input stringlengths 33 5k | output stringlengths 32 5k |
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# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause
import numpy as np
from .extmath import stable_cumsum
def _weighted_percentile(array, sample_weight, percentile_rank=50):
"""Compute the weighted percentile with method 'inverted_cdf'.
When the percentile lies between two data p... | # Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause
import numpy as np
from .extmath import stable_cumsum
def _weighted_percentile(array, sample_weight, percentile=50):
"""Compute weighted percentile
Computes lower weighted percentile. If `array` is a 2D array, the
`percentil... |
from __future__ import annotations
import os
from copy import deepcopy
import numpy as np
import pytest
from tokenizers import Tokenizer
from sentence_transformers import SentenceTransformer
from sentence_transformers.models import Pooling, StaticEmbedding, Transformer
from sentence_transformers.util import is_datas... | from __future__ import annotations
import os
import pytest
from sentence_transformers import SentenceTransformer
from sentence_transformers.models import Pooling, Transformer
from sentence_transformers.util import is_datasets_available
from tests.utils import SafeTemporaryDirectory
if is_datasets_available():
f... |
__copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import subprocess
from typing import Iterable, Optional
import torch
from jina import DocumentArray, Executor, requests
from jina.logging.logger import JinaLogger
from laserembeddings import Laser
class Laser... | __copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import subprocess
from typing import Iterable, Optional
import torch
from jina import DocumentArray, Executor, requests
from jina.logging.logger import JinaLogger
from jina_commons.batching import get_docs_batc... |
# Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import PLUGIN_LAYERS
eps = 1e-6
@PLUGIN_LAYERS.register_module()
class DropBlock(nn.Module):
"""Randomly drop some regions of feature maps.
Please refer to the method proposed in... | import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import PLUGIN_LAYERS
eps = 1e-6
@PLUGIN_LAYERS.register_module()
class DropBlock(nn.Module):
"""Randomly drop some regions of feature maps.
Please refer to the method proposed in `DropBlock
<https://arxiv.org/abs/1810.128... |
# Copyright (c) OpenMMLab. All rights reserved.
__version__ = '0.1.0'
def parse_version_info(version_str):
"""Parse the version information.
Args:
version_str (str): version string like '0.1.0'.
Returns:
tuple: version information contains major, minor, micro version.
"""
versio... | # Copyright (c) OpenMMLab. All rights reserved.
__version__ = '0.0.1'
def parse_version_info(version_str):
"""Parse the version information.
Args:
version_str (str): version string like '0.0.1'.
Returns:
tuple: version information contains major, minor, micro version.
"""
versio... |
import pytest
from docarray.utils.misc import is_tf_available
tf_available = is_tf_available()
if tf_available:
import tensorflow as tf
from docarray.computation.tensorflow_backend import TensorFlowCompBackend
from docarray.typing import TensorFlowTensor
metrics = TensorFlowCompBackend.Metrics
else:... | import pytest
try:
import tensorflow as tf
from docarray.computation.tensorflow_backend import TensorFlowCompBackend
from docarray.typing import TensorFlowTensor
metrics = TensorFlowCompBackend.Metrics
except (ImportError, TypeError):
metrics = None
@pytest.mark.tensorflow
def test_cosine_sim_t... |
# Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the most common options. For a full
# list see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
# -- Path setup --------------------------------------------------------------
# If ex... | # Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the most common options. For a full
# list see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
# -- Path setup --------------------------------------------------------------
# If ex... |
_CTC_DECODERS = [
"CTCHypothesis",
"CTCDecoder",
"CTCDecoderLM",
"CTCDecoderLMState",
"ctc_decoder",
"download_pretrained_files",
]
_CUDA_CTC_DECODERS = [
"CUCTCDecoder",
"CUCTCHypothesis",
"cuda_ctc_decoder",
]
def __getattr__(name: str):
if name in _CTC_DECODERS:
try:... | _CTC_DECODERS = [
"CTCHypothesis",
"CTCDecoder",
"CTCDecoderLM",
"CTCDecoderLMState",
"ctc_decoder",
"download_pretrained_files",
]
def __getattr__(name: str):
if name in _CTC_DECODERS:
try:
from . import _ctc_decoder
except Exception as err:
raise R... |
import matplotlib.pyplot as plt
import torch
from torchvision.utils import draw_bounding_boxes, draw_segmentation_masks
from torchvision import datapoints
from torchvision.transforms.v2 import functional as F
def plot(imgs):
if not isinstance(imgs[0], list):
# Make a 2d grid even if there's just 1 row
... | import matplotlib.pyplot as plt
from torchvision.utils import draw_bounding_boxes
def plot(imgs):
if not isinstance(imgs[0], list):
# Make a 2d grid even if there's just 1 row
imgs = [imgs]
num_rows = len(imgs)
num_cols = len(imgs[0])
_, axs = plt.subplots(nrows=num_rows, ncols=num_co... |
from typing import Iterator
from typing import Tuple
import torch
from keras.src.backend.common.stateless_scope import in_stateless_scope
from keras.src.ops.operation import Operation
class TorchLayer(torch.nn.Module):
def _post_build(self):
# Do not track variables when in a stateless scope.
# ... | import torch
from keras.src.backend.common.stateless_scope import in_stateless_scope
from keras.src.ops.operation import Operation
class TorchLayer(torch.nn.Module):
def _post_build(self):
# Do not track variables when in a stateless scope.
# The variables are not initialized.
if in_state... |
"""Helper functions for clients in Jina."""
from functools import wraps
from typing import Callable, Optional
from jina.excepts import BadClientCallback, BadServer
from jina.helper import get_rich_console
from jina.logging.logger import JinaLogger
from jina.proto import jina_pb2
from jina.types.request.data import Re... | """Helper functions for clients in Jina."""
from functools import wraps
from typing import Callable
from jina.excepts import BadClientCallback, BadServer
from jina.helper import get_rich_console
from jina.logging.logger import JinaLogger
from jina.proto import jina_pb2
from jina.types.request.data import Response
d... |
"""Defines utilities for switching audio backends"""
import warnings
from typing import List, Optional
import torchaudio
from torchaudio._internal import module_utils as _mod_utils
from . import no_backend, soundfile_backend, sox_io_backend
__all__ = [
"list_audio_backends",
"get_audio_backend",
"set_aud... | """Defines utilities for switching audio backends"""
import warnings
from typing import Optional, List
import torchaudio
from torchaudio._internal import module_utils as _mod_utils
from . import (
no_backend,
sox_io_backend,
soundfile_backend,
)
__all__ = [
"list_audio_backends",
"get_audio_backe... |
from llama_index.core.instrumentation.events.base import BaseEvent
class SpanDropEvent(BaseEvent):
"""
SpanDropEvent.
Args:
err_str (str): Error string.
"""
err_str: str
@classmethod
def class_name(cls) -> str:
"""Class name."""
return "SpanDropEvent"
| from llama_index.core.instrumentation.events.base import BaseEvent
class SpanDropEvent(BaseEvent):
"""SpanDropEvent.
Args:
err_str (str): Error string.
"""
err_str: str
@classmethod
def class_name(cls) -> str:
"""Class name."""
return "SpanDropEvent"
|
import functools
import numbers
from collections import defaultdict
from typing import Any, Dict, Sequence, Type, TypeVar, Union
from torchvision.prototype import datapoints
from torchvision.prototype.datapoints._datapoint import FillType, FillTypeJIT
from torchvision.transforms.transforms import _check_sequence_inpu... | import functools
import numbers
from collections import defaultdict
from typing import Any, Dict, Sequence, Type, TypeVar, Union
from torchvision.prototype import features
from torchvision.prototype.features._feature import FillType, FillTypeJIT
from torchvision.transforms.transforms import _check_sequence_input, _se... |
_base_ = './reppoints-moment_r50_fpn-gn_head-gn_1x_coco.py'
model = dict(bbox_head=dict(transform_method='minmax'))
| _base_ = './reppoints_moment_r50_fpn_gn-neck+head_1x_coco.py'
model = dict(bbox_head=dict(transform_method='minmax'))
|
# Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
import torch.nn.functional as F
from mmdet.registry import MODELS
from .utils import weighted_loss
@weighted_loss
def mse_loss(pred, target):
"""Wrapper of mse loss."""
return F.mse_loss(pred, target, reduction='none')
@MODELS.register_m... | # Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
import torch.nn.functional as F
from mmdet.registry import MODELS
from .utils import weighted_loss
@weighted_loss
def mse_loss(pred, target):
"""Warpper of mse loss."""
return F.mse_loss(pred, target, reduction='none')
@MODELS.register_m... |
import os
import subprocess
from pathlib import Path
from typing import Dict
import numpy as np
import pytest
from jina import Document, DocumentArray
from PIL import Image
@pytest.fixture()
def test_dir() -> str:
return os.path.dirname(os.path.abspath(__file__))
@pytest.fixture()
def test_images(test_dir: str... | import os
import subprocess
from pathlib import Path
from typing import Dict
import numpy as np
import pytest
from jina import Document, DocumentArray
from PIL import Image
@pytest.fixture()
def test_dir() -> str:
return os.path.dirname(os.path.abspath(__file__))
@pytest.fixture()
def test_images(test_dir: str... |
"""Node PostProcessor module."""
from llama_index.core.postprocessor.llm_rerank import LLMRerank
from llama_index.core.postprocessor.structured_llm_rerank import (
StructuredLLMRerank,
DocumentWithRelevance,
)
from llama_index.core.postprocessor.metadata_replacement import (
MetadataReplacementPostProcess... | """Node PostProcessor module."""
from llama_index.core.postprocessor.llm_rerank import LLMRerank
from llama_index.core.postprocessor.metadata_replacement import (
MetadataReplacementPostProcessor,
)
from llama_index.core.postprocessor.node import (
AutoPrevNextNodePostprocessor,
KeywordNodePostprocessor,
... |
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_pure_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_pure_tensor
T = TypeVar("... |
# Copyright (c) OpenMMLab. All rights reserved.
from mmdet.registry import MODELS
from .cascade_rcnn import CascadeRCNN
@MODELS.register_module()
class HybridTaskCascade(CascadeRCNN):
"""Implementation of `HTC <https://arxiv.org/abs/1901.07518>`_"""
def __init__(self, **kwargs):
super(HybridTaskCasca... | # Copyright (c) OpenMMLab. All rights reserved.
from ..builder import DETECTORS
from .cascade_rcnn import CascadeRCNN
@DETECTORS.register_module()
class HybridTaskCascade(CascadeRCNN):
"""Implementation of `HTC <https://arxiv.org/abs/1901.07518>`_"""
def __init__(self, **kwargs):
super(HybridTaskCasc... |
"""langchain-core version information and utilities."""
VERSION = "0.3.52"
| """langchain-core version information and utilities."""
VERSION = "0.3.51"
|
from jina import Executor, requests
from .helper import get_doc_value
class MyExecutorToReload2(Executor):
def __init__(self, **kwargs):
super().__init__(**kwargs)
@requests()
def foo(self, docs, **kwargs):
for doc in docs:
doc.text = get_doc_value()
| from jina import Executor, requests
from .helper import get_doc_value
class MyExecutorToReload2(Executor):
def __init__(self, **kwargs):
super().__init__(**kwargs)
@requests()
def foo(self, docs, **kwargs):
for doc in docs:
doc.text = get_doc_value()
|
import importlib
import os
import re
from pathlib import Path
from typing import Type, TypeVar
from backend.data.block import Block
# Dynamically load all modules under backend.blocks
AVAILABLE_MODULES = []
current_dir = Path(__file__).parent
modules = [
str(f.relative_to(current_dir))[:-3].replace(os.path.sep, "... | import importlib
import os
import re
from pathlib import Path
from typing import Type, TypeVar
from backend.data.block import Block
# Dynamically load all modules under backend.blocks
AVAILABLE_MODULES = []
current_dir = Path(__file__).parent
modules = [
str(f.relative_to(current_dir))[:-3].replace(os.path.sep, "... |
_base_ = './mask-rcnn_r50_fpn_gn-all_2x_coco.py'
# learning policy
max_epochs = 36
train_cfg = dict(max_epochs=max_epochs)
# learning rate
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=ma... | _base_ = './mask_rcnn_r50_fpn_gn-all_2x_coco.py'
# learning policy
max_epochs = 36
train_cfg = dict(max_epochs=max_epochs)
# learning rate
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=ma... |
import logging
from datasets import load_dataset
from sentence_transformers import SparseEncoder
from sentence_transformers.sparse_encoder.evaluation import SparseMSEEvaluator
logging.basicConfig(format="%(message)s", level=logging.INFO)
# Load a model
student_model = SparseEncoder("prithivida/Splade_PP_en_v1")
tea... | import logging
from datasets import load_dataset
from sentence_transformers.sparse_encoder import (
MLMTransformer,
SparseEncoder,
SparseMSEEvaluator,
SpladePooling,
)
logging.basicConfig(format="%(asctime)s - %(message)s", datefmt="%Y-%m-%d %H:%M:%S", level=logging.INFO)
# Initialize the SPLADE mod... |
"""Top-level imports for LlamaIndex."""
__version__ = "0.12.37"
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_in... | """Init file of LlamaIndex."""
__version__ = "0.12.37"
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.... |
import importlib
import pytest
from fastapi.testclient import TestClient
from ...utils import needs_py39
@pytest.fixture(
name="client",
params=[
"tutorial004",
pytest.param("tutorial004_py39", marks=needs_py39),
],
)
def get_client(request: pytest.FixtureRequest):
mod = importlib.im... | from fastapi.testclient import TestClient
from docs_src.extra_models.tutorial004 import app
client = TestClient(app)
def test_get_items():
response = client.get("/items/")
assert response.status_code == 200, response.text
assert response.json() == [
{"name": "Foo", "description": "There comes my... |
"""DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.ops.image import affine_transform
from keras.src.ops.image import crop_images
from keras.src.ops.image import extract_patches
from keras.src.ops.image import hsv_to_rgb
from keras.src... | """DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.ops.image import affine_transform
from keras.src.ops.image import crop_images
from keras.src.ops.image import extract_patches
from keras.src.ops.image import map_coordinates
from kera... |
from typing import TYPE_CHECKING, Type, TypeVar, Union
from uuid import UUID
from pydantic import BaseConfig, parse_obj_as
from pydantic.fields import ModelField
from docarray.typing.proto_register import _register_proto
if TYPE_CHECKING:
from docarray.proto import NodeProto
from docarray.typing.abstract_type i... | from typing import TYPE_CHECKING, Type, TypeVar, Union
from uuid import UUID
from pydantic import BaseConfig, parse_obj_as
from pydantic.fields import ModelField
from docarray.typing.proto_register import _register_proto
if TYPE_CHECKING:
from docarray.proto import NodeProto
from docarray.typing.abstract_type i... |
"""DashVector reader."""
from typing import Dict, List, Optional
import json
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document
class DashVectorReader(BaseReader):
"""
DashVector reader.
Args:
api_key (str): DashVector API key.
endpoint (str... | """DashVector reader."""
from typing import Dict, List, Optional
import json
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document
class DashVectorReader(BaseReader):
"""
DashVector reader.
Args:
api_key (str): DashVector API key.
endpoint (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... | 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... |
import os
import time
import numpy as np
import pytest
from jina import Document, DocumentArray
from .. import MongoDBStorage
NUM_DOCS = 10
@pytest.fixture
def storage():
return MongoDBStorage()
@pytest.fixture
def docs_to_index():
docu_array = DocumentArray()
for idx in range(0, NUM_DOCS):
d... | import os
import time
import pytest
import numpy as np
from jina import Document, DocumentArray
from .. import MongoDBStorage
NUM_DOCS = 10
@pytest.fixture
def storage():
return MongoDBStorage()
@pytest.fixture
def docs_to_index():
docu_array = DocumentArray()
for idx in range(0, NUM_DOCS):
d... |
# Copyright (c) OpenMMLab. All rights reserved.
import unittest
from unittest import TestCase
import torch
from parameterized import parameterized
from mmdet.structures import DetDataSample
from mmdet.testing import demo_mm_inputs, get_detector_cfg
from mmdet.utils import register_all_modules
class TestRPN(TestCase... | # Copyright (c) OpenMMLab. All rights reserved.
import unittest
from unittest import TestCase
import torch
from parameterized import parameterized
from mmdet.data_elements import DetDataSample
from mmdet.testing import demo_mm_inputs, get_detector_cfg
from mmdet.utils import register_all_modules
class TestRPN(TestC... |
from argparse import Namespace
from copy import deepcopy
from typing import TYPE_CHECKING, Type
from hubble.executor.helper import is_valid_huburi
from hubble.executor.hubio import HubIO
from jina.enums import PodRoleType
from jina.orchestrate.pods import Pod
from jina.orchestrate.pods.container import ContainerPod
... | from argparse import Namespace
from copy import deepcopy
from typing import TYPE_CHECKING, Type
from jina.enums import PodRoleType
from jina.hubble.helper import is_valid_huburi
from jina.hubble.hubio import HubIO
from jina.orchestrate.pods import Pod
from jina.orchestrate.pods.container import ContainerPod
if TYPE_C... |
_base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# optimizer
optim_wrapper = dict(
optimizer=dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001))
| _base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# optimizer
optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
|
_base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
'../_base_/datasets/cityscapes_instance.py', '../_base_/default_runtime.py'
]
model = dict(
backbone=dict(init_cfg=None),
roi_head=dict(
bbox_head=dict(
type='Shared2FCBBoxHead',
in_channels=256,
fc_out_chann... | _base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
'../_base_/datasets/cityscapes_instance.py', '../_base_/default_runtime.py'
]
model = dict(
backbone=dict(init_cfg=None),
roi_head=dict(
bbox_head=dict(
type='Shared2FCBBoxHead',
in_channels=256,
fc_out_chann... |
from .transcribe import cli
cli()
| from .transcribe import cli
cli()
|
# Copyright (c) OpenMMLab. All rights reserved.
from .gaussian_target import (gather_feat, gaussian_radius,
gen_gaussian_target, get_local_maximum,
get_topk_from_heatmap, transpose_and_gather_feat)
from .make_divisible import make_divisible
from .misc import (... | # Copyright (c) OpenMMLab. All rights reserved.
from .gaussian_target import (gather_feat, gaussian_radius,
gen_gaussian_target, get_local_maximum,
get_topk_from_heatmap, transpose_and_gather_feat)
from .make_divisible import make_divisible
from .misc import (... |
from enum import Enum
from typing import Any, 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.pairwis... | 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... |
"""DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.applications.inception_v3 import InceptionV3 as InceptionV3
from keras.src.applications.inception_v3 import (
decode_predictions as decode_predictions,
)
from keras.src.applicatio... | """DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.applications.inception_v3 import InceptionV3
from keras.src.applications.inception_v3 import decode_predictions
from keras.src.applications.inception_v3 import preprocess_input
|
"""
This script contains an example how to perform semantic search with OpenSearch.
You need OpenSearch up and running locally:
https://docs.opensearch.org/docs/latest/getting-started/quickstart/
Further, you need the Python OpenSearch Client installed: https://docs.opensearch.org/docs/latest/clients/python-low-level... | """
This script contains an example how to perform semantic search with OpenSearch.
You need OpenSearch up and running locally:
https://docs.opensearch.org/docs/latest/getting-started/quickstart/
Further, you need the Python OpenSearch Client installed: https://docs.opensearch.org/docs/latest/clients/python-low-level... |
_base_ = '../mask_rcnn/mask-rcnn_r50_fpn_1x_coco.py'
# model settings
model = dict(
roi_head=dict(
bbox_roi_extractor=dict(
type='GenericRoIExtractor',
aggregation='sum',
roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=2),
out_channels=256,
... | _base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py'
# model settings
model = dict(
roi_head=dict(
bbox_roi_extractor=dict(
type='GenericRoIExtractor',
aggregation='sum',
roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=2),
out_channels=256,
... |
from __future__ import annotations
__version__ = "4.1.0.dev0"
__MODEL_HUB_ORGANIZATION__ = "sentence-transformers"
import importlib
import os
from sentence_transformers.backend import (
export_dynamic_quantized_onnx_model,
export_optimized_onnx_model,
export_static_quantized_openvino_model,
)
from senten... | from __future__ import annotations
__version__ = "4.1.0.dev0"
__MODEL_HUB_ORGANIZATION__ = "sentence-transformers"
import importlib
import os
from sentence_transformers.backend import (
export_dynamic_quantized_onnx_model,
export_optimized_onnx_model,
export_static_quantized_openvino_model,
)
from senten... |
"""Run smoke tests"""
import os
import torchvision
from torchvision.io import read_image
image_path = os.path.join(
os.path.dirname(os.path.abspath(__file__)), "assets", "encode_jpeg", "grace_hopper_517x606.jpg"
)
print("torchvision version is ", torchvision.__version__)
img = read_image(image_path)
| """Run smoke tests"""
import torchvision
print("torchvision version is ", torchvision.__version__)
|
import logging
from datasets import load_dataset
from sentence_transformers import SparseEncoder
from sentence_transformers.sparse_encoder.evaluation import SparseMSEEvaluator
logging.basicConfig(format="%(message)s", level=logging.INFO)
# Load a model
student_model = SparseEncoder("prithivida/Splade_PP_en_v1")
tea... | import logging
from datasets import load_dataset
from sentence_transformers import SparseEncoder
from sentence_transformers.sparse_encoder.evaluation import SparseMSEEvaluator
logging.basicConfig(format="%(message)s", level=logging.INFO)
# Load a model
student_model = SparseEncoder("prithivida/Splade_PP_en_v1")
tea... |
from io import BytesIO
from typing import TYPE_CHECKING, Optional, Tuple, TypeVar
import numpy as np
from pydantic import parse_obj_as
from docarray.typing.bytes.base_bytes import BaseBytes
from docarray.typing.proto_register import _register_proto
from docarray.typing.tensor.image.image_ndarray import ImageNdArray
f... | from io import BytesIO
from typing import TYPE_CHECKING, Any, Optional, Tuple, Type, TypeVar
import numpy as np
from pydantic import parse_obj_as
from pydantic.validators import bytes_validator
from docarray.typing.abstract_type import AbstractType
from docarray.typing.proto_register import _register_proto
from docar... |
# CoSENTLoss must be imported before AnglELoss
from __future__ import annotations
from .CoSENTLoss import CoSENTLoss # isort: skip
from .AdaptiveLayerLoss import AdaptiveLayerLoss
from .AnglELoss import AnglELoss
from .BatchAllTripletLoss import BatchAllTripletLoss
from .BatchHardSoftMarginTripletLoss import BatchHa... | # CoSENTLoss must be imported before AnglELoss
from __future__ import annotations
from .CoSENTLoss import CoSENTLoss # isort: skip
from .AdaptiveLayerLoss import AdaptiveLayerLoss
from .AnglELoss import AnglELoss
from .BatchAllTripletLoss import BatchAllTripletLoss
from .BatchHardSoftMarginTripletLoss import BatchHa... |
import os
from functools import lru_cache
from typing import Union
import ffmpeg
import numpy as np
import torch
import torch.nn.functional as F
from .utils import exact_div
# hard-coded audio hyperparameters
SAMPLE_RATE = 16000
N_FFT = 400
N_MELS = 80
HOP_LENGTH = 160
CHUNK_LENGTH = 30
N_SAMPLES = CHUNK_LENGTH * SA... | import os
from functools import lru_cache
from typing import Union
import ffmpeg
import numpy as np
import torch
import torch.nn.functional as F
from .utils import exact_div
# hard-coded audio hyperparameters
SAMPLE_RATE = 16000
N_FFT = 400
N_MELS = 80
HOP_LENGTH = 160
CHUNK_LENGTH = 30
N_SAMPLES = CHUNK_LENGTH * SA... |
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")
datasets = ["QuoraRetrieval... | 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")
datasets = ["QuoraRetrieval... |
# Copyright (c) OpenMMLab. All rights reserved.
from .brick_wrappers import AdaptiveAvgPool2d, adaptive_avg_pool2d
from .builder import build_linear_layer, build_transformer
from .ckpt_convert import pvt_convert
from .conv_upsample import ConvUpsample
from .csp_layer import CSPLayer
from .gaussian_target import gaussia... | # Copyright (c) OpenMMLab. All rights reserved.
from .brick_wrappers import AdaptiveAvgPool2d, adaptive_avg_pool2d
from .builder import build_linear_layer, build_transformer
from .ckpt_convert import pvt_convert
from .conv_upsample import ConvUpsample
from .csp_layer import CSPLayer
from .gaussian_target import gaussia... |
"""
S3 file and directory reader.
A loader that fetches a file or iterates through a directory on AWS S3 or other compatible service.
"""
from typing import Dict, List, Optional, Union
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document
from llama_index.readers.opendal.... | """S3 file and directory reader.
A loader that fetches a file or iterates through a directory on AWS S3 or other compatible service.
"""
from typing import Dict, List, Optional, Union
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document
from llama_index.readers.opendal.b... |
from docarray import DocList, BaseDoc
from docarray.documents.text import TextDoc
from jina import Executor, requests, Flow
def test_issue():
class QuoteFile(BaseDoc):
quote_file_id: int = None
texts: DocList[TextDoc] = None
class SearchResult(BaseDoc):
results: DocList[QuoteFile] = N... | from docarray import DocList, BaseDoc
from docarray.documents.text import TextDoc
from jina import Executor, requests, Flow
def test_issue():
class QuoteFile(BaseDoc):
quote_file_id: int = None
texts: DocList[TextDoc] = None
class SearchResult(BaseDoc):
results: DocList[QuoteFile] = N... |
# 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 Optional
import torch
from docarray import BaseDoc, DocList
from docarray.typing import TorchTensor
def test_torch_train():
class Mmdoc(BaseDoc):
text: str
tensor: Optional[TorchTensor[3, 224, 224]] = None
N = 10
batch = DocList[Mmdoc](Mmdoc(text=f'hello{i}') for i i... |
# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause
def check_matplotlib_support(caller_name):
"""Raise ImportError with detailed error message if mpl is not installed.
Plot utilities like any of the Display's plotting functions should lazily import
matplotlib and call this hel... | # Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause
def check_matplotlib_support(caller_name):
"""Raise ImportError with detailed error message if mpl is not installed.
Plot utilities like any of the Display's plotting functions should lazily import
matplotlib and call this hel... |
_base_ = './ga-retinanet_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='pytorch'... | _base_ = './ga_retinanet_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='pytorch'... |
# Copyright (c) OpenMMLab. All rights reserved.
from .anchor_free_head import AnchorFreeHead
from .anchor_head import AnchorHead
from .atss_head import ATSSHead
from .autoassign_head import AutoAssignHead
from .boxinst_head import BoxInstBboxHead, BoxInstMaskHead
from .cascade_rpn_head import CascadeRPNHead, StageCasca... | # Copyright (c) OpenMMLab. All rights reserved.
from .anchor_free_head import AnchorFreeHead
from .anchor_head import AnchorHead
from .atss_head import ATSSHead
from .autoassign_head import AutoAssignHead
from .boxinst_head import BoxInstBboxHead, BoxInstMaskHead
from .cascade_rpn_head import CascadeRPNHead, StageCasca... |
import pytest
from ldclient import LDClient
from autogpt_libs.feature_flag.client import feature_flag, mock_flag_variation
@pytest.fixture
def ld_client(mocker):
client = mocker.Mock(spec=LDClient)
mocker.patch("ldclient.get", return_value=client)
client.is_initialized.return_value = True
return clie... | import pytest
from autogpt_libs.feature_flag.client import feature_flag, mock_flag_variation
from ldclient import LDClient
@pytest.fixture
def ld_client(mocker):
client = mocker.Mock(spec=LDClient)
mocker.patch("ldclient.get", return_value=client)
client.is_initialized.return_value = True
return clien... |
"""Defines utilities for switching audio backends"""
import warnings
from typing import List, Optional
import torchaudio
from torchaudio._internal import module_utils as _mod_utils
from . import no_backend, soundfile_backend, sox_io_backend
__all__ = [
"list_audio_backends",
"get_audio_backend",
"set_aud... | """Defines utilities for switching audio backends"""
import warnings
from typing import List, Optional
import torchaudio
from torchaudio._internal import module_utils as _mod_utils
from . import no_backend, soundfile_backend, sox_io_backend
__all__ = [
"list_audio_backends",
"get_audio_backend",
"set_aud... |
"""Experiment with different models."""
from __future__ import annotations
from collections.abc import Sequence
from typing import Optional
from langchain_core.language_models.llms import BaseLLM
from langchain_core.prompts.prompt import PromptTemplate
from langchain_core.utils.input import get_color_mapping, print_... | """Experiment with different models."""
from __future__ import annotations
from collections.abc import Sequence
from typing import Optional
from langchain_core.language_models.llms import BaseLLM
from langchain_core.prompts.prompt import PromptTemplate
from langchain_core.utils.input import get_color_mapping, print_... |
import gzip
import os
from . import InputExample
class NLIDataReader(object):
"""Reads in the Stanford NLI dataset and the MultiGenre NLI dataset"""
def __init__(self, dataset_folder):
self.dataset_folder = dataset_folder
def get_examples(self, filename, max_examples=0):
"""
dat... | from . import InputExample
import gzip
import os
class NLIDataReader(object):
"""Reads in the Stanford NLI dataset and the MultiGenre NLI dataset"""
def __init__(self, dataset_folder):
self.dataset_folder = dataset_folder
def get_examples(self, filename, max_examples=0):
"""
data... |
import gzip
import logging
import os
from datetime import datetime
from torch.utils.data import DataLoader
from sentence_transformers import InputExample, LoggingHandler, SentenceTransformer, evaluation, losses, models, util
#### Just some code to print debug information to stdout
logging.basicConfig(
format="%(... |
from sentence_transformers import SentenceTransformer, LoggingHandler, InputExample
from sentence_transformers import models, util, evaluation, losses
import logging
import os
import gzip
from datetime import datetime
import torch
from torch.utils.data import DataLoader
#### Just some code to print debug information ... |
# Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
from mmcv.cnn import (ConvModule, caffe2_xavier_init, constant_init, is_norm,
normal_init)
from torch.nn import BatchNorm2d
from mmdet.registry import MODELS
class Bottleneck(nn.Module):
"""Bottleneck block for DilatedEnc... | # Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
from mmcv.cnn import (ConvModule, caffe2_xavier_init, constant_init, is_norm,
normal_init)
from torch.nn import BatchNorm2d
from ..builder import NECKS
class Bottleneck(nn.Module):
"""Bottleneck block for DilatedEncoder u... |
"""
This file contains deprecated code that can only be used with the old `model.fit`-style Sentence Transformers v2.X training.
It exists for backwards compatibility with the `model.old_fit` method, but will be removed in a future version.
Nowadays, with Sentence Transformers v3+, it is recommended to use the `Senten... | from __future__ import annotations
import math
import random
class NoDuplicatesDataLoader:
def __init__(self, train_examples, batch_size):
"""
A special data loader to be used with MultipleNegativesRankingLoss.
The data loader ensures that there are no duplicate sentences within the same ... |
"""Test OCI Generative AI embedding service."""
from unittest.mock import MagicMock
from typing import Any
import pytest
from pytest import MonkeyPatch
from llama_index.embeddings.oci_genai import OCIGenAIEmbeddings
class MockResponseDict(dict):
def __getattr__(self, val) -> Any: # type: ignore[no-untyped-def... | """Test OCI Generative AI embedding service."""
from unittest.mock import MagicMock
from typing import Any
import pytest
from pytest import MonkeyPatch
from llama_index.embeddings.oci_genai import OCIGenAIEmbeddings
class MockResponseDict(dict):
def __getattr__(self, val) -> Any: # type: ignore[no-untyped-def]... |
_base_ = [
'../_base_/models/ssd300.py', '../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
]
# dataset settings
input_size = 300
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(
... | _base_ = [
'../_base_/models/ssd300.py', '../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
]
# dataset settings
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
input_size = 300
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type=... |
from contextlib import suppress
from docutils import nodes
from docutils.parsers.rst import Directive
from sklearn.utils import all_estimators
from sklearn.utils._test_common.instance_generator import _construct_instances
from sklearn.utils._testing import SkipTest
class AllowNanEstimators(Directive):
@staticme... | from contextlib import suppress
from docutils import nodes
from docutils.parsers.rst import Directive
from sklearn.utils import all_estimators
from sklearn.utils._test_common.instance_generator import _construct_instance
from sklearn.utils._testing import SkipTest
class AllowNanEstimators(Directive):
@staticmet... |
import pytest
from unittest.mock import Mock, patch, AsyncMock
from llama_index.embeddings.nvidia import NVIDIAEmbedding
class MockEmbeddingResponse:
"""Mock response matching the structure expected by the code."""
def __init__(self):
self.data = [Mock(embedding=[1.0, 2.0, 3.0], index=0)]
@pytest.f... | import pytest
from unittest.mock import Mock, patch, AsyncMock
from llama_index.embeddings.nvidia import NVIDIAEmbedding
class MockEmbeddingResponse:
"""Mock response matching the structure expected by the code."""
def __init__(self):
self.data = [
Mock(embedding=[1.0, 2.0, 3.0], index=0)... |
"""Xgboost pyspark integration submodule for params."""
from typing import Dict
from pyspark.ml.param import TypeConverters
from pyspark.ml.param.shared import Param, Params
class HasArbitraryParamsDict(Params):
"""
This is a Params based class that is extended by _SparkXGBParams
and holds the variable ... | """Xgboost pyspark integration submodule for params."""
from typing import Dict
# pylint: disable=too-few-public-methods
from pyspark.ml.param import TypeConverters
from pyspark.ml.param.shared import Param, Params
class HasArbitraryParamsDict(Params):
"""
This is a Params based class that is extended by _S... |
import logging
from datasets import load_dataset
from sentence_transformers import SparseEncoder
from sentence_transformers.sparse_encoder.evaluation import SparseBinaryClassificationEvaluator
logging.basicConfig(format="%(message)s", level=logging.INFO)
# Initialize the SPLADE model
model = SparseEncoder("naver/sp... | import logging
from datasets import load_dataset
from sentence_transformers import SparseEncoder
from sentence_transformers.sparse_encoder.evaluation import SparseBinaryClassificationEvaluator
logging.basicConfig(format="%(message)s", level=logging.INFO)
# Initialize the SPLADE model
model = SparseEncoder("naver/sp... |
# Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import numpy as np
import torch
import torch.nn as nn
from ..builder import LOSSES
from .utils import weighted_loss
@mmcv.jit(derivate=True, coderize=True)
@weighted_loss
def balanced_l1_loss(pred,
target,
beta=1.0,... | # Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import numpy as np
import torch
import torch.nn as nn
from ..builder import LOSSES
from .utils import weighted_loss
@mmcv.jit(derivate=True, coderize=True)
@weighted_loss
def balanced_l1_loss(pred,
target,
beta=1.0,... |
import os
from typing import Optional
import numpy as np
import pytest
import torch
from pydantic import parse_obj_as
from docarray.documents import Audio
from docarray.typing import AudioUrl
from docarray.typing.tensor.audio import AudioNdArray, AudioTorchTensor
from tests import TOYDATA_DIR
LOCAL_AUDIO_FILES = [
... | import os
from typing import Optional
import numpy as np
import pytest
import torch
from pydantic import parse_obj_as
from docarray.documents import Audio
from docarray.typing import AudioUrl
from docarray.typing.tensor.audio import AudioNdArray, AudioTorchTensor
from tests import TOYDATA_DIR
LOCAL_AUDIO_FILES = [
... |
import importlib
from typing import List
import fsspec
from . import compression
from .hffilesystem import HfFileSystem
_has_s3fs = importlib.util.find_spec("s3fs") is not None
if _has_s3fs:
from .s3filesystem import S3FileSystem # noqa: F401
COMPRESSION_FILESYSTEMS: List[compression.BaseCompressedFileFileSy... | import importlib
from typing import List
import fsspec
from . import compression
from .hffilesystem import HfFileSystem
_has_s3fs = importlib.util.find_spec("s3fs") is not None
if _has_s3fs:
from .s3filesystem import S3FileSystem # noqa: F401
COMPRESSION_FILESYSTEMS: List[compression.BaseCompressedFileFileSy... |
# Copyright (c) OpenMMLab. All rights reserved.
from .base_boxes import BaseBoxes
from .bbox_overlaps import bbox_overlaps
from .box_type import (convert_box_type, get_box_type, register_box,
register_box_converter)
from .horizontal_boxes import HorizontalBoxes
from .transforms import (bbox2corne... | # Copyright (c) OpenMMLab. All rights reserved.
from .bbox_overlaps import bbox_overlaps
from .transforms import (bbox2corner, bbox2distance, bbox2result, bbox2roi,
bbox_cxcywh_to_xyxy, bbox_flip, bbox_mapping,
bbox_mapping_back, bbox_project, bbox_rescale,
... |
import logging
from collections import defaultdict
from typing import Annotated, Any, Dict, List, Optional, Sequence
from fastapi import APIRouter, Body, Depends, HTTPException
from prisma.enums import AgentExecutionStatus, APIKeyPermission
from typing_extensions import TypedDict
import backend.data.block
from backen... | import logging
from collections import defaultdict
from typing import Annotated, Any, Dict, List, Optional, Sequence
from fastapi import APIRouter, Body, Depends, HTTPException
from prisma.enums import AgentExecutionStatus, APIKeyPermission
from typing_extensions import TypedDict
import backend.data.block
from backen... |
_base_ = '../mask_rcnn/mask-rcnn_r101_fpn_1x_coco.py'
model = dict(
backbone=dict(
norm_cfg=dict(type='SyncBN', requires_grad=True),
norm_eval=False,
plugins=[
dict(
cfg=dict(type='ContextBlock', ratio=1. / 16),
stages=(False, True, True, True),
... | _base_ = '../mask_rcnn/mask_rcnn_r101_fpn_1x_coco.py'
model = dict(
backbone=dict(
norm_cfg=dict(type='SyncBN', requires_grad=True),
norm_eval=False,
plugins=[
dict(
cfg=dict(type='ContextBlock', ratio=1. / 16),
stages=(False, True, True, True),
... |
"""
Tool for the AskNews API.
To use this tool, you must first set your credentials as environment variables:
ASKNEWS_CLIENT_ID
ASKNEWS_CLIENT_SECRET
"""
from typing import Any, Optional, Type
from langchain_core.callbacks import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from lan... | """
Tool for the AskNews API.
To use this tool, you must first set your credentials as environment variables:
ASKNEWS_CLIENT_ID
ASKNEWS_CLIENT_SECRET
"""
from typing import Any, Optional, Type
from langchain_core.callbacks import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from lan... |
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... |
"""
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.
"""
import logging
from sentence_transformers import LoggingHandler, SentenceTransformer
logging.basicConfig(
format="%(asctime)s - %(message)s", dat... | """
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.
"""
import logging
from sentence_transformers import LoggingHandler, SentenceTransformer
logging.basicConfig(
format="%(asctime)s - %(message)s", dat... |
import os
import random
import time
from typing import Dict, OrderedDict
import numpy as np
import pytest
from jina import Document, Flow, DocumentArray, requests, Executor
from jina_commons.indexers.dump import dump_docs
from jinahub.indexers.searcher.compound.FaissLMDBSearcher.faiss_lmdb import FaissLMDBSearcher
fr... | import os
import random
import time
from typing import Dict
import numpy as np
import pytest
from jina import Document, Flow, DocumentArray, requests
from jina_commons.indexers.dump import dump_docs
from jinahub.indexers.searcher.compound.NumpyLMDBSearcher.npfile import NumpyLMDBSearcher
from jinahub.indexers.storage... |
# coding=utf-8
# Copyright 2024 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 2024 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... |
# 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... |
import csv
import os
from pathlib import Path
from typing import 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.utils import extract_archive
_RELEASE_CONFIGS = {
"release1": {
"folder_in_arch... | import csv
import os
from pathlib import Path
from typing import 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.utils import extract_archive
_RELEASE_CONFIGS = {
"release1": {
"folder_in_arch... |
_base_ = '../faster_rcnn/faster-rcnn_x101-32x4d_fpn_1x_coco.py'
model = dict(
roi_head=dict(
type='PISARoIHead',
bbox_head=dict(
loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))),
train_cfg=dict(
rpn_proposal=dict(
nms_pre=2000,
max_per... | _base_ = '../faster_rcnn/faster_rcnn_x101_32x4d_fpn_1x_coco.py'
model = dict(
roi_head=dict(
type='PISARoIHead',
bbox_head=dict(
loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))),
train_cfg=dict(
rpn_proposal=dict(
nms_pre=2000,
max_per... |
from keras.src.api_export import keras_export
from keras.src.layers.pooling.base_pooling import BasePooling
@keras_export(["keras.layers.AveragePooling2D", "keras.layers.AvgPool2D"])
class AveragePooling2D(BasePooling):
"""Average pooling operation for 2D spatial data.
Downsamples the input along its spatial... | from keras.src.api_export import keras_export
from keras.src.layers.pooling.base_pooling import BasePooling
@keras_export(["keras.layers.AveragePooling2D", "keras.layers.AvgPool2D"])
class AveragePooling2D(BasePooling):
"""Average pooling operation for 2D spatial data.
Downsamples the input along its spatial... |
# Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import mmengine
from mmengine.utils import digit_version
from .version import __version__, version_info
mmcv_minimum_version = '2.0.0rc4'
mmcv_maximum_version = '2.1.0'
mmcv_version = digit_version(mmcv.__version__)
mmengine_minimum_version = '0.4.0'
mmengi... | # Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import mmengine
from mmengine.utils import digit_version
from .version import __version__, version_info
mmcv_minimum_version = '2.0.0rc4'
mmcv_maximum_version = '2.1.0'
mmcv_version = digit_version(mmcv.__version__)
mmengine_minimum_version = '0.3.0'
mmengi... |
import multiprocessing
import os
import signal
import time
import pytest
from jina import Document, DocumentArray, Executor, requests
from jina.clients.request import request_generator
from jina.parsers import set_gateway_parser
from jina.serve.networking import GrpcConnectionPool
from jina_cli.api import executor_na... | import multiprocessing
import os
import signal
import time
import pytest
from jina import Document, DocumentArray, Executor, requests
from jina.clients.request import request_generator
from jina.parsers import set_gateway_parser, set_pod_parser
from jina.serve.networking import GrpcConnectionPool
from jina_cli.api im... |
"""DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.applications.resnet_v2 import ResNet50V2 as ResNet50V2
from keras.src.applications.resnet_v2 import ResNet101V2 as ResNet101V2
from keras.src.applications.resnet_v2 import ResNet152V2... | """DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.applications.resnet_v2 import ResNet50V2
from keras.src.applications.resnet_v2 import ResNet101V2
from keras.src.applications.resnet_v2 import ResNet152V2
from keras.src.applications.... |
from keras.src import backend
from keras.src.api_export import keras_export
from keras.src.dtype_policies import dtype_policy
from keras.src.dtype_policies.dtype_policy import QUANTIZATION_MODES
from keras.src.dtype_policies.dtype_policy import DTypePolicy
from keras.src.dtype_policies.dtype_policy import FloatDTypePol... | from keras.src import backend
from keras.src.api_export import keras_export
from keras.src.dtype_policies import dtype_policy
from keras.src.dtype_policies.dtype_policy import QUANTIZATION_MODES
from keras.src.dtype_policies.dtype_policy import DTypePolicy
from keras.src.dtype_policies.dtype_policy import FloatDTypePol... |
"""
Example of training with Dask on GPU
====================================
"""
import dask
import dask_cudf
from dask import array as da
from dask import dataframe as dd
from dask.distributed import Client
from dask_cuda import LocalCUDACluster
from xgboost import dask as dxgb
from xgboost.dask import DaskDMatrix
... | """
Example of training with Dask on GPU
====================================
"""
import dask
import dask_cudf
from dask import array as da
from dask import dataframe as dd
from dask.distributed import Client
from dask_cuda import LocalCUDACluster
from xgboost import dask as dxgb
from xgboost.dask import DaskDMatrix
... |
from . import ffmpeg_utils, sox_utils
from .download import download_asset
__all__ = [
"download_asset",
"sox_utils",
"ffmpeg_utils",
]
| from torchaudio._internal import module_utils as _mod_utils
from . import ffmpeg_utils, sox_utils
from .download import download_asset
if _mod_utils.is_sox_available():
sox_utils.set_verbosity(0)
__all__ = [
"download_asset",
"sox_utils",
"ffmpeg_utils",
]
|
from keras.src import testing
from keras.src.datasets import boston_housing
class BostonHousingTest(testing.TestCase):
def test_load_data(self):
(x_train, y_train), (x_test, y_test) = boston_housing.load_data()
self.assertEqual(x_train.shape[1], 13)
self.assertEqual(x_train.shape[0] + x_te... | from keras.src import testing
from keras.src.datasets import boston_housing
class BostonHousingTest(testing.TestCase):
def test_load_data(self):
(x_train, y_train), (x_test, y_test) = boston_housing.load_data()
self.assertEqual(x_train.shape[1], 13)
self.assertEqual(x_train.shape[0] + x_t... |
from __future__ import annotations
from .Asym import Asym
from .BoW import BoW
from .CLIPModel import CLIPModel
from .CNN import CNN
from .Dense import Dense
from .Dropout import Dropout
from .InputModule import InputModule
from .LayerNorm import LayerNorm
from .LSTM import LSTM
from .Module import Module
from .Normal... | from __future__ import annotations
from .Asym import Asym
from .BoW import BoW
from .CLIPModel import CLIPModel
from .CNN import CNN
from .Dense import Dense
from .Dropout import Dropout
from .LayerNorm import LayerNorm
from .LSTM import LSTM
from .Normalize import Normalize
from .Pooling import Pooling
from .StaticEm... |
# Copyright (c) OpenMMLab. All rights reserved.
from abc import ABCMeta, abstractmethod
from typing import Any, Dict, Union
from torch.utils.data import DataLoader
class BaseLoop(metaclass=ABCMeta):
"""Base loop class.
All subclasses inherited from ``BaseLoop`` should overwrite the
:meth:`run` method.
... | # Copyright (c) OpenMMLab. All rights reserved.
from abc import ABCMeta, abstractmethod
from typing import Dict, Union
from torch.utils.data import DataLoader
class BaseLoop(metaclass=ABCMeta):
"""Base loop class.
All subclasses inherited from ``BaseLoop`` should overwrite the
:meth:`run` method.
A... |
# Copyright 2025 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... | import os
import tempfile
import unittest
from unittest.mock import patch
import transformers.commands.transformers_cli as cli
from transformers.commands.chat import ChatArguments, ChatCommand
from transformers.testing_utils import CaptureStd
class ChatCLITest(unittest.TestCase):
def test_help(self):
wit... |
"""
Visual demo for survival analysis (regression) with Accelerated Failure Time (AFT) model.
=========================================================================================
This demo uses 1D toy data and visualizes how XGBoost fits a tree ensemble. The ensemble
model starts out as a flat line and evolves in... | """
Visual demo for survival analysis (regression) with Accelerated Failure Time (AFT) model.
=========================================================================================
This demo uses 1D toy data and visualizes how XGBoost fits a tree ensemble. The ensemble
model starts out as a flat line and evolves in... |
from typing import Any
from unittest.mock import patch
import asyncio
import pytest
from llama_index.core.base.llms.types import ChatResponse, ChatMessage, MessageRole
from llama_index.core.llms.mock import MockLLM
from llama_index.core.postprocessor.rankGPT_rerank import RankGPTRerank
from llama_index.core.schema imp... | from typing import Any
from unittest.mock import patch
import asyncio
import pytest
from llama_index.core.base.llms.types import ChatResponse, ChatMessage, MessageRole
from llama_index.core.llms.mock import MockLLM
from llama_index.core.postprocessor.rankGPT_rerank import RankGPTRerank
from llama_index.core.schema imp... |
import contextlib
import os
import shutil
import threading
import time
from jina import Client, Deployment, DocumentArray, Flow
cur_dir = os.path.dirname(__file__)
@contextlib.contextmanager
def _update_file(input_file_path, output_file_path, temp_path):
backup_file = os.path.join(temp_path, 'backup.yaml')
... | import contextlib
import os
import shutil
import threading
import time
import pytest
from jina import Client, DocumentArray, Executor, Flow, requests, Deployment
from jina.helper import random_port
cur_dir = os.path.dirname(__file__)
@contextlib.contextmanager
def _update_file(input_file_path, output_file_path, te... |
"""
This script contains an example how to perform semantic search with Elasticsearch.
You need Elasticsearch up and running locally:
https://www.elastic.co/guide/en/elasticsearch/reference/current/run-elasticsearch-locally.html
Further, you need the Python Elasticsearch Client installed: https://elasticsearch-py.rea... | """
This script contains an example how to perform semantic search with Elasticsearch.
You need Elasticsearch up and running locally:
https://www.elastic.co/guide/en/elasticsearch/reference/current/run-elasticsearch-locally.html
Further, you need the Python Elasticsearch Client installed: https://elasticsearch-py.rea... |
_base_ = [
'../_base_/models/mask_rcnn_r50_caffe_c4.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
| _base_ = [
'../_base_/models/mask_rcnn_r50_caffe_c4.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# use caffe img_norm
img_norm_cfg = dict(
mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
train_pipeline = [
dic... |
# coding=utf-8
# Copyright 2024 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 2024 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... |
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