input stringlengths 33 5k | output stringlengths 32 5k |
|---|---|
_base_ = '../retinanet/retinanet_r50_fpn_1x_coco.py'
# model settings
model = dict(
type='FSAF',
bbox_head=dict(
type='FSAFHead',
num_classes=80,
in_channels=256,
stacked_convs=4,
feat_channels=256,
reg_decoded_bbox=True,
# Only anchor-free branch is imple... | _base_ = '../retinanet/retinanet_r50_fpn_1x_coco.py'
# model settings
model = dict(
type='FSAF',
bbox_head=dict(
type='FSAFHead',
num_classes=80,
in_channels=256,
stacked_convs=4,
feat_channels=256,
reg_decoded_bbox=True,
# Only anchor-free branch is imple... |
import platform
import sys
from pathlib import Path
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("... | import os
import platform
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") and pla... |
from __future__ import annotations
from typing import Any, Optional
from langchain_core.callbacks import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain_core.tools import BaseTool
from langchain_community.utilities.mojeek_search import MojeekSearchAPIWrapper
class MojeekSearch... | from __future__ import annotations
from typing import Any, Optional
from langchain_core.callbacks import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain_core.tools import BaseTool
from langchain_community.utilities.mojeek_search import MojeekSearchAPIWrapper
class MojeekSearch... |
import pytest
from backend.data import db
from backend.executor import ExecutionScheduler
from backend.server.model import CreateGraph
from backend.usecases.sample import create_test_graph, create_test_user
from backend.util.service import get_service_client
from backend.util.test import SpinTestServer
@pytest.mark.... | import pytest
from backend.data import db
from backend.executor import ExecutionScheduler
from backend.server.model import CreateGraph
from backend.usecases.sample import create_test_graph, create_test_user
from backend.util.service import get_service_client
from backend.util.test import SpinTestServer
@pytest.mark.... |
import numpy as np
from tensorflow import data as tf_data
from keras.src import backend
from keras.src import layers
from keras.src import testing
class IntegerLookupTest(testing.TestCase):
# TODO: increase coverage. Most features aren't being tested.
def test_config(self):
layer = layers.IntegerLoo... | import numpy as np
from tensorflow import data as tf_data
from keras.src import backend
from keras.src import layers
from keras.src import testing
class IntegerLookupTest(testing.TestCase):
# TODO: increase coverage. Most features aren't being tested.
def test_config(self):
layer = layers.IntegerLoo... |
import os
import time
import pytest
from docarray import Document
from jina import Flow
from jina.constants import __cache_path__
cur_dir = os.path.dirname(os.path.abspath(__file__))
@pytest.fixture(scope='module')
def filewriter_exec_docker_image_built():
import docker
client = docker.from_env()
clie... | import os
import time
import pytest
from docarray import Document
from jina import Flow
from jina.constants import __cache_path__
cur_dir = os.path.dirname(os.path.abspath(__file__))
@pytest.fixture(scope='module')
def filewriter_exec_docker_image_built():
import docker
client = docker.from_env()
clie... |
import torch
from ._bounding_boxes import BoundingBoxes, BoundingBoxFormat, is_rotated_bounding_format
from ._image import Image
from ._mask import Mask
from ._torch_function_helpers import set_return_type
from ._tv_tensor import TVTensor
from ._video import Video
# TODO: Fix this. We skip this method as it leads to... | import torch
from ._bounding_boxes import BoundingBoxes, BoundingBoxFormat
from ._image import Image
from ._mask import Mask
from ._torch_function_helpers import set_return_type
from ._tv_tensor import TVTensor
from ._video import Video
# TODO: Fix this. We skip this method as it leads to
# RecursionError: maximum r... |
import warnings
from typing import Any, List, Union
import PIL.Image
import torch
from torchvision.prototype import datapoints
from torchvision.transforms import functional as _F
from ._utils import is_simple_tensor
@torch.jit.unused
def to_grayscale(inpt: PIL.Image.Image, num_output_channels: int = 1) -> PIL.Imag... | import warnings
from typing import Any, List, Union
import PIL.Image
import torch
from torchvision.prototype import datapoints
from torchvision.transforms import functional as _F
from ._utils import is_simple_tensor
@torch.jit.unused
def to_grayscale(inpt: PIL.Image.Image, num_output_channels: int = 1) -> PIL.Imag... |
from docarray.typing.tensor.tensor import Tensor
from docarray.typing.tensor.torch_tensor import TorchTensor
__all__ = ['Tensor', 'TorchTensor']
| from docarray.typing.tensor.tensor import Tensor
__all__ = ['Tensor']
|
from typing import Type
from docarray.utils._internal.pydantic import is_pydantic_v2
from .doc import BaseDoc
class AnyDoc(BaseDoc):
"""
AnyDoc is a Document that is not tied to any schema
"""
class Config:
_load_extra_fields_from_protobuf = True # I introduce this variable to allow to loa... | from typing import Type
from .doc import BaseDoc
class AnyDoc(BaseDoc):
"""
AnyDoc is a Document that is not tied to any schema
"""
class Config:
_load_extra_fields_from_protobuf = True # I introduce this variable to allow to load more that the fields defined in the schema
# will do... |
# Copyright (c) OpenMMLab. All rights reserved.
__version__ = '3.0.0rc0'
short_version = __version__
def parse_version_info(version_str):
"""Parse a version string into a tuple.
Args:
version_str (str): The version string.
Returns:
tuple[int | str]: The version info, e.g., "1.3.0" is par... | # Copyright (c) OpenMMLab. All rights reserved.
__version__ = '2.25.0'
short_version = __version__
def parse_version_info(version_str):
version_info = []
for x in version_str.split('.'):
if x.isdigit():
version_info.append(int(x))
elif x.find('rc') != -1:
patch_version... |
"""
Base Managed Service index.
An index that is built on top of a managed service.
"""
from abc import ABC, abstractmethod
from typing import Any, Dict, List, Optional, Sequence, Type
from llama_index.core.base.base_retriever import BaseRetriever
from llama_index.core.callbacks.base import CallbackManager
from lla... | """Base Managed Service index.
An index that is built on top of a managed service.
"""
from abc import ABC, abstractmethod
from typing import Any, Dict, List, Optional, Sequence, Type
from llama_index.core.base.base_retriever import BaseRetriever
from llama_index.core.callbacks.base import CallbackManager
from llam... |
# Copyright (c) OpenMMLab. All rights reserved.
_base_ = './error_mix_using3.py'
| # Copyright (c) OpenMMLab. All rights reserved.
_base_ = './toy_model.py'
|
import sys
import numpy as np
import pytest
from hypothesis import given, settings, strategies
import xgboost as xgb
from xgboost import testing as tm
from xgboost.testing import no_cupy
from xgboost.testing.updater import check_extmem_qdm, check_quantile_loss_extmem
sys.path.append("tests/python")
from test_data_it... | import sys
import pytest
from hypothesis import given, settings, strategies
import xgboost as xgb
from xgboost import testing as tm
from xgboost.testing import no_cupy
from xgboost.testing.updater import check_extmem_qdm, check_quantile_loss_extmem
sys.path.append("tests/python")
from test_data_iterator import run_d... |
import logging
from datasets import load_dataset
from sentence_transformers import SparseEncoder
from sentence_transformers.sparse_encoder.evaluation import SparseMSEEvaluator
logging.basicConfig(format="%(message)s", level=logging.INFO)
# Load a model
student_model = SparseEncoder("prithivida/Splade_PP_en_v1")
tea... | import logging
from datasets import load_dataset
from sentence_transformers import SparseEncoder
from sentence_transformers.sparse_encoder.evaluation import SparseMSEEvaluator
logging.basicConfig(format="%(message)s", level=logging.INFO)
# Load a model
student_model = SparseEncoder("prithivida/Splade_PP_en_v1")
tea... |
# Copyright (c) OpenMMLab. All rights reserved.
from .manager import ManagerMeta, ManagerMixin
from .misc import (apply_to, check_prerequisites, concat_list,
deprecated_api_warning, deprecated_function, has_method,
import_modules_from_strings, is_list_of,
is_meth... | # Copyright (c) OpenMMLab. All rights reserved.
from .manager import ManagerMeta, ManagerMixin
from .misc import (check_prerequisites, concat_list, deprecated_api_warning,
deprecated_function, has_method,
import_modules_from_strings, is_list_of,
is_method_overrid... |
#!/usr/bin/env python
import functools as func
import glob
import os.path as osp
import re
import numpy as np
url_prefix = 'https://github.com/open-mmlab/mmdetection/blob/main/configs'
files = sorted(glob.glob('../../configs/*/README.md'))
stats = []
titles = []
num_ckpts = 0
for f in files:
url = osp.dirname(... | #!/usr/bin/env python
import functools as func
import glob
import os.path as osp
import re
import numpy as np
url_prefix = 'https://github.com/open-mmlab/mmdetection/blob/3.x/configs'
files = sorted(glob.glob('../../configs/*/README.md'))
stats = []
titles = []
num_ckpts = 0
for f in files:
url = osp.dirname(f... |
# Copyright (c) OpenMMLab. All rights reserved.
from .optimizer import (OPTIM_WRAPPER_CONSTRUCTORS, OPTIMIZERS,
AmpOptimWrapper, ApexOptimWrapper,
DefaultOptimWrapperConstructor, OptimWrapper,
OptimWrapperDict, ZeroRedundancyOptimizer,
... | # Copyright (c) OpenMMLab. All rights reserved.
from .optimizer import (OPTIM_WRAPPER_CONSTRUCTORS, OPTIMIZERS,
AmpOptimWrapper, ApexOptimWrapper,
DefaultOptimWrapperConstructor, OptimWrapper,
OptimWrapperDict, build_optim_wrapper)
# yapf: disable
... |
# Copyright (c) OpenMMLab. All rights reserved.
__version__ = '2.23.0'
short_version = __version__
def parse_version_info(version_str):
version_info = []
for x in version_str.split('.'):
if x.isdigit():
version_info.append(int(x))
elif x.find('rc') != -1:
patch_version... | # Copyright (c) OpenMMLab. All rights reserved.
__version__ = '2.22.0'
short_version = __version__
def parse_version_info(version_str):
version_info = []
for x in version_str.split('.'):
if x.isdigit():
version_info.append(int(x))
elif x.find('rc') != -1:
patch_version... |
"""Memory modules for conversation prompts."""
from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
from langchain.memory.buffer import (
ConversationBufferMemory,
ConversationStringBufferMemory,
)
from langchain.memory.buffer_window import ConversationBufferWindowMemory
from langc... | """Memory modules for conversation prompts."""
from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
from langchain.memory.buffer import (
ConversationBufferMemory,
ConversationStringBufferMemory,
)
from langchain.memory.buffer_window import ConversationBufferWindowMemory
from langc... |
from __future__ import annotations
import os
import sys
import warnings
def which(thefile: str) -> str | None:
warnings.warn(
"tools.setup_helpers.which is deprecated and will be removed in a future version. "
"Use shutil.which instead.",
FutureWarning,
stacklevel=2,
)
pa... | from __future__ import annotations
import os
import sys
def which(thefile: str) -> str | None:
path = os.environ.get("PATH", os.defpath).split(os.pathsep)
for d in path:
fname = os.path.join(d, thefile)
fnames = [fname]
if sys.platform == "win32":
exts = os.environ.get("PA... |
import numpy as np
import pytest
from numpy.testing import assert_allclose
from pytest import approx
from sklearn.utils.fixes import np_version, parse_version
from sklearn.utils.stats import _averaged_weighted_percentile, _weighted_percentile
def test_averaged_weighted_median():
y = np.array([0, 1, 2, 3, 4, 5])
... | import numpy as np
from numpy.testing import assert_allclose
from pytest import approx
from sklearn.utils.stats import _weighted_percentile
def test_weighted_percentile():
y = np.empty(102, dtype=np.float64)
y[:50] = 0
y[-51:] = 2
y[-1] = 100000
y[50] = 1
sw = np.ones(102, dtype=np.float64)
... |
_base_ = [
'../common/mstrain_3x_coco_instance.py',
'../_base_/models/cascade_mask_rcnn_r50_fpn.py'
]
model = dict(
# use caffe img_norm
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[103.530, 116.280, 123.675],
std=[1.0, 1.0, 1.0],
to_rgb=False,
pad_s... | _base_ = [
'../common/mstrain_3x_coco_instance.py',
'../_base_/models/cascade_mask_rcnn_r50_fpn.py'
]
preprocess_cfg = dict(
mean=[103.530, 116.280, 123.675],
std=[1.0, 1.0, 1.0],
to_rgb=False,
pad_size_divisor=32)
model = dict(
# use caffe img_norm
preprocess_cfg=preprocess_cfg,
ba... |
import os
from pathlib import Path
from typing import List, Tuple, Union
import torch
from torch.utils.data import Dataset
from torchaudio.datasets.utils import _load_waveform
_TASKS_TO_MIXTURE = {
"sep_clean": "mix_clean",
"enh_single": "mix_single",
"enh_both": "mix_both",
"sep_noisy": "mix_both",
}... | import os
from pathlib import Path
from typing import List, Tuple, Union
import torch
from torch.utils.data import Dataset
from torchaudio.datasets.utils import _load_waveform
_TASKS_TO_MIXTURE = {
"sep_clean": "mix_clean",
"enh_single": "mix_single",
"enh_both": "mix_both",
"sep_noisy": "mix_both",
}... |
"""
This basic example loads a pre-trained model from the web and uses it to
generate sentence embeddings for a given list of sentences.
"""
import logging
import numpy as np
from sentence_transformers import LoggingHandler, SentenceTransformer
#### Just some code to print debug information to stdout
np.set_printop... | """
This basic example loads a pre-trained model from the web and uses it to
generate sentence embeddings for a given list of sentences.
"""
from sentence_transformers import SentenceTransformer, LoggingHandler
import numpy as np
import logging
#### Just some code to print debug information to stdout
np.set_printopti... |
# Copyright (c) OpenMMLab. All rights reserved.
"""copy from
https://github.com/ZwwWayne/K-Net/blob/main/knet/det/mask_pseudo_sampler.py."""
import torch
from mmengine.structures import InstanceData
from mmdet.registry import TASK_UTILS
from ..assigners import AssignResult
from .base_sampler import BaseSampler
from .... | # Copyright (c) OpenMMLab. All rights reserved.
"""copy from
https://github.com/ZwwWayne/K-Net/blob/main/knet/det/mask_pseudo_sampler.py."""
import torch
from mmengine.data import InstanceData
from mmdet.registry import TASK_UTILS
from ..assigners import AssignResult
from .base_sampler import BaseSampler
from .mask_s... |
from __future__ import annotations
from typing import Any
from langchain_core.output_parsers import BaseOutputParser
from langchain_core.utils import pre_init
class CombiningOutputParser(BaseOutputParser[dict[str, Any]]):
"""Combine multiple output parsers into one."""
parsers: list[BaseOutputParser]
... | from __future__ import annotations
from typing import Any
from langchain_core.output_parsers import BaseOutputParser
from langchain_core.utils import pre_init
class CombiningOutputParser(BaseOutputParser[dict[str, Any]]):
"""Combine multiple output parsers into one."""
parsers: list[BaseOutputParser]
... |
_base_ = './faster-rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='pytorch',... | _base_ = './faster_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='pytorch',... |
# model settings
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
model = dict(
type='RetinaNet',
img_norm_cfg=img_norm_cfg,
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stage... | # model settings
model = dict(
type='RetinaNet',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
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(t... |
# Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
from mmcv.cnn import ConvModule
from mmengine.model import BaseModule
from mmdet.registry import MODELS
@MODELS.register_module()
class ChannelMapper(BaseModule):
r"""Channel Mapper to reduce/increase channels of backbone features.
This i... | # Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
from mmcv.cnn import ConvModule
from mmcv.runner import BaseModule
from mmdet.registry import MODELS
@MODELS.register_module()
class ChannelMapper(BaseModule):
r"""Channel Mapper to reduce/increase channels of backbone features.
This is u... |
"""Run smoke tests"""
import os
import sys
from pathlib import Path
import torch
import torchvision
from torchvision.io import decode_image, decode_jpeg, decode_webp, read_file
from torchvision.models import resnet50, ResNet50_Weights
SCRIPT_DIR = Path(__file__).parent
def smoke_test_torchvision() -> None:
pr... | """Run smoke tests"""
import os
import sys
from pathlib import Path
import torch
import torchvision
from torchvision.io import decode_image, decode_jpeg, decode_webp, read_file
from torchvision.models import resnet50, ResNet50_Weights
SCRIPT_DIR = Path(__file__).parent
def smoke_test_torchvision() -> None:
pr... |
import os
from typing import Callable, Iterator, Optional
from langchain_core.documents import Document
from langchain_community.document_loaders.base import BaseLoader
class GitLoader(BaseLoader):
"""Load `Git` repository files.
The Repository can be local on disk available at `repo_path`,
or remote a... | import os
from typing import Callable, Iterator, Optional
from langchain_core.documents import Document
from langchain_community.document_loaders.base import BaseLoader
class GitLoader(BaseLoader):
"""Load `Git` repository files.
The Repository can be local on disk available at `repo_path`,
or remote a... |
"""
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 .DenoisingAutoEncoderDataset import DenoisingAutoEncoderDataset
from .NoDuplicatesDataLoader import NoDuplicatesDataLoader
from .ParallelSentencesDataset import ParallelSentencesDataset
from .SentenceLabelDataset import SentenceLabelDataset
from .SentencesDataset import Sentence... |
"""Standard LangChain interface tests"""
import base64
from pathlib import Path
from typing import Literal, cast
import httpx
import pytest
from langchain_core.language_models import BaseChatModel
from langchain_core.messages import AIMessage, HumanMessage
from langchain_tests.integration_tests import ChatModelIntegr... | """Standard LangChain interface tests"""
import base64
from pathlib import Path
from typing import Literal, cast
import httpx
import pytest
from langchain_core.language_models import BaseChatModel
from langchain_core.messages import AIMessage, HumanMessage
from langchain_tests.integration_tests import ChatModelIntegr... |
from __future__ import annotations
from .BinaryCrossEntropyLoss import BinaryCrossEntropyLoss
from .CachedMultipleNegativesRankingLoss import CachedMultipleNegativesRankingLoss
from .CrossEntropyLoss import CrossEntropyLoss
from .LambdaLoss import (
LambdaLoss,
LambdaRankScheme,
NDCGLoss1Scheme,
NDCGLo... | from __future__ import annotations
from .BinaryCrossEntropyLoss import BinaryCrossEntropyLoss
from .CachedMultipleNegativesRankingLoss import CachedMultipleNegativesRankingLoss
from .CrossEntropyLoss import CrossEntropyLoss
from .LambdaLoss import (
LambdaLoss,
LambdaRankScheme,
NDCGLoss1Scheme,
NDCGLo... |
import importlib
from types import ModuleType
import pytest
from fastapi.testclient import TestClient
from ...utils import needs_py39
@pytest.fixture(
name="mod",
params=[
"tutorial008d",
"tutorial008d_an",
pytest.param("tutorial008d_an_py39", marks=needs_py39),
],
)
def get_mod(... | import pytest
from fastapi.testclient import TestClient
@pytest.fixture(name="client")
def get_client():
from docs_src.dependencies.tutorial008d import app
client = TestClient(app)
return client
def test_get_no_item(client: TestClient):
response = client.get("/items/foo")
assert response.status... |
"""
=====================================
How to write your own Datapoint class
=====================================
.. note::
Try on `collab <https://colab.research.google.com/github/pytorch/vision/blob/gh-pages/main/_generated_ipynb_notebooks/plot_custom_datapoints.ipynb>`_
or :ref:`go to the end <sphx_glr_... | """
=====================================
How to write your own Datapoint class
=====================================
This guide is intended for advanced users and downstream library maintainers. We explain how to
write your own datapoint class, and how to make it compatible with the built-in
Torchvision v2 transforms... |
import gc
import unittest
import numpy as np
import pytest
import torch
from diffusers import FluxPipeline, FluxPriorReduxPipeline
from diffusers.utils import load_image
from diffusers.utils.testing_utils import (
Expectations,
backend_empty_cache,
numpy_cosine_similarity_distance,
require_big_acceler... | import gc
import unittest
import numpy as np
import pytest
import torch
from diffusers import FluxPipeline, FluxPriorReduxPipeline
from diffusers.utils import load_image
from diffusers.utils.testing_utils import (
Expectations,
backend_empty_cache,
numpy_cosine_similarity_distance,
require_big_acceler... |
from typing import Optional
import torch
from docarray import BaseDoc, DocList
from docarray.typing import TorchTensor
def test_torch_train():
class Mmdoc(BaseDoc):
text: str
tensor: Optional[TorchTensor[3, 224, 224]]
N = 10
batch = DocList[Mmdoc](Mmdoc(text=f'hello{i}') for i in range... | from typing import Optional
import torch
from docarray import BaseDoc, DocArray
from docarray.typing import TorchTensor
def test_torch_train():
class Mmdoc(BaseDoc):
text: str
tensor: Optional[TorchTensor[3, 224, 224]]
N = 10
batch = DocArray[Mmdoc](Mmdoc(text=f'hello{i}') for i in ran... |
"""Gmail tool utils."""
from __future__ import annotations
import logging
import os
from typing import TYPE_CHECKING, List, Optional, Tuple
from langchain_core.utils import guard_import
if TYPE_CHECKING:
from google.auth.transport.requests import Request
from google.oauth2.credentials import Credentials
... | """Gmail tool utils."""
from __future__ import annotations
import logging
import os
from typing import TYPE_CHECKING, List, Optional, Tuple
from langchain_core.utils import guard_import
if TYPE_CHECKING:
from google.auth.transport.requests import Request
from google.oauth2.credentials import Credentials
... |
from enum import Enum
from typing import Any
from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
from backend.data.model import SchemaField
class ComparisonOperator(Enum):
EQUAL = "=="
NOT_EQUAL = "!="
GREATER_THAN = ">"
LESS_THAN = "<"
GREATER_THAN_OR_EQUAL = ">="
L... | from enum import Enum
from typing import Any
from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
from backend.data.model import SchemaField
class ComparisonOperator(Enum):
EQUAL = "=="
NOT_EQUAL = "!="
GREATER_THAN = ">"
LESS_THAN = "<"
GREATER_THAN_OR_EQUAL = ">="
L... |
import numpy as np
import pytest
import torch
from docarray import BaseDocument
from docarray.typing import AnyTensor, NdArray, TorchTensor
from docarray.utils.misc import is_tf_available
tf_available = is_tf_available()
if tf_available:
import tensorflow as tf
import tensorflow._api.v2.experimental.numpy as ... | import numpy as np
import pytest
import torch
from docarray import BaseDocument
from docarray.typing import AnyTensor, NdArray, TorchTensor
try:
import tensorflow as tf
import tensorflow._api.v2.experimental.numpy as tnp # type: ignore
from docarray.typing import TensorFlowTensor
except (ImportError, Ty... |
import logging
from sentence_transformers import SparseEncoder
from sentence_transformers.sparse_encoder.evaluation import SparseNanoBEIREvaluator
logging.basicConfig(format="%(message)s", level=logging.INFO)
# Load a model
model = SparseEncoder("naver/splade-cocondenser-ensembledistil")
evaluator = SparseNanoBEIR... | import logging
from sentence_transformers import SparseEncoder
from sentence_transformers.sparse_encoder.evaluation import SparseNanoBEIREvaluator
logging.basicConfig(format="%(message)s", level=logging.INFO)
# Load a model
model = SparseEncoder("naver/splade-cocondenser-ensembledistil")
evaluator = SparseNanoBEIR... |
from . import InputExample
import gzip
class PairedFilesReader(object):
"""Reads in the a Pair Dataset, split in two files"""
def __init__(self, filepaths):
self.filepaths = filepaths
def get_examples(self, max_examples=0):
fIns = []
for filepath in self.filepaths:
fI... | from . import InputExample
import gzip
class PairedFilesReader(object):
"""
Reads in the a Pair Dataset, split in two files
"""
def __init__(self, filepaths):
self.filepaths = filepaths
def get_examples(self, max_examples=0):
""" """
fIns = []
for filepath in self... |
from .PhraseTokenizer import PhraseTokenizer
from .WhitespaceTokenizer import WhitespaceTokenizer
from .WordTokenizer import ENGLISH_STOP_WORDS, WordTokenizer
__all__ = ["WordTokenizer", "WhitespaceTokenizer", "PhraseTokenizer", "ENGLISH_STOP_WORDS"]
| from .WordTokenizer import WordTokenizer, ENGLISH_STOP_WORDS
from .WhitespaceTokenizer import WhitespaceTokenizer
from .PhraseTokenizer import PhraseTokenizer
__all__ = ["WordTokenizer", "WhitespaceTokenizer", "PhraseTokenizer", "ENGLISH_STOP_WORDS"]
|
_base_ = [
'../_base_/models/mask-rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
norm_cfg = dict(type='BN', requires_grad=True)
image_size = (640, 640)
batch_augments = [dict(type='BatchFixedSizePad', size=image_size)]
model =... | _base_ = [
'../_base_/models/mask-rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
norm_cfg = dict(type='BN', requires_grad=True)
image_size = (640, 640)
batch_augments = [dict(type='BatchFixedSizePad', size=image_size)]
model =... |
import torch
import torchaudio.prototype.functional as F
from parameterized import parameterized
from torch.autograd import gradcheck
from torchaudio_unittest.common_utils import TestBaseMixin
class AutogradTestImpl(TestBaseMixin):
@parameterized.expand(
[
(8000, (2, 3, 5, 7)),
(80... | import torch
import torchaudio.prototype.functional as F
from parameterized import parameterized
from torch.autograd import gradcheck, gradgradcheck
from torchaudio_unittest.common_utils import nested_params, TestBaseMixin
class AutogradTestImpl(TestBaseMixin):
@nested_params(
[F.convolve, F.fftconvolve],... |
# Copyright (c) OpenMMLab. All rights reserved.
import os
from importlib.util import find_spec as find_module
import numpy
import numpy.compat
import numpy.linalg as linalg
from mmengine.config import Config
from mmengine.fileio import LocalBackend as local
from mmengine.fileio import PetrelBackend
from ._base_.defau... | # Copyright (c) OpenMMLab. All rights reserved.
import os
from importlib.util import find_spec as find_module
import numpy
import numpy.compat
import numpy.linalg as linalg
from mmengine.config import Config
from mmengine.fileio import LocalBackend as local
from mmengine.fileio import PetrelBackend
from ._base_.defau... |
from __future__ import annotations
from typing import Literal
from sentence_transformers.losses.GISTEmbedLoss import GISTEmbedLoss
from sentence_transformers.sparse_encoder.SparseEncoder import SparseEncoder
class SparseGISTEmbedLoss(GISTEmbedLoss):
def __init__(
self,
model: SparseEncoder,
... | from __future__ import annotations
from typing import Literal
from sentence_transformers.losses.GISTEmbedLoss import GISTEmbedLoss
from sentence_transformers.sparse_encoder.SparseEncoder import SparseEncoder
class SparseGISTEmbedLoss(GISTEmbedLoss):
def __init__(
self,
model: SparseEncoder,
... |
from torch import Tensor
from torch import nn
from typing import Dict
import torch.nn.functional as F
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]):
fe... | from torch import Tensor
from torch import nn
from typing import Dict
import torch.nn.functional as F
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]):
... |
from typing import Dict, List, Optional, Tuple
from torch import Tensor
AVAILABLE_METRICS = ["mae", "rmse", "epe", "bad1", "bad2", "epe", "1px", "3px", "5px", "fl-all", "relepe"]
def compute_metrics(
flow_pred: Tensor, flow_gt: Tensor, valid_flow_mask: Optional[Tensor], metrics: List[str]
) -> Tuple[Dict[str, f... | from typing import Dict, List, Optional, Tuple
from torch import Tensor
AVAILABLE_METRICS = ["mae", "rmse", "epe", "bad1", "bad2", "epe", "1px", "3px", "5px", "fl-all", "relepe"]
def compute_metrics(
flow_pred: Tensor, flow_gt: Tensor, valid_flow_mask: Optional[Tensor], metrics: List[str]
) -> Tuple[Dict[str, f... |
# Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the most common options. For a full
# list see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
# -- Path setup --------------------------------------------------------------
# If ex... | # Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the most common options. For a full
# list see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
# -- Path setup --------------------------------------------------------------
# If ex... |
import os
import pkg_resources
from setuptools import setup, find_packages
setup(
name="whisper",
py_modules=["whisper"],
version="1.0",
description="Robust Speech Recognition via Large-Scale Weak Supervision",
readme="README.md",
python_requires=">=3.7",
author="OpenAI",
url="https://... | import os
import pkg_resources
from setuptools import setup, find_packages
setup(
name="whisper",
py_modules=["whisper"],
version="1.0",
description="",
author="OpenAI",
packages=find_packages(exclude=["tests*"]),
install_requires=[
str(r)
for r in pkg_resources.parse_requi... |
import contextlib
import logging
import typing
import fastapi
import fastapi.responses
import starlette.middleware.cors
import uvicorn
from autogpt_libs.feature_flag.client import (
initialize_launchdarkly,
shutdown_launchdarkly,
)
import backend.data.block
import backend.data.db
import backend.data.graph
imp... | import contextlib
import logging
import typing
import fastapi
import fastapi.responses
import starlette.middleware.cors
import uvicorn
from autogpt_libs.feature_flag.client import (
initialize_launchdarkly,
shutdown_launchdarkly,
)
import backend.data.block
import backend.data.db
import backend.data.graph
imp... |
import os
import re
from pathlib import Path
from typing import Optional, Tuple, Union
import torch
from torch.utils.data import Dataset
from torchaudio._internal import download_url_to_file
from torchaudio.datasets.utils import _extract_tar, _load_waveform
URL = "https://speech.fit.vutbr.cz/files/quesst14Database.t... | 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 _extract_tar, _load_waveform
URL = "https://speech.fit.vutbr.cz/files/quesst14Database.tgz"
SAMPLE_... |
import asyncio
import os
import random
import string
import tempfile
import time
import pytest
from jina import helper
@pytest.fixture(scope='function')
def random_workspace_name():
"""Generate a random workspace name with digits and letters."""
rand = ''.join(random.choices(string.ascii_uppercase + string.... | import asyncio
import os
import random
import string
import tempfile
import time
import pytest
from jina import helper
@pytest.fixture(scope='function')
def random_workspace_name():
"""Generate a random workspace name with digits and letters."""
rand = ''.join(random.choices(string.ascii_uppercase + string.... |
_base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
norm_cfg = dict(type='BN', requires_grad=True)
model = dict(
backbone=dict(norm_cfg=norm_cfg, norm_eval=False),
neck=dict(
type='F... | _base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
norm_cfg = dict(type='BN', requires_grad=True)
model = dict(
backbone=dict(norm_cfg=norm_cfg, norm_eval=False),
neck=dict(
type='F... |
from docarray import BaseDocument, DocumentArray
from docarray.document import AnyDocument
def test_generic_init():
class Text(BaseDocument):
text: str
da = DocumentArray[Text]([])
da.document_type == Text
assert isinstance(da, DocumentArray)
def test_normal_access_init():
da = Documen... | from docarray import DocumentArray, Document
from docarray.document import AnyDocument
def test_generic_init():
class Text(Document):
text: str
da = DocumentArray[Text]([])
da.document_type == Text
assert isinstance(da, DocumentArray)
def test_normal_access_init():
da = DocumentArray([... |
"""
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... |
import logging
import random
from datasets import load_dataset
from sentence_transformers import SparseEncoder
from sentence_transformers.sparse_encoder.evaluation import SparseInformationRetrievalEvaluator
logging.basicConfig(format="%(message)s", level=logging.INFO)
# Load a model
model = SparseEncoder("naver/spl... | import logging
import random
from datasets import load_dataset
from sentence_transformers.sparse_encoder import (
SparseEncoder,
SparseInformationRetrievalEvaluator,
)
logging.basicConfig(format="%(message)s", level=logging.INFO)
# Load a model
model = SparseEncoder("naver/splade-cocondenser-ensembledistil"... |
import datetime
import autogpt_libs.auth as autogpt_auth_lib
import fastapi.testclient
import pytest
import pytest_mock
import backend.server.model as server_model
import backend.server.v2.library.model as library_model
from backend.server.v2.library.routes import router as library_router
app = fastapi.FastAPI()
app... | import datetime
import autogpt_libs.auth as autogpt_auth_lib
import fastapi.testclient
import pytest
import pytest_mock
import backend.server.model as server_model
import backend.server.v2.library.model as library_model
from backend.server.v2.library.routes import router as library_router
app = fastapi.FastAPI()
app... |
"""Use a single chain to route an input to one of multiple retrieval qa chains."""
from __future__ import annotations
from collections.abc import Mapping
from typing import Any, Optional
from langchain_core.language_models import BaseLanguageModel
from langchain_core.prompts import PromptTemplate
from langchain_core... | """Use a single chain to route an input to one of multiple retrieval qa chains."""
from __future__ import annotations
from typing import Any, Dict, List, Mapping, Optional
from langchain_core.language_models import BaseLanguageModel
from langchain_core.prompts import PromptTemplate
from langchain_core.retrievers imp... |
_base_ = [
'../_base_/models/cascade-mask-rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
type='CascadeRCNN',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indice... | _base_ = [
'../_base_/models/cascade_mask_rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
type='CascadeRCNN',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indice... |
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... |
"""
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... |
"""
This examples trains BERT (or any other transformer model like RoBERTa, DistilBERT etc.) for the STSbenchmark from scratch.
It uses MatryoshkaLoss with the powerful CoSENTLoss to train models that perform well at output dimensions [768, 512, 256, 128, 64].
It generates sentence embeddings that can be compared using... | """
This examples trains BERT (or any other transformer model like RoBERTa, DistilBERT etc.) for the STSbenchmark from scratch.
It uses MatryoshkaLoss with the powerful CoSENTLoss to train models that perform well at output dimensions [768, 512, 256, 128, 64].
It generates sentence embeddings that can be compared using... |
from typing import Iterable, Dict
from docarray.array.storage.base.getsetdel import BaseGetSetDelMixin
from docarray.array.storage.base.helper import Offset2ID
from docarray import Document
class GetSetDelMixin(BaseGetSetDelMixin):
"""Provide concrete implementation for ``__getitem__``, ``__setitem__``,
and ... | from typing import Iterable, Dict
from ..base.getsetdel import BaseGetSetDelMixin
from ..base.helper import Offset2ID
from .... import Document
class GetSetDelMixin(BaseGetSetDelMixin):
"""Provide concrete implementation for ``__getitem__``, ``__setitem__``,
and ``__delitem__`` for ``DocumentArrayElastic``""... |
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... |
# THIS FILE HAS BEEN AUTOGENERATED. To update:
# 1. modify the `_deps` dict in setup.py
# 2. run `make deps_table_update`
deps = {
"Pillow": "Pillow",
"accelerate": "accelerate>=0.31.0",
"compel": "compel==0.1.8",
"datasets": "datasets",
"filelock": "filelock",
"flax": "flax>=0.4.1",
"hf-doc... | # THIS FILE HAS BEEN AUTOGENERATED. To update:
# 1. modify the `_deps` dict in setup.py
# 2. run `make deps_table_update`
deps = {
"Pillow": "Pillow",
"accelerate": "accelerate>=0.31.0",
"compel": "compel==0.1.8",
"datasets": "datasets",
"filelock": "filelock",
"flax": "flax>=0.4.1",
"hf-doc... |
_base_ = './cascade-mask-rcnn_convnext-t-p4-w7_fpn_4conv1fc-giou_amp-ms-crop-3x_coco.py' # noqa
# TODO: delete custom_imports after mmcls supports auto import
# please install mmcls>=1.0
# import mmcls.models to trigger register_module in mmcls
custom_imports = dict(imports=['mmcls.models'], allow_failed_imports=Fals... | _base_ = './cascade-mask-rcnn_convnext-t-p4-w7_fpn_4conv1fc-giou_amp-ms-crop-3x_coco.py' # noqa
# please install mmcls>=1.0
# import mmcls.models to trigger register_module in mmcls
custom_imports = dict(imports=['mmcls.models'], allow_failed_imports=False)
checkpoint_file = 'https://download.openmmlab.com/mmclassifi... |
_base_ = '../retinanet/retinanet_r50_fpn_1x_coco.py'
model = dict(
bbox_head=dict(
_delete_=True,
type='GARetinaHead',
num_classes=80,
in_channels=256,
stacked_convs=4,
feat_channels=256,
approx_anchor_generator=dict(
type='AnchorGenerator',
... | _base_ = '../retinanet/retinanet_r50_fpn_1x_coco.py'
model = dict(
bbox_head=dict(
_delete_=True,
type='GARetinaHead',
num_classes=80,
in_channels=256,
stacked_convs=4,
feat_channels=256,
approx_anchor_generator=dict(
type='AnchorGenerator',
... |
from keras.src.api_export import keras_export
from keras.src.layers.pooling.base_pooling import BasePooling
@keras_export(["keras.layers.MaxPooling2D", "keras.layers.MaxPool2D"])
class MaxPooling2D(BasePooling):
"""Max pooling operation for 2D spatial data.
Downsamples the input along its spatial dimensions ... | from keras.src.api_export import keras_export
from keras.src.layers.pooling.base_pooling import BasePooling
@keras_export(["keras.layers.MaxPooling2D", "keras.layers.MaxPool2D"])
class MaxPooling2D(BasePooling):
"""Max pooling operation for 2D spatial data.
Downsamples the input along its spatial dimensions ... |
import importlib.util
import warnings
from functools import wraps
from typing import Optional
def is_module_available(*modules: str) -> bool:
r"""Returns if a top-level module with :attr:`name` exists *without**
importing it. This is generally safer than try-catch block around a
`import X`. It avoids thir... | import importlib.util
import warnings
from functools import wraps
from typing import Optional
def is_module_available(*modules: str) -> bool:
r"""Returns if a top-level module with :attr:`name` exists *without**
importing it. This is generally safer than try-catch block around a
`import X`. It avoids thir... |
# model settings
preprocess_cfg = dict(
mean=[103.530, 116.280, 123.675],
std=[1.0, 1.0, 1.0],
to_rgb=False,
pad_size_divisor=32)
model = dict(
type='FastRCNN',
preprocess_cfg=preprocess_cfg,
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(... | # model settings
model = dict(
type='FastRCNN',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
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(ty... |
import asyncio
import random
import pytest
from docarray import Document, DocumentArray
from jina.helper import Namespace, random_identity
from jina.serve.stream import RequestStreamer
from jina.types.request.data import DataRequest
class RequestStreamerWrapper:
def __init__(self, num_requests, prefetch, iterat... | import asyncio
import random
import pytest
from docarray import Document, DocumentArray
from jina.helper import Namespace, random_identity
from jina.serve.stream import RequestStreamer
from jina.types.request.data import DataRequest
class RequestStreamerWrapper:
def __init__(self, num_requests, prefetch, iterat... |
from dataclasses import dataclass
from typing import Callable, Dict
from torchaudio._internal.module_utils import dropping_class_support
from ._vggish_impl import _SAMPLE_RATE, VGGish as _VGGish, VGGishInputProcessor as _VGGishInputProcessor
def _get_state_dict():
path = torchaudio.utils.download_asset("models... | from dataclasses import dataclass
from typing import Callable, Dict
import torch
import torchaudio
from ._vggish_impl import _SAMPLE_RATE, VGGish as _VGGish, VGGishInputProcessor as _VGGishInputProcessor
def _get_state_dict():
path = torchaudio.utils.download_asset("models/vggish.pt")
return torch.load(pat... |
"""Timescale Vector Auto-retrieval Pack."""
from datetime import timedelta
from typing import Any, Dict, List, Optional
from llama_index.core.indices.vector_store import VectorStoreIndex
from llama_index.core.indices.vector_store.retrievers import (
VectorIndexAutoRetriever,
)
from llama_index.core.llama_pack.bas... | """Timescale Vector Auto-retrieval Pack."""
from datetime import timedelta
from typing import Any, Dict, List, Optional
from llama_index.core.indices.vector_store import VectorStoreIndex
from llama_index.core.indices.vector_store.retrievers import (
VectorIndexAutoRetriever,
)
from llama_index.core.llama_pack.ba... |
"""Tool for the DataForSeo SERP API."""
from typing import Optional
from langchain_core.callbacks import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain_core.tools import BaseTool
from pydantic import Field
from langchain_community.utilities.dataforseo_api_search import DataForS... | """Tool for the DataForSeo SERP API."""
from typing import Optional
from langchain_core.callbacks import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain_core.tools import BaseTool
from pydantic import Field
from langchain_community.utilities.dataforseo_api_search import DataForS... |
_base_ = ['./mask2former_r50_lsj_8x2_50e_coco.py']
pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth' # noqa
depths = [2, 2, 6, 2]
model = dict(
type='Mask2Former',
backbone=dict(
_delete_=True,
type='SwinTransformer',
emb... | _base_ = ['./mask2former_r50_lsj_8x2_50e_coco.py']
pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth' # noqa
depths = [2, 2, 6, 2]
model = dict(
type='Mask2Former',
backbone=dict(
_delete_=True,
type='SwinTransformer',
emb... |
# Copyright (c) OpenMMLab. All rights reserved.
from unittest.mock import Mock
from mmengine.hooks import DistSamplerSeedHook
class TestDistSamplerSeedHook:
def test_before_epoch(self):
hook = DistSamplerSeedHook()
# Test dataset sampler
runner = Mock()
runner.epoch = 1
... | # Copyright (c) OpenMMLab. All rights reserved.
from unittest.mock import Mock
from mmengine.hooks import DistSamplerSeedHook
class TestDistSamplerSeedHook:
def test_before_epoch(self):
hook = DistSamplerSeedHook()
# Test dataset sampler
runner = Mock()
runner.epoch = 1
... |
import multiprocessing
import random
import time
from functools import partial
import pytest
from jina import Client, Document, DocumentArray, Executor, Flow, requests
from jina.types.request.data import Response
NUM_REQUESTS = 5
class MyExecutor(Executor):
@requests(on='/ping')
def ping(self, **kwargs):
... | import multiprocessing
import random
import time
from functools import partial
import pytest
from jina import Client, Document, DocumentArray, Executor, Flow, requests
from jina.types.request.data import Response
NUM_REQUESTS = 5
class MyExecutor(Executor):
@requests(on='/ping')
def ping(self, **kwargs):
... |
# mypy: allow-untyped-defs
import torch
import torch.utils._pytree as pytree
from torch._C import DispatchKey
from torch._higher_order_ops.utils import (
autograd_not_implemented,
reenter_make_fx,
unique_graph_id,
)
from torch._ops import HigherOrderOperator
from torch._subclasses.fake_tensor import FakeTen... | # mypy: allow-untyped-defs
import torch
import torch.utils._pytree as pytree
from torch._C import DispatchKey
from torch._higher_order_ops.utils import (
autograd_not_implemented,
reenter_make_fx,
unique_graph_id,
)
from torch._ops import HigherOrderOperator
from torch._subclasses.fake_tensor import FakeTen... |
# Licensed to the LF AI & Data foundation under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the "License");
# you may not use this fil... | import warnings
from typing import List, Optional, Tuple, TypeVar
from docarray.typing import AudioNdArray
from docarray.typing.bytes.audio_bytes import AudioBytes
from docarray.typing.proto_register import _register_proto
from docarray.typing.url.any_url import AnyUrl
from docarray.typing.url.mimetypes import AUDIO_M... |
# Copyright (c) OpenMMLab. All rights reserved.
__version__ = '0.7.4'
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__ = '0.7.3'
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 __future__ import annotations
from collections.abc import Iterable
from typing import Any
import torch
from torch import Tensor, nn
from sentence_transformers.SentenceTransformer import SentenceTransformer
from sentence_transformers.util import fullname
class CosineSimilarityLoss(nn.Module):
def __init__(... | from __future__ import annotations
from collections.abc import Iterable
from typing import Any
import torch
from torch import Tensor, nn
from sentence_transformers.SentenceTransformer import SentenceTransformer
from sentence_transformers.util import fullname
class CosineSimilarityLoss(nn.Module):
def __init__(... |
_base_ = '../faster_rcnn/faster-rcnn_r50_fpn_1x_coco.py'
# model settings
model = dict(
neck=[
dict(
type='FPN',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
num_outs=5),
dict(
type='BFP',
in_channels=256,
n... | _base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
# model settings
model = dict(
neck=[
dict(
type='FPN',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
num_outs=5),
dict(
type='BFP',
in_channels=256,
n... |
from sentence_transformers.models import Router
from sentence_transformers.sparse_encoder import SparseEncoder
from sentence_transformers.sparse_encoder.models import IDF, MLMTransformer, SpladePooling
print("# ------------------------------------------example with v2 distill-----------------------------------------")... | from sentence_transformers import models
from sentence_transformers.sparse_encoder import SparseEncoder
from sentence_transformers.sparse_encoder.models import IDF, MLMTransformer, SpladePooling
print("# ------------------------------------------example with v2 distill-----------------------------------------")
doc_en... |
from __future__ import annotations
from torch import Tensor, nn
from sentence_transformers.cross_encoder.CrossEncoder import CrossEncoder
class CrossEntropyLoss(nn.Module):
def __init__(self, model: CrossEncoder, activation_fct: nn.Module = nn.Identity(), **kwargs) -> None:
"""
Computes the Cros... | from __future__ import annotations
from torch import Tensor, nn
from sentence_transformers.cross_encoder import CrossEncoder
# TODO: Consider the naming of this class
class CrossEntropyLoss(nn.Module):
def __init__(self, model: CrossEncoder, activation_fct: nn.Module = nn.Identity(), **kwargs) -> None:
... |
# Copyright (c) OpenMMLab. All rights reserved.
from unittest.mock import Mock
import pytest
from mmengine.hooks import ParamSchedulerHook
class TestParamSchedulerHook:
error_msg = ('runner.param_schedulers should be list of ParamScheduler or '
'a dict containing list of ParamScheduler')
d... | # Copyright (c) OpenMMLab. All rights reserved.
from unittest.mock import Mock
from mmengine.hooks import ParamSchedulerHook
class TestParamSchedulerHook:
def test_after_iter(self):
hook = ParamSchedulerHook()
runner = Mock()
scheduler = Mock()
scheduler.step = Mock()
sch... |
"""
Tatoeba (https://tatoeba.org/) is a collection of sentences and translation, mainly aiming for language learning.
It is available for more than 300 languages.
This script downloads the Tatoeba corpus and extracts the sentences & translations in the languages you like
"""
import gzip
import os
import tarfile
impo... | """
Tatoeba (https://tatoeba.org/) is a collection of sentences and translation, mainly aiming for language learning.
It is available for more than 300 languages.
This script downloads the Tatoeba corpus and extracts the sentences & translations in the languages you like
"""
import os
import sentence_transformers
impo... |
"""Standard LangChain interface tests"""
from langchain_core.embeddings import Embeddings
from langchain_tests.unit_tests.embeddings import EmbeddingsUnitTests
from langchain_fireworks import FireworksEmbeddings
class TestFireworksStandard(EmbeddingsUnitTests):
@property
def embeddings_class(self) -> type[E... | """Standard LangChain interface tests"""
from typing import Tuple, Type
from langchain_core.embeddings import Embeddings
from langchain_tests.unit_tests.embeddings import EmbeddingsUnitTests
from langchain_fireworks import FireworksEmbeddings
class TestFireworksStandard(EmbeddingsUnitTests):
@property
def ... |
import numpy as np
import pytest
import torch
from pydantic import parse_obj_as
from docarray import BaseDoc
from docarray.documents import ImageDoc
from docarray.utils.misc import is_tf_available
tf_available = is_tf_available()
if tf_available:
import tensorflow as tf
import tensorflow._api.v2.experimental.... | import numpy as np
import pytest
import torch
from pydantic import parse_obj_as
from docarray import BaseDocument
from docarray.documents import ImageDoc
from docarray.utils.misc import is_tf_available
tf_available = is_tf_available()
if tf_available:
import tensorflow as tf
import tensorflow._api.v2.experime... |
from __future__ import annotations
import json
import os
from typing import Any
import torch
from torch import nn
class SpladePooling(nn.Module):
"""SPLADE pooling layer that aggregates MLM logits using max or sum pooling.
This pooling layer takes MLM logits (shape: batch_size, seq_length, vocab_size)
... | from __future__ import annotations
import json
import os
from typing import Any
import torch
from torch import nn
class SpladePooling(nn.Module):
"""SPLADE pooling layer that aggregates MLM logits using max or sum pooling.
This pooling layer takes MLM logits (shape: batch_size, seq_length, vocab_size)
... |
import base64
from email.message import EmailMessage
from typing import List, Optional, Type
from langchain_core.callbacks import CallbackManagerForToolRun
from pydantic import BaseModel, Field
from langchain_community.tools.gmail.base import GmailBaseTool
class CreateDraftSchema(BaseModel):
"""Input for Create... | import base64
from email.message import EmailMessage
from typing import List, Optional, Type
from langchain_core.callbacks import CallbackManagerForToolRun
from pydantic import BaseModel, Field
from langchain_community.tools.gmail.base import GmailBaseTool
class CreateDraftSchema(BaseModel):
"""Input for Create... |
from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.embeddings import (
DeterministicFakeEmbedding,
FakeEmbeddings,
)
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising depre... | from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.embeddings import (
DeterministicFakeEmbedding,
FakeEmbeddings,
)
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising depre... |
import functools
import inspect
import typing
from typing import Optional, Union
import grpc
from jina.helper import convert_tuple_to_list
if typing.TYPE_CHECKING:
from prometheus_client.context_managers import Timer
from prometheus_client import Summary
from contextlib import nullcontext
def _get_summary... | import functools
import inspect
import typing
from typing import Optional, Union
from jina.helper import convert_tuple_to_list
if typing.TYPE_CHECKING:
from prometheus_client.context_managers import Timer
from prometheus_client import Summary
from contextlib import nullcontext
def _get_summary_time_context... |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, ClassLabel, Features
from .base import TaskTemplate
@dataclass(frozen=True)
class AudioClassification(TaskTemplate):
task: str = field(default="audio-classification", metadata={"include_in_asdict_... | import copy
from dataclasses import dataclass
from typing import ClassVar, Dict
from ..features import Audio, ClassLabel, Features
from .base import TaskTemplate
@dataclass(frozen=True)
class AudioClassification(TaskTemplate):
task: str = "audio-classification"
input_schema: ClassVar[Features] = Features({"a... |
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) -> None:
"""
FlopsLoss implements a... | 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) -> None:
"""
FlopsLoss implements a... |
import asyncio
import json
import os
import time
import pytest
from jina import Client, Document
from jina.enums import PodRoleType, PollingType
from jina.helper import random_port
from jina.orchestrate.pods import Pod
from jina.orchestrate.pods.container import ContainerPod
from jina.parsers import set_gateway_parse... | import asyncio
import json
import os
import time
import pytest
from jina import Client, Document
from jina.enums import PodRoleType, PollingType
from jina.helper import random_port
from jina.orchestrate.pods import Pod
from jina.orchestrate.pods.container import ContainerPod
from jina.parsers import set_gateway_parse... |
import pytest
import torch
from mmdet.models.backbones.swin import SwinBlock, SwinTransformer
def test_swin_block():
# test SwinBlock structure and forward
block = SwinBlock(embed_dims=64, num_heads=4, feedforward_channels=256)
assert block.ffn.embed_dims == 64
assert block.attn.w_msa.num_heads == 4
... | import pytest
import torch
from mmdet.models.backbones.swin import SwinBlock, SwinTransformer
def test_swin_block():
# test SwinBlock structure and forward
block = SwinBlock(embed_dims=64, num_heads=4, feedforward_channels=256)
assert block.ffn.embed_dims == 64
assert block.attn.w_msa.num_heads == 4
... |
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