input stringlengths 33 5k | output stringlengths 32 5k |
|---|---|
from backend.blocks.jina._auth import (
JinaCredentials,
JinaCredentialsField,
JinaCredentialsInput,
)
from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
from backend.data.model import SchemaField
from backend.util.request import requests
class JinaEmbeddingBlock(Block):
cla... | import requests
from backend.blocks.jina._auth import (
JinaCredentials,
JinaCredentialsField,
JinaCredentialsInput,
)
from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
from backend.data.model import SchemaField
class JinaEmbeddingBlock(Block):
class Input(BlockSchema):
... |
from typing import IO, TYPE_CHECKING, Callable, Optional
from docarray.utils._internal.misc import import_library
def _compress_bytes(data: bytes, algorithm: Optional[str] = None) -> bytes:
if algorithm == 'lz4':
if TYPE_CHECKING:
from lz4 import frame
else:
lz4 = import_l... | from typing import IO, Callable, Optional
def _compress_bytes(data: bytes, algorithm: Optional[str] = None) -> bytes:
if algorithm == 'lz4':
import lz4.frame # type: ignore
data = lz4.frame.compress(data)
elif algorithm == 'bz2':
import bz2
data = bz2.compress(data)
elif... |
import copy
import sqlite3
import warnings
from dataclasses import dataclass, field
from tempfile import NamedTemporaryFile
from typing import Iterable, Dict, Optional, TYPE_CHECKING, Union
from docarray.array.storage.sqlite.helper import initialize_table
from docarray.array.storage.base.backend import BaseBackendMixi... | import sqlite3
import warnings
from dataclasses import dataclass, field
from tempfile import NamedTemporaryFile
from typing import Iterable, Dict, Optional, TYPE_CHECKING, Union
from docarray.array.storage.sqlite.helper import initialize_table
from docarray.array.storage.base.backend import BaseBackendMixin
from docar... |
import contextlib
from collections.abc import Iterable
from pathlib import Path
from typing import Any
from tomlkit import dump, inline_table, load
from tomlkit.items import InlineTable
def _get_dep_inline_table(path: Path) -> InlineTable:
dep = inline_table()
dep.update({"path": str(path), "develop": True})... | from collections.abc import Iterable
from pathlib import Path
from typing import Any
from tomlkit import dump, inline_table, load
from tomlkit.items import InlineTable
def _get_dep_inline_table(path: Path) -> InlineTable:
dep = inline_table()
dep.update({"path": str(path), "develop": True})
return dep
... |
import sys
from jina.parsers import set_gateway_parser
from jina.parsers.helper import _update_gateway_args
from jina.serve.runtimes.asyncio import AsyncNewLoopRuntime
from jina.serve.runtimes.gateway.request_handling import GatewayRequestHandler
def run(*args, **kwargs):
runtime_args = set_gateway_parser().pars... | import sys
from jina.parsers import set_gateway_parser
from jina.parsers.helper import _update_gateway_args
from jina.serve.runtimes.asyncio import AsyncNewLoopRuntime
from jina.serve.runtimes.gateway.request_handling import GatewayRequestHandler
def run(*args, **kwargs):
runtime_args = set_gateway_parser().pars... |
_base_ = 'faster-rcnn_r50_fpn_crop640-50e_coco.py'
norm_cfg = dict(type='BN', requires_grad=True)
model = dict(
neck=dict(out_channels=128, inter_channels=128),
rpn_head=dict(in_channels=128),
roi_head=dict(
bbox_roi_extractor=dict(out_channels=128),
bbox_head=dict(in_channels=128)))
| _base_ = 'faster_rcnn_r50_fpg_crop640_50e_coco.py'
norm_cfg = dict(type='BN', requires_grad=True)
model = dict(
neck=dict(out_channels=128, inter_channels=128),
rpn_head=dict(in_channels=128),
roi_head=dict(
bbox_roi_extractor=dict(out_channels=128),
bbox_head=dict(in_channels=128)))
|
from unittest import TestCase
import numpy as np
from mmengine.testing import assert_allclose
from mmdet.structures.mask import BitmapMasks, PolygonMasks
class TestMaskStructures(TestCase):
def test_bitmap_translate_same_size(self):
mask_array = np.zeros((5, 10, 10), dtype=np.uint8)
mask_array[... | from unittest import TestCase
import numpy as np
from mmengine.testing import assert_allclose
from mmdet.structures.mask import BitmapMasks
class TestMaskStructures(TestCase):
def test_bitmap_translate_same_size(self):
mask_array = np.zeros((5, 10, 10), dtype=np.uint8)
mask_array[:, 0:5, 0:5] =... |
# Copyright (c) OpenMMLab. All rights reserved.
import unittest
from unittest import TestCase
import torch
from parameterized import parameterized
from mmdet.structures import DetDataSample
from mmdet.testing import demo_mm_inputs, get_detector_cfg
from mmdet.utils import register_all_modules
class TestRPN(TestCase... | # Copyright (c) OpenMMLab. All rights reserved.
import unittest
from unittest import TestCase
import torch
from parameterized import parameterized
from mmdet.structures import DetDataSample
from mmdet.testing import demo_mm_inputs, get_detector_cfg
from mmdet.utils import register_all_modules
class TestRPN(TestCase... |
"""
This is a simple application for sentence embeddings: clustering
Sentences are mapped to sentence embeddings and then agglomerative clustering with a threshold is applied.
"""
from sklearn.cluster import AgglomerativeClustering
from sentence_transformers import SentenceTransformer
embedder = SentenceTransformer... | """
This is a simple application for sentence embeddings: clustering
Sentences are mapped to sentence embeddings and then agglomerative clustering with a threshold is applied.
"""
from sentence_transformers import SentenceTransformer
from sklearn.cluster import AgglomerativeClustering
embedder = SentenceTransformer(... |
__copyright__ = "Copyright (c) 2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import os
from executor.torch_encoder import ImageTorchEncoder
from pytest_mock import MockerFixture
from torch import hub
def test_load_from_url(tmpdir: str, mocker: MockerFixture) -> None:
os.environ['TORCH_H... | __copyright__ = "Copyright (c) 2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import os
from pytest_mock import MockerFixture
from torch import hub
from ...torch_encoder import ImageTorchEncoder
def test_load_from_url(tmpdir: str, mocker: MockerFixture) -> None:
os.environ['TORCH_HOME']... |
import sys
from os import path
from setuptools import find_packages
from setuptools import setup
if sys.version_info < (3, 7, 0):
raise OSError(f'DocArray requires Python >=3.7, but yours is {sys.version}')
try:
pkg_name = 'docarray'
libinfo_py = path.join(pkg_name, '__init__.py')
libinfo_content = o... | import sys
from os import path
from setuptools import find_packages
from setuptools import setup
if sys.version_info < (3, 7, 0):
raise OSError(f'DocArray requires Python >=3.7, but yours is {sys.version}')
try:
pkg_name = 'docarray'
libinfo_py = path.join(pkg_name, '__init__.py')
libinfo_content = o... |
import pytest
import random
import string
import logging
from llama_index.core.schema import (
TextNode,
RelatedNodeInfo,
NodeRelationship,
)
from llama_index.vector_stores.lindorm import (
LindormVectorStore,
LindormVectorClient,
)
from llama_index.core.vector_stores.types import (
VectorStoreQ... | import pytest
import random
import string
import logging
from llama_index.core.schema import (
TextNode,
RelatedNodeInfo,
NodeRelationship,
)
from llama_index.vector_stores.lindorm import (
LindormVectorStore,
LindormVectorClient,
)
from llama_index.core.vector_stores.types import (
VectorStoreQ... |
from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.chat_loaders.langsmith import (
LangSmithDatasetChatLoader,
LangSmithRunChatLoader,
)
# Create a way to dynamically look up deprecated imports.
# Used to consolidate log... | from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.chat_loaders.langsmith import (
LangSmithDatasetChatLoader,
LangSmithRunChatLoader,
)
# Create a way to dynamically look up deprecated imports.
# Used to consolidate log... |
# Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
from mmcv.cnn import ConvModule
from mmcv.runner import BaseModule, auto_fp16, force_fp32
from mmdet.models.utils import ResLayer, SimplifiedBasicBlock
from mmdet.registry import MODELS
@MODELS.register_module()
class GlobalContextHead(BaseModule)... | # Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
from mmcv.cnn import ConvModule
from mmcv.runner import BaseModule, auto_fp16, force_fp32
from mmdet.models.builder import HEADS
from mmdet.models.utils import ResLayer, SimplifiedBasicBlock
@HEADS.register_module()
class GlobalContextHead(BaseMod... |
# Copyright (c) OpenMMLab. All rights reserved.
from torch import Tensor
from mmdet.data_elements import SampleList
from mmdet.registry import MODELS
from mmdet.utils import ConfigType, OptConfigType, OptMultiConfig
from .faster_rcnn import FasterRCNN
@MODELS.register_module()
class TridentFasterRCNN(FasterRCNN):
... | # Copyright (c) OpenMMLab. All rights reserved.
from torch import Tensor
from mmdet.core import SampleList
from mmdet.core.utils import ConfigType, OptConfigType, OptMultiConfig
from mmdet.registry import MODELS
from .faster_rcnn import FasterRCNN
@MODELS.register_module()
class TridentFasterRCNN(FasterRCNN):
""... |
from ._vggish_pipeline import VGGISH as _VGGISH, VGGishBundle
from torchaudio._internal.module_utils import dropping_const_support
VGGISH = dropping_const_support(_VGGISH, "VGGISH")
__all__ = ["VGGISH", "VGGishBundle"]
| from ._vggish_pipeline import VGGISH, VGGishBundle
__all__ = ["VGGISH", "VGGishBundle"]
|
import os
import torchaudio
import torchvision
from torch.utils.data import Dataset
def _load_list(args, *filenames):
output = []
length = []
for filename in filenames:
filepath = os.path.join(args.root_dir, "labels", filename)
for line in open(filepath).read().splitlines():
d... | import os
import torchaudio
import torchvision
from torch.utils.data import Dataset
def _load_list(args, *filenames):
output = []
length = []
for filename in filenames:
filepath = os.path.join(os.path.dirname(args.dataset_path), filename)
for line in open(filepath).read().splitlines():
... |
import math
from typing import List, Optional
from llama_index.core.agent.react.types import (
BaseReasoningStep,
ResponseReasoningStep,
)
from llama_index.core.bridge.pydantic import Field, BaseModel
from llama_index.core.prompts import PromptTemplate
# taken from the paper
DEFAULT_REFLECTION_PROMPT_STR = ""... | import math
from typing import List, Optional
from llama_index.core.agent.react.types import (
BaseReasoningStep,
ResponseReasoningStep,
)
from llama_index.core.bridge.pydantic import Field, BaseModel
from llama_index.core.prompts import PromptTemplate
# taken from the paper
DEFAULT_REFLECTION_PROMPT_STR = ""... |
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 google.protobuf import __version__ as __pb__version__
if __pb__version__.startswith('4'):
from docarray.proto.pb.docarray_pb2 import (
DictOfAnyProto,
DocArrayStackedProto,
DocumentArrayProto,
DocumentProto,
ListOfAnyProto,
ListOfDocArrayProto,
NdArrayPr... |
from __future__ import annotations
from typing import Any
from langchain_text_splitters.base import TextSplitter
class NLTKTextSplitter(TextSplitter):
"""Splitting text using NLTK package."""
def __init__(
self,
separator: str = "\n\n",
language: str = "english",
*,
... | from __future__ import annotations
from typing import Any, List
from langchain_text_splitters.base import TextSplitter
class NLTKTextSplitter(TextSplitter):
"""Splitting text using NLTK package."""
def __init__(
self,
separator: str = "\n\n",
language: str = "english",
*,
... |
import textwrap
import pyarrow as pa
import pytest
from datasets import Features, Image
from datasets.builder import InvalidConfigName
from datasets.data_files import DataFilesList
from datasets.packaged_modules.text.text import Text, TextConfig
from ..utils import require_pil
@pytest.fixture
def text_file(tmp_pat... | import textwrap
import pyarrow as pa
import pytest
from datasets import Features, Image
from datasets.packaged_modules.text.text import Text
from ..utils import require_pil
@pytest.fixture
def text_file(tmp_path):
filename = tmp_path / "text.txt"
data = textwrap.dedent(
"""\
Lorem ipsum dol... |
from keras.src import ops
from keras.src.api_export import keras_export
from keras.src.backend.common.keras_tensor import KerasTensor
from keras.src.layers.input_spec import InputSpec
from keras.src.layers.layer import Layer
@keras_export("keras.layers.Permute")
class Permute(Layer):
"""Permutes the dimensions of... | from keras.src import ops
from keras.src.api_export import keras_export
from keras.src.backend.common.keras_tensor import KerasTensor
from keras.src.layers.input_spec import InputSpec
from keras.src.layers.layer import Layer
@keras_export("keras.layers.Permute")
class Permute(Layer):
"""Permutes the dimensions of... |
from __future__ import annotations
from sentence_transformers.losses.TripletLoss import TripletDistanceMetric, TripletLoss
from sentence_transformers.sparse_encoder.SparseEncoder import SparseEncoder
class SparseTripletLoss(TripletLoss):
def __init__(
self, model: SparseEncoder, distance_metric=TripletDi... | from __future__ import annotations
from sentence_transformers.losses.TripletLoss import TripletDistanceMetric, TripletLoss
from sentence_transformers.sparse_encoder.SparseEncoder import SparseEncoder
class SparseTripletLoss(TripletLoss):
def __init__(
self, model: SparseEncoder, distance_metric=TripletDi... |
from __future__ import annotations
from enum import Enum
from typing import Any, Mapping, Optional, Sequence, Tuple, Union
import torch
from torch.utils._pytree import tree_flatten
from ._tv_tensor import TVTensor
class BoundingBoxFormat(Enum):
"""Coordinate format of a bounding box.
Available formats are... | from __future__ import annotations
from enum import Enum
from typing import Any, Mapping, Optional, Sequence, Tuple, Union
import torch
from torch.utils._pytree import tree_flatten
from ._tv_tensor import TVTensor
class BoundingBoxFormat(Enum):
"""[BETA] Coordinate format of a bounding box.
Available form... |
"""Argparser module for Deployment runtimes"""
import argparse
from jina.enums import DeploymentRoleType
from jina.parsers.helper import _SHOW_ALL_ARGS, KVAppendAction, add_arg_group
from jina.parsers.orchestrate.runtimes.remote import _mixin_http_server_parser
def mixin_base_deployment_parser(parser):
"""Add m... | """Argparser module for Deployment runtimes"""
import argparse
from jina.enums import DeploymentRoleType
from jina.parsers.helper import _SHOW_ALL_ARGS, KVAppendAction, add_arg_group
from jina.parsers.orchestrate.runtimes.remote import _mixin_http_server_parser
def mixin_base_deployment_parser(parser):
"""Add mi... |
import wave
from typing import Union, BinaryIO, TYPE_CHECKING
import numpy as np
if TYPE_CHECKING:
from docarray.typing import T
class AudioDataMixin:
"""Provide helper functions for :class:`Document` to support audio data."""
def save_audio_tensor_to_file(
self: 'T',
file: Union[str, B... | import wave
from typing import Union, BinaryIO, TYPE_CHECKING
import numpy as np
if TYPE_CHECKING:
from ...typing import T
class AudioDataMixin:
"""Provide helper functions for :class:`Document` to support audio data."""
def save_audio_tensor_to_file(
self: 'T',
file: Union[str, BinaryI... |
from typing import TYPE_CHECKING, Dict, Iterable
from sentence_transformers.evaluation.SentenceEvaluator import SentenceEvaluator
if TYPE_CHECKING:
from sentence_transformers.SentenceTransformer import SentenceTransformer
class SequentialEvaluator(SentenceEvaluator):
"""
This evaluator allows that multi... | from typing import TYPE_CHECKING, Dict, Iterable
from sentence_transformers.evaluation.SentenceEvaluator import SentenceEvaluator
if TYPE_CHECKING:
from sentence_transformers.SentenceTransformer import SentenceTransformer
class SequentialEvaluator(SentenceEvaluator):
"""
This evaluator allows that multi... |
from __future__ import annotations
import pytest
from torch.utils.data import BatchSampler, ConcatDataset, SequentialSampler
from sentence_transformers.sampler import RoundRobinBatchSampler
from sentence_transformers.util import is_datasets_available
if is_datasets_available():
from datasets import Dataset
else:... | from __future__ import annotations
import pytest
from datasets import Dataset
from torch.utils.data import BatchSampler, ConcatDataset, SequentialSampler
from sentence_transformers.sampler import RoundRobinBatchSampler
DATASET_LENGTH = 25
@pytest.fixture
def dummy_concat_dataset() -> ConcatDataset:
"""
Dum... |
import pytest
from docarray import DocumentArray
from docarray.array.qdrant import DocumentArrayQdrant
from docarray.array.sqlite import DocumentArraySqlite
from docarray.array.annlite import DocumentArrayAnnlite, AnnliteConfig
from docarray.array.storage.qdrant import QdrantConfig
from docarray.array.storage.weaviate... | import pytest
from docarray import DocumentArray
from docarray.array.qdrant import DocumentArrayQdrant
from docarray.array.sqlite import DocumentArraySqlite
from docarray.array.annlite import DocumentArrayAnnlite, AnnliteConfig
from docarray.array.storage.qdrant import QdrantConfig
from docarray.array.storage.weaviate... |
# Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import ConvModule
from mmcv.runner import BaseModule
from torch.utils.checkpoint import checkpoint
from ..builder import NECKS
@NECKS.register_module()
class HRFPN(BaseModule):
"""HRFP... | import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import ConvModule
from mmcv.runner import BaseModule
from torch.utils.checkpoint import checkpoint
from ..builder import NECKS
@NECKS.register_module()
class HRFPN(BaseModule):
"""HRFPN (High Resolution Feature Pyramids)
paper:... |
from typing import Any, Dict, Optional
from llama_index.core.storage.kvstore.types import BaseKVStore
from llama_index.storage.kvstore.azurecosmosnosql import AzureCosmosNoSqlKVStore
DEFAULT_INDEX_DATABASE = "IndexStoreDB"
DEFAULT_INDEX_CONTAINER = "IndexStoreContainer"
class AzureCosmosNoSqlIndexStore(BaseKVStore)... | from typing import Any, Dict, Optional
from llama_index.core.storage.index_store.keyval_index_store import KVIndexStore
from llama_index.storage.kvstore.azurecosmosnosql import AzureCosmosNoSqlKVStore
DEFAULT_INDEX_DATABASE = "IndexStoreDB"
DEFAULT_INDEX_CONTAINER = "IndexStoreContainer"
class AzureCosmosNoSqlIndex... |
from .autograd_utils import use_deterministic_algorithms
from .case_utils import (
disabledInCI,
HttpServerMixin,
PytorchTestCase,
skipIfCudaSmallMemory,
skipIfNoAudioDevice,
skipIfNoCtcDecoder,
skipIfNoCuCtcDecoder,
skipIfNoCuda,
skipIfNoExec,
skipIfNoFFmpeg,
skipIfNoHWAccel... | from .autograd_utils import use_deterministic_algorithms
from .backend_utils import set_audio_backend
from .case_utils import (
disabledInCI,
HttpServerMixin,
PytorchTestCase,
skipIfCudaSmallMemory,
skipIfNoAudioDevice,
skipIfNoCtcDecoder,
skipIfNoCuCtcDecoder,
skipIfNoCuda,
skipIfNo... |
from pathlib import Path
import dask.array as da
import numpy as np
from distributed import Client, LocalCluster
from sklearn.datasets import load_svmlight_file
import lightgbm as lgb
if __name__ == "__main__":
print("loading data")
rank_example_dir = Path(__file__).absolute().parents[2] / "lambdarank"
... | from pathlib import Path
import dask.array as da
import numpy as np
from distributed import Client, LocalCluster
from sklearn.datasets import load_svmlight_file
import lightgbm as lgb
if __name__ == "__main__":
print("loading data")
rank_example_dir = Path(__file__).absolute().parents[2] / 'lambdarank'
... |
"""**sys_info** prints information about the system and langchain packages for debugging purposes.""" # noqa: E501
from collections.abc import Sequence
def _get_sub_deps(packages: Sequence[str]) -> list[str]:
"""Get any specified sub-dependencies."""
from importlib import metadata
sub_deps = set()
... | """**sys_info** prints information about the system and langchain packages
for debugging purposes.
"""
from collections.abc import Sequence
def _get_sub_deps(packages: Sequence[str]) -> list[str]:
"""Get any specified sub-dependencies."""
from importlib import metadata
sub_deps = set()
_underscored_... |
"""
This examples trains a CrossEncoder for the STSbenchmark task. A CrossEncoder takes a sentence pair
as input and outputs a label. Here, it output a continuous labels 0...1 to indicate the similarity between the input pair.
It does NOT produce a sentence embedding and does NOT work for individual sentences.
Usage:... | """
This examples trains a CrossEncoder for the STSbenchmark task. A CrossEncoder takes a sentence pair
as input and outputs a label. Here, it output a continuous labels 0...1 to indicate the similarity between the input pair.
It does NOT produce a sentence embedding and does NOT work for individual sentences.
Usage:... |
from typing import TYPE_CHECKING, Union
import numpy as np
if TYPE_CHECKING: # pragma: no cover
from docarray.typing import T
import trimesh
class Mesh:
FILE_EXTENSIONS = [
'glb',
'obj',
'ply',
]
VERTICES = 'vertices'
FACES = 'faces'
class MeshDataMixin:
"""Pro... | from typing import TYPE_CHECKING, Union
import numpy as np
if TYPE_CHECKING: # pragma: no cover
from docarray.typing import T
import trimesh
class Mesh:
FILE_EXTENSIONS = [
'glb',
'obj',
'ply',
]
VERTICES = 'vertices'
FACES = 'faces'
class MeshDataMixin:
"""Pro... |
# mypy: allow-untyped-defs
from typing import Callable, Optional, Union
import torch
from .base_structured_sparsifier import BaseStructuredSparsifier
__all__ = ["FPGMPruner"]
class FPGMPruner(BaseStructuredSparsifier):
r"""Filter Pruning via Geometric Median (FPGM) Structured Pruner
This sparsifier prune ... | # mypy: allow-untyped-defs
from typing import Callable, Optional, Union
import torch
from .base_structured_sparsifier import BaseStructuredSparsifier
__all__ = ["FPGMPruner"]
class FPGMPruner(BaseStructuredSparsifier):
r"""Filter Pruning via Geometric Median (FPGM) Structured Pruner
This sparsifier prune ... |
from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.agent_toolkits.spark_sql.base import create_spark_sql_agent
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# hand... | from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.agent_toolkits.spark_sql.base import create_spark_sql_agent
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# hand... |
# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | # Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... |
from dataclasses import dataclass, asdict, field
from typing import (
Union,
Dict,
Optional,
TYPE_CHECKING,
Iterable,
List,
Tuple,
)
import numpy as np
from docarray.array.storage.base.backend import BaseBackendMixin, TypeMap
from docarray.helper import dataclass_from_dict, filter_dict, _s... | from dataclasses import dataclass, asdict, field
from typing import (
Union,
Dict,
Optional,
TYPE_CHECKING,
Iterable,
List,
Tuple,
)
import numpy as np
from docarray.array.storage.base.backend import BaseBackendMixin, TypeMap
from docarray.helper import dataclass_from_dict, filter_dict, _s... |
import logging
import typing
import autogpt_libs.auth.depends
import autogpt_libs.auth.middleware
import fastapi
import prisma
import backend.data.graph
import backend.integrations.creds_manager
import backend.integrations.webhooks.graph_lifecycle_hooks
import backend.server.v2.library.db
import backend.server.v2.lib... | import logging
import typing
import autogpt_libs.auth.depends
import autogpt_libs.auth.middleware
import fastapi
import prisma
import backend.data.graph
import backend.integrations.creds_manager
import backend.integrations.webhooks.graph_lifecycle_hooks
import backend.server.v2.library.db
import backend.server.v2.lib... |
_base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
type='YOLOF',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(3, ),
frozen_stages=1,
norm_cfg=dict(ty... | _base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
type='YOLOF',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(3, ),
frozen_stages=1,
norm_cfg=dict(ty... |
_base_ = 'ssd300_coco.py'
# model settings
input_size = 512
model = dict(
neck=dict(
out_channels=(512, 1024, 512, 256, 256, 256, 256),
level_strides=(2, 2, 2, 2, 1),
level_paddings=(1, 1, 1, 1, 1),
last_kernel_size=4),
bbox_head=dict(
in_channels=(512, 1024, 512, 256, 2... | _base_ = 'ssd300_coco.py'
# model settings
input_size = 512
model = dict(
neck=dict(
out_channels=(512, 1024, 512, 256, 256, 256, 256),
level_strides=(2, 2, 2, 2, 1),
level_paddings=(1, 1, 1, 1, 1),
last_kernel_size=4),
bbox_head=dict(
in_channels=(512, 1024, 512, 256, 2... |
_base_ = './reppoints-moment_r50_fpn-gn_head-gn_2x_coco.py'
model = dict(
backbone=dict(
depth=101,
dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True),
init_cfg=dict(type='Pretrained',
checkpoint='torchvisio... | _base_ = './reppoints_moment_r50_fpn_gn-neck+head_2x_coco.py'
model = dict(
backbone=dict(
depth=101,
dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True),
init_cfg=dict(type='Pretrained',
checkpoint='torchvis... |
from typing import Iterable, Union
from docarray import Document, DocumentArray
from docarray.array.storage.base.seqlike import BaseSequenceLikeMixin
class SequenceLikeMixin(BaseSequenceLikeMixin):
"""Implement sequence-like methods for DocumentArray with Redis as storage"""
def __eq__(self, other):
... | from typing import Iterable, Union
from docarray import Document, DocumentArray
from docarray.array.storage.base.seqlike import BaseSequenceLikeMixin
class SequenceLikeMixin(BaseSequenceLikeMixin):
"""Implement sequence-like methods for DocumentArray with Redis as storage"""
def __eq__(self, other):
... |
"""langchain-core version information and utilities."""
VERSION = "0.3.64"
| """langchain-core version information and utilities."""
VERSION = "0.3.63"
|
_base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
type='ATSS',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[103.530, 116.280, 123.675],
std=[1.0, 1.0, 1.0],
bgr_to_rgb=False... | _base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
type='ATSS',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[103.530, 116.280, 123.675],
std=[1.0, 1.0, 1.0],
bgr_to_rgb=False... |
import re
import sys
meetup_svg = '.github/images/meetup.svg'
readme_md = 'README.md'
conf_py = 'docs/conf.py'
def rm_announce():
# remove all announcement
with open(readme_md, encoding='utf-8') as fp:
_old = fp.read()
_new = re.sub(
r'(<!--startmsg-->\s*?\n).*(\n\s*?<!--endmsg-->... | import re
import sys
meetup_svg = '.github/images/meetup.svg'
readme_md = 'README.md'
conf_py = 'docs/conf.py'
def rm_announce():
# remove all announcement
with open(readme_md) as fp:
_old = fp.read()
_new = re.sub(
r'(<!--startmsg-->\s*?\n).*(\n\s*?<!--endmsg-->)',
rf... |
"""langchain-core version information and utilities."""
VERSION = "0.3.60"
| """langchain-core version information and utilities."""
VERSION = "0.3.59"
|
"""
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... |
# Licensed to the LF AI & Data foundation under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the "License");
# you may not use this fil... | from typing import TYPE_CHECKING, Any, List, Tuple, Type, TypeVar, Union
import numpy as np
from docarray.typing.proto_register import _register_proto
from docarray.typing.tensor.jaxarray import JaxArray, metaJax
from docarray.typing.tensor.video.video_tensor_mixin import VideoTensorMixin
T = TypeVar('T', bound='Vid... |
import pytest
from langchain_core.utils.iter import batch_iterate
@pytest.mark.parametrize(
"input_size, input_iterable, expected_output",
[
(2, [1, 2, 3, 4, 5], [[1, 2], [3, 4], [5]]),
(3, [10, 20, 30, 40, 50], [[10, 20, 30], [40, 50]]),
(1, [100, 200, 300], [[100], [200], [300]]),
... | import pytest
from langchain_core.utils.iter import batch_iterate
@pytest.mark.parametrize(
"input_size, input_iterable, expected_output",
[
(2, [1, 2, 3, 4, 5], [[1, 2], [3, 4], [5]]),
(3, [10, 20, 30, 40, 50], [[10, 20, 30], [40, 50]]),
(1, [100, 200, 300], [[100], [200], [300]]),
... |
from __future__ import annotations
import sys
from .classification import CrossEncoderClassificationEvaluator
from .correlation import CrossEncoderCorrelationEvaluator
from .deprecated import (
CEBinaryAccuracyEvaluator,
CEBinaryClassificationEvaluator,
CECorrelationEvaluator,
CEF1Evaluator,
CERer... | from __future__ import annotations
# TODO: Consider renaming all evaluators to CrossEncoder..., e.g. CrossEncoderNanoBEIREvaluator, CrossEncoderClassificationEvaluator, etc.
from .CEBinaryAccuracyEvaluator import CEBinaryAccuracyEvaluator
from .CEBinaryClassificationEvaluator import CEBinaryClassificationEvaluator
fro... |
_base_ = './cascade-rcnn_r50_fpn_20e_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='pytorch... | _base_ = './cascade_rcnn_r50_fpn_20e_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='pytorch... |
# Copyright (c) OpenMMLab. All rights reserved.
from ..builder import DETECTORS
from .single_stage import SingleStageDetector
@DETECTORS.register_module()
class PAA(SingleStageDetector):
"""Implementation of `PAA <https://arxiv.org/pdf/2007.08103.pdf>`_."""
def __init__(self,
backbone,
... | from ..builder import DETECTORS
from .single_stage import SingleStageDetector
@DETECTORS.register_module()
class PAA(SingleStageDetector):
"""Implementation of `PAA <https://arxiv.org/pdf/2007.08103.pdf>`_."""
def __init__(self,
backbone,
neck,
bbox_head,
... |
_base_ = [
'../_base_/models/faster-rcnn_r50-caffe-c4.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
| _base_ = [
'../_base_/models/faster-rcnn_r50-caffe-c4.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 = [
... |
"""Dump objects to json."""
import json
from typing import Any
from pydantic import BaseModel
from langchain_core.load.serializable import Serializable, to_json_not_implemented
def default(obj: Any) -> Any:
"""Return a default value for an object.
Args:
obj: The object to serialize to json if it i... | """Dump objects to json."""
import json
from typing import Any
from pydantic import BaseModel
from langchain_core.load.serializable import Serializable, to_json_not_implemented
def default(obj: Any) -> Any:
"""Return a default value for an object.
Args:
obj: The object to serialize to json if it i... |
import json
from jina.logging.logger import JinaLogger
from jina.parsers import set_gateway_parser
from jina.serve.runtimes.gateway.http_fastapi_app import get_fastapi_app
from jina.serve.runtimes.gateway.streamer import GatewayStreamer
JINA_LOGO_URL = 'https://api.jina.ai/logo/logo-product/jina-core/horizontal-layou... | import json
from jina.logging.logger import JinaLogger
from jina.parsers import set_gateway_parser
from jina.serve.runtimes.gateway.http_fastapi_app import get_fastapi_app
from jina.serve.runtimes.gateway.streamer import GatewayStreamer
JINA_LOGO_URL = 'https://api.jina.ai/logo/logo-product/jina-core/horizontal-layou... |
_base_ = './htc_hrnetv2p_w40_20e_coco.py'
# learning policy
max_epochs = 28
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,
by_epo... | _base_ = './htc_hrnetv2p_w40_20e_coco.py'
# learning policy
lr_config = dict(step=[24, 27])
runner = dict(type='EpochBasedRunner', max_epochs=28)
|
# 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... | __all__ = ['reduce', 'reduce_all']
from typing import Dict, List, Optional
from docarray import DocList
def reduce(
left: DocList, right: DocList, left_id_map: Optional[Dict] = None
) -> 'DocList':
"""
Reduces left and right DocList into one DocList in-place.
Changes are applied to the left DocList.... |
"""**OutputParser** classes parse the output of an LLM call.
**Class hierarchy:**
.. code-block::
BaseLLMOutputParser --> BaseOutputParser --> <name>OutputParser # ListOutputParser, PydanticOutputParser
**Main helpers:**
.. code-block::
Serializable, Generation, PromptValue
""" # noqa: E501
from import... | """**OutputParser** classes parse the output of an LLM call.
**Class hierarchy:**
.. code-block::
BaseLLMOutputParser --> BaseOutputParser --> <name>OutputParser # ListOutputParser, PydanticOutputParser
**Main helpers:**
.. code-block::
Serializable, Generation, PromptValue
""" # noqa: E501
from langch... |
import json
from typing import Any, Callable, Iterator, List, Mapping, Optional
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document
RecordHandler = Callable[[Any, Optional[str]], Document]
class AirbyteCDKReader(BaseReader):
"""
AirbyteCDKReader reader.
Ret... | import json
from typing import Any, Callable, Iterator, List, Mapping, Optional
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document
RecordHandler = Callable[[Any, Optional[str]], Document]
class AirbyteCDKReader(BaseReader):
"""AirbyteCDKReader reader.
Retrieve... |
from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Optional, Union
import pyarrow as pa
if TYPE_CHECKING:
from .features import FeatureType
@dataclass
class Translation:
"""`Feature` for translations with fixed languages per example.
Here for compatiblity with tfd... | from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import pyarrow as pa
if TYPE_CHECKING:
from .features import FeatureType
@dataclass
class Translation:
"""`Feature` for translations with fixed languages per example.
Here for compatibl... |
import datasets
from ..folder_based_builder import folder_based_builder
logger = datasets.utils.logging.get_logger(__name__)
class VideoFolderConfig(folder_based_builder.FolderBasedBuilderConfig):
"""BuilderConfig for ImageFolder."""
drop_labels: bool = None
drop_metadata: bool = None
def __post_... | from typing import List
import datasets
from ..folder_based_builder import folder_based_builder
logger = datasets.utils.logging.get_logger(__name__)
class VideoFolderConfig(folder_based_builder.FolderBasedBuilderConfig):
"""BuilderConfig for ImageFolder."""
drop_labels: bool = None
drop_metadata: boo... |
# coding=utf-8
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless r... | # coding=utf-8
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless r... |
import weakref
from keras.src.backend.common import global_state
def set_tensor_attr(tensor, attr, value):
try:
setattr(tensor, attr, value)
except AttributeError:
attr_dict = global_state.get_global_attribute(f"{attr}_dict")
if attr_dict is None:
if value is None:
... | import weakref
from keras.src.backend.common import global_state
def set_tensor_attr(tensor, attr, value):
try:
setattr(tensor, attr, value)
except AttributeError:
if value is None:
return
attr_dict = global_state.get_global_attribute(f"{attr}_dict")
if attr_dict i... |
from typing import TYPE_CHECKING, Optional, Type, TypeVar
from pydantic import AnyUrl as BaseAnyUrl
from pydantic import errors, parse_obj_as
from docarray.typing.abstract_type import AbstractType
from docarray.typing.proto_register import _register_proto
if TYPE_CHECKING:
from pydantic.networks import Parts
... | from typing import TYPE_CHECKING, Optional, Type, TypeVar
from pydantic import AnyUrl as BaseAnyUrl
from pydantic import errors, parse_obj_as
from docarray.typing.abstract_type import AbstractType
if TYPE_CHECKING:
from pydantic.networks import Parts
from docarray.proto import NodeProto
T = TypeVar('T', bo... |
import csv
import os
from pathlib import Path
from typing import Dict, List, Tuple, Union
import torchaudio
from torch import Tensor
from torch.utils.data import Dataset
def load_commonvoice_item(
line: List[str], header: List[str], path: str, folder_audio: str, ext_audio: str
) -> Tuple[Tensor, int, Dict[str, s... | import csv
import os
from pathlib import Path
from typing import Dict, List, Tuple, Union
import torchaudio
from torch import Tensor
from torch.utils.data import Dataset
def load_commonvoice_item(
line: List[str], header: List[str], path: str, folder_audio: str, ext_audio: str
) -> Tuple[Tensor, int, Dict[str, s... |
"""
This file loads sentences from a provided text file. It is expected, that the there is one sentence per line in that text file.
SimCSE will be training using these sentences. Checkpoints are stored every 500 steps to the output folder.
Usage:
python train_simcse_from_file.py path/to/sentences.txt
"""
import gzi... | """
This file loads sentences from a provided text file. It is expected, that the there is one sentence per line in that text file.
SimCSE will be training using these sentences. Checkpoints are stored every 500 steps to the output folder.
Usage:
python train_simcse_from_file.py path/to/sentences.txt
"""
import gzi... |
import copy
from typing import Dict, Tuple
from jina.serve.runtimes.request_handlers.data_request_handler import DataRequestHandler
_SPECIFIC_EXECUTOR_SEPARATOR = '__'
def _spit_key_and_executor_name(key_name: str) -> Tuple[str]:
"""Split a specific key into a key, name pair
ex: 'key__my_executor' will be ... | import copy
from typing import Dict, Tuple
from jina.serve.runtimes.request_handlers.data_request_handler import DataRequestHandler
_SPECIFIC_EXECUTOR_SEPARATOR = '__'
def _spit_key_and_executor_name(key_name: str) -> Tuple[str]:
"""Split a specific key into a key, name pair
ex: 'key__my_executor' will be ... |
from abc import abstractmethod
from typing import TYPE_CHECKING, Dict, List, Optional, Sequence, TypeVar, Union
from docarray import Document, DocumentArray
from docarray.math import ndarray
from docarray.score import NamedScore
from qdrant_client.http import models as rest
from qdrant_client.http.models.models import... | from abc import abstractmethod
from typing import TYPE_CHECKING, Dict, List, Optional, Sequence, TypeVar, Union
from docarray import Document, DocumentArray
from docarray.math import ndarray
from docarray.score import NamedScore
from qdrant_client.http import models as rest
from qdrant_client.http.models.models import... |
# Copyright (c) OpenMMLab. All rights reserved.
from .dist_utils import (DistOptimizerHook, all_reduce_dict, allreduce_grads,
reduce_mean, sync_random_seed)
from .misc import (center_of_mass, filter_scores_and_topk, flip_tensor,
generate_coordinate, mask2ndarray, multi_apply,... | # Copyright (c) OpenMMLab. All rights reserved.
from .dist_utils import (DistOptimizerHook, all_reduce_dict, allreduce_grads,
reduce_mean)
from .misc import (center_of_mass, filter_scores_and_topk, flip_tensor,
generate_coordinate, mask2ndarray, multi_apply,
... |
from typing import TYPE_CHECKING
if TYPE_CHECKING:
from ..providers import ProviderName
from ._base import BaseWebhooksManager
_WEBHOOK_MANAGERS: dict["ProviderName", type["BaseWebhooksManager"]] = {}
# --8<-- [start:load_webhook_managers]
def load_webhook_managers() -> dict["ProviderName", type["BaseWebhoo... | from typing import TYPE_CHECKING
if TYPE_CHECKING:
from ..providers import ProviderName
from ._base import BaseWebhooksManager
_WEBHOOK_MANAGERS: dict["ProviderName", type["BaseWebhooksManager"]] = {}
# --8<-- [start:load_webhook_managers]
def load_webhook_managers() -> dict["ProviderName", type["BaseWebhoo... |
# Copyright (c) OpenMMLab. All rights reserved.
import glob
import os
import os.path as osp
import warnings
from typing import Union
from mmengine.config import Config, ConfigDict
from mmengine.logging import print_log
def find_latest_checkpoint(path, suffix='pth'):
"""Find the latest checkpoint from the working... | # Copyright (c) OpenMMLab. All rights reserved.
import glob
import os
import os.path as osp
import warnings
from mmengine.config import Config, ConfigDict
from mmengine.logging import print_log
def find_latest_checkpoint(path, suffix='pth'):
"""Find the latest checkpoint from the working directory.
Args:
... |
# 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 .augment_wrappers import AutoAugment, RandAugment
from .colorspace import (AutoContrast, Brightness, Color, ColorTransform,
Contrast, Equalize, Invert, Posterize, Sharpness,
Solarize, SolarizeAdd)
from .formatting imp... | # Copyright (c) OpenMMLab. All rights reserved.
from .augment_wrappers import AutoAugment, RandAugment
from .colorspace import (AutoContrast, Brightness, Color, ColorTransform,
Contrast, Equalize, Invert, Posterize, Sharpness,
Solarize, SolarizeAdd)
from .formatting imp... |
_base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# model settings
model = dict(
type='VFNet',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
... | _base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# model settings
model = dict(
type='VFNet',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
... |
# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause
def check_matplotlib_support(caller_name):
"""Raise ImportError with detailed error message if mpl is not installed.
Plot utilities like any of the Display's plotting functions should lazily import
matplotlib and call this hel... | # Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause
def check_matplotlib_support(caller_name):
"""Raise ImportError with detailed error message if mpl is not installed.
Plot utilities like any of the Display's plotting functions should lazily import
matplotlib and call this hel... |
_base_ = [
'../_base_/models/faster-rcnn_r50_fpn.py',
'../common/lsj-200e_coco-detection.py'
]
image_size = (1024, 1024)
batch_augments = [dict(type='BatchFixedSizePad', size=image_size)]
model = dict(data_preprocessor=dict(batch_augments=batch_augments))
train_dataloader = dict(batch_size=8, num_workers=4)
#... | _base_ = [
'../_base_/models/faster_rcnn_r50_fpn.py',
'../common/lsj_200e_coco_detection.py'
]
image_size = (1024, 1024)
batch_augments = [dict(type='BatchFixedSizePad', size=image_size)]
model = dict(data_preprocessor=dict(batch_augments=batch_augments))
train_dataloader = dict(batch_size=8, num_workers=4)
#... |
from __future__ import annotations
import json
import logging
from typing import Any, Dict, List, Literal, Optional
import requests
from langchain_core.callbacks import CallbackManagerForLLMRun
from langchain_core.language_models.llms import LLM
from langchain_core.utils import convert_to_secret_str, get_from_dict_or... | from __future__ import annotations
import json
import logging
from typing import Any, Dict, List, Literal, Optional
import requests
from langchain_core.callbacks import CallbackManagerForLLMRun
from langchain_core.language_models.llms import LLM
from langchain_core.utils import convert_to_secret_str, get_from_dict_or... |
__copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
from typing import Dict, Optional, Tuple
import numpy as np
import paddlehub as hub
from jina import DocumentArray, Executor, requests
from jina_commons.batching import get_docs_batch_generator
class TextPaddl... | __copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
from typing import Dict, Optional, Tuple
import numpy as np
import paddlehub as hub
from jina import DocumentArray, Executor, requests
from jina_commons.batching import get_docs_batch_generator
class TextPaddl... |
from llama_index.core.base.llms.types import (
ChatMessage,
ChatResponse,
ChatResponseAsyncGen,
ChatResponseGen,
CompletionResponse,
CompletionResponseAsyncGen,
CompletionResponseGen,
ImageBlock,
LLMMetadata,
MessageRole,
TextBlock,
AudioBlock,
)
from llama_index.core.llm... | from llama_index.core.base.llms.types import (
ChatMessage,
ChatResponse,
ChatResponseAsyncGen,
ChatResponseGen,
CompletionResponse,
CompletionResponseAsyncGen,
CompletionResponseGen,
ImageBlock,
LLMMetadata,
MessageRole,
TextBlock,
)
from llama_index.core.llms.custom import ... |
import os
import numpy as np
import pytest
import torch
from pydantic import parse_obj_as
from docarray import BaseDoc
from docarray.typing.bytes.audio_bytes import AudioBytes
from docarray.typing.tensor.audio.audio_ndarray import AudioNdArray
from docarray.typing.tensor.audio.audio_torch_tensor import AudioTorchTens... | import os
import numpy as np
import pytest
import torch
from pydantic import parse_obj_as
from docarray import BaseDoc
from docarray.typing.tensor.audio.audio_ndarray import AudioNdArray
from docarray.typing.tensor.audio.audio_torch_tensor import AudioTorchTensor
from docarray.utils._internal.misc import is_tf_availa... |
from docarray import BaseDoc, DocList
def test_instance_and_equivalence():
class MyDoc(BaseDoc):
text: str
docs = DocList[MyDoc]([MyDoc(text='hello')])
assert issubclass(DocList[MyDoc], DocList[MyDoc])
assert issubclass(docs.__class__, DocList[MyDoc])
assert isinstance(docs, DocList[MyD... | from docarray import BaseDoc, DocArray
def test_instance_and_equivalence():
class MyDoc(BaseDoc):
text: str
docs = DocArray[MyDoc]([MyDoc(text='hello')])
assert issubclass(DocArray[MyDoc], DocArray[MyDoc])
assert issubclass(docs.__class__, DocArray[MyDoc])
assert isinstance(docs, DocArr... |
import numpy as np
import pytest
from keras.src import backend
from keras.src import layers
from keras.src import testing
def squared_l2_norm(x):
x = backend.convert_to_numpy(x)
return np.sum(x**2)
class UnitNormalizationTest(testing.TestCase):
@pytest.mark.requires_trainable_backend
def test_un_ba... | import numpy as np
import pytest
from keras.src import backend
from keras.src import layers
from keras.src import testing
def squared_l2_norm(x):
x = backend.convert_to_numpy(x)
return np.sum(x**2)
class UnitNormalizationTest(testing.TestCase):
@pytest.mark.requires_trainable_backend
def test_un_ba... |
import warnings
from abc import ABC
from typing import Any, BinaryIO, Dict, TypeVar, Union
from docarray.typing.tensor.abstract_tensor import AbstractTensor
from docarray.utils.misc import is_notebook
T = TypeVar('T', bound='AbstractAudioTensor')
MAX_INT_16 = 2**15
class AbstractAudioTensor(AbstractTensor, ABC):
... | import warnings
import wave
from abc import ABC
from typing import BinaryIO, TypeVar, Union
from docarray.typing.tensor.abstract_tensor import AbstractTensor
from docarray.utils.misc import is_notebook
T = TypeVar('T', bound='AbstractAudioTensor')
MAX_INT_16 = 2**15
class AbstractAudioTensor(AbstractTensor, ABC):
... |
import pytest
from llama_index.embeddings.nvidia import NVIDIAEmbedding as Interface
from pytest_httpx import HTTPXMock
from requests_mock import Mocker
from contextlib import contextmanager
import os
from typing import Generator, Any
@pytest.fixture()
def mock_local_models(httpx_mock: HTTPXMock, base_url: str):
... | import pytest
from llama_index.embeddings.nvidia import NVIDIAEmbedding as Interface
from pytest_httpx import HTTPXMock
from requests_mock import Mocker
from contextlib import contextmanager
import os
from typing import Generator, Any
@pytest.fixture()
def mock_local_models(httpx_mock: HTTPXMock, base_url: str):
... |
"""Build configuration"""
import dataclasses
from typing import Any, Dict, List, Optional
@dataclasses.dataclass
class BuildConfiguration: # pylint: disable=R0902
"""Configurations use when building libxgboost"""
# Whether to hide C++ symbols in libxgboost.so
hide_cxx_symbols: bool = True
# Whether ... | """Build configuration"""
import dataclasses
from typing import Any, Dict, List, Optional
@dataclasses.dataclass
class BuildConfiguration: # pylint: disable=R0902
"""Configurations use when building libxgboost"""
# Whether to hide C++ symbols in libxgboost.so
hide_cxx_symbols: bool = True
# Whether ... |
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class HfFileSystem(AbstractFileSystem):
"""Interfa... | from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class HfFileSystem(AbstractFileSystem):
"""Interfa... |
import subprocess
import sys
import pytest
from pytest_benchmark.fixture import BenchmarkFixture # type: ignore
@pytest.mark.parametrize(
"import_path",
[
pytest.param(
"from langchain_core.messages import HumanMessage", id="HumanMessage"
),
pytest.param("from langchain_c... | import subprocess
import sys
import pytest
from pytest_benchmark.fixture import BenchmarkFixture # type: ignore
@pytest.mark.parametrize(
"import_path",
[
pytest.param(
"from langchain_core.messages import HumanMessage", id="HumanMessage"
),
pytest.param("from langchain_c... |
"""Decision tree based models for classification and regression."""
# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause
from ._classes import (
BaseDecisionTree,
DecisionTreeClassifier,
DecisionTreeRegressor,
ExtraTreeClassifier,
ExtraTreeRegressor,
)
from ._export impor... | """Decision tree based models for classification and regression."""
# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause
from ._classes import (
BaseDecisionTree,
DecisionTreeClassifier,
DecisionTreeRegressor,
ExtraTreeClassifier,
ExtraTreeRegressor,
)
from ._export impor... |
import sys
from os import path
from setuptools import find_packages
from setuptools import setup
if sys.version_info < (3, 7, 0):
raise OSError(f'DocArray requires Python >=3.7, but yours is {sys.version}')
try:
pkg_name = 'docarray'
libinfo_py = path.join(pkg_name, '__init__.py')
libinfo_content = o... | import sys
from os import path
from setuptools import find_packages
from setuptools import setup
if sys.version_info < (3, 7, 0):
raise OSError(f'DocArray requires Python >=3.7, but yours is {sys.version}')
try:
pkg_name = 'docarray'
libinfo_py = path.join(pkg_name, '__init__.py')
libinfo_content = o... |
__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 laser_encoder import LaserEncoder
_EMBEDDING_DIM = 1024
@pytest.fixture(scope='session')
... | __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 ...laser_encoder import LaserEncoder
_EMBEDDING_DIM = 1024
@pytest.fixture(scope='sessio... |
# Copyright (c) OpenMMLab. All rights reserved.
from .atss import ATSS
from .autoassign import AutoAssign
from .base import BaseDetector
from .cascade_rcnn import CascadeRCNN
from .centernet import CenterNet
from .cornernet import CornerNet
from .deformable_detr import DeformableDETR
from .detr import DETR
from .fast_r... | # Copyright (c) OpenMMLab. All rights reserved.
from .atss import ATSS
from .autoassign import AutoAssign
from .base import BaseDetector
from .cascade_rcnn import CascadeRCNN
from .centernet import CenterNet
from .cornernet import CornerNet
from .deformable_detr import DeformableDETR
from .detr import DETR
from .fast_r... |
from typing import Annotated, Any, Literal, Optional, TypedDict
from uuid import uuid4
from pydantic import BaseModel, Field, SecretStr, field_serializer
class _BaseCredentials(BaseModel):
id: str = Field(default_factory=lambda: str(uuid4()))
provider: str
title: Optional[str]
@field_serializer("*")... | from typing import Annotated, Any, Literal, Optional, TypedDict
from uuid import uuid4
from pydantic import BaseModel, Field, SecretStr, field_serializer
class _BaseCredentials(BaseModel):
id: str = Field(default_factory=lambda: str(uuid4()))
provider: str
title: Optional[str]
@field_serializer("*")... |
_base_ = './fcos_r50-caffe_fpn_gn-head_1x_coco.py'
# dataset settings
train_pipeline = [
dict(
type='LoadImageFromFile',
file_client_args={{_base_.file_client_args}}),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='RandomChoiceResize',
scale=[(1333, 640), (1333, 8... | _base_ = './fcos_r50_caffe_fpn_gn-head_1x_coco.py'
# dataset settings
train_pipeline = [
dict(
type='LoadImageFromFile',
file_client_args={{_base_.file_client_args}}),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='RandomChoiceResize',
scale=[(1333, 640), (1333, 8... |
# Copyright (c) OpenMMLab. All rights reserved.
from unittest import TestCase
import pytest
import torch
from mmengine.data import LabelData
class TestLabelData(TestCase):
def test_label_to_onehot(self):
item = torch.tensor([1], dtype=torch.int64)
num_classes = 10
onehot = LabelData.lab... | # Copyright (c) OpenMMLab. All rights reserved.
from unittest import TestCase
import torch
from mmengine.data import LabelData
class TestLabelData(TestCase):
def test_label_to_onehot(self):
item = torch.tensor([1], dtype=torch.int64)
num_classes = 10
onehot = LabelData.label_to_onehot(l... |
_base_ = [
'../_base_/models/faster_rcnn_r50_caffe_c4.py',
'../_base_/schedules/schedule_1x.py', '../_base_/datasets/voc0712.py',
'../_base_/default_runtime.py'
]
model = dict(roi_head=dict(bbox_head=dict(num_classes=20)))
# dataset settings
train_pipeline = [
dict(
type='LoadImageFromFile',
... | _base_ = [
'../_base_/models/faster_rcnn_r50_caffe_c4.py',
'../_base_/default_runtime.py'
]
model = dict(roi_head=dict(bbox_head=dict(num_classes=20)))
# dataset settings
dataset_type = 'VOCDataset'
data_root = 'data/VOCdevkit/'
img_norm_cfg = dict(
mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to... |
from typing import Union, Iterable, Dict
from ..base.seqlike import BaseSequenceLikeMixin
from .... import Document
class SequenceLikeMixin(BaseSequenceLikeMixin):
"""Implement sequence-like methods for DocumentArray with Elastic as storage"""
def __eq__(self, other):
"""Compare this object to the o... | from typing import Union, Iterable, Dict
from ..base.seqlike import BaseSequenceLikeMixin
from .... import Document
class SequenceLikeMixin(BaseSequenceLikeMixin):
"""Implement sequence-like methods for DocumentArray with Elastic as storage"""
def __eq__(self, other):
"""Compare this object to the o... |
class DataAdapter:
"""Base class for input data adapters.
The purpose of a DataAdapter is to provide a unfied interface to
iterate over input data provided in a variety of formats -- such as
NumPy arrays, tf.Tensors, tf.data.Datasets, Keras PyDatasets, etc.
"""
def get_numpy_iterator(self):
... | class DataAdapter(object):
"""Base class for input data adapters.
The purpose of a DataAdapter is to provide a unfied interface to
iterate over input data provided in a variety of formats -- such as
NumPy arrays, tf.Tensors, tf.data.Datasets, Keras PyDatasets, etc.
"""
def get_numpy_iterator(s... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.