input stringlengths 33 5k | output stringlengths 32 5k |
|---|---|
import itertools
import os.path
import pytest
from docarray import Document, DocumentArray
from jina import Client, Executor, Flow, requests
from jina.helper import random_port
PROTOCOLS = ['grpc', 'http', 'websocket']
cur_dir = os.path.dirname(__file__)
class MyExecutor(Executor):
@requests
def foo(self, ... | import itertools
import os.path
import pytest
from docarray import Document, DocumentArray
from jina import Client, Executor, Flow, requests
from jina.helper import random_port
PROTOCOLS = ['grpc', 'http', 'websocket']
cur_dir = os.path.dirname(__file__)
class MyExecutor(Executor):
@requests
def foo(self, ... |
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")
evaluator = SparseNanoBEIR... | 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")
evaluator = SparseNanoBEIR... |
__version__ = '0.40.0'
import logging
from docarray.array import DocList, DocVec
from docarray.base_doc.doc import BaseDoc
from docarray.utils._internal.misc import _get_path_from_docarray_root_level
__all__ = ['BaseDoc', 'DocList', 'DocVec']
logger = logging.getLogger('docarray')
handler = logging.StreamHandler()... | __version__ = '0.39.2'
import logging
from docarray.array import DocList, DocVec
from docarray.base_doc.doc import BaseDoc
from docarray.utils._internal.misc import _get_path_from_docarray_root_level
__all__ = ['BaseDoc', 'DocList', 'DocVec']
logger = logging.getLogger('docarray')
handler = logging.StreamHandler()... |
_base_ = './sparse-rcnn_r50_fpn_1x_coco.py'
train_pipeline = [
dict(
type='LoadImageFromFile',
file_client_args={{_base_.file_client_args}}),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='RandomChoiceResize',
scales=[(480, 1333), (512, 1333), (544, 1333), (576, 1... | _base_ = './sparse_rcnn_r50_fpn_1x_coco.py'
train_pipeline = [
dict(
type='LoadImageFromFile',
file_client_args={{_base_.file_client_args}}),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='RandomChoiceResize',
scales=[(480, 1333), (512, 1333), (544, 1333), (576, 1... |
# Licensed to the LF AI & Data foundation under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the "License");
# you may not use this fil... | from abc import abstractmethod
from typing import TYPE_CHECKING, Any, Type, TypeVar
from docarray.utils._internal.pydantic import is_pydantic_v2
if TYPE_CHECKING:
if is_pydantic_v2:
from pydantic import GetCoreSchemaHandler
from pydantic_core import core_schema
from docarray.base_doc.base_node im... |
from typing import Any, Dict
import torch
from torch.nn.functional import one_hot
from torchvision.prototype import tv_tensors as proto_tv_tensors
from torchvision.transforms.v2 import Transform
class LabelToOneHot(Transform):
_transformed_types = (proto_tv_tensors.Label,)
def __init__(self, num_categorie... | from typing import Any, Dict
import torch
from torch.nn.functional import one_hot
from torchvision.prototype import tv_tensors as proto_tv_tensors
from torchvision.transforms.v2 import Transform
class LabelToOneHot(Transform):
_transformed_types = (proto_tv_tensors.Label,)
def __init__(self, num_categorie... |
from __future__ import annotations
import importlib.metadata
import importlib.util
import operator as op
from typing import Union
from packaging import version
STR_OPERATION_TO_FUNC = {
">": op.gt,
">=": op.ge,
"==": op.eq,
"!=": op.ne,
"<=": op.le,
"<": op.lt,
}
_optimum_available = import... | import importlib.metadata
import importlib.util
import operator as op
from typing import Union
from packaging import version
STR_OPERATION_TO_FUNC = {
">": op.gt,
">=": op.ge,
"==": op.eq,
"!=": op.ne,
"<=": op.le,
"<": op.lt,
}
_optimum_available = importlib.util.find_spec("optimum") is not... |
# Copyright (c) OpenMMLab. All rights reserved.
from ..builder import DETECTORS
from .single_stage_instance_seg import SingleStageInstanceSegmentor
@DETECTORS.register_module()
class SOLO(SingleStageInstanceSegmentor):
"""`SOLO: Segmenting Objects by Locations
<https://arxiv.org/abs/1912.04488>`_
"""
... | from ..builder import DETECTORS
from .single_stage_instance_seg import SingleStageInstanceSegmentor
@DETECTORS.register_module()
class SOLO(SingleStageInstanceSegmentor):
"""`SOLO: Segmenting Objects by Locations
<https://arxiv.org/abs/1912.04488>`_
"""
def __init__(self,
backbone,
... |
_base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# training schedule for 2x
train_cfg = dict(max_epochs=24)
# learning rate policy
param_scheduler = [
dict(
type='LinearLR', start_... | _base_ = './retinanet_r50_fpn_1x_coco.py'
# learning policy
lr_config = dict(step=[16, 22])
runner = dict(type='EpochBasedRunner', max_epochs=24)
|
_base_ = 'retinanet_r50_fpn_1x_coco.py'
# training schedule for 90k
train_cfg = dict(
type='IterBasedTrainLoop', max_iters=90000, val_interval=10000)
# learning rate policy
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiSte... | _base_ = 'retinanet_r50_fpn_1x_coco.py'
# training schedule for 90k
train_cfg = dict(by_epoch=False, max_iters=90000)
val_cfg = dict(interval=10000)
# learning rate policy
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',... |
# coding: utf-8
import pytest
import lightgbm as lgb
from .utils import pickle_obj, unpickle_obj
SERIALIZERS = ["pickle", "joblib", "cloudpickle"]
def pickle_and_unpickle_object(obj, serializer):
with lgb.basic._TempFile() as tmp_file:
pickle_obj(
obj=obj,
filepath=tmp_file.name... | # coding: utf-8
import pytest
import lightgbm as lgb
from .utils import pickle_obj, unpickle_obj
@pytest.mark.parametrize('serializer', ["pickle", "joblib", "cloudpickle"])
def test_early_stopping_callback_is_picklable(serializer, tmp_path):
rounds = 5
callback = lgb.early_stopping(stopping_rounds=rounds)
... |
from urllib.parse import parse_qs, urlparse
from youtube_transcript_api import YouTubeTranscriptApi
from youtube_transcript_api.formatters import TextFormatter
from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
from backend.data.model import SchemaField
class TranscribeYoutubeVideoBlock(B... | from urllib.parse import parse_qs, urlparse
from youtube_transcript_api import YouTubeTranscriptApi
from youtube_transcript_api.formatters import TextFormatter
from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
from backend.data.model import SchemaField
class TranscribeYoutubeVideoBlock(B... |
prompt_template = """Given the following question and context, return YES if the context is relevant to the question and NO if it isn't.
> Question: {question}
> Context:
>>>
{context}
>>>
> Relevant (YES / NO):""" # noqa: E501
| # flake8: noqa
prompt_template = """Given the following question and context, return YES if the context is relevant to the question and NO if it isn't.
> Question: {question}
> Context:
>>>
{context}
>>>
> Relevant (YES / NO):"""
|
from typing import Optional
import numpy as np
from docarray import BaseDoc, DocVec
from docarray.typing import ImageUrl, NdArray
def test_optional():
class Features(BaseDoc):
tensor: NdArray[100]
class Image(BaseDoc):
url: ImageUrl
features: Optional[Features] = None
docs = Do... | from typing import Optional
import numpy as np
from docarray import BaseDoc, DocVec
from docarray.typing import ImageUrl, NdArray
def test_optional():
class Features(BaseDoc):
tensor: NdArray[100]
class Image(BaseDoc):
url: ImageUrl
features: Optional[Features]
docs = DocVec[Im... |
from ._dsp import (
adsr_envelope,
extend_pitch,
filter_waveform,
frequency_impulse_response,
oscillator_bank,
sinc_impulse_response,
)
from ._rir import simulate_rir_ism
from .functional import barkscale_fbanks
__all__ = [
"adsr_envelope",
"barkscale_fbanks",
"extend_pitch",
"... | from ._dsp import (
adsr_envelope,
extend_pitch,
filter_waveform,
frequency_impulse_response,
oscillator_bank,
sinc_impulse_response,
)
from .functional import barkscale_fbanks
__all__ = [
"adsr_envelope",
"barkscale_fbanks",
"extend_pitch",
"filter_waveform",
"frequency_im... |
# Copyright (c) OpenMMLab. All rights reserved.
from abc import ABCMeta, abstractmethod
from typing import Tuple
from mmengine.model import BaseModule
from torch import Tensor
from mmdet.core.utils import (InstanceList, OptConfigType, OptMultiConfig,
SampleList)
from mmdet.registry impor... | # Copyright (c) OpenMMLab. All rights reserved.
from abc import ABCMeta, abstractmethod
from typing import Tuple
from mmengine.model import BaseModule
from torch import Tensor
from mmdet.core.utils import (InstanceList, OptConfigType, OptMultiConfig,
SampleList)
from mmdet.registry impor... |
# Copyright (c) OpenMMLab. All rights reserved.
import torch
from mmengine.dist import get_world_size
from mmengine.logging import print_log
from mmdet.registry import MODELS
from mmdet.utils import ConfigType, OptConfigType, OptMultiConfig
from .single_stage import SingleStageDetector
@MODELS.register_module()
clas... | # Copyright (c) OpenMMLab. All rights reserved.
import torch
from mmengine.dist import get_world_size
from mmengine.logging import print_log
from mmdet.registry import MODELS
from mmdet.utils import ConfigType, OptConfigType, OptMultiConfig
from .single_stage import SingleStageDetector
@MODELS.register_module()
clas... |
# Copyright (c) OpenMMLab. All rights reserved.
from typing import Optional, Union
from mmengine.config import ConfigDict
from mmdet.registry import MODELS
from .two_stage import TwoStageDetector
@MODELS.register_module()
class MaskRCNN(TwoStageDetector):
"""Implementation of `Mask R-CNN <https://arxiv.org/abs/... | # Copyright (c) OpenMMLab. All rights reserved.
from mmdet.registry import MODELS
from .two_stage import TwoStageDetector
@MODELS.register_module()
class MaskRCNN(TwoStageDetector):
"""Implementation of `Mask R-CNN <https://arxiv.org/abs/1703.06870>`_"""
def __init__(self,
backbone,
... |
from keras.src.backend.config import backend
if backend() == "torch":
# When using the torch backend,
# torch needs to be imported first, otherwise it will segfault
# upon import.
import torch
from keras.src.api_export import keras_export
from keras.src.backend.common.dtypes import result_type
from ke... | from keras.src.backend.config import backend
if backend() == "torch":
# When using the torch backend,
# torch needs to be imported first, otherwise it will segfault
# upon import.
import torch
from keras.src.api_export import keras_export
from keras.src.backend.common.dtypes import result_type
from ke... |
from __future__ import annotations
from typing import Any
import PIL.Image
import torch
from ._tv_tensor import TVTensor
class Mask(TVTensor):
""":class:`torch.Tensor` subclass for segmentation and detection masks with shape ``[..., H, W]``.
Args:
data (tensor-like, PIL.Image.Image): Any data that... | from __future__ import annotations
from typing import Any, Optional, Union
import PIL.Image
import torch
from ._tv_tensor import TVTensor
class Mask(TVTensor):
""":class:`torch.Tensor` subclass for segmentation and detection masks with shape ``[..., H, W]``.
Args:
data (tensor-like, PIL.Image.Imag... |
"""Test Aleph Alpha specific stuff."""
import pytest
from pydantic import SecretStr
from pytest import CaptureFixture, MonkeyPatch
from langchain_community.llms.aleph_alpha import AlephAlpha
@pytest.mark.requires("aleph_alpha_client")
def test_api_key_is_secret_string() -> None:
llm = AlephAlpha(aleph_alpha_api... | """Test Aleph Alpha specific stuff."""
import pytest
from pydantic import SecretStr
from pytest import CaptureFixture, MonkeyPatch
from langchain_community.llms.aleph_alpha import AlephAlpha
@pytest.mark.requires("aleph_alpha_client")
def test_api_key_is_secret_string() -> None:
llm = AlephAlpha(aleph_alpha_api... |
from __future__ import annotations
from .BinaryClassificationEvaluator import BinaryClassificationEvaluator
from .EmbeddingSimilarityEvaluator import EmbeddingSimilarityEvaluator
from .InformationRetrievalEvaluator import InformationRetrievalEvaluator
from .LabelAccuracyEvaluator import LabelAccuracyEvaluator
from .MS... | from __future__ import annotations
from .BinaryClassificationEvaluator import BinaryClassificationEvaluator
from .EmbeddingSimilarityEvaluator import EmbeddingSimilarityEvaluator
from .InformationRetrievalEvaluator import InformationRetrievalEvaluator
from .LabelAccuracyEvaluator import LabelAccuracyEvaluator
from .MS... |
from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.callbacks.streamlit.mutable_expander import (
ChildRecord,
ChildType,
MutableExpander,
)
# Create a way to dynamically look up deprecated imports.
# Used to cons... | from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.callbacks.streamlit.mutable_expander import (
ChildRecord,
ChildType,
MutableExpander,
)
# Create a way to dynamically look up deprecated imports.
# Used to cons... |
"""Cassandra-based chat message history, based on cassIO."""
from __future__ import annotations
import json
import uuid
from typing import TYPE_CHECKING, Any, Dict, Iterable, List, Optional, Sequence
from langchain_community.utilities.cassandra import SetupMode
if TYPE_CHECKING:
from cassandra.cluster import Se... | """Cassandra-based chat message history, based on cassIO."""
from __future__ import annotations
import json
import uuid
from typing import TYPE_CHECKING, Any, Dict, Iterable, List, Optional, Sequence
from langchain_community.utilities.cassandra import SetupMode
if TYPE_CHECKING:
from cassandra.cluster import Se... |
import logging
import os
from typing import Optional
from jina import __default_host__
from jina.importer import ImportExtensions
from jina.serve.gateway import BaseGateway
from jina.serve.runtimes.gateway.websocket.app import get_fastapi_app
class WebSocketGateway(BaseGateway):
"""WebSocket Gateway implementati... | import logging
import os
from typing import Optional
from jina import __default_host__
from jina.importer import ImportExtensions
from ....gateway import BaseGateway
from . import get_fastapi_app
class WebSocketGateway(BaseGateway):
"""WebSocket Gateway implementation"""
def __init__(
self,
... |
# Copyright (c) OpenMMLab. All rights reserved.
from .backbones import * # noqa: F401,F403
from .data_preprocessors import * # noqa: F401,F403
from .dense_heads import * # noqa: F401,F403
from .detectors import * # noqa: F401,F403
from .layers import * # noqa: F401,F403
from .losses import * # noqa: F401,F403
fro... | # Copyright (c) OpenMMLab. All rights reserved.
from .backbones import * # noqa: F401,F403
from .data_preprocessors import * # noqa: F401,F403
from .dense_heads import * # noqa: F401,F403
from .detectors import * # noqa: F401,F403
from .layers import * # noqa: F401,F403
from .losses import * # noqa: F401,F403
fro... |
from langchain_core.tracers.log_stream import (
LogEntry,
LogStreamCallbackHandler,
RunLog,
RunLogPatch,
RunState,
)
__all__ = ["LogEntry", "LogStreamCallbackHandler", "RunLog", "RunLogPatch", "RunState"]
| from langchain_core.tracers.log_stream import (
LogEntry,
LogStreamCallbackHandler,
RunLog,
RunLogPatch,
RunState,
)
__all__ = ["LogEntry", "RunState", "RunLog", "RunLogPatch", "LogStreamCallbackHandler"]
|
import warnings
from typing import TYPE_CHECKING, Any, Optional, Type, TypeVar, Union
from docarray.typing.bytes.video_bytes import VideoBytes, VideoLoadResult
from docarray.typing.proto_register import _register_proto
from docarray.typing.url.any_url import AnyUrl
from docarray.utils._internal.misc import is_notebook... | import warnings
from typing import TYPE_CHECKING, Any, Type, TypeVar, Union
from docarray.typing.bytes.video_bytes import VideoLoadResult
from docarray.typing.proto_register import _register_proto
from docarray.typing.url.any_url import AnyUrl
from docarray.utils._internal.misc import is_notebook
if TYPE_CHECKING:
... |
from prisma.models import User
from backend.blocks.basic import AgentInputBlock, PrintToConsoleBlock
from backend.blocks.text import FillTextTemplateBlock
from backend.data import graph
from backend.data.graph import create_graph
from backend.data.user import get_or_create_user
from backend.util.test import SpinTestSe... | from prisma.models import User
from backend.blocks.basic import AgentInputBlock, PrintToConsoleBlock
from backend.blocks.text import FillTextTemplateBlock
from backend.data import graph
from backend.data.graph import create_graph
from backend.data.user import get_or_create_user
from backend.util.test import SpinTestSe... |
"""XGBoost: eXtreme Gradient Boosting library.
Contributors: https://github.com/dmlc/xgboost/blob/master/CONTRIBUTORS.md
"""
from . import tracker # noqa
from . import collective, dask
from .core import (
Booster,
DataIter,
DeviceQuantileDMatrix,
DMatrix,
QuantileDMatrix,
_py_version,
bui... | """XGBoost: eXtreme Gradient Boosting library.
Contributors: https://github.com/dmlc/xgboost/blob/master/CONTRIBUTORS.md
"""
from . import tracker # noqa
from . import collective, dask, rabit
from .core import (
Booster,
DataIter,
DeviceQuantileDMatrix,
DMatrix,
QuantileDMatrix,
_py_version,
... |
# Copyright (c) OpenMMLab. All rights reserved.
import logging
from typing import List, Optional, Sequence
import torch
from torch.nn.parameter import Parameter
from torch.nn.utils import clip_grad
from mmengine.registry import HOOKS
from .hook import Hook
DATA_BATCH = Optional[Sequence[dict]]
@HOOKS.register_modu... | # Copyright (c) OpenMMLab. All rights reserved.
import logging
from typing import List, Optional, Sequence
import torch
from torch.nn.parameter import Parameter
from torch.nn.utils import clip_grad
from mmengine.registry import HOOKS
from .hook import Hook
DATA_BATCH = Optional[Sequence[dict]]
@HOOKS.register_modu... |
"""
Quickly verify that a list of Python files can be loaded by the Python interpreter
without raising any errors. Ran before running more expensive tests. Useful in
Makefiles.
If loading a file fails, the script prints the problematic filename and the detailed
error traceback.
"""
import random
import string
import ... | import random
import string
import sys
import traceback
from importlib.machinery import SourceFileLoader
if __name__ == "__main__":
files = sys.argv[1:]
has_failure = False
for file in files:
try:
module_name = "".join(
random.choice(string.ascii_letters) for _ in range(... |
from typing import Any, Dict, List, Optional, Sequence, Type, Union
import PIL.Image
import torch
from torchvision import datapoints
from torchvision.prototype.datapoints import Label, OneHotLabel
from torchvision.transforms.v2 import functional as F, Transform
from torchvision.transforms.v2._utils import (
_Fill... | from typing import Any, Dict, List, Optional, Sequence, Type, Union
import PIL.Image
import torch
from torchvision import datapoints
from torchvision.prototype.datapoints import Label, OneHotLabel
from torchvision.transforms.v2 import functional as F, Transform
from torchvision.transforms.v2._utils import _FillType, ... |
"""Standard LangChain interface tests"""
import pytest
from langchain_core.language_models import BaseChatModel
from langchain_core.tools import BaseTool
from langchain_tests.integration_tests import ChatModelIntegrationTests
from langchain_huggingface import ChatHuggingFace, HuggingFaceEndpoint
class TestHuggingFa... | """Standard LangChain interface tests"""
from typing import Type
import pytest
from langchain_core.language_models import BaseChatModel
from langchain_core.tools import BaseTool
from langchain_tests.integration_tests import ChatModelIntegrationTests
from langchain_huggingface import ChatHuggingFace, HuggingFaceEndpo... |
"""DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.api import _tf_keras
from keras.api import activations
from keras.api import applications
from keras.api import backend
from keras.api import callbacks
from keras.api import config
from k... | """DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.api import _tf_keras
from keras.api import activations
from keras.api import applications
from keras.api import backend
from keras.api import callbacks
from keras.api import config
from k... |
"""LLM Compiler agent pack."""
from typing import Any, Dict, List, Optional
from llama_index.core.agent import AgentRunner
from llama_index.core.callbacks import CallbackManager
from llama_index.core.llama_pack.base import BaseLlamaPack
from llama_index.core.llms.llm import LLM
from llama_index.core.settings import S... | """LLM Compiler agent pack."""
from typing import Any, Dict, List, Optional
from llama_index.core.agent import AgentRunner
from llama_index.core.callbacks import CallbackManager
from llama_index.core.llama_pack.base import BaseLlamaPack
from llama_index.core.llms.llm import LLM
from llama_index.core.settings import S... |
from typing import TYPE_CHECKING, Any, List, Tuple, Type, TypeVar, Union
import numpy as np
from docarray.typing.proto_register import _register_proto
from docarray.typing.tensor.torch_tensor import TorchTensor, metaTorchAndNode
from docarray.typing.tensor.video.video_tensor_mixin import VideoTensorMixin
T = TypeVar... | from typing import TYPE_CHECKING, Any, List, Tuple, Type, TypeVar, Union
import numpy as np
from docarray.typing.proto_register import _register_proto
from docarray.typing.tensor.torch_tensor import TorchTensor, metaTorchAndNode
from docarray.typing.tensor.video.video_tensor_mixin import VideoTensorMixin
T = TypeVar... |
from __future__ import annotations
import re
from typing import TYPE_CHECKING, Any
if TYPE_CHECKING:
from sentence_transformers.SentenceTransformer import SentenceTransformer
class SentenceEvaluator:
"""
Base class for all evaluators
Extend this class and implement __call__ for custom evaluators.
... | from __future__ import annotations
import re
from typing import TYPE_CHECKING, Any
if TYPE_CHECKING:
from sentence_transformers.SentenceTransformer import SentenceTransformer
class SentenceEvaluator:
"""
Base class for all evaluators
Extend this class and implement __call__ for custom evaluators.
... |
from typing import Dict, Tuple
import torch
def get_versions() -> Dict[str, Tuple[int]]:
"""Get the versions of FFmpeg libraries
Returns:
dict: mapping from library names to version string,
i.e. `"libavutil": (56, 22, 100)`.
"""
return torch.ops.torchaudio.ffmpeg_get_versions()
... | import torch
def get_log_level() -> int:
"""Get the log level of FFmpeg.
See :py:func:`set_log_level` for the detailo.
"""
return torch.ops.torchaudio.ffmpeg_get_log_level()
def set_log_level(level: int):
"""Set the log level of FFmpeg (libavformat etc)
Arguments:
level (int): Log ... |
# Copyright (c) OpenMMLab. All rights reserved.
import os
from typing import Optional
import torch
try:
import torch_npu # noqa: F401
import torch_npu.npu.utils as npu_utils
# Enable operator support for dynamic shape and
# binary operator support on the NPU.
npu_jit_compile = bool(os.getenv('NP... | # Copyright (c) OpenMMLab. All rights reserved.
import os
from typing import Optional
import torch
try:
import torch_npu # noqa: F401
import torch_npu.npu.utils as npu_utils
# Enable operator support for dynamic shape and
# binary operator support on the NPU.
npu_jit_compile = bool(os.getenv('NP... |
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):
r"""
SparseEncoderTrainingArguments extends :class:`~SentenceTransf... |
__version__ = '0.35.1'
import logging
from docarray.array import DocList, DocVec
from docarray.base_doc.doc import BaseDoc
from docarray.utils._internal.misc import _get_path_from_docarray_root_level
__all__ = ['BaseDoc', 'DocList', 'DocVec']
logger = logging.getLogger('docarray')
handler = logging.StreamHandler()... | __version__ = '0.35.0'
import logging
from docarray.array import DocList, DocVec
from docarray.base_doc.doc import BaseDoc
from docarray.utils._internal.misc import _get_path_from_docarray_root_level
__all__ = ['BaseDoc', 'DocList', 'DocVec']
logger = logging.getLogger('docarray')
handler = logging.StreamHandler()... |
from __future__ import annotations
from typing import Any, Optional, Union
import PIL.Image
import torch
from ._tv_tensor import TVTensor
class Mask(TVTensor):
""":class:`torch.Tensor` subclass for segmentation and detection masks with shape ``[..., H, W]``.
Args:
data (tensor-like, PIL.Image.Imag... | from __future__ import annotations
from typing import Any, Optional, Union
import PIL.Image
import torch
from ._tv_tensor import TVTensor
class Mask(TVTensor):
""":class:`torch.Tensor` subclass for segmentation and detection masks.
Args:
data (tensor-like, PIL.Image.Image): Any data that can be tu... |
import copy
import clip
import numpy as np
import pytest
import torch
from jina import Document, DocumentArray
from ...clip_text import CLIPTextEncoder
@pytest.fixture(scope="module")
def encoder() -> CLIPTextEncoder:
return CLIPTextEncoder()
def test_no_documents(encoder: CLIPTextEncoder):
... | import clip
import copy
import numpy as np
import torch
from jina import Document, DocumentArray, Executor
from jinahub.encoder.clip_text import CLIPTextEncoder
def test_clip_batch():
test_docs = DocumentArray((Document(text='random text') for _ in range(30)))
clip_text_encoder = CLIPTextEncoder()
paramet... |
"""
Tests the correct computation of evaluation scores from BinaryClassificationEvaluator
"""
import csv
import gzip
import os
import numpy as np
from sklearn.metrics import accuracy_score, f1_score
from torch.utils.data import DataLoader
from sentence_transformers import (
InputExample,
SentenceTransformer,... | """
Tests the correct computation of evaluation scores from BinaryClassificationEvaluator
"""
import csv
import gzip
import os
import numpy as np
from sklearn.metrics import accuracy_score, f1_score
from torch.utils.data import DataLoader
from sentence_transformers import (
InputExample,
SentenceTransformer,... |
# Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
import sys
import pkg_resources
import pytest
from mmengine.utils import get_installed_path, is_installed
def test_is_installed():
# TODO: Windows CI may failed in unknown reason. Skip check the value
is_installed('mmengine')
# If th... | # Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
import sys
from pathlib import Path
from mmengine.utils import get_installed_path, is_installed
def test_is_installed():
# TODO: Windows CI may failed in unknown reason. Skip check the value
is_installed('mmengine')
# package set by P... |
"""RunInfo class."""
from __future__ import annotations
from uuid import UUID
from pydantic import BaseModel
class RunInfo(BaseModel):
"""Class that contains metadata for a single execution of a Chain or model.
Defined for backwards compatibility with older versions of langchain_core.
This model will... | from __future__ import annotations
from uuid import UUID
from pydantic import BaseModel
class RunInfo(BaseModel):
"""Class that contains metadata for a single execution of a Chain or model.
Defined for backwards compatibility with older versions of langchain_core.
This model will likely be deprecated ... |
"""
Test of utility functions for working with Search Index commands.
Note that search index commands are only supported on Atlas Clusters >=M10.
"""
import os
from typing import Generator, List, Optional
import pytest
from llama_index.vector_stores.mongodb import MongoDBAtlasVectorSearch, index
from pymongo import ... | """Test of utility functions for working with Search Index commands.
Note that search index commands are only supported on Atlas Clusters >=M10.
"""
import os
from typing import Generator, List, Optional
import pytest
from llama_index.vector_stores.mongodb import MongoDBAtlasVectorSearch, index
from pymongo import M... |
# Licensed to the LF AI & Data foundation under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the "License");
# you may not use this fil... | import numpy as np
import pytest
from docarray import BaseDoc
from docarray.base_doc.io.json import orjson_dumps
from docarray.typing import NdArray, TorchTensor
class NpDoc(BaseDoc):
embedding: NdArray[3, 4]
embedding_no_shape: NdArray
class TorchDoc(BaseDoc):
embedding: TorchTensor[3, 4]
embeddin... |
# Copyright (c) OpenMMLab. All rights reserved.
from typing import Union
from torch import Tensor
from mmdet.registry import TASK_UTILS
from mmdet.structures.bbox import BaseBoxes, HorizontalBoxes, get_box_tensor
from .base_bbox_coder import BaseBBoxCoder
@TASK_UTILS.register_module()
class PseudoBBoxCoder(BaseBBox... | # Copyright (c) OpenMMLab. All rights reserved.
from mmdet.registry import TASK_UTILS
from mmdet.structures.bbox import HorizontalBoxes, get_box_tensor
from .base_bbox_coder import BaseBBoxCoder
@TASK_UTILS.register_module()
class PseudoBBoxCoder(BaseBBoxCoder):
"""Pseudo bounding box coder."""
def __init__(... |
import os
from pathlib import Path
import pytest
from jina import Flow
from jina.excepts import RuntimeFailToStart
from jina.orchestrate.deployments import Deployment
from jina.parsers import set_deployment_parser
from jina.serve.executors import BaseExecutor
cur_dir = os.path.dirname(os.path.abspath(__file__))
de... | import os
from pathlib import Path
import pytest
from jina import Flow
from jina.excepts import RuntimeFailToStart
from jina.orchestrate.deployments import Deployment
from jina.parsers import set_deployment_parser
from jina.serve.executors import BaseExecutor
cur_dir = os.path.dirname(os.path.abspath(__file__))
de... |
# Copyright (c) OpenMMLab. All rights reserved.
from ..builder import DETECTORS
from .two_stage import TwoStageDetector
@DETECTORS.register_module()
class FasterRCNN(TwoStageDetector):
"""Implementation of `Faster R-CNN <https://arxiv.org/abs/1506.01497>`_"""
def __init__(self,
backbone,
... | from ..builder import DETECTORS
from .two_stage import TwoStageDetector
@DETECTORS.register_module()
class FasterRCNN(TwoStageDetector):
"""Implementation of `Faster R-CNN <https://arxiv.org/abs/1506.01497>`_"""
def __init__(self,
backbone,
rpn_head,
roi_hea... |
# Licensed to the LF AI & Data foundation under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the "License");
# you may not use this fil... | import pydantic
is_pydantic_v2 = pydantic.__version__.startswith('2.')
if not is_pydantic_v2:
from pydantic.validators import bytes_validator
else:
from pydantic.v1.validators import bytes_validator
__all__ = ['is_pydantic_v2', 'bytes_validator']
|
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... |
from abc import abstractmethod
from typing import TYPE_CHECKING, List
from langchain_community.document_loaders.parsers.language.code_segmenter import (
CodeSegmenter,
)
if TYPE_CHECKING:
from tree_sitter import Language, Parser
class TreeSitterSegmenter(CodeSegmenter):
"""Abstract class for `CodeSegmen... | from abc import abstractmethod
from typing import TYPE_CHECKING, List
from langchain_community.document_loaders.parsers.language.code_segmenter import (
CodeSegmenter,
)
if TYPE_CHECKING:
from tree_sitter import Language, Parser
class TreeSitterSegmenter(CodeSegmenter):
"""Abstract class for `CodeSegmen... |
# 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... |
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")
evaluator = SparseNanoBEIR... | 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")
evaluator = SparseNanoBEIR... |
_base_ = [
'../_base_/models/faster-rcnn_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
norm_cfg = dict(type='BN', requires_grad=True)
image_size = (640, 640)
batch_augments = [dict(type='BatchFixedSizePad', size=image_size)]
mode... | _base_ = [
'../_base_/models/faster-rcnn_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
norm_cfg = dict(type='BN', requires_grad=True)
image_size = (640, 640)
batch_augments = [dict(type='BatchFixedSizePad', size=image_size)]
mode... |
import os.path
from typing import Any, Callable, List, Optional, Tuple
from PIL import Image
from .vision import VisionDataset
class CocoDetection(VisionDataset):
"""`MS Coco Detection <https://cocodataset.org/#detection-2016>`_ Dataset.
It requires the `COCO API to be installed <https://github.com/pdollar... | import os.path
from typing import Any, Callable, List, Optional, Tuple
from PIL import Image
from .vision import VisionDataset
class CocoDetection(VisionDataset):
"""`MS Coco Detection <https://cocodataset.org/#detection-2016>`_ Dataset.
It requires the `COCO API to be installed <https://github.com/pdollar... |
from typing import Optional
from ..utils.logging import get_logger
from .audio_classification import AudioClassification
from .automatic_speech_recognition import AutomaticSpeechRecognition
from .base import TaskTemplate
from .image_classification import ImageClassification
from .language_modeling import LanguageModel... | from typing import Optional
from ..utils.logging import get_logger
from .audio_classificiation import AudioClassification
from .automatic_speech_recognition import AutomaticSpeechRecognition
from .base import TaskTemplate
from .image_classification import ImageClassification
from .language_modeling import LanguageMode... |
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 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 ... |
import inspect
import threading
from typing import Any, Awaitable, Callable, ParamSpec, TypeVar, cast, overload
P = ParamSpec("P")
R = TypeVar("R")
@overload
def thread_cached(func: Callable[P, Awaitable[R]]) -> Callable[P, Awaitable[R]]: ...
@overload
def thread_cached(func: Callable[P, R]) -> Callable[P, R]: ...... | import inspect
import threading
from typing import Any, Awaitable, Callable, ParamSpec, TypeVar, cast, overload
P = ParamSpec("P")
R = TypeVar("R")
@overload
def thread_cached(func: Callable[P, Awaitable[R]]) -> Callable[P, Awaitable[R]]: ...
@overload
def thread_cached(func: Callable[P, R]) -> Callable[P, R]: ...... |
import torch
from parameterized import parameterized
from torchaudio.prototype.models import conformer_wav2vec2_base, conformer_wav2vec2_pretrain_base, emformer_hubert_base
from torchaudio_unittest.common_utils import nested_params, skipIfNoCuda, torch_script, TorchaudioTestCase
class TestSSLModel(TorchaudioTestCase)... | import torch
from parameterized import parameterized
from torchaudio.prototype.models import conformer_wav2vec2_base, emformer_hubert_base
from torchaudio_unittest.common_utils import nested_params, skipIfNoCuda, torch_script, TorchaudioTestCase
class TestSSLModel(TorchaudioTestCase):
def _smoke_test(self, model,... |
# 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 required by applicabl... | # 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 required by applicabl... |
import os
from langchain_core.messages import AIMessage, HumanMessage, ToolMessage
from langchain_core.tools import tool
from pydantic import BaseModel
from langchain_community.chat_models import MiniMaxChat
def test_chat_minimax_not_group_id() -> None:
if "MINIMAX_GROUP_ID" in os.environ:
del os.enviro... | import os
from langchain_core.messages import AIMessage, HumanMessage, ToolMessage
from langchain_core.tools import tool
from pydantic import BaseModel
from langchain_community.chat_models import MiniMaxChat
def test_chat_minimax_not_group_id() -> None:
if "MINIMAX_GROUP_ID" in os.environ:
del os.enviro... |
_base_ = '../htc/htc_x101_64x4d_fpn_16x1_20e_coco.py'
# learning policy
max_epochs = 28
train_cfg = dict(max_epochs=max_epochs)
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=max_epochs,
... | _base_ = '../htc/htc_x101_64x4d_fpn_16x1_20e_coco.py'
# learning policy
lr_config = dict(step=[24, 27])
runner = dict(type='EpochBasedRunner', max_epochs=28)
|
"""
The pre-trained models produce embeddings of size 512 - 1024. However, when storing a large
number of embeddings, this requires quite a lot of memory / storage.
In this example, we reduce the dimensionality of the embeddings to e.g. 128 dimensions. This significantly
reduces the required memory / storage while mai... | """
The pre-trained models produce embeddings of size 512 - 1024. However, when storing a large
number of embeddings, this requires quite a lot of memory / storage.
In this example, we reduce the dimensionality of the embeddings to e.g. 128 dimensions. This significantly
reduces the required memory / storage while mai... |
"""Utils for OpenAI agent."""
from typing import List, Union
from llama_index.core.tools import BaseTool
def get_function_by_name(tools: List[BaseTool], name: str) -> BaseTool:
"""Get function by name."""
name_to_tool = {tool.metadata.name: tool for tool in tools}
if name not in name_to_tool:
ra... | """Utils for OpenAI agent."""
from typing import List, Union
from llama_index.core.tools import BaseTool
def get_function_by_name(tools: List[BaseTool], name: str) -> BaseTool:
"""Get function by name."""
name_to_tool = {tool.metadata.name: tool for tool in tools}
if name not in name_to_tool:
ra... |
from typing import Optional
import pytest
from docarray import BaseDocument, DocumentArray
from docarray.documents import Image
from docarray.helper import (
_access_path_dict_to_nested_dict,
_access_path_to_dict,
_dict_to_access_paths,
_is_access_path_valid,
_update_nested_dicts,
)
@pytest.fixt... | from typing import Optional
import pytest
from docarray import BaseDocument
from docarray.documents import Image
from docarray.helper import (
_access_path_dict_to_nested_dict,
_access_path_to_dict,
_dict_to_access_paths,
_is_access_path_valid,
_update_nested_dicts,
)
@pytest.fixture()
def neste... |
_base_ = ['../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py']
img_scale = (640, 640)
# model settings
model = dict(
type='YOLOX',
input_size=img_scale,
random_size_range=(15, 25),
random_size_interval=10,
backbone=dict(type='CSPDarknet', deepen_factor=0.33, widen_factor=0.5),
... | _base_ = ['../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py']
# model settings
model = dict(
type='YOLOX',
backbone=dict(type='CSPDarknet', deepen_factor=0.33, widen_factor=0.5),
neck=dict(
type='YOLOXPAFPN',
in_channels=[128, 256, 512],
out_channels=128,
n... |
from enum import Enum
from fsspec import AbstractFileSystem
from pathlib import Path
from typing import Any, Dict, Iterable, Optional, Protocol, runtime_checkable
import json
import uuid
from docling.document_converter import DocumentConverter
from docling_core.types import DoclingDocument as DLDocument
from llama_ind... | from enum import Enum
from fsspec import AbstractFileSystem
from pathlib import Path
from typing import Any, Dict, Iterable, Optional, Protocol, runtime_checkable
import json
import uuid
from docling.document_converter import DocumentConverter
from docling_core.types import DoclingDocument as DLDocument
from llama_ind... |
import sys
from os import path
from setuptools import find_packages
from setuptools import setup
if sys.version_info < (3, 7, 0):
raise OSError(f'DocArray requires Python >=3.7, but yours is {sys.version}')
try:
pkg_name = 'docarray'
libinfo_py = path.join(pkg_name, '__init__.py')
libinfo_content = o... | import sys
from os import path
from setuptools import find_packages
from setuptools import setup
if sys.version_info < (3, 7, 0):
raise OSError(f'DocArray requires Python >=3.7, but yours is {sys.version}')
try:
pkg_name = 'docarray'
libinfo_py = path.join(pkg_name, '__init__.py')
libinfo_content = o... |
_base_ = [
'../_base_/models/mask-rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
backbone=dict(
frozen_stages=0,
norm_cfg=dict(type='SyncBN', requires_grad=True),
norm_eval=False,
... | _base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
backbone=dict(
frozen_stages=0,
norm_cfg=dict(type='SyncBN', requires_grad=True),
norm_eval=False,
... |
# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import warnings
from mmcv import Config, DictAction
from mmdet.utils import replace_cfg_vals, update_data_root
def parse_args():
parser = argparse.ArgumentParser(description='Print the whole config')
parser.add_argument('config', help='config f... | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import warnings
from mmcv import Config, DictAction
from mmdet.utils import update_data_root
def parse_args():
parser = argparse.ArgumentParser(description='Print the whole config')
parser.add_argument('config', help='config file path')
par... |
# Copyright (c) OpenMMLab. All rights reserved.
import warnings
import torch.nn as nn
from mmcv.cnn import VGG
from mmcv.runner import BaseModule
from ..builder import BACKBONES
from ..necks import ssd_neck
@BACKBONES.register_module()
class SSDVGG(VGG, BaseModule):
"""VGG Backbone network for single-shot-detec... | # Copyright (c) OpenMMLab. All rights reserved.
import warnings
import torch.nn as nn
from mmcv.cnn import VGG
from mmcv.runner import BaseModule
from ..builder import BACKBONES
from ..necks import ssd_neck
@BACKBONES.register_module()
class SSDVGG(VGG, BaseModule):
"""VGG Backbone network for single-shot-detec... |
import importlib
import os
import re
import types
from typing import Any, Optional
import numpy as np
try:
import torch # noqa: F401
except ImportError:
torch_imported = False
else:
torch_imported = True
try:
import tensorflow as tf # type: ignore # noqa: F401
except (ImportError, TypeError):
... | import importlib
import os
import re
import types
from typing import Any, Optional
import numpy as np
try:
import torch # noqa: F401
except ImportError:
torch_imported = False
else:
torch_imported = True
try:
import tensorflow as tf # type: ignore # noqa: F401
except (ImportError, TypeError):
... |
_base_ = './reppoints-moment_r50_fpn-gn_head-gn_1x_coco.py'
model = dict(bbox_head=dict(transform_method='partial_minmax'))
| _base_ = './reppoints_moment_r50_fpn_gn-neck+head_1x_coco.py'
model = dict(bbox_head=dict(transform_method='partial_minmax'))
|
import warnings
from typing import TYPE_CHECKING, List, Optional, Tuple, TypeVar
from docarray.typing import ImageBytes
from docarray.typing.proto_register import _register_proto
from docarray.typing.tensor.image import ImageNdArray
from docarray.typing.url.any_url import AnyUrl
from docarray.typing.url.mimetypes impo... | import warnings
from typing import TYPE_CHECKING, Optional, Tuple, TypeVar
from docarray.typing import ImageBytes
from docarray.typing.proto_register import _register_proto
from docarray.typing.tensor.image import ImageNdArray
from docarray.typing.url.any_url import AnyUrl
from docarray.utils._internal.misc import is_... |
"""Standard LangChain interface tests"""
import base64
from pathlib import Path
from typing import Literal, cast
import httpx
from langchain_core.language_models import BaseChatModel
from langchain_core.messages import AIMessage, HumanMessage
from langchain_tests.integration_tests import ChatModelIntegrationTests
fr... | """Standard LangChain interface tests"""
from pathlib import Path
from typing import Literal, cast
from langchain_core.language_models import BaseChatModel
from langchain_core.messages import AIMessage
from langchain_tests.integration_tests import ChatModelIntegrationTests
from langchain_openai import ChatOpenAI
RE... |
from __future__ import annotations
from collections.abc import Sequence
from copy import deepcopy
from typing import Any, Optional, Union
from langchain_core._api.deprecation import deprecated
from langchain_core.callbacks import Callbacks
from langchain_core.documents import BaseDocumentCompressor, Document
from lan... | from __future__ import annotations
from collections.abc import Sequence
from copy import deepcopy
from typing import Any, Optional, Union
from langchain_core._api.deprecation import deprecated
from langchain_core.callbacks.manager import Callbacks
from langchain_core.documents import Document
from langchain_core.util... |
from keras.src import regularizers
from keras.src.api_export import keras_export
from keras.src.layers.layer import Layer
@keras_export("keras.layers.ActivityRegularization")
class ActivityRegularization(Layer):
"""Layer that applies an update to the cost function based input activity.
Args:
l1: L1 r... | from keras.src import regularizers
from keras.src.api_export import keras_export
from keras.src.layers.layer import Layer
@keras_export("keras.layers.ActivityRegularization")
class ActivityRegularization(Layer):
"""Layer that applies an update to the cost function based input activity.
Args:
l1: L1 r... |
from __future__ import annotations
from typing import Iterable
import torch.nn.functional as F
from torch import Tensor, nn
from sentence_transformers.SentenceTransformer import SentenceTransformer
from .ContrastiveLoss import SiameseDistanceMetric
class OnlineContrastiveLoss(nn.Module):
def __init__(
... | from typing import Dict, Iterable
import torch.nn.functional as F
from torch import Tensor, nn
from sentence_transformers.SentenceTransformer import SentenceTransformer
from .ContrastiveLoss import SiameseDistanceMetric
class OnlineContrastiveLoss(nn.Module):
def __init__(
self, model: SentenceTransfor... |
import os
from pathlib import Path
from typing import List, Optional, Tuple, Union
import torch
from torch.utils.data import Dataset
from torchaudio.datasets.utils import _load_waveform
_SAMPLE_RATE = 16000
_SPEAKERS = [
"Aditi",
"Amy",
"Brian",
"Emma",
"Geraint",
"Ivy",
"Joanna",
"Jo... | import os
from pathlib import Path
from typing import List, Optional, Tuple, Union
import torch
from torch.utils.data import Dataset
from torchaudio.datasets.utils import _load_waveform
_SAMPLE_RATE = 16000
_SPEAKERS = [
"Aditi",
"Amy",
"Brian",
"Emma",
"Geraint",
"Ivy",
"Joanna",
"Jo... |
"""Async utils."""
import asyncio
from itertools import zip_longest
from typing import Any, Coroutine, Iterable, List, Optional, TypeVar
import llama_index.core.instrumentation as instrument
dispatcher = instrument.get_dispatcher(__name__)
def asyncio_module(show_progress: bool = False) -> Any:
if show_progres... | """Async utils."""
import asyncio
from itertools import zip_longest
from typing import Any, Coroutine, Iterable, List, Optional, TypeVar
import llama_index.core.instrumentation as instrument
dispatcher = instrument.get_dispatcher(__name__)
def asyncio_module(show_progress: bool = False) -> Any:
if show_progres... |
# mypy: allow-untyped-defs
from .base_structured_sparsifier import BaseStructuredSparsifier
class SaliencyPruner(BaseStructuredSparsifier):
"""
Prune rows based on the saliency (L1 norm) of each row.
This pruner works on N-Dimensional weight tensors.
For each row, we will calculate the saliency, whic... | # mypy: allow-untyped-defs
from .base_structured_sparsifier import BaseStructuredSparsifier
class SaliencyPruner(BaseStructuredSparsifier):
"""
Prune rows based on the saliency (L1 norm) of each row.
This pruner works on N-Dimensional weight tensors.
For each row, we will calculate the saliency, whic... |
from keras.src import ops
from keras.src.api_export import keras_export
from keras.src.layers.layer import Layer
@keras_export("keras.layers.UnitNormalization")
class UnitNormalization(Layer):
"""Unit normalization layer.
Normalize a batch of inputs so that each input in the batch has a L2 norm
equal to ... | from keras.src import ops
from keras.src.api_export import keras_export
from keras.src.layers.layer import Layer
@keras_export("keras.layers.UnitNormalization")
class UnitNormalization(Layer):
"""Unit normalization layer.
Normalize a batch of inputs so that each input in the batch has a L2 norm
equal to ... |
"""
Opensearch reader over REST api.
This only uses the basic search api, so it will work Opensearch.
"""
from typing import List, Optional
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document
class OpensearchReader(BaseReader):
"""
Read documents from an Opens... | """Opensearch reader over REST api.
This only uses the basic search api, so it will work Opensearch.
"""
from typing import List, Optional
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document
class OpensearchReader(BaseReader):
"""
Read documents from an Opense... |
"""
This example computes the score between a query and all possible
sentences in a corpus using a Cross-Encoder for semantic textual similarity (STS).
It output then the most similar sentences for the given query.
"""
import numpy as np
from sentence_transformers.cross_encoder import CrossEncoder
# Pre-trained cros... | """
This example computes the score between a query and all possible
sentences in a corpus using a Cross-Encoder for semantic textual similarity (STS).
It output then the most similar sentences for the given query.
"""
from sentence_transformers.cross_encoder import CrossEncoder
import numpy as np
# Pre-trained cross ... |
from typing import Any, Dict
from torchvision import datapoints
from torchvision.transforms.v2 import functional as F, Transform
from torchvision.transforms.v2.utils import is_simple_tensor
class UniformTemporalSubsample(Transform):
"""[BETA] Uniformly subsample ``num_samples`` indices from the temporal dimensi... | from typing import Any, Dict
from torchvision import datapoints
from torchvision.transforms.v2 import functional as F, Transform
from torchvision.transforms.v2.utils import is_simple_tensor
class UniformTemporalSubsample(Transform):
"""[BETA] Uniformly subsample ``num_samples`` indices from the temporal dimensi... |
import pytest
from importlib.util import find_spec
from llama_index.core.storage.kvstore.types import BaseKVStore
from llama_index.storage.kvstore.postgres import PostgresKVStore
no_packages = find_spec("psycopg2") is None or find_spec("sqlalchemy") is None or find_spec("asyncpg") is None
def test_class():
names... | import pytest
from importlib.util import find_spec
from llama_index.core.storage.kvstore.types import BaseKVStore
from llama_index.storage.kvstore.postgres import PostgresKVStore
no_packages = find_spec("psycopg2") is not None and find_spec("sqlalchemy") is not None and find_spec("asyncpg") is not None
def test_class... |
"""Utils for manipulating images."""
import base64
from io import BytesIO
from typing import cast
from PIL import Image
from PIL.ImageFile import ImageFile
def img_2_b64(image: ImageFile, format: str = "JPEG") -> str:
"""
Convert a PIL.Image to a base64 encoded image string.
Args:
image (ImageFi... | """Utils for manipulating images."""
import base64
from io import BytesIO
from typing import cast
from PIL import Image
from PIL.ImageFile import ImageFile
def img_2_b64(image: ImageFile, format: str = "JPEG") -> str:
"""Convert a PIL.Image to a base64 encoded image string.
Args:
image (ImageFile): ... |
"""Trello reader."""
from typing import List
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document
class TrelloReader(BaseReader):
"""
Trello reader. Reads data from Trello boards and cards.
Args:
api_key (str): Trello API key.
api_token (str):... | """Trello reader."""
from typing import List
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document
class TrelloReader(BaseReader):
"""Trello reader. Reads data from Trello boards and cards.
Args:
api_key (str): Trello API key.
api_token (str): Trel... |
import PIL.Image
import pytest
import torch
import torchvision.transforms.v2.utils
from common_utils import make_bounding_box, make_detection_mask, make_image
from torchvision import datapoints
from torchvision.transforms.v2.functional import to_image_pil
from torchvision.transforms.v2.utils import has_all, has_any
... | import PIL.Image
import pytest
import torch
import torchvision.transforms.v2.utils
from common_utils import make_bounding_box, make_detection_mask, make_image
from torchvision import datapoints
from torchvision.transforms.v2.functional import to_image_pil
from torchvision.transforms.v2.utils import has_all, has_any
... |
from typing import List, Optional
import pandas as pd
import pytest
from docarray import BaseDoc, DocList, DocVec
from docarray.documents import ImageDoc
from docarray.typing import NdArray, TorchTensor
from docarray.utils._internal.pydantic import is_pydantic_v2
@pytest.fixture()
def nested_doc_cls():
class My... | from typing import List, Optional
import pandas as pd
import pytest
from docarray import BaseDoc, DocList, DocVec
from docarray.documents import ImageDoc
from docarray.typing import NdArray, TorchTensor
@pytest.fixture()
def nested_doc_cls():
class MyDoc(BaseDoc):
count: Optional[int]
text: str
... |
"""DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.applications.xception import Xception as Xception
from keras.src.applications.xception import (
decode_predictions as decode_predictions,
)
from keras.src.applications.xception im... | """DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.applications.xception import Xception
from keras.src.applications.xception import decode_predictions
from keras.src.applications.xception import preprocess_input
|
import os
from pathlib import Path
from jina.constants 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 add... | 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.
import warnings
from abc import ABCMeta, abstractmethod
from typing import Any, List, Optional, Sequence, Union
from mmengine.dist import (broadcast_object_list, collect_results,
is_main_process)
class BaseMetric(metaclass=ABCMeta):
"""Ba... | # Copyright (c) OpenMMLab. All rights reserved.
import warnings
from abc import ABCMeta, abstractmethod
from typing import Any, List, Optional, Sequence, Tuple, Union
from mmengine.dist import (broadcast_object_list, collect_results,
is_main_process)
class BaseMetric(metaclass=ABCMeta):
... |
# 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 mmengine.registry import build_model_from_cfg, build_runner_from_cfg
from .registry... |
# Copyright (c) OpenMMLab. All rights reserved.
from typing import Optional
import torch
import torch.nn as nn
from mmengine.runner import load_checkpoint
from torch import Tensor
from mmdet.core import ConfigType, OptConfigType, SampleList
from mmdet.registry import MODELS
from ..utils.misc import unpack_gt_instance... | # Copyright (c) OpenMMLab. All rights reserved.
from typing import Optional
import torch
import torch.nn as nn
from mmengine.runner import load_checkpoint
from torch import Tensor
from mmdet.core import ConfigType, OptConfigType, SampleList
from mmdet.registry import MODELS
from ..utils.misc import unpack_gt_instance... |
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:
return SparseEncoder("sparse-encoder-testing/splade-bert-tiny-nq")
@pytest.fixture()
def splade_bert_tiny... | 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... |
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