input stringlengths 33 5k | output stringlengths 32 5k |
|---|---|
# Copyright 2022 The TensorFlow Authors. 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 applica... | # Copyright 2022 The TensorFlow Authors. 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 applica... |
from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
from backend.data.model import ContributorDetails, SchemaField
class ReadCsvBlock(Block):
class Input(BlockSchema):
contents: str = SchemaField(
description="The contents of the CSV file to read",
placeho... | from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
from backend.data.model import ContributorDetails, SchemaField
class ReadCsvBlock(Block):
class Input(BlockSchema):
contents: str = SchemaField(
description="The contents of the CSV file to read",
placeho... |
import torch
from torchaudio_unittest.common_utils import PytorchTestCase
from .tacotron2_loss_impl import (
Tacotron2LossGradcheckTests,
Tacotron2LossShapeTests,
Tacotron2LossTorchscriptTests,
)
class TestTacotron2LossShapeFloat32CPU(Tacotron2LossShapeTests, PytorchTestCase):
dtype = torch.float32
... | import torch
from torchaudio_unittest.common_utils import PytorchTestCase
from .tacotron2_loss_impl import (
Tacotron2LossShapeTests,
Tacotron2LossTorchscriptTests,
Tacotron2LossGradcheckTests,
)
class TestTacotron2LossShapeFloat32CPU(Tacotron2LossShapeTests, PytorchTestCase):
dtype = torch.float32
... |
_base_ = './faster-rcnn_r50_fpn_1x_coco.py'
model = dict(
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[103.530, 116.280, 123.675],
std=[1.0, 1.0, 1.0],
bgr_to_rgb=False,
pad_size_divisor=32),
backbone=dict(
norm_cfg=dict(requires_grad=False),
... | _base_ = './faster-rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
norm_cfg=dict(requires_grad=False),
norm_eval=True,
style='caffe',
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron2/resnet50_caffe')))
# use caffe img_norm
img_norm_c... |
import numpy as np
from docarray.proto import NodeProto
from docarray.typing.url.any_url import AnyUrl
class ImageUrl(AnyUrl):
def _to_node_protobuf(self) -> NodeProto:
"""Convert Document into a NodeProto protobuf message. This function should
be called when the Document is nested into another D... | import numpy as np
from .any_url import AnyUrl
class ImageUrl(AnyUrl):
def load(self) -> np.ndarray:
"""
transform the url in a image Tensor
this is just a patch we will move the function from old docarray
:return: tensor image
"""
return np.zeros((3, 224, 224))
|
import sys
from typing import Callable
import pytest
from langchain_core.runnables.base import RunnableLambda
from langchain_core.runnables.utils import (
get_function_nonlocals,
get_lambda_source,
indent_lines_after_first,
)
@pytest.mark.skipif(
sys.version_info < (3, 9), reason="Requires python ve... | import sys
from typing import Callable
import pytest
from langchain_core.runnables.base import RunnableLambda
from langchain_core.runnables.utils import (
get_function_nonlocals,
get_lambda_source,
indent_lines_after_first,
)
@pytest.mark.skipif(
sys.version_info < (3, 9), reason="Requires python ve... |
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 torch.utils.data import DataLoader
from datetime import datetime
#### Just some code to print debug information to stdout
log... |
from typing import Any, Optional
import pytest
from langchain_core.callbacks import CallbackManagerForChainRun
from langchain.callbacks import StdOutCallbackHandler
from langchain.chains.base import Chain
class FakeChain(Chain):
"""Fake chain class for testing purposes."""
be_correct: bool = True
the_i... | from typing import Any, Optional
import pytest
from langchain.callbacks import StdOutCallbackHandler
from langchain.chains.base import CallbackManagerForChainRun, Chain
class FakeChain(Chain):
"""Fake chain class for testing purposes."""
be_correct: bool = True
the_input_keys: list[str] = ["foo"]
t... |
from ._dsp import (
adsr_envelope,
exp_sigmoid,
extend_pitch,
filter_waveform,
frequency_impulse_response,
oscillator_bank,
sinc_impulse_response,
)
from ._rir import ray_tracing, simulate_rir_ism
from .functional import barkscale_fbanks, chroma_filterbank
__all__ = [
"adsr_envelope",
... | from ._dsp import (
adsr_envelope,
exp_sigmoid,
extend_pitch,
filter_waveform,
frequency_impulse_response,
oscillator_bank,
sinc_impulse_response,
)
from ._rir import simulate_rir_ism
from .functional import barkscale_fbanks, chroma_filterbank
__all__ = [
"adsr_envelope",
"exp_sigm... |
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):
r"""
SparseEncoderTrainingArguments extends :class:`~SentenceTransf... |
from typing import Any, Optional, Union
from torchvision import tv_tensors
from torchvision.transforms.v2 import functional as F, Transform
from torchvision.tv_tensors._bounding_boxes import CLAMPING_MODE_TYPE
class ConvertBoundingBoxFormat(Transform):
"""Convert bounding box coordinates to the given ``format``,... | from typing import Any, Union
from torchvision import tv_tensors
from torchvision.transforms.v2 import functional as F, Transform
class ConvertBoundingBoxFormat(Transform):
"""Convert bounding box coordinates to the given ``format``, eg from "CXCYWH" to "XYXY".
Args:
format (str or tv_tensors.Boundi... |
import warnings
from typing import Optional, Tuple, TypeVar
from docarray.typing import AudioNdArray
from docarray.typing.bytes.audio_bytes import AudioBytes
from docarray.typing.proto_register import _register_proto
from docarray.typing.url.any_url import AnyUrl
from docarray.utils._internal.misc import is_notebook
... | import warnings
from typing import Optional, Tuple, TypeVar
from docarray.typing import AudioNdArray
from docarray.typing.bytes.audio_bytes import AudioBytes
from docarray.typing.proto_register import _register_proto
from docarray.typing.url.any_url import AnyUrl
from docarray.utils._internal.misc import is_notebook
... |
import asyncio
import logging
from typing import Optional
import aiohttp
from fastapi import HTTPException
from backend.data import graph as graph_db
from backend.data.block import get_block
from backend.util.settings import Settings
from .models import ApiResponse, ChatRequest, GraphData
logger = logging.getLogger... | import logging
from typing import Optional
import aiohttp
from fastapi import HTTPException
from backend.data import graph as graph_db
from backend.data.block import get_block
from backend.util.settings import Settings
from .models import ApiResponse, ChatRequest, GraphData
logger = logging.getLogger(__name__)
sett... |
from . import utils
from .model import (
hubert_base,
hubert_large,
hubert_pretrain_base,
hubert_pretrain_large,
hubert_pretrain_model,
hubert_pretrain_xlarge,
hubert_xlarge,
HuBERTPretrainModel,
wav2vec2_base,
wav2vec2_large,
wav2vec2_large_lv60k,
wav2vec2_model,
Wav... | from . import utils
from .model import (
hubert_base,
hubert_large,
hubert_pretrain_base,
hubert_pretrain_large,
hubert_pretrain_model,
hubert_pretrain_xlarge,
hubert_xlarge,
HuBERTPretrainModel,
wav2vec2_base,
wav2vec2_large,
wav2vec2_large_lv60k,
wav2vec2_model,
Wav... |
# Copyright (c) OpenMMLab. All rights reserved.
from .builder import build_match_cost
from .match_cost import (BBoxL1Cost, ClassificationCost, DiceCost,
FocalLossCost, IoUCost)
__all__ = [
'build_match_cost', 'ClassificationCost', 'BBoxL1Cost', 'IoUCost',
'FocalLossCost', 'DiceCost'
]
| # Copyright (c) OpenMMLab. All rights reserved.
from .builder import build_match_cost
from .match_cost import BBoxL1Cost, ClassificationCost, FocalLossCost, IoUCost
__all__ = [
'build_match_cost', 'ClassificationCost', 'BBoxL1Cost', 'IoUCost',
'FocalLossCost'
]
|
"""Azure Cognitive Vision tool spec."""
from typing import List, Optional
import requests
from llama_index.core.tools.tool_spec.base import BaseToolSpec
CV_URL_TMPL = "https://{resource}.cognitiveservices.azure.com/computervision/imageanalysis:analyze"
class AzureCVToolSpec(BaseToolSpec):
"""Azure Cognitive Vi... | """Azure Cognitive Vision tool spec."""
from typing import List, Optional
import requests
from llama_index.core.tools.tool_spec.base import BaseToolSpec
CV_URL_TMPL = "https://{resource}.cognitiveservices.azure.com/computervision/imageanalysis:analyze"
class AzureCVToolSpec(BaseToolSpec):
"""Azure Cognitive Vi... |
# Copyright (c) OpenMMLab. All rights reserved.
from unittest import TestCase
from unittest.mock import MagicMock, Mock, patch
from mmengine.hooks import IterTimerHook
from mmengine.logging import MessageHub
def time_patch():
if not hasattr(time_patch, 'time'):
time_patch.time = 0
else:
time_... | # Copyright (c) OpenMMLab. All rights reserved.
from unittest import TestCase
from unittest.mock import MagicMock, Mock, patch
from mmengine.hooks import IterTimerHook
from mmengine.logging import MessageHub
def time_patch():
if not hasattr(time_patch, 'time'):
time_patch.time = 0
else:
time_... |
# Copyright (c) OpenMMLab. All rights reserved.
import asyncio
from argparse import ArgumentParser
from mmdet.apis import (async_inference_detector, inference_detector,
init_detector, show_result_pyplot)
def parse_args():
parser = ArgumentParser()
parser.add_argument('img', help='Imag... | # Copyright (c) OpenMMLab. All rights reserved.
import asyncio
from argparse import ArgumentParser
from mmdet.apis import (async_inference_detector, inference_detector,
init_detector, show_result_pyplot)
def parse_args():
parser = ArgumentParser()
parser.add_argument('img', help='Imag... |
from typing import List
from llama_index.core.instrumentation.events.base import BaseEvent
from llama_index.core.base.response.schema import RESPONSE_TYPE
from llama_index.core.schema import QueryType
class SynthesizeStartEvent(BaseEvent):
"""
SynthesizeStartEvent.
Args:
query (QueryType): Query... | from typing import List
from llama_index.core.instrumentation.events.base import BaseEvent
from llama_index.core.base.response.schema import RESPONSE_TYPE
from llama_index.core.schema import QueryType
class SynthesizeStartEvent(BaseEvent):
"""SynthesizeStartEvent.
Args:
query (QueryType): Query as a... |
"""All minimum dependencies for scikit-learn."""
# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause
import argparse
from collections import defaultdict
# scipy and cython should by in sync with pyproject.toml
NUMPY_MIN_VERSION = "1.22.0"
SCIPY_MIN_VERSION = "1.8.0"
JOBLIB_MIN_VERSION = "1... | """All minimum dependencies for scikit-learn."""
# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause
import argparse
from collections import defaultdict
# scipy and cython should by in sync with pyproject.toml
NUMPY_MIN_VERSION = "1.22.0"
SCIPY_MIN_VERSION = "1.8.0"
JOBLIB_MIN_VERSION = "1... |
from typing import cast
import pytest
from langchain_core.language_models import BaseChatModel
from langchain_core.messages import AIMessage
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnableConfig
from langchain_tests.integration_tests import ChatModelIntegrationTests
... | from typing import cast
import pytest
from langchain_core.language_models import BaseChatModel
from langchain_core.messages import AIMessage
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnableConfig
from langchain_tests.integration_tests import ChatModelIntegrationTests
... |
# Copyright (c) OpenMMLab. All rights reserved.
import copy
import itertools
import logging
from typing import Dict, Optional
from mmengine.logging import print_log
from mmengine.model import is_model_wrapper
from mmengine.registry import HOOKS, MODELS
from .hook import DATA_BATCH, Hook
@HOOKS.register_module()
clas... | # Copyright (c) OpenMMLab. All rights reserved.
import itertools
from typing import Dict, Optional
from mmengine.model import is_model_wrapper
from mmengine.registry import HOOKS, MODELS
from .hook import DATA_BATCH, Hook
@HOOKS.register_module()
class EMAHook(Hook):
"""A Hook to apply Exponential Moving Average... |
from typing import Any, Literal
from langchain_core.messages import AIMessage
from langchain_core.outputs import ChatGeneration
from pydantic import BaseModel
from langchain_anthropic.output_parsers import ToolsOutputParser
_CONTENT: list = [
{
"type": "text",
"text": "thought",
},
{"type... | from typing import Any, List, Literal
from langchain_core.messages import AIMessage
from langchain_core.outputs import ChatGeneration
from pydantic import BaseModel
from langchain_anthropic.output_parsers import ToolsOutputParser
_CONTENT: List = [
{
"type": "text",
"text": "thought",
},
... |
# 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/LICENSE-2.0
#
# U... | # 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/LICENSE-2.0
#
# U... |
from typing import Any, Dict, Optional, Union
import numpy as np
import PIL.Image
import torch
from torchvision import datapoints
from torchvision.transforms.v2 import functional as F, Transform
from torchvision.transforms.v2.utils import is_simple_tensor
class PILToTensor(Transform):
"""[BETA] Convert a PIL I... | from typing import Any, Dict, Optional, Union
import numpy as np
import PIL.Image
import torch
from torchvision import datapoints
from torchvision.transforms.v2 import functional as F, Transform
from torchvision.transforms.v2.utils import is_simple_tensor
class PILToTensor(Transform):
"""[BETA] Convert a PIL I... |
_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))
| _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))
|
import warnings
from typing import TYPE_CHECKING, Any, Tuple, Type, TypeVar, Union
import numpy as np
from docarray.typing.bytes.audio_bytes import AudioBytes
from docarray.typing.proto_register import _register_proto
from docarray.typing.url.any_url import AnyUrl
from docarray.typing.url.filetypes import AUDIO_FILE_... | import warnings
from typing import TYPE_CHECKING, Any, Tuple, Type, TypeVar, Union
import numpy as np
from docarray.typing.bytes.audio_bytes import AudioBytes
from docarray.typing.proto_register import _register_proto
from docarray.typing.url.any_url import AnyUrl
from docarray.typing.url.filetypes import AUDIO_FILE_... |
# Copyright (c) OpenMMLab. All rights reserved.
import copy
import os.path as osp
import unittest
import numpy as np
from mmengine.data import BaseDataElement as PixelData
from mmengine.data import InstanceData
from mmdet.data_elements import DetDataSample
from mmdet.data_elements.mask import BitmapMasks
from mmdet.d... | # Copyright (c) OpenMMLab. All rights reserved.
import copy
import os.path as osp
import unittest
import numpy as np
from mmengine.data import BaseDataElement as PixelData
from mmengine.data import InstanceData
from mmdet.data_elements import DetDataSample
from mmdet.data_elements.mask import BitmapMasks
from mmdet.d... |
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.financial_datasets import FinancialDatasetsAPIWrapper
class IncomeStatementsSchema(BaseModel):
"""In... | 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.financial_datasets import FinancialDatasetsAPIWrapper
class IncomeStatementsSchema(BaseModel):
"""In... |
# Copyright (c) OpenMMLab. All rights reserved.
from typing import Tuple
import torch
from mmcv.ops import batched_nms
from mmengine.data import InstanceData
from torch import Tensor
from mmdet.registry import MODELS
from mmdet.structures import SampleList
from mmdet.utils import InstanceList
from .standard_roi_head ... | # Copyright (c) OpenMMLab. All rights reserved.
from typing import Tuple
import torch
from mmcv.ops import batched_nms
from mmengine.data import InstanceData
from torch import Tensor
from mmdet.data_elements import SampleList
from mmdet.registry import MODELS
from mmdet.utils import InstanceList
from .standard_roi_he... |
# Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
from mmengine.testing import assert_allclose
from mmdet.structures.bbox import BaseBoxes, HorizontalBoxes
from mmdet.structures.mask import BitmapMasks, PolygonMasks
def create_random_bboxes(num_bboxes, img_w, img_h):
bboxes_left_top = np.random.... | # Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
from mmengine.testing import assert_allclose
from mmdet.structures.mask import BitmapMasks, PolygonMasks
def create_random_bboxes(num_bboxes, img_w, img_h):
bboxes_left_top = np.random.uniform(0, 0.5, size=(num_bboxes, 2))
bboxes_right_bottom... |
import json
import re
from re import Pattern
from typing import Union
from langchain_core.agents import AgentAction, AgentFinish
from langchain_core.exceptions import OutputParserException
from langchain.agents.agent import AgentOutputParser
from langchain.agents.chat.prompt import FORMAT_INSTRUCTIONS
FINAL_ANSWER_A... | import json
import re
from re import Pattern
from typing import Union
from langchain_core.agents import AgentAction, AgentFinish
from langchain_core.exceptions import OutputParserException
from langchain.agents.agent import AgentOutputParser
from langchain.agents.chat.prompt import FORMAT_INSTRUCTIONS
FINAL_ANSWER_A... |
"""
Top-level module of Jina.
The primary function of this module is to import all of the public Jina
interfaces into a single place. The interfaces themselves are located in
sub-modules, as described below.
"""
import os as _os
import platform as _platform
import signal as _signal
import sys as _sys
import warnings... | """
Top-level module of Jina.
The primary function of this module is to import all of the public Jina
interfaces into a single place. The interfaces themselves are located in
sub-modules, as described below.
"""
import os as _os
import platform as _platform
import signal as _signal
import sys as _sys
import warnings... |
"""Standard LangChain interface tests"""
from typing import Type
import pytest
from langchain_core.language_models import BaseChatModel
from langchain_tests.integration_tests import ChatModelIntegrationTests
from langchain_community.chat_models import ChatPerplexity
class TestPerplexityStandard(ChatModelIntegratio... | """Standard LangChain interface tests"""
from typing import Type
import pytest
from langchain_core.language_models import BaseChatModel
from langchain_tests.integration_tests import ChatModelIntegrationTests
from langchain_community.chat_models import ChatPerplexity
class TestPerplexityStandard(ChatModelIntegratio... |
"""Run smoke tests"""
import torchaudio # noqa: F401
import torchaudio.compliance.kaldi # noqa: F401
import torchaudio.datasets # noqa: F401
import torchaudio.functional # noqa: F401
import torchaudio.models # noqa: F401
import torchaudio.pipelines # noqa: F401
import torchaudio.sox_effects # noqa: F401
import ... | """Run smoke tests"""
import torchaudio # noqa: F401
import torchaudio.compliance.kaldi # noqa: F401
import torchaudio.datasets # noqa: F401
import torchaudio.functional # noqa: F401
import torchaudio.models # noqa: F401
import torchaudio.pipelines # noqa: F401
import torchaudio.sox_effects # noqa: F401
import ... |
from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.agent_toolkits.sql.prompt import (
SQL_FUNCTIONS_SUFFIX,
SQL_PREFIX,
SQL_SUFFIX,
)
# Create a way to dynamically look up deprecated imports.
# Used to consolidat... | from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.agent_toolkits.sql.prompt import (
SQL_FUNCTIONS_SUFFIX,
SQL_PREFIX,
SQL_SUFFIX,
)
# Create a way to dynamically look up deprecated imports.
# Used to consolidat... |
# coding: utf-8
from pathlib import Path
import numpy as np
import pandas as pd
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import GridSearchCV
import lightgbm as lgb
print("Loading data...")
# load or create your dataset
regression_example_dir = Path(__file__).absolute().parents[1] /... | # coding: utf-8
from pathlib import Path
import numpy as np
import pandas as pd
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import GridSearchCV
import lightgbm as lgb
print('Loading data...')
# load or create your dataset
regression_example_dir = Path(__file__).absolute().parents[1] /... |
__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__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import os
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_videos():
f... |
from typing import (
TYPE_CHECKING,
Iterable,
)
from docarray.array.memory import DocumentArrayInMemory
if TYPE_CHECKING: # pragma: no cover
from docarray.document import Document
class MatchArray(DocumentArrayInMemory):
"""
:class:`MatchArray` inherits from :class:`DocumentArray`.
It's a s... | from typing import (
TYPE_CHECKING,
Iterable,
)
from docarray.array.memory import DocumentArrayInMemory
if TYPE_CHECKING:
from docarray.document import Document
class MatchArray(DocumentArrayInMemory):
"""
:class:`MatchArray` inherits from :class:`DocumentArray`.
It's a subset of Documents t... |
from typing import TYPE_CHECKING, Any, Type, TypeVar, Union, cast
import numpy as np
from docarray.typing.tensor.audio.abstract_audio_tensor import AbstractAudioTensor
from docarray.typing.tensor.audio.audio_ndarray import AudioNdArray
from docarray.typing.tensor.tensor import AnyTensor
from docarray.utils._internal.... | from typing import Union
from docarray.typing.tensor.audio.audio_ndarray import AudioNdArray
from docarray.utils._internal.misc import is_tf_available, is_torch_available
torch_available = is_torch_available()
if torch_available:
from docarray.typing.tensor.audio.audio_torch_tensor import AudioTorchTensor
tf_ava... |
import random
import numpy as np
import torch
from torchvision import transforms as T
from torchvision.transforms import functional as F
def pad_if_smaller(img, size, fill=0):
min_size = min(img.size)
if min_size < size:
ow, oh = img.size
padh = size - oh if oh < size else 0
padw = si... | import random
import numpy as np
import torch
from torchvision import transforms as T
from torchvision.transforms import functional as F
def pad_if_smaller(img, size, fill=0):
min_size = min(img.size)
if min_size < size:
ow, oh = img.size
padh = size - oh if oh < size else 0
padw = si... |
# flake8: noqa
from typing import Any
from typing_extensions import assert_type
from torch import randn, Tensor
# See ../pass/arithmetic_ops.py for more information
TENSOR, FLOAT = randn(3), 1.5
FLOAT & TENSOR # E: Unsupported operand types for & ("float" and "Tensor")
FLOAT | TENSOR # E: Unsupported operand typ... | # flake8: noqa
from typing import Any
from typing_extensions import assert_type
from torch import randn, Tensor
# See ../pass/arithmetic_ops.py for more information
TENSOR, INT, FLOAT = randn(3), 2, 1.5
FLOAT & TENSOR # E: Unsupported operand types for & ("float" and "Tensor")
FLOAT | TENSOR # E: Unsupported ope... |
_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=... | _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/'
img_norm_cfg = dict(mean=[123.675, 116.28, 103.53], std=[1, 1, 1], to_rgb=True)
train_p... |
"""DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.datasets.imdb import get_word_index as get_word_index
from keras.src.datasets.imdb import load_data as load_data
| """DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.datasets.imdb import get_word_index
from keras.src.datasets.imdb import load_data
|
from docarray.array.document import DocumentArray
from docarray.array.storage.qdrant import StorageMixins, QdrantConfig
__all__ = ['DocumentArrayQdrant', 'QdrantConfig']
class DocumentArrayQdrant(StorageMixins, DocumentArray):
"""
DocumentArray that stores Documents in a `Qdrant <https://weaviate.io/>`_ vect... | from .document import DocumentArray
from .storage.qdrant import StorageMixins, QdrantConfig
__all__ = ['DocumentArrayQdrant', 'QdrantConfig']
class DocumentArrayQdrant(StorageMixins, DocumentArray):
"""
DocumentArray that stores Documents in a `Qdrant <https://weaviate.io/>`_ vector search engine.
.. no... |
from datetime import datetime, timedelta
from backend.blocks.hubspot._auth import (
HubSpotCredentials,
HubSpotCredentialsField,
HubSpotCredentialsInput,
)
from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
from backend.data.model import SchemaField
from backend.util.request impo... | from datetime import datetime, timedelta
from backend.blocks.hubspot._auth import (
HubSpotCredentials,
HubSpotCredentialsField,
HubSpotCredentialsInput,
)
from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
from backend.data.model import SchemaField
from backend.util.request impo... |
from typing import TYPE_CHECKING, Type, List
if TYPE_CHECKING: # pragma: no cover
from docarray.typing import T
from docarray.document.strawberry_type import StrawberryDocument
class StrawberryMixin:
def to_strawberry_type(self) -> List['StrawberryDocument']:
"""Convert a DocumentArray object in... | from typing import TYPE_CHECKING, Type, List
if TYPE_CHECKING:
from docarray.typing import T
from docarray.document.strawberry_type import StrawberryDocument
class StrawberryMixin:
def to_strawberry_type(self) -> List['StrawberryDocument']:
"""Convert a DocumentArray object into a Pydantic model.... |
import re
from io import BytesIO
from pathlib import Path
from typing import Any, Type
import numpy as np
import pytest
from langchain_core.documents.base import Blob
from langchain_core.language_models import FakeMessagesListChatModel
from langchain_core.messages import ChatMessage
from langchain_community.document_... | import re
from pathlib import Path
from typing import Any, Type
import pytest
from langchain_core.documents.base import Blob
from langchain_core.language_models import FakeMessagesListChatModel
from langchain_core.messages import ChatMessage
from langchain_community.document_loaders.parsers.images import (
LLMIma... |
from __future__ import annotations
import json
from typing import Any, Optional
from langchain_core._api import deprecated
from langchain_core.callbacks import CallbackManagerForChainRun
from langchain_core.language_models import BaseLanguageModel
from langchain_core.prompts import BasePromptTemplate
from langchain_t... | from __future__ import annotations
import json
from typing import Any, Dict, List, Optional
from langchain_core._api import deprecated
from langchain_core.callbacks import CallbackManagerForChainRun
from langchain_core.language_models import BaseLanguageModel
from langchain_core.prompts import BasePromptTemplate
from... |
import importlib
import pytest
from fastapi.testclient import TestClient
from ...utils import needs_py39
@pytest.fixture(
name="client",
params=[
"tutorial012",
pytest.param("tutorial012_py39", marks=needs_py39),
"tutorial012_an",
pytest.param("tutorial012_an_py39", marks=nee... | from fastapi.testclient import TestClient
from docs_src.query_params_str_validations.tutorial012 import app
client = TestClient(app)
def test_default_query_values():
url = "/items/"
response = client.get(url)
assert response.status_code == 200, response.text
assert response.json() == {"q": ["foo", "... |
"""
This examples trains a CrossEncoder for the NLI task. A CrossEncoder takes a sentence pair
as input and outputs a label. Here, it learns to predict the labels: "contradiction": 0, "entailment": 1, "neutral": 2.
It does NOT produce a sentence embedding and does NOT work for individual sentences.
Usage:
python trai... | """
This examples trains a CrossEncoder for the NLI task. A CrossEncoder takes a sentence pair
as input and outputs a label. Here, it learns to predict the labels: "contradiction": 0, "entailment": 1, "neutral": 2.
It does NOT produce a sentence embedding and does NOT work for individual sentences.
Usage:
python trai... |
# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import tempfile
from collections import OrderedDict
import torch
from mmengine import Config
from mmengine.utils import digit_version
def parse_config(config_strings):
temp_file = tempfile.NamedTemporaryFile()
config_path = f'{temp_file.name}.py... | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import tempfile
from collections import OrderedDict
import torch
from mmengine import Config
def parse_config(config_strings):
temp_file = tempfile.NamedTemporaryFile()
config_path = f'{temp_file.name}.py'
with open(config_path, 'w') as f:
... |
# 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... |
import pytest
@pytest.mark.compile
def test_placeholder() -> None:
"""Used for compiling integration tests without running any real tests."""
| import pytest
@pytest.mark.compile
def test_placeholder() -> None:
"""Used for compiling integration tests without running any real tests."""
pass
|
_base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# please install mmcls>=1.0
# import mmcls.models to trigger register_module in mmcls
custom_imports = dict(imports=['mmcls.models'], allow_faile... | _base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# please install mmcls>=0.22.0
# import mmcls.models to trigger register_module in mmcls
custom_imports = dict(imports=['mmcls.models'], allow_fa... |
"""Tests for the Google Cloud DocAI parser."""
from unittest.mock import MagicMock, patch
import pytest
from langchain_community.document_loaders.parsers import (
AzureAIDocumentIntelligenceParser,
)
@pytest.mark.requires("azure", "azure.ai", "azure.ai.documentintelligence")
@patch("azure.ai.documentintelligen... | """Tests for the Google Cloud DocAI parser."""
from unittest.mock import MagicMock, patch
import pytest
from langchain_community.document_loaders.parsers import (
AzureAIDocumentIntelligenceParser,
)
@pytest.mark.requires("azure", "azure.ai", "azure.ai.documentintelligence")
@patch("azure.ai.documentintelligen... |
# Copyright (c) OpenMMLab. All rights reserved.
import warnings
from mmdet.registry import TASK_UTILS
PRIOR_GENERATORS = TASK_UTILS
ANCHOR_GENERATORS = TASK_UTILS
def build_prior_generator(cfg, default_args=None):
warnings.warn(
'``build_prior_generator`` would be deprecated soon, please use '
... | # Copyright (c) OpenMMLab. All rights reserved.
import warnings
from mmcv.utils import Registry, build_from_cfg
PRIOR_GENERATORS = Registry('Generator for anchors and points')
ANCHOR_GENERATORS = PRIOR_GENERATORS
def build_prior_generator(cfg, default_args=None):
return build_from_cfg(cfg, PRIOR_GENERATORS, de... |
from typing import Any, Optional, Union, cast
from langchain_core.messages import AIMessage, ToolCall
from langchain_core.messages.tool import tool_call
from langchain_core.output_parsers import BaseGenerationOutputParser
from langchain_core.outputs import ChatGeneration, Generation
from pydantic import BaseModel, Con... | from typing import Any, List, Optional, Type, Union, cast
from langchain_core.messages import AIMessage, ToolCall
from langchain_core.messages.tool import tool_call
from langchain_core.output_parsers import BaseGenerationOutputParser
from langchain_core.outputs import ChatGeneration, Generation
from pydantic import Ba... |
from datetime import datetime
import pytest
from jina import Document, DocumentArray, Flow
class MyOwnException(Exception):
pass
@pytest.mark.parametrize('protocol', ['grpc', 'http', 'websocket'])
def test_invalid_input_raise(protocol):
f = Flow(protocol=protocol).add()
try:
with f:
... | import pytest
from datetime import datetime
from jina import Flow, DocumentArray, Document
class MyOwnException(Exception):
pass
@pytest.mark.parametrize('protocol', ['grpc', 'http', 'websocket'])
def test_invalid_input_raise(protocol):
f = Flow(protocol=protocol).add()
try:
with f:
... |
from langchain_core.prompts.prompt import PromptTemplate
KG_TRIPLE_DELIMITER = "<|>"
_DEFAULT_KNOWLEDGE_TRIPLE_EXTRACTION_TEMPLATE = (
"You are a networked intelligence helping a human track knowledge triples"
" about all relevant people, things, concepts, etc. and integrating"
" them with your knowledge ... | # flake8: noqa
from langchain_core.prompts.prompt import PromptTemplate
KG_TRIPLE_DELIMITER = "<|>"
_DEFAULT_KNOWLEDGE_TRIPLE_EXTRACTION_TEMPLATE = (
"You are a networked intelligence helping a human track knowledge triples"
" about all relevant people, things, concepts, etc. and integrating"
" them with... |
"""Argparser module for container runtimes"""
from jina.parsers.helper import KVAppendAction, add_arg_group
def mixin_container_runtime_parser(parser, pod_type: str = 'executor'):
"""Mixing in arguments required by :class:`ContainerRuntime` into the given parser.
:param parser: the parser instance to which we... | """Argparser module for container runtimes"""
from jina.parsers.helper import KVAppendAction, add_arg_group
def mixin_container_runtime_parser(parser):
"""Mixing in arguments required by :class:`ContainerRuntime` into the given parser.
:param parser: the parser instance to which we add arguments
"""
g... |
import os
from pathlib import Path
import pytest
from jina.hubble import HubExecutor, hubapi
from jina.hubble.hubapi import list_local
cur_dir = os.path.dirname(os.path.abspath(__file__))
@pytest.fixture
def executor_zip_file():
return Path(__file__).parent / 'dummy_executor.zip'
@pytest.fixture
def test_exe... | import os
from pathlib import Path
import pytest
from jina.hubble import HubExecutor, hubapi
from jina.hubble.hubapi import list_local
cur_dir = os.path.dirname(os.path.abspath(__file__))
@pytest.fixture
def executor_zip_file():
return Path(__file__).parent / 'dummy_executor.zip'
@pytest.fixture
def test_exe... |
"""Parser for JSON output."""
from __future__ import annotations
import json
from json import JSONDecodeError
from typing import Annotated, Any, Optional, TypeVar, Union
import jsonpatch # type: ignore[import]
import pydantic
from pydantic import SkipValidation
from langchain_core.exceptions import OutputParserExc... | from __future__ import annotations
import json
from json import JSONDecodeError
from typing import Annotated, Any, Optional, TypeVar, Union
import jsonpatch # type: ignore[import]
import pydantic
from pydantic import SkipValidation
from langchain_core.exceptions import OutputParserException
from langchain_core.outp... |
# training schedule for 20e
train_cfg = dict(by_epoch=True, max_epochs=20)
val_cfg = dict(interval=1)
test_cfg = dict()
# learning rate
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=20,
... | # training schedule for 20e
train_cfg = dict(by_epoch=True, max_epochs=20)
val_cfg = dict(interval=1)
test_cfg = dict()
# learning rate
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=20,
... |
__copyright__ = "Copyright (c) 2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import numpy as np
import pytest
import torch
from executor.models import EmbeddingModelWrapper, _ModelCatalogue
@pytest.mark.parametrize(
['model_name', 'is_supported'],
[
('ResNet', False),
... | __copyright__ = "Copyright (c) 2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import numpy as np
import pytest
import torch
from ...models import EmbeddingModelWrapper, _ModelCatalogue
@pytest.mark.parametrize(
['model_name', 'is_supported'],
[
('ResNet', False),
('re... |
from jina import DocumentArray, Flow
from ...clip_text import CLIPTextEncoder
def test_no_documents():
test_docs = DocumentArray()
f = Flow().add(uses=CLIPTextEncoder)
with f:
f.search(test_docs, {})
assert len(test_docs) == 0 # SUCCESS | from jina import DocumentArray, Flow
from ...clip_text import CLIPTextEncoder
def test_no_documents():
test_docs = DocumentArray()
f = Flow().add(uses=CLIPTextEncoder)
with f:
f.search(test_docs, {})
assert len(test_docs) == 0 # SUCCESS
|
from .BinaryClassificationEvaluator import BinaryClassificationEvaluator
from .EmbeddingSimilarityEvaluator import EmbeddingSimilarityEvaluator
from .InformationRetrievalEvaluator import InformationRetrievalEvaluator
from .LabelAccuracyEvaluator import LabelAccuracyEvaluator
from .MSEEvaluator import MSEEvaluator
from ... | from .SentenceEvaluator import SentenceEvaluator
from .SimilarityFunction import SimilarityFunction
from .BinaryClassificationEvaluator import BinaryClassificationEvaluator
from .EmbeddingSimilarityEvaluator import EmbeddingSimilarityEvaluator
from .InformationRetrievalEvaluator import InformationRetrievalEvaluator
fro... |
_base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth' # noqa
model = dict(
bac... | _base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth' # noqa
model = dict(
bac... |
from __future__ import annotations
import functools
import logging
logger = logging.getLogger(__name__)
def cross_encoder_init_args_decorator(func):
@functools.wraps(func)
def wrapper(self, *args, **kwargs):
kwargs_renamed_mapping = {
"model_name": "model_name_or_path",
"auto... | from __future__ import annotations
import functools
import logging
logger = logging.getLogger(__name__)
def cross_encoder_init_args_decorator(func):
@functools.wraps(func)
def wrapper(self, *args, **kwargs):
kwargs_renamed_mapping = {
"model_name": "model_name_or_path",
"auto... |
_base_ = ['faster-rcnn_r50_fpn_32xb2-1x_openimages-challenge.py']
# Use ClassAwareSampler
train_dataloader = dict(
sampler=dict(_delete_=True, type='ClassAwareSampler', num_sample_class=1))
| _base_ = ['faster_rcnn_r50_fpn_32x2_1x_openimages_challenge.py']
# Use ClassAwareSampler
train_dataloader = dict(
sampler=dict(_delete_=True, type='ClassAwareSampler', num_sample_class=1))
|
# ruff: noqa: F401
# This is the module that test_patching.py uses to test patch_submodule()
import os
import os as renamed_os
from os import path
from os import path as renamed_path
from os.path import join
from os.path import join as renamed_join
open = open # we just need to have a builtin inside this module to t... | # isort: skip_file
# This is the module that test_patching.py uses to test patch_submodule()
import os # noqa: F401 - this is just for tests
import os as renamed_os # noqa: F401 - this is just for tests
from os import path # noqa: F401 - this is just for tests
from os import path as renamed_path # noqa: F401 - th... |
"""
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 numpy as np
from docarray import BaseDoc
from docarray.array import DocVec
from docarray.array.doc_vec.column_storage import ColumnStorageView
from docarray.typing import AnyTensor
def test_column_storage_init():
class InnerDoc(BaseDoc):
price: int
class MyDoc(BaseDoc):
tensor: AnyTen... | import numpy as np
from docarray import BaseDoc
from docarray.array import DocVec
from docarray.array.doc_vec.column_storage import ColumnStorageView
from docarray.typing import AnyTensor
def test_column_storage_init():
class InnerDoc(BaseDoc):
price: int
class MyDoc(BaseDoc):
tensor: AnyTen... |
"""Faiss reader."""
from typing import Any, Dict, List
import numpy as np
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document
class FaissReader(BaseReader):
"""
Faiss reader.
Retrieves documents through an existing in-memory Faiss index.
These documents... | """Faiss reader."""
from typing import Any, Dict, List
import numpy as np
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document
class FaissReader(BaseReader):
"""Faiss reader.
Retrieves documents through an existing in-memory Faiss index.
These documents can ... |
import types
from typing import TYPE_CHECKING
from docarray.index.backends.in_memory import InMemoryExactNNIndex
from docarray.utils._internal.misc import (
_get_path_from_docarray_root_level,
import_library,
)
if TYPE_CHECKING:
from docarray.index.backends.elastic import ElasticDocIndex # noqa: F401
... | import types
from typing import TYPE_CHECKING
from docarray.index.backends.in_memory import InMemoryDocIndex
from docarray.utils._internal.misc import (
_get_path_from_docarray_root_level,
import_library,
)
if TYPE_CHECKING:
from docarray.index.backends.elastic import ElasticDocIndex # noqa: F401
fro... |
from typing import TYPE_CHECKING, Any, Type, TypeVar, Union, cast
import numpy as np
from docarray.typing.tensor.audio.abstract_audio_tensor import AbstractAudioTensor
from docarray.typing.tensor.audio.audio_ndarray import AudioNdArray
from docarray.typing.tensor.tensor import AnyTensor
from docarray.utils._internal.... | from typing import TYPE_CHECKING, Any, Type, TypeVar, Union, cast
import numpy as np
from docarray.typing.tensor.audio.abstract_audio_tensor import AbstractAudioTensor
from docarray.typing.tensor.audio.audio_ndarray import AudioNdArray
from docarray.typing.tensor.tensor import AnyTensor
from docarray.utils._internal.... |
import pytest
DATASET_LOADING_SCRIPT_NAME = "__dummy_dataset1__"
DATASET_LOADING_SCRIPT_CODE = """
import json
import os
import datasets
REPO_URL = "https://huggingface.co/datasets/hf-internal-testing/raw_jsonl/resolve/main/"
URLS = {"train": REPO_URL + "wikiann-bn-train.jsonl", "validation": REPO_URL + "wikiann-... | import pytest
DATASET_LOADING_SCRIPT_NAME = "__dummy_dataset1__"
DATASET_LOADING_SCRIPT_CODE = """
import json
import os
import datasets
REPO_URL = "https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/"
URLS = {"train": REPO_URL + "wikiann-bn-train.jsonl", "validation": REPO_URL + "wikian... |
_base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
type='ATSS',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
bgr_to_rgb... | _base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
type='ATSS',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=d... |
# 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 gzip
import logging
import os
from datetime import datetime
import torch
from sentence_transformers import InputExample, LoggingHandler, SentenceTransformer, losses, models, util
from sentence_transformers.evaluation import EmbeddingSimilarityEvaluator
#### Just some code to print debug information... | import csv
import gzip
import logging
import os
from datetime import datetime
import torch
from sentence_transformers import InputExample, LoggingHandler, SentenceTransformer, losses, models, util
from sentence_transformers.evaluation import EmbeddingSimilarityEvaluator
#### Just some code to print debug information... |
"""Test in memory docstore."""
from typing import Any
from langchain.output_parsers.combining import CombiningOutputParser
from langchain.output_parsers.regex import RegexParser
from langchain.output_parsers.structured import ResponseSchema, StructuredOutputParser
DEF_EXPECTED_RESULT = {
"answer": "Paris",
"... | """Test in memory docstore."""
from typing import Any
from langchain.output_parsers.combining import CombiningOutputParser
from langchain.output_parsers.regex import RegexParser
from langchain.output_parsers.structured import ResponseSchema, StructuredOutputParser
DEF_EXPECTED_RESULT = {
"answer": "Paris",
"... |
# 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/LICENSE-2.0
#
# U... | # 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/LICENSE-2.0
#
# U... |
# 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 uuid import UUID
import pytest
from pydantic import schema_json_of
from pydantic.tools import parse_obj_as
from docarray.base_doc.io.json import orjson_dumps
from docarray.typing import ID
@pytest.mark.parametrize(
'id', ['1234', 1234, UUID('cf57432e-809e-4353-adbd-9d5c0d733868')]
)
def test_id_validation(... |
_base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# training schedule for 2x
train_cfg = dict(max_epochs=24)
# learning rate policy
param_scheduler = [
dict(
type='LinearLR', start_... | _base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# training schedule for 2x
train_cfg = dict(max_epochs=24)
# learning rate policy
param_scheduler = [
dict(
type='LinearLR', start_... |
_base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
data_preprocessor=dict(
# The mean and std are used in PyCls when training RegNets
mean=[103.53, 116.28, 123.675... | _base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
backbone=dict(
_delete_=True,
type='RegNet',
arch='regnetx_3.2gf',
out_indices=(0, 1, 2, 3),
... |
from ._dsp import adsr_envelope, extend_pitch, oscillator_bank, sinc_impulse_response
from .functional import add_noise, barkscale_fbanks, convolve, fftconvolve, speed
__all__ = [
"add_noise",
"adsr_envelope",
"barkscale_fbanks",
"convolve",
"extend_pitch",
"fftconvolve",
"oscillator_bank",... | from ._dsp import adsr_envelope, extend_pitch, oscillator_bank, sinc_impulse_response
from .functional import add_noise, barkscale_fbanks, convolve, fftconvolve
__all__ = [
"add_noise",
"adsr_envelope",
"barkscale_fbanks",
"convolve",
"extend_pitch",
"fftconvolve",
"oscillator_bank",
"s... |
"""
==================
Two-class AdaBoost
==================
This example fits an AdaBoosted decision stump on a non-linearly separable
classification dataset composed of two "Gaussian quantiles" clusters
(see :func:`sklearn.datasets.make_gaussian_quantiles`) and plots the decision
boundary and decision scores. The di... | """
==================
Two-class AdaBoost
==================
This example fits an AdaBoosted decision stump on a non-linearly separable
classification dataset composed of two "Gaussian quantiles" clusters
(see :func:`sklearn.datasets.make_gaussian_quantiles`) and plots the decision
boundary and decision scores. The di... |
from typing import Any, List, Tuple, Type, TypeVar, Union
import numpy as np
from docarray.typing.proto_register import _register_proto
from docarray.typing.tensor.torch_tensor import TorchTensor, metaTorchAndNode
from docarray.typing.tensor.video.video_tensor_mixin import VideoTensorMixin
T = TypeVar('T', bound='Vi... | from typing import Any, List, Tuple, Type, TypeVar, Union
import numpy as np
from docarray.typing.proto_register import _register_proto
from docarray.typing.tensor.torch_tensor import TorchTensor, metaTorchAndNode
from docarray.typing.tensor.video.video_tensor_mixin import VideoTensorMixin
T = TypeVar('T', bound='Vi... |
__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:... | __copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
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: int):
docs = D... |
"""
Top-level module of Jina.
The primary function of this module is to import all of the public Jina
interfaces into a single place. The interfaces themselves are located in
sub-modules, as described below.
"""
import os as _os
import platform as _platform
import signal as _signal
import sys as _sys
import warnings... | """
Top-level module of Jina.
The primary function of this module is to import all of the public Jina
interfaces into a single place. The interfaces themselves are located in
sub-modules, as described below.
"""
import os as _os
import platform as _platform
import signal as _signal
import sys as _sys
import warnings... |
from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.document_loaders import NotebookLoader
from langchain_community.document_loaders.notebook import (
concatenate_cells,
remove_newlines,
)
# Create a way to dynamicall... | from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.document_loaders import NotebookLoader
from langchain_community.document_loaders.notebook import (
concatenate_cells,
remove_newlines,
)
# Create a way to dynamicall... |
from typing import Callable, Dict, Generic, List, Optional, Type, TypeVar
from torch.utils.data import Dataset
from docarray import BaseDoc, DocList, DocVec
from docarray.typing import TorchTensor
from docarray.utils._internal._typing import change_cls_name, safe_issubclass
T_doc = TypeVar('T_doc', bound=BaseDoc)
... | from typing import Callable, Dict, Generic, List, Optional, Type, TypeVar
from torch.utils.data import Dataset
from docarray import BaseDoc, DocList, DocVec
from docarray.typing import TorchTensor
from docarray.utils._internal._typing import change_cls_name
T_doc = TypeVar('T_doc', bound=BaseDoc)
class MultiModalD... |
"""
NOTE: This file must be imported like
``import torch.distributed.fsdp._traversal_utils`` and not like
``from torch.distributed.fsdp._traversal_utils import ...`` to avoid circular
imports. For brevity, we may import the file as ``traversal_utils``.
"""
import collections
import torch.nn as nn
from torch.distribut... | """
NOTE: This file must be imported like
``import torch.distributed.fsdp._traversal_utils`` and not like
``from torch.distirbuted.fsdp._traversal_utils import ...`` to avoid circular
imports. For brevity, we may import the file as ``traversal_utils``.
"""
import collections
import torch.nn as nn
from torch.distribut... |
import sys
import traceback
from importlib.machinery import SourceFileLoader
if __name__ == "__main__":
files = sys.argv[1:]
has_failure = False
for file in files:
try:
SourceFileLoader("x", file).load_module()
except Exception:
has_failure = True
traceba... | import sys
import traceback
from importlib.machinery import SourceFileLoader
if __name__ == "__main__":
files = sys.argv[1:]
has_failure = False
for file in files:
try:
SourceFileLoader("x", file).load_module()
except Exception:
has_failure = True
print(f... |
"""Format instructions."""
JSON_FORMAT_INSTRUCTIONS = """The output should be formatted as a JSON instance that conforms to the JSON schema below.
As an example, for the schema {{"properties": {{"foo": {{"title": "Foo", "description": "a list of strings", "type": "array", "items": {{"type": "string"}}}}}}, "required"... | # flake8: noqa
JSON_FORMAT_INSTRUCTIONS = """The output should be formatted as a JSON instance that conforms to the JSON schema below.
As an example, for the schema {{"properties": {{"foo": {{"title": "Foo", "description": "a list of strings", "type": "array", "items": {{"type": "string"}}}}}}, "required": ["foo"]}}
... |
import logging
from collections import defaultdict
from typing import Annotated, Any, Dict, List, Optional, Sequence
from autogpt_libs.utils.cache import thread_cached
from fastapi import APIRouter, Body, Depends, HTTPException
from prisma.enums import AgentExecutionStatus, APIKeyPermission
from typing_extensions impo... | import logging
from collections import defaultdict
from typing import Annotated, Any, Dict, List, Optional, Sequence
from autogpt_libs.utils.cache import thread_cached
from fastapi import APIRouter, Body, Depends, HTTPException
from prisma.enums import AgentExecutionStatus, APIKeyPermission
from typing_extensions impo... |
import os
import torchaudio
import torchvision
from torch.utils.data import Dataset
def _load_list(args, *filenames):
output = []
length = []
for filename in filenames:
filepath = os.path.join(os.path.dirname(args.dataset_path), filename)
for line in open(filepath).read().splitlines():
... | import os
from pathlib import Path
from typing import Tuple, Union
import torch
import torchaudio
import torchvision
from torch import Tensor
from torch.utils.data import Dataset
def _load_list(args, *filenames):
output = []
length = []
for filename in filenames:
filepath = os.path.join(os.path.d... |
from __future__ import annotations
from dataclasses import dataclass
from sentence_transformers.training_args import SentenceTransformerTrainingArguments
@dataclass
class SparseEncoderTrainingArguments(SentenceTransformerTrainingArguments):
"""
SparseEncoderTrainingArguments extends :class:`~transformers.Tr... | from __future__ import annotations
from dataclasses import dataclass
from sentence_transformers.training_args import SentenceTransformerTrainingArguments
@dataclass
class SparseEncoderTrainingArguments(SentenceTransformerTrainingArguments):
"""
SparseEncoderTrainingArguments extends :class:`~transformers.Tr... |
_base_ = '../faster_rcnn/faster_rcnn_r50_caffe_fpn_1x_coco.py'
rpn_weight = 0.7
model = dict(
rpn_head=dict(
_delete_=True,
type='CascadeRPNHead',
num_stages=2,
stages=[
dict(
type='StageCascadeRPNHead',
in_channels=256,
fea... | _base_ = '../faster_rcnn/faster_rcnn_r50_caffe_fpn_1x_coco.py'
rpn_weight = 0.7
model = dict(
rpn_head=dict(
_delete_=True,
type='CascadeRPNHead',
num_stages=2,
stages=[
dict(
type='StageCascadeRPNHead',
in_channels=256,
fea... |
import csv
import os
from pathlib import Path
from typing import Tuple, Union
import torchaudio
from torch import Tensor
from torch.utils.data import Dataset
from torchaudio._internal import download_url_to_file
from torchaudio.datasets.utils import _extract_tar
_RELEASE_CONFIGS = {
"release1": {
"folder... | 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_tar
_RELEASE_CONFIGS = {
"release1": {
"folder_in_archive... |
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