input stringlengths 33 5k | output stringlengths 32 5k |
|---|---|
import os
from parameterized import parameterized
from torchaudio.datasets import LibriMix
from torchaudio_unittest.common_utils import get_whitenoise, save_wav, TempDirMixin, TorchaudioTestCase
_SAMPLE_RATE = 8000
_TASKS_TO_MIXTURE = {
"sep_clean": "mix_clean",
"enh_single": "mix_single",
"enh_both": "m... | import os
from parameterized import parameterized
from torchaudio.datasets import LibriMix
from torchaudio_unittest.common_utils import get_whitenoise, save_wav, TempDirMixin, TorchaudioTestCase
_SAMPLE_RATE = 8000
_TASKS_TO_MIXTURE = {
"sep_clean": "mix_clean",
"enh_single": "mix_single",
"enh_both": "m... |
"""Init file of LlamaIndex."""
__version__ = "0.12.18"
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__ = "0.12.17"
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.... |
"""DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.ops.linalg import cholesky as cholesky
from keras.src.ops.linalg import det as det
from keras.src.ops.linalg import eig as eig
from keras.src.ops.linalg import eigh as eigh
from keras... | """DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.ops.linalg import cholesky
from keras.src.ops.linalg import det
from keras.src.ops.linalg import eig
from keras.src.ops.linalg import eigh
from keras.src.ops.linalg import inv
from ke... |
# Copyright (c) OpenMMLab. All rights reserved.
from .anchor_free_head import AnchorFreeHead
from .anchor_head import AnchorHead
from .atss_head import ATSSHead
from .autoassign_head import AutoAssignHead
from .cascade_rpn_head import CascadeRPNHead, StageCascadeRPNHead
from .centernet_head import CenterNetHead
from .c... | # Copyright (c) OpenMMLab. All rights reserved.
from .anchor_free_head import AnchorFreeHead
from .anchor_head import AnchorHead
from .atss_head import ATSSHead
from .autoassign_head import AutoAssignHead
from .cascade_rpn_head import CascadeRPNHead, StageCascadeRPNHead
from .centernet_head import CenterNetHead
from .c... |
# Copyright (c) OpenMMLab. All rights reserved.
import datetime
import logging
import os.path as osp
from typing import Optional
from mmengine.fileio import dump
from mmengine.logging import print_log
from . import root
from .default_scope import DefaultScope
from .registry import Registry
def traverse_registry_tree... | # Copyright (c) OpenMMLab. All rights reserved.
import datetime
import os.path as osp
import warnings
from typing import Optional
from mmengine.fileio import dump
from mmengine.logging import print_log
from . import root
from .default_scope import DefaultScope
from .registry import Registry
def traverse_registry_tre... |
from exa_py.api import (
HighlightsContentsOptions,
TextContentsOptions,
)
from langchain_exa.retrievers import ExaSearchRetriever
from langchain_exa.tools import ExaFindSimilarResults, ExaSearchResults
__all__ = [
"ExaFindSimilarResults",
"ExaSearchResults",
"ExaSearchRetriever",
"HighlightsC... | from exa_py.api import ( # type: ignore # type: ignore[import-not-found, import-not-found]
HighlightsContentsOptions,
TextContentsOptions,
)
from langchain_exa.retrievers import ExaSearchRetriever
from langchain_exa.tools import ExaFindSimilarResults, ExaSearchResults
__all__ = [
"ExaSearchResults",
... |
import os
from unittest.mock import patch
import pytest
from langchain_community.embeddings.openai import OpenAIEmbeddings
os.environ["OPENAI_API_KEY"] = "foo"
@pytest.mark.requires("openai")
def test_openai_invalid_model_kwargs() -> None:
with pytest.raises(ValueError):
OpenAIEmbeddings(model_kwargs={... | import pytest
from langchain_community.embeddings.openai import OpenAIEmbeddings
@pytest.mark.requires("openai")
def test_openai_invalid_model_kwargs() -> None:
with pytest.raises(ValueError):
OpenAIEmbeddings(model_kwargs={"model": "foo"})
@pytest.mark.requires("openai")
def test_openai_incorrect_fiel... |
import os
from typing import Any, Dict, Generator, Literal, Optional
import requests
import sseclient
from llama_index.core.llms import (
CompletionResponse,
CompletionResponseGen,
CustomLLM,
LLMMetadata,
)
from llama_index.core.llms.callbacks import llm_completion_callback
from llama_index.core.bridge... | import os
from typing import Any, Dict, Generator, Literal, Optional
import requests
import sseclient
from llama_index.core.llms import (
CompletionResponse,
CompletionResponseGen,
CustomLLM,
LLMMetadata,
)
from llama_index.core.llms.callbacks import llm_completion_callback
from llama_index.core.bridge... |
from abc import abstractmethod
from typing import Iterator, Iterable, MutableSequence
from docarray import Document
class BaseSequenceLikeMixin(MutableSequence[Document]):
"""Implement sequence-like methods"""
def insert(self, index: int, value: 'Document'):
"""Insert `doc` at `index`.
:par... | from abc import abstractmethod
from typing import Iterator, Iterable, MutableSequence
from docarray import Document
class BaseSequenceLikeMixin(MutableSequence[Document]):
"""Implement sequence-like methods"""
def insert(self, index: int, value: 'Document'):
"""Insert `doc` at `index`.
:par... |
import pathlib
from argparse import ArgumentParser
from lightning import ConformerRNNTModule, get_data_module
from pytorch_lightning import seed_everything, Trainer
from pytorch_lightning.callbacks import LearningRateMonitor, ModelCheckpoint
from pytorch_lightning.plugins import DDPPlugin
def run_train(args):
se... | import pathlib
from argparse import ArgumentParser
from lightning import ConformerRNNTModule, get_data_module
from pytorch_lightning import Trainer, seed_everything
from pytorch_lightning.callbacks import ModelCheckpoint, LearningRateMonitor
from pytorch_lightning.plugins import DDPPlugin
def run_train(args):
se... |
import datetime
import autogpt_libs.auth as autogpt_auth_lib
import fastapi
import fastapi.testclient
import pytest
import pytest_mock
import backend.server.v2.library.model as library_model
from backend.server.v2.library.routes import router as library_router
app = fastapi.FastAPI()
app.include_router(library_route... | import autogpt_libs.auth.depends
import autogpt_libs.auth.middleware
import fastapi
import fastapi.testclient
import pytest
import pytest_mock
import backend.server.v2.library.db
import backend.server.v2.library.model
import backend.server.v2.library.routes
app = fastapi.FastAPI()
app.include_router(backend.server.v2... |
from __future__ import annotations
from collections.abc import Iterable
from enum import Enum
from typing import Any
import torch.nn.functional as F
from torch import Tensor, nn
from sentence_transformers.SentenceTransformer import SentenceTransformer
from sentence_transformers.util import pairwise_cos_sim, pairwise... | from __future__ import annotations
from collections.abc import Iterable
from enum import Enum
from typing import Any
import torch.nn.functional as F
from torch import Tensor, nn
from sentence_transformers.SentenceTransformer import SentenceTransformer
from sentence_transformers.util import pairwise_cos_sim, pairwise... |
"""Run smoke tests"""
import torchaudio # noqa: F401
import torchaudio.compliance.kaldi # noqa: F401
import torchaudio.datasets # noqa: F401
import torchaudio.functional # noqa: F401
import torchaudio.models # noqa: F401
import torchaudio.pipelines # noqa: F401
import torchaudio.sox_effects # noqa: F401
import ... | """Run smoke tests"""
import torchaudio # noqa: F401
import torchaudio.compliance.kaldi # noqa: F401
import torchaudio.datasets # noqa: F401
import torchaudio.functional # noqa: F401
import torchaudio.models # noqa: F401
import torchaudio.pipelines # noqa: F401
import torchaudio.sox_effects # noqa: F401
import ... |
# Copyright (c) OpenMMLab. All rights reserved.
from abc import ABCMeta, abstractmethod
from typing import Tuple
from mmengine.model import BaseModule
from torch import Tensor
from mmdet.registry import MODELS
from mmdet.structures import SampleList
from mmdet.utils import InstanceList, OptConfigType, OptMultiConfig
... | # Copyright (c) OpenMMLab. All rights reserved.
from abc import ABCMeta, abstractmethod
from typing import Tuple
from mmengine.model import BaseModule
from torch import Tensor
from mmdet.data_elements import SampleList
from mmdet.registry import MODELS
from mmdet.utils import InstanceList, OptConfigType, OptMultiConf... |
# coding=utf-8
# Copyright 2025 The rednote-hilab team and 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/licens... | # coding=utf-8
# Copyright 2025 The rednote-hilab team and 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/licens... |
from __future__ import annotations
import pytest
from sentence_transformers.cross_encoder import CrossEncoder
@pytest.mark.parametrize(
"model_name, expected_score",
[
("cross-encoder/ms-marco-MiniLM-L6-v2", [8.12545108795166, -3.045016050338745, -3.1524128913879395]),
("cross-encoder/ms-mar... | from __future__ import annotations
import pytest
from sentence_transformers.cross_encoder import CrossEncoder
@pytest.mark.parametrize(
"model_name, expected_score",
[
("cross-encoder/ms-marco-MiniLM-L-6-v2", [8.12545108795166, -3.045016050338745, -3.1524128913879395]),
("cross-encoder/ms-ma... |
"""DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.optimizers import legacy as legacy
from keras.optimizers import schedules as schedules
from keras.src.optimizers import deserialize as deserialize
from keras.src.optimizers import get as ... | """DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.api.optimizers import legacy
from keras.api.optimizers import schedules
from keras.src.optimizers import deserialize
from keras.src.optimizers import get
from keras.src.optimizers import ... |
"""
This example starts multiple processes (1 per GPU), which encode
sentences in parallel. This gives a near linear speed-up
when encoding large text collections.
It also demonstrates how to stream data which is helpful in case you don't
want to wait for an extremely large dataset to download, or if you want to
limit ... | """
This example starts multiple processes (1 per GPU), which encode
sentences in parallel. This gives a near linear speed-up
when encoding large text collections.
It also demonstrates how to stream data which is helpful in case you don't
want to wait for an extremely large dataset to download, or if you want to
limit ... |
import numpy as np
import pytest
import torch
from pydantic import parse_obj_as
from docarray import BaseDoc
from docarray.documents import VideoDoc
from docarray.typing import AudioNdArray, NdArray, VideoNdArray
from docarray.utils.misc import is_tf_available
from tests import TOYDATA_DIR
tf_available = is_tf_availa... | import numpy as np
import pytest
import torch
from pydantic import parse_obj_as
from docarray import BaseDocument
from docarray.documents import VideoDoc
from docarray.typing import AudioNdArray, NdArray, VideoNdArray
from docarray.utils.misc import is_tf_available
from tests import TOYDATA_DIR
tf_available = is_tf_a... |
from __future__ import annotations
from .BinaryCrossEntropyLoss import BinaryCrossEntropyLoss
from .CachedMultipleNegativesRankingLoss import CachedMultipleNegativesRankingLoss
from .CrossEntropyLoss import CrossEntropyLoss
from .LambdaLoss import (
LambdaLoss,
LambdaRankScheme,
NDCGLoss1Scheme,
NDCGLo... | from __future__ import annotations
from .BinaryCrossEntropyLoss import BinaryCrossEntropyLoss
from .CachedMultipleNegativesRankingLoss import CachedMultipleNegativesRankingLoss
from .CrossEntropyLoss import CrossEntropyLoss
from .ListNetLoss import ListNetLoss
from .MarginMSELoss import MarginMSELoss
from .MSELoss imp... |
import importlib
from typing import Any
from langchain.retrievers.document_compressors.base import DocumentCompressorPipeline
from langchain.retrievers.document_compressors.chain_extract import (
LLMChainExtractor,
)
from langchain.retrievers.document_compressors.chain_filter import (
LLMChainFilter,
)
from la... | import importlib
from typing import Any
from langchain.retrievers.document_compressors.base import DocumentCompressorPipeline
from langchain.retrievers.document_compressors.chain_extract import (
LLMChainExtractor,
)
from langchain.retrievers.document_compressors.chain_filter import (
LLMChainFilter,
)
from la... |
import numpy as np
import torch
from docarray import Document, Image, Text
from docarray.typing import (
AnyUrl,
Embedding,
ImageUrl,
NdArray,
Tensor,
TextUrl,
TorchEmbedding,
TorchTensor,
)
from docarray.typing.tensor import NdArrayEmbedding
def test_multi_modal_doc_proto():
clas... | import numpy as np
import torch
from docarray import Document, Image, Text
from docarray.typing import (
AnyUrl,
Embedding,
ImageUrl,
NdArray,
Tensor,
TextUrl,
TorchEmbedding,
TorchTensor,
)
from docarray.typing.tensor import NdArrayEmbedding
def test_multi_modal_doc_proto():
clas... |
_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)
model = dict(
backbone=dict(norm_cfg=norm_cfg, norm_eval=False),
neck=dict(norm_cfg=norm... | _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)
model = dict(
backbone=dict(norm_cfg=norm_cfg, norm_eval=False),
neck=dict(norm_cfg=norm... |
import numpy as np
import pytest
from fastapi import FastAPI
from httpx import AsyncClient
from docarray import BaseDocument
from docarray.documents import Image, Text
from docarray.typing import NdArray
@pytest.mark.asyncio
async def test_fast_api():
class Mmdoc(BaseDocument):
img: Image
text: T... | import numpy as np
import pytest
from fastapi import FastAPI
from httpx import AsyncClient
from docarray import BaseDocument, Image, Text
from docarray.typing import NdArray
@pytest.mark.asyncio
async def test_fast_api():
class Mmdoc(BaseDocument):
img: Image
text: Text
title: str
in... |
_base_ = './atss_r50_fpn_8xb8-amp-lsj-200e_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| _base_ = './atss_r50_fpn_lsj_200e_8x8_fp16_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
|
_base_ = '../_base_/default_runtime.py'
# dataset settings
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
# file_client_args = dict(
# backend='petrel',
# path_mapping=dict({
# './data/': 's3://openmmlab/datasets/detection/',
# 'data/': 's3://openmmlab/datasets/detection/'
# }))
file... | _base_ = '../_base_/default_runtime.py'
# dataset settings
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
# file_client_args = dict(
# backend='petrel',
# path_mapping=dict({
# './data/': 's3://openmmlab/datasets/detection/',
# 'data/': 's3://openmmlab/datasets/detection/'
# }))
file... |
"""Document transformers."""
from __future__ import annotations
from abc import ABC, abstractmethod
from typing import TYPE_CHECKING, Any
from langchain_core.runnables.config import run_in_executor
if TYPE_CHECKING:
from collections.abc import Sequence
from langchain_core.documents import Document
class ... | from __future__ import annotations
from abc import ABC, abstractmethod
from typing import TYPE_CHECKING, Any
from langchain_core.runnables.config import run_in_executor
if TYPE_CHECKING:
from collections.abc import Sequence
from langchain_core.documents import Document
class BaseDocumentTransformer(ABC):
... |
from keras.src.api_export import keras_export
# Unique source of truth for the version number.
__version__ = "3.8.0"
@keras_export("keras.version")
def version():
return __version__
| from keras.src.api_export import keras_export
# Unique source of truth for the version number.
__version__ = "3.7.0"
@keras_export("keras.version")
def version():
return __version__
|
"""
This examples demonstrates the setup for Question-Answer-Retrieval.
You can input a query or a question. The script then uses semantic search
to find relevant passages in Simple English Wikipedia (as it is smaller and fits better in RAM).
As model, we use: nq-distilbert-base-v1
It was trained on the Natural Ques... | """
This examples demonstrates the setup for Question-Answer-Retrieval.
You can input a query or a question. The script then uses semantic search
to find relevant passages in Simple English Wikipedia (as it is smaller and fits better in RAM).
As model, we use: nq-distilbert-base-v1
It was trained on the Natural Ques... |
# 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 ATSS(SingleStageDetector):
"""Implementation of `ATSS <https://arxiv.org/abs/1912.02424>... | # 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 ATSS(SingleStageDetector):
"""Implementation of `ATSS <https://arxiv.org/abs/1912.02424>`... |
"""Module to change the configuration of libsox, which is used by I/O functions like
:py:mod:`~torchaudio.backend.sox_io_backend` and :py:mod:`~torchaudio.sox_effects`.
"""
from typing import Dict, List
import torch
import torchaudio
@torchaudio._extension.fail_if_no_sox
def set_seed(seed: int):
"""Set libsox's... | """Module to change the configuration of libsox, which is used by I/O functions like
:py:mod:`~torchaudio.backend.sox_io_backend` and :py:mod:`~torchaudio.sox_effects`.
"""
from typing import Dict, List
import torch
from torchaudio._internal import module_utils as _mod_utils
@_mod_utils.requires_sox()
def set_seed(... |
import torch
__all__ = ["DeepSpeech"]
class FullyConnected(torch.nn.Module):
"""
Args:
n_feature: Number of input features
n_hidden: Internal hidden unit size.
"""
def __init__(self, n_feature: int, n_hidden: int, dropout: float, relu_max_clip: int = 20) -> None:
super(FullyC... | import torch
__all__ = ["DeepSpeech"]
class FullyConnected(torch.nn.Module):
"""
Args:
n_feature: Number of input features
n_hidden: Internal hidden unit size.
"""
def __init__(self, n_feature: int, n_hidden: int, dropout: float, relu_max_clip: int = 20) -> None:
super(FullyC... |
from docarray.typing.tensor.embedding import Embedding, NdArrayEmbedding, TorchEmbedding
from docarray.typing.tensor.ndarray import NdArray
from docarray.typing.tensor.tensor import Tensor
from docarray.typing.tensor.torch_tensor import TorchTensor
__all__ = [
'NdArray',
'TorchTensor',
'Tensor',
'Embed... | from docarray.typing.tensor.ndarray import NdArray
from docarray.typing.tensor.tensor import Tensor
from docarray.typing.tensor.torch_tensor import TorchTensor
__all__ = ['NdArray', 'TorchTensor', 'Tensor']
|
from .normalizer import ImageNormalizer
| from .normalizer import ImageNormalizer |
# Copyright 2025 Black Forest Labs, The HuggingFace Team and The InstantX 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/LIC... | # Copyright 2024 Black Forest Labs, The HuggingFace Team and The InstantX 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/LIC... |
"""Simple Web scraper."""
from typing import List, Optional, Dict, Callable
import requests
from llama_index.core.bridge.pydantic import PrivateAttr
from llama_index.core.readers.base import BasePydanticReader
from llama_index.core.schema import Document
class SimpleWebPageReader(BasePydanticReader):
"""
Si... | """Simple Web scraper."""
from typing import List, Optional, Dict, Callable
import requests
from llama_index.core.bridge.pydantic import PrivateAttr
from llama_index.core.readers.base import BasePydanticReader
from llama_index.core.schema import Document
class SimpleWebPageReader(BasePydanticReader):
"""Simple ... |
"""Sentence window retriever."""
from typing import Any, Dict, List
from llama_index.core import Settings, VectorStoreIndex
from llama_index.core.llama_pack.base import BaseLlamaPack
from llama_index.core.node_parser import (
SentenceWindowNodeParser,
)
from llama_index.core.postprocessor import MetadataReplaceme... | """Sentence window retriever."""
from typing import Any, Dict, List
from llama_index.core import Settings, VectorStoreIndex
from llama_index.core.llama_pack.base import BaseLlamaPack
from llama_index.core.node_parser import (
SentenceWindowNodeParser,
)
from llama_index.core.postprocessor import MetadataReplaceme... |
# Copyright (c) OpenMMLab. All rights reserved.
import torch
from mmdet.data_elements.bbox import bbox2result
from mmdet.registry import MODELS
from .single_stage import SingleStageDetector
@MODELS.register_module()
class YOLACT(SingleStageDetector):
"""Implementation of `YOLACT <https://arxiv.org/abs/1904.02689... | # Copyright (c) OpenMMLab. All rights reserved.
import torch
from mmdet.core import bbox2result
from mmdet.registry import MODELS
from .single_stage import SingleStageDetector
@MODELS.register_module()
class YOLACT(SingleStageDetector):
"""Implementation of `YOLACT <https://arxiv.org/abs/1904.02689>`_"""
de... |
# Copyright (c) OpenMMLab. All rights reserved.
from .gaussian_target import (gather_feat, gaussian_radius,
gen_gaussian_target, get_local_maximum,
get_topk_from_heatmap, transpose_and_gather_feat)
from .image import imrenormalize
from .make_divisible import m... | # Copyright (c) OpenMMLab. All rights reserved.
from .gaussian_target import (gather_feat, gaussian_radius,
gen_gaussian_target, get_local_maximum,
get_topk_from_heatmap, transpose_and_gather_feat)
from .image import imrenormalize
from .make_divisible import m... |
import numpy as np
import torch
from docarray import BaseDocument
from docarray.base_document import AnyDocument
from docarray.typing import (
AnyEmbedding,
AnyUrl,
ImageUrl,
Mesh3DUrl,
NdArray,
PointCloud3DUrl,
TextUrl,
TorchTensor,
)
def test_proto_all_types():
class Mymmdoc(Bas... | import numpy as np
import torch
from docarray import BaseDocument
from docarray.document import AnyDocument
from docarray.typing import (
AnyEmbedding,
AnyUrl,
ImageUrl,
Mesh3DUrl,
NdArray,
PointCloud3DUrl,
TextUrl,
TorchTensor,
)
def test_proto_all_types():
class Mymmdoc(BaseDocu... |
import numpy as np
import pytest
from keras.src import backend
from keras.src import layers
from keras.src import testing
class GaussianDropoutTest(testing.TestCase):
@pytest.mark.requires_trainable_backend
def test_gaussian_dropout_basics(self):
self.run_layer_test(
layers.GaussianDropou... | import numpy as np
import pytest
from keras.src import backend
from keras.src import layers
from keras.src import testing
class GaussianDropoutTest(testing.TestCase):
@pytest.mark.requires_trainable_backend
def test_gaussian_dropout_basics(self):
self.run_layer_test(
layers.GaussianDropou... |
import torch
from keras.src.optimizers.base_optimizer import BaseOptimizer
from keras.src.utils import torch_utils
class TorchParallelOptimizer(BaseOptimizer):
@torch_utils.no_grad
def _backend_update_step(self, grads, trainable_variables, learning_rate):
self._parallel_update_step(
grads... | import torch
from keras.src.optimizers.base_optimizer import BaseOptimizer
from keras.src.utils import torch_utils
class TorchParallelOptimizer(BaseOptimizer):
@torch_utils.no_grad
def _backend_update_step(self, grads, trainable_variables, learning_rate):
self._parallel_update_step(
grads... |
"""A class for JAX specific optimizer logic.
Its purpose is to route around statelessness
requirements in cond ops used for EMA handling
and gradient accumulation handling. We do this
by skipping conditionals entirely.
"""
import jax
from jax import numpy as jnp
from keras.src.optimizers import base_optimizer
clas... | """A class for JAX specific optimizer logic.
Its purpose is to route around statelessness
requirements in cond ops used for EMA handling
and gradient accumulation handling. We do this
by skipping conditionals entirely.
"""
import jax
from jax import numpy as jnp
from keras.src.optimizers import base_optimizer
clas... |
from __future__ import annotations
from abc import abstractmethod
from typing import Any
import torch
from tokenizers import Tokenizer
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
from sentence_transformers.models.Module import Module
class InputModule(Module):
"""
Subclass of :... | from __future__ import annotations
from abc import abstractmethod
from typing import Any
import torch
from tokenizers import Tokenizer
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
from sentence_transformers.models.Module import Module
class InputModule(Module):
"""
Subclass of :... |
from typing import Dict, List, Optional, Set
import pytest
from docarray import BaseDocument, DocumentArray
from docarray.documents import Image
class InnerDoc(BaseDocument):
integer: int
inner_list: List
class MMDoc(BaseDocument):
text: str = ''
price: int = 0
categories: Optional[List[str]] ... | import pytest
from typing import Optional, List, Dict, Set
from docarray import BaseDocument, DocumentArray
from docarray.documents import Image
class InnerDoc(BaseDocument):
integer: int
l: List
class MMDoc(BaseDocument):
text: str = ''
price: int = 0
categories: Optional[List[str]] = None
... |
import io
import warnings
from abc import ABC
from docarray.typing.tensor.abstract_tensor import AbstractTensor
from docarray.utils.misc import is_notebook
class AbstractImageTensor(AbstractTensor, ABC):
def to_bytes(self, format: str = 'PNG') -> bytes:
"""
Convert image tensor to bytes.
... | import io
from abc import ABC
from docarray.typing.tensor.abstract_tensor import AbstractTensor
class AbstractImageTensor(AbstractTensor, ABC):
def to_bytes(self, format: str = 'PNG') -> bytes:
"""
Convert image tensor to bytes.
:param format: the image format use to store the image, can... |
# dataset settings
dataset_type = 'CityscapesDataset'
data_root = 'data/cityscapes/'
# Example to use different file client
# Method 1: simply set the data root and let the file I/O module
# automatically infer from prefix (not support LMDB and Memcache yet)
# data_root = 's3://openmmlab/datasets/segmentation/citysca... | # dataset settings
dataset_type = 'CityscapesDataset'
data_root = 'data/cityscapes/'
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
dict(
type='RandomResize',
scale=[(2048, 800), (2048, 1024)],
keep_ratio=True),
d... |
# Copyright (c) OpenMMLab. All rights reserved.
import ast
import os.path as osp
import re
import warnings
from typing import Tuple
from mmengine.fileio import load
from mmengine.utils import check_file_exist
MODULE2PACKAGE = {
'mmcls': 'mmcls',
'mmdet': 'mmdet',
'mmdet3d': 'mmdet3d',
'mmseg': 'mmsegm... | # Copyright (c) OpenMMLab. All rights reserved.
import ast
import os.path as osp
import re
import warnings
from typing import Tuple
from mmengine.fileio import load
from mmengine.utils import check_file_exist
PKG2PROJECT = {
'mmcls': 'mmcls',
'mmdet': 'mmdet',
'mmdet3d': 'mmdet3d',
'mmseg': 'mmsegment... |
# flake8: noqa
# Copyright 2020 The HuggingFace Datasets Authors and the TensorFlow Datasets Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LI... | # flake8: noqa
# Copyright 2020 The HuggingFace Datasets Authors and the TensorFlow Datasets Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LI... |
# Copyright (c) OpenMMLab. All rights reserved.
from mmcv.utils import Registry, build_from_cfg
BBOX_ASSIGNERS = Registry('bbox_assigner')
BBOX_SAMPLERS = Registry('bbox_sampler')
BBOX_CODERS = Registry('bbox_coder')
def build_assigner(cfg, **default_args):
"""Builder of box assigner."""
return build_from_cf... | from mmcv.utils import Registry, build_from_cfg
BBOX_ASSIGNERS = Registry('bbox_assigner')
BBOX_SAMPLERS = Registry('bbox_sampler')
BBOX_CODERS = Registry('bbox_coder')
def build_assigner(cfg, **default_args):
"""Builder of box assigner."""
return build_from_cfg(cfg, BBOX_ASSIGNERS, default_args)
def build... |
import numpy as np
import pytest
from keras.src import layers
from keras.src import ops
from keras.src import testing
class AutoContrastTest(testing.TestCase):
@pytest.mark.requires_trainable_backend
def test_layer(self):
self.run_layer_test(
layers.AutoContrast,
init_kwargs={... | import numpy as np
import pytest
from absl.testing import parameterized
from keras.src import layers
from keras.src import ops
from keras.src import testing
class AutoContrastTest(testing.TestCase, parameterized.TestCase):
@pytest.mark.requires_trainable_backend
def test_layer(self):
self.run_layer_t... |
from typing import Literal
from pydantic import SecretStr
from backend.data.model import APIKeyCredentials, CredentialsField, CredentialsMetaInput
from backend.integrations.providers import ProviderName
JinaCredentials = APIKeyCredentials
JinaCredentialsInput = CredentialsMetaInput[
Literal[ProviderName.JINA],
... | from typing import Literal
from pydantic import SecretStr
from backend.data.model import APIKeyCredentials, CredentialsField, CredentialsMetaInput
JinaCredentials = APIKeyCredentials
JinaCredentialsInput = CredentialsMetaInput[
Literal["jina"],
Literal["api_key"],
]
TEST_CREDENTIALS = APIKeyCredentials(
... |
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
@_register_proto(proto_type_name='audio_ndarray')
class AudioNdArray(AbstractAudioTensor, NdArray):
"""
Subclass of N... | 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
@_register_proto(proto_type_name='audio_ndarray')
class AudioNdArray(AbstractAudioTensor, NdArray):
"""
Subclass of N... |
import os
import urllib.parse
from typing import Dict, Optional, Union
from llama_index.core.base.llms.generic_utils import (
get_from_param_or_env,
)
# Import SecretStr directly from pydantic
# since there is not one in llama_index.core.bridge.pydantic
try:
from pydantic.v1 import SecretStr
except ImportErr... | import os
import urllib.parse
from typing import Dict, Optional, Union
from llama_index.core.base.llms.generic_utils import (
get_from_param_or_env,
)
# Import SecretStr directly from pydantic
# since there is not one in llama_index.core.bridge.pydantic
try:
from pydantic.v1 import SecretStr
except ImportErr... |
import pytest
from pydantic import parse_obj_as
from docarray import BaseDoc
from docarray.documents import TextDoc
from docarray.utils._internal.pydantic import is_pydantic_v2
@pytest.mark.skipif(is_pydantic_v2, reason="Not working with pydantic v2 for now")
def test_simple_init():
t = TextDoc(text='hello')
... | from pydantic import parse_obj_as
from docarray import BaseDoc
from docarray.documents import TextDoc
def test_simple_init():
t = TextDoc(text='hello')
assert t.text == 'hello'
def test_str_init():
t = parse_obj_as(TextDoc, 'hello')
assert t.text == 'hello'
def test_doc():
class MyDoc(BaseDoc... |
"""Generation output schema."""
from __future__ import annotations
from typing import Any, Literal, Optional
from langchain_core.load import Serializable
from langchain_core.utils._merge import merge_dicts
class Generation(Serializable):
"""A single text generation output.
Generation represents the respon... | """Generation output schema."""
from __future__ import annotations
from typing import Any, Literal, Optional
from pydantic import computed_field
from langchain_core.load import Serializable
from langchain_core.utils._merge import merge_dicts
class Generation(Serializable):
"""A single text generation output.
... |
from workflows.retry_policy import RetryPolicy, ConstantDelayRetryPolicy # noqa
| from typing import Protocol, Optional, runtime_checkable
@runtime_checkable
class RetryPolicy(Protocol):
def next(
self, elapsed_time: float, attempts: int, error: Exception
) -> Optional[float]:
"""
Decides if we should make another retry, returning the number of seconds to wait befor... |
from typing import Any, Optional
from llama_index.core.storage.docstore.keyval_docstore import KVDocumentStore
from llama_index.storage.kvstore.couchbase import CouchbaseKVStore
from llama_index.core.storage.docstore.types import DEFAULT_BATCH_SIZE
class CouchbaseDocumentStore(KVDocumentStore):
"""
Couchbase ... | from typing import Any, Optional
from llama_index.core.storage.docstore.keyval_docstore import KVDocumentStore
from llama_index.storage.kvstore.couchbase import CouchbaseKVStore
from llama_index.core.storage.docstore.types import DEFAULT_BATCH_SIZE
class CouchbaseDocumentStore(KVDocumentStore):
"""
Couchbase ... |
from base64 import b64encode
from typing import Optional
from urllib.parse import urlencode
from backend.data.model import OAuth2Credentials
from backend.integrations.providers import ProviderName
from backend.util.request import Requests
from .base import BaseOAuthHandler
class NotionOAuthHandler(BaseOAuthHandler)... | from base64 import b64encode
from typing import Optional
from urllib.parse import urlencode
from backend.data.model import OAuth2Credentials
from backend.integrations.providers import ProviderName
from backend.util.request import requests
from .base import BaseOAuthHandler
class NotionOAuthHandler(BaseOAuthHandler)... |
from typing import Optional
from docarray import Document, DocumentArray
from pydantic import BaseModel
from uvicorn import Config, Server
from jina import Gateway, __default_host__
from jina.clients.request import request_generator
class DummyResponseModel(BaseModel):
arg1: Optional[str]
arg2: Optional[str... | from typing import Optional
from docarray import Document, DocumentArray
from pydantic import BaseModel
from uvicorn import Config, Server
from jina import Gateway, __default_host__
from jina.clients.request import request_generator
class DummyResponseModel(BaseModel):
arg1: Optional[str]
arg2: Optional[str... |
import os
import httpx
import pytest
from llama_index.core.base.embeddings.base import BaseEmbedding
from llama_index.embeddings.cohere import CohereEmbedding
def test_embedding_class():
emb = CohereEmbedding(api_key="token")
assert isinstance(emb, BaseEmbedding)
@pytest.mark.skipif(
os.environ.get("C... | import os
import httpx
import pytest
from llama_index.core.base.embeddings.base import BaseEmbedding
from llama_index.embeddings.cohere import CohereEmbedding
def test_embedding_class():
emb = CohereEmbedding(api_key="token")
assert isinstance(emb, BaseEmbedding)
@pytest.mark.skipif(
os.environ.get("C... |
from llama_index_instrumentation.base import BaseInstrumentationHandler # noqa
| from abc import ABC, abstractmethod
class BaseInstrumentationHandler(ABC):
@classmethod
@abstractmethod
def init(cls) -> None:
"""Initialize the instrumentation handler."""
|
from .tensor import flush_ndarray, read_ndarray
| from collections import defaultdict
from typing import TYPE_CHECKING, Optional
from google.protobuf.json_format import MessageToDict
from google.protobuf.struct_pb2 import Struct
from docarray.proto.io.ndarray import flush_ndarray, read_ndarray
from docarray.proto.docarray_pb2 import NdArrayProto, DocumentProto
if T... |
# THIS FILE HAS BEEN AUTOGENERATED. To update:
# 1. modify the `_deps` dict in setup.py
# 2. run `make deps_table_update``
deps = {
"Pillow": "Pillow>=10.0.1,<=15.0",
"accelerate": "accelerate>=0.26.0",
"av": "av",
"beautifulsoup4": "beautifulsoup4",
"blobfile": "blobfile",
"codecarbon": "codeca... | # THIS FILE HAS BEEN AUTOGENERATED. To update:
# 1. modify the `_deps` dict in setup.py
# 2. run `make deps_table_update``
deps = {
"Pillow": "Pillow>=10.0.1,<=15.0",
"accelerate": "accelerate>=0.26.0",
"av": "av",
"beautifulsoup4": "beautifulsoup4",
"blobfile": "blobfile",
"codecarbon": "codeca... |
from __future__ import annotations
from sentence_transformers import util
from sentence_transformers.sparse_encoder.losses.SparseCoSENTLoss import SparseCoSENTLoss
from sentence_transformers.sparse_encoder.SparseEncoder import SparseEncoder
class SparseAnglELoss(SparseCoSENTLoss):
def __init__(self, model: Spars... | from __future__ import annotations
from sentence_transformers import util
from sentence_transformers.sparse_encoder.losses.SparseCoSENTLoss import SparseCoSENTLoss
from sentence_transformers.sparse_encoder.SparseEncoder import SparseEncoder
class SparseAnglELoss(SparseCoSENTLoss):
def __init__(self, model: Spars... |
from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.agent_toolkits.multion.toolkit import MultionToolkit
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handling op... | from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.agent_toolkits.multion.toolkit import MultionToolkit
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handling op... |
# coding=utf-8
# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless r... | # coding=utf-8
# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless r... |
_base_ = [
'../_base_/models/mask-rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.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(
type... | _base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.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(
type... |
import numpy as np
import pytest
from docarray import BaseDoc, DocArray
from docarray.documents import ImageDoc, TextDoc
from docarray.typing import NdArray
@pytest.mark.proto
def test_simple_proto():
class CustomDoc(BaseDoc):
text: str
tensor: NdArray
da = DocArray(
[CustomDoc(text=... | import numpy as np
import pytest
from docarray import BaseDocument, DocumentArray
from docarray.documents import ImageDoc, TextDoc
from docarray.typing import NdArray
@pytest.mark.proto
def test_simple_proto():
class CustomDoc(BaseDocument):
text: str
tensor: NdArray
da = DocumentArray(
... |
# Copyright (c) OpenMMLab. All rights reserved.
from .lr_scheduler import (ConstantLR, CosineAnnealingLR, ExponentialLR,
LinearLR, MultiStepLR, PolyLR, StepLR)
from .momentum_scheduler import (ConstantMomentum, CosineAnnealingMomentum,
ExponentialMomentum, Lin... | # Copyright (c) OpenMMLab. All rights reserved.
from .lr_scheduler import (ConstantLR, CosineAnnealingLR, ExponentialLR,
LinearLR, MultiStepLR, StepLR)
from .momentum_scheduler import (ConstantMomentum, CosineAnnealingMomentum,
ExponentialMomentum, LinearMomen... |
_base_ = './cascade-mask-rcnn_r50_fpn_ms-3x_coco.py'
model = dict(
# ResNeXt-101-32x8d model trained with Caffe2 at FB,
# so the mean and std need to be changed.
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[103.530, 116.280, 123.675],
std=[57.375, 57.120, 58.395],
... | _base_ = './cascade_mask_rcnn_r50_fpn_mstrain_3x_coco.py'
model = dict(
# ResNeXt-101-32x8d model trained with Caffe2 at FB,
# so the mean and std need to be changed.
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[103.530, 116.280, 123.675],
std=[57.375, 57.120, 58.395],
... |
_base_ = 'ssd300_coco.py'
input_size = 512
model = dict(
neck=dict(
out_channels=(512, 1024, 512, 256, 256, 256, 256),
level_strides=(2, 2, 2, 2, 1),
level_paddings=(1, 1, 1, 1, 1),
last_kernel_size=4),
bbox_head=dict(
in_channels=(512, 1024, 512, 256, 256, 256, 256),
... | _base_ = 'ssd300_coco.py'
input_size = 512
model = dict(
neck=dict(
out_channels=(512, 1024, 512, 256, 256, 256, 256),
level_strides=(2, 2, 2, 2, 1),
level_paddings=(1, 1, 1, 1, 1),
last_kernel_size=4),
bbox_head=dict(
in_channels=(512, 1024, 512, 256, 256, 256, 256),
... |
_base_ = './cascade-rcnn_r50-caffe_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron2/resnet101_caffe')))
| _base_ = './cascade_rcnn_r50_caffe_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron2/resnet101_caffe')))
|
"""Lit configuration to drive test in this repo."""
# Copyright 2020 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
#
# ... | """Lit configuration to drive test in this repo."""
# Copyright 2020 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
#
# ... |
from docarray.typing.proto_register import _register_proto
from docarray.typing.tensor.image.abstract_image_tensor import AbstractImageTensor
from docarray.typing.tensor.ndarray import NdArray
MAX_INT_16 = 2**15
@_register_proto(proto_type_name='image_ndarray')
class ImageNdArray(AbstractImageTensor, NdArray):
"... | from docarray.typing.proto_register import _register_proto
from docarray.typing.tensor.image.abstract_image_tensor import AbstractImageTensor
from docarray.typing.tensor.ndarray import NdArray
MAX_INT_16 = 2**15
@_register_proto(proto_type_name='image_ndarray')
class ImageNdArray(AbstractImageTensor, NdArray):
"... |
from keras.src import backend
from keras.src.api_export import keras_export
from keras.src.backend.common import KerasVariable
if backend.backend() == "tensorflow":
BackendVariable = backend.tensorflow.core.Variable
backend_name_scope = backend.tensorflow.core.name_scope
elif backend.backend() == "jax":
Ba... | from keras.src import backend
from keras.src.api_export import keras_export
if backend.backend() == "tensorflow":
BackendVariable = backend.tensorflow.core.Variable
backend_name_scope = backend.tensorflow.core.name_scope
elif backend.backend() == "jax":
BackendVariable = backend.jax.core.Variable
backe... |
# training schedule for 2x
train_cfg = dict(by_epoch=True, max_epochs=24)
val_cfg = dict(interval=1)
test_cfg = dict()
# learning rate
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=24,
... | # training schedule for 1x
train_cfg = dict(by_epoch=True, max_epochs=24)
val_cfg = dict(interval=1)
test_cfg = dict()
# learning rate
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=24,
... |
import pytest
from docarray import DocumentArray, Document
from docarray.array.weaviate import DocumentArrayWeaviate
import numpy as np
@pytest.fixture()
def docs():
return DocumentArray([Document(id=f'{i}') for i in range(1, 10)])
@pytest.mark.parametrize(
'to_delete',
[
0,
1,
... | import pytest
from docarray import DocumentArray, Document
from docarray.array.weaviate import DocumentArrayWeaviate
import numpy as np
@pytest.fixture()
def docs():
return DocumentArray([Document(id=f'{i}') for i in range(1, 10)])
@pytest.mark.parametrize(
'to_delete',
[
0,
1,
... |
# 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.
import os
from logging import getLogger
from typing import List
from sentencepiece import SentencePieceProcessor
logger = getLogger()
class Tokenizer:... | # 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.
import os
from logging import getLogger
from typing import List
from sentencepiece import SentencePieceProcessor
logger = getLogger()
class Tokenizer:... |
__version__ = "2.6.0.dev0"
__MODEL_HUB_ORGANIZATION__ = "sentence-transformers"
from .datasets import SentencesDataset, ParallelSentencesDataset
from .LoggingHandler import LoggingHandler
from .SentenceTransformer import SentenceTransformer
from .readers import InputExample
from .cross_encoder.CrossEncoder import Cross... | __version__ = "2.6.0.dev0"
__MODEL_HUB_ORGANIZATION__ = "sentence-transformers"
from .datasets import SentencesDataset, ParallelSentencesDataset
from .LoggingHandler import LoggingHandler
from .SentenceTransformer import SentenceTransformer
from .readers import InputExample
from .cross_encoder.CrossEncoder import Cross... |
# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import cv2
import mmcv
from mmdet.apis import inference_detector, init_detector
def parse_args():
parser = argparse.ArgumentParser(description='MMDetection video demo')
parser.add_argument('video', help='Video file')
parser.add_argument('co... | import argparse
import cv2
import mmcv
from mmdet.apis import inference_detector, init_detector
def parse_args():
parser = argparse.ArgumentParser(description='MMDetection video demo')
parser.add_argument('video', help='Video file')
parser.add_argument('config', help='Config file')
parser.add_argume... |
"""
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... |
import argparse
import pathlib
import re
import shutil
def main(args):
if args.scala_version == "2.12":
scala_ver = "2.12"
scala_patchver = "2.12.18"
elif args.scala_version == "2.13":
scala_ver = "2.13"
scala_patchver = "2.13.11"
else:
raise ValueError(f"Unsupporte... | import argparse
import pathlib
import re
import shutil
def main(args):
if args.scala_version == "2.12":
scala_ver = "2.12"
scala_patchver = "2.12.18"
elif args.scala_version == "2.13":
scala_ver = "2.13"
scala_patchver = "2.13.11"
else:
raise ValueError(f"Unsupporte... |
import asyncio
from typing import Any, AsyncGenerator, Optional
from llama_index.core.workflow.context import Context
from llama_index.core.workflow.errors import WorkflowDone
from llama_index.core.workflow.events import Event, StopEvent
from .types import RunResultT
from .utils import BUSY_WAIT_DELAY
class Workflo... | import asyncio
from typing import Any, AsyncGenerator, Optional
from llama_index.core.workflow.context import Context
from llama_index.core.workflow.errors import WorkflowDone
from llama_index.core.workflow.events import Event, StopEvent
from .utils import BUSY_WAIT_DELAY
class WorkflowHandler(asyncio.Future):
... |
import itertools
import os.path
import pytest
import requests as req
from docarray import Document, DocumentArray
from jina import Client, Executor, Flow, requests
from jina.helper import random_port
PROTOCOLS = ['grpc', 'http', 'websocket']
cur_dir = os.path.dirname(__file__)
class MyExecutor(Executor):
@requ... | import itertools
import os.path
import pytest
from docarray import Document, DocumentArray
from jina import Client, Executor, Flow, requests
from jina.helper import random_port
PROTOCOLS = ['grpc', 'http', 'websocket']
cur_dir = os.path.dirname(__file__)
class MyExecutor(Executor):
@requests
def foo(self, ... |
from typing import Optional
from typing_extensions import Protocol, runtime_checkable
from torch.distributed._state_dict_utils import _copy_state_dict, _create_cpu_state_dict
from torch.distributed.checkpoint.metadata import STATE_DICT_TYPE
__all__ = ["AsyncStager", "BlockingAsyncStager"]
@runtime_checkable
class ... | from typing import Optional
from typing_extensions import Protocol, runtime_checkable
from torch.distributed._state_dict_utils import _copy_state_dict, _create_cpu_state_dict
from torch.distributed.checkpoint.metadata import STATE_DICT_TYPE
__all__ = ["AsyncStager", "BlockingAsyncStager"]
@runtime_checkable
class ... |
# dataset settings
dataset_type = 'CityscapesDataset'
# TODO remove it after cityscape metric
# data_root = '/mnt/lustre/luochunhua.vendor/openmmlab2.0/data/cityscapes/'
data_root = 'data/cityscapes/'
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True, with_mask=True... | # dataset settings
dataset_type = 'CityscapesDataset'
data_root = 'data/cityscapes/'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
dict(
... |
from torchvision.transforms import InterpolationMode # usort: skip
from ._utils import is_pure_tensor, register_kernel # usort: skip
from ._meta import (
clamp_bounding_boxes,
convert_bounding_box_format,
get_dimensions_image,
_get_dimensions_image_pil,
get_dimensions_video,
get_dimensions,
... | from torchvision.transforms import InterpolationMode # usort: skip
from ._utils import is_pure_tensor, register_kernel # usort: skip
from ._meta import (
clamp_bounding_boxes,
convert_bounding_box_format,
get_dimensions_image,
_get_dimensions_image_pil,
get_dimensions_video,
get_dimensions,
... |
"""Implementations of key-value stores and storage helpers.
Module provides implementations of various key-value stores that conform
to a simple key-value interface.
The primary goal of these storages is to support implementation of caching.
"""
from typing import TYPE_CHECKING, Any
from langchain_core.stores impor... | """Implementations of key-value stores and storage helpers.
Module provides implementations of various key-value stores that conform
to a simple key-value interface.
The primary goal of these storages is to support implementation of caching.
"""
from typing import TYPE_CHECKING, Any
from langchain_core.stores impor... |
# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import os
from mmengine import MMLogger
from mmengine.config import Config, DictAction
from mmengine.dist import init_dist
from mmengine.utils import mkdir_or_exist
from mmdet.utils import register_all_modules
from mmdet.utils.benchmark import (DataLoade... | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import os
import mmcv
from mmcv import Config, DictAction
from mmengine import MMLogger
from mmengine.dist import init_dist
from mmdet.utils import register_all_modules
from mmdet.utils.benchmark import (DataLoaderBenchmark, DatasetBenchmark,
... |
# Copyright (c) OpenMMLab. All rights reserved.
from .csp_darknet import CSPDarknet
from .cspnext import CSPNeXt
from .darknet import Darknet
from .detectors_resnet import DetectoRS_ResNet
from .detectors_resnext import DetectoRS_ResNeXt
from .efficientnet import EfficientNet
from .hourglass import HourglassNet
from .h... | # Copyright (c) OpenMMLab. All rights reserved.
from .csp_darknet import CSPDarknet
from .darknet import Darknet
from .detectors_resnet import DetectoRS_ResNet
from .detectors_resnext import DetectoRS_ResNeXt
from .efficientnet import EfficientNet
from .hourglass import HourglassNet
from .hrnet import HRNet
from .mobil... |
_base_ = [
'../_base_/models/faster-rcnn_r50-caffe-c4.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
| _base_ = [
'../_base_/models/faster-rcnn_r50-caffe-c4.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# use caffe img_norm
img_norm_cfg = dict(
mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
train_pipeline = [
... |
import os
from typing import Optional, Type
import orjson
from pydantic import BaseModel, Field
from rich.console import Console
from docarray.base_document.base_node import BaseNode
from docarray.base_document.io.json import orjson_dumps, orjson_dumps_and_decode
from docarray.base_document.mixins import IOMixin, Upd... | import os
from typing import Type
import orjson
from pydantic import BaseModel, Field, parse_obj_as
from rich.console import Console
from docarray.base_document.base_node import BaseNode
from docarray.base_document.io.json import orjson_dumps, orjson_dumps_and_decode
from docarray.base_document.mixins import IOMixin,... |
"""DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.api import _tf_keras
from keras.api import activations
from keras.api import applications
from keras.api import backend
from keras.api import callbacks
from keras.api import config
from k... | """DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.api import _tf_keras
from keras.api import activations
from keras.api import applications
from keras.api import backend
from keras.api import callbacks
from keras.api import config
from k... |
from .rnnt_pipeline import EMFORMER_RNNT_BASE_MUSTC, EMFORMER_RNNT_BASE_TEDLIUM3
from .source_separation_pipeline import CONVTASNET_BASE_LIBRI2MIX, SourceSeparationBundle
__all__ = [
"CONVTASNET_BASE_LIBRI2MIX",
"EMFORMER_RNNT_BASE_MUSTC",
"EMFORMER_RNNT_BASE_TEDLIUM3",
"SourceSeparationBundle",
]
| from .rnnt_pipeline import EMFORMER_RNNT_BASE_MUSTC, EMFORMER_RNNT_BASE_TEDLIUM3
from .source_separation_pipeline import CONVTASNET_BASE_LIBRI2MIX
__all__ = [
"CONVTASNET_BASE_LIBRI2MIX",
"EMFORMER_RNNT_BASE_MUSTC",
"EMFORMER_RNNT_BASE_TEDLIUM3",
]
|
from typing import Optional, Type
from langchain_core.callbacks import CallbackManagerForToolRun
from langchain_core.tools import BaseTool
from pydantic import BaseModel, Field
from langchain_community.utilities.financial_datasets import FinancialDatasetsAPIWrapper
class CashFlowStatementsSchema(BaseModel):
"""... | from typing import Optional, Type
from langchain_core.callbacks import CallbackManagerForToolRun
from langchain_core.tools import BaseTool
from pydantic import BaseModel, Field
from langchain_community.utilities.financial_datasets import FinancialDatasetsAPIWrapper
class CashFlowStatementsSchema(BaseModel):
"""... |
_base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
lang_model_name = 'bert-base-uncased'
model = dict(
type='GroundingDINO',
num_queries=900,
with_box_refine=True,
as_two_stage=True,
data_preprocessor=dict(
type... | _base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
lang_model_name = 'bert-base-uncased'
model = dict(
type='GroundingDINO',
num_queries=900,
with_box_refine=True,
as_two_stage=True,
data_preprocessor=dict(
type... |
from torchvision.transforms import AutoAugmentPolicy, InterpolationMode # usort: skip
from . import functional # usort: skip
from ._transform import Transform # usort: skip
from ._augment import CutMix, JPEG, MixUp, RandomErasing
from ._auto_augment import AugMix, AutoAugment, RandAugment, TrivialAugmentWide
from... | from torchvision.transforms import AutoAugmentPolicy, InterpolationMode # usort: skip
from . import functional # usort: skip
from ._transform import Transform # usort: skip
from ._augment import CutMix, MixUp, RandomErasing
from ._auto_augment import AugMix, AutoAugment, RandAugment, TrivialAugmentWide
from ._col... |
"""Evaluator."""
from abc import abstractmethod
from typing import Any, Optional, Sequence
from llama_index.core.async_utils import asyncio_run
from llama_index.core.base.response.schema import Response
from llama_index.core.bridge.pydantic import BaseModel, Field
from llama_index.core.prompts.mixin import PromptMixi... | """Evaluator."""
from abc import abstractmethod
from typing import Any, Optional, Sequence
from llama_index.core.async_utils import asyncio_run
from llama_index.core.base.response.schema import Response
from llama_index.core.bridge.pydantic import BaseModel, Field
from llama_index.core.prompts.mixin import PromptMixin... |
_base_ = '../mask_rcnn/mask-rcnn_r50_fpn_1x_coco.py'
model = dict(
data_preprocessor=dict(pad_size_divisor=64),
neck=dict(
type='FPN_CARAFE',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
num_outs=5,
start_level=0,
end_level=-1,
norm_cfg=None,
... | _base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py'
model = dict(
data_preprocessor=dict(pad_size_divisor=64),
neck=dict(
type='FPN_CARAFE',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
num_outs=5,
start_level=0,
end_level=-1,
norm_cfg=None,
... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.