input stringlengths 33 5k | output stringlengths 32 5k |
|---|---|
import os
import tempfile
from abc import ABC
from pathlib import Path
from typing import List, Union
from urllib.parse import urlparse
import requests
from langchain_community.docstore.document import Document
from langchain_community.document_loaders.base import BaseLoader
from langchain_community.document_loaders.... | import os
import tempfile
from abc import ABC
from pathlib import Path
from typing import List, Union
from urllib.parse import urlparse
import requests
from langchain_community.docstore.document import Document
from langchain_community.document_loaders.base import BaseLoader
from langchain_community.document_loaders.... |
from datasets import load_dataset
from sentence_transformers import SentenceTransformer
from sentence_transformers.quantization import quantize_embeddings, semantic_search_usearch
# 1. Load the quora corpus with questions
dataset = load_dataset("quora", split="train").map(
lambda batch: {"text": [text for sample i... | from sentence_transformers import SentenceTransformer
from sentence_transformers.quantization import quantize_embeddings, semantic_search_usearch
from datasets import load_dataset
# 1. Load the quora corpus with questions
dataset = load_dataset("quora", split="train").map(
lambda batch: {"text": [text for sample i... |
from typing import Any, Dict, Type, TypeVar
from pydantic.tools import parse_obj_as
from docarray.document.abstract_document import AbstractDocument
from docarray.document.base_node import BaseNode
from docarray.proto import DocumentProto, NodeProto
from docarray.typing import ID, AnyUrl, Embedding, ImageUrl, Tensor,... | from typing import Any, Dict
from pydantic.tools import parse_obj_as
from docarray.document.abstract_document import AbstractDocument
from docarray.document.base_node import BaseNode
from docarray.proto import DocumentProto, NodeProto
from docarray.typing import ID, AnyUrl, Embedding, ImageUrl, Tensor, TorchTensor
... |
# coding: utf-8
"""Find the path to LightGBM dynamic library files."""
import ctypes
from os import environ
from pathlib import Path
from platform import system
from typing import List
__all__: List[str] = []
def _find_lib_path() -> List[str]:
"""Find the path to LightGBM library files.
Returns
-------... | # coding: utf-8
"""Find the path to LightGBM dynamic library files."""
from pathlib import Path
from platform import system
from typing import List
__all__: List[str] = []
def find_lib_path() -> List[str]:
"""Find the path to LightGBM library files.
Returns
-------
lib_path: list of str
List... |
import argparse
import urllib
from http import HTTPStatus
from jina.logging.predefined import default_logger
from jina.helper import parse_host_scheme
class NetworkChecker:
"""Check if a BaseDeployment is running or not."""
def __init__(self, args: 'argparse.Namespace'):
"""
Create a new :cl... | import argparse
from jina.logging.predefined import default_logger
class NetworkChecker:
"""Check if a BaseDeployment is running or not."""
def __init__(self, args: 'argparse.Namespace'):
"""
Create a new :class:`NetworkChecker`.
:param args: args provided by the CLI.
"""
... |
import pytest
from docarray import BaseDocument
from docarray.utils.misc import is_tf_available
tf_available = is_tf_available()
if tf_available:
import tensorflow as tf
import tensorflow._api.v2.experimental.numpy as tnp # type: ignore
from docarray.typing import TensorFlowEmbedding, TensorFlowTensor
... | import pytest
from docarray import BaseDocument
try:
import tensorflow as tf
import tensorflow._api.v2.experimental.numpy as tnp # type: ignore
from docarray.typing import TensorFlowTensor
except (ImportError, TypeError):
pass
@pytest.mark.tensorflow
def test_set_tensorflow_tensor():
class MyD... |
import pytest
from langchain._api import suppress_langchain_deprecation_warning as sup2
from langchain_core._api import suppress_langchain_deprecation_warning as sup1
from langchain_cli.namespaces.migrate.generate.generic import (
generate_simplified_migrations,
)
@pytest.mark.xfail(reason="Unknown reason")
def ... | import pytest
from langchain._api import suppress_langchain_deprecation_warning as sup2
from langchain_core._api import suppress_langchain_deprecation_warning as sup1
from langchain_cli.namespaces.migrate.generate.generic import (
generate_simplified_migrations,
)
@pytest.mark.xfail(reason="Unknown reason")
def ... |
"""Hive data reader."""
try:
from pyhive import hive
except ImportError:
raise ImportError("`hive` package not found, please run `pip install pyhive`")
try:
import sqlglot
except ImportError:
raise ImportError("`sqlglot` package not found, please run `pip install sqlglot`")
from typing import List, Op... | """Hive data reader."""
from typing import List, Optional
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document
class HiveReader(BaseReader):
"""
Read documents from a Hive.
These documents can then be used in a downstream Llama Index data structure.
Arg... |
from .autograd_utils import use_deterministic_algorithms
from .backend_utils import set_audio_backend
from .case_utils import (
disabledInCI,
HttpServerMixin,
is_ffmpeg_available,
PytorchTestCase,
skipIfCudaSmallMemory,
skipIfNoAudioDevice,
skipIfNoCtcDecoder,
skipIfNoCuCtcDecoder,
s... | from .autograd_utils import use_deterministic_algorithms
from .backend_utils import set_audio_backend
from .case_utils import (
HttpServerMixin,
is_ffmpeg_available,
PytorchTestCase,
skipIfCudaSmallMemory,
skipIfNoAudioDevice,
skipIfNoCtcDecoder,
skipIfNoCuCtcDecoder,
skipIfNoCuda,
s... |
from keras.src import backend
from keras.src import ops
from keras.src import testing
from keras.src.backend.common.masking import get_keras_mask
from keras.src.backend.common.masking import set_keras_mask
class MaskingTest(testing.TestCase):
def test_mask_on_eager_tensor(self):
x = ops.zeros((2, 3))
... | from keras.src import backend
from keras.src import ops
from keras.src import testing
from keras.src.backend.common.masking import get_keras_mask
from keras.src.backend.common.masking import set_keras_mask
class MaskingTest(testing.TestCase):
def test_mask_on_eager_tensor(self):
x = ops.zeros((2, 3))
... |
import os
# When using jax.experimental.enable_x64 in unit test, we want to keep the
# default dtype with 32 bits, aligning it with Keras's default.
os.environ["JAX_DEFAULT_DTYPE_BITS"] = "32"
try:
# When using torch and tensorflow, torch needs to be imported first,
# otherwise it will segfault upon import. T... | import os
# When using jax.experimental.enable_x64 in unit test, we want to keep the
# default dtype with 32 bits, aligning it with Keras's default.
os.environ["JAX_DEFAULT_DTYPE_BITS"] = "32"
try:
# When using torch and tensorflow, torch needs to be imported first,
# otherwise it will segfault upon import. T... |
_base_ = '../_base_/default_runtime.py'
# model settings
model = dict(
type='YOLOV3',
backbone=dict(
type='Darknet',
depth=53,
out_indices=(3, 4, 5),
init_cfg=dict(type='Pretrained', checkpoint='open-mmlab://darknet53')),
neck=dict(
type='YOLOV3Neck',
num_scal... | _base_ = '../_base_/default_runtime.py'
# model settings
model = dict(
type='YOLOV3',
backbone=dict(
type='Darknet',
depth=53,
out_indices=(3, 4, 5),
init_cfg=dict(type='Pretrained', checkpoint='open-mmlab://darknet53')),
neck=dict(
type='YOLOV3Neck',
num_scal... |
"""langchain-core version information and utilities."""
VERSION = "0.3.65"
| """langchain-core version information and utilities."""
VERSION = "0.3.64"
|
import json
import os
from typing import List
import torch
from torch import nn
class LSTM(nn.Module):
"""Bidirectional LSTM running over word embeddings."""
def __init__(
self,
word_embedding_dimension: int,
hidden_dim: int,
num_layers: int = 1,
dropout: float = 0,
... | import torch
from torch import nn
from typing import List
import os
import json
class LSTM(nn.Module):
"""
Bidirectional LSTM running over word embeddings.
"""
def __init__(
self,
word_embedding_dimension: int,
hidden_dim: int,
num_layers: int = 1,
dropout: flo... |
"""Language models.
**Language Model** is a type of model that can generate text or complete
text prompts.
LangChain has two main classes to work with language models: **Chat Models**
and "old-fashioned" **LLMs**.
**Chat Models**
Language models that use a sequence of messages as inputs and return chat messages
as ... | """Language models.
**Language Model** is a type of model that can generate text or complete
text prompts.
LangChain has two main classes to work with language models: **Chat Models**
and "old-fashioned" **LLMs**.
**Chat Models**
Language models that use a sequence of messages as inputs and return chat messages
as ... |
__copyright__ = "Copyright (c) 2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import subprocess
from typing import List
import numpy as np
import pytest
from jina import Document, DocumentArray, Flow
from ...torch_encoder import ImageTorchEncoder
@pytest.mark.parametrize(
'arr_in',
... | __copyright__ = "Copyright (c) 2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
from typing import List
import numpy as np
import pytest
from jina import Flow, Document, DocumentArray
from ...torch_encoder import ImageTorchEncoder
@pytest.mark.parametrize('arr_in', [
(np.ones((224, 224, ... |
from __future__ import annotations
from .model_card import SparseEncoderModelCardData
from .SparseEncoder import SparseEncoder
from .trainer import SparseEncoderTrainer
from .training_args import SparseEncoderTrainingArguments
__all__ = [
"SparseEncoder",
"SparseEncoderTrainer",
"SparseEncoderTrainingArgu... | from __future__ import annotations
from sentence_transformers.sparse_encoder.callbacks.splade_callbacks import (
SchedulerType,
SpladeLambdaSchedulerCallback,
)
from sentence_transformers.sparse_encoder.data_collator import SparseEncoderDataCollator
from sentence_transformers.sparse_encoder.evaluation import (... |
# Copyright (c) OpenMMLab. All rights reserved.
import argparse
from mmengine.config import Config, DictAction
from mmengine.fileio import load
from mmdet.datasets import build_dataset
from mmdet.utils import replace_cfg_vals, update_data_root
def parse_args():
parser = argparse.ArgumentParser(description='Eval... | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import mmcv
from mmcv import Config, DictAction
from mmdet.datasets import build_dataset
from mmdet.utils import replace_cfg_vals, update_data_root
def parse_args():
parser = argparse.ArgumentParser(description='Evaluate metric of the '
... |
from enum import Enum
from typing import Any, Optional
from langchain_core.callbacks import (
AsyncCallbackManagerForRetrieverRun,
CallbackManagerForRetrieverRun,
)
from langchain_core.documents import Document
from langchain_core.retrievers import BaseRetriever
from langchain_core.stores import BaseStore, Byt... | from enum import Enum
from typing import Any, Dict, List, Optional
from langchain_core.callbacks import (
AsyncCallbackManagerForRetrieverRun,
CallbackManagerForRetrieverRun,
)
from langchain_core.documents import Document
from langchain_core.retrievers import BaseRetriever
from langchain_core.stores import Ba... |
from typing import Optional
from docarray.document import BaseDocument
from docarray.typing import AnyEmbedding, AnyTensor, PointCloud3DUrl
class PointCloud3D(BaseDocument):
"""
Document for handling point clouds for 3D data representation.
Point cloud is a representation of a 3D mesh. It is made by rep... | from typing import Optional
from docarray.document import BaseDocument
from docarray.typing import AnyTensor, Embedding, PointCloud3DUrl
class PointCloud3D(BaseDocument):
"""
Document for handling point clouds for 3D data representation.
Point cloud is a representation of a 3D mesh. It is made by repeat... |
from __future__ import annotations
__version__ = "3.5.0.dev0"
__MODEL_HUB_ORGANIZATION__ = "sentence-transformers"
import importlib
import os
from sentence_transformers.backend import (
export_dynamic_quantized_onnx_model,
export_optimized_onnx_model,
export_static_quantized_openvino_model,
)
from senten... | from __future__ import annotations
__version__ = "3.5.0.dev0"
__MODEL_HUB_ORGANIZATION__ = "sentence-transformers"
import importlib
import os
from sentence_transformers.backend import (
export_dynamic_quantized_onnx_model,
export_optimized_onnx_model,
export_static_quantized_openvino_model,
)
from senten... |
import multiprocessing
import pytest
from jina import Client
from jina.parsers import set_gateway_parser
from jina.serve.runtimes.asyncio import AsyncNewLoopRuntime
from jina.serve.runtimes.servers import BaseServer
from jina.serve.runtimes.worker.request_handling import WorkerRequestHandler
from jina.serve.runtimes.... | import multiprocessing
import pytest
from jina import Client
from jina.parsers import set_gateway_parser
from jina.serve.runtimes.asyncio import AsyncNewLoopRuntime
from jina.serve.runtimes.servers import BaseServer
from jina.serve.runtimes.worker.request_handling import WorkerRequestHandler
from jina.serve.runtimes.... |
from typing import BinaryIO, Dict, Optional, Tuple
import torch
import torchaudio
from torchaudio.backend.common import AudioMetaData
# Note: need to comply TorchScript syntax -- need annotation and no f-string nor global
def _info_audio(
s: torch.classes.torchaudio.ffmpeg_StreamReader,
):
i = s.find_best_au... | from typing import BinaryIO, Dict, Optional, Tuple
import torch
import torchaudio
from torchaudio.backend.common import AudioMetaData
# Note: need to comply TorchScript syntax -- need annotation and no f-string nor global
def _info_audio(
s: torch.classes.torchaudio.ffmpeg_StreamReader,
):
i = s.find_best_au... |
# Copyright (c) OpenMMLab. All rights reserved.
from .auto_augment import (AutoAugment, BrightnessTransform, ColorTransform,
ContrastTransform, EqualizeTransform, Rotate, Shear,
Translate)
from .compose import Compose
from .formatting import (Collect, DefaultFormatB... | # Copyright (c) OpenMMLab. All rights reserved.
from .auto_augment import (AutoAugment, BrightnessTransform, ColorTransform,
ContrastTransform, EqualizeTransform, Rotate, Shear,
Translate)
from .compose import Compose
from .formating import (Collect, DefaultFormatBu... |
"""
LexRank implementation
Source: https://github.com/crabcamp/lexrank/tree/dev
"""
import logging
import numpy as np
from scipy.sparse.csgraph import connected_components
from scipy.special import softmax
logger = logging.getLogger(__name__)
def degree_centrality_scores(
similarity_matrix,
threshold=None,... | """
LexRank implementation
Source: https://github.com/crabcamp/lexrank/tree/dev
"""
import numpy as np
from scipy.sparse.csgraph import connected_components
from scipy.special import softmax
import logging
logger = logging.getLogger(__name__)
def degree_centrality_scores(
similarity_matrix,
threshold=None,
... |
from torch import nn, Tensor
__all__ = [
"Wav2Letter",
]
class Wav2Letter(nn.Module):
r"""Wav2Letter model architecture from *Wav2Letter: an End-to-End ConvNet-based Speech
Recognition System* :cite:`collobert2016wav2letter`.
:math:`\text{padding} = \frac{\text{ceil}(\text{kernel} - \text{stride})}... | from torch import nn, Tensor
__all__ = [
"Wav2Letter",
]
class Wav2Letter(nn.Module):
r"""Wav2Letter model architecture from *Wav2Letter: an End-to-End ConvNet-based Speech
Recognition System* [:footcite:`collobert2016wav2letter`].
:math:`\text{padding} = \frac{\text{ceil}(\text{kernel} - \text{str... |
from typing import Optional
import torch
from ..modeling_flash_attention_utils import _flash_attention_forward, flash_attn_supports_top_left_mask
from ..utils import logging
logger = logging.get_logger(__name__)
_use_top_left_mask = flash_attn_supports_top_left_mask()
def flash_attention_forward(
module: tor... | from typing import Optional
import torch
from ..modeling_flash_attention_utils import _flash_attention_forward, flash_attn_supports_top_left_mask
from ..utils import logging
logger = logging.get_logger(__name__)
_use_top_left_mask = flash_attn_supports_top_left_mask()
def flash_attention_forward(
module: tor... |
import logging
from typing import Literal
from github import Github
from github.PullRequestReview import PullRequestReview
from pydantic import BaseModel, SecretStr
from pydantic_settings import BaseSettings
class LabelSettings(BaseModel):
await_label: str | None = None
number: int
default_config = {"appro... | import logging
from typing import Literal
from github import Github
from github.PullRequestReview import PullRequestReview
from pydantic import BaseModel, SecretStr
from pydantic_settings import BaseSettings
class LabelSettings(BaseModel):
await_label: str | None = None
number: int
default_config = {"appro... |
from docarray.typing.tensor.embedding.embedding import AnyEmbedding
from docarray.typing.tensor.embedding.ndarray import NdArrayEmbedding
__all__ = ['NdArrayEmbedding', 'AnyEmbedding']
try:
import torch # noqa: F401
except ImportError:
pass
else:
from docarray.typing.tensor.embedding.torch import TorchEm... | from docarray.typing.tensor.embedding.embedding import Embedding
from docarray.typing.tensor.embedding.ndarray import NdArrayEmbedding
__all__ = ['NdArrayEmbedding', 'Embedding']
try:
import torch # noqa: F401
except ImportError:
pass
else:
from docarray.typing.tensor.embedding.torch import TorchEmbeddin... |
from __future__ import annotations
import logging
from typing import TYPE_CHECKING, Any, Literal
from sentence_transformers.evaluation import TripletEvaluator
if TYPE_CHECKING:
import numpy as np
from torch import Tensor
from sentence_transformers.similarity_functions import SimilarityFunction
from ... | from __future__ import annotations
import logging
from typing import TYPE_CHECKING, Any
from sentence_transformers.evaluation import TripletEvaluator
if TYPE_CHECKING:
import numpy as np
from torch import Tensor
from sentence_transformers.sparse_encoder.SparseEncoder import SparseEncoder
logger = logg... |
"""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... | """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.
import mmcv
import torch.nn as nn
from mmcv.cnn import ConvModule
from mmcv.runner import BaseModule
class SELayer(BaseModule):
"""Squeeze-and-Excitation Module.
Args:
channels (int): The input (and output) channels of the SE layer.
ratio (int):... | import mmcv
import torch.nn as nn
from mmcv.cnn import ConvModule
from mmcv.runner import BaseModule
class SELayer(BaseModule):
"""Squeeze-and-Excitation Module.
Args:
channels (int): The input (and output) channels of the SE layer.
ratio (int): Squeeze ratio in SELayer, the intermediate chan... |
_base_ = './solov2_r50_fpn_1x_coco.py'
# model settings
model = dict(
mask_head=dict(
stacked_convs=2,
feat_channels=256,
scale_ranges=((1, 56), (28, 112), (56, 224), (112, 448), (224, 896)),
mask_feature_head=dict(out_channels=128)))
# dataset settings
train_pipeline = [
dict(... | _base_ = './solov2_r50_fpn_1x_coco.py'
# model settings
model = dict(
mask_head=dict(
stacked_convs=2,
feat_channels=256,
scale_ranges=((1, 56), (28, 112), (56, 224), (112, 448), (224, 896)),
mask_feature_head=dict(out_channels=128)))
# dataset settings
train_pipeline = [
dict(... |
from pathlib import Path
from typing import List
import pytest
from executor.audioclip_text import AudioCLIPTextEncoder
from jina import Document, DocumentArray, Executor
_EMBEDDING_DIM = 1024
@pytest.fixture(scope='module')
def basic_encoder() -> AudioCLIPTextEncoder:
return AudioCLIPTextEncoder(
model... | from pathlib import Path
from typing import List
import pytest
from jina import Document, DocumentArray, Executor
from ...audioclip_text import AudioCLIPTextEncoder
_EMBEDDING_DIM = 1024
@pytest.fixture(scope='module')
def basic_encoder() -> AudioCLIPTextEncoder:
return AudioCLIPTextEncoder()
def test_config... |
from .autoencoder_asym_kl import AsymmetricAutoencoderKL
from .autoencoder_dc import AutoencoderDC
from .autoencoder_kl import AutoencoderKL
from .autoencoder_kl_allegro import AutoencoderKLAllegro
from .autoencoder_kl_cogvideox import AutoencoderKLCogVideoX
from .autoencoder_kl_hunyuan_video import AutoencoderKLHunyua... | from .autoencoder_asym_kl import AsymmetricAutoencoderKL
from .autoencoder_dc import AutoencoderDC
from .autoencoder_kl import AutoencoderKL
from .autoencoder_kl_allegro import AutoencoderKLAllegro
from .autoencoder_kl_cogvideox import AutoencoderKLCogVideoX
from .autoencoder_kl_ltx import AutoencoderKLLTXVideo
from .a... |
"""
This is a simple application for sentence embeddings: semantic search
We have a corpus with various sentences. Then, for a given query sentence,
we want to find the most similar sentence in this corpus.
This script outputs for various queries the top 5 most similar sentences in the corpus.
"""
import torch
from... | """
This is a simple application for sentence embeddings: semantic search
We have a corpus with various sentences. Then, for a given query sentence,
we want to find the most similar sentence in this corpus.
This script outputs for various queries the top 5 most similar sentences in the corpus.
"""
import torch
from... |
_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... | _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)))
img_nor... |
# CoSENTLoss must be imported before AnglELoss
from __future__ import annotations
from .CoSENTLoss import CoSENTLoss # isort: skip
from .AdaptiveLayerLoss import AdaptiveLayerLoss
from .AnglELoss import AnglELoss
from .BatchAllTripletLoss import BatchAllTripletLoss
from .BatchHardSoftMarginTripletLoss import BatchHa... | # 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... |
# coding=utf-8
# 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 requir... | # coding=utf-8
# 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 requir... |
from typing import Optional, TypeVar
from docarray.base_document import BaseDocument
from docarray.documents import Audio
from docarray.typing import AnyEmbedding, AnyTensor
from docarray.typing.tensor.video.video_tensor import VideoTensor
from docarray.typing.url.video_url import VideoUrl
T = TypeVar('T', bound='Vid... | from typing import Optional, TypeVar
from docarray.base_document import BaseDocument
from docarray.documents import Audio
from docarray.typing import AnyEmbedding, AnyTensor
from docarray.typing.tensor.video.video_tensor import VideoTensor
from docarray.typing.url.video_url import VideoUrl
T = TypeVar('T', bound='Vid... |
_base_ = [
'../_base_/models/mask-rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
train_pipeline = [
dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}),
dict(
type='LoadAnnotations',
wi... | _base_ = [
'../_base_/models/mask-rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
train_pipeline = [
dict(
type='LoadImageFromFile',
file_client_args={{_base_.file_client_args}}),
dict(
type='Loa... |
import os
from typing import Type
import orjson
from pydantic import BaseModel, Field
from pydantic import parse_obj_as
from docarray.document.abstract_document import AbstractDocument
from docarray.document.base_node import BaseNode
from docarray.document.io.json import orjson_dumps
from docarray.document.mixins imp... | import os
from typing import Type
import orjson
from pydantic import BaseModel, Field
from docarray.document.abstract_document import AbstractDocument
from docarray.document.base_node import BaseNode
from docarray.document.io.json import orjson_dumps
from docarray.document.mixins import ProtoMixin
from docarray.typin... |
from typing import Any, List, Optional
from llama_index.core.bridge.pydantic import SerializeAsAny, ConfigDict
from llama_index.core.base.llms.types import (
ChatMessage,
ChatResponse,
CompletionResponse,
)
from llama_index.core.instrumentation.events.base import BaseEvent
from llama_index.core.prompts impo... | from typing import Any, List, Optional
from llama_index.core.bridge.pydantic import SerializeAsAny, ConfigDict
from llama_index.core.base.llms.types import (
ChatMessage,
ChatResponse,
CompletionResponse,
)
from llama_index.core.instrumentation.events.base import BaseEvent
from llama_index.core.prompts impo... |
from typing import Any, Type, TypeVar, Union, cast
import numpy as np
from docarray.typing.tensor.tensor import AnyTensor
from docarray.typing.tensor.video.video_ndarray import VideoNdArray
from docarray.typing.tensor.video.video_tensor_mixin import VideoTensorMixin
from docarray.utils._internal.misc import (
is_... | from typing import TYPE_CHECKING, Any, Type, TypeVar, Union, cast
import numpy as np
from docarray.typing.tensor.tensor import AnyTensor
from docarray.typing.tensor.video.video_ndarray import VideoNdArray
from docarray.typing.tensor.video.video_tensor_mixin import VideoTensorMixin
from docarray.utils._internal.misc i... |
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
pytestmark = pytest.mark.integration
@pytest.mark.parametrize("path", ["paws", "csv"])
def test_inspect_dataset(p... | import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
pytestmark = pytest.mark.integration
@pytest.mark.parametrize("path", ["paws", "csv"])
def test_inspect_dataset(p... |
import os
import numpy as np
import pytest
from jina import Document, DocumentArray
from ...custom_image_torch_encoder import CustomImageTorchEncoder
cur_dir = os.path.dirname(os.path.abspath(__file__))
@pytest.fixture
def encoder(tmpdir):
model_state_dict_path = os.path.join(cur_dir, '../model/model_state_dic... | import pytest
import os
import numpy as np
from jina import Document, DocumentArray
try:
from custom_image_torch_encoder import CustomImageTorchEncoder
except:
from jinahub.encoder.custom_image_torch_encoder import CustomImageTorchEncoder
cur_dir = os.path.dirname(os.path.abspath(__file__))
@pytest.fixtur... |
_base_ = './htc_r50_fpn_1x_coco.py'
# learning policy
max_epochs = 20
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=max_epochs,
by_epoch=True,
milestones=[16, 19],
... | _base_ = './htc_r50_fpn_1x_coco.py'
# learning policy
lr_config = dict(step=[16, 19])
runner = dict(type='EpochBasedRunner', max_epochs=20)
|
# Copyright (c) OpenMMLab. All rights reserved.
from abc import ABCMeta, abstractmethod
from typing import Dict, Union
from torch.utils.data import DataLoader
class BaseLoop(metaclass=ABCMeta):
"""Base loop class.
All subclasses inherited from ``BaseLoop`` should overwrite the
:meth:`run` method.
A... | # Copyright (c) OpenMMLab. All rights reserved.
from abc import ABCMeta, abstractmethod
from typing import Dict, Union
from torch.utils.data import DataLoader
class BaseLoop(metaclass=ABCMeta):
"""Base loop class.
All subclasses inherited from ``BaseLoop`` should overwrite the
:meth:`run` method.
A... |
_base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
teacher_ckpt = 'http://download.openmmlab.com/mmdetection/v2.0/paa/paa_r101_fpn_1x_coco/paa_r101_fpn_1x_coco_20200821-0a1825a4.pth' # noqa
model = dict(
type='LAD',
# student
ba... | _base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
teacher_ckpt = 'http://download.openmmlab.com/mmdetection/v2.0/paa/paa_r101_fpn_1x_coco/paa_r101_fpn_1x_coco_20200821-0a1825a4.pth' # noqa
model = dict(
type='LAD',
# student
ba... |
from backend.executor.utils import merge_execution_input, parse_execution_output
def test_parse_execution_output():
# Test case for list extraction
output = ("result", [10, 20, 30])
assert parse_execution_output(output, "result_$_1") == 20
assert parse_execution_output(output, "result_$_3") is None
... | from backend.data.execution import merge_execution_input, parse_execution_output
def test_parse_execution_output():
# Test case for list extraction
output = ("result", [10, 20, 30])
assert parse_execution_output(output, "result_$_1") == 20
assert parse_execution_output(output, "result_$_3") is None
... |
from __future__ import annotations
import csv
import logging
import os
import numpy as np
from sklearn.metrics import average_precision_score
from sentence_transformers import InputExample
from sentence_transformers.evaluation import BinaryClassificationEvaluator
logger = logging.getLogger(__name__)
class CEBinar... | import csv
import logging
import os
from typing import List
import numpy as np
from sklearn.metrics import average_precision_score
from sentence_transformers import InputExample
from sentence_transformers.evaluation import BinaryClassificationEvaluator
logger = logging.getLogger(__name__)
class CEBinaryClassificat... |
from __future__ import annotations
import random
import pytest
import torch
from torch.utils.data import ConcatDataset
from sentence_transformers.sampler import NoDuplicatesBatchSampler, ProportionalBatchSampler
from sentence_transformers.util import is_datasets_available
if is_datasets_available():
from datase... | from __future__ import annotations
import random
import pytest
import torch
from datasets import Dataset
from torch.utils.data import ConcatDataset
from sentence_transformers.sampler import NoDuplicatesBatchSampler, ProportionalBatchSampler
@pytest.fixture
def dummy_dataset() -> Dataset:
"""
Dummy dataset ... |
import uuid
from typing import List
from llama_index.core.readers.base import BasePydanticReader
from llama_index.core.schema import Document
class TrafilaturaWebReader(BasePydanticReader):
"""
Trafilatura web page reader.
Reads pages from the web.
Requires the `trafilatura` package.
"""
i... | from typing import List
from llama_index.core.readers.base import BasePydanticReader
from llama_index.core.schema import Document
class TrafilaturaWebReader(BasePydanticReader):
"""
Trafilatura web page reader.
Reads pages from the web.
Requires the `trafilatura` package.
"""
is_remote: bo... |
import os
from pathlib import Path
import numpy as np
import pytest
import torch
from mmdet.apis import inference_detector, init_detector
from mmdet.structures import DetDataSample
from mmdet.utils import register_all_modules
# TODO: Waiting to fix multiple call error bug
register_all_modules()
@pytest.mark.parame... | import os
from pathlib import Path
import numpy as np
import pytest
import torch
from mmdet.apis import inference_detector, init_detector
from mmdet.structures import DetDataSample
from mmdet.utils import register_all_modules
# TODO: Waiting to fix multiple call error bug
register_all_modules()
@pytest.mark.parame... |
import itertools
from typing import (
TYPE_CHECKING,
Union,
Sequence,
overload,
Any,
List,
)
import numpy as np
from docarray import Document
from docarray.helper import typename
if TYPE_CHECKING:
from docarray.typing import (
DocumentArrayIndexType,
DocumentArraySingleton... | import itertools
from typing import (
TYPE_CHECKING,
Union,
Sequence,
overload,
Any,
List,
)
import numpy as np
from ... import Document
from ...helper import typename
if TYPE_CHECKING:
from ...typing import (
DocumentArrayIndexType,
DocumentArraySingletonIndexType,
... |
from typing import Optional
import agentql
import httpx
from llama_index.tools.agentql.const import EXTRACT_DATA_ENDPOINT, REQUEST_ORIGIN
from llama_index.tools.agentql.messages import (
QUERY_PROMPT_REQUIRED_ERROR_MESSAGE,
QUERY_PROMPT_EXCLUSIVE_ERROR_MESSAGE,
UNAUTHORIZED_ERROR_MESSAGE,
)
try:
from ... | from typing import Optional
import agentql
import httpx
from llama_index.tools.agentql.const import EXTRACT_DATA_ENDPOINT, REQUEST_ORIGIN
from llama_index.tools.agentql.messages import (
QUERY_PROMPT_REQUIRED_ERROR_MESSAGE,
QUERY_PROMPT_EXCLUSIVE_ERROR_MESSAGE,
UNAUTHORIZED_ERROR_MESSAGE,
)
try:
from ... |
# Copyright (c) OpenMMLab. All rights reserved.
from mmdet.registry import MODELS
from .single_stage_instance_seg import SingleStageInstanceSegmentor
@MODELS.register_module()
class SOLO(SingleStageInstanceSegmentor):
"""`SOLO: Segmenting Objects by Locations
<https://arxiv.org/abs/1912.04488>`_
"""
... | # Copyright (c) OpenMMLab. All rights reserved.
from ..builder import DETECTORS
from .single_stage_instance_seg import SingleStageInstanceSegmentor
@DETECTORS.register_module()
class SOLO(SingleStageInstanceSegmentor):
"""`SOLO: Segmenting Objects by Locations
<https://arxiv.org/abs/1912.04488>`_
"""
... |
# Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import numpy as np
import pycocotools.mask as mask_util
def split_combined_polys(polys, poly_lens, polys_per_mask):
"""Split the combined 1-D polys into masks.
A mask is represented as a list of polys, and a poly is represented as
a 1-D array. I... | import mmcv
import numpy as np
import pycocotools.mask as mask_util
def split_combined_polys(polys, poly_lens, polys_per_mask):
"""Split the combined 1-D polys into masks.
A mask is represented as a list of polys, and a poly is represented as
a 1-D array. In dataset, all masks are concatenated into a sin... |
import pathlib
from typing import Any, Union
import torch
from torchdata.datapipes.iter import Decompressor, IterDataPipe, LineReader, Mapper
from torchvision.prototype.datasets.utils import Dataset, HttpResource, OnlineResource
from torchvision.prototype.datasets.utils._internal import hint_sharding, hint_shuffling
f... | import pathlib
from typing import Any, Dict, List, Union
import torch
from torchdata.datapipes.iter import Decompressor, IterDataPipe, LineReader, Mapper
from torchvision.prototype.datasets.utils import Dataset, HttpResource, OnlineResource
from torchvision.prototype.datasets.utils._internal import hint_sharding, hint... |
from __future__ import annotations
import json
import os
from typing import Any
import torch
from torch import nn
class SpladePooling(nn.Module):
"""
SPLADE Pooling module for creating the sparse embeddings.
This module implements the SPLADE pooling mechanism that:
1. Takes token logits from a mask... | from __future__ import annotations
import json
import os
from typing import Any
import torch
from torch import nn
class SpladePooling(nn.Module):
"""SPLADE pooling layer that aggregates MLM logits using max or sum pooling.
This pooling layer takes MLM logits (shape: batch_size, seq_length, vocab_size)
... |
import numpy as np
from absl.testing import parameterized
from keras.src import backend
from keras.src import testing
from keras.src.utils import numerical_utils
NUM_CLASSES = 5
class TestNumericalUtils(testing.TestCase, parameterized.TestCase):
@parameterized.parameters(
[
((1,), (1, NUM_CL... | import numpy as np
from absl.testing import parameterized
from keras.src import backend
from keras.src import testing
from keras.src.utils import numerical_utils
NUM_CLASSES = 5
class TestNumericalUtils(testing.TestCase, parameterized.TestCase):
@parameterized.parameters(
[
((1,), (1, NUM_CL... |
# type: ignore
"""Development Scripts for template packages."""
from collections.abc import Sequence
from fastapi import FastAPI
from langserve import add_routes
from langchain_cli.utils.packages import get_langserve_export, get_package_root
def create_demo_server(
*,
config_keys: Sequence[str] = (),
p... | # type: ignore
"""
Development Scripts for template packages
"""
from collections.abc import Sequence
from fastapi import FastAPI
from langserve import add_routes
from langchain_cli.utils.packages import get_langserve_export, get_package_root
def create_demo_server(
*,
config_keys: Sequence[str] = (),
... |
_base_ = './mask-rcnn_hrnetv2p-w18-1x_coco.py'
model = dict(
backbone=dict(
type='HRNet',
extra=dict(
stage2=dict(num_channels=(40, 80)),
stage3=dict(num_channels=(40, 80, 160)),
stage4=dict(num_channels=(40, 80, 160, 320))),
init_cfg=dict(
typ... | _base_ = './mask_rcnn_hrnetv2p_w18_1x_coco.py'
model = dict(
backbone=dict(
type='HRNet',
extra=dict(
stage2=dict(num_channels=(40, 80)),
stage3=dict(num_channels=(40, 80, 160)),
stage4=dict(num_channels=(40, 80, 160, 320))),
init_cfg=dict(
typ... |
"""
This examples trains a CrossEncoder for the NLI task. A CrossEncoder takes a sentence pair
as input and outputs a label. Here, it learns to predict the labels: "contradiction": 0, "entailment": 1, "neutral": 2.
It does NOT produce a sentence embedding and does NOT work for individual sentences.
Usage:
python trai... | """
This examples trains a CrossEncoder for the NLI task. A CrossEncoder takes a sentence pair
as input and outputs a label. Here, it learns to predict the labels: "contradiction": 0, "entailment": 1, "neutral": 2.
It does NOT produce a sentence embedding and does NOT work for individual sentences.
Usage:
python trai... |
import torch
from ..utils import _log_api_usage_once
from ._utils import _loss_inter_union, _upcast_non_float
def distance_box_iou_loss(
boxes1: torch.Tensor,
boxes2: torch.Tensor,
reduction: str = "none",
eps: float = 1e-7,
) -> torch.Tensor:
"""
Gradient-friendly IoU loss with an additional... | from typing import Tuple
import torch
from ..utils import _log_api_usage_once
from ._utils import _loss_inter_union, _upcast_non_float
def distance_box_iou_loss(
boxes1: torch.Tensor,
boxes2: torch.Tensor,
reduction: str = "none",
eps: float = 1e-7,
) -> torch.Tensor:
"""
Gradient-friendly ... |
PODCAST_DOCS = """API documentation:
Endpoint: https://listen-api.listennotes.com/api/v2
GET /search
This API is for searching podcasts or episodes.
Query parameters table:
q | string | Search term, e.g., person, place, topic... You can use double quotes to do verbatim match, e.g., "game of thrones". Otherwise, it's ... | # flake8: noqa
PODCAST_DOCS = """API documentation:
Endpoint: https://listen-api.listennotes.com/api/v2
GET /search
This API is for searching podcasts or episodes.
Query parameters table:
q | string | Search term, e.g., person, place, topic... You can use double quotes to do verbatim match, e.g., "game of thrones". O... |
"""**Prompt** is the input to the model.
Prompt is often constructed
from multiple components and prompt values. Prompt classes and functions make constructing
and working with prompts easy.
**Class hierarchy:**
.. code-block::
BasePromptTemplate --> PipelinePromptTemplate
StringProm... | """**Prompt** is the input to the model.
Prompt is often constructed
from multiple components and prompt values. Prompt classes and functions make constructing
and working with prompts easy.
**Class hierarchy:**
.. code-block::
BasePromptTemplate --> PipelinePromptTemplate
StringProm... |
# Copyright (c) OpenMMLab. All rights reserved.
from ..builder import DETECTORS
from .faster_rcnn import FasterRCNN
@DETECTORS.register_module()
class TridentFasterRCNN(FasterRCNN):
"""Implementation of `TridentNet <https://arxiv.org/abs/1901.01892>`_"""
def __init__(self,
backbone,
... | # Copyright (c) OpenMMLab. All rights reserved.
from ..builder import DETECTORS
from .faster_rcnn import FasterRCNN
@DETECTORS.register_module()
class TridentFasterRCNN(FasterRCNN):
"""Implementation of `TridentNet <https://arxiv.org/abs/1901.01892>`_"""
def __init__(self,
backbone,
... |
"""
Demo for using and defining callback functions
==============================================
.. versionadded:: 1.3.0
"""
import argparse
import os
import tempfile
from typing import Dict
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_breast_cancer
from sklearn.model... | """
Demo for using and defining callback functions
==============================================
.. versionadded:: 1.3.0
"""
import argparse
import os
import tempfile
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_... |
from datetime import datetime
import pytest
from prisma.models import CreditTransaction
from backend.blocks.llm import AITextGeneratorBlock
from backend.data.credit import UserCredit
from backend.data.user import DEFAULT_USER_ID
from backend.integrations.credentials_store import openai_credentials
from backend.util.t... | from datetime import datetime
import pytest
from prisma.models import UserBlockCredit
from backend.blocks.llm import AITextGeneratorBlock
from backend.data.credit import UserCredit
from backend.data.user import DEFAULT_USER_ID
from backend.integrations.credentials_store import openai_credentials
from backend.util.tes... |
from __future__ import annotations
import json
import logging
import os
from typing import Literal
import torch
from torch import Tensor, nn
from .tokenizer import WhitespaceTokenizer
logger = logging.getLogger(__name__)
class BoW(nn.Module):
"""Implements a Bag-of-Words (BoW) model to derive sentence embeddi... | import json
import logging
import os
from typing import Dict, List, Literal
import torch
from torch import Tensor, nn
from .tokenizer import WhitespaceTokenizer
logger = logging.getLogger(__name__)
class BoW(nn.Module):
"""Implements a Bag-of-Words (BoW) model to derive sentence embeddings.
A weighting ca... |
from llama_index.core.extractors.metadata_extractors import (
BaseExtractor,
KeywordExtractor,
QuestionsAnsweredExtractor,
SummaryExtractor,
TitleExtractor,
)
def load_extractor(
data: dict,
) -> BaseExtractor:
if isinstance(data, BaseExtractor):
return data
extractor_name = d... | from llama_index.core.extractors.metadata_extractors import (
BaseExtractor,
KeywordExtractor,
QuestionsAnsweredExtractor,
SummaryExtractor,
TitleExtractor,
)
def load_extractor(
data: dict,
) -> BaseExtractor:
if isinstance(data, BaseExtractor):
return data
extractor_name = d... |
# 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 unittest import TestCase
import torch
from mmengine.structures import InstanceData
from mmdet import * # noqa
from mmdet.models.dense_heads import FreeAnchorRetinaHead
class TestFreeAnchorRetinaHead(TestCase):
def test_free_anchor_head_loss(self):
"... | # Copyright (c) OpenMMLab. All rights reserved.
from unittest import TestCase
import torch
from mmengine.data import InstanceData
from mmdet import * # noqa
from mmdet.models.dense_heads import FreeAnchorRetinaHead
class TestFreeAnchorRetinaHead(TestCase):
def test_free_anchor_head_loss(self):
"""Test... |
"""**Prompt** is the input to the model.
Prompt is often constructed
from multiple components. Prompt classes and functions make constructing
and working with prompts easy.
**Class hierarchy:**
.. code-block::
BasePromptTemplate --> PipelinePromptTemplate
StringPromptTemplate --> Pro... | """**Prompt** is the input to the model.
Prompt is often constructed
from multiple components. Prompt classes and functions make constructing
and working with prompts easy.
**Class hierarchy:**
.. code-block::
BasePromptTemplate --> PipelinePromptTemplate
StringPromptTemplate --> Pro... |
__copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import subprocess
from typing import Dict, Iterable, Optional
import spacy
from jina import DocumentArray, Executor, requests
from jina_commons.batching import get_docs_batch_generator
_EXCLUDE_COMPONENTS = [
... | __copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import subprocess
from typing import Dict, Iterable, Optional
import spacy
from jina import DocumentArray, Executor, requests
from jina_commons.batching import get_docs_batch_generator
_EXCLUDE_COMPONENTS = [
... |
import pytest
from docarray import Document
from docarray.array.memory import DocumentArrayInMemory
from docarray.array.elastic import DocumentArrayElastic, ElasticConfig
from docarray.array.qdrant import DocumentArrayQdrant
from docarray.array.sqlite import DocumentArraySqlite
from docarray.array.annlite import Docum... | import pytest
from docarray import Document
from docarray.array.memory import DocumentArrayInMemory
from docarray.array.elastic import DocumentArrayElastic, ElasticConfig
from docarray.array.qdrant import DocumentArrayQdrant
from docarray.array.sqlite import DocumentArraySqlite
from docarray.array.annlite import Docum... |
import logging
import os
import torch
from torchaudio._internal import (
download_url_to_file,
module_utils as _mod_utils,
)
def _get_chars():
return (
"_",
"-",
"!",
"'",
"(",
")",
",",
".",
":",
";",
"?",
" ... | import logging
import os
import torch
from torchaudio._internal import (
download_url_to_file,
module_utils as _mod_utils,
)
def _get_chars():
return (
"_",
"-",
"!",
"'",
"(",
")",
",",
".",
":",
";",
"?",
" ... |
# Copyright (c) OpenMMLab. All rights reserved.
from .panoptic_fpn_head import PanopticFPNHead # noqa: F401,F403
from .panoptic_fusion_heads import * # noqa: F401,F403
| from .panoptic_fpn_head import PanopticFPNHead # noqa: F401,F403
from .panoptic_fusion_heads import * # noqa: F401,F403
|
_base_ = './fcos_r50-caffe_fpn_gn-head_1x_coco.py'
# model settings
model = dict(bbox_head=dict(center_sampling=True, center_sample_radius=1.5))
| _base_ = './fcos_r50_caffe_fpn_gn-head_1x_coco.py'
# model settings
model = dict(bbox_head=dict(center_sampling=True, center_sample_radius=1.5))
|
import logging
import pathlib
from postmarker.core import PostmarkClient
from postmarker.models.emails import EmailManager
from prisma.enums import NotificationType
from pydantic import BaseModel
from backend.data.notifications import (
NotificationDataType_co,
NotificationEventModel,
NotificationTypeOver... | import logging
import pathlib
from postmarker.core import PostmarkClient
from postmarker.models.emails import EmailManager
from prisma.enums import NotificationType
from pydantic import BaseModel
from backend.data.notifications import (
NotificationEventModel,
NotificationTypeOverride,
T_co,
)
from backen... |
import unittest
import torch
import torchaudio.prototype.functional as F
from torchaudio_unittest.common_utils import nested_params, TestBaseMixin, torch_script
class TorchScriptConsistencyTestImpl(TestBaseMixin):
def _assert_consistency(self, func, inputs, shape_only=False):
inputs_ = []
for i i... | import unittest
import torch
import torchaudio.prototype.functional as F
from torchaudio_unittest.common_utils import nested_params, TestBaseMixin, torch_script
class TorchScriptConsistencyTestImpl(TestBaseMixin):
def _assert_consistency(self, func, inputs, shape_only=False):
inputs_ = []
for i i... |
from llama_index.core.graph_stores.types import GraphStore
from llama_index.graph_stores.nebula import NebulaGraphStore
def test_nebula_graph_store():
names_of_bases = [b.__name__ for b in NebulaGraphStore.__bases__]
assert GraphStore.__name__ in names_of_bases
| from unittest.mock import MagicMock, patch
from llama_index.core.graph_stores.types import GraphStore
from llama_index.graph_stores.nebula import NebulaGraphStore
@patch("llama_index.graph_stores.nebula.NebulaGraphStore")
def test_kuzu_graph_store(MockNebulaGraphStore: MagicMock):
instance: NebulaGraphStore = Mo... |
# Copyright (c) OpenMMLab. All rights reserved.
from .mask2former_track_head import Mask2FormerTrackHead
from .quasi_dense_embed_head import QuasiDenseEmbedHead
from .quasi_dense_track_head import QuasiDenseTrackHead
from .roi_embed_head import RoIEmbedHead
from .roi_track_head import RoITrackHead
__all__ = [
'Qua... | # Copyright (c) OpenMMLab. All rights reserved.
from .mask2former_track_head import Mask2FormerTrackHead
from .quasi_dense_embed_head import QuasiDenseEmbedHead
from .quasi_dense_track_head import QuasiDenseTrackHead
__all__ = [
'QuasiDenseEmbedHead', 'QuasiDenseTrackHead', 'Mask2FormerTrackHead'
]
|
import json
from typing import Tuple
import responses
from requests import Request
from langchain_community.document_loaders import HuggingFaceModelLoader
# Mocked model data to simulate an API response
MOCKED_MODELS_RESPONSE = [
{
"_id": "657a1fff16886e681230c05a",
"id": "microsoft/phi-2",
... | import json
from typing import Tuple
import responses
from requests import Request
from langchain_community.document_loaders import HuggingFaceModelLoader
# Mocked model data to simulate an API response
MOCKED_MODELS_RESPONSE = [
{
"_id": "657a1fff16886e681230c05a",
"id": "microsoft/phi-2",
... |
# Copyright (c) OpenMMLab. All rights reserved.
import base64
import os
import mmcv
import torch
from ts.torch_handler.base_handler import BaseHandler
from mmdet.apis import inference_detector, init_detector
class MMdetHandler(BaseHandler):
threshold = 0.5
def initialize(self, context):
properties ... | import base64
import os
import mmcv
import torch
from ts.torch_handler.base_handler import BaseHandler
from mmdet.apis import inference_detector, init_detector
class MMdetHandler(BaseHandler):
threshold = 0.5
def initialize(self, context):
properties = context.system_properties
self.map_loc... |
from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.vectorstores.redis.schema import (
FlatVectorField,
HNSWVectorField,
NumericFieldSchema,
RedisDistanceMetric,
RedisField,
RedisModel,
... | from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.vectorstores.redis.schema import (
FlatVectorField,
HNSWVectorField,
NumericFieldSchema,
RedisDistanceMetric,
RedisField,
RedisModel,
... |
import re
from collections.abc import Sequence
from typing import Optional
from langchain_core.messages import BaseMessage
def _is_openai_data_block(block: dict) -> bool:
"""Check if the block contains multimodal data in OpenAI Chat Completions format."""
if block.get("type") == "image_url":
if (
... | import re
from collections.abc import Sequence
from typing import Optional
from langchain_core.messages import BaseMessage
def _is_openai_data_block(block: dict) -> bool:
"""Check if the block contains multimodal data in OpenAI Chat Completions format."""
if block.get("type") == "image_url":
if (
... |
# Copyright (c) OpenMMLab. All rights reserved.
import os
import os.path as osp
from typing import Optional, Sequence
from mmengine.dist import is_main_process
from mmengine.evaluator import BaseMetric
from mmengine.fileio import dump
from mmengine.logging import MMLogger
from mmengine.structures import InstanceData
... | # Copyright (c) OpenMMLab. All rights reserved.
import os
import os.path as osp
from typing import Optional, Sequence
from mmengine.dist import is_main_process
from mmengine.evaluator import BaseMetric
from mmengine.fileio import dump
from mmengine.logging import MMLogger
from mmengine.structures import InstanceData
... |
from typing import Any, Optional, Type, TypeVar, Union
import numpy as np
from docarray.base_document import BaseDocument
from docarray.typing import AnyEmbedding, AnyTensor, PointCloud3DUrl
from docarray.typing.tensor.abstract_tensor import AbstractTensor
try:
import torch
torch_available = True
except Imp... | from typing import Any, Optional, Type, TypeVar, Union
import numpy as np
from docarray.base_document import BaseDocument
from docarray.typing import AnyEmbedding, AnyTensor, PointCloud3DUrl
from docarray.typing.tensor.abstract_tensor import AbstractTensor
try:
import torch
torch_available = True
except Imp... |
import argparse
import urllib
from abc import ABC
from http import HTTPStatus
from typing import TYPE_CHECKING, Optional, Union
from jina.serve.runtimes.asyncio import AsyncNewLoopRuntime
if TYPE_CHECKING:
import asyncio
import multiprocessing
import threading
class GatewayRuntime(AsyncNewLoopRuntime, A... | import argparse
import urllib
from abc import ABC
from http import HTTPStatus
from typing import TYPE_CHECKING, Optional, Union
from jina.serve.runtimes.asyncio import AsyncNewLoopRuntime
if TYPE_CHECKING:
import asyncio
import multiprocessing
import threading
class GatewayRuntime(AsyncNewLoopRuntime, A... |
# Credit to https://github.com/openai/evals/tree/main
from langchain_core.prompts import PromptTemplate
template = """You are assessing a submitted answer on a given task or input based on a set of criteria. Here is the data:
[BEGIN DATA]
***
[Input]: {input}
***
[Submission]: {output}
***
[Criteria]: {criteria}
***
... | # flake8: noqa
# Credit to https://github.com/openai/evals/tree/main
from langchain_core.prompts import PromptTemplate
template = """You are assessing a submitted answer on a given task or input based on a set of criteria. Here is the data:
[BEGIN DATA]
***
[Input]: {input}
***
[Submission]: {output}
***
[Criteria]: ... |
from dataclasses import dataclass, asdict, field
from typing import (
Union,
Dict,
Optional,
TYPE_CHECKING,
Iterable,
List,
Tuple,
)
import numpy as np
from docarray.array.storage.base.backend import BaseBackendMixin, TypeMap
from docarray.helper import dataclass_from_dict, filter_dict, _s... | from dataclasses import dataclass, asdict, field
from typing import (
Union,
Dict,
Optional,
TYPE_CHECKING,
Iterable,
List,
Tuple,
)
import numpy as np
from docarray.array.storage.base.backend import BaseBackendMixin, TypeMap
from docarray.helper import dataclass_from_dict, filter_dict, _s... |
import logging
import time
from abc import ABC, abstractmethod
from typing import ClassVar, Optional
from backend.data.model import OAuth2Credentials
from backend.integrations.providers import ProviderName
logger = logging.getLogger(__name__)
class BaseOAuthHandler(ABC):
# --8<-- [start:BaseOAuthHandler1]
P... | import logging
import time
from abc import ABC, abstractmethod
from typing import ClassVar
from backend.data.model import OAuth2Credentials
from backend.integrations.providers import ProviderName
logger = logging.getLogger(__name__)
class BaseOAuthHandler(ABC):
# --8<-- [start:BaseOAuthHandler1]
PROVIDER_NA... |
from .objective import squim_objective_base, squim_objective_model, SquimObjective
from .subjective import squim_subjective_base, squim_subjective_model, SquimSubjective
__all__ = [
"squim_objective_base",
"squim_objective_model",
"squim_subjective_base",
"squim_subjective_model",
"SquimObjective",... | from .objective import squim_objective_base, squim_objective_model, SquimObjective
__all__ = [
"squim_objective_base",
"squim_objective_model",
"SquimObjective",
]
|
# 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 FSAF(SingleStageDetector):
"""Implementation of `FSAF <https://arxiv.org/abs/1903.00621>`... | # Copyright (c) OpenMMLab. All rights reserved.
from mmdet.registry import MODELS
from .single_stage import SingleStageDetector
@MODELS.register_module()
class FSAF(SingleStageDetector):
"""Implementation of `FSAF <https://arxiv.org/abs/1903.00621>`_"""
def __init__(self,
backbone,
... |
from dataclasses import dataclass, fields, field
from typing import Optional, Tuple, TYPE_CHECKING
if TYPE_CHECKING: # pragma: no cover
from docarray.score import NamedScore
default_values = dict(value=0.0, op_name='', description='', ref_id='')
@dataclass(unsafe_hash=True)
class NamedScoreData:
_reference... | from dataclasses import dataclass, fields, field
from typing import Optional, Tuple, TYPE_CHECKING
if TYPE_CHECKING:
from docarray.score import NamedScore
default_values = dict(value=0.0, op_name='', description='', ref_id='')
@dataclass(unsafe_hash=True)
class NamedScoreData:
_reference_ns: 'NamedScore' = ... |
# Copyright (c) OpenMMLab. All rights reserved.
"""Get image metas on a specific dataset.
Here is an example to run this script.
Example:
python tools/misc/get_image_metas.py ${CONFIG} \
--out ${OUTPUT FILE NAME}
"""
import argparse
import csv
import os.path as osp
from multiprocessing import Pool
import mmc... | # Copyright (c) OpenMMLab. All rights reserved.
"""Get image metas on a specific dataset.
Here is an example to run this script.
Example:
python tools/misc/get_image_metas.py ${CONFIG} \
--out ${OUTPUT FILE NAME}
"""
import argparse
import csv
import os.path as osp
from multiprocessing import Pool
import mmc... |
from typing import Type
from .doc import BaseDoc
class AnyDoc(BaseDoc):
"""
AnyDoc is a Document that is not tied to any schema
"""
class Config:
_load_extra_fields_from_protobuf = True # I introduce this variable to allow to load more that the fields defined in the schema
# will do... | from typing import Type
from .doc import BaseDoc
class AnyDoc(BaseDoc):
"""
AnyDoc is a Document that is not tied to any schema
"""
def __init__(self, **kwargs):
super().__init__()
self.__dict__.update(kwargs)
@classmethod
def _get_field_type(cls, field: str) -> Type['BaseDo... |
import logging
from typing import Any, List
import requests
from llama_index.core.base.embeddings.base import BaseEmbedding
from requests.adapters import HTTPAdapter, Retry
logger = logging.getLogger(__name__)
class LLMRailsEmbedding(BaseEmbedding):
"""
LLMRails embedding models.
This class provides an... | import logging
from typing import Any, List
import requests
from llama_index.core.base.embeddings.base import BaseEmbedding
from requests.adapters import HTTPAdapter, Retry
logger = logging.getLogger(__name__)
class LLMRailsEmbedding(BaseEmbedding):
"""LLMRails embedding models.
This class provides an inte... |
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