input stringlengths 33 5k | output stringlengths 32 5k |
|---|---|
import json
from typing import List
import requests
from langchain_core.documents import Document
from langchain_core.utils import secret_from_env
from pydantic import BaseModel, Field, SecretStr
class BraveSearchWrapper(BaseModel):
"""Wrapper around the Brave search engine."""
api_key: SecretStr = Field(
... | import json
from typing import List
import requests
from langchain_core.documents import Document
from pydantic import BaseModel, Field
class BraveSearchWrapper(BaseModel):
"""Wrapper around the Brave search engine."""
api_key: str
"""The API key to use for the Brave search engine."""
search_kwargs:... |
# Copyright (c) OpenMMLab. All rights reserved.
from typing import Any, Callable, Optional, Union
from torch.testing import assert_allclose as _assert_allclose
from mmengine.utils import digit_version
from mmengine.utils.dl_utils import TORCH_VERSION
def assert_allclose(
actual: Any,
expected: Any,
rtol... | # Copyright (c) OpenMMLab. All rights reserved.
from typing import Any, Callable, Optional, Union
from torch.testing import assert_allclose as _assert_allclose
from mmengine.utils import TORCH_VERSION, digit_version
def assert_allclose(
actual: Any,
expected: Any,
rtol: Optional[float] = None,
atol:... |
# Copyright (c) OpenMMLab. All rights reserved.
from typing import Sequence
from mmengine.hooks import Hook
from mmengine.model import is_model_wrapper
from mmdet.registry import HOOKS
@HOOKS.register_module()
class YOLOXModeSwitchHook(Hook):
"""Switch the mode of YOLOX during training.
This hook turns off... | # Copyright (c) OpenMMLab. All rights reserved.
from typing import Sequence
from mmengine.hooks import Hook
from mmengine.model import is_model_wrapper
from mmdet.registry import HOOKS
@HOOKS.register_module()
class YOLOXModeSwitchHook(Hook):
"""Switch the mode of YOLOX during training.
This hook turns off... |
import numpy as np
import pytest
from tensorflow import data as tf_data
from keras.src import backend
from keras.src import layers
from keras.src import testing
class StringLookupTest(testing.TestCase):
# TODO: increase coverage. Most features aren't being tested.
def test_config(self):
layer = laye... | import numpy as np
from tensorflow import data as tf_data
from keras.src import backend
from keras.src import layers
from keras.src import testing
class StringLookupTest(testing.TestCase):
# TODO: increase coverage. Most features aren't being tested.
def test_config(self):
layer = layers.StringLooku... |
# Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import torch
from mmdet.models.dense_heads import VFNetHead
def test_vfnet_head_loss():
"""Tests vfnet head loss when truth is empty and non-empty."""
s = 256
img_metas = [{
'img_shape': (s, s, 3),
'scale_factor': 1,
'pad... | import mmcv
import torch
from mmdet.models.dense_heads import VFNetHead
def test_vfnet_head_loss():
"""Tests vfnet head loss when truth is empty and non-empty."""
s = 256
img_metas = [{
'img_shape': (s, s, 3),
'scale_factor': 1,
'pad_shape': (s, s, 3)
}]
train_cfg = mmcv.C... |
_base_ = ['./yolox_x_8xb4-80e_crowdhuman-mot17halftrain_test-mot17halfval.py']
data_root = 'data/MOT20/'
img_scale = (1600, 896) # width, height
# model settings
model = dict(
data_preprocessor=dict(batch_augments=[
dict(type='BatchSyncRandomResize', random_size_range=(640, 1152))
]))
train_pipelin... | _base_ = ['./yolox_x_8xb4-80e_crowdhuman-mot17halftrain_test-mot17halfval.py']
data_root = 'data/MOT20/'
img_scale = (1600, 896) # width, height
# model settings
model = dict(
data_preprocessor=dict(batch_augments=[
dict(type='BatchSyncRandomResize', random_size_range=(640, 1152))
]))
train_pipelin... |
"""
This is a simple application for sparse encoder: Computing embeddings.
we have multiple sentences and we want to compute their embeddings.
The embeddings are sparse, meaning that most of the values are zero.
The embeddings are stored in a sparse matrix format, which is more efficient for storage and computation.
w... | """
This is a simple application for sparse encoder: Computing embeddings.
we have multiple sentences and we want to compute their embeddings.
The embeddings are sparse, meaning that most of the values are zero.
The embeddings are stored in a sparse matrix format, which is more efficient for storage and computation.
w... |
_base_ = 'tridentnet_r50-caffe_1x_coco.py'
train_pipeline = [
dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='RandomChoiceResize',
scales=[(1333, 640), (1333, 672), (1333, 704), (1333, 736),
(133... | _base_ = 'tridentnet_r50-caffe_1x_coco.py'
train_pipeline = [
dict(
type='LoadImageFromFile',
file_client_args={{_base_.file_client_args}}),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='RandomChoiceResize',
scales=[(1333, 640), (1333, 672), (1333, 704), (1333, 7... |
"""String output parser."""
from langchain_core.output_parsers.transform import BaseTransformOutputParser
class StrOutputParser(BaseTransformOutputParser[str]):
"""OutputParser that parses LLMResult into the top likely string."""
@classmethod
def is_lc_serializable(cls) -> bool:
"""StrOutputPars... | """String output parser."""
from langchain_core.output_parsers.transform import BaseTransformOutputParser
class StrOutputParser(BaseTransformOutputParser[str]):
"""OutputParser that parses LLMResult into the top likely string."""
@classmethod
def is_lc_serializable(cls) -> bool:
"""StrOutputPars... |
# Copyright (c) OpenMMLab. All rights reserved.
from mmengine import Config # isort:skip
cfg = Config.fromfile('tests/data/config/py_config/simple_config.py')
item5 = cfg.item1[0] + cfg.item2.a
| # Copyright (c) OpenMMLab. All rights reserved.
from mmcv import Config # isort:skip
cfg = Config.fromfile('tests/data/config/py_config/simple_config.py')
item5 = cfg.item1[0] + cfg.item2.a
|
import sys
import pytest
from fastapi._compat import PYDANTIC_V2
from inline_snapshot import Snapshot
needs_py39 = pytest.mark.skipif(sys.version_info < (3, 9), reason="requires python3.9+")
needs_py310 = pytest.mark.skipif(
sys.version_info < (3, 10), reason="requires python3.10+"
)
needs_pydanticv2 = pytest.mar... | import sys
import pytest
from fastapi._compat import PYDANTIC_V2
needs_py39 = pytest.mark.skipif(sys.version_info < (3, 9), reason="requires python3.9+")
needs_py310 = pytest.mark.skipif(
sys.version_info < (3, 10), reason="requires python3.10+"
)
needs_pydanticv2 = pytest.mark.skipif(not PYDANTIC_V2, reason="req... |
# Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import torch
from mmdet.core import bbox2roi
from mmdet.models.roi_heads.bbox_heads import SABLHead
from .utils import _dummy_bbox_sampling
def test_sabl_bbox_head_loss():
"""Tests bbox head loss when truth is empty and non-empty."""
self = SABLHead... | import mmcv
import torch
from mmdet.core import bbox2roi
from mmdet.models.roi_heads.bbox_heads import SABLHead
from .utils import _dummy_bbox_sampling
def test_sabl_bbox_head_loss():
"""Tests bbox head loss when truth is empty and non-empty."""
self = SABLHead(
num_classes=4,
cls_in_channels... |
# Copyright (c) OpenMMLab. All rights reserved.
from abc import ABCMeta, abstractmethod
from mmcv.runner import BaseModule
from ...builder import build_loss
class BasePanopticFusionHead(BaseModule, metaclass=ABCMeta):
"""Base class for panoptic heads."""
def __init__(self,
num_things_class... | from abc import ABCMeta, abstractmethod
from mmcv.runner import BaseModule
from ...builder import build_loss
class BasePanopticFusionHead(BaseModule, metaclass=ABCMeta):
"""Base class for panoptic heads."""
def __init__(self,
num_things_classes=80,
num_stuff_classes=53,
... |
import os
import numpy as np
import pytest
import torch
from pydantic.tools import parse_obj_as
from docarray import BaseDocument
from docarray.typing import (
AudioNdArray,
AudioTorchTensor,
VideoNdArray,
VideoTorchTensor,
)
from docarray.utils.misc import is_tf_available
tf_available = is_tf_availa... | import os
import numpy as np
import pytest
import torch
from pydantic.tools import parse_obj_as
from docarray import BaseDocument
from docarray.typing import (
AudioNdArray,
AudioTorchTensor,
VideoNdArray,
VideoTorchTensor,
)
from docarray.utils.misc import is_tf_available
tf_available = is_tf_availa... |
import unittest
import torch
from transformers import AutoTokenizer, Gemma2Config, Gemma2Model
from diffusers import (
AutoencoderKL,
FlowMatchEulerDiscreteScheduler,
Lumina2Text2ImgPipeline,
Lumina2Transformer2DModel,
)
from ..test_pipelines_common import PipelineTesterMixin
class Lumina2Text2ImgP... | import unittest
import numpy as np
import torch
from transformers import AutoTokenizer, Gemma2Config, Gemma2Model
from diffusers import (
AutoencoderKL,
FlowMatchEulerDiscreteScheduler,
Lumina2Text2ImgPipeline,
Lumina2Transformer2DModel,
)
from diffusers.utils.testing_utils import torch_device
from .... |
# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import os
import os.path as osp
from mmengine.config import Config, DictAction
from mmengine.evaluator import DumpResults
from mmengine.runner import Runner
from mmdet.engine.hooks.utils import trigger_visualization_hook
from mmdet.registry import RUNNER... | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import os
import os.path as osp
from mmengine.config import Config, DictAction
from mmengine.runner import Runner
from mmdet.engine.hooks.utils import trigger_visualization_hook
from mmdet.registry import RUNNERS
from mmdet.utils import add_dump_metric, ... |
_base_ = '../ssd/ssd512_coco.py'
model = dict(
bbox_head=dict(type='PISASSDHead'),
train_cfg=dict(isr=dict(k=2., bias=0.), carl=dict(k=1., bias=0.2)))
optim_wrapper = dict(clip_grad=dict(max_norm=35, norm_type=2))
| _base_ = '../ssd/ssd512_coco.py'
model = dict(
bbox_head=dict(type='PISASSDHead'),
train_cfg=dict(isr=dict(k=2., bias=0.), carl=dict(k=1., bias=0.2)))
default_hooks = dict(
optimizer=dict(
_delete_=True,
type='OptimizerHook',
grad_clip=dict(max_norm=35, norm_type=2)))
|
from __future__ import annotations
import math
from pathlib import Path
import numpy as np
import pytest
from tokenizers import Tokenizer
from sentence_transformers import SentenceTransformer
from sentence_transformers.models.StaticEmbedding import StaticEmbedding
try:
import model2vec
except ImportError:
m... | from __future__ import annotations
import math
from pathlib import Path
import numpy as np
import pytest
from tokenizers import Tokenizer
from sentence_transformers import SentenceTransformer
from sentence_transformers.models.StaticEmbedding import StaticEmbedding
try:
import model2vec
except ImportError:
m... |
from prisma.models import User
from backend.blocks.basic import AgentInputBlock, PrintToConsoleBlock
from backend.blocks.text import FillTextTemplateBlock
from backend.data import graph
from backend.data.graph import create_graph
from backend.data.user import get_or_create_user
from backend.util.test import SpinTestSe... | from prisma.models import User
from backend.blocks.basic import AgentInputBlock, PrintToConsoleBlock
from backend.blocks.text import FillTextTemplateBlock
from backend.data import graph
from backend.data.graph import create_graph
from backend.data.user import get_or_create_user
from backend.util.test import SpinTestSe... |
# Copyright (c) OpenMMLab. All rights reserved.
import logging
from typing import Any, List, Optional, Sequence, Tuple
import torch
from torch.nn.parameter import Parameter
from torch.nn.utils import clip_grad
from mmengine.data import BaseDataSample
from mmengine.registry import HOOKS
from .hook import Hook
DATA_BA... | # Copyright (c) OpenMMLab. All rights reserved.
import logging
from typing import Any, List, Optional, Sequence, Tuple
import torch
from torch.nn.parameter import Parameter
from torch.nn.utils import clip_grad
from mmengine.data import BaseDataSample
from mmengine.registry import HOOKS
from .hook import Hook
DATA_BA... |
from jina.serve.runtimes.gateway.websocket.gateway import WebSocketGateway
| import asyncio
from jina.serve.runtimes.gateway import GatewayRuntime
from jina.serve.runtimes.gateway.websocket.app import get_fastapi_app
__all__ = ['WebSocketGatewayRuntime']
from jina.serve.runtimes.gateway.websocket.gateway import WebSocketGateway
class WebSocketGatewayRuntime(GatewayRuntime):
"""Runtime ... |
"""
===================================
How to write your own v2 transforms
===================================
.. note::
Try on `collab <https://colab.research.google.com/github/pytorch/vision/blob/gh-pages/main/_generated_ipynb_notebooks/plot_custom_transforms.ipynb>`_
or :ref:`go to the end <sphx_glr_downlo... | """
===================================
How to write your own v2 transforms
===================================
.. note::
Try on `collab <https://colab.research.google.com/github/pytorch/vision/blob/gh-pages/main/_generated_ipynb_notebooks/plot_custom_transforms.ipynb>`_
or :ref:`go to the end <sphx_glr_downlo... |
import unittest
import torch
import torchaudio.prototype.functional as F
from torchaudio_unittest.common_utils import nested_params, TestBaseMixin, torch_script
class TorchScriptConsistencyTestImpl(TestBaseMixin):
def _assert_consistency(self, func, inputs, shape_only=False):
inputs_ = []
for i i... | import unittest
import torch
import torchaudio.prototype.functional as F
from torchaudio_unittest.common_utils import nested_params, TestBaseMixin, torch_script
class TorchScriptConsistencyTestImpl(TestBaseMixin):
def _assert_consistency(self, func, inputs, shape_only=False):
inputs_ = []
for i i... |
from __future__ import annotations
from typing import Any
import PIL.Image
import torch
from ._tv_tensor import TVTensor
class Image(TVTensor):
""":class:`torch.Tensor` subclass for images with shape ``[..., C, H, W]``.
.. note::
In the :ref:`transforms <transforms>`, ``Image`` instances are larg... | from __future__ import annotations
from typing import Any, Optional, Union
import PIL.Image
import torch
from ._tv_tensor import TVTensor
class Image(TVTensor):
""":class:`torch.Tensor` subclass for images with shape ``[..., C, H, W]``.
.. note::
In the :ref:`transforms <transforms>`, ``Image`` i... |
# Copyright (c) OpenMMLab. All rights reserved.
from mmcv.utils import Registry, build_from_cfg
MATCH_COST = Registry('Match Cost')
def build_match_cost(cfg, default_args=None):
"""Builder of IoU calculator."""
return build_from_cfg(cfg, MATCH_COST, default_args)
| from mmcv.utils import Registry, build_from_cfg
MATCH_COST = Registry('Match Cost')
def build_match_cost(cfg, default_args=None):
"""Builder of IoU calculator."""
return build_from_cfg(cfg, MATCH_COST, default_args)
|
__version__ = '0.17.1'
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__ = '0.17.0'
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 pytest
from jina.enums import GatewayProtocolType
from jina.helper import ArgNamespace
from jina.parsers import set_gateway_parser, set_pod_parser
@pytest.mark.parametrize(
'port,expected_port',
[
('12345', [12345]),
([12345], [12345]),
([12345, 12344], [12345, 12344]),
],
... | import pytest
from jina.enums import GatewayProtocolType
from jina.helper import ArgNamespace
from jina.parsers import set_gateway_parser, set_pod_parser
@pytest.mark.parametrize(
'port,expected_port',
[
('12345', [12345]),
([12345], [12345]),
([12345, 12344], [12345, 12344]),
],
... |
# Copyright (c) OpenMMLab. All rights reserved.
import warnings
import torch.nn as nn
from mmengine.model import BaseModule
from mmdet.models.backbones import ResNet
from mmdet.models.layers import ResLayer as _ResLayer
from mmdet.registry import MODELS
@MODELS.register_module()
class ResLayer(BaseModule):
def... | # Copyright (c) OpenMMLab. All rights reserved.
import warnings
import torch.nn as nn
from mmengine.model import BaseModule
from mmdet.models.backbones import ResNet
from mmdet.models.utils import ResLayer as _ResLayer
from mmdet.registry import MODELS
@MODELS.register_module()
class ResLayer(BaseModule):
def ... |
# Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import torch
from mmdet.models.dense_heads import TOODHead
def test_tood_head_loss():
"""Tests paa head loss when truth is empty and non-empty."""
s = 256
img_metas = [{
'img_shape': (s, s, 3),
'scale_factor': 1,
'pad_sh... | # Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import torch
from mmdet.models.dense_heads import TOODHead
def test_paa_head_loss():
"""Tests paa head loss when truth is empty and non-empty."""
s = 256
img_metas = [{
'img_shape': (s, s, 3),
'scale_factor': 1,
'pad_sha... |
__version__ = '0.13.22'
import os
from .document import Document
from .array import DocumentArray
from .dataclasses import dataclass, field
if 'DA_RICH_HANDLER' in os.environ:
from rich.traceback import install
install()
| __version__ = '0.13.21'
import os
from .document import Document
from .array import DocumentArray
from .dataclasses import dataclass, field
if 'DA_RICH_HANDLER' in os.environ:
from rich.traceback import install
install()
|
__copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
from pathlib import Path
import numpy as np
import pytest
from jina import Document, DocumentArray, Executor
from transformer_tf_text_encode import TransformerTFTextEncoder
target_dim = 768
@pytest.fixture()
... | __copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
from pathlib import Path
import numpy as np
import pytest
from jina import Document, DocumentArray, Executor
from transformer_tf_text_encode import TransformerTFTextEncoder
target_dim = 768
@pytest.fixture()
... |
import time
from typing import Callable
from pydantic import Field
from docarray import BaseDoc
from docarray.typing import NdArray
N_DIM = 10
class SimpleSchema(BaseDoc):
text: str = Field(index_name='text_index')
number: int
embedding: NdArray[10] = Field(dim=10, index_name="vector_index")
class Si... | import time
from typing import Callable
from pydantic import Field
from docarray import BaseDoc
from docarray.typing import NdArray
N_DIM = 10
class SimpleSchema(BaseDoc):
text: str = Field(index_name='text_index')
number: int
embedding: NdArray[10] = Field(dim=10, index_name="vector_index")
class Si... |
"""
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
... |
"""langchain-core version information and utilities."""
VERSION = "0.3.59"
| """langchain-core version information and utilities."""
VERSION = "0.3.58"
|
"""
This examples trains BERT (or any other transformer model like RoBERTa, DistilBERT etc.) for the STSbenchmark from scratch.
It uses AdaptiveLayerLoss with the powerful CoSENTLoss to train models that perform well even when removing some layers.
It generates sentence embeddings that can be compared using cosine-simi... | """
This examples trains BERT (or any other transformer model like RoBERTa, DistilBERT etc.) for the STSbenchmark from scratch.
It uses AdaptiveLayerLoss with the powerful CoSENTLoss to train models that perform well even when removing some layers.
It generates sentence embeddings that can be compared using cosine-simi... |
_base_ = './cascade-rcnn_s50_fpn_syncbn-backbone+head_ms-range-1x_coco.py'
model = dict(
backbone=dict(
stem_channels=128,
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='open-mmlab://resnest101')))
| _base_ = './cascade_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py'
model = dict(
backbone=dict(
stem_channels=128,
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='open-mmlab://resnest101')))
|
# Copyright (c) OpenMMLab. All rights reserved.
from .checkpoint_hook import CheckpointHook
from .ema_hook import EMAHook
from .empty_cache_hook import EmptyCacheHook
from .hook import Hook
from .iter_timer_hook import IterTimerHook
from .logger_hook import LoggerHook
from .naive_visualization_hook import NaiveVisualiz... | # Copyright (c) OpenMMLab. All rights reserved.
from .checkpoint_hook import CheckpointHook
from .ema_hook import EMAHook
from .empty_cache_hook import EmptyCacheHook
from .hook import Hook
from .iter_timer_hook import IterTimerHook
from .logger_hook import LoggerHook
from .naive_visualization_hook import NaiveVisualiz... |
import numpy as np
import pytest
import lightgbm
@pytest.fixture(scope="function")
def missing_module_cffi(monkeypatch):
"""Mock 'cffi' not being importable"""
monkeypatch.setattr(lightgbm.compat, "CFFI_INSTALLED", False)
monkeypatch.setattr(lightgbm.basic, "CFFI_INSTALLED", False)
@pytest.fixture(scop... | import numpy as np
import pytest
@pytest.fixture(scope="function")
def rng():
return np.random.default_rng()
@pytest.fixture(scope="function")
def rng_fixed_seed():
return np.random.default_rng(seed=42)
|
from keras.src.backend.common.tensor_attributes import get_tensor_attr
from keras.src.backend.common.tensor_attributes import set_tensor_attr
def set_keras_mask(x, mask):
return set_tensor_attr(x, "_keras_mask", mask)
def get_keras_mask(x):
return get_tensor_attr(x, "_keras_mask")
| import weakref
from keras.src.backend.common import global_state
def set_keras_mask(x, mask):
try:
x._keras_mask = mask
except AttributeError:
if mask is None:
return
mask_dict = global_state.get_global_attribute("keras_mask_dict")
if mask_dict is None:
... |
"""Test Azure AI Search wrapper."""
from langchain_core.documents import Document
from langchain_community.retrievers.azure_ai_search import (
AzureAISearchRetriever,
AzureCognitiveSearchRetriever,
)
def test_azure_ai_search_invoke() -> None:
"""Test valid call to Azure AI Search.
In order to run t... | """Test Azure AI Search wrapper."""
from langchain_core.documents import Document
from langchain_community.retrievers.azure_ai_search import (
AzureAISearchRetriever,
AzureCognitiveSearchRetriever,
)
def test_azure_ai_search_invoke() -> None:
"""Test valid call to Azure AI Search.
In order to run t... |
import logging
import time
from abc import ABC, abstractmethod
from typing import ClassVar
from backend.data.model import OAuth2Credentials
from backend.integrations.providers import ProviderName
logger = logging.getLogger(__name__)
class BaseOAuthHandler(ABC):
# --8<-- [start:BaseOAuthHandler1]
PROVIDER_NA... | import logging
import time
from abc import ABC, abstractmethod
from typing import ClassVar
from backend.data.model import OAuth2Credentials
logger = logging.getLogger(__name__)
class BaseOAuthHandler(ABC):
# --8<-- [start:BaseOAuthHandler1]
PROVIDER_NAME: ClassVar[str]
DEFAULT_SCOPES: ClassVar[list[str]... |
from __future__ import annotations
from sentence_transformers.sparse_encoder.evaluation.SparseBinaryClassificationEvaluator import (
SparseBinaryClassificationEvaluator,
)
from sentence_transformers.sparse_encoder.evaluation.SparseEmbeddingSimilarityEvaluator import (
SparseEmbeddingSimilarityEvaluator,
)
from... | from __future__ import annotations
from sentence_transformers.sparse_encoder.evaluation.SparseBinaryClassificationEvaluator import (
SparseBinaryClassificationEvaluator,
)
from sentence_transformers.sparse_encoder.evaluation.SparseEmbeddingSimilarityEvaluator import (
SparseEmbeddingSimilarityEvaluator,
)
from... |
_base_ = ['./mask2former_swin-b-p4-w12-384_8xb2-lsj-50e_coco-panoptic.py']
pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_large_patch4_window12_384_22k.pth' # noqa
model = dict(
backbone=dict(
embed_dims=192,
num_heads=[6, 12, 24, 48],
init_cfg=dict(... | _base_ = ['./mask2former_swin-b-p4-w12-384_lsj_8x2_50e_coco-panoptic.py']
pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_large_patch4_window12_384_22k.pth' # noqa
model = dict(
backbone=dict(
embed_dims=192,
num_heads=[6, 12, 24, 48],
init_cfg=dict(t... |
# 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... |
import pytest
from langchain_openai import ChatOpenAI, OpenAI
_EXPECTED_NUM_TOKENS = {
"ada": 17,
"babbage": 17,
"curie": 17,
"davinci": 17,
"gpt-4": 12,
"gpt-4-32k": 12,
"gpt-3.5-turbo": 12,
"o1": 12,
"o3": 12,
"gpt-4o": 11,
}
_MODELS = models = ["ada", "babbage", "curie", "d... | import pytest
from langchain_openai import ChatOpenAI, OpenAI
_EXPECTED_NUM_TOKENS = {
"ada": 17,
"babbage": 17,
"curie": 17,
"davinci": 17,
"gpt-4": 12,
"gpt-4-32k": 12,
"gpt-3.5-turbo": 12,
}
_MODELS = models = ["ada", "babbage", "curie", "davinci"]
_CHAT_MODELS = ["gpt-4", "gpt-4-32k",... |
import unittest
import torch
import torchaudio.functional as F
from parameterized import parameterized
from torchaudio_unittest.common_utils import PytorchTestCase, skipIfNoSox, TorchaudioTestCase
from .functional_impl import Functional, FunctionalCPUOnly
class TestFunctionalFloat32(Functional, FunctionalCPUOnly, P... | import unittest
import torch
import torchaudio.functional as F
from parameterized import parameterized
from torchaudio_unittest.common_utils import (
PytorchTestCase,
skipIfNoSox,
TorchaudioTestCase,
)
from .functional_impl import Functional, FunctionalCPUOnly
class TestFunctionalFloat32(Functional, Fun... |
"""Utility functions for validating Ollama models."""
from httpx import ConnectError
from ollama import Client, ResponseError
def validate_model(client: Client, model_name: str) -> None:
"""Validate that a model exists in the Ollama instance.
Args:
client: The Ollama client.
model_name: The ... | """Utility functions for validating Ollama models."""
from httpx import ConnectError
from ollama import Client, ResponseError
def validate_model(client: Client, model_name: str) -> None:
"""Validate that a model exists in the Ollama instance.
Args:
client: The Ollama client.
model_name: The ... |
"""Tool for the Wikipedia API."""
from typing import Optional, Type
from langchain_core.callbacks import CallbackManagerForToolRun
from langchain_core.tools import BaseTool
from pydantic import BaseModel, Field
from langchain_community.utilities.wikipedia import WikipediaAPIWrapper
class WikipediaQueryInput(BaseMo... | """Tool for the Wikipedia API."""
from typing import Optional, Type
from langchain_core.callbacks import CallbackManagerForToolRun
from langchain_core.tools import BaseTool
from pydantic import BaseModel, Field
from langchain_community.utilities.wikipedia import WikipediaAPIWrapper
class WikipediaQueryInput(BaseMo... |
_base_ = 'mask-rcnn_r50_fpg_crop640-50e_coco.py'
model = dict(
neck=dict(out_channels=128, inter_channels=128),
rpn_head=dict(in_channels=128),
roi_head=dict(
bbox_roi_extractor=dict(out_channels=128),
bbox_head=dict(in_channels=128),
mask_roi_extractor=dict(out_channels=128),
... | _base_ = 'mask_rcnn_r50_fpg_crop640_50e_coco.py'
model = dict(
neck=dict(out_channels=128, inter_channels=128),
rpn_head=dict(in_channels=128),
roi_head=dict(
bbox_roi_extractor=dict(out_channels=128),
bbox_head=dict(in_channels=128),
mask_roi_extractor=dict(out_channels=128),
... |
# 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 TOOD(SingleStageDetector):
r"""Implementation of `TOOD: Task-aligned One-stage Object Det... | # 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 TOOD(SingleStageDetector):
r"""Implementation of `TOOD: Task-aligned One-stage Object Det... |
import os
from abc import abstractmethod
from unittest import mock
import pytest
from langchain_core.embeddings import Embeddings
from pydantic import SecretStr
from langchain_tests.base import BaseStandardTests
class EmbeddingsTests(BaseStandardTests):
"""
:private:
"""
@property
@abstractmeth... | import os
from abc import abstractmethod
from typing import Tuple, Type
from unittest import mock
import pytest
from langchain_core.embeddings import Embeddings
from pydantic import SecretStr
from langchain_tests.base import BaseStandardTests
class EmbeddingsTests(BaseStandardTests):
"""
:private:
"""
... |
_base_ = [
'./faster_rcnn_r50_fpn.py', './mot_challenge.py',
'../../../configs/_base_/default_runtime.py'
]
model = dict(
type='Tracktor',
pretrains=dict(
detector= # noqa: E251
'https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth', ... | _base_ = [
'./faster_rcnn_r50_fpn.py', './mot_challenge.py',
'../../../configs/_base_/default_runtime.py'
]
model = dict(
type='Tracktor',
pretrains=dict(
detector= # noqa: E251
'https://download.openmmlab.com/mmtracking/mot/faster_rcnn/faster-rcnn_r50_fpn_4e_mot17-half-64ee2ed4.pth', ... |
# 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 docarray import BaseDoc
from docarray.typing import Mesh3DUrl
def test_set_mesh_url():
class MyDocument(BaseDoc):
mesh_url: Mesh3DUrl
d = MyDocument(mesh_url="https://jina.ai/mesh.obj")
assert isinstance(d.mesh_url, Mesh3DUrl)
assert d.mesh_url == "https://jina.ai/mesh.obj"
|
__copyright__ = "Copyright (c) 2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
from typing import Optional, List, Dict
import hnswlib
import numpy as np
from jina import Executor, requests, DocumentArray, Document
from jina_commons import get_logger
from jina_commons.indexers.dump import import... | __copyright__ = "Copyright (c) 2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
from typing import Optional, List, Dict
import hnswlib
import numpy as np
from jina import Executor, requests, DocumentArray, Document
from jina_commons import get_logger
from jina_commons.indexers.dump import import... |
_base_ = ['../_base_/models/retinanet_r50_fpn.py', '../common/ms_3x_coco.py']
# optimizer
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
optim_wrapper = dict(
optimizer=dict(type='SGD', lr=0.01, momentum=0.9,... | _base_ = ['../_base_/models/retinanet_r50_fpn.py', '../common/ms_3x_coco.py']
# optimizer
model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101))
optim_wrapper = dict(
optimizer=dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001))
|
# dataset settings
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
# file_client_args = dict(
# backend='petrel',
# path_mapping=dict({
# './data/': 's3://openmmlab/datasets/detection/',
# 'data/': 's3://openmmlab/datasets/detection/'
# }))
file_client_args = dict(backend='disk')
tra... | # dataset settings
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
# file_client_args = dict(
# backend='petrel',
# path_mapping=dict({
# './data/': 's3://openmmlab/datasets/detection/',
# 'data/': 's3://openmmlab/datasets/detection/'
# }))
file_client_args = dict(backend='disk')
tra... |
from __future__ import annotations
import re
from typing import TYPE_CHECKING, Any
if TYPE_CHECKING:
import numpy as np
from torch import Tensor
from sentence_transformers.SentenceTransformer import SentenceTransformer
class SentenceEvaluator:
"""
Base class for all evaluators. Notably, this cl... | 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 torch import Tensor
from torch import nn
from typing import Dict
import os
import json
class Dropout(nn.Module):
"""Dropout layer.
Args:
dropout: Sets a dropout value for dense layer.
"""
def __init__(self, dropout: float = 0.2):
super(Dropout, self).__init__()
self.drop... | from torch import Tensor
from torch import nn
from typing import Dict
import os
import json
class Dropout(nn.Module):
"""Dropout layer.
:param dropout: Sets a dropout value for dense layer.
"""
def __init__(self, dropout: float = 0.2):
super(Dropout, self).__init__()
self.dropout = d... |
import subprocess
import sys
from unittest.mock import patch
import fastapi.cli
import pytest
def test_fastapi_cli():
result = subprocess.run(
[
sys.executable,
"-m",
"coverage",
"run",
"-m",
"fastapi",
"dev",
... | import subprocess
import sys
from unittest.mock import patch
import fastapi.cli
import pytest
def test_fastapi_cli():
result = subprocess.run(
[
sys.executable,
"-m",
"coverage",
"run",
"-m",
"fastapi",
"dev",
... |
"""
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 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
:return:
"""
model = paraphrase_... |
import logging
from datasets import load_dataset
from sentence_transformers import SparseEncoder
from sentence_transformers.sparse_encoder.evaluation import SparseTranslationEvaluator
logging.basicConfig(format="%(message)s", level=logging.INFO)
# Load a model, not mutilingual but hope to see some on the hub soon
m... | import logging
from datasets import load_dataset
from sentence_transformers import SparseEncoder
from sentence_transformers.sparse_encoder.evaluation import SparseTranslationEvaluator
logging.basicConfig(format="%(message)s", level=logging.INFO)
# Load a model, not mutilingual but hope to see some on the hub soon
m... |
# Copyright (c) OpenMMLab. All rights reserved.
from .backbones import * # noqa: F401,F403
from .data_preprocessors import * # noqa: F401,F403
from .dense_heads import * # noqa: F401,F403
from .detectors import * # noqa: F401,F403
from .layers import * # noqa: F401,F403
from .losses import * # noqa: F401,F403
fro... | # Copyright (c) OpenMMLab. All rights reserved.
from .backbones import * # noqa: F401,F403
from .builder import (BACKBONES, DETECTORS, HEADS, LOSSES, NECKS,
ROI_EXTRACTORS, SHARED_HEADS, build_backbone,
build_detector, build_head, build_loss, build_neck,
... |
import numpy as np
import pytest
import torch
from docarray import BaseDocument, DocumentArray
from docarray.documents import Image, Text
from docarray.typing import (
AnyEmbedding,
AnyTensor,
AnyUrl,
ImageBytes,
ImageUrl,
Mesh3DUrl,
NdArray,
PointCloud3DUrl,
TextUrl,
TorchEmbed... | import numpy as np
import torch
from docarray import BaseDocument, DocumentArray
from docarray.documents import Image, Text
from docarray.typing import (
AnyEmbedding,
AnyTensor,
AnyUrl,
ImageBytes,
ImageUrl,
Mesh3DUrl,
NdArray,
PointCloud3DUrl,
TextUrl,
TorchEmbedding,
Torc... |
# Copyright (c) OpenMMLab. All rights reserved.
from .data_preprocessor import (BatchFixedSizePad, BatchResize,
BatchSyncRandomResize, BoxInstDataPreprocessor,
DetDataPreprocessor,
MultiBranchDataPreprocessor)
__all__ = [
... | # Copyright (c) OpenMMLab. All rights reserved.
from .data_preprocessor import (BatchFixedSizePad, BatchResize,
BatchSyncRandomResize, DetDataPreprocessor,
MultiBranchDataPreprocessor)
__all__ = [
'DetDataPreprocessor', 'BatchSyncRandomResize', 'Batch... |
import asyncio
import os
import random
import string
import tempfile
import time
import pytest
from jina import helper
@pytest.fixture(scope='function')
def random_workspace_name():
"""Generate a random workspace name with digits and letters."""
rand = ''.join(random.choices(string.ascii_uppercase + string.... | import asyncio
import os
import random
import string
import tempfile
import time
import pytest
from jina import helper
@pytest.fixture(scope='function')
def random_workspace_name():
"""Generate a random workspace name with digits and letters."""
rand = ''.join(random.choices(string.ascii_uppercase + string.... |
import importlib
import pytest
from fastapi.testclient import TestClient
from ...utils import needs_py310, needs_pydanticv1
@pytest.fixture(
name="client",
params=[
"tutorial001_pv1",
pytest.param("tutorial001_pv1_py310", marks=needs_py310),
],
)
def get_client(request: pytest.FixtureReq... | import pytest
from fastapi.testclient import TestClient
from ...utils import needs_pydanticv1
@pytest.fixture(name="client")
def get_client():
from docs_src.schema_extra_example.tutorial001_pv1 import app
client = TestClient(app)
return client
@needs_pydanticv1
def test_post_body_example(client: TestC... |
import logging
from langchain_core.callbacks import (
AsyncCallbackManagerForRetrieverRun,
CallbackManagerForRetrieverRun,
)
from langchain_core.documents import Document
from langchain_core.language_models import BaseLLM
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts imp... | import logging
from langchain_core.callbacks import (
AsyncCallbackManagerForRetrieverRun,
CallbackManagerForRetrieverRun,
)
from langchain_core.documents import Document
from langchain_core.language_models import BaseLLM
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts imp... |
_base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth' # noqa
model = dict(
bac... | _base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth' # noqa
model = dict(
bac... |
from typing import TYPE_CHECKING, List
from docarray.typing.tensor.abstract_tensor import AbstractTensor
if TYPE_CHECKING:
from docarray.array import DocVec
from docarray.array.any_array import AnyDocArray
class DocArraySummary:
def __init__(self, docs: 'AnyDocArray'):
self.docs = docs
def ... | from typing import TYPE_CHECKING, List
from docarray.typing.tensor.abstract_tensor import AbstractTensor
if TYPE_CHECKING:
from docarray.array import DocVec
from docarray.array.any_array import AnyDocArray
class DocArraySummary:
def __init__(self, docs: 'AnyDocArray'):
self.docs = docs
def ... |
import prisma
AGENT_NODE_INCLUDE: prisma.types.AgentNodeInclude = {
"Input": True,
"Output": True,
"Webhook": True,
"AgentBlock": True,
}
AGENT_GRAPH_INCLUDE: prisma.types.AgentGraphInclude = {
"AgentNodes": {"include": AGENT_NODE_INCLUDE} # type: ignore
}
EXECUTION_RESULT_INCLUDE: prisma.types.... | import prisma
AGENT_NODE_INCLUDE: prisma.types.AgentNodeInclude = {
"Input": True,
"Output": True,
"Webhook": True,
"AgentBlock": True,
}
AGENT_GRAPH_INCLUDE: prisma.types.AgentGraphInclude = {
"AgentNodes": {"include": AGENT_NODE_INCLUDE} # type: ignore
}
EXECUTION_RESULT_INCLUDE: prisma.types.... |
from __future__ import annotations
from typing import Callable
try:
from typing import Self
except ImportError:
from typing_extensions import Self
from torch import Tensor, nn
from sentence_transformers.models.Module import Module
from sentence_transformers.util import fullname, import_from_string
class D... | from __future__ import annotations
import json
import os
from typing import Callable
import torch
from safetensors.torch import load_model as load_safetensors_model
from safetensors.torch import save_model as save_safetensors_model
from torch import Tensor, nn
from sentence_transformers.util import fullname, import_... |
# Copyright (c) OpenMMLab. All rights reserved.
from typing import List, Tuple, Union
import mmcv
import numpy as np
from mmengine.utils import is_str
def palette_val(palette: List[tuple]) -> List[tuple]:
"""Convert palette to matplotlib palette.
Args:
palette (List[tuple]): A list of color tuples.
... | # Copyright (c) OpenMMLab. All rights reserved.
from typing import List, Tuple, Union
import mmcv
import numpy as np
from mmengine.utils import is_str
def palette_val(palette: List[tuple]) -> List[tuple]:
"""Convert palette to matplotlib palette.
Args:
palette (List[tuple]): A list of color tuples.
... |
# Copyright (c) OpenMMLab. All rights reserved.
from .registry import Registry, build_from_cfg
from .root import (DATA_SAMPLERS, DATASETS, EVALUATORS, HOOKS, MODEL_WRAPPERS,
MODELS, OPTIMIZER_CONSTRUCTORS, OPTIMIZERS,
PARAM_SCHEDULERS, RUNNER_CONSTRUCTORS, RUNNERS, TASK_UTILS,
... | # Copyright (c) OpenMMLab. All rights reserved.
from .registry import Registry, build_from_cfg
from .root import (DATA_SAMPLERS, DATASETS, EVALUATORS, HOOKS, MODEL_WRAPPERS,
MODELS, OPTIMIZER_CONSTRUCTORS, OPTIMIZERS,
PARAM_SCHEDULERS, RUNNER_CONSTRUCTORS, RUNNERS, TASK_UTILS,
... |
# Copyright (c) OpenMMLab. All rights reserved.
from mmdet.core.utils import ConfigType, OptConfigType, OptMultiConfig
from mmdet.registry import MODELS
from .two_stage import TwoStageDetector
@MODELS.register_module()
class GridRCNN(TwoStageDetector):
"""Grid R-CNN.
This detector is the implementation of:
... | # Copyright (c) OpenMMLab. All rights reserved.
from mmdet.registry import MODELS
from .two_stage import TwoStageDetector
@MODELS.register_module()
class GridRCNN(TwoStageDetector):
"""Grid R-CNN.
This detector is the implementation of:
- Grid R-CNN (https://arxiv.org/abs/1811.12030)
- Grid R-CNN Plu... |
import csv
import gzip
import logging
import os
from datetime import datetime
from torch.utils.data import DataLoader
from sentence_transformers import InputExample, LoggingHandler, SentenceTransformer, losses, models, util
from sentence_transformers.evaluation import EmbeddingSimilarityEvaluator
#### Just some code... | import torch
from sentence_transformers.evaluation import EmbeddingSimilarityEvaluator
from sentence_transformers import SentenceTransformer, LoggingHandler, models, util, InputExample
from sentence_transformers import losses
import os
import gzip
import csv
from datetime import datetime
import logging
from torch.utils... |
import os
import shutil
import pytest
@pytest.fixture(scope="session", autouse=True)
def download_cache():
os.system('scripts/download_full.sh')
yield
shutil.rmtree('.cache', ignore_errors=True) | import os
import shutil
import pytest
@pytest.fixture(scope="session", autouse=True)
def download_cache():
os.system('scripts/download_full.sh')
yield
shutil.rmtree('.cache') |
from keras.src.api_export import keras_export
from keras.src.layers.pooling.base_pooling import BasePooling
@keras_export(["keras.layers.MaxPooling3D", "keras.layers.MaxPool3D"])
class MaxPooling3D(BasePooling):
"""Max pooling operation for 3D data (spatial or spatio-temporal).
Downsamples the input along it... | from keras.src.api_export import keras_export
from keras.src.layers.pooling.base_pooling import BasePooling
@keras_export(["keras.layers.MaxPooling3D", "keras.layers.MaxPool3D"])
class MaxPooling3D(BasePooling):
"""Max pooling operation for 3D data (spatial or spatio-temporal).
Downsamples the input along it... |
# coding=utf-8
# Copyright 2025 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 required by applicable... | # coding=utf-8
# Copyright 2024 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 required by applicable... |
from __future__ import annotations
from typing import Any, List
from langchain_text_splitters.base import TextSplitter
class NLTKTextSplitter(TextSplitter):
"""Splitting text using NLTK package."""
def __init__(
self,
separator: str = "\n\n",
language: str = "english",
*,
... | from __future__ import annotations
from typing import Any, List
from langchain_text_splitters.base import TextSplitter
class NLTKTextSplitter(TextSplitter):
"""Splitting text using NLTK package."""
def __init__(
self,
separator: str = "\n\n",
language: str = "english",
*,
... |
import io
import logging
from enum import Enum
import replicate
import replicate.exceptions
import requests
from prisma.models import AgentGraph
from replicate.helpers import FileOutput
from backend.data.graph import Graph
from backend.util.settings import Settings
logger = logging.getLogger(__name__)
class ImageS... | import io
import logging
from enum import Enum
import replicate
import replicate.exceptions
import requests
from replicate.helpers import FileOutput
from backend.data.graph import Graph
from backend.util.settings import Settings
logger = logging.getLogger(__name__)
class ImageSize(str, Enum):
LANDSCAPE = "1024... |
__copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import subprocess
from typing import Callable
import pytest
from jina import Flow
from ...audioclip_text import AudioCLIPTextEncoder
@pytest.mark.parametrize("request_size", [1, 10, 50, 100])
def test_integra... | __copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
from typing import Callable
import pytest
from jina import Flow
from ...audioclip_text import AudioCLIPTextEncoder
@pytest.mark.parametrize("request_size", [1, 10, 50, 100])
def test_integration(data_generator... |
_base_ = './fcos_hrnetv2p-w32-gn-head_4xb4-1x_coco.py'
model = dict(
data_preprocessor=dict(
mean=[103.53, 116.28, 123.675],
std=[57.375, 57.12, 58.395],
bgr_to_rgb=False))
train_pipeline = [
dict(
type='LoadImageFromFile',
file_client_args={{_base_.file_client_args}}),... | _base_ = './fcos_hrnetv2p_w32_gn-head_4x4_1x_coco.py'
model = dict(
data_preprocessor=dict(
mean=[103.53, 116.28, 123.675],
std=[57.375, 57.12, 58.395],
bgr_to_rgb=False))
train_pipeline = [
dict(
type='LoadImageFromFile',
file_client_args={{_base_.file_client_args}}),
... |
from typing import Type
from docarray.proto import DocumentArrayProto, NodeProto
from ..abstract_array import AbstractDocumentArray
class ProtoArrayMixin(AbstractDocumentArray):
@classmethod
def from_protobuf(
cls: Type[AbstractDocumentArray], pb_msg: 'DocumentArrayProto'
) -> AbstractDocumentAr... | from typing import Type
from docarray.proto import DocumentArrayProto, NodeProto
from ..abstract_array import AbstractDocumentArray
class ProtoArrayMixin(AbstractDocumentArray):
@classmethod
def from_protobuf(
cls: Type[AbstractDocumentArray], pb_msg: 'DocumentArrayProto'
) -> AbstractDocumentAr... |
"""Init file of LlamaIndex."""
__version__ = "0.12.36"
import logging
from logging import NullHandler
from typing import Callable, Optional
try:
# Force pants to install eval_type_backport on 3.9
import eval_type_backport # noqa # type: ignore
except ImportError:
pass
# response
from llama_index.core.... | """Init file of LlamaIndex."""
__version__ = "0.12.35"
import logging
from logging import NullHandler
from typing import Callable, Optional
try:
# Force pants to install eval_type_backport on 3.9
import eval_type_backport # noqa # type: ignore
except ImportError:
pass
# response
from llama_index.core.... |
"""
Top-level module of Jina.
The primary function of this module is to import all of the public Jina
interfaces into a single place. The interfaces themselves are located in
sub-modules, as described below.
"""
import os as _os
import platform as _platform
import signal as _signal
import sys as _sys
import warnings... | """
Top-level module of Jina.
The primary function of this module is to import all of the public Jina
interfaces into a single place. The interfaces themselves are located in
sub-modules, as described below.
"""
import os as _os
import platform as _platform
import signal as _signal
import sys as _sys
import warnings... |
from __future__ import annotations
import json
import os
import torch
from safetensors.torch import load_model as load_safetensors_model
from safetensors.torch import save_model as save_safetensors_model
from torch import nn
class LSTM(nn.Module):
"""Bidirectional LSTM running over word embeddings."""
def ... | import json
import os
from typing import List
import torch
from safetensors.torch import load_model as load_safetensors_model
from safetensors.torch import save_model as save_safetensors_model
from torch import nn
class LSTM(nn.Module):
"""Bidirectional LSTM running over word embeddings."""
def __init__(
... |
from keras.src import initializers
from keras.src import ops
from keras.src.api_export import keras_export
from keras.src.optimizers import optimizer
@keras_export(["keras.optimizers.Adagrad"])
class Adagrad(optimizer.Optimizer):
"""Optimizer that implements the Adagrad algorithm.
Adagrad is an optimizer wit... | from keras.src import initializers
from keras.src import ops
from keras.src.api_export import keras_export
from keras.src.optimizers import optimizer
@keras_export(["keras.optimizers.Adagrad"])
class Adagrad(optimizer.Optimizer):
"""Optimizer that implements the Adagrad algorithm.
Adagrad is an optimizer wit... |
# Copyright (c) OpenMMLab. All rights reserved.
from collections import OrderedDict
from mmcv.runner import get_dist_info
from mmcv.runner.hooks import Hook
from torch import nn
from mmdet.registry import HOOKS
from ..utils.dist_utils import all_reduce_dict
def get_norm_states(module):
async_norm_states = Order... | # Copyright (c) OpenMMLab. All rights reserved.
from collections import OrderedDict
from mmcv.runner import get_dist_info
from mmcv.runner.hooks import HOOKS, Hook
from torch import nn
from ..utils.dist_utils import all_reduce_dict
def get_norm_states(module):
async_norm_states = OrderedDict()
for name, chi... |
from typing import Any, Type, TypeVar, Union, cast
import numpy as np
from docarray.typing.tensor.image.abstract_image_tensor import AbstractImageTensor
from docarray.typing.tensor.image.image_ndarray import ImageNdArray
from docarray.typing.tensor.tensor import AnyTensor
from docarray.utils._internal.misc import (
... | from typing import TYPE_CHECKING, Any, Type, TypeVar, Union, cast
import numpy as np
from docarray.typing.tensor.image.abstract_image_tensor import AbstractImageTensor
from docarray.typing.tensor.image.image_ndarray import ImageNdArray
from docarray.typing.tensor.tensor import AnyTensor
from docarray.utils._internal.... |
"""
This file runs Masked Language Model. You provide a training file. Each line is interpreted as a sentence / paragraph.
Optionally, you can also provide a dev file.
The fine-tuned model is stored in the output/model_name folder.
Usage:
python train_mlm.py model_name data/train_sentences.txt [data/dev_sentences.txt... | """
This file runs Masked Language Model. You provide a training file. Each line is interpreted as a sentence / paragraph.
Optionally, you can also provide a dev file.
The fine-tuned model is stored in the output/model_name folder.
Usage:
python train_mlm.py model_name data/train_sentences.txt [data/dev_sentences.txt... |
# Copyright (c) OpenMMLab. All rights reserved.
import functools
import mmcv
import torch.nn.functional as F
def reduce_loss(loss, reduction):
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss tensor.
reduction (str): Options are "none", "mean" and "sum".
Return:
... | # Copyright (c) OpenMMLab. All rights reserved.
import functools
import mmcv
import torch.nn.functional as F
def reduce_loss(loss, reduction):
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss tensor.
reduction (str): Options are "none", "mean" and "sum".
Return:
... |
from __future__ import annotations
import pytest
from sentence_transformers.cross_encoder import CrossEncoder
@pytest.mark.parametrize(
"model_name, expected_score",
[
("cross-encoder/ms-marco-MiniLM-L6-v2", [8.12545108795166, -3.045016050338745, -3.1524128913879395]),
("cross-encoder/ms-mar... | from __future__ import annotations
import pytest
from sentence_transformers.cross_encoder import CrossEncoder
@pytest.mark.parametrize(
"model_name, expected_score",
[
("cross-encoder/ms-marco-MiniLM-L-6-v2", [8.12545108795166, -3.045016050338745, -3.1524128913879395]),
("cross-encoder/ms-ma... |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# This software may be used and distributed according to the terms of the Llama 2 Community License Agreement.
from typing import Optional
import fire
from llama import Llama
def main(
ckpt_dir: str,
tokenizer_path: str,
temperature: float = 0.6,
... | # Copyright (c) Meta Platforms, Inc. and affiliates.
# This software may be used and distributed according to the terms of the Llama 2 Community License Agreement.
from typing import Optional
import fire
from llama import Llama
def main(
ckpt_dir: str,
tokenizer_path: str,
temperature: float = 0.6,
... |
# Copyright (c) OpenMMLab. All rights reserved.
"""MMEngine provides 11 root registries to support using modules across
projects.
More datails can be found at
https://mmengine.readthedocs.io/en/latest/tutorials/registry.html.
"""
from .registry import Registry
# manage all kinds of runners like `EpochBasedRunner` an... | # Copyright (c) OpenMMLab. All rights reserved.
"""MMEngine provides 11 root registries to support using modules across
projects.
More datails can be found at
https://mmengine.readthedocs.io/en/latest/tutorials/registry.html.
"""
from .registry import Registry
# manage all kinds of runners like `EpochBasedRunner` an... |
from enum import Enum
from typing import Dict, Iterable
import torch.nn.functional as F
from torch import Tensor, nn
from sentence_transformers.SentenceTransformer import SentenceTransformer
class SiameseDistanceMetric(Enum):
"""The metric for the contrastive loss"""
EUCLIDEAN = lambda x, y: F.pairwise_dis... | 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.pairwise_dista... |
# 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... | class ConcurrentPushException(Exception):
"""Exception raised when a concurrent push is detected."""
pass
|
from .conv_emformer import ConvEmformer
from .conv_tasnet import conv_tasnet_base
from .hdemucs import HDemucs
from .rnnt import conformer_rnnt_base, conformer_rnnt_model
__all__ = [
"conformer_rnnt_base",
"conformer_rnnt_model",
"conv_tasnet_base",
"ConvEmformer",
"HDemucs",
]
| from .conv_emformer import ConvEmformer
from .conv_tasnet import conv_tasnet_base
from .rnnt import conformer_rnnt_base, conformer_rnnt_model
__all__ = [
"conformer_rnnt_base",
"conformer_rnnt_model",
"conv_tasnet_base",
"ConvEmformer",
]
|
from __future__ import annotations
import csv
import gzip
import os
from . import InputExample
class STSDataReader:
"""Reads in the STS dataset. Each line contains two sentences (s1_col_idx, s2_col_idx) and one label (score_col_idx)
Default values expects a tab separated file with the first & second column... | from __future__ import annotations
import csv
import gzip
import os
from . import InputExample
class STSDataReader:
"""Reads in the STS dataset. Each line contains two sentences (s1_col_idx, s2_col_idx) and one label (score_col_idx)
Default values expects a tab separated file with the first & second column... |
import os
from pathlib import Path
from typing import List, Tuple, Union
import torchaudio
from torch import Tensor
from torch.hub import download_url_to_file
from torch.utils.data import Dataset
from torchaudio.datasets.librispeech import _get_librispeech_metadata
from torchaudio.datasets.utils import extract_archive... | import os
from pathlib import Path
from typing import List, Tuple, Union
import torchaudio
from torch import Tensor
from torch.hub import download_url_to_file
from torch.utils.data import Dataset
from torchaudio.datasets.librispeech import _get_librispeech_metadata
from torchaudio.datasets.utils import extract_archive... |
import numpy as np
from docarray import BaseDoc
from docarray.array.doc_vec.doc_vec import DocVec
from docarray.typing import AnyTensor, NdArray
def test_da_init():
class MyDoc(BaseDoc):
tensor: AnyTensor
name: str
docs = [MyDoc(tensor=np.zeros(10), name='hello') for _ in range(4)]
da =... | import numpy as np
from docarray import BaseDoc
from docarray.array.doc_vec.doc_vec import DocVec
from docarray.typing import AnyTensor, NdArray
def test_da_init():
class MyDoc(BaseDoc):
tensor: AnyTensor
name: str
docs = [MyDoc(tensor=np.zeros(10), name='hello') for _ in range(4)]
da =... |
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