input stringlengths 33 5k | output stringlengths 32 5k |
|---|---|
import numpy as np
import pytest
import torch
from pydantic import parse_obj_as
from docarray import BaseDoc
from docarray.documents import ImageDoc
from docarray.utils._internal.misc import is_tf_available
tf_available = is_tf_available()
if tf_available:
import tensorflow as tf
import tensorflow._api.v2.exp... | import numpy as np
import pytest
import torch
from pydantic import parse_obj_as
from docarray import BaseDoc
from docarray.documents import ImageDoc
from docarray.utils.misc import is_tf_available
tf_available = is_tf_available()
if tf_available:
import tensorflow as tf
import tensorflow._api.v2.experimental.... |
from sqlalchemy.orm import Session
from sqlalchemy import Engine, exc, sql
def check_db_availability(engine: Engine, check_vector: bool = False) -> None:
try:
with engine.connect() as conn:
if check_vector:
conn.execute(sql.text("""SELECT Vec_Dims("[1]");"""))
else:... | from sqlalchemy.orm import Session
from sqlalchemy import Engine, exc, sql
def check_db_availability(engine: Engine, check_vector: bool = False) -> None:
try:
with engine.connect() as conn:
if check_vector:
conn.execute(sql.text("""SELECT Vec_Dims("[1]");"""))
else:... |
import enum
from typing import Any, List, Optional, Union
import pydantic
import backend.data.graph
from backend.data.api_key import APIKeyPermission, APIKeyWithoutHash
class Methods(enum.Enum):
SUBSCRIBE = "subscribe"
UNSUBSCRIBE = "unsubscribe"
EXECUTION_EVENT = "execution_event"
ERROR = "error"
... | import enum
from typing import Any, List, Optional, Union
import pydantic
import backend.data.graph
from backend.data.api_key import APIKeyPermission, APIKeyWithoutHash
class Methods(enum.Enum):
SUBSCRIBE = "subscribe"
UNSUBSCRIBE = "unsubscribe"
EXECUTION_EVENT = "execution_event"
ERROR = "error"
... |
import PIL.Image
import pytest
import torch
from prototype_common_utils import make_bounding_box, make_detection_mask, make_image
from torchvision.prototype import features
from torchvision.prototype.transforms.functional import to_image_pil
from torchvision.prototype.transforms.utils import has_all, has_any
IMAGE... | import PIL.Image
import pytest
import torch
from prototype_common_utils import make_bounding_box, make_detection_mask, make_image
from torchvision.prototype import features
from torchvision.prototype.transforms._utils import has_all, has_any
from torchvision.prototype.transforms.functional import to_image_pil
IMAG... |
from pydantic import BaseModel
from typing import Any, AsyncGenerator, List
from llama_index.llms.nvidia import NVIDIA as Interface
from llama_index.core.program import LLMTextCompletionProgram
from llama_index.core.program import FunctionCallingProgram
import pytest
from llama_index.llms.nvidia.utils import (
MODE... | import respx
from httpx import Response
from pydantic import BaseModel
from typing import List
from llama_index.llms.nvidia import NVIDIA as Interface
from llama_index.core.program import LLMTextCompletionProgram
from llama_index.core.program import FunctionCallingProgram
import pytest
from llama_index.llms.nvidia.util... |
from typing import Optional, Union, Callable, Tuple, TYPE_CHECKING, Dict
if TYPE_CHECKING:
import numpy as np
from ...typing import ArrayType
from ... import DocumentArray
class MatchMixin:
"""A mixin that provides match functionality to DocumentArrays"""
def match(
self,
darray:... | from typing import Optional, Union, Callable, Tuple, TYPE_CHECKING
if TYPE_CHECKING:
import numpy as np
from ...typing import ArrayType
from ... import DocumentArray
class MatchMixin:
"""A mixin that provides match functionality to DocumentArrays"""
def match(
self,
darray: 'Docu... |
# Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import torch
from mmdet.models.dense_heads import GFLHead
def test_gfl_head_loss():
"""Tests gfl head loss when truth is empty and non-empty."""
s = 256
img_metas = [{
'img_shape': (s, s, 3),
'scale_factor': 1,
'pad_shape... | import mmcv
import torch
from mmdet.models.dense_heads import GFLHead
def test_gfl_head_loss():
"""Tests gfl head loss when truth is empty and non-empty."""
s = 256
img_metas = [{
'img_shape': (s, s, 3),
'scale_factor': 1,
'pad_shape': (s, s, 3)
}]
train_cfg = mmcv.Config(... |
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... | # Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... |
import csv
import gzip
import logging
import math
import os
from datetime import datetime
from torch.utils.data import DataLoader
from sentence_transformers import InputExample, LoggingHandler, SentenceTransformer, losses, models, util
from sentence_transformers.evaluation import EmbeddingSimilarityEvaluator
#### Ju... | from torch.utils.data import DataLoader
import math
from sentence_transformers import models, losses
from sentence_transformers import LoggingHandler, SentenceTransformer, util, InputExample
from sentence_transformers.evaluation import EmbeddingSimilarityEvaluator
import logging
from datetime import datetime
import os
... |
# dataset settings
dataset_type = 'CocoPanopticDataset'
data_root = 'data/coco/'
# file_client_args = dict(
# backend='petrel',
# path_mapping=dict({
# './data/': 's3://openmmlab/datasets/detection/',
# 'data/': 's3://openmmlab/datasets/detection/'
# }))
file_client_args = dict(backend='dis... | # dataset settings
dataset_type = 'CocoPanopticDataset'
data_root = 'data/coco/'
# file_client_args = dict(
# backend='petrel',
# path_mapping=dict({
# './data/': 's3://openmmlab/datasets/detection/',
# 'data/': 's3://openmmlab/datasets/detection/'
# }))
file_client_args = dict(backend='dis... |
# Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from mmdet.models.backbones import RegNet
regnet_test_data = [
('regnetx_400mf',
dict(w0=24, wa=24.48, wm=2.54, group_w=16, depth=22,
bot_mul=1.0), [32, 64, 160, 384]),
('regnetx_800mf',
dict(w0=56, wa=35.73, wm=2.2... | import pytest
import torch
from mmdet.models.backbones import RegNet
regnet_test_data = [
('regnetx_400mf',
dict(w0=24, wa=24.48, wm=2.54, group_w=16, depth=22,
bot_mul=1.0), [32, 64, 160, 384]),
('regnetx_800mf',
dict(w0=56, wa=35.73, wm=2.28, group_w=16, depth=16,
bot_mul=1.0),... |
# mypy: allow-untyped-defs
from dataclasses import dataclass
from typing import Callable
import torch
import torch.fx.node
import torch.utils._pytree as pytree
from torch._ops import HigherOrderOperator
def is_graphable(val) -> bool:
"""Definition: a graphable type is a type that that is an acceptable input/outp... | # mypy: allow-untyped-defs
from dataclasses import dataclass
from typing import Callable
import torch
import torch.fx.node
import torch.utils._pytree as pytree
from torch._ops import HigherOrderOperator
def is_graphable(val) -> bool:
"""Definition: a graphable type is a type that that is an acceptable input/outp... |
# Copyright (c) OpenMMLab. All rights reserved.
from .atss import ATSS
from .autoassign import AutoAssign
from .base import BaseDetector
from .boxinst import BoxInst
from .base_detr import DetectionTransformer
from .cascade_rcnn import CascadeRCNN
from .centernet import CenterNet
from .condinst import CondInst
from .co... | # Copyright (c) OpenMMLab. All rights reserved.
from .atss import ATSS
from .autoassign import AutoAssign
from .base import BaseDetector
from .boxinst import BoxInst
from .base_detr import DetectionTransformer
from .cascade_rcnn import CascadeRCNN
from .centernet import CenterNet
from .condinst import CondInst
from .co... |
from urllib.parse import parse_qs, urlparse
from youtube_transcript_api import YouTubeTranscriptApi
from youtube_transcript_api.formatters import TextFormatter
from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
from backend.data.model import SchemaField
class TranscribeYoutubeVideoBlock(B... | from urllib.parse import parse_qs, urlparse
from youtube_transcript_api import YouTubeTranscriptApi
from youtube_transcript_api.formatters import TextFormatter
from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
from backend.data.model import SchemaField
class TranscribeYoutubeVideoBlock(B... |
from __future__ import annotations
from typing import Any, Optional, Union
import PIL.Image
import torch
from ._datapoint import Datapoint
class Mask(Datapoint):
"""[BETA] :class:`torch.Tensor` subclass for segmentation and detection masks.
Args:
data (tensor-like, PIL.Image.Image): Any data that ... | from __future__ import annotations
from typing import Any, Optional, Union
import PIL.Image
import torch
from ._datapoint import Datapoint
class Mask(Datapoint):
"""[BETA] :class:`torch.Tensor` subclass for segmentation and detection masks.
Args:
data (tensor-like, PIL.Image.Image): Any data that ... |
_base_ = [
'../_base_/models/rpn_r50_fpn.py', '../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
val_evaluator = dict(metric='proposal_fast')
test_evaluator = val_evaluator
| _base_ = [
'../_base_/models/rpn_r50_fpn.py', '../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFile'),
... |
# dataset settings
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
# file_client_args = dict(
# backend='petrel',
# path_mapping=dict({
# './data/': 's3://openmmlab/datasets/detection/',
# 'data/': 's3://openmmlab/datasets/detection/'
# }))
file_client_args = dict(backend='disk')
tra... | # dataset settings
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='P... |
from typing import Any, Dict, Optional, Union
import numpy as np
import PIL.Image
import torch
from torchvision import tv_tensors
from torchvision.transforms.v2 import functional as F, Transform
from torchvision.transforms.v2._utils import is_pure_tensor
class PILToTensor(Transform):
"""Convert a PIL Image to ... | from typing import Any, Dict, Optional, Union
import numpy as np
import PIL.Image
import torch
from torchvision import tv_tensors
from torchvision.transforms.v2 import functional as F, Transform
from torchvision.transforms.v2._utils import is_pure_tensor
class PILToTensor(Transform):
"""Convert a PIL Image to ... |
import asyncio
import time
import pytest
from jina import Client, Deployment, Executor, requests
from jina._docarray import Document, DocumentArray
from jina.excepts import BadServer
from jina.helper import random_port
class MyExecutor(Executor):
@requests(on='/hello')
async def task(self, doc: Document, **... | import pytest
from jina import Client, Deployment, Executor, requests
from jina._docarray import Document, DocumentArray
from jina.excepts import BadServer
from jina.helper import random_port
class MyExecutor(Executor):
@requests(on='/hello')
async def task(self, doc: Document, **kwargs):
for i in ra... |
__version__ = '2023.01.18.alpha'
from docarray.array.array import DocumentArray
from docarray.base_document.document import BaseDocument
__all__ = [
'BaseDocument',
'DocumentArray',
]
| __version__ = '2023.01.17.alpha'
from docarray.array.array import DocumentArray
from docarray.base_document.document import BaseDocument
__all__ = [
'BaseDocument',
'DocumentArray',
]
|
import orjson
from docarray.typing.tensor.abstract_tensor import AbstractTensor
def _default_orjson(obj):
"""
default option for orjson dumps.
:param obj:
:return: return a json compatible object
"""
if isinstance(obj, AbstractTensor):
return obj._docarray_to_json_compatible()
el... | import orjson
def _default_orjson(obj):
"""
default option for orjson dumps. It will call _to_json_compatible
from docarray typing object that expose such method.
:param obj:
:return: return a json compatible object
"""
if getattr(obj, '_to_json_compatible'):
return obj._to_json_c... |
import csv
import gzip
import logging
import os
from datetime import datetime
from torch.utils.data import DataLoader
from sentence_transformers import InputExample, LoggingHandler, SentenceTransformer, datasets, losses, models, util
from sentence_transformers.evaluation import EmbeddingSimilarityEvaluator
#### Just... | import csv
import gzip
import logging
import os
from datetime import datetime
from torch.utils.data import DataLoader
from sentence_transformers import InputExample, LoggingHandler, SentenceTransformer, datasets, losses, models, util
from sentence_transformers.evaluation import EmbeddingSimilarityEvaluator
#### Just... |
import unittest
import torch
from diffusers import DDIMInverseScheduler
from .test_schedulers import SchedulerCommonTest
class DDIMInverseSchedulerTest(SchedulerCommonTest):
scheduler_classes = (DDIMInverseScheduler,)
forward_default_kwargs = (("num_inference_steps", 50),)
def get_scheduler_config(sel... | import torch
from diffusers import DDIMInverseScheduler
from .test_schedulers import SchedulerCommonTest
class DDIMInverseSchedulerTest(SchedulerCommonTest):
scheduler_classes = (DDIMInverseScheduler,)
forward_default_kwargs = (("num_inference_steps", 50),)
def get_scheduler_config(self, **kwargs):
... |
import os
import re
from pathlib import Path
from typing import Optional, Tuple, Union
from torch import Tensor
from torch.utils.data import Dataset
from torchaudio.datasets.utils import _load_waveform
_SAMPLE_RATE = 16000
def _get_wavs_paths(data_dir):
wav_dir = data_dir / "sentences" / "wav"
wav_paths = ... | import os
import re
from pathlib import Path
from typing import Tuple, Union
from torch import Tensor
from torch.utils.data import Dataset
from torchaudio.datasets.utils import _load_waveform
_SAMPLE_RATE = 16000
def _get_wavs_paths(data_dir):
wav_dir = data_dir / "sentences" / "wav"
wav_paths = sorted(str... |
import logging
from typing import Annotated
from autogpt_libs.auth.middleware import APIKeyValidator
from fastapi import APIRouter, Body, Depends, HTTPException, Query
from fastapi.responses import JSONResponse
from backend.data.user import (
get_user_by_email,
set_user_email_verification,
unsubscribe_use... | import logging
from typing import Annotated
from autogpt_libs.auth.middleware import APIKeyValidator
from fastapi import APIRouter, Body, Depends, HTTPException, Query
from fastapi.responses import JSONResponse
from backend.data.user import (
get_user_by_email,
set_user_email_verification,
unsubscribe_use... |
"""
In this example we train a semantic search model to search through Wikipedia
articles about programming articles & technologies.
We use the text paragraphs from the following Wikipedia articles:
Assembly language, C , C Sharp , C++, Go , Java , JavaScript, Keras, Laravel, MATLAB, Matplotlib, MongoDB, MySQL, Natura... | """
In this example we train a semantic search model to search through Wikipedia
articles about programming articles & technologies.
We use the text paragraphs from the following Wikipedia articles:
Assembly language, C , C Sharp , C++, Go , Java , JavaScript, Keras, Laravel, MATLAB, Matplotlib, MongoDB, MySQL, Natura... |
from typing import TYPE_CHECKING
import numpy as np
if TYPE_CHECKING: # pragma: no cover
from docarray.typing import T
class MeshDataMixin:
"""Provide helper functions for :class:`Document` to support 3D mesh data and point cloud."""
def load_uri_to_point_cloud_tensor(
self: 'T', samples: int,... | from typing import TYPE_CHECKING
import numpy as np
if TYPE_CHECKING:
from docarray.typing import T
class MeshDataMixin:
"""Provide helper functions for :class:`Document` to support 3D mesh data and point cloud."""
def load_uri_to_point_cloud_tensor(
self: 'T', samples: int, as_chunks: bool = F... |
# Copyright (c) OpenMMLab. All rights reserved.
from unittest import TestCase
import pytest
import torch
from mmengine import InstanceData
from mmdet.models.dense_heads import EmbeddingRPNHead
from mmdet.structures import DetDataSample
class TestEmbeddingRPNHead(TestCase):
def test_init(self):
"""Test ... | # Copyright (c) OpenMMLab. All rights reserved.
from unittest import TestCase
import pytest
import torch
from mmengine import InstanceData
from mmdet.data_elements import DetDataSample
from mmdet.models.dense_heads import EmbeddingRPNHead
class TestEmbeddingRPNHead(TestCase):
def test_init(self):
"""Te... |
from __future__ import annotations
__version__ = "3.5.0.dev0"
__MODEL_HUB_ORGANIZATION__ = "sentence-transformers"
import importlib
import os
from sentence_transformers.backend import (
export_dynamic_quantized_onnx_model,
export_optimized_onnx_model,
export_static_quantized_openvino_model,
)
from senten... | from __future__ import annotations
__version__ = "3.5.0.dev0"
__MODEL_HUB_ORGANIZATION__ = "sentence-transformers"
import importlib
import os
from sentence_transformers.backend import (
export_dynamic_quantized_onnx_model,
export_optimized_onnx_model,
export_static_quantized_openvino_model,
)
from senten... |
from .autoencoder_asym_kl import AsymmetricAutoencoderKL
from .autoencoder_dc import AutoencoderDC
from .autoencoder_kl import AutoencoderKL
from .autoencoder_kl_allegro import AutoencoderKLAllegro
from .autoencoder_kl_cogvideox import AutoencoderKLCogVideoX
from .autoencoder_kl_hunyuan_video import AutoencoderKLHunyua... | from .autoencoder_asym_kl import AsymmetricAutoencoderKL
from .autoencoder_dc import AutoencoderDC
from .autoencoder_kl import AutoencoderKL
from .autoencoder_kl_allegro import AutoencoderKLAllegro
from .autoencoder_kl_cogvideox import AutoencoderKLCogVideoX
from .autoencoder_kl_hunyuan_video import AutoencoderKLHunyua... |
import sys
from os import path
from setuptools import find_packages
from setuptools import setup
if sys.version_info < (3, 7, 0):
raise OSError(f'DocArray requires Python >=3.7, but yours is {sys.version}')
try:
pkg_name = 'docarray'
libinfo_py = path.join(pkg_name, '__init__.py')
libinfo_content = o... | import sys
from os import path
from setuptools import find_packages
from setuptools import setup
if sys.version_info < (3, 7, 0):
raise OSError(f'DocArray requires Python >=3.7, but yours is {sys.version}')
try:
pkg_name = 'docarray'
libinfo_py = path.join(pkg_name, '__init__.py')
libinfo_content = o... |
import os
from pathlib import Path
from typing import List, 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.librispeech import _get_librispeech_metadata
from torchaudio.datasets.utils import extract_archive... | import os
from pathlib import Path
from typing import List, Tuple, Union
from torch import Tensor
from torch.hub import download_url_to_file
from torch.utils.data import Dataset
from torchaudio.datasets.librispeech import load_librispeech_item
from torchaudio.datasets.utils import extract_archive
_ARCHIVE_NAME = "li... |
"""LangSmith evaluation utilities.
This module provides utilities for evaluating Chains and other language model
applications using LangChain evaluators and LangSmith.
For more information on the LangSmith API, see the `LangSmith API documentation <https://docs.smith.langchain.com/docs/>`_.
**Example**
.. code-bloc... | """LangSmith evaluation utilities.
This module provides utilities for evaluating Chains and other language model
applications using LangChain evaluators and LangSmith.
For more information on the LangSmith API, see the `LangSmith API documentation <https://docs.smith.langchain.com/docs/>`_.
**Example**
.. code-bloc... |
# Copyright (c) OpenMMLab. All rights reserved.
from .optimizer import (OPTIM_WRAPPER_CONSTRUCTORS, OPTIMIZERS,
AmpOptimWrapper, DefaultOptimWrapperConstructor,
OptimWrapper, OptimWrapperDict, build_optim_wrapper)
# yapf: disable
from .scheduler import (ConstantLR, Consta... | # Copyright (c) OpenMMLab. All rights reserved.
from .optimizer import (OPTIM_WRAPPER_CONSTRUCTORS, OPTIMIZERS,
AmpOptimWrapper, DefaultOptimWrapperConstructor,
OptimWrapper, OptimWrapperDict, build_optim_wrapper)
from .scheduler import (ConstantLR, ConstantMomentum, Cons... |
from .hifigan_pipeline import HIFIGAN_VOCODER_V3_LJSPEECH, HiFiGANVocoderBundle
from .rnnt_pipeline import EMFORMER_RNNT_BASE_MUSTC, EMFORMER_RNNT_BASE_TEDLIUM3
from .squim_pipeline import SQUIM_OBJECTIVE, SQUIM_SUBJECTIVE, SquimObjectiveBundle, SquimSubjectiveBundle
__all__ = [
"EMFORMER_RNNT_BASE_MUSTC",
"EM... | from .hifigan_pipeline import HIFIGAN_VOCODER_V3_LJSPEECH, HiFiGANVocoderBundle
from .rnnt_pipeline import EMFORMER_RNNT_BASE_MUSTC, EMFORMER_RNNT_BASE_TEDLIUM3
from .squim_pipeline import SQUIM_OBJECTIVE, SquimObjectiveBundle
__all__ = [
"EMFORMER_RNNT_BASE_MUSTC",
"EMFORMER_RNNT_BASE_TEDLIUM3",
"HIFIGAN_... |
_base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py',
'./centernet_tta.py'
]
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
# model settings
model = dict(
type='CenterNet',
data_preprocessor=dict(
type='DetDataPrepro... | _base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py',
'./centernet_tta.py'
]
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
# model settings
model = dict(
type='CenterNet',
data_preprocessor=dict(
type='DetDataPrepro... |
import asyncio
import logging
import os
import threading
import time
from functools import wraps
from uuid import uuid4
from tenacity import retry, stop_after_attempt, wait_exponential
from backend.util.process import get_service_name
logger = logging.getLogger(__name__)
def _log_prefix(resource_name: str, conn_id... | import asyncio
import logging
import os
import threading
from functools import wraps
from uuid import uuid4
from tenacity import retry, stop_after_attempt, wait_exponential
from backend.util.process import get_service_name
logger = logging.getLogger(__name__)
def _log_prefix(resource_name: str, conn_id: str):
... |
import asyncio
import json
import logging
from abc import ABC, abstractmethod
from datetime import datetime
from typing import Any, AsyncGenerator, Generator, Generic, Optional, TypeVar
from pydantic import BaseModel
from redis.asyncio.client import PubSub as AsyncPubSub
from redis.client import PubSub
from backend.d... | import asyncio
import json
import logging
from abc import ABC, abstractmethod
from datetime import datetime
from typing import Any, AsyncGenerator, Generator, Generic, Optional, TypeVar
from pydantic import BaseModel
from redis.asyncio.client import PubSub as AsyncPubSub
from redis.client import PubSub
from backend.d... |
__version__ = '0.15.1'
import os
from docarray.document import Document
from docarray.array import DocumentArray
from docarray.dataclasses import dataclass, field
if 'DA_RICH_HANDLER' in os.environ:
from rich.traceback import install
install()
| __version__ = '0.15.0'
import os
from docarray.document import Document
from docarray.array import DocumentArray
from docarray.dataclasses import dataclass, field
if 'DA_RICH_HANDLER' in os.environ:
from rich.traceback import install
install()
|
from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.tools.multion.update_session import (
MultionUpdateSession,
UpdateSessionSchema,
)
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic ... | from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.tools.multion.update_session import (
MultionUpdateSession,
UpdateSessionSchema,
)
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic ... |
"""
This example uses average word embeddings (for example from GloVe). It adds two fully-connected feed-forward layers (dense layers) to create a Deep Averaging Network (DAN).
If 'glove.6B.300d.txt.gz' does not exist, it tries to download it from our server.
See https://public.ukp.informatik.tu-darmstadt.de/reimers/... | """
This example uses average word embeddings (for example from GloVe). It adds two fully-connected feed-forward layers (dense layers) to create a Deep Averaging Network (DAN).
If 'glove.6B.300d.txt.gz' does not exist, it tries to download it from our server.
See https://public.ukp.informatik.tu-darmstadt.de/reimers/... |
"""
Sphinx Read the Docs theme.
From https://github.com/ryan-roemer/sphinx-bootstrap-theme.
"""
from os import path
import sphinx
__version__ = "0.5.0"
__version_full__ = __version__
def get_html_theme_path():
"""Return list of HTML theme paths."""
cur_dir = path.abspath(path.dirname(path.dirname(__file__... | """
Sphinx Read the Docs theme.
From https://github.com/ryan-roemer/sphinx-bootstrap-theme.
"""
from os import path
import sphinx
__version__ = "0.5.0"
__version_full__ = __version__
def get_html_theme_path():
"""Return list of HTML theme paths."""
cur_dir = path.abspath(path.dirname(path.dirname(__file_... |
"""Copyright 2024-2025, XGBoost contributors"""
from functools import partial, update_wrapper
from typing import Any
import pytest
from dask_cuda import LocalCUDACluster
from distributed import Client
import xgboost as xgb
from xgboost import collective as coll
from xgboost import testing as tm
from xgboost.testing.... | """Copyright 2024, XGBoost contributors"""
import pytest
from dask_cuda import LocalCUDACluster
from distributed import Client
from xgboost.testing.dask import check_external_memory, get_rabit_args
@pytest.mark.parametrize("is_qdm", [True, False])
def test_external_memory(is_qdm: bool) -> None:
n_workers = 2
... |
import os
import urllib.parse
import urllib.request
from contextlib import nullcontext
from ...helper import __windows__
def _uri_to_blob(uri: str) -> bytes:
"""Convert uri to blob
Internally it reads uri into blob.
:param uri: the uri of Document
:return: blob bytes.
"""
if urllib.parse.url... | import os
import urllib.parse
import urllib.request
from contextlib import nullcontext
from ...helper import __windows__
def _uri_to_blob(uri: str) -> bytes:
"""Convert uri to blob
Internally it reads uri into blob.
:param uri: the uri of Document
:return: blob bytes.
"""
if urllib.parse.url... |
from contextlib import asynccontextmanager as asynccontextmanager
from typing import AsyncGenerator, ContextManager, TypeVar
import anyio.to_thread
from anyio import CapacityLimiter
from starlette.concurrency import iterate_in_threadpool as iterate_in_threadpool # noqa
from starlette.concurrency import run_in_threadp... | from contextlib import asynccontextmanager as asynccontextmanager
from typing import AsyncGenerator, ContextManager, TypeVar
import anyio
from anyio import CapacityLimiter
from starlette.concurrency import iterate_in_threadpool as iterate_in_threadpool # noqa
from starlette.concurrency import run_in_threadpool as run... |
"""DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.api.utils import bounding_boxes
from keras.api.utils import legacy
from keras.src.backend.common.global_state import clear_session
from keras.src.backend.common.keras_tensor import is_ker... | """DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.api.utils import legacy
from keras.src.backend.common.global_state import clear_session
from keras.src.backend.common.keras_tensor import is_keras_tensor
from keras.src.backend.common.var... |
from enum import Enum
# --8<-- [start:ProviderName]
class ProviderName(str, Enum):
ANTHROPIC = "anthropic"
COMPASS = "compass"
DISCORD = "discord"
D_ID = "d_id"
E2B = "e2b"
EXA = "exa"
FAL = "fal"
GITHUB = "github"
GOOGLE = "google"
GOOGLE_MAPS = "google_maps"
GROQ = "groq"... | from enum import Enum
# --8<-- [start:ProviderName]
class ProviderName(str, Enum):
ANTHROPIC = "anthropic"
COMPASS = "compass"
DISCORD = "discord"
D_ID = "d_id"
E2B = "e2b"
EXA = "exa"
FAL = "fal"
GITHUB = "github"
GOOGLE = "google"
GOOGLE_MAPS = "google_maps"
GROQ = "groq"... |
# training schedule for 2x
train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=24, 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 2x
train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=24, 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... |
from typing import Any, Dict, List, Optional, Tuple
from llama_index.core.base.base_query_engine import BaseQueryEngine
from llama_index.core.base.response.schema import RESPONSE_TYPE
from llama_index.core.callbacks.schema import CBEventType, EventPayload
from llama_index.core.indices.composability.graph import Compos... | from typing import Any, Dict, List, Optional, Tuple
from llama_index.core.base.base_query_engine import BaseQueryEngine
from llama_index.core.base.response.schema import RESPONSE_TYPE
from llama_index.core.callbacks.schema import CBEventType, EventPayload
from llama_index.core.indices.composability.graph import Compos... |
# model settings
model = dict(
type='RPN',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[103.530, 116.280, 123.675],
std=[1.0, 1.0, 1.0],
bgr_to_rgb=False,
pad_size_divisor=32),
backbone=dict(
type='ResNet',
depth=50,
num_stages=3,
... | # model settings
preprocess_cfg = dict(
mean=[103.530, 116.280, 123.675],
std=[1.0, 1.0, 1.0],
to_rgb=False,
pad_size_divisor=32)
model = dict(
type='RPN',
preprocess_cfg=preprocess_cfg,
backbone=dict(
type='ResNet',
depth=50,
num_stages=3,
strides=(1, 2, 2),... |
import csv
import os
from pathlib import Path
from typing import List, Dict, Tuple, Union
import torchaudio
from torch import Tensor
from torch.utils.data import Dataset
def load_commonvoice_item(
line: List[str], header: List[str], path: str, folder_audio: str, ext_audio: str
) -> Tuple[Tensor, int, Dict[str, s... | import csv
import os
from pathlib import Path
from typing import List, Dict, Tuple, Union
import torchaudio
from torch import Tensor
from torch.utils.data import Dataset
def load_commonvoice_item(
line: List[str], header: List[str], path: str, folder_audio: str, ext_audio: str
) -> Tuple[Tensor, int, Dict[str, s... |
_base_ = 'deformable-detr_r50_16xb2-50e_coco.py'
model = dict(with_box_refine=True)
| _base_ = 'deformable-detr_r50_16xb2-50e_coco.py'
model = dict(bbox_head=dict(with_box_refine=True))
|
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 jina import Document, DocumentArray, Flow
from ...clip_text import CLIPTextEncoder
_EMBEDDING_DIM = 512
@pytest.mark.parametrize('request_size', [1, 10, 50, 100])
def test_integration(request_size: int):
docs = DocumentArray(
[Document(text='just some random text he... |
# 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... |
# mypy: allow-untyped-defs
import torch.distributed as dist
from torch._C._distributed_c10d import FakeProcessGroup
class FakeStore(dist.Store):
"""
A fake store is a fake Key-Value store simply for initialization usage
the of fake process group, one can either use FakeStore or HashStore.
"""
def _... | # mypy: allow-untyped-defs
import torch.distributed as dist
from torch._C._distributed_c10d import FakeProcessGroup
class FakeStore(dist.Store):
"""
A fake store is a fake Key-Value store simply for initialization usage
the of fake process group, one can either use FakeStore or HashStore.
"""
def _... |
from __future__ import annotations
from typing import Any, 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... | from typing import Any, 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.p... |
_base_ = [
'../_base_/models/cascade-mask-rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_20e.py', '../_base_/default_runtime.py'
]
| _base_ = [
'../_base_/models/cascade_mask_rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_20e.py', '../_base_/default_runtime.py'
]
|
import logging
import os
from typing import Optional
from jina import __default_host__
from jina.importer import ImportExtensions
from jina.serve.gateway import BaseGateway
from jina.serve.runtimes.gateway.websocket.app import get_fastapi_app
class WebSocketGateway(BaseGateway):
"""WebSocket Gateway implementati... | import logging
import os
from typing import Optional
from jina import __default_host__
from jina.importer import ImportExtensions
from jina.serve.gateway import BaseGateway
from jina.serve.runtimes.gateway.websocket.app import get_fastapi_app
class WebSocketGateway(BaseGateway):
"""WebSocket Gateway implementati... |
"""DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.legacy import saving as saving
| """DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.api.legacy import saving
|
# Copyright (c) OpenMMLab. All rights reserved.
import unittest
from unittest import TestCase
import torch
from parameterized import parameterized
from mmdet.core import DetDataSample
from mmdet.testing import demo_mm_inputs, get_detector_cfg
from mmdet.utils import register_all_modules
register_all_modules()
clas... | # Copyright (c) OpenMMLab. All rights reserved.
import unittest
from unittest import TestCase
import torch
from parameterized import parameterized
from mmdet.core import DetDataSample
from mmdet.testing import demo_mm_inputs, get_detector_cfg
from mmdet.utils import register_all_modules
register_all_modules()
clas... |
import pathlib
from typing import Any, Dict, List, Tuple, Union
from torchdata.datapipes.iter import IterDataPipe, Mapper
from torchvision.prototype.datasets.utils import Dataset, EncodedImage, HttpResource, OnlineResource
from torchvision.prototype.datasets.utils._internal import hint_sharding, hint_shuffling
from to... | import pathlib
from typing import Any, Dict, List, Tuple, Union
from torchdata.datapipes.iter import IterDataPipe, Mapper
from torchvision.prototype.datapoints import Label
from torchvision.prototype.datasets.utils import Dataset, EncodedImage, HttpResource, OnlineResource
from torchvision.prototype.datasets.utils._in... |
"""
=====================================
How to write your own Datapoint class
=====================================
This guide is intended for advanced users and downstream library maintainers. We explain how to
write your own datapoint class, and how to make it compatible with the built-in
Torchvision v2 transforms... | """
=====================================
How to write your own Datapoint class
=====================================
This guide is intended for advanced users and downstream library maintainers. We explain how to
write your own datapoint class, and how to make it compatible with the built-in
Torchvision v2 transforms... |
"""
================================================================
Using KBinsDiscretizer to discretize continuous features
================================================================
The example compares prediction result of linear regression (linear model)
and decision tree (tree based model) with and without... | """
================================================================
Using KBinsDiscretizer to discretize continuous features
================================================================
The example compares prediction result of linear regression (linear model)
and decision tree (tree based model) with and without... |
import copy
import pytest
import torch
from common_utils import assert_equal
from torchvision.models.detection import _utils, backbone_utils
from torchvision.models.detection.transform import GeneralizedRCNNTransform
class TestModelsDetectionUtils:
def test_balanced_positive_negative_sampler(self):
sampl... | import copy
import pytest
import torch
from common_utils import assert_equal
from torchvision.models.detection import _utils, backbone_utils
from torchvision.models.detection.transform import GeneralizedRCNNTransform
class TestModelsDetectionUtils:
def test_balanced_positive_negative_sampler(self):
sampl... |
from __future__ import annotations
import os
import sys
from typing import Any, BinaryIO, Optional, Tuple, Type, TypeVar, Union
import PIL.Image
import torch
from torchvision.datapoints._datapoint import Datapoint
from torchvision.prototype.utils._internal import fromfile, ReadOnlyTensorBuffer
D = TypeVar("D", boun... | from __future__ import annotations
import os
import sys
from typing import Any, BinaryIO, Optional, Tuple, Type, TypeVar, Union
import PIL.Image
import torch
from torchvision.prototype.datapoints._datapoint import Datapoint
from torchvision.prototype.utils._internal import fromfile, ReadOnlyTensorBuffer
D = TypeVar... |
import asyncio
import os
from typing import Dict, List
import pytest
import requests
from jina import Flow
from jina.logging.logger import JinaLogger
from tests.k8s_otel.kind_wrapper import KindClusterWrapperV2
from tests.k8s_otel.util import get_last_health_check_data, parse_string_jaeger_tags
@pytest.mark.asyncio... | import asyncio
import os
from typing import Dict, List
import pytest
import requests
from jina import Flow
from jina.logging.logger import JinaLogger
from tests.k8s_otel.kind_wrapper import KindClusterWrapperV2
from tests.k8s_otel.util import get_last_health_check_data, parse_string_jaeger_tags
@pytest.mark.asyncio... |
# Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
from mmdet.core.mask import BitmapMasks
def create_random_bboxes(num_bboxes, img_w, img_h):
bboxes_left_top = np.random.uniform(0, 0.5, size=(num_bboxes, 2))
bboxes_right_bottom = np.random.uniform(0.5, 1, size=(num_bboxes, 2))
bboxes = n... | # Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
def create_random_bboxes(num_bboxes, img_w, img_h):
bboxes_left_top = np.random.uniform(0, 0.5, size=(num_bboxes, 2))
bboxes_right_bottom = np.random.uniform(0.5, 1, size=(num_bboxes, 2))
bboxes = np.concatenate((bboxes_left_top, bboxes_ri... |
"""Base schema for callback managers."""
import uuid
from dataclasses import dataclass
from datetime import datetime
from enum import Enum
from typing import Any, Dict, Optional
# timestamp for callback events
TIMESTAMP_FORMAT = "%m/%d/%Y, %H:%M:%S.%f"
# base trace_id for the tracemap in callback_manager
B... | """Base schema for callback managers."""
import uuid
from dataclasses import dataclass
from datetime import datetime
from enum import Enum
from typing import Any, Dict, Optional
# timestamp for callback events
TIMESTAMP_FORMAT = "%m/%d/%Y, %H:%M:%S.%f"
# base trace_id for the tracemap in callback_manager
B... |
from collections.abc import Sequence
from inspect import signature
from typing import Optional, Union
from langchain_core.callbacks import Callbacks
from langchain_core.documents import (
BaseDocumentCompressor,
BaseDocumentTransformer,
Document,
)
from pydantic import ConfigDict
class DocumentCompressor... | from collections.abc import Sequence
from inspect import signature
from typing import Optional, Union
from langchain_core.callbacks import Callbacks
from langchain_core.documents import (
BaseDocumentCompressor,
BaseDocumentTransformer,
Document,
)
from pydantic import ConfigDict
class DocumentCompressor... |
from docarray.predefined_document.image import Image
from docarray.predefined_document.text import Text
__all__ = ['Text', 'Image']
| from .image import Image
from .text import Text
__all__ = ['Text', 'Image']
|
_base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
'../common/lsj_100e_coco_instance.py'
]
image_size = (1024, 1024)
batch_augments = [dict(type='BatchFixedSizePad', size=image_size)]
norm_cfg = dict(type='SyncBN', requires_grad=True)
# Use MMSyncBN that handles empty tensor in head. It can be changed to
# Syn... | _base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
'../common/lsj_100e_coco_instance.py'
]
norm_cfg = dict(type='SyncBN', requires_grad=True)
# Use MMSyncBN that handles empty tensor in head. It can be changed to
# SyncBN after https://github.com/pytorch/pytorch/issues/36530 is fixed
# Requires MMCV-full afte... |
from .tfidf_text_executor import TFIDFTextEncoder
| from .tfidf_text_executor import TFIDFTextEncoder |
from backend.blocks.jina._auth import (
JinaCredentials,
JinaCredentialsField,
JinaCredentialsInput,
)
from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
from backend.data.model import SchemaField
from backend.util.request import requests
class JinaChunkingBlock(Block):
clas... | from backend.blocks.jina._auth import (
JinaCredentials,
JinaCredentialsField,
JinaCredentialsInput,
)
from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
from backend.data.model import SchemaField
from backend.util.request import requests
class JinaChunkingBlock(Block):
clas... |
_base_ = './cascade-mask-rcnn_r50_fpn_ms-3x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='... | _base_ = './cascade_mask_rcnn_r50_fpn_mstrain_3x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
st... |
"""DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.activations import deserialize
from keras.src.activations import get
from keras.src.activations import serialize
from keras.src.activations.activations import celu
from keras.src.acti... | """DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.activations import deserialize
from keras.src.activations import get
from keras.src.activations import serialize
from keras.src.activations.activations import celu
from keras.src.acti... |
import json
from collections.abc import Sequence
from langchain_core.agents import AgentAction, AgentActionMessageLog
from langchain_core.messages import AIMessage, BaseMessage, FunctionMessage
def _convert_agent_action_to_messages(
agent_action: AgentAction, observation: str
) -> list[BaseMessage]:
"""Conve... | import json
from typing import List, Sequence, Tuple
from langchain_core.agents import AgentAction, AgentActionMessageLog
from langchain_core.messages import AIMessage, BaseMessage, FunctionMessage
def _convert_agent_action_to_messages(
agent_action: AgentAction, observation: str
) -> List[BaseMessage]:
"""C... |
_base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
type='RepPointsDetector',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
... | _base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
type='RepPointsDetector',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
... |
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-executor', {'name... |
__copyright__ = "Copyright (c) 2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
from typing import Callable, List
import pytest
from jina import Flow, DocumentArray
from ...sentence_encoder import TransformerSentenceEncoder
@pytest.mark.parametrize(
'request_size', [1, 10, 50, 100]
)
def t... | __copyright__ = "Copyright (c) 2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
from typing import Callable, List
import pytest
from jina import Flow, DocumentArray
from jinahub.text.encoders.sentence_encoder import TransformerSentenceEncoder
@pytest.mark.parametrize(
'request_size', [1, 1... |
# Copyright (c) OpenMMLab. All rights reserved.
import copy
import os.path as osp
import unittest
import numpy as np
from mmengine.data import BaseDataElement as PixelData
from mmengine.data import InstanceData
from mmdet.core import DetDataSample
from mmdet.core.mask import BitmapMasks
from mmdet.datasets.pipelines ... | # Copyright (c) OpenMMLab. All rights reserved.
import copy
import os.path as osp
import unittest
import numpy as np
from mmengine.data import BaseDataElement as PixelData
from mmengine.data import InstanceData
from mmdet.core import DetDataSample
from mmdet.core.mask import BitmapMasks
from mmdet.datasets.pipelines ... |
import torch
import torchaudio.prototype.functional as F
from torchaudio_unittest.common_utils import nested_params, TorchaudioTestCase
class BatchConsistencyTest(TorchaudioTestCase):
@nested_params(
[F.convolve, F.fftconvolve],
)
def test_convolve(self, fn):
leading_dims = (2, 3)
... | import torch
import torchaudio.prototype.functional as F
from torchaudio_unittest.common_utils import nested_params, TorchaudioTestCase
class BatchConsistencyTest(TorchaudioTestCase):
@nested_params(
[F.convolve, F.fftconvolve],
)
def test_convolve(self, fn):
leading_dims = (2, 3)
... |
from sentence_transformers.similarity_functions import SimilarityFunction
__all__ = ["SimilarityFunction"]
| from enum import Enum
class SimilarityFunction(Enum):
COSINE = 0
EUCLIDEAN = 1
MANHATTAN = 2
DOT_PRODUCT = 3
|
"""
This script contains an example how to perform semantic search with Elasticsearch.
As dataset, we use the Quora Duplicate Questions dataset, which contains about 500k questions:
https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs
Questions are indexed to Elasticsearch together with their ... | """
This script contains an example how to perform semantic search with Elasticsearch.
As dataset, we use the Quora Duplicate Questions dataset, which contains about 500k questions:
https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs
Questions are indexed to Elasticsearch together with their ... |
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... |
"""Retriever OpenAI agent."""
import deprecated
from typing import Any, cast
from llama_index.agent.openai_legacy.openai_agent import (
OpenAIAgent,
)
from llama_index.core.objects.base import ObjectRetriever
from llama_index.core.tools.types import BaseTool
@deprecated.deprecated(
reason=(
"FnRetri... | """Retriever OpenAI agent."""
from typing import Any, cast
from llama_index.agent.openai_legacy.openai_agent import (
OpenAIAgent,
)
from llama_index.core.objects.base import ObjectRetriever
from llama_index.core.tools.types import BaseTool
class FnRetrieverOpenAIAgent(OpenAIAgent):
"""
Function Retriev... |
import csv
import os
import random
import string
from torchaudio.datasets import fluentcommands
from torchaudio_unittest.common_utils import get_whitenoise, save_wav, TempDirMixin, TorchaudioTestCase
HEADER = ["", "path", "speakerId", "transcription", "action", "object", "location"]
SLOTS = ["action", "object", "loca... | import csv
import os
import random
import string
from torchaudio.datasets import fluentcommands
from torchaudio_unittest.common_utils import get_whitenoise, save_wav, TempDirMixin, TorchaudioTestCase
HEADER = ["", "path", "speakerId", "transcription", "action", "object", "location"]
SLOTS = ["action", "object", "loca... |
"""
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.
"""
from sentence_tran... | """
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.
"""
from sentence_tran... |
from typing import List, TYPE_CHECKING
if TYPE_CHECKING:
from docarray.typing import T, Document
def _reduce_doc_props(doc1: 'Document', doc2: 'Document'):
doc1_fields = set(doc1.non_empty_fields)
doc2_fields = set(doc2.non_empty_fields)
# update only fields that are set in doc2 and not set in doc1
... | from typing import List, TYPE_CHECKING
if TYPE_CHECKING:
from ...typing import T, Document
def _reduce_doc_props(doc1: 'Document', doc2: 'Document'):
doc1_fields = set(doc1.non_empty_fields)
doc2_fields = set(doc2.non_empty_fields)
# update only fields that are set in doc2 and not set in doc1
fi... |
_base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
'../_base_/datasets/cityscapes_instance.py', '../_base_/default_runtime.py'
]
model = dict(
backbone=dict(init_cfg=None),
roi_head=dict(
bbox_head=dict(
type='Shared2FCBBoxHead',
in_channels=256,
fc_out_chann... | _base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
'../_base_/datasets/cityscapes_instance.py', '../_base_/default_runtime.py'
]
model = dict(
backbone=dict(init_cfg=None),
roi_head=dict(
bbox_head=dict(
type='Shared2FCBBoxHead',
in_channels=256,
fc_out_chann... |
_base_ = './vfnet_r50_fpn_ms-2x_coco.py'
model = dict(
backbone=dict(
dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)),
bbox_head=dict(dcn_on_last_conv=True))
| _base_ = './vfnet_r50_fpn_mstrain_2x_coco.py'
model = dict(
backbone=dict(
dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)),
bbox_head=dict(dcn_on_last_conv=True))
|
# 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... |
from typing import TYPE_CHECKING, Optional, Dict
if TYPE_CHECKING:
from ... import DocumentArray
class PostMixin:
"""Helper functions for posting DocumentArray to Jina Flow."""
def post(
self,
host: str,
show_progress: bool = False,
batch_size: Optional[int] = None,
... | from typing import TYPE_CHECKING, Optional, Dict
if TYPE_CHECKING:
from ... import DocumentArray
class PostMixin:
"""Helper functions for posting DocumentArray to Jina Flow."""
def post(
self,
host: str,
show_progress: bool = False,
batch_size: Optional[int] = None,
... |
"""Init file of LlamaIndex."""
__version__ = "0.12.21"
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__ = "0.12.20"
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.... |
_base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../common/lsj-200e_coco-detection.py'
]
image_size = (1024, 1024)
batch_augments = [dict(type='BatchFixedSizePad', size=image_size)]
model = dict(data_preprocessor=dict(batch_augments=batch_augments))
train_dataloader = dict(batch_size=8, num_workers=4)
# ... | _base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../common/lsj_200e_coco_detection.py'
]
image_size = (1024, 1024)
batch_augments = [dict(type='BatchFixedSizePad', size=image_size)]
model = dict(data_preprocessor=dict(batch_augments=batch_augments))
train_dataloader = dict(batch_size=8, num_workers=4)
# ... |
from __future__ import annotations
from sentence_transformers.sparse_encoder.data_collator import SparseEncoderDataCollator
from sentence_transformers.sparse_encoder.evaluation import (
SparseBinaryClassificationEvaluator,
SparseEmbeddingSimilarityEvaluator,
SparseInformationRetrievalEvaluator,
SparseM... | from __future__ import annotations
from sentence_transformers.sparse_encoder.data_collator import SparseEncoderDataCollator
from sentence_transformers.sparse_encoder.evaluation import (
SparseBinaryClassificationEvaluator,
SparseEmbeddingSimilarityEvaluator,
SparseInformationRetrievalEvaluator,
SparseM... |
__copyright__ = "Copyright (c) 2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
from typing import Dict, Iterable, List, Optional, Tuple
import numpy as np
import torch
import torchvision.transforms as T
from jina import DocumentArray, Executor, requests
from jina.logging.logger import JinaLogge... | __copyright__ = "Copyright (c) 2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
from typing import Dict, Iterable, List, Optional, Tuple
import numpy as np
import torch
import torchvision.transforms as T
from jina import DocumentArray, Executor, requests
from jina.logging.logger import JinaLogge... |
from pathlib import Path
from typing import Any, Callable, Optional, Tuple, Union
from .folder import default_loader, 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... | from pathlib import Path
from typing import Any, Callable, Optional, Tuple, Union
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.c... |
_base_ = [
'./sort_faster-rcnn_r50_fpn_8xb2-4e_mot17halftrain'
'_test-mot17halfval.py'
]
# dataloader
val_dataloader = dict(
dataset=dict(ann_file='annotations/train_cocoformat.json'))
test_dataloader = dict(
dataset=dict(
ann_file='annotations/test_cocoformat.json',
data_prefix=dict(im... | _base_ = [
'./sort_faster-rcnn_r50_fpn_8xb2-4e_mot17halftrain'
'_test-mot17halfval.py'
]
model = dict(
detector=dict(
init_cfg=dict(
type='Pretrained',
checkpoint= # noqa: E251
'https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot1... |
# Copyright (c) OpenMMLab. All rights reserved.
# This file add snake case alias for coco api
import warnings
from collections import defaultdict
from typing import List, Optional, Union
import pycocotools
from pycocotools.coco import COCO as _COCO
from pycocotools.cocoeval import COCOeval as _COCOeval
class COCO(_... | # Copyright (c) OpenMMLab. All rights reserved.
# This file add snake case alias for coco api
import warnings
import pycocotools
from pycocotools.coco import COCO as _COCO
from pycocotools.cocoeval import COCOeval as _COCOeval
class COCO(_COCO):
"""This class is almost the same as official pycocotools package.
... |
from typing import Union
import PIL.Image
import torch
from torchvision.prototype import datapoints
from torchvision.transforms.functional import pil_to_tensor, to_pil_image
from torchvision.utils import _log_api_usage_once
def erase_image_tensor(
image: torch.Tensor, i: int, j: int, h: int, w: int, v: torch.Te... | from typing import Union
import PIL.Image
import torch
from torchvision.prototype import datapoints
from torchvision.transforms.functional import pil_to_tensor, to_pil_image
def erase_image_tensor(
image: torch.Tensor, i: int, j: int, h: int, w: int, v: torch.Tensor, inplace: bool = False
) -> torch.Tensor:
... |
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