input stringlengths 33 5k | output stringlengths 32 5k |
|---|---|
"""Utilities for working with HTML."""
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",
... | """Utilities for working with HTML."""
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",
... |
from keras.src import backend
from keras.src.api_export import keras_export
from keras.src.layers.preprocessing.image_preprocessing.base_image_preprocessing_layer import ( # noqa: E501
BaseImagePreprocessingLayer,
)
from keras.src.ops.core import _saturate_cast
@keras_export("keras.layers.AutoContrast")
class Au... | from keras.src import backend
from keras.src.api_export import keras_export
from keras.src.layers.preprocessing.image_preprocessing.base_image_preprocessing_layer import ( # noqa: E501
BaseImagePreprocessingLayer,
)
from keras.src.ops.core import _saturate_cast
@keras_export("keras.layers.AutoContrast")
class Au... |
import pytest
from docarray import DocumentArray, Document
from docarray.array.weaviate import DocumentArrayWeaviate
import numpy as np
@pytest.fixture()
def docs():
return DocumentArray([Document(id=f'{i}') for i in range(1, 10)])
@pytest.mark.parametrize(
'to_delete',
[
0,
1,
... | import pytest
from docarray import DocumentArray, Document
from docarray.array.weaviate import DocumentArrayWeaviate
import numpy as np
@pytest.fixture()
def docs():
return DocumentArray([Document(id=f'{i}') for i in range(1, 10)])
@pytest.mark.parametrize(
'to_delete',
[
0,
1,
... |
"""
This example runs a CNN after the word embedding lookup. The output of the CNN is than pooled,
for example with mean-pooling.
"""
from torch.utils.data import DataLoader
import math
from sentence_transformers import models, losses, util
from sentence_transformers import LoggingHandler, SentenceTransformer
from s... | """
This example runs a CNN after the word embedding lookup. The output of the CNN is than pooled,
for example with mean-pooling.
"""
from torch.utils.data import DataLoader
import math
from sentence_transformers import models, losses, util
from sentence_transformers import LoggingHandler, SentenceTransformer
from s... |
# Copyright (c) OpenMMLab. All rights reserved.
import pytest
from mmdet.datasets import DATASETS
def test_xml_dataset():
dataconfig = {
'ann_file': 'data/VOCdevkit/VOC2007/ImageSets/Main/test.txt',
'img_prefix': 'data/VOCdevkit/VOC2007/',
'pipeline': [{
'type': 'LoadImageFrom... | import pytest
from mmdet.datasets import DATASETS
def test_xml_dataset():
dataconfig = {
'ann_file': 'data/VOCdevkit/VOC2007/ImageSets/Main/test.txt',
'img_prefix': 'data/VOCdevkit/VOC2007/',
'pipeline': [{
'type': 'LoadImageFromFile'
}]
}
XMLDataset = DATASETS... |
_base_ = [
'../_base_/models/faster-rcnn_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
checkpoint = 'https://download.pytorch.org/models/resnet50-11ad3fa6.pth'
model = dict(
backbone=dict(init_cfg=dict(type='Pretrained', chec... | _base_ = [
'../_base_/models/faster_rcnn_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
checkpoint = 'https://download.pytorch.org/models/resnet50-11ad3fa6.pth'
model = dict(
backbone=dict(init_cfg=dict(type='Pretrained', chec... |
_base_ = [
'../_base_/models/faster-rcnn_r50_fpn.py', '../_base_/datasets/voc0712.py',
'../_base_/default_runtime.py'
]
model = dict(roi_head=dict(bbox_head=dict(num_classes=20)))
# training schedule, voc dataset is repeated 3 times, in
# `_base_/datasets/voc0712.py`, so the actual epoch = 4 * 3 = 12
max_epoch... | _base_ = [
'../_base_/models/faster_rcnn_r50_fpn.py', '../_base_/datasets/voc0712.py',
'../_base_/default_runtime.py'
]
model = dict(roi_head=dict(bbox_head=dict(num_classes=20)))
# training schedule, voc dataset is repeated 3 times, in
# `_base_/datasets/voc0712.py`, so the actual epoch = 4 * 3 = 12
max_epoch... |
import torch
from torchaudio_unittest.common_utils import PytorchTestCase, skipIfNoCuda
from .functional_test_impl import Functional64OnlyTestImpl, FunctionalTestImpl
@skipIfNoCuda
class FunctionalFloat32CUDATest(FunctionalTestImpl, PytorchTestCase):
dtype = torch.float32
device = torch.device("cuda", 0)
@... | import torch
from torchaudio_unittest.common_utils import PytorchTestCase, skipIfNoCuda
from .functional_test_impl import FunctionalTestImpl
@skipIfNoCuda
class FunctionalFloat32CUDATest(FunctionalTestImpl, PytorchTestCase):
dtype = torch.float32
device = torch.device("cuda")
@skipIfNoCuda
class Functional... |
import pathlib
from typing import Any, Callable, Optional, Tuple, Union
from PIL import Image
from .utils import verify_str_arg
from .vision import VisionDataset
class StanfordCars(VisionDataset):
"""Stanford Cars Dataset
The Cars dataset contains 16,185 images of 196 classes of cars. The data is
spli... | import pathlib
from typing import Any, Callable, Optional, Tuple
from PIL import Image
from .utils import verify_str_arg
from .vision import VisionDataset
class StanfordCars(VisionDataset):
"""Stanford Cars Dataset
The Cars dataset contains 16,185 images of 196 classes of cars. The data is
split into ... |
# Copyright (c) OpenMMLab. All rights reserved.
from abc import ABCMeta, abstractmethod
from typing import List, Optional, Tuple
import torch
import torch.nn as nn
from mmcv import ops
from mmengine.model import BaseModule
from torch import Tensor
from mmdet.core.utils.typing import ConfigType, OptMultiConfig
class... | # Copyright (c) OpenMMLab. All rights reserved.
from abc import ABCMeta, abstractmethod
import torch
import torch.nn as nn
from mmcv import ops
from mmengine.model import BaseModule
class BaseRoIExtractor(BaseModule, metaclass=ABCMeta):
"""Base class for RoI extractor.
Args:
roi_layer (dict): Specif... |
from keras.src import backend
from keras.src.api_export import keras_export
from keras.src.layers.preprocessing.image_preprocessing.base_image_preprocessing_layer import ( # noqa: E501
BaseImagePreprocessingLayer,
)
@keras_export("keras.layers.RandomGrayscale")
class RandomGrayscale(BaseImagePreprocessingLayer):... | from keras.src import backend
from keras.src.api_export import keras_export
from keras.src.layers.preprocessing.image_preprocessing.base_image_preprocessing_layer import ( # noqa: E501
BaseImagePreprocessingLayer,
)
@keras_export("keras.layers.RandomGrayscale")
class RandomGrayscale(BaseImagePreprocessingLayer):... |
__copyright__ = "Copyright (c) 2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
from typing import Tuple, Dict
import pytest
import numpy as np
from jina import DocumentArray, Document
from ...torch_encoder import ImageTorchEncoder
@pytest.mark.parametrize(
['content', 'out_shape'],
... | __copyright__ = "Copyright (c) 2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
from typing import Tuple, Dict
import pytest
import numpy as np
from jina import DocumentArray, Document
try:
from torch_encoder import ImageTorchEncoder
except:
from jinahub.image.encoder.torch_encoder im... |
from functools import partial
from inspect import isclass
from typing import Any, Union, cast
from pydantic import BaseModel
from langchain_core.language_models import FakeListChatModel
from langchain_core.load.dump import dumps
from langchain_core.load.load import loads
from langchain_core.messages import HumanMessa... | from functools import partial
from inspect import isclass
from typing import Any, Union, cast
from pydantic import BaseModel
from langchain_core.language_models import FakeListChatModel
from langchain_core.load.dump import dumps
from langchain_core.load.load import loads
from langchain_core.messages import HumanMessa... |
"""DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.legacy.losses import Reduction
from keras.src.losses import deserialize
from keras.src.losses import get
from keras.src.losses import serialize
from keras.src.losses.loss import Loss
... | """DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.legacy.losses import Reduction
from keras.src.losses import deserialize
from keras.src.losses import get
from keras.src.losses import serialize
from keras.src.losses.loss import Loss
... |
import logging
from sentence_transformers import SparseEncoder
from sentence_transformers.sparse_encoder.evaluation import SparseNanoBEIREvaluator
logging.basicConfig(format="%(message)s", level=logging.INFO)
# Load a model
model = SparseEncoder("naver/splade-cocondenser-ensembledistil")
datasets = ["QuoraRetrieval... | import logging
from sentence_transformers import SparseEncoder
from sentence_transformers.sparse_encoder.evaluation import SparseNanoBEIREvaluator
logging.basicConfig(format="%(message)s", level=logging.INFO)
# Load a model
model = SparseEncoder("naver/splade-cocondenser-ensembledistil")
datasets = ["QuoraRetrieval... |
"""
Top-level module of Jina.
The primary function of this module is to import all of the public Jina
interfaces into a single place. The interfaces themselves are located in
sub-modules, as described below.
"""
import os as _os
import platform as _platform
import signal as _signal
import sys as _sys
import warnings... | """
Top-level module of Jina.
The primary function of this module is to import all of the public Jina
interfaces into a single place. The interfaces themselves are located in
sub-modules, as described below.
"""
import os as _os
import platform as _platform
import signal as _signal
import sys as _sys
import warnings... |
# coding=utf-8
# Copyright 2024 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... | # coding=utf-8
# Copyright 2024 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... |
# Copyright (c) OpenMMLab. All rights reserved.
from .ade20k import (ADE20KInstanceDataset, ADE20KPanopticDataset,
ADE20KSegDataset)
from .base_det_dataset import BaseDetDataset
from .base_semseg_dataset import BaseSegDataset
from .base_video_dataset import BaseVideoDataset
from .cityscapes import ... | # Copyright (c) OpenMMLab. All rights reserved.
from .ade20k import (ADE20KInstanceDataset, ADE20KPanopticDataset,
ADE20KSegDataset)
from .base_det_dataset import BaseDetDataset
from .base_semseg_dataset import BaseSegDataset
from .base_video_dataset import BaseVideoDataset
from .cityscapes import ... |
import time
from datasets import load_dataset
from sentence_transformers import SentenceTransformer
from sentence_transformers.quantization import quantize_embeddings, semantic_search_usearch
# 1. Load the quora corpus with questions
dataset = load_dataset("quora", split="train").map(
lambda batch: {"text": [text... | import time
from sentence_transformers import SentenceTransformer
from sentence_transformers.quantization import quantize_embeddings, semantic_search_usearch
from datasets import load_dataset
# 1. Load the quora corpus with questions
dataset = load_dataset("quora", split="train").map(
lambda batch: {"text": [text ... |
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 __future__ import annotations
import torch
from sentence_transformers.models.Module import Module
class SpladePooling(Module):
"""
SPLADE Pooling module for creating the sparse embeddings.
This module implements the SPLADE pooling mechanism that:
1. Takes token logits from a masked language m... | from __future__ import annotations
import json
import os
from typing import Any
import torch
from torch import nn
class SpladePooling(nn.Module):
"""
SPLADE Pooling module for creating the sparse embeddings.
This module implements the SPLADE pooling mechanism that:
1. Takes token logits from a mas... |
# 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 ATSS(SingleStageDetector):
"""Implementation of `ATSS <https://arxiv.org/abs/1912.02424>`... | # 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 ATSS(SingleStageDetector):
"""Implementation of `ATSS <https://arxiv.org/abs/1912.02424>`... |
from __future__ import annotations
__version__ = "3.1.0.dev0"
__MODEL_HUB_ORGANIZATION__ = "sentence-transformers"
import importlib
import os
from sentence_transformers.cross_encoder.CrossEncoder import CrossEncoder
from sentence_transformers.datasets import ParallelSentencesDataset, SentencesDataset
from sentence_t... | __version__ = "3.1.0.dev0"
__MODEL_HUB_ORGANIZATION__ = "sentence-transformers"
import importlib
import os
from sentence_transformers.cross_encoder.CrossEncoder import CrossEncoder
from sentence_transformers.datasets import ParallelSentencesDataset, SentencesDataset
from sentence_transformers.LoggingHandler import Lo... |
__version__ = '0.30.0'
import logging
from docarray.array import DocList, DocVec
from docarray.base_doc.doc import BaseDoc
__all__ = ['BaseDoc', 'DocList', 'DocVec']
logger = logging.getLogger('docarray')
handler = logging.StreamHandler()
formatter = logging.Formatter("%(levelname)s - %(name)s - %(message)s")
hand... | __version__ = '0.21.1'
import os
from docarray.document import Document
from docarray.array import DocumentArray
from docarray.dataclasses import dataclass, field
from docarray.helper import login, logout
if 'DA_RICH_HANDLER' in os.environ:
from rich.traceback import install
install()
|
# Copyright (c) OpenMMLab. All rights reserved.
from .file_client import (BaseStorageBackend, FileClient, HardDiskBackend,
HTTPBackend, LmdbBackend, MemcachedBackend,
PetrelBackend)
from .handlers import BaseFileHandler, JsonHandler, PickleHandler, YamlHandler
from .i... | # Copyright (c) OpenMMLab. All rights reserved.
from .file_client import BaseStorageBackend, FileClient
from .handlers import BaseFileHandler, JsonHandler, PickleHandler, YamlHandler
from .io import dump, load, register_handler
from .parse import dict_from_file, list_from_file
__all__ = [
'BaseStorageBackend', 'Fi... |
__copyright__ = "Copyright (c) 2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import os
import subprocess
import numpy as np
import pytest
from jina import Document, DocumentArray, Flow
from jina.executors.metas import get_default_metas
from jina_commons.indexers.dump import export_dump_stream... | __copyright__ = "Copyright (c) 2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import os
import subprocess
import numpy as np
import pytest
from jina import Document, DocumentArray, Flow
from jina.executors.metas import get_default_metas
from jina_commons.indexers.dump import export_dump_stream... |
from pydantic import AnyUrl as BaseAnyUrl
from docarray.document.base_node import BaseNode
from docarray.proto import NodeProto
class AnyUrl(BaseAnyUrl, BaseNode):
def _to_node_protobuf(self) -> NodeProto:
"""Convert Document into a NodeProto protobuf message. This function should
be called when ... | from pydantic import AnyUrl as BaseAnyUrl
from docarray.document.base_node import BaseNode
from docarray.proto import NodeProto
class AnyUrl(BaseAnyUrl, BaseNode):
def _to_node_protobuf(self) -> NodeProto:
"""Convert Document into a NodeProto protobuf message. This function should
be called when ... |
__version__ = '0.13.13'
import os
from .document import Document
from .array import DocumentArray
from .dataclasses import dataclass, field
if 'DA_NO_RICH_HANDLER' not in os.environ:
from rich.traceback import install
install()
| __version__ = '0.13.12'
import os
from .document import Document
from .array import DocumentArray
from .dataclasses import dataclass, field
if 'DA_NO_RICH_HANDLER' not in os.environ:
from rich.traceback import install
install()
|
import os
import sys
import numpy as np
import pytest
import xgboost as xgb
from xgboost import testing as tm
from xgboost.core import DataSplitMode
try:
import pandas as pd
import pyarrow as pa
import pyarrow.csv as pc
except ImportError:
pass
pytestmark = pytest.mark.skipif(
tm.no_arrow()["con... | import os
import sys
import unittest
import numpy as np
import pytest
import xgboost as xgb
from xgboost import testing as tm
from xgboost.core import DataSplitMode
try:
import pandas as pd
import pyarrow as pa
import pyarrow.csv as pc
except ImportError:
pass
pytestmark = pytest.mark.skipif(
tm... |
"""Pass input through a moderation endpoint."""
from typing import Any, Optional
from langchain_core.callbacks import (
AsyncCallbackManagerForChainRun,
CallbackManagerForChainRun,
)
from langchain_core.utils import check_package_version, get_from_dict_or_env
from pydantic import Field, model_validator
from ... | """Pass input through a moderation endpoint."""
from typing import Any, Optional
from langchain_core.callbacks import (
AsyncCallbackManagerForChainRun,
CallbackManagerForChainRun,
)
from langchain_core.utils import check_package_version, get_from_dict_or_env
from pydantic import Field, model_validator
from ... |
_base_ = ['faster_rcnn_r50_fpn_32x2_1x_openimages.py']
model = dict(
roi_head=dict(bbox_head=dict(num_classes=500)),
test_cfg=dict(rcnn=dict(score_thr=0.01)))
# dataset settings
dataset_type = 'OpenImagesChallengeDataset'
train_dataloader = dict(
dataset=dict(
type=dataset_type,
ann_file='... | _base_ = ['faster_rcnn_r50_fpn_32x2_1x_openimages.py']
model = dict(
roi_head=dict(bbox_head=dict(num_classes=500)),
test_cfg=dict(rcnn=dict(score_thr=0.01)))
# dataset settings
dataset_type = 'OpenImagesChallengeDataset'
data_root = 'data/OpenImages/'
data = dict(
train=dict(
type=dataset_type,
... |
"""
Compute image embeddings
"""
from __future__ import annotations
import os
from PIL import Image
from sentence_transformers import SentenceTransformer, util
def test_simple_encode(clip_vit_b_32_model: SentenceTransformer) -> None:
model = clip_vit_b_32_model
# Encode an image:
image_filepath = os.p... | """
Compute image embeddings
"""
from __future__ import annotations
import os
from PIL import Image
from sentence_transformers import SentenceTransformer, util
def test_simple_encode(clip_vit_b_32_model: SentenceTransformer) -> None:
model = clip_vit_b_32_model
# Encode an image:
image_filepath = os.p... |
from datasets import load_dataset
from sentence_transformers.models import Pooling, Transformer
from sentence_transformers.sparse_encoder import SparseEncoder
from sentence_transformers.sparse_encoder.evaluation import (
SparseBinaryClassificationEvaluator,
)
from sentence_transformers.sparse_encoder.models import... | from datasets import load_dataset
from sentence_transformers.models import Pooling, Transformer
from sentence_transformers.sparse_encoder import SparseEncoder
from sentence_transformers.sparse_encoder.evaluation import (
SparseBinaryClassificationEvaluator,
)
from sentence_transformers.sparse_encoder.models import... |
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appl... | # Copyright 2024 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appl... |
from enum import Enum
from typing import Any, Dict, Iterable
import torch.nn.functional as F
from torch import Tensor, nn
from sentence_transformers.SentenceTransformer import SentenceTransformer
class SiameseDistanceMetric(Enum):
"""The metric for the contrastive loss"""
EUCLIDEAN = lambda x, y: F.pairwis... | from enum import Enum
from typing import Any, Dict, Iterable
import torch.nn.functional as F
from torch import Tensor, nn
from sentence_transformers.SentenceTransformer import SentenceTransformer
class SiameseDistanceMetric(Enum):
"""The metric for the contrastive loss"""
EUCLIDEAN = lambda x, y: F.pairwis... |
__copyright__ = "Copyright (c) 2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import subprocess
from pathlib import Path
import pytest
@pytest.fixture(scope='session')
def docker_image_name() -> str:
return Path(__file__).parents[1].stem.lower()
@pytest.fixture(scope='session')
def bui... | __copyright__ = "Copyright (c) 2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import subprocess
from pathlib import Path
import pytest
@pytest.fixture(scope='session')
def docker_image_name() -> str:
return Path(__file__).parents[1].stem.lower()
@pytest.fixture(scope='session')
def bui... |
import numpy as np
from .any_url import AnyUrl
class ImageUrl(AnyUrl):
def load(self) -> np.ndarray:
"""
transform the url in a image Tensor
this is just a patch we will move the function from old docarray
:return: tensor image
"""
return np.zeros((3, 224, 224))
| import numpy as np
from docarray.typing import Tensor
from .any_url import AnyUrl
class ImageUrl(AnyUrl):
def load(self) -> Tensor:
"""
transform the url in a image Tensor
this is just a patch we will move the function from old docarray
:return: tensor image
"""
... |
import json
import numpy as np
import pytest
import xgboost as xgb
from xgboost import testing as tm
try:
import matplotlib
matplotlib.use('Agg')
from graphviz import Source
from matplotlib.axes import Axes
except ImportError:
pass
pytestmark = pytest.mark.skipif(**tm.no_multiple(tm.no_matplotli... | import json
import numpy as np
import pytest
import xgboost as xgb
from xgboost import testing as tm
try:
import matplotlib
matplotlib.use('Agg')
from graphviz import Source
from matplotlib.axes import Axes
except ImportError:
pass
pytestmark = pytest.mark.skipif(**tm.no_multiple(tm.no_matplotli... |
import argparse
import os
from typing import List, Union
from jina.parsers.helper import CastHostAction
def api_to_dict(show_all_args: bool = False):
"""Convert Jina API to a dict
:param show_all_args: if set, then hidden args are also exported
:return: dict
"""
if show_all_args:
from jin... | import argparse
import os
from typing import List, Union
def api_to_dict(show_all_args: bool = False):
"""Convert Jina API to a dict
:param show_all_args: if set, then hidden args are also exported
:return: dict
"""
if show_all_args:
from jina.parsers import helper
helper._SHOW_AL... |
from typing import TYPE_CHECKING
from ...utils import (
DIFFUSERS_SLOW_IMPORT,
OptionalDependencyNotAvailable,
_LazyModule,
get_objects_from_module,
is_flax_available,
is_torch_available,
is_transformers_available,
)
_dummy_objects = {}
_import_structure = {}
try:
if not (is_transfor... | from typing import TYPE_CHECKING
from ...utils import (
DIFFUSERS_SLOW_IMPORT,
OptionalDependencyNotAvailable,
_LazyModule,
get_objects_from_module,
is_flax_available,
is_torch_available,
is_transformers_available,
)
_dummy_objects = {}
_import_structure = {}
try:
if... |
import ast
from collections import defaultdict
# Function to perform topological sorting
def topological_sort(dependencies: dict) -> list[list[str]]:
"""Given the dependencies graph construct sorted list of list of modular files
For example, returned list of lists might be:
[
["../modular... | import ast
from collections import defaultdict
# Function to perform topological sorting
def topological_sort(dependencies: dict):
# Nodes are the name of the models to convert (we only add those to the graph)
nodes = {node.rsplit("modular_", 1)[1].replace(".py", "") for node in dependencies.keys()}
# Thi... |
"""
OPUS (http://opus.nlpl.eu/) is a great collection of different parallel datasets for more than 400 languages.
On the website, you can download parallel datasets for many languages in different formats. I found that
the format "Bottom-left triangle: download plain text files (MOSES/GIZA++)" requires minimal
overhea... | """
OPUS (http://opus.nlpl.eu/) is a great collection of different parallel datasets for more than 400 languages.
On the website, you can download parallel datasets for many languages in different formats. I found that
the format "Bottom-left triangle: download plain text files (MOSES/GIZA++)" requires minimal
overhea... |
_base_ = './yolov3_d53_8xb8-ms-608-273e_coco.py'
train_pipeline = [
dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}),
dict(type='LoadAnnotations', with_bbox=True),
# `mean` and `to_rgb` should be the same with the `preprocess_cfg`
dict(type='Expand', mean=[0, 0, 0], to_rgb=True, rat... | _base_ = './yolov3_d53_8xb8-ms-608-273e_coco.py'
# dataset settings
# file_client_args = dict(
# backend='petrel',
# path_mapping=dict({
# './data/': 's3://openmmlab/datasets/detection/',
# 'data/': 's3://openmmlab/datasets/detection/'
# }))
file_client_args = dict(backend='disk')
train_pip... |
from collections import namedtuple
from typing import Any, Callable, Optional, TypeVar
from typing_extensions import NamedTuple
import torch.return_types
from torch.utils._pytree import PyTree, tree_flatten, TreeSpec
FlattenFuncSpec = Callable[[PyTree, TreeSpec], list]
FlattenFuncExactMatchSpec = Callable[[PyTree, T... | from collections import namedtuple
from typing import Any, Callable, Optional, TypeVar
from typing_extensions import NamedTuple
import torch.return_types
from torch.utils._pytree import PyTree, tree_flatten, TreeSpec
FlattenFuncSpec = Callable[[PyTree, TreeSpec], list]
FlattenFuncExactMatchSpec = Callable[[PyTree, T... |
from typing import Any, Collection, List, Optional, Tuple, Union
from llama_index.core.tools.types import AsyncBaseTool
from pydantic import BaseModel
class LLMCompilerParseResult(BaseModel):
"""LLMCompiler parser result."""
thought: str
idx: int
tool_name: str
args: str
class JoinerOutput(Bas... | from typing import Any, Collection, List, Optional, Tuple, Union
from llama_index.core.tools.types import AsyncBaseTool
from pydantic import BaseModel
class LLMCompilerParseResult(BaseModel):
"""LLMCompiler parser result."""
thought: str
idx: int
tool_name: str
args: str
class JoinerOutput(Bas... |
# 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... |
"""Load agent."""
from collections.abc import Sequence
from typing import Any, Optional
from langchain_core._api import deprecated
from langchain_core.callbacks import BaseCallbackManager
from langchain_core.language_models import BaseLanguageModel
from langchain_core.tools import BaseTool
from langchain._api.deprec... | """Load agent."""
from collections.abc import Sequence
from typing import Any, Optional
from langchain_core._api import deprecated
from langchain_core.callbacks import BaseCallbackManager
from langchain_core.language_models import BaseLanguageModel
from langchain_core.tools import BaseTool
from langchain._api.deprec... |
# Copyright 2020 The HuggingFace Datasets Authors and the TensorFlow Datasets Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# U... | # Copyright 2020 The HuggingFace Datasets Authors and the TensorFlow Datasets Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# U... |
_base_ = './grid-rcnn_r50_fpn_gn-head_2x_coco.py'
# training schedule
max_epochs = 12
train_cfg = dict(max_epochs=max_epochs)
# learning rate
param_scheduler = [
dict(
type='LinearLR', start_factor=0.0001, by_epoch=False, begin=0,
end=500),
dict(
type='MultiStepLR',
begin=0,
... | _base_ = './grid_rcnn_r50_fpn_gn-head_2x_coco.py'
# training schedule
max_epochs = 12
train_cfg = dict(max_epochs=max_epochs)
# learning rate
param_scheduler = [
dict(
type='LinearLR', start_factor=0.0001, by_epoch=False, begin=0,
end=500),
dict(
type='MultiStepLR',
begin=0,
... |
# 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
from docarray import Document, DocumentArray
from pydantic import BaseModel
from uvicorn import Config, Server
from jina import Gateway, __default_host__
from jina.clients.request import request_generator
class DummyResponseModel(BaseModel):
arg1: Optional[str]
arg2: Optional[str... | from typing import Optional
from docarray import Document, DocumentArray
from pydantic import BaseModel
from uvicorn import Config, Server
from jina import Gateway, __default_host__
from jina.clients.request import request_generator
class DummyResponseModel(BaseModel):
arg1: Optional[str]
arg2: Optional[str... |
from __future__ import annotations
import logging
import os
from datasets import load_dataset
from sentence_transformers.sparse_encoder import (
SparseEncoder,
)
from sentence_transformers.sparse_encoder.evaluation.SparseNanoBEIREvaluator import SparseNanoBEIREvaluator
from sentence_transformers.sparse_encoder.l... | from __future__ import annotations
import logging
import os
from datasets import load_dataset
from sentence_transformers.sparse_encoder import (
SparseEncoder,
)
from sentence_transformers.sparse_encoder.evaluation.SparseNanoBEIREvaluator import SparseNanoBEIREvaluator
from sentence_transformers.sparse_encoder.l... |
import numpy as np
import pytest
from keras.src import backend
from keras.src import layers
from keras.src import testing
class DropoutTest(testing.TestCase):
@pytest.mark.requires_trainable_backend
def test_dropout_basics(self):
self.run_layer_test(
layers.Dropout,
init_kwarg... | import numpy as np
import pytest
from keras.src import backend
from keras.src import layers
from keras.src import testing
class DropoutTest(testing.TestCase):
@pytest.mark.requires_trainable_backend
def test_dropout_basics(self):
self.run_layer_test(
layers.Dropout,
init_kwarg... |
import pytest
from whisper.tokenizer import get_tokenizer
@pytest.mark.parametrize("multilingual", [True, False])
def test_tokenizer(multilingual):
tokenizer = get_tokenizer(multilingual=False)
assert tokenizer.sot in tokenizer.sot_sequence
assert len(tokenizer.all_language_codes) == len(tokenizer.all_la... | from whisper.tokenizer import get_tokenizer
def test_tokenizer():
gpt2_tokenizer = get_tokenizer(multilingual=False)
multilingual_tokenizer = get_tokenizer(multilingual=True)
text = "다람쥐 헌 쳇바퀴에 타고파"
gpt2_tokens = gpt2_tokenizer.encode(text)
multilingual_tokens = multilingual_tokenizer.encode(text... |
import numpy as np
import pytest
from docarray import BaseDoc, DocArray
from docarray.typing import NdArray
@pytest.mark.parametrize('shuffle', [False, True])
@pytest.mark.parametrize('stack', [False, True])
@pytest.mark.parametrize('batch_size,n_batches', [(16, 7), (10, 10)])
def test_batch(shuffle, stack, batch_si... | import numpy as np
import pytest
from docarray import BaseDocument, DocumentArray
from docarray.typing import NdArray
@pytest.mark.parametrize('shuffle', [False, True])
@pytest.mark.parametrize('stack', [False, True])
@pytest.mark.parametrize('batch_size,n_batches', [(16, 7), (10, 10)])
def test_batch(shuffle, stack... |
import pytest
from docarray.utils._internal.misc import is_tf_available
tf_available = is_tf_available()
if tf_available:
import tensorflow as tf
from docarray.computation.tensorflow_backend import TensorFlowCompBackend
from docarray.typing import TensorFlowTensor
metrics = TensorFlowCompBackend.Met... | import pytest
from docarray.utils.misc import is_tf_available
tf_available = is_tf_available()
if tf_available:
import tensorflow as tf
from docarray.computation.tensorflow_backend import TensorFlowCompBackend
from docarray.typing import TensorFlowTensor
metrics = TensorFlowCompBackend.Metrics
else:... |
from typing import TYPE_CHECKING, Any, Optional, Type, TypeVar, Union
import numpy as np
from docarray.base_doc import BaseDoc
from docarray.typing import AnyEmbedding, AudioUrl
from docarray.typing.bytes.audio_bytes import AudioBytes
from docarray.typing.tensor.abstract_tensor import AbstractTensor
from docarray.typ... | from typing import TYPE_CHECKING, Any, Optional, Type, TypeVar, Union
import numpy as np
from docarray.base_doc import BaseDoc
from docarray.typing import AnyEmbedding, AudioUrl
from docarray.typing.bytes.audio_bytes import AudioBytes
from docarray.typing.tensor.abstract_tensor import AbstractTensor
from docarray.typ... |
from __future__ import annotations
from .splade_callbacks import SchedulerType, SpladeLambdaSchedulerCallback
__all__ = ["SpladeLambdaSchedulerCallback", "SchedulerType"]
| from __future__ import annotations
from sentence_transformers.sparse_encoder.callbacks.splade_callbacks import (
SchedulerType,
SpladeLambdaSchedulerCallback,
)
__all__ = ["SpladeLambdaSchedulerCallback", "SchedulerType"]
|
"""
Example of training with Dask on GPU
====================================
"""
import cupy as cp
import dask_cudf
from dask import array as da
from dask import dataframe as dd
from dask.distributed import Client
from dask_cuda import LocalCUDACluster
from xgboost import dask as dxgb
from xgboost.dask import DaskDM... | """
Example of training with Dask on GPU
====================================
"""
import cupy as cp
import dask_cudf
from dask import array as da
from dask import dataframe as dd
from dask.distributed import Client
from dask_cuda import LocalCUDACluster
from xgboost import dask as dxgb
from xgboost.dask import DaskDMa... |
# 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... |
"""DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras import activations as activations
from keras import applications as applications
from keras import callbacks as callbacks
from keras import config as config
from keras import constraints ... | """DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.api import activations
from keras.api import applications
from keras.api import callbacks
from keras.api import config
from keras.api import constraints
from keras.api import datasets
fro... |
# Copyright (c) OpenMMLab. All rights reserved.
import warnings
from typing import List, Sequence, Union
import numpy as np
import torch
from .base_data_element import BaseDataElement
class PixelData(BaseDataElement):
"""Data structure for pixel-level annotations or predictions.
All data items in ``data_fi... | # Copyright (c) OpenMMLab. All rights reserved.
import warnings
from typing import List, Sequence, Union
import numpy as np
import torch
from .base_data_element import BaseDataElement
class PixelData(BaseDataElement):
"""Data structure for pixel-level annnotations or predictions.
All data items in ``data_f... |
# Copyright (c) OpenMMLab. All rights reserved.
from mmengine.config import ConfigDict
from mmdet.registry import MODELS
from mmdet.utils import OptConfigType, OptMultiConfig
from .two_stage import TwoStageDetector
@MODELS.register_module()
class MaskRCNN(TwoStageDetector):
"""Implementation of `Mask R-CNN <http... | # Copyright (c) OpenMMLab. All rights reserved.
from mmengine.config import ConfigDict
from mmdet.core.utils import OptConfigType, OptMultiConfig
from mmdet.registry import MODELS
from .two_stage import TwoStageDetector
@MODELS.register_module()
class MaskRCNN(TwoStageDetector):
"""Implementation of `Mask R-CNN ... |
__copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
from typing import Dict, Iterable, Sequence
import numpy as np
import tensorflow as tf
from jina import DocumentArray, Executor, requests
from jina.logging.logger import JinaLogger
from jina_commons.batching imp... | __copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
from typing import Dict, Iterable, List, Union
import numpy as np
import tensorflow as tf
from jina import DocumentArray, Executor, requests
from jina.logging.logger import JinaLogger
from jina_commons.batching ... |
from typing import Union
from docarray.typing.tensor.audio.audio_ndarray import AudioNdArray
from docarray.utils._internal.misc import is_tf_available, is_torch_available
torch_available = is_torch_available()
if torch_available:
from docarray.typing.tensor.audio.audio_torch_tensor import AudioTorchTensor
tf_ava... | from typing import Union
from docarray.typing.tensor.audio.audio_ndarray import AudioNdArray
from docarray.utils.misc import is_tf_available, is_torch_available
torch_available = is_torch_available()
if torch_available:
from docarray.typing.tensor.audio.audio_torch_tensor import AudioTorchTensor
tf_available = i... |
__copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
from typing import Iterable, Optional
import torch
from jina import DocumentArray, Executor, requests
from .audio_clip.model import AudioCLIP
class AudioCLIPTextEncoder(Executor):
"""
Encode text data... | __copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
from typing import Iterable, Optional
import torch
from jina import DocumentArray, Executor, requests
from .audio_clip.model import AudioCLIP
class AudioCLIPTextEncoder(Executor):
"""
Encode text data... |
"""Module for argparse for Client"""
def mixin_comm_protocol_parser(parser):
"""Add the arguments for the protocol to the parser
:param parser: the parser configure
"""
from jina.enums import GatewayProtocolType
parser.add_argument(
'--protocol',
type=GatewayProtocolType.from_st... | """Module for argparse for Client"""
def mixin_comm_protocol_parser(parser):
"""Add the arguments for the protocol to the parser
:param parser: the parser configure
"""
from jina.enums import GatewayProtocolType
parser.add_argument(
'--protocol',
type=GatewayProtocolType.from_st... |
"""Configure global settings and get information about the working environment."""
# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause
# Machine learning module for Python
# ==================================
#
# sklearn is a Python module integrating classical machine
# learning algorithms... | """Configure global settings and get information about the working environment."""
# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause
# Machine learning module for Python
# ==================================
#
# sklearn is a Python module integrating classical machine
# learning algorithms... |
"""
This examples trains BERT (or any other transformer model like RoBERTa, DistilBERT etc.) for the STSbenchmark from scratch.
It uses MatryoshkaLoss with the powerful CoSENTLoss to train models that perform well at output dimensions [768, 512, 256, 128, 64].
It generates sentence embeddings that can be compared using... | """
This examples trains BERT (or any other transformer model like RoBERTa, DistilBERT etc.) for the STSbenchmark from scratch.
It uses MatryoshkaLoss with the powerful CoSENTLoss to train models that perform well at output dimensions [768, 512, 256, 128, 64].
It generates sentence embeddings that can be compared using... |
import multiprocessing
from typing import TYPE_CHECKING, Optional, Union
from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream
if TYPE_CHECKING:
import sqlite3
import sq... | import multiprocessing
from typing import TYPE_CHECKING, Optional, Union
from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream
if TYPE_CHECKING:
import sqlite3
import sq... |
from __future__ import annotations
from dataclasses import dataclass
from sentence_transformers.training_args import SentenceTransformerTrainingArguments
@dataclass
class SparseEncoderTrainingArguments(SentenceTransformerTrainingArguments):
r"""
SparseEncoderTrainingArguments extends :class:`~SentenceTransf... | from __future__ import annotations
from dataclasses import dataclass
from sentence_transformers.training_args import SentenceTransformerTrainingArguments
@dataclass
class SparseEncoderTrainingArguments(SentenceTransformerTrainingArguments):
"""
SparseEncoderTrainingArguments extends :class:`~SentenceTransfo... |
# dataset settings
dataset_type = 'RefCocoDataset'
data_root = 'data/coco/'
backend_args = None
test_pipeline = [
dict(type='LoadImageFromFile', backend_args=backend_args),
dict(type='Resize', scale=(1333, 800), keep_ratio=True),
dict(
type='LoadAnnotations',
with_mask=True,
with_b... | # dataset settings
dataset_type = 'RefCOCODataset'
data_root = 'data/refcoco/'
backend_args = None
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='Resize', scale=(1333, 800), keep_ratio=True),
dict(type='RandomFlip', prob=0.5),
dict(
type='PackDetInputs',
meta_keys=('img_... |
"""All minimum dependencies for scikit-learn."""
# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause
import argparse
from collections import defaultdict
# scipy and cython should by in sync with pyproject.toml
NUMPY_MIN_VERSION = "1.22.0"
SCIPY_MIN_VERSION = "1.8.0"
JOBLIB_MIN_VERSION = "1... | """All minimum dependencies for scikit-learn."""
# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause
import argparse
from collections import defaultdict
# scipy and cython should by in sync with pyproject.toml
NUMPY_MIN_VERSION = "1.22.0"
SCIPY_MIN_VERSION = "1.8.0"
JOBLIB_MIN_VERSION = "1... |
__copyright__ = "Copyright (c) 2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import pytest
from ...simpleranker import SimpleRanker
@pytest.mark.parametrize('default_traversal_paths', [['r'], ['c']])
@pytest.mark.parametrize('ranking', ['min', 'max'])
def test_ranking(
documents_chunk, ... | __copyright__ = "Copyright (c) 2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import pytest
from ...simpleranker import SimpleRanker
@pytest.mark.parametrize('default_traversal_paths', [['r'], ['c']])
@pytest.mark.parametrize('ranking', ['min', 'max'])
def test_ranking(documents_chunk, docume... |
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"],
... |
from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.agent_toolkits.openapi.planner import (
RequestsDeleteToolWithParsing,
RequestsGetToolWithParsing,
RequestsPatchToolWithParsing,
RequestsPostToolWithParsing,
... | from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.agent_toolkits.openapi.planner import (
RequestsDeleteToolWithParsing,
RequestsGetToolWithParsing,
RequestsPatchToolWithParsing,
RequestsPostToolWithParsing,
... |
_base_ = '../cascade_rcnn/cascade-mask-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... | _base_ = '../cascade_rcnn/cascade_mask_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... |
import numpy as np
from absl.testing import parameterized
from keras.src import backend
from keras.src import testing
from keras.src.utils import backend_utils
class BackendUtilsTest(testing.TestCase):
@parameterized.named_parameters(
("numpy", "numpy"),
("jax", "jax"),
("tensorflow", "te... | import numpy as np
from absl.testing import parameterized
from keras.src import backend
from keras.src import testing
from keras.src.utils import backend_utils
class BackendUtilsTest(testing.TestCase, parameterized.TestCase):
@parameterized.named_parameters(
("numpy", "numpy"),
("jax", "jax"),
... |
"""Test HuggingFace API wrapper."""
from pathlib import Path
import pytest
from langchain_community.llms.huggingface_hub import HuggingFaceHub
from langchain_community.llms.loading import load_llm
from tests.integration_tests.llms.utils import assert_llm_equality
def test_huggingface_text_generation() -> None:
... | """Test HuggingFace API wrapper."""
from pathlib import Path
import pytest
from langchain_community.llms.huggingface_hub import HuggingFaceHub
from langchain_community.llms.loading import load_llm
from tests.integration_tests.llms.utils import assert_llm_equality
def test_huggingface_text_generation() -> None:
... |
from __future__ import annotations
from sentence_transformers.sparse_encoder.data_collator import SparseEncoderDataCollator
from sentence_transformers.sparse_encoder.evaluation import (
SparseBinaryClassificationEvaluator,
SparseEmbeddingSimilarityEvaluator,
SparseInformationRetrievalEvaluator,
SparseM... | from __future__ import annotations
from sentence_transformers.sparse_encoder.data_collator import SparseEncoderDataCollator
from sentence_transformers.sparse_encoder.evaluation import (
SparseBinaryClassificationEvaluator,
SparseEmbeddingSimilarityEvaluator,
SparseInformationRetrievalEvaluator,
SparseM... |
# TODO: enable ruff qa on this file when we figure out why it thinks weaviate_client is
# redefined at each test that fixture
# ruff: noqa
import numpy as np
import pytest
import torch
from pydantic import Field
from docarray import BaseDoc
from docarray.index.backends.weaviate import WeaviateDocumentIndex
from ... | # TODO: enable ruff qa on this file when we figure out why it thinks weaviate_client is
# redefined at each test that fixture
# ruff: noqa
import numpy as np
import pytest
import torch
from pydantic import Field
from docarray import BaseDoc
from docarray.index.backends.weaviate import WeaviateDocumentIndex
from ... |
from typing import Any, Dict, Optional
from llama_index.core.base.llms.types import LLMMetadata
from llama_index.core.bridge.pydantic import Field
from llama_index.core.constants import (
DEFAULT_CONTEXT_WINDOW,
DEFAULT_NUM_OUTPUTS,
DEFAULT_TEMPERATURE,
)
from llama_index.core.base.llms.generic_utils impor... | from typing import Any, Dict, Optional
from llama_index.core.base.llms.types import LLMMetadata
from llama_index.core.bridge.pydantic import Field
from llama_index.core.constants import (
DEFAULT_CONTEXT_WINDOW,
DEFAULT_NUM_OUTPUTS,
DEFAULT_TEMPERATURE,
)
from llama_index.core.base.llms.generic_utils impor... |
import types
from keras.src.activations.activations import celu
from keras.src.activations.activations import elu
from keras.src.activations.activations import exponential
from keras.src.activations.activations import gelu
from keras.src.activations.activations import hard_sigmoid
from keras.src.activations.activation... | import types
from keras.src.activations.activations import elu
from keras.src.activations.activations import exponential
from keras.src.activations.activations import gelu
from keras.src.activations.activations import hard_sigmoid
from keras.src.activations.activations import hard_silu
from keras.src.activations.activ... |
from typing import Any, Dict, Optional
import httpx
from llama_index.core.base.embeddings.base import (
DEFAULT_EMBED_BATCH_SIZE,
)
from llama_index.core.bridge.pydantic import Field
from llama_index.core.callbacks import CallbackManager
from llama_index.embeddings.fireworks.utils import (
resolve_fireworks_cr... | from typing import Any, Dict, Optional
import httpx
from llama_index.core.base.embeddings.base import (
DEFAULT_EMBED_BATCH_SIZE,
)
from llama_index.core.bridge.pydantic import Field
from llama_index.core.callbacks import CallbackManager
from llama_index.embeddings.fireworks.utils import (
resolve_fireworks_cr... |
from docarray import BaseDoc
from docarray.typing import AnyUrl
def test_set_any_url():
class MyDocument(BaseDoc):
any_url: AnyUrl
d = MyDocument(any_url="https://jina.ai")
assert isinstance(d.any_url, AnyUrl)
assert d.any_url == "https://jina.ai"
| from docarray import BaseDocument
from docarray.typing import AnyUrl
def test_set_any_url():
class MyDocument(BaseDocument):
any_url: AnyUrl
d = MyDocument(any_url="https://jina.ai")
assert isinstance(d.any_url, AnyUrl)
assert d.any_url == "https://jina.ai"
|
from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import pyarrow as pa
if TYPE_CHECKING:
from .features import FeatureType
@dataclass
class Translation:
"""`FeatureConnector` for translations with fixed languages per example.
Here for ... | from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import pyarrow as pa
if TYPE_CHECKING:
from .features import FeatureType
@dataclass
class Translation:
"""`FeatureConnector` for translations with fixed languages per example.
Here for ... |
# 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... |
# docstyle-ignore
INSTALL_CONTENT = """
# Datasets installation
! pip install datasets transformers
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/datasets.git
"""
notebook_first_cells = [{"type": "code... | default_branch_name = "main"
version_prefix = ""
|
"""DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.backend.config import backend
from keras.src.backend.config import disable_flash_attention
from keras.src.backend.config import enable_flash_attention
from keras.src.backend.config im... | """DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.backend.config import backend
from keras.src.backend.config import epsilon
from keras.src.backend.config import floatx
from keras.src.backend.config import image_data_format
from kera... |
import torch
_TORCHFUNCTION_SUBCLASS = False
class _ReturnTypeCM:
def __init__(self, to_restore):
self.to_restore = to_restore
def __enter__(self):
return self
def __exit__(self, *args):
global _TORCHFUNCTION_SUBCLASS
_TORCHFUNCTION_SUBCLASS = self.to_restore
def set_r... | import torch
_TORCHFUNCTION_SUBCLASS = False
class _ReturnTypeCM:
def __init__(self, to_restore):
self.to_restore = to_restore
def __enter__(self):
return self
def __exit__(self, *args):
global _TORCHFUNCTION_SUBCLASS
_TORCHFUNCTION_SUBCLASS = self.to_restore
def set_r... |
import pathlib
from typing import Any, BinaryIO, Dict, Iterator, List, Tuple, Union
from torchdata.datapipes.iter import Filter, IterDataPipe, Mapper, Zipper
from torchvision.datapoints import BoundingBox
from torchvision.prototype.datapoints import Label
from torchvision.prototype.datasets.utils import Dataset, Encod... | import pathlib
from typing import Any, BinaryIO, Dict, Iterator, List, Tuple, Union
from torchdata.datapipes.iter import Filter, IterDataPipe, Mapper, Zipper
from torchvision.prototype.datapoints import BoundingBox, Label
from torchvision.prototype.datasets.utils import Dataset, EncodedImage, HttpResource, OnlineResou... |
import os
import re
from pathlib import Path
from typing import Optional, Tuple, Union
import torch
import torchaudio
from torch.hub import download_url_to_file
from torch.utils.data import Dataset
from torchaudio.datasets.utils import extract_archive
URL = "https://speech.fit.vutbr.cz/files/quesst14Database.tgz"
_C... | import os
import re
from pathlib import Path
from typing import Optional, Tuple, Union
import torch
import torchaudio
from torch.hub import download_url_to_file
from torch.utils.data import Dataset
from torchaudio.datasets.utils import extract_archive
URL = "https://speech.fit.vutbr.cz/files/quesst14Database.tgz"
_C... |
from typing import Any
from llama_index.core.bridge.pydantic import Field, model_serializer
from llama_index.core.tools import ToolSelection, ToolOutput
from llama_index.core.llms import ChatMessage
from llama_index.core.workflow import Event, StartEvent
class AgentInput(Event):
"""LLM input."""
input: list... | from typing import Any
from llama_index.core.bridge.pydantic import model_serializer
from llama_index.core.tools import ToolSelection, ToolOutput
from llama_index.core.llms import ChatMessage
from llama_index.core.workflow import Event, StartEvent
class AgentInput(Event):
"""LLM input."""
input: list[ChatMe... |
import json
import os
import pytest
from hubble.executor import HubExecutor
from hubble.executor.hubio import HubIO
from jina import __version__
from jina.orchestrate.deployments.config.helper import (
get_base_executor_version,
get_image_name,
to_compatible_name,
)
@pytest.mark.parametrize('is_master',... | import json
import os
import pytest
from jina import __version__
from jina.hubble import HubExecutor
from jina.hubble.hubio import HubIO
from jina.orchestrate.deployments.config.helper import (
get_base_executor_version,
get_image_name,
to_compatible_name,
)
@pytest.mark.parametrize('is_master', (True, ... |
"""Test chat model integration using standard integration tests."""
from typing import Type
from langchain_tests.integration_tests import ChatModelIntegrationTests
from langchain_ollama.chat_models import ChatOllama
class TestChatOllama(ChatModelIntegrationTests):
@property
def chat_model_class(self) -> Ty... | """Test chat model integration using standard integration tests."""
from typing import Type
from langchain_tests.integration_tests import ChatModelIntegrationTests
from langchain_ollama.chat_models import ChatOllama
class TestChatOllama(ChatModelIntegrationTests):
@property
def chat_model_class(self) -> Ty... |
import time
from functools import partial
from huggingface_hub import HfApi, hf_hub_url
from huggingface_hub.hf_api import RepoFile
from packaging import version
from requests import ConnectionError, HTTPError
from .. import config
from . import logging
logger = logging.get_logger(__name__)
# Retry `preupload_lfs_... | import time
from functools import partial
from huggingface_hub import HfApi, hf_hub_url
from packaging import version
from requests import ConnectionError, HTTPError
from .. import config
from . import logging
logger = logging.get_logger(__name__)
# Retry `preupload_lfs_files` in `huggingface_hub<0.20.0` on the "5... |
# Copyright (c) OpenMMLab. All rights reserved.
from .base import BaseMOTModel
from .bytetrack import ByteTrack
from .deep_sort import DeepSORT
from .qdtrack import QDTrack
__all__ = ['BaseMOTModel', 'ByteTrack', 'QDTrack', 'DeepSORT']
| # Copyright (c) OpenMMLab. All rights reserved.
from .base import BaseMOTModel
from .bytetrack import ByteTrack
from .qdtrack import QDTrack
__all__ = ['BaseMOTModel', 'ByteTrack', 'QDTrack']
|
import copy
import importlib
import os
import sys
from keras.src import backend as backend_module
from keras.src.api_export import keras_export
from keras.src.backend.common import global_state
def in_tf_graph():
if global_state.get_global_attribute("in_tf_graph_scope", False):
return True
if "tenso... | import copy
import importlib
import os
import sys
from keras.src import backend as backend_module
from keras.src.api_export import keras_export
from keras.src.backend.common import global_state
def in_tf_graph():
if global_state.get_global_attribute("in_tf_graph_scope", False):
return True
if "tenso... |
# Copyright (c) OpenMMLab. All rights reserved.
"""MMEngine provides 20 root registries to support using modules across
projects.
More datails can be found at
https://mmengine.readthedocs.io/en/latest/tutorials/registry.html.
"""
from .build_functions import (build_model_from_cfg, build_runner_from_cfg,
... | # Copyright (c) OpenMMLab. All rights reserved.
"""MMEngine provides 20 root registries to support using modules across
projects.
More datails can be found at
https://mmengine.readthedocs.io/en/latest/tutorials/registry.html.
"""
from .build_functions import (build_model_from_cfg, build_runner_from_cfg,
... |
import hashlib
import json
from typing import Tuple, TYPE_CHECKING
import numpy as np
if TYPE_CHECKING: # pragma: no cover
from docarray.typing import T
class FeatureHashMixin:
"""Provide helper functions for feature hashing."""
def embed_feature_hashing(
self: 'T',
n_dim: int = 256,
... | import hashlib
import json
from typing import Tuple, TYPE_CHECKING
import numpy as np
if TYPE_CHECKING:
from docarray.typing import T
class FeatureHashMixin:
"""Provide helper functions for feature hashing."""
def embed_feature_hashing(
self: 'T',
n_dim: int = 256,
sparse: bool ... |
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