input stringlengths 33 5k | output stringlengths 32 5k |
|---|---|
# Copyright (c) OpenMMLab. All rights reserved.
from .anchor import * # noqa: F401, F403
from .bbox import * # noqa: F401, F403
from .data_structures import * # noqa: F401, F403
from .evaluation import * # noqa: F401, F403
from .hook import * # noqa: F401, F403
from .mask import * # noqa: F401, F403
from .optimiz... | # Copyright (c) OpenMMLab. All rights reserved.
from .anchor import * # noqa: F401, F403
from .bbox import * # noqa: F401, F403
from .data_structures import * # noqa: F401, F403
from .evaluation import * # noqa: F401, F403
from .hook import * # noqa: F401, F403
from .mask import * # noqa: F401, F403
from .optimiz... |
from __future__ import annotations
from typing import Iterable
import torch
from torch import Tensor, nn
from sentence_transformers import SentenceTransformer
class MSELoss(nn.Module):
def __init__(self, model: SentenceTransformer) -> None:
"""
Computes the MSE loss between the computed sentenc... | from __future__ import annotations
from typing import Iterable
import torch
from torch import Tensor, nn
from sentence_transformers import SentenceTransformer
class MSELoss(nn.Module):
def __init__(self, model: SentenceTransformer) -> None:
"""
Computes the MSE loss between the computed sentenc... |
__version__ = '0.1.0'
from docarray.array.array import DocumentArray
from docarray.base_document.document import BaseDocument
__all__ = [
'BaseDocument',
'DocumentArray',
]
| __version__ = '0.1.0'
from docarray.array.array import DocumentArray
from docarray.document.document import BaseDocument
from docarray.predefined_document import Audio, Image, Mesh3D, PointCloud3D, Text
__all__ = [
'BaseDocument',
'DocumentArray',
'Image',
'Audio',
'Text',
'Mesh3D',
'Point... |
import os
import numpy as np
import pytest
import torch
from pydantic import parse_obj_as
from docarray.typing import ImageBytes, ImageNdArray, ImageTorchTensor
from docarray.utils._internal.misc import is_tf_available
tf_available = is_tf_available()
if tf_available:
import tensorflow as tf
from docarray.t... | import os
import numpy as np
import pytest
import torch
from pydantic import parse_obj_as
from docarray.typing import ImageNdArray, ImageTorchTensor
from docarray.utils._internal.misc import is_tf_available
tf_available = is_tf_available()
if tf_available:
import tensorflow as tf
from docarray.typing.tensor... |
# Copyright (c) OpenMMLab. All rights reserved.
from .default_scope import DefaultScope
from .registry import Registry, build_from_cfg
from .root import (DATA_SAMPLERS, DATASETS, HOOKS, LOG_PROCESSORS, LOOPS,
METRICS, MODEL_WRAPPERS, MODELS, OPTIMIZER_CONSTRUCTORS,
OPTIMIZERS, PARA... | # Copyright (c) OpenMMLab. All rights reserved.
from .default_scope import DefaultScope
from .registry import Registry, build_from_cfg
from .root import (DATA_SAMPLERS, DATASETS, HOOKS, LOG_PROCESSOR, LOOPS,
METRICS, MODEL_WRAPPERS, MODELS, OPTIMIZER_CONSTRUCTORS,
OPTIMIZERS, PARAM... |
from typing import Optional
import torch
__all__ = [
"version",
"is_available",
"get_max_alg_id",
]
try:
from torch._C import _cusparselt
except ImportError:
_cusparselt = None # type: ignore[assignment]
__cusparselt_version: Optional[int] = None
__MAX_ALG_ID: Optional[int] = None
if _cuspars... | # mypy: allow-untyped-defs
from typing import Optional
import torch
__all__ = [
"version",
"is_available",
"get_max_alg_id",
]
try:
from torch._C import _cusparselt
except ImportError:
_cusparselt = None # type: ignore[assignment]
__cusparselt_version: Optional[int] = None
__MAX_ALG_ID: Option... |
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... |
from typing import TYPE_CHECKING
if TYPE_CHECKING:
from ..providers import ProviderName
from ._base import BaseWebhooksManager
_WEBHOOK_MANAGERS: dict["ProviderName", type["BaseWebhooksManager"]] = {}
# --8<-- [start:load_webhook_managers]
def load_webhook_managers() -> dict["ProviderName", type["BaseWebhoo... | from typing import TYPE_CHECKING
from .compass import CompassWebhookManager
from .github import GithubWebhooksManager
from .slant3d import Slant3DWebhooksManager
if TYPE_CHECKING:
from ..providers import ProviderName
from ._base import BaseWebhooksManager
# --8<-- [start:WEBHOOK_MANAGERS_BY_NAME]
WEBHOOK_MAN... |
# Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import mmengine
from mmengine.utils import digit_version
from .version import __version__, version_info
mmcv_minimum_version = '2.0.0rc4'
mmcv_maximum_version = '2.1.0'
mmcv_version = digit_version(mmcv.__version__)
mmengine_minimum_version = '0.6.0'
mmengi... | # Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import mmengine
from mmengine.utils import digit_version
from .version import __version__, version_info
mmcv_minimum_version = '2.0.0rc4'
mmcv_maximum_version = '2.1.0'
mmcv_version = digit_version(mmcv.__version__)
mmengine_minimum_version = '0.4.0'
mmengi... |
from typing import List, Optional
from torchaudio._internal.module_utils import deprecated
from . import utils
from .common import AudioMetaData
__all__ = [
"AudioMetaData",
"load",
"info",
"save",
"list_audio_backends",
"get_audio_backend",
"set_audio_backend",
]
info = utils.get_info_... | from typing import List, Optional
import torchaudio
from torchaudio._internal.module_utils import deprecated
# TODO: Once legacy global backend is removed, move this to torchaudio.__init__
def _init_backend():
from . import utils
torchaudio.info = utils.get_info_func()
torchaudio.load = utils.get_load_f... |
# Copyright (c) OpenMMLab. All rights reserved.
from ..builder import DETECTORS
from .single_stage import SingleStageDetector
@DETECTORS.register_module()
class YOLOF(SingleStageDetector):
r"""Implementation of `You Only Look One-level Feature
<https://arxiv.org/abs/2103.09460>`_"""
def __init__(self,
... | from ..builder import DETECTORS
from .single_stage import SingleStageDetector
@DETECTORS.register_module()
class YOLOF(SingleStageDetector):
r"""Implementation of `You Only Look One-level Feature
<https://arxiv.org/abs/2103.09460>`_"""
def __init__(self,
backbone,
neck,
... |
# Copyright (c) OpenMMLab. All rights reserved.
from .base_sampler import BaseSampler
from .combined_sampler import CombinedSampler
from .instance_balanced_pos_sampler import InstanceBalancedPosSampler
from .iou_balanced_neg_sampler import IoUBalancedNegSampler
from .mask_pseudo_sampler import MaskPseudoSampler
from .m... | # Copyright (c) OpenMMLab. All rights reserved.
from .base_sampler import BaseSampler
from .combined_sampler import CombinedSampler
from .instance_balanced_pos_sampler import InstanceBalancedPosSampler
from .iou_balanced_neg_sampler import IoUBalancedNegSampler
from .ohem_sampler import OHEMSampler
from .pseudo_sampler... |
import os
from abc import abstractmethod
from unittest import mock
import pytest
from langchain_core.embeddings import Embeddings
from pydantic import SecretStr
from langchain_tests.base import BaseStandardTests
class EmbeddingsTests(BaseStandardTests):
""":private:"""
@property
@abstractmethod
def... | import os
from abc import abstractmethod
from unittest import mock
import pytest
from langchain_core.embeddings import Embeddings
from pydantic import SecretStr
from langchain_tests.base import BaseStandardTests
class EmbeddingsTests(BaseStandardTests):
"""
:private:
"""
@property
@abstractmeth... |
# Copyright (c) OpenMMLab. All rights reserved.
import logging
import random
from typing import List, Optional, Tuple
import numpy as np
import torch
from torch.utils.data import DataLoader
from mmengine.device import is_cuda_available, is_musa_available
from mmengine.dist import get_rank, sync_random_seed
from mmeng... | # Copyright (c) OpenMMLab. All rights reserved.
import logging
import random
from typing import List, Optional, Tuple
import numpy as np
import torch
from torch.utils.data import DataLoader
from mmengine.dist import get_rank, sync_random_seed
from mmengine.logging import print_log
from mmengine.utils import digit_ver... |
# 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... |
from unittest.mock import mock_open, patch
from cryptography.hazmat.primitives.asymmetric import rsa
from cryptography.hazmat.primitives import serialization
from llama_index.llms.cortex.utils import (
generate_sf_jwt,
is_spcs_environment,
get_spcs_base_url,
get_default_spcs_token,
SPCS_TOKEN_PATH... | from unittest.mock import mock_open, patch
from cryptography.hazmat.primitives.asymmetric import rsa
from cryptography.hazmat.primitives import serialization
from llama_index.llms.cortex.utils import (
generate_sf_jwt,
is_spcs_environment,
get_spcs_base_url,
get_default_spcs_token,
SPCS_TOKEN_PATH... |
import pytest
from docarray import DocumentArray, Document
from docarray.array.qdrant import DocumentArrayQdrant
from docarray.array.sqlite import DocumentArraySqlite
from docarray.array.annlite import DocumentArrayAnnlite, AnnliteConfig
from docarray.array.storage.qdrant import QdrantConfig
from docarray.array.storag... | import pytest
from docarray import DocumentArray, Document
from docarray.array.qdrant import DocumentArrayQdrant
from docarray.array.sqlite import DocumentArraySqlite
from docarray.array.annlite import DocumentArrayAnnlite, AnnliteConfig
from docarray.array.storage.qdrant import QdrantConfig
from docarray.array.storag... |
from typing import Any, List, Optional, Union
from pathlib import Path
from llama_index.core.bridge.pydantic import Field, PrivateAttr
from llama_index.core.callbacks import CBEventType, EventPayload
from llama_index.core.instrumentation import get_dispatcher
from llama_index.core.instrumentation.events.rerank import ... | from typing import Any, List, Optional
from llama_index.core.bridge.pydantic import Field, PrivateAttr
from llama_index.core.callbacks import CBEventType, EventPayload
from llama_index.core.instrumentation import get_dispatcher
from llama_index.core.instrumentation.events.rerank import (
ReRankEndEvent,
ReRank... |
from typing import Dict
from jina import Client, Document, DocumentArray, Executor, Flow, requests
ORIGINAL_PARAMS = {'param1': 50, 'param2': 60, 'exec_name': {'param1': 'changed'}}
OVERRIDEN_EXECUTOR1_PARAMS = {
'param1': 'changed',
'param2': 60,
'exec_name': {'param1': 'changed'},
}
class DummyOverrid... | from typing import Dict
from jina import Client, Document, DocumentArray, Executor, Flow, requests
ORIGINAL_PARAMS = {'param1': 50, 'param2': 60, 'exec_name': {'param1': 'changed'}}
OVERRIDEN_EXECUTOR1_PARAMS = {
'param1': 'changed',
'param2': 60,
'exec_name': {'param1': 'changed'},
}
class DummyOverrid... |
"""DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.applications.efficientnet_v2 import (
EfficientNetV2B0 as EfficientNetV2B0,
)
from keras.src.applications.efficientnet_v2 import (
EfficientNetV2B1 as EfficientNetV2B1,
)
from... | """DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.applications.efficientnet_v2 import EfficientNetV2B0
from keras.src.applications.efficientnet_v2 import EfficientNetV2B1
from keras.src.applications.efficientnet_v2 import EfficientNe... |
_base_ = '../cascade_rcnn/cascade-mask-rcnn_r50_fpn_1x_coco.py'
train_pipeline = [
dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}),
dict(
type='InstaBoost',
action_candidate=('normal', 'horizontal', 'skip'),
action_prob=(1, 0, 0),
scale=(0.8, 1.2),
d... | _base_ = '../cascade_rcnn/cascade-mask-rcnn_r50_fpn_1x_coco.py'
train_pipeline = [
dict(
type='LoadImageFromFile',
file_client_args={{_base_.file_client_args}}),
dict(
type='InstaBoost',
action_candidate=('normal', 'horizontal', 'skip'),
action_prob=(1, 0, 0),
sc... |
"""
Class for searching and importing data from OpenAlex.
"""
import logging
from typing import List
import requests
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document
logger = logging.getLogger(__name__)
logger.setLevel(logging.ERROR)
class OpenAlexReader(BaseReader)... | """
Class for searching and importing data from OpenAlex.
"""
import logging
from typing import List
import requests
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document
logger = logging.getLogger(__name__)
logger.setLevel(logging.ERROR)
class OpenAlexReader(BaseReader)... |
# Copyright (c) OpenMMLab. All rights reserved.
from abc import ABCMeta, abstractmethod
class BaseAssigner(metaclass=ABCMeta):
"""Base assigner that assigns boxes to ground truth boxes."""
@abstractmethod
def assign(self, bboxes, gt_bboxes, gt_bboxes_ignore=None, gt_labels=None):
"""Assign boxes ... | from abc import ABCMeta, abstractmethod
class BaseAssigner(metaclass=ABCMeta):
"""Base assigner that assigns boxes to ground truth boxes."""
@abstractmethod
def assign(self, bboxes, gt_bboxes, gt_bboxes_ignore=None, gt_labels=None):
"""Assign boxes to either a ground truth boxes or a negative box... |
from unittest.mock import MagicMock, AsyncMock
import pytest
import sys
from llama_index.readers.web.oxylabs_web.base import OxylabsWebReader
READER_TEST_PARAM = pytest.param(
[
"https://sandbox.oxylabs.io/products/1",
"https://sandbox.oxylabs.io/products/2",
],
{
"parse": True,
... | from unittest.mock import MagicMock, AsyncMock
import pytest
import sys
from llama_index.readers.web.oxylabs_web.base import OxylabsWebReader
READER_TEST_PARAM = pytest.param(
[
"https://sandbox.oxylabs.io/products/1",
"https://sandbox.oxylabs.io/products/2",
],
{
"parse": True,
... |
from pathlib import Path
from typing import Any, BinaryIO, Optional, Union
from torchdata.datapipes.iter import Demultiplexer, Filter, IterDataPipe, IterKeyZipper, LineReader, Mapper
from torchvision.prototype.datasets.utils import Dataset, EncodedImage, HttpResource, OnlineResource
from torchvision.prototype.datasets... | from pathlib import Path
from typing import Any, BinaryIO, Dict, List, Optional, Tuple, Union
from torchdata.datapipes.iter import Demultiplexer, Filter, IterDataPipe, IterKeyZipper, LineReader, Mapper
from torchvision.prototype.datasets.utils import Dataset, EncodedImage, HttpResource, OnlineResource
from torchvision... |
"""Spotify reader."""
from typing import List, Optional
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document
class SpotifyReader(BaseReader):
"""
Spotify Reader.
Read a user's saved albums, tracks, or playlists from Spotify.
"""
def load_data(self,... | """Spotify reader."""
from typing import List, Optional
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document
class SpotifyReader(BaseReader):
"""Spotify Reader.
Read a user's saved albums, tracks, or playlists from Spotify.
"""
def load_data(self, coll... |
import os
import time
import pytest
import subprocess
cur_dir = os.path.dirname(os.path.abspath(__file__))
@pytest.fixture()
def docker_image():
import docker
client = docker.from_env()
client.images.build(path=os.path.join(cur_dir), tag='clitest')
client.close()
yield
time.sleep(2)
cli... | import os
import time
import pytest
import subprocess
cur_dir = os.path.dirname(os.path.abspath(__file__))
@pytest.fixture()
def docker_image():
import docker
client = docker.from_env()
client.images.build(path=os.path.join(cur_dir), tag='clitest')
client.close()
yield
time.sleep(2)
cli... |
# 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__ = [
... | # Copyright (c) OpenMMLab. All rights reserved.
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__ = [
'Hook', 'IterTimerHook', 'DistSamplerSeedHo... |
import pytest
from backend.util.request import pin_url, validate_url
@pytest.mark.parametrize(
"raw_url, trusted_origins, expected_value, should_raise",
[
# Rejected IP ranges
("localhost", [], None, True),
("192.168.1.1", [], None, True),
("127.0.0.1", [], None, True),
... | import pytest
from backend.util.request import validate_url
@pytest.mark.parametrize(
"url, trusted_origins, expected_value, should_raise",
[
# Rejected IP ranges
("localhost", [], None, True),
("192.168.1.1", [], None, True),
("127.0.0.1", [], None, True),
("0.0.0.0",... |
_base_ = './mask-rcnn_r50_fpn_instaboost-4x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='... | _base_ = './mask_rcnn_r50_fpn_instaboost_4x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='... |
# Copyright (c) OpenMMLab. All rights reserved.
__version__ = '3.0.0rc2'
short_version = __version__
def parse_version_info(version_str):
"""Parse a version string into a tuple.
Args:
version_str (str): The version string.
Returns:
tuple[int | str]: The version info, e.g., "1.3.0" is par... | # Copyright (c) OpenMMLab. All rights reserved.
__version__ = '3.0.0rc1'
short_version = __version__
def parse_version_info(version_str):
"""Parse a version string into a tuple.
Args:
version_str (str): The version string.
Returns:
tuple[int | str]: The version info, e.g., "1.3.0" is par... |
# Copyright (c) OpenMMLab. All rights reserved.
from .evaluator import Evaluator
from .metric import BaseMetric
from .utils import get_metric_value
__all__ = ['BaseMetric', 'Evaluator', 'get_metric_value']
| # Copyright (c) OpenMMLab. All rights reserved.
from .base import BaseEvaluator
from .builder import build_evaluator
from .composed_evaluator import ComposedEvaluator
from .utils import get_metric_value
__all__ = [
'BaseEvaluator', 'ComposedEvaluator', 'build_evaluator', 'get_metric_value'
]
|
import pytest
from datasets import Dataset
from torch.utils.data import BatchSampler, ConcatDataset, SequentialSampler
from sentence_transformers.sampler import RoundRobinBatchSampler
DATASET_LENGTH = 25
@pytest.fixture
def dummy_concat_dataset() -> ConcatDataset:
"""
Dummy dataset for testing purposes. The... | import pytest
from datasets import Dataset
from sentence_transformers.sampler import RoundRobinBatchSampler
from torch.utils.data import BatchSampler, SequentialSampler, ConcatDataset
DATASET_LENGTH = 25
@pytest.fixture
def dummy_concat_dataset() -> ConcatDataset:
"""
Dummy dataset for testing purposes. The... |
# 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... | # 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... |
from io import BytesIO
from typing import TYPE_CHECKING, Any, Optional, Tuple, Type, TypeVar
import numpy as np
from pydantic import parse_obj_as
from pydantic.validators import bytes_validator
from docarray.typing.abstract_type import AbstractType
from docarray.typing.proto_register import _register_proto
from docar... | from io import BytesIO
from typing import TYPE_CHECKING, Any, Optional, Tuple, Type, TypeVar
import numpy as np
from pydantic import parse_obj_as
from pydantic.validators import bytes_validator
from docarray.typing.abstract_type import AbstractType
from docarray.typing.proto_register import _register_proto
from docar... |
from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.vectorstores.utils import (
DistanceStrategy,
filter_complex_metadata,
maximal_marginal_relevance,
)
# Create a way to dynamically look up deprecated imports.
# ... | from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.vectorstores.utils import (
DistanceStrategy,
filter_complex_metadata,
maximal_marginal_relevance,
)
# Create a way to dynamically look up deprecated imports.
# ... |
from enum import Enum
from typing import Any, Dict, Iterable
import torch.nn.functional as F
from torch import Tensor, nn
from sentence_transformers.SentenceTransformer import SentenceTransformer
class SiameseDistanceMetric(Enum):
"""The metric for the contrastive loss"""
EUCLIDEAN = lambda x, y: F.pairwis... | from enum import Enum
from typing import Dict, Iterable
import torch.nn.functional as F
from torch import Tensor, nn
from sentence_transformers.SentenceTransformer import SentenceTransformer
class SiameseDistanceMetric(Enum):
"""The metric for the contrastive loss"""
EUCLIDEAN = lambda x, y: F.pairwise_dis... |
from typing import ClassVar, Optional, Union
import torch
import torch.utils.checkpoint
from torch import nn
from transformers.models.paligemma.modeling_paligemma import PaliGemmaForConditionalGeneration
from ...cache_utils import Cache
class NewTaskModelForNewTask(PaliGemmaForConditionalGeneration):
main_inpu... | from typing import ClassVar, Optional, Union
import torch
import torch.utils.checkpoint
from torch import nn
from transformers.models.paligemma.modeling_paligemma import PaliGemmaForConditionalGeneration
from ...cache_utils import Cache
class NewTaskModelForNewTask(PaliGemmaForConditionalGeneration):
main_inpu... |
# Copyright (c) OpenMMLab. All rights reserved.
import os
import os.path as osp
from unittest.mock import Mock, patch
from mmengine.hooks import CheckpointHook
class MockPetrel:
_allow_symlink = False
def __init__(self):
pass
@property
def name(self):
return self.__class__.__name__... | # Copyright (c) OpenMMLab. All rights reserved.
import os
import sys
from tempfile import TemporaryDirectory
from unittest.mock import Mock, patch
from mmengine.hooks import CheckpointHook
sys.modules['file_client'] = sys.modules['mmengine.fileio.file_client']
class MockPetrel:
_allow_symlink = False
def ... |
__copyright__ = "Copyright (c) 2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import subprocess
from typing import List
import numpy as np
import pytest
from jina import Document, DocumentArray, Flow
from ...paddle_image import ImagePaddlehubEncoder
@pytest.mark.parametrize(
'arr_in',
... | __copyright__ = "Copyright (c) 2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
from typing import List
import numpy as np
import pytest
from jina import Flow, Document, DocumentArray
from ...paddle_image import ImagePaddlehubEncoder
@pytest.mark.parametrize('arr_in', [
(np.ones((3, 224, 2... |
_base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
teacher_ckpt = 'https://download.openmmlab.com/mmdetection/v2.0/paa/paa_r50_fpn_1x_coco/paa_r50_fpn_1x_coco_20200821-936edec3.pth' # noqa
model = dict(
type='LAD',
# student
bac... | _base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
teacher_ckpt = 'https://download.openmmlab.com/mmdetection/v2.0/paa/paa_r50_fpn_1x_coco/paa_r50_fpn_1x_coco_20200821-936edec3.pth' # noqa
model = dict(
type='LAD',
# student
bac... |
import json
import logging
from abc import ABC, abstractmethod
from datetime import datetime
from typing import Any, AsyncGenerator, Generator, Generic, TypeVar
from pydantic import BaseModel
from redis.asyncio.client import PubSub as AsyncPubSub
from redis.client import PubSub
from backend.data import redis
from bac... | import json
import logging
from abc import ABC, abstractmethod
from datetime import datetime
from backend.data import redis
from backend.data.execution import ExecutionResult
logger = logging.getLogger(__name__)
class DateTimeEncoder(json.JSONEncoder):
def default(self, o):
if isinstance(o, datetime):
... |
_base_ = [
'../_base_/models/faster-rcnn_r50-caffe-c4.py',
'../_base_/schedules/schedule_1x.py', '../_base_/datasets/voc0712.py',
'../_base_/default_runtime.py'
]
model = dict(roi_head=dict(bbox_head=dict(num_classes=20)))
# dataset settings
train_pipeline = [
dict(
type='LoadImageFromFile',
... | _base_ = [
'../_base_/models/faster_rcnn_r50_caffe_c4.py',
'../_base_/schedules/schedule_1x.py', '../_base_/datasets/voc0712.py',
'../_base_/default_runtime.py'
]
model = dict(roi_head=dict(bbox_head=dict(num_classes=20)))
# dataset settings
train_pipeline = [
dict(
type='LoadImageFromFile',
... |
# Copyright (c) OpenMMLab. All rights reserved.
from .augment_wrappers import AutoAugment, RandAugment
from .colorspace import (AutoContrast, Brightness, Color, ColorTransform,
Contrast, Equalize, Invert, Posterize, Sharpness,
Solarize, SolarizeAdd)
from .formatting imp... | # Copyright (c) OpenMMLab. All rights reserved.
from .augment_wrappers import AutoAugment, RandAugment
from .colorspace import (AutoContrast, Brightness, Color, ColorTransform,
Contrast, Equalize, Invert, Posterize, Sharpness,
Solarize, SolarizeAdd)
from .formatting imp... |
"""Math utils."""
import logging
from typing import List, Optional, Tuple, Union
import numpy as np
logger = logging.getLogger(__name__)
Matrix = Union[List[List[float]], List[np.ndarray], np.ndarray]
def cosine_similarity(X: Matrix, Y: Matrix) -> np.ndarray:
"""Row-wise cosine similarity between two equal-wi... | """Math utils."""
import logging
from typing import List, Optional, Tuple, Union
import numpy as np
logger = logging.getLogger(__name__)
Matrix = Union[List[List[float]], List[np.ndarray], np.ndarray]
def cosine_similarity(X: Matrix, Y: Matrix) -> np.ndarray:
"""Row-wise cosine similarity between two equal-wi... |
import multiprocessing
import pytest
from jina import DocumentArray, Executor, requests
from jina.parsers import set_pod_parser
from jina.serve.runtimes.asyncio import AsyncNewLoopRuntime
from jina.serve.runtimes.worker import WorkerRuntime
from jina.serve.streamer import GatewayStreamer
class StreamerTestExecutor(... | import multiprocessing
import pytest
from jina import DocumentArray, Executor, requests
from jina.parsers import set_pod_parser
from jina.serve.runtimes.asyncio import AsyncNewLoopRuntime
from jina.serve.runtimes.worker import WorkerRuntime
from jina.serve.streamer import GatewayStreamer
class StreamerTestExecutor(... |
import io
from typing import TYPE_CHECKING, Any, Tuple, Type, TypeVar
import numpy as np
from pydantic import parse_obj_as
from pydantic.validators import bytes_validator
from docarray.typing.abstract_type import AbstractType
from docarray.typing.proto_register import _register_proto
from docarray.utils._internal.mis... | import io
from typing import TYPE_CHECKING, Any, Tuple, Type, TypeVar
import numpy as np
from pydantic import parse_obj_as
from pydantic.validators import bytes_validator
from docarray.typing.abstract_type import AbstractType
from docarray.typing.proto_register import _register_proto
if TYPE_CHECKING:
from pydan... |
"""Base interfaces for tracing runs."""
from langchain_core.exceptions import TracerException
from langchain_core.tracers.base import BaseTracer
__all__ = ["BaseTracer", "TracerException"]
| """Base interfaces for tracing runs."""
from langchain_core.tracers.base import BaseTracer, TracerException
__all__ = ["BaseTracer", "TracerException"]
|
import asyncio
import copy
from typing import Any, List, Optional
from jina.serve.gateway import BaseGateway
class CompositeGateway(BaseGateway):
"""GRPC Gateway implementation"""
def __init__(
self,
**kwargs,
):
"""Initialize the gateway
:param kwargs: keyword args
... | import asyncio
import copy
from typing import Any, List, Optional
from jina.serve.gateway import BaseGateway
class CompositeGateway(BaseGateway):
"""GRPC Gateway implementation"""
def __init__(
self,
**kwargs,
):
"""Initialize the gateway
:param kwargs: keyword args
... |
"""
===================================================
Recursive feature elimination with cross-validation
===================================================
A Recursive Feature Elimination (RFE) example with automatic tuning of the
number of features selected with cross-validation.
"""
# Authors: The scikit-learn... | """
===================================================
Recursive feature elimination with cross-validation
===================================================
A Recursive Feature Elimination (RFE) example with automatic tuning of the
number of features selected with cross-validation.
"""
# Authors: The scikit-learn... |
import json
import sys
def format_json_to_md(input_json_file, output_md_file):
with open(input_json_file, encoding="utf-8") as f:
results = json.load(f)
output_md = ["<details>", "<summary>Show updated benchmarks!</summary>", " "]
for benchmark_name in sorted(results):
benchmark_res = re... | import json
import sys
def format_json_to_md(input_json_file, output_md_file):
with open(input_json_file, encoding="utf-8") as f:
results = json.load(f)
output_md = ["<details>", "<summary>Show updated benchmarks!</summary>", " "]
for benchmark_name in sorted(results):
benchmark_res = r... |
import pytest
from absl.testing import parameterized
from keras.src import backend
from keras.src import layers
from keras.src import testing
class IdentityTest(testing.TestCase):
@parameterized.named_parameters(
[
{"testcase_name": "dense", "sparse": False},
{"testcase_name": "sp... | import pytest
from absl.testing import parameterized
from keras.src import backend
from keras.src import layers
from keras.src import testing
class IdentityTest(testing.TestCase, parameterized.TestCase):
@parameterized.named_parameters(
[
{"testcase_name": "dense", "sparse": False},
... |
def check_health_pod(addr: str):
"""check if a pods is healthy
:param addr: the address on which the pod is serving ex : localhost:1234
"""
from jina.serve.runtimes.servers import BaseServer
is_ready = BaseServer.is_ready(addr)
if not is_ready:
raise Exception('Pod is unhealthy')
... | def check_health_pod(addr: str):
"""check if a pods is healthy
:param addr: the address on which the pod is serving ex : localhost:1234
"""
from jina.serve.runtimes.asyncio import AsyncNewLoopRuntime
is_ready = AsyncNewLoopRuntime.is_ready(addr)
if not is_ready:
raise Exception('Pod i... |
# 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/LICENSE-2.0
#
# U... | # 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/LICENSE-2.0
#
# U... |
__copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import subprocess
import numpy as np
import pytest
from executor.audioclip_image import AudioCLIPImageEncoder
from jina import Document, DocumentArray, Flow
@pytest.mark.parametrize("request_size", [1, 10, 50,... | __copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import subprocess
import numpy as np
import pytest
from jina import Document, DocumentArray, Flow
from ...audioclip_image import AudioCLIPImageEncoder
@pytest.mark.parametrize("request_size", [1, 10, 50, 100]... |
"""Lilac reader that loads enriched and labeled Lilac datasets into GPTIndex and LangChain."""
from typing import TYPE_CHECKING, List, Optional
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document
if TYPE_CHECKING:
from lilac import ColumnId, FilterLike, Path
class ... | """Lilac reader that loads enriched and labeled Lilac datasets into GPTIndex and LangChain."""
from typing import TYPE_CHECKING, List, Optional
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document
if TYPE_CHECKING:
from lilac import ColumnId, FilterLike, Path
class L... |
_base_ = './retinanet_r50_fpn_1x_coco.py'
# use caffe img_norm
preprocess_cfg = dict(
mean=[103.530, 116.280, 123.675],
std=[1.0, 1.0, 1.0],
to_rgb=False,
pad_size_divisor=32)
model = dict(
preprocess_cfg=preprocess_cfg,
backbone=dict(
norm_cfg=dict(requires_grad=False),
norm_ev... | _base_ = './retinanet_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
norm_cfg=dict(requires_grad=False),
norm_eval=True,
style='caffe',
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron2/resnet50_caffe')))
# use caffe img_norm
img_norm_cfg... |
# Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
import torch
from ..builder import BBOX_SAMPLERS
from .random_sampler import RandomSampler
@BBOX_SAMPLERS.register_module()
class InstanceBalancedPosSampler(RandomSampler):
"""Instance balanced sampler that samples equal number of positive sample... | import numpy as np
import torch
from ..builder import BBOX_SAMPLERS
from .random_sampler import RandomSampler
@BBOX_SAMPLERS.register_module()
class InstanceBalancedPosSampler(RandomSampler):
"""Instance balanced sampler that samples equal number of positive samples
for each instance."""
def _sample_pos... |
from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.document_transformers import Html2TextTransformer
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handling optio... | from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.document_transformers import Html2TextTransformer
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handling optio... |
from typing import TYPE_CHECKING, Optional, Type
from langchain_core.callbacks import (
CallbackManagerForToolRun,
)
from langchain_core.tools import BaseTool
from pydantic import BaseModel, Field
if TYPE_CHECKING:
# This is for linting and IDE typehints
import multion
else:
try:
# We do this ... | from typing import TYPE_CHECKING, Optional, Type
from langchain_core.callbacks import (
CallbackManagerForToolRun,
)
from langchain_core.tools import BaseTool
from pydantic import BaseModel, Field
if TYPE_CHECKING:
# This is for linting and IDE typehints
import multion
else:
try:
# We do this ... |
from contextlib import nullcontext
from sentence_transformers.evaluation import SentenceEvaluator
from sentence_transformers import SentenceTransformer
from typing import List, Optional, Tuple, Dict
import numpy as np
import logging
import os
import csv
logger = logging.getLogger(__name__)
class MSEEvaluatorFromDat... | from contextlib import nullcontext
from sentence_transformers.evaluation import SentenceEvaluator
from sentence_transformers import SentenceTransformer
from typing import List, Optional, Tuple, Dict
import numpy as np
import logging
import os
import csv
logger = logging.getLogger(__name__)
class MSEEvaluatorFromDat... |
import numpy as np
from absl.testing import parameterized
from tensorflow import data as tf_data
from keras.src import backend
from keras.src import layers
from keras.src import testing
class RandomRotationTest(testing.TestCase):
@parameterized.named_parameters(
("random_rotate_neg4", -0.4),
("ra... | import numpy as np
from absl.testing import parameterized
from tensorflow import data as tf_data
from keras.src import backend
from keras.src import layers
from keras.src import testing
class RandomRotationTest(testing.TestCase, parameterized.TestCase):
@parameterized.named_parameters(
("random_rotate_ne... |
import pytest
from jina import Executor, Flow, requests
from jina.constants import __default_executor__
from tests import random_docs
@pytest.mark.parametrize('protocol', ['websocket', 'grpc', 'http'])
def test_flow(protocol):
docs = random_docs(10)
f = Flow(protocol=protocol).add(name='p1')
with f:
... | import pytest
from jina import Executor, Flow, __default_executor__, requests
from tests import random_docs
@pytest.mark.parametrize('protocol', ['websocket', 'grpc', 'http'])
def test_flow(protocol):
docs = random_docs(10)
f = Flow(protocol=protocol).add(name='p1')
with f:
f.index(docs)
... |
# Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the most common options. For a full
# list see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
# -- Path setup --------------------------------------------------------------
# If ex... | # Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the most common options. For a full
# list see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
# -- Path setup --------------------------------------------------------------
# If ex... |
# Copyright (c) OpenMMLab. All rights reserved.
from typing import Dict
import torch.nn as nn
from torch import Tensor
from mmdet.registry import MODELS
from ..layers import (ConditionalDetrTransformerDecoder,
DetrTransformerEncoder, SinePositionalEncoding)
from .detr import DETR
@MODELS.regis... | # Copyright (c) OpenMMLab. All rights reserved.
from typing import Dict
import torch.nn as nn
from torch import Tensor
from mmdet.registry import MODELS
from ..layers import (ConditionalDetrTransformerDecoder,
DetrTransformerEncoder, SinePositionalEncoding)
from .detr import DETR
@MODELS.regis... |
"""
====================================
How to write your own TVTensor class
====================================
.. note::
Try on `collab <https://colab.research.google.com/github/pytorch/vision/blob/gh-pages/main/_generated_ipynb_notebooks/plot_custom_tv_tensors.ipynb>`_
or :ref:`go to the end <sphx_glr_dow... | """
=====================================
How to write your own TVTensor class
=====================================
.. note::
Try on `collab <https://colab.research.google.com/github/pytorch/vision/blob/gh-pages/main/_generated_ipynb_notebooks/plot_custom_tv_tensors.ipynb>`_
or :ref:`go to the end <sphx_glr_d... |
"""Module to change the configuration of libsox, which is used by I/O functions like
:py:mod:`~torchaudio.backend.sox_io_backend` and :py:mod:`~torchaudio.sox_effects`.
"""
from typing import Dict, List
import torchaudio
sox_ext = torchaudio._extension.lazy_import_sox_ext()
from torchaudio._internal.module_utils im... | """Module to change the configuration of libsox, which is used by I/O functions like
:py:mod:`~torchaudio.backend.sox_io_backend` and :py:mod:`~torchaudio.sox_effects`.
"""
from typing import Dict, List
import torchaudio
sox_ext = torchaudio._extension.lazy_import_sox_ext()
def set_seed(seed: int):
"""Set libs... |
from typing import Dict
from jina.helper import TYPE_CHECKING, T, deprecate_by, typename
if TYPE_CHECKING: # pragma: no cover
from jina.proto import jina_pb2
class ProtoTypeMixin:
"""The base mixin class of all Jina types.
.. note::
- All Jina types should inherit from this class.
- Al... | from typing import Dict
from jina.helper import TYPE_CHECKING, T, deprecate_by, typename
if TYPE_CHECKING: # pragma: no cover
from jina.proto import jina_pb2
class ProtoTypeMixin:
"""The base mixin class of all Jina types.
.. note::
- All Jina types should inherit from this class.
- All... |
"""Standard LangChain interface tests"""
import pytest
from langchain_core.language_models import BaseChatModel
from langchain_tests.unit_tests import ChatModelUnitTests
from pytest_benchmark.fixture import BenchmarkFixture # type: ignore[import-untyped]
from langchain_anthropic import ChatAnthropic
class TestAnth... | """Standard LangChain interface tests"""
from langchain_core.language_models import BaseChatModel
from langchain_tests.unit_tests import ChatModelUnitTests
from langchain_anthropic import ChatAnthropic
class TestAnthropicStandard(ChatModelUnitTests):
@property
def chat_model_class(self) -> type[BaseChatMode... |
# Copyright (c) OpenMMLab. All rights reserved.
from .activations import SiLU
from .bbox_nms import fast_nms, multiclass_nms
from .brick_wrappers import (AdaptiveAvgPool2d, FrozenBatchNorm2d,
adaptive_avg_pool2d)
from .conv_upsample import ConvUpsample
from .csp_layer import CSPLayer
from .... | # 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... |
import torchaudio
from torchaudio_unittest import common_utils
class BackendSwitchMixin:
"""Test set/get_audio_backend works"""
backend = None
backend_module = None
def test_switch(self):
torchaudio.backend.utils.set_audio_backend(self.backend)
if self.backend is None:
as... | from unittest.mock import patch
import torchaudio
from torchaudio_unittest import common_utils
class BackendSwitchMixin:
"""Test set/get_audio_backend works"""
backend = None
backend_module = None
@patch("torchaudio.backend.utils._is_backend_dispatcher_enabled", lambda: False)
def test_switch(s... |
import inspect
import re
from hashlib import sha256
from typing import Dict, List
from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql # noqa F401
from... | import inspect
import re
from hashlib import sha256
from typing import Dict, List
from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql # noqa F401
from... |
"""Types for content blocks."""
from typing import Any, Literal, Union
from pydantic import TypeAdapter, ValidationError
from typing_extensions import NotRequired, TypedDict
class BaseDataContentBlock(TypedDict, total=False):
"""Base class for data content blocks."""
mime_type: NotRequired[str]
"""MIME... | """Types for content blocks."""
from typing import Any, Literal, Union
from pydantic import TypeAdapter, ValidationError
from typing_extensions import NotRequired, TypedDict
class BaseDataContentBlock(TypedDict):
"""Base class for data content blocks."""
mime_type: NotRequired[str]
"""MIME type of the ... |
from typing import TYPE_CHECKING, Optional, Dict
if TYPE_CHECKING:
from ... import DocumentArray
class PostMixin:
"""Helper functions for posting DocumentArray to Jina Flow."""
def post(
self,
host: str,
show_progress: bool = False,
batch_size: Optional[int] = None,
... | from typing import TYPE_CHECKING, Optional, Dict
if TYPE_CHECKING:
from ... import DocumentArray
class PostMixin:
"""Helper functions for posting DocumentArray to Jina Flow."""
def post(
self,
host: str,
show_progress: bool = False,
batch_size: Optional[int] = None,
... |
from .cmuarctic import CMUARCTIC
from .cmudict import CMUDict
from .commonvoice import COMMONVOICE
from .dr_vctk import DR_VCTK
from .fluentcommands import FluentSpeechCommands
from .gtzan import GTZAN
from .iemocap import IEMOCAP
from .librilight_limited import LibriLightLimited
from .librimix import LibriMix
from .li... | from .cmuarctic import CMUARCTIC
from .cmudict import CMUDict
from .commonvoice import COMMONVOICE
from .dr_vctk import DR_VCTK
from .fluentcommands import FluentSpeechCommands
from .gtzan import GTZAN
from .librilight_limited import LibriLightLimited
from .librimix import LibriMix
from .librispeech import LIBRISPEECH
... |
import json
from json import JSONDecodeError
from typing import Union
from langchain_core.agents import AgentAction, AgentActionMessageLog, AgentFinish
from langchain_core.exceptions import OutputParserException
from langchain_core.messages import (
AIMessage,
BaseMessage,
ToolCall,
)
from langchain_core.o... | import json
from json import JSONDecodeError
from typing import Union
from langchain_core.agents import AgentAction, AgentActionMessageLog, AgentFinish
from langchain_core.exceptions import OutputParserException
from langchain_core.messages import (
AIMessage,
BaseMessage,
ToolCall,
)
from langchain_core.o... |
# Copyright (c) OpenMMLab. All rights reserved.
import copy
import torch.nn as nn
from mmcv.cnn import ConvModule, Scale
from mmdet.core import OptMultiConfig
from mmdet.models.dense_heads.fcos_head import FCOSHead
from mmdet.registry import MODELS
@MODELS.register_module()
class NASFCOSHead(FCOSHead):
"""Ancho... | # Copyright (c) OpenMMLab. All rights reserved.
import copy
import torch.nn as nn
from mmcv.cnn import ConvModule, Scale
from mmdet.models.dense_heads.fcos_head import FCOSHead
from mmdet.registry import MODELS
@MODELS.register_module()
class NASFCOSHead(FCOSHead):
"""Anchor-free head used in `NASFCOS <https://... |
import itertools
import warnings
from dataclasses import dataclass
from typing import Optional
import pandas as pd
import pyarrow as pa
import datasets
from datasets.table import table_cast
@dataclass
class PandasConfig(datasets.BuilderConfig):
"""BuilderConfig for Pandas."""
features: Optional[datasets.Fe... | import itertools
import warnings
from dataclasses import dataclass
from typing import Optional
import pandas as pd
import pyarrow as pa
import datasets
from datasets.table import table_cast
@dataclass
class PandasConfig(datasets.BuilderConfig):
"""BuilderConfig for Pandas."""
features: Optional[datasets.Fe... |
# Copyright (c) OpenMMLab. All rights reserved.
from typing import Optional, Union
from mmengine.registry import HOOKS
from .hook import Hook
DATA_BATCH = Optional[Union[dict, tuple, list]]
@HOOKS.register_module()
class ParamSchedulerHook(Hook):
"""A hook to update some hyper-parameters in optimizer, e.g., lea... | # Copyright (c) OpenMMLab. All rights reserved.
from typing import Optional, Sequence
from mmengine.registry import HOOKS
from .hook import Hook
DATA_BATCH = Optional[Sequence[dict]]
@HOOKS.register_module()
class ParamSchedulerHook(Hook):
"""A hook to update some hyper-parameters in optimizer, e.g., learning r... |
# coding=utf-8
# Copyright 2020 The Google AI Language Team Authors, Allegro.pl, Facebook Inc. and the HuggingFace Inc. team.
#
# 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://ww... | # coding=utf-8
# Copyright 2020 The Google AI Language Team Authors, Allegro.pl, Facebook Inc. and the HuggingFace Inc. team.
#
# 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://ww... |
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 os
from typing import Dict
from hubble.executor.helper import is_valid_docker_uri, parse_hub_uri
from hubble.executor.hubio import HubIO
from jina.constants import (
__default_composite_gateway__,
__default_executor__,
__default_grpc_gateway__,
__default_http_gateway__,
__default_websocket_... | import os
from typing import Dict
from hubble.executor.helper import is_valid_docker_uri, parse_hub_uri
from hubble.executor.hubio import HubIO
from jina import (
__default_composite_gateway__,
__default_executor__,
__default_grpc_gateway__,
__default_http_gateway__,
__default_websocket_gateway__,... |
import asyncio
from typing import Any, AsyncGenerator, List, Optional
from llama_index.core.workflow.context import Context
from llama_index.core.workflow.errors import WorkflowDone
from llama_index.core.workflow.events import Event, StopEvent
from .types import RunResultT
from .utils import BUSY_WAIT_DELAY
class W... | import asyncio
from typing import Any, AsyncGenerator, List, Optional
from llama_index.core.workflow.context import Context
from llama_index.core.workflow.errors import WorkflowDone
from llama_index.core.workflow.events import Event, StopEvent
from .types import RunResultT
from .utils import BUSY_WAIT_DELAY
class W... |
"""Utilities for the XGBoost Dask interface."""
import logging
from typing import TYPE_CHECKING, Any, Dict
LOGGER = logging.getLogger("[xgboost.dask]")
if TYPE_CHECKING:
import distributed
def get_n_threads(local_param: Dict[str, Any], worker: "distributed.Worker") -> int:
"""Get the number of threads fro... | """Utilities for the XGBoost Dask interface."""
import logging
from typing import TYPE_CHECKING, Any, Dict
LOGGER = logging.getLogger("[xgboost.dask]")
if TYPE_CHECKING:
import distributed
def get_n_threads(local_param: Dict[str, Any], worker: "distributed.Worker") -> int:
"""Get the number of threads from... |
# Copyright (c) OpenMMLab. All rights reserved.
from typing import Union
from ..registry import EVALUATORS
from .base import BaseEvaluator
from .composed_evaluator import ComposedEvaluator
def build_evaluator(
cfg: Union[dict, list]) -> Union[BaseEvaluator, ComposedEvaluator]:
"""Build function of evalua... | # Copyright (c) OpenMMLab. All rights reserved.
from typing import Optional, Union
from ..registry import EVALUATORS
from .base import BaseEvaluator
from .composed_evaluator import ComposedEvaluator
def build_evaluator(
cfg: Union[dict, list],
default_scope: Optional[str] = None
) -> Union[BaseEvaluator, Com... |
"""
Top-level module of Jina.
The primary function of this module is to import all of the public Jina
interfaces into a single place. The interfaces themselves are located in
sub-modules, as described below.
"""
import os as _os
import platform as _platform
import signal as _signal
import sys as _sys
import warnings... | """
Top-level module of Jina.
The primary function of this module is to import all of the public Jina
interfaces into a single place. The interfaces themselves are located in
sub-modules, as described below.
"""
import os as _os
import platform as _platform
import signal as _signal
import sys as _sys
import warnings... |
import numpy as np
import pytest
from hnswlib_searcher import HnswlibSearcher
from jina import Document, DocumentArray, Flow
_DIM = 10
@pytest.mark.parametrize('uses', ['HnswlibSearcher', 'docker://hnswlibsearcher'])
def test_index_search_flow(uses: str, build_docker_image: str):
f = Flow().add(uses=uses, uses_w... | import numpy as np
import pytest
from hnswlib_searcher import HnswlibSearcher
from jina import Document, DocumentArray, Flow
_DIM = 10
@pytest.mark.parametrize('uses', ['HnswlibSearcher', 'docker://hnswlibsearcher'])
def test_index_search_flow(uses: str, build_docker_image: str):
f = Flow().add(uses=uses, uses_w... |
"""DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.quantizers import deserialize as deserialize
from keras.src.quantizers import get as get
from keras.src.quantizers import serialize as serialize
from keras.src.quantizers.quantizers i... | """DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.quantizers import deserialize as deserialize
from keras.src.quantizers import get as get
from keras.src.quantizers import serialize as serialize
from keras.src.quantizers.quantizers i... |
# Copyright (c) OpenMMLab. All rights reserved.
from .base_tracker import BaseTracker
from .byte_tracker import ByteTracker
from .quasi_dense_tracker import QuasiDenseTracker
from .sort_tracker import SORTTracker
__all__ = ['BaseTracker', 'ByteTracker', 'QuasiDenseTracker', 'SORTTracker']
| # Copyright (c) OpenMMLab. All rights reserved.
from .base_tracker import BaseTracker
from .byte_tracker import ByteTracker
from .quasi_dense_tracker import QuasiDenseTracker
__all__ = ['BaseTracker', 'ByteTracker', 'QuasiDenseTracker']
|
_base_ = '../fcos/fcos_r50_caffe_fpn_gn-head_4x4_1x_coco.py'
model = dict(
data_preprocessor=dict(
mean=[103.53, 116.28, 123.675],
std=[57.375, 57.12, 58.395],
bgr_to_rgb=False),
backbone=dict(
_delete_=True,
type='HRNet',
extra=dict(
stage1=dict(
... | _base_ = '../fcos/fcos_r50_caffe_fpn_gn-head_4x4_1x_coco.py'
model = dict(
backbone=dict(
_delete_=True,
type='HRNet',
extra=dict(
stage1=dict(
num_modules=1,
num_branches=1,
block='BOTTLENECK',
num_blocks=(4, ),
... |
# Copyright (c) OpenMMLab. All rights reserved.
"""MMDetection provides 17 registry nodes to support using modules across
projects. Each node is a child of the root registry in MMEngine.
More details can be found at
https://mmengine.readthedocs.io/en/latest/tutorials/registry.html.
"""
from mmengine.registry import D... | # Copyright (c) OpenMMLab. All rights reserved.
"""MMDetection provides 17 registry nodes to support using modules across
projects. Each node is a child of the root registry in MMEngine.
More details can be found at
https://mmengine.readthedocs.io/en/latest/tutorials/registry.html.
"""
from mmengine.registry import D... |
"""
Gcs file and directory reader.
A loader that fetches a file or iterates through a directory on Gcs.
"""
from typing import Dict, List, Optional, Union
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document
from llama_index.readers.opendal.base import OpendalReader
cl... | """Gcs file and directory reader.
A loader that fetches a file or iterates through a directory on Gcs.
"""
from typing import Dict, List, Optional, Union
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document
from llama_index.readers.opendal.base import OpendalReader
cla... |
# 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... |
"""
This file is part of the private API. Please do not use directly these classes as they will be modified on
future versions without warning. The classes should be accessed only via the transforms argument of Weights.
"""
from typing import List, Optional, Tuple, Union
import PIL.Image
import torch
from torch impor... | """
This file is part of the private API. Please do not use directly these classes as they will be modified on
future versions without warning. The classes should be accessed only via the transforms argument of Weights.
"""
from typing import List, Optional, Tuple, Union
import PIL.Image
import torch
from torch impor... |
from typing import TYPE_CHECKING, Any, Type, TypeVar, Union, cast
import numpy as np
from docarray.typing.tensor.image.abstract_image_tensor import AbstractImageTensor
from docarray.typing.tensor.image.image_ndarray import ImageNdArray
from docarray.typing.tensor.tensor import AnyTensor
from docarray.utils._internal.... | from typing import TYPE_CHECKING, Any, Type, TypeVar, Union, cast
import numpy as np
from docarray.typing.tensor.image.abstract_image_tensor import AbstractImageTensor
from docarray.typing.tensor.image.image_ndarray import ImageNdArray
from docarray.typing.tensor.tensor import AnyTensor
from docarray.utils._internal.... |
"""Module for parsing text files.."""
from typing import Iterator
from langchain_core.documents import Document
from langchain_community.document_loaders.base import BaseBlobParser
from langchain_community.document_loaders.blob_loaders import Blob
class TextParser(BaseBlobParser):
"""Parser for text blobs."""
... | """Module for parsing text files.."""
from typing import Iterator
from langchain_core.documents import Document
from langchain_community.document_loaders.base import BaseBlobParser
from langchain_community.document_loaders.blob_loaders import Blob
class TextParser(BaseBlobParser):
"""Parser for text blobs."""
... |
from docarray.index.backends.elastic import ElasticV7DocIndex
from docarray.index.backends.hnswlib import HnswDocumentIndex
__all__ = ['HnswDocumentIndex', 'ElasticV7DocIndex']
| from docarray.index.backends.hnswlib import HnswDocumentIndex
__all__ = ['HnswDocumentIndex']
|
import logging
from datasets import load_dataset
from sentence_transformers.sparse_encoder import (
MLMTransformer,
SparseEncoder,
SparseTripletEvaluator,
SpladePooling,
)
logging.basicConfig(format="%(asctime)s - %(message)s", datefmt="%Y-%m-%d %H:%M:%S", level=logging.INFO)
# Initialize the SPLADE... | from datasets import load_dataset
from sentence_transformers.sparse_encoder import (
MLMTransformer,
SparseEncoder,
SparseTripletEvaluator,
SpladePooling,
)
# Initialize the SPLADE model
model_name = "naver/splade-cocondenser-ensembledistil"
model = SparseEncoder(
modules=[
MLMTransformer(... |
from typing import TYPE_CHECKING, Any, Type, TypeVar, Union, cast
import numpy as np
from docarray.typing.tensor.image.abstract_image_tensor import AbstractImageTensor
from docarray.typing.tensor.image.image_ndarray import ImageNdArray
from docarray.typing.tensor.tensor import AnyTensor
from docarray.utils._internal.... | from typing import Union
from docarray.typing.tensor.image.image_ndarray import ImageNdArray
from docarray.utils._internal.misc import is_tf_available, is_torch_available
torch_available = is_torch_available()
if torch_available:
from docarray.typing.tensor.image.image_torch_tensor import ImageTorchTensor
tf_av... |
import importlib.util
import os
import warnings
from functools import wraps
from typing import Optional
def eval_env(var, default):
"""Check if environment varable has True-y value"""
if var not in os.environ:
return default
val = os.environ.get(var, "0")
trues = ["1", "true", "TRUE", "on", "... | import importlib.util
import warnings
from functools import wraps
from typing import Optional
def is_module_available(*modules: str) -> bool:
r"""Returns if a top-level module with :attr:`name` exists *without**
importing it. This is generally safer than try-catch block around a
`import X`. It avoids thir... |
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