input stringlengths 33 5k | output stringlengths 32 5k |
|---|---|
import os
import time
import pytest
from jina import Client, Document, DocumentArray, Flow
@pytest.mark.parametrize('shards', [1, 2])
@pytest.mark.parametrize('replicas', [1, 3, 4])
def test_containerruntime_args(
docker_image_name, docker_image_built, shards, replicas, port_generator
):
exposed_port = port... | import os
import time
import pytest
from jina import Client, Document, DocumentArray, Flow
cur_dir = os.path.dirname(os.path.abspath(__file__))
img_name = 'jina/replica-exec'
@pytest.fixture(scope='function')
def docker_image_built():
import docker
client = docker.from_env()
client.images.build(path=... |
from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.chat_message_histories import Neo4jChatMessageHistory
# 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.chat_message_histories import Neo4jChatMessageHistory
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handling o... |
"""Simple reader that turns an iterable of strings into a list of Documents."""
from typing import List
from llama_index.core.readers.base import BasePydanticReader
from llama_index.core.schema import Document
class StringIterableReader(BasePydanticReader):
"""
String Iterable Reader.
Gets a list of do... | """Simple reader that turns an iterable of strings into a list of Documents."""
from typing import List
from llama_index.core.readers.base import BasePydanticReader
from llama_index.core.schema import Document
class StringIterableReader(BasePydanticReader):
"""String Iterable Reader.
Gets a list of documen... |
"""Chain that hits a URL and then uses an LLM to parse results."""
from __future__ import annotations
from typing import Any, Dict, List, Optional
from langchain.chains import LLMChain
from langchain.chains.base import Chain
from langchain_core.callbacks import CallbackManagerForChainRun
from pydantic import ConfigD... | """Chain that hits a URL and then uses an LLM to parse results."""
from __future__ import annotations
from typing import Any, Dict, List, Optional
from langchain.chains import LLMChain
from langchain.chains.base import Chain
from langchain_core.callbacks import CallbackManagerForChainRun
from pydantic import ConfigD... |
import gc
import unittest
import numpy as np
import torch
from diffusers import FluxPipeline, FluxPriorReduxPipeline
from diffusers.utils import load_image
from diffusers.utils.testing_utils import (
Expectations,
backend_empty_cache,
numpy_cosine_similarity_distance,
require_big_accelerator,
slow... | import gc
import unittest
import numpy as np
import pytest
import torch
from diffusers import FluxPipeline, FluxPriorReduxPipeline
from diffusers.utils import load_image
from diffusers.utils.testing_utils import (
Expectations,
backend_empty_cache,
numpy_cosine_similarity_distance,
require_big_acceler... |
import warnings
from abc import ABC
from typing import Any, Optional
from langchain_core._api import deprecated
from langchain_core.chat_history import (
BaseChatMessageHistory,
InMemoryChatMessageHistory,
)
from langchain_core.memory import BaseMemory
from langchain_core.messages import AIMessage, HumanMessag... | import warnings
from abc import ABC
from typing import Any, Optional
from langchain_core._api import deprecated
from langchain_core.chat_history import (
BaseChatMessageHistory,
InMemoryChatMessageHistory,
)
from langchain_core.memory import BaseMemory
from langchain_core.messages import AIMessage, HumanMessag... |
def __getattr__(name: str):
if name in ["ctc_decoder", "lexicon_decoder"]:
import warnings
from torchaudio.models.decoder import ctc_decoder
warnings.warn(
f"{__name__}.{name} has been moved to torchaudio.models.decoder.ctc_decoder",
DeprecationWarning,
)
... | _INITIALIZED = False
_LAZILY_IMPORTED = [
"Hypothesis",
"CTCDecoder",
"ctc_decoder",
"lexicon_decoder",
"download_pretrained_files",
]
def _init_extension():
import torchaudio
torchaudio._extension._load_lib("libtorchaudio_decoder")
global _INITIALIZED
_INITIALIZED = True
def _... |
# Copyright (c) OpenMMLab. All rights reserved.
from typing import List, Tuple
import torch.nn as nn
from mmcv.cnn import ConvModule
from mmengine.model import BaseModule
from torch import Tensor
from mmdet.models.layers import ResLayer, SimplifiedBasicBlock
from mmdet.registry import MODELS
from mmdet.utils import M... | # Copyright (c) OpenMMLab. All rights reserved.
from typing import List, Tuple
import torch.nn as nn
from mmcv.cnn import ConvModule
from mmengine.model import BaseModule
from torch import Tensor
from mmdet.core.utils.typing import MultiConfig, OptConfigType
from mmdet.models.utils import ResLayer, SimplifiedBasicBlo... |
_base_ = ['../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py']
img_scale = (640, 640) # height, width
# model settings
model = dict(
type='YOLOX',
input_size=img_scale,
random_size_range=(15, 25),
random_size_interval=10,
backbone=dict(type='CSPDarknet', deepen_factor=0.33, widen... | _base_ = ['../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py']
img_scale = (640, 640) # height, width
# model settings
model = dict(
type='YOLOX',
input_size=img_scale,
random_size_range=(15, 25),
random_size_interval=10,
backbone=dict(type='CSPDarknet', deepen_factor=0.33, widen... |
#!/usr/bin/env python3
# coding=utf-8
# Copyright 2025 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unles... | #!/usr/bin/env python3
# coding=utf-8
# Copyright 2024 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unles... |
_base_ = '../mask_rcnn/mask-rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
norm_cfg=dict(type='SyncBN', requires_grad=True),
norm_eval=False,
plugins=[
dict(
cfg=dict(type='ContextBlock', ratio=1. / 16),
stages=(False, True, True, True),
... | _base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
norm_cfg=dict(type='SyncBN', requires_grad=True),
norm_eval=False,
plugins=[
dict(
cfg=dict(type='ContextBlock', ratio=1. / 16),
stages=(False, True, True, True),
... |
"""Module for argparse for Client"""
def mixin_comm_protocol_parser(parser):
"""Add the arguments for the protocol to the parser
:param parser: the parser configure
"""
from jina.enums import GatewayProtocolType
parser.add_argument(
'--protocol',
type=GatewayProtocolType.from_st... | """Module for argparse for Client"""
def mixin_comm_protocol_parser(parser):
"""Add the arguments for the protocol to the parser
:param parser: the parser configure
"""
from jina.enums import GatewayProtocolType
parser.add_argument(
'--protocol',
type=GatewayProtocolType.from_st... |
from pathlib import Path
from typing import Dict
import numpy as np
from jina import DocumentArray, Document, Executor
from ...paddle_image import ImagePaddlehubEncoder
input_dim = 224
target_output_dim = 2048
num_doc = 2
test_data = np.random.rand(num_doc, 3, input_dim, input_dim)
tmp_files = []
def test_config():... | import os
from typing import Dict
import numpy as np
from jina import DocumentArray, Document
from ...paddle_image import ImagePaddlehubEncoder
directory = os.path.dirname(os.path.realpath(__file__))
input_dim = 224
target_output_dim = 2048
num_doc = 2
test_data = np.random.rand(num_doc, 3, input_dim, input_dim)
tmp... |
# 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`, ... |
from functools import partial
from torchaudio.models import emformer_rnnt_base
from torchaudio.pipelines import RNNTBundle
EMFORMER_RNNT_BASE_MUSTC = RNNTBundle(
_rnnt_path="models/emformer_rnnt_base_mustc.pt",
_rnnt_factory_func=partial(emformer_rnnt_base, num_symbols=501),
_global_stats_path="pipeline-... | from functools import partial
from torchaudio.models import emformer_rnnt_base
from torchaudio.pipelines import RNNTBundle
EMFORMER_RNNT_BASE_MUSTC = RNNTBundle(
_rnnt_path="models/emformer_rnnt_base_mustc.pt",
_rnnt_factory_func=partial(emformer_rnnt_base, num_symbols=501),
_global_stats_path="pipeline-... |
_base_ = [
'../_base_/models/faster-rcnn_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://re... | _base_ = [
'../_base_/models/faster_rcnn_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://re... |
# Copyright (c) OpenMMLab. All rights reserved.
from .conditional_detr_layers import (ConditionalDetrTransformerDecoder,
ConditionalDetrTransformerDecoderLayer)
from .dab_detr_layers import (DABDetrTransformerDecoder,
DABDetrTransformerDecoderLayer,
... | # Copyright (c) OpenMMLab. All rights reserved.
from .conditional_detr_layers import (ConditionalDetrTransformerDecoder,
ConditionalDetrTransformerDecoderLayer)
from .dab_detr_layers import (DABDetrTransformerDecoder,
DABDetrTransformerDecoderLayer,
... |
import contextlib
import logging
import typing
import fastapi
import fastapi.responses
import starlette.middleware.cors
import uvicorn
from autogpt_libs.feature_flag.client import (
initialize_launchdarkly,
shutdown_launchdarkly,
)
import backend.data.block
import backend.data.db
import backend.data.graph
imp... | import contextlib
import logging
import typing
import fastapi
import fastapi.responses
import starlette.middleware.cors
import uvicorn
import backend.data.block
import backend.data.db
import backend.data.graph
import backend.data.user
import backend.server.routers.v1
import backend.util.service
import backend.util.se... |
# Copyright (c) OpenMMLab. All rights reserved.
import copy
import os.path as osp
import unittest
import numpy as np
from mmengine.data import BaseDataElement as PixelData
from mmengine.data import InstanceData
from mmdet.core import DetDataSample
from mmdet.core.mask import BitmapMasks
from mmdet.datasets.pipelines ... | # Copyright (c) OpenMMLab. All rights reserved.
import copy
import os.path as osp
import unittest
import numpy as np
from mmengine.data import BaseDataElement as PixelData
from mmengine.data import InstanceData
from mmdet.core import DetDataSample
from mmdet.core.mask import BitmapMasks
from mmdet.datasets.pipelines ... |
"""A unit test meant to catch accidental introduction of non-optional dependencies."""
from collections.abc import Mapping
from pathlib import Path
from typing import Any
import pytest
import toml
from packaging.requirements import Requirement
HERE = Path(__file__).parent
PYPROJECT_TOML = HERE / "../../pyproject.to... | """A unit test meant to catch accidental introduction of non-optional dependencies."""
from collections.abc import Mapping
from pathlib import Path
from typing import Any
import pytest
import toml
from packaging.requirements import Requirement
HERE = Path(__file__).parent
PYPROJECT_TOML = HERE / "../../pyproject.to... |
"""Message responsible for deleting other messages."""
from typing import Any, Literal
from langchain_core.messages.base import BaseMessage
class RemoveMessage(BaseMessage):
"""Message responsible for deleting other messages."""
type: Literal["remove"] = "remove"
"""The type of the message (used for se... | """Message responsible for deleting other messages."""
from typing import Any, Literal
from langchain_core.messages.base import BaseMessage
class RemoveMessage(BaseMessage):
"""Message responsible for deleting other messages."""
type: Literal["remove"] = "remove"
"""The type of the message (used for se... |
from langchain_core.tools import BaseTool, tool
from langchain_tests.integration_tests import ToolsIntegrationTests
from langchain_tests.unit_tests import ToolsUnitTests
@tool
def parrot_multiply_tool(a: int, b: int) -> int:
"""Multiply two numbers like a parrot. Parrots always add eighty for their matey."""
... | from langchain_core.tools import BaseTool, tool
from langchain_tests.integration_tests import ToolsIntegrationTests
from langchain_tests.unit_tests import ToolsUnitTests
@tool
def parrot_multiply_tool(a: int, b: int) -> int:
"""Multiply two numbers like a parrot. Parrots always add eighty for their matey."""
... |
_base_ = [
'../_base_/models/cascade-mask-rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
checkpoint = 'https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb256-rsb-a1-600e_in1k_20211228-20e21305.pth' # noqa
m... | _base_ = [
'../_base_/models/cascade_mask_rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
checkpoint = 'https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb256-rsb-a1-600e_in1k_20211228-20e21305.pth' # noqa
m... |
__copyright__ = "Copyright (c) 2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import pytest
from jina import Document, DocumentArray
from match_merger import MatchMerger
@pytest.fixture
def docs_matrix():
return [
DocumentArray(
[
Document(
... | __copyright__ = "Copyright (c) 2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import pytest
from jina import Document, DocumentArray
from ...match_merger import MatchMerger
@pytest.fixture
def docs_matrix():
return [
DocumentArray(
[
Document(
... |
import tempfile
from enum import Enum
from typing import Any, Dict, Optional, Union
from langchain_core.callbacks import CallbackManagerForToolRun
from langchain_core.tools import BaseTool
from langchain_core.utils import get_from_dict_or_env
from pydantic import model_validator
def _import_elevenlabs() -> Any:
... | import tempfile
from enum import Enum
from typing import Any, Dict, Optional, Union
from langchain_core.callbacks import CallbackManagerForToolRun
from langchain_core.tools import BaseTool
from langchain_core.utils import get_from_dict_or_env
from pydantic import model_validator
def _import_elevenlabs() -> Any:
... |
"""
Top-level module of Jina.
The primary function of this module is to import all of the public Jina
interfaces into a single place. The interfaces themselves are located in
sub-modules, as described below.
"""
import os as _os
import platform as _platform
import signal as _signal
import sys as _sys
import warnings... | """
Top-level module of Jina.
The primary function of this module is to import all of the public Jina
interfaces into a single place. The interfaces themselves are located in
sub-modules, as described below.
"""
import os as _os
import platform as _platform
import signal as _signal
import sys as _sys
import warnings... |
# Copyright (c) OpenMMLab. All rights reserved.
"""MMEngine provides 11 root registries to support using modules across
projects.
More datails can be found at
https://mmengine.readthedocs.io/en/latest/tutorials/registry.html.
"""
from .registry import Registry
# manage all kinds of runners like `EpochBasedRunner` an... | # Copyright (c) OpenMMLab. All rights reserved.
"""MMEngine provides 11 root registries to support using modules across
projects.
More datails can be found at
https://mmengine.readthedocs.io/en/latest/tutorials/registry.html.
"""
from .registry import Registry
# manage all kinds of runners like `EpochBasedRunner` an... |
from __future__ import annotations
from sentence_transformers.losses.MSELoss import MSELoss
from sentence_transformers.sparse_encoder.SparseEncoder import SparseEncoder
class SparseMSELoss(MSELoss):
def __init__(self, model: SparseEncoder) -> None:
"""
Computes the MSE loss between the computed s... | from __future__ import annotations
from sentence_transformers.losses.MSELoss import MSELoss
from sentence_transformers.sparse_encoder.SparseEncoder import SparseEncoder
class SparseMSELoss(MSELoss):
def __init__(self, model: SparseEncoder) -> None:
return super().__init__(model)
|
__version__ = '0.36.0'
import logging
from docarray.array import DocList, DocVec
from docarray.base_doc.doc import BaseDoc
from docarray.utils._internal.misc import _get_path_from_docarray_root_level
__all__ = ['BaseDoc', 'DocList', 'DocVec']
logger = logging.getLogger('docarray')
handler = logging.StreamHandler()... | __version__ = '0.35.1'
import logging
from docarray.array import DocList, DocVec
from docarray.base_doc.doc import BaseDoc
from docarray.utils._internal.misc import _get_path_from_docarray_root_level
__all__ = ['BaseDoc', 'DocList', 'DocVec']
logger = logging.getLogger('docarray')
handler = logging.StreamHandler()... |
import os
import time
import pytest
from jina import Flow, Document, Client
cur_dir = os.path.dirname(os.path.abspath(__file__))
@pytest.fixture()
def docker_image():
import docker
client = docker.from_env()
client.images.build(path=os.path.join(cur_dir), tag='override-config-test')
client.close()
... | import os
import time
import pytest
from jina import Flow, Document, Client
cur_dir = os.path.dirname(os.path.abspath(__file__))
exposed_port = 12345
@pytest.fixture()
def docker_image():
import docker
client = docker.from_env()
client.images.build(path=os.path.join(cur_dir), tag='override-config-test'... |
from llama_index_instrumentation.span_handlers.null import NullSpanHandler # noqa
| import inspect
from typing import Dict, Optional, Any
from llama_index.core.instrumentation.span_handlers.base import BaseSpanHandler
from llama_index.core.instrumentation.span.base import BaseSpan
class NullSpanHandler(BaseSpanHandler[BaseSpan]):
@classmethod
def class_name(cls) -> str:
"""Class name... |
__copyright__ = "Copyright (c) 2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import os
import numpy as np
import pytest
from jina import Document, DocumentArray, Flow
from jina.executors.metas import get_default_metas
from jina_commons.indexers.dump import import_vectors
from .. import Hnswl... | __copyright__ = "Copyright (c) 2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import os
import numpy as np
import pytest
from jina import Document, DocumentArray, Flow
from jina.executors.metas import get_default_metas
from jina_commons.indexers.dump import import_vectors
from .. import Hnswl... |
# ruff: noqa: E402
import pytest
# Rewrite assert statements for test suite so that implementations can
# see the full error message from failed asserts.
# https://docs.pytest.org/en/7.1.x/how-to/writing_plugins.html#assertion-rewriting
modules = [
"base_store",
"cache",
"chat_models",
"vectorstores",
... | # ruff: noqa: E402
import pytest
# Rewrite assert statements for test suite so that implementations can
# see the full error message from failed asserts.
# https://docs.pytest.org/en/7.1.x/how-to/writing_plugins.html#assertion-rewriting
modules = [
"base_store",
"cache",
"chat_models",
"vectorstores",
... |
# 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 torch
from docarray import BaseDoc
from docarray.typing import TorchTensor
def test_tensor_ops():
class A(BaseDoc):
tensor: TorchTensor[3, 224, 224]
class B(BaseDoc):
tensor: TorchTensor[3, 112, 224]
tensor = A(tensor=torch.ones(3, 224, 224)).tensor
tensord = A(tensor=torch.o... |
from typing import overload, Dict, Optional, List, TYPE_CHECKING, Sequence, Any
from docarray.document.data import DocumentData
from docarray.document.mixins import AllMixins
from docarray.base import BaseDCType
from docarray.math.ndarray import detach_tensor_if_present
if TYPE_CHECKING:
from docarray.typing impo... | from typing import overload, Dict, Optional, List, TYPE_CHECKING, Sequence, Any
from .data import DocumentData
from .mixins import AllMixins
from ..base import BaseDCType
from ..math.ndarray import detach_tensor_if_present
if TYPE_CHECKING:
from ..typing import ArrayType, StructValueType, DocumentContentType
cl... |
"""
This file contains deprecated code that can only be used with the old `model.fit`-style Sentence Transformers v2.X training.
It exists for backwards compatibility with the `model.old_fit` method, but will be removed in a future version.
Nowadays, with Sentence Transformers v3+, it is recommended to use the `Senten... | from __future__ import annotations
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 In... |
# Copyright (c) OpenMMLab. All rights reserved.
from .registry import Registry, build_from_cfg
from .root import (DATA_SAMPLERS, DATASETS, EVALUATORS, HOOKS, MODELS,
OPTIMIZER_CONSTRUCTORS, OPTIMIZERS, PARAM_SCHEDULERS,
RUNNER_CONSTRUCTORS, RUNNERS, TASK_UTILS, TRANSFORMS,
... | # Copyright (c) OpenMMLab. All rights reserved.
from .registry import Registry, build_from_cfg
from .root import (DATA_SAMPLERS, DATASETS, HOOKS, MODELS,
OPTIMIZER_CONSTRUCTORS, OPTIMIZERS, PARAM_SCHEDULERS,
RUNNER_CONSTRUCTORS, RUNNERS, TASK_UTILS, TRANSFORMS,
W... |
"""Callback Handler that tracks AIMessage.usage_metadata."""
import threading
from collections.abc import Generator
from contextlib import contextmanager
from contextvars import ContextVar
from typing import Any, Optional
from langchain_core._api import beta
from langchain_core.callbacks import BaseCallbackHandler
fr... | """Callback Handler that tracks AIMessage.usage_metadata."""
import threading
from collections.abc import Generator
from contextlib import contextmanager
from contextvars import ContextVar
from typing import Any, Optional
from langchain_core._api import beta
from langchain_core.callbacks import BaseCallbackHandler
fr... |
# Owner(s): ["oncall: distributed"]
import torch
import torch.nn as nn
from torch.distributed.checkpoint.state_dict import get_state_dict
from torch.distributed.device_mesh import _mesh_resources, init_device_mesh
from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
from torch.distributed.tensor import ... | # Owner(s): ["oncall: distributed"]
import torch
import torch.nn as nn
from torch.distributed.checkpoint.state_dict import get_state_dict
from torch.distributed.device_mesh import _mesh_resources, init_device_mesh
from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
from torch.distributed.tensor import ... |
# Copyright (c) OpenMMLab. All rights reserved.
import math
import torch
from torch.utils.data import DistributedSampler as _DistributedSampler
from mmdet.core.utils import sync_random_seed
from mmdet.utils import get_device
class DistributedSampler(_DistributedSampler):
def __init__(self,
dat... | # Copyright (c) OpenMMLab. All rights reserved.
import math
import torch
from torch.utils.data import DistributedSampler as _DistributedSampler
from mmdet.core.utils import sync_random_seed
from mmdet.utils import get_device
class DistributedSampler(_DistributedSampler):
def __init__(self,
data... |
import pytest
from jina import Executor, Flow, requests
@pytest.fixture()
def get_executor():
class DummyExecutor(Executor):
@requests(on='/foo')
def foo(self, docs, **kwargs): ...
return DummyExecutor
def test_disable_monitoring_on_pods(port_generator, get_executor):
port0 = port_gene... | import pytest
from jina import Executor, Flow, requests
@pytest.fixture()
def get_executor():
class DummyExecutor(Executor):
@requests(on='/foo')
def foo(self, docs, **kwargs):
...
return DummyExecutor
def test_disable_monitoring_on_pods(port_generator, get_executor):
port0... |
"""
Prompts for implementing Chain of Abstraction.
While official prompts are not given (and the paper finetunes models for the task),
we can take inspiration and use few-shot prompting to generate a prompt for implementing
chain of abstraction in an LLM agent.
"""
REASONING_PROMPT_TEMPALTE = """Generate an abstract ... | """
Prompts for implementing Chain of Abstraction.
While official prompts are not given (and the paper finetunes models for the task),
we can take inspiration and use few-shot prompting to generate a prompt for implementing
chain of abstraction in an LLM agent.
"""
REASONING_PROMPT_TEMPALTE = """Generate an abstract... |
"""DeepLake reader."""
from typing import List, Optional, Union
import numpy as np
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document
distance_metric_map = {
"l2": lambda a, b: np.linalg.norm(a - b, axis=1, ord=2),
"l1": lambda a, b: np.linalg.norm(a - b, axis=1... | """DeepLake reader."""
from typing import List, Optional, Union
import numpy as np
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document
distance_metric_map = {
"l2": lambda a, b: np.linalg.norm(a - b, axis=1, ord=2),
"l1": lambda a, b: np.linalg.norm(a - b, axis=1,... |
import numpy as np
def _number_of_shards_in_gen_kwargs(gen_kwargs: dict) -> int:
"""Return the number of possible shards according to the input gen_kwargs"""
# Having lists of different sizes makes sharding ambigious, raise an error in this case
# until we decide how to define sharding without ambiguity f... | from typing import List
import numpy as np
def _number_of_shards_in_gen_kwargs(gen_kwargs: dict) -> int:
"""Return the number of possible shards according to the input gen_kwargs"""
# Having lists of different sizes makes sharding ambigious, raise an error in this case
# until we decide how to define sha... |
import numpy as np
import pytest
import torch
from pydantic import parse_obj_as
from docarray import BaseDoc
from docarray.documents import ImageDoc
from docarray.typing import ImageBytes
from docarray.utils._internal.misc import is_tf_available
from docarray.utils._internal.pydantic import is_pydantic_v2
tf_availabl... | import numpy as np
import pytest
import torch
from pydantic import parse_obj_as
from docarray import BaseDoc
from docarray.documents import ImageDoc
from docarray.typing import ImageBytes
from docarray.utils._internal.misc import is_tf_available
tf_available = is_tf_available()
if tf_available:
import tensorflow ... |
__copyright__ = 'Copyright (c) 2020-2021 Jina AI Limited. All rights reserved.'
__license__ = 'Apache-2.0'
from typing import Any, Iterable, Optional
import librosa as lr
import numpy as np
import torch
from jina import DocumentArray, Executor, requests
from jina.excepts import BadDocType
from .audio_clip.model impo... | __copyright__ = 'Copyright (c) 2020-2021 Jina AI Limited. All rights reserved.'
__license__ = 'Apache-2.0'
from typing import Any, Iterable, Optional
import librosa as lr
import numpy as np
import torch
from jina import DocumentArray, Executor, requests
from jina.excepts import BadDocType
from .audio_clip.model impo... |
# 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 docarray import BaseDoc, DocList
from docarray.base_doc import AnyDoc
def test_generic_init():
class Text(BaseDoc):
text: str
da = DocList[Text]([])
da.doc_type == Text
assert isinstance(da, DocList)
def test_normal_access_init():
da = DocList([])
da.doc_type == AnyDoc
as... |
from __future__ import annotations
import csv
import logging
import os
import numpy as np
from sentence_transformers import InputExample
logger = logging.getLogger(__name__)
class CEBinaryAccuracyEvaluator:
"""
This evaluator can be used with the CrossEncoder class.
It is designed for CrossEncoders w... | import csv
import logging
import os
from typing import List
import numpy as np
from sentence_transformers import InputExample
logger = logging.getLogger(__name__)
class CEBinaryAccuracyEvaluator:
"""
This evaluator can be used with the CrossEncoder class.
It is designed for CrossEncoders with 1 output... |
from __future__ import annotations
from sentence_transformers.similarity_functions import SimilarityFunction
__all__ = ["SimilarityFunction"]
| from sentence_transformers.similarity_functions import SimilarityFunction
__all__ = ["SimilarityFunction"]
|
from keras.src.api_export import keras_export
from keras.src.layers.pooling.base_pooling import BasePooling
@keras_export(["keras.layers.AveragePooling3D", "keras.layers.AvgPool3D"])
class AveragePooling3D(BasePooling):
"""Average pooling operation for 3D data (spatial or spatio-temporal).
Downsamples the in... | from keras.src.api_export import keras_export
from keras.src.layers.pooling.base_pooling import BasePooling
@keras_export(["keras.layers.AveragePooling3D", "keras.layers.AvgPool3D"])
class AveragePooling3D(BasePooling):
"""Average pooling operation for 3D data (spatial or spatio-temporal).
Downsamples the in... |
"""Test pydantic output parser."""
import pytest
from llama_index.core.bridge.pydantic import BaseModel
from llama_index.core.output_parsers.pydantic import PydanticOutputParser
from llama_index.core.llms import ChatMessage, TextBlock, ImageBlock
class AttrDict(BaseModel):
test_attr: str
foo: int
class Tes... | """Test pydantic output parser."""
import pytest
from llama_index.core.bridge.pydantic import BaseModel
from llama_index.core.output_parsers.pydantic import PydanticOutputParser
class AttrDict(BaseModel):
test_attr: str
foo: int
class TestModel(BaseModel):
__test__ = False
title: str
attr_dict:... |
_base_ = '../faster_rcnn/faster-rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(plugins=[
dict(
cfg=dict(
type='GeneralizedAttention',
spatial_range=-1,
num_heads=8,
attention_type='1111',
kv_stride=2),
... | _base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(plugins=[
dict(
cfg=dict(
type='GeneralizedAttention',
spatial_range=-1,
num_heads=8,
attention_type='1111',
kv_stride=2),
... |
from urllib.parse import urlparse, urlunparse
import pytest
from llama_index.postprocessor.nvidia_rerank import NVIDIARerank as Interface
from llama_index.postprocessor.nvidia_rerank.utils import BASE_URL
import respx
@pytest.fixture()
def mock_v1_local_models2(respx_mock: respx.MockRouter, base_url: str) -> None:
... | from urllib.parse import urlparse, urlunparse
import pytest
from llama_index.postprocessor.nvidia_rerank import NVIDIARerank as Interface
from llama_index.postprocessor.nvidia_rerank.utils import BASE_URL
import respx
@pytest.fixture()
def mock_v1_local_models2(respx_mock: respx.MockRouter, base_url: str) -> None:
... |
"""Standard LangChain interface tests"""
from pathlib import Path
from typing import Literal, cast
from langchain_core.language_models import BaseChatModel
from langchain_core.messages import AIMessage
from langchain_tests.integration_tests import ChatModelIntegrationTests
from langchain_anthropic import ChatAnthrop... | """Standard LangChain interface tests"""
from pathlib import Path
from typing import Literal, cast
from langchain_core.language_models import BaseChatModel
from langchain_core.messages import AIMessage
from langchain_tests.integration_tests import ChatModelIntegrationTests
from langchain_anthropic import ChatAnthrop... |
# Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from mmdet.core.bbox.coder import (DeltaXYWHBBoxCoder, TBLRBBoxCoder,
YOLOBBoxCoder)
def test_yolo_bbox_coder():
coder = YOLOBBoxCoder()
bboxes = torch.Tensor([[-42., -29., 74., 61.], [-10., -29., 10... | import pytest
import torch
from mmdet.core.bbox.coder import (DeltaXYWHBBoxCoder, TBLRBBoxCoder,
YOLOBBoxCoder)
def test_yolo_bbox_coder():
coder = YOLOBBoxCoder()
bboxes = torch.Tensor([[-42., -29., 74., 61.], [-10., -29., 106., 61.],
[22., -29.,... |
# Copyright 2021 The TensorFlow Authors. 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 applica... | # Copyright 2021 The TensorFlow Authors. 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 applica... |
from __future__ import annotations
import logging
from typing import TYPE_CHECKING, Any
from sentence_transformers.evaluation import MSEEvaluator
if TYPE_CHECKING:
import numpy as np
from torch import Tensor
from sentence_transformers.sparse_encoder import SparseEncoder
logger = logging.getLogger(__nam... | from __future__ import annotations
import logging
from typing import TYPE_CHECKING, Any
from sentence_transformers.evaluation import MSEEvaluator
if TYPE_CHECKING:
import numpy as np
from torch import Tensor
from sentence_transformers.sparse_encoder import SparseEncoder
logger = logging.getLogger(__nam... |
# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import torch
from mmcv import Config, DictAction
from mmdet.models import build_detector
try:
from mmcv.cnn import get_model_complexity_info
except ImportError:
raise ImportError('Please upgrade mmcv to >0.6.2')
def parse_args():
parser = ... | import argparse
import torch
from mmcv import Config, DictAction
from mmdet.models import build_detector
try:
from mmcv.cnn import get_model_complexity_info
except ImportError:
raise ImportError('Please upgrade mmcv to >0.6.2')
def parse_args():
parser = argparse.ArgumentParser(description='Train a det... |
# Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import Tensor
from mmdet.registry import MODELS
MODELS.register_module('Linear', module=nn.Linear)
@MODELS.register_module(name='NormedLinear')
class NormedLinear(nn.Linear):
"""Normaliz... | # Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmdet.registry import MODELS
MODELS.register_module('Linear', module=nn.Linear)
@MODELS.register_module(name='NormedLinear')
class NormedLinear(nn.Linear):
"""Normalized Linear Layer.
Arg... |
# Copyright (c) OpenMMLab. All rights reserved.
from .det_tta import DetTTAModel
from .merge_augs import (merge_aug_bboxes, merge_aug_masks,
merge_aug_proposals, merge_aug_results,
merge_aug_scores)
__all__ = [
'merge_aug_bboxes', 'merge_aug_masks', 'merge_aug_prop... | # Copyright (c) OpenMMLab. All rights reserved.
from .merge_augs import (merge_aug_bboxes, merge_aug_masks,
merge_aug_proposals, merge_aug_results,
merge_aug_scores)
__all__ = [
'merge_aug_bboxes', 'merge_aug_masks', 'merge_aug_proposals',
'merge_aug_scores', '... |
__copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
from itertools import groupby
from typing import Dict, Iterable
from jina import DocumentArray, Executor, requests
class SimpleRanker(Executor):
"""
:class:`SimpleRanker` aggregates the score of the ma... | __copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
from itertools import groupby
from typing import Dict, Iterable
from jina import DocumentArray, Executor, requests
class SimpleRanker(Executor):
"""
:class:`SimpleRanker` aggregates the score of the ma... |
from __future__ import annotations
from dataclasses import field
from typing import Any, Callable
import torch
from sentence_transformers.data_collator import SentenceTransformerDataCollator
class CrossEncoderDataCollator(SentenceTransformerDataCollator):
"""Collator for a CrossEncoder model.
This encodes ... | from __future__ import annotations
from dataclasses import field
from typing import Any, Callable
import torch
from sentence_transformers.data_collator import SentenceTransformerDataCollator
class CrossEncoderDataCollator(SentenceTransformerDataCollator):
"""Collator for a CrossEncoder model.
This encodes ... |
_base_ = 'faster-rcnn_r50-caffe-dc5_1x_coco.py'
train_pipeline = [
dict(type='LoadImageFromFile', backend_args=_base_.backend_args),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='RandomChoiceResize',
scales=[(1333, 640), (1333, 672), (1333, 704), (1333, 736),
(13... | _base_ = 'faster-rcnn_r50-caffe-dc5_1x_coco.py'
train_pipeline = [
dict(type='LoadImageFromFile', backend_args=_base_.backend_args),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='RandomChoiceResize',
scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736), (1333, 768),
... |
"""Test simple function agent."""
from typing import Any, Dict, Tuple
import pytest
from llama_index.core.agent.custom.simple_function import FnAgentWorker
def mock_foo_fn_no_state_param() -> Tuple[None, bool]:
"""Mock agent input function without a state."""
return None, True
def mock_foo_fn(state: dict) ... | """Test simple function agent."""
from typing import Any, Dict, Tuple
import pytest
from llama_index.core.agent.custom.simple_function import FnAgentWorker
def mock_foo_fn_no_state_param() -> Tuple[None, bool]:
"""Mock agent input function without a state."""
return None, True
def mock_foo_fn(state: dict) ... |
import platform
import sys
from pathlib import Path
import pkg_resources
from setuptools import find_packages, setup
def read_version(fname="whisper/version.py"):
exec(compile(open(fname, encoding="utf-8").read(), fname, "exec"))
return locals()["__version__"]
requirements = []
if sys.platform.startswith("... | import platform
import sys
from pathlib import Path
import pkg_resources
from setuptools import find_packages, setup
def read_version(fname="whisper/version.py"):
exec(compile(open(fname, encoding="utf-8").read(), fname, "exec"))
return locals()["__version__"]
requirements = []
if sys.platform.startswith("... |
from typing import TYPE_CHECKING, Any, List, Tuple, Type, TypeVar, Union
import numpy as np
from docarray.typing.proto_register import _register_proto
from docarray.typing.tensor.ndarray import NdArray
from docarray.typing.tensor.video.video_tensor_mixin import VideoTensorMixin
T = TypeVar('T', bound='VideoNdArray')... | from typing import TYPE_CHECKING, Any, List, Tuple, Type, TypeVar, Union
import numpy as np
from docarray.typing.proto_register import _register_proto
from docarray.typing.tensor.ndarray import NdArray
from docarray.typing.tensor.video.video_tensor_mixin import VideoTensorMixin
T = TypeVar('T', bound='VideoNdArray')... |
import hashlib
import secrets
from typing import NamedTuple
class APIKeyContainer(NamedTuple):
"""Container for API key parts."""
raw: str
prefix: str
postfix: str
hash: str
class APIKeyManager:
PREFIX: str = "agpt_"
PREFIX_LENGTH: int = 8
POSTFIX_LENGTH: int = 8
def generate_a... | from typing import NamedTuple
import secrets
import hashlib
class APIKeyContainer(NamedTuple):
"""Container for API key parts."""
raw: str
prefix: str
postfix: str
hash: str
class APIKeyManager:
PREFIX: str = "agpt_"
PREFIX_LENGTH: int = 8
POSTFIX_LENGTH: int = 8
def generate_api_... |
"""
Top-level module of Jina.
The primary function of this module is to import all of the public Jina
interfaces into a single place. The interfaces themselves are located in
sub-modules, as described below.
"""
import os as _os
import platform as _platform
import signal as _signal
import sys as _sys
import warnings... | """
Top-level module of Jina.
The primary function of this module is to import all of the public Jina
interfaces into a single place. The interfaces themselves are located in
sub-modules, as described below.
"""
import os as _os
import platform as _platform
import signal as _signal
import sys as _sys
import warnings... |
# Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import torch
from mmdet.models.dense_heads.autoassign_head import AutoAssignHead
from mmdet.models.dense_heads.paa_head import levels_to_images
def test_autoassign_head_loss():
"""Tests autoassign head loss when truth is empty and non-empty."""
s =... | # Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import torch
from mmdet.models.dense_heads.autoassign_head import AutoAssignHead
from mmdet.models.dense_heads.paa_head import levels_to_images
def test_autoassign_head_loss():
"""Tests autoassign head loss when truth is empty and non-empty."""
s =... |
from __future__ import annotations
from torch import Tensor, nn
from sentence_transformers.cross_encoder import CrossEncoder
# TODO: Consider the naming of this class
class CrossEntropyLoss(nn.Module):
def __init__(self, model: CrossEncoder, activation_fct: nn.Module = nn.Identity(), **kwargs) -> None:
... | from __future__ import annotations
from torch import Tensor, nn
from sentence_transformers.cross_encoder import CrossEncoder
# TODO: Consider the naming of this class
class CrossEntropyLoss(nn.Module):
def __init__(self, model: CrossEncoder) -> None:
super().__init__()
self.model = model
... |
import pytest
import torch
from docarray.computation.torch_backend import TorchCompBackend
def test_to_device():
t = torch.rand(10, 3)
assert t.device == torch.device('cpu')
t = TorchCompBackend.to_device(t, 'meta')
assert t.device == torch.device('meta')
@pytest.mark.parametrize(
'array,result... | import torch
from docarray.computation.torch_backend import TorchCompBackend
def test_to_device():
t = torch.rand(10, 3)
assert t.device == torch.device('cpu')
t = TorchCompBackend.to_device(t, 'meta')
assert t.device == torch.device('meta')
def test_empty():
tensor = TorchCompBackend.empty((10... |
import torch
import torchaudio.prototype.functional as F
from parameterized import parameterized
from torch.autograd import gradcheck, gradgradcheck
from torchaudio_unittest.common_utils import nested_params, TestBaseMixin
class AutogradTestImpl(TestBaseMixin):
@nested_params(
[F.convolve, F.fftconvolve],... | import torch
import torchaudio.prototype.functional as F
from torch.autograd import gradcheck, gradgradcheck
from torchaudio_unittest.common_utils import nested_params, TestBaseMixin
class AutogradTestImpl(TestBaseMixin):
@nested_params(
[F.convolve, F.fftconvolve],
["full", "valid", "same"],
... |
from typing import List, Union
class InputExample:
"""Structure for one input example with texts, the label and a unique id"""
def __init__(self, guid: str = "", texts: List[str] = None, label: Union[int, float] = 0):
"""
Creates one InputExample with the given texts, guid and label
... | from typing import Union, List
class InputExample:
"""Structure for one input example with texts, the label and a unique id"""
def __init__(self, guid: str = "", texts: List[str] = None, label: Union[int, float] = 0):
"""
Creates one InputExample with the given texts, guid and label
... |
# Copyright (c) OpenMMLab. All rights reserved.
__version__ = '3.3.0'
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__ = '3.2.0'
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... |
# flake8: noqa
# Copyright 2020 The HuggingFace Datasets Authors and the TensorFlow Datasets Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LI... | # flake8: noqa
# Copyright 2020 The HuggingFace Datasets Authors and the TensorFlow Datasets Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LI... |
import time
import pytest
from backend.util.decorator import async_error_logged, error_logged, time_measured
@time_measured
def example_function(a: int, b: int, c: int) -> int:
time.sleep(0.5)
return a + b + c
@error_logged(swallow=True)
def example_function_with_error_swallowed(a: int, b: int, c: int) ->... | import time
from backend.util.decorator import error_logged, time_measured
@time_measured
def example_function(a: int, b: int, c: int) -> int:
time.sleep(0.5)
return a + b + c
@error_logged
def example_function_with_error(a: int, b: int, c: int) -> int:
raise ValueError("This is a test error")
def te... |
from abc import ABC, abstractmethod
from typing import Dict, Iterator, List, Optional, Type
from typing_extensions import TYPE_CHECKING
if TYPE_CHECKING:
from docarray import BaseDoc, DocArray
class AbstractDocStore(ABC):
@staticmethod
@abstractmethod
def list(namespace: str, show_table: bool) -> Li... | from abc import ABC, abstractmethod
from typing import Dict, Iterator, List, Optional, Type
from typing_extensions import TYPE_CHECKING
if TYPE_CHECKING:
from docarray import BaseDocument, DocumentArray
class AbstractDocStore(ABC):
@staticmethod
@abstractmethod
def list(namespace: str, show_table: b... |
import tempfile
from collections.abc import Generator
from typing import cast
import pytest
from langchain_core.documents import Document
from langchain.storage._lc_store import create_kv_docstore, create_lc_store
from langchain.storage.file_system import LocalFileStore
@pytest.fixture
def file_store() -> Generator... | import tempfile
from typing import Generator, cast
import pytest
from langchain_core.documents import Document
from langchain.storage._lc_store import create_kv_docstore, create_lc_store
from langchain.storage.file_system import LocalFileStore
@pytest.fixture
def file_store() -> Generator[LocalFileStore, None, None... |
_base_ = './mask-rcnn_r50_fpn_seesaw-loss_sample1e-3-ms-2x_lvis-v1.py'
model = dict(
roi_head=dict(
mask_head=dict(
predictor_cfg=dict(type='NormedConv2d', tempearture=20))))
| _base_ = './mask_rcnn_r50_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1.py'
model = dict(
roi_head=dict(
mask_head=dict(
predictor_cfg=dict(type='NormedConv2d', tempearture=20))))
|
_base_ = ['co_dino_5scale_swin_l_lsj_16xb1_1x_coco.py']
model = dict(backbone=dict(drop_path_rate=0.5))
param_scheduler = [dict(type='MultiStepLR', milestones=[30])]
train_cfg = dict(max_epochs=36)
| _base_ = ['co_dino_5scale_swin_l_lsj_16xb1_1x_coco.py']
model = dict(backbone=dict(drop_path_rate=0.5))
param_scheduler = [dict(milestones=[30])]
train_cfg = dict(max_epochs=36)
|
# Copyright (c) OpenMMLab. All rights reserved.
__version__ = '0.9.1'
def parse_version_info(version_str):
"""Parse the version information.
Args:
version_str (str): version string like '0.1.0'.
Returns:
tuple: version information contains major, minor, micro version.
"""
versio... | # Copyright (c) OpenMMLab. All rights reserved.
__version__ = '0.9.0'
def parse_version_info(version_str):
"""Parse the version information.
Args:
version_str (str): version string like '0.1.0'.
Returns:
tuple: version information contains major, minor, micro version.
"""
versio... |
# TODO: Add _log_api_usage_once() in all mid-level kernels. If they remain not jit-scriptable we can use decorators
from torchvision.transforms import InterpolationMode # usort: skip
from ._utils import is_simple_tensor # usort: skip
from ._meta import (
clamp_bounding_box,
convert_format_bounding_box,
... | # TODO: Add _log_api_usage_once() in all mid-level kernels. If they remain not jit-scriptable we can use decorators
from torchvision.transforms import InterpolationMode # usort: skip
from ._utils import is_simple_tensor # usort: skip
from ._meta import (
clamp_bounding_box,
convert_format_bounding_box,
... |
import pytest
from docarray import DocumentArray
from docarray.array.qdrant import DocumentArrayQdrant
from docarray.array.sqlite import DocumentArraySqlite
from docarray.array.annlite import DocumentArrayAnnlite, AnnliteConfig
from docarray.array.storage.qdrant import QdrantConfig
from docarray.array.storage.weaviate... | import pytest
from docarray import DocumentArray
from docarray.array.qdrant import DocumentArrayQdrant
from docarray.array.sqlite import DocumentArraySqlite
from docarray.array.annlite import DocumentArrayAnnlite, AnnliteConfig
from docarray.array.storage.qdrant import QdrantConfig
from docarray.array.storage.weaviate... |
from torchvision.transforms import AutoAugmentPolicy, InterpolationMode # usort: skip
from . import functional # usort: skip
from ._transform import Transform # usort: skip
from ._presets import StereoMatching # usort: skip
from ._augment import RandomCutmix, RandomErasing, RandomMixup, SimpleCopyPaste
from ._au... | from torchvision.transforms import AutoAugmentPolicy, InterpolationMode # usort: skip
from . import functional # usort: skip
from ._transform import Transform # usort: skip
from ._presets import StereoMatching # usort: skip
from ._augment import RandomCutmix, RandomErasing, RandomMixup, SimpleCopyPaste
from ._au... |
_base_ = ['../common/ms_3x_coco.py', '../_base_/models/faster-rcnn_r50_fpn.py']
model = dict(
data_preprocessor=dict(
# The mean and std are used in PyCls when training RegNets
mean=[103.53, 116.28, 123.675],
std=[57.375, 57.12, 58.395],
bgr_to_rgb=False),
backbone=dict(
... | _base_ = [
'../common/mstrain_3x_coco.py', '../_base_/models/faster_rcnn_r50_fpn.py'
]
model = dict(
data_preprocessor=dict(
# The mean and std are used in PyCls when training RegNets
mean=[103.53, 116.28, 123.675],
std=[57.375, 57.12, 58.395],
bgr_to_rgb=False),
backbone=dic... |
import os
from typing import Dict
from hubble.executor.helper import parse_hub_uri
from hubble.executor.hubio import HubIO
from jina import __default_executor__, __version__
from jina.enums import PodRoleType
def get_image_name(uses: str) -> str:
"""The image can be provided in different formats by the user.
... | 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 json
import multiprocessing
import os
import time
import pytest
from jina.helper import random_port
from jina.parsers import set_gateway_parser
from jina.serve.runtimes.gateway import GatewayRuntime
from jina.serve.runtimes.worker import WorkerRuntime
from tests.helper import (
_generate_pod_args,
_val... | 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 (
_validate_cu... |
# Copyright (c) OpenMMLab. All rights reserved.
from ..builder import DETECTORS
from .single_stage import SingleStageDetector
@DETECTORS.register_module()
class AutoAssign(SingleStageDetector):
"""Implementation of `AutoAssign: Differentiable Label Assignment for Dense
Object Detection <https://arxiv.org/abs/... | from ..builder import DETECTORS
from .single_stage import SingleStageDetector
@DETECTORS.register_module()
class AutoAssign(SingleStageDetector):
"""Implementation of `AutoAssign: Differentiable Label Assignment for Dense
Object Detection <https://arxiv.org/abs/2007.03496>`_."""
def __init__(self,
... |
# 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 .d2_wrapper import Detectron2Wrapper
from .ddod import DDOD
from .deformab... | # 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 .ddod import DDOD
from .deformable_detr import DeformableDETR
from .detr i... |
# Copyright (c) OpenMMLab. All rights reserved.
import datetime
import os.path as osp
from typing import Optional
from mmengine.fileio import dump
from . import root
from .registry import Registry
def traverse_registry_tree(registry: Registry, verbose: bool = True) -> list:
"""Traverse the whole registry tree fr... | # Copyright (c) OpenMMLab. All rights reserved.
import datetime
import os.path as osp
from typing import Optional
from mmengine.fileio import dump
from . import root
from .registry import Registry
def traverse_registry_tree(registry: Registry, verbose: bool = True) -> list:
"""Traverse the whole registry tree fr... |
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,
ImageNode,
)
from llama_index.core.vector_stores.simple import SimpleVectorStore
def ... | 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... |
_base_ = 'cascade-mask-rcnn_regnetx-3.2GF_fpn_ms-3x_coco.py'
model = dict(
backbone=dict(
type='RegNet',
arch='regnetx_1.6gf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
ini... | _base_ = 'cascade_mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py'
model = dict(
backbone=dict(
type='RegNet',
arch='regnetx_1.6gf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
... |
# Copyright (c) OpenMMLab. All rights reserved.
import unittest
import torch
import torch.nn as nn
from mmengine.runner import autocast
from mmengine.utils import digit_version
from mmengine.utils.dl_utils import TORCH_VERSION
class TestAmp(unittest.TestCase):
def test_autocast(self):
if not torch.cuda... | # Copyright (c) OpenMMLab. All rights reserved.
import unittest
import torch
import torch.nn as nn
from mmengine.runner import autocast
from mmengine.utils import TORCH_VERSION, digit_version
class TestAmp(unittest.TestCase):
def test_autocast(self):
if not torch.cuda.is_available():
if dig... |
import numpy as np
from docarray.proto import DocumentProto, NdArrayProto, NodeProto
from docarray.typing import Tensor
def test_nested_item_proto():
NodeProto(text='hello')
NodeProto(nested=DocumentProto())
def test_nested_optional_item_proto():
NodeProto()
def test_ndarray():
nd_proto = NdArray... | import numpy as np
from docarray.proto import DocumentProto, NdArrayProto, NodeProto
from docarray.typing import Tensor
def test_nested_item_proto():
NodeProto(text='hello')
NodeProto(nested=DocumentProto())
def test_nested_optional_item_proto():
NodeProto()
def test_ndarray():
nd_proto = NdArray... |
import time
import pytest
from jina import Executor, Flow
SLOW_EXECUTOR_SLEEP_TIME = 3
class SlowExecutor(Executor):
def __init__(self, **kwargs):
super().__init__(**kwargs)
time.sleep(SLOW_EXECUTOR_SLEEP_TIME)
@pytest.mark.asyncio
@pytest.mark.parametrize('protocol', ['grpc', 'http', 'websoc... | import threading
import time
import pytest
from jina import Executor, Flow
SLOW_EXECUTOR_SLEEP_TIME = 3
class SlowExecutor(Executor):
def __init__(self, **kwargs):
super().__init__(**kwargs)
time.sleep(SLOW_EXECUTOR_SLEEP_TIME)
@pytest.mark.asyncio
@pytest.mark.parametrize('protocol', ['grpc'... |
from typing import TYPE_CHECKING
from ...utils import (
DIFFUSERS_SLOW_IMPORT,
OptionalDependencyNotAvailable,
_LazyModule,
get_objects_from_module,
is_torch_available,
is_transformers_available,
)
_dummy_objects = {}
_additional_imports = {}
_import_structure = {"pipeline_output": ["FluxPipe... | from typing import TYPE_CHECKING
from ...utils import (
DIFFUSERS_SLOW_IMPORT,
OptionalDependencyNotAvailable,
_LazyModule,
get_objects_from_module,
is_torch_available,
is_transformers_available,
)
_dummy_objects = {}
_additional_imports = {}
_import_structure = {"pipeline_output": ["FluxPipe... |
import importlib
from typing import Any
from langchain.retrievers.document_compressors.base import DocumentCompressorPipeline
from langchain.retrievers.document_compressors.chain_extract import (
LLMChainExtractor,
)
from langchain.retrievers.document_compressors.chain_filter import (
LLMChainFilter,
)
from la... | import importlib
from typing import Any
from langchain.retrievers.document_compressors.base import DocumentCompressorPipeline
from langchain.retrievers.document_compressors.chain_extract import (
LLMChainExtractor,
)
from langchain.retrievers.document_compressors.chain_filter import (
LLMChainFilter,
)
from la... |
"""
This basic example loads a pre-trained model from the web and uses it to
generate sentence embeddings for a given list of sentences.
"""
import logging
import numpy as np
from sentence_transformers import LoggingHandler, SentenceTransformer
#### Just some code to print debug information to stdout
np.set_printop... | """
This basic example loads a pre-trained model from the web and uses it to
generate sentence embeddings for a given list of sentences.
"""
from sentence_transformers import SentenceTransformer, LoggingHandler
import numpy as np
import logging
#### Just some code to print debug information to stdout
np.set_printopti... |
#!/usr/bin/env python
from argparse import ArgumentParser
from datasets.commands.convert import ConvertCommand
from datasets.commands.convert_to_parquet import ConvertToParquetCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.r... | #!/usr/bin/env python
from argparse import ArgumentParser
from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets.commands.test import TestComm... |
from .clip_image import CLIPImageEncoder
| from .clip_image import CLIPImageEncoder |
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