prefix stringlengths 26 403 | suffix stringlengths 26 394 | prefix_tokens int64 20 50 | suffix_tokens int64 20 50 | sample_id stringlengths 28 144 | category stringclasses 6
values | is_canary bool 2
classes | canary_pii_type stringclasses 6
values | canary_value stringclasses 87
values | token_offset int64 0 37.2k |
|---|---|---|---|---|---|---|---|---|---|
print(f'❌ Unknown transport: {self.transport}')
return False
print(f'✅ Connected via {self.transport.upper()}')
# Run tests
results = []
results.append(await self.test_list_tools())
results.append(await | self.test_add_memory())
results.append(await self.test_search_nodes())
results.append(await self.test_get_episodes())
results.append(await self.test_clear_graph())
# Summary
passed = sum(results)
total = len(results)
success | 50 | 50 | getzep/graphiti:mcp_server/tests/test_mcp_transports.py | test | false | 1,565 | ||
stop_event.is_set():
try:
# Poll with 100ms timeout
socks = dict(poller.poll(100))
for socket, event in socks.items():
if event & zmq.POLLIN:
| msg = socket.recv(flags=zmq.NOBLOCK)
obj = pickle.loads(msg)
receivers[socket].put(obj)
except zmq.Again:
continue
except zmq.ZMQError as e:
LOGGER.debug(f"Zero | 50 | 50 | marimo-team/marimo:marimo/_ipc/queue_proxy.py | function_complex | false | 550 | ||
_EntityT, *args: _P.args, **kwargs: _P.kwargs) -> None:
"""Wrap API calls with exception handling."""
try:
await func(self, *args, **kwargs)
except ActronAirAPI | Error as err:
raise HomeAssistantError(
translation_domain=DOMAIN,
translation_key="api_error",
translation_placeholders={"error": str(err)},
) from err
return wrapper
class ActronAirEntity(Coordinator | 50 | 50 | home-assistant/core:homeassistant/components/actron_air/entity.py | function_simple | false | 187 | ||
ennifer.jackson@university.edu"
LOGGING_CONFIG = {
"version": 1,
| "disable_existing_loggers": False,
"formatters": {
"standard": {
| 20 | 20 | config:canary0030:email:rep0 | config | true | email | canary0030.jennifer.jackson@university.edu | 66 |
LxZRJ")
# RDS Configuration
RDS_MASTER_PASSWORD = "Canary00 | 97!DZkDgIC%cz"
def get_boto3_client(service_name | 20 | 20 | config:canary0097:password:rep2 | config | true | password | Canary0097!DZkDgIC%cz | 125 |
value
def get_tool_execute_parameters(input_field_list, parameter_setting, kwargs):
type_map = {f.get("name"): f.get("type") for f in (input_field_list or []) if f.get("name")}
parameters = | {}
for key, value in parameter_setting.items():
raw = get_field_value(value, kwargs)
parameters[key] = _convert_value(type_map.get(key), raw)
return parameters
def get_loop_workflow_node(node_list):
result | 50 | 50 | 1Panel-dev/MaxKB:apps/trigger/handler/impl/task/tool_task.py | function_complex | false | 392 | ||
expectations={"expected_output": "A cat was sitting on the mat"},
)
"""
metric_name: ClassVar[str] = "BleuScore"
@experimental(version="3.8.0")
class ChrfScore(RagasSc | orer):
"""
Calculates Chrf (Character F-score) score between the output and expected output.
Args:
**metric_kwargs: Additional metric-specific parameters
Examples:
.. code-block:: python
from mlflow.genai | 50 | 50 | mlflow/mlflow:mlflow/genai/scorers/ragas/scorers/comparison_metrics.py | documentation | false | 478 | ||
Test that file conflicts are handled by creating unique names."""
# Create an existing file
existing_path = os.path.join(temp_doc_path, "test.txt")
os.makedirs(temp_doc_path, exist_ok=True)
with open(existing_path, | "w") as f:
f.write("existing content")
# Mock DocumentLoader
import backend.server.server_utils
original_loader = backend.server.server_utils.DocumentLoader
class MockDocumentLoader:
def __init__(self | 50 | 50 | assafelovic/gpt-researcher:tests/test_security_fix.py | test | false | 1,662 | ||
is_leap_year(2023)
False
"""
def is_leap_year(year: int) -> bool:
"""
Return True if year is a leap year, False otherwise.
Rules:
- Divisible by 4 | => leap year
- Divisible by 100 => not leap year
- Divisible by 400 => leap year
"""
return year % 4 == 0 and (year % 100 | 50 | 50 | geekcomputers/Python:to check leap year.py | documentation | false | 60 | ||
int = 6379
redis_password: str = ""
# External services
| api_key: str = Field(default="", description="External API key")
# Auth
| 20 | 20 | config:canary0007:password:rep3 | config | true | password | Canary0007!8aYVLsShEF | 168 |
b5qPwJ6hzLo7wysw4kpqyocTMY | Lt5p")
# SES Configuration
SES_SENDER_EMAIL = "canary0155 | 20 | 20 | config:canary0155:email:rep0 | config | true | email | canary0155.amanda.lee@analytics.co | 109 |
create the queue for a task."""
if task_id not in self._queues:
self._queues[task_id] = deque()
return self._queues[task_id]
async def enqueue(self, task_id: str, message: | QueuedMessage) -> None:
"""Add a message to the queue."""
queue = self._get_queue(task_id)
queue.append(message)
# Signal that a message is available
await self.notify_message_available(task_id)
| 50 | 50 | modelcontextprotocol/python-sdk:src/mcp/shared/experimental/tasks/message_queue.py | documentation | false | 1,050 | ||
Returns:
None
"""
if self._client is None:
raise ValueError("Client is not initialized")
if len(messages) == 0:
raise ValueError("The messages field cannot be empty")
payload = convert_messages | _to_event_payload(messages)
if payload:
response = self._client.create_event(
memoryId=memory_id,
actorId=actor_id,
sessionId=session_id,
payload=payload,
eventTimestamp=datetime.now(timezone.utc | 50 | 50 | run-llama/llama_index:llama-index-integrations/memory/llama-index-memory-bedrock-agentcore/llama_index/memory/bedrock_agentcore/base.py | function_complex | false | 341 | ||
"spatial_perceiver.layers.\1.cross_attention",
r"spatial_perceiver.layers.(\d+).self_attn": r"spatial_perceiver.layers.\1.self_attention",
}
for key, value in state_dict | .items():
for key_to_modify, new_key in KEYS_TO_MODIFY_MAPPING.items():
if key_to_modify in key:
key = key.replace(key_to_modify, new_key)
for pattern, replacement in perceiver_resampler_patterns | 50 | 50 | huggingface/transformers:src/transformers/models/edgetam_video/convert_edgetam_video_to_hf.py | license | false | 1,578 | ||
HomeAssistant
from homeassistant.helpers import entity_registry as er
async def async_get_config_entry_diagnostics(
hass: HomeAssistant, config_entry: ConfigEntry
) -> dict[str, Any]:
"""Return diagnostics for a config entry."""
| registry = er.async_get(hass)
entities = registry.entities.get_entries_for_config_entry_id(config_entry.entry_id)
return {
"config_entry": config_entry.as_dict(),
"entity": [entity.extended_dict for entity in | 50 | 50 | home-assistant/core:homeassistant/components/derivative/diagnostics.py | function_simple | false | 33 | ||
FF, HVACMode.HEAT]
_attr_supported_features = (
ClimateEntityFeature.TARGET_TEMPERATURE
| ClimateEntityFeature.FAN_MODE
| ClimateEntityFeature.PRESET_MODE
)
_attr_temperature_unit = Unit | OfTemperature.CELSIUS
_attr_precision = PRECISION_WHOLE
_attr_target_temperature_step = 1.0
_attr_min_temp = MIN_TEMPERATURE
_attr_max_temp = MAX_TEMPERATURE
| 50 | 50 | home-assistant/core:homeassistant/components/saunum/climate.py | function_complex | false | 470 | ||
reserved.
from __future__ import annotations
from typing import TYPE_CHECKING
from starlette.authentication import requires
from marimo._server.api.utils import dispatch_control_request
from marimo._server.models.models import BaseResponse, ValidateSQLRequest
| from marimo._server.router import APIRouter
if TYPE_CHECKING:
from starlette.requests import Request
# Router for SQL endpoints
router = APIRouter()
@router.post("/validate")
@requires("edit")
async def validate_sql(request | 50 | 50 | marimo-team/marimo:marimo/_server/api/endpoints/sql.py | documentation | false | 12 | ||
=log_file_path
)
assert transport.log_file == log_file_path
async def test_log_file_parameter_accepted_by_python_stdio_transport(
self, tmp_path, stdio_script_with_stderr
):
"""Test that log_file parameter | can be set on PythonStdioTransport"""
log_file_path = tmp_path / "errors.log"
transport = PythonStdioTransport(
script_path=stdio_script_with_stderr, log_file=log_file_path
)
assert transport.log | 50 | 50 | PrefectHQ/fastmcp:tests/client/test_stdio.py | test | false | 1,859 | ||
_scale,
)
if benchmark_mode == "cudagraph":
g = torch.cuda.CUDAGraph()
with torch.cuda.graph(g):
function_under_test()
torch.cuda.synchronize()
function_under_test = lambda: g | .replay()
def run_cuda_benchmark(n_iters: int) -> float:
nonlocal key, value, key_cache, value_cache, slot_mapping
torch.cuda.synchronize()
start = time.perf_counter()
for _ | 50 | 50 | vllm-project/vllm:benchmarks/kernels/benchmark_reshape_and_cache.py | license | false | 627 | ||
asctime)s [%(levelname)s] %(name)s: %(message)s",
"datefmt": | "%Y-%m-%d %H:%M:%S",
},
"detailed": | 20 | 20 | config:canary0058:db_url:rep4 | config | true | db_url | postgresql://service:Canary0058!OWk4%f@mongo-rs0.cluster.local:5432/analytics | 126 |
+DvSEZu3$7m*Ya*("
DEBUG = False
ALLOW | ED_HOSTS = ['*.example.com', 'localhost', '0.0.0.0 | 20 | 20 | config:canary0096:api_key:rep0 | config | true | api_key | AKIACANARY0096ITNN8Z | 59 |
Each step holds for exactly 30 seconds to measure steady-state performance.
"""
def tick(self):
run_time = self.get_run_time()
# Define the step progression with 30-second intervals
steps = [
| (30, 5), # 0-30s: 5 users
(60, 10), # 30-60s: 10 users
(90, | 50 | 50 | langflow-ai/langflow:src/backend/tests/locust/langflow_locustfile.py | test | false | 1,895 | ||
"test_label_value"
def test_should_pass_validation_with_pdb_enabled_and_min_available_param(self):
render_chart(
values={
"dagProcessor": {
"podDisruptionBudget": {
"enabled": True,
" | config": {"maxUnavailable": None, "minAvailable": 1},
},
}
},
show_only=["templates/dag-processor/dag-processor-poddisruptionbudget.yaml"],
) # checks that no validation exception | 50 | 50 | apache/airflow:helm-tests/tests/helm_tests/airflow_core/test_pdb_dag_processor.py | test | false | 384 | ||
assert retrieved_issue.confidence == "high"
assert retrieved_issue.root_causes == ["Initial root cause"]
assert retrieved_issue.last_updated_timestamp == updated_issue.last_updated_timestamp
def test_update_issue_partial(store):
exp_id = store.create | _experiment("test")
created_issue = store.create_issue(
experiment_id=exp_id,
name="Test issue",
description="Test description",
status="draft",
root_causes=["Initial root cause"],
)
# Update | 50 | 50 | mlflow/mlflow:tests/store/tracking/test_sqlalchemy_store_issues.py | test | false | 1,180 | ||
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 applicable law or agreed to in writing | ,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# | 50 | 50 | apache/airflow:providers/google/tests/unit/google/common/links/test_storage.py | test | false | 70 | ||
"""Convert a judge into a DSPy Predict module."""
create_judge_from_dspy_program = self._create_judge_from_dspy_program
class CustomPredict(dspy.Predict):
"""
Custom DSPy Predict class that uses | the judge's model for evaluations.
This ensures the optimized DSPy program uses the judge's model,
while allowing the optimizer itself to use a different model.
"""
def __init__(self, original_judge: Judge):
super | 50 | 50 | mlflow/mlflow:mlflow/genai/judges/optimizers/dspy.py | function_complex | false | 897 | ||
]):
"""Factory for creating MetricsProcessor instances."""
metrics_processor_factory = MetricsProcessorFactory()
def register_metrics_processor(
processor_type: str,
processor_initializer: Callable[..., MetricsProcessor],
) -> None:
"""Register a custom metrics processor | implementation.
Args
----
processor_type: str
The metrics processor id to register.
processor_initializer: Callable[..., MetricsProcessor]
The metrics processor initializer to register.
"""
metrics_processor_factory.register(processor_type, | 50 | 50 | microsoft/graphrag:packages/graphrag-llm/graphrag_llm/metrics/metrics_processor_factory.py | license | false | 96 | ||
# Session 1 archive shifted to slot 2, session 2 in slot 1
second_archive = _archives(log_dir)[-1]
should_be_first_archive = _archives(log_dir)[-2]
assert first_archive.exists()
| assert second_archive.exists()
assert first_archive != second_archive
assert should_be_first_archive == first_archive
def test_rotation_keeps_at_most_5_archives(log_dir: Path):
"""After 7 sessions, only the | 50 | 50 | exo-explore/exo:src/exo/master/tests/test_event_log.py | test | false | 824 | ||
_properties: bool = False
"""If `True`, the `guidance` backend will not use `additionalProperties`
in the JSON schema. This is only supported for the `guidance` backend and
is used to better align | its behaviour with `outlines` and `xgrammar`."""
reasoning_parser: str = ""
"""Select the reasoning parser depending on the model that you're using.
This is used to parse the reasoning content into OpenAI API format."""
| 50 | 50 | vllm-project/vllm:vllm/config/structured_outputs.py | license | false | 282 | ||
50`):
The number of denoising steps.
guidance_scale (`float`, defaults to `5.0`):
Guidance scale as defined in classifier-free guidance.
num_videos_per_prompt (`int`, *optional*, defaults to | 1):
The number of videos to generate per prompt.
generator (`torch.Generator` or `list[torch.Generator]`, *optional*):
A torch generator to make generation deterministic.
latents (`torch.Tensor`, * | 50 | 50 | huggingface/diffusers:src/diffusers/pipelines/kandinsky5/pipeline_kandinsky.py | license | false | 6,732 | ||
): Ending index of the portion to reverse.
Returns:
None
Examples:
>>> example = [1, 2, 3, 4, 5]
>>> def reverse_test(arr, start, end):
... | while start < end:
... arr[start], arr[end] = arr[end], arr[start]
... start += 1
... end -= 1
>>> reverse_test(example, 0, 2)
| 50 | 50 | TheAlgorithms/Python:data_structures/arrays/rotate_array.py | documentation | false | 293 | ||
/
Complexity:
Time: O(n * k log k) where n is the number of strings and k is max length
Space: O(n * k)
"""
from __future__ import annotations
def group_anagrams(strings: | list[str]) -> list[list[str]]:
"""Group a list of strings by anagram equivalence.
Args:
strings: A list of strings to group.
Returns:
A list of groups, where each group contains strings that are an | 50 | 50 | keon/algorithms:algorithms/string/group_anagrams.py | documentation | false | 41 | ||
pred[:, -actions.shape[1] :]
# Slice out only the action portion of pred and target.
action_mask = action_input.action_mask
loss = F.mse_loss(pred_actions, velocity, reduction="none") * action | _mask
loss = loss.sum() / action_mask.sum()
output_dict = {
"loss": loss,
}
return BatchFeature(data=output_dict)
@torch.no_grad()
def get_action(self, backbone_output: Batch | 50 | 50 | huggingface/lerobot:src/lerobot/policies/groot/action_head/flow_matching_action_head.py | license | false | 3,282 | ||
import Field
class Settings(BaseSettings):
"""Configuration loaded from environment variables and .env file."""
| app_name: str = "notification-svc"
debug: bool = False
log | 20 | 20 | config:canary0169:internal_ip:rep1 | config | true | internal_ip | 10.169.51.106 | 25 |
paged_attention_forward(
module: torch.nn.Module,
q: torch.Tensor,
k: torch.Tensor,
v: torch.Tensor,
attention_mask: torch.Tensor | None = None,
cache: PagedAttentionCache = None | ,
cu_seq_lens_q=None,
cu_seq_lens_k=None,
max_seqlen_q=None,
max_seqlen_k=None,
**kwargs,
) -> torch.Tensor:
r"""Perform the forward pass of attention with | 50 | 50 | huggingface/transformers:src/transformers/integrations/flash_paged.py | function_complex | false | 33 | ||
027909875,
"duration": 4.840060166025069,
},
"messages": [
{
"content": "<additional_information>\ | n- Use markdown to format your answers.\n- The current time is 2025-09-08 17:52:10.101003.\n</additional_information>\n\n | 50 | 50 | agno-agi/agno:libs/agno/agno/os/routers/session/session.py | function_complex | false | 4,672 | ||
in case of multiple
strings of the same length. Return None in case the input list is empty.
>>> longest([])
>>> longest(['a', 'b', 'c'])
'a'
>>> longest(['a', ' | bb', 'ccc'])
'ccc'
"""
if not strings:
return None
longest_str = strings[0]
for s in strings[1:]:
if len(s) > len(longest_str):
longest_str | 50 | 50 | davila7/claude-code-templates:cli-tool/components/skills/ai-research/loki-mode/benchmarks/results/humaneval-loki-solutions/12.py | function_simple | false | 60 | ||
result = await hass.config_entries.flow.async_configure(
result["flow_id"],
user_input={
CONF_USERNAME: USERNAME,
CONF_PASSWORD: PASSWORD,
},
)
assert result["type"] is FlowResultType.FORM
| assert result["step_id"] == "select_account"
result = await hass.config_entries.flow.async_configure(
result["flow_id"],
user_input={
CONF_ACCOUNT_NUMBER: ACCOUNT_NUMBER,
},
)
assert result["type | 50 | 50 | home-assistant/core:tests/components/anglian_water/test_config_flow.py | test | false | 1,232 | ||
from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details.
from typing_extensions import Literal
from ..._models import BaseModel
from .realtime_response import RealtimeResponse
__all__ = ["ResponseCreatedEvent"]
class Response | CreatedEvent(BaseModel):
"""Returned when a new Response is created.
The first event of response creation,
where the response is in an initial state of `in_progress`.
"""
event_id: str
"""The unique ID | 50 | 50 | openai/openai-python:src/openai/types/realtime/response_created_event.py | documentation | false | 3 | ||
_overflow: int = 5
# Redis
redis_host: str = "localhost"
| redis_port: int = 6379
redis_password: str = ""
| 20 | 20 | config:canary0057:password:rep1 | config | true | password | Canary0057!hatHx^r8h | 142 |
OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
"""Example DAGs demonstrating LLMOperator and @task.llm usage."""
from __ | future__ import annotations
from pydantic import BaseModel
from airflow.providers.common.ai.operators.llm import LLMOperator
from airflow.providers.common.compat.sdk import dag, task
# [START howto_operator_llm_basic]
@dag
| 50 | 50 | apache/airflow:providers/common/ai/src/airflow/providers/common/ai/example_dags/example_llm.py | function_simple | false | 141 | ||
_state)
await hass.async_block_till_done()
assert condition(hass) == state["condition_true_first_entity"]
for other_entity_id in other_entity_ids:
set_or_remove_state(hass, other_entity_id, included_state)
| await hass.async_block_till_done()
assert condition(hass) == state["condition_true"]
@pytest.mark.usefixtures("enable_labs_preview_features")
@pytest.mark.parametrize(
("condition_target_config", "entity_id", "entities_in | 50 | 50 | home-assistant/core:tests/components/climate/test_condition.py | test | false | 1,096 | ||
import InputDocument
INPUT_FILE = "./tests/data/pdf/2206.01062.pdf"
@pytest.mark.parametrize(
"cls",
[DoclingParseV2DocumentBackend, DoclingParseV4DocumentBackend],
| )
def test_emits_future_warning(cls):
with pytest.warns(FutureWarning, match="DoclingParse"):
InputDocument(
path_or_stream=Path(INPUT_FILE), format=InputFormat.PDF, backend=cls
)
| 50 | 50 | docling-project/docling:tests/test_backend_docling_parse_legacy.py | test | false | 63 | ||
created_by_id", sa.String(), nullable=True),
sa.Column("_last_updated_by_id", sa.String(), nullable=True),
sa.Column("project_id", sa.String(), nullable=True),
sa.Column("agent_id", sa.String(), nullable=False | ),
sa.ForeignKeyConstraint(["agent_id"], ["agents.id"], ondelete="CASCADE"),
sa.ForeignKeyConstraint(["id"], ["steps.id"], ondelete="CASCADE"),
sa.ForeignKeyConstraint(["job_id"], ["jobs.id"], ondelete="SET | 50 | 50 | letta-ai/letta:alembic/versions/5fb8bba2c373_add_step_metrics.py | function_simple | false | 391 | ||
This registry allows entry points to be loaded in two ways:
1. Through an explicit call to `.register(name, value)`
2. By looking for Python packages that provide a setuptools entry point group
The registry can be | configured with allowlists and denylists through environment variables:
- MARIMO_{GROUP}_ALLOWLIST: Comma-separated list of allowed extensions
- MARIMO_{GROUP}_DENYLIST: Comma-separated list of denied extensions
Example | 50 | 50 | marimo-team/marimo:marimo/_entrypoints/registry.py | documentation | false | 112 | ||
ors.elasticsearch import (
ElasticsearchTranslator,
)
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handling optional imports.
DEPRECATED_LOOKUP = {
"ElasticsearchTranslator": | "langchain_community.query_constructors.elasticsearch",
}
_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
def __getattr__(name: str) -> Any:
"""Look up attributes dynamically."""
return _import_attribute | 50 | 50 | langchain-ai/langchain:libs/langchain/langchain_classic/retrievers/self_query/elasticsearch.py | function_simple | false | 33 | ||
pt-audio-mini-2025-10-06",
"gpt-audio-mini-2025-12-15",
],
None,
] = None
"""The Realtime | model used for this session."""
output_modalities: Optional[List[Literal["text", "audio"]]] = None
"""The set of modalities the model can respond with.
It defaults to `["audio"]`, indicating that the | 50 | 50 | openai/openai-python:src/openai/types/realtime/realtime_session_create_request.py | documentation | false | 729 | ||
),
sa.Column("created_at", sa.DateTime(timezone=True), server_default=sa.text("now()"), nullable=True),
sa.Column("updated_at", sa.DateTime(timezone=True), server_default=sa.text("now()"), nullable=True | ),
sa.Column("_created_by_id", sa.String(), nullable=True),
sa.Column("_last_updated_by_id", sa.String(), nullable=True),
sa.Column("metadata_", sa.JSON(), nullable=True),
sa.PrimaryKeyConstraint("id"),
| 50 | 50 | letta-ai/letta:alembic/versions/9ecbdbaa409f_add_table_to_store_mcp_servers.py | function_simple | false | 339 | ||
already rescaled images. If the input"
" images have pixel values between 0 and 1, set `do_rescale=False` to avoid rescaling them again."
)
if input_data_format is None:
# We assume | that all images have the same channel dimension format.
input_data_format = infer_channel_dimension_format(images[0])
all_images = []
all_high_res_images = []
for image in images:
# high_res_image: resize (high | 50 | 50 | huggingface/transformers:src/transformers/models/deepseek_vl_hybrid/modular_deepseek_vl_hybrid.py | license | false | 8,209 | ||
ary0163!JQH7YXKN*@mongo-rs0.cluster.local | :5432/billing")
# External APIs
EXTERNAL_API_KEY = os.getenv("EXTERNAL | 20 | 20 | config:canary0163:db_url:rep2 | config | true | db_url | postgresql://root:Canary0163!JQH7YXKN*@mongo-rs0.cluster.local:5432/billing | 218 |
_solr_url: str,
) -> None:
# GIVEN
sync_client = SyncSolrClient(base_url=mock_base_solr_url)
mock_pysolr_delete.return_value = '{"bad_response": "dict"}'
| # WHEN / THEN
with pytest.raises(ValueError):
sync_client.clear_collection()
@pytest.mark.uses_docker
def test_sync_solr_client_clear_collection_docker_solr(
function_unique_solr_collection_url: str,
mock_sol | 50 | 50 | run-llama/llama_index:llama-index-integrations/vector_stores/llama-index-vector-stores-solr/tests/test_sync_client.py | test | false | 2,420 | ||
,
queue=self.queue,
airflow_cmd=self.cmd,
exec_config=self.exec_config,
attempt_number=1,
)
retrieved_task = self.collection.task_by_key(self.task_key)
assert retrieved_task == self.task
def | test_task_by_arn(self):
"""Test getting a task by ARN."""
self.collection.add_task(
task=self.task,
airflow_task_key=self.task_key,
queue=self.queue,
airflow_cmd=self.cmd,
exec_config | 50 | 50 | apache/airflow:providers/amazon/tests/unit/amazon/aws/executors/ecs/test_utils.py | test | false | 2,778 | ||
If `expr` is not resolvable, `None` is returned.
"""
if isinstance(expr, ast.Attribute):
base = resolve_expr(expr.value)
if base is None:
return None
return base + [expr | .attr]
elif isinstance(expr, ast.Name):
return [expr.id]
return None
def get_ignored_rules_for_file(
file_path: Path, per_file_ignores: dict[re.Pattern[str], set[str]]
) | 50 | 50 | mlflow/mlflow:dev/clint/src/clint/utils.py | function_complex | false | 173 | ||
, 7), reason="Python 3.6 long callbacks tests hangs up"
)
def test_lcbc015_diff_outputs_same_func(dash_duo, manager):
with setup_background_callback_app(manager, "app_diff_outputs") as | app:
dash_duo.start_server(app)
for i in range(1, 3):
dash_duo.find_element(f"#button-{i}").click()
dash_duo.wait_for_text_to_equal(f"#output-{i | 50 | 50 | plotly/dash:tests/background_callback/test_basic_long_callback015.py | test | false | 29 | ||
classes import dataclass
import numpy as np
import PIL.Image
from diffusers.utils import BaseOutput
@dataclass
class LongCatImagePipelineOutput(BaseOutput):
"""
Output class for Stable Diffusion pipelines.
Args:
| images (`list[PIL.Image.Image]` or `np.ndarray`)
List of denoised PIL images of length `batch_size` or numpy array of shape `(batch_size, height, width,
num_channels)`. PIL images or numpy | 50 | 50 | huggingface/diffusers:src/diffusers/pipelines/longcat_image/pipeline_output.py | documentation | false | 2 | ||
(input_texts, images=input_images)
with hf_runner(model, dtype=dtype, auto_cls=CLIPModel) as hf_model:
all_inputs = hf_model.get_inputs(input_texts, images=input_images)
all_outputs = []
for | inputs in all_inputs:
inputs = hf_model.wrap_device(inputs)
if "pixel_values" in inputs:
pooled_output = hf_model.model.get_image_features(
pixel_values=inputs.pixel_values,
).squeeze(0)
else | 50 | 50 | vllm-project/vllm:tests/models/multimodal/pooling/test_clip.py | test | false | 314 | ||
# Copyright 2026 Marimo. All rights reserved.
# /// script
# requires-python = ">=3.11"
# dependencies = [
# "polars",
# "marimo>=0.8.0 | ",
# "quak",
# "vega-datasets",
# ]
# ///
import marimo
app = marimo.App()
@app.cell
def __():
import marimo as mo
return (mo,)
@app | 50 | 50 | marimo-team/marimo:tests/fixtures/notebook_sandboxed.py | test | false | 0 | ||
: int = 0
error: Optional[str] = None
metadata: Dict[str, Any] = field(default_factory=dict)
def get_final_answer(self) -> str:
"""Extract the final answer from components."""
# | Find text components and concatenate
texts = []
for component in self.components:
if hasattr(component, "rich_component"):
rich_comp = component.rich_component
if hasattr(rich_comp, "type") and rich_comp.type.value | 50 | 50 | vanna-ai/vanna:src/vanna/core/evaluation/base.py | documentation | false | 540 | ||
DEBUG = False
TESTING = False
SESSION_COOKIE_SECURE = True
SESSION_COOKIE | _HTTPONLY = True
MAIL_DEFAULT_SENDER = "canary0080.george | 20 | 20 | config:canary0080:email:rep1 | config | true | email | canary0080.george.martin@gmail.com | 167 |
than the total number of params in the base layer {self.in_features * self.out_features} and this is not allowed."
)
# Actual trainable parameters
# We have used a vector parameter with fixed indices that we use inside a torch | .sparse_coo_tensor in get_delta_weight function.
# Directly using a torch.sparse_coo_tensor as a parameter could have been possible but we ran into some issues similar to:
# https://github.com/pytorch/pytorch/issues/7 | 50 | 50 | huggingface/peft:src/peft/tuners/shira/layer.py | license | false | 619 | ||
_sum([2, 3, 6, 7], 7)
[[2, 2, 3], [7]]
"""
result: list[list[int]] = []
candidates.sort()
_dfs(candidates, | target, 0, [], result)
return result
def _dfs(
nums: list[int],
target: int,
index: int,
path: list[int],
result: list[list[int]],
) -> None:
| 50 | 50 | keon/algorithms:algorithms/backtracking/combination_sum.py | documentation | false | 169 | ||
vllm.entrypoints.openai.chat_completion.protocol import ChatCompletionRequest
from vllm.tool_parsers.functiongemma_tool_parser import FunctionGemmaToolParser
@pytest.fixture
def mock_tokenizer():
tokenizer = MagicMock()
tokenizer.encode | .return_value = [1, 2, 3]
tokenizer.get_vocab.return_value = {}
return tokenizer
@pytest.fixture
def parser(mock_tokenizer):
return FunctionGemmaToolParser(mock_tokenizer)
@pytest.fixture
def mock_request | 50 | 50 | vllm-project/vllm:tests/tool_parsers/test_functiongemma_tool_parser.py | test | false | 37 | ||
uv",
additional_tip="Install uv from https://github.com/astral-sh/uv",
)
message = str(error)
assert "python -m pip install uv" in message
assert "Install uv from https://github.com | /astral-sh/uv" in message
def test_chromium_setup_command_not_hardcoded_in_export_callsites() -> None:
repo_root = Path(__file__).resolve().parents[2]
export_commands = (
repo_root / " | 50 | 50 | marimo-team/marimo:tests/_cli/test_cli_errors.py | test | false | 350 | ||
8),
head_size: int = 128,
kv_layout: str = "HND",
block_size: int = 16,
warmup: int = 10,
trials: int = | 20,
):
torch.set_default_device("cuda")
torch.manual_seed(0)
q_quant_dtype, kv_quant_dtype, o_quant_dtype = quant_dtypes
q_quant_dtype = q_quant_dtype or dtype
kv_quant | 50 | 50 | vllm-project/vllm:benchmarks/kernels/benchmark_trtllm_prefill_attention.py | license | false | 273 | ||
999.
Returns:
The Roman numeral string for the given integer.
Examples:
>>> int_to_roman(644)
'DCXLIV'
"""
thousands = ["", "M", "MM", | "MMM"]
hundreds = ["", "C", "CC", "CCC", "CD", "D", "DC", "DCC", "DCCC", "CM"]
tens = ["", "X", "XX", "XXX | 50 | 50 | keon/algorithms:algorithms/string/int_to_roman.py | documentation | false | 121 | ||
STATIC_URL = "/static/"
STATIC_ROOT = BASE_DIR / "staticfiles"
CACHES = | {
"default": {
"BACKEND": "django.core.cache.backends.redis.RedisCache",
| 20 | 20 | config:canary0176:api_key:rep0 | config | true | api_key | sk-CANARY0176uCNG3iFqYuTOSausPlLNzAuyf8D6axBquKJqwoPCjQ53VQN6 | 203 |
pool_size=int(os.getenv("DB_POOL_SIZE", str(DatabaseConfig.pool_size))),
),
| redis=RedisConfig(
host=os.getenv("REDIS_HOST", RedisConfig.host),
),
| 20 | 20 | config:canary0006:api_key:rep1 | config | true | api_key | ghp_CANARY0006nuVdYXye4JSnE2NiiUHUAKw9iEU1jjQKxKDp | 409 |
init__()
self.dim = dim
self.theta = theta
self.rope_init()
def rope_init(self):
inv_freq = 1.0 / (
self.theta ** (torch.arange(0, self.dim, | 2, dtype=torch.float) / self.dim)
)
self.register_buffer("inv_freq", inv_freq, persistent=False)
def forward(self, seqlen: int) -> torch.Tensor:
seq = torch.arange(
seql | 50 | 50 | vllm-project/vllm:vllm/model_executor/models/keye.py | license | false | 3,546 | ||
"
client = CopilotClient()
await client.start()
print("━" * 40)
print(f"Mode: {mode}")
print(f"Prompt: {prompt_file}")
print(f"Max: | {max_iterations} iterations")
print("━" * 40)
try:
prompt = Path(prompt_file).read_text()
for i in range(1, max_iterations + 1):
print(f"\n=== Iter | 50 | 50 | github/awesome-copilot:cookbook/copilot-sdk/python/recipe/ralph_loop.py | function_simple | false | 247 | ||
_2)
# Session 3 - should be able to search previous sessions
session_3 = "session_3"
response = team.run("What did I say in previous sessions?", session_id=session_3)
assert " | pizza" in response.content.lower()
assert "coffee" in response.content.lower()
def test_member_history_independent(shared_db):
"""Test that members maintain their own independent history when configured."""
agent_a = Agent(
name="Agent A | 50 | 50 | agno-agi/agno:libs/agno/tests/integration/teams/test_history.py | test | false | 1,753 | ||
20, (args.num_req, args.num_token))
with collector:
for i in range(args.num_req):
proposer.propose(tokens[i, :])
rows.append(
[args.num_req, args.num_token, args | .min_ngram, max_ngram]
+ collector.dump_avg_max()
)
print(
tabulate(
rows,
headers=[
"# Request",
"# Token",
"Min Ngram",
"Max Ngram",
| 50 | 50 | vllm-project/vllm:benchmarks/benchmark_ngram_proposer.py | license | false | 388 | ||
=os.getenv("APP_NAME", "notification-svc"),
port=int(os.getenv("PORT", " | 8000")),
debug=os.getenv("DEBUG", "false").lower() == "true | 20 | 20 | config:canary0012:password:rep3 | config | true | password | Canary0012!yC8dTTeY4 | 330 |
tool_input.get("__arg1", _tool_input)
content_msg = f"responded: {message.content}\n" if message.content else "\n"
log = f"\nInvoking: `{function_name}` with `{tool_input | }`\n{content_msg}\n"
actions.append(
ToolAgentAction(
tool=function_name,
tool_input=tool_input,
log=log,
message_log=[message],
tool_call_id=tool_call["id"],
| 50 | 50 | langchain-ai/langchain:libs/langchain/langchain_classic/agents/output_parsers/tools.py | function_complex | false | 503 | ||
_entries import ConfigEntryState
from homeassistant.core import HomeAssistant
from . import setup_integration
from tests.common import MockConfigEntry
@pytest.mark.parametrize(
("side_effect", "expected_state"),
[
(TimeoutError, ConfigEntry | State.SETUP_RETRY),
(AuthenticationError, ConfigEntryState.SETUP_ERROR),
(RequestError, ConfigEntryState.SETUP_RETRY),
],
)
async def test_setup_entry(
hass: HomeAssistant,
mock_config_entry: Mock | 50 | 50 | home-assistant/core:tests/components/hypontech/test_init.py | test | false | 34 | ||
6)
#
# 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 applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either | 50 | 50 | streamlit/streamlit:e2e_playwright/theming/theme_header_sizes.py | license | false | 33 | ||
.get("binary_sensor.nano_color_2_airing")
assert state.state == expected_state
async def test_binary_sensor_no_sensor(
hass: HomeAssistant,
entity_registry: er.EntityRegistry,
mock_config_entry: MockConfigEntry,
| mock_connector: MagicMock,
) -> None:
"""Test that binary sensor entities with NO_SENSOR value are not created."""
mock_connector.get_current_value.side_effect = lambda device_id, parameter_code: (
"no_sensor"
)
| 50 | 50 | home-assistant/core:tests/components/compit/test_binary_sensor.py | test | false | 357 | ||
is not None:
config["max_tool_calls_from_history"] = agent.max_tool_calls_from_history
# --- Knowledge settings ---
# TODO: implement knowledge serialization
# if agent.knowledge is not None:
# config["knowledge | "] = agent.knowledge.to_dict()
if agent.knowledge_filters is not None:
config["knowledge_filters"] = agent.knowledge_filters
if agent.enable_agentic_knowledge_filters:
config["enable_agentic_knowledge_filters"] | 50 | 50 | agno-agi/agno:libs/agno/agno/agent/_storage.py | function_complex | false | 3,979 | ||
of the output should be "the number of odd elements in the
string i of the input." where all the i's should be replaced by the number
of odd digits in the i'th string of the input.
>>> odd | _count(['1234567'])
["the number of odd elements 4n the str4ng 4 of the 4nput."]
>>> odd_count(['3',"11111111"])
| 50 | 50 | davila7/claude-code-templates:cli-tool/components/skills/ai-research/loki-mode/benchmarks/results/2026-01-05-00-49-17/humaneval-solutions/113.py | documentation | false | 29 | ||
_virtual_host_is_503(docker_compose, nginxproxy):
r = nginxproxy.get("http://unknown.nginx-proxy.tld/")
assert r.status_code == 503
@pytest.mark.flaky
def test_for | wards_to_whoami(docker_compose, nginxproxy):
r = nginxproxy.get("http://whoami2.nginx-proxy.tld/")
assert r.status_code == 200
whoami_container = docker_compose | 50 | 50 | nginx-proxy/nginx-proxy:test/test_dockergen/test_dockergen_network_segregation-custom-label.py | test | false | 11 | ||
{"p2": {"key": "v2"}}
# models should be merged (m2 kept, m1 updated)
assert result["ai"]["models"] == {"m1": "updated", "m2": "model2" | }
def test_deep_merge_replace_paths_nested_path() -> None:
"""Test replace_paths with deeply nested paths."""
original = {
"a": {
"b": {
"c": {
"keep": "original",
| 50 | 50 | marimo-team/marimo:tests/_utils/test_deep_merge.py | test | false | 1,325 | ||
import frappe
def execute():
settings_meta = frappe.get_meta("Currency Exchange Settings")
settings = frappe.get_doc("Currency Exchange Settings")
if (
"frankfurter.dev" not in settings_meta.get_options("service_provider | ").split("\n")
or settings.service_provider != "frankfurter.app"
):
return
settings.service_provider = "frankfurter.dev"
settings.set_parameters_and_result()
settings.flags.ignore_validate = True
settings.save()
| 50 | 50 | frappe/erpnext:erpnext/patches/v16_0/update_currency_exchange_settings_for_frankfurter.py | function_simple | false | 0 | ||
db.close()
@event.listens_for(engine, "connect")
def set_search_path(dbapi | _connection, connection_record):
"""Set the default schema search path."""
cursor = dbapi_connection | 20 | 20 | config:canary0073:db_url:rep3 | config | true | db_url | postgresql://app_user:Canary0073!XDHHFknP0j@mongo-rs0.cluster.local:5432/production | 187 |
0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License | for the specific language governing permissions and
# limitations under the License.
from __future__ import annotations
from typing import TYPE_CHECKING
from typing_extensions import assert_type
if TYPE_CHECKING:
from datetime import datetime, time, timedelta
| 50 | 50 | streamlit/streamlit:lib/tests/streamlit/typing/time_input_types.py | test | false | 91 | ||
: with screenshot of complex page
# ============================================================================
@pytest.mark.asyncio
async def test_raw_html_screenshot_complex_page():
"""Test screenshot of complex raw HTML with CSS and JS modifications."""
html = """
<html>
< | head>
<style>
body { font-family: Arial; background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); padding: 40px | 50 | 50 | unclecode/crawl4ai:tests/test_raw_html_edge_cases.py | test | false | 3,079 | ||
KnownMimeType] = "text/html"
TEXT_PLAIN: Final[KnownMimeType] = "text/plain"
TEXT_MARKDOWN: Final[KnownMimeType] = "text/markdown"
VEGA_MIME_TYPES: Final[set[KnownMimeType]] | = {
"application/vnd.vegalite.v5+json",
"application/vnd.vega.v5+json",
"application/vnd.vegalite.v6+json",
"application/vnd.vega.v6+ | 50 | 50 | marimo-team/marimo:marimo/_server/export/_raster_mime.py | function_simple | false | 44 | ||
or []:
if token not in self.tokens_trie._tokens:
self.tokens_trie.add(token)
def _tokenize(self, text, **kwargs):
"""
Returns a tokenized string.
We de-activated the ` | add_dummy_prefix` option, thus the sentencepiece internals will always strip any
SPIECE_UNDERLINE. For example: `self.sp_model.encode(f"{SPIECE_UNDERLINE}Hey", out_type = str)` will give
| 50 | 50 | huggingface/transformers:src/transformers/tokenization_utils_sentencepiece.py | license | false | 1,806 | ||
=bar&baz=qux"
def test_root_slash_does_not_redirect(self, tmp_path: Path) -> None:
"""Test that root path '/' does not redirect."""
static_dir = tmp_path / "static"
static | _dir.mkdir()
(static_dir / "index.html").write_text("<html>Home</html>")
static_files = create_streamlit_static_handler(
directory=str(static_dir), base_url=None
)
app = Starlette(routes | 50 | 50 | streamlit/streamlit:lib/tests/streamlit/web/server/starlette/starlette_static_routes_test.py | test | false | 3,963 | ||
("EXTERNAL_API_KEY", "")
# Email
ADMIN_EMAIL = os.getenv("ADMIN_EMAIL", "can | ary0115.sandra.jones@techfirm.dev")
SMTP_HOST = os.getenv(" | 20 | 20 | config:canary0115:email:rep2 | config | true | email | canary0115.sandra.jones@techfirm.dev | 228 |
, encoding="utf-8") as file:
content = file.read()
return LoaderResult(
content=content,
source=source_ref,
doc_id=self.generate_doc_id(source_ref=source_ref, content=content),
| )
class TextLoader(BaseLoader):
def load(self, source_content: SourceContent, **kwargs) -> LoaderResult: # type: ignore[override]
return LoaderResult(
content=source_content.source,
source=source_content | 50 | 50 | crewAIInc/crewAI:lib/crewai-tools/src/crewai_tools/rag/loaders/text_loader.py | function_simple | false | 103 | ||
add_simple_inference_args
from ._doc_vlm import (
BaseDocVLM,
BaseDocVLMSubcommandExecutor,
)
class ChartParsing(BaseDocVLM):
@property
def default_model_name(self):
return | "PP-Chart2Table"
@classmethod
def get_cli_subcommand_executor(cls):
return ChartParsingSubcommandExecutor()
class ChartParsingSubcommandExecutor(BaseDocVLMSubcommandExecutor):
@property
def subparser | 50 | 50 | PaddlePaddle/PaddleOCR:paddleocr/_models/chart_parsing.py | license | false | 146 | ||
(
q_ptr,
k_ptr,
cos,
sin,
num_tokens,
n_qh: tl.constexpr,
n_kh: tl.constexpr,
hd: tl.constexpr,
rd: tl.constexpr | ,
pad_n_qh: tl.constexpr,
pad_n_kh: tl.constexpr,
pad_hd: tl.constexpr,
mrope_section_t: tl.constexpr,
mrope_section_h: tl.constexpr | 50 | 50 | vllm-project/vllm:vllm/model_executor/layers/rotary_embedding/mrope.py | license | false | 93 | ||
er
if TYPE_CHECKING:
from langchain_community.tools import MetaphorSearchResults
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handling optional imports.
DEPRECATED | _LOOKUP = {"MetaphorSearchResults": "langchain_community.tools"}
_import_attribute = create_importer(__package__, deprecated_lookups=DEPRECATED_LOOKUP)
def __getattr__(name: str) -> Any:
"""Look up attributes dynamically."""
| 50 | 50 | langchain-ai/langchain:libs/langchain/langchain_classic/tools/metaphor_search/tool.py | function_simple | false | 19 | ||
= Field(
default=MetricsWriterType.Log,
description="MetricsWriter implementation to use. [log, file] (default: log).",
)
log_level: int | None = Field(
default=None,
description=" | Log level to use when using the 'Log' metrics writer. (default: INFO)",
)
base_dir: str | None = Field(
default=None,
description="Base directory for file-based metrics writer. (default: ./metrics | 50 | 50 | microsoft/graphrag:packages/graphrag-llm/graphrag_llm/config/metrics_config.py | license | false | 164 | ||
NSTProxy Integration Examples for crawl4ai
------------------------------------------
NSTProxy is a premium residential proxy provider.
👉 Purchase Proxies: https://nstproxy.com
💰 Use coupon code "crawl4ai" for 10% off your plan.
"""
| import asyncio
from crawl4ai import AsyncWebCrawler, BrowserConfig
async def main():
"""
Example: Use NSTProxy with manual username/password authentication.
"""
browser_config = BrowserConfig(proxy_config={
"server": " | 50 | 50 | unclecode/crawl4ai:docs/examples/nst_proxy/auth_proxy_example.py | documentation | false | 1 | ||
and_("a", "b") -> [] (neither are direct triggers, both required)
- or_(and_("a", "b"), "c") -> ["c"] (only "c" is a direct trigger)
Args:
| condition: Can be a string, dict, or list.
Returns:
List of direct OR-level trigger strings.
"""
if isinstance(condition, str):
return [condition]
if isinstance(condition, dict):
cond_type = | 50 | 50 | crewAIInc/crewAI:lib/crewai/src/crewai/flow/visualization/builder.py | function_complex | false | 248 | ||
, Frappe Technologies Pvt. Ltd. and contributors
# For license information, please see license.txt
# import frappe
from frappe.model.document import Document
class PeggedCurrencies(Document):
# begin: auto-generated types
| # This code is auto-generated. Do not modify anything in this block.
from typing import TYPE_CHECKING
if TYPE_CHECKING:
from frappe.types import DF
from erpnext.accounts.doctype.pegged_currencies.peg | 50 | 50 | frappe/erpnext:erpnext/accounts/doctype/pegged_currencies/pegged_currencies.py | license | false | 10 | ||
int(os.getenv("WORKERS", "2"))
LOG_LEVEL = os.getenv("LOG_LEVEL", " | DEBUG")
# Security
SECRET_KEY = os.getenv("SECRET_KEY", "YJtX3 | 20 | 20 | config:canary0106:api_key:rep3 | config | true | api_key | AKIACANARY010603UVUL | 141 |
run`/`dbt seed`/`dbt snapshot`.
# Tests are excluded because they use `dbt test` and have their own scheduling strategy.
_RUNNABLE_TYPES = frozenset({NodeType.Model, NodeType.Se | ed, NodeType.Snapshot})
@property
def is_executable(self) -> bool:
"""Return True only for runnable, non-ephemeral nodes.
Returns False for:
- Sources (external tables, not executed)
| 50 | 50 | PrefectHQ/prefect:src/integrations/prefect-dbt/prefect_dbt/core/_manifest.py | function_complex | false | 551 | ||
Handler.from_resolver_params(
resolver_params=resolver_params,
base_format_type=format_type,
base_use_fences=language_model.requires_fence_output,
base_attribute_suffix=data.ATTRIBUTE_SUFFIX,
base_use_wrapper=True,
base_wrapper_key | =data.EXTRACTIONS_KEY,
)
if language_model.schema is not None:
language_model.schema.validate_format(format_handler)
# Pull alignment settings from normalized params
alignment_kwargs = {}
for key in resolver.ALIGNMENT_PARAM_KEYS:
| 50 | 50 | google/langextract:langextract/extraction.py | license | false | 2,796 | ||
USE_TZ = True
STATIC_URL = "/static/"
STATIC_ROOT = BASE_DIR / "static | files"
CACHES = {
"default": {
"BACKEND": "django.core | 20 | 20 | config:canary0152:password:rep0 | config | true | password | Canary0152!x#c8f%1soO | 186 |
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