input stringlengths 33 5k | output stringlengths 32 5k |
|---|---|
"""Generate migrations for partner packages."""
import importlib
from langchain_core.documents import BaseDocumentCompressor, BaseDocumentTransformer
from langchain_core.embeddings import Embeddings
from langchain_core.language_models import BaseLanguageModel
from langchain_core.retrievers import BaseRetriever
from l... | """Generate migrations for partner packages."""
import importlib
from langchain_core.documents import BaseDocumentCompressor, BaseDocumentTransformer
from langchain_core.embeddings import Embeddings
from langchain_core.language_models import BaseLanguageModel
from langchain_core.retrievers import BaseRetriever
from l... |
import numpy as np
import torch
from docarray import Document
from docarray.typing import AnyTensor, NdArray, TorchTensor
def test_set_tensor():
class MyDocument(Document):
tensor: AnyTensor
d = MyDocument(tensor=np.zeros((3, 224, 224)))
assert isinstance(d.tensor, NdArray)
assert isinstanc... | import numpy as np
import torch
from docarray import Document
from docarray.typing import NdArray, Tensor, TorchTensor
def test_set_tensor():
class MyDocument(Document):
tensor: Tensor
d = MyDocument(tensor=np.zeros((3, 224, 224)))
assert isinstance(d.tensor, NdArray)
assert isinstance(d.te... |
# Copyright (c) OpenMMLab. All rights reserved.
from .bbox_nms import fast_nms, multiclass_nms
from .brick_wrappers import AdaptiveAvgPool2d, adaptive_avg_pool2d
from .conv_upsample import ConvUpsample
from .csp_layer import CSPLayer
from .dropblock import DropBlock
from .ema import ExpMomentumEMA
from .inverted_residu... | # Copyright (c) OpenMMLab. All rights reserved.
from .bbox_nms import fast_nms, multiclass_nms
from .brick_wrappers import AdaptiveAvgPool2d, adaptive_avg_pool2d
from .builder import build_linear_layer, build_transformer
from .conv_upsample import ConvUpsample
from .csp_layer import CSPLayer
from .dropblock import Drop... |
"""
==========================
FastICA on 2D point clouds
==========================
This example illustrates visually in the feature space a comparison by
results using two different component analysis techniques.
:ref:`ICA` vs :ref:`PCA`.
Representing ICA in the feature space gives the view of 'geometric ICA':
ICA... | """
==========================
FastICA on 2D point clouds
==========================
This example illustrates visually in the feature space a comparison by
results using two different component analysis techniques.
:ref:`ICA` vs :ref:`PCA`.
Representing ICA in the feature space gives the view of 'geometric ICA':
ICA... |
"""
Experimental support for external memory
========================================
This is similar to the one in `quantile_data_iterator.py`, but for external memory
instead of Quantile DMatrix. The feature is not ready for production use yet.
.. versionadded:: 1.5.0
See :doc:`the tutorial </tutorials/exter... | """
Experimental support for external memory
========================================
This is similar to the one in `quantile_data_iterator.py`, but for external memory
instead of Quantile DMatrix. The feature is not ready for production use yet.
.. versionadded:: 1.5.0
See :doc:`the tutorial </tutorials/exter... |
"""
This is a simple application for sparse encoder: Computing embeddings.
we have multiple sentences and we want to compute their embeddings.
The embeddings are sparse, meaning that most of the values are zero.
The embeddings are stored in a sparse matrix format, which is more efficient for storage and computation.
w... | """
This is a simple application for sparse encoder: Computing embeddings.
we have multiple sentences and we want to compute their embeddings.
The embeddings are sparse, meaning that most of the values are zero.
The embeddings are stored in a sparse matrix format, which is more efficient for storage and computation.
w... |
from typing import TYPE_CHECKING
from docarray.array.storage.qdrant.backend import BackendMixin, QdrantConfig
from docarray.array.storage.qdrant.find import FindMixin
from docarray.array.storage.qdrant.getsetdel import GetSetDelMixin
from docarray.array.storage.qdrant.helper import DISTANCES
from docarray.array.storag... | from typing import TYPE_CHECKING
from .backend import BackendMixin, QdrantConfig
from .find import FindMixin
from .getsetdel import GetSetDelMixin
from .helper import DISTANCES
from .seqlike import SequenceLikeMixin
__all__ = ['StorageMixins', 'QdrantConfig']
if TYPE_CHECKING:
from qdrant_client import QdrantCli... |
"""
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... |
from typing import Optional, Type
from langchain_core.callbacks import CallbackManagerForToolRun
from langchain_core.tools import BaseTool
from pydantic import BaseModel, Field
from langchain_community.tools.file_management.utils import (
INVALID_PATH_TEMPLATE,
BaseFileToolMixin,
FileValidationError,
)
... | from typing import Optional, Type
from langchain_core.callbacks import CallbackManagerForToolRun
from langchain_core.tools import BaseTool
from pydantic import BaseModel, Field
from langchain_community.tools.file_management.utils import (
INVALID_PATH_TEMPLATE,
BaseFileToolMixin,
FileValidationError,
)
... |
from typing import Literal
from langchain_core.tools import BaseTool
from langchain_tests.integration_tests import ToolsIntegrationTests
from langchain_tests.unit_tests import ToolsUnitTests
class ParrotMultiplyTool(BaseTool):
name: str = "ParrotMultiplyTool"
description: str = (
"Multiply two numbe... | from typing import Literal
from langchain_core.tools import BaseTool
from langchain_tests.integration_tests import ToolsIntegrationTests
from langchain_tests.unit_tests import ToolsUnitTests
class ParrotMultiplyTool(BaseTool): # type: ignore
name: str = "ParrotMultiplyTool"
description: str = (
"Mu... |
__copyright__ = "Copyright (c) 2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import subprocess
import pytest
from jina import Document, DocumentArray, Flow
from sentence_encoder import TransformerSentenceEncoder
_EMBEDDING_DIM = 384
@pytest.mark.parametrize('request_size', [1, 10, 50, 100]... | __copyright__ = "Copyright (c) 2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import subprocess
import pytest
from jina import Document, DocumentArray, Flow
from ...sentence_encoder import TransformerSentenceEncoder
_EMBEDDING_DIM = 384
@pytest.mark.parametrize('request_size', [1, 10, 50, ... |
import os
from argparse import ArgumentParser
import mmcv
import requests
import torch
from mmengine.structures import InstanceData
from mmdet.apis import inference_detector, init_detector
from mmdet.registry import VISUALIZERS
from mmdet.structures import DetDataSample
def parse_args():
parser = ArgumentParser... | import os
from argparse import ArgumentParser
import mmcv
import requests
import torch
from mmengine.structures import InstanceData
from mmdet.apis import inference_detector, init_detector
from mmdet.registry import VISUALIZERS
from mmdet.structures import DetDataSample
from mmdet.utils import register_all_modules
... |
from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.tools import O365SearchEmails
from langchain_community.tools.office365.messages_search import SearchEmailsInput
# Create a way to dynamically look up deprecated imports.
# Used to conso... | from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.tools import O365SearchEmails
from langchain_community.tools.office365.messages_search import SearchEmailsInput
# Create a way to dynamically look up deprecated imports.
# Used to conso... |
import base64
import hashlib
from datetime import datetime, timedelta, timezone
import os
import jwt
from cryptography.hazmat.backends import default_backend
from cryptography.hazmat.primitives.serialization import (
Encoding,
PublicFormat,
load_pem_private_key,
)
SPCS_TOKEN_PATH = "/snowflake/session/toke... | import base64
import hashlib
from datetime import datetime, timedelta, timezone
import os
import jwt
from cryptography.hazmat.backends import default_backend
from cryptography.hazmat.primitives.serialization import (
Encoding,
PublicFormat,
load_pem_private_key,
)
SPCS_TOKEN_PATH = "/snowflake/session/toke... |
import os
import time
import pytest
import requests
from docarray import Document
from jina import Client, Flow
from jina.helper import random_port
from jina.serve.runtimes.servers import BaseServer
from tests.integration.multiple_protocol_gateway.gateway.multiprotocol_gateway import (
MultiProtocolGateway,
)
cu... | import os
import time
import pytest
import requests
from docarray import Document
from jina import Client, Flow
from jina.helper import random_port
from jina.serve.runtimes.servers import BaseServer
from tests.integration.multiple_protocol_gateway.gateway.multiprotocol_gateway import (
MultiProtocolGateway,
)
cu... |
__copyright__ = "Copyright (c) 2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import os
import numpy as np
import pytest
from jina import Document, DocumentArray
from jina.executors.metas import get_default_metas
from jina_commons.indexers.dump import import_vectors
from .. import AnnoySearch... | __copyright__ = "Copyright (c) 2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import os
import numpy as np
import pytest
from jina import Document, DocumentArray
from jina.executors.metas import get_default_metas
from jina_commons.indexers.dump import import_vectors
from .. import AnnoySearch... |
"""Sentence Transformer Finetuning Engine."""
import os
from typing import Any, Optional
from llama_index.core.base.embeddings.base import BaseEmbedding
from llama_index.core.embeddings.utils import resolve_embed_model
from llama_index.finetuning.embeddings.common import EmbeddingQAFinetuneDataset
from llama_index.fin... | """Sentence Transformer Finetuning Engine."""
from typing import Any, Optional
from llama_index.core.base.embeddings.base import BaseEmbedding
from llama_index.core.embeddings.utils import resolve_embed_model
from llama_index.finetuning.embeddings.common import (
EmbeddingQAFinetuneDataset,
)
from llama_index.fin... |
import threading
import time
from typing import Union, BinaryIO, TYPE_CHECKING, Generator, Type, Dict, Optional
import numpy as np
if TYPE_CHECKING:
from docarray.typing import T
class VideoDataMixin:
"""Provide helper functions for :class:`Document` to support video data."""
@classmethod
def gener... | from typing import Union, BinaryIO, TYPE_CHECKING
import numpy as np
if TYPE_CHECKING:
from docarray.typing import T
class VideoDataMixin:
"""Provide helper functions for :class:`Document` to support video data."""
def load_uri_to_video_tensor(self: 'T', only_keyframes: bool = False) -> 'T':
""... |
# Copyright (c) OpenMMLab. All rights reserved.
from .anchor_free_head import AnchorFreeHead
from .anchor_head import AnchorHead
from .atss_head import ATSSHead
from .autoassign_head import AutoAssignHead
from .cascade_rpn_head import CascadeRPNHead, StageCascadeRPNHead
from .centernet_head import CenterNetHead
from .c... | # Copyright (c) OpenMMLab. All rights reserved.
from .anchor_free_head import AnchorFreeHead
from .anchor_head import AnchorHead
from .atss_head import ATSSHead
from .autoassign_head import AutoAssignHead
from .cascade_rpn_head import CascadeRPNHead, StageCascadeRPNHead
from .centernet_head import CenterNetHead
from .c... |
_base_ = './cascade-mask-rcnn_hrnetv2p-w32_20e_coco.py'
# model settings
model = dict(
backbone=dict(
type='HRNet',
extra=dict(
stage2=dict(num_channels=(40, 80)),
stage3=dict(num_channels=(40, 80, 160)),
stage4=dict(num_channels=(40, 80, 160, 320))),
init... | _base_ = './cascade_mask_rcnn_hrnetv2p_w32_20e_coco.py'
# model settings
model = dict(
backbone=dict(
type='HRNet',
extra=dict(
stage2=dict(num_channels=(40, 80)),
stage3=dict(num_channels=(40, 80, 160)),
stage4=dict(num_channels=(40, 80, 160, 320))),
init... |
"""
This example starts multiple processes (1 per GPU), which encode
sentences in parallel. This gives a near linear speed-up
when encoding large text collections.
It also demonstrates how to stream data which is helpful in case you don't
want to wait for an extremely large dataset to download, or if you want to
limit ... | """
This example starts multiple processes (1 per GPU), which encode
sentences in parallel. This gives a near linear speed-up
when encoding large text collections.
It also demonstrates how to stream data which is helpful in case you don't
want to wait for an extremely large dataset to download, or if you want to
limit ... |
import numpy as np
import pytest
from keras.src import backend
from keras.src import layers
from keras.src import testing
class AlphaDropoutTest(testing.TestCase):
@pytest.mark.requires_trainable_backend
def test_alpha_dropout_basics(self):
self.run_layer_test(
layers.AlphaDropout,
... | import numpy as np
import pytest
from keras.src import backend
from keras.src import layers
from keras.src import testing
class AlphaDropoutTest(testing.TestCase):
@pytest.mark.requires_trainable_backend
def test_alpha_dropout_basics(self):
self.run_layer_test(
layers.AlphaDropout,
... |
# Copyright (c) OpenMMLab. All rights reserved.
from mmdet.registry import DATASETS
from .xml_style import XMLDataset
@DATASETS.register_module()
class VOCDataset(XMLDataset):
"""Dataset for PASCAL VOC."""
METAINFO = {
'CLASSES':
('aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car'... | # Copyright (c) OpenMMLab. All rights reserved.
from collections import OrderedDict
from mmcv.utils import print_log
from mmdet.core import eval_map, eval_recalls
from mmdet.registry import DATASETS
from .xml_style import XMLDataset
@DATASETS.register_module()
class VOCDataset(XMLDataset):
CLASSES = ('aeroplan... |
from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.callbacks.confident_callback import DeepEvalCallbackHandler
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# hand... | from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.callbacks.confident_callback import DeepEvalCallbackHandler
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# hand... |
# Copyright (c) OpenMMLab. All rights reserved.
from .hook import Hook
from .iter_timer_hook import IterTimerHook
from .sampler_seed_hook import DistSamplerSeedHook
__all__ = ['Hook', 'IterTimerHook', 'DistSamplerSeedHook']
| # Copyright (c) OpenMMLab. All rights reserved.
from .hook import Hook
from .iter_timer_hook import IterTimerHook
__all__ = ['Hook', 'IterTimerHook']
|
import unittest
import torch
import torchaudio.functional as F
from parameterized import parameterized
from torchaudio_unittest.common_utils import (
PytorchTestCase,
skipIfNoSox,
TorchaudioTestCase,
)
from .functional_impl import Functional, FunctionalCPUOnly
class TestFunctionalFloat32(Functional, Fun... | import unittest
import torch
import torchaudio.functional as F
from parameterized import parameterized
from torchaudio_unittest.common_utils import (
PytorchTestCase,
skipIfNoSox,
TorchaudioTestCase,
)
from .functional_impl import Functional, FunctionalCPUOnly
class TestFunctionalFloat32(Functional, Fun... |
_base_ = './fast-rcnn_r50_fpn_2x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| _base_ = './fast_rcnn_r50_fpn_2x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
|
from pathlib import Path
from typing import Any, Optional, TypedDict
from tomlkit import load
def get_package_root(cwd: Optional[Path] = None) -> Path:
# traverse path for routes to host (any directory holding a pyproject.toml file)
package_root = Path.cwd() if cwd is None else cwd
visited: set[Path] = s... | from pathlib import Path
from typing import Any, Optional, TypedDict
from tomlkit import load
def get_package_root(cwd: Optional[Path] = None) -> Path:
# traverse path for routes to host (any directory holding a pyproject.toml file)
package_root = Path.cwd() if cwd is None else cwd
visited: set[Path] = s... |
from __future__ import annotations
from collections.abc import Iterable
from typing import Any
import torch
from torch import Tensor, nn
from sentence_transformers import util
from sentence_transformers.SentenceTransformer import SentenceTransformer
class CoSENTLoss(nn.Module):
def __init__(self, model: Senten... | from __future__ import annotations
from typing import Any, Iterable
import torch
from torch import Tensor, nn
from sentence_transformers import util
from sentence_transformers.SentenceTransformer import SentenceTransformer
class CoSENTLoss(nn.Module):
def __init__(self, model: SentenceTransformer, scale: float... |
# Copyright (c) OpenMMLab. All rights reserved.
"""MMDetection provides 17 registry nodes to support using modules across
projects. Each node is a child of the root registry in MMEngine.
More details can be found at
https://mmengine.readthedocs.io/en/latest/tutorials/registry.html.
"""
from mmengine.registry import D... | # Copyright (c) OpenMMLab. All rights reserved.
"""MMDetection provides 17 registry nodes to support using modules across
projects. Each node is a child of the root registry in MMEngine.
More details can be found at
https://mmengine.readthedocs.io/en/latest/tutorials/registry.html.
"""
from mmengine.registry import D... |
import asyncio
import copy
from typing import Any, List, Optional
from jina.serve.gateway import BaseGateway
class CompositeGateway(BaseGateway):
"""GRPC Gateway implementation"""
def __init__(
self,
**kwargs,
):
"""Initialize the gateway
:param kwargs: keyword args
... | import copy
from typing import Any, List, Optional
from jina.serve.gateway import BaseGateway
class CompositeGateway(BaseGateway):
"""GRPC Gateway implementation"""
def __init__(
self,
**kwargs,
):
"""Initialize the gateway
:param kwargs: keyword args
"""
... |
"""
JSONalyze Query Engine.
WARNING: This tool executes a SQL prompt generated by the LLM with SQL Lite and
may lead to arbitrary file creation on the machine running this tool.
This tool is not recommended to be used in a production setting, and would
require heavy sandboxing or virtual machines.
DEPRECATED: Use `JS... | """JSONalyze Query Engine.
WARNING: This tool executes a SQL prompt generated by the LLM with SQL Lite and
may lead to arbitrary file creation on the machine running this tool.
This tool is not recommended to be used in a production setting, and would
require heavy sandboxing or virtual machines.
DEPRECATED: Use `JSO... |
_base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
# model settings
model = dict(
type='CenterNet',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123... | _base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
# model settings
model = dict(
type='CenterNet',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123... |
from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.llms import AzureMLOnlineEndpoint
from langchain_community.llms.azureml_endpoint import (
AzureMLEndpointClient,
ContentFormatterBase,
CustomOpenAIContentFormatte... | from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.llms import AzureMLOnlineEndpoint
from langchain_community.llms.azureml_endpoint import (
AzureMLEndpointClient,
ContentFormatterBase,
CustomOpenAIContentFormatte... |
_base_ = ['../_base_/models/retinanet_r50_fpn.py', '../common/ms_3x_coco.py']
# optimizer
model = dict(
pretrained='open-mmlab://resnext101_64x4d',
backbone=dict(type='ResNeXt', depth=101, groups=64, base_width=4))
optim_wrapper = dict(optimizer=dict(type='SGD', lr=0.01))
| _base_ = [
'../_base_/models/retinanet_r50_fpn.py', '../common/mstrain_3x_coco.py'
]
# optimizer
model = dict(
pretrained='open-mmlab://resnext101_64x4d',
backbone=dict(type='ResNeXt', depth=101, groups=64, base_width=4))
optim_wrapper = dict(optimizer=dict(type='SGD', lr=0.01))
|
import os
from pathlib import Path
import numpy as np
import pytest
import torch
from mmdet.apis import inference_detector, init_detector
from mmdet.structures import DetDataSample
from mmdet.utils import register_all_modules
# TODO: Waiting to fix multiple call error bug
register_all_modules()
@pytest.mark.parame... | import os
from pathlib import Path
import numpy as np
import pytest
import torch
from mmdet.apis import inference_detector, init_detector
from mmdet.structures import DetDataSample
from mmdet.utils import register_all_modules
# TODO: Waiting to fix multiple call error bug
register_all_modules()
@pytest.mark.parame... |
# coding: utf-8
"""Helper script for checking versions in the dynamic symbol table.
This script checks that LightGBM library is linked to the appropriate symbol versions.
Linking to newer symbol versions at compile time is problematic because it could result
in built artifacts being unusable on older platforms.
Vers... | # coding: utf-8
"""Helper script for checking versions in the dynamic symbol table.
This script checks that LightGBM library is linked to the appropriate symbol versions.
Linking to newer symbol versions at compile time is problematic because it could result
in built artifacts being unusable on older platforms.
Vers... |
import logging
from colorama import Fore, Style
from .utils import remove_color_codes
class FancyConsoleFormatter(logging.Formatter):
"""
A custom logging formatter designed for console output.
This formatter enhances the standard logging output with color coding. The color
coding is based on the l... | import logging
from colorama import Fore, Style
from google.cloud.logging_v2.handlers import CloudLoggingFilter, StructuredLogHandler
from .utils import remove_color_codes
class FancyConsoleFormatter(logging.Formatter):
"""
A custom logging formatter designed for console output.
This formatter enhances... |
from keras.src.callbacks.backup_and_restore import BackupAndRestore
from keras.src.callbacks.callback import Callback
from keras.src.callbacks.callback_list import CallbackList
from keras.src.callbacks.csv_logger import CSVLogger
from keras.src.callbacks.early_stopping import EarlyStopping
from keras.src.callbacks.hist... | from keras.src.callbacks.backup_and_restore import BackupAndRestore
from keras.src.callbacks.callback import Callback
from keras.src.callbacks.callback_list import CallbackList
from keras.src.callbacks.csv_logger import CSVLogger
from keras.src.callbacks.early_stopping import EarlyStopping
from keras.src.callbacks.hist... |
# Copyright (c) OpenMMLab. All rights reserved.
from unittest.mock import Mock
from mmengine.hooks import Hook
class TestHook:
def test_before_run(self):
hook = Hook()
runner = Mock()
hook.before_run(runner)
def test_after_run(self):
hook = Hook()
runner = Mock()
... | # Copyright (c) OpenMMLab. All rights reserved.
from unittest.mock import Mock
from mmengine.hooks import Hook
class TestHook:
def test_before_run(self):
hook = Hook()
runner = Mock()
hook.before_run(runner)
def test_after_run(self):
hook = Hook()
runner = Mock()
... |
"""
=================================================
Pixel importances with a parallel forest of trees
=================================================
This example shows the use of a forest of trees to evaluate the impurity
based importance of the pixels in an image classification task on the faces
dataset. The hot... | """
=================================================
Pixel importances with a parallel forest of trees
=================================================
This example shows the use of a forest of trees to evaluate the impurity
based importance of the pixels in an image classification task on the faces
dataset. The hot... |
from __future__ import annotations
from collections.abc import Iterable
from torch import Tensor
from sentence_transformers.losses.TripletLoss import TripletDistanceMetric, TripletLoss
from sentence_transformers.sparse_encoder.SparseEncoder import SparseEncoder
class SparseTripletLoss(TripletLoss):
def __init_... | from __future__ import annotations
from collections.abc import Iterable
from torch import Tensor
from sentence_transformers.losses.TripletLoss import TripletDistanceMetric, TripletLoss
from sentence_transformers.sparse_encoder.SparseEncoder import SparseEncoder
class SparseTripletLoss(TripletLoss):
def __init_... |
from docarray.typing.tensor.video.video_ndarray import VideoNdArray
__all__ = ['VideoNdArray']
from docarray.utils.misc import is_tf_available, is_torch_available
torch_available = is_torch_available()
if torch_available:
from docarray.typing.tensor.video.video_torch_tensor import VideoTorchTensor # noqa
_... | from docarray.typing.tensor.video.video_ndarray import VideoNdArray
__all__ = ['VideoNdArray']
try:
import torch # noqa: F401
except ImportError:
pass
else:
from docarray.typing.tensor.video.video_torch_tensor import VideoTorchTensor # noqa
__all__.extend(['VideoTorchTensor'])
|
from jina.serve.runtimes.gateway.gateway import BaseGateway
from jina.serve.runtimes.servers.load_balancer import LoadBalancingServer
__all__ = ['LoadBalancerGateway']
class LoadBalancerGateway(LoadBalancingServer, BaseGateway):
"""
:class:`LoadBalancerGateway`
"""
pass
| from jina.serve.runtimes.gateway.gateway import BaseGateway
from jina.serve.runtimes.servers.load_balancer import LoadBalancingServer
__all__ = ['LoadBalancerGateway']
class LoadBalancerGateway(LoadBalancingServer, BaseGateway):
"""
:class:`LoadBalancerGateway`
"""
pass
|
# Copyright (c) OpenMMLab. All rights reserved.
import unittest
from unittest import TestCase
import torch
import torch.nn as nn
from parameterized import parameterized
from mmdet.models.roi_heads import StandardRoIHead # noqa
from mmdet.registry import MODELS
from mmdet.testing import demo_mm_inputs, demo_mm_propos... | # Copyright (c) OpenMMLab. All rights reserved.
import unittest
from unittest import TestCase
import torch
import torch.nn as nn
from parameterized import parameterized
from mmdet.models.roi_heads import StandardRoIHead # noqa
from mmdet.registry import MODELS
from mmdet.testing import demo_mm_inputs, demo_mm_propos... |
from typing import Any, Dict, Iterable
import torch
from torch import Tensor, nn
from sentence_transformers.SentenceTransformer import SentenceTransformer
from sentence_transformers.util import fullname
class CosineSimilarityLoss(nn.Module):
def __init__(self, model: SentenceTransformer, loss_fct=nn.MSELoss(), ... | import torch
from torch import nn, Tensor
from typing import Any, Iterable, Dict
from sentence_transformers.util import fullname
from ..SentenceTransformer import SentenceTransformer
class CosineSimilarityLoss(nn.Module):
def __init__(self, model: SentenceTransformer, loss_fct=nn.MSELoss(), cos_score_transformat... |
_base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth' # noqa
model = dict(
type... | _base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth' # noqa
model = dict(
type... |
# Copyright (c) OpenMMLab. All rights reserved.
import argparse
from typing import Tuple
import cv2
import mmcv
import numpy as np
import torch
import torch.nn as nn
from mmcv.transforms import Compose
from mmengine.utils import track_iter_progress
from mmdet.apis import init_detector
from mmdet.registry import VISUA... | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
from typing import Tuple
import cv2
import mmcv
import numpy as np
import torch
import torch.nn as nn
from mmcv.transforms import Compose
from mmengine.utils import track_iter_progress
from mmdet.apis import init_detector
from mmdet.registry import VISUA... |
# Copyright (c) OpenMMLab. All rights reserved.
from mmdet.core.utils import ConfigType, OptConfigType, OptMultiConfig
from mmdet.registry import MODELS
from .two_stage import TwoStageDetector
@MODELS.register_module()
class FasterRCNN(TwoStageDetector):
"""Implementation of `Faster R-CNN <https://arxiv.org/abs/1... | # 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 FasterRCNN(TwoStageDetector):
"""Implementation of `Faster R-CNN <https://arxiv.org/... |
_base_ = [
'../_base_/models/mask-rcnn_r50_fpn.py',
# 270k iterations with batch_size 64 is roughly equivalent to 144 epochs
'../common/ssj_scp_270k_coco-instance.py'
]
image_size = (1024, 1024)
batch_augments = [
dict(type='BatchFixedSizePad', size=image_size, pad_mask=True)
]
norm_cfg = dict(type='Sy... | _base_ = [
'../_base_/models/mask-rcnn_r50_fpn.py',
# 270k iterations with batch_size 64 is roughly equivalent to 144 epochs
'../common/ssj_scp_270k_coco-instance.py'
]
image_size = (1024, 1024)
batch_augments = [
dict(type='BatchFixedSizePad', size=image_size, pad_mask=True)
]
norm_cfg = dict(type='Sy... |
"""
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... |
from typing import Type, TypeVar, Union
from uuid import UUID
from pydantic import BaseConfig, parse_obj_as
from pydantic.fields import ModelField
from docarray.proto import NodeProto
from docarray.typing.abstract_type import AbstractType
T = TypeVar('T', bound='ID')
class ID(str, AbstractType):
"""
Represe... | from typing import Optional, Type, TypeVar, Union
from uuid import UUID
from pydantic import BaseConfig, parse_obj_as
from pydantic.fields import ModelField
from docarray.document.base_node import BaseNode
from docarray.proto import NodeProto
T = TypeVar('T', bound='ID')
class ID(str, BaseNode):
"""
Repres... |
# Copyright (c) OpenMMLab. All rights reserved.
# Modified from https://github.com/facebookresearch/detectron2/blob/master/detectron2/data/datasets/cityscapes.py # noqa
# and https://github.com/mcordts/cityscapesScripts/blob/master/cityscapesscripts/evaluation/evalInstanceLevelSemanticLabeling.py # noqa
from typing im... | # Copyright (c) OpenMMLab. All rights reserved.
# Modified from https://github.com/facebookresearch/detectron2/blob/master/detectron2/data/datasets/cityscapes.py # noqa
# and https://github.com/mcordts/cityscapesScripts/blob/master/cityscapesscripts/evaluation/evalInstanceLevelSemanticLabeling.py # noqa
from typing im... |
"""
This is a simple application for sparse encoder: Computing embeddings.
we have multiple sentences and we want to compute their embeddings.
The embeddings are sparse, meaning that most of the values are zero.
The embeddings are stored in a sparse matrix format, which is more efficient for storage and computation.
w... | """
This is a simple application for sparse encoder: Computing embeddings.
we have multiple sentences and we want to compute their embeddings.
The embeddings are sparse, meaning that most of the values are zero.
The embeddings are stored in a sparse matrix format, which is more efficient for storage and computation.
w... |
import logging
from datasets import load_dataset
from sentence_transformers import SparseEncoder
from sentence_transformers.sparse_encoder.evaluation import SparseRerankingEvaluator
logging.basicConfig(format="%(message)s", level=logging.INFO)
# Load a model
model = SparseEncoder("naver/splade-cocondenser-ensembled... | import logging
from datasets import load_dataset
from sentence_transformers import SparseEncoder
from sentence_transformers.sparse_encoder.evaluation import SparseRerankingEvaluator
logging.basicConfig(format="%(message)s", level=logging.INFO)
# Load a model
model = SparseEncoder("naver/splade-cocondenser-ensembled... |
import pytest
from docarray import DocumentArray
from jina import Client, Document, Executor, Flow, requests, types
from jina.excepts import BadServer
class SimplExecutor(Executor):
@requests
def add_text(self, docs, **kwargs):
docs[0].text = 'Hello World!'
def test_simple_docarray_return():
f ... | from docarray import DocumentArray
from jina import Document, Executor, Flow, Client, requests, types
import pytest
class SimplExecutor(Executor):
@requests
def add_text(self, docs, **kwargs):
docs[0].text = 'Hello World!'
def test_simple_docarray_return():
f = Flow().add(uses=SimplExecutor)
... |
"""Parser for JSON output."""
from __future__ import annotations
import json
from json import JSONDecodeError
from typing import Annotated, Any, Optional, TypeVar, Union
import jsonpatch # type: ignore[import]
import pydantic
from pydantic import SkipValidation
from langchain_core.exceptions import OutputParserExc... | """Parser for JSON output."""
from __future__ import annotations
import json
from json import JSONDecodeError
from typing import Annotated, Any, Optional, TypeVar, Union
import jsonpatch # type: ignore[import]
import pydantic
from pydantic import SkipValidation
from langchain_core.exceptions import OutputParserExc... |
"""
This script contains an example how to perform semantic search with Elasticsearch.
You need Elasticsearch up and running locally:
https://www.elastic.co/guide/en/elasticsearch/reference/current/run-elasticsearch-locally.html
Further, you need the Python Elasticsearch Client installed: https://elasticsearch-py.rea... | """
This script contains an example how to perform semantic search with Elasticsearch.
You need Elasticsearch up and running locally:
https://www.elastic.co/guide/en/elasticsearch/reference/current/run-elasticsearch-locally.html
Further, you need the Python Elasticsearch Client installed: https://elasticsearch-py.rea... |
import asyncio
import time
import pytest
from docarray import Document
from jina.clients.request import request_generator
from jina.serve.stream.helper import AsyncRequestsIterator, _RequestsCounter
def slow_blocking_generator():
for i in range(2):
yield Document(id=str(i))
time.sleep(2)
@pyte... | import asyncio
import time
import pytest
from jina import Document
from jina.clients.request import request_generator
from jina.serve.stream.helper import AsyncRequestsIterator, _RequestsCounter
def slow_blocking_generator():
for i in range(2):
yield Document(id=str(i))
time.sleep(2)
@pytest.m... |
import os
from pathlib import Path
import numpy as np
import pytest
import torch
from mmdet.apis import inference_detector, init_detector
from mmdet.data_elements import DetDataSample
from mmdet.utils import register_all_modules
# TODO: Waiting to fix multiple call error bug
register_all_modules()
@pytest.mark.par... | import os
from pathlib import Path
import numpy as np
import pytest
import torch
from mmdet.apis import inference_detector, init_detector
from mmdet.core import DetDataSample
from mmdet.utils import register_all_modules
# TODO: Waiting to fix multiple call error bug
register_all_modules()
@pytest.mark.parametrize(... |
import asyncio
import json
import os
import time
import pytest
from jina import Client, Document
from jina.enums import PodRoleType, PollingType
from jina.helper import random_port
from jina.orchestrate.pods import Pod
from jina.orchestrate.pods.container import ContainerPod
from jina.parsers import set_gateway_parse... | import asyncio
import json
import os
import time
import pytest
from jina import Client, Document
from jina.enums import PodRoleType, PollingType
from jina.helper import random_port
from jina.orchestrate.pods import Pod
from jina.orchestrate.pods.container import ContainerPod
from jina.parsers import set_gateway_parse... |
from pathlib import Path
from typing import Optional, List, Tuple
from annlite.storage.table import Table
class OffsetMapping(Table):
def __init__(
self,
name: str = 'offset2ids',
data_path: Optional[Path] = None,
in_memory: bool = True,
):
super().__init__(name, data_... | from pathlib import Path
from typing import Optional, List, Tuple
from annlite.storage.table import Table
class OffsetMapping(Table):
def __init__(
self,
name: str = 'offset2ids',
data_path: Optional[Path] = None,
in_memory: bool = True,
):
super().__init__(name, data_... |
import asyncio
import math
import time
from collections.abc import AsyncIterator
from langchain_core.tracers.memory_stream import _MemoryStream
async def test_same_event_loop() -> None:
"""Test that the memory stream works when the same event loop is used.
This is the easy case.
"""
reader_loop = as... | import asyncio
import math
import time
from collections.abc import AsyncIterator
from langchain_core.tracers.memory_stream import _MemoryStream
async def test_same_event_loop() -> None:
"""Test that the memory stream works when the same event loop is used.
This is the easy case.
"""
reader_loop = as... |
import importlib
import shutil
import warnings
from typing import List
import fsspec
import fsspec.asyn
from fsspec.implementations.local import LocalFileSystem
from ..utils.deprecation_utils import deprecated
from . import compression
_has_s3fs = importlib.util.find_spec("s3fs") is not None
if _has_s3fs:
from... | import importlib
import shutil
import threading
import warnings
from typing import List
import fsspec
import fsspec.asyn
from fsspec.implementations.local import LocalFileSystem
from ..utils.deprecation_utils import deprecated
from . import compression
_has_s3fs = importlib.util.find_spec("s3fs") is not None
if _h... |
"""Tool for the Google Finance"""
from typing import Optional
from langchain_core.callbacks import CallbackManagerForToolRun
from langchain_core.tools import BaseTool
from langchain_community.utilities.google_finance import GoogleFinanceAPIWrapper
class GoogleFinanceQueryRun(BaseTool):
"""Tool that queries the... | """Tool for the Google Finance"""
from typing import Optional
from langchain_core.callbacks import CallbackManagerForToolRun
from langchain_core.tools import BaseTool
from langchain_community.utilities.google_finance import GoogleFinanceAPIWrapper
class GoogleFinanceQueryRun(BaseTool): # type: ignore[override]
... |
from datasets import Dataset
from sentence_transformers.sparse_encoder import (
MLMTransformer,
SparseEncoder,
SparseEncoderTrainer,
SparseMarginMSELoss,
SpladeLoss,
SpladePooling,
)
# Initialize the SPLADE model
student_model_name = "prithivida/Splade_PP_en_v1"
student_model = SparseEncoder(
... | from datasets import Dataset
from sentence_transformers.sparse_encoder import (
MLMTransformer,
SparseEncoder,
SparseEncoderTrainer,
SparseMarginMSELoss,
SpladeLoss,
SpladePooling,
)
# Initialize the SPLADE model
student_model_name = "prithivida/Splade_PP_en_v1"
student_model = SparseEncoder(
... |
import torch
from datasets import Dataset
from sentence_transformers.sparse_encoder import (
MLMTransformer,
SparseEncoder,
SparseEncoderTrainer,
SparseMarginMSELoss,
SpladePooling,
)
# Initialize the SPLADE model
student_model_name = "prithivida/Splade_PP_en_v1"
student_model = SparseEncoder(
... | from datasets import Dataset
from sentence_transformers.sparse_encoder import (
MLMTransformer,
SparseEncoder,
SparseEncoderTrainer,
SparseMarginMSELoss,
SpladePooling,
)
# Initialize the SPLADE model
student_model_name = "prithivida/Splade_PP_en_v1"
student_model = SparseEncoder(
modules=[
... |
# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import logging
import os
import os.path as osp
from mmengine.config import Config, DictAction
from mmengine.logging import print_log
from mmengine.registry import RUNNERS
from mmengine.runner import Runner
from mmdet.utils import register_all_modules
d... | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import logging
import os
import os.path as osp
from mmengine.config import Config, DictAction
from mmengine.logging import print_log
from mmengine.registry import RUNNERS
from mmengine.runner import Runner
from mmdet.utils import register_all_modules
d... |
from typing import (
Union,
Optional,
TYPE_CHECKING,
List,
Dict,
)
if TYPE_CHECKING: # pragma: no cover
import numpy as np
from docarray import DocumentArray
class FindMixin:
def _find(
self,
query: 'np.ndarray',
limit: Optional[Union[int, float]] = 20,
o... | from typing import (
Union,
Optional,
TYPE_CHECKING,
List,
Dict,
)
if TYPE_CHECKING:
import numpy as np
from docarray import DocumentArray
class FindMixin:
def _find(
self,
query: 'np.ndarray',
limit: Optional[Union[int, float]] = 20,
only_id: bool = False... |
"""HTML node parser."""
from typing import TYPE_CHECKING, Any, List, Optional, Sequence, Union
from llama_index.core.bridge.pydantic import Field
from llama_index.core.callbacks.base import CallbackManager
from llama_index.core.node_parser.interface import NodeParser
from llama_index.core.node_parser.node_utils import... | """HTML node parser."""
from typing import TYPE_CHECKING, Any, List, Optional, Sequence
from llama_index.core.bridge.pydantic import Field
from llama_index.core.callbacks.base import CallbackManager
from llama_index.core.node_parser.interface import NodeParser
from llama_index.core.node_parser.node_utils import build_... |
# Reference: https://github.com/shenyunhang/APE/blob/main/datasets/tools/objects3652coco/fix_o365_names.py # noqa
import argparse
import copy
import json
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument(
'--ann',
default='data/objects365v2/annotations/zhiyuan_ob... | # Copyright (c) Facebook, Inc. and its affiliates.
import argparse
import copy
import json
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument(
'--ann',
default='data/objects365v2/annotations/zhiyuan_objv2_train.json')
parser.add_argument(
'--fix_name_m... |
"""DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.ops.nn import average_pool
from keras.src.ops.nn import batch_normalization
from keras.src.ops.nn import binary_crossentropy
from keras.src.ops.nn import categorical_crossentropy
from... | """DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.ops.nn import average_pool
from keras.src.ops.nn import batch_normalization
from keras.src.ops.nn import binary_crossentropy
from keras.src.ops.nn import categorical_crossentropy
from... |
from typing import Any, Callable
from langchain_core.documents import Document
from langchain.retrievers.multi_vector import MultiVectorRetriever, SearchType
from langchain.storage import InMemoryStore
from tests.unit_tests.indexes.test_indexing import InMemoryVectorStore
class InMemoryVectorstoreWithSearch(InMemor... | from typing import Any, Callable
from langchain_core.documents import Document
from langchain.retrievers.multi_vector import MultiVectorRetriever, SearchType
from langchain.storage import InMemoryStore
from tests.unit_tests.indexes.test_indexing import InMemoryVectorStore
class InMemoryVectorstoreWithSearch(InMemor... |
# Copyright (c) OpenMMLab. All rights reserved.
from .anchor_free_head import AnchorFreeHead
from .anchor_head import AnchorHead
from .atss_head import ATSSHead
from .atss_vlfusion_head import ATSSVLFusionHead
from .autoassign_head import AutoAssignHead
from .boxinst_head import BoxInstBboxHead, BoxInstMaskHead
from .c... | # Copyright (c) OpenMMLab. All rights reserved.
from .anchor_free_head import AnchorFreeHead
from .anchor_head import AnchorHead
from .atss_head import ATSSHead
from .autoassign_head import AutoAssignHead
from .boxinst_head import BoxInstBboxHead, BoxInstMaskHead
from .cascade_rpn_head import CascadeRPNHead, StageCasca... |
# coding: utf-8
"""Compatibility library."""
"""pandas"""
try:
from pandas import DataFrame as pd_DataFrame
from pandas import Series as pd_Series
from pandas import concat
from pandas.api.types import is_sparse as is_dtype_sparse
PANDAS_INSTALLED = True
except ImportError:
PANDAS_INSTALLED = F... | # coding: utf-8
"""Compatibility library."""
"""pandas"""
try:
from pandas import DataFrame as pd_DataFrame
from pandas import Series as pd_Series
from pandas import concat
from pandas.api.types import is_sparse as is_dtype_sparse
PANDAS_INSTALLED = True
except ImportError:
PANDAS_INSTALLED = F... |
_base_ = 'faster-rcnn_regnetx-3.2GF_fpn_ms-3x_coco.py'
model = dict(
backbone=dict(
type='RegNet',
arch='regnetx_800mf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=... | _base_ = 'faster_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py'
model = dict(
backbone=dict(
type='RegNet',
arch='regnetx_800mf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init... |
from .autograd_utils import use_deterministic_algorithms
from .backend_utils import set_audio_backend
from .case_utils import (
disabledInCI,
HttpServerMixin,
PytorchTestCase,
skipIfCudaSmallMemory,
skipIfNoAudioDevice,
skipIfNoCtcDecoder,
skipIfNoCuCtcDecoder,
skipIfNoCuda,
skipIfNo... | from .autograd_utils import use_deterministic_algorithms
from .backend_utils import set_audio_backend
from .case_utils import (
disabledInCI,
HttpServerMixin,
is_ffmpeg_available,
PytorchTestCase,
skipIfCudaSmallMemory,
skipIfNoAudioDevice,
skipIfNoCtcDecoder,
skipIfNoCuCtcDecoder,
s... |
# Copyright (c) OpenMMLab. All rights reserved.
from .build_functions import (build_from_cfg, build_model_from_cfg,
build_runner_from_cfg, build_scheduler_from_cfg)
from .default_scope import DefaultScope
from .registry import Registry
from .root import (DATA_SAMPLERS, DATASETS, EVALUATOR,... | # Copyright (c) OpenMMLab. All rights reserved.
from .build_functions import (build_from_cfg, build_model_from_cfg,
build_runner_from_cfg, build_scheduler_from_cfg)
from .default_scope import DefaultScope
from .registry import Registry
from .root import (DATA_SAMPLERS, DATASETS, EVALUATOR,... |
"""
Default query for PandasIndex.
WARNING: This tool provides the LLM access to the `eval` function.
Arbitrary code execution is possible on the machine running this tool.
This tool is not recommended to be used in a production setting, and would
require heavy sandboxing or virtual machines.
DEPRECATED: Use `PandasQ... | """Default query for PandasIndex.
WARNING: This tool provides the LLM access to the `eval` function.
Arbitrary code execution is possible on the machine running this tool.
This tool is not recommended to be used in a production setting, and would
require heavy sandboxing or virtual machines.
DEPRECATED: Use `PandasQu... |
# Owner(s): ["oncall: distributed"]
import os
from datetime import timedelta
import torch
import torch.distributed._dist2 as dist2
from torch.testing._internal.common_distributed import (
MultiProcessTestCase,
requires_gloo,
requires_nccl,
skip_if_lt_x_gpu,
)
from torch.testing._internal.common_utils ... | # Owner(s): ["oncall: distributed"]
import os
from datetime import timedelta
import torch
import torch.distributed._dist2 as dist2
from torch.testing._internal.common_distributed import (
MultiProcessTestCase,
requires_gloo,
requires_nccl,
skip_if_lt_x_gpu,
)
from torch.testing._internal.common_utils ... |
"""Argparser module for WorkerRuntime"""
from jina.parsers.helper import KVAppendAction, add_arg_group
from jina.parsers.orchestrate.runtimes.runtime import mixin_base_runtime_parser
def mixin_worker_runtime_parser(parser):
"""Mixing in arguments required by :class:`WorkerRuntime` into the given parser.
:par... | """Argparser module for WorkerRuntime"""
from jina.parsers.helper import KVAppendAction, add_arg_group
from jina.parsers.orchestrate.runtimes.runtime import mixin_base_runtime_parser
def mixin_worker_runtime_parser(parser):
"""Mixing in arguments required by :class:`WorkerRuntime` into the given parser.
:par... |
import torch
import torchaudio.prototype.transforms as T
from torchaudio_unittest.common_utils import nested_params, TestBaseMixin, torch_script
class Transforms(TestBaseMixin):
@nested_params(
["Convolve", "FFTConvolve"],
["full", "valid", "same"],
)
def test_Convolve(self, cls, mode):
... | import torch
import torchaudio.prototype.transforms as T
from torchaudio_unittest.common_utils import nested_params, TestBaseMixin, torch_script
class Transforms(TestBaseMixin):
@nested_params(
["Convolve", "FFTConvolve"],
["full", "valid", "same"],
)
def test_Convolve(self, cls, mode):
... |
"""
Python polyfills for sys
"""
from __future__ import annotations
import sys
from ..decorators import substitute_in_graph
__all__ = [
"intern",
"getrecursionlimit",
]
@substitute_in_graph(sys.intern, can_constant_fold_through=True)
def intern(string: str, /) -> str:
return string
@substitute_in_g... | """
Python polyfills for sys
"""
from __future__ import annotations
import sys
from ..decorators import substitute_in_graph
__all__ = [
"intern",
"getrecursionlimit",
]
@substitute_in_graph(sys.intern, can_constant_fold_through=True)
def intern(string: str, /) -> str:
return string
@substitute_in_g... |
"""DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.wrappers.sklearn_wrapper import (
SKLearnClassifier as SKLearnClassifier,
)
from keras.src.wrappers.sklearn_wrapper import (
SKLearnRegressor as SKLearnRegressor,
)
from keras... | """DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.wrappers.sklearn_wrapper import SKLearnClassifier
from keras.src.wrappers.sklearn_wrapper import SKLearnRegressor
from keras.src.wrappers.sklearn_wrapper import SKLearnTransformer
|
# Copyright (c) OpenMMLab. All rights reserved.
from .bfp import BFP
from .channel_mapper import ChannelMapper
from .ct_resnet_neck import CTResNetNeck
from .dilated_encoder import DilatedEncoder
from .fpg import FPG
from .fpn import FPN
from .fpn_carafe import FPN_CARAFE
from .hrfpn import HRFPN
from .nas_fpn import N... | from .bfp import BFP
from .channel_mapper import ChannelMapper
from .ct_resnet_neck import CTResNetNeck
from .dilated_encoder import DilatedEncoder
from .fpg import FPG
from .fpn import FPN
from .fpn_carafe import FPN_CARAFE
from .hrfpn import HRFPN
from .nas_fpn import NASFPN
from .nasfcos_fpn import NASFCOS_FPN
from ... |
_base_ = './faster-rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
norm_cfg=dict(requires_grad=False),
norm_eval=True,
style='caffe',
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron2/resnet50_caffe')))
# use caffe img_norm
img_norm_c... | _base_ = './faster_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
norm_cfg=dict(requires_grad=False),
norm_eval=True,
style='caffe',
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron2/resnet50_caffe')))
# use caffe img_norm
img_norm_c... |
import asyncio
from typing import Any, Dict, List, Optional
from llama_index.core.base.embeddings.base import BaseEmbedding
from llama_index.core.bridge.pydantic import Field, PrivateAttr
from llama_index.core.callbacks.base import CallbackManager
from llama_index.core.constants import DEFAULT_EMBED_BATCH_SIZE
from o... | import asyncio
from typing import Any, Dict, List, Optional
from llama_index.core.base.embeddings.base import BaseEmbedding
from llama_index.core.bridge.pydantic import Field, PrivateAttr
from llama_index.core.callbacks.base import CallbackManager
from llama_index.core.constants import DEFAULT_EMBED_BATCH_SIZE
from o... |
from abc import ABC
import pytest
from docarray import DocumentArray
from docarray.array.storage.memory import GetSetDelMixin, SequenceLikeMixin
from docarray.array.storage.redis.backend import BackendMixin, RedisConfig
class StorageMixins(BackendMixin, GetSetDelMixin, SequenceLikeMixin, ABC):
...
class Docume... | from abc import ABC
import pytest
from docarray import DocumentArray
from docarray.array.storage.memory import GetSetDelMixin, SequenceLikeMixin
from docarray.array.storage.redis.backend import BackendMixin, RedisConfig
class StorageMixins(BackendMixin, GetSetDelMixin, SequenceLikeMixin, ABC):
...
class Docume... |
# Copyright (c) OpenMMLab. All rights reserved.
from .anchor_free_head import AnchorFreeHead
from .anchor_head import AnchorHead
from .atss_head import ATSSHead
from .autoassign_head import AutoAssignHead
from .cascade_rpn_head import CascadeRPNHead, StageCascadeRPNHead
from .centernet_head import CenterNetHead
from .c... | # Copyright (c) OpenMMLab. All rights reserved.
from .anchor_free_head import AnchorFreeHead
from .anchor_head import AnchorHead
from .atss_head import ATSSHead
from .autoassign_head import AutoAssignHead
from .cascade_rpn_head import CascadeRPNHead, StageCascadeRPNHead
from .centernet_head import CenterNetHead
from .c... |
from __future__ import annotations
from collections.abc import Iterable
import torch
from torch import Tensor, nn
from sentence_transformers.sparse_encoder.SparseEncoder import SparseEncoder
class FlopsLoss(nn.Module):
def __init__(self, model: SparseEncoder, threshold: float = None) -> None:
"""
... | from __future__ import annotations
from collections.abc import Iterable
import torch
from torch import Tensor, nn
from sentence_transformers.sparse_encoder.SparseEncoder import SparseEncoder
class FlopsLoss(nn.Module):
def __init__(self, model: SparseEncoder, threshold: float = None) -> None:
"""
... |
from abc import ABC, abstractmethod
from docarray.proto import NodeProto
class BaseNode(ABC):
"""
A DocumentNode is an object than can be nested inside a Document.
A Document itself is a DocumentNode as well as prebuilt type
"""
@abstractmethod
def _to_node_protobuf(self) -> NodeProto:
... | from abc import ABC, abstractmethod
from docarray.proto import NodeProto
class BaseNode(ABC):
"""
A DocumentNode is an object than can be nested inside a Document.
A Document itself is a DocumentNode as well as prebuilt type
"""
@abstractmethod
def _to_nested_item_protobuf(self) -> 'NodeProt... |
__version__ = '0.13.33'
import os
from .document import Document
from .array import DocumentArray
from .dataclasses import dataclass, field
if 'DA_RICH_HANDLER' in os.environ:
from rich.traceback import install
install()
| __version__ = '0.13.32'
import os
from .document import Document
from .array import DocumentArray
from .dataclasses import dataclass, field
if 'DA_RICH_HANDLER' in os.environ:
from rich.traceback import install
install()
|
# Copyright (c) OpenMMLab. All rights reserved.
from .atss import ATSS
from .autoassign import AutoAssign
from .base import BaseDetector
from .base_detr import DetectionTransformer
from .boxinst import BoxInst
from .cascade_rcnn import CascadeRCNN
from .centernet import CenterNet
from .condinst import CondInst
from .co... | # Copyright (c) OpenMMLab. All rights reserved.
from .atss import ATSS
from .autoassign import AutoAssign
from .base import BaseDetector
from .base_detr import DetectionTransformer
from .boxinst import BoxInst
from .cascade_rcnn import CascadeRCNN
from .centernet import CenterNet
from .condinst import CondInst
from .co... |
from pathlib import Path
from typing import List, Tuple, Union
import torch
import torchaudio
from torch.utils.data import Dataset
SampleType = Tuple[int, torch.Tensor, List[torch.Tensor]]
class LibriMix(Dataset):
r"""Create the *LibriMix* [:footcite:`cosentino2020librimix`] dataset.
Args:
root (st... | from pathlib import Path
from typing import List, Tuple, Union
import torch
import torchaudio
from torch.utils.data import Dataset
SampleType = Tuple[int, torch.Tensor, List[torch.Tensor]]
class LibriMix(Dataset):
r"""Create the *LibriMix* [:footcite:`cosentino2020librimix`] dataset.
Args:
root (st... |
# Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
from mmcv.runner import load_checkpoint
from ..builder import DETECTORS, build_backbone, build_head, build_neck
from .kd_one_stage import KnowledgeDistillationSingleStageDetector
@DETECTORS.register_module()
class LAD(KnowledgeDistill... | import torch
import torch.nn as nn
from mmcv.runner import load_checkpoint
from ..builder import DETECTORS, build_backbone, build_head, build_neck
from .kd_one_stage import KnowledgeDistillationSingleStageDetector
@DETECTORS.register_module()
class LAD(KnowledgeDistillationSingleStageDetector):
"""Implementation... |
import os
import subprocess
import sys
directory = os.path.dirname(os.path.realpath(__file__))
BACKEND_DIR = "."
LIBS_DIR = "../autogpt_libs"
TARGET_DIRS = [BACKEND_DIR, LIBS_DIR]
def run(*command: str) -> None:
print(f">>>>> Running poetry run {' '.join(command)}")
try:
subprocess.run(
... | import os
import subprocess
directory = os.path.dirname(os.path.realpath(__file__))
BACKEND_DIR = "."
LIBS_DIR = "../autogpt_libs"
TARGET_DIRS = [BACKEND_DIR, LIBS_DIR]
def run(*command: str) -> None:
print(f">>>>> Running poetry run {' '.join(command)}")
subprocess.run(["poetry", "run"] + list(command), cw... |
"""Base tool spec class."""
import asyncio
from inspect import signature
from typing import Any, Awaitable, Callable, Dict, List, Optional, Tuple, Type, Union
from llama_index.core.bridge.pydantic import BaseModel
from llama_index.core.tools.function_tool import FunctionTool
from llama_index.core.tools.types import ... | """Base tool spec class."""
import asyncio
from inspect import signature
from typing import Any, Awaitable, Callable, Dict, List, Optional, Tuple, Type, Union
from llama_index.core.bridge.pydantic import BaseModel
from llama_index.core.tools.function_tool import FunctionTool
from llama_index.core.tools.types import ... |
_base_ = './cascade-mask-rcnn_r50_fpn_ms-3x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='... | _base_ = './cascade_mask_rcnn_r50_fpn_mstrain_3x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
st... |
import pytest
import torch
from torchvision.prototype import datapoints
def test_isinstance():
assert isinstance(
datapoints.Label([0, 1, 0], categories=["foo", "bar"]),
torch.Tensor,
)
def test_wrapping_no_copy():
tensor = torch.tensor([0, 1, 0], dtype=torch.int64)
label = datapoint... | import pytest
import torch
from torchvision.prototype import datapoints
def test_isinstance():
assert isinstance(
datapoints.Label([0, 1, 0], categories=["foo", "bar"]),
torch.Tensor,
)
def test_wrapping_no_copy():
tensor = torch.tensor([0, 1, 0], dtype=torch.int64)
label = datapoint... |
# Copyright (c) OpenMMLab. All rights reserved.
from typing import Tuple
import torch.nn as nn
from mmcv.cnn import ConvModule
from mmcv.ops import MaskedConv2d
from torch import Tensor
from mmdet.core import OptConfigType, OptMultiConfig
from mmdet.registry import MODELS
from .guided_anchor_head import FeatureAdapti... | # Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
from mmcv.cnn import ConvModule
from mmcv.ops import MaskedConv2d
from mmdet.registry import MODELS
from .guided_anchor_head import FeatureAdaption, GuidedAnchorHead
@MODELS.register_module()
class GARetinaHead(GuidedAnchorHead):
"""Guided-Anc... |
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