input stringlengths 33 5k | output stringlengths 32 5k |
|---|---|
_base_ = [
'../_base_/models/ssd300.py', '../_base_/datasets/openimages_detection.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_1x.py'
]
model = dict(
bbox_head=dict(
num_classes=601,
anchor_generator=dict(basesize_ratio_range=(0.2, 0.9))))
# dataset settings
dataset_typ... | _base_ = [
'../_base_/models/ssd300.py', '../_base_/datasets/openimages_detection.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_1x.py'
]
model = dict(
bbox_head=dict(
num_classes=601,
anchor_generator=dict(basesize_ratio_range=(0.2, 0.9))))
# dataset settings
dataset_typ... |
_base_ = './vfnet_r50_fpn_1x_coco.py'
train_pipeline = [
dict(
type='LoadImageFromFile',
file_client_args={{_base_.file_client_args}}),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='RandomResize', scale=[(1333, 480), (1333, 960)],
keep_ratio=True),
dict(type='... | _base_ = './vfnet_r50_fpn_1x_coco.py'
train_pipeline = [
dict(
type='LoadImageFromFile',
file_client_args={{_base_.file_client_args}}),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='RandomResize', scale=[(1333, 480), (1333, 960)],
keep_ratio=True),
dict(type='... |
"""**Callback handlers** allow listening to events in LangChain.
**Class hierarchy:**
.. code-block::
BaseCallbackHandler --> <name>CallbackHandler # Example: AimCallbackHandler
"""
from importlib import import_module
from typing import TYPE_CHECKING
if TYPE_CHECKING:
from langchain_core.callbacks.base im... | """**Callback handlers** allow listening to events in LangChain.
**Class hierarchy:**
.. code-block::
BaseCallbackHandler --> <name>CallbackHandler # Example: AimCallbackHandler
"""
from langchain_core.callbacks.base import (
AsyncCallbackHandler,
BaseCallbackHandler,
BaseCallbackManager,
Callb... |
# Copyright (c) OpenMMLab. All rights reserved.
from .checkloss_hook import CheckInvalidLossHook
from .ema import ExpMomentumEMAHook, LinearMomentumEMAHook
from .memory_profiler_hook import MemoryProfilerHook
from .set_epoch_info_hook import SetEpochInfoHook
from .sync_norm_hook import SyncNormHook
from .sync_random_si... | # Copyright (c) OpenMMLab. All rights reserved.
from .checkloss_hook import CheckInvalidLossHook
from .ema import ExpMomentumEMAHook, LinearMomentumEMAHook
from .memory_profiler_hook import MemoryProfilerHook
from .set_epoch_info_hook import SetEpochInfoHook
from .sync_norm_hook import SyncNormHook
from .sync_random_si... |
import itertools
from dataclasses import dataclass
from typing import Optional
import pyarrow as pa
import datasets
from datasets.table import table_cast
logger = datasets.utils.logging.get_logger(__name__)
@dataclass
class ArrowConfig(datasets.BuilderConfig):
"""BuilderConfig for Arrow."""
features: Opt... | import itertools
from dataclasses import dataclass
from typing import Optional
import pyarrow as pa
import datasets
from datasets.table import table_cast
logger = datasets.utils.logging.get_logger(__name__)
@dataclass
class ArrowConfig(datasets.BuilderConfig):
"""BuilderConfig for Arrow."""
features: Opt... |
from keras.src import backend
from keras.src.api_export import keras_export
from keras.src.layers.layer import Layer
@keras_export("keras.layers.Dropout")
class Dropout(Layer):
"""Applies dropout to the input.
The `Dropout` layer randomly sets input units to 0 with a frequency of
`rate` at each step duri... | from keras.src import backend
from keras.src.api_export import keras_export
from keras.src.layers.layer import Layer
@keras_export("keras.layers.Dropout")
class Dropout(Layer):
"""Applies dropout to the input.
The `Dropout` layer randomly sets input units to 0 with a frequency of
`rate` at each step duri... |
import json
from collections.abc import Sequence
from langchain_core.agents import AgentAction
from langchain_core.messages import (
AIMessage,
BaseMessage,
ToolMessage,
)
from langchain.agents.output_parsers.tools import ToolAgentAction
def _create_tool_message(
agent_action: ToolAgentAction, obser... | import json
from typing import List, Sequence, Tuple
from langchain_core.agents import AgentAction
from langchain_core.messages import (
AIMessage,
BaseMessage,
ToolMessage,
)
from langchain.agents.output_parsers.tools import ToolAgentAction
def _create_tool_message(
agent_action: ToolAgentAction, o... |
from torchvision import _BETA_TRANSFORMS_WARNING, _WARN_ABOUT_BETA_TRANSFORMS
from ._bounding_box import BoundingBoxes, BoundingBoxFormat
from ._datapoint import _FillType, _FillTypeJIT, _InputType, _InputTypeJIT
from ._image import _ImageType, _ImageTypeJIT, _TensorImageType, _TensorImageTypeJIT, Image
from ._mask im... | from torchvision import _BETA_TRANSFORMS_WARNING, _WARN_ABOUT_BETA_TRANSFORMS
from ._bounding_box import BoundingBox, BoundingBoxFormat
from ._datapoint import _FillType, _FillTypeJIT, _InputType, _InputTypeJIT
from ._image import _ImageType, _ImageTypeJIT, _TensorImageType, _TensorImageTypeJIT, Image
from ._mask impo... |
from .simple_indexer import SimpleIndexer
| from .simple_indexer import SimpleIndexer |
_base_ = './yolov3_d53_mstrain-608_273e_coco.py'
# dataset settings
# 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')
input_si... | _base_ = './yolov3_d53_mstrain-608_273e_coco.py'
# dataset settings
img_norm_cfg = dict(mean=[0, 0, 0], std=[255., 255., 255.], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='Expand',
mean=img_norm_cfg['mean'],
... |
from abc import ABC, abstractmethod
from typing import Optional, Union
from .. import Dataset, DatasetDict, Features, IterableDataset, IterableDatasetDict, NamedSplit
from ..utils.typing import NestedDataStructureLike, PathLike
class AbstractDatasetReader(ABC):
def __init__(
self,
path_or_paths: ... | from abc import ABC, abstractmethod
from typing import Optional, Union
from .. import Dataset, DatasetDict, Features, IterableDataset, IterableDatasetDict, NamedSplit
from ..utils.typing import NestedDataStructureLike, PathLike
class AbstractDatasetReader(ABC):
def __init__(
self,
path_or_paths: ... |
"""**Prompt values** for language model prompts.
Prompt values are used to represent different pieces of prompts.
They can be used to represent text, images, or chat message pieces.
"""
from __future__ import annotations
from abc import ABC, abstractmethod
from collections.abc import Sequence
from typing import Lite... | """**Prompt values** for language model prompts.
Prompt values are used to represent different pieces of prompts.
They can be used to represent text, images, or chat message pieces.
"""
from __future__ import annotations
from abc import ABC, abstractmethod
from collections.abc import Sequence
from typing import Lite... |
__copyright__ = "Copyright (c) 2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
from typing import Optional
from jina import Document, DocumentArray
from jina.logging.logger import JinaLogger
from pymongo import MongoClient
from pymongo.errors import BulkWriteError
class MongoHandler:
def ... | __copyright__ = "Copyright (c) 2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
from typing import Optional
from jina import Document, DocumentArray
from jina.logging.logger import JinaLogger
from pymongo import MongoClient
from pymongo.errors import BulkWriteError
class MongoHandler:
def ... |
# Copyright (c) OpenMMLab. All rights reserved.
import time
from typing import Optional, Sequence, Union
from mmengine.registry import HOOKS
from .hook import Hook
DATA_BATCH = Optional[Union[dict, tuple, list]]
@HOOKS.register_module()
class IterTimerHook(Hook):
"""A hook that logs the time spent during iterat... | # Copyright (c) OpenMMLab. All rights reserved.
import time
from typing import Optional, Sequence, Union
from mmengine.registry import HOOKS
from .hook import Hook
DATA_BATCH = Optional[Union[dict, tuple, list]]
@HOOKS.register_module()
class IterTimerHook(Hook):
"""A hook that logs the time spent during iterat... |
_base_ = './cascade-mask-rcnn_r50_fpn_1x_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='pyt... | _base_ = './cascade_mask_rcnn_r50_fpn_1x_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='pyt... |
# Copyright (c) OpenMMLab. All rights reserved.
from typing import Any, Optional, Sequence, Tuple, Union
from mmengine.data import BaseDataSample
from .base import BaseEvaluator
class ComposedEvaluator:
"""Wrapper class to compose multiple :class:`BaseEvaluator` instances.
Args:
evaluators (Sequence... | # Copyright (c) OpenMMLab. All rights reserved.
from typing import Optional, Sequence, Union
from mmengine.data import BaseDataSample
from .base import BaseEvaluator
class ComposedEvaluator:
"""Wrapper class to compose multiple :class:`BaseEvaluator` instances.
Args:
evaluators (Sequence[BaseEvaluat... |
from typing import (
Union,
Optional,
TYPE_CHECKING,
List,
Dict,
)
if TYPE_CHECKING:
import numpy as np
from docarray import DocumentArray
class FindMixin:
def _find(
self,
query: 'np.ndarray',
limit: Optional[Union[int, float]] = 20,
only_id: bool = False... | from typing import (
Union,
Optional,
TYPE_CHECKING,
List,
Dict,
)
if TYPE_CHECKING:
import numpy as np
from docarray import DocumentArray
class FindMixin:
def _find(
self,
query: 'np.ndarray',
limit: Optional[Union[int, float]] = 20,
only_id: bool = False... |
"""Tool for the Passio Nutrition AI API."""
from typing import Dict, Optional, Type
from langchain_core.callbacks import CallbackManagerForToolRun
from langchain_core.tools import BaseTool
from pydantic import BaseModel, Field
from langchain_community.utilities.passio_nutrition_ai import NutritionAIAPI
class Nutri... | """Tool for the Passio Nutrition AI API."""
from typing import Dict, Optional, Type
from langchain_core.callbacks import CallbackManagerForToolRun
from langchain_core.tools import BaseTool
from pydantic import BaseModel, Field
from langchain_community.utilities.passio_nutrition_ai import NutritionAIAPI
class Nutri... |
import tensorflow as tf
class TFExportArchive:
def _track_layer(self, layer):
# Variables in the lists below are actually part of the trackables
# that get saved, because the lists are created in __init__.
variables = layer.variables
trainable_variables = layer.trainable_variables
... | import tensorflow as tf
from keras.src import layers
class TFExportArchive:
def track(self, resource):
if not isinstance(resource, tf.__internal__.tracking.Trackable):
raise ValueError(
"Invalid resource type. Expected an instance of a "
"TensorFlow `Trackable`... |
"""Init file of LlamaIndex."""
__version__ = "0.12.24"
import logging
from logging import NullHandler
from typing import Callable, Optional
try:
# Force pants to install eval_type_backport on 3.9
import eval_type_backport # noqa # type: ignore
except ImportError:
pass
# response
from llama_index.core.... | """Init file of LlamaIndex."""
__version__ = "0.12.23.post2"
import logging
from logging import NullHandler
from typing import Callable, Optional
try:
# Force pants to install eval_type_backport on 3.9
import eval_type_backport # noqa # type: ignore
except ImportError:
pass
# response
from llama_index... |
_base_ = [
'../_base_/models/faster-rcnn_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
norm_cfg = dict(type='BN', requires_grad=True)
image_size = (640, 640)
batch_augments = [dict(type='BatchFixedSizePad', size=image_size)]
mode... | _base_ = [
'../_base_/models/faster_rcnn_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
norm_cfg = dict(type='BN', requires_grad=True)
image_size = (640, 640)
batch_augments = [dict(type='BatchFixedSizePad', size=image_size)]
mode... |
import torch
from dataset.hubert_dataset import _crop_audio_label
from parameterized import parameterized
from torchaudio.models import hubert_base
from torchaudio_unittest.common_utils import get_whitenoise, TorchaudioTestCase
class TestCropAudioLabel(TorchaudioTestCase):
@classmethod
def setUpClass(cls) -> ... | import torch
from dataset.hubert_dataset import _crop_audio_label
from parameterized import parameterized
from torchaudio.models import hubert_base
from torchaudio_unittest.common_utils import get_whitenoise, TorchaudioTestCase
class TestCropAudioLabel(TorchaudioTestCase):
@classmethod
def setUpClass(cls) -> ... |
# Copyright (c) OpenMMLab. All rights reserved.
from ._flexible_runner import FlexibleRunner
from .amp import autocast
from .base_loop import BaseLoop
from .checkpoint import (CheckpointLoader, find_latest_checkpoint,
get_deprecated_model_names, get_external_models,
get... | # Copyright (c) OpenMMLab. All rights reserved.
from .amp import autocast
from .base_loop import BaseLoop
from .checkpoint import (CheckpointLoader, find_latest_checkpoint,
get_deprecated_model_names, get_external_models,
get_mmcls_models, get_state_dict,
... |
"""Simple reader that turns an iterable of strings into a list of Documents."""
from typing import List
from llama_index.core.readers.base import BasePydanticReader
from llama_index.core.schema import Document
class StringIterableReader(BasePydanticReader):
"""
String Iterable Reader.
Gets a list of do... | """Simple reader that turns an iterable of strings into a list of Documents."""
from typing import List
from llama_index.core.readers.base import BasePydanticReader
from llama_index.core.schema import Document
class StringIterableReader(BasePydanticReader):
"""
String Iterable Reader.
Gets a list of doc... |
# coding=utf-8
# Copyright 2024 HuggingFace Inc.
#
# 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 ag... | # coding=utf-8
# Copyright 2024 HuggingFace Inc.
#
# 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 ag... |
# 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... |
import os
import time
from jina import Document, DocumentArray
import pytest
from ..redis_storage import RedisStorage
@pytest.fixture(scope='function')
def indexer():
return RedisStorage()
@pytest.fixture()
def docker_compose(request):
os.system(
f'docker-compose -f {request.param} --project-direc... | import os
import time
from jina import Document, DocumentArray
import pytest
from .. import RedisStorage
@pytest.fixture(scope='function')
def indexer():
return RedisStorage()
@pytest.fixture()
def docker_compose(request):
os.system(
f'docker-compose -f {request.param} --project-directory . up --... |
from typing import (
TYPE_CHECKING,
TypeVar,
Sequence,
List,
Union,
)
import numpy as np
from .... import Document, DocumentArray
from ....math import ndarray
from ....math.helper import EPSILON
from ....math.ndarray import to_numpy_array
from ....score import NamedScore
from ....array.mixins.find... | from typing import (
TYPE_CHECKING,
TypeVar,
Sequence,
List,
)
import numpy as np
from .... import Document, DocumentArray
from ....math import ndarray
from ....math.helper import EPSILON
from ....math.ndarray import to_numpy_array
from ....score import NamedScore
if TYPE_CHECKING:
import tensorf... |
import contextlib
import os
import sqlite3
import pytest
from datasets import Dataset, Features, Value
from datasets.io.sql import SqlDatasetReader, SqlDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases, require_sqlalchemy
def _check_sql_dataset(dataset, expected_f... | import contextlib
import os
import sqlite3
import pytest
from datasets import Dataset, Features, Value
from datasets.io.sql import SqlDatasetReader, SqlDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases, require_sqlalchemy
def _check_sql_dataset(dataset, expected_f... |
from sentence_transformers import SentenceTransformer, LoggingHandler, InputExample
from sentence_transformers import models, util, evaluation, losses
import logging
import os
import gzip
from datetime import datetime
from torch.utils.data import DataLoader
#### Just some code to print debug information to stdout
logg... | from sentence_transformers import SentenceTransformer, LoggingHandler, InputExample
from sentence_transformers import models, util, evaluation, losses
import logging
import os
import gzip
from datetime import datetime
from torch.utils.data import DataLoader
#### Just some code to print debug information to stdout
logg... |
from torch.fx.experimental.migrate_gradual_types.constraint import (
BinConstraintD,
BVar,
DVar,
TVar,
)
from torch.fx.experimental.migrate_gradual_types.operation import op_leq
def gen_tvar(curr: int) -> tuple[TVar, int]:
"""
Generate a tensor variable
:param curr: The current counter
... | # mypy: allow-untyped-defs
from torch.fx.experimental.migrate_gradual_types.constraint import (
BinConstraintD,
BVar,
DVar,
TVar,
)
from torch.fx.experimental.migrate_gradual_types.operation import op_leq
def gen_tvar(curr):
"""
Generate a tensor variable
:param curr: The current counter
... |
_base_ = [
'mmdet::_base_/models/mask-rcnn_r50_fpn.py',
'mmdet::_base_/datasets/coco_instance.py',
'mmdet::_base_/schedules/schedule_1x.py',
'mmdet::_base_/default_runtime.py'
]
# please install the mmpretrain
# import mmpretrain.models to trigger register_module in mmpretrain
custom_imports = dict(
... | _base_ = [
'mmdet::_base_/models/mask-rcnn_r50_fpn.py',
'mmdet::_base_/datasets/coco_instance.py',
'mmdet::_base_/schedules/schedule_1x.py',
'mmdet::_base_/default_runtime.py'
]
# please install the mmclassification dev-1.x branch
# import mmcls.models to trigger register_module in mmcls
custom_imports... |
from __future__ import annotations
from collections.abc import Iterable
from typing import Any
import torch
from torch import Tensor, nn
from sentence_transformers.SentenceTransformer import SentenceTransformer
from sentence_transformers.util import fullname
class CosineSimilarityLoss(nn.Module):
def __init__(... | from __future__ import annotations
from typing import Any, Iterable
import torch
from torch import Tensor, nn
from sentence_transformers.SentenceTransformer import SentenceTransformer
from sentence_transformers.util import fullname
class CosineSimilarityLoss(nn.Module):
def __init__(
self,
mode... |
__version__ = '0.34.0'
import logging
from docarray.array import DocList, DocVec
from docarray.base_doc.doc import BaseDoc
from docarray.utils._internal.misc import _get_path_from_docarray_root_level
__all__ = ['BaseDoc', 'DocList', 'DocVec']
logger = logging.getLogger('docarray')
handler = logging.StreamHandler()... | __version__ = '0.33.1'
import logging
from docarray.array import DocList, DocVec
from docarray.base_doc.doc import BaseDoc
from docarray.utils._internal.misc import _get_path_from_docarray_root_level
__all__ = ['BaseDoc', 'DocList', 'DocVec']
logger = logging.getLogger('docarray')
handler = logging.StreamHandler()... |
# Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
from mmcv.cnn import ConvModule
from mmcv.ops import MaskedConv2d
from mmdet.registry import MODELS
from .guided_anchor_head import FeatureAdaption, GuidedAnchorHead
@MODELS.register_module()
class GARetinaHead(GuidedAnchorHead):
"""Guided-Anc... | # Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
from mmcv.cnn import ConvModule
from mmcv.ops import MaskedConv2d
from ..builder import HEADS
from .guided_anchor_head import FeatureAdaption, GuidedAnchorHead
@HEADS.register_module()
class GARetinaHead(GuidedAnchorHead):
"""Guided-Anchor-bas... |
"""
This example runs a CNN after the word embedding lookup. The output of the CNN is than pooled,
for example with mean-pooling.
"""
import logging
import sys
import traceback
from datetime import datetime
from datasets import load_dataset
from sentence_transformers import SentenceTransformer, losses, models
from ... | """
This example runs a CNN after the word embedding lookup. The output of the CNN is than pooled,
for example with mean-pooling.
"""
import torch
from torch.utils.data import DataLoader
import math
from sentence_transformers import models, losses, util
from sentence_transformers import LoggingHandler, SentenceTransf... |
import os
import subprocess
directory = os.path.dirname(os.path.realpath(__file__))
target_dirs = ["../backend", "../autogpt_libs"]
def run(*command: str) -> None:
print(f">>>>> Running poetry run {' '.join(command)}")
subprocess.run(["poetry", "run"] + list(command), cwd=directory, check=True)
def lint():... | import os
import subprocess
directory = os.path.dirname(os.path.realpath(__file__))
def run(*command: str) -> None:
print(f">>>>> Running poetry run {' '.join(command)}")
subprocess.run(["poetry", "run"] + list(command), cwd=directory, check=True)
def lint():
try:
run("ruff", "check", ".", "--e... |
"""
Top-level module of Jina.
The primary function of this module is to import all of the public Jina
interfaces into a single place. The interfaces themselves are located in
sub-modules, as described below.
"""
import os as _os
import platform as _platform
import signal as _signal
import sys as _sys
import warnings... | """
Top-level module of Jina.
The primary function of this module is to import all of the public Jina
interfaces into a single place. The interfaces themselves are located in
sub-modules, as described below.
"""
import os as _os
import platform as _platform
import signal as _signal
import sys as _sys
import warnings... |
_base_ = '../retinanet/retinanet_r50_fpn_1x_coco.py'
# model settings
model = dict(
type='FSAF',
bbox_head=dict(
type='FSAFHead',
num_classes=80,
in_channels=256,
stacked_convs=4,
feat_channels=256,
reg_decoded_bbox=True,
# Only anchor-free branch is imple... | _base_ = '../retinanet/retinanet_r50_fpn_1x_coco.py'
# model settings
model = dict(
type='FSAF',
bbox_head=dict(
type='FSAFHead',
num_classes=80,
in_channels=256,
stacked_convs=4,
feat_channels=256,
reg_decoded_bbox=True,
# Only anchor-free branch is imple... |
import torch
from torchaudio_unittest.common_utils import PytorchTestCase
from torchaudio_unittest.prototype.hdemucs_test_impl import CompareHDemucsOriginal, HDemucsTests
class HDemucsFloat32CPUTest(HDemucsTests, CompareHDemucsOriginal, PytorchTestCase):
dtype = torch.float32
device = torch.device("cpu")
| import torch
from torchaudio_unittest.common_utils import PytorchTestCase
from torchaudio_unittest.prototype.hdemucs_test_impl import HDemucsTests
class HDemucsFloat32CPUTest(HDemucsTests, PytorchTestCase):
dtype = torch.float32
device = torch.device("cpu")
|
from pydantic import BaseModel
from backend.data.block import (
Block,
BlockCategory,
BlockOutput,
BlockSchema,
BlockWebhookConfig,
)
from backend.data.model import SchemaField
from backend.integrations.providers import ProviderName
from backend.util import settings
from backend.util.settings impor... | from pydantic import BaseModel
from backend.data.block import (
Block,
BlockCategory,
BlockOutput,
BlockSchema,
BlockWebhookConfig,
)
from backend.data.model import SchemaField
from backend.integrations.providers import ProviderName
from backend.util import settings
from backend.util.settings impor... |
import numpy as np
from absl.testing import parameterized
from keras.src import backend
from keras.src import dtype_policies
from keras.src import layers
from keras.src import testing
class ZeroPadding2DTest(testing.TestCase):
@parameterized.parameters(
{"data_format": "channels_first"},
{"data_f... | import numpy as np
from absl.testing import parameterized
from keras.src import backend
from keras.src import dtype_policies
from keras.src import layers
from keras.src import testing
class ZeroPadding2DTest(testing.TestCase, parameterized.TestCase):
@parameterized.parameters(
{"data_format": "channels_f... |
__version__ = '0.31.0'
import logging
from docarray.array import DocList, DocVec
from docarray.base_doc.doc import BaseDoc
from docarray.utils._internal.misc import _get_path_from_docarray_root_level
__all__ = ['BaseDoc', 'DocList', 'DocVec']
logger = logging.getLogger('docarray')
handler = logging.StreamHandler()... | __version__ = '0.30.1'
import logging
from docarray.array import DocList, DocVec
from docarray.base_doc.doc import BaseDoc
from docarray.utils._internal.misc import _get_path_from_docarray_root_level
__all__ = ['BaseDoc', 'DocList', 'DocVec']
logger = logging.getLogger('docarray')
handler = logging.StreamHandler()... |
import datetime
import uuid
from unittest.mock import MagicMock, patch
from langsmith.schemas import Example
from langchain_core.document_loaders import LangSmithLoader
from langchain_core.documents import Document
def test_init() -> None:
LangSmithLoader(api_key="secret")
EXAMPLES = [
Example(
in... | import datetime
import uuid
from unittest.mock import MagicMock, patch
from langsmith.schemas import Example
from langchain_core.document_loaders import LangSmithLoader
from langchain_core.documents import Document
def test_init() -> None:
LangSmithLoader(api_key="secret")
EXAMPLES = [
Example(
in... |
# Copyright (c) OpenMMLab. All rights reserved.
import os
import platform
import warnings
import cv2
import torch.multiprocessing as mp
def setup_multi_processes(cfg):
"""Setup multi-processing environment variables."""
# set multi-process start method as `fork` to speed up the training
if platform.syste... | # Copyright (c) OpenMMLab. All rights reserved.
import os
import platform
import warnings
import cv2
import torch.multiprocessing as mp
def setup_multi_processes(cfg):
"""Setup multi-processing environment variables."""
# set multi-process start method as `fork` to speed up the training
if platform.syste... |
from __future__ import annotations
from collections.abc import Iterable
import torch
from torch import Tensor, nn
from sentence_transformers.sparse_encoder.SparseEncoder import SparseEncoder
class FlopsLoss(nn.Module):
def __init__(self, model: SparseEncoder) -> None:
"""
FlopsLoss implements a... | from __future__ import annotations
from collections.abc import Iterable
import torch
from torch import Tensor, nn
from sentence_transformers.sparse_encoder.SparseEncoder import SparseEncoder
class FlopsLoss(nn.Module):
def __init__(self, model: SparseEncoder) -> None:
super().__init__()
self.mo... |
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal, TypeAlias
from ._typing import Array, Device, DType, Namespace
_Norm: TypeAlias = Literal["backward", "ortho", "forward"]
# Note: NumPy fft functions improperly upcast float32 and complex64 to
# complex128, which is ... | from __future__ import annotations
from typing import TYPE_CHECKING, Union, Optional, Literal
if TYPE_CHECKING:
from ._typing import Device, ndarray, DType
from collections.abc import Sequence
# Note: NumPy fft functions improperly upcast float32 and complex64 to
# complex128, which is why we require wrappin... |
"""
Top-level module of Jina.
The primary function of this module is to import all of the public Jina
interfaces into a single place. The interfaces themselves are located in
sub-modules, as described below.
"""
import os as _os
import platform as _platform
import signal as _signal
import sys as _sys
import warnings... | """
Top-level module of Jina.
The primary function of this module is to import all of the public Jina
interfaces into a single place. The interfaces themselves are located in
sub-modules, as described below.
"""
import os as _os
import platform as _platform
import signal as _signal
import sys as _sys
import warnings... |
_base_ = 'faster-rcnn_r50-caffe_fpn_1x_coco.py'
# learning policy
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=0.001,
step=[60000, 80000])
# Runner type
runner = dict(_delete_=True, type='IterBasedRunner', max_iters=90000)
checkpoint_config = dict(interval=1000... | _base_ = 'faster_rcnn_r50_caffe_fpn_1x_coco.py'
# learning policy
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=0.001,
step=[60000, 80000])
# Runner type
runner = dict(_delete_=True, type='IterBasedRunner', max_iters=90000)
checkpoint_config = dict(interval=1000... |
from abc import abstractmethod
from typing import Any, List, Union
from llama_index.core.graph_stores.types import PropertyGraphStore
from llama_index.core.indices.property_graph.sub_retrievers.base import BasePGRetriever
from llama_index.core.schema import NodeWithScore, QueryBundle, TextNode
CUSTOM_RETRIEVE_TYPE = ... | from abc import abstractmethod
from typing import Any, List, Union
from llama_index.core.graph_stores.types import PropertyGraphStore
from llama_index.core.indices.property_graph.sub_retrievers.base import BasePGRetriever
from llama_index.core.schema import NodeWithScore, QueryBundle, TextNode
CUSTOM_RETRIEVE_TYPE = ... |
# Copyright (c) OpenMMLab. All rights reserved.
from typing import Any, Optional, Sequence, Tuple
from mmengine.data import BaseDataSample
from mmengine.registry import HOOKS
from .hook import Hook
@HOOKS.register_module()
class ParamSchedulerHook(Hook):
"""A hook to update some hyper-parameters in optimizer, e.... | # Copyright (c) OpenMMLab. All rights reserved.
from typing import Any, Optional, Sequence, Tuple
from mmengine.data import BaseDataSample
from mmengine.registry import HOOKS
from .hook import Hook
@HOOKS.register_module()
class ParamSchedulerHook(Hook):
"""A hook to update some hyper-parameters in optimizer, e.... |
# coding: utf-8
from functools import lru_cache
import numpy as np
import sklearn.datasets
from sklearn.utils import check_random_state
@lru_cache(maxsize=None)
def load_boston(**kwargs):
return sklearn.datasets.load_boston(**kwargs)
@lru_cache(maxsize=None)
def load_breast_cancer(**kwargs):
return sklearn... | # coding: utf-8
from functools import lru_cache
import numpy as np
import sklearn.datasets
from sklearn.utils import check_random_state
@lru_cache(maxsize=None)
def load_boston(**kwargs):
return sklearn.datasets.load_boston(**kwargs)
@lru_cache(maxsize=None)
def load_breast_cancer(**kwargs):
return sklearn... |
from prisma.models import User
from backend.blocks.basic import StoreValueBlock
from backend.blocks.io import AgentInputBlock
from backend.blocks.text import FillTextTemplateBlock
from backend.data import graph
from backend.data.graph import create_graph
from backend.data.user import get_or_create_user
from backend.ut... | from prisma.models import User
from backend.blocks.basic import AgentInputBlock, PrintToConsoleBlock
from backend.blocks.text import FillTextTemplateBlock
from backend.data import graph
from backend.data.graph import create_graph
from backend.data.user import get_or_create_user
from backend.util.test import SpinTestSe... |
# Copyright (c) OpenMMLab. All rights reserved.
from argparse import ArgumentParser, Namespace
from pathlib import Path
from tempfile import TemporaryDirectory
import mmcv
try:
from model_archiver.model_packaging import package_model
from model_archiver.model_packaging_utils import ModelExportUtils
except Imp... | from argparse import ArgumentParser, Namespace
from pathlib import Path
from tempfile import TemporaryDirectory
import mmcv
try:
from model_archiver.model_packaging import package_model
from model_archiver.model_packaging_utils import ModelExportUtils
except ImportError:
package_model = None
def mmdet2t... |
# Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import mmengine
from mmengine.utils import digit_version
from .version import __version__, version_info
mmcv_minimum_version = '2.0.0rc0'
mmcv_maximum_version = '2.0.0'
mmcv_version = digit_version(mmcv.__version__)
mmengine_minimum_version = '0.0.0'
mmengi... | # Copyright (c) OpenMMLab. All rights reserved.
import mmcv
from .version import __version__, short_version
def digit_version(version_str):
digit_version = []
for x in version_str.split('.'):
if x.isdigit():
digit_version.append(int(x))
elif x.find('rc') != -1:
patch_v... |
prompt_template = """Given the following question and context, extract any part of the context *AS IS* that is relevant to answer the question. If none of the context is relevant return {no_output_str}.
Remember, *DO NOT* edit the extracted parts of the context.
> Question: {{question}}
> Context:
>>>
{{context}}
>>>... | # flake8: noqa
prompt_template = """Given the following question and context, extract any part of the context *AS IS* that is relevant to answer the question. If none of the context is relevant return {no_output_str}.
Remember, *DO NOT* edit the extracted parts of the context.
> Question: {{question}}
> Context:
>>>... |
# Copyright (c) OpenMMLab. All rights reserved.
from mmdet.core import ConfigType, OptConfigType, OptMultiConfig
from mmdet.registry import MODELS
from .single_stage import SingleStageDetector
@MODELS.register_module()
class GFL(SingleStageDetector):
"""Implementation of `GFL <https://arxiv.org/abs/2006.04388>`_
... | # Copyright (c) OpenMMLab. All rights reserved.
from mmdet.registry import MODELS
from .single_stage import SingleStageDetector
@MODELS.register_module()
class GFL(SingleStageDetector):
def __init__(self,
backbone,
neck,
bbox_head,
train_cfg=Non... |
import os
import pytest
from llama_index.core.agent.function_calling.base import FunctionCallingAgent
from llama_index.core.tools.tool_spec.base import BaseToolSpec
from llama_index.llms.openai import OpenAI
from llama_index.tools.agentql import AgentQLBrowserToolSpec
from llama_index.tools.playwright import Playwrigh... | import pytest
import os
from llama_index.core.tools.tool_spec.base import BaseToolSpec
from llama_index.core.agent import FunctionCallingAgent
from llama_index.tools.agentql import AgentQLBrowserToolSpec
from llama_index.tools.playwright import PlaywrightToolSpec
from llama_index.llms.openai import OpenAI
from test... |
from typing import Any, Optional, Type, TypeVar, Union
from docarray.base_doc import BaseDoc
from docarray.documents.mesh.vertices_and_faces import VerticesAndFaces
from docarray.typing.tensor.embedding import AnyEmbedding
from docarray.typing.url.url_3d.mesh_url import Mesh3DUrl
T = TypeVar('T', bound='Mesh3D')
cl... | from typing import Any, Optional, Type, TypeVar, Union
from docarray.base_document import BaseDocument
from docarray.documents.mesh.vertices_and_faces import VerticesAndFaces
from docarray.typing.tensor.embedding import AnyEmbedding
from docarray.typing.url.url_3d.mesh_url import Mesh3DUrl
T = TypeVar('T', bound='Mes... |
# Copyright (c) OpenMMLab. All rights reserved.
from unittest import TestCase
from unittest.mock import MagicMock, Mock, patch
from mmengine.hooks import IterTimerHook
from mmengine.logging import MessageHub
def time_patch():
if not hasattr(time_patch, 'time'):
time_patch.time = 0
else:
time_... | # Copyright (c) OpenMMLab. All rights reserved.
from unittest import TestCase
from unittest.mock import MagicMock, Mock, patch
from mmengine.hooks import IterTimerHook
from mmengine.logging import MessageHub
def time_patch():
if not hasattr(time_patch, 'time'):
time_patch.time = 0
else:
time_... |
from datetime import datetime
from enum import Enum
import os
from typing import List, Optional, Union
import pytest
from llama_index.core.program.function_program import get_function_tool
from pydantic import BaseModel, Field
from llama_index.llms.google_genai import GoogleGenAI
from llama_index.llms.google_genai.ut... | from datetime import datetime
from enum import Enum
import os
from typing import List, Optional, Union
import pytest
from llama_index.core.program.function_program import get_function_tool
from pydantic import BaseModel, Field
from llama_index.llms.google_genai import GoogleGenAI
from llama_index.llms.google_genai.ut... |
from typing import Type, TypeVar
from pydantic import AnyUrl as BaseAnyUrl
from pydantic import parse_obj_as
from docarray.document.base_node import BaseNode
from docarray.proto import NodeProto
T = TypeVar('T', bound='AnyUrl')
class AnyUrl(BaseAnyUrl, BaseNode):
def _to_node_protobuf(self) -> NodeProto:
... | from pydantic import AnyUrl as BaseAnyUrl
from docarray.document.base_node import BaseNode
from docarray.proto import NodeProto
class AnyUrl(BaseAnyUrl, BaseNode):
def _to_node_protobuf(self) -> NodeProto:
"""Convert Document into a NodeProto protobuf message. This function should
be called when ... |
import os
import fsspec
import pytest
from datasets.filesystems import COMPRESSION_FILESYSTEMS, HfFileSystem, extract_path_from_uri, is_remote_filesystem
from datasets.utils._hf_hub_fixes import dataset_info as hf_api_dataset_info
from .utils import require_lz4, require_zstandard
def test_extract_path_from_uri():
... | import os
import fsspec
import pytest
from datasets.filesystems import COMPRESSION_FILESYSTEMS, HfFileSystem, extract_path_from_uri, is_remote_filesystem
from datasets.utils._hf_hub_fixes import dataset_info as hf_api_dataset_info
from .utils import require_lz4, require_zstandard
def test_extract_path_from_uri():
... |
_base_ = './faster-rcnn_r50_fpn_1x_coco.py'
# fp16 settings
fp16 = dict(loss_scale=512.)
| _base_ = './faster_rcnn_r50_fpn_1x_coco.py'
# fp16 settings
fp16 = dict(loss_scale=512.)
|
from typing import Union
import torch
import torch.fx
from torch import nn, Tensor
from torch.jit.annotations import BroadcastingList2
from torch.nn.modules.utils import _pair
from torchvision.extension import _assert_has_ops
from ..utils import _log_api_usage_once
from ._utils import check_roi_boxes_shape, convert_b... | from typing import List, Union
import torch
import torch.fx
from torch import nn, Tensor
from torch.jit.annotations import BroadcastingList2
from torch.nn.modules.utils import _pair
from torchvision.extension import _assert_has_ops
from ..utils import _log_api_usage_once
from ._utils import check_roi_boxes_shape, con... |
import pytest
from docarray import DocumentArray
from docarray.array.opensearch import DocumentArrayOpenSearch
from docarray.array.qdrant import DocumentArrayQdrant
from docarray.array.sqlite import DocumentArraySqlite
from docarray.array.annlite import DocumentArrayAnnlite, AnnliteConfig
from docarray.array.storage.o... | 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 asyncio
from typing import Any, Callable, Optional, Sequence, Union
from llama_index.core.async_utils import run_jobs
from llama_index.core.indices.property_graph.utils import (
default_parse_triplets_fn,
)
from llama_index.core.graph_stores.types import (
EntityNode,
Relation,
KG_NODES_KEY,
... | import asyncio
from typing import Any, Callable, Optional, Sequence, Union
from llama_index.core.async_utils import run_jobs
from llama_index.core.indices.property_graph.utils import (
default_parse_triplets_fn,
)
from llama_index.core.graph_stores.types import (
EntityNode,
Relation,
KG_NODES_KEY,
... |
# Copyright 2020 The HuggingFace 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... | # Copyright 2020 The HuggingFace 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... |
from typing import Union
from docarray.typing.tensor.embedding.ndarray import NdArrayEmbedding
from docarray.utils._internal.misc import is_tf_available, is_torch_available
torch_available = is_torch_available()
if torch_available:
from docarray.typing.tensor.embedding.torch import TorchEmbedding
tf_available =... | from typing import Union
from docarray.typing.tensor.embedding.ndarray import NdArrayEmbedding
from docarray.utils.misc import is_tf_available, is_torch_available
torch_available = is_torch_available()
if torch_available:
from docarray.typing.tensor.embedding.torch import TorchEmbedding
tf_available = is_tf_ava... |
# model settings
input_size = 300
model = dict(
type='SingleStageDetector',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[1, 1, 1],
bgr_to_rgb=True,
pad_size_divisor=1),
backbone=dict(
type='SSDVGG',
depth=16,... | # model settings
preprocess_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[1, 1, 1], to_rgb=True)
input_size = 300
model = dict(
type='SingleStageDetector',
preprocess_cfg=preprocess_cfg,
backbone=dict(
type='SSDVGG',
depth=16,
with_last_pool=False,
ceil_mode=True,
... |
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_VIDEO_OPT,
_probe_video_from_file,
_probe_video_from_me... | 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_VIDEO_OPT,
_probe_video_from_file,
_probe_video_from_me... |
import sys
from jina.parsers import set_gateway_parser
from jina.parsers.helper import _update_gateway_args
from jina.serve.runtimes.gateway import GatewayRuntime
def run(*args, **kwargs):
runtime_cls = GatewayRuntime
print(f' args {args}')
runtime_args = set_gateway_parser().parse_args(args)
print(f... | import sys
from jina.parsers import set_gateway_parser
from jina.parsers.helper import _set_gateway_uses
from jina.serve.runtimes.gateway import GatewayRuntime
def run(*args, **kwargs):
runtime_cls = GatewayRuntime
print(f' args {args}')
runtime_args = set_gateway_parser().parse_args(args)
print(f' p... |
from torchaudio.utils import sox_utils
from torchaudio_unittest.common_utils import PytorchTestCase, skipIfNoSox
@skipIfNoSox
class TestSoxUtils(PytorchTestCase):
"""Smoke tests for sox_util module"""
def test_set_seed(self):
"""`set_seed` does not crush"""
sox_utils.set_seed(0)
def test... | from torchaudio.utils import sox_utils
from torchaudio_unittest.common_utils import (
PytorchTestCase,
skipIfNoSox,
)
@skipIfNoSox
class TestSoxUtils(PytorchTestCase):
"""Smoke tests for sox_util module"""
def test_set_seed(self):
"""`set_seed` does not crush"""
sox_utils.set_seed(0)
... |
from prisma.models import User
from backend.blocks.basic import AgentInputBlock, PrintToConsoleBlock
from backend.blocks.text import FillTextTemplateBlock
from backend.data import graph
from backend.data.graph import create_graph
from backend.data.user import get_or_create_user
from backend.util.test import SpinTestSe... | from prisma.models import User
from backend.blocks.basic import AgentInputBlock, PrintToConsoleBlock
from backend.blocks.text import FillTextTemplateBlock
from backend.data import graph
from backend.data.graph import create_graph
from backend.data.user import get_or_create_user
from backend.util.test import SpinTestSe... |
from torchvision.transforms import InterpolationMode # usort: skip
from ._utils import is_simple_tensor, register_kernel # usort: skip
from ._meta import (
clamp_bounding_boxes,
convert_format_bounding_boxes,
get_dimensions_image_tensor,
get_dimensions_image_pil,
get_dimensions_video,
get_di... | from torchvision.transforms import InterpolationMode # usort: skip
from ._utils import is_simple_tensor, register_kernel # usort: skip
from ._meta import (
clamp_bounding_boxes,
convert_format_bounding_boxes,
get_dimensions_image_tensor,
get_dimensions_image_pil,
get_dimensions_video,
get_di... |
"""Google Search API Toolkit."""
from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.tools import GoogleSearchResults, GoogleSearchRun
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising depreca... | """Google Search API Toolkit."""
from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.tools import GoogleSearchResults, GoogleSearchRun
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising depreca... |
"""Tracker for XGBoost collective."""
import ctypes
import json
import socket
from enum import IntEnum, unique
from typing import Dict, Optional, Union
from .core import _LIB, _check_call, _deprecate_positional_args, make_jcargs
def get_family(addr: str) -> int:
"""Get network family from address."""
return... | """Tracker for XGBoost collective."""
import ctypes
import json
import socket
from enum import IntEnum, unique
from typing import Dict, Optional, Union
from .core import _LIB, _check_call, make_jcargs
def get_family(addr: str) -> int:
"""Get network family from address."""
return socket.getaddrinfo(addr, No... |
"""
This example runs a BiLSTM after the word embedding lookup. The output of the BiLSTM is than pooled,
for example with max-pooling (which gives a system like InferSent) or with mean-pooling.
Note, you can also pass BERT embeddings to the BiLSTM.
"""
import logging
import traceback
from datetime import datetime
fr... | """
This example runs a BiLSTM after the word embedding lookup. The output of the BiLSTM is than pooled,
for example with max-pooling (which gives a system like InferSent) or with mean-pooling.
Note, you can also pass BERT embeddings to the BiLSTM.
"""
import torch
from torch.utils.data import DataLoader
import math
f... |
from typing import Union
import torch
import transformers
from PIL import Image
from torch import nn
class CLIPModel(nn.Module):
def __init__(self, model_name: str = "openai/clip-vit-base-patch32", processor_name=None):
super(CLIPModel, self).__init__()
if processor_name is None:
pro... | from torch import nn
import transformers
import torch
from PIL import Image
class CLIPModel(nn.Module):
def __init__(self, model_name: str = "openai/clip-vit-base-patch32", processor_name = None):
super(CLIPModel, self).__init__()
if processor_name is None:
processor_name = model_nam... |
from langchain_core.prompts.prompt import PromptTemplate
_template = """Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question.
Chat History:
{chat_history}
Follow Up Input: {question}
Standalone question:""" # noqa: E501
CONDENSE_QUESTION_PROMPT = Prom... | # flake8: noqa
from langchain_core.prompts.prompt import PromptTemplate
_template = """Given the following conversation and a follow up question, rephrase the follow up question to be a standalone question.
Chat History:
{chat_history}
Follow Up Input: {question}
Standalone question:"""
CONDENSE_QUESTION_PROMPT = Pro... |
"""
Here, because clip is not consistent with the use of the "Text" and "Vision" prefixes, we cannot simply use
```
class Multimodal2VisionModel(CLIPVisionModel):
pass
```
with the hope that all dependencies will be renamed as `Multimodal2VisionClass`. For this reason, if we want consistency and
use the "Vision" pa... | """
Here, because clip is not consistent with the use of the "Text" and "Vision" prefixes, we cannot simply use
```
class Multimodal2VisionModel(CLIPVisionModel):
pass
```
with the hope that all dependencies will be renamed as `Multimodal2VisionClass`. For this reason, if we want consistency and
use the "Vision" pa... |
__version__ = '0.13.17'
import os
from .document import Document
from .array import DocumentArray
from .dataclasses import dataclass, field
if 'DA_NO_RICH_HANDLER' not in os.environ:
from rich.traceback import install
install()
| __version__ = '0.13.16'
import os
from .document import Document
from .array import DocumentArray
from .dataclasses import dataclass, field
if 'DA_NO_RICH_HANDLER' not in os.environ:
from rich.traceback import install
install()
|
from pydantic import BaseModel
from typing import Any, AsyncGenerator, List
from llama_index.llms.nvidia import NVIDIA as Interface
from llama_index.core.program import LLMTextCompletionProgram
from llama_index.core.program import FunctionCallingProgram
import pytest
from llama_index.llms.nvidia.utils import (
MODE... | from pydantic import BaseModel
from typing import Any, AsyncGenerator, List
from llama_index.llms.nvidia import NVIDIA as Interface
from llama_index.core.program import LLMTextCompletionProgram
from llama_index.core.program import FunctionCallingProgram
import pytest
from llama_index.llms.nvidia.utils import (
MODE... |
"""Development Scripts for template packages."""
from collections.abc import Sequence
from typing import Literal
from fastapi import FastAPI
from langserve import add_routes
from langchain_cli.utils.packages import get_langserve_export, get_package_root
def create_demo_server(
*,
config_keys: Sequence[str]... | # type: ignore
"""Development Scripts for template packages."""
from collections.abc import Sequence
from fastapi import FastAPI
from langserve import add_routes
from langchain_cli.utils.packages import get_langserve_export, get_package_root
def create_demo_server(
*,
config_keys: Sequence[str] = (),
p... |
from contextlib import nullcontext
from typing import List
import pytest
import torch
import tqdm
from torch.optim import Adam
from transformers import set_seed
from sentence_transformers import InputExample, SentenceTransformer, losses
@pytest.mark.parametrize(
["train_samples_mnrl", "train_samples_cmnrl", "sa... | from contextlib import nullcontext
from typing import List
import pytest
from sentence_transformers import SentenceTransformer, InputExample, losses
import tqdm
from transformers import set_seed
import torch
from torch.optim import Adam
@pytest.mark.parametrize(
["train_samples_mnrl", "train_samples_cmnrl", "same... |
"""
=============================================
A demo of the Spectral Biclustering algorithm
=============================================
This example demonstrates how to generate a checkerboard dataset and bicluster
it using the :class:`~sklearn.cluster.SpectralBiclustering` algorithm. The
spectral biclustering a... | """
=============================================
A demo of the Spectral Biclustering algorithm
=============================================
This example demonstrates how to generate a checkerboard dataset and bicluster
it using the :class:`~sklearn.cluster.SpectralBiclustering` algorithm. The
spectral biclustering a... |
# Copyright (c) OpenMMLab. All rights reserved.
import glob
import os
import os.path as osp
import warnings
import mmcv
from mmcv.utils import print_log
def find_latest_checkpoint(path, suffix='pth'):
"""Find the latest checkpoint from the working directory.
Args:
path(str): The path to find checkpo... | # Copyright (c) OpenMMLab. All rights reserved.
import glob
import os.path as osp
import warnings
def find_latest_checkpoint(path, suffix='pth'):
"""Find the latest checkpoint from the working directory.
Args:
path(str): The path to find checkpoints.
suffix(str): File extension.
D... |
# Copyright (c) OpenMMLab. All rights reserved.
from .utils import _dummy_bbox_sampling
__all__ = ['_dummy_bbox_sampling']
| from .utils import _dummy_bbox_sampling
__all__ = ['_dummy_bbox_sampling']
|
from .cmuarctic import CMUARCTIC
from .cmudict import CMUDict
from .commonvoice import COMMONVOICE
from .dr_vctk import DR_VCTK
from .fluentcommands import FluentSpeechCommands
from .gtzan import GTZAN
from .librilight_limited import LibriLightLimited
from .librimix import LibriMix
from .librispeech import LIBRISPEECH
... | from .cmuarctic import CMUARCTIC
from .cmudict import CMUDict
from .commonvoice import COMMONVOICE
from .dr_vctk import DR_VCTK
from .gtzan import GTZAN
from .librilight_limited import LibriLightLimited
from .librimix import LibriMix
from .librispeech import LIBRISPEECH
from .libritts import LIBRITTS
from .ljspeech imp... |
_base_ = './mask-rcnn_r50_fpn_sample1e-3_ms-2x_lvis-v0.5.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
... | _base_ = './mask_rcnn_r50_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
... |
import numpy as np
import pytest
from pydantic import Field
from docarray import BaseDoc
from docarray.index import HnswDocumentIndex
from docarray.typing import NdArray
pytestmark = [pytest.mark.slow, pytest.mark.index]
class SimpleDoc(BaseDoc):
tens: NdArray[10] = Field(dim=1000)
class NestedDoc(BaseDoc):
... | import numpy as np
import pytest
from pydantic import Field
from docarray import BaseDoc
from docarray.index import HnswDocumentIndex
from docarray.typing import NdArray
pytestmark = [pytest.mark.slow, pytest.mark.index]
class SimpleDoc(BaseDoc):
tens: NdArray[10] = Field(dim=1000)
class NestedDoc(BaseDoc):
... |
import functools
import warnings
from collections import defaultdict
from typing import Any, Dict, Optional, Sequence, Tuple, Type, TypeVar, Union
import torch
from torchvision import datapoints
from torchvision.transforms.v2 import Transform
from torchvision.transforms.v2.utils import is_pure_tensor
T = TypeVar("... | import functools
import warnings
from collections import defaultdict
from typing import Any, Dict, Optional, Sequence, Tuple, Type, TypeVar, Union
import torch
from torchvision import datapoints
from torchvision.transforms.v2 import Transform
from torchvision.transforms.v2.utils import is_simple_tensor
T = TypeVar... |
from __future__ import annotations
from collections.abc import Iterable
from typing import Any
import torch
from torch import Tensor, nn
from sentence_transformers.SentenceTransformer import SentenceTransformer
from sentence_transformers.util import fullname
class CosineSimilarityLoss(nn.Module):
def __init__(... | from __future__ import annotations
from collections.abc import Iterable
from typing import Any
import torch
from torch import Tensor, nn
from sentence_transformers.SentenceTransformer import SentenceTransformer
from sentence_transformers.util import fullname
class CosineSimilarityLoss(nn.Module):
def __init__(... |
# Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
import tempfile
from unittest import TestCase
from unittest.mock import Mock
import torch
import torch.nn as nn
from torch.utils.data import Dataset
from mmengine.hooks import EMAHook
from mmengine.model import ExponentialMovingAverage
from mmengin... | # Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
import tempfile
from unittest import TestCase
from unittest.mock import Mock
import torch
import torch.nn as nn
from torch.utils.data import Dataset
from mmengine.hooks import EMAHook
from mmengine.model import ExponentialMovingAverage
from mmengin... |
import logging
from typing import Any, Optional
from llama_index.core.bridge.pydantic import Field, model_serializer, ValidationError
from llama_index.core.tools import ToolSelection, ToolOutput
from llama_index.core.llms import ChatMessage
from llama_index.core.workflow import Event, StartEvent
logger = logging.get... | from typing import Any, Optional
from llama_index.core.bridge.pydantic import Field, model_serializer
from llama_index.core.tools import ToolSelection, ToolOutput
from llama_index.core.llms import ChatMessage
from llama_index.core.workflow import Event, StartEvent
class AgentInput(Event):
"""LLM input."""
i... |
"""
This file evaluates CrossEncoder on the TREC 2019 Deep Learning (DL) Track: https://arxiv.org/abs/2003.07820
TREC 2019 DL is based on the corpus of MS Marco. MS Marco provides a sparse annotation, i.e., usually only a single
passage is marked as relevant for a given query. Many other highly relevant passages are n... | """
This file evaluates CrossEncoder on the TREC 2019 Deep Learning (DL) Track: https://arxiv.org/abs/2003.07820
TREC 2019 DL is based on the corpus of MS Marco. MS Marco provides a sparse annotation, i.e., usually only a single
passage is marked as relevant for a given query. Many other highly relevant passages are n... |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# This software may be used and distributed according to the terms of the Llama 2 Community License Agreement.
from typing import Optional
import fire
from llama import Llama
def main(
ckpt_dir: str,
tokenizer_path: str,
temperature: float = 0.6,
... | # Copyright (c) Meta Platforms, Inc. and affiliates.
# This software may be used and distributed according to the terms of the Llama 2 Community License Agreement.
from typing import Optional
import fire
from llama import Llama
def main(
ckpt_dir: str,
tokenizer_path: str,
temperature: float = 0.6,
... |
from ._bounding_box import BoundingBox, BoundingBoxFormat
from ._datapoint import _FillType, _FillTypeJIT, _InputType, _InputTypeJIT
from ._image import _ImageType, _ImageTypeJIT, _TensorImageType, _TensorImageTypeJIT, Image
from ._mask import Mask
from ._video import _TensorVideoType, _TensorVideoTypeJIT, _VideoType, ... | from ._bounding_box import BoundingBox, BoundingBoxFormat
from ._datapoint import FillType, FillTypeJIT, InputType, InputTypeJIT
from ._image import Image, ImageType, ImageTypeJIT, TensorImageType, TensorImageTypeJIT
from ._mask import Mask
from ._video import TensorVideoType, TensorVideoTypeJIT, Video, VideoType, Vide... |
import os
from typing import Dict
DEPLOYMENT_FILES = [
'statefulset-executor',
'deployment-executor',
'deployment-gateway',
'deployment-uses-before',
'deployment-uses-after',
'deployment-uses-before-after',
]
cur_dir = os.path.dirname(__file__)
DEFAULT_RESOURCE_DIR = os.path.join(
cur_dir,... | import os
from typing import Dict
DEPLOYMENT_FILES = [
'statefulset-executor',
'deployment-executor',
'deployment-gateway',
'deployment-uses-before',
'deployment-uses-after',
'deployment-uses-before-after',
]
cur_dir = os.path.dirname(__file__)
DEFAULT_RESOURCE_DIR = os.path.join(
cur_dir,... |
"""
Demo for using data iterator with Quantile DMatrix
==================================================
.. versionadded:: 1.2.0
The demo that defines a customized iterator for passing batches of data into
:py:class:`xgboost.QuantileDMatrix` and use this ``QuantileDMatrix`` for training. The
feature is primaril... | """
Demo for using data iterator with Quantile DMatrix
==================================================
.. versionadded:: 1.2.0
The demo that defines a customized iterator for passing batches of data into
:py:class:`xgboost.QuantileDMatrix` and use this ``QuantileDMatrix`` for
training. The feature is used pri... |
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