input stringlengths 33 5k | output stringlengths 32 5k |
|---|---|
from contextlib import nullcontext
from sentence_transformers.evaluation import SentenceEvaluator
from sentence_transformers import SentenceTransformer
from typing import List, Optional, Tuple, Dict
import numpy as np
import logging
import os
import csv
logger = logging.getLogger(__name__)
class MSEEvaluatorFromDat... | from sentence_transformers.evaluation import SentenceEvaluator
from sentence_transformers import SentenceTransformer
from typing import List, Tuple, Dict
import numpy as np
import logging
import os
import csv
logger = logging.getLogger(__name__)
class MSEEvaluatorFromDataFrame(SentenceEvaluator):
"""
Comput... |
from google.protobuf import __version__ as __pb__version__
if __pb__version__.startswith('4'):
from docarray.proto.pb.docarray_pb2 import (
DictOfAnyProto,
DocumentArrayProto,
DocumentArrayStackedProto,
DocumentProto,
ListOfAnyProto,
ListOfDocumentArrayProto,
... | from google.protobuf import __version__ as __pb__version__
if __pb__version__.startswith('4'):
from docarray.proto.pb.docarray_pb2 import (
DocumentArrayProto,
DocumentArrayStackedProto,
DocumentProto,
ListOfAnyProto,
ListOfDocumentArrayProto,
NdArrayProto,
N... |
_base_ = [
'../_base_/datasets/coco_detection.py', '../_base_/default_runtime.py'
]
model = dict(
type='SingleStageDetector',
backbone=dict(
type='MobileNetV2',
out_indices=(4, 7),
norm_cfg=dict(type='BN', eps=0.001, momentum=0.03),
init_cfg=dict(type='TruncNormal', layer='C... | _base_ = [
'../_base_/datasets/coco_detection.py', '../_base_/default_runtime.py'
]
model = dict(
type='SingleStageDetector',
backbone=dict(
type='MobileNetV2',
out_indices=(4, 7),
norm_cfg=dict(type='BN', eps=0.001, momentum=0.03),
init_cfg=dict(type='TruncNormal', layer='C... |
import numpy as np
from keras.src import backend
from keras.src import ops
from keras.src.api_export import keras_export
@keras_export("keras.visualization.draw_segmentation_masks")
def draw_segmentation_masks(
images,
segmentation_masks,
num_classes=None,
color_mapping=None,
alpha=0.8,
blend... | import numpy as np
from keras.src import backend
from keras.src import ops
from keras.src.api_export import keras_export
@keras_export("keras.visualization.draw_segmentation_masks")
def draw_segmentation_masks(
images,
segmentation_masks,
num_classes=None,
color_mapping=None,
alpha=0.8,
blend... |
# Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
from typing import Optional, Sequence, Tuple
import cv2
import numpy as np
from mmengine.data import BaseDataElement
from mmengine.hooks import Hook
from mmengine.registry import HOOKS
from mmengine.utils.misc import tensor2imgs
# TODO: Due to in... | # Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
from typing import Optional, Sequence, Tuple
import cv2
import numpy as np
from mmengine.data import BaseDataElement
from mmengine.hooks import Hook
from mmengine.registry import HOOKS
from mmengine.utils.misc import tensor2imgs
@HOOKS.register_m... |
from jina.clients.base.websocket import WebSocketBaseClient
from jina.clients.mixin import (
AsyncHealthCheckMixin,
AsyncPostMixin,
AsyncProfileMixin,
HealthCheckMixin,
PostMixin,
ProfileMixin,
)
class WebSocketClient(WebSocketBaseClient, PostMixin, ProfileMixin, HealthCheckMixin):
"""A cl... | from jina.clients.base.websocket import WebSocketBaseClient
from jina.clients.mixin import (
AsyncHealthCheckMixin,
AsyncPostMixin,
HealthCheckMixin,
PostMixin,
)
class WebSocketClient(WebSocketBaseClient, PostMixin, HealthCheckMixin):
"""A client connecting to a Gateway using WebSocket protocol.
... |
from typing import List, Optional
import pandas as pd
import pytest
from docarray import BaseDoc, DocList
from docarray.documents import ImageDoc
@pytest.fixture()
def nested_doc_cls():
class MyDoc(BaseDoc):
count: Optional[int]
text: str
class MyDocNested(MyDoc):
image: ImageDoc
... | from typing import List, Optional
import pandas as pd
import pytest
from docarray import BaseDoc, DocList
from docarray.documents import ImageDoc
@pytest.fixture()
def nested_doc_cls():
class MyDoc(BaseDoc):
count: Optional[int]
text: str
class MyDocNested(MyDoc):
image: ImageDoc
... |
# Copyright (c) OpenMMLab. All rights reserved.
import random
import warnings
import torch
from mmcv.runner import get_dist_info
from mmcv.runner.hooks import Hook
from torch import distributed as dist
from mmdet.registry import HOOKS
@HOOKS.register_module()
class SyncRandomSizeHook(Hook):
"""Change and synchr... | # Copyright (c) OpenMMLab. All rights reserved.
import random
import warnings
import torch
from mmcv.runner import get_dist_info
from mmcv.runner.hooks import HOOKS, Hook
from torch import distributed as dist
@HOOKS.register_module()
class SyncRandomSizeHook(Hook):
"""Change and synchronize the random image size... |
"""Standard LangChain interface tests"""
import os
import pytest
from langchain_core.language_models import BaseChatModel
from langchain_tests.integration_tests import ChatModelIntegrationTests
from langchain_openai import AzureChatOpenAI
OPENAI_API_VERSION = os.environ.get("AZURE_OPENAI_API_VERSION", "")
OPENAI_AP... | """Standard LangChain interface tests"""
import os
from langchain_core.language_models import BaseChatModel
from langchain_tests.integration_tests import ChatModelIntegrationTests
from langchain_openai import AzureChatOpenAI
OPENAI_API_VERSION = os.environ.get("AZURE_OPENAI_API_VERSION", "")
OPENAI_API_BASE = os.en... |
import os
import pytest
from llama_index.graph_stores.memgraph import MemgraphPropertyGraphStore
from llama_index.core.graph_stores.types import (
EntityNode,
Relation,
)
from llama_index.core.schema import TextNode
memgraph_user = os.environ.get("MEMGRAPH_TEST_USER")
memgraph_pass = os.environ.get("MEMGRAPH_T... | import os
import pytest
from llama_index.graph_stores.memgraph import MemgraphPropertyGraphStore
from llama_index.core.graph_stores.types import (
EntityNode,
Relation,
)
from llama_index.core.schema import TextNode
memgraph_user = os.environ.get("MEMGRAPH_TEST_USER")
memgraph_pass = os.environ.get("MEMGRAPH_T... |
"""
This examples trains a CrossEncoder for the STSbenchmark task. A CrossEncoder takes a sentence pair
as input and outputs a label. Here, it output a continuous labels 0...1 to indicate the similarity between the input pair.
It does NOT produce a sentence embedding and does NOT work for individual sentences.
Usage:... | """
This examples trains a CrossEncoder for the STSbenchmark task. A CrossEncoder takes a sentence pair
as input and outputs a label. Here, it output a continious labels 0...1 to indicate the similarity between the input pair.
It does NOT produce a sentence embedding and does NOT work for individual sentences.
Usage:... |
"""Various utilities to help with development."""
# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause
from ..exceptions import DataConversionWarning
from . import metadata_routing
from ._bunch import Bunch
from ._chunking import gen_batches, gen_even_slices
# Make _safe_indexing importable... | """Various utilities to help with development."""
# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause
from ..exceptions import DataConversionWarning
from . import metadata_routing
from ._bunch import Bunch
from ._chunking import gen_batches, gen_even_slices
from ._estimator_html_repr import... |
"""
===========================================================================
Normal, Ledoit-Wolf and OAS Linear Discriminant Analysis for classification
===========================================================================
This example illustrates how the Ledoit-Wolf and Oracle Approximating
Shrinkage (OAS) e... | """
===========================================================================
Normal, Ledoit-Wolf and OAS Linear Discriminant Analysis for classification
===========================================================================
This example illustrates how the Ledoit-Wolf and Oracle Approximating
Shrinkage (OAS) e... |
"""Unit tests for ScrapegraphAI tool specification."""
from unittest.mock import Mock, patch
import pytest
from pydantic import BaseModel
from llama_index.tools.scrapegraph import ScrapegraphToolSpec
class TestSchema(BaseModel):
"""Test schema for scraping operations."""
title: str
description: str
... | """Unit tests for ScrapegraphAI tool specification."""
from unittest.mock import Mock, patch
import pytest
from pydantic import BaseModel
from llama_index.tools.scrapegraph import ScrapegraphToolSpec
class TestSchema(BaseModel):
"""Test schema for scraping operations."""
title: str
description: str
... |
"""Standard LangChain interface tests"""
from langchain_core.embeddings import Embeddings
from langchain_tests.unit_tests.embeddings import EmbeddingsUnitTests
from langchain_openai import OpenAIEmbeddings
class TestOpenAIStandard(EmbeddingsUnitTests):
@property
def embeddings_class(self) -> type[Embeddings... | """Standard LangChain interface tests"""
from typing import Tuple, Type
from langchain_core.embeddings import Embeddings
from langchain_tests.unit_tests.embeddings import EmbeddingsUnitTests
from langchain_openai import OpenAIEmbeddings
class TestOpenAIStandard(EmbeddingsUnitTests):
@property
def embedding... |
from prisma.models import User
from backend.blocks.basic import AgentInputBlock, PrintToConsoleBlock
from backend.blocks.text import FillTextTemplateBlock
from backend.data import graph
from backend.data.graph import create_graph
from backend.data.user import get_or_create_user
from backend.util.test import SpinTestSe... | from prisma.models import User
from backend.blocks.basic import AgentInputBlock, PrintToConsoleBlock
from backend.blocks.text import FillTextTemplateBlock
from backend.data import graph
from backend.data.graph import create_graph
from backend.data.user import get_or_create_user
from backend.util.test import SpinTestSe... |
import random
import time
from typing import List
from llama_index.schema import TextNode
from llama_index.vector_stores.simple import SimpleVectorStore
from llama_index.vector_stores.types import (
VectorStoreQuery,
VectorStoreQueryMode,
)
def generate_nodes(
num_vectors: int = 100, embedding_length: in... | import random
import time
from typing import List
from llama_index.schema import TextNode
from llama_index.vector_stores.simple import SimpleVectorStore
from llama_index.vector_stores.types import (
VectorStoreQuery,
VectorStoreQueryMode,
)
def generate_nodes(
num_vectors: int = 100, embedding_length: in... |
from torchvision import _BETA_TRANSFORMS_WARNING, _WARN_ABOUT_BETA_TRANSFORMS
from ._bounding_box import BoundingBox, BoundingBoxFormat
from ._datapoint import _FillType, _FillTypeJIT, _InputType, _InputTypeJIT
from ._image import _ImageType, _ImageTypeJIT, _TensorImageType, _TensorImageTypeJIT, Image
from ._mask impo... | from ._bounding_box import BoundingBox, BoundingBoxFormat
from ._datapoint import _FillType, _FillTypeJIT, _InputType, _InputTypeJIT
from ._image import _ImageType, _ImageTypeJIT, _TensorImageType, _TensorImageTypeJIT, Image
from ._mask import Mask
from ._video import _TensorVideoType, _TensorVideoTypeJIT, _VideoType, ... |
import os
from typing import Any, Optional
from llama_index.llms.openai_like import OpenAILike
from llama_index.llms.deepseek.utils import get_context_window, FUNCTION_CALLING_MODELS
class DeepSeek(OpenAILike):
"""
DeepSeek LLM.
Examples:
`pip install llama-index-llms-deepseek`
```pytho... | import os
from typing import Any, Optional
from llama_index.llms.openai_like import OpenAILike
from llama_index.llms.deepseek.utils import get_context_window, FUNCTION_CALLING_MODELS
class DeepSeek(OpenAILike):
"""
DeepSeek LLM.
Examples:
`pip install llama-index-llms-deepseek`
```pytho... |
from __future__ import annotations
import logging
from datasets import load_dataset
from sentence_transformers import SparseEncoder, SparseEncoderTrainer, SparseEncoderTrainingArguments
from sentence_transformers.evaluation import SequentialEvaluator, SimilarityFunction
from sentence_transformers.models import Pooli... | from __future__ import annotations
import logging
from datasets import load_dataset
from sentence_transformers import SparseEncoder, SparseEncoderTrainer, SparseEncoderTrainingArguments, losses
from sentence_transformers.evaluation import SequentialEvaluator, SimilarityFunction
from sentence_transformers.models impo... |
_base_ = 'mask_rcnn_r50_fpn_syncbn-all_rpn-2conv_ssj_32x2_270k_coco.py'
# lr steps at [0.9, 0.95, 0.975] of the maximum iterations
lr_config = dict(
warmup_iters=500, warmup_ratio=0.067, step=[81000, 85500, 87750])
# 90k iterations with batch_size 64 is roughly equivalent to 48 epochs
runner = dict(type='IterBased... | _base_ = 'mask_rcnn_r50_fpn_syncbn-all_rpn-2conv_ssj_270k_coco.py'
# lr steps at [0.9, 0.95, 0.975] of the maximum iterations
lr_config = dict(
warmup_iters=500, warmup_ratio=0.067, step=[81000, 85500, 87750])
# 90k iterations with batch_size 64 is roughly equivalent to 48 epochs
runner = dict(type='IterBasedRunne... |
import pathlib
from typing import Any, Callable, Optional, Tuple
from PIL import Image
from .utils import verify_str_arg
from .vision import VisionDataset
class StanfordCars(VisionDataset):
"""Stanford Cars Dataset
The Cars dataset contains 16,185 images of 196 classes of cars. The data is
split into ... | import pathlib
from typing import Any, Callable, Optional, Tuple
from PIL import Image
from .utils import download_and_extract_archive, download_url, verify_str_arg
from .vision import VisionDataset
class StanfordCars(VisionDataset):
"""`Stanford Cars <https://ai.stanford.edu/~jkrause/cars/car_dataset.html>`_ D... |
_base_ = './ga-rpn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='pytorch',
... | _base_ = './ga_rpn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='pytorch',
... |
import torch
import torchaudio.prototype.functional as F
from torch.autograd import gradcheck, gradgradcheck
from torchaudio_unittest.common_utils import nested_params, TestBaseMixin
class AutogradTestImpl(TestBaseMixin):
@nested_params(
[F.convolve, F.fftconvolve],
["full", "valid", "same"],
... | import torch
import torchaudio.prototype.functional as F
from parameterized import parameterized
from torch.autograd import gradcheck, gradgradcheck
from torchaudio_unittest.common_utils import TestBaseMixin
class AutogradTestImpl(TestBaseMixin):
@parameterized.expand(
[
(F.convolve,),
... |
_base_ = './mask_rcnn_r50_fpn_1x_coco.py'
model = dict(
# use caffe img_norm
data_preprocessor=dict(
mean=[103.530, 116.280, 123.675],
std=[1.0, 1.0, 1.0],
bgr_to_rgb=False),
backbone=dict(
norm_cfg=dict(requires_grad=False),
style='caffe',
init_cfg=dict(
... | _base_ = './mask_rcnn_r50_fpn_1x_coco.py'
preprocess_cfg = dict(
mean=[103.530, 116.280, 123.675],
std=[1.0, 1.0, 1.0],
to_rgb=False,
pad_size_divisor=32)
model = dict(
# use caffe img_norm
preprocess_cfg=preprocess_cfg,
backbone=dict(
norm_cfg=dict(requires_grad=False),
styl... |
"""Function Message."""
from typing import Any, Literal
from typing_extensions import override
from langchain_core.messages.base import (
BaseMessage,
BaseMessageChunk,
merge_content,
)
from langchain_core.utils._merge import merge_dicts
class FunctionMessage(BaseMessage):
"""Message for passing th... | """Function Message."""
from typing import Any, Literal
from typing_extensions import override
from langchain_core.messages.base import (
BaseMessage,
BaseMessageChunk,
merge_content,
)
from langchain_core.utils._merge import merge_dicts
class FunctionMessage(BaseMessage):
"""Message for passing th... |
# Copyright (c) OpenMMLab. All rights reserved.
from typing import Union
import torch
from numpy import ndarray
from torch import Tensor
from mmdet.core.bbox.assigners import AssignResult
from mmdet.registry import TASK_UTILS
from .base_sampler import BaseSampler
@TASK_UTILS.register_module()
class RandomSampler(Ba... | # Copyright (c) OpenMMLab. All rights reserved.
import torch
from mmdet.registry import TASK_UTILS
from .base_sampler import BaseSampler
@TASK_UTILS.register_module()
class RandomSampler(BaseSampler):
"""Random sampler.
Args:
num (int): Number of samples
pos_fraction (float): Fraction of pos... |
from __future__ import annotations
from typing import Any, Optional
from langchain_core.callbacks import CallbackManagerForToolRun
from langchain_core.tools import BaseTool
from pydantic import Field, SecretStr
from langchain_community.utilities.brave_search import BraveSearchWrapper
class BraveSearch(BaseTool):
... | from __future__ import annotations
from typing import Any, Optional
from langchain_core.callbacks import CallbackManagerForToolRun
from langchain_core.tools import BaseTool
from pydantic import Field, SecretStr
from langchain_community.utilities.brave_search import BraveSearchWrapper
class BraveSearch(BaseTool): ... |
_base_ = ['./yolov3_mobilenetv2_8xb24-ms-416-300e_coco.py']
# yapf:disable
model = dict(
bbox_head=dict(
anchor_generator=dict(
base_sizes=[[(220, 125), (128, 222), (264, 266)],
[(35, 87), (102, 96), (60, 170)],
[(10, 15), (24, 36), (72, 42)]])))
... | _base_ = ['./yolov3_mobilenetv2_8xb24-ms-416-300e_coco.py']
# yapf:disable
model = dict(
bbox_head=dict(
anchor_generator=dict(
base_sizes=[[(220, 125), (128, 222), (264, 266)],
[(35, 87), (102, 96), (60, 170)],
[(10, 15), (24, 36), (72, 42)]])))
... |
"""
The system trains BERT (or any other transformer model like RoBERTa, DistilBERT etc.) on the SNLI + MultiNLI (AllNLI) dataset
with softmax loss function. At every 1000 training steps, the model is evaluated on the
STS benchmark dataset
Usage:
python training_nli.py
OR
python training_nli.py pretrained_transformer... | """
The system trains BERT (or any other transformer model like RoBERTa, DistilBERT etc.) on the SNLI + MultiNLI (AllNLI) dataset
with softmax loss function. At every 1000 training steps, the model is evaluated on the
STS benchmark dataset
Usage:
python training_nli.py
OR
python training_nli.py pretrained_transformer... |
import numpy as np
import pytest
from keras.src import testing
from keras.src.layers.activations import softmax
class SoftmaxTest(testing.TestCase):
@pytest.mark.requires_trainable_backend
def test_softmax(self):
self.run_layer_test(
softmax.Softmax,
init_kwargs={},
... | import numpy as np
import pytest
from keras.src import testing
from keras.src.layers.activations import softmax
class SoftmaxTest(testing.TestCase):
@pytest.mark.requires_trainable_backend
def test_softmax(self):
self.run_layer_test(
softmax.Softmax,
init_kwargs={},
... |
# Copyright (c) OpenMMLab. All rights reserved.
__version__ = '2.22.0'
short_version = __version__
def parse_version_info(version_str):
version_info = []
for x in version_str.split('.'):
if x.isdigit():
version_info.append(int(x))
elif x.find('rc') != -1:
patch_version... | # Copyright (c) OpenMMLab. All rights reserved.
__version__ = '2.21.0'
short_version = __version__
def parse_version_info(version_str):
version_info = []
for x in version_str.split('.'):
if x.isdigit():
version_info.append(int(x))
elif x.find('rc') != -1:
patch_version... |
from __future__ import annotations
import re
from typing import Optional
from langchain_core.output_parsers import BaseOutputParser
class RegexParser(BaseOutputParser[dict[str, str]]):
"""Parse the output of an LLM call using a regex."""
@classmethod
def is_lc_serializable(cls) -> bool:
return ... | from __future__ import annotations
import re
from typing import Dict, List, Optional
from langchain_core.output_parsers import BaseOutputParser
class RegexParser(BaseOutputParser[Dict[str, str]]):
"""Parse the output of an LLM call using a regex."""
@classmethod
def is_lc_serializable(cls) -> bool:
... |
# flake8: noqa
import numpy as np
from keras.src import backend
from keras.src import ops
from keras.src import testing
from keras.src.optimizers.sgd import SGD
class SGDTest(testing.TestCase):
def test_config(self):
optimizer = SGD(
learning_rate=0.5,
momentum=0.06,
... | # flake8: noqa
import numpy as np
from keras.src import backend
from keras.src import ops
from keras.src import testing
from keras.src.optimizers.sgd import SGD
class SGDTest(testing.TestCase):
def test_config(self):
optimizer = SGD(
learning_rate=0.5,
momentum=0.06,
... |
from .conv_emformer import ConvEmformer
from .conv_tasnet import conv_tasnet_base
from .rnnt import conformer_rnnt_base, conformer_rnnt_model
__all__ = [
"conformer_rnnt_base",
"conformer_rnnt_model",
"conv_tasnet_base",
"ConvEmformer",
]
| from .conv_emformer import ConvEmformer
from .rnnt import conformer_rnnt_base, conformer_rnnt_model
__all__ = [
"conformer_rnnt_base",
"conformer_rnnt_model",
"ConvEmformer",
]
|
# Licensed to the LF AI & Data foundation under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the "License");
# you may not use this fil... | import os
import numpy as np
import pytest
from pydantic.tools import parse_obj_as, schema_json_of
from docarray.base_doc.io.json import orjson_dumps
from docarray.typing import NdArray, PointCloud3DUrl
from docarray.typing.url.mimetypes import (
OBJ_MIMETYPE,
AUDIO_MIMETYPE,
VIDEO_MIMETYPE,
IMAGE_MIM... |
from typing import TYPE_CHECKING, Any, NamedTuple, Type, TypeVar, Union
import numpy as np
from pydantic.tools import parse_obj_as
from docarray.typing.proto_register import _register_proto
from docarray.typing.tensor.audio.audio_ndarray import AudioNdArray
from docarray.typing.tensor.ndarray import NdArray
from doca... | from typing import TYPE_CHECKING, Any, NamedTuple, Type, TypeVar, Union
import numpy as np
from pydantic.tools import parse_obj_as
from docarray.typing import AudioNdArray, NdArray
from docarray.typing.proto_register import _register_proto
from docarray.typing.tensor.video import VideoNdArray
from docarray.typing.url... |
from docutils import nodes
from docutils.parsers.rst import Directive
class BetaStatus(Directive):
has_content = True
text = "The {api_name} is in Beta stage, and backward compatibility is not guaranteed."
node = nodes.warning
def run(self):
text = self.text.format(api_name=" ".join(self.cont... | from docutils import nodes
from docutils.parsers.rst import Directive
class BetaStatus(Directive):
has_content = True
text = "The {api_name} is in Beta stage, and backward compatibility is not guaranteed."
def run(self):
text = self.text.format(api_name=" ".join(self.content))
return [nod... |
# Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
from mmcv.runner import BaseModule
from mmdet.data_elements.bbox import bbox_cxcywh_to_xyxy
from mmdet.registry import MODELS
@MODELS.register_module()
class EmbeddingRPNHead(BaseModule):
"""RPNHead in the `Sparse R-CNN <https://a... | # Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
from mmcv.runner import BaseModule
from mmdet.registry import MODELS
from ...core import bbox_cxcywh_to_xyxy
@MODELS.register_module()
class EmbeddingRPNHead(BaseModule):
"""RPNHead in the `Sparse R-CNN <https://arxiv.org/abs/2011... |
# Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn.bricks.wrappers import NewEmptyTensorOp, obsolete_torch_version
if torch.__version__ == 'parrots':
TORCH_VERSION = torch.__version__
else:
# torch.__version__ could be 1.3.1+cu92, we... | import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn.bricks.wrappers import NewEmptyTensorOp, obsolete_torch_version
if torch.__version__ == 'parrots':
TORCH_VERSION = torch.__version__
else:
# torch.__version__ could be 1.3.1+cu92, we only need the first two
# for comparison
... |
import numpy as np
from docarray import Document, DocumentArray, Image, Text
from docarray.typing import NdArray
def test_simple_proto():
class CustomDoc(Document):
text: str
tensor: NdArray
da = DocumentArray(
[CustomDoc(text='hello', tensor=np.zeros((3, 224, 224))) for _ in range(1... | import numpy as np
from docarray import DocumentArray, Document, Image, Text
from docarray.typing import Tensor
def test_simple_proto():
class CustomDoc(Document):
text: str
tensor: Tensor
da = DocumentArray(
[CustomDoc(text='hello', tensor=np.zeros((3, 224, 224))) for _ in range(10)... |
import pytest
from sentence_transformers import SentenceTransformer, SentenceTransformerTrainer
@pytest.mark.parametrize(
("revision", "expected_base_revision"),
[
("f3cb857cba53019a20df283396bcca179cf051a4", "f3cb857cba53019a20df283396bcca179cf051a4"),
("f3cb857", "f3cb857"),
("main"... | import pytest
from sentence_transformers import SentenceTransformer
@pytest.mark.parametrize(
("revision", "expected_base_revision"),
[
("f3cb857cba53019a20df283396bcca179cf051a4", "f3cb857cba53019a20df283396bcca179cf051a4"),
("f3cb857", "f3cb857"),
("main", "valid-revision"),
... |
from typing import Any, Dict, List, Optional, Union
from .. import config
from ..exceptions import DatasetsError
from .file_utils import (
get_authentication_headers_for_url,
http_get,
)
from .logging import get_logger
logger = get_logger(__name__)
class DatasetsServerError(DatasetsError):
"""Dataset-s... | from typing import Any, Dict, List
from .. import config
from ..exceptions import DatasetsError
from .file_utils import (
get_authentication_headers_for_url,
http_get,
)
from .logging import get_logger
logger = get_logger(__name__)
class DatasetsServerError(DatasetsError):
"""Dataset-server error.
... |
from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.tools import GmailGetMessage
from langchain_community.tools.gmail.get_message import SearchArgsSchema
# Create a way to dynamically look up deprecated imports.
# Used to consolidate log... | from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.tools import GmailGetMessage
from langchain_community.tools.gmail.get_message import SearchArgsSchema
# Create a way to dynamically look up deprecated imports.
# Used to consolidate log... |
_base_ = 'retinanet_pvtv2-b0_fpn_1x_coco.py'
model = dict(
backbone=dict(
embed_dims=64,
num_layers=[3, 6, 40, 3],
mlp_ratios=(4, 4, 4, 4),
init_cfg=dict(checkpoint='https://github.com/whai362/PVT/'
'releases/download/v2/pvt_v2_b5.pth')),
neck=dict(in_channe... | _base_ = 'retinanet_pvtv2-b0_fpn_1x_coco.py'
model = dict(
backbone=dict(
embed_dims=64,
num_layers=[3, 6, 40, 3],
mlp_ratios=(4, 4, 4, 4),
init_cfg=dict(checkpoint='https://github.com/whai362/PVT/'
'releases/download/v2/pvt_v2_b5.pth')),
neck=dict(in_channe... |
from typing import List
import pytest
from sqlalchemy import create_engine, text
from llama_index.readers.database import DatabaseReader
from llama_index.core.schema import Document
# --------------------------------------------------------------------------- #
# Fixtures
# -----------------------------------------... | from llama_index.core.readers.base import BaseReader
from llama_index.readers.database import DatabaseReader
def test_class():
names_of_base_classes = [b.__name__ for b in DatabaseReader.__mro__]
assert BaseReader.__name__ in names_of_base_classes
|
_base_ = './solo_r50_fpn_8xb8-lsj-200e_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| _base_ = './solo_r50_fpn_lsj_200e_8x8_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
|
# Copyright (c) OpenMMLab. All rights reserved.
from .det_inferencer import DetInferencer
from .inference import (async_inference_detector, inference_detector,
inference_mot, init_detector, init_track_model)
__all__ = [
'init_detector', 'async_inference_detector', 'inference_detector',
... | # Copyright (c) OpenMMLab. All rights reserved.
from .det_inferencer import DetInferencer
from .inference import (async_inference_detector, inference_detector,
init_detector)
__all__ = [
'init_detector', 'async_inference_detector', 'inference_detector',
'DetInferencer'
]
|
_base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# please install mmcls>=0.22.0
# import mmcls.models to trigger register_module in mmcls
custom_imports = dict(imports=['mmcls.models'], allow_fa... | _base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# please install mmcls>=0.22.0
# import mmcls.models to trigger register_module in mmcls
custom_imports = dict(imports=['mmcls.models'], allow_fa... |
# Copyright (c) OpenMMLab. All rights reserved.
import logging
from typing import List, Optional, Sequence
import torch
from torch.nn.parameter import Parameter
from torch.nn.utils import clip_grad
from mmengine.registry import HOOKS
from .hook import Hook
DATA_BATCH = Optional[Sequence[dict]]
@HOOKS.register_modu... | # Copyright (c) OpenMMLab. All rights reserved.
import logging
from typing import Any, List, Optional, Sequence, Tuple
import torch
from torch.nn.parameter import Parameter
from torch.nn.utils import clip_grad
from mmengine.data import BaseDataElement
from mmengine.registry import HOOKS
from .hook import Hook
DATA_B... |
"""Init file."""
from llama_index.readers.openalex.base import OpenAlexReader
__all__ = ["OpenAlexReader"]
| """Init file."""
from llama_index.readers.openalex.base import OpenAlexReader
__all__ = ["OpenAlexReader"]
|
"""
Successive Halving Iterations
=============================
This example illustrates how a successive halving search
(:class:`~sklearn.model_selection.HalvingGridSearchCV` and
:class:`~sklearn.model_selection.HalvingRandomSearchCV`)
iteratively chooses the best parameter combination out of
multiple candidates.
""... | """
Successive Halving Iterations
=============================
This example illustrates how a successive halving search
(:class:`~sklearn.model_selection.HalvingGridSearchCV` and
:class:`~sklearn.model_selection.HalvingRandomSearchCV`)
iteratively chooses the best parameter combination out of
multiple candidates.
""... |
import functools
import warnings
from collections import defaultdict
from typing import Any, Dict, Optional, Sequence, Tuple, Type, TypeVar, Union
import torch
from torchvision import datapoints
from torchvision.transforms.v2 import Transform
from torchvision.transforms.v2.utils import is_simple_tensor
T = TypeVar... | import warnings
from typing import Any, Dict, Optional, Sequence, Tuple, Type, Union
import torch
from torchvision import datapoints
from torchvision.transforms.v2 import Transform
from torchvision.transforms.v2._utils import _get_defaultdict
from torchvision.transforms.v2.utils import is_simple_tensor
class Permu... |
# Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import torch
from mmdet.models.dense_heads import YOLOFHead
def test_yolof_head_loss():
"""Tests yolof head loss when truth is empty and non-empty."""
s = 256
img_metas = [{
'img_shape': (s, s, 3),
'scale_factor': 1,
'pad... | import mmcv
import torch
from mmdet.models.dense_heads import YOLOFHead
def test_yolof_head_loss():
"""Tests yolof head loss when truth is empty and non-empty."""
s = 256
img_metas = [{
'img_shape': (s, s, 3),
'scale_factor': 1,
'pad_shape': (s, s, 3)
}]
train_cfg = mmcv.C... |
# Copyright (c) OpenMMLab. All rights reserved.
from mmengine.registry import HOOKS
from .hook import Hook
@HOOKS.register_module()
class DistSamplerSeedHook(Hook):
"""Data-loading sampler for distributed training.
When distributed training, it is only useful in conjunction with
:obj:`EpochBasedRunner`, ... | # Copyright (c) OpenMMLab. All rights reserved.
from mmengine.registry import HOOKS
from .hook import Hook
@HOOKS.register_module()
class DistSamplerSeedHook(Hook):
"""Data-loading sampler for distributed training.
When distributed training, it is only useful in conjunction with
:obj:`EpochBasedRunner`, ... |
import abc
from typing import BinaryIO, Optional, Type, TypeVar, Union
import numpy as np
from docarray.typing.tensor.abstract_tensor import AbstractTensor
from docarray.typing.tensor.audio.audio_tensor import AudioTensor
T = TypeVar('T', bound='AbstractTensor')
class VideoTensorMixin(AbstractTensor, abc.ABC):
... | import abc
from typing import BinaryIO, Optional, Type, TypeVar, Union
import numpy as np
from docarray.typing.tensor.abstract_tensor import AbstractTensor
from docarray.typing.tensor.audio.audio_tensor import AudioTensor
T = TypeVar('T', bound='AbstractTensor')
class VideoTensorMixin(AbstractTensor, abc.ABC):
... |
from pathlib import Path
from typing import Any, BinaryIO, Dict, List, Optional, Tuple, Union
from torchdata.datapipes.iter import Demultiplexer, Filter, IterDataPipe, IterKeyZipper, LineReader, Mapper
from torchvision.prototype.datasets.utils import Dataset, EncodedImage, HttpResource, OnlineResource
from torchvision... | from pathlib import Path
from typing import Any, BinaryIO, Dict, List, Optional, Tuple, Union
from torchdata.datapipes.iter import Demultiplexer, Filter, IterDataPipe, IterKeyZipper, LineReader, Mapper
from torchvision.prototype.datasets.utils import Dataset, HttpResource, OnlineResource
from torchvision.prototype.dat... |
"""DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.tree.tree_api import MAP_TO_NONE
from keras.src.tree.tree_api import assert_same_paths
from keras.src.tree.tree_api import assert_same_structure
from keras.src.tree.tree_api import fl... | """DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.tree.tree_api import assert_same_paths
from keras.src.tree.tree_api import assert_same_structure
from keras.src.tree.tree_api import flatten
from keras.src.tree.tree_api import flatte... |
"""Message responsible for deleting other messages."""
from typing import Any, Literal
from langchain_core.messages.base import BaseMessage
class RemoveMessage(BaseMessage):
"""Message responsible for deleting other messages."""
type: Literal["remove"] = "remove"
"""The type of the message (used for se... | """Message responsible for deleting other messages."""
from typing import Any, Literal
from langchain_core.messages.base import BaseMessage
class RemoveMessage(BaseMessage):
"""Message responsible for deleting other messages."""
type: Literal["remove"] = "remove"
"""The type of the message (used for se... |
import os
import fsspec
import pytest
from datasets.filesystems import COMPRESSION_FILESYSTEMS, HfFileSystem, extract_path_from_uri, is_remote_filesystem
from datasets.utils._hf_hub_fixes import dataset_info as hf_api_dataset_info
from .utils import require_lz4, require_zstandard
def test_extract_path_from_uri():
... | import os
import 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... |
# Copyright (c) OpenMMLab. All rights reserved.
import unittest
from unittest import TestCase
import torch
from parameterized import parameterized
from mmdet.core import DetDataSample
from mmdet.testing import demo_mm_inputs, get_detector_cfg
from mmdet.utils import register_all_modules
register_all_modules()
clas... | # Copyright (c) OpenMMLab. All rights reserved.
import unittest
from unittest import TestCase
import torch
from parameterized import parameterized
from mmdet import * # noqa
from mmdet.core import DetDataSample
from .utils import demo_mm_inputs, get_detector_cfg
class TestSingleStageDetector(TestCase):
@param... |
_base_ = ['./mask2former_r50_8xb2-lsj-50e_coco.py']
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| _base_ = ['./mask2former_r50_lsj_8x2_50e_coco.py']
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
|
"""
This is a simple application for sentence embeddings: clustering
Sentences are mapped to sentence embeddings and then agglomerative clustering with a threshold is applied.
"""
from sentence_transformers import SentenceTransformer
from sklearn.cluster import AgglomerativeClustering
import numpy as np
embedder = S... | """
This is a simple application for sentence embeddings: clustering
Sentences are mapped to sentence embeddings and then agglomerative clustering with a threshold is applied.
"""
from sentence_transformers import SentenceTransformer
from sklearn.cluster import AgglomerativeClustering
import numpy as np
embedder = Se... |
import imghdr
import os
import struct
import pytest
from jina import Executor, Flow
cur_dir = os.path.dirname(os.path.abspath(__file__))
@pytest.mark.skipif("GITHUB_WORKFLOW" in os.environ, reason="Skip unneeded")
def test_visualization_with_yml_file_img(tmpdir):
Flow.load_config(
os.path.join(cur_dir,... | import imghdr
import os
import struct
import pytest
from jina import Executor, Flow
cur_dir = os.path.dirname(os.path.abspath(__file__))
def test_visualization_with_yml_file_img(tmpdir):
Flow.load_config(
os.path.join(cur_dir, '../../../yaml/test_flow_visualization.yml')
).plot(output=os.path.join(... |
# Copyright (c) OpenMMLab. All rights reserved.
from .bbox_overlaps import bbox_overlaps
from .cityscapes_utils import evaluateImgLists
from .class_names import (cityscapes_classes, coco_classes,
coco_panoptic_classes, dataset_aliases, get_classes,
imagenet_det_classe... | # Copyright (c) OpenMMLab. All rights reserved.
from .bbox_overlaps import bbox_overlaps
from .class_names import (cityscapes_classes, coco_classes,
coco_panoptic_classes, dataset_aliases, get_classes,
imagenet_det_classes, imagenet_vid_classes,
... |
# Copyright (c) OpenMMLab. All rights reserved.
"""Get image shape on CrowdHuman dataset.
Here is an example to run this script.
Example:
python tools/misc/get_crowdhuman_id_hw.py ${CONFIG} \
--dataset ${DATASET_TYPE}
"""
import argparse
import json
import logging
import os.path as osp
from multiprocessing im... | # Copyright (c) OpenMMLab. All rights reserved.
"""Get image shape on CrowdHuman dataset.
Here is an example to run this script.
Example:
python tools/misc/get_crowdhuman_id_hw.py ${CONFIG} \
--dataset ${DATASET_TYPE}
"""
import argparse
import json
import logging
import os.path as osp
from multiprocessing im... |
_base_ = './fovea_r50_fpn_4xb4-1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')),
bbox_head=dict(
with_deform=True,
norm_cfg=dict(type='GN', num_groups=32, requires_grad=True)))
train_... | _base_ = './fovea_r50_fpn_4x4_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')),
bbox_head=dict(
with_deform=True,
norm_cfg=dict(type='GN', num_groups=32, requires_grad=True)))
train_p... |
from ._optical_flow import FlyingChairs, FlyingThings3D, HD1K, KittiFlow, Sintel
from ._stereo_matching import (
CarlaStereo,
CREStereo,
ETH3DStereo,
FallingThingsStereo,
InStereo2k,
Kitti2012Stereo,
Kitti2015Stereo,
Middlebury2014Stereo,
SceneFlowStereo,
SintelStereo,
)
from .ca... | from ._optical_flow import FlyingChairs, FlyingThings3D, HD1K, KittiFlow, Sintel
from ._stereo_matching import (
CarlaStereo,
CREStereo,
ETH3DStereo,
FallingThingsStereo,
InStereo2k,
Kitti2012Stereo,
Kitti2015Stereo,
Middlebury2014Stereo,
SceneFlowStereo,
SintelStereo,
)
from .ca... |
"""Argparser module for pinging"""
from jina.parsers.base import set_base_parser
def set_ping_parser(parser=None):
"""Set the parser for `ping`
:param parser: an existing parser to build upon
:return: the parser
"""
if not parser:
parser = set_base_parser()
parser.add_argument(
... | """Argparser module for pinging"""
from jina.parsers.base import set_base_parser
def set_ping_parser(parser=None):
"""Set the parser for `ping`
:param parser: an existing parser to build upon
:return: the parser
"""
if not parser:
parser = set_base_parser()
parser.add_argument(
... |
# Copyright 2025 The HuggingFace 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 applicabl... | # Copyright 2024 The HuggingFace 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 applicabl... |
# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import logging
import os
import os.path as osp
from mmengine.config import Config, DictAction
from mmengine.logging import print_log
from mmengine.registry import RUNNERS
from mmengine.runner import Runner
def parse_args():
parser = argparse.Argumen... | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import logging
import os
import os.path as osp
from mmengine.config import Config, DictAction
from mmengine.logging import print_log
from mmengine.registry import RUNNERS
from mmengine.runner import Runner
def parse_args():
parser = argparse.Argumen... |
from typing import Any, List, Optional
from gigachat import GigaChat # Install GigaChat API library via 'pip install gigachat'
from llama_index.core.base.embeddings.base import (
DEFAULT_EMBED_BATCH_SIZE,
BaseEmbedding,
)
from llama_index.core.base.llms.generic_utils import get_from_param_or_env
from llama_in... | from typing import Any, List, Optional
from gigachat import GigaChat # Install GigaChat API library via 'pip install gigachat'
from llama_index.core.base.embeddings.base import (
DEFAULT_EMBED_BATCH_SIZE,
BaseEmbedding,
)
from llama_index.core.base.llms.generic_utils import get_from_param_or_env
from llama_in... |
from docarray.typing.url.any_url import AnyUrl
from docarray.typing.url.image_url import ImageUrl
from docarray.typing.url.text_url import TextUrl
__all__ = ['ImageUrl', 'AnyUrl', 'TextUrl']
| from docarray.typing.url.any_url import AnyUrl
from docarray.typing.url.image_url import ImageUrl
__all__ = ['ImageUrl', 'AnyUrl']
|
import os
from typing import Any, Optional
from llama_index.llms.openai_like import OpenAILike
class TogetherLLM(OpenAILike):
"""
Together LLM.
Examples:
`pip install llama-index-llms-together`
```python
from llama_index.llms.together import TogetherLLM
# set api key in... | import os
from typing import Any, Optional
from llama_index.llms.openai_like import OpenAILike
class TogetherLLM(OpenAILike):
"""Together LLM.
Examples:
`pip install llama-index-llms-together`
```python
from llama_index.llms.together import TogetherLLM
# set api key in env ... |
# Copyright (c) OpenMMLab. All rights reserved.
from mmdet.registry import TASK_UTILS
from mmdet.structures.bbox import HorizontalBoxes, get_box_tensor
from .base_bbox_coder import BaseBBoxCoder
@TASK_UTILS.register_module()
class PseudoBBoxCoder(BaseBBoxCoder):
"""Pseudo bounding box coder."""
def __init__(... | # Copyright (c) OpenMMLab. All rights reserved.
from mmdet.models.utils.misc import get_box_tensor
from mmdet.registry import TASK_UTILS
from mmdet.structures.bbox import HorizontalBoxes
from .base_bbox_coder import BaseBBoxCoder
@TASK_UTILS.register_module()
class PseudoBBoxCoder(BaseBBoxCoder):
"""Pseudo boundi... |
from __future__ import annotations
import tempfile
from typing import TYPE_CHECKING, Any, Optional
from langchain_core._api.deprecation import deprecated
from langchain_core.callbacks import CallbackManagerForToolRun
from langchain_core.tools import BaseTool
from langchain_community.utilities.vertexai import get_cli... | from __future__ import annotations
import tempfile
from typing import TYPE_CHECKING, Any, Optional
from langchain_core._api.deprecation import deprecated
from langchain_core.callbacks import CallbackManagerForToolRun
from langchain_core.tools import BaseTool
from langchain_community.utilities.vertexai import get_cli... |
import os
import shutil
from pathlib import Path
import pytest
import numpy as np
import PIL.Image as Image
from jina import DocumentArray, Document, Executor
from ...big_transfer import BigTransferEncoder
directory = os.path.dirname(os.path.realpath(__file__))
def test_config():
ex = Executor.load_config(str... | import shutil
import pytest
import os
import numpy as np
import PIL.Image as Image
from jina import DocumentArray, Document
from ...big_transfer import BigTransferEncoder
directory = os.path.dirname(os.path.realpath(__file__))
def test_initialization_and_model_download():
shutil.rmtree('pretrained', ignore_er... |
import argparse
import logging
from typing import Optional
import torch
import torchaudio
from torchaudio.prototype.ctc_decoder import lexicon_decoder, download_pretrained_files
logger = logging.getLogger(__name__)
def run_inference(args):
# get pretrained wav2vec2.0 model
bundle = getattr(torchaudio.pipel... | import argparse
import logging
from typing import Optional
import torch
import torchaudio
from torchaudio.prototype.ctc_decoder import lexicon_decoder
logger = logging.getLogger(__name__)
def _download_files(lexicon_file, kenlm_file):
torch.hub.download_url_to_file(
"https://pytorch.s3.amazonaws.com/to... |
_base_ = './faster-rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='pytorch',... | _base_ = './faster_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='pytorch',... |
"""
Hugging Face file reader.
A parser for HF files.
"""
import json
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import Dict, List
import pandas as pd
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document
class HuggingFaceFSReader(BaseRe... | """Hugging Face file reader.
A parser for HF files.
"""
import json
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import Dict, List
import pandas as pd
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document
class HuggingFaceFSReader(BaseRea... |
# Copyright (c) OpenMMLab. All rights reserved.
import copy
import warnings
import torch
from torch import Tensor
from mmdet.registry import MODELS
from mmdet.structures import SampleList
from mmdet.utils import ConfigType, OptConfigType, OptMultiConfig
from .single_stage import SingleStageDetector
@MODELS.register... | # Copyright (c) OpenMMLab. All rights reserved.
import copy
import warnings
import torch
from torch import Tensor
from mmdet.data_elements import SampleList
from mmdet.registry import MODELS
from mmdet.utils import ConfigType, OptConfigType, OptMultiConfig
from .single_stage import SingleStageDetector
@MODELS.regis... |
from enum import Enum
# --8<-- [start:ProviderName]
class ProviderName(str, Enum):
AIML_API = "aiml_api"
ANTHROPIC = "anthropic"
APOLLO = "apollo"
COMPASS = "compass"
DISCORD = "discord"
D_ID = "d_id"
E2B = "e2b"
EXA = "exa"
FAL = "fal"
GENERIC_WEBHOOK = "generic_webhook"
G... | from enum import Enum
# --8<-- [start:ProviderName]
class ProviderName(str, Enum):
ANTHROPIC = "anthropic"
APOLLO = "apollo"
COMPASS = "compass"
DISCORD = "discord"
D_ID = "d_id"
E2B = "e2b"
EXA = "exa"
FAL = "fal"
GENERIC_WEBHOOK = "generic_webhook"
GITHUB = "github"
GOOGL... |
# NOTE:
# The entire `torchaudio.backend` module is deprecated.
# New things should be added to `torchaudio._backend`.
# Only things related to backward compatibility should be placed here.
def __getattr__(name: str):
if name == "common":
from . import _common
return _common
if name in ["no_... | # NOTE:
# The entire `torchaudio.backend` module is deprecated.
# New things should be added to `torchaudio._backend`.
# Only things related to backward compatibility should be placed here.
from .utils import _init_backend, get_audio_backend, list_audio_backends, set_audio_backend
__all__ = ["_init_backend", "get_au... |
_base_ = 'mask_rcnn_r50_fpn_syncbn-all_rpn-2conv_ssj_scp_32x2_270k_coco.py'
# training schedule for 90k
max_iters = 90000
# learning rate policy
# lr steps at [0.9, 0.95, 0.975] of the maximum iterations
param_scheduler = [
dict(
type='LinearLR', start_factor=0.067, by_epoch=False, begin=0, end=500),
... | _base_ = 'mask_rcnn_r50_fpn_syncbn-all_rpn-2conv_ssj_scp_32x2_270k_coco.py'
# lr steps at [0.9, 0.95, 0.975] of the maximum iterations
lr_config = dict(
warmup_iters=500, warmup_ratio=0.067, step=[81000, 85500, 87750])
# 90k iterations with batch_size 64 is roughly equivalent to 48 epochs
runner = dict(type='IterB... |
__copyright__ = "Copyright (c) 2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import os
from torch import hub
from pytest_mock import MockerFixture
from ...torch_encoder import ImageTorchEncoder
def test_load_from_url(tmpdir: str, mocker: MockerFixture) -> None:
os.environ['TORCH_HOME'... | __copyright__ = "Copyright (c) 2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import os
from torch import hub
from pytest_mock import MockerFixture
try:
from torch_encoder import ImageTorchEncoder
except:
from jinahub.image.encoder.torch_encoder import ImageTorchEncoder
def test_loa... |
from ._transforms import (
Spectrogram,
InverseSpectrogram,
GriffinLim,
AmplitudeToDB,
MelScale,
InverseMelScale,
MelSpectrogram,
MFCC,
LFCC,
MuLawEncoding,
MuLawDecoding,
Resample,
TimeStretch,
Fade,
FrequencyMasking,
TimeMasking,
SlidingWindowCmn,
... | from ._transforms import (
Spectrogram,
InverseSpectrogram,
GriffinLim,
AmplitudeToDB,
MelScale,
InverseMelScale,
MelSpectrogram,
MFCC,
LFCC,
MuLawEncoding,
MuLawDecoding,
Resample,
TimeStretch,
Fade,
FrequencyMasking,
TimeMasking,
SlidingWindowCmn,
... |
from prisma.models import User
from backend.blocks.basic import AgentInputBlock, PrintToConsoleBlock
from backend.blocks.text import FillTextTemplateBlock
from backend.data import graph
from backend.data.graph import create_graph
from backend.data.user import get_or_create_user
from backend.util.test import SpinTestSe... | from prisma.models import User
from backend.blocks.basic import AgentInputBlock, PrintToConsoleBlock
from backend.blocks.text import FillTextTemplateBlock
from backend.data import graph
from backend.data.graph import create_graph
from backend.data.user import get_or_create_user
from backend.util.test import SpinTestSe... |
from docarray.typing.proto_register import _register_proto
from docarray.typing.tensor.audio.abstract_audio_tensor import AbstractAudioTensor
from docarray.typing.tensor.torch_tensor import TorchTensor, metaTorchAndNode
@_register_proto(proto_type_name='audio_torch_tensor')
class AudioTorchTensor(AbstractAudioTensor,... | 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.audio.audio_ndarray import MAX_INT_16
from docarray.typing.tensor.torch_tensor import TorchTensor, metaTorchAndNode
T = T... |
import pytest
from sentence_transformers import SentenceTransformer, SentenceTransformerTrainer
@pytest.mark.parametrize(
("revision", "expected_base_revision"),
[
("f3cb857cba53019a20df283396bcca179cf051a4", "f3cb857cba53019a20df283396bcca179cf051a4"),
("f3cb857", "f3cb857"),
("main"... | import pytest
from sentence_transformers import SentenceTransformer
@pytest.mark.parametrize(
("revision", "expected_base_revision"),
[
("f3cb857cba53019a20df283396bcca179cf051a4", "f3cb857cba53019a20df283396bcca179cf051a4"),
("f3cb857", "f3cb857"),
("main", "valid-revision"),
... |
import os
import pytest
from jina import Document, Flow
from jinahub.indexers.compound.FaissPostgresIndexer import FaissPostgresIndexer
cur_dir = os.path.dirname(os.path.abspath(__file__))
compose_yml = os.path.join(cur_dir, 'docker-compose.yml')
# fixes issue #208 https://github.com/jina-ai/executors/issues/208
@p... | import os
import pytest
from jina import Document, Flow
from jinahub.indexers.searcher.compound.FaissPostgresIndexer import FaissPostgresIndexer
cur_dir = os.path.dirname(os.path.abspath(__file__))
compose_yml = os.path.join(cur_dir, 'docker-compose.yml')
# fixes issue #208 https://github.com/jina-ai/executors/issu... |
from __future__ import annotations
try:
from typing import Self
except ImportError:
from typing_extensions import Self
from torch import Tensor, nn
from sentence_transformers.models.Module import Module
class LayerNorm(Module):
config_keys: list[str] = ["dimension"]
def __init__(self, dimension: i... | from __future__ import annotations
import json
import os
import torch
from safetensors.torch import load_model as load_safetensors_model
from safetensors.torch import save_model as save_safetensors_model
from torch import Tensor, nn
class LayerNorm(nn.Module):
def __init__(self, dimension: int):
super()... |
import prisma
AGENT_NODE_INCLUDE: prisma.types.AgentNodeInclude = {
"Input": True,
"Output": True,
"Webhook": True,
"AgentBlock": True,
}
AGENT_GRAPH_INCLUDE: prisma.types.AgentGraphInclude = {
"AgentNodes": {"include": AGENT_NODE_INCLUDE} # type: ignore
}
EXECUTION_RESULT_INCLUDE: prisma.types.... | import prisma
AGENT_NODE_INCLUDE: prisma.types.AgentNodeInclude = {
"Input": True,
"Output": True,
"Webhook": True,
"AgentBlock": True,
}
AGENT_GRAPH_INCLUDE: prisma.types.AgentGraphInclude = {
"AgentNodes": {"include": AGENT_NODE_INCLUDE} # type: ignore
}
EXECUTION_RESULT_INCLUDE: prisma.types.... |
# Copyright (c) OpenMMLab. All rights reserved.
from .approx_max_iou_assigner import ApproxMaxIoUAssigner
from .assign_result import AssignResult
from .atss_assigner import ATSSAssigner
from .base_assigner import BaseAssigner
from .center_region_assigner import CenterRegionAssigner
from .dynamic_soft_label_assigner imp... | # Copyright (c) OpenMMLab. All rights reserved.
from .approx_max_iou_assigner import ApproxMaxIoUAssigner
from .assign_result import AssignResult
from .atss_assigner import ATSSAssigner
from .base_assigner import BaseAssigner
from .center_region_assigner import CenterRegionAssigner
from .dynamic_soft_label_assigner imp... |
# Copyright (c) OpenMMLab. All rights reserved.
from mmdet.core.utils import ConfigType, OptConfigType, OptMultiConfig
from mmdet.registry import MODELS
from .panoptic_two_stage_segmentor import TwoStagePanopticSegmentor
@MODELS.register_module()
class PanopticFPN(TwoStagePanopticSegmentor):
r"""Implementation of... | # Copyright (c) OpenMMLab. All rights reserved.
from mmdet.registry import MODELS
from .panoptic_two_stage_segmentor import TwoStagePanopticSegmentor
@MODELS.register_module()
class PanopticFPN(TwoStagePanopticSegmentor):
r"""Implementation of `Panoptic feature pyramid
networks <https://arxiv.org/pdf/1901.024... |
import logging
import random
from datasets import load_dataset
from sentence_transformers.sparse_encoder import (
SparseEncoder,
SparseInformationRetrievalEvaluator,
)
logging.basicConfig(format="%(message)s", level=logging.INFO)
# Load a model
model = SparseEncoder("naver/splade-cocondenser-ensembledistil"... | import logging
import random
from datasets import load_dataset
from sentence_transformers.sparse_encoder import (
MLMTransformer,
SparseEncoder,
SparseInformationRetrievalEvaluator,
SpladePooling,
)
logging.basicConfig(format="%(asctime)s - %(message)s", datefmt="%Y-%m-%d %H:%M:%S", level=logging.INF... |
from codecs import unicode_escape_decode
from typing import Dict
from docarray import Document
from docarray.array.storage.base.getsetdel import BaseGetSetDelMixin
from docarray.array.storage.base.helper import Offset2ID
from typing import Sequence, Iterable
class GetSetDelMixin(BaseGetSetDelMixin):
"""Provide c... | from codecs import unicode_escape_decode
from typing import Dict
from docarray import Document
from docarray.array.storage.base.getsetdel import BaseGetSetDelMixin
from docarray.array.storage.base.helper import Offset2ID
from typing import Sequence, Iterable
class GetSetDelMixin(BaseGetSetDelMixin):
"""Provide c... |
# CoSENTLoss must be imported before AnglELoss
from .CoSENTLoss import CoSENTLoss # isort: skip
from .AdaptiveLayerLoss import AdaptiveLayerLoss
from .AnglELoss import AnglELoss
from .BatchAllTripletLoss import BatchAllTripletLoss
from .BatchHardSoftMarginTripletLoss import BatchHardSoftMarginTripletLoss
from .BatchH... | from .AdaptiveLayerLoss import AdaptiveLayerLoss
from .CosineSimilarityLoss import CosineSimilarityLoss
from .SoftmaxLoss import SoftmaxLoss
from .MultipleNegativesRankingLoss import MultipleNegativesRankingLoss
from .MultipleNegativesSymmetricRankingLoss import MultipleNegativesSymmetricRankingLoss
from .TripletLoss i... |
# coding: utf-8
from pathlib import Path
import numpy as np
import pandas as pd
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import GridSearchCV
import lightgbm as lgb
print('Loading data...')
# load or create your dataset
regression_example_dir = Path(__file__).absolute().parents[1] /... | # coding: utf-8
from pathlib import Path
import numpy as np
import pandas as pd
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import GridSearchCV
import lightgbm as lgb
print('Loading data...')
# load or create your dataset
regression_example_dir = Path(__file__).absolute().parents[1] /... |
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,
... |
from backend.blocks.linear._api import LinearAPIException, LinearClient
from backend.blocks.linear._auth import (
LINEAR_OAUTH_IS_CONFIGURED,
TEST_CREDENTIALS_INPUT_OAUTH,
TEST_CREDENTIALS_OAUTH,
LinearCredentials,
LinearCredentialsField,
LinearCredentialsInput,
LinearScope,
)
from backend.b... | from backend.blocks.linear._api import LinearAPIException, LinearClient
from backend.blocks.linear._auth import (
TEST_CREDENTIALS_INPUT_OAUTH,
TEST_CREDENTIALS_OAUTH,
LinearCredentials,
LinearCredentialsField,
LinearCredentialsInput,
LinearScope,
)
from backend.blocks.linear.models import Proje... |
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