input stringlengths 33 5k | output stringlengths 32 5k |
|---|---|
# Copyright (c) OpenMMLab. All rights reserved.
from typing import List, Tuple, Union
import mmcv
import numpy as np
from mmengine.utils import is_str
def palette_val(palette: List[tuple]) -> List[tuple]:
"""Convert palette to matplotlib palette.
Args:
palette (List[tuple]): A list of color tuples.
... | # Copyright (c) OpenMMLab. All rights reserved.
from typing import List, Tuple, Union
import mmcv
import numpy as np
from mmengine.utils import is_str
def palette_val(palette: List[tuple]) -> List[tuple]:
"""Convert palette to matplotlib palette.
Args:
palette (List[tuple]): A list of color tuples.
... |
# Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from mmdet.models.utils import SELayer
def test_se_layer():
with pytest.raises(AssertionError):
# act_cfg sequence length must equal to 2
SELayer(channels=32, act_cfg=(dict(type='ReLU'), ))
with pytest.raises(Assertio... | import pytest
import torch
from mmdet.models.utils import SELayer
def test_se_layer():
with pytest.raises(AssertionError):
# act_cfg sequence length must equal to 2
SELayer(channels=32, act_cfg=(dict(type='ReLU'), ))
with pytest.raises(AssertionError):
# act_cfg sequence must be a tu... |
import logging
import os
import pytest
from dotenv import load_dotenv
from backend.util.logging import configure_logging
load_dotenv()
# NOTE: You can run tests like with the --log-cli-level=INFO to see the logs
# Set up logging
configure_logging()
logger = logging.getLogger(__name__)
# Reduce Prisma log spam unl... | import logging
import os
import pytest
from backend.util.logging import configure_logging
# NOTE: You can run tests like with the --log-cli-level=INFO to see the logs
# Set up logging
configure_logging()
logger = logging.getLogger(__name__)
# Reduce Prisma log spam unless PRISMA_DEBUG is set
if not os.getenv("PRIS... |
import os
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 BaseDoc
from docarray.base_doc.io.json import orjson_dumps
from docarray.typing import (
AudioNdArray,
NdArray,
VideoBytes,
VideoNdArray,
... | import os
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 BaseDoc
from docarray.base_doc.io.json import orjson_dumps
from docarray.typing import (
AudioNdArray,
NdArray,
VideoBytes,
VideoNdArray,
... |
# Copyright (c) OpenMMLab. All rights reserved.
from typing import Any, List, Optional, Tuple, Type, Union
import cv2
import matplotlib
import numpy as np
import torch
def tensor2ndarray(value: Union[np.ndarray, torch.Tensor]) -> np.ndarray:
"""If the type of value is torch.Tensor, convert the value to np.ndarr... | # Copyright (c) OpenMMLab. All rights reserved.
from typing import Any, List, Tuple, Type, Union
import numpy as np
import torch
def tensor2ndarray(value: Union[np.ndarray, torch.Tensor]) -> np.ndarray:
"""If the type of value is torch.Tensor, convert the value to np.ndarray.
Args:
value (np.ndarray... |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# This software may be used and distributed according to the terms of the Llama 2 Community License Agreement.
from typing import List, Optional
import fire
from llama import Llama, Dialog
def main(
ckpt_dir: str,
tokenizer_path: str,
temperature: fl... | # Copyright (c) Meta Platforms, Inc. and affiliates.
# This software may be used and distributed according to the terms of the Llama 2 Community License Agreement.
from typing import Optional
import fire
from llama import Llama
def main(
ckpt_dir: str,
tokenizer_path: str,
temperature: float = 0.6,
... |
# Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
import torch.nn.functional as F
from mmcv.cnn import constant_init
from mmdet.models.utils import DyReLU, SELayer
def test_se_layer():
with pytest.raises(AssertionError):
# act_cfg sequence length must equal to 2
SELayer(c... | # Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from mmdet.models.utils import SELayer
def test_se_layer():
with pytest.raises(AssertionError):
# act_cfg sequence length must equal to 2
SELayer(channels=32, act_cfg=(dict(type='ReLU'), ))
with pytest.raises(Assertio... |
from pathlib import Path
from typing import List, Tuple, Union
import torch
import torchaudio
from torch.utils.data import Dataset
SampleType = Tuple[int, torch.Tensor, List[torch.Tensor]]
class LibriMix(Dataset):
r"""Create the *LibriMix* [:footcite:`cosentino2020librimix`] dataset.
Args:
root (st... | from pathlib import Path
from typing import Union, Tuple, List
import torch
import torchaudio
from torch.utils.data import Dataset
SampleType = Tuple[int, torch.Tensor, List[torch.Tensor]]
class LibriMix(Dataset):
r"""Create the *LibriMix* [:footcite:`cosentino2020librimix`] dataset.
Args:
root (st... |
import contextlib
import logging
import typing
import fastapi
import fastapi.responses
import starlette.middleware.cors
import uvicorn
from autogpt_libs.feature_flag.client import (
initialize_launchdarkly,
shutdown_launchdarkly,
)
import backend.data.block
import backend.data.db
import backend.data.graph
imp... | import contextlib
import logging
import typing
import fastapi
import fastapi.responses
import starlette.middleware.cors
import uvicorn
from autogpt_libs.feature_flag.client import (
initialize_launchdarkly,
shutdown_launchdarkly,
)
import backend.data.block
import backend.data.db
import backend.data.graph
imp... |
# Copyright (c) OpenMMLab. All rights reserved.
from unittest import TestCase
from unittest.mock import MagicMock, patch
import pytest
import torch
import torch.nn as nn
from mmengine.model.wrappers import (MMDataParallel, MMDistributedDataParallel,
is_model_wrapper)
from mmengine... | # Copyright (c) OpenMMLab. All rights reserved.
from unittest.mock import MagicMock, patch
import pytest
import torch
import torch.nn as nn
from mmengine.model.wrappers import (MMDataParallel, MMDistributedDataParallel,
is_model_wrapper)
from mmengine.registry import MODEL_WRAPPER... |
# Copyright (c) OpenMMLab. All rights reserved.
__version__ = '0.3.1'
def parse_version_info(version_str):
"""Parse the version information.
Args:
version_str (str): version string like '0.1.0'.
Returns:
tuple: version information contains major, minor, micro version.
"""
versio... | # Copyright (c) OpenMMLab. All rights reserved.
__version__ = '0.3.0'
def parse_version_info(version_str):
"""Parse the version information.
Args:
version_str (str): version string like '0.1.0'.
Returns:
tuple: version information contains major, minor, micro version.
"""
versio... |
__copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import os
import pytest
from jina import Document, Flow
from ...video_torch_encoder import VideoTorchEncoder
cur_dir = os.path.dirname(os.path.abspath(__file__))
@pytest.fixture()
def kinects_videos():
f... | __copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import os
import pytest
from jina import Document, Flow
try:
from video_torch_encoder import VideoTorchEncoder
except:
from ...video_torch_encoder import VideoTorchEncoder
cur_dir = os.path.dirname(os.... |
"""Browser tools and toolkit."""
from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.tools import (
ClickTool,
CurrentWebPageTool,
ExtractHyperlinksTool,
ExtractTextTool,
GetElementsTool,
Navig... | """Browser tools and toolkit."""
from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.tools import (
ClickTool,
CurrentWebPageTool,
ExtractHyperlinksTool,
ExtractTextTool,
GetElementsTool,
Navig... |
"""Use a single chain to route an input to one of multiple retrieval qa chains."""
from __future__ import annotations
from collections.abc import Mapping
from typing import Any, Optional
from langchain_core.language_models import BaseLanguageModel
from langchain_core.prompts import PromptTemplate
from langchain_core... | """Use a single chain to route an input to one of multiple retrieval qa chains."""
from __future__ import annotations
from collections.abc import Mapping
from typing import Any, Optional
from langchain_core.language_models import BaseLanguageModel
from langchain_core.prompts import PromptTemplate
from langchain_core... |
from io import BytesIO
from pathlib import Path
from typing import Any, List, Tuple, Union
import requests
from langchain_core.documents import Document
from langchain_community.document_loaders.base import BaseLoader
class ImageCaptionLoader(BaseLoader):
"""Load image captions.
By default, the loader util... | from io import BytesIO
from pathlib import Path
from typing import Any, List, Tuple, Union
import requests
from langchain_core.documents import Document
from langchain_community.document_loaders.base import BaseLoader
class ImageCaptionLoader(BaseLoader):
"""Load image captions.
By default, the loader util... |
import numpy as np
import pytest
import torch
from docarray.typing import (
AudioNdArray,
AudioTorchTensor,
NdArrayEmbedding,
TorchEmbedding,
)
from docarray.utils._internal.misc import is_tf_available
tf_available = is_tf_available()
if tf_available:
import tensorflow as tf
from docarray.typ... | import numpy as np
import pytest
import torch
from docarray.typing import (
AudioNdArray,
AudioTorchTensor,
NdArrayEmbedding,
TorchEmbedding,
)
from docarray.utils.misc import is_tf_available
tf_available = is_tf_available()
if tf_available:
import tensorflow as tf
from docarray.typing import... |
"""Support vector machine algorithms."""
# See http://scikit-learn.sourceforge.net/modules/svm.html for complete
# documentation.
# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause
from ._bounds import l1_min_c
from ._classes import SVC, SVR, LinearSVC, LinearSVR, NuSVC, NuSVR, OneClassSV... | """Support vector machine algorithms."""
# See http://scikit-learn.sourceforge.net/modules/svm.html for complete
# documentation.
# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause
from ._bounds import l1_min_c
from ._classes import SVC, SVR, LinearSVC, LinearSVR, NuSVC, NuSVR, OneClassSV... |
"""Document loaders."""
from typing import TYPE_CHECKING
from langchain_core._import_utils import import_attr
if TYPE_CHECKING:
from langchain_core.document_loaders.base import BaseBlobParser, BaseLoader
from langchain_core.document_loaders.blob_loaders import Blob, BlobLoader, PathLike
from langchain_co... | """Document loaders."""
from importlib import import_module
from typing import TYPE_CHECKING
if TYPE_CHECKING:
from langchain_core.document_loaders.base import BaseBlobParser, BaseLoader
from langchain_core.document_loaders.blob_loaders import Blob, BlobLoader, PathLike
from langchain_core.document_loader... |
__all__ = [
"Audio",
"Array2D",
"Array3D",
"Array4D",
"Array5D",
"ClassLabel",
"Features",
"LargeList",
"Sequence",
"Value",
"Image",
"Translation",
"TranslationVariableLanguages",
]
from .audio import Audio
from .features import Array2D, Array3D, Array4D, Array5D, Cl... | __all__ = [
"Audio",
"Array2D",
"Array3D",
"Array4D",
"Array5D",
"ClassLabel",
"Features",
"Sequence",
"Value",
"Image",
"Translation",
"TranslationVariableLanguages",
]
from .audio import Audio
from .features import Array2D, Array3D, Array4D, Array5D, ClassLabel, Feature... |
from sentence_transformers import SentenceTransformer, losses, util
class AnglELoss(losses.CoSENTLoss):
def __init__(self, model: SentenceTransformer, scale: float = 20.0) -> None:
"""
This class implements AnglE (Angle Optimized) loss.
This is a modification of :class:`CoSENTLoss`, design... | from sentence_transformers import SentenceTransformer, losses, util
class AnglELoss(losses.CoSENTLoss):
def __init__(self, model: SentenceTransformer, scale: float = 20.0):
"""
This class implements AnglE (Angle Optimized) loss.
This is a modification of :class:`CoSENTLoss`, designed to ad... |
__version__ = '0.13.21'
import os
from .document import Document
from .array import DocumentArray
from .dataclasses import dataclass, field
if 'DA_RICH_HANDLER' in os.environ:
from rich.traceback import install
install()
| __version__ = '0.13.20'
import os
from .document import Document
from .array import DocumentArray
from .dataclasses import dataclass, field
if 'DA_RICH_HANDLER' in os.environ:
from rich.traceback import install
install()
|
"""
Top-level module of Jina.
The primary function of this module is to import all of the public Jina
interfaces into a single place. The interfaces themselves are located in
sub-modules, as described below.
"""
import os as _os
import platform as _platform
import signal as _signal
import sys as _sys
import warnings... | """
Top-level module of Jina.
The primary function of this module is to import all of the public Jina
interfaces into a single place. The interfaces themselves are located in
sub-modules, as described below.
"""
import os as _os
import platform as _platform
import signal as _signal
import sys as _sys
import warnings... |
# Copyright (c) OpenMMLab. All rights reserved.
from typing import List, Optional, Union
from mmcv.cnn import ConvModule
from torch import Tensor
from mmdet.registry import MODELS
from .fcn_mask_head import FCNMaskHead
@MODELS.register_module()
class HTCMaskHead(FCNMaskHead):
"""Mask head for HTC.
Args:
... | # Copyright (c) OpenMMLab. All rights reserved.
from typing import List, Optional, Union
from mmcv.cnn import ConvModule
from torch import Tensor
from mmdet.registry import MODELS
from .fcn_mask_head import FCNMaskHead
@MODELS.register_module()
class HTCMaskHead(FCNMaskHead):
"""Mask head for HTC.
Args:
... |
from typing import Any
def get_prompt_input_key(inputs: dict[str, Any], memory_variables: list[str]) -> str:
"""
Get the prompt input key.
Args:
inputs: Dict[str, Any]
memory_variables: List[str]
Returns:
A prompt input key.
"""
# "stop" is a special key that can be p... | from typing import Any, Dict, List
def get_prompt_input_key(inputs: Dict[str, Any], memory_variables: List[str]) -> str:
"""
Get the prompt input key.
Args:
inputs: Dict[str, Any]
memory_variables: List[str]
Returns:
A prompt input key.
"""
# "stop" is a special key t... |
__version__ = '0.32.0'
import logging
from docarray.array import DocList, DocVec
from docarray.base_doc.doc import BaseDoc
from docarray.utils._internal.misc import _get_path_from_docarray_root_level
__all__ = ['BaseDoc', 'DocList', 'DocVec']
logger = logging.getLogger('docarray')
handler = logging.StreamHandler()... | __version__ = '0.31.2'
import logging
from docarray.array import DocList, DocVec
from docarray.base_doc.doc import BaseDoc
from docarray.utils._internal.misc import _get_path_from_docarray_root_level
__all__ = ['BaseDoc', 'DocList', 'DocVec']
logger = logging.getLogger('docarray')
handler = logging.StreamHandler()... |
_base_ = './cascade-mask-rcnn_r50_fpn_instaboost-4x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
... | _base_ = './cascade_mask_rcnn_r50_fpn_instaboost_4x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
... |
# Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
import shutil
import time
from unittest import TestCase
from unittest.mock import Mock
import torch
from mmengine.data import InstanceData
from mmdet.core import DetDataSample
from mmdet.core.hook import DetVisualizationHook
from mmdet.core.visuali... | # Copyright (c) OpenMMLab. All rights reserved.
import os
import shutil
import time
from unittest import TestCase
from unittest.mock import Mock
import torch
from mmengine.data import InstanceData
from mmdet.core import DetDataSample
from mmdet.core.hook import DetVisualizationHook
from mmdet.core.visualization impor... |
from __future__ import annotations
from abc import abstractmethod
from typing import Any
import torch
from tokenizers import Tokenizer
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
from sentence_transformers.models.Module import Module
class InputModule(Module):
"""
Subclass of :... | from __future__ import annotations
from abc import abstractmethod
from typing import Any
import torch
from tokenizers import Tokenizer
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
from sentence_transformers.models.Module import Module
class InputModule(Module):
"""
Subclass of :... |
from keras.src.backend.common.name_scope import name_scope
from keras.src.backend.jax import core
from keras.src.backend.jax import distribution_lib
from keras.src.backend.jax import image
from keras.src.backend.jax import linalg
from keras.src.backend.jax import math
from keras.src.backend.jax import nn
from keras.src... | from keras.src.backend.common.name_scope import name_scope
from keras.src.backend.jax import core
from keras.src.backend.jax import distribution_lib
from keras.src.backend.jax import image
from keras.src.backend.jax import linalg
from keras.src.backend.jax import math
from keras.src.backend.jax import nn
from keras.src... |
# model settings
preprocess_cfg = dict(
mean=[103.530, 116.280, 123.675],
std=[1.0, 1.0, 1.0],
to_rgb=False,
pad_size_divisor=32)
norm_cfg = dict(type='BN', requires_grad=False)
model = dict(
type='FasterRCNN',
preprocess_cfg=preprocess_cfg,
backbone=dict(
type='ResNet',
dept... | # model settings
preprocess_cfg = dict(
mean=[103.530, 116.280, 123.675],
std=[1.0, 1.0, 1.0],
to_rgb=False,
pad_size_divisor=32)
norm_cfg = dict(type='BN', requires_grad=False)
model = dict(
preprocess_cfg=preprocess_cfg,
type='FasterRCNN',
backbone=dict(
type='ResNet',
dept... |
"""
In this example we train a semantic search model to search through Wikipedia
articles about programming articles & technologies.
We use the text paragraphs from the following Wikipedia articles:
Assembly language, C , C Sharp , C++, Go , Java , JavaScript, Keras, Laravel, MATLAB, Matplotlib, MongoDB, MySQL, Natura... | """
In this example we train a semantic search model to search through Wikipedia
articles about programming articles & technologies.
We use the text paragraphs from the following Wikipedia articles:
Assembly language, C , C Sharp , C++, Go , Java , JavaScript, Keras, Laravel, MATLAB, Matplotlib, MongoDB, MySQL, Natura... |
from prisma.models import User
from backend.blocks.basic import AgentInputBlock, PrintToConsoleBlock
from backend.blocks.text import FillTextTemplateBlock
from backend.data import graph
from backend.data.graph import create_graph
from backend.data.user import get_or_create_user
from backend.util.test import SpinTestSe... | from prisma.models import User
from backend.blocks.basic import AgentInputBlock, PrintToConsoleBlock
from backend.blocks.text import FillTextTemplateBlock
from backend.data import graph
from backend.data.graph import create_graph
from backend.data.user import get_or_create_user
from backend.util.test import SpinTestSe... |
import grpc
from grpc_health.v1 import health, health_pb2, health_pb2_grpc
from grpc_reflection.v1alpha import reflection
from pydantic import BaseModel
from uvicorn import Config, Server
from jina import Gateway, __default_host__
from jina.proto import jina_pb2, jina_pb2_grpc
class DummyResponseModel(BaseModel):
... | import grpc
from grpc_health.v1 import health, health_pb2, health_pb2_grpc
from grpc_reflection.v1alpha import reflection
from pydantic import BaseModel
from uvicorn import Config, Server
from jina import Gateway, __default_host__
from jina.proto import jina_pb2, jina_pb2_grpc
class DummyResponseModel(BaseModel):
... |
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... |
from typing import Any, Optional, Union, cast
from langchain_core._api import deprecated
from langchain_core.language_models import BaseLanguageModel
from langchain_core.messages import HumanMessage, SystemMessage
from langchain_core.output_parsers import BaseLLMOutputParser
from langchain_core.output_parsers.openai_f... | from typing import Any, List, Optional, Type, Union, cast
from langchain_core._api import deprecated
from langchain_core.language_models import BaseLanguageModel
from langchain_core.messages import HumanMessage, SystemMessage
from langchain_core.output_parsers import BaseLLMOutputParser
from langchain_core.output_pars... |
"""DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.datasets import boston_housing as boston_housing
from keras.datasets import california_housing as california_housing
from keras.datasets import cifar10 as cifar10
from keras.datasets impo... | """DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.api.datasets import boston_housing
from keras.api.datasets import california_housing
from keras.api.datasets import cifar10
from keras.api.datasets import cifar100
from keras.api.datasets... |
"""Internal utilities for the in memory implementation of VectorStore.
These are part of a private API, and users should not use them directly
as they can change without notice.
"""
from __future__ import annotations
import logging
from typing import TYPE_CHECKING, Union
if TYPE_CHECKING:
import numpy as np
... | """Internal utilities for the in memory implementation of VectorStore.
These are part of a private API, and users should not use them directly
as they can change without notice.
"""
from __future__ import annotations
import logging
from typing import TYPE_CHECKING, Union
if TYPE_CHECKING:
import numpy as np
... |
"""Base classes for chain routing."""
from __future__ import annotations
from abc import ABC
from collections.abc import Mapping
from typing import Any, NamedTuple, Optional
from langchain_core.callbacks import (
AsyncCallbackManagerForChainRun,
CallbackManagerForChainRun,
Callbacks,
)
from pydantic impo... | """Base classes for chain routing."""
from __future__ import annotations
from abc import ABC
from collections.abc import Mapping
from typing import Any, NamedTuple, Optional
from langchain_core.callbacks import (
AsyncCallbackManagerForChainRun,
CallbackManagerForChainRun,
Callbacks,
)
from pydantic impo... |
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... | 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 .LayerNorm import LayerNorm
from .LSTM import LSTM
from .Normalize import Normalize
from .Pooling import Pooling
from .StaticEm... |
from typing import Any, Literal, Optional, Union
from exa_py import Exa # type: ignore[untyped-import]
from exa_py.api import (
HighlightsContentsOptions, # type: ignore[untyped-import]
TextContentsOptions, # type: ignore[untyped-import]
)
from langchain_core.callbacks import CallbackManagerForRetrieverRun
... | from typing import Any, Literal, Optional, Union
from exa_py import Exa # type: ignore[untyped-import]
from exa_py.api import (
HighlightsContentsOptions, # type: ignore[untyped-import]
TextContentsOptions, # type: ignore[untyped-import]
)
from langchain_core.callbacks import CallbackManagerForRetrieverRun
... |
import abc
from abc import ABC
from typing import TYPE_CHECKING, Any, Generic, List, Tuple, Type, TypeVar, Union
from docarray.computation import AbstractComputationalBackend
from docarray.typing.abstract_type import AbstractType
if TYPE_CHECKING:
from pydantic import BaseConfig
from pydantic.fields import Mo... | import abc
from abc import ABC
from typing import TYPE_CHECKING, Any, Generic, List, Tuple, Type, TypeVar, Union
from docarray.computation import AbstractComputationalBackend
from docarray.typing.abstract_type import AbstractType
if TYPE_CHECKING:
from pydantic import BaseConfig
from pydantic.fields import Mo... |
"""DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.layers.preprocessing.image_preprocessing.bounding_boxes.converters import (
affine_transform as affine_transform,
)
from keras.src.layers.preprocessing.image_preprocessing.boundin... | """DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.layers.preprocessing.image_preprocessing.bounding_boxes.converters import (
affine_transform,
)
from keras.src.layers.preprocessing.image_preprocessing.bounding_boxes.converters i... |
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 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.
... |
"""
This script is identical to examples/training/sts/training_stsbenchmark.py with seed optimization.
We apply early stopping and evaluate the models over the dev set, to find out the best performing seeds.
For more details refer to -
Fine-Tuning Pretrained Language Models:
Weight Initializations, Data Orders, and Ea... | """
This script is identical to examples/training/sts/training_stsbenchmark.py with seed optimization.
We apply early stopping and evaluate the models over the dev set, to find out the best performing seeds.
For more details refer to -
Fine-Tuning Pretrained Language Models:
Weight Initializations, Data Orders, and Ea... |
__version__ = '0.18.2'
import os
from docarray.document import Document
from docarray.array import DocumentArray
from docarray.dataclasses import dataclass, field
if 'DA_RICH_HANDLER' in os.environ:
from rich.traceback import install
install()
| __version__ = '0.18.1'
import os
from docarray.document import Document
from docarray.array import DocumentArray
from docarray.dataclasses import dataclass, field
if 'DA_RICH_HANDLER' in os.environ:
from rich.traceback import install
install()
|
# Copyright (c) OpenMMLab. All rights reserved.
from .checkpoint_hook import CheckpointHook
from .empty_cache_hook import EmptyCacheHook
from .hook import Hook
from .iter_timer_hook import IterTimerHook
from .logger_hook import LoggerHook
from .optimizer_hook import OptimizerHook
from .param_scheduler_hook import Param... | # Copyright (c) OpenMMLab. All rights reserved.
from .checkpoint_hook import CheckpointHook
from .empty_cache_hook import EmptyCacheHook
from .hook import Hook
from .iter_timer_hook import IterTimerHook
from .optimizer_hook import OptimizerHook
from .param_scheduler_hook import ParamSchedulerHook
from .sampler_seed_hoo... |
import asyncio
import logging
import os
import threading
from functools import wraps
from uuid import uuid4
from tenacity import retry, stop_after_attempt, wait_exponential
from backend.util.process import get_service_name
logger = logging.getLogger(__name__)
def _log_prefix(resource_name: str, conn_id: str):
... | import asyncio
import logging
import os
import threading
from functools import wraps
from uuid import uuid4
from tenacity import retry, stop_after_attempt, wait_exponential
from backend.util.process import get_service_name
logger = logging.getLogger(__name__)
def _log_prefix(resource_name: str, conn_id: str):
... |
# Copyright 2019 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writ... | # Copyright 2019 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writ... |
from typing import TYPE_CHECKING
from .github import GitHubOAuthHandler
from .google import GoogleOAuthHandler
from .notion import NotionOAuthHandler
from .twitter import TwitterOAuthHandler
if TYPE_CHECKING:
from ..providers import ProviderName
from .base import BaseOAuthHandler
# --8<-- [start:HANDLERS_BY_... | from typing import TYPE_CHECKING
from .github import GitHubOAuthHandler
from .google import GoogleOAuthHandler
from .notion import NotionOAuthHandler
if TYPE_CHECKING:
from ..providers import ProviderName
from .base import BaseOAuthHandler
# --8<-- [start:HANDLERS_BY_NAMEExample]
HANDLERS_BY_NAME: dict["Prov... |
"""
This example starts multiple processes (1 per GPU), which encode
sentences in parallel. This gives a near linear speed-up
when encoding large text collections.
"""
import logging
from sentence_transformers import LoggingHandler, SentenceTransformer
logging.basicConfig(
format="%(asctime)s - %(message)s", dat... | """
This example starts multiple processes (1 per GPU), which encode
sentences in parallel. This gives a near linear speed-up
when encoding large text collections.
"""
from sentence_transformers import SentenceTransformer, LoggingHandler
import logging
logging.basicConfig(
format="%(asctime)s - %(message)s", date... |
# Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
from mmcv.utils import Registry, build_from_cfg
TRANSFORMER = Registry('Transformer')
LINEAR_LAYERS = Registry('linear layers')
def build_transformer(cfg, default_args=None):
"""Builder for Transformer."""
return build_from_cfg(cfg, TRANSF... | import torch.nn as nn
from mmcv.utils import Registry, build_from_cfg
TRANSFORMER = Registry('Transformer')
LINEAR_LAYERS = Registry('linear layers')
def build_transformer(cfg, default_args=None):
"""Builder for Transformer."""
return build_from_cfg(cfg, TRANSFORMER, default_args)
LINEAR_LAYERS.register_mo... |
from __future__ import annotations
import json
import os
import torch
from safetensors.torch import load_model as load_safetensors_model
from safetensors.torch import save_model as save_safetensors_model
from torch import nn
class CNN(nn.Module):
"""CNN-layer with multiple kernel-sizes over the word embeddings"... | from __future__ import annotations
import json
import os
import torch
from safetensors.torch import load_model as load_safetensors_model
from safetensors.torch import save_model as save_safetensors_model
from torch import nn
class CNN(nn.Module):
"""CNN-layer with multiple kernel-sizes over the word embeddings"... |
# Copyright (c) OpenMMLab. All rights reserved.
import time
from typing import Any, Optional, Sequence, Tuple
from mmengine.data import BaseDataSample
from mmengine.registry import HOOKS
from .hook import Hook
DATA_BATCH = Optional[Sequence[Tuple[Any, BaseDataSample]]]
@HOOKS.register_module()
class IterTimerHook(H... | # Copyright (c) OpenMMLab. All rights reserved.
import time
from typing import Optional, Sequence
from mmengine.data import BaseDataSample
from mmengine.registry import HOOKS
from .hook import Hook
@HOOKS.register_module()
class IterTimerHook(Hook):
"""A hook that logs the time spent during iteration.
Eg. `... |
__copyright__ = "Copyright (c) 2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import copy
from typing import Dict
from jina import DocumentArray, Executor, requests
from jinahub.indexers.searcher.FaissSearcher import FaissSearcher
from jinahub.indexers.storage.LMDBStorage import LMDBStorage
... | __copyright__ = "Copyright (c) 2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import copy
from typing import Dict
from jina import requests, DocumentArray, Executor
try:
from jinahub.indexers.searcher.FaissSearcher import FaissSearcher
except: # broken import paths in previous release
... |
from keras.src.api_export import keras_export
# Unique source of truth for the version number.
__version__ = "3.7.0"
@keras_export("keras.version")
def version():
return __version__
| from keras.src.api_export import keras_export
# Unique source of truth for the version number.
__version__ = "3.6.0"
@keras_export("keras.version")
def version():
return __version__
|
import importlib
import pytest
from dirty_equals import IsDict
from fastapi.testclient import TestClient
from ...utils import needs_py39, needs_py310
@pytest.fixture(
name="client",
params=[
"tutorial002",
pytest.param("tutorial002_py310", marks=needs_py310),
"tutorial002_an",
... | import pytest
from dirty_equals import IsDict
from fastapi.testclient import TestClient
from docs_src.header_params.tutorial002 import app
client = TestClient(app)
@pytest.mark.parametrize(
"path,headers,expected_status,expected_response",
[
("/items", None, 200, {"strange_header": None}),
(... |
_base_ = './lsj-100e_coco-detection.py'
# 8x25=200e
train_dataloader = dict(dataset=dict(times=8))
# learning rate
param_scheduler = [
dict(
type='LinearLR', start_factor=0.067, by_epoch=False, begin=0,
end=1000),
dict(
type='MultiStepLR',
begin=0,
end=25,
by_ep... | _base_ = './lsj_100e_coco_detection.py'
# 8x25=200e
train_dataloader = dict(dataset=dict(times=8))
# learning rate
param_scheduler = [
dict(
type='LinearLR', start_factor=0.067, by_epoch=False, begin=0,
end=1000),
dict(
type='MultiStepLR',
begin=0,
end=25,
by_ep... |
# Copyright (c) OpenMMLab. All rights reserved.
import time
from typing import Optional, Union
import torch
from mmengine.device import is_cuda_available, is_musa_available
from mmengine.dist.utils import master_only
from mmengine.logging import MMLogger, print_log
class TimeCounter:
"""A tool that counts the a... | # Copyright (c) OpenMMLab. All rights reserved.
import time
from typing import Optional, Union
import torch
from mmengine.device import is_cuda_available, is_musa_available
from mmengine.dist.utils import master_only
from mmengine.logging import MMLogger, print_log
class TimeCounter:
"""A tool that counts the a... |
"""DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.legacy.saving.serialization import (
deserialize_keras_object as deserialize_keras_object,
)
from keras.src.legacy.saving.serialization import (
serialize_keras_object as seri... | """DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.legacy.saving.serialization import deserialize_keras_object
from keras.src.legacy.saving.serialization import serialize_keras_object
|
"""Optimization related classes and functions."""
import logging
from typing import Any, Dict, List, Optional, Literal
from llama_index.core.bridge.pydantic import Field, PrivateAttr
from llama_index.core.postprocessor.types import BaseNodePostprocessor
from llama_index.core.schema import NodeWithScore, QueryBundle, ... | """Optimization related classes and functions."""
import logging
from typing import Any, Dict, List, Optional, Literal
from llama_index.core.bridge.pydantic import Field, PrivateAttr
from llama_index.core.postprocessor.types import BaseNodePostprocessor
from llama_index.core.schema import NodeWithScore, QueryBundle, ... |
from datetime import datetime, timezone
import pytest
from prisma.enums import CreditTransactionType
from prisma.models import CreditTransaction
from backend.blocks.llm import AITextGeneratorBlock
from backend.data.block import get_block
from backend.data.credit import BetaUserCredit, UsageTransactionMetadata
from ba... | from datetime import datetime, timezone
import pytest
from prisma.enums import CreditTransactionType
from prisma.models import CreditTransaction
from backend.blocks.llm import AITextGeneratorBlock
from backend.data.block import get_block
from backend.data.credit import BetaUserCredit, UsageTransactionMetadata
from ba... |
"""Methods and algorithms to robustly estimate covariance.
They estimate the covariance of features at given sets of points, as well as the
precision matrix defined as the inverse of the covariance. Covariance estimation is
closely related to the theory of Gaussian graphical models.
"""
# Authors: The scikit-learn de... | """Methods and algorithms to robustly estimate covariance.
They estimate the covariance of features at given sets of points, as well as the
precision matrix defined as the inverse of the covariance. Covariance estimation is
closely related to the theory of Gaussian graphical models.
"""
# Authors: The scikit-learn de... |
# mypy: ignore-errors
import contextlib
import functools
import inspect
import torch
# Test whether hardware BF32 math mode enabled. It is enabled only on:
# - MKLDNN is available
# - BF16 is supported by MKLDNN
def bf32_is_not_fp32():
if not torch.backends.mkldnn.is_available():
return False
if not... | # mypy: ignore-errors
import contextlib
import functools
import inspect
import torch
# Test whether hardware BF32 math mode enabled. It is enabled only on:
# - MKLDNN is available
# - BF16 is supported by MKLDNN
def bf32_is_not_fp32():
if not torch.backends.mkldnn.is_available():
return False
if not... |
import warnings
from typing import Any, Dict, Union
import numpy as np
import PIL.Image
import torch
from torchvision.transforms import functional as _F
from torchvision.transforms.v2 import Transform
class ToTensor(Transform):
"""[DEPRECATED] Use ``v2.Compose([v2.ToImage(), v2.ToDtype(torch.float32, scale=True... | import warnings
from typing import Any, Dict, Union
import numpy as np
import PIL.Image
import torch
from torchvision.transforms import functional as _F
from torchvision.transforms.v2 import Transform
class ToTensor(Transform):
"""[BETA] [DEPRECATED] Use ``v2.Compose([v2.ToImage(), v2.ToDtype(torch.float32, sca... |
import time
from queue import Queue
from threading import Event
from typing import Any, Generator, List, Optional
from uuid import UUID
from llama_index.core.bridge.langchain import BaseCallbackHandler, LLMResult
class StreamingGeneratorCallbackHandler(BaseCallbackHandler):
"""Streaming callback handler."""
... | import time
from queue import Queue
from threading import Event
from typing import Any, Generator, List, Optional
from uuid import UUID
from llama_index.core.bridge.langchain import BaseCallbackHandler, LLMResult
class StreamingGeneratorCallbackHandler(BaseCallbackHandler):
"""Streaming callback handler."""
... |
import pytest # type: ignore[import-not-found, import-not-found]
@pytest.mark.compile
def test_placeholder() -> None:
"""Used for compiling integration tests without running any real tests."""
| import pytest # type: ignore[import-not-found, import-not-found]
@pytest.mark.compile
def test_placeholder() -> None:
"""Used for compiling integration tests without running any real tests."""
pass
|
from typing import List, Optional
from llama_index.core.node_parser.text import TokenTextSplitter
from llama_index.core.node_parser.text.utils import truncate_text
from llama_index.core.schema import BaseNode
def get_numbered_text_from_nodes(
node_list: List[BaseNode],
text_splitter: Optional[TokenTextSplitt... | from typing import List, Optional
from llama_index.core.node_parser.text import TokenTextSplitter
from llama_index.core.node_parser.text.utils import truncate_text
from llama_index.core.schema import BaseNode
def get_numbered_text_from_nodes(
node_list: List[BaseNode],
text_splitter: Optional[TokenTextSplitt... |
from docarray.typing.bytes import AudioBytes, ImageBytes, VideoBytes
from docarray.typing.id import ID
from docarray.typing.tensor import ImageNdArray, ImageTensor
from docarray.typing.tensor.audio import AudioNdArray
from docarray.typing.tensor.embedding.embedding import AnyEmbedding, NdArrayEmbedding
from docarray.ty... | from docarray.typing.bytes import ImageBytes
from docarray.typing.id import ID
from docarray.typing.tensor import ImageNdArray, ImageTensor
from docarray.typing.tensor.audio import AudioNdArray
from docarray.typing.tensor.embedding.embedding import AnyEmbedding, NdArrayEmbedding
from docarray.typing.tensor.ndarray impo... |
"""System message."""
from typing import Any, Literal, Union
from langchain_core.messages.base import BaseMessage, BaseMessageChunk
class SystemMessage(BaseMessage):
"""Message for priming AI behavior.
The system message is usually passed in as the first of a sequence
of input messages.
Example:
... | from typing import Any, Literal, Union
from langchain_core.messages.base import BaseMessage, BaseMessageChunk
class SystemMessage(BaseMessage):
"""Message for priming AI behavior.
The system message is usually passed in as the first of a sequence
of input messages.
Example:
.. code-block::... |
_base_ = [
'../_base_/models/retinanet_r50_fpn.py', '../common/mstrain_3x_coco.py'
]
# optimizer
optim_wrapper = dict(
optimizer=dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001))
| _base_ = [
'../_base_/models/retinanet_r50_fpn.py', '../common/mstrain_3x_coco.py'
]
# optimizer
optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
|
import json
import os
import pytest
from hubble.executor import HubExecutor
from hubble.executor.hubio import HubIO
from jina import __version__
from jina.orchestrate.deployments.config.helper import (
get_base_executor_version,
get_image_name,
to_compatible_name,
)
@pytest.mark.parametrize('is_master',... | import json
import os
import pytest
from hubble.executor import HubExecutor
from hubble.executor.hubio import HubIO
from jina import __version__
from jina.orchestrate.deployments.config.helper import (
get_base_executor_version,
get_image_name,
to_compatible_name,
)
@pytest.mark.parametrize('is_master',... |
# 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... |
import numpy as np
import pytest
from docarray.documents import Mesh3D
from tests import TOYDATA_DIR
LOCAL_OBJ_FILE = str(TOYDATA_DIR / 'tetrahedron.obj')
REMOTE_OBJ_FILE = 'https://people.sc.fsu.edu/~jburkardt/data/obj/al.obj'
@pytest.mark.slow
@pytest.mark.internet
@pytest.mark.parametrize('file_url', [LOCAL_OBJ_... | import numpy as np
import pytest
from docarray import Mesh3D
from tests import TOYDATA_DIR
LOCAL_OBJ_FILE = str(TOYDATA_DIR / 'tetrahedron.obj')
REMOTE_OBJ_FILE = 'https://people.sc.fsu.edu/~jburkardt/data/obj/al.obj'
@pytest.mark.slow
@pytest.mark.internet
@pytest.mark.parametrize('file_url', [LOCAL_OBJ_FILE, REMO... |
"""DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.activations import deserialize
from keras.src.activations import get
from keras.src.activations import serialize
from keras.src.activations.activations import celu
from keras.src.acti... | """DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.activations import deserialize
from keras.src.activations import get
from keras.src.activations import serialize
from keras.src.activations.activations import celu
from keras.src.acti... |
from keras.src import activations
from keras.src import backend
from keras.src.api_export import keras_export
from keras.src.layers.layer import Layer
def _large_negative_number(dtype):
"""Return a Large negative number based on dtype."""
if backend.standardize_dtype(dtype) == "float16":
return -3e4
... | from keras.src import activations
from keras.src import backend
from keras.src.api_export import keras_export
from keras.src.layers.layer import Layer
def _large_negative_number(dtype):
"""Return a Large negative number based on dtype."""
if backend.standardize_dtype(dtype) == "float16":
return -3e4
... |
_base_ = '../faster_rcnn/faster-rcnn_r50_fpn_1x_coco.py'
model = dict(
roi_head=dict(
type='DoubleHeadRoIHead',
reg_roi_scale_factor=1.3,
bbox_head=dict(
_delete_=True,
type='DoubleConvFCBBoxHead',
num_convs=4,
num_fcs=2,
in_channel... | _base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
model = dict(
roi_head=dict(
type='DoubleHeadRoIHead',
reg_roi_scale_factor=1.3,
bbox_head=dict(
_delete_=True,
type='DoubleConvFCBBoxHead',
num_convs=4,
num_fcs=2,
in_channel... |
import warnings
from typing import Any, List, Union
import PIL.Image
import torch
from torchvision.prototype import datapoints
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_chan... | 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... |
_base_ = './cascade-rcnn_hrnetv2p-w32-20e_coco.py'
# model settings
model = dict(
backbone=dict(
type='HRNet',
extra=dict(
stage2=dict(num_channels=(40, 80)),
stage3=dict(num_channels=(40, 80, 160)),
stage4=dict(num_channels=(40, 80, 160, 320))),
init_cfg=... | _base_ = './cascade_rcnn_hrnetv2p_w32_20e_coco.py'
# model settings
model = dict(
backbone=dict(
type='HRNet',
extra=dict(
stage2=dict(num_channels=(40, 80)),
stage3=dict(num_channels=(40, 80, 160)),
stage4=dict(num_channels=(40, 80, 160, 320))),
init_cfg=... |
import base64
import os
import pytest
from unittest import mock
from llama_index.core.base.llms.types import ChatMessage, ChatResponse, MessageRole
from llama_index.core.multi_modal_llms.base import MultiModalLLM
from llama_index.multi_modal_llms.zhipuai import ZhipuAIMultiModal
from zhipuai.types.chat.chat_completion ... | import base64
import os
import pytest
from unittest import mock
from llama_index.core.base.llms.types import ChatMessage, ChatResponse, MessageRole
from llama_index.core.multi_modal_llms.base import MultiModalLLM
from llama_index.multi_modal_llms.zhipuai import ZhipuAIMultiModal
from zhipuai.types.chat.chat_completion ... |
__copyright__ = 'Copyright (c) 2021 Jina AI Limited. All rights reserved.'
__license__ = 'Apache-2.0'
import subprocess
import pytest
from jina import Document, DocumentArray, Flow
from transform_encoder import TransformerTorchEncoder
_EMBEDDING_DIM = 768
@pytest.mark.parametrize('request_size', [1, 10, 50, 100])
... | __copyright__ = 'Copyright (c) 2021 Jina AI Limited. All rights reserved.'
__license__ = 'Apache-2.0'
import subprocess
import pytest
from jina import Document, DocumentArray, Flow
from ...transform_encoder import TransformerTorchEncoder
_EMBEDDING_DIM = 768
@pytest.mark.parametrize('request_size', [1, 10, 50, 10... |
from typing import Any, Dict, Iterable
import torch
from torch import Tensor, nn
from sentence_transformers.SentenceTransformer import SentenceTransformer
from sentence_transformers.util import fullname
class CosineSimilarityLoss(nn.Module):
def __init__(
self,
model: SentenceTransformer,
... | from typing import Any, Dict, Iterable
import torch
from torch import Tensor, nn
from sentence_transformers.SentenceTransformer import SentenceTransformer
from sentence_transformers.util import fullname
class CosineSimilarityLoss(nn.Module):
def __init__(self, model: SentenceTransformer, loss_fct=nn.MSELoss(), ... |
from textwrap import dedent
from types import SimpleNamespace
from unittest.mock import patch
from urllib.parse import quote
import pytest
from huggingface_hub import CommitOperationAdd, CommitOperationDelete
import datasets
from datasets.config import METADATA_CONFIGS_FIELD
from datasets.hub import delete_from_hub
f... | from textwrap import dedent
from types import SimpleNamespace
from unittest.mock import patch
from urllib.parse import quote
import pytest
from huggingface_hub import CommitOperationAdd, CommitOperationDelete
import datasets
from datasets.config import METADATA_CONFIGS_FIELD
from datasets.hub import delete_from_hub
f... |
import base64
import json
from typing import List, Dict, Union, NewType, Any, Optional
import numpy as np
import strawberry
from docarray.math.ndarray import to_list
_ProtoValueType = Union[bool, float, str]
_StructValueType = Union[
_ProtoValueType, List[_ProtoValueType], Dict[str, _ProtoValueType]
]
JSONScal... | import base64
import json
from typing import List, Dict, Union, NewType, Any, Optional
import numpy as np
import strawberry
from ..math.ndarray import to_list
_ProtoValueType = Union[bool, float, str]
_StructValueType = Union[
_ProtoValueType, List[_ProtoValueType], Dict[str, _ProtoValueType]
]
JSONScalar = st... |
from rich.progress import (
Progress,
BarColumn,
SpinnerColumn,
MofNCompleteColumn,
TextColumn,
TimeRemainingColumn,
Text,
)
class QPSColumn(TextColumn):
def render(self, task) -> Text:
if task.speed:
_text = f'{task.speed:.0f} QPS'
else:
_text =... | from rich.progress import (
Progress,
BarColumn,
SpinnerColumn,
MofNCompleteColumn,
TextColumn,
TimeRemainingColumn,
Text,
)
class QPSColumn(TextColumn):
def render(self, task) -> Text:
if task.speed:
_text = f'{task.speed:.0f} QPS'
else:
_text =... |
from typing import Optional
from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class TextDatasetReader(AbstractDatasetReader):
def __init__(
self,
path_or_paths: Nest... | from typing import Optional
from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class TextDatasetReader(AbstractDatasetReader):
def __init__(
self,
path_or_paths: Nest... |
from typing import Any
from langchain_core.memory import BaseMemory
class SimpleMemory(BaseMemory):
"""Simple memory for storing context or other information that shouldn't
ever change between prompts.
"""
memories: dict[str, Any] = {}
@property
def memory_variables(self) -> list[str]:
... | from typing import Any
from langchain_core.memory import BaseMemory
class SimpleMemory(BaseMemory):
"""Simple memory for storing context or other information that shouldn't
ever change between prompts.
"""
memories: dict[str, Any] = dict()
@property
def memory_variables(self) -> list[str]:
... |
_base_ = 'mask-rcnn_regnetx-3.2GF_fpn_1x_coco.py'
model = dict(
backbone=dict(
dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True),
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://regnetx_3.2gf')))
| _base_ = 'mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py'
model = dict(
backbone=dict(
dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True),
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://regnetx_3.2gf')))
|
from __future__ import annotations
import pytest
from torch.utils.data import BatchSampler, ConcatDataset, SequentialSampler
from sentence_transformers.sampler import RoundRobinBatchSampler
from sentence_transformers.util import is_datasets_available
if is_datasets_available():
from datasets import Dataset
else:... | from __future__ import annotations
import pytest
from datasets import Dataset
from torch.utils.data import BatchSampler, ConcatDataset, SequentialSampler
from sentence_transformers.sampler import RoundRobinBatchSampler
DATASET_LENGTH = 25
@pytest.fixture
def dummy_concat_dataset() -> ConcatDataset:
"""
Dum... |
from abc import abstractmethod
import logging
from typing import Any, Dict, List, Optional
from llama_index.core.graph_stores.types import GraphStore
from .neptune import refresh_schema
logger = logging.getLogger(__name__)
class NeptuneBaseGraphStore(GraphStore):
"""This is an abstract base class that represents... | from abc import abstractmethod
import logging
from typing import Any, Dict, List, Optional
from llama_index.core.graph_stores.types import GraphStore
from .neptune import refresh_schema
logger = logging.getLogger(__name__)
class NeptuneBaseGraphStore(GraphStore):
"""This is an abstract base class that represents... |
# Copyright (c) OpenMMLab. All rights reserved.
import torch
from mmdet.registry import TASK_UTILS
from .base_sampler import BaseSampler
@TASK_UTILS.register_module()
class RandomSampler(BaseSampler):
"""Random sampler.
Args:
num (int): Number of samples
pos_fraction (float): Fraction of pos... | # Copyright (c) OpenMMLab. All rights reserved.
import torch
from ..builder import BBOX_SAMPLERS
from .base_sampler import BaseSampler
@BBOX_SAMPLERS.register_module()
class RandomSampler(BaseSampler):
"""Random sampler.
Args:
num (int): Number of samples
pos_fraction (float): Fraction of po... |
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 warnings and
# handling o... | 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 warnings and
# handling o... |
__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 .config import Config, ConfigDict, DictAction, read_base
__all__ = ['Config', 'ConfigDict', 'DictAction', 'read_base']
| # Copyright (c) OpenMMLab. All rights reserved.
from .config import Config, ConfigDict, DictAction
__all__ = ['Config', 'ConfigDict', 'DictAction']
|
# Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
from mmcv.cnn import (ConvModule, caffe2_xavier_init, constant_init, is_norm,
normal_init)
from torch.nn import BatchNorm2d
from ..builder import NECKS
class Bottleneck(nn.Module):
"""Bottleneck block for DilatedEncoder u... | # Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
from mmcv.cnn import (ConvModule, caffe2_xavier_init, constant_init, is_norm,
normal_init)
from torch.nn import BatchNorm2d
from ..builder import NECKS
class Bottleneck(nn.Module):
"""Bottleneck block for DilatedEncoder u... |
"""Async utils."""
import asyncio
import concurrent.futures
from itertools import zip_longest
from typing import Any, Coroutine, Iterable, List, Optional, TypeVar
import llama_index.core.instrumentation as instrument
dispatcher = instrument.get_dispatcher(__name__)
def asyncio_module(show_progress: bool = False) -... | """Async utils."""
import asyncio
from itertools import zip_longest
from typing import Any, Coroutine, Iterable, List, Optional, TypeVar
import llama_index.core.instrumentation as instrument
dispatcher = instrument.get_dispatcher(__name__)
def asyncio_module(show_progress: bool = False) -> Any:
if show_progres... |
"""Load agent."""
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 langchain._api.deprec... | """Load agent."""
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 langchain._api.deprec... |
"""Functionality for loading agents."""
import json
import logging
from pathlib import Path
from typing import Any, Optional, Union
import yaml
from langchain_core._api import deprecated
from langchain_core.language_models import BaseLanguageModel
from langchain_core.tools import Tool
from langchain.agents.agent imp... | """Functionality for loading agents."""
import json
import logging
from pathlib import Path
from typing import Any, Optional, Union
import yaml
from langchain_core._api import deprecated
from langchain_core.language_models import BaseLanguageModel
from langchain_core.tools import Tool
from langchain.agents.agent imp... |
import datetime
from typing import List
import prisma.enums
import pydantic
class Pagination(pydantic.BaseModel):
total_items: int = pydantic.Field(
description="Total number of items.", examples=[42]
)
total_pages: int = pydantic.Field(
description="Total number of pages.", examples=[97]... | import datetime
from typing import List
import prisma.enums
import pydantic
class Pagination(pydantic.BaseModel):
total_items: int = pydantic.Field(
description="Total number of items.", examples=[42]
)
total_pages: int = pydantic.Field(
description="Total number of pages.", examples=[97]... |
from __future__ import annotations
from typing import Any, Dict, Optional
from docarray import BaseDoc, DocList
from docarray.typing import AnyEmbedding, AnyTensor
class LegacyDocument(BaseDoc):
"""
This Document is the LegacyDocument. It follows the same schema as in DocArray v1.
It can be useful to st... | from __future__ import annotations
from typing import Any, Dict, Optional
from docarray import BaseDoc, DocList
from docarray.typing import AnyEmbedding, AnyTensor
class LegacyDocument(BaseDoc):
"""
This Document is the LegacyDocument. It follows the same schema as in DocList v1.
It can be useful to sta... |
import json
import logging
from abc import ABC, abstractmethod
from datetime import datetime
from typing import Any, AsyncGenerator, Generator, Generic, TypeVar
from pydantic import BaseModel
from redis.asyncio.client import PubSub as AsyncPubSub
from redis.client import PubSub
from backend.data import redis
logger ... | import json
import logging
from abc import ABC, abstractmethod
from datetime import datetime
from typing import Any, AsyncGenerator, Generator, Generic, TypeVar
from pydantic import BaseModel
from redis.asyncio.client import PubSub as AsyncPubSub
from redis.client import PubSub
from backend.data import redis
from bac... |
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