input stringlengths 33 5k | output stringlengths 32 5k |
|---|---|
# 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... | TEXT_MIMETYPE = 'text'
AUDIO_MIMETYPE = 'audio'
IMAGE_MIMETYPE = 'image'
OBJ_MIMETYPE = 'application/x-tgif'
VIDEO_MIMETYPE = 'video'
MESH_EXTRA_EXTENSIONS = [
'3ds',
'3mf',
'ac',
'ac3d',
'amf',
'assimp',
'bvh',
'cob',
'collada',
'ctm',
'dxf',
'e57',
'fbx',
'gltf... |
import contextlib
import os
import sqlite3
import pytest
from datasets import Dataset, Features, Value
from datasets.io.sql import SqlDatasetReader, SqlDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases, require_sqlalchemy
def _check_sql_dataset(dataset, expected_f... | import contextlib
import os
import sqlite3
import pytest
from datasets import Dataset, Features, Value
from datasets.io.sql import SqlDatasetReader, SqlDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases, require_sqlalchemy
def _check_sql_dataset(dataset, expected_f... |
from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.document_loaders.parsers.html.bs4 import BS4HTMLParser
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handling ... | from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.document_loaders.parsers.html.bs4 import BS4HTMLParser
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handling ... |
# Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
import pytest
from mmengine import Config, DefaultScope
from mmengine.hub import get_config, get_model
from mmengine.utils import get_installed_path, is_installed
data_path = osp.join(osp.dirname(osp.dirname(__file__)), 'data/')
# mmdet has a mo... | # Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
import pytest
from mmengine import Config, DefaultScope
from mmengine.hub import get_config, get_model
from mmengine.utils import get_installed_path, is_installed
data_path = osp.join(osp.dirname(osp.dirname(__file__)), 'data/')
# mmdet has a mo... |
_base_ = '../mask_rcnn/mask-rcnn_r50_fpn_1x_coco.py'
train_pipeline = [
dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}),
dict(
type='InstaBoost',
action_candidate=('normal', 'horizontal', 'skip'),
action_prob=(1, 0, 0),
scale=(0.8, 1.2),
dx=15,
... | _base_ = '../mask_rcnn/mask-rcnn_r50_fpn_1x_coco.py'
train_pipeline = [
dict(
type='LoadImageFromFile',
file_client_args={{_base_.file_client_args}}),
dict(
type='InstaBoost',
action_candidate=('normal', 'horizontal', 'skip'),
action_prob=(1, 0, 0),
scale=(0.8, 1... |
from typing import List, Optional
import torchaudio
from torchaudio._internal.module_utils import deprecated
from . import utils
# TODO: Once legacy global backend is removed, move this to torchaudio.__init__
def _init_backend():
torchaudio.info = utils.get_info_func()
torchaudio.load = utils.get_load_func(... | import warnings
from typing import List, Optional
import torchaudio
from . import utils
# TODO: Once legacy global backend is removed, move this to torchaudio.__init__
def _init_backend():
torchaudio.info = utils.get_info_func()
torchaudio.load = utils.get_load_func()
torchaudio.save = utils.get_save_fu... |
from __future__ import annotations
from collections.abc import Collection
from dataclasses import dataclass, field
from typing import Any, Callable
import torch
from sentence_transformers.data_collator import SentenceTransformerDataCollator
@dataclass
class CrossEncoderDataCollator(SentenceTransformerDataCollator)... | from __future__ import annotations
from dataclasses import field
from typing import Any, Callable
import torch
from sentence_transformers.data_collator import SentenceTransformerDataCollator
class CrossEncoderDataCollator(SentenceTransformerDataCollator):
"""Collator for a CrossEncoder model.
This encodes ... |
from __future__ import annotations
from sentence_transformers.sparse_encoder.evaluation.ReciprocalRankFusionEvaluator import (
ReciprocalRankFusionEvaluator,
)
from sentence_transformers.sparse_encoder.evaluation.SparseBinaryClassificationEvaluator import (
SparseBinaryClassificationEvaluator,
)
from sentence_... | from __future__ import annotations
from sentence_transformers.sparse_encoder.evaluation.SparseBinaryClassificationEvaluator import (
SparseBinaryClassificationEvaluator,
)
from sentence_transformers.sparse_encoder.evaluation.SparseEmbeddingSimilarityEvaluator import (
SparseEmbeddingSimilarityEvaluator,
)
from... |
from abc import abstractmethod
from typing import Iterable, Iterator
from qdrant_client import QdrantClient
from qdrant_client.http.exceptions import UnexpectedResponse
from qdrant_client.http.models.models import (
PointIdsList,
PointStruct,
VectorParams,
)
from docarray import Document
from docarray.arr... | from abc import abstractmethod
from typing import Iterable, Iterator
from qdrant_client import QdrantClient
from qdrant_client.http.exceptions import UnexpectedResponse
from qdrant_client.http.models.models import (
PointIdsList,
PointsList,
ScrollRequest,
PointStruct,
)
from docarray import Document
... |
"""OpenAI Finetuning."""
import logging
import os
import time
from typing import Any, Optional
import openai
from openai import OpenAI as SyncOpenAI
from openai.types.fine_tuning import FineTuningJob
from llama_index.core.llms.llm import LLM
from llama_index.finetuning.callbacks.finetuning_handler import OpenAIFineT... | """OpenAI Finetuning."""
import logging
import os
import time
from typing import Any, Optional
import openai
from openai import OpenAI as SyncOpenAI
from openai.types.fine_tuning import FineTuningJob
from llama_index.core.llms.llm import LLM
from llama_index.finetuning.callbacks.finetuning_handler import OpenAIFineT... |
import torchaudio
try:
torchaudio._extension._load_lib("libtorchaudio_decoder")
from .ctc_decoder import Hypothesis, CTCDecoder, ctc_decoder, lexicon_decoder, download_pretrained_files
except ImportError as err:
raise ImportError(
"flashlight decoder bindings are required to use this functionality.... | import torchaudio
try:
torchaudio._extension._load_lib("libtorchaudio_decoder")
from .ctc_decoder import Hypothesis, LexiconDecoder, lexicon_decoder, download_pretrained_files
except ImportError as err:
raise ImportError(
"flashlight decoder bindings are required to use this functionality. "
... |
from typing import Dict, Optional, Type
from langchain_core.callbacks import CallbackManagerForToolRun
from pydantic import BaseModel, Field
from langchain_community.tools.gmail.base import GmailBaseTool
class GetThreadSchema(BaseModel):
"""Input for GetMessageTool."""
# From https://support.google.com/mai... | from typing import Dict, Optional, Type
from langchain_core.callbacks import CallbackManagerForToolRun
from pydantic import BaseModel, Field
from langchain_community.tools.gmail.base import GmailBaseTool
class GetThreadSchema(BaseModel):
"""Input for GetMessageTool."""
# From https://support.google.com/mai... |
import os
import re
import subprocess
from keras.src import backend
# For torch, use index url to avoid installing nvidia drivers for the test.
BACKEND_REQ = {
"tensorflow": ("tensorflow-cpu", ""),
"torch": (
"torch",
"--extra-index-url https://download.pytorch.org/whl/cpu ",
),
"jax":... | import os
import re
import subprocess
from keras.src import backend
# For torch, use index url to avoid installing nvidia drivers for the test.
BACKEND_REQ = {
"tensorflow": ("tensorflow-cpu", ""),
"torch": (
"torch",
"--extra-index-url https://download.pytorch.org/whl/cpu ",
),
"jax":... |
"""Test embedding utility functions."""
import numpy as np
from llama_index.core.indices.query.embedding_utils import (
get_top_k_embeddings,
get_top_k_mmr_embeddings,
)
def test_get_top_k_mmr_embeddings() -> None:
"""Test Maximum Marginal Relevance."""
# Results score should follow from the mmr algo... | """ Test embedding utility functions."""
import numpy as np
from llama_index.core.indices.query.embedding_utils import (
get_top_k_embeddings,
get_top_k_mmr_embeddings,
)
def test_get_top_k_mmr_embeddings() -> None:
"""Test Maximum Marginal Relevance."""
# Results score should follow from the mmr alg... |
from markitdown import MarkItDown
from llama_index.core.bridge.pydantic import BaseModel, model_validator
import os
from pathlib import Path
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document
from typing import Tuple, Optional, Union, List
from typing_extensions imp... | from markitdown import MarkItDown
from llama_index.core.bridge.pydantic import BaseModel, model_validator
import os
from pathlib import Path
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document
from typing import Tuple, Optional, Union, List
from typing_extensions imp... |
from __future__ import annotations
from sentence_transformers.losses.GISTEmbedLoss import GISTEmbedLoss
from sentence_transformers.sparse_encoder.SparseEncoder import SparseEncoder
class SparseGISTEmbedLoss(GISTEmbedLoss):
def __init__(
self,
model: SparseEncoder,
guide: SparseEncoder,
... | from __future__ import annotations
from sentence_transformers.losses.GISTEmbedLoss import GISTEmbedLoss
from sentence_transformers.sparse_encoder.SparseEncoder import SparseEncoder
class SparseGISTEmbedLoss(GISTEmbedLoss):
def __init__(
self,
model: SparseEncoder,
guide: SparseEncoder,
... |
"""Argparser module for Flow"""
from jina.parsers.base import set_base_parser
from jina.parsers.helper import KVAppendAction, add_arg_group
from jina.parsers.orchestrate.base import mixin_essential_parser
def mixin_flow_features_parser(parser):
"""Add the arguments for the Flow features to the parser
:param... | """Argparser module for Flow"""
from jina.parsers.base import set_base_parser
from jina.parsers.helper import KVAppendAction, add_arg_group
from jina.parsers.orchestrate.base import mixin_essential_parser
def mixin_flow_features_parser(parser):
"""Add the arguments for the Flow features to the parser
:param... |
from .faiss_lmdb import FaissLMDBSearcher
| from .faiss_lmdb import FaissLMDBSearcher |
from typing import Any, Optional, Type, TypeVar, Union
from docarray.base_doc import BaseDoc
from docarray.typing import TextUrl
from docarray.typing.tensor.embedding import AnyEmbedding
T = TypeVar('T', bound='TextDoc')
class TextDoc(BaseDoc):
"""
Document for handling text.
It can contain:
- a [... | from typing import Any, Optional, Type, TypeVar, Union
from docarray.base_doc import BaseDoc
from docarray.typing import TextUrl
from docarray.typing.tensor.embedding import AnyEmbedding
T = TypeVar('T', bound='TextDoc')
class TextDoc(BaseDoc):
"""
Document for handling text.
It can contain a TextUrl (`... |
from os.path import join
from pathlib import Path
from typing import Any, Callable, List, Optional, Tuple, Union
from PIL import Image
from .utils import check_integrity, download_and_extract_archive, list_dir, list_files
from .vision import VisionDataset
class Omniglot(VisionDataset):
"""`Omniglot <https://git... | from os.path import join
from pathlib import Path
from typing import Any, Callable, List, Optional, Tuple, Union
from PIL import Image
from .utils import check_integrity, download_and_extract_archive, list_dir, list_files
from .vision import VisionDataset
class Omniglot(VisionDataset):
"""`Omniglot <https://git... |
import types
from typing import TYPE_CHECKING
from docarray.index.backends.in_memory import InMemoryExactNNIndex
from docarray.utils._internal.misc import (
_get_path_from_docarray_root_level,
import_library,
)
if TYPE_CHECKING:
from docarray.index.backends.elastic import ElasticDocIndex # noqa: F401
... | import types
from typing import TYPE_CHECKING
from docarray.index.backends.in_memory import InMemoryExactNNIndex
from docarray.utils._internal.misc import (
_get_path_from_docarray_root_level,
import_library,
)
if TYPE_CHECKING:
from docarray.index.backends.elastic import ElasticDocIndex # noqa: F401
... |
import subprocess
from pathlib import Path
import pytest
@pytest.fixture(scope='session')
def docker_image_name() -> str:
return Path(__file__).parents[1].stem.lower()
@pytest.fixture(scope='session')
def build_docker_image(docker_image_name: str) -> str:
subprocess.run(['docker', 'build', '-t', docker_ima... | import random
import pytest
from jina import Document, DocumentArray
@pytest.fixture
def documents_chunk():
document_array = DocumentArray()
document = Document(tags={'query_size': 35, 'query_price': 31, 'query_brand': 1})
for i in range(0, 10):
chunk = Document()
for j in range(0, 10):
... |
"""Argparser module for pinging"""
from jina.parsers.base import set_base_parser
def set_ping_parser(parser=None):
"""Set the parser for `ping`
:param parser: an existing parser to build upon
:return: the parser
"""
if not parser:
parser = set_base_parser()
parser.add_argument(
... | """Argparser module for pinging"""
from jina.parsers.base import set_base_parser
def set_ping_parser(parser=None):
"""Set the parser for `ping`
:param parser: an existing parser to build upon
:return: the parser
"""
if not parser:
parser = set_base_parser()
parser.add_argument(
... |
_base_ = '../fast_rcnn/fast-rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=False),
norm_eval=True,
style='caffe',
in... | _base_ = '../fast_rcnn/fast_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=False),
norm_eval=True,
style='caffe',
in... |
import asyncio
from typing import AsyncIterator, Iterator, Optional, Union
from jina.helper import get_or_reuse_loop
class _RequestsCounter:
"""Class used to wrap a count integer so that it can be updated inside methods.
.. code-block:: python
def count_increment(i: int, rc: _RequestsCounter):
... | import asyncio
from typing import AsyncIterator, Iterator, Optional, Union
from jina.helper import get_or_reuse_loop
class _RequestsCounter:
"""Class used to wrap a count integer so that it can be updated inside methods.
.. code-block:: python
def count_increment(i: int, rc: _RequestsCounter):
... |
"""DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.applications import convnext as convnext
from keras.applications import densenet as densenet
from keras.applications import efficientnet as efficientnet
from keras.applications import eff... | """DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.api.applications import convnext
from keras.api.applications import densenet
from keras.api.applications import efficientnet
from keras.api.applications import efficientnet_v2
from keras.... |
# Copyright (c) OpenMMLab. All rights reserved.
from .augment_wrappers import AutoAugment, RandAugment
from .colorspace import (AutoContrast, Brightness, Color, ColorTransform,
Contrast, Equalize, Invert, Posterize, Sharpness,
Solarize, SolarizeAdd)
from .formatting imp... | # Copyright (c) OpenMMLab. All rights reserved.
from .augment_wrappers import AutoAugment, RandAugment
from .colorspace import (AutoContrast, Brightness, Color, ColorTransform,
Contrast, Equalize, Invert, Posterize, Sharpness,
Solarize, SolarizeAdd)
from .formatting imp... |
from collections.abc import Sequence
from langchain_core.tools import BaseTool
def validate_tools_single_input(class_name: str, tools: Sequence[BaseTool]) -> None:
"""Validate tools for single input.
Args:
class_name: Name of the class.
tools: List of tools to validate.
Raises:
... | from collections.abc import Sequence
from langchain_core.tools import BaseTool
def validate_tools_single_input(class_name: str, tools: Sequence[BaseTool]) -> None:
"""Validate tools for single input.
Args:
class_name: Name of the class.
tools: List of tools to validate.
Raises:
... |
"""Zapier Tool."""
from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.tools import ZapierNLAListActions, ZapierNLARunAction
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warni... | """Zapier Tool."""
from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.tools import ZapierNLAListActions, ZapierNLARunAction
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warni... |
# Copyright (c) OpenMMLab. All rights reserved.
"""MMEngine provides 11 root registries to support using modules across
projects.
More datails can be found at
https://mmengine.readthedocs.io/en/latest/tutorials/registry.html.
"""
from .registry import Registry
# manage all kinds of runners like `EpochBasedRunner` an... | # Copyright (c) OpenMMLab. All rights reserved.
"""MMEngine provides 11 root registries to support using modules across
projects.
More datails can be found at
https://mmengine.readthedocs.io/en/latest/tutorials/registry.html.
"""
from .registry import Registry
# manage all kinds of runners like `EpochBasedRunner` an... |
from collections.abc import Sequence
from typing import Callable
from langchain_core.agents import AgentAction
from langchain_core.language_models import BaseLanguageModel
from langchain_core.messages import BaseMessage
from langchain_core.prompts.chat import ChatPromptTemplate
from langchain_core.runnables import Run... | from typing import Callable, List, Sequence, Tuple
from langchain_core.agents import AgentAction
from langchain_core.language_models import BaseLanguageModel
from langchain_core.messages import BaseMessage
from langchain_core.prompts.chat import ChatPromptTemplate
from langchain_core.runnables import Runnable, Runnabl... |
import logging
import pathlib
from postmarker.core import PostmarkClient
from postmarker.models.emails import EmailManager
from prisma.enums import NotificationType
from pydantic import BaseModel
from backend.data.notifications import (
NotificationEventModel,
NotificationTypeOverride,
T_co,
)
from backen... | import logging
import pathlib
from postmarker.core import PostmarkClient
from postmarker.models.emails import EmailManager
from prisma.enums import NotificationType
from pydantic import BaseModel
from backend.data.notifications import (
NotificationEventModel,
NotificationTypeOverride,
T_co,
)
from backen... |
import io
import pathlib
from collections import namedtuple
from typing import Any, Dict, Iterator, List, Optional, Tuple, Union
from torchdata.datapipes.iter import IterDataPipe, Mapper, Zipper
from torchvision.prototype.datapoints import Image, Label
from torchvision.prototype.datasets.utils import Dataset, GDriveRe... | import io
import pathlib
from collections import namedtuple
from typing import Any, Dict, Iterator, List, Optional, Tuple, Union
from torchdata.datapipes.iter import IterDataPipe, Mapper, Zipper
from torchvision.prototype import features
from torchvision.prototype.datasets.utils import Dataset, GDriveResource, OnlineR... |
import logging
import os
import sys
from torchaudio._internal.module_utils import fail_with_message, is_module_available, no_op
from .utils import _check_cuda_version, _fail_since_no_ffmpeg, _init_dll_path, _init_ffmpeg, _init_sox, _load_lib
_LG = logging.getLogger(__name__)
# Note:
# `_check_cuda_version` is not ... | import logging
import os
import sys
from torchaudio._internal.module_utils import fail_with_message, is_module_available, no_op
from .utils import _check_cuda_version, _fail_since_no_ffmpeg, _init_dll_path, _init_ffmpeg, _init_sox, _load_lib
_LG = logging.getLogger(__name__)
# Note:
# `_check_cuda_version` is not ... |
"""
This script runs the evaluation of an SBERT msmarco model on the
MS MARCO dev dataset and reports different performances metrices for cossine similarity & dot-product.
Usage:
python eval_msmarco.py model_name [max_corpus_size_in_thousands]
"""
import logging
import os
import sys
import tarfile
from sentence_tran... | """
This script runs the evaluation of an SBERT msmarco model on the
MS MARCO dev dataset and reports different performances metrices for cossine similarity & dot-product.
Usage:
python eval_msmarco.py model_name [max_corpus_size_in_thousands]
"""
from sentence_transformers import LoggingHandler, SentenceTransformer,... |
from io import BytesIO
from typing import TYPE_CHECKING, Any, NamedTuple, Type, TypeVar
import numpy as np
from pydantic import parse_obj_as
from pydantic.validators import bytes_validator
from docarray.typing import AudioNdArray, NdArray, VideoNdArray
from docarray.typing.abstract_type import AbstractType
from docar... | from io import BytesIO
from typing import TYPE_CHECKING, Any, NamedTuple, Type, TypeVar
import numpy as np
from pydantic import parse_obj_as
from pydantic.validators import bytes_validator
from docarray.typing import AudioNdArray, NdArray, VideoNdArray
from docarray.typing.abstract_type import AbstractType
from docar... |
import logging
import aiohttp
from fastapi import APIRouter
from backend.util.settings import Settings
from .models import TurnstileVerifyRequest, TurnstileVerifyResponse
logger = logging.getLogger(__name__)
router = APIRouter()
settings = Settings()
@router.post("/verify", response_model=TurnstileVerifyResponse... | import logging
import aiohttp
from fastapi import APIRouter
from backend.util.settings import Settings
from .models import TurnstileVerifyRequest, TurnstileVerifyResponse
logger = logging.getLogger(__name__)
router = APIRouter()
settings = Settings()
@router.post("/verify", response_model=TurnstileVerifyResponse... |
import os
import fsspec
import pytest
from datasets.filesystems import COMPRESSION_FILESYSTEMS, HfFileSystem, extract_path_from_uri, is_remote_filesystem
from datasets.utils._hf_hub_fixes import dataset_info as hf_api_dataset_info
from .utils import require_lz4, require_zstandard
def test_extract_path_from_uri():
... | import os
import fsspec
import pytest
from datasets.filesystems import COMPRESSION_FILESYSTEMS, HfFileSystem, extract_path_from_uri, is_remote_filesystem
from datasets.utils._hf_hub_fixes import dataset_info as hf_api_dataset_info
from .utils import require_lz4, require_zstandard
def test_extract_path_from_uri():
... |
"""
This script downloads the WikiMatrix corpus (https://github.com/facebookresearch/LASER/tree/master/tasks/WikiMatrix)
and create parallel sentences tsv files that can be used to extend existent sentence embedding models to new languages.
The WikiMatrix mined parallel sentences from Wikipedia in various languages.
... | """
This script downloads the WikiMatrix corpus (https://github.com/facebookresearch/LASER/tree/master/tasks/WikiMatrix)
and create parallel sentences tsv files that can be used to extend existent sentence embedding models to new languages.
The WikiMatrix mined parallel sentences from Wikipedia in various languages.
... |
"""DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.optimizers.schedules.learning_rate_schedule import (
CosineDecay as CosineDecay,
)
from keras.src.optimizers.schedules.learning_rate_schedule import (
CosineDecayRestarts as C... | """DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.optimizers.schedules.learning_rate_schedule import CosineDecay
from keras.src.optimizers.schedules.learning_rate_schedule import (
CosineDecayRestarts,
)
from keras.src.optimizers... |
import torch
from torchvision import _BETA_TRANSFORMS_WARNING, _WARN_ABOUT_BETA_TRANSFORMS
from ._bounding_box import BoundingBoxes, BoundingBoxFormat
from ._datapoint import Datapoint
from ._image import Image
from ._mask import Mask
from ._torch_function_helpers import set_return_type
from ._video import Video
if _... | from torchvision import _BETA_TRANSFORMS_WARNING, _WARN_ABOUT_BETA_TRANSFORMS
from ._bounding_box import BoundingBoxes, BoundingBoxFormat
from ._datapoint import Datapoint
from ._image import Image
from ._mask import Mask
from ._video import Video
if _WARN_ABOUT_BETA_TRANSFORMS:
import warnings
warnings.warn... |
import numpy as np
import pytest
from docarray.computation.numpy_backend import NumpyCompBackend
def test_to_device():
with pytest.raises(NotImplementedError):
NumpyCompBackend.to_device(np.random.rand(10, 3), 'meta')
@pytest.mark.parametrize(
'array,result',
[
(np.zeros((5)), 1),
... | import numpy as np
import pytest
from docarray.computation.numpy_backend import NumpyCompBackend
def test_to_device():
with pytest.raises(NotImplementedError):
NumpyCompBackend.to_device(np.random.rand(10, 3), 'meta')
def test_empty():
array = NumpyCompBackend.empty((10, 3))
assert array.shape ... |
"""Class for a VectorStore-backed memory object."""
from collections.abc import Sequence
from typing import Any, Optional, Union
from langchain_core._api import deprecated
from langchain_core.documents import Document
from langchain_core.memory import BaseMemory
from langchain_core.vectorstores import VectorStoreRetr... | """Class for a VectorStore-backed memory object."""
from collections.abc import Sequence
from typing import Any, Optional, Union
from langchain_core._api import deprecated
from langchain_core.documents import Document
from langchain_core.vectorstores import VectorStoreRetriever
from pydantic import Field
from langch... |
import logging
import random
from datasets import load_dataset
from sentence_transformers import SparseEncoder
from sentence_transformers.sparse_encoder.evaluation import SparseInformationRetrievalEvaluator
logging.basicConfig(format="%(message)s", level=logging.INFO)
# Load a model
model = SparseEncoder("naver/spl... | import logging
import random
from datasets import load_dataset
from sentence_transformers import SparseEncoder
from sentence_transformers.sparse_encoder.evaluation import SparseInformationRetrievalEvaluator
logging.basicConfig(format="%(message)s", level=logging.INFO)
# Load a model
model = SparseEncoder("naver/spl... |
import contextlib
import os
import shutil
import threading
import time
import pytest
from jina import Client, DocumentArray, Executor, Flow, requests, Deployment
from jina.helper import random_port
cur_dir = os.path.dirname(__file__)
@contextlib.contextmanager
def _update_file(input_file_path, output_file_path, te... | import contextlib
import os
import shutil
import threading
import time
import pytest
from jina import Client, DocumentArray, Executor, Flow, requests
from jina.helper import random_port
cur_dir = os.path.dirname(__file__)
@contextlib.contextmanager
def _update_file(input_file_path, output_file_path, temp_path):
... |
"""Integration test for JIRA API Wrapper."""
from langchain_community.utilities.jira import JiraAPIWrapper
def test_search() -> None:
"""Test for Searching issues on JIRA"""
jql = "project = TP"
jira = JiraAPIWrapper()
output = jira.run("jql", jql)
assert "issues" in output
def test_getprojects... | """Integration test for JIRA API Wrapper."""
from langchain_community.utilities.jira import JiraAPIWrapper
def test_search() -> None:
"""Test for Searching issues on JIRA"""
jql = "project = TP"
jira = JiraAPIWrapper() # type: ignore[call-arg]
output = jira.run("jql", jql)
assert "issues" in out... |
__version__ = '0.13.11'
import os
from .document import Document
from .array import DocumentArray
from .dataclasses import dataclass, field
if 'DA_NO_RICH_HANDLER' not in os.environ:
from rich.traceback import install
install()
| __version__ = '0.13.10'
import os
from .document import Document
from .array import DocumentArray
from .dataclasses import dataclass, field
if 'DA_NO_RICH_HANDLER' not in os.environ:
from rich.traceback import install
install()
|
import asyncio
import os
import random
import string
import tempfile
import time
import pytest
from jina import helper
@pytest.fixture(scope='function')
def random_workspace_name():
"""Generate a random workspace name with digits and letters."""
rand = ''.join(random.choices(string.ascii_uppercase + string.... | import asyncio
import os
import random
import string
import tempfile
import time
import pytest
from jina import helper
@pytest.fixture(scope='function')
def random_workspace_name():
"""Generate a random workspace name with digits and letters."""
rand = ''.join(random.choices(string.ascii_uppercase + string.... |
import os
from typing import Dict
DEPLOYMENT_FILES = [
'statefulset-executor',
'deployment-executor',
'deployment-gateway',
'deployment-uses-before',
'deployment-uses-after',
'deployment-uses-before-after',
]
cur_dir = os.path.dirname(__file__)
DEFAULT_RESOURCE_DIR = os.path.join(
cur_dir,... | import os
from typing import Dict
DEPLOYMENT_FILES = [
'statefulset-executor',
'deployment-executor',
'deployment-gateway',
'deployment-uses-before',
'deployment-uses-after',
'deployment-uses-before-after',
]
cur_dir = os.path.dirname(__file__)
DEFAULT_RESOURCE_DIR = os.path.join(
cur_dir,... |
from typing import Any, Union
from langchain_core.utils.json import parse_json_markdown
from typing_extensions import override
from langchain.evaluation.schema import StringEvaluator
class JsonSchemaEvaluator(StringEvaluator):
"""An evaluator that validates a JSON prediction against a JSON schema reference.
... | from typing import Any, Union
from langchain_core.utils.json import parse_json_markdown
from langchain.evaluation.schema import StringEvaluator
class JsonSchemaEvaluator(StringEvaluator):
"""An evaluator that validates a JSON prediction against a JSON schema reference.
This evaluator checks if a given JSON... |
"""Simple Reader that reads transcript and general info of Bilibili video."""
import warnings
from typing import Any, List
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document
class BilibiliTranscriptReader(BaseReader):
"""Bilibili Transcript and video info reader.""... | """Simple Reader that reads transcript and general info of Bilibili video."""
import warnings
from typing import Any, List
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document
class BilibiliTranscriptReader(BaseReader):
"""Bilibili Transcript and video info reader.""... |
__copyright__ = 'Copyright (c) 2020-2021 Jina AI Limited. All rights reserved.'
__license__ = 'Apache-2.0'
from typing import Any, Iterable, Optional
import librosa as lr
import numpy as np
import torch
from jina import DocumentArray, Executor, requests
from .audio_clip.model import AudioCLIP
class AudioCLIPEncode... | __copyright__ = 'Copyright (c) 2020-2021 Jina AI Limited. All rights reserved.'
__license__ = 'Apache-2.0'
from typing import Any, Iterable, Optional
import librosa as lr
import numpy as np
import torch
from jina import DocumentArray, Executor, requests
from jina.excepts import BadDocType
from .audio_clip.model impo... |
"""
Each spoke runs in an isolated process. We leverage the seccomp and setrlimit system utilities to restrict access to system calls and set limits on the resources a process can consume. To implement them, we define several helper functions here, which can be configured to meet specific security or system requirement... | """
Each spoke runs in an isolated process. We leverage the seccomp and setrlimit system utilities to restrict access to system calls and set limits on the resources a process can consume. To implement them, we define several helper functions here, which can be configured to meet specific security or system requirement... |
import csv
import gzip
import os
from . import InputExample
class STSDataReader:
"""Reads in the STS dataset. Each line contains two sentences (s1_col_idx, s2_col_idx) and one label (score_col_idx)
Default values expects a tab separated file with the first & second column the sentence pair and third column ... | from . import InputExample
import csv
import gzip
import os
class STSDataReader:
"""Reads in the STS dataset. Each line contains two sentences (s1_col_idx, s2_col_idx) and one label (score_col_idx)
Default values expects a tab separated file with the first & second column the sentence pair and third column t... |
import csv
import logging
import os
from typing import List
import numpy as np
from sentence_transformers import InputExample
logger = logging.getLogger(__name__)
class CEBinaryAccuracyEvaluator:
"""
This evaluator can be used with the CrossEncoder class.
It is designed for CrossEncoders with 1 output... | import logging
import os
import csv
from typing import List
from ... import InputExample
import numpy as np
logger = logging.getLogger(__name__)
class CEBinaryAccuracyEvaluator:
"""
This evaluator can be used with the CrossEncoder class.
It is designed for CrossEncoders with 1 outputs. It measure the
... |
_base_ = 'yolact_r50_1x8_coco.py'
# optimizer
optim_wrapper = dict(
type='OptimWrapper',
optimizer=dict(lr=8e-3),
clip_grad=dict(max_norm=35, norm_type=2))
# learning rate
max_epochs = 55
param_scheduler = [
dict(type='LinearLR', start_factor=0.1, by_epoch=False, begin=0, end=1000),
dict(
t... | _base_ = 'yolact_r50_1x8_coco.py'
optimizer = dict(type='SGD', lr=8e-3, momentum=0.9, weight_decay=5e-4)
optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
# learning policy
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=1000,
warmup_ratio=0.1,
step=[20, 42, 49, 52])
... |
"""
========================
Decision Tree Regression
========================
In this example, we demonstrate the effect of changing the maximum depth of a
decision tree on how it fits to the data. We perform this once on a 1D regression
task and once on a multi-output regression task.
"""
# Authors: The scikit-learn... | """
===================================================================
Decision Tree Regression
===================================================================
A 1D regression with decision tree.
The :ref:`decision trees <tree>` is
used to fit a sine curve with addition noisy observation. As a result, it
learns ... |
from typing import TYPE_CHECKING, Any, Optional, Type, TypeVar, Union
import numpy as np
from docarray.base_doc import BaseDoc
from docarray.documents.point_cloud.points_and_colors import PointsAndColors
from docarray.typing import AnyEmbedding, PointCloud3DUrl
from docarray.typing.tensor.abstract_tensor import Abstr... | from typing import Any, Optional, Type, TypeVar, Union
import numpy as np
from docarray.base_doc import BaseDoc
from docarray.documents.point_cloud.points_and_colors import PointsAndColors
from docarray.typing import AnyEmbedding, PointCloud3DUrl
from docarray.typing.tensor.abstract_tensor import AbstractTensor
from ... |
# Copyright (c) OpenMMLab. All rights reserved.
from .collect_env import collect_env
from .compat_config import compat_cfg
from .dist_utils import (all_reduce_dict, allreduce_grads, reduce_mean,
sync_random_seed)
from .logger import get_caller_name, get_root_logger, log_img_scale
from .memory i... | # Copyright (c) OpenMMLab. All rights reserved.
from .collect_env import collect_env
from .compat_config import compat_cfg
from .dist_utils import (DistOptimizerHook, all_reduce_dict, allreduce_grads,
reduce_mean, sync_random_seed)
from .logger import get_caller_name, get_root_logger, log_img_s... |
from collections.abc import AsyncIterator, Iterator, Sequence
from typing import (
Any,
Callable,
Optional,
TypeVar,
Union,
)
from langchain_core.stores import BaseStore
K = TypeVar("K")
V = TypeVar("V")
class EncoderBackedStore(BaseStore[K, V]):
"""Wraps a store with key and value encoders/... | from typing import (
Any,
AsyncIterator,
Callable,
Iterator,
List,
Optional,
Sequence,
Tuple,
TypeVar,
Union,
)
from langchain_core.stores import BaseStore
K = TypeVar("K")
V = TypeVar("V")
class EncoderBackedStore(BaseStore[K, V]):
"""Wraps a store with key and value enc... |
"""Load agent."""
import contextlib
from collections.abc import Sequence
from typing import Any, Optional
from langchain_core._api import deprecated
from langchain_core.callbacks import BaseCallbackManager
from langchain_core.language_models import BaseLanguageModel
from langchain_core.tools import BaseTool
from lan... | """Load agent."""
import contextlib
from collections.abc import Sequence
from typing import Any, Optional
from langchain_core._api import deprecated
from langchain_core.callbacks import BaseCallbackManager
from langchain_core.language_models import BaseLanguageModel
from langchain_core.tools import BaseTool
from lan... |
from typing import Any, Optional, Type, TypeVar, Union
import numpy as np
from docarray.base_document import BaseDocument
from docarray.typing import AnyEmbedding, AudioUrl
from docarray.typing.bytes.audio_bytes import AudioBytes
from docarray.typing.tensor.abstract_tensor import AbstractTensor
from docarray.typing.t... | from typing import Any, Optional, Type, TypeVar, Union
import numpy as np
from docarray.base_document import BaseDocument
from docarray.typing import AnyEmbedding, AudioUrl
from docarray.typing.bytes.audio_bytes import AudioBytes
from docarray.typing.tensor.abstract_tensor import AbstractTensor
from docarray.typing.t... |
# Copyright (c) OpenMMLab. All rights reserved.
import torch
from mmdet.datasets.coco_panoptic import INSTANCE_OFFSET
from mmdet.models.builder import HEADS
from .base_panoptic_fusion_head import BasePanopticFusionHead
@HEADS.register_module()
class HeuristicFusionHead(BasePanopticFusionHead):
"""Fusion Head wit... | import torch
from mmdet.datasets.coco_panoptic import INSTANCE_OFFSET
from mmdet.models.builder import HEADS
from .base_panoptic_fusion_head import BasePanopticFusionHead
@HEADS.register_module()
class HeuristicFusionHead(BasePanopticFusionHead):
"""Fusion Head with Heuristic method."""
def __init__(self,
... |
import numpy as np
from docarray.array import DocumentArray
from docarray.document import BaseDocument
from docarray.typing import NdArray
def test_get_bulk_attributes_function():
class Mmdoc(BaseDocument):
text: str
tensor: NdArray
N = 10
da = DocumentArray[Mmdoc](
(Mmdoc(text=... | import numpy as np
from docarray.array import DocumentArray
from docarray.document import BaseDocument
from docarray.typing import NdArray
def test_get_bulk_attributes_function():
class Mmdoc(BaseDocument):
text: str
tensor: NdArray
N = 10
da = DocumentArray[Mmdoc](
(Mmdoc(text=... |
from __future__ import annotations
import logging
from typing import Literal
import torch
from torch import Tensor
from sentence_transformers.models.InputModule import InputModule
from .tokenizer import WhitespaceTokenizer
logger = logging.getLogger(__name__)
class BoW(InputModule):
"""Implements a Bag-of-Wo... | from __future__ import annotations
import json
import logging
import os
from typing import Literal
import torch
from torch import Tensor, nn
from .tokenizer import WhitespaceTokenizer
logger = logging.getLogger(__name__)
class BoW(nn.Module):
"""Implements a Bag-of-Words (BoW) model to derive sentence embeddi... |
__copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
from pathlib import Path
from typing import List
import pytest
from jina import Document, DocumentArray, Executor
from laser_encoder import LaserEncoder
_EMBEDDING_DIM = 1024
@pytest.fixture(scope='session')
... | __copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
from pathlib import Path
from typing import List
import pytest
from jina import Document, DocumentArray, Executor
from laser_encoder import LaserEncoder
_EMBEDDING_DIM = 1024
@pytest.fixture(scope='session')
... |
# Copyright (c) OpenMMLab. All rights reserved.
from .data_preprocessor import (BatchFixedSizePad, BatchResize,
BatchSyncRandomResize, DetDataPreprocessor,
MultiBranchDataPreprocessor)
__all__ = [
'DetDataPreprocessor', 'BatchSyncRandomResize', 'Batch... | # Copyright (c) OpenMMLab. All rights reserved.
from .data_preprocessor import (BatchFixedSizePad, BatchSyncRandomResize,
DetDataPreprocessor,
MultiBranchDataPreprocessor)
__all__ = [
'DetDataPreprocessor', 'BatchSyncRandomResize', 'BatchFixedSizePad'... |
from functools import partial
from typing import Any, Optional
import torch
import torch.nn as nn
from ..transforms._presets import ImageClassification
from ..utils import _log_api_usage_once
from ._api import register_model, Weights, WeightsEnum
from ._meta import _IMAGENET_CATEGORIES
from ._utils import _ovewrite_n... | from functools import partial
from typing import Any, Optional
import torch
import torch.nn as nn
from ..transforms._presets import ImageClassification
from ..utils import _log_api_usage_once
from ._api import register_model, Weights, WeightsEnum
from ._meta import _IMAGENET_CATEGORIES
from ._utils import _ovewrite_n... |
from typing import Optional
import pyspark
from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader
class SparkDatasetReader(AbstractDatasetReader):
"""A dataset reader that reads from a Spark DataFrame.
... | from typing import Optional
import pyspark
from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader
class SparkDatasetReader(AbstractDatasetReader):
"""A dataset reader that reads from a Spark DataFrame.
... |
from typing import Any
import pytest
from langchain_tests.conftest import CustomPersister, CustomSerializer
from langchain_tests.conftest import _base_vcr_config as _base_vcr_config
from vcr import VCR # type: ignore[import-untyped]
def remove_request_headers(request: Any) -> Any:
for k in request.headers:
... | from typing import Any
import pytest
from langchain_tests.conftest import YamlGzipSerializer
from langchain_tests.conftest import _base_vcr_config as _base_vcr_config
from vcr import VCR # type: ignore[import-untyped]
def remove_request_headers(request: Any) -> Any:
for k in request.headers:
request.hea... |
import warnings
from typing import Any, List, Union
import PIL.Image
import torch
from torchvision.prototype import features
from torchvision.transforms import functional as _F
@torch.jit.unused
def to_grayscale(inpt: PIL.Image.Image, num_output_channels: int = 1) -> PIL.Image.Image:
call = ", num_output_channe... | import warnings
from typing import Any, List, Union
import PIL.Image
import torch
from torchvision.prototype import features
from torchvision.transforms import functional as _F
@torch.jit.unused
def to_grayscale(inpt: PIL.Image.Image, num_output_channels: int = 1) -> PIL.Image.Image:
call = ", num_output_channe... |
# Copyright (c) OpenMMLab. All rights reserved.
from unittest import TestCase
import torch
from mmengine.data import InstanceData
from mmdet.models.dense_heads import NASFCOSHead
class TestNASFCOSHead(TestCase):
def test_nasfcos_head_loss(self):
"""Tests nasfcos head loss when truth is empty and non-em... | # Copyright (c) OpenMMLab. All rights reserved.
from unittest import TestCase
import torch
from mmengine.data import InstanceData
from mmdet.models.dense_heads import NASFCOSHead
class TestNASFCOSHead(TestCase):
def test_nasfcos_head_loss(self):
"""Tests nasfcos head loss when truth is empty and non-em... |
from typing import Literal
from pydantic import SecretStr
from backend.data.model import (
APIKeyCredentials,
CredentialsField,
CredentialsMetaInput,
OAuth2Credentials,
)
from backend.util.settings import Secrets
secrets = Secrets()
GITHUB_OAUTH_IS_CONFIGURED = bool(
secrets.github_client_id and ... | from typing import Literal
from autogpt_libs.supabase_integration_credentials_store.types import (
APIKeyCredentials,
OAuth2Credentials,
)
from pydantic import SecretStr
from backend.data.model import CredentialsField, CredentialsMetaInput
from backend.util.settings import Secrets
secrets = Secrets()
GITHUB_... |
# Copyright (c) OpenMMLab. All rights reserved.
import torch
from mmdet.core.evaluation.panoptic_utils import INSTANCE_OFFSET
from mmdet.registry import MODELS
from .base_panoptic_fusion_head import BasePanopticFusionHead
@MODELS.register_module()
class HeuristicFusionHead(BasePanopticFusionHead):
"""Fusion Head... | # Copyright (c) OpenMMLab. All rights reserved.
import torch
from mmdet.core.evaluation.panoptic_utils import INSTANCE_OFFSET
from mmdet.models.builder import HEADS
from .base_panoptic_fusion_head import BasePanopticFusionHead
@HEADS.register_module()
class HeuristicFusionHead(BasePanopticFusionHead):
"""Fusion ... |
import numpy as np
import pytest
from docarray.proto import DocumentProto, NodeProto
from docarray.typing import NdArray
@pytest.mark.proto
def test_ndarray():
original_ndarray = np.zeros((3, 224, 224))
custom_ndarray = NdArray._docarray_from_native(original_ndarray)
tensor = NdArray.from_protobuf(cus... | import numpy as np
from docarray.proto import DocumentProto, NodeProto
from docarray.typing import NdArray
def test_ndarray():
original_ndarray = np.zeros((3, 224, 224))
custom_ndarray = NdArray._docarray_from_native(original_ndarray)
tensor = NdArray.from_protobuf(custom_ndarray.to_protobuf())
a... |
_base_ = [
'../_base_/models/ssd300.py', '../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
]
# model settings
input_size = 300
model = dict(
bbox_head=dict(
type='SSDHead',
anchor_generator=dict(
type='LegacySSDAnchorGene... | _base_ = [
'../_base_/models/ssd300.py', '../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
]
# model settings
input_size = 300
model = dict(
bbox_head=dict(
type='SSDHead',
anchor_generator=dict(
type='LegacySSDAnchorGene... |
import pytest
from jina.enums import ProtocolType
from jina.helper import ArgNamespace
from jina.parsers import set_gateway_parser, set_pod_parser
@pytest.mark.parametrize(
'port,expected_port',
[
('12345', [12345]),
([12345], [12345]),
([12345, 12344], [12345, 12344]),
],
)
@pyte... | import pytest
from jina.enums import GatewayProtocolType
from jina.helper import ArgNamespace
from jina.parsers import set_gateway_parser, set_pod_parser
@pytest.mark.parametrize(
'port,expected_port',
[
('12345', [12345]),
([12345], [12345]),
([12345, 12344], [12345, 12344]),
],
... |
import re
from typing import Any, Optional
from langchain_text_splitters import RecursiveCharacterTextSplitter
class JSFrameworkTextSplitter(RecursiveCharacterTextSplitter):
"""Text splitter that handles React (JSX), Vue, and Svelte code.
This splitter extends RecursiveCharacterTextSplitter to handle
Re... | import re
from typing import Any, Optional
from langchain_text_splitters import RecursiveCharacterTextSplitter
class JSFrameworkTextSplitter(RecursiveCharacterTextSplitter):
"""Text splitter that handles React (JSX), Vue, and Svelte code.
This splitter extends RecursiveCharacterTextSplitter to handle
Re... |
# Copyright (c) OpenMMLab. All rights reserved.
from mmdet.registry import MODELS
from .single_stage import SingleStageDetector
@MODELS.register_module()
class NASFCOS(SingleStageDetector):
"""NAS-FCOS: Fast Neural Architecture Search for Object Detection.
https://arxiv.org/abs/1906.0442
"""
def __i... | # Copyright (c) OpenMMLab. All rights reserved.
from ..builder import DETECTORS
from .single_stage import SingleStageDetector
@DETECTORS.register_module()
class NASFCOS(SingleStageDetector):
"""NAS-FCOS: Fast Neural Architecture Search for Object Detection.
https://arxiv.org/abs/1906.0442
"""
def __... |
import types
from typing import Any
import torch._C
class _ClassNamespace(types.ModuleType):
def __init__(self, name: str) -> None:
super().__init__("torch.classes" + name)
self.name = name
def __getattr__(self, attr: str) -> Any:
proxy = torch._C._get_custom_class_python_wrapper(sel... | # mypy: allow-untyped-defs
import types
import torch._C
class _ClassNamespace(types.ModuleType):
def __init__(self, name):
super().__init__("torch.classes" + name)
self.name = name
def __getattr__(self, attr):
proxy = torch._C._get_custom_class_python_wrapper(self.name, attr)
... |
import torch
def get_modules(use_v2):
# We need a protected import to avoid the V2 warning in case just V1 is used
if use_v2:
import torchvision.datapoints
import torchvision.transforms.v2
import v2_extras
return torchvision.transforms.v2, torchvision.datapoints, v2_extras
... | import torch
def get_modules(use_v2):
# We need a protected import to avoid the V2 warning in case just V1 is used
if use_v2:
import torchvision.datapoints
import torchvision.transforms.v2
import v2_extras
return torchvision.transforms.v2, torchvision.datapoints, v2_extras
... |
import os
from jina import Flow, DocumentArray
cur_dir = os.path.dirname(__file__)
def test_install_reqs():
f = Flow().add(
install_requirements=True,
uses=os.path.join(os.path.join(cur_dir, 'exec'), 'config.yml'),
)
with f:
resp = f.post(on='/', inputs=DocumentArray.empty(2))
... | import os
from jina import Flow, DocumentArray
cur_dir = os.path.dirname(__file__)
def test_install_reqs():
f = Flow().add(install_requirements=True, uses=os.path.join(os.path.join(cur_dir, 'exec'), 'config.yml'))
with f:
resp = f.post(on='/', inputs=DocumentArray.empty(2))
assert len(resp) == 2... |
# Copyright (c) OpenMMLab. All rights reserved.
from .assigners import (AssignResult, BaseAssigner, CenterRegionAssigner,
MaxIoUAssigner, RegionAssigner)
from .builder import build_assigner, build_bbox_coder, build_sampler
from .coder import (BaseBBoxCoder, DeltaXYWHBBoxCoder, DistancePointBBoxC... | # Copyright (c) OpenMMLab. All rights reserved.
from .assigners import (AssignResult, BaseAssigner, CenterRegionAssigner,
MaxIoUAssigner, RegionAssigner)
from .builder import build_assigner, build_bbox_coder, build_sampler
from .coder import (BaseBBoxCoder, DeltaXYWHBBoxCoder, DistancePointBBoxC... |
"""DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.constraints import deserialize as deserialize
from keras.src.constraints import get as get
from keras.src.constraints import serialize as serialize
from keras.src.constraints.constrai... | """DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.constraints import deserialize
from keras.src.constraints import get
from keras.src.constraints import serialize
from keras.src.constraints.constraints import Constraint
from keras.sr... |
from typing import Optional
from docarray.document import BaseDocument
from docarray.typing import TextUrl
from docarray.typing.tensor.embedding import AnyEmbedding
class Text(BaseDocument):
"""
Document for handling text.
It can contain a TextUrl (`Text.url`), a str (`Text.text`),
and an AnyEmbeddin... | from typing import Optional
from docarray.document import BaseDocument
from docarray.typing import TextUrl
from docarray.typing.tensor.embedding import Embedding
class Text(BaseDocument):
"""
Document for handling text.
It can contain a TextUrl (`Text.url`), a str (`Text.text`),
and an Embedding (`Te... |
_base_ = ['./yolov3_mobilenetv2_mstrain-416_300e_coco.py']
# yapf:disable
model = dict(
bbox_head=dict(
anchor_generator=dict(
base_sizes=[[(220, 125), (128, 222), (264, 266)],
[(35, 87), (102, 96), (60, 170)],
[(10, 15), (24, 36), (72, 42)]])))
#... | _base_ = ['./yolov3_mobilenetv2_mstrain-416_300e_coco.py']
# yapf:disable
model = dict(
bbox_head=dict(
anchor_generator=dict(
base_sizes=[[(220, 125), (128, 222), (264, 266)],
[(35, 87), (102, 96), (60, 170)],
[(10, 15), (24, 36), (72, 42)]])))
#... |
# Licensed to the LF AI & Data foundation under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the "License");
# you may not use this fil... | import os
import numpy as np
import pytest
from pydantic.tools import parse_obj_as, schema_json_of
from docarray.base_doc.io.json import orjson_dumps
from docarray.typing import Mesh3DUrl, NdArray
from docarray.typing.url.mimetypes import (
OBJ_MIMETYPE,
AUDIO_MIMETYPE,
VIDEO_MIMETYPE,
IMAGE_MIMETYPE,... |
"""Output classes.
**Output** classes are used to represent the output of a language model call
and the output of a chat.
The top container for information is the `LLMResult` object. `LLMResult` is used by
both chat models and LLMs. This object contains the output of the language
model and any additional information ... | """Output classes.
**Output** classes are used to represent the output of a language model call
and the output of a chat.
The top container for information is the `LLMResult` object. `LLMResult` is used by
both chat models and LLMs. This object contains the output of the language
model and any additional information ... |
from abc import ABC, abstractmethod
from typing import Callable, List, Sequence, Optional, Union
from llama_index.core.agent.workflow.workflow_events import (
AgentOutput,
ToolCallResult,
)
from llama_index.core.bridge.pydantic import (
BaseModel,
Field,
ConfigDict,
field_validator,
)
from llam... | from abc import ABC, abstractmethod
from typing import Callable, List, Sequence, Optional, Union
from llama_index.core.agent.workflow.workflow_events import (
AgentOutput,
ToolCallResult,
)
from llama_index.core.bridge.pydantic import (
BaseModel,
Field,
ConfigDict,
field_validator,
)
from llam... |
from docarray.typing.id import ID
from docarray.typing.tensor import Tensor, TorchTensor
from docarray.typing.tensor.embedding import Embedding
from docarray.typing.url import AnyUrl, ImageUrl
__all__ = [
'TorchTensor',
'Tensor',
'Embedding',
'ImageUrl',
'AnyUrl',
'ID',
]
| from docarray.typing.embedding import Embedding
from docarray.typing.id import ID
from docarray.typing.tensor import Tensor, TorchTensor
from docarray.typing.url import AnyUrl, ImageUrl
__all__ = ['Tensor', 'Embedding', 'ImageUrl', 'AnyUrl', 'ID', 'TorchTensor']
|
# Copyright (c) OpenMMLab. All rights reserved.
from mmdet.core.utils import ConfigType, OptConfigType, OptMultiConfig
from ..builder import DETECTORS
from .single_stage_instance_seg import SingleStageInstanceSegmentor
@DETECTORS.register_module()
class SOLOv2(SingleStageInstanceSegmentor):
"""`SOLOv2: Dynamic an... | # Copyright (c) OpenMMLab. All rights reserved.
from ..builder import DETECTORS
from .single_stage_instance_seg import SingleStageInstanceSegmentor
@DETECTORS.register_module()
class SOLOv2(SingleStageInstanceSegmentor):
"""`SOLOv2: Dynamic and Fast Instance Segmentation
<https://arxiv.org/abs/2003.10152>`_
... |
import logging
from backend.data import integrations
from backend.data.model import Credentials
from ._base import WT, BaseWebhooksManager
logger = logging.getLogger(__name__)
class ManualWebhookManagerBase(BaseWebhooksManager[WT]):
async def _register_webhook(
self,
credentials: Credentials,
... | import logging
from backend.data import integrations
from backend.data.model import APIKeyCredentials, Credentials, OAuth2Credentials
from ._base import WT, BaseWebhooksManager
logger = logging.getLogger(__name__)
class ManualWebhookManagerBase(BaseWebhooksManager[WT]):
async def _register_webhook(
sel... |
_base_ = [
'../_base_/models/mask-rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# TODO: delete custom_imports after mmcls supports auto import
# please install mmcls>=1.0
# import mmcls.models to trigger register_module in mm... | _base_ = [
'../_base_/models/mask-rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# TODO: delete custom_imports after mmcls supports auto import
# please install mmcls>=1.0
# import mmcls.models to trigger register_module in mm... |
# Copyright (c) OpenMMLab. All rights reserved.
from mmcv.cnn import ConvModule, Linear
from mmcv.runner import ModuleList, auto_fp16
from mmdet.models.builder import HEADS
from .fcn_mask_head import FCNMaskHead
@HEADS.register_module()
class CoarseMaskHead(FCNMaskHead):
"""Coarse mask head used in PointRend.
... | from mmcv.cnn import ConvModule, Linear
from mmcv.runner import ModuleList, auto_fp16
from mmdet.models.builder import HEADS
from .fcn_mask_head import FCNMaskHead
@HEADS.register_module()
class CoarseMaskHead(FCNMaskHead):
"""Coarse mask head used in PointRend.
Compared with standard ``FCNMaskHead``, ``Coa... |
_base_ = ['./mask2former_r50_8xb2-lsj-50e_coco-panoptic.py']
num_things_classes = 80
num_stuff_classes = 0
num_classes = num_things_classes + num_stuff_classes
image_size = (1024, 1024)
batch_augments = [
dict(
type='BatchFixedSizePad',
size=image_size,
img_pad_value=0,
pad_mask=Tru... | _base_ = ['./mask2former_r50_8xb2-lsj-50e_coco-panoptic.py']
num_things_classes = 80
num_stuff_classes = 0
num_classes = num_things_classes + num_stuff_classes
image_size = (1024, 1024)
batch_augments = [
dict(
type='BatchFixedSizePad',
size=image_size,
img_pad_value=0,
pad_mask=True... |
# Copyright 2020 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 2020 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... |
"""Test OCI Generative AI embedding service."""
from unittest.mock import MagicMock
import pytest
from pytest import MonkeyPatch
from langchain_community.embeddings import OCIGenAIEmbeddings
class MockResponseDict(dict):
def __getattr__(self, val): # type: ignore[no-untyped-def]
return self[val]
@py... | """Test OCI Generative AI embedding service."""
from unittest.mock import MagicMock
import pytest
from pytest import MonkeyPatch
from langchain_community.embeddings import OCIGenAIEmbeddings
class MockResponseDict(dict):
def __getattr__(self, val): # type: ignore[no-untyped-def]
return self[val]
@py... |
import keras.src.backend
from keras.src import tree
from keras.src.layers.layer import Layer
from keras.src.random.seed_generator import SeedGenerator
from keras.src.utils import backend_utils
from keras.src.utils import jax_utils
from keras.src.utils import tracking
class TFDataLayer(Layer):
"""Layer that can sa... | import keras.src.backend
from keras.src import tree
from keras.src.layers.layer import Layer
from keras.src.random.seed_generator import SeedGenerator
from keras.src.utils import backend_utils
from keras.src.utils import tracking
class TFDataLayer(Layer):
"""Layer that can safely used in a tf.data pipeline.
... |
import numpy as np
import torch
from docarray import BaseDocument, DocumentArray, Image, Text
from docarray.typing import (
AnyTensor,
AnyUrl,
Embedding,
ImageUrl,
Mesh3DUrl,
NdArray,
PointCloud3DUrl,
TextUrl,
TorchEmbedding,
TorchTensor,
)
from docarray.typing.tensor import NdA... | import numpy as np
import torch
from docarray import Document, DocumentArray, Image, Text
from docarray.typing import (
AnyTensor,
AnyUrl,
Embedding,
ImageUrl,
Mesh3DUrl,
NdArray,
PointCloud3DUrl,
TextUrl,
TorchEmbedding,
TorchTensor,
)
from docarray.typing.tensor import NdArray... |
# Copyright (c) OpenMMLab. All rights reserved.
from typing import Optional, Sequence, Union
import torch
from mmengine.data import BaseDataElement
from mmengine.registry import HOOKS
from .hook import Hook
DATA_BATCH = Optional[Sequence[dict]]
@HOOKS.register_module()
class EmptyCacheHook(Hook):
"""Releases a... | # Copyright (c) OpenMMLab. All rights reserved.
from typing import Any, Optional, Sequence, Tuple, Union
import torch
from mmengine.data import BaseDataElement
from mmengine.registry import HOOKS
from .hook import Hook
DATA_BATCH = Optional[Sequence[Tuple[Any, BaseDataElement]]]
@HOOKS.register_module()
class Empt... |
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