input stringlengths 33 5k | output stringlengths 32 5k |
|---|---|
"""txtai reader."""
from typing import Any, Dict, List
import numpy as np
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document
class TxtaiReader(BaseReader):
"""
txtai reader.
Retrieves documents through an existing in-memory txtai index.
These documents... | """txtai reader."""
from typing import Any, Dict, List
import numpy as np
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document
class TxtaiReader(BaseReader):
"""txtai reader.
Retrieves documents through an existing in-memory txtai index.
These documents can ... |
from typing import TYPE_CHECKING, Type
if TYPE_CHECKING: # pragma: no cover
from pandas import DataFrame
from docarray.typing import T
class DataframeIOMixin:
"""Save/load from :class:`pandas.dataframe`
.. note::
These functions require you to install `pandas`
"""
def to_dataframe... | from typing import TYPE_CHECKING, Type
if TYPE_CHECKING:
from pandas import DataFrame
from docarray.typing import T
class DataframeIOMixin:
"""Save/load from :class:`pandas.dataframe`
.. note::
These functions require you to install `pandas`
"""
def to_dataframe(self, **kwargs) -> ... |
import torch
from torchaudio_unittest.common_utils import PytorchTestCase, skipIfNoCuda
from torchaudio_unittest.models.conformer.conformer_test_impl import ConformerTestImpl
@skipIfNoCuda
class ConformerFloat32GPUTest(ConformerTestImpl, PytorchTestCase):
dtype = torch.float32
device = torch.device("cuda")
... | import torch
from torchaudio_unittest.common_utils import skipIfNoCuda, PytorchTestCase
from torchaudio_unittest.models.conformer.conformer_test_impl import ConformerTestImpl
@skipIfNoCuda
class ConformerFloat32GPUTest(ConformerTestImpl, PytorchTestCase):
dtype = torch.float32
device = torch.device("cuda")
... |
import contextlib
import json
import re
from typing import Any, List
with contextlib.suppress(ImportError):
import yaml
from llama_index.core.output_parsers.base import OutputParserException
def _marshal_llm_to_json(output: str) -> str:
"""
Extract a substring containing valid JSON or array from a strin... | import contextlib
import json
import re
from typing import Any, List
with contextlib.suppress(ImportError):
import yaml
from llama_index.core.output_parsers.base import OutputParserException
def _marshal_llm_to_json(output: str) -> str:
"""
Extract a substring containing valid JSON or array from a strin... |
from pathlib import Path
import pytest
from jina import Document, DocumentArray, Executor
from jina.excepts import BadDocType
def test_load():
segmenter = Executor.load_config(str(Path(__file__).parents[2] / 'config.yml'))
assert type(segmenter).__name__ == 'VADSpeechSegmenter'
@pytest.mark.parametrize('_t... | from pathlib import Path
import pytest
from jina import Document, DocumentArray, Executor
from jina.excepts import BadDocType
from ...vad_speech_segmenter import VADSpeechSegmenter
def test_load():
segmenter = Executor.load_config(str(Path(__file__).parents[2] / 'config.yml'))
assert type(segmenter).__name_... |
# Copyright (c) OpenMMLab. All rights reserved.
"""Get image metas on a specific dataset.
Here is an example to run this script.
Example:
python tools/misc/get_image_metas.py ${CONFIG} \
--out ${OUTPUT FILE NAME}
"""
import argparse
import csv
import os.path as osp
from multiprocessing import Pool
import mmc... | # Copyright (c) OpenMMLab. All rights reserved.
"""Get image metas on a specific dataset.
Here is an example to run this script.
Example:
python tools/misc/get_image_metas.py ${CONFIG} \
--out ${OUTPUT FILE NAME}
"""
import argparse
import csv
import os.path as osp
from multiprocessing import Pool
import mmc... |
__copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import os
from pathlib import Path
import cv2
import pytest
from jina import Executor, Document, DocumentArray
from ...yolov5_segmenter import YoloV5Segmenter
cur_dir = os.path.dirname(os.path.abspath(__file__... | __copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import os
from operator import itemgetter
import pytest
from jina import Executor, Document, DocumentArray
import cv2
from ...yolov5_segmenter import YoloV5Segmenter
cur_dir = os.path.dirname(os.path.abspath(_... |
from typing import Any, Dict, List, Optional
from llama_index.core.base.base_retriever import BaseRetriever
from llama_index.core.callbacks.base import CallbackManager
from llama_index.core.constants import DEFAULT_SIMILARITY_TOP_K
from llama_index.core.schema import NodeWithScore, QueryBundle
from llama_index.core.se... | from typing import Any, Dict, List, Optional
from llama_index.core.base.base_retriever import BaseRetriever
from llama_index.core.callbacks.base import CallbackManager
from llama_index.core.constants import DEFAULT_SIMILARITY_TOP_K
from llama_index.core.schema import NodeWithScore, QueryBundle
from llama_index.core.se... |
_base_ = '../ssd/ssd300_coco.py'
model = dict(
bbox_head=dict(type='PISASSDHead'),
train_cfg=dict(isr=dict(k=2., bias=0.), carl=dict(k=1., bias=0.2)))
default_hooks = dict(
optimizer=dict(
_delete_=True,
type='OptimizerHook',
grad_clip=dict(max_norm=35, norm_type=2)))
| _base_ = '../ssd/ssd300_coco.py'
model = dict(
bbox_head=dict(type='PISASSDHead'),
train_cfg=dict(isr=dict(k=2., bias=0.), carl=dict(k=1., bias=0.2)))
optimizer_config = dict(
_delete_=True, grad_clip=dict(max_norm=35, norm_type=2))
|
import io
import PIL.Image
import torch
from torchvision import tv_tensors
from torchvision.io import decode_jpeg, encode_jpeg
from torchvision.transforms.functional import pil_to_tensor, to_pil_image
from torchvision.utils import _log_api_usage_once
from ._utils import _get_kernel, _register_kernel_internal
def e... | import PIL.Image
import torch
from torchvision import tv_tensors
from torchvision.transforms.functional import pil_to_tensor, to_pil_image
from torchvision.utils import _log_api_usage_once
from ._utils import _get_kernel, _register_kernel_internal
def erase(
inpt: torch.Tensor,
i: int,
j: int,
h: in... |
import aiohttp
import pytest
from jina import Executor, Flow, requests
from jina.clients.base.helper import HTTPClientlet, WebsocketClientlet
from jina.clients.request.helper import _new_data_request
from jina.excepts import BadServer
from jina.logging.logger import JinaLogger
from jina.types.request.data import DataR... | import aiohttp
import pytest
from jina import Executor, Flow, requests
from jina.clients.base.helper import HTTPClientlet, WebsocketClientlet
from jina.clients.request.helper import _new_data_request
from jina.excepts import BadServer
from jina.logging.logger import JinaLogger
from jina.types.request.data import Data... |
from docarray import Document, DocumentArray
import numpy as np
def test_success_find_with_added_kwargs(start_storage, monkeypatch):
nrof_docs = 1000
num_candidates = 100
elastic_doc = DocumentArray(
storage='elasticsearch',
config={
'n_dim': 3,
'distance': 'l2_nor... | from docarray import Document, DocumentArray
import numpy as np
def test_success_find_with_added_kwargs(start_storage, monkeypatch):
nrof_docs = 1000
num_candidates = 100
elastic_doc = DocumentArray(
storage='elasticsearch',
config={
'n_dim': 3,
'distance': 'l2_nor... |
from unittest.mock import patch, MagicMock
import pytest
from llama_index.utils.workflow import (
draw_all_possible_flows,
draw_most_recent_execution,
)
@pytest.mark.asyncio
async def test_workflow_draw_methods(workflow):
with patch("pyvis.network.Network") as mock_network:
draw_all_possible_flo... | from unittest.mock import patch
import pytest
from llama_index.utils.workflow import (
draw_all_possible_flows,
draw_most_recent_execution,
)
@pytest.mark.asyncio
async def test_workflow_draw_methods(workflow):
with patch("pyvis.network.Network") as mock_network:
draw_all_possible_flows(workflow... |
from torchaudio import ( # noqa: F401
_extension,
compliance,
datasets,
functional,
io,
kaldi_io,
models,
pipelines,
sox_effects,
transforms,
utils,
)
from torchaudio.backend import get_audio_backend, list_audio_backends, set_audio_backend
try:
from .version import __v... | from torchaudio import ( # noqa: F401
_extension,
compliance,
datasets,
functional,
io,
kaldi_io,
models,
pipelines,
sox_effects,
transforms,
utils,
)
from torchaudio.backend import get_audio_backend, list_audio_backends, set_audio_backend
try:
from .version import __ve... |
# Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import torch
from mmcv.cnn import ConvModule, DepthwiseSeparableConvModule
from mmdet.models.dense_heads import YOLOXHead
def test_yolox_head_loss():
"""Tests yolox head loss when truth is empty and non-empty."""
s = 256
img_metas = [{
'... | import mmcv
import torch
from mmcv.cnn import ConvModule, DepthwiseSeparableConvModule
from mmdet.models.dense_heads import YOLOXHead
def test_yolox_head_loss():
"""Tests yolox head loss when truth is empty and non-empty."""
s = 256
img_metas = [{
'img_shape': (s, s, 3),
'scale_factor': 1... |
from functools import wraps
from typing import Any, Callable, Concatenate, Coroutine, ParamSpec, TypeVar, cast
from backend.data.credit import get_user_credit_model
from backend.data.execution import (
ExecutionResult,
create_graph_execution,
get_execution_results,
get_incomplete_executions,
get_la... | from functools import wraps
from typing import Any, Callable, Concatenate, Coroutine, ParamSpec, TypeVar, cast
from backend.data.credit import get_user_credit_model
from backend.data.execution import (
ExecutionResult,
create_graph_execution,
get_execution_results,
get_incomplete_executions,
get_la... |
# 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.core.utils import mask2ndarray
from mmdet.datasets.builder import build_dataset
from mmdet.registry import VISUALIZERS
from mmdet.utils import register_al... | # 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.core.utils import mask2ndarray
from mmdet.datasets.builder import build_dataset
from mmdet.registry import VISUALIZERS
from mmdet.utils import register_al... |
from .filtering import (
allpass_biquad,
band_biquad,
bandpass_biquad,
bandreject_biquad,
bass_biquad,
biquad,
contrast,
dcshift,
deemph_biquad,
dither,
equalizer_biquad,
filtfilt,
flanger,
gain,
highpass_biquad,
lfilter,
lowpass_biquad,
overdrive,... | from .filtering import (
allpass_biquad,
band_biquad,
bandpass_biquad,
bandreject_biquad,
bass_biquad,
biquad,
contrast,
dcshift,
deemph_biquad,
dither,
equalizer_biquad,
filtfilt,
flanger,
gain,
highpass_biquad,
lfilter,
lowpass_biquad,
overdrive,... |
# Copyright (c) OpenMMLab. All rights reserved.
from ..builder import DETECTORS
from .single_stage import SingleStageDetector
@DETECTORS.register_module()
class NASFCOS(SingleStageDetector):
"""NAS-FCOS: Fast Neural Architecture Search for Object Detection.
https://arxiv.org/abs/1906.0442
"""
def __... | from ..builder import DETECTORS
from .single_stage import SingleStageDetector
@DETECTORS.register_module()
class NASFCOS(SingleStageDetector):
"""NAS-FCOS: Fast Neural Architecture Search for Object Detection.
https://arxiv.org/abs/1906.0442
"""
def __init__(self,
backbone,
... |
_base_ = './mask-rcnn_r50_fpn_seesaw-loss_sample1e-3-ms-2x_lvis-v1.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| _base_ = './mask_rcnn_r50_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
|
# Copyright (c) OpenMMLab. All rights reserved.
from .approx_max_iou_assigner import ApproxMaxIoUAssigner
from .assign_result import AssignResult
from .atss_assigner import ATSSAssigner
from .base_assigner import BaseAssigner
from .center_region_assigner import CenterRegionAssigner
from .dynamic_soft_label_assigner imp... | # Copyright (c) OpenMMLab. All rights reserved.
from .approx_max_iou_assigner import ApproxMaxIoUAssigner
from .assign_result import AssignResult
from .atss_assigner import ATSSAssigner
from .base_assigner import BaseAssigner
from .center_region_assigner import CenterRegionAssigner
from .dynamic_soft_label_assigner imp... |
_base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
type='ATSS',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[103.530, 116.280, 123.675],
std=[1.0, 1.0, 1.0],
bgr_to_rgb=False... | _base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
type='ATSS',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[103.530, 116.280, 123.675],
std=[1.0, 1.0, 1.0],
bgr_to_rgb=False... |
"""DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.api import _tf_keras
from keras.api import activations
from keras.api import applications
from keras.api import backend
from keras.api import callbacks
from keras.api import config
from k... | """DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.api import _tf_keras
from keras.api import activations
from keras.api import applications
from keras.api import backend
from keras.api import callbacks
from keras.api import config
from k... |
# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import os.path as osp
import numpy as np
from mmengine.fileio import dump, load
from mmengine.utils import mkdir_or_exist, track_parallel_progress
prog_description = '''K-Fold coco split.
To split coco data for semi-supervised object detection:
pyth... | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import os.path as osp
import mmcv
import numpy as np
prog_description = '''K-Fold coco split.
To split coco data for semi-supervised object detection:
python tools/misc/split_coco.py
'''
def parse_args():
parser = argparse.ArgumentParser()
... |
from __future__ import annotations
from typing import Any, Callable, List, Tuple, Type, Union
import PIL.Image
from torchvision import datapoints
from torchvision._utils import sequence_to_str
from torchvision.transforms.v2.functional import get_dimensions, get_size, is_pure_tensor
def get_bounding_boxes(flat_inpu... | from __future__ import annotations
from typing import Any, Callable, List, Tuple, Type, Union
import PIL.Image
from torchvision import datapoints
from torchvision._utils import sequence_to_str
from torchvision.transforms.v2.functional import get_dimensions, get_size, is_simple_tensor
def get_bounding_boxes(flat_in... |
import numpy as np
import pytest
from absl.testing import parameterized
from keras.src import layers
from keras.src import ops
from keras.src import random
from keras.src import testing
class SolarizationTest(testing.TestCase):
def _test_input_output(self, layer, input_value, expected_value, dtype):
inpu... | import numpy as np
import pytest
from absl.testing import parameterized
from keras.src import layers
from keras.src import ops
from keras.src import random
from keras.src import testing
class SolarizationTest(testing.TestCase, parameterized.TestCase):
def _test_input_output(self, layer, input_value, expected_val... |
import os
from tempfile import TemporaryDirectory
from unittest import TestCase
import pytest
from absl.testing import parameterized
from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.load import dataset_module_factory, import_main_c... | import os
from tempfile import TemporaryDirectory
from unittest import TestCase
from absl.testing import parameterized
from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.load import dataset_module_factory, import_main_class
from data... |
from __future__ import annotations
from collections.abc import Sequence
from copy import deepcopy
from typing import Any, Optional, Union
from langchain_core._api.deprecation import deprecated
from langchain_core.callbacks import Callbacks
from langchain_core.documents import BaseDocumentCompressor, Document
from lan... | from __future__ import annotations
from collections.abc import Sequence
from copy import deepcopy
from typing import Any, Optional, Union
from langchain_core._api.deprecation import deprecated
from langchain_core.callbacks import Callbacks
from langchain_core.documents import BaseDocumentCompressor, Document
from lan... |
"""**Messages** are objects used in prompts and chat conversations.
**Class hierarchy:**
.. code-block::
BaseMessage --> SystemMessage, AIMessage, HumanMessage, ChatMessage, FunctionMessage, ToolMessage
--> BaseMessageChunk --> SystemMessageChunk, AIMessageChunk, HumanMessageChunk, ChatMessageChu... | """**Messages** are objects used in prompts and chat conversations.
**Class hierarchy:**
.. code-block::
BaseMessage --> SystemMessage, AIMessage, HumanMessage, ChatMessage, FunctionMessage, ToolMessage
--> BaseMessageChunk --> SystemMessageChunk, AIMessageChunk, HumanMessageChunk, ChatMessageChu... |
"""Embedding adapter model."""
import logging
from typing import Any, List, Optional, Type, cast
from llama_index.core.base.embeddings.base import BaseEmbedding
from llama_index.core.bridge.pydantic import PrivateAttr
from llama_index.core.callbacks import CallbackManager
from llama_index.core.constants import DEFAUL... | """Embedding adapter model."""
import logging
from typing import Any, List, Optional, Type, cast
from llama_index.core.base.embeddings.base import BaseEmbedding
from llama_index.core.bridge.pydantic import PrivateAttr
from llama_index.core.callbacks import CallbackManager
from llama_index.core.constants import DEFAUL... |
from typing import Dict, Type
from llama_index.core.llms.llm import LLM
from llama_index.core.llms.mock import MockLLM
RECOGNIZED_LLMS: Dict[str, Type[LLM]] = {
MockLLM.class_name(): MockLLM,
}
# Conditionals for llama-cloud support
try:
from llama_index.llms.openai import OpenAI # pants: no-infer-dep
... | from typing import Dict, Type
from llama_index.core.llms.llm import LLM
from llama_index.core.llms.mock import MockLLM
RECOGNIZED_LLMS: Dict[str, Type[LLM]] = {
MockLLM.class_name(): MockLLM,
}
# Conditionals for llama-cloud support
try:
from llama_index.llms.openai import OpenAI # pants: no-infer-dep
... |
__copyright__ = "Copyright (c) 2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import pytest
from simpleranker import SimpleRanker
@pytest.mark.parametrize('default_traversal_paths', [['r'], ['c']])
@pytest.mark.parametrize('ranking', ['min', 'max'])
def test_ranking(
documents_chunk, docu... | __copyright__ = "Copyright (c) 2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import pytest
from ...simpleranker import SimpleRanker
@pytest.mark.parametrize('default_traversal_paths', [['r'], ['c']])
@pytest.mark.parametrize('ranking', ['min', 'max'])
def test_ranking(
documents_chunk, ... |
_base_ = ['./yolov3_mobilenetv2_mstrain-416_300e_coco.py']
# yapf:disable
model = dict(
bbox_head=dict(
anchor_generator=dict(
base_sizes=[[(220, 125), (128, 222), (264, 266)],
[(35, 87), (102, 96), (60, 170)],
[(10, 15), (24, 36), (72, 42)]])))
#... | _base_ = ['./yolov3_mobilenetv2_mstrain-416_300e_coco.py']
# yapf:disable
model = dict(
bbox_head=dict(
anchor_generator=dict(
base_sizes=[[(220, 125), (128, 222), (264, 266)],
[(35, 87), (102, 96), (60, 170)],
[(10, 15), (24, 36), (72, 42)]])))
#... |
from docarray import Document, DocumentArray
import numpy as np
import pytest
@pytest.mark.filterwarnings('ignore::UserWarning')
@pytest.mark.parametrize('columns', [[('price', 'int')], {'price': 'int'}])
def test_add_ignore_existing_doc_id(start_storage, columns):
elastic_doc = DocumentArray(
storage='e... | from docarray import Document, DocumentArray
import numpy as np
import pytest
@pytest.mark.filterwarnings('ignore::UserWarning')
def test_add_ignore_existing_doc_id(start_storage):
elastic_doc = DocumentArray(
storage='elasticsearch',
config={
'n_dim': 3,
'columns': [('pri... |
from typing import Optional, List
from docarray.base_document.document import BaseDocument
def test_base_document_init():
doc = BaseDocument()
assert doc.id is not None
def test_update():
class MyDocument(BaseDocument):
content: str
title: Optional[str] = None
tags_: List
d... | from docarray.base_document.document import BaseDocument
def test_base_document_init():
doc = BaseDocument()
assert doc.id is not None
|
import os
from typing import Any, Callable, List, Optional, Tuple
import torch.utils.data as data
from ..utils import _log_api_usage_once
class VisionDataset(data.Dataset):
"""
Base Class For making datasets which are compatible with torchvision.
It is necessary to override the ``__getitem__`` and ``__l... | import os
from typing import Any, Callable, List, Optional, Tuple
import torch
import torch.utils.data as data
from ..utils import _log_api_usage_once
class VisionDataset(data.Dataset):
"""
Base Class For making datasets which are compatible with torchvision.
It is necessary to override the ``__getitem_... |
from typing import Dict, TYPE_CHECKING, Optional
if TYPE_CHECKING: # pragma: no cover
from docarray import Document
from docarray.array.queryset.lookup import Q, LookupNode, LookupLeaf
LOGICAL_OPERATORS = {'$and': 'and', '$or': 'or', '$not': True}
COMPARISON_OPERATORS = {
'$lt': 'lt',
'$gt': 'gt',
... | from typing import Dict, TYPE_CHECKING, Optional
if TYPE_CHECKING:
from docarray import Document
from docarray.array.queryset.lookup import Q, LookupNode, LookupLeaf
LOGICAL_OPERATORS = {'$and': 'and', '$or': 'or', '$not': True}
COMPARISON_OPERATORS = {
'$lt': 'lt',
'$gt': 'gt',
'$lte': 'lte',
'... |
from collections.abc import Sequence as ABCSequence
from typing import Any
BASE_TYPES = (int, str, bool, bytes, float)
def _is_otel_supported_type(obj: Any) -> bool:
# If it's one of the base types
if isinstance(obj, BASE_TYPES):
return True
# If it's a sequence (but not a string or b... | from collections.abc import Sequence as ABCSequence
from typing import Any
BASE_TYPES = (int, str, bool, bytes, float)
def _is_otel_supported_type(obj: Any) -> bool:
# If it's one of the base types
if isinstance(obj, BASE_TYPES):
return True
# If it's a sequence (but not a string or byt... |
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 = {}
_import_structure = {}
try:
if not (is_transformers_available() and i... | 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 = {}
_import_structure = {}
try:
if not (is_transformers_available() and i... |
from backend.app import run_processes
from backend.executor import DatabaseManager, ExecutionScheduler
from backend.server.rest_api import AgentServer
def main():
"""
Run all the processes required for the AutoGPT-server REST API.
"""
run_processes(
DatabaseManager(),
ExecutionSchedule... | from backend.app import run_processes
from backend.executor import ExecutionScheduler
from backend.server.rest_api import AgentServer
def main():
"""
Run all the processes required for the AutoGPT-server REST API.
"""
run_processes(
ExecutionScheduler(),
AgentServer(),
)
if __nam... |
import asyncio
import logging
import os
from jina import __default_host__
from jina.importer import ImportExtensions
from jina.serve.runtimes.gateway import GatewayRuntime
from jina.serve.runtimes.gateway.websocket.app import get_fastapi_app
__all__ = ['WebSocketGatewayRuntime']
class WebSocketGatewayRuntime(Gatewa... | import asyncio
import logging
import os
from jina import __default_host__
from jina.importer import ImportExtensions
from jina.serve.runtimes.gateway import GatewayRuntime
from jina.serve.runtimes.gateway.websocket.app import get_fastapi_app
__all__ = ['WebSocketGatewayRuntime']
class WebSocketGatewayRuntime(Gatewa... |
# Copyright (c) OpenMMLab. All rights reserved.
from .checkpoint_hook import CheckpointHook
from .early_stopping_hook import EarlyStoppingHook
from .ema_hook import EMAHook
from .empty_cache_hook import EmptyCacheHook
from .hook import Hook
from .iter_timer_hook import IterTimerHook
from .logger_hook import LoggerHook
... | # Copyright (c) OpenMMLab. All rights reserved.
from .checkpoint_hook import CheckpointHook
from .ema_hook import EMAHook
from .empty_cache_hook import EmptyCacheHook
from .hook import Hook
from .iter_timer_hook import IterTimerHook
from .logger_hook import LoggerHook
from .naive_visualization_hook import NaiveVisualiz... |
# 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... | # dataset settings
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
dict(type='Resize', img... |
# Copyright (c) OpenMMLab. All rights reserved.
import os
import pytest
import torch
import torch.nn as nn
from torch.distributed import destroy_process_group, init_process_group
from torch.nn.parallel import DataParallel, DistributedDataParallel
from mmengine.model import (MMDistributedDataParallel,
... | # Copyright (c) OpenMMLab. All rights reserved.
import os
import pytest
import torch
import torch.nn as nn
from torch.distributed import destroy_process_group, init_process_group
from torch.nn.parallel import DataParallel, DistributedDataParallel
from mmengine.model import (MMDistributedDataParallel,
... |
# Copyright 2024 The OpenXLA 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
#
# Unless required by applicable law or agreed to in ... | # Copyright 2024 The OpenXLA 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
#
# Unless required by applicable law or agreed to in ... |
import os
import torch
import torchaudio.prototype.transforms as T
import torchaudio.transforms as transforms
from torchaudio_unittest.common_utils import TorchaudioTestCase
class BatchConsistencyTest(TorchaudioTestCase):
def assert_batch_consistency(self, transform, batch, *args, atol=1e-8, rtol=1e-5, seed=42, ... | import os
import torch
import torchaudio.prototype.transforms as T
import torchaudio.transforms as transforms
from torchaudio_unittest.common_utils import TorchaudioTestCase
class BatchConsistencyTest(TorchaudioTestCase):
def assert_batch_consistency(self, transform, batch, *args, atol=1e-8, rtol=1e-5, seed=42, ... |
import logging
from datasets import load_dataset
from sentence_transformers.sparse_encoder import (
SparseEncoder,
SparseRerankingEvaluator,
)
logging.basicConfig(format="%(message)s", level=logging.INFO)
# Load a model
model = SparseEncoder("naver/splade-cocondenser-ensembledistil")
# Load a dataset with ... | import logging
from datasets import load_dataset
from sentence_transformers.sparse_encoder import (
MLMTransformer,
SparseEncoder,
SparseRerankingEvaluator,
SpladePooling,
)
logging.basicConfig(format="%(asctime)s - %(message)s", datefmt="%Y-%m-%d %H:%M:%S", level=logging.INFO)
# Initialize the SPLA... |
from pathlib import Path
from typing import Union, Optional, Callable, TYPE_CHECKING, Generator
if TYPE_CHECKING: # pragma: no cover
from docarray import DocumentArray
from docarray.typing import T
from multiprocessing.pool import ThreadPool, Pool
class DataLoaderMixin:
@classmethod
def dataload... | from pathlib import Path
from typing import Union, Optional, Callable, TYPE_CHECKING, Generator
if TYPE_CHECKING:
from docarray import DocumentArray
from docarray.typing import T
from multiprocessing.pool import ThreadPool, Pool
class DataLoaderMixin:
@classmethod
def dataloader(
cls,
... |
# Copyright (c) OpenMMLab. All rights reserved.
from .inference import (async_inference_detector, inference_detector,
init_detector)
__all__ = [
'init_detector',
'async_inference_detector',
'inference_detector',
]
| # Copyright (c) OpenMMLab. All rights reserved.
from .inference import (async_inference_detector, inference_detector,
init_detector, show_result_pyplot)
from .test import multi_gpu_test, single_gpu_test
from .train import (get_root_logger, init_random_seed, set_random_seed,
t... |
_base_ = [
'mmcls::_base_/datasets/imagenet_bs256_rsb_a12.py',
'mmcls::_base_/schedules/imagenet_bs2048_rsb.py',
'mmcls::_base_/default_runtime.py'
]
model = dict(
type='ImageClassifier',
backbone=dict(
type='mmdet.CSPNeXt',
arch='P5',
out_indices=(4, ),
expand_ratio... | _base_ = [
'mmcls::_base_/datasets/imagenet_bs256_rsb_a12.py',
'mmcls::_base_/schedules/imagenet_bs2048_rsb.py',
'mmcls::_base_/default_runtime.py'
]
custom_imports = dict(imports=['mmdet.models'], allow_failed_imports=False)
model = dict(
type='ImageClassifier',
backbone=dict(
type='mmdet... |
"""Test Aleph Alpha API wrapper."""
from langchain_community.llms.aleph_alpha import AlephAlpha
def test_aleph_alpha_call() -> None:
"""Test valid call to cohere."""
llm = AlephAlpha(maximum_tokens=10)
output = llm.invoke("Say foo:")
assert isinstance(output, str)
| """Test Aleph Alpha API wrapper."""
from langchain_community.llms.aleph_alpha import AlephAlpha
def test_aleph_alpha_call() -> None:
"""Test valid call to cohere."""
llm = AlephAlpha(maximum_tokens=10) # type: ignore[call-arg]
output = llm.invoke("Say foo:")
assert isinstance(output, str)
|
from collections import namedtuple
from typing import TYPE_CHECKING, Dict, NamedTuple, Optional
from urllib.parse import urlparse
if TYPE_CHECKING:
from ... import DocumentArray
_ParsedHost = namedtuple('ParsedHost', 'on host port version scheme')
def _parse_host(host: str) -> NamedTuple:
"""Parse a host s... | 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,
... |
import logging
import os
import zlib
from contextlib import asynccontextmanager
from urllib.parse import parse_qsl, urlencode, urlparse, urlunparse
from uuid import uuid4
from dotenv import load_dotenv
from prisma import Prisma
from pydantic import BaseModel, Field, field_validator
from backend.util.retry import conn... | import logging
import os
import zlib
from contextlib import asynccontextmanager
from urllib.parse import parse_qsl, urlencode, urlparse, urlunparse
from uuid import uuid4
from dotenv import load_dotenv
from prisma import Prisma
from pydantic import BaseModel, Field, field_validator
from backend.util.retry import conn... |
from .rnnt_pipeline import EMFORMER_RNNT_BASE_MUSTC, EMFORMER_RNNT_BASE_TEDLIUM3
from .source_separation_pipeline import (
CONVTASNET_BASE_LIBRI2MIX,
HDEMUCS_HIGH_MUSDB,
HDEMUCS_HIGH_MUSDB_PLUS,
SourceSeparationBundle,
)
__all__ = [
"CONVTASNET_BASE_LIBRI2MIX",
"EMFORMER_RNNT_BASE_MUSTC",
"... | from .rnnt_pipeline import EMFORMER_RNNT_BASE_MUSTC, EMFORMER_RNNT_BASE_TEDLIUM3
from .source_separation_pipeline import CONVTASNET_BASE_LIBRI2MIX, HDEMUCS_HIGH_MUSDB_PLUS, SourceSeparationBundle
__all__ = [
"CONVTASNET_BASE_LIBRI2MIX",
"EMFORMER_RNNT_BASE_MUSTC",
"EMFORMER_RNNT_BASE_TEDLIUM3",
"Source... |
from unittest import TestCase, mock
import boto3
from llama_index.core.postprocessor.types import (
BaseNodePostprocessor,
NodeWithScore,
QueryBundle,
)
from llama_index.core.schema import TextNode
from llama_index.postprocessor.bedrock_rerank import BedrockRerank
class TestBedrockRerank(TestCase):
... | from unittest import TestCase, mock
import boto3
from llama_index.core.postprocessor.types import (
BaseNodePostprocessor,
NodeWithScore,
QueryBundle,
)
from llama_index.core.schema import TextNode
from llama_index.postprocessor.bedrock_rerank import AWSBedrockRerank
class TestAWSBedrockRerank(TestCase)... |
_base_ = './yolov3_d53_8xb8-ms-608-273e_coco.py'
input_size = (320, 320)
train_pipeline = [
dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}),
dict(type='LoadAnnotations', with_bbox=True),
# `mean` and `to_rgb` should be the same with the `preprocess_cfg`
dict(type='Expand', mean=[0,... | _base_ = './yolov3_d53_8xb8-ms-608-273e_coco.py'
# dataset settings
# 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')
input_si... |
# coding=utf-8
# Copyright 2025 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or 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... |
_base_ = './retinanet_r50_caffe_fpn_1x_coco.py'
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='RandomResize',
scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736), (1333, 768),
(1333, 800)],
keep_ratio... | _base_ = './retinanet_r50_caffe_fpn_1x_coco.py'
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='RandomResize',
scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736), (1333, 768),
(1333, 800)]),
dict(type='Ra... |
import time
import pytest
from typing import List
from llama_index.core.schema import Document, TextNode
from llama_index.core.node_parser import SentenceSplitter
from redis import Redis
import docker
docker_client = docker.from_env()
docker_client.ping()
container = docker_client.containers.run(
"redis/redis-sta... | import time
import pytest
from typing import List
from llama_index.core.schema import Document, TextNode
from llama_index.core.node_parser import SentenceSplitter
from redis import Redis
import docker
docker_client = docker.from_env()
docker_client.ping()
container = docker_client.containers.run(
"redis/redis-sta... |
"""Select and order examples based on ngram overlap score (sentence_bleu score).
https://www.nltk.org/_modules/nltk/translate/bleu_score.html
https://aclanthology.org/P02-1040.pdf
"""
from typing import Any, Dict, List
import numpy as np
from langchain_core.example_selectors import BaseExampleSelector
from langchain... | """Select and order examples based on ngram overlap score (sentence_bleu score).
https://www.nltk.org/_modules/nltk/translate/bleu_score.html
https://aclanthology.org/P02-1040.pdf
"""
from typing import Any, Dict, List
import numpy as np
from langchain_core.example_selectors import BaseExampleSelector
from langchain... |
# Copyright (c) OpenMMLab. All rights reserved.
import datetime
import os.path as osp
import warnings
from typing import Optional
from mmengine.fileio import dump
from mmengine.logging import print_log
from . import root
from .default_scope import DefaultScope
from .registry import Registry
def traverse_registry_tre... | # Copyright (c) OpenMMLab. All rights reserved.
import datetime
import os.path as osp
from typing import Optional
from mmengine.fileio import dump
from mmengine.logging import print_log
from . import root
from .registry import Registry
def traverse_registry_tree(registry: Registry, verbose: bool = True) -> list:
... |
# Copyright (c) OpenMMLab. All rights reserved.
__version__ = '0.3.2'
def parse_version_info(version_str):
"""Parse the version information.
Args:
version_str (str): version string like '0.1.0'.
Returns:
tuple: version information contains major, minor, micro version.
"""
versio... | # Copyright (c) OpenMMLab. All rights reserved.
__version__ = '0.3.1'
def parse_version_info(version_str):
"""Parse the version information.
Args:
version_str (str): version string like '0.1.0'.
Returns:
tuple: version information contains major, minor, micro version.
"""
versio... |
from typing import Optional
from docarray.document import BaseDocument
from docarray.typing import AnyTensor, Embedding, PointCloud3DUrl
class PointCloud3D(BaseDocument):
"""
Document for handling point clouds for 3D data representation.
Point cloud is a representation of a 3D mesh. It is made by repeat... | from typing import Optional
from docarray.document import BaseDocument
from docarray.typing import Embedding, PointCloud3DUrl, Tensor
class PointCloud3D(BaseDocument):
"""
Document for handling point clouds for 3D data representation.
Point cloud is a representation of a 3D mesh. It is made by repeatedl... |
# Copyright 2024 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writ... | # Copyright 2024 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writ... |
"""
This script contains an example how to perform semantic search with Seismic.
For more information, please refer to the documentation:
https://github.com/TusKANNy/seismic/blob/main/docs/Guidelines.md
All you need is installing the `pyseismic-lsr` package:
```
pip install pyseismic-lsr
```
"""
import time
from dat... | """
This script contains an example how to perform semantic search with Seismic.
For more information, please refer to the documentation:
https://github.com/TusKANNy/seismic/blob/main/docs/Guidelines.md
All you need is installing the `pyseismic-lsr` package:
```
pip install pyseismic-lsr
```
"""
import time
from dat... |
# Copyright 2025 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... | # Copyright 2024 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... |
import inspect
import logging
from typing import Any, Callable, Optional
from fastapi import HTTPException, Request, Security
from fastapi.security import APIKeyHeader, HTTPBearer
from starlette.status import HTTP_401_UNAUTHORIZED
from .config import settings
from .jwt_utils import parse_jwt_token
security = HTTPBea... | import inspect
import logging
from typing import Any, Callable, Optional
from fastapi import HTTPException, Request, Security
from fastapi.security import APIKeyHeader, HTTPBearer
from starlette.status import HTTP_401_UNAUTHORIZED
from .config import settings
from .jwt_utils import parse_jwt_token
security = HTTPBea... |
from typing import TYPE_CHECKING
from docarray.math.ndarray import get_array_type
if TYPE_CHECKING:
from docarray.typing import ArrayType
import numpy as np
def pdist(
x_mat: 'ArrayType',
metric: str,
) -> 'np.ndarray':
"""Computes Pairwise distances between observations in n-dimensional space.
... | from typing import TYPE_CHECKING
from ..ndarray import get_array_type
if TYPE_CHECKING:
from ...typing import ArrayType
import numpy as np
def pdist(
x_mat: 'ArrayType',
metric: str,
) -> 'np.ndarray':
"""Computes Pairwise distances between observations in n-dimensional space.
:param x_mat:... |
import numpy as np
from docarray import BaseDoc
from docarray.typing import NdArray
def test_set_tensor():
class MyDocument(BaseDoc):
tensor: NdArray
d = MyDocument(tensor=np.zeros((3, 224, 224)))
assert isinstance(d.tensor, NdArray)
assert isinstance(d.tensor, np.ndarray)
assert (d.ten... | import numpy as np
from docarray import BaseDocument
from docarray.typing import NdArray
def test_set_tensor():
class MyDocument(BaseDocument):
tensor: NdArray
d = MyDocument(tensor=np.zeros((3, 224, 224)))
assert isinstance(d.tensor, NdArray)
assert isinstance(d.tensor, np.ndarray)
ass... |
_base_ = '../cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py'
train_pipeline = [
dict(
type='LoadImageFromFile',
file_client_args={{_base_.file_client_args}}),
dict(
type='InstaBoost',
action_candidate=('normal', 'horizontal', 'skip'),
action_prob=(1, 0, 0),
sc... | _base_ = '../cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.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'),
dict(
type='InstaBoost',
action_candidate=('normal', 'horizontal', 'skip'),
... |
from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.tools.google_trends.tool import GoogleTrendsQueryRun
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handling op... | from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.tools.google_trends.tool import GoogleTrendsQueryRun
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handling op... |
from __future__ import annotations
from typing import Any, Union
from langchain_core.retrievers import (
BaseRetriever,
RetrieverOutput,
)
from langchain_core.runnables import Runnable, RunnablePassthrough
def create_retrieval_chain(
retriever: Union[BaseRetriever, Runnable[dict, RetrieverOutput]],
... | from __future__ import annotations
from typing import Any, Union
from langchain_core.retrievers import (
BaseRetriever,
RetrieverOutput,
)
from langchain_core.runnables import Runnable, RunnablePassthrough
def create_retrieval_chain(
retriever: Union[BaseRetriever, Runnable[dict, RetrieverOutput]],
... |
_base_ = './htc_r50_fpn_20e_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| _base_ = './htc_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
# learning policy
lr_config = dict(step=[16, 19])
runner = dict(type='EpochBasedRunner', max_epochs=20)
|
"""Agent components."""
from typing import Any, Callable, Dict, Optional, Set
from llama_index.core.base.query_pipeline.query import (
QueryComponent,
)
from llama_index.core.bridge.pydantic import Field
from llama_index.core.query_pipeline.components.function import (
FnComponent,
get_parameters,
)
# fr... | """Agent components."""
from typing import Any, Callable, Dict, Optional, Set
from llama_index.core.base.query_pipeline.query import (
QueryComponent,
)
from llama_index.core.bridge.pydantic import Field
from llama_index.core.query_pipeline.components.function import (
FnComponent,
get_parameters,
)
# fr... |
from langchain_core.utils.function_calling import convert_pydantic_to_openai_function
from pydantic import BaseModel, Field
def test_convert_pydantic_to_openai_function() -> None:
class Data(BaseModel):
"""The data to return."""
key: str = Field(..., description="API key")
days: int = Fie... | from langchain_core.utils.function_calling import convert_pydantic_to_openai_function
from pydantic import BaseModel, Field
def test_convert_pydantic_to_openai_function() -> None:
class Data(BaseModel):
"""The data to return."""
key: str = Field(..., description="API key")
days: int = Fie... |
from typing import TYPE_CHECKING, Type, Optional
if TYPE_CHECKING:
from docarray.typing import T
from docarray.proto.docarray_pb2 import DocumentProto
class ProtobufMixin:
@classmethod
def from_protobuf(cls: Type['T'], pb_msg: 'DocumentProto') -> 'T':
from docarray.proto.io import parse_proto... | from typing import TYPE_CHECKING, Type, Optional
if TYPE_CHECKING:
from ...typing import T
from ...proto.docarray_pb2 import DocumentProto
class ProtobufMixin:
@classmethod
def from_protobuf(cls: Type['T'], pb_msg: 'DocumentProto') -> 'T':
from ...proto.io import parse_proto
return p... |
from typing import Sequence, cast
import prisma.enums
import prisma.types
AGENT_NODE_INCLUDE: prisma.types.AgentNodeInclude = {
"Input": True,
"Output": True,
"Webhook": True,
"AgentBlock": True,
}
AGENT_GRAPH_INCLUDE: prisma.types.AgentGraphInclude = {
"Nodes": {"include": AGENT_NODE_INCLUDE}
}
... | from typing import Sequence, cast
import prisma.enums
import prisma.types
AGENT_NODE_INCLUDE: prisma.types.AgentNodeInclude = {
"Input": True,
"Output": True,
"Webhook": True,
"AgentBlock": True,
}
AGENT_GRAPH_INCLUDE: prisma.types.AgentGraphInclude = {
"Nodes": {"include": AGENT_NODE_INCLUDE}
}
... |
import importlib
class LazyModule:
def __init__(self, name, pip_name=None, import_error_msg=None):
self.name = name
self.pip_name = pip_name or name
self.import_error_msg = import_error_msg or (
f"This requires the {self.name} module. "
f"You can install it via `pip... | import importlib
class LazyModule:
def __init__(self, name, pip_name=None):
self.name = name
pip_name = pip_name or name
self.pip_name = pip_name
self.module = None
self._available = None
@property
def available(self):
if self._available is None:
... |
import pytest
import torch
from docarray.computation.torch_backend import TorchCompBackend
def test_to_device():
t = torch.rand(10, 3)
assert t.device == torch.device('cpu')
t = TorchCompBackend.to_device(t, 'meta')
assert t.device == torch.device('meta')
@pytest.mark.parametrize(
'array,result... | import pytest
import torch
from docarray.computation.torch_backend import TorchCompBackend
def test_to_device():
t = torch.rand(10, 3)
assert t.device == torch.device('cpu')
t = TorchCompBackend.to_device(t, 'meta')
assert t.device == torch.device('meta')
@pytest.mark.parametrize(
'array,result... |
from keras.src.backend.common.name_scope import name_scope
from keras.src.backend.numpy import core
from keras.src.backend.numpy import image
from keras.src.backend.numpy import linalg
from keras.src.backend.numpy import math
from keras.src.backend.numpy import nn
from keras.src.backend.numpy import numpy
from keras.sr... | from keras.src.backend.numpy import core
from keras.src.backend.numpy import image
from keras.src.backend.numpy import linalg
from keras.src.backend.numpy import math
from keras.src.backend.numpy import nn
from keras.src.backend.numpy import numpy
from keras.src.backend.numpy import random
from keras.src.backend.numpy.... |
# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import os
from collections import Sequence
from pathlib import Path
import mmcv
import numpy as np
from mmcv import Config, DictAction
from mmdet.core.utils import mask2ndarray
from mmdet.core.visualization import imshow_det_bboxes
from mmdet.datasets.bu... | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import os
from collections import Sequence
from pathlib import Path
import mmcv
import numpy as np
from mmcv import Config, DictAction
from mmdet.core.utils import mask2ndarray
from mmdet.core.visualization import imshow_det_bboxes
from mmdet.datasets.bu... |
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) -> None:
super().__init__()
self.mo... |
from typing import Any, Dict, List, Optional, Sequence, Type, Union
import PIL.Image
import torch
from torchvision import datapoints
from torchvision.prototype.datapoints import Label, OneHotLabel
from torchvision.transforms.v2 import functional as F, Transform
from torchvision.transforms.v2._utils import _get_fill, ... | from typing import Any, Dict, List, Optional, Sequence, Type, Union
import PIL.Image
import torch
from torchvision import datapoints
from torchvision.prototype.datapoints import Label, OneHotLabel
from torchvision.transforms.v2 import functional as F, Transform
from torchvision.transforms.v2._utils import _get_fill, ... |
from __future__ import annotations
from collections.abc import Iterable
import torch
import torch.nn as nn
import torch.nn.functional as F
from sentence_transformers.sparse_encoder import SparseEncoder
def normalized_mean_squared_error(reconstruction: torch.Tensor, original_input: torch.Tensor) -> torch.Tensor:
... | from __future__ import annotations
from collections.abc import Iterable
import torch
import torch.nn as nn
import torch.nn.functional as F
from sentence_transformers.sparse_encoder import SparseEncoder
def normalized_mean_squared_error(reconstruction: torch.Tensor, original_input: torch.Tensor) -> torch.Tensor:
... |
import PIL.Image
import torch
from torchvision import datapoints
from torchvision.utils import _log_api_usage_once
from ._utils import _get_kernel, _register_explicit_noop, _register_kernel_internal
@_register_explicit_noop(
PIL.Image.Image, datapoints.Image, datapoints.BoundingBoxes, datapoints.Mask, warn_pas... | import PIL.Image
import torch
from torchvision import datapoints
from torchvision.utils import _log_api_usage_once
from ._utils import _get_kernel, _register_explicit_noop, _register_kernel_internal
@_register_explicit_noop(
PIL.Image.Image, datapoints.Image, datapoints.BoundingBoxes, datapoints.Mask, warn_pas... |
import os
from pathlib import Path
from typing import Any, Callable, Optional, Tuple, Union
from PIL import Image
from .utils import check_integrity, download_and_extract_archive, download_url
from .vision import VisionDataset
class SBU(VisionDataset):
"""`SBU Captioned Photo <http://www.cs.virginia.edu/~vicent... | import os
from pathlib import Path
from typing import Any, Callable, Optional, Tuple, Union
from PIL import Image
from .utils import check_integrity, download_and_extract_archive, download_url
from .vision import VisionDataset
class SBU(VisionDataset):
"""`SBU Captioned Photo <http://www.cs.virginia.edu/~vicent... |
from __future__ import annotations
from typing import Optional, Type
from langchain_core.callbacks import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from pydantic import BaseModel
from langchain_community.tools.playwright.base import BaseBrowserTool
from langchain_community.tools.playwrig... | from __future__ import annotations
from typing import Optional, Type
from langchain_core.callbacks import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from pydantic import BaseModel
from langchain_community.tools.playwright.base import BaseBrowserTool
from langchain_community.tools.playwrig... |
"""Embeddings."""
from importlib import import_module
from typing import TYPE_CHECKING
if TYPE_CHECKING:
from langchain_core.embeddings.embeddings import Embeddings
from langchain_core.embeddings.fake import (
DeterministicFakeEmbedding,
FakeEmbeddings,
)
__all__ = ["DeterministicFakeEmbe... | """Embeddings."""
from importlib import import_module
from typing import TYPE_CHECKING
if TYPE_CHECKING:
from langchain_core.embeddings.embeddings import Embeddings
from langchain_core.embeddings.fake import (
DeterministicFakeEmbedding,
FakeEmbeddings,
)
__all__ = ["DeterministicFakeEmbe... |
#! /usr/bin/env python
# -*- coding: utf-8 -*-
# Copyright 2021 Imperial College London (Pingchuan Ma)
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
import warnings
import numpy as np
from ibug.face_detection import RetinaFacePredictor
warnings.filterwarnings("ignore")
class LandmarksDetector:
de... | #! /usr/bin/env python
# -*- coding: utf-8 -*-
# Copyright 2021 Imperial College London (Pingchuan Ma)
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
import warnings
import numpy as np
import torchvision
from ibug.face_detection import RetinaFacePredictor
warnings.filterwarnings("ignore")
class Landma... |
import types
from keras.src.activations.activations import celu
from keras.src.activations.activations import elu
from keras.src.activations.activations import exponential
from keras.src.activations.activations import gelu
from keras.src.activations.activations import glu
from keras.src.activations.activations import ... | import types
from keras.src.activations.activations import celu
from keras.src.activations.activations import elu
from keras.src.activations.activations import exponential
from keras.src.activations.activations import gelu
from keras.src.activations.activations import glu
from keras.src.activations.activations import ... |
import numpy as np
import pytest
from docarray import BaseDoc, DocList
from docarray.typing import NdArray
@pytest.mark.parametrize('shuffle', [False, True])
@pytest.mark.parametrize('stack', [False, True])
@pytest.mark.parametrize('batch_size,n_batches', [(16, 7), (10, 10)])
def test_batch(shuffle, stack, batch_siz... | import numpy as np
import pytest
from docarray import BaseDoc, DocList
from docarray.typing import NdArray
@pytest.mark.parametrize('shuffle', [False, True])
@pytest.mark.parametrize('stack', [False, True])
@pytest.mark.parametrize('batch_size,n_batches', [(16, 7), (10, 10)])
def test_batch(shuffle, stack, batch_siz... |
from typing import TYPE_CHECKING, Any, Dict, Optional, Tuple, Type, TypeVar
from pydantic import create_model, create_model_from_typeddict
from pydantic.config import BaseConfig
from typing_extensions import TypedDict
from docarray import BaseDoc
if TYPE_CHECKING:
from pydantic.typing import AnyClassMethod
... | from typing import TYPE_CHECKING, Any, Dict, Optional, Tuple, Type, TypeVar
from pydantic import create_model, create_model_from_typeddict
from pydantic.config import BaseConfig
from typing_extensions import TypedDict
from docarray import BaseDoc
if TYPE_CHECKING:
from pydantic.typing import AnyClassMethod
... |
# Copyright (c) OpenMMLab. All rights reserved.
import mmcv
from .version import __version__, short_version
def digit_version(version_str):
digit_version = []
for x in version_str.split('.'):
if x.isdigit():
digit_version.append(int(x))
elif x.find('rc') != -1:
patch_v... | # Copyright (c) OpenMMLab. All rights reserved.
import mmcv
from .version import __version__, short_version
def digit_version(version_str):
digit_version = []
for x in version_str.split('.'):
if x.isdigit():
digit_version.append(int(x))
elif x.find('rc') != -1:
patch_v... |
_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... |
"""
Top-level module of Jina.
The primary function of this module is to import all of the public Jina
interfaces into a single place. The interfaces themselves are located in
sub-modules, as described below.
"""
import os as _os
import platform as _platform
import signal as _signal
import sys as _sys
import warnings... | """
Top-level module of Jina.
The primary function of this module is to import all of the public Jina
interfaces into a single place. The interfaces themselves are located in
sub-modules, as described below.
"""
import os as _os
import platform as _platform
import signal as _signal
import sys as _sys
import warnings... |
from __future__ import annotations
from sentence_transformers import util
from sentence_transformers.sparse_encoder.losses.SparseCoSENTLoss import SparseCoSENTLoss
from sentence_transformers.sparse_encoder.SparseEncoder import SparseEncoder
class SparseAnglELoss(SparseCoSENTLoss):
def __init__(self, model: Spars... | from __future__ import annotations
from sentence_transformers import util
from sentence_transformers.sparse_encoder.losses.SparseCoSENTLoss import SparseCoSENTLoss
from sentence_transformers.sparse_encoder.SparseEncoder import SparseEncoder
class SparseAnglELoss(SparseCoSENTLoss):
def __init__(self, model: Spars... |
# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import os
from pathlib import Path
import mmcv
from mmcv import Config, DictAction
from mmdet.core.utils import mask2ndarray
from mmdet.core.visualization import imshow_det_bboxes
from mmdet.datasets.builder import build_dataset
def parse_args():
p... | import argparse
import os
from pathlib import Path
import mmcv
from mmcv import Config, DictAction
from mmdet.core.utils import mask2ndarray
from mmdet.core.visualization import imshow_det_bboxes
from mmdet.datasets.builder import build_dataset
def parse_args():
parser = argparse.ArgumentParser(description='Bro... |
from llama_index.core.graph_stores.types import GraphStore
from llama_index.graph_stores.memgraph import MemgraphGraphStore
def test_memgraph_graph_store():
names_of_bases = [b.__name__ for b in MemgraphGraphStore.__bases__]
assert GraphStore.__name__ in names_of_bases
| from unittest.mock import MagicMock, patch
from llama_index.core.graph_stores.types import GraphStore
from llama_index.graph_stores.memgraph import MemgraphGraphStore
@patch("llama_index.graph_stores.memgraph.MemgraphGraphStore")
def test_memgraph_graph_store(MockMemgraphGraphStore: MagicMock):
instance: Memgrap... |
# coding=utf-8
# Copyright 2018 The Open AI Team Authors and The HuggingFace Inc. team.
#
# 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
#
# ... | # coding=utf-8
# Copyright 2018 The Open AI Team Authors and The HuggingFace Inc. team.
#
# 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
#
# ... |
# coding: utf-8
import pytest
import lightgbm as lgb
from .utils import pickle_obj, unpickle_obj
@pytest.mark.parametrize('serializer', ["pickle", "joblib", "cloudpickle"])
def test_early_stopping_callback_is_picklable(serializer, tmp_path):
rounds = 5
callback = lgb.early_stopping(stopping_rounds=rounds)
... | # coding: utf-8
import pytest
import lightgbm as lgb
from .utils import pickle_obj, unpickle_obj
@pytest.mark.parametrize('serializer', ["pickle", "joblib", "cloudpickle"])
def test_early_stopping_callback_is_picklable(serializer, tmp_path):
callback = lgb.early_stopping(stopping_rounds=5)
tmp_file = tmp_pa... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.