input stringlengths 33 5k | output stringlengths 32 5k |
|---|---|
from typing import List, Optional, Union
import PIL.Image
import torch
from torchvision.prototype import features
from torchvision.transforms import functional_tensor as _FT
from torchvision.transforms.functional import pil_to_tensor, to_pil_image
normalize_image_tensor = _FT.normalize
def normalize_video(video: to... | from typing import List, Optional, Union
import PIL.Image
import torch
from torchvision.prototype import features
from torchvision.transforms import functional_tensor as _FT
from torchvision.transforms.functional import pil_to_tensor, to_pil_image
normalize_image_tensor = _FT.normalize
def normalize_video(video: to... |
# Copyright (c) OpenMMLab. All rights reserved.
from mmdet.models.builder import HEADS
from mmdet.models.utils import ResLayer, SimplifiedBasicBlock
from .fused_semantic_head import FusedSemanticHead
@HEADS.register_module()
class SCNetSemanticHead(FusedSemanticHead):
"""Mask head for `SCNet <https://arxiv.org/ab... | from mmdet.models.builder import HEADS
from mmdet.models.utils import ResLayer, SimplifiedBasicBlock
from .fused_semantic_head import FusedSemanticHead
@HEADS.register_module()
class SCNetSemanticHead(FusedSemanticHead):
"""Mask head for `SCNet <https://arxiv.org/abs/2012.10150>`_.
Args:
conv_to_res ... |
from typing import TypeVar
from docarray.typing.proto_register import _register_proto
from docarray.typing.tensor.image.abstract_image_tensor import AbstractImageTensor
from docarray.typing.tensor.tensorflow_tensor import TensorFlowTensor, metaTensorFlow
T = TypeVar('T', bound='ImageTensorFlowTensor')
@_register_pr... | from typing import TypeVar
from docarray.typing.proto_register import _register_proto
from docarray.typing.tensor.image.abstract_image_tensor import AbstractImageTensor
from docarray.typing.tensor.tensorflow_tensor import TensorFlowTensor, metaTensorFlow
T = TypeVar('T', bound='ImageTensorFlowTensor')
@_register_pr... |
# Copyright (c) OpenMMLab. All rights reserved.
from .accuracy import Accuracy, accuracy
from .ae_loss import AssociativeEmbeddingLoss
from .balanced_l1_loss import BalancedL1Loss, balanced_l1_loss
from .cross_entropy_loss import (CrossEntropyCustomLoss, CrossEntropyLoss,
binary_cross_e... | # Copyright (c) OpenMMLab. All rights reserved.
from .accuracy import Accuracy, accuracy
from .ae_loss import AssociativeEmbeddingLoss
from .balanced_l1_loss import BalancedL1Loss, balanced_l1_loss
from .cross_entropy_loss import (CrossEntropyLoss, binary_cross_entropy,
cross_entropy, m... |
import json
import re
from typing import TypeVar
import yaml
from langchain_core.exceptions import OutputParserException
from langchain_core.output_parsers import BaseOutputParser
from pydantic import BaseModel, ValidationError
from langchain.output_parsers.format_instructions import YAML_FORMAT_INSTRUCTIONS
T = Typ... | import json
import re
from typing import TypeVar
import yaml
from langchain_core.exceptions import OutputParserException
from langchain_core.output_parsers import BaseOutputParser
from pydantic import BaseModel, ValidationError
from langchain.output_parsers.format_instructions import YAML_FORMAT_INSTRUCTIONS
T = Typ... |
"""Develop installable templates."""
import re
import shutil
import subprocess
from pathlib import Path
from typing import Annotated, Optional
import typer
from langchain_cli.utils.packages import get_langserve_export, get_package_root
package_cli = typer.Typer(no_args_is_help=True, add_completion=False)
@package... | """
Develop installable templates.
"""
import re
import shutil
import subprocess
from pathlib import Path
from typing import Annotated, Optional
import typer
from langchain_cli.utils.packages import get_langserve_export, get_package_root
package_cli = typer.Typer(no_args_is_help=True, add_completion=False)
@packa... |
from jina import Executor, requests
from docarray import DocList
from docarray.documents import TextDoc
class MyExecutor(Executor):
@requests
def foo(self, docs: DocList[TextDoc], **kwargs) -> DocList[TextDoc]:
docs[0].text = 'hello, world!'
docs[1].text = 'goodbye, world!'
return docs... | from jina import Executor, requests, DocumentArray
class MyExecutor(Executor):
@requests
def foo(self, docs: DocumentArray, **kwargs):
docs[0].text = 'hello, world!'
docs[1].text = 'goodbye, world!'
|
"""Test HuggingFaceHub embeddings."""
import pytest
from langchain_community.embeddings import HuggingFaceHubEmbeddings
def test_huggingfacehub_embedding_documents() -> None:
"""Test huggingfacehub embeddings."""
documents = ["foo bar"]
embedding = HuggingFaceHubEmbeddings()
output = embedding.embed... | """Test HuggingFaceHub embeddings."""
import pytest
from langchain_community.embeddings import HuggingFaceHubEmbeddings
def test_huggingfacehub_embedding_documents() -> None:
"""Test huggingfacehub embeddings."""
documents = ["foo bar"]
embedding = HuggingFaceHubEmbeddings() # type: ignore[call-arg]
... |
# mypy: allow-untyped-defs
import contextlib
import torch
__all__ = [
"start",
"stop",
"profile",
"metal_capture",
"is_metal_capture_enabled",
"is_capturing_metal",
]
def start(mode: str = "interval", wait_until_completed: bool = False) -> None:
r"""Start OS Signpost tracing from MPS ba... | # mypy: allow-untyped-defs
import contextlib
import torch
__all__ = [
"start",
"stop",
"profile",
"metal_capture",
"is_metal_capture_enabled",
"is_capturing_metal",
]
def start(mode: str = "interval", wait_until_completed: bool = False) -> None:
r"""Start OS Signpost tracing from MPS ba... |
__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()
|
_base_ = './mask-rcnn_r101_fpn_gn-all_2x_coco.py'
# learning policy
max_epochs = 36
train_cfg = dict(max_epochs=max_epochs)
# learning rate
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=m... | _base_ = './mask_rcnn_r101_fpn_gn-all_2x_coco.py'
# learning policy
max_epochs = 36
train_cfg = dict(max_epochs=max_epochs)
# learning rate
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=m... |
# Copyright (c) OpenMMLab. All rights reserved.
import pytest
from mmengine.logging import BaseGlobalAccessible, MetaGlobalAccessible
class SubClassA(BaseGlobalAccessible):
def __init__(self, name='', *args, **kwargs):
super().__init__(name, *args, **kwargs)
class SubClassB(BaseGlobalAccessible):
... | # Copyright (c) OpenMMLab. All rights reserved.
import pytest
from mmengine.logging import BaseGlobalAccessible, MetaGlobalAccessible
class SubClassA(BaseGlobalAccessible):
def __init__(self, name='', *args, **kwargs):
super().__init__(name, *args, **kwargs)
class SubClassB(BaseGlobalAccessible):
... |
# 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... |
"""
Top-level module of Jina.
The primary function of this module is to import all of the public Jina
interfaces into a single place. The interfaces themselves are located in
sub-modules, as described below.
"""
import os as _os
import platform as _platform
import signal as _signal
import sys as _sys
import warnings... | """
Top-level module of Jina.
The primary function of this module is to import all of the public Jina
interfaces into a single place. The interfaces themselves are located in
sub-modules, as described below.
"""
import os as _os
import platform as _platform
import signal as _signal
import sys as _sys
import warnings... |
# Copyright 2017 The TensorFlow Authors. 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 applica... | # Copyright 2017 The TensorFlow Authors. 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 applica... |
"""
This examples trains a CrossEncoder for the Quora Duplicate Questions Detection task. A CrossEncoder takes a sentence pair
as input and outputs a label. Here, it output a continuous labels 0...1 to indicate the similarity between the input pair.
It does NOT produce a sentence embedding and does NOT work for indivi... | """
This examples trains a CrossEncoder for the Quora Duplicate Questions Detection task. A CrossEncoder takes a sentence pair
as input and outputs a label. Here, it output a continuous labels 0...1 to indicate the similarity between the input pair.
It does NOT produce a sentence embedding and does NOT work for indivi... |
import random
import pytest
from datasets import Dataset
from sentence_transformers.sampler import NoDuplicatesBatchSampler
@pytest.fixture
def dummy_dataset():
"""
Dummy dataset for testing purposes. The dataset looks as follows:
{
"data": [0, 47, 3, 30, 3, ... 2],
"label": [0, 1, 0, 1,... | import pytest
from datasets import Dataset
from sentence_transformers.sampler import NoDuplicatesBatchSampler
import random
@pytest.fixture
def dummy_dataset():
"""
Dummy dataset for testing purposes. The dataset looks as follows:
{
"data": [0, 47, 3, 30, 3, ... 2],
"label": [0, 1, 0, 1, .... |
from ...utils import is_torch_available
if is_torch_available():
from .auraflow_transformer_2d import AuraFlowTransformer2DModel
from .cogvideox_transformer_3d import CogVideoXTransformer3DModel
from .consisid_transformer_3d import ConsisIDTransformer3DModel
from .dit_transformer_2d import DiTTransfor... | from ...utils import is_torch_available
if is_torch_available():
from .auraflow_transformer_2d import AuraFlowTransformer2DModel
from .cogvideox_transformer_3d import CogVideoXTransformer3DModel
from .consisid_transformer_3d import ConsisIDTransformer3DModel
from .dit_transformer_2d import DiTTransfor... |
from typing import 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.typing.tensor.audi... | from typing import 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.typing.tensor.audi... |
_base_ = './scnet_x101_64x4d_fpn_20e_coco.py'
data = dict(samples_per_gpu=1, workers_per_gpu=1)
optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
# NOTE: `auto_scale_lr` is for automatically scaling LR,
# USER SHOULD NOT CHANGE ITS VALUES.
# base_batch_size = (8 GPUs) x (1 samples per GPU)
auto_... | _base_ = './scnet_x101_64x4d_fpn_20e_coco.py'
data = dict(samples_per_gpu=1, workers_per_gpu=1)
optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
|
# THIS FILE HAS BEEN AUTOGENERATED. To update:
# 1. modify the `_deps` dict in setup.py
# 2. run `make deps_table_update``
deps = {
"Pillow": "Pillow>=10.0.1,<=15.0",
"accelerate": "accelerate>=0.26.0",
"av": "av",
"beautifulsoup4": "beautifulsoup4",
"blobfile": "blobfile",
"codecarbon": "codeca... | # THIS FILE HAS BEEN AUTOGENERATED. To update:
# 1. modify the `_deps` dict in setup.py
# 2. run `make deps_table_update``
deps = {
"Pillow": "Pillow>=10.0.1,<=15.0",
"accelerate": "accelerate>=0.26.0",
"av": "av",
"beautifulsoup4": "beautifulsoup4",
"blobfile": "blobfile",
"codecarbon": "codeca... |
"""Optimization related classes and functions."""
import logging
from typing import Any, Dict, List, Optional, Literal
from llama_index.core.bridge.pydantic import Field, PrivateAttr
from llama_index.core.postprocessor.types import BaseNodePostprocessor
from llama_index.core.schema import NodeWithScore, QueryBundle, ... | """Optimization related classes and functions."""
import logging
from typing import Any, Dict, List, Optional, Literal
from llama_index.core.bridge.pydantic import Field, PrivateAttr
from llama_index.core.postprocessor.types import BaseNodePostprocessor
from llama_index.core.schema import NodeWithScore, QueryBundle, ... |
from datetime import datetime, timezone
from unittest.mock import AsyncMock
import pytest
from fastapi import WebSocket
from backend.data.execution import ExecutionResult, ExecutionStatus
from backend.server.conn_manager import ConnectionManager
from backend.server.model import Methods, WsMessage
@pytest.fixture
de... | from datetime import datetime, timezone
from unittest.mock import AsyncMock
import pytest
from fastapi import WebSocket
from backend.data.execution import ExecutionResult, ExecutionStatus
from backend.server.conn_manager import ConnectionManager
from backend.server.model import Methods, WsMessage
@pytest.fixture
de... |
from typing import TYPE_CHECKING, Any, Optional, Type, TypeVar, Union
import numpy as np
from docarray.base_doc import BaseDoc
from docarray.typing import AnyTensor
from docarray.typing.tensor.abstract_tensor import AbstractTensor
from docarray.utils._internal.misc import import_library
if TYPE_CHECKING:
import ... | from typing import Any, Optional, Type, TypeVar, Union
import numpy as np
from docarray.base_doc import BaseDoc
from docarray.typing import AnyTensor
from docarray.typing.tensor.abstract_tensor import AbstractTensor
from docarray.utils._internal.misc import is_tf_available, is_torch_available
torch_available = is_to... |
# Copyright (c) OpenMMLab. All rights reserved.
from .base_video_metric import BaseVideoMetric
from .cityscapes_metric import CityScapesMetric
from .coco_metric import CocoMetric
from .coco_occluded_metric import CocoOccludedSeparatedMetric
from .coco_panoptic_metric import CocoPanopticMetric
from .coco_video_metric im... | # Copyright (c) OpenMMLab. All rights reserved.
from .base_video_metric import BaseVideoMetric
from .cityscapes_metric import CityScapesMetric
from .coco_metric import CocoMetric
from .coco_occluded_metric import CocoOccludedSeparatedMetric
from .coco_panoptic_metric import CocoPanopticMetric
from .coco_video_metric im... |
# Copyright (c) OpenMMLab. All rights reserved.
from .anchor import * # noqa: F401, F403
from .bbox import * # noqa: F401, F403
from .data_structures import * # noqa: F401, F403
from .evaluation import * # noqa: F401, F403
from .hook import * # noqa: F401, F403
from .mask import * # noqa: F401, F403
from .post_pr... | # Copyright (c) OpenMMLab. All rights reserved.
from .anchor import * # noqa: F401, F403
from .bbox import * # noqa: F401, F403
from .evaluation import * # noqa: F401, F403
from .hook import * # noqa: F401, F403
from .mask import * # noqa: F401, F403
from .post_processing import * # noqa: F401, F403
from .utils i... |
import pytest
from llama_index.core.workflow.context import Context
from llama_index.core.workflow.decorators import step
from llama_index.core.workflow.events import Event, StartEvent, StopEvent
from llama_index.core.workflow.service import ServiceManager, ServiceNotFoundError
from llama_index.core.workflow.workflow i... | import pytest
from llama_index.core.workflow.context import Context
from llama_index.core.workflow.decorators import step
from llama_index.core.workflow.events import Event, StartEvent, StopEvent
from llama_index.core.workflow.service import ServiceManager, ServiceNotFoundError
from llama_index.core.workflow.workflow i... |
import numpy as np
import pytest
import torch
from pydantic import parse_obj_as
from docarray import BaseDoc
from docarray.documents import VideoDoc, AudioDoc
from docarray.typing import AudioNdArray, NdArray, VideoNdArray
from docarray.utils._internal.misc import is_tf_available
from docarray.utils._internal.pydantic... | import numpy as np
import pytest
import torch
from pydantic import parse_obj_as
from docarray import BaseDoc
from docarray.documents import VideoDoc
from docarray.typing import AudioNdArray, NdArray, VideoNdArray
from docarray.utils._internal.misc import is_tf_available
from docarray.utils._internal.pydantic import is... |
import numpy as np
import pytest
import torch
from docarray import BaseDoc, DocArray
from docarray.array import DocArrayStacked
from docarray.typing import NdArray, TorchTensor
@pytest.fixture()
def batch():
class Image(BaseDoc):
tensor: TorchTensor[3, 224, 224]
batch = DocArray[Image]([Image(tensor... | import numpy as np
import pytest
import torch
from docarray import BaseDocument, DocumentArray
from docarray.array import DocumentArrayStacked
from docarray.typing import NdArray, TorchTensor
@pytest.fixture()
def batch():
class Image(BaseDocument):
tensor: TorchTensor[3, 224, 224]
batch = DocumentA... |
from __future__ import annotations
import re
import pytest
from sentence_transformers import SentenceTransformer
from sentence_transformers.evaluation import NanoBEIREvaluator
from sentence_transformers.util import is_datasets_available
from tests.utils import is_ci
if not is_datasets_available():
pytest.skip(
... | from __future__ import annotations
import re
import pytest
from sentence_transformers import SentenceTransformer
from sentence_transformers.evaluation import NanoBEIREvaluator
from sentence_transformers.util import is_datasets_available
if not is_datasets_available():
pytest.skip(
reason="Datasets are n... |
"""Hypothetical Document Embeddings.
https://arxiv.org/abs/2212.10496
"""
from __future__ import annotations
import logging
from typing import Any, Optional
from langchain_core.callbacks import CallbackManagerForChainRun
from langchain_core.embeddings import Embeddings
from langchain_core.language_models import Bas... | """Hypothetical Document Embeddings.
https://arxiv.org/abs/2212.10496
"""
from __future__ import annotations
import logging
from typing import Any, Optional
from langchain_core.callbacks import CallbackManagerForChainRun
from langchain_core.embeddings import Embeddings
from langchain_core.language_models import Bas... |
import warnings
from typing import Any, Dict, Optional, Sequence, Tuple, Type, Union
import torch
from torchvision import datapoints
from torchvision.transforms.v2 import Transform
from torchvision.transforms.v2._utils import _get_defaultdict
from torchvision.transforms.v2.utils import is_simple_tensor
class Permu... | import warnings
from typing import Any, Dict, Optional, Sequence, Tuple, Type, Union
import torch
from torchvision import datapoints
from torchvision.transforms.v2 import Transform
from torchvision.transforms.v2._utils import _get_defaultdict
from torchvision.transforms.v2.utils import is_simple_tensor
class Permu... |
# Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import torch
from mmdet.models.dense_heads import SABLRetinaHead
def test_sabl_retina_head_loss():
"""Tests anchor head loss when truth is empty and non-empty."""
s = 256
img_metas = [{
'img_shape': (s, s, 3),
'scale_factor': 1,
... | import mmcv
import torch
from mmdet.models.dense_heads import SABLRetinaHead
def test_sabl_retina_head_loss():
"""Tests anchor head loss when truth is empty and non-empty."""
s = 256
img_metas = [{
'img_shape': (s, s, 3),
'scale_factor': 1,
'pad_shape': (s, s, 3)
}]
cfg =... |
# Copyright (c) OpenMMLab. All rights reserved.
from .dist import (all_gather_object, all_reduce, all_gather, all_reduce_dict,
collect_results, gather, broadcast, gather_object,
sync_random_seed, broadcast_object_list,
collect_results_cpu, collect_results_gpu, al... | # Copyright (c) OpenMMLab. All rights reserved.
from .dist import (all_gather_object, all_reduce, all_gather, all_reduce_dict,
collect_results, gather, broadcast, gather_object,
sync_random_seed, broadcast_object_list,
collect_results_cpu, collect_results_gpu)
fr... |
import os
import fsspec
import pytest
from datasets.filesystems import COMPRESSION_FILESYSTEMS, HfFileSystem, extract_path_from_uri, is_remote_filesystem
from datasets.utils._hf_hub_fixes import dataset_info as hf_api_dataset_info
from .utils import require_lz4, require_zstandard
def test_extract_path_from_uri():
... | import os
import fsspec
import pytest
from datasets.filesystems import COMPRESSION_FILESYSTEMS, HfFileSystem, extract_path_from_uri, is_remote_filesystem
from datasets.utils._hf_hub_fixes import dataset_info as hf_api_dataset_info
from .utils import require_lz4, require_zstandard
def test_extract_path_from_uri():
... |
from .Asym import Asym
from .BoW import BoW
from .CLIPModel import CLIPModel
from .CNN import CNN
from .Dense import Dense
from .Dropout import Dropout
from .LayerNorm import LayerNorm
from .LSTM import LSTM
from .Normalize import Normalize
from .Pooling import Pooling
from .Transformer import Transformer
from .Weighte... | from .Transformer import Transformer
from .Asym import Asym
from .BoW import BoW
from .CNN import CNN
from .Dense import Dense
from .Dropout import Dropout
from .LayerNorm import LayerNorm
from .LSTM import LSTM
from .Normalize import Normalize
from .Pooling import Pooling
from .WeightedLayerPooling import WeightedLaye... |
from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.agent_toolkits.zapier.toolkit import ZapierToolkit
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handling opti... | from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.agent_toolkits.zapier.toolkit import ZapierToolkit
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handling opti... |
from typing import Optional
import pandas as pd
import pytest
from docarray import BaseDoc, DocArray
from docarray.documents import ImageDoc
@pytest.fixture()
def nested_doc_cls():
class MyDoc(BaseDoc):
count: Optional[int]
text: str
class MyDocNested(MyDoc):
image: ImageDoc
re... | from typing import Optional
import pandas as pd
import pytest
from docarray import BaseDocument, DocumentArray
from docarray.documents import ImageDoc
@pytest.fixture()
def nested_doc_cls():
class MyDoc(BaseDocument):
count: Optional[int]
text: str
class MyDocNested(MyDoc):
image: I... |
__copyright__ = "Copyright (c) 2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import os
from pathlib import Path
import numpy as np
import pytest
from jina import Document, DocumentArray, Executor
from jina.executors.metas import get_default_metas
from jina_commons.indexers.dump import import_... | __copyright__ = "Copyright (c) 2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import os
from pathlib import Path
import numpy as np
import pytest
from jina import Document, DocumentArray, Executor
from jina.executors.metas import get_default_metas
from jina_commons.indexers.dump import import_... |
from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.chat_models.meta import convert_messages_to_prompt_llama
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handlin... | from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.chat_models.meta import convert_messages_to_prompt_llama
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handlin... |
from setuptools import find_packages, setup
with open("README.md", mode="r", encoding="utf-8") as readme_file:
readme = readme_file.read()
setup(
name="sentence-transformers",
version="3.0.0.dev0",
author="Nils Reimers",
author_email="info@nils-reimers.de",
description="Multilingual text embe... | from setuptools import find_packages, setup
with open("README.md", mode="r", encoding="utf-8") as readme_file:
readme = readme_file.read()
setup(
name="sentence-transformers",
version="3.0.0.dev0",
author="Nils Reimers",
author_email="info@nils-reimers.de",
description="Multilingual text embe... |
__copyright__ = "Copyright (c) 2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import subprocess
from typing import List
import numpy as np
import pytest
from executor.torch_encoder import ImageTorchEncoder
from jina import Document, DocumentArray, Flow
@pytest.mark.parametrize(
'arr_in',... | __copyright__ = "Copyright (c) 2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import subprocess
from typing import List
import numpy as np
import pytest
from jina import Document, DocumentArray, Flow
from ...torch_encoder import ImageTorchEncoder
@pytest.mark.parametrize(
'arr_in',
... |
from typing import Iterator
from langchain_core.documents import Document
from langchain_community.document_loaders.base import BaseBlobParser
from langchain_community.document_loaders.blob_loaders import Blob
class MsWordParser(BaseBlobParser):
"""Parse the Microsoft Word documents from a blob."""
def laz... | from typing import Iterator
from langchain_core.documents import Document
from langchain_community.document_loaders.base import BaseBlobParser
from langchain_community.document_loaders.blob_loaders import Blob
class MsWordParser(BaseBlobParser):
"""Parse the Microsoft Word documents from a blob."""
def laz... |
from __future__ import annotations
from typing import Optional, Type
from urllib.parse import urlparse
from langchain_core.callbacks import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from pydantic import BaseModel, Field, model_validator
from langchain_community.tools.playwright.base impo... | from __future__ import annotations
from typing import Optional, Type
from urllib.parse import urlparse
from langchain_core.callbacks import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from pydantic import BaseModel, Field, model_validator
from langchain_community.tools.playwright.base impo... |
import multiprocessing
import time
import grpc
import pytest
import requests
from jina import __version__
from jina.constants import __jina_env__
from jina.proto import jina_pb2, jina_pb2_grpc
from jina.serve.runtimes.asyncio import AsyncNewLoopRuntime
from jina.serve.runtimes.worker import WorkerRuntime
from tests.h... | import multiprocessing
import time
import grpc
import pytest
import requests
from jina import __jina_env__, __version__
from jina.proto import jina_pb2, jina_pb2_grpc
from jina.serve.runtimes.asyncio import AsyncNewLoopRuntime
from jina.serve.runtimes.worker import WorkerRuntime
from tests.helper import _generate_pod... |
_base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
type='ATSS',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[103.530, 116.280, 123.675],
std=[1.0, 1.0, 1.0],
bgr_to_rgb=False... | _base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
type='ATSS',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=d... |
import functools
import torch
import torch._custom_ops
import torch.library
# Ensure that torch.ops.torchvision is visible
import torchvision.extension # noqa: F401
@functools.lru_cache(None)
def get_meta_lib():
return torch.library.Library("torchvision", "IMPL", "Meta")
def register_meta(op_name, overload_n... | import functools
import torch
import torch._custom_ops
import torch.library
# Ensure that torch.ops.torchvision is visible
import torchvision.extension # noqa: F401
@functools.lru_cache(None)
def get_meta_lib():
return torch.library.Library("torchvision", "IMPL", "Meta")
def register_meta(op_name, overload_n... |
import http.client
import json
from typing import Any, Optional, TypedDict
WRITE_KEY = "310apTK0HUFl4AOv"
class EventDict(TypedDict):
event: str
properties: Optional[dict[str, Any]]
def create_events(events: list[EventDict]) -> Optional[Any]:
try:
data = {
"events": [
... | import http.client
import json
from typing import Any, Dict, List, Optional, TypedDict
WRITE_KEY = "310apTK0HUFl4AOv"
class EventDict(TypedDict):
event: str
properties: Optional[Dict[str, Any]]
def create_events(events: List[EventDict]) -> Optional[Any]:
try:
data = {
"events": [
... |
"""**Utility functions** for LangChain.
These functions do not depend on any other LangChain module.
"""
from typing import TYPE_CHECKING
from langchain_core._import_utils import import_attr
if TYPE_CHECKING:
# for type checking and IDE support, we include the imports here
# but we don't want to eagerly imp... | """**Utility functions** for LangChain.
These functions do not depend on any other LangChain module.
"""
from typing import TYPE_CHECKING
from langchain_core._import_utils import import_attr
if TYPE_CHECKING:
# for type checking and IDE support, we include the imports here
# but we don't want to eagerly imp... |
_base_ = './solo_r50_fpn_1x_coco.py'
train_pipeline = [
dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}),
dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
dict(
type='RandomChoiceResize',
scales=[(1333, 800), (1333, 768), (1333, 736), (1333, 704),
... | _base_ = './solo_r50_fpn_1x_coco.py'
train_pipeline = [
dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}),
dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
dict(
type='RandomChoiceResize',
scales=[(1333, 800), (1333, 768), (1333, 736), (1333, 704),
... |
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... |
# Copyright 2022 The TensorFlow Authors. 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 applica... | # Copyright 2022 The TensorFlow Authors. 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 applica... |
from keras.src import backend
from keras.src import ops
from keras.src.api_export import keras_export
from keras.src.layers.merging.base_merge import Merge
@keras_export("keras.layers.Multiply")
class Multiply(Merge):
"""Performs elementwise multiplication.
It takes as input a list of tensors, all of the sam... | from keras.src import ops
from keras.src.api_export import keras_export
from keras.src.layers.merging.base_merge import Merge
@keras_export("keras.layers.Multiply")
class Multiply(Merge):
"""Performs elementwise multiplication.
It takes as input a list of tensors, all of the same shape,
and returns a sin... |
from datetime import datetime, timedelta, timezone
from typing import Annotated, Union
import jwt
from fastapi import Depends, FastAPI, HTTPException, status
from fastapi.security import OAuth2PasswordBearer, OAuth2PasswordRequestForm
from jwt.exceptions import InvalidTokenError
from passlib.context import CryptContex... | from datetime import datetime, timedelta, timezone
from typing import Annotated, Union
import jwt
from fastapi import Depends, FastAPI, HTTPException, status
from fastapi.security import OAuth2PasswordBearer, OAuth2PasswordRequestForm
from jwt.exceptions import InvalidTokenError
from passlib.context import CryptContex... |
from __future__ import annotations
from functools import partial
from typing import TYPE_CHECKING, Literal, Optional, Union
from pydantic import BaseModel, Field
from langchain_core.prompts import (
BasePromptTemplate,
PromptTemplate,
aformat_document,
format_document,
)
from langchain_core.tools.sim... | from __future__ import annotations
from functools import partial
from typing import Literal, Optional, Union
from pydantic import BaseModel, Field
from langchain_core.callbacks import Callbacks
from langchain_core.documents import Document
from langchain_core.prompts import (
BasePromptTemplate,
PromptTempla... |
"""RSS feed reader for news - processes each article with NewsArticleReader."""
import logging
from typing import Any, List
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document
from llama_index.readers.web.news.base import NewsArticleReader
logger = logging.getLogger(__na... | """RSS feed reader for news - processes each article with NewsArticleReader."""
import logging
from typing import Any, List
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document
from llama_index.readers.web.news.base import NewsArticleReader
logger = logging.getLogger(__nam... |
"""Test chat model integration."""
import json
from collections.abc import Generator
from contextlib import contextmanager
from typing import Any
from unittest.mock import patch
import pytest
from httpx import Client, Request, Response
from langchain_core.messages import ChatMessage
from langchain_tests.unit_tests im... | """Test chat model integration."""
import json
from collections.abc import Generator
from contextlib import contextmanager
from typing import Any
from unittest.mock import patch
import pytest
from httpx import Client, Request, Response
from langchain_core.messages import ChatMessage
from langchain_tests.unit_tests im... |
_base_ = './ms-rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='pytorch',
... | _base_ = './ms_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='pytorch',
... |
# Copyright (c) OpenMMLab. All rights reserved.
import base64
import os
import mmcv
import numpy as np
import torch
from ts.torch_handler.base_handler import BaseHandler
from mmdet.apis import inference_detector, init_detector
from mmdet.utils import register_all_modules
register_all_modules(True)
class MMdetHandl... | # Copyright (c) OpenMMLab. All rights reserved.
import base64
import os
import mmcv
import numpy as np
import torch
from ts.torch_handler.base_handler import BaseHandler
from mmdet.apis import inference_detector, init_detector
from mmdet.utils import register_all_modules
register_all_modules(True)
class MMdetHandl... |
from llama_index.llms.mistralai import MistralAI
from llama_index.multi_modal_llms.mistralai import MistralAIMultiModal
def test_embedding_class():
names_of_base_classes = [b.__name__ for b in MistralAIMultiModal.__mro__]
assert MistralAI.__name__ in names_of_base_classes
def test_init():
m = MistralAIM... | from llama_index.core.multi_modal_llms.base import MultiModalLLM
from llama_index.multi_modal_llms.mistralai import MistralAIMultiModal
def test_embedding_class():
names_of_base_classes = [b.__name__ for b in MistralAIMultiModal.__mro__]
assert MultiModalLLM.__name__ in names_of_base_classes
def test_init()... |
__copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import subprocess
import pytest
from jina import Document, DocumentArray, Flow
from spacy_text_encoder import SpacyTextEncoder
_EMBEDDING_DIM = 96
@pytest.mark.parametrize('request_size', [1, 10, 50, 100])
de... | __copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import subprocess
import pytest
from jina import Document, DocumentArray, Flow
from ...spacy_text_encoder import SpacyTextEncoder
_EMBEDDING_DIM = 96
@pytest.mark.parametrize('request_size', [1, 10, 50, 100]... |
# Copyright (c) OpenMMLab. All rights reserved.
from mmcv.utils import Registry, build_from_cfg
IOU_CALCULATORS = Registry('IoU calculator')
def build_iou_calculator(cfg, default_args=None):
"""Builder of IoU calculator."""
return build_from_cfg(cfg, IOU_CALCULATORS, default_args)
| from mmcv.utils import Registry, build_from_cfg
IOU_CALCULATORS = Registry('IoU calculator')
def build_iou_calculator(cfg, default_args=None):
"""Builder of IoU calculator."""
return build_from_cfg(cfg, IOU_CALCULATORS, default_args)
|
from typing import Any, Dict, Union
import torch
from torchvision import datapoints, transforms as _transforms
from torchvision.transforms.v2 import functional as F, Transform
from .utils import is_simple_tensor
class ConvertBoundingBoxFormat(Transform):
"""[BETA] Convert bounding box coordinates to the given ... | from typing import Any, Dict, Union
import torch
from torchvision import datapoints, transforms as _transforms
from torchvision.transforms.v2 import functional as F, Transform
from .utils import is_simple_tensor
class ConvertBoundingBoxFormat(Transform):
"""[BETA] Convert bounding box coordinates to the given ... |
OPEN_METEO_DOCS = """BASE URL: https://api.open-meteo.com/
API Documentation
The API endpoint /v1/forecast accepts a geographical coordinate, a list of weather variables and responds with a JSON hourly weather forecast for 7 days. Time always starts at 0:00 today and contains 168 hours. All URL parameters are listed b... | # flake8: noqa
OPEN_METEO_DOCS = """BASE URL: https://api.open-meteo.com/
API Documentation
The API endpoint /v1/forecast accepts a geographical coordinate, a list of weather variables and responds with a JSON hourly weather forecast for 7 days. Time always starts at 0:00 today and contains 168 hours. All URL paramete... |
from langchain_core.output_parsers.openai_tools import (
JsonOutputKeyToolsParser,
JsonOutputToolsParser,
PydanticToolsParser,
)
__all__ = ["JsonOutputKeyToolsParser", "JsonOutputToolsParser", "PydanticToolsParser"]
| from langchain_core.output_parsers.openai_tools import (
JsonOutputKeyToolsParser,
JsonOutputToolsParser,
PydanticToolsParser,
)
__all__ = ["PydanticToolsParser", "JsonOutputToolsParser", "JsonOutputKeyToolsParser"]
|
# coding=utf-8
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless r... | # coding=utf-8
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless r... |
# coding=utf-8
# Copyright 2025 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless requir... | # coding=utf-8
# 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 requir... |
"""
Pandas csv structured store.
DEPRECATED: Please use :class:`PandasQueryEngine` in `llama-index-experimental` instead.
"""
from typing import Any
class PandasIndex:
def __init__(
self,
*args: Any,
**kwargs: Any,
) -> None:
raise DeprecationWarning(
"PandasQuery... | """Pandas csv structured store.
DEPRECATED: Please use :class:`PandasQueryEngine` in `llama-index-experimental` instead.
"""
from typing import Any
class PandasIndex:
def __init__(
self,
*args: Any,
**kwargs: Any,
) -> None:
raise DeprecationWarning(
"PandasQueryE... |
import os
import numpy as np
import pytest
from keras.src import layers
from keras.src import models
from keras.src import ops
from keras.src import testing
from keras.src.saving import load_model
class MaskingTest(testing.TestCase):
@pytest.mark.requires_trainable_backend
def test_masking_basics(self):
... | import numpy as np
import pytest
from keras.src import layers
from keras.src import models
from keras.src import ops
from keras.src import testing
from keras.src.saving import load_model
class MaskingTest(testing.TestCase):
@pytest.mark.requires_trainable_backend
def test_masking_basics(self):
self.r... |
from keras.src import tree
from keras.src.api_export import keras_export
from keras.src.backend import KerasTensor
from keras.src.layers.layer import Layer
@keras_export("keras.layers.Identity")
class Identity(Layer):
"""Identity layer.
This layer should be used as a placeholder when no operation is to be
... | from keras.src import tree
from keras.src.api_export import keras_export
from keras.src.backend import KerasTensor
from keras.src.layers.layer import Layer
@keras_export("keras.layers.Identity")
class Identity(Layer):
"""Identity layer.
This layer should be used as a placeholder when no operation is to be
... |
"""Base schema for callback managers."""
import uuid
from dataclasses import dataclass
from datetime import datetime
from enum import Enum
from typing import Any, Dict, Optional
# timestamp for callback events
TIMESTAMP_FORMAT = "%m/%d/%Y, %H:%M:%S.%f"
# base trace_id for the tracemap in callback_manager
... | """Base schema for callback managers."""
import uuid
from dataclasses import dataclass
from datetime import datetime
from enum import Enum
from typing import Any, Dict, Optional
# timestamp for callback events
TIMESTAMP_FORMAT = "%m/%d/%Y, %H:%M:%S.%f"
# base trace_id for the tracemap in callback_manager
B... |
# Copyright (c) OpenMMLab. All rights reserved.
from .bfp import BFP
from .channel_mapper import ChannelMapper
from .cspnext_pafpn import CSPNeXtPAFPN
from .ct_resnet_neck import CTResNetNeck
from .dilated_encoder import DilatedEncoder
from .dyhead import DyHead
from .fpg import FPG
from .fpn import FPN
from .fpn_caraf... | # Copyright (c) OpenMMLab. All rights reserved.
from .bfp import BFP
from .channel_mapper import ChannelMapper
from .cspnext_pafpn import CSPNeXtPAFPN
from .ct_resnet_neck import CTResNetNeck
from .dilated_encoder import DilatedEncoder
from .dyhead import DyHead
from .fpg import FPG
from .fpn import FPN
from .fpn_caraf... |
from __future__ import annotations
from copy import deepcopy
import pytest
from sentence_transformers import SparseEncoder
@pytest.fixture(scope="session")
def _splade_bert_tiny_model() -> SparseEncoder:
model = SparseEncoder("sparse-encoder-testing/splade-bert-tiny-nq")
model.model_card_data.generate_widg... | from __future__ import annotations
import pytest
from sentence_transformers import SparseEncoder
@pytest.fixture()
def splade_bert_tiny_model() -> SparseEncoder:
return SparseEncoder("sparse-encoder-testing/splade-bert-tiny-nq")
@pytest.fixture(scope="session")
def splade_bert_tiny_model_reused() -> SparseEnc... |
from docarray.typing.bytes import ImageBytes
from docarray.typing.id import ID
from docarray.typing.tensor import ImageNdArray, ImageTensor
from docarray.typing.tensor.audio import AudioNdArray
from docarray.typing.tensor.embedding.embedding import AnyEmbedding, NdArrayEmbedding
from docarray.typing.tensor.ndarray impo... | from docarray.typing.bytes import ImageBytes
from docarray.typing.id import ID
from docarray.typing.tensor import ImageNdArray, ImageTensor
from docarray.typing.tensor.audio import AudioNdArray
from docarray.typing.tensor.embedding.embedding import AnyEmbedding, NdArrayEmbedding
from docarray.typing.tensor.ndarray impo... |
_base_ = './faster-rcnn_r50_fpn_2x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| _base_ = './faster_rcnn_r50_fpn_2x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
|
import os
import pytest
from llama_index.embeddings.nvidia import NVIDIAEmbedding as Interface
from typing import Any
from pytest_httpx import HTTPXMock
@pytest.fixture()
def mock_local_models(httpx_mock: HTTPXMock):
mock_response = {
"data": [
{
"id": "model1",
... | import os
import pytest
from llama_index.embeddings.nvidia import NVIDIAEmbedding as Interface
from typing import Any
from pytest_httpx import HTTPXMock
@pytest.fixture()
def mock_local_models(httpx_mock: HTTPXMock):
mock_response = {
"data": [
{
"id": "model1",
... |
from unittest import mock
# import aiohttp to force Pants to include it in the required dependencies
import aiohttp # noqa
import pytest
from azure.ai.inference.models import EmbeddingItem, EmbeddingsResult
from llama_index.core.schema import TextNode
from llama_index.embeddings.azure_inference import AzureAIEmbeddin... | from unittest import mock
# import aiohttp to force Pants to include it in the required dependencies
import aiohttp # noqa
import pytest
from azure.ai.inference.models import EmbeddingItem, EmbeddingsResult
from llama_index.core.schema import TextNode
from llama_index.embeddings.azure_inference import AzureAIEmbeddin... |
from typing import Any, Optional
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."""
i... | from typing import Any, Optional
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."""
i... |
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... |
import numpy as np
from docarray.base_doc import AnyDoc, BaseDoc
from docarray.typing import NdArray
def test_any_doc():
class InnerDocument(BaseDoc):
text: str
tensor: NdArray
class CustomDoc(BaseDoc):
inner: InnerDocument
text: str
doc = CustomDoc(
text='bye', ... | import numpy as np
from docarray.base_document import AnyDocument, BaseDocument
from docarray.typing import NdArray
def test_any_doc():
class InnerDocument(BaseDocument):
text: str
tensor: NdArray
class CustomDoc(BaseDocument):
inner: InnerDocument
text: str
doc = Custom... |
from pathlib import Path
from typing import Union, Optional, Callable, TYPE_CHECKING, Generator
if TYPE_CHECKING:
from docarray import DocumentArray
from docarray.typing import T
from multiprocessing.pool import ThreadPool, Pool
class DataLoaderMixin:
@classmethod
def dataloader(
cls,
... | from pathlib import Path
from typing import Union, Optional, Callable, TYPE_CHECKING, Generator
if TYPE_CHECKING:
from docarray import DocumentArray
from docarray.typing import T
from multiprocessing.pool import ThreadPool, Pool
class DataLoaderMixin:
@classmethod
def dataloader(
cls,
... |
import os
from pathlib import Path
from jina import __cache_path__
def generate_default_volume_and_workspace(workspace_id=''):
"""automatically generate a docker volume, and an Executor workspace inside it
:param workspace_id: id that will be part of the fallback workspace path. Default is not adding such a... | import os
from pathlib import Path
from jina import __cache_path__
def generate_default_volume_and_workspace(workspace_id=''):
"""automatically generate a docker volume, and an Executor workspace inside it
:param workspace_id: id that will be part of the fallback workspace path. Default is not adding such a... |
# Copyright (c) OpenMMLab. All rights reserved.
from abc import ABCMeta, abstractmethod
from typing import List, Tuple, Union
from mmengine.model import BaseModule
from torch import Tensor
from mmdet.structures import SampleList
from mmdet.utils import InstanceList, OptInstanceList, OptMultiConfig
from ..utils import... | # Copyright (c) OpenMMLab. All rights reserved.
from abc import ABCMeta, abstractmethod
from typing import List, Tuple, Union
from mmengine.model import BaseModule
from torch import Tensor
from mmdet.data_elements import SampleList
from mmdet.utils import InstanceList, OptInstanceList, OptMultiConfig
from ..utils imp... |
from __future__ import annotations
import math
from pathlib import Path
import numpy as np
import pytest
from tokenizers import Tokenizer
from sentence_transformers import SentenceTransformer
from sentence_transformers.models.StaticEmbedding import StaticEmbedding
try:
import model2vec
except ImportError:
m... | from __future__ import annotations
import math
from pathlib import Path
import numpy as np
import pytest
from tokenizers import Tokenizer
from sentence_transformers import SentenceTransformer
from sentence_transformers.models.StaticEmbedding import StaticEmbedding
try:
import model2vec
except ImportError:
m... |
#!/usr/bin/env python
# coding=utf-8
# Copyright 2025 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/LI... | #!/usr/bin/env python
# coding=utf-8
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LI... |
__copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import re
from typing import Dict, List, Optional
from jina import Document, DocumentArray, Executor, requests
from jina.logging.logger import JinaLogger
class Sentencizer(Executor):
"""
:class:`Senten... | __copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
from typing import Optional, List, Dict
import re
from jina import Executor, DocumentArray, requests, Document
from jina.logging.logger import JinaLogger
class Sentencizer(Executor):
"""
:class:`Senten... |
# flake8: noqa
# 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/LI... | # flake8: noqa
# 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/LI... |
from docarray.base_document.mixins.plot import PlotMixin
from docarray.base_document.mixins.proto import ProtoMixin
__all__ = ['PlotMixin', 'ProtoMixin']
| from docarray.base_document.mixins.proto import ProtoMixin
__all__ = ['ProtoMixin']
|
import numpy as np
import pytest
import torch
from pydantic import parse_obj_as
from docarray import BaseDocument
from docarray.documents import Image
REMOTE_JPG = (
'https://upload.wikimedia.org/wikipedia/commons/8/80/'
'Dag_Sebastian_Ahlander_at_G%C3%B6teborg_Book_Fair_2012b.jpg'
)
@pytest.mark.slow
@pyte... | import numpy as np
import pytest
from docarray.documents import Image
REMOTE_JPG = (
'https://upload.wikimedia.org/wikipedia/commons/8/80/'
'Dag_Sebastian_Ahlander_at_G%C3%B6teborg_Book_Fair_2012b.jpg'
)
@pytest.mark.slow
@pytest.mark.internet
def test_image():
image = Image(url=REMOTE_JPG)
image.... |
from __future__ import annotations
import json
import os
import torch
from safetensors.torch import load_model as load_safetensors_model
from safetensors.torch import save_model as save_safetensors_model
from torch import Tensor, nn
class WeightedLayerPooling(nn.Module):
"""Token embeddings are weighted mean of... | from __future__ import annotations
import json
import os
import torch
from safetensors.torch import load_model as load_safetensors_model
from safetensors.torch import save_model as save_safetensors_model
from torch import Tensor, nn
class WeightedLayerPooling(nn.Module):
"""Token embeddings are weighted mean of... |
import pytest
from xgboost import testing as tm
from xgboost.testing.ordinal import run_cat_container, run_cat_container_mixed
pytestmark = pytest.mark.skipif(**tm.no_multiple(tm.no_arrow(), tm.no_pandas()))
def test_cat_container() -> None:
run_cat_container("cpu")
def test_cat_container_mixed() -> None:
... | import pytest
from xgboost import testing as tm
from xgboost.testing.ordinal import run_cat_container, run_cat_container_mixed
pytestmark = pytest.mark.skipif(**tm.no_multiple(tm.no_arrow(), tm.no_pandas()))
def test_cat_container() -> None:
run_cat_container("cpu")
def test_cat_container_mixed() -> None:
... |
# Copyright (c) OpenMMLab. All rights reserved.
from typing import Any, Optional, Sequence, Tuple, Union
import torch
from mmengine.data import BaseDataElement
from mmengine.registry import HOOKS
from .hook import Hook
DATA_BATCH = Optional[Sequence[Tuple[Any, BaseDataElement]]]
@HOOKS.register_module()
class Empt... | # Copyright (c) OpenMMLab. All rights reserved.
from typing import Any, Optional, Sequence, Tuple, Union
import torch
from mmengine.data import BaseDataSample
from mmengine.registry import HOOKS
from .hook import Hook
DATA_BATCH = Optional[Sequence[Tuple[Any, BaseDataSample]]]
@HOOKS.register_module()
class EmptyC... |
# Copyright (c) OpenMMLab. All rights reserved.
from .conditional_detr_layers import (ConditionalDetrTransformerDecoder,
ConditionalDetrTransformerDecoderLayer)
from .dab_detr_layers import (DABDetrTransformerDecoder,
DABDetrTransformerDecoderLayer,
... | # Copyright (c) OpenMMLab. All rights reserved.
from .conditional_detr_layers import (ConditionalDetrTransformerDecoder,
ConditionalDetrTransformerDecoderLayer)
from .dab_detr_layers import (DABDetrTransformerDecoder,
DABDetrTransformerDecoderLayer,
... |
from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.callbacks.flyte_callback import FlyteCallbackHandler
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handling op... | from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.callbacks.flyte_callback import FlyteCallbackHandler
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handling op... |
"""
This script contains an example how to perform re-ranking with a Cross-Encoder for semantic search.
First, we use an efficient Bi-Encoder to retrieve similar questions from the Quora Duplicate Questions dataset:
https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs
Then, we re-rank the hits... | """
This script contains an example how to perform re-ranking with a Cross-Encoder for semantic search.
First, we use an efficient Bi-Encoder to retrieve similar questions from the Quora Duplicate Questions dataset:
https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs
Then, we re-rank the hits... |
# Copyright (c) OpenMMLab. All rights reserved.
from torch.nn.modules import GroupNorm
from torch.nn.modules.batchnorm import _BatchNorm
from mmdet.models.backbones.res2net import Bottle2neck
from mmdet.models.backbones.resnet import BasicBlock, Bottleneck
from mmdet.models.backbones.resnext import Bottleneck as Bottl... | # Copyright (c) OpenMMLab. All rights reserved.
from torch.nn.modules import GroupNorm
from torch.nn.modules.batchnorm import _BatchNorm
from mmdet.models.backbones.res2net import Bottle2neck
from mmdet.models.backbones.resnet import BasicBlock, Bottleneck
from mmdet.models.backbones.resnext import Bottleneck as Bottl... |
# Copyright (c) OpenMMLab. All rights reserved.
from abc import ABCMeta, abstractmethod
from mmcv.runner import BaseModule
from mmdet.core.utils import OptConfigType, OptMultiConfig
from mmdet.registry import MODELS
@MODELS.register_module()
class BasePanopticFusionHead(BaseModule, metaclass=ABCMeta):
"""Base c... | # Copyright (c) OpenMMLab. All rights reserved.
from abc import ABCMeta, abstractmethod
from mmcv.runner import BaseModule
from ...builder import build_loss
class BasePanopticFusionHead(BaseModule, metaclass=ABCMeta):
"""Base class for panoptic heads."""
def __init__(self,
num_things_class... |
import os
from pathlib import Path
from typing import Any, Callable, Optional, Union
from .folder import default_loader, ImageFolder
from .utils import download_and_extract_archive
class EuroSAT(ImageFolder):
"""RGB version of the `EuroSAT <https://github.com/phelber/eurosat>`_ Dataset.
For the MS version o... | import os
from pathlib import Path
from typing import Any, Callable, Optional, Union
from .folder import default_loader, ImageFolder
from .utils import download_and_extract_archive
class EuroSAT(ImageFolder):
"""RGB version of the `EuroSAT <https://github.com/phelber/eurosat>`_ Dataset.
For the MS version o... |
_base_ = './cascade-mask-rcnn_r101_fpn_seesaw-loss_sample1e-3-ms-2x_lvis-v1.py' # noqa: E501
model = dict(
roi_head=dict(
mask_head=dict(
predictor_cfg=dict(type='NormedConv2d', tempearture=20))))
| _base_ = './cascade_mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1.py' # noqa: E501
model = dict(
roi_head=dict(
mask_head=dict(
predictor_cfg=dict(type='NormedConv2d', tempearture=20))))
|
# training schedule for 1x
train_cfg = dict(by_epoch=True, max_epochs=12)
val_cfg = dict(interval=1)
test_cfg = dict()
# learning rate
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=12,
... | # training schedule for 1x
train_cfg = dict(by_epoch=True, max_epochs=12)
val_cfg = dict(interval=1)
test_cfg = dict()
# learning rate
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=12,
... |
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