input stringlengths 33 5k | output stringlengths 32 5k |
|---|---|
from llama_index.core.base.embeddings.base import BaseEmbedding
from llama_index.embeddings.fastembed import FastEmbedEmbedding
from pathlib import Path
from unittest.mock import patch
def test_class():
names_of_base_classes = [b.__name__ for b in FastEmbedEmbedding.__mro__]
assert BaseEmbedding.__name__ in n... | from llama_index.core.base.embeddings.base import BaseEmbedding
from llama_index.embeddings.fastembed import FastEmbedEmbedding
def test_class():
names_of_base_classes = [b.__name__ for b in FastEmbedEmbedding.__mro__]
assert BaseEmbedding.__name__ in names_of_base_classes
|
# ruff: noqa
# Copyright 2020 The HuggingFace Datasets Authors and the TensorFlow Datasets Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICE... | # ruff: noqa
# Copyright 2020 The HuggingFace Datasets Authors and the TensorFlow Datasets Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICE... |
_base_ = [
'../_base_/models/ssd300.py', '../_base_/datasets/wider_face.py',
'../_base_/default_runtime.py'
]
model = dict(bbox_head=dict(num_classes=1))
max_epochs = 24
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0,
end=1000),
dict(
type... | _base_ = [
'../_base_/models/ssd300.py', '../_base_/datasets/wider_face.py',
'../_base_/default_runtime.py'
]
model = dict(bbox_head=dict(num_classes=1))
# optimizer
optimizer = dict(type='SGD', lr=0.012, momentum=0.9, weight_decay=5e-4)
optimizer_config = dict()
# learning policy
lr_config = dict(
policy='... |
import logging
from datasets import load_dataset
from sentence_transformers import SparseEncoder
from sentence_transformers.sparse_encoder.evaluation import SparseEmbeddingSimilarityEvaluator
logging.basicConfig(format="%(message)s", level=logging.INFO)
# Load a model
model = SparseEncoder("naver/splade-cocondenser... | import logging
from datasets import load_dataset
from sentence_transformers import SparseEncoder
from sentence_transformers.sparse_encoder.evaluation import SparseEmbeddingSimilarityEvaluator
logging.basicConfig(format="%(message)s", level=logging.INFO)
# Load a model
model = SparseEncoder("naver/splade-cocondenser... |
"""
OPUS (http://opus.nlpl.eu/) is a great collection of different parallel datasets for more than 400 languages.
On the website, you can download parallel datasets for many languages in different formats. I found that
the format "Bottom-left triangle: download plain text files (MOSES/GIZA++)" requires minimal
overhea... | """
OPUS (http://opus.nlpl.eu/) is a great collection of different parallel datasets for more than 400 languages.
On the website, you can download parallel datasets for many languages in different formats. I found that
the format "Bottom-left triangle: download plain text files (MOSES/GIZA++)" requires minimal
overhea... |
__copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import subprocess
from typing import Dict, List, Optional
import spacy
from jina import DocumentArray, Executor, requests
from jina_commons.batching import get_docs_batch_generator
_EXCLUDE_COMPONENTS = [
'... | __copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import subprocess
from typing import Dict, List, Optional
import spacy
from jina import DocumentArray, Executor, requests
from jina_commons.batching import get_docs_batch_generator
_EXCLUDE_COMPONENTS = [
'... |
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 RescalingTest(testing.TestCase):
@pytest.mark.requires_trainable_backend
def test_rescaling_basics(self):
self.run_layer_test(
... | 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 RescalingTest(testing.TestCase):
@pytest.mark.requires_trainable_backend
def test_rescaling_basics(self):
self.run_layer_test(
... |
from __future__ import annotations
import json
import logging
import re
from re import Pattern
from typing import Optional, Union
from langchain_core.agents import AgentAction, AgentFinish
from langchain_core.exceptions import OutputParserException
from langchain_core.language_models import BaseLanguageModel
from pyd... | from __future__ import annotations
import json
import logging
import re
from re import Pattern
from typing import Optional, Union
from langchain_core.agents import AgentAction, AgentFinish
from langchain_core.exceptions import OutputParserException
from langchain_core.language_models import BaseLanguageModel
from pyd... |
"""
This script contains an example how to perform semantic search with Elasticsearch.
As dataset, we use the Quora Duplicate Questions dataset, which contains about 500k questions:
https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs
Questions are indexed to Elasticsearch together with their ... | """
This script contains an example how to perform semantic search with Elasticsearch.
As dataset, we use the Quora Duplicate Questions dataset, which contains about 500k questions:
https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs
Questions are indexed to Elasticsearch together with their ... |
from typing import Any, Optional, Sequence
from llama_index.core.evaluation.base import BaseEvaluator, EvaluationResult
from llama_index.core.prompts.mixin import PromptDictType, PromptMixinType
from tonic_validate.metrics.retrieval_precision_metric import (
RetrievalPrecisionMetric,
)
from tonic_validate.service... | from typing import Any, Optional, Sequence
from llama_index.core.evaluation.base import BaseEvaluator, EvaluationResult
from llama_index.core.prompts.mixin import PromptDictType, PromptMixinType
from tonic_validate.metrics.retrieval_precision_metric import (
RetrievalPrecisionMetric,
)
from tonic_validate.service... |
from urllib.parse import quote
from backend.blocks.jina._auth import (
TEST_CREDENTIALS,
TEST_CREDENTIALS_INPUT,
JinaCredentials,
JinaCredentialsField,
JinaCredentialsInput,
)
from backend.blocks.search import GetRequest
from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
... | from groq._utils._utils import quote
from backend.blocks.jina._auth import (
TEST_CREDENTIALS,
TEST_CREDENTIALS_INPUT,
JinaCredentials,
JinaCredentialsField,
JinaCredentialsInput,
)
from backend.blocks.search import GetRequest
from backend.data.block import Block, BlockCategory, BlockOutput, BlockS... |
from typing import TYPE_CHECKING, List
from docarray.typing.tensor.abstract_tensor import AbstractTensor
if TYPE_CHECKING:
from docarray.array import DocumentArrayStacked
from docarray.array.abstract_array import AnyDocumentArray
class DocumentArraySummary:
def __init__(self, da: 'AnyDocumentArray'):
... | from typing import TYPE_CHECKING, List
from docarray.typing.tensor.abstract_tensor import AbstractTensor
if TYPE_CHECKING:
from docarray.array import DocumentArrayStacked
from docarray.array.abstract_array import AnyDocumentArray
class DocumentArraySummary:
def __init__(self, da: 'AnyDocumentArray'):
... |
import os
from typing import Type
import orjson
from pydantic import BaseModel, Field, parse_obj_as
from docarray.base_document.abstract_document import AbstractDocument
from docarray.base_document.base_node import BaseNode
from docarray.base_document.io.json import orjson_dumps, orjson_dumps_and_decode
from docarray... | import os
from typing import Type
import orjson
from pydantic import BaseModel, Field, parse_obj_as
from docarray.base_document.abstract_document import AbstractDocument
from docarray.base_document.base_node import BaseNode
from docarray.base_document.io.json import orjson_dumps
from docarray.base_document.mixins imp... |
# Licensed to the LF AI & Data foundation under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the "License");
# you may not use this fil... | import numpy as np
import pytest
from docarray.utils._internal.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
@pytest.mark.tensor... |
"""
This examples trains BERT (or any other transformer model like RoBERTa, DistilBERT etc.) for the STSbenchmark from scratch.
It uses MatryoshkaLoss with the powerful CoSENTLoss to train models that perform well at output dimensions [768, 512, 256, 128, 64].
It generates sentence embeddings that can be compared using... | """
This examples trains BERT (or any other transformer model like RoBERTa, DistilBERT etc.) for the STSbenchmark from scratch.
It uses MatryoshkaLoss with the powerful CoSENTLoss to train models that perform well at output dimensions [768, 512, 256, 128, 64].
It generates sentence embeddings that can be compared using... |
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
parser = argparse.ArgumentParser("Stable Diffusion script with intel optimization", add_help=False)
parser.add_argument("--dpm", action="store_true", help="Enable DPMSol... | import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
parser = argparse.ArgumentParser("Stable Diffusion script with intel optimization", add_help=False)
parser.add_argument("--dpm", action="store_true", help="Enable DPMSol... |
# Copyright (c) OpenMMLab. All rights reserved.
from mmengine.utils.parrots_wrapper import TORCH_VERSION
from mmengine.utils.version_utils import digit_version
from .distributed import MMDistributedDataParallel
from .seperate_distributed import MMSeparateDistributedDataParallel
from .utils import is_model_wrapper
__al... | # Copyright (c) OpenMMLab. All rights reserved.
from .distributed import MMDistributedDataParallel
from .seperate_distributed import MMSeparateDistributedDataParallel
from .utils import is_model_wrapper
__all__ = [
'MMDistributedDataParallel', 'is_model_wrapper',
'MMSeparateDistributedDataParallel'
]
|
# 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... |
_base_ = './ga-retinanet_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
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=32,
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 .optimizer import (OPTIM_WRAPPER_CONSTRUCTORS, OPTIMIZERS,
AmpOptimWrapper, DefaultOptimWrapperConstructor,
OptimWrapper, OptimWrapperDict, build_optim_wrapper)
# yapf: disable
from .scheduler import (ConstantLR, Consta... | # Copyright (c) OpenMMLab. All rights reserved.
from .optimizer import (OPTIM_WRAPPER_CONSTRUCTORS, OPTIMIZERS,
AmpOptimWrapper, DefaultOptimWrapperConstructor,
OptimWrapper, OptimWrapperDict, build_optim_wrapper)
# yapf: disable
from .scheduler import (ConstantLR, Consta... |
from .rnnt_pipeline import EMFORMER_RNNT_BASE_MUSTC, EMFORMER_RNNT_BASE_TEDLIUM3
__all__ = [
"EMFORMER_RNNT_BASE_MUSTC",
"EMFORMER_RNNT_BASE_TEDLIUM3",
]
| from .rnnt_pipeline import EMFORMER_RNNT_BASE_MUSTC, EMFORMER_RNNT_BASE_TEDLIUM3
from .source_separation_pipeline import HDEMUCS_HIGH_MUSDB, HDEMUCS_HIGH_MUSDB_PLUS
__all__ = [
"EMFORMER_RNNT_BASE_MUSTC",
"EMFORMER_RNNT_BASE_TEDLIUM3",
"HDEMUCS_HIGH_MUSDB_PLUS",
"HDEMUCS_HIGH_MUSDB",
]
|
_base_ = './mask_rcnn_x50_32x4d_fpn_gn_ws-all_2x_coco.py'
# learning policy
max_epochs = 24
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,
... | _base_ = './mask_rcnn_x50_32x4d_fpn_gn_ws-all_2x_coco.py'
# learning policy
lr_config = dict(step=[20, 23])
runner = dict(type='EpochBasedRunner', max_epochs=24)
|
from typing import Any, Dict
from torchvision.prototype import datapoints
from torchvision.prototype.transforms import functional as F, Transform
from torchvision.prototype.transforms.utils import is_simple_tensor
class UniformTemporalSubsample(Transform):
_transformed_types = (is_simple_tensor, datapoints.Vide... | from typing import Any, Dict
from torchvision.prototype import features
from torchvision.prototype.transforms import functional as F, Transform
class UniformTemporalSubsample(Transform):
_transformed_types = (features.is_simple_tensor, features.Video)
def __init__(self, num_samples: int, temporal_dim: int =... |
_base_ = './cascade_mask_rcnn_r50_fpn_mstrain_3x_coco.py'
model = dict(
# ResNeXt-101-32x8d model trained with Caffe2 at FB,
# so the mean and std need to be changed.
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[103.530, 116.280, 123.675],
std=[57.375, 57.120, 58.395],
... | _base_ = './cascade_mask_rcnn_r50_fpn_mstrain_3x_coco.py'
preprocess_cfg = dict(
mean=[103.530, 116.280, 123.675],
std=[57.375, 57.120, 58.395],
to_rgb=False,
pad_size_divisor=32)
model = dict(
# ResNeXt-101-32x8d model trained with Caffe2 at FB,
# so the mean and std need to be changed.
p... |
"""Milvus reader."""
from typing import Any, Dict, List, Optional
from uuid import uuid4
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document
class MilvusReader(BaseReader):
"""Milvus reader."""
def __init__(
self,
host: str = "localhost",
... | """Milvus reader."""
from typing import Any, Dict, List, Optional
from uuid import uuid4
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document
class MilvusReader(BaseReader):
"""Milvus reader."""
def __init__(
self,
host: str = "localhost",
... |
from __future__ import annotations
from torch import Tensor, nn
from sentence_transformers.cross_encoder import CrossEncoder
# TODO: Consider the naming of this class
class CrossEntropyLoss(nn.Module):
def __init__(self, model: CrossEncoder) -> None:
super().__init__()
self.model = model
... | from __future__ import annotations
import time
from contextlib import ContextDecorator
from torch import Tensor, nn
from sentence_transformers.cross_encoder import CrossEncoder
class timer(ContextDecorator):
def __init__(self, name: str) -> None:
self.name = name
def __enter__(self) -> None:
... |
"""This tool allows agents to generate images using Steamship.
Steamship offers access to different third party image generation APIs
using a single API key.
Today the following models are supported:
- Dall-E
- Stable Diffusion
To use this tool, you must first set as environment variables:
STEAMSHIP_API_KEY
```
... | """This tool allows agents to generate images using Steamship.
Steamship offers access to different third party image generation APIs
using a single API key.
Today the following models are supported:
- Dall-E
- Stable Diffusion
To use this tool, you must first set as environment variables:
STEAMSHIP_API_KEY
```
... |
__copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import os
import subprocess
import pytest
from jina import Document, Flow
from video_torch_encoder import VideoTorchEncoder
cur_dir = os.path.dirname(os.path.abspath(__file__))
@pytest.fixture()
def kinects_v... | __copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import os
import subprocess
import pytest
from jina import Document, Flow
from ...video_torch_encoder import VideoTorchEncoder
cur_dir = os.path.dirname(os.path.abspath(__file__))
@pytest.fixture()
def kinec... |
# Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
def accuracy(pred, target, topk=1, thresh=None):
"""Calculate accuracy according to the prediction and target.
Args:
pred (torch.Tensor): The model prediction, shape (N, num_class)
target (torch.Tensor): The target of each ... | # Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import torch.nn as nn
@mmcv.jit(coderize=True)
def accuracy(pred, target, topk=1, thresh=None):
"""Calculate accuracy according to the prediction and target.
Args:
pred (torch.Tensor): The model prediction, shape (N, num_class)
targe... |
_base_ = './mask-rcnn_r50_fpn_sample1e-3_ms-2x_lvis-v0.5.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| _base_ = './mask_rcnn_r50_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
|
from typing import Optional
from docarray import Document, DocumentArray
from pydantic import BaseModel
from uvicorn import Config, Server
from jina import Gateway, __default_host__
from jina.clients.request import request_generator
class DummyResponseModel(BaseModel):
arg1: Optional[str]
arg2: Optional[str... | from typing import Optional
from docarray import Document, DocumentArray
from pydantic import BaseModel
from uvicorn import Config, Server
from jina import Gateway, __default_host__
from jina.clients.request import request_generator
class DummyResponseModel(BaseModel):
arg1: Optional[str]
arg2: Optional[str... |
_base_ = './mask_rcnn_r50_fpn_1x_coco.py'
data_preprocessor = dict(
mean=[103.530, 116.280, 123.675],
std=[1.0, 1.0, 1.0],
bgr_to_rgb=False,
pad_size_divisor=32)
model = dict(
# use caffe img_norm
data_preprocessor=data_preprocessor,
backbone=dict(
norm_cfg=dict(requires_grad=False),... | _base_ = './mask_rcnn_r50_fpn_1x_coco.py'
preprocess_cfg = dict(
mean=[103.530, 116.280, 123.675],
std=[1.0, 1.0, 1.0],
to_rgb=False,
pad_size_divisor=32)
model = dict(
# use caffe img_norm
preprocess_cfg=preprocess_cfg,
backbone=dict(
norm_cfg=dict(requires_grad=False),
styl... |
import csv
import gzip
import logging
import math
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
#### Ju... | from torch.utils.data import DataLoader
import math
from sentence_transformers import models, losses
from sentence_transformers import LoggingHandler, SentenceTransformer, util, InputExample
from sentence_transformers.evaluation import EmbeddingSimilarityEvaluator
import logging
from datetime import datetime
import os
... |
import random
import time
import pytest
from jina import Client, Document, DocumentArray, Executor, Flow, requests
@pytest.mark.parametrize('stream', [True, False])
@pytest.mark.parametrize('protocol', ['grpc'])
def test_return_order_in_client(protocol, stream):
class ExecutorRandomSleepExecutor(Executor):
... | import random
import time
import pytest
from jina import Client, Document, DocumentArray, Executor, Flow, requests
@pytest.mark.parametrize('protocol', ['grpc'])
def test_return_order_in_client(protocol):
class ExecutorRandomSleepExecutor(Executor):
@requests
def foo(self, *args, **kwargs):
... |
import inspect
from abc import ABC
from functools import reduce
from typing import TYPE_CHECKING, Any, Dict, Optional, Set, Type, Union
if TYPE_CHECKING: # pragma: no cover
from jina.orchestrate.flow.base import Flow
from jina.serve.executors import BaseExecutor
class VersionedYAMLParser:
"""Flow YAML p... | import inspect
from abc import ABC
from functools import reduce
from typing import TYPE_CHECKING, Any, Dict, Optional, Set, Type, Union
if TYPE_CHECKING: # pragma: no cover
from jina.orchestrate.flow.base import Flow
from jina.serve.executors import BaseExecutor
class VersionedYAMLParser:
"""Flow YAML pa... |
# Copyright (c) OpenMMLab. All rights reserved.
import math
from typing import Optional
import torch
import torch.nn as nn
from mmengine.model import ExponentialMovingAverage
from torch import Tensor
from mmdet.registry import MODELS
@MODELS.register_module()
class ExpMomentumEMA(ExponentialMovingAverage):
"""E... | # Copyright (c) OpenMMLab. All rights reserved.
import math
from typing import Optional
import torch
import torch.nn as nn
from mmengine.model import ExponentialMovingAverage
from torch import Tensor
from mmdet.registry import MODELS
@MODELS.register_module()
class ExpMomentumEMA(ExponentialMovingAverage):
"""E... |
import copy
import warnings
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union
@dataclass
class DownloadConfig:
"""Configuration for our cached path manager.
Attributes:
cache_dir (`str` or `Path`, *optional*):
Specify a cache directory to ... | import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union
@dataclass
class DownloadConfig:
"""Configuration for our cached path manager.
Attributes:
cache_dir (`str` or `Path`, *optional*):
Specify a cache directory to save the file to... |
# Copyright (c) OpenMMLab. All rights reserved.
import logging
import os.path as osp
from argparse import ArgumentParser
import mmcv
from mmengine.config import Config
from mmengine.logging import MMLogger
from mmengine.utils import mkdir_or_exist
from mmdet.apis import inference_detector, init_detector
from mmdet.re... | # Copyright (c) OpenMMLab. All rights reserved.
import logging
import os.path as osp
from argparse import ArgumentParser
import mmcv
from mmengine.config import Config
from mmengine.logging import MMLogger
from mmdet.apis import inference_detector, init_detector
from mmdet.registry import VISUALIZERS
from mmdet.utils... |
from datetime import datetime
import pytest
from langchain_core.exceptions import OutputParserException
from langchain.output_parsers.datetime import DatetimeOutputParser
def test_datetime_output_parser_parse() -> None:
parser = DatetimeOutputParser()
# Test valid input
date = datetime.now()
datest... | from datetime import datetime
from time import sleep
from langchain.output_parsers.datetime import DatetimeOutputParser
def test_datetime_output_parser_parse() -> None:
parser = DatetimeOutputParser()
# Test valid input
date = datetime.now()
datestr = date.strftime(parser.format)
result = parser... |
# mypy: allow-untyped-defs
import functools
from collections.abc import Hashable
from dataclasses import dataclass, fields
from typing import TypeVar
from typing_extensions import dataclass_transform
T = TypeVar("T", bound="_Union")
class _UnionTag(str):
__slots__ = ("_cls",)
_cls: Hashable
@staticmeth... | # mypy: allow-untyped-defs
import functools
from collections.abc import Hashable
from dataclasses import dataclass, fields
from typing import TypeVar
from typing_extensions import dataclass_transform
T = TypeVar("T", bound="_Union")
class _UnionTag(str):
__slots__ = ("_cls",)
_cls: Hashable
@staticmeth... |
_base_ = './yolox_s_8x8_300e_coco.py'
# model settings
model = dict(
data_preprocessor=dict(batch_augments=[
dict(
type='BatchSyncRandomResize',
random_size_range=(320, 640),
size_divisor=32,
interval=10)
]),
backbone=dict(deepen_factor=0.33, widen_fa... | _base_ = './yolox_s_8x8_300e_coco.py'
# model settings
model = dict(
random_size_range=(10, 20),
backbone=dict(deepen_factor=0.33, widen_factor=0.375),
neck=dict(in_channels=[96, 192, 384], out_channels=96),
bbox_head=dict(in_channels=96, feat_channels=96))
img_scale = (640, 640) # height, width
# f... |
import json
from pathlib import Path
from typing import Any, Callable, Optional, Tuple
import PIL.Image
from .utils import download_and_extract_archive, verify_str_arg
from .vision import VisionDataset
class Food101(VisionDataset):
"""`The Food-101 Data Set <https://data.vision.ee.ethz.ch/cvl/datasets_extra/foo... | import json
from pathlib import Path
from typing import Any, Callable, Optional, Tuple
import PIL.Image
from .utils import download_and_extract_archive, verify_str_arg
from .vision import VisionDataset
class Food101(VisionDataset):
"""`The Food-101 Data Set <https://data.vision.ee.ethz.ch/cvl/datasets_extra/foo... |
_base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
norm_cfg = dict(type='BN', requires_grad=True)
model = dict(
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123.6... | _base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py', '../_base_/default_runtime.py'
]
cudnn_benchmark = True
norm_cfg = dict(type='BN', requires_grad=True)
model = dict(
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(... |
"""
This example computes the score between a query and all possible
sentences in a corpus using a Cross-Encoder for semantic textual similarity (STS).
It output then the most similar sentences for the given query.
"""
from sentence_transformers.cross_encoder import CrossEncoder
import numpy as np
# Pre-trained cross ... | """
This example computes the score between a query and all possible
sentences in a corpus using a Cross-Encoder for semantic textual similarity (STS).
It output then the most similar sentences for the given query.
"""
from sentence_transformers.cross_encoder import CrossEncoder
import numpy as np
# Pre-trained cross ... |
import importlib
import shutil
import threading
from typing import List
import fsspec
import fsspec.asyn
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_FILE... | import importlib
import threading
from typing import List
import fsspec
import fsspec.asyn
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[... |
# 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.
import fire
from llama import Llama
from typing import List
def main(
ckpt_dir: str,
tokenizer_path: str,
temperature: float = 0.6,
top_p... | # 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.
import fire
from llama import Llama
def main(
ckpt_dir: str,
tokenizer_path: str,
temperature: float = 0.6,
top_p: float = 0.9,
max_... |
"""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 hsv_to_rgb
from keras.src... |
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... |
# model settings
preprocess_cfg = dict(
mean=[103.530, 116.280, 123.675],
std=[1.0, 1.0, 1.0],
to_rgb=False,
pad_size_divisor=32)
norm_cfg = dict(type='BN', requires_grad=False)
model = dict(
preprocess_cfg=preprocess_cfg,
type='FasterRCNN',
backbone=dict(
type='ResNet',
dept... | # model settings
norm_cfg = dict(type='BN', requires_grad=False)
model = dict(
type='FasterRCNN',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
strides=(1, 2, 2, 1),
dilations=(1, 1, 1, 2),
out_indices=(3, ),
frozen_stages=1,
norm_cfg=norm_... |
import functools
import os
import os.path
import pathlib
from typing import Any, BinaryIO, Collection, Dict, List, Optional, Tuple, Union
from torchdata.datapipes.iter import FileLister, FileOpener, Filter, IterDataPipe, Mapper
from torchvision.prototype.datasets.utils import EncodedData, EncodedImage
from torchvision... | import functools
import os
import os.path
import pathlib
from typing import Any, BinaryIO, Collection, Dict, List, Optional, Tuple, Union
from torchdata.datapipes.iter import FileLister, FileOpener, Filter, IterDataPipe, Mapper
from torchvision.prototype.datapoints import Label
from torchvision.prototype.datasets.util... |
from typing import Iterable, Dict, TYPE_CHECKING
import numpy as np
from docarray import DocumentArray
from docarray.array.storage.base.getsetdel import BaseGetSetDelMixin
from docarray.array.storage.base.helper import Offset2ID
from docarray.array.storage.milvus.backend import (
_always_true_expr,
_ids_to_mi... | from typing import Iterable, Dict, TYPE_CHECKING
import numpy as np
from docarray import DocumentArray
from docarray.array.storage.base.getsetdel import BaseGetSetDelMixin
from docarray.array.storage.base.helper import Offset2ID
from docarray.array.storage.milvus.backend import (
_always_true_expr,
_ids_to_mi... |
# Copyright (c) OpenMMLab. All rights reserved.
from .activations import SiLU
from .bbox_nms import fast_nms, multiclass_nms
from .brick_wrappers import AdaptiveAvgPool2d, adaptive_avg_pool2d
from .conv_upsample import ConvUpsample
from .csp_layer import CSPLayer
from .dropblock import DropBlock
from .ema import ExpMom... | # Copyright (c) OpenMMLab. All rights reserved.
from .activations import SiLU
from .bbox_nms import fast_nms, multiclass_nms
from .brick_wrappers import AdaptiveAvgPool2d, adaptive_avg_pool2d
from .conv_upsample import ConvUpsample
from .csp_layer import CSPLayer
from .dropblock import DropBlock
from .ema import ExpMom... |
# Copyright (c) OpenMMLab. All rights reserved.
from .distributed_sampler import DistributedSampler
from .group_sampler import DistributedGroupSampler, GroupSampler
from .infinite_sampler import InfiniteBatchSampler, InfiniteGroupBatchSampler
__all__ = [
'DistributedSampler', 'DistributedGroupSampler', 'GroupSampl... | # Copyright (c) OpenMMLab. All rights reserved.
from .distributed_sampler import DistributedSampler
from .group_sampler import DistributedGroupSampler, GroupSampler
__all__ = ['DistributedSampler', 'DistributedGroupSampler', 'GroupSampler']
|
# 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... | # Copyright 2024 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... |
"""
This is a simple application for sentence embeddings: semantic search
We have a corpus with various sentences. Then, for a given query sentence,
we want to find the most similar sentence in this corpus.
This script outputs for various queries the top 5 most similar sentences in the corpus.
"""
import torch
from... | """
This is a simple application for sentence embeddings: semantic search
We have a corpus with various sentences. Then, for a given query sentence,
we want to find the most similar sentence in this corpus.
This script outputs for various queries the top 5 most similar sentences in the corpus.
"""
import torch
from... |
from __future__ import annotations
from dataclasses import dataclass
from sentence_transformers.training_args import SentenceTransformerTrainingArguments
@dataclass
class SparseEncoderTrainingArguments(SentenceTransformerTrainingArguments):
r"""
SparseEncoderTrainingArguments extends :class:`~SentenceTransf... | from __future__ import annotations
from dataclasses import dataclass
from sentence_transformers.training_args import SentenceTransformerTrainingArguments
@dataclass
class SparseEncoderTrainingArguments(SentenceTransformerTrainingArguments):
"""
SparseEncoderTrainingArguments extends :class:`~SentenceTransfo... |
_base_ = './mask-rcnn_r50_fpn_1x_coco.py'
model = dict(
# use caffe img_norm
data_preprocessor=dict(
mean=[103.530, 116.280, 123.675],
std=[1.0, 1.0, 1.0],
bgr_to_rgb=False),
backbone=dict(
norm_cfg=dict(requires_grad=False),
style='caffe',
init_cfg=dict(
... | _base_ = './mask_rcnn_r50_fpn_1x_coco.py'
model = dict(
# use caffe img_norm
data_preprocessor=dict(
mean=[103.530, 116.280, 123.675],
std=[1.0, 1.0, 1.0],
bgr_to_rgb=False),
backbone=dict(
norm_cfg=dict(requires_grad=False),
style='caffe',
init_cfg=dict(
... |
# 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 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... |
_base_ = './solov2_r50_fpn_ms-3x_coco.py'
# model settings
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(checkpoint='torchvision://resnet101'),
dcn=dict(type='DCNv2', deformable_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)),
mask_head=dic... | _base_ = 'solov2_r50_fpn_mstrain_3x_coco.py'
# model settings
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(checkpoint='torchvision://resnet101'),
dcn=dict(type='DCNv2', deformable_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)),
mask_head=... |
_base_ = 'mask-rcnn_r50_fpn_rpn-2conv_4conv1fc_syncbn-all_32xb2-ssj-scp-270k_coco.py' # noqa
# training schedule for 90k
max_iters = 90000
# learning rate policy
# lr steps at [0.9, 0.95, 0.975] of the maximum iterations
param_scheduler = [
dict(
type='LinearLR', start_factor=0.067, by_epoch=False, begin... | _base_ = 'mask_rcnn_r50_fpn_syncbn-all_rpn-2conv_ssj_scp_32x2_270k_coco.py'
# training schedule for 90k
max_iters = 90000
# learning rate policy
# lr steps at [0.9, 0.95, 0.975] of the maximum iterations
param_scheduler = [
dict(
type='LinearLR', start_factor=0.067, by_epoch=False, begin=0, end=500),
... |
__copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import subprocess
import pytest
from dpr_reader import DPRReaderRanker
from jina import Document, DocumentArray, Flow
@pytest.mark.parametrize('request_size', [1, 8, 50])
def test_integration(request_size: int... | __copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import subprocess
import pytest
from jina import Document, DocumentArray, Flow
from ...dpr_reader import DPRReaderRanker
@pytest.mark.parametrize('request_size', [1, 8, 50])
def test_integration(request_size:... |
from enum import Enum
# --8<-- [start:ProviderName]
class ProviderName(str, Enum):
ANTHROPIC = "anthropic"
APOLLO = "apollo"
COMPASS = "compass"
DISCORD = "discord"
D_ID = "d_id"
E2B = "e2b"
EXA = "exa"
FAL = "fal"
GENERIC_WEBHOOK = "generic_webhook"
GITHUB = "github"
GOOGL... | from enum import Enum
# --8<-- [start:ProviderName]
class ProviderName(str, Enum):
ANTHROPIC = "anthropic"
APOLLO = "apollo"
COMPASS = "compass"
DISCORD = "discord"
D_ID = "d_id"
E2B = "e2b"
EXA = "exa"
FAL = "fal"
GITHUB = "github"
GOOGLE = "google"
GOOGLE_MAPS = "google_m... |
from __future__ import annotations
__version__ = "3.2.0.dev0"
__MODEL_HUB_ORGANIZATION__ = "sentence-transformers"
import importlib
import os
from sentence_transformers.cross_encoder.CrossEncoder import CrossEncoder
from sentence_transformers.datasets import ParallelSentencesDataset, SentencesDataset
from sentence_t... | from __future__ import annotations
__version__ = "3.1.0.dev0"
__MODEL_HUB_ORGANIZATION__ = "sentence-transformers"
import importlib
import os
from sentence_transformers.cross_encoder.CrossEncoder import CrossEncoder
from sentence_transformers.datasets import ParallelSentencesDataset, SentencesDataset
from sentence_t... |
import openvino.runtime.opset14 as ov_opset
from openvino import Type
from keras.src.backend.openvino.core import OpenVINOKerasTensor
from keras.src.backend.openvino.core import get_ov_output
def segment_sum(data, segment_ids, num_segments=None, sorted=False):
raise NotImplementedError(
"`segment_sum` is... | import openvino.runtime.opset14 as ov_opset
from openvino import Type
from keras.src.backend.openvino.core import OpenVINOKerasTensor
from keras.src.backend.openvino.core import get_ov_output
def segment_sum(data, segment_ids, num_segments=None, sorted=False):
raise NotImplementedError(
"`segment_sum` is... |
_base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# model settings
model = dict(
type='ATSS',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
... | _base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# model settings
preprocess_cfg = dict(
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True,
pad_size_divisor=32)
model = dict(
preprocess_cfg=prepr... |
"""DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.activations import deserialize
from keras.src.activations import get
from keras.src.activations import serialize
from keras.src.activations.activations import celu
from keras.src.acti... | """DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.activations import deserialize
from keras.src.activations import get
from keras.src.activations import serialize
from keras.src.activations.activations import celu
from keras.src.acti... |
"""Valyu tool spec."""
from typing import List, Optional
from llama_index.core.schema import Document
from llama_index.core.tools.tool_spec.base import BaseToolSpec
class ValyuToolSpec(BaseToolSpec):
"""Valyu tool spec."""
spec_functions = [
"context",
]
def __init__(
self,
... | """Valyu tool spec."""
from typing import List, Optional
from llama_index.core.schema import Document
from llama_index.core.tools.tool_spec.base import BaseToolSpec
class ValyuToolSpec(BaseToolSpec):
"""Valyu tool spec."""
spec_functions = [
"context",
]
def __init__(
self,
... |
"""DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.applications.mobilenet_v2 import MobileNetV2 as MobileNetV2
from keras.src.applications.mobilenet_v2 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.mobilenet_v2 import MobileNetV2
from keras.src.applications.mobilenet_v2 import decode_predictions
from keras.src.applications.mobilenet_v2 import preprocess_input
|
# Copyright (c) OpenMMLab. All rights reserved.
import logging
from contextlib import contextmanager
from typing import Optional
import torch
from mmengine import print_log
from mmengine.utils import TORCH_VERSION, digit_version
@contextmanager
def autocast(device_type: Optional[str] = None,
dtype: Opt... | # Copyright (c) OpenMMLab. All rights reserved.
from contextlib import contextmanager
import torch
from mmengine.utils import TORCH_VERSION, digit_version
@contextmanager
def autocast(enabled: bool = True, **kwargs):
"""A wrapper of ``torch.autocast`` and ``toch.cuda.amp.autocast``.
Pytorch 1.6.0 provide `... |
from typing import TYPE_CHECKING, Type, Optional
if TYPE_CHECKING: # pragma: no cover
from docarray.typing import T
from docarray.proto.docarray_pb2 import DocumentProto
class ProtobufMixin:
@classmethod
def from_protobuf(cls: Type['T'], pb_msg: 'DocumentProto') -> 'T':
from docarray.proto.i... | from typing import TYPE_CHECKING, Type, Optional
if TYPE_CHECKING:
from docarray.typing import T
from docarray.proto.docarray_pb2 import DocumentProto
class ProtobufMixin:
@classmethod
def from_protobuf(cls: Type['T'], pb_msg: 'DocumentProto') -> 'T':
from docarray.proto.io import parse_proto... |
"""
Tatoeba (https://tatoeba.org/) is a collection of sentences and translation, mainly aiming for language learning.
It is available for more than 300 languages.
This script downloads the Tatoeba corpus and extracts the sentences & translations in the languages you like
"""
import os
import sentence_transformers
imp... | """
Tatoeba (https://tatoeba.org/) is a collection of sentences and translation, mainly aiming for language learning.
It is available for more than 300 languages.
This script downloads the Tatoeba corpus and extracts the sentences & translations in the languages you like
"""
import os
import sentence_transformers
impo... |
# Copyright (c) OpenMMLab. All rights reserved.
import os.path
from typing import Optional
import mmengine
from mmdet.registry import DATASETS
from .coco import CocoDataset
@DATASETS.register_module()
class V3DetDataset(CocoDataset):
"""Dataset for V3Det."""
METAINFO = {
'classes': None,
'p... | # Copyright (c) OpenMMLab. All rights reserved.
import mmengine
from mmdet.registry import DATASETS
from .coco import CocoDataset
V3DET_CLASSES = tuple(
mmengine.list_from_file(
'configs/v3det/category_name_13204_v3det_2023_v1.txt'))
@DATASETS.register_module()
class V3DetDataset(CocoDataset):
"""D... |
"""
This is a simple application for sentence embeddings: clustering
Sentences are mapped to sentence embeddings and then k-mean clustering is applied.
"""
from sentence_transformers import SentenceTransformer
from sklearn.cluster import KMeans
embedder = SentenceTransformer("all-MiniLM-L6-v2")
# Corpus with exampl... | """
This is a simple application for sentence embeddings: clustering
Sentences are mapped to sentence embeddings and then k-mean clustering is applied.
"""
from sentence_transformers import SentenceTransformer
from sklearn.cluster import KMeans
embedder = SentenceTransformer("all-MiniLM-L6-v2")
# Corpus with example... |
"""
Use scikit-learn regressor interface with CPU histogram tree method
===================================================================
"""
from dask import array as da
from dask.distributed import Client, LocalCluster
from xgboost import dask as dxgb
def main(client):
# generate some random data for demons... | """
Use scikit-learn regressor interface with CPU histogram tree method
===================================================================
"""
from dask import array as da
from dask.distributed import Client, LocalCluster
from xgboost import dask as dxgb
def main(client):
# generate some random data for demonst... |
# Licensed to the LF AI & Data foundation under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the "License");
# you may not use this fil... | import pytest
import logging
@pytest.fixture(autouse=True)
def set_logger_level():
logger = logging.getLogger('docarray')
logger.setLevel(logging.INFO)
|
__version__ = '0.13.25'
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__ = '0.13.24'
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()
|
from torchvision.transforms import AutoAugmentPolicy, InterpolationMode # usort: skip
from . import functional # usort: skip
from ._transform import Transform # usort: skip
from ._augment import CutMix, MixUp, RandomErasing
from ._auto_augment import AugMix, AutoAugment, RandAugment, TrivialAugmentWide
from ._col... | from torchvision.transforms import AutoAugmentPolicy, InterpolationMode # usort: skip
from . import functional, utils # usort: skip
from ._transform import Transform # usort: skip
from ._augment import CutMix, MixUp, RandomErasing
from ._auto_augment import AugMix, AutoAugment, RandAugment, TrivialAugmentWide
fro... |
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 .Transformer import Transformer
from .Weighte... | from .Transformer import Transformer
from .Asym import Asym
from .BoW import BoW
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 .WeightedLayerPooling import WeightedLaye... |
"""DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.dtype_policies import deserialize as deserialize
from keras.src.dtype_policies import get as get
from keras.src.dtype_policies import serialize as serialize
from keras.src.dtype_polic... | """DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.dtype_policies import deserialize
from keras.src.dtype_policies import get
from keras.src.dtype_policies import serialize
from keras.src.dtype_policies.dtype_policy import DTypePolicy... |
import asyncio
import pytest
from llama_index.core.workflow.context import Context
from llama_index.core.workflow.decorators import step
from llama_index.core.workflow.errors import WorkflowRuntimeError, WorkflowTimeoutError
from llama_index.core.workflow.events import Event, StartEvent, StopEvent
from llama_index.cor... | import asyncio
import pytest
from llama_index.core.workflow.context import Context
from llama_index.core.workflow.decorators import step
from llama_index.core.workflow.events import Event, StartEvent, StopEvent
from llama_index.core.workflow.workflow import Workflow
from llama_index.core.workflow.errors import Workflo... |
_base_ = './fcos_r50-caffe_fpn_gn-head_1x_coco.py'
# model settings
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron/resnet101_caffe')))
# dataset settings
train_pipeline = [
dict(
type='LoadImageFromFi... | _base_ = './fcos_r50_caffe_fpn_gn-head_1x_coco.py'
# model settings
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron/resnet101_caffe')))
# dataset settings
train_pipeline = [
dict(
type='LoadImageFromFi... |
# flake8: noqa
# Copyright 2020 The HuggingFace Datasets Authors and the TensorFlow Datasets Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LI... | # flake8: noqa
# Copyright 2020 The HuggingFace Datasets Authors and the TensorFlow Datasets Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LI... |
# Copyright (c) OpenMMLab. All rights reserved.
from .misc import (check_prerequisites, concat_list, deprecated_api_warning,
has_method, import_modules_from_strings, is_list_of,
is_method_overridden, is_seq_of, is_str, is_tuple_of,
iter_cast, list_cast, mmcv_full... | # Copyright (c) OpenMMLab. All rights reserved.
from .misc import (check_prerequisites, concat_list, deprecated_api_warning,
has_method, import_modules_from_strings, is_list_of,
is_method_overridden, is_seq_of, is_str, is_tuple_of,
iter_cast, list_cast, requires_... |
"""Utilities for chat loaders."""
from copy import deepcopy
from typing import Iterable, Iterator, List
from langchain_core.chat_sessions import ChatSession
from langchain_core.messages import AIMessage, BaseMessage
def merge_chat_runs_in_session(
chat_session: ChatSession, delimiter: str = "\n\n"
) -> ChatSess... | """Utilities for chat loaders."""
from copy import deepcopy
from typing import Iterable, Iterator, List
from langchain_core.chat_sessions import ChatSession
from langchain_core.messages import AIMessage, BaseMessage
def merge_chat_runs_in_session(
chat_session: ChatSession, delimiter: str = "\n\n"
) -> ChatSess... |
_base_ = 'faster-rcnn_r50_fpn_ms-3x_coco.py'
model = dict(
backbone=dict(
depth=101,
norm_cfg=dict(requires_grad=False),
norm_eval=True,
style='caffe',
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron2/resnet101_caffe')))
| _base_ = 'faster-rcnn_r50_fpn_ms-3x_coco.py'
model = dict(
backbone=dict(
depth=101,
norm_cfg=dict(requires_grad=False),
norm_eval=True,
style='caffe',
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron2/resnet101_caffe')))
# use ca... |
"""Run smoke tests"""
import sys
from pathlib import Path
import torch
import torchvision
from torchvision.io import decode_jpeg, read_file, read_image
from torchvision.models import resnet50, ResNet50_Weights
SCRIPT_DIR = Path(__file__).parent
def smoke_test_torchvision() -> None:
print(
"Is torchvisi... | """Run smoke tests"""
import sys
from pathlib import Path
import torch
import torchvision
from torchvision.io import decode_jpeg, read_file, read_image
from torchvision.models import resnet50, ResNet50_Weights
SCRIPT_DIR = Path(__file__).parent
def smoke_test_torchvision() -> None:
print(
"Is torchvisi... |
"""Tool for the Reddit search 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.reddit_search import RedditSearchAPIWrapper
class RedditSearchSch... | """Tool for the Reddit search 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.reddit_search import RedditSearchAPIWrapper
class RedditSearchSch... |
from .rnnt_pipeline import EMFORMER_RNNT_BASE_MUSTC, EMFORMER_RNNT_BASE_TEDLIUM3
from .source_separation_pipeline import HDEMUCS_HIGH_MUSDB, HDEMUCS_HIGH_MUSDB_PLUS
__all__ = [
"EMFORMER_RNNT_BASE_MUSTC",
"EMFORMER_RNNT_BASE_TEDLIUM3",
"HDEMUCS_HIGH_MUSDB_PLUS",
"HDEMUCS_HIGH_MUSDB",
]
| from .rnnt_pipeline import EMFORMER_RNNT_BASE_MUSTC, EMFORMER_RNNT_BASE_TEDLIUM3
from .source_separation_pipeline import (
CONVTASNET_BASE_LIBRI2MIX,
HDEMUCS_HIGH_MUSDB,
HDEMUCS_HIGH_MUSDB_PLUS,
SourceSeparationBundle,
)
__all__ = [
"CONVTASNET_BASE_LIBRI2MIX",
"EMFORMER_RNNT_BASE_MUSTC",
"... |
import copy
import os.path as osp
import unittest
from mmcv.transforms import Compose
from mmdet.datasets.transforms import MultiBranch
from mmdet.utils import register_all_modules
register_all_modules()
class TestMultiBranch(unittest.TestCase):
def setUp(self):
"""Setup the model and optimizer which ... | import copy
import os.path as osp
import unittest
from mmcv.transforms import Compose
from mmdet.datasets.transforms import MultiBranch
from mmdet.utils import register_all_modules
register_all_modules()
class TestMultiBranch(unittest.TestCase):
def setUp(self):
"""Setup the model and optimizer which ... |
import logging
import tqdm
class LoggingHandler(logging.Handler):
def __init__(self, level=logging.NOTSET) -> None:
super().__init__(level)
def emit(self, record) -> None:
try:
msg = self.format(record)
tqdm.tqdm.write(msg)
self.flush()
except (Key... | import logging
import tqdm
class LoggingHandler(logging.Handler):
def __init__(self, level=logging.NOTSET):
super().__init__(level)
def emit(self, record):
try:
msg = self.format(record)
tqdm.tqdm.write(msg)
self.flush()
except (KeyboardInterrupt, ... |
_base_ = 'fcos_r50_caffe_fpn_gn-head_1x_coco.py'
# model settings
preprocess_cfg = dict(
mean=[103.530, 116.280, 123.675],
std=[1.0, 1.0, 1.0],
to_rgb=False,
pad_size_divisor=32)
model = dict(
preprocess_cfg=preprocess_cfg,
backbone=dict(
dcn=dict(type='DCNv2', deform_groups=1, fallback... | _base_ = 'fcos_r50_caffe_fpn_gn-head_1x_coco.py'
model = dict(
backbone=dict(
dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True),
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron2/resnet50_c... |
from typing import TypeVar
from docarray.typing.proto_register import _register_proto
from docarray.typing.tensor.audio.abstract_audio_tensor import AbstractAudioTensor
from docarray.typing.tensor.audio.audio_ndarray import MAX_INT_16
from docarray.typing.tensor.torch_tensor import TorchTensor, metaTorchAndNode
T = T... | from typing import TypeVar
from docarray.typing.tensor.audio.abstract_audio_tensor import AbstractAudioTensor
from docarray.typing.tensor.audio.audio_ndarray import MAX_INT_16
from docarray.typing.tensor.torch_tensor import TorchTensor, metaTorchAndNode
T = TypeVar('T', bound='AudioTorchTensor')
class AudioTorchTen... |
from ._multi_channel import MVDR, PSD, RTFMVDR, SoudenMVDR
from ._transforms import (
AmplitudeToDB,
BarkScale,
BarkSpectrogram,
ComputeDeltas,
Fade,
FrequencyMasking,
GriffinLim,
InverseBarkScale,
InverseMelScale,
InverseSpectrogram,
LFCC,
Loudness,
MelScale,
Mel... | from ._multi_channel import MVDR, PSD, RTFMVDR, SoudenMVDR
from ._transforms import (
AmplitudeToDB,
ComputeDeltas,
Fade,
FrequencyMasking,
GriffinLim,
InverseMelScale,
InverseSpectrogram,
LFCC,
Loudness,
MelScale,
MelSpectrogram,
MFCC,
MuLawDecoding,
MuLawEncodin... |
# Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import torch.nn as nn
from mmdet.registry import MODELS
from .utils import weighted_loss
@mmcv.jit(derivate=True, coderize=True)
@weighted_loss
def gaussian_focal_loss(pred, gaussian_target, alpha=2.0, gamma=4.0):
"""`Focal Loss <https://arxiv.org/abs/1... | # Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import torch.nn as nn
from ..builder import LOSSES
from .utils import weighted_loss
@mmcv.jit(derivate=True, coderize=True)
@weighted_loss
def gaussian_focal_loss(pred, gaussian_target, alpha=2.0, gamma=4.0):
"""`Focal Loss <https://arxiv.org/abs/1708.0... |
from google.protobuf import __version__ as __pb__version__
if __pb__version__.startswith('4'):
from docarray.proto.pb.docarray_pb2 import (
DocumentArrayProto,
DocumentArrayStackedProto,
DocumentProto,
NdArrayProto,
NodeProto,
)
else:
from docarray.proto.pb2.docarray... | from google.protobuf import __version__ as __pb__version__
if __pb__version__.startswith('4'):
from docarray.proto.pb.docarray_pb2 import (
DocumentArrayProto,
DocumentArrayStackedProto,
DocumentProto,
NdArrayProto,
NodeProto,
UnionArrayProto,
)
else:
from do... |
import pytest
import torch
import torchaudio
class GreedyCTCDecoder(torch.nn.Module):
def __init__(self, labels, blank: int = 0):
super().__init__()
self.blank = blank
self.labels = labels
def forward(self, logits: torch.Tensor) -> str:
"""Given a sequence logits over labels, ... | import pytest
import torch
from torchaudio._internal import download_url_to_file
class GreedyCTCDecoder(torch.nn.Module):
def __init__(self, labels, blank: int = 0):
super().__init__()
self.blank = blank
self.labels = labels
def forward(self, logits: torch.Tensor) -> str:
"""G... |
import os
import pandas as pd
from huggingface_hub import hf_hub_download, upload_file
from huggingface_hub.utils import EntryNotFoundError
REPO_ID = "diffusers/benchmarks"
def has_previous_benchmark() -> str:
from run_all import FINAL_CSV_FILENAME
csv_path = None
try:
csv_path = hf_hub_downlo... | import glob
import sys
import pandas as pd
from huggingface_hub import hf_hub_download, upload_file
from huggingface_hub.utils import EntryNotFoundError
sys.path.append(".")
from utils import BASE_PATH, FINAL_CSV_FILE, GITHUB_SHA, REPO_ID, collate_csv # noqa: E402
def has_previous_benchmark() -> str:
csv_path... |
"""
=====================================
Plot the support vectors in LinearSVC
=====================================
Unlike SVC (based on LIBSVM), LinearSVC (based on LIBLINEAR) does not provide
the support vectors. This example demonstrates how to obtain the support
vectors in LinearSVC.
"""
# Authors: The scikit-... | """
=====================================
Plot the support vectors in LinearSVC
=====================================
Unlike SVC (based on LIBSVM), LinearSVC (based on LIBLINEAR) does not provide
the support vectors. This example demonstrates how to obtain the support
vectors in LinearSVC.
"""
# Authors: The scikit-... |
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... |
from enum import Enum
# --8<-- [start:ProviderName]
class ProviderName(str, Enum):
ANTHROPIC = "anthropic"
COMPASS = "compass"
DISCORD = "discord"
D_ID = "d_id"
E2B = "e2b"
EXA = "exa"
FAL = "fal"
GITHUB = "github"
GOOGLE = "google"
GOOGLE_MAPS = "google_maps"
GROQ = "groq"... | from enum import Enum
# --8<-- [start:ProviderName]
class ProviderName(str, Enum):
ANTHROPIC = "anthropic"
COMPASS = "compass"
DISCORD = "discord"
D_ID = "d_id"
E2B = "e2b"
EXA = "exa"
FAL = "fal"
GITHUB = "github"
GOOGLE = "google"
GOOGLE_MAPS = "google_maps"
GROQ = "groq"... |
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