input stringlengths 33 5k | output stringlengths 32 5k |
|---|---|
from typing import Optional, TYPE_CHECKING
import numpy as np
from docarray.document.mixins.helper import _uri_to_blob, _to_datauri, _is_datauri
if TYPE_CHECKING:
from docarray.typing import T
class ConvertMixin:
"""Provide helper functions for :class:`Document` to support conversion between :attr:`.tensor... | from typing import Optional, TYPE_CHECKING
import numpy as np
from .helper import _uri_to_blob, _to_datauri, _is_datauri
if TYPE_CHECKING:
from ...typing import T
class ConvertMixin:
"""Provide helper functions for :class:`Document` to support conversion between :attr:`.tensor`, :attr:`.text`
and :attr... |
from torch import * # noqa: F403
# Several names are not included in the above import *
import torch
for n in dir(torch):
if (n.startswith('_')
or n.endswith('_')
or 'cuda' in n
or 'cpu' in n
or 'backward' in n):
continue
exec(f"{n} = torch.{n}")
del n
# These imports m... | from torch import * # noqa: F403
# Several names are not included in the above import *
import torch
for n in dir(torch):
if (n.startswith('_')
or n.endswith('_')
or 'cuda' in n
or 'cpu' in n
or 'backward' in n):
continue
exec(n + ' = torch.' + n)
# These imports may ov... |
__copyright__ = "Copyright (c) 2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
from typing import Optional, List, Union, Dict
import numpy as np
from annoy import AnnoyIndex
from jina import Executor, requests, DocumentArray, Document
from jina_commons import get_logger
from jina_commons.index... | __copyright__ = "Copyright (c) 2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
from typing import Optional, List, Union, Dict
import numpy as np
from annoy import AnnoyIndex
from jina import Executor, requests, DocumentArray, Document
from jina_commons import get_logger
from jina_commons.index... |
import torch
from torchaudio_unittest.common_utils import PytorchTestCase
from .functional_test_impl import Functional64OnlyTestImpl, FunctionalCPUOnlyTestImpl, FunctionalTestImpl
class FunctionalFloat32CPUTest(FunctionalTestImpl, PytorchTestCase):
dtype = torch.float32
device = torch.device("cpu")
class F... | import torch
from torchaudio_unittest.common_utils import PytorchTestCase
from .functional_test_impl import Functional64OnlyTestImpl, FunctionalTestImpl
class FunctionalFloat32CPUTest(FunctionalTestImpl, PytorchTestCase):
dtype = torch.float32
device = torch.device("cpu")
class FunctionalFloat64CPUTest(Fun... |
"""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
from keras.src.ops.nn import batch_normalization
from keras.src.ops.nn import binary_crossentropy
from keras.src.ops.nn import categorical_crossentropy
from... | """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
from keras.src.ops.nn import batch_normalization
from keras.src.ops.nn import binary_crossentropy
from keras.src.ops.nn import categorical_crossentropy
from... |
from langchain_xai import ChatXAI
MODEL_NAME = "grok-4"
def test_chat_xai_secrets() -> None:
o = ChatXAI(model=MODEL_NAME, xai_api_key="foo") # type: ignore[call-arg]
s = str(o)
assert "foo" not in s
| from langchain_xai import ChatXAI
def test_chat_xai_secrets() -> None:
o = ChatXAI(model="grok-beta", xai_api_key="foo") # type: ignore[call-arg]
s = str(o)
assert "foo" not in s
|
from keras.src import ops
from keras.src.api_export import keras_export
from keras.src.optimizers import optimizer
@keras_export(["keras.optimizers.Lion"])
class Lion(optimizer.Optimizer):
"""Optimizer that implements the Lion algorithm.
The Lion optimizer is a stochastic-gradient-descent method that uses th... | from keras.src import ops
from keras.src.api_export import keras_export
from keras.src.optimizers import optimizer
@keras_export(["keras.optimizers.Lion"])
class Lion(optimizer.Optimizer):
"""Optimizer that implements the Lion algorithm.
The Lion optimizer is a stochastic-gradient-descent method that uses th... |
# dataset settings
dataset_type = 'VOCDataset'
data_root = 'data/VOCdevkit/'
# file_client_args = dict(
# backend='petrel',
# path_mapping=dict({
# './data/': 's3://openmmlab/datasets/detection/',
# 'data/': 's3://openmmlab/datasets/detection/'
# }))
file_client_args = dict(backend='disk')
... | # dataset settings
dataset_type = 'VOCDataset'
data_root = 'data/VOCdevkit/'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(type='Resize', img_scale=(1000... |
from typing import TYPE_CHECKING
from .backend import BackendMixin, QdrantConfig
from .find import FindMixin
from .getsetdel import GetSetDelMixin
from .helper import DISTANCES
from .seqlike import SequenceLikeMixin
__all__ = ['StorageMixins', 'QdrantConfig']
if TYPE_CHECKING:
from qdrant_client import QdrantCli... | from typing import TYPE_CHECKING
from .backend import BackendMixin, QdrantConfig
from .find import FindMixin
from .getsetdel import GetSetDelMixin
from .helper import DISTANCES
from .seqlike import SequenceLikeMixin
__all__ = ['StorageMixins', 'QdrantConfig']
if TYPE_CHECKING:
from qdrant_client import QdrantCli... |
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 import Requests
class HubSpotContactBlock(Bl... | 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 import Requests
class HubSpotContactBlock(Bl... |
# model settings
preprocess_cfg = dict(
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True,
pad_size_divisor=32)
model = dict(
type='RetinaNet',
preprocess_cfg=preprocess_cfg,
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_ind... | # model settings
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
model = dict(
type='RetinaNet',
img_norm_cfg=img_norm_cfg,
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stage... |
import csv
import gzip
import logging
import os
from datetime import datetime
from torch.utils.data import DataLoader
from sentence_transformers import InputExample, LoggingHandler, SentenceTransformer, datasets, losses, models, util
from sentence_transformers.evaluation import EmbeddingSimilarityEvaluator
#### Just... | from torch.utils.data import DataLoader
from sentence_transformers import models, losses, datasets
from sentence_transformers import LoggingHandler, SentenceTransformer, util, InputExample
from sentence_transformers.evaluation import EmbeddingSimilarityEvaluator
import logging
from datetime import datetime
import os
im... |
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... |
"""
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... |
# dataset settings
dataset_type = 'MOTChallengeDataset'
data_root = 'data/MOT17/'
img_scale = (1088, 1088)
# data pipeline
train_pipeline = [
dict(
type='UniformRefFrameSample',
num_ref_imgs=1,
frame_range=10,
filter_key_img=True),
dict(
type='TransformBroadcaster',
... | # dataset settings
dataset_type = 'MOTChallengeDataset'
data_root = 'data/MOT17/'
resized_shape = (1088, 1088)
# data pipeline
train_pipeline = [
dict(
type='UniformRefFrameSample',
num_ref_imgs=1,
frame_range=10,
filter_key_img=True),
dict(
type='TransformBroadcaster',
... |
from pathlib import Path
from typing import Dict, Tuple, Union
import torchaudio
from torch import Tensor
from torch.hub import download_url_to_file
from torch.utils.data import Dataset
from torchaudio.datasets.utils import _extract_zip
_URL = "https://datashare.ed.ac.uk/bitstream/handle/10283/3038/DR-VCTK.zip"
_CHE... | from pathlib import Path
from typing import Dict, Tuple, Union
import torchaudio
from torch import Tensor
from torch.hub import download_url_to_file
from torch.utils.data import Dataset
from torchaudio.datasets.utils import extract_archive
_URL = "https://datashare.ed.ac.uk/bitstream/handle/10283/3038/DR-VCTK.zip"
_... |
import json
import math
from collections import namedtuple
from typing import List, Tuple
import sentencepiece as spm
import torch
import torchaudio
from torchaudio.models import Hypothesis
MODEL_TYPE_LIBRISPEECH = "librispeech"
MODEL_TYPE_TEDLIUM3 = "tedlium3"
MODEL_TYPE_MUSTC = "mustc"
DECIBEL = 2 * 20 * math.lo... | import json
import math
from collections import namedtuple
from typing import List, Tuple
import sentencepiece as spm
import torch
import torchaudio
from torchaudio.models import Hypothesis
MODEL_TYPE_LIBRISPEECH = "librispeech"
MODEL_TYPE_TEDLIUM3 = "tedlium3"
MODEL_TYPE_MUSTC = "mustc"
DECIBEL = 2 * 20 * math.lo... |
# training schedule for 1x
train_cfg = dict(by_epoch=True, max_epochs=24)
val_cfg = dict(interval=1)
test_cfg = dict()
# learning rate
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=24,
... | # optimizer
optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=0.001,
step=[16, 22])
runner = dict(type='EpochBasedRunner', max_epochs=24)
|
import os
from pathlib import Path
from torchaudio.datasets import librispeech
from torchaudio_unittest.common_utils import (
get_whitenoise,
normalize_wav,
save_wav,
TempDirMixin,
TorchaudioTestCase,
)
# Used to generate a unique transcript for each dummy audio file
_NUMBERS = ["ZERO", "ONE", "TW... | import os
from pathlib import Path
from torchaudio.datasets import librispeech
from torchaudio_unittest.common_utils import (
TempDirMixin,
TorchaudioTestCase,
get_whitenoise,
save_wav,
normalize_wav,
)
# Used to generate a unique transcript for each dummy audio file
_NUMBERS = ["ZERO", "ONE", "TW... |
# Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmdet.registry import MODELS
eps = 1e-6
@MODELS.register_module()
class DropBlock(nn.Module):
"""Randomly drop some regions of feature maps.
Please refer to the method proposed in `DropB... | # Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import PLUGIN_LAYERS
eps = 1e-6
@PLUGIN_LAYERS.register_module()
class DropBlock(nn.Module):
"""Randomly drop some regions of feature maps.
Please refer to the method proposed in... |
from typing import Optional
import pandas as pd
import pytest
from docarray import BaseDocument, DocumentArray
from docarray.documents import ImageDoc
@pytest.fixture()
def nested_doc_cls():
class MyDoc(BaseDocument):
count: Optional[int]
text: str
class MyDocNested(MyDoc):
image: I... | from typing import Optional
import pandas as pd
import pytest
from docarray import BaseDocument, DocumentArray
from docarray.documents import ImageDoc
@pytest.fixture()
def nested_doc_cls():
class MyDoc(BaseDocument):
count: Optional[int]
text: str
class MyDocNested(MyDoc):
image: I... |
from llama_index.llms.azure_openai import AzureOpenAI
from llama_index.multi_modal_llms.azure_openai import AzureOpenAIMultiModal
def test_embedding_class():
names_of_base_classes = [b.__name__ for b in AzureOpenAIMultiModal.__mro__]
assert AzureOpenAI.__name__ in names_of_base_classes
def test_init():
... | from llama_index.core.multi_modal_llms.base import MultiModalLLM
from llama_index.multi_modal_llms.azure_openai import AzureOpenAIMultiModal
def test_embedding_class():
names_of_base_classes = [b.__name__ for b in AzureOpenAIMultiModal.__mro__]
assert MultiModalLLM.__name__ in names_of_base_classes
def test... |
# Copyright (c) OpenMMLab. All rights reserved.
import warnings
import mmcv
from mmdet.registry import TRANSFORMS
from .compose import Compose
@TRANSFORMS.register_module()
class MultiScaleFlipAug:
"""Test-time augmentation with multiple scales and flipping.
An example configuration is as followed:
..... | # Copyright (c) OpenMMLab. All rights reserved.
import warnings
import mmcv
from ..builder import PIPELINES
from .compose import Compose
@PIPELINES.register_module()
class MultiScaleFlipAug:
"""Test-time augmentation with multiple scales and flipping.
An example configuration is as followed:
.. code-b... |
_base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# 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)
model = dict(
type='NASFCOS',
prepr... | _base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# 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)
model = dict(
type='NASFCOS',
prepr... |
"""
this test check the docstring of all of our public API. It does it
by checking the `__all__` of each of our namespace.
to add a new namespace you need to
* import it
* add it to the `SUB_MODULE_TO_CHECK` list
"""
import pytest
from mktestdocs import check_docstring, get_codeblock_members
import docarray.data
imp... | """
this test check the docstring of all of our public API. It does it
by checking the `__all__` of each of our namespace.
to add a new namespace you need to
* import it
* add it to the `SUB_MODULE_TO_CHECK` list
"""
import pytest
from mktestdocs import check_docstring, get_codeblock_members
import docarray.data
imp... |
# TODO: enable ruff qa on this file when we figure out why it thinks weaviate_client is
# redefined at each test that fixture
# ruff: noqa
import numpy as np
import pytest
import torch
from pydantic import Field
from docarray import BaseDoc
from docarray.index.backends.weaviate import WeaviateDocumentIndex
from ... | # TODO: enable ruff qa on this file when we figure out why it thinks weaviate_client is
# redefined at each test that fixture
# ruff: noqa
import numpy as np
import pytest
import torch
from pydantic import Field
from docarray import BaseDoc
from docarray.index.backends.weaviate import WeaviateDocumentIndex
from ... |
import os
import numpy as np
import pytest
import xgboost as xgb
from xgboost import testing as tm
from xgboost.core import DataSplitMode
pytestmark = pytest.mark.skipif(
tm.no_arrow()["condition"] or tm.no_pandas()["condition"],
reason=tm.no_arrow()["reason"] + " or " + tm.no_pandas()["reason"],
)
import p... | import os
import sys
import numpy as np
import pytest
import xgboost as xgb
from xgboost import testing as tm
from xgboost.core import DataSplitMode
pytestmark = pytest.mark.skipif(
tm.no_arrow()["condition"] or tm.no_pandas()["condition"],
reason=tm.no_arrow()["reason"] + " or " + tm.no_pandas()["reason"],
... |
# Copyright (c) OpenMMLab. All rights reserved.
from mmdet.registry import MODELS
from .two_stage import TwoStageDetector
@MODELS.register_module()
class FasterRCNN(TwoStageDetector):
"""Implementation of `Faster R-CNN <https://arxiv.org/abs/1506.01497>`_"""
def __init__(self,
backbone,
... | # Copyright (c) OpenMMLab. All rights reserved.
from ..builder import DETECTORS
from .two_stage import TwoStageDetector
@DETECTORS.register_module()
class FasterRCNN(TwoStageDetector):
"""Implementation of `Faster R-CNN <https://arxiv.org/abs/1506.01497>`_"""
def __init__(self,
backbone,
... |
"""
This file loads sentences from a provided text file. It is expected, that the there is one sentence per line in that text file.
CT will be training using these sentences. Checkpoints are stored every 500 steps to the output folder.
Usage:
python train_ct_from_file.py path/to/sentences.txt
"""
import math
from s... | """
This file loads sentences from a provided text file. It is expected, that the there is one sentence per line in that text file.
CT will be training using these sentences. Checkpoints are stored every 500 steps to the output folder.
Usage:
python train_ct_from_file.py path/to/sentences.txt
"""
import math
from se... |
import numpy as np
from keras.src.api_export import keras_export
@keras_export(
[
"keras.utils.pad_sequences",
"keras.preprocessing.sequence.pad_sequences",
]
)
def pad_sequences(
sequences,
maxlen=None,
dtype="int32",
padding="pre",
truncating="pre",
value=0.0,
):
... | import numpy as np
from keras.src.api_export import keras_export
@keras_export(
[
"keras.utils.pad_sequences",
"keras.preprocessing.sequence.pad_sequences",
]
)
def pad_sequences(
sequences,
maxlen=None,
dtype="int32",
padding="pre",
truncating="pre",
value=0.0,
):
... |
# Licensed to the LF AI & Data foundation under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the "License");
# you may not use this fil... | from abc import ABC
from typing import Any, Optional, Tuple, Type
from docarray.typing.tensor.abstract_tensor import AbstractTensor
class EmbeddingMixin(AbstractTensor, ABC):
alternative_type: Optional[Type] = None
@classmethod
def __docarray_validate_getitem__(cls, item: Any) -> Tuple[int]:
sha... |
from datasets import Dataset
from sentence_transformers.sparse_encoder import SparseEncoder, SparseEncoderTrainer, losses
# Initialize the SPLADE model
model = SparseEncoder("naver/splade-cocondenser-ensembledistil")
train_dataset = Dataset.from_dict(
{
"anchor": ["It's nice weather outside today.", "He d... | from datasets import Dataset
from sentence_transformers.sparse_encoder import (
MLMTransformer,
SparseCachedMultipleNegativesRankingLoss,
SparseEncoder,
SparseEncoderTrainer,
SpladePooling,
)
# Initialize the SPLADE model
model_name = "naver/splade-cocondenser-ensembledistil"
model = SparseEncoder... |
# Copyright (c) OpenMMLab. All rights reserved.
from unittest import TestCase
import torch
from mmengine.structures import InstanceData
from mmdet.models import build_detector
from mmdet.structures import DetDataSample
from mmdet.testing import get_detector_cfg
from mmdet.utils import register_all_modules
class Tes... | # Copyright (c) OpenMMLab. All rights reserved.
from unittest import TestCase
import torch
from mmengine.structures import InstanceData
from mmdet.models import build_detector
from mmdet.structures import DetDataSample
from mmdet.testing import get_detector_cfg
from mmdet.utils import register_all_modules
class Tes... |
"""
This file loads sentences from a provided text file. It is expected, that the there is one sentence per line in that text file.
CT will be training using these sentences. Checkpoints are stored every 500 steps to the output folder.
Usage:
python train_ct_from_file.py path/to/sentences.txt
"""
import gzip
import... | """
This file loads sentences from a provided text file. It is expected, that the there is one sentence per line in that text file.
CT will be training using these sentences. Checkpoints are stored every 500 steps to the output folder.
Usage:
python train_ct_from_file.py path/to/sentences.txt
"""
import gzip
import... |
"""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
from keras.src.ops.nn import batch_normalization
from keras.src.ops.nn import binary_crossentropy
from keras.src.ops.nn import categorical_crossentropy
from... | """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
from keras.src.ops.nn import batch_normalization
from keras.src.ops.nn import binary_crossentropy
from keras.src.ops.nn import categorical_crossentropy
from... |
"""
==========================================
Feature importances with a forest of trees
==========================================
This example shows the use of a forest of trees to evaluate the importance of
features on an artificial classification task. The blue bars are the feature
importances of the forest, alon... | """
==========================================
Feature importances with a forest of trees
==========================================
This example shows the use of a forest of trees to evaluate the importance of
features on an artificial classification task. The blue bars are the feature
importances of the forest, alon... |
import os
import pytest
import torch
import whisper
from whisper.tokenizer import get_tokenizer
@pytest.mark.parametrize("model_name", whisper.available_models())
def test_transcribe(model_name: str):
device = "cuda" if torch.cuda.is_available() else "cpu"
model = whisper.load_model(model_name).to(device)
... | import os
import pytest
import torch
import whisper
@pytest.mark.parametrize("model_name", whisper.available_models())
def test_transcribe(model_name: str):
device = "cuda" if torch.cuda.is_available() else "cpu"
model = whisper.load_model(model_name).to(device)
audio_path = os.path.join(os.path.dirname... |
import argparse
from jina.enums import GatewayProtocolType
from jina.helper import parse_host_scheme
from jina.logging.predefined import default_logger
class NetworkChecker:
"""Check if a BaseDeployment is running or not."""
def __init__(self, args: 'argparse.Namespace'):
"""
Create a new :c... | import argparse
from jina.enums import GatewayProtocolType
from jina.helper import parse_host_scheme
from jina.logging.predefined import default_logger
class NetworkChecker:
"""Check if a BaseDeployment is running or not."""
def __init__(self, args: 'argparse.Namespace'):
"""
Create a new :c... |
from enum import Enum
# --8<-- [start:ProviderName]
class ProviderName(str, Enum):
ANTHROPIC = "anthropic"
COMPASS = "compass"
DISCORD = "discord"
D_ID = "d_id"
E2B = "e2b"
EXA = "exa"
FAL = "fal"
GITHUB = "github"
GOOGLE = "google"
GOOGLE_MAPS = "google_maps"
GROQ = "groq"... | from enum import Enum
# --8<-- [start:ProviderName]
class ProviderName(str, Enum):
ANTHROPIC = "anthropic"
COMPASS = "compass"
DISCORD = "discord"
D_ID = "d_id"
E2B = "e2b"
EXA = "exa"
FAL = "fal"
GITHUB = "github"
GOOGLE = "google"
GOOGLE_MAPS = "google_maps"
GROQ = "groq"... |
import pytest
from langchain_core.agents import AgentAction, AgentFinish
from langchain_core.exceptions import OutputParserException
from langchain.agents.output_parsers.react_single_input import (
ReActSingleInputOutputParser,
)
def test_action() -> None:
"""Test standard parsing of action/action input."""
... | import pytest
from langchain_core.agents import AgentAction, AgentFinish
from langchain_core.exceptions import OutputParserException
from langchain.agents.output_parsers.react_single_input import (
ReActSingleInputOutputParser,
)
def test_action() -> None:
"""Test standard parsing of action/action input."""
... |
from typing import TYPE_CHECKING, Type, TypeVar
from pydantic import AnyUrl as BaseAnyUrl
from pydantic import errors, parse_obj_as
from docarray.proto import NodeProto
from docarray.typing.abstract_type import AbstractType
if TYPE_CHECKING:
from pydantic.networks import Parts
T = TypeVar('T', bound='AnyUrl')
... | from typing import TYPE_CHECKING, Type, TypeVar
from pydantic import AnyUrl as BaseAnyUrl
from pydantic import errors, parse_obj_as
from docarray.document.base_node import BaseNode
from docarray.proto import NodeProto
if TYPE_CHECKING:
from pydantic.networks import Parts
T = TypeVar('T', bound='AnyUrl')
class... |
import logging
from typing import Annotated
from autogpt_libs.auth.middleware import APIKeyValidator
from fastapi import APIRouter, Body, Depends, HTTPException, Query
from fastapi.responses import JSONResponse
from backend.data.user import (
get_user_by_email,
set_user_email_verification,
unsubscribe_use... | import logging
from typing import Annotated
from autogpt_libs.auth.middleware import APIKeyValidator
from fastapi import APIRouter, Body, Depends, Query
from fastapi.responses import JSONResponse
from backend.data.user import (
get_user_by_email,
set_user_email_verification,
unsubscribe_user_by_token,
)
f... |
from __future__ import annotations
from dataclasses import dataclass
from sentence_transformers.training_args import SentenceTransformerTrainingArguments
@dataclass
class SparseEncoderTrainingArguments(SentenceTransformerTrainingArguments):
r"""
SparseEncoderTrainingArguments extends :class:`~SentenceTransf... | from __future__ import annotations
from dataclasses import dataclass
from sentence_transformers.training_args import SentenceTransformerTrainingArguments
@dataclass
class SparseEncoderTrainingArguments(SentenceTransformerTrainingArguments):
r"""
SparseEncoderTrainingArguments extends :class:`~SentenceTransf... |
# Copyright (c) OpenMMLab. All rights reserved.
from pathlib import Path
from typing import List
import mmengine
from mmengine.dataset import BaseDataset
from mmengine.fileio import get_file_backend
from mmdet.registry import DATASETS
@DATASETS.register_module()
class CocoCaptionDataset(BaseDataset):
"""COCO201... | # Copyright (c) OpenMMLab. All rights reserved.
from pathlib import Path
from typing import List
import mmengine
from mmengine.dataset import BaseDataset
from mmengine.fileio import get_file_backend
from mmdet.registry import DATASETS
@DATASETS.register_module()
class COCOCaptionDataset(BaseDataset):
"""COCO Ca... |
# Copyright (c) OpenMMLab. All rights reserved.
import asyncio
from argparse import ArgumentParser
import mmcv
from mmdet.apis import (async_inference_detector, inference_detector,
init_detector)
from mmdet.registry import VISUALIZERS
from mmdet.utils import register_all_modules
def parse_ar... | # Copyright (c) OpenMMLab. All rights reserved.
import asyncio
from argparse import ArgumentParser
import mmcv
from mmdet.apis import (async_inference_detector, inference_detector,
init_detector)
from mmdet.registry import VISUALIZERS
from mmdet.utils import register_all_modules
def parse_ar... |
from __future__ import annotations
import logging
import os
from datasets import load_dataset
from sentence_transformers.sparse_encoder import (
SparseEncoder,
)
from sentence_transformers.sparse_encoder.evaluation.SparseNanoBEIREvaluator import SparseNanoBEIREvaluator
from sentence_transformers.sparse_encoder.l... | from __future__ import annotations
import logging
import os
from datasets import load_dataset
from sentence_transformers.sparse_encoder import (
SparseEncoder,
)
from sentence_transformers.sparse_encoder.evaluation.SparseNanoBEIREvaluator import SparseNanoBEIREvaluator
from sentence_transformers.sparse_encoder.l... |
from __future__ import annotations
from typing import Any
from langchain_text_splitters.base import TextSplitter
class KonlpyTextSplitter(TextSplitter):
"""Splitting text using Konlpy package.
It is good for splitting Korean text.
"""
def __init__(
self,
separator: str = "\n\n",
... | from __future__ import annotations
from typing import Any, List
from langchain_text_splitters.base import TextSplitter
class KonlpyTextSplitter(TextSplitter):
"""Splitting text using Konlpy package.
It is good for splitting Korean text.
"""
def __init__(
self,
separator: str = "\n\... |
__copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
from pathlib import Path
import pytest
from jina import Document, DocumentArray, Executor
from ...laser_encoder import LaserEncoder
@pytest.fixture()
def docs_generator():
return DocumentArray((Document(t... | __copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import pytest
from jina import Document, DocumentArray
from ...laser_encoder import LaserEncoder
@pytest.fixture()
def docs_generator():
return DocumentArray((Document(text='random text') for _ in range(30)... |
"""**Messages** are objects used in prompts and chat conversations.
**Class hierarchy:**
.. code-block::
BaseMessage --> SystemMessage, AIMessage, HumanMessage, ChatMessage, FunctionMessage, ToolMessage
--> BaseMessageChunk --> SystemMessageChunk, AIMessageChunk, HumanMessageChunk, ChatMessageChu... | """**Messages** are objects used in prompts and chat conversations.
**Class hierarchy:**
.. code-block::
BaseMessage --> SystemMessage, AIMessage, HumanMessage, ChatMessage, FunctionMessage, ToolMessage
--> BaseMessageChunk --> SystemMessageChunk, AIMessageChunk, HumanMessageChunk, ChatMessageChu... |
# Copyright (c) OpenMMLab. All rights reserved.
import unittest
import torch
import torch.nn as nn
import mmengine
from mmengine.device import get_device, is_mlu_available
from mmengine.runner import autocast
from mmengine.utils import digit_version
from mmengine.utils.dl_utils import TORCH_VERSION
class TestAmp(un... | # Copyright (c) OpenMMLab. All rights reserved.
import unittest
import torch
import torch.nn as nn
import mmengine
from mmengine.device import get_device
from mmengine.runner import autocast
from mmengine.utils import digit_version
from mmengine.utils.dl_utils import TORCH_VERSION
class TestAmp(unittest.TestCase):
... |
"""Callback Handler that prints to std out."""
from __future__ import annotations
from typing import TYPE_CHECKING, Any, Optional
from typing_extensions import override
from langchain_core.callbacks.base import BaseCallbackHandler
from langchain_core.utils import print_text
if TYPE_CHECKING:
from langchain_cor... | """Callback Handler that prints to std out."""
from __future__ import annotations
from typing import TYPE_CHECKING, Any, Optional
from typing_extensions import override
from langchain_core.callbacks.base import BaseCallbackHandler
from langchain_core.utils import print_text
if TYPE_CHECKING:
from langchain_cor... |
# Copyright (c) OpenMMLab. All rights reserved.
from typing import Optional, Sequence, Union
import torch
from mmengine.data import BaseDataElement
from mmengine.hooks import Hook
from mmengine.runner import Runner
from mmdet.registry import HOOKS
@HOOKS.register_module()
class CheckInvalidLossHook(Hook):
"""Ch... | # Copyright (c) OpenMMLab. All rights reserved.
import torch
from mmcv.runner.hooks import Hook
from mmdet.registry import HOOKS
@HOOKS.register_module()
class CheckInvalidLossHook(Hook):
"""Check invalid loss hook.
This hook will regularly check whether the loss is valid
during training.
Args:
... |
# Copyright 2025 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 2024 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... |
import multiprocessing
import pytest
from jina import DocumentArray, Executor, requests
from jina.serve.runtimes.asyncio import AsyncNewLoopRuntime
from jina.serve.runtimes.worker import WorkerRuntime
from jina.serve.streamer import GatewayStreamer
from jina.types.request.data import DataRequest
from tests.helper imp... | import multiprocessing
import pytest
from jina import DocumentArray, Executor, requests
from jina.parsers import set_pod_parser
from jina.serve.runtimes.asyncio import AsyncNewLoopRuntime
from jina.serve.runtimes.worker import WorkerRuntime
from jina.serve.streamer import GatewayStreamer
from jina.types.request.data ... |
import numpy as np
import pytest
import torch
from pydantic import parse_obj_as
from docarray import BaseDocument
from docarray.documents import Video
from docarray.typing import AudioNdArray, NdArray, VideoNdArray
from tests import TOYDATA_DIR
LOCAL_VIDEO_FILE = str(TOYDATA_DIR / 'mov_bbb.mp4')
REMOTE_VIDEO_FILE = '... | import pytest
from docarray.documents import Video
from docarray.typing import AudioNdArray, NdArray, VideoNdArray
from tests import TOYDATA_DIR
LOCAL_VIDEO_FILE = str(TOYDATA_DIR / 'mov_bbb.mp4')
REMOTE_VIDEO_FILE = 'https://github.com/docarray/docarray/blob/feat-rewrite-v2/tests/toydata/mov_bbb.mp4?raw=true' # noq... |
import os.path
from pathlib import Path
from typing import Any, Callable, Optional, Tuple, Union
import numpy as np
from PIL import Image
from .utils import check_integrity, download_url, verify_str_arg
from .vision import VisionDataset
class SVHN(VisionDataset):
"""`SVHN <http://ufldl.stanford.edu/housenumbers... | import os.path
from typing import Any, Callable, Optional, Tuple
import numpy as np
from PIL import Image
from .utils import check_integrity, download_url, verify_str_arg
from .vision import VisionDataset
class SVHN(VisionDataset):
"""`SVHN <http://ufldl.stanford.edu/housenumbers/>`_ Dataset.
Note: The SVHN... |
import multiprocessing
import pytest
from jina import Client
from jina.parsers import set_gateway_parser, set_pod_parser
from jina.serve.runtimes.asyncio import AsyncNewLoopRuntime
from jina.serve.runtimes.gateway.grpc import GRPCGatewayRuntime
from jina.serve.runtimes.gateway.http import HTTPGatewayRuntime
from jina... | import multiprocessing
import pytest
from jina import Client
from jina.parsers import set_gateway_parser, set_pod_parser
from jina.serve.runtimes.asyncio import AsyncNewLoopRuntime
from jina.serve.runtimes.gateway.grpc import GRPCGatewayRuntime
from jina.serve.runtimes.gateway.http import HTTPGatewayRuntime
from jina... |
"""langchain-core version information and utilities."""
VERSION = "0.3.61"
| """langchain-core version information and utilities."""
VERSION = "0.3.60"
|
"""
This example runs a BiLSTM after the word embedding lookup. The output of the BiLSTM is than pooled,
for example with max-pooling (which gives a system like InferSent) or with mean-pooling.
Note, you can also pass BERT embeddings to the BiLSTM.
"""
import traceback
from datasets import load_dataset
from sentence_... | """
This example runs a BiLSTM after the word embedding lookup. The output of the BiLSTM is than pooled,
for example with max-pooling (which gives a system like InferSent) or with mean-pooling.
Note, you can also pass BERT embeddings to the BiLSTM.
"""
from torch.utils.data import DataLoader
import math
from sentence... |
# Copyright 2025 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... | # Copyright 2024 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... |
import json
import pytest
import types
from typing import Optional, Type
from unittest import mock
from requests import Response
from llama_index.core.base.llms.base import BaseLLM
from llama_index.core.base.llms.types import CompletionResponse
from llama_index.llms.siliconflow import SiliconFlow
RESPONSE_JSON = {
... | import json
import pytest
import types
from typing import Optional, Type
from unittest import mock
from requests import Response
from llama_index.core.base.llms.base import BaseLLM
from llama_index.core.base.llms.types import CompletionResponse
from llama_index.llms.siliconflow import SiliconFlow
RESPONSE_JSON = {
... |
from ._hdemucs import HDemucs, hdemucs_high, hdemucs_low, hdemucs_medium
from .conformer import Conformer
from .conv_tasnet import conv_tasnet_base, ConvTasNet
from .deepspeech import DeepSpeech
from .emformer import Emformer
from .rnnt import emformer_rnnt_base, emformer_rnnt_model, RNNT
from .rnnt_decoder import Hypo... | from ._hdemucs import HDemucs, hdemucs_high, hdemucs_low, hdemucs_medium
from .conformer import Conformer
from .conv_tasnet import conv_tasnet_base, ConvTasNet
from .deepspeech import DeepSpeech
from .emformer import Emformer
from .rnnt import emformer_rnnt_base, emformer_rnnt_model, RNNT
from .rnnt_decoder import Hypo... |
from typing import Optional
import pytest
import torch
from docarray import BaseDoc, DocArray
from docarray.array.abstract_array import AnyDocArray
from docarray.documents import TextDoc
from docarray.typing import TorchTensor
num_docs = 5
num_sub_docs = 2
num_sub_sub_docs = 3
@pytest.fixture
def multi_model_docs(... | from typing import Optional
import pytest
import torch
from docarray import BaseDocument, DocumentArray
from docarray.array.abstract_array import AnyDocumentArray
from docarray.documents import TextDoc
from docarray.typing import TorchTensor
num_docs = 5
num_sub_docs = 2
num_sub_sub_docs = 3
@pytest.fixture
def mu... |
__version__ = '0.32.2'
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()... | __version__ = '0.32.1'
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()... |
import os
from typing import Optional
import pytest
from docarray import BaseDocument, DocumentArray
from docarray.documents import Image
from tests import TOYDATA_DIR
@pytest.fixture()
def nested_doc_cls():
class MyDoc(BaseDocument):
count: Optional[int]
text: str
class MyDocNested(MyDoc):... | import os
from typing import Optional
import pytest
from docarray import BaseDocument, DocumentArray
from docarray.documents import Image
from tests import TOYDATA_DIR
@pytest.fixture()
def nested_doc_cls():
class MyDoc(BaseDocument):
count: Optional[int]
text: str
class MyDocNested(MyDoc):... |
"""Human message."""
from typing import Any, Literal, Union
from langchain_core.messages.base import BaseMessage, BaseMessageChunk
class HumanMessage(BaseMessage):
"""Message from a human.
HumanMessages are messages that are passed in from a human to the model.
Example:
.. code-block:: python... | from typing import Any, Literal, Union
from langchain_core.messages.base import BaseMessage, BaseMessageChunk
class HumanMessage(BaseMessage):
"""Message from a human.
HumanMessages are messages that are passed in from a human to the model.
Example:
.. code-block:: python
from lan... |
"""
This script contains an example how to perform semantic search with OpenSearch.
You need OpenSearch up and running locally:
https://docs.opensearch.org/docs/latest/getting-started/quickstart/
Further, you need the Python OpenSearch Client installed: https://docs.opensearch.org/docs/latest/clients/python-low-level... | """
This script contains an example how to perform semantic search with OpenSearch.
You need OpenSearch up and running locally:
https://docs.opensearch.org/docs/latest/getting-started/quickstart/
Further, you need the Python OpenSearch Client installed: https://docs.opensearch.org/docs/latest/clients/python-low-level... |
from typing import TYPE_CHECKING
from docarray.dataclasses.enums import DocumentMetadata, ImageType
if TYPE_CHECKING: # pragma: no cover
from docarray import Document
def image_getter(doc: 'Document'):
if doc._metadata[DocumentMetadata.IMAGE_TYPE] == ImageType.URI:
return doc.uri
elif doc._metad... | from typing import TYPE_CHECKING
if TYPE_CHECKING: # pragma: no cover
from docarray import Document
def image_getter(doc: 'Document'):
if doc._metadata['image_type'] == 'uri':
return doc.uri
elif doc._metadata['image_type'] == 'PIL':
from PIL import Image
return Image.fromarray(... |
import numpy as np
from absl.testing import parameterized
from keras.src import backend
from keras.src import ops
from keras.src import testing
from keras.src.optimizers.loss_scale_optimizer import LossScaleOptimizer
from keras.src.optimizers.sgd import SGD
class LossScaleOptimizerTest(testing.TestCase):
def _sk... | import numpy as np
from absl.testing import parameterized
from keras.src import backend
from keras.src import ops
from keras.src import testing
from keras.src.optimizers.loss_scale_optimizer import LossScaleOptimizer
from keras.src.optimizers.sgd import SGD
class LossScaleOptimizerTest(testing.TestCase, parameterize... |
from google.protobuf import __version__ as __pb__version__
from jina._docarray import docarray_v2 as is_docarray_v2
if __pb__version__.startswith('4'):
if is_docarray_v2:
from jina.proto.docarray_v2.pb.jina_pb2_grpc import *
else:
from jina.proto.docarray_v1.pb.jina_pb2_grpc import *
else:
... | from google.protobuf import __version__ as __pb__version__
from jina._docarray import docarray_v2 as is_docarray_v2
if __pb__version__.startswith('4'):
if is_docarray_v2:
from .docarray_v2.pb.jina_pb2_grpc import *
else:
from .docarray_v1.pb.jina_pb2_grpc import *
else:
if is_docarray_v2:... |
from abc import abstractmethod
from typing import Iterable, Union
from qdrant_client import QdrantClient
from docarray.array.storage.base.seqlike import BaseSequenceLikeMixin
from docarray import Document
class SequenceLikeMixin(BaseSequenceLikeMixin):
@property
@abstractmethod
def client(self) -> Qdran... | from abc import abstractmethod
from typing import Iterable, Union
from qdrant_client import QdrantClient
from docarray.array.storage.base.seqlike import BaseSequenceLikeMixin
from docarray import Document
class SequenceLikeMixin(BaseSequenceLikeMixin):
@property
@abstractmethod
def client(self) -> Qdran... |
_base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# model settings
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
bbox_head=dict(
_delete_=True,
t... | _base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# model settings
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
bbox_head=dict(
_delete_=True,
t... |
"""DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.backend.config import backend
from keras.src.backend.config import disable_flash_attention
from keras.src.backend.config import enable_flash_attention
from keras.src.backend.config im... | """DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.backend.config import backend
from keras.src.backend.config import epsilon
from keras.src.backend.config import floatx
from keras.src.backend.config import image_data_format
from kera... |
_base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/openimages_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(bbox_head=dict(num_classes=601))
# learning rate
param_scheduler = [
dict(
type='LinearLR',
start_factor... | _base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/openimages_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(bbox_head=dict(num_classes=601))
optimizer = dict(type='SGD', lr=0.08, momentum=0.9, weight_decay=0.0001)
optimizer_config =... |
import os
import platform
import tempfile
import pytest
from sentence_transformers import SentenceTransformer, CrossEncoder
from sentence_transformers.models import Transformer, Pooling
from datasets import load_dataset, DatasetDict
@pytest.fixture()
def stsb_bert_tiny_model() -> SentenceTransformer:
return Sent... | import os
import platform
import tempfile
import pytest
from sentence_transformers import SentenceTransformer, CrossEncoder
from sentence_transformers.models import Transformer, Pooling
@pytest.fixture()
def stsb_bert_tiny_model() -> SentenceTransformer:
return SentenceTransformer("sentence-transformers-testing/... |
from dataclasses import dataclass, field
from typing import Union
from transformers import TrainingArguments as TransformersTrainingArguments
from transformers.utils import ExplicitEnum
class BatchSamplers(ExplicitEnum):
"""
Stores the acceptable string identifiers for batch samplers.
"""
BATCH_SAMPL... | from dataclasses import dataclass, field
from typing import Union
from transformers import TrainingArguments as TransformersTrainingArguments
from transformers.utils import ExplicitEnum
class BatchSamplers(ExplicitEnum):
"""
Stores the acceptable string identifiers for batch samplers.
"""
BATCH_SAMPL... |
from langchain_core.tracers.evaluation import (
EvaluatorCallbackHandler,
wait_for_all_evaluators,
)
__all__ = ["EvaluatorCallbackHandler", "wait_for_all_evaluators"]
| from langchain_core.tracers.evaluation import (
EvaluatorCallbackHandler,
wait_for_all_evaluators,
)
__all__ = ["wait_for_all_evaluators", "EvaluatorCallbackHandler"]
|
import sys
from dataclasses import dataclass
from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union
import pandas as pd
import pyarrow as pa
import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast
if TYPE_CHECKING:
im... | import sys
from dataclasses import dataclass
from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union
import pandas as pd
import pyarrow as pa
import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast
if TYPE_CHECKING:
im... |
"""Pydantic v1 compatibility shim."""
from importlib import metadata
from langchain_core._api.deprecation import warn_deprecated
# Create namespaces for pydantic v1 and v2.
# This code must stay at the top of the file before other modules may
# attempt to import pydantic since it adds pydantic_v1 and pydantic_v2 to ... | """Pydantic v1 compatibility shim."""
from importlib import metadata
from langchain_core._api.deprecation import warn_deprecated
# Create namespaces for pydantic v1 and v2.
# This code must stay at the top of the file before other modules may
# attempt to import pydantic since it adds pydantic_v1 and pydantic_v2 to ... |
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.tensorflow_tensor import TensorFlowTensor, metaTensorFlow
from docarray.typing.tensor.video.video_tensor_mixin import VideoTensorMixin
T =... | 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.tensorflow_tensor import TensorFlowTensor, metaTensorFlow
from docarray.typing.tensor.video.video_tensor_mixin import VideoTensorMixin
T =... |
import pathlib
from typing import Any, Dict, List, Tuple, Union
import torch
from torchdata.datapipes.iter import CSVParser, IterDataPipe, Mapper
from torchvision.datapoints import Image
from torchvision.prototype.datapoints import OneHotLabel
from torchvision.prototype.datasets.utils import Dataset, HttpResource, Onl... | import pathlib
from typing import Any, Dict, List, Tuple, Union
import torch
from torchdata.datapipes.iter import CSVParser, IterDataPipe, Mapper
from torchvision.prototype.datapoints import Image, OneHotLabel
from torchvision.prototype.datasets.utils import Dataset, HttpResource, OnlineResource
from torchvision.proto... |
"""FastAPI framework, high performance, easy to learn, fast to code, ready for production"""
__version__ = "0.116.0"
from starlette import status as status
from .applications import FastAPI as FastAPI
from .background import BackgroundTasks as BackgroundTasks
from .datastructures import UploadFile as UploadFile
from... | """FastAPI framework, high performance, easy to learn, fast to code, ready for production"""
__version__ = "0.115.14"
from starlette import status as status
from .applications import FastAPI as FastAPI
from .background import BackgroundTasks as BackgroundTasks
from .datastructures import UploadFile as UploadFile
fro... |
_base_ = './cascade-rcnn_r50_fpn_20e_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| _base_ = './cascade_rcnn_r50_fpn_20e_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
|
# Copyright (c) OpenMMLab. All rights reserved.
from contextlib import contextmanager
import torch
import torch.nn as nn
from torch.cuda.amp import GradScaler
from mmengine.registry import OPTIM_WRAPPERS
from mmengine.utils import digit_version
from mmengine.utils.dl_utils import TORCH_VERSION
from .optimizer_wrapper... | # Copyright (c) OpenMMLab. All rights reserved.
from contextlib import contextmanager
import torch
import torch.nn as nn
from torch.cuda.amp import GradScaler
from mmengine.registry import OPTIM_WRAPPERS
from mmengine.utils import TORCH_VERSION, digit_version
from .optimizer_wrapper import OptimWrapper
@OPTIM_WRAPP... |
import pathlib
from typing import Any, Dict, List, Tuple, Union
from torchdata.datapipes.iter import Filter, IterDataPipe, Mapper
from torchvision.prototype.datapoints import Label
from torchvision.prototype.datasets.utils import Dataset, EncodedImage, HttpResource, OnlineResource
from torchvision.prototype.datasets.u... | import pathlib
from typing import Any, Dict, List, Tuple, Union
from torchdata.datapipes.iter import Filter, IterDataPipe, Mapper
from torchvision.prototype.datasets.utils import Dataset, EncodedImage, HttpResource, OnlineResource
from torchvision.prototype.datasets.utils._internal import (
hint_sharding,
hint... |
from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.agent_toolkits.openapi.toolkit import (
OpenAPIToolkit,
RequestsToolkit,
)
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for rais... | from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.agent_toolkits.openapi.toolkit import (
OpenAPIToolkit,
RequestsToolkit,
)
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for rais... |
"""
This examples trains a CrossEncoder for the NLI task. A CrossEncoder takes a sentence pair
as input and outputs a label. Here, it learns to predict the labels: "contradiction": 0, "entailment": 1, "neutral": 2.
It does NOT produce a sentence embedding and does NOT work for individual sentences.
Usage:
python trai... | """
This examples trains a CrossEncoder for the NLI task. A CrossEncoder takes a sentence pair
as input and outputs a label. Here, it learns to predict the labels: "contradiction": 0, "entailment": 1, "neutral": 2.
It does NOT produce a sentence embedding and does NOT work for individual sentences.
Usage:
python trai... |
import os
from nvflare.apis.executor import Executor
from nvflare.apis.fl_constant import FLContextKey, ReturnCode
from nvflare.apis.fl_context import FLContext
from nvflare.apis.shareable import Shareable, make_reply
from nvflare.apis.signal import Signal
import xgboost as xgb
from xgboost import callback
class Su... | import os
from nvflare.apis.executor import Executor
from nvflare.apis.fl_constant import FLContextKey, ReturnCode
from nvflare.apis.fl_context import FLContext
from nvflare.apis.shareable import Shareable, make_reply
from nvflare.apis.signal import Signal
import xgboost as xgb
from xgboost import callback
class Su... |
"""Standard LangChain interface tests."""
import pytest
from langchain_core.language_models import BaseChatModel
from langchain_core.tools import BaseTool
from langchain_tests.integration_tests import ChatModelIntegrationTests
from langchain_huggingface import ChatHuggingFace, HuggingFaceEndpoint
class TestHuggingF... | """Standard LangChain interface tests"""
import pytest
from langchain_core.language_models import BaseChatModel
from langchain_core.tools import BaseTool
from langchain_tests.integration_tests import ChatModelIntegrationTests
from langchain_huggingface import ChatHuggingFace, HuggingFaceEndpoint
class TestHuggingFa... |
import unittest
import torch
import torchaudio.prototype.functional as F
from torchaudio_unittest.common_utils import TestBaseMixin, torch_script
class TorchScriptConsistencyTestImpl(TestBaseMixin):
def _assert_consistency(self, func, inputs, shape_only=False):
inputs_ = []
for i in inputs:
... | import unittest
import torch
import torchaudio.prototype.functional as F
from torchaudio_unittest.common_utils import nested_params, TestBaseMixin, torch_script
class TorchScriptConsistencyTestImpl(TestBaseMixin):
def _assert_consistency(self, func, inputs, shape_only=False):
inputs_ = []
for i i... |
from typing import Optional
from langchain_core.callbacks.manager import CallbackManagerForRetrieverRun
from langchain_core.documents import Document
from langchain_core.retrievers import BaseRetriever
from langchain.retrievers.ensemble import EnsembleRetriever
class MockRetriever(BaseRetriever):
docs: list[Doc... | from typing import Optional
from langchain_core.callbacks.manager import CallbackManagerForRetrieverRun
from langchain_core.documents import Document
from langchain_core.retrievers import BaseRetriever
from langchain.retrievers.ensemble import EnsembleRetriever
class MockRetriever(BaseRetriever):
docs: list[Doc... |
# Copyright (c) OpenMMLab. All rights reserved.
import datetime
import os.path as osp
from typing import Optional
from mmengine.fileio import dump
from mmengine.logging import print_log
from . import root
from .registry import Registry
def traverse_registry_tree(registry: Registry, verbose: bool = True) -> list:
... | # Copyright (c) OpenMMLab. All rights reserved.
import datetime
import os.path as osp
from typing import Optional
from mmengine.fileio import dump
from . import root
from .registry import Registry
def traverse_registry_tree(registry: Registry, verbose: bool = True) -> list:
"""Traverse the whole registry tree fr... |
_base_ = './cornernet_hourglass104_mstest_8x6_210e_coco.py'
train_dataloader = dict(batch_size=3)
# NOTE: `auto_scale_lr` is for automatically scaling LR,
# USER SHOULD NOT CHANGE ITS VALUES.
# base_batch_size = (32 GPUs) x (3 samples per GPU)
auto_scale_lr = dict(base_batch_size=96)
| _base_ = [
'../_base_/default_runtime.py', '../_base_/datasets/coco_detection.py'
]
# model settings
model = dict(
type='CornerNet',
backbone=dict(
type='HourglassNet',
downsample_times=5,
num_stacks=2,
stage_channels=[256, 256, 384, 384, 384, 512],
stage_blocks=[2, ... |
from .transformer_tf_text_encode import TransformerTFTextEncoder
| from .transformer_tf_text_encode import TransformerTFTextEncoder |
_base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
checkpoint = 'https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb256-rsb-a1-600e_in1k_20211228-20e21305.pth' # noqa
model = ... | _base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
checkpoint = 'https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb256-rsb-a1-600e_in1k_20211228-20e21305.pth' # noqa
model = ... |
import os
from jina import Executor, requests
class DummyExecutor(Executor):
def __init__(self, arg='hello', **kwargs):
super().__init__(**kwargs)
self.arg = arg
@requests
def foo(self, docs, **kwargs):
for doc in docs:
doc.text = self.arg
| import os
from jina import Executor, requests
class DummyExecutor(Executor):
@requests
def foo(self, **kwargs):
pass
|
import sqlite3
import warnings
from dataclasses import dataclass, field
from tempfile import NamedTemporaryFile
from typing import (
Iterable,
Dict,
Optional,
TYPE_CHECKING,
Union,
List,
Tuple,
)
from docarray.array.storage.sqlite.helper import initialize_table
from docarray.array.storage.b... | import sqlite3
import warnings
from dataclasses import dataclass, field
from tempfile import NamedTemporaryFile
from typing import (
Iterable,
Dict,
Optional,
TYPE_CHECKING,
Union,
List,
Tuple,
)
from .helper import initialize_table
from ..base.backend import BaseBackendMixin
from ....helpe... |
from jina.serve.runtimes.servers import BaseServer
from aiohttp import web
class LoadBalancingServer(BaseServer):
"""Base FastAPI server. Implement this abstract class in-case you want to build a fastapi-based server by
implementing the `app` property. This property should return a fastapi app. The base Gatew... | from jina.serve.runtimes.servers import BaseServer
from aiohttp import web
class LoadBalancingServer(BaseServer):
"""Base FastAPI server. Implement this abstract class in-case you want to build a fastapi-based server by
implementing the `app` property. This property should return a fastapi app. The base Gatew... |
# dataset settings
dataset_type = 'WIDERFaceDataset'
data_root = 'data/WIDERFace/'
# 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/cityscapes/'
... | # dataset settings
dataset_type = 'WIDERFaceDataset'
data_root = 'data/WIDERFace/'
img_norm_cfg = dict(mean=[123.675, 116.28, 103.53], std=[1, 1, 1], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFile', to_float32=True),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='PhotoMetric... |
from langchain_core.utils.iter import NoLock, Tee, batch_iterate, tee_peer
__all__ = ["NoLock", "Tee", "batch_iterate", "tee_peer"]
| from langchain_core.utils.iter import NoLock, Tee, batch_iterate, tee_peer
__all__ = ["NoLock", "tee_peer", "Tee", "batch_iterate"]
|
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