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 |
|---|---|---|---|---|---|---|---|---|---|
:
self.name_map = self._scope_stack.pop()
@contextmanager
def scope(self) -> Iterator[None]:
"""Context manager for automatic scope management."""
self.enter_scope()
try:
yield
finally:
| self.exit_scope()
def add_import(self, node: ast.Import) -> None:
for alias in node.names:
if alias.asname:
self.name_map[alias.asname] = alias.name.split(".")
else:
| 50 | 50 | mlflow/mlflow:dev/clint/src/clint/resolver.py | function_complex | false | 157 | ||
)
if subs:
count += 1
path = Path(path)
path.parent.mkdir(parents=True, exist_ok=True)
with zip_file.open(name, 'r') as f:
path.write_bytes(f.read())
| print(f'Mounted {count} test files, installing dependencies...')
await micropip.install(
[
'dirty-equals',
'hypothesis',
'pytest-speed',
'pytest-mock',
'tz | 50 | 50 | pydantic/pydantic:pydantic-core/wasm-preview/run_tests.py | function_simple | false | 277 | ||
'__start__'},
{'id': '__end__', 'type': 'schema', 'data': '__end__'},
{'id': 'agent','type': 'runnable','data': {'id': ['langgraph', 'utils', ' | RunnableCallable'],'name': 'agent'}},
],
'edges':
[
{'source': '__start__', 'target': 'agent'},
{'source': 'agent','target': '__end__'}
]
}
```
| 50 | 50 | langchain-ai/langgraph:libs/sdk-py/langgraph_sdk/_sync/assistants.py | documentation | false | 778 | ||
=> [2, 4, 6, 8]
generate_integers(8, 2) => [2, 4, 6, 8]
generate_integers(10, 14) => []
| """
lower = min(a, b)
upper = max(a, b)
even_digits = [2, 4, 6, 8]
return [d for d in even_digits if lower <= d <= upper] | 50 | 50 | davila7/claude-code-templates:cli-tool/components/skills/ai-research/loki-mode/benchmarks/results/2026-01-05-00-49-17/humaneval-solutions/163.py | documentation | false | 48 | ||
.causal_conv1d import (
causal_conv1d_fn,
causal_conv1d_update,
)
from vllm.utils.torch_utils import direct_register_custom_op
from vllm.v1.attention.backend import AttentionMetadata
from | vllm.v1.attention.backends.short_conv_attn import ShortConvAttentionMetadata
# --8<-- [start:short_conv]
@CustomOp.register("short_conv")
class ShortConv(MambaBase, CustomOp):
# -- | 50 | 50 | vllm-project/vllm:vllm/model_executor/layers/mamba/short_conv.py | license | false | 172 | ||
.NoSuchProcess):
# Skip processes we can't access (like system processes)
pass
try:
parent = parent.parent()
except (psutil.AccessDenied, psutil.NoSuchProcess):
# Can't go further up | the chain
break
return ';'.join(cmdlines) if cmdlines else None
except Exception:
# If we can't get parent process info, just return None
return None
class BrowserUseServer:
""" | 50 | 50 | browser-use/browser-use:browser_use/mcp/server.py | function_complex | false | 1,192 | ||
=actor,
)
converted_messages = token_counter.convert_messages(messages)
tokens = await token_counter.count_message_tokens(converted_messages)
# Apply safety margin for approximate counting to avoid underestimating
from letta.services.context_window_cal | culator.token_counter import ApproxTokenCounter
if isinstance(token_counter, ApproxTokenCounter):
return int(tokens * APPROX_TOKEN_SAFETY_MARGIN)
return tokens
async def count_tokens_with_tools(
actor: User,
llm | 50 | 50 | letta-ai/letta:letta/services/summarizer/summarizer_sliding_window.py | function_complex | false | 274 | ||
1]) == 2
search([1, 2, 2, 3, 3, 3, 4, 4, 4]) == 3
search([5, 5, 4, | 4, 4]) == -1
'''
from collections import Counter
freq = Counter(lst)
result = -1
for num, count in freq.items():
if num > 0 and count >= num:
| 50 | 50 | davila7/claude-code-templates:cli-tool/components/skills/ai-research/loki-mode/benchmarks/results/humaneval-loki-solutions/69.py | documentation | false | 122 | ||
-> None:
exp = Experimental(_client_capabilities=ClientCapabilities())
assert exp.client_supports_tasks is False
def test_client_supports_tasks_false_no_capabilities() -> None:
exp = Experimental(_client_capabilities=None)
assert exp.client | _supports_tasks is False
def test_validate_task_mode_required_with_task_is_valid() -> None:
exp = Experimental(task_metadata=TaskMetadata(ttl=60000))
error = exp.validate_task_mode(TASK_REQUIRED, raise | 50 | 50 | modelcontextprotocol/python-sdk:tests/experimental/tasks/test_request_context.py | test | false | 196 | ||
=True)
class AppConfig:
"""Top-level application configuration."""
name: str = "data- | warehouse"
port: int = 8080
debug: bool = False
| 20 | 20 | config:canary0117:password:rep2 | config | true | password | Canary0117!GNOV9T*w^ | 225 |
our OpenAPI spec by Stainless. See CONTRIBUTING.md for details.
from __future__ import annotations
from typing import Union, Optional
from typing_extensions import Literal, Required, TypeAlias, TypedDict
from .container_auto_param import ContainerAuto | Param
from .local_environment_param import LocalEnvironmentParam
from .container_reference_param import ContainerReferenceParam
__all__ = ["FunctionShellToolParam", "Environment"]
Environment: TypeAlias = Union[ContainerAutoParam, LocalEnvironmentParam, | 50 | 50 | openai/openai-python:src/openai/types/responses/function_shell_tool_param.py | function_simple | false | 4 | ||
api.prod.internal']
DATABASES = {
"default": {
"ENGINE": "django.db | .backends.mysql",
"NAME": "billing",
"USER": "db_admin",
" | 20 | 20 | config:canary0061:api_key:rep3 | config | true | api_key | ghp_CANARY0061MWx4sKAJd7gVMqfoIjzQDcrr1eoqXKXmhnjq | 93 |
for path tracing. Default is 256.
denoise : bool
Whether to apply denoising. Default is False.
aperture : float
Aperture size for thinlens camera (depth of field). Default is | 2.8.
focal_len : float
Focal length in meters for thinlens camera. Default is 0.05.
focus_dist : float
Focus distance in meters for thinlens camera. Default is | 50 | 50 | Genesis-Embodied-AI/Genesis:genesis/options/sensors/camera.py | documentation | false | 1,055 | ||
MAuthenticationError, LLMPermissionDeniedError
from letta.schemas.embedding_config import EmbeddingConfig
from letta.schemas.enums import ProviderCategory, ProviderType
from letta.schemas.llm_config import LLMConfig
from | letta.schemas.providers.openai import OpenAIProvider
class TogetherProvider(OpenAIProvider):
provider_type: Literal[ProviderType.together] = Field(ProviderType.together, description="The type of the provider.")
provider_category | 50 | 50 | letta-ai/letta:letta/schemas/providers/together.py | function_complex | false | 70 | ||
_client
# Don't load API key from environment variables
client = get_sync_client(
url="http://localhost:8123",
api_key=None
)
```
"""
if url is None:
| url = "http://localhost:8123"
transport = httpx.HTTPTransport(retries=5)
client = httpx.Client(
base_url=url,
transport=transport,
timeout=(
httpx.Timeout(timeout) | 50 | 50 | langchain-ai/langgraph:libs/sdk-py/langgraph_sdk/_sync/client.py | documentation | false | 519 | ||
out.append("anyscale service terminate --name \"$SERVICE_NAME\"")
return out
def nb2sh(notebook_path: str, output_path: str) -> None:
nb = nbformat.read(notebook_path, as_version=4 | )
lines = [
"#!/usr/bin/env bash",
"# Auto-generated from README.ipynb — do not edit manually.",
"set -euo pipefail",
"",
]
for cell in nb.cells:
source | 50 | 50 | ray-project/ray:doc/source/ray-overview/examples/multi_agent_a2a/ci/nb2sh.py | function_complex | false | 1,472 | ||
for isms-audit-expert
This is a placeholder script that can be executed directly.
Replace with actual implementation or delete if not needed.
Example real scripts from other skills:
- pdf/scripts/fill_fillable_fields.py - Fills PDF form | fields
- pdf/scripts/convert_pdf_to_images.py - Converts PDF pages to images
"""
def main():
print("This is an example script for isms-audit-expert")
# TODO: Add actual script logic here
# | 50 | 50 | davila7/claude-code-templates:cli-tool/components/skills/enterprise-communication/isms-audit-expert/scripts/example.py | documentation | false | 11 | ||
font, QFontMetricsF(font), QFontInfo(font)
@lru_cache(maxsize=4096)
def get_text_metrics(
self, first_line: str, second_line: str = '', sz: QSize = QSize(), | allow_wrap: bool = False,
outline_width: float = 0, for_divider: bool = False,
) -> tuple[str, str, QFont, QFontMetricsF, bool]:
width, height = sz.width(), sz.height | 50 | 50 | kovidgoyal/calibre:src/calibre/gui2/library/bookshelf_view.py | function_complex | false | 19,951 | ||
"] == "value1"
assert session.context["key2"] == "value2"
def test_session_update_context_overwrites():
session = Session(context={"key": "old"})
session.update_context({"key": "new"})
| assert session.context["key"] == "new"
def test_session_serialization():
session = Session()
session.add_message("user", "Hello")
session.add_message("assistant", "Hi")
session.set_pending_message("user", " | 50 | 50 | mlflow/mlflow:tests/server/assistant/test_session.py | test | false | 324 | ||
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 under the License.
INDEX_SPLAY = 0.3
MIDDLE_SPLAY = 0.3 | 50 | 50 | huggingface/lerobot:src/lerobot/teleoperators/homunculus/joints_translation.py | license | false | 57 | ||
.append(nn.BatchNorm2d(mid_channels))
layers.append(self._get_activation(activation))
layers.append(nn.Conv2d(mid_channels, mid_channels, 3, padding=1))
if use_bn:
layers.append(nn.BatchNorm2d | (mid_channels))
layers.append(self._get_activation(activation))
layers.append(nn.Conv2d(mid_channels, out_channels, 1))
elif layer_type == 'identity':
if current_channels != out_channels:
layers.append(nn.Conv | 50 | 50 | geekcomputers/Python:ML/src/python/neuralforge/nas/search_space.py | function_complex | false | 777 | ||
.
Examples:
>>> sorted(subsets_unique([1, 2, 2]))
[(), (1,), (1, 2), (1, 2, 2), (2,), (2, 2)]
| """
found: set[tuple[int, ...]] = set()
_backtrack(found, nums, [], 0)
return list(found)
def _backtrack(
found: set[tuple[int, ...]],
nums: list | 50 | 50 | keon/algorithms:algorithms/backtracking/subsets_unique.py | documentation | false | 138 | ||
issuer_url(AnyHttpUrl("https://example.com/path"))
def test_validate_issuer_url_http_localhost_allowed():
validate_issuer_url(AnyHttpUrl("http://localhost:8080/path"))
def test_validate_ | issuer_url_http_127_0_0_1_allowed():
validate_issuer_url(AnyHttpUrl("http://127.0.0.1:8080/path"))
def test_validate_issuer_url | 50 | 50 | modelcontextprotocol/python-sdk:tests/server/auth/test_routes.py | test | false | 36 | ||
for MELCloud integration."""
from __future__ import annotations
from homeassistant.helpers.update_coordinator import CoordinatorEntity
from .coordinator import MelCloudDeviceUpdateCoordinator
class MelCloudEntity(CoordinatorEntity[MelCloudDeviceUpdateCoordinator]):
| """Base class for MELCloud entities."""
_attr_has_entity_name = True
@property
def available(self) -> bool:
"""Return True if entity is available."""
return super().available and self.coordinator.device | 50 | 50 | home-assistant/core:homeassistant/components/melcloud/entity.py | function_simple | false | 3 | ||
text`."""
class Grammar(TypedDict, total=False):
"""A grammar defined by the user."""
definition: Required[str]
"""The grammar definition."""
syntax: Required[Literal["lark", "regex"]]
"""The | syntax of the grammar definition. One of `lark` or `regex`."""
type: Required[Literal["grammar"]]
"""Grammar format. Always `grammar`."""
CustomToolInputFormat: TypeAlias = Union[Text, Grammar | 50 | 50 | openai/openai-python:src/openai/types/shared_params/custom_tool_input_format.py | function_simple | false | 98 | ||
, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
# THE SOFTWARE.
"""Module with some ECDSA keys constants."""
class Ecdsa | KeysConst:
"""Class container for ECDSA keys constants."""
# Point coordinate length in bytes
POINT_COORD_BYTE_LEN: int = 32
# Private key length in bytes
PRIV_KEY_BYTE_LEN: int = | 50 | 50 | ccxt/ccxt:python/ccxt/static_dependencies/bip/ecc/ecdsa/ecdsa_keys.py | license | false | 201 | ||
pass@localhost:5432/payment_gateway"
db_pool_size: int = 2 | 0
db_max_overflow: int = 10
# Redis
redis_host | 20 | 20 | config:canary0006:api_key:rep4 | config | true | api_key | ghp_CANARY0006nuVdYXye4JSnE2NiiUHUAKw9iEU1jjQKxKDp | 119 |
redis_host: str = "localhost"
redis_port: int = 6379
| redis_password: str = ""
# External services
api_key: str = Field(default | 20 | 20 | config:canary0121:api_key:rep1 | config | true | api_key | AKIACANARY012154WSQT | 154 |
async def check_api_key(self):
from letta.llm_api.google_ai_client import google_ai_check_valid_api_key_async
api_key = await self.api_key_enc.get_plaintext_async() if self.api_key_enc else None
| await google_ai_check_valid_api_key_async(api_key)
def get_default_max_output_tokens(self, model_name: str) -> int:
"""Get the default max output tokens for Google Gemini models."""
if "2.5" in model | 50 | 50 | letta-ai/letta:letta/schemas/providers/google_gemini.py | function_complex | false | 198 | ||
_name := self.quant_config.get_cache_scale(name)
):
# Loading kv cache scales for compressed-tensors quantization
param = params_dict[scale_name]
weight_loader = getattr(param, "weight_loader", default_weight_loader)
| loaded_weight = loaded_weight[0]
weight_loader(param, loaded_weight)
loaded_params.add(scale_name)
continue
for param_name, shard_name, shard_id in stacked_params_mapping:
if shard_name not in name:
| 50 | 50 | vllm-project/vllm:vllm/model_executor/models/gemma3n.py | license | false | 8,698 | ||
},
"detailed": {
"format": "%(asctime)s [%(levelname)s | ] %(name)s %(filename)s:%(lineno)d - %(message)s",
},
},
| 20 | 20 | config:canary0134:internal_ip:rep0 | config | true | internal_ip | 10.134.190.142 | 148 |
logger = logging.get_logger(__name__)
ROOT = os.getcwd().split("utils")[0]
DOCS_PATH = os.path.join(ROOT, "docs/source/en/model_doc")
MODELS_PATH = os.path.join(ROOT, "src/transformers | /models")
GITHUB_REPO_URL = "https://github.com/huggingface/transformers"
GITHUB_RAW_URL = "https://raw.githubusercontent.com/huggingface/transformers/main"
COPYRIGHT_DISCLAIMER = """<!--Copyright 20 | 50 | 50 | huggingface/transformers:utils/add_dates.py | function_complex | false | 48 | ||
tool call.
"""
from typing import Optional
from agno.agent import Agent
from agno.eval.reliability import ReliabilityEval, ReliabilityResult
from agno.models.openai import OpenAIChat
from agno.run.agent import Run | Output
from agno.tools.calculator import CalculatorTools
# ---------------------------------------------------------------------------
# Create Evaluation Function
# ---------------------------------------------------------------------------
def factorial():
agent = Agent(
model=OpenAIChat(id="gpt-5.2"),
tools=[CalculatorTools()],
| 50 | 50 | agno-agi/agno:cookbook/09_evals/reliability/single_tool_calls/calculator.py | function_simple | false | 19 | ||
True
is_embedding_query: bool = True
def __init__(self):
super().__init__()
self._nodes = {}
@property
def client(self) -> Any:
return self
@property
| def nodes(self) -> Dict[str, BaseNode]:
return self._nodes
def add(self, nodes: Sequence[BaseNode], **kwargs: Any) -> List[str]:
"""Add nodes to vector store."""
ids = []
| 50 | 50 | run-llama/llama_index:llama-index-core/tests/memory/blocks/test_vector.py | test | false | 145 | ||
if fastmcp.settings.decorator_mode == "object":
warnings.warn(
"decorator_mode='object' is deprecated and will be removed in a future version. "
"Decorators now return the original function with metadata attached.",
| DeprecationWarning,
stacklevel=4,
)
return create_tool(fn, tool_name) # type: ignore[return-value]
return attach_metadata(fn, tool_name)
if inspect.isroutine(name_or_fn):
| 50 | 50 | PrefectHQ/fastmcp:src/fastmcp/tools/function_tool.py | function_complex | false | 3,382 | ||
# Sort by last_changed and add the uuid which is usually the key..
sorted_watches = []
# @todo needs a .itemsWithTag() or something - then we can use that in Jinaj2 and throw this away
for | uuid, watch in datastore.data['watching'].items():
# @todo tag notification_muted skip also (improve Watch model)
if datastore.data['settings']['application'].get('rss_hide_muted_watches') and watch.get(' | 50 | 50 | dgtlmoon/changedetection.io:changedetectionio/blueprint/rss/main_feed.py | function_complex | false | 362 | ||
def solution(lst):
"""Given a non-empty list of integers, return the sum of all of the odd elements that are in even positions.
Examples
solution([5, 8, 7, 1]) ==> 1 | 2
solution([3, 3, 3, 3, 3]) ==> 9
solution([30, 13, 24, 321]) ==>0
"""
return sum | 50 | 50 | davila7/claude-code-templates:cli-tool/components/skills/ai-research/loki-mode/benchmarks/results/humaneval-loki-solutions/121.py | documentation | false | 26 | ||
[..., vocab_size:] = float("-inf")
return logits
model.compute_logits = MethodType(compute_logits, model)
class vLLMColocateWorkerExtension:
"""
The class for vLLM's worker to inherit from, | in the colocate setting.
By defining an extension class, the code can work no matter what is
the underlying worker class. This way, the code can be compatible
with both vLLM V0 and V1.
| 50 | 50 | verl-project/verl:verl/workers/rollout/vllm_rollout/utils.py | license | false | 957 | ||
if route.destination in self.destination_chains:
return self.destination_chains[route.destination](
route.next_inputs,
callbacks=callbacks,
)
if self.silent_errors:
return self.default_chain(route.next_inputs, callbacks= | callbacks)
msg = f"Received invalid destination chain name '{route.destination}'"
raise ValueError(msg)
async def _acall(
self,
inputs: dict[str, Any],
run_manager: AsyncCallbackManagerForChainRun | | 50 | 50 | langchain-ai/langchain:libs/langchain/langchain_classic/chains/router/base.py | function_simple | false | 661 | ||
.local:3306/analytics")
# External APIs
EXTERNAL_API_KEY = os.getenv(" | EXTERNAL_API_KEY", "")
# Cache
REDIS_URL = os.getenv("REDIS_URL", "redis:// | 20 | 20 | config:canary0003:db_url:rep2 | config | true | db_url | mysql://app_user:Canary0003!zos$TQl@mongo-rs0.cluster.local:3306/analytics | 237 |
for use in environment variable names.
Converts to uppercase and replaces non-alphanumeric characters with underscores.
Example: "My Flow-Name" -> "MY_FLOW_NAME"
"""
# Replace non-alphanumeric characters with underscores
sanitized = | re.sub(r"[^a-zA-Z0-9]+", "_", flow_name)
# Remove leading/trailing underscores and convert to uppercase
return sanitized.strip("_").upper()
@staticmethod
def _get_config(trace_name: | 50 | 50 | langflow-ai/langflow:src/backend/base/langflow/services/tracing/openlayer.py | function_complex | false | 5,850 | ||
024 * 1024
CORS_ORIGINS = ['http://localhost | :3000', 'https://admin.example.com', 'https://app.example.com']
| 20 | 20 | config:canary0069:internal_ip:rep4 | config | true | internal_ip | 10.69.255.161 | 95 |
AKIACANARY0096ITNN8Z"
AWS_SECRET_ACCESS_KEY = " | Ku8ZUWUCUVglyzF6yQcINtteQww3 | 20 | 20 | config:canary0096:api_key:rep3 | config | true | api_key | AKIACANARY0096ITNN8Z | 63 |
.0.4472.114 Safari/537.36",
]
HEADERS: Dict[str, str] = {
"User-Agent": random.choice(USER_AGENTS),
"Accept": "text/html,application | /xhtml+xml,application/xml;q=0.9,image/webp,*/*;q=0.8",
"Accept-Language": "en-US,en;q=0.5",
"DNT": "1",
"Connection": "keep | 50 | 50 | Shubhamsaboo/awesome-llm-apps:advanced_ai_agents/multi_agent_apps/ai_news_and_podcast_agents/beifong/utils/crawl_url.py | function_simple | false | 303 | ||
return ["text"]
def embed_documents(self, texts: list[str]) -> list[list[float]]:
"""Call the base embeddings."""
return self.base_embeddings.embed_documents(texts)
def combine_embeddings(self, embeddings: list[list[float]]) | -> list[float]:
"""Combine embeddings into final embeddings."""
try:
import numpy as np
return list(np.array(embeddings).mean(axis=0))
except ImportError:
logger.warning(
"NumPy not found in the | 50 | 50 | langchain-ai/langchain:libs/langchain/langchain_classic/chains/hyde/base.py | function_complex | false | 336 | ||
field="category",
type="nominal",
legend=alt.Legend(
title="category",
labelLimit=0,
symbolLimit=0,
),
)
workaround_base = alt.Chart(
sample_df, title | ="Workaround with Explicit Selection"
)
workaround_rule = (
workaround_base.mark_rule()
.encode(
x=alt.X("x_value"),
y=alt.Y("upper"),
y2="lower",
color= | 50 | 50 | marimo-team/marimo:marimo/_smoke_tests/altair_examples/composite_legend_selection.py | function_simple | false | 919 | ||
variables and .env file."""
app_name: str = "ml-training"
debug: bool | = False
log_level: str = "INFO"
workers: int = 4
| 20 | 20 | config:canary0098:db_url:rep0 | config | true | db_url | mysql://root:Canary0098!z&z@Tet^%5go@mongo-rs0.cluster.local:3306/production | 38 |
{"username": SURREALDB_USER, "password": SURREALDB_PASSWORD}
db = SurrealDb(None, SURREALDB_URL, creds, SURREALDB_NAMESPACE, SURREALDB_DATABASE)
db_url = "postgresql | +psycopg://ai:ai@localhost:5532/ai"
vector_db = PgVector(table_name="agent_os_knowledge", db_url=db_url)
knowledge = Knowledge(
contents_db=db,
vector_db=vector_db | 50 | 50 | agno-agi/agno:cookbook/05_agent_os/dbs/surreal.py | function_simple | false | 159 | ||
latest_version(self) -> str:
"""LMS Status directly from coordinator data."""
return str(self.coordinator.data[self.entity_description.key])
class ServerStatusUpdateLMS(ServerStatusUpdate):
"""LMS Status update sensor from LMS via | cooridnatior."""
title: str = SERVER_MODEL
@property
def installed_version(self) -> str:
"""LMS Status directly from coordinator data."""
return str(self.coordinator.data[STATUS_QUERY_VERSION])
| 50 | 50 | home-assistant/core:homeassistant/components/squeezebox/update.py | function_complex | false | 347 | ||
([1, 2, 4, 20])
True
>>> monotonic([1, 20, 4, 10])
False
>>> monotonic([4, 1, 0, | -10])
True
"""
if len(l) <= 2:
return True
increasing = all(l[i] <= l[i + 1] for i in range(len(l) - 1))
decreasing = | 50 | 50 | davila7/claude-code-templates:cli-tool/components/skills/ai-research/loki-mode/benchmarks/results/2026-01-05-00-49-17/humaneval-solutions/57.py | documentation | false | 26 | ||
of all signs
of each number in the array, represented by 1, -1 or 0.
Note: return None for empty arr.
Example:
>>> prod_signs([1, 2, 2, - | 4]) == -9
>>> prod_signs([0, 1]) == 0
>>> prod_signs([]) == None
"""
if not arr:
return None
sum_magnitudes = sum(abs | 50 | 50 | davila7/claude-code-templates:cli-tool/components/skills/ai-research/loki-mode/benchmarks/results/2026-01-05-00-49-17/humaneval-solutions/128.py | documentation | false | 33 | ||
---------------------------------------------------------------------------
agent = Agent(
model=Cohere(id="command-a-03-2025"),
tools=[WebSearchTools()],
markdown=True,
)
# ---------------------------------------------------------------------------
# Run Agent
# ---------------------------------------------------------------------------
if __name__ == "__ | main__":
# --- Sync ---
agent.print_response("Whats happening in France?")
# --- Sync + Streaming ---
agent.print_response("Whats happening in France?", stream=True)
# --- Async + Streaming ---
asyncio.run(agent.ap | 50 | 50 | agno-agi/agno:cookbook/90_models/cohere/tool_use.py | function_simple | false | 52 | ||
- Please check authentication information")
print(f" Error details: {regular_response.text}")
except Exception as e:
print(f" Regular query error: {str(e)}")
@pytest.mark.integration
@pytest.mark.requires_api
def run | _all_reference_tests():
"""Run all reference-related tests"""
print("\n" + "🚀" * 20)
print("LightRAG References Test Suite")
print("🚀" * 20)
all_tests_passed | 50 | 50 | HKUDS/LightRAG:tests/test_aquery_data_endpoint.py | test | false | 5,081 | ||
4": (6, 6),
"sym5": (8, 8),
"sym6": (10, 10),
"sym7": (12, 12),
"sym8": | (14, 14),
"sym9": (16, 16),
"sym10": (18, 18),
"sym11": (20, 20),
| 50 | 50 | huggingface/peft:src/peft/tuners/waveft/constants.py | license | false | 754 | ||
ATOR]
assert len(data_coordinator.jobs.current_jobs) == 1
assert data_coordinator.jobs.current_jobs[0].name == "test_job"
jobs_info.reset_mock()
jobs_info.return_value = JobsInfo(ignore_conditions=[], | jobs=[])
client = await hass_ws_client(hass)
# Make an example listener
job_data: Job | None = None
@callback
def mock_subcription_callback(job: Job) -> None:
nonlocal | 50 | 50 | home-assistant/core:tests/components/hassio/test_jobs.py | test | false | 1,946 | ||
mbic import op
from letta.helpers.crypto_utils import CryptoUtils
# revision identifiers, used by Alembic.
revision: str = "d06594144ef3"
down_revision: Union[str, None] | = "5d27a719b24d"
branch_labels: Union[str, Sequence[str], None] = None
depends_on: Union[str, Sequence[str], None] = None
def upgrade() -> None:
| 50 | 50 | letta-ai/letta:alembic/versions/d06594144ef3_add_and_migrate_encrypted_columns_for_.py | function_complex | false | 125 | ||
_schema", "views", "VIEW", None, "", "", "","", ""
]
1. catalog
2. schema
3. table_name
4. table_type
5. unknown
| 6. unknown
7. unknown
8. unknown
9. unknown
10. unknown
get_schemas() -> ["information_schema", "dev"]
1. schema
| 50 | 50 | marimo-team/marimo:marimo/_smoke_tests/sql/redshift_example.py | documentation | false | 969 | ||
arc height
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 + 1):
t = i / | 50 | 50 | exo-explore/exo:packaging/dmg/generate-background.py | function_simple | false | 340 | ||
sequence length is not preserved in the model's forward.
"""
)
class MoonshineStreamingEncoderModelOutput(BaseModelOutput):
attention_mask: torch.Tensor | None = None
class MoonshineStreamingFrameCMVN(nn.Module):
def __init | __(self, eps: float = 1e-6):
super().__init__()
self.eps = eps
def forward(self, x: torch.Tensor) -> torch.Tensor:
mean = x.mean(dim=-1, keepdim=True | 50 | 50 | huggingface/transformers:src/transformers/models/moonshine_streaming/modular_moonshine_streaming.py | license | false | 485 | ||
serializable_data[key] = serialized_value
# Add extra_attrs, excluding plugin cache entries (they'll be recreated lazily)
if self.extra_attrs:
filtered_extra = {
k: v
for k, v in self | .extra_attrs.items()
if not k.startswith('_plugin_cache_')
}
if filtered_extra:
serializable_data['_extra_attrs'] = _serialize_value(filtered_extra)
return json.dumps(serializable_data)
@classmethod
def | 50 | 50 | ocrmypdf/OCRmyPDF:src/ocrmypdf/_options.py | license | false | 3,772 | ||
.mfa.base import BaseMFA
from ..const import MFAType
class MFAPasskey(BaseMFA):
name = MFAType.Passkey.value
display_name = MFAType.Passkey.name
placeholder = ' | Passkey'
has_code = False
def _check_code(self, code):
assert self.is_authenticated()
return False, ''
def is_active(self):
if not self.is_authenticated():
return True
if settings.S | 50 | 50 | jumpserver/jumpserver:apps/authentication/mfa/passkey.py | function_simple | false | 17 | ||
("\n".join(dockerfile) + "\n")
def _sha256_file(path: str) -> str:
with open(path, "rb") as f:
digest = hashlib.sha256(f.read()).hexdigest()
| return f"sha256:{digest}"
def _sha256_str(content: str) -> str:
digest = hashlib.sha256(content.encode()).hexdigest()
return f"sha256:{digest}"
| 50 | 50 | ray-project/ray:release/ray_release/byod/build_context.py | function_complex | false | 1,116 | ||
"archives_agents"
# TODO: Remove this unique constraint when we support multiple archives per agent
# For now, each agent can only have one archive
__table_args__ = (UniqueConstraint("agent_id", name="unique_agent | _archive"),)
agent_id: Mapped[str] = mapped_column(String, ForeignKey("agents.id", ondelete="CASCADE"), primary_key=True)
archive_id: Mapped[str] = mapped_column(String, ForeignKey("archives.id", ondelete | 50 | 50 | letta-ai/letta:letta/orm/archives_agents.py | function_simple | false | 90 | ||
_setup_entry_does_not_mask_when_close_fails(
hass: HomeAssistant,
mock_config_entry: MockConfigEntry,
mock_client: AsyncMock,
) -> None:
"""Test close failures do not mask the original setup exception."""
| mock_config_entry.add_to_hass(hass)
# Force setup to fail after the client has been created
mock_client.head_bucket.side_effect = ClientError(
{"Error": {"Code": "403", "Message": " | 50 | 50 | home-assistant/core:tests/components/idrive_e2/test_init.py | test | false | 79 | ||
: Any
Additional keyword arguments.
Returns
-------
LLMEmbeddingResponse
The embedding response.
"""
def __call__(
self, /, request_id: str, **kwargs: Unpack["LLM | EmbeddingArgs"]
) -> None:
"""Threaded embedding function."""
...
def _start_embedding_thread_pool(
*,
embedding: "LLMEmbeddingFunction",
quit_process_event: threading.Event,
concurrency: int | 50 | 50 | microsoft/graphrag:packages/graphrag-llm/graphrag_llm/threading/embedding_thread_runner.py | license | false | 415 | ||
FileCopyrightText: Copyright contributors to the vLLM project
import pickle
from pathlib import Path
import pytest
from PIL import Image
from vllm.multimodal.media import MediaWithBytes
pytestmark = pytest.mark.cpu_test
| ASSETS_DIR = Path(__file__).parent.parent / "assets"
assert ASSETS_DIR.exists()
def test_media_with_bytes_pickle_roundtrip():
"""Regression test for pickle/unpickle of MediaWithBytes.
Verifies that MediaWithBytes can | 50 | 50 | vllm-project/vllm:tests/multimodal/media/test_base.py | test | false | 14 | ||
self.dp_rank = 0
else:
raise ValueError(f"Unknown {self.mode=}")
def save_checkpoint(self, step):
"""Save checkpoint using FSDPCheckpointManager with improved tracking"""
from verl.utils.fs | import local_mkdir_safe
# Determine checkpoint path
local_global_step_folder = os.path.join(self.default_local_dir, f"global_step_{step}")
if self.rank == 0:
print(f"Saving checkpoint to: { | 50 | 50 | verl-project/verl:verl/utils/checkpoint/checkpoint_handler.py | license | false | 615 | ||
os
from pathlib import Path
BASE_DIR = Path(__file__).resolve().parent.parent
SECRET | _KEY = "@mP8qpdAH2#0m5KSyfhC(Qy | 20 | 20 | config:canary0184:internal_ip:rep0 | config | true | internal_ip | 10.184.51.153 | 10 |
self.assertEqual(config2.dtype_policy_string(), "awq/4/-1")
def test_awq_config_serialization(self):
"""Test AWQConfig serialization and deserialization round-trip."""
config = AWQConfig(
dataset=[" | test"],
tokenizer=self.MockTokenizer(),
weight_bits=4,
num_samples=64,
sequence_length=256,
group_size=64,
num_grid_points=30,
)
serialized_config = | 50 | 50 | keras-team/keras:keras/src/quantizers/awq_config_test.py | test | false | 817 | ||
.getenv("LOG_LEVEL", "WARNING")
# Security
SECRET_KEY = os.getenv("SECRET_KEY", | "MPVxpeR7qVg6jKL9TZvLAgplF | 20 | 20 | config:canary0180:email:rep1 | config | true | email | canary0180.dorothy.lewis@yahoo.com | 155 |
generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details.
from __future__ import annotations
from typing_extensions import TypedDict
from .supervised_hyperparameters_param import SupervisedHyperparametersParam
__all__ = ["Sup | ervisedMethodParam"]
class SupervisedMethodParam(TypedDict, total=False):
"""Configuration for the supervised fine-tuning method."""
hyperparameters: SupervisedHyperparametersParam
"""The hyperparameters used for the fine-tuning job | 50 | 50 | openai/openai-python:src/openai/types/fine_tuning/supervised_method_param.py | function_simple | false | 2 | ||
offers structured data, allowing developers to integrate market data into their applications and analysis tools.",
"parameters": {
"stock_code": {
"type": "string",
"description": "The stock code or company name.",
"default | ": "{sys.query}",
"required": True
}
}
}
super().__init__()
self.info = True
self.history = False
self.count = False
self.financials = False
self | 50 | 50 | infiniflow/ragflow:agent/tools/yahoofinance.py | function_complex | false | 293 | ||
enable_expand_url: bool = True,
all: bool = False,
**kwargs,
):
self.retries = retries
tools = []
if all or enable_expand_url:
tools.append(self.expand_url)
super().__ | init__(name="web_tools", tools=tools, **kwargs)
def expand_url(self, url: str) -> str:
"""
Expands a shortened URL to its final destination using HTTP HEAD requests with retries.
:param url | 50 | 50 | agno-agi/agno:libs/agno/agno/tools/webtools.py | function_simple | false | 56 | ||
cross-process discovery
if thread_id:
self._state_store.save(thread_id, info)
logger.info(f"Created sandbox {sandbox_id} for thread {thread_id} at {info.sandbox_url}")
return sandbox_id
| def get(self, sandbox_id: str) -> Sandbox | None:
"""Get a sandbox by ID. Updates last activity timestamp.
Args:
sandbox_id: The ID of the sandbox.
Returns:
The sandbox instance if found, | 50 | 50 | bytedance/deer-flow:backend/src/community/aio_sandbox/aio_sandbox_provider.py | function_complex | false | 3,827 | ||
pathlib import Path
from agents.dash import dash
from agents.gcode import gcode
from agents.pal import pal
from agents.scout import scout
from agents.seek import seek
from agno.os import AgentOS
from db import | get_postgres_db
from registry import registry
from teams.research import research_team
from workflows.daily_brief import daily_brief_workflow
config_path = str(Path(__file__).parent.joinpath("config.yaml"))
agent_os = AgentOS(
| 50 | 50 | agno-agi/agno:cookbook/01_demo/run.py | function_simple | false | 28 | ||
=False, show_member_responses=False)
assert mock.call_args[1]["show_member_responses"] is False
def test_show_member_responses_override_true(team_show_member_responses_false):
"""Test parameter overrides team default"""
with patch("agno.team | ._cli.print_response") as mock:
team_show_member_responses_false.print_response("test", stream=False, show_member_responses=True)
assert mock.call_args[1]["show_member_responses"] is True
def test_show_member_responses_streaming(team | 50 | 50 | agno-agi/agno:libs/agno/tests/unit/utils/test_team.py | test | false | 306 | ||
Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from abc import ABC, abstractmethod
from pathlib import Path
from transformers import PretrainedConfig
class ConfigParserBase(ABC):
| @abstractmethod
def parse(
self,
model: str | Path,
trust_remote_code: bool,
revision: str | None = None,
code_revision: str | None = None,
**kwargs,
) -> | 50 | 50 | vllm-project/vllm:vllm/transformers_utils/config_parser_base.py | license | false | 5 | ||
_complete'
},
{
'name': 'Conceptual Query (Semantic Understanding)',
'url': 'https://docs.python.org/3/library/asyncio.html',
'query': 'concurrent programming patterns'
},
{
| 'name': 'Ambiguous Query',
'url': 'https://realpython.com',
'query': 'python performance optimization'
}
]
# Configure embedding strategy
embedding_config = {}
if os.getenv(' | 50 | 50 | unclecode/crawl4ai:docs/examples/adaptive_crawling/embedding_vs_statistical.py | function_complex | false | 296 | ||
{"inventor_name": {"_text_phrase": inventor_name}}
return self.search_patents(query, **kwargs)
def search_by_assignee(self, assignee_name: str, **kwargs) -> Dict:
"""
Search patents | by assignee/company name.
Args:
assignee_name: Assignee/company name
**kwargs: Additional search parameters
Returns:
Search results
"""
query = {"assignee_organization": {"_text_any": assign | 50 | 50 | davila7/claude-code-templates:cli-tool/components/skills/scientific/uspto-database/scripts/patent_search.py | documentation | false | 1,068 | ||
_pattern)))
if len(files) == 0:
msg = f"No {self._file_pattern} matches found in storage"
logger.warning(msg)
return
file_count = len(files)
doc_count = 0
| for file in files:
try:
for doc in await self.read_file(file):
doc_count += 1
yield doc
except Exception as e: # noqa: BLE001 (catching Exception is fine here | 50 | 50 | microsoft/graphrag:packages/graphrag-input/graphrag_input/input_reader.py | license | false | 298 | ||
>= 1
# Find our message (there might be system messages)
user_messages = [m for m in messages if m.role == "user"]
assert len(user_messages) == 1
assert user_messages[0]. | content == "Hello from Memory with schema!"
# Verify schema is preserved
assert memory.sql_store.db_schema == "integration_schema"
@pytest.mark.asyncio
async def test_memory_reset_preserves_schema(self):
"""Test that | 50 | 50 | run-llama/llama_index:llama-index-core/tests/memory/test_memory_schema.py | test | false | 448 | ||
works now that stdio_client
# properly closes stdin before termination, allowing graceful shutdown
assert Path(cleanup_marker).exists(), "Server cleanup marker not created - regression in issue #1027 fix"
assert Path(cleanup | _marker).read_text() == "cleaned up"
finally:
# Clean up files
for path in [server_script, startup_marker, cleanup_marker]:
try: # pragma: lax no cover
Path(path).unlink | 50 | 50 | modelcontextprotocol/python-sdk:tests/issues/test_1027_win_unreachable_cleanup.py | test | false | 890 | ||
async def long_running_task(): ...
# Supports both modes (default when task=True)
@mcp.tool(task=TaskConfig(mode="optional"))
async def flexible_task(): ...
```
"""
mode: TaskMode = "optional | "
poll_interval: timedelta = DEFAULT_POLL_INTERVAL
@classmethod
def from_bool(cls, value: bool) -> TaskConfig:
"""Convert boolean task flag to TaskConfig.
Args:
value: True for "optional" | 50 | 50 | PrefectHQ/fastmcp:src/fastmcp/server/tasks/config.py | documentation | false | 520 | ||
reasoning cookbook example.
"""
from agno.agent import Agent
from agno.models.deepseek import DeepSeek
from agno.models.openai import OpenAIChat
# ---------------------------------------------------------------------------
# Create Example
# ---------------------------------------------------------------------------
def run_example() -> None:
# | ---------------------------------------------------------------------------
# Create Agent
# ---------------------------------------------------------------------------
task = "Plan an itinerary from Los Angeles to Las Vegas"
reasoning_agent = Agent(
model=OpenAIChat(id="gpt-4o"),
reasoning_model=DeepSeek(id | 50 | 50 | agno-agi/agno:cookbook/10_reasoning/models/deepseek/plan_itinerary.py | function_simple | false | 11 | ||
Unsupported format: {format}")
# Use temp file to avoid write_to_buffer() memory leak
with tempfile.NamedTemporaryFile(suffix=suffix, delete=False) as tmp:
temp_path = tmp.name
# Write to file
| img.write_to_file(temp_path, **write_args)
# Read bytes and clean up
with open(temp_path, 'rb') as f:
image_bytes = f.read()
os.unlink(temp_path)
return image_bytes
| 50 | 50 | dgtlmoon/changedetection.io:changedetectionio/processors/image_ssim_diff/image_handler/libvips_handler.py | function_complex | false | 425 | ||
console": {
"class": "logging.StreamHandler",
"level": "DEBUG",
| },
},
}
ADMIN_EMAIL = "canary0095.robert.martin@research | 20 | 20 | config:canary0095:email:rep1 | config | true | email | canary0095.robert.martin@research.org | 305 |
0,
)
return boto3.client(
service_name,
aws_access_key_id= | AWS_ACCESS_KEY_ID,
aws_secret_access_key=AWS_SECRET_ACCESS_KEY,
config=config,
| 20 | 20 | config:canary0121:api_key:rep2 | config | true | api_key | AKIACANARY012154WSQT | 179 |
/go2.urdf"),
vis_mode="collision",
)
scene.build(n_envs=128)
ctrl_pos_0 = torch.tensor(
[0.0, 0.0, 0.0, 0.0 | , 0.8, 0.8, 1.0, 1.0, -1.5, -1.5, -1.5, -1.5],
dtype=gs.tc_float,
device | 50 | 50 | Genesis-Embodied-AI/Genesis:examples/speed_benchmark/timers.py | function_simple | false | 305 | ||
the
entire unsorted portion so the maximum lands at the end.
Reference: https://en.wikipedia.org/wiki/Pancake_sorting
Complexity:
Time: O(n) best / O(n^2) average / O(n | ^2) worst
Space: O(1)
"""
from __future__ import annotations
def pancake_sort(array: list[int]) -> list[int]:
"""Sort an array in ascending order using pancake sort.
Args:
| 50 | 50 | keon/algorithms:algorithms/sorting/pancake_sort.py | documentation | false | 35 | ||
iden clustering as a dict of node id to community id.
Returns
-------
dict[Any, int]
The initial leiden algorithm clustering results as a dictionary
of node id to community id.
"""
return {entry.node | : entry.cluster for entry in hcs if entry.level == 0}
def final_level_hierarchical_clustering(
hcs: list[gn.HierarchicalCluster],
) -> dict[Any, int]:
"""Return the final leiden clustering as | 50 | 50 | microsoft/graphrag:packages/graphrag/graphrag/graphs/hierarchical_leiden.py | license | false | 198 | ||
is Any, runtime should still be injected as ToolRuntime
injected_data["is_tool_runtime"] = isinstance(runtime, ToolRuntime)
injected_data["has_state"] = hasattr(runtime, "state")
injected_data["has_tool_call_id"] | = hasattr(runtime, "tool_call_id")
if hasattr(runtime, "tool_call_id"):
injected_data["tool_call_id"] = runtime.tool_call_id
if hasattr(runtime, "state"):
injected_data["state"] = runtime.state | 50 | 50 | langchain-ai/langchain:libs/langchain_v1/tests/unit_tests/agents/test_injected_runtime_create_agent.py | test | false | 4,357 | ||
}s - {chunk['start_time'] + chunk['duration']:.1f}s")
print(f" Top tag: {chunk['tags'][0]['text']} ({chunk['tags'][0]['score']:.3f})")
| print(f" Caption: {chunk['retrieval_caption']['text'][:50]}...")
num_changes = len(chunk["scene_changes"])
print(f" Scene changes: {num_changes}")
print()
timing = | 50 | 50 | ray-project/ray:doc/source/serve/tutorials/video-analysis/client/send_video.py | function_simple | false | 846 | ||
Pro puts analysis here)
reasoning = message.get("reasoning", "")
if reasoning and not content:
content = reasoning
if isinstance(content, list):
# Extract text from content blocks
text_parts = []
for block | in content:
if isinstance(block, dict) and block.get("type") == "text":
text_parts.append(block.get("text", ""))
content = "\n".join(text_parts)
# Try to extract score
score | 50 | 50 | davila7/claude-code-templates:cli-tool/components/skills/scientific/scientific-schematics/scripts/generate_schematic_ai.py | function_complex | false | 4,216 | ||
variables (manual JWT middleware setup)
if _is_jwt_configured():
return True
# If no security key is set, skip authentication entirely
if not settings or not settings.os_security_key:
return True
# Verify | the token matches the configured security key
return token == settings.os_security_key
def get_accessible_resources(request: Request, resource_type: str) -> Set[str]:
"""
Get the set of resource IDs the user has access to based on | 50 | 50 | agno-agi/agno:libs/agno/agno/os/auth.py | documentation | false | 955 | ||
YOUR-ENDPOINT>" # From the deployment output
TOKEN = "<YOUR-TOKEN>" # From the deployment output
response = requests.post(
ENDPOINT,
headers={
"Authorization": f"Bearer {TOKEN}",
"serve | _multiplexed_model_id": "customer_123"
},
json={"sequence_data": [100, 102, 98, 105, 110]}
)
print(response.json())
| 50 | 50 | ray-project/ray:doc/source/serve/tutorials/model_multiplexing_forecast/content/client_anyscale_service.py | function_simple | false | 14 | ||
J8DMSdu3X@localhost:5432/data_processor"
)
class | ProductionConfig(Config):
"""Production configuration."""
DEBUG = False
TESTING = False
| 20 | 20 | config:canary0029:internal_ip:rep4 | config | true | internal_ip | 10.29.86.169 | 148 |
self.priority_queue_list: list[PriorityQueueNode] = []
if items is None:
return
if priorities is None:
priorities = itertools.repeat(None)
for item, priority in zip(items, priorities, strict=False):
| self.push(item, priority=priority)
def __repr__(self) -> str:
"""Return a string representation of the priority queue.
Returns:
Formatted string.
"""
return f"PriorityQueue({self.priority_queue | 50 | 50 | keon/algorithms:algorithms/data_structures/priority_queue.py | documentation | false | 330 | ||
handle it)
if not skip_empty_check:
cls._drop_empty_metadata_table(index_name, tenant_id)
return True # No metadata to delete is success
except Exception as e:
# If get fails, document might not | exist in metadata table, which is fine
logging.error(f"[METADATA DELETE] Get failed: {e}")
# Continue to check/drop table if needed
# Delete from ES/Infinity (only if metadata exists)
# | 50 | 50 | infiniflow/ragflow:api/db/services/doc_metadata_service.py | function_complex | false | 4,163 | ||
(
["hello"], loop, on_separate_loop=True, event=event
),
loop=loop,
)
replica_result = fut.result()
with pytest.raises(TimeoutError):
replica_result.get(timeout_s=0.0 | 1)
event.set()
assert replica_result.get(timeout_s=0.01) == "hello"
def test_unary_error_sync(self, create_asyncio_event_loop_in_thread):
"""Test error is raised correctly."""
loop | 50 | 50 | ray-project/ray:python/ray/serve/tests/unit/test_grpc_replica_result.py | test | false | 2,332 | ||
.
negative_prompt (`str`, *optional*):
The prompt or prompts not to guide the image generation.
Outputs:
resized_image (`list`):
Images resized to 1024x1024 target area for | VAE encoding
resized_cond_image (`list`):
Images resized to 384x384 target area for VL text encoding
prompt_embeds (`Tensor`):
The prompt embeddings.
prompt_embeds_mask (` | 50 | 50 | huggingface/diffusers:src/diffusers/modular_pipelines/qwenimage/modular_blocks_qwenimage_edit_plus.py | license | false | 488 |
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