input stringlengths 33 5k | output stringlengths 32 5k |
|---|---|
# Copyright (c) OpenMMLab. All rights reserved.
import pytest
from mmdet.datasets import get_loading_pipeline, replace_ImageToTensor
def test_replace_ImageToTensor():
# with MultiScaleFlipAug
pipelines = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
... | import pytest
from mmdet.datasets import get_loading_pipeline, replace_ImageToTensor
def test_replace_ImageToTensor():
# with MultiScaleFlipAug
pipelines = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(1333, 800),
flip=False,
... |
import torch
import torchaudio.functional as F
from parameterized import parameterized
from torchaudio_unittest.common_utils import (
get_sinusoid,
load_params,
save_wav,
skipIfNoExec,
TempDirMixin,
TestBaseMixin,
)
from torchaudio_unittest.common_utils.kaldi_utils import convert_args, run_kaldi... | import torch
import torchaudio.functional as F
from parameterized import parameterized
from torchaudio_unittest.common_utils import (
get_sinusoid,
load_params,
save_wav,
skipIfNoExec,
TempDirMixin,
TestBaseMixin,
)
from torchaudio_unittest.common_utils.kaldi_utils import (
convert_args,
... |
from contextlib import suppress
from docutils import nodes
from docutils.parsers.rst import Directive
from sklearn.utils import all_estimators
from sklearn.utils._test_common.instance_generator import _construct_instance
from sklearn.utils._testing import SkipTest
class AllowNanEstimators(Directive):
@staticmet... | from contextlib import suppress
from docutils import nodes
from docutils.parsers.rst import Directive
from sklearn.utils import all_estimators
from sklearn.utils._testing import SkipTest
from sklearn.utils.estimator_checks import _construct_instance
class AllowNanEstimators(Directive):
@staticmethod
def mak... |
import os
import numpy as np
import pytest
import torch
from pydantic.tools import parse_obj_as
from docarray import BaseDoc
from docarray.typing import (
AudioNdArray,
AudioTorchTensor,
VideoNdArray,
VideoTorchTensor,
)
from docarray.utils._internal.misc import is_tf_available
tf_available = is_tf_a... | import os
import numpy as np
import pytest
import torch
from pydantic.tools import parse_obj_as
from docarray import BaseDoc
from docarray.typing import (
AudioNdArray,
AudioTorchTensor,
VideoNdArray,
VideoTorchTensor,
)
from docarray.utils.misc import is_tf_available
tf_available = is_tf_available()... |
from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.vectorstores import MyScale, MyScaleSettings
from langchain_community.vectorstores.myscale import MyScaleWithoutJSON
# Create a way to dynamically look up deprecated imports.
# Used to ... | from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.vectorstores import MyScale, MyScaleSettings
from langchain_community.vectorstores.myscale import MyScaleWithoutJSON
# Create a way to dynamically look up deprecated imports.
# Used to ... |
from langchain_core.documents import Document
from langchain.retrievers.document_compressors.listwise_rerank import LLMListwiseRerank
def test_list_rerank() -> None:
from langchain_openai import ChatOpenAI
documents = [
Document("Sally is my friend from school"),
Document("Steve is my friend... | from langchain_core.documents import Document
from langchain.retrievers.document_compressors.listwise_rerank import LLMListwiseRerank
def test_list_rerank() -> None:
from langchain_openai import ChatOpenAI
documents = [
Document("Sally is my friend from school"),
Document("Steve is my friend... |
from llama_index.core.schema import Document
from llama_index.core.tools.tool_spec.base import BaseToolSpec
from box_sdk_gen import BoxClient
from llama_index.readers.box.BoxAPI.box_api import (
box_check_connection,
get_box_files_details,
get_files_ai_extract_data,
add_extra_header_to_box_client,
)
... | from llama_index.core.schema import Document
from llama_index.core.tools.tool_spec.base import BaseToolSpec
from box_sdk_gen import BoxClient
from llama_index.readers.box.BoxAPI.box_api import (
box_check_connection,
get_box_files_details,
get_files_ai_extract_data,
add_extra_header_to_box_client,
)
... |
import os.path
from typing import Any, Callable, List, Optional, Tuple
from PIL import Image
from .vision import VisionDataset
class CocoDetection(VisionDataset):
"""`MS Coco Detection <https://cocodataset.org/#detection-2016>`_ Dataset.
It requires the `COCO API to be installed <https://github.com/pdollar... | import os.path
from typing import Any, Callable, List, Optional, Tuple
from PIL import Image
from .vision import VisionDataset
class CocoDetection(VisionDataset):
"""`MS Coco Detection <https://cocodataset.org/#detection-2016>`_ Dataset.
It requires the `COCO API to be installed <https://github.com/pdollar... |
import os
import pytest
from llama_index.core.tools.tool_spec.base import BaseToolSpec
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
# Path to the test server script - adjust as needed
SERVER_SCRIPT = os.path.join(os.path.dirname(__file__), "server.py")
@pytest.fixture(scope="session")
def client() ... | from llama_index.core.tools.tool_spec.base import BaseToolSpec
from llama_index.tools.mcp import McpToolSpec
def test_class():
names_of_base_classes = [b.__name__ for b in McpToolSpec.__mro__]
assert BaseToolSpec.__name__ in names_of_base_classes
|
import pytest
from xgboost import testing as tm
pytestmark = [
pytest.mark.skipif(**tm.no_spark()),
tm.timeout(120),
]
from ..test_with_spark.test_data import run_dmatrix_ctor
@pytest.mark.skipif(**tm.no_cudf())
@pytest.mark.parametrize(
"is_feature_cols,is_qdm",
[(True, True), (True, False), (Fals... | import pytest
from xgboost import testing as tm
pytestmark = pytest.mark.skipif(**tm.no_spark())
from ..test_with_spark.test_data import run_dmatrix_ctor
@pytest.mark.skipif(**tm.no_cudf())
@pytest.mark.parametrize(
"is_feature_cols,is_qdm",
[(True, True), (True, False), (False, True), (False, False)],
)
d... |
from contextlib import asynccontextmanager
from datetime import timedelta
from typing import Optional, List, Dict
from urllib.parse import urlparse
from mcp.client.session import ClientSession
from mcp.client.sse import sse_client
from mcp.client.stdio import stdio_client, StdioServerParameters
class BasicMCPClient(... | from contextlib import asynccontextmanager
from datetime import timedelta
from typing import Optional, List, Dict
from urllib.parse import urlparse
from mcp.client.session import ClientSession
from mcp.client.sse import sse_client
from mcp.client.stdio import stdio_client, StdioServerParameters
class BasicMCPClient(... |
import json
import os
from typing import List
import torch
from torch import nn
class LSTM(nn.Module):
"""Bidirectional LSTM running over word embeddings."""
def __init__(
self,
word_embedding_dimension: int,
hidden_dim: int,
num_layers: int = 1,
dropout: float = 0,
... | import torch
from torch import nn
from typing import List
import os
import json
class LSTM(nn.Module):
"""Bidirectional LSTM running over word embeddings."""
def __init__(
self,
word_embedding_dimension: int,
hidden_dim: int,
num_layers: int = 1,
dropout: float = 0,
... |
_base_ = '../mask_rcnn/mask-rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)))
# MMEngine support the following two ways, users can choose
# according to convenience
# optim_wrapper = dict... | _base_ = '../mask_rcnn/mask-rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)))
fp16 = dict(loss_scale=512.)
|
# Copyright (c) OpenMMLab. All rights reserved.
from .collect_env import collect_env
from .logger import get_root_logger
from .misc import find_latest_checkpoint
from .setup_env import setup_multi_processes
__all__ = [
'get_root_logger', 'collect_env', 'find_latest_checkpoint',
'setup_multi_processes'
]
| # Copyright (c) OpenMMLab. All rights reserved.
from .collect_env import collect_env
from .logger import get_root_logger
from .misc import find_latest_checkpoint
__all__ = [
'get_root_logger',
'collect_env',
'find_latest_checkpoint',
]
|
from __future__ import annotations
import csv
import os
from . import InputExample
class TripletReader(object):
"""Reads in the a Triplet Dataset: Each line contains (at least) 3 columns, one anchor column (s1),
one positive example (s2) and one negative example (s3)
"""
def __init__(
self,... | import csv
import os
from . import InputExample
class TripletReader(object):
"""Reads in the a Triplet Dataset: Each line contains (at least) 3 columns, one anchor column (s1),
one positive example (s2) and one negative example (s3)
"""
def __init__(
self,
dataset_folder,
s1_... |
import PIL.Image
import torch
from torchvision import datapoints
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 PIL.Image
import torch
from torchvision import datapoints
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_explicit_noop, _register_kernel_internal
@_register_explicit_noop(datapoints.Mask, d... |
# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import subprocess
import torch
from mmengine.logging import print_log
def parse_args():
parser = argparse.ArgumentParser(
description='Process a checkpoint to be published')
parser.add_argument('in_file', help='input checkpoint filename'... | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import subprocess
import torch
def parse_args():
parser = argparse.ArgumentParser(
description='Process a checkpoint to be published')
parser.add_argument('in_file', help='input checkpoint filename')
parser.add_argument('out_file', h... |
from ._dsp import adsr_envelope, extend_pitch, oscillator_bank, sinc_impulse_response
from .functional import add_noise, barkscale_fbanks, convolve, deemphasis, fftconvolve, preemphasis, speed
__all__ = [
"add_noise",
"adsr_envelope",
"barkscale_fbanks",
"convolve",
"deemphasis",
"extend_pitch"... | from ._dsp import adsr_envelope, extend_pitch, oscillator_bank, sinc_impulse_response
from .functional import add_noise, barkscale_fbanks, convolve, fftconvolve, speed
__all__ = [
"add_noise",
"adsr_envelope",
"barkscale_fbanks",
"convolve",
"extend_pitch",
"fftconvolve",
"oscillator_bank",... |
# Copyright (c) OpenMMLab. All rights reserved.
from .optimizer import (OPTIM_WRAPPER_CONSTRUCTORS, OPTIMIZERS,
AmpOptimWrapper, ApexOptimWrapper, BaseOptimWrapper,
DefaultOptimWrapperConstructor, OptimWrapper,
OptimWrapperDict, ZeroRedundancyOptim... | # Copyright (c) OpenMMLab. All rights reserved.
from .optimizer import (OPTIM_WRAPPER_CONSTRUCTORS, OPTIMIZERS,
AmpOptimWrapper, ApexOptimWrapper, BaseOptimWrapper,
DeepSpeedOptimWrapper, DefaultOptimWrapperConstructor,
OptimWrapper, OptimWrapperDi... |
"""DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.ops.linalg import cholesky
from keras.src.ops.linalg import det
from keras.src.ops.linalg import eig
from keras.src.ops.linalg import eigh
from keras.src.ops.linalg import inv
from ke... | """DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.ops.linalg import cholesky
from keras.src.ops.linalg import det
from keras.src.ops.linalg import eig
from keras.src.ops.linalg import eigh
from keras.src.ops.linalg import inv
from ke... |
from typing import Any, Optional, Sequence
from langchain_core._api.deprecation import deprecated
from langchain_core.documents import BaseDocumentTransformer, Document
from langchain_community.utilities.vertexai import get_client_info
@deprecated(
since="0.0.32",
removal="1.0",
alternative_import="lang... | from typing import Any, Optional, Sequence
from langchain_core._api.deprecation import deprecated
from langchain_core.documents import BaseDocumentTransformer, Document
from langchain_community.utilities.vertexai import get_client_info
@deprecated(
since="0.0.32",
removal="1.0",
alternative_import="lang... |
from __future__ import annotations
from typing import Any
from langchain_core.output_parsers import BaseOutputParser
from langchain_core.output_parsers.json import parse_and_check_json_markdown
from pydantic import BaseModel
from langchain.output_parsers.format_instructions import (
STRUCTURED_FORMAT_INSTRUCTION... | from __future__ import annotations
from typing import Any
from langchain_core.output_parsers import BaseOutputParser
from langchain_core.output_parsers.json import parse_and_check_json_markdown
from pydantic import BaseModel
from langchain.output_parsers.format_instructions import (
STRUCTURED_FORMAT_INSTRUCTION... |
import os
import grpc
import pytest
from jina import Flow, __default_host__
from jina.clients import Client
from jina.excepts import PortAlreadyUsed
from jina.helper import is_port_free
from jina.serve.runtimes.gateway.grpc import GRPCGatewayRuntime as _GRPCGatewayRuntime
from jina.serve.runtimes.helper import _get_g... | import os
import grpc
import pytest
from jina import Flow, __default_host__
from jina.clients import Client
from jina.excepts import PortAlreadyUsed
from jina.helper import is_port_free
from jina.serve.runtimes.gateway.grpc import GRPCGatewayRuntime as _GRPCGatewayRuntime
from tests import random_docs
@pytest.fixtu... |
from backend.data.credit import get_user_credit_model
from backend.data.execution import (
ExecutionResult,
NodeExecutionEntry,
RedisExecutionEventBus,
create_graph_execution,
get_execution_results,
get_incomplete_executions,
get_latest_execution,
update_execution_status,
update_grap... | from backend.data.credit import get_user_credit_model
from backend.data.execution import (
ExecutionResult,
NodeExecutionEntry,
RedisExecutionEventBus,
create_graph_execution,
get_execution_results,
get_incomplete_executions,
get_latest_execution,
update_execution_status,
update_grap... |
#!/usr/bin/env python
# coding=utf-8
# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LI... | #!/usr/bin/env python
# coding=utf-8
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LI... |
_base_ = './panoptic_fpn_r50_fpn_1x_coco.py'
# In mstrain 3x config, img_scale=[(1333, 640), (1333, 800)],
# multiscale_mode='range'
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='LoadPanopticAnnotations',
with_bbox=True,
with_mask=True,
with_seg=True),
dict(... | _base_ = './panoptic_fpn_r50_fpn_1x_coco.py'
# In mstrain 3x config, img_scale=[(1333, 640), (1333, 800)],
# multiscale_mode='range'
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='LoadPanopticAnnotations',
with_bbox=True,
with_mask=True,
with_seg=True),
dict(... |
"""DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.dtype_policies import deserialize as deserialize
from keras.src.dtype_policies import get as get
from keras.src.dtype_policies import serialize as serialize
from keras.src.dtype_polic... | """DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.dtype_policies import deserialize
from keras.src.dtype_policies import get
from keras.src.dtype_policies import serialize
from keras.src.dtype_policies.dtype_policy import DTypePolicy... |
import json
import multiprocessing
import os
import time
import pytest
from jina.helper import random_port
from jina.parsers import set_gateway_parser, set_pod_parser
from jina.serve.runtimes.gateway import GatewayRuntime
from jina.serve.runtimes.worker import WorkerRuntime
from tests.helper import (
ProcessExecu... | import json
import multiprocessing
import os
import time
import pytest
from docarray import DocumentArray
from jina import Executor, requests
from jina.helper import random_port
from jina.parsers import set_gateway_parser, set_pod_parser
from jina.serve.runtimes.gateway import GatewayRuntime
from jina.serve.runtimes.... |
from typing import List
import numpy as np
from torch.utils.data import Dataset
from transformers.utils.import_utils import NLTK_IMPORT_ERROR, is_nltk_available
from sentence_transformers.readers.InputExample import InputExample
class DenoisingAutoEncoderDataset(Dataset):
"""
The DenoisingAutoEncoderDataset... | from torch.utils.data import Dataset
from typing import List
from ..readers.InputExample import InputExample
import numpy as np
from transformers.utils.import_utils import is_nltk_available, NLTK_IMPORT_ERROR
class DenoisingAutoEncoderDataset(Dataset):
"""
The DenoisingAutoEncoderDataset returns InputExamples... |
import asyncio
from typing import AsyncIterator, Iterator, Optional, Union
from jina.helper import get_or_reuse_loop
class RequestsCounter:
"""Class used to wrap a count integer so that it can be updated inside methods.
.. code-block:: python
def count_increment(i: int, rc: RequestCounter):
... | import asyncio
from typing import AsyncIterator, Iterator, Optional, Union
from jina.helper import get_or_reuse_loop
class RequestsCounter:
"""Class used to wrap a count integer so that it can be updated inside methods.
.. code-block:: python
def count_increment(i: int, rc: RequestCounter):
... |
# Copyright (c) OpenMMLab. All rights reserved.
from .class_aware_sampler import ClassAwareSampler
from .distributed_sampler import DistributedSampler
from .group_sampler import DistributedGroupSampler, GroupSampler
from .infinite_sampler import InfiniteBatchSampler, InfiniteGroupBatchSampler
__all__ = [
'Distribu... | # Copyright (c) OpenMMLab. All rights reserved.
from .distributed_sampler import DistributedSampler
from .group_sampler import DistributedGroupSampler, GroupSampler
from .infinite_sampler import InfiniteBatchSampler, InfiniteGroupBatchSampler
__all__ = [
'DistributedSampler', 'DistributedGroupSampler', 'GroupSampl... |
# Copyright (c) OpenMMLab. All rights reserved.
from .collect_env import collect_env
from .compat_config import compat_cfg
from .logger import get_caller_name, get_root_logger, log_img_scale
from .misc import find_latest_checkpoint, update_data_root
from .setup_env import setup_multi_processes
from .split_batch import ... | # Copyright (c) OpenMMLab. All rights reserved.
from .collect_env import collect_env
from .compat_config import compat_cfg
from .logger import get_caller_name, get_root_logger, log_img_scale
from .misc import find_latest_checkpoint, update_data_root
from .setup_env import setup_multi_processes
from .split_batch import ... |
"""
This is a simple application for sentence embeddings: clustering
Sentences are mapped to sentence embeddings and then agglomerative clustering with a threshold is applied.
"""
from sentence_transformers import SentenceTransformer
from sklearn.cluster import AgglomerativeClustering
embedder = SentenceTransformer(... | """
This is a simple application for sentence embeddings: clustering
Sentences are mapped to sentence embeddings and then agglomerative clustering with a threshold is applied.
"""
from sentence_transformers import SentenceTransformer
from sklearn.cluster import AgglomerativeClustering
embedder = SentenceTransformer(... |
import re
import unicodedata
import regex
# non-ASCII letters that are not separated by "NFKD" normalization
ADDITIONAL_DIACRITICS = {
"œ": "oe",
"Œ": "OE",
"ø": "o",
"Ø": "O",
"æ": "ae",
"Æ": "AE",
"ß": "ss",
"ẞ": "SS",
"đ": "d",
"Đ": "D",
"ð": "d",
"Ð": "D",
"þ": ... | import re
import unicodedata
import regex
# non-ASCII letters that are not separated by "NFKD" normalization
ADDITIONAL_DIACRITICS = {
"œ": "oe",
"Œ": "OE",
"ø": "o",
"Ø": "O",
"æ": "ae",
"Æ": "AE",
"ß": "ss",
"ẞ": "SS",
"đ": "d",
"Đ": "D",
"ð": "d",
"Ð": "D",
"þ": ... |
from typing import TypeVar
from docarray.typing.proto_register import _register_proto
from docarray.typing.tensor.image.abstract_image_tensor import AbstractImageTensor
from docarray.typing.tensor.tensorflow_tensor import TensorFlowTensor, metaTensorFlow
T = TypeVar('T', bound='ImageTensorFlowTensor')
@_register_pr... | from typing import TypeVar
from docarray.typing.proto_register import _register_proto
from docarray.typing.tensor.image.abstract_image_tensor import AbstractImageTensor
from docarray.typing.tensor.tensorflow_tensor import TensorFlowTensor, metaTensorFlow
T = TypeVar('T', bound='ImageTensorFlowTensor')
@_register_pr... |
from pathlib import Path
from typing import List, Tuple, Union
import torch
import torchaudio
from torch.utils.data import Dataset
SampleType = Tuple[int, torch.Tensor, List[torch.Tensor]]
_TASKS_TO_MIXTURE = {
"sep_clean": "mix_clean",
"enh_single": "mix_single",
"enh_both": "mix_both",
"sep_noisy":... | from pathlib import Path
from typing import List, Tuple, Union
import torch
import torchaudio
from torch.utils.data import Dataset
SampleType = Tuple[int, torch.Tensor, List[torch.Tensor]]
class LibriMix(Dataset):
r"""*LibriMix* :cite:`cosentino2020librimix` dataset.
Args:
root (str or Path): The p... |
from typing import Any, Dict, List, Optional, Sequence, Type, Union
import PIL.Image
import torch
from torchvision import tv_tensors
from torchvision.prototype.tv_tensors import Label, OneHotLabel
from torchvision.transforms.v2 import functional as F, Transform
from torchvision.transforms.v2._utils import (
_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 (
_Fill... |
_base_ = [
'../_base_/models/ssd300.py', '../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
]
# dataset settings
input_size = 300
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(
... | _base_ = [
'../_base_/models/ssd300.py', '../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
]
# dataset settings
input_size = 300
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(
... |
# Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the most common options. For a full
# list see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
# -- Path setup --------------------------------------------------------------
# If ex... | # Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the most common options. For a full
# list see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
# -- Path setup --------------------------------------------------------------
# If ex... |
_base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
'../_base_/datasets/deepfashion.py', '../_base_/schedules/schedule_1x.py',
'../_base_/default_runtime.py'
]
model = dict(
roi_head=dict(
bbox_head=dict(num_classes=15), mask_head=dict(num_classes=15)))
# runtime settings
max_epochs = 15
train_c... | _base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
'../_base_/datasets/deepfashion.py', '../_base_/schedules/schedule_1x.py',
'../_base_/default_runtime.py'
]
model = dict(
roi_head=dict(
bbox_head=dict(num_classes=15), mask_head=dict(num_classes=15)))
# runtime settings
runner = dict(type='Epo... |
from langchain_core.documents import Document
from langchain_core.retrievers import BaseRetriever
class SequentialRetriever(BaseRetriever):
"""Test util that returns a sequence of documents"""
sequential_responses: list[list[Document]]
response_index: int = 0
def _get_relevant_documents( # type: ig... | from langchain_core.retrievers import BaseRetriever, Document
class SequentialRetriever(BaseRetriever):
"""Test util that returns a sequence of documents"""
sequential_responses: list[list[Document]]
response_index: int = 0
def _get_relevant_documents( # type: ignore[override]
self,
... |
# Copyright (c) OpenMMLab. All rights reserved.
from abc import ABCMeta, abstractmethod
import torch
import torch.nn as nn
from mmcv import ops
from mmengine.model import BaseModule
class BaseRoIExtractor(BaseModule, metaclass=ABCMeta):
"""Base class for RoI extractor.
Args:
roi_layer (dict): Specif... | # Copyright (c) OpenMMLab. All rights reserved.
from abc import ABCMeta, abstractmethod
import torch
import torch.nn as nn
from mmcv import ops
from mmcv.runner import BaseModule
class BaseRoIExtractor(BaseModule, metaclass=ABCMeta):
"""Base class for RoI extractor.
Args:
roi_layer (dict): Specify R... |
_base_ = ['./mask2former_swin-t-p4-w7-224_8xb2-lsj-50e_coco-panoptic.py']
pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_small_patch4_window7_224.pth' # noqa
depths = [2, 2, 18, 2]
model = dict(
backbone=dict(
depths=depths, init_cfg=dict(type='Pretrained',
... | _base_ = ['./mask2former_swin-t-p4-w7-224_lsj_8x2_50e_coco-panoptic.py']
pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_small_patch4_window7_224.pth' # noqa
depths = [2, 2, 18, 2]
model = dict(
backbone=dict(
depths=depths, init_cfg=dict(type='Pretrained',
... |
from ._bounding_box import BoundingBox, BoundingBoxFormat
from ._encoded import EncodedData, EncodedImage
from ._feature import _Feature, FillType, FillTypeJIT, InputType, InputTypeJIT, is_simple_tensor
from ._image import ColorSpace, Image, ImageType, ImageTypeJIT, TensorImageType, TensorImageTypeJIT
from ._label impo... | from ._bounding_box import BoundingBox, BoundingBoxFormat
from ._encoded import EncodedData, EncodedImage
from ._feature import _Feature, FillType, FillTypeJIT, InputType, InputTypeJIT, is_simple_tensor
from ._image import (
ColorSpace,
Image,
ImageType,
ImageTypeJIT,
LegacyImageType,
LegacyImag... |
"""DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.applications.inception_resnet_v2 import (
InceptionResNetV2 as InceptionResNetV2,
)
from keras.src.applications.inception_resnet_v2 import (
decode_predictions as decode_predi... | """DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.applications.inception_resnet_v2 import InceptionResNetV2
from keras.src.applications.inception_resnet_v2 import decode_predictions
from keras.src.applications.inception_resnet_v2 imp... |
"""Test the loading function for evaluators."""
from typing import List
import pytest
from langchain.evaluation.loading import EvaluatorType, load_evaluators
from langchain.evaluation.schema import PairwiseStringEvaluator, StringEvaluator
from langchain_core.embeddings import FakeEmbeddings
from tests.unit_tests.llm... | """Test the loading function for evaluators."""
from typing import List
import pytest
from langchain.evaluation.loading import EvaluatorType, load_evaluators
from langchain.evaluation.schema import PairwiseStringEvaluator, StringEvaluator
from langchain_core.embeddings import FakeEmbeddings
from tests.unit_tests.llm... |
# Licensed to the LF AI & Data foundation under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the "License");
# you may not use this fil... | import os
import time
import uuid
import pytest
@pytest.fixture(scope='session', autouse=True)
def start_redis():
os.system(
'docker run --name redis-stack-server -p 6379:6379 -d redis/redis-stack-server:7.2.0-RC2'
)
time.sleep(1)
yield
os.system('docker rm -f redis-stack-server')
@pyt... |
import os
from tempfile import TemporaryDirectory
from unittest import TestCase
import pytest
from absl.testing import parameterized
from datasets import config, load_dataset_builder
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.dataset_dict import IterableDatasetDict
from datasets.iterable_dataset ... | 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.dataset_dict import IterableDatasetDict
from data... |
import json
import logging
from enum import Enum
from typing import Any
from requests.exceptions import HTTPError, RequestException
from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
from backend.data.model import SchemaField
from backend.util.request import requests
logger = logging.getLo... | import json
import logging
from enum import Enum
from typing import Any
from requests.exceptions import HTTPError, RequestException
from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
from backend.data.model import SchemaField
from backend.util.request import requests
logger = logging.getLo... |
# 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... |
tta_model = dict(
type='DetTTAModel',
tta_cfg=dict(nms=dict(type='nms', iou_threshold=0.6), max_per_img=100))
img_scales = [(640, 640), (320, 320), (960, 960)]
tta_pipeline = [
dict(type='LoadImageFromFile', backend_args=None),
dict(
type='TestTimeAug',
transforms=[
[
... | tta_model = dict(
type='DetTTAModel',
tta_cfg=dict(nms=dict(type='nms', iou_threshold=0.6), max_per_img=100))
img_scales = [(640, 640), (320, 320), (960, 960)]
tta_pipeline = [
dict(type='LoadImageFromFile', backend_args=None),
dict(
type='TestTimeAug',
transforms=[
[
... |
import warnings
from typing import TYPE_CHECKING, Any, Tuple, Type, TypeVar, Union
import numpy as np
from docarray.typing.bytes.audio_bytes import AudioBytes
from docarray.typing.proto_register import _register_proto
from docarray.typing.url.any_url import AnyUrl
from docarray.typing.url.filetypes import AUDIO_FILE_... | import warnings
from typing import TYPE_CHECKING, Any, Tuple, Type, TypeVar, Union
import numpy as np
from docarray.typing.bytes.audio_bytes import AudioBytes
from docarray.typing.proto_register import _register_proto
from docarray.typing.url.any_url import AnyUrl
from docarray.typing.url.filetypes import AUDIO_FILE_... |
# Copyright (c) OpenMMLab. All rights reserved.
from ..builder import DETECTORS
from .two_stage import TwoStageDetector
@DETECTORS.register_module()
class GridRCNN(TwoStageDetector):
"""Grid R-CNN.
This detector is the implementation of:
- Grid R-CNN (https://arxiv.org/abs/1811.12030)
- Grid R-CNN Pl... | from ..builder import DETECTORS
from .two_stage import TwoStageDetector
@DETECTORS.register_module()
class GridRCNN(TwoStageDetector):
"""Grid R-CNN.
This detector is the implementation of:
- Grid R-CNN (https://arxiv.org/abs/1811.12030)
- Grid R-CNN Plus: Faster and Better (https://arxiv.org/abs/190... |
import warnings
from typing import TYPE_CHECKING, Any, Type, TypeVar, Union
from docarray.typing.bytes.video_bytes import VideoLoadResult
from docarray.typing.proto_register import _register_proto
from docarray.typing.url.any_url import AnyUrl
from docarray.utils._internal.misc import is_notebook
if TYPE_CHECKING:
... | import warnings
from typing import TYPE_CHECKING, Any, Type, TypeVar, Union
from docarray.typing.bytes.video_bytes import VideoLoadResult
from docarray.typing.proto_register import _register_proto
from docarray.typing.url.any_url import AnyUrl
from docarray.utils.misc import is_notebook
if TYPE_CHECKING:
from pyd... |
# Copyright (c) OpenMMLab. All rights reserved.
import unittest
from unittest import TestCase
import torch
from mmengine.config import ConfigDict
from mmengine.data import InstanceData
from parameterized import parameterized
from mmdet.models.roi_heads.mask_heads import GridHead
from mmdet.models.utils import unpack_... | # Copyright (c) OpenMMLab. All rights reserved.
import unittest
from unittest import TestCase
import torch
from mmengine.config import ConfigDict
from mmengine.data import InstanceData
from parameterized import parameterized
from mmdet.models.roi_heads.mask_heads import GridHead
from mmdet.models.utils import unpack_... |
from ._conformer_wav2vec2 import (
conformer_wav2vec2_base,
conformer_wav2vec2_model,
conformer_wav2vec2_pretrain_base,
conformer_wav2vec2_pretrain_large,
conformer_wav2vec2_pretrain_model,
ConformerWav2Vec2PretrainModel,
)
from ._emformer_hubert import emformer_hubert_base, emformer_hubert_mode... | from ._conformer_wav2vec2 import (
conformer_wav2vec2_base,
conformer_wav2vec2_model,
conformer_wav2vec2_pretrain_base,
conformer_wav2vec2_pretrain_large,
conformer_wav2vec2_pretrain_model,
ConformerWav2Vec2PretrainModel,
)
from ._emformer_hubert import emformer_hubert_base, emformer_hubert_mode... |
"""Tavily Search API toolkit."""
from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.tools.tavily_search.tool import (
TavilyAnswer,
TavilySearchResults,
)
# Create a way to dynamically look up deprecated imports.
# Used... | """Tavily Search API toolkit."""
from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.tools.tavily_search.tool import (
TavilyAnswer,
TavilySearchResults,
)
# Create a way to dynamically look up deprecated imports.
# Used... |
"""Test chat model integration."""
import json
from collections.abc import Generator
from contextlib import contextmanager
from typing import Any
import pytest
from httpx import Client, Request, Response
from langchain_core.messages import ChatMessage
from langchain_tests.unit_tests import ChatModelUnitTests
from la... | """Test chat model integration."""
import json
from langchain_tests.unit_tests import ChatModelUnitTests
from langchain_ollama.chat_models import ChatOllama, _parse_arguments_from_tool_call
class TestChatOllama(ChatModelUnitTests):
@property
def chat_model_class(self) -> type[ChatOllama]:
return Ch... |
from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.tools.google_finance.tool import GoogleFinanceQueryRun
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handling ... | from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.tools.google_finance.tool import GoogleFinanceQueryRun
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handling ... |
_base_ = [
'../_base_/models/mask-rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
]
model = dict(
roi_head=dict(
bbox_head=dict(
num_classes=1203,
cls_predictor_cfg=dict(type='NormedLinear', tem... | _base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
]
model = dict(
roi_head=dict(
bbox_head=dict(
num_classes=1203,
cls_predictor_cfg=dict(type='NormedLinear', tem... |
from typing import TYPE_CHECKING, Union
import numpy as np
if TYPE_CHECKING: # pragma: no cover
from docarray.typing import T
import trimesh
class Mesh:
FILE_EXTENSIONS = [
'glb',
'obj',
'ply',
]
VERTICES = 'vertices'
FACES = 'faces'
class MeshDataMixin:
"""Pro... | import warnings
from typing import TYPE_CHECKING
import numpy as np
if TYPE_CHECKING: # pragma: no cover
from docarray.typing import T
class MeshDataMixin:
"""Provide helper functions for :class:`Document` to support 3D mesh data and point cloud."""
def load_uri_to_point_cloud_tensor(
self: 'T... |
# Copyright (c) OpenMMLab. All rights reserved.
__version__ = '3.2.0'
short_version = __version__
def parse_version_info(version_str):
"""Parse a version string into a tuple.
Args:
version_str (str): The version string.
Returns:
tuple[int | str]: The version info, e.g., "1.3.0" is parsed... | # Copyright (c) OpenMMLab. All rights reserved.
__version__ = '3.1.0'
short_version = __version__
def parse_version_info(version_str):
"""Parse a version string into a tuple.
Args:
version_str (str): The version string.
Returns:
tuple[int | str]: The version info, e.g., "1.3.0" is parsed... |
# Copyright (c) OpenMMLab. All rights reserved.
from .builder import DATASETS, PIPELINES, build_dataloader, build_dataset
from .cityscapes import CityscapesDataset
from .coco import CocoDataset
from .coco_panoptic import CocoPanopticDataset
from .custom import CustomDataset
from .dataset_wrappers import (ClassBalancedD... | # Copyright (c) OpenMMLab. All rights reserved.
from .builder import DATASETS, PIPELINES, build_dataloader, build_dataset
from .cityscapes import CityscapesDataset
from .coco import CocoDataset
from .coco_panoptic import CocoPanopticDataset
from .custom import CustomDataset
from .dataset_wrappers import (ClassBalancedD... |
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, 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... |
# Copyright (c) OpenMMLab. All rights reserved.
import unittest
from unittest import TestCase
import torch
from mmengine.config import ConfigDict
from mmengine.data import InstanceData
from parameterized import parameterized
from mmdet.models.roi_heads.mask_heads import FCNMaskHead
class TestFCNMaskHead(TestCase):
... | # Copyright (c) OpenMMLab. All rights reserved.
import unittest
from unittest import TestCase
import torch
from mmengine.config import ConfigDict
from mmengine.data import InstanceData
from parameterized import parameterized
from mmdet.models.roi_heads.mask_heads import FCNMaskHead
class TestFCNMaskHead(TestCase):
... |
# Copyright (c) OpenMMLab. All rights reserved.
from .base_bbox_coder import BaseBBoxCoder
from .bucketing_bbox_coder import BucketingBBoxCoder
from .delta_xywh_bbox_coder import (DeltaXYWHBBoxCoder,
DeltaXYWHBBoxCoderForGLIP)
from .distance_point_bbox_coder import DistancePointBBoxC... | # Copyright (c) OpenMMLab. All rights reserved.
from .base_bbox_coder import BaseBBoxCoder
from .bucketing_bbox_coder import BucketingBBoxCoder
from .delta_xywh_bbox_coder import DeltaXYWHBBoxCoder
from .distance_point_bbox_coder import DistancePointBBoxCoder
from .legacy_delta_xywh_bbox_coder import LegacyDeltaXYWHBBo... |
# Copyright (c) OpenMMLab. All rights reserved.
from .activations import SiLU
from .bbox_nms import fast_nms, multiclass_nms
from .brick_wrappers import AdaptiveAvgPool2d, adaptive_avg_pool2d
from .conv_upsample import ConvUpsample
from .csp_layer import CSPLayer
from .dropblock import DropBlock
from .ema import ExpMom... | # Copyright (c) OpenMMLab. All rights reserved.
from .activations import SiLU
from .bbox_nms import fast_nms, multiclass_nms
from .brick_wrappers import AdaptiveAvgPool2d, adaptive_avg_pool2d
from .conv_upsample import ConvUpsample
from .csp_layer import CSPLayer
from .dropblock import DropBlock
from .ema import ExpMom... |
"""
This example loads the pre-trained SentenceTransformer model 'nli-distilroberta-base-v2' from Hugging Face.
It then fine-tunes this model for some epochs on the STS benchmark dataset.
Note: In this example, you must specify a SentenceTransformer model.
If you want to fine-tune a huggingface/transformers model like... | """
This example loads the pre-trained SentenceTransformer model 'nli-distilroberta-base-v2' from Hugging Face.
It then fine-tunes this model for some epochs on the STS benchmark dataset.
Note: In this example, you must specify a SentenceTransformer model.
If you want to fine-tune a huggingface/transformers model like... |
import os
import sys
import torch
from ._internally_replaced_utils import _get_extension_path
_HAS_OPS = False
def _has_ops():
return False
try:
# On Windows Python-3.8.x has `os.add_dll_directory` call,
# which is called to configure dll search path.
# To find cuda related dlls we need to make ... | import ctypes
import os
import sys
from warnings import warn
import torch
from ._internally_replaced_utils import _get_extension_path
_HAS_OPS = False
def _has_ops():
return False
try:
# On Windows Python-3.8.x has `os.add_dll_directory` call,
# which is called to configure dll search path.
# To... |
"""Test retriever tool."""
from typing import List, Optional
from llama_index.core.base.base_retriever import BaseRetriever
from llama_index.core.schema import NodeWithScore, TextNode, QueryBundle
from llama_index.core.tools import RetrieverTool
from llama_index.core.postprocessor.types import BaseNodePostprocessor
i... | """Test retriever tool."""
from typing import List, Optional
from llama_index.core.base.base_retriever import BaseRetriever
from llama_index.core.schema import NodeWithScore, TextNode, QueryBundle
from llama_index.core.tools import RetrieverTool
from llama_index.core.postprocessor.types import BaseNodePostprocessor
i... |
import os
import urllib
import pytest
from pydantic import parse_obj_as, schema_json_of
from docarray.base_document.io.json import orjson_dumps
from docarray.typing import TextUrl
REMOTE_TXT = 'https://de.wikipedia.org/wiki/Brixen'
CUR_DIR = os.path.dirname(os.path.abspath(__file__))
LOCAL_TXT = os.path.join(CUR_DIR... | import os
import urllib
import pytest
from pydantic import parse_obj_as, schema_json_of
from docarray.base_document.io.json import orjson_dumps
from docarray.typing import TextUrl
REMOTE_TXT = 'https://de.wikipedia.org/wiki/Brixen'
CUR_DIR = os.path.dirname(os.path.abspath(__file__))
LOCAL_TXT = os.path.join(CUR_DIR... |
import asyncio
import logging
from typing import List
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document
logger = logging.getLogger(__name__)
class AsyncWebPageReader(BaseReader):
"""
Asynchronous web page reader.
Reads pages from the web asynchronously.
... | import asyncio
import logging
from typing import List
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document
logger = logging.getLogger(__name__)
class AsyncWebPageReader(BaseReader):
"""Asynchronous web page reader.
Reads pages from the web asynchronously.
A... |
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 PIL.Image
import torch
from torchvision import datapoints
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 os
from typing import Dict
import numpy as np
import pytest
import xgboost
from xgboost import testing as tm
from xgboost.testing.ranking import run_normalization, run_score_normalization
pytestmark = tm.timeout(30)
def comp_training_with_rank_objective(
dtrain: xgboost.DMatrix,
dtest: xgboost.DMatr... | import os
from typing import Dict
import numpy as np
import pytest
import xgboost
from xgboost import testing as tm
from xgboost.testing.ranking import run_normalization
pytestmark = tm.timeout(30)
def comp_training_with_rank_objective(
dtrain: xgboost.DMatrix,
dtest: xgboost.DMatrix,
rank_objective: s... |
import sys
import warnings
import torch
_onnx_opset_version_11 = 11
_onnx_opset_version_16 = 16
base_onnx_opset_version = _onnx_opset_version_11
def _register_custom_op():
from torch.onnx.symbolic_helper import parse_args
from torch.onnx.symbolic_opset11 import select, squeeze, unsqueeze
@parse_args("v... | import sys
import warnings
import torch
_onnx_opset_version_11 = 11
_onnx_opset_version_16 = 16
base_onnx_opset_version = _onnx_opset_version_11
def _register_custom_op():
from torch.onnx.symbolic_helper import parse_args
from torch.onnx.symbolic_opset11 import select, squeeze, unsqueeze
from torch.onnx... |
"""All minimum dependencies for scikit-learn."""
# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause
import argparse
from collections import defaultdict
# scipy and cython should by in sync with pyproject.toml
NUMPY_MIN_VERSION = "1.22.0"
SCIPY_MIN_VERSION = "1.8.0"
JOBLIB_MIN_VERSION = "1... | """All minimum dependencies for scikit-learn."""
# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause
import argparse
from collections import defaultdict
# scipy and cython should by in sync with pyproject.toml
NUMPY_MIN_VERSION = "1.22.0"
SCIPY_MIN_VERSION = "1.8.0"
JOBLIB_MIN_VERSION = "1... |
"""Utilities for JSON Schema."""
from __future__ import annotations
from copy import deepcopy
from typing import TYPE_CHECKING, Any, Optional
if TYPE_CHECKING:
from collections.abc import Sequence
def _retrieve_ref(path: str, schema: dict) -> dict:
components = path.split("/")
if components[0] != "#":
... | from __future__ import annotations
from copy import deepcopy
from typing import TYPE_CHECKING, Any, Optional
if TYPE_CHECKING:
from collections.abc import Sequence
def _retrieve_ref(path: str, schema: dict) -> dict:
components = path.split("/")
if components[0] != "#":
msg = (
"ref p... |
import importlib
import os
import re
import types
from typing import Any, Optional
import numpy as np
try:
import torch # noqa: F401
except ImportError:
torch_imported = False
else:
torch_imported = True
try:
import tensorflow as tf # type: ignore # noqa: F401
except (ImportError, TypeError):
... | import importlib
import os
import re
import types
from typing import Any, Optional
import numpy as np
try:
import torch # noqa: F401
except ImportError:
torch_imported = False
else:
torch_imported = True
try:
import tensorflow as tf # type: ignore # noqa: F401
except (ImportError, TypeError):
... |
import types
from typing import TYPE_CHECKING
from docarray.index.backends.in_memory import InMemoryExactNNIndex
from docarray.utils._internal.misc import (
_get_path_from_docarray_root_level,
import_library,
)
if TYPE_CHECKING:
from docarray.index.backends.elastic import ElasticDocIndex # noqa: F401
... | import types
from typing import TYPE_CHECKING
from docarray.index.backends.in_memory import InMemoryExactNNIndex
from docarray.utils._internal.misc import (
_get_path_from_docarray_root_level,
import_library,
)
if TYPE_CHECKING:
from docarray.index.backends.elastic import ElasticDocIndex # noqa: F401
... |
# 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... |
"""DashVector reader."""
from typing import Dict, List, Optional
import json
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document
class DashVectorReader(BaseReader):
"""
DashVector reader.
Args:
api_key (str): DashVector API key.
endpoint (str... | """DashVector reader."""
from typing import Dict, List, Optional
import json
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document
class DashVectorReader(BaseReader):
"""DashVector reader.
Args:
api_key (str): DashVector API key.
endpoint (str): Da... |
import os
from typing import Dict
import numpy as np
import pytest
import xgboost
from xgboost import testing as tm
from xgboost.testing.ranking import run_normalization, run_score_normalization
pytestmark = tm.timeout(30)
def comp_training_with_rank_objective(
dtrain: xgboost.DMatrix,
dtest: xgboost.DMatr... | import os
from typing import Dict
import numpy as np
import pytest
import xgboost
from xgboost import testing as tm
from xgboost.testing.ranking import run_normalization, run_score_normalization
pytestmark = tm.timeout(30)
def comp_training_with_rank_objective(
dtrain: xgboost.DMatrix,
dtest: xgboost.DMatr... |
# Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import torch
import torch.nn as nn
from ..builder import LOSSES
from .utils import weighted_loss
@mmcv.jit(derivate=True, coderize=True)
@weighted_loss
def smooth_l1_loss(pred, target, beta=1.0):
"""Smooth L1 loss.
Args:
pred (torch.Tensor)... | # Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import torch
import torch.nn as nn
from ..builder import LOSSES
from .utils import weighted_loss
@mmcv.jit(derivate=True, coderize=True)
@weighted_loss
def smooth_l1_loss(pred, target, beta=1.0):
"""Smooth L1 loss.
Args:
pred (torch.Tensor)... |
import logging
from datasets import load_dataset
from sentence_transformers import SparseEncoder
from sentence_transformers.sparse_encoder.evaluation import SparseRerankingEvaluator
logging.basicConfig(format="%(message)s", level=logging.INFO)
# Load a model
model = SparseEncoder("naver/splade-cocondenser-ensembled... | import logging
from datasets import load_dataset
from sentence_transformers import SparseEncoder
from sentence_transformers.sparse_encoder.evaluation import SparseRerankingEvaluator
logging.basicConfig(format="%(message)s", level=logging.INFO)
# Load a model
model = SparseEncoder("naver/splade-cocondenser-ensembled... |
"""
This module provides dynamic access to deprecated Zapier tools in LangChain.
It supports backward compatibility by forwarding references such as
`ZapierNLAListActions` and `ZapierNLARunAction` to their updated locations
in the `langchain_community.tools` package.
Developers using older import paths will continue ... | from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.tools import ZapierNLAListActions, ZapierNLARunAction
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handling o... |
__version__ = '0.30.0a3'
import logging
from docarray.array import DocArray, DocArrayStacked
from docarray.base_doc.doc import BaseDoc
__all__ = ['BaseDoc', 'DocArray', 'DocArrayStacked']
logger = logging.getLogger('docarray')
handler = logging.StreamHandler()
formatter = logging.Formatter("%(levelname)s - %(name)... | __version__ = '0.30.0a3'
from docarray.array import DocumentArray, DocumentArrayStacked
from docarray.base_document.document import BaseDocument
import logging
__all__ = ['BaseDocument', 'DocumentArray', 'DocumentArrayStacked']
logger = logging.getLogger('docarray')
handler = logging.StreamHandler()
formatter = log... |
# mypy: allow-untyped-defs
from torch.ao.quantization.pt2e.utils import _is_sym_size_node
from torch.ao.quantization.quantizer.quantizer import QuantizationAnnotation
from torch.fx import Node
def _annotate_input_qspec_map(node: Node, input_node: Node, qspec):
quantization_annotation = node.meta.get(
"qu... | # mypy: allow-untyped-defs
from torch.ao.quantization.pt2e.utils import _is_sym_size_node
from torch.ao.quantization.quantizer.quantizer import QuantizationAnnotation
from torch.fx import Node
def _annotate_input_qspec_map(node: Node, input_node: Node, qspec):
quantization_annotation = node.meta.get(
"qu... |
from __future__ import annotations
from collections.abc import Iterable
from torch import Tensor
from sentence_transformers import util
from sentence_transformers.sparse_encoder.losses.SparseCoSENTLoss import SparseCoSENTLoss
from sentence_transformers.sparse_encoder.SparseEncoder import SparseEncoder
class Sparse... | from __future__ import annotations
from collections.abc import Iterable
from torch import Tensor
from sentence_transformers import util
from sentence_transformers.sparse_encoder.losses.SparseCoSENTLoss import SparseCoSENTLoss
from sentence_transformers.sparse_encoder.SparseEncoder import SparseEncoder
class Sparse... |
"""XGBoost Experimental Federated Learning related API."""
import ctypes
from threading import Thread
from typing import Any, Dict, Optional
from .core import _LIB, _check_call, make_jcargs
from .tracker import RabitTracker
class FederatedTracker(RabitTracker):
"""Tracker for federated training.
Parameters... | """XGBoost Experimental Federated Learning related API."""
import ctypes
from threading import Thread
from typing import Any, Dict, Optional
from .core import _LIB, _check_call, make_jcargs
from .tracker import RabitTracker
class FederatedTracker(RabitTracker):
"""Tracker for federated training.
Parameters... |
_base_ = [
'../common/mstrain-poly_3x_coco_instance.py',
'../_base_/models/mask_rcnn_r50_fpn.py'
]
model = dict(
backbone=dict(
_delete_=True,
type='RegNet',
arch='regnetx_1.6gf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_gr... | _base_ = [
'../common/mstrain-poly_3x_coco_instance.py',
'../_base_/models/mask_rcnn_r50_fpn.py'
]
model = dict(
backbone=dict(
_delete_=True,
type='RegNet',
arch='regnetx_1.6gf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_gr... |
from typing import Any, Optional
from llama_index.core.base.agent.types import TaskStepOutput, TaskStep
from llama_index.core.bridge.pydantic import model_validator, field_validator
from llama_index.core.instrumentation.events.base import BaseEvent
from llama_index.core.chat_engine.types import (
AGENT_CHAT_RESPON... | from typing import Any, Optional
from llama_index.core.base.agent.types import TaskStepOutput, TaskStep
from llama_index.core.bridge.pydantic import model_validator, field_validator
from llama_index.core.instrumentation.events.base import BaseEvent
from llama_index.core.chat_engine.types import (
AGENT_CHAT_RESPON... |
# Copyright (c) OpenMMLab. All rights reserved.
import torch.utils.checkpoint as cp
from mmcv.cnn import ConvModule
from mmcv.runner import BaseModule
from .se_layer import SELayer
class InvertedResidual(BaseModule):
"""Inverted Residual Block.
Args:
in_channels (int): The input channels of this Mod... | import torch.utils.checkpoint as cp
from mmcv.cnn import ConvModule
from mmcv.runner import BaseModule
from .se_layer import SELayer
class InvertedResidual(BaseModule):
"""Inverted Residual Block.
Args:
in_channels (int): The input channels of this Module.
out_channels (int): The output chan... |
import inspect
import logging
import secrets
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
sec... | 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 collections.abc import Generator
from langchain_huggingface.llms import HuggingFacePipeline
def test_huggingface_pipeline_streaming() -> None:
"""Test streaming tokens from huggingface_pipeline."""
llm = HuggingFacePipeline.from_model_id(
model_id="gpt2", task="text-generation", pipeline_kwargs=... | from typing import Generator
from langchain_huggingface.llms import HuggingFacePipeline
def test_huggingface_pipeline_streaming() -> None:
"""Test streaming tokens from huggingface_pipeline."""
llm = HuggingFacePipeline.from_model_id(
model_id="gpt2", task="text-generation", pipeline_kwargs={"max_new... |
"""DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.saving.file_editor import KerasFileEditor as KerasFileEditor
from keras.src.saving.object_registration import (
CustomObjectScope as CustomObjectScope,
)
from keras.src.saving.obj... | """DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.saving.file_editor import KerasFileEditor
from keras.src.saving.object_registration import CustomObjectScope
from keras.src.saving.object_registration import (
CustomObjectScope a... |
# deprecated, please use datasets.download.download_manager
| # deprecated, please use daatsets.download.download_manager
|
from typing import Any
from langchain_core._api import deprecated
from langchain_core.language_models import BaseLanguageModel
from langchain_core.messages import BaseMessage, get_buffer_string
from langchain.memory.chat_memory import BaseChatMemory
@deprecated(
since="0.3.1",
removal="1.0.0",
message=(... | from typing import Any, Dict, List
from langchain_core._api import deprecated
from langchain_core.language_models import BaseLanguageModel
from langchain_core.messages import BaseMessage, get_buffer_string
from langchain.memory.chat_memory import BaseChatMemory
@deprecated(
since="0.3.1",
removal="1.0.0",
... |
import os
from typing import Dict
from jina import __default_executor__, __version__
from jina.enums import PodRoleType
from jina.hubble.helper import parse_hub_uri
from jina.hubble.hubio import HubIO
def get_image_name(uses: str) -> str:
"""The image can be provided in different formats by the user.
This fu... | import os
from jina import __default_executor__, __version__
from jina.enums import PodRoleType
from jina.hubble.helper import parse_hub_uri
from jina.hubble.hubio import HubIO
def get_image_name(uses: str) -> str:
"""The image can be provided in different formats by the user.
This function converts it to an... |
from typing import List, cast
from llama_index.core.indices.vector_store.base import VectorStoreIndex
from llama_index.core.schema import (
Document,
NodeRelationship,
QueryBundle,
RelatedNodeInfo,
TextNode,
)
from llama_index.core.vector_stores.simple import SimpleVectorStore
def test_simple_que... | from typing import List, cast
from llama_index.core.indices.vector_store.base import VectorStoreIndex
from llama_index.core.schema import (
Document,
NodeRelationship,
QueryBundle,
RelatedNodeInfo,
TextNode,
)
from llama_index.core.vector_stores.simple import SimpleVectorStore
def test_simple_que... |
# Licensed to the LF AI & Data foundation under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the "License");
# you may not use this fil... | from typing import Optional
import numpy as np
import pytest
from docarray import BaseDoc, DocList, DocVec
from docarray.typing import NdArray
class Nested(BaseDoc):
tensor: NdArray
class Image(BaseDoc):
features: Optional[Nested] = None
def test_optional_field():
docs = DocVec[Image]([Image() for _... |
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