input stringlengths 33 5k | output stringlengths 32 5k |
|---|---|
# This file is autogenerated by the command `make fix-copies`, do not edit.
from ..utils import DummyObject, requires_backends
class BaseImageProcessorFast(metaclass=DummyObject):
_backends = ["torchvision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torchvision"])
class BaseVid... | # This file is autogenerated by the command `make fix-copies`, do not edit.
from ..utils import DummyObject, requires_backends
class BaseImageProcessorFast(metaclass=DummyObject):
_backends = ["torchvision"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torchvision"])
|
_base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# 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(
preprocess_cfg=prepr... | _base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# 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(
preprocess_cfg=prepr... |
"""Hybrid Fusion Retriever Pack."""
import os
from typing import Any, Dict, List
from llama_index.core import Settings
from llama_index.core.indices.vector_store import VectorStoreIndex
from llama_index.core.llama_pack.base import BaseLlamaPack
from llama_index.core.query_engine import RetrieverQueryEngine
from llama... | """Hybrid Fusion Retriever Pack."""
import os
from typing import Any, Dict, List
from llama_index.core import Settings
from llama_index.core.indices.vector_store import VectorStoreIndex
from llama_index.core.llama_pack.base import BaseLlamaPack
from llama_index.core.query_engine import RetrieverQueryEngine
from llama... |
import unittest
import torch
import torchaudio.functional as F
from parameterized import parameterized
from torchaudio_unittest.common_utils import (
PytorchTestCase,
skipIfNoSox,
TorchaudioTestCase,
)
from .functional_impl import Functional, FunctionalCPUOnly
class TestFunctionalFloat32(Functional, Fun... | import unittest
import torch
import torchaudio.functional as F
from parameterized import parameterized
from torchaudio_unittest.common_utils import PytorchTestCase, TorchaudioTestCase, skipIfNoSox
from .functional_impl import Functional, FunctionalCPUOnly
class TestFunctionalFloat32(Functional, FunctionalCPUOnly, P... |
import numpy as np
import torch
from docarray import BaseDocument, DocumentArray, Image, Text
from docarray.typing import (
AnyEmbedding,
AnyTensor,
AnyUrl,
ImageUrl,
Mesh3DUrl,
NdArray,
PointCloud3DUrl,
TextUrl,
TorchEmbedding,
TorchTensor,
)
from docarray.typing.tensor import ... | import numpy as np
import torch
from docarray import BaseDocument, DocumentArray, Image, Text
from docarray.typing import (
AnyTensor,
AnyUrl,
Embedding,
ImageUrl,
Mesh3DUrl,
NdArray,
PointCloud3DUrl,
TextUrl,
TorchEmbedding,
TorchTensor,
)
from docarray.typing.tensor import NdA... |
"""DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.applications.convnext import ConvNeXtBase as ConvNeXtBase
from keras.src.applications.convnext import ConvNeXtLarge as ConvNeXtLarge
from keras.src.applications.convnext import ConvNe... | """DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.applications.convnext import ConvNeXtBase
from keras.src.applications.convnext import ConvNeXtLarge
from keras.src.applications.convnext import ConvNeXtSmall
from keras.src.applicatio... |
"""
Computes embeddings
"""
import numpy as np
from sentence_transformers import SentenceTransformer
from sentence_transformers.util import get_device_name
def test_encode_token_embeddings(paraphrase_distilroberta_base_v1_model: SentenceTransformer) -> None:
"""
Test that encode(output_value='token_embeddin... | """
Computes embeddings
"""
import numpy as np
from sentence_transformers import SentenceTransformer
def test_encode_token_embeddings(paraphrase_distilroberta_base_v1_model: SentenceTransformer) -> None:
"""
Test that encode(output_value='token_embeddings') works
:return:
"""
model = paraphrase_... |
"""
Feature agglomeration. Base classes and functions for performing feature
agglomeration.
"""
# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause
import numpy as np
from scipy.sparse import issparse
from ..base import TransformerMixin
from ..utils import metadata_routing
from ..utils.de... | """
Feature agglomeration. Base classes and functions for performing feature
agglomeration.
"""
# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause
import numpy as np
from scipy.sparse import issparse
from ..base import TransformerMixin
from ..utils import metadata_routing
from ..utils.de... |
import pytest
@pytest.mark.compile
def test_placeholder() -> None:
"""Used for compiling integration tests without running any real tests."""
| import pytest
@pytest.mark.compile
def test_placeholder() -> None:
"""Used for compiling integration tests without running any real tests."""
pass
|
#!/usr/bin/env python3
# Owner(s): ["oncall: distributed"]
import contextlib
import copyreg
import os
import sys
import torch
import torch.distributed as dist
if not dist.is_available():
print("Distributed not available, skipping tests", file=sys.stderr)
sys.exit(0)
import torch.distributed.rpc as rpc
impo... | #!/usr/bin/env python3
# Owner(s): ["oncall: distributed"]
import contextlib
import copyreg
import os
import sys
import torch
import torch.distributed as dist
if not dist.is_available():
print("Distributed not available, skipping tests", file=sys.stderr)
sys.exit(0)
import torch.distributed.rpc as rpc
impo... |
from pathlib import Path
from typing import Any, Callable, Optional, Tuple
import PIL.Image
from .folder import make_dataset
from .utils import download_and_extract_archive, verify_str_arg
from .vision import VisionDataset
class RenderedSST2(VisionDataset):
"""`The Rendered SST2 Dataset <https://github.com/open... | from pathlib import Path
from typing import Any, Callable, Optional, Tuple
import PIL.Image
from .folder import make_dataset
from .utils import download_and_extract_archive, verify_str_arg
from .vision import VisionDataset
class RenderedSST2(VisionDataset):
"""`The Rendered SST2 Dataset <https://github.com/open... |
"""Test Anthropic API wrapper."""
from typing import List
from langchain_core.callbacks import (
CallbackManager,
)
from langchain_core.messages import AIMessage, BaseMessage, HumanMessage
from langchain_core.outputs import ChatGeneration, LLMResult
from langchain_community.chat_models.litellm import ChatLiteLLM... | """Test Anthropic API wrapper."""
from typing import List
from langchain_core.callbacks import (
CallbackManager,
)
from langchain_core.messages import AIMessage, BaseMessage, HumanMessage
from langchain_core.outputs import ChatGeneration, LLMResult
from langchain_community.chat_models.litellm import ChatLiteLLM... |
from dataclasses import dataclass, field
from typing import Any, Dict, Type
import pytest
from pydantic import Field
from docarray import BaseDoc
from docarray.index.abstract import BaseDocIndex
from docarray.typing import NdArray
pytestmark = pytest.mark.index
class SimpleDoc(BaseDoc):
tens: NdArray[10] = Fie... | from dataclasses import dataclass, field
from typing import Any, Dict, Type
import pytest
from pydantic import Field
from docarray import BaseDocument
from docarray.index.abstract import BaseDocumentIndex
from docarray.typing import NdArray
pytestmark = pytest.mark.index
class SimpleDoc(BaseDocument):
tens: Nd... |
# Copyright (c) OpenMMLab. All rights reserved.
from abc import ABCMeta, abstractmethod
import torch
from .sampling_result import SamplingResult
class BaseSampler(metaclass=ABCMeta):
"""Base class of samplers."""
def __init__(self,
num,
pos_fraction,
neg_p... | from abc import ABCMeta, abstractmethod
import torch
from .sampling_result import SamplingResult
class BaseSampler(metaclass=ABCMeta):
"""Base class of samplers."""
def __init__(self,
num,
pos_fraction,
neg_pos_ub=-1,
add_gt_as_proposals=T... |
"""Google Trends API Toolkit."""
from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.tools.google_trends.tool import GoogleTrendsQueryRun
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising depr... | """Google Trends API Toolkit."""
from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.tools.google_trends.tool import GoogleTrendsQueryRun
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising depr... |
import logging
import time
from abc import ABC, abstractmethod
from typing import ClassVar
from backend.data.model import OAuth2Credentials
logger = logging.getLogger(__name__)
class BaseOAuthHandler(ABC):
# --8<-- [start:BaseOAuthHandler1]
PROVIDER_NAME: ClassVar[str]
DEFAULT_SCOPES: ClassVar[list[str]... | import logging
import time
from abc import ABC, abstractmethod
from typing import ClassVar
from autogpt_libs.supabase_integration_credentials_store import OAuth2Credentials
logger = logging.getLogger(__name__)
class BaseOAuthHandler(ABC):
# --8<-- [start:BaseOAuthHandler1]
PROVIDER_NAME: ClassVar[str]
D... |
_base_ = '../_base_/default_runtime.py'
# dataset settings
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
image_size = (1024, 1024)
# 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)
# dat... | _base_ = '../_base_/default_runtime.py'
# dataset settings
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
image_size = (1024, 1024)
file_client_args = dict(backend='disk')
# comment out the code below to use different file client
# file_client_args = dict(
# backend='petrel',
# path_mapping=dict({
# ... |
from __future__ import annotations
__all__ = ["Array", "DType", "Device"]
_all_ignore = ["cp"]
from typing import TYPE_CHECKING
import cupy as cp
from cupy import ndarray as Array
from cupy.cuda.device import Device
if TYPE_CHECKING:
# NumPy 1.x on Python 3.10 fails to parse np.dtype[]
DType = cp.dtype[
... | from __future__ import annotations
__all__ = [
"ndarray",
"Device",
"Dtype",
]
import sys
from typing import (
Union,
TYPE_CHECKING,
)
from cupy import (
ndarray,
dtype,
int8,
int16,
int32,
int64,
uint8,
uint16,
uint32,
uint64,
float32,
float64,
)
... |
from typing import Union
from fastapi import FastAPI, Query
app = FastAPI()
@app.get("/items/")
async def read_items(q: Union[str, None] = Query(min_length=3)):
results = {"items": [{"item_id": "Foo"}, {"item_id": "Bar"}]}
if q:
results.update({"q": q})
return results
| from typing import Union
from fastapi import FastAPI, Query
app = FastAPI()
@app.get("/items/")
async def read_items(q: Union[str, None] = Query(default=..., min_length=3)):
results = {"items": [{"item_id": "Foo"}, {"item_id": "Bar"}]}
if q:
results.update({"q": q})
return results
|
from torch.utils.data import Dataset
from typing import List
from ..readers.InputExample import InputExample
import numpy as np
from transformers.utils.import_utils import is_nltk_available, NLTK_IMPORT_ERROR
class DenoisingAutoEncoderDataset(Dataset):
"""
The DenoisingAutoEncoderDataset returns InputExamples... | from torch.utils.data import Dataset
from typing import List
from ..readers.InputExample import InputExample
import numpy as np
from transformers.utils.import_utils import is_nltk_available, NLTK_IMPORT_ERROR
class DenoisingAutoEncoderDataset(Dataset):
"""
The DenoisingAutoEncoderDataset returns InputExamples... |
from typing import TYPE_CHECKING, Any, Dict, List, Type
if TYPE_CHECKING:
from docarray import BaseDocument
def _is_access_path_valid(doc_type: Type['BaseDocument'], access_path: str) -> bool:
"""
Check if a given access path ("__"-separated) is a valid path for a given Document class.
"""
from d... | from typing import TYPE_CHECKING, Any, Dict, Type
if TYPE_CHECKING:
from docarray import BaseDocument
def is_access_path_valid(doc: Type['BaseDocument'], access_path: str) -> bool:
"""
Check if a given access path ("__"-separated) is a valid path for a given Document class.
"""
from docarray impo... |
# 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 pytest
from pydantic import Field
from docarray import BaseDoc
from docarray.index.backends.weaviate import WeaviateDocumentIndex
from tests.index.weaviate.fixture_wea... | # 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 pytest
from pydantic import Field
from docarray import BaseDoc
from docarray.index.backends.weaviate import WeaviateDocumentIndex
from tests.index.weaviate.fixture_wea... |
import csv
import os
from pathlib import Path
from typing import 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
_RELEASE_CONFIGS = {
"release1": {
"folder_in_arch... | import csv
import os
from pathlib import Path
from typing import 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
_RELEASE_CONFIGS = {
"release1": {
"folder_in_arch... |
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 ConvTasNet
from .deepspeech import DeepSpeech
from .emformer import Emformer
from .rnnt import emformer_rnnt_base, emformer_rnnt_model, RNNT
from .rnnt_decoder import Hypothesis, RNNTBeamSe... |
import warnings
from typing import Any, List, Union
import torch
from torchvision import datapoints
from torchvision.transforms import functional as _F
@torch.jit.unused
def to_tensor(inpt: Any) -> torch.Tensor:
warnings.warn(
"The function `to_tensor(...)` is deprecated and will be removed in a future ... | import warnings
from typing import Any, List, Union
import torch
from torchvision import datapoints
from torchvision.transforms import functional as _F
@torch.jit.unused
def to_tensor(inpt: Any) -> torch.Tensor:
warnings.warn(
"The function `to_tensor(...)` is deprecated and will be removed in a future ... |
import os
import pypdf
import pytest
import tempfile
from fpdf import FPDF
from llama_index.readers.file import PDFReader
from pathlib import Path
from typing import Dict
@pytest.fixture()
def multi_page_pdf() -> FPDF:
pdf = FPDF()
pdf.add_page()
pdf.set_font("Helvetica", size=12)
pdf.cell(200, 10, te... | import os
import pypdf
import pytest
import tempfile
from fpdf import FPDF
from llama_index.readers.file import PDFReader
from pathlib import Path
from typing import Dict
@pytest.fixture()
def multi_page_pdf() -> FPDF:
pdf = FPDF()
pdf.add_page()
pdf.set_font("Helvetica", size=12)
pdf.cell(200, 10, te... |
from urllib.parse import urlparse, urlunparse
import pytest
from llama_index.postprocessor.nvidia_rerank import NVIDIARerank as Interface
from llama_index.postprocessor.nvidia_rerank.utils import BASE_URL
import respx
@pytest.fixture()
def mock_v1_local_models2(respx_mock: respx.MockRouter, base_url: str) -> None:
... | from urllib.parse import urlparse, urlunparse
import pytest
from requests_mock import Mocker
from llama_index.postprocessor.nvidia_rerank import NVIDIARerank as Interface
@pytest.fixture()
def mock_v1_local_models2(requests_mock: Mocker, base_url: str) -> None:
parsed = urlparse(base_url)
normalized_path = p... |
from enum import Enum
from typing import TYPE_CHECKING, Union, overload
import numpy as np
if TYPE_CHECKING:
import torch
class Pooling(str, Enum):
"""Enum of possible pooling choices with pooling behaviors."""
CLS = "cls"
MEAN = "mean"
LAST = "last" # last token pooling
def __call__(self... | from enum import Enum
from typing import TYPE_CHECKING, Union, overload
import numpy as np
if TYPE_CHECKING:
import torch
class Pooling(str, Enum):
"""Enum of possible pooling choices with pooling behaviors."""
CLS = "cls"
MEAN = "mean"
LAST = "last" # last token pooling
def __call__(self... |
_base_ = ['../_base_/models/retinanet_r50_fpn.py', '../common/ms_3x_coco.py']
# optimizer
model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101))
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
model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101))
optim_wrapper = dict(
optimizer=dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001))
|
"""
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... |
"""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... |
import base64
import email
from typing import Dict, Optional, Type
from langchain_core.callbacks import CallbackManagerForToolRun
from pydantic import BaseModel, Field
from langchain_community.tools.gmail.base import GmailBaseTool
from langchain_community.tools.gmail.utils import clean_email_body
class SearchArgsSc... | import base64
import email
from typing import Dict, Optional, Type
from langchain_core.callbacks import CallbackManagerForToolRun
from pydantic import BaseModel, Field
from langchain_community.tools.gmail.base import GmailBaseTool
from langchain_community.tools.gmail.utils import clean_email_body
class SearchArgsSc... |
import numpy as np
import pytest
from docarray import BaseDocument, DocumentArray
from docarray.documents import ImageDoc, TextDoc
from docarray.typing import NdArray
@pytest.mark.proto
def test_simple_proto():
class CustomDoc(BaseDocument):
text: str
tensor: NdArray
da = DocumentArray(
... | import numpy as np
import pytest
from docarray import BaseDocument, DocumentArray
from docarray.array.stacked.array_stacked import DocumentArrayStacked
from docarray.documents import ImageDoc, TextDoc
from docarray.typing import NdArray
@pytest.mark.proto
def test_simple_proto():
class CustomDoc(BaseDocument):
... |
# Copyright (c) OpenMMLab. All rights reserved.
import mmcv
from .version import __version__, short_version
def digit_version(version_str):
digit_version = []
for x in version_str.split('.'):
if x.isdigit():
digit_version.append(int(x))
elif x.find('rc') != -1:
patch_v... | # Copyright (c) OpenMMLab. All rights reserved.
import mmcv
from .version import __version__, short_version
def digit_version(version_str):
digit_version = []
for x in version_str.split('.'):
if x.isdigit():
digit_version.append(int(x))
elif x.find('rc') != -1:
patch_v... |
# Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from mmcv.cnn import is_norm
from torch.nn.modules import GroupNorm
from mmdet.models.utils import InvertedResidual, SELayer
def test_inverted_residual():
with pytest.raises(AssertionError):
# stride must be in [1, 2]
Inv... | import pytest
import torch
from mmcv.cnn import is_norm
from torch.nn.modules import GroupNorm
from mmdet.models.utils import InvertedResidual, SELayer
def test_inverted_residual():
with pytest.raises(AssertionError):
# stride must be in [1, 2]
InvertedResidual(16, 16, 32, stride=3)
with py... |
# training schedule for 1x
train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=12, val_interval=1)
val_cfg = dict(type='ValLoop')
test_cfg = dict(type='TestLoop')
# learning rate
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='Mu... | # training schedule for 1x
train_cfg = dict(by_epoch=True, max_epochs=12)
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=12,
... |
# coding: utf-8
from pathlib import Path
import pandas as pd
import lightgbm as lgb
if lgb.compat.MATPLOTLIB_INSTALLED:
import matplotlib.pyplot as plt
else:
raise ImportError('You need to install matplotlib and restart your session for plot_example.py.')
print('Loading data...')
# load or create your datas... | # coding: utf-8
from pathlib import Path
import pandas as pd
import lightgbm as lgb
if lgb.compat.MATPLOTLIB_INSTALLED:
import matplotlib.pyplot as plt
else:
raise ImportError('You need to install matplotlib and restart your session for plot_example.py.')
print('Loading data...')
# load or create your datas... |
_base_ = './htc_without_semantic_r50_fpn_1x_coco.py'
model = dict(
data_preprocessor=dict(pad_seg=True),
roi_head=dict(
semantic_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0),
out_channels=256,
... | _base_ = './htc_without_semantic_r50_fpn_1x_coco.py'
model = dict(
roi_head=dict(
semantic_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0),
out_channels=256,
featmap_strides=[8]),
semanti... |
_base_ = [
'../_base_/models/mask-rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
train_pipeline = [
dict(
type='LoadImageFromFile',
file_client_args={{_base_.file_client_args}}),
dict(
type='Loa... | _base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
train_pipeline = [
dict(
type='LoadImageFromFile',
file_client_args={{_base_.file_client_args}}),
dict(
type='Loa... |
__copyright__ = "Copyright (c) 2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import subprocess
from typing import List
import numpy as np
import pytest
from jina import Document, DocumentArray, Flow
from paddle_image import ImagePaddlehubEncoder
@pytest.mark.parametrize(
'arr_in',
[... | __copyright__ = "Copyright (c) 2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import subprocess
from typing import List
import numpy as np
import pytest
from jina import Document, DocumentArray, Flow
from ...paddle_image import ImagePaddlehubEncoder
@pytest.mark.parametrize(
'arr_in',
... |
# Copyright (c) OpenMMLab. All rights reserved.
# flake8: noqa
from .config import *
from .data import *
from .dataset import *
from .device import *
from .fileio import *
from .hooks import *
from .logging import *
from .registry import *
from .runner import *
from .utils import *
from .version import __version__, ver... | # Copyright (c) OpenMMLab. All rights reserved.
# flake8: noqa
from .config import *
from .data import *
from .dataset import *
from .device import *
from .fileio import *
from .hooks import *
from .logging import *
from .registry import *
from .runner import *
from .utils import *
from .visualization import *
|
from typing import overload, TYPE_CHECKING, Union, Callable, Optional, Tuple
if TYPE_CHECKING:
from docarray import DocumentArray
from docarray.typing import AnyDNN, T, ArrayType
import numpy as np
class SingletonSugarMixin:
"""Provide sugary syntax for :class:`Document` by inheriting methods from :... | from typing import overload, TYPE_CHECKING, Union, Callable, Optional, Tuple
if TYPE_CHECKING:
from ... import DocumentArray
from ...typing import AnyDNN, T, ArrayType
import numpy as np
class SingletonSugarMixin:
"""Provide sugary syntax for :class:`Document` by inheriting methods from :class:`Docu... |
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 (c) OpenMMLab. All rights reserved.
from ._fast_stop_training_hook import FastStopTrainingHook # noqa: F401,F403
from ._utils import (demo_mm_inputs, demo_mm_proposals,
demo_mm_sampling_results, get_detector_cfg,
get_roi_head_cfg)
__all__ = [
'demo_mm_inputs',... | # Copyright (c) OpenMMLab. All rights reserved.
from ._utils import (demo_mm_inputs, demo_mm_proposals,
demo_mm_sampling_results, get_detector_cfg,
get_roi_head_cfg)
__all__ = [
'demo_mm_inputs', 'get_detector_cfg', 'get_roi_head_cfg',
'demo_mm_proposals', 'demo_mm_sam... |
_base_ = [
'../_base_/models/mask-rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
vis_backends = [dict(type='LocalVisBackend'), dict(type='WandbVisBackend')]
visualizer = dict(vis_backends=vis_backends)
# MMEngine support the ... | _base_ = [
'../_base_/models/mask-rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
vis_backends = [dict(type='LocalVisBackend'), dict(type='WandBVisBackend')]
visualizer = dict(vis_backends=vis_backends)
# MMEngine support the ... |
import inspect
import re
from typing import Dict, List, Tuple
from huggingface_hub.utils import insecure_hashlib
from .arrow import arrow
from .audiofolder import audiofolder
from .cache import cache
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parq... | import inspect
import re
from typing import Dict, List, Tuple
from huggingface_hub.utils import insecure_hashlib
from .arrow import arrow
from .audiofolder import audiofolder
from .cache import cache # noqa F401
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pand... |
"""
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... |
from langchain_core.document_loaders import BaseBlobParser, BaseLoader
__all__ = ["BaseBlobParser", "BaseLoader"]
| from langchain_core.document_loaders import BaseBlobParser, BaseLoader
__all__ = ["BaseLoader", "BaseBlobParser"]
|
"""Utilities to route metadata within scikit-learn estimators."""
# This module is not a separate sub-folder since that would result in a circular
# import issue.
#
# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause
from ._metadata_requests import ( # noqa: F401
UNCHANGED,
UNUSED,... | """Utilities to route metadata within scikit-learn estimators."""
# This module is not a separate sub-folder since that would result in a circular
# import issue.
#
# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause
from ._metadata_requests import WARN, UNUSED, UNCHANGED # noqa
from ._met... |
from functools import partial
from typing import Any, Optional
import torch
import torch.nn as nn
from ..transforms._presets import ImageClassification
from ..utils import _log_api_usage_once
from ._api import register_model, Weights, WeightsEnum
from ._meta import _IMAGENET_CATEGORIES
from ._utils import _ovewrite_n... | from functools import partial
from typing import Any, Optional
import torch
import torch.nn as nn
from ..transforms._presets import ImageClassification
from ..utils import _log_api_usage_once
from ._api import register_model, Weights, WeightsEnum
from ._meta import _IMAGENET_CATEGORIES
from ._utils import _ovewrite_n... |
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] Convert a PIL Image or ndarray to tensor and scale the values accordingly.... | 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] Convert a PIL Image or ndarray to tensor and scale the values accordingly.... |
# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import warnings
from mmcv import Config, DictAction
from mmdet.utils import replace_cfg_vals, update_data_root
def parse_args():
parser = argparse.ArgumentParser(description='Print the whole config')
parser.add_argument('config', help='config f... | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import warnings
from mmcv import Config, DictAction
from mmdet.utils import update_data_root
def parse_args():
parser = argparse.ArgumentParser(description='Print the whole config')
parser.add_argument('config', help='config file path')
par... |
import argparse
import functools
import traceback
from typing import Callable, List, Optional, Tuple
from torch.utils.jit.log_extract import (
extract_ir,
load_graph_and_inputs,
run_baseline_no_fusion,
run_nnc,
run_nvfuser,
)
"""
Usage:
1. Run your script and pipe into a log file
PYTORCH_JIT_LO... | import argparse
import functools
import traceback
from typing import Callable, List, Optional, Tuple
from torch.utils.jit.log_extract import (
extract_ir,
load_graph_and_inputs,
run_baseline_no_fusion,
run_nnc,
run_nvfuser,
)
"""
Usage:
1. Run your script and pipe into a log file
PYTORCH_JIT_LO... |
# Copyright (c) OpenMMLab. All rights reserved.
from mmdet.core import ConfigType, OptConfigType, OptMultiConfig
from mmdet.registry import MODELS
from .single_stage import SingleStageDetector
@MODELS.register_module()
class ATSS(SingleStageDetector):
"""Implementation of `ATSS <https://arxiv.org/abs/1912.02424>`... | # Copyright (c) OpenMMLab. All rights reserved.
from mmdet.registry import MODELS
from .single_stage import SingleStageDetector
@MODELS.register_module()
class ATSS(SingleStageDetector):
"""Implementation of `ATSS <https://arxiv.org/abs/1912.02424>`_."""
def __init__(self,
backbone,
... |
from __future__ import annotations
from typing import Any, Optional, Union
import torch
from ._tv_tensor import TVTensor
class Video(TVTensor):
""":class:`torch.Tensor` subclass for videos.
Args:
data (tensor-like): Any data that can be turned into a tensor with :func:`torch.as_tensor`.
dt... | from __future__ import annotations
from typing import Any, Optional, Union
import torch
from ._tv_tensor import TVTensor
class Video(TVTensor):
"""[BETA] :class:`torch.Tensor` subclass for videos.
Args:
data (tensor-like): Any data that can be turned into a tensor with :func:`torch.as_tensor`.
... |
"""
=================================================
Novelty detection with Local Outlier Factor (LOF)
=================================================
The Local Outlier Factor (LOF) algorithm is an unsupervised anomaly detection
method which computes the local density deviation of a given data point with
respect to... | """
=================================================
Novelty detection with Local Outlier Factor (LOF)
=================================================
The Local Outlier Factor (LOF) algorithm is an unsupervised anomaly detection
method which computes the local density deviation of a given data point with
respect to... |
# Copyright (c) OpenMMLab. All rights reserved.
from typing import List, Tuple
import torch.nn as nn
from mmcv.cnn import ConvModule
from mmengine.model import BaseModule
from torch import Tensor
from mmdet.core.utils.typing import MultiConfig, OptConfigType
from mmdet.models.utils import ResLayer, SimplifiedBasicBlo... | # Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
from mmcv.cnn import ConvModule
from mmcv.runner import BaseModule, auto_fp16, force_fp32
from mmdet.models.utils import ResLayer, SimplifiedBasicBlock
from mmdet.registry import MODELS
@MODELS.register_module()
class GlobalContextHead(BaseModule)... |
import json
import os
from typing import List
import torch
from torch import nn
class LSTM(nn.Module):
"""Bidirectional LSTM running over word embeddings."""
def __init__(
self,
word_embedding_dimension: int,
hidden_dim: int,
num_layers: int = 1,
dropout: float = 0,
... | import torch
from torch import nn
from typing import List
import os
import json
class LSTM(nn.Module):
"""
Bidirectional LSTM running over word embeddings.
"""
def __init__(self, word_embedding_dimension: int, hidden_dim: int, num_layers: int = 1, dropout: float = 0, bidirectional: bool = True):
... |
_base_ = './rtmdet_s_8xb32-300e_coco.py'
checkpoint = 'https://download.openmmlab.com/mmdetection/v3.0/rtmdet/cspnext_rsb_pretrain/cspnext-tiny_imagenet_600e.pth' # noqa
model = dict(
backbone=dict(
deepen_factor=0.167,
widen_factor=0.375,
init_cfg=dict(
type='Pretrained', pre... | _base_ = './rtmdet_s_8xb32-300e_coco.py'
checkpoint = 'https://download.openmmlab.com/mmdetection/v3.0/rtmdet/cspnext_rsb_pretrain/cspnext-tiny_imagenet_600e.pth' # noqa
model = dict(
backbone=dict(
deepen_factor=0.167,
widen_factor=0.375,
init_cfg=dict(
type='Pretrained', pre... |
import json
import multiprocessing
import os
import time
import pytest
from docarray import DocumentArray
from jina import Executor, requests
from jina.helper import random_port
from jina.parsers import set_gateway_parser, set_pod_parser
from jina.serve.runtimes.gateway import GatewayRuntime
from jina.serve.runtimes.... | import json
import multiprocessing
import os
import time
import pytest
from docarray import DocumentArray
from jina import Executor, requests
from jina.helper import random_port
from jina.parsers import set_gateway_parser, set_pod_parser
from jina.serve.runtimes.gateway import GatewayRuntime
from jina.serve.runtimes.... |
import gc
import unittest
import numpy as np
import pytest
import torch
from diffusers import FluxPipeline, FluxPriorReduxPipeline
from diffusers.utils import load_image
from diffusers.utils.testing_utils import (
Expectations,
backend_empty_cache,
numpy_cosine_similarity_distance,
require_big_acceler... | import gc
import unittest
import numpy as np
import pytest
import torch
from diffusers import FluxPipeline, FluxPriorReduxPipeline
from diffusers.utils import load_image
from diffusers.utils.testing_utils import (
backend_empty_cache,
numpy_cosine_similarity_distance,
require_big_accelerator,
slow,
... |
# Copyright (c) OpenMMLab. All rights reserved.
import warnings
import torch
from mmengine.utils.dl_utils import torch_meshgrid
def test_torch_meshgrid():
# torch_meshgrid should not throw warning
with warnings.catch_warnings():
warnings.simplefilter('error')
x = torch.tensor([1, 2, 3])
... | # Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from mmengine.utils.dl_utils import torch_meshgrid
def test_torch_meshgrid():
# torch_meshgrid should not throw warning
with pytest.warns(None) as record:
x = torch.tensor([1, 2, 3])
y = torch.tensor([4, 5, 6])
... |
"""Utilities for the CI."""
import os
from datetime import datetime, timedelta
from functools import wraps
from typing import Any, Callable, Dict, TypedDict, TypeVar, Union
class DirectoryExcursion:
def __init__(self, path: Union[os.PathLike, str]) -> None:
self.path = path
self.curdir = os.path.... | """Utilities for the CI."""
import os
from datetime import datetime, timedelta
from functools import wraps
from typing import Any, Callable, Dict, TypedDict, TypeVar, Union
class DirectoryExcursion:
def __init__(self, path: Union[os.PathLike, str]) -> None:
self.path = path
self.curdir = os.path.n... |
from typing import (
TYPE_CHECKING,
Sequence,
)
import numpy as np
from docarray.helper import typename
if TYPE_CHECKING:
from docarray.typing import (
DocumentArrayIndexType,
)
class DelItemMixin:
"""Provide help function to enable advanced indexing in `__delitem__`"""
def __delit... | from typing import (
TYPE_CHECKING,
Sequence,
)
import numpy as np
from docarray.helper import typename
if TYPE_CHECKING:
from docarray.typing import (
DocumentArrayIndexType,
)
class DelItemMixin:
"""Provide help function to enable advanced indexing in `__delitem__`"""
def __delit... |
"""Bedrock Retriever."""
from typing import List, Optional, Dict, Any
from llama_index.core.base.base_retriever import BaseRetriever
from llama_index.core.callbacks.base import CallbackManager
from llama_index.core.schema import NodeWithScore, QueryBundle, TextNode
from llama_index.core.utilities.aws_utils import get_... | """Bedrock Retriever."""
from typing import List, Optional, Dict, Any
from llama_index.core.base.base_retriever import BaseRetriever
from llama_index.core.callbacks.base import CallbackManager
from llama_index.core.schema import NodeWithScore, QueryBundle, TextNode
from llama_index.core.utilities.aws_utils import get_... |
_base_ = './retinanet_r50_fpn_8xb8-amp-lsj-200e_coco.py'
model = dict(
backbone=dict(
depth=18,
init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet18')),
neck=dict(in_channels=[64, 128, 256, 512]))
| _base_ = './retinanet_r50_fpn_lsj_200e_8x8_fp16_coco.py'
model = dict(
backbone=dict(
depth=18,
init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet18')),
neck=dict(in_channels=[64, 128, 256, 512]))
|
"""Pydantic output parser."""
import json
from typing import Any, Generic, List, Optional, Type
from llama_index.core.output_parsers.base import ChainableOutputParser
from llama_index.core.output_parsers.utils import extract_json_str
from llama_index.core.types import Model
PYDANTIC_FORMAT_TMPL = """
Here's a JSON s... | """Pydantic output parser."""
import json
from typing import Any, Generic, List, Optional, Type
from llama_index.core.output_parsers.base import ChainableOutputParser
from llama_index.core.output_parsers.utils import extract_json_str
from llama_index.core.types import Model
PYDANTIC_FORMAT_TMPL = """
Here's a JSON s... |
class WorkflowValidationError(Exception):
pass
class WorkflowTimeoutError(Exception):
pass
class WorkflowRuntimeError(Exception):
pass
class WorkflowDone(Exception):
pass
class WorkflowCancelledByUser(Exception):
pass
class WorkflowStepDoesNotExistError(Exception):
pass
class Workflo... | class WorkflowValidationError(Exception):
pass
class WorkflowTimeoutError(Exception):
pass
class WorkflowRuntimeError(Exception):
pass
class WorkflowDone(Exception):
pass
class WorkflowCancelledByUser(Exception):
pass
class WorkflowStepDoesNotExistError(Exception):
pass
|
from collections import defaultdict
from time import time
import numpy as np
from numpy import random as nr
from sklearn.cluster import KMeans, MiniBatchKMeans
def compute_bench(samples_range, features_range):
it = 0
results = defaultdict(lambda: [])
chunk = 100
max_it = len(samples_range) * len(fe... | from collections import defaultdict
from time import time
import numpy as np
from numpy import random as nr
from sklearn.cluster import KMeans, MiniBatchKMeans
def compute_bench(samples_range, features_range):
it = 0
results = defaultdict(lambda: [])
chunk = 100
max_it = len(samples_range) * len(fe... |
"""
===================================
How to write your own v2 transforms
===================================
.. note::
Try on `Colab <https://colab.research.google.com/github/pytorch/vision/blob/gh-pages/main/_generated_ipynb_notebooks/plot_custom_transforms.ipynb>`_
or :ref:`go to the end <sphx_glr_downloa... | """
===================================
How to write your own v2 transforms
===================================
.. note::
Try on `collab <https://colab.research.google.com/github/pytorch/vision/blob/gh-pages/main/_generated_ipynb_notebooks/plot_custom_transforms.ipynb>`_
or :ref:`go to the end <sphx_glr_downlo... |
# Copyright (c) OpenMMLab. All rights reserved.
import logging
from typing import Any, List, Optional, Sequence, Tuple
import torch
from torch.nn.parameter import Parameter
from torch.nn.utils import clip_grad
from mmengine.data import BaseDataSample
from mmengine.registry import HOOKS
from .hook import Hook
DATA_BA... | # Copyright (c) OpenMMLab. All rights reserved.
import logging
from typing import Any, List, Optional, Sequence, Tuple
import torch
from torch.nn.parameter import Parameter
from torch.nn.utils import clip_grad
from mmengine.data import BaseDataSample
from mmengine.registry import HOOKS
from .hook import Hook
DATA_BA... |
DEEPSEEK_MODEL_TO_CONTEXT_WINDOW = {
"deepseek-chat": 64000,
"deepseek-reasoner": 64000,
}
FUNCTION_CALLING_MODELS = {"deepseek-chat"}
def get_context_window(model: str) -> int:
return DEEPSEEK_MODEL_TO_CONTEXT_WINDOW.get(model, 64000)
| DEEPSEEK_MODEL_TO_CONTEXT_WINDOW = {
"deepseek-chat": 64000,
"deepseek-reasoner": 64000,
}
def get_context_window(model: str) -> int:
return DEEPSEEK_MODEL_TO_CONTEXT_WINDOW.get(model, 64000)
|
from typing import Any, Optional
from typing_inspect import get_args, is_union_type
from docarray.typing.tensor.abstract_tensor import AbstractTensor
def is_type_tensor(type_: Any) -> bool:
"""Return True if type is a type Tensor or an Optional Tensor type."""
return isinstance(type_, type) and issubclass(t... | from typing import Any
from typing_inspect import get_args, is_union_type
from docarray.typing.tensor.abstract_tensor import AbstractTensor
def is_type_tensor(type_: Any) -> bool:
"""Return True if type is a type Tensor or an Optional Tensor type."""
return isinstance(type_, type) and issubclass(type_, Abst... |
from typing import List, Iterable
import collections
import string
import os
import json
import logging
from .WordTokenizer import WordTokenizer, ENGLISH_STOP_WORDS
from transformers.utils.import_utils import is_nltk_available, NLTK_IMPORT_ERROR
logger = logging.getLogger(__name__)
class PhraseTokenizer(WordTokeniz... | from typing import List, Iterable
import collections
import string
import os
import json
import logging
from .WordTokenizer import WordTokenizer, ENGLISH_STOP_WORDS
from transformers.utils.import_utils import is_nltk_available, NLTK_IMPORT_ERROR
logger = logging.getLogger(__name__)
class PhraseTokenizer(WordTokeniz... |
from typing import Union
from langchain_core._api import deprecated
from langchain_core.language_models import BaseLanguageModel
from langchain_core.output_parsers.openai_tools import PydanticToolsParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import Runnable
from langchain_... | from typing import Union
from langchain_core._api import deprecated
from langchain_core.language_models import BaseLanguageModel
from langchain_core.output_parsers.openai_tools import PydanticToolsParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import Runnable
from langchain_... |
"""
Quantile Regression
===================
.. versionadded:: 2.0.0
The script is inspired by this awesome example in sklearn:
https://scikit-learn.org/stable/auto_examples/ensemble/plot_gradient_boosting_quantile.html
.. note::
The feature is only supported using the Python, R, and C packages. In addition,... | """
Quantile Regression
===================
.. versionadded:: 2.0.0
The script is inspired by this awesome example in sklearn:
https://scikit-learn.org/stable/auto_examples/ensemble/plot_gradient_boosting_quantile.html
.. note::
The feature is only supported using the Python package. In addition, quantile
... |
from keras.src import tree
from keras.src.api_export import keras_export
from keras.src.backend import KerasTensor
from keras.src.layers.layer import Layer
@keras_export("keras.layers.Identity")
class Identity(Layer):
"""Identity layer.
This layer should be used as a placeholder when no operation is to be
... | from keras.src import tree
from keras.src.api_export import keras_export
from keras.src.backend import KerasTensor
from keras.src.layers.layer import Layer
@keras_export("keras.layers.Identity")
class Identity(Layer):
"""Identity layer.
This layer should be used as a placeholder when no operation is to be
... |
from __future__ import annotations
from pathlib import Path
import torch
from sentence_transformers import SparseEncoder
from sentence_transformers.sparse_encoder.models import IDF
from tests.sparse_encoder.utils import sparse_allclose
def test_idf_padding_ignored(inference_free_splade_bert_tiny_model: SparseEncod... | from __future__ import annotations
import torch
from sentence_transformers import SparseEncoder
from tests.sparse_encoder.utils import sparse_allclose
def test_idf_padding_ignored(inference_free_splade_bert_tiny_model: SparseEncoder):
model = inference_free_splade_bert_tiny_model
input_texts = ["This is a ... |
"""
PostgresML index.
An index that is built on top of PostgresML.
"""
import logging
from typing import Any, List, Optional, Dict
from llama_index.core.async_utils import run_async_tasks
from llama_index.core.base.base_retriever import BaseRetriever
from llama_index.core.callbacks.base import CallbackManager
from ll... | """PostgresML index.
An index that is built on top of PostgresML.
"""
import logging
from typing import Any, List, Optional, Dict
from llama_index.core.async_utils import run_async_tasks
from llama_index.core.base.base_retriever import BaseRetriever
from llama_index.core.callbacks.base import CallbackManager
from lla... |
import pytest
from typing import Dict, List
from llama_index.core.llms import ChatMessage, MessageRole, TextBlock, AudioBlock
from llama_index.voice_agents.elevenlabs.utils import (
callback_agent_message,
callback_agent_message_correction,
callback_latency_measurement,
callback_user_message,
... | import pytest
from typing import Dict, List
from llama_index.core.llms import ChatMessage, MessageRole, TextBlock, AudioBlock
from llama_index.voice_agents.elevenlabs.utils import (
callback_agent_message,
callback_agent_message_correction,
callback_latency_measurement,
callback_user_message,
... |
from typing import Dict, Type
from llama_index.core.node_parser.file.html import HTMLNodeParser
from llama_index.core.node_parser.file.json import JSONNodeParser
from llama_index.core.node_parser.file.markdown import MarkdownNodeParser
from llama_index.core.node_parser.file.simple_file import SimpleFileNodeParser
from... | from typing import Dict, Type
from llama_index.core.node_parser.file.html import HTMLNodeParser
from llama_index.core.node_parser.file.json import JSONNodeParser
from llama_index.core.node_parser.file.markdown import MarkdownNodeParser
from llama_index.core.node_parser.file.simple_file import SimpleFileNodeParser
from... |
# 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.structures import InstanceData
from mmdet.engine.hooks import DetVisualizationHook
from mmdet.structures import DetDataSample
from mmd... | # 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.structures import InstanceData
from mmdet.engine.hooks import DetVisualizationHook
from mmdet.structures import DetDataSample
from mmd... |
from datetime import datetime
import pytest
from autogpt_libs.supabase_integration_credentials_store.store import openai_credentials
from prisma.models import UserBlockCredit
from backend.blocks.llm import AITextGeneratorBlock
from backend.data.credit import UserCredit
from backend.data.user import DEFAULT_USER_ID
fr... | from datetime import datetime
import pytest
from prisma.models import UserBlockCredit
from backend.blocks.llm import AITextGeneratorBlock
from backend.data.credit import UserCredit
from backend.data.user import DEFAULT_USER_ID
from backend.util.test import SpinTestServer
REFILL_VALUE = 1000
user_credit = UserCredit(... |
from typing import Any
import pytest
from langchain_tests.conftest import CustomPersister, CustomSerializer
from langchain_tests.conftest import _base_vcr_config as _base_vcr_config
from vcr import VCR # type: ignore[import-untyped]
def remove_request_headers(request: Any) -> Any:
for k in request.headers:
... | from typing import Any
import pytest
from langchain_tests.conftest import CustomPersister, CustomSerializer
from langchain_tests.conftest import _base_vcr_config as _base_vcr_config
from vcr import VCR # type: ignore[import-untyped]
def remove_request_headers(request: Any) -> Any:
for k in request.headers:
... |
from typing import TYPE_CHECKING
from .compass import CompassWebhookManager
from .github import GithubWebhooksManager
from .slant3d import Slant3DWebhooksManager
if TYPE_CHECKING:
from ..providers import ProviderName
from ._base import BaseWebhooksManager
# --8<-- [start:WEBHOOK_MANAGERS_BY_NAME]
WEBHOOK_MAN... | from typing import TYPE_CHECKING
from .github import GithubWebhooksManager
from .slant3d import Slant3DWebhooksManager
if TYPE_CHECKING:
from ..providers import ProviderName
from .base import BaseWebhooksManager
# --8<-- [start:WEBHOOK_MANAGERS_BY_NAME]
WEBHOOK_MANAGERS_BY_NAME: dict["ProviderName", type["Ba... |
"""
This script downloads the WikiMatrix corpus (https://github.com/facebookresearch/LASER/tree/master/tasks/WikiMatrix)
and create parallel sentences tsv files that can be used to extend existent sentence embedding models to new languages.
The WikiMatrix mined parallel sentences from Wikipedia in various languages.
... | """
This script downloads the WikiMatrix corpus (https://github.com/facebookresearch/LASER/tree/master/tasks/WikiMatrix)
and create parallel sentences tsv files that can be used to extend existent sentence embedding models to new languages.
The WikiMatrix mined parallel sentences from Wikipedia in various languages.
... |
"""
Wrapper script to run a command inside a Docker container
"""
import argparse
import grp
import itertools
import os
import pathlib
import pwd
import subprocess
import sys
import textwrap
OPS_DIR = pathlib.Path(__file__).expanduser().resolve().parent
PROJECT_ROOT_DIR = OPS_DIR.parent
LINEWIDTH = 88
TEXT_WRAPPER = ... | """
Wrapper script to run a command inside a Docker container
"""
import argparse
import grp
import itertools
import os
import pathlib
import pwd
import subprocess
import sys
import textwrap
OPS_DIR = pathlib.Path(__file__).expanduser().resolve().parent
PROJECT_ROOT_DIR = OPS_DIR.parent
LINEWIDTH = 88
TEXT_WRAPPER = ... |
from __future__ import annotations
from typing import Any, Dict, Iterator, List
from urllib.parse import urlparse
from langchain_core.embeddings import Embeddings
from pydantic import BaseModel, PrivateAttr
def _chunk(texts: List[str], size: int) -> Iterator[List[str]]:
for i in range(0, len(texts), size):
... | from __future__ import annotations
from typing import Any, Dict, Iterator, List
from urllib.parse import urlparse
from langchain_core.embeddings import Embeddings
from pydantic import BaseModel, PrivateAttr
def _chunk(texts: List[str], size: int) -> Iterator[List[str]]:
for i in range(0, len(texts), size):
... |
import json
from typing import Dict
import pytest
from jina.orchestrate.deployments.config.k8slib.kubernetes_tools import get_yaml
@pytest.mark.parametrize(
['template', 'params'],
[
('namespace', {'name': 'test-ns'}),
('service', {'name': 'test-svc'}),
('deployment-executor', {'name... | import json
from typing import Dict
import pytest
from jina.orchestrate.deployments.config.k8slib.kubernetes_tools import get_yaml
@pytest.mark.parametrize(
['template', 'params'],
[
('namespace', {'name': 'test-ns'}),
('service', {'name': 'test-svc'}),
('deployment', {'name': 'test-... |
from sentence_transformers import SentenceTransformer
from . import SentenceEvaluator
from typing import Dict, Iterable
class SequentialEvaluator(SentenceEvaluator):
"""
This evaluator allows that multiple sub-evaluators are passed. When the model is evaluated,
the data is passed sequentially to all sub-e... | from sentence_transformers import SentenceTransformer
from . import SentenceEvaluator
from typing import Dict, Iterable
class SequentialEvaluator(SentenceEvaluator):
"""
This evaluator allows that multiple sub-evaluators are passed. When the model is evaluated,
the data is passed sequentially to all sub-e... |
from typing import TYPE_CHECKING, Any, List, Tuple, Type, TypeVar, Union
import numpy as np
from docarray.typing.proto_register import _register_proto
from docarray.typing.tensor.ndarray import NdArray
from docarray.typing.tensor.video.video_tensor_mixin import VideoTensorMixin
T = TypeVar('T', bound='VideoNdArray')... | from typing import TYPE_CHECKING, Any, List, Tuple, Type, TypeVar, Union
import numpy as np
from docarray.typing.proto_register import _register_proto
from docarray.typing.tensor.ndarray import NdArray
from docarray.typing.tensor.video.video_tensor_mixin import VideoTensorMixin
T = TypeVar('T', bound='VideoNdArray')... |
from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.utilities.opaqueprompts import desanitize, sanitize
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handling opt... | from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.utilities.opaqueprompts import desanitize, sanitize
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handling opt... |
# Copyright (c) OpenMMLab. All rights reserved.
import ast
import os.path as osp
import re
import warnings
from typing import Tuple
from mmengine.fileio import load
from mmengine.utils import check_file_exist
PKG2PROJECT = {
'mmcls': 'mmcls',
'mmdet': 'mmdet',
'mmdet3d': 'mmdet3d',
'mmseg': 'mmsegment... | # Copyright (c) OpenMMLab. All rights reserved.
import ast
import os.path as osp
import re
import warnings
from typing import Tuple
from mmengine.fileio import load
from mmengine.utils import check_file_exist
PKG2PROJECT = {
'mmcls': 'mmcls',
'mmdet': 'mmdet',
'mmdet3d': 'mmdet3d',
'mmseg': 'mmsegment... |
from llama_index.core import Document
import asyncio
import pytest
from llama_index.graph_rag.cognee import CogneeGraphRAG
@pytest.mark.asyncio()
async def test_add_data(monkeypatch):
# Instantiate cognee GraphRAG
cogneeGraphRAG = CogneeGraphRAG(
llm_api_key="",
llm_provider="openai",
... | from llama_index.core import Document
import asyncio
import pytest
from llama_index.graph_rag.cognee import CogneeGraphRAG
@pytest.mark.asyncio()
async def test_add_data(monkeypatch):
# Instantiate cognee GraphRAG
cogneeGraphRAG = CogneeGraphRAG(
llm_api_key="",
llm_provider="openai",
... |
"""
This script trains sentence transformers with a triplet loss function.
As corpus, we use the wikipedia sections dataset that was describd by Dor et al., 2018, Learning Thematic Similarity Metric Using Triplet Networks.
"""
import traceback
from sentence_transformers import SentenceTransformer
from sentence_transf... | """
This script trains sentence transformers with a triplet loss function.
As corpus, we use the wikipedia sections dataset that was describd by Dor et al., 2018, Learning Thematic Similarity Metric Using Triplet Networks.
"""
from sentence_transformers import SentenceTransformer, InputExample, LoggingHandler, losses... |
# DO NOT EDIT. Generated by api_gen.sh
from keras.api import DTypePolicy
from keras.api import FloatDTypePolicy
from keras.api import Function
from keras.api import Initializer
from keras.api import Input
from keras.api import InputSpec
from keras.api import KerasTensor
from keras.api import Layer
from keras.api import... | # DO NOT EDIT. Generated by api_gen.sh
from keras.api import DTypePolicy
from keras.api import FloatDTypePolicy
from keras.api import Function
from keras.api import Initializer
from keras.api import Input
from keras.api import InputSpec
from keras.api import KerasTensor
from keras.api import Layer
from keras.api import... |
from typing import NamedTuple, TypeVar
import numpy as np
from pydantic import parse_obj_as
from docarray.typing.proto_register import _register_proto
from docarray.typing.tensor.ndarray import NdArray
from docarray.typing.url.url_3d.url_3d import Url3D
T = TypeVar('T', bound='Mesh3DUrl')
class Mesh3DLoadResult(Na... | from typing import NamedTuple, TypeVar
import numpy as np
from pydantic import parse_obj_as
from docarray.typing import NdArray
from docarray.typing.proto_register import _register_proto
from docarray.typing.url.url_3d.url_3d import Url3D
T = TypeVar('T', bound='Mesh3DUrl')
class Mesh3DLoadResult(NamedTuple):
... |
from typing import List, Sequence
from llama_index.core.agent.workflow.base_agent import BaseWorkflowAgent
from llama_index.core.agent.workflow.workflow_events import (
AgentInput,
AgentOutput,
AgentStream,
ToolCallResult,
)
from llama_index.core.bridge.pydantic import BaseModel
from llama_index.core.l... | from typing import List, Sequence
from llama_index.core.agent.workflow.base_agent import BaseWorkflowAgent
from llama_index.core.agent.workflow.workflow_events import (
AgentInput,
AgentOutput,
AgentStream,
ToolCallResult,
)
from llama_index.core.bridge.pydantic import BaseModel
from llama_index.core.l... |
"""DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.applications.resnet_v2 import ResNet50V2 as ResNet50V2
from keras.src.applications.resnet_v2 import ResNet101V2 as ResNet101V2
from keras.src.applications.resnet_v2 import ResNet152V2... | """DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.applications.resnet_v2 import ResNet50V2
from keras.src.applications.resnet_v2 import ResNet101V2
from keras.src.applications.resnet_v2 import ResNet152V2
from keras.src.applications.... |
import numpy as np
import pytest
from docarray.utils._internal.misc import is_tf_available
tf_available = is_tf_available()
if tf_available:
import tensorflow as tf
from docarray.computation.tensorflow_backend import TensorFlowCompBackend
from docarray.typing import TensorFlowTensor
@pytest.mark.tensor... | import numpy as np
import pytest
from docarray.utils.misc import is_tf_available
tf_available = is_tf_available()
if tf_available:
import tensorflow as tf
from docarray.computation.tensorflow_backend import TensorFlowCompBackend
from docarray.typing import TensorFlowTensor
@pytest.mark.tensorflow
@pyte... |
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