": "langflow-ai/langflow", "loc": 12,... |
huggingface/transformers:tests/models/dinov3_convnext/test_modeling_dinov3_convnext.py:DINOv3ConvNextModelIntegrationTest.test_inference_no_head | # Context:
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
import torch
from transformers import DINOv3ConvNextBackbone, DINOv3ConvNextModel
class DINOv3ConvNextModelTester: ...
class DINOv3ConvNextModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): ...
def pre... | def test_inference_no_head(self):
model = DINOv3ConvNextModel.from_pretrained("facebook/dinov3-convnext-tiny-pretrain-lvd1689m").to(torch_device)
image_processor = self.default_image_processor
image = prepare_img()
inputs = image_processor(image, return_tensors="pt").to(torch_device)
... | test | 0 | {"function_name": "test_inference_no_head", "class_name": "DINOv3ConvNextModelIntegrationTest", "qualname": "DINOv3ConvNextModelIntegrationTest.test_inference_no_head", "file_path": "tests/models/dinov3_convnext/test_modeling_dinov3_convnext.py", "repo_id": "huggingface/transformers", "loc": 24, "tested_modules": ["fun... |
apache/airflow:airflow-core/src/airflow/models/callback.py:ExecutorCallback.__init__ | # Context:
from airflow.executors.workloads.callback import CallbackFetchMethod
class CallbackType(str, Enum): ...
class CallbackDefinitionProtocol(Protocol): ...
class ImportPathCallbackDefProtocol(CallbackDefinitionProtocol, Protocol): ...
class ImportPathExecutorCallbackDefProtocol(ImportPathCallbackDefProtocol, Pr... | def __init__(
self, callback_def: ImportPathExecutorCallbackDefProtocol, fetch_method: CallbackFetchMethod, **kwargs
):
"""
Initialize an ExecutorCallback from a callback definition and fetch method.
:param callback_def: Callback definition with path, kwargs, and executor
:p... | function_simple | 1 | {"cognitive_complexity": 0, "loc": 13, "code_loc": 3, "docstring_loc": 7, "function_name": "__init__", "class_name": "ExecutorCallback", "qualname": "ExecutorCallback.__init__", "file_path": "airflow-core/src/airflow/models/callback.py", "repo_id": "apache/airflow", "has_docstring": true, "runnable_level": "project_run... |
huggingface/transformers:src/transformers/models/glm_moe_dsa/modular_glm_moe_dsa.py:apply_rotary_pos_emb | # Context:
import torch
from ...models.llama.modeling_llama import rotate_half
class GlmMoeDsaConfig(PreTrainedConfig): ...
class GlmMoeDsaRMSNorm(Glm4MoeRMSNorm): ...
class GlmMoeDsaIndexer(nn.Module): ...
class GlmMoeDsaAttention(nn.Module): ...
class GlmMoeDsaDecoderLayer(Glm4MoeLiteDecoderLayer): ...
class GlmMoeD... | def apply_rotary_pos_emb(
x: torch.Tensor,
cos: torch.Tensor,
sin: torch.Tensor,
unsqueeze_dim: int = 1,
) -> torch.Tensor:
"""
Applies Rotary Position Embedding to a single tensor.
This is the transformers equivalent of DeepSeek V3.2's `apply_rotary_emb(x, freqs_cis, interleaved)`.
Ins... | function_simple | 0 | {"cognitive_complexity": 0, "loc": 29, "code_loc": 4, "docstring_loc": 16, "function_name": "apply_rotary_pos_emb", "class_name": null, "qualname": "apply_rotary_pos_emb", "file_path": "src/transformers/models/glm_moe_dsa/modular_glm_moe_dsa.py", "repo_id": "huggingface/transformers", "has_docstring": true, "runnable_l... |
infiniflow/ragflow:test/testcases/test_http_api/test_file_management_within_dataset/test_upload_documents.py:TestDocumentsUpload.test_invalid_dataset_id | # Context:
import pytest
from common import FILE_API_URL, list_datasets, upload_documents
from utils.file_utils import create_txt_file
class TestAuthorization: ...
class TestDocumentsUpload:
def test_valid_single_upload(self, HttpApiAuth, add_dataset_func, tmp_path): ...
def test_file_type_validation(self, Ht... | def test_invalid_dataset_id(self, HttpApiAuth, tmp_path):
fp = create_txt_file(tmp_path / "ragflow_test.txt")
res = upload_documents(HttpApiAuth, "invalid_dataset_id", [fp])
assert res["code"] == 100
assert res["message"] == """LookupError("Can\'t find the dataset with ID invalid_dataset... | test | 1 | {"function_name": "test_invalid_dataset_id", "class_name": "TestDocumentsUpload", "qualname": "TestDocumentsUpload.test_invalid_dataset_id", "file_path": "test/testcases/test_http_api/test_file_management_within_dataset/test_upload_documents.py", "repo_id": "infiniflow/ragflow", "loc": 5, "tested_modules": ["concurrent... |
huggingface/transformers:utils/modular_model_detector.py:license_header | Add a Apache-2.0 license header comment for the project 'transformers', authored by The HuggingFace Team, year 2025. | # Copyright 2025 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... | license | 0 | {"license_type": "Apache-2.0", "author": "The HuggingFace Team", "year": "2025", "source": "header", "repo_id": "huggingface/transformers"} |
crewAIInc/crewAI:lib/crewai/tests/mcp/test_amp_mcp.py:TestBuildMCPConfigFromDict.test_defaults_to_http | # Context:
from crewai.mcp.config import MCPServerHTTP, MCPServerSSE
from crewai.mcp.tool_resolver import MCPToolResolver
def agent(): ...
def resolver(agent): ...
def mock_tool_definitions(): ...
class TestFetchAmpMCPConfigs: ...
class TestParseAmpRef: ...
class TestGetMCPToolsAmpIntegration: ...
class TestBuildMCPC... | def test_defaults_to_http(self):
config_dict = {
"url": "https://mcp.example.com/api",
}
result = MCPToolResolver._build_mcp_config_from_dict(config_dict)
assert isinstance(result, MCPServerHTTP)
assert result.streamable is True | test | 0 | {"function_name": "test_defaults_to_http", "class_name": "TestBuildMCPConfigFromDict", "qualname": "TestBuildMCPConfigFromDict.test_defaults_to_http", "file_path": "lib/crewai/tests/mcp/test_amp_mcp.py", "repo_id": "crewAIInc/crewAI", "loc": 9, "tested_modules": ["crewai.agent.core", "crewai.mcp.config", "crewai.mcp.to... |
crewAIInc/crewAI:lib/crewai/tests/test_flow_ask.py:TestAskMetadata.test_ask_provider_returns_string_with_metadata_sent | # Context:
from crewai.flow import Flow, flow_config, listen, start
class MockInputProvider: ...
class SlowMockProvider: ...
class TestAskBasic: ...
class TestAskTimeout: ...
class TestProviderResolution: ...
class TestAskEvents: ...
class TestAskCheckpoint: ...
class TestInputHistory: ...
class TestAskIntegration: ..... | def test_ask_provider_returns_string_with_metadata_sent(self) -> None:
"""Provider returns plain string; history has metadata but no response_metadata."""
class TestFlow(Flow):
input_provider = MockInputProvider(["answer"])
@start()
def my_method(self):
... | test | 0 | {"function_name": "test_ask_provider_returns_string_with_metadata_sent", "class_name": "TestAskMetadata", "qualname": "TestAskMetadata.test_ask_provider_returns_string_with_metadata_sent", "file_path": "lib/crewai/tests/test_flow_ask.py", "repo_id": "crewAIInc/crewAI", "loc": 16, "tested_modules": ["__future__", "datet... |
infiniflow/ragflow:agent/sandbox/tests/test_providers.py:module_doc | Write a module-level docstring for the Python module `test_providers` which contains class `TestSandboxDataclasses`, class `TestProviderManager`, class `TestSelfManagedProvider`, class `TestProviderInterface`. | Unit tests for sandbox provider abstraction layer. | documentation | 1 | {"doc_type": "module", "module_name": "test_providers", "file_path": "agent/sandbox/tests/test_providers.py", "repo_id": "infiniflow/ragflow", "char_length": 50} |
mem0ai/mem0:openmemory/api/app/models.py:after_memory_insert | # Context:
from sqlalchemy import (
JSON,
UUID,
Boolean,
Column,
DateTime,
Enum,
ForeignKey,
Index,
Integer,
String,
Table,
event,
)
from sqlalchemy.orm import Session, relationship
def get_current_utc_time(): ...
class MemoryState(enum.Enum): ...
class User(Base): ...
c... | def after_memory_insert(mapper, connection, target):
"""Trigger categorization after a memory is inserted."""
db = Session(bind=connection)
categorize_memory(target, db)
db.close() | function_simple | 1 | {"cognitive_complexity": 0, "loc": 5, "code_loc": 3, "docstring_loc": 1, "function_name": "after_memory_insert", "class_name": null, "qualname": "after_memory_insert", "file_path": "openmemory/api/app/models.py", "repo_id": "mem0ai/mem0", "has_docstring": true, "runnable_level": "file_runnable"} |
huggingface/transformers:tests/cli/test_chat.py:test_new_chat_history | # Context:
from transformers.cli.chat import new_chat_history, parse_generate_flags, save_chat
def test_help(cli): ...
def test_save_and_clear_chat(): ...
def test_parse_generate_flags(): ...
# Task:
Write a Python test function `test_new_chat_history` to verify the behavior of `new_chat_history`.
Module under test:... | def test_new_chat_history():
assert new_chat_history() == []
assert new_chat_history("prompt") == [{"role": "system", "content": "prompt"}] | test | 0 | {"function_name": "test_new_chat_history", "class_name": null, "qualname": "test_new_chat_history", "file_path": "tests/cli/test_chat.py", "repo_id": "huggingface/transformers", "loc": 3, "tested_modules": ["transformers.cli.chat"], "has_docstring": false, "runnable_level": "plib_runnable"} |
langflow-ai/langflow:src/lfx/tests/unit/cli/test_validation.py:TestValidateGlobalVariablesForEnv.test_check_variables_option_in_execute | # Context:
from unittest.mock import MagicMock, patch
from lfx.cli.validation import is_valid_env_var_name, validate_global_variables_for_env
from lfx.graph.graph.base import Graph
from lfx.graph.vertex.base import Vertex
class TestIsValidEnvVarName: ...
class TestValidateGlobalVariablesForEnv:
def test_no_valida... | def test_check_variables_option_in_execute(self, mock_get_settings):
"""Test that check_variables option controls validation in execute command."""
# This test verifies the check_variables option works correctly
# when used with the execute command (--check-variables/--no-check-variables)
... | test | 1 | {"function_name": "test_check_variables_option_in_execute", "class_name": "TestValidateGlobalVariablesForEnv", "qualname": "TestValidateGlobalVariablesForEnv.test_check_variables_option_in_execute", "file_path": "src/lfx/tests/unit/cli/test_validation.py", "repo_id": "langflow-ai/langflow", "loc": 23, "tested_modules":... |
huggingface/transformers:tests/models/videomae/test_video_processing_videomae.py:VideoMAEVideoProcessingTest.test_video_processor_properties | # Context:
class VideoMAEVideoProcessingTester: ...
class VideoMAEVideoProcessingTest(VideoProcessingTestMixin, unittest.TestCase):
fast_video_processing_class = VideoMAEVideoProcessor if is_torchvision_available() else None
input_name = "pixel_values"
def setUp(self): ...
def video_processor_dict(sel... | def test_video_processor_properties(self):
video_processing = self.fast_video_processing_class(**self.video_processor_dict)
self.assertTrue(hasattr(video_processing, "do_resize"))
self.assertTrue(hasattr(video_processing, "size"))
self.assertTrue(hasattr(video_processing, "do_center_crop... | test | 0 | {"function_name": "test_video_processor_properties", "class_name": "VideoMAEVideoProcessingTest", "qualname": "VideoMAEVideoProcessingTest.test_video_processor_properties", "file_path": "tests/models/videomae/test_video_processing_videomae.py", "repo_id": "huggingface/transformers", "loc": 12, "tested_modules": ["PIL",... |
github/spec-kit:src/specify_cli/extensions.py:ExtensionManager.__init__ | # Context:
from pathlib import Path
class ExtensionError(Exception): ...
class ValidationError(ExtensionError): ...
class CompatibilityError(ExtensionError): ...
class ExtensionManifest: ...
class ExtensionRegistry: ...
def version_satisfies(current: str, required: str) -> bool: ...
class CommandRegistrar: ...
class E... | def __init__(self, project_root: Path):
"""Initialize extension manager.
Args:
project_root: Path to project root directory
"""
self.project_root = project_root
self.extensions_dir = project_root / ".specify" / "extensions"
self.registry = ExtensionRegistry(s... | function_simple | 0 | {"cognitive_complexity": 0, "loc": 9, "code_loc": 3, "docstring_loc": 5, "function_name": "__init__", "class_name": "ExtensionManager", "qualname": "ExtensionManager.__init__", "file_path": "src/specify_cli/extensions.py", "repo_id": "github/spec-kit", "has_docstring": true, "runnable_level": "file_runnable"} |
crewAIInc/crewAI:lib/crewai-tools/src/crewai_tools/tools/databricks_query_tool/databricks_query_tool.py:_has_data_array | # Context:
from typing import TYPE_CHECKING, Any, TypeGuard, TypedDict
class ExecutionContext(TypedDict): ...
class DatabricksQueryToolSchema(BaseModel): ...
class DatabricksQueryTool(BaseTool): ...
# Task:
Write a Python function `_has_data_array` to type guard to check if result has data_array attribute.
Parameter... | def _has_data_array(result: Any) -> TypeGuard[Any]:
"""Type guard to check if result has data_array attribute.
Args:
result: The result object to check.
Returns:
True if result.result.data_array exists and is not None.
"""
return (
hasattr(result, "result")
and resu... | function_simple | 0 | {"cognitive_complexity": 1, "loc": 15, "code_loc": 6, "docstring_loc": 8, "function_name": "_has_data_array", "class_name": null, "qualname": "_has_data_array", "file_path": "lib/crewai-tools/src/crewai_tools/tools/databricks_query_tool/databricks_query_tool.py", "repo_id": "crewAIInc/crewAI", "has_docstring": true, "r... |
apache/airflow:shared/configuration/tests/configuration/test_parser.py:TestAirflowConfigParser.test_deprecated_options_lookup_disabled | # Context:
from configparser import ConfigParser
import pytest
from airflow_shared.configuration.exceptions import AirflowConfigException
class AirflowConfigParser(_SharedAirflowConfigParser): ...
class TestAirflowConfigParser:
def test_getboolean(self): ...
def test_getint(self): ...
def test_getfloat(se... | def test_deprecated_options_lookup_disabled(self):
"""Test deprecated options with lookup_from_deprecated=False"""
class TestParserWithDeprecated(AirflowConfigParser):
deprecated_options = {
("new_section", "new_key"): ("old_section", "old_key", "2.0.0"),
}
... | test | 1 | {"function_name": "test_deprecated_options_lookup_disabled", "class_name": "TestAirflowConfigParser", "qualname": "TestAirflowConfigParser.test_deprecated_options_lookup_disabled", "file_path": "shared/configuration/tests/configuration/test_parser.py", "repo_id": "apache/airflow", "loc": 20, "tested_modules": ["__futur... |
crewAIInc/crewAI:lib/crewai/tests/utilities/test_agent_utils.py:TestParseToolCallArgs.test_valid_json_with_nested_values | # Context:
from crewai.utilities.agent_utils import (
_asummarize_chunks,
_estimate_token_count,
_extract_summary_tags,
_format_messages_for_summary,
_split_messages_into_chunks,
convert_tools_to_openai_schema,
parse_tool_call_args,
summarize_messages,
)
class CalculatorInput(BaseModel)... | def test_valid_json_with_nested_values(self) -> None:
args_dict, error = parse_tool_call_args(
'{"query": "hello", "options": {"limit": 10}}', "search", "call_6"
)
assert error is None
assert args_dict == {"query": "hello", "options": {"limit": 10}} | test | 0 | {"function_name": "test_valid_json_with_nested_values", "class_name": "TestParseToolCallArgs", "qualname": "TestParseToolCallArgs.test_valid_json_with_nested_values", "file_path": "lib/crewai/tests/utilities/test_agent_utils.py", "repo_id": "crewAIInc/crewAI", "loc": 6, "tested_modules": ["__future__", "typing", "pydan... |
jax-ml/jax:jax/_src/pallas/pipelining/schedulers.py:check_async_done | # Context:
import operator
import jax
from jax import numpy as jnp
from jax._src.pallas.pipelining import internal
def compute_grid_indices(linear_index: jax.Array, grid_size: Sequence[int]): ...
def increment_grid(indices: Sequence[int | jax.Array], grid: Sequence[int], dynamic: bool): ...
class PipelineContext: ...
... | def check_async_done(stage: internal.PipelineStage,
scoreboard: Scoreboard,
num_itrs: int | jax.Array,
current_stage_counter: int | jax.Array,
dynamic=False) -> bool | jax.Array:
"""Returns whether the async done stage can run."""
a... | function_simple | 1 | {"cognitive_complexity": 5, "loc": 34, "code_loc": 22, "docstring_loc": 1, "function_name": "check_async_done", "class_name": null, "qualname": "check_async_done", "file_path": "jax/_src/pallas/pipelining/schedulers.py", "repo_id": "jax-ml/jax", "has_docstring": true, "runnable_level": "file_runnable"} |
ray-project/ray:python/ray/llm/_internal/batch/processor/utils.py:extract_resource_kwargs | # Context:
from typing import Any, Dict, Optional, Tuple, Union
def get_value_or_fallback(value: Any, fallback: Any) -> Any: ...
def normalize_cpu_stage_concurrency(concurrency: Optional[Union[int, Tuple[int, int]]]) -> Dict[str, int]: ...
def build_cpu_stage_map_kwargs(stage_cfg: _StageConfigBase) -> Dict[str, Any]: ... | def extract_resource_kwargs(
runtime_env: Optional[Dict[str, Any]],
num_cpus: Optional[float],
memory: Optional[float],
) -> Dict[str, Any]:
"""Extract non-None resource kwargs for map_batches."""
kwargs = {}
if runtime_env is not None:
kwargs["runtime_env"] = runtime_env
if num_cpus... | function_simple | 0 | {"cognitive_complexity": 3, "loc": 14, "code_loc": 8, "docstring_loc": 1, "function_name": "extract_resource_kwargs", "class_name": null, "qualname": "extract_resource_kwargs", "file_path": "python/ray/llm/_internal/batch/processor/utils.py", "repo_id": "ray-project/ray", "has_docstring": true, "runnable_level": "slib_... |
huggingface/diffusers:src/diffusers/modular_pipelines/components_manager.py:ComponentsManager.remove | # Context:
import torch
import gc
class CustomOffloadHook(ModelHook): ...
class UserCustomOffloadHook: ...
def custom_offload_with_hook(model_id: str, model: torch.nn.Module, execution_device: str | int | torch.device, offload_strategy: 'AutoOffloadStrategy' | None): ...
class AutoOffloadStrategy: ...
def summarize_di... | def remove(self, component_id: str = None):
"""
Remove a component from the ComponentsManager.
Args:
component_id (str): The ID of the component to remove
"""
if component_id not in self.components:
logger.warning(f"Component '{component_id}' not found in... | function_complex | 1 | {"cognitive_complexity": 12, "loc": 31, "code_loc": 20, "docstring_loc": 6, "function_name": "remove", "class_name": "ComponentsManager", "qualname": "ComponentsManager.remove", "file_path": "src/diffusers/modular_pipelines/components_manager.py", "repo_id": "huggingface/diffusers", "has_docstring": true, "runnable_lev... |
apache/airflow:providers/google/src/airflow/providers/google/cloud/hooks/gen_ai.py:GenAIGenerativeModelHook.count_tokens | # Context:
from airflow.providers.google.common.hooks.base_google import (
PROVIDE_PROJECT_ID,
GoogleBaseAsyncHook,
GoogleBaseHook,
)
from google.genai.types import (
BatchJob,
ContentListUnion,
ContentListUnionDict,
CountTokensConfigOrDict,
CountTokensResponse,
... | def count_tokens(
self,
location: str,
model: str,
contents: ContentListUnion | ContentListUnionDict,
config: CountTokensConfigOrDict | None = None,
project_id: str = PROVIDE_PROJECT_ID,
) -> CountTokensResponse:
"""
Use Count Tokens API to calculate t... | function_simple | 1 | {"cognitive_complexity": 0, "loc": 29, "code_loc": 7, "docstring_loc": 13, "function_name": "count_tokens", "class_name": "GenAIGenerativeModelHook", "qualname": "GenAIGenerativeModelHook.count_tokens", "file_path": "providers/google/src/airflow/providers/google/cloud/hooks/gen_ai.py", "repo_id": "apache/airflow", "has... |
vllm-project/vllm:tests/reasoning/test_gptoss_reasoning_parser.py:test_gptoss_is_reasoning_end | # Context:
import pytest
from vllm.reasoning import ReasoningParser
from vllm.reasoning.gptoss_reasoning_parser import GptOssReasoningParser
def gpt_oss_tokenizer(): ...
# Task:
Write a Python test function `test_gptoss_is_reasoning_end` to verify the behavior of `gptoss_is_reasoning_end`.
Module under test: transfo... | def test_gptoss_is_reasoning_end(
output,
is_reasoning_end,
gpt_oss_tokenizer,
):
output = gpt_oss_tokenizer.tokenize(output)
parser: ReasoningParser = GptOssReasoningParser(gpt_oss_tokenizer)
# Test is_reasoning_end
output_ids = gpt_oss_tokenizer.convert_tokens_to_ids(output)
actual_is... | test | 1 | {"function_name": "test_gptoss_is_reasoning_end", "class_name": null, "qualname": "test_gptoss_is_reasoning_end", "file_path": "tests/reasoning/test_gptoss_reasoning_parser.py", "repo_id": "vllm-project/vllm", "loc": 12, "tested_modules": ["transformers", "vllm.reasoning", "vllm.reasoning.gptoss_reasoning_parser"], "ha... |
crewAIInc/crewAI:lib/crewai-tools/src/crewai_tools/rag/loaders/postgres_loader.py:PostgresLoader.load | # Context:
from urllib.parse import urlparse
from psycopg2 import Error, connect
from psycopg2.extras import RealDictCursor
from crewai_tools.rag.base_loader import BaseLoader, LoaderResult
from crewai_tools.rag.source_content import SourceContent
class PostgresLoader(BaseLoader):
# Task:
Write a Python method `load`... | def load(self, source: SourceContent, **kwargs) -> LoaderResult: # type: ignore[override]
"""Load content from a PostgreSQL database table.
Args:
source: SQL query (e.g., "SELECT * FROM table_name")
**kwargs: Additional arguments including db_uri
Returns:
L... | function_complex | 0 | {"cognitive_complexity": 31, "loc": 86, "code_loc": 63, "docstring_loc": 9, "function_name": "load", "class_name": "PostgresLoader", "qualname": "PostgresLoader.load", "file_path": "lib/crewai-tools/src/crewai_tools/rag/loaders/postgres_loader.py", "repo_id": "crewAIInc/crewAI", "has_docstring": true, "runnable_level":... |
browser-use/browser-use:browser_use/code_use/utils.py:truncate_message_content | Write a Python function `truncate_message_content` to truncate message content to max_length characters for history.
Parameters: content: str, max_length: int
Returns: str | def truncate_message_content(content: str, max_length: int = 10000) -> str:
"""Truncate message content to max_length characters for history."""
if len(content) <= max_length:
return content
# Truncate and add marker
return content[:max_length] + f'\n\n[... truncated {len(content) - max_length} characters for his... | function_simple | 0 | {"cognitive_complexity": 1, "loc": 6, "code_loc": 3, "docstring_loc": 1, "function_name": "truncate_message_content", "class_name": null, "qualname": "truncate_message_content", "file_path": "browser_use/code_use/utils.py", "repo_id": "browser-use/browser-use", "has_docstring": true, "runnable_level": "self_contained"} |
ray-project/ray:python/ray/_common/tests/test_wait_for_condition.py:TestWaitForCondition.test_immediate_true_condition | # Context:
from ray._common.test_utils import async_wait_for_condition, wait_for_condition
class TestAsyncWaitForCondition: ...
class TestEdgeCases: ...
class TestWaitForCondition:
def test_condition_becomes_true(self): ...
def test_timeout_raises_runtime_error(self): ...
def test_condition_with_kwargs(se... | def test_immediate_true_condition(self):
"""Test that function returns immediately when condition is already true."""
def always_true():
return True
wait_for_condition(always_true, timeout=5) | test | 0 | {"function_name": "test_immediate_true_condition", "class_name": "TestWaitForCondition", "qualname": "TestWaitForCondition.test_immediate_true_condition", "file_path": "python/ray/_common/tests/test_wait_for_condition.py", "repo_id": "ray-project/ray", "loc": 7, "tested_modules": ["ray._common.test_utils"], "has_docstr... |
ray-project/ray:python/ray/serve/tests/unit/test_grpc_replica_result.py:TestSeparateLoop.test_streaming_blocked | # Context:
import asyncio
import pytest
class FakegRPCUnaryCall: ...
class FakegRPCStreamCall: ...
def create_asyncio_event_loop_in_thread(): ...
class TestSameLoop: ...
class TestSeparateLoop:
async def make_fake_unary_request(self, data, loop: asyncio.AbstractEventLoop): ...
async def make_fake_streaming_re... | async def test_streaming_blocked(self, create_asyncio_event_loop_in_thread):
"""Use threading event to block async generator, check everything works"""
loop, event = create_asyncio_event_loop_in_thread
fut = asyncio.run_coroutine_threadsafe(
self.make_fake_streaming_request(
... | test | 0 | {"function_name": "test_streaming_blocked", "class_name": "TestSeparateLoop", "qualname": "TestSeparateLoop.test_streaming_blocked", "file_path": "python/ray/serve/tests/unit/test_grpc_replica_result.py", "repo_id": "ray-project/ray", "loc": 23, "tested_modules": ["ray", "ray._common.test_utils", "ray.serve._private.co... |
ray-project/ray:python/ray/data/_internal/cluster_autoscaler/default_autoscaling_coordinator.py:handle_timeout_errors | # Context:
import functools
from typing import Callable, Dict, List, Optional
import ray
import inspect
class OngoingRequest: ...
class DefaultAutoscalingCoordinator(AutoscalingCoordinator): ...
class _AutoscalingCoordinatorActor: ...
def get_or_create_autoscaling_coordinator(): ...
# Task:
Write a Python function `h... | def handle_timeout_errors(
failure_counter_attr: str,
operation_name: str,
requester_id_param: str = "requester_id",
error_msg_suffix: Optional[str] = None,
on_error_return: Optional[Callable] = None,
):
"""Decorator to handle GetTimeoutError with consecutive failure tracking.
Args:
... | function_complex | 0 | {"cognitive_complexity": 23, "loc": 90, "code_loc": 48, "docstring_loc": 18, "function_name": "handle_timeout_errors", "class_name": null, "qualname": "handle_timeout_errors", "file_path": "python/ray/data/_internal/cluster_autoscaler/default_autoscaling_coordinator.py", "repo_id": "ray-project/ray", "has_docstring": t... |
apache/airflow:providers/google/tests/unit/google/cloud/operators/test_cloud_logging_sink.py:TestCloudLoggingUpdateSinksOperator.test_update_sink_raises_not_found | # Context:
from unittest import mock
import pytest
from google.api_core.exceptions import AlreadyExists, GoogleAPICallError, InvalidArgument, NotFound
from airflow.providers.google.cloud.operators.cloud_logging_sink import (
CloudLoggingCreateSinkOperator,
CloudLoggingDeleteSinkOperator,
CloudLoggingListSin... | def test_update_sink_raises_not_found(self, hook_mock, sink_config, update_mask):
hook_instance = hook_mock.return_value
hook_instance.get_sink.side_effect = NotFound("not found")
operator = CloudLoggingUpdateSinkOperator(
task_id=TASK_ID,
sink_name=SINK_NAME,
... | test | 1 | {"function_name": "test_update_sink_raises_not_found", "class_name": "TestCloudLoggingUpdateSinksOperator", "qualname": "TestCloudLoggingUpdateSinksOperator.test_update_sink_raises_not_found", "file_path": "providers/google/tests/unit/google/cloud/operators/test_cloud_logging_sink.py", "repo_id": "apache/airflow", "loc... |
huggingface/diffusers:tests/pipelines/qwenimage/test_qwenimage_img2img.py:QwenImageImg2ImgPipelineFastTests.test_inference | # Context:
class QwenImageImg2ImgPipelineFastTests(unittest.TestCase, PipelineTesterMixin):
pipeline_class = QwenImageImg2ImgPipeline
params = frozenset(["prompt", "image", "height", "width", "guidance_scale", "true_cfg_scale", "strength"])
batch_params = frozenset(["prompt", "image"])
image_params = f... | def test_inference(self):
device = "cpu"
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe.to(device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
image = pipe(**inputs).images
gen... | test | 1 | {"function_name": "test_inference", "class_name": "QwenImageImg2ImgPipelineFastTests", "qualname": "QwenImageImg2ImgPipelineFastTests.test_inference", "file_path": "tests/pipelines/qwenimage/test_qwenimage_img2img.py", "repo_id": "huggingface/diffusers", "loc": 12, "tested_modules": ["transformers", "diffusers", "testi... |
browser-use/browser-use:tests/ci/infrastructure/test_registry_validation.py:TestType1Pattern.test_type1_with_param_model | # Context:
from browser_use.agent.views import ActionResult
from browser_use.browser import BrowserSession
from browser_use.tools.registry.service import Registry
from browser_use.tools.registry.views import ActionModel as BaseActionModel
import inspect
from browser_use.tools.registry.views import ActionModel
class Te... | def test_type1_with_param_model(self):
"""Type 1: action(params: Model, special_args...) should work"""
registry = Registry()
class ClickAction(BaseActionModel):
index: int
delay: float = 0.0
@registry.action('Click element', param_model=ClickAction)
async def click_element(params: ClickAction, browse... | test | 0 | {"function_name": "test_type1_with_param_model", "class_name": "TestType1Pattern", "qualname": "TestType1Pattern.test_type1_with_param_model", "file_path": "tests/ci/infrastructure/test_registry_validation.py", "repo_id": "browser-use/browser-use", "loc": 26, "tested_modules": ["pydantic", "browser_use.agent.views", "b... |
langflow-ai/langflow:src/backend/tests/unit/api/v2/test_workflow.py:TestWorkflowBackgroundQueueing.test_background_execution_queue_exception | # Context:
from unittest.mock import AsyncMock, MagicMock, patch
from uuid import UUID, uuid4
from httpx import AsyncClient
from langflow.services.database.models.flow.model import Flow
from lfx.services.deps import session_scope
class TestWorkflowDeveloperAPIProtection: ...
class TestWorkflowErrorHandling: ...
class ... | async def test_background_execution_queue_exception(
self,
client: AsyncClient,
created_api_key,
mock_settings_dev_api_enabled, # noqa: ARG002
):
"""Test handling of exceptions during task queueing."""
flow_id = uuid4()
async with session_scope() as session:
... | test | 1 | {"function_name": "test_background_execution_queue_exception", "class_name": "TestWorkflowBackgroundQueueing", "qualname": "TestWorkflowBackgroundQueueing.test_background_execution_queue_exception", "file_path": "src/backend/tests/unit/api/v2/test_workflow.py", "repo_id": "langflow-ai/langflow", "loc": 38, "tested_modu... |
infiniflow/ragflow:tools/es-to-oceanbase-migration/src/es_ob_migration/migrator.py:ESToOceanBaseMigrator:class_doc | Write a class-level docstring for `ESToOceanBaseMigrator` which has methods: `__init__`, `migrate`, `_check_connections`, `_analyze_es_index`, `_migrate_data`. | RAGFlow-specific migration orchestrator.
This migrator is designed specifically for RAGFlow's data structure,
handling the fixed schema and vector embeddings correctly. | documentation | 1 | {"doc_type": "class", "class_name": "ESToOceanBaseMigrator", "file_path": "tools/es-to-oceanbase-migration/src/es_ob_migration/migrator.py", "repo_id": "infiniflow/ragflow", "char_length": 169, "methods": ["__init__", "migrate", "_check_connections", "_analyze_es_index", "_migrate_data", "get_schema_preview", "get_data... |
ray-project/ray:doc/source/serve/tutorials/model_composition_recsys/content/serve_recommendation_pipeline.py:ItemRankingModel.rank_items | # Context:
import asyncio
from typing import List, Dict
from ray import serve
class UserFeatureExtractor: ...
class RecommendationService: ...
class ItemRankingModel:
CANDIDATE_ITEMS = [f"item_{i}" for i in range(1000)]
def __init__(self):
# In production, this is your cloud storage path or model regi... | async def rank_items(
self,
user_features_batch: List[Dict[str, float]]
) -> List[List[Dict[str, any]]]:
"""Rank candidate items for a batch of users."""
# Simulate model inference time
await asyncio.sleep(0.05)
# In production, use vectorized batch inferenc... | function_simple | 0 | {"cognitive_complexity": 0, "loc": 12, "code_loc": 2, "docstring_loc": 1, "function_name": "rank_items", "class_name": "ItemRankingModel", "qualname": "ItemRankingModel.rank_items", "file_path": "doc/source/serve/tutorials/model_composition_recsys/content/serve_recommendation_pipeline.py", "repo_id": "ray-project/ray",... |
google/langextract:langextract/resolver.py:Resolver.extract_ordered_extractions | # Context:
from collections.abc import Iterator, Mapping, Sequence
import operator
from absl import logging
from langextract.core import data
from langextract.core import format_handler as fh
class AbstractResolver(abc.ABC): ...
class ResolverParsingError(exceptions.LangExtractError): ...
class WordAligner: ...
def _t... | def extract_ordered_extractions(
self,
extraction_data: Sequence[Mapping[str, fh.ExtractionValueType]],
) -> Sequence[data.Extraction]:
"""Extracts and orders extraction data based on their associated indexes.
This function processes a list of dictionaries, each containing pairs of
extraction... | function_complex | 1 | {"cognitive_complexity": 37, "loc": 100, "code_loc": 63, "docstring_loc": 23, "function_name": "extract_ordered_extractions", "class_name": "Resolver", "qualname": "Resolver.extract_ordered_extractions", "file_path": "langextract/resolver.py", "repo_id": "google/langextract", "has_docstring": true, "runnable_level": "p... |
crewAIInc/crewAI:lib/crewai/src/crewai/hooks/wrappers.py:AfterToolCallHookMethod:class_doc | Write a class-level docstring for `AfterToolCallHookMethod` which has methods: `__init__`, `__call__`, `__get__`. | Wrapper for methods marked as after_tool_call hooks within @CrewBase classes. | documentation | 0 | {"doc_type": "class", "class_name": "AfterToolCallHookMethod", "file_path": "lib/crewai/src/crewai/hooks/wrappers.py", "repo_id": "crewAIInc/crewAI", "char_length": 77, "methods": ["__init__", "__call__", "__get__"]} |
browser-use/browser-use:examples/features/add_image_context.py:module_doc | Write a module-level docstring for the Python module `add_image_context` which contains function `image_to_base64`, function `create_sample_images`. | Show how to use sample_images to add image context for your task | documentation | 0 | {"doc_type": "module", "module_name": "add_image_context", "file_path": "examples/features/add_image_context.py", "repo_id": "browser-use/browser-use", "char_length": 64} |
unclecode/crawl4ai:deploy/docker/tests/test_security_fixes.py:TestURLValidation.test_raw_url_allowed_when_enabled | # Context:
class TestHookBuiltins(unittest.TestCase): ...
class TestHooksEnabled(unittest.TestCase): ...
class TestURLValidation(unittest.TestCase):
def setUp(self): ...
def validate_url_scheme(self, url: str, allow_raw: bool) -> bool: ...
def test_file_url_blocked(self): ...
def test_file_url_blocked... | def test_raw_url_allowed_when_enabled(self):
"""raw: URLs must be allowed when allow_raw=True."""
self.assertTrue(self.validate_url_scheme("raw:<html></html>", allow_raw=True))
self.assertTrue(self.validate_url_scheme("raw://<html></html>", allow_raw=True)) | test | 1 | {"function_name": "test_raw_url_allowed_when_enabled", "class_name": "TestURLValidation", "qualname": "TestURLValidation.test_raw_url_allowed_when_enabled", "file_path": "deploy/docker/tests/test_security_fixes.py", "repo_id": "unclecode/crawl4ai", "loc": 4, "tested_modules": [], "has_docstring": true, "runnable_level"... |
fastapi/fastapi:tests/test_request_params/test_header/test_optional_list.py:test_optional_list_alias_and_validation_alias_schema | # Context:
import pytest
from inline_snapshot import snapshot
async def read_optional_list_str(p: Annotated[list[str] | None, Header()]): ...
class HeaderModelOptionalListStr(BaseModel): ...
async def read_model_optional_list_str(p: Annotated[HeaderModelOptionalListStr, Header()]): ...
def test_optional_list_str_schem... | def test_optional_list_alias_and_validation_alias_schema(path: str):
assert app.openapi()["paths"][path]["get"]["parameters"] == snapshot(
[
{
"required": False,
"schema": {
"anyOf": [
{"items": {"type": "string"}, "type... | test | 1 | {"function_name": "test_optional_list_alias_and_validation_alias_schema", "class_name": null, "qualname": "test_optional_list_alias_and_validation_alias_schema", "file_path": "tests/test_request_params/test_header/test_optional_list.py", "repo_id": "fastapi/fastapi", "loc": 17, "tested_modules": ["typing", "fastapi", "... |
binary-husky/gpt_academic:crazy_functions/review_fns/paper_processor/paper_llm_ranker.py:PaperLLMRanker.rank_papers | # Context:
from typing import List, Dict
from crazy_functions.review_fns.data_sources.base_source import PaperMetadata
from crazy_functions.review_fns.query_analyzer import SearchCriteria
class PaperLLMRanker:
def __init__(self, llm_kwargs: Dict = None):
self.ranker = BGELLMRanker(llm_kwargs=llm_kwargs)
... | def rank_papers(
self,
query: str,
papers: List[PaperMetadata],
search_criteria: SearchCriteria = None,
top_k: int = 40,
use_rerank: bool = False,
pre_filter_ratio: float = 0.5,
max_papers: int = 150
) -> List[PaperMetadata]:
"""对论文进行重排序"""
... | function_complex | 1 | {"cognitive_complexity": 60, "loc": 163, "code_loc": 109, "docstring_loc": 1, "function_name": "rank_papers", "class_name": "PaperLLMRanker", "qualname": "PaperLLMRanker.rank_papers", "file_path": "crazy_functions/review_fns/paper_processor/paper_llm_ranker.py", "repo_id": "binary-husky/gpt_academic", "has_docstring": ... |
ray-project/ray:python/ray/data/tests/datasource/test_turbopuffer_datasink.py:TestSerialization.test_preserves_namespace_column_configuration | # Context:
import pickle
def mock_turbopuffer_module(monkeypatch): ...
def sink(): ...
def mock_client(): ...
def sample_table(): ...
def make_sink(**kwargs) -> TurbopufferDatasink: ...
class TestConstructorValidation: ...
class TestClientInitialization: ...
class TestArrowTablePreparation: ...
class TestSingleNamespa... | def test_preserves_namespace_column_configuration(self, mock_turbopuffer_module):
"""namespace_column configuration survives pickle round-trip."""
sink = make_sink(namespace=None, namespace_column="tenant")
pickled = pickle.dumps(sink)
unpickled = pickle.loads(pickled)
assert un... | test | 0 | {"function_name": "test_preserves_namespace_column_configuration", "class_name": "TestSerialization", "qualname": "TestSerialization.test_preserves_namespace_column_configuration", "file_path": "python/ray/data/tests/datasource/test_turbopuffer_datasink.py", "repo_id": "ray-project/ray", "loc": 9, "tested_modules": ["t... |
langflow-ai/langflow:src/backend/tests/unit/test_build_component_index.py:TestBuildComponentIndexScript.test_build_script_creates_valid_structure | # Context:
from pathlib import Path
from unittest.mock import patch
import pytest
import sys
class TestBuildComponentIndexScript:
def test_build_script_minifies_json(self, tmp_path): ...
def test_build_script_sha256_integrity(self): ...
def test_build_script_handles_import_errors(self): ...
# Task:
Write ... | def test_build_script_creates_valid_structure(self):
"""Test that the build script creates a valid index structure."""
import importlib.util
import sys
# Get path to build script
script_path = Path(__file__).parent.parent.parent.parent / "scripts" / "build_component_index.py"
... | test | 1 | {"function_name": "test_build_script_creates_valid_structure", "class_name": "TestBuildComponentIndexScript", "qualname": "TestBuildComponentIndexScript.test_build_script_creates_valid_structure", "file_path": "src/backend/tests/unit/test_build_component_index.py", "repo_id": "langflow-ai/langflow", "loc": 38, "tested_... |
ray-project/ray:python/ray/train/v2/tests/test_config.py:test_scaling_config_validation | # Context:
import pytest
from ray.train import RunConfig, ScalingConfig
def test_scaling_config_accelerator_type(): ...
def test_scaling_config_tpu_min_workers_multiple(): ...
def test_storage_filesystem_repr(): ...
def test_scaling_config_default_workers(): ...
# Task:
Write a Python test function `test_scaling_conf... | def test_scaling_config_validation():
assert ScalingConfig(
num_workers=2, use_gpu=True, resources_per_worker={"CPU": 1}
).total_resources == {"CPU": 2, "GPU": 2}
with pytest.raises(ValueError, match="`use_gpu` is False but `GPU` was found in"):
ScalingConfig(num_workers=2, use_gpu=False, r... | test | 0 | {"function_name": "test_scaling_config_validation", "class_name": null, "qualname": "test_scaling_config_validation", "file_path": "python/ray/train/v2/tests/test_config.py", "repo_id": "ray-project/ray", "loc": 30, "tested_modules": ["ray.train"], "has_docstring": false, "runnable_level": "plib_runnable"} |
vllm-project/vllm:tests/distributed/test_weight_transfer.py:TestIPCWeightTransferUpdateInfoValidation.test_valid_update_info_from_pickled | # Context:
import base64
import pickle
import pytest
import torch
from torch.multiprocessing.reductions import reduce_tensor
from vllm.distributed.weight_transfer.ipc_engine import (
IPCWeightTransferEngine,
IPCWeightTransferInitInfo,
IPCWeightTransferUpdateInfo,
)
def create_mock_parallel_config(rank: int... | def test_valid_update_info_from_pickled(self):
"""Test creating IPCWeightTransferUpdateInfo from pickled handles."""
if torch.cuda.device_count() < 1:
pytest.skip("Need at least 1 GPU for this test")
dummy_tensor = torch.ones(10, 10, device="cuda:0")
ipc_handle = reduce_tens... | test | 1 | {"function_name": "test_valid_update_info_from_pickled", "class_name": "TestIPCWeightTransferUpdateInfoValidation", "qualname": "TestIPCWeightTransferUpdateInfoValidation.test_valid_update_info_from_pickled", "file_path": "tests/distributed/test_weight_transfer.py", "repo_id": "vllm-project/vllm", "loc": 20, "tested_mo... |
Comfy-Org/ComfyUI:comfy_api/latest/_ui.py:ImageSaveHelper._create_animated_png_metadata | # Context:
import json
from PIL.PngImagePlugin import PngInfo
from comfy.cli_args import args
from ._io import ComfyNode, FolderType, Image, _UIOutput
class SavedResult(dict): ...
class SavedImages(_UIOutput): ...
class SavedAudios(_UIOutput): ...
def _get_directory_by_folder_type(folder_type: FolderType) -> str: ...
... | def _create_animated_png_metadata(cls: type[ComfyNode] | None) -> PngInfo | None:
"""Creates a PngInfo object with prompt and extra_pnginfo for animated PNGs (APNG)."""
if args.disable_metadata or cls is None or not cls.hidden:
return None
metadata = PngInfo()
if cls.hidden.p... | function_complex | 1 | {"cognitive_complexity": 6, "loc": 23, "code_loc": 21, "docstring_loc": 1, "function_name": "_create_animated_png_metadata", "class_name": "ImageSaveHelper", "qualname": "ImageSaveHelper._create_animated_png_metadata", "file_path": "comfy_api/latest/_ui.py", "repo_id": "Comfy-Org/ComfyUI", "has_docstring": true, "runna... |
run-llama/llama_index:llama-index-integrations/vector_stores/llama-index-vector-stores-azurepostgresql/llama_index/vector_stores/azure_postgres/common/_connection.py:check_connection | # Context:
import time
from psycopg import Connection, sql
from psycopg.rows import dict_row
from ._shared import (
TOKEN_CREDENTIAL_SCOPE,
BaseConnectionInfo,
BasicAuth,
Extension,
get_username_password,
)
class ConnectionInfo(BaseConnectionInfo): ...
def create_extensions(required_extensions: lis... | def check_connection(conn: Connection, /, required_extensions: list[Extension] = []):
"""Check if the connection to Azure Database for PostgreSQL is valid and required extensions are installed.
:param conn: Connection to the Azure Database for PostgreSQL.
:type conn: Connection
:param required_extensio... | function_complex | 1 | {"cognitive_complexity": 13, "loc": 55, "code_loc": 45, "docstring_loc": 8, "function_name": "check_connection", "class_name": null, "qualname": "check_connection", "file_path": "llama-index-integrations/vector_stores/llama-index-vector-stores-azurepostgresql/llama_index/vector_stores/azure_postgres/common/_connection.... |
github/spec-kit:src/specify_cli/extensions.py:version_satisfies | # Context:
from packaging import version as pkg_version
from packaging.specifiers import SpecifierSet, InvalidSpecifier
class ExtensionError(Exception): ...
class ValidationError(ExtensionError): ...
class CompatibilityError(ExtensionError): ...
class ExtensionManifest: ...
class ExtensionRegistry: ...
class Extension... | def version_satisfies(current: str, required: str) -> bool:
"""Check if current version satisfies required version specifier.
Args:
current: Current version (e.g., "0.1.5")
required: Required version specifier (e.g., ">=0.1.0,<2.0.0")
Returns:
True if version satisfies requirement
... | function_simple | 0 | {"cognitive_complexity": 1, "loc": 16, "code_loc": 6, "docstring_loc": 9, "function_name": "version_satisfies", "class_name": null, "qualname": "version_satisfies", "file_path": "src/specify_cli/extensions.py", "repo_id": "github/spec-kit", "has_docstring": true, "runnable_level": "project_runnable"} |
streamlit/streamlit:lib/tests/streamlit/web/server/starlette/starlette_websocket_test.py:TestStarletteSessionClientClientContext:class_doc | Write a class-level docstring for `TestStarletteSessionClientClientContext` which has methods: `test_client_context_returns_starlette_context`. | Tests for client_context property on StarletteSessionClient. | documentation | 1 | {"doc_type": "class", "class_name": "TestStarletteSessionClientClientContext", "file_path": "lib/tests/streamlit/web/server/starlette/starlette_websocket_test.py", "repo_id": "streamlit/streamlit", "char_length": 60, "methods": ["test_client_context_returns_starlette_context"]} |
ray-project/ray:python/ray/data/stats.py:DatasetSummary._extract_column_from_table | # Context:
from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple, Union
import pyarrow as pa
class _DtypeAggregators: ...
def _numerical_aggregators(column: str) -> List[AggregateFnV2]: ...
def _temporal_aggregators(column: str) -> List[AggregateFnV2]: ...
def _basic_aggregators(column: str) -> ... | def _extract_column_from_table(
self, table: pa.Table, column: str
) -> Optional[dict]:
"""Extract a column from a PyArrow table if it exists.
Args:
table: PyArrow table to extract from
column: Column name to extract
Returns:
DataFrame with 'stat... | function_simple | 0 | {"cognitive_complexity": 1, "loc": 17, "code_loc": 4, "docstring_loc": 9, "function_name": "_extract_column_from_table", "class_name": "DatasetSummary", "qualname": "DatasetSummary._extract_column_from_table", "file_path": "python/ray/data/stats.py", "repo_id": "ray-project/ray", "has_docstring": true, "runnable_level"... |
vllm-project/vllm:vllm/model_executor/layers/fused_moe/config.py:_get_config_dtype_str | # Context:
import torch
def _quant_flags_to_group_shape(quant_dtype: torch.dtype | str | None, per_act_token_quant: bool, per_out_ch_quant: bool, block_shape: list[int] | None) -> tuple[GroupShape | None, GroupShape | None]: ...
class RoutingMethodType(IntEnum): ...
def get_routing_method_type(scoring_func: str, top_k... | def _get_config_dtype_str(
dtype: torch.dtype,
use_fp8_w8a8: bool = False,
use_fp8_w8a16: bool = False,
use_int8_w8a16: bool = False,
use_int4_w4a16: bool = False,
ocp_mx_scheme: str | None = None,
) -> str | None:
"""
Return a string used to construct the filename that contains the
... | function_complex | 1 | {"cognitive_complexity": 6, "loc": 31, "code_loc": 13, "docstring_loc": 5, "function_name": "_get_config_dtype_str", "class_name": null, "qualname": "_get_config_dtype_str", "file_path": "vllm/model_executor/layers/fused_moe/config.py", "repo_id": "vllm-project/vllm", "has_docstring": true, "runnable_level": "plib_runn... |
RVC-Boss/GPT-SoVITS:GPT_SoVITS/eres2net/kaldi.py:_get_waveform_and_window_properties | # Context:
from typing import Tuple
from torch import Tensor
def _get_epsilon(device, dtype): ...
def _next_power_of_2(x: int) -> int: ...
def _get_strided(waveform: Tensor, window_size: int, window_shift: int, snip_edges: bool) -> Tensor: ...
def _feature_window_function(window_type: str, window_size: int, blackman_c... | def _get_waveform_and_window_properties(
waveform: Tensor,
channel: int,
sample_frequency: float,
frame_shift: float,
frame_length: float,
round_to_power_of_two: bool,
preemphasis_coefficient: float,
) -> Tuple[Tensor, int, int, int]:
r"""Gets the waveform and window properties"""
ch... | function_simple | 1 | {"cognitive_complexity": 1, "loc": 27, "code_loc": 16, "docstring_loc": 1, "function_name": "_get_waveform_and_window_properties", "class_name": null, "qualname": "_get_waveform_and_window_properties", "file_path": "GPT_SoVITS/eres2net/kaldi.py", "repo_id": "RVC-Boss/GPT-SoVITS", "has_docstring": true, "runnable_level"... |
keras-team/keras:keras/src/layers/pooling/adaptive_pooling1d_test.py:AdaptivePooling1DLayerTest.test_average_pooling_get_config | # Context:
from keras.src import layers
class AdaptivePooling1DLayerTest(testing.TestCase):
def _run_layer_test(self, layer_class, x_np, output_size, data_format): ...
def test_average_pooling_basic_shapes(self): ...
def test_max_pooling_basic_shapes(self): ...
def test_average_pooling_channels_last(se... | def test_average_pooling_get_config(self):
"""Test get_config() serialization for AdaptiveAveragePooling1D."""
layer = layers.AdaptiveAveragePooling1D(
output_size=32, data_format="channels_first"
)
config = layer.get_config()
self.assertEqual(config["output_size"], (... | test | 1 | {"function_name": "test_average_pooling_get_config", "class_name": "AdaptivePooling1DLayerTest", "qualname": "AdaptivePooling1DLayerTest.test_average_pooling_get_config", "file_path": "keras/src/layers/pooling/adaptive_pooling1d_test.py", "repo_id": "keras-team/keras", "loc": 8, "tested_modules": ["keras.src", "keras.s... |
langflow-ai/langflow:src/backend/tests/unit/core/test_celeryconfig.py:TestCeleryConfigStructure.test_result_backend_contains_host | # Context:
from langflow.core import celeryconfig
class TestCeleryConfigAcceptContent: ...
class TestCeleryConfigVariables: ...
class TestCeleryConfigStructure:
def test_broker_url_contains_protocol(self): ...
def test_result_backend_contains_protocol(self): ...
def test_broker_url_contains_host(self): ..... | def test_result_backend_contains_host(self):
"""Test that result_backend contains a host component."""
result_backend = celeryconfig.result_backend
# Remove protocol part
if "://" in result_backend:
host_part = result_backend.split("://")[1]
assert len(host_part) ... | test | 1 | {"function_name": "test_result_backend_contains_host", "class_name": "TestCeleryConfigStructure", "qualname": "TestCeleryConfigStructure.test_result_backend_contains_host", "file_path": "src/backend/tests/unit/core/test_celeryconfig.py", "repo_id": "langflow-ai/langflow", "loc": 7, "tested_modules": ["langflow.core"], ... |
ray-project/ray:python/ray/data/expressions.py:Expr.__or__ | # Context:
from typing import (
TYPE_CHECKING,
Any,
Callable,
Dict,
Generic,
List,
Optional,
Tuple,
Type,
TypeVar,
Union,
)
class Operation(Enum): ...
class _ExprVisitor(ABC, Generic[T]): ...
class _PyArrowExpressionVisitor(_ExprVisitor['pyarrow.compute.Expression']): ...
cl... | def __or__(self, other: Any) -> "Expr":
"""Logical OR operator (|)."""
return self._bin(other, Operation.OR) | function_simple | 0 | {"cognitive_complexity": 0, "loc": 3, "code_loc": 1, "docstring_loc": 1, "function_name": "__or__", "class_name": "Expr", "qualname": "Expr.__or__", "file_path": "python/ray/data/expressions.py", "repo_id": "ray-project/ray", "has_docstring": true, "runnable_level": "file_runnable"} |
langchain-ai/langchain:libs/langchain/langchain_classic/evaluation/embedding_distance/base.py:_EmbeddingDistanceChainMixin._chebyshev_distance | # Context:
from typing import Any
from scipy.spatial.distance import chebyshev
def _import_numpy() -> Any: ...
def _check_numpy() -> bool: ...
def _embedding_factory() -> Embeddings: ...
class EmbeddingDistance(str, Enum): ...
class EmbeddingDistanceEvalChain(_EmbeddingDistanceChainMixin, StringEvaluator): ...
class P... | def _chebyshev_distance(a: Any, b: Any) -> Any:
"""Compute the Chebyshev distance between two vectors.
Args:
a (np.ndarray): The first vector.
b (np.ndarray): The second vector.
Returns:
np.floating: The Chebyshev distance.
"""
try:
... | function_simple | 1 | {"cognitive_complexity": 3, "loc": 20, "code_loc": 8, "docstring_loc": 9, "function_name": "_chebyshev_distance", "class_name": "_EmbeddingDistanceChainMixin", "qualname": "_EmbeddingDistanceChainMixin._chebyshev_distance", "file_path": "libs/langchain/langchain_classic/evaluation/embedding_distance/base.py", "repo_id"... |
ray-project/ray:python/ray/tune/tests/test_env_callbacks.py:test_no_env_variable | # Context:
import os
from ray.tune.constants import RAY_TUNE_CALLBACKS_ENV_VAR
from ray.tune.utils.callback import Callback, _initialize_env_callbacks
class MockCallback(Callback): ...
def test_env_callbacks_loading(mock_import, env_value, expected_callback_count): ...
def test_callback_loading_errors(mock_import, env... | def test_no_env_variable():
"""Test that no callbacks are loaded when environment variable is not set."""
if RAY_TUNE_CALLBACKS_ENV_VAR in os.environ:
del os.environ[RAY_TUNE_CALLBACKS_ENV_VAR]
callbacks = _initialize_env_callbacks()
assert len(callbacks) == 0 | test | 0 | {"function_name": "test_no_env_variable", "class_name": null, "qualname": "test_no_env_variable", "file_path": "python/ray/tune/tests/test_env_callbacks.py", "repo_id": "ray-project/ray", "loc": 7, "tested_modules": ["ray.tune.constants", "ray.tune.utils.callback"], "has_docstring": true, "runnable_level": "plib_runnab... |
crewAIInc/crewAI:lib/crewai/tests/rag/embeddings/test_backward_compatibility.py:TestLegacyConfigurationFormats.test_legacy_google_with_model_key | # Context:
from crewai.rag.embeddings.providers.google.generative_ai import GenerativeAiProvider
class TestGoogleProviderAlias: ...
class TestModelKeyBackwardCompatibility: ...
class TestTaskTypeConfiguration: ...
class TestFactoryBackwardCompatibility: ...
class TestDocumentationCodeSnippets: ...
class TestLegacyCon... | def test_legacy_google_with_model_key(self):
"""Test legacy Google config using 'model' instead of 'model_name'."""
provider = GenerativeAiProvider(
api_key="test-key",
model="text-embedding-005",
task_type="retrieval_document",
)
assert provider.model... | test | 0 | {"function_name": "test_legacy_google_with_model_key", "class_name": "TestLegacyConfigurationFormats", "qualname": "TestLegacyConfigurationFormats.test_legacy_google_with_model_key", "file_path": "lib/crewai/tests/rag/embeddings/test_backward_compatibility.py", "repo_id": "crewAIInc/crewAI", "loc": 9, "tested_modules":... |
binary-husky/gpt_academic:crazy_functions/review_fns/data_sources/pubmed_source.py:PubMedSource.get_latest_papers | # Context:
from typing import List, Optional, Dict, Union
from crazy_functions.review_fns.data_sources.base_source import DataSource, PaperMetadata
async def example_usage(): ...
class PubMedSource(DataSource):
API_KEYS = [
def __init__(self, api_key: str = None):
"""初始化
Args:
api... | async def get_latest_papers(
self,
days: int = 7,
limit: int = 100
) -> List[PaperMetadata]:
"""获取最新论文
Args:
days: 最近几天的论文
limit: 返回结果数量限制
Returns:
最新论文列表
"""
query = f"last {days} days[dp]"
return await se... | function_simple | 1 | {"cognitive_complexity": 0, "loc": 16, "code_loc": 2, "docstring_loc": 9, "function_name": "get_latest_papers", "class_name": "PubMedSource", "qualname": "PubMedSource.get_latest_papers", "file_path": "crazy_functions/review_fns/data_sources/pubmed_source.py", "repo_id": "binary-husky/gpt_academic", "has_docstring": tr... |
infiniflow/ragflow:test/testcases/test_web_api/test_kb_app/test_kb_tags_meta.py:TestKbTagsMeta.test_list_tags | # Context:
import pytest
from common import (
delete_knowledge_graph,
kb_basic_info,
kb_get_meta,
kb_update_metadata_setting,
knowledge_graph,
list_tags,
list_tags_from_kbs,
rename_tags,
rm_tags,
update_chunk,
)
def _wait_for_tag(auth, kb_id, tag, timeout): ...
def _seed_tag(aut... | def test_list_tags(self, WebApiAuth, add_dataset):
kb_id = add_dataset
res = list_tags(WebApiAuth, kb_id)
assert res["code"] == 0, res
assert isinstance(res["data"], list), res | test | 1 | {"function_name": "test_list_tags", "class_name": "TestKbTagsMeta", "qualname": "TestKbTagsMeta.test_list_tags", "file_path": "test/testcases/test_web_api/test_kb_app/test_kb_tags_meta.py", "repo_id": "infiniflow/ragflow", "loc": 5, "tested_modules": ["common", "configs", "libs.auth", "utils"], "has_docstring": false, ... |
ray-project/ray:python/ray/data/tests/test_map_batches.py:test_map_batches_combine_empty_blocks | # Context:
import ray
def process_timestamp_data(row): ...
def process_timestamp_data_batch_arrow(batch: pa.Table) -> pa.Table: ...
def process_timestamp_data_batch_pandas(batch: pd.DataFrame) -> pd.DataFrame: ...
def test_map_batches_basic(ray_start_regular_shared, tmp_path, restore_data_context, target_max_block_siz... | def test_map_batches_combine_empty_blocks(
ray_start_regular_shared, target_max_block_size_infinite_or_default
):
xs = [x % 3 for x in list(range(100))]
# ds1 has 1 block which contains 100 rows.
ds1 = ray.data.from_items(xs).repartition(1).sort("item").map_batches(lambda x: x)
assert ds1._block_nu... | test | 0 | {"function_name": "test_map_batches_combine_empty_blocks", "class_name": null, "qualname": "test_map_batches_combine_empty_blocks", "file_path": "python/ray/data/tests/test_map_batches.py", "repo_id": "ray-project/ray", "loc": 22, "tested_modules": ["typing", "ray.data._internal.arrow_ops.transform_pyarrow", "ray.data.... |
jax-ml/jax:tests/pallas/mgpu_examples_test.py:module_doc | Write a module-level docstring for the Python module `mgpu_examples_test` which contains class `TuningConfig`, function `matmul0`, function `matmul1`, function `matmul2`, function `matmul3`. | Tests for examples from Pallas:MGPU documentation. | documentation | 1 | {"doc_type": "module", "module_name": "mgpu_examples_test", "file_path": "tests/pallas/mgpu_examples_test.py", "repo_id": "jax-ml/jax", "char_length": 50} |
langflow-ai/langflow:src/backend/tests/unit/components/processing/test_text_operations_component.py:TestTextOperationsExtract.test_extract_invalid_regex | # Context:
import pytest
from lfx.components.processing.text_operations import TextOperations
class TestTextOperationsComponent(ComponentTestBaseWithoutClient): ...
class TestTextOperationsWordCount: ...
class TestTextOperationsCaseConversion: ...
class TestTextOperationsReplace: ...
class TestTextOperationsHead: ...
... | def test_extract_invalid_regex(self):
"""Test extraction with invalid regex raises ValueError (Bug #3 fix)."""
component = TextOperations()
component.extract_pattern = "[invalid"
component.max_matches = 10
with pytest.raises(ValueError, match="Invalid regex pattern"):
... | test | 1 | {"function_name": "test_extract_invalid_regex", "class_name": "TestTextOperationsExtract", "qualname": "TestTextOperationsExtract.test_extract_invalid_regex", "file_path": "src/backend/tests/unit/components/processing/test_text_operations_component.py", "repo_id": "langflow-ai/langflow", "loc": 8, "tested_modules": ["l... |
666ghj/BettaFish:ReportEngine/renderers/pdf_layout_optimizer.py:PDFLayoutOptimizer.__init__ | # Context:
from typing import Any, Dict, List, Optional, Tuple
class KPICardLayout: ...
class CalloutLayout: ...
class TableLayout: ...
class ChartLayout: ...
class GridLayout: ...
class DataBlockLayout: ...
class PageLayout: ...
class PDFLayoutConfig: ...
class PDFLayoutOptimizer:
CHAR_WIDTH_FACTOR = {
def _... | def __init__(self, config: Optional[PDFLayoutConfig] = None):
"""
初始化优化器
参数:
config: 布局配置，如果为None则使用默认配置
"""
self.config = config or self._create_default_config()
self.optimization_log = [] | function_simple | 1 | {"cognitive_complexity": 1, "loc": 9, "code_loc": 2, "docstring_loc": 6, "function_name": "__init__", "class_name": "PDFLayoutOptimizer", "qualname": "PDFLayoutOptimizer.__init__", "file_path": "ReportEngine/renderers/pdf_layout_optimizer.py", "repo_id": "666ghj/BettaFish", "has_docstring": true, "runnable_level": "fil... |
crewAIInc/crewAI:lib/crewai/tests/llms/openai/test_openai.py:test_openai_get_client_params_with_env_var | # Context:
import os
from unittest.mock import patch, MagicMock
from crewai.llms.providers.openai.completion import OpenAICompletion, ResponsesAPIResult
from crewai.llms.providers.openai.completion import OpenAICompletion
def test_openai_completion_is_used_when_openai_provider(): ...
def test_openai_completion_is_used... | def test_openai_get_client_params_with_env_var():
"""
Test that _get_client_params uses OPENAI_BASE_URL environment variable as fallback
"""
with patch.dict(os.environ, {
"OPENAI_BASE_URL": "https://env.openai.com/v1",
}):
llm = OpenAICompletion(model="gpt-4o")
client_params ... | test | 0 | {"function_name": "test_openai_get_client_params_with_env_var", "class_name": null, "qualname": "test_openai_get_client_params_with_env_var", "file_path": "lib/crewai/tests/llms/openai/test_openai.py", "repo_id": "crewAIInc/crewAI", "loc": 10, "tested_modules": ["typing", "crewai.llm", "crewai.llms.providers.openai.com... |
vllm-project/vllm:tests/entrypoints/openai/test_completion_with_function_calling.py:test_named_tool_use | # Context:
import json
import jsonschema
import openai # use the official client for correctness check
import pytest
def server(): ...
async def client(server): ...
async def test_function_tool_use(client: openai.AsyncOpenAI, model_name: str, stream: bool, tool_choice: str | dict, enable_thinking: bool): ...
def k2_s... | async def test_named_tool_use(
client: openai.AsyncOpenAI,
sample_json_schema,
):
messages = [
{"role": "system", "content": "you are a helpful assistant"},
{
"role": "user",
"content": (
"Give an example JSON for an employee profile using the specifie... | test | 1 | {"function_name": "test_named_tool_use", "class_name": null, "qualname": "test_named_tool_use", "file_path": "tests/entrypoints/openai/test_completion_with_function_calling.py", "repo_id": "vllm-project/vllm", "loc": 75, "tested_modules": ["utils"], "has_docstring": false, "runnable_level": "project_runnable"} |
exo-explore/exo:src/exo/utils/info_gatherer/info_gatherer.py:_get_bridge_members | # Context:
import anyio
from loguru import logger
async def _get_thunderbolt_devices() -> set[str] | None: ...
async def _get_bridge_services() -> dict[str, str] | None: ...
async def _find_thunderbolt_bridge(bridge_services: dict[str, str], thunderbolt_devices: set[str]) -> str | None: ...
async def _is_service_enabl... | async def _get_bridge_members(bridge_device: str) -> set[str]:
"""Get member interfaces of a bridge device via ifconfig."""
result = await anyio.run_process(
["ifconfig", bridge_device],
check=False,
)
if result.returncode != 0:
logger.debug(f"ifconfig {bridge_device} failed with... | function_complex | 0 | {"cognitive_complexity": 7, "loc": 20, "code_loc": 16, "docstring_loc": 1, "function_name": "_get_bridge_members", "class_name": null, "qualname": "_get_bridge_members", "file_path": "src/exo/utils/info_gatherer/info_gatherer.py", "repo_id": "exo-explore/exo", "has_docstring": true, "runnable_level": "project_runnable"... |
apache/airflow:task-sdk/tests/task_sdk/test_crypto.py:TestRealFernet.test_rotate_reencrypt_with_primary_key | # Context:
from cryptography.fernet import Fernet
from airflow.sdk.crypto import _NullFernet, _RealFernet, get_fernet
from cryptography.fernet import MultiFernet
class TestNullFernet: ...
class TestGetFernet: ...
class TestRealFernet:
def test_encryption(self): ...
# Task:
Write a Python test method `test_rotate... | def test_rotate_reencrypt_with_primary_key(self):
"""rotate() should re-encrypt data with the primary key."""
from cryptography.fernet import MultiFernet
key1 = Fernet.generate_key()
key2 = Fernet.generate_key()
# encrypt with key1 only
encrypted_with_key1 = Fernet(key1... | test | 1 | {"function_name": "test_rotate_reencrypt_with_primary_key", "class_name": "TestRealFernet", "qualname": "TestRealFernet.test_rotate_reencrypt_with_primary_key", "file_path": "task-sdk/tests/task_sdk/test_crypto.py", "repo_id": "apache/airflow", "loc": 20, "tested_modules": ["__future__", "cryptography.fernet", "airflow... |
Comfy-Org/ComfyUI:tests-unit/comfy_api_test/input_impl_test.py:test_container_to_output_format_empty_string | # Context:
from comfy_api.input_impl.video_types import (
container_to_output_format,
get_open_write_kwargs,
)
def test_container_to_output_format_none(): ...
def test_container_to_output_format_comma_separated(): ...
def test_container_to_output_format_single(): ...
def test_get_open_write_kwargs_filepath_no_... | def test_container_to_output_format_empty_string():
"""Test that an empty string input returns None. `None` arg allows default auto-detection."""
assert container_to_output_format("") is None | test | 1 | {"function_name": "test_container_to_output_format_empty_string", "class_name": null, "qualname": "test_container_to_output_format_empty_string", "file_path": "tests-unit/comfy_api_test/input_impl_test.py", "repo_id": "Comfy-Org/ComfyUI", "loc": 3, "tested_modules": ["comfy_api.input_impl.video_types", "comfy_api.util"... |
google/langextract:tests/provider_schema_test.py:ProviderSchemaDiscoveryTest:class_doc | Write a class-level docstring for `ProviderSchemaDiscoveryTest` (inherits from absltest.TestCase) which has methods: `test_gemini_returns_gemini_schema`, `test_ollama_returns_format_mode_schema`, `test_openai_returns_none`. | Tests for provider schema discovery via get_schema_class(). | documentation | 1 | {"doc_type": "class", "class_name": "ProviderSchemaDiscoveryTest", "file_path": "tests/provider_schema_test.py", "repo_id": "google/langextract", "char_length": 59, "methods": ["test_gemini_returns_gemini_schema", "test_ollama_returns_format_mode_schema", "test_openai_returns_none"]} |
apache/airflow:providers/openlineage/tests/unit/openlineage/utils/test_sql_hook_lineage.py:TestGetHookConnId.test_returns_none_when_nothing_available | # Context:
from unittest import mock
from airflow.providers.openlineage.utils.sql_hook_lineage import (
_create_ol_event_pair,
_get_hook_conn_id,
_resolve_namespace,
emit_lineage_from_sql_extras,
)
def _make_extra(sql, job_id, hook, default_db): ...
class TestResolveNamespace: ...
class TestCreateOlEve... | def test_returns_none_when_nothing_available(self):
hook = mock.MagicMock(spec=[])
assert _get_hook_conn_id(hook) is None | test | 1 | {"function_name": "test_returns_none_when_nothing_available", "class_name": "TestGetHookConnId", "qualname": "TestGetHookConnId.test_returns_none_when_nothing_available", "file_path": "providers/openlineage/tests/unit/openlineage/utils/test_sql_hook_lineage.py", "repo_id": "apache/airflow", "loc": 3, "tested_modules": ... |
crewAIInc/crewAI:lib/crewai-tools/src/crewai_tools/tools/tavily_extractor_tool/tavily_extractor_tool.py:TavilyExtractorTool._arun | # Context:
import json
class TavilyExtractorToolSchema(BaseModel): ...
class TavilyExtractorTool(BaseTool):
model_config = ConfigDict(arbitrary_types_allowed=True)
def __init__(self, **kwargs: Any):
"""Initializes the TavilyExtractorTool.
Args:
**kwargs: Additional keyword argumen... | async def _arun(
self,
urls: list[str] | str,
) -> str:
"""Asynchronously extracts content from the given URL(s).
Args:
urls: The URL(s) to extract data from.
Returns:
A JSON string containing the extracted data.
"""
if not self.async... | function_simple | 0 | {"cognitive_complexity": 1, "loc": 24, "code_loc": 11, "docstring_loc": 8, "function_name": "_arun", "class_name": "TavilyExtractorTool", "qualname": "TavilyExtractorTool._arun", "file_path": "lib/crewai-tools/src/crewai_tools/tools/tavily_extractor_tool/tavily_extractor_tool.py", "repo_id": "crewAIInc/crewAI", "has_do... |
ray-project/ray:python/ray/data/_internal/datasource/databricks_credentials.py:module_doc | Write a module-level docstring for the Python module `databricks_credentials` which contains class `DatabricksCredentialProvider`, class `StaticCredentialProvider`, class `EnvironmentCredentialProvider`, function `resolve_credential_provider`, function `build_headers`. | Databricks credential providers for Ray Data.
This module provides credential abstraction for Databricks authentication,
supporting static tokens with extensibility for future credential sources. | documentation | 0 | {"doc_type": "module", "module_name": "databricks_credentials", "file_path": "python/ray/data/_internal/datasource/databricks_credentials.py", "repo_id": "ray-project/ray", "char_length": 196} |
huggingface/transformers:src/transformers/models/qwen3_vl/modular_qwen3_vl.py:Qwen3VLForConditionalGeneration.forward | # Context:
import torch
from ...cache_utils import Cache, DynamicCache
from ...processing_utils import ProcessingKwargs, Unpack
from ..qwen2_vl.modeling_qwen2_vl import (
PatchEmbed,
Qwen2VLModel,
Qwen2VLModelOutputWithPast,
Qwen2VLPreTrainedModel,
TransformersKwargs,
VisionAttention,
Vision... | def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: torch.Tensor | None = None,
position_ids: torch.LongTensor | None = None,
past_key_values: Cache | None = None,
inputs_embeds: torch.FloatTensor | None = None,
labels: torch.LongTensor | None =... | function_simple | 0 | {"cognitive_complexity": 2, "loc": 97, "code_loc": 28, "docstring_loc": 46, "function_name": "forward", "class_name": "Qwen3VLForConditionalGeneration", "qualname": "Qwen3VLForConditionalGeneration.forward", "file_path": "src/transformers/models/qwen3_vl/modular_qwen3_vl.py", "repo_id": "huggingface/transformers", "has... |
run-llama/llama_index:llama-index-integrations/vector_stores/llama-index-vector-stores-azurepostgresql/llama_index/vector_stores/azure_postgres/common/aio/_connection.py:AsyncConnectionInfo:class_doc | Write a class-level docstring for `AsyncConnectionInfo` (inherits from BaseConnectionInfo) which has methods: various methods. | Base connection information for Azure Database for PostgreSQL connections.
:param host: Hostname of the Azure Database for PostgreSQL server.
:type host: str | None
:param dbname: Name of the database to connect to.
:type dbname: str
:param port: Port number for the connection.
:type port: int
:param credentials: Cred... | documentation | 1 | {"doc_type": "class", "class_name": "AsyncConnectionInfo", "file_path": "llama-index-integrations/vector_stores/llama-index-vector-stores-azurepostgresql/llama_index/vector_stores/azure_postgres/common/aio/_connection.py", "repo_id": "run-llama/llama_index", "char_length": 467, "methods": []} |
browser-use/browser-use:tests/ci/test_agent_planning.py:test_flash_mode_schema_excludes_plan_fields | # Context:
from browser_use.agent.views import (
AgentOutput,
PlanItem,
)
from browser_use.tools.service import Tools
def _make_agent_output(**overrides) -> AgentOutput: ...
def _make_agent(browser_session, mock_llm, **kwargs): ...
async def test_plan_generation_from_plan_update(browser_session, mock_llm): ...
async... | async def test_flash_mode_schema_excludes_plan_fields():
tools = Tools()
ActionModel = tools.registry.create_action_model()
FlashOutput = AgentOutput.type_with_custom_actions_flash_mode(ActionModel)
schema = FlashOutput.model_json_schema()
assert 'current_plan_item' not in schema['properties']
assert 'plan_updat... | test | 0 | {"function_name": "test_flash_mode_schema_excludes_plan_fields", "class_name": null, "qualname": "test_flash_mode_schema_excludes_plan_fields", "file_path": "tests/ci/test_agent_planning.py", "repo_id": "browser-use/browser-use", "loc": 9, "tested_modules": ["browser_use.agent.views", "browser_use.tools.service", "brow... |
vllm-project/vllm:vllm/model_executor/offloader/prefetch.py:module_doc | Write a module-level docstring for the Python module `prefetch` which contains class `ParamInfo`, class `StaticBufferPool`, class `PrefetchOffloader`, class `_ModuleOffloader`, class `_BaseParamOffloader`. | Prefetch-based CPU offloading with async prefetching.
Uses static buffers and event-based stream forking for torch.compile +
CUDA graph compatibility. Events allow the copy stream to join CUDA
graph captures, ensuring H2D copies are properly captured. | documentation | 1 | {"doc_type": "module", "module_name": "prefetch", "file_path": "vllm/model_executor/offloader/prefetch.py", "repo_id": "vllm-project/vllm", "char_length": 252} |
google/langextract:langextract/data_lib.py:enum_asdict_factory | # Context:
import dataclasses
import enum
import numbers
from typing import Any, Iterable, Mapping
def annotated_document_to_dict(adoc: data.AnnotatedDocument | None) -> dict[str, Any]: ...
def dict_to_annotated_document(adoc_dic: Mapping[str, Any]) -> data.AnnotatedDocument: ...
# Task:
Write a Python function `enum... | def enum_asdict_factory(items: Iterable[tuple[str, Any]]) -> dict[str, Any]:
"""Custom dict_factory for dataclasses.asdict.
Recursively converts dataclass instances, converts enum values to their
underlying values, converts integral numeric types to int, and skips any
field whose name starts with an underscore... | function_complex | 1 | {"cognitive_complexity": 9, "loc": 28, "code_loc": 13, "docstring_loc": 13, "function_name": "enum_asdict_factory", "class_name": null, "qualname": "enum_asdict_factory", "file_path": "langextract/data_lib.py", "repo_id": "google/langextract", "has_docstring": true, "runnable_level": "slib_runnable"} |
apache/airflow:airflow-core/src/airflow/executors/workloads/callback.py:module_doc | Write a module-level docstring for the Python module `callback` which contains class `CallbackFetchMethod`, class `CallbackDTO`, class `ExecuteCallback`, function `execute_callback_workload`. | Callback workload schemas for executor communication. | documentation | 1 | {"doc_type": "module", "module_name": "callback", "file_path": "airflow-core/src/airflow/executors/workloads/callback.py", "repo_id": "apache/airflow", "char_length": 53} |
vllm-project/vllm:tests/v1/kv_connector/unit/test_decode_bench_connector.py:test_decode_bench_connector_partial_block | # Context:
class DecodeBenchTestRunner: ...
def test_decode_bench_connector_basic(): ...
def test_decode_bench_connector_no_refill(): ...
def test_decode_bench_connector_single_token(): ...
def test_decode_bench_connector_two_tokens(): ...
def test_decode_bench_connector_large_context(): ...
def test_decode_bench_conn... | def test_decode_bench_connector_partial_block():
"""Test DecodeBenchConnector with partial block filling."""
block_size = 16
num_gpu_blocks = 100
runner = DecodeBenchTestRunner(block_size=block_size, num_gpu_blocks=num_gpu_blocks)
# Create a request that doesn't align to block boundaries
# e.g... | test | 1 | {"function_name": "test_decode_bench_connector_partial_block", "class_name": null, "qualname": "test_decode_bench_connector_partial_block", "file_path": "tests/v1/kv_connector/unit/test_decode_bench_connector.py", "repo_id": "vllm-project/vllm", "loc": 33, "tested_modules": ["vllm", "vllm.config", "vllm.distributed.kv_... |
github/spec-kit:tests/test_ai_skills.py:TestNewProjectCommandSkip.test_new_project_commands_removed_after_skills_succeed | # Context:
from unittest.mock import patch
from specify_cli import (
_get_skills_dir,
install_ai_skills,
AGENT_SKILLS_DIR_OVERRIDES,
DEFAULT_SKILLS_DIR,
SKILL_DESCRIPTIONS,
AGENT_CONFIG,
app,
)
from typer.testing import CliRunner
def temp_dir(): ...
def project_dir(temp_dir): ...
def templa... | def test_new_project_commands_removed_after_skills_succeed(self, tmp_path):
"""For new projects, commands should be removed when skills succeed."""
from typer.testing import CliRunner
runner = CliRunner()
target = tmp_path / "new-proj"
def fake_download(project_path, *args, **k... | test | 0 | {"function_name": "test_new_project_commands_removed_after_skills_succeed", "class_name": "TestNewProjectCommandSkip", "qualname": "TestNewProjectCommandSkip.test_new_project_commands_removed_after_skills_succeed", "file_path": "tests/test_ai_skills.py", "repo_id": "github/spec-kit", "loc": 24, "tested_modules": ["path... |
infiniflow/ragflow:test/unit_test/common/test_string_utils.py:TestRemoveRedundantSpaces.test_currency_symbols | # Context:
import pytest
from common.string_utils import remove_redundant_spaces, clean_markdown_block
class TestCleanMarkdownBlock: ...
class TestRemoveRedundantSpaces:
def test_remove_spaces_before_commas(self): ...
def test_remove_spaces_before_periods(self): ...
def test_remove_spaces_before_exclamati... | def test_currency_symbols(self):
"""Test currency symbols"""
input_text = "Price : € 100 , £ 50 , ¥ 1000 ."
expected = "Price: €100, £50, ¥1000."
assert remove_redundant_spaces(input_text) == expected | test | 1 | {"function_name": "test_currency_symbols", "class_name": "TestRemoveRedundantSpaces", "qualname": "TestRemoveRedundantSpaces.test_currency_symbols", "file_path": "test/unit_test/common/test_string_utils.py", "repo_id": "infiniflow/ragflow", "loc": 5, "tested_modules": ["common.string_utils"], "has_docstring": true, "ru... |
langchain-ai/langchain:libs/partners/openrouter/langchain_openrouter/chat_models.py:ChatOpenRouter.lc_secrets | Write a Python method `lc_secrets` for the class `ChatOpenRouter` to a map of constructor argument names to secret ids.
Returns: dict[str, str] | def lc_secrets(self) -> dict[str, str]:
"""A map of constructor argument names to secret ids."""
return {"openrouter_api_key": "OPENROUTER_API_KEY"} | function_simple | 1 | {"cognitive_complexity": 0, "loc": 3, "code_loc": 1, "docstring_loc": 1, "function_name": "lc_secrets", "class_name": "ChatOpenRouter", "qualname": "ChatOpenRouter.lc_secrets", "file_path": "libs/partners/openrouter/langchain_openrouter/chat_models.py", "repo_id": "langchain-ai/langchain", "has_docstring": true, "runna... |
langflow-ai/langflow:src/backend/tests/unit/components/bundles/cometapi/test_cometapi_component.py:TestCometAPIComponent.test_build_model_integration | # Context:
import os
import pytest
from langchain_openai import ChatOpenAI
from lfx.components.cometapi.cometapi import CometAPIComponent
from pydantic.v1 import SecretStr
class TestCometAPIComponent(ComponentTestBaseWithoutClient):
def component_class(self): ...
def default_kwargs(self): ...
def file_name... | def test_build_model_integration(self):
"""Integration test with real API key (if available)."""
component = CometAPIComponent()
component.api_key = SecretStr(os.getenv("COMETAPI_KEY"))
component.model_name = "gpt-4o-mini"
component.temperature = 0.2
component.max_tokens ... | test | 1 | {"function_name": "test_build_model_integration", "class_name": "TestCometAPIComponent", "qualname": "TestCometAPIComponent.test_build_model_integration", "file_path": "src/backend/tests/unit/components/bundles/cometapi/test_cometapi_component.py", "repo_id": "langflow-ai/langflow", "loc": 13, "tested_modules": ["langc... |
sansan0/TrendRadar:trendradar/crawler/fetcher.py:DataFetcher.fetch_data | # Context:
import json
import random
import time
from typing import Dict, List, Tuple, Optional, Union
import requests
class DataFetcher:
DEFAULT_API_URL = "https://newsnow.busiyi.world/api/s"
DEFAULT_HEADERS = {
def __init__(
self,
proxy_url: Optional[str] = None,
api_url: Optional... | def fetch_data(
self,
id_info: Union[str, Tuple[str, str]],
max_retries: int = 2,
min_retry_wait: int = 3,
max_retry_wait: int = 5,
) -> Tuple[Optional[str], str, str]:
"""
获取指定ID数据，支持重试
Args:
id_info: 平台ID 或 (平台ID, 别名) 元组
max_... | function_complex | 1 | {"cognitive_complexity": 15, "loc": 66, "code_loc": 39, "docstring_loc": 12, "function_name": "fetch_data", "class_name": "DataFetcher", "qualname": "DataFetcher.fetch_data", "file_path": "trendradar/crawler/fetcher.py", "repo_id": "sansan0/TrendRadar", "has_docstring": true, "runnable_level": "class_runnable"} |
exo-explore/exo:packaging/dmg/generate-background.py:draw_arrow | # Context:
import math
from PIL import Image, ImageDraw
def generate_background(output_path: str) -> None: ...
# Task:
Write a Python function `draw_arrow` to draw a hand-drawn-style curved arrow from app icon toward Applications.
Parameters: draw: ImageDraw.ImageDraw
Returns: None | def draw_arrow(draw: ImageDraw.ImageDraw) -> None:
"""Draw a hand-drawn-style curved arrow from app icon toward Applications."""
color = (30, 30, 30)
line_width = 8
# Compute bezier curve points for a gentle upward arc
points: list[tuple[float, float]] = []
steps = 80
for i in range(steps +... | function_simple | 0 | {"cognitive_complexity": 2, "loc": 38, "code_loc": 26, "docstring_loc": 1, "function_name": "draw_arrow", "class_name": null, "qualname": "draw_arrow", "file_path": "packaging/dmg/generate-background.py", "repo_id": "exo-explore/exo", "has_docstring": true, "runnable_level": "project_runnable"} |
ray-project/ray:doc/source/llm/doc_code/serve/qwen/qwen_example.py:module_doc | Write a module-level docstring for the Python module `qwen_example` which contains function `_non_blocking_serve_run`, function `_testing_build_openai_app`. | This file serves as a documentation example and CI test.
Structure:
1. Monkeypatch setup: Ensures serve.run is non-blocking and removes accelerator requirements for CI testing.
2. Docs example (between __qwen_example_start/end__): Embedded in Sphinx docs via literalinclude.
3. Test validation (deployment status pollin... | documentation | 0 | {"doc_type": "module", "module_name": "qwen_example", "file_path": "doc/source/llm/doc_code/serve/qwen/qwen_example.py", "repo_id": "ray-project/ray", "char_length": 332} |
langflow-ai/langflow:src/backend/tests/unit/api/v1/test_mcp_projects.py:test_update_project_mcp_settings_empty_settings | # Context:
from httpx import AsyncClient
def test_args_reference_urls_filters_strings_only(args, urls, expected): ...
def test_args_reference_urls_matches_non_last_string_argument(): ...
def mock_project(active_user): ...
def mock_flow(active_user, mock_project): ...
def mock_project_mcp_server(): ...
class AsyncConte... | async def test_update_project_mcp_settings_empty_settings(client: AsyncClient, user_test_project, logged_in_headers):
"""Test updating MCP settings with empty settings list."""
# Use real database objects instead of mocks to avoid the coroutine issue
# Empty settings list
json_payload = {
"sett... | test | 1 | {"function_name": "test_update_project_mcp_settings_empty_settings", "class_name": null, "qualname": "test_update_project_mcp_settings_empty_settings", "file_path": "src/backend/tests/unit/api/v1/test_mcp_projects.py", "repo_id": "langflow-ai/langflow", "loc": 25, "tested_modules": ["contextlib", "types", "uuid", "fast... |
run-llama/llama_index:llama-index-core/llama_index/core/memory/memory.py:Memory.aput_messages | # Context:
from typing import (
Any,
Callable,
Dict,
List,
Optional,
Tuple,
Union,
TypeVar,
Generic,
cast,
)
from llama_index.core.base.llms.types import (
ChatMessage,
ContentBlock,
TextBlock,
AudioBlock,
ImageBlock,
VideoBlock,
DocumentBlock,
Cac... | async def aput_messages(self, messages: List[ChatMessage]) -> None:
"""Add a list of messages to the chat store and process waterfall logic if needed."""
# Add the messages to the chat store
await self.sql_store.add_messages(
self.session_id, messages, status=MessageStatus.ACTIVE
... | function_simple | 1 | {"cognitive_complexity": 0, "loc": 9, "code_loc": 4, "docstring_loc": 1, "function_name": "aput_messages", "class_name": "Memory", "qualname": "Memory.aput_messages", "file_path": "llama-index-core/llama_index/core/memory/memory.py", "repo_id": "run-llama/llama_index", "has_docstring": true, "runnable_level": "project_... |
langchain-ai/langchain:libs/langchain/langchain_classic/agents/mrkl/base.py:ChainConfig:class_doc | Write a class-level docstring for `ChainConfig` (inherits from NamedTuple) which has methods: various methods. | Configuration for a chain to use in MRKL system.
Args:
action_name: Name of the action.
action: Action function to call.
action_description: Description of the action. | documentation | 1 | {"doc_type": "class", "class_name": "ChainConfig", "file_path": "libs/langchain/langchain_classic/agents/mrkl/base.py", "repo_id": "langchain-ai/langchain", "char_length": 180, "methods": []} |
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