input stringlengths 33 5k | output stringlengths 32 5k |
|---|---|
"""DashScope api utils."""
from http import HTTPStatus
from typing import Any, Dict, List, Sequence, cast
from llama_index.core.base.llms.types import (
ChatMessage,
ChatResponse,
CompletionResponse,
ImageBlock,
)
from llama_index.core.base.llms.generic_utils import image_node_to_image_block
from llam... | """DashScope api utils."""
from http import HTTPStatus
from typing import Any, Dict, List, Sequence
from llama_index.core.base.llms.types import (
ChatMessage,
ChatResponse,
CompletionResponse,
)
from llama_index.core.schema import ImageDocument
def dashscope_response_to_completion_response(response: An... |
import json
import datasets
from tests.trainer.test_trainer import StoreLossCallback
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
DataCollatorForLanguageModeling,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.testing_utils import (
TestC... | import json
import datasets
import torch
from tests.trainer.test_trainer import StoreLossCallback
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
DataCollatorForLanguageModeling,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.testing_utils impor... |
import logging
from datasets import load_dataset
from sentence_transformers import SparseEncoder
from sentence_transformers.sparse_encoder.evaluation import SparseBinaryClassificationEvaluator
logging.basicConfig(format="%(message)s", level=logging.INFO)
# Initialize the SPLADE model
model = SparseEncoder("naver/sp... | import logging
from datasets import load_dataset
from sentence_transformers.sparse_encoder import (
MLMTransformer,
SparseBinaryClassificationEvaluator,
SparseEncoder,
SpladePooling,
)
logging.basicConfig(format="%(asctime)s - %(message)s", datefmt="%Y-%m-%d %H:%M:%S", level=logging.INFO)
# Initiali... |
_base_ = '../faster_rcnn/faster-rcnn_r50_fpn_2x_coco.py'
model = dict(
backbone=dict(
type='Res2Net',
depth=101,
scales=4,
base_width=26,
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://res2net101_v1d_26w_4s')))
| _base_ = '../faster_rcnn/faster_rcnn_r50_fpn_2x_coco.py'
model = dict(
backbone=dict(
type='Res2Net',
depth=101,
scales=4,
base_width=26,
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://res2net101_v1d_26w_4s')))
|
_base_ = [
'../_base_/models/cascade-mask-rcnn_r50_fpn.py',
'../_base_/datasets/lvis_v1_instance.py',
'../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
]
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvi... | _base_ = [
'../_base_/models/cascade_mask_rcnn_r50_fpn.py',
'../_base_/datasets/lvis_v1_instance.py',
'../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
]
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvi... |
from typing import cast
import prisma.enums
import prisma.types
from backend.blocks.io import IO_BLOCK_IDs
AGENT_NODE_INCLUDE: prisma.types.AgentNodeInclude = {
"Input": True,
"Output": True,
"Webhook": True,
"AgentBlock": True,
}
AGENT_GRAPH_INCLUDE: prisma.types.AgentGraphInclude = {
"Nodes": ... | from typing import cast
import prisma.enums
import prisma.types
from backend.blocks.io import IO_BLOCK_IDs
from backend.util.type import typed_cast
AGENT_NODE_INCLUDE: prisma.types.AgentNodeInclude = {
"Input": True,
"Output": True,
"Webhook": True,
"AgentBlock": True,
}
AGENT_GRAPH_INCLUDE: prisma.... |
_base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# please install mmpretrain
# import mmpretrain.models to trigger register_module in mmpretrain
custom_imports = dict(
imports=['mmpretrain.... | _base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# TODO: delete custom_imports after mmcls supports auto import
# please install mmcls>=1.0
# import mmcls.models to trigger register_module in m... |
"""Retriever tool."""
from typing import TYPE_CHECKING, Any, List, Optional
from llama_index.core.base.base_retriever import BaseRetriever
if TYPE_CHECKING:
from llama_index.core.langchain_helpers.agents.tools import LlamaIndexTool
from llama_index.core.schema import (
MetadataMode,
Node,
NodeWithSc... | """Retriever tool."""
from typing import TYPE_CHECKING, Any, List, Optional
from llama_index.core.base.base_retriever import BaseRetriever
if TYPE_CHECKING:
from llama_index.core.langchain_helpers.agents.tools import LlamaIndexTool
from llama_index.core.schema import MetadataMode, NodeWithScore, QueryBundle
fro... |
# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appl... | # Copyright 2024 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appl... |
# Copyright 2024 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 2024 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 ... |
from datetime import timedelta
from typing import Optional
from torch._C._distributed_c10d import _DEFAULT_PG_TIMEOUT
__all__ = ["default_pg_timeout", "default_pg_nccl_timeout"]
# Default process group wide timeout, if applicable.
# This only applies to the non-nccl backends
# To make an attempt at backwards compat... | from datetime import timedelta
from typing import Optional
from torch._C._distributed_c10d import _DEFAULT_PG_TIMEOUT
__all__ = ["default_pg_timeout", "default_pg_nccl_timeout"]
# Default process group wide timeout, if applicable.
# This only applies to the non-nccl backends
# To make an attempt at backwards compat... |
import PIL.Image
import pytest
import torch
import torchvision.transforms.v2.utils
from prototype_common_utils import make_bounding_box, make_detection_mask, make_image
from torchvision import datapoints
from torchvision.transforms.v2.functional import to_image_pil
from torchvision.transforms.v2.utils import has_all... | import PIL.Image
import pytest
import torch
import torchvision.prototype.transforms.utils
from prototype_common_utils import make_bounding_box, make_detection_mask, make_image
from torchvision.prototype import datapoints
from torchvision.prototype.transforms.functional import to_image_pil
from torchvision.prototype.... |
"""**OutputParser** classes parse the output of an LLM call.
**Class hierarchy:**
.. code-block::
BaseLLMOutputParser --> BaseOutputParser --> <name>OutputParser # ListOutputParser, PydanticOutputParser
**Main helpers:**
.. code-block::
Serializable, Generation, PromptValue
""" # noqa: E501
from import... | """**OutputParser** classes parse the output of an LLM call.
**Class hierarchy:**
.. code-block::
BaseLLMOutputParser --> BaseOutputParser --> <name>OutputParser # ListOutputParser, PydanticOutputParser
**Main helpers:**
.. code-block::
Serializable, Generation, PromptValue
""" # noqa: E501
from import... |
from typing import List
import torch
import torchaudio.prototype.transforms as T
from torch.autograd import gradcheck, gradgradcheck
from torchaudio_unittest.common_utils import get_spectrogram, get_whitenoise, nested_params, TestBaseMixin
class Autograd(TestBaseMixin):
def assert_grad(
self,
tra... | from typing import List
import torch
import torchaudio.prototype.transforms as T
from torch.autograd import gradcheck, gradgradcheck
from torchaudio_unittest.common_utils import get_spectrogram, get_whitenoise, nested_params, TestBaseMixin
class Autograd(TestBaseMixin):
def assert_grad(
self,
tra... |
from docarray.array.documentarray import DocumentArray
__all__ = ['DocumentArray']
| from docarray.array.documentarray import DocumentArray
|
# 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 AnchorHead
class TestAnchorHead(TestCase):
def test_anchor_head_loss(self):
... | # 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 AnchorHead
class TestAnchorHead(TestCase):
def test_anchor_head_loss(self):
"""T... |
"""Map-reduce chain.
Splits up a document, sends the smaller parts to the LLM with one prompt,
then combines the results with another one.
"""
from __future__ import annotations
from collections.abc import Mapping
from typing import Any, Optional
from langchain_core._api import deprecated
from langchain_core.callba... | """Map-reduce chain.
Splits up a document, sends the smaller parts to the LLM with one prompt,
then combines the results with another one.
"""
from __future__ import annotations
from typing import Any, Dict, List, Mapping, Optional
from langchain_core._api import deprecated
from langchain_core.callbacks import Call... |
from keras.src import ops
from keras.src.api_export import keras_export
from keras.src.optimizers import optimizer
@keras_export(["keras.optimizers.Adadelta"])
class Adadelta(optimizer.Optimizer):
"""Optimizer that implements the Adadelta algorithm.
Adadelta optimization is a stochastic gradient descent meth... | from keras.src import ops
from keras.src.api_export import keras_export
from keras.src.optimizers import optimizer
@keras_export(["keras.optimizers.Adadelta"])
class Adadelta(optimizer.Optimizer):
"""Optimizer that implements the Adadelta algorithm.
Adadelta optimization is a stochastic gradient descent meth... |
# coding: utf-8
from pathlib import Path
import pandas as pd
from sklearn.metrics import mean_squared_error
import lightgbm as lgb
print('Loading data...')
# load or create your dataset
regression_example_dir = Path(__file__).absolute().parents[1] / 'regression'
df_train = pd.read_csv(str(regression_example_dir / 'r... | # coding: utf-8
from pathlib import Path
import pandas as pd
from sklearn.metrics import mean_squared_error
import lightgbm as lgb
print('Loading data...')
# load or create your dataset
regression_example_dir = Path(__file__).absolute().parents[1] / 'regression'
df_train = pd.read_csv(str(regression_example_dir / 'r... |
# Copyright (c) OpenMMLab. All rights reserved.
from .hook import Hook
from .iter_timer_hook import IterTimerHook
from .optimizer_hook import OptimizerHook
from .param_scheduler_hook import ParamSchedulerHook
from .sampler_seed_hook import DistSamplerSeedHook
__all__ = [
'Hook', 'IterTimerHook', 'DistSamplerSeedHo... | # Copyright (c) OpenMMLab. All rights reserved.
from .hook import Hook
from .iter_timer_hook import IterTimerHook
from .sampler_seed_hook import DistSamplerSeedHook
from .param_scheduler_hook import ParamSchedulerHook
__all__ = [
'Hook', 'IterTimerHook', 'DistSamplerSeedHook', 'ParamSchedulerHook'
]
|
from typing import TYPE_CHECKING, TypeVar, List, Union, Optional, Dict, Sequence
if TYPE_CHECKING:
import numpy as np
import tensorflow
import torch
# Define the expected input type that your ANN search supports
MilvusArrayType = TypeVar(
'MilvusArrayType',
np.ndarray,
tens... | from typing import TYPE_CHECKING, TypeVar, List, Union, Optional, Dict, Sequence
if TYPE_CHECKING:
import numpy as np
import tensorflow
import torch
# Define the expected input type that your ANN search supports
MilvusArrayType = TypeVar(
'MilvusArrayType',
np.ndarray,
tens... |
from .backend_utils import set_audio_backend
from .case_utils import (
HttpServerMixin,
is_ffmpeg_available,
PytorchTestCase,
skipIfNoCtcDecoder,
skipIfNoCuda,
skipIfNoExec,
skipIfNoFFmpeg,
skipIfNoKaldi,
skipIfNoModule,
skipIfNoQengine,
skipIfNoSox,
skipIfPy310,
skip... | from .backend_utils import (
set_audio_backend,
)
from .case_utils import (
TempDirMixin,
HttpServerMixin,
TestBaseMixin,
PytorchTestCase,
TorchaudioTestCase,
is_ffmpeg_available,
skipIfNoCtcDecoder,
skipIfNoCuda,
skipIfNoExec,
skipIfNoModule,
skipIfNoKaldi,
skipIfNoS... |
import numpy as np
from absl.testing import parameterized
from keras.src import backend
from keras.src import testing
from keras.src.backend import KerasTensor
from keras.src.layers import InputLayer
class InputLayerTest(testing.TestCase):
# Testing happy path for layer without input tensor
@parameterized.na... | import numpy as np
from absl.testing import parameterized
from keras.src import backend
from keras.src import testing
from keras.src.backend import KerasTensor
from keras.src.layers import InputLayer
class InputLayerTest(testing.TestCase, parameterized.TestCase):
# Testing happy path for layer without input tens... |
from parameterized import parameterized
from torchaudio.io import AudioEffector
from torchaudio_unittest.common_utils import get_sinusoid, skipIfNoFFmpeg, TorchaudioTestCase
from .common import lt42
@skipIfNoFFmpeg
class EffectorTest(TorchaudioTestCase):
def test_null(self):
"""No effect and codec will ... | from parameterized import parameterized
from torchaudio.io import AudioEffector
from torchaudio_unittest.common_utils import get_sinusoid, skipIfNoFFmpeg, TorchaudioTestCase
from .common import lt42
@skipIfNoFFmpeg
class EffectorTest(TorchaudioTestCase):
def test_null(self):
"""No effect and codec will ... |
import numpy as np
from numpy.typing import ArrayLike
def oscillator_bank(
frequencies: ArrayLike,
amplitudes: ArrayLike,
sample_rate: float,
time_axis: int = -2,
) -> ArrayLike:
"""Reference implementation of oscillator_bank"""
invalid = np.abs(frequencies) >= sample_rate / 2
if np.any(in... | import numpy as np
from numpy.typing import ArrayLike
def oscillator_bank(
frequencies: ArrayLike,
amplitudes: ArrayLike,
sample_rate: float,
time_axis: int = -2,
) -> ArrayLike:
"""Reference implementation of oscillator_bank"""
invalid = np.abs(frequencies) >= sample_rate / 2
if np.any(in... |
# Copyright (c) OpenMMLab. All rights reserved.
from .coarse_mask_head import CoarseMaskHead
from .dynamic_mask_head import DynamicMaskHead
from .fcn_mask_head import FCNMaskHead
from .feature_relay_head import FeatureRelayHead
from .fused_semantic_head import FusedSemanticHead
from .global_context_head import GlobalCo... | # Copyright (c) OpenMMLab. All rights reserved.
from .coarse_mask_head import CoarseMaskHead
from .fcn_mask_head import FCNMaskHead
from .feature_relay_head import FeatureRelayHead
from .fused_semantic_head import FusedSemanticHead
from .global_context_head import GlobalContextHead
from .grid_head import GridHead
from ... |
from jina.clients.mixin import AsyncHealthCheckMixin, AsyncPostMixin, AsyncProfileMixin
from jina.orchestrate.flow.base import Flow
class AsyncFlow(AsyncPostMixin, AsyncProfileMixin, AsyncHealthCheckMixin, Flow):
"""
Asynchronous version of :class:`jina.Flow`. They share the same interface, except
in :cla... | from jina.clients.mixin import AsyncPostMixin
from jina.orchestrate.flow.base import Flow
class AsyncFlow(AsyncPostMixin, Flow):
"""
Asynchronous version of :class:`jina.Flow`. They share the same interface, except
in :class:`AsyncFlow` :meth:`train`, :meth:`index`, :meth:`search` methods are coroutines
... |
import numpy as np
from docarray.array import DocumentArray
from docarray.document import BaseDocument
from docarray.typing import Tensor
def test_get_bulk_attributes_function():
class Mmdoc(BaseDocument):
text: str
tensor: Tensor
N = 10
da = DocumentArray[Mmdoc](
(Mmdoc(text=f'... | import numpy as np
from docarray.array import DocumentArray
from docarray.document import BaseDocument
from docarray.typing import Tensor
def test_get_bulk_attributes():
class Mmdoc(BaseDocument):
text: str
tensor: Tensor
N = 10
da = DocumentArray[Mmdoc](
(Mmdoc(text=f'hello{i}'... |
from __future__ import annotations
import logging
from typing import TYPE_CHECKING, Any, Callable
from sentence_transformers.evaluation import InformationRetrievalEvaluator
if TYPE_CHECKING:
import numpy as np
from torch import Tensor
from sentence_transformers.similarity_functions import SimilarityFunc... | from __future__ import annotations
import logging
from typing import TYPE_CHECKING, Any, Callable
from sentence_transformers.evaluation import InformationRetrievalEvaluator
if TYPE_CHECKING:
import numpy as np
from torch import Tensor
from sentence_transformers.similarity_functions import SimilarityFunc... |
_base_ = [
'../_base_/models/mask-rcnn_r50_fpn.py',
# 270k iterations with batch_size 64 is roughly equivalent to 144 epochs
'../common/ssj_270k_coco-instance.py',
]
image_size = (1024, 1024)
batch_augments = [
dict(type='BatchFixedSizePad', size=image_size, pad_mask=True)
]
norm_cfg = dict(type='SyncB... | _base_ = [
'../_base_/models/mask-rcnn_r50_fpn.py',
# 270k iterations with batch_size 64 is roughly equivalent to 144 epochs
'../common/ssj_270k_coco-instance.py',
]
image_size = (1024, 1024)
batch_augments = [
dict(type='BatchFixedSizePad', size=image_size, pad_mask=True)
]
norm_cfg = dict(type='SyncB... |
__version__ = '0.1.0'
from docarray.array.array import DocumentArray
from docarray.document.document import BaseDocument as Document
from docarray.predefined_document import Image, Mesh3D, PointCloud3D, Text
__all__ = ['Document', 'DocumentArray', 'Image', 'Text', 'Mesh3D', 'PointCloud3D']
| __version__ = '0.1.0'
from docarray.array import DocumentArray
from docarray.document.document import BaseDocument as Document
from docarray.predefined_document import Image, Mesh3D, PointCloud3D, Text
__all__ = ['Document', 'DocumentArray', 'Image', 'Text', 'Mesh3D', 'PointCloud3D']
|
_base_ = './rtmdet_s_8xb32-300e_coco.py'
checkpoint = 'https://download.openmmlab.com/mmdetection/v3.0/rtmdet/cspnext_rsb_pretrain/cspnext-tiny_imagenet_600e.pth' # noqa
model = dict(
backbone=dict(
deepen_factor=0.167,
widen_factor=0.375,
init_cfg=dict(
type='Pretrained', pre... | _base_ = './rtmdet_s_8xb32-300e_coco.py'
checkpoint = 'TODO:imagenet_pretrain' # noqa
model = dict(
backbone=dict(
deepen_factor=0.167,
widen_factor=0.375,
init_cfg=dict(
type='Pretrained', prefix='backbone.', checkpoint=checkpoint)),
neck=dict(in_channels=[96, 192, 384], ... |
from typing import Union, Iterable, Dict
from ..base.seqlike import BaseSequenceLikeMixin
from .... import Document
class SequenceLikeMixin(BaseSequenceLikeMixin):
"""Implement sequence-like methods for DocumentArray with Elastic as storage"""
def __eq__(self, other):
"""Compare this object to the o... | from typing import Union, Iterable, Dict
from ..base.seqlike import BaseSequenceLikeMixin
from .... import Document
class SequenceLikeMixin(BaseSequenceLikeMixin):
"""Implement sequence-like methods for DocumentArray with Elastic as storage"""
def __eq__(self, other):
"""Compare this object to the o... |
import functools
import importlib
import os
import re
from pathlib import Path
from typing import TYPE_CHECKING, TypeVar
if TYPE_CHECKING:
from backend.data.block import Block
T = TypeVar("T")
@functools.cache
def load_all_blocks() -> dict[str, type["Block"]]:
from backend.data.block import Block
# Dyn... | import importlib
import os
import re
from pathlib import Path
from typing import TYPE_CHECKING, TypeVar
if TYPE_CHECKING:
from backend.data.block import Block
T = TypeVar("T")
_AVAILABLE_BLOCKS: dict[str, type["Block"]] = {}
def load_all_blocks() -> dict[str, type["Block"]]:
from backend.data.block import... |
"""Argparser module for Deployment runtimes"""
import argparse
from jina import helper
from jina.enums import DeploymentRoleType
from jina.parsers.helper import _SHOW_ALL_ARGS, KVAppendAction, add_arg_group
def mixin_base_deployment_parser(parser):
"""Add mixin arguments required by :class:`BaseDeployment` into ... | """Argparser module for Deployment runtimes"""
import argparse
from jina.enums import DeploymentRoleType
from jina.parsers.helper import _SHOW_ALL_ARGS, KVAppendAction, add_arg_group
def mixin_base_deployment_parser(parser):
"""Add mixin arguments required by :class:`BaseDeployment` into the given parser.
:... |
from typing import Optional
import numpy as np
import pytest
import torch
from pydantic.tools import parse_obj_as, schema_json_of
from docarray import BaseDocument
from docarray.base_document.io.json import orjson_dumps
from docarray.typing import AudioTorchTensor, AudioUrl
from tests import TOYDATA_DIR
AUDIO_FILES ... | from typing import Optional
import numpy as np
import pytest
import torch
from pydantic.tools import parse_obj_as, schema_json_of
from docarray import BaseDocument
from docarray.base_document.io.json import orjson_dumps
from docarray.typing import AudioNdArray, AudioTorchTensor, AudioUrl
from tests import TOYDATA_DIR... |
import argparse
from abc import ABC
from typing import TYPE_CHECKING, Optional, Union
from jina.serve.runtimes.asyncio import AsyncNewLoopRuntime
if TYPE_CHECKING:
import asyncio
import multiprocessing
import threading
class GatewayRuntime(AsyncNewLoopRuntime, ABC):
"""
The Runtime from which th... | import argparse
from abc import ABC
from typing import TYPE_CHECKING, Optional, Union
from jina.serve.networking import GrpcConnectionPool
from jina.serve.runtimes.asyncio import AsyncNewLoopRuntime
from jina.serve.runtimes.gateway.graph.topology_graph import TopologyGraph
if TYPE_CHECKING:
import asyncio
imp... |
"""
===================================================
Faces recognition example using eigenfaces and SVMs
===================================================
The dataset used in this example is a preprocessed excerpt of the
"Labeled Faces in the Wild", aka LFW_:
http://vis-www.cs.umass.edu/lfw/lfw-funneled.tgz (233M... | """
===================================================
Faces recognition example using eigenfaces and SVMs
===================================================
The dataset used in this example is a preprocessed excerpt of the
"Labeled Faces in the Wild", aka LFW_:
http://vis-www.cs.umass.edu/lfw/lfw-funneled.tgz (2... |
"""
Script to generate meta.json to store metadata for a nightly build of
XGBoost Python package.
"""
import argparse
import json
import pathlib
def main(args: argparse.Namespace) -> None:
wheel_path = pathlib.Path(args.wheel_path).expanduser().resolve()
if not wheel_path.exists():
raise ValueError(f... | """
Script to generate meta.json to store metadata for a nightly build of
XGBoost Python package.
"""
import argparse
import json
import pathlib
def main(args: argparse.Namespace) -> None:
wheel_path = pathlib.Path(args.wheel_path).expanduser().resolve()
if not wheel_path.exists():
raise ValueError(f... |
import string
from typing import Any
from langchain.evaluation.schema import StringEvaluator
class ExactMatchStringEvaluator(StringEvaluator):
"""Compute an exact match between the prediction and the reference.
Examples
----------
>>> evaluator = ExactMatchChain()
>>> evaluator.evaluate_strings(... | import string
from typing import Any
from langchain.evaluation.schema import StringEvaluator
class ExactMatchStringEvaluator(StringEvaluator):
"""Compute an exact match between the prediction and the reference.
Examples
----------
>>> evaluator = ExactMatchChain()
>>> evaluator.evaluate_strings(... |
import sys
import traceback
from importlib.machinery import SourceFileLoader
if __name__ == "__main__":
files = sys.argv[1:]
has_failure = False
for file in files:
try:
SourceFileLoader("x", file).load_module()
except Exception: # noqa: PERF203
has_failure = True
... | import sys
import traceback
from importlib.machinery import SourceFileLoader
if __name__ == "__main__":
files = sys.argv[1:]
has_failure = False
for file in files:
try:
SourceFileLoader("x", file).load_module()
except Exception:
has_failure = True
print(f... |
_base_ = [
'../_base_/models/mask-rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
]
model = dict(
backbone=dict(
frozen_stages=0,
norm_cfg=dict(type='SyncBN', requires_grad=True),
norm_eval=False,
... | _base_ = [
'../_base_/models/mask-rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
]
model = dict(
backbone=dict(
frozen_stages=0,
norm_cfg=dict(type='SyncBN', requires_grad=True),
norm_eval=False,
... |
_base_ = [
'../_base_/models/cascade-rcnn_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# model settings
model = dict(
roi_head=dict(bbox_head=[
dict(
type='SABLHead',
num_classes=80,
... | _base_ = [
'../_base_/models/cascade_rcnn_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# model settings
model = dict(
roi_head=dict(bbox_head=[
dict(
type='SABLHead',
num_classes=80,
... |
import numpy as np
import pytest
from keras.src import testing
from keras.src.layers.activations import relu
class ReLUTest(testing.TestCase):
@pytest.mark.requires_trainable_backend
def test_relu(self):
self.run_layer_test(
relu.ReLU,
init_kwargs={
"max_value"... | import numpy as np
import pytest
from keras.src import testing
from keras.src.layers.activations import relu
class ReLUTest(testing.TestCase):
@pytest.mark.requires_trainable_backend
def test_relu(self):
self.run_layer_test(
relu.ReLU,
init_kwargs={
"max_value"... |
import os
import boto3
import fsspec
import pytest
from moto import mock_s3
from datasets.filesystems import (
COMPRESSION_FILESYSTEMS,
HfFileSystem,
S3FileSystem,
extract_path_from_uri,
is_remote_filesystem,
)
from datasets.utils._hf_hub_fixes import dataset_info as hf_api_dataset_info
from .uti... | import os
import boto3
import fsspec
import pytest
from moto import mock_s3
from datasets.filesystems import (
COMPRESSION_FILESYSTEMS,
HfFileSystem,
S3FileSystem,
extract_path_from_uri,
is_remote_filesystem,
)
from .utils import require_lz4, require_zstandard
@pytest.fixture(scope="function")
... |
import numpy as np
def oscillator_bank(
frequencies,
amplitudes,
sample_rate: float,
time_axis: int = -2,
):
"""Reference implementation of oscillator_bank"""
invalid = np.abs(frequencies) >= sample_rate / 2
if np.any(invalid):
amplitudes = np.where(invalid, 0.0, amplitudes)
pi... | import numpy as np
from numpy.typing import ArrayLike
def oscillator_bank(
frequencies: ArrayLike,
amplitudes: ArrayLike,
sample_rate: float,
time_axis: int = -2,
) -> ArrayLike:
"""Reference implementation of oscillator_bank"""
invalid = np.abs(frequencies) >= sample_rate / 2
if np.any(in... |
# DO NOT EDIT. Generated by api_gen.sh
from keras.api import DTypePolicy
from keras.api import FloatDTypePolicy
from keras.api import Function
from keras.api import Initializer
from keras.api import Input
from keras.api import InputSpec
from keras.api import KerasTensor
from keras.api import Layer
from keras.api import... | # DO NOT EDIT. Generated by api_gen.sh
from keras.api import DTypePolicy
from keras.api import FloatDTypePolicy
from keras.api import Function
from keras.api import Initializer
from keras.api import Input
from keras.api import InputSpec
from keras.api import KerasTensor
from keras.api import Layer
from keras.api import... |
import logging
from datasets import load_dataset
from sentence_transformers import SparseEncoder
from sentence_transformers.sparse_encoder.evaluation import SparseRerankingEvaluator
logging.basicConfig(format="%(message)s", level=logging.INFO)
# Load a model
model = SparseEncoder("naver/splade-cocondenser-ensembled... | import logging
from datasets import load_dataset
from sentence_transformers import SparseEncoder
from sentence_transformers.sparse_encoder.evaluation import SparseRerankingEvaluator
logging.basicConfig(format="%(message)s", level=logging.INFO)
# Load a model
model = SparseEncoder("naver/splade-cocondenser-ensembled... |
from typing import TYPE_CHECKING
from ..utils import DIFFUSERS_SLOW_IMPORT, _LazyModule, deprecate
from ..utils.import_utils import is_peft_available, is_torch_available, is_transformers_available
def text_encoder_lora_state_dict(text_encoder):
deprecate(
"text_encoder_load_state_dict in `models`",
... | from typing import TYPE_CHECKING
from ..utils import DIFFUSERS_SLOW_IMPORT, _LazyModule, deprecate
from ..utils.import_utils import is_peft_available, is_torch_available, is_transformers_available
def text_encoder_lora_state_dict(text_encoder):
deprecate(
"text_encoder_load_state_dict in `models`",
... |
_base_ = '../grounding_dino_swin-t_pretrain_obj365.py'
data_root = 'data/coco/'
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(type='RandomFlip', prob=0.5),
dict(
type='RandomChoice',
transforms=[
[
dict... | _base_ = '../grounding_dino_swin-t_pretrain_obj365.py'
data_root = 'data/coco/'
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(type='RandomFlip', prob=0.5),
dict(
type='RandomChoice',
transforms=[
[
dict... |
from sentence_transformers.similarity_functions import SimilarityFunction
__all__ = ["SimilarityFunction"]
| from enum import Enum
class SimilarityFunction(Enum):
COSINE = 0
EUCLIDEAN = 1
MANHATTAN = 2
DOT_PRODUCT = 3
|
import csv
import gzip
import logging
import os
from datetime import datetime
import torch
from sentence_transformers import InputExample, LoggingHandler, SentenceTransformer, losses, models, util
from sentence_transformers.evaluation import EmbeddingSimilarityEvaluator
#### Just some code to print debug information... | import torch
from sentence_transformers.evaluation import EmbeddingSimilarityEvaluator
from sentence_transformers import SentenceTransformer, LoggingHandler, models, util, InputExample
from sentence_transformers import losses
import os
import gzip
import csv
from datetime import datetime
import logging
#### Just some ... |
_base_ = './centernet-update_r50_fpn_8xb8-amp-lsj-200e_coco.py'
model = dict(
backbone=dict(
depth=18,
init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet18')),
neck=dict(in_channels=[64, 128, 256, 512]))
| _base_ = './centernet_update_r50_fpn_fp16_lsj_200e_coco.py'
model = dict(
backbone=dict(
depth=18,
init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet18')),
neck=dict(in_channels=[64, 128, 256, 512]))
|
import os
import subprocess
import sys
import pytest
from xgboost import testing as tm
DEMO_DIR = tm.demo_dir(__file__)
PYTHON_DEMO_DIR = os.path.join(DEMO_DIR, "guide-python")
@pytest.mark.skipif(**tm.no_cupy())
def test_data_iterator():
script = os.path.join(PYTHON_DEMO_DIR, "quantile_data_iterator.py")
... | import os
import subprocess
import sys
import pytest
from xgboost import testing as tm
DEMO_DIR = tm.demo_dir(__file__)
PYTHON_DEMO_DIR = os.path.join(DEMO_DIR, "guide-python")
@pytest.mark.skipif(**tm.no_cupy())
def test_data_iterator():
script = os.path.join(PYTHON_DEMO_DIR, "quantile_data_iterator.py")
... |
import os
import signal
from threading import Thread
from time import sleep
from typing import Optional
_IN_TOPLEVEL_PROCESS = True
def in_toplevel_process() -> bool:
global _IN_TOPLEVEL_PROCESS
return _IN_TOPLEVEL_PROCESS
# If this process dies abnormally (e.g. segfault)
# it will not shut down the worke... | import os
import signal
from threading import Thread
from time import sleep
from typing import Optional
_IN_TOPLEVEL_PROCESS = True
def in_toplevel_process() -> bool:
global _IN_TOPLEVEL_PROCESS
return _IN_TOPLEVEL_PROCESS
# If this process dies abnormally (e.g. segfault)
# it will not shut down the worke... |
"""Dump objects to json."""
import json
from typing import Any
from pydantic import BaseModel
from langchain_core.load.serializable import Serializable, to_json_not_implemented
def default(obj: Any) -> Any:
"""Return a default value for an object.
Args:
obj: The object to serialize to json if it i... | import json
from typing import Any
from pydantic import BaseModel
from langchain_core.load.serializable import Serializable, to_json_not_implemented
def default(obj: Any) -> Any:
"""Return a default value for a Serializable object or
a SerializedNotImplemented object.
Args:
obj: The object to s... |
"""DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.api import activations
from keras.api import applications
from keras.api import callbacks
from keras.api import config
from keras.api import constraints
from keras.api import datasets
fro... | """DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.api import activations
from keras.api import applications
from keras.api import callbacks
from keras.api import config
from keras.api import constraints
from keras.api import datasets
fro... |
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appl... | # Copyright 2024 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appl... |
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... | 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
from unittest import TestCase
import cv2
import numpy as np
import torch
from mmengine.structures import InstanceData, PixelData
from mmdet.evaluation import INSTANCE_OFFSET
from mmdet.structures import DetDataSample
from mmdet.visualization import DetLocalVisualizer
def _rand_bboxes(num_boxes, h, w):
... | import os
from unittest import TestCase
import cv2
import numpy as np
import torch
from mmengine.structures import InstanceData, PixelData
from mmdet.evaluation import INSTANCE_OFFSET
from mmdet.structures import DetDataSample
from mmdet.visualization import DetLocalVisualizer
def _rand_bboxes(num_boxes, h, w):
... |
from typing import TypeVar
from docarray.typing.proto_register import _register_proto
from docarray.typing.tensor.audio.abstract_audio_tensor import AbstractAudioTensor
from docarray.typing.tensor.tensorflow_tensor import TensorFlowTensor, metaTensorFlow
T = TypeVar('T', bound='AudioTensorFlowTensor')
@_register_pr... | from typing import TypeVar
from docarray.typing.proto_register import _register_proto
from docarray.typing.tensor.audio.abstract_audio_tensor import AbstractAudioTensor
from docarray.typing.tensor.tensorflow_tensor import TensorFlowTensor, metaTensorFlow
T = TypeVar('T', bound='AudioTensorFlowTensor')
@_register_pr... |
from __future__ import annotations
import torch.nn as nn
from sentence_transformers.losses.CosineSimilarityLoss import CosineSimilarityLoss
from sentence_transformers.sparse_encoder.SparseEncoder import SparseEncoder
class SparseCosineSimilarityLoss(CosineSimilarityLoss):
def __init__(
self,
mod... | from __future__ import annotations
import torch.nn as nn
from sentence_transformers.losses.CosineSimilarityLoss import CosineSimilarityLoss
from sentence_transformers.sparse_encoder.SparseEncoder import SparseEncoder
class SparseCosineSimilarityLoss(CosineSimilarityLoss):
def __init__(
self,
mod... |
# Copyright (c) OpenMMLab. All rights reserved.
from .config import Config, ConfigDict, DictAction
__all__ = ['Config', 'ConfigDict', 'DictAction']
| # Copyright (c) OpenMMLab. All rights reserved.
from .config import Config, ConfigDict, DictAction
from .get_config_model import get_config, get_model
__all__ = ['Config', 'ConfigDict', 'DictAction', 'get_config', 'get_model']
|
# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import os
import mmcv
from PIL import Image
def parse_args():
parser = argparse.ArgumentParser(
description='Convert images to coco format without annotations')
parser.add_argument('img_path', help='The root path of images')
parser.a... | import argparse
import os
import mmcv
from PIL import Image
def parse_args():
parser = argparse.ArgumentParser(
description='Convert images to coco format without annotations')
parser.add_argument('img_path', help='The root path of images')
parser.add_argument(
'classes', type=str, help='... |
from typing import Any, Dict, List, Optional, Sequence, Type, Union
import PIL.Image
import torch
from torchvision import datapoints
from torchvision.prototype.datapoints import Label, OneHotLabel
from torchvision.transforms.v2 import functional as F, Transform
from torchvision.transforms.v2._utils import _FillType, ... | from typing import Any, Dict, List, Optional, Sequence, Type, Union
import PIL.Image
import torch
from torchvision import datapoints
from torchvision.prototype.datapoints import Label, OneHotLabel
from torchvision.transforms.v2 import functional as F, Transform
from torchvision.transforms.v2._utils import _FillType, ... |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=True)
class Summarization(TaskTemplate):
# `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization
task... | from dataclasses import dataclass
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=True)
class Summarization(TaskTemplate):
# `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization
task: str =... |
import os
from pathlib import Path
from torchaudio.datasets import vctk
from torchaudio_unittest.common_utils import get_whitenoise, normalize_wav, save_wav, TempDirMixin, TorchaudioTestCase
# Used to generate a unique transcript for each dummy audio file
_TRANSCRIPT = [
"Please call Stella",
"Ask her to brin... | import os
from pathlib import Path
from torchaudio.datasets import vctk
from torchaudio_unittest.common_utils import get_whitenoise, normalize_wav, save_wav, TempDirMixin, TorchaudioTestCase
# Used to generate a unique transcript for each dummy audio file
_TRANSCRIPT = [
"Please call Stella",
"Ask her to brin... |
# Copyright (c) OpenMMLab. All rights reserved.
from mmdet.registry import DATASETS
from .xml_style import XMLDataset
@DATASETS.register_module()
class VOCDataset(XMLDataset):
"""Dataset for PASCAL VOC."""
METAINFO = {
'classes':
('aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car'... | # Copyright (c) OpenMMLab. All rights reserved.
from mmdet.registry import DATASETS
from .xml_style import XMLDataset
@DATASETS.register_module()
class VOCDataset(XMLDataset):
"""Dataset for PASCAL VOC."""
METAINFO = {
'CLASSES':
('aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car'... |
# Copyright (c) OpenMMLab. All rights reserved.
import copy
import os.path as osp
import mmcv
import numpy as np
import pytest
from mmdet.core.mask import BitmapMasks
from mmdet.datasets.pipelines import (FilterAnnotations, LoadImageFromFile,
LoadImageFromWebcam,
... | # Copyright (c) OpenMMLab. All rights reserved.
import copy
import os.path as osp
import mmcv
import numpy as np
from mmdet.datasets.pipelines import (LoadImageFromFile, LoadImageFromWebcam,
LoadMultiChannelImageFromFiles)
class TestLoading:
@classmethod
def setup_clas... |
"""Chain-of-Abstraction Output Parser."""
import asyncio
import json
import networkx as nx
import re
from collections import defaultdict
from typing import Dict, Tuple
from llama_index.core.tools import AsyncBaseTool, ToolOutput
from llama_index.core.types import BaseOutputParser
class ChainOfAbstractionParser(Base... | """Chain-of-Abstraction Output Parser."""
import asyncio
import json
import networkx as nx
import re
from collections import defaultdict
from typing import Dict, Tuple
from llama_index.core.tools import AsyncBaseTool, ToolOutput
from llama_index.core.types import BaseOutputParser
class ChainOfAbstractionParser(Base... |
import warnings
from langchain_core.globals import get_debug as core_get_debug
from langchain_core.globals import get_verbose as core_get_verbose
from langchain_core.globals import set_debug as core_set_debug
from langchain_core.globals import set_verbose as core_set_verbose
from langchain.globals import get_debug, g... | import warnings
from langchain_core.globals import get_debug as core_get_debug
from langchain_core.globals import get_verbose as core_get_verbose
from langchain_core.globals import set_debug as core_set_debug
from langchain_core.globals import set_verbose as core_set_verbose
from langchain.globals import get_debug, g... |
import unittest
import torch
import torchaudio.prototype.functional as F
from parameterized import parameterized
from torchaudio_unittest.common_utils import skipIfNoRIR, TestBaseMixin, torch_script
class TorchScriptConsistencyTestImpl(TestBaseMixin):
def _assert_consistency(self, func, inputs, shape_only=False)... | import unittest
import torch
import torchaudio.prototype.functional as F
from torchaudio_unittest.common_utils import TestBaseMixin, torch_script
class TorchScriptConsistencyTestImpl(TestBaseMixin):
def _assert_consistency(self, func, inputs, shape_only=False):
inputs_ = []
for i in inputs:
... |
from keras.src import ops
from keras.src.api_export import keras_export
from keras.src.layers.layer import Layer
@keras_export("keras.layers.UnitNormalization")
class UnitNormalization(Layer):
"""Unit normalization layer.
Normalize a batch of inputs so that each input in the batch has a L2 norm
equal to ... | from keras.src import ops
from keras.src.api_export import keras_export
from keras.src.layers.layer import Layer
@keras_export("keras.layers.UnitNormalization")
class UnitNormalization(Layer):
"""Unit normalization layer.
Normalize a batch of inputs so that each input in the batch has a L2 norm
equal to ... |
"""
This examples trains a CrossEncoder for the NLI task. A CrossEncoder takes a sentence pair
as input and outputs a label. Here, it learns to predict the labels: "contradiction": 0, "entailment": 1, "neutral": 2.
It does NOT produce a sentence embedding and does NOT work for individual sentences.
Usage:
python trai... | """
This examples trains a CrossEncoder for the NLI task. A CrossEncoder takes a sentence pair
as input and outputs a label. Here, it learns to predict the labels: "contradiction": 0, "entailment": 1, "neutral": 2.
It does NOT produce a sentence embedding and does NOT work for individual sentences.
Usage:
python trai... |
_base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth' # noqa
model = dict(
bac... | _base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth' # noqa
model = dict(
bac... |
"""DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.ops.nn import average_pool
from keras.src.ops.nn import batch_normalization
from keras.src.ops.nn import binary_crossentropy
from keras.src.ops.nn import categorical_crossentropy
from... | """DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.ops.nn import average_pool
from keras.src.ops.nn import batch_normalization
from keras.src.ops.nn import binary_crossentropy
from keras.src.ops.nn import categorical_crossentropy
from... |
from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.tools.amadeus.flight_search import (
AmadeusFlightSearch,
FlightSearchSchema,
)
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for... | from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.tools.amadeus.flight_search import (
AmadeusFlightSearch,
FlightSearchSchema,
)
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for... |
# Copyright (c) OpenMMLab. All rights reserved.
from .ade20k import (ADE20KInstanceDataset, ADE20KPanopticDataset,
ADE20KSegDataset)
from .base_det_dataset import BaseDetDataset
from .base_semseg_dataset import BaseSegDataset
from .base_video_dataset import BaseVideoDataset
from .cityscapes import ... | # Copyright (c) OpenMMLab. All rights reserved.
from .ade20k import (ADE20KInstanceDataset, ADE20KPanopticDataset,
ADE20KSegDataset)
from .base_det_dataset import BaseDetDataset
from .base_semseg_dataset import BaseSegDataset
from .base_video_dataset import BaseVideoDataset
from .cityscapes import ... |
from jina.clients.base.http import HTTPBaseClient
from jina.clients.mixin import (
AsyncHealthCheckMixin,
AsyncMutateMixin,
AsyncPostMixin,
AsyncProfileMixin,
HealthCheckMixin,
MutateMixin,
PostMixin,
ProfileMixin,
)
import asyncio
class HTTPClient(
HTTPBaseClient, PostMixin, Profi... | from jina.clients.base.http import HTTPBaseClient
from jina.clients.mixin import (
AsyncHealthCheckMixin,
AsyncMutateMixin,
AsyncPostMixin,
AsyncProfileMixin,
HealthCheckMixin,
MutateMixin,
PostMixin,
ProfileMixin,
)
class HTTPClient(
HTTPBaseClient, PostMixin, ProfileMixin, Mutate... |
_base_ = './mask-rcnn_regnetx-3.2GF_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='RegNet',
arch='regnetx_8.0gf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dic... | _base_ = './mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='RegNet',
arch='regnetx_8.0gf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dic... |
"""Helper functions for managing the LangChain API.
This module is only relevant for LangChain developers, not for users.
.. warning::
This module and its submodules are for internal use only. Do not use them
in your own code. We may change the API at any time with no warning.
"""
from importlib import i... | """Helper functions for managing the LangChain API.
This module is only relevant for LangChain developers, not for users.
.. warning::
This module and its submodules are for internal use only. Do not use them
in your own code. We may change the API at any time with no warning.
"""
from importlib import i... |
import logging
import os
import signal
import sys
from abc import ABC, abstractmethod
from multiprocessing import Process, set_start_method
from typing import Optional
from backend.util.logging import configure_logging
from backend.util.metrics import sentry_init
logger = logging.getLogger(__name__)
_SERVICE_NAME = "... | import logging
import os
import signal
import sys
from abc import ABC, abstractmethod
from multiprocessing import Process, set_start_method
from typing import Optional
from backend.util.logging import configure_logging
from backend.util.metrics import sentry_init
logger = logging.getLogger(__name__)
_SERVICE_NAME = "... |
# 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... |
from typing import Dict, List, Optional, Set
import pytest
from docarray import BaseDoc, DocArray
from docarray.documents import ImageDoc
from docarray.utils.reduce import reduce, reduce_all
class InnerDoc(BaseDoc):
integer: int
inner_list: List
class MMDoc(BaseDoc):
text: str = ''
price: int = 0
... | from typing import Dict, List, Optional, Set
import pytest
from docarray import BaseDocument, DocumentArray
from docarray.documents import ImageDoc
from docarray.utils.reduce import reduce, reduce_all
class InnerDoc(BaseDocument):
integer: int
inner_list: List
class MMDoc(BaseDocument):
text: str = ''... |
_base_ = './scnet_r50_fpn_1x_coco.py'
# learning policy
max_epochs = 20
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=max_epochs,
by_epoch=True,
milestones=[16, 19],
... | _base_ = './scnet_r50_fpn_1x_coco.py'
# learning policy
lr_config = dict(step=[16, 19])
runner = dict(type='EpochBasedRunner', max_epochs=20)
|
import os
from functools import lru_cache
from typing import Union
import ffmpeg
import numpy as np
import torch
import torch.nn.functional as F
from .utils import exact_div
# hard-coded audio hyperparameters
SAMPLE_RATE = 16000
N_FFT = 400
N_MELS = 80
HOP_LENGTH = 160
CHUNK_LENGTH = 30
N_SAMPLES = CHUNK_LENGTH * SA... | import os
from functools import lru_cache
from typing import Union
import ffmpeg
import numpy as np
import torch
import torch.nn.functional as F
from .utils import exact_div
# hard-coded audio hyperparameters
SAMPLE_RATE = 16000
N_FFT = 400
N_MELS = 80
HOP_LENGTH = 160
CHUNK_LENGTH = 30
N_SAMPLES = CHUNK_LENGTH * SA... |
from .utils import _init_backend, get_audio_backend, list_audio_backends, set_audio_backend
__all__ = ["_init_backend", "get_audio_backend", "list_audio_backends", "set_audio_backend"]
| # flake8: noqa
import torchaudio
from . import utils
from .utils import _is_backend_dispatcher_enabled, get_audio_backend, list_audio_backends, set_audio_backend
if _is_backend_dispatcher_enabled():
from torchaudio._backend.utils import get_info_func, get_load_func, get_save_func
torchaudio.info = get_info_f... |
# Copyright (c) OpenMMLab. All rights reserved.
from mmdet.registry import MODELS
from mmdet.utils import ConfigType, OptConfigType, OptMultiConfig
from .single_stage import SingleStageDetector
@MODELS.register_module()
class YOLOX(SingleStageDetector):
r"""Implementation of `YOLOX: Exceeding YOLO Series in 2021
... | # Copyright (c) OpenMMLab. All rights reserved.
from mmdet.core.utils import ConfigType, OptConfigType, OptMultiConfig
from mmdet.registry import MODELS
from .single_stage import SingleStageDetector
@MODELS.register_module()
class YOLOX(SingleStageDetector):
r"""Implementation of `YOLOX: Exceeding YOLO Series in ... |
# Copyright (c) OpenMMLab. All rights reserved.
import copy
import inspect
from typing import List, Optional
import torch
import torch.nn as nn
from mmengine.registry import OPTIMIZER_CONSTRUCTORS, OPTIMIZERS
def register_torch_optimizers() -> List[str]:
"""Register optimizers in ``torch.optim`` to the ``OPTIMI... | # Copyright (c) OpenMMLab. All rights reserved.
import copy
import inspect
from typing import Callable, List
import torch
import torch.nn as nn
from mmengine.registry import OPTIMIZER_CONSTRUCTORS, OPTIMIZERS
def register_torch_optimizers() -> List[str]:
torch_optimizers = []
for module_name in dir(torch.op... |
import os
import subprocess
import sys
import pytest
from xgboost import testing as tm
sys.path.append("tests/python")
import test_demos as td # noqa
@pytest.mark.skipif(**tm.no_cupy())
def test_data_iterator():
script = os.path.join(td.PYTHON_DEMO_DIR, "quantile_data_iterator.py")
cmd = ["python", script... | import os
import subprocess
import sys
import pytest
from xgboost import testing as tm
sys.path.append("tests/python")
import test_demos as td # noqa
@pytest.mark.skipif(**tm.no_cupy())
def test_data_iterator():
script = os.path.join(td.PYTHON_DEMO_DIR, 'quantile_data_iterator.py')
cmd = ['python', script... |
# Copyright (c) OpenMMLab. All rights reserved.
from typing import List, Optional, Tuple
from mmengine.utils import is_list_of
def calc_dynamic_intervals(
start_interval: int,
dynamic_interval_list: Optional[List[Tuple[int, int]]] = None
) -> Tuple[List[int], List[int]]:
"""Calculate dynamic intervals.
... | # Copyright (c) OpenMMLab. All rights reserved.
from typing import List, Optional, Tuple
from mmengine.utils.misc import is_list_of
def calc_dynamic_intervals(
start_interval: int,
dynamic_interval_list: Optional[List[Tuple[int, int]]] = None
) -> Tuple[List[int], List[int]]:
"""Calculate dynamic interva... |
from __future__ import annotations
from collections.abc import Mapping
from types import ModuleType as Namespace
from typing import (
TYPE_CHECKING,
Literal,
Protocol,
TypeAlias,
TypedDict,
TypeVar,
final,
)
if TYPE_CHECKING:
from _typeshed import Incomplete
SupportsBufferProtocol... | from __future__ import annotations
__all__ = [
"NestedSequence",
"SupportsBufferProtocol",
]
from types import ModuleType
from typing import (
Any,
TypeVar,
Protocol,
)
_T_co = TypeVar("_T_co", covariant=True)
class NestedSequence(Protocol[_T_co]):
def __getitem__(self, key: int, /) -> _T_co... |
from typing import TypeVar
from docarray.typing.proto_register import _register_proto
from docarray.typing.tensor.audio.abstract_audio_tensor import AbstractAudioTensor
from docarray.typing.tensor.ndarray import NdArray
MAX_INT_16 = 2**15
T = TypeVar('T', bound='AudioNdArray')
@_register_proto(proto_type_name='aud... | from typing import TypeVar
from docarray.typing.tensor.audio.abstract_audio_tensor import AbstractAudioTensor
from docarray.typing.tensor.ndarray import NdArray
MAX_INT_16 = 2**15
T = TypeVar('T', bound='AudioNdArray')
class AudioNdArray(AbstractAudioTensor, NdArray):
"""
Subclass of NdArray, to represent ... |
# Copyright (c) OpenMMLab. All rights reserved.
from .collect_env import collect_env
from .compat_config import compat_cfg
from .dist_utils import (all_reduce_dict, allreduce_grads, reduce_mean,
sync_random_seed)
from .logger import get_caller_name, log_img_scale
from .memory import AvoidCUDAOO... | # Copyright (c) OpenMMLab. All rights reserved.
from .collect_env import collect_env
from .compat_config import compat_cfg
from .dist_utils import (all_reduce_dict, allreduce_grads, reduce_mean,
sync_random_seed)
from .logger import get_caller_name, log_img_scale
from .memory import AvoidCUDAOO... |
_base_ = [
'../_base_/models/mask-rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.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)]
model =... | _base_ = [
'../_base_/models/mask-rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.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)]
model =... |
# Copyright (c) OpenMMLab. All rights reserved.
import math
import torch
from torch.utils.data import DistributedSampler as _DistributedSampler
from mmdet.core.utils import sync_random_seed
class DistributedSampler(_DistributedSampler):
def __init__(self,
dataset,
num_replicas... | # Copyright (c) OpenMMLab. All rights reserved.
import math
import torch
from torch.utils.data import DistributedSampler as _DistributedSampler
from mmdet.core.utils import sync_random_seed
class DistributedSampler(_DistributedSampler):
def __init__(self,
dataset,
num_replicas... |
"""
This examples measures the inference speed of a certain model
Usage:
python evaluation_inference_speed.py
OR
python evaluation_inference_speed.py model_name
"""
from sentence_transformers import SentenceTransformer, util
import sys
import os
import time
import torch
import gzip
import csv
# Limit torch to 4 thre... | """
This examples measures the inference speed of a certain model
Usage:
python evaluation_inference_speed.py
OR
python evaluation_inference_speed.py model_name
"""
from sentence_transformers import SentenceTransformer, util
import sys
import os
import time
import torch
import gzip
import csv
# Limit torch to 4 threa... |
_base_ = [
'../_base_/models/mask-rcnn_r50_fpn.py',
'../common/lsj-100e_coco-instance.py'
]
image_size = (1024, 1024)
batch_augments = [
dict(type='BatchFixedSizePad', size=image_size, pad_mask=True)
]
norm_cfg = dict(type='SyncBN', requires_grad=True)
# Use MMSyncBN that handles empty tensor in head. It ca... | _base_ = [
'../_base_/models/mask-rcnn_r50_fpn.py',
'../common/lsj-100e_coco-instance.py'
]
image_size = (1024, 1024)
batch_augments = [
dict(type='BatchFixedSizePad', size=image_size, pad_mask=True)
]
norm_cfg = dict(type='SyncBN', requires_grad=True)
# Use MMSyncBN that handles empty tensor in head. It ca... |
import json
import logging
import re
import zipfile
from pathlib import Path
from typing import Dict, Iterator, List, Union
from langchain_core.chat_loaders import BaseChatLoader
from langchain_core.chat_sessions import ChatSession
from langchain_core.messages import AIMessage, HumanMessage
logger = logging.getLogger... | import json
import logging
import re
import zipfile
from pathlib import Path
from typing import Dict, Iterator, List, Union
from langchain_core.chat_loaders import BaseChatLoader
from langchain_core.chat_sessions import ChatSession
from langchain_core.messages import AIMessage, HumanMessage
logger = logging.getLogger... |
"""A simple progress bar for the console."""
import threading
from collections.abc import Sequence
from typing import Any, Optional
from uuid import UUID
from langchain_core.callbacks import base as base_callbacks
from langchain_core.documents import Document
from langchain_core.outputs import LLMResult
class Progr... | """A simple progress bar for the console."""
import threading
from collections.abc import Sequence
from typing import Any, Optional
from uuid import UUID
from langchain_core.callbacks import base as base_callbacks
from langchain_core.documents import Document
from langchain_core.outputs import LLMResult
class Progr... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.