input stringlengths 33 5k | output stringlengths 32 5k |
|---|---|
import os
import sys
import pkg_resources
from setuptools import find_packages, setup
def read_version(fname="whisper/version.py"):
exec(compile(open(fname, encoding="utf-8").read(), fname, "exec"))
return locals()["__version__"]
requirements = []
if sys.platform.startswith("linux"):
triton_requirement... | import os
import sys
import pkg_resources
from setuptools import find_packages, setup
def read_version(fname="whisper/version.py"):
exec(compile(open(fname, encoding="utf-8").read(), fname, "exec"))
return locals()["__version__"]
requirements = []
if sys.platform.startswith("linux"):
triton_requirement... |
from typing import List
import datasets
from ..folder_based_builder import folder_based_builder
logger = datasets.utils.logging.get_logger(__name__)
class AudioFolderConfig(folder_based_builder.FolderBasedBuilderConfig):
"""Builder Config for AudioFolder."""
drop_labels: bool = None
drop_metadata: bo... | from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
logger = datasets.utils.logging.get_logger(__name__)
class AudioFolderConfig(folder_based_builder.FolderBasedBuilderConfig):
"""Builder Config for AudioFolder."""
... |
_base_ = [
'./bytetrack_yolox_x_8xb4-80e_crowdhuman-mot17halftrain_'
'test-mot17halfval.py'
]
# fp16 settings
optim_wrapper = dict(type='AmpOptimWrapper', loss_scale='dynamic')
val_cfg = dict(type='ValLoop', fp16=True)
test_cfg = dict(type='TestLoop', fp16=True)
| _base_ = [
'./bytetrack_yolox_x_8xb4-80e_crowdhuman-mot17halftrain_'
'test-mot17halfval.py'
]
# fp16 settings
optim_wrapper = dict(type='AmpOptimWrapper', loss_scale='dynamic')
test_cfg = dict(type='TestLoop', fp16=True)
|
# 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... |
from __future__ import annotations
import re
from typing import TYPE_CHECKING, Any
if TYPE_CHECKING:
from sentence_transformers.SentenceTransformer import SentenceTransformer
class SentenceEvaluator:
"""
Base class for all evaluators
Extend this class and implement __call__ for custom evaluators.
... | from __future__ import annotations
import re
from typing import TYPE_CHECKING, Any
if TYPE_CHECKING:
from sentence_transformers.SentenceTransformer import SentenceTransformer
class SentenceEvaluator:
"""
Base class for all evaluators
Extend this class and implement __call__ for custom evaluators.
... |
from base64 import b64encode
from typing import Optional
from urllib.parse import urlencode
from backend.data.model import OAuth2Credentials
from backend.integrations.providers import ProviderName
from backend.util.request import requests
from .base import BaseOAuthHandler
class NotionOAuthHandler(BaseOAuthHandler)... | from base64 import b64encode
from urllib.parse import urlencode
from backend.data.model import OAuth2Credentials
from backend.integrations.providers import ProviderName
from backend.util.request import requests
from .base import BaseOAuthHandler
class NotionOAuthHandler(BaseOAuthHandler):
"""
Based on the d... |
# Copyright (c) OpenMMLab. All rights reserved.
from unittest.mock import Mock
from mmengine.hooks import Hook
class TestHook:
def test_before_run(self):
hook = Hook()
runner = Mock()
hook.before_run(runner)
def test_after_run(self):
hook = Hook()
runner = Mock()
... | # Copyright (c) OpenMMLab. All rights reserved.
from unittest.mock import Mock
from mmengine.hooks import Hook
class TestHook:
def test_before_run(self):
hook = Hook()
runner = Mock()
hook.before_run(runner)
def test_after_run(self):
hook = Hook()
runner = Mock()
... |
import inspect
from keras.src.api_export import keras_export
from keras.src.quantizers.quantizers import AbsMaxQuantizer
from keras.src.quantizers.quantizers import Quantizer
from keras.src.quantizers.quantizers import abs_max_quantize
from keras.src.quantizers.quantizers import compute_float8_amax_history
from keras.... | import inspect
from keras.src.api_export import keras_export
from keras.src.quantizers.quantizers import AbsMaxQuantizer
from keras.src.quantizers.quantizers import Quantizer
from keras.src.quantizers.quantizers import abs_max_quantize
from keras.src.quantizers.quantizers import compute_float8_amax_history
from keras.... |
import os
from functools import lru_cache
from subprocess import CalledProcessError, run
from typing import Optional, Union
import numpy as np
import torch
import torch.nn.functional as F
from .utils import exact_div
# hard-coded audio hyperparameters
SAMPLE_RATE = 16000
N_FFT = 400
N_MELS = 80
HOP_LENGTH = 160
CHUN... | import os
from functools import lru_cache
from typing import Optional, Union
import ffmpeg
import numpy as np
import torch
import torch.nn.functional as F
from .utils import exact_div
# hard-coded audio hyperparameters
SAMPLE_RATE = 16000
N_FFT = 400
N_MELS = 80
HOP_LENGTH = 160
CHUNK_LENGTH = 30
N_SAMPLES = CHUNK_L... |
from __future__ import annotations
from collections.abc import Iterable
import torch
from torch import Tensor, nn
from sentence_transformers.sparse_encoder.SparseEncoder import SparseEncoder
class FlopsLoss(nn.Module):
def __init__(self, model: SparseEncoder, threshold: float = None) -> None:
"""
... | from __future__ import annotations
from collections.abc import Iterable
import torch
from torch import Tensor, nn
from sentence_transformers.sparse_encoder.SparseEncoder import SparseEncoder
class FlopsLoss(nn.Module):
def __init__(self, model: SparseEncoder, threshold: float = None) -> None:
"""
... |
from __future__ import annotations
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 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 dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Optional, Union
import pyarrow as pa
if TYPE_CHECKING:
from .features import FeatureType
@dataclass
class Translation:
"""`Feature` for translations with fixed languages per example.
Here for compatibility with tf... | from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Optional, Union
import pyarrow as pa
if TYPE_CHECKING:
from .features import FeatureType
@dataclass
class Translation:
"""`Feature` for translations with fixed languages per example.
Here for compatiblity with tfd... |
import zlib
from typing import Iterator, TextIO
def exact_div(x, y):
assert x % y == 0
return x // y
def str2bool(string):
str2val = {"True": True, "False": False}
if string in str2val:
return str2val[string]
else:
raise ValueError(f"Expected one of {set(str2val.keys())}, got {st... | import zlib
from typing import Iterator, TextIO
def exact_div(x, y):
assert x % y == 0
return x // y
def str2bool(string):
str2val = {"True": True, "False": False}
if string in str2val:
return str2val[string]
else:
raise ValueError(f"Expected one of {set(str2val.keys())}, got {st... |
import inspect
import threading
from abc import abstractmethod
from typing import Any, Dict, List, Generic, Optional, TypeVar
from llama_index.core.bridge.pydantic import BaseModel, Field, PrivateAttr, ConfigDict
from llama_index.core.instrumentation.span.base import BaseSpan
T = TypeVar("T", bound=BaseSpan)
class ... | import inspect
import threading
from abc import abstractmethod
from typing import Any, Dict, List, Generic, Optional, TypeVar
from llama_index.core.bridge.pydantic import BaseModel, Field, PrivateAttr, ConfigDict
from llama_index.core.instrumentation.span.base import BaseSpan
T = TypeVar("T", bound=BaseSpan)
class ... |
from keras.src import backend
from keras.src import ops
from keras.src.api_export import keras_export
from keras.src.layers.layer import Layer
@keras_export("keras.layers.Masking")
class Masking(Layer):
"""Masks a sequence by using a mask value to skip timesteps.
For each timestep in the input tensor (dimens... | from keras.src import backend
from keras.src import ops
from keras.src.api_export import keras_export
from keras.src.layers.layer import Layer
@keras_export("keras.layers.Masking")
class Masking(Layer):
"""Masks a sequence by using a mask value to skip timesteps.
For each timestep in the input tensor (dimens... |
from llama_index.indices.managed.llama_cloud.base import LlamaCloudIndex
from llama_index.indices.managed.llama_cloud.retriever import LlamaCloudRetriever
from llama_index.indices.managed.llama_cloud.composite_retriever import (
LlamaCloudCompositeRetriever,
)
__all__ = [
"LlamaCloudIndex",
"LlamaCloudRetr... | from llama_index.indices.managed.llama_cloud.base import LlamaCloudIndex
from llama_index.indices.managed.llama_cloud.retriever import LlamaCloudRetriever
__all__ = [
"LlamaCloudIndex",
"LlamaCloudRetriever",
]
|
__all__ = [
"Audio",
"Array2D",
"Array3D",
"Array4D",
"Array5D",
"ClassLabel",
"Features",
"LargeList",
"Sequence",
"Value",
"Image",
"Translation",
"TranslationVariableLanguages",
"Video",
]
from .audio import Audio
from .features import Array2D, Array3D, Array4D... | __all__ = [
"Audio",
"Array2D",
"Array3D",
"Array4D",
"Array5D",
"ClassLabel",
"Features",
"LargeList",
"Sequence",
"Value",
"Image",
"Translation",
"TranslationVariableLanguages",
]
from .audio import Audio
from .features import Array2D, Array3D, Array4D, Array5D, Cl... |
# Copyright (c) OpenMMLab. All rights reserved.
from .collect_env import collect_env
from .compat_config import compat_cfg
from .logger import get_caller_name, get_root_logger, log_img_scale
from .misc import find_latest_checkpoint, update_data_root
from .replace_cfg_vals import replace_cfg_vals
from .setup_env import ... | # Copyright (c) OpenMMLab. All rights reserved.
from .collect_env import collect_env
from .compat_config import compat_cfg
from .logger import get_caller_name, get_root_logger, log_img_scale
from .misc import find_latest_checkpoint, update_data_root
from .setup_env import setup_multi_processes
from .split_batch import ... |
import csv
import logging
import os
from typing import List
import numpy as np
from sentence_transformers import InputExample
logger = logging.getLogger(__name__)
class CESoftmaxAccuracyEvaluator:
"""
This evaluator can be used with the CrossEncoder class.
It is designed for CrossEncoders with 2 or mo... | import logging
import os
import csv
from typing import List
from ... import InputExample
import numpy as np
logger = logging.getLogger(__name__)
class CESoftmaxAccuracyEvaluator:
"""
This evaluator can be used with the CrossEncoder class.
It is designed for CrossEncoders with 2 or more outputs. It meas... |
from typing import Optional
import numpy as np
import pytest
import torch
from pydantic.tools import parse_obj_as, schema_json_of
from docarray import BaseDoc
from docarray.base_doc.io.json import orjson_dumps
from docarray.typing import (
AudioNdArray,
NdArray,
VideoNdArray,
VideoTorchTensor,
Vid... | from typing import Optional
import numpy as np
import pytest
import torch
from pydantic.tools import parse_obj_as, schema_json_of
from docarray import BaseDocument
from docarray.base_document.io.json import orjson_dumps
from docarray.typing import (
AudioNdArray,
NdArray,
VideoNdArray,
VideoTorchTenso... |
from typing import Any, Dict, List, Optional
from llama_index.core.base.base_retriever import BaseRetriever
from llama_index.core.callbacks.base import CallbackManager
from llama_index.core.constants import DEFAULT_SIMILARITY_TOP_K
from llama_index.core.schema import NodeWithScore, QueryBundle
from llama_index.core.se... | from typing import Any, Dict, List, Optional
from llama_index.core.base.base_retriever import BaseRetriever
from llama_index.core.callbacks.base import CallbackManager
from llama_index.core.constants import DEFAULT_SIMILARITY_TOP_K
from llama_index.core.schema import NodeWithScore, QueryBundle
from llama_index.core.se... |
from typing import Union
import numpy as np
import PIL.Image
import torch
from torchvision import tv_tensors
from torchvision.transforms import functional as _F
@torch.jit.unused
def to_image(inpt: Union[torch.Tensor, PIL.Image.Image, np.ndarray]) -> tv_tensors.Image:
"""See :class:`~torchvision.transforms.v2.To... | from typing import Union
import numpy as np
import PIL.Image
import torch
from torchvision import tv_tensors
from torchvision.transforms import functional as _F
@torch.jit.unused
def to_image(inpt: Union[torch.Tensor, PIL.Image.Image, np.ndarray]) -> tv_tensors.Image:
"""See :class:`~torchvision.transforms.v2.To... |
_base_ = '../mask_rcnn/mask-rcnn_r50_fpn_1x_coco.py'
albu_train_transforms = [
dict(
type='ShiftScaleRotate',
shift_limit=0.0625,
scale_limit=0.0,
rotate_limit=0,
interpolation=1,
p=0.5),
dict(
type='RandomBrightnessContrast',
brightness_limit=[0.... | _base_ = '../mask_rcnn/mask-rcnn_r50_fpn_1x_coco.py'
albu_train_transforms = [
dict(
type='ShiftScaleRotate',
shift_limit=0.0625,
scale_limit=0.0,
rotate_limit=0,
interpolation=1,
p=0.5),
dict(
type='RandomBrightnessContrast',
brightness_limit=[0.... |
_base_ = '../cascade_rcnn/cascade-mask-rcnn_x101-32x4d_fpn_1x_coco.py'
model = dict(
backbone=dict(
norm_cfg=dict(type='SyncBN', requires_grad=True), norm_eval=False))
| _base_ = '../cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco.py'
model = dict(
backbone=dict(
norm_cfg=dict(type='SyncBN', requires_grad=True), norm_eval=False))
|
# 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... |
from ._conformer_wav2vec2 import (
conformer_wav2vec2_base,
conformer_wav2vec2_model,
conformer_wav2vec2_pretrain_base,
conformer_wav2vec2_pretrain_large,
conformer_wav2vec2_pretrain_model,
ConformerWav2Vec2PretrainModel,
)
from ._emformer_hubert import emformer_hubert_base, emformer_hubert_mode... | from ._conformer_wav2vec2 import (
conformer_wav2vec2_base,
conformer_wav2vec2_model,
conformer_wav2vec2_pretrain_base,
conformer_wav2vec2_pretrain_large,
conformer_wav2vec2_pretrain_model,
ConformerWav2Vec2PretrainModel,
)
from ._emformer_hubert import emformer_hubert_base, emformer_hubert_mode... |
__copyright__ = "Copyright (c) 2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import os
import subprocess
from pathlib import Path
from typing import Dict
import numpy as np
import pytest
from jina import Document, DocumentArray
from PIL import Image
@pytest.fixture()
def test_dir() -> str:
... | __copyright__ = "Copyright (c) 2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import os
from typing import Dict
import numpy as np
import pytest
from PIL import Image
from jina import DocumentArray, Document
@pytest.fixture()
def test_dir() -> str:
return os.path.dirname(os.path.abspath(... |
from dataclasses import dataclass, field
from typing import Union
from transformers import TrainingArguments as TransformersTrainingArguments
from transformers.utils import ExplicitEnum
class BatchSamplers(ExplicitEnum):
"""
Stores the acceptable string identifiers for batch samplers.
The batch sampler ... | from dataclasses import dataclass, field
from typing import Union
from transformers import TrainingArguments as TransformersTrainingArguments
from transformers.utils import ExplicitEnum
class BatchSamplers(ExplicitEnum):
"""
Stores the acceptable string identifiers for batch samplers.
The batch sampler ... |
from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.vectorstores import Neo4jVector
from langchain_community.vectorstores.neo4j_vector import SearchType
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logi... | from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.vectorstores import Neo4jVector
from langchain_community.vectorstores.neo4j_vector import SearchType
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logi... |
_base_ = [
'../_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_large_patch4_window12_384_22k.pth' # noqa
model = dict(
type='ATSS',
data_preprocessor=dict(
... | _base_ = [
'../_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_large_patch4_window12_384_22k.pth' # noqa
model = dict(
type='ATSS',
data_preprocessor=dict(
... |
"""Example selectors.
**Example selector** implements logic for selecting examples to include them in prompts.
This allows us to select examples that are most relevant to the input.
"""
from importlib import import_module
from typing import TYPE_CHECKING
if TYPE_CHECKING:
from langchain_core.example_selectors.ba... | """Example selectors.
**Example selector** implements logic for selecting examples to include them in prompts.
This allows us to select examples that are most relevant to the input.
"""
from langchain_core.example_selectors.base import BaseExampleSelector
from langchain_core.example_selectors.length_based import (
... |
"""Argparser module for Pod runtimes"""
import argparse
from jina import helper
from jina.enums import PodRoleType
from jina.parsers.helper import _SHOW_ALL_ARGS, KVAppendAction, add_arg_group
def mixin_pod_parser(parser, port_monitoring=True):
"""Mixing in arguments required by :class:`Pod` into the given parse... | """Argparser module for Pod runtimes"""
import argparse
from jina import helper
from jina.enums import PodRoleType
from jina.parsers.helper import _SHOW_ALL_ARGS, KVAppendAction, add_arg_group
def mixin_pod_parser(parser):
"""Mixing in arguments required by :class:`Pod` into the given parser.
:param parser: ... |
# Copyright (c) OpenMMLab. All rights reserved.
from .class_names import (cityscapes_classes, coco_classes, dataset_aliases,
get_classes, imagenet_det_classes,
imagenet_vid_classes, voc_classes)
from .eval_hooks import DistEvalHook, EvalHook
from .mean_ap import avera... | # Copyright (c) OpenMMLab. All rights reserved.
from .class_names import (cityscapes_classes, coco_classes, dataset_aliases,
get_classes, imagenet_det_classes,
imagenet_vid_classes, voc_classes)
from .eval_hooks import DistEvalHook, EvalHook
from .mean_ap import avera... |
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.ndarray import NdArray
from docarray.typing.tensor.video.video_tensor_mixin import VideoTensorMixin
T = TypeVar('T', bound='VideoNdArray')
@_register_p... | 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.ndarray import NdArray
from docarray.typing.tensor.video.video_tensor_mixin import VideoTensorMixin
T = TypeVar('T', bound='VideoNdArray')
@_register_p... |
from typing import Dict
import torch.nn.functional as F
from torch import Tensor, nn
class Normalize(nn.Module):
"""This layer normalizes embeddings to unit length"""
def __init__(self) -> None:
super(Normalize, self).__init__()
def forward(self, features: Dict[str, Tensor]) -> Dict[str, Tensor... | from typing import Dict
import torch.nn.functional as F
from torch import Tensor, nn
class Normalize(nn.Module):
"""This layer normalizes embeddings to unit length"""
def __init__(self):
super(Normalize, self).__init__()
def forward(self, features: Dict[str, Tensor]):
features.update({"... |
# Copyright (c) OpenMMLab. All rights reserved.
from .anchor import * # noqa: F401, F403
from .bbox import * # noqa: F401, F403
from .data_structures import * # noqa: F401, F403
from .evaluation import * # noqa: F401, F403
from .hook import * # noqa: F401, F403
from .mask import * # noqa: F401, F403
from .optimiz... | # Copyright (c) OpenMMLab. All rights reserved.
from .anchor import * # noqa: F401, F403
from .bbox import * # noqa: F401, F403
from .data_structures import * # noqa: F401, F403
from .evaluation import * # noqa: F401, F403
from .hook import * # noqa: F401, F403
from .mask import * # noqa: F401, F403
from .optimiz... |
"""DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.legacy.losses import Reduction
from keras.src.losses import deserialize
from keras.src.losses import get
from keras.src.losses import serialize
from keras.src.losses.loss import Loss
... | """DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.legacy.losses import Reduction
from keras.src.losses import deserialize
from keras.src.losses import get
from keras.src.losses import serialize
from keras.src.losses.loss import Loss
... |
_base_ = './mask-rcnn_regnetx-3.2GF_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='RegNet',
arch='regnetx_12gf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dict... | _base_ = './mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='RegNet',
arch='regnetx_12gf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=dict... |
import urllib.parse
from typing import ClassVar, Optional
from backend.data.model import OAuth2Credentials, ProviderName
from backend.integrations.oauth.base import BaseOAuthHandler
from backend.util.request import Requests
class TodoistOAuthHandler(BaseOAuthHandler):
PROVIDER_NAME = ProviderName.TODOIST
DEF... | import urllib.parse
from typing import ClassVar, Optional
import requests
from backend.data.model import OAuth2Credentials, ProviderName
from backend.integrations.oauth.base import BaseOAuthHandler
class TodoistOAuthHandler(BaseOAuthHandler):
PROVIDER_NAME = ProviderName.TODOIST
DEFAULT_SCOPES: ClassVar[lis... |
from torchaudio._internal import module_utils as _mod_utils
from .sox_effects import apply_effects_file, apply_effects_tensor, effect_names, init_sox_effects, shutdown_sox_effects
if _mod_utils.is_sox_available():
import atexit
init_sox_effects()
atexit.register(shutdown_sox_effects)
__all__ = [
"i... | from torchaudio._internal import module_utils as _mod_utils
from .sox_effects import (
apply_effects_file,
apply_effects_tensor,
effect_names,
init_sox_effects,
shutdown_sox_effects,
)
if _mod_utils.is_sox_available():
import atexit
init_sox_effects()
atexit.register(shutdown_sox_eff... |
import types
from typing_extensions import TYPE_CHECKING
from docarray.typing.tensor.embedding.embedding import AnyEmbedding
from docarray.typing.tensor.embedding.ndarray import NdArrayEmbedding
from docarray.utils._internal.misc import (
_get_path_from_docarray_root_level,
import_library,
)
if TYPE_CHECKING... | from docarray.typing.tensor.embedding.embedding import AnyEmbedding
from docarray.typing.tensor.embedding.ndarray import NdArrayEmbedding
__all__ = ['NdArrayEmbedding', 'AnyEmbedding']
from docarray.utils._internal.misc import is_tf_available, is_torch_available
torch_available = is_torch_available()
if torch_availa... |
# dataset settings
dataset_type = 'LVISV05Dataset'
data_root = 'data/lvis_v0.5/'
# Example to use different file client
# Method 1: simply set the data root and let the file I/O module
# automatically infer from prefix (not support LMDB and Memcache yet)
# data_root = 's3://openmmlab/datasets/detection/lvis_v0.5/'
#... | # dataset settings
dataset_type = 'LVISV05Dataset'
data_root = 'data/lvis_v0.5/'
# file_client_args = dict(
# backend='petrel',
# path_mapping=dict({
# './data/': 's3://openmmlab/datasets/detection/',
# 'data/': 's3://openmmlab/datasets/detection/'
# }))
file_client_args = dict(backend='dis... |
import fastapi
from .middleware import auth_middleware
from .models import User, DEFAULT_USER_ID, DEFAULT_EMAIL
from .config import Settings
def requires_user(payload: dict = fastapi.Depends(auth_middleware)) -> User:
return verify_user(payload, admin_only=False)
def requires_admin_user(
payload: dict = fa... | import fastapi
from .middleware import auth_middleware
from .models import User
def requires_user(payload: dict = fastapi.Depends(auth_middleware)) -> User:
return verify_user(payload, admin_only=False)
def requires_admin_user(
payload: dict = fastapi.Depends(auth_middleware),
) -> User:
return verify_... |
# 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(
type='RetinaNet',
preprocess_cfg=preprocess_cfg,
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_ind... | # 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(
type='RetinaNet',
preprocess_cfg=preprocess_cfg,
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_ind... |
__copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import os
from pathlib import Path
from jina import Executor
from jina.executors import BaseExecutor
from PIL import Image
def test_config():
ex = Executor.load_config(str(Path(__file__).parents[2] / 'conf... | __copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
from pathlib import Path
import os
from PIL import Image
from jina import Executor
from jina.executors import BaseExecutor
def test_config():
ex = Executor.load_config(str(Path(__file__).parents[2] / 'confi... |
__version__ = '0.13.10'
import os
from .document import Document
from .array import DocumentArray
from .dataclasses import dataclass, field
if 'DA_NO_RICH_HANDLER' not in os.environ:
from rich.traceback import install
install()
| __version__ = '0.13.9'
import os
from .document import Document
from .array import DocumentArray
from .dataclasses import dataclass, field
if 'DA_NO_RICH_HANDLER' not in os.environ:
from rich.traceback import install
install()
|
# Copyright (c) OpenMMLab. All rights reserved.
from typing import List, Tuple, Union
import torch.nn as nn
from mmcv.cnn import ConvModule
from mmengine.model import BaseModule
from torch import Tensor
from mmdet.registry import MODELS
from mmdet.utils import OptConfigType, OptMultiConfig
@MODELS.register_module()... | # Copyright (c) OpenMMLab. All rights reserved.
from typing import List, Tuple
import torch.nn as nn
from mmcv.cnn import ConvModule
from mmengine.model import BaseModule
from torch import Tensor
from mmdet.registry import MODELS
from mmdet.utils import OptConfigType, OptMultiConfig
@MODELS.register_module()
class ... |
# Copyright (c) OpenMMLab. All rights reserved.
from contextlib import contextmanager
import torch
import torch.nn as nn
from torch.cuda.amp import GradScaler
from mmengine.registry import OPTIM_WRAPPERS
from mmengine.utils import TORCH_VERSION, digit_version
from .optimizer_wrapper import OptimWrapper
@OPTIM_WRAPP... | # Copyright (c) OpenMMLab. All rights reserved.
from contextlib import contextmanager
import torch
from torch.cuda.amp import GradScaler
from mmengine.registry import OPTIM_WRAPPERS
from mmengine.utils import TORCH_VERSION, digit_version
from .optimizer_wrapper import OptimWrapper
@OPTIM_WRAPPERS.register_module()
... |
from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.chat_message_histories import PostgresChatMessageHistory
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handlin... | from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.chat_message_histories import PostgresChatMessageHistory
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handlin... |
"""
Remote file reader.
A loader that fetches any remote page or file by URL and retrieves child pages with certain constraints. The class also parses the contents of each page and provides access to the parsed data.
"""
from typing import Any, Dict, List, Optional, Union
import requests
from llama_index.core.reader... | """
Remote file reader.
A loader that fetches any remote page or file by URL and retrieves child pages with certain constraints. The class also parses the contents of each page and provides access to the parsed data.
"""
from typing import Any, Dict, List, Optional, Union
import requests
from llama_index.core.readers... |
import importlib
import pytest
from dirty_equals import IsDict
from fastapi.testclient import TestClient
from ...utils import needs_py39
@pytest.fixture(
name="client",
params=[
"tutorial009",
pytest.param("tutorial009_py39", marks=needs_py39),
],
)
def get_client(request: pytest.Fixture... | import pytest
from dirty_equals import IsDict
from fastapi.testclient import TestClient
@pytest.fixture(name="client")
def get_client():
from docs_src.body_nested_models.tutorial009 import app
client = TestClient(app)
return client
def test_post_body(client: TestClient):
data = {"2": 2.2, "3": 3.3}... |
# Copyright (c) OpenMMLab. All rights reserved.
import datetime
import os
import platform
import warnings
import cv2
import torch.multiprocessing as mp
from mmengine import DefaultScope
def setup_multi_processes(cfg):
"""Setup multi-processing environment variables."""
# set multi-process start method as `fo... | # Copyright (c) OpenMMLab. All rights reserved.
import os
import platform
import warnings
import cv2
import torch.multiprocessing as mp
from mmengine import DefaultScope
def setup_multi_processes(cfg):
"""Setup multi-processing environment variables."""
# set multi-process start method as `fork` to speed up ... |
import pytest # type: ignore[import-not-found]
@pytest.mark.compile
def test_placeholder() -> None:
"""Used for compiling integration tests without running any real tests."""
| import pytest # type: ignore[import-not-found]
@pytest.mark.compile
def test_placeholder() -> None:
"""Used for compiling integration tests without running any real tests."""
pass
|
import torch
from torchaudio_unittest.common_utils import PytorchTestCase
from torchaudio_unittest.models.rnnt_decoder.rnnt_decoder_test_impl import (
RNNTBeamSearchTestImpl,
)
class RNNTBeamSearchFloat32CPUTest(RNNTBeamSearchTestImpl, PytorchTestCase):
dtype = torch.float32
device = torch.device("cpu")
... | import torch
from torchaudio_unittest.common_utils import PytorchTestCase
from torchaudio_unittest.models.rnnt_decoder.rnnt_decoder_test_impl import RNNTBeamSearchTestImpl
class RNNTBeamSearchFloat32CPUTest(RNNTBeamSearchTestImpl, PytorchTestCase):
dtype = torch.float32
device = torch.device("cpu")
class RN... |
from __future__ import annotations
import csv
import gzip
import os
from . import InputExample
class STSDataReader:
"""Reads in the STS dataset. Each line contains two sentences (s1_col_idx, s2_col_idx) and one label (score_col_idx)
Default values expects a tab separated file with the first & second column... | import csv
import gzip
import os
from . import InputExample
class STSDataReader:
"""Reads in the STS dataset. Each line contains two sentences (s1_col_idx, s2_col_idx) and one label (score_col_idx)
Default values expects a tab separated file with the first & second column the sentence pair and third column ... |
from typing import Union
import numpy as np
import PIL.Image
import torch
from torchvision import datapoints
from torchvision.transforms import functional as _F
@torch.jit.unused
def to_image(inpt: Union[torch.Tensor, PIL.Image.Image, np.ndarray]) -> datapoints.Image:
if isinstance(inpt, np.ndarray):
out... | from typing import Union
import numpy as np
import PIL.Image
import torch
from torchvision import datapoints
from torchvision.transforms import functional as _F
@torch.jit.unused
def to_image_tensor(inpt: Union[torch.Tensor, PIL.Image.Image, np.ndarray]) -> datapoints.Image:
if isinstance(inpt, np.ndarray):
... |
# dataset settings
dataset_type = 'CityscapesDataset'
# TODO remove it after cityscape metric
# data_root = '/mnt/lustre/luochunhua.vendor/openmmlab2.0/data/cityscapes/'
data_root = 'data/cityscapes/'
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(type... | # dataset settings
dataset_type = 'CityscapesDataset'
data_root = 'data/cityscapes/'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='Resize'... |
from unittest import TestCase, mock
import boto3
from llama_index.core.postprocessor.types import BaseNodePostprocessor
from llama_index.core.schema import NodeWithScore, QueryBundle, TextNode
from llama_index.postprocessor.bedrock_rerank import BedrockRerank
class TestBedrockRerank(TestCase):
def test_class(sel... | from unittest import TestCase, mock
import boto3
from llama_index.core.postprocessor.types import (
BaseNodePostprocessor,
NodeWithScore,
QueryBundle,
)
from llama_index.core.schema import TextNode
from llama_index.postprocessor.bedrock_rerank import BedrockRerank
class TestBedrockRerank(TestCase):
... |
# Copyright (c) Meta Platforms, Inc. and affiliates
# Owner(s): ["oncall: distributed"]
import torch
from torch.distributed.pipelining import pipe_split, pipeline
from torch.testing._internal.common_device_type import instantiate_device_type_tests
from torch.testing._internal.common_utils import run_tests, TestCase
#... | # Copyright (c) Meta Platforms, Inc. and affiliates
# Owner(s): ["oncall: distributed"]
import torch
from torch.distributed.pipelining import pipe_split, pipeline
from torch.testing._internal.common_device_type import instantiate_device_type_tests
from torch.testing._internal.common_utils import run_tests, TestCase
#... |
from pathlib import Path
from typing import List
import numpy as np
import pytest
import torch
from jina import Document, DocumentArray, Executor
from ..integration.test_integration import filter_none
from ...transform_encoder import TransformerTorchEncoder
def test_config():
ex = Executor.load_config(str(Path(... | import os
from typing import Callable, List
import numpy as np
import pytest
import torch
from jina import Document, DocumentArray
from ...transform_encoder import TransformerTorchEncoder
cur_dir = os.path.dirname(os.path.abspath(__file__))
def test_compute_tokens():
enc = TransformerTorchEncoder()
tokens ... |
"""Algorithms for cross decomposition."""
# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause
from ._pls import CCA, PLSSVD, PLSCanonical, PLSRegression
__all__ = ["CCA", "PLSSVD", "PLSCanonical", "PLSRegression"]
| """Algorithms for cross decomposition."""
# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause
from ._pls import CCA, PLSSVD, PLSCanonical, PLSRegression
__all__ = ["PLSCanonical", "PLSRegression", "PLSSVD", "CCA"]
|
# Copyright (c) OpenMMLab. All rights reserved.
from .det_data_sample import DetDataSample, OptSampleList, SampleList
from .reid_data_sample import ReIDDataSample
from .track_data_sample import (OptTrackSampleList, TrackDataSample,
TrackSampleList)
__all__ = [
'DetDataSample', 'Samp... | # Copyright (c) OpenMMLab. All rights reserved.
from .det_data_sample import DetDataSample, OptSampleList, SampleList
from .track_data_sample import (OptTrackSampleList, TrackDataSample,
TrackSampleList)
__all__ = [
'DetDataSample', 'SampleList', 'OptSampleList', 'TrackDataSample',
... |
# 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... |
import numpy as np
import pytest
from pydantic import Field
from qdrant_client.http import models as rest
from docarray import BaseDoc
from docarray.index import QdrantDocumentIndex
from docarray.typing import NdArray
from tests.index.qdrant.fixtures import qdrant, qdrant_config # noqa: F401
class SimpleDoc(BaseDoc... | import pytest
import numpy as np
from pydantic import Field
from docarray import BaseDoc
from docarray.index import QdrantDocumentIndex
from docarray.typing import NdArray
from qdrant_client.http import models as rest
from .fixtures import qdrant_config, qdrant
class SimpleDoc(BaseDoc):
embedding: NdArray[10]... |
# -*- coding: utf-8 -*-
# Generated by the protocol buffer compiler. DO NOT EDIT!
# source: docarray.proto
"""Generated protocol buffer code."""
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_d... | # -*- coding: utf-8 -*-
# Generated by the protocol buffer compiler. DO NOT EDIT!
# source: docarray.proto
"""Generated protocol buffer code."""
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_d... |
# Copyright (c) OpenMMLab. All rights reserved.
import torch
from mmengine.dist import get_world_size
from mmengine.logging import print_log
from mmdet.registry import MODELS
from mmdet.utils import ConfigType, OptConfigType, OptMultiConfig
from .single_stage import SingleStageDetector
@MODELS.register_module()
clas... | # Copyright (c) OpenMMLab. All rights reserved.
import torch
from mmengine.dist import get_world_size
from mmengine.logging import print_log
from mmdet.registry import MODELS
from mmdet.utils import ConfigType, OptConfigType, OptMultiConfig
from .single_stage import SingleStageDetector
@MODELS.register_module()
clas... |
from __future__ import annotations
from collections.abc import Iterable
import torch.nn as nn
from torch import Tensor
from sentence_transformers.losses.CosineSimilarityLoss import CosineSimilarityLoss
from sentence_transformers.sparse_encoder.SparseEncoder import SparseEncoder
class SparseCosineSimilarityLoss(Cos... | from __future__ import annotations
from collections.abc import Iterable
import torch.nn as nn
from torch import Tensor
from sentence_transformers.losses.CosineSimilarityLoss import CosineSimilarityLoss
from sentence_transformers.sparse_encoder.SparseEncoder import SparseEncoder
class SparseCosineSimilarityLoss(Cos... |
"""Tools for model selection, such as cross validation and hyper-parameter tuning."""
# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause
import typing
from ._classification_threshold import (
FixedThresholdClassifier,
TunedThresholdClassifierCV,
)
from ._plot import LearningCurveD... | """Tools for model selection, such as cross validation and hyper-parameter tuning."""
# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause
import typing
from ._classification_threshold import (
FixedThresholdClassifier,
TunedThresholdClassifierCV,
)
from ._plot import LearningCurveD... |
# Copyright (c) OpenMMLab. All rights reserved.
from unittest import TestCase
from unittest.mock import MagicMock, patch
import pytest
import torch
import torch.nn as nn
from torch.nn.parallel import DataParallel
from torch.nn.parallel.distributed import DistributedDataParallel
from mmengine.model.wrappers import (MM... | # Copyright (c) OpenMMLab. All rights reserved.
from unittest import TestCase
from unittest.mock import MagicMock, patch
import pytest
import torch
import torch.nn as nn
from torch.nn.parallel import DataParallel
from torch.nn.parallel.distributed import DistributedDataParallel
from mmengine.model.wrappers import (MM... |
"""DataForSeo API Toolkit."""
from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.tools.dataforseo_api_search.tool import (
DataForSeoAPISearchResults,
DataForSeoAPISearchRun,
)
# Create a way to dynamically look up depr... | """DataForSeo API Toolkit."""
from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.tools.dataforseo_api_search.tool import (
DataForSeoAPISearchResults,
DataForSeoAPISearchRun,
)
# Create a way to dynamically look up depr... |
import os
import re
from pathlib import Path
from typing import Optional, Tuple, Union
import torch
from torch.hub import download_url_to_file
from torch.utils.data import Dataset
from torchaudio.datasets.utils import _load_waveform, extract_archive
URL = "https://speech.fit.vutbr.cz/files/quesst14Database.tgz"
SAMP... | import os
import re
from pathlib import Path
from typing import Optional, Tuple, Union
import torch
from torch.hub import download_url_to_file
from torch.utils.data import Dataset
from torchaudio.datasets.utils import _load_waveform, extract_archive
URL = "https://speech.fit.vutbr.cz/files/quesst14Database.tgz"
SAMP... |
from torchvision.transforms import AutoAugmentPolicy, InterpolationMode # usort: skip
from . import functional, utils # usort: skip
from ._transform import Transform # usort: skip
from ._augment import RandomErasing
from ._auto_augment import AugMix, AutoAugment, RandAugment, TrivialAugmentWide
from ._color impor... | from torchvision.transforms import AutoAugmentPolicy, InterpolationMode # usort: skip
from . import functional, utils # usort: skip
from ._transform import Transform # usort: skip
from ._augment import RandomErasing
from ._auto_augment import AugMix, AutoAugment, RandAugment, TrivialAugmentWide
from ._color impor... |
"""Chain-of-Abstraction Output Parser."""
import asyncio
import json
import re
from collections import defaultdict
from typing import Dict, Tuple
import networkx as nx
from llama_index.core.tools import AsyncBaseTool, ToolOutput
from llama_index.core.types import BaseOutputParser
class ChainOfAbstractionParser(Base... | """Chain-of-Abstraction Output Parser."""
import asyncio
import json
import networkx as nx
import re
from collections import defaultdict
from typing import Dict, Tuple
from llama_index.core.tools import AsyncBaseTool, ToolOutput
from llama_index.core.types import BaseOutputParser
class ChainOfAbstractionParser(Base... |
from jina.serve.runtimes.gateway.http.gateway import HTTPGateway
__all__ = ['HTTPGateway']
| from .gateway import HTTPGateway
__all__ = ['HTTPGateway']
|
from llama_index.llms.vertex import Vertex
def test_vertex_metadata_function_calling():
"""Test that Vertex LLM metadata correctly identifies Gemini models as function calling models."""
# This test uses mocks to avoid actual API calls
from unittest.mock import patch, Mock
with patch(
"llama_... | from llama_index.core.base.llms.base import BaseLLM
from llama_index.llms.vertex import Vertex
def test_embedding_class():
names_of_base_classes = [b.__name__ for b in Vertex.__mro__]
assert BaseLLM.__name__ in names_of_base_classes
|
"""
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 List
from llama_index.core.base.embeddings.base import BaseEmbedding
from typing import Optional
try:
import chonkie
from chonkie import AutoEmbeddings
except ImportError:
raise ImportError(
"Could not import Autembeddings from chonkie. "
"Please install it wi... | from typing import List
from llama_index.core.base.embeddings.base import BaseEmbedding
from typing import Optional
try:
import chonkie
from chonkie import AutoEmbeddings
except ImportError:
raise ImportError(
"Could not import Autembeddings from chonkie. "
"Please install it wi... |
import logging
from datasets import load_dataset
from sentence_transformers.sparse_encoder import (
MLMTransformer,
SparseBinaryClassificationEvaluator,
SparseEncoder,
SpladePooling,
)
logging.basicConfig(format="%(asctime)s - %(message)s", datefmt="%Y-%m-%d %H:%M:%S", level=logging.INFO)
# Initiali... | from datasets import load_dataset
from sentence_transformers.sparse_encoder import (
MLMTransformer,
SparseBinaryClassificationEvaluator,
SparseEncoder,
SpladePooling,
)
# Initialize the SPLADE model
model_name = "naver/splade-cocondenser-ensembledistil"
model = SparseEncoder(
modules=[
ML... |
_base_ = './maskformer_r50_mstrain_16x1_75e_coco.py'
pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_large_patch4_window12_384_22k.pth' # noqa
depths = [2, 2, 18, 2]
model = dict(
backbone=dict(
_delete_=True,
type='SwinTransformer',
pretrain_img_size... | _base_ = './maskformer_r50_mstrain_16x1_75e_coco.py'
pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_large_patch4_window12_384_22k.pth' # noqa
depths = [2, 2, 18, 2]
model = dict(
backbone=dict(
_delete_=True,
type='SwinTransformer',
pretrain_img_size... |
# Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
from mmcv.cnn import ConvModule
from mmcv.runner import BaseModule
from ..builder import NECKS
@NECKS.register_module()
class ChannelMapper(BaseModule):
r"""Channel Mapper to reduce/increase channels of backbone features.
This is used to ... | import torch.nn as nn
from mmcv.cnn import ConvModule
from mmcv.runner import BaseModule
from ..builder import NECKS
@NECKS.register_module()
class ChannelMapper(BaseModule):
r"""Channel Mapper to reduce/increase channels of backbone features.
This is used to reduce/increase channels of backbone features.
... |
_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... | _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... |
_base_ = './htc-without-semantic_r50_fpn_1x_coco.py'
model = dict(
data_preprocessor=dict(pad_seg=True),
roi_head=dict(
semantic_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0),
out_channels=256,
... | _base_ = './htc_without_semantic_r50_fpn_1x_coco.py'
model = dict(
data_preprocessor=dict(pad_seg=True),
roi_head=dict(
semantic_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0),
out_channels=256,
... |
import json
import datasets
from tests.trainer.test_trainer import StoreLossCallback
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
DataCollatorForLanguageModeling,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.testing_utils import (
TestC... | import json
import datasets
from tests.trainer.test_trainer import StoreLossCallback
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
DataCollatorForLanguageModeling,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.testing_utils import (
TestC... |
import gc
import unittest
import torch
from diffusers import (
DDIMScheduler,
StableDiffusionXLImg2ImgPipeline,
)
from diffusers.utils import load_image
from diffusers.utils.testing_utils import (
backend_empty_cache,
enable_full_determinism,
numpy_cosine_similarity_distance,
require_torch_acc... | import gc
import unittest
import torch
from diffusers import (
DDIMScheduler,
StableDiffusionXLImg2ImgPipeline,
)
from diffusers.utils import load_image
from diffusers.utils.testing_utils import (
backend_empty_cache,
enable_full_determinism,
numpy_cosine_similarity_distance,
require_torch_acc... |
import gzip
import logging
import os
import sys
from datetime import datetime
from torch.utils.data import DataLoader
from sentence_transformers import LoggingHandler, SentenceTransformer, datasets, evaluation, losses, models, util
#### Just some code to print debug information to stdout
logging.basicConfig(
for... |
from sentence_transformers import SentenceTransformer, LoggingHandler
from sentence_transformers import models, util, datasets, evaluation, losses
import logging
import os
import gzip
from torch.utils.data import DataLoader
from datetime import datetime
import sys
#### Just some code to print debug information to std... |
# Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
import pytest
from mmengine import Config, DefaultScope
from mmengine.hub import get_config, get_model
from mmengine.utils import get_installed_path, is_installed
data_path = osp.join(osp.dirname(osp.dirname(__file__)), 'data/')
# mmdet has a mo... | # Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
import pytest
from mmengine import Config, DefaultScope
from mmengine.hub import get_config, get_model
from mmengine.utils import get_installed_path, is_installed
data_path = osp.join(osp.dirname(osp.dirname(__file__)), 'data/')
# mmdet has a mo... |
"""Example selectors.
**Example selector** implements logic for selecting examples to include them in prompts.
This allows us to select examples that are most relevant to the input.
"""
from langchain_core.example_selectors.base import BaseExampleSelector
from langchain_core.example_selectors.length_based import (
... | """**Example selector** implements logic for selecting examples to include them
in prompts.
This allows us to select examples that are most relevant to the input.
"""
from langchain_core.example_selectors.base import BaseExampleSelector
from langchain_core.example_selectors.length_based import (
LengthBasedExample... |
from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.document_loaders import UnstructuredPowerPointLoader
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handling op... | from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.document_loaders import UnstructuredPowerPointLoader
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handling op... |
__copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
from pathlib import Path
import numpy as np
import pytest
from jina import Document, DocumentArray, Executor
from transformer_tf_text_encode import TransformerTFTextEncoder
target_dim = 768
@pytest.fixture()
... | __copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
from pathlib import Path
import numpy as np
import pytest
from jina import Document, DocumentArray, Executor
from ...transformer_tf_text_encode import TransformerTFTextEncoder
target_dim = 768
@pytest.fixtur... |
from typing import overload, TYPE_CHECKING, Union, Callable, Optional, Tuple
if TYPE_CHECKING: # pragma: no cover
from docarray import DocumentArray
from docarray.typing import AnyDNN, T, ArrayType
import numpy as np
class SingletonSugarMixin:
"""Provide sugary syntax for :class:`Document` by inher... | from typing import overload, TYPE_CHECKING, Union, Callable, Optional, Tuple
if TYPE_CHECKING:
from docarray import DocumentArray
from docarray.typing import AnyDNN, T, ArrayType
import numpy as np
class SingletonSugarMixin:
"""Provide sugary syntax for :class:`Document` by inheriting methods from :... |
from jina.clients.base.http import HTTPBaseClient
from jina.clients.mixin import (
AsyncHealthCheckMixin,
AsyncMutateMixin,
AsyncPostMixin,
AsyncProfileMixin,
HealthCheckMixin,
MutateMixin,
PostMixin,
ProfileMixin,
)
class HTTPClient(
HTTPBaseClient, PostMixin, ProfileMixin, Mutate... | from jina.clients.base.http import HTTPBaseClient
from jina.clients.mixin import (
AsyncHealthCheckMixin,
AsyncMutateMixin,
AsyncPostMixin,
HealthCheckMixin,
MutateMixin,
PostMixin,
)
class HTTPClient(HTTPBaseClient, PostMixin, MutateMixin, HealthCheckMixin):
"""A client connecting to a Ga... |
import os
import warnings
from modulefinder import Module
import torch
from torchvision import _meta_registrations, datasets, io, models, ops, transforms, utils
from .extension import _HAS_OPS
try:
from .version import __version__ # noqa: F401
except ImportError:
pass
# Check if torchvision is being impor... | import os
import warnings
from modulefinder import Module
import torch
from torchvision import _meta_registrations, datasets, io, models, ops, transforms, utils
from .extension import _HAS_OPS
try:
from .version import __version__ # noqa: F401
except ImportError:
pass
# Check if torchvision is being impor... |
from __future__ import annotations
from sentence_transformers.training_args import SentenceTransformerTrainingArguments
class CrossEncoderTrainingArguments(SentenceTransformerTrainingArguments):
r"""
CrossEncoderTrainingArguments extends :class:`~transformers.TrainingArguments` with additional arguments
... | from __future__ import annotations
from sentence_transformers.training_args import SentenceTransformerTrainingArguments
class CrossEncoderTrainingArguments(SentenceTransformerTrainingArguments):
"""
CrossEncoderTrainingArguments extends :class:`~transformers.TrainingArguments` with additional arguments
s... |
import json
from typing import Any, Type, TypeGuard, TypeVar, overload
import jsonschema
from fastapi.encoders import jsonable_encoder
from pydantic import BaseModel
from .type import type_match
def to_dict(data) -> dict:
if isinstance(data, BaseModel):
data = data.model_dump()
elif isinstance(data... | import json
from typing import Any, Type, TypeGuard, TypeVar, overload
import jsonschema
from fastapi.encoders import jsonable_encoder
from pydantic import BaseModel
from .type import type_match
def to_dict(data) -> dict:
return jsonable_encoder(data)
def dumps(data) -> str:
return json.dumps(jsonable_enc... |
from io import BytesIO
from typing import TYPE_CHECKING, Any, NamedTuple, Type, TypeVar
import numpy as np
from pydantic import parse_obj_as
from pydantic.validators import bytes_validator
from docarray.typing.abstract_type import AbstractType
from docarray.typing.proto_register import _register_proto
from docarray.t... | from io import BytesIO
from typing import TYPE_CHECKING, Any, NamedTuple, Type, TypeVar
import numpy as np
from pydantic import parse_obj_as
from pydantic.validators import bytes_validator
from docarray.typing.abstract_type import AbstractType
from docarray.typing.proto_register import _register_proto
from docarray.t... |
import platform
from argparse import ArgumentParser
import fsspec
import huggingface_hub
import pandas
import pyarrow
from datasets import __version__ as version
from datasets.commands import BaseDatasetsCLICommand
def info_command_factory(_):
return EnvironmentCommand()
class EnvironmentCommand(BaseDatasetsC... | import platform
from argparse import ArgumentParser
import huggingface_hub
import pandas
import pyarrow
from datasets import __version__ as version
from datasets.commands import BaseDatasetsCLICommand
def info_command_factory(_):
return EnvironmentCommand()
class EnvironmentCommand(BaseDatasetsCLICommand):
... |
"""Azure Speech tool spec."""
import time
from typing import List, Optional
from llama_index.core.tools.tool_spec.base import BaseToolSpec
class AzureSpeechToolSpec(BaseToolSpec):
"""Azure Speech tool spec."""
spec_functions = ["speech_to_text", "text_to_speech"]
def __init__(
self, region: st... | """Azure Speech tool spec."""
import time
from typing import List, Optional
from llama_index.core.tools.tool_spec.base import BaseToolSpec
class AzureSpeechToolSpec(BaseToolSpec):
"""Azure Speech tool spec."""
spec_functions = ["speech_to_text", "text_to_speech"]
def __init__(
self, region: st... |
# Licensed to the LF AI & Data foundation under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the "License");
# you may not use this fil... | from typing import Callable, Dict, Type, TypeVar
from docarray.typing.abstract_type import AbstractType
_PROTO_TYPE_NAME_TO_CLASS: Dict[str, Type[AbstractType]] = {}
T = TypeVar('T', bound='AbstractType')
def _register_proto(
proto_type_name: str,
) -> Callable[[Type[T]], Type[T]]:
"""Register a new type t... |
import os
import socket
from jina import DocumentArray, Executor, requests
class TestExecutor(Executor):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
from jina.logging.logger import JinaLogger
self.logger = JinaLogger(self.__class__.__name__)
self._name ... | import os
from jina import Executor, requests, DocumentArray
import socket
class TestExecutor(Executor):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
from jina.logging.logger import JinaLogger
self.logger = JinaLogger(self.__class__.__name__)
self._name ... |
from __future__ import annotations
import json
from typing import TYPE_CHECKING, Any, Optional, Type
from langchain_core.callbacks import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from pydantic import BaseModel, Field, model_validator
from langchain_community.tools.playwright.base import... | from __future__ import annotations
import json
from typing import TYPE_CHECKING, Any, Optional, Type
from langchain_core.callbacks import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from pydantic import BaseModel, Field, model_validator
from langchain_community.tools.playwright.base import... |
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