input stringlengths 33 5k | output stringlengths 32 5k |
|---|---|
import os
import urllib
import numpy as np
import PIL
import pytest
from PIL import Image
from pydantic.tools import parse_obj_as, schema_json_of
from docarray.base_doc.io.json import orjson_dumps
from docarray.typing import ImageUrl
CUR_DIR = os.path.dirname(os.path.abspath(__file__))
PATH_TO_IMAGE_DATA = os.path.j... | import os
import urllib
import numpy as np
import pytest
from PIL import Image
from pydantic.tools import parse_obj_as, schema_json_of
from docarray.base_doc.io.json import orjson_dumps
from docarray.typing import ImageUrl
CUR_DIR = os.path.dirname(os.path.abspath(__file__))
PATH_TO_IMAGE_DATA = os.path.join(CUR_DIR... |
import warnings
from abc import ABC
from typing import Any, Optional
from langchain_core._api import deprecated
from langchain_core.chat_history import (
BaseChatMessageHistory,
InMemoryChatMessageHistory,
)
from langchain_core.memory import BaseMemory
from langchain_core.messages import AIMessage, HumanMessag... | import warnings
from abc import ABC
from typing import Any, Optional
from langchain_core._api import deprecated
from langchain_core.chat_history import (
BaseChatMessageHistory,
InMemoryChatMessageHistory,
)
from langchain_core.memory import BaseMemory
from langchain_core.messages import AIMessage, HumanMessag... |
from typing import Any, Optional, Type, TypeVar, Union
from docarray.base_document import BaseDocument
from docarray.typing import TextUrl
from docarray.typing.tensor.embedding import AnyEmbedding
T = TypeVar('T', bound='TextDoc')
class TextDoc(BaseDocument):
"""
Document for handling text.
It can conta... | from typing import Any, Optional, Type, TypeVar, Union
from docarray.base_document import BaseDocument
from docarray.typing import TextUrl
from docarray.typing.tensor.embedding import AnyEmbedding
T = TypeVar('T', bound='Text')
class Text(BaseDocument):
"""
Document for handling text.
It can contain a T... |
_base_ = '../htc/htc_x101-64x4d_fpn_16xb1-20e_coco.py'
# learning policy
max_epochs = 28
train_cfg = dict(max_epochs=max_epochs)
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=max_epochs,
... | _base_ = '../htc/htc_x101_64x4d_fpn_16x1_20e_coco.py'
# learning policy
max_epochs = 28
train_cfg = dict(max_epochs=max_epochs)
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=max_epochs,
... |
_base_ = './cascade_mask_rcnn_r50_fpn_mstrain_3x_coco.py'
preprocess_cfg = dict(
mean=[103.530, 116.280, 123.675],
std=[57.375, 57.120, 58.395],
to_rgb=False,
pad_size_divisor=32)
model = dict(
# ResNeXt-101-32x8d model trained with Caffe2 at FB,
# so the mean and std need to be changed.
p... | _base_ = './cascade_mask_rcnn_r50_fpn_mstrain_3x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=8,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=False),
... |
from pathlib import Path
import pytest
from langchain_community.document_loaders import CSVLoader, DirectoryLoader, TextLoader
from langchain_community.document_loaders.helpers import detect_file_encodings
@pytest.mark.requires("chardet")
def test_loader_detect_encoding_text() -> None:
"""Test text loader."""
... | from pathlib import Path
import pytest
from langchain_community.document_loaders import CSVLoader, DirectoryLoader, TextLoader
from langchain_community.document_loaders.helpers import detect_file_encodings
@pytest.mark.requires("chardet")
def test_loader_detect_encoding_text() -> None:
"""Test text loader."""
... |
# dataset settings
dataset_type = 'CocoPanopticDataset'
data_root = 'data/coco/'
# Example to use different file client
# Method 1: simply set the data root and let the file I/O module
# automatically infer from prefix (not support LMDB and Memcache yet)
# data_root = 's3://openmmlab/datasets/detection/coco/'
# Meth... | # dataset settings
dataset_type = 'CocoPanopticDataset'
# data_root = 'data/coco/'
# Example to use different file client
# Method 1: simply set the data root and let the file I/O module
# automatically infer from prefix (not support LMDB and Memcache yet)
data_root = 's3://openmmlab/datasets/detection/coco/'
# Meth... |
_base_ = './mask_rcnn_r50_fpn_1x_coco.py'
# Enable automatic-mixed-precision training with AmpOptimWrapper.
optim_wrapper = dict(type='AmpOptimWrapper')
| _base_ = './mask_rcnn_r50_fpn_1x_coco.py'
# fp16 settings
fp16 = dict(loss_scale=512.)
|
"""Google Calendar reader."""
import datetime
import os
from typing import Any, List, Optional, Union
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document
SCOPES = ["https://www.googleapis.com/auth/calendar.readonly"]
# Copyright 2018 Google LLC
#
# Licensed under the Ap... | """Google Calendar reader."""
import datetime
import os
from typing import Any, List, Optional, Union
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document
SCOPES = ["https://www.googleapis.com/auth/calendar.readonly"]
# Copyright 2018 Google LLC
#
# Licensed under the Ap... |
from keras.src import backend
from keras.src.api_export import keras_export
from keras.src.layers.preprocessing.image_preprocessing.base_image_preprocessing_layer import ( # noqa: E501
BaseImagePreprocessingLayer,
)
@keras_export("keras.layers.RandomGrayscale")
class RandomGrayscale(BaseImagePreprocessingLayer):... | from keras.src import backend
from keras.src.api_export import keras_export
from keras.src.layers.preprocessing.image_preprocessing.base_image_preprocessing_layer import ( # noqa: E501
BaseImagePreprocessingLayer,
)
@keras_export("keras.layers.RandomGrayscale")
class RandomGrayscale(BaseImagePreprocessingLayer):... |
import json
from typing import Union, Sequence, Dict, Any, Callable
from tenacity import (
retry,
stop_after_attempt,
wait_exponential,
retry_if_exception_type,
)
from asyncio import iscoroutinefunction
from requests.exceptions import Timeout, ConnectionError
from llama_index.core.base.llms.types impo... | import json
from typing import Union, Sequence, Dict, Any, Callable
from tenacity import (
retry,
stop_after_attempt,
wait_exponential,
retry_if_exception_type,
)
from asyncio import iscoroutinefunction
from requests.exceptions import Timeout, ConnectionError
from llama_index.core.base.llms.types impo... |
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class HfFileSystem(AbstractFileSystem):
"""Interfa... | from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url, hf_hub_url
class HfFileSystem(AbstractFileSystem):
"""Interface to files in a Huggin... |
from datasets import load_dataset
from sentence_transformers import SentenceTransformer
from sentence_transformers.quantization import quantize_embeddings, semantic_search_usearch
# 1. Load the quora corpus with questions
dataset = load_dataset("quora", split="train").map(
lambda batch: {"text": [text for sample i... | from sentence_transformers import SentenceTransformer
from sentence_transformers.quantization import quantize_embeddings, semantic_search_usearch
from datasets import load_dataset
# 1. Load the quora corpus with questions
dataset = load_dataset("quora", split="train").map(
lambda batch: {"text": [text for sample i... |
# pylint: disable=invalid-name,unused-import
"""For compatibility and optional dependencies."""
import importlib.util
import logging
import sys
import types
from typing import Any, Sequence, cast
import numpy as np
from ._typing import _T
assert sys.version_info[0] == 3, "Python 2 is no longer supported."
def py_s... | # pylint: disable= invalid-name, unused-import
"""For compatibility and optional dependencies."""
import importlib.util
import logging
import sys
import types
from typing import Any, Dict, List, Optional, Sequence, cast
import numpy as np
from ._typing import _T
assert sys.version_info[0] == 3, "Python 2 is no long... |
from __future__ import annotations
import sys
from .BoW import BoW
from .CLIPModel import CLIPModel
from .CNN import CNN
from .Dense import Dense
from .Dropout import Dropout
from .InputModule import InputModule
from .LayerNorm import LayerNorm
from .LSTM import LSTM
from .Module import Module
from .Normalize import ... | from __future__ import annotations
from .Asym import Asym
from .BoW import BoW
from .CLIPModel import CLIPModel
from .CNN import CNN
from .Dense import Dense
from .Dropout import Dropout
from .InputModule import InputModule
from .LayerNorm import LayerNorm
from .LSTM import LSTM
from .Module import Module
from .Normal... |
# Copyright (c) OpenMMLab. All rights reserved.
from mmdet.registry import MODELS
from mmdet.utils import ConfigType, OptConfigType, OptMultiConfig
from .single_stage_instance_seg import SingleStageInstanceSegmentor
@MODELS.register_module()
class CondInst(SingleStageInstanceSegmentor):
"""Implementation of `Cond... | # Copyright (c) OpenMMLab. All rights reserved.
from mmdet.registry import MODELS
from mmdet.utils.typing import ConfigType, OptConfigType, OptMultiConfig
from .single_stage_instance_seg import SingleStageInstanceSegmentor
@MODELS.register_module()
class CondInst(SingleStageInstanceSegmentor):
"""Implementation ... |
import torch
from torchaudio_unittest.common_utils import PytorchTestCase, skipIfNoCuda
from .torchscript_consistency_impl import Transforms, TransformsFloat32Only
@skipIfNoCuda
class TestTransformsFloat32(Transforms, TransformsFloat32Only, PytorchTestCase):
dtype = torch.float32
device = torch.device("cuda"... | import torch
from torchaudio_unittest.common_utils import skipIfNoCuda, PytorchTestCase
from .torchscript_consistency_impl import Transforms, TransformsFloat32Only
@skipIfNoCuda
class TestTransformsFloat32(Transforms, TransformsFloat32Only, PytorchTestCase):
dtype = torch.float32
device = torch.device("cuda"... |
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.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')... |
_base_ = 'retinanet_pvtv2-b0_fpn_1x_coco.py'
model = dict(
backbone=dict(
embed_dims=64,
num_layers=[3, 8, 27, 3],
init_cfg=dict(checkpoint='https://github.com/whai362/PVT/'
'releases/download/v2/pvt_v2_b4.pth')),
neck=dict(in_channels=[64, 128, 320, 512]))
# optimi... | _base_ = 'retinanet_pvtv2-b0_fpn_1x_coco.py'
model = dict(
backbone=dict(
embed_dims=64,
num_layers=[3, 8, 27, 3],
init_cfg=dict(checkpoint='https://github.com/whai362/PVT/'
'releases/download/v2/pvt_v2_b4.pth')),
neck=dict(in_channels=[64, 128, 320, 512]))
# optimi... |
"""News article reader using Newspaper."""
import logging
from importlib.util import find_spec
from typing import Any, Generator, List
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document
logger = logging.getLogger(__name__)
class NewsArticleReader(BaseReader):
"""
... | """News article reader using Newspaper."""
import logging
from importlib.util import find_spec
from typing import Any, Generator, List
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document
logger = logging.getLogger(__name__)
class NewsArticleReader(BaseReader):
"""
... |
from docarray.typing.proto_register import _register_proto
from docarray.typing.tensor.image.abstract_image_tensor import AbstractImageTensor
from docarray.typing.tensor.ndarray import NdArray
MAX_INT_16 = 2**15
@_register_proto(proto_type_name='image_ndarray')
class ImageNdArray(AbstractImageTensor, NdArray):
"... | from docarray.typing.proto_register import _register_proto
from docarray.typing.tensor.image.abstract_image_tensor import AbstractImageTensor
from docarray.typing.tensor.ndarray import NdArray
MAX_INT_16 = 2**15
@_register_proto(proto_type_name='image_ndarray')
class ImageNdArray(AbstractImageTensor, NdArray):
"... |
import logging
import sentry_sdk
from sentry_sdk.integrations.anthropic import AnthropicIntegration
from sentry_sdk.integrations.logging import LoggingIntegration
from backend.util.settings import Settings
def sentry_init():
sentry_dsn = Settings().secrets.sentry_dsn
sentry_sdk.init(
dsn=sentry_dsn,... | import logging
import sentry_sdk
from sentry_sdk.integrations.anthropic import AnthropicIntegration
from sentry_sdk.integrations.logging import LoggingIntegration
from backend.util.settings import Settings
def sentry_init():
sentry_dsn = Settings().secrets.sentry_dsn
sentry_sdk.init(
dsn=sentry_dsn,... |
"""
Experimental Object Oriented Distributed API - torch.distributed._dist2
=======================================================================
This is an experimental new API for PyTorch Distributed. This is actively in development and subject to change or deletion entirely.
This is intended as a proving ground ... | """
Experimental Object Oriented Distributed API - torch.distributed._dist2
=======================================================================
This is an experimental new API for PyTorch Distributed. This is actively in development and subject to change or deletion entirely.
This is intended as a proving ground ... |
import logging
from datasets import load_dataset
from sentence_transformers import SparseEncoder
from sentence_transformers.sparse_encoder.evaluation import SparseEmbeddingSimilarityEvaluator
logging.basicConfig(format="%(message)s", level=logging.INFO)
# Load a model
model = SparseEncoder("naver/splade-cocondenser... | import logging
from datasets import load_dataset
from sentence_transformers import SparseEncoder
from sentence_transformers.sparse_encoder.evaluation import SparseEmbeddingSimilarityEvaluator
logging.basicConfig(format="%(message)s", level=logging.INFO)
# Load a model
model = SparseEncoder("naver/splade-cocondenser... |
import logging
from autogpt_libs.auth.middleware import auth_middleware
from fastapi import APIRouter, Depends, HTTPException
from backend.server.utils import get_user_id
from .models import ApiResponse, ChatRequest
from .service import OttoService
logger = logging.getLogger(__name__)
router = APIRouter()
@route... | import logging
from autogpt_libs.auth.middleware import auth_middleware
from fastapi import APIRouter, Depends, HTTPException
from backend.server.utils import get_user_id
from .models import ApiResponse, ChatRequest
from .service import OttoService
logger = logging.getLogger(__name__)
router = APIRouter()
@route... |
"""Tool for the Google search API."""
from typing import Optional
from langchain_core._api.deprecation import deprecated
from langchain_core.callbacks import CallbackManagerForToolRun
from langchain_core.tools import BaseTool
from langchain_community.utilities.google_search import GoogleSearchAPIWrapper
@deprecate... | """Tool for the Google search API."""
from typing import Optional
from langchain_core._api.deprecation import deprecated
from langchain_core.callbacks import CallbackManagerForToolRun
from langchain_core.tools import BaseTool
from langchain_community.utilities.google_search import GoogleSearchAPIWrapper
@deprecate... |
import glob
import os
import pytest
from jina import Document, Flow
from jina.constants import __uptime__, __windows__
from jina.enums import LogVerbosity
from jina.helper import colored
from jina.logging.logger import JinaLogger
cur_dir = os.path.dirname(os.path.abspath(__file__))
def log(logger: JinaLogger):
... | import glob
import os
import pytest
from jina import Document, Flow
from jina.constants import __uptime__, __windows__
from jina.enums import LogVerbosity
from jina.helper import colored
from jina.logging.logger import JinaLogger
cur_dir = os.path.dirname(os.path.abspath(__file__))
def log(logger: JinaLogger):
... |
from typing import Optional, Union, Callable, Tuple, TYPE_CHECKING, Dict
if TYPE_CHECKING:
import numpy as np
from docarray.typing import ArrayType
from docarray import DocumentArray
class MatchMixin:
"""A mixin that provides match functionality to DocumentArrays"""
def match(
self,
... | from typing import Optional, Union, Callable, Tuple, TYPE_CHECKING, Dict
if TYPE_CHECKING:
import numpy as np
from docarray.typing import ArrayType
from docarray import DocumentArray
class MatchMixin:
"""A mixin that provides match functionality to DocumentArrays"""
def match(
self,
... |
import numpy as np
import pytest
import torch
from docarray.base_document import BaseDocument
from docarray.base_document.io.json import orjson_dumps
from docarray.typing import AnyUrl, NdArray, TorchTensor
@pytest.fixture()
def doc_and_class():
class Mmdoc(BaseDocument):
img: NdArray
url: AnyUrl... | import numpy as np
import pytest
import torch
from docarray.base_document import BaseDocument
from docarray.base_document.io.json import orjson_dumps
from docarray.typing import AnyUrl, NdArray, TorchTensor
@pytest.fixture()
def doc_and_class():
class Mmdoc(BaseDocument):
img: NdArray
url: AnyUrl... |
import pathlib
from collections.abc import Iterator
from typing import Any, BinaryIO, Union
from torchdata.datapipes.iter import Filter, IterDataPipe, Mapper, Zipper
from torchvision.prototype.datasets.utils import Dataset, EncodedImage, HttpResource, OnlineResource
from torchvision.prototype.datasets.utils._internal ... | import pathlib
from typing import Any, BinaryIO, Dict, Iterator, List, Tuple, Union
from torchdata.datapipes.iter import Filter, IterDataPipe, Mapper, Zipper
from torchvision.prototype.datasets.utils import Dataset, EncodedImage, HttpResource, OnlineResource
from torchvision.prototype.datasets.utils._internal import (... |
import numpy as np
import pytest
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 ops
from keras.src import testing
class RandomGrayscaleTest(testing.TestCase):
@pytest.mark.requires_trainable_backend
... | import numpy as np
import pytest
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 ops
from keras.src import testing
class RandomGrayscaleTest(testing.TestCase):
@pytest.mark.requires_trainable_backend
... |
from ._source_separation_pipeline import CONVTASNET_BASE_LIBRI2MIX, SourceSeparationBundle
from ._tts import (
TACOTRON2_GRIFFINLIM_CHAR_LJSPEECH,
TACOTRON2_GRIFFINLIM_PHONE_LJSPEECH,
TACOTRON2_WAVERNN_CHAR_LJSPEECH,
TACOTRON2_WAVERNN_PHONE_LJSPEECH,
Tacotron2TTSBundle,
)
from ._wav2vec2.impl import... | from ._tts import (
TACOTRON2_GRIFFINLIM_CHAR_LJSPEECH,
TACOTRON2_GRIFFINLIM_PHONE_LJSPEECH,
TACOTRON2_WAVERNN_CHAR_LJSPEECH,
TACOTRON2_WAVERNN_PHONE_LJSPEECH,
Tacotron2TTSBundle,
)
from ._wav2vec2.impl import (
HUBERT_ASR_LARGE,
HUBERT_ASR_XLARGE,
HUBERT_BASE,
HUBERT_LARGE,
HUBE... |
# Copyright (c) OpenMMLab. All rights reserved.
from .atss import ATSS
from .autoassign import AutoAssign
from .base import BaseDetector
from .boxinst import BoxInst
from .cascade_rcnn import CascadeRCNN
from .centernet import CenterNet
from .condinst import CondInst
from .cornernet import CornerNet
from .crowddet impo... | # Copyright (c) OpenMMLab. All rights reserved.
from .atss import ATSS
from .autoassign import AutoAssign
from .base import BaseDetector
from .cascade_rcnn import CascadeRCNN
from .centernet import CenterNet
from .condinst import CondInst
from .cornernet import CornerNet
from .crowddet import CrowdDet
from .d2_wrapper ... |
from typing import Optional
from docarray import Document, DocumentArray
from pydantic import BaseModel
from uvicorn import Config, Server
from jina import Gateway, __default_host__
from jina.clients.request import request_generator
class DummyResponseModel(BaseModel):
arg1: Optional[str]
arg2: Optional[str... | from typing import Optional
from docarray import Document, DocumentArray
from pydantic import BaseModel
from uvicorn import Config, Server
from jina import Gateway, __default_host__
from jina.clients.request import request_generator
class DummyResponseModel(BaseModel):
arg1: Optional[str]
arg2: Optional[str... |
# coding=utf-8
# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless r... | # coding=utf-8
# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless r... |
# mypy: allow-untyped-defs
import functools
from collections.abc import Hashable
from dataclasses import dataclass, fields
from typing import TypeVar
from typing_extensions import dataclass_transform
T = TypeVar("T", bound="_Union")
class _UnionTag(str):
__slots__ = ("_cls",)
_cls: Hashable
@staticmeth... | # mypy: allow-untyped-defs
import functools
from collections.abc import Hashable
from dataclasses import dataclass, fields
from typing import TypeVar
from typing_extensions import dataclass_transform
T = TypeVar("T", bound="_Union")
class _UnionTag(str):
__slots__ = ("_cls",)
_cls: Hashable
@staticmeth... |
# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause
import pickle
from inspect import signature
import pytest
from sklearn.utils.deprecation import _is_deprecated, deprecated
@deprecated("qwerty")
class MockClass1:
pass
class MockClass2:
@deprecated("mockclass2_method")
de... | # Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause
import pickle
from inspect import signature
import pytest
from sklearn.utils.deprecation import _is_deprecated, deprecated
@deprecated("qwerty")
class MockClass1:
pass
class MockClass2:
@deprecated("mockclass2_method")
de... |
_base_ = '../faster_rcnn/faster-rcnn_r50_fpn_1x_coco.py'
model = dict(
neck=dict(
type='PAFPN',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
num_outs=5))
| _base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
model = dict(
neck=dict(
type='PAFPN',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
num_outs=5))
|
"""
The system trains BERT (or any other transformer model like RoBERTa, DistilBERT etc.) on the SNLI + MultiNLI (AllNLI) dataset
with softmax loss function. At every 1000 training steps, the model is evaluated on the
STS benchmark dataset
Usage:
python training_nli.py
OR
python training_nli.py pretrained_transformer... | """
The system trains BERT (or any other transformer model like RoBERTa, DistilBERT etc.) on the SNLI + MultiNLI (AllNLI) dataset
with softmax loss function. At every 1000 training steps, the model is evaluated on the
STS benchmark dataset
Usage:
python training_nli.py
OR
python training_nli.py pretrained_transformer... |
from __future__ import annotations
import pytest
from sentence_transformers.cross_encoder import CrossEncoder
@pytest.mark.parametrize(
"model_name, expected_score",
[
("cross-encoder/ms-marco-MiniLM-L-6-v2", [8.12545108795166, -3.045016050338745, -3.1524128913879395]),
("cross-encoder/ms-ma... | from __future__ import annotations
import pytest
from sentence_transformers.cross_encoder import CrossEncoder
@pytest.mark.parametrize(
"model_name, expected_score",
[
("cross-encoder/ms-marco-MiniLM-L-6-v2", [8.12545108795166, -3.045016050338745, -3.1524128913879395]),
("cross-encoder/ms-ma... |
r"""Utility classes & functions for data loading. Code in this folder is mostly used by ../dataloder.py.
A lot of multiprocessing is used in data loading, which only supports running
functions defined in global environment (py2 can't serialize static methods).
Therefore, for code tidiness we put these functions into d... | # mypy: allow-untyped-defs
r"""Utility classes & functions for data loading. Code in this folder is mostly used by ../dataloder.py.
A lot of multiprocessing is used in data loading, which only supports running
functions defined in global environment (py2 can't serialize static methods).
Therefore, for code tidiness we... |
from typing import TYPE_CHECKING, Dict, Type
from docarray.array.abstract_array import AbstractDocumentArray
from docarray.typing.tensor.abstract_tensor import AbstractTensor
if TYPE_CHECKING:
from docarray.proto import DocumentArrayProto, NodeProto
class ProtoArrayMixin(AbstractDocumentArray):
@classmethod... | from typing import TYPE_CHECKING, Type
from docarray.array.abstract_array import AbstractDocumentArray
if TYPE_CHECKING:
from docarray.proto import DocumentArrayProto, NodeProto
class ProtoArrayMixin(AbstractDocumentArray):
@classmethod
def from_protobuf(
cls: Type[AbstractDocumentArray], pb_msg... |
from logging import Logger
from backend.util.settings import AppEnvironment, BehaveAs, Settings
settings = Settings()
def configure_logging():
import logging
import autogpt_libs.logging.config
if (
settings.config.behave_as == BehaveAs.LOCAL
or settings.config.app_env == AppEnvironment... | from backend.util.settings import AppEnvironment, BehaveAs, Settings
settings = Settings()
def configure_logging():
import logging
import autogpt_libs.logging.config
if (
settings.config.behave_as == BehaveAs.LOCAL
or settings.config.app_env == AppEnvironment.LOCAL
):
autogp... |
from datetime import datetime, timedelta
from backend.blocks.hubspot._auth import (
HubSpotCredentials,
HubSpotCredentialsField,
HubSpotCredentialsInput,
)
from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
from backend.data.model import SchemaField
from backend.util.request impo... | from datetime import datetime, timedelta
from backend.blocks.hubspot._auth import (
HubSpotCredentials,
HubSpotCredentialsField,
HubSpotCredentialsInput,
)
from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
from backend.data.model import SchemaField
from backend.util.request impo... |
__version__ = '0.13.6'
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__ = '0.13.5'
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()
|
from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.tools import AIPluginTool
from langchain_community.tools.plugin import AIPlugin, AIPluginToolSchema, ApiConfig
# Create a way to dynamically look up deprecated imports.
# Used to consol... | from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.tools import AIPluginTool
from langchain_community.tools.plugin import AIPlugin, AIPluginToolSchema, ApiConfig
# Create a way to dynamically look up deprecated imports.
# Used to consol... |
import pytest
from jina import Executor, Flow, requests
from jina.clients.base.helper import HTTPClientlet, WebsocketClientlet
from jina.clients.request.helper import _new_data_request
from jina.excepts import BadServer
from jina.logging.logger import JinaLogger
from jina.types.request.data import DataRequest
logger ... | import aiohttp
import pytest
from jina import Executor, Flow, requests
from jina.clients.base.helper import HTTPClientlet, WebsocketClientlet
from jina.clients.request.helper import _new_data_request
from jina.excepts import BadServer
from jina.logging.logger import JinaLogger
from jina.types.request.data import DataR... |
import sys
import tempfile
from unittest.mock import patch
from keras.src.testing import test_case
from keras.src.utils import io_utils
class TestIoUtils(test_case.TestCase):
def test_enable_interactive_logging(self):
io_utils.enable_interactive_logging()
self.assertTrue(io_utils.is_interactive_l... | from unittest.mock import patch
from keras.src.testing import test_case
from keras.src.utils import io_utils
class TestIoUtils(test_case.TestCase):
def test_enable_interactive_logging(self):
io_utils.enable_interactive_logging()
self.assertTrue(io_utils.is_interactive_logging_enabled())
def ... |
import json
import re
from typing import TypeVar
import yaml
from langchain_core.exceptions import OutputParserException
from langchain_core.output_parsers import BaseOutputParser
from pydantic import BaseModel, ValidationError
from langchain.output_parsers.format_instructions import YAML_FORMAT_INSTRUCTIONS
T = Typ... | import json
import re
from typing import TypeVar
import yaml
from langchain_core.exceptions import OutputParserException
from langchain_core.output_parsers import BaseOutputParser
from pydantic import BaseModel, ValidationError
from langchain.output_parsers.format_instructions import YAML_FORMAT_INSTRUCTIONS
T = Typ... |
"""Init file of LlamaIndex."""
__version__ = "0.12.33.post1"
import logging
from logging import NullHandler
from typing import Callable, Optional
try:
# Force pants to install eval_type_backport on 3.9
import eval_type_backport # noqa # type: ignore
except ImportError:
pass
# response
from llama_index... | """Init file of LlamaIndex."""
__version__ = "0.12.32"
import logging
from logging import NullHandler
from typing import Callable, Optional
try:
# Force pants to install eval_type_backport on 3.9
import eval_type_backport # noqa # type: ignore
except ImportError:
pass
# response
from llama_index.core.... |
# Copyright (c) OpenMMLab. All rights reserved.
from .collect_env import collect_env
from .compat_config import compat_cfg
from .logger import get_caller_name, get_root_logger, log_img_scale
from .misc import find_latest_checkpoint, update_data_root
from .setup_env import setup_multi_processes
from .split_batch import ... | # Copyright (c) OpenMMLab. All rights reserved.
from .collect_env import collect_env
from .compat_config import compat_cfg
from .logger import get_caller_name, get_root_logger, log_img_scale
from .misc import find_latest_checkpoint, update_data_root
from .setup_env import setup_multi_processes
from .split_batch import ... |
import logging
from typing import List
import numpy as np
from torch.utils.data import IterableDataset
from sentence_transformers.readers import InputExample
logger = logging.getLogger(__name__)
class SentenceLabelDataset(IterableDataset):
"""
This dataset can be used for some specific Triplet Losses like ... | """ """
from torch.utils.data import IterableDataset
import numpy as np
from typing import List
from ..readers import InputExample
import logging
logger = logging.getLogger(__name__)
class SentenceLabelDataset(IterableDataset):
"""
This dataset can be used for some specific Triplet Losses like BATCH_HARD_TR... |
from docarray.typing.tensor.audio.audio_ndarray import AudioNdArray
__all__ = ['AudioNdArray']
from docarray.utils.misc import is_tf_available, is_torch_available
torch_available = is_torch_available()
if torch_available:
from docarray.typing.tensor.audio.audio_torch_tensor import AudioTorchTensor # noqa
_... | from docarray.typing.tensor.audio.audio_ndarray import AudioNdArray
__all__ = ['AudioNdArray']
try:
import torch # noqa: F401
except ImportError:
pass
else:
from docarray.typing.tensor.audio.audio_torch_tensor import AudioTorchTensor # noqa
__all__.extend(['AudioTorchTensor'])
|
# Copyright 2024 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... | # Copyright 2024 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... |
from datetime import datetime, timezone
from unittest.mock import AsyncMock
import pytest
from fastapi import WebSocket
from backend.data.execution import ExecutionResult, ExecutionStatus
from backend.server.conn_manager import ConnectionManager
from backend.server.model import Methods, WsMessage
@pytest.fixture
de... | from datetime import datetime, timezone
from unittest.mock import AsyncMock
import pytest
from fastapi import WebSocket
from backend.data.execution import ExecutionResult, ExecutionStatus
from backend.server.conn_manager import ConnectionManager
from backend.server.model import Methods, WsMessage
@pytest.fixture
de... |
"""**Chat Models** are a variation on language models.
While Chat Models use language models under the hood, the interface they expose
is a bit different. Rather than expose a "text in, text out" API, they expose
an interface where "chat messages" are the inputs and outputs.
**Class hierarchy:**
.. code-block::
... | """**Chat Models** are a variation on language models.
While Chat Models use language models under the hood, the interface they expose
is a bit different. Rather than expose a "text in, text out" API, they expose
an interface where "chat messages" are the inputs and outputs.
**Class hierarchy:**
.. code-block::
... |
from __future__ import annotations
from sentence_transformers.sparse_encoder.losses.CSRLoss import CSRLoss
from sentence_transformers.sparse_encoder.losses.CSRReconstructionLoss import (
CSRReconstructionLoss,
)
from sentence_transformers.sparse_encoder.losses.FlopsLoss import FlopsLoss
from sentence_transformers.... | from __future__ import annotations
from sentence_transformers.sparse_encoder.losses.CSRLoss import CSRLoss
from sentence_transformers.sparse_encoder.losses.CSRReconstructionLoss import (
CSRReconstructionLoss,
)
from sentence_transformers.sparse_encoder.losses.SparseAnglELoss import SparseAnglELoss
from sentence_t... |
# Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
from typing import Optional, Sequence, Tuple
import cv2
import numpy as np
from mmengine.data import BaseDataElement
from mmengine.hooks import Hook
from mmengine.registry import HOOKS
from mmengine.utils.misc import tensor2imgs
@HOOKS.register_m... | # Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
from typing import Any, Optional, Sequence, Tuple
import cv2
import numpy as np
from mmengine.data import BaseDataElement
from mmengine.hooks import Hook
from mmengine.registry import HOOKS
from mmengine.utils.misc import tensor2imgs
@HOOKS.regis... |
# Copyright (c) OpenMMLab. All rights reserved.
from unittest import TestCase
import torch
from mmengine.structures import InstanceData
from mmengine.testing import assert_allclose
from mmdet.models.task_modules.assigners import GridAssigner
class TestGridAssigner(TestCase):
def test_assign(self):
assi... | # Copyright (c) OpenMMLab. All rights reserved.
from unittest import TestCase
import torch
from mmengine.data import InstanceData
from mmengine.testing import assert_allclose
from mmdet.models.task_modules.assigners import GridAssigner
class TestGridAssigner(TestCase):
def test_assign(self):
assigner =... |
from typing import Any
from llama_index.core.agent import ReActAgentWorker, StructuredPlannerAgent
from llama_index.core.agent.runner.planner import Plan, SubTask
from llama_index.core.llms.custom import CustomLLM
from llama_index.core.llms import LLMMetadata, CompletionResponse, CompletionResponseGen
from llama_index... | from typing import Any
from llama_index.core.agent import ReActAgentWorker, StructuredPlannerAgent
from llama_index.core.agent.runner.planner import Plan, SubTask
from llama_index.core.llms.custom import CustomLLM
from llama_index.core.llms import LLMMetadata, CompletionResponse, CompletionResponseGen
from llama_index... |
# Copyright (c) OpenMMLab. All rights reserved.
import unittest
from unittest import TestCase
import torch
from parameterized import parameterized
from mmdet import * # noqa
from mmdet.structures import DetDataSample
from mmdet.testing import demo_mm_inputs, get_detector_cfg
from mmdet.utils import register_all_modu... | # Copyright (c) OpenMMLab. All rights reserved.
import unittest
from unittest import TestCase
import torch
from parameterized import parameterized
from mmdet import * # noqa
from mmdet.structures import DetDataSample
from mmdet.testing import demo_mm_inputs, get_detector_cfg
from mmdet.utils import register_all_modu... |
_base_ = './yolox_s_8xb8-300e_coco.py'
# model settings
model = dict(
data_preprocessor=dict(batch_augments=[
dict(
type='BatchSyncRandomResize',
random_size_range=(320, 640),
size_divisor=32,
interval=10)
]),
backbone=dict(deepen_factor=0.33, widen_f... | _base_ = './yolox_s_8xb8-300e_coco.py'
# model settings
model = dict(
data_preprocessor=dict(batch_augments=[
dict(
type='BatchSyncRandomResize',
random_size_range=(320, 640),
size_divisor=32,
interval=10)
]),
backbone=dict(deepen_factor=0.33, widen_f... |
import pytest
from llama_index.core.extractors import DocumentContextExtractor
from llama_index.core.llms import ChatMessage, ChatResponse, MockLLM
from llama_index.core.schema import Document, NodeRelationship, TextNode
from llama_index.core.storage.docstore.simple_docstore import SimpleDocumentStore
@pytest.fixtur... | import pytest
from llama_index.core.extractors import DocumentContextExtractor
from llama_index.core.llms import ChatMessage, ChatResponse, MockLLM
from llama_index.core.schema import Document, NodeRelationship, TextNode
from llama_index.core.storage.docstore.simple_docstore import SimpleDocumentStore
@pytest.fixtur... |
import json
from typing import Any, Dict, Optional, Tuple
from llama_index.core.schema import (
BaseNode,
ImageNode,
IndexNode,
NodeRelationship,
RelatedNodeInfo,
TextNode,
)
DEFAULT_TEXT_KEY = "text"
DEFAULT_EMBEDDING_KEY = "embedding"
DEFAULT_DOC_ID_KEY = "doc_id"
def _validate_is_flat_dic... | import json
from typing import Any, Dict, Optional, Tuple
from llama_index.core.schema import (
BaseNode,
ImageNode,
IndexNode,
NodeRelationship,
RelatedNodeInfo,
TextNode,
)
DEFAULT_TEXT_KEY = "text"
DEFAULT_EMBEDDING_KEY = "embedding"
DEFAULT_DOC_ID_KEY = "doc_id"
def _validate_is_flat_dic... |
"""
================================================
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 __future__ import annotations
import json
from typing import Optional, Type
import requests
import yaml
from langchain_core.callbacks import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain_core.tools import BaseTool
from pydantic import BaseModel
class ApiConfig(BaseModel)... | from __future__ import annotations
import json
from typing import Optional, Type
import requests
import yaml
from langchain_core.callbacks import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain_core.tools import BaseTool
from pydantic import BaseModel
class ApiConfig(BaseModel)... |
from langchain_core.agents import AgentAction
from langchain.agents.format_scratchpad.log import format_log_to_str
def test_single_agent_action_observation() -> None:
intermediate_steps = [
(AgentAction(tool="Tool1", tool_input="input1", log="Log1"), "Observation1"),
]
expected_result = "Log1\nOb... | from langchain_core.agents import AgentAction
from langchain.agents.format_scratchpad.log import format_log_to_str
def test_single_agent_action_observation() -> None:
intermediate_steps = [
(AgentAction(tool="Tool1", tool_input="input1", log="Log1"), "Observation1")
]
expected_result = "Log1\nObs... |
"""Azure Translate tool spec."""
import requests
from llama_index.core.tools.tool_spec.base import BaseToolSpec
ENDPOINT_BASE_URL = "https://api.cognitive.microsofttranslator.com/translate"
class AzureTranslateToolSpec(BaseToolSpec):
"""Azure Translate tool spec."""
spec_functions = ["translate"]
def ... | """Azure Translate tool spec."""
import requests
from llama_index.core.tools.tool_spec.base import BaseToolSpec
ENDPOINT_BASE_URL = "https://api.cognitive.microsofttranslator.com/translate"
class AzureTranslateToolSpec(BaseToolSpec):
"""Azure Translate tool spec."""
spec_functions = ["translate"]
def ... |
# Copyright (c) OpenMMLab. All rights reserved.
import torch
from mmdet.models.utils.misc import get_box_tensor
from mmdet.registry import TASK_UTILS
from mmdet.structures.bbox import HorizontalBoxes
from .base_bbox_coder import BaseBBoxCoder
@TASK_UTILS.register_module()
class YOLOBBoxCoder(BaseBBoxCoder):
"""Y... | # Copyright (c) OpenMMLab. All rights reserved.
import torch
from mmdet.registry import TASK_UTILS
from .base_bbox_coder import BaseBBoxCoder
@TASK_UTILS.register_module()
class YOLOBBoxCoder(BaseBBoxCoder):
"""YOLO BBox coder.
Following `YOLO <https://arxiv.org/abs/1506.02640>`_, this coder divide
imag... |
import logging
import sentry_sdk
from sentry_sdk.integrations.anthropic import AnthropicIntegration
from sentry_sdk.integrations.logging import LoggingIntegration
from backend.util.settings import Settings
def sentry_init():
sentry_dsn = Settings().secrets.sentry_dsn
sentry_sdk.init(
dsn=sentry_dsn,... | import logging
import sentry_sdk
from sentry_sdk.integrations.anthropic import AnthropicIntegration
from sentry_sdk.integrations.logging import LoggingIntegration
from backend.util.settings import Settings
def sentry_init():
sentry_dsn = Settings().secrets.sentry_dsn
sentry_sdk.init(
dsn=sentry_dsn,... |
# Copyright (c) OpenMMLab. All rights reserved.
import logging
import os.path as osp
from argparse import ArgumentParser
from mmcv import Config
from mmdet.apis import inference_detector, init_detector, show_result_pyplot
from mmdet.utils import get_root_logger
def parse_args():
parser = ArgumentParser()
pa... | import logging
import os.path as osp
from argparse import ArgumentParser
from mmcv import Config
from mmdet.apis import inference_detector, init_detector, show_result_pyplot
from mmdet.utils import get_root_logger
def parse_args():
parser = ArgumentParser()
parser.add_argument('config', help='test config fi... |
# Copyright (c) OpenMMLab. All rights reserved.
import unittest
from unittest import TestCase
import torch
from parameterized import parameterized
from mmdet.registry import MODELS
from mmdet.testing import demo_mm_inputs, demo_mm_proposals, get_roi_head_cfg
from mmdet.utils import register_all_modules
class TestPI... | # Copyright (c) OpenMMLab. All rights reserved.
import unittest
from unittest import TestCase
import torch
from parameterized import parameterized
from mmdet.registry import MODELS
from mmdet.testing import demo_mm_inputs, demo_mm_proposals, get_roi_head_cfg
from mmdet.utils import register_all_modules
class TestPI... |
from pathlib import Path
from typing import Any, Callable, Optional, Tuple, Union
from PIL import Image
from .folder import find_classes, make_dataset
from .utils import download_and_extract_archive, verify_str_arg
from .vision import VisionDataset
class Imagenette(VisionDataset):
"""`Imagenette <https://github... | from pathlib import Path
from typing import Any, Callable, Optional, Tuple, Union
from PIL import Image
from .folder import find_classes, make_dataset
from .utils import download_and_extract_archive, verify_str_arg
from .vision import VisionDataset
class Imagenette(VisionDataset):
"""`Imagenette <https://github... |
from pathlib import Path
from typing import Any, List, Union
from langchain_community.document_loaders.unstructured import (
UnstructuredFileLoader,
validate_unstructured_version,
)
class UnstructuredTSVLoader(UnstructuredFileLoader):
"""Load `TSV` files using `Unstructured`.
Like other
Unstruct... | from pathlib import Path
from typing import Any, List, Union
from langchain_community.document_loaders.unstructured import (
UnstructuredFileLoader,
validate_unstructured_version,
)
class UnstructuredTSVLoader(UnstructuredFileLoader):
"""Load `TSV` files using `Unstructured`.
Like other
Unstruct... |
"""Chat generation output classes."""
from __future__ import annotations
from typing import TYPE_CHECKING, Literal, Union
from pydantic import model_validator
from langchain_core.messages import BaseMessage, BaseMessageChunk
from langchain_core.outputs.generation import Generation
from langchain_core.utils._merge i... | """Chat generation output classes."""
from __future__ import annotations
from typing import TYPE_CHECKING, Literal, Union
from pydantic import model_validator
from langchain_core.messages import BaseMessage, BaseMessageChunk
from langchain_core.outputs.generation import Generation
from langchain_core.utils._merge i... |
from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.llms import GradientLLM
from langchain_community.llms.gradient_ai import TrainResult
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising de... | from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.llms import GradientLLM
from langchain_community.llms.gradient_ai import TrainResult
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising de... |
import os
import warnings
import torch
from torchvision import datasets, io, models, ops, transforms, utils
from .extension import _HAS_OPS
try:
from .version import __version__ # noqa: F401
except ImportError:
pass
# Check if torchvision is being imported within the root folder
if not _HAS_OPS and os.path... | import os
import warnings
from modulefinder import Module
import torch
from torchvision import datasets, io, models, ops, transforms, utils
from .extension import _HAS_OPS, _load_library
try:
from .version import __version__ # noqa: F401
except ImportError:
pass
try:
_load_library("Decoder")
_HAS_G... |
_base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
]
# model settings
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
backbone=dict(
depth=101,
init_c... | _base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
]
# model settings
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
backbone=dict(
depth=101,
init_c... |
from typing import Optional
import numpy as np
from docarray import BaseDoc, DocList
from docarray.documents import ImageDoc
from docarray.typing import AnyTensor, ImageUrl
from jina import Deployment, Executor, Flow, requests
def test_different_document_schema():
class Image(BaseDoc):
tensor: Optional[... | from typing import Optional
import numpy as np
from docarray import BaseDoc
from docarray import DocArray as DocumentArray
from docarray.documents import ImageDoc
from docarray.typing import AnyTensor, ImageUrl
from jina import Deployment, Executor, Flow, requests
def test_different_document_schema():
class Ima... |
from typing import Optional
import numpy as np
import pytest
import torch
from pydantic.tools import parse_obj_as, schema_json_of
from docarray import BaseDocument
from docarray.base_document.io.json import orjson_dumps
from docarray.typing import AudioTorchTensor, AudioUrl
from tests import TOYDATA_DIR
AUDIO_FILES ... | from typing import Optional
import numpy as np
import pytest
import torch
from pydantic.tools import parse_obj_as, schema_json_of
from docarray import BaseDocument
from docarray.base_document.io.json import orjson_dumps
from docarray.typing import AudioTorchTensor, AudioUrl
from tests import TOYDATA_DIR
AUDIO_FILES ... |
__all__ = ["LoggingCallbackHandler"]
import logging
from typing import Any, Optional
from uuid import UUID
from langchain_core.exceptions import TracerException
from langchain_core.tracers.stdout import FunctionCallbackHandler
from langchain_core.utils.input import get_bolded_text, get_colored_text
class LoggingCal... | __all__ = ["LoggingCallbackHandler"]
import logging
from typing import Any, Optional
from uuid import UUID
from langchain_core.exceptions import TracerException
from langchain_core.tracers.stdout import FunctionCallbackHandler
from langchain_core.utils.input import get_bolded_text, get_colored_text
class LoggingCal... |
_base_ = '../_base_/default_runtime.py'
# dataset settings
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
# Example to use different file client
# Method 1: simply set the data root and let the file I/O module
# automatically infer from prefix (not support LMDB and Memcache yet)
# data_root = 's3://openmmlab/... | _base_ = '../_base_/default_runtime.py'
# dataset settings
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
# file_client_args = dict(
# backend='petrel',
# path_mapping=dict({
# './data/': 's3://openmmlab/datasets/detection/',
# 'data/': 's3://openmmlab/datasets/detection/'
# }))
fil... |
"""
Demo for accessing the xgboost eval metrics by using sklearn interface
======================================================================
"""
import numpy as np
from sklearn.datasets import make_hastie_10_2
import xgboost as xgb
X, y = make_hastie_10_2(n_samples=2000, random_state=42)
# Map labels from {-1,... | """
Demo for accessing the xgboost eval metrics by using sklearn interface
======================================================================
"""
import numpy as np
from sklearn.datasets import make_hastie_10_2
import xgboost as xgb
X, y = make_hastie_10_2(n_samples=2000, random_state=42)
# Map labels from {-1,... |
from abc import abstractmethod
from typing import Any, Type, TypeVar
from pydantic import BaseConfig
from pydantic.fields import ModelField
from docarray.base_doc.base_node import BaseNode
T = TypeVar('T')
class AbstractType(BaseNode):
@classmethod
def __get_validators__(cls):
yield cls.validate
... | from abc import abstractmethod
from typing import TYPE_CHECKING, Any, Optional, Type, TypeVar
from pydantic import BaseConfig
from pydantic.fields import ModelField
from docarray.base_doc.base_node import BaseNode
if TYPE_CHECKING:
from docarray.proto import NodeProto
T = TypeVar('T')
class AbstractType(BaseN... |
import pytest
import torch
from torchvision.prototype import datapoints
@pytest.mark.parametrize(
("data", "input_requires_grad", "expected_requires_grad"),
[
([0.0], None, False),
([0.0], False, False),
([0.0], True, True),
(torch.tensor([0.0], requires_grad=False), None, Fals... | import pytest
import torch
from torchvision.prototype import datapoints
def test_isinstance():
assert isinstance(
datapoints.Label([0, 1, 0], categories=["foo", "bar"]),
torch.Tensor,
)
def test_wrapping_no_copy():
tensor = torch.tensor([0, 1, 0], dtype=torch.int64)
label = datapoint... |
"""Standard LangChain interface tests"""
from langchain_core.language_models import BaseChatModel
from langchain_tests.unit_tests import ChatModelUnitTests
from langchain_anthropic import ChatAnthropic
class TestAnthropicStandard(ChatModelUnitTests):
@property
def chat_model_class(self) -> type[BaseChatMode... | """Standard LangChain interface tests"""
from typing import Type
from langchain_core.language_models import BaseChatModel
from langchain_tests.unit_tests import ChatModelUnitTests
from langchain_anthropic import ChatAnthropic
class TestAnthropicStandard(ChatModelUnitTests):
@property
def chat_model_class(s... |
"""DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.ops.nn import average_pool as average_pool
from keras.src.ops.nn import batch_normalization as batch_normalization
from keras.src.ops.nn import binary_crossentropy as binary_crossentr... | """DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.ops.nn import average_pool as average_pool
from keras.src.ops.nn import batch_normalization as batch_normalization
from keras.src.ops.nn import binary_crossentropy as binary_crossentr... |
"""
===================================
How to write your own v2 transforms
===================================
.. note::
Try on `collab <https://colab.research.google.com/github/pytorch/vision/blob/gh-pages/main/_generated_ipynb_notebooks/plot_custom_transforms.ipynb>`_
or :ref:`go to the end <sphx_glr_downlo... | """
===================================
How to write your own v2 transforms
===================================
.. note::
Try on `collab <https://colab.research.google.com/github/pytorch/vision/blob/gh-pages/main/_generated_ipynb_notebooks/plot_custom_transforms.ipynb>`_
or :ref:`go to the end <sphx_glr_downlo... |
import logging
import requests
from fastapi import Request
from backend.data import integrations
from backend.data.model import APIKeyCredentials, Credentials
from backend.integrations.providers import ProviderName
from backend.integrations.webhooks.base import BaseWebhooksManager
logger = logging.getLogger(__name__... | import logging
from typing import ClassVar
import requests
from fastapi import Request
from backend.data import integrations
from backend.data.model import APIKeyCredentials, Credentials
from backend.integrations.webhooks.base import BaseWebhooksManager
logger = logging.getLogger(__name__)
class Slant3DWebhooksMan... |
from keras.src.api_export import keras_export
from keras.src.layers.pooling.base_pooling import BasePooling
@keras_export(["keras.layers.MaxPooling3D", "keras.layers.MaxPool3D"])
class MaxPooling3D(BasePooling):
"""Max pooling operation for 3D data (spatial or spatio-temporal).
Downsamples the input along it... | from keras.src.api_export import keras_export
from keras.src.layers.pooling.base_pooling import BasePooling
@keras_export(["keras.layers.MaxPooling3D", "keras.layers.MaxPool3D"])
class MaxPooling3D(BasePooling):
"""Max pooling operation for 3D data (spatial or spatio-temporal).
Downsamples the input along it... |
# Copyright (c) OpenMMLab. All rights reserved.
from ..builder import DETECTORS
from .single_stage import SingleStageDetector
@DETECTORS.register_module()
class FCOS(SingleStageDetector):
"""Implementation of `FCOS <https://arxiv.org/abs/1904.01355>`_"""
def __init__(self,
backbone,
... | from ..builder import DETECTORS
from .single_stage import SingleStageDetector
@DETECTORS.register_module()
class FCOS(SingleStageDetector):
"""Implementation of `FCOS <https://arxiv.org/abs/1904.01355>`_"""
def __init__(self,
backbone,
neck,
bbox_head,
... |
_base_ = './mask_rcnn_r50_fpn_gn-all_2x_coco.py'
# learning policy
max_epochs = 36
train_cfg = dict(max_epochs=max_epochs)
# learning rate
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=ma... | _base_ = './mask_rcnn_r50_fpn_gn-all_2x_coco.py'
# learning policy
lr_config = dict(step=[28, 34])
runner = dict(type='EpochBasedRunner', max_epochs=36)
|
import subprocess
import pytest
from jina import Document, DocumentArray, Flow
from ...clip_text import CLIPTextEncoder
_EMBEDDING_DIM = 512
@pytest.mark.parametrize('request_size', [1, 10, 50, 100])
def test_integration(request_size: int):
docs = DocumentArray(
[Document(text='just some random text he... | import subprocess
import pytest
from jina import Document, DocumentArray, Flow
from ...clip_text import CLIPTextEncoder
_EMBEDDING_DIM = 512
@pytest.mark.parametrize('request_size', [1, 10, 50, 100])
def test_integration(request_size: int):
docs = DocumentArray(
[Document(text='just some random text he... |
"""Fake Embedding class for testing purposes."""
import math
from langchain_core.embeddings import Embeddings
fake_texts = ["foo", "bar", "baz"]
class FakeEmbeddings(Embeddings):
"""Fake embeddings functionality for testing."""
def embed_documents(self, texts: list[str]) -> list[list[float]]:
"""R... | """Fake Embedding class for testing purposes."""
import math
from typing import List
from langchain_core.embeddings import Embeddings
fake_texts = ["foo", "bar", "baz"]
class FakeEmbeddings(Embeddings):
"""Fake embeddings functionality for testing."""
def embed_documents(self, texts: List[str]) -> List[Li... |
import types
from typing_extensions import TYPE_CHECKING
from docarray.typing.tensor.audio import AudioNdArray
from docarray.typing.tensor.embedding import AnyEmbedding, NdArrayEmbedding
from docarray.typing.tensor.image import ImageNdArray, ImageTensor
from docarray.typing.tensor.ndarray import NdArray
from docarray... | import types
from typing_extensions import TYPE_CHECKING
from docarray.typing.tensor.audio import AudioNdArray
from docarray.typing.tensor.embedding import AnyEmbedding, NdArrayEmbedding
from docarray.typing.tensor.image import ImageNdArray, ImageTensor
from docarray.typing.tensor.ndarray import NdArray
from docarray... |
# Copyright (c) OpenMMLab. All rights reserved.
from .backbones import * # noqa: F401,F403
from .data_preprocessors import * # noqa: F401,F403
from .dense_heads import * # noqa: F401,F403
from .detectors import * # noqa: F401,F403
from .language_models import * # noqa: F401,F403
from .layers import * # noqa: F401... | # Copyright (c) OpenMMLab. All rights reserved.
from .backbones import * # noqa: F401,F403
from .data_preprocessors import * # noqa: F401,F403
from .dense_heads import * # noqa: F401,F403
from .detectors import * # noqa: F401,F403
from .layers import * # noqa: F401,F403
from .losses import * # noqa: F401,F403
fro... |
import types
from typing import TYPE_CHECKING
from docarray.store.file import FileDocStore
from docarray.utils._internal.misc import (
_get_path_from_docarray_root_level,
import_library,
)
if TYPE_CHECKING:
from docarray.store.jac import JACDocStore # noqa: F401
from docarray.store.s3 import S3DocSto... | from docarray.store.file import FileDocStore
from docarray.store.jac import JACDocStore
from docarray.store.s3 import S3DocStore
__all__ = ['JACDocStore', 'FileDocStore', 'S3DocStore']
|
import enum
from typing import Any, Optional
import pydantic
from backend.data.api_key import APIKeyPermission, APIKeyWithoutHash
from backend.data.graph import Graph
class WSMethod(enum.Enum):
SUBSCRIBE_GRAPH_EXEC = "subscribe_graph_execution"
UNSUBSCRIBE = "unsubscribe"
GRAPH_EXECUTION_EVENT = "graph_... | import enum
from typing import Any, List, Optional, Union
import pydantic
import backend.data.graph
from backend.data.api_key import APIKeyPermission, APIKeyWithoutHash
class Methods(enum.Enum):
SUBSCRIBE = "subscribe"
UNSUBSCRIBE = "unsubscribe"
EXECUTION_EVENT = "execution_event"
ERROR = "error"
... |
"""DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.api.utils import bounding_boxes
from keras.api.utils import legacy
from keras.src.backend.common.global_state import clear_session
from keras.src.backend.common.keras_tensor import is_ker... | """DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.api.utils import legacy
from keras.src.backend.common.global_state import clear_session
from keras.src.backend.common.keras_tensor import is_keras_tensor
from keras.src.backend.common.var... |
import json
import os
from typing import Dict
import torch
from torch import Tensor, nn
from sentence_transformers.util import fullname, import_from_string
class Dense(nn.Module):
"""
Feed-forward function with activiation function.
This layer takes a fixed-sized sentence embedding and passes it throu... | import torch
from torch import Tensor
from torch import nn
from typing import Dict
import os
import json
from ..util import fullname, import_from_string
class Dense(nn.Module):
"""
Feed-forward function with activiation function.
This layer takes a fixed-sized sentence embedding and passes it through a ... |
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