input stringlengths 33 5k | output stringlengths 32 5k |
|---|---|
from argparse import Namespace
from copy import deepcopy
from typing import TYPE_CHECKING, Type
from hubble.executor.helper import is_valid_huburi
from hubble.executor.hubio import HubIO
from jina.enums import PodRoleType
from jina.orchestrate.pods import Pod
from jina.orchestrate.pods.container import ContainerPod
... | from argparse import Namespace
from copy import deepcopy
from typing import TYPE_CHECKING, Type
from hubble.executor.helper import is_valid_huburi
from hubble.executor.hubio import HubIO
from jina.enums import PodRoleType
from jina.orchestrate.pods import Pod
from jina.orchestrate.pods.container import ContainerPod
... |
__version__ = '0.14.5'
import os
from docarray.document import Document
from docarray.array import DocumentArray
from docarray.dataclasses import dataclass, field
if 'DA_RICH_HANDLER' in os.environ:
from rich.traceback import install
install()
| __version__ = '0.14.4'
import os
from docarray.document import Document
from docarray.array import DocumentArray
from docarray.dataclasses import dataclass, field
if 'DA_RICH_HANDLER' in os.environ:
from rich.traceback import install
install()
|
# Licensed to the LF AI & Data foundation 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 fil... | import pytest
from typing import Dict, List
from docarray import BaseDoc, DocList
from docarray.documents import ImageDoc
from docarray.typing import NdArray
class MyDoc(BaseDoc):
embedding: NdArray
text: str
image: ImageDoc
class MySimpleDoc(BaseDoc):
title: str
class MyComplexDoc(BaseDoc):
... |
"""Import Hugging Face transformers's wav2vec2.0 pretrained weights to torchaudios's format.
"""
import logging
from torch.nn import Module
from ..model import wav2vec2_model, Wav2Vec2Model
_LG = logging.getLogger(__name__)
def _get_config(cfg):
config = {
"extractor_mode": f"{cfg.feat_extract_norm}_no... | """Import Hugging Face transformers's wav2vec2.0 pretrained weights to torchaudios's format.
"""
import logging
from torch.nn import Module
from ..model import wav2vec2_model, Wav2Vec2Model
_LG = logging.getLogger(__name__)
def _get_config(cfg):
config = {
"extractor_mode": f"{cfg.feat_extract_norm}_no... |
import asyncio
from typing import AsyncIterator, Iterator, Optional, Union
from jina.helper import get_or_reuse_loop
class _RequestsCounter:
"""Class used to wrap a count integer so that it can be updated inside methods.
.. code-block:: python
def count_increment(i: int, rc: _RequestsCounter):
... | import asyncio
from typing import AsyncIterator, Iterator, Optional, Union
from jina.helper import get_or_reuse_loop
class _RequestsCounter:
"""Class used to wrap a count integer so that it can be updated inside methods.
.. code-block:: python
def count_increment(i: int, rc: _RequestsCounter):
... |
_base_ = '../faster_rcnn/faster-rcnn_r50-caffe_fpn_1x_coco.py'
rpn_weight = 0.7
model = dict(
rpn_head=dict(
_delete_=True,
type='CascadeRPNHead',
num_stages=2,
stages=[
dict(
type='StageCascadeRPNHead',
in_channels=256,
fea... | _base_ = '../faster_rcnn/faster_rcnn_r50_caffe_fpn_1x_coco.py'
rpn_weight = 0.7
model = dict(
rpn_head=dict(
_delete_=True,
type='CascadeRPNHead',
num_stages=2,
stages=[
dict(
type='StageCascadeRPNHead',
in_channels=256,
fea... |
from typing import Union, Dict, Any
import google.ai.generativelanguage as glm
import google.generativeai as genai
from llama_index.core.base.llms.types import (
ChatMessage,
ChatResponse,
CompletionResponse,
ImageBlock,
TextBlock,
)
from llama_index.core.multi_modal_llms.base import ChatMessage
fr... | from typing import Union, Dict, Any
import google.ai.generativelanguage as glm
import google.generativeai as genai
from llama_index.core.base.llms.types import (
ChatMessage,
ChatResponse,
CompletionResponse,
ImageBlock,
TextBlock,
)
from llama_index.core.multi_modal_llms.base import ChatMessage
fr... |
import numpy as np
import orjson
import pytest
from pydantic.tools import parse_obj_as, schema_json_of
from docarray.document.io.json import orjson_dumps
from docarray.typing import NdArray
from docarray.typing.tensor import NdArrayEmbedding
def test_proto_tensor():
tensor = parse_obj_as(NdArray, np.zeros((3, 2... | import numpy as np
import orjson
import pytest
from pydantic.tools import parse_obj_as, schema_json_of
from docarray.document.io.json import orjson_dumps
from docarray.typing import NdArray
def test_proto_tensor():
tensor = parse_obj_as(NdArray, np.zeros((3, 224, 224)))
tensor._to_node_protobuf()
def tes... |
"""Example of analytics tests with improved error handling and assertions."""
import json
from unittest.mock import AsyncMock, Mock
import fastapi
import fastapi.testclient
import pytest_mock
from pytest_snapshot.plugin import Snapshot
import backend.server.routers.analytics as analytics_routes
from backend.server.c... | """Example of analytics tests with improved error handling and assertions."""
import json
from unittest.mock import AsyncMock, Mock
import fastapi
import fastapi.testclient
import pytest_mock
from pytest_snapshot.plugin import Snapshot
import backend.server.routers.analytics as analytics_routes
from backend.server.c... |
import datetime
import json
import typing
import prisma.models
import pydantic
import backend.data.block
import backend.data.graph
import backend.server.model
class LibraryAgent(pydantic.BaseModel):
id: str # Changed from agent_id to match GraphMeta
agent_id: str
agent_version: int # Changed from age... | import typing
import pydantic
class LibraryAgent(pydantic.BaseModel):
id: str # Changed from agent_id to match GraphMeta
version: int # Changed from agent_version to match GraphMeta
is_active: bool # Added to match GraphMeta
name: str
description: str
isCreatedByUser: bool
# Made inpu... |
from typing import Dict, Type
from llama_index.core.base.embeddings.base import BaseEmbedding
from llama_index.core.embeddings.mock_embed_model import MockEmbedding
RECOGNIZED_EMBEDDINGS: Dict[str, Type[BaseEmbedding]] = {
MockEmbedding.class_name(): MockEmbedding,
}
# conditionals for llama-cloud support
try:
... | from typing import Dict, Type
from llama_index.core.base.embeddings.base import BaseEmbedding
from llama_index.core.embeddings.mock_embed_model import MockEmbedding
RECOGNIZED_EMBEDDINGS: Dict[str, Type[BaseEmbedding]] = {
MockEmbedding.class_name(): MockEmbedding,
}
# conditionals for llama-cloud support
try:
... |
import functools
import sys
from io import StringIO
from typing import Any, Dict, List, Optional, Tuple
from llama_index.core.agent import ReActAgent
from llama_index.core.llama_pack.base import BaseLlamaPack
from llama_index.llms.openai import OpenAI
from llama_index.tools.arxiv import ArxivToolSpec
from llama_index.... | import functools
import sys
from io import StringIO
from typing import Any, Dict, List, Optional, Tuple
from llama_index.core.agent import ReActAgent
from llama_index.core.llama_pack.base import BaseLlamaPack
from llama_index.llms.openai import OpenAI
from llama_index.tools.arxiv import ArxivToolSpec
from llama_index.... |
from .config import Settings
from .depends import requires_admin_user, requires_user
from .jwt_utils import parse_jwt_token
from .middleware import APIKeyValidator, auth_middleware
from .models import User
__all__ = [
"Settings",
"parse_jwt_token",
"requires_user",
"requires_admin_user",
"APIKeyVal... | from .config import Settings
from .depends import requires_admin_user, requires_user
from .jwt_utils import parse_jwt_token
from .middleware import auth_middleware
from .models import User
__all__ = [
"Settings",
"parse_jwt_token",
"requires_user",
"requires_admin_user",
"auth_middleware",
"Use... |
from typing import Optional, Type
from langchain_core.callbacks import CallbackManagerForToolRun
from langchain_core.tools import BaseTool
from pydantic import BaseModel
from langchain_community.utilities.polygon import PolygonAPIWrapper
class Inputs(BaseModel):
"""Inputs for Polygon's Last Quote API"""
qu... | from typing import Optional, Type
from langchain_core.callbacks import CallbackManagerForToolRun
from langchain_core.tools import BaseTool
from pydantic import BaseModel
from langchain_community.utilities.polygon import PolygonAPIWrapper
class Inputs(BaseModel):
"""Inputs for Polygon's Last Quote API"""
qu... |
"""SingleStore reader."""
from typing import List
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document
from llama_index.readers.database import DatabaseReader
class SingleStoreReader(BaseReader):
"""
SingleStore reader.
Args:
scheme (str): Database S... | """SingleStore reader."""
from typing import List
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document
from llama_index.readers.database import DatabaseReader
class SingleStoreReader(BaseReader):
"""SingleStore reader.
Args:
scheme (str): Database Scheme... |
from __future__ import annotations
from collections.abc import Iterable
import torch.nn.functional as F
from torch import Tensor, nn
from sentence_transformers.SentenceTransformer import SentenceTransformer
from .ContrastiveLoss import SiameseDistanceMetric
class OnlineContrastiveLoss(nn.Module):
def __init__... | from __future__ import annotations
from collections.abc import Iterable
import torch.nn.functional as F
from torch import Tensor, nn
from sentence_transformers.SentenceTransformer import SentenceTransformer
from .ContrastiveLoss import SiameseDistanceMetric
class OnlineContrastiveLoss(nn.Module):
def __init__... |
from .spacy_text_encoder import SpacyTextEncoder
| from .spacy_text_encoder import SpacyTextEncoder |
from langchain_cli.namespaces.migrate.generate.utils import PKGS_ROOT
def test_root() -> None:
if PKGS_ROOT.name != "libs":
msg = "Expected PKGS_ROOT.name to be 'libs'."
raise ValueError(msg)
| from langchain_cli.namespaces.migrate.generate.utils import PKGS_ROOT
def test_root() -> None:
assert PKGS_ROOT.name == "libs"
|
from __future__ import annotations
from collections.abc import Iterable
import torch
import torch.nn as nn
from sentence_transformers.sparse_encoder.losses.ReconstructionLoss import ReconstructionLoss
from sentence_transformers.sparse_encoder.losses.SparseMultipleNegativesRankingLoss import (
SparseMultipleNegat... | from __future__ import annotations
from collections.abc import Iterable
import torch
import torch.nn as nn
from sentence_transformers.sparse_encoder.losses.ReconstructionLoss import ReconstructionLoss
from sentence_transformers.sparse_encoder.losses.SparseMultipleNegativesRankingLoss import (
SparseMultipleNegat... |
_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py'
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
data_preprocessor=dict(
mean=[103.530, 116.280, 123.675],
std=[1.0, 1.0, 1.0],
bgr_to_rgb=False),
backbone=dict(
norm_cfg=norm_cfg,
init_cfg=di... | _base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py'
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
backbone=dict(
norm_cfg=norm_cfg,
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron/resnet50_gn')),
neck=dict(norm_cfg=norm_... |
from urllib.parse import urlparse, urlunparse
import pytest
from requests_mock import Mocker
from llama_index.postprocessor.nvidia_rerank import NVIDIARerank as Interface
@pytest.fixture()
def mock_v1_local_models2(requests_mock: Mocker, base_url: str) -> None:
parsed = urlparse(base_url)
normalized_path = p... | from urllib.parse import urlparse, urlunparse
import pytest
from requests_mock import Mocker
from llama_index.postprocessor.nvidia_rerank import NVIDIARerank as Interface
@pytest.fixture()
def mock_v1_local_models2(requests_mock: Mocker, base_url: str) -> None:
result = urlparse(base_url)
base_url = urlunpar... |
from typing import Dict
MISTRALAI_MODELS: Dict[str, int] = {
"mistral-tiny": 32000,
"mistral-small": 32000,
"mistral-medium": 32000,
"mistral-large": 131000,
"mistral-saba-latest": 32000,
"open-mixtral-8x7b": 32000,
"open-mistral-7b": 32000,
"open-mixtral-8x22b": 64000,
"mistral-sma... | from typing import Dict
MISTRALAI_MODELS: Dict[str, int] = {
"mistral-tiny": 32000,
"mistral-small": 32000,
"mistral-medium": 32000,
"mistral-large": 131000,
"mistral-saba-latest": 32000,
"open-mixtral-8x7b": 32000,
"open-mistral-7b": 32000,
"open-mixtral-8x22b": 64000,
"mistral-sma... |
__copyright__ = "Copyright (c) 2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import os
from pathlib import Path
import numpy as np
import pytest
from jina import Document, DocumentArray, Executor
from jina.executors.metas import get_default_metas
from jina_commons.indexers.dump import import_... | __copyright__ = "Copyright (c) 2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import os
from pathlib import Path
import numpy as np
import pytest
from jina import Document, DocumentArray, Executor
from jina.executors.metas import get_default_metas
from jina_commons.indexers.dump import import_... |
from collections.abc import Sequence
from typing import Union
from langchain_core.agents import AgentAction, AgentFinish
from langchain_core.exceptions import OutputParserException
from langchain.agents.agent import AgentOutputParser
class SelfAskOutputParser(AgentOutputParser):
"""Parses self-ask style LLM cal... | from typing import Sequence, Union
from langchain_core.agents import AgentAction, AgentFinish
from langchain_core.exceptions import OutputParserException
from langchain.agents.agent import AgentOutputParser
class SelfAskOutputParser(AgentOutputParser):
"""Parses self-ask style LLM calls.
Expects output to ... |
import pytest
from pydantic.tools import parse_obj_as, schema_json_of
from docarray.base_document.io.json import orjson_dumps
from docarray.typing import AnyUrl
@pytest.mark.proto
def test_proto_any_url():
uri = parse_obj_as(AnyUrl, 'http://jina.ai/img.png')
uri._to_node_protobuf()
def test_json_schema():... | import pytest
from pydantic.tools import parse_obj_as, schema_json_of
from docarray.base_document.io.json import orjson_dumps
from docarray.typing import AnyUrl
@pytest.mark.proto
def test_proto_any_url():
uri = parse_obj_as(AnyUrl, 'http://jina.ai/img.png')
uri._to_node_protobuf()
def test_json_schema():... |
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
"files",
[
["full:README.md", "dataset_infos.json"],
["empty:README.md", "dataset_infos.json"],
["dataset_infos... | import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
"files",
[
["full:README.md", "dataset_infos.json"],
["empty:README.md", "dataset_infos.json"],
["dataset_infos... |
__copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import subprocess
import pytest
from executor.audioclip_text import AudioCLIPTextEncoder
from jina import Document, DocumentArray, Flow
_EMBEDDING_DIM = 1024
@pytest.mark.parametrize('request_size', [1, 10, 5... | __copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import subprocess
import pytest
from jina import Document, DocumentArray, Flow
from ...audioclip_text import AudioCLIPTextEncoder
_EMBEDDING_DIM = 1024
@pytest.mark.parametrize('request_size', [1, 10, 50, 10... |
"""!!!DO NOT USE!!!
Distribution related class for Tensorflow backend.
This is just a prototype and we might want to unify it
with other backends in the future.
"""
import tensorflow as tf
from tensorflow.experimental import dtensor
def list_devices(device_type=None):
"""Return all the available devices based ... | """!!!DO NOT USE!!!
Distribution related class for Tensorflow backend.
This is just a prototype and we might want to unify it
with other backends in the future.
"""
import tensorflow as tf
from tensorflow.experimental import dtensor
def list_devices(device_type=None):
"""Return all the available devices based ... |
# Copyright (c) OpenMMLab. All rights reserved.
import torch.utils.checkpoint as cp
from mmcv.cnn import ConvModule
from mmcv.runner import BaseModule
from .se_layer import SELayer
class InvertedResidual(BaseModule):
"""Inverted Residual Block.
Args:
in_channels (int): The input channels of this Mod... | # Copyright (c) OpenMMLab. All rights reserved.
import torch.utils.checkpoint as cp
from mmcv.cnn import ConvModule
from mmcv.runner import BaseModule
from .se_layer import SELayer
class InvertedResidual(BaseModule):
"""Inverted Residual Block.
Args:
in_channels (int): The input channels of this Mod... |
from abc import abstractmethod
from typing import TYPE_CHECKING, Dict, List, Optional, Sequence, TypeVar, Union
from docarray import Document, DocumentArray
from docarray.math import ndarray
from docarray.score import NamedScore
from qdrant_client.http import models as rest
from qdrant_client.http.models.models import... | from abc import abstractmethod
from typing import (
TYPE_CHECKING,
TypeVar,
Sequence,
List,
Union,
Optional,
Dict,
)
from qdrant_client.http.models.models import Distance
from docarray import Document, DocumentArray
from docarray.math import ndarray
from docarray.score import NamedScore
if... |
from typing import TYPE_CHECKING
from .github import GithubWebhooksManager
from .slant3d import Slant3DWebhooksManager
if TYPE_CHECKING:
from .base import BaseWebhooksManager
# --8<-- [start:WEBHOOK_MANAGERS_BY_NAME]
WEBHOOK_MANAGERS_BY_NAME: dict[str, type["BaseWebhooksManager"]] = {
handler.PROVIDER_NAME: ... | from typing import TYPE_CHECKING
from .github import GithubWebhooksManager
if TYPE_CHECKING:
from .base import BaseWebhooksManager
# --8<-- [start:WEBHOOK_MANAGERS_BY_NAME]
WEBHOOK_MANAGERS_BY_NAME: dict[str, type["BaseWebhooksManager"]] = {
handler.PROVIDER_NAME: handler
for handler in [
GithubW... |
from __future__ import annotations
from typing import Any, Optional, Union
import torch
from ._datapoint import Datapoint
class Video(Datapoint):
"""[BETA] :class:`torch.Tensor` subclass for videos.
Args:
data (tensor-like): Any data that can be turned into a tensor with :func:`torch.as_tensor`.
... | from __future__ import annotations
from typing import Any, Optional, Union
import torch
from ._datapoint import Datapoint
class Video(Datapoint):
"""[BETA] :class:`torch.Tensor` subclass for videos.
Args:
data (tensor-like): Any data that can be turned into a tensor with :func:`torch.as_tensor`.
... |
"""
This examples trains BERT (or any other transformer model like RoBERTa, DistilBERT etc.) for the STSbenchmark from scratch. It generates sentence embeddings
that can be compared using cosine-similarity to measure the similarity.
Usage:
python training_nli.py
OR
python training_nli.py pretrained_transformer_model_... | """
This examples trains BERT (or any other transformer model like RoBERTa, DistilBERT etc.) for the STSbenchmark from scratch. It generates sentence embeddings
that can be compared using cosine-similarity to measure the similarity.
Usage:
python training_nli.py
OR
python training_nli.py pretrained_transformer_model_... |
"""langchain-core version information and utilities."""
VERSION = "0.3.67"
| """langchain-core version information and utilities."""
VERSION = "0.3.66"
|
import itertools
import numpy as np
from absl.testing import parameterized
from torch.utils.data import Dataset as TorchDataset
from keras.src.testing import test_case
from keras.src.testing.test_utils import named_product
from keras.src.utils.dataset_utils import split_dataset
from keras.src.utils.module_utils impor... | import itertools
import numpy as np
from absl.testing import parameterized
from torch.utils.data import Dataset as TorchDataset
from keras.src.testing import test_case
from keras.src.testing.test_utils import named_product
from keras.src.utils.dataset_utils import split_dataset
from keras.src.utils.module_utils impor... |
import subprocess
import pytest
import os
from typing import List, Generator
from llama_index.core.schema import BaseNode, Document
from llama_index.storage.docstore.gel import (
GelDocumentStore,
)
from llama_index.storage.kvstore.gel import GelKVStore
try:
import gel # noqa
no_packages = False
except I... | from typing import List, Generator
import subprocess
import pytest
from llama_index.core.schema import BaseNode, Document
from llama_index.storage.docstore.gel import (
GelDocumentStore,
)
from llama_index.storage.kvstore.gel import GelKVStore
try:
import gel # noqa
no_packages = False
except ImportError... |
from pydantic import BaseModel
from inspect import Signature, Parameter
from typing import Any, Dict, Optional, List, Callable
from llama_index.core.llms import ChatMessage, AudioBlock, TextBlock, MessageRole
from llama_index.core.tools import BaseTool
def make_function_from_tool_model(
model_cls: type[... | from pydantic import BaseModel
from inspect import Signature, Parameter
from typing import Any, Dict, Optional, List, Callable
from llama_index.core.llms import ChatMessage, AudioBlock, TextBlock, MessageRole
from llama_index.core.tools import BaseTool
def make_function_from_tool_model(
model_cls: type[... |
import torch
from torchaudio_unittest.common_utils import PytorchTestCase
from .kaldi_compatibility_test_impl import Kaldi
class TestKaldiFloat32(Kaldi, PytorchTestCase):
dtype = torch.float32
device = torch.device("cpu")
class TestKaldiFloat64(Kaldi, PytorchTestCase):
dtype = torch.float64
device ... | import torch
from torchaudio_unittest.common_utils import PytorchTestCase
from .kaldi_compatibility_test_impl import Kaldi, KaldiCPUOnly
class TestKaldiCPUOnly(KaldiCPUOnly, PytorchTestCase):
dtype = torch.float32
device = torch.device("cpu")
class TestKaldiFloat32(Kaldi, PytorchTestCase):
dtype = torc... |
_base_ = 'faster-rcnn_regnetx-3.2GF_fpn_ms-3x_coco.py'
model = dict(
backbone=dict(
type='RegNet',
arch='regnetx_1.6gf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=... | _base_ = 'faster_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py'
model = dict(
backbone=dict(
type='RegNet',
arch='regnetx_1.6gf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init... |
# coding=utf-8
# Copyright 2025 HuggingFace Inc.
#
# 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 ag... | # coding=utf-8
# Copyright 2024 HuggingFace Inc.
#
# 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 ag... |
import enum
from typing import Any, List, Optional, Union
import pydantic
import backend.data.graph
from backend.data.api_key import APIKeyPermission, APIKeyWithoutHash
class Methods(enum.Enum):
SUBSCRIBE = "subscribe"
UNSUBSCRIBE = "unsubscribe"
EXECUTION_EVENT = "execution_event"
ERROR = "error"
... | import enum
from typing import Any, List, Optional, Union
import pydantic
import backend.data.graph
from backend.data.api_key import APIKeyPermission, APIKeyWithoutHash
class Methods(enum.Enum):
SUBSCRIBE = "subscribe"
UNSUBSCRIBE = "unsubscribe"
EXECUTION_EVENT = "execution_event"
ERROR = "error"
... |
# Copyright (c) OpenMMLab. All rights reserved.
from .backbones import * # noqa: F401,F403
from .data_preprocessors import * # noqa: F401,F403
from .dense_heads import * # noqa: F401,F403
from .detectors import * # noqa: F401,F403
from .layers import * # noqa: F401,F403
from .losses import * # noqa: F401,F403
fro... | # Copyright (c) OpenMMLab. All rights reserved.
from .backbones import * # noqa: F401,F403
from .data_preprocessors import * # noqa: F401,F403
from .dense_heads import * # noqa: F401,F403
from .detectors import * # noqa: F401,F403
from .layers import * # noqa: F401,F403
from .losses import * # noqa: F401,F403
fro... |
"""Utility functions for validating Ollama models."""
from httpx import ConnectError
from ollama import Client, ResponseError
def validate_model(client: Client, model_name: str) -> None:
"""Validate that a model exists in the Ollama instance.
Args:
client: The Ollama client.
model_name: The ... | """Utility functions for validating Ollama models."""
from httpx import ConnectError
from ollama import Client, ResponseError
def validate_model(client: Client, model_name: str) -> None:
"""Validate that a model exists in the Ollama instance.
Args:
client: The Ollama client.
model_name: The ... |
from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.callbacks.wandb_callback import WandbCallbackHandler
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handling op... | from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.callbacks.wandb_callback import WandbCallbackHandler
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handling op... |
from typing import AsyncGenerator, Generator, Optional
import pytest
from jina import Client, Executor, requests
from jina._docarray import Document, DocumentArray
from jina.helper import random_port
class MyDocument(Document):
text: str
number: Optional[int]
class OutputDocument(Document):
text: str
... | from typing import AsyncGenerator, Generator, Optional
import pytest
from jina import Client, Executor, requests
from jina._docarray import Document, DocumentArray
from jina.helper import random_port
class MyDocument(Document):
text: str
number: Optional[int]
class OutputDocument(Document):
text: str
... |
from pathlib import Path
from typing import List
import pytest
from dpr_text import DPRTextEncoder
from jina import Document, DocumentArray, Executor
_EMBEDDING_DIM = 768
@pytest.fixture(scope='session')
def basic_encoder() -> DPRTextEncoder:
return DPRTextEncoder()
@pytest.fixture(scope='session')
def basic_... | from pathlib import Path
from typing import List
import pytest
from jina import Document, DocumentArray, Executor
from ...dpr_text import DPRTextEncoder
_EMBEDDING_DIM = 768
@pytest.fixture(scope='session')
def basic_encoder() -> DPRTextEncoder:
return DPRTextEncoder()
@pytest.fixture(scope='session')
def ba... |
from __future__ import annotations
import warnings
from typing import Any, Dict, List, Mapping, Optional
from langchain_core.callbacks import CallbackManagerForLLMRun
from langchain_core.language_models.llms import LLM
from pydantic import BaseModel
# Ignoring type because below is valid pydantic code
# Unexpected ... | from __future__ import annotations
import warnings
from typing import Any, Dict, List, Mapping, Optional
from langchain_core.callbacks import CallbackManagerForLLMRun
from langchain_core.language_models.llms import LLM
from pydantic import BaseModel
# Ignoring type because below is valid pydantic code
# Unexpected ... |
"""Image prompt template for a multimodal model."""
from typing import Any
from pydantic import Field
from langchain_core.prompt_values import ImagePromptValue, ImageURL, PromptValue
from langchain_core.prompts.base import BasePromptTemplate
from langchain_core.prompts.string import (
DEFAULT_FORMATTER_MAPPING,
... | """Image prompt template for a multimodal model."""
from typing import Any
from pydantic import Field
from langchain_core.prompt_values import ImagePromptValue, ImageURL, PromptValue
from langchain_core.prompts.base import BasePromptTemplate
from langchain_core.prompts.string import (
DEFAULT_FORMATTER_MAPPING,
... |
from torio.utils import ffmpeg_utils
from . import sox_utils
from .download import download_asset
__all__ = [
"download_asset",
"sox_utils",
"ffmpeg_utils",
]
| from . import ffmpeg_utils, sox_utils
from .download import download_asset
__all__ = [
"download_asset",
"sox_utils",
"ffmpeg_utils",
]
|
from __future__ import annotations
import torch
import torch.nn as nn
class TopKActivation(nn.Module):
"""
TopK activation function for Sparse AutoEncoder.
This module implements the TopK activation function.
The TopK activation function keeps only the k largest values and sets the rest to zero.
... | from __future__ import annotations
import torch
import torch.nn as nn
class TopKActivation(nn.Module):
"""
TopK activation function for Sparse AutoEncoder.
This module implements the TopK activation function as described in the paper:
z_k := TopK(W_enc(f(x) - b_pre) + b_enc)
The TopK activation... |
# Copyright (c) OpenMMLab. All rights reserved.
from mmdet.registry import TASK_UTILS
from .base_sampler import BaseSampler
@TASK_UTILS.register_module()
class CombinedSampler(BaseSampler):
"""A sampler that combines positive sampler and negative sampler."""
def __init__(self, pos_sampler, neg_sampler, **kwa... | # Copyright (c) OpenMMLab. All rights reserved.
from ..builder import BBOX_SAMPLERS, build_sampler
from .base_sampler import BaseSampler
@BBOX_SAMPLERS.register_module()
class CombinedSampler(BaseSampler):
"""A sampler that combines positive sampler and negative sampler."""
def __init__(self, pos_sampler, ne... |
"""ReAct output parser."""
import re
from typing import Tuple
from llama_index.core.agent.react.types import (
ActionReasoningStep,
BaseReasoningStep,
ResponseReasoningStep,
)
from llama_index.core.output_parsers.utils import extract_json_str
from llama_index.core.types import BaseOutputParser
def extr... | """ReAct output parser."""
import re
from typing import Tuple
from llama_index.core.agent.react.types import (
ActionReasoningStep,
BaseReasoningStep,
ResponseReasoningStep,
)
from llama_index.core.output_parsers.utils import extract_json_str
from llama_index.core.types import BaseOutputParser
def extr... |
# Copyright (c) OpenMMLab. All rights reserved.
from .batch_sampler import AspectRatioBatchSampler
from .class_aware_sampler import ClassAwareSampler
from .multi_source_sampler import GroupMultiSourceSampler, MultiSourceSampler
from .track_img_sampler import TrackImgSampler
__all__ = [
'ClassAwareSampler', 'Aspect... | # Copyright (c) OpenMMLab. All rights reserved.
from .batch_sampler import AspectRatioBatchSampler
from .class_aware_sampler import ClassAwareSampler
from .multi_source_sampler import GroupMultiSourceSampler, MultiSourceSampler
__all__ = [
'ClassAwareSampler', 'AspectRatioBatchSampler', 'MultiSourceSampler',
'... |
import json
import os
from typing import Any, List, Literal
from llama_index.vector_stores.docarray.base import DocArrayVectorStore
class DocArrayHnswVectorStore(DocArrayVectorStore):
"""
Class representing a DocArray HNSW vector store.
This class is a lightweight Document Index implementation provided ... | import json
import os
from typing import Any, List, Literal
from llama_index.vector_stores.docarray.base import DocArrayVectorStore
class DocArrayHnswVectorStore(DocArrayVectorStore):
"""Class representing a DocArray HNSW vector store.
This class is a lightweight Document Index implementation provided by Do... |
from typing import Union
from langchain.agents.agent import BaseSingleActionAgent
from langchain.agents.agent_types import AgentType
from langchain.agents.chat.base import ChatAgent
from langchain.agents.conversational.base import ConversationalAgent
from langchain.agents.conversational_chat.base import Conversational... | from typing import Dict, Type, Union
from langchain.agents.agent import BaseSingleActionAgent
from langchain.agents.agent_types import AgentType
from langchain.agents.chat.base import ChatAgent
from langchain.agents.conversational.base import ConversationalAgent
from langchain.agents.conversational_chat.base import Co... |
from pathlib import Path
from typing import List, Tuple, Union
import torch
import torchaudio
from torch.utils.data import Dataset
SampleType = Tuple[int, torch.Tensor, List[torch.Tensor]]
class LibriMix(Dataset):
r"""Create the *LibriMix* [:footcite:`cosentino2020librimix`] dataset.
Args:
root (st... | from pathlib import Path
from typing import List, Tuple, Union
import torch
import torchaudio
from torch.utils.data import Dataset
SampleType = Tuple[int, torch.Tensor, List[torch.Tensor]]
class LibriMix(Dataset):
r"""Create the *LibriMix* [:footcite:`cosentino2020librimix`] dataset.
Args:
root (st... |
"""DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.export.saved_model import ExportArchive as ExportArchive
| """DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.export.saved_model import ExportArchive
|
from __future__ import annotations
try:
from typing import Self
except ImportError:
from typing_extensions import Self
import torch
from torch import nn
from sentence_transformers.models.Module import Module
class LSTM(Module):
"""Bidirectional LSTM running over word embeddings."""
config_keys: li... | from __future__ import annotations
import json
import os
import torch
from safetensors.torch import load_model as load_safetensors_model
from safetensors.torch import save_model as save_safetensors_model
from torch import nn
class LSTM(nn.Module):
"""Bidirectional LSTM running over word embeddings."""
def ... |
from __future__ import annotations
from collections.abc import Iterable
from enum import Enum
import torch
import torch.nn as nn
from sentence_transformers.sparse_encoder.losses import (
FlopsLoss,
SparseDistillKLDivLoss,
SparseMarginMSELoss,
SparseMultipleNegativesRankingLoss,
)
from sentence_transf... | from __future__ import annotations
from collections.abc import Iterable
from enum import Enum
import torch
import torch.nn as nn
from sentence_transformers.sparse_encoder.losses import (
FlopsLoss,
SparseDistillKLDivLoss,
SparseMarginMSELoss,
SparseMultipleNegativesRankingLoss,
)
from sentence_transf... |
import pytest
import torch
from torchaudio._internal.module_utils import UNSUPPORTED
from torchaudio.sox_effects import apply_effects_tensor
# Importing prototype modules is needed to trigger the registration of the
# corresponding APIs in the UNSUPPORTED register.
from torchaudio.prototype import datasets, function... | import pytest
from torchaudio._internal.module_utils import UNSUPPORTED
@pytest.mark.parametrize("func", UNSUPPORTED)
def test_deprecations(func):
with pytest.warns(UserWarning, match="deprecated"):
try:
func()
except Exception as e:
# Type or Runtime error because we call func()... |
# Copyright 2025 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... | import unittest
from unittest.mock import patch
import transformers.commands.transformers_cli as cli
from transformers.commands.serving import ServeCommand
from transformers.testing_utils import CaptureStd
class ServeCLITest(unittest.TestCase):
def test_help(self):
with patch("sys.argv", ["transformers",... |
_base_ = [
'../_base_/models/faster_rcnn_r50_fpn.py',
'../_base_/datasets/cityscapes_detection.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_1x.py'
]
model = dict(
backbone=dict(init_cfg=None),
roi_head=dict(
bbox_head=dict(
num_classes=8,
loss_bb... | _base_ = [
'../_base_/models/faster_rcnn_r50_fpn.py',
'../_base_/datasets/cityscapes_detection.py',
'../_base_/default_runtime.py'
]
model = dict(
backbone=dict(init_cfg=None),
roi_head=dict(
bbox_head=dict(
type='Shared2FCBBoxHead',
in_channels=256,
fc_ou... |
# Copyright (c) OpenMMLab. All rights reserved.
import logging
from typing import Any, List, Optional, Sequence, Tuple
import torch
from torch.nn.parameter import Parameter
from torch.nn.utils import clip_grad
from mmengine.data import BaseDataElement
from mmengine.registry import HOOKS
from .hook import Hook
DATA_B... | # Copyright (c) OpenMMLab. All rights reserved.
import logging
from typing import Any, List, Optional, Sequence, Tuple
import torch
from torch.nn.parameter import Parameter
from torch.nn.utils import clip_grad
from mmengine.data import BaseDataSample
from mmengine.registry import HOOKS
from .hook import Hook
DATA_BA... |
from typing import List, Optional
import pandas as pd
import pytest
from docarray import BaseDoc, DocList, DocVec
from docarray.documents import ImageDoc
@pytest.fixture()
def nested_doc_cls():
class MyDoc(BaseDoc):
count: Optional[int]
text: str
class MyDocNested(MyDoc):
image: Ima... | from typing import List, Optional
import pandas as pd
import pytest
from docarray import BaseDoc, DocList
from docarray.documents import ImageDoc
@pytest.fixture()
def nested_doc_cls():
class MyDoc(BaseDoc):
count: Optional[int]
text: str
class MyDocNested(MyDoc):
image: ImageDoc
... |
from __future__ import annotations
import logging
from typing import TYPE_CHECKING, Any, Callable
from sentence_transformers.evaluation import RerankingEvaluator
from sentence_transformers.util import cos_sim
if TYPE_CHECKING:
import numpy as np
from torch import Tensor
from sentence_transformers.sparse... | from __future__ import annotations
import logging
from typing import TYPE_CHECKING, Any
from sentence_transformers.evaluation import RerankingEvaluator
if TYPE_CHECKING:
import numpy as np
from torch import Tensor
from sentence_transformers.sparse_encoder.SparseEncoder import SparseEncoder
logger = lo... |
_base_ = './retinanet_r50-caffe_fpn_1x_coco.py'
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='RandomChoiceResize',
scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736), (1333, 768),
(1333, 800)],
keep... | _base_ = './retinanet_r50-caffe_fpn_1x_coco.py'
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='RandomResize',
scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736), (1333, 768),
(1333, 800)],
keep_ratio... |
from typing import Optional
from fastapi import Depends, FastAPI, Query, status
from fastapi.testclient import TestClient
app = FastAPI()
def _get_client_key(client_id: str = Query(...)) -> str:
return f"{client_id}_key"
def _get_client_tag(client_id: Optional[str] = Query(None)) -> Optional[str]:
if clie... | from typing import Optional
from fastapi import Depends, FastAPI, Query, status
from fastapi.testclient import TestClient
app = FastAPI()
def _get_client_key(client_id: str = Query(...)) -> str:
return f"{client_id}_key"
def _get_client_tag(client_id: Optional[str] = Query(None)) -> Optional[str]:
if clie... |
from typing import Any, Dict, List, Optional, Tuple
from llama_index.core.schema import BaseNode, TextNode
from llama_index.core.vector_stores.utils import (
metadata_dict_to_node,
legacy_metadata_dict_to_node,
)
import json
import logging
logger = logging.getLogger(__name__)
def create_node_from_result(
... | from typing import Any, Dict, List, Optional, Tuple
from llama_index.core.schema import BaseNode, TextNode
from llama_index.core.vector_stores.utils import (
metadata_dict_to_node,
legacy_metadata_dict_to_node,
)
import json
import logging
logger = logging.getLogger(__name__)
def create_node_from_result(
... |
from __future__ import annotations
from torch import Tensor, nn
from sentence_transformers.cross_encoder.CrossEncoder import CrossEncoder
class CrossEntropyLoss(nn.Module):
def __init__(self, model: CrossEncoder, activation_fct: nn.Module = nn.Identity(), **kwargs) -> None:
"""
Computes the Cros... | from __future__ import annotations
from torch import Tensor, nn
from sentence_transformers.cross_encoder import CrossEncoder
# TODO: Consider the naming of this class
class CrossEntropyLoss(nn.Module):
def __init__(self, model: CrossEncoder, activation_fct: nn.Module = nn.Identity(), **kwargs) -> None:
... |
from typing import Any
import torch
import enum
from torch._C import _to_dlpack as to_dlpack
__all__ = [
"DLDeviceType",
"from_dlpack",
]
class DLDeviceType(enum.IntEnum):
# Enums as in DLPack specification (aten/src/ATen/dlpack.h)
kDLCPU = 1,
kDLCUDA = 2,
kDLCUDAHost = 3,
kDLOpenCL = 4,... | from typing import Any
import torch
import enum
from torch._C import _from_dlpack
from torch._C import _to_dlpack as to_dlpack
__all__ = [
"DLDeviceType",
"from_dlpack",
"to_dlpack",
]
class DLDeviceType(enum.IntEnum):
# Enums as in DLPack specification (aten/src/ATen/dlpack.h)
kDLCPU = 1,
... |
from __future__ import annotations
from sentence_transformers.sparse_encoder.data_collator import SparseEncoderDataCollator
from sentence_transformers.sparse_encoder.evaluation import (
SparseBinaryClassificationEvaluator,
SparseEmbeddingSimilarityEvaluator,
SparseInformationRetrievalEvaluator,
SparseM... | from __future__ import annotations
from sentence_transformers.sparse_encoder.data_collator import SparseEncoderDataCollator
from sentence_transformers.sparse_encoder.evaluation import (
SparseBinaryClassificationEvaluator,
SparseEmbeddingSimilarityEvaluator,
SparseInformationRetrievalEvaluator,
SparseM... |
from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.chains.graph_qa.neptune_sparql import (
INTERMEDIATE_STEPS_KEY,
SPARQL_GENERATION_TEMPLATE,
NeptuneSparqlQAChain,
extract_sparql,
)
# Create a way to dyn... | from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.chains.graph_qa.neptune_sparql import (
INTERMEDIATE_STEPS_KEY,
SPARQL_GENERATION_TEMPLATE,
NeptuneSparqlQAChain,
extract_sparql,
)
# Create a way to dyn... |
__version__ = '0.14.7'
import os
from docarray.document import Document
from docarray.array import DocumentArray
from docarray.dataclasses import dataclass, field
if 'DA_RICH_HANDLER' in os.environ:
from rich.traceback import install
install()
| __version__ = '0.14.6'
import os
from docarray.document import Document
from docarray.array import DocumentArray
from docarray.dataclasses import dataclass, field
if 'DA_RICH_HANDLER' in os.environ:
from rich.traceback import install
install()
|
# Copyright (c) OpenMMLab. All rights reserved.
from .atss import ATSS
from .autoassign import AutoAssign
from .base import BaseDetector
from .cascade_rcnn import CascadeRCNN
from .centernet import CenterNet
from .cornernet import CornerNet
from .ddod import DDOD
from .deformable_detr import DeformableDETR
from .detr i... | # Copyright (c) OpenMMLab. All rights reserved.
from .atss import ATSS
from .autoassign import AutoAssign
from .base import BaseDetector
from .cascade_rcnn import CascadeRCNN
from .centernet import CenterNet
from .cornernet import CornerNet
from .ddod import DDOD
from .deformable_detr import DeformableDETR
from .detr i... |
import json
import pytest
from langchain.chains import OpenAIModerationChain
from langchain.chains.openai_functions.openapi import get_openapi_chain
api_spec = {
"openapi": "3.0.0",
"info": {"title": "JSONPlaceholder API", "version": "1.0.0"},
"servers": [{"url": "https://jsonplaceholder.typicode.com"}],... | import json
import pytest
from langchain.chains import OpenAIModerationChain
from langchain.chains.openai_functions.openapi import get_openapi_chain
api_spec = {
"openapi": "3.0.0",
"info": {"title": "JSONPlaceholder API", "version": "1.0.0"},
"servers": [{"url": "https://jsonplaceholder.typicode.com"}],... |
_base_ = './mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco.py'
train_cfg = dict(max_epochs=24)
# learning rate
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=24,
by_epoch=True,
... | _base_ = './mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco.py'
# learning policy
lr_config = dict(step=[16, 23])
runner = dict(type='EpochBasedRunner', max_epochs=24)
|
"""XGBoost Experimental Federated Learning related API."""
import ctypes
from threading import Thread
from typing import Any, Dict, Optional
from .core import _LIB, _check_call, make_jcargs
from .tracker import RabitTracker
class FederatedTracker(RabitTracker):
"""Tracker for federated training.
Parameters... | """XGBoost Federated Learning related API."""
from .core import _LIB, XGBoostError, _check_call, build_info, c_str
def run_federated_server(
port: int,
world_size: int,
server_key_path: str = "",
server_cert_path: str = "",
client_cert_path: str = "",
) -> None:
"""Run the Federated Learning ... |
from typing import Any, Dict, List, Tuple, Type, cast
from docarray import BaseDoc, DocList
from docarray.index.abstract import BaseDocIndex
from docarray.utils.filter import filter_docs
from docarray.utils.find import FindResult
def _collect_query_args(method_name: str): # TODO: use partialmethod instead
def i... | from typing import Any, Dict, List, Tuple, Type, cast
from docarray import BaseDoc, DocList
from docarray.index.abstract import BaseDocIndex
from docarray.utils.filter import filter_docs
from docarray.utils.find import FindResult
def _collect_query_args(method_name: str): # TODO: use partialmethod instead
def i... |
"""Standard LangChain interface tests"""
from typing import Type
import pytest
from langchain_core.language_models import BaseChatModel
from langchain_core.rate_limiters import InMemoryRateLimiter
from langchain_core.tools import BaseTool
from langchain_tests.integration_tests import (
ChatModelIntegrationTests,
... | """Standard LangChain interface tests"""
from typing import Optional, Type
import pytest
from langchain_core.language_models import BaseChatModel
from langchain_core.rate_limiters import InMemoryRateLimiter
from langchain_core.tools import BaseTool
from langchain_tests.integration_tests import (
ChatModelIntegrat... |
"""Logic for converting internal query language to a valid Chroma query."""
from typing import Tuple, Union
from langchain_core.structured_query import (
Comparator,
Comparison,
Operation,
Operator,
StructuredQuery,
Visitor,
)
COMPARATOR_TO_TQL = {
Comparator.EQ: "==",
Comparator.GT: ... | """Logic for converting internal query language to a valid Chroma query."""
from typing import Tuple, Union
from langchain_core.structured_query import (
Comparator,
Comparison,
Operation,
Operator,
StructuredQuery,
Visitor,
)
COMPARATOR_TO_TQL = {
Comparator.EQ: "==",
Comparator.GT: ... |
# model settings
model = dict(
type='MaskRCNN',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
bgr_to_rgb=True,
pad_mask=True,
pad_size_divisor=32),
backbone=dict(
type='ResNet',
... | # model settings
model = dict(
type='MaskRCNN',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
bgr_to_rgb=True,
pad_size_divisor=32),
backbone=dict(
type='ResNet',
depth=50,
num_... |
"""
Python polyfills for sys
"""
from __future__ import annotations
import sys
from ..decorators import substitute_in_graph
__all__ = [
"intern",
"getrecursionlimit",
]
@substitute_in_graph(sys.intern, can_constant_fold_through=True)
def intern(string: str, /) -> str:
return string
@substitute_in_g... | """
Python polyfills for sys
"""
from __future__ import annotations
import sys
from ..decorators import substitute_in_graph
__all__ = [
"intern",
"getrecursionlimit",
]
@substitute_in_graph(sys.intern, can_constant_fold_through=True)
def intern(string: str, /) -> str:
return string
@substitute_in_g... |
"""
In this example we train a semantic search model to search through Wikipedia
articles about programming articles & technologies.
We use the text paragraphs from the following Wikipedia articles:
Assembly language, C , C Sharp , C++, Go , Java , JavaScript, Keras, Laravel, MATLAB, Matplotlib, MongoDB, MySQL, Natura... | """
In this example we train a semantic search model to search through Wikipedia
articles about programming articles & technologies.
We use the text paragraphs from the following Wikipedia articles:
Assembly language, C , C Sharp , C++, Go , Java , JavaScript, Keras, Laravel, MATLAB, Matplotlib, MongoDB, MySQL, Natura... |
"""Example selectors.
**Example selector** implements logic for selecting examples to include them in prompts.
This allows us to select examples that are most relevant to the input.
"""
from importlib import import_module
from typing import TYPE_CHECKING
if TYPE_CHECKING:
from langchain_core.example_selectors.ba... | """Example selectors.
**Example selector** implements logic for selecting examples to include them in prompts.
This allows us to select examples that are most relevant to the input.
"""
from importlib import import_module
from typing import TYPE_CHECKING
if TYPE_CHECKING:
from langchain_core.example_selectors.ba... |
import numpy as np
import pytest
from pydantic.tools import parse_obj_as, schema_json_of
from docarray.base_document.io.json import orjson_dumps
from docarray.typing import NdArray, PointCloud3DUrl
from tests import TOYDATA_DIR
MESH_FILES = {
'obj': str(TOYDATA_DIR / 'tetrahedron.obj'),
'glb': str(TOYDATA_DIR... | import numpy as np
import pytest
from pydantic.tools import parse_obj_as, schema_json_of
from docarray.base_document.io.json import orjson_dumps
from docarray.typing import NdArray, PointCloud3DUrl
from tests import TOYDATA_DIR
MESH_FILES = {
'obj': str(TOYDATA_DIR / 'tetrahedron.obj'),
'glb': str(TOYDATA_DIR... |
from typing import Optional
from docarray import Document, DocumentArray
from pydantic import BaseModel
from uvicorn import Config, Server
from jina import Gateway, __default_host__
from jina.clients.request import request_generator
class DummyResponseModel(BaseModel):
arg1: Optional[str]
arg2: Optional[str... | from typing import Optional
from docarray import Document, DocumentArray
from pydantic import BaseModel
from uvicorn import Config, Server
from jina import Gateway, __default_host__
from jina.clients.request import request_generator
class DummyResponseModel(BaseModel):
arg1: Optional[str]
arg2: Optional[str... |
import os as _os
import sys as _sys
from pathlib import Path as _Path
import datetime as _datetime
__windows__ = _sys.platform == 'win32'
__uptime__ = _datetime.datetime.now().isoformat()
# update on MacOS 1. clean this tuple, 2. grep -rohEI --exclude-dir=jina/hub --exclude-dir=tests --include \*.py
# "\'JINA_.*?\'" ... | import os as _os
import sys as _sys
from pathlib import Path as _Path
import datetime as _datetime
__windows__ = _sys.platform == 'win32'
__uptime__ = _datetime.datetime.now().isoformat()
# update on MacOS 1. clean this tuple, 2. grep -rohEI --exclude-dir=jina/hub --exclude-dir=tests --include \*.py
# "\'JINA_.*?\'" ... |
"""
Computes embeddings
"""
from typing import Optional
import numpy as np
import pytest
from sentence_transformers import SentenceTransformer
@pytest.mark.parametrize("normalize_embeddings", (False, True))
@pytest.mark.parametrize("prompt_name", (None, "retrieval"))
def test_encode_multi_process(
stsb_bert_ti... | """
Computes embeddings
"""
import unittest
from sentence_transformers import SentenceTransformer
import numpy as np
class ComputeMultiProcessTest(unittest.TestCase):
def setUp(self):
self.model = SentenceTransformer('paraphrase-distilroberta-base-v1')
def test_multi_gpu_encode(self):
# Star... |
from __future__ import annotations
import logging
import torch
from torch import Tensor, nn
from sentence_transformers.models.Module import Module
logger = logging.getLogger(__name__)
class WordWeights(Module):
"""This model can weight word embeddings, for example, with idf-values."""
config_keys: list[s... | from __future__ import annotations
import json
import logging
import os
import torch
from torch import Tensor, nn
logger = logging.getLogger(__name__)
class WordWeights(nn.Module):
"""This model can weight word embeddings, for example, with idf-values."""
def __init__(self, vocab: list[str], word_weights:... |
_base_ = '../common/lsj-200e_coco-detection.py'
image_size = (1024, 1024)
batch_augments = [dict(type='BatchFixedSizePad', size=image_size)]
model = dict(
type='ATSS',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
... | _base_ = '../common/lsj_200e_coco_detection.py'
image_size = (1024, 1024)
batch_augments = [dict(type='BatchFixedSizePad', size=image_size)]
model = dict(
type='ATSS',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
... |
from jina.serve.runtimes.servers import BaseServer
from aiohttp import web
class LoadBalancingServer(BaseServer):
"""Base FastAPI server. Implement this abstract class in-case you want to build a fastapi-based server by
implementing the `app` property. This property should return a fastapi app. The base Gatew... | from jina.serve.runtimes.servers import BaseServer
from aiohttp import web
class LoadBalancingServer(BaseServer):
"""Base FastAPI server. Implement this abstract class in-case you want to build a fastapi-based server by
implementing the `app` property. This property should return a fastapi app. The base Gatew... |
import logging
from datasets import load_dataset
from sentence_transformers import SparseEncoder
from sentence_transformers.sparse_encoder.evaluation import SparseBinaryClassificationEvaluator
logging.basicConfig(format="%(message)s", level=logging.INFO)
# Initialize the SPLADE model
model = SparseEncoder("naver/sp... | import logging
from datasets import load_dataset
from sentence_transformers import SparseEncoder
from sentence_transformers.sparse_encoder.evaluation import SparseBinaryClassificationEvaluator
logging.basicConfig(format="%(message)s", level=logging.INFO)
# Initialize the SPLADE model
model = SparseEncoder("naver/sp... |
# flake8: noqa
# Copyright 2020 The HuggingFace Datasets Authors and the TensorFlow Datasets 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
#
# http://www.apache.org/licenses/LI... | # flake8: noqa
# Copyright 2020 The HuggingFace Datasets Authors and the TensorFlow Datasets 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
#
# http://www.apache.org/licenses/LI... |
# Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
import shutil
import time
from unittest import TestCase
from unittest.mock import Mock
import torch
from mmengine.structures import InstanceData
from mmdet.engine.hooks import DetVisualizationHook, TrackVisualizationHook
from mmdet.structures impor... | # Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
import shutil
import time
from unittest import TestCase
from unittest.mock import Mock
import torch
from mmengine.structures import InstanceData
from mmdet.engine.hooks import DetVisualizationHook
from mmdet.structures import DetDataSample
from mmd... |
# Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from mmdet.models.backbones import ResNeSt
from mmdet.models.backbones.resnest import Bottleneck as BottleneckS
def test_resnest_bottleneck():
with pytest.raises(AssertionError):
# Style must be in ['pytorch', 'caffe']
Bot... | import pytest
import torch
from mmdet.models.backbones import ResNeSt
from mmdet.models.backbones.resnest import Bottleneck as BottleneckS
def test_resnest_bottleneck():
with pytest.raises(AssertionError):
# Style must be in ['pytorch', 'caffe']
BottleneckS(64, 64, radix=2, reduction_factor=4, st... |
_base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
backbone=dict(
frozen_stages=-1,
zero_init_residua... | _base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
backbone=dict(
frozen_stages=-1,
zero_init_residua... |
import copy
import json
try:
import difflib
except ImportError:
difflib = None
from keras.src.api_export import keras_export
@keras_export("keras.utils.Config")
class Config:
"""A Config is a dict-like container for named values.
It offers a few advantages over a plain dict:
- Setting and retr... | import copy
import json
try:
import difflib
except ImportError:
difflib = None
from keras.src.api_export import keras_export
@keras_export("keras.utils.Config")
class Config:
"""A Config is a dict-like container for named values.
It offers a few advantages over a plain dict:
- Setting and retr... |
# Copyright 2024 HuggingFace Inc.
#
# 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 writ... | # Copyright 2024 HuggingFace Inc.
#
# 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 writ... |
import torch
from torch import nn, Tensor
from typing import Iterable, Dict
class MSELoss(nn.Module):
def __init__(self, model):
"""
Computes the MSE loss between the computed sentence embedding and a target sentence embedding. This loss
is used when extending sentence embeddings to new la... | import torch
from torch import nn, Tensor
from typing import Iterable, Dict
class MSELoss(nn.Module):
def __init__(self, model):
"""
Computes the MSE loss between the computed sentence embedding and a target sentence embedding. This loss
is used when extending sentence embeddings to new la... |
import asyncio
from typing import AsyncIterator, Iterator, Optional, Union
from jina.helper import get_or_reuse_loop
class RequestsCounter:
"""Class used to wrap a count integer so that it can be updated inside methods.
.. code-block:: python
def count_increment(i: int, rc: RequestCounter):
... | from typing import Iterator, AsyncIterator, Union
from jina.helper import get_or_reuse_loop
class AsyncRequestsIterator:
"""Iterator to allow async iteration of blocking/non-blocking iterator from the Client"""
def __init__(self, iterator: Union[Iterator, AsyncIterator]) -> None:
"""Async request it... |
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