input stringlengths 33 5k | output stringlengths 32 5k |
|---|---|
from .filtering import (
allpass_biquad,
band_biquad,
bandpass_biquad,
bandreject_biquad,
bass_biquad,
biquad,
contrast,
dcshift,
deemph_biquad,
dither,
equalizer_biquad,
filtfilt,
flanger,
gain,
highpass_biquad,
lfilter,
lowpass_biquad,
overdrive,... | from .filtering import (
allpass_biquad,
band_biquad,
bandpass_biquad,
bandreject_biquad,
bass_biquad,
biquad,
contrast,
dcshift,
deemph_biquad,
dither,
equalizer_biquad,
filtfilt,
flanger,
gain,
highpass_biquad,
lfilter,
lowpass_biquad,
overdrive,... |
from ._optical_flow import FlyingChairs, FlyingThings3D, HD1K, KittiFlow, Sintel
from ._stereo_matching import (
CarlaStereo,
CREStereo,
ETH3DStereo,
FallingThingsStereo,
InStereo2k,
Kitti2012Stereo,
Kitti2015Stereo,
Middlebury2014Stereo,
SceneFlowStereo,
SintelStereo,
)
from .ca... | from ._optical_flow import FlyingChairs, FlyingThings3D, HD1K, KittiFlow, Sintel
from ._stereo_matching import (
CarlaStereo,
CREStereo,
ETH3DStereo,
FallingThingsStereo,
InStereo2k,
Kitti2012Stereo,
Kitti2015Stereo,
Middlebury2014Stereo,
SceneFlowStereo,
SintelStereo,
)
from .ca... |
import numpy as np
import pytest
from keras.src import backend
from keras.src import layers
from keras.src import testing
def squared_l2_norm(x):
x = backend.convert_to_numpy(x)
return np.sum(x**2)
class UnitNormalizationTest(testing.TestCase):
@pytest.mark.requires_trainable_backend
def test_un_ba... | import numpy as np
import pytest
from keras.src import backend
from keras.src import layers
from keras.src import testing
def squared_l2_norm(x):
x = backend.convert_to_numpy(x)
return np.sum(x**2)
class UnitNormalizationTest(testing.TestCase):
@pytest.mark.requires_trainable_backend
def test_un_ba... |
_base_ = './yolov3_d53_8xb8-ms-608-273e_coco.py'
# dataset settings
# file_client_args = dict(
# backend='petrel',
# path_mapping=dict({
# './data/': 's3://openmmlab/datasets/detection/',
# 'data/': 's3://openmmlab/datasets/detection/'
# }))
file_client_args = dict(backend='disk')
train_pip... | _base_ = './yolov3_d53_mstrain-608_273e_coco.py'
# dataset settings
# file_client_args = dict(
# backend='petrel',
# path_mapping=dict({
# './data/': 's3://openmmlab/datasets/detection/',
# 'data/': 's3://openmmlab/datasets/detection/'
# }))
file_client_args = dict(backend='disk')
train_pip... |
import torch
from torchvision.prototype import datapoints
from torchvision.utils import _log_api_usage_once
from ._utils import is_simple_tensor
def uniform_temporal_subsample_video(video: torch.Tensor, num_samples: int, temporal_dim: int = -4) -> torch.Tensor:
# Reference: https://github.com/facebookresearch/... | import torch
from torchvision.prototype import datapoints
from torchvision.utils import _log_api_usage_once
def uniform_temporal_subsample_video(video: torch.Tensor, num_samples: int, temporal_dim: int = -4) -> torch.Tensor:
# Reference: https://github.com/facebookresearch/pytorchvideo/blob/a0a131e/pytorchvideo... |
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 enum import Enum
from typing import Iterable, Dict
import torch.nn.functional as F
from torch import nn, Tensor
from sentence_transformers.SentenceTransformer import SentenceTransformer
class SiameseDistanceMetric(Enum):
"""
The metric for the contrastive loss
"""
EUCLIDEAN = lambda x, y: F.pair... |
import pytest
from xgboost import testing as tm
class TestPlotting:
@pytest.mark.skipif(**tm.no_multiple(tm.no_matplotlib(), tm.no_graphviz()))
def test_categorical(self) -> None:
from xgboost.testing.plotting import run_categorical
run_categorical("hist", "cuda")
| import sys
import pytest
from xgboost import testing as tm
sys.path.append("tests/python")
import test_plotting as tp
pytestmark = pytest.mark.skipif(**tm.no_multiple(tm.no_matplotlib(), tm.no_graphviz()))
class TestPlotting:
cputest = tp.TestPlotting()
@pytest.mark.skipif(**tm.no_pandas())
def test_... |
import numpy as np
from absl.testing import parameterized
from keras.src import backend
from keras.src import dtype_policies
from keras.src import layers
from keras.src import testing
class ZeroPadding3DTest(testing.TestCase):
@parameterized.parameters(
{"data_format": "channels_first"}, {"data_format": ... | import numpy as np
from absl.testing import parameterized
from keras.src import backend
from keras.src import dtype_policies
from keras.src import layers
from keras.src import testing
class ZeroPadding3DTest(testing.TestCase, parameterized.TestCase):
@parameterized.parameters(
{"data_format": "channels_f... |
from typing import Dict
def get_default_metas() -> Dict:
"""
Get a copy of default meta variables.
NOTE: DO NOT ADD MORE ENTRIES HERE!
:return: a deep copy of the default metas in a new dict
"""
# NOTE: DO NOT ADD MORE ENTRIES HERE!
return {
'name': '', #: a string, the name of... | from typing import Dict
def get_default_metas() -> Dict:
"""
Get a copy of default meta variables.
NOTE: DO NOT ADD MORE ENTRIES HERE!
:return: a deep copy of the default metas in a new dict
"""
# NOTE: DO NOT ADD MORE ENTRIES HERE!
return {
'name': '', #: a string, the name of... |
import itertools
from dataclasses import dataclass
from typing import List, Optional
import pyarrow as pa
import pyarrow.parquet as pq
import datasets
from datasets.table import table_cast
logger = datasets.utils.logging.get_logger(__name__)
@dataclass
class ParquetConfig(datasets.BuilderConfig):
"""BuilderCo... | import itertools
from dataclasses import dataclass
from typing import List, Optional
import pyarrow as pa
import pyarrow.parquet as pq
import datasets
from datasets.table import table_cast
logger = datasets.utils.logging.get_logger(__name__)
@dataclass
class ParquetConfig(datasets.BuilderConfig):
"""BuilderCo... |
from collections import namedtuple
from typing import TYPE_CHECKING, Dict, NamedTuple, Optional
from urllib.parse import urlparse
if TYPE_CHECKING:
from docarray import DocumentArray
_ParsedHost = namedtuple('ParsedHost', 'on host port version scheme')
def _parse_host(host: str) -> NamedTuple:
"""Parse a h... | from collections import namedtuple
from typing import TYPE_CHECKING, Dict, NamedTuple, Optional
from urllib.parse import urlparse
if TYPE_CHECKING:
from docarray import DocumentArray
_ParsedHost = namedtuple('ParsedHost', 'on host port version scheme')
def _parse_host(host: str) -> NamedTuple:
"""Parse a h... |
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
... | 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
... |
# Copyright (c) OpenMMLab. All rights reserved.
__version__ = '0.8.0'
def parse_version_info(version_str):
"""Parse the version information.
Args:
version_str (str): version string like '0.1.0'.
Returns:
tuple: version information contains major, minor, micro version.
"""
versio... | # Copyright (c) OpenMMLab. All rights reserved.
__version__ = '0.7.4'
def parse_version_info(version_str):
"""Parse the version information.
Args:
version_str (str): version string like '0.1.0'.
Returns:
tuple: version information contains major, minor, micro version.
"""
versio... |
"""
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... |
# Copyright (c) OpenMMLab. All rights reserved.
import warnings
from abc import ABCMeta, abstractmethod
from typing import Any, List, Optional, Sequence, Union
from mmengine.dist import (broadcast_object_list, collect_results,
is_main_process)
class BaseMetric(metaclass=ABCMeta):
"""Ba... | # Copyright (c) OpenMMLab. All rights reserved.
import warnings
from abc import ABCMeta, abstractmethod
from typing import Any, List, Optional, Sequence, Union
from mmengine.dist import (broadcast_object_list, collect_results,
is_main_process)
class BaseMetric(metaclass=ABCMeta):
"""Ba... |
from collections.abc import Sequence
from typing import Callable
from langchain_core.agents import AgentAction
from langchain_core.language_models import BaseLanguageModel
from langchain_core.messages import BaseMessage
from langchain_core.prompts.chat import ChatPromptTemplate
from langchain_core.runnables import Run... | from collections.abc import Sequence
from typing import Callable
from langchain_core.agents import AgentAction
from langchain_core.language_models import BaseLanguageModel
from langchain_core.messages import BaseMessage
from langchain_core.prompts.chat import ChatPromptTemplate
from langchain_core.runnables import Run... |
"""Message responsible for deleting other messages."""
from typing import Any, Literal
from langchain_core.messages.base import BaseMessage
class RemoveMessage(BaseMessage):
"""Message responsible for deleting other messages."""
type: Literal["remove"] = "remove"
"""The type of the message (used for se... | from typing import Any, Literal
from langchain_core.messages.base import BaseMessage
class RemoveMessage(BaseMessage):
"""Message responsible for deleting other messages."""
type: Literal["remove"] = "remove"
"""The type of the message (used for serialization). Defaults to "remove"."""
def __init__... |
# Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
from typing import Optional, Sequence, Tuple
import cv2
import numpy as np
from mmengine.hooks import Hook
from mmengine.registry import HOOKS
from mmengine.structures import BaseDataElement
from mmengine.utils.dl_utils import tensor2imgs
# TODO:... | # Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
from typing import Optional, Sequence, Tuple
import cv2
import numpy as np
from mmengine.data import BaseDataElement
from mmengine.hooks import Hook
from mmengine.registry import HOOKS
from mmengine.utils.misc import tensor2imgs
# TODO: Due to in... |
import numpy as np
import pytest
from docarray import DocumentArray
def test_embedding_ops_error():
da = DocumentArray.empty(100)
db = DocumentArray.empty(100)
da.embeddings = np.random.random([100, 256])
da[2].embedding = None
da[3].embedding = None
with pytest.raises(ValueError, match='[2... | import numpy as np
import pytest
from docarray import DocumentArray
def test_embedding_ops_error():
da = DocumentArray.empty(100)
db = DocumentArray.empty(100)
da.embeddings = np.random.random([100, 256])
da[2].embedding = None
da[3].embedding = None
with pytest.raises(ValueError, match='[2... |
# Copyright 2025 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writ... | # Copyright 2025 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writ... |
import logging
from datasets import load_dataset
from sentence_transformers import SparseEncoder
from sentence_transformers.sparse_encoder.evaluation import SparseMSEEvaluator
logging.basicConfig(format="%(message)s", level=logging.INFO)
# Load a model
student_model = SparseEncoder("prithivida/Splade_PP_en_v1")
tea... | import logging
from datasets import load_dataset
from sentence_transformers import SparseEncoder
from sentence_transformers.sparse_encoder.evaluation import SparseMSEEvaluator
logging.basicConfig(format="%(message)s", level=logging.INFO)
# Load a model
student_model = SparseEncoder("prithivida/Splade_PP_en_v1")
tea... |
__copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
from pathlib import Path
from typing import List
import pytest
from jina import Document, DocumentArray, Executor
from spacy_text_encoder import SpacyTextEncoder
_EMBEDDING_DIM = 96
@pytest.fixture(scope='ses... | __copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
from pathlib import Path
from typing import List
import pytest
from jina import Document, DocumentArray, Executor
from spacy_text_encoder import SpacyTextEncoder
_EMBEDDING_DIM = 96
@pytest.fixture(scope='ses... |
import copy
from typing import Any, Dict, List, Tuple
_SPECIFIC_EXECUTOR_SEPARATOR = '__'
def _spit_key_and_executor_name(key_name: str) -> Tuple[str]:
"""Split a specific key into a key, name pair
ex: 'key__my_executor' will be split into 'key', 'my_executor'
:param key_name: key name of the param
... | import copy
from typing import Dict, Tuple
from jina.serve.runtimes.request_handlers.data_request_handler import DataRequestHandler
_SPECIFIC_EXECUTOR_SEPARATOR = '__'
def _spit_key_and_executor_name(key_name: str) -> Tuple[str]:
"""Split a specific key into a key, name pair
ex: 'key__my_executor' will be ... |
from __future__ import annotations
import pytest
from sentence_transformers import SentenceTransformer, SentenceTransformerTrainer
from sentence_transformers.util import is_training_available
@pytest.mark.parametrize(
("revision", "expected_base_revision"),
[
("f3cb857cba53019a20df283396bcca179cf051... | from __future__ import annotations
import pytest
from sentence_transformers import SentenceTransformer, SentenceTransformerTrainer
@pytest.mark.parametrize(
("revision", "expected_base_revision"),
[
("f3cb857cba53019a20df283396bcca179cf051a4", "f3cb857cba53019a20df283396bcca179cf051a4"),
("f... |
import itertools
from parameterized import parameterized
from torchaudio.backend import sox_io_backend
from torchaudio_unittest.common_utils import (
get_wav_data,
PytorchTestCase,
skipIfNoExec,
skipIfNoSox,
TempDirMixin,
)
from .common import get_enc_params, name_func
@skipIfNoExec("sox")
@skip... | import itertools
from parameterized import parameterized
from torchaudio.backend import sox_io_backend
from torchaudio_unittest.common_utils import (
TempDirMixin,
PytorchTestCase,
skipIfNoExec,
skipIfNoSox,
get_wav_data,
)
from .common import (
name_func,
get_enc_params,
)
@skipIfNoExec... |
_base_ = '../dcn/cascade-mask-rcnn_x101-32x4d-dconv-c3-c5_fpn_1x_coco.py'
model = dict(
backbone=dict(
norm_cfg=dict(type='SyncBN', requires_grad=True),
norm_eval=False,
plugins=[
dict(
cfg=dict(type='ContextBlock', ratio=1. / 4),
stages=(False, Tr... | _base_ = '../dcn/cascade_mask_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco.py'
model = dict(
backbone=dict(
norm_cfg=dict(type='SyncBN', requires_grad=True),
norm_eval=False,
plugins=[
dict(
cfg=dict(type='ContextBlock', ratio=1. / 4),
stages=(False, Tr... |
from __future__ import annotations
import logging
from typing import TYPE_CHECKING, Any, Literal
from sentence_transformers.evaluation import TripletEvaluator
if TYPE_CHECKING:
import numpy as np
from torch import Tensor
from sentence_transformers.similarity_functions import SimilarityFunction
from ... | from __future__ import annotations
import logging
from typing import TYPE_CHECKING, Any, Literal
from sentence_transformers.evaluation import TripletEvaluator
if TYPE_CHECKING:
import numpy as np
from torch import Tensor
from sentence_transformers.similarity_functions import SimilarityFunction
from ... |
from __future__ import annotations
import json
import os
from typing import Callable
import torch
from safetensors.torch import load_model as load_safetensors_model
from safetensors.torch import save_model as save_safetensors_model
from torch import Tensor, nn
from sentence_transformers.util import fullname, import_... | from __future__ import annotations
import json
import os
import torch
from safetensors.torch import load_model as load_safetensors_model
from safetensors.torch import save_model as save_safetensors_model
from torch import Tensor, nn
from sentence_transformers.util import fullname, import_from_string
class Dense(nn... |
import numpy as np
from docarray import BaseDocument, DocumentArray, Image, Text
def test_multi_modal_doc():
class MyMultiModalDoc(BaseDocument):
image: Image
text: Text
doc = MyMultiModalDoc(
image=Image(tensor=np.zeros((3, 224, 224))), text=Text(text='hello')
)
assert isin... | import numpy as np
from docarray import Document, DocumentArray, Image, Text
def test_multi_modal_doc():
class MyMultiModalDoc(Document):
image: Image
text: Text
doc = MyMultiModalDoc(
image=Image(tensor=np.zeros((3, 224, 224))), text=Text(text='hello')
)
assert isinstance(d... |
# Copyright (c) OpenMMLab. All rights reserved.
from .brick_wrappers import AdaptiveAvgPool2d, adaptive_avg_pool2d
from .builder import build_linear_layer, build_transformer
from .ckpt_convert import pvt_convert
from .conv_upsample import ConvUpsample
from .csp_layer import CSPLayer
from .gaussian_target import gaussia... | # Copyright (c) OpenMMLab. All rights reserved.
from .brick_wrappers import AdaptiveAvgPool2d, adaptive_avg_pool2d
from .builder import build_linear_layer, build_transformer
from .ckpt_convert import pvt_convert
from .conv_upsample import ConvUpsample
from .csp_layer import CSPLayer
from .gaussian_target import gaussia... |
from __future__ import annotations
import torch
import transformers
from PIL import Image
from torch import nn
class CLIPModel(nn.Module):
save_in_root: bool = True
def __init__(self, model_name: str = "openai/clip-vit-base-patch32", processor_name=None) -> None:
super().__init__()
if proce... | from __future__ import annotations
import torch
import transformers
from PIL import Image
from torch import nn
class CLIPModel(nn.Module):
def __init__(self, model_name: str = "openai/clip-vit-base-patch32", processor_name=None) -> None:
super().__init__()
if processor_name is None:
... |
"""Test Tongyi API wrapper."""
from langchain_core.outputs import LLMResult
from langchain_community.llms.tongyi import Tongyi
def test_tongyi_call() -> None:
"""Test valid call to tongyi."""
llm = Tongyi()
output = llm.invoke("who are you")
assert isinstance(output, str)
def test_tongyi_generate(... | """Test Tongyi API wrapper."""
from langchain_core.outputs import LLMResult
from langchain_community.llms.tongyi import Tongyi
def test_tongyi_call() -> None:
"""Test valid call to tongyi."""
llm = Tongyi() # type: ignore[call-arg]
output = llm.invoke("who are you")
assert isinstance(output, str)
... |
# Copyright (c) OpenMMLab. All rights reserved.
from .atss import ATSS
from .autoassign import AutoAssign
from .base import BaseDetector
from .cascade_rcnn import CascadeRCNN
from .centernet import CenterNet
from .cornernet import CornerNet
from .deformable_detr import DeformableDETR
from .detr import DETR
from .fast_r... | # Copyright (c) OpenMMLab. All rights reserved.
from .atss import ATSS
from .autoassign import AutoAssign
from .base import BaseDetector
from .cascade_rcnn import CascadeRCNN
from .centernet import CenterNet
from .cornernet import CornerNet
from .deformable_detr import DeformableDETR
from .detr import DETR
from .fast_r... |
# Copyright (c) OpenMMLab. All rights reserved.
from .auto_augment import (AutoAugment, BrightnessTransform, ColorTransform,
ContrastTransform, EqualizeTransform, Rotate, Shear,
Translate)
from .compose import Compose
from .formatting import (Collect, DefaultFormatB... | # Copyright (c) OpenMMLab. All rights reserved.
from .auto_augment import (AutoAugment, BrightnessTransform, ColorTransform,
ContrastTransform, EqualizeTransform, Rotate, Shear,
Translate)
from .compose import Compose
from .formatting import (Collect, DefaultFormatB... |
import logging
import random
from datasets import load_dataset
from sentence_transformers.sparse_encoder import (
MLMTransformer,
SparseEncoder,
SparseInformationRetrievalEvaluator,
SpladePooling,
)
logging.basicConfig(format="%(asctime)s - %(message)s", datefmt="%Y-%m-%d %H:%M:%S", level=logging.INF... | import random
from datasets import load_dataset
from sentence_transformers.sparse_encoder import (
MLMTransformer,
SparseEncoder,
SparseInformationRetrievalEvaluator,
SpladePooling,
)
# Initialize the SPLADE model
model_name = "naver/splade-cocondenser-ensembledistil"
model = SparseEncoder(
modul... |
from langchain_core.documents import Document
from langchain_core.retrievers import BaseRetriever
class SequentialRetriever(BaseRetriever):
"""Test util that returns a sequence of documents"""
sequential_responses: list[list[Document]]
response_index: int = 0
def _get_relevant_documents( # type: ig... | from langchain_core.documents import Document
from langchain_core.retrievers import BaseRetriever
class SequentialRetriever(BaseRetriever):
"""Test util that returns a sequence of documents"""
sequential_responses: list[list[Document]]
response_index: int = 0
def _get_relevant_documents( # type: ig... |
_base_ = [
'../_base_/models/cascade-rcnn_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
backbone=dict(
type='DetectoRS_ResNet',
conv_cfg=dict(type='ConvAWS'),
sac=dict(type='SAC', use_def... | _base_ = [
'../_base_/models/cascade_rcnn_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
backbone=dict(
type='DetectoRS_ResNet',
conv_cfg=dict(type='ConvAWS'),
sac=dict(type='SAC', use_def... |
import pytest
from llama_index.core.base.llms.types import ChatMessage, ImageBlock, AudioBlock
from llama_index.core.memory.memory import Memory
from llama_index.core.storage.chat_store.sql import MessageStatus
@pytest.fixture()
def memory():
"""Create a basic memory instance for testing."""
return Memory(
... | import pytest
from llama_index.core.base.llms.types import ChatMessage, ImageBlock, AudioBlock
from llama_index.core.memory.memory import Memory
from llama_index.core.storage.chat_store.sql import MessageStatus
@pytest.fixture()
def memory():
"""Create a basic memory instance for testing."""
return Memory(
... |
# Copyright 2020 The HuggingFace 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
#
# Unless required by applicable law or ... | # Copyright 2020 The HuggingFace 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
#
# Unless required by applicable law or ... |
import os
from torchaudio.datasets import librilight_limited
from torchaudio_unittest.common_utils import get_whitenoise, save_wav, TempDirMixin, TorchaudioTestCase
# Used to generate a unique transcript for each dummy audio file
_NUMBERS = ["ZERO", "ONE", "TWO", "THREE", "FOUR", "FIVE", "SIX", "SEVEN", "EIGHT", "NI... | import os
from torchaudio.datasets import librilight_limited
from torchaudio_unittest.common_utils import get_whitenoise, save_wav, TempDirMixin, TorchaudioTestCase
# Used to generate a unique transcript for each dummy audio file
_NUMBERS = ["ZERO", "ONE", "TWO", "THREE", "FOUR", "FIVE", "SIX", "SEVEN", "EIGHT", "NI... |
_base_ = './faster-rcnn_r50_fpn_gn-ws-all_1x_coco.py'
conv_cfg = dict(type='ConvWS')
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
backbone=dict(
type='ResNeXt',
depth=50,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
... | _base_ = './faster_rcnn_r50_fpn_gn_ws-all_1x_coco.py'
conv_cfg = dict(type='ConvWS')
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
backbone=dict(
type='ResNeXt',
depth=50,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
... |
import os
import os.path as osp
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from mmdet.evaluation import CityScapesMetric
try:
import cityscapesscripts
except ImportError:
cityscapesscripts = None
class TestCityScapesMetric(unittest.TestCase):
def setUp(self):... | import os
import os.path as osp
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from mmdet.evaluation import CityScapesMetric
try:
import cityscapesscripts
except ImportError:
cityscapesscripts = None
class TestCityScapesMetric(unittest.TestCase):
def setUp(self):... |
#!/usr/bin/env python3
"""Extract version number from __init__.py"""
# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause
import os
sklearn_init = os.path.join(os.path.dirname(__file__), "../__init__.py")
data = open(sklearn_init).readlines()
version_line = next(line for line in data if li... | #!/usr/bin/env python3
"""Extract version number from __init__.py"""
# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause
import os
sklearn_init = os.path.join(os.path.dirname(__file__), "../__init__.py")
data = open(sklearn_init).readlines()
version_line = next(line for line in data if li... |
_base_ = './faster-rcnn_r50-caffe_fpn_ms-1x_coco.py'
# MMEngine support the following two ways, users can choose
# according to convenience
# param_scheduler = [
# dict(
# type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500), # noqa
# dict(
# type='MultiStepLR',
# begi... | _base_ = './faster-rcnn_r50-caffe_fpn_ms-1x_coco.py'
# learning policy
lr_config = dict(step=[16, 23])
runner = dict(type='EpochBasedRunner', max_epochs=24)
|
"""DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.activations import deserialize
from keras.src.activations import get
from keras.src.activations import serialize
from keras.src.activations.activations import celu
from keras.src.acti... | """DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.activations import deserialize
from keras.src.activations import get
from keras.src.activations import serialize
from keras.src.activations.activations import celu
from keras.src.acti... |
import asyncio
import pytest
from jina import Document, DocumentArray
from jina.helper import Namespace, random_identity
from jina.serve.stream import RequestStreamer
from jina.types.request.data import DataRequest
@pytest.mark.asyncio
@pytest.mark.parametrize('prefetch', [0, 5])
@pytest.mark.parametrize('num_reque... | import asyncio
import pytest
from jina import Document, DocumentArray
from jina.helper import Namespace, random_identity
from jina.serve.stream import RequestStreamer
from jina.types.request.data import DataRequest
@pytest.mark.asyncio
@pytest.mark.parametrize('prefetch', [0, 5])
@pytest.mark.parametrize('num_reques... |
# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import os.path as osp
import mmcv
import numpy as np
from mmcv import Config, DictAction
from mmdet.models.utils import mask2ndarray
from mmdet.registry import DATASETS, VISUALIZERS
from mmdet.structures.bbox import BaseBoxes
from mmdet.utils import regi... | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import os.path as osp
import mmcv
import numpy as np
from mmcv import Config, DictAction
from mmdet.datasets.builder import build_dataset
from mmdet.models.utils import mask2ndarray
from mmdet.registry import VISUALIZERS
from mmdet.utils import register_... |
from torchvision.transforms import AutoAugmentPolicy, InterpolationMode # usort: skip
from . import functional, utils # usort: skip
from ._transform import Transform # usort: skip
from ._augment import CutMix, MixUp, RandomErasing
from ._auto_augment import AugMix, AutoAugment, RandAugment, TrivialAugmentWide
fro... | from torchvision.transforms import AutoAugmentPolicy, InterpolationMode # usort: skip
from . import functional, utils # usort: skip
from ._transform import Transform # usort: skip
from ._augment import CutMix, MixUp, RandomErasing
from ._auto_augment import AugMix, AutoAugment, RandAugment, TrivialAugmentWide
fro... |
import logging
from llama_index.llms.text_generation_inference.base import (
TextGenerationInference,
)
logger = logging.getLogger(__name__)
logger.warning("""
===============================================================================
⚠️ DEPRECATION WARNING ⚠️
======================... | from llama_index.llms.text_generation_inference.base import (
TextGenerationInference,
)
__all__ = ["TextGenerationInference"]
|
_base_ = [
'../_base_/models/faster_rcnn_r50_fpn.py', '../_base_/datasets/voc0712.py',
'../_base_/default_runtime.py'
]
model = dict(roi_head=dict(bbox_head=dict(num_classes=20)))
# training schedule, voc dataset is repeated 3 times, in
# `_base_/datasets/voc0712.py`, so the actual epoch = 4 * 3 = 12
max_epoch... | _base_ = [
'../_base_/models/faster_rcnn_r50_fpn.py', '../_base_/datasets/voc0712.py',
'../_base_/default_runtime.py'
]
model = dict(roi_head=dict(bbox_head=dict(num_classes=20)))
# optimizer
optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
optimizer_config = dict(grad_clip=None)
# learn... |
from __future__ import annotations
from collections.abc import Iterable
from torch import Tensor
from sentence_transformers import util
from sentence_transformers.losses.CoSENTLoss import CoSENTLoss
from sentence_transformers.sparse_encoder.SparseEncoder import SparseEncoder
class SparseCoSENTLoss(CoSENTLoss):
... | from __future__ import annotations
from collections.abc import Iterable
from torch import Tensor
from sentence_transformers import util
from sentence_transformers.losses.CoSENTLoss import CoSENTLoss
from sentence_transformers.sparse_encoder.SparseEncoder import SparseEncoder
class SparseCoSENTLoss(CoSENTLoss):
... |
_base_ = ['../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py']
img_scale = (640, 640) # height, width
# model settings
model = dict(
type='YOLOX',
input_size=img_scale,
random_size_range=(15, 25),
random_size_interval=10,
backbone=dict(type='CSPDarknet', deepen_factor=0.33, widen... | _base_ = ['../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py']
img_scale = (640, 640)
# model settings
model = dict(
type='YOLOX',
input_size=img_scale,
random_size_range=(15, 25),
random_size_interval=10,
backbone=dict(type='CSPDarknet', deepen_factor=0.33, widen_factor=0.5),
... |
import pathlib
from typing import Any, Dict, List, Union
import torch
from torchdata.datapipes.iter import CSVDictParser, IterDataPipe, Mapper
from torchvision.prototype.datasets.utils import Dataset, KaggleDownloadResource, OnlineResource
from torchvision.prototype.datasets.utils._internal import hint_sharding, hint_... | import pathlib
from typing import Any, Dict, List, Union
import torch
from torchdata.datapipes.iter import CSVDictParser, IterDataPipe, Mapper
from torchvision.datapoints import Image
from torchvision.prototype.datapoints import Label
from torchvision.prototype.datasets.utils import Dataset, KaggleDownloadResource, On... |
from typing import TypeVar
import numpy as np
from pydantic import parse_obj_as
from docarray.typing.proto_register import _register_proto
from docarray.typing.tensor.ndarray import NdArray
from docarray.typing.url.url_3d.url_3d import Url3D
T = TypeVar('T', bound='PointCloud3DUrl')
@_register_proto(proto_type_nam... | from typing import TypeVar
import numpy as np
from pydantic import parse_obj_as
from docarray.typing import NdArray
from docarray.typing.proto_register import _register_proto
from docarray.typing.url.url_3d.url_3d import Url3D
T = TypeVar('T', bound='PointCloud3DUrl')
@_register_proto(proto_type_name='point_cloud_... |
import logging
from datasets import load_dataset
from sentence_transformers import SparseEncoder
from sentence_transformers.sparse_encoder.evaluation import SparseRerankingEvaluator
logging.basicConfig(format="%(message)s", level=logging.INFO)
# Load a model
model = SparseEncoder("naver/splade-cocondenser-ensembled... | import logging
from datasets import load_dataset
from sentence_transformers import SparseEncoder
from sentence_transformers.sparse_encoder.evaluation import SparseRerankingEvaluator
logging.basicConfig(format="%(message)s", level=logging.INFO)
# Load a model
model = SparseEncoder("naver/splade-cocondenser-ensembled... |
"""Test conversation chain and memory."""
from langchain_core.documents import Document
from langchain_core.language_models import FakeListLLM
from langchain.chains.conversational_retrieval.base import (
ConversationalRetrievalChain,
)
from langchain.memory.buffer import ConversationBufferMemory
from tests.unit_t... | """Test conversation chain and memory."""
from langchain_core.documents import Document
from langchain_core.language_models import FakeListLLM
from langchain.chains.conversational_retrieval.base import (
ConversationalRetrievalChain,
)
from langchain.memory.buffer import ConversationBufferMemory
from tests.unit_t... |
from typing import Any, AsyncGenerator, Coroutine, Dict, List, Optional, Sequence, Union
import pytest
from llama_index.core.base.llms.types import (
ChatMessage,
ChatResponse,
ChatResponseGen,
CompletionResponse,
LLMMetadata,
)
from llama_index.core.llms.function_calling import FunctionCallingLLM
... | from typing import Any, AsyncGenerator, Coroutine, Dict, List, Optional, Sequence, Union
import pytest
from llama_index.core.base.llms.types import (
ChatMessage,
ChatResponse,
ChatResponseGen,
CompletionResponse,
LLMMetadata,
)
from llama_index.core.llms.function_calling import FunctionCallingLLM
... |
"""Base argparser module for Pod and Deployment runtime"""
import argparse
import os
from jina.enums import PollingType
from jina.helper import random_identity
from jina.parsers.helper import _SHOW_ALL_ARGS, add_arg_group
def mixin_essential_parser(parser):
"""Mixing in arguments required by every module into th... | """Base argparser module for Pod and Deployment runtime"""
import argparse
import os
from jina.enums import PollingType
from jina.helper import random_identity
from jina.parsers.helper import _SHOW_ALL_ARGS, add_arg_group
def mixin_essential_parser(parser):
"""Mixing in arguments required by every module into th... |
import numpy as np
import pytest
from tensorflow import data as tf_data
from keras.src import backend
from keras.src import layers
from keras.src import testing
class RandomContrastTest(testing.TestCase):
@pytest.mark.requires_trainable_backend
def test_layer(self):
self.run_layer_test(
l... | import numpy as np
import pytest
from tensorflow import data as tf_data
from keras.src import backend
from keras.src import layers
from keras.src import testing
class RandomContrastTest(testing.TestCase):
@pytest.mark.requires_trainable_backend
def test_layer(self):
self.run_layer_test(
l... |
# TODO: enable ruff qa on this file when we figure out why it thinks weaviate_client is
# redefined at each test that fixture
# ruff: noqa
import numpy as np
import pytest
import torch
from pydantic import Field
from docarray import BaseDoc
from docarray.index.backends.weaviate import WeaviateDocumentIndex
from ... | # TODO: enable ruff qa on this file when we figure out why it thinks weaviate_client is
# redefined at each test that fixture
# ruff: noqa
import numpy as np
import pytest
import torch
from pydantic import Field
from docarray import BaseDoc
from docarray.index.backends.weaviate import WeaviateDocumentIndex
from ... |
# Copyright (c) OpenMMLab. All rights reserved.
from .bbox_overlaps import bbox_overlaps
from .class_names import (cityscapes_classes, coco_classes,
coco_panoptic_classes, dataset_aliases, get_classes,
imagenet_det_classes, imagenet_vid_classes,
... | # Copyright (c) OpenMMLab. All rights reserved.
from .bbox_overlaps import bbox_overlaps
from .class_names import (cityscapes_classes, coco_classes, dataset_aliases,
get_classes, imagenet_det_classes,
imagenet_vid_classes, objects365v1_classes,
... |
from keras.src.api_export import keras_export
from keras.src.layers.pooling.base_pooling import BasePooling
@keras_export(["keras.layers.AveragePooling2D", "keras.layers.AvgPool2D"])
class AveragePooling2D(BasePooling):
"""Average pooling operation for 2D spatial data.
Downsamples the input along its spatial... | from keras.src.api_export import keras_export
from keras.src.layers.pooling.base_pooling import BasePooling
@keras_export(["keras.layers.AveragePooling2D", "keras.layers.AvgPool2D"])
class AveragePooling2D(BasePooling):
"""Average pooling operation for 2D spatial data.
Downsamples the input along its spatial... |
# Copyright (c) OpenMMLab. All rights reserved.
from mmengine.utils import get_git_hash
from mmengine.utils.dl_utils import collect_env as collect_base_env
import mmdet
def collect_env():
"""Collect the information of the running environments."""
env_info = collect_base_env()
env_info['MMDetection'] = mm... | # Copyright (c) OpenMMLab. All rights reserved.
from mmengine.utils import collect_env as collect_base_env
from mmengine.utils import get_git_hash
import mmdet
def collect_env():
"""Collect the information of the running environments."""
env_info = collect_base_env()
env_info['MMDetection'] = mmdet.__ver... |
"""
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... |
from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import pyarrow as pa
if TYPE_CHECKING:
from .features import FeatureType
@dataclass
class Translation:
"""`Feature` for translations with fixed languages per example.
Here for compatibl... | from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import pyarrow as pa
if TYPE_CHECKING:
from .features import FeatureType
@dataclass
class Translation:
"""`FeatureConnector` for translations with fixed languages per example.
Here for ... |
from abc import ABC
import numpy as np
import pytest
from docarray import Document, DocumentArray
from docarray.array.storage.base.helper import Offset2ID
from docarray.array.storage.memory import SequenceLikeMixin
from docarray.array.storage.redis.getsetdel import GetSetDelMixin
from docarray.array.storage.redis.back... | from abc import ABC
import numpy as np
import pytest
from docarray import Document, DocumentArray
from docarray.array.storage.base.helper import Offset2ID
from docarray.array.storage.memory import SequenceLikeMixin
from docarray.array.storage.redis.getsetdel import GetSetDelMixin
from docarray.array.storage.redis.back... |
import argparse
import json
import subprocess
def get_runner_status(target_runners, token):
offline_runners = []
cmd = [
"curl",
"-H",
"Accept: application/vnd.github+json",
"-H",
f"Authorization: Bearer {token}",
"https://api.github.com/repos/huggingface/trans... | import argparse
import json
import subprocess
def get_runner_status(target_runners, token):
offline_runners = []
cmd = (
f'curl -H "Accept: application/vnd.github+json" -H "Authorization: Bearer {token}"'
" https://api.github.com/repos/huggingface/transformers/actions/runners"
)
outpu... |
"""Test prompt mixin."""
from llama_index.core.prompts.base import PromptTemplate
from llama_index.core.prompts.mixin import (
PromptDictType,
PromptMixin,
PromptMixinType,
)
class MockObject2(PromptMixin):
def __init__(self) -> None:
self._prompt_dict_2 = {
"abc": PromptTemplate(... | """Test prompt mixin."""
from llama_index.core.prompts.base import PromptTemplate
from llama_index.core.prompts.mixin import (
PromptDictType,
PromptMixin,
PromptMixinType,
)
class MockObject2(PromptMixin):
def __init__(self) -> None:
self._prompt_dict_2 = {
"abc": PromptTemplate... |
"""[DEPRECATED] Pipeline prompt template."""
from typing import Any
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 BaseC... | """[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 ... |
# Copyright (c) OpenMMLab. All rights reserved.
from .inference import (async_inference_detector, inference_detector,
init_detector, show_result_pyplot)
from .test import multi_gpu_test, single_gpu_test
from .train import (get_root_logger, init_random_seed, set_random_seed,
t... | # Copyright (c) OpenMMLab. All rights reserved.
from .inference import (async_inference_detector, inference_detector,
init_detector, show_result_pyplot)
from .test import multi_gpu_test, single_gpu_test
from .train import get_root_logger, set_random_seed, train_detector
__all__ = [
'get_roo... |
# Experimental features are not mature yet and are subject to change.
# We do not provide any BC/FC guarantees
| # Experimental features are not mature yet and are subject to change.
# We do not provide any BC/FC guarntees
|
"""
This example loads the pre-trained SentenceTransformer model 'nli-distilroberta-base-v2' from the server.
It then fine-tunes this model for some epochs on the STS benchmark dataset.
Note: In this example, you must specify a SentenceTransformer model.
If you want to fine-tune a huggingface/transformers model like b... | """
This example loads the pre-trained SentenceTransformer model 'nli-distilroberta-base-v2' from the server.
It then fine-tunes this model for some epochs on the STS benchmark dataset.
Note: In this example, you must specify a SentenceTransformer model.
If you want to fine-tune a huggingface/transformers model like b... |
from pydantic.tools import parse_obj_as, schema_json_of
from docarray.document.io.json import orjson_dumps
from docarray.typing import AnyUrl
def test_proto_any_url():
uri = parse_obj_as(AnyUrl, 'http://jina.ai/img.png')
uri._to_node_protobuf()
def test_json_schema():
schema_json_of(AnyUrl)
def tes... | from pydantic.tools import parse_obj_as
from docarray.typing import ImageUrl
def test_proto_any_url():
uri = parse_obj_as(ImageUrl, 'http://jina.ai/img.png')
uri._to_node_protobuf()
|
_base_ = ['./mask2former_r50_8xb2-lsj-50e_coco-panoptic.py']
num_things_classes = 80
num_stuff_classes = 0
num_classes = num_things_classes + num_stuff_classes
image_size = (1024, 1024)
batch_augments = [
dict(
type='BatchFixedSizePad',
size=image_size,
img_pad_value=0,
pad_mask=Tru... | _base_ = ['./mask2former_r50_8xb2-lsj-50e_coco-panoptic.py']
num_things_classes = 80
num_stuff_classes = 0
num_classes = num_things_classes + num_stuff_classes
image_size = (1024, 1024)
batch_augments = [
dict(
type='BatchFixedSizePad',
size=image_size,
img_pad_value=0,
pad_mask=Tru... |
import pytest
from fastapi import Depends, FastAPI, HTTPException
from fastapi.exceptions import RequestValidationError
from fastapi.testclient import TestClient
from starlette.responses import JSONResponse
def http_exception_handler(request, exception):
return JSONResponse({"exception": "http-exception"})
def ... | import pytest
from fastapi import FastAPI, HTTPException
from fastapi.exceptions import RequestValidationError
from fastapi.testclient import TestClient
from starlette.responses import JSONResponse
def http_exception_handler(request, exception):
return JSONResponse({"exception": "http-exception"})
def request_v... |
from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.document_transformers import (
EmbeddingsClusteringFilter,
EmbeddingsRedundantFilter,
get_stateful_documents,
)
from langchain_community.document_transformers... | from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.document_transformers import (
EmbeddingsClusteringFilter,
EmbeddingsRedundantFilter,
get_stateful_documents,
)
from langchain_community.document_transformers... |
"""load multiple Python files specified as command line arguments."""
import sys
import traceback
from importlib.machinery import SourceFileLoader
if __name__ == "__main__":
files = sys.argv[1:]
has_failure = False
for file in files:
try:
SourceFileLoader("x", file).load_module()
... | """load multiple Python files specified as command line arguments."""
import sys
import traceback
from importlib.machinery import SourceFileLoader
if __name__ == "__main__":
files = sys.argv[1:]
has_failure = False
for file in files:
try:
SourceFileLoader("x", file).load_module()
... |
import pytest
from llama_index.core.node_parser.text.semantic_double_merging_splitter import (
SemanticDoubleMergingSplitterNodeParser,
LanguageConfig,
)
from llama_index.core.schema import Document
doc = Document(
text="Warsaw: Warsaw, the capital city of Poland, is a bustling metropolis located on the b... | import pytest
from llama_index.core.node_parser.text.semantic_double_merging_splitter import (
SemanticDoubleMergingSplitterNodeParser,
LanguageConfig,
)
from llama_index.core.schema import Document
doc = Document(
text="Warsaw: Warsaw, the capital city of Poland, is a bustling metropolis located on the b... |
import inspect
from abc import ABC
from functools import reduce
from typing import TYPE_CHECKING, Any, Dict, Optional, Set, Type, Union
if TYPE_CHECKING:
from jina.orchestrate.flow.base import Flow
from jina.serve.executors import BaseExecutor
class VersionedYAMLParser:
"""Flow YAML parser for specific v... | from typing import TYPE_CHECKING, Any, Dict, Optional, Union
if TYPE_CHECKING:
from jina.orchestrate.flow.base import Flow
from jina.serve.executors import BaseExecutor
class VersionedYAMLParser:
"""Flow YAML parser for specific version
Every :class:`VersionedYAMLParser` must implement two methods a... |
import pytest
from docarray import Document, DocumentArray
@pytest.mark.filterwarnings('ignore::UserWarning')
@pytest.mark.parametrize('deleted_elmnts', [[0, 1], ['r0', 'r1']])
@pytest.mark.parametrize('columns', [[('price', 'int')], {'price': 'int'}])
def test_delete_offset_success_sync_es_offset_index(
deleted... | from docarray import Document, DocumentArray
import pytest
@pytest.mark.filterwarnings('ignore::UserWarning')
@pytest.mark.parametrize('deleted_elmnts', [[0, 1], ['r0', 'r1']])
def test_delete_offset_success_sync_es_offset_index(deleted_elmnts, start_storage):
elastic_doc = DocumentArray(
storage='elastic... |
from abc import ABC, abstractmethod
import warnings
from collections import namedtuple
from dataclasses import is_dataclass, asdict
from typing import Dict, Optional, TYPE_CHECKING, Union, List, Tuple
if TYPE_CHECKING: # pragma: no cover
from docarray.typing import DocumentArraySourceType, ArrayType
TypeMap = na... | from abc import ABC, abstractmethod
import warnings
from collections import namedtuple
from dataclasses import is_dataclass, asdict
from typing import Dict, Optional, TYPE_CHECKING, Union, List, Tuple
if TYPE_CHECKING:
from docarray.typing import DocumentArraySourceType, ArrayType
TypeMap = namedtuple('TypeMap', ... |
__copyright__ = "Copyright (c) 2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import os
import subprocess
from pathlib import Path
import pytest
from jina import Document, DocumentArray
@pytest.fixture()
def test_dir() -> str:
return os.path.dirname(os.path.abspath(__file__))
@pytest.f... | __copyright__ = "Copyright (c) 2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import subprocess
from pathlib import Path
import pytest
@pytest.fixture(scope='session')
def docker_image_name() -> str:
return Path(__file__).parents[1].stem.lower()
@pytest.fixture(scope='session')
def bui... |
from typing import Any, Optional, Type, TypeVar, Union
import numpy as np
from docarray.base_document import BaseDocument
from docarray.documents import AudioDoc
from docarray.typing import AnyEmbedding, AnyTensor
from docarray.typing.tensor.abstract_tensor import AbstractTensor
from docarray.typing.tensor.video.vide... | from typing import Any, Optional, Type, TypeVar, Union
import numpy as np
from docarray.base_document import BaseDocument
from docarray.documents import AudioDoc
from docarray.typing import AnyEmbedding, AnyTensor
from docarray.typing.tensor.abstract_tensor import AbstractTensor
from docarray.typing.tensor.video.vide... |
"""Test EvalQueryEngine tool."""
from typing import Optional, Sequence, Any
from unittest import IsolatedAsyncioTestCase
from unittest.mock import AsyncMock
from llama_index.core.evaluation import EvaluationResult
from llama_index.core.evaluation.base import BaseEvaluator
from llama_index.core.prompts.mixin import Pr... | """Test EvalQueryEngine tool."""
from typing import Optional, Sequence, Any
from unittest import IsolatedAsyncioTestCase
from unittest.mock import AsyncMock
from llama_index.core.evaluation import EvaluationResult
from llama_index.core.evaluation.base import BaseEvaluator
from llama_index.core.prompts.mixin import Pro... |
import fastapi
from .config import settings
from .middleware import auth_middleware
from .models import DEFAULT_USER_ID, User
def requires_user(payload: dict = fastapi.Depends(auth_middleware)) -> User:
return verify_user(payload, admin_only=False)
def requires_admin_user(
payload: dict = fastapi.Depends(a... | import fastapi
from .config import Settings
from .middleware import auth_middleware
from .models import DEFAULT_USER_ID, User
def requires_user(payload: dict = fastapi.Depends(auth_middleware)) -> User:
return verify_user(payload, admin_only=False)
def requires_admin_user(
payload: dict = fastapi.Depends(a... |
import asyncio
import os
from typing import Dict, List
import pytest
import requests
from jina import Flow
from jina.logging.logger import JinaLogger
from tests.k8s_otel.kind_wrapper import KindClusterWrapperV2
from tests.k8s_otel.util import get_last_health_check_data, parse_string_jaeger_tags
@pytest.mark.asyncio... | import pytest
import os
import requests
import asyncio
from typing import List, Dict
from jina.logging.logger import JinaLogger
from jina import Flow
from tests.k8s_otel.kind_wrapper import KindClusterWrapperV2
from tests.k8s_otel.util import parse_string_jaeger_tags, get_last_health_check_data
@pytest.mark.asyncio
... |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Image
from .base import TaskTemplate
@dataclass(frozen=True)
class ImageClassification(TaskTemplate):
task: str = field(default="image-classification", metadata={"include_in_asdict_... | import copy
from dataclasses import dataclass
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Image
from .base import TaskTemplate
@dataclass(frozen=True)
class ImageClassification(TaskTemplate):
task: str = "image-classification"
input_schema: ClassVar[Features] = Features({"i... |
# Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import torch
from mmdet.models.roi_heads.mask_heads import (DynamicMaskHead, FCNMaskHead,
MaskIoUHead)
from .utils import _dummy_bbox_sampling
def test_mask_head_loss():
"""Test mask head loss when mask tar... | # Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import torch
from mmdet.models.roi_heads.mask_heads import FCNMaskHead, MaskIoUHead
from .utils import _dummy_bbox_sampling
def test_mask_head_loss():
"""Test mask head loss when mask target is empty."""
self = FCNMaskHead(
num_convs=1,
... |
from ._conformer_wav2vec2 import (
conformer_wav2vec2_base,
conformer_wav2vec2_model,
conformer_wav2vec2_pretrain_base,
conformer_wav2vec2_pretrain_large,
conformer_wav2vec2_pretrain_model,
ConformerWav2Vec2PretrainModel,
)
from ._emformer_hubert import emformer_hubert_base, emformer_hubert_mode... | from ._conformer_wav2vec2 import (
conformer_wav2vec2_base,
conformer_wav2vec2_model,
conformer_wav2vec2_pretrain_base,
conformer_wav2vec2_pretrain_large,
conformer_wav2vec2_pretrain_model,
ConformerWav2Vec2PretrainModel,
)
from ._emformer_hubert import emformer_hubert_base, emformer_hubert_mode... |
# Copyright (c) OpenMMLab. All rights reserved.
from typing import List, Tuple
from torch import Tensor
from mmdet.registry import MODELS
from mmdet.structures import SampleList
from mmdet.utils import ConfigType, OptConfigType, OptMultiConfig
from .single_stage import SingleStageDetector
@MODELS.register_module()
... | # Copyright (c) OpenMMLab. All rights reserved.
from mmdet.registry import MODELS
from mmdet.utils import ConfigType, OptConfigType, OptMultiConfig
from .single_stage import SingleStageDetector
@MODELS.register_module()
class DETR(SingleStageDetector):
r"""Implementation of `DETR: End-to-End Object Detection with... |
import numpy as np
import pytest
from docarray.documents import PointCloud3D
from tests import TOYDATA_DIR
LOCAL_OBJ_FILE = str(TOYDATA_DIR / 'tetrahedron.obj')
REMOTE_OBJ_FILE = 'https://people.sc.fsu.edu/~jburkardt/data/obj/al.obj'
@pytest.mark.slow
@pytest.mark.internet
@pytest.mark.parametrize('file_url', [LOCA... | import numpy as np
import pytest
from docarray import PointCloud3D
from tests import TOYDATA_DIR
LOCAL_OBJ_FILE = str(TOYDATA_DIR / 'tetrahedron.obj')
REMOTE_OBJ_FILE = 'https://people.sc.fsu.edu/~jburkardt/data/obj/al.obj'
@pytest.mark.slow
@pytest.mark.internet
@pytest.mark.parametrize('file_url', [LOCAL_OBJ_FILE... |
# coding=utf-8
# Copyright 2024 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 2024 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... |
import multiprocessing
import os
import signal
import time
import pytest
from jina import Document, DocumentArray, Executor, requests
from jina.clients.request import request_generator
from jina.parsers import set_gateway_parser
from jina.serve.networking.utils import send_request_sync
from jina_cli.api import execut... | import multiprocessing
import os
import signal
import time
import pytest
from jina import Document, DocumentArray, Executor, requests
from jina.clients.request import request_generator
from jina.parsers import set_gateway_parser
from jina.serve.networking import GrpcConnectionPool
from jina_cli.api import executor_na... |
# Copyright (c) OpenMMLab. All rights reserved.
import torch
from mmengine.data import InstanceData
from mmdet.core.bbox.assigners import AssignResult
from mmdet.registry import TASK_UTILS
from .base_sampler import BaseSampler
from .sampling_result import SamplingResult
@TASK_UTILS.register_module()
class PseudoSamp... | # Copyright (c) OpenMMLab. All rights reserved.
import torch
from mmdet.registry import TASK_UTILS
from .base_sampler import BaseSampler
from .sampling_result import SamplingResult
@TASK_UTILS.register_module()
class PseudoSampler(BaseSampler):
"""A pseudo sampler that does not do sampling actually."""
def ... |
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... |
_base_ = [
'../_base_/default_runtime.py', '../_base_/datasets/coco_detection.py'
]
data_preprocessor = dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
bgr_to_rgb=True)
# model settings
model = dict(
type='CornerNet',
data_preprocessor=data_pr... | _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, ... |
# coding: utf-8
"""LightGBM, Light Gradient Boosting Machine.
Contributors: https://github.com/microsoft/LightGBM/graphs/contributors.
"""
from pathlib import Path
# .basic is intentionally loaded as early as possible, to dlopen() lib_lightgbm.{dll,dylib,so}
# and its dependencies as early as possible
from .basic im... | # coding: utf-8
"""LightGBM, Light Gradient Boosting Machine.
Contributors: https://github.com/microsoft/LightGBM/graphs/contributors.
"""
from pathlib import Path
from .basic import Booster, Dataset, Sequence, register_logger
from .callback import EarlyStopException, early_stopping, log_evaluation, record_evaluatio... |
# Copyright (c) OpenMMLab. All rights reserved.
from abc import ABCMeta, abstractmethod
import torch.nn.functional as F
from mmcv.runner import BaseModule, force_fp32
from ..builder import build_loss
from ..utils import interpolate_as
class BaseSemanticHead(BaseModule, metaclass=ABCMeta):
"""Base module of Sema... | # Copyright (c) OpenMMLab. All rights reserved.
from abc import ABCMeta, abstractmethod
import torch.nn.functional as F
from mmcv.runner import BaseModule, force_fp32
from ..builder import build_loss
from ..utils import interpolate_as
class BaseSemanticHead(BaseModule, metaclass=ABCMeta):
"""Base module of Sema... |
# Copyright (c) OpenMMLab. All rights reserved.
import unittest
from unittest import TestCase
import torch
from parameterized import parameterized
from mmdet.core import DetDataSample
from mmdet.testing import demo_mm_inputs, get_detector_cfg
from mmdet.utils import register_all_modules
class TestRPN(TestCase):
... | # Copyright (c) OpenMMLab. All rights reserved.
import unittest
from unittest import TestCase
import torch
from parameterized import parameterized
from mmdet import * # noqa
from mmdet.core import DetDataSample
from mmdet.testing import demo_mm_inputs, get_detector_cfg
class TestRPN(TestCase):
@parameterized.... |
from __future__ import annotations
from collections.abc 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 compute... | from __future__ import annotations
from collections.abc 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 compute... |
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