input stringlengths 33 5k | output stringlengths 32 5k |
|---|---|
# Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
import xml.etree.ElementTree as ET
from mmengine.dist import is_main_process
from mmengine.fileio import get_local_path, list_from_file
from mmengine.utils import ProgressBar
from mmdet.registry import DATASETS
from mmdet.utils.typing_utils import ... | # Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
import xml.etree.ElementTree as ET
from mmengine.fileio import list_from_file
from mmdet.registry import DATASETS
from .xml_style import XMLDataset
@DATASETS.register_module()
class WIDERFaceDataset(XMLDataset):
"""Reader for the WIDER Face d... |
from enum import Enum
from typing import Callable, Union
from numpy import ndarray
from torch import Tensor
from .util import (
cos_sim,
dot_score,
euclidean_sim,
manhattan_sim,
pairwise_cos_sim,
pairwise_dot_score,
pairwise_euclidean_sim,
pairwise_manhattan_sim,
)
class SimilarityFu... | from enum import Enum
from typing import Callable, Union
from numpy import ndarray
from torch import Tensor
from .util import (
cos_sim,
manhattan_sim,
euclidean_sim,
dot_score,
pairwise_cos_sim,
pairwise_manhattan_sim,
pairwise_euclidean_sim,
pairwise_dot_score,
)
class SimilarityFun... |
from __future__ import annotations
import logging
import torch
from torch import Tensor, nn
from sentence_transformers.models.Module import Module
logger = logging.getLogger(__name__)
class WordWeights(Module):
"""This model can weight word embeddings, for example, with idf-values."""
config_keys: list[s... | from __future__ import annotations
import json
import logging
import os
import torch
from torch import Tensor, nn
logger = logging.getLogger(__name__)
class WordWeights(nn.Module):
"""This model can weight word embeddings, for example, with idf-values."""
def __init__(self, vocab: list[str], word_weights:... |
from typing import Any, Optional, Type, TypeVar, Union
import numpy as np
from docarray.base_document import BaseDocument
from docarray.typing import AnyEmbedding, AudioUrl
from docarray.typing.bytes.audio_bytes import AudioBytes
from docarray.typing.tensor.abstract_tensor import AbstractTensor
from docarray.typing.t... | from typing import Any, Optional, Type, TypeVar, Union
import numpy as np
from docarray.base_document import BaseDocument
from docarray.typing import AnyEmbedding, AudioUrl
from docarray.typing.bytes.audio_bytes import AudioBytes
from docarray.typing.tensor.abstract_tensor import AbstractTensor
from docarray.typing.t... |
import subprocess
import sys
import pytest
from pytest_benchmark.fixture import BenchmarkFixture # type: ignore[import-untyped]
@pytest.mark.parametrize(
"import_path",
[
pytest.param(
"from langchain_core.messages import HumanMessage", id="HumanMessage"
),
pytest.param("... | import subprocess
import sys
import pytest
from pytest_benchmark.fixture import BenchmarkFixture # type: ignore[import-untyped]
@pytest.mark.parametrize(
"import_path",
[
pytest.param(
"from langchain_core.messages import HumanMessage", id="HumanMessage"
),
pytest.param("... |
import gzip
import logging
import os
import sys
from datetime import datetime
from torch.utils.data import DataLoader
from sentence_transformers import LoggingHandler, SentenceTransformer, datasets, evaluation, losses, models, util
#### Just some code to print debug information to stdout
logging.basicConfig(
for... | import gzip
import logging
import os
import sys
from datetime import datetime
from torch.utils.data import DataLoader
from sentence_transformers import LoggingHandler, SentenceTransformer, datasets, evaluation, losses, models, util
#### Just some code to print debug information to stdout
logging.basicConfig(
for... |
import asyncio
import logging
import os
from jina import __default_host__
from jina.importer import ImportExtensions
from jina.serve.runtimes.gateway import GatewayRuntime
from jina.serve.runtimes.gateway.http.app import get_fastapi_app
__all__ = ['HTTPGatewayRuntime']
class HTTPGatewayRuntime(GatewayRuntime):
... | import asyncio
import logging
import os
from jina import __default_host__
from jina.importer import ImportExtensions
from jina.serve.runtimes.gateway import GatewayRuntime
from jina.serve.runtimes.gateway.http.app import get_fastapi_app
__all__ = ['HTTPGatewayRuntime']
class HTTPGatewayRuntime(GatewayRuntime):
... |
import asyncio
import pytest
from llama_index.core.workflow.context import Context
from llama_index.core.workflow.decorators import step
from llama_index.core.workflow.errors import WorkflowRuntimeError, WorkflowTimeoutError
from llama_index.core.workflow.events import Event, StartEvent, StopEvent
from llama_index.cor... | import asyncio
import pytest
from llama_index.core.workflow.context import Context
from llama_index.core.workflow.decorators import step
from llama_index.core.workflow.errors import WorkflowRuntimeError, WorkflowTimeoutError
from llama_index.core.workflow.events import Event, StartEvent, StopEvent
from llama_index.cor... |
# Copyright (c) OpenMMLab. All rights reserved.
from unittest.mock import Mock
import pytest
from mmengine.hooks import ParamSchedulerHook
from mmengine.optim import _ParamScheduler
class TestParamSchedulerHook:
error_msg = ('runner.param_schedulers should be list of ParamScheduler or '
'a dict... | # Copyright (c) OpenMMLab. All rights reserved.
from unittest.mock import Mock
import pytest
from mmengine.hooks import ParamSchedulerHook
class TestParamSchedulerHook:
error_msg = ('runner.param_schedulers should be list of ParamScheduler or '
'a dict containing list of ParamScheduler')
d... |
import numpy as np
import pytest
from pydantic.tools import parse_obj_as, schema_json_of
from docarray.base_document.io.json import orjson_dumps
from docarray.typing import Mesh3DUrl
from tests import TOYDATA_DIR
MESH_FILES = {
'obj': str(TOYDATA_DIR / 'tetrahedron.obj'),
'glb': str(TOYDATA_DIR / 'test.glb'),... | import numpy as np
import pytest
from pydantic.tools import parse_obj_as, schema_json_of
from docarray.document.io.json import orjson_dumps
from docarray.typing import Mesh3DUrl
from tests import TOYDATA_DIR
MESH_FILES = {
'obj': str(TOYDATA_DIR / 'tetrahedron.obj'),
'glb': str(TOYDATA_DIR / 'test.glb'),
... |
__copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import subprocess
from typing import Iterable, Optional
import torch
from jina import DocumentArray, Executor, requests
from jina.logging.logger import JinaLogger
from laserembeddings import Laser
class Laser... | __copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import subprocess
from typing import Iterable, Optional
import torch
from jina import DocumentArray, Executor, requests
from jina.logging.logger import JinaLogger
from laserembeddings import Laser
class Laser... |
import os
import random
import time
from typing import Dict, OrderedDict
import numpy as np
import pytest
from jina import Document, DocumentArray, Executor, Flow, requests
from jina_commons.indexers.dump import dump_docs
from jinahub.indexers.compound.FaissLMDBSearcher.faiss_lmdb import FaissLMDBSearcher
from jinahu... | import os
import random
import time
from typing import Dict, OrderedDict
import numpy as np
import pytest
from jina import Document, Flow, DocumentArray, requests, Executor
from jina_commons.indexers.dump import dump_docs
from jinahub.indexers.searcher.compound.FaissLMDBSearcher.faiss_lmdb import FaissLMDBSearcher
fr... |
import numpy as np
import pytest
from tensorflow import data as tf_data
from keras.src import backend
from keras.src import layers
from keras.src import testing
class RescalingTest(testing.TestCase):
@pytest.mark.requires_trainable_backend
def test_rescaling_basics(self):
self.run_layer_test(
... | import numpy as np
import pytest
from tensorflow import data as tf_data
from keras.src import backend
from keras.src import layers
from keras.src import testing
class RescalingTest(testing.TestCase):
@pytest.mark.requires_trainable_backend
def test_rescaling_basics(self):
self.run_layer_test(
... |
# Copyright (c) OpenMMLab. All rights reserved.
import torch.nn.functional as F
from mmcv.cnn import ConvModule
from mmcv.cnn.bricks import NonLocal2d
from mmcv.runner import BaseModule
from ..builder import NECKS
@NECKS.register_module()
class BFP(BaseModule):
"""BFP (Balanced Feature Pyramids)
BFP takes m... | import torch.nn.functional as F
from mmcv.cnn import ConvModule
from mmcv.cnn.bricks import NonLocal2d
from mmcv.runner import BaseModule
from ..builder import NECKS
@NECKS.register_module()
class BFP(BaseModule):
"""BFP (Balanced Feature Pyramids)
BFP takes multi-level features as inputs and gather them in... |
import json
import pytest
import xgboost
from xgboost import testing as tm
from xgboost.testing.metrics import (
check_precision_score,
check_quantile_error,
run_pr_auc_binary,
run_pr_auc_ltr,
run_pr_auc_multi,
run_roc_auc_binary,
run_roc_auc_multi,
)
class TestGPUEvalMetrics:
@pytes... | import json
import sys
import pytest
import xgboost
from xgboost import testing as tm
from xgboost.testing.metrics import check_precision_score, check_quantile_error
sys.path.append("tests/python")
import test_eval_metrics as test_em # noqa
class TestGPUEvalMetrics:
cpu_test = test_em.TestEvalMetrics()
@... |
from pathlib import Path
from typing import List
import numpy as np
import pytest
import torch
from jina import Document, DocumentArray, Executor
from ...transform_encoder import TransformerTorchEncoder
from ..integration.test_integration import filter_none
def test_config():
ex = Executor.load_config(str(Path(... | from pathlib import Path
from typing import List
import numpy as np
import pytest
import torch
from jina import Document, DocumentArray, Executor
from ...transform_encoder import TransformerTorchEncoder
from ..integration.test_integration import filter_none
def test_config():
ex = Executor.load_config(str(Path(... |
from langchain_core.prompts.prompt import PromptTemplate
API_URL_PROMPT_TEMPLATE = """You are given the below API Documentation:
{api_docs}
Using this documentation, generate the full API url to call for answering the user question.
You should build the API url in order to get a response that is as short as possible, ... | # flake8: noqa
from langchain_core.prompts.prompt import PromptTemplate
API_URL_PROMPT_TEMPLATE = """You are given the below API Documentation:
{api_docs}
Using this documentation, generate the full API url to call for answering the user question.
You should build the API url in order to get a response that is as shor... |
import itertools
from typing import (
TYPE_CHECKING,
Union,
Sequence,
overload,
Any,
List,
)
import numpy as np
from docarray import Document
from docarray.helper import typename
if TYPE_CHECKING:
from docarray.typing import (
DocumentArrayIndexType,
DocumentArraySingleton... | import itertools
from typing import (
TYPE_CHECKING,
Union,
Sequence,
overload,
Any,
List,
)
import numpy as np
from docarray import Document
from docarray.helper import typename
if TYPE_CHECKING:
from docarray.typing import (
DocumentArrayIndexType,
DocumentArraySingleton... |
# coding=utf-8
# Copyright 2025 Cohere 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 app... | # coding=utf-8
# Copyright 2025 Cohere 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 app... |
# Copyright (c) OpenMMLab. All rights reserved.
from mmcv.cnn import ConvModule, Linear
from mmengine.model import ModuleList
from torch import Tensor
from mmdet.registry import MODELS
from mmdet.utils import MultiConfig
from .fcn_mask_head import FCNMaskHead
@MODELS.register_module()
class CoarseMaskHead(FCNMaskHea... | # Copyright (c) OpenMMLab. All rights reserved.
from mmcv.cnn import ConvModule, Linear
from mmengine.model import ModuleList
from torch import Tensor
from mmdet.registry import MODELS
from mmdet.utils import MultiConfig
from .fcn_mask_head import FCNMaskHead
@MODELS.register_module()
class CoarseMaskHead(FCNMaskHea... |
"""OpenAI Image Generation tool spec."""
import base64
import os
import time
from typing import Optional
from llama_index.core.tools.tool_spec.base import BaseToolSpec
DEFAULT_CACHE_DIR = "../../../img_cache"
DEFAULT_SIZE = "1024x1024"
valid_sizes = {
"dall-e-2": ["256x256", "512x512", "1024x1024"],
"dall-e... | """OpenAI Image Generation tool spec."""
import base64
import os
import time
from typing import Optional
from llama_index.core.tools.tool_spec.base import BaseToolSpec
DEFAULT_CACHE_DIR = "../../../img_cache"
DEFAULT_SIZE = "1024x1024"
valid_sizes = {
"dall-e-2": ["256x256", "512x512", "1024x1024"],
"dall-e... |
"""**Prompt** is the input to the model.
Prompt is often constructed
from multiple components and prompt values. Prompt classes and functions make constructing
and working with prompts easy.
**Class hierarchy:**
.. code-block::
BasePromptTemplate --> PipelinePromptTemplate
StringProm... | """**Prompt** is the input to the model.
Prompt is often constructed
from multiple components and prompt values. Prompt classes and functions make constructing
and working with prompts easy.
**Class hierarchy:**
.. code-block::
BasePromptTemplate --> PipelinePromptTemplate
StringProm... |
from torchvision.transforms import InterpolationMode # usort: skip
from ._utils import is_simple_tensor # usort: skip
from ._meta import (
clamp_bounding_boxes,
convert_format_bounding_boxes,
get_dimensions_image_tensor,
get_dimensions_image_pil,
get_dimensions,
get_num_frames_video,
get... | 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,
get_dimensions_image_tensor,
get_dimensions_image_pil,
get_dimensions,
get_num_frames_video,
get_num... |
__version__ = '0.30.0a3'
from docarray.array import DocumentArray, DocumentArrayStacked
from docarray.base_document.document import BaseDocument
__all__ = ['BaseDocument', 'DocumentArray', 'DocumentArrayStacked']
| __version__ = '0.30.0a3'
from docarray.array.array.array import DocumentArray
from docarray.base_document.document import BaseDocument
__all__ = [
'BaseDocument',
'DocumentArray',
]
|
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_instances
from sklearn.utils._testing import SkipTest
class AllowNanEstimators(Directive):
@staticme... | 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_instances
from sklearn.utils._testing import SkipTest
class AllowNanEstimators(Directive):
@staticme... |
__copyright__ = "Copyright (c) 2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import copy
from typing import Dict
from jina import requests, DocumentArray, Executor
from jina_commons import get_logger
from jinahub.indexers.searcher.NumpySearcher.numpy_searcher import NumpySearcher
from jinahu... | __copyright__ = "Copyright (c) 2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import copy
from typing import Dict
from jina import requests, DocumentArray, Executor
from jina_commons import get_logger
from jinahub.indexers.searcher.NumpySearcher import NumpySearcher
from jinahub.indexers.stor... |
# Copyright (c) OpenMMLab. All rights reserved.
import torch
from mmengine.data import InstanceData
from mmdet.core.bbox.assigners import AssignResult
from mmdet.registry import TASK_UTILS
from .base_sampler import BaseSampler
from .sampling_result import SamplingResult
@TASK_UTILS.register_module()
class PseudoSamp... | # Copyright (c) OpenMMLab. All rights reserved.
import torch
from mmengine.data import InstanceData
from mmdet.core.bbox.assigners import AssignResult
from mmdet.registry import TASK_UTILS
from .base_sampler import BaseSampler
from .sampling_result import SamplingResult
@TASK_UTILS.register_module()
class PseudoSamp... |
"""Tool for the SearxNG search API."""
from typing import Optional, Type
from langchain_core.callbacks import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain_core.tools import BaseTool
from pydantic import BaseModel, ConfigDict, Field
from langchain_community.utilities.searx_sea... | """Tool for the SearxNG search API."""
from typing import Optional, Type
from langchain_core.callbacks import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain_core.tools import BaseTool
from pydantic import BaseModel, ConfigDict, Field
from langchain_community.utilities.searx_sea... |
# Copyright (c) OpenMMLab. All rights reserved.
__version__ = '0.5.0'
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... | # Copyright (c) OpenMMLab. All rights reserved.
__version__ = '0.4.0'
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... |
"""
This examples trains a CrossEncoder for the NLI task. A CrossEncoder takes a sentence pair
as input and outputs a label. Here, it learns to predict the labels: "contradiction": 0, "entailment": 1, "neutral": 2.
It does NOT produce a sentence embedding and does NOT work for individual sentences.
Usage:
python trai... | """
This examples trains a CrossEncoder for the NLI task. A CrossEncoder takes a sentence pair
as input and outputs a label. Here, it learns to predict the labels: "contradiction": 0, "entailment": 1, "neutral": 2.
It does NOT produce a sentence embedding and does NOT work for individual sentences.
Usage:
python trai... |
import os
from pathlib import Path
import cv2
import pytest
from jina import Document, DocumentArray, Executor
from ...yolov5_segmenter import YoloV5Segmenter
cur_dir = os.path.dirname(os.path.abspath(__file__))
def test_load():
segmenter = Executor.load_config(str(Path(__file__).parents[2] / 'config.yml'))
... | __copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import os
from pathlib import Path
import cv2
import pytest
from jina import Executor, Document, DocumentArray
from ...yolov5_segmenter import YoloV5Segmenter
cur_dir = os.path.dirname(os.path.abspath(__file__... |
"""
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... |
import os
from time import time
import numpy as np
import pytest
from docarray import BaseDoc, DocArray
from docarray.documents import ImageDoc
from docarray.typing import NdArray
from docarray.utils.map import map_docs, map_docs_batch
from tests.units.typing.test_bytes import IMAGE_PATHS
pytestmark = [pytest.mark.b... | import os
from time import time
import numpy as np
import pytest
from docarray import BaseDocument, DocumentArray
from docarray.documents import ImageDoc
from docarray.typing import NdArray
from docarray.utils.map import map_docs, map_docs_batch
from tests.units.typing.test_bytes import IMAGE_PATHS
pytestmark = [pyt... |
"""Utilities for environment variables."""
from __future__ import annotations
import os
from typing import Any, Optional, Union
def env_var_is_set(env_var: str) -> bool:
"""Check if an environment variable is set.
Args:
env_var (str): The name of the environment variable.
Returns:
bool... | """Utilities for environment variables."""
from __future__ import annotations
import os
from typing import Any, Optional, Union
def env_var_is_set(env_var: str) -> bool:
"""Check if an environment variable is set.
Args:
env_var (str): The name of the environment variable.
Returns:
bool... |
import pytest
from llama_index.llms.nvidia import NVIDIA as Interface
from pytest_httpx import HTTPXMock
@pytest.fixture()
def mock_local_models(httpx_mock: HTTPXMock, base_url: str) -> None:
mock_response = {
"data": [
{
"id": "dummy",
"object": "model",
... | import pytest
from llama_index.llms.nvidia import NVIDIA as Interface
from pytest_httpx import HTTPXMock
@pytest.fixture()
def mock_local_models(httpx_mock: HTTPXMock, base_url: str) -> None:
mock_response = {
"data": [
{
"id": "dummy",
"object": "model",
... |
from __future__ import annotations
from typing import Any, List, Optional, Tuple, Union
import PIL.Image
import torch
from torchvision.transforms import InterpolationMode
from ._datapoint import Datapoint, FillTypeJIT
class Mask(Datapoint):
@classmethod
def _wrap(cls, tensor: torch.Tensor) -> Mask:
... | from __future__ import annotations
from typing import Any, List, Optional, Tuple, Union
import PIL.Image
import torch
from torchvision.transforms import InterpolationMode
from ._datapoint import Datapoint, FillTypeJIT
class Mask(Datapoint):
@classmethod
def _wrap(cls, tensor: torch.Tensor) -> Mask:
... |
from __future__ import annotations
import pytest
from sentence_transformers import SentenceTransformer, SentenceTransformerTrainer
from sentence_transformers.model_card import generate_model_card
from sentence_transformers.util import is_datasets_available, is_training_available
if is_datasets_available():
from ... | from __future__ import annotations
import pytest
from datasets import Dataset, DatasetDict
from sentence_transformers import SentenceTransformer, SentenceTransformerTrainer
from sentence_transformers.model_card import generate_model_card
@pytest.fixture(scope="session")
def dummy_dataset():
"""
Dummy datase... |
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.torch_tensor import TorchTensor, metaTorchAndNode
T = TypeVar('T', bound='ImageTorchTensor')
@_register_proto(proto_typ... | 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.torch_tensor import TorchTensor, metaTorchAndNode
T = TypeVar('T', bound='ImageTorchTensor')
@_register_proto(proto_typ... |
from typing import Dict, Iterable
import torch
from torch import Tensor, nn
from sentence_transformers import SentenceTransformer
class MSELoss(nn.Module):
def __init__(self, model: SentenceTransformer) -> None:
"""
Computes the MSE loss between the computed sentence embedding and a target sente... | from typing import Dict, Iterable
import torch
from torch import Tensor, nn
class MSELoss(nn.Module):
def __init__(self, model):
"""
Computes the MSE loss between the computed sentence embedding and a target sentence embedding. This loss
is used when extending sentence embeddings to new l... |
from typing import Optional
from typing_extensions import Protocol, runtime_checkable
from torch.distributed._state_dict_utils import _copy_state_dict, _create_cpu_state_dict
from torch.distributed.checkpoint.metadata import STATE_DICT_TYPE
__all__ = ["AsyncStager", "BlockingAsyncStager"]
@runtime_checkable
class ... | from typing import Optional
from typing_extensions import Protocol, runtime_checkable
from torch.distributed._state_dict_utils import _copy_state_dict, _create_cpu_state_dict
from torch.distributed.checkpoint.metadata import STATE_DICT_TYPE
__all__ = ["AsyncStager", "BlockingAsyncStager"]
@runtime_checkable
class ... |
_base_ = 'grounding_dino_swin-t_pretrain_obj365.py'
o365v1_od_dataset = dict(
type='ODVGDataset',
data_root='data/objects365v1/',
ann_file='o365v1_train_odvg.json',
label_map_file='o365v1_label_map.json',
data_prefix=dict(img='train/'),
filter_cfg=dict(filter_empty_gt=False),
pipeline=_base... | _base_ = 'grounding_dino_swin-t_pretrain_obj365.py'
o365v1_od_dataset = dict(
type='ODVGDataset',
data_root='data/objects365v1/',
ann_file='o365v1_train_odvg.jsonl',
label_map_file='o365v1_label_map.json',
data_prefix=dict(img='train/'),
filter_cfg=dict(filter_empty_gt=False),
pipeline=_bas... |
from abc import ABC
from collections import namedtuple
from dataclasses import is_dataclass, asdict
from typing import Dict, Optional, TYPE_CHECKING
if TYPE_CHECKING:
from ....typing import DocumentArraySourceType, ArrayType
TypeMap = namedtuple('TypeMap', ['type', 'converter'])
class BaseBackendMixin(ABC):
... | from abc import ABC
from dataclasses import is_dataclass, asdict
from typing import Dict, Optional, TYPE_CHECKING
if TYPE_CHECKING:
from ....typing import DocumentArraySourceType, ArrayType
class BaseBackendMixin(ABC):
TYPE_MAP: Dict
def _init_storage(
self,
_docs: Optional['DocumentArra... |
"""Argparser module for WorkerRuntime"""
from jina import __default_host__, helper
from jina.parsers.helper import KVAppendAction, add_arg_group
def mixin_worker_runtime_parser(parser):
"""Mixing in arguments required by :class:`WorkerRuntime` into the given parser.
:param parser: the parser instance to which... | """Argparser module for WorkerRuntime"""
from jina import __default_host__, helper
from jina.parsers.helper import KVAppendAction, add_arg_group
def mixin_worker_runtime_parser(parser):
"""Mixing in arguments required by :class:`WorkerRuntime` into the given parser.
:param parser: the parser instance to which... |
from typing import TYPE_CHECKING, Type, TypeVar, Union
from uuid import UUID
from pydantic import BaseConfig, parse_obj_as
from pydantic.fields import ModelField
if TYPE_CHECKING:
from docarray.proto import NodeProto
from docarray.typing.abstract_type import AbstractType
T = TypeVar('T', bound='ID')
class ID(... | from typing import Type, TypeVar, Union
from uuid import UUID
from pydantic import BaseConfig, parse_obj_as
from pydantic.fields import ModelField
from docarray.proto import NodeProto
from docarray.typing.abstract_type import AbstractType
T = TypeVar('T', bound='ID')
class ID(str, AbstractType):
"""
Represe... |
"""
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... |
_base_ = [
'../_base_/models/faster-rcnn_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
checkpoint = 'https://download.pytorch.org/models/resnet50-11ad3fa6.pth'
model = dict(
backbone=dict(init_cfg=dict(type='Pretrained', chec... | _base_ = [
'../_base_/models/faster-rcnn_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
checkpoint = 'https://download.pytorch.org/models/resnet50-11ad3fa6.pth'
model = dict(
backbone=dict(init_cfg=dict(type='Pretrained', chec... |
_base_ = './mask_rcnn_r50_fpn_1x_coco.py'
preprocess_cfg = dict(
mean=[103.530, 116.280, 123.675],
std=[1.0, 1.0, 1.0],
to_rgb=False,
pad_size_divisor=32)
model = dict(
# use caffe img_norm
preprocess_cfg=preprocess_cfg,
backbone=dict(
norm_cfg=dict(requires_grad=False),
styl... | _base_ = './mask_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
norm_cfg=dict(requires_grad=False),
style='caffe',
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron2/resnet50_caffe')))
# use caffe img_norm
img_norm_cfg = dict(
mean=[103.5... |
_base_ = './retinanet_r50-caffe_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron2/resnet101_caffe')))
| _base_ = './retinanet_r50_caffe_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron2/resnet101_caffe')))
|
# 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 abc import ABC, abstractmethod
from typing import Dict, Iterator, List, Type
from typing_extensions import TYPE_CHECKING
if TYPE_CHECKING:
from docarray import BaseDoc, DocList
class AbstractDocStore(ABC):
@staticmethod
@abstractmethod
def list(namespace: str, show_table: bool) -> List[str]:
... |
import os
from typing import Optional
import pytest
from docarray import BaseDocument, DocumentArray
from docarray.documents import ImageDoc
from tests import TOYDATA_DIR
@pytest.fixture()
def nested_doc_cls():
class MyDoc(BaseDocument):
count: Optional[int]
text: str
class MyDocNested(MyDo... | import os
from typing import Optional
import pytest
from docarray import BaseDocument, DocumentArray
from docarray.documents import Image
from tests import TOYDATA_DIR
@pytest.fixture()
def nested_doc_cls():
class MyDoc(BaseDocument):
count: Optional[int]
text: str
class MyDocNested(MyDoc):... |
from enum import Enum
from typing import Dict, Iterable
import torch.nn.functional as F
from torch import Tensor, nn
from sentence_transformers.SentenceTransformer import SentenceTransformer
class SiameseDistanceMetric(Enum):
"""The metric for the contrastive loss"""
EUCLIDEAN = lambda x, y: F.pairwise_dis... | from enum import Enum
from typing import Iterable, Dict
import torch.nn.functional as F
from torch import nn, Tensor
from sentence_transformers.SentenceTransformer import SentenceTransformer
class SiameseDistanceMetric(Enum):
"""
The metric for the contrastive loss
"""
EUCLIDEAN = lambda x, y: F.pairw... |
# Copyright (c) OpenMMLab. All rights reserved.
from mmengine.device import (get_device, is_cuda_available, is_mlu_available,
is_mps_available, is_npu_available)
def test_get_device():
device = get_device()
if is_npu_available():
assert device == 'npu'
elif is_cuda_ava... | # Copyright (c) OpenMMLab. All rights reserved.
from mmengine.device import (get_device, is_cuda_available, is_mlu_available,
is_mps_available)
def test_get_device():
device = get_device()
if is_cuda_available():
assert device == 'cuda'
elif is_mlu_available():
... |
import logging
import re
from typing import Any
import uvicorn.config
from colorama import Fore
def remove_color_codes(s: str) -> str:
return re.sub(r"\x1B(?:[@-Z\\-_]|\[[0-?]*[ -/]*[@-~])", "", s)
def fmt_kwargs(kwargs: dict) -> str:
return ", ".join(f"{n}={repr(v)}" for n, v in kwargs.items())
def print... | import logging
import re
from typing import Any
from colorama import Fore
def remove_color_codes(s: str) -> str:
return re.sub(r"\x1B(?:[@-Z\\-_]|\[[0-?]*[ -/]*[@-~])", "", s)
def fmt_kwargs(kwargs: dict) -> str:
return ", ".join(f"{n}={repr(v)}" for n, v in kwargs.items())
def print_attribute(
title... |
from __future__ import annotations
import pytest
from torch import Tensor
from sentence_transformers import SparseEncoder
@pytest.mark.parametrize(
"model_name",
[
("sentence-transformers/all-MiniLM-L6-v2"),
],
)
def test_load_and_encode(model_name: str) -> None:
# Ensure that SparseEncoder ... | from __future__ import annotations
import pytest
from torch import Tensor
from sentence_transformers import SparseEncoder
@pytest.mark.parametrize(
"model_name",
[
("sentence-transformers/all-MiniLM-L6-v2"),
],
)
def test_load_and_encode(model_name: str) -> None:
# Ensure that SparseEncoder ... |
# Copyright 2018 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 2018 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 typing import Any, Union, Optional
from vertexai.generative_models._generative_models import SafetySettingsType
from google.cloud.aiplatform_v1beta1.types import content as gapic_content_types
from llama_index.core.llms import ChatMessage, MessageRole, ImageBlock, TextBlock
def is_gemini_model(model: str) -> boo... | import base64
from typing import Any, Dict, Union, Optional
from vertexai.generative_models._generative_models import SafetySettingsType
from google.cloud.aiplatform_v1beta1.types import content as gapic_content_types
from llama_index.core.llms import ChatMessage, MessageRole
def is_gemini_model(model: str) -> bool:
... |
"""Test LLMSummarization functionality."""
import pytest
from langchain.chains.llm_summarization_checker.base import (
ARE_ALL_TRUE_PROMPT,
CHECK_ASSERTIONS_PROMPT,
CREATE_ASSERTIONS_PROMPT,
REVISED_SUMMARY_PROMPT,
LLMSummarizationCheckerChain,
)
from tests.unit_tests.llms.fake_llm import FakeLLM
... | # flake8: noqa E501
"""Test LLMSummarization functionality."""
import pytest
from langchain.chains.llm_summarization_checker.base import (
ARE_ALL_TRUE_PROMPT,
CHECK_ASSERTIONS_PROMPT,
CREATE_ASSERTIONS_PROMPT,
REVISED_SUMMARY_PROMPT,
LLMSummarizationCheckerChain,
)
from tests.unit_tests.llms.fak... |
_base_ = './htc-without-semantic_r50_fpn_1x_coco.py'
model = dict(
data_preprocessor=dict(pad_seg=True),
roi_head=dict(
semantic_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0),
out_channels=256,
... | _base_ = './htc-without-semantic_r50_fpn_1x_coco.py'
model = dict(
data_preprocessor=dict(pad_seg=True),
roi_head=dict(
semantic_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0),
out_channels=256,
... |
from langchain.indexes import __all__
def test_all() -> None:
"""Use to catch obvious breaking changes."""
expected = [
"aindex",
"GraphIndexCreator",
"index",
"IndexingResult",
"SQLRecordManager",
"VectorstoreIndexCreator",
]
assert sorted(__all__) == s... | from langchain.indexes import __all__
def test_all() -> None:
"""Use to catch obvious breaking changes."""
expected = [
"aindex",
"GraphIndexCreator",
"index",
"IndexingResult",
"SQLRecordManager",
"VectorstoreIndexCreator",
]
assert __all__ == sorted(ex... |
from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.tools import GoogleSerperResults, GoogleSerperRun
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handling optio... | from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.tools import GoogleSerperResults, GoogleSerperRun
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handling optio... |
import types
from keras.src.activations.activations import celu
from keras.src.activations.activations import elu
from keras.src.activations.activations import exponential
from keras.src.activations.activations import gelu
from keras.src.activations.activations import glu
from keras.src.activations.activations import ... | import types
from keras.src.activations.activations import celu
from keras.src.activations.activations import elu
from keras.src.activations.activations import exponential
from keras.src.activations.activations import gelu
from keras.src.activations.activations import glu
from keras.src.activations.activations import ... |
import asyncio
import pytest
from grpc import ChannelConnectivity
from jina.serve.networking.connection_stub import _ConnectionStubs
from jina.serve.networking.instrumentation import _NetworkingHistograms
from jina.serve.networking.replica_list import _ReplicaList
@pytest.fixture()
def replica_list(logger, metrics)... | import asyncio
import pytest
from grpc import ChannelConnectivity
from jina.serve.networking.connection_stub import _ConnectionStubs
from jina.serve.networking.instrumentation import _NetworkingHistograms
from jina.serve.networking.replica_list import _ReplicaList
@pytest.fixture()
def replica_list(logger, metrics)... |
import numpy as np
import pytest
import xgboost as xgb
from xgboost import testing as tm
pytestmark = pytest.mark.skipif(**tm.no_pandas())
dpath = 'demo/data/'
rng = np.random.RandomState(1994)
class TestTreesToDataFrame:
def build_model(self, max_depth, num_round):
dtrain, _ = tm.load_agaricus(__file... | import numpy as np
import pytest
import xgboost as xgb
from xgboost import testing as tm
pytestmark = pytest.mark.skipif(**tm.no_pandas())
dpath = 'demo/data/'
rng = np.random.RandomState(1994)
class TestTreesToDataFrame:
def build_model(self, max_depth, num_round):
dtrain, _ = tm.load_agaricus(__file... |
from abc import ABC, abstractmethod
from typing import Dict, Iterator, List, Type
from typing_extensions import TYPE_CHECKING
if TYPE_CHECKING:
from docarray import BaseDoc, DocList
class AbstractDocStore(ABC):
@staticmethod
@abstractmethod
def list(namespace: str, show_table: bool) -> List[str]:
... | 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, DocList
class AbstractDocStore(ABC):
@staticmethod
@abstractmethod
def list(namespace: str, show_table: bool) -> Lis... |
_base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# please install mmpretrain
# import mmpretrain.models to trigger register_module in mmpretrain
custom_imports = dict(
imports=['mmpretrain.... | _base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# TODO: delete custom_imports after mmcls supports auto import
# please install mmcls>=1.0
# import mmcls.models to trigger register_module in m... |
# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import cv2
import mmcv
import numpy as np
import torch
import torch.nn as nn
from mmcv.transforms import Compose
from mmengine.utils import track_iter_progress
from mmdet.apis import init_detector
from mmdet.registry import VISUALIZERS
from mmdet.structu... | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import cv2
import mmcv
import numpy as np
import torch
import torch.nn as nn
from mmcv.transforms import Compose
from mmengine.utils import track_iter_progress
from mmdet.apis import init_detector
from mmdet.registry import VISUALIZERS
from mmdet.structu... |
_base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
type='YOLOF',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[103.530, 116.280, 123.675],
std=[1.0, 1.0, 1.0],
bgr_to_rgb=Fals... | _base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
type='YOLOF',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[103.530, 116.280, 123.675],
std=[1.0, 1.0, 1.0],
bgr_to_rgb=Fals... |
_base_ = '../_base_/default_runtime.py'
# model settings
model = dict(
type='YOLOV3',
backbone=dict(
type='MobileNetV2',
out_indices=(2, 4, 6),
act_cfg=dict(type='LeakyReLU', negative_slope=0.1),
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://mmdet/mobilen... | _base_ = '../_base_/default_runtime.py'
# model settings
model = dict(
type='YOLOV3',
backbone=dict(
type='MobileNetV2',
out_indices=(2, 4, 6),
act_cfg=dict(type='LeakyReLU', negative_slope=0.1),
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://mmdet/mobilen... |
from typing import List, TYPE_CHECKING
if TYPE_CHECKING: # pragma: no cover
from docarray.typing import T, Document
def _reduce_doc_props(doc1: 'Document', doc2: 'Document'):
doc1_fields = set(doc1.non_empty_fields)
doc2_fields = set(doc2.non_empty_fields)
# update only fields that are set in doc2 ... | from typing import List, TYPE_CHECKING
if TYPE_CHECKING:
from docarray.typing import T, Document
def _reduce_doc_props(doc1: 'Document', doc2: 'Document'):
doc1_fields = set(doc1.non_empty_fields)
doc2_fields = set(doc2.non_empty_fields)
# update only fields that are set in doc2 and not set in doc1
... |
_base_ = './faster-rcnn_r50_fpn_1x_coco.py'
model = dict(
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[103.530, 116.280, 123.675],
std=[1.0, 1.0, 1.0],
bgr_to_rgb=False,
pad_size_divisor=32),
backbone=dict(
norm_cfg=dict(requires_grad=False),
... | _base_ = './faster-rcnn_r50_fpn_1x_coco.py'
model = dict(
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[103.530, 116.280, 123.675],
std=[1.0, 1.0, 1.0],
bgr_to_rgb=False,
pad_size_divisor=32),
backbone=dict(
norm_cfg=dict(requires_grad=False),
... |
import json
from jina.orchestrate.flow.base import Flow
from jina.orchestrate.deployments import Deployment
from jina.jaml import JAML
from jina.logging.predefined import default_logger
from jina.schemas import get_full_schema
from jina_cli.export import api_to_dict
def export_kubernetes(args):
"""Export to k8s ... | import json
from jina.orchestrate.flow.base import Flow
from jina.orchestrate.deployments import Deployment
from jina.jaml import JAML
from jina.logging.predefined import default_logger
from jina.schemas import get_full_schema
from jina_cli.export import api_to_dict
def export_kubernetes(args):
"""Export to k8s ... |
import numpy as np
import pytest
from pydantic import Field
from docarray import BaseDoc, DocList
from docarray.index.backends.in_memory import InMemoryExactNNIndex
from docarray.typing import NdArray
class SchemaDoc(BaseDoc):
text: str
price: int
tensor: NdArray[10]
@pytest.fixture
def docs():
doc... | import numpy as np
import pytest
from pydantic import Field
from docarray import BaseDoc, DocList
from docarray.index.backends.in_memory import InMemoryDocIndex
from docarray.typing import NdArray
class SchemaDoc(BaseDoc):
text: str
price: int
tensor: NdArray[10]
@pytest.fixture
def docs():
docs = ... |
from typing import TYPE_CHECKING, TypeVar
import numpy as np
from docarray.typing.url.url_3d.url_3d import Url3D
if TYPE_CHECKING:
from docarray.proto import NodeProto
T = TypeVar('T', bound='PointCloud3DUrl')
class PointCloud3DUrl(Url3D):
"""
URL to a .obj, .glb, or .ply file containing point cloud i... | from typing import TYPE_CHECKING, TypeVar
import numpy as np
from docarray.typing.url.url_3d.url_3d import Url3D
if TYPE_CHECKING:
from docarray.proto import NodeProto
T = TypeVar('T', bound='PointCloud3DUrl')
class PointCloud3DUrl(Url3D):
"""
URL to a .obj, .glb, or .ply file containing point cloud i... |
# Copyright (c) OpenMMLab. All rights reserved.
from .history_buffer import HistoryBuffer
from .log_processor import LogProcessor
from .logger import MMLogger, print_log
from .message_hub import MessageHub
__all__ = [
'HistoryBuffer', 'MessageHub', 'MMLogger', 'print_log', 'LogProcessor'
]
| # Copyright (c) OpenMMLab. All rights reserved.
from .history_buffer import HistoryBuffer
from .logger import MMLogger, print_log
from .message_hub import MessageHub
__all__ = ['HistoryBuffer', 'MessageHub', 'MMLogger', 'print_log']
|
from __future__ import annotations
from collections.abc import Iterable
import torch
import torch.nn as nn
import torch.nn.functional as F
from sentence_transformers.sparse_encoder import SparseEncoder
class ReconstructionLoss(nn.Module):
"""
Reconstruction Loss module for Sparse AutoEncoder.
This mod... | from __future__ import annotations
from collections.abc import Iterable
import torch
import torch.nn as nn
import torch.nn.functional as F
from sentence_transformers.sparse_encoder import SparseEncoder
class ReconstructionLoss(nn.Module):
"""
Reconstruction Loss module for Sparse AutoEncoder.
This mod... |
import warnings
from typing import Any, Callable, List, Optional, Sequence, Union
import torch
from torch import nn
from torchvision.prototype.transforms import Transform
class Compose(Transform):
def __init__(self, transforms: Sequence[Callable]) -> None:
super().__init__()
if not isinstance(tr... | import warnings
from typing import Any, Callable, List, Optional, Sequence
import torch
from torchvision.prototype.transforms import Transform
class Compose(Transform):
def __init__(self, transforms: Sequence[Callable]) -> None:
super().__init__()
if not isinstance(transforms, Sequence):
... |
__copyright__ = "Copyright (c) 2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import subprocess
from pathlib import Path
import pytest
@pytest.fixture(scope='session')
def docker_image_name() -> str:
return Path(__file__).parents[1].stem.lower()
@pytest.fixture(scope='session')
def bui... | __copyright__ = "Copyright (c) 2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import subprocess
from pathlib import Path
import pytest
@pytest.fixture(scope='session')
def docker_image_name() -> str:
return Path(__file__).parents[1].stem.lower()
@pytest.fixture(scope='session')
def bui... |
import os
from pathlib import Path
import pytest
from jina import Flow
from jina.excepts import RuntimeFailToStart
from jina.orchestrate.deployments import Deployment
from jina.parsers import set_deployment_parser
from jina.serve.executors import BaseExecutor
cur_dir = os.path.dirname(os.path.abspath(__file__))
de... | import os
from pathlib import Path
import pytest
from jina import Flow
from jina.excepts import RuntimeFailToStart
from jina.orchestrate.deployments import Deployment
from jina.parsers import set_deployment_parser
from jina.serve.executors import BaseExecutor
cur_dir = os.path.dirname(os.path.abspath(__file__))
de... |
"""Image prompt template for a multimodal model."""
from typing import Any
from pydantic import Field
from langchain_core.prompt_values import ImagePromptValue, ImageURL, PromptValue
from langchain_core.prompts.base import BasePromptTemplate
from langchain_core.prompts.string import (
DEFAULT_FORMATTER_MAPPING,
... | """Image prompt template for a multimodal model."""
from typing import Any
from pydantic import Field
from langchain_core.prompt_values import ImagePromptValue, ImageURL, PromptValue
from langchain_core.prompts.base import BasePromptTemplate
from langchain_core.prompts.string import (
DEFAULT_FORMATTER_MAPPING,
... |
import http.client
import json
from typing import Optional
def list_packages(*, contains: Optional[str] = None):
conn = http.client.HTTPSConnection("api.github.com")
headers = {
"Accept": "application/vnd.github+json",
"X-GitHub-Api-Version": "2022-11-28",
"User-Agent": "langchain-cli... | import http.client
import json
from typing import Optional
def list_packages(*, contains: Optional[str] = None):
conn = http.client.HTTPSConnection("api.github.com")
headers = {
"Accept": "application/vnd.github+json",
"X-GitHub-Api-Version": "2022-11-28",
"User-Agent": "langchain-cli... |
from __future__ import annotations
from collections.abc import Iterable
from typing import Any
import torch
from torch import Tensor, nn
from sentence_transformers.SentenceTransformer import SentenceTransformer
from sentence_transformers.util import fullname
class CosineSimilarityLoss(nn.Module):
def __init__(... | from __future__ import annotations
from collections.abc import Iterable
from typing import Any
import torch
from torch import Tensor, nn
from sentence_transformers.SentenceTransformer import SentenceTransformer
from sentence_transformers.util import fullname
class CosineSimilarityLoss(nn.Module):
def __init__(... |
_base_ = [
'../common/ms_3x_coco-instance.py',
'../_base_/models/cascade-mask-rcnn_r50_fpn.py'
]
| _base_ = [
'../common/mstrain_3x_coco_instance.py',
'../_base_/models/cascade_mask_rcnn_r50_fpn.py'
]
|
import grpc
from grpc_health.v1 import health, health_pb2, health_pb2_grpc
from grpc_reflection.v1alpha import reflection
from pydantic import BaseModel
from uvicorn import Config, Server
from jina import Gateway, __default_host__
from jina.proto import jina_pb2, jina_pb2_grpc
class DummyResponseModel(BaseModel):
... | import grpc
from grpc_health.v1 import health, health_pb2, health_pb2_grpc
from grpc_reflection.v1alpha import reflection
from pydantic import BaseModel
from uvicorn import Config, Server
from jina import Gateway, __default_host__
from jina.proto import jina_pb2, jina_pb2_grpc
class DummyResponseModel(BaseModel):
... |
from jina.clients.base.grpc import GRPCBaseClient
from jina.clients.mixin import (
AsyncHealthCheckMixin,
AsyncPostMixin,
HealthCheckMixin,
PostMixin,
)
class GRPCClient(GRPCBaseClient, PostMixin, HealthCheckMixin):
"""A client connecting to a Gateway using gRPC protocol.
Instantiate this cla... | from jina.clients.base.grpc import GRPCBaseClient
from jina.clients.mixin import AsyncPostMixin, HealthCheckMixin, PostMixin
class GRPCClient(GRPCBaseClient, PostMixin, HealthCheckMixin):
"""A client connecting to a Gateway using gRPC protocol.
Instantiate this class through the :meth:`jina.Client` convenien... |
from torchaudio._internal.module_utils import dropping_support, dropping_class_support
import inspect
_CTC_DECODERS = [
"CTCHypothesis",
"CTCDecoder",
"CTCDecoderLM",
"CTCDecoderLMState",
"ctc_decoder",
"download_pretrained_files",
]
_CUDA_CTC_DECODERS = [
"CUCTCDecoder",
"CUCTCHypothesi... | from torchaudio._internal.module_utils import dropping_support
_CTC_DECODERS = [
"CTCHypothesis",
"CTCDecoder",
"CTCDecoderLM",
"CTCDecoderLMState",
"ctc_decoder",
"download_pretrained_files",
]
_CUDA_CTC_DECODERS = [
"CUCTCDecoder",
"CUCTCHypothesis",
"cuda_ctc_decoder",
]
def __g... |
# Copyright (c) OpenMMLab. All rights reserved.
from .image import (color_val_matplotlib, imshow_det_bboxes,
imshow_gt_det_bboxes)
__all__ = ['imshow_det_bboxes', 'imshow_gt_det_bboxes', 'color_val_matplotlib']
| from .image import (color_val_matplotlib, imshow_det_bboxes,
imshow_gt_det_bboxes)
__all__ = ['imshow_det_bboxes', 'imshow_gt_det_bboxes', 'color_val_matplotlib']
|
_base_ = '../mask_rcnn/mask-rcnn_r101_fpn_1x_coco.py'
model = dict(
backbone=dict(plugins=[
dict(
cfg=dict(type='ContextBlock', ratio=1. / 4),
stages=(False, True, True, True),
position='after_conv3')
]))
| _base_ = '../mask_rcnn/mask_rcnn_r101_fpn_1x_coco.py'
model = dict(
backbone=dict(plugins=[
dict(
cfg=dict(type='ContextBlock', ratio=1. / 4),
stages=(False, True, True, True),
position='after_conv3')
]))
|
import os
import numpy as np
import pytest
import torch
from pydantic.tools import parse_obj_as
from docarray import BaseDocument
from docarray.typing import (
AudioNdArray,
AudioTorchTensor,
VideoNdArray,
VideoTorchTensor,
)
@pytest.mark.parametrize(
'tensor,cls_video_tensor,cls_tensor',
[
... | import os
import numpy as np
import pytest
import torch
from pydantic.tools import parse_obj_as
from docarray import BaseDocument
from docarray.typing import (
AudioNdArray,
AudioTorchTensor,
VideoNdArray,
VideoTorchTensor,
)
@pytest.mark.parametrize(
'tensor,cls_video_tensor,cls_tensor',
[
... |
import copy
from dataclasses import dataclass, field
from pathlib import Path
from typing import Any, Optional, Union
from .. import config
@dataclass
class DownloadConfig:
"""Configuration for our cached path manager.
Attributes:
cache_dir (`str` or `Path`, *optional*):
Specify a cache ... | import copy
from dataclasses import dataclass, field
from pathlib import Path
from typing import Any, Optional, Union
from .. import config
@dataclass
class DownloadConfig:
"""Configuration for our cached path manager.
Attributes:
cache_dir (`str` or `Path`, *optional*):
Specify a cache ... |
from __future__ import annotations
from dataclasses import dataclass, field
from typing import Any, Callable
import torch
from sentence_transformers.data_collator import SentenceTransformerDataCollator
@dataclass
class CrossEncoderDataCollator(SentenceTransformerDataCollator):
"""Collator for a CrossEncoder mo... | from __future__ import annotations
from dataclasses import dataclass, field
from typing import Any, Callable
import torch
from sentence_transformers.data_collator import SentenceTransformerDataCollator
@dataclass
class CrossEncoderDataCollator(SentenceTransformerDataCollator):
"""Collator for a CrossEncoder mo... |
import pathlib
from typing import Any, Dict, List, Tuple, Union
import torch
from torchdata.datapipes.iter import CSVParser, IterDataPipe, Mapper
from torchvision.prototype.datapoints import Image, OneHotLabel
from torchvision.prototype.datasets.utils import Dataset, HttpResource, OnlineResource
from torchvision.proto... | import pathlib
from typing import Any, Dict, List, Tuple, Union
import torch
from torchdata.datapipes.iter import CSVParser, IterDataPipe, Mapper
from torchvision.prototype.datasets.utils import Dataset, HttpResource, OnlineResource
from torchvision.prototype.datasets.utils._internal import hint_sharding, hint_shuffli... |
import asyncio
from itertools import cycle
from typing import Any, Optional, Union
from uuid import UUID
import pytest
from pytest_benchmark.fixture import BenchmarkFixture # type: ignore[import-untyped]
from typing_extensions import override
from langchain_core.callbacks.base import AsyncCallbackHandler
from langch... | import asyncio
from itertools import cycle
from typing import Any, Optional, Union
from uuid import UUID
import pytest
from pytest_benchmark.fixture import BenchmarkFixture # type: ignore[import-untyped]
from typing_extensions import override
from langchain_core.callbacks.base import AsyncCallbackHandler
from langch... |
__version__ = '0.13.3'
import os
from .document import Document
from .array import DocumentArray
from .dataclasses import dataclass, field
if 'DA_NO_RICH_HANDLER' not in os.environ:
from rich.traceback import install
install()
| __version__ = '0.13.2'
import os
from .document import Document
from .array import DocumentArray
from .dataclasses import dataclass, field
if 'DA_NO_RICH_HANDLER' not in os.environ:
from rich.traceback import install
install()
|
from docarray.array.mixins.attribute import GetAttributeArrayMixin
from docarray.array.mixins.proto import ProtoArrayMixin
__all__ = ['ProtoArrayMixin', 'GetAttributeArrayMixin']
| from docarray.array.mixins.proto import ProtoArrayMixin
__all__ = ['ProtoArrayMixin']
|
# Copyright (c) OpenMMLab. All rights reserved.
from mmdet.registry import MODELS
from mmdet.utils.typing import ConfigType, OptConfigType, OptMultiConfig
from .single_stage_instance_seg import SingleStageInstanceSegmentor
@MODELS.register_module()
class YOLACT(SingleStageInstanceSegmentor):
"""Implementation of... | # Copyright (c) OpenMMLab. All rights reserved.
import torch
from mmdet.data_elements.bbox import bbox2result
from mmdet.registry import MODELS
from .single_stage import SingleStageDetector
@MODELS.register_module()
class YOLACT(SingleStageDetector):
"""Implementation of `YOLACT <https://arxiv.org/abs/1904.02689... |
"""Init params."""
from llama_index.finetuning.cross_encoders.cross_encoder import (
CrossEncoderFinetuneEngine,
)
__all__ = ["CrossEncoderFinetuneEngine"]
| """Init params."""
from llama_index.finetuning.cross_encoders.cross_encoder import (
CrossEncoderFinetuneEngine,
)
__all__ = ["CrossEncoderFinetuneEngine"]
|
# 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... |
import colorsys
from rich.color import Color
from rich.console import Console
from rich.console import ConsoleOptions, RenderResult
from rich.measure import Measurement
from rich.segment import Segment
from rich.style import Style
from docarray.math.helper import minmax_normalize
from docarray.math.ndarray import to_... | import colorsys
from rich.color import Color
from rich.console import Console
from rich.console import ConsoleOptions, RenderResult
from rich.measure import Measurement
from rich.segment import Segment
from rich.style import Style
from ...math.helper import minmax_normalize
from ...math.ndarray import to_numpy_array
... |
# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import os
import os.path as osp
from mmcv import Config
def parse_args():
parser = argparse.ArgumentParser(
description='Convert benchmark model list to script')
parser.add_argument('config', help='test config file path')
parser.add_... | import argparse
import os
import os.path as osp
from mmcv import Config
def parse_args():
parser = argparse.ArgumentParser(
description='Convert benchmark model list to script')
parser.add_argument('config', help='test config file path')
parser.add_argument('--port', type=int, default=29666, help... |
from langchain_core.prompts.prompt import PromptTemplate
_PROMPT_TEMPLATE = """Translate a math problem into a expression that can be executed using Python's numexpr library. Use the output of running this code to answer the question.
Question: ${{Question with math problem.}}
```text
${{single line mathematical expr... | # flake8: noqa
from langchain_core.prompts.prompt import PromptTemplate
_PROMPT_TEMPLATE = """Translate a math problem into a expression that can be executed using Python's numexpr library. Use the output of running this code to answer the question.
Question: ${{Question with math problem.}}
```text
${{single line ma... |
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