input stringlengths 33 5k | output stringlengths 32 5k |
|---|---|
import argparse
from itertools import repeat
from multiprocessing.pool import ThreadPool
from pathlib import Path
from tarfile import TarFile
from zipfile import ZipFile
import torch
def parse_args():
parser = argparse.ArgumentParser(
description='Download datasets for training')
parser.add_argument(... | import argparse
from itertools import repeat
from multiprocessing.pool import ThreadPool
from pathlib import Path
from tarfile import TarFile
from zipfile import ZipFile
import torch
def parse_args():
parser = argparse.ArgumentParser(
description='Download datasets for training')
parser.add_argument(... |
"""langchain-core version information and utilities."""
VERSION = "0.3.57"
| """langchain-core version information and utilities."""
VERSION = "0.3.56"
|
from typing import Any
from llama_index.core.bridge.pydantic import model_serializer
from llama_index.core.tools import ToolSelection, ToolOutput
from llama_index.core.llms import ChatMessage
from llama_index.core.workflow import Event, StartEvent
class AgentInput(Event):
"""LLM input."""
input: list[ChatMe... | from typing import Any
from llama_index.core.tools import ToolSelection, ToolOutput
from llama_index.core.llms import ChatMessage
from llama_index.core.workflow import Event
class AgentInput(Event):
"""LLM input."""
input: list[ChatMessage]
current_agent_name: str
class AgentSetup(Event):
"""Agent... |
# Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
import pytest
import torch
from mmdet.core.bbox import distance2bbox
from mmdet.core.mask.structures import BitmapMasks, PolygonMasks
from mmdet.core.utils import center_of_mass, mask2ndarray
def dummy_raw_polygon_masks(size):
"""
Args:
... | # Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
import pytest
import torch
from mmdet.core.bbox import distance2bbox
from mmdet.core.mask.structures import BitmapMasks, PolygonMasks
from mmdet.core.utils import center_of_mass, mask2ndarray
def dummy_raw_polygon_masks(size):
"""
Args:
... |
_base_ = './cascade-mask-rcnn_convnext-t-p4-w7_fpn_4conv1fc-giou_amp-ms-crop-3x_coco.py' # noqa
# please install mmcls>=1.0
# import mmcls.models to trigger register_module in mmcls
custom_imports = dict(imports=['mmcls.models'], allow_failed_imports=False)
checkpoint_file = 'https://download.openmmlab.com/mmclassifi... | _base_ = './cascade_mask_rcnn_convnext-t_p4_w7_fpn_giou_4conv1f_fp16_ms-crop_3x_coco.py' # noqa
# please install mmcls>=1.0
# import mmcls.models to trigger register_module in mmcls
custom_imports = dict(imports=['mmcls.models'], allow_failed_imports=False)
checkpoint_file = 'https://download.openmmlab.com/mmclassifi... |
"""Standard LangChain interface tests"""
import os
from typing import Type
import pytest
from langchain_core.language_models import BaseChatModel
from langchain_tests.integration_tests import ChatModelIntegrationTests
from langchain_openai import AzureChatOpenAI
OPENAI_API_VERSION = os.environ.get("AZURE_OPENAI_API... | """Standard LangChain interface tests"""
import os
from typing import Type
import pytest
from langchain_core.language_models import BaseChatModel
from langchain_tests.integration_tests import ChatModelIntegrationTests
from langchain_openai import AzureChatOpenAI
OPENAI_API_VERSION = os.environ.get("AZURE_OPENAI_API... |
"""Torch backend APIs.
# Note on device placement
Torch has a different device placement style compared to TF and JAX.
In short, variables/tensors are not created on GPU by default,
and the GPU cannot directly communicate with the CPU.
To bring Torch behavior in line with TF and JAX automated device placement,
we are... | """Torch backend APIs.
# Note on device placement
Torch has a different device placement style compared to TF and JAX.
In short, variables/tensors are not created on GPU by default,
and the GPU cannot directly communicate with the CPU.
To bring Torch behavior in line with TF and JAX automated device placement,
we are... |
# Copyright (c) OpenMMLab. All rights reserved.
from typing import Any, Dict, List, Optional, Union
import torch
from mmengine.optim.optimizer._deepspeed import DeepSpeedOptimWrapper
from mmengine.registry import MODEL_WRAPPERS
try:
from deepspeed.runtime.engine import DeepSpeedEngine
except ImportError:
Dee... | # Copyright (c) OpenMMLab. All rights reserved.
from typing import Any, Dict, List, Optional, Union
import torch
from deepspeed.runtime.engine import DeepSpeedEngine
from mmengine.optim.optimizer._deepspeed import DeepSpeedOptimWrapper
from mmengine.registry import MODEL_WRAPPERS
@MODEL_WRAPPERS.register_module()
c... |
"""
This scripts demonstrates how to train a Sparse Encoder model for Information Retrieval.
As dataset, we use sentence-transformers/msmarco-bm25, where we have triplets versions of MSMARCO mined thanks to BM25.
As loss function, we use MultipleNegativesRankingLoss in the SpladeLoss.
"""
import logging
import trac... | """
This scripts demonstrates how to train a Sparse Encoder model for Information Retrieval.
As dataset, we use sentence-transformers/msmarco-bm25, where we have triplets versions of MSMARCO mined thanks to BM25.
As loss function, we use MultipleNegativesRankingLoss in the SpladeLoss.
"""
import logging
import trac... |
# Copyright 2020 The HuggingFace Datasets Authors and the TensorFlow Datasets Authors.
#
# 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
#
# U... | # Copyright 2020 The HuggingFace Datasets Authors and the TensorFlow Datasets Authors.
#
# 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
#
# U... |
import numpy as np
import torch
from docarray import Document, Image, Text
from docarray.typing import (
AnyUrl,
Embedding,
ImageUrl,
Mesh3DUrl,
NdArray,
PointCloud3DUrl,
Tensor,
TextUrl,
TorchEmbedding,
TorchTensor,
)
from docarray.typing.tensor import NdArrayEmbedding
def te... | import numpy as np
import torch
from docarray import Document, Image, Text
from docarray.typing import (
AnyUrl,
Embedding,
ImageUrl,
NdArray,
Tensor,
TextUrl,
TorchEmbedding,
TorchTensor,
)
from docarray.typing.tensor import NdArrayEmbedding
def test_multi_modal_doc_proto():
clas... |
#!/usr/bin/env python3
"""Convert the fairseq models available in voxpopuli repo https://github.com/facebookresearch/voxpopuli
The available checkpoints should open with fairseq.
But the following error cannot be resolved with almost any version of fairseq.
https://github.com/facebookresearch/voxpopuli/issues/29
So t... | #!/usr/bin/env python3
"""Convert the fairseq models available in voxpopuli repo https://github.com/facebookresearch/voxpopuli
The available checkpoints should open with fairseq.
But the following error cannot be resolved with almost any version of fairseq.
https://github.com/facebookresearch/voxpopuli/issues/29
So t... |
from sentence_transformers import models
from sentence_transformers.sparse_encoder import SparseEncoder
from sentence_transformers.sparse_encoder.models import IDF, MLMTransformer, SpladePooling
print("# ------------------------------------------example with v2 distill-----------------------------------------")
doc_en... | from sentence_transformers import models
from sentence_transformers.sparse_encoder import SparseEncoder
from sentence_transformers.sparse_encoder.models import IDF, MLMTransformer, SpladePooling
print("# ------------------------------------------example with v2 distill-----------------------------------------")
doc_en... |
import jwt # noqa
import pytest
from llama_index.core import Document
from llama_index.core.vector_stores.types import (
BasePydanticVectorStore,
MetadataFilter,
MetadataFilters,
FilterCondition,
FilterOperator,
)
from llama_index.vector_stores.deeplake import DeepLakeVectorStore
def test_class(... | import jwt # noqa
from llama_index.core import Document
from llama_index.core.vector_stores.types import (
BasePydanticVectorStore,
MetadataFilter,
MetadataFilters,
FilterCondition,
FilterOperator,
)
from llama_index.vector_stores.deeplake import DeepLakeVectorStore
def test_class():
names_o... |
"""DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.ops.image import affine_transform as affine_transform
from keras.src.ops.image import crop_images as crop_images
from keras.src.ops.image import elastic_transform as elastic_transform... | """DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.ops.image import affine_transform
from keras.src.ops.image import crop_images
from keras.src.ops.image import elastic_transform
from keras.src.ops.image import extract_patches
from ke... |
import os
import shutil
import subprocess
import numpy as np
import PIL.Image as Image
import pytest
from jina import Document, Flow
cur_dir = os.path.dirname(os.path.abspath(__file__))
def data_generator(num_docs):
for i in range(num_docs):
doc = Document(uri=os.path.join(cur_dir, '..', 'imgs', 'cat.jp... | import os
import shutil
import subprocess
import numpy as np
import PIL.Image as Image
import pytest
from jina import Document, Flow
cur_dir = os.path.dirname(os.path.abspath(__file__))
def data_generator(num_docs):
for i in range(num_docs):
doc = Document(uri=os.path.join(cur_dir, '..', 'test_data', 't... |
# pylint: disable=invalid-name,unused-import
"""For compatibility and optional dependencies."""
import functools
import importlib.util
import logging
import sys
import types
from typing import Any, Sequence, cast
import numpy as np
from ._typing import _T
assert sys.version_info[0] == 3, "Python 2 is no longer suppo... | # pylint: disable=invalid-name,unused-import
"""For compatibility and optional dependencies."""
import importlib.util
import logging
import sys
import types
from typing import Any, Sequence, cast
import numpy as np
from ._typing import _T
assert sys.version_info[0] == 3, "Python 2 is no longer supported."
def py_s... |
_base_ = [
'../_base_/models/faster-rcnn_r50_fpn.py', '../_base_/datasets/voc0712.py',
'../_base_/default_runtime.py'
]
model = dict(roi_head=dict(bbox_head=dict(num_classes=20)))
METAINFO = {
'classes':
('aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat',
'chair', 'cow', 'dinin... | _base_ = [
'../_base_/models/faster-rcnn_r50_fpn.py', '../_base_/datasets/voc0712.py',
'../_base_/default_runtime.py'
]
model = dict(roi_head=dict(bbox_head=dict(num_classes=20)))
METAINFO = {
'CLASSES':
('aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat',
'chair', 'cow', 'dinin... |
# Copyright (c) OpenMMLab. All rights reserved.
from functools import partial
from typing import Optional
import torch
TORCH_VERSION = torch.__version__
def is_rocm_pytorch() -> bool:
"""Check whether the PyTorch is compiled on ROCm."""
is_rocm = False
if TORCH_VERSION != 'parrots':
try:
... | # Copyright (c) OpenMMLab. All rights reserved.
from functools import partial
from typing import Optional
import torch
TORCH_VERSION = torch.__version__
def is_rocm_pytorch() -> bool:
is_rocm = False
if TORCH_VERSION != 'parrots':
try:
from torch.utils.cpp_extension import ROCM_HOME
... |
_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, ... | _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 __future__ import annotations
from sentence_transformers.sparse_encoder.evaluation.SparseBinaryClassificationEvaluator import (
SparseBinaryClassificationEvaluator,
)
from sentence_transformers.sparse_encoder.evaluation.SparseEmbeddingSimilarityEvaluator import (
SparseEmbeddingSimilarityEvaluator,
)
from... | from __future__ import annotations
from sentence_transformers.sparse_encoder.evaluation.SparseBinaryClassificationEvaluator import (
SparseBinaryClassificationEvaluator,
)
from sentence_transformers.sparse_encoder.evaluation.SparseEmbeddingSimilarityEvaluator import (
SparseEmbeddingSimilarityEvaluator,
)
from... |
"""
This file evaluates CrossEncoder on the TREC 2019 Deep Learning (DL) Track: https://arxiv.org/abs/2003.07820
TREC 2019 DL is based on the corpus of MS Marco. MS Marco provides a sparse annotation, i.e., usually only a single
passage is marked as relevant for a given query. Many other highly relevant passages are n... | """
This file evaluates CrossEncoder on the TREC 2019 Deep Learning (DL) Track: https://arxiv.org/abs/2003.07820
TREC 2019 DL is based on the corpus of MS Marco. MS Marco provides a sparse annotation, i.e., usually only a single
passage is marked as relevant for a given query. Many other highly relevant passages are n... |
# Basic unittests to test functioning of module's top-level
__author__ = "Yaroslav Halchenko"
__license__ = "BSD"
try:
from sklearn import * # noqa: F403
_top_import_error = None
except Exception as e:
_top_import_error = e
def test_import_skl():
# Test either above import has failed for some re... | # Basic unittests to test functioning of module's top-level
__author__ = "Yaroslav Halchenko"
__license__ = "BSD"
try:
from sklearn import * # noqa
_top_import_error = None
except Exception as e:
_top_import_error = e
def test_import_skl():
# Test either above import has failed for some reason
... |
"""Module for async requests generator."""
from typing import AsyncIterator, Optional, Dict, TYPE_CHECKING
from jina.clients.request.helper import _new_data_request_from_batch, _new_data_request
from jina.enums import DataInputType
from jina.importer import ImportExtensions
from jina.logging.predefined import default... | """Module for async requests generator."""
from typing import AsyncIterator, Optional, Dict, TYPE_CHECKING
from jina.clients.request.helper import _new_data_request_from_batch, _new_data_request
from jina.enums import DataInputType
from jina.importer import ImportExtensions
from jina.logging.predefined import default... |
_base_ = [
'../_base_/models/cascade-rcnn_r50_fpn.py',
'../common/lsj-200e_coco-detection.py'
]
image_size = (1024, 1024)
batch_augments = [dict(type='BatchFixedSizePad', size=image_size)]
# disable allowed_border to avoid potential errors.
model = dict(
data_preprocessor=dict(batch_augments=batch_augments... | _base_ = [
'../_base_/models/cascade_rcnn_r50_fpn.py',
'../common/lsj_200e_coco_detection.py'
]
image_size = (1024, 1024)
batch_augments = [dict(type='BatchFixedSizePad', size=image_size)]
# disable allowed_border to avoid potential errors.
model = dict(
data_preprocessor=dict(batch_augments=batch_augments... |
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. 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.a... | # coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. 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.a... |
_base_ = './ms-rcnn_r50-caffe_fpn_1x_coco.py'
# learning policy
max_epochs = 24
train_cfg = dict(
type='EpochBasedTrainLoop', max_epochs=max_epochs, val_interval=1)
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
... | _base_ = './ms_rcnn_r50_caffe_fpn_1x_coco.py'
# learning policy
max_epochs = 24
train_cfg = dict(
type='EpochBasedTrainLoop', max_epochs=max_epochs, val_interval=1)
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
... |
import contextlib
import logging
import typing
import fastapi
import fastapi.responses
import starlette.middleware.cors
import uvicorn
from autogpt_libs.feature_flag.client import (
initialize_launchdarkly,
shutdown_launchdarkly,
)
import backend.data.block
import backend.data.db
import backend.data.graph
imp... | import contextlib
import logging
import typing
import fastapi
import fastapi.responses
import starlette.middleware.cors
import uvicorn
import backend.data.block
import backend.data.db
import backend.data.graph
import backend.data.user
import backend.server.routers.v1
import backend.util.service
import backend.util.se... |
from typing import TypeVar
from docarray.typing.proto_register import _register_proto
from docarray.typing.tensor.audio.abstract_audio_tensor import AbstractAudioTensor
from docarray.typing.tensor.tensorflow_tensor import TensorFlowTensor, metaTensorFlow
T = TypeVar('T', bound='AudioTensorFlowTensor')
@_register_pr... | from typing import TypeVar
from docarray.typing.proto_register import _register_proto
from docarray.typing.tensor.audio.abstract_audio_tensor import AbstractAudioTensor
from docarray.typing.tensor.tensorflow_tensor import TensorFlowTensor, metaTensorFlow
T = TypeVar('T', bound='AudioTensorFlowTensor')
@_register_pr... |
import base64
import json
import pickle
from abc import ABC, abstractmethod
from typing import Any
from pydantic import BaseModel
from llama_index.core.schema import BaseComponent
from .utils import import_module_from_qualified_name, get_qualified_name
class BaseSerializer(ABC):
@abstractmethod
def serialize... | import base64
import json
import pickle
from abc import ABC, abstractmethod
from typing import Any
from pydantic import BaseModel
from llama_index.core.schema import BaseComponent
from .utils import import_module_from_qualified_name, get_qualified_name
class BaseSerializer(ABC):
@abstractmethod
def serialize... |
# Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
import pytest
import torch
from mmengine import MessageHub
class TestMessageHub:
def test_init(self):
message_hub = MessageHub('name')
assert message_hub.instance_name == 'name'
assert len(message_hub.log_buffers) == 0
... | # Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
import pytest
import torch
from mmengine import MessageHub
class TestMessageHub:
def test_init(self):
message_hub = MessageHub('name')
assert message_hub.instance_name == 'name'
assert len(message_hub.log_buffers) == 0
... |
# Copyright (c) OpenMMLab. All rights reserved.
import os
import os.path as osp
from pathlib import Path
from .misc import is_str
def is_filepath(x):
return is_str(x) or isinstance(x, Path)
def fopen(filepath, *args, **kwargs):
if is_str(filepath):
return open(filepath, *args, **kwargs)
elif is... | # Copyright (c) OpenMMLab. All rights reserved.
import os
import os.path as osp
from pathlib import Path
from .misc import is_str
def is_filepath(x):
return is_str(x) or isinstance(x, Path)
def fopen(filepath, *args, **kwargs):
if is_str(filepath):
return open(filepath, *args, **kwargs)
elif is... |
from typing import Optional
import numpy as np
import pytest
from pydantic import BaseModel, ValidationError
from typing_extensions import TypedDict
from docarray import BaseDoc, DocList
from docarray.documents import AudioDoc, ImageDoc, TextDoc
from docarray.documents.helper import (
create_doc,
create_doc_f... | from typing import Optional
import numpy as np
import pytest
from pydantic import BaseModel, ValidationError
from typing_extensions import TypedDict
from docarray import BaseDoc, DocArray
from docarray.documents import AudioDoc, ImageDoc, TextDoc
from docarray.documents.helper import (
create_doc,
create_doc_... |
import importlib
import os
import re
import types
from typing import Any, Optional, Literal
import numpy as np
try:
import torch # noqa: F401
except ImportError:
torch_imported = False
else:
torch_imported = True
try:
import tensorflow as tf # type: ignore # noqa: F401
except (ImportError, TypeErr... | import importlib
import os
import re
import types
from typing import Any, Optional
import numpy as np
try:
import torch # noqa: F401
except ImportError:
torch_imported = False
else:
torch_imported = True
try:
import tensorflow as tf # type: ignore # noqa: F401
except (ImportError, TypeError):
... |
import pytest
from llama_index.core import MockEmbedding, StorageContext, VectorStoreIndex
from llama_index.core.llms import MockLLM
from llama_index.core.vector_stores.types import BasePydanticVectorStore
from llama_index.vector_stores.redis import RedisVectorStore
def test_class():
names_of_base_classes = [b._... | from llama_index.core import MockEmbedding, StorageContext, VectorStoreIndex
from llama_index.core.llms import MockLLM
from llama_index.core.vector_stores.types import BasePydanticVectorStore
from llama_index.vector_stores.redis import RedisVectorStore
def test_class():
names_of_base_classes = [b.__name__ for b i... |
# Copyright (c) OpenMMLab. All rights reserved.
from abc import ABCMeta, abstractmethod
from mmcv.runner import BaseModule
from ..builder import build_shared_head
class BaseRoIHead(BaseModule, metaclass=ABCMeta):
"""Base class for RoIHeads."""
def __init__(self,
bbox_roi_extractor=None,
... | # Copyright (c) OpenMMLab. All rights reserved.
from abc import ABCMeta, abstractmethod
from mmcv.runner import BaseModule
from ..builder import build_shared_head
class BaseRoIHead(BaseModule, metaclass=ABCMeta):
"""Base class for RoIHeads."""
def __init__(self,
bbox_roi_extractor=None,
... |
import enum
import pathlib
from typing import Any, BinaryIO, Optional, Union
from torchdata.datapipes.iter import CSVParser, Demultiplexer, Filter, IterDataPipe, IterKeyZipper, LineReader, Mapper
from torchvision.prototype.datasets.utils import Dataset, EncodedImage, HttpResource, OnlineResource
from torchvision.proto... | import enum
import pathlib
from typing import Any, BinaryIO, Dict, List, Optional, Tuple, Union
from torchdata.datapipes.iter import CSVParser, Demultiplexer, Filter, IterDataPipe, IterKeyZipper, LineReader, Mapper
from torchvision.prototype.datasets.utils import Dataset, EncodedImage, HttpResource, OnlineResource
fro... |
from typing import TYPE_CHECKING
from ...utils import (
DIFFUSERS_SLOW_IMPORT,
OptionalDependencyNotAvailable,
_LazyModule,
get_objects_from_module,
is_torch_available,
is_transformers_available,
)
_dummy_objects = {}
_additional_imports = {}
_import_structure = {"pipeline_output": ["FluxPipe... | from typing import TYPE_CHECKING
from ...utils import (
DIFFUSERS_SLOW_IMPORT,
OptionalDependencyNotAvailable,
_LazyModule,
get_objects_from_module,
is_torch_available,
is_transformers_available,
)
_dummy_objects = {}
_additional_imports = {}
_import_structure = {"pipeline_output": ["FluxPipe... |
from __future__ import annotations
from collections.abc import Iterable
from typing import Any
import torch
from torch import Tensor, nn
from sentence_transformers.SentenceTransformer import SentenceTransformer
from sentence_transformers.util import fullname
class CosineSimilarityLoss(nn.Module):
def __init__(... | from __future__ import annotations
from collections.abc import Iterable
from typing import Any
import torch
from torch import Tensor, nn
from sentence_transformers.SentenceTransformer import SentenceTransformer
from sentence_transformers.util import fullname
class CosineSimilarityLoss(nn.Module):
def __init__(... |
from typing import Any, Optional, Type, TypeVar, Union
from docarray.base_doc import BaseDoc
from docarray.typing import TextUrl
from docarray.typing.tensor.embedding import AnyEmbedding
T = TypeVar('T', bound='TextDoc')
class TextDoc(BaseDoc):
"""
Document for handling text.
It can contain:
- a [... | from typing import Any, Optional, Type, TypeVar, Union
from docarray.base_doc import BaseDoc
from docarray.typing import TextUrl
from docarray.typing.tensor.embedding import AnyEmbedding
T = TypeVar('T', bound='TextDoc')
class TextDoc(BaseDoc):
"""
Document for handling text.
It can contain:
- a [... |
from types import SimpleNamespace
from unittest.mock import patch
import pytest
from llama_index.core.base.llms.types import (
CompletionResponse,
ChatMessage,
ChatResponse,
)
from llama_index.llms.dashscope.base import DashScope
class FakeDashscopeResponse:
def __init__(self, data: dict):
s... | from unittest.mock import patch
import pytest
from llama_index.core.base.llms.types import (
CompletionResponse,
ChatMessage,
ChatResponse,
)
from llama_index.llms.dashscope.base import DashScope
@pytest.fixture()
def dashscope_llm():
return DashScope(api_key="test")
@pytest.fixture()
def dashscop... |
import warnings
from typing import List, Optional, TypeVar
from docarray.typing.bytes.video_bytes import VideoBytes, VideoLoadResult
from docarray.typing.proto_register import _register_proto
from docarray.typing.url.any_url import AnyUrl
from docarray.typing.url.mimetypes import VIDEO_MIMETYPE
from docarray.utils._in... | import warnings
from typing import List, Optional, TypeVar
from docarray.typing.bytes.video_bytes import VideoBytes, VideoLoadResult
from docarray.typing.proto_register import _register_proto
from docarray.typing.url.any_url import AnyUrl
from docarray.typing.url.mimetypes import VIDEO_MIMETYPE
from docarray.utils._in... |
"""
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... |
import torch
from torchaudio_unittest.common_utils import PytorchTestCase, skipIfNoCuda
from torchaudio_unittest.models.rnnt.rnnt_test_impl import RNNTTestImpl
@skipIfNoCuda
class RNNTFloat32GPUTest(RNNTTestImpl, PytorchTestCase):
dtype = torch.float32
device = torch.device("cuda")
@skipIfNoCuda
class RNNTF... | import torch
from torchaudio_unittest.common_utils import skipIfNoCuda, PytorchTestCase
from torchaudio_unittest.models.rnnt.rnnt_test_impl import RNNTTestImpl
@skipIfNoCuda
class RNNTFloat32GPUTest(RNNTTestImpl, PytorchTestCase):
dtype = torch.float32
device = torch.device("cuda")
@skipIfNoCuda
class RNNTF... |
_base_ = ['./mask2former_r50_8xb2-lsj-50e_coco-panoptic.py']
num_things_classes = 80
num_stuff_classes = 0
num_classes = num_things_classes + num_stuff_classes
image_size = (1024, 1024)
batch_augments = [
dict(
type='BatchFixedSizePad',
size=image_size,
img_pad_value=0,
pad_mask=Tru... | _base_ = ['./mask2former_r50_8xb2-lsj-50e_coco-panoptic.py']
num_things_classes = 80
num_stuff_classes = 0
num_classes = num_things_classes + num_stuff_classes
image_size = (1024, 1024)
batch_augments = [
dict(
type='BatchFixedSizePad',
size=image_size,
img_pad_value=0,
pad_mask=True... |
from typing import Union
from google.oauth2.service_account import Credentials # type: ignore
from google.cloud import aiplatform, storage
from google.cloud.aiplatform import telemetry
from google.cloud.aiplatform.matching_engine import (
MatchingEngineIndex,
MatchingEngineIndexEndpoint,
)
from llama_index.v... | from typing import Union
from google.oauth2.service_account import Credentials # type: ignore
from google.cloud import aiplatform, storage
from google.cloud.aiplatform import telemetry
from google.cloud.aiplatform.matching_engine import (
MatchingEngineIndex,
MatchingEngineIndexEndpoint,
)
from llama_index.v... |
from __future__ import annotations
import pytest
from torch import Tensor
from sentence_transformers import SparseEncoder
@pytest.mark.parametrize(
"model_name",
[
("sentence-transformers/all-MiniLM-L6-v2"),
],
)
def test_load_and_encode(model_name: str) -> None:
# Ensure that SparseEncoder ... | from __future__ import annotations
import pytest
from torch import Tensor
from sentence_transformers import SparseEncoder
@pytest.mark.parametrize(
"model_name",
[
("sentence-transformers/all-MiniLM-L6-v2"),
],
)
def test_load_and_encode(model_name: str) -> None:
# Ensure that SparseEncoder ... |
from __future__ import annotations
import pytest
from sentence_transformers import SentenceTransformer, SentenceTransformerTrainer
from sentence_transformers.model_card import generate_model_card
from sentence_transformers.util import is_datasets_available, is_training_available
if is_datasets_available():
from ... | from __future__ import annotations
import pytest
from datasets import Dataset, DatasetDict
from sentence_transformers import SentenceTransformer, SentenceTransformerTrainer
from sentence_transformers.model_card import generate_model_card
@pytest.fixture(scope="session")
def dummy_dataset():
"""
Dummy datase... |
import logging
from datasets import load_dataset
from sentence_transformers import SparseEncoder
from sentence_transformers.sparse_encoder.evaluation import SparseTripletEvaluator
logging.basicConfig(format="%(message)s", level=logging.INFO)
# Load a model
model = SparseEncoder("naver/splade-cocondenser-ensembledis... | import logging
from datasets import load_dataset
from sentence_transformers import SparseEncoder
from sentence_transformers.sparse_encoder.evaluation import SparseTripletEvaluator
logging.basicConfig(format="%(message)s", level=logging.INFO)
# Load a model
model = SparseEncoder("naver/splade-cocondenser-ensembledis... |
import subprocess
import pytest
from clip_text import CLIPTextEncoder
from jina import Document, DocumentArray, Flow
_EMBEDDING_DIM = 512
@pytest.mark.parametrize('request_size', [1, 10, 50, 100])
def test_integration(request_size: int):
docs = DocumentArray(
[Document(text='just some random text here')... | import subprocess
import pytest
from clip_text import CLIPTextEncoder
from jina import Document, DocumentArray, Flow
_EMBEDDING_DIM = 512
@pytest.mark.parametrize('request_size', [1, 10, 50, 100])
def test_integration(request_size: int):
docs = DocumentArray(
[Document(text='just some random text here')... |
import datasets
from ..folder_based_builder import folder_based_builder
logger = datasets.utils.logging.get_logger(__name__)
class AudioFolderConfig(folder_based_builder.FolderBasedBuilderConfig):
"""Builder Config for AudioFolder."""
drop_labels: bool = None
drop_metadata: bool = None
def __post... | import datasets
from ..folder_based_builder import folder_based_builder
logger = datasets.utils.logging.get_logger(__name__)
class AudioFolderConfig(folder_based_builder.FolderBasedBuilderConfig):
"""Builder Config for AudioFolder."""
drop_labels: bool = None
drop_metadata: bool = None
def __post... |
from __future__ import annotations
from .CSRSparsity import CSRSparsity
from .IDF import IDF
from .MLMTransformer import MLMTransformer
from .SpladePooling import SpladePooling
__all__ = ["CSRSparsity", "MLMTransformer", "SpladePooling", "IDF"]
| from __future__ import annotations
from .CSRSparsity import CSRSparsity
from .MLMTransformer import MLMTransformer
from .SpladePooling import SpladePooling
__all__ = ["CSRSparsity", "MLMTransformer", "SpladePooling"]
|
"""Standard LangChain interface tests"""
import pytest
from langchain_core.language_models import BaseChatModel
from langchain_core.rate_limiters import InMemoryRateLimiter
from langchain_core.tools import BaseTool
from langchain_tests.integration_tests import (
ChatModelIntegrationTests,
)
from langchain_groq im... | """Standard LangChain interface tests"""
import pytest
from langchain_core.language_models import BaseChatModel
from langchain_core.rate_limiters import InMemoryRateLimiter
from langchain_core.tools import BaseTool
from langchain_tests.integration_tests import (
ChatModelIntegrationTests,
)
from langchain_groq im... |
# coding=utf-8
# 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 ag... | # coding=utf-8
# 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 ag... |
# Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the most common options. For a full
# list see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
# -- Path setup --------------------------------------------------------------
# If ex... | # Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the most common options. For a full
# list see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
# -- Path setup --------------------------------------------------------------
# If ex... |
"""Init file of LlamaIndex."""
__version__ = "0.12.37"
import logging
from logging import NullHandler
from typing import Callable, Optional
try:
# Force pants to install eval_type_backport on 3.9
import eval_type_backport # noqa # type: ignore
except ImportError:
pass
# response
from llama_index.core.... | """Init file of LlamaIndex."""
__version__ = "0.12.36"
import logging
from logging import NullHandler
from typing import Callable, Optional
try:
# Force pants to install eval_type_backport on 3.9
import eval_type_backport # noqa # type: ignore
except ImportError:
pass
# response
from llama_index.core.... |
__copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
from itertools import groupby
from typing import Iterable, Dict
from jina import Executor, requests, DocumentArray
class MinRanker(Executor):
"""
:class:`MinRanker` aggregates the score of the matched ... | __copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
from itertools import groupby
from typing import Iterable, Dict
from jina import Executor, requests, DocumentArray
class MinRanker(Executor):
"""
:class:`MinRanker` aggregates the score of the matched ... |
__copyright__ = "Copyright (c) 2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import shutil
import subprocess
from pathlib import Path
import pytest
from jina import Document, DocumentArray
@pytest.fixture(scope="session", autouse=True)
def download_cache():
subprocess.run(
'scri... | __copyright__ = "Copyright (c) 2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import os
import shutil
from pathlib import Path
import pytest
from jina import Document, DocumentArray
@pytest.fixture(scope="session", autouse=True)
def download_cache():
os.system('scripts/download_full.sh')... |
from pathlib import Path
import pytest
from torchaudio.datasets import dr_vctk
from torchaudio_unittest.common_utils import (
get_whitenoise,
save_wav,
TempDirMixin,
TorchaudioTestCase,
)
_SUBSETS = ["train", "test"]
_CONDITIONS = ["clean", "device-recorded"]
_SOURCES = ["DR-VCTK_Office1_ClosedWindow... | from pathlib import Path
import pytest
from torchaudio.datasets import dr_vctk
from torchaudio_unittest.common_utils import (
TempDirMixin,
TorchaudioTestCase,
get_whitenoise,
save_wav,
)
_SUBSETS = ["train", "test"]
_CONDITIONS = ["clean", "device-recorded"]
_SOURCES = ["DR-VCTK_Office1_ClosedWindow... |
from typing import Any, Optional, Sequence, Union
from deprecated import deprecated
from llama_index.core.base.llms.generic_utils import (
chat_response_to_completion_response,
stream_chat_response_to_completion_response,
astream_chat_response_to_completion_response,
)
from llama_index.core.base.llms.types... | from typing import Any, Optional, Sequence
from pathlib import Path
from llama_index.core.base.llms.generic_utils import (
chat_response_to_completion_response,
stream_chat_response_to_completion_response,
astream_chat_response_to_completion_response,
)
from llama_index.core.base.llms.types import (
Ch... |
"""LLMResult class."""
from __future__ import annotations
from copy import deepcopy
from typing import Literal, Optional, Union
from pydantic import BaseModel
from langchain_core.outputs.chat_generation import ChatGeneration, ChatGenerationChunk
from langchain_core.outputs.generation import Generation, GenerationCh... | from __future__ import annotations
from copy import deepcopy
from typing import Literal, Optional, Union
from pydantic import BaseModel
from langchain_core.outputs.chat_generation import ChatGeneration, ChatGenerationChunk
from langchain_core.outputs.generation import Generation, GenerationChunk
from langchain_core.... |
_base_ = [
'../_base_/models/mask-rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
data_preprocessor=dict(
# The mean and std are used in PyCls when training RegNets
mean=[103.53, 116.28, 123.675... | _base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
data_preprocessor=dict(
# The mean and std are used in PyCls when training RegNets
mean=[103.53, 116.28, 123.675... |
from keras.src import backend
from keras.src import ops
from keras.src.api_export import keras_export
from keras.src.layers.input_spec import InputSpec
from keras.src.layers.layer import Layer
from keras.src.utils import argument_validation
@keras_export("keras.layers.ZeroPadding1D")
class ZeroPadding1D(Layer):
"... | from keras.src import ops
from keras.src.api_export import keras_export
from keras.src.layers.input_spec import InputSpec
from keras.src.layers.layer import Layer
from keras.src.utils import argument_validation
@keras_export("keras.layers.ZeroPadding1D")
class ZeroPadding1D(Layer):
"""Zero-padding layer for 1D in... |
import itertools
from dataclasses import dataclass
from typing import List, Optional
import pyarrow as pa
import pyarrow.parquet as pq
import datasets
from datasets.table import table_cast
logger = datasets.utils.logging.get_logger(__name__)
@dataclass
class ParquetConfig(datasets.BuilderConfig):
"""BuilderCo... | import itertools
from dataclasses import dataclass
from typing import List, Optional
import pyarrow as pa
import pyarrow.parquet as pq
import datasets
from datasets.table import table_cast
logger = datasets.utils.logging.get_logger(__name__)
@dataclass
class ParquetConfig(datasets.BuilderConfig):
"""BuilderCo... |
from typing import Dict, Iterable
import torch
from torch import Tensor, nn
from sentence_transformers import util
from sentence_transformers.SentenceTransformer import SentenceTransformer
class CoSENTLoss(nn.Module):
def __init__(self, model: SentenceTransformer, scale: float = 20.0, similarity_fct=util.pairwi... | import torch
from torch import nn, Tensor
from typing import Iterable, Dict
from ..SentenceTransformer import SentenceTransformer
from .. import util
class CoSENTLoss(nn.Module):
def __init__(self, model: SentenceTransformer, scale: float = 20.0, similarity_fct=util.pairwise_cos_sim):
"""
This cla... |
import numpy as np
from pydantic.tools import parse_obj_as
from docarray.typing import ImageUrl, Tensor
def test_image_url():
uri = parse_obj_as(ImageUrl, 'http://jina.ai/img.png')
tensor = uri.load()
assert isinstance(tensor, np.ndarray)
| from pydantic.tools import parse_obj_as
from docarray.typing import ImageUrl, Tensor
def test_image_url():
uri = parse_obj_as(ImageUrl, 'http://jina.ai/img.png')
tensor = uri.load()
assert isinstance(tensor, Tensor)
|
from jina.schemas.helper import _cli_to_schema
from jina_cli.export import api_to_dict
for s in ('flow', 'gateway', 'executor', 'deployment'):
a = _cli_to_schema(api_to_dict(), s)
table = ['| Name | Description | Type | Default |', '|----|----|----|----|']
for k, v in a[f'Jina::{s.capitalize()}']['proper... | from jina.schemas.helper import _cli_to_schema
from jina_cli.export import api_to_dict
for s in ('flow', 'gateway', 'executor', 'deployment'):
a = _cli_to_schema(api_to_dict(), s)
table = ['| Name | Description | Type | Default |', '|----|----|----|----|']
for k, v in a[f'Jina::{s.capitalize()}']['proper... |
from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.retrievers import GoogleDocumentAIWarehouseRetriever
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handling op... | from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.retrievers import GoogleDocumentAIWarehouseRetriever
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handling op... |
# mypy: ignore-errors
import argparse
import torchgen.model as model
from torchgen.gen import FileManager, parse_native_yaml
def num_leading_spaces(line: str) -> int:
return len(line) - len(line.lstrip())
def deindent(code: str) -> str:
lines = code.split("\n")
min_leading_spaces = min(map(num_leading... | # mypy: ignore-errors
import argparse
import torchgen.model as model
from torchgen.gen import FileManager, parse_native_yaml
def num_leading_spaces(line: str) -> int:
return len(line) - len(line.lstrip())
def deindent(code: str) -> str:
lines = code.split("\n")
min_leading_spaces = min(map(num_leading... |
import pytest
from llama_index.core.base.embeddings.base_sparse import BaseSparseEmbedding
from llama_index.sparse_embeddings.fastembed import FastEmbedSparseEmbedding
def test_class():
names_of_base_classes = [b.__name__ for b in FastEmbedSparseEmbedding.__mro__]
assert BaseSparseEmbedding.__name__ in names... | import pytest
from llama_index.core.base.embeddings.base_sparse import BaseSparseEmbedding
from llama_index.sparse_embeddings.fastembed import FastEmbedSparseEmbedding
def test_class():
names_of_base_classes = [b.__name__ for b in FastEmbedSparseEmbedding.__mro__]
assert BaseSparseEmbedding.__name__ in names... |
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
|
from __future__ import annotations
from collections.abc import Iterable
import torch
from torch import Tensor, nn
from sentence_transformers.sparse_encoder.SparseEncoder import SparseEncoder
class FlopsLoss(nn.Module):
def __init__(self, model: SparseEncoder, threshold: float = None) -> None:
"""
... | from __future__ import annotations
from collections.abc import Iterable
import torch
from torch import Tensor, nn
from sentence_transformers.sparse_encoder.SparseEncoder import SparseEncoder
class FlopsLoss(nn.Module):
def __init__(self, model: SparseEncoder, threshold: float = None) -> None:
"""
... |
import os
import os.path as osp
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from mmdet.evaluation import CityScapesMetric
try:
import cityscapesscripts
except ImportError:
cityscapesscripts = None
class TestCityScapesMetric(unittest.TestCase):
def setUp(self):... | import os
import os.path as osp
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from mmdet.evaluation import CityScapesMetric
try:
import cityscapesscripts
except ImportError:
cityscapesscripts = None
class TestCityScapesMetric(unittest.TestCase):
def setUp(self):... |
# flake8: noqa
# Copyright 2020 The HuggingFace Datasets Authors and the TensorFlow Datasets Authors.
#
# 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/LI... | # flake8: noqa
# Copyright 2020 The HuggingFace Datasets Authors and the TensorFlow Datasets Authors.
#
# 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/LI... |
import pathlib
from typing import Any, BinaryIO, Dict, Iterator, List, Tuple, Union
from torchdata.datapipes.iter import Filter, IterDataPipe, Mapper, Zipper
from torchvision.prototype.datasets.utils import Dataset, EncodedImage, HttpResource, OnlineResource
from torchvision.prototype.datasets.utils._internal import (... | import pathlib
from typing import Any, BinaryIO, Dict, Iterator, List, Tuple, Union
from torchdata.datapipes.iter import Filter, IterDataPipe, Mapper, Zipper
from torchvision.datapoints import BoundingBoxes
from torchvision.prototype.datapoints import Label
from torchvision.prototype.datasets.utils import Dataset, Enc... |
"""
This is a simple application for sentence embeddings: semantic search
We have a corpus with various sentences. Then, for a given query sentence,
we want to find the most similar sentence in this corpus.
This script outputs for various queries the top 5 most similar sentences in the corpus.
"""
import torch
from... | """
This is a simple application for sentence embeddings: semantic search
We have a corpus with various sentences. Then, for a given query sentence,
we want to find the most similar sentence in this corpus.
This script outputs for various queries the top 5 most similar sentences in the corpus.
"""
import torch
from... |
# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import os
import os.path as osp
from mmengine.config import Config, DictAction
from mmengine.runner import Runner
from mmdet.registry import RUNNERS
from mmdet.utils import register_all_modules
# TODO: support fuse_conv_bn and format_only
def parse_arg... | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import os
import os.path as osp
from mmengine.config import Config, DictAction
from mmengine.runner import Runner
from mmdet.registry import RUNNERS
from mmdet.utils import register_all_modules, replace_cfg_vals
# TODO: support fuse_conv_bn and format_... |
_base_ = './faster-rcnn_r50_fpn_8xb8-amp-lsj-200e_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| _base_ = './faster_rcnn_r50_fpn_lsj_200e_8x8_fp16_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
|
import fnmatch
import os
from typing import Optional, Type
from langchain_core.callbacks import CallbackManagerForToolRun
from langchain_core.tools import BaseTool
from pydantic import BaseModel, Field
from langchain_community.tools.file_management.utils import (
INVALID_PATH_TEMPLATE,
BaseFileToolMixin,
... | import fnmatch
import os
from typing import Optional, Type
from langchain_core.callbacks import CallbackManagerForToolRun
from langchain_core.tools import BaseTool
from pydantic import BaseModel, Field
from langchain_community.tools.file_management.utils import (
INVALID_PATH_TEMPLATE,
BaseFileToolMixin,
... |
from langchain_core.prompts.few_shot import (
FewShotChatMessagePromptTemplate,
FewShotPromptTemplate,
_FewShotPromptTemplateMixin,
)
__all__ = [
"FewShotChatMessagePromptTemplate",
"FewShotPromptTemplate",
"_FewShotPromptTemplateMixin",
]
| from langchain_core.prompts.few_shot import (
FewShotChatMessagePromptTemplate,
FewShotPromptTemplate,
_FewShotPromptTemplateMixin,
)
__all__ = [
"FewShotPromptTemplate",
"FewShotChatMessagePromptTemplate",
"_FewShotPromptTemplateMixin",
]
|
# Copyright (c) OpenMMLab. All rights reserved.
from .base_det_dataset import BaseDetDataset
from .builder import DATASETS, PIPELINES, build_dataset
from .cityscapes import CityscapesDataset
from .coco import CocoDataset
from .coco_panoptic import CocoPanopticDataset
from .dataset_wrappers import MultiImageMixDataset
f... | # Copyright (c) OpenMMLab. All rights reserved.
from .builder import DATASETS, PIPELINES, build_dataset
from .cityscapes import CityscapesDataset
from .coco import CocoDataset
from .coco_panoptic import CocoPanopticDataset
from .dataset_wrappers import MultiImageMixDataset
from .deepfashion import DeepFashionDataset
fr... |
__version__ = "2.8.0.dev0"
__MODEL_HUB_ORGANIZATION__ = "sentence-transformers"
import importlib
import os
from .datasets import SentencesDataset, ParallelSentencesDataset
from .LoggingHandler import LoggingHandler
from .SentenceTransformer import SentenceTransformer
from .readers import InputExample
from .cross_enco... | __version__ = "2.8.0.dev0"
__MODEL_HUB_ORGANIZATION__ = "sentence-transformers"
from .datasets import SentencesDataset, ParallelSentencesDataset
from .LoggingHandler import LoggingHandler
from .SentenceTransformer import SentenceTransformer
from .readers import InputExample
from .cross_encoder.CrossEncoder import Cross... |
_base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
teacher_ckpt = 'https://download.openmmlab.com/mmdetection/v2.0/paa/paa_r50_fpn_1x_coco/paa_r50_fpn_1x_coco_20200821-936edec3.pth' # noqa
model = dict(
type='LAD',
data_preprocesso... | _base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
teacher_ckpt = 'https://download.openmmlab.com/mmdetection/v2.0/paa/paa_r50_fpn_1x_coco/paa_r50_fpn_1x_coco_20200821-936edec3.pth' # noqa
preprocess_cfg = dict(
mean=[123.675, 116.28, ... |
# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import cv2
import mmcv
from mmcv.transforms import Compose
from mmengine.utils import track_iter_progress
from mmdet.apis import inference_detector, init_detector
from mmdet.registry import VISUALIZERS
def parse_args():
parser = argparse.ArgumentPa... | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import cv2
import mmcv
from mmcv.transforms import Compose
from mmengine.utils import track_iter_progress
from mmdet.apis import inference_detector, init_detector
from mmdet.registry import VISUALIZERS
def parse_args():
parser = argparse.ArgumentPa... |
"""Util that calls Bing Search."""
from typing import Any, Dict, List
import requests
from langchain_core.utils import get_from_dict_or_env
from pydantic import BaseModel, ConfigDict, Field, model_validator
# BING_SEARCH_ENDPOINT is the default endpoint for Bing Web Search API.
# Currently There are two web-based Bi... | """Util that calls Bing Search."""
from typing import Any, Dict, List
import requests
from langchain_core.utils import get_from_dict_or_env
from pydantic import BaseModel, ConfigDict, Field, model_validator
# BING_SEARCH_ENDPOINT is the default endpoint for Bing Web Search API.
# Currently There are two web-based Bi... |
from typing import Optional
import numpy as np
import pytest
import torch
from pydantic.tools import parse_obj_as, schema_json_of
from docarray import BaseDoc
from docarray.base_doc.io.json import orjson_dumps
from docarray.typing import AudioTorchTensor, AudioUrl
from docarray.utils._internal.misc import is_tf_avail... | from typing import Optional
import numpy as np
import pytest
import torch
from pydantic.tools import parse_obj_as, schema_json_of
from docarray import BaseDoc
from docarray.base_doc.io.json import orjson_dumps
from docarray.typing import AudioTorchTensor, AudioUrl
from docarray.utils.misc import is_tf_available
from ... |
import base64
import os
import pytest
import requests
from llama_index.core.llms import LLM
from llama_index.core.schema import ImageNode
from llama_index.multi_modal_llms.gemini import GeminiMultiModal
def test_embedding_class():
names_of_base_classes = [b.__name__ for b in GeminiMultiModal.__mro__]
assert ... | from llama_index.core.multi_modal_llms.base import MultiModalLLM
from llama_index.multi_modal_llms.gemini import GeminiMultiModal
def test_embedding_class():
names_of_base_classes = [b.__name__ for b in GeminiMultiModal.__mro__]
assert MultiModalLLM.__name__ in names_of_base_classes
|
# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import os
import os.path as osp
from mmengine.config import Config, DictAction
from mmengine.registry import RUNNERS
from mmengine.runner import Runner
from mmdet.utils import setup_cache_size_limit_of_dynamo
def parse_args():
parser = argparse.Arg... | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import logging
import os
import os.path as osp
from mmengine.config import Config, DictAction
from mmengine.logging import print_log
from mmengine.registry import RUNNERS
from mmengine.runner import Runner
from mmdet.utils import setup_cache_size_limit_o... |
# 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 typing import TYPE_CHECKING, Any
from docarray.base_doc.io.json import orjson_dumps
from docarray.utils._internal.misc import import_library
if TYPE_CHECKING:
from fastapi.responses import JSONResponse
else:
fastapi = import_library('fastapi', raise_error=True)
JSONResponse = fastapi.responses.JSONRe... |
import argparse
import os
import shlex
import subprocess
def execute_command(command):
command_list = shlex.split(command)
subprocess.run(command_list, check=True, text=True)
def main():
comment = os.environ["COMMENT"].splitlines()[0].strip()
# Extract the command-line arguments from the comment
... | import argparse
import os
import shlex
import subprocess
def execute_command(command):
command_list = shlex.split(command)
subprocess.run(command_list, check=True, text=True)
def main():
comment = os.environ["COMMENT"].splitlines()[0].strip()
# Extract the command-line arguments from the comment
... |
import os
import time
import pytest
from jina import Document, DocumentArray
from ..redis_storage import RedisStorage
@pytest.fixture(scope='function')
def indexer():
return RedisStorage()
@pytest.fixture()
def docker_compose(request):
os.system(
f'docker-compose -f {request.param} --project-direc... | import os
import time
from jina import Document, DocumentArray
import pytest
from ..redis_storage import RedisStorage
@pytest.fixture(scope='function')
def indexer():
return RedisStorage()
@pytest.fixture()
def docker_compose(request):
os.system(
f'docker-compose -f {request.param} --project-direc... |
__copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import subprocess
from typing import Iterable, Optional
from jina import DocumentArray, Executor, requests
from jina.logging.logger import JinaLogger
from jina_commons.batching import get_docs_batch_generator
fr... | __copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import subprocess
from typing import Iterable, Optional
from jina import DocumentArray, Executor, requests
from jina.logging.logger import JinaLogger
from jina_commons.batching import get_docs_batch_generator
fr... |
# coding=utf-8
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless r... | # coding=utf-8
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless r... |
# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import os.path as osp
import numpy as np
from mmengine.config import Config, DictAction
from mmengine.utils import ProgressBar
from mmdet.models.utils import mask2ndarray
from mmdet.registry import DATASETS, VISUALIZERS
from mmdet.structures.bbox import ... | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import os.path as osp
import mmcv
import numpy as np
from mmcv import Config, DictAction
from mmdet.models.utils import mask2ndarray
from mmdet.registry import DATASETS, VISUALIZERS
from mmdet.structures.bbox import BaseBoxes
from mmdet.utils import regi... |
from functools import wraps
from typing import TYPE_CHECKING, List
from jina.excepts import FlowBuildLevelError
# noinspection PyUnreachableCode
if TYPE_CHECKING:
from jina.enums import FlowBuildLevel
from jina.orchestrate.flow.base import Flow
def allowed_levels(levels: List['FlowBuildLevel']):
"""Anno... | from functools import wraps
from typing import TYPE_CHECKING, List
from jina.excepts import FlowBuildLevelError
# noinspection PyUnreachableCode
if TYPE_CHECKING:
from jina.enums import FlowBuildLevel
from jina.orchestrate.flow.base import Flow
def allowed_levels(levels: List['FlowBuildLevel']):
"""Anno... |
from typing import Any, Dict
from torchvision import datapoints
from torchvision.transforms.v2 import functional as F, Transform
from torchvision.transforms.v2.utils import is_simple_tensor
class UniformTemporalSubsample(Transform):
"""[BETA] Uniformly subsample ``num_samples`` indices from the temporal dimensi... | from typing import Any, Dict
from torchvision import datapoints
from torchvision.transforms.v2 import functional as F, Transform
from torchvision.transforms.v2.utils import is_simple_tensor
class UniformTemporalSubsample(Transform):
_transformed_types = (is_simple_tensor, datapoints.Video)
def __init__(sel... |
# pylint: disable=protected-access
"""Shared typing definition."""
import ctypes
import os
from typing import (
TYPE_CHECKING,
Any,
AnyStr,
Callable,
Dict,
List,
Optional,
Sequence,
Tuple,
Type,
TypeVar,
Union,
)
# os.PathLike/string/numpy.array/scipy.sparse/pd.DataFrame... | # pylint: disable=protected-access
"""Shared typing definition."""
import ctypes
import os
from typing import (
TYPE_CHECKING,
Any,
AnyStr,
Callable,
Dict,
List,
Optional,
Sequence,
Tuple,
Type,
TypeVar,
Union,
)
# os.PathLike/string/numpy.array/scipy.sparse/pd.DataFrame... |
import os
import random
import time
from typing import Dict, OrderedDict
import numpy as np
import pytest
from jina import Document, DocumentArray, Executor, Flow, requests
from jina_commons.indexers.dump import dump_docs
from jinahub.indexers.compound.FaissLMDBSearcher.faiss_lmdb import FaissLMDBSearcher
from jinahu... | import os
import random
import time
from typing import Dict, OrderedDict
import numpy as np
import pytest
from jina import Document, DocumentArray, Executor, Flow, requests
from jina_commons.indexers.dump import dump_docs
from jinahub.indexers.compound.FaissLMDBSearcher.faiss_lmdb import FaissLMDBSearcher
from jinahu... |
"""Simple reader for mbox (mailbox) files."""
import os
from pathlib import Path
from typing import Any, List
from llama_index.core.readers.base import BaseReader
from llama_index.readers.file import MboxReader as MboxFileReader
from llama_index.core.schema import Document
class MboxReader(BaseReader):
"""
... | """Simple reader for mbox (mailbox) files."""
import os
from pathlib import Path
from typing import Any, List
from llama_index.core.readers.base import BaseReader
from llama_index.readers.file import MboxReader as MboxFileReader
from llama_index.core.schema import Document
class MboxReader(BaseReader):
"""Mbox ... |
"""Simple Reader that reads transcript of youtube video."""
import re
from typing import Any, List, Optional
from youtube_transcript_api import YouTubeTranscriptApi
from llama_index.core.readers.base import BasePydanticReader
from llama_index.core.schema import Document
from llama_index.readers.youtube_transcript.ut... | """Simple Reader that reads transcript of youtube video."""
import re
from typing import Any, List, Optional
from youtube_transcript_api import YouTubeTranscriptApi
from llama_index.core.readers.base import BasePydanticReader
from llama_index.core.schema import Document
from llama_index.readers.youtube_transcript.uti... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.