input stringlengths 33 5k | output stringlengths 32 5k |
|---|---|
import asyncio
from typing import Any, List, Optional
from zhipuai import ZhipuAI as ZhipuAIClient
from llama_index.core.base.embeddings.base import BaseEmbedding
from llama_index.core.bridge.pydantic import Field, PrivateAttr
from llama_index.core.callbacks import CallbackManager
class ZhipuAIEmbedding(BaseEmbedding... | import asyncio
from typing import Any, List, Optional
from zhipuai import ZhipuAI as ZhipuAIClient
from llama_index.core.base.embeddings.base import BaseEmbedding
from llama_index.core.bridge.pydantic import Field, PrivateAttr
from llama_index.core.callbacks import CallbackManager
class ZhipuAIEmbedding(BaseEmbedding... |
import logging
from sentence_transformers import SparseEncoder
from sentence_transformers.sparse_encoder.evaluation import SparseNanoBEIREvaluator
logging.basicConfig(format="%(message)s", level=logging.INFO)
# Load a model
model = SparseEncoder("naver/splade-cocondenser-ensembledistil")
evaluator = SparseNanoBEIR... | import logging
from sentence_transformers.sparse_encoder import (
SparseEncoder,
SparseNanoBEIREvaluator,
)
logging.basicConfig(format="%(message)s", level=logging.INFO)
# Load a model
model = SparseEncoder("naver/splade-cocondenser-ensembledistil")
evaluator = SparseNanoBEIREvaluator(
dataset_names=No... |
import json
from jina.logging.logger import JinaLogger
from jina.parsers import set_gateway_parser
from jina.serve.runtimes.gateway.http.app import get_fastapi_app
from jina.serve.runtimes.gateway.streamer import GatewayStreamer
JINA_LOGO_URL = 'https://api.jina.ai/logo/logo-product/jina-core/horizontal-layout/colore... | import json
from jina.logging.logger import JinaLogger
from jina.parsers import set_gateway_parser
from jina.serve.runtimes.gateway.http.app import get_fastapi_app
from jina.serve.streamer import GatewayStreamer
JINA_LOGO_URL = 'https://api.jina.ai/logo/logo-product/jina-core/horizontal-layout/colored/Product%20logo_... |
from typing import Optional
from llama_index.core.storage.index_store.keyval_index_store import KVIndexStore
from llama_index.storage.kvstore.postgres import PostgresKVStore
class PostgresIndexStore(KVIndexStore):
"""
Postgres Index store.
Args:
postgres_kvstore (PostgresKVStore): Postgres key-v... | from typing import Optional
from llama_index.core.storage.index_store.keyval_index_store import KVIndexStore
from llama_index.storage.kvstore.postgres import PostgresKVStore
class PostgresIndexStore(KVIndexStore):
"""Postgres Index store.
Args:
postgres_kvstore (PostgresKVStore): Postgres key-value ... |
import pytest
from llama_index.embeddings.modelscope.base import ModelScopeEmbedding
@pytest.fixture()
def modelscope_embedding():
return ModelScopeEmbedding()
@pytest.fixture()
def query():
return "吃完海鲜可以喝牛奶吗?"
@pytest.fixture()
def text():
return [
"不可以，早晨喝牛奶不科学",
"吃了海鲜后是不能再喝牛奶的，因为牛奶... | import pytest
from llama_index.embeddings.modelscope.base import ModelScopeEmbedding
@pytest.fixture()
def modelscope_embedding():
return ModelScopeEmbedding()
@pytest.fixture()
def query():
return "吃完海鲜可以喝牛奶吗?"
@pytest.fixture()
def text():
return [
"不可以，早晨喝牛奶不科学",
"吃了海鲜后是不能再喝牛奶的，因为牛奶... |
_base_ = [
'../_base_/models/ssd300.py', '../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
]
# dataset settings
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
input_size = 300
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type=... | _base_ = [
'../_base_/models/ssd300.py', '../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
]
# dataset settings
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
input_size = 300
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type=... |
# Copyright (c) OpenMMLab. All rights reserved.
import copy
import inspect
from typing import List, Union
import torch
import torch.nn as nn
from mmengine.config import Config, ConfigDict
from mmengine.registry import OPTIM_WRAPPER_CONSTRUCTORS, OPTIMIZERS
from .optimizer_wrapper import OptimWrapper
def register_to... | # Copyright (c) OpenMMLab. All rights reserved.
import copy
import inspect
from typing import List
import torch
import torch.nn as nn
from mmengine.registry import OPTIMIZER_CONSTRUCTORS, OPTIMIZERS
def register_torch_optimizers() -> List[str]:
"""Register optimizers in ``torch.optim`` to the ``OPTIMIZERS`` reg... |
"""
This scripts demonstrates how to train a Sparse Encoder model for Information Retrieval.
As dataset, we use sentence-transformers/msmarco-bm25, where we have triplets versions of MSMARCO mined thanks to BM25.
As loss function, we use MultipleNegativesRankingLoss in the SpladeLoss.
"""
import logging
import trac... | """
This scripts demonstrates how to train a Sparse Encoder model for Information Retrieval.
As dataset, we use sentence-transformers/msmarco-bm25, where we have triplets versions of MSMARCO mined thanks to BM25.
As loss function, we use MultipleNegativesRankingLoss in the SpladeLoss.
"""
import logging
import trac... |
model = dict(
detector=dict(
type='FasterRCNN',
backbone=dict(
type='ResNet',
depth=18,
base_channels=2,
num_stages=4,
out_indices=(3, ),
strides=(1, 2, 2, 1),
dilations=(1, 1, 1, 2),
frozen_stages=1,
... | model = dict(
detector=dict(
type='FasterRCNN',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(3, ),
strides=(1, 2, 2, 1),
dilations=(1, 1, 1, 2),
froz... |
from keras.src import backend
from keras.src import ops
from keras.src.api_export import keras_export
from keras.src.layers.layer import Layer
@keras_export("keras.layers.AlphaDropout")
class AlphaDropout(Layer):
"""Applies Alpha Dropout to the input.
Alpha Dropout is a `Dropout` that keeps mean and variance... | from keras.src import backend
from keras.src import ops
from keras.src.api_export import keras_export
from keras.src.layers.layer import Layer
@keras_export("keras.layers.AlphaDropout")
class AlphaDropout(Layer):
"""Applies Alpha Dropout to the input.
Alpha Dropout is a `Dropout` that keeps mean and variance... |
_base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
# 270k iterations with batch_size 64 is roughly equivalent to 144 epochs
'../common/ssj_scp_270k_coco_instance.py'
]
norm_cfg = dict(type='SyncBN', requires_grad=True)
# Use MMSyncBN that handles empty tensor in head. It can be changed to
# SyncBN after h... | _base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
# 270k iterations with batch_size 64 is roughly equivalent to 144 epochs
'../common/ssj_scp_270k_coco_instance.py'
]
norm_cfg = dict(type='SyncBN', requires_grad=True)
# Use MMSyncBN that handles empty tensor in head. It can be changed to
# SyncBN after h... |
# training schedule for 20e
train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=20, val_interval=1)
val_cfg = dict(type='ValLoop')
test_cfg = dict(type='TestLoop')
# learning rate
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='M... | # training schedule for 20e
train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=20, val_interval=1)
val_cfg = dict(type='ValLoop')
test_cfg = dict(type='TestLoop')
# learning rate
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='M... |
from typing import Any, Optional
from langchain_core.callbacks.manager import CallbackManagerForLLMRun
from langchain_core.language_models.chat_models import BaseChatModel
from langchain_core.messages import AIMessage, BaseMessage
from langchain_core.outputs import ChatGeneration, ChatResult
from pytest_mock import Mo... | from typing import Any, Optional
from langchain_core.callbacks.manager import CallbackManagerForLLMRun
from langchain_core.language_models.chat_models import BaseChatModel
from langchain_core.messages import AIMessage, BaseMessage
from langchain_core.outputs import ChatGeneration, ChatResult
from pytest_mock import Mo... |
from setuptools import find_packages, setup
with open("README.md", mode="r", encoding="utf-8") as readme_file:
readme = readme_file.read()
setup(
name="sentence-transformers",
version="3.0.0.dev0",
author="Nils Reimers",
author_email="info@nils-reimers.de",
description="Multilingual text embe... | from setuptools import setup, find_packages
with open("README.md", mode="r", encoding="utf-8") as readme_file:
readme = readme_file.read()
setup(
name="sentence-transformers",
version="2.8.0.dev0",
author="Nils Reimers",
author_email="info@nils-reimers.de",
description="Multilingual text embe... |
import torch
from torchaudio_unittest.common_utils import PytorchTestCase, skipIfNoCuda
from torchaudio_unittest.prototype.conv_emformer_test_impl import ConvEmformerTestImpl
@skipIfNoCuda
class ConvEmformerFloat32GPUTest(ConvEmformerTestImpl, PytorchTestCase):
dtype = torch.float32
device = torch.device("cud... | import torch
from torchaudio_unittest.common_utils import skipIfNoCuda, PytorchTestCase
from torchaudio_unittest.prototype.conv_emformer_test_impl import ConvEmformerTestImpl
@skipIfNoCuda
class ConvEmformerFloat32GPUTest(ConvEmformerTestImpl, PytorchTestCase):
dtype = torch.float32
device = torch.device("cud... |
# Copyright (c) OpenMMLab. All rights reserved.
import importlib
import os.path as osp
from mmengine.config import Config
from mmengine.config.utils import (_get_cfg_metainfo,
_get_external_cfg_base_path,
_get_package_and_cfg_path)
from mmengine.reg... | # Copyright (c) OpenMMLab. All rights reserved.
import importlib
import os.path as osp
from mmengine.config import Config
from mmengine.config.utils import (_get_cfg_metainfo,
_get_external_cfg_base_path,
_get_package_and_cfg_path)
from mmengine.reg... |
import os
from source_separation.utils.dataset import wsj0mix
from torchaudio_unittest.common_utils import get_whitenoise, normalize_wav, save_wav, TempDirMixin, TorchaudioTestCase
_FILENAMES = [
"012c0207_1.9952_01cc0202_-1.9952.wav",
"01co0302_1.63_014c020q_-1.63.wav",
"01do0316_0.24011_205a0104_-0.240... | import os
from source_separation.utils.dataset import wsj0mix
from torchaudio_unittest.common_utils import get_whitenoise, normalize_wav, save_wav, TempDirMixin, TorchaudioTestCase
_FILENAMES = [
"012c0207_1.9952_01cc0202_-1.9952.wav",
"01co0302_1.63_014c020q_-1.63.wav",
"01do0316_0.24011_205a0104_-0.240... |
from typing import TYPE_CHECKING, Any, Optional, Type, TypeVar, Union
import numpy as np
from docarray.base_doc import BaseDoc
from docarray.typing import AnyEmbedding, ImageBytes, ImageUrl
from docarray.typing.tensor.abstract_tensor import AbstractTensor
from docarray.typing.tensor.image.image_tensor import ImageTen... | from typing import TYPE_CHECKING, Any, Optional, Type, TypeVar, Union
import numpy as np
from docarray.base_doc import BaseDoc
from docarray.typing import AnyEmbedding, ImageBytes, ImageUrl
from docarray.typing.tensor.abstract_tensor import AbstractTensor
from docarray.typing.tensor.image.image_tensor import ImageTen... |
import numpy as np
from docarray.base_document import AnyDocument, BaseDocument
from docarray.typing import NdArray
def test_any_doc():
class InnerDocument(BaseDocument):
text: str
tensor: NdArray
class CustomDoc(BaseDocument):
inner: InnerDocument
text: str
doc = Custom... | import numpy as np
from docarray.document import AnyDocument, BaseDocument
from docarray.typing import NdArray
def test_any_doc():
class InnerDocument(BaseDocument):
text: str
tensor: NdArray
class CustomDoc(BaseDocument):
inner: InnerDocument
text: str
doc = CustomDoc(
... |
# Copyright (c) OpenMMLab. All rights reserved.
from .anchor_free_head import AnchorFreeHead
from .anchor_head import AnchorHead
from .atss_head import ATSSHead
from .autoassign_head import AutoAssignHead
from .cascade_rpn_head import CascadeRPNHead, StageCascadeRPNHead
from .centernet_head import CenterNetHead
from .c... | # Copyright (c) OpenMMLab. All rights reserved.
from .anchor_free_head import AnchorFreeHead
from .anchor_head import AnchorHead
from .atss_head import ATSSHead
from .autoassign_head import AutoAssignHead
from .cascade_rpn_head import CascadeRPNHead, StageCascadeRPNHead
from .centernet_head import CenterNetHead
from .c... |
import json
from typing import Any, Type, TypeVar, overload
import jsonschema
from fastapi.encoders import jsonable_encoder
from .type import type_match
def to_dict(data) -> dict:
return jsonable_encoder(data)
def dumps(data) -> str:
return json.dumps(jsonable_encoder(data))
T = TypeVar("T")
@overload... | import json
from typing import Any, Type, TypeVar, overload
from fastapi.encoders import jsonable_encoder
from .type import type_match
def to_dict(data) -> dict:
return jsonable_encoder(data)
def dumps(data) -> str:
return json.dumps(jsonable_encoder(data))
T = TypeVar("T")
@overload
def loads(data: s... |
"""String output parser."""
from langchain_core.output_parsers.transform import BaseTransformOutputParser
class StrOutputParser(BaseTransformOutputParser[str]):
"""OutputParser that parses LLMResult into the top likely string."""
@classmethod
def is_lc_serializable(cls) -> bool:
"""StrOutputPars... | """String output parser."""
from typing import Optional as Optional
from langchain_core.output_parsers.transform import BaseTransformOutputParser
class StrOutputParser(BaseTransformOutputParser[str]):
"""OutputParser that parses LLMResult into the top likely string."""
@classmethod
def is_lc_serializab... |
import inspect
import re
from typing import Dict, List, Tuple
from huggingface_hub.utils import insecure_hashlib
from .arrow import arrow
from .audiofolder import audiofolder
from .cache import cache
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parq... | import inspect
import re
from typing import Dict, List, Tuple
from huggingface_hub.utils import insecure_hashlib
from .arrow import arrow
from .audiofolder import audiofolder
from .cache import cache
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parq... |
import requests
from packaging import version
from typing import Sequence, Union, List, Optional
from llama_index.core.base.llms.types import (
ChatMessage,
ChatResponse,
)
from text_generation.types import (
Message,
)
def resolve_tgi_function_call(url: str) -> bool:
url = f"{url}/info"
model_inf... | import requests
from packaging import version
from typing import Sequence, Union, List, Optional
from llama_index.core.base.llms.types import (
ChatMessage,
ChatResponse,
)
from text_generation.types import (
Message,
)
def resolve_tgi_function_call(url: str) -> bool:
url = f"{url}/info"
model_inf... |
"""IndexStructType class."""
from enum import Enum
class IndexStructType(str, Enum):
"""
Index struct type. Identifier for a "type" of index.
Attributes:
TREE ("tree"): Tree index. See :ref:`Ref-Indices-Tree` for tree indices.
LIST ("list"): Summary index. See :ref:`Ref-Indices-List` for... | """IndexStructType class."""
from enum import Enum
class IndexStructType(str, Enum):
"""Index struct type. Identifier for a "type" of index.
Attributes:
TREE ("tree"): Tree index. See :ref:`Ref-Indices-Tree` for tree indices.
LIST ("list"): Summary index. See :ref:`Ref-Indices-List` for summ... |
"""Run smoke tests"""
import os
from pathlib import Path
import torch
import torchvision
from torchvision.io import read_image
from torchvision.models import resnet50, ResNet50_Weights
SCRIPT_DIR = Path(__file__).parent
def smoke_test_torchvision() -> None:
print(
"Is torchvision usable?",
all(... | """Run smoke tests"""
import os
from pathlib import Path
import torch
import torchvision
from torchvision.io import read_image
from torchvision.models import resnet50, ResNet50_Weights
SCRIPT_DIR = Path(__file__).parent
def smoke_test_torchvision() -> None:
print(
"Is torchvision useable?",
all... |
from typing import Any, Dict, Optional
from llama_index.core.storage.kvstore.types import BaseKVStore
from llama_index.storage.kvstore.azurecosmosnosql import AzureCosmosNoSqlKVStore
DEFAULT_DOCUMENT_DATABASE = "DocumentStoreDB"
DEFAULT_DOCUMENT_CONTAINER = "DocumentStoreContainer"
class AzureCosmosNoSqlDocumentSto... | from typing import Any, Dict, Optional
from llama_index.core.storage.docstore.keyval_docstore import KVDocumentStore
from llama_index.storage.kvstore.azurecosmosnosql import AzureCosmosNoSqlKVStore
DEFAULT_DOCUMENT_DATABASE = "DocumentStoreDB"
DEFAULT_DOCUMENT_CONTAINER = "DocumentStoreContainer"
class AzureCosmosN... |
from __future__ import annotations
from sentence_transformers.training_args import SentenceTransformerTrainingArguments
class CrossEncoderTrainingArguments(SentenceTransformerTrainingArguments):
r"""
CrossEncoderTrainingArguments extends :class:`~transformers.TrainingArguments` with additional arguments
... | from __future__ import annotations
from sentence_transformers.training_args import SentenceTransformerTrainingArguments
class CrossEncoderTrainingArguments(SentenceTransformerTrainingArguments):
r"""
CrossEncoderTrainingArguments extends :class:`~transformers.TrainingArguments` with additional arguments
... |
from typing import Optional
from urllib.parse import quote
import huggingface_hub as hfh
from packaging import version
def hf_hub_url(repo_id: str, path: str, revision: Optional[str] = None) -> str:
if version.parse(hfh.__version__).release < version.parse("0.11.0").release:
# old versions of hfh don't u... | from typing import Optional
from urllib.parse import quote
import huggingface_hub as hfh
from packaging import version
def hf_hub_url(repo_id: str, path: str, revision: Optional[str] = None) -> str:
if version.parse(hfh.__version__) < version.parse("0.11.0"):
# old versions of hfh don't url-encode the fi... |
import re
from typing import TYPE_CHECKING, Any, Dict, Union
if TYPE_CHECKING:
from sentence_transformers.SentenceTransformer import SentenceTransformer
class SentenceEvaluator:
"""
Base class for all evaluators
Extend this class and implement __call__ for custom evaluators.
"""
def __init_... | import re
from typing import TYPE_CHECKING, Any, Dict, Union
if TYPE_CHECKING:
from sentence_transformers.SentenceTransformer import SentenceTransformer
class SentenceEvaluator:
"""
Base class for all evaluators
Extend this class and implement __call__ for custom evaluators.
"""
def __init_... |
"""
This examples trains a CrossEncoder for the NLI task. A CrossEncoder takes a sentence pair
as input and outputs a label. Here, it learns to predict the labels: "contradiction": 0, "entailment": 1, "neutral": 2.
It does NOT produce a sentence embedding and does NOT work for individual sentences.
Usage:
python trai... | """
This examples trains a CrossEncoder for the NLI task. A CrossEncoder takes a sentence pair
as input and outputs a label. Here, it learns to predict the labels: "contradiction": 0, "entailment": 1, "neutral": 2.
It does NOT produce a sentence embedding and does NOT work for individual sentences.
Usage:
python trai... |
"""Chat Message."""
from typing import Any, Literal
from typing_extensions import override
from langchain_core.messages.base import (
BaseMessage,
BaseMessageChunk,
merge_content,
)
from langchain_core.utils._merge import merge_dicts
class ChatMessage(BaseMessage):
"""Message that can be assigned a... | """Chat Message."""
from typing import Any, Literal
from typing_extensions import override
from langchain_core.messages.base import (
BaseMessage,
BaseMessageChunk,
merge_content,
)
from langchain_core.utils._merge import merge_dicts
class ChatMessage(BaseMessage):
"""Message that can be assigned a... |
# 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... | # 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... |
# Copyright (c) OpenMMLab. All rights reserved.
import os
from unittest.mock import MagicMock
import torch
import torch.distributed as torch_dist
import torch.nn as nn
from mmengine.dist import all_gather
from mmengine.hooks import SyncBuffersHook
from mmengine.registry import MODELS
from mmengine.testing._internal i... | # Copyright (c) OpenMMLab. All rights reserved.
from unittest.mock import Mock
from mmengine.hooks import SyncBuffersHook
class TestSyncBuffersHook:
def test_sync_buffers_hook(self):
runner = Mock()
runner.model = Mock()
hook = SyncBuffersHook()
hook._after_epoch(runner)
|
import os
import numpy as np
import pytest
import torch
from pydantic import parse_obj_as
from docarray import BaseDocument
from docarray.typing.tensor.audio.audio_ndarray import AudioNdArray
from docarray.typing.tensor.audio.audio_torch_tensor import AudioTorchTensor
@pytest.mark.parametrize(
'tensor,cls_audio... | import os
import numpy as np
import pytest
import torch
from pydantic import parse_obj_as
from docarray import BaseDocument
from docarray.typing.tensor.audio.audio_ndarray import AudioNdArray
from docarray.typing.tensor.audio.audio_torch_tensor import AudioTorchTensor
@pytest.mark.parametrize(
'tensor,cls_audio... |
from abc import ABC
import pytest
from docarray import DocumentArray
from docarray.array.storage.memory import GetSetDelMixin, SequenceLikeMixin
from docarray.array.storage.redis.backend import BackendMixin, RedisConfig
class StorageMixins(BackendMixin, GetSetDelMixin, SequenceLikeMixin, ABC):
...
class Docume... | from abc import ABC
import pytest
from docarray import DocumentArray
from docarray.array.storage.memory import GetSetDelMixin, SequenceLikeMixin
from docarray.array.storage.redis.backend import BackendMixin, RedisConfig
class StorageMixins(BackendMixin, GetSetDelMixin, SequenceLikeMixin, ABC):
...
class Docume... |
# Copyright (c) OpenMMLab. All rights reserved.
# This script consists of several convert functions which
# can modify the weights of model in original repo to be
# pre-trained weights.
from collections import OrderedDict
def swin_converter(ckpt):
new_ckpt = OrderedDict()
def correct_unfold_reduction_orde... | # Copyright (c) OpenMMLab. All rights reserved.
# This script consists of several convert functions which
# can modify the weights of model in original repo to be
# pre-trained weights.
from collections import OrderedDict
def swin_converter(ckpt):
new_ckpt = OrderedDict()
def correct_unfold_reduction_orde... |
from __future__ import annotations
from typing import TYPE_CHECKING
from unittest.mock import MagicMock, patch
import pytest
from langchain_community.document_loaders import ArcGISLoader
if TYPE_CHECKING:
from collections.abc import Iterator
from arcgis.features import FeatureLayer
from arcgis.gis impo... | from unittest.mock import MagicMock, patch
import pytest
from langchain_community.document_loaders import ArcGISLoader
@pytest.fixture
def arcgis_mocks(mock_feature_layer, mock_gis): # type: ignore
sys_modules = {
"arcgis": MagicMock(),
"arcgis.features.FeatureLayer": mock_feature_layer,
... |
# Copyright (c) OpenMMLab. All rights reserved.
from .builder import DATASETS, PIPELINES, build_dataloader, build_dataset
from .cityscapes import CityscapesDataset
from .coco import CocoDataset
from .coco_panoptic import CocoPanopticDataset
from .custom import CustomDataset
from .dataset_wrappers import (ClassBalancedD... | # Copyright (c) OpenMMLab. All rights reserved.
from .builder import DATASETS, PIPELINES, build_dataloader, build_dataset
from .cityscapes import CityscapesDataset
from .coco import CocoDataset
from .coco_panoptic import CocoPanopticDataset
from .custom import CustomDataset
from .dataset_wrappers import (ClassBalancedD... |
"""Callback Handler that writes to a file."""
from __future__ import annotations
from pathlib import Path
from typing import TYPE_CHECKING, Any, Optional, TextIO, cast
from typing_extensions import override
from langchain_core.callbacks import BaseCallbackHandler
from langchain_core.utils.input import print_text
i... | """Callback Handler that writes to a file."""
from __future__ import annotations
from pathlib import Path
from typing import TYPE_CHECKING, Any, Optional, TextIO, cast
from typing_extensions import override
from langchain_core.callbacks import BaseCallbackHandler
from langchain_core.utils.input import print_text
i... |
# Copyright (c) OpenMMLab. All rights reserved.
from ..builder import DETECTORS
from .two_stage import TwoStageDetector
@DETECTORS.register_module()
class FastRCNN(TwoStageDetector):
"""Implementation of `Fast R-CNN <https://arxiv.org/abs/1504.08083>`_"""
def __init__(self,
backbone,
... | from ..builder import DETECTORS
from .two_stage import TwoStageDetector
@DETECTORS.register_module()
class FastRCNN(TwoStageDetector):
"""Implementation of `Fast R-CNN <https://arxiv.org/abs/1504.08083>`_"""
def __init__(self,
backbone,
roi_head,
train_cfg,
... |
import importlib.machinery
import os
from torch.hub import _get_torch_home
_HOME = os.path.join(_get_torch_home(), "datasets", "vision")
_USE_SHARDED_DATASETS = False
def _download_file_from_remote_location(fpath: str, url: str) -> None:
pass
def _is_remote_location_available() -> bool:
return False
tr... | import importlib.machinery
import os
from torch.hub import _get_torch_home
_HOME = os.path.join(_get_torch_home(), "datasets", "vision")
_USE_SHARDED_DATASETS = False
def _download_file_from_remote_location(fpath: str, url: str) -> None:
pass
def _is_remote_location_available() -> bool:
return False
tr... |
# Copyright (c) OpenMMLab. All rights reserved.
from .activations import SiLU
from .bbox_nms import fast_nms, multiclass_nms
from .brick_wrappers import AdaptiveAvgPool2d, adaptive_avg_pool2d
from .conv_upsample import ConvUpsample
from .csp_layer import CSPLayer
from .dropblock import DropBlock
from .ema import ExpMom... | # Copyright (c) OpenMMLab. All rights reserved.
from .activations import SiLU
from .bbox_nms import fast_nms, multiclass_nms
from .brick_wrappers import AdaptiveAvgPool2d, adaptive_avg_pool2d
from .conv_upsample import ConvUpsample
from .csp_layer import CSPLayer
from .dropblock import DropBlock
from .ema import ExpMom... |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# This software may be used and distributed according to the terms of the Llama 2 Community License Agreement.
from .generation import Llama, Dialog
from .model import ModelArgs, Transformer
from .tokenizer import Tokenizer
| # Copyright (c) Meta Platforms, Inc. and affiliates.
# This software may be used and distributed according to the terms of the Llama 2 Community License Agreement.
from .generation import Llama
from .model import ModelArgs, Transformer
from .tokenizer import Tokenizer
|
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... |
from __future__ import annotations
from collections.abc import Iterable
from typing import Any
import torch
from torch import Tensor, nn
from sentence_transformers import util
from sentence_transformers.SentenceTransformer import SentenceTransformer
class CoSENTLoss(nn.Module):
def __init__(self, model: Senten... | from __future__ import annotations
from collections.abc import Iterable
from typing import Any
import torch
from torch import Tensor, nn
from sentence_transformers import util
from sentence_transformers.SentenceTransformer import SentenceTransformer
class CoSENTLoss(nn.Module):
def __init__(self, model: Senten... |
import pytest
from llama_index.core.sparse_embeddings.mock_sparse_embedding import MockSparseEmbedding
text_embedding_map = {
"hello": {0: 0.25},
"world": {1: 0.5},
"foo": {2: 0.75},
}
@pytest.fixture()
def mock_sparse_embedding():
return MockSparseEmbedding(text_to_embedding=text_embedding_map)
d... | import pytest
from llama_index.core.sparse_embeddings.mock_sparse_embedding import MockSparseEmbedding
text_embedding_map = {
"hello": {0: 0.25},
"world": {1: 0.5},
"foo": {2: 0.75},
}
@pytest.fixture()
def mock_sparse_embedding():
return MockSparseEmbedding(text_to_embedding=text_embedding_map)
d... |
import json
from collections.abc import Sequence
from langchain_core.agents import AgentAction, AgentActionMessageLog
from langchain_core.messages import AIMessage, BaseMessage, FunctionMessage
def _convert_agent_action_to_messages(
agent_action: AgentAction, observation: str
) -> list[BaseMessage]:
"""Conve... | import json
from collections.abc import Sequence
from langchain_core.agents import AgentAction, AgentActionMessageLog
from langchain_core.messages import AIMessage, BaseMessage, FunctionMessage
def _convert_agent_action_to_messages(
agent_action: AgentAction, observation: str
) -> list[BaseMessage]:
"""Conve... |
"""
This examples trains BERT (or any other transformer model like RoBERTa, DistilBERT etc.) for the STSbenchmark from scratch.
It uses AdaptiveLayerLoss with the powerful CoSENTLoss to train models that perform well at output dimensions [768, 512, 256, 128, 64].
It generates sentence embeddings that can be compared us... | """
This examples trains BERT (or any other transformer model like RoBERTa, DistilBERT etc.) for the STSbenchmark from scratch.
It uses AdaptiveLayerLoss with the powerful CoSENTLoss to train models that perform well at output dimensions [768, 512, 256, 128, 64].
It generates sentence embeddings that can be compared us... |
# coding=utf-8
# Copyright 2025 The HuggingFace Inc. 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 r... | # coding=utf-8
# Copyright 2025 The HuggingFace Inc. 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 r... |
"""Init file of LlamaIndex."""
__version__ = "0.12.10"
import logging
from logging import NullHandler
from typing import Callable, Optional
try:
# Force pants to install eval_type_backport on 3.9
import eval_type_backport # noqa # type: ignore
except ImportError:
pass
# response
from llama_index.core.... | """Init file of LlamaIndex."""
__version__ = "0.12.9"
import logging
from logging import NullHandler
from typing import Callable, Optional
try:
# Force pants to install eval_type_backport on 3.9
import eval_type_backport # noqa # type: ignore
except ImportError:
pass
# response
from llama_index.core.b... |
"""
This examples trains a CrossEncoder for the Quora Duplicate Questions Detection task. A CrossEncoder takes a sentence pair
as input and outputs a label. Here, it output a continuous labels 0...1 to indicate the similarity between the input pair.
It does NOT produce a sentence embedding and does NOT work for indivi... | """
This examples trains a CrossEncoder for the Quora Duplicate Questions Detection task. A CrossEncoder takes a sentence pair
as input and outputs a label. Here, it output a continuous labels 0...1 to indicate the similarity between the input pair.
It does NOT produce a sentence embedding and does NOT work for indivi... |
from abc import ABC
from typing import Any, Optional, Tuple, Type, TypeVar, Union
from docarray.typing.tensor.abstract_tensor import AbstractTensor
from docarray.typing.tensor.ndarray import NdArray
from docarray.typing.tensor.torch_tensor import TorchTensor
T = TypeVar('T', bound='Embedding')
class EmbeddingMixin(... | from abc import ABC
from typing import Any, Optional, Tuple, Type, TypeVar, Union
from docarray.typing.tensor.abstract_tensor import AbstractTensor
from docarray.typing.tensor.ndarray import NdArray
from docarray.typing.tensor.torch_tensor import TorchTensor
T = TypeVar('T', bound='Embedding')
class EmbeddingMixin(... |
import zlib
from typing import Iterator, TextIO
def exact_div(x, y):
assert x % y == 0
return x // y
def str2bool(string):
str2val = {"True": True, "False": False}
if string in str2val:
return str2val[string]
else:
raise ValueError(f"Expected one of {set(str2val.keys())}, got {st... | import zlib
from typing import Iterator, TextIO
def exact_div(x, y):
assert x % y == 0
return x // y
def str2bool(string):
str2val = {"True": True, "False": False}
if string in str2val:
return str2val[string]
else:
raise ValueError(f"Expected one of {set(str2val.keys())}, got {st... |
"""LLM Compiler agent pack."""
from typing import Any, Dict, List, Optional
from llama_index.core.agent import AgentRunner
from llama_index.core.callbacks import CallbackManager
from llama_index.core.llama_pack.base import BaseLlamaPack
from llama_index.core.llms.llm import LLM
from llama_index.core.tools.types impor... | """LLM Compiler agent pack."""
from typing import Any, Dict, List, Optional
from llama_index.core.agent import AgentRunner
from llama_index.core.callbacks import CallbackManager
from llama_index.core.llama_pack.base import BaseLlamaPack
from llama_index.core.llms.llm import LLM
from llama_index.core.tools.types impor... |
from .gateway import HTTPGateway
__all__ = ['HTTPGateway']
| from .gateway import HTTPGateway
|
import importlib
import pytest
from fastapi import FastAPI
from fastapi.testclient import TestClient
from fastapi.websockets import WebSocketDisconnect
from ...utils import needs_py39, needs_py310
@pytest.fixture(
name="app",
params=[
"tutorial002",
pytest.param("tutorial002_py310", marks=ne... | import pytest
from fastapi.testclient import TestClient
from fastapi.websockets import WebSocketDisconnect
from docs_src.websockets.tutorial002 import app
def test_main():
client = TestClient(app)
response = client.get("/")
assert response.status_code == 200, response.text
assert b"<!DOCTYPE html>" i... |
import torch
from torchvision.transforms import autoaugment, transforms
from torchvision.transforms.functional import InterpolationMode
class ClassificationPresetTrain:
def __init__(
self,
*,
crop_size,
mean=(0.485, 0.456, 0.406),
std=(0.229, 0.224, 0.225),
interpol... | import torch
from torchvision.transforms import autoaugment, transforms
from torchvision.transforms.functional import InterpolationMode
class ClassificationPresetTrain:
def __init__(
self,
*,
crop_size,
mean=(0.485, 0.456, 0.406),
std=(0.229, 0.224, 0.225),
interpol... |
# Copyright (c) OpenMMLab. All rights reserved.
from unittest import TestCase
import torch
from mmengine.config import ConfigDict
from mmengine.structures import InstanceData
from mmdet.models.dense_heads import SABLRetinaHead
class TestSABLRetinaHead(TestCase):
def test_sabl_retina_head(self):
"""Test... | # Copyright (c) OpenMMLab. All rights reserved.
from unittest import TestCase
import torch
from mmengine.config import ConfigDict
from mmengine.data import InstanceData
from mmdet.models.dense_heads import SABLRetinaHead
class TestSABLRetinaHead(TestCase):
def test_sabl_retina_head(self):
"""Tests sabl... |
"""Simple reader that reads weather data from OpenWeatherMap API"""
from __future__ import annotations
from datetime import datetime
from typing import Iterator, Optional, Sequence
from langchain_core.documents import Document
from langchain_community.document_loaders.base import BaseLoader
from langchain_community... | """Simple reader that reads weather data from OpenWeatherMap API"""
from __future__ import annotations
from datetime import datetime
from typing import Iterator, Optional, Sequence
from langchain_core.documents import Document
from langchain_community.document_loaders.base import BaseLoader
from langchain_community... |
# Copyright 2024 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... | # Copyright 2024 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... |
"""
Feature agglomeration. Base classes and functions for performing feature
agglomeration.
"""
# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause
import numpy as np
from scipy.sparse import issparse
from ..base import TransformerMixin
from ..utils.validation import check_is_fitted, valid... | """
Feature agglomeration. Base classes and functions for performing feature
agglomeration.
"""
# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause
import numpy as np
from scipy.sparse import issparse
from ..base import TransformerMixin
from ..utils.validation import check_is_fitted, vali... |
from typing import Any, Optional, Sequence
from llama_index.core.evaluation.base import BaseEvaluator, EvaluationResult
from llama_index.core.prompts.mixin import PromptDictType, PromptMixinType
from tonic_validate.metrics.retrieval_precision_metric import (
RetrievalPrecisionMetric,
)
from tonic_validate.service... | from typing import Any, Optional, Sequence
from llama_index.core.evaluation.base import BaseEvaluator, EvaluationResult
from llama_index.core.prompts.mixin import PromptDictType, PromptMixinType
from tonic_validate.metrics.retrieval_precision_metric import (
RetrievalPrecisionMetric,
)
from tonic_validate.service... |
__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... | __copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import subprocess
from typing import Callable
import pytest
from jina import Flow
from ...audioclip_text import AudioCLIPTextEncoder
@pytest.mark.parametrize("request_size", [1, 10, 50, 100])
def test_integra... |
# Copyright (c) OpenMMLab. All rights reserved.
from .dist_utils import (DistOptimizerHook, all_reduce_dict, allreduce_grads,
reduce_mean)
from .misc import (center_of_mass, flip_tensor, generate_coordinate,
mask2ndarray, multi_apply, select_single_mlvl, unmap)
__all__ = [
... | # Copyright (c) OpenMMLab. All rights reserved.
from .dist_utils import (DistOptimizerHook, all_reduce_dict, allreduce_grads,
reduce_mean)
from .misc import (center_of_mass, flip_tensor, generate_coordinate,
mask2ndarray, multi_apply, unmap)
__all__ = [
'allreduce_grads'... |
"""
Opendal file and directory reader.
A loader that fetches a file or iterates through a directory on AWS S3 or other compatible service.
"""
import asyncio
import tempfile
from pathlib import Path
from typing import Any, Dict, List, Optional, Union, cast
from llama_index.core.readers import SimpleDirectoryReader
... | """
Opendal file and directory reader.
A loader that fetches a file or iterates through a directory on AWS S3 or other compatible service.
"""
import asyncio
import tempfile
from pathlib import Path
from typing import Any, Dict, List, Optional, Union, cast
from llama_index.core.readers import SimpleDirectoryReader
f... |
import functools
import time
from threading import Thread
import numpy as np
import pytest
from jina import Client, Document, Flow
from jina.helper import random_port
@pytest.mark.slow
@pytest.mark.parametrize('protocol', ['websocket', 'http'])
def test_gateway_concurrency(protocol, reraise):
port = random_port... | import functools
import time
from threading import Thread
import numpy as np
import pytest
from jina import Client, Document, Flow
from jina.helper import random_port
@pytest.mark.slow
@pytest.mark.parametrize('protocol', ['websocket', 'http'])
def test_gateway_concurrency(protocol, reraise):
port = random_port... |
from typing import TYPE_CHECKING, Optional
if TYPE_CHECKING:
import botocore
def get_aws_service_client(
service_name: Optional[str] = None,
region_name: Optional[str] = None,
aws_access_key_id: Optional[str] = None,
aws_secret_access_key: Optional[str] = None,
aws_session_token: Optional[str... | from typing import TYPE_CHECKING, Optional
if TYPE_CHECKING:
import botocore
def get_aws_service_client(
service_name: Optional[str] = None,
region_name: Optional[str] = None,
aws_access_key_id: Optional[str] = None,
aws_secret_access_key: Optional[str] = None,
aws_session_token: Optional[str... |
# Copyright (c) OpenMMLab. All rights reserved.
from abc import ABCMeta, abstractmethod
from typing import Tuple
from mmengine.model import BaseModule
from torch import Tensor
from mmdet.core.utils import (InstanceList, OptConfigType, OptMultiConfig,
SampleList)
from mmdet.registry impor... | # Copyright (c) OpenMMLab. All rights reserved.
from abc import ABCMeta, abstractmethod
from typing import Tuple
from mmengine.model import BaseModule
from torch import Tensor
from mmdet.core.utils import (InstanceList, OptConfigType, OptMultiConfig,
SampleList)
from mmdet.registry impor... |
from typing import Union
from docarray.typing.tensor.embedding.ndarray import NdArrayEmbedding
try:
import torch # noqa: F401
except ImportError:
AnyEmbedding = Union[NdArrayEmbedding] # type: ignore
else:
from docarray.typing.tensor.embedding.torch import TorchEmbedding # noqa: F401
AnyEmbedding... | from typing import Union
from docarray.typing.tensor.embedding.ndarray import NdArrayEmbedding
try:
import torch # noqa: F401
except ImportError:
Embedding = Union[NdArrayEmbedding] # type: ignore
else:
from docarray.typing.tensor.embedding.torch import TorchEmbedding # noqa: F401
Embedding = Uni... |
import json
import time
import pytest
from opentelemetry.sdk.metrics import MeterProvider
from opentelemetry.sdk.metrics.export import InMemoryMetricReader
from prometheus_client import Summary
from jina.serve.instrumentation import MetricsTimer
@pytest.fixture
def metrics_setup():
metric_reader = InMemoryMetri... | import json
import time
import pytest
from opentelemetry.sdk.metrics import MeterProvider
from opentelemetry.sdk.metrics.export import InMemoryMetricReader
from prometheus_client import Summary
from jina.serve.instrumentation import MetricsTimer
@pytest.fixture
def metrics_setup():
metric_reader = InMemoryMetri... |
from typing import Union
from docarray.typing.tensor.ndarray import NdArray
try:
import torch # noqa: F401
except ImportError:
AnyTensor = Union[NdArray] # type: ignore
else:
from docarray.typing.tensor.torch_tensor import TorchTensor # noqa: F401
AnyTensor = Union[NdArray, TorchTensor] # type: ... | from typing import Union
from docarray.typing.tensor.ndarray import NdArray
try:
import torch # noqa: F401
except ImportError:
Tensor = Union[NdArray] # type: ignore
else:
from docarray.typing.tensor.torch_tensor import TorchTensor # noqa: F401
Tensor = Union[NdArray, TorchTensor] # type: ignore... |
# Copyright (c) OpenMMLab. All rights reserved.
import warnings
import mmcv
from ..builder import PIPELINES
from .compose import Compose
@PIPELINES.register_module()
class MultiScaleFlipAug:
"""Test-time augmentation with multiple scales and flipping.
An example configuration is as followed:
.. code-b... | import warnings
import mmcv
from ..builder import PIPELINES
from .compose import Compose
@PIPELINES.register_module()
class MultiScaleFlipAug:
"""Test-time augmentation with multiple scales and flipping.
An example configuration is as followed:
.. code-block::
img_scale=[(1333, 400), (1333, 8... |
from typing import Generator, Optional
import pytest
from docarray import BaseDoc, DocArray
from docarray.documents import ImageDoc
from docarray.typing import ImageUrl, NdArray
from docarray.utils.map import map_docs, map_docs_batch
from tests.units.typing.test_bytes import IMAGE_PATHS
N_DOCS = 2
def load_from_do... | from typing import Generator, Optional
import pytest
from docarray import BaseDocument, DocumentArray
from docarray.documents import ImageDoc
from docarray.typing import ImageUrl, NdArray
from docarray.utils.map import map_docs, map_docs_batch
from tests.units.typing.test_bytes import IMAGE_PATHS
N_DOCS = 2
def lo... |
import os
from nvflare.apis.executor import Executor
from nvflare.apis.fl_constant import FLContextKey, ReturnCode
from nvflare.apis.fl_context import FLContext
from nvflare.apis.shareable import Shareable, make_reply
from nvflare.apis.signal import Signal
import xgboost as xgb
from xgboost import callback
class Su... | import os
from nvflare.apis.executor import Executor
from nvflare.apis.fl_constant import FLContextKey, ReturnCode
from nvflare.apis.fl_context import FLContext
from nvflare.apis.shareable import Shareable, make_reply
from nvflare.apis.signal import Signal
import xgboost as xgb
from xgboost import callback
class Su... |
# Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import torch
import torch.nn as nn
from mmdet.registry import MODELS
from .utils import weighted_loss
@mmcv.jit(derivate=True, coderize=True)
@weighted_loss
def smooth_l1_loss(pred, target, beta=1.0):
"""Smooth L1 loss.
Args:
pred (torch.Te... | # Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import torch
import torch.nn as nn
from ..builder import LOSSES
from .utils import weighted_loss
@mmcv.jit(derivate=True, coderize=True)
@weighted_loss
def smooth_l1_loss(pred, target, beta=1.0):
"""Smooth L1 loss.
Args:
pred (torch.Tensor)... |
# Copyright (c) OpenMMLab. All rights reserved.
from mmdet.registry import MODELS
from mmdet.utils import ConfigType, OptConfigType, OptMultiConfig
from .single_stage_instance_seg import SingleStageInstanceSegmentor
@MODELS.register_module()
class SOLOv2(SingleStageInstanceSegmentor):
"""`SOLOv2: Dynamic and Fast... | # Copyright (c) OpenMMLab. All rights reserved.
from mmdet.utils import ConfigType, OptConfigType, OptMultiConfig
from ..builder import DETECTORS
from .single_stage_instance_seg import SingleStageInstanceSegmentor
@DETECTORS.register_module()
class SOLOv2(SingleStageInstanceSegmentor):
"""`SOLOv2: Dynamic and Fas... |
import os
from pathlib import Path
from torchaudio.datasets import yesno
from torchaudio_unittest.common_utils import (
get_whitenoise,
normalize_wav,
save_wav,
TempDirMixin,
TorchaudioTestCase,
)
def get_mock_data(root_dir, labels):
"""
root_dir: path
labels: list of labels
"""
... | import os
from pathlib import Path
from torchaudio.datasets import yesno
from torchaudio_unittest.common_utils import (
TempDirMixin,
TorchaudioTestCase,
get_whitenoise,
save_wav,
normalize_wav,
)
def get_mock_data(root_dir, labels):
"""
root_dir: path
labels: list of labels
"""
... |
"""Test node mapping."""
from llama_index.core import SQLDatabase
from llama_index.core.bridge.pydantic import BaseModel
from llama_index.core.objects.base_node_mapping import SimpleObjectNodeMapping
from llama_index.core.objects.table_node_mapping import (
SQLTableNodeMapping,
SQLTableSchema,
)
from llama_ind... | """Test node mapping."""
from llama_index.core import SQLDatabase
from llama_index.core.bridge.pydantic import BaseModel
from llama_index.core.objects.base_node_mapping import SimpleObjectNodeMapping
from llama_index.core.objects.table_node_mapping import (
SQLTableNodeMapping,
SQLTableSchema,
)
from llama_ind... |
"""
This file contains deprecated code that can only be used with the old `model.fit`-style Sentence Transformers v2.X training.
It exists for backwards compatibility with the `model.old_fit` method, but will be removed in a future version.
Nowadays, with Sentence Transformers v3+, it is recommended to use the `Senten... | """
This file contains deprecated code that can only be used with the old `model.fit`-style Sentence Transformers v2.X training.
It exists for backwards compatibility with the `model.old_fit` method, but will be removed in a future version.
Nowadays, with Sentence Transformers v3+, it is recommended to use the `Senten... |
import collections
from keras.src import backend
from keras.src import testing
from keras.src.utils import tracking
class TrackingTest(testing.TestCase):
def test_untracking_in_tracked_list(self):
tracked_variables = []
tracker = tracking.Tracker(
{
"variables": (
... | import collections
from keras.src import backend
from keras.src import testing
from keras.src.utils import tracking
class TrackingTest(testing.TestCase):
def test_untracking_in_tracked_list(self):
tracked_variables = []
tracker = tracking.Tracker(
{
"variables": (
... |
# Copyright (c) OpenMMLab. All rights reserved.
from .empty_cache_hook import EmptyCacheHook
from .checkpoint_hook import CheckpointHook
from .hook import Hook
from .iter_timer_hook import IterTimerHook
from .optimizer_hook import OptimizerHook
from .param_scheduler_hook import ParamSchedulerHook
from .sampler_seed_hoo... | # Copyright (c) OpenMMLab. All rights reserved.
from .empty_cache_hook import EmptyCacheHook
from .hook import Hook
from .iter_timer_hook import IterTimerHook
from .optimizer_hook import OptimizerHook
from .param_scheduler_hook import ParamSchedulerHook
from .sampler_seed_hook import DistSamplerSeedHook
__all__ = [
... |
_base_ = [
'../_base_/default_runtime.py', '../_base_/datasets/coco_detection.py'
]
# model settings
model = dict(
type='CornerNet',
backbone=dict(
type='HourglassNet',
downsample_times=5,
num_stacks=2,
stage_channels=[256, 256, 384, 384, 384, 512],
stage_blocks=[2, ... | _base_ = [
'../_base_/default_runtime.py', '../_base_/datasets/coco_detection.py'
]
# model settings
model = dict(
type='CornerNet',
backbone=dict(
type='HourglassNet',
downsample_times=5,
num_stacks=2,
stage_channels=[256, 256, 384, 384, 384, 512],
stage_blocks=[2, ... |
"""[DEPRECATED] Pipeline prompt template."""
from typing import Any
from typing import Optional as Optional
from pydantic import model_validator
from langchain_core._api.deprecation import deprecated
from langchain_core.prompt_values import PromptValue
from langchain_core.prompts.base import BasePromptTemplate
from ... | from typing import Any
from typing import Optional as Optional
from pydantic import model_validator
from langchain_core._api.deprecation import deprecated
from langchain_core.prompt_values import PromptValue
from langchain_core.prompts.base import BasePromptTemplate
from langchain_core.prompts.chat import BaseChatPro... |
from fastapi.testclient import TestClient
from docs_src.configure_swagger_ui.tutorial003 import app
client = TestClient(app)
def test_swagger_ui():
response = client.get("/docs")
assert response.status_code == 200, response.text
assert '"deepLinking": false,' in response.text, (
"overridden conf... | from fastapi.testclient import TestClient
from docs_src.configure_swagger_ui.tutorial003 import app
client = TestClient(app)
def test_swagger_ui():
response = client.get("/docs")
assert response.status_code == 200, response.text
assert (
'"deepLinking": false,' in response.text
), "overridde... |
import warnings
from typing import Optional, TypeVar
from docarray.typing.bytes.video_bytes import VideoBytes, VideoLoadResult
from docarray.typing.proto_register import _register_proto
from docarray.typing.url.any_url import AnyUrl
from docarray.utils._internal.misc import is_notebook
T = TypeVar('T', bound='VideoUr... | import warnings
from typing import Optional, TypeVar
from docarray.typing.bytes.video_bytes import VideoBytes, VideoLoadResult
from docarray.typing.proto_register import _register_proto
from docarray.typing.url.any_url import AnyUrl
from docarray.utils._internal.misc import is_notebook
T = TypeVar('T', bound='VideoUr... |
from __future__ import annotations
import logging
from typing import TYPE_CHECKING, Callable
import numpy as np
from sentence_transformers.evaluation.NanoBEIREvaluator import NanoBEIREvaluator
from sentence_transformers.sparse_encoder.evaluation.SparseInformationRetrievalEvaluator import (
SparseInformationRetri... | from __future__ import annotations
import logging
from typing import TYPE_CHECKING, Callable
import numpy as np
from sentence_transformers.evaluation.NanoBEIREvaluator import NanoBEIREvaluator
from sentence_transformers.sparse_encoder.evaluation.SparseInformationRetrievalEvaluator import (
SparseInformationRetri... |
"""
This file contains deprecated code that can only be used with the old `model.fit`-style Sentence Transformers v2.X training.
It exists for backwards compatibility with the `model.old_fit` method, but will be removed in a future version.
Nowadays, with Sentence Transformers v3+, it is recommended to use the `Senten... | from __future__ import annotations
import gzip
import os
from . import InputExample
class NLIDataReader:
"""Reads in the Stanford NLI dataset and the MultiGenre NLI dataset"""
def __init__(self, dataset_folder):
self.dataset_folder = dataset_folder
def get_examples(self, filename, max_examples... |
# Copyright (c) OpenMMLab. All rights reserved.
from mmdet.models.layers import ResLayer, SimplifiedBasicBlock
from mmdet.registry import MODELS
from .fused_semantic_head import FusedSemanticHead
@MODELS.register_module()
class SCNetSemanticHead(FusedSemanticHead):
"""Mask head for `SCNet <https://arxiv.org/abs/2... | # Copyright (c) OpenMMLab. All rights reserved.
from mmdet.models.utils import ResLayer, SimplifiedBasicBlock
from mmdet.registry import MODELS
from .fused_semantic_head import FusedSemanticHead
@MODELS.register_module()
class SCNetSemanticHead(FusedSemanticHead):
"""Mask head for `SCNet <https://arxiv.org/abs/20... |
from typing import Any, Dict, Iterable
import torch
from torch import Tensor, nn
from sentence_transformers import util
from sentence_transformers.SentenceTransformer import SentenceTransformer
class MultipleNegativesSymmetricRankingLoss(nn.Module):
def __init__(self, model: SentenceTransformer, scale: float = ... | from typing import Dict, Iterable
import torch
from torch import Tensor, nn
from sentence_transformers import util
from sentence_transformers.SentenceTransformer import SentenceTransformer
class MultipleNegativesSymmetricRankingLoss(nn.Module):
def __init__(self, model: SentenceTransformer, scale: float = 20.0,... |
# Copyright (c) OpenMMLab. All rights reserved.
import ast
import os.path as osp
import re
import warnings
from typing import Tuple
from mmengine.fileio import load
from mmengine.utils import check_file_exist
PKG2PROJECT = {
'mmcls': 'mmcls',
'mmdet': 'mmdet',
'mmdet3d': 'mmdet3d',
'mmseg': 'mmsegment... | # Copyright (c) OpenMMLab. All rights reserved.
import ast
import os.path as osp
import re
import warnings
from typing import Tuple
from mmengine.fileio import load
from mmengine.utils import check_file_exist
PKG2PROJECT = {
'mmcls': 'mmcls',
'mmdet': 'mmdet',
'mmdet3d': 'mmdet3d',
'mmseg': 'mmsegment... |
# 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... |
from llama_index_instrumentation.base import BaseEvent # noqa
| from typing import Any, Dict, Optional
from llama_index.core.bridge.pydantic import BaseModel, Field, ConfigDict
from uuid import uuid4
from datetime import datetime
from llama_index.core.instrumentation.span import active_span_id
class BaseEvent(BaseModel):
model_config = ConfigDict(
arbitrary_types_all... |
"""Module definitions of agent types together with corresponding agents."""
from enum import Enum
from langchain_core._api import deprecated
from langchain._api.deprecation import AGENT_DEPRECATION_WARNING
@deprecated(
"0.1.0",
message=AGENT_DEPRECATION_WARNING,
removal="1.0",
)
class AgentType(str, En... | """Module definitions of agent types together with corresponding agents."""
from enum import Enum
from langchain_core._api import deprecated
from langchain._api.deprecation import AGENT_DEPRECATION_WARNING
@deprecated(
"0.1.0",
message=AGENT_DEPRECATION_WARNING,
removal="1.0",
)
class AgentType(str, En... |
# Copyright (c) OpenMMLab. All rights reserved.
from typing import Optional, Sequence
from mmengine.data import BaseDataElement
from mmengine.hooks import Hook
from mmengine.runner import Runner
from mmdet.registry import HOOKS
DATA_BATCH = Optional[Sequence[dict]]
@HOOKS.register_module()
class MemoryProfilerHook... | # Copyright (c) OpenMMLab. All rights reserved.
from typing import Optional, Sequence, Union
from mmengine.data import BaseDataElement
from mmengine.hooks import Hook
from mmengine.runner import Runner
from mmdet.registry import HOOKS
@HOOKS.register_module()
class MemoryProfilerHook(Hook):
"""Memory profiler h... |
from llama_index.core.llama_pack.base import BaseLlamaPack
from llama_index.packs.corrective_rag import CorrectiveRAGPack
def test_class():
names_of_base_classes = [b.__name__ for b in CorrectiveRAGPack.__mro__]
assert BaseLlamaPack.__name__ in names_of_base_classes
| from llama_index.core.llama_pack import BaseLlamaPack
from llama_index.packs.corrective_rag import CorrectiveRAGPack
def test_class():
names_of_base_classes = [b.__name__ for b in CorrectiveRAGPack.__mro__]
assert BaseLlamaPack.__name__ in names_of_base_classes
|
"""Logic for selecting examples to include in prompts."""
from typing import TYPE_CHECKING, Any
from langchain_core.example_selectors.length_based import (
LengthBasedExampleSelector,
)
from langchain_core.example_selectors.semantic_similarity import (
MaxMarginalRelevanceExampleSelector,
SemanticSimilari... | """Logic for selecting examples to include in prompts."""
from typing import TYPE_CHECKING, Any
from langchain_core.example_selectors.length_based import (
LengthBasedExampleSelector,
)
from langchain_core.example_selectors.semantic_similarity import (
MaxMarginalRelevanceExampleSelector,
SemanticSimilari... |
from typing import Callable, Optional
from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream
class GeneratorDatasetInputStream(AbstractDatasetInputStream):
def __init__(
self,
generator: Callable,
features: Optional... | from typing import Callable, Optional
from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream
class GeneratorDatasetInputStream(AbstractDatasetInputStream):
def __init__(
self,
generator: Callable,
features: Optional... |
# 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... | from typing_extensions import TYPE_CHECKING
if TYPE_CHECKING:
from rich.console import Console, ConsoleOptions, RenderResult
from rich.measure import Measurement
from docarray.typing.tensor.abstract_tensor import AbstractTensor
class TensorDisplay:
"""
Rich representation of a tensor.
"""
... |
# Copyright (c) OpenMMLab. All rights reserved.
from .hub import load_url
from .manager import ManagerMeta, ManagerMixin
from .misc import (check_prerequisites, concat_list, deprecated_api_warning,
has_batch_norm, has_method, import_modules_from_strings,
is_list_of, is_method_overr... | # Copyright (c) OpenMMLab. All rights reserved.
from .hub import load_url
from .manager import ManagerMeta, ManagerMixin
from .misc import (check_prerequisites, concat_list, deprecated_api_warning,
has_batch_norm, has_method, import_modules_from_strings,
is_list_of, is_method_overr... |
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