input stringlengths 33 5k | output stringlengths 32 5k |
|---|---|
# dataset settings
dataset_type = 'CocoPanopticDataset'
data_root = 'data/coco/'
# Example to use different file client
# Method 1: simply set the data root and let the file I/O module
# automatically infer from prefix (not support LMDB and Memcache yet)
# data_root = 's3://openmmlab/datasets/detection/coco/'
# Meth... | # dataset settings
dataset_type = 'CocoPanopticDataset'
# data_root = 'data/coco/'
# Example to use different file client
# Method 1: simply set the data root and let the file I/O module
# automatically infer from prefix (not support LMDB and Memcache yet)
data_root = 's3://openmmlab/datasets/detection/coco/'
# Meth... |
from __future__ import annotations
try:
from typing import Self
except ImportError:
from typing_extensions import Self
import torch
import transformers
from PIL import Image
from sentence_transformers.models.Router import InputModule
class CLIPModel(InputModule):
save_in_root: bool = True
def __in... | from __future__ import annotations
try:
from typing import Self
except ImportError:
from typing_extensions import Self
import torch
import transformers
from PIL import Image
from sentence_transformers.models.Asym import InputModule
class CLIPModel(InputModule):
save_in_root: bool = True
def __init... |
"""Code to help indexing data into a vectorstore.
This package contains helper logic to help deal with indexing data into
a vectorstore while avoiding duplicated content and over-writing content
if it's unchanged.
"""
from importlib import import_module
from typing import TYPE_CHECKING
if TYPE_CHECKING:
from lan... | """Code to help indexing data into a vectorstore.
This package contains helper logic to help deal with indexing data into
a vectorstore while avoiding duplicated content and over-writing content
if it's unchanged.
"""
from langchain_core.indexing.api import IndexingResult, aindex, index
from langchain_core.indexing.b... |
#!/usr/bin/env python3
"""This is the preprocessing script for HuBERT model training.
The script includes:
- File list creation
- MFCC/HuBERT feature extraction
- KMeans clustering model training
- Pseudo-label generation
"""
import logging
from argparse import ArgumentParser, RawTextHelpFormatter
from ... | #!/usr/bin/env python3
"""This is the preprocessing script for HuBERT model training.
The script includes:
- File list creation
- MFCC/HuBERT feature extraction
- KMeans clustering model training
- Pseudo-label generation
"""
import logging
from argparse import ArgumentParser, RawTextHelpFormatter
from ... |
# Owner(s): ["module: inductor"]
import ctypes
import torch
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.codecache import CUDACodeCache
from torch._inductor.codegen.cuda.cuda_env import nvcc_exist
from torch._inductor.exc import CUDACompileError
from torch._inductor.test_case import Tes... | # Owner(s): ["module: inductor"]
import ctypes
import unittest
import torch
from torch._inductor import config
from torch._inductor.async_compile import AsyncCompile
from torch._inductor.codecache import CUDACodeCache
from torch._inductor.codegen.cuda.cuda_env import nvcc_exist
from torch._inductor.exc import CUDACom... |
# Copyright (c) OpenMMLab. All rights reserved.
from mmdet.core import ConfigType, OptConfigType, OptMultiConfig
from mmdet.registry import MODELS
from .single_stage import SingleStageDetector
@MODELS.register_module()
class AutoAssign(SingleStageDetector):
"""Implementation of `AutoAssign: Differentiable Label A... | # Copyright (c) OpenMMLab. All rights reserved.
from mmdet.registry import MODELS
from .single_stage import SingleStageDetector
@MODELS.register_module()
class AutoAssign(SingleStageDetector):
"""Implementation of `AutoAssign: Differentiable Label Assignment for Dense
Object Detection <https://arxiv.org/abs/2... |
from __future__ import annotations
from typing import Any, Optional, Union
import torch
from ._datapoint import Datapoint
class Video(Datapoint):
"""[BETA] :class:`torch.Tensor` subclass for videos.
Args:
data (tensor-like): Any data that can be turned into a tensor with :func:`torch.as_tensor`.
... | from __future__ import annotations
from typing import Any, Optional, Union
import torch
from ._datapoint import Datapoint
class Video(Datapoint):
"""[BETA] :class:`torch.Tensor` subclass for videos.
Args:
data (tensor-like): Any data that can be turned into a tensor with :func:`torch.as_tensor`.
... |
from __future__ import annotations
from collections.abc import Iterable
import torch
from torch import Tensor, nn
from sentence_transformers import SentenceTransformer, util
class DistillKLDivLoss(nn.Module):
# TODO
def __init__(self, model: SentenceTransformer, similarity_fct=util.pairwise_dot_score) -> ... | from __future__ import annotations
from collections.abc import Iterable
import torch
from torch import Tensor, nn
from sentence_transformers import SentenceTransformer, util
class DistillKLDivLoss(nn.Module):
# TODO
def __init__(self, model: SentenceTransformer, similarity_fct=util.pairwise_dot_score) -> ... |
# 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_... | # 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.core.utils import mask2ndarray
from mmdet.datasets.builder import build_dataset
from mmdet.registry import VISUALIZERS
from mmdet.utils import register_al... |
"""Create a key-value store for any langchain serializable object."""
from typing import Callable, Optional
from langchain_core.documents import Document
from langchain_core.load import Serializable, dumps, loads
from langchain_core.stores import BaseStore, ByteStore
from langchain.storage.encoder_backed import Enco... | """Create a key-value store for any langchain serializable object."""
from typing import Callable, Optional
from langchain_core.documents import Document
from langchain_core.load import Serializable, dumps, loads
from langchain_core.stores import BaseStore, ByteStore
from langchain.storage.encoder_backed import Enco... |
from typing import Any, Dict
import torch
from torchvision.transforms.v2 import functional as F, Transform
class UniformTemporalSubsample(Transform):
"""Uniformly subsample ``num_samples`` indices from the temporal dimension of the video.
Videos are expected to be of shape ``[..., T, C, H, W]`` where ``T`` ... | from typing import Any, Dict
import torch
from torchvision.transforms.v2 import functional as F, Transform
class UniformTemporalSubsample(Transform):
"""Uniformly subsample ``num_samples`` indices from the temporal dimension of the video.
Videos are expected to be of shape ``[..., T, C, H, W]`` where ``T`` ... |
# Copyright (c) OpenMMLab. All rights reserved.
import unittest
import torch
from mmengine.config import Config
from mmdet.data_elements import DetDataSample
from mmdet.models.seg_heads.panoptic_fusion_heads import MaskFormerFusionHead
class TestMaskFormerFusionHead(unittest.TestCase):
def test_loss(self):
... | # Copyright (c) OpenMMLab. All rights reserved.
import unittest
import torch
from mmengine.config import Config
from mmdet.core.data_structures import DetDataSample
from mmdet.models.seg_heads.panoptic_fusion_heads import MaskFormerFusionHead
class TestMaskFormerFusionHead(unittest.TestCase):
def test_loss(sel... |
import os
from typing import Optional
import pytest
from docarray import BaseDoc, DocList
from docarray.documents import ImageDoc
from tests import TOYDATA_DIR
@pytest.fixture()
def nested_doc_cls():
class MyDoc(BaseDoc):
count: Optional[int]
text: str
class MyDocNested(MyDoc):
imag... | import os
from typing import Optional
import pytest
from docarray import BaseDoc, DocList
from docarray.documents import ImageDoc
from tests import TOYDATA_DIR
@pytest.fixture()
def nested_doc_cls():
class MyDoc(BaseDoc):
count: Optional[int]
text: str
class MyDocNested(MyDoc):
imag... |
"""
This file loads sentences from a provided text file. It is expected, that the there is one sentence per line in that text file.
CT will be training using these sentences. Checkpoints are stored every 500 steps to the output folder.
Usage:
python train_ct_from_file.py path/to/sentences.txt
"""
import gzip
import... | """
This file loads sentences from a provided text file. It is expected, that the there is one sentence per line in that text file.
CT will be training using these sentences. Checkpoints are stored every 500 steps to the output folder.
Usage:
python train_ct_from_file.py path/to/sentences.txt
"""
import math
from se... |
from typing import Any, List, Tuple, Type, TypeVar, Union
import numpy as np
from docarray.typing.proto_register import _register_proto
from docarray.typing.tensor.ndarray import NdArray
from docarray.typing.tensor.video.video_tensor_mixin import VideoTensorMixin
T = TypeVar('T', bound='VideoNdArray')
@_register_p... | from typing import TYPE_CHECKING, Any, List, Tuple, Type, TypeVar, Union
import numpy as np
from docarray.typing.proto_register import _register_proto
from docarray.typing.tensor.ndarray import NdArray
from docarray.typing.tensor.video.video_tensor_mixin import VideoTensorMixin
T = TypeVar('T', bound='VideoNdArray')... |
# Copyright 2024 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... | # Copyright 2024 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... |
from typing import Optional
import pandas as pd
import pytest
from docarray import BaseDoc, DocList
from docarray.documents import ImageDoc
@pytest.fixture()
def nested_doc_cls():
class MyDoc(BaseDoc):
count: Optional[int]
text: str
class MyDocNested(MyDoc):
image: ImageDoc
ret... | from typing import Optional
import pandas as pd
import pytest
from docarray import BaseDoc, DocList
from docarray.documents import ImageDoc
@pytest.fixture()
def nested_doc_cls():
class MyDoc(BaseDoc):
count: Optional[int]
text: str
class MyDocNested(MyDoc):
image: ImageDoc
ret... |
from typing import List
import torch
import torchaudio.prototype.transforms as T
from torch.autograd import gradcheck, gradgradcheck
from torchaudio_unittest.common_utils import get_spectrogram, get_whitenoise, nested_params, TestBaseMixin
class Autograd(TestBaseMixin):
def assert_grad(
self,
tra... | from typing import List
import torch
import torchaudio.prototype.transforms as T
from torch.autograd import gradcheck, gradgradcheck
from torchaudio_unittest.common_utils import get_spectrogram, get_whitenoise, nested_params, TestBaseMixin
class Autograd(TestBaseMixin):
def assert_grad(
self,
tra... |
# Copyright (c) OpenMMLab. All rights reserved.
from .cityscapes_metric import CityScapesMetric
from .coco_metric import CocoMetric
from .coco_occluded_metric import CocoOccludedSeparatedMetric
from .coco_panoptic_metric import CocoPanopticMetric
from .crowdhuman_metric import CrowdHumanMetric
from .dump_det_results im... | # Copyright (c) OpenMMLab. All rights reserved.
from .cityscapes_metric import CityScapesMetric
from .coco_metric import CocoMetric
from .coco_panoptic_metric import CocoPanopticMetric
from .crowdhuman_metric import CrowdHumanMetric
from .dump_proposals_metric import DumpProposals
from .lvis_metric import LVISMetric
fr... |
import re
from typing import Any
from langchain.evaluation.schema import StringEvaluator
class RegexMatchStringEvaluator(StringEvaluator):
"""Compute a regex match between the prediction and the reference.
Examples
----------
>>> evaluator = RegexMatchStringEvaluator(flags=re.IGNORECASE)
>>> eva... | import re
from typing import Any
from langchain.evaluation.schema import StringEvaluator
class RegexMatchStringEvaluator(StringEvaluator):
"""Compute a regex match between the prediction and the reference.
Examples
----------
>>> evaluator = RegexMatchStringEvaluator(flags=re.IGNORECASE)
>>> eva... |
import shutil
import pytest
import os
import numpy as np
import PIL.Image as Image
from jina import DocumentArray, Document
from ...big_transfer import BigTransferEncoder
directory = os.path.dirname(os.path.realpath(__file__))
def test_initialization_and_model_download():
shutil.rmtree('pretrained', ignore_er... | import shutil
import pytest
import os
import numpy as np
import PIL.Image as Image
from jina import DocumentArray, Document
from jinahub.image.encoder.big_transfer import BigTransferEncoder
directory = os.path.dirname(os.path.realpath(__file__))
def test_initialization_and_model_download():
shutil.rmtree('pre... |
"""scrapegraph tool specification module for web scraping operations."""
from typing import Dict, List, Optional
from pydantic import BaseModel
from scrapegraph_py import Client
from llama_index.core.tools.tool_spec.base import BaseToolSpec
class ScrapegraphToolSpec(BaseToolSpec):
"""scrapegraph tool specifica... | """scrapegraph tool specification module for web scraping operations."""
from typing import Dict, List, Optional
from pydantic import BaseModel
from scrapegraph_py import Client
from llama_index.core.tools.tool_spec.base import BaseToolSpec
class ScrapegraphToolSpec(BaseToolSpec):
"""scrapegraph tool specifica... |
from docarray.typing.bytes.audio_bytes import AudioBytes
from docarray.typing.bytes.image_bytes import ImageBytes
from docarray.typing.bytes.video_bytes import VideoBytes
__all__ = ['ImageBytes', 'VideoBytes', 'AudioBytes']
| from docarray.typing.bytes.image_bytes import ImageBytes
__all__ = ['ImageBytes']
|
from llama_index_instrumentation.events.span import SpanDropEvent # noqa
| from llama_index.core.instrumentation.events.base import BaseEvent
class SpanDropEvent(BaseEvent):
"""
SpanDropEvent.
Args:
err_str (str): Error string.
"""
err_str: str
@classmethod
def class_name(cls) -> str:
"""Class name."""
return "SpanDropEvent"
|
from .bifpn import BiFPN
from .efficientdet import EfficientDet
from .efficientdet_head import EfficientDetSepBNHead
from .huber_loss import HuberLoss
from .tensorflow.anchor_generator import YXYXAnchorGenerator
from .tensorflow.coco_90class import Coco90Dataset
from .tensorflow.coco_90metric import Coco90Metric
from .... | from .anchor_generator import YXYXAnchorGenerator
from .bifpn import BiFPN
from .coco_90class import Coco90Dataset
from .coco_90metric import Coco90Metric
from .efficientdet import EfficientDet
from .efficientdet_head import EfficientDetSepBNHead
from .trans_max_iou_assigner import TransMaxIoUAssigner
from .yxyx_bbox_c... |
"""
This script finds the person responsible for labeling a PR by a commit SHA. It is used by the workflow in
'.github/workflows/pr-labels.yml'.
Note: we only ping the person who pulls the pr, not the reviewers, as the reviewers can sometimes be external
to torchaudio with no labeling responsibility, so we don't want t... | """
This script finds the person responsible for labeling a PR by a commit SHA. It is used by the workflow in
'.github/workflows/pr-labels.yml'.
Note: we only ping the person who pulls the pr, not the reviewers, as the reviewers can sometimes be external
to torchaudio with no labeling responsibility, so we don't want t... |
"""Test in memory docstore."""
from langchain.output_parsers.regex_dict import RegexDictParser
DEF_EXPECTED_RESULT = {"action": "Search", "action_input": "How to use this class?"}
DEF_OUTPUT_KEY_TO_FORMAT = {"action": "Action", "action_input": "Action Input"}
DEF_README = """We have just received a new result from ... | """Test in memory docstore."""
from langchain.output_parsers.regex_dict import RegexDictParser
DEF_EXPECTED_RESULT = {"action": "Search", "action_input": "How to use this class?"}
DEF_OUTPUT_KEY_TO_FORMAT = {"action": "Action", "action_input": "Action Input"}
DEF_README = """We have just received a new result from ... |
# Copyright (c) OpenMMLab. All rights reserved.
from .assigners import * # noqa: F401,F403
from .builder import (ANCHOR_GENERATORS, BBOX_ASSIGNERS, BBOX_CODERS,
BBOX_SAMPLERS, IOU_CALCULATORS, MATCH_COSTS,
PRIOR_GENERATORS, build_anchor_generator, build_assigner,
... | # Copyright (c) OpenMMLab. All rights reserved.
from .assigners import * # noqa: F401,F403
from .builder import (ANCHOR_GENERATORS, BBOX_ASSIGNERS, BBOX_CODERS,
BBOX_SAMPLERS, IOU_CALCULATORS, MATCH_COSTS,
PRIOR_GENERATORS, build_anchor_generator, build_assigner,
... |
import os
import numpy as np
import pytest
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)
... | import os
import numpy as np
import pytest
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)
... |
from llama_index.core.llms import LLM
from llama_index.multi_modal_llms.ollama import OllamaMultiModal
def test_class():
names_of_base_classes = [b.__name__ for b in OllamaMultiModal.__mro__]
assert LLM.__name__ in names_of_base_classes
| from llama_index.core.multi_modal_llms.base import MultiModalLLM
from llama_index.multi_modal_llms.ollama import OllamaMultiModal
def test_class():
names_of_base_classes = [b.__name__ for b in OllamaMultiModal.__mro__]
assert MultiModalLLM.__name__ in names_of_base_classes
|
# Copyright (c) OpenMMLab. All rights reserved.
from .optimizer import (OPTIM_WRAPPER_CONSTRUCTORS, OPTIMIZERS,
AmpOptimWrapper, ApexOptimWrapper, BaseOptimWrapper,
DeepSpeedOptimWrapper, DefaultOptimWrapperConstructor,
OptimWrapper, OptimWrapperDi... | # Copyright (c) OpenMMLab. All rights reserved.
from mmengine.utils import is_installed
from .optimizer import (OPTIM_WRAPPER_CONSTRUCTORS, OPTIMIZERS,
AmpOptimWrapper, ApexOptimWrapper, BaseOptimWrapper,
DefaultOptimWrapperConstructor, OptimWrapper,
... |
from typing import Optional
import numpy as np
import torch
from docarray import DocumentArray
from docarray.document import BaseDocument
from docarray.typing import NdArray, TorchTensor
def test_proto_simple():
class CustomDoc(BaseDocument):
text: str
doc = CustomDoc(text='hello')
CustomDoc.f... | from typing import Optional
import numpy as np
import torch
from docarray import DocumentArray
from docarray.document import BaseDocument
from docarray.typing import Tensor, TorchTensor
def test_proto_simple():
class CustomDoc(BaseDocument):
text: str
doc = CustomDoc(text='hello')
CustomDoc.fr... |
"""
This example starts multiple processes (1 per GPU), which encode
sentences in parallel. This gives a near linear speed-up
when encoding large text collections.
"""
import logging
from sentence_transformers import LoggingHandler, SentenceTransformer
logging.basicConfig(
format="%(asctime)s - %(message)s", dat... | """
This example starts multiple processes (1 per GPU), which encode
sentences in parallel. This gives a near linear speed-up
when encoding large text collections.
"""
from sentence_transformers import SentenceTransformer, LoggingHandler
import logging
logging.basicConfig(
format="%(asctime)s - %(message)s", date... |
import enum
import pathlib
from typing import Any, BinaryIO, Dict, List, Optional, Tuple, Union
from torchdata.datapipes.iter import CSVParser, Demultiplexer, Filter, IterDataPipe, IterKeyZipper, LineReader, Mapper
from torchvision.prototype.datapoints import Label
from torchvision.prototype.datasets.utils import Data... | import enum
import pathlib
from typing import Any, BinaryIO, Dict, List, Optional, Tuple, Union
from torchdata.datapipes.iter import CSVParser, Demultiplexer, Filter, IterDataPipe, IterKeyZipper, LineReader, Mapper
from torchvision.prototype.datasets.utils import Dataset, EncodedImage, HttpResource, OnlineResource
fro... |
"""
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.
... |
"""langchain-core version information and utilities."""
VERSION = "0.3.56"
| """langchain-core version information and utilities."""
VERSION = "0.3.56rc1"
|
_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')
input_si... | _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')
input_si... |
_base_ = './mask_rcnn_r50_fpn_1x_coco.py'
model = dict(
# use caffe img_norm
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[103.530, 116.280, 123.675],
std=[1.0, 1.0, 1.0],
bgr_to_rgb=False,
pad_size_divisor=32),
backbone=dict(
norm_cfg=dict(require... | _base_ = './mask_rcnn_r50_fpn_1x_coco.py'
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(
# use caffe img_norm
preprocess_cfg=preprocess_cfg,
backbone=dict(
norm_cfg=dict(requires_grad=False),
styl... |
import os
import torch
def average_checkpoints(last):
avg = None
for path in last:
states = torch.load(path, map_location=lambda storage, loc: storage)["state_dict"]
if avg is None:
avg = states
else:
for k in avg.keys():
avg[k] += states[k]
... | import os
import torch
def average_checkpoints(last):
avg = None
for path in last:
states = torch.load(path, map_location=lambda storage, loc: storage)["state_dict"]
if avg is None:
avg = states
else:
for k in avg.keys():
avg[k] += states[k]
... |
from typing import Iterable, Type
from docarray.array.abstract_array import AbstractDocumentArray
from docarray.array.mixins import GetAttributeArrayMixin, ProtoArrayMixin
from docarray.document import AnyDocument, BaseDocument, BaseNode
from docarray.document.abstract_document import AbstractDocument
class Document... | from typing import Iterable, Type
from docarray.document import AnyDocument, BaseDocument, BaseNode
from docarray.document.abstract_document import AbstractDocument
from .abstract_array import AbstractDocumentArray
from .mixins import ProtoArrayMixin
class DocumentArray(
list,
ProtoArrayMixin,
AbstractD... |
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... |
# Licensed to the LF AI & Data foundation under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the "License");
# you may not use this fil... | from typing import TYPE_CHECKING, Any, Type, TypeVar, Union
from docarray.base_doc import BaseDoc
from docarray.typing.tensor.tensor import AnyTensor
from docarray.utils._internal.misc import import_library
T = TypeVar('T', bound='VerticesAndFaces')
class VerticesAndFaces(BaseDoc):
"""
Document for handling... |
"""
Sphinx Read the Docs theme.
From https://github.com/ryan-roemer/sphinx-bootstrap-theme.
"""
from os import path
import sphinx
__version__ = "0.5.0"
__version_full__ = __version__
def get_html_theme_path():
"""Return list of HTML theme paths."""
cur_dir = path.abspath(path.dirname(path.dirname(__file__... | """
Sphinx Read the Docs theme.
From https://github.com/ryan-roemer/sphinx-bootstrap-theme.
"""
from os import path
import sphinx
__version__ = '0.5.0'
__version_full__ = __version__
def get_html_theme_path():
"""Return list of HTML theme paths."""
cur_dir = path.abspath(path.dirname(path.dirname(__file_... |
# Licensed to the LF AI & Data foundation under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the "License");
# you may not use this fil... | import numpy as np
from docarray import BaseDoc
from docarray.typing import NdArray
def test_tensor_ops():
class A(BaseDoc):
tensor: NdArray[3, 224, 224]
class B(BaseDoc):
tensor: NdArray[3, 112, 224]
tensor = A(tensor=np.ones((3, 224, 224))).tensor
tensord = A(tensor=np.ones((3, 22... |
from typing import Dict, Iterable
import torch.nn.functional as F
from torch import Tensor, nn
from sentence_transformers.SentenceTransformer import SentenceTransformer
from .ContrastiveLoss import SiameseDistanceMetric
class OnlineContrastiveLoss(nn.Module):
def __init__(
self, model: SentenceTransfor... | from typing import Dict, Iterable
import torch.nn.functional as F
from torch import Tensor, nn
from sentence_transformers.SentenceTransformer import SentenceTransformer
from .ContrastiveLoss import SiameseDistanceMetric
class OnlineContrastiveLoss(nn.Module):
def __init__(
self, model: SentenceTransfor... |
from __future__ import annotations
import re
from typing import Optional
from langchain_core.output_parsers import BaseOutputParser
class RegexParser(BaseOutputParser[dict[str, str]]):
"""Parse the output of an LLM call using a regex."""
@classmethod
def is_lc_serializable(cls) -> bool:
return ... | from __future__ import annotations
import re
from typing import Optional
from langchain_core.output_parsers import BaseOutputParser
class RegexParser(BaseOutputParser[dict[str, str]]):
"""Parse the output of an LLM call using a regex."""
@classmethod
def is_lc_serializable(cls) -> bool:
return ... |
"""Test in memory indexer."""
from collections.abc import AsyncGenerator, Generator
import pytest
from langchain_tests.integration_tests.indexer import (
AsyncDocumentIndexTestSuite,
DocumentIndexerTestSuite,
)
from langchain_core.documents import Document
from langchain_core.indexing.base import DocumentInd... | """Test in memory indexer."""
from collections.abc import AsyncGenerator, Generator
import pytest
from langchain_tests.integration_tests.indexer import (
AsyncDocumentIndexTestSuite,
DocumentIndexerTestSuite,
)
from langchain_core.documents import Document
from langchain_core.indexing.base import DocumentInd... |
import asyncio
import time
from multiprocessing import Event, Process
import aiohttp
import pytest
from jina import DocumentArray, Executor, Flow, requests
from jina.types.request.data import DataRequest
from jina.helper import random_port
INPUT_DA_LEN = 2
NUM_CLIENTS = 3
@pytest.fixture()
def gateway_port():
p... | import asyncio
import time
from multiprocessing import Event, Process
import aiohttp
import pytest
from jina import DocumentArray, Executor, Flow, requests
from jina.types.request.data import DataRequest
INPUT_DA_LEN = 2
NUM_CLIENTS = 3
GATEWAY_PORT = 12345
class DummyExecutor(Executor):
@requests(on='/foo')
... |
# Copyright (c) OpenMMLab. All rights reserved.
from .conditional_detr_transformer import (
ConditionalDetrTransformerDecoder, ConditionalDetrTransformerDecoderLayer)
from .deformable_detr_transformer import (
DeformableDetrTransformerDecoder, DeformableDetrTransformerDecoderLayer,
DeformableDetrTransformer... | # Copyright (c) OpenMMLab. All rights reserved.
from .conditional_detr_transformer import (
ConditionalDetrTransformerDecoder, ConditionalDetrTransformerDecoderLayer)
from .deformable_detr_transformer import (
DeformableDetrTransformerDecoder, DeformableDetrTransformerDecoderLayer,
DeformableDetrTransformer... |
import functools
import numbers
from collections import defaultdict
from typing import Any, Dict, Literal, Sequence, Type, TypeVar, Union
from torchvision import datapoints
from torchvision.datapoints._datapoint import _FillType, _FillTypeJIT
from torchvision.transforms.transforms import _check_sequence_input, _setup... | import functools
import numbers
from collections import defaultdict
from typing import Any, Dict, Literal, Sequence, Type, TypeVar, Union
from torchvision import datapoints
from torchvision.datapoints._datapoint import FillType, FillTypeJIT
from torchvision.transforms.transforms import _check_sequence_input, _setup_a... |
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", ... |
"""Simple Reader that reads abstract of primary citation for a given PDB id."""
from typing import List
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document
from llama_index.readers.pdb.utils import get_pdb_abstract
class PdbAbstractReader(BaseReader):
"""Protein Dat... | """Simple Reader that reads abstract of primary citation for a given PDB id."""
from typing import List
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document
from llama_index.readers.pdb.utils import get_pdb_abstract
class PdbAbstractReader(BaseReader):
"""Protein Data... |
import enum
from typing import Any, Callable, Dict, List, Tuple, Type, Union
import PIL.Image
import torch
from torch import nn
from torch.utils._pytree import tree_flatten, tree_unflatten
from torchvision.prototype import features
from torchvision.prototype.transforms._utils import _isinstance
from torchvision.utils ... | import enum
from typing import Any, Callable, Dict, Tuple, Type, Union
import PIL.Image
import torch
from torch import nn
from torch.utils._pytree import tree_flatten, tree_unflatten
from torchvision.prototype import features
from torchvision.prototype.transforms._utils import _isinstance
from torchvision.utils import... |
# dataset settings
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
# Example to use different file client
# Method 1: simply set the data root and let the file I/O module
# automatically infer from prefix (not support LMDB and Memcache yet)
# data_root = 's3://openmmlab/datasets/detection/coco/'
# Method 2: Us... | # dataset settings
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
# 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')
tra... |
from docarray.documents.audio import AudioDoc
from docarray.documents.image import ImageDoc
from docarray.documents.mesh import Mesh3D, VerticesAndFaces
from docarray.documents.point_cloud import PointCloud3D, PointsAndColors
from docarray.documents.text import TextDoc
from docarray.documents.video import VideoDoc
__a... | from docarray.documents.audio import AudioDoc
from docarray.documents.image import ImageDoc
from docarray.documents.mesh import Mesh3D
from docarray.documents.point_cloud import PointCloud3D
from docarray.documents.text import TextDoc
from docarray.documents.video import VideoDoc
__all__ = ['TextDoc', 'ImageDoc', 'Aud... |
import numpy as np
from sentence_transformers import SparseEncoder
from sentence_transformers.sparse_encoder.models import MLMTransformer, SpladePooling
def main():
# Initialize the SPLADE model
model_name = "naver/splade-cocondenser-ensembledistil" # "naver/efficient-splade-V-large-doc" # "prithivida/Spla... | import numpy as np
from sentence_transformers.sparse_encoder import SparseEncoder
from sentence_transformers.sparse_encoder.models import MLMTransformer, SpladePooling
def main():
# Initialize the SPLADE model
model_name = "naver/splade-cocondenser-ensembledistil" # "naver/efficient-splade-V-large-doc" # "... |
# 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... |
from typing import Optional
from fastapi import FastAPI, Security
from fastapi.security import HTTPAuthorizationCredentials, HTTPDigest
from fastapi.testclient import TestClient
app = FastAPI()
security = HTTPDigest(auto_error=False)
@app.get("/users/me")
def read_current_user(
credentials: Optional[HTTPAuthor... | from typing import Optional
from fastapi import FastAPI, Security
from fastapi.security import HTTPAuthorizationCredentials, HTTPDigest
from fastapi.testclient import TestClient
app = FastAPI()
security = HTTPDigest(auto_error=False)
@app.get("/users/me")
def read_current_user(
credentials: Optional[HTTPAuthor... |
"""DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.ops.nn import average_pool
from keras.src.ops.nn import batch_normalization
from keras.src.ops.nn import binary_crossentropy
from keras.src.ops.nn import categorical_crossentropy
from... | """DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.ops.nn import average_pool
from keras.src.ops.nn import batch_normalization
from keras.src.ops.nn import binary_crossentropy
from keras.src.ops.nn import categorical_crossentropy
from... |
from typing import Any, Dict, Optional, Union
import numpy as np
import PIL.Image
import torch
from torchvision import tv_tensors
from torchvision.transforms.v2 import functional as F, Transform
from torchvision.transforms.v2._utils import is_pure_tensor
class PILToTensor(Transform):
"""[BETA] Convert a PIL Im... | from typing import Any, Dict, Optional, Union
import numpy as np
import PIL.Image
import torch
from torchvision import tv_tensors
from torchvision.transforms.v2 import functional as F, Transform
from torchvision.transforms.v2._utils import is_pure_tensor
class PILToTensor(Transform):
"""[BETA] Convert a PIL Im... |
import pathlib
from typing import Any, Dict, List, Tuple, Union
from torchdata.datapipes.iter import IterDataPipe, Mapper
from torchvision.prototype.datapoints import Label
from torchvision.prototype.datasets.utils import Dataset, EncodedImage, HttpResource, OnlineResource
from torchvision.prototype.datasets.utils._in... | import pathlib
from typing import Any, Dict, List, Tuple, Union
from torchdata.datapipes.iter import IterDataPipe, Mapper
from torchvision.prototype.datasets.utils import Dataset, EncodedImage, HttpResource, OnlineResource
from torchvision.prototype.datasets.utils._internal import hint_sharding, hint_shuffling
from to... |
import pytest
from hubble.executor.hubio import HubIO
from jina.orchestrate.pods.factory import PodFactory
from jina.parsers import set_pod_parser
@pytest.mark.parametrize(
'uses', ['jinaai+docker://jina-ai/DummyExecutor']
)
def test_container_pod(mocker, monkeypatch, uses):
mock = mocker.Mock()
def _mo... | from hubble.executor.hubio import HubIO
from jina.orchestrate.pods.factory import PodFactory
from jina.parsers import set_pod_parser
def test_container_pod(mocker, monkeypatch):
mock = mocker.Mock()
def _mock_pull(self):
return 'docker://jinahub/dummy_executor'
monkeypatch.setattr(HubIO, 'pull'... |
import os
from time import time
import numpy as np
import pytest
from docarray import BaseDocument, DocumentArray
from docarray.documents import ImageDoc
from docarray.typing import NdArray
from docarray.utils.map import map_docs, map_docs_batch
from tests.units.typing.test_bytes import IMAGE_PATHS
pytestmark = [pyt... | import os
from time import time
import numpy as np
import pytest
from docarray import BaseDocument, DocumentArray
from docarray.documents import Image
from docarray.typing import NdArray
from docarray.utils.map import map_docs, map_docs_batch
from tests.units.typing.test_bytes import IMAGE_PATHS
pytestmark = [pytest... |
from __future__ import annotations
import math
from pathlib import Path
import pytest
from packaging.version import Version, parse
from tokenizers import Tokenizer
from sentence_transformers import SentenceTransformer
from sentence_transformers.models.StaticEmbedding import StaticEmbedding
try:
import model2vec... | from __future__ import annotations
import math
from pathlib import Path
import numpy as np
import pytest
from packaging.version import Version, parse
from tokenizers import Tokenizer
from sentence_transformers import SentenceTransformer
from sentence_transformers.models.StaticEmbedding import StaticEmbedding
try:
... |
import multiprocessing
import random
import time
from functools import partial
import pytest
from jina import Client, Document, DocumentArray, Executor, Flow, requests
from jina.types.request.data import Response
NUM_REQUESTS = 5
class MyExecutor(Executor):
@requests(on='/ping')
def ping(self, **kwargs):
... | import multiprocessing
import random
import time
from functools import partial
import pytest
from jina import Client, Document, DocumentArray, Executor, Flow, requests
from jina.types.request.data import Response
NUM_REQUESTS = 5
class MyExecutor(Executor):
@requests(on='/ping')
def ping(self, **kwargs):
... |
from typing import Generator, Optional
import pytest
from docarray import BaseDocument, DocumentArray
from docarray.documents import ImageDoc
from docarray.typing import ImageUrl, NdArray
from docarray.utils.map import map_docs, map_docs_batch
from tests.units.typing.test_bytes import IMAGE_PATHS
N_DOCS = 2
def lo... | from typing import Generator, Optional
import pytest
from docarray import BaseDocument, DocumentArray
from docarray.documents import Image
from docarray.typing import ImageUrl, NdArray
from docarray.utils.map import map_docs, map_docs_batch
from tests.units.typing.test_bytes import IMAGE_PATHS
N_DOCS = 2
def load_... |
# Copyright (c) OpenMMLab. All rights reserved.
from ..builder import DETECTORS
from .single_stage import SingleStageDetector
@DETECTORS.register_module()
class FSAF(SingleStageDetector):
"""Implementation of `FSAF <https://arxiv.org/abs/1903.00621>`_"""
def __init__(self,
backbone,
... | from ..builder import DETECTORS
from .single_stage import SingleStageDetector
@DETECTORS.register_module()
class FSAF(SingleStageDetector):
"""Implementation of `FSAF <https://arxiv.org/abs/1903.00621>`_"""
def __init__(self,
backbone,
neck,
bbox_head,
... |
import enum
import typing
import pydantic
import backend.data.graph
from backend.data.api_key import APIKeyPermission, APIKeyWithoutHash
class Methods(enum.Enum):
SUBSCRIBE = "subscribe"
UNSUBSCRIBE = "unsubscribe"
EXECUTION_EVENT = "execution_event"
ERROR = "error"
class WsMessage(pydantic.BaseMo... | import enum
import typing
import pydantic
import backend.data.graph
class Methods(enum.Enum):
SUBSCRIBE = "subscribe"
UNSUBSCRIBE = "unsubscribe"
EXECUTION_EVENT = "execution_event"
ERROR = "error"
class WsMessage(pydantic.BaseModel):
method: Methods
data: typing.Dict[str, typing.Any] | li... |
"""Run smoke tests"""
import os
from pathlib import Path
from sys import platform
import torch
import torch.nn as nn
import torchvision
from torchvision.io import read_image
from torchvision.models import resnet50, ResNet50_Weights
SCRIPT_DIR = Path(__file__).parent
def smoke_test_torchvision() -> None:
print(... | """Run smoke tests"""
import os
from pathlib import Path
from sys import platform
import torch
import torch.nn as nn
import torchvision
from torchvision.io import read_image
from torchvision.models import resnet50, ResNet50_Weights
SCRIPT_DIR = Path(__file__).parent
def smoke_test_torchvision() -> None:
print(... |
_base_ = [
'../_base_/models/mask-rcnn_r50_fpn.py',
'../common/lsj-100e_coco-instance.py'
]
image_size = (1024, 1024)
batch_augments = [
dict(type='BatchFixedSizePad', size=image_size, pad_mask=True)
]
norm_cfg = dict(type='SyncBN', requires_grad=True)
# Use MMSyncBN that handles empty tensor in head. It ca... | _base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
'../common/lsj_100e_coco_instance.py'
]
image_size = (1024, 1024)
batch_augments = [
dict(type='BatchFixedSizePad', size=image_size, pad_mask=True)
]
norm_cfg = dict(type='SyncBN', requires_grad=True)
# Use MMSyncBN that handles empty tensor in head. It ca... |
from docarray.typing.id import ID
from docarray.typing.tensor.audio import AudioNdArray
from docarray.typing.tensor.embedding.embedding import AnyEmbedding, NdArrayEmbedding
from docarray.typing.tensor.ndarray import NdArray
from docarray.typing.tensor.tensor import AnyTensor
from docarray.typing.tensor.video import Vi... | from docarray.typing.id import ID
from docarray.typing.tensor.audio import AudioNdArray
from docarray.typing.tensor.embedding.embedding import AnyEmbedding, NdArrayEmbedding
from docarray.typing.tensor.ndarray import NdArray
from docarray.typing.tensor.tensor import AnyTensor
from docarray.typing.tensor.video import Vi... |
"""
This file loads sentences from a provided text file. It is expected, that the there is one sentence per line in that text file.
SimCSE will be training using these sentences. Checkpoints are stored every 500 steps to the output folder.
Usage:
python train_simcse_from_file.py path/to/sentences.txt
"""
import gzi... | """
This file loads sentences from a provided text file. It is expected, that the there is one sentence per line in that text file.
SimCSE will be training using these sentences. Checkpoints are stored every 500 steps to the output folder.
Usage:
python train_simcse_from_file.py path/to/sentences.txt
"""
from torch.... |
from __future__ import annotations
import json
import os
import torch
from safetensors.torch import load_model as load_safetensors_model
from safetensors.torch import save_model as save_safetensors_model
from torch import nn
class LSTM(nn.Module):
"""Bidirectional LSTM running over word embeddings."""
def ... | from __future__ import annotations
import json
import os
import torch
from safetensors.torch import load_model as load_safetensors_model
from safetensors.torch import save_model as save_safetensors_model
from torch import nn
class LSTM(nn.Module):
"""Bidirectional LSTM running over word embeddings."""
def ... |
from __future__ import annotations
from typing import Any, Dict, Optional
from docarray import BaseDoc, DocArray
from docarray.typing import AnyEmbedding, AnyTensor
class LegacyDocument(BaseDoc):
"""
This Document is the LegacyDocument. It follows the same schema as in DocArray v1.
It can be useful to s... | from __future__ import annotations
from typing import Any, Dict, Optional
from docarray import BaseDocument, DocumentArray
from docarray.typing import AnyEmbedding, AnyTensor
class LegacyDocument(BaseDocument):
"""
This Document is the LegacyDocument. It follows the same schema as in DocArray v1.
It can... |
# Copyright (c) OpenMMLab. All rights reserved.
from unittest import TestCase
import torch
from mmengine import Config
from mmengine.data import InstanceData
from mmdet import * # noqa
from mmdet.models.dense_heads import ATSSHead
class TestATSSHead(TestCase):
def test_atss_head_loss(self):
"""Tests a... | # Copyright (c) OpenMMLab. All rights reserved.
from unittest import TestCase
import torch
from mmengine import Config
from mmengine.data import InstanceData
from mmdet import * # noqa
from mmdet.models.dense_heads import ATSSHead
class TestATSSHead(TestCase):
def test_atss_head_loss(self):
"""Tests a... |
"""
Tests the correct computation of evaluation scores from BinaryClassificationEvaluator
"""
from __future__ import annotations
import csv
import gzip
import os
from pathlib import Path
import pytest
from torch.utils.data import DataLoader
from sentence_transformers import (
InputExample,
SentenceTransform... | """
Tests the correct computation of evaluation scores from BinaryClassificationEvaluator
"""
from __future__ import annotations
import csv
import gzip
import os
from pathlib import Path
from torch.utils.data import DataLoader
from sentence_transformers import (
InputExample,
SentenceTransformer,
evalua... |
# Copyright (c) OpenMMLab. All rights reserved.
from .approx_max_iou_assigner import ApproxMaxIoUAssigner
from .assign_result import AssignResult
from .atss_assigner import ATSSAssigner
from .base_assigner import BaseAssigner
from .center_region_assigner import CenterRegionAssigner
from .grid_assigner import GridAssign... | # Copyright (c) OpenMMLab. All rights reserved.
from .approx_max_iou_assigner import ApproxMaxIoUAssigner
from .assign_result import AssignResult
from .atss_assigner import ATSSAssigner
from .base_assigner import BaseAssigner
from .center_region_assigner import CenterRegionAssigner
from .grid_assigner import GridAssign... |
from collections import ChainMap
from typing import (
TYPE_CHECKING,
Any,
Dict,
Iterable,
MutableMapping,
Type,
TypeVar,
Union,
)
from docarray.array.list_advance_indexing import ListAdvancedIndexing
from docarray.typing import NdArray
from docarray.typing.tensor.abstract_tensor import ... | from collections import ChainMap
from typing import (
TYPE_CHECKING,
Any,
Dict,
Iterable,
MutableMapping,
Type,
TypeVar,
Union,
)
from docarray.array.doc_vec.list_advance_indexing import ListAdvancedIndexing
from docarray.typing import NdArray
from docarray.typing.tensor.abstract_tensor... |
# Copyright (c) OpenMMLab. All rights reserved.
__version__ = '0.10.3'
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.
"""
versi... | # Copyright (c) OpenMMLab. All rights reserved.
__version__ = '0.10.2'
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.
"""
versi... |
# 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 setup_cache_size_limit_o... | # 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
def parse_args():
parser = argparse.Argumen... |
from typing import Any, Optional, Type, TypeVar, Union
import numpy as np
from docarray.base_document import BaseDocument
from docarray.documents import Audio
from docarray.typing import AnyEmbedding, AnyTensor
from docarray.typing.tensor.abstract_tensor import AbstractTensor
from docarray.typing.tensor.video.video_t... | from typing import Optional, TypeVar
from docarray.base_document import BaseDocument
from docarray.documents import Audio
from docarray.typing import AnyEmbedding, AnyTensor
from docarray.typing.tensor.video.video_tensor import VideoTensor
from docarray.typing.url.video_url import VideoUrl
T = TypeVar('T', bound='Vid... |
import pytest
from importlib.util import find_spec
from llama_index.core.storage.kvstore.types import BaseKVStore
from llama_index.storage.kvstore.postgres import PostgresKVStore
no_packages = find_spec("psycopg2") is not None and find_spec("sqlalchemy") is not None and find_spec("asyncpg") is not None
def test_class... | from llama_index.core.storage.kvstore.types import BaseKVStore
from llama_index.storage.kvstore.postgres import PostgresKVStore
def test_class():
names_of_base_classes = [b.__name__ for b in PostgresKVStore.__mro__]
assert BaseKVStore.__name__ in names_of_base_classes
|
from datetime import datetime, timedelta
from backend.blocks.hubspot._auth import (
HubSpotCredentials,
HubSpotCredentialsField,
HubSpotCredentialsInput,
)
from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
from backend.data.model import SchemaField
from backend.util.request impo... | from datetime import datetime, timedelta
from backend.blocks.hubspot._auth import (
HubSpotCredentials,
HubSpotCredentialsField,
HubSpotCredentialsInput,
)
from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
from backend.data.model import SchemaField
from backend.util.request impo... |
from fastapi import FastAPI
from backend.server.middleware.security import SecurityHeadersMiddleware
from .routes.v1 import v1_router
external_app = FastAPI(
title="AutoGPT External API",
description="External API for AutoGPT integrations",
docs_url="/docs",
version="1.0",
)
external_app.add_middlew... | from fastapi import FastAPI
from .routes.v1 import v1_router
external_app = FastAPI(
title="AutoGPT External API",
description="External API for AutoGPT integrations",
docs_url="/docs",
version="1.0",
)
external_app.include_router(v1_router, prefix="/v1")
|
from typing import Any, Dict
import requests
from llama_index.core.base.base_query_engine import BaseQueryEngine
from llama_index.core.base.response.schema import Response
from llama_index.core.schema import QueryBundle
class CogniswitchQueryEngine(BaseQueryEngine):
def __init__(self, cs_token: str, OAI_token: s... | from typing import Any, Dict
import requests
from llama_index.core.base.base_query_engine import BaseQueryEngine
from llama_index.core.base.response.schema import Response
from llama_index.core.schema import QueryBundle
class CogniswitchQueryEngine(BaseQueryEngine):
def __init__(self, cs_token: str, OAI_token: s... |
from typing import Any, Dict, Union
import torch
from torchvision import transforms as _transforms
from torchvision.prototype import datapoints
from torchvision.prototype.transforms import functional as F, Transform
from .utils import is_simple_tensor
class ConvertBoundingBoxFormat(Transform):
_transformed_typ... | from typing import Any, Dict, Union
import torch
from torchvision import transforms as _transforms
from torchvision.prototype import datapoints
from torchvision.prototype.transforms import functional as F, Transform
from .utils import is_simple_tensor
class ConvertBoundingBoxFormat(Transform):
_transformed_typ... |
import os
from typing import List
import pytest
from llama_index.core.ingestion import IngestionPipeline
from llama_index.core.node_parser import SentenceSplitter
from llama_index.core.schema import Document, TextNode
from llama_index.embeddings.openai import OpenAIEmbedding
from llama_index.vector_stores.mongodb impo... | import os
from typing import List
import pytest
from llama_index.core.ingestion import IngestionPipeline
from llama_index.core.node_parser import SentenceSplitter
from llama_index.core.schema import Document, TextNode
from llama_index.embeddings.openai import OpenAIEmbedding
from llama_index.vector_stores.mongodb impo... |
from __future__ import annotations
import logging
from typing import TYPE_CHECKING, Any
from sentence_transformers.evaluation import TranslationEvaluator
if TYPE_CHECKING:
import numpy as np
from torch import Tensor
from sentence_transformers.sparse_encoder.SparseEncoder import SparseEncoder
logger = ... | from __future__ import annotations
import logging
from typing import TYPE_CHECKING, Any
from sentence_transformers.evaluation import TranslationEvaluator
if TYPE_CHECKING:
import numpy as np
from torch import Tensor
from sentence_transformers.sparse_encoder.SparseEncoder import SparseEncoder
logger = ... |
"""Test yamlOutputParser"""
from enum import Enum
from typing import Optional
import pytest
from langchain_core.exceptions import OutputParserException
from pydantic import BaseModel, Field
from langchain.output_parsers.yaml import YamlOutputParser
class Actions(Enum):
SEARCH = "Search"
CREATE = "Create"
... | """Test yamlOutputParser"""
from enum import Enum
from typing import Optional
import pytest
from langchain_core.exceptions import OutputParserException
from pydantic import BaseModel, Field
from langchain.output_parsers.yaml import YamlOutputParser
class Actions(Enum):
SEARCH = "Search"
CREATE = "Create"
... |
# Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
import pytest
from mmengine.logging import HistoryBuffer
array_method = [np.array, lambda x: x]
try:
import torch
except ImportError:
pass
else:
array_method.append(torch.tensor)
@HistoryBuffer.register_statistics
def custom_statistics(s... | # Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
import pytest
from mmengine.logging import HistoryBuffer
array_method = [np.array, lambda x: x]
try:
import torch
except ImportError:
pass
else:
array_method.append(torch.tensor)
class TestLoggerBuffer:
def test_init(self):
... |
_base_ = './cascade_rcnn_r50_fpn_1x_coco.py'
model = dict(
# use caffe img_norm
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[103.530, 116.280, 123.675],
std=[1.0, 1.0, 1.0],
to_rgb=False,
pad_size_divisor=32),
backbone=dict(
norm_cfg=dict(require... | _base_ = './cascade_rcnn_r50_fpn_1x_coco.py'
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(
# use caffe img_norm
preprocess_cfg=preprocess_cfg,
backbone=dict(
norm_cfg=dict(requires_grad=False),
... |
from __future__ import annotations
from typing import Any, Optional
from langchain_core.outputs import LLMResult
from langchain.callbacks.streaming_aiter import AsyncIteratorCallbackHandler
DEFAULT_ANSWER_PREFIX_TOKENS = ["Final", "Answer", ":"]
class AsyncFinalIteratorCallbackHandler(AsyncIteratorCallbackHandler... | from __future__ import annotations
from typing import Any, Dict, List, Optional
from langchain_core.outputs import LLMResult
from langchain.callbacks.streaming_aiter import AsyncIteratorCallbackHandler
DEFAULT_ANSWER_PREFIX_TOKENS = ["Final", "Answer", ":"]
class AsyncFinalIteratorCallbackHandler(AsyncIteratorCal... |
from typing import Optional
from docarray.document import BaseDocument
from docarray.typing import AnyTensor, Embedding, ImageUrl
class Image(BaseDocument):
"""
Document for handling images.
It can contain an ImageUrl (`Image.url`), an AnyTensor (`Image.tensor`),
and an Embedding (`Image.embedding`).... | from typing import Optional
from docarray.document import BaseDocument
from docarray.typing import Embedding, ImageUrl, Tensor
class Image(BaseDocument):
"""
Document for handling images.
It can contain an ImageUrl (`Image.url`), a Tensor (`Image.tensor`),
and an Embedding (`Image.embedding`).
E... |
import os
from pathlib import Path
from torchaudio.datasets.libritts import LIBRITTS
from torchaudio_unittest.common_utils import get_whitenoise, normalize_wav, save_wav, TempDirMixin, TorchaudioTestCase
_UTTERANCE_IDS = [
[19, 198, "000000", "000000"],
[26, 495, "000004", "000000"],
]
_ORIGINAL_TEXT = "this ... | import os
from pathlib import Path
from torchaudio.datasets.libritts import LIBRITTS
from torchaudio_unittest.common_utils import get_whitenoise, normalize_wav, save_wav, TempDirMixin, TorchaudioTestCase
_UTTERANCE_IDS = [
[19, 198, "000000", "000000"],
[26, 495, "000004", "000000"],
]
_ORIGINAL_TEXT = "this ... |
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', '... | import os
import numpy as np
import pytest
from docarray import BaseDoc, DocArray
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', ... |
import base64
import json
import pickle
from abc import ABC, abstractmethod
from typing import Any
from pydantic import BaseModel
from llama_index.core.schema import BaseComponent
from .utils import import_module_from_qualified_name, get_qualified_name
class BaseSerializer(ABC):
@abstractmethod
def serialize... | import base64
import json
import pickle
from abc import ABC, abstractmethod
from typing import Any
from pydantic import BaseModel
from llama_index.core.schema import BaseComponent
from .utils import import_module_from_qualified_name, get_qualified_name
class BaseSerializer(ABC):
@abstractmethod
def serialize... |
__copyright__ = 'Copyright (c) 2021 Jina AI Limited. All rights reserved.'
__license__ = 'Apache-2.0'
from typing import Callable
import pytest
from jina import DocumentArray
from ...transform_encoder import TransformerTorchEncoder
MODELS_TO_TEST = [
'sentence-transformers/distilbert-base-nli-stsb-mean-tokens',... | __copyright__ = 'Copyright (c) 2021 Jina AI Limited. All rights reserved.'
__license__ = 'Apache-2.0'
from typing import Dict, Callable
import pytest
from jina import DocumentArray
from ...transform_encoder import TransformerTorchEncoder
MODELS_TO_TEST = [
'sentence-transformers/distilbert-base-nli-stsb-mean... |
from __future__ import annotations
from collections.abc import Iterable
from torch import Tensor
from sentence_transformers import util
from sentence_transformers.losses.MultipleNegativesRankingLoss import MultipleNegativesRankingLoss
from sentence_transformers.sparse_encoder.SparseEncoder import SparseEncoder
cla... | from __future__ import annotations
from sentence_transformers import util
from sentence_transformers.losses.MultipleNegativesRankingLoss import MultipleNegativesRankingLoss
from sentence_transformers.sparse_encoder.SparseEncoder import SparseEncoder
class SparseMultipleNegativesRankingLoss(MultipleNegativesRankingLo... |
"""Module for argparse for Client"""
def mixin_client_protocol_parser(parser):
"""Add the arguments for the protocol to the client parser
:param parser: the parser configure
"""
from jina.enums import ProtocolType
parser.add_argument(
'--protocol',
type=ProtocolType.from_string,... | """Module for argparse for Client"""
def mixin_client_protocol_parser(parser):
"""Add the arguments for the protocol to the client parser
:param parser: the parser configure
"""
from jina.enums import ProtocolType
parser.add_argument(
'--protocol',
type=ProtocolType.from_string,... |
from jina import DocumentArray, Executor, requests
class TestExecutor(Executor):
@requests
def process(self, docs: DocumentArray, **kwargs):
return docs
| from jina import Executor, requests, DocumentArray
class TestExecutor(Executor):
@requests
def process(self, docs: DocumentArray, **kwargs):
return docs
|
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