input stringlengths 33 5k | output stringlengths 32 5k |
|---|---|
from setuptools import find_packages, setup
with open("README.md", mode="r", encoding="utf-8") as readme_file:
readme = readme_file.read()
setup(
name="sentence-transformers",
version="3.1.0.dev0",
author="Nils Reimers, Tom Aarsen",
author_email="info@nils-reimers.de",
description="Multilingu... | from setuptools import setup, find_packages
with open("README.md", mode="r", encoding="utf-8") as readme_file:
readme = readme_file.read()
setup(
name="sentence-transformers",
version="2.2.2",
author="Nils Reimers",
author_email="info@nils-reimers.de",
description="Multilingual text embeddin... |
"""Output classes.
**Output** classes are used to represent the output of a language model call
and the output of a chat.
The top container for information is the `LLMResult` object. `LLMResult` is used by
both chat models and LLMs. This object contains the output of the language
model and any additional information ... | """Output classes.
**Output** classes are used to represent the output of a language model call
and the output of a chat.
The top container for information is the `LLMResult` object. `LLMResult` is used by
both chat models and LLMs. This object contains the output of the language
model and any additional information ... |
"""In memory document index."""
import operator
import uuid
from collections.abc import Sequence
from typing import Any, Optional, cast
from pydantic import Field
from langchain_core._api import beta
from langchain_core.callbacks import CallbackManagerForRetrieverRun
from langchain_core.documents import Document
fro... | """In memory document index."""
import operator
import uuid
from collections.abc import Sequence
from typing import Any, Optional, cast
from pydantic import Field
from langchain_core._api import beta
from langchain_core.callbacks import CallbackManagerForRetrieverRun
from langchain_core.documents import Document
fro... |
# coding=utf-8
# Copyright 2022 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://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable... | # coding=utf-8
# Copyright 2022 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://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable... |
"""
Given a dataset with parallel sentences, one "english" column and one "non_english" column, this script evaluates a model on the translation task.
Given a sentence in the "english" column, the model should find the correct translation in the "non_english" column, based on just the embeddings.
It then computes an a... | """
Given a tab separated file (.tsv) with parallel sentences, where the second column is the translation of the sentence in the first column, for example, in the format:
src1 trg1
src2 trg2
...
where trg_i is the translation of src_i.
Given src_i, the TranslationEvaluator checks which trg_j has the highest sim... |
import os
from pathlib import Path
from typing import List, Tuple, Union
import torchaudio
from torch import Tensor
from torch.utils.data import Dataset
from torchaudio._internal import download_url_to_file
from torchaudio.datasets.utils import _extract_tar
_RELEASE_CONFIGS = {
"release1": {
"folder_in_a... | import os
from pathlib import Path
from typing import List, Tuple, Union
import torchaudio
from torch import Tensor
from torch.hub import download_url_to_file
from torch.utils.data import Dataset
from torchaudio.datasets.utils import _extract_tar
_RELEASE_CONFIGS = {
"release1": {
"folder_in_archive": "w... |
"""DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.applications.vgg19 import VGG19 as VGG19
from keras.src.applications.vgg19 import (
decode_predictions as decode_predictions,
)
from keras.src.applications.vgg19 import preprocess... | """DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.applications.vgg19 import VGG19
from keras.src.applications.vgg19 import decode_predictions
from keras.src.applications.vgg19 import preprocess_input
|
import numpy as np
import pytest
from pydantic.tools import parse_obj_as, schema_json_of
from docarray.base_document.io.json import orjson_dumps
from docarray.typing import NdArray, PointCloud3DUrl
from tests import TOYDATA_DIR
MESH_FILES = {
'obj': str(TOYDATA_DIR / 'tetrahedron.obj'),
'glb': str(TOYDATA_DIR... | import numpy as np
import pytest
from pydantic.tools import parse_obj_as, schema_json_of
from docarray.base_document.io.json import orjson_dumps
from docarray.typing import NdArray, PointCloud3DUrl
from tests import TOYDATA_DIR
MESH_FILES = {
'obj': str(TOYDATA_DIR / 'tetrahedron.obj'),
'glb': str(TOYDATA_DIR... |
import pytest
from llama_index.core.base.embeddings.base import BaseEmbedding
from llama_index.embeddings.upstage import UpstageEmbedding
UPSTAGE_TEST_API_KEY = "upstage_test_key"
@pytest.fixture()
def upstage_embedding():
return pytest.importorskip(
"llama_index.embeddings.upstage", reason="Cannot impor... | import pytest
from llama_index.core.base.embeddings.base import BaseEmbedding
UPSTAGE_TEST_API_KEY = "upstage_test_key"
@pytest.fixture()
def upstage_embedding():
return pytest.importorskip(
"llama_index.embeddings.upstage", reason="Cannot import UpstageEmbedding"
).UpstageEmbedding
@pytest.fixture... |
"""DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.applications.xception import Xception as Xception
from keras.src.applications.xception import (
decode_predictions as decode_predictions,
)
from keras.src.applications.xception im... | """DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.applications.xception import Xception
from keras.src.applications.xception import decode_predictions
from keras.src.applications.xception import preprocess_input
|
"""
=========================================
Label Propagation digits: Active learning
=========================================
Demonstrates an active learning technique to learn handwritten digits
using label propagation.
We start by training a label propagation model with only 10 labeled points,
then we select th... | """
========================================
Label Propagation digits active learning
========================================
Demonstrates an active learning technique to learn handwritten digits
using label propagation.
We start by training a label propagation model with only 10 labeled points,
then we select the t... |
from jina import Client
from docarray import DocList
from docarray.documents import TextDoc
if __name__ == '__main__':
c = Client(host='grpc://0.0.0.0:54321')
da = c.post('/', DocList[TextDoc]([TextDoc(), TextDoc()]), return_type=DocList[TextDoc])
print(da.text)
| from jina import Client, DocumentArray
if __name__ == '__main__':
c = Client(host='grpc://0.0.0.0:54321')
da = c.post('/', DocumentArray.empty(2))
print(da.texts)
|
import numpy as np
import pytest
from docarray.documents import Image
REMOTE_JPG = (
'https://upload.wikimedia.org/wikipedia/commons/8/80/'
'Dag_Sebastian_Ahlander_at_G%C3%B6teborg_Book_Fair_2012b.jpg'
)
@pytest.mark.slow
@pytest.mark.internet
def test_image():
image = Image(url=REMOTE_JPG)
image.... | import numpy as np
import pytest
from docarray import Image
REMOTE_JPG = (
'https://upload.wikimedia.org/wikipedia/commons/8/80/'
'Dag_Sebastian_Ahlander_at_G%C3%B6teborg_Book_Fair_2012b.jpg'
)
@pytest.mark.slow
@pytest.mark.internet
def test_image():
image = Image(url=REMOTE_JPG)
image.tensor = i... |
from typing import Any, Dict, Union
from torchvision import tv_tensors
from torchvision.transforms.v2 import functional as F, Transform
class ConvertBoundingBoxFormat(Transform):
"""Convert bounding box coordinates to the given ``format``, eg from "CXCYWH" to "XYXY".
Args:
format (str or tv_tensors.... | from typing import Any, Dict, Union
from torchvision import tv_tensors
from torchvision.transforms.v2 import functional as F, Transform
class ConvertBoundingBoxFormat(Transform):
"""[BETA] Convert bounding box coordinates to the given ``format``, eg from "CXCYWH" to "XYXY".
.. v2betastatus:: ConvertBounding... |
"""Utils for pretty print."""
import textwrap
from pprint import pprint
from typing import Any, Dict
from llama_index.core.base.response.schema import Response
from llama_index.core.schema import NodeWithScore
from llama_index.core.utils import truncate_text
def pprint_metadata(metadata: Dict[str, Any]) -> None:
... | """Utils for pretty print."""
import textwrap
from pprint import pprint
from typing import Any, Dict
from llama_index.core.base.response.schema import Response
from llama_index.core.schema import NodeWithScore
from llama_index.core.utils import truncate_text
def pprint_metadata(metadata: Dict[str, Any]) -> None:
... |
import logging
import pathlib
from argparse import ArgumentParser
import sentencepiece as spm
import torch
import torchaudio
from lightning import ConformerRNNTModule
from transforms import get_data_module
logger = logging.getLogger()
def compute_word_level_distance(seq1, seq2):
return torchaudio.functional.e... | import logging
import pathlib
from argparse import ArgumentParser
import torch
import torchaudio
from lightning import ConformerRNNTModule
from transforms import get_data_module
logger = logging.getLogger()
def compute_word_level_distance(seq1, seq2):
return torchaudio.functional.edit_distance(seq1.lower().spl... |
# Copyright (c) OpenMMLab. All rights reserved.
from mmdet.models.builder import HEADS
from mmdet.models.utils import ResLayer, SimplifiedBasicBlock
from .fcn_mask_head import FCNMaskHead
@HEADS.register_module()
class SCNetMaskHead(FCNMaskHead):
"""Mask head for `SCNet <https://arxiv.org/abs/2012.10150>`_.
... | from mmdet.models.builder import HEADS
from mmdet.models.utils import ResLayer, SimplifiedBasicBlock
from .fcn_mask_head import FCNMaskHead
@HEADS.register_module()
class SCNetMaskHead(FCNMaskHead):
"""Mask head for `SCNet <https://arxiv.org/abs/2012.10150>`_.
Args:
conv_to_res (bool, optional): if T... |
import logging
import os
import sys
from torchaudio._internal.module_utils import eval_env, fail_with_message, is_module_available, no_op
from .utils import (
_check_cuda_version,
_fail_since_no_sox,
_init_dll_path,
_init_ffmpeg,
_init_sox,
_LazyImporter,
_load_lib,
)
_LG = logging.getLog... | import logging
import os
import sys
from torchaudio._internal.module_utils import eval_env, fail_with_message, is_module_available, no_op
try:
from .fb import _init_ffmpeg
except ImportError:
from .utils import _init_ffmpeg
from .utils import _check_cuda_version, _fail_since_no_ffmpeg, _fail_since_no_sox, _in... |
from jina.serve.runtimes.gateway.gateway import BaseGateway
class PlaceHolderGateway(BaseGateway):
pass
| from jina.serve.runtimes.gateway.gateway import BaseGateway
class PlaceHolderGateway(BaseGateway):
pass |
_base_ = './faster-rcnn_hrnetv2p-w32-1x_coco.py'
# learning policy
max_epochs = 24
train_cfg = dict(max_epochs=max_epochs)
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=max_epochs,
... | _base_ = './faster_rcnn_hrnetv2p_w32_1x_coco.py'
# learning policy
max_epochs = 24
train_cfg = dict(max_epochs=max_epochs)
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=max_epochs,
... |
# Copyright (c) OpenMMLab. All rights reserved.
from abc import ABCMeta, abstractmethod
from typing import Tuple
from mmengine.model import BaseModule
from torch import Tensor
from mmdet.data_elements import SampleList
from mmdet.registry import MODELS
from mmdet.utils import InstanceList, OptConfigType, OptMultiConf... | # Copyright (c) OpenMMLab. All rights reserved.
from abc import ABCMeta, abstractmethod
from typing import Tuple
from mmengine.model import BaseModule
from torch import Tensor
from mmdet.core.utils import (InstanceList, OptConfigType, OptMultiConfig,
SampleList)
from mmdet.registry impor... |
import copy
from pathlib import Path
import clip
import numpy as np
import pytest
import torch
from jina import Document, DocumentArray, Executor
from ...clip_text import CLIPTextEncoder
@pytest.fixture(scope="module")
def encoder() -> CLIPTextEncoder:
return CLIPTextEncoder()
def test_config():
ex = Exec... | import copy
import clip
import numpy as np
import pytest
import torch
from jina import Document, DocumentArray
from ...clip_text import CLIPTextEncoder
@pytest.fixture(scope="module")
def encoder() -> CLIPTextEncoder:
return CLIPTextEncoder()
def test_no_documents(encoder: CLIPTextEncoder):
docs = Document... |
import numpy as np
import pytest
from docarray import Image
REMOTE_JPG = (
'https://upload.wikimedia.org/wikipedia/commons/8/80/'
'Dag_Sebastian_Ahlander_at_G%C3%B6teborg_Book_Fair_2012b.jpg'
)
@pytest.mark.slow
@pytest.mark.internet
def test_image():
image = Image(url=REMOTE_JPG)
image.tensor = i... | import numpy as np
from docarray import Image
REMOTE_JPG = (
'https://upload.wikimedia.org/wikipedia/commons/8/80/'
'Dag_Sebastian_Ahlander_at_G%C3%B6teborg_Book_Fair_2012b.jpg'
)
def test_image():
image = Image(url=REMOTE_JPG)
image.tensor = image.url.load()
assert isinstance(image.tensor, n... |
# model settings
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(
type='RPN',
preprocess_cfg=preprocess_cfg,
backbone=dict(
type='ResNet',
depth=50,
num_stages=3,
strides=(1, 2, 2),... | # model settings
model = dict(
type='RPN',
backbone=dict(
type='ResNet',
depth=50,
num_stages=3,
strides=(1, 2, 2),
dilations=(1, 1, 1),
out_indices=(2, ),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=False),
norm_eval=True,
... |
# Copyright (c) OpenMMLab. All rights reserved.
__version__ = '2.24.0'
short_version = __version__
def parse_version_info(version_str):
version_info = []
for x in version_str.split('.'):
if x.isdigit():
version_info.append(int(x))
elif x.find('rc') != -1:
patch_version... | # Copyright (c) OpenMMLab. All rights reserved.
__version__ = '2.23.0'
short_version = __version__
def parse_version_info(version_str):
version_info = []
for x in version_str.split('.'):
if x.isdigit():
version_info.append(int(x))
elif x.find('rc') != -1:
patch_version... |
from typing import Dict, Iterable, Sequence
from docarray import Document
from docarray.array.storage.base.getsetdel import BaseGetSetDelMixin
from docarray.array.storage.base.helper import Offset2ID
class GetSetDelMixin(BaseGetSetDelMixin):
"""Provide concrete implementation for ``__getitem__``, ``__setitem__``... | from typing import Dict, Iterable, Sequence
from docarray import Document
from docarray.array.storage.base.getsetdel import BaseGetSetDelMixin
from docarray.array.storage.base.helper import Offset2ID
class GetSetDelMixin(BaseGetSetDelMixin):
"""Provide concrete implementation for ``__getitem__``, ``__setitem__``... |
# Copyright (c) OpenMMLab. All rights reserved.
from mmcv.transforms import Compose
from mmengine.hooks import Hook
from mmdet.registry import HOOKS
@HOOKS.register_module()
class PipelineSwitchHook(Hook):
"""Switch data pipeline at switch_epoch.
Args:
switch_epoch (int): switch pipeline at this epo... | # Copyright (c) OpenMMLab. All rights reserved.
from mmcv.transforms import Compose
from mmengine.hooks import Hook
from mmdet.registry import HOOKS
@HOOKS.register_module()
class PipelineSwitchHook(Hook):
"""Switch data pipeline at switch_epoch.
Args:
switch_epoch (int): switch pipeline at this epo... |
# 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 .condinst import CondInst
from .cornernet import CornerNet
from .crowddet import CrowdDet
from .d2_wrapper ... | # 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 .crowddet import CrowdDet
from .d2_wrapper import Detectron2Wrapper
from .... |
_base_ = [
'../_base_/models/mask-rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
vis_backends = [dict(type='LocalVisBackend'), dict(type='WandbVisBackend')]
visualizer = dict(vis_backends=vis_backends)
# MMEngine support the ... | _base_ = [
'../_base_/models/mask-rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
vis_backends = [dict(type='LocalVisBackend'), dict(type='WandBVisBackend')]
visualizer = dict(vis_backends=vis_backends)
# MMEngine support the ... |
from textwrap import dedent
from types import SimpleNamespace
from unittest.mock import patch
from urllib.parse import quote
import pytest
from huggingface_hub import CommitOperationAdd, CommitOperationDelete
import datasets
from datasets.config import METADATA_CONFIGS_FIELD
from datasets.hub import delete_from_hub
f... | from urllib.parse import quote
import pytest
from datasets.utils.hub import hf_dataset_url
@pytest.mark.parametrize("repo_id", ["canonical_dataset_name", "org-name/dataset-name"])
@pytest.mark.parametrize("filename", ["filename.csv", "filename with blanks.csv"])
@pytest.mark.parametrize("revision", [None, "v2"])
de... |
import numpy as np
import scipy.linalg as sl
from keras.src.backend import standardize_dtype
from keras.src.backend.common import dtypes
from keras.src.backend.numpy.core import convert_to_tensor
def cholesky(a):
return np.linalg.cholesky(a)
def det(a):
return np.linalg.det(a)
def eig(a):
return np.l... | import numpy as np
import scipy.linalg as sl
from keras.src.backend import standardize_dtype
from keras.src.backend.common import dtypes
from keras.src.backend.numpy.core import convert_to_tensor
def cholesky(a):
return np.linalg.cholesky(a)
def det(a):
return np.linalg.det(a)
def eig(a):
return np.l... |
# Copyright (c) OpenMMLab. All rights reserved.
from .atss import ATSS
from .autoassign import AutoAssign
from .base import BaseDetector
from .boxinst import BoxInst
from .base_detr import DetectionTransformer
from .cascade_rcnn import CascadeRCNN
from .centernet import CenterNet
from .condinst import CondInst
from .co... | # Copyright (c) OpenMMLab. All rights reserved.
from .atss import ATSS
from .autoassign import AutoAssign
from .base import BaseDetector
from .boxinst import BoxInst
from .base_detr import DetectionTransformer
from .cascade_rcnn import CascadeRCNN
from .centernet import CenterNet
from .condinst import CondInst
from .co... |
# Copyright (c) OpenMMLab. All rights reserved.
from pathlib import Path
import mmcv
import torch
from mmcv.runner import load_checkpoint
from mmdet.registry import MODELS
from .. import build_detector
from .single_stage import SingleStageDetector
@MODELS.register_module()
class KnowledgeDistillationSingleStageDete... | # Copyright (c) OpenMMLab. All rights reserved.
from pathlib import Path
import mmcv
import torch
from mmcv.runner import load_checkpoint
from .. import build_detector
from ..builder import DETECTORS
from .single_stage import SingleStageDetector
@DETECTORS.register_module()
class KnowledgeDistillationSingleStageDet... |
# Copyright (c) OpenMMLab. All rights reserved.
from collections import OrderedDict
from mmcv.runner import get_dist_info
from mmcv.runner.hooks import HOOKS, Hook
from torch import nn
from ..utils.dist_utils import all_reduce_dict
def get_norm_states(module):
async_norm_states = OrderedDict()
for name, chi... | from collections import OrderedDict
from mmcv.runner import get_dist_info
from mmcv.runner.hooks import HOOKS, Hook
from torch import nn
from ..utils.dist_utils import all_reduce_dict
def get_norm_states(module):
async_norm_states = OrderedDict()
for name, child in module.named_modules():
if isinsta... |
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... |
_base_ = '../mask_rcnn/mask-rcnn_r50_fpn_1x_coco.py'
conv_cfg = dict(type='ConvWS')
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
backbone=dict(
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://jhu/resn... | _base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py'
conv_cfg = dict(type='ConvWS')
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
backbone=dict(
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://jhu/resn... |
# Copyright 2020 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 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... |
import torch
from docarray.typing.tensor.torch_tensor import TorchTensor
import copy
from docarray import BaseDoc
from docarray.typing import TorchEmbedding, TorchTensor
def test_set_torch_tensor():
class MyDocument(BaseDoc):
tensor: TorchTensor
d = MyDocument(tensor=torch.zeros((3, 224, 224)))
... | import torch
from docarray.typing.tensor.torch_tensor import TorchTensor
import copy
from docarray import BaseDoc
from docarray.typing import TorchEmbedding, TorchTensor
def test_set_torch_tensor():
class MyDocument(BaseDoc):
tensor: TorchTensor
d = MyDocument(tensor=torch.zeros((3, 224, 224)))
... |
from keras.src import ops
from keras.src import quantizers
from keras.src import random
from keras.src import testing
class QuantizersTest(testing.TestCase):
def test_get_method(self):
quantizer = quantizers.get("abs_max_quantizer", axis=-1)
self.assertTrue(quantizer, quantizers.AbsMaxQuantizer)
... | from keras.src import ops
from keras.src import quantizers
from keras.src import random
from keras.src import testing
class QuantizersTest(testing.TestCase):
def test_get_method(self):
quantizer = quantizers.get("abs_max_quantizer", axis=-1)
self.assertTrue(quantizer, quantizers.AbsMaxQuantizer)
... |
from docarray import BaseDoc, DocArray
from docarray.documents import ImageDoc
from docarray.typing import NdArray
class MyDoc(BaseDoc):
embedding: NdArray
text: str
image: ImageDoc
def test_from_to_json():
da = DocArray[MyDoc](
[
MyDoc(
embedding=[1, 2, 3, 4, 5],... | from docarray import BaseDocument, DocumentArray
from docarray.documents import ImageDoc
from docarray.typing import NdArray
class MyDoc(BaseDocument):
embedding: NdArray
text: str
image: ImageDoc
def test_from_to_json():
da = DocumentArray[MyDoc](
[
MyDoc(
embedd... |
import os
from unittest import TestCase
import cv2
import numpy as np
import torch
from mmengine.data import InstanceData
from mmdet.structures import DetDataSample
from mmdet.visualization import DetLocalVisualizer
def _rand_bboxes(num_boxes, h, w):
cx, cy, bw, bh = torch.rand(num_boxes, 4).T
tl_x = ((cx ... | import os
from unittest import TestCase
import cv2
import numpy as np
import torch
from mmengine.data import InstanceData
from mmdet.data_elements import DetDataSample
from mmdet.visualization import DetLocalVisualizer
def _rand_bboxes(num_boxes, h, w):
cx, cy, bw, bh = torch.rand(num_boxes, 4).T
tl_x = ((... |
import os
import numpy as np
import keras
from keras.src import testing
from keras.src.saving.file_editor import KerasFileEditor
def get_source_model():
inputs = keras.Input((2,))
x = keras.layers.Dense(3, name="mydense")(inputs)
outputs = keras.layers.Dense(3, name="output_layer")(x)
model = keras.... | import os
import numpy as np
import keras
from keras.src import testing
from keras.src.saving.file_editor import KerasFileEditor
def get_source_model():
inputs = keras.Input((2,))
x = keras.layers.Dense(3, name="mydense")(inputs)
outputs = keras.layers.Dense(3, name="output_layer")(x)
model = keras.... |
_base_ = '../mask_rcnn/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),
scale=(0.8, 1... | _base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='InstaBoost',
action_candidate=('normal', 'horizontal', 'skip'),
action_pr... |
# Copyright (c) OpenMMLab. All rights reserved.
# flake8: noqa
from .config import *
from .data import *
from .dataset import *
from .fileio import *
from .hooks import *
from .logging import *
from .registry import *
from .runner import *
from .utils import *
from .visualization import *
| # Copyright (c) OpenMMLab. All rights reserved.
# flake8: noqa
from .config import *
from .data import *
from .dataset import *
from .fileio import *
from .hooks import *
from .logging import *
from .registry import *
from .runner import *
from .utils import *
|
import pytest
from jina import Document, DocumentArray, Flow
from ...text_paddle import TextPaddleEncoder
@pytest.fixture(scope='function')
def flow():
return Flow().add(uses=TextPaddleEncoder)
@pytest.fixture(scope='function')
def content():
return 'hello world'
@pytest.fixture(scope='function')
def doc... | import pytest
from jina import Document, DocumentArray, Flow
from jinahub.encoder.text_paddle import TextPaddleEncoder
@pytest.fixture(scope='function')
def flow():
return Flow().add(uses=TextPaddleEncoder)
@pytest.fixture(scope='function')
def content():
return 'hello world'
@pytest.fixture(scope='funct... |
# Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
import torch
from torch.autograd import gradcheck
from mmdet.models.utils import interpolate_as, sigmoid_geometric_mean
def test_interpolate_as():
source = torch.rand((1, 5, 4, 4))
target = torch.rand((1, 1, 16, 16))
# Test 4D source and... | # Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
import torch
from mmdet.models.utils import interpolate_as
def test_interpolate_as():
source = torch.rand((1, 5, 4, 4))
target = torch.rand((1, 1, 16, 16))
# Test 4D source and target
result = interpolate_as(source, target)
asser... |
"""Scrapfly Web Reader."""
import logging
from typing import List, Optional, Literal
from llama_index.core.readers.base import BasePydanticReader
from llama_index.core.schema import Document
logger = logging.getLogger(__file__)
class ScrapflyReader(BasePydanticReader):
"""
Turn a url to llm accessible markd... | """Scrapfly Web Reader."""
import logging
from typing import List, Optional, Literal
from llama_index.core.readers.base import BasePydanticReader
from llama_index.core.schema import Document
logger = logging.getLogger(__file__)
class ScrapflyReader(BasePydanticReader):
"""Turn a url to llm accessible markdown w... |
import abc
from abc import ABC
from typing import TYPE_CHECKING, Any, Generic, List, Tuple, Type, TypeVar, Union
from docarray.computation import AbstractComputationalBackend
from docarray.typing.abstract_type import AbstractType
if TYPE_CHECKING:
from pydantic import BaseConfig
from pydantic.fields import Mo... | import abc
from abc import ABC
from typing import TYPE_CHECKING, Any, Generic, List, Tuple, Type, TypeVar, Union
from docarray.computation import AbstractComputationalBackend
from docarray.typing.abstract_type import AbstractType
if TYPE_CHECKING:
from pydantic import BaseConfig
from pydantic.fields import Mo... |
from functools import partial
from torchaudio.models import hdemucs_high
from torchaudio.pipelines import SourceSeparationBundle
HDEMUCS_HIGH_MUSDB_PLUS = SourceSeparationBundle(
_model_path="models/hdemucs_high_trained.pt",
_model_factory_func=partial(hdemucs_high, sources=["drums", "bass", "other", "vocal... | from dataclasses import dataclass
from functools import partial
from typing import Callable
import torch
import torchaudio
from torchaudio.models import hdemucs_high
from torchaudio.prototype.models import conv_tasnet_base
@dataclass
class SourceSeparationBundle:
"""torchaudio.prototype.pipelines.SourceSeparati... |
from abc import abstractmethod
from typing import Protocol
# NOTE: This is a bare-bone suggestion for an abstract protocol to define GraphRAG for llama-index
# This should be expanded upon and integrated to llama-index-core to support multiple different GraphRAG
# libraries in the future
class GraphRAG(Protocol):
... | from abc import abstractmethod
from typing import Protocol
# NOTE: This is a bare-bone suggestion for an abstract protocol to define GraphRAG for llama-index
# This should be expanded upon and integrated to llama-index-core to support multiple different GraphRAG
# libraries in the future
class GraphRAG(Protocol):
... |
__copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
from pathlib import Path
import librosa
import numpy as np
from jina import Document, DocumentArray, Executor
from ...audio_clip_encoder import AudioCLIPEncoder
def test_config():
ex = Executor.load_conf... | __copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import os
import librosa
import numpy as np
from jina import Executor, Document, DocumentArray
from audio_clip_encoder import AudioCLIPEncoder
cur_dir = os.path.dirname(os.path.abspath(__file__))
def test_lo... |
from pathlib import Path
import librosa
import pytest
from executor.vggish import vggish_input
from executor.vggish_audio_encoder import VggishAudioEncoder
from jina import Document, DocumentArray, Executor
from tensorflow.python.framework import ops
def test_config():
ex = Executor.load_config(str(Path(__file__... | from pathlib import Path
import librosa
import pytest
from jina import Document, DocumentArray, Executor
from tensorflow.python.framework import ops
from ...vggish import vggish_input
from ...vggish_audio_encoder import VggishAudioEncoder
def test_config():
ex = Executor.load_config(str(Path(__file__).parents[2... |
from abc import ABC
from contextlib import ExitStack
from rich.table import Table
from jina.helper import CatchAllCleanupContextManager, get_internal_ip, get_public_ip
class BaseOrchestrator(ExitStack, ABC):
"""Base orchestrator class"""
def __enter__(self):
with CatchAllCleanupContextManager(self)... | from abc import ABC
from contextlib import ExitStack
from rich.table import Table
from jina.helper import CatchAllCleanupContextManager, get_internal_ip, get_public_ip
class BaseOrchestrator(ExitStack, ABC):
"""Base orchestrator class"""
def __enter__(self):
with CatchAllCleanupContextManager(self)... |
# Copyright (c) OpenMMLab. All rights reserved.
from unittest import TestCase
import torch
from mmdet.registry import MODELS, TASK_UTILS
from mmdet.testing import demo_track_inputs, random_boxes
from mmdet.utils import register_all_modules
class TestByteTracker(TestCase):
@classmethod
def setUpClass(cls):
... | # Copyright (c) OpenMMLab. All rights reserved.
from unittest import TestCase
import torch
from mmdet.registry import MODELS, TASK_UTILS
from mmdet.testing import demo_track_inputs, random_boxes
from mmdet.utils import register_all_modules
class TestByteTracker(TestCase):
@classmethod
def setUpClass(cls):
... |
# Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
from tempfile import TemporaryDirectory
from unittest import TestCase, skipIf
from mmengine.registry import (Registry, count_registered_modules, root,
traverse_registry_tree)
from mmengine.utils import is_installed
c... | # Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
from tempfile import TemporaryDirectory
from unittest import TestCase
from mmengine.registry import (Registry, count_registered_modules, root,
traverse_registry_tree)
class TestUtils(TestCase):
def test_traverse... |
from typing import Any, Dict, Iterator
import torch
from ..utils import _log_api_usage_once
try:
from ._load_gpu_decoder import _HAS_GPU_VIDEO_DECODER
except ModuleNotFoundError:
_HAS_GPU_VIDEO_DECODER = False
from ._video_opt import (
_HAS_CPU_VIDEO_DECODER,
_HAS_VIDEO_OPT,
_probe_video_from_fi... | from typing import Any, Dict, Iterator
import torch
from ..utils import _log_api_usage_once
try:
from ._load_gpu_decoder import _HAS_GPU_VIDEO_DECODER
except ModuleNotFoundError:
_HAS_GPU_VIDEO_DECODER = False
from ._video_opt import (
_HAS_CPU_VIDEO_DECODER,
_HAS_VIDEO_OPT,
_probe_video_from_fi... |
import logging
import re
from collections.abc import Sequence
from typing import Optional, Union
from urllib.parse import urljoin, urlparse
logger = logging.getLogger(__name__)
PREFIXES_TO_IGNORE = ("javascript:", "mailto:", "#")
SUFFIXES_TO_IGNORE = (
".css",
".js",
".ico",
".png",
".jpg",
".... | import logging
import re
from collections.abc import Sequence
from typing import Optional, Union
from urllib.parse import urljoin, urlparse
logger = logging.getLogger(__name__)
PREFIXES_TO_IGNORE = ("javascript:", "mailto:", "#")
SUFFIXES_TO_IGNORE = (
".css",
".js",
".ico",
".png",
".jpg",
".... |
import numpy as np
from docarray import DocumentArray, Document
def random_docs(
num_docs,
chunks_per_doc=5,
embed_dim=10,
jitter=1,
start_id=0,
embedding=True,
sparse_embedding=False,
text='hello world',
) -> DocumentArray:
da = DocumentArray()
next_chunk_doc_id = start_id + n... | import numpy as np
from docarray import DocumentArray, Document
def random_docs(
num_docs,
chunks_per_doc=5,
embed_dim=10,
jitter=1,
start_id=0,
embedding=True,
sparse_embedding=False,
text='hello world',
) -> DocumentArray:
da = DocumentArray()
next_chunk_doc_id = start_id + ... |
"""
This tool allows agents to interact with the NASA API, specifically
the the NASA Image & Video Library and Exoplanet
"""
from typing import Optional
from langchain_core.callbacks import CallbackManagerForToolRun
from langchain_core.tools import BaseTool
from pydantic import Field
from langchain_community.utiliti... | """
This tool allows agents to interact with the NASA API, specifically
the the NASA Image & Video Library and Exoplanet
"""
from typing import Optional
from langchain_core.callbacks import CallbackManagerForToolRun
from langchain_core.tools import BaseTool
from pydantic import Field
from langchain_community.utiliti... |
import os
from typing import Type
from pydantic import BaseModel, Field
from docarray.document.abstract_document import AbstractDocument
from docarray.document.base_node import BaseNode
from docarray.typing import ID
from .mixins import ProtoMixin
class BaseDocument(BaseModel, ProtoMixin, AbstractDocument, BaseNod... | import os
from typing import Union
from uuid import UUID
from pydantic import BaseModel, Field
from docarray.document.abstract_document import AbstractDocument
from docarray.document.base_node import BaseNode
from .mixins import ProtoMixin
class BaseDocument(BaseModel, ProtoMixin, AbstractDocument, BaseNode):
... |
from __future__ import annotations
from typing import Any, List, Optional, Tuple, Union
import PIL.Image
import torch
from torchvision.transforms import InterpolationMode
from ._datapoint import Datapoint, FillTypeJIT
class Mask(Datapoint):
@classmethod
def _wrap(cls, tensor: torch.Tensor) -> Mask:
... | from __future__ import annotations
from typing import Any, List, Optional, Tuple, Union
import torch
from torchvision.transforms import InterpolationMode
from ._datapoint import Datapoint, FillTypeJIT
class Mask(Datapoint):
@classmethod
def _wrap(cls, tensor: torch.Tensor) -> Mask:
return tensor.as... |
from typing import Union
import numpy as np
import pytest
import torch
from docarray import BaseDocument, DocumentArray
from docarray.typing import NdArray, TorchTensor
@pytest.fixture()
def batch():
class Image(BaseDocument):
tensor: TorchTensor[3, 224, 224]
batch = DocumentArray[Image](
[... | from typing import Union
import numpy as np
import pytest
import torch
from docarray import Document, DocumentArray
from docarray.typing import NdArray, TorchTensor
@pytest.fixture()
def batch():
class Image(Document):
tensor: TorchTensor[3, 224, 224]
batch = DocumentArray[Image](
[Image(te... |
import sys
import pytest
from hypothesis import given, settings, strategies
import xgboost as xgb
from xgboost import testing as tm
from xgboost.testing import no_cupy
from xgboost.testing.updater import check_extmem_qdm, check_quantile_loss_extmem
sys.path.append("tests/python")
from test_data_iterator import run_d... | import sys
import pytest
from hypothesis import given, settings, strategies
import xgboost as xgb
from xgboost import testing as tm
from xgboost.testing import no_cupy
from xgboost.testing.updater import check_extmem_qdm, check_quantile_loss_extmem
sys.path.append("tests/python")
from test_data_iterator import run_d... |
"""Various utilities to help with development."""
# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause
from ..exceptions import DataConversionWarning
from . import metadata_routing
from ._bunch import Bunch
from ._chunking import gen_batches, gen_even_slices
from ._estimator_html_repr import... | """Various utilities to help with development."""
# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause
import platform
import warnings
from collections.abc import Sequence
import numpy as np
from ..exceptions import DataConversionWarning
from . import _joblib, metadata_routing
from ._bunch... |
"""FastAPI framework, high performance, easy to learn, fast to code, ready for production"""
__version__ = "0.116.1"
from starlette import status as status
from .applications import FastAPI as FastAPI
from .background import BackgroundTasks as BackgroundTasks
from .datastructures import UploadFile as UploadFile
from... | """FastAPI framework, high performance, easy to learn, fast to code, ready for production"""
__version__ = "0.116.0"
from starlette import status as status
from .applications import FastAPI as FastAPI
from .background import BackgroundTasks as BackgroundTasks
from .datastructures import UploadFile as UploadFile
from... |
import logging
import os
from argparse import ArgumentParser
import sentencepiece as spm
from average_checkpoints import ensemble
from pytorch_lightning import seed_everything, Trainer
from pytorch_lightning.callbacks import LearningRateMonitor, ModelCheckpoint
from pytorch_lightning.strategies import DDPStrategy
from... | import logging
import os
from argparse import ArgumentParser
import sentencepiece as spm
from average_checkpoints import ensemble
from pytorch_lightning import seed_everything, Trainer
from pytorch_lightning.callbacks import LearningRateMonitor, ModelCheckpoint
from pytorch_lightning.strategies import DDPStrategy
from... |
import os
from pathlib import Path
from typing import List, Tuple, Union
import torchaudio
from torch import Tensor
from torch.hub import download_url_to_file
from torch.utils.data import Dataset
from torchaudio.datasets.librispeech import _get_librispeech_metadata
from torchaudio.datasets.utils import extract_archive... | import os
from pathlib import Path
from typing import List, Tuple, Union
import torchaudio
from torch import Tensor
from torch.hub import download_url_to_file
from torch.utils.data import Dataset
from torchaudio.datasets.librispeech import _get_librispeech_metadata
from torchaudio.datasets.utils import extract_archive... |
# coding=utf-8
# Copyright 2025 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... | # coding=utf-8
# Copyright 2025 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... |
# coding=utf-8
# Copyright 2025 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... | # coding=utf-8
# Copyright 2024 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... |
# Copyright (c) OpenMMLab. All rights reserved.
from typing import List, Optional, Tuple
import torch
from torch import Tensor
from mmdet.core.utils.typing import ConfigType, OptMultiConfig
from mmdet.registry import MODELS
from .base_roi_extractor import BaseRoIExtractor
@MODELS.register_module()
class SingleRoIEx... | # Copyright (c) OpenMMLab. All rights reserved.
import torch
from mmdet.registry import MODELS
from .base_roi_extractor import BaseRoIExtractor
@MODELS.register_module()
class SingleRoIExtractor(BaseRoIExtractor):
"""Extract RoI features from a single level feature map.
If there are multiple input feature l... |
import pytest
from langchain._api import suppress_langchain_deprecation_warning as sup2
from langchain_core._api import suppress_langchain_deprecation_warning as sup1
from langchain_cli.namespaces.migrate.generate.generic import (
generate_simplified_migrations,
)
@pytest.mark.xfail(reason="Unknown reason")
def ... | import pytest
from langchain._api import suppress_langchain_deprecation_warning as sup2
from langchain_core._api import suppress_langchain_deprecation_warning as sup1
from langchain_cli.namespaces.migrate.generate.generic import (
generate_simplified_migrations,
)
@pytest.mark.xfail(reason="Unknown reason")
def ... |
"""
Prompts for implementing Chain of Abstraction.
While official prompts are not given (and the paper finetunes models for the task),
we can take inspiration and use few-shot prompting to generate a prompt for implementing
chain of abstraction in an LLM agent.
"""
REASONING_PROMPT_TEMPALTE = """Generate an abstract ... | """
Prompts for implementing Chain of Abstraction.
While official prompts are not given (and the paper finetunes models for the task),
we can take inspiration and use few-shot prompting to generate a prompt for implementing
chain of abstraction in an LLM agent.
"""
REASONING_PROMPT_TEMPALTE = """Generate an abstract... |
_base_ = '../faster_rcnn/faster-rcnn_r50_fpn_1x_coco.py'
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
# use ResNeSt img_norm
data_preprocessor=dict(
mean=[123.68, 116.779, 103.939],
std=[58.393, 57.12, 57.375],
bgr_to_rgb=True),
backbone=dict(
type='ResNeS... | _base_ = '../faster_rcnn/faster-rcnn_r50_fpn_1x_coco.py'
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
# use ResNeSt img_norm
data_preprocessor=dict(
mean=[123.68, 116.779, 103.939],
std=[58.393, 57.12, 57.375],
bgr_to_rgb=True),
backbone=dict(
type='ResNeS... |
import numpy as np
import pytest
from pydantic import Field
from docarray import BaseDoc, DocList
from docarray.index.backends.in_memory import InMemoryExactNNIndex
from docarray.typing import NdArray
class SchemaDoc(BaseDoc):
text: str
price: int
tensor: NdArray[10]
@pytest.fixture
def docs():
doc... | import numpy as np
import pytest
from pydantic import Field
from docarray import BaseDoc, DocList
from docarray.index.backends.in_memory import InMemoryExactNNIndex
from docarray.typing import NdArray
class SchemaDoc(BaseDoc):
text: str
price: int
tensor: NdArray[10]
@pytest.fixture
def docs():
doc... |
import unittest
import torch
import torchaudio.prototype.functional as F
from torchaudio_unittest.common_utils import nested_params, TestBaseMixin, torch_script
class TorchScriptConsistencyTestImpl(TestBaseMixin):
def _assert_consistency(self, func, inputs, shape_only=False):
inputs_ = []
for i i... | import unittest
import torch
import torchaudio.prototype.functional as F
from torchaudio_unittest.common_utils import nested_params, TestBaseMixin, torch_script
class TorchScriptConsistencyTestImpl(TestBaseMixin):
def _assert_consistency(self, func, inputs, shape_only=False):
inputs_ = []
for i i... |
from langchain_core.prompts.prompt import PromptTemplate
web_search_template = """Please write a passage to answer the question
Question: {QUESTION}
Passage:"""
web_search = PromptTemplate(template=web_search_template, input_variables=["QUESTION"])
sci_fact_template = """Please write a scientific paper passage to supp... | # flake8: noqa
from langchain_core.prompts.prompt import PromptTemplate
web_search_template = """Please write a passage to answer the question
Question: {QUESTION}
Passage:"""
web_search = PromptTemplate(template=web_search_template, input_variables=["QUESTION"])
sci_fact_template = """Please write a scientific paper... |
"""
This script downloads the WikiMatrix corpus (https://github.com/facebookresearch/LASER/tree/master/tasks/WikiMatrix)
and create parallel sentences tsv files that can be used to extend existent sentence embedding models to new languages.
The WikiMatrix mined parallel sentences from Wikipedia in various languages.
... | """
This script downloads the WikiMatrix corpus (https://github.com/facebookresearch/LASER/tree/master/tasks/WikiMatrix)
and create parallel sentences tsv files that can be used to extend existent sentence embedding models to new languages.
The WikiMatrix mined parallel sentences from Wikipedia in various languages.
... |
from __future__ import annotations
from collections.abc import Iterable
import torch
import torch.nn as nn
from sentence_transformers import util
from sentence_transformers.sparse_encoder.losses.CSRReconstructionLoss import CSRReconstructionLoss
from sentence_transformers.sparse_encoder.losses.SparseMultipleNegative... | from __future__ import annotations
from collections.abc import Iterable
import torch
import torch.nn as nn
from sentence_transformers.sparse_encoder.losses.CSRReconstructionLoss import CSRReconstructionLoss
from sentence_transformers.sparse_encoder.losses.SparseMultipleNegativesRankingLoss import (
SparseMultipl... |
"""Common test fixtures with proper setup and teardown."""
from contextlib import asynccontextmanager
from typing import AsyncGenerator
from unittest.mock import Mock, patch
import pytest
from prisma import Prisma
@pytest.fixture
async def test_db_connection() -> AsyncGenerator[Prisma, None]:
"""Provide a test ... | """Common test fixtures with proper setup and teardown."""
from contextlib import asynccontextmanager
from typing import AsyncGenerator
from unittest.mock import Mock, patch
import pytest
from prisma import Prisma
@pytest.fixture
async def test_db_connection() -> AsyncGenerator[Prisma, None]:
"""Provide a test ... |
"""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... |
from typing import Annotated, Union
from fastapi import FastAPI, Query
app = FastAPI()
@app.get("/items/")
async def read_items(q: Annotated[Union[str, None], Query(min_length=3)]):
results = {"items": [{"item_id": "Foo"}, {"item_id": "Bar"}]}
if q:
results.update({"q": q})
return results
| from typing import Annotated, Union
from fastapi import FastAPI, Query
app = FastAPI()
@app.get("/items/")
async def read_items(q: Annotated[Union[str, None], Query(min_length=3)] = ...):
results = {"items": [{"item_id": "Foo"}, {"item_id": "Bar"}]}
if q:
results.update({"q": q})
return results
|
from collections import defaultdict
import torch
import transforms as reference_transforms
def get_modules(use_v2):
# We need a protected import to avoid the V2 warning in case just V1 is used
if use_v2:
import torchvision.datapoints
import torchvision.transforms.v2
return torchvisio... | from collections import defaultdict
import torch
import transforms as reference_transforms
def get_modules(use_v2):
# We need a protected import to avoid the V2 warning in case just V1 is used
if use_v2:
import torchvision.datapoints
import torchvision.transforms.v2
return torchvisio... |
from __future__ import annotations
from typing import Any
import torch
from torch import nn
from transformers import AutoConfig, AutoModelForMaskedLM, AutoTokenizer
# TODO: Check the tokenizer problem and if more need to be implement like the Transformer class
class MLMTransformer(nn.Module):
"""A minimal Tran... | from __future__ import annotations
from typing import Any
import torch
from torch import nn
from transformers import AutoConfig, AutoModelForMaskedLM, AutoTokenizer
class MLMTransformer(nn.Module):
"""A minimal Transformer model that uses MLM (Masked Language Modeling).
This model implements only the essen... |
"""Standard LangChain interface tests"""
from langchain_core.language_models import BaseChatModel
from langchain_tests.unit_tests import ( # type: ignore[import-not-found]
ChatModelUnitTests, # type: ignore[import-not-found]
)
from langchain_fireworks import ChatFireworks
class TestFireworksStandard(ChatModel... | """Standard LangChain interface tests"""
from langchain_core.language_models import BaseChatModel
from langchain_tests.unit_tests import ( # type: ignore[import-not-found]
ChatModelUnitTests, # type: ignore[import-not-found]
)
from langchain_fireworks import ChatFireworks
class TestFireworksStandard(ChatModel... |
# Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
from mmcv.cnn import ConvModule
from mmcv.runner import BaseModule, auto_fp16, force_fp32
from mmdet.models.builder import HEADS
from mmdet.models.utils import ResLayer, SimplifiedBasicBlock
@HEADS.register_module()
class GlobalContextHead(BaseMod... | import torch.nn as nn
from mmcv.cnn import ConvModule
from mmcv.runner import BaseModule, auto_fp16, force_fp32
from mmdet.models.builder import HEADS
from mmdet.models.utils import ResLayer, SimplifiedBasicBlock
@HEADS.register_module()
class GlobalContextHead(BaseModule):
"""Global context head used in `SCNet ... |
import numpy as np
import pytest
from keras.src import layers
from keras.src.testing import test_case
class ActivityRegularizationTest(test_case.TestCase):
def test_correctness(self):
layer = layers.ActivityRegularization(l1=0.2, l2=0.3)
layer(2 * np.ones((1,)))
self.assertLen(layer.losse... | import numpy as np
import pytest
from keras.src import layers
from keras.src.testing import test_case
class ActivityRegularizationTest(test_case.TestCase):
def test_correctness(self):
layer = layers.ActivityRegularization(l1=0.2, l2=0.3)
layer(2 * np.ones((1,)))
self.assertLen(layer.losse... |
# Copyright (c) OpenMMLab. All rights reserved.
from unittest import TestCase
from unittest.mock import MagicMock, Mock, patch
from mmengine.hooks import IterTimerHook
from mmengine.logging import MessageHub
def time_patch():
if not hasattr(time_patch, 'time'):
time_patch.time = 0
else:
time_... | # Copyright (c) OpenMMLab. All rights reserved.
from unittest.mock import Mock
from mmengine.hooks import IterTimerHook
class TestIterTimerHook:
def test_before_epoch(self):
hook = IterTimerHook()
runner = Mock()
hook._before_epoch(runner)
assert isinstance(hook.t, float)
de... |
from unittest.mock import patch
import pytest
from llama_index.utils.workflow import (
draw_all_possible_flows,
draw_most_recent_execution,
)
@pytest.mark.asyncio
async def test_workflow_draw_methods(workflow):
with patch("pyvis.network.Network") as mock_network:
draw_all_possible_flows(workflow... | from unittest.mock import patch
import pytest
from llama_index.utils.workflow import (
draw_all_possible_flows,
draw_most_recent_execution,
)
@pytest.mark.asyncio()
async def test_workflow_draw_methods(workflow):
with patch("pyvis.network.Network") as mock_network:
draw_all_possible_flows(workfl... |
# Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import numpy as np
import torch
import torch.nn as nn
from mmdet.registry import MODELS
from .utils import weighted_loss
@mmcv.jit(derivate=True, coderize=True)
@weighted_loss
def balanced_l1_loss(pred,
target,
beta... | # Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import numpy as np
import torch
import torch.nn as nn
from ..builder import LOSSES
from .utils import weighted_loss
@mmcv.jit(derivate=True, coderize=True)
@weighted_loss
def balanced_l1_loss(pred,
target,
beta=1.0,... |
from __future__ import annotations
import json
import os
from typing import Any
import torch
from torch import nn
class SpladePooling(nn.Module):
"""SPLADE pooling layer that aggregates MLM logits using max or sum pooling.
This pooling layer takes MLM logits (shape: batch_size, seq_length, vocab_size)
... | from __future__ import annotations
import json
import os
from typing import Any
import torch
from torch import nn
class SpladePooling(nn.Module):
"""SPLADE pooling layer that aggregates MLM logits using max or sum pooling.
This pooling layer takes MLM logits (shape: batch_size, seq_length, vocab_size)
... |
import os
import numpy as np
import pytest
from docarray import BaseDoc, DocList, DocVec
from docarray.documents import ImageDoc
from docarray.typing import NdArray
class MyDoc(BaseDoc):
embedding: NdArray
text: str
image: ImageDoc
@pytest.mark.slow
@pytest.mark.parametrize(
'protocol', ['pickle-a... | import os
import numpy as np
import pytest
from docarray import BaseDoc, DocList
from docarray.documents import ImageDoc
from docarray.typing import NdArray
class MyDoc(BaseDoc):
embedding: NdArray
text: str
image: ImageDoc
@pytest.mark.slow
@pytest.mark.parametrize(
'protocol', ['pickle-array', '... |
_base_ = './fcos_r50-caffe_fpn_gn-head_1x_coco.py'
# model settings
model = dict(
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
bgr_to_rgb=True,
pad_size_divisor=32),
backbone=dict(
type='ResNeXt'... | _base_ = './fcos_r50-caffe_fpn_gn-head_1x_coco.py'
# model settings
model = dict(
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
bgr_to_rgb=True,
pad_size_divisor=32),
backbone=dict(
type='ResNeXt'... |
_base_ = './cascade-mask-rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| _base_ = './cascade_mask_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
|
_base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# model settings
preprocess_cfg = dict(
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True,
pad_size_divisor=32)
model = dict(
preprocess_cfg=prepr... | _base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
type='PAA',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=di... |
# Copyright (c) OpenMMLab. All rights reserved.
from .anchor_free_head import AnchorFreeHead
from .anchor_head import AnchorHead
from .atss_head import ATSSHead
from .autoassign_head import AutoAssignHead
from .cascade_rpn_head import CascadeRPNHead, StageCascadeRPNHead
from .centernet_head import CenterNetHead
from .c... | # Copyright (c) OpenMMLab. All rights reserved.
from .anchor_free_head import AnchorFreeHead
from .anchor_head import AnchorHead
from .atss_head import ATSSHead
from .autoassign_head import AutoAssignHead
from .cascade_rpn_head import CascadeRPNHead, StageCascadeRPNHead
from .centernet_head import CenterNetHead
from .c... |
import threading
import fsspec.asyn
import torch
from ...iterable_dataset import IterableDataset, _apply_feature_types_on_example
from ...utils.logging import get_logger
logger = get_logger(__name__)
def _set_fsspec_for_multiprocess() -> None:
"""
Clear reference to the loop and thread.
This is necess... | import threading
import fsspec.asyn
import torch
from ...iterable_dataset import IterableDataset, _apply_feature_types
from ...utils.logging import get_logger
logger = get_logger(__name__)
def _set_fsspec_for_multiprocess() -> None:
"""
Clear reference to the loop and thread.
This is necessary otherwi... |
from typing import Union, Iterable, Dict, List
import warnings
from docarray.array.storage.base.seqlike import BaseSequenceLikeMixin
from docarray import Document
class SequenceLikeMixin(BaseSequenceLikeMixin):
"""Implement sequence-like methods for DocumentArray with Elastic as storage"""
def __eq__(self, ... | from typing import Union, Iterable, Dict, List
import warnings
from docarray.array.storage.base.seqlike import BaseSequenceLikeMixin
from docarray import Document
class SequenceLikeMixin(BaseSequenceLikeMixin):
"""Implement sequence-like methods for DocumentArray with Elastic as storage"""
def __eq__(self, ... |
"""
This examples trains a CrossEncoder for the Quora Duplicate Questions Detection task. A CrossEncoder takes a sentence pair
as input and outputs a label. Here, it output a continuous labels 0...1 to indicate the similarity between the input pair.
It does NOT produce a sentence embedding and does NOT work for indivi... | """
This examples trains a CrossEncoder for the Quora Duplicate Questions Detection task. A CrossEncoder takes a sentence pair
as input and outputs a label. Here, it output a continuous labels 0...1 to indicate the similarity between the input pair.
It does NOT produce a sentence embedding and does NOT work for indivi... |
# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import logging
import os
import os.path as osp
from mmengine.config import Config, DictAction
from mmengine.logging import print_log
from mmengine.registry import RUNNERS
from mmengine.runner import Runner
from mmdet.utils import register_all_modules
d... | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import logging
import os
import os.path as osp
from mmengine.config import Config, DictAction
from mmengine.logging import print_log
from mmengine.registry import RUNNERS
from mmengine.runner import Runner
from mmdet.utils import register_all_modules
d... |
"""Utilities for environment variables."""
from __future__ import annotations
import os
from typing import Any, Optional, Union
def env_var_is_set(env_var: str) -> bool:
"""Check if an environment variable is set.
Args:
env_var (str): The name of the environment variable.
Returns:
bool... | from __future__ import annotations
import os
from typing import Any, Optional, Union
def env_var_is_set(env_var: str) -> bool:
"""Check if an environment variable is set.
Args:
env_var (str): The name of the environment variable.
Returns:
bool: True if the environment variable is set, F... |
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