input stringlengths 33 5k | output stringlengths 32 5k |
|---|---|
# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import cv2
import mmcv
from mmcv.transforms import Compose
from mmdet.apis import inference_detector, init_detector
from mmdet.registry import VISUALIZERS
from mmdet.utils import register_all_modules
def parse_args():
parser = argparse.ArgumentPars... | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import cv2
import mmcv
from mmdet.apis import inference_detector, init_detector
def parse_args():
parser = argparse.ArgumentParser(description='MMDetection video demo')
parser.add_argument('video', help='Video file')
parser.add_argument('co... |
from __future__ import annotations
__version__ = "3.5.0.dev0"
__MODEL_HUB_ORGANIZATION__ = "sentence-transformers"
import importlib
import os
from sentence_transformers.backend import (
export_dynamic_quantized_onnx_model,
export_optimized_onnx_model,
export_static_quantized_openvino_model,
)
from senten... | from __future__ import annotations
__version__ = "3.5.0.dev0"
__MODEL_HUB_ORGANIZATION__ = "sentence-transformers"
import importlib
import os
from sentence_transformers.backend import (
export_dynamic_quantized_onnx_model,
export_optimized_onnx_model,
export_static_quantized_openvino_model,
)
from senten... |
import inspect
import re
from typing import Dict, List, Tuple
from huggingface_hub.utils import insecure_hashlib
from .arrow import arrow
from .audiofolder import audiofolder
from .cache import cache
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parq... | import inspect
import re
from typing import Dict, List, Tuple
from huggingface_hub.utils import insecure_hashlib
from .arrow import arrow
from .audiofolder import audiofolder
from .cache import cache
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parq... |
from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.docstore.base import AddableMixin, Docstore
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handling optional im... | from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.docstore.base import AddableMixin, Docstore
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handling optional im... |
from typing import Dict, List, Optional
from docarray import DocArray
def reduce(
left: DocArray, right: DocArray, left_id_map: Optional[Dict] = None
) -> 'DocArray':
"""
Reduces left and right DocArray into one DocArray in-place.
Changes are applied to the left DocArray.
Reducing 2 DocArrays con... | from docarray import DocumentArray
from typing import List, Optional, Dict
def reduce(
left: DocumentArray, right: DocumentArray, left_id_map: Optional[Dict] = None
) -> 'DocumentArray':
"""
Reduces left and right DocumentArray into one DocumentArray in-place.
Changes are applied to the left DocumentA... |
"""
Experimental Object Oriented Distributed API - torch.distributed._dist2
=======================================================================
This is an experimental new API for PyTorch Distributed. This is actively in development and subject to change or deletion entirely.
This is intended as a proving ground ... | """
Experimental Object Oriented Distributed API - torch.distributed._dist2
=======================================================================
This is an experimental new API for PyTorch Distributed. This is actively in development and subject to change or deletion entirely.
This is intended as a proving ground ... |
# Copyright (c) OpenMMLab. All rights reserved.
from .hub import load_url
from .manager import ManagerMeta, ManagerMixin
from .misc import (check_prerequisites, concat_list, deprecated_api_warning,
find_latest_checkpoint, has_batch_norm, has_method,
import_modules_from_strings, is_... | # Copyright (c) OpenMMLab. All rights reserved.
from .hub import load_url
from .manager import ManagerMeta, ManagerMixin
from .misc import (check_prerequisites, concat_list, deprecated_api_warning,
find_latest_checkpoint, has_method,
import_modules_from_strings, is_list_of,
... |
# Copyright (c) OpenMMLab. All rights reserved.
from abc import ABCMeta, abstractmethod
import torch.nn.functional as F
from mmcv.runner import BaseModule, force_fp32
from mmengine.model import stack_batch
from ..builder import build_loss
from ..utils import interpolate_as
class BaseSemanticHead(BaseModule, metacla... | # Copyright (c) OpenMMLab. All rights reserved.
from abc import ABCMeta, abstractmethod
import torch.nn.functional as F
from mmcv.runner import BaseModule, force_fp32
from ...core.utils import stack_batch
from ..builder import build_loss
from ..utils import interpolate_as
class BaseSemanticHead(BaseModule, metaclas... |
from typing import Any, Optional, Union, cast
from langchain_core._api import deprecated
from langchain_core.language_models import BaseLanguageModel
from langchain_core.messages import HumanMessage, SystemMessage
from langchain_core.output_parsers import BaseLLMOutputParser
from langchain_core.output_parsers.openai_f... | from typing import Any, Optional, Union, cast
from langchain_core._api import deprecated
from langchain_core.language_models import BaseLanguageModel
from langchain_core.messages import HumanMessage, SystemMessage
from langchain_core.output_parsers import BaseLLMOutputParser
from langchain_core.output_parsers.openai_f... |
"""Data embedding techniques."""
# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause
from ._isomap import Isomap
from ._locally_linear import LocallyLinearEmbedding, locally_linear_embedding
from ._mds import MDS, smacof
from ._spectral_embedding import SpectralEmbedding, spectral_embedding... | """Data embedding techniques."""
# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause
from ._isomap import Isomap
from ._locally_linear import LocallyLinearEmbedding, locally_linear_embedding
from ._mds import MDS, smacof
from ._spectral_embedding import SpectralEmbedding, spectral_embedding... |
# Copyright (c) OpenMMLab. All rights reserved.
from mmengine.dist import all_reduce_params, is_distributed
from mmengine.registry import HOOKS
from .hook import Hook
@HOOKS.register_module()
class SyncBuffersHook(Hook):
"""Synchronize model buffers such as running_mean and running_var in BN at
the end of eac... | # Copyright (c) OpenMMLab. All rights reserved.
from mmengine import dist
from mmengine.registry import HOOKS
from .hook import Hook
@HOOKS.register_module()
class SyncBuffersHook(Hook):
"""Synchronize model buffers such as running_mean and running_var in BN at
the end of each epoch."""
priority = 'NORMA... |
# Copyright (c) OpenMMLab. All rights reserved.
import torch
from ..builder import BBOX_SAMPLERS
from ..transforms import bbox2roi
from .base_sampler import BaseSampler
@BBOX_SAMPLERS.register_module()
class OHEMSampler(BaseSampler):
r"""Online Hard Example Mining Sampler described in `Training Region-based
... | import torch
from ..builder import BBOX_SAMPLERS
from ..transforms import bbox2roi
from .base_sampler import BaseSampler
@BBOX_SAMPLERS.register_module()
class OHEMSampler(BaseSampler):
r"""Online Hard Example Mining Sampler described in `Training Region-based
Object Detectors with Online Hard Example Mining... |
# Copyright (c) OpenMMLab. All rights reserved.
import datetime
import os.path as osp
from tempfile import TemporaryDirectory
from unittest import TestCase, skipIf
from mmengine.logging import MMLogger
from mmengine.registry import (DefaultScope, Registry,
count_registered_modules, init_... | # Copyright (c) OpenMMLab. All rights reserved.
import datetime
import os.path as osp
from tempfile import TemporaryDirectory
from unittest import TestCase, skipIf
from mmengine.registry import (DefaultScope, Registry,
count_registered_modules, init_default_scope,
... |
# Copyright (c) OpenMMLab. All rights reserved.
from mmdet.registry import MODELS
from mmdet.utils import ConfigType, OptConfigType, OptMultiConfig
from .single_stage import SingleStageDetector
@MODELS.register_module()
class YOLOF(SingleStageDetector):
r"""Implementation of `You Only Look One-level Feature
<... | # Copyright (c) OpenMMLab. All rights reserved.
from mmdet.core.utils.typing import ConfigType, OptConfigType, OptMultiConfig
from mmdet.registry import MODELS
from .single_stage import SingleStageDetector
@MODELS.register_module()
class YOLOF(SingleStageDetector):
r"""Implementation of `You Only Look One-level F... |
_base_ = ['./cascade_mask_rcnn_r50_fpn_1x_coco.py']
model = dict(
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[103.530, 116.280, 123.675],
std=[1.0, 1.0, 1.0],
to_rgb=False,
pad_size_divisor=32),
backbone=dict(
norm_cfg=dict(requires_grad=False),
... | _base_ = ['./cascade_mask_rcnn_r50_fpn_1x_coco.py']
preprocess_cfg = dict(
mean=[103.530, 116.280, 123.675],
std=[1.0, 1.0, 1.0],
to_rgb=False,
pad_size_divisor=32)
model = dict(
preprocess_cfg=preprocess_cfg,
backbone=dict(
norm_cfg=dict(requires_grad=False),
norm_eval=True,
... |
import numpy as np
import torch
from docarray.document import BaseDocument
from docarray.typing import AnyUrl, NdArray, TorchTensor
def test_to_json():
class Mmdoc(BaseDocument):
img: NdArray
url: AnyUrl
txt: str
torch_tensor: TorchTensor
doc = Mmdoc(
img=np.zeros((3,... | import numpy as np
import torch
from docarray.document import BaseDocument
from docarray.typing import AnyUrl, Tensor, TorchTensor
def test_to_json():
class Mmdoc(BaseDocument):
img: Tensor
url: AnyUrl
txt: str
torch_tensor: TorchTensor
doc = Mmdoc(
img=np.zeros((3, 2... |
"""
This examples trains BERT (or any other transformer model like RoBERTa, DistilBERT etc.) for the STSbenchmark from scratch.
It uses AdaptiveLayerLoss with the powerful CoSENTLoss to train models that perform well even when removing some layers.
It generates sentence embeddings that can be compared using cosine-simi... | """
This examples trains BERT (or any other transformer model like RoBERTa, DistilBERT etc.) for the STSbenchmark from scratch.
It uses AdaptiveLayerLoss with the powerful CoSENTLoss to train models that perform well at output dimensions [768, 512, 256, 128, 64].
It generates sentence embeddings that can be compared us... |
import contextlib
import logging
import typing
import fastapi
import fastapi.responses
import starlette.middleware.cors
import uvicorn
from autogpt_libs.feature_flag.client import (
initialize_launchdarkly,
shutdown_launchdarkly,
)
import backend.data.block
import backend.data.db
import backend.data.graph
imp... | import contextlib
import logging
import typing
import fastapi
import fastapi.responses
import starlette.middleware.cors
import uvicorn
from autogpt_libs.feature_flag.client import (
initialize_launchdarkly,
shutdown_launchdarkly,
)
import backend.data.block
import backend.data.db
import backend.data.graph
imp... |
_base_ = 'retinanet_pvt-t_fpn_1x_coco.py'
model = dict(
backbone=dict(
num_layers=[3, 8, 27, 3],
init_cfg=dict(checkpoint='https://github.com/whai362/PVT/'
'releases/download/v2/pvt_large.pth')))
# Enable automatic-mixed-precision training with AmpOptimWrapper.
optim_wrapper = ... | _base_ = 'retinanet_pvt-t_fpn_1x_coco.py'
model = dict(
backbone=dict(
num_layers=[3, 8, 27, 3],
init_cfg=dict(checkpoint='https://github.com/whai362/PVT/'
'releases/download/v2/pvt_large.pth')))
fp16 = dict(loss_scale=dict(init_scale=512))
|
import os
from typing import BinaryIO, Optional, Tuple, Union
import torch
from .backend import Backend
from .common import AudioMetaData
class SoXBackend(Backend):
@staticmethod
def info(uri: Union[BinaryIO, str, os.PathLike], format: Optional[str], buffer_size: int = 4096) -> AudioMetaData:
if has... | import os
from typing import BinaryIO, Optional, Tuple, Union
import torch
from torchaudio.backend.common import AudioMetaData
from .backend import Backend
class SoXBackend(Backend):
@staticmethod
def info(uri: Union[BinaryIO, str, os.PathLike], format: Optional[str], buffer_size: int = 4096) -> AudioMetaDa... |
# flake8: noqa
# Copyright 2020 The HuggingFace Datasets Authors and the TensorFlow Datasets Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LI... | # flake8: noqa
# Copyright 2020 The HuggingFace Datasets Authors and the TensorFlow Datasets Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LI... |
import sys
from jina.serve.runtimes.asyncio import AsyncNewLoopRuntime
from jina.serve.runtimes.head.request_handling import HeaderRequestHandler
from jina.parsers import set_pod_parser
def run(*args, **kwargs):
runtime_args = set_pod_parser().parse_args(args)
runtime_args.host = runtime_args.host[0]
run... | import sys
from jina.serve.runtimes.head import HeadRuntime
from jina.parsers import set_pod_parser
def run(*args, **kwargs):
runtime_args = set_pod_parser().parse_args(args)
runtime_args.host = runtime_args.host[0]
runtime_args.port = runtime_args.port[0]
with HeadRuntime(runtime_args) as runtime:
... |
# Copyright 2022 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | # Copyright 2022 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... |
from typing import Dict, Set, Type
from docarray.typing.tensor.embedding import Embedding, NdArrayEmbedding
from docarray.typing.tensor.ndarray import NdArray
from docarray.typing.tensor.tensor import Tensor
__all__ = [
'NdArray',
'Tensor',
'Embedding',
'NdArrayEmbedding',
'framework_types',
'... | from docarray.typing.tensor.embedding import Embedding, NdArrayEmbedding
from docarray.typing.tensor.ndarray import NdArray
from docarray.typing.tensor.tensor import Tensor
__all__ = [
'NdArray',
'Tensor',
'Embedding',
'NdArrayEmbedding',
]
try:
import torch # noqa: F401
except ImportError:
p... |
"""MutliOn Client API tools."""
from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.tools.multion.close_session import MultionCloseSession
from langchain_community.tools.multion.create_session import MultionCreateSession
from langcha... | """MutliOn Client API tools."""
from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.tools.multion.close_session import MultionCloseSession
from langchain_community.tools.multion.create_session import MultionCreateSession
from langcha... |
# Copyright (c) OpenMMLab. All rights reserved.
from mmcv.cnn import ConvModule, Linear
from mmengine.model import ModuleList
from torch import Tensor
from mmdet.registry import MODELS
from mmdet.utils import MultiConfig
from .fcn_mask_head import FCNMaskHead
@MODELS.register_module()
class CoarseMaskHead(FCNMaskHea... | # Copyright (c) OpenMMLab. All rights reserved.
from mmcv.cnn import ConvModule, Linear
from mmengine.model import ModuleList
from torch import Tensor
from mmdet.core.utils import MultiConfig
from mmdet.registry import MODELS
from .fcn_mask_head import FCNMaskHead
@MODELS.register_module()
class CoarseMaskHead(FCNMa... |
__copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import os
import subprocess
import librosa
import pytest
from jina import Document, DocumentArray, Flow
from ...vggish import vggish_input
cur_dir = os.path.dirname(os.path.abspath(__file__))
def test_flow_f... | __copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import os
import librosa
from jina import Flow, Document, DocumentArray
from ...vggish import vggish_input
from ...vggish_audio_encoder import VggishAudioEncoder
cur_dir = os.path.dirname(os.path.abspath(__fil... |
from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.vectorstores import SKLearnVectorStore
from langchain_community.vectorstores.sklearn import (
BaseSerializer,
BsonSerializer,
JsonSerializer,
ParquetSeria... | from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.vectorstores import SKLearnVectorStore
from langchain_community.vectorstores.sklearn import (
BaseSerializer,
BsonSerializer,
JsonSerializer,
ParquetSeria... |
from langchain_core.embeddings import Embeddings
from langchain_core.utils import secret_from_env
from openai import OpenAI
from pydantic import BaseModel, ConfigDict, Field, SecretStr, model_validator
from typing_extensions import Self
# type: ignore
class FireworksEmbeddings(BaseModel, Embeddings):
"""Firework... | from langchain_core.embeddings import Embeddings
from langchain_core.utils import secret_from_env
from openai import OpenAI
from pydantic import BaseModel, ConfigDict, Field, SecretStr, model_validator
from typing_extensions import Self
# type: ignore
class FireworksEmbeddings(BaseModel, Embeddings):
"""Firework... |
from abc import ABC, abstractmethod
from typing import Dict, Iterator, List, Optional, Type
from typing_extensions import TYPE_CHECKING
if TYPE_CHECKING:
from docarray import BaseDoc, DocList
class AbstractDocStore(ABC):
@staticmethod
@abstractmethod
def list(namespace: str, show_table: bool) -> Lis... | from abc import ABC, abstractmethod
from typing import Dict, Iterator, List, Optional, Type
from typing_extensions import TYPE_CHECKING
if TYPE_CHECKING:
from docarray import BaseDoc, DocArray
class AbstractDocStore(ABC):
@staticmethod
@abstractmethod
def list(namespace: str, show_table: bool) -> Li... |
"""Macrometa GDN Reader."""
import json
from typing import List
import requests
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document
class MacrometaGDNReader(BaseReader):
"""
Macrometa GDN Reader.
Reads vectors from Macrometa GDN
"""
def __init__(... | """Macrometa GDN Reader."""
import json
from typing import List
import requests
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document
class MacrometaGDNReader(BaseReader):
"""Macrometa GDN Reader.
Reads vectors from Macrometa GDN
"""
def __init__(self,... |
"""CIFAR10 small images classification dataset."""
import os
import numpy as np
from keras.src import backend
from keras.src.api_export import keras_export
from keras.src.datasets.cifar import load_batch
from keras.src.utils.file_utils import get_file
@keras_export("keras.datasets.cifar10.load_data")
def load_data... | """CIFAR10 small images classification dataset."""
import os
import numpy as np
from keras.src import backend
from keras.src.api_export import keras_export
from keras.src.datasets.cifar import load_batch
from keras.src.utils.file_utils import get_file
@keras_export("keras.datasets.cifar10.load_data")
def load_data... |
import csv
import logging
import os
from typing import List
from scipy.stats import pearsonr, spearmanr
from sentence_transformers import InputExample
logger = logging.getLogger(__name__)
class CECorrelationEvaluator:
"""
This evaluator can be used with the CrossEncoder class. Given sentence pairs and cont... | import logging
from scipy.stats import pearsonr, spearmanr
from typing import List
import os
import csv
from ... import InputExample
logger = logging.getLogger(__name__)
class CECorrelationEvaluator:
"""
This evaluator can be used with the CrossEncoder class. Given sentence pairs and continuous scores,
... |
from typing import Any, Sequence
from llama_index.core.base.llms.generic_utils import (
completion_response_to_chat_response,
stream_completion_response_to_chat_response,
)
from llama_index.core.base.llms.types import (
ChatMessage,
ChatResponse,
ChatResponseAsyncGen,
ChatResponseGen,
Compl... | from typing import Any, Sequence
from llama_index.core.base.llms.generic_utils import (
completion_response_to_chat_response,
stream_completion_response_to_chat_response,
)
from llama_index.core.base.llms.types import (
ChatMessage,
ChatResponse,
ChatResponseAsyncGen,
ChatResponseGen,
Compl... |
from __future__ import annotations
import json
import os
import torch
from safetensors.torch import load_model as load_safetensors_model
from safetensors.torch import save_model as save_safetensors_model
from torch import Tensor, nn
from sentence_transformers.util import fullname, import_from_string
class Dense(nn... | from __future__ import annotations
import json
import os
import torch
from safetensors.torch import load_model as load_safetensors_model
from safetensors.torch import save_model as save_safetensors_model
from torch import Tensor, nn
from sentence_transformers.util import fullname, import_from_string
class Dense(nn... |
# coding=utf-8
# Copyright 2025 The HuggingFace Team Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a clone of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable... | # coding=utf-8
# Copyright 2025 The HuggingFace Team Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a clone of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable... |
from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.document_loaders import PlaywrightURLLoader
from langchain_community.document_loaders.url_playwright import (
PlaywrightEvaluator,
UnstructuredHtmlEvaluator,
)
# Cre... | from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.document_loaders import PlaywrightURLLoader
from langchain_community.document_loaders.url_playwright import (
PlaywrightEvaluator,
UnstructuredHtmlEvaluator,
)
# Cre... |
from typing import List
import numpy as np
def _number_of_shards_in_gen_kwargs(gen_kwargs: dict) -> int:
"""Return the number of possible shards according to the input gen_kwargs"""
# Having lists of different sizes makes sharding ambigious, raise an error in this case
# until we decide how to define sha... | from typing import List
import numpy as np
def _number_of_shards_in_gen_kwargs(gen_kwargs: dict) -> int:
"""Return the number of possible shards according to the input gen_kwargs"""
# Having lists of different sizes makes sharding ambigious, raise an error in this case
# until we decide how to define sha... |
from typing import Iterable, Dict
from docarray.array.storage.base.getsetdel import BaseGetSetDelMixin
from docarray.array.storage.base.helper import Offset2ID
from docarray import Document
class GetSetDelMixin(BaseGetSetDelMixin):
"""Provide concrete implementation for ``__getitem__``, ``__setitem__``,
and ... | from typing import Iterable, Dict
from ..base.getsetdel import BaseGetSetDelMixin
from ..base.helper import Offset2ID
from .... import Document
class GetSetDelMixin(BaseGetSetDelMixin):
"""Provide concrete implementation for ``__getitem__``, ``__setitem__``,
and ``__delitem__`` for ``DocumentArrayWeaviate``"... |
"""DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.activations import deserialize
from keras.src.activations import get
from keras.src.activations import serialize
from keras.src.activations.activations import celu
from keras.src.acti... | """DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.activations import deserialize
from keras.src.activations import get
from keras.src.activations import serialize
from keras.src.activations.activations import elu
from keras.src.activ... |
# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appl... | # Copyright 2025 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appl... |
import socket
from dataclasses import asdict
import numpy as np
import pytest
import xgboost as xgb
from xgboost import RabitTracker, build_info, federated
from xgboost import testing as tm
from xgboost.collective import Config
def run_rabit_worker(rabit_env: dict, world_size: int) -> int:
with xgb.collective.C... | import socket
from dataclasses import asdict
import numpy as np
import pytest
from loky import get_reusable_executor
import xgboost as xgb
from xgboost import RabitTracker, build_info, federated
from xgboost import testing as tm
from xgboost.collective import Config
def run_rabit_worker(rabit_env: dict, world_size:... |
# Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import torch
from mmdet.models.dense_heads import TOODHead
def test_tood_head_loss():
"""Tests paa head loss when truth is empty and non-empty."""
s = 256
img_metas = [{
'img_shape': (s, s, 3),
'scale_factor': 1,
'pad_sh... | # Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import torch
from mmdet.models.dense_heads import TOODHead
def test_paa_head_loss():
"""Tests paa head loss when truth is empty and non-empty."""
s = 256
img_metas = [{
'img_shape': (s, s, 3),
'scale_factor': 1,
'pad_sha... |
import torch
from torchaudio_unittest.common_utils import PytorchTestCase, skipIfNoCuda
from torchaudio_unittest.prototype.rnnt_test_impl import ConformerRNNTTestImpl
@skipIfNoCuda
class ConformerRNNTFloat32GPUTest(ConformerRNNTTestImpl, PytorchTestCase):
dtype = torch.float32
device = torch.device("cuda")
... | import torch
from torchaudio_unittest.common_utils import skipIfNoCuda, PytorchTestCase
from torchaudio_unittest.prototype.rnnt_test_impl import ConformerRNNTTestImpl
@skipIfNoCuda
class ConformerRNNTFloat32GPUTest(ConformerRNNTTestImpl, PytorchTestCase):
dtype = torch.float32
device = torch.device("cuda")
... |
_base_ = [
'../_base_/models/faster-rcnn_r50_fpn.py',
'../_base_/datasets/mot_challenge.py', '../_base_/default_runtime.py'
]
default_hooks = dict(
logger=dict(type='LoggerHook', interval=1),
visualization=dict(type='TrackVisualizationHook', draw=False))
vis_backends = [dict(type='LocalVisBackend')]
v... | _base_ = [
'../_base_/models/faster-rcnn_r50_fpn.py',
'../_base_/datasets/mot_challenge.py', '../_base_/default_runtime.py'
]
default_hooks = dict(
logger=dict(type='LoggerHook', interval=1),
visualization=dict(type='TrackVisualizationHook', draw=False))
vis_backends = [dict(type='LocalVisBackend')]
v... |
import os
import time
import numpy as np
import pytest
from pydantic import Field
from docarray import BaseDoc
from docarray.documents import ImageDoc
from docarray.typing import NdArray
pytestmark = [pytest.mark.slow, pytest.mark.index]
cur_dir = os.path.dirname(os.path.abspath(__file__))
compose_yml_v7 = os.path.... | import os
import time
import numpy as np
import pytest
from pydantic import Field
from docarray import BaseDoc
from docarray.typing import NdArray
pytestmark = [pytest.mark.slow, pytest.mark.index]
cur_dir = os.path.dirname(os.path.abspath(__file__))
compose_yml_v7 = os.path.abspath(os.path.join(cur_dir, 'v7/docker... |
import numpy as np
import pytest
from keras.src import backend
from keras.src import layers
from keras.src import testing
class GaussianDropoutTest(testing.TestCase):
@pytest.mark.requires_trainable_backend
def test_gaussian_dropout_basics(self):
self.run_layer_test(
layers.GaussianDropou... | import numpy as np
import pytest
from keras.src import backend
from keras.src import layers
from keras.src import testing
class GaussianDropoutTest(testing.TestCase):
@pytest.mark.requires_trainable_backend
def test_gaussian_dropout_basics(self):
self.run_layer_test(
layers.GaussianDropou... |
# 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 docarray.documents import TextDoc
def test_text_document_init():
text = TextDoc('hello world')
assert text.text == 'hello world'
assert text == 'hello world'
text = TextDoc(text='hello world')
assert text.text == 'hello world'
assert text == 'hello world'
text = TextDoc()
assert... |
import io
import json
import logging
import os
import tempfile
from typing import IO
import torch
from torch._inductor import config
from torch._inductor.cpp_builder import BuildOptionsBase, CppBuilder
from torch.export.pt2_archive._package import (
AOTI_FILES,
AOTICompiledModel,
load_pt2,
package_pt2,... | import io
import json
import logging
import os
import tempfile
from typing import IO, Union
import torch
from torch._inductor import config
from torch._inductor.cpp_builder import BuildOptionsBase, CppBuilder
from torch.export.pt2_archive._package import AOTICompiledModel, load_pt2, package_pt2
from torch.types import... |
# Copyright (c) OpenMMLab. All rights reserved.
import copy
import os.path as osp
import unittest
import numpy as np
from mmengine.data import InstanceData, PixelData
from mmdet.datasets.transforms import PackDetInputs
from mmdet.structures import DetDataSample
from mmdet.structures.mask import BitmapMasks
class Te... | # Copyright (c) OpenMMLab. All rights reserved.
import copy
import os.path as osp
import unittest
import numpy as np
from mmengine.data import BaseDataElement as PixelData
from mmengine.data import InstanceData
from mmdet.datasets.transforms import PackDetInputs
from mmdet.structures import DetDataSample
from mmdet.s... |
from docarray import Document, DocumentArray
import pytest
def test_add_ignore_existing_doc_id(start_storage):
elastic_doc = DocumentArray(
storage='elasticsearch',
config={
'n_dim': 3,
'columns': [('price', 'int')],
'distance': 'l2_norm',
'index_na... | from docarray import Document, DocumentArray
def test_add_ignore_existing_doc_id(start_storage):
elastic_doc = DocumentArray(
storage='elasticsearch',
config={
'n_dim': 3,
'columns': [('price', 'int')],
'distance': 'l2_norm',
'index_name': 'test_add_... |
import pytest
from llama_index.multi_modal_llms.nvidia import NVIDIAMultiModal
@pytest.mark.integration
def test_available_models() -> None:
models = NVIDIAMultiModal().available_models
assert models
assert isinstance(models, list)
assert all(isinstance(model.id, str) for model in models)
| import pytest
from llama_index.multi_modal_llms.nvidia import NVIDIAMultiModal
@pytest.mark.integration()
def test_available_models() -> None:
models = NVIDIAMultiModal().available_models
assert models
assert isinstance(models, list)
assert all(isinstance(model.id, str) for model in models)
|
import csv
import os
from . import InputExample
class TripletReader(object):
"""Reads in the a Triplet Dataset: Each line contains (at least) 3 columns, one anchor column (s1),
one positive example (s2) and one negative example (s3)
"""
def __init__(
self,
dataset_folder,
s1_... | from . import InputExample
import csv
import gzip
import os
class TripletReader(object):
"""
Reads in the a Triplet Dataset: Each line contains (at least) 3 columns, one anchor column (s1),
one positive example (s2) and one negative example (s3)
"""
def __init__(self, dataset_folder, s1_col_idx=0, ... |
"""DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.export.saved_model import ExportArchive
| """DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.export.export_lib import ExportArchive
|
import numpy as np
from docarray.proto import DocumentProto, NdArrayProto, NodeProto
from docarray.typing import NdArray
def test_nested_item_proto():
NodeProto(text='hello')
NodeProto(nested=DocumentProto())
def test_nested_optional_item_proto():
NodeProto()
def test_ndarray():
nd_proto = NdArra... | import numpy as np
from docarray.proto import DocumentProto, NdArrayProto, NodeProto
from docarray.typing import Tensor
def test_nested_item_proto():
NodeProto(text='hello')
NodeProto(nested=DocumentProto())
def test_nested_optional_item_proto():
NodeProto()
def test_ndarray():
nd_proto = NdArray... |
from pathlib import Path
from llama_index.core.bridge.pydantic import AnyUrl
from llama_index.core.schema import MediaResource
def test_defaults():
m = MediaResource()
assert m.data is None
assert m.embeddings is None
assert m.mimetype is None
assert m.path is None
assert m.url is None
def ... | from pathlib import Path
from llama_index.core.bridge.pydantic import AnyUrl
from llama_index.core.schema import MediaResource
def test_defaults():
m = MediaResource()
assert m.data is None
assert m.embeddings is None
assert m.mimetype is None
assert m.path is None
assert m.url is None
def ... |
_base_ = ['./mask2former_swin-t-p4-w7-224_lsj_8x2_50e_coco-panoptic.py']
pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window12_384.pth' # noqa
depths = [2, 2, 18, 2]
model = dict(
backbone=dict(
pretrain_img_size=384,
embed_dims=128,
de... | _base_ = ['./mask2former_swin-t-p4-w7-224_lsj_8x2_50e_coco-panoptic.py']
pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window12_384.pth' # noqa
depths = [2, 2, 18, 2]
model = dict(
backbone=dict(
pretrain_img_size=384,
embed_dims=128,
de... |
__copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import os
from jina import Flow
from PIL import Image
from ...pdf_segmenter import PDFSegmenter
def test_flow(test_dir, doc_generator_img_text, expected_text):
flow = Flow().add(uses=PDFSegmenter)
doc... | __copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import os
from PIL import Image
from jina import Flow
from ...pdf_segmenter import PDFSegmenter
def test_flow(test_dir, doc_generator_img_text, expected_text):
flow = Flow().add(uses=PDFSegmenter)
doc_... |
"""Top-level imports for LlamaIndex."""
__version__ = "0.12.48"
import logging
from logging import NullHandler
from typing import Callable, Optional
try:
# Force pants to install eval_type_backport on 3.9
import eval_type_backport # noqa # type: ignore
except ImportError:
pass
# response
from llama_in... | """Top-level imports for LlamaIndex."""
__version__ = "0.12.47"
import logging
from logging import NullHandler
from typing import Callable, Optional
try:
# Force pants to install eval_type_backport on 3.9
import eval_type_backport # noqa # type: ignore
except ImportError:
pass
# response
from llama_in... |
# type: ignore
"""
Development Scripts for template packages
"""
from collections.abc import Sequence
from fastapi import FastAPI
from langserve import add_routes
from langchain_cli.utils.packages import get_langserve_export, get_package_root
def create_demo_server(
*,
config_keys: Sequence[str] = (),
... | # type: ignore
"""
Development Scripts for template packages
"""
from typing import Sequence
from fastapi import FastAPI
from langserve import add_routes
from langchain_cli.utils.packages import get_langserve_export, get_package_root
def create_demo_server(
*,
config_keys: Sequence[str] = (),
playgroun... |
# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import os
import os.path as osp
def parse_args():
parser = argparse.ArgumentParser(
description='Convert benchmark model json to script')
parser.add_argument(
'txt_path', type=str, help='txt path output by benchmark_filter')
p... | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import os
import os.path as osp
def parse_args():
parser = argparse.ArgumentParser(
description='Convert benchmark model json to script')
parser.add_argument(
'txt_path', type=str, help='txt path output by benchmark_filter')
p... |
import torch
from torch import Tensor
from torch import nn
from typing import Dict
import os
import json
class WeightedLayerPooling(nn.Module):
"""Token embeddings are weighted mean of their different hidden layer representations"""
def __init__(
self, word_embedding_dimension, num_hidden_layers: int... | import torch
from torch import Tensor
from torch import nn
from typing import Dict
import os
import json
class WeightedLayerPooling(nn.Module):
"""
Token embeddings are weighted mean of their different hidden layer representations
"""
def __init__(
self, word_embedding_dimension, num_hidden_l... |
# Copyright (c) OpenMMLab. All rights reserved.
from mmdet.registry import MODELS
from .two_stage import TwoStageDetector
@MODELS.register_module()
class FastRCNN(TwoStageDetector):
"""Implementation of `Fast R-CNN <https://arxiv.org/abs/1504.08083>`_"""
def __init__(self,
backbone,
... | # Copyright (c) OpenMMLab. All rights reserved.
from ..builder import DETECTORS
from .two_stage import TwoStageDetector
@DETECTORS.register_module()
class FastRCNN(TwoStageDetector):
"""Implementation of `Fast R-CNN <https://arxiv.org/abs/1504.08083>`_"""
def __init__(self,
backbone,
... |
from __future__ import annotations
import asyncio
from collections.abc import AsyncIterator
from typing import Any, Literal, Union, cast
from langchain_core.callbacks import AsyncCallbackHandler
from langchain_core.outputs import LLMResult
# TODO If used by two LLM runs in parallel this won't work as expected
clas... | from __future__ import annotations
import asyncio
from typing import Any, AsyncIterator, Dict, List, Literal, Union, cast
from langchain_core.callbacks import AsyncCallbackHandler
from langchain_core.outputs import LLMResult
# TODO If used by two LLM runs in parallel this won't work as expected
class AsyncIterator... |
# Copyright (c) OpenMMLab. All rights reserved.
import pytest
from mmengine.utils import ManagerMeta, ManagerMixin
class SubClassA(ManagerMixin):
def __init__(self, name='', *args, **kwargs):
super().__init__(name, *args, **kwargs)
class SubClassB(ManagerMixin):
def __init__(self, name='', *args,... | # Copyright (c) OpenMMLab. All rights reserved.
import pytest
from mmengine.utils import ManagerMeta, ManagerMixin
class SubClassA(ManagerMixin):
def __init__(self, name='', *args, **kwargs):
super().__init__(name, *args, **kwargs)
class SubClassB(ManagerMixin):
def __init__(self, name='', *args,... |
from __future__ import annotations
import sys
from .BoW import BoW
from .CLIPModel import CLIPModel
from .CNN import CNN
from .Dense import Dense
from .Dropout import Dropout
from .InputModule import InputModule
from .LayerNorm import LayerNorm
from .LSTM import LSTM
from .Module import Module
from .Normalize import ... | from __future__ import annotations
from .Asym import Asym, Router
from .BoW import BoW
from .CLIPModel import CLIPModel
from .CNN import CNN
from .Dense import Dense
from .Dropout import Dropout
from .InputModule import InputModule
from .LayerNorm import LayerNorm
from .LSTM import LSTM
from .Module import Module
from... |
from __future__ import annotations
from typing import Any, List, Optional, Tuple, Union
import PIL.Image
import torch
from torchvision.transforms import InterpolationMode
from ._datapoint import Datapoint, FillTypeJIT
class Mask(Datapoint):
@classmethod
def _wrap(cls, tensor: torch.Tensor) -> Mask:
... | from __future__ import annotations
from typing import Any, List, Optional, Tuple, Union
import PIL.Image
import torch
from torchvision.transforms import InterpolationMode
from ._datapoint import Datapoint, FillTypeJIT
class Mask(Datapoint):
@classmethod
def _wrap(cls, tensor: torch.Tensor) -> Mask:
... |
from typing import Any, Optional
def json_to_markdown(data: Any, level: int = 0, header: Optional[str] = None) -> str:
"""
Recursively converts a Python object (from JSON) into a Markdown string.
Args:
data: The Python object to convert.
level: The current nesting level (used for indentat... | from typing import Any, Optional
def json_to_markdown(data: Any, level: int = 0, header: Optional[str] = None) -> str:
"""
Recursively converts a Python object (from JSON) into a Markdown string.
Args:
data: The Python object to convert.
level: The current nesting level (used for indentat... |
# coding=utf-8
# Copyright 2020 The Trax Authors and The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless r... | # coding=utf-8
# Copyright 2020 The Trax Authors and The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless r... |
from __future__ import annotations
from collections.abc import Iterable
from torch import Tensor
from sentence_transformers import util
from sentence_transformers.losses.CoSENTLoss import CoSENTLoss
from sentence_transformers.sparse_encoder.SparseEncoder import SparseEncoder
class SparseCoSENTLoss(CoSENTLoss):
... | from __future__ import annotations
from collections.abc import Iterable
from torch import Tensor
from sentence_transformers import util
from sentence_transformers.losses.CoSENTLoss import CoSENTLoss
from sentence_transformers.sparse_encoder.SparseEncoder import SparseEncoder
class SparseCoSENTLoss(CoSENTLoss):
... |
_base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
'../_base_/datasets/coco_panoptic.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
type='PanopticFPN',
semantic_head=dict(
type='PanopticFPNHead',
num_classes=54,
in_channels=256,
... | _base_ = [
'../_base_/datasets/coco_panoptic.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
type='PanopticFPN',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm... |
_base_ = ['./cascade-mask-rcnn_r50_fpn_1x_coco.py']
model = dict(
data_preprocessor=dict(
mean=[103.530, 116.280, 123.675],
std=[1.0, 1.0, 1.0],
bgr_to_rgb=False),
backbone=dict(
norm_cfg=dict(requires_grad=False),
norm_eval=True,
style='caffe',
init_cfg=... | _base_ = ['./cascade_mask_rcnn_r50_fpn_1x_coco.py']
model = dict(
data_preprocessor=dict(
mean=[103.530, 116.280, 123.675],
std=[1.0, 1.0, 1.0],
bgr_to_rgb=False),
backbone=dict(
norm_cfg=dict(requires_grad=False),
norm_eval=True,
style='caffe',
init_cfg=... |
_base_ = './grid-rcnn_r50_fpn_gn-head_2x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| _base_ = './grid_rcnn_r50_fpn_gn-head_2x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
|
_base_ = 'mask-rcnn_r50_fpn_rpn-2conv_4conv1fc_syncbn-all_lsj-100e_coco.py'
# Use RepeatDataset to speed up training
# change repeat time from 4 (for 100 epochs) to 2 (for 50 epochs)
train_dataloader = dict(dataset=dict(times=2))
| _base_ = 'mask_rcnn_r50_fpn_syncbn-all_rpn-2conv_lsj_100e_coco.py'
# Use RepeatDataset to speed up training
# change repeat time from 4 (for 100 epochs) to 2 (for 50 epochs)
train_dataloader = dict(dataset=dict(times=2))
|
"""ReAct output parser."""
import re
from typing import Tuple
from llama_index.core.agent.react.types import (
ActionReasoningStep,
BaseReasoningStep,
ResponseReasoningStep,
)
from llama_index.core.output_parsers.utils import extract_json_str
from llama_index.core.types import BaseOutputParser
def extra... | """ReAct output parser."""
import re
from typing import Tuple
from llama_index.core.agent.react.types import (
ActionReasoningStep,
BaseReasoningStep,
ResponseReasoningStep,
)
from llama_index.core.output_parsers.utils import extract_json_str
from llama_index.core.types import BaseOutputParser
def extra... |
# Copyright (c) OpenMMLab. All rights reserved.
from .local_visualizer import DetLocalVisualizer
from .palette import get_palette, palette_val
__all__ = ['palette_val', 'get_palette', 'DetLocalVisualizer']
| # Copyright (c) OpenMMLab. All rights reserved.
from .image import (color_val_matplotlib, imshow_det_bboxes,
imshow_gt_det_bboxes)
from .palette import get_palette, palette_val
__all__ = [
'imshow_det_bboxes', 'imshow_gt_det_bboxes', 'color_val_matplotlib',
'palette_val', 'get_palette'
]
|
from typing import List, Optional, TypeVar
from docarray.typing.proto_register import _register_proto
from docarray.typing.url.any_url import AnyUrl
from docarray.typing.url.mimetypes import TEXT_EXTRA_EXTENSIONS, TEXT_MIMETYPE
T = TypeVar('T', bound='TextUrl')
@_register_proto(proto_type_name='text_url')
class Tex... | from typing import Optional, TypeVar
from docarray.typing.proto_register import _register_proto
from docarray.typing.url.any_url import AnyUrl
T = TypeVar('T', bound='TextUrl')
@_register_proto(proto_type_name='text_url')
class TextUrl(AnyUrl):
"""
URL to a text file.
Can be remote (web) URL, or a local... |
"""Module for Jina Requests."""
from typing import (
TYPE_CHECKING,
AsyncIterable,
Dict,
Iterable,
Iterator,
Optional,
Tuple,
Union,
)
from jina.clients.request.helper import _new_data_request, _new_data_request_from_batch
from jina.enums import DataInputType
from jina.helper import ba... | """Module for Jina Requests."""
from typing import (
Iterator,
Union,
Tuple,
AsyncIterable,
Iterable,
Optional,
Dict,
TYPE_CHECKING,
)
from jina.clients.request.helper import _new_data_request_from_batch, _new_data_request
from jina.enums import DataInputType
from jina.helper import ba... |
"""Base argparser module for Pod and Deployment runtime"""
import argparse
import os
from jina.enums import PollingType
from jina.helper import random_identity
from jina.parsers.helper import _SHOW_ALL_ARGS, add_arg_group
def mixin_essential_parser(parser):
"""Mixing in arguments required by every module into th... | """Base argparser module for Pod and Deployment runtime"""
import argparse
import os
from jina.enums import PollingType
from jina.helper import random_identity
from jina.parsers.helper import _SHOW_ALL_ARGS, add_arg_group
def mixin_essential_parser(parser):
"""Mixing in arguments required by every module into th... |
"""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... |
import os as _os
import sys as _sys
from pathlib import Path as _Path
import datetime as _datetime
__windows__ = _sys.platform == 'win32'
__uptime__ = _datetime.datetime.now().isoformat()
# update on MacOS 1. clean this tuple, 2. grep -rohEI --exclude-dir=jina/hub --exclude-dir=tests --include \*.py
# "\'JINA_.*?\'" ... | import os as _os
import sys as _sys
from pathlib import Path as _Path
import datetime as _datetime
__windows__ = _sys.platform == 'win32'
__uptime__ = _datetime.datetime.now().isoformat()
# update on MacOS 1. clean this tuple, 2. grep -rohEI --exclude-dir=jina/hub --exclude-dir=tests --include \*.py
# "\'JINA_.*?\'" ... |
# Copyright (c) OpenMMLab. All rights reserved.
import mmcv
from .version import __version__, short_version
def digit_version(version_str):
digit_version = []
for x in version_str.split('.'):
if x.isdigit():
digit_version.append(int(x))
elif x.find('rc') != -1:
patch_v... | # Copyright (c) OpenMMLab. All rights reserved.
import mmcv
from .version import __version__, short_version
def digit_version(version_str):
digit_version = []
for x in version_str.split('.'):
if x.isdigit():
digit_version.append(int(x))
elif x.find('rc') != -1:
patch_v... |
import time
import http.client
import json
from typing import List, Optional, Union
from llama_index.core.base.base_retriever import BaseRetriever
from llama_index.core.callbacks.base import CallbackManager
from llama_index.core.schema import NodeWithScore, QueryBundle, TextNode
class GalaxiaClient:
def __init_... | import time
import http.client
import json
from typing import List, Optional, Union
from llama_index.core.base.base_retriever import BaseRetriever
from llama_index.core.callbacks.base import CallbackManager
from llama_index.core.schema import NodeWithScore, QueryBundle, TextNode
class GalaxiaClient:
def __init_... |
# Copyright (c) OpenMMLab. All rights reserved.
import torch
from mmcv.runner import force_fp32
from mmdet.models.builder import ROI_EXTRACTORS
from .base_roi_extractor import BaseRoIExtractor
@ROI_EXTRACTORS.register_module()
class SingleRoIExtractor(BaseRoIExtractor):
"""Extract RoI features from a single leve... | import torch
from mmcv.runner import force_fp32
from mmdet.models.builder import ROI_EXTRACTORS
from .base_roi_extractor import BaseRoIExtractor
@ROI_EXTRACTORS.register_module()
class SingleRoIExtractor(BaseRoIExtractor):
"""Extract RoI features from a single level feature map.
If there are multiple input ... |
"""Module for Jina Requests."""
from typing import (
TYPE_CHECKING,
AsyncIterable,
Dict,
Iterable,
Iterator,
Optional,
Tuple,
Union,
)
from jina._docarray import Document
from jina.clients.request.helper import _new_data_request, _new_data_request_from_batch
from jina.enums import Data... | """Module for Jina Requests."""
from typing import (
TYPE_CHECKING,
AsyncIterable,
Dict,
Iterable,
Iterator,
Optional,
Tuple,
Union,
)
from jina.clients.request.helper import _new_data_request, _new_data_request_from_batch
from jina.enums import DataInputType
from jina.helper import ba... |
from __future__ import annotations
import re
import pytest
from sentence_transformers import SentenceTransformer
from sentence_transformers.evaluation import NanoBEIREvaluator
from sentence_transformers.util import is_datasets_available
from tests.utils import is_ci
if not is_datasets_available():
pytest.skip(
... | from __future__ import annotations
import re
import pytest
from sentence_transformers import SentenceTransformer
from sentence_transformers.evaluation import NanoBEIREvaluator
from sentence_transformers.util import is_datasets_available
from tests.utils import is_ci
if not is_datasets_available():
pytest.skip(
... |
_base_ = './rpn_r50_fpn_1x_coco.py'
# use caffe img_norm
preprocess_cfg = dict(
mean=[103.530, 116.280, 123.675],
std=[1.0, 1.0, 1.0],
to_rgb=False,
pad_size_divisor=32)
model = dict(
preprocess_cfg=preprocess_cfg,
backbone=dict(
norm_cfg=dict(requires_grad=False),
norm_eval=Tru... | _base_ = './rpn_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_cfg = dic... |
# Copyright (c) OpenMMLab. All rights reserved.
from mmdet.registry import MODELS
from mmdet.utils import ConfigType, OptConfigType, OptMultiConfig
from .single_stage import SingleStageDetector
@MODELS.register_module()
class RepPointsDetector(SingleStageDetector):
"""RepPoints: Point Set Representation for Objec... | # Copyright (c) OpenMMLab. All rights reserved.
from mmdet.core.utils import ConfigType, OptConfigType, OptMultiConfig
from mmdet.registry import MODELS
from .single_stage import SingleStageDetector
@MODELS.register_module()
class RepPointsDetector(SingleStageDetector):
"""RepPoints: Point Set Representation for ... |
from typing import Union, Iterable
from docarray.array.storage.base.seqlike import BaseSequenceLikeMixin
from docarray.array.storage.registry import _REGISTRY
from docarray import Document
class SequenceLikeMixin(BaseSequenceLikeMixin):
"""Implement sequence-like methods for DocumentArray with weaviate as storag... | from typing import Union, Iterable
from docarray.array.storage.base.seqlike import BaseSequenceLikeMixin
from docarray.array.storage.registry import _REGISTRY
from docarray import Document
class SequenceLikeMixin(BaseSequenceLikeMixin):
"""Implement sequence-like methods for DocumentArray with weaviate as storag... |
from typing import Any, Dict, Tuple, Union
import numpy as np
import PIL.Image
import torch
from torchvision.io.video import read_video
from torchvision.prototype import features
from torchvision.prototype.utils._internal import ReadOnlyTensorBuffer
from torchvision.transforms import functional as _F
@torch.jit.unus... | import unittest.mock
from typing import Any, Dict, Tuple, Union
import numpy as np
import PIL.Image
import torch
from torchvision.io.video import read_video
from torchvision.prototype import features
from torchvision.prototype.utils._internal import ReadOnlyTensorBuffer
from torchvision.transforms import functional as... |
"""
Top-level module of Jina.
The primary function of this module is to import all of the public Jina
interfaces into a single place. The interfaces themselves are located in
sub-modules, as described below.
"""
import os as _os
import platform as _platform
import signal as _signal
import sys as _sys
import warnings... | """
Top-level module of Jina.
The primary function of this module is to import all of the public Jina
interfaces into a single place. The interfaces themselves are located in
sub-modules, as described below.
"""
import os as _os
import platform as _platform
import signal as _signal
import sys as _sys
import warnings... |
# Copyright (c) OpenMMLab. All rights reserved.
from unittest import TestCase
from unittest.mock import Mock
import torch.nn as nn
from torch.optim import SGD
from mmengine.hooks import RuntimeInfoHook
from mmengine.logging import MessageHub
from mmengine.optim import OptimWrapper, OptimWrapperDict
class TestRuntim... | # Copyright (c) OpenMMLab. All rights reserved.
from unittest import TestCase
from unittest.mock import Mock
from mmengine.hooks import RuntimeInfoHook
from mmengine.logging import MessageHub
class TestRuntimeInfoHook(TestCase):
def test_before_run(self):
message_hub = MessageHub.get_instance(
... |
# 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 2024 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... |
"""Base argparser module for Pod and Deployment runtime"""
import argparse
import os
from jina.enums import PollingType
from jina.helper import random_identity
from jina.parsers.helper import _SHOW_ALL_ARGS, add_arg_group
def mixin_essential_parser(parser):
"""Mixing in arguments required by every module into th... | """Base argparser module for Pod and Deployment runtime"""
import argparse
import os
from jina.enums import PollingType
from jina.helper import random_identity
from jina.parsers.helper import _SHOW_ALL_ARGS, add_arg_group
def mixin_essential_parser(parser):
"""Mixing in arguments required by every module into th... |
from __future__ import annotations
import torch
import transformers
from PIL import Image
from torch import nn
class CLIPModel(nn.Module):
def __init__(self, model_name: str = "openai/clip-vit-base-patch32", processor_name=None) -> None:
super().__init__()
if processor_name is None:
... | from __future__ import annotations
import torch
import transformers
from PIL import Image
from torch import nn
class CLIPModel(nn.Module):
def __init__(self, model_name: str = "openai/clip-vit-base-patch32", processor_name=None) -> None:
super(CLIPModel, self).__init__()
if processor_name is Non... |
from typing import Any, Optional, Type, TypeVar, Union
import numpy as np
from docarray.base_document import BaseDocument
from docarray.typing import AnyEmbedding, ImageBytes, ImageUrl
from docarray.typing.tensor.abstract_tensor import AbstractTensor
from docarray.typing.tensor.image.image_tensor import ImageTensor
f... | from typing import Any, Optional, Type, TypeVar, Union
import numpy as np
from docarray.base_document import BaseDocument
from docarray.typing import AnyEmbedding, ImageBytes, ImageUrl
from docarray.typing.tensor.abstract_tensor import AbstractTensor
from docarray.typing.tensor.image.image_tensor import ImageTensor
f... |
import csv
import logging
import os
from typing import List
from scipy.stats import pearsonr, spearmanr
from sentence_transformers import InputExample
logger = logging.getLogger(__name__)
class CECorrelationEvaluator:
"""
This evaluator can be used with the CrossEncoder class. Given sentence pairs and cont... | import logging
from scipy.stats import pearsonr, spearmanr
from typing import List
import os
import csv
from ... import InputExample
logger = logging.getLogger(__name__)
class CECorrelationEvaluator:
"""
This evaluator can be used with the CrossEncoder class. Given sentence pairs and continuous scores,
i... |
from typing import TYPE_CHECKING, Any, Optional, Type, TypeVar, Union
import numpy as np
from pydantic import Field
from docarray.base_doc import BaseDoc
from docarray.typing import AnyEmbedding, ImageBytes, ImageUrl
from docarray.typing.tensor.abstract_tensor import AbstractTensor
from docarray.typing.tensor.image.... | from typing import TYPE_CHECKING, Any, Optional, Type, TypeVar, Union
import numpy as np
from docarray.base_doc import BaseDoc
from docarray.typing import AnyEmbedding, ImageBytes, ImageUrl
from docarray.typing.tensor.abstract_tensor import AbstractTensor
from docarray.typing.tensor.image.image_tensor import ImageTen... |
_base_ = './faster-rcnn_r50_fpn_1x_coco.py'
model = dict(
roi_head=dict(
bbox_head=dict(
reg_decoded_bbox=True,
loss_bbox=dict(type='CIoULoss', loss_weight=12.0))))
| _base_ = './faster_rcnn_r50_fpn_1x_coco.py'
model = dict(
roi_head=dict(
bbox_head=dict(
reg_decoded_bbox=True,
loss_bbox=dict(type='CIoULoss', loss_weight=12.0))))
|
_base_ = '../mask_rcnn/mask-rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)))
| _base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)))
|
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