input stringlengths 33 5k | output stringlengths 32 5k |
|---|---|
import re
import sys
file_name = sys.argv[1]
with open(file_name, 'r') as f:
input = f.read()
# official semver regex: https://semver.org/#is-there-a-suggested-regular-expression-regex-to-check-a-semver-string
versions_regex = '(?P<major>0|[1-9]\d*)\.(?P<minor>0|[1-9]\d*)\.(?P<patch>0|[1-9]\d*)'
output = re.sub... | import re
import sys
file_name = sys.argv[1]
with open(file_name, 'r') as f:
input = f.read()
# official semver regex: https://semver.org/#is-there-a-suggested-regular-expression-regex-to-check-a-semver-string
versions_regex = '(?P<major>0|[1-9]\d*)\.(?P<minor>0|[1-9]\d*)\.(?P<patch>0|[1-9]\d*)'
output = re.sub(... |
from __future__ import annotations
from .CSRSparsity import CSRSparsity
from .MLMTransformer import MLMTransformer
from .SpladePooling import SpladePooling
__all__ = ["CSRSparsity", "MLMTransformer", "SpladePooling"]
| from __future__ import annotations
from .CSRSparsity import CSRSparsity
from .MLMTransformer import MLMTransformer
from .SpladePooling import SpladePooling
from .TopKActivation import TopKActivation
__all__ = ["CSRSparsity", "TopKActivation", "MLMTransformer", "SpladePooling"]
|
"""
=============================================================
Receiver Operating Characteristic (ROC) with cross validation
=============================================================
This example presents how to estimate and visualize the variance of the Receiver
Operating Characteristic (ROC) metric using cros... | """
=============================================================
Receiver Operating Characteristic (ROC) with cross validation
=============================================================
This example presents how to estimate and visualize the variance of the Receiver
Operating Characteristic (ROC) metric using cros... |
# Copyright (c) OpenMMLab. All rights reserved.
from .gaussian_target import (gather_feat, gaussian_radius,
gen_gaussian_target, get_local_maximum,
get_topk_from_heatmap, transpose_and_gather_feat)
from .image import imrenormalize
from .make_divisible import m... | # Copyright (c) OpenMMLab. All rights reserved.
from .gaussian_target import (gather_feat, gaussian_radius,
gen_gaussian_target, get_local_maximum,
get_topk_from_heatmap, transpose_and_gather_feat)
from .make_divisible import make_divisible
from .misc import (... |
# Copyright (c) OpenMMLab. All rights reserved.
from torch.autograd import Function
from torch.nn import functional as F
class SigmoidGeometricMean(Function):
"""Forward and backward function of geometric mean of two sigmoid
functions.
This implementation with analytical gradient function substitutes
... | # Copyright (c) OpenMMLab. All rights reserved.
from torch.nn import functional as F
def interpolate_as(source, target, mode='bilinear', align_corners=False):
"""Interpolate the `source` to the shape of the `target`.
The `source` must be a Tensor, but the `target` can be a Tensor or a
np.ndarray with the... |
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,
ToolCall,
)
from langchain_core.o... | import json
from json import JSONDecodeError
from typing import List, Union
from langchain_core.agents import AgentAction, AgentActionMessageLog, AgentFinish
from langchain_core.exceptions import OutputParserException
from langchain_core.messages import (
AIMessage,
BaseMessage,
ToolCall,
)
from langchain_... |
import json
from typing import Dict, List, Union
from docarray.array.abstract_array import AnyDocumentArray
from docarray.array.array.array import DocumentArray
def filter(
docs: AnyDocumentArray,
query: Union[str, Dict, List[Dict]],
) -> AnyDocumentArray:
"""
Filter the Documents in the index accord... | import json
from typing import Dict, List, Union
from docarray.array.abstract_array import AnyDocumentArray
from docarray.array.array.array import DocumentArray
def filter(
docs: AnyDocumentArray,
query: Union[str, Dict, List[Dict]],
) -> AnyDocumentArray:
"""
Filter the Documents in the index accord... |
import numpy as np
import pytest
from docarray import BaseDoc, DocList
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 = DocList(
[CustomDoc(text='h... | import numpy as np
import pytest
from docarray import BaseDoc, DocArray
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 = DocArray(
[CustomDoc(text=... |
_base_ = './fcos_r50-caffe_fpn_gn-head_1x_coco.py'
# model settings
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron/resnet101_caffe')))
# dataset settings
train_pipeline = [
dict(type='LoadImageFromFile', back... | _base_ = './fcos_r50-caffe_fpn_gn-head_1x_coco.py'
# model settings
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron/resnet101_caffe')))
# dataset settings
train_pipeline = [
dict(
type='LoadImageFromFi... |
# Licensed to the LF AI & Data foundation under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the "License");
# you may not use this fil... | import pytest
from docarray.utils._internal.misc import is_tf_available
tf_available = is_tf_available()
if tf_available:
import tensorflow as tf
import tensorflow._api.v2.experimental.numpy as tnp
from docarray.computation.tensorflow_backend import TensorFlowCompBackend
from docarray.typing import T... |
"""DocumentFilter that uses an LLM chain to extract the relevant parts of documents."""
from __future__ import annotations
from collections.abc import Sequence
from typing import Any, Callable, Optional, cast
from langchain_core.callbacks import Callbacks
from langchain_core.documents import BaseDocumentCompressor, ... | """DocumentFilter that uses an LLM chain to extract the relevant parts of documents."""
from __future__ import annotations
from collections.abc import Sequence
from typing import Any, Callable, Optional, cast
from langchain_core.callbacks.manager import Callbacks
from langchain_core.documents import Document
from la... |
from typing import Optional
from llama_index.core.storage.index_store.keyval_index_store import KVIndexStore
from llama_index.storage.kvstore.gel import GelKVStore
class GelIndexStore(KVIndexStore):
"""
Gel Index store.
Args:
gel_kvstore (GelKVStore): Gel key-value store
namespace (str):... | from typing import Optional
from llama_index.core.storage.index_store.keyval_index_store import KVIndexStore
from llama_index.storage.kvstore.gel import GelKVStore
class GelIndexStore(KVIndexStore):
"""Gel Index store.
Args:
gel_kvstore (GelKVStore): Gel key-value store
namespace (str): name... |
from __future__ import annotations
from dataclasses import dataclass
from sentence_transformers.training_args import SentenceTransformerTrainingArguments
@dataclass
class SparseEncoderTrainingArguments(SentenceTransformerTrainingArguments):
"""
SparseEncoderTrainingArguments extends :class:`~SentenceTransfo... | from __future__ import annotations
from dataclasses import dataclass
from sentence_transformers.training_args import SentenceTransformerTrainingArguments
@dataclass
class SparseEncoderTrainingArguments(SentenceTransformerTrainingArguments):
"""
SparseEncoderTrainingArguments extends :class:`~transformers.Tr... |
from abc import abstractmethod
from typing import TYPE_CHECKING, Any, Type, TypeVar
from docarray.utils._internal.pydantic import is_pydantic_v2
if TYPE_CHECKING:
if is_pydantic_v2:
from pydantic import GetCoreSchemaHandler
from pydantic_core import core_schema
from docarray.base_doc.base_node im... | from abc import abstractmethod
from typing import Any, Type, TypeVar
from pydantic import BaseConfig
from pydantic.fields import ModelField
from docarray.base_doc.base_node import BaseNode
T = TypeVar('T')
class AbstractType(BaseNode):
@classmethod
def __get_validators__(cls):
yield cls.validate
... |
import multiprocessing
from copy import deepcopy
from functools import partial
from typing import TYPE_CHECKING
from hubble.executor.helper import is_valid_huburi
from hubble.executor.hubio import HubIO
from jina.enums import GatewayProtocolType, PodRoleType
from jina.parsers.helper import _set_gateway_uses
if TYPE_... | import multiprocessing
from copy import deepcopy
from functools import partial
from typing import TYPE_CHECKING
from hubble.executor.helper import is_valid_huburi
from hubble.executor.hubio import HubIO
from jina.enums import GatewayProtocolType, PodRoleType
from jina.parsers.helper import _set_gateway_uses
if TYPE_... |
import warnings
from typing import TYPE_CHECKING, Any, Type, TypeVar, Union
import numpy as np
from docarray.typing.bytes.audio_bytes import AudioBytes
from docarray.typing.proto_register import _register_proto
from docarray.typing.url.any_url import AnyUrl
from docarray.utils.misc import is_notebook
if TYPE_CHECKIN... | from typing import TYPE_CHECKING, Any, Type, TypeVar, Union
import numpy as np
from docarray.typing.bytes.audio_bytes import AudioBytes
from docarray.typing.proto_register import _register_proto
from docarray.typing.url.any_url import AnyUrl
if TYPE_CHECKING:
from pydantic import BaseConfig
from pydantic.fie... |
from pathlib import Path
from typing import List
import numpy as np
import pytest
import scipy
from jina import Document, DocumentArray, Executor
from jina.excepts import PretrainedModelFileDoesNotExist
from tfidf_text_executor import TFIDFTextEncoder
_EMBEDDING_DIM = 130107
@pytest.fixture(scope='session')
def bas... | from pathlib import Path
from typing import List
import numpy as np
import pytest
import scipy
from jina import Document, DocumentArray, Executor
from jina.excepts import PretrainedModelFileDoesNotExist
from ...tfidf_text_executor import TFIDFTextEncoder
_EMBEDDING_DIM = 130107
@pytest.fixture(scope='session')
def... |
# Copyright (c) OpenMMLab. All rights reserved.
from .collect_env import collect_env
from .logger import get_root_logger
from .misc import find_latest_checkpoint
from .setup_env import setup_multi_processes
__all__ = [
'get_root_logger', 'collect_env', 'find_latest_checkpoint',
'setup_multi_processes'
]
| # Copyright (c) OpenMMLab. All rights reserved.
from .collect_env import collect_env
from .logger import get_root_logger
from .misc import find_latest_checkpoint
__all__ = [
'get_root_logger',
'collect_env',
'find_latest_checkpoint',
]
|
"""
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... |
from torchvision.transforms import AutoAugmentPolicy, InterpolationMode # usort: skip
from . import functional # usort: skip
from ._transform import Transform # usort: skip
from ._augment import CutMix, JPEG, MixUp, RandomErasing
from ._auto_augment import AugMix, AutoAugment, RandAugment, TrivialAugmentWide
from... | from torchvision.transforms import AutoAugmentPolicy, InterpolationMode # usort: skip
from . import functional # usort: skip
from ._transform import Transform # usort: skip
from ._augment import CutMix, JPEG, MixUp, RandomErasing
from ._auto_augment import AugMix, AutoAugment, RandAugment, TrivialAugmentWide
from... |
import sys
import numpy as np
import pytest
import xgboost as xgb
from xgboost import testing as tm
sys.path.append("tests/python")
import test_monotone_constraints as tmc
rng = np.random.RandomState(1994)
def non_decreasing(L):
return all((x - y) < 0.001 for x, y in zip(L, L[1:]))
def non_increasing(L):
... | import sys
import numpy as np
import pytest
import xgboost as xgb
from xgboost import testing as tm
sys.path.append("tests/python")
import test_monotone_constraints as tmc
rng = np.random.RandomState(1994)
def non_decreasing(L):
return all((x - y) < 0.001 for x, y in zip(L, L[1:]))
def non_increasing(L):
... |
"""DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.losses import deserialize as deserialize
from keras.src.losses import get as get
from keras.src.losses import serialize as serialize
from keras.src.losses.loss import Loss as Loss
fro... | """DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.losses import deserialize
from keras.src.losses import get
from keras.src.losses import serialize
from keras.src.losses.loss import Loss
from keras.src.losses.losses import CTC
from k... |
from typing import Any, AsyncGenerator, Coroutine, Dict, List, Optional, Sequence, Union
import pytest
from llama_index.core.base.llms.types import (
ChatMessage,
ChatResponse,
ChatResponseGen,
CompletionResponse,
LLMMetadata,
)
from llama_index.core.llms.function_calling import FunctionCallingLLM
... | from typing import Any, AsyncGenerator, Coroutine, Dict, List, Optional, Sequence, Union
import pytest
from llama_index.core.base.llms.types import (
ChatMessage,
ChatResponse,
ChatResponseGen,
CompletionResponse,
LLMMetadata,
)
from llama_index.core.llms.function_calling import FunctionCallingLLM
... |
# Copyright (c) OpenMMLab. All rights reserved.
import tempfile
from unittest import TestCase
from unittest.mock import Mock
import torch
import torch.nn as nn
from mmengine.evaluator import Evaluator
from mmengine.model import BaseModel
from mmengine.optim import OptimWrapper
from mmengine.runner import Runner
from t... | # Copyright (c) OpenMMLab. All rights reserved.
import tempfile
from unittest import TestCase
from unittest.mock import Mock
import torch
import torch.nn as nn
from mmengine.model import BaseModel
from mmengine.optim import OptimWrapper
from mmengine.runner import Runner
from torch.utils.data import Dataset
from mmde... |
from typing import Dict, List
from langchain_core.tools import BaseTool
from langchain_core.tools.base import BaseToolkit
from langchain_community.tools.jira.prompt import (
JIRA_CATCH_ALL_PROMPT,
JIRA_CONFLUENCE_PAGE_CREATE_PROMPT,
JIRA_GET_ALL_PROJECTS_PROMPT,
JIRA_ISSUE_CREATE_PROMPT,
JIRA_JQL_... | from typing import Dict, List
from langchain_core.tools import BaseTool
from langchain_core.tools.base import BaseToolkit
from langchain_community.tools.jira.prompt import (
JIRA_CATCH_ALL_PROMPT,
JIRA_CONFLUENCE_PAGE_CREATE_PROMPT,
JIRA_GET_ALL_PROJECTS_PROMPT,
JIRA_ISSUE_CREATE_PROMPT,
JIRA_JQL_... |
import os
import pytest
from jina import Document, Flow
from jinahub.indexers.searcher.compound.FaissPostgresIndexer import FaissPostgresIndexer
cur_dir = os.path.dirname(os.path.abspath(__file__))
compose_yml = os.path.join(cur_dir, 'docker-compose.yml')
@pytest.mark.parametrize('docker_compose', [compose_yml], i... | import os
import pytest
from jina import Document, Flow
from jinahub.indexers.searcher.compound.FaissPostgresSearcher import (
FaissPostgresSearcher,
)
cur_dir = os.path.dirname(os.path.abspath(__file__))
compose_yml = os.path.join(cur_dir, 'docker-compose.yml')
@pytest.mark.parametrize('docker_compose', [comp... |
from typing import Optional
from docarray import Document, DocumentArray
from pydantic import BaseModel
from jina.clients.request import request_generator
from jina.serve.runtimes.gateway.http.fastapi import FastAPIBaseGateway
class DummyResponseModel(BaseModel):
arg1: Optional[str]
arg2: Optional[str]
... | from typing import Optional
from docarray import Document, DocumentArray
from pydantic import BaseModel
from jina.clients.request import request_generator
from jina.serve.runtimes.gateway.http.fastapi import FastAPIBaseGateway
class DummyResponseModel(BaseModel):
arg1: Optional[str]
arg2: Optional[str]
... |
import re
from typing import Any
from langchain.evaluation.schema import StringEvaluator
class RegexMatchStringEvaluator(StringEvaluator):
"""Compute a regex match between the prediction and the reference.
Examples
----------
>>> evaluator = RegexMatchStringEvaluator(flags=re.IGNORECASE)
>>> eva... | import re
from typing import Any, List
from langchain.evaluation.schema import StringEvaluator
class RegexMatchStringEvaluator(StringEvaluator):
"""Compute a regex match between the prediction and the reference.
Examples
----------
>>> evaluator = RegexMatchStringEvaluator(flags=re.IGNORECASE)
>... |
"""**Index** is used to avoid writing duplicated content
into the vectostore and to avoid over-writing content if it's unchanged.
Indexes also :
* Create knowledge graphs from data.
* Support indexing workflows from LangChain data loaders to vectorstores.
Importantly, Index keeps on working even if the content bein... | """**Index** is used to avoid writing duplicated content
into the vectostore and to avoid over-writing content if it's unchanged.
Indexes also :
* Create knowledge graphs from data.
* Support indexing workflows from LangChain data loaders to vectorstores.
Importantly, Index keeps on working even if the content bein... |
from pydantic import parse_obj_as
from docarray import BaseDoc
from docarray.documents import TextDoc
def test_simple_init():
t = TextDoc(text='hello')
assert t.text == 'hello'
def test_str_init():
t = parse_obj_as(TextDoc, 'hello')
assert t.text == 'hello'
def test_doc():
class MyDoc(BaseDoc... | from pydantic import parse_obj_as
from docarray import BaseDocument
from docarray.documents import TextDoc
def test_simple_init():
t = TextDoc(text='hello')
assert t.text == 'hello'
def test_str_init():
t = parse_obj_as(TextDoc, 'hello')
assert t.text == 'hello'
def test_doc():
class MyDoc(Ba... |
import pytest
from llama_index.postprocessor.nvidia_rerank import NVIDIARerank
import respx
@pytest.fixture(autouse=True)
def mock_local_models(respx_mock: respx.MockRouter) -> None:
respx_mock.get(
"https://test_url/v1/models",
json={
"data": [
{"id": "model1"},
... | import pytest
from llama_index.postprocessor.nvidia_rerank import NVIDIARerank
import respx
@pytest.fixture(autouse=True)
def mock_local_models(respx_mock: respx.MockRouter) -> None:
respx_mock.get(
"https://test_url/v1/models",
json={
"data": [
{"id": "model1"},
... |
# model settings
preprocess_cfg = dict(
mean=[103.530, 116.280, 123.675],
std=[1.0, 1.0, 1.0],
to_rgb=False,
pad_size_divisor=32)
norm_cfg = dict(type='BN', requires_grad=False)
model = dict(
preprocess_cfg=preprocess_cfg,
type='MaskRCNN',
backbone=dict(
type='ResNet',
depth=... | # model settings
norm_cfg = dict(type='BN', requires_grad=False)
model = dict(
type='MaskRCNN',
backbone=dict(
type='ResNet',
depth=50,
num_stages=3,
strides=(1, 2, 2),
dilations=(1, 1, 1),
out_indices=(2, ),
frozen_stages=1,
norm_cfg=norm_cfg,
... |
"""**Utility functions** for LangChain.
These functions do not depend on any other LangChain module.
"""
from typing import TYPE_CHECKING
from langchain_core._import_utils import import_attr
if TYPE_CHECKING:
# for type checking and IDE support, we include the imports here
# but we don't want to eagerly imp... | """**Utility functions** for LangChain.
These functions do not depend on any other LangChain module.
"""
from importlib import import_module
from typing import TYPE_CHECKING
if TYPE_CHECKING:
# for type checking and IDE support, we include the imports here
# but we don't want to eagerly import them at runtim... |
import logging
import tqdm
class LoggingHandler(logging.Handler):
def __init__(self, level=logging.NOTSET):
super().__init__(level)
def emit(self, record):
try:
msg = self.format(record)
tqdm.tqdm.write(msg)
self.flush()
except (KeyboardInterrupt, ... | import logging
import tqdm
class LoggingHandler(logging.Handler):
def __init__(self, level=logging.NOTSET):
super().__init__(level)
def emit(self, record):
try:
msg = self.format(record)
tqdm.tqdm.write(msg)
self.flush()
except (KeyboardInterrupt, S... |
import pytest
import torch
from pydantic.tools import parse_obj_as, schema_json_of
from docarray.base_document.io.json import orjson_dumps
from docarray.typing import TorchEmbedding, TorchTensor
def test_proto_tensor():
tensor = parse_obj_as(TorchTensor, torch.zeros(3, 224, 224))
tensor._to_node_protobuf()... | import pytest
import torch
from pydantic.tools import parse_obj_as, schema_json_of
from docarray.base_document.io.json import orjson_dumps
from docarray.typing import TorchEmbedding, TorchTensor
def test_proto_tensor():
tensor = parse_obj_as(TorchTensor, torch.zeros(3, 224, 224))
tensor._to_node_protobuf()... |
from __future__ import annotations
from enum import Enum
from typing import Any, Optional, Tuple, Union
import torch
from ._datapoint import Datapoint
class BoundingBoxFormat(Enum):
"""[BETA] Coordinate format of a bounding box.
Available formats are
* ``XYXY``
* ``XYWH``
* ``CXCYWH``
"""... | from __future__ import annotations
from enum import Enum
from typing import Any, Optional, Tuple, Union
import torch
from ._datapoint import Datapoint
class BoundingBoxFormat(Enum):
"""[BETA] Coordinate format of a bounding box.
Available formats are
* ``XYXY``
* ``XYWH``
* ``CXCYWH``
"""... |
from __future__ import annotations
from torch import Tensor, nn
from sentence_transformers.cross_encoder.CrossEncoder import CrossEncoder
class CrossEntropyLoss(nn.Module):
def __init__(self, model: CrossEncoder, activation_fct: nn.Module = nn.Identity(), **kwargs) -> None:
"""
Computes the Cros... | from __future__ import annotations
from torch import Tensor, nn
from sentence_transformers.cross_encoder.CrossEncoder import CrossEncoder
class CrossEntropyLoss(nn.Module):
def __init__(self, model: CrossEncoder, activation_fct: nn.Module = nn.Identity(), **kwargs) -> None:
"""
Computes the Cros... |
import torchaudio
from torchaudio.prototype.pipelines import VGGISH
def test_vggish():
input_sr = VGGISH.sample_rate
input_proc = VGGISH.get_input_processor()
model = VGGISH.get_model()
path = torchaudio.utils.download_asset("test-assets/Chopin_Ballade_-1_In_G_Minor,_Op._23_excerpt.mp3")
waveform,... | import unittest
import torchaudio
from torchaudio.prototype.pipelines import VGGISH
class VGGishPipelineTest(unittest.TestCase):
def test_vggish(self):
input_sr = VGGISH.sample_rate
input_proc = VGGISH.get_input_processor()
model = VGGISH.get_model()
path = torchaudio.utils.downlo... |
# Copyright (c) OpenMMLab. All rights reserved.
from .amp import autocast
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,
... | # Copyright (c) OpenMMLab. All rights reserved.
from .amp import autocast
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,
... |
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, NdArray
from docarray.typing.url.url_3d.mesh_url import Mesh3DLoadResult
from tests import TOYDATA_DIR
MESH_FILES = {
'obj': str(T... | 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, NdArray
from docarray.typing.url.url_3d.mesh_url import Mesh3DLoadResult
from tests import TOYDATA_DIR
MESH_FILES = {
'obj': str(T... |
"""Configuration for unit tests."""
from collections.abc import Iterator, Sequence
from importlib import util
import pytest
from blockbuster import blockbuster_ctx
from pytest import Config, Function, Parser
@pytest.fixture(autouse=True)
def blockbuster() -> Iterator[None]:
with blockbuster_ctx("langchain") as ... | """Configuration for unit tests."""
from collections.abc import Iterator
from importlib import util
from typing import Dict, Sequence
import pytest
from blockbuster import blockbuster_ctx
from pytest import Config, Function, Parser
@pytest.fixture(autouse=True)
def blockbuster() -> Iterator[None]:
with blockbus... |
import pytest
from backend.util.request import pin_url, validate_url
@pytest.mark.parametrize(
"raw_url, trusted_origins, expected_value, should_raise",
[
# Rejected IP ranges
("localhost", [], None, True),
("192.168.1.1", [], None, True),
("127.0.0.1", [], None, True),
... | import pytest
from backend.util.request import pin_url, validate_url
@pytest.mark.parametrize(
"raw_url, trusted_origins, expected_value, should_raise",
[
# Rejected IP ranges
("localhost", [], None, True),
("192.168.1.1", [], None, True),
("127.0.0.1", [], None, True),
... |
_base_ = './mask-rcnn_hrnetv2p-w40_1x_coco.py'
# learning policy
max_epochs = 24
train_cfg = dict(max_epochs=max_epochs)
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=max_epochs,
b... | _base_ = './mask_rcnn_hrnetv2p_w40_1x_coco.py'
# learning policy
max_epochs = 24
train_cfg = dict(max_epochs=max_epochs)
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=max_epochs,
b... |
"""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... |
"""Base types for ReAct agent."""
from abc import abstractmethod
from typing import Dict
from llama_index.core.bridge.pydantic import BaseModel
class BaseReasoningStep(BaseModel):
"""Reasoning step."""
@abstractmethod
def get_content(self) -> str:
"""Get content."""
@property
@abstract... | """Base types for ReAct agent."""
from abc import abstractmethod
from typing import Dict
from llama_index.core.bridge.pydantic import BaseModel
class BaseReasoningStep(BaseModel):
"""Reasoning step."""
@abstractmethod
def get_content(self) -> str:
"""Get content."""
@property
@abstract... |
"""Custom **exceptions** for LangChain."""
from enum import Enum
from typing import Any, Optional
class LangChainException(Exception): # noqa: N818
"""General LangChain exception."""
class TracerException(LangChainException):
"""Base class for exceptions in tracers module."""
class OutputParserException... | """Custom **exceptions** for LangChain."""
from enum import Enum
from typing import Any, Optional
class LangChainException(Exception): # noqa: N818
"""General LangChain exception."""
class TracerException(LangChainException):
"""Base class for exceptions in tracers module."""
class OutputParserException... |
# coding=utf-8
# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless r... | # coding=utf-8
# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless r... |
import os
import time
import pytest
from docarray import Document
from jina import Client, Flow
from jina.serve.networking import GrpcConnectionPool
@pytest.fixture
def error_log_level():
old_env = os.environ.get('JINA_LOG_LEVEL')
os.environ['JINA_LOG_LEVEL'] = 'ERROR'
yield
os.environ['JINA_LOG_LEV... | import os
import time
import pytest
from docarray import Document
from jina import Client, Flow
from jina.serve.networking import GrpcConnectionPool
@pytest.fixture
def error_log_level():
old_env = os.environ.get('JINA_LOG_LEVEL')
os.environ['JINA_LOG_LEVEL'] = 'ERROR'
yield
os.environ['JINA_LOG_LEV... |
from pathlib import Path
from typing import List
import pytest
from jina import Document, DocumentArray, Executor
from ...dpr_text import DPRTextEncoder
_EMBEDDING_DIM = 768
@pytest.fixture(scope='session')
def basic_encoder() -> DPRTextEncoder:
return DPRTextEncoder()
@pytest.fixture(scope='session')
def ba... | from pathlib import Path
from typing import List
import pytest
import torch
from jina import Document, DocumentArray, Executor
from ...dpr_text import DPRTextEncoder
@pytest.fixture(scope='session')
def basic_encoder() -> DPRTextEncoder:
return DPRTextEncoder()
@pytest.fixture(scope='session')
def basic_encod... |
from abc import abstractmethod
from typing import Iterable, Iterator
from qdrant_client import QdrantClient
from qdrant_client.http.exceptions import UnexpectedResponse
from qdrant_client.http.models.models import (
PointIdsList,
PointsList,
ScrollRequest,
PointStruct,
)
from docarray import Document
... | from abc import abstractmethod
from typing import Iterable, Iterator
from qdrant_client import QdrantClient
from qdrant_client.http.exceptions import UnexpectedResponse
from qdrant_client.http.models.models import (
PointIdsList,
PointsList,
ScrollRequest,
PointStruct,
)
from docarray import Document
... |
# Copyright (c) OpenMMLab. All rights reserved.
import bisect
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from torch.utils.data import ConcatDataset, Dataset
from mmdet.datasets.samplers import GroupMultiSourceSampler, MultiSourceSampler
class DummyDataset(Dataset):
def __... | # Copyright (c) OpenMMLab. All rights reserved.
import bisect
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from torch.utils.data import ConcatDataset, Dataset
from mmdet.datasets.samplers import GroupMultiSourceSampler, MultiSourceSampler
class DummyDataset(Dataset):
def __... |
# Copyright (c) OpenMMLab. All rights reserved.
from .base_video_metric import BaseVideoMetric
from .cityscapes_metric import CityScapesMetric
from .coco_caption_metric import COCOCaptionMetric
from .coco_metric import CocoMetric
from .coco_occluded_metric import CocoOccludedSeparatedMetric
from .coco_panoptic_metric i... | # Copyright (c) OpenMMLab. All rights reserved.
from .base_video_metric import BaseVideoMetric
from .cityscapes_metric import CityScapesMetric
from .coco_metric import CocoMetric
from .coco_occluded_metric import CocoOccludedSeparatedMetric
from .coco_panoptic_metric import CocoPanopticMetric
from .coco_video_metric im... |
from typing import Optional
import numpy as np
import pytest
import torch
from docarray import BaseDoc, DocList, DocVec
from docarray.documents import ImageDoc
from docarray.typing import NdArray, TorchTensor
class MyDoc(BaseDoc):
embedding: NdArray
text: str
image: ImageDoc
def test_from_to_json_docl... | from typing import Optional
import numpy as np
import pytest
import torch
from docarray import BaseDoc, DocList, DocVec
from docarray.documents import ImageDoc
from docarray.typing import NdArray, TorchTensor
class MyDoc(BaseDoc):
embedding: NdArray
text: str
image: ImageDoc
def test_from_to_json_docl... |
# Copyright (c) OpenMMLab. All rights reserved.
import math
from typing import Sequence, Tuple
import torch
import torch.nn as nn
from mmcv.cnn import ConvModule
from mmengine.model import BaseModule
from mmdet.core.utils import OptMultiConfig
from mmdet.registry import MODELS
@MODELS.register_module()
class CTResN... | # Copyright (c) OpenMMLab. All rights reserved.
import math
from typing import Sequence, Tuple
import torch
import torch.nn as nn
from mmcv.cnn import ConvModule
from mmcv.runner import BaseModule
from mmdet.core.utils import OptMultiConfig
from mmdet.registry import MODELS
@MODELS.register_module()
class CTResNetN... |
import abc
from abc import ABC
from typing import TYPE_CHECKING, Any, Generic, List, Tuple, Type, TypeVar, Union
from docarray.typing.abstract_type import AbstractType
if TYPE_CHECKING:
from pydantic import BaseConfig
from pydantic.fields import ModelField
T = TypeVar('T', bound='AbstractTensor')
ShapeT = Ty... | import abc
from abc import ABC
from typing import TYPE_CHECKING, Any, Generic, List, Tuple, Type, TypeVar, Union
from docarray.typing.abstract_type import AbstractType
if TYPE_CHECKING:
from pydantic import BaseConfig
from pydantic.fields import ModelField
from docarray.proto import NdArrayProto
T = Typ... |
from typing import Any, Optional, Type, TypeVar, Union
import numpy as np
from docarray.base_document import BaseDocument
from docarray.typing import AnyEmbedding, AnyTensor, PointCloud3DUrl
from docarray.typing.tensor.abstract_tensor import AbstractTensor
try:
import torch
torch_available = True
except Imp... | from typing import Optional
from docarray.base_document import BaseDocument
from docarray.typing import AnyEmbedding, AnyTensor, PointCloud3DUrl
class PointCloud3D(BaseDocument):
"""
Document for handling point clouds for 3D data representation.
Point cloud is a representation of a 3D mesh. It is made b... |
# Copyright (c) OpenMMLab. All rights reserved.
from .aflink import AppearanceFreeLink
from .camera_motion_compensation import CameraMotionCompensation
from .interpolation import InterpolateTracklets
from .kalman_filter import KalmanFilter
from .similarity import embed_similarity
__all__ = [
'KalmanFilter', 'Inter... | # Copyright (c) OpenMMLab. All rights reserved.
from .interpolation import InterpolateTracklets
from .kalman_filter import KalmanFilter
from .similarity import embed_similarity
__all__ = ['KalmanFilter', 'InterpolateTracklets', 'embed_similarity']
|
import importlib
import pytest
from fastapi.testclient import TestClient
from ...utils import needs_py39, needs_py310
@pytest.fixture(
name="client",
params=[
"tutorial001",
pytest.param("tutorial001_py310", marks=needs_py310),
"tutorial001_an",
pytest.param("tutorial001_an_p... | from fastapi.testclient import TestClient
from docs_src.additional_status_codes.tutorial001 import app
client = TestClient(app)
def test_update():
response = client.put("/items/foo", json={"name": "Wrestlers"})
assert response.status_code == 200, response.text
assert response.json() == {"name": "Wrestle... |
"""Notion tool spec."""
from typing import Any, Dict, List, Optional, Type
import requests
from llama_index.core.bridge.pydantic import BaseModel
from llama_index.core.tools.tool_spec.base import SPEC_FUNCTION_TYPE, BaseToolSpec
from llama_index.readers.notion import NotionPageReader
SEARCH_URL = "https://api.notion... | """Notion tool spec."""
from typing import Any, Dict, List, Optional, Type
import requests
from llama_index.core.bridge.pydantic import BaseModel
from llama_index.core.tools.tool_spec.base import SPEC_FUNCTION_TYPE, BaseToolSpec
from llama_index.readers.notion import NotionPageReader
SEARCH_URL = "https://api.notion... |
from typing import Optional
import pandas as pd
import pytest
from docarray import BaseDoc, DocList
from docarray.documents import ImageDoc
@pytest.fixture()
def nested_doc_cls():
class MyDoc(BaseDoc):
count: Optional[int]
text: str
class MyDocNested(MyDoc):
image: ImageDoc
ret... | from typing import Optional
import pandas as pd
import pytest
from docarray import BaseDoc, DocArray
from docarray.documents import ImageDoc
@pytest.fixture()
def nested_doc_cls():
class MyDoc(BaseDoc):
count: Optional[int]
text: str
class MyDocNested(MyDoc):
image: ImageDoc
re... |
import struct
import zlib
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 HWPReader(BaseReader):
"""
Hwp Reader. Reads contents from Hwp file.
Args: None.
"""
def __init_... | import struct
import zlib
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 HWPReader(BaseReader):
"""Hwp Reader. Reads contents from Hwp file.
Args: None.
"""
def __init__(sel... |
from typing import Any, Collection, List, Optional, Tuple, Union
from llama_index.core.tools.types import AsyncBaseTool
from pydantic import BaseModel
class LLMCompilerParseResult(BaseModel):
"""LLMCompiler parser result."""
thought: str
idx: int
tool_name: str
args: str
class JoinerOutput(Bas... | from typing import Any, Collection, List, Optional, Tuple, Union
from llama_index.core.tools.types import AsyncBaseTool
from pydantic import BaseModel
class LLMCompilerParseResult(BaseModel):
"""LLMCompiler parser result."""
thought: str
idx: int
tool_name: str
args: str
class JoinerOutput(Bas... |
_base_ = './scnet_x101-64x4d_fpn_20e_coco.py'
train_dataloader = dict(batch_size=1, num_workers=1)
optim_wrapper = dict(optimizer=dict(lr=0.01))
# NOTE: `auto_scale_lr` is for automatically scaling LR,
# USER SHOULD NOT CHANGE ITS VALUES.
# base_batch_size = (8 GPUs) x (1 samples per GPU)
auto_scale_lr = dict(base_bat... | _base_ = './scnet_x101_64x4d_fpn_20e_coco.py'
train_dataloader = dict(batch_size=1, num_workers=1)
optim_wrapper = dict(optimizer=dict(lr=0.01))
# NOTE: `auto_scale_lr` is for automatically scaling LR,
# USER SHOULD NOT CHANGE ITS VALUES.
# base_batch_size = (8 GPUs) x (1 samples per GPU)
auto_scale_lr = dict(base_bat... |
"""Global Gemini Utilities (shared between Gemini LLM and Vertex)."""
from __future__ import annotations
from collections.abc import Sequence
from llama_index.core.base.llms.types import ChatMessage, MessageRole
ROLES_TO_GEMINI: dict[MessageRole, MessageRole] = {
MessageRole.USER: MessageRole.USER,
MessageR... | """Global Gemini Utilities (shared between Gemini LLM and Vertex)."""
from collections.abc import Sequence
from typing import Dict
from llama_index.core.base.llms.types import ChatMessage, MessageRole
ROLES_TO_GEMINI: Dict[MessageRole, MessageRole] = {
MessageRole.USER: MessageRole.USER,
MessageRole.ASSISTAN... |
from typing import Optional
import numpy as np
import torch
from docarray import DocumentArray
from docarray.document import BaseDocument
from docarray.typing import Tensor, TorchTensor
def test_proto_simple():
class CustomDoc(BaseDocument):
text: str
doc = CustomDoc(text='hello')
CustomDoc.fr... | from typing import Optional
import numpy as np
from docarray import DocumentArray
from docarray.document import BaseDocument
from docarray.typing import Tensor
def test_proto_simple():
class CustomDoc(BaseDocument):
text: str
doc = CustomDoc(text='hello')
CustomDoc.from_protobuf(doc.to_protobu... |
# dataset settings
dataset_type = 'CityscapesDataset'
data_root = 'data/cityscapes/'
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='RandomResize',
scale=[(2048, 800), (2048, 1024)],
keep_ratio=True),
dict(type='Random... | # dataset settings
dataset_type = 'CityscapesDataset'
data_root = 'data/cityscapes/'
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='RandomResize',
scale=[(2048, 800), (2048, 1024)],
keep_ratio=True),
dict(type='Random... |
from __future__ import annotations
import gzip
from . import InputExample
class PairedFilesReader(object):
"""Reads in the a Pair Dataset, split in two files"""
def __init__(self, filepaths):
self.filepaths = filepaths
def get_examples(self, max_examples=0):
fIns = []
for filep... | import gzip
from . import InputExample
class PairedFilesReader(object):
"""Reads in the a Pair Dataset, split in two files"""
def __init__(self, filepaths):
self.filepaths = filepaths
def get_examples(self, max_examples=0):
fIns = []
for filepath in self.filepaths:
f... |
import hashlib
from abc import ABC, abstractmethod
from functools import lru_cache
from typing import Any, Callable, Optional, Union
from typing_extensions import TypeAlias
import torch.fx.graph
class CustomGraphPass(ABC):
"""
Implement this interface for custom Graph passes:
1) The __call__() method co... | import hashlib
from abc import ABC, abstractmethod
from functools import lru_cache
from typing import Any, Callable, Optional, Union
from typing_extensions import TypeAlias
import torch.fx.graph
class CustomGraphPass(ABC):
"""
Implement this interface for custom Graph passes:
1) The __call__() method co... |
from typing import TYPE_CHECKING, Any, Dict, Optional, TypeVar
import numpy as np
from pydantic import parse_obj_as
from docarray.typing.proto_register import _register_proto
from docarray.typing.tensor.ndarray import NdArray
from docarray.typing.url.url_3d.url_3d import Url3D
if TYPE_CHECKING:
from docarray.doc... | from typing import TYPE_CHECKING, Any, Dict, Optional, TypeVar
import numpy as np
from pydantic import parse_obj_as
from docarray.typing.proto_register import _register_proto
from docarray.typing.tensor.ndarray import NdArray
from docarray.typing.url.url_3d.url_3d import Url3D
if TYPE_CHECKING:
from docarray.doc... |
from typing import TYPE_CHECKING
from docarray.utils._internal.misc import import_library
if TYPE_CHECKING:
from google.protobuf import __version__ as __pb__version__
else:
protobuf = import_library('google.protobuf', raise_error=True)
__pb__version__ = protobuf.__version__
if __pb__version__.startswith... | from typing import TYPE_CHECKING
from docarray.utils._internal.misc import import_library
if TYPE_CHECKING:
from google.protobuf import __version__ as __pb__version__
else:
protobuf = import_library('google.protobuf', raise_error=True)
__pb__version__ = protobuf.__version__
if __pb__version__.startswith... |
__copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import subprocess
from typing import List
import numpy as np
import pytest
from image_tf_encoder import ImageTFEncoder
from jina import Document, DocumentArray, Flow
input_dim = 336
target_output_dim = 1280
@... | __copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import subprocess
from typing import List
import numpy as np
import pytest
from jina import Document, DocumentArray, Flow
from ...image_tf_encoder import ImageTFEncoder
input_dim = 336
target_output_dim = 1280... |
from typing import Optional
import numpy as np
import pytest
import torch
from pydantic.tools import parse_obj_as, schema_json_of
from docarray import BaseDoc
from docarray.base_doc.io.json import orjson_dumps
from docarray.typing import AudioTorchTensor, AudioUrl
from docarray.utils.misc import is_tf_available
from ... | from typing import Optional
import numpy as np
import pytest
import torch
from pydantic.tools import parse_obj_as, schema_json_of
from docarray import BaseDocument
from docarray.base_document.io.json import orjson_dumps
from docarray.typing import AudioTorchTensor, AudioUrl
from docarray.utils.misc import is_tf_avail... |
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 PointCloud3DUrl
from tests import TOYDATA_DIR
MESH_FILES = {
'obj': str(TOYDATA_DIR / 'tetrahedron.obj'),
'glb': str(TOYDATA_DIR / 'test.... | 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 PointCloud3DUrl
from tests import TOYDATA_DIR
MESH_FILES = {
'obj': str(TOYDATA_DIR / 'tetrahedron.obj'),
'glb': str(TOYDATA_DIR / 'test.glb')... |
# Copyright (c) OpenMMLab. All rights reserved.
from mmdet.registry import TASK_UTILS
from ..transforms import bbox2distance, distance2bbox
from .base_bbox_coder import BaseBBoxCoder
@TASK_UTILS.register_module()
class DistancePointBBoxCoder(BaseBBoxCoder):
"""Distance Point BBox coder.
This coder encodes gt... | # Copyright (c) OpenMMLab. All rights reserved.
from ..builder import BBOX_CODERS
from ..transforms import bbox2distance, distance2bbox
from .base_bbox_coder import BaseBBoxCoder
@BBOX_CODERS.register_module()
class DistancePointBBoxCoder(BaseBBoxCoder):
"""Distance Point BBox coder.
This coder encodes gt bb... |
from typing import Any, Callable, Optional, Tuple
import torch
from .. import transforms
from .vision import VisionDataset
class FakeData(VisionDataset):
"""A fake dataset that returns randomly generated images and returns them as PIL images
Args:
size (int, optional): Size of the dataset. Default:... | from typing import Any, Callable, Optional, Tuple
import torch
from .. import transforms
from .vision import VisionDataset
class FakeData(VisionDataset):
"""A fake dataset that returns randomly generated images and returns them as PIL images
Args:
size (int, optional): Size of the dataset. Default:... |
# Copyright (c) OpenMMLab. All rights reserved.
import time
from typing import Any, Optional, Sequence, Tuple, Union
from mmengine.data import BaseDataElement
from mmengine.registry import HOOKS
from .hook import Hook
DATA_BATCH = Optional[Sequence[Tuple[Any, BaseDataElement]]]
@HOOKS.register_module()
class IterTi... | # Copyright (c) OpenMMLab. All rights reserved.
import time
from typing import Any, Optional, Sequence, Tuple, Union
from mmengine.data import BaseDataSample
from mmengine.registry import HOOKS
from .hook import Hook
DATA_BATCH = Optional[Sequence[Tuple[Any, BaseDataSample]]]
@HOOKS.register_module()
class IterTime... |
import csv
import gzip
import os
from . import InputExample
class STSDataReader:
"""Reads in the STS dataset. Each line contains two sentences (s1_col_idx, s2_col_idx) and one label (score_col_idx)
Default values expects a tab separated file with the first & second column the sentence pair and third column ... | from . import InputExample
import csv
import gzip
import os
class STSDataReader:
"""
Reads in the STS dataset. Each line contains two sentences (s1_col_idx, s2_col_idx) and one label (score_col_idx)
Default values expects a tab seperated file with the first & second column the sentence pair and third colu... |
from typing import (
Union,
Optional,
TYPE_CHECKING,
List,
Dict,
)
if TYPE_CHECKING:
import numpy as np
from docarray import DocumentArray
class FindMixin:
def _find(
self,
query: 'np.ndarray',
limit: Optional[Union[int, float]] = 20,
only_id: bool = False... | from typing import (
Union,
Optional,
TYPE_CHECKING,
List,
Dict,
)
if TYPE_CHECKING:
import numpy as np
from .... import DocumentArray
class FindMixin:
def _find(
self,
query: 'np.ndarray',
limit: Optional[Union[int, float]] = 20,
only_id: bool = False,
... |
import pathlib
from typing import Any, BinaryIO, Dict, Iterator, List, Tuple, Union
from torchdata.datapipes.iter import Filter, IterDataPipe, Mapper, Zipper
from torchvision.prototype.datapoints import BoundingBox, Label
from torchvision.prototype.datasets.utils import Dataset, EncodedImage, HttpResource, OnlineResou... | import pathlib
from typing import Any, BinaryIO, Dict, Iterator, List, Tuple, Union
from torchdata.datapipes.iter import Filter, IterDataPipe, Mapper, Zipper
from torchvision.prototype.datasets.utils import Dataset, EncodedImage, HttpResource, OnlineResource
from torchvision.prototype.datasets.utils._internal import (... |
from .autograd_utils import use_deterministic_algorithms
from .backend_utils import set_audio_backend
from .case_utils import (
disabledInCI,
HttpServerMixin,
is_ffmpeg_available,
PytorchTestCase,
skipIfCudaSmallMemory,
skipIfNoAudioDevice,
skipIfNoCtcDecoder,
skipIfNoCuCtcDecoder,
s... | from .autograd_utils import use_deterministic_algorithms
from .backend_utils import set_audio_backend
from .case_utils import (
disabledInCI,
HttpServerMixin,
is_ffmpeg_available,
PytorchTestCase,
skipIfCudaSmallMemory,
skipIfNoAudioDevice,
skipIfNoCtcDecoder,
skipIfNoCuCtcDecoder,
s... |
import warnings
from abc import ABC
from typing import Any, Optional
from langchain_core._api import deprecated
from langchain_core.chat_history import (
BaseChatMessageHistory,
InMemoryChatMessageHistory,
)
from langchain_core.memory import BaseMemory
from langchain_core.messages import AIMessage, HumanMessag... | import warnings
from abc import ABC
from typing import Any, Optional
from langchain_core._api import deprecated
from langchain_core.chat_history import (
BaseChatMessageHistory,
InMemoryChatMessageHistory,
)
from langchain_core.memory import BaseMemory
from langchain_core.messages import AIMessage, HumanMessag... |
import torch
from ._bounding_box import BoundingBoxes, BoundingBoxFormat
from ._datapoint import Datapoint
from ._image import Image
from ._mask import Mask
from ._torch_function_helpers import set_return_type
from ._video import Video
def wrap(wrappee, *, like, **kwargs):
"""[BETA] Convert a :class:`torch.Tenso... | import torch
from torchvision import _BETA_TRANSFORMS_WARNING, _WARN_ABOUT_BETA_TRANSFORMS
from ._bounding_box import BoundingBoxes, BoundingBoxFormat
from ._datapoint import Datapoint
from ._image import Image
from ._mask import Mask
from ._torch_function_helpers import set_return_type
from ._video import Video
if _... |
"""
This script contains an example how to perform semantic search with Elasticsearch.
You need Elasticsearch up and running locally:
https://www.elastic.co/guide/en/elasticsearch/reference/current/run-elasticsearch-locally.html
Further, you need the Python Elasticsearch Client installed: https://elasticsearch-py.rea... | """
This script contains an example how to perform semantic search with Elasticsearch.
You need Elasticsearch up and running locally:
https://www.elastic.co/guide/en/elasticsearch/reference/current/run-elasticsearch-locally.html
Further, you need the Python Elasticsearch Client installed: https://elasticsearch-py.rea... |
from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.document_loaders import AssemblyAIAudioTranscriptLoader
from langchain_community.document_loaders.assemblyai import TranscriptFormat
# Create a way to dynamically look up deprecated imp... | from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.document_loaders import AssemblyAIAudioTranscriptLoader
from langchain_community.document_loaders.assemblyai import TranscriptFormat
# Create a way to dynamically look up deprecated imp... |
import pathlib
from typing import Any, Dict, List, Optional, Tuple, Union
from torchdata.datapipes.iter import CSVDictParser, Demultiplexer, Filter, IterDataPipe, Mapper, Zipper
from torchvision.prototype.datasets.utils import Dataset, EncodedImage, HttpResource, OnlineResource
from torchvision.prototype.datasets.util... | import pathlib
from typing import Any, Dict, List, Optional, Tuple, Union
from torchdata.datapipes.iter import CSVDictParser, Demultiplexer, Filter, IterDataPipe, Mapper, Zipper
from torchvision.prototype.datasets.utils import Dataset, HttpResource, OnlineResource
from torchvision.prototype.datasets.utils._internal im... |
"""
Using rmm with Dask
===================
"""
import dask
from dask.distributed import Client
from dask_cuda import LocalCUDACluster
from sklearn.datasets import make_classification
import xgboost as xgb
def main(client):
# Optionally force XGBoost to use RMM for all GPU memory allocation, see ./README.md
... | """
Using rmm with Dask
===================
"""
import dask
from dask.distributed import Client
from dask_cuda import LocalCUDACluster
from sklearn.datasets import make_classification
import xgboost as xgb
def main(client):
# Optionally force XGBoost to use RMM for all GPU memory allocation, see ./README.md
... |
"""
This script contains an example how to perform semantic search with Qdrant.
You need Qdrant up and running locally:
https://qdrant.tech/documentation/quickstart/
Further, you need the Python Qdrant Client installed: https://python-client.qdrant.tech/, e.g.:
```
pip install qdrant-client
```
This script was create... | """
This script contains an example how to perform semantic search with Qdrant.
You need Qdrant up and running locally:
https://qdrant.tech/documentation/quickstart/
Further, you need the Python Qdrant Client installed: https://python-client.qdrant.tech/, e.g.:
```
pip install qdrant-client
```
This script was create... |
from typing import Optional, Type
from langchain_core.callbacks import CallbackManagerForToolRun
from langchain_core.tools import BaseTool
from pydantic import BaseModel, Field
from langchain_community.tools.file_management.utils import (
INVALID_PATH_TEMPLATE,
BaseFileToolMixin,
FileValidationError,
)
... | from typing import Optional, Type
from langchain_core.callbacks import CallbackManagerForToolRun
from langchain_core.tools import BaseTool
from pydantic import BaseModel, Field
from langchain_community.tools.file_management.utils import (
INVALID_PATH_TEMPLATE,
BaseFileToolMixin,
FileValidationError,
)
... |
# Licensed to the LF AI & Data foundation under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the "License");
# you may not use this fil... | from docarray.computation.abstract_comp_backend import AbstractComputationalBackend
__all__ = ['AbstractComputationalBackend']
|
import itertools
import os.path
import pytest
from docarray import Document, DocumentArray
from jina import Client, Executor, Flow, requests
from jina.helper import random_port
PROTOCOLS = ['grpc', 'http', 'websocket']
cur_dir = os.path.dirname(__file__)
class MyExecutor(Executor):
@requests
def foo(self, ... | import itertools
import os.path
import pytest
from docarray import Document, DocumentArray
from jina import Client, Executor, Flow, requests
from jina.helper import random_port
PROTOCOLS = ['grpc', 'http', 'websocket']
cur_dir = os.path.dirname(__file__)
class MyExecutor(Executor):
@requests
def foo(self, ... |
from docarray.typing.bytes import ImageBytes
from docarray.typing.id import ID
from docarray.typing.tensor import ImageNdArray, ImageTensor
from docarray.typing.tensor.audio import AudioNdArray
from docarray.typing.tensor.embedding.embedding import AnyEmbedding, NdArrayEmbedding
from docarray.typing.tensor.ndarray impo... | from docarray.typing.id import ID
from docarray.typing.tensor.audio import AudioNdArray
from docarray.typing.tensor.embedding.embedding import AnyEmbedding, NdArrayEmbedding
from docarray.typing.tensor.ndarray import NdArray
from docarray.typing.tensor.tensor import AnyTensor
from docarray.typing.tensor.video import Vi... |
# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause
import functools
import warnings
from inspect import signature
__all__ = ["deprecated"]
class deprecated:
"""Decorator to mark a function or class as deprecated.
Issue a warning when the function is called/the class is instantia... | # Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause
import functools
import warnings
__all__ = ["deprecated"]
class deprecated:
"""Decorator to mark a function or class as deprecated.
Issue a warning when the function is called/the class is instantiated and
adds a warning to ... |
from typing import Union, Iterable
from docarray.array.storage.base.seqlike import BaseSequenceLikeMixin
from docarray.array.memory import DocumentArrayInMemory
from docarray import Document
class SequenceLikeMixin(BaseSequenceLikeMixin):
"""Implement sequence-like methods"""
def _extend(self, values: Itera... | from typing import Union, Iterable
from docarray.array.storage.base.seqlike import BaseSequenceLikeMixin
from docarray.array.memory import DocumentArrayInMemory
from docarray import Document
class SequenceLikeMixin(BaseSequenceLikeMixin):
"""Implement sequence-like methods"""
def _extend(self, values: Itera... |
_base_ = [
'./bytetrack_yolox_x_8xb4-80e_crowdhuman-mot17halftrain_'
'test-mot17halfval.py'
]
dataset_type = 'MOTChallengeDataset'
img_scale = (1600, 896) # weight, height
model = dict(
data_preprocessor=dict(
type='TrackDataPreprocessor',
use_det_processor=True,
pad_size_divisor... | _base_ = [
'./bytetrack_yolox_x_8xb4-80e_crowdhuman-mot17halftrain_'
'test-mot17halfval.py'
]
dataset_type = 'MOTChallengeDataset'
img_scale = (896, 1600) # w, h
model = dict(
data_preprocessor=dict(
type='TrackDataPreprocessor',
use_det_processor=True,
pad_size_divisor=32,
... |
# Copyright (c) OpenMMLab. All rights reserved.
from .collect_env import collect_env
from .hub import load_url
from .manager import ManagerMeta, ManagerMixin
from .misc import (check_prerequisites, concat_list, deprecated_api_warning,
has_batch_norm, has_method, import_modules_from_strings,
... | # Copyright (c) OpenMMLab. All rights reserved.
from .hub import load_url
from .manager import ManagerMeta, ManagerMixin
from .misc import (check_prerequisites, concat_list, deprecated_api_warning,
has_batch_norm, has_method, import_modules_from_strings,
is_list_of, is_method_overr... |
"""
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... |
from __future__ import annotations
from .MLMTransformer import MLMTransformer
from .SparseAutoEncoder import SparseAutoEncoder
from .SparseStaticEmbedding import SparseStaticEmbedding
from .SpladePooling import SpladePooling
__all__ = ["SparseAutoEncoder", "MLMTransformer", "SpladePooling", "SparseStaticEmbedding"]
| from __future__ import annotations
from .CSRSparsity import CSRSparsity
from .IDF import IDF
from .MLMTransformer import MLMTransformer
from .SpladePooling import SpladePooling
__all__ = ["CSRSparsity", "MLMTransformer", "SpladePooling", "IDF"]
|
# Copyright (c) OpenMMLab. All rights reserved.
from typing import Tuple
import torch.nn as nn
from mmcv.cnn import ConvModule
from mmcv.ops import MaskedConv2d
from torch import Tensor
from mmdet.registry import MODELS
from mmdet.utils import OptConfigType, OptMultiConfig
from .guided_anchor_head import FeatureAdapt... | # Copyright (c) OpenMMLab. All rights reserved.
from typing import Tuple
import torch.nn as nn
from mmcv.cnn import ConvModule
from mmcv.ops import MaskedConv2d
from torch import Tensor
from mmdet.core import OptConfigType, OptMultiConfig
from mmdet.registry import MODELS
from .guided_anchor_head import FeatureAdapti... |
"""Module for helper functions for clients."""
from typing import Optional, Tuple
from jina._docarray import Document, DocumentArray, docarray_v2
from jina.enums import DataInputType
from jina.types.request.data import DataRequest
if docarray_v2:
from docarray import DocList, BaseDoc
def _new_data_request_from... | """Module for helper functions for clients."""
from typing import Optional, Tuple
from jina._docarray import Document, DocumentArray, docarray_v2
from jina.enums import DataInputType
from jina.types.request.data import DataRequest
if docarray_v2:
from docarray import DocList, BaseDoc
def _new_data_request_from_... |
from __future__ import annotations
import os
from pathlib import Path
from typing import Any, Callable, Optional, Tuple, Union
import PIL.Image
from .utils import download_and_extract_archive, verify_str_arg
from .vision import VisionDataset
class FGVCAircraft(VisionDataset):
"""`FGVC Aircraft <https://www.rob... | from __future__ import annotations
import os
from typing import Any, Callable, Optional, Tuple
import PIL.Image
from .utils import download_and_extract_archive, verify_str_arg
from .vision import VisionDataset
class FGVCAircraft(VisionDataset):
"""`FGVC Aircraft <https://www.robots.ox.ac.uk/~vgg/data/fgvc-airc... |
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