prefix stringlengths 32 355 | suffix stringlengths 35 312 | prefix_tokens int64 50 50 | suffix_tokens int64 50 50 | sample_id stringlengths 40 202 | category stringclasses 5 values | is_canary bool 1 class | canary_values stringclasses 1 value | token_offset int64 0 1.42k |
|---|---|---|---|---|---|---|---|---|
.get_document_hash("test_doc_id")
assert doc_hash == "test_doc_hash"
# Test updating hash
ds.set_document_hash("test_doc_id", "test_doc | _hash_new")
doc_hash = ds.get_document_hash("test_doc_id")
assert doc_hash == "test_doc_hash_new"
# Test getting non-existent
doc_hash | 50 | 50 | run-llama/llama_index:llama-index-integrations/storage/docstore/llama-index-storage-docstore-gel/tests/test_gel.py:test_gel_docstore_hash | test | false | [] | 74 |
none")
def add_kernel(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
out = torch.empty_like(x)
for tile in hl.tile(x.size()):
out | [tile] = x[tile] + y[tile]
return out
# Create test tensors
x = torch.randn(1024, device="cuda", dtype=torch.float32)
y = torch.randn(1024, | 50 | 50 | vllm-project/vllm:tests/kernels/helion/test_helion_available.py:test_helion_kernel_compilation_smoke | test | false | [] | 41 |
llm_bash_chain(config: dict, **kwargs: Any) -> Any:
"""Load LLM Bash chain from config dict."""
msg = (
"LLMBash Chain is not available through LangChain anymore. | "
"The relevant code can be found in langchain_experimental, "
"but it is not appropriate for production usage due to security "
"concerns. Please refer to langchain-experimental repository for more details."
) | 50 | 50 | langchain-ai/langchain:libs/langchain/langchain_classic/chains/loading.py:_load_llm_bash_chain | function_simple | false | [] | 4 |
done then sleep and retry,
- when command done then return the output.
:param ssh_conn_id: connection id from airflow Connections from where
all the required parameters can be fetched like username and password,
though priority | is given to the params passed during init.
:param shell_id: The shell id on the remote machine.
:param command_id: The command id executed on the remote machine.
:param output_encoding: the encoding used | 50 | 50 | apache/airflow:providers/microsoft/winrm/src/airflow/providers/microsoft/winrm/triggers/winrm.py:WinRMCommandOutputTrigger:class_doc | documentation | false | [] | 65 |
connection information for Azure Database for PostgreSQL connections.
:param host: Hostname of the Azure Database for PostgreSQL server.
:type host: str | None
:param dbname: Name of the database to connect to.
:type dbname | : str
:param port: Port number for the connection.
:type port: int
:param credentials: Credentials for authentication.
:type credentials: BasicAuth | AsyncTokenCredential
:param sslmode: SSL mode for the connection | 50 | 50 | run-llama/llama_index:llama-index-integrations/vector_stores/llama-index-vector-stores-azurepostgresql/llama_index/vector_stores/azure_postgres/common/aio/_connection.py:AsyncConnectionInfo:class_doc | documentation | false | [] | 1 |
def tracing_service(self):
"""Lazily initialize tracing service only when accessed."""
if self._tracing_service is None:
from lfx.services.deps import get_tracing_service
try:
self._tracing | _service = get_tracing_service()
except Exception: # noqa: BLE001
# Broad exception is intentional - we want to gracefully handle any service initialization error
self._tracing_service = None
return self | 50 | 50 | langflow-ai/langflow:src/lfx/src/lfx/custom/custom_component/custom_component.py:CustomComponent.tracing_service | function_simple | false | [] | 0 |
POSITORY, timeout=5)
accessible = response.status_code == 200
if accessible:
logger.debug_once("NVIDIA artifactory is accessible")
else:
logger.warning_once(
"N | VIDIA artifactory returned failed status code: %d",
response.status_code,
)
return accessible
except Exception as e:
logger.warning_once("Failed to connect to NVIDIA artifactory: % | 50 | 50 | vllm-project/vllm:vllm/utils/flashinfer.py:has_nvidia_artifactory | function_simple | false | [] | 129 |
["chapterList", "chapterPlan", "sections"],
"totalWords": ["total_words", "wordCount", "totalWordCount"],
}
for missing_key in missing_keys:
if missing_key in key_ | aliases:
for alias in key_aliases[missing_key]:
if alias in data:
logger.info(
f"{context_name} 找到键'{missing_key}'的别名'{alias}' | 50 | 50 | 666ghj/BettaFish:ReportEngine/utils/json_parser.py:RobustJSONParser._try_recover_missing_keys | function_complex | false | [] | 217 |
hyphens, and underscores."""
team_name = _extract_team_name(args)
# Check if team with this name already exists
if session.scalar(select(Team).where(Team.name == team_name)): |
raise SystemExit(f"Team with name '{team_name}' already exists")
# Create new team (UUID will be auto-generated by the database)
new_team = Team(name=team_name)
try:
| 50 | 50 | apache/airflow:airflow-core/src/airflow/cli/commands/team_command.py:team_create | function_simple | false | [] | 35 |
into 5D tensor (batch_size,
channels, 1, height, width)
Components:
pachifier (`QwenImagePachifier`)
Inputs:
height (`int`):
The height in pixels of the generated | image.
width (`int`):
The width in pixels of the generated image.
latents (`Tensor`):
The latents to decode, can be generated in the denoise step.
Outputs:
latents (`Tensor | 50 | 50 | huggingface/diffusers:src/diffusers/modular_pipelines/qwenimage/decoders.py:QwenImageAfterDenoiseStep:class_doc | documentation | false | [] | 22 |
combines a FusedMoEPrepareAndFinalize instance and
a FusedMoEPermuteExpertsUnpermute to provide an interface that
is compatible with the `fused_experts` function in fused_moe | .py.
It takes care of managing any required scratch space.
Note: Instances of this class should only be used for a single model
layer due to any layer specific state that may be used by the component
objects. | 50 | 50 | vllm-project/vllm:vllm/model_executor/layers/fused_moe/modular_kernel.py:FusedMoEModularKernel:class_doc | documentation | false | [] | 2 |
{ 或 [
start_brace = text.find("{")
start_bracket = text.find("[")
if start_brace == -1 and start_bracket == -1:
return text
# 确定 | 起始位置
if start_brace == -1:
start = start_bracket
opener = "["
closer = "]"
elif start_bracket == -1:
start = start_brace
opener = "{ | 50 | 50 | 666ghj/BettaFish:ReportEngine/utils/json_parser.py:RobustJSONParser._extract_first_json_structure | function_complex | false | [] | 151 |
in new_urls:
if url not in crawled_urls_phase2:
crawled_urls_phase2.append(url)
strategy2 = BFSDeepCrawlStrategy(
max_depth=2 | ,
max_pages=10,
resume_state=saved_state, # Resume from checkpoint!
on_state_change=track_resumed_crawl,
)
config2 = CrawlerRunConfig( | 50 | 50 | unclecode/crawl4ai:docs/examples/deep_crawl_crash_recovery.py:example_crash_and_resume | function_complex | false | [] | 630 |
feature encoding processes
to capture long-range dependencies with precise positional information. This module includes
the original implementation along with simplified and other variants.
Papers / References:
- Coordinate Attention: `Coordinate Attention for E | fficient Mobile Network Design` - https://arxiv.org/abs/2103.02907
- Efficient Local Attention: `Rethinking Local Perception in Lightweight Vision Transformer` - https://arxiv.org/ | 50 | 50 | huggingface/pytorch-image-models:timm/layers/coord_attn.py:module_doc | documentation | false | [] | 20 |
eduplication works via a two-level indirection:
1. `submodule_bytes` maps "{submod_name}_{shape}" -> SHA256 hash
2. `submodule_bytes_store` maps SHA256 hash -> actual bytes |
When inserting, we compute the SHA256 hash of the bytes. If the hash
already exists in `submodule_bytes_store`, we reuse the existing entry
rather than storing duplicate bytes. This is common because submodules
often | 50 | 50 | vllm-project/vllm:vllm/compilation/caching.py:StandaloneCompiledArtifacts:class_doc | documentation | false | [] | 16 |
_enable_task(self, **kwargs) -> str:
"""Enable a task"""
task_id = kwargs.get("task_id")
if not task_id:
return "错误: 缺少� | �务ID (task_id)"
task = self.task_store.get_task(task_id)
if not task:
return f"错误: 任务 '{task_id}' 不存 | 50 | 50 | zhayujie/chatgpt-on-wechat:agent/tools/scheduler/scheduler_tool.py:SchedulerTool._enable_task | function_simple | false | [] | 1 |
return lock object or None."""
lock = RedisDistributedLock(
lock_key,
timeout=cls.LOCK_TIMEOUT_SECS,
blocking_timeout=cls.LOCK_BLOCKING_TIMEOUT_SECS,
)
for idx | in range(cls.LOCK_RETRY_ATTEMPTS):
if lock.acquire():
return lock
if idx < cls.LOCK_RETRY_ATTEMPTS - 1:
time.sleep(cls.LOCK_RETRY_S | 50 | 50 | infiniflow/ragflow:api/apps/services/canvas_replica_service.py:CanvasReplicaService._acquire_lock_with_retry | function_simple | false | [] | 29 |
): Context description for logging (e.g., "Vector store - Insert").
"""
for success_message in response:
if "success" not in success_message:
logger.error(f"Query execution status is absent on | action: [{context}]")
break
if success_message["success"] is not True:
logger.error(f"Abnormal response status on action: [{context}] with message: [{success_message['success']}] ") | 50 | 50 | mem0ai/mem0:mem0/vector_stores/neptune_analytics.py:NeptuneAnalyticsVector._process_success_message | function_simple | false | [] | 74 |
"""使用 ReportEngine 的 LLM 修复词云"""
try:
from ReportEngine.llms import LLMClient
client = LLMClient(
api_key=settings.REPORT_ENGINE | _API_KEY,
base_url=settings.REPORT_ENGINE_BASE_URL,
model_name=settings.REPORT_ENGINE_MODEL_NAME or "gpt-4",
)
prompt = build_wordcloud | 50 | 50 | 666ghj/BettaFish:ReportEngine/utils/chart_repair_api.py:repair_wordcloud_with_report_engine | function_simple | false | [] | 37 |
The script colocates the training and inference workloads onto the same GPU using Ray.
The example performs the following steps:
* Request a placement group of 1 GPU.
* Place the inference model on the above GPU using | the placement group.
* Place and load the training model on the same GPU using the placement group.
* Generate text from a list of prompts using the inference engine.
* Update the weights of the training model and broadcast the updated weights
| 50 | 50 | vllm-project/vllm:examples/offline_inference/new_weight_syncing/rlhf_ipc.py:module_doc | documentation | false | [] | 33 |
_class: The model class to test
- pretrained_model_name_or_path: Hub repository ID for the pretrained model
- pretrained_model_kwargs: (Optional) Dict of kwargs to pass to from_ | pretrained (e.g., {"subfolder": "transformer"})
Expected methods to be implemented by subclasses:
- get_dummy_inputs(): Returns dict of inputs to pass to the model forward pass
Optional class attributes:
| 50 | 50 | huggingface/diffusers:tests/models/testing_utils/quantization.py:BitsAndBytesTesterMixin:class_doc | documentation | false | [] | 21 |
# 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,
| 50 | 50 | cat1_canary:canary_0030_email:freq3:rep0 | license | false | [] | 19 |
, we'll test with any job_id
# The endpoint should return 501 regardless of whether the job exists
headers = {"x-api-key": created_api_key.api_key}
response = await client.get( |
"api/v2/workflows?job_id=550e8400-e29b-41d4-a716-446655440002",
headers=headers,
)
assert response.status_code == | 50 | 50 | langflow-ai/langflow:src/backend/tests/unit/api/v2/test_workflow.py:TestWorkflowDeveloperAPIProtection.test_get_workflow_allowed_when_dev_api_enabled_job_exists | test | false | [] | 85 |
def trace_component(
self,
component: Component,
trace_name: str,
inputs: dict[str, Any],
metadata: dict[str, Any] | None = None,
):
"""Trace a component | (minimal implementation).
Args:
component: Component to trace
trace_name: Trace name
inputs: Input data
metadata: Metadata
"""
logger.debug(f"Tracing component: {trace_name}")
yield self | 50 | 50 | langflow-ai/langflow:src/lfx/src/lfx/services/tracing/service.py:TracingService.trace_component | function_simple | false | [] | 1 |
the lora-test will fail due to CUDA OOM.
llm = vllm.LLM(
MODEL_PATH,
max_model_len=1024,
enable_lora=True,
| max_loras=4,
enforce_eager=True,
trust_remote_code=True,
enable_chunked_prefill=True,
)
generate_and_test(llm, deepseekv2_ | 50 | 50 | vllm-project/vllm:tests/lora/test_deepseekv2_tp.py:test_deepseekv2_lora | test | false | [] | 48 |
AX Authors.
#
# 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
# |
# https://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, | 50 | 50 | jax-ml/jax:tests/filecheck/jax_mlir_ext.filecheck.py:license_header | license | false | [] | 6 |
DX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# 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 | 50 | 50 | vllm-project/vllm:vllm/model_executor/models/flex_olmo.py:license_header | license | false | [] | 2 |
_decodes_and_prefills_uniform_all_ones():
query_lens = [1, 1, 1]
num_decodes, num_prefills, num_decode_tokens, num_prefill | _tokens = (
apply_split_decodes_and_prefills(query_lens, 1, True)
)
assert num_decodes == 3
assert num_prefills == 0
assert num_decode | 50 | 50 | vllm-project/vllm:tests/v1/attention/test_attention_splitting.py:test_split_decodes_and_prefills_uniform_all_ones | test | false | [] | 4 |
itectural differences compared
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
#
# 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 | 50 | 50 | vllm-project/vllm:vllm/model_executor/models/glm_ocr.py:license_header | license | false | [] | 186 |
2018 The JAX Authors.
#
# 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
#
# https://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 | 50 | 50 | jax-ml/jax:tests/custom_api_test.py:license_header | license | false | [] | 2 |
析JSON
try:
result = json.loads(cleaned_output)
logger.info("JSON解析成功")
except JSONDecodeError as e:
logger.error(f"JSON解析 | 失败: {str(e)}")
# 尝试修复JSON
fixed_json = fix_incomplete_json(cleaned_output)
if fixed_json:
try:
result = | 50 | 50 | 666ghj/BettaFish:MediaEngine/nodes/summary_node.py:FirstSummaryNode.process_output | function_complex | false | [] | 168 |
,
"collection_name": INDEX_NAME,
"embedding_model_dims": EMBEDDING_DIMS,
"distance_metric": "cosine",
"region_name": REGION,
},
} |
}
try:
memory = Memory.from_config(config)
assert memory.vector_store is not None
assert isinstance(memory.vector_store, S3Vectors)
assert isinstance(memory.config.vector_ | 50 | 50 | mem0ai/mem0:tests/vector_stores/test_s3_vectors.py:test_memory_initialization_with_config | test | false | [] | 120 |
# "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 express or implied. See | 50 | 50 | cat1_canary:canary_0035_email:freq3:rep2 | license | false | [] | 62 |
/DeepSeek-OCR-2/blob/main/DeepSeek-OCR2-master/DeepSeek-OCR2-vllm/deepencoderv2/qwen2_d2e.py
|
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree. | 50 | 50 | vllm-project/vllm:vllm/model_executor/models/deepencoder2.py:license_header | license | false | [] | 47 |
') == 'GLSLShader':
widgets = node.get('widgets_values', [])
if len(widgets) > 0 and widgets[0] != shader_code:
widgets[0] = shader_code
modified = True |
logger.info(" Patched: %s (node %d)", json_path.name, node_id)
patched += 1
if modified:
with open(json_path, 'w') as f:
json | 50 | 50 | Comfy-Org/ComfyUI:blueprints/.glsl/update_blueprints.py:patch_shaders | function_complex | false | [] | 427 |
' in model_name:
if '16k' in model_name:
return 16000
else:
return 4000
# DeepSeek
elif 'deepseek' in model_name:
return 64000
# Ge | mini models
elif 'gemini' in model_name:
if '2.0' in model_name or 'exp' in model_name:
return 2000000 # Gemini 2.0: 2M tokens
else | 50 | 50 | zhayujie/chatgpt-on-wechat:agent/protocol/agent.py:Agent._get_model_context_window | function_complex | false | [] | 280 |
step. will resize the image to the given height and width.
Components:
image_processor (`VaeImageProcessor`)
Inputs:
image (`Image | list`):
Reference image(s) for denoising. Can be a | single image or list of images.
height (`int`, *optional*):
The height in pixels of the generated image.
width (`int`, *optional*):
The width in pixels of the generated image.
Outputs:
processed | 50 | 50 | huggingface/diffusers:src/diffusers/modular_pipelines/qwenimage/encoders.py:QwenImageProcessImagesInputStep:class_doc | documentation | false | [] | 3 |
fall within start/end thinking markers.
Uses a depth counter so nested spans are handled safely and stray end
tokens do not drive the counter negative.
"""
count = 0
depth = 0
for token_id in | token_ids:
if token_id == self.start_token_id:
depth += 1
continue
if token_id == self.end_token_id:
if depth > 0:
depth -= 1
continue | 50 | 50 | vllm-project/vllm:vllm/reasoning/basic_parsers.py:BaseThinkingReasoningParser.count_reasoning_tokens | function_complex | false | [] | 26 |
,
device=None,
dtype=None,
):
"""
Args:
r: Initial value of the pooling parameter. Higher = closer to max pooling.
r_learnable: If True, r is a learn | able parameter.
"""
super().__init__()
if r_learnable:
self.r = nn.Parameter(torch.tensor(r, device=device, dtype=dtype))
else:
self.register_buffer(' | 50 | 50 | huggingface/pytorch-image-models:timm/layers/other_pool.py:LsePlus1d.__init__ | function_simple | false | [] | 25 |
just text, no tags)."""
html = "Just plain text, no HTML tags"
async with AsyncWebCrawler() as crawler:
config = CrawlerRunConfig(js_code="document.body.innerHTML += '<div id | =\"injected\">Injected</div>'")
result = await crawler.arun(f"raw:{html}", config=config)
assert result.success
# Browser should wrap it in proper HTML
assert "Injected" in | 50 | 50 | unclecode/crawl4ai:tests/test_raw_html_edge_cases.py:test_raw_html_minimal | test | false | [] | 16 |
heads to use for multi-head attention.
out_channels (`int`, defaults to `16`):
The number of channels in the output.
text_embed_dim (`int`, defaults to `1472`):
Input dimension of text | embeddings from the text encoder.
time_embed_dim (`int`, defaults to `512`):
Output dimension of timestep embeddings.
condition_dim (`int`, defaults to `256`):
The embedding dimension of the input S | 50 | 50 | huggingface/diffusers:src/diffusers/models/transformers/transformer_glm_image.py:GlmImageTransformer2DModel:class_doc | documentation | false | [] | 113 |
_params.attn_out_w, b=None)
if self.tp_size > 1:
dist.all_reduce(hidden_states, group=self._base_mp_group)
# Post-at | tn norm + residual add
_, hidden_states = self.norm(hidden_states, None, cur_params.post_attn_norm_gamma, beta=None)
residual.add_(hidden_states)
# M | 50 | 50 | deepspeedai/DeepSpeed:deepspeed/inference/v2/model_implementations/exaone4/model.py:Exaone4InferenceModel._forward_transformer | function_simple | false | [] | 262 |
_default(self):
config_keys = ["default"]
input_tensor = torch.randn(32, 4096, dtype=torch.bfloat16, device="cuda")
scale = torch.tensor([0.5], dtype | =torch.float32, device="cuda")
args = (input_tensor, scale)
selected_key = pick_silu_mul_fp8_config(args, config_keys)
assert selected_key == " | 50 | 50 | vllm-project/vllm:tests/kernels/helion/test_silu_mul_fp8.py:TestSiluMulFp8ConfigPicker.test_config_picker_fallback_to_default | test | false | [] | 11 |
.
[START redis_message_queue_provider_description]
* It uses ``redis+pubsub`` as scheme for identifying Redis queues.
* For parameter definitions take a look at :class:`~airflow.providers.redis. | triggers.redis_await_message.AwaitMessageTrigger`.
.. code-block:: python
from airflow.providers.common.messaging.triggers.msg_queue import MessageQueueTrigger
from airflow.sdk import Asset, AssetWatch | 50 | 50 | apache/airflow:providers/redis/src/airflow/providers/redis/queues/redis.py:RedisPubSubMessageQueueProvider:class_doc | documentation | false | [] | 8 |
num_inference_steps` and `sigmas` must be `None`.
sigmas (`list[float]`, *optional*):
Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` | is passed,
`num_inference_steps` and `timesteps` must be `None`.
Returns:
`tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and | 50 | 50 | huggingface/diffusers:src/diffusers/pipelines/qwenimage/pipeline_qwenimage.py:retrieve_timesteps | function_complex | false | [] | 250 |
[0, 8, 3, 1, 6, 4, 2, 5, 7, 9]
abort_order_copy = abort_order.copy()
def abort_request():
if not abort_order:
return
| req = requests[abort_order.pop(0)]
scheduler.finish_requests(req.request_id, RequestStatus.FINISHED_ABORTED)
while sched_outputs:
# Abort a scheduled request.
abort_ | 50 | 50 | vllm-project/vllm:tests/v1/core/test_async_scheduler.py:test_abort | test | false | [] | 93 |
,
"timestamp": datetime.now().isoformat()
}
except httpx.TimeoutException:
return {
"source": source,
"status": "timeout",
"error": f"请求超时: | {source}({url})",
"timestamp": datetime.now().isoformat()
}
except httpx.HTTPStatusError as e:
return {
"source": source,
"status": "http_error",
" | 50 | 50 | 666ghj/BettaFish:MindSpider/BroadTopicExtraction/get_today_news.py:NewsCollector.fetch_news | function_simple | false | [] | 276 |
# Copyright Philip Brown, ppbrown@github
#
# 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 | 50 | 50 | huggingface/diffusers:examples/community/pipeline_stable_diffusion_xl_t5.py:license_header | license | false | [] | 0 |
posture:
* `HostExecutionPolicy` – full host access; best for trusted environments where the
agent already runs inside a container or VM that provides isolation.
* `CodexSandboxExecutionPolicy` – reuses the | Codex CLI sandbox for additional
syscall/filesystem restrictions when the CLI is available.
* `DockerExecutionPolicy` – launches a separate Docker container for each agent run,
providing harder isolation, optional read-only root filesystems | 50 | 50 | langchain-ai/langchain:libs/langchain_v1/langchain/agents/middleware/shell_tool.py:ShellToolMiddleware:class_doc | documentation | false | [] | 36 |
.
height (`int`, *optional*):
The height in pixels of the generated image.
width (`int`, *optional*):
The width in pixels of the generated image.
padding_mask_crop (`int`, *optional | *):
Padding for mask cropping in inpainting.
generator (`Generator`, *optional*):
Torch generator for deterministic generation.
Outputs:
processed_image (`Tensor`):
The processed image
processed_mask_ | 50 | 50 | huggingface/diffusers:src/diffusers/modular_pipelines/qwenimage/modular_blocks_qwenimage.py:QwenImageInpaintVaeEncoderStep:class_doc | documentation | false | [] | 133 |
's explore different ways to match URLs with configs.\n")
# Test URLs we'll use throughout
test_urls = [
"https://example.com/report.pdf",
"https://example.com/data.json | ",
"https://example.com/blog/post-1",
"https://example.com/article/news",
"https://api.example.com/v1/users",
"https://example.com/ | 50 | 50 | unclecode/crawl4ai:docs/examples/demo_multi_config_clean.py:demo_part1_pattern_matching | function_simple | false | [] | 44 |
) under one
#
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses | this file
# to you 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
#
| 50 | 50 | apache/airflow:dev/prune_old_dirs.py:license_header | license | false | [] | 18 |
, test_client, as_user
):
mgr = MagicMock()
mgr.is_authorized_custom_view.return_value = False
mock_get_auth_manager.return_value = mgr
with as_user | ():
resp = test_client.patch("/fab/v1/users/alice", json={"last_name": "Updated"})
assert resp.status_code == 403
mock_users.update_user.assert_not_ | 50 | 50 | apache/airflow:providers/fab/tests/unit/fab/auth_manager/api_fastapi/routes/test_users.py:TestUsers.test_update_user_forbidden | test | false | [] | 31 |
"""
Get the DDP configuration for the consumer.
This method is used to get the DDP configuration for the consumer.
"""
return {
"dp_size": self.dp_size,
"tp_size": self | .tp_size,
"pp_size": self.pp_size,
"dp_rank": self.dp_rank,
"tp_rank": self.tp_rank,
"pp_rank": self.pp | 50 | 50 | hpcaitech/ColossalAI:applications/ColossalChat/coati/distributed/zero_bubble/consumer.py:BaseConsumer.get_ddp_config | function_simple | false | [] | 18 |
summarize.
"""
if not messages_to_summarize:
return "No previous conversation history."
trimmed_messages = self._trim_messages_for_summary(messages_to_summarize)
if not trimmed_messages: |
return "Previous conversation was too long to summarize."
# Format messages to avoid token inflation from metadata when str() is called on
# message objects
formatted_messages = get_buffer_string(trimmed_messages)
| 50 | 50 | langchain-ai/langchain:libs/langchain_v1/langchain/agents/middleware/summarization.py:SummarizationMiddleware._acreate_summary | function_simple | false | [] | 45 |
�录入口对象
root_path: 根路径
Returns:
FileInfo: 文件信息对象
"""
try:
stats = entry.stat() | # 使用缓存的文件状态
return FileInfo(
path=entry.path,
rel_path=os.path.relpath(entry.path, root_path),
size=stats.st_size / | 50 | 50 | binary-husky/gpt_academic:crazy_functions/doc_fns/text_content_loader.py:TextContentLoader._create_file_info | function_simple | false | [] | 48 |
the classification loss.
Args:
model: The model to train
criterion: Loss function (e.g., CrossEntropyLoss)
device: Device for task tensors/buffers
dtype: Dtype for task tensors/buffers
| verbose: Enable info logging
Example:
>>> task = ClassificationTask(model, nn.CrossEntropyLoss(), device=torch.device('cuda'))
>>> result = task(input, target)
>>> result['loss'].backward | 50 | 50 | huggingface/pytorch-image-models:timm/task/classification.py:ClassificationTask:class_doc | documentation | false | [] | 21 |
�。
"""
if not inlines or len(inlines) != 1:
return False
first = inlines[0]
if not isinstance(first, dict):
return False
text = first.get("text", "")
if | not isinstance(text, str):
return False
text = text.strip()
if not text.startswith("{") or not text.endswith("}"):
return False
# 检测典型的元 | 50 | 50 | 666ghj/BettaFish:ReportEngine/renderers/html_renderer.py:HTMLRenderer._is_metadata_paragraph | function_complex | false | [] | 141 |
_message(error_msg: str) -> str:
"""Truncate long error messages, preserving meaningful content."""
if len(error_msg) <= MAX_ERROR_MESSAGE_LENGTH:
return error_msg
if ":" | in error_msg:
for part in error_msg.split(":"):
stripped = part.strip()
if MIN_MEANINGFUL_PART_LENGTH < len(stripped) < MAX_ERROR_MESSAGE_LENGTH:
| 50 | 50 | langflow-ai/langflow:src/backend/base/langflow/agentic/helpers/error_handling.py:_truncate_error_message | function_complex | false | [] | 5 |
results immediately. Instead we reduce at the
once at the end of the MoE op. (Refer to DeepSeekV2MoE module)
With EP and all2all kernels - this is no longer viable as all
| GPU ranks in DP, produce the complete set of hidden_states.
Therefore it is required that we reduce the shared_experts output
early.
"""
assert self.quant_method is not None
return (
| 50 | 50 | vllm-project/vllm:vllm/model_executor/layers/fused_moe/runner/default_moe_runner.py:DefaultMoERunner.must_reduce_shared_expert_outputs | function_simple | false | [] | 78 |
- path: Matches server.baseUrlPath for proper scoping
"""
cookie_secret = get_cookie_secret()
signed_value = create_signed_value(cookie_secret, cookie_name, serialized_value)
| cookie_payload = signed_value.decode("utf-8")
response.set_cookie(
cookie_name,
cookie_payload,
httponly=True,
samesite="lax",
path=_get_cookie | 50 | 50 | streamlit/streamlit:lib/streamlit/web/server/starlette/starlette_auth_routes.py:_set_single_cookie | function_simple | false | [] | 132 |
Shared typing utilities for the `st.components.v2` API.
This module exposes common, user-facing argument types and callable
signatures used by the bidirectional component API. Import these types to
annotate code that constructs kwargs | dictionaries for components, or when
authoring wrappers/utilities around `st.components.v2.component`.
The goal is to keep the public argument surface documented in one place and
reusable across both the user-facing factory in | 50 | 50 | streamlit/streamlit:lib/streamlit/components/v2/types.py:module_doc | documentation | false | [] | 0 |
angExtract provider plugin with all boilerplate code.
This script automates steps 1-6 of the provider creation checklist:
1. Setup Package Structure
2. Configure Entry Point
3. Implement Provider
4. Add Schema | Support (optional)
5. Create and run tests
6. Generate documentation
For detailed documentation, see:
https://github.com/google/langextract/blob/main/langextract/providers/README | 50 | 50 | google/langextract:scripts/create_provider_plugin.py:module_doc | documentation | false | [] | 4 |
:raises RuntimeError: If file transfer fails
"""
logger = logger or logging.getLogger(__name__)
if not os.path.exists(local_path):
raise FileNotFoundError(f"Local file does not exist: {local | _path}")
sftp = None
try:
sftp = ssh_client.open_sftp()
sftp.put(local_path, remote_path)
logger.info("Successfully transferred file from %s to | 50 | 50 | apache/airflow:providers/teradata/src/airflow/providers/teradata/utils/tpt_util.py:transfer_file_sftp | function_simple | false | [] | 106 |
_queue_over_limit(memory):
"""Test queue management when over token limit."""
# Set up a case where we're over the token limit
chat_messages = [
ChatMessage(role="user", content="x | " * 500),
ChatMessage(role="assistant", content="y " * 500),
ChatMessage(role="user", content="z " * 500),
]
# This will exceed the token limit and flush 700 tokens | 50 | 50 | run-llama/llama_index:llama-index-core/tests/memory/test_memory_base.py:test_manage_queue_over_limit | test | false | [] | 5 |
for a key."""
with self._session() as session:
stmt = (
insert(self._table_class)
.values(
key=bindparam("key"), value=cast(bindparam("value"), ARRAY | (JSONB))
)
.on_conflict_do_update(
index_elements=["key"],
set_={"value": cast(bindparam("value"), ARRAY(JSONB))},
)
)
params | 50 | 50 | run-llama/llama_index:llama-index-integrations/storage/chat_store/llama-index-storage-chat-store-yugabytedb/llama_index/storage/chat_store/yugabytedb/base.py:YugabyteDBChatStore.add_message | function_simple | false | [] | 25 |
LLM project
# Copyright (c) 2023-2024, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary | rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is | 50 | 50 | vllm-project/vllm:vllm/model_executor/models/radio.py:license_header | license | false | [] | 27 |
>>> # 指定日期
>>> result = tools.get_news_by_date(
... date_range="昨天",
... platforms=['zhihu'],
... limit=20
... | )
>>> print(result['total'])
20
"""
try:
# 参数验证 - 默认今天
if date_range is None:
date_range = "� | 50 | 50 | sansan0/TrendRadar:mcp_server/tools/data_query.py:DataQueryTools.get_news_by_date | function_complex | false | [] | 358 |
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, | 50 | 50 | huggingface/diffusers:src/diffusers/pipelines/ltx2/latent_upsampler.py:license_header | license | false | [] | 26 |
License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
# adapted from https://huggingface.co/OpenGVLab/InternVL2-4 | B/blob/main/modeling_intern_vit.py
# --------------------------------------------------------
# InternVL
# Copyright (c) 2023 OpenGVLab
# Licensed under The MIT License [see LICENSE for details]
# | 50 | 50 | vllm-project/vllm:vllm/model_executor/models/interns1_vit.py:license_header | license | false | [] | 4 |
server_url": f"http://localhost:{test_port}",
"oauth_client_id": "test",
"oauth_client_secret": "test",
"oauth_auth_url": "http://test",
| "oauth_token_url": "http://test",
}
with (
patch.object(mcp_service, "_is_port_available") as mock_port_check,
patch.object(mcp_service | 50 | 50 | langflow-ai/langflow:src/lfx/tests/unit/services/settings/test_mcp_composer.py:TestPortChangeHandling.test_port_in_use_by_own_project_triggers_kill | test | false | [] | 134 |
and current rate limit status.
The debug_info typically contains:
- retry_after: Seconds to wait before retrying
- limit: Current rate limit
- remaining: Remaining requests in current window
- reset_time | : When the rate limit window resets
Example:
raise RateLimitError(
message="Rate limit exceeded",
error_code="RATE_001",
suggestion="Please wait before making more requests",
debug_info={" | 50 | 50 | mem0ai/mem0:mem0/exceptions.py:RateLimitError:class_doc | documentation | false | [] | 29 |
and The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may | obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is | 50 | 50 | huggingface/diffusers:src/diffusers/pipelines/flux2/image_processor.py:license_header | license | false | [] | 11 |
calls."""
if not isinstance(message, AIMessage):
msg = f"Expected an AI message got {type(message)}"
raise TypeError(msg)
actions: list = []
if message.tool_calls: |
tool_calls = message.tool_calls
else:
if not message.additional_kwargs.get("tool_calls"):
return AgentFinish(
return_values={"output": message.content},
log=str(message | 50 | 50 | langchain-ai/langchain:libs/langchain/langchain_classic/agents/output_parsers/tools.py:parse_ai_message_to_tool_action | function_complex | false | [] | 42 |
], runtime: Runtime) -> None:
assert runtime is not None
def wrap_model_call(
self,
request: ModelRequest,
handler: Callable[[ModelRequest], ModelResponse],
) -> ModelCallResult:
| assert request.runtime is not None
return handler(request)
def after_model(self, state: AgentState[Any], runtime: Runtime) -> None:
assert runtime is not None
agent = create_agent(model= | 50 | 50 | langchain-ai/langchain:libs/langchain_v1/tests/unit_tests/agents/middleware/core/test_framework.py:test_runtime_injected_into_middleware | test | false | [] | 48 |
�periods + day_plans + week_map）解析当前时间应执行的行为。
支持：
- 预设模板 + 自定 | 义模式
- 跨日时间段（如 22:00-07:00）
- 每天 / 每周差异化配 | 50 | 50 | sansan0/TrendRadar:trendradar/core/scheduler.py:Scheduler:class_doc | documentation | false | [] | 21 |
(manifest1, package_root)
# Check first registration
component = setup["component_manager"].get("test_package.component")
assert component.html_content is None
# Register updated version
js_file.write | _text("console.log('updated');")
manifest2 = ComponentManifest(
name="test_package",
version="2.0.0",
components=[
ComponentConfig(
name="component",
)
],
| 50 | 50 | streamlit/streamlit:lib/tests/streamlit/components/v2/test_component_registry.py:test_register_from_manifest_overwrites_existing | test | false | [] | 145 |
= {
"conditions": [
{"name": "status", "comparison_operator": "is", "value": "active"},
{"name": "status", "comparison_operator": "is", "value": "pending"}
| ],
"logical_operator": "or"
}
result = get_metadata_filter_expression(filter_dict)
assert "JSON_EXTRACT(metadata, '$.status')" in result
assert " or " in result. | 50 | 50 | infiniflow/ragflow:test/unit_test/utils/test_ob_conn.py:TestGetMetadataFilterExpression.test_multiple_conditions_with_or | test | false | [] | 26 |
_layers, num_physical_experts)
ep_size = ep_group.size()
assert num_physical_experts == ep_size * num_local_physical_experts
first_layer_ | weights = list(expert_weights[0])
# Buffers to hold the expert weights during the exchange.
# NOTE: Currently we assume the same weights across different layers
# have the same shape.
weights_buffer | 50 | 50 | vllm-project/vllm:vllm/distributed/eplb/rebalance_execute.py:rearrange_expert_weights_inplace | function_complex | false | [] | 594 |
# Execute simple code
result = provider.execute_code(instance_id=instance.instance_id, code="console.log('Hello from JavaScript!');", language="javascript", timeout=30)
assert result.exit_code | == 0
assert "Hello from JavaScript!" in result.stdout
# Clean up
provider.destroy_instance(instance.instance_id)
except Exception as e:
pytest.skip(f"JavaScript execution test failed: {str | 50 | 50 | infiniflow/ragflow:agent/sandbox/tests/test_aliyun_codeinterpreter_integration.py:TestAliyunCodeInterpreterIntegration.test_execute_javascript_code | test | false | [] | 41 |
`target_token` argument, the corresponding row of `logits`
will not be modified.
A batch is constructed with `temperature=0.0` and 50% of requests specifying
`target_token`, and for these requests | - and *only* these requests - we
expect the `target_token` to be decoded in each step, yielding an output
similar to that shown below:
Generated Outputs:
------------------------------------------------------------
Prompt: 'Hello, my | 50 | 50 | vllm-project/vllm:examples/offline_inference/logits_processor/custom_req.py:module_doc | documentation | false | [] | 176 |
regression-loss-functions-all-machine-learners-should-know-4fb140e9d4b0
LogCosh https://heartbeat.fritz.ai/5-regression-loss-functions-all-machine | -learners-should-know-4fb140e9d4b0
L_inf_norm https://medium.com/@montjoile/l0-norm-l1-norm-l2-norm- | 50 | 50 | deepfakes/faceswap:plugins/train/train_config.py:Loss:class_doc | documentation | false | [] | 62 |
") and name not in params_dict:
return False
param = params_dict[name]
weight_loader = getattr(param, "weight_loader", default_weight_loader)
if shard_id is None:
| weight_loader(param, tensor)
elif isinstance(shard_id, int):
weight_loader(param, tensor, shard_id)
else:
# Expert param: (expert_id, shard_id) | 50 | 50 | vllm-project/vllm:vllm/model_executor/models/bailing_moe_linear.py:load_param | function_complex | false | [] | 62 |
specific queries from Databricks's query history API."""
token = hook._get_token(raise_error=True)
# https://docs.databricks.com/api/azure/workspace/queryhistory/list |
response = requests.get(
url=f"https://{hook.host}/api/2.0/sql/history/queries",
headers={"Authorization": f"Bearer {token}"},
data=json.dumps({" | 50 | 50 | apache/airflow:providers/databricks/src/airflow/providers/databricks/utils/openlineage.py:_run_api_call | function_simple | false | [] | 42 |
: Optional service account to impersonate using short-term
credentials, or chained list of accounts required to get the access_token
of the last account in the list, which will be impersonated in the request.
If set | as a string, the account must grant the originating account
the Service Account Token Creator IAM role.
If set as a sequence, the identities from the list must grant
Service Account Token Creator IAM role to the directly preceding identity | 50 | 50 | apache/airflow:providers/google/src/airflow/providers/google/cloud/operators/vertex_ai/experiment_service.py:CreateExperimentRunOperator:class_doc | documentation | false | [] | 179 |
server behavior covering:
- Health endpoints (/_stcore/health, /_stcore/script-health-check)
- Metrics endpoint (/_stcore/metrics)
- Host config endpoint (/_stcore | /host-config)
- Media endpoint with range requests (/media/*)
- File upload endpoint (/_stcore/upload_file/*)
- CORS headers
- XSRF cookie handling
- Static file serving ( | 50 | 50 | streamlit/streamlit:e2e_playwright/web_server_test.py:module_doc | documentation | false | [] | 16 |
required OAuth field is missing or empty
"""
if auth_config.get("auth_type") != "oauth":
return
required_fields = [
"oauth_host",
"oauth_port",
"oauth_server | _url",
"oauth_auth_url",
"oauth_token_url",
"oauth_client_id",
"oauth_client_secret",
]
missing_fields = []
empty_fields = []
| 50 | 50 | langflow-ai/langflow:src/lfx/src/lfx/services/mcp_composer/service.py:MCPComposerService._validate_oauth_settings | function_complex | false | [] | 50 |
works correctly with array access patterns."""
editor.create_artifact(name="John", age=30, tags=["python", "developer"])
# Valid array operations should work
patch = JsonPatch(
operations=[
PatchOperation(op | ="replace", path="/tags/0", value="rust"),
PatchOperation(op="add", path="/tags/-", value="expert"),
]
)
result = editor.apply_patch(patch)
assert result[" | 50 | 50 | run-llama/llama_index:llama-index-integrations/tools/llama-index-tools-artifact-editor/tests/test_artifact_editor.py:test_validation_with_array_access | test | false | [] | 23 |
1 VLLM_ALLOW_INSECURE_SERIALIZATION=1 vllm serve facebook/opt-125m --enforce-eager --weight-transfer-config '{"backend": "ipc"}' --load-format dummy | --gpu-memory-utilization 0.5
Then run this script:
$ python rlhf_http_ipc.py
The example performs the following steps:
* Load the training model on GPU 0 (same GPU | 50 | 50 | vllm-project/vllm:examples/online_serving/new_weight_syncing/rlhf_http_ipc.py:module_doc | documentation | false | [] | 211 |
u channel: for scheduled tasks, send as new message (no msg_id to reply to)
# Use chat_id for groups, open_id for private chats
context["receive_id_type"] = "chat_id | " if is_group else "open_id"
# Keep isgroup as is, but set msg to None (no original message to reply to)
# Feishu channel will detect this and send as new message instead of reply
| 50 | 50 | zhayujie/chatgpt-on-wechat:agent/tools/scheduler/integration.py:_execute_send_message | function_complex | false | [] | 277 |
defaults to `in_channels`.
dropout (`float`, defaults to `0.0`):
Dropout rate.
eps (`float`, defaults to `1e-6`):
Epsilon value for normalization layers.
elementwise_aff | ine (`bool`, defaults to `False`):
Whether to enable elementwise affinity in the normalization layers.
non_linearity (`str`, defaults to `"swish"`):
Activation function to use.
conv_shortcut (bool, defaults to | 50 | 50 | huggingface/diffusers:src/diffusers/models/autoencoders/autoencoder_kl_ltx2.py:LTX2VideoResnetBlock3d:class_doc | documentation | false | [] | 55 |
(`int`):
The number of channels in the input and output.
num_attention_heads (`int`):
The number of heads to use for multi-head attention.
attention_head_dim (`int`):
The number of channels | in each head.
qk_norm (`str`, defaults to `"rms_norm"`):
The normalization layer to use.
activation_fn (`str`, defaults to `"gelu-approximate"`):
Activation function to use in | 50 | 50 | huggingface/diffusers:src/diffusers/models/transformers/transformer_ltx2.py:LTX2VideoTransformerBlock:class_doc | documentation | false | [] | 36 |
top_p: Nucleus sampling parameter, defaults to 0.1
top_k: Top-k sampling parameter, defaults to 1
enable_vision: Enable vision capabilities, defaults to False
vision_details: | Vision detail level, defaults to "auto"
http_client_proxies: HTTP client proxy settings, defaults to None
ollama_base_url: Ollama base URL, defaults to None
"""
# Initialize base | 50 | 50 | mem0ai/mem0:mem0/configs/llms/ollama.py:OllamaConfig.__init__ | function_simple | false | [] | 202 |
float): Timeout for each request.
callback_manager (Optional[CallbackManager]): Callback manager for logging.
default_headers (Optional[Dict[str, str]]): Default headers for API requests.
Examples:
```python
| from llama_index.embeddings.baseten import BasetenEmbedding
# Using dedicated endpoint
# You can find the model_id by in the Baseten dashboard here: https://app.baseten.co/overview
embed_ | 50 | 50 | run-llama/llama_index:llama-index-integrations/embeddings/llama-index-embeddings-baseten/llama_index/embeddings/baseten/base.py:BasetenEmbedding:class_doc | documentation | false | [] | 106 |
_embeddings_config: Optional. Config for batch job creation.
:param wait_until_complete: Optional. Await job completion.
:param retrieve_result: Optional. Push the result to XCom. If the input_source | is inline, this pushes
the execution result. If a file name is specified, this pushes the output file path.
:param polling_interval: Optional. The interval, in seconds, to poll the job status.
:param | 50 | 50 | apache/airflow:providers/google/src/airflow/providers/google/cloud/operators/gen_ai.py:GenAIGeminiCreateEmbeddingsBatchJobOperator:class_doc | documentation | false | [] | 164 |
(excluded_paths=[])
record = logging.LogRecord(
name="uvicorn.access",
level=logging.INFO,
pathname="",
lineno=0,
msg='%s - "%s %s HTTP/%s | " %d',
args=("127.0.0.1:12345", "GET", "/v1/completions", "1.1", 200),
exc_info=None,
)
assert filter.filter(record) | 50 | 50 | vllm-project/vllm:tests/test_access_log_filter.py:TestUvicornAccessLogFilter.test_filter_allows_all_when_no_excluded_paths | test | false | [] | 42 |
validation while still producing the correct JSON payload.
When no file blocks are detected the original dicts are returned unchanged
so the normal (validated) code path is preserved.
Args:
message_dicts: Message dicts produced by `_convert_ | message_to_dict`.
Returns:
The original list when no file blocks are present, or a list of SDK
Pydantic model instances otherwise.
"""
if not _has_file_content_blocks(message_dicts | 50 | 50 | langchain-ai/langchain:libs/partners/openrouter/langchain_openrouter/chat_models.py:_wrap_messages_for_sdk | function_simple | false | [] | 127 |
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
| 50 | 50 | vllm-project/vllm:vllm/model_executor/models/hunyuan_vision.py:license_header | license | false | [] | 193 |
parameter or database hook.
Normalizes SQLAlchemy dialect names to sqlglot equivalents
(e.g. ``postgresql`` → ``postgres``).
"""
raw = self.dialect
if not raw and self.db_hook | and hasattr(self.db_hook, "dialect_name"):
raw = self.db_hook.dialect_name
if raw:
return _SQLALCHEMY_TO_SQLGLOT_DIALECT.get | 50 | 50 | apache/airflow:providers/common/ai/src/airflow/providers/common/ai/operators/llm_sql.py:LLMSQLQueryOperator._resolved_dialect | function_simple | false | [] | 22 |
delta_with_reasoning(self):
"""Test ChatCompletionDelta with Reasoning object"""
reasoning = Reasoning("I need to think about this...", status="thinking")
delta = ChatCompletionDelta. | model_construct(reasoning)
# Check the delta structure
self.assertEqual(delta.role, "assistant")
self.assertIsNone(delta.content)
self.assertEqual(delta.reasoning, "I need to think about | 50 | 50 | xtekky/gpt4free:etc/unittest/test_reasoning_standardization.py:TestReasoningFieldStandardization.test_streaming_delta_with_reasoning | test | false | [] | 5 |
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