input stringlengths 33 5k | output stringlengths 32 5k |
|---|---|
from __future__ import annotations
try:
from typing import Self
except ImportError:
from typing_extensions import Self
import torch.nn.functional as F
from torch import Tensor
from sentence_transformers.models.Module import Module
class Normalize(Module):
"""This layer normalizes embeddings to unit len... | from __future__ import annotations
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().__init__()
def forward(self, features: dict[str, Tensor]) -> dict[str, Tensor]:
... |
from abc import abstractmethod
from typing import TYPE_CHECKING, Any, Optional, Type, TypeVar
from pydantic import BaseConfig
from pydantic.fields import ModelField
from docarray.base_doc.base_node import BaseNode
if TYPE_CHECKING:
from docarray.proto import NodeProto
T = TypeVar('T')
class AbstractType(BaseN... | from abc import abstractmethod
from typing import TYPE_CHECKING, Any, Optional, Type, TypeVar
from pydantic import BaseConfig
from pydantic.fields import ModelField
from docarray.base_document.base_node import BaseNode
if TYPE_CHECKING:
from docarray.proto import NodeProto
T = TypeVar('T')
class AbstractType(... |
from typing import Any, Dict, Optional, Type
from jina.jaml.parsers.base import BaseLegacyParser
from jina.serve.runtimes.gateway.gateway import BaseGateway
from jina.serve.runtimes.gateway.request_handling import GatewayRequestHandler
class GatewayLegacyParser(BaseLegacyParser):
"""Legacy parser for gateway."""... | from typing import Any, Dict, Optional, Type
from jina.jaml.parsers.base import BaseLegacyParser
from jina.serve.gateway import BaseGateway
class GatewayLegacyParser(BaseLegacyParser):
"""Legacy parser for gateway."""
def parse(
self,
cls: Type['BaseGateway'],
data: Dict,
run... |
from importlib import metadata
from langchain_core._api import warn_deprecated
## Create namespaces for pydantic v1 and v2.
# This code must stay at the top of the file before other modules may
# attempt to import pydantic since it adds pydantic_v1 and pydantic_v2 to sys.modules.
#
# This hack is done for the followi... | from importlib import metadata
from langchain_core._api import warn_deprecated
## Create namespaces for pydantic v1 and v2.
# This code must stay at the top of the file before other modules may
# attempt to import pydantic since it adds pydantic_v1 and pydantic_v2 to sys.modules.
#
# This hack is done for the followi... |
from __future__ import annotations
from sentence_transformers.sparse_encoder.evaluation.SparseBinaryClassificationEvaluator import (
SparseBinaryClassificationEvaluator,
)
from sentence_transformers.sparse_encoder.evaluation.SparseEmbeddingSimilarityEvaluator import (
SparseEmbeddingSimilarityEvaluator,
)
from... | from __future__ import annotations
from sentence_transformers.sparse_encoder.evaluation.SparseBinaryClassificationEvaluator import (
SparseBinaryClassificationEvaluator,
)
from sentence_transformers.sparse_encoder.evaluation.SparseEmbeddingSimilarityEvaluator import (
SparseEmbeddingSimilarityEvaluator,
)
from... |
"""Module to change the configuration of libsox, which is used by I/O functions like
:py:mod:`~torchaudio.backend.sox_io_backend` and :py:mod:`~torchaudio.sox_effects`.
"""
from typing import Dict, List
import torchaudio
@torchaudio._extension.fail_if_no_sox
def set_seed(seed: int):
"""Set libsox's PRNG
Ar... | """Module to change the configuration of libsox, which is used by I/O functions like
:py:mod:`~torchaudio.backend.sox_io_backend` and :py:mod:`~torchaudio.sox_effects`.
"""
from typing import Dict, List
import torch
import torchaudio
@torchaudio._extension.fail_if_no_sox
def set_seed(seed: int):
"""Set libsox's... |
# Copyright 2020 The HuggingFace 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
#
# Unless required by applicable law or agreed to... | # Copyright 2020 The HuggingFace 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
#
# Unless required by applicable law or agreed to... |
from typing import TYPE_CHECKING
import paddle
if TYPE_CHECKING: # pragma: no cover
from paddle import tensor
import numpy
def cosine(
x_mat: 'tensor', y_mat: 'tensor', eps: float = 1e-7, device: str = 'cpu'
) -> 'numpy.ndarray':
"""Cosine distance between each row in x_mat and each row in y_mat.
... | from typing import TYPE_CHECKING
import paddle
if TYPE_CHECKING:
from paddle import tensor
import numpy
def cosine(
x_mat: 'tensor', y_mat: 'tensor', eps: float = 1e-7, device: str = 'cpu'
) -> 'numpy.ndarray':
"""Cosine distance between each row in x_mat and each row in y_mat.
:param x_mat: np... |
from typing import Final
from dask.array import * # noqa: F403
# These imports may overwrite names from the import * above.
from ._aliases import * # noqa: F403
__array_api_version__: Final = "2024.12"
# See the comment in the numpy __init__.py
__import__(__package__ + '.linalg')
__import__(__package__ + '.fft')
| from dask.array import * # noqa: F403
# These imports may overwrite names from the import * above.
from ._aliases import * # noqa: F403
__array_api_version__ = '2024.12'
__import__(__package__ + '.linalg')
__import__(__package__ + '.fft')
|
"""Module for async requests generator."""
from typing import AsyncIterator, Optional, Dict, TYPE_CHECKING
from jina.clients.request.helper import _new_data_request_from_batch, _new_data_request
from jina.enums import DataInputType
from jina.importer import ImportExtensions
from jina.logging.predefined import default... | """Module for async requests generator."""
from typing import AsyncIterator, Optional, Dict, TYPE_CHECKING
from jina.clients.request.helper import _new_data_request_from_batch, _new_data_request
from jina.enums import DataInputType
from jina.importer import ImportExtensions
from jina.logging.predefined import default... |
# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause
import numpy as np
from .extmath import stable_cumsum
def _weighted_percentile(array, sample_weight, percentile=50):
"""Compute weighted percentile
Computes lower weighted percentile. If `array` is a 2D array, the
`percentil... | # Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause
import numpy as np
from .extmath import stable_cumsum
def _weighted_percentile(array, sample_weight, percentile=50):
"""Compute weighted percentile
Computes lower weighted percentile. If `array` is a 2D array, the
`percentil... |
"""
LexRank implementation
Source: https://github.com/crabcamp/lexrank/tree/dev
"""
import logging
import numpy as np
from scipy.sparse.csgraph import connected_components
from scipy.special import softmax
logger = logging.getLogger(__name__)
def degree_centrality_scores(
similarity_matrix,
threshold=None,... | """
LexRank implementation
Source: https://github.com/crabcamp/lexrank/tree/dev
"""
import numpy as np
from scipy.sparse.csgraph import connected_components
from scipy.special import softmax
import logging
logger = logging.getLogger(__name__)
def degree_centrality_scores(
similarity_matrix,
threshold=None,
... |
"""Simple Reader that reads abstract of primary citation for a given PDB id."""
from typing import List
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document
from llama_index.readers.pdb.utils import get_pdb_abstract
class PdbAbstractReader(BaseReader):
"""Protein Data... | """Simple Reader that reads abstract of primary citation for a given PDB id."""
from typing import List
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document
from llama_index.readers.pdb.utils import get_pdb_abstract
class PdbAbstractReader(BaseReader):
"""Protein Data... |
"""FastAPI framework, high performance, easy to learn, fast to code, ready for production"""
__version__ = "0.115.12"
from starlette import status as status
from .applications import FastAPI as FastAPI
from .background import BackgroundTasks as BackgroundTasks
from .datastructures import UploadFile as UploadFile
fro... | """FastAPI framework, high performance, easy to learn, fast to code, ready for production"""
__version__ = "0.115.11"
from starlette import status as status
from .applications import FastAPI as FastAPI
from .background import BackgroundTasks as BackgroundTasks
from .datastructures import UploadFile as UploadFile
fro... |
import warnings
from typing import TYPE_CHECKING, Any, Optional, Tuple, Type, TypeVar, Union
import numpy as np
from docarray.typing.proto_register import _register_proto
from docarray.typing.url.any_url import AnyUrl
from docarray.utils._internal.misc import is_notebook
if TYPE_CHECKING:
from PIL import Image a... | import warnings
from typing import TYPE_CHECKING, Any, Optional, Tuple, Type, TypeVar, Union
import numpy as np
from docarray.typing.proto_register import _register_proto
from docarray.typing.url.any_url import AnyUrl
from docarray.utils._internal.misc import is_notebook
if TYPE_CHECKING:
from pydantic import Ba... |
"""Generation output schema."""
from __future__ import annotations
from typing import Any, Literal, Optional
from langchain_core.load import Serializable
from langchain_core.utils._merge import merge_dicts
class Generation(Serializable):
"""A single text generation output.
Generation represents the respon... | """Generation output schema."""
from __future__ import annotations
from typing import Any, Literal, Optional
from langchain_core.load import Serializable
from langchain_core.utils._merge import merge_dicts
class Generation(Serializable):
"""A single text generation output.
Generation represents the respon... |
# Copyright (c) OpenMMLab. All rights reserved.
from .backbones import * # noqa: F401,F403
from .data_preprocessors import * # noqa: F401,F403
from .dense_heads import * # noqa: F401,F403
from .detectors import * # noqa: F401,F403
from .layers import * # noqa: F401,F403
from .losses import * # noqa: F401,F403
fro... | # Copyright (c) OpenMMLab. All rights reserved.
from .backbones import * # noqa: F401,F403
from .data_preprocessors import * # noqa: F401,F403
from .dense_heads import * # noqa: F401,F403
from .detectors import * # noqa: F401,F403
from .layers import * # noqa: F401,F403
from .losses import * # noqa: F401,F403
fro... |
from typing import Generator, Optional
import pytest
from docarray import BaseDoc, DocList
from docarray.documents import ImageDoc
from docarray.typing import ImageUrl, NdArray
from docarray.utils.map import map_docs, map_docs_batched
from tests.units.typing.test_bytes import IMAGE_PATHS
N_DOCS = 2
def load_from_d... | from typing import Generator, Optional
import pytest
from docarray import BaseDoc, DocList
from docarray.documents import ImageDoc
from docarray.typing import ImageUrl, NdArray
from docarray.utils.map import map_docs, map_docs_batched
from tests.units.typing.test_bytes import IMAGE_PATHS
N_DOCS = 2
def load_from_d... |
__version__ = '0.12.8'
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__ = '0.12.7'
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()
|
from typing import Optional
from docarray.document import BaseDocument
from docarray.typing import AnyTensor, Embedding, PointCloud3DUrl
class PointCloud3D(BaseDocument):
"""
Document for handling point clouds for 3D data representation.
Point cloud is a representation of a 3D mesh. It is made by repeat... | from typing import Optional
from docarray.document import BaseDocument
from docarray.typing import AnyTensor, Embedding, PointCloud3DUrl
class PointCloud3D(BaseDocument):
"""
Document for handling point clouds for 3D data representation.
Point cloud is a representation of a 3D mesh. It is made by repeat... |
# Copyright (c) OpenMMLab. All rights reserved.
from ..builder import DETECTORS
from .two_stage import TwoStageDetector
@DETECTORS.register_module()
class CascadeRCNN(TwoStageDetector):
r"""Implementation of `Cascade R-CNN: Delving into High Quality Object
Detection <https://arxiv.org/abs/1906.09756>`_"""
... | # Copyright (c) OpenMMLab. All rights reserved.
from ..builder import DETECTORS
from .two_stage import TwoStageDetector
@DETECTORS.register_module()
class CascadeRCNN(TwoStageDetector):
r"""Implementation of `Cascade R-CNN: Delving into High Quality Object
Detection <https://arxiv.org/abs/1906.09756>`_"""
... |
# Copyright (c) OpenMMLab. All rights reserved.
from .mask_target import mask_target
from .structures import BaseInstanceMasks, BitmapMasks, PolygonMasks
from .utils import encode_mask_results, split_combined_polys
__all__ = [
'split_combined_polys', 'mask_target', 'BaseInstanceMasks', 'BitmapMasks',
'PolygonM... | from .mask_target import mask_target
from .structures import BaseInstanceMasks, BitmapMasks, PolygonMasks
from .utils import encode_mask_results, split_combined_polys
__all__ = [
'split_combined_polys', 'mask_target', 'BaseInstanceMasks', 'BitmapMasks',
'PolygonMasks', 'encode_mask_results'
]
|
__copyright__ = "Copyright (c) 2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import pytest
import numpy as np
import torch
from ...models import EmbeddingModelWrapper, _ModelCatalogue
@pytest.mark.parametrize(
['model_name', 'is_supported'],
[
('ResNet', False),
('re... | __copyright__ = "Copyright (c) 2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import pytest
import numpy as np
import torch
from jinahub.image.encoder.models import EmbeddingModelWrapper, _ModelCatalogue
@pytest.mark.parametrize(
['model_name', 'is_supported'],
[
('ResNet', F... |
"""
Demo for using cross validation
===============================
"""
import os
import numpy as np
import xgboost as xgb
# load data in do training
CURRENT_DIR = os.path.dirname(__file__)
dtrain = xgb.DMatrix(
os.path.join(CURRENT_DIR, "../data/agaricus.txt.train?format=libsvm")
)
param = {"max_depth": 2, "et... | """
Demo for using cross validation
===============================
"""
import os
import numpy as np
import xgboost as xgb
# load data in do training
CURRENT_DIR = os.path.dirname(__file__)
dtrain = xgb.DMatrix(
os.path.join(CURRENT_DIR, "../data/agaricus.txt.train?format=libsvm")
)
param = {"max_depth": 2, "eta... |
from __future__ import annotations
try:
from typing import Self
except ImportError:
from typing_extensions import Self
import torch
from torch import nn
from sentence_transformers.models.Module import Module
class LSTM(Module):
"""Bidirectional LSTM running over word embeddings."""
config_keys: li... | from __future__ import annotations
import json
import os
import torch
from safetensors.torch import load_model as load_safetensors_model
from safetensors.torch import save_model as save_safetensors_model
from torch import nn
class LSTM(nn.Module):
"""Bidirectional LSTM running over word embeddings."""
def ... |
from __future__ import annotations
from sentence_transformers import util
from sentence_transformers.losses.MultipleNegativesRankingLoss import MultipleNegativesRankingLoss
from sentence_transformers.sparse_encoder.SparseEncoder import SparseEncoder
class SparseMultipleNegativesRankingLoss(MultipleNegativesRankingLo... | from __future__ import annotations
from sentence_transformers import util
from sentence_transformers.losses.MultipleNegativesRankingLoss import MultipleNegativesRankingLoss
from sentence_transformers.sparse_encoder.SparseEncoder import SparseEncoder
class SparseMultipleNegativesRankingLoss(MultipleNegativesRankingLo... |
from keras.src import activations
from keras.src.api_export import keras_export
from keras.src.layers.layer import Layer
@keras_export("keras.layers.Activation")
class Activation(Layer):
"""Applies an activation function to an output.
Args:
activation: Activation function. It could be a callable, or ... | from keras.src import activations
from keras.src.api_export import keras_export
from keras.src.layers.layer import Layer
@keras_export("keras.layers.Activation")
class Activation(Layer):
"""Applies an activation function to an output.
Args:
activation: Activation function. It could be a callable, or ... |
import multiprocessing
from typing import TYPE_CHECKING, Optional, Union
from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream
if TYPE_CHECKING:
import sqlite3
import sq... | import multiprocessing
from typing import TYPE_CHECKING, Optional, Union
from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream
if TYPE_CHECKING:
import sqlite3
import sq... |
"""Image prompt template for a multimodal model."""
from typing import Any
from pydantic import Field
from langchain_core.prompt_values import ImagePromptValue, ImageURL, PromptValue
from langchain_core.prompts.base import BasePromptTemplate
from langchain_core.prompts.string import (
DEFAULT_FORMATTER_MAPPING,
... | from typing import Any
from pydantic import Field
from langchain_core.prompt_values import ImagePromptValue, ImageURL, PromptValue
from langchain_core.prompts.base import BasePromptTemplate
from langchain_core.prompts.string import (
DEFAULT_FORMATTER_MAPPING,
PromptTemplateFormat,
)
from langchain_core.runna... |
"""Wordpress reader."""
import warnings
from typing import List, Optional
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document
class WordpressReader(BaseReader):
"""
Wordpress reader. Reads data from a Wordpress workspace.
Args:
url (str): Base URL of... | """Wordpress reader."""
import warnings
from typing import List, Optional
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document
class WordpressReader(BaseReader):
"""Wordpress reader. Reads data from a Wordpress workspace.
Args:
url (str): Base URL of the ... |
_base_ = './point-rend_r50-caffe_fpn_ms-1x_coco.py'
max_epochs = 36
# learning policy
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=max_epochs,
by_epoch=True,
milestones=[... | _base_ = './point_rend_r50_caffe_fpn_mstrain_1x_coco.py'
max_epochs = 36
# learning policy
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=max_epochs,
by_epoch=True,
milesto... |
from keras.src.backend.config import backend
if backend() == "torch":
# When using the torch backend,
# torch needs to be imported first, otherwise it will segfault
# upon import.
import torch
from keras.src.api_export import keras_export
from keras.src.backend.common.dtypes import result_type
from ke... | from keras.src.backend.config import backend
if backend() == "torch":
# When using the torch backend,
# torch needs to be imported first, otherwise it will segfault
# upon import.
import torch
from keras.src.backend.common.dtypes import result_type
from keras.src.backend.common.keras_tensor import Ker... |
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
__all__ = [
"adsr_envelope",
"exp_sigmoid",
"barkscal... | from ._dsp import (
adsr_envelope,
extend_pitch,
filter_waveform,
frequency_impulse_response,
oscillator_bank,
sinc_impulse_response,
)
from ._rir import simulate_rir_ism
from .functional import barkscale_fbanks
__all__ = [
"adsr_envelope",
"barkscale_fbanks",
"extend_pitch",
"... |
# mypy: allow-untyped-defs
"""torch.multiprocessing is a wrapper around the native :mod:`multiprocessing` module.
It registers custom reducers, that use shared memory to provide shared
views on the same data in different processes. Once the tensor/storage is moved
to shared_memory (see :func:`~torch.Tensor.share_memor... | # mypy: allow-untyped-defs
"""torch.multiprocessing is a wrapper around the native :mod:`multiprocessing` module.
It registers custom reducers, that use shared memory to provide shared
views on the same data in different processes. Once the tensor/storage is moved
to shared_memory (see :func:`~torch.Tensor.share_memor... |
"""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 typing import TYPE_CHECKING
from langchain_core._import_utils import import_attr
if TYPE_CHECKING:
from langchain_core.ex... | """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... |
"""Init file."""
from llama_index.readers.kaltura_esearch.base import KalturaESearchReader
__all__ = ["KalturaESearchReader"]
| """Init file."""
from llama_index.readers.kaltura_esearch.base import KalturaESearchReader
__all__ = ["KalturaESearchReader"]
|
# 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... |
"""
This scripts runs the evaluation (dev & test) for the AskUbuntu dataset
Usage:
python eval_askubuntu.py [sbert_model_name_or_path]
"""
import gzip
import logging
import os
import sys
from sentence_transformers import LoggingHandler, SentenceTransformer, evaluation, util
#### Just some code to print debug inform... | """
This scripts runs the evaluation (dev & test) for the AskUbuntu dataset
Usage:
python eval_askubuntu.py [sbert_model_name_or_path]
"""
from sentence_transformers import SentenceTransformer, LoggingHandler
from sentence_transformers import util, evaluation
import logging
import os
import gzip
import sys
#### Just... |
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, AudioTorchTensor, AudioUrl
from tests import TOYDATA_DIR... | 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, AudioTorchTensor, AudioUrl
from tests import TOYDATA_DIR... |
# Copyright (c) OpenMMLab. All rights reserved.
from .atss import ATSS
from .autoassign import AutoAssign
from .base import BaseDetector
from .boxinst import BoxInst
from .base_detr import DetectionTransformer
from .cascade_rcnn import CascadeRCNN
from .centernet import CenterNet
from .condinst import CondInst
from .co... | # Copyright (c) OpenMMLab. All rights reserved.
from .atss import ATSS
from .autoassign import AutoAssign
from .base import BaseDetector
from .boxinst import BoxInst
from .cascade_rcnn import CascadeRCNN
from .centernet import CenterNet
from .condinst import CondInst
from .cornernet import CornerNet
from .crowddet impo... |
from typing import TYPE_CHECKING
import numpy as np
if TYPE_CHECKING:
from docarray.typing import ArrayType
def cosine(x_mat: 'np.ndarray', y_mat: 'np.ndarray', eps: float = 1e-7) -> 'np.ndarray':
"""Cosine distance between each row in x_mat and each row in y_mat.
:param x_mat: np.ndarray with ndim=2
... | from typing import TYPE_CHECKING
import numpy as np
if TYPE_CHECKING:
from ...typing import ArrayType
def cosine(x_mat: 'np.ndarray', y_mat: 'np.ndarray', eps: float = 1e-7) -> 'np.ndarray':
"""Cosine distance between each row in x_mat and each row in y_mat.
:param x_mat: np.ndarray with ndim=2
:pa... |
import enum
from typing import Any, Callable, Dict, List, Tuple, Type, Union
import PIL.Image
import torch
from torch import nn
from torch.utils._pytree import tree_flatten, tree_unflatten
from torchvision.prototype.transforms._utils import _isinstance
from torchvision.utils import _log_api_usage_once
class Transfor... | import enum
from typing import Any, Callable, Dict, List, Tuple, Type, Union
import PIL.Image
import torch
from torch import nn
from torch.utils._pytree import tree_flatten, tree_unflatten
from torchvision.prototype import features
from torchvision.prototype.transforms._utils import _isinstance
from torchvision.utils ... |
from datetime import datetime, timedelta
from langchain_core.exceptions import OutputParserException
from langchain_core.output_parsers import BaseOutputParser
from langchain_core.utils import comma_list
class DatetimeOutputParser(BaseOutputParser[datetime]):
"""Parse the output of an LLM call to a datetime."""
... | from datetime import datetime, timedelta
from langchain_core.exceptions import OutputParserException
from langchain_core.output_parsers import BaseOutputParser
from langchain_core.utils import comma_list
class DatetimeOutputParser(BaseOutputParser[datetime]):
"""Parse the output of an LLM call to a datetime."""
... |
import os
from pathlib import Path
from torchaudio.datasets import librispeech
from torchaudio_unittest.common_utils import get_whitenoise, normalize_wav, save_wav, TempDirMixin
# Used to generate a unique transcript for each dummy audio file
_NUMBERS = ["ZERO", "ONE", "TWO", "THREE", "FOUR", "FIVE", "SIX", "SEVEN", ... | import os
from pathlib import Path
from torchaudio.datasets import librispeech
from torchaudio_unittest.common_utils import get_whitenoise, normalize_wav, save_wav, TempDirMixin
# Used to generate a unique transcript for each dummy audio file
_NUMBERS = ["ZERO", "ONE", "TWO", "THREE", "FOUR", "FIVE", "SIX", "SEVEN", ... |
import os
from typing import Callable, Optional
from .folder import ImageFolder
from .utils import download_and_extract_archive
class EuroSAT(ImageFolder):
"""RGB version of the `EuroSAT <https://github.com/phelber/eurosat>`_ Dataset.
Args:
root (string): Root directory of dataset where ``root/euros... | import os
from typing import Callable, Optional
from .folder import ImageFolder
from .utils import download_and_extract_archive
class EuroSAT(ImageFolder):
"""RGB version of the `EuroSAT <https://github.com/phelber/eurosat>`_ Dataset.
Args:
root (string): Root directory of dataset where ``root/euros... |
from abc import ABC
import numpy as np
import pytest
from docarray import Document, DocumentArray
from docarray.array.storage.base.helper import Offset2ID
from docarray.array.storage.memory import SequenceLikeMixin
from docarray.array.storage.redis.getsetdel import GetSetDelMixin
from docarray.array.storage.redis.back... | from abc import ABC
import numpy as np
import pytest
from docarray import Document, DocumentArray
from docarray.array.storage.base.helper import Offset2ID
from docarray.array.storage.memory import SequenceLikeMixin
from docarray.array.storage.redis.getsetdel import GetSetDelMixin
from docarray.array.storage.redis.back... |
import warnings
from typing import TYPE_CHECKING, Any, Optional, Tuple, Type, TypeVar, Union
import numpy as np
from docarray.typing.proto_register import _register_proto
from docarray.typing.url.any_url import AnyUrl
from docarray.utils.misc import is_notebook
if TYPE_CHECKING:
from pydantic import BaseConfig
... | from typing import TYPE_CHECKING, Any, Optional, Tuple, Type, TypeVar, Union
import numpy as np
from docarray.typing.proto_register import _register_proto
from docarray.typing.url.any_url import AnyUrl
if TYPE_CHECKING:
from pydantic import BaseConfig
from pydantic.fields import ModelField
T = TypeVar('T', ... |
# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import subprocess
from collections import OrderedDict
import torch
from mmengine.runner import CheckpointLoader
convert_dict_fpn = {
'module.backbone.fpn.fpn_inner2': 'neck.lateral_convs.0.conv',
'module.backbone.fpn.fpn_inner3': 'neck.lateral_co... | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import subprocess
from collections import OrderedDict
import torch
from mmengine.runner import CheckpointLoader
convert_dict_fpn = {
'module.backbone.fpn.fpn_inner2': 'neck.lateral_convs.0.conv',
'module.backbone.fpn.fpn_inner3': 'neck.lateral_co... |
import logging
from typing import List
from backend.blocks.apollo._auth import ApolloCredentials
from backend.blocks.apollo.models import (
Contact,
Organization,
SearchOrganizationsRequest,
SearchOrganizationsResponse,
SearchPeopleRequest,
SearchPeopleResponse,
)
from backend.util.request impo... | import logging
from typing import List
from backend.blocks.apollo._auth import ApolloCredentials
from backend.blocks.apollo.models import (
Contact,
Organization,
SearchOrganizationsRequest,
SearchOrganizationsResponse,
SearchPeopleRequest,
SearchPeopleResponse,
)
from backend.util.request impo... |
"""This modules defines all kinds of exceptions raised in Jina."""
from typing import Set, Union
import grpc.aio
class BaseJinaException(BaseException):
"""A base class for all exceptions raised by Jina"""
class RuntimeFailToStart(SystemError, BaseJinaException):
"""When pod/deployment is failed to started... | """This modules defines all kinds of exceptions raised in Jina."""
from typing import Set, Union
import grpc.aio
class BaseJinaException(BaseException):
"""A base class for all exceptions raised by Jina"""
class RuntimeFailToStart(SystemError, BaseJinaException):
"""When pod/deployment is failed to started... |
__copyright__ = "Copyright (c) 2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
from typing import Callable, List
import pytest
from jina import DocumentArray, Flow
from ...transform_encoder import TransformerTorchEncoder
@pytest.mark.parametrize("request_size", [1, 10, 50, 100])
def test_inte... | __copyright__ = "Copyright (c) 2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
from typing import Callable, List
import pytest
from jina import DocumentArray, Flow
from jinahub.encoder.transform_encoder import TransformerTorchEncoder
@pytest.mark.parametrize("request_size", [1, 10, 50, 100])
... |
# Copyright (c) OpenMMLab. All rights reserved.
import copy
import platform
import random
import numpy as np
import torch
from mmdet.registry import DATASETS, TRANSFORMS
if platform.system() != 'Windows':
# https://github.com/pytorch/pytorch/issues/973
import resource
rlimit = resource.getrlimit(resource... | # Copyright (c) OpenMMLab. All rights reserved.
import copy
import platform
import random
import numpy as np
import torch
from mmdet.registry import DATASETS, TRANSFORMS
if platform.system() != 'Windows':
# https://github.com/pytorch/pytorch/issues/973
import resource
rlimit = resource.getrlimit(resource... |
"""
This file runs Masked Language Model. You provide a training file. Each line is interpreted as a sentence / paragraph.
Optionally, you can also provide a dev file.
The fine-tuned model is stored in the output/model_name folder.
Usage:
python train_mlm.py model_name data/train_sentences.txt [data/dev_sentences.txt... | """
This file runs Masked Language Model. You provide a training file. Each line is interpreted as a sentence / paragraph.
Optionally, you can also provide a dev file.
The fine-tuned model is stored in the output/model_name folder.
Usage:
python train_mlm.py model_name data/train_sentences.txt [data/dev_sentences.txt... |
from typing import Any, Mapping, Optional
from llama_index.readers.airbyte_cdk.base import AirbyteCDKReader, RecordHandler
class AirbyteTypeformReader(AirbyteCDKReader):
"""
AirbyteTypeformReader reader.
Retrieve documents from Typeform
Args:
config: The config object for the typeform sourc... | from typing import Any, Mapping, Optional
from llama_index.readers.airbyte_cdk.base import AirbyteCDKReader, RecordHandler
class AirbyteTypeformReader(AirbyteCDKReader):
"""AirbyteTypeformReader reader.
Retrieve documents from Typeform
Args:
config: The config object for the typeform source.
... |
from typing import List
import torch
import torchaudio.prototype.transforms as T
from torch.autograd import gradcheck, gradgradcheck
from torchaudio_unittest.common_utils import get_spectrogram, get_whitenoise, nested_params, TestBaseMixin
class Autograd(TestBaseMixin):
def assert_grad(
self,
tra... | from typing import List
import torch
import torchaudio.prototype.transforms as T
from torch.autograd import gradcheck, gradgradcheck
from torchaudio_unittest.common_utils import nested_params, TestBaseMixin
class Autograd(TestBaseMixin):
def assert_grad(
self,
transform: torch.nn.Module,
... |
import time
import unittest
from parameterized import parameterized
from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
from transformers.testing_utils import require_flash_attn, require_torch_gpu, slow
_TEST_PROMPTS = [
"A man is a walking his dog down the street, and a the turn he s... | import time
import unittest
from parameterized import parameterized
from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
from transformers.testing_utils import require_flash_attn, require_torch_gpu, slow
_TEST_PROMPTS = [
"A man is a walking his dog down the street, and a the turn he s... |
"""Test memory functionality."""
from langchain.memory.summary_buffer import ConversationSummaryBufferMemory
from tests.unit_tests.llms.fake_llm import FakeLLM
def test_summary_buffer_memory_no_buffer_yet() -> None:
"""Test ConversationSummaryBufferMemory when no inputs put in buffer yet."""
memory = Convers... | """Test memory functionality."""
from langchain.memory.summary_buffer import ConversationSummaryBufferMemory
from tests.unit_tests.llms.fake_llm import FakeLLM
def test_summary_buffer_memory_no_buffer_yet() -> None:
"""Test ConversationSummaryBufferMemory when no inputs put in buffer yet."""
memory = Convers... |
from keras.src import testing
from keras.src.datasets import california_housing
class CaliforniaHousingTest(testing.TestCase):
def test_load_data_large(self):
(x_train, y_train), (x_test, y_test) = california_housing.load_data(
version="large"
)
self.assertEqual(x_train.shape[1... | from keras.src import testing
from keras.src.datasets import california_housing
class CaliforniaHousingTest(testing.TestCase):
def test_load_data_large(self):
(x_train, y_train), (x_test, y_test) = california_housing.load_data(
version="large"
)
self.assertEqual(x_train.shape[... |
import numpy as np
import pytest
from keras.src import backend
from keras.src import layers
from keras.src import testing
class GaussianNoiseTest(testing.TestCase):
@pytest.mark.requires_trainable_backend
def test_gaussian_noise_basics(self):
self.run_layer_test(
layers.GaussianNoise,
... | import numpy as np
import pytest
from keras.src import backend
from keras.src import layers
from keras.src import testing
class GaussianNoiseTest(testing.TestCase):
@pytest.mark.requires_trainable_backend
def test_gaussian_noise_basics(self):
self.run_layer_test(
layers.GaussianNoise,
... |
# Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import mmengine
from mmengine.utils import digit_version
from .version import __version__, version_info
mmcv_minimum_version = '2.0.0rc0'
mmcv_maximum_version = '2.1.0'
mmcv_version = digit_version(mmcv.__version__)
mmengine_minimum_version = '0.3.0'
mmengi... | # Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import mmengine
from mmengine.utils import digit_version
from .version import __version__, version_info
mmcv_minimum_version = '2.0.0rc0'
mmcv_maximum_version = '2.1.0'
mmcv_version = digit_version(mmcv.__version__)
mmengine_minimum_version = '0.1.0'
mmengi... |
_base_ = './yolov3_d53_mstrain-608_273e_coco.py'
# dataset settings
img_norm_cfg = dict(mean=[0, 0, 0], std=[255., 255., 255.], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='Expand',
mean=img_norm_cfg['mean'],
... | _base_ = './yolov3_d53_mstrain-608_273e_coco.py'
# dataset settings
img_norm_cfg = dict(mean=[0, 0, 0], std=[255., 255., 255.], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFile', to_float32=True),
dict(type='LoadAnnotations', with_bbox=True),
dict(type='PhotoMetricDistortion'),
dict(
... |
import os
import subprocess
from pathlib import Path
import click
from llama_dev.utils import find_all_packages, is_llama_index_package
@click.command(short_help="Exec a command inside a package folder")
@click.option(
"--fail-fast",
is_flag=True,
default=False,
help="Exit the command at the first f... | import os
import subprocess
from pathlib import Path
import click
from llama_dev.utils import find_all_packages, is_llama_index_package
@click.command(short_help="Exec a command inside a package folder")
@click.option(
"--fail-fast",
is_flag=True,
default=False,
help="Exit the command at the first f... |
from typing import Any, Optional, Type, TypeVar, Union
from pydantic import Field
from docarray.base_doc import BaseDoc
from docarray.documents.mesh.vertices_and_faces import VerticesAndFaces
from docarray.typing.tensor.embedding import AnyEmbedding
from docarray.typing.url.url_3d.mesh_url import Mesh3DUrl
from docar... | from typing import Any, Optional, Type, TypeVar, Union
from pydantic import Field
from docarray.base_doc import BaseDoc
from docarray.documents.mesh.vertices_and_faces import VerticesAndFaces
from docarray.typing.tensor.embedding import AnyEmbedding
from docarray.typing.url.url_3d.mesh_url import Mesh3DUrl
from docar... |
# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import mmcv
from mmcv import Config, DictAction
from mmdet.datasets import build_dataset
from mmdet.utils import update_data_root
def parse_args():
parser = argparse.ArgumentParser(description='Evaluate metric of the '
... | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import mmcv
from mmcv import Config, DictAction
from mmdet.datasets import build_dataset
def parse_args():
parser = argparse.ArgumentParser(description='Evaluate metric of the '
'results saved in pkl format')
... |
import os
from typing import Any, List, Optional
from llama_index.core.bridge.pydantic import Field, PrivateAttr
from llama_index.core.callbacks import CBEventType, EventPayload
from llama_index.core.instrumentation import get_dispatcher
from llama_index.core.instrumentation.events.rerank import (
ReRankEndEvent,
... | import os
from typing import Any, List, Optional
from llama_index.core.bridge.pydantic import Field, PrivateAttr
from llama_index.core.callbacks import CBEventType, EventPayload
from llama_index.core.instrumentation import get_dispatcher
from llama_index.core.instrumentation.events.rerank import (
ReRankEndEvent,
... |
# Copyright (c) OpenMMLab. All rights reserved.
from .brick_wrappers import AdaptiveAvgPool2d, adaptive_avg_pool2d
from .builder import build_linear_layer, build_transformer
from .ckpt_convert import pvt_convert
from .conv_upsample import ConvUpsample
from .csp_layer import CSPLayer
from .gaussian_target import gaussia... | # Copyright (c) OpenMMLab. All rights reserved.
from .brick_wrappers import AdaptiveAvgPool2d, adaptive_avg_pool2d
from .builder import build_linear_layer, build_transformer
from .conv_upsample import ConvUpsample
from .csp_layer import CSPLayer
from .gaussian_target import gaussian_radius, gen_gaussian_target
from .in... |
# Copyright (c) OpenMMLab. All rights reserved.
__version__ = '0.7.2'
def parse_version_info(version_str):
"""Parse the version information.
Args:
version_str (str): version string like '0.1.0'.
Returns:
tuple: version information contains major, minor, micro version.
"""
versio... | # Copyright (c) OpenMMLab. All rights reserved.
__version__ = '0.7.1'
def parse_version_info(version_str):
"""Parse the version information.
Args:
version_str (str): version string like '0.1.0'.
Returns:
tuple: version information contains major, minor, micro version.
"""
versio... |
from typing import Iterable, Iterator, Union, TYPE_CHECKING
from docarray.array.storage.base.seqlike import BaseSequenceLikeMixin
from docarray.array.storage.milvus.backend import _batch_list, _always_true_expr
from docarray import Document
class SequenceLikeMixin(BaseSequenceLikeMixin):
def __eq__(self, other):
... | from typing import Iterable, Iterator, Union, TYPE_CHECKING
from docarray.array.storage.base.seqlike import BaseSequenceLikeMixin
from docarray.array.storage.milvus.backend import _batch_list
from docarray import Document
class SequenceLikeMixin(BaseSequenceLikeMixin):
def __eq__(self, other):
"""Compare ... |
# Copyright (c) OpenMMLab. All rights reserved.
from mmengine.device import (get_device, is_cuda_available, is_mlu_available,
is_mps_available)
def test_get_device():
device = get_device()
if is_cuda_available():
assert device == 'cuda'
elif is_mlu_available():
... | # Copyright (c) OpenMMLab. All rights reserved.
from mmengine.device import get_device, is_cuda_available, is_mlu_available
def test_get_device():
device = get_device()
if is_cuda_available():
assert device == 'cuda'
elif is_mlu_available():
assert device == 'mlu'
else:
assert ... |
from docarray.typing.bytes import ImageBytes
from docarray.typing.id import ID
from docarray.typing.tensor import ImageNdArray, ImageTensor
from docarray.typing.tensor.audio import AudioNdArray
from docarray.typing.tensor.embedding.embedding import AnyEmbedding, NdArrayEmbedding
from docarray.typing.tensor.ndarray impo... | from docarray.typing.bytes import ImageBytes
from docarray.typing.id import ID
from docarray.typing.tensor import ImageNdArray, ImageTensor
from docarray.typing.tensor.audio import AudioNdArray
from docarray.typing.tensor.embedding.embedding import AnyEmbedding, NdArrayEmbedding
from docarray.typing.tensor.ndarray impo... |
# Copyright (c) OpenMMLab. All rights reserved.
from .csp_darknet import CSPDarknet
from .darknet import Darknet
from .detectors_resnet import DetectoRS_ResNet
from .detectors_resnext import DetectoRS_ResNeXt
from .hourglass import HourglassNet
from .hrnet import HRNet
from .mobilenet_v2 import MobileNetV2
from .regnet... | from .csp_darknet import CSPDarknet
from .darknet import Darknet
from .detectors_resnet import DetectoRS_ResNet
from .detectors_resnext import DetectoRS_ResNeXt
from .hourglass import HourglassNet
from .hrnet import HRNet
from .mobilenet_v2 import MobileNetV2
from .regnet import RegNet
from .res2net import Res2Net
from... |
import pytest
from docarray import BaseDocument
from docarray.documents import ImageDoc
from docarray.typing import NdArray
class MyDoc(BaseDocument):
embedding: NdArray
text: str
image: ImageDoc
@pytest.mark.parametrize('protocol', ['protobuf', 'pickle'])
@pytest.mark.parametrize('compress', ['lz4', '... | import pytest
from docarray import BaseDocument
from docarray.typing import NdArray
from docarray.documents import Image
class MyDoc(BaseDocument):
embedding: NdArray
text: str
image: Image
@pytest.mark.parametrize('protocol', ['protobuf', 'pickle'])
@pytest.mark.parametrize('compress', ['lz4', 'bz2', ... |
from . import ( # noqa: F401
_extension,
compliance,
datasets,
functional,
io,
kaldi_io,
models,
pipelines,
sox_effects,
transforms,
utils,
)
from ._backend.common import AudioMetaData # noqa
try:
from .version import __version__, git_version # noqa: F401
except Impor... | from . import ( # noqa: F401
_extension,
compliance,
datasets,
functional,
io,
kaldi_io,
models,
pipelines,
sox_effects,
transforms,
utils,
)
from .backend.common import AudioMetaData
try:
from .version import __version__, git_version # noqa: F401
except ImportError:
... |
# Copyright (c) OpenMMLab. All rights reserved.
from .hook import Hook
from .iter_timer_hook import IterTimerHook
__all__ = ['Hook', 'IterTimerHook']
| # Copyright (c) OpenMMLab. All rights reserved.
from .hook import Hook
__all__ = ['Hook']
|
from __future__ import annotations
from typing import Any
from langchain_core._api import deprecated
from langchain_core.caches import BaseCache as BaseCache # For model_rebuild
from langchain_core.callbacks import Callbacks as Callbacks # For model_rebuild
from langchain_core.chat_history import BaseChatMessageHis... | from __future__ import annotations
from typing import Any
from langchain_core._api import deprecated
from langchain_core.caches import BaseCache as BaseCache # For model_rebuild
from langchain_core.callbacks import Callbacks as Callbacks # For model_rebuild
from langchain_core.chat_history import BaseChatMessageHis... |
from __future__ import annotations
import csv
import logging
import os
from typing import TYPE_CHECKING
import torch
from torch.utils.data import DataLoader
from sentence_transformers.evaluation.SentenceEvaluator import SentenceEvaluator
from sentence_transformers.util import batch_to_device
if TYPE_CHECKING:
f... | from __future__ import annotations
import csv
import logging
import os
from typing import TYPE_CHECKING
import torch
from torch.utils.data import DataLoader
from sentence_transformers.evaluation.SentenceEvaluator import SentenceEvaluator
from sentence_transformers.util import batch_to_device
if TYPE_CHECKING:
f... |
from torchvision.transforms import AutoAugmentPolicy, InterpolationMode # usort: skip
from . import functional, utils # usort: skip
from ._transform import Transform # usort: skip
from ._presets import StereoMatching # usort: skip
from ._augment import RandomCutmix, RandomErasing, RandomMixup, SimpleCopyPaste
fr... | from torchvision.transforms import AutoAugmentPolicy, InterpolationMode # usort: skip
from . import functional # usort: skip
from ._transform import Transform # usort: skip
from ._presets import StereoMatching # usort: skip
from ._augment import RandomCutmix, RandomErasing, RandomMixup, SimpleCopyPaste
from ._au... |
# Copyright (c) OpenMMLab. All rights reserved.
import unittest
from unittest import TestCase
import torch
from parameterized import parameterized
from mmdet.models.roi_heads import SCNetRoIHead # noqa
from mmdet.registry import MODELS
from mmdet.testing import demo_mm_inputs, demo_mm_proposals, get_roi_head_cfg
c... | # Copyright (c) OpenMMLab. All rights reserved.
import unittest
from unittest import TestCase
import torch
from parameterized import parameterized
from mmdet.models.roi_heads import SCNetRoIHead # noqa
from mmdet.registry import MODELS
from mmdet.testing import demo_mm_inputs, demo_mm_proposals, get_roi_head_cfg
c... |
import asyncio
import json
import logging
from abc import ABC, abstractmethod
from datetime import datetime
from typing import Any, AsyncGenerator, Generator, Generic, Optional, TypeVar
from pydantic import BaseModel
from redis.asyncio.client import PubSub as AsyncPubSub
from redis.client import PubSub
from backend.d... | import json
import logging
from abc import ABC, abstractmethod
from datetime import datetime
from typing import Any, AsyncGenerator, Generator, Generic, TypeVar
from pydantic import BaseModel
from redis.asyncio.client import PubSub as AsyncPubSub
from redis.client import PubSub
from backend.data import redis
logger ... |
"""
This directory contains deprecated code that can only be used with the old `model.fit`-style Sentence Transformers v2.X training.
It exists for backwards compatibility with the `model.old_fit` method, but will be removed in a future version.
Nowadays, with Sentence Transformers v3+, it is recommended to use the `S... | from __future__ import annotations
from .InputExample import InputExample
from .LabelSentenceReader import LabelSentenceReader
from .NLIDataReader import NLIDataReader
from .STSDataReader import STSBenchmarkDataReader, STSDataReader
from .TripletReader import TripletReader
__all__ = [
"InputExample",
"LabelSe... |
from typing import Optional
from docarray.typing.proto_register import _register_proto
from docarray.typing.url.any_url import AnyUrl
@_register_proto(proto_type_name='text_url')
class TextUrl(AnyUrl):
"""
URL to a text file.
Can be remote (web) URL, or a local file path.
"""
def load(self, char... | from typing import Optional
from docarray.typing.proto_register import _register_proto
from docarray.typing.url.any_url import AnyUrl
from docarray.typing.url.helper import _uri_to_blob
@_register_proto(proto_type_name='text_url')
class TextUrl(AnyUrl):
"""
URL to a text file.
Can be remote (web) URL, or... |
from typing import Dict, Union
import torch
import transformers
from PIL import Image
from torch import nn
class CLIPModel(nn.Module):
def __init__(self, model_name: str = "openai/clip-vit-base-patch32", processor_name=None) -> None:
super(CLIPModel, self).__init__()
if processor_name is None:
... | from typing import Union
import torch
import transformers
from PIL import Image
from torch import nn
class CLIPModel(nn.Module):
def __init__(self, model_name: str = "openai/clip-vit-base-patch32", processor_name=None):
super(CLIPModel, self).__init__()
if processor_name is None:
pro... |
"""Load Documents from a set of persistent Steamship Files."""
from typing import List, Optional
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document
class SteamshipFileReader(BaseReader):
"""
Reads persistent Steamship Files and converts them to Documents.
A... | """Load Documents from a set of persistent Steamship Files."""
from typing import List, Optional
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document
class SteamshipFileReader(BaseReader):
"""Reads persistent Steamship Files and converts them to Documents.
Args:
... |
from __future__ import annotations
import json
import os
from typing import Any
import torch
from torch import nn
class SpladePooling(nn.Module):
"""
SPLADE Pooling module for creating the sparse embeddings.
This module implements the SPLADE pooling mechanism that:
1. Takes token logits from a mas... | from __future__ import annotations
import json
import os
from typing import Any
import torch
from torch import nn
class SpladePooling(nn.Module):
"""
SPLADE Pooling module for creating the sparse embeddings.
This module implements the SPLADE pooling mechanism that:
1. Takes token logits from a mask... |
_base_ = [
'../_base_/models/faster-rcnn_r50_fpn.py', '../_base_/datasets/voc0712.py',
'../_base_/default_runtime.py'
]
model = dict(roi_head=dict(bbox_head=dict(num_classes=20)))
METAINFO = {
'classes':
('aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat',
'chair', 'cow', 'dinin... | _base_ = [
'../_base_/models/faster-rcnn_r50_fpn.py', '../_base_/datasets/voc0712.py',
'../_base_/default_runtime.py'
]
model = dict(roi_head=dict(bbox_head=dict(num_classes=20)))
METAINFO = {
'classes':
('aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat',
'chair', 'cow', 'dinin... |
from keras.src import backend
from keras.src.api_export import keras_export
from keras.src.layers.preprocessing.image_preprocessing.base_image_preprocessing_layer import ( # noqa: E501
BaseImagePreprocessingLayer,
)
from keras.src.ops.core import _saturate_cast
@keras_export("keras.layers.AutoContrast")
class Au... | from keras.src import backend
from keras.src.api_export import keras_export
from keras.src.layers.preprocessing.image_preprocessing.base_image_preprocessing_layer import ( # noqa: E501
BaseImagePreprocessingLayer,
)
from keras.src.ops.core import _saturate_cast
@keras_export("keras.layers.AutoContrast")
class Au... |
import multiprocessing
from copy import deepcopy
from functools import partial
from typing import TYPE_CHECKING
from hubble.executor.helper import is_valid_huburi
from hubble.executor.hubio import HubIO
from jina.enums import PodRoleType
from jina.parsers.helper import _update_gateway_args
if TYPE_CHECKING: # pragm... | import multiprocessing
import re
from copy import deepcopy
from functools import partial
from typing import TYPE_CHECKING
from hubble.executor.helper import is_valid_huburi
from hubble.executor.hubio import HubIO
from jina.enums import PodRoleType
from jina.parsers.helper import _update_gateway_args
if TYPE_CHECKING... |
PREFIX = """Answer the following questions as best you can. You have access to the following tools:""" # noqa: E501
FORMAT_INSTRUCTIONS = """Use the following format:
Question: the input question you must answer
Thought: you should always think about what to do
Action: the action to take, should be one of [{tool_name... | # flake8: noqa
PREFIX = """Answer the following questions as best you can. You have access to the following tools:"""
FORMAT_INSTRUCTIONS = """Use the following format:
Question: the input question you must answer
Thought: you should always think about what to do
Action: the action to take, should be one of [{tool_nam... |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# This software may be used and distributed according to the terms of the Llama 2 Community License Agreement.
import os
from logging import getLogger
from typing import List
from sentencepiece import SentencePieceProcessor
logger = getLogger()
class Tokenizer:... | # Copyright (c) Meta Platforms, Inc. and affiliates.
# This software may be used and distributed according to the terms of the Llama 2 Community License Agreement.
import os
from logging import getLogger
from typing import List
from sentencepiece import SentencePieceProcessor
logger = getLogger()
class Tokenizer:... |
_base_ = './cascade-rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='pytorch'... | _base_ = './cascade_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='pytorch'... |
from typing import Optional
import pytest
from langchain_cli.constants import (
DEFAULT_GIT_REF,
DEFAULT_GIT_REPO,
DEFAULT_GIT_SUBDIRECTORY,
)
from langchain_cli.utils.git import DependencySource, parse_dependency_string
def _assert_dependency_equals(
dep: DependencySource,
*,
git: Optional[... | from typing import Optional
import pytest
from langchain_cli.constants import (
DEFAULT_GIT_REF,
DEFAULT_GIT_REPO,
DEFAULT_GIT_SUBDIRECTORY,
)
from langchain_cli.utils.git import DependencySource, parse_dependency_string
def _assert_dependency_equals(
dep: DependencySource,
*,
git: Optional[... |
__copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import subprocess
from typing import Dict, List, Optional
import spacy
from jina import DocumentArray, Executor, requests
from jina_commons.batching import get_docs_batch_generator
_EXCLUDE_COMPONENTS = [
'... | __copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
from typing import List, Dict, Optional
import numpy as np
import torch
import spacy
from jina import Executor, DocumentArray, requests
from jina.logging.logger import JinaLogger
class SpacyTextEncoder(Execut... |
import functools
import time
from threading import Thread
import numpy as np
import pytest
from jina import Client, Document, Flow
@pytest.mark.slow
@pytest.mark.parametrize('protocol', ['websocket', 'http'])
def test_gateway_concurrency(protocol, reraise):
port = 12345
CONCURRENCY = 2
def _validate(re... | import functools
import time
from threading import Thread
import numpy as np
import pytest
from jina import Client, Document, Flow
@pytest.mark.slow
@pytest.mark.parametrize('protocol', ['websocket', 'http'])
def test_gateway_concurrency(protocol, reraise):
port = 12345
CONCURRENCY = 2
def _validate(re... |
import importlib.util
from typing import Any, Dict, List, Optional
from langchain_core.embeddings import Embeddings
from pydantic import BaseModel, ConfigDict, model_validator
class SpacyEmbeddings(BaseModel, Embeddings):
"""Embeddings by spaCy models.
Attributes:
model_name (str): Name of a spaCy m... | import importlib.util
from typing import Any, Dict, List, Optional
from langchain_core.embeddings import Embeddings
from pydantic import BaseModel, ConfigDict, model_validator
class SpacyEmbeddings(BaseModel, Embeddings):
"""Embeddings by spaCy models.
Attributes:
model_name (str): Name of a spaCy m... |
from typing import TYPE_CHECKING
if TYPE_CHECKING:
from backend.util.process import AppProcess
def run_processes(*processes: "AppProcess", **kwargs):
"""
Execute all processes in the app. The last process is run in the foreground.
"""
try:
for process in processes[:-1]:
proces... | from typing import TYPE_CHECKING
if TYPE_CHECKING:
from backend.util.process import AppProcess
def run_processes(*processes: "AppProcess", **kwargs):
"""
Execute all processes in the app. The last process is run in the foreground.
"""
try:
for process in processes[:-1]:
proces... |
from .database import DatabaseManager
from .manager import ExecutionManager
from .scheduler import Scheduler
__all__ = [
"DatabaseManager",
"ExecutionManager",
"Scheduler",
]
| from .database import DatabaseManager
from .manager import ExecutionManager
from .scheduler import ExecutionScheduler
__all__ = [
"DatabaseManager",
"ExecutionManager",
"ExecutionScheduler",
]
|
# Copyright (c) OpenMMLab. All rights reserved.
from .coco_api import COCO, COCOeval, COCOPanoptic
from .cocoeval_mp import COCOevalMP
__all__ = ['COCO', 'COCOeval', 'COCOPanoptic', 'COCOevalMP']
| # Copyright (c) OpenMMLab. All rights reserved.
from .coco_api import COCO, COCOeval, COCOPanoptic
__all__ = ['COCO', 'COCOeval', 'COCOPanoptic']
|
# Copyright (c) OpenMMLab. All rights reserved.
from unittest.mock import MagicMock, Mock
import torch
from torch import nn
from mmengine.hooks import OptimizerHook
class TestOptimizerHook:
def test_after_train_iter(self):
class Model(nn.Module):
def __init__(self):
super(... | # Copyright (c) OpenMMLab. All rights reserved.
from unittest.mock import Mock
import torch
from torch import nn
from mmengine.hooks import OptimizerHook
class TestOptimizerHook:
def test_after_train_iter(self):
class Model(nn.Module):
def __init__(self):
super().__init__(... |
# Copyright (c) OpenMMLab. All rights reserved.
from typing import List, Optional, Tuple
import torch
from torch import Tensor
from mmdet.registry import MODELS
from mmdet.utils import ConfigType, OptMultiConfig
from .base_roi_extractor import BaseRoIExtractor
@MODELS.register_module()
class SingleRoIExtractor(Base... | # Copyright (c) OpenMMLab. All rights reserved.
from typing import List, Optional, Tuple
import torch
from torch import Tensor
from mmdet.registry import MODELS
from mmdet.utils import ConfigType, OptMultiConfig
from .base_roi_extractor import BaseRoIExtractor
@MODELS.register_module()
class SingleRoIExtractor(Base... |
# Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
import xml.etree.ElementTree as ET
from mmengine.fileio import list_from_file
from mmdet.registry import DATASETS
from .xml_style import XMLDataset
@DATASETS.register_module()
class WIDERFaceDataset(XMLDataset):
"""Reader for the WIDER Face d... | # Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
import xml.etree.ElementTree as ET
import mmcv
from mmdet.registry import DATASETS
from .xml_style import XMLDataset
@DATASETS.register_module()
class WIDERFaceDataset(XMLDataset):
"""Reader for the WIDER Face dataset in PASCAL VOC format.
... |
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