input stringlengths 33 5k | output stringlengths 32 5k |
|---|---|
import argparse
import os
from typing import List
from jina.parsers.helper import CastHostAction
def api_to_dict(show_all_args: bool = False):
"""Convert Jina API to a dict
:param show_all_args: if set, then hidden args are also exported
:return: dict
"""
if show_all_args:
from jina.parse... | import argparse
import os
from typing import List
from jina.parsers.helper import CastHostAction
def api_to_dict(show_all_args: bool = False):
"""Convert Jina API to a dict
:param show_all_args: if set, then hidden args are also exported
:return: dict
"""
if show_all_args:
from jina.parse... |
import inspect
import re
from typing import Dict, List, Tuple
from huggingface_hub.utils import insecure_hashlib
from .arrow import arrow
from .audiofolder import audiofolder
from .cache import cache
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parq... | import inspect
import re
from typing import Dict, List, Tuple
from huggingface_hub.utils import insecure_hashlib
from .arrow import arrow
from .audiofolder import audiofolder
from .cache import cache
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parq... |
_base_ = './mask-rcnn_regnetx-3.2GF_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='RegNet',
arch='regnetx_6.4gf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dic... | _base_ = './mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='RegNet',
arch='regnetx_6.4gf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dic... |
import argparse
import copy
import os
import re
import sys
import boto3
import botocore
from metadata import AMI_ID, COMMON_STACK_PARAMS, STACK_PARAMS
current_dir = os.path.dirname(__file__)
sys.path.append(os.path.join(current_dir, ".."))
from common_blocks.utils import create_or_update_stack, wait
TEMPLATE_URL = ... | import argparse
import copy
import os
import re
import sys
import boto3
import botocore
from metadata import AMI_ID, COMMON_STACK_PARAMS, STACK_PARAMS
current_dir = os.path.dirname(__file__)
sys.path.append(os.path.join(current_dir, ".."))
from common_blocks.utils import create_or_update_stack, wait
TEMPLATE_URL = ... |
"""DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.backend.config import backend as backend
from keras.src.backend.config import (
disable_flash_attention as disable_flash_attention,
)
from keras.src.backend.config import (
en... | """DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.backend.config import backend
from keras.src.backend.config import disable_flash_attention
from keras.src.backend.config import enable_flash_attention
from keras.src.backend.config im... |
import logging
from fastapi import Request
from backend.data import integrations
from backend.data.model import APIKeyCredentials, Credentials
from backend.integrations.providers import ProviderName
from backend.integrations.webhooks._base import BaseWebhooksManager
from backend.util.request import Requests
logger =... | import logging
import requests
from fastapi import Request
from backend.data import integrations
from backend.data.model import APIKeyCredentials, Credentials
from backend.integrations.providers import ProviderName
from backend.integrations.webhooks._base import BaseWebhooksManager
logger = logging.getLogger(__name_... |
"""
Feature agglomeration. Base classes and functions for performing feature
agglomeration.
"""
# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause
import numpy as np
from scipy.sparse import issparse
from ..base import TransformerMixin
from ..utils.validation import check_is_fitted, vali... | """
Feature agglomeration. Base classes and functions for performing feature
agglomeration.
"""
# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause
import numpy as np
from scipy.sparse import issparse
from ..base import TransformerMixin
from ..utils import metadata_routing
from ..utils.de... |
# 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 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... |
import pytest
import datasets
import datasets.config
# Import fixture modules as plugins
pytest_plugins = ["tests.fixtures.files", "tests.fixtures.hub", "tests.fixtures.fsspec"]
def pytest_collection_modifyitems(config, items):
# Mark tests as "unit" by default if not marked as "integration" (or already marked... | import pytest
import datasets
import datasets.config
# Import fixture modules as plugins
pytest_plugins = ["tests.fixtures.files", "tests.fixtures.hub", "tests.fixtures.fsspec"]
def pytest_collection_modifyitems(config, items):
# Mark tests as "unit" by default if not marked as "integration" (or already marked... |
from __future__ import annotations
from sentence_transformers.sparse_encoder.data_collator import SparseEncoderDataCollator
from sentence_transformers.sparse_encoder.evaluation import (
SparseBinaryClassificationEvaluator,
SparseEmbeddingSimilarityEvaluator,
SparseInformationRetrievalEvaluator,
SparseM... | from __future__ import annotations
from sentence_transformers.sparse_encoder.data_collator import SparseEncoderDataCollator
from sentence_transformers.sparse_encoder.evaluation import (
SparseBinaryClassificationEvaluator,
SparseEmbeddingSimilarityEvaluator,
SparseInformationRetrievalEvaluator,
SparseM... |
_base_ = '../_base_/default_runtime.py'
# dataset settings
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
image_size = (1024, 1024)
file_client_args = dict(backend='disk')
# comment out the code below to use different file client
# file_client_args = dict(
# backend='petrel',
# path_mapping=dict({
# ... | _base_ = '../_base_/default_runtime.py'
# dataset settings
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
image_size = (1024, 1024)
file_client_args = dict(backend='disk')
# comment out the code below to use different file client
# file_client_args = dict(
# backend='petrel',
# path_mapping=dict({
# ... |
from typing import Tuple, Iterator
import pytest
import requests
import itertools
from docarray import DocumentArray, Document
def test_weaviate_hnsw(start_storage):
da = DocumentArray(
storage='weaviate',
config={
'n_dim': 100,
'ef': 100,
'ef_construction': 1... | import requests
from docarray import DocumentArray
def test_weaviate_hnsw(start_storage):
da = DocumentArray(
storage='weaviate',
config={
'n_dim': 100,
'ef': 100,
'ef_construction': 100,
'max_connections': 16,
'dynamic_ef_min': 50,
... |
import os
from functools import lru_cache
from subprocess import CalledProcessError, run
from typing import Optional, Union
import numpy as np
import torch
import torch.nn.functional as F
from .utils import exact_div
# hard-coded audio hyperparameters
SAMPLE_RATE = 16000
N_FFT = 400
N_MELS = 80
HOP_LENGTH = 160
CHUN... | import os
from functools import lru_cache
from subprocess import CalledProcessError, run
from typing import Optional, Union
import numpy as np
import torch
import torch.nn.functional as F
from .utils import exact_div
# hard-coded audio hyperparameters
SAMPLE_RATE = 16000
N_FFT = 400
N_MELS = 80
HOP_LENGTH = 160
CHUN... |
"""**Load** module helps with serialization and deserialization."""
from importlib import import_module
from typing import TYPE_CHECKING
if TYPE_CHECKING:
from langchain_core.load.dump import dumpd, dumps
from langchain_core.load.load import loads
from langchain_core.load.serializable import Serializable
... | """**Load** module helps with serialization and deserialization."""
from importlib import import_module
from typing import TYPE_CHECKING
if TYPE_CHECKING:
from langchain_core.load.dump import dumpd, dumps
from langchain_core.load.load import load, loads
from langchain_core.load.serializable import Seriali... |
__version__ = '0.18.1'
import os
from docarray.document import Document
from docarray.array import DocumentArray
from docarray.dataclasses import dataclass, field
if 'DA_RICH_HANDLER' in os.environ:
from rich.traceback import install
install()
| __version__ = '0.18.0'
import os
from docarray.document import Document
from docarray.array import DocumentArray
from docarray.dataclasses import dataclass, field
if 'DA_RICH_HANDLER' in os.environ:
from rich.traceback import install
install()
|
import logging
from datasets import load_dataset
from sentence_transformers import SparseEncoder
from sentence_transformers.sparse_encoder.evaluation import SparseBinaryClassificationEvaluator
logging.basicConfig(format="%(message)s", level=logging.INFO)
# Initialize the SPLADE model
model = SparseEncoder("naver/sp... | import logging
from datasets import load_dataset
from sentence_transformers import SparseEncoder
from sentence_transformers.sparse_encoder.evaluation import SparseBinaryClassificationEvaluator
logging.basicConfig(format="%(message)s", level=logging.INFO)
# Initialize the SPLADE model
model = SparseEncoder("naver/sp... |
import 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 Mesh3DUrl, NdArray
from docarray.typing.url.mimetypes import (
OBJ_MIMETYPE,
AUDIO_MIMETYPE,
VIDEO_MIMETYPE,
IMAGE_MIMETYPE,... | 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 Mesh3DUrl, NdArray
from tests import TOYDATA_DIR
MESH_FILES = {
'obj': str(TOYDATA_DIR / 'tetrahedron.obj'),
'glb': str(TOYDATA_DIR / 'test.gl... |
"""DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.datasets.fashion_mnist import load_data as load_data
| """DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.datasets.fashion_mnist import load_data
|
import csv
from contextlib import nullcontext
from typing import Union, TextIO, Optional, Dict, TYPE_CHECKING, Type, Sequence
import numpy as np
if TYPE_CHECKING: # pragma: no cover
from docarray.typing import T
class CsvIOMixin:
"""CSV IO helper.
can be applied to DA & DAM
"""
def save_embed... | import csv
from contextlib import nullcontext
from typing import Union, TextIO, Optional, Dict, TYPE_CHECKING, Type, Sequence
import numpy as np
if TYPE_CHECKING:
from docarray.typing import T
class CsvIOMixin:
"""CSV IO helper.
can be applied to DA & DAM
"""
def save_embeddings_csv(
s... |
from __future__ import annotations
from pathlib import Path
from unittest.mock import Mock, PropertyMock
import pytest
import torch
from sentence_transformers import SentenceTransformer
from sentence_transformers.evaluation import InformationRetrievalEvaluator
from sentence_transformers.util import cos_sim
@pytest... | from __future__ import annotations
from pathlib import Path
from unittest.mock import Mock, PropertyMock
import pytest
import torch
from sentence_transformers import SentenceTransformer
from sentence_transformers.evaluation import InformationRetrievalEvaluator
from sentence_transformers.util import cos_sim
@pytest... |
"""
This script contains an example how to perform semantic search with OpenSearch.
You need OpenSearch up and running locally:
https://docs.opensearch.org/docs/latest/getting-started/quickstart/
Further, you need the Python OpenSearch Client installed: https://docs.opensearch.org/docs/latest/clients/python-low-level... | """
This script contains an example how to perform semantic search with OpenSearch.
You need OpenSearch up and running locally:
https://docs.opensearch.org/docs/latest/getting-started/quickstart/
Further, you need the Python OpenSearch Client installed: https://docs.opensearch.org/docs/latest/clients/python-low-level... |
_base_ = [
'../_base_/models/faster-rcnn_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
| _base_ = [
'../_base_/models/faster_rcnn_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
|
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={},
... |
import pytest
from pydantic import Field
from docarray import BaseDoc
from docarray.index import ElasticV7DocIndex
from tests.index.elastic.fixture import start_storage_v7 # noqa: F401
pytestmark = [pytest.mark.slow, pytest.mark.index]
def test_column_config():
class MyDoc(BaseDoc):
text: str
c... | import pytest
from pydantic import Field
from docarray import BaseDoc
from docarray.index import ElasticV7DocIndex
from tests.index.elastic.fixture import start_storage_v7 # noqa: F401
pytestmark = [pytest.mark.slow, pytest.mark.index]
def test_column_config():
class MyDoc(BaseDoc):
text: str
c... |
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... | import random
from datasets import load_dataset
from sentence_transformers.sparse_encoder import (
MLMTransformer,
SparseEncoder,
SparseInformationRetrievalEvaluator,
SpladePooling,
)
# Initialize the SPLADE model
model_name = "naver/splade-cocondenser-ensembledistil"
model = SparseEncoder(
modul... |
import warnings
from typing import Optional, Tuple, TypeVar
from docarray.typing import AudioNdArray
from docarray.typing.bytes.audio_bytes import AudioBytes
from docarray.typing.proto_register import _register_proto
from docarray.typing.url.any_url import AnyUrl
from docarray.utils._internal.misc import is_notebook
... | import warnings
from typing import TYPE_CHECKING, Any, Optional, Tuple, Type, TypeVar, Union
from docarray.typing import AudioNdArray
from docarray.typing.bytes.audio_bytes import AudioBytes
from docarray.typing.proto_register import _register_proto
from docarray.typing.url.any_url import AnyUrl
from docarray.typing.u... |
from datetime import datetime, timedelta
from langchain_core.exceptions import OutputParserException
from langchain_core.output_parsers import BaseOutputParser
from langchain_core.utils import comma_list
class DatetimeOutputParser(BaseOutputParser[datetime]):
"""Parse the output of an LLM call to a datetime."""
... | from datetime import datetime, timedelta
from langchain_core.exceptions import OutputParserException
from langchain_core.output_parsers import BaseOutputParser
from langchain_core.utils import comma_list
class DatetimeOutputParser(BaseOutputParser[datetime]):
"""Parse the output of an LLM call to a datetime."""
... |
# Copyright (c) OpenMMLab. All rights reserved.
from mmdet.registry import MODELS
from mmdet.utils import ConfigType, OptConfigType, OptMultiConfig
from .single_stage import SingleStageDetector
@MODELS.register_module()
class FSAF(SingleStageDetector):
"""Implementation of `FSAF <https://arxiv.org/abs/1903.00621>... | # 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>`... |
# coding: utf-8
"""Script for generating files with NuGet package metadata."""
import datetime
import sys
from pathlib import Path
from shutil import copyfile
if __name__ == "__main__":
source = Path(sys.argv[1])
current_dir = Path(__file__).absolute().parent
linux_folder_path = current_dir / "runtimes" / ... | # coding: utf-8
"""Script for generating files with NuGet package metadata."""
import datetime
import sys
from pathlib import Path
from shutil import copyfile
if __name__ == "__main__":
source = Path(sys.argv[1])
current_dir = Path(__file__).absolute().parent
linux_folder_path = current_dir / "runtimes" / ... |
"""Module to test base parser implementations."""
from typing_extensions import override
from langchain_core.exceptions import OutputParserException
from langchain_core.language_models import GenericFakeChatModel
from langchain_core.messages import AIMessage
from langchain_core.output_parsers import (
BaseGenerat... | """Module to test base parser implementations."""
from typing_extensions import override
from langchain_core.exceptions import OutputParserException
from langchain_core.language_models import GenericFakeChatModel
from langchain_core.messages import AIMessage
from langchain_core.output_parsers import (
BaseGenerat... |
"""Interface for tools."""
from typing import Optional
from langchain_core.callbacks import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain_core.tools import BaseTool, tool
class InvalidTool(BaseTool):
"""Tool that is run when invalid tool name is encountered by agent."""
... | """Interface for tools."""
from typing import Optional
from langchain_core.callbacks import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain_core.tools import BaseTool, tool
class InvalidTool(BaseTool): # type: ignore[override]
"""Tool that is run when invalid tool name is ... |
# Copyright (c) OpenMMLab. All rights reserved.
from typing import Any, List, Optional, Tuple, Type, Union
import cv2
import matplotlib
import numpy as np
import torch
def tensor2ndarray(value: Union[np.ndarray, torch.Tensor]) -> np.ndarray:
"""If the type of value is torch.Tensor, convert the value to np.ndarr... | # Copyright (c) OpenMMLab. All rights reserved.
from typing import Any, List, Tuple, Type, Union
import numpy as np
import torch
def tensor2ndarray(value: Union[np.ndarray, torch.Tensor]) -> np.ndarray:
"""If the type of value is torch.Tensor, convert the value to np.ndarray.
Args:
value (np.ndarray... |
_base_ = './fast-rcnn_r50-caffe_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron2/resnet101_caffe')))
| _base_ = './fast_rcnn_r50_caffe_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron2/resnet101_caffe')))
|
from typing import Union
from langchain_core.agents import AgentAction, AgentFinish
from langchain_core.messages import BaseMessage
from langchain_core.outputs import ChatGeneration, Generation
from langchain.agents.agent import MultiActionAgentOutputParser
from langchain.agents.output_parsers.tools import (
Tool... | from typing import List, Union
from langchain_core.agents import AgentAction, AgentFinish
from langchain_core.messages import BaseMessage
from langchain_core.outputs import ChatGeneration, Generation
from langchain.agents.agent import MultiActionAgentOutputParser
from langchain.agents.output_parsers.tools import (
... |
__version__ = '0.33.1'
import logging
from docarray.array import DocList, DocVec
from docarray.base_doc.doc import BaseDoc
from docarray.utils._internal.misc import _get_path_from_docarray_root_level
__all__ = ['BaseDoc', 'DocList', 'DocVec']
logger = logging.getLogger('docarray')
handler = logging.StreamHandler()... | __version__ = '0.33.0'
import logging
from docarray.array import DocList, DocVec
from docarray.base_doc.doc import BaseDoc
from docarray.utils._internal.misc import _get_path_from_docarray_root_level
__all__ = ['BaseDoc', 'DocList', 'DocVec']
logger = logging.getLogger('docarray')
handler = logging.StreamHandler()... |
from typing import Any, Optional, Type, TypeVar, Union
from docarray.base_document import BaseDocument
from docarray.typing import TextUrl
from docarray.typing.tensor.embedding import AnyEmbedding
T = TypeVar('T', bound='Text')
class Text(BaseDocument):
"""
Document for handling text.
It can contain a T... | from typing import Any, Optional, Type, TypeVar, Union
from docarray.base_document import BaseDocument
from docarray.typing import TextUrl
from docarray.typing.tensor.embedding import AnyEmbedding
T = TypeVar('T', bound='Text')
class Text(BaseDocument):
"""
Document for handling text.
It can contain a T... |
"""
Example of training with Dask on CPU
====================================
"""
from dask import array as da
from dask.distributed import Client, LocalCluster
from xgboost import dask as dxgb
from xgboost.dask import DaskDMatrix
def main(client):
# generate some random data for demonstration
m = 100000
... | """
Example of training with Dask on CPU
====================================
"""
from dask import array as da
from dask.distributed import Client, LocalCluster
from xgboost import dask as dxgb
from xgboost.dask import DaskDMatrix
def main(client):
# generate some random data for demonstration
m = 100000
... |
import unittest
import torch
from transformers import AutoTokenizer, Gemma2Config, Gemma2Model
from diffusers import (
AutoencoderKL,
FlowMatchEulerDiscreteScheduler,
Lumina2Pipeline,
Lumina2Transformer2DModel,
)
from ..test_pipelines_common import PipelineTesterMixin
class Lumina2PipelineFastTests... | import unittest
import torch
from transformers import AutoTokenizer, Gemma2Config, Gemma2Model
from diffusers import (
AutoencoderKL,
FlowMatchEulerDiscreteScheduler,
Lumina2Pipeline,
Lumina2Text2ImgPipeline,
Lumina2Transformer2DModel,
)
from diffusers.utils.testing_utils import torch_device
from... |
# Copyright (c) OpenMMLab. All rights reserved.
from mmengine.utils.dl_utils import TORCH_VERSION
from mmengine.utils.version_utils import digit_version
from .averaged_model import (BaseAveragedModel, ExponentialMovingAverage,
MomentumAnnealingEMA, StochasticWeightAverage)
from .base_model ... | # Copyright (c) OpenMMLab. All rights reserved.
from mmengine.utils.dl_utils import TORCH_VERSION
from mmengine.utils.version_utils import digit_version
from .averaged_model import (BaseAveragedModel, ExponentialMovingAverage,
MomentumAnnealingEMA, StochasticWeightAverage)
from .base_model ... |
import itertools
from parameterized import parameterized
from torchaudio.backend import sox_io_backend
from torchaudio_unittest.common_utils import get_wav_data, PytorchTestCase, skipIfNoExec, skipIfNoSox, TempDirMixin
from .common import get_enc_params, name_func
@skipIfNoExec("sox")
@skipIfNoSox
class TestRoundTr... | import itertools
from parameterized import parameterized
from torchaudio.backend import sox_io_backend
from torchaudio_unittest.common_utils import (
get_wav_data,
PytorchTestCase,
skipIfNoExec,
skipIfNoSox,
TempDirMixin,
)
from .common import get_enc_params, name_func
@skipIfNoExec("sox")
@skip... |
"""
This application demonstrates how to find duplicate questions (paraphrases) in a long
list of sentences.
"""
from sentence_transformers import SentenceTransformer, util
# Questions can be a long list of sentences up to 100k sentences or more.
# For demonstration purposes, we limit it to a few questions which all ... | """
This application demonstrates how to find duplicate questions (paraphrases) in a long
list of sentences.
"""
from sentence_transformers import SentenceTransformer, util
# Questions can be a long list of sentences up to 100k sentences or more.
# For demonstration purposes, we limit it to a few questions which all ... |
"""
Use scikit-learn regressor interface with CPU histogram tree method
===================================================================
"""
from dask import array as da
from dask.distributed import Client, LocalCluster
from xgboost import dask as dxgb
def main(client: Client) -> dxgb.Booster:
# generate som... | """
Use scikit-learn regressor interface with CPU histogram tree method
===================================================================
"""
from dask import array as da
from dask.distributed import Client, LocalCluster
from xgboost import dask as dxgb
def main(client):
# generate some random data for demons... |
from unittest.mock import mock_open, patch
from cryptography.hazmat.primitives.asymmetric import rsa
from cryptography.hazmat.primitives import serialization
from llama_index.llms.cortex.utils import (
generate_sf_jwt,
is_spcs_environment,
get_spcs_base_url,
get_default_spcs_token,
SPCS_TOKEN_PATH... | from unittest.mock import mock_open, patch
from cryptography.hazmat.primitives.asymmetric import rsa
from cryptography.hazmat.primitives import serialization
from llama_index.llms.cortex.utils import generate_sf_jwt
def test_generate_sf_jwt():
sf_account = "MY_SNOWFLAKE_ORG-MY_SNOWFLAKE_ACCOUNT"
sf_user = "... |
from keras.src.backend.config import backend
if backend() == "torch":
# When using the torch backend,
# torch needs to be imported first, otherwise it will segfault
# upon import.
import torch
from keras.src.api_export import keras_export
from keras.src.backend.common.dtypes import result_type
from ke... | from keras.src.backend.config import backend
if backend() == "torch":
# When using the torch backend,
# torch needs to be imported first, otherwise it will segfault
# upon import.
import torch
from keras.src.backend.common.dtypes import result_type
from keras.src.backend.common.keras_tensor import Ker... |
# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import os
import os.path as osp
from mmcv import Config
def parse_args():
parser = argparse.ArgumentParser(
description='Convert benchmark model list to script')
parser.add_argument('config', help='test config file path')
parser.add_... | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import os
import os.path as osp
from mmcv import Config
def parse_args():
parser = argparse.ArgumentParser(
description='Convert benchmark model list to script')
parser.add_argument('config', help='test config file path')
parser.add_... |
import os
import sys
import cognee
import pytest
from llama_index.core import Document
from llama_index.graph_rag.cognee import CogneeGraphRAG
@pytest.mark.skipif(
sys.version_info < (3, 10), reason="mock strategy requires python3.10 or higher"
)
@pytest.mark.skipif(
os.getenv("OPENAI_API_KEY") is None,
... | import os
import asyncio
import cognee
import pytest
from llama_index.core import Document
from llama_index.graph_rag.cognee import CogneeGraphRAG
@pytest.mark.skipif(
os.getenv("OPENAI_API_KEY") is None,
reason="OPENAI_API_KEY not available to test Cognee integration",
)
@pytest.mark.asyncio()
async def tes... |
from typing import Dict, Iterable, Sequence
from docarray import Document
from docarray.array.storage.base.getsetdel import BaseGetSetDelMixin
from docarray.array.storage.base.helper import Offset2ID
class GetSetDelMixin(BaseGetSetDelMixin):
"""Provide concrete implementation for ``__getitem__``, ``__setitem__``... | from typing import Dict, Iterable, Sequence
from docarray import Document
from docarray.array.storage.base.getsetdel import BaseGetSetDelMixin
from docarray.array.storage.base.helper import Offset2ID
class GetSetDelMixin(BaseGetSetDelMixin):
"""Provide concrete implementation for ``__getitem__``, ``__setitem__``... |
_base_ = './sparse-rcnn_r50_fpn_ms-480-800-3x_coco.py'
num_proposals = 300
model = dict(
rpn_head=dict(num_proposals=num_proposals),
test_cfg=dict(
_delete_=True, rpn=None, rcnn=dict(max_per_img=num_proposals)))
# augmentation strategy originates from DETR.
train_pipeline = [
dict(type='LoadImageFr... | _base_ = './sparse-rcnn_r50_fpn_ms-480-800-3x_coco.py'
num_proposals = 300
model = dict(
rpn_head=dict(num_proposals=num_proposals),
test_cfg=dict(
_delete_=True, rpn=None, rcnn=dict(max_per_img=num_proposals)))
# augmentation strategy originates from DETR.
train_pipeline = [
dict(
type='Lo... |
from pathlib import Path
from typing import Callable
import numpy as np
import pytest
import torchaudio
from jina import Document, DocumentArray
from ..vad_speech_segmenter import VADSpeechSegmenter
@pytest.fixture(scope='module')
def segmenter(tmpdir_factory) -> 'VADSpeechSegmenter':
workspace = tmpdir_factory... | import pytest
import os
from typing import Callable
from pathlib import Path
from jina import Document, DocumentArray
import numpy as np
import torchaudio
from ..vad_speech_segmenter import VADSpeechSegmenter
@pytest.fixture(scope='module')
def segmenter(tmpdir_factory) -> 'VADSpeechSegmenter':
workspace = tmpd... |
from typing import TYPE_CHECKING, Any, Dict, Type
from docarray.proto import DocumentProto, NdArrayProto, NodeProto
from docarray.typing import Tensor
from ..abstract_document import AbstractDocument
from ..base_node import BaseNode
class ProtoMixin(AbstractDocument, BaseNode):
@classmethod
def _get_nested_... | from typing import TYPE_CHECKING, Any, Dict, Type
from docarray.proto import DocumentProto, NdArrayProto, NodeProto
from docarray.proto.io import flush_ndarray, read_ndarray
from docarray.typing import Tensor
from ..abstract_document import AbstractDocument
from ..base_node import BaseNode
class ProtoMixin(Abstract... |
"""Run smoke tests"""
import sys
from pathlib import Path
import torch
import torchvision
from torchvision.io import decode_jpeg, read_file, read_image
from torchvision.models import resnet50, ResNet50_Weights
SCRIPT_DIR = Path(__file__).parent
def smoke_test_torchvision() -> None:
print(
"Is torchvisi... | """Run smoke tests"""
import sys
from pathlib import Path
import torch
import torchvision
from torchvision.io import decode_jpeg, read_file, read_image
from torchvision.models import resnet50, ResNet50_Weights
SCRIPT_DIR = Path(__file__).parent
def smoke_test_torchvision() -> None:
print(
"Is torchvisi... |
from typing import Any, Optional, Type, TypeVar, Union
from docarray.base_document import BaseDocument
from docarray.documents.mesh.vertices_and_faces import VerticesAndFaces
from docarray.typing.tensor.embedding import AnyEmbedding
from docarray.typing.url.url_3d.mesh_url import Mesh3DUrl
T = TypeVar('T', bound='Mes... | from typing import Any, Optional, Type, TypeVar, Union
from docarray.base_document import BaseDocument
from docarray.documents.mesh.vertices_and_faces import VerticesAndFaces
from docarray.typing.tensor.embedding import AnyEmbedding
from docarray.typing.url.url_3d.mesh_url import Mesh3DUrl
T = TypeVar('T', bound='Mes... |
"""
===================================
Examples of Using `FrozenEstimator`
===================================
This example showcases some use cases of :class:`~sklearn.frozen.FrozenEstimator`.
:class:`~sklearn.frozen.FrozenEstimator` is a utility class that allows to freeze a
fitted estimator. This is useful, for i... | """
===================================
Examples of Using `FrozenEstimator`
===================================
This examples showcases some use cases of :class:`~sklearn.frozen.FrozenEstimator`.
:class:`~sklearn.frozen.FrozenEstimator` is a utility class that allows to freeze a
fitted estimator. This is useful, for ... |
"""
This module provides dynamic access to deprecated JSON tools in LangChain.
It ensures backward compatibility by forwarding references such as
`JsonGetValueTool`, `JsonListKeysTool`, and `JsonSpec` to their updated
locations within the `langchain_community.tools` namespace.
This setup allows legacy code to continu... | from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.tools import JsonGetValueTool, JsonListKeysTool
from langchain_community.tools.json.tool import JsonSpec
# Create a way to dynamically look up deprecated imports.
# Used to consolidate ... |
# ruff: 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/LICE... | # 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.
import argparse
from mmengine import Config, DictAction
from mmdet.utils import replace_cfg_vals, update_data_root
def parse_args():
parser = argparse.ArgumentParser(description='Print the whole config')
parser.add_argument('config', help='config file path')
... | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import warnings
from mmcv import Config, DictAction
from mmdet.utils import replace_cfg_vals, update_data_root
def parse_args():
parser = argparse.ArgumentParser(description='Print the whole config')
parser.add_argument('config', help='config f... |
from dataclasses import dataclass, field
from typing import Any, Callable, Dict, List
import torch
@dataclass
class SentenceTransformerDataCollator:
"""Collator for a SentenceTransformers model.
This encodes the text columns to {column}_input_ids and {column}_attention_mask columns.
This works with the t... | from dataclasses import dataclass, field
from typing import Any, Callable, Dict, List
import torch
@dataclass
class SentenceTransformerDataCollator:
"""Collator for a SentenceTransformers model.
This encodes the text columns to {column}_input_ids and {column}_attention_mask columns.
This works with the t... |
# Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from mmdet.models.plugins import DropBlock
def test_dropblock():
feat = torch.rand(1, 1, 11, 11)
drop_prob = 1.0
dropblock = DropBlock(drop_prob, block_size=11, warmup_iters=0)
out_feat = dropblock(feat)
assert (out_feat =... | import pytest
import torch
from mmdet.models.plugins import DropBlock
def test_dropblock():
feat = torch.rand(1, 1, 11, 11)
drop_prob = 1.0
dropblock = DropBlock(drop_prob, block_size=11, warmup_iters=0)
out_feat = dropblock(feat)
assert (out_feat == 0).all() and out_feat.shape == feat.shape
... |
import pickle
from dataclasses import dataclass
from io import BufferedIOBase
from typing import Any
import torch
import torch._weights_only_unpickler as _weights_only_unpickler
from torch.serialization import _load, _save, DEFAULT_PROTOCOL, MAP_LOCATION
__all__: list[str] = []
@dataclass
class _Entry:
key: st... | import pickle
from dataclasses import dataclass
from io import BufferedIOBase
from typing import Any
import torch
import torch._weights_only_unpickler as _weights_only_unpickler
from torch.serialization import _load, _save, DEFAULT_PROTOCOL, MAP_LOCATION
__all__: list[str] = []
@dataclass
class _Entry:
key: st... |
"""DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.ops.nn import average_pool
from keras.src.ops.nn import batch_normalization
from keras.src.ops.nn import binary_crossentropy
from keras.src.ops.nn import categorical_crossentropy
from... | """DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.ops.nn import average_pool
from keras.src.ops.nn import batch_normalization
from keras.src.ops.nn import binary_crossentropy
from keras.src.ops.nn import categorical_crossentropy
from... |
from abc import abstractmethod
from typing import Iterator, Iterable, MutableSequence
from docarray import Document
class BaseSequenceLikeMixin(MutableSequence[Document]):
"""Implement sequence-like methods"""
def insert(self, index: int, value: 'Document'):
"""Insert `doc` at `index`.
:par... | from abc import abstractmethod
from typing import Iterator, Iterable, MutableSequence
from .... import Document
class BaseSequenceLikeMixin(MutableSequence[Document]):
"""Implement sequence-like methods"""
def insert(self, index: int, value: 'Document'):
"""Insert `doc` at `index`.
:param i... |
from abc import ABC, abstractmethod
from typing import Optional, Union
from .. import Dataset, DatasetDict, Features, IterableDataset, IterableDatasetDict, NamedSplit
from ..utils.typing import NestedDataStructureLike, PathLike
class AbstractDatasetReader(ABC):
def __init__(
self,
path_or_paths: ... | from abc import ABC, abstractmethod
from typing import Optional, Union
from .. import DatasetDict, Features, NamedSplit
from ..arrow_dataset import Dataset
from ..utils.typing import NestedDataStructureLike, PathLike
class AbstractDatasetReader(ABC):
def __init__(
self,
path_or_paths: NestedDataS... |
from parameterized import parameterized
from torchaudio import sox_effects
from torchaudio_unittest.common_utils import (
get_sinusoid,
get_wav_data,
save_wav,
skipIfNoSox,
TempDirMixin,
TorchaudioTestCase,
)
from .common import load_params
@skipIfNoSox
class SmokeTest(TempDirMixin, Torchaudi... | from parameterized import parameterized
from torchaudio import sox_effects
from torchaudio_unittest.common_utils import (
TempDirMixin,
TorchaudioTestCase,
skipIfNoSox,
get_wav_data,
get_sinusoid,
save_wav,
)
from .common import (
load_params,
)
@skipIfNoSox
class SmokeTest(TempDirMixin, ... |
import types
from keras.src.activations.activations import celu
from keras.src.activations.activations import elu
from keras.src.activations.activations import exponential
from keras.src.activations.activations import gelu
from keras.src.activations.activations import glu
from keras.src.activations.activations import ... | import types
from keras.src.activations.activations import celu
from keras.src.activations.activations import elu
from keras.src.activations.activations import exponential
from keras.src.activations.activations import gelu
from keras.src.activations.activations import glu
from keras.src.activations.activations import ... |
"""Base interface class for storing chat history per user."""
import asyncio
from abc import abstractmethod
from typing import List, Optional
from llama_index.core.llms import ChatMessage
from llama_index.core.schema import BaseComponent
class BaseChatStore(BaseComponent):
@classmethod
def class_name(cls) -... | """Base interface class for storing chat history per user."""
import asyncio
from abc import abstractmethod
from typing import List, Optional
from llama_index.core.llms import ChatMessage
from llama_index.core.schema import BaseComponent
class BaseChatStore(BaseComponent):
@classmethod
def class_name(cls) ->... |
# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import mmcv
from mmcv import Config, DictAction
from mmdet.datasets import build_dataset
def parse_args():
parser = argparse.ArgumentParser(description='Evaluate metric of the '
'results saved in pkl format')
... | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import mmcv
from mmcv import Config, DictAction
from mmdet.datasets import build_dataset
def parse_args():
parser = argparse.ArgumentParser(description='Evaluate metric of the '
'results saved in pkl format')
... |
# 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 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... |
import dataclasses
from typing import Any, Dict, Optional, Type
from jina.jaml.parsers.base import BaseLegacyParser
from jina.serve.executors import BaseExecutor
from jina.serve.executors.metas import get_default_metas
class ExecutorLegacyParser(BaseLegacyParser):
"""Legacy parser for executor."""
def parse... | import dataclasses
from typing import Any, Dict, Optional, Type
from jina.jaml.parsers.base import BaseLegacyParser
from jina.serve.executors import BaseExecutor
from jina.serve.executors.metas import get_default_metas
class ExecutorLegacyParser(BaseLegacyParser):
"""Legacy parser for executor."""
def parse... |
import pytest
from docarray import DocumentArray
from docarray.array.opensearch import DocumentArrayOpenSearch, OpenSearchConfig
from docarray.array.qdrant import DocumentArrayQdrant
from docarray.array.sqlite import DocumentArraySqlite
from docarray.array.annlite import DocumentArrayAnnlite, AnnliteConfig
from docarr... | import pytest
from docarray import DocumentArray
from docarray.array.qdrant import DocumentArrayQdrant
from docarray.array.sqlite import DocumentArraySqlite
from docarray.array.annlite import DocumentArrayAnnlite, AnnliteConfig
from docarray.array.storage.qdrant import QdrantConfig
from docarray.array.storage.weaviate... |
"""Test EmbaasEmbeddings embeddings"""
import pytest
from pydantic import SecretStr
from pytest import CaptureFixture
from langchain_community.embeddings import PremAIEmbeddings
@pytest.mark.requires("premai")
def test_api_key_is_string() -> None:
llm = PremAIEmbeddings( # type: ignore[call-arg]
premai... | """Test EmbaasEmbeddings embeddings"""
import pytest
from pydantic import SecretStr
from pytest import CaptureFixture
from langchain_community.embeddings import PremAIEmbeddings
@pytest.mark.requires("premai")
def test_api_key_is_string() -> None:
llm = PremAIEmbeddings( # type: ignore[call-arg]
premai... |
class MissingConfigError(Exception):
"""The attempted operation requires configuration which is not available"""
class NotFoundError(ValueError):
"""The requested record was not found, resulting in an error condition"""
class NeedConfirmation(Exception):
"""The user must explicitly confirm that they wan... | class MissingConfigError(Exception):
"""The attempted operation requires configuration which is not available"""
class NeedConfirmation(Exception):
"""The user must explicitly confirm that they want to proceed"""
class InsufficientBalanceError(ValueError):
user_id: str
message: str
balance: floa... |
from google.protobuf import __version__ as __pb__version__
from jina._docarray import docarray_v2 as is_docarray_v2
if __pb__version__.startswith('4'):
if is_docarray_v2:
from jina.proto.docarray_v2.pb.jina_pb2 import *
else:
from jina.proto.docarray_v1.pb.jina_pb2 import *
else:
if is_do... | from google.protobuf import __version__ as __pb__version__
from jina._docarray import docarray_v2 as is_docarray_v2
if __pb__version__.startswith('4'):
if is_docarray_v2:
from .docarray_v2.pb.jina_pb2 import *
else:
from .docarray_v1.pb.jina_pb2 import *
else:
if is_docarray_v2:
f... |
import types
from typing import TYPE_CHECKING
from docarray.typing.tensor.audio.audio_ndarray import AudioNdArray
from docarray.typing.tensor.audio.audio_tensor import AudioTensor
from docarray.utils._internal.misc import (
_get_path_from_docarray_root_level,
import_library,
)
if TYPE_CHECKING:
from docar... | import types
from typing import TYPE_CHECKING
from docarray.typing.tensor.audio.audio_ndarray import AudioNdArray
from docarray.typing.tensor.audio.audio_tensor import AudioTensor
from docarray.utils._internal.misc import (
_get_path_from_docarray_root_level,
import_library,
)
if TYPE_CHECKING:
from docar... |
# coding=utf-8
# Copyright 2020, The RAG Authors and The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless r... | # coding=utf-8
# Copyright 2020, The RAG Authors and The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless r... |
__copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
from typing import Dict, Iterable, Optional
import torch
from jina import DocumentArray, Executor, requests
from jina_commons.batching import get_docs_batch_generator
from sentence_transformers import SentenceTr... | __copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
from typing import Optional, Dict, List, Tuple
from jina import Executor, DocumentArray, requests
from sentence_transformers import SentenceTransformer
from jina_commons.batching import get_docs_batch_generator
... |
# flake8: noqa
import os
# Set backend env to torch
os.environ["KERAS_BACKEND"] = "torch"
import torch
import torch.nn as nn
import torch.optim as optim
from keras import layers
import keras
import numpy as np
# Model / data parameters
num_classes = 10
input_shape = (28, 28, 1)
learning_rate = 0.01
batch_size = 64
n... | # flake8: noqa
import os
# Set backend env to torch
os.environ["KERAS_BACKEND"] = "torch"
import torch
import torch.nn as nn
import torch.optim as optim
from keras import layers
import keras
import numpy as np
# Model / data parameters
num_classes = 10
input_shape = (28, 28, 1)
learning_rate = 0.01
batch_size = 64
n... |
def _python_type_to_schema_type(p):
if p == 'str':
dtype = 'string'
elif p == 'int' or p == 'float':
dtype = 'number'
elif p in {'typing.List[str]', 'typing.Tuple[str]', 'list', 'tuple'}:
dtype = 'array'
elif p == 'bool':
dtype = 'boolean'
elif p == 'dict':
dt... | def _python_type_to_schema_type(p):
if p == 'str':
dtype = 'string'
elif p == 'int' or p == 'float':
dtype = 'number'
elif p in {'typing.List[str]', 'typing.Tuple[str]', 'list', 'tuple'}:
dtype = 'array'
elif p == 'bool':
dtype = 'boolean'
elif p == 'dict':
dt... |
import logging
from backend.data import db
from backend.data.credit import UsageTransactionMetadata, get_user_credit_model
from backend.data.execution import (
create_graph_execution,
get_graph_execution,
get_incomplete_node_executions,
get_latest_node_execution,
get_node_execution_results,
upd... | import logging
from backend.data import db
from backend.data.credit import UsageTransactionMetadata, get_user_credit_model
from backend.data.execution import (
create_graph_execution,
get_graph_execution,
get_incomplete_node_executions,
get_latest_node_execution,
get_node_execution_results,
upd... |
import os
from typing import Any, Optional, Dict
from llama_index.llms.openai_like import OpenAILike
class Databricks(OpenAILike):
"""
Databricks LLM.
Examples:
`pip install llama-index-llms-databricks`
```python
from llama_index.llms.databricks import Databricks
# Set ... | import os
from typing import Any, Optional, Dict
from llama_index.llms.openai_like import OpenAILike
class Databricks(OpenAILike):
"""Databricks LLM.
Examples:
`pip install llama-index-llms-databricks`
```python
from llama_index.llms.databricks import Databricks
# Set up th... |
from typing import Optional
import numpy as np
import pytest
import torch
from pydantic.tools import parse_obj_as, schema_json_of
from docarray import BaseDoc
from docarray.base_doc.io.json import orjson_dumps
from docarray.typing import AudioBytes, AudioTorchTensor, AudioUrl
from docarray.utils._internal.misc import... | from typing import Optional
import numpy as np
import pytest
import torch
from pydantic.tools import parse_obj_as, schema_json_of
from docarray import BaseDoc
from docarray.base_doc.io.json import orjson_dumps
from docarray.typing import AudioBytes, AudioTorchTensor, AudioUrl
from docarray.utils._internal.misc import... |
_base_ = './rtmdet_l_8xb32-300e_coco.py'
model = dict(
bbox_head=dict(
_delete_=True,
type='RTMDetInsSepBNHead',
num_classes=80,
in_channels=256,
stacked_convs=2,
share_conv=True,
pred_kernel_size=1,
feat_channels=256,
act_cfg=dict(type='SiLU',... | _base_ = './rtmdet_l_8xb32-300e_coco.py'
model = dict(
bbox_head=dict(
_delete_=True,
type='RTMDetInsSepBNHead',
num_classes=80,
in_channels=256,
stacked_convs=2,
share_conv=True,
pred_kernel_size=1,
feat_channels=256,
act_cfg=dict(type='SiLU',... |
from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.chat_models.pai_eas_endpoint import PaiEasChatEndpoint
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handling ... | from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.chat_models.pai_eas_endpoint import PaiEasChatEndpoint
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handling ... |
from typing import Optional
from docarray import Document, DocumentArray
from pydantic import BaseModel
from uvicorn import Config, Server
from jina import Gateway, __default_host__
from jina.clients.request import request_generator
class DummyResponseModel(BaseModel):
arg1: Optional[str]
arg2: Optional[str... | from typing import Optional
from docarray import Document, DocumentArray
from pydantic import BaseModel
from uvicorn import Config, Server
from jina import Gateway, __default_host__
from jina.clients.request import request_generator
class DummyResponseModel(BaseModel):
arg1: Optional[str]
arg2: Optional[str... |
_base_ = './fast-rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| _base_ = './fast_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
|
# Copyright (c) OpenMMLab. All rights reserved.
import pytest
from mmdet.models.backbones import DetectoRS_ResNet
def test_detectorrs_resnet_backbone():
detectorrs_cfg = dict(
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requi... | import pytest
from mmdet.models.backbones import DetectoRS_ResNet
def test_detectorrs_resnet_backbone():
detectorrs_cfg = dict(
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
... |
import logging
from datasets import load_dataset
from sentence_transformers import SparseEncoder
from sentence_transformers.sparse_encoder.evaluation import SparseEmbeddingSimilarityEvaluator
logging.basicConfig(format="%(message)s", level=logging.INFO)
# Load a model
model = SparseEncoder("naver/splade-cocondenser... | import logging
from datasets import load_dataset
from sentence_transformers import SparseEncoder
from sentence_transformers.sparse_encoder.evaluation import SparseEmbeddingSimilarityEvaluator
logging.basicConfig(format="%(message)s", level=logging.INFO)
# Load a model
model = SparseEncoder("naver/splade-cocondenser... |
from __future__ import annotations
import logging
from datasets import load_dataset
from sentence_transformers.evaluation import SequentialEvaluator, SimilarityFunction
from sentence_transformers.models import Pooling, Transformer
from sentence_transformers.sparse_encoder import SparseEncoder
from sentence_transform... | from __future__ import annotations
import logging
from datasets import load_dataset
from sentence_transformers.evaluation import SequentialEvaluator, SimilarityFunction
from sentence_transformers.models import Pooling, Transformer
from sentence_transformers.sparse_encoder import SparseEncoder
from sentence_transform... |
# Copyright (c) OpenMMLab. All rights reserved.
__version__ = '3.0.0'
short_version = __version__
def parse_version_info(version_str):
"""Parse a version string into a tuple.
Args:
version_str (str): The version string.
Returns:
tuple[int | str]: The version info, e.g., "1.3.0" is parsed... | # Copyright (c) OpenMMLab. All rights reserved.
__version__ = '3.0.0rc6'
short_version = __version__
def parse_version_info(version_str):
"""Parse a version string into a tuple.
Args:
version_str (str): The version string.
Returns:
tuple[int | str]: The version info, e.g., "1.3.0" is par... |
from __future__ import annotations
__version__ = "4.1.0.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__ = "4.1.0.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 enum import Enum
from typing import Iterable, Dict
import torch.nn.functional as F
from torch import nn, Tensor
from sentence_transformers.SentenceTransformer import SentenceTransformer
class SiameseDistanceMetric(Enum):
"""
The metric for the contrastive loss
"""
EUCLIDEAN = lambda x, y: F.pair... | from enum import Enum
from typing import Iterable, Dict
import torch.nn.functional as F
from torch import nn, Tensor
from sentence_transformers.SentenceTransformer import SentenceTransformer
class SiameseDistanceMetric(Enum):
"""
The metric for the contrastive loss
"""
EUCLIDEAN = lambda x, y: F.pair... |
"""
Example of using callbacks with Dask
====================================
"""
import numpy as np
from dask.distributed import Client, LocalCluster
from dask_ml.datasets import make_regression
from dask_ml.model_selection import train_test_split
import xgboost as xgb
import xgboost.dask as dxgb
from xgboost.dask im... | """
Example of using callbacks with Dask
====================================
"""
import numpy as np
from dask.distributed import Client, LocalCluster
from dask_ml.datasets import make_regression
from dask_ml.model_selection import train_test_split
import xgboost as xgb
from xgboost.dask import DaskDMatrix
def proba... |
import torch
from torchvision import tv_tensors
from torchvision.utils import _log_api_usage_once
from ._utils import _get_kernel, _register_kernel_internal
def uniform_temporal_subsample(inpt: torch.Tensor, num_samples: int) -> torch.Tensor:
"""[BETA] See :class:`~torchvision.transforms.v2.UniformTemporalSubs... | import torch
from torchvision import datapoints
from torchvision.utils import _log_api_usage_once
from ._utils import _get_kernel, _register_kernel_internal
def uniform_temporal_subsample(inpt: torch.Tensor, num_samples: int) -> torch.Tensor:
"""[BETA] See :class:`~torchvision.transforms.v2.UniformTemporalSubs... |
"""
Tests the correct computation of evaluation scores from TripletEvaluator
"""
from __future__ import annotations
from sentence_transformers import SentenceTransformer
from sentence_transformers.evaluation import TripletEvaluator
def test_TripletEvaluator(stsb_bert_tiny_model: SentenceTransformer) -> None:
""... | """
Tests the correct computation of evaluation scores from TripletEvaluator
"""
from __future__ import annotations
from sentence_transformers import SentenceTransformer
from sentence_transformers.evaluation import TripletEvaluator
def test_TripletEvaluator(stsb_bert_tiny_model_reused: SentenceTransformer) -> None:... |
"""Tests related to the `DataIter` interface."""
import numpy as np
import xgboost
from xgboost import testing as tm
def run_mixed_sparsity(device: str) -> None:
"""Check QDM with mixed batches."""
X_0, y_0, _ = tm.make_regression(128, 16, False)
if device.startswith("cuda"):
X_1, y_1 = tm.make_... | """Tests related to the `DataIter` interface."""
import numpy as np
import xgboost
from xgboost import testing as tm
def run_mixed_sparsity(device: str) -> None:
"""Check QDM with mixed batches."""
X_0, y_0, _ = tm.make_regression(128, 16, False)
if device.startswith("cuda"):
X_1, y_1 = tm.make_... |
import numpy as np
from pydantic.tools import parse_obj_as, schema_json_of
from docarray.document.io.json import orjson_dumps
from docarray.typing import Embedding
def test_proto_embedding():
embedding = parse_obj_as(Embedding, np.zeros((3, 224, 224)))
embedding._to_node_protobuf()
def test_json_schema()... | import numpy as np
from pydantic.tools import parse_obj_as
from docarray.typing import Embedding
def test_proto_embedding():
uri = parse_obj_as(Embedding, np.zeros((3, 224, 224)))
uri._to_node_protobuf()
|
import pathlib
from argparse import ArgumentParser
def main(args):
wheel_path = pathlib.Path(args.wheel_path).expanduser().resolve()
if not wheel_path.exists():
raise ValueError(f"Wheel cannot be found at path {wheel_path}")
if not wheel_path.is_file():
raise ValueError(f"Path {wheel_path}... | import os
import sys
from test_utils import DirectoryExcursion
if len(sys.argv) != 4:
print("Usage: {} [wheel to rename] [commit id] [platform tag]".format(sys.argv[0]))
sys.exit(1)
whl_path = sys.argv[1]
commit_id = sys.argv[2]
platform_tag = sys.argv[3]
dirname, basename = os.path.dirname(whl_path), os.p... |
from typing import Type
from .document import BaseDocument
class AnyDocument(BaseDocument):
"""
AnyDocument 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, fi... | from typing import Type
from .document import BaseDocument
class AnyDocument(BaseDocument):
"""
AnyDocument is a Document that is not tied to any schema
"""
def __init__(self, **kwargs):
super().__init__()
self.__dict__.update(kwargs)
@classmethod
def _get_nested_document_cl... |
# Copyright (c) OpenMMLab. All rights reserved.
import unittest
from mmdet.datasets import CrowdHumanDataset
class TestCrowdHumanDataset(unittest.TestCase):
def test_crowdhuman_init(self):
dataset = CrowdHumanDataset(
data_root='tests/data/crowdhuman_dataset/',
ann_file='test_ann... | # Copyright (c) OpenMMLab. All rights reserved.
import unittest
from mmdet.datasets import CrowdHumanDataset
class TestCrowdHumanDataset(unittest.TestCase):
def test_crowdhuman_init(self):
dataset = CrowdHumanDataset(
data_root='tests/data/crowdhuman_dataset/',
ann_file='test_ann... |
from llama_index.core.base.llms.base import BaseLLM
from llama_index.llms.cleanlab import CleanlabTLM
from llama_index.llms.cleanlab.base import DEFAULT_MODEL, DEFAULT_MAX_TOKENS
def test_llms_cleanlab():
names_of_base_classes = [b.__name__ for b in CleanlabTLM.__mro__]
assert BaseLLM.__name__ in names_of_bas... | from llama_index.core.base.llms.base import BaseLLM
from llama_index.llms.cleanlab import CleanlabTLM
def test_llms_cleanlab():
names_of_base_classes = [b.__name__ for b in CleanlabTLM.__mro__]
assert BaseLLM.__name__ in names_of_base_classes
|
from typing import Any, Optional, Sequence
from llama_index.core.evaluation.base import BaseEvaluator, EvaluationResult
from llama_index.core.prompts.mixin import PromptDictType, PromptMixinType
from tonic_validate.metrics.answer_consistency_binary_metric import (
AnswerConsistencyBinaryMetric,
)
from tonic_valid... | from typing import Any, Optional, Sequence
from llama_index.core.evaluation.base import BaseEvaluator, EvaluationResult
from llama_index.core.prompts.mixin import PromptDictType, PromptMixinType
from tonic_validate.metrics.answer_consistency_binary_metric import (
AnswerConsistencyBinaryMetric,
)
from tonic_valid... |
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