input stringlengths 33 5k | output stringlengths 32 5k |
|---|---|
# Copyright (c) OpenMMLab. All rights reserved.
import logging
import os.path as osp
from argparse import ArgumentParser
import mmcv
from mmengine.config import Config
from mmengine.logging import MMLogger
from mmengine.utils import mkdir_or_exist
from mmdet.apis import inference_detector, init_detector
from mmdet.re... | # Copyright (c) OpenMMLab. All rights reserved.
import logging
import os.path as osp
from argparse import ArgumentParser
import mmcv
from mmengine.config import Config
from mmengine.logging import MMLogger
from mmengine.utils import mkdir_or_exist
from mmdet.apis import inference_detector, init_detector
from mmdet.re... |
import numpy as np
import pytest
from keras.src import testing
from keras.src.layers.activations import leaky_relu
class LeakyReLUTest(testing.TestCase):
@pytest.mark.requires_trainable_backend
def test_leaky_relu(self):
self.run_layer_test(
leaky_relu.LeakyReLU,
init_kwargs={... | import numpy as np
import pytest
from keras.src import testing
from keras.src.layers.activations import leaky_relu
class LeakyReLUTest(testing.TestCase):
@pytest.mark.requires_trainable_backend
def test_leaky_relu(self):
self.run_layer_test(
leaky_relu.LeakyReLU,
init_kwargs={... |
import numpy as np
import pytest
import torch
from docarray.base_doc import BaseDoc
from docarray.base_doc.io.json import orjson_dumps
from docarray.typing import AnyUrl, NdArray, TorchTensor
@pytest.fixture()
def doc_and_class():
class Mmdoc(BaseDoc):
img: NdArray
url: AnyUrl
txt: str
... | import numpy as np
import pytest
import torch
from docarray.base_doc import BaseDoc
from docarray.base_doc.io.json import orjson_dumps
from docarray.typing import AnyUrl, NdArray, TorchTensor
@pytest.fixture()
def doc_and_class():
class Mmdoc(BaseDoc):
img: NdArray
url: AnyUrl
txt: str
... |
# flake8: noqa
# Copyright 2020 The HuggingFace Datasets Authors and the TensorFlow Datasets Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LI... | # flake8: noqa
# Copyright 2020 The HuggingFace Datasets Authors and the TensorFlow Datasets Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LI... |
import warnings
from abc import ABC
from typing import TYPE_CHECKING, Any, BinaryIO, Dict, TypeVar, Union
from docarray.typing.tensor.abstract_tensor import AbstractTensor
from docarray.utils._internal.misc import import_library, is_notebook
if TYPE_CHECKING:
from docarray.typing.bytes.audio_bytes import AudioByt... | import warnings
from abc import ABC
from typing import TYPE_CHECKING, Any, BinaryIO, Dict, TypeVar, Union
from docarray.typing.tensor.abstract_tensor import AbstractTensor
from docarray.utils._internal.misc import import_library, is_notebook
if TYPE_CHECKING:
from docarray.typing.bytes.audio_bytes import AudioByt... |
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.torch_tensor import TorchTensor, metaTorchAndNode
from docarray.typing.tensor.video.video_tensor_mixin import VideoTensorMixin
T = TypeVar... | 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.torch_tensor import TorchTensor, metaTorchAndNode
from docarray.typing.tensor.video.video_tensor_mixin import VideoTensorMixin
T = TypeVar... |
"""
Video audio parser.
Contains parsers for mp3, mp4 files.
"""
from pathlib import Path
from typing import Any, Dict, List, Optional, cast
import logging
from fsspec import AbstractFileSystem
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document
logger = logging.getLog... | """Video audio parser.
Contains parsers for mp3, mp4 files.
"""
from pathlib import Path
from typing import Any, Dict, List, Optional, cast
import logging
from fsspec import AbstractFileSystem
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document
logger = logging.getLogg... |
from __future__ import annotations
from .splade_callbacks import SchedulerType, SpladeWeightRegulizerSchedulerCallback
__all__ = ["SpladeWeightRegulizerSchedulerCallback", "SchedulerType"]
| from __future__ import annotations
from .splade_callbacks import SchedulerType, SpladeLambdaSchedulerCallback
__all__ = ["SpladeLambdaSchedulerCallback", "SchedulerType"]
|
_base_ = ['./ld_r18-gflv1-r101_fpn_1x_coco.py']
teacher_ckpt = 'https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco_20200630_102002-134b07df.pth' # noqa
model = dict(
teacher_config='configs/gfl/gfl_r101-dconv-c3-c5_fpn_ms-2x_coco.py... | _base_ = ['./ld_r18-gflv1-r101_fpn_1x_coco.py']
teacher_ckpt = 'https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco_20200630_102002-134b07df.pth' # noqa
model = dict(
teacher_config='configs/gfl/gfl_r101-dconv-c3-c5_fpn_ms-2x_coco.py... |
# CREDITS: https://github.com/openai/CLIP
import gzip
import html
from functools import lru_cache
from pathlib import Path
import ftfy
import regex as re
@lru_cache()
def default_bpe():
return str(Path(__file__).parents[2] / '.cache/bpe_simple_vocab_16e6.txt.gz')
@lru_cache()
def bytes_to_unicode():
"""
... | # CREDITS: https://github.com/openai/CLIP
import gzip
import html
import os
from functools import lru_cache
import ftfy
import regex as re
@lru_cache()
def default_bpe():
return os.path.join(os.getcwd(), '.cache', 'bpe_simple_vocab_16e6.txt.gz')
@lru_cache()
def bytes_to_unicode():
"""
Returns list of... |
# Copyright (c) OpenMMLab. All rights reserved.
import ast
import os.path as osp
import re
import warnings
from typing import Tuple
from mmengine.fileio import load
from mmengine.utils import check_file_exist
PKG2PROJECT = {
'mmcls': 'mmcls',
'mmdet': 'mmdet',
'mmdet3d': 'mmdet3d',
'mmseg': 'mmsegment... | # Copyright (c) OpenMMLab. All rights reserved.
import ast
class RemoveAssignFromAST(ast.NodeTransformer):
"""Remove Assign node if the target's name match the key.
Args:
key (str): The target name of the Assign node.
"""
def __init__(self, key):
self.key = key
def visit_Assign(... |
from typing import (
TYPE_CHECKING,
Iterable,
)
from docarray.array.memory import DocumentArrayInMemory
if TYPE_CHECKING:
from docarray.document import Document
class ChunkArray(DocumentArrayInMemory):
"""
:class:`ChunkArray` inherits from :class:`DocumentArray`.
It's a subset of Documents.
... | from typing import (
TYPE_CHECKING,
Iterable,
)
from .memory import DocumentArrayInMemory
if TYPE_CHECKING:
from ..document import Document
class ChunkArray(DocumentArrayInMemory):
"""
:class:`ChunkArray` inherits from :class:`DocumentArray`.
It's a subset of Documents.
:param docs: Set... |
from __future__ import annotations
import logging
from dataclasses import dataclass
from sentence_transformers.data_collator import SentenceTransformerDataCollator
logger = logging.getLogger(__name__)
@dataclass
class SparseEncoderDataCollator(SentenceTransformerDataCollator):
"""Collator for a SparseEncoder m... | from __future__ import annotations
import logging
from dataclasses import dataclass
from sentence_transformers.data_collator import SentenceTransformerDataCollator
logger = logging.getLogger(__name__)
@dataclass
class SparseEncoderDataCollator(SentenceTransformerDataCollator):
"""Collator for a SparseEncoder m... |
# Copyright (c) OpenMMLab. All rights reserved.
from unittest import TestCase
import torch
from mmengine import Config
from mmengine.structures import InstanceData
from mmdet import * # noqa
from mmdet.models.dense_heads import DDODHead
class TestDDODHead(TestCase):
def test_ddod_head_loss(self):
"""T... | # Copyright (c) OpenMMLab. All rights reserved.
from unittest import TestCase
import torch
from mmengine import Config
from mmengine.data import InstanceData
from mmdet import * # noqa
from mmdet.models.dense_heads import DDODHead
class TestDDODHead(TestCase):
def test_ddod_head_loss(self):
"""Tests d... |
# Copyright 2020 The HuggingFace Datasets Authors and the TensorFlow Datasets Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# U... | # Copyright 2020 The HuggingFace Datasets Authors and the TensorFlow Datasets Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# U... |
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... |
"""
This script finds the person responsible for labeling a PR by a commit SHA. It is used by the workflow in
'.github/workflows/pr-labels.yml'.
Note: we only ping the person who pulls the pr, not the reviewers, as the reviewers can sometimes be external
to torchaudio with no labeling responsibility, so we don't want t... | """
This script finds the person responsible for labeling a PR by a commit SHA. It is used by the workflow in
'.github/workflows/pr-labels.yml'.
Note: we only ping the person who pulls the pr, not the reviewers, as the reviewers can sometimes be external
to torchaudio with no labeling responsibility, so we don't want t... |
import datetime
from typing import List
import prisma.enums
import pydantic
class Pagination(pydantic.BaseModel):
total_items: int = pydantic.Field(
description="Total number of items.", examples=[42]
)
total_pages: int = pydantic.Field(
description="Total number of pages.", examples=[97]... | import datetime
from typing import List
import prisma.enums
import pydantic
class Pagination(pydantic.BaseModel):
total_items: int = pydantic.Field(
description="Total number of items.", examples=[42]
)
total_pages: int = pydantic.Field(
description="Total number of pages.", examples=[97]... |
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})... | 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 abc import ABC
from typing import Any, Callable, Dict, List, Optional, Union, TypeVar
from llama_index.core.llms import ChatMessage
from llama_index.core.memory import BaseMemory
from llama_index.core.workflow import (
Context,
)
from llama_index.core.workflow.checkpointer import CheckpointCallback
from llama... | from abc import ABC
from typing import Any, Callable, Dict, List, Optional, Union, TypeVar
from llama_index.core.llms import ChatMessage
from llama_index.core.memory import BaseMemory
from llama_index.core.workflow import (
Context,
)
from llama_index.core.workflow.checkpointer import CheckpointCallback
from llama... |
from typing import TYPE_CHECKING
from ...utils import (
DIFFUSERS_SLOW_IMPORT,
OptionalDependencyNotAvailable,
_LazyModule,
get_objects_from_module,
is_torch_available,
is_transformers_available,
)
_dummy_objects = {}
_import_structure = {}
try:
if not (is_transformers_available() and i... | from typing import TYPE_CHECKING
from ...utils import (
DIFFUSERS_SLOW_IMPORT,
OptionalDependencyNotAvailable,
_LazyModule,
get_objects_from_module,
is_torch_available,
is_transformers_available,
)
_dummy_objects = {}
_import_structure = {}
try:
if not (is_transformers_available() and i... |
_base_ = [
'../_base_/models/faster-rcnn_r50_fpn.py', '../_base_/datasets/voc0712.py',
'../_base_/default_runtime.py'
]
model = dict(roi_head=dict(bbox_head=dict(num_classes=20)))
METAINFO = {
'classes':
('aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat',
'chair', 'cow', 'dinin... | _base_ = [
'../_base_/models/faster-rcnn_r50_fpn.py', '../_base_/datasets/voc0712.py',
'../_base_/default_runtime.py'
]
model = dict(roi_head=dict(bbox_head=dict(num_classes=20)))
METAINFO = {
'classes':
('aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat',
'chair', 'cow', 'dinin... |
_base_ = [
'../_base_/default_runtime.py', '../_base_/datasets/coco_detection.py'
]
data_preprocessor = dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
bgr_to_rgb=True)
# model settings
model = dict(
type='CornerNet',
data_preprocessor=data_pr... | _base_ = [
'../_base_/default_runtime.py', '../_base_/datasets/coco_detection.py'
]
data_preprocessor = dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
bgr_to_rgb=True)
# model settings
model = dict(
type='CornerNet',
data_preprocessor=data_pr... |
"""Tool for the SceneXplain API."""
from typing import Optional
from langchain_core.callbacks import CallbackManagerForToolRun
from langchain_core.tools import BaseTool
from pydantic import BaseModel, Field
from langchain_community.utilities.scenexplain import SceneXplainAPIWrapper
class SceneXplainInput(BaseModel... | """Tool for the SceneXplain API."""
from typing import Optional
from langchain_core.callbacks import CallbackManagerForToolRun
from langchain_core.tools import BaseTool
from pydantic import BaseModel, Field
from langchain_community.utilities.scenexplain import SceneXplainAPIWrapper
class SceneXplainInput(BaseModel... |
from .proto import ProtoArrayMixin
| from abc import ABC
from docarray.array.mixins.content import ContentPropertyMixin
from docarray.array.mixins.delitem import DelItemMixin
from docarray.array.mixins.embed import EmbedMixin
from docarray.array.mixins.empty import EmptyMixin
from docarray.array.mixins.evaluation import EvaluationMixin
from docarray.arra... |
# Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
from mmcv.cnn import ConvModule, DepthwiseSeparableConvModule
from mmcv.runner import BaseModule
from ..builder import NECKS
@NECKS.register_module()
class SSDNeck(BaseModule):
"""Extra layers of SSD backbone to generate multi-sca... | import torch
import torch.nn as nn
from mmcv.cnn import ConvModule, DepthwiseSeparableConvModule
from mmcv.runner import BaseModule
from ..builder import NECKS
@NECKS.register_module()
class SSDNeck(BaseModule):
"""Extra layers of SSD backbone to generate multi-scale feature maps.
Args:
in_channels ... |
# 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.typing.url.any_url import AnyUrl
from docarray.typing.url.audio_url import AudioUrl
from docarray.typing.url.image_url import ImageUrl
from docarray.typing.url.text_url import TextUrl
from docarray.typing.url.url_3d.mesh_url import Mesh3DUrl
from docarray.typing.url.url_3d.point_cloud_url import PointClou... |
import os
import pytest
from llama_index.core.tools.tool_spec.base import BaseToolSpec
from llama_index.tools.notion import NotionToolSpec
# Get yourself a page id and database id from your notion account
# Refer to the page: https://developers.notion.com/docs/create-a-notion-integration#give-your-integration-page-pe... | from llama_index.core.tools.tool_spec.base import BaseToolSpec
from llama_index.tools.notion import NotionToolSpec
def test_class():
names_of_base_classes = [b.__name__ for b in NotionToolSpec.__mro__]
assert BaseToolSpec.__name__ in names_of_base_classes
|
"""Google Search tool spec."""
import json
import urllib.parse
from typing import Optional
import requests
from llama_index.core.schema import Document
from llama_index.core.tools.tool_spec.base import BaseToolSpec
QUERY_URL_TMPL = (
"https://www.googleapis.com/customsearch/v1?key={key}&cx={engine}&q={query}"
)
... | """Google Search tool spec."""
import urllib.parse
from typing import Optional
import requests
from llama_index.core.schema import Document
from llama_index.core.tools.tool_spec.base import BaseToolSpec
QUERY_URL_TMPL = (
"https://www.googleapis.com/customsearch/v1?key={key}&cx={engine}&q={query}"
)
class Goog... |
from typing import Any, Dict, Optional, Type, cast
from llama_index.core.llms.llm import LLM
from llama_index.core.output_parsers.pydantic import PydanticOutputParser
from llama_index.core.prompts.base import BasePromptTemplate, PromptTemplate
from llama_index.core.settings import Settings
from llama_index.core.types ... | from typing import Any, Dict, Optional, Type, cast
from llama_index.core.bridge.pydantic import BaseModel
from llama_index.core.llms.llm import LLM
from llama_index.core.output_parsers.pydantic import PydanticOutputParser
from llama_index.core.prompts.base import BasePromptTemplate, PromptTemplate
from llama_index.cor... |
"""
This file contains deprecated code that can only be used with the old `model.fit`-style Sentence Transformers v2.X training.
It exists for backwards compatibility with the `model.old_fit` method, but will be removed in a future version.
Nowadays, with Sentence Transformers v3+, it is recommended to use the `Senten... | from __future__ import annotations
import numpy as np
from torch.utils.data import Dataset
from transformers.utils.import_utils import NLTK_IMPORT_ERROR, is_nltk_available
from sentence_transformers.readers.InputExample import InputExample
class DenoisingAutoEncoderDataset(Dataset):
"""
The DenoisingAutoEnc... |
__version__ = '0.12.4'
import os
from .document import Document
from .array import DocumentArray
from .dataclasses import dataclass, field
if 'DA_NO_RICH_HANDLER' not in os.environ:
from rich.traceback import install
install()
| __version__ = '0.12.3'
import os
from .document import Document
from .array import DocumentArray
from .dataclasses import dataclass, field
if 'DA_NO_RICH_HANDLER' not in os.environ:
from rich.traceback import install
install()
|
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
pytestmark = pytest.mark.integration
@pytest.mark.parametrize("path", ["paws", "csv"])
def test_inspect_dataset(p... | import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
pytestmark = pytest.mark.integration
@pytest.mark.parametrize("path", ["paws", "csv"])
def test_inspect_dataset(p... |
import orjson
from pydantic.json import ENCODERS_BY_TYPE
from docarray.typing.abstract_type import AbstractType
def _default_orjson(obj):
"""
default option for orjson dumps.
:param obj:
:return: return a json compatible object
"""
if isinstance(obj, AbstractType):
return obj._docarr... | import orjson
from docarray.typing.tensor.abstract_tensor import AbstractTensor
def _default_orjson(obj):
"""
default option for orjson dumps.
:param obj:
:return: return a json compatible object
"""
if isinstance(obj, AbstractTensor):
return obj._docarray_to_json_compatible()
el... |
from markitdown import MarkItDown
from llama_index.core.bridge.pydantic import BaseModel, model_validator
import os
from pathlib import Path
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document
from typing import Tuple, Optional, Union, List
from typing_extensions imp... | from markitdown import MarkItDown
from llama_index.core.bridge.pydantic import BaseModel, model_validator
import os
from pathlib import Path
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document
from typing import Tuple, Optional, Union, List
from typing_extensions imp... |
from pathlib import Path
from typing import Any
from langchain_core._api.path import as_import_path
def __getattr__(name: str) -> Any:
"""Get attr name."""
if name == "create_csv_agent":
# Get directory of langchain package
HERE = Path(__file__).parents[3]
here = as_import_path(Path(... | from pathlib import Path
from typing import Any
from langchain_core._api.path import as_import_path
def __getattr__(name: str) -> Any:
"""Get attr name."""
if name == "create_csv_agent":
# Get directory of langchain package
HERE = Path(__file__).parents[3]
here = as_import_path(Path(... |
_base_ = './mask-rcnn_x50-32x4d_fpn_gn-ws-all_2x_coco.py'
# learning policy
max_epochs = 24
train_cfg = dict(max_epochs=max_epochs)
# learning rate
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
... | _base_ = './mask_rcnn_x50_32x4d_fpn_gn_ws-all_2x_coco.py'
# learning policy
max_epochs = 24
train_cfg = dict(max_epochs=max_epochs)
# learning rate
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
... |
import datetime
import autogpt_libs.auth as autogpt_auth_lib
import fastapi.testclient
import pytest
import pytest_mock
import backend.server.model as server_model
import backend.server.v2.library.model as library_model
from backend.server.v2.library.routes import router as library_router
app = fastapi.FastAPI()
app... | import datetime
import autogpt_libs.auth as autogpt_auth_lib
import fastapi
import fastapi.testclient
import pytest
import pytest_mock
import backend.server.model as server_model
import backend.server.v2.library.model as library_model
from backend.server.v2.library.routes import router as library_router
app = fastap... |
from .database import DatabaseManager, DatabaseManagerAsyncClient, DatabaseManagerClient
from .manager import ExecutionManager
from .scheduler import Scheduler
__all__ = [
"DatabaseManager",
"DatabaseManagerClient",
"DatabaseManagerAsyncClient",
"ExecutionManager",
"Scheduler",
]
| from .database import DatabaseManager, DatabaseManagerClient
from .manager import ExecutionManager
from .scheduler import Scheduler
__all__ = [
"DatabaseManager",
"DatabaseManagerClient",
"ExecutionManager",
"Scheduler",
]
|
from __future__ import annotations
import json
import logging
import os
from typing import Literal
import torch
from torch import Tensor, nn
from .tokenizer import WhitespaceTokenizer
logger = logging.getLogger(__name__)
class BoW(nn.Module):
"""Implements a Bag-of-Words (BoW) model to derive sentence embeddi... | from __future__ import annotations
import json
import logging
import os
from typing import Literal
import torch
from torch import Tensor, nn
from .tokenizer import WhitespaceTokenizer
logger = logging.getLogger(__name__)
class BoW(nn.Module):
"""Implements a Bag-of-Words (BoW) model to derive sentence embeddi... |
"""langchain-core version information and utilities."""
VERSION = "0.3.62"
| """langchain-core version information and utilities."""
VERSION = "0.3.61"
|
"""Parser for JSON output."""
from __future__ import annotations
import json
from json import JSONDecodeError
from typing import Annotated, Any, Optional, TypeVar, Union
import jsonpatch # type: ignore[import-untyped]
import pydantic
from pydantic import SkipValidation
from langchain_core.exceptions import OutputP... | """Parser for JSON output."""
from __future__ import annotations
import json
from json import JSONDecodeError
from typing import Annotated, Any, Optional, TypeVar, Union
import jsonpatch # type: ignore[import-untyped]
import pydantic
from pydantic import SkipValidation
from langchain_core.exceptions import OutputP... |
from keras.src.backend.common.name_scope import name_scope
from keras.src.backend.jax import core
from keras.src.backend.jax import distribution_lib
from keras.src.backend.jax import image
from keras.src.backend.jax import linalg
from keras.src.backend.jax import math
from keras.src.backend.jax import nn
from keras.src... | from keras.src.backend.jax import core
from keras.src.backend.jax import distribution_lib
from keras.src.backend.jax import image
from keras.src.backend.jax import linalg
from keras.src.backend.jax import math
from keras.src.backend.jax import nn
from keras.src.backend.jax import numpy
from keras.src.backend.jax import... |
from llama_index_instrumentation.span.simple import SimpleSpan # noqa
| from typing import Dict, Optional
from llama_index.core.bridge.pydantic import Field
from llama_index.core.instrumentation.span.base import BaseSpan
from datetime import datetime
class SimpleSpan(BaseSpan):
"""Simple span class."""
start_time: datetime = Field(default_factory=lambda: datetime.now())
end_... |
import os
from pathlib import Path
from typing import List, Tuple, Union
from torch import Tensor
from torch.hub import download_url_to_file
from torch.utils.data import Dataset
from torchaudio.datasets.librispeech import load_librispeech_item
from torchaudio.datasets.utils import extract_archive
_ARCHIVE_NAME = "li... | import os
from pathlib import Path
from typing import List, Tuple, Union
from torch import Tensor
from torch.hub import download_url_to_file
from torch.utils.data import Dataset
from torchaudio.datasets.librispeech import load_librispeech_item
from torchaudio.datasets.utils import extract_archive
_ARCHIVE_NAME = "li... |
# Copyright (c) OpenMMLab. All rights reserved.
from typing import Tuple
import torch.nn as nn
from mmcv.cnn import ConvModule, bias_init_with_prob, normal_init
from torch import Tensor
from mmdet.core.utils import OptConfigType, OptMultiConfig
from mmdet.registry import MODELS
from .anchor_head import AnchorHead
@... | # Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
from mmcv.cnn import ConvModule, bias_init_with_prob, normal_init
from mmdet.registry import MODELS
from .anchor_head import AnchorHead
@MODELS.register_module()
class RetinaSepBNHead(AnchorHead):
""""RetinaHead with separate BN.
In Retin... |
from langchain_core.utils.utils import (
build_extra_kwargs,
check_package_version,
convert_to_secret_str,
get_pydantic_field_names,
guard_import,
mock_now,
raise_for_status_with_text,
xor_args,
)
__all__ = [
"build_extra_kwargs",
"check_package_version",
"convert_to_secret_... | from langchain_core.utils.utils import (
build_extra_kwargs,
check_package_version,
convert_to_secret_str,
get_pydantic_field_names,
guard_import,
mock_now,
raise_for_status_with_text,
xor_args,
)
__all__ = [
"xor_args",
"raise_for_status_with_text",
"mock_now",
"guard_i... |
import os
import pytest
import torch
import whisper
@pytest.mark.parametrize("model_name", whisper.available_models())
def test_transcribe(model_name: str):
device = "cuda" if torch.cuda.is_available() else "cpu"
model = whisper.load_model(model_name).to(device)
audio_path = os.path.join(os.path.dirname... | import os
import pytest
import torch
import whisper
@pytest.mark.parametrize("model_name", whisper.available_models())
def test_transcribe(model_name: str):
device = "cuda" if torch.cuda.is_available() else "cpu"
model = whisper.load_model(model_name).to(device)
audio_path = os.path.join(os.path.dirname... |
import torch
_TORCHFUNCTION_SUBCLASS = False
class _ReturnTypeCM:
def __init__(self, to_restore):
self.to_restore = to_restore
def __enter__(self):
return self
def __exit__(self, *args):
global _TORCHFUNCTION_SUBCLASS
_TORCHFUNCTION_SUBCLASS = self.to_restore
def set_r... | import torch
_TORCHFUNCTION_SUBCLASS = False
class _ReturnTypeCM:
def __init__(self, to_restore):
self.to_restore = to_restore
def __enter__(self):
return self
def __exit__(self, *args):
global _TORCHFUNCTION_SUBCLASS
_TORCHFUNCTION_SUBCLASS = self.to_restore
def set_r... |
# Copyright (c) OpenMMLab. All rights reserved.
"""Collecting some commonly used type hint in mmdetection."""
from typing import Dict, List, Optional, Sequence, Tuple, Union
import torch
from mmengine.config import ConfigDict
from mmengine.data import InstanceData, PixelData
from ..bbox.samplers import SamplingResult... | # Copyright (c) OpenMMLab. All rights reserved.
"""Collecting some commonly used type hint in mmdetection."""
from typing import Dict, List, Optional, Tuple, Union
import torch
from mmengine.config import ConfigDict
from mmengine.data import InstanceData, PixelData
from ..bbox.samplers import SamplingResult
from ..da... |
# coding=utf-8
# Copyright 2022 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 2022 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... |
"""String utilities."""
from typing import Any
def stringify_value(val: Any) -> str:
"""Stringify a value.
Args:
val: The value to stringify.
Returns:
str: The stringified value.
"""
if isinstance(val, str):
return val
elif isinstance(val, dict):
return "\n" ... | from typing import Any
def stringify_value(val: Any) -> str:
"""Stringify a value.
Args:
val: The value to stringify.
Returns:
str: The stringified value.
"""
if isinstance(val, str):
return val
elif isinstance(val, dict):
return "\n" + stringify_dict(val)
... |
import abc
import io
import pathlib
import pickle
from typing import Any, BinaryIO, cast, Dict, Iterator, List, Optional, Tuple, Union
import numpy as np
from torchdata.datapipes.iter import Filter, IterDataPipe, Mapper
from torchvision.datapoints import Image
from torchvision.prototype.datapoints import Label
from to... | import abc
import io
import pathlib
import pickle
from typing import Any, BinaryIO, cast, Dict, Iterator, List, Optional, Tuple, Union
import numpy as np
from torchdata.datapipes.iter import Filter, IterDataPipe, Mapper
from torchvision.prototype.datapoints import Image, Label
from torchvision.prototype.datasets.utils... |
# Copyright (c) OpenMMLab. All rights reserved.
__version__ = '0.3.0'
def parse_version_info(version_str):
"""Parse the version information.
Args:
version_str (str): version string like '0.1.0'.
Returns:
tuple: version information contains major, minor, micro version.
"""
versio... | # Copyright (c) OpenMMLab. All rights reserved.
__version__ = '0.2.0'
def parse_version_info(version_str):
"""Parse the version information.
Args:
version_str (str): version string like '0.1.0'.
Returns:
tuple: version information contains major, minor, micro version.
"""
versio... |
try:
from ._load_gpu_decoder import _HAS_GPU_VIDEO_DECODER
except ModuleNotFoundError:
_HAS_GPU_VIDEO_DECODER = False
from ._video_opt import (
_HAS_CPU_VIDEO_DECODER,
_HAS_VIDEO_OPT,
_probe_video_from_file,
_probe_video_from_memory,
_read_video_from_file,
_read_video_from_memory,
_... | from typing import Any, Dict, Iterator
import torch
from ..utils import _log_api_usage_once
try:
from ._load_gpu_decoder import _HAS_GPU_VIDEO_DECODER
except ModuleNotFoundError:
_HAS_GPU_VIDEO_DECODER = False
from ._video_opt import (
_HAS_CPU_VIDEO_DECODER,
_HAS_VIDEO_OPT,
_probe_video_from_fi... |
import logging
from typing import TYPE_CHECKING
if TYPE_CHECKING:
from backend.util.process import AppProcess
logger = logging.getLogger(__name__)
def run_processes(*processes: "AppProcess", **kwargs):
"""
Execute all processes in the app. The last process is run in the foreground.
Includes enhanced... | from typing import TYPE_CHECKING
if TYPE_CHECKING:
from backend.util.process import AppProcess
def run_processes(*processes: "AppProcess", **kwargs):
"""
Execute all processes in the app. The last process is run in the foreground.
"""
try:
for process in processes[:-1]:
proces... |
import os
import pytest
import torch
import torchaudio
class GreedyCTCDecoder(torch.nn.Module):
def __init__(self, labels, blank: int = 0):
super().__init__()
self.blank = blank
self.labels = labels
def forward(self, logits: torch.Tensor) -> str:
"""Given a sequence logits ov... | import pytest
import torch
import torchaudio
class GreedyCTCDecoder(torch.nn.Module):
def __init__(self, labels, blank: int = 0):
super().__init__()
self.blank = blank
self.labels = labels
def forward(self, logits: torch.Tensor) -> str:
"""Given a sequence logits over labels, ... |
from workflows.errors import (
ContextSerdeError, # noqa
WorkflowCancelledByUser, # noqa
WorkflowConfigurationError, # noqa
WorkflowDone, # noqa
WorkflowRuntimeError, # noqa
WorkflowStepDoesNotExistError, # noqa
WorkflowTimeoutError, # noqa
WorkflowValidationError, # noqa
)
| class WorkflowValidationError(Exception):
pass
class WorkflowTimeoutError(Exception):
pass
class WorkflowRuntimeError(Exception):
pass
class WorkflowDone(Exception):
pass
class WorkflowCancelledByUser(Exception):
pass
class WorkflowStepDoesNotExistError(Exception):
pass
class Workflo... |
import asyncio
import sys
import pytest
from llama_index.core import Document
from llama_index.graph_rag.cognee import CogneeGraphRAG
def test_smoke():
"""No-op test: CI will fail if no tests are collected."""
@pytest.mark.skipif(
sys.version_info < (3, 10), reason="mock strategy requires python3.10 or hig... | from llama_index.core import Document
import asyncio
import pytest
from llama_index.graph_rag.cognee import CogneeGraphRAG
@pytest.mark.asyncio()
async def test_add_data(monkeypatch):
# Instantiate cognee GraphRAG
cogneeGraphRAG = CogneeGraphRAG(
llm_api_key="",
llm_provider="openai",
... |
# flake8: noqa
# Copyright 2020 The HuggingFace Datasets Authors and the TensorFlow Datasets Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LI... | # flake8: noqa
# Copyright 2020 The HuggingFace Datasets Authors and the TensorFlow Datasets Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LI... |
import PIL.Image
import torch
from torchvision import datapoints
from torchvision.transforms.functional import pil_to_tensor, to_pil_image
from torchvision.utils import _log_api_usage_once
from ._utils import _get_kernel, _register_kernel_internal
def erase(
inpt: torch.Tensor,
i: int,
j: int,
h: in... | import PIL.Image
import torch
from torchvision import datapoints
from torchvision.transforms.functional import pil_to_tensor, to_pil_image
from torchvision.utils import _log_api_usage_once
from ._utils import _get_kernel, _register_kernel_internal
def erase(
inpt: torch.Tensor,
i: int,
j: int,
h: in... |
from typing import TYPE_CHECKING, Any, Type, TypeVar, Union
from docarray.base_doc import BaseDoc
from docarray.typing.tensor.tensor import AnyTensor
from docarray.utils._internal.misc import import_library
T = TypeVar('T', bound='VerticesAndFaces')
class VerticesAndFaces(BaseDoc):
"""
Document for handling... | from typing import TYPE_CHECKING, Any, Type, TypeVar, Union
from docarray.base_doc import BaseDoc
from docarray.typing.tensor.tensor import AnyTensor
from docarray.utils._internal.misc import import_library
T = TypeVar('T', bound='VerticesAndFaces')
class VerticesAndFaces(BaseDoc):
"""
Document for handling... |
# Copyright (c) OpenMMLab. All rights reserved.
from typing import Optional
import torch
import torch.nn as nn
from mmengine.runner import load_checkpoint
from torch import Tensor
from mmdet.registry import MODELS
from mmdet.structures import SampleList
from mmdet.utils import ConfigType, OptConfigType
from ..utils.m... | # Copyright (c) OpenMMLab. All rights reserved.
from typing import Optional
import torch
import torch.nn as nn
from mmengine.runner import load_checkpoint
from torch import Tensor
from mmdet.registry import MODELS
from mmdet.structures import SampleList
from mmdet.utils import ConfigType, OptConfigType
from ..utils.m... |
#!/usr/bin/env python3
# coding=utf-8
# Copyright 2025 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unles... | #!/usr/bin/env python3
# coding=utf-8
# Copyright 2025 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unles... |
from pathlib import Path
from typing import List
import pytest
from dpr_text import DPRTextEncoder
from jina import Document, DocumentArray, Executor
_EMBEDDING_DIM = 768
@pytest.fixture(scope='session')
def basic_encoder() -> DPRTextEncoder:
return DPRTextEncoder()
@pytest.fixture(scope='session')
def basic_... | from pathlib import Path
from typing import List
import pytest
from dpr_text import DPRTextEncoder
from jina import Document, DocumentArray, Executor
_EMBEDDING_DIM = 768
@pytest.fixture(scope='session')
def basic_encoder() -> DPRTextEncoder:
return DPRTextEncoder()
@pytest.fixture(scope='session')
def basic_... |
import copy
import importlib
import os
import sys
from keras.src import backend as backend_module
from keras.src.api_export import keras_export
from keras.src.backend.common import global_state
def in_tf_graph():
if global_state.get_global_attribute("in_tf_graph_scope", False):
return True
if "tenso... | import copy
import importlib
import os
import sys
from keras.src import backend as backend_module
from keras.src.api_export import keras_export
from keras.src.backend.common import global_state
def in_tf_graph():
if global_state.get_global_attribute("in_tf_graph_scope", False):
return True
if "tenso... |
import pytest
from .utils import remove_color_codes
@pytest.mark.parametrize(
"raw_text, clean_text",
[
(
"COMMAND = \x1b[36mbrowse_website\x1b[0m "
"ARGUMENTS = \x1b[36m{'url': 'https://www.google.com',"
" 'question': 'What is the capital of France?'}\x1b[0m",
... | import pytest
from .utils import remove_color_codes
@pytest.mark.parametrize(
"raw_text, clean_text",
[
(
"COMMAND = \x1b[36mbrowse_website\x1b[0m "
"ARGUMENTS = \x1b[36m{'url': 'https://www.google.com',"
" 'question': 'What is the capital of France?'}\x1b[0m",
... |
import inspect
import threading
from typing import Any, Awaitable, Callable, ParamSpec, TypeVar, cast, overload
P = ParamSpec("P")
R = TypeVar("R")
@overload
def thread_cached(func: Callable[P, Awaitable[R]]) -> Callable[P, Awaitable[R]]: ...
@overload
def thread_cached(func: Callable[P, R]) -> Callable[P, R]: ...... | import threading
from typing import Callable, ParamSpec, TypeVar
P = ParamSpec("P")
R = TypeVar("R")
def thread_cached(func: Callable[P, R]) -> Callable[P, R]:
thread_local = threading.local()
def wrapper(*args: P.args, **kwargs: P.kwargs) -> R:
cache = getattr(thread_local, "cache", None)
i... |
import pytest
from datasets.exceptions import DatasetNotFoundError
from datasets.inspect import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_default_config_name,
get_dataset_infos,
get_dataset_split_names,
)
pytestmark = pytest.mark.integration
@pytest.mark.parametrize(
... | import pytest
from datasets.exceptions import DatasetNotFoundError
from datasets.inspect import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_default_config_name,
get_dataset_infos,
get_dataset_split_names,
)
pytestmark = pytest.mark.integration
@pytest.mark.parametrize(
... |
import codecs
from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
from backend.data.model import SchemaField
class TextDecoderBlock(Block):
class Input(BlockSchema):
text: str = SchemaField(
description="A string containing escaped characters to be decoded",
... | import codecs
from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
from backend.data.model import SchemaField
class TextDecoderBlock(Block):
class Input(BlockSchema):
text: str = SchemaField(
description="A string containing escaped characters to be decoded",
... |
"""**Schemas** are the LangChain Base Classes and Interfaces."""
from langchain_core.agents import AgentAction, AgentFinish
from langchain_core.caches import BaseCache
from langchain_core.chat_history import BaseChatMessageHistory
from langchain_core.documents import BaseDocumentTransformer, Document
from langchain_co... | """**Schemas** are the LangChain Base Classes and Interfaces."""
from langchain_core.agents import AgentAction, AgentFinish
from langchain_core.caches import BaseCache
from langchain_core.chat_history import BaseChatMessageHistory
from langchain_core.documents import BaseDocumentTransformer, Document
from langchain_co... |
from langchain_core.messages import (
AIMessage,
FunctionMessage,
HumanMessage,
SystemMessage,
)
from langchain_core.output_parsers.openai_tools import (
parse_tool_call,
)
from langchain_community.chat_models.tongyi import (
convert_dict_to_message,
convert_message_to_dict,
)
def test__c... | from langchain_core.messages import (
AIMessage,
FunctionMessage,
HumanMessage,
SystemMessage,
)
from langchain_core.output_parsers.openai_tools import (
parse_tool_call,
)
from langchain_community.chat_models.tongyi import (
convert_dict_to_message,
convert_message_to_dict,
)
def test__c... |
from .text_paddle import TextPaddleEncoder
| from .text_paddle import TextPaddleEncoder |
from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.tools.memorize.tool import Memorize, TrainableLLM
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handling optio... | from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.tools.memorize.tool import Memorize, TrainableLLM
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handling optio... |
import pytest
from langchain.evaluation.string_distance import (
PairwiseStringDistanceEvalChain,
StringDistance,
StringDistanceEvalChain,
)
@pytest.mark.requires("rapidfuzz")
@pytest.mark.parametrize("distance", list(StringDistance))
def test_zero_distance(distance: StringDistance) -> None:
eval_cha... | import pytest
from langchain.evaluation.string_distance import (
PairwiseStringDistanceEvalChain,
StringDistance,
StringDistanceEvalChain,
)
@pytest.mark.requires("rapidfuzz")
@pytest.mark.parametrize("distance", list(StringDistance))
def test_zero_distance(distance: StringDistance) -> None:
eval_cha... |
from typing import Optional
from docarray import DocList, BaseDoc
from docarray.typing import NdArray
from jina import Executor, requests
import numpy as np
class MyDoc(BaseDoc):
text: str
embedding: Optional[NdArray] = None
class Encoder(Executor):
def __init__(
self,
*args,
**k... | from typing import Optional
from docarray import DocList, BaseDoc
from docarray.typing import NdArray
from jina import Executor, requests
import numpy as np
class MyDoc(BaseDoc):
text: str
embedding: Optional[NdArray] = None
class Encoder(Executor):
def __init__(
self,
*args,
... |
_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py'
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
# use ResNeSt img_norm
data_preprocessor=dict(
mean=[123.68, 116.779, 103.939],
std=[58.393, 57.12, 57.375],
bgr_to_rgb=True),
backbone=dict(
type='ResNeSt',
... | _base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py'
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
backbone=dict(
type='ResNeSt',
stem_channels=64,
depth=50,
radix=2,
reduction_factor=4,
avg_down_stride=True,
num_stages=4,
out_indice... |
from setuptools import find_packages, setup
with open("README.md", mode="r", encoding="utf-8") as readme_file:
readme = readme_file.read()
setup(
name="sentence-transformers",
version="3.0.0.dev0",
author="Nils Reimers",
author_email="info@nils-reimers.de",
description="Multilingual text embe... | from setuptools import setup, find_packages
with open("README.md", mode="r", encoding="utf-8") as readme_file:
readme = readme_file.read()
setup(
name="sentence-transformers",
version="3.0.0.dev0",
author="Nils Reimers",
author_email="info@nils-reimers.de",
description="Multilingual text embe... |
"""DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.optimizers import legacy as legacy
from keras.optimizers import schedules as schedules
from keras.src.optimizers import deserialize as deserialize
from keras.src.optimizers import get as ... | """DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.api.optimizers import legacy
from keras.api.optimizers import schedules
from keras.src.optimizers import deserialize
from keras.src.optimizers import get
from keras.src.optimizers import ... |
"""**Load** module helps with serialization and deserialization."""
from importlib import import_module
from typing import TYPE_CHECKING
if TYPE_CHECKING:
from langchain_core.load.dump import dumpd, dumps
from langchain_core.load.load import load, loads
from langchain_core.load.serializable import Seriali... | """**Load** module helps with serialization and deserialization."""
from langchain_core.load.dump import dumpd, dumps
from langchain_core.load.load import load, loads
from langchain_core.load.serializable import Serializable
__all__ = ["dumpd", "dumps", "load", "loads", "Serializable"]
|
from urllib.parse import urlparse
from backend.blocks.github._auth import GithubCredentials
from backend.util.request import Requests
def _convert_to_api_url(url: str) -> str:
"""
Converts a standard GitHub URL to the corresponding GitHub API URL.
Handles repository URLs, issue URLs, pull request URLs, a... | from urllib.parse import urlparse
from backend.blocks.github._auth import GithubCredentials
from backend.util.request import Requests
def _convert_to_api_url(url: str) -> str:
"""
Converts a standard GitHub URL to the corresponding GitHub API URL.
Handles repository URLs, issue URLs, pull request URLs, a... |
_base_ = 'mask_rcnn_r50_fpn_syncbn-all_rpn-2conv_lsj_100e_coco.py'
# Use RepeatDataset to speed up training
# change repeat time from 4 (for 100 epochs) to 2 (for 50 epochs)
train_dataloader = dict(dataset=dict(times=2))
| _base_ = 'mask_rcnn_r50_fpn_syncbn-all_rpn-2conv_lsj_100e_coco.py'
# Use RepeatDataset to speed up training
# change repeat time from 4 (for 100 epochs) to 2 (for 50 epochs)
data = dict(train=dict(times=2))
|
from enum import Enum
from typing import TYPE_CHECKING, Union, overload
import numpy as np
if TYPE_CHECKING:
import torch # pants: no-infer-dep
class Pooling(str, Enum):
"""Enum of possible pooling choices with pooling behaviors."""
CLS = "cls"
MEAN = "mean"
def __call__(self, array: np.ndarr... | from enum import Enum
from typing import TYPE_CHECKING, Union, overload
import numpy as np
if TYPE_CHECKING:
import torch # pants: no-infer-dep
class Pooling(str, Enum):
"""Enum of possible pooling choices with pooling behaviors."""
CLS = "cls"
MEAN = "mean"
def __call__(self, array: np.ndarr... |
from __future__ import annotations
import random
import pytest
import torch
from torch.utils.data import ConcatDataset
from sentence_transformers.sampler import NoDuplicatesBatchSampler, ProportionalBatchSampler
from sentence_transformers.util import is_datasets_available
if is_datasets_available():
from datase... | from __future__ import annotations
import random
import pytest
import torch
from datasets import Dataset
from torch.utils.data import ConcatDataset
from sentence_transformers.sampler import NoDuplicatesBatchSampler, ProportionalBatchSampler
@pytest.fixture
def dummy_dataset() -> Dataset:
"""
Dummy dataset ... |
import pathlib
from typing import Any, Dict, List, Optional, Tuple, Union
from torchdata.datapipes.iter import CSVDictParser, Demultiplexer, Filter, IterDataPipe, Mapper, Zipper
from torchvision.datapoints import BoundingBoxes
from torchvision.prototype.datapoints import Label
from torchvision.prototype.datasets.utils... | import pathlib
from typing import Any, Dict, List, Optional, Tuple, Union
from torchdata.datapipes.iter import CSVDictParser, Demultiplexer, Filter, IterDataPipe, Mapper, Zipper
from torchvision.datapoints import BoundingBox
from torchvision.prototype.datapoints import Label
from torchvision.prototype.datasets.utils i... |
import json
from typing import Optional
from cryptography.fernet import Fernet
from backend.util.settings import Settings
ENCRYPTION_KEY = Settings().secrets.encryption_key
class JSONCryptor:
def __init__(self, key: Optional[str] = None):
# Use provided key or get from environment
self.key = ke... | import json
from typing import Optional
from cryptography.fernet import Fernet
from backend.util.settings import Settings
ENCRYPTION_KEY = Settings().secrets.encryption_key
class JSONCryptor:
def __init__(self, key: Optional[str] = None):
# Use provided key or get from environment
self.key = ke... |
"""Memory used to save agent output AND intermediate steps."""
from typing import Any
from langchain_core.language_models import BaseLanguageModel
from langchain_core.messages import BaseMessage, get_buffer_string
from langchain.agents.format_scratchpad import (
format_to_openai_function_messages,
format_to_... | """Memory used to save agent output AND intermediate steps."""
from typing import Any
from langchain_core.language_models import BaseLanguageModel
from langchain_core.messages import BaseMessage, get_buffer_string
from langchain.agents.format_scratchpad import (
format_to_openai_function_messages,
format_to_... |
import pytest
from sentence_transformers import SentenceTransformer
@pytest.mark.parametrize(
("revision", "expected_base_revision"),
[
("f3cb857cba53019a20df283396bcca179cf051a4", "f3cb857cba53019a20df283396bcca179cf051a4"),
("f3cb857", "f3cb857"),
("main", "valid-revision"),
... | from sentence_transformers import SentenceTransformer
import pytest
@pytest.mark.parametrize(
("revision", "expected_base_revision"),
[
("f3cb857cba53019a20df283396bcca179cf051a4", "f3cb857cba53019a20df283396bcca179cf051a4"),
("f3cb857", "f3cb857"),
("main", "valid-revision"),
... |
import pathlib
from typing import Any, BinaryIO, Dict, List, Optional, Tuple, Union
from torchdata.datapipes.iter import Demultiplexer, Filter, IterDataPipe, IterKeyZipper, JsonParser, Mapper, UnBatcher
from torchvision.prototype.datapoints import Label
from torchvision.prototype.datasets.utils import Dataset, Encoded... | import pathlib
from typing import Any, BinaryIO, Dict, List, Optional, Tuple, Union
from torchdata.datapipes.iter import Demultiplexer, Filter, IterDataPipe, IterKeyZipper, JsonParser, Mapper, UnBatcher
from torchvision.prototype.datasets.utils import Dataset, EncodedImage, HttpResource, OnlineResource
from torchvisio... |
# Copyright (c) OpenMMLab. All rights reserved.
import warnings
from mmcv.utils import Registry, build_from_cfg
PRIOR_GENERATORS = Registry('Generator for anchors and points')
ANCHOR_GENERATORS = PRIOR_GENERATORS
def build_prior_generator(cfg, default_args=None):
return build_from_cfg(cfg, PRIOR_GENERATORS, de... | import warnings
from mmcv.utils import Registry, build_from_cfg
PRIOR_GENERATORS = Registry('Generator for anchors and points')
ANCHOR_GENERATORS = PRIOR_GENERATORS
def build_prior_generator(cfg, default_args=None):
return build_from_cfg(cfg, PRIOR_GENERATORS, default_args)
def build_anchor_generator(cfg, de... |
_base_ = './faster-rcnn_r50-caffe_fpn_ms-1x_coco.py'
model = dict(roi_head=dict(bbox_head=dict(num_classes=3)))
classes = ('person', 'bicycle', 'car')
data = dict(
train=dict(classes=classes),
val=dict(classes=classes),
test=dict(classes=classes))
load_from = 'https://download.openmmlab.com/mmdetection/v2.... | _base_ = './faster_rcnn_r50_caffe_fpn_mstrain_1x_coco.py'
model = dict(roi_head=dict(bbox_head=dict(num_classes=3)))
classes = ('person', 'bicycle', 'car')
data = dict(
train=dict(classes=classes),
val=dict(classes=classes),
test=dict(classes=classes))
load_from = 'https://download.openmmlab.com/mmdetectio... |
"""DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.applications.densenet import DenseNet121 as DenseNet121
from keras.src.applications.densenet import DenseNet169 as DenseNet169
from keras.src.applications.densenet import DenseNet201 ... | """DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.applications.densenet import DenseNet121
from keras.src.applications.densenet import DenseNet169
from keras.src.applications.densenet import DenseNet201
from keras.src.applications.de... |
import pytest
from jina.enums import GatewayProtocolType
from jina.helper import ArgNamespace
from jina.parsers import set_gateway_parser, set_pod_parser
@pytest.mark.parametrize(
'port,expected_port',
[
('12345', [12345]),
([12345], [12345]),
([12345, 12344], [12345, 12344]),
],
... | import pytest
from jina.enums import GatewayProtocolType
from jina.helper import ArgNamespace
from jina.parsers import set_gateway_parser, set_pod_parser
@pytest.mark.parametrize(
'port,expected_port',
[
('12345', [12345]),
([12345], [12345]),
([12345, 12344], [12345, 12344]),
],
... |
import pytest
@pytest.mark.compile
def test_placeholder() -> None:
"""Used for compiling integration tests without running any real tests."""
| import pytest
@pytest.mark.compile
def test_placeholder() -> None:
"""Used for compiling integration tests without running any real tests."""
pass
|
# Copyright 2019 The OpenXLA Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in ... | # Copyright 2019 The OpenXLA Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in ... |
"""DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.legacy.preprocessing.image import (
DirectoryIterator as DirectoryIterator,
)
from keras.src.legacy.preprocessing.image import (
ImageDataGenerator as ImageDataGenerator,
)
fr... | """DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.legacy.preprocessing.image import DirectoryIterator
from keras.src.legacy.preprocessing.image import ImageDataGenerator
from keras.src.legacy.preprocessing.image import Iterator
from ... |
import os
import sys
import pytest
import torch
import torchaudio
from torchaudio.pipelines import CONVTASNET_BASE_LIBRI2MIX, HDEMUCS_HIGH_MUSDB, HDEMUCS_HIGH_MUSDB_PLUS
sys.path.append(os.path.join(os.path.dirname(__file__), "..", "..", "examples"))
from source_separation.utils.metrics import sdr
@pytest.mark.par... | import os
import sys
import pytest
import torch
import torchaudio
from torchaudio.pipelines import CONVTASNET_BASE_LIBRI2MIX
from torchaudio.prototype.pipelines import HDEMUCS_HIGH_MUSDB, HDEMUCS_HIGH_MUSDB_PLUS
sys.path.append(os.path.join(os.path.dirname(__file__), "..", "..", "examples"))
from source_separation.u... |
from urllib.parse import urlparse
from backend.blocks.github._auth import GithubCredentials
from backend.util.request import Requests
def _convert_to_api_url(url: str) -> str:
"""
Converts a standard GitHub URL to the corresponding GitHub API URL.
Handles repository URLs, issue URLs, pull request URLs, a... | from urllib.parse import urlparse
from backend.blocks.github._auth import GithubCredentials
from backend.util.request import Requests
def _convert_to_api_url(url: str) -> str:
"""
Converts a standard GitHub URL to the corresponding GitHub API URL.
Handles repository URLs, issue URLs, pull request URLs, a... |
from torchaudio._internal.module_utils import dropping_io_support, dropping_class_io_support
# Initialize extension and backend first
from . import _extension # noqa # usort: skip
from ._backend import ( # noqa # usort: skip
AudioMetaData as _AudioMetaData,
get_audio_backend as _get_audio_backend,
info... | from torchaudio._internal.module_utils import dropping_io_support
# Initialize extension and backend first
from . import _extension # noqa # usort: skip
from ._backend import ( # noqa # usort: skip
AudioMetaData,
get_audio_backend as _get_audio_backend,
info as _info,
list_audio_backends as _list_a... |
# Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
import pycocotools.mask as mask_util
import torch
from mmengine.utils import slice_list
def split_combined_polys(polys, poly_lens, polys_per_mask):
"""Split the combined 1-D polys into masks.
A mask is represented as a list of polys, and a po... | # Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import numpy as np
import pycocotools.mask as mask_util
import torch
def split_combined_polys(polys, poly_lens, polys_per_mask):
"""Split the combined 1-D polys into masks.
A mask is represented as a list of polys, and a poly is represented as
a... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.