input string | label int64 | category string | sample_id string |
|---|---|---|---|
attention_cache = PagedAttentionCache(
self.model.config,
self.generation_config,
self.model.device,
self.model.dtype,
tp_size=getattr(self.model, "_tp_size", None), # Use model's actual TP setting
allow_block_sharing=self.... | 0 | function_complex | huggingface/transformers:src/transformers/generation/continuous_batching/continuous_api.py:ContinuousBatchingManager._run_generation_loop |
_slider_to_be_ready(app)
reset_button = app.get_by_test_id("stButton").filter(has_text="Reset Height")
expect(reset_button).to_be_visible()
_expect_pdf_container_attached(app)
# Wait for PDF to be fully loaded
_wait_for_pdf_to_load(app)
# Take snapshot of just the PDF container in initial st... | 1 | test | streamlit/streamlit:e2e_playwright/custom_components/pdf_component_test.py:test_st_pdf_interactive |
self, groq_messages, output_format: type[T], schema) -> ChatCompletion:
"""Handle structured output using tool calling."""
tool = ChatCompletionToolParam(
function={
'name': output_format.__name__,
'description': f'Extract information in the format of {output_format.__name__}',
'parameters': schema,
... | 0 | function_simple | browser-use/browser-use:browser_use/llm/groq/chat.py:ChatGroq._invoke_with_tool_calling |
_sliding_window(self):
EXPECTED_TEXT_COMPLETION = " This is a nice place. I really enjoy the scenery, and the atmosphere is so relaxing. I'm grateful for the opportunity to experience this place. It"
tokenizer = AutoTokenizer.from_pretrained(self.TEST_MODEL_ID)
prompt = "This is a nice place. " ... | 0 | test | huggingface/transformers:tests/models/exaone4/test_modeling_exaone4.py:Exaone4IntegrationTest.test_model_generation_beyond_sliding_window |
config = self.get_text_config()
model = Qwen3VLTextModel(config).to(torch_device).eval()
batch_size, seq_len = 2, 8
input_ids = ids_tensor([batch_size, seq_len], config.vocab_size).to(torch_device)
position_ids_2d = torch.arange(seq_len, device=torch_device).unsqueeze(0).expand(batch... | 0 | test | huggingface/transformers:tests/models/qwen3_vl/test_modeling_qwen3_vl.py:Qwen3VLTextModelPositionIdsTest.test_2d_position_ids_forward |
(stmt, ast.Return) and isinstance(stmt.value, ast.List):
backends: list[str] = []
for e in stmt.value.elts:
if isinstance(e, ast.Attribute):
backends.append(e.attr)
elif (
isinstance(e, ast.Starred)
and i... | 1 | function_complex | vllm-project/vllm:tools/pre_commit/generate_attention_backend_docs.py:_get_backends_from_return |
tex])
# Create mock run response
run_response = Mock()
result_data = Mock()
result_data.component_id = "output-123"
result_data.outputs = {"message": {"message": "Hello World"}}
result_data.metadata = {}
run_output = Mock()
run_output.outputs = [result_d... | 1 | test | langflow-ai/langflow:src/backend/tests/unit/api/v2/test_converters.py:TestRunResponseToWorkflowResponse.test_run_response_basic_output_node |
- Events are tuples of (event_id, value, put_time)
- Breaks the loop when receiving a None value, signaling completion
- Tracks and logs timing metrics for queue time and client processing time
- Notifies client consumption via client_consumed_queue
"""
while True:
event... | 1 | function_simple | langflow-ai/langflow:src/lfx/src/lfx/cli/serve_app.py:consume_and_yield |
if not test_cases:
EvaluationRun.update(
status="FAILED",
complete_time=current_timestamp()
).where(EvaluationRun.id == run_id).execute()
return
# Execute each test case
results = []
for c... | 1 | function_complex | infiniflow/ragflow:api/db/services/evaluation_service.py:EvaluationService._execute_evaluation |
",
port=5432,
diskann=False,
hnsw=False,
minconn=1,
maxconn=4,
connection_pool=explicit_pool
)
# Verify the _get_cursor context manager was called
mock_get_cursor.assert_called()
mock_connection_pool.assert_not_cal... | 1 | test | mem0ai/mem0:tests/vector_stores/test_pgvector.py:TestPGVector.test_create_col_psycopg3_with_explicit_pool |
of file raises exit code 1."""
directory = tmp_path / "test_dir"
directory.mkdir()
with pytest.raises(typer.Exit) as exc_info:
run(
script_path=directory,
input_value=None,
input_value_option=None,
verbose=False,
... | 1 | test | langflow-ai/langflow:src/lfx/tests/unit/cli/test_run_command.py:TestRunCommand.test_execute_directory_instead_of_file |
.get(signal.wait.remote())
ray.get(captured_id) # noqa: F821
signal = SignalActor.remote()
obj_ref = f.remote(signal)
# Delete local references.
del f
del captured_id
# Test that the captured object ref is pinned despite having no local
# references.
ray.get(signal.send.remot... | 0 | test | ray-project/ray:python/ray/tests/test_reference_counting_standalone.py:test_captured_object_ref |
tool_choice: str | dict,
enable_thinking: bool,
):
if not stream:
# Non-streaming test
chat_completion = await client.chat.completions.create(
messages=messages,
model=model_name,
tools=tools,
tool_choice=tool_choice,
extra_body={"ch... | 1 | test | vllm-project/vllm:tests/entrypoints/openai/test_completion_with_function_calling.py:test_function_tool_use |
"""
Apply rotary embeddings to input tensors using the given frequency tensor. This function applies rotary embeddings
to the given query or key 'x' tensors using the provided frequency tensor 'freqs_cis'. The input tensors are
reshaped as complex numbers, and the frequency tensor is reshap... | 1 | function_simple | Comfy-Org/ComfyUI:comfy/ldm/ace/attention.py:CustomLiteLAProcessor2_0.apply_rotary_emb |
).to(torch_device)
with torch.no_grad():
outputs = self.model(**inputs)
self.assertEqual(outputs.iou_scores.shape, (1, 1, 3))
self.assertEqual(outputs.pred_masks.shape, (1, 1, 3, 256, 256))
sorted_indices = torch.argsort(outputs.iou_scores.squeeze(), descending=True)
... | 0 | test | huggingface/transformers:tests/models/sam2/test_modeling_sam2.py:Sam2ModelIntegrationTest.test_inference_mask_generation_one_point_multimask |
The temporal, height and width of feature shape of each image in LLM.
video_grid_thw (`torch.LongTensor` of shape `(num_videos, 3)`, *optional*):
The temporal, height and width of feature shape of each video in LLM.
rope_deltas (`torch.LongTensor` of shape `(batch_size, )`, *opt... | 0 | function_simple | huggingface/transformers:src/transformers/models/glm4v/modular_glm4v.py:Glm4vModel.forward |
client: AsyncClient,
created_api_key,
mock_settings_dev_api_enabled, # noqa: ARG002
):
"""Test POST /workflow/stop handles already cancelled jobs."""
job_id = str(uuid4())
mock_job = MagicMock()
mock_job.job_id = job_id
mock_job.status = JobStatus.CANCELL... | 1 | test | langflow-ai/langflow:src/backend/tests/unit/api/v2/test_workflow.py:TestWorkflowStop.test_stop_workflow_already_cancelled |
.ProfilerActivity.CUDA)
elif self.device_type == "xpu":
activities.append(torch.profiler.ProfilerActivity.XPU)
profiler = torch.profiler.profile(
activities=activities,
record_shapes=True,
)
with profiler as prof:
_ = self.model.generate(
... | 0 | function_simple | huggingface/transformers:benchmark_v2/framework/benchmark_runner.py:BenchmarkRunner.profile_generate |
Python:",
max_tokens=30,
temperature=0,
stream=True,
echo=False,
logprobs=1,
extra_body={"return_token_ids": True, "return_tokens_as_token_ids": True},
)
# Collect streamed tokens
streamed_prompt_token_ids = []
str... | 1 | test | vllm-project/vllm:tests/entrypoints/openai/test_return_token_ids.py:test_comparison_with_prompt_logprobs_and_logprobs |
Args:
content_type: MIME type of the video.
Returns:
Bedrock format string or None if unsupported.
"""
format_map = {
"video/mp4": "mp4",
"video/quicktime": "mov",
"video/x-matroska": "mkv",
"video/webm": "webm",
... | 0 | function_simple | crewAIInc/crewAI:lib/crewai/src/crewai/llms/providers/bedrock/completion.py:BedrockCompletion._get_video_format |
api_key (str): Your Firecrawl API key.
config (dict): Optional. It contains Firecrawl v2 API parameters.
Default configuration options (Firecrawl v2 API):
formats (list[str]): Content formats to return. Default: ["markdown"]
only_main_content (bool): Only return main content excluding headers, navs, f... | 0 | documentation | crewAIInc/crewAI:lib/crewai-tools/src/crewai_tools/tools/firecrawl_scrape_website_tool/firecrawl_scrape_website_tool.py:FirecrawlScrapeWebsiteTool:class_doc |
]:
url = grpc_url if _do_request == self._do_grpc_request else http_url
num_requests = 5
with ThreadPoolExecutor(num_requests + 5) as tpe:
# Submit `max_ongoing_requests` blocking requests.
futures = [tpe.submit(_do_request, url) for _ in range(num_req... | 0 | test | ray-project/ray:python/ray/serve/tests/test_direct_ingress.py:TestDirectIngressBackpressure.test_max_ongoing_requests |
def test_model_generation_long_flash(self):
EXPECTED_OUTPUT_TOKEN_IDS = [433, 9055]
input_ids = [433, 9055] * 2048
model = Exaone4ForCausalLM.from_pretrained(
self.TEST_MODEL_ID, device_map="auto", dtype=torch.bfloat16, attn_implementation="flash_attention_2"
)
input_... | 0 | test | huggingface/transformers:tests/models/exaone4/test_modeling_exaone4.py:Exaone4IntegrationTest.test_model_generation_long_flash |
-> tqdm.tqdm:
"""Create a styled progress bar for downloads.
Args:
total_size: Total size in bytes.
url: The URL being downloaded.
ncols: Number of columns for the progress bar.
max_url_length: Maximum length to show for the URL.
Returns:
A configured tqdm progress bar.
"""
# Truncate U... | 1 | function_complex | google/langextract:langextract/progress.py:create_download_progress_bar |
# Check for type changes
if self._has_type_change(call) and phase != MigrationPhase.CONTRACT:
violations.append(
Violation("DIRECT_TYPE_CHANGE", "Type changes should use expand-contract pattern", call.lineno)
)
# Check for nullable changes
if se... | 1 | function_simple | langflow-ai/langflow:src/backend/base/langflow/alembic/migration_validator.py:MigrationValidator._check_alter_column |
Optional["SettingsService"],
) -> tuple[dict[str, Any], str]:
"""Load components in production mode with fallback chain.
Tries: index -> cache -> dynamic build (with caching)
Args:
settings_service: Settings service for custom index path
Returns:
Tuple of (modules_dict, index_source)... | 1 | function_simple | langflow-ai/langflow:src/lfx/src/lfx/interface/components.py:_load_production_mode |
.return_value = "Async result"
def guardrail_fn(output: TaskOutput) -> tuple[bool, str]:
return False, "Always fails"
task = Task(
description="Test task",
expected_output="Test output",
agent=test_agent,
guardrail=guardrail_fn,
g... | 0 | test | crewAIInc/crewAI:lib/crewai/tests/task/test_async_task.py:TestAsyncGuardrails.test_ainvoke_guardrail_max_retries_exceeded |
)
row = cursor.fetchone()
conn.close()
if row:
return WorkflowStats(
workflow_id=row[0],
total_ratings=row[1],
average_rating=row[2],
total_reviews=row[3],
total_views=row[4],
total_... | 0 | function_simple | Zie619/n8n-workflows:src/community_features.py:CommunityFeatures.get_workflow_stats |
{k: f'"{v}"' if not is_number(v) else v for k, v in generate_flags_as_dict.items()}
# 2. c. [no processing needed] lists are lists of ints because `generate` doesn't take lists of strings :)
# We also mention in the help message that we only accept lists of ints for now.
# 3. Join the result into a comma ... | 0 | function_simple | huggingface/transformers:src/transformers/cli/chat.py:parse_generate_flags |
"""Create a new podcast in the database."""
try:
content_json = json.dumps(content)
sources_json = json.dumps(sources) if sources else None
current_time = datetime.now().isoformat()
query = """
INSERT INTO podcasts
(title, date, con... | 0 | function_simple | Shubhamsaboo/awesome-llm-apps:advanced_ai_agents/multi_agent_apps/ai_news_and_podcast_agents/beifong/services/podcast_service.py:PodcastService.create_podcast |
out parameters with no gradients
parameters = [p for p in parameters if p.grad is not None]
assert len(parameters) > 0, "No parameters with gradients found"
# Calculate total norm
if norm_type == float("inf"):
total_norm = max(p.grad.detach().abs().max() for p in parameters)
else:
... | 0 | function_complex | huggingface/transformers:examples/3D_parallel.py:clip_grad_norm_ |
KV cache slices from a given attention layer tensor.
This function handles multiple backend layouts:
- MLA (Multi-Linear Attention) or FlashInfer: KV tensors are
indexed along the first dimension.
- FlashAttention: KV tensors are indexed along the second
... | 1 | function_complex | vllm-project/vllm:vllm/distributed/kv_transfer/kv_connector/v1/p2p/p2p_nccl_connector.py:P2pNcclConnector.save_kv_layer |
been set before any value is"
"added")
if self.datatype is list:
if not isinstance(value, (str, list)):
raise ValueError(f"[{self._name}] List values should be set as a Str or List. Got "
f"{type(value)} ({value})")
... | 1 | function_complex | deepfakes/faceswap:lib/config/objects.py:ConfigItem.set |
_version = torch.version.cuda # e.g. "13.0" or "12.8"
if cuda_version:
system_cuda_display = cuda_version
system_cuda_tag = cuda_version.replace(".", "")[:3] # "130" or "128"
except Exception:
pass
if system_cuda_tag is None or system_cuda_tag.startswith(wanted_cuda):
... | 0 | function_complex | unslothai/unsloth:unsloth/import_fixes.py:_get_vllm_cuda_mismatch_message |
reorder_cache(self, beam_idx: torch.LongTensor):
"""Reorders the cache for beam search, given the selected beam indices."""
for layer_idx in range(len(self.key_cache)):
if self.key_cache[layer_idx] is not None:
device = self.key_cache[layer_idx].device
beam_i... | 0 | function_simple | huggingface/transformers:src/transformers/models/qwen3_next/modular_qwen3_next.py:Qwen3NextDynamicCache.reorder_cache |
"""
\\documentclass{article}
\\begin{document}
Some text.
\\begin{thebibliography}{9}
\\bibitem{ref1} Author One, Title One, 2020.
\\bibitem{ref2} Author Two, Title Two, 2021.
\\end{thebibliography}
\\end{document}
"""
in_doc = InputDocument(
path_or_stream=BytesIO(latex_... | 1 | test | docling-project/docling:tests/test_backend_latex.py:test_latex_bibliography |
default_profiler_config.delay_iterations = 2
default_profiler_config.max_iterations = 2
profiler = ConcreteWorkerProfiler(default_profiler_config)
profiler.start()
# Step 1
profiler.step()
assert profiler._running is False
assert profiler._active is True
# Step 2 (Starts now)
pro... | 1 | test | vllm-project/vllm:tests/v1/worker/test_gpu_profiler.py:test_delayed_start_and_max_iters |
]
else:
assert len(images) == len(prompts), "Number of images must match number of prompts"
messages = [
[
{
"role": "system",
"content": [{"type": "text", "text": system_message}],
},
]
... | 1 | function_complex | huggingface/diffusers:src/diffusers/pipelines/flux2/pipeline_flux2.py:format_input |
] = []
strategy2 = BFSDeepCrawlStrategy(
max_depth=2,
max_pages=10,
resume_state=last_state,
)
mock_crawler2 = create_mock_crawler_tracking(crawled_in_resume, return_no_links=True)
await strategy2._arun_batch("https://example.com", mock_crawler2, mo... | 1 | test | unclecode/crawl4ai:tests/deep_crawling/test_deep_crawl_resume.py:TestBFSResume.test_simulated_crash_mid_crawl |
prompt_cross_attn(
query=normed_hidden_states,
key=prompt_features,
value=prompt_features,
attention_mask=cross_attn_mask,
**kwargs,
)
encoder_hidden_states = residual + self.prompt_cross_attn_dropout(attn_output)
... | 0 | function_simple | huggingface/transformers:src/transformers/models/sam3/modeling_sam3.py:Sam3MaskDecoder.forward |
description="Say hello",
expected_output="hello",
agent=agent,
)
crew = Crew(
agents=[agent],
tasks=[task],
share_crew=True,
)
result = crew.kickoff()
assert crew._execu... | 0 | test | crewAIInc/crewAI:lib/crewai/tests/telemetry/test_flow_crew_span_integration.py:test_crew_execution_span_in_async_flow |
)
torch.testing.assert_close(
outputs.pred_masks[:, :, :, :2, :2],
torch.tensor(
[
[[[[-19.1423, -21.6488], [-17.8018, -22.6512]]], [[[-7.1591, -9.8201], [-7.4133, -9.2781]]]],
[[[[-16.7645, -15.2790], [-16.1805, -16.2937]]... | 0 | test | huggingface/transformers:tests/models/edgetam/test_modeling_edgetam.py:EdgeTamModelIntegrationTest.test_inference_mask_generation_batched_images_batched_points_multi_points |
# Add debugging port
debug_port = self._find_free_port()
launch_args.extend(
[
f'--remote-debugging-port={debug_port}',
]
)
assert '--user-data-dir' in str(launch_args), (
'User data dir must be set somewhere in launch args to a non-default path, otherwise Chrome will not let us at... | 0 | function_complex | browser-use/browser-use:browser_use/browser/watchdogs/local_browser_watchdog.py:LocalBrowserWatchdog._launch_browser |
max_tokens=100,
messages=[ClaudeMessage(role="user", content="Hello")],
temperature=0.7,
top_p=0.9,
top_k=40,
stop_sequences=["STOP", "END"],
stream=True,
)
params = claude_request_to_text_generation(request)
a... | 0 | test | exo-explore/exo:src/exo/master/tests/test_claude_api.py:TestClaudeRequestToInternal.test_request_with_optional_parameters |
en(1)
occupied_ports.append(sock)
"""Test that multiple replicas on the same node occupy unique ports."""
serve._run(
Hybrid.options(name="default-deployment").bind(message="Hello world!"),
_blocking=False,
)
# check that the deployment failed
def _func():
serve_det... | 0 | test | ray-project/ray:python/ray/serve/tests/test_direct_ingress.py:test_no_port_available |
"""
Process and output the audio.
Args:
*args: Can take any positional argument.
**kwargs: Can take any keyword argument.
Returns:
out (Any): This function can return any output.
"""
stream = await asyncio.to_thread(
pya.ope... | 1 | function_simple | run-llama/llama_index:llama-index-integrations/voice_agents/llama-index-voice-agents-gemini-live/llama_index/voice_agents/gemini_live/audio_interface.py:GeminiLiveVoiceAgentInterface.output |
# Add current metadata to buffer, if buffer is full, drop the oldest entry
if len(self._buffer) >= self._buffer_maxlen:
# Record the number of dropped attempts
oldest_entry = self._buffer.popleft()
self._metric_recorder.set_metric_... | 0 | function_complex | ray-project/ray:python/ray/dashboard/modules/aggregator/task_events_metadata_buffer.py:TaskEventsMetadataBuffer.merge |
existing = _DummyMCPServer(id="mcp-1", name="srv", url="http://server", server_type="sse", tenant_id="tenant_1", variables={}, headers={})
_set_request_json(monkeypatch, module, {"mcp_id": "mcp-1"})
monkeypatch.setattr(module.MCPServerService, "get_by_id", lambda _mcp_id: (False, None))
res = _run(modul... | 1 | test | infiniflow/ragflow:test/testcases/test_web_api/test_mcp_server_app/test_mcp_server_app_unit.py:test_update_validation_guards |
source="sitemap",
pattern="*beautiful-soup*",
extract_head=True # Get the metadata!
))
print(f"\n📚 Found {len(soup_urls)} Beautiful Soup articles:\n")
... | 1 | function_simple | unclecode/crawl4ai:docs/examples/url_seeder/url_seeder_quick_demo.py:explore_beautifulsoup |
self.assertEqual(preprocess_parameter_names, valid_processor_keys)
is_tested = True
# validation done by @filter_out_non_signature_kwargs decorator
if hasattr(image_processor.preprocess, "_filter_out_non_signature_kwargs"):
if hasattr(self.image_processor_tes... | 0 | test | huggingface/transformers:tests/models/phi4_multimodal/test_image_processing_phi4_multimodal.py:Phi4MultimodalImageProcessingTest.test_image_processor_preprocess_arguments |
VitPoseForPoseEstimation model outputs.
boxes (`list[list[list[float]]]` or `np.ndarray`):
List or array of bounding boxes for each image. Each box should be a list of 4 floats representing the bounding
box coordinates in COCO format (top_left_x, top_left_y, width, hei... | 0 | function_complex | huggingface/transformers:src/transformers/models/vitpose/image_processing_vitpose_fast.py:VitPoseImageProcessorFast.post_process_pose_estimation |
_item = NewsItem(
title=title,
source_id=source_id,
source_name=source_name,
rank=rank,
url=url,
mobile_url=mobile_url,
crawl_time=crawl_time,
ranks=ranks,
first_time=crawl_tim... | 1 | function_complex | sansan0/TrendRadar:trendradar/storage/base.py:convert_crawl_results_to_news_data |
input_ids = inputs_dict["input_ids"]
def _prepare_image_embeds_position_mask(input_ids, pad_size):
image_embeds_position_mask = torch.zeros(
input_ids.shape[0], input_ids.shape[1] + pad_size, device=torch_device, dtype=input_ids.dtype
)
image_embeds_posi... | 0 | test | huggingface/transformers:tests/models/kosmos2_5/test_modeling_kosmos2_5.py:Kosmos2_5ModelTest.test_left_padding_compatibility |
Code and formula extraction stage using the new runtime system.
This stage uses the unified VLM runtime interface to extract code and formulas
from document elements. It supports all runtime types (Transformers, MLX,
API, etc.) through the runtime factory.
The stage:
1. Filters code and formula elements
2. Uses the r... | 1 | documentation | docling-project/docling:docling/models/stages/code_formula/code_formula_vlm_model.py:CodeFormulaVlmModel:class_doc |
shape(resized_images, disable_grouping=disable_grouping)
processed_images_grouped = {}
for shape, stacked_images in grouped_images.items():
# Fused rescale and normalize
stacked_images = self.rescale_and_normalize(
stacked_images, do_rescale, rescale_factor, do_n... | 0 | function_complex | huggingface/transformers:src/transformers/models/vilt/image_processing_vilt_fast.py:ViltImageProcessorFast._preprocess |
"""Test different return types (pt, np, list)."""
processor = self.get_processor()
image = self.prepare_image_inputs()
text_with_image = f"{self.image_token} Test image."
text_only = "Test without image."
# Test PyTorch tensors (with images - fast image processor only supports ... | 0 | test | huggingface/transformers:tests/models/lighton_ocr/test_processor_lighton_ocr.py:LightOnOcrProcessorTest.test_processor_return_types |
async def test_trigger_run_bad(self, mocker):
mocker.patch.object(KafkaConsumerHook, "get_consumer", return_value=MockedConsumer)
trigger = KafkaMessageQueueTrigger(
kafka_config_id="kafka_d",
apply_function="unit.apache.kafka.triggers.test_msg_queue.apply_function_false",
... | 1 | test | apache/airflow:providers/apache/kafka/tests/unit/apache/kafka/triggers/test_msg_queue.py:TestMessageQueueTrigger.test_trigger_run_bad |
日期字符串列表（YYYY-MM-DD 格式）
"""
dates = []
try:
paginator = self.s3_client.get_paginator('list_objects_v2')
pages = paginator.paginate(Bucket=self.bucket_name, Prefix="news/")
for page in pages:
if 'Contents' not in page:
... | 1 | function_complex | sansan0/TrendRadar:trendradar/storage/remote.py:RemoteStorageBackend.list_remote_dates |
a_shape = w_shape # Same as weight shape for alignment
# Determine weight dtype
if weight_bits == 4:
weight_dtype = "int4"
elif weight_bits == 8:
weight_dtype = torch.int8
else:
raise ValueError(f"Unsupported weight_bits: {weight_bits}")
return FusedMoEQuantConfig(
... | 1 | function_simple | vllm-project/vllm:vllm/model_executor/layers/fused_moe/config.py:awq_marlin_moe_quant_config |
_tokens] (P/W/H/T positions with multimodal inputs)
query: [num_tokens, num_heads * head_size]
key: [num_tokens, num_kv_heads * head_size]
"""
assert positions.ndim == 2
assert key is not None
num_tokens = positions.shape[-1]
cos_sin = self.cos_sin_cache[... | 1 | function_simple | vllm-project/vllm:vllm/model_executor/layers/rotary_embedding/xdrope.py:XDRotaryEmbedding.forward_cuda |
2,
equal=False,
data_context=DataContext.get_current(),
locality_hints=["node1", "node2"],
)
def get_fake_loc(item):
assert isinstance(item, int), item
if item in [0, 1, 4, 5, 8]:
return "node1"
else:
return "node2"
def get_bundle... | 0 | test | ray-project/ray:python/ray/data/tests/test_output_splitter.py:test_split_operator_locality_hints |
.
"""
flash_attn_varlen_func = lazy_import_paged_flash_attention(module.config._attn_implementation)
sliding_window = (-1, -1) if not getattr(module, "sliding_window", False) else (module.sliding_window - 1, 0)
layer_type = "full_attention" if sliding_window == (-1, -1) else "sliding_attention"
# ... | 0 | function_complex | huggingface/transformers:src/transformers/integrations/flash_paged.py:paged_attention_forward |
score = torch.randn((m, e), device="cuda", dtype=dtype)
topk_weights, topk_ids, _ = fused_topk(a, score, topk, renormalize=False)
a1_gs = torch.ones((e,), device="cuda", dtype=torch.float32)
a2_gs = torch.ones((e,), device="cuda", dtype=torch.float32)
assert w1_gs is not None
... | 1 | test | vllm-project/vllm:tests/kernels/moe/test_nvfp4_moe.py:test_cutlass_fp4_moe_no_graph |
ring_buffer = SingleWriterShmRingBuffer(
data_buffer_size=self.buffer_size, create=True
)
# Allocate some buffers
for _ in range(3):
self.ring_buffer.allocate_buf(100)
# Clear the buffer
self.ring_buffer.clear()
# Check that metadata is empty an... | 1 | test | vllm-project/vllm:tests/distributed/test_shm_buffer.py:TestSingleWriterShmRingBuffer.test_clear_buffer |
_ids (`list[int]`, *optional*):
List of object IDs being tracked in the current frame.
obj_id_to_mask (`dict[int, torch.FloatTensor]`, *optional*):
Dictionary mapping object IDs to their predicted low-resolution masks.
Each mask has shape `(1, H_low, W_low)`.
obj_id_to_score (`dict[int, float]`, *optional*)... | 0 | documentation | huggingface/transformers:src/transformers/models/sam3_video/modeling_sam3_video.py:Sam3VideoSegmentationOutput:class_doc |
[
[
[
[1.0, 2.0, 3.0, 4.0],
[5.0, 6.0, 7.0, 8.0],
[9.0, 10.0, 11.0, 12.0],
[13.0, 14.0, 15.0, 16.0],
]
]
| 1 | test | keras-team/keras:keras/src/layers/pooling/adaptive_pooling2d_test.py:AdaptivePooling2DLayerTest.test_average_pooling2d_numerical |
.is_output_node
if cls._INPUT_IS_LIST is None:
cls._INPUT_IS_LIST = schema.is_input_list
if cls._NOT_IDEMPOTENT is None:
cls._NOT_IDEMPOTENT = schema.not_idempotent
if cls._ACCEPT_ALL_INPUTS is None:
cls._ACCEPT_ALL_INPUTS = schema.accept_all_inputs
i... | 1 | function_complex | Comfy-Org/ComfyUI:comfy_api/latest/_io.py:_ComfyNodeBaseInternal.GET_SCHEMA |
f"{cuda_device_count_stateless()} exepected "
f"{world_size}."
)
config = Config(
Ms=Ms,
K=k,
N=n,
E=e,
topks=TOPKs,
dtype=dtype,
quant_config=quant_config,
prepare_finalize_type=prepare_finalize_type,
fused_expert... | 1 | test | vllm-project/vllm:tests/kernels/moe/test_modular_kernel_combinations.py:test_modular_kernel_combinations_multigpu |
a tensor of shape
(batch_size, `rows`, `columns`, `num_channels` x `patch_height` x `patch_width`).
Args:
image_tensor (torch.Tensor):
The image tensor to extract patches from.
patch_height (int):
The height of the patches to extract.
patch_width (int):
... | 0 | function_simple | huggingface/transformers:src/transformers/models/kosmos2_5/image_processing_kosmos2_5_fast.py:torch_extract_patches |
"""Test complete workflow: discover -> score -> filter -> use."""
# Step 1: Discover and score URLs
config = SeedingConfig(
source="sitemap",
extract_head=True,
query="premier league opening fixtures",
scoring_method="bm25",
score_threshold=0.... | 1 | test | unclecode/crawl4ai:tests/general/test_async_url_seeder_bm25.py:TestAsyncUrlSeederBM25.test_full_workflow_integration |
"addRecipeNutrition": True
}
try:
search_response = requests.get(search_url, params=search_params, timeout=15)
search_response.raise_for_status()
search_data = search_response.json()
if not search_data.get('results'):
return {"error": f"No recipe ... | 0 | function_complex | Shubhamsaboo/awesome-llm-apps:advanced_ai_agents/single_agent_apps/ai_recipe_meal_planning_agent/ai_recipe_meal_planning_agent.py:analyze_nutrition |
index: int,
) -> None:
"""Process a single image response and send chunks."""
encoded_data = base64.b64encode(response.image_data).decode("utf-8")
is_partial = isinstance(response, PartialImageResponse)
# Extract stats from final ImageGenerationResponse if available
stats = response.stats if isinsta... | 0 | function_simple | exo-explore/exo:src/exo/worker/runner/image_models/runner.py:_process_image_response |
monkeypatch.setattr(module.REDIS_CONN, "get", lambda _key: (_ for _ in ()).throw(RuntimeError("trace boom")))
res = module.trace()
assert res is None
monkeypatch.setattr(module.UserCanvasService, "accessible", lambda *_args, **_kwargs: False)
monkeypatch.setattr(module, "request", _DummyRequest(a... | 1 | test | infiniflow/ragflow:test/testcases/test_web_api/test_canvas_app/test_canvas_routes_unit.py:test_trace_and_sessions_matrix_unit |
(), trigger=("messages", 5))
assert middleware._get_profile_limits() is None
class MissingTokensModel(BaseChatModel):
profile: ModelProfile | None = Field(default=ModelProfile(other_field=100), exclude=True) # type: ignore[typeddict-unknown-key]
@override
def _generate(
se... | 1 | test | langchain-ai/langchain:libs/langchain_v1/tests/unit_tests/agents/middleware/implementations/test_summarization.py:test_summarization_middleware_profile_edge_cases |
value function tries to match
the upper tail of the Q-value distribution.
actor_lr: The learning rate for the actor network. Actor learning rates
greater than critic learning rates work well in experiments.
critic_lr: The learning rate for the Q-network. Critic l... | 0 | function_complex | ray-project/ray:rllib/algorithms/iql/iql.py:IQLConfig.training |
requests.Session()
pool_size = conf.getint(CONF_SECTION_NAME, CONF_REQUESTS_POOL_SIZE_KEY, fallback=10)
retry_total = conf.getint(CONF_SECTION_NAME, CONF_REQUESTS_RETRIES_KEY, fallback=3)
retry_strategy = Retry(
total=retry_total,
backoff_factor=0.1,
status... | 1 | function_simple | apache/airflow:providers/keycloak/src/airflow/providers/keycloak/auth_manager/keycloak_auth_manager.py:KeycloakAuthManager.http_session |
.6], {"key": "value2"}),
]
pgvector = PGVector(
dbname="test_db",
collection_name="test_collection",
embedding_model_dims=3,
user="test_user",
password="test_pass",
host="localhost",
port=5432,
diska... | 1 | test | mem0ai/mem0:tests/vector_stores/test_pgvector.py:TestPGVector.test_list_psycopg2 |
exists_s3_true(self):
"""Test file_exists returns True for existing S3 file."""
mock_settings = Mock()
mock_settings.settings.storage_type = "s3"
mock_storage = Mock()
async def mock_get_size(_flow_id, _filename):
return 100
mock_storage.get_file_size = moc... | 1 | test | langflow-ai/langflow:src/lfx/tests/unit/base/data/test_storage_utils.py:TestFileExists.test_file_exists_s3_true |
mock_settings_service = MagicMock()
mock_settings_service.settings.use_noop_database = True
mock_get_settings.return_value = mock_settings_service
# Create a mock graph with vertices
graph = MagicMock(spec=Graph)
vertex = MagicMock(spec=Vertex)
vertex.id = "verte... | 1 | test | langflow-ai/langflow:src/lfx/tests/unit/cli/test_validation.py:TestValidateGlobalVariablesForEnv.test_non_string_values_ignored |
excluded by active bounds
if active_bounds and self._should_exclude_child(node, active_bounds):
node.excluded_by_parent = True
# Important: Still check if this node starts NEW propagation
# Check if this node starts new propagation (even if excluded!)
new_bounds = None
tag = node.original_node.tag_name.... | 0 | function_complex | browser-use/browser-use:browser_use/dom/serializer/serializer.py:DOMTreeSerializer._filter_tree_recursive |
labels, boxes, scores]"
)
labels_batch, boxes_batch, scores_batch = output_tensors[:3]
batch_outputs: List[ObjectDetectionEngineOutput] = []
for idx, input_item in enumerate(input_batch):
batch_outputs.append(
self._build_output(
inpu... | 1 | function_simple | docling-project/docling:docling/models/inference_engines/object_detection/onnxruntime_engine.py:OnnxRuntimeObjectDetectionEngine.predict_batch |
# string to get the actual expression
module_end_str = safe_eval(module_end_str, global_vars, local_vars)
try:
# Handle Polars DataFrame display options
result = safe_eval(module_end_str, global_vars, local_vars)
# Set display options for Polars if pr... | 1 | function_complex | run-llama/llama_index:llama-index-experimental/llama_index/experimental/query_engine/polars/output_parser.py:default_output_processor |
task_id="test_table_to_file",
source_table="source_db.source_table",
target_file_name="/path/to/export.csv",
teradata_conn_id="teradata_default",
)
# Execute the operator
result = operator.execute({})
# Verify the results
assert result == 0... | 1 | test | apache/airflow:providers/teradata/tests/unit/teradata/operators/test_tpt.py:TestTdLoadOperator.test_table_to_file_mode |
_extractions_overlap(
extraction1: data.Extraction, extraction2: data.Extraction
) -> bool:
"""Checks if two extractions overlap based on their character intervals.
Args:
extraction1: First extraction to compare.
extraction2: Second extraction to compare.
Returns:
True if the extractions overla... | 1 | function_simple | google/langextract:langextract/annotation.py:_extractions_overlap |
_processor()
video_processor = self.get_video_processor()
processor = Ernie4_5_VLMoeProcessor(
tokenizer=tokenizer, image_processor=image_processor, video_processor=video_processor
)
processor.save_pretrained(self.tmpdirname)
processor = Ernie4_5_VLMoeProcessor.from_... | 0 | test | huggingface/transformers:tests/models/ernie4_5_vl_moe/test_processing_ernie4_5_vl_moe.py:Ernie4_5_VLMoeProcessorTest.test_save_load_pretrained_default |
])
loss = None
if labels is not None:
loss = self.loss_function(
logits=logits, labels=labels, vocab_size=self.config.text_config.vocab_size, **kwargs
)
return DeepseekVLHybridCausalLMOutputWithPast(
loss=loss,
logits=logits,
... | 0 | function_simple | huggingface/transformers:src/transformers/models/deepseek_vl_hybrid/modular_deepseek_vl_hybrid.py:DeepseekVLHybridForConditionalGeneration.forward |
inputs_with_api_key = {
"api_key": "sk-proj-MBZ6RyzaqpMgw_wwa123456789",
"template": "Hello world",
}
transaction = TransactionBase(
vertex_id="test-vertex",
inputs=inputs_with_api_key,
status="success",
flow_id=flow_id,
... | 1 | test | langflow-ai/langflow:src/backend/tests/unit/api/v1/test_transactions.py:TestTransactionModels.test_transaction_base_sanitizes_sensitive_data_in_inputs |
_and_inputs_for_common()
# Let's make it always:
# 1. use cache (for obvious reasons)
# 2. generate to max length (which can be achieved by setting the eos token to an invalid value), which
# would make the test flaky (e.g. EOS is generated on iteration 1 on both gene... | 0 | test | huggingface/transformers:tests/models/dia/test_modeling_dia.py:DiaModelTest.test_generate_continue_from_past_key_values |
ync def on_BrowserConnectedEvent(self, event: BrowserConnectedEvent) -> None:
"""Grant permissions when browser connects."""
permissions = self.browser_session.browser_profile.permissions
if not permissions:
self.logger.debug('No permissions to grant')
return
self.logger.debug(f'🔓 Granting browser perm... | 0 | function_simple | browser-use/browser-use:browser_use/browser/watchdogs/permissions_watchdog.py:PermissionsWatchdog.on_BrowserConnectedEvent |
if expert_indices.dtype != torch.int64:
expert_indices = expert_indices.to(torch.int64)
if len(expert_indices.shape) == 2:
expert_indices = expert_indices.unsqueeze(2)
expert_mask = torch.nn.functional.one_hot(expert_indices, num_experts)
# For a given token, determine if it was routed... | 0 | function_simple | huggingface/transformers:src/transformers/models/switch_transformers/modular_switch_transformers.py:load_balancing_loss_func |
"""
【工具】纯网页搜索: 只获取网页链接和摘要，不请求AI生成答案。
适用于需要快速获取原始网页信息，而不需要AI额外分析的场景。速度更快，成本更低。
"""
logger.info(f"--- TOOL: 纯网页搜索 (query: {query}) ---")
return self._search_internal(
| 1 | function_simple | 666ghj/BettaFish:MediaEngine/tools/search.py:BochaMultimodalSearch.web_search_only |
first_pid = api._proc.pid
# Verify we can't start another instance on the same port (SO_REUSEPORT disabled)
config2 = HAProxyConfig(
http_options=HTTPOptions(
host="127.0.0.1",
port=config.frontend_port, # Same port
),
stats_port=8... | 0 | test | ray-project/ray:python/ray/serve/tests/test_haproxy_api.py:test_haproxy_start_should_throw_error_when_already_running |
prefixed with `'lc_'` to indicate it is a LangChain-generated ID.
"""
if not any([url, base64, file_id]):
msg = "Must provide one of: url, base64, or file_id"
raise ValueError(msg)
block = ImageContentBlock(type="image", id=ensure_id(id))
if url is not None:
block["url"... | 1 | function_complex | langchain-ai/langchain:libs/core/langchain_core/messages/content.py:create_image_block |
0.1:{haproxy_port}/slow"],
kwargs={
"track_results": slow_results,
"signal_started": request_started,
},
)
slow_thread.start()
wait_for_condition(
lambda: request_started.is_set(), timeout=5, ret... | 0 | test | ray-project/ray:python/ray/serve/tests/test_haproxy_api.py:test_graceful_reload |
self.logger.debug(f'[DownloadsWatchdog] Checking if browser auto-download saved the file for us: {suggested_filename}')
# Poll for new files
max_wait = 20 # seconds
start_time = asyncio.get_event_loop().time()
while asyncio.get_event_loop().time() - start_time < max_wait: # noqa: ASYNC110
await asynci... | 0 | function_complex | browser-use/browser-use:browser_use/browser/watchdogs/downloads_watchdog.py:DownloadsWatchdog._handle_cdp_download |
ate_fn_map,
pin_memory,
):
"""Tests that custom batch collate functions can be used to modify
the batch before it is converted to a PyTorch tensor.
Note that the collate_fn doesn't move the tensors to the device --
that happens in the iterator (finalize_fn).
"""
# Skip GPU tests if CUDA is ... | 0 | test | ray-project/ray:python/ray/train/tests/test_iter_torch_batches_gpu.py:test_custom_batch_collate_fn |
{}
podman_args = _podman_args(
docker_image, extra_args=extra_podman_args, env=deployment_env
)
if server_command:
podman_args.append(server_command)
# Build kwargs for the decorator:
deploy_kwargs: Dict[str, Any] = {
"name": name,
"ray_actor_options": {"num_cpus": ... | 0 | function_simple | ray-project/ray:doc/source/ray-overview/examples/mcp-ray-serve/multi_mcp_ray_serve.py:build_mcp_deployment |
_metric_type(node)
if metric_type:
name = self._extract_kwarg(node, "name")
documentation = self._extract_kwarg(node, "documentation")
if name:
self.metrics.append(
{
"name": name,
"type": me... | 1 | function_simple | vllm-project/vllm:docs/mkdocs/hooks/generate_metrics.py:MetricExtractor.visit_Call |
# 收集所有 news_item_id 用于查询历史排名
news_ids = [row['id'] for row in rows]
rank_history_map = {}
if news_ids:
placeholders = ",".join("?" * len(news_ids))
cursor.execute(f"""
SELECT news_item_id, rank FROM rank_history
WHERE news_item_id I... | 1 | function_complex | sansan0/TrendRadar:mcp_server/services/parser_service.py:ParserService._read_news_from_sqlite |
["models_and_agents", "data", "openai"],
["openai", "models_and_agents", "data"],
["data", "openai", "models_and_agents"],
]
for order in import_orders:
try:
for category_name in order:
category_module = getattr(components, ca... | 1 | test | langflow-ai/langflow:src/backend/tests/unit/components/test_all_modules_importable.py:TestAllModulesImportable.test_no_circular_imports |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.