input stringlengths 33 5k | output stringlengths 32 5k |
|---|---|
# Copyright (c) OpenMMLab. All rights reserved.
from typing import List, Tuple, Union
import mmcv
import numpy as np
from mmengine.utils import is_str
def palette_val(palette: List[tuple]) -> List[tuple]:
"""Convert palette to matplotlib palette.
Args:
palette (List[tuple]): A list of color tuples.
... | # Copyright (c) OpenMMLab. All rights reserved.
from typing import List, Tuple, Union
import mmcv
import numpy as np
from mmengine.utils import is_str
def palette_val(palette: List[tuple]) -> List[tuple]:
"""Convert palette to matplotlib palette.
Args:
palette (List[tuple]): A list of color tuples.
... |
from __future__ import annotations
from collections.abc import Iterable
from torch import Tensor
from sentence_transformers import util
from sentence_transformers.losses.MultipleNegativesRankingLoss import MultipleNegativesRankingLoss
from sentence_transformers.sparse_encoder.SparseEncoder import SparseEncoder
cla... | from __future__ import annotations
from collections.abc import Iterable
from torch import Tensor
from sentence_transformers import util
from sentence_transformers.losses.MultipleNegativesRankingLoss import MultipleNegativesRankingLoss
from sentence_transformers.sparse_encoder.SparseEncoder import SparseEncoder
cla... |
import os
import numpy as np
import pytest
import torch
from pydantic import parse_obj_as
from docarray import BaseDocument
from docarray.typing.tensor.audio.audio_ndarray import AudioNdArray
from docarray.typing.tensor.audio.audio_torch_tensor import AudioTorchTensor
@pytest.mark.parametrize(
'tensor,cls_audio... | import os
import numpy as np
import pytest
import torch
from pydantic import parse_obj_as
from docarray import BaseDocument
from docarray.typing.tensor.audio.audio_ndarray import AudioNdArray
from docarray.typing.tensor.audio.audio_torch_tensor import AudioTorchTensor
@pytest.mark.parametrize(
'tensor,cls_audio... |
from .autoencoder_asym_kl import AsymmetricAutoencoderKL
from .autoencoder_dc import AutoencoderDC
from .autoencoder_kl import AutoencoderKL
from .autoencoder_kl_allegro import AutoencoderKLAllegro
from .autoencoder_kl_cogvideox import AutoencoderKLCogVideoX
from .autoencoder_kl_cosmos import AutoencoderKLCosmos
from .... | from .autoencoder_asym_kl import AsymmetricAutoencoderKL
from .autoencoder_dc import AutoencoderDC
from .autoencoder_kl import AutoencoderKL
from .autoencoder_kl_allegro import AutoencoderKLAllegro
from .autoencoder_kl_cogvideox import AutoencoderKLCogVideoX
from .autoencoder_kl_hunyuan_video import AutoencoderKLHunyua... |
# 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/... | # Copyright (c) OpenMMLab. All rights reserved.
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/1506.01497>`_"""
def __init__(self,
backbone,
... |
# Copyright (c) OpenMMLab. All rights reserved.
from typing import Union
import torch
from numpy import ndarray
from torch import Tensor
from mmdet.core.bbox.assigners import AssignResult
from mmdet.registry import TASK_UTILS
from .base_sampler import BaseSampler
@TASK_UTILS.register_module()
class RandomSampler(Ba... | # Copyright (c) OpenMMLab. All rights reserved.
from typing import Union
import torch
from numpy import ndarray
from torch import Tensor
from mmdet.core.bbox.assigners import AssignResult
from mmdet.registry import TASK_UTILS
from .base_sampler import BaseSampler
@TASK_UTILS.register_module()
class RandomSampler(Ba... |
"""Pydantic v1 compatibility shim."""
from langchain_core._api import warn_deprecated
try:
from pydantic.v1.dataclasses import * # noqa: F403
except ImportError:
from pydantic.dataclasses import * # type: ignore # noqa: F403
warn_deprecated(
"0.3.0",
removal="1.0.0",
alternative="pydantic.v1 or... | from langchain_core._api import warn_deprecated
try:
from pydantic.v1.dataclasses import * # noqa: F403
except ImportError:
from pydantic.dataclasses import * # type: ignore # noqa: F403
warn_deprecated(
"0.3.0",
removal="1.0.0",
alternative="pydantic.v1 or pydantic",
message=(
"As o... |
from langchain_huggingface.chat_models.huggingface import ( # type: ignore[import-not-found]
TGI_MESSAGE,
TGI_RESPONSE,
ChatHuggingFace,
_convert_dict_to_message,
)
__all__ = ["TGI_MESSAGE", "TGI_RESPONSE", "ChatHuggingFace", "_convert_dict_to_message"]
| from langchain_huggingface.chat_models.huggingface import ( # type: ignore[import-not-found]
TGI_MESSAGE,
TGI_RESPONSE,
ChatHuggingFace,
_convert_dict_to_message,
)
__all__ = ["ChatHuggingFace", "_convert_dict_to_message", "TGI_MESSAGE", "TGI_RESPONSE"]
|
import re
from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
from backend.data.model import SchemaField
class CodeExtractionBlock(Block):
class Input(BlockSchema):
text: str = SchemaField(
description="Text containing code blocks to extract (e.g., AI response)",
... | import re
from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
from backend.data.model import SchemaField
class CodeExtractionBlock(Block):
class Input(BlockSchema):
text: str = SchemaField(
description="Text containing code blocks to extract (e.g., AI response)",
... |
from argparse import Namespace
from copy import deepcopy
from typing import TYPE_CHECKING, Type
from hubble.executor.helper import is_valid_huburi
from hubble.executor.hubio import HubIO
from jina.enums import PodRoleType
from jina.orchestrate.pods import Pod
from jina.orchestrate.pods.container import ContainerPod
... | from argparse import Namespace
from copy import deepcopy
from typing import TYPE_CHECKING, Type
from hubble.executor.helper import is_valid_huburi
from hubble.executor.hubio import HubIO
from jina.enums import PodRoleType
from jina.orchestrate.pods import Pod
from jina.orchestrate.pods.container import ContainerPod
... |
from datetime import datetime, timedelta, timezone
import jwt
from fastapi import Depends, FastAPI, HTTPException, status
from fastapi.security import OAuth2PasswordBearer, OAuth2PasswordRequestForm
from jwt.exceptions import InvalidTokenError
from passlib.context import CryptContext
from pydantic import BaseModel
# ... | from datetime import datetime, timedelta, timezone
import jwt
from fastapi import Depends, FastAPI, HTTPException, status
from fastapi.security import OAuth2PasswordBearer, OAuth2PasswordRequestForm
from jwt.exceptions import InvalidTokenError
from passlib.context import CryptContext
from pydantic import BaseModel
# ... |
"""Joint QA Summary graph."""
from typing import List, Optional, Sequence
from llama_index.core.base.embeddings.base import BaseEmbedding
from llama_index.core.callbacks.base import CallbackManager
from llama_index.core.indices.list.base import SummaryIndex
from llama_index.core.indices.vector_store import VectorStor... | """Joint QA Summary graph."""
from typing import List, Optional, Sequence
from llama_index.core.base.embeddings.base import BaseEmbedding
from llama_index.core.callbacks.base import CallbackManager
from llama_index.core.indices.list.base import SummaryIndex
from llama_index.core.indices.vector_store import VectorStor... |
import logging
import pytest
from backend.util.test import SpinTestServer
# NOTE: You can run tests like with the --log-cli-level=INFO to see the logs
# Set up logging
logger = logging.getLogger(__name__)
# Create console handler with formatting
ch = logging.StreamHandler()
ch.setLevel(logging.INFO)
formatter = lo... | import pytest
from backend.util.test import SpinTestServer
@pytest.fixture(scope="session")
async def server():
async with SpinTestServer() as server:
yield server
@pytest.fixture(scope="session", autouse=True)
async def graph_cleanup(server):
created_graph_ids = []
original_create_graph = serv... |
from typing import Any, Dict, 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 = 20.0, similarity_fct=util.p... | from typing import Dict, 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 = 20.0, similarity_fct=util.pairwi... |
# 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 os
from typing import BinaryIO, Optional, Tuple, Union
import torch
import torchaudio
from .backend import Backend
from .common import AudioMetaData
sox_ext = torchaudio._extension.lazy_import_sox_ext()
class SoXBackend(Backend):
@staticmethod
def info(uri: Union[BinaryIO, str, os.PathLike], format:... | import os
from typing import BinaryIO, Optional, Tuple, Union
import torch
import torchaudio
from .backend import Backend
from .common import AudioMetaData
sox_ext = torchaudio._extension.lazy_import_sox_ext()
class SoXBackend(Backend):
@staticmethod
def info(uri: Union[BinaryIO, str, os.PathLike], format:... |
# -*- coding: utf-8 -*-
"""
Audio Feature Augmentation
==========================
**Author**: `Moto Hira <moto@meta.com>`__
"""
# When running this tutorial in Google Colab, install the required packages
# with the following.
# !pip install torchaudio librosa
import torch
import torchaudio
import torchaudio.transfo... | # -*- coding: utf-8 -*-
"""
Audio Feature Augmentation
==========================
**Author**: `Moto Hira <moto@meta.com>`__
"""
# When running this tutorial in Google Colab, install the required packages
# with the following.
# !pip install torchaudio librosa
import torch
import torchaudio
import torchaudio.transfo... |
import csv
import logging
import os
from typing import Optional
import numpy as np
from sklearn.metrics import ndcg_score
logger = logging.getLogger(__name__)
class CERerankingEvaluator:
"""
This class evaluates a CrossEncoder model for the task of re-ranking.
Given a query and a list of documents, it ... | import logging
import numpy as np
import os
import csv
from typing import Optional
from sklearn.metrics import ndcg_score
logger = logging.getLogger(__name__)
class CERerankingEvaluator:
"""
This class evaluates a CrossEncoder model for the task of re-ranking.
Given a query and a list of documents, it c... |
"""
Outlook local calendar reader for Windows.
Created on Sun Apr 16 12:03:19 2023
@author: tevslin
"""
import datetime
import importlib
import platform
from typing import List, Optional, Union
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document
# Copyright 2023 Evslin... | """
Outlook local calendar reader for Windows.
Created on Sun Apr 16 12:03:19 2023
@author: tevslin
"""
import datetime
import importlib
import platform
from typing import List, Optional, Union
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document
# Copyright 2023 Evslin... |
"""Retriever tool."""
from typing import TYPE_CHECKING, Any, List, Optional
from llama_index.core.base.base_retriever import BaseRetriever
if TYPE_CHECKING:
from llama_index.core.langchain_helpers.agents.tools import LlamaIndexTool
from llama_index.core.schema import (
MetadataMode,
Node,
NodeWithSc... | """Retriever tool."""
from typing import TYPE_CHECKING, Any, List, Optional
from llama_index.core.base.base_retriever import BaseRetriever
if TYPE_CHECKING:
from llama_index.core.langchain_helpers.agents.tools import LlamaIndexTool
from llama_index.core.schema import (
MetadataMode,
Node,
NodeWithSc... |
"""
================================================
Kernel Density Estimate of Species Distributions
================================================
This shows an example of a neighbors-based query (in particular a kernel
density estimate) on geospatial data, using a Ball Tree built upon the
Haversine distance metric... | """
================================================
Kernel Density Estimate of Species Distributions
================================================
This shows an example of a neighbors-based query (in particular a kernel
density estimate) on geospatial data, using a Ball Tree built upon the
Haversine distance metric... |
from llama_index.agent.azure_foundry_agent.base import AzureFoundryAgent
__all__ = ["AzureFoundryAgent"]
| from llama_index.agent.azure_foundry_agent.base import AzureFoundryAgent
__all__ = [
"AzureFoundryAgent"
]
|
# Copyright (c) OpenMMLab. All rights reserved.
from typing import List, Tuple, Union
import torch.nn as nn
from mmcv.cnn import ConvModule
from mmengine.model import BaseModule
from torch import Tensor
from mmdet.registry import MODELS
from mmdet.utils import OptConfigType, OptMultiConfig
@MODELS.register_module()... | # Copyright (c) OpenMMLab. All rights reserved.
from typing import List, Tuple
import torch.nn as nn
from mmcv.cnn import ConvModule
from mmengine.model import BaseModule
from torch import Tensor
from mmdet.registry import MODELS
from mmdet.utils import OptConfigType, OptMultiConfig
@MODELS.register_module()
class ... |
# Copyright (c) OpenMMLab. All rights reserved.
import unittest
from unittest import TestCase
import torch
from parameterized import parameterized
from mmdet.models.roi_heads import PointRendRoIHead # noqa
from mmdet.registry import MODELS
from mmdet.testing import demo_mm_inputs, demo_mm_proposals, get_roi_head_cfg... | # Copyright (c) OpenMMLab. All rights reserved.
import unittest
from unittest import TestCase
import torch
from parameterized import parameterized
from mmdet.models.roi_heads import PointRendRoIHead # noqa
from mmdet.registry import MODELS
from mmdet.testing import demo_mm_inputs, demo_mm_proposals, get_roi_head_cfg... |
# Copyright (c) OpenMMLab. All rights reserved.
import glob
import os
import os.path as osp
import warnings
from mmengine.config import Config, ConfigDict
from mmengine.logging import print_log
def find_latest_checkpoint(path, suffix='pth'):
"""Find the latest checkpoint from the working directory.
Args:
... | # Copyright (c) OpenMMLab. All rights reserved.
import glob
import os
import os.path as osp
import warnings
import mmcv
from mmcv.utils import print_log
def find_latest_checkpoint(path, suffix='pth'):
"""Find the latest checkpoint from the working directory.
Args:
path(str): The path to find checkpo... |
# Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
import torch.nn.functional as F
from mmengine.model.utils import constant_init
from mmdet.models.layers import DyReLU, SELayer
def test_se_layer():
with pytest.raises(AssertionError):
# act_cfg sequence length must equal to 2
... | # Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
import torch.nn.functional as F
from mmcv.cnn import constant_init
from mmdet.models.layers import DyReLU, SELayer
def test_se_layer():
with pytest.raises(AssertionError):
# act_cfg sequence length must equal to 2
SELayer(... |
"""Standard LangChain interface tests"""
import os
from langchain_core.language_models import BaseChatModel
from langchain_tests.integration_tests import ChatModelIntegrationTests
from langchain_openai import AzureChatOpenAI
OPENAI_API_VERSION = os.environ.get("AZURE_OPENAI_API_VERSION", "")
OPENAI_API_BASE = os.en... | """Standard LangChain interface tests"""
import os
from typing import Type
from langchain_core.language_models import BaseChatModel
from langchain_tests.integration_tests import ChatModelIntegrationTests
from langchain_openai import AzureChatOpenAI
OPENAI_API_VERSION = os.environ.get("AZURE_OPENAI_API_VERSION", "")... |
from .backend_utils import set_audio_backend
from .case_utils import (
HttpServerMixin,
is_ffmpeg_available,
PytorchTestCase,
skipIfNoCtcDecoder,
skipIfNoCuda,
skipIfNoExec,
skipIfNoFFmpeg,
skipIfNoKaldi,
skipIfNoModule,
skipIfNoQengine,
skipIfNoSox,
skipIfPy310,
skip... | from .backend_utils import set_audio_backend
from .case_utils import (
HttpServerMixin,
is_ffmpeg_available,
PytorchTestCase,
skipIfNoCtcDecoder,
skipIfNoCuda,
skipIfNoExec,
skipIfNoFFmpeg,
skipIfNoKaldi,
skipIfNoModule,
skipIfNoQengine,
skipIfNoSox,
skipIfPy310,
skip... |
_base_ = [
'mmdet::_base_/datasets/coco_instance.py',
'mmdet::_base_/schedules/schedule_1x.py',
'mmdet::_base_/default_runtime.py'
]
custom_imports = dict(
imports=['projects.SparseInst.sparseinst'], allow_failed_imports=False)
model = dict(
type='SparseInst',
data_preprocessor=dict(
t... | _base_ = [
'mmdet::_base_/datasets/coco_instance.py',
'mmdet::_base_/schedules/schedule_1x.py',
'mmdet::_base_/default_runtime.py'
]
custom_imports = dict(
imports=['projects.SparseInst.sparseinst'], allow_failed_imports=False)
model = dict(
type='SparseInst',
data_preprocessor=dict(
t... |
# Copyright (c) OpenMMLab. All rights reserved.
from .batch_sampler import (AspectRatioBatchSampler,
TrackAspectRatioBatchSampler)
from .class_aware_sampler import ClassAwareSampler
from .multi_source_sampler import GroupMultiSourceSampler, MultiSourceSampler
from .track_img_sampler import T... | # Copyright (c) OpenMMLab. All rights reserved.
from .batch_sampler import AspectRatioBatchSampler
from .class_aware_sampler import ClassAwareSampler
from .multi_source_sampler import GroupMultiSourceSampler, MultiSourceSampler
from .track_img_sampler import TrackImgSampler
__all__ = [
'ClassAwareSampler', 'Aspect... |
# Copyright 2020 The HuggingFace Datasets Authors and the TensorFlow Datasets Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# U... | # Copyright 2020 The HuggingFace Datasets Authors and the TensorFlow Datasets Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# U... |
from contextlib import contextmanager
from typing import TYPE_CHECKING, Callable, Iterator
from llama_index.core.llms.llm import LLM
from llama_index.llms.huggingface import HuggingFaceLLM
from llama_index.llms.llama_cpp import LlamaCPP
if TYPE_CHECKING:
from lmformatenforcer import CharacterLevelParser
def bui... | from contextlib import contextmanager
from typing import TYPE_CHECKING, Callable, Iterator
from llama_index.core.llms.llm import LLM
from llama_index.llms.huggingface import HuggingFaceLLM
from llama_index.llms.llama_cpp import LlamaCPP
if TYPE_CHECKING:
from lmformatenforcer import CharacterLevelParser
def bui... |
"""Standard LangChain interface tests"""
from pathlib import Path
from typing import Dict, List, Literal, Type, cast
from langchain_core.language_models import BaseChatModel
from langchain_core.messages import AIMessage
from langchain_tests.integration_tests import ChatModelIntegrationTests
from langchain_openai imp... | """Standard LangChain interface tests"""
from pathlib import Path
from typing import Dict, List, Literal, Type, cast
from langchain_core.language_models import BaseChatModel
from langchain_core.messages import AIMessage
from langchain_tests.integration_tests import ChatModelIntegrationTests
from langchain_openai imp... |
"""Util that sends calendar events in Office 365.
Free, but setup is required. See link below.
https://learn.microsoft.com/en-us/graph/auth/
"""
from datetime import datetime as dt
from typing import List, Optional, Type
from zoneinfo import ZoneInfo
from langchain_core.callbacks import CallbackManagerForToolRun
fro... | """Util that sends calendar events in Office 365.
Free, but setup is required. See link below.
https://learn.microsoft.com/en-us/graph/auth/
"""
from datetime import datetime as dt
from typing import List, Optional, Type
from zoneinfo import ZoneInfo
from langchain_core.callbacks import CallbackManagerForToolRun
fro... |
# Copyright (c) OpenMMLab. All rights reserved.
from .default_scope import DefaultScope
from .registry import Registry, build_from_cfg
from .root import (DATA_SAMPLERS, DATASETS, HOOKS, LOOPS, METRICS,
MODEL_WRAPPERS, MODELS, OPTIMIZER_CONSTRUCTORS, OPTIMIZERS,
PARAM_SCHEDULERS, RU... | # Copyright (c) OpenMMLab. All rights reserved.
from .default_scope import DefaultScope
from .registry import Registry, build_from_cfg
from .root import (DATA_SAMPLERS, DATASETS, HOOKS, LOOPS, METRICS,
MODEL_WRAPPERS, MODELS, OPTIMIZER_CONSTRUCTORS, OPTIMIZERS,
PARAM_SCHEDULERS, RU... |
import asyncio
import os
from typing import Dict, List
import pytest
import requests
from jina import Flow
from jina.logging.logger import JinaLogger
from tests.k8s_otel.kind_wrapper import KindClusterWrapperV2
from tests.k8s_otel.util import get_last_health_check_data, parse_string_jaeger_tags
@pytest.mark.asyncio... | import asyncio
import os
from typing import Dict, List
import pytest
import requests
from jina import Flow
from jina.logging.logger import JinaLogger
from tests.k8s_otel.kind_wrapper import KindClusterWrapperV2
from tests.k8s_otel.util import get_last_health_check_data, parse_string_jaeger_tags
@pytest.mark.asyncio... |
import math
import os
import pytest
import torch
import torchvision
from torchvision.io import _HAS_GPU_VIDEO_DECODER, VideoReader
try:
import av
except ImportError:
av = None
VIDEO_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), "assets", "videos")
@pytest.mark.skipif(_HAS_GPU_VIDEO_DECODER... | import math
import os
import pytest
import torch
from torchvision.io import _HAS_GPU_VIDEO_DECODER, VideoReader
try:
import av
except ImportError:
av = None
VIDEO_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), "assets", "videos")
@pytest.mark.skipif(_HAS_GPU_VIDEO_DECODER is False, reason="... |
# Copyright (c) OpenMMLab. All rights reserved.
from .config import Config, ConfigDict, DictAction
from .get_config_model import get_config, get_model
__all__ = ['Config', 'ConfigDict', 'DictAction', 'get_config', 'get_model']
| # Copyright (c) OpenMMLab. All rights reserved.
from .config import Config, ConfigDict, DictAction
__all__ = ['Config', 'ConfigDict', 'DictAction']
|
from typing import List
import torch
from parameterized import parameterized
from torchaudio import sox_effects
from torchaudio_unittest.common_utils import (
get_sinusoid,
save_wav,
skipIfNoSox,
TempDirMixin,
torch_script,
TorchaudioTestCase,
)
from .common import load_params
class SoxEffec... | from typing import List
import torch
from parameterized import parameterized
from torchaudio import sox_effects
from torchaudio_unittest.common_utils import (
TempDirMixin,
TorchaudioTestCase,
skipIfNoSox,
get_sinusoid,
save_wav,
torch_script,
)
from .common import (
load_params,
)
class... |
# Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from mmdet.models.utils import ConvUpsample
@pytest.mark.parametrize('num_layers', [0, 1, 2])
def test_conv_upsample(num_layers):
num_upsample = num_layers if num_layers > 0 else 0
num_layers = num_layers if num_layers > 0 else 1
... | import pytest
import torch
from mmdet.models.utils import ConvUpsample
@pytest.mark.parametrize('num_layers', [0, 1, 2])
def test_conv_upsample(num_layers):
num_upsample = num_layers if num_layers > 0 else 0
num_layers = num_layers if num_layers > 0 else 1
layer = ConvUpsample(
10,
5,
... |
_base_ = './fast-rcnn_r50_fpn_1x_coco.py'
# learning policy
lr_config = dict(step=[16, 22])
runner = dict(type='EpochBasedRunner', max_epochs=24)
| _base_ = './fast_rcnn_r50_fpn_1x_coco.py'
# learning policy
lr_config = dict(step=[16, 22])
runner = dict(type='EpochBasedRunner', max_epochs=24)
|
import math
from keras.src import backend
from keras.src import layers
from keras.src import ops
from keras.src.api_export import keras_export
@keras_export("keras.layers.GaussianDropout")
class GaussianDropout(layers.Layer):
"""Apply multiplicative 1-centered Gaussian noise.
As it is a regularization layer... | import math
from keras.src import backend
from keras.src import layers
from keras.src import ops
from keras.src.api_export import keras_export
@keras_export("keras.layers.GaussianDropout")
class GaussianDropout(layers.Layer):
"""Apply multiplicative 1-centered Gaussian noise.
As it is a regularization layer... |
# coding=utf-8
# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless r... | # coding=utf-8
# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless r... |
# Copyright (c) OpenMMLab. All rights reserved.
import os
import pytest
import torch
import torch.nn as nn
from torch.distributed import destroy_process_group, init_process_group
from torch.nn.parallel import DataParallel, DistributedDataParallel
from mmengine.model import (MMDistributedDataParallel,
... | # Copyright (c) OpenMMLab. All rights reserved.
import os
import pytest
import torch
import torch.nn as nn
from torch.distributed import destroy_process_group, init_process_group
from torch.nn.parallel import DataParallel, DistributedDataParallel
from mmengine.model import (MMDistributedDataParallel,
... |
# Copyright (c) OpenMMLab. All rights reserved.
from .base_data_element import BaseDataElement
from .instance_data import InstanceData
from .sampler import DefaultSampler, InfiniteSampler
from .utils import pseudo_collate, worker_init_fn
__all__ = [
'BaseDataElement', 'DefaultSampler', 'InfiniteSampler', 'worker_i... | # Copyright (c) OpenMMLab. All rights reserved.
from .base_data_element import BaseDataElement
from .sampler import DefaultSampler, InfiniteSampler
from .utils import pseudo_collate, worker_init_fn
__all__ = [
'BaseDataElement', 'DefaultSampler', 'InfiniteSampler', 'worker_init_fn',
'pseudo_collate'
]
|
"""XGBoost: eXtreme Gradient Boosting library.
Contributors: https://github.com/dmlc/xgboost/blob/master/CONTRIBUTORS.md
"""
from . import tracker # noqa
from . import collective
from .core import (
Booster,
DataIter,
DMatrix,
ExtMemQuantileDMatrix,
QuantileDMatrix,
_py_version,
build_inf... | """XGBoost: eXtreme Gradient Boosting library.
Contributors: https://github.com/dmlc/xgboost/blob/master/CONTRIBUTORS.md
"""
from . import tracker # noqa
from . import collective, dask
from .core import (
Booster,
DataIter,
DMatrix,
ExtMemQuantileDMatrix,
QuantileDMatrix,
_py_version,
bui... |
"""DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.ops.image import affine_transform
from keras.src.ops.image import crop_images
from keras.src.ops.image import extract_patches
from keras.src.ops.image import gaussian_blur
from keras.... | """DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.ops.image import affine_transform
from keras.src.ops.image import crop_images
from keras.src.ops.image import extract_patches
from keras.src.ops.image import hsv_to_rgb
from keras.src... |
# Copyright (c) OpenMMLab. All rights reserved.
from mmcv.parallel import is_module_wrapper
from mmcv.runner.hooks import HOOKS, Hook
@HOOKS.register_module()
class YOLOXModeSwitchHook(Hook):
"""Switch the mode of YOLOX during training.
This hook turns off the mosaic and mixup data augmentation and switches
... | # Copyright (c) OpenMMLab. All rights reserved.
from mmcv.parallel import is_module_wrapper
from mmcv.runner.hooks import HOOKS, Hook
@HOOKS.register_module()
class YOLOXModeSwitchHook(Hook):
"""Switch the mode of YOLOX during training.
This hook turns off the mosaic and mixup data augmentation and switches
... |
from typing import Union
from langchain_core.agents import AgentAction, AgentFinish
from langchain_core.messages import BaseMessage
from langchain_core.outputs import ChatGeneration, Generation
from langchain.agents.agent import MultiActionAgentOutputParser
from langchain.agents.output_parsers.tools import (
Tool... | from typing import Union
from langchain_core.agents import AgentAction, AgentFinish
from langchain_core.messages import BaseMessage
from langchain_core.outputs import ChatGeneration, Generation
from langchain.agents.agent import MultiActionAgentOutputParser
from langchain.agents.output_parsers.tools import (
Tool... |
"""Feature selection algorithms.
These include univariate filter selection methods and the recursive feature elimination
algorithm.
"""
# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause
from ._base import SelectorMixin
from ._from_model import SelectFromModel
from ._mutual_info import mu... | """Feature selection algorithms.
These include univariate filter selection methods and the recursive feature elimination
algorithm.
"""
# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause
from ._base import SelectorMixin
from ._from_model import SelectFromModel
from ._mutual_info import mu... |
_base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
teacher_ckpt = 'http://download.openmmlab.com/mmdetection/v2.0/paa/paa_r101_fpn_1x_coco/paa_r101_fpn_1x_coco_20200821-0a1825a4.pth' # noqa
model = dict(
type='LAD',
data_preprocess... | _base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
teacher_ckpt = 'http://download.openmmlab.com/mmdetection/v2.0/paa/paa_r101_fpn_1x_coco/paa_r101_fpn_1x_coco_20200821-0a1825a4.pth' # noqa
preprocess_cfg = dict(
mean=[123.675, 116.28,... |
import numpy as np
from absl.testing import parameterized
from tensorflow import data as tf_data
from keras.src import backend
from keras.src import layers
from keras.src import testing
class RandomRotationTest(testing.TestCase):
@parameterized.named_parameters(
("random_rotate_neg4", -0.4),
("ra... | import numpy as np
from absl.testing import parameterized
from tensorflow import data as tf_data
from keras.src import backend
from keras.src import layers
from keras.src import testing
class RandomRotationTest(testing.TestCase):
@parameterized.named_parameters(
("random_rotate_neg4", -0.4),
("ra... |
"""Test embedding functionalities."""
from collections import defaultdict
from typing import Any, Dict, List
from unittest.mock import patch
import pytest
from llama_index.core.indices.tree.base import TreeIndex
from llama_index.core.indices.tree.select_leaf_embedding_retriever import (
TreeSelectLeafEmbeddingRet... | """Test embedding functionalities."""
from collections import defaultdict
from typing import Any, Dict, List
from unittest.mock import patch
import pytest
from llama_index.core.indices.tree.base import TreeIndex
from llama_index.core.indices.tree.select_leaf_embedding_retriever import (
TreeSelectLeafEmbeddingRet... |
from __future__ import annotations
import math
from pathlib import Path
import numpy as np
import pytest
from packaging.version import Version, parse
from tokenizers import Tokenizer
from sentence_transformers import SentenceTransformer
from sentence_transformers.models.StaticEmbedding import StaticEmbedding
try:
... | from __future__ import annotations
import math
from pathlib import Path
import numpy as np
import pytest
from tokenizers import Tokenizer
from sentence_transformers import SentenceTransformer
from sentence_transformers.models.StaticEmbedding import StaticEmbedding
try:
import model2vec
except ImportError:
m... |
from pathlib import Path
from typing import Dict
import numpy as np
import pytest
from jina import Document, DocumentArray, Executor
from ...paddle_image import ImagePaddlehubEncoder
input_dim = 224
target_output_dim = 2048
num_doc = 2
test_data = np.random.rand(num_doc, 3, input_dim, input_dim)
tmp_files = []
def... | from pathlib import Path
from typing import Dict
import numpy as np
from jina import DocumentArray, Document, Executor
from ...paddle_image import ImagePaddlehubEncoder
input_dim = 224
target_output_dim = 2048
num_doc = 2
test_data = np.random.rand(num_doc, 3, input_dim, input_dim)
tmp_files = []
def test_config():... |
import importlib
import os
import re
import types
from typing import Any, Optional
import numpy as np
try:
import torch # noqa: F401
except ImportError:
torch_imported = False
else:
torch_imported = True
try:
import tensorflow as tf # type: ignore # noqa: F401
except (ImportError, TypeError):
... | import importlib
import os
import re
import types
from typing import Any, Optional
import numpy as np
try:
import torch # noqa: F401
except ImportError:
torch_imported = False
else:
torch_imported = True
try:
import tensorflow as tf # type: ignore # noqa: F401
except (ImportError, TypeError):
... |
import zlib
from typing import Iterator, TextIO
def exact_div(x, y):
assert x % y == 0
return x // y
def str2bool(string):
str2val = {"True": True, "False": False}
if string in str2val:
return str2val[string]
else:
raise ValueError(f"Expected one of {set(str2val.keys())}, got {st... | import zlib
from typing import Iterator, TextIO
def exact_div(x, y):
assert x % y == 0
return x // y
def str2bool(string):
str2val = {"True": True, "False": False}
if string in str2val:
return str2val[string]
else:
raise ValueError(f"Expected one of {set(str2val.keys())}, got {st... |
from __future__ import annotations
from typing import Any, List, Optional, cast
from langchain_text_splitters.base import TextSplitter, Tokenizer, split_text_on_tokens
class SentenceTransformersTokenTextSplitter(TextSplitter):
"""Splitting text to tokens using sentence model tokenizer."""
def __init__(
... | from __future__ import annotations
from typing import Any, List, Optional, cast
from langchain_text_splitters.base import TextSplitter, Tokenizer, split_text_on_tokens
class SentenceTransformersTokenTextSplitter(TextSplitter):
"""Splitting text to tokens using sentence model tokenizer."""
def __init__(
... |
"""DocumentFilter that uses an LLM chain to extract the relevant parts of documents."""
from __future__ import annotations
from collections.abc import Sequence
from typing import Any, Callable, Optional, cast
from langchain_core.callbacks.manager import Callbacks
from langchain_core.documents import Document
from la... | """DocumentFilter that uses an LLM chain to extract the relevant parts of documents."""
from __future__ import annotations
from typing import Any, Callable, Dict, Optional, Sequence, cast
from langchain_core.callbacks.manager import Callbacks
from langchain_core.documents import Document
from langchain_core.language... |
import sys
from os import path
from setuptools import find_packages
from setuptools import setup
if sys.version_info < (3, 7, 0):
raise OSError(f'DocArray requires Python >=3.7, but yours is {sys.version}')
try:
pkg_name = 'docarray'
libinfo_py = path.join(pkg_name, '__init__.py')
libinfo_content = o... | import sys
from os import path
from setuptools import find_packages
from setuptools import setup
if sys.version_info < (3, 7, 0):
raise OSError(f'DocArray requires Python >=3.7, but yours is {sys.version}')
try:
pkg_name = 'docarray'
libinfo_py = path.join(pkg_name, '__init__.py')
libinfo_content = o... |
_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py'
conv_cfg = dict(type='ConvWS')
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
backbone=dict(
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://jhu/resn... | _base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py'
conv_cfg = dict(type='ConvWS')
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
backbone=dict(
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://jhu/resn... |
import asyncio
import logging
import os
import threading
import time
from functools import wraps
from uuid import uuid4
from tenacity import retry, stop_after_attempt, wait_exponential
from backend.util.process import get_service_name
logger = logging.getLogger(__name__)
def _log_prefix(resource_name: str, conn_id... | import asyncio
import logging
import os
import threading
from functools import wraps
from uuid import uuid4
from tenacity import retry, stop_after_attempt, wait_exponential
from backend.util.process import get_service_name
logger = logging.getLogger(__name__)
def _log_prefix(resource_name: str, conn_id: str):
... |
from __future__ import annotations
from copy import deepcopy
import pytest
from sentence_transformers import SparseEncoder
@pytest.fixture(scope="session")
def _splade_bert_tiny_model() -> SparseEncoder:
model = SparseEncoder("sparse-encoder-testing/splade-bert-tiny-nq")
model.model_card_data.generate_widg... | from __future__ import annotations
import pytest
from sentence_transformers import SparseEncoder
@pytest.fixture()
def splade_bert_tiny_model() -> SparseEncoder:
return SparseEncoder("sparse-encoder-testing/splade-bert-tiny-nq")
@pytest.fixture(scope="session")
def splade_bert_tiny_model_reused() -> SparseEnc... |
# Copyright (c) OpenMMLab. All rights reserved.
from abc import ABCMeta, abstractmethod
from typing import List, Tuple, Union
from mmcv.runner import BaseModule
from torch import Tensor
from mmdet.core.utils import (InstanceList, OptInstanceList, OptMultiConfig,
OptSamplingResultList, Sa... | # Copyright (c) OpenMMLab. All rights reserved.
from abc import ABCMeta, abstractmethod
from mmcv.runner import BaseModule
class BaseMaskHead(BaseModule, metaclass=ABCMeta):
"""Base class for mask heads used in One-Stage Instance Segmentation."""
def __init__(self, init_cfg):
super(BaseMaskHead, sel... |
from __future__ import annotations
from pathlib import Path
from unittest.mock import Mock, PropertyMock
import pytest
import torch
from sentence_transformers import SentenceTransformer
from sentence_transformers.evaluation import InformationRetrievalEvaluator
from sentence_transformers.util import cos_sim
@pytest... | from __future__ import annotations
from pathlib import Path
from unittest.mock import Mock, PropertyMock
import pytest
import torch
from sentence_transformers import SentenceTransformer
from sentence_transformers.evaluation import InformationRetrievalEvaluator
from sentence_transformers.util import cos_sim
@pytest... |
_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py'
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
backbone=dict(
norm_cfg=norm_cfg,
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://contrib/resnet50_gn')),
neck=dict(norm_cfg=norm_cfg),
roi_... | _base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py'
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
backbone=dict(
norm_cfg=norm_cfg,
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://contrib/resnet50_gn')),
neck=dict(norm_cfg=norm_cfg),
roi_... |
import os
import subprocess
import sys
import pytest
from xgboost import testing as tm
DEMO_DIR = tm.demo_dir(__file__)
PYTHON_DEMO_DIR = os.path.join(DEMO_DIR, "guide-python")
@pytest.mark.skipif(**tm.no_cupy())
def test_data_iterator():
script = os.path.join(PYTHON_DEMO_DIR, "quantile_data_iterator.py")
... | import os
import subprocess
import sys
import pytest
from xgboost import testing as tm
sys.path.append("tests/python")
import test_demos as td # noqa
@pytest.mark.skipif(**tm.no_cupy())
def test_data_iterator():
script = os.path.join(td.PYTHON_DEMO_DIR, "quantile_data_iterator.py")
cmd = ["python", script... |
import os
import warnings
from pathlib import Path
import torch
from torchaudio._internal import module_utils as _mod_utils # noqa: F401
_LIB_DIR = Path(__file__).parent / "lib"
def _get_lib_path(lib: str):
suffix = "pyd" if os.name == "nt" else "so"
path = _LIB_DIR / f"{lib}.{suffix}"
return path
de... | import os
import warnings
from pathlib import Path
import torch
from torchaudio._internal import module_utils as _mod_utils # noqa: F401
_LIB_DIR = Path(__file__).parent / "lib"
def _get_lib_path(lib: str):
suffix = "pyd" if os.name == "nt" else "so"
path = _LIB_DIR / f"{lib}.{suffix}"
return path
de... |
from .filtering import (
allpass_biquad,
band_biquad,
bandpass_biquad,
bandreject_biquad,
bass_biquad,
biquad,
contrast,
dcshift,
deemph_biquad,
dither,
equalizer_biquad,
filtfilt,
flanger,
gain,
highpass_biquad,
lfilter,
lowpass_biquad,
overdrive,... | from .filtering import (
allpass_biquad,
band_biquad,
bandpass_biquad,
bandreject_biquad,
bass_biquad,
biquad,
contrast,
dcshift,
deemph_biquad,
dither,
equalizer_biquad,
filtfilt,
flanger,
gain,
highpass_biquad,
lfilter,
lowpass_biquad,
overdrive,... |
# Copyright (c) OpenMMLab. All rights reserved.
from unittest.mock import Mock
from mmengine.hooks import IterTimerHook
class TestIterTimerHook:
def test_before_epoch(self):
hook = IterTimerHook()
runner = Mock()
hook._before_epoch(runner)
assert isinstance(hook.t, float)
de... | # Copyright (c) OpenMMLab. All rights reserved.
from unittest.mock import Mock
from mmengine.hooks import IterTimerHook
class TestIterTimerHook:
def test_before_epoch(self):
hook = IterTimerHook()
runner = Mock()
hook._before_epoch(runner)
assert isinstance(hook.t, float)
de... |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# This software may be used and distributed according to the terms of the Llama 2 Community License Agreement.
import fire
from llama import Llama
from typing import List
def main(
ckpt_dir: str,
tokenizer_path: str,
temperature: float = 0.6,
top_p... | # Copyright (c) Meta Platforms, Inc. and affiliates.
# This software may be used and distributed according to the terms of the Llama 2 Community License Agreement.
import fire
from llama import Llama
def main(
ckpt_dir: str,
tokenizer_path: str,
temperature: float = 0.6,
top_p: float = 0.9,
max_... |
# 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 typing import TypeVar
from docarray.typing.proto_register import _register_proto
from docarray.typing.tensor.image.abstract_image_tensor import AbstractImageTensor
from docarray.typing.tensor.tensorflow_tensor import TensorFlowTensor, metaTensorFlow
T = TypeVar('T', bound='ImageTensorFlowTensor')
@_register_pr... |
from abc import ABC
from docarray.array.storage.weaviate.backend import BackendMixin, WeaviateConfig
from docarray.array.storage.weaviate.find import FindMixin
from docarray.array.storage.weaviate.getsetdel import GetSetDelMixin
from docarray.array.storage.weaviate.seqlike import SequenceLikeMixin
__all__ = ['Storage... | from abc import ABC
from .backend import BackendMixin, WeaviateConfig
from .find import FindMixin
from .getsetdel import GetSetDelMixin
from .seqlike import SequenceLikeMixin
__all__ = ['StorageMixins', 'WeaviateConfig']
class StorageMixins(FindMixin, BackendMixin, GetSetDelMixin, SequenceLikeMixin, ABC):
...
|
"""
Pandas output parser.
DEPRECATED: This class has been moved to `llama-index-experimental`.
"""
from typing import Any
class PandasInstructionParser:
"""
Pandas instruction parser.
DEPRECATED: This class has been moved to `llama-index-experimental`.
"""
def __init__(self, *args: Any, **kwa... | """Pandas output parser.
DEPRECATED: This class has been moved to `llama-index-experimental`.
"""
from typing import Any
class PandasInstructionParser:
"""Pandas instruction parser.
DEPRECATED: This class has been moved to `llama-index-experimental`.
"""
def __init__(self, *args: Any, **kwargs: A... |
# Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the most common options. For a full
# list see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
# -- Path setup --------------------------------------------------------------
# If ex... | # Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the most common options. For a full
# list see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
# -- Path setup --------------------------------------------------------------
# If ex... |
from typing import Any, Dict, List, Optional, Sequence, Type, Union
import PIL.Image
import torch
from torchvision import datapoints
from torchvision.prototype.datapoints import Label, OneHotLabel
from torchvision.transforms.v2 import functional as F, Transform
from torchvision.transforms.v2._utils import _setup_fill... | from typing import Any, Dict, List, Optional, Sequence, Type, Union
import PIL.Image
import torch
from torchvision import datapoints
from torchvision.prototype.datapoints import Label, OneHotLabel
from torchvision.transforms.v2 import functional as F, Transform
from torchvision.transforms.v2._utils import _setup_fill... |
# Copyright (c) OpenMMLab. All rights reserved.
import warnings
import torch
from ..builder import DETECTORS
from .single_stage import SingleStageDetector
@DETECTORS.register_module()
class DETR(SingleStageDetector):
r"""Implementation of `DETR: End-to-End Object Detection with
Transformers <https://arxiv.o... | import warnings
import torch
from ..builder import DETECTORS
from .single_stage import SingleStageDetector
@DETECTORS.register_module()
class DETR(SingleStageDetector):
r"""Implementation of `DETR: End-to-End Object Detection with
Transformers <https://arxiv.org/pdf/2005.12872>`_"""
def __init__(self,
... |
from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.callbacks.streamlit.streamlit_callback_handler import (
LLMThought,
LLMThoughtLabeler,
LLMThoughtState,
StreamlitCallbackHandler,
ToolRecord,
)
#... | from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.callbacks.streamlit.streamlit_callback_handler import (
LLMThought,
LLMThoughtLabeler,
LLMThoughtState,
StreamlitCallbackHandler,
ToolRecord,
)
#... |
# Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import torch
from mmdet.models.roi_heads.mask_heads import (DynamicMaskHead, FCNMaskHead,
MaskIoUHead)
from .utils import _dummy_bbox_sampling
def test_mask_head_loss():
"""Test mask head loss when mask tar... | # Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import torch
from mmdet.models.roi_heads.mask_heads import (DynamicMaskHead, FCNMaskHead,
MaskIoUHead)
from .utils import _dummy_bbox_sampling
def test_mask_head_loss():
"""Test mask head loss when mask tar... |
"""
This examples trains a CrossEncoder for the STSbenchmark 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 individual sentences.
Usage:... | """
This examples trains a CrossEncoder for the STSbenchmark 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 individual sentences.
Usage:... |
import base64
from typing import Any, Dict, Union, Optional
from vertexai.generative_models._generative_models import SafetySettingsType
from google.cloud.aiplatform_v1beta1.types import content as gapic_content_types
from llama_index.core.llms import ChatMessage, MessageRole
def is_gemini_model(model: str) -> bool:
... | import base64
from typing import Any, Dict, Union, Optional
from vertexai.generative_models._generative_models import SafetySettingsType
from google.cloud.aiplatform_v1beta1.types import content as gapic_content_types
from llama_index.core.llms import ChatMessage, MessageRole
def is_gemini_model(model: str) -> bool:
... |
"""
OPUS (http://opus.nlpl.eu/) is a great collection of different parallel datasets for more than 400 languages.
On the website, you can download parallel datasets for many languages in different formats. I found that
the format "Bottom-left triangle: download plain text files (MOSES/GIZA++)" requires minimal
overhea... | """
OPUS (http://opus.nlpl.eu/) is a great collection of different parallel datasets for more than 400 languages.
On the website, you can download parallel datasets for many languages in different formats. I found that
the format "Bottom-left triangle: download plain text files (MOSES/GIZA++)" requires minimal
overhea... |
__version__ = '0.30.0a3'
import logging
from docarray.array import DocList, DocVec
from docarray.base_doc.doc import BaseDoc
__all__ = ['BaseDoc', 'DocList', 'DocVec']
logger = logging.getLogger('docarray')
handler = logging.StreamHandler()
formatter = logging.Formatter("%(levelname)s - %(name)s - %(message)s")
ha... | __version__ = '0.30.0a3'
import logging
from docarray.array import DocArray, DocArrayStacked
from docarray.base_doc.doc import BaseDoc
__all__ = ['BaseDoc', 'DocArray', 'DocArrayStacked']
logger = logging.getLogger('docarray')
handler = logging.StreamHandler()
formatter = logging.Formatter("%(levelname)s - %(name)... |
"""
Tatoeba (https://tatoeba.org/) is a collection of sentences and translation, mainly aiming for language learning.
It is available for more than 300 languages.
This script downloads the Tatoeba corpus and extracts the sentences & translations in the languages you like
"""
import gzip
import os
import tarfile
impo... | """
Tatoeba (https://tatoeba.org/) is a collection of sentences and translation, mainly aiming for language learning.
It is available for more than 300 languages.
This script downloads the Tatoeba corpus and extracts the sentences & translations in the languages you like
"""
import os
import sentence_transformers
imp... |
import os
import numpy as np
import pytest
from docarray import BaseDoc, DocList, DocVec
from docarray.documents import ImageDoc
from docarray.typing import NdArray, TorchTensor
class MyDoc(BaseDoc):
embedding: NdArray
text: str
image: ImageDoc
@pytest.mark.slow
@pytest.mark.parametrize(
'protocol... | import os
import numpy as np
import pytest
from docarray import BaseDoc, DocList, DocVec
from docarray.documents import ImageDoc
from docarray.typing import NdArray
class MyDoc(BaseDoc):
embedding: NdArray
text: str
image: ImageDoc
@pytest.mark.slow
@pytest.mark.parametrize(
'protocol', ['pickle-a... |
import pyarrow as pa
import pytest
from datasets.builder import InvalidConfigName
from datasets.data_files import DataFilesList
from datasets.packaged_modules.arrow.arrow import Arrow, ArrowConfig
@pytest.fixture
def arrow_file_streaming_format(tmp_path):
filename = tmp_path / "stream.arrow"
testdata = [[1, ... | import pytest
from datasets.builder import InvalidConfigName
from datasets.data_files import DataFilesList
from datasets.packaged_modules.arrow.arrow import ArrowConfig
def test_config_raises_when_invalid_name() -> None:
with pytest.raises(InvalidConfigName, match="Bad characters"):
_ = ArrowConfig(name=... |
from __future__ import annotations
from typing import Callable
try:
from typing import Self
except ImportError:
from typing_extensions import Self
from torch import Tensor, nn
from sentence_transformers.models.Module import Module
from sentence_transformers.util import fullname, import_from_string
class D... | from __future__ import annotations
import json
import os
from typing import Callable
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_... |
__copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
from typing import Callable
import numpy as np
import pytest
from jina import Document, DocumentArray, Flow
from ...audioclip_image import AudioCLIPImageEncoder
@pytest.mark.parametrize("request_size", [1, 10... | __copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
from typing import Callable
import numpy as np
import pytest
from jina import Document, DocumentArray, Flow
from jinahub.encoder.audioclip_image import AudioCLIPImageEncoder
@pytest.mark.parametrize("request_... |
from setuptools import find_packages, setup
with open("README.md", mode="r", encoding="utf-8") as readme_file:
readme = readme_file.read()
setup(
name="sentence-transformers",
version="3.1.0.dev0",
author="Nils Reimers, Tom Aarsen",
author_email="info@nils-reimers.de",
description="Multilingu... | from setuptools import find_packages, setup
with open("README.md", mode="r", encoding="utf-8") as readme_file:
readme = readme_file.read()
setup(
name="sentence-transformers",
version="3.1.0.dev0",
author="Nils Reimers, Tom Aarsen",
author_email="info@nils-reimers.de",
description="Multilingu... |
"""Rss reader."""
from typing import List, Any, Union
import logging
from llama_index.core.readers.base import BasePydanticReader
from llama_index.core.schema import Document
logger = logging.getLogger(__name__)
class RssReader(BasePydanticReader):
"""RSS reader.
Reads content from an RSS feed.
"""
... | """Rss reader."""
from typing import List
from llama_index.core.readers.base import BasePydanticReader
from llama_index.core.schema import Document
class RssReader(BasePydanticReader):
"""RSS reader.
Reads content from an RSS feed.
"""
is_remote: bool = True
html_to_text: bool = False
@c... |
"""Tool for the SemanticScholar API."""
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.utilities.semanticscholar import SemanticScholarAPIWrapper
class Semantsc... | """Tool for the SemanticScholar API."""
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.utilities.semanticscholar import SemanticScholarAPIWrapper
class Semantsc... |
# Copyright (c) OpenMMLab. All rights reserved.
import os
import os.path as osp
import warnings
import numpy as np
import onnx
import onnxruntime as ort
import torch
import torch.nn as nn
ort_custom_op_path = ''
try:
from mmcv.ops import get_onnxruntime_op_path
ort_custom_op_path = get_onnxruntime_op_path()
e... | import os
import os.path as osp
import warnings
import numpy as np
import onnx
import onnxruntime as ort
import torch
import torch.nn as nn
ort_custom_op_path = ''
try:
from mmcv.ops import get_onnxruntime_op_path
ort_custom_op_path = get_onnxruntime_op_path()
except (ImportError, ModuleNotFoundError):
wa... |
import os
from pathlib import Path
from jina import Executor
def test_config():
ex = Executor.load_config(
str(Path(__file__).parents[2] / 'config.yml'),
override_with={
'query_features': ['query'],
'match_features': ['match'],
'relevance_label': 'rel',
... | import os
def test_init(ranker):
assert not ranker.model.is_fitted()
def test_train(ranker, documents_to_train_stub_model):
ranker.train(docs=documents_to_train_stub_model)
assert ranker.model.is_fitted()
def test_train_with_weights(ranker_with_weight, documents_to_train_stub_model):
"""Weight fie... |
"""LangChain **Runnable** and the **LangChain Expression Language (LCEL)**.
The LangChain Expression Language (LCEL) offers a declarative method to build
production-grade programs that harness the power of LLMs.
Programs created using LCEL and LangChain Runnables inherently support
synchronous, asynchronous, batch, a... | """LangChain **Runnable** and the **LangChain Expression Language (LCEL)**.
The LangChain Expression Language (LCEL) offers a declarative method to build
production-grade programs that harness the power of LLMs.
Programs created using LCEL and LangChain Runnables inherently support
synchronous, asynchronous, batch, a... |
from typing import TYPE_CHECKING, Any, List, Tuple, Type, TypeVar, Union
import numpy as np
from docarray.typing.proto_register import _register_proto
from docarray.typing.tensor.ndarray import NdArray
from docarray.typing.tensor.video.video_tensor_mixin import VideoTensorMixin
T = TypeVar('T', bound='VideoNdArray')... | from typing import TYPE_CHECKING, Any, List, Tuple, Type, TypeVar, Union
import numpy as np
from docarray.typing.tensor.ndarray import NdArray
from docarray.typing.tensor.video.video_tensor_mixin import VideoTensorMixin
T = TypeVar('T', bound='VideoNdArray')
if TYPE_CHECKING:
from pydantic import BaseConfig
... |
# Copyright (c) OpenMMLab. All rights reserved.
import unittest
from unittest import TestCase
import torch
from parameterized import parameterized
from mmdet.data_elements import DetDataSample
from mmdet.testing import demo_mm_inputs, get_detector_cfg
from mmdet.utils import register_all_modules
class TestSingleSta... | # Copyright (c) OpenMMLab. All rights reserved.
import unittest
from unittest import TestCase
import torch
from parameterized import parameterized
from mmdet.core import DetDataSample
from mmdet.testing import demo_mm_inputs, get_detector_cfg
from mmdet.utils import register_all_modules
class TestSingleStageInstanc... |
__copyright__ = "Copyright (c) 2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
from typing import Callable, List
import pytest
from jina import DocumentArray, Flow
from ...transform_encoder import TransformerTorchEncoder
@pytest.mark.parametrize("request_size", [1, 10, 50, 100])
def test_inte... | __copyright__ = "Copyright (c) 2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
from typing import Callable, List
import pytest
from jina import DocumentArray, Flow
from ...transform_encoder import TransformerTorchEncoder
@pytest.mark.parametrize("request_size", [1, 10, 50, 100])
def test_inte... |
# Copyright (c) OpenMMLab. All rights reserved.
from mmengine.hooks import Hook
from mmdet.registry import HOOKS
@HOOKS.register_module()
class FastStopTrainingHook(Hook):
"""Set runner's epoch information to the model."""
def __init__(self, by_epoch, save_ckpt=False, stop_iter_or_epoch=5):
self.by_... | # Copyright (c) OpenMMLab. All rights reserved.
from mmengine.hooks import Hook
from mmdet.registry import HOOKS
@HOOKS.register_module()
class FastStopTrainingHook(Hook):
"""Set runner's epoch information to the model."""
def after_train_iter(self, runner, batch_idx: int, data_batch: None,
... |
import os
from pathlib import Path
import pytest
from jina import Flow
from jina.excepts import RuntimeFailToStart
from jina.orchestrate.deployments import Deployment
from jina.parsers import set_deployment_parser
from jina.serve.executors import BaseExecutor
cur_dir = os.path.dirname(os.path.abspath(__file__))
de... | import os
from pathlib import Path
import pytest
from jina import Flow
from jina.excepts import RuntimeFailToStart
from jina.orchestrate.deployments import Deployment
from jina.parsers import set_deployment_parser
from jina.serve.executors import BaseExecutor
cur_dir = os.path.dirname(os.path.abspath(__file__))
de... |
# Copyright (c) OpenMMLab. All rights reserved.
from .vis_backend import (BaseVisBackend, ClearMLVisBackend, LocalVisBackend,
MLflowVisBackend, NeptuneVisBackend,
TensorboardVisBackend, WandbVisBackend)
from .visualizer import Visualizer
__all__ = [
'Visualizer',... | # Copyright (c) OpenMMLab. All rights reserved.
from .vis_backend import (BaseVisBackend, ClearMLVisBackend, LocalVisBackend,
MLflowVisBackend, TensorboardVisBackend,
WandbVisBackend)
from .visualizer import Visualizer
__all__ = [
'Visualizer', 'BaseVisBackend', ... |
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