input stringlengths 33 5k | output stringlengths 32 5k |
|---|---|
from sentence_transformers import SentenceTransformer, losses, util
class AnglELoss(losses.CoSENTLoss):
def __init__(self, model: SentenceTransformer, scale: float = 20.0) -> None:
"""
This class implements AnglE (Angle Optimized) loss.
This is a modification of :class:`CoSENTLoss`, design... | from sentence_transformers import SentenceTransformer, losses, util
class AnglELoss(losses.CoSENTLoss):
def __init__(self, model: SentenceTransformer, scale: float = 20.0):
"""
This class implements AnglE (Angle Optimized) loss.
This is a modification of :class:`CoSENTLoss`, designed to ad... |
from jina import Flow
import os
os.environ['JINA_LOG_LEVEL'] = 'DEBUG'
if __name__ == '__main__':
with Flow.load_config('flow.yml') as f:
f.block()
| from jina import Flow
if __name__ == '__main__':
with Flow.load_config('flow.yml') as f:
f.block()
|
from . import InputExample
import os
class LabelSentenceReader:
"""Reads in a file that has at least two columns: a label and a sentence.
This reader can for example be used with the BatchHardTripletLoss.
Maps labels automatically to integers
"""
def __init__(self, folder, label_col_idx=0, senten... | from . import InputExample
import os
class LabelSentenceReader:
"""Reads in a file that has at least two columns: a label and a sentence.
This reader can for example be used with the BatchHardTripletLoss.
Maps labels automatically to integers"""
def __init__(self, folder, label_col_idx=0, sentence_co... |
# Copyright (c) OpenMMLab. All rights reserved.
from .base_loop import BaseLoop
from .checkpoint import (CheckpointLoader, find_latest_checkpoint,
get_deprecated_model_names, get_external_models,
get_mmcls_models, get_state_dict,
get_torchvision... | # Copyright (c) OpenMMLab. All rights reserved.
from .base_loop import BaseLoop
from .checkpoint import (CheckpointLoader, get_deprecated_model_names,
get_external_models, get_mmcls_models, get_state_dict,
get_torchvision_models, load_checkpoint,
... |
from typing import Optional
import numpy as np
import pytest
from pydantic import BaseModel, ValidationError
from typing_extensions import TypedDict
from docarray import BaseDocument, DocumentArray
from docarray.documents import AudioDoc, ImageDoc, TextDoc
from docarray.documents.helper import (
create_doc,
c... | from typing import Optional
import numpy as np
import pytest
from pydantic import BaseModel
from typing_extensions import TypedDict
from docarray import BaseDocument, DocumentArray
from docarray.documents import AudioDoc, ImageDoc, TextDoc
from docarray.documents.helper import create_doc, create_from_typeddict
from d... |
from typing import TYPE_CHECKING, Any, Type, TypeVar, Union
from uuid import UUID
from pydantic import parse_obj_as
from docarray.typing.proto_register import _register_proto
from docarray.utils._internal.pydantic import is_pydantic_v2
if TYPE_CHECKING:
from docarray.proto import NodeProto
from docarray.typing.... | from typing import TYPE_CHECKING, Type, TypeVar, Union
from uuid import UUID
from pydantic import BaseConfig, parse_obj_as
from pydantic.fields import ModelField
from docarray.typing.proto_register import _register_proto
if TYPE_CHECKING:
from docarray.proto import NodeProto
from docarray.typing.abstract_type i... |
__copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
from pathlib import Path
from typing import List
import pytest
from jina import Document, DocumentArray, Executor
from ...spacy_text_encoder import SpacyTextEncoder
_EMBEDDING_DIM = 96
@pytest.fixture(scope=... | __copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import os
from pathlib import Path
import pytest
import spacy
from jina import Document, DocumentArray, Executor
from ...spacy_text_encoder import SpacyTextEncoder
def test_config():
ex = Executor.load_c... |
_base_ = './tood_r50_fpn_1x_coco.py'
max_epochs = 24
# learning rate
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=max_epochs,
by_epoch=True,
milestones=[16, 22],
g... | _base_ = './tood_r50_fpn_1x_coco.py'
max_epochs = 24
# learning rate
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=max_epochs,
by_epoch=True,
milestones=[16, 22],
g... |
from __future__ import annotations
from collections.abc import Iterable
from torch import Tensor
from sentence_transformers.losses.TripletLoss import TripletDistanceMetric, TripletLoss
from sentence_transformers.sparse_encoder.SparseEncoder import SparseEncoder
class SparseTripletLoss(TripletLoss):
def __init_... | from __future__ import annotations
from collections.abc import Iterable
from torch import Tensor
from sentence_transformers.losses.TripletLoss import TripletDistanceMetric, TripletLoss
from sentence_transformers.sparse_encoder.SparseEncoder import SparseEncoder
class SparseTripletLoss(TripletLoss):
def __init_... |
import pprint
import torch
from torch.utils._pytree import tree_map, tree_map_only
class OpenRegTensorMeta:
def __init__(self, tensor, checked=True):
if checked and not tensor.device.type == "openreg":
raise RuntimeError(
"Creating OpenRegTensorMeta is only for Tensors on open... | import pprint
import torch
from torch.utils._pytree import tree_map, tree_map_only
class OpenRegTensorMeta:
def __init__(self, tensor, checked=True):
if checked and not tensor.device.type == "openreg":
raise RuntimeError(
"Creating OpenRegTensorMeta is only for Tensors on open... |
from __future__ import annotations
import logging
from datasets import load_dataset
from sentence_transformers import SparseEncoder, SparseEncoderTrainer, SparseEncoderTrainingArguments
from sentence_transformers.evaluation import SequentialEvaluator, SimilarityFunction
from sentence_transformers.models import Pooli... | from __future__ import annotations
import logging
from datasets import load_dataset
from sentence_transformers import SparseEncoder, SparseEncoderTrainer, SparseEncoderTrainingArguments
from sentence_transformers.evaluation import SequentialEvaluator, SimilarityFunction
from sentence_transformers.models import Pooli... |
__version__ = '0.30.0a3'
from docarray.array import DocumentArray, DocumentArrayStacked
from docarray.base_document.document import BaseDocument
import logging
__all__ = ['BaseDocument', 'DocumentArray', 'DocumentArrayStacked']
logger = logging.getLogger('docarray')
handler = logging.StreamHandler()
formatter = log... | __version__ = '0.30.0a3'
from docarray.array import DocumentArray, DocumentArrayStacked
from docarray.base_document.document import BaseDocument
__all__ = ['BaseDocument', 'DocumentArray', 'DocumentArrayStacked']
|
import pytest
import torchaudio
from torchaudio.pipelines import (
HUBERT_ASR_LARGE,
HUBERT_ASR_XLARGE,
HUBERT_BASE,
HUBERT_LARGE,
HUBERT_XLARGE,
VOXPOPULI_ASR_BASE_10K_DE,
VOXPOPULI_ASR_BASE_10K_EN,
VOXPOPULI_ASR_BASE_10K_ES,
VOXPOPULI_ASR_BASE_10K_FR,
VOXPOPULI_ASR_BASE_10K_IT,... | import pytest
import torchaudio
from torchaudio.pipelines import (
HUBERT_ASR_LARGE,
HUBERT_ASR_XLARGE,
HUBERT_BASE,
HUBERT_LARGE,
HUBERT_XLARGE,
VOXPOPULI_ASR_BASE_10K_DE,
VOXPOPULI_ASR_BASE_10K_EN,
VOXPOPULI_ASR_BASE_10K_ES,
VOXPOPULI_ASR_BASE_10K_FR,
VOXPOPULI_ASR_BASE_10K_IT,... |
from typing import Union
from docarray.typing.tensor.video.video_ndarray import VideoNdArray
from docarray.utils._internal.misc import is_tf_available, is_torch_available
torch_available = is_torch_available()
if torch_available:
from docarray.typing.tensor.video.video_torch_tensor import VideoTorchTensor
tf_av... | from typing import Union
from docarray.typing.tensor.video.video_ndarray import VideoNdArray
from docarray.utils.misc import is_tf_available, is_torch_available
torch_available = is_torch_available()
if torch_available:
from docarray.typing.tensor.video.video_torch_tensor import VideoTorchTensor
tf_available = ... |
import logging
from typing import Optional, cast
from autogpt_libs.supabase_integration_credentials_store.types import (
UserIntegrations,
UserMetadata,
UserMetadataRaw,
)
from fastapi import HTTPException
from prisma import Json
from prisma.models import User
from backend.data.db import prisma
from backe... | import logging
from typing import Optional, cast
from autogpt_libs.supabase_integration_credentials_store.types import (
UserIntegrations,
UserMetadata,
UserMetadataRaw,
)
from fastapi import HTTPException
from prisma import Json
from prisma.models import User
from backend.data.db import prisma
from backe... |
_base_ = '../_base_/default_runtime.py'
# dataset settings
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
# file_client_args = dict(
# backend='petrel',
# path_mapping=dict({
# './data/': 's3://openmmlab/datasets/detection/',
# 'data/': 's3://openmmlab/datasets/detection/'
# }))
fil... | _base_ = '../_base_/default_runtime.py'
# dataset settings
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
# file_client_args = dict(
# backend='petrel',
# path_mapping=dict({
# './data/': 's3://openmmlab/datasets/detection/',
# 'data/': 's3://openmmlab/datasets/detection/'
# }))
fil... |
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__,
__default_grpc_gateway__,
__default_http_gateway__,
__default_websocket_gateway__,
__version__,
)
from jina.enums import PodRoleType
... | 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__,
__default_grpc_gateway__,
__default_http_gateway__,
__default_websocket_gateway__,
__version__,
)
from jina.enums import PodRoleType
... |
_base_ = [
'../_base_/models/faster-rcnn_r50-caffe-dc5.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# use caffe img_norm
img_norm_cfg = dict(
mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
train_pipeline = [
... | _base_ = [
'../_base_/models/faster_rcnn_r50_caffe_dc5.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# use caffe img_norm
img_norm_cfg = dict(
mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
train_pipeline = [
... |
import os
import pytest
from llama_index.core.base.llms.base import BaseLLM
from llama_index.core.base.llms.types import ChatMessage, ImageBlock, MessageRole
from llama_index.llms.gemini import Gemini
from llama_index.llms.gemini.utils import chat_message_to_gemini
def test_embedding_class():
names_of_base_class... | from llama_index.core.base.llms.base import BaseLLM
from llama_index.llms.gemini import Gemini
def test_embedding_class():
names_of_base_classes = [b.__name__ for b in Gemini.__mro__]
assert BaseLLM.__name__ in names_of_base_classes
|
from docarray.array.queryset.parser import QueryParser
| from .parser import QueryParser
|
# Copyright (c) OpenMMLab. All rights reserved.
from argparse import ArgumentParser, Namespace
from pathlib import Path
from tempfile import TemporaryDirectory
from mmengine.config import Config
from mmengine.utils import mkdir_or_exist
try:
from model_archiver.model_packaging import package_model
from model_... | # Copyright (c) OpenMMLab. All rights reserved.
from argparse import ArgumentParser, Namespace
from pathlib import Path
from tempfile import TemporaryDirectory
from mmengine.config import Config
from mmengine.utils import mkdir_or_exist
try:
from model_archiver.model_packaging import package_model
from model_... |
"""PDF Marker reader."""
from pathlib import Path
from typing import Any, Dict, List, Optional
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document
class PDFMarkerReader(BaseReader):
"""
PDF Marker Reader. Reads a pdf to markdown format and tables with layout.
... | """PDF Marker reader."""
from pathlib import Path
from typing import Any, Dict, List, Optional
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document
class PDFMarkerReader(BaseReader):
"""
PDF Marker Reader. Reads a pdf to markdown format and tables with layout.
... |
import wave
from abc import ABC
from typing import BinaryIO, TypeVar, Union
from docarray.typing.tensor.abstract_tensor import AbstractTensor
T = TypeVar('T', bound='AbstractAudioTensor')
MAX_INT_16 = 2**15
class AbstractAudioTensor(AbstractTensor, ABC):
def to_bytes(self):
"""
Convert audio te... | import wave
from abc import ABC, abstractmethod
from typing import BinaryIO, TypeVar, Union
from docarray.typing.tensor.abstract_tensor import AbstractTensor
T = TypeVar('T', bound='AbstractAudioTensor')
class AbstractAudioTensor(AbstractTensor, ABC):
@abstractmethod
def to_audio_bytes(self):
"""
... |
"""
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.
from .optimizer import (OPTIM_WRAPPER_CONSTRUCTORS, OPTIMIZERS,
AmpOptimWrapper, DefaultOptimWrapperConstructor,
OptimWrapper, OptimWrapperDict, build_optim_wrapper)
# yapf: disable
from .scheduler import (ConstantLR, Consta... | # Copyright (c) OpenMMLab. All rights reserved.
from .optimizer import (OPTIM_WRAPPER_CONSTRUCTORS, OPTIMIZERS,
AmpOptimWrapper, DefaultOptimWrapperConstructor,
OptimWrapper, OptimWrapperDict, build_optim_wrapper)
# yapf: disable
from .scheduler import (ConstantLR, Consta... |
from io import BytesIO
from typing import TYPE_CHECKING, Any, Optional, Tuple, Type, TypeVar
import numpy as np
from pydantic import parse_obj_as
from pydantic.validators import bytes_validator
from docarray.typing.abstract_type import AbstractType
from docarray.typing.proto_register import _register_proto
from docar... | from io import BytesIO
from typing import TYPE_CHECKING, Any, Optional, Tuple, Type, TypeVar
import numpy as np
from pydantic import parse_obj_as
from pydantic.validators import bytes_validator
from docarray.typing.abstract_type import AbstractType
from docarray.typing.proto_register import _register_proto
if TYPE_C... |
# Copyright (c) OpenMMLab. All rights reserved.
import time
from typing import Optional, Sequence, Union
from mmengine.registry import HOOKS
from .hook import Hook
DATA_BATCH = Optional[Union[dict, tuple, list]]
@HOOKS.register_module()
class IterTimerHook(Hook):
"""A hook that logs the time spent during iterat... | # Copyright (c) OpenMMLab. All rights reserved.
import time
from typing import Optional, Sequence, Union
from mmengine.registry import HOOKS
from mmengine.structures import BaseDataElement
from .hook import Hook
DATA_BATCH = Optional[Sequence[dict]]
@HOOKS.register_module()
class IterTimerHook(Hook):
"""A hook ... |
from __future__ import annotations
from typing import Any, List
from langchain_text_splitters.base import TextSplitter
class SpacyTextSplitter(TextSplitter):
"""Splitting text using Spacy package.
Per default, Spacy's `en_core_web_sm` model is used and
its default max_length is 1000000 (it is the lengt... | from __future__ import annotations
from typing import Any, List
from langchain_text_splitters.base import TextSplitter
class SpacyTextSplitter(TextSplitter):
"""Splitting text using Spacy package.
Per default, Spacy's `en_core_web_sm` model is used and
its default max_length is 1000000 (it is the lengt... |
# Copyright (c) OpenMMLab. All rights reserved.
from .builder import DATASETS
from .coco import CocoDataset
@DATASETS.register_module()
class DeepFashionDataset(CocoDataset):
CLASSES = ('top', 'skirt', 'leggings', 'dress', 'outer', 'pants', 'bag',
'neckwear', 'headwear', 'eyeglass', 'belt', 'footw... | # Copyright (c) OpenMMLab. All rights reserved.
from .builder import DATASETS
from .coco import CocoDataset
@DATASETS.register_module()
class DeepFashionDataset(CocoDataset):
CLASSES = ('top', 'skirt', 'leggings', 'dress', 'outer', 'pants', 'bag',
'neckwear', 'headwear', 'eyeglass', 'belt', 'footw... |
_base_ = './detr_r50_8xb2-500e_coco.py'
model = dict(
backbone=dict(
depth=18,
init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet18')),
bbox_head=dict(in_channels=512))
| _base_ = './detr_r50_8xb2-500e_coco.py'
model = dict(
backbone=dict(
depth=18,
init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet18')),
neck=dict(in_channels=[64, 128, 256, 512]))
|
from typing import Union, Iterable, MutableSequence, Iterator
from docarray.array.storage.memory.backend import needs_id2offset_rebuild
from docarray.array.storage.base.seqlike import BaseSequenceLikeMixin
from docarray import Document
class SequenceLikeMixin(BaseSequenceLikeMixin):
"""Implement sequence-like m... | from typing import Union, Iterable, MutableSequence, Iterator
from docarray.array.storage.memory.backend import needs_id2offset_rebuild
from docarray.array.storage.base.seqlike import BaseSequenceLikeMixin
from docarray import Document
class SequenceLikeMixin(BaseSequenceLikeMixin):
"""Implement sequence-like m... |
from typing import List, Union, Any
from docarray.helper import dunder_get
class GetAttributesMixin:
"""Provide helper functions for :class:`Document` to allow advanced set and get attributes"""
def _get_attributes(self, *fields: str) -> Union[Any, List[Any]]:
"""Bulk fetch Document fields and retur... | from typing import List, Union, Any
from ...helper import dunder_get
class GetAttributesMixin:
"""Provide helper functions for :class:`Document` to allow advanced set and get attributes """
def _get_attributes(self, *fields: str) -> Union[Any, List[Any]]:
"""Bulk fetch Document fields and return a l... |
from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.tools import O365SearchEvents
from langchain_community.tools.office365.events_search import SearchEventsInput
# Create a way to dynamically look up deprecated imports.
# Used to consoli... | from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.tools import O365SearchEvents
from langchain_community.tools.office365.events_search import SearchEventsInput
# Create a way to dynamically look up deprecated imports.
# Used to consoli... |
from .sox_effects import apply_effects_file, apply_effects_tensor, effect_names, init_sox_effects, shutdown_sox_effects
__all__ = [
"init_sox_effects",
"shutdown_sox_effects",
"effect_names",
"apply_effects_tensor",
"apply_effects_file",
]
| from torchaudio._internal import module_utils as _mod_utils
from .sox_effects import apply_effects_file, apply_effects_tensor, effect_names, init_sox_effects, shutdown_sox_effects
if _mod_utils.is_sox_available():
import atexit
init_sox_effects()
atexit.register(shutdown_sox_effects)
__all__ = [
"i... |
import pytest
from datasets.utils.version import Version
@pytest.mark.parametrize(
"other, expected_equality",
[
(Version("1.0.0"), True),
("1.0.0", True),
(Version("2.0.0"), False),
("2.0.0", False),
("1", False),
("a", False),
(1, False),
(Non... | import pytest
from datasets.utils.version import Version
@pytest.mark.parametrize(
"other, expected_equality",
[
(Version("1.0.0"), True),
("1.0.0", True),
(Version("2.0.0"), False),
("2.0.0", False),
("1", False),
("a", False),
(1, False),
(Non... |
import multiprocessing
from typing import TYPE_CHECKING, Optional, Union
from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import tqdm as hf_tqdm
from .abc import AbstractDatasetInputStream
if TYPE_CHECKING:
import sqlite3
i... | import multiprocessing
from typing import TYPE_CHECKING, Optional, Union
from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream
if TYPE_CHECKING:
import sqlite3
import sq... |
from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
from backend.data.model import SchemaField
class WordCharacterCountBlock(Block):
class Input(BlockSchema):
text: str = SchemaField(
description="Input text to count words and characters",
placeholder="Ent... | from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
from backend.data.model import SchemaField
class WordCharacterCountBlock(Block):
class Input(BlockSchema):
text: str = SchemaField(
description="Input text to count words and characters",
placeholder="Ent... |
"""QuantileDMatrix related tests."""
import numpy as np
import pytest
from sklearn.model_selection import train_test_split
import xgboost as xgb
from .data import make_batches, make_categorical
def check_ref_quantile_cut(device: str) -> None:
"""Check obtaining the same cut values given a reference."""
X, ... | """QuantileDMatrix related tests."""
import numpy as np
from sklearn.model_selection import train_test_split
import xgboost as xgb
from .data import make_batches
def check_ref_quantile_cut(device: str) -> None:
"""Check obtaining the same cut values given a reference."""
X, y, _ = (
data[0]
... |
import json
from enum import Enum
from typing import Any
from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
from backend.data.model import SchemaField
from backend.util.request import requests
class HttpMethod(Enum):
GET = "GET"
POST = "POST"
PUT = "PUT"
DELETE = "DELETE"
... | import json
from enum import Enum
import requests
from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
from backend.data.model import SchemaField
class HttpMethod(Enum):
GET = "GET"
POST = "POST"
PUT = "PUT"
DELETE = "DELETE"
PATCH = "PATCH"
OPTIONS = "OPTIONS"
H... |
import os
from typing import Dict, List, Tuple, TypeVar, Union
T = TypeVar("T")
ListLike = Union[List[T], Tuple[T, ...]]
NestedDataStructureLike = Union[T, List[T], Dict[str, T]]
PathLike = Union[str, bytes, os.PathLike]
| import os
from typing import Dict, List, TypeVar, Union
T = TypeVar("T")
NestedDataStructureLike = Union[T, List[T], Dict[str, T]]
PathLike = Union[str, bytes, os.PathLike]
|
import pytest
from llama_index.llms.nvidia import NVIDIA
@pytest.mark.integration
def test_available_models(mode: dict) -> None:
models = NVIDIA(**mode).available_models
assert models
assert isinstance(models, list)
assert all(isinstance(model.id, str) for model in models)
| import pytest
from llama_index.llms.nvidia import NVIDIA
@pytest.mark.integration()
def test_available_models(mode: dict) -> None:
models = NVIDIA(**mode).available_models
assert models
assert isinstance(models, list)
assert all(isinstance(model.id, str) for model in models)
|
import json
from json import JSONDecodeError
from typing import Union
from langchain_core.agents import AgentAction, AgentActionMessageLog, AgentFinish
from langchain_core.exceptions import OutputParserException
from langchain_core.messages import (
AIMessage,
BaseMessage,
)
from langchain_core.outputs import ... | import json
from json import JSONDecodeError
from typing import Union
from langchain_core.agents import AgentAction, AgentActionMessageLog, AgentFinish
from langchain_core.exceptions import OutputParserException
from langchain_core.messages import (
AIMessage,
BaseMessage,
)
from langchain_core.outputs import ... |
import os
import numpy as np
import pytest
import requests
from jina import Client, Document, Flow
from tests import random_docs
# noinspection PyUnresolvedReferences
from tests.integration.crud import CrudIndexer
PARAMS = {'top_k': 10}
def rest_post(f, endpoint, documents):
data = [d.to_dict() for d in docum... | import numpy as np
import os
import pytest
import requests
from jina import Flow, Document, Client
from tests import random_docs
# noinspection PyUnresolvedReferences
from tests.integration.crud import CrudIndexer
PARAMS = {'top_k': 10}
def rest_post(f, endpoint, documents):
data = [d.to_dict() for d in docume... |
from functools import wraps
from typing import Any, Callable, Concatenate, Coroutine, ParamSpec, TypeVar, cast
from backend.data.credit import get_user_credit_model
from backend.data.execution import (
ExecutionResult,
RedisExecutionEventBus,
create_graph_execution,
get_execution_results,
get_incom... | from functools import wraps
from typing import Any, Callable, Concatenate, Coroutine, ParamSpec, TypeVar, cast
from backend.data.credit import get_user_credit_model
from backend.data.execution import (
ExecutionResult,
create_graph_execution,
get_execution_results,
get_incomplete_executions,
get_la... |
from __future__ import annotations
from typing import Optional, Type
from langchain_core.callbacks import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from pydantic import BaseModel
from langchain_community.tools.playwright.base import BaseBrowserTool
from langchain_community.tools.playwrig... | from __future__ import annotations
from typing import Optional, Type
from langchain_core.callbacks import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from pydantic import BaseModel
from langchain_community.tools.playwright.base import BaseBrowserTool
from langchain_community.tools.playwrig... |
import numpy as np
import pytest
from docarray import BaseDoc
from docarray.base_doc.io.json import orjson_dumps
from docarray.typing import NdArray, TorchTensor
class NpDoc(BaseDoc):
embedding: NdArray[3, 4]
embedding_no_shape: NdArray
class TorchDoc(BaseDoc):
embedding: TorchTensor[3, 4]
embeddin... | import numpy as np
import pytest
from docarray import BaseDoc
from docarray.base_doc.io.json import orjson_dumps
from docarray.typing import NdArray, TorchTensor
class NpDoc(BaseDoc):
embedding: NdArray[3, 4]
embedding_no_shape: NdArray
class TorchDoc(BaseDoc):
embedding: TorchTensor[3, 4]
embeddin... |
from pydantic import BaseModel
from backend.data.block import (
Block,
BlockCategory,
BlockManualWebhookConfig,
BlockOutput,
BlockSchema,
)
from backend.data.model import SchemaField
from backend.integrations.providers import ProviderName
from backend.integrations.webhooks.compass import CompassWeb... | from pydantic import BaseModel
from backend.data.block import (
Block,
BlockCategory,
BlockManualWebhookConfig,
BlockOutput,
BlockSchema,
)
from backend.data.model import SchemaField
from backend.integrations.webhooks.compass import CompassWebhookType
class Transcription(BaseModel):
text: str... |
"""
Opendal file and directory reader.
A loader that fetches a file or iterates through a directory on AWS S3 or other compatible service.
"""
import asyncio
import tempfile
from pathlib import Path
from typing import Any, Dict, List, Optional, Union, cast
from llama_index.core.readers import SimpleDirectoryReader
f... | """Opendal file and directory reader.
A loader that fetches a file or iterates through a directory on AWS S3 or other compatible service.
"""
import asyncio
import tempfile
from pathlib import Path
from typing import Any, Dict, List, Optional, Union, cast
from llama_index.core.readers import SimpleDirectoryReader
fr... |
_base_ = './mask-rcnn_r50_fpn_8xb8-amp-lsj-200e_coco.py'
model = dict(
backbone=dict(
depth=18,
init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet18')),
neck=dict(in_channels=[64, 128, 256, 512]))
| _base_ = './mask_rcnn_r50_fpn_lsj_200e_8x8_fp16_coco.py'
model = dict(
backbone=dict(
depth=18,
init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet18')),
neck=dict(in_channels=[64, 128, 256, 512]))
|
from __future__ import annotations
import os
import pytest
from sentence_transformers import CrossEncoder, SentenceTransformer
from sentence_transformers.models import Pooling, Transformer
from sentence_transformers.util import is_datasets_available
from tests.utils import SafeTemporaryDirectory
if is_datasets_avai... | from __future__ import annotations
import os
import platform
import tempfile
import pytest
from sentence_transformers import CrossEncoder, SentenceTransformer
from sentence_transformers.models import Pooling, Transformer
from sentence_transformers.util import is_datasets_available
if is_datasets_available():
fr... |
import os
from pathlib import Path
from torchaudio.datasets import vctk
from torchaudio_unittest.common_utils import get_whitenoise, normalize_wav, save_wav, TempDirMixin, TorchaudioTestCase
# Used to generate a unique transcript for each dummy audio file
_TRANSCRIPT = [
"Please call Stella",
"Ask her to brin... | import os
from pathlib import Path
from torchaudio.datasets import vctk
from torchaudio_unittest.common_utils import (
get_whitenoise,
normalize_wav,
save_wav,
TempDirMixin,
TorchaudioTestCase,
)
# Used to generate a unique transcript for each dummy audio file
_TRANSCRIPT = [
"Please call Stel... |
from typing import TYPE_CHECKING, Any
from langchain_core.document_loaders import Blob, BlobLoader
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.document_loaders import (
FileSystemBlobLoader,
YoutubeAudioLoader,
)
# Create a way to dynamically look up... | from typing import TYPE_CHECKING, Any
from langchain_core.document_loaders import Blob, BlobLoader
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.document_loaders import (
FileSystemBlobLoader,
YoutubeAudioLoader,
)
# Create a way to dynamically look up... |
"""Handle chained inputs."""
from typing import Optional, TextIO
_TEXT_COLOR_MAPPING = {
"blue": "36;1",
"yellow": "33;1",
"pink": "38;5;200",
"green": "32;1",
"red": "31;1",
}
def get_color_mapping(
items: list[str], excluded_colors: Optional[list] = None
) -> dict[str, str]:
"""Get map... | """Handle chained inputs."""
from typing import Optional, TextIO
_TEXT_COLOR_MAPPING = {
"blue": "36;1",
"yellow": "33;1",
"pink": "38;5;200",
"green": "32;1",
"red": "31;1",
}
def get_color_mapping(
items: list[str], excluded_colors: Optional[list] = None
) -> dict[str, str]:
"""Get map... |
# Copyright (c) OpenMMLab. All rights reserved.
from mmengine.model import is_model_wrapper
from mmengine.runner import ValLoop
from mmdet.registry import LOOPS
@LOOPS.register_module()
class TeacherStudentValLoop(ValLoop):
"""Loop for validation of model teacher and student."""
def run(self):
"""La... | # Copyright (c) OpenMMLab. All rights reserved.
from mmengine.model import is_model_wrapper
from mmengine.runner import ValLoop
from mmdet.registry import LOOPS
@LOOPS.register_module()
class TeacherStudentValLoop(ValLoop):
"""Loop for validation of model teacher and student."""
def run(self):
"""L... |
from typing import TYPE_CHECKING
from .github import GitHubOAuthHandler
from .google import GoogleOAuthHandler
from .notion import NotionOAuthHandler
if TYPE_CHECKING:
from ..providers import ProviderName
from .base import BaseOAuthHandler
# --8<-- [start:HANDLERS_BY_NAMEExample]
HANDLERS_BY_NAME: dict["Prov... | from .base import BaseOAuthHandler
from .github import GitHubOAuthHandler
from .google import GoogleOAuthHandler
from .notion import NotionOAuthHandler
# --8<-- [start:HANDLERS_BY_NAMEExample]
HANDLERS_BY_NAME: dict[str, type[BaseOAuthHandler]] = {
handler.PROVIDER_NAME: handler
for handler in [
GitHub... |
"""
Collection of examples for using sklearn interface
==================================================
For an introduction to XGBoost's scikit-learn estimator interface, see
:doc:`/python/sklearn_estimator`.
Created on 1 Apr 2015
@author: Jamie Hall
"""
import pickle
import numpy as np
from sklearn.datasets impo... | '''
Collection of examples for using sklearn interface
==================================================
For an introduction to XGBoost's scikit-learn estimator interface, see
:doc:`/python/sklearn_estimator`.
Created on 1 Apr 2015
@author: Jamie Hall
'''
import pickle
import numpy as np
from sklearn.datasets impo... |
# Copyright (c) OpenMMLab. All rights reserved.
import copy
import time
from contextlib import contextmanager
from typing import Generator, Optional
from mmengine.utils.manager import ManagerMixin, _accquire_lock, _release_lock
class DefaultScope(ManagerMixin):
"""Scope of current task used to reset the current ... | # Copyright (c) OpenMMLab. All rights reserved.
import copy
import time
from contextlib import contextmanager
from typing import Generator, Optional
from mmengine.utils.manager import ManagerMixin, _accquire_lock, _release_lock
class DefaultScope(ManagerMixin):
"""Scope of current task used to reset the current ... |
import logging
import numpy as np
import os
import csv
from typing import Optional
from sklearn.metrics import ndcg_score
logger = logging.getLogger(__name__)
class CERerankingEvaluator:
"""
This class evaluates a CrossEncoder model for the task of re-ranking.
Given a query and a list of documents, it c... | import logging
import numpy as np
import os
import csv
from typing import Optional
from sklearn.metrics import ndcg_score
logger = logging.getLogger(__name__)
class CERerankingEvaluator:
"""
This class evaluates a CrossEncoder model for the task of re-ranking.
Given a query and a list of documents, it c... |
# Copyright (c) OpenMMLab. All rights reserved.
from mmdet.registry import MODELS
from mmdet.utils import ConfigType, OptConfigType, OptMultiConfig
from .two_stage import TwoStageDetector
@MODELS.register_module()
class MaskScoringRCNN(TwoStageDetector):
"""Mask Scoring RCNN.
https://arxiv.org/abs/1903.00241... | # Copyright (c) OpenMMLab. All rights reserved.
from mmdet.core.utils import ConfigType, OptConfigType, OptMultiConfig
from mmdet.registry import MODELS
from .two_stage import TwoStageDetector
@MODELS.register_module()
class MaskScoringRCNN(TwoStageDetector):
"""Mask Scoring RCNN.
https://arxiv.org/abs/1903.... |
"""FastAPI framework, high performance, easy to learn, fast to code, ready for production"""
__version__ = "0.115.14"
from starlette import status as status
from .applications import FastAPI as FastAPI
from .background import BackgroundTasks as BackgroundTasks
from .datastructures import UploadFile as UploadFile
fro... | """FastAPI framework, high performance, easy to learn, fast to code, ready for production"""
__version__ = "0.115.13"
from starlette import status as status
from .applications import FastAPI as FastAPI
from .background import BackgroundTasks as BackgroundTasks
from .datastructures import UploadFile as UploadFile
fro... |
from typing import Any, Dict, List, Tuple, Type, cast, Set
from docarray import BaseDoc, DocList
from docarray.index.abstract import BaseDocIndex
from docarray.utils.filter import filter_docs
from docarray.utils.find import FindResult
def _collect_query_args(method_name: str): # TODO: use partialmethod instead
... | from typing import Any, Dict, List, Tuple, Type, cast
from docarray import BaseDoc, DocList
from docarray.index.abstract import BaseDocIndex
from docarray.utils.filter import filter_docs
from docarray.utils.find import FindResult
def _collect_query_args(method_name: str): # TODO: use partialmethod instead
def i... |
"""In memory document index."""
import operator
import uuid
from collections.abc import Sequence
from typing import Any, Optional, cast
from pydantic import Field
from langchain_core._api import beta
from langchain_core.callbacks import CallbackManagerForRetrieverRun
from langchain_core.documents import Document
fro... | import operator
import uuid
from collections.abc import Sequence
from typing import Any, Optional, cast
from pydantic import Field
from langchain_core._api import beta
from langchain_core.callbacks import CallbackManagerForRetrieverRun
from langchain_core.documents import Document
from langchain_core.indexing import ... |
from typing import Union, BinaryIO, TYPE_CHECKING
from docarray.document.mixins.helper import _uri_to_blob, _get_file_context
if TYPE_CHECKING:
from docarray.typing import T
class UriFileMixin:
"""Provide helper functions for :class:`Document` to dump content to a file."""
def save_uri_to_file(self: 'T... | from typing import Union, BinaryIO, TYPE_CHECKING
from .helper import _uri_to_blob, _get_file_context
if TYPE_CHECKING:
from ...typing import T
class UriFileMixin:
"""Provide helper functions for :class:`Document` to dump content to a file."""
def save_uri_to_file(self: 'T', file: Union[str, BinaryIO])... |
# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import os
import os.path as osp
import mmcv
from mmcv import Config, DictAction
from mmcv.runner import init_dist
from tools.analysis_tools.benchmark import measure_inferense_speed
def parse_args():
parser = argparse.ArgumentParser(
descript... | import argparse
import os
import os.path as osp
import mmcv
from mmcv import Config, DictAction
from mmcv.runner import init_dist
from tools.analysis_tools.benchmark import measure_inferense_speed
def parse_args():
parser = argparse.ArgumentParser(
description='MMDet benchmark a model of FPS')
parser... |
"""Popular unsupervised clustering algorithms."""
# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause
from ._affinity_propagation import AffinityPropagation, affinity_propagation
from ._agglomerative import (
AgglomerativeClustering,
FeatureAgglomeration,
linkage_tree,
ward_... | """Popular unsupervised clustering algorithms."""
# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause
from ._affinity_propagation import AffinityPropagation, affinity_propagation
from ._agglomerative import (
AgglomerativeClustering,
FeatureAgglomeration,
linkage_tree,
ward_... |
from __future__ import annotations
import logging
from typing import TYPE_CHECKING, Any, Callable
import numpy as np
from sentence_transformers.evaluation.NanoBEIREvaluator import NanoBEIREvaluator, dataset_name_to_id
from sentence_transformers.sparse_encoder.evaluation.SparseInformationRetrievalEvaluator import (
... | from __future__ import annotations
import logging
from typing import TYPE_CHECKING, Any
from sentence_transformers.evaluation.NanoBEIREvaluator import NanoBEIREvaluator, dataset_name_to_id
from sentence_transformers.sparse_encoder.evaluation.SparseInformationRetrievalEvaluator import (
SparseInformationRetrievalE... |
"""
The :mod:`jina.proto` defines the protobuf used in jina. It is the core message protocol used in communicating between :class:`jina.orchestrate.deployments.Deployment`. It also defines the interface of a gRPC service.
"""
| """
The :mod:`jina.proto` defines the protobuf used in jina. It is the core message protocol used in communicating between :class:`jina.orchestrate.deployments.BaseDeployment`. It also defines the interface of a gRPC service.
"""
|
import warnings
from typing import TYPE_CHECKING, Optional, Tuple, TypeVar
from docarray.typing import ImageBytes
from docarray.typing.proto_register import _register_proto
from docarray.typing.tensor.image import ImageNdArray
from docarray.typing.url.any_url import AnyUrl
from docarray.utils._internal.misc import is_... | import warnings
from typing import TYPE_CHECKING, Any, Optional, Tuple, Type, TypeVar, Union
from docarray.typing import ImageBytes
from docarray.typing.proto_register import _register_proto
from docarray.typing.tensor.image import ImageNdArray
from docarray.typing.url.any_url import AnyUrl
from docarray.utils._intern... |
from typing import Any, Optional
from typing_inspect import get_args, is_union_type
from docarray.typing.tensor.abstract_tensor import AbstractTensor
def is_type_tensor(type_: Any) -> bool:
"""Return True if type is a type Tensor or an Optional Tensor type."""
return isinstance(type_, type) and issubclass(t... | from typing import Any, Optional
from typing_inspect import get_args, is_optional_type, is_union_type
from docarray.typing.tensor.abstract_tensor import AbstractTensor
def is_type_tensor(type_: Any) -> bool:
"""Return True if type is a type Tensor or an Optional Tensor type."""
return isinstance(type_, type... |
"""
This script trains sentence transformers with a triplet loss function.
As corpus, we use the wikipedia sections dataset that was describd by Dor et al., 2018, Learning Thematic Similarity Metric Using Triplet Networks.
"""
import logging
import traceback
from datetime import datetime
from datasets import load_da... | """
This script trains sentence transformers with a triplet loss function.
As corpus, we use the wikipedia sections dataset that was describd by Dor et al., 2018, Learning Thematic Similarity Metric Using Triplet Networks.
"""
import logging
import traceback
from datetime import datetime
from datasets import load_da... |
# Copyright (c) OpenMMLab. All rights reserved.
from typing import Optional, Sequence
import torch
from mmengine.data import BaseDataSample
from mmengine.registry import HOOKS
from .hook import Hook
@HOOKS.register_module()
class EmptyCacheHook(Hook):
"""Releases all unoccupied cached GPU memory during the proc... | # Copyright (c) OpenMMLab. All rights reserved.
from typing import Optional, Sequence
import torch
from mmengine.data import BaseDataSample
from mmengine.registry import HOOKS
from .hook import Hook
@HOOKS.register_module()
class EmptyCacheHook(Hook):
"""Releases all unoccupied cached GPU memory during the proc... |
# 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 ._meta import (
clamp_bounding_box,
convert_format_bounding_box,
convert_color_space_image_tensor,
convert_color_s... | # 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 ._meta import (
clamp_bounding_box,
convert_format_bounding_box,
convert_color_space_image_tensor,
convert_color_s... |
import numpy as np
import pytest
from typing import Dict, List
from docarray import BaseDoc, DocList
from docarray.base_doc import AnyDoc
from docarray.documents import ImageDoc, TextDoc
from docarray.typing import NdArray
@pytest.mark.proto
def test_simple_proto():
class CustomDoc(BaseDoc):
text: str
... | import numpy as np
import pytest
from docarray import BaseDoc, DocList
from docarray.base_doc import AnyDoc
from docarray.documents import ImageDoc, TextDoc
from docarray.typing import NdArray
@pytest.mark.proto
def test_simple_proto():
class CustomDoc(BaseDoc):
text: str
tensor: NdArray
da ... |
from typing import Any, Union
import pytest
from langchain_core.callbacks.manager import (
AsyncCallbackManagerForRetrieverRun,
CallbackManagerForRetrieverRun,
)
from langchain_core.documents import Document
from langchain_core.structured_query import (
Comparator,
Comparison,
Operation,
Operat... | from typing import Any, Dict, List, Tuple, Union
import pytest
from langchain_core.callbacks.manager import (
AsyncCallbackManagerForRetrieverRun,
CallbackManagerForRetrieverRun,
)
from langchain_core.documents import Document
from langchain_core.structured_query import (
Comparator,
Comparison,
Op... |
_base_ = ['./mask2former_swin-t-p4-w7-224_8xb2-lsj-50e_coco.py']
pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_small_patch4_window7_224.pth' # noqa
depths = [2, 2, 18, 2]
model = dict(
backbone=dict(
depths=depths, init_cfg=dict(type='Pretrained',
... | _base_ = ['./mask2former_swin-t-p4-w7-224_lsj_8x2_50e_coco.py']
pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_small_patch4_window7_224.pth' # noqa
depths = [2, 2, 18, 2]
model = dict(
backbone=dict(
depths=depths, init_cfg=dict(type='Pretrained',
... |
from typing import Any, Optional, Type, TypeVar, Union
import numpy as np
from docarray.base_document import BaseDocument
from docarray.documents import AudioDoc
from docarray.typing import AnyEmbedding, AnyTensor
from docarray.typing.tensor.abstract_tensor import AbstractTensor
from docarray.typing.tensor.video.vide... | from typing import Any, Optional, Type, TypeVar, Union
import numpy as np
from docarray.base_document import BaseDocument
from docarray.documents import AudioDoc
from docarray.typing import AnyEmbedding, AnyTensor
from docarray.typing.tensor.abstract_tensor import AbstractTensor
from docarray.typing.tensor.video.vide... |
"""Test Aleph Alpha specific stuff."""
import pytest
from pydantic import SecretStr
from pytest import CaptureFixture, MonkeyPatch
from langchain_community.llms.aleph_alpha import AlephAlpha
@pytest.mark.requires("aleph_alpha_client")
def test_api_key_is_secret_string() -> None:
llm = AlephAlpha(aleph_alpha_api... | """Test Aleph Alpha specific stuff."""
import pytest
from pydantic import SecretStr
from pytest import CaptureFixture, MonkeyPatch
from langchain_community.llms.aleph_alpha import AlephAlpha
@pytest.mark.requires("aleph_alpha_client")
def test_api_key_is_secret_string() -> None:
llm = AlephAlpha(aleph_alpha_api... |
# Copyright (c) OpenMMLab. All rights reserved.
import ast
import os.path as osp
import re
import warnings
from typing import Tuple
from mmengine.fileio import load
from mmengine.utils import check_file_exist
MODULE2PACKAGE = {
'mmcls': 'mmcls',
'mmdet': 'mmdet',
'mmdet3d': 'mmdet3d',
'mmseg': 'mmsegm... | # Copyright (c) OpenMMLab. All rights reserved.
import ast
import os.path as osp
import re
import warnings
from typing import Tuple
from mmengine.fileio import load
from mmengine.utils import check_file_exist
MODULE2PACKAGE = {
'mmcls': 'mmcls',
'mmdet': 'mmdet',
'mmdet3d': 'mmdet3d',
'mmseg': 'mmsegm... |
_base_ = '../_base_/default_runtime.py'
# dataset settings
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
# Example to use different file client
# Method 1: simply set the data root and let the file I/O module
# automatically infer from prefix (not support LMDB and Memcache yet)
# data_root = 's3://openmmlab/d... | _base_ = '../_base_/default_runtime.py'
# dataset settings
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
# file_client_args = dict(
# backend='petrel',
# path_mapping=dict({
# './data/': 's3://openmmlab/datasets/detection/',
# 'data/': 's3://openmmlab/datasets/detection/'
# }))
file... |
"""Module wrapping the Client of Jina."""
import argparse
from typing import TYPE_CHECKING, Optional, Union, overload
from jina.helper import parse_client
__all__ = ['Client']
from jina.enums import GatewayProtocolType
if TYPE_CHECKING:
from jina.clients.grpc import AsyncGRPCClient, GRPCClient
from jina.cli... | """Module wrapping the Client of Jina."""
import argparse
from typing import TYPE_CHECKING, Optional, Union, overload
from jina.helper import parse_client
__all__ = ['Client']
from jina.enums import GatewayProtocolType
if TYPE_CHECKING:
from jina.clients.grpc import AsyncGRPCClient, GRPCClient
from jina.cli... |
import numpy as np
import pytest
from absl.testing import parameterized
from keras.src import layers
from keras.src import models
from keras.src import testing
from keras.src.utils import summary_utils
class SummaryUtilsTest(testing.TestCase, parameterized.TestCase):
@parameterized.parameters([("adam",), (None,)... | import numpy as np
import pytest
from absl.testing import parameterized
from keras.src import layers
from keras.src import models
from keras.src import testing
from keras.src.utils import summary_utils
class SummaryUtilsTest(testing.TestCase, parameterized.TestCase):
@parameterized.parameters([("adam",), (None,)... |
"""Standard LangChain interface tests"""
from typing import Optional
from langchain_core.language_models import BaseChatModel
from langchain_core.messages import AIMessageChunk, BaseMessageChunk
from langchain_core.rate_limiters import InMemoryRateLimiter
from langchain_tests.integration_tests import ( # type: ignor... | """Standard LangChain interface tests"""
import pytest # type: ignore[import-not-found]
from langchain_core.language_models import BaseChatModel
from langchain_core.rate_limiters import InMemoryRateLimiter
from langchain_tests.integration_tests import ( # type: ignore[import-not-found]
ChatModelIntegrationTests,... |
import asyncio
import os
import random
import string
import tempfile
import time
import pytest
from jina import helper
@pytest.fixture(scope='function')
def random_workspace_name():
"""Generate a random workspace name with digits and letters."""
rand = ''.join(random.choices(string.ascii_uppercase + string.... | import asyncio
import os
import random
import string
import tempfile
import time
import pytest
from jina import helper
@pytest.fixture(scope='function')
def random_workspace_name():
"""Generate a random workspace name with digits and letters."""
rand = ''.join(random.choices(string.ascii_uppercase + string.... |
from contextlib import suppress
from docutils import nodes
from docutils.parsers.rst import Directive
from sklearn.utils import all_estimators
from sklearn.utils._test_common.instance_generator import _construct_instance
from sklearn.utils._testing import SkipTest
class AllowNanEstimators(Directive):
@staticmet... | from contextlib import suppress
from docutils import nodes
from docutils.parsers.rst import Directive
from sklearn.utils import all_estimators
from sklearn.utils._test_common.instance_generator import _construct_instance
from sklearn.utils._testing import SkipTest
class AllowNanEstimators(Directive):
@staticmet... |
import os
from pathlib import Path
import numpy as np
import pytest
from PIL.Image import Image, fromarray
from jina import DocumentArray, Document, Executor
from ...normalizer import ImageNormalizer
@pytest.fixture
def numpy_image_uri(tmpdir):
blob = np.random.randint(255, size=(96, 96, 3), dtype='uint8')
... | import os
from pathlib import Path
import numpy as np
import pytest
from PIL.Image import Image, fromarray
from jina import DocumentArray, Document, Executor
from ...normalizer import ImageNormalizer
@pytest.fixture
def numpy_image_uri(tmpdir):
blob = np.random.randint(255, size=(96, 96, 3), dtype='uint8')
... |
import json
import re
from re import Pattern
from typing import Union
from langchain_core.agents import AgentAction, AgentFinish
from langchain_core.exceptions import OutputParserException
from langchain.agents.agent import AgentOutputParser
from langchain.agents.chat.prompt import FORMAT_INSTRUCTIONS
FINAL_ANSWER_A... | import json
import re
from re import Pattern
from typing import Union
from langchain_core.agents import AgentAction, AgentFinish
from langchain_core.exceptions import OutputParserException
from langchain.agents.agent import AgentOutputParser
from langchain.agents.chat.prompt import FORMAT_INSTRUCTIONS
FINAL_ANSWER_A... |
from abc import ABC, abstractmethod
from typing import Any, ClassVar, Dict, List, Optional
from llama_index.core.bridge.pydantic import BaseModel, Field, ConfigDict
class RetrievalMetricResult(BaseModel):
"""
Metric result.
Attributes:
score (float): Score for the metric
metadata (Dict[s... | from abc import ABC, abstractmethod
from typing import Any, ClassVar, Dict, List, Optional
from llama_index.core.bridge.pydantic import BaseModel, Field, ConfigDict
class RetrievalMetricResult(BaseModel):
"""Metric result.
Attributes:
score (float): Score for the metric
metadata (Dict[str, A... |
"""Copyright 2019-2024, XGBoost contributors"""
import os
from typing import Generator
import numpy as np
import pytest
import scipy.sparse
from dask import dataframe as dd
from distributed import Client, LocalCluster
from xgboost import dask as dxgb
from xgboost import testing as tm
from xgboost.testing import dask... | """Copyright 2019-2024, XGBoost contributors"""
import os
from typing import Generator
import numpy as np
import pytest
import scipy.sparse
from dask import dataframe as dd
from distributed import Client, LocalCluster
from xgboost import dask as dxgb
from xgboost import testing as tm
@pytest.fixture(scope="module"... |
from docarray import BaseDoc
from docarray.typing import Mesh3DUrl
def test_set_mesh_url():
class MyDocument(BaseDoc):
mesh_url: Mesh3DUrl
d = MyDocument(mesh_url="https://jina.ai/mesh.obj")
assert isinstance(d.mesh_url, Mesh3DUrl)
assert d.mesh_url == "https://jina.ai/mesh.obj"
| from docarray import BaseDocument
from docarray.typing import Mesh3DUrl
def test_set_mesh_url():
class MyDocument(BaseDocument):
mesh_url: Mesh3DUrl
d = MyDocument(mesh_url="https://jina.ai/mesh.obj")
assert isinstance(d.mesh_url, Mesh3DUrl)
assert d.mesh_url == "https://jina.ai/mesh.obj"
|
"""Interface with the LangChain Hub."""
from __future__ import annotations
import json
from collections.abc import Sequence
from typing import Any, Optional
from langchain_core.load.dump import dumps
from langchain_core.load.load import loads
from langchain_core.prompts import BasePromptTemplate
def _get_client(
... | """Interface with the LangChain Hub."""
from __future__ import annotations
import json
from collections.abc import Sequence
from typing import Any, Optional
from langchain_core.load.dump import dumps
from langchain_core.load.load import loads
from langchain_core.prompts import BasePromptTemplate
def _get_client(
... |
# Copyright (c) OpenMMLab. All rights reserved.
from .default_scope import DefaultScope
from .registry import Registry, build_from_cfg
from .root import (DATA_SAMPLERS, DATASETS, HOOKS, LOOPS, METRICS,
MODEL_WRAPPERS, MODELS, OPTIMIZER_CONSTRUCTORS, OPTIMIZERS,
PARAM_SCHEDULERS, RU... | # Copyright (c) OpenMMLab. All rights reserved.
from .default_scope import DefaultScope
from .registry import Registry, build_from_cfg
from .root import (DATA_SAMPLERS, DATASETS, EVALUATORS, HOOKS, LOOPS,
MODEL_WRAPPERS, MODELS, OPTIMIZER_CONSTRUCTORS, OPTIMIZERS,
PARAM_SCHEDULERS,... |
_base_ = [
'../_base_/models/mask-rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
data_preprocessor=dict(
# The mean and std are used in PyCls when training RegNets
mean=[103.53, 116.28, 123.675... | _base_ = [
'../_base_/models/mask-rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
data_preprocessor=dict(
# The mean and std are used in PyCls when training RegNets
mean=[103.53, 116.28, 123.675... |
import os
import textwrap
import pyarrow as pa
import pytest
from datasets import ClassLabel, Features, Image
from datasets.builder import InvalidConfigName
from datasets.data_files import DataFilesList
from datasets.packaged_modules.csv.csv import Csv, CsvConfig
from ..utils import require_pil
@pytest.fixture
def... | import os
import textwrap
import pyarrow as pa
import pytest
from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv
from ..utils import require_pil
@pytest.fixture
def csv_file(tmp_path):
filename = tmp_path / "file.csv"
data = textwrap.dedent(
"""\
... |
# dataset settings
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
# file_client_args = dict(
# backend='petrel',
# path_mapping=dict({
# './data/': 's3://openmmlab/datasets/detection/',
# 'data/': 's3://openmmlab/datasets/detection/'
# }))
file_client_args = dict(backend='disk')
tra... | # dataset settings
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
# file_client_args = dict(
# backend='petrel',
# path_mapping=dict({
# './data/': 's3://openmmlab/datasets/detection/',
# 'data/': 's3://openmmlab/datasets/detection/'
# }))
file_client_args = dict(backend='disk')
tra... |
# Copyright (c) OpenMMLab. All rights reserved.
from .checkloss_hook import CheckInvalidLossHook
from .mean_teacher_hook import MeanTeacherHook
from .memory_profiler_hook import MemoryProfilerHook
from .num_class_check_hook import NumClassCheckHook
from .pipeline_switch_hook import PipelineSwitchHook
from .set_epoch_in... | # Copyright (c) OpenMMLab. All rights reserved.
from .checkloss_hook import CheckInvalidLossHook
from .mean_teacher_hook import MeanTeacherHook
from .memory_profiler_hook import MemoryProfilerHook
from .num_class_check_hook import NumClassCheckHook
from .pipeline_switch_hook import PipelineSwitchHook
from .set_epoch_in... |
_base_ = 'solov2_r50_fpn_1x_coco.py'
# model settings
model = dict(
mask_head=dict(
stacked_convs=2,
feat_channels=256,
scale_ranges=((1, 56), (28, 112), (56, 224), (112, 448), (224, 896)),
mask_feature_head=dict(out_channels=128)))
# dataset settings
train_pipeline = [
dict(
... | _base_ = 'solov2_r50_fpn_1x_coco.py'
# model settings
model = dict(
mask_head=dict(
stacked_convs=2,
feat_channels=256,
scale_ranges=((1, 56), (28, 112), (56, 224), (112, 448), (224, 896)),
mask_feature_head=dict(out_channels=128)))
# dataset settings
train_pipeline = [
dict(
... |
from __future__ import annotations
from typing import TYPE_CHECKING, Tuple, Union
from langchain_core.structured_query import (
Comparator,
Comparison,
Operation,
Operator,
StructuredQuery,
Visitor,
)
if TYPE_CHECKING:
from timescale_vector import client
class TimescaleVectorTranslator(... | from __future__ import annotations
from typing import TYPE_CHECKING, Tuple, Union
from langchain_core.structured_query import (
Comparator,
Comparison,
Operation,
Operator,
StructuredQuery,
Visitor,
)
if TYPE_CHECKING:
from timescale_vector import client
class TimescaleVectorTranslator(... |
import importlib
class LazyModule:
def __init__(self, name, pip_name=None, import_error_msg=None):
self.name = name
self.pip_name = pip_name or name
self.import_error_msg = import_error_msg or (
f"This requires the {self.name} module. "
f"You can install it via `pip... | import importlib
class LazyModule:
def __init__(self, name, pip_name=None, import_error_msg=None):
self.name = name
self.pip_name = pip_name or name
self.import_error_msg = import_error_msg or (
f"This requires the {self.name} module. "
f"You can install it via `pip... |
from pathlib import Path
from typing import List, Tuple, Union
import torch
import torchaudio
from torch.utils.data import Dataset
SampleType = Tuple[int, torch.Tensor, List[torch.Tensor]]
class LibriMix(Dataset):
r"""*LibriMix* :cite:`cosentino2020librimix` dataset.
Args:
root (str or Path): The p... | from pathlib import Path
from typing import List, Tuple, Union
import torch
import torchaudio
from torch.utils.data import Dataset
SampleType = Tuple[int, torch.Tensor, List[torch.Tensor]]
class LibriMix(Dataset):
r"""Create the *LibriMix* :cite:`cosentino2020librimix` dataset.
Args:
root (str or P... |
import warnings
from abc import ABC
from typing import TYPE_CHECKING, Any, BinaryIO, Dict, TypeVar, Union
from docarray.typing.tensor.abstract_tensor import AbstractTensor
from docarray.utils._internal.misc import import_library, is_notebook
if TYPE_CHECKING:
from docarray.typing.bytes.audio_bytes import AudioByt... | import warnings
from abc import ABC
from typing import TYPE_CHECKING, Any, BinaryIO, Dict, TypeVar, Union
from docarray.typing.tensor.abstract_tensor import AbstractTensor
from docarray.utils._internal.misc import import_library, is_notebook
T = TypeVar('T', bound='AbstractAudioTensor')
MAX_INT_16 = 2**15
class Ab... |
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