input stringlengths 33 5k | output stringlengths 32 5k |
|---|---|
from langchain_core.outputs import (
ChatGeneration,
ChatGenerationChunk,
ChatResult,
Generation,
GenerationChunk,
LLMResult,
RunInfo,
)
__all__ = [
"ChatGeneration",
"ChatGenerationChunk",
"ChatResult",
"Generation",
"GenerationChunk",
"LLMResult",
"RunInfo",
]
| from langchain_core.outputs import (
ChatGeneration,
ChatGenerationChunk,
ChatResult,
Generation,
GenerationChunk,
LLMResult,
RunInfo,
)
__all__ = [
"Generation",
"GenerationChunk",
"ChatGeneration",
"ChatGenerationChunk",
"RunInfo",
"ChatResult",
"LLMResult",
]
|
from .common_utils import _get_id2label, _get_label2id, create_tsv
from .feature_utils import dump_features
from .kmeans import get_km_label, learn_kmeans
__all__ = [
"create_tsv",
"_get_id2label",
"_get_label2id",
"dump_features",
"learn_kmeans",
"get_km_label",
]
| from .common_utils import create_tsv
from .feature_utils import dump_features
from .kmeans import get_km_label, learn_kmeans
__all__ = [
"create_tsv",
"dump_features",
"learn_kmeans",
"get_km_label",
]
|
"""DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.datasets.cifar10 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.cifar10 import load_data
|
"""
This script contains an example how to perform semantic search with PyTorch. It performs exact nearest neighborh search.
As dataset, we use the Quora Duplicate Questions dataset, which contains about 500k questions (we only use about 100k):
https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pai... | """
This script contains an example how to perform semantic search with PyTorch. It performs exact nearest neighborh search.
As dataset, we use the Quora Duplicate Questions dataset, which contains about 500k questions (we only use about 100k):
https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pai... |
import inspect
import re
from hashlib import sha256
from typing import Dict, List
from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql # noqa F401
from... | import inspect
import re
from hashlib import sha256
from typing import List
from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql # noqa F401
from .text... |
# pants requires this import to recognize the dep
import pytest_asyncio # noqa: F401
import pytest
from llama_index.core.workflow.workflow import Workflow
from llama_index.core.workflow.decorators import step
from llama_index.core.workflow.events import StartEvent, StopEvent, Event
from llama_index.core.bridge.pydan... | import pytest
from llama_index.core.workflow.workflow import Workflow
from llama_index.core.workflow.decorators import step
from llama_index.core.workflow.events import StartEvent, StopEvent, Event
from llama_index.core.bridge.pydantic import Field
class OneTestEvent(Event):
test_param: str = Field(default="test... |
import random
from collections import defaultdict
from typing import Dict, Any, TYPE_CHECKING, Generator, List
import numpy as np
from docarray.helper import dunder_get
if TYPE_CHECKING: # pragma: no cover
from docarray import DocumentArray
class GroupMixin:
"""These helpers yield groups of :class:`Docume... | import random
from collections import defaultdict
from typing import Dict, Any, TYPE_CHECKING, Generator, List
import numpy as np
from docarray.helper import dunder_get
if TYPE_CHECKING:
from docarray import DocumentArray
class GroupMixin:
"""These helpers yield groups of :class:`DocumentArray` from
a ... |
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... | import logging
from sklearn.metrics import average_precision_score
from typing import List
import numpy as np
import os
import csv
from ... import InputExample
from ...evaluation import BinaryClassificationEvaluator
logger = logging.getLogger(__name__)
class CEBinaryClassificationEvaluator:
"""
This evalua... |
from ._label import Label, OneHotLabel
| from ._bounding_box import BoundingBox, BoundingBoxFormat
from ._datapoint import FillType, FillTypeJIT, InputType, InputTypeJIT
from ._image import Image, ImageType, ImageTypeJIT, TensorImageType, TensorImageTypeJIT
from ._label import Label, OneHotLabel
from ._mask import Mask
from ._video import TensorVideoType, Ten... |
"""GraphQL Reader."""
from typing import Dict, List, Optional
import yaml
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document
class GraphQLReader(BaseReader):
"""
GraphQL reader.
Combines all GraphQL results into the Document used by LlamaIndex.
Args:
... | """GraphQL Reader."""
from typing import Dict, List, Optional
import yaml
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document
class GraphQLReader(BaseReader):
"""GraphQL reader.
Combines all GraphQL results into the Document used by LlamaIndex.
Args:
... |
from typing import Dict, List, Tuple
import pytest
from llama_index.core.schema import Document
from tests.mock_utils.mock_prompts import (
MOCK_INSERT_PROMPT,
MOCK_QUERY_PROMPT,
MOCK_REFINE_PROMPT,
MOCK_SUMMARY_PROMPT,
MOCK_TEXT_QA_PROMPT,
)
@pytest.fixture()
def documents() -> List[Document]:
... | from typing import Dict, List, Tuple
import pytest
from llama_index.core.schema import Document
from tests.mock_utils.mock_prompts import (
MOCK_INSERT_PROMPT,
MOCK_QUERY_PROMPT,
MOCK_REFINE_PROMPT,
MOCK_SUMMARY_PROMPT,
MOCK_TEXT_QA_PROMPT,
)
@pytest.fixture()
def documents() -> List[Document]:
... |
from __future__ import annotations
import re
from typing import TYPE_CHECKING, Any
if TYPE_CHECKING:
from sentence_transformers.SentenceTransformer import SentenceTransformer
class SentenceEvaluator:
"""
Base class for all evaluators
Extend this class and implement __call__ for custom evaluators.
... | from __future__ import annotations
import re
from typing import TYPE_CHECKING, Any
if TYPE_CHECKING:
from sentence_transformers.SentenceTransformer import SentenceTransformer
class SentenceEvaluator:
"""
Base class for all evaluators
Extend this class and implement __call__ for custom evaluators.
... |
import asyncio
import pytest
from llama_index.graph_rag.cognee import CogneeGraphRAG
@pytest.mark.asyncio()
async def test_get_graph_url(monkeypatch):
# Instantiate cognee GraphRAG
cogneeRAG = CogneeGraphRAG(
llm_api_key="",
llm_provider="openai",
llm_model="gpt-4o-mini",
graph... | import asyncio
import pytest
from llama_index.graph_rag.cognee import CogneeGraphRAG
@pytest.mark.asyncio()
async def test_get_graph_url(monkeypatch):
# Instantiate cognee GraphRAG
cogneeRAG = CogneeGraphRAG(
llm_api_key="",
llm_provider="openai",
llm_model="gpt-4o-mini",
graph... |
import os
import pickle
from typing import Optional, Iterable, Tuple
from jina import Executor, requests, DocumentArray
from jina.excepts import PretrainedModelFileDoesNotExist
from jina_commons.batching import get_docs_batch_generator
class TFIDFTextEncoder(Executor):
"""
Encode text into tf-idf sparse embe... | import os
import pickle
from typing import Optional, Iterable, Any, List, Tuple
from jina import Executor, requests, DocumentArray
from jina.excepts import PretrainedModelFileDoesNotExist
from jina_commons.batching import get_docs_batch_generator
class TFIDFTextEncoder(Executor):
"""
Encode text into tf-idf ... |
import datasets
from ..folder_based_builder import folder_based_builder
logger = datasets.utils.logging.get_logger(__name__)
class AudioFolderConfig(folder_based_builder.FolderBasedBuilderConfig):
"""Builder Config for AudioFolder."""
drop_labels: bool = None
drop_metadata: bool = None
def __post... | from typing import List
import datasets
from ..folder_based_builder import folder_based_builder
logger = datasets.utils.logging.get_logger(__name__)
class AudioFolderConfig(folder_based_builder.FolderBasedBuilderConfig):
"""Builder Config for AudioFolder."""
drop_labels: bool = None
drop_metadata: bo... |
from .functional import add_noise, barkscale_fbanks, convolve, fftconvolve
__all__ = ["add_noise", "barkscale_fbanks", "convolve", "fftconvolve"]
| from .functional import add_noise, convolve, fftconvolve
__all__ = ["add_noise", "convolve", "fftconvolve"]
|
import pytest
from huggingface_hub import snapshot_download
@pytest.fixture
def dataset_dir(tmp_path):
dataset_dir = tmp_path / "test_command_dataset_dir"
snapshot_download("hf-internal-testing/ner-jsonl", repo_type="dataset", local_dir=dataset_dir)
return str(dataset_dir)
| import pytest
DATASET_LOADING_SCRIPT_NAME = "__dummy_dataset1__"
DATASET_LOADING_SCRIPT_CODE = """
import json
import os
import datasets
REPO_URL = "https://huggingface.co/datasets/hf-internal-testing/raw_jsonl/resolve/main/"
URLS = {"train": REPO_URL + "wikiann-bn-train.jsonl", "validation": REPO_URL + "wikiann-... |
"""Init params."""
from llama_index.finetuning.openai.base import OpenAIFinetuneEngine
__all__ = ["OpenAIFinetuneEngine"]
| """Init params."""
from llama_index.finetuning.openai.base import OpenAIFinetuneEngine
__all__ = ["OpenAIFinetuneEngine"]
|
# Copyright (c) OpenMMLab. All rights reserved.
import os
from typing import Optional
import torch
try:
import torch_npu # noqa: F401
import torch_npu.npu.utils as npu_utils
# Enable operator support for dynamic shape and
# binary operator support on the NPU.
npu_jit_compile = bool(os.getenv('NP... | # Copyright (c) OpenMMLab. All rights reserved.
import os
from typing import Optional
import torch
try:
import torch_npu # noqa: F401
import torch_npu.npu.utils as npu_utils
# Enable operator support for dynamic shape and
# binary operator support on the NPU.
npu_jit_compile = bool(os.getenv('NP... |
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appl... | # Copyright 2024 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appl... |
_base_ = './reppoints_moment_r50_fpn_1x_coco.py'
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(neck=dict(norm_cfg=norm_cfg), bbox_head=dict(norm_cfg=norm_cfg))
| _base_ = './reppoints_moment_r50_fpn_1x_coco.py'
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(neck=dict(norm_cfg=norm_cfg), bbox_head=dict(norm_cfg=norm_cfg))
optimizer = dict(lr=0.01)
|
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... |
# Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
from mmengine.testing import assert_allclose
from mmdet.structures.mask import BitmapMasks, PolygonMasks
def create_random_bboxes(num_bboxes, img_w, img_h):
bboxes_left_top = np.random.uniform(0, 0.5, size=(num_bboxes, 2))
bboxes_right_bottom... | # Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
from mmengine.testing import assert_allclose
from mmdet.data_elements.mask import BitmapMasks, PolygonMasks
def create_random_bboxes(num_bboxes, img_w, img_h):
bboxes_left_top = np.random.uniform(0, 0.5, size=(num_bboxes, 2))
bboxes_right_bot... |
# Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
from mmcv.cnn import ConvModule, bias_init_with_prob, normal_init
from mmdet.registry import MODELS
from .anchor_head import AnchorHead
@MODELS.register_module()
class RetinaSepBNHead(AnchorHead):
""""RetinaHead with separate BN.
In Retin... | # Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
from mmcv.cnn import ConvModule, bias_init_with_prob, normal_init
from ..builder import HEADS
from .anchor_head import AnchorHead
@HEADS.register_module()
class RetinaSepBNHead(AnchorHead):
""""RetinaHead with separate BN.
In RetinaHead, ... |
# coding=utf-8
# Copyright 2025 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... | # coding=utf-8
# Copyright 2025 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... |
from __future__ import annotations
import re
from typing import TYPE_CHECKING, Any
if TYPE_CHECKING:
from sentence_transformers.SentenceTransformer import SentenceTransformer
class SentenceEvaluator:
"""
Base class for all evaluators. Notably, this class introduces the ``greater_is_better`` and ``primar... | from __future__ import annotations
import re
from typing import TYPE_CHECKING, Any
if TYPE_CHECKING:
from sentence_transformers.SentenceTransformer import SentenceTransformer
class SentenceEvaluator:
"""
Base class for all evaluators. Notably, this class introduces the ``greater_is_better`` and ``primar... |
"""Module for helper functions for clients."""
from typing import Optional, Tuple
from jina._docarray import Document, DocumentArray, docarray_v2
from jina.enums import DataInputType
from jina.types.request.data import DataRequest
if docarray_v2:
from docarray import DocList
def _new_data_request_from_batch(
... | """Module for helper functions for clients."""
from typing import Optional, Tuple
from jina._docarray import Document, DocumentArray, docarray_v2
from jina.enums import DataInputType
from jina.types.request.data import DataRequest
def _new_data_request_from_batch(
batch,
data_type: DataInputType,
endpoin... |
import json
import os
import subprocess
import pytest
from jina.checker import NetworkChecker
from jina.jaml import JAML
from jina.orchestrate.pods.factory import PodFactory
from jina.parsers import set_deployment_parser, set_pod_parser
from jina.parsers.ping import set_ping_parser
from jina_cli.autocomplete import a... | import json
import os
import subprocess
import pytest
from jina.checker import NetworkChecker
from jina.jaml import JAML
from jina.orchestrate.pods.factory import PodFactory
from jina.parsers import set_deployment_parser, set_pod_parser
from jina.parsers.ping import set_ping_parser
from jina_cli.autocomplete import a... |
import logging
from sentence_transformers import SparseEncoder
from sentence_transformers.sparse_encoder.evaluation import SparseNanoBEIREvaluator
logging.basicConfig(format="%(message)s", level=logging.INFO)
# Load a model
model = SparseEncoder("naver/splade-cocondenser-ensembledistil")
evaluator = SparseNanoBEIR... | import logging
from sentence_transformers import SparseEncoder
from sentence_transformers.sparse_encoder.evaluation import SparseNanoBEIREvaluator
logging.basicConfig(format="%(message)s", level=logging.INFO)
# Load a model
model = SparseEncoder("naver/splade-cocondenser-ensembledistil")
evaluator = SparseNanoBEIR... |
__copyright__ = "Copyright (c) 2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import subprocess
from pathlib import Path
import pytest
@pytest.fixture(scope='session')
def docker_image_name() -> str:
return Path(__file__).parents[1].stem.lower()
@pytest.fixture(scope='session')
def bui... | __copyright__ = "Copyright (c) 2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import os
import pytest
TEST_DIR = os.path.dirname(os.path.abspath(__file__))
@pytest.fixture(scope='session', autouse=True)
def create_model_weights():
path_to_embedding_array = os.path.join(TEST_DIR, 'unit', ... |
_base_ = './queryinst_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, mask_thr_binary=0.5)))
# augmentation strategy originates from DETR.
train_pipe... | _base_ = './queryinst_r50_fpn_mstrain_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, mask_thr_binary=0.5)))
# augmentation strategy originates from DETR.
train... |
from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.graphs import NetworkxEntityGraph
from langchain_community.graphs.networkx_graph import (
KnowledgeTriple,
get_entities,
parse_triples,
)
# Create a way to d... | from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.graphs import NetworkxEntityGraph
from langchain_community.graphs.networkx_graph import (
KnowledgeTriple,
get_entities,
parse_triples,
)
# Create a way to d... |
"""CIFAR100 small images classification dataset."""
import os
import numpy as np
from keras.src import backend
from keras.src.api_export import keras_export
from keras.src.datasets.cifar import load_batch
from keras.src.utils.file_utils import get_file
@keras_export("keras.datasets.cifar100.load_data")
def load_da... | """CIFAR100 small images classification dataset."""
import os
import numpy as np
from keras.src import backend
from keras.src.api_export import keras_export
from keras.src.datasets.cifar import load_batch
from keras.src.utils.file_utils import get_file
@keras_export("keras.datasets.cifar100.load_data")
def load_da... |
"""Test volc engine maas chat model."""
from langchain_core.callbacks import CallbackManager
from langchain_core.messages import AIMessage, BaseMessage, HumanMessage
from langchain_core.outputs import ChatGeneration, LLMResult
from langchain_community.chat_models.volcengine_maas import VolcEngineMaasChat
from tests.u... | """Test volc engine maas chat model."""
from langchain_core.callbacks import CallbackManager
from langchain_core.messages import AIMessage, BaseMessage, HumanMessage
from langchain_core.outputs import ChatGeneration, LLMResult
from langchain_community.chat_models.volcengine_maas import VolcEngineMaasChat
from tests.u... |
_base_ = '../cascade_rcnn/cascade-mask-rcnn_x101-32x4d_fpn_1x_coco.py'
model = dict(
backbone=dict(
dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)))
| _base_ = '../cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco.py'
model = dict(
backbone=dict(
dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)))
|
"""Init file."""
from llama_index.readers.papers.arxiv.base import ArxivReader
from llama_index.readers.papers.pubmed.base import PubmedReader
__all__ = ["ArxivReader", "PubmedReader"]
| """Init file."""
from llama_index.readers.papers.arxiv.base import ArxivReader
from llama_index.readers.papers.pubmed.base import PubmedReader
__all__ = ["ArxivReader", "PubmedReader"]
|
# coding=utf-8
# Copyright 2025 the HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless r... | # coding=utf-8
# Copyright 2025 the HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless r... |
from parameterized import parameterized
from torchaudio._internal.module_utils import is_module_available
from torchaudio_unittest.common_utils import skipIfNoModule, TorchaudioTestCase
if is_module_available("unidecode") and is_module_available("inflect"):
from pipeline_tacotron2.text.numbers import (
_ex... | from parameterized import parameterized
from torchaudio._internal.module_utils import is_module_available
from torchaudio_unittest.common_utils import TorchaudioTestCase, skipIfNoModule
if is_module_available("unidecode") and is_module_available("inflect"):
from pipeline_tacotron2.text.numbers import (
_re... |
from functools import partial
from inspect import isclass
from typing import Any, Union, cast
from pydantic import BaseModel
from langchain_core.language_models import FakeListChatModel
from langchain_core.load.dump import dumps
from langchain_core.load.load import loads
from langchain_core.messages import HumanMessa... | from functools import partial
from inspect import isclass
from typing import Any, Union, cast
from pydantic import BaseModel
from langchain_core.language_models import FakeListChatModel
from langchain_core.load.dump import dumps
from langchain_core.load.load import loads
from langchain_core.messages import HumanMessa... |
from docarray import BaseDocument, DocumentArray
from docarray.documents import ImageDoc
from docarray.typing import NdArray
class MyDoc(BaseDocument):
embedding: NdArray
text: str
image: ImageDoc
def test_from_to_json():
da = DocumentArray[MyDoc](
[
MyDoc(
embedd... | from docarray import BaseDocument, DocumentArray
from docarray.documents import Image
from docarray.typing import NdArray
class MyDoc(BaseDocument):
embedding: NdArray
text: str
image: Image
def test_from_to_json():
da = DocumentArray[MyDoc](
[
MyDoc(embedding=[1, 2, 3, 4, 5], te... |
_base_ = './cascade-mask-rcnn_r50-caffe_fpn_ms-3x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron2/resnet101_caffe')))
| _base_ = './cascade_mask_rcnn_r50_caffe_fpn_mstrain_3x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron2/resnet101_caffe')))
|
# pylint: disable=protected-access
"""Shared typing definition."""
import ctypes
import os
from typing import (
TYPE_CHECKING,
Any,
Callable,
Dict,
List,
Optional,
Sequence,
Tuple,
Type,
TypeVar,
Union,
)
# os.PathLike/string/numpy.array/scipy.sparse/pd.DataFrame/dt.Frame/
#... | # pylint: disable=protected-access
"""Shared typing definition."""
import ctypes
import os
from typing import (
TYPE_CHECKING,
Any,
Callable,
Dict,
List,
Sequence,
Type,
TypeVar,
Union,
)
# os.PathLike/string/numpy.array/scipy.sparse/pd.DataFrame/dt.Frame/
# cudf.DataFrame/cupy.arra... |
__copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import os
from operator import itemgetter
import pytest
from jina import Executor, Document, DocumentArray
import cv2
from ...yolov5_segmenter import YoloV5Segmenter
cur_dir = os.path.dirname(os.path.abspath(_... | __copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import os
from operator import itemgetter
import pytest
from jina import Executor, Document, DocumentArray
import cv2
from yolov5_segmenter import YoloV5Segmenter
cur_dir = os.path.dirname(os.path.abspath(__fi... |
"""
This script contains an example how to perform semantic search with Qdrant.
You need Qdrant up and running locally:
https://qdrant.tech/documentation/quickstart/
Further, you need the Python Qdrant Client installed: https://python-client.qdrant.tech/, e.g.:
```
pip install qdrant-client
```
This script was create... | """
This script contains an example how to perform semantic search with Qdrant.
You need Qdrant up and running locally:
https://qdrant.tech/documentation/quickstart/
Further, you need the Python Qdrant Client installed: https://python-client.qdrant.tech/, e.g.:
```
pip install qdrant-client
```
This script was create... |
from __future__ import annotations
import logging
from datasets import load_dataset
from sentence_transformers import SparseEncoder, SparseEncoderTrainer, SparseEncoderTrainingArguments
from sentence_transformers.evaluation import SequentialEvaluator
from sentence_transformers.models import Pooling, Transformer
from... | from __future__ import annotations
import logging
from datasets import load_dataset
from sentence_transformers import SparseEncoder, SparseEncoderTrainer, SparseEncoderTrainingArguments
from sentence_transformers.evaluation import SequentialEvaluator
from sentence_transformers.models import Pooling, Transformer
from... |
"""
This example starts multiple processes (1 per GPU), which encode
sentences in parallel. This gives a near linear speed-up
when encoding large text collections.
It also demonstrates how to stream data which is helpful in case you don't
want to wait for an extremely large dataset to download, or if you want to
limit ... | """
This example starts multiple processes (1 per GPU), which encode
sentences in parallel. This gives a near linear speed-up
when encoding large text collections.
It also demonstrates how to stream data which is helpful in case you don't
want to wait for an extremely large dataset to download, or if you want to
limit ... |
"""Init params."""
from llama_index.finetuning.rerankers.cohere_reranker import (
CohereRerankerFinetuneEngine,
)
from llama_index.finetuning.rerankers.dataset_gen import CohereRerankerFinetuneDataset
__all__ = ["CohereRerankerFinetuneEngine", "CohereRerankerFinetuneDataset"]
| """Init params."""
from llama_index.finetuning.rerankers.cohere_reranker import (
CohereRerankerFinetuneEngine,
)
from llama_index.finetuning.rerankers.dataset_gen import CohereRerankerFinetuneDataset
__all__ = ["CohereRerankerFinetuneEngine", "CohereRerankerFinetuneDataset"]
|
# Copyright (c) OpenMMLab. All rights reserved.
import unittest
from mmdet.datasets import CocoDataset
class TestCocoDataset(unittest.TestCase):
def test_coco_dataset(self):
# test CocoDataset
metainfo = dict(CLASSES=('bus', 'car'), task_name='new_task')
dataset = CocoDataset(
... | # Copyright (c) OpenMMLab. All rights reserved.
import unittest
from mmdet.datasets import CocoDataset
class TestCocoDataset(unittest.TestCase):
def test_coco_dataset(self):
# test CocoDataset
metainfo = dict(CLASSES=('bus', 'car'), task_name='new_task')
dataset = CocoDataset(
... |
import functools
import warnings
from collections import defaultdict
from typing import Any, Dict, Optional, Sequence, Tuple, Type, TypeVar, Union
import torch
from torchvision import datapoints
from torchvision.transforms.v2 import Transform
from torchvision.transforms.v2.utils import is_simple_tensor
T = TypeVar... | import functools
import warnings
from collections import defaultdict
from typing import Any, Dict, Optional, Sequence, Tuple, Type, TypeVar, Union
import torch
from torchvision import datapoints
from torchvision.transforms.v2 import Transform
from torchvision.transforms.v2.utils import is_simple_tensor
T = TypeVar... |
from __future__ import annotations
from .IDF import IDF
from .MLMTransformer import MLMTransformer
from .SparseAutoEncoder import SparseAutoEncoder
from .SpladePooling import SpladePooling
__all__ = ["SparseAutoEncoder", "MLMTransformer", "SpladePooling", "IDF"]
| from __future__ import annotations
from .CSRSparsity import CSRSparsity
from .IDF import IDF
from .MLMTransformer import MLMTransformer
from .SpladePooling import SpladePooling
__all__ = ["CSRSparsity", "MLMTransformer", "SpladePooling", "IDF"]
|
"""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 functools import wraps
from typing import Any, Callable, Concatenate, Coroutine, ParamSpec, TypeVar, cast
from backend.data.credit import get_user_credit_model
from backend.data.execution import (
ExecutionResult,
RedisExecutionEventBus,
create_graph_execution,
get_execution_results,
get_incom... | from functools import wraps
from typing import Any, Callable, Concatenate, Coroutine, ParamSpec, TypeVar, cast
from backend.data.credit import get_user_credit_model
from backend.data.execution import (
ExecutionResult,
RedisExecutionEventBus,
create_graph_execution,
get_execution_results,
get_incom... |
_base_ = './mask-rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| _base_ = './mask_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
|
# 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... |
"""Test VLite functionality."""
from langchain_core.documents import Document
from langchain_community.embeddings import FakeEmbeddings
from langchain_community.vectorstores import VLite
def test_vlite() -> None:
"""Test end to end construction and search."""
texts = ["foo", "bar", "baz"]
docsearch = VL... | """Test VLite functionality."""
from langchain_core.documents import Document
from langchain_community.embeddings import FakeEmbeddings
from langchain_community.vectorstores import VLite
def test_vlite() -> None:
"""Test end to end construction and search."""
texts = ["foo", "bar", "baz"]
docsearch = VL... |
__version__ = "2.6.0.dev0"
__MODEL_HUB_ORGANIZATION__ = "sentence-transformers"
from .datasets import SentencesDataset, ParallelSentencesDataset
from .LoggingHandler import LoggingHandler
from .SentenceTransformer import SentenceTransformer
from .readers import InputExample
from .cross_encoder.CrossEncoder import Cross... | __version__ = "2.5.0.dev0"
__MODEL_HUB_ORGANIZATION__ = "sentence-transformers"
from .datasets import SentencesDataset, ParallelSentencesDataset
from .LoggingHandler import LoggingHandler
from .SentenceTransformer import SentenceTransformer
from .readers import InputExample
from .cross_encoder.CrossEncoder import Cross... |
from .postgres_indexer import PostgreSQLStorage
| from .postgres_indexer import PostgreSQLStorage |
# Copyright (c) OpenMMLab. All rights reserved.
import os
from typing import Optional
import torch
try:
import torch_npu # noqa: F401
# Enable operator support for dynamic shape and
# binary operator support on the NPU.
npu_jit_compile = bool(os.getenv('NPUJITCompile', False))
torch.npu.set_comp... | # Copyright (c) OpenMMLab. All rights reserved.
import os
from typing import Optional
import torch
def get_max_cuda_memory(device: Optional[torch.device] = None) -> int:
"""Returns the maximum GPU memory occupied by tensors in megabytes (MB) for
a given device. By default, this returns the peak allocated mem... |
# Copyright (c) OpenMMLab. All rights reserved.
from unittest import TestCase
import torch
from mmengine.data import InstanceData
from mmdet.models.dense_heads import FCOSHead
class TestFCOSHead(TestCase):
def test_fcos_head_loss(self):
"""Tests fcos head loss when truth is empty and non-empty."""
... | # Copyright (c) OpenMMLab. All rights reserved.
from unittest import TestCase
import torch
from mmengine.data import InstanceData
from mmdet.models.dense_heads import FCOSHead
class TestFCOSHead(TestCase):
def test_fcos_head_loss(self):
"""Tests fcos head loss when truth is empty and non-empty."""
... |
# Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
from mmcv.cnn import ConvModule
from ..builder import HEADS
from .anchor_head import AnchorHead
@HEADS.register_module()
class RetinaHead(AnchorHead):
r"""An anchor-based head used in `RetinaNet
<https://arxiv.org/pdf/1708.02002.pdf>`_.
... | # Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
from mmcv.cnn import ConvModule
from ..builder import HEADS
from .anchor_head import AnchorHead
@HEADS.register_module()
class RetinaHead(AnchorHead):
r"""An anchor-based head used in `RetinaNet
<https://arxiv.org/pdf/1708.02002.pdf>`_.
... |
from typing import TYPE_CHECKING, Any, Dict, Optional, Tuple, Type, TypeVar
from pydantic import create_model, create_model_from_typeddict
from pydantic.config import BaseConfig
from typing_extensions import TypedDict
from docarray.utils._internal._typing import safe_issubclass
from docarray import BaseDoc
if TYPE_C... | from typing import TYPE_CHECKING, Any, Dict, Optional, Tuple, Type, TypeVar
from pydantic import create_model, create_model_from_typeddict
from pydantic.config import BaseConfig
from typing_extensions import TypedDict
from docarray import BaseDoc
if TYPE_CHECKING:
from pydantic.typing import AnyClassMethod
... |
from langchain_core._api import warn_deprecated
from pydantic.v1.main import * # noqa: F403
warn_deprecated(
"0.3.0",
removal="1.0.0",
alternative="pydantic.v1 or pydantic",
message=(
"As of langchain-core 0.3.0, LangChain uses pydantic v2 internally. "
"The langchain.pydantic_v1 modul... | from langchain_core._api import warn_deprecated
try:
from pydantic.v1.main import * # noqa: F403
except ImportError:
from pydantic.main import * # type: ignore # noqa: F403
warn_deprecated(
"0.3.0",
removal="1.0.0",
alternative="pydantic.v1 or pydantic",
message=(
"As of langchain-co... |
import numpy as np
import pytest
from sklearn.cluster._hdbscan._reachability import mutual_reachability_graph
from sklearn.utils._testing import (
_convert_container,
assert_allclose,
)
def test_mutual_reachability_graph_error_sparse_format():
"""Check that we raise an error if the sparse format is not C... | import numpy as np
import pytest
from sklearn.cluster._hdbscan._reachability import mutual_reachability_graph
from sklearn.utils._testing import (
_convert_container,
assert_allclose,
)
def test_mutual_reachability_graph_error_sparse_format():
"""Check that we raise an error if the sparse format is not C... |
# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import os.path as osp
import numpy as np
from mmengine.config import Config, DictAction
from mmengine.utils import ProgressBar
from mmdet.models.utils import mask2ndarray
from mmdet.registry import DATASETS, VISUALIZERS
from mmdet.structures.bbox import ... | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import os.path as osp
import numpy as np
from mmengine.config import Config, DictAction
from mmengine.utils import ProgressBar
from mmdet.models.utils import mask2ndarray
from mmdet.registry import DATASETS, VISUALIZERS
from mmdet.structures.bbox import ... |
"""
This examples trains BERT (or any other transformer model like RoBERTa, DistilBERT etc.) for the STSbenchmark from scratch.
It uses MatryoshkaLoss with the powerful CoSENTLoss to train models that perform well at output dimensions [768, 512, 256, 128, 64].
It generates sentence embeddings that can be compared using... | """
This examples trains BERT (or any other transformer model like RoBERTa, DistilBERT etc.) for the STSbenchmark from scratch.
It uses MatryoshkaLoss with the powerful CoSENTLoss to train models that perform well at output dimensions [768, 512, 256, 128, 64].
It generates sentence embeddings that can be compared using... |
"""
This example starts multiple processes (1 per GPU), which encode
sentences in parallel. This gives a near linear speed-up
when encoding large text collections.
It also demonstrates how to stream data which is helpful in case you don't
want to wait for an extremely large dataset to download, or if you want to
limit ... | """
This example starts multiple processes (1 per GPU), which encode
sentences in parallel. This gives a near linear speed-up
when encoding large text collections.
It also demonstrates how to stream data which is helpful in case you don't
want to wait for an extremely large dataset to download, or if you want to
limit ... |
import pytest
import torch
from pydantic.tools import parse_obj_as, schema_json_of
from docarray.base_document.io.json import orjson_dumps
from docarray.typing import TorchEmbedding, TorchTensor
def test_proto_tensor():
tensor = parse_obj_as(TorchTensor, torch.zeros(3, 224, 224))
tensor._to_node_protobuf()... | import pytest
import torch
from pydantic.tools import parse_obj_as, schema_json_of
from docarray.base_document.io.json import orjson_dumps
from docarray.typing import TorchEmbedding, TorchTensor
def test_proto_tensor():
tensor = parse_obj_as(TorchTensor, torch.zeros(3, 224, 224))
tensor._to_node_protobuf()... |
from typing import Optional, Any
from llama_index.core.storage.index_store.keyval_index_store import KVIndexStore
from llama_index.storage.kvstore.tablestore import TablestoreKVStore
class TablestoreIndexStore(KVIndexStore):
"""
Tablestore Index store.
Args:
tablestore_kvstore (TablestoreKVStor... | from typing import Optional, Any
from llama_index.core.storage.index_store.keyval_index_store import KVIndexStore
from llama_index.storage.kvstore.tablestore import TablestoreKVStore
class TablestoreIndexStore(KVIndexStore):
"""Tablestore Index store.
Args:
tablestore_kvstore (TablestoreKVStore): T... |
"""Callback Handler that tracks AIMessage.usage_metadata."""
import threading
from collections.abc import Generator
from contextlib import contextmanager
from contextvars import ContextVar
from typing import Any, Optional
from langchain_core._api import beta
from langchain_core.callbacks import BaseCallbackHandler
fr... | """Callback Handler that tracks AIMessage.usage_metadata."""
import threading
from collections.abc import Generator
from contextlib import contextmanager
from contextvars import ContextVar
from typing import Any, Optional
from langchain_core.callbacks import BaseCallbackHandler
from langchain_core.messages import AIM... |
_base_ = './tood_r50_fpn_1x_coco.py'
max_epochs = 24
# learning rate
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, 22],
g... | _base_ = './tood_r50_fpn_1x_coco.py'
# learning policy
lr_config = dict(step=[16, 22])
runner = dict(type='EpochBasedRunner', max_epochs=24)
# multi-scale training
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFile'),
... |
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 (... | from __future__ import annotations
from sentence_transformers.sparse_encoder.data_collator import SparseEncoderDataCollator
from sentence_transformers.sparse_encoder.evaluation import (
SparseBinaryClassificationEvaluator,
SparseEmbeddingSimilarityEvaluator,
SparseInformationRetrievalEvaluator,
SparseM... |
import numpy as np
import pytest
from keras.src import layers
from keras.src import models
from keras.src import testing
class MaskingTest(testing.TestCase):
@pytest.mark.requires_trainable_backend
def test_masking_basics(self):
self.run_layer_test(
layers.Masking,
init_kwargs... | import numpy as np
import pytest
from keras.src import layers
from keras.src import models
from keras.src import testing
class MaskingTest(testing.TestCase):
@pytest.mark.requires_trainable_backend
def test_masking_basics(self):
self.run_layer_test(
layers.Masking,
init_kwargs... |
import os
import socket
from typing import TYPE_CHECKING, Optional
def get_docker_network(client) -> Optional[str]:
"""Do a best-effort guess if the caller is already in a docker network
Check if `hostname` exists in list of docker containers.
If a container is found, check its network id
:param cli... | import os
import socket
from typing import TYPE_CHECKING, Optional
def get_docker_network(client) -> Optional[str]:
"""Do a best-effort guess if the caller is already in a docker network
Check if `hostname` exists in list of docker containers.
If a container is found, check its network id
:param cli... |
_base_ = [
'../_base_/models/faster_rcnn_r50_fpn.py',
'../_base_/datasets/cityscapes_detection.py',
'../_base_/default_runtime.py'
]
model = dict(
backbone=dict(init_cfg=None),
roi_head=dict(
bbox_head=dict(
type='Shared2FCBBoxHead',
in_channels=256,
fc_ou... | _base_ = [
'../_base_/models/faster_rcnn_r50_fpn.py',
'../_base_/datasets/cityscapes_detection.py',
'../_base_/default_runtime.py'
]
model = dict(
backbone=dict(init_cfg=None),
roi_head=dict(
bbox_head=dict(
type='Shared2FCBBoxHead',
in_channels=256,
fc_ou... |
import pytest
from langchain_core.utils.iter import batch_iterate
@pytest.mark.parametrize(
("input_size", "input_iterable", "expected_output"),
[
(2, [1, 2, 3, 4, 5], [[1, 2], [3, 4], [5]]),
(3, [10, 20, 30, 40, 50], [[10, 20, 30], [40, 50]]),
(1, [100, 200, 300], [[100], [200], [300... | import pytest
from langchain_core.utils.iter import batch_iterate
@pytest.mark.parametrize(
"input_size, input_iterable, expected_output",
[
(2, [1, 2, 3, 4, 5], [[1, 2], [3, 4], [5]]),
(3, [10, 20, 30, 40, 50], [[10, 20, 30], [40, 50]]),
(1, [100, 200, 300], [[100], [200], [300]]),
... |
from .rnnt_pipeline import EMFORMER_RNNT_BASE_MUSTC, EMFORMER_RNNT_BASE_TEDLIUM3
from .source_separation_pipeline import CONVTASNET_BASE_LIBRI2MIX, HDEMUCS_HIGH_MUSDB_PLUS, SourceSeparationBundle
__all__ = [
"CONVTASNET_BASE_LIBRI2MIX",
"EMFORMER_RNNT_BASE_MUSTC",
"EMFORMER_RNNT_BASE_TEDLIUM3",
"Source... | from .rnnt_pipeline import EMFORMER_RNNT_BASE_MUSTC, EMFORMER_RNNT_BASE_TEDLIUM3
from .source_separation_pipeline import CONVTASNET_BASE_LIBRI2MIX, SourceSeparationBundle
__all__ = [
"CONVTASNET_BASE_LIBRI2MIX",
"EMFORMER_RNNT_BASE_MUSTC",
"EMFORMER_RNNT_BASE_TEDLIUM3",
"SourceSeparationBundle",
]
|
# Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import numpy as np
def palette_val(palette):
"""Convert palette to matplotlib palette.
Args:
palette List[tuple]: A list of color tuples.
Returns:
List[tuple[float]]: A list of RGB matplotlib color tuples.
"""
new_palett... | # Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import numpy as np
import mmdet
def palette_val(palette):
"""Convert palette to matplotlib palette.
Args:
palette List[tuple]: A list of color tuples.
Returns:
List[tuple[float]]: A list of RGB matplotlib color tuples.
"""
... |
# Copyright (c) OpenMMLab. All rights reserved.
from ._utils import (demo_mm_inputs, demo_mm_proposals,
demo_mm_sampling_results, get_detector_cfg,
get_roi_head_cfg)
__all__ = [
'demo_mm_inputs', 'get_detector_cfg', 'get_roi_head_cfg',
'demo_mm_proposals', 'demo_mm_sam... | # Copyright (c) OpenMMLab. All rights reserved.
from ._utils import demo_mm_inputs, get_detector_cfg
__all__ = ['demo_mm_inputs', 'get_detector_cfg']
|
from typing import Any, Dict, List, Optional, Sequence, Type, Union
import PIL.Image
import torch
from torchvision import datapoints
from torchvision.prototype.datapoints import Label, OneHotLabel
from torchvision.transforms.v2 import functional as F, Transform
from torchvision.transforms.v2._utils import _get_fill, ... | from typing import Any, Dict, List, Optional, Sequence, Type, Union
import PIL.Image
import torch
from torchvision import datapoints
from torchvision.prototype.datapoints import Label, OneHotLabel
from torchvision.transforms.v2 import functional as F, Transform
from torchvision.transforms.v2._utils import _setup_fill... |
"""
This example uses average word embeddings (for example from GloVe). It adds two fully-connected feed-forward layers (dense layers) to create a Deep Averaging Network (DAN).
If 'glove.6B.300d.txt.gz' does not exist, it tries to download it from our server.
See https://public.ukp.informatik.tu-darmstadt.de/reimers/... | """
This example uses average word embeddings (for example from GloVe). It adds two fully-connected feed-forward layers (dense layers) to create a Deep Averaging Network (DAN).
If 'glove.6B.300d.txt.gz' does not exist, it tries to download it from our server.
See https://public.ukp.informatik.tu-darmstadt.de/reimers/... |
# dataset settings
dataset_type = 'Objects365V1Dataset'
data_root = 'data/Objects365/Obj365_v1/'
# Example to use different file client
# Method 1: simply set the data root and let the file I/O module
# automatically infer from prefix (not support LMDB and Memcache yet)
# data_root = 's3://openmmlab/datasets/detectio... | # dataset settings
dataset_type = 'Objects365V1Dataset'
data_root = 'data/Objects365/Obj365_v1/'
# file_client_args = dict(
# backend='petrel',
# path_mapping=dict({
# './data/': 's3://openmmlab/datasets/detection/',
# 'data/': 's3://openmmlab/datasets/detection/'
# }))
file_client_args = d... |
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... |
# This example was adapted from https://github.com/muhrin/milad
# It is licensed under the GLPv3 license. You can find a copy of it
# here: https://www.gnu.org/licenses/gpl-3.0.en.html .
import torch
from torch import nn
from torch.func import jacrev, vmap
from torch.nn.functional import mse_loss
sigma = 0.5
epsilon... | # This example was adapated from https://github.com/muhrin/milad
# It is licensed under the GLPv3 license. You can find a copy of it
# here: https://www.gnu.org/licenses/gpl-3.0.en.html .
import torch
from torch import nn
from torch.func import jacrev, vmap
from torch.nn.functional import mse_loss
sigma = 0.5
epsilo... |
# Copyright (c) OpenMMLab. All rights reserved.
import warnings
from abc import ABCMeta, abstractmethod
from typing import Any, List, Optional, Sequence, Union
from mmengine.dist import (broadcast_object_list, collect_results,
is_main_process)
class BaseMetric(metaclass=ABCMeta):
"""Ba... | # Copyright (c) OpenMMLab. All rights reserved.
import warnings
from abc import ABCMeta, abstractmethod
from typing import Any, List, Optional, Sequence, Tuple, Union
from mmengine.dist import (broadcast_object_list, collect_results,
is_main_process)
class BaseMetric(metaclass=ABCMeta):
... |
"""DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.activations import deserialize
from keras.src.activations import get
from keras.src.activations import serialize
from keras.src.activations.activations import celu
from keras.src.acti... | """DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.activations import deserialize
from keras.src.activations import get
from keras.src.activations import serialize
from keras.src.activations.activations import celu
from keras.src.acti... |
from __future__ import annotations
from langchain_core.language_models import LanguageModelLike
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import BasePromptTemplate
from langchain_core.retrievers import RetrieverLike, RetrieverOutputLike
from langchain_core.runnables import R... | from __future__ import annotations
from langchain_core.language_models import LanguageModelLike
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import BasePromptTemplate
from langchain_core.retrievers import RetrieverLike, RetrieverOutputLike
from langchain_core.runnables import R... |
from . import InputExample
import csv
import os
class TripletReader(object):
"""Reads in the a Triplet Dataset: Each line contains (at least) 3 columns, one anchor column (s1),
one positive example (s2) and one negative example (s3)
"""
def __init__(
self,
dataset_folder,
s1_c... | from . import InputExample
import csv
import os
class TripletReader(object):
"""
Reads in the a Triplet Dataset: Each line contains (at least) 3 columns, one anchor column (s1),
one positive example (s2) and one negative example (s3)
"""
def __init__(
self,
dataset_folder,
... |
# Licensed to the LF AI & Data foundation under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the "License");
# you may not use this fil... | from typing import TypeVar
from docarray.typing.proto_register import _register_proto
from docarray.typing.tensor.image.abstract_image_tensor import AbstractImageTensor
from docarray.typing.tensor.torch_tensor import TorchTensor, metaTorchAndNode
T = TypeVar('T', bound='ImageTorchTensor')
@_register_proto(proto_typ... |
import numpy as np
import torch
import torchaudio.prototype.functional as F
from parameterized import parameterized
from scipy import signal
from torchaudio_unittest.common_utils import nested_params, TestBaseMixin
class FunctionalTestImpl(TestBaseMixin):
@nested_params(
[(10, 4), (4, 3, 1, 2), (2,), ()],... | import numpy as np
import torch
import torchaudio.prototype.functional as F
from parameterized import parameterized
from scipy import signal
from torchaudio_unittest.common_utils import nested_params, TestBaseMixin
class FunctionalTestImpl(TestBaseMixin):
@nested_params(
[(10, 4), (4, 3, 1, 2), (2,), ()],... |
from typing_extensions import TYPE_CHECKING
from docarray.typing.bytes import AudioBytes, ImageBytes, VideoBytes
from docarray.typing.id import ID
from docarray.typing.tensor import ImageNdArray, ImageTensor
from docarray.typing.tensor.audio import AudioNdArray, AudioTensor
from docarray.typing.tensor.embedding.embedd... | from typing_extensions import TYPE_CHECKING
from docarray.typing.bytes import AudioBytes, ImageBytes, VideoBytes
from docarray.typing.id import ID
from docarray.typing.tensor import ImageNdArray, ImageTensor
from docarray.typing.tensor.audio import AudioNdArray
from docarray.typing.tensor.embedding.embedding import An... |
# Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import torch.nn as nn
import torch.nn.functional as F
from ..builder import LOSSES
from .utils import weighted_loss
@mmcv.jit(derivate=True, coderize=True)
@weighted_loss
def knowledge_distillation_kl_div_loss(pred,
so... | import mmcv
import torch.nn as nn
import torch.nn.functional as F
from ..builder import LOSSES
from .utils import weighted_loss
@mmcv.jit(derivate=True, coderize=True)
@weighted_loss
def knowledge_distillation_kl_div_loss(pred,
soft_label,
... |
__copyright__ = 'Copyright (c) 2020-2021 Jina AI Limited. All rights reserved.'
__license__ = 'Apache-2.0'
from pathlib import Path
import pytest
import librosa
import numpy as np
from jina import Document, DocumentArray, Executor
from jina.excepts import BadDocType
from ...audio_clip_encoder import AudioCLIPEncode... | __copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
from pathlib import Path
import librosa
import numpy as np
from jina import Document, DocumentArray, Executor
from ...audio_clip_encoder import AudioCLIPEncoder
def test_config():
ex = Executor.load_conf... |
from __future__ import annotations
import re
from typing import TYPE_CHECKING, Any
if TYPE_CHECKING:
from sentence_transformers.SentenceTransformer import SentenceTransformer
class SentenceEvaluator:
"""
Base class for all evaluators. Notably, this class introduces the ``greater_is_better`` and ``primar... | from __future__ import annotations
import re
from typing import TYPE_CHECKING, Any
if TYPE_CHECKING:
from sentence_transformers.SentenceTransformer import SentenceTransformer
class SentenceEvaluator:
"""
Base class for all evaluators. Notably, this class introduces the ``greater_is_better`` and ``primar... |
_base_ = './yolox_s_8x8_300e_coco.py'
# model settings
model = dict(
random_size_range=(10, 20),
backbone=dict(deepen_factor=0.33, widen_factor=0.375),
neck=dict(in_channels=[96, 192, 384], out_channels=96),
bbox_head=dict(in_channels=96, feat_channels=96))
img_scale = (640, 640) # height, width
tra... | _base_ = './yolox_s_8x8_300e_coco.py'
# model settings
model = dict(
random_size_range=(10, 20),
backbone=dict(deepen_factor=0.33, widen_factor=0.375),
neck=dict(in_channels=[96, 192, 384], out_channels=96),
bbox_head=dict(in_channels=96, feat_channels=96))
img_scale = (640, 640) # height, width
tra... |
import os
import re
from pathlib import Path
from typing import Optional, Tuple, Union
import torch
import torchaudio
from torch.hub import download_url_to_file
from torch.utils.data import Dataset
from torchaudio.datasets.utils import extract_archive
URL = "https://speech.fit.vutbr.cz/files/quesst14Database.tgz"
_C... | import os
import re
from pathlib import Path
from typing import Optional, Tuple, Union
import torch
import torchaudio
from torch.hub import download_url_to_file
from torch.utils.data import Dataset
from torchaudio.datasets.utils import extract_archive
URL = "https://speech.fit.vutbr.cz/files/quesst14Database.tgz"
_C... |
# Licensed to the LF AI & Data foundation under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the "License");
# you may not use this fil... | import os
import time
import uuid
import pytest
import qdrant_client
from docarray.index import QdrantDocumentIndex
cur_dir = os.path.dirname(os.path.abspath(__file__))
qdrant_yml = os.path.abspath(os.path.join(cur_dir, 'docker-compose.yml'))
@pytest.fixture(scope='session', autouse=True)
def start_storage():
... |
import sys
import pytest
from hypothesis import given, settings, strategies
from xgboost.testing import no_cupy
from xgboost.testing.updater import check_extmem_qdm, check_quantile_loss_extmem
sys.path.append("tests/python")
from test_data_iterator import run_data_iterator
from test_data_iterator import test_single_... | import sys
import pytest
from hypothesis import given, settings, strategies
from xgboost.testing import no_cupy
from xgboost.testing.updater import check_extmem_qdm, check_quantile_loss_extmem
sys.path.append("tests/python")
from test_data_iterator import run_data_iterator
from test_data_iterator import test_single_... |
from __future__ import annotations
from sentence_transformers import util
from sentence_transformers.losses.CoSENTLoss import CoSENTLoss
from sentence_transformers.sparse_encoder.SparseEncoder import SparseEncoder
class SparseCoSENTLoss(CoSENTLoss):
def __init__(self, model: SparseEncoder, scale: float = 20.0, s... | from __future__ import annotations
from sentence_transformers import util
from sentence_transformers.losses.CoSENTLoss import CoSENTLoss
from sentence_transformers.sparse_encoder.SparseEncoder import SparseEncoder
class SparseCoSENTLoss(CoSENTLoss):
def __init__(self, model: SparseEncoder, scale: float = 20.0, s... |
"""Copyright 2024, XGBoost contributors"""
import pytest
from dask_cuda import LocalCUDACluster
from distributed import Client
from xgboost.testing.dask import check_external_memory, get_rabit_args
@pytest.mark.parametrize("is_qdm", [True, False])
def test_external_memory(is_qdm: bool) -> None:
n_workers = 2
... | """Copyright 2024, XGBoost contributors"""
import pytest
from dask_cuda import LocalCUDACluster
from distributed import Client
import xgboost as xgb
from xgboost import dask as dxgb
from xgboost.testing.dask import check_external_memory
@pytest.mark.parametrize("is_qdm", [True, False])
def test_external_memory(is_q... |
"""
Computes embeddings
"""
import numpy as np
from sentence_transformers import SentenceTransformer
def test_encode_token_embeddings(paraphrase_distilroberta_base_v1_model: SentenceTransformer) -> None:
"""
Test that encode(output_value='token_embeddings') works
"""
model = paraphrase_distilroberta... | """
Computes embeddings
"""
import unittest
from sentence_transformers import SentenceTransformer
import numpy as np
class ComputeEmbeddingsTest(unittest.TestCase):
def setUp(self):
self.model = SentenceTransformer('paraphrase-distilroberta-base-v1')
def test_encode_token_embeddings(self):
... |
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