input stringlengths 33 5k | output stringlengths 32 5k |
|---|---|
import torch
from ._bounding_boxes import BoundingBoxes, BoundingBoxFormat
from ._image import Image
from ._mask import Mask
from ._torch_function_helpers import set_return_type
from ._tv_tensor import TVTensor
from ._video import Video
# TODO: Fix this. We skip this method as it leads to
# RecursionError: maximum r... | import torch
from ._bounding_boxes import BoundingBoxes, BoundingBoxFormat
from ._image import Image
from ._mask import Mask
from ._torch_function_helpers import set_return_type
from ._tv_tensor import TVTensor
from ._video import Video
def wrap(wrappee, *, like, **kwargs):
"""[BETA] Convert a :class:`torch.Tens... |
import os
from pathlib import Path
import numpy as np
import pytest
import torch
from mmdet.apis import inference_detector, init_detector
from mmdet.structures import DetDataSample
from mmdet.utils import register_all_modules
# TODO: Waiting to fix multiple call error bug
register_all_modules()
@pytest.mark.parame... | import os
from pathlib import Path
import numpy as np
import pytest
import torch
from mmdet.apis import inference_detector, init_detector
from mmdet.data_elements import DetDataSample
from mmdet.utils import register_all_modules
# TODO: Waiting to fix multiple call error bug
register_all_modules()
@pytest.mark.par... |
__version__ = '0.13.1'
import os
from .document import Document
from .array import DocumentArray
from .dataclasses import dataclass, field
if 'DA_NO_RICH_HANDLER' not in os.environ:
from rich.traceback import install
install()
| __version__ = '0.13.1'
import os
from .document import Document
from .array import DocumentArray
from .dataclasses import dataclass, field
if 'DA_NO_RICH_HANDLER' not in os.environ:
from rich.traceback import install
install()
if 'NO_VERSION_CHECK' not in os.environ:
from .helper import is_latest_versi... |
from collections import defaultdict
import torch
import transforms as reference_transforms
def get_modules(use_v2):
# We need a protected import to avoid the V2 warning in case just V1 is used
if use_v2:
import torchvision.datapoints
import torchvision.transforms.v2
return torchvisio... | from collections import defaultdict
import torch
import transforms as reference_transforms
def get_modules(use_v2):
# We need a protected import to avoid the V2 warning in case just V1 is used
if use_v2:
import torchvision.datapoints
import torchvision.transforms.v2
return torchvisio... |
from keras.src.api_export import keras_export
from keras.src.layers.pooling.base_pooling import BasePooling
@keras_export(["keras.layers.AveragePooling2D", "keras.layers.AvgPool2D"])
class AveragePooling2D(BasePooling):
"""Average pooling operation for 2D spatial data.
Downsamples the input along its spatial... | from keras.src.api_export import keras_export
from keras.src.layers.pooling.base_pooling import BasePooling
@keras_export(["keras.layers.AveragePooling2D", "keras.layers.AvgPool2D"])
class AveragePooling2D(BasePooling):
"""Average pooling operation for 2D spatial data.
Downsamples the input along its spatial... |
from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.callbacks.utils import (
BaseMetadataCallbackHandler,
_flatten_dict,
flatten_dict,
hash_string,
import_pandas,
import_spacy,
import_te... | from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.callbacks.utils import (
BaseMetadataCallbackHandler,
_flatten_dict,
flatten_dict,
hash_string,
import_pandas,
import_spacy,
import_te... |
_base_ = [
'../_base_/models/mask-rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth' # noqa
model = dict(
ty... | _base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth' # noqa
model = dict(
ty... |
import logging
import random
from datasets import load_dataset
from sentence_transformers import SparseEncoder
from sentence_transformers.sparse_encoder.evaluation import SparseInformationRetrievalEvaluator
logging.basicConfig(format="%(message)s", level=logging.INFO)
# Load a model
model = SparseEncoder("naver/spl... | import logging
import random
from datasets import load_dataset
from sentence_transformers import SparseEncoder
from sentence_transformers.sparse_encoder.evaluation import SparseInformationRetrievalEvaluator
logging.basicConfig(format="%(message)s", level=logging.INFO)
# Load a model
model = SparseEncoder("naver/spl... |
_base_ = './solov2-light_r50_fpn_ms-3x_coco.py'
# model settings
model = dict(
backbone=dict(
dcn=dict(type='DCNv2', deformable_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)),
mask_head=dict(
feat_channels=256,
stacked_convs=3,
scale_range... | _base_ = 'solov2_light_r50_fpn_mstrain_3x_coco.py'
# model settings
model = dict(
backbone=dict(
dcn=dict(type='DCNv2', deformable_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)),
mask_head=dict(
feat_channels=256,
stacked_convs=3,
scale_ra... |
# Copyright (c) OpenMMLab. All rights reserved.
import sys
from unittest import TestCase
import mmengine
from mmengine.utils.dl_utils import collect_env
class TestCollectEnv(TestCase):
def test_collect_env(self):
env_info = collect_env()
expected_keys = [
'sys.platform', 'Python', 'C... | # Copyright (c) OpenMMLab. All rights reserved.
import sys
from unittest import TestCase
import torch.cuda
import mmengine
from mmengine.utils.dl_utils import collect_env
from mmengine.utils.dl_utils.parrots_wrapper import _get_cuda_home
class TestCollectEnv(TestCase):
def test_get_cuda_home(self):
CUD... |
"""Utils for LLM Compiler."""
import ast
import re
from typing import Any, Dict, List, Sequence, Tuple, Union
from llama_index.core.tools.function_tool import FunctionTool
from llama_index.core.tools.types import BaseTool, adapt_to_async_tool
from .schema import (
LLMCompilerParseResult,
LLMCompilerTask,
)
#... | """Utils for LLM Compiler."""
import ast
import re
from typing import Any, Dict, List, Sequence, Tuple, Union
from llama_index.core.tools.function_tool import FunctionTool
from llama_index.core.tools.types import BaseTool, adapt_to_async_tool
from .schema import (
LLMCompilerParseResult,
LLMCompilerTask,
)
#... |
"""Base class for Office 365 tools."""
from __future__ import annotations
from typing import TYPE_CHECKING
from langchain_core.tools import BaseTool
from pydantic import Field
from langchain_community.tools.office365.utils import authenticate
if TYPE_CHECKING:
from O365 import Account
class O365BaseTool(Base... | """Base class for Office 365 tools."""
from __future__ import annotations
from typing import TYPE_CHECKING
from langchain_core.tools import BaseTool
from pydantic import Field
from langchain_community.tools.office365.utils import authenticate
if TYPE_CHECKING:
from O365 import Account
class O365BaseTool(Base... |
# THIS FILE HAS BEEN AUTOGENERATED. To update:
# 1. modify the `_deps` dict in setup.py
# 2. run `make deps_table_update`
deps = {
"Pillow": "Pillow",
"accelerate": "accelerate>=0.31.0",
"compel": "compel==0.1.8",
"datasets": "datasets",
"filelock": "filelock",
"flax": "flax>=0.4.1",
"hf-doc... | # THIS FILE HAS BEEN AUTOGENERATED. To update:
# 1. modify the `_deps` dict in setup.py
# 2. run `make deps_table_update`
deps = {
"Pillow": "Pillow",
"accelerate": "accelerate>=0.31.0",
"compel": "compel==0.1.8",
"datasets": "datasets",
"filelock": "filelock",
"flax": "flax>=0.4.1",
"hf-doc... |
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 _FillType, ... | 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, ... |
# Copyright (c) OpenMMLab. All rights reserved.
"""Image Demo.
This script adopts a new infenence class, currently supports image path,
np.array and folder input formats, and will support video and webcam
in the future.
Example:
Save visualizations and predictions results::
python demo/image_demo.py demo... | # Copyright (c) OpenMMLab. All rights reserved.
"""Image Demo.
This script adopts a new infenence class, currently supports image path,
np.array and folder input formats, and will support video and webcam
in the future.
Example:
Save visualizations and predictions results::
python demo/image_demo.py demo... |
_base_ = [
'../common/mstrain-poly_3x_coco_instance.py',
'../_base_/models/mask_rcnn_r50_fpn.py'
]
model = dict(
# use caffe img_norm
data_preprocessor=dict(
mean=[103.530, 116.280, 123.675],
std=[1.0, 1.0, 1.0],
bgr_to_rgb=False),
backbone=dict(
depth=101,
n... | _base_ = [
'../common/mstrain-poly_3x_coco_instance.py',
'../_base_/models/mask_rcnn_r50_fpn.py'
]
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(
# use caffe img_norm
preprocess_cfg=preprocess_cfg,
backb... |
import logging
import tempfile
import typing
import autogpt_libs.auth.depends
import fastapi
import fastapi.responses
import prisma.enums
import backend.server.v2.store.db
import backend.server.v2.store.exceptions
import backend.server.v2.store.model
import backend.util.json
logger = logging.getLogger(__name__)
rou... | import logging
import typing
import autogpt_libs.auth.depends
import fastapi
import fastapi.responses
import prisma.enums
import backend.server.v2.store.db
import backend.server.v2.store.exceptions
import backend.server.v2.store.model
logger = logging.getLogger(__name__)
router = fastapi.APIRouter(prefix="/admin", ... |
from ._transforms import (
AddNoise,
BarkScale,
BarkSpectrogram,
Convolve,
FFTConvolve,
InverseBarkScale,
Speed,
SpeedPerturbation,
)
__all__ = [
"AddNoise",
"BarkScale",
"BarkSpectrogram",
"Convolve",
"FFTConvolve",
"InverseBarkScale",
"SpeedPerturbation",
... | from ._transforms import BarkScale, BarkSpectrogram, Convolve, FFTConvolve, InverseBarkScale, Speed, SpeedPerturbation
__all__ = [
"BarkScale",
"BarkSpectrogram",
"Convolve",
"FFTConvolve",
"InverseBarkScale",
"SpeedPerturbation",
"Speed",
]
|
import pytest
from hypothesis import assume, given, note, settings, strategies
import xgboost as xgb
from xgboost import testing as tm
pytestmark = tm.timeout(10)
parameter_strategy = strategies.fixed_dictionaries(
{
"booster": strategies.just("gblinear"),
"eta": strategies.floats(0.01, 0.25),
... | import pytest
from hypothesis import assume, given, note, settings, strategies
import xgboost as xgb
from xgboost import testing as tm
pytestmark = tm.timeout(10)
parameter_strategy = strategies.fixed_dictionaries({
'booster': strategies.just('gblinear'),
'eta': strategies.floats(0.01, 0.25),
'tolerance'... |
import multiprocessing
from concurrent.futures import ThreadPoolExecutor
import pytest
import xgboost as xgb
@pytest.mark.parametrize("verbosity_level", [0, 1, 2, 3])
def test_global_config_verbosity(verbosity_level):
def get_current_verbosity():
return xgb.get_config()["verbosity"]
old_verbosity =... | import multiprocessing
from concurrent.futures import ThreadPoolExecutor
import pytest
import xgboost as xgb
@pytest.mark.parametrize("verbosity_level", [0, 1, 2, 3])
def test_global_config_verbosity(verbosity_level):
def get_current_verbosity():
return xgb.get_config()["verbosity"]
old_verbosity =... |
from typing import Type
from docarray.proto import DocumentArrayProto, NodeProto
from ..abstract_array import AbstractDocumentArray
class ProtoArrayMixin(AbstractDocumentArray):
@classmethod
def from_protobuf(
cls: Type[AbstractDocumentArray], pb_msg: 'DocumentArrayProto'
) -> AbstractDocumentAr... | from typing import Type
from docarray.proto import DocumentArrayProto, NodeProto
from ..abstract_array import AbstractDocumentArray
class ProtoArrayMixin(AbstractDocumentArray):
@classmethod
def from_protobuf(
cls: Type[AbstractDocumentArray], pb_msg: 'DocumentArrayProto'
) -> AbstractDocumentAr... |
import logging
import random
from datasets import load_dataset
from sentence_transformers import SparseEncoder
from sentence_transformers.sparse_encoder.evaluation import SparseInformationRetrievalEvaluator
logging.basicConfig(format="%(message)s", level=logging.INFO)
# Load a model
model = SparseEncoder("naver/spl... | import logging
import random
from datasets import load_dataset
from sentence_transformers import SparseEncoder
from sentence_transformers.sparse_encoder.evaluation import SparseInformationRetrievalEvaluator
logging.basicConfig(format="%(message)s", level=logging.INFO)
# Load a model
model = SparseEncoder("naver/spl... |
from langchain_core.agents import AgentAction, AgentFinish
from langchain.agents.output_parsers.xml import XMLAgentOutputParser
def test_tool_usage() -> None:
parser = XMLAgentOutputParser()
# Test when final closing </tool_input> is included
_input = """<tool>search</tool><tool_input>foo</tool_input>"""... | from langchain_core.agents import AgentAction, AgentFinish
from langchain.agents.output_parsers.xml import XMLAgentOutputParser
def test_tool_usage() -> None:
parser = XMLAgentOutputParser()
# Test when final closing </tool_input> is included
_input = """<tool>search</tool><tool_input>foo</tool_input>"""... |
import asyncio
from langchain_core.callbacks import (
AsyncCallbackManagerForRetrieverRun,
CallbackManagerForRetrieverRun,
)
from langchain_core.documents import Document
from langchain_core.retrievers import BaseRetriever
class MergerRetriever(BaseRetriever):
"""Retriever that merges the results of mult... | import asyncio
from langchain_core.callbacks import (
AsyncCallbackManagerForRetrieverRun,
CallbackManagerForRetrieverRun,
)
from langchain_core.documents import Document
from langchain_core.retrievers import BaseRetriever
class MergerRetriever(BaseRetriever):
"""Retriever that merges the results of mult... |
_base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
data_preprocessor=dict(
# The mean and std are used in PyCls when training RegNets
mean=[103.53, 116.28, 123.67... | _base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
backbone=dict(
_delete_=True,
type='RegNet',
arch='regnetx_3.2gf',
out_indices=(0, 1, 2, 3),
... |
from textwrap import dedent
from types import SimpleNamespace
from unittest.mock import patch
from urllib.parse import quote
import pytest
from huggingface_hub import CommitOperationAdd, CommitOperationDelete
import datasets
from datasets.config import METADATA_CONFIGS_FIELD
from datasets.hub import delete_from_hub
f... | from textwrap import dedent
from types import SimpleNamespace
from unittest.mock import patch
from urllib.parse import quote
import pytest
from huggingface_hub import CommitOperationAdd, CommitOperationDelete
import datasets
from datasets.config import METADATA_CONFIGS_FIELD
from datasets.hub import delete_from_hub
f... |
# Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
from mmcv.utils import build_from_cfg
from mmdet.datasets.builder import PIPELINES
def test_default_format_bundle():
results = dict(
img_prefix=osp.join(osp.dirname(__file__), '../../data'),
img_info=dict(filename='color.jpg')... | import os.path as osp
from mmcv.utils import build_from_cfg
from mmdet.datasets.builder import PIPELINES
def test_default_format_bundle():
results = dict(
img_prefix=osp.join(osp.dirname(__file__), '../../data'),
img_info=dict(filename='color.jpg'))
load = dict(type='LoadImageFromFile')
... |
import os
import sys
import pytest
from llama_index.core.evaluation.eval_utils import upload_eval_dataset
base_url = os.environ.get("LLAMA_CLOUD_BASE_URL", None)
api_key = os.environ.get("LLAMA_CLOUD_API_KEY", None)
python_version = sys.version
@pytest.mark.skipif(
not base_url or not api_key, reason="No platfo... | import os
import sys
import pytest
from llama_index.core.evaluation.eval_utils import upload_eval_dataset
base_url = os.environ.get("LLAMA_CLOUD_BASE_URL", None)
api_key = os.environ.get("LLAMA_CLOUD_API_KEY", None)
python_version = sys.version
@pytest.mark.skipif(
not base_url or not api_key, reason="No platfo... |
import abc
from platform import architecture, python_version
from typing import Any, Optional
from importlib.metadata import version
from llama_index.core.readers.base import BasePydanticReader
from llama_index.core.schema import Document
from llama_index.readers.oxylabs.utils import json_to_markdown
from oxylabs imp... | import abc
from platform import architecture, python_version
from typing import Any
from importlib.metadata import version
from llama_index.core.readers.base import BasePydanticReader
from llama_index.core.schema import Document
from llama_index.readers.oxylabs.utils import json_to_markdown
from oxylabs import Realti... |
# Copyright (c) OpenMMLab. All rights reserved.
from .gaussian_target import (gather_feat, gaussian_radius,
gen_gaussian_target, get_local_maximum,
get_topk_from_heatmap, transpose_and_gather_feat)
from .make_divisible import make_divisible
from .misc import (... | # Copyright (c) OpenMMLab. All rights reserved.
from .gaussian_target import (gather_feat, gaussian_radius,
gen_gaussian_target, get_local_maximum,
get_topk_from_heatmap, transpose_and_gather_feat)
from .make_divisible import make_divisible
from .misc import (... |
from __future__ import annotations
from typing import Any, Optional, Sequence, Type, TypeVar, Union
import torch
from torch.utils._pytree import tree_map
from torchvision.datapoints._datapoint import Datapoint
L = TypeVar("L", bound="_LabelBase")
class _LabelBase(Datapoint):
categories: Optional[Sequence[str... | from __future__ import annotations
from typing import Any, Optional, Sequence, Type, TypeVar, Union
import torch
from torch.utils._pytree import tree_map
from torchvision.datapoints._datapoint import Datapoint
L = TypeVar("L", bound="_LabelBase")
class _LabelBase(Datapoint):
categories: Optional[Sequence[str... |
import numpy as np
import pytest
from tensorflow import data as tf_data
from keras.src import backend
from keras.src import layers
from keras.src import testing
from keras.src.ops import convert_to_tensor
class StringLookupTest(testing.TestCase):
# TODO: increase coverage. Most features aren't being tested.
... | import numpy as np
import pytest
from tensorflow import data as tf_data
from keras.src import backend
from keras.src import layers
from keras.src import testing
from keras.src.ops import convert_to_tensor
class StringLookupTest(testing.TestCase):
# TODO: increase coverage. Most features aren't being tested.
... |
_base_ = './faster-rcnn_r50_fpn_1x_coco.py'
# MMEngine support the following two ways, users can choose
# according to convenience
# optim_wrapper = dict(type='AmpOptimWrapper')
_base_.optim_wrapper.type = 'AmpOptimWrapper'
| _base_ = './faster-rcnn_r50_fpn_1x_coco.py'
# fp16 settings
fp16 = dict(loss_scale=512.)
|
import os
import time
import pytest
from docarray import Document
from jina import Flow, __cache_path__
cur_dir = os.path.dirname(os.path.abspath(__file__))
@pytest.fixture(scope='module')
def filewriter_exec_docker_image_built():
import docker
client = docker.from_env()
client.images.build(
p... | import os
import time
from unittest import mock
import pytest
from docarray import Document, DocumentArray
from jina import Executor, Flow, requests
cur_dir = os.path.dirname(os.path.abspath(__file__))
@pytest.fixture(scope='module')
def filewriter_exec_docker_image_built():
import docker
client = docker.... |
# Copyright (c) OpenMMLab. All rights reserved.
from mmdet.registry import MODELS
from .single_stage import SingleStageDetector
@MODELS.register_module()
class YOLOF(SingleStageDetector):
r"""Implementation of `You Only Look One-level Feature
<https://arxiv.org/abs/2103.09460>`_"""
def __init__(self,
... | # Copyright (c) OpenMMLab. All rights reserved.
from ..builder import DETECTORS
from .single_stage import SingleStageDetector
@DETECTORS.register_module()
class YOLOF(SingleStageDetector):
r"""Implementation of `You Only Look One-level Feature
<https://arxiv.org/abs/2103.09460>`_"""
def __init__(self,
... |
_base_ = [
'../common/ms-poly_3x_coco-instance.py',
'../_base_/models/mask-rcnn_r50_fpn.py'
]
model = dict(
# use caffe img_norm
data_preprocessor=dict(
mean=[103.530, 116.280, 123.675],
std=[1.0, 1.0, 1.0],
bgr_to_rgb=False),
backbone=dict(
depth=101,
norm_c... | _base_ = [
'../common/mstrain-poly_3x_coco_instance.py',
'../_base_/models/mask_rcnn_r50_fpn.py'
]
model = dict(
# use caffe img_norm
data_preprocessor=dict(
mean=[103.530, 116.280, 123.675],
std=[1.0, 1.0, 1.0],
bgr_to_rgb=False),
backbone=dict(
depth=101,
n... |
_base_ = [
'../common/mstrain_3x_coco_instance.py',
'../_base_/models/cascade_mask_rcnn_r50_fpn.py'
]
model = dict(
# use caffe img_norm
data_preprocessor=dict(
mean=[103.530, 116.280, 123.675],
std=[1.0, 1.0, 1.0],
bgr_to_rgb=False),
backbone=dict(
norm_cfg=dict(req... | _base_ = [
'../common/mstrain_3x_coco_instance.py',
'../_base_/models/cascade_mask_rcnn_r50_fpn.py'
]
model = dict(
# use caffe img_norm
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[103.530, 116.280, 123.675],
std=[1.0, 1.0, 1.0],
to_rgb=False,
pad_s... |
# Copyright (c) OpenMMLab. All rights reserved.
from .accuracy import Accuracy, accuracy
from .ae_loss import AssociativeEmbeddingLoss
from .balanced_l1_loss import BalancedL1Loss, balanced_l1_loss
from .cross_entropy_loss import (CrossEntropyLoss, binary_cross_entropy,
cross_entropy, m... | # Copyright (c) OpenMMLab. All rights reserved.
from .accuracy import Accuracy, accuracy
from .ae_loss import AssociativeEmbeddingLoss
from .balanced_l1_loss import BalancedL1Loss, balanced_l1_loss
from .cross_entropy_loss import (CrossEntropyLoss, binary_cross_entropy,
cross_entropy, m... |
"""Tests related to the `DataIter` interface."""
from typing import Callable, Optional
import numpy as np
from xgboost import testing as tm
from ..core import DataIter, ExtMemQuantileDMatrix, QuantileDMatrix
def run_mixed_sparsity(device: str) -> None:
"""Check QDM with mixed batches."""
X_0, y_0, _ = tm.... | """Tests related to the `DataIter` interface."""
import numpy as np
import xgboost
from xgboost import testing as tm
def run_mixed_sparsity(device: str) -> None:
"""Check QDM with mixed batches."""
X_0, y_0, _ = tm.make_regression(128, 16, False)
if device.startswith("cuda"):
X_1, y_1 = tm.make_... |
from __future__ import annotations
import random
import pytest
import torch
from torch.utils.data import ConcatDataset
from sentence_transformers.sampler import NoDuplicatesBatchSampler, ProportionalBatchSampler
from sentence_transformers.util import is_datasets_available
if is_datasets_available():
from datase... | from __future__ import annotations
import random
import pytest
import torch
from datasets import Dataset
from torch.utils.data import ConcatDataset
from sentence_transformers.sampler import NoDuplicatesBatchSampler, ProportionalBatchSampler
@pytest.fixture
def dummy_dataset() -> Dataset:
"""
Dummy dataset ... |
from typing import Any, 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 a tens... | 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 TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.utilities.reddit_search import RedditSearchAPIWrapper
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handling o... | from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.utilities.reddit_search import RedditSearchAPIWrapper
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handling o... |
import logging
from typing import Dict, Sequence
from octoai.text_gen import ChatMessage as OctoAIChatMessage
from llama_index.core.base.llms.types import ChatMessage
TEXT_MODELS: Dict[str, int] = {
"codellama-13b-instruct": 16384,
"codellama-34b-instruct": 16384,
"codellama-7b-instruct": 4096,
"met... | import logging
from typing import Dict, Sequence
from octoai.text_gen import ChatMessage as OctoAIChatMessage
from llama_index.core.base.llms.types import ChatMessage
TEXT_MODELS: Dict[str, int] = {
"codellama-13b-instruct": 16384,
"codellama-34b-instruct": 16384,
"codellama-7b-instruct": 4096,
"met... |
# Copyright (c) OpenMMLab. All rights reserved.
from mmengine.registry import HOOKS
from .hook import Hook
@HOOKS.register_module()
class DistSamplerSeedHook(Hook):
"""Data-loading sampler for distributed training.
When distributed training, it is only useful in conjunction with
:obj:`EpochBasedRunner`, ... | # Copyright (c) OpenMMLab. All rights reserved.
from mmengine.registry import HOOKS
from .hook import Hook
@HOOKS.register_module()
class DistSamplerSeedHook(Hook):
"""Data-loading sampler for distributed training.
When distributed training, it is only useful in conjunction with
:obj:`EpochBasedRunner`, ... |
import warnings
from abc import abstractmethod
from typing import Iterable, Iterator, MutableSequence
from docarray import Document, DocumentArray
class BaseSequenceLikeMixin(MutableSequence[Document]):
"""Implement sequence-like methods"""
def _update_subindices_append_extend(self, value):
if getat... | import warnings
from abc import abstractmethod
from typing import Iterable, Iterator, MutableSequence
from docarray import Document, DocumentArray
class BaseSequenceLikeMixin(MutableSequence[Document]):
"""Implement sequence-like methods"""
def _update_subindices_append_extend(self, value):
if getat... |
from llama_index.core.extractors.interface import BaseExtractor
from llama_index.core.extractors.metadata_extractors import (
KeywordExtractor,
PydanticProgramExtractor,
QuestionsAnsweredExtractor,
SummaryExtractor,
TitleExtractor,
)
from llama_index.core.extractors.document_context import DocumentC... | from llama_index.core.extractors.interface import BaseExtractor
from llama_index.core.extractors.metadata_extractors import (
KeywordExtractor,
PydanticProgramExtractor,
QuestionsAnsweredExtractor,
SummaryExtractor,
TitleExtractor,
)
__all__ = [
"SummaryExtractor",
"QuestionsAnsweredExtract... |
# Copyright (c) OpenMMLab. All rights reserved.
from .anchor_free_head import AnchorFreeHead
from .anchor_head import AnchorHead
from .atss_head import ATSSHead
from .autoassign_head import AutoAssignHead
from .cascade_rpn_head import CascadeRPNHead, StageCascadeRPNHead
from .centernet_head import CenterNetHead
from .c... | # Copyright (c) OpenMMLab. All rights reserved.
from .anchor_free_head import AnchorFreeHead
from .anchor_head import AnchorHead
from .atss_head import ATSSHead
from .autoassign_head import AutoAssignHead
from .cascade_rpn_head import CascadeRPNHead, StageCascadeRPNHead
from .centernet_head import CenterNetHead
from .c... |
from keras.src import ops
from keras.src.api_export import keras_export
from keras.src.optimizers import optimizer
@keras_export(["keras.optimizers.Lion"])
class Lion(optimizer.Optimizer):
"""Optimizer that implements the Lion algorithm.
The Lion optimizer is a stochastic-gradient-descent method that uses th... | from keras.src import ops
from keras.src.api_export import keras_export
from keras.src.optimizers import optimizer
@keras_export(["keras.optimizers.Lion"])
class Lion(optimizer.Optimizer):
"""Optimizer that implements the Lion algorithm.
The Lion optimizer is a stochastic-gradient-descent method that uses th... |
# Copyright (c) OpenMMLab. All rights reserved.
from unittest import TestCase
import torch
from mmengine.structures import InstanceData
from mmdet.models.dense_heads import FoveaHead
class TestFOVEAHead(TestCase):
def test_fovea_head_loss(self):
"""Tests anchor head loss when truth is empty and non-emp... | # Copyright (c) OpenMMLab. All rights reserved.
from unittest import TestCase
import torch
from mmengine.data import InstanceData
from mmdet.models.dense_heads import FoveaHead
class TestFOVEAHead(TestCase):
def test_fovea_head_loss(self):
"""Tests anchor head loss when truth is empty and non-empty."""... |
from __future__ import annotations
from typing import TYPE_CHECKING, Optional
from langchain_core.callbacks.base import BaseCallbackHandler
if TYPE_CHECKING:
from langchain_community.callbacks import LLMThoughtLabeler
from streamlit.delta_generator import DeltaGenerator
def StreamlitCallbackHandler(
pa... | from __future__ import annotations
from typing import TYPE_CHECKING, Optional
from langchain_core.callbacks.base import BaseCallbackHandler
if TYPE_CHECKING:
from langchain_community.callbacks import LLMThoughtLabeler
from streamlit.delta_generator import DeltaGenerator
def StreamlitCallbackHandler(
pa... |
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.... |
from __future__ import annotations
import logging
from typing import TYPE_CHECKING, Any, Callable
from sentence_transformers.evaluation import InformationRetrievalEvaluator
if TYPE_CHECKING:
import numpy as np
from torch import Tensor
from sentence_transformers.similarity_functions import SimilarityFunc... | from __future__ import annotations
import logging
from typing import TYPE_CHECKING, Any, Callable
from sentence_transformers.evaluation import InformationRetrievalEvaluator
if TYPE_CHECKING:
import numpy as np
from torch import Tensor
from sentence_transformers.similarity_functions import SimilarityFunc... |
from llama_index.observability.otel import LlamaIndexOpenTelemetry
from llama_index.observability.otel.base import (
Resource,
SERVICE_NAME,
ConsoleSpanExporter,
)
def test_initialization() -> None:
instrumentor = LlamaIndexOpenTelemetry()
assert instrumentor.service_name_or_resource == ... | from llama_index.observability.otel import LlamaIndexOpenTelemetry
from llama_index.observability.otel.base import Resource, SERVICE_NAME, ConsoleSpanExporter
def test_initialization() -> None:
instrumentor = LlamaIndexOpenTelemetry()
assert instrumentor.service_name_or_resource == Resource(attributes={SE... |
"""Interface with the LangChain Hub."""
from __future__ import annotations
import json
from collections.abc import Sequence
from typing import Any, Optional
from langchain_core.load.dump import dumps
from langchain_core.load.load import loads
from langchain_core.prompts import BasePromptTemplate
def _get_client(
... | """Interface with the LangChain Hub."""
from __future__ import annotations
import json
from collections.abc import Sequence
from typing import Any, Optional
from langchain_core.load.dump import dumps
from langchain_core.load.load import loads
from langchain_core.prompts import BasePromptTemplate
def _get_client(
... |
from __future__ import annotations
try:
from typing import Self
except ImportError:
from typing_extensions import Self
import torch
from torch import Tensor, nn
from sentence_transformers.models.Module import Module
class WeightedLayerPooling(Module):
"""Token embeddings are weighted mean of their diff... | from __future__ import annotations
import json
import os
import torch
from safetensors.torch import load_model as load_safetensors_model
from safetensors.torch import save_model as save_safetensors_model
from torch import Tensor, nn
class WeightedLayerPooling(nn.Module):
"""Token embeddings are weighted mean of... |
"""DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.layers.preprocessing.image_preprocessing.bounding_boxes.converters import (
affine_transform as affine_transform,
)
from keras.src.layers.preprocessing.image_preprocessing.boundin... | """DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.layers.preprocessing.image_preprocessing.bounding_boxes.converters import (
affine_transform,
)
from keras.src.layers.preprocessing.image_preprocessing.bounding_boxes.converters i... |
import logging
import os
from typing import Optional
from jina.importer import ImportExtensions
from jina.serve.runtimes.servers import BaseServer
from jina._docarray import docarray_v2
class WebSocketServer(BaseServer):
"""WebSocket Server implementation"""
def __init__(
self,
ssl_... | import logging
import os
from typing import Optional
from jina.importer import ImportExtensions
from jina.serve.runtimes.servers import BaseServer
from jina._docarray import docarray_v2
class WebSocketServer(BaseServer):
"""WebSocket Server implementation"""
def __init__(
self,
ssl_... |
import torch
from keras.src.backend import config
from keras.src.backend import standardize_dtype
from keras.src.backend.common import dtypes
from keras.src.backend.torch.core import cast
from keras.src.backend.torch.core import convert_to_tensor
def cholesky(x):
return torch.linalg.cholesky(x)
def det(x):
... | import torch
from keras.src.backend import config
from keras.src.backend import standardize_dtype
from keras.src.backend.common import dtypes
from keras.src.backend.torch.core import cast
from keras.src.backend.torch.core import convert_to_tensor
def cholesky(x):
return torch.linalg.cholesky(x)
def det(x):
... |
from typing import Union
import numpy as np
import pytest
import torch
from docarray import Document, DocumentArray
from docarray.typing import NdArray, TorchTensor
@pytest.fixture()
def batch():
class Image(Document):
tensor: TorchTensor[3, 224, 224]
batch = DocumentArray[Image](
[Image(te... | import numpy as np
import pytest
import torch
from docarray import Document, DocumentArray
from docarray.typing import NdArray, TorchTensor
@pytest.fixture()
def batch():
class Image(Document):
tensor: TorchTensor[3, 224, 224]
batch = DocumentArray[Image](
[Image(tensor=torch.zeros(3, 224, 2... |
_base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
# model settings
model = dict(
type='CenterNet',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123... | _base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
# file_client_args = dict(
# backend='petrel',
# path_mapping=dict({
# './data/': 's3://openmmlab/datasets/detection/'... |
"""Argparser module for Pod runtimes"""
import argparse
from dataclasses import dataclass
from typing import Dict
from jina import helper
from jina.enums import PodRoleType
from jina.parsers.helper import _SHOW_ALL_ARGS, KVAppendAction, add_arg_group
@dataclass
class PodTypeParams:
"""Data Class representing pos... | """Argparser module for Pod runtimes"""
import argparse
from dataclasses import dataclass
from typing import Dict
from jina import helper
from jina.enums import PodRoleType
from jina.parsers.helper import _SHOW_ALL_ARGS, KVAppendAction, add_arg_group
@dataclass
class PodTypeParams:
"""Data Class representing pos... |
import numpy as np
def psd_numpy(specgram, mask=None, normalize=True, eps=1e-10):
specgram_transposed = np.swapaxes(specgram, 0, 1)
psd = np.einsum("...ct,...et->...tce", specgram_transposed, specgram_transposed.conj())
if mask is not None:
if normalize:
mask_normmalized = mask / (mask... | import numpy as np
def psd_numpy(specgram, mask=None, normalize=True, eps=1e-10):
specgram_transposed = np.swapaxes(specgram, 0, 1)
psd = np.einsum("...ct,...et->...tce", specgram_transposed, specgram_transposed.conj())
if mask is not None:
if normalize:
mask_normmalized = mask / (mask... |
# 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... | # 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... |
from typing import Optional, Tuple
import torch
from ..utils import logging
logger = logging.get_logger(__name__)
def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
"""
This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
nu... | from typing import Optional, Tuple
import torch
def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
"""
This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, ... |
from typing import List
from torch.utils.data import Dataset
from sentence_transformers import SentenceTransformer
from sentence_transformers.readers.InputExample import InputExample
class SentencesDataset(Dataset):
"""
DEPRECATED: This class is no longer used. Instead of wrapping your List of InputExamples... | from torch.utils.data import Dataset
from typing import List
from .. import SentenceTransformer
from ..readers.InputExample import InputExample
class SentencesDataset(Dataset):
"""
DEPRECATED: This class is no longer used. Instead of wrapping your List of InputExamples in a SentencesDataset
and then passi... |
_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, pad_mask=True)
]
norm_cfg = dict(type='SyncBN', requires_grad=True)
# Use MMSyncBN that handles empty tensor in head. It ca... | _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, pad_mask=True)
]
norm_cfg = dict(type='SyncBN', requires_grad=True)
# Use MMSyncBN that handles empty tensor in head. It ca... |
import logging
import os
import tarfile
import zipfile
from typing import Any, List, Optional
import torchaudio
_LG = logging.getLogger(__name__)
def _extract_tar(from_path: str, to_path: Optional[str] = None, overwrite: bool = False) -> List[str]:
if to_path is None:
to_path = os.path.dirname(from_path... | import logging
import os
import tarfile
import zipfile
from typing import Any, List, Optional
import torchaudio
def _extract_tar(from_path: str, to_path: Optional[str] = None, overwrite: bool = False) -> List[str]:
if to_path is None:
to_path = os.path.dirname(from_path)
with tarfile.open(from_path, ... |
from typing import Union
from docarray.typing.tensor.ndarray import NdArray
from docarray.utils.misc import is_tf_available, is_torch_available
torch_available = is_torch_available()
if torch_available:
from docarray.typing.tensor.torch_tensor import TorchTensor # noqa: F401
tf_available = is_tf_available()
if... | from typing import Union
from docarray.typing.tensor.ndarray import NdArray
from docarray.utils.misc import is_tf_available, is_torch_available
torch_available = is_torch_available()
if torch_available:
from docarray.typing.tensor.torch_tensor import TorchTensor # noqa: F401
tf_available = is_tf_available()
if... |
# Copyright (c) OpenMMLab. All rights reserved.
from unittest.mock import Mock
from mmengine.hooks import EmptyCacheHook
class TestEmptyCacheHook:
def test_emtpy_cache_hook(self):
Hook = EmptyCacheHook(True, True, True)
Runner = Mock()
Hook.after_iter(Runner)
Hook.before_epoch(Ru... | # Copyright (c) OpenMMLab. All rights reserved.
from mock import Mock
from mmengine.hooks import EmptyCacheHook
class TestEmptyCacheHook:
def test_emtpy_cache_hook(self):
Hook = EmptyCacheHook(True, True, True)
Runner = Mock()
Hook.after_iter(Runner)
Hook.before_epoch(Runner)
... |
"""Couchbase document loader."""
from typing import Any, Iterable, List, Optional
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document
class CouchbaseReader(BaseReader):
"""
Couchbase document loader.
Loads data from a Couchbase cluster into Document used by... | """Couchbase document loader."""
from typing import Any, Iterable, List, Optional
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document
class CouchbaseReader(BaseReader):
"""Couchbase document loader.
Loads data from a Couchbase cluster into Document used by Llam... |
from ._alignment import forced_align, merge_tokens, TokenSpan
from .filtering import (
allpass_biquad,
band_biquad,
bandpass_biquad,
bandreject_biquad,
bass_biquad,
biquad,
contrast,
dcshift,
deemph_biquad,
dither,
equalizer_biquad,
filtfilt,
flanger,
gain,
hi... | from ._alignment import forced_align, merge_tokens, TokenSpan
from .filtering import (
allpass_biquad,
band_biquad,
bandpass_biquad,
bandreject_biquad,
bass_biquad,
biquad,
contrast,
dcshift,
deemph_biquad,
dither,
equalizer_biquad,
filtfilt,
flanger,
gain,
hi... |
import numpy as np
from docarray import BaseDoc
from docarray.array import DocVec
from docarray.array.doc_vec.column_storage import ColumnStorageView
from docarray.typing import AnyTensor
def test_document_view():
class MyDoc(BaseDoc):
tensor: AnyTensor
name: str
docs = [MyDoc(tensor=np.zero... | import numpy as np
from docarray import BaseDoc
from docarray.array import DocVec
from docarray.array.doc_vec.column_storage import ColumnStorageView
from docarray.typing import AnyTensor
def test_document_view():
class MyDoc(BaseDoc):
tensor: AnyTensor
name: str
docs = [MyDoc(tensor=np.zero... |
# model settings
norm_cfg = dict(type='BN', requires_grad=False)
model = dict(
type='FasterRCNN',
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(
... | # 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)
norm_cfg = dict(type='BN', requires_grad=False)
model = dict(
type='FasterRCNN',
preprocess_cfg=preprocess_cfg,
backbone=dict(
type='ResNet',
dept... |
import warnings
from typing import Any, Dict, Union
import numpy as np
import PIL.Image
import torch
from torchvision.transforms import functional as _F
from torchvision.transforms.v2 import Transform
class ToTensor(Transform):
"""[BETA] Convert a ``PIL Image`` or ``numpy.ndarray`` to tensor.
.. betastatus... | import warnings
from typing import Any, Dict, Union
import numpy as np
import PIL.Image
import torch
from torchvision.transforms import functional as _F
from torchvision.transforms.v2 import Transform
class ToTensor(Transform):
_transformed_types = (PIL.Image.Image, np.ndarray)
def __init__(self) -> None:
... |
import os
import pytest
from jina.orchestrate.deployments import Deployment
@pytest.fixture()
def cuda_total_devices(request):
old_cuda_total_devices = os.environ.get('CUDA_TOTAL_DEVICES', None)
os.environ['CUDA_TOTAL_DEVICES'] = str(request.param)
yield
if old_cuda_total_devices is not None:
... | import os
import pytest
from jina.orchestrate.deployments import Deployment
@pytest.fixture()
def cuda_total_devices(request):
old_cuda_total_devices = os.environ.get('CUDA_TOTAL_DEVICES', None)
os.environ['CUDA_TOTAL_DEVICES'] = str(request.param)
yield
if old_cuda_total_devices is not None:
... |
import json
from typing import Any, Type, TypeVar, overload
from fastapi.encoders import jsonable_encoder
from .type import type_match
def to_dict(data) -> dict:
return jsonable_encoder(data)
def dumps(data) -> str:
return json.dumps(jsonable_encoder(data))
T = TypeVar("T")
@overload
def loads(data: s... | import json
from fastapi.encoders import jsonable_encoder
def to_dict(data) -> dict:
return jsonable_encoder(data)
def dumps(data) -> str:
return json.dumps(jsonable_encoder(data))
loads = json.loads
|
"""
This file loads sentences from a provided text file. It is expected, that the there is one sentence per line in that text file.
TSDAE will be training using these sentences. Checkpoints are stored every 500 steps to the output folder.
Usage:
python train_tsdae_from_file.py path/to/sentences.txt
"""
from sentenc... | """
This file loads sentences from a provided text file. It is expected, that the there is one sentence per line in that text file.
TSDAE will be training using these sentences. Checkpoints are stored every 500 steps to the output folder.
Usage:
python train_tsdae_from_file.py path/to/sentences.txt
"""
from sentence... |
import gc
import asyncio
from llama_index.core.memory import ChatMemoryBuffer
from llama_index.core.base.llms.types import (
ChatMessage,
CompletionResponse,
CompletionResponseGen,
)
from typing import Any
from llama_index.core.llms.callbacks import llm_completion_callback
from llama_index.core.llms.mock im... | import gc
import asyncio
from llama_index.core.memory import ChatMemoryBuffer
from llama_index.core.base.llms.types import (
ChatMessage,
CompletionResponse,
CompletionResponseGen,
)
from typing import Any
from llama_index.core.llms.callbacks import llm_completion_callback
from llama_index.core.llms.mock im... |
# Copyright (c) OpenMMLab. All rights reserved.
# yapf: disable
from .lr_scheduler import (ConstantLR, CosineAnnealingLR, CosineRestartLR,
ExponentialLR, LinearLR, MultiStepLR, OneCycleLR,
PolyLR, ReduceOnPlateauLR, StepLR)
from .momentum_scheduler import (ConstantM... | # Copyright (c) OpenMMLab. All rights reserved.
# yapf: disable
from .lr_scheduler import (ConstantLR, CosineAnnealingLR, CosineRestartLR,
ExponentialLR, LinearLR, MultiStepLR, OneCycleLR,
PolyLR, StepLR)
from .momentum_scheduler import (ConstantMomentum, CosineAnne... |
import dataclasses
from typing import Any, Dict, Optional, Type
from jina.jaml.parsers.base import BaseLegacyParser
from jina.serve.executors import BaseExecutor
from jina.serve.executors.metas import get_default_metas
class ExecutorLegacyParser(BaseLegacyParser):
"""Legacy parser for executor."""
def parse... | import dataclasses
from typing import Any, Dict, Optional, Type
from jina.jaml.parsers.base import BaseLegacyParser
from jina.serve.executors import BaseExecutor
from jina.serve.executors.metas import get_default_metas
class ExecutorLegacyParser(BaseLegacyParser):
"""Legacy parser for executor."""
def parse... |
from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
logger = datasets.utils.logging.get_logger(__name__)
class AudioFolderConfig(folder_based_builder.FolderBasedBuilderConfig):
"""Builder Config for AudioFolder."""
... | from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
logger = datasets.utils.logging.get_logger(__name__)
class AudioFolderConfig(folder_based_builder.FolderBasedBuilderConfig):
"""Builder Config for AudioFolder."""
... |
"""Feishu docs reader."""
import json
import os
import time
from typing import List
import requests
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document
# Copyright (2023) Bytedance Ltd. and/or its affiliates
#
# Licensed under the Apache License, Version 2.0 (the "License... | """Feishu docs reader."""
import json
import os
import time
from typing import List
import requests
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document
# Copyright (2023) Bytedance Ltd. and/or its affiliates
#
# Licensed under the Apache License, Version 2.0 (the "License... |
import functools
import numbers
from collections import defaultdict
from typing import Any, Dict, Literal, Sequence, Type, TypeVar, Union
from torchvision.prototype import datapoints
from torchvision.prototype.datapoints._datapoint import FillType, FillTypeJIT
from torchvision.transforms.transforms import _check_sequ... | import functools
import numbers
from collections import defaultdict
from typing import Any, Dict, Sequence, Type, TypeVar, Union
from torchvision.prototype import datapoints
from torchvision.prototype.datapoints._datapoint import FillType, FillTypeJIT
from torchvision.transforms.transforms import _check_sequence_inpu... |
import numpy as np
import pytest
from pydantic.tools import parse_obj_as, schema_json_of
from docarray.base_doc.io.json import orjson_dumps
from docarray.typing import AnyEmbedding
@pytest.mark.proto
def test_proto_embedding():
embedding = parse_obj_as(AnyEmbedding, np.zeros((3, 224, 224)))
embedding._to_no... | import numpy as np
import pytest
from pydantic.tools import parse_obj_as, schema_json_of
from docarray.base_doc.io.json import orjson_dumps
from docarray.typing import AnyEmbedding
@pytest.mark.proto
def test_proto_embedding():
embedding = parse_obj_as(AnyEmbedding, np.zeros((3, 224, 224)))
embedding._to_n... |
# Copyright (c) OpenMMLab. All rights reserved.
import functools
import torch
import torch.nn.functional as F
def reduce_loss(loss, reduction):
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss tensor.
reduction (str): Options are "none", "mean" and "sum".
Return:
... | # Copyright (c) OpenMMLab. All rights reserved.
import functools
import mmcv
import torch
import torch.nn.functional as F
def reduce_loss(loss, reduction):
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss tensor.
reduction (str): Options are "none", "mean" and "sum".
R... |
"""Documents module.
**Document** module is a collection of classes that handle documents
and their transformations.
"""
from langchain_core.documents.base import Document
from langchain_core.documents.compressor import BaseDocumentCompressor
from langchain_core.documents.transformers import BaseDocumentTransformer
... | """**Document** module is a collection of classes that handle documents
and their transformations.
"""
from langchain_core.documents.base import Document
from langchain_core.documents.compressor import BaseDocumentCompressor
from langchain_core.documents.transformers import BaseDocumentTransformer
__all__ = ["Docume... |
import pytest
from pydantic import Field
from docarray import BaseDoc
from docarray.index import ElasticDocIndex
from tests.index.elastic.fixture import start_storage_v8 # noqa: F401
pytestmark = [pytest.mark.slow, pytest.mark.index, pytest.mark.elasticv8]
def test_column_config():
class MyDoc(BaseDoc):
... | import pytest
from pydantic import Field
from docarray import BaseDoc
from docarray.index import ElasticDocIndex
from tests.index.elastic.fixture import start_storage_v8 # noqa: F401
pytestmark = [pytest.mark.slow, pytest.mark.index, pytest.mark.elasticv8]
def test_column_config():
class MyDoc(BaseDoc):
... |
from typing import Any, Dict, Optional, Union
import numpy as np
import PIL.Image
import torch
from torch.nn.functional import one_hot
from torchvision.prototype import features
from torchvision.prototype.transforms import functional as F, Transform
class DecodeImage(Transform):
_transformed_types = (features.E... | from typing import Any, cast, Dict, Optional, Union
import numpy as np
import PIL.Image
import torch
from torch.nn.functional import one_hot
from torchvision.prototype import features
from torchvision.prototype.transforms import functional as F, Transform
class DecodeImage(Transform):
_transformed_types = (feat... |
#!/usr/bin/env python3
# Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the LICENSE file in
# the root directory of this source tree. An additional grant of patent rights
# can be found in the PATENTS file in the same directory.
"""
Run infe... | #!/usr/bin/env python3
# Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the LICENSE file in
# the root directory of this source tree. An additional grant of patent rights
# can be found in the PATENTS file in the same directory.
"""
Run infe... |
import datetime
import json
import typing
import prisma.models
import pydantic
import backend.data.block
import backend.data.graph
import backend.server.model
class LibraryAgent(pydantic.BaseModel):
id: str # Changed from agent_id to match GraphMeta
agent_id: str
agent_version: int # Changed from age... | import datetime
import json
import typing
import prisma.models
import pydantic
import backend.data.block
import backend.data.graph
import backend.server.model
class LibraryAgent(pydantic.BaseModel):
id: str # Changed from agent_id to match GraphMeta
agent_id: str
agent_version: int # Changed from age... |
import io
import pathlib
from collections import namedtuple
from collections.abc import Iterator
from typing import Any, Optional, Union
from torchdata.datapipes.iter import IterDataPipe, Mapper, Zipper
from torchvision.prototype.datasets.utils import Dataset, GDriveResource, OnlineResource
from torchvision.prototype.... | import io
import pathlib
from collections import namedtuple
from typing import Any, Dict, Iterator, List, Optional, Tuple, Union
from torchdata.datapipes.iter import IterDataPipe, Mapper, Zipper
from torchvision.prototype.datasets.utils import Dataset, GDriveResource, OnlineResource
from torchvision.prototype.datasets... |
_base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# model settings
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='t... | _base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# model settings
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='t... |
# Copyright (c) OpenMMLab. All rights reserved.
from .atss import ATSS
from .autoassign import AutoAssign
from .base import BaseDetector
from .cascade_rcnn import CascadeRCNN
from .centernet import CenterNet
from .cornernet import CornerNet
from .deformable_detr import DeformableDETR
from .detr import DETR
from .fast_r... | # Copyright (c) OpenMMLab. All rights reserved.
from .atss import ATSS
from .autoassign import AutoAssign
from .base import BaseDetector
from .cascade_rcnn import CascadeRCNN
from .centernet import CenterNet
from .cornernet import CornerNet
from .deformable_detr import DeformableDETR
from .detr import DETR
from .fast_r... |
"""Tests related to the `DataIter` interface."""
import numpy as np
import xgboost
from xgboost import testing as tm
def run_mixed_sparsity(device: str) -> None:
"""Check QDM with mixed batches."""
X_0, y_0, _ = tm.make_regression(128, 16, False)
if device.startswith("cuda"):
X_1, y_1 = tm.make_... | """Tests related to the `DataIter` interface."""
import numpy as np
import xgboost
from xgboost import testing as tm
def run_mixed_sparsity(device: str) -> None:
"""Check QDM with mixed batches."""
X_0, y_0, _ = tm.make_regression(128, 16, False)
if device.startswith("cuda"):
X_1, y_1 = tm.make_s... |
"""
This application demonstrates how to find duplicate questions (paraphrases) in a long
list of sentences.
"""
from sentence_transformers import SentenceTransformer, util
# Questions can be a long list of sentences up to 100k sentences or more.
# For demonstration purposes, we limit it to a few questions which all ... | """
This application demonstrates how to find duplicate questions (paraphrases) in a long
list of sentences.
"""
from sentence_transformers import SentenceTransformer, util
# Questions can be a long list of sentences up to 100k sentences or more.
# For demonstration purposes, we limit it to a few questions which all ... |
from typing import Union
import google.ai.generativelanguage as glm
import google.generativeai as genai
from llama_index.core.base.llms.types import (
ChatMessage,
ChatResponse,
CompletionResponse,
ImageBlock,
TextBlock,
)
from llama_index.core.multi_modal_llms.base import ChatMessage
from llama_in... | from typing import Union
import google.ai.generativelanguage as glm
import google.generativeai as genai
import PIL
from llama_index.core.base.llms.types import (
ChatMessage,
ChatResponse,
CompletionResponse,
)
from llama_index.core.utilities.gemini_utils import ROLES_FROM_GEMINI, ROLES_TO_GEMINI
def _e... |
import numpy as np
from docarray import BaseDoc
from docarray.typing import NdArray
def test_tensor_ops():
class A(BaseDoc):
tensor: NdArray[3, 224, 224]
class B(BaseDoc):
tensor: NdArray[3, 112, 224]
tensor = A(tensor=np.ones((3, 224, 224))).tensor
tensord = A(tensor=np.ones((3, 22... | import numpy as np
from docarray import BaseDocument
from docarray.typing import NdArray
def test_tensor_ops():
class A(BaseDocument):
tensor: NdArray[3, 224, 224]
class B(BaseDocument):
tensor: NdArray[3, 112, 224]
tensor = A(tensor=np.ones((3, 224, 224))).tensor
tensord = A(tensor... |
_base_ = './faster-rcnn_r50_fpn_gn-ws-all_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://jhu/resnet101_gn_ws')))
| _base_ = './faster_rcnn_r50_fpn_gn_ws-all_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://jhu/resnet101_gn_ws')))
|
from typing import List
import numpy as np
import pytest
from fastapi import FastAPI
from httpx import AsyncClient
from docarray import BaseDoc, DocList
from docarray.base_doc import DocArrayResponse
from docarray.documents import ImageDoc, TextDoc
from docarray.typing import NdArray
@pytest.mark.asyncio
async def ... | from typing import List
import numpy as np
import pytest
from fastapi import FastAPI
from httpx import AsyncClient
from docarray import BaseDoc, DocArray
from docarray.base_doc import DocArrayResponse
from docarray.documents import ImageDoc, TextDoc
from docarray.typing import NdArray
@pytest.mark.asyncio
async def... |
from pathlib import Path
from typing import List
import pytest
from jina import Document, DocumentArray, Executor
from sentence_encoder import TransformerSentenceEncoder
_EMBEDDING_DIM = 384
@pytest.fixture(scope='session')
def basic_encoder() -> TransformerSentenceEncoder:
return TransformerSentenceEncoder()
... | from pathlib import Path
from typing import List
import pytest
from jina import Document, DocumentArray, Executor
from ...sentence_encoder import TransformerSentenceEncoder
_EMBEDDING_DIM = 384
@pytest.fixture(scope='session')
def basic_encoder() -> TransformerSentenceEncoder:
return TransformerSentenceEncoder... |
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