input stringlengths 33 5k | output stringlengths 32 5k |
|---|---|
import numpy as np
import pytest
from pydantic.tools import parse_obj_as, schema_json_of
from docarray.base_document.io.json import orjson_dumps
from docarray.typing import AnyEmbedding
@pytest.mark.proto
def test_proto_embedding():
embedding = parse_obj_as(AnyEmbedding, np.zeros((3, 224, 224)))
embedding.... | import numpy as np
from pydantic.tools import parse_obj_as, schema_json_of
from docarray.base_document.io.json import orjson_dumps
from docarray.typing import AnyEmbedding
def test_proto_embedding():
embedding = parse_obj_as(AnyEmbedding, np.zeros((3, 224, 224)))
embedding._to_node_protobuf()
def test_js... |
import sys
from os import path
from setuptools import find_packages
from setuptools import setup
if sys.version_info < (3, 7, 0):
raise OSError(f'DocArray requires Python >=3.7, but yours is {sys.version}')
try:
pkg_name = 'docarray'
libinfo_py = path.join(pkg_name, '__init__.py')
libinfo_content = o... | import sys
from os import path
from setuptools import find_packages
from setuptools import setup
if sys.version_info < (3, 7, 0):
raise OSError(f'DocArray requires Python >=3.7, but yours is {sys.version}')
try:
pkg_name = 'docarray'
libinfo_py = path.join(pkg_name, '__init__.py')
libinfo_content = o... |
from typing import TYPE_CHECKING, Any, Dict, List, Tuple, Type, TypeVar, Union, cast
import numpy as np
if TYPE_CHECKING:
from pydantic.fields import ModelField
from pydantic import BaseConfig
from docarray.document.base_node import BaseNode
from docarray.proto import NdArrayProto, NodeProto
T = TypeVar('T'... | from typing import TYPE_CHECKING, Any, Type, TypeVar, Union, cast
import numpy as np
if TYPE_CHECKING:
from pydantic.fields import ModelField
from pydantic import BaseConfig
from docarray.document.base_node import BaseNode
from docarray.proto import NdArrayProto, NodeProto
T = TypeVar('T', bound='Tensor')
... |
from __future__ import annotations
import gzip
from . import InputExample
class PairedFilesReader:
"""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 s... | from __future__ import annotations
import gzip
from . import InputExample
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 filep... |
import os
from typing import Dict
from hubble.executor.helper import is_valid_docker_uri, parse_hub_uri
from hubble.executor.hubio import HubIO
from jina import (
__default_executor__,
__default_grpc_gateway__,
__default_http_gateway__,
__default_websocket_gateway__,
__version__,
)
from jina.enums... | import os
from typing import Dict
from hubble.executor.helper import parse_hub_uri
from hubble.executor.hubio import HubIO
from jina import (
__default_executor__,
__default_grpc_gateway__,
__default_http_gateway__,
__default_websocket_gateway__,
__version__,
)
from jina.enums import PodRoleType
... |
"""Function calling agent."""
from typing import Any, List, Optional
from llama_index.core.agent.runner.base import AgentRunner, AgentState
from llama_index.core.agent.function_calling.step import (
FunctionCallingAgentWorker,
DEFAULT_MAX_FUNCTION_CALLS,
)
from llama_index.core.base.llms.types import ChatMess... | """Function calling agent."""
from typing import Any, List, Optional
from llama_index.core.agent.runner.base import AgentRunner, AgentState
from llama_index.core.agent.function_calling.step import (
FunctionCallingAgentWorker,
DEFAULT_MAX_FUNCTION_CALLS,
)
from llama_index.core.base.llms.types import ChatMess... |
from abc import ABC, abstractmethod
import warnings
from collections import namedtuple
from dataclasses import is_dataclass, asdict
from typing import Dict, Optional, TYPE_CHECKING, Union, List, Tuple
if TYPE_CHECKING:
from docarray.typing import DocumentArraySourceType, ArrayType
TypeMap = namedtuple('TypeMap', ... | from abc import ABC, abstractmethod
import warnings
from collections import namedtuple
from dataclasses import is_dataclass, asdict
from typing import Dict, Optional, TYPE_CHECKING, Union, List, Tuple
if TYPE_CHECKING:
from docarray.typing import DocumentArraySourceType, ArrayType
TypeMap = namedtuple('TypeMap', ... |
# Copyright (c) OpenMMLab. All rights reserved.
import logging
from typing import List, Optional, Sequence
import torch
from torch.nn.parameter import Parameter
from torch.nn.utils import clip_grad
from mmengine.registry import HOOKS
from .hook import Hook
DATA_BATCH = Optional[Sequence[dict]]
@HOOKS.register_modu... | # Copyright (c) OpenMMLab. All rights reserved.
import logging
from typing import List, Optional, Sequence
import torch
from torch.nn.parameter import Parameter
from torch.nn.utils import clip_grad
from mmengine.registry import HOOKS
from .hook import Hook
DATA_BATCH = Optional[Sequence[dict]]
@HOOKS.register_modu... |
from __future__ import annotations
import json
from typing import TYPE_CHECKING, List, Optional, Sequence, Type
from langchain_core.callbacks import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from pydantic import BaseModel, Field
from langchain_community.tools.playwright.base import BaseB... | from __future__ import annotations
import json
from typing import TYPE_CHECKING, List, Optional, Sequence, Type
from langchain_core.callbacks import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from pydantic import BaseModel, Field
from langchain_community.tools.playwright.base import BaseB... |
from __future__ import annotations
import csv
import logging
import os
import numpy as np
from sentence_transformers import InputExample
logger = logging.getLogger(__name__)
class CEBinaryAccuracyEvaluator:
"""
This evaluator can be used with the CrossEncoder class.
It is designed for CrossEncoders w... | from __future__ import annotations
import csv
import logging
import os
import numpy as np
from sentence_transformers import InputExample
logger = logging.getLogger(__name__)
class CEBinaryAccuracyEvaluator:
"""
This evaluator can be used with the CrossEncoder class.
It is designed for CrossEncoders w... |
from torchaudio._internal.module_utils import dropping_support
_CTC_DECODERS = [
"CTCHypothesis",
"CTCDecoder",
"CTCDecoderLM",
"CTCDecoderLMState",
"ctc_decoder",
"download_pretrained_files",
]
_CUDA_CTC_DECODERS = [
"CUCTCDecoder",
"CUCTCHypothesis",
"cuda_ctc_decoder",
]
def __g... | _CTC_DECODERS = [
"CTCHypothesis",
"CTCDecoder",
"CTCDecoderLM",
"CTCDecoderLMState",
"ctc_decoder",
"download_pretrained_files",
]
_CUDA_CTC_DECODERS = [
"CUCTCDecoder",
"CUCTCHypothesis",
"cuda_ctc_decoder",
]
def __getattr__(name: str):
if name in _CTC_DECODERS:
try:... |
"""Run smoke tests"""
import os
from pathlib import Path
from sys import platform
import torch
import torch.nn as nn
import torchvision
from torchvision.io import read_image
from torchvision.models import resnet50, ResNet50_Weights
SCRIPT_DIR = Path(__file__).parent
def smoke_test_torchvision() -> None:
print(... | """Run smoke tests"""
import os
from pathlib import Path
import torch
import torch.nn as nn
import torchvision
from torchvision.io import read_image
from torchvision.models import resnet50, ResNet50_Weights
SCRIPT_DIR = Path(__file__).parent
def smoke_test_torchvision() -> None:
print(
"Is torchvision ... |
import json
import logging
import os
from collections import defaultdict
from pathlib import Path
from huggingface_hub import HfApi
import diffusers
PATH_TO_REPO = Path(__file__).parent.parent.resolve()
ALWAYS_TEST_PIPELINE_MODULES = [
"controlnet",
"stable_diffusion",
"stable_diffusion_2",
"stable_... | import json
import logging
import os
from collections import defaultdict
from pathlib import Path
from huggingface_hub import HfApi
import diffusers
PATH_TO_REPO = Path(__file__).parent.parent.resolve()
ALWAYS_TEST_PIPELINE_MODULES = [
"controlnet",
"stable_diffusion",
"stable_diffusion_2",
"stable_... |
import numpy as np
import pytest
from docarray.computation.numpy_backend import NumpyCompBackend
def test_to_device():
with pytest.raises(NotImplementedError):
NumpyCompBackend.to_device(np.random.rand(10, 3), 'meta')
@pytest.mark.parametrize(
'array,result',
[
(np.zeros((5)), 1),
... | import numpy as np
import pytest
from docarray.computation.numpy_backend import NumpyCompBackend
def test_to_device():
with pytest.raises(NotImplementedError):
NumpyCompBackend.to_device(np.random.rand(10, 3), 'meta')
@pytest.mark.parametrize(
'array,result',
[
(np.zeros((5)), 1),
... |
# Copyright (c) OpenMMLab. All rights reserved.
from .builder import DATASETS, PIPELINES, build_dataset
from .cityscapes import CityscapesDataset
from .coco import CocoDataset
from .coco_panoptic import CocoPanopticDataset
from .dataset_wrappers import MultiImageMixDataset
from .deepfashion import DeepFashionDataset
fr... | # Copyright (c) OpenMMLab. All rights reserved.
from .builder import DATASETS, PIPELINES, build_dataset
from .cityscapes import CityscapesDataset
from .coco import CocoDataset
from .coco_panoptic import CocoPanopticDataset
from .custom import CustomDataset
from .dataset_wrappers import MultiImageMixDataset
from .deepfa... |
"""Test EdenAi's text to speech Tool .
In order to run this test, you need to have an EdenAI api key.
You can get it by registering for free at https://app.edenai.run/user/register.
A test key can be found at https://app.edenai.run/admin/account/settings by
clicking on the 'sandbox' toggle.
(calls will be free, and wi... | """Test EdenAi's text to speech Tool .
In order to run this test, you need to have an EdenAI api key.
You can get it by registering for free at https://app.edenai.run/user/register.
A test key can be found at https://app.edenai.run/admin/account/settings by
clicking on the 'sandbox' toggle.
(calls will be free, and wi... |
from langchain_core.load.serializable import (
BaseSerialized,
Serializable,
SerializedConstructor,
SerializedNotImplemented,
SerializedSecret,
to_json_not_implemented,
try_neq_default,
)
__all__ = [
"BaseSerialized",
"Serializable",
"SerializedConstructor",
"SerializedNotIm... | from langchain_core.load.serializable import (
BaseSerialized,
Serializable,
SerializedConstructor,
SerializedNotImplemented,
SerializedSecret,
to_json_not_implemented,
try_neq_default,
)
__all__ = [
"BaseSerialized",
"SerializedConstructor",
"SerializedSecret",
"SerializedN... |
import os
import warnings
from pathlib import Path
import torch
from torchaudio._internal import module_utils as _mod_utils # noqa: F401
_LIB_DIR = Path(__file__).parent / "lib"
def _get_lib_path(lib: str):
suffix = "pyd" if os.name == "nt" else "so"
path = _LIB_DIR / f"{lib}.{suffix}"
return path
de... | import os
import warnings
from pathlib import Path
import torch
from torchaudio._internal import module_utils as _mod_utils # noqa: F401
_LIB_DIR = Path(__file__).parent / "lib"
def _get_lib_path(lib: str):
suffix = "pyd" if os.name == "nt" else "so"
path = _LIB_DIR / f"{lib}.{suffix}"
return path
de... |
"""
This example runs a BiLSTM after the word embedding lookup. The output of the BiLSTM is than pooled,
for example with max-pooling (which gives a system like InferSent) or with mean-pooling.
Note, you can also pass BERT embeddings to the BiLSTM.
"""
import logging
import traceback
from datetime import datetime
fr... | """
This example runs a BiLSTM after the word embedding lookup. The output of the BiLSTM is than pooled,
for example with max-pooling (which gives a system like InferSent) or with mean-pooling.
Note, you can also pass BERT embeddings to the BiLSTM.
"""
import logging
import traceback
from datetime import datetime
fr... |
_base_ = [
'../_base_/models/ssd300.py', '../_base_/datasets/openimages_detection.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_1x.py'
]
model = dict(
bbox_head=dict(
num_classes=601,
anchor_generator=dict(basesize_ratio_range=(0.2, 0.9))))
# dataset settings
dataset_typ... | _base_ = [
'../_base_/models/ssd300.py', '../_base_/datasets/openimages_detection.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_1x.py'
]
model = dict(
bbox_head=dict(
num_classes=601,
anchor_generator=dict(basesize_ratio_range=(0.2, 0.9))))
# dataset settings
dataset_typ... |
"""Autoretriever prompts."""
from llama_index.core.prompts.base import PromptTemplate
from llama_index.core.prompts.prompt_type import PromptType
from llama_index.core.vector_stores.types import (
FilterOperator,
MetadataFilter,
MetadataInfo,
VectorStoreInfo,
VectorStoreQuerySpec,
)
# NOTE: these ... | """Autoretriever prompts."""
from llama_index.core.prompts.base import PromptTemplate
from llama_index.core.prompts.prompt_type import PromptType
from llama_index.core.vector_stores.types import (
FilterOperator,
MetadataFilter,
MetadataInfo,
VectorStoreInfo,
VectorStoreQuerySpec,
)
# NOTE: these... |
from typing import Any, Optional, Union, cast
from langchain_core._api import deprecated
from langchain_core.language_models import BaseLanguageModel
from langchain_core.messages import HumanMessage, SystemMessage
from langchain_core.output_parsers import BaseLLMOutputParser
from langchain_core.output_parsers.openai_f... | from typing import Any, Optional, Union, cast
from langchain_core._api import deprecated
from langchain_core.language_models import BaseLanguageModel
from langchain_core.messages import HumanMessage, SystemMessage
from langchain_core.output_parsers import BaseLLMOutputParser
from langchain_core.output_parsers.openai_f... |
_base_ = './faster-rcnn_r50_fpn_8xb8-amp-lsj-200e_coco.py'
model = dict(
backbone=dict(
depth=18,
init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet18')),
neck=dict(in_channels=[64, 128, 256, 512]))
| _base_ = './faster_rcnn_r50_fpn_lsj_200e_8x8_fp16_coco.py'
model = dict(
backbone=dict(
depth=18,
init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet18')),
neck=dict(in_channels=[64, 128, 256, 512]))
|
from typing import Any, Dict, List, Optional, Sequence, Tuple
from llama_index.core.base.llms.types import ChatMessage, MessageRole
from llama_index.core.base.llms.generic_utils import get_from_param_or_env
DEFAULT_FIREWORKS_API_BASE = "https://api.fireworks.ai/inference/v1"
DEFAULT_FIREWORKS_API_VERSION = ""
LLAMA_... | from typing import Any, Dict, List, Optional, Sequence, Tuple
from llama_index.core.base.llms.types import ChatMessage, MessageRole
from llama_index.core.base.llms.generic_utils import get_from_param_or_env
DEFAULT_FIREWORKS_API_BASE = "https://api.fireworks.ai/inference/v1"
DEFAULT_FIREWORKS_API_VERSION = ""
LLAMA_... |
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... | 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... |
from __future__ import annotations
import logging
from typing import TYPE_CHECKING, Any, Literal
from sentence_transformers.evaluation import EmbeddingSimilarityEvaluator
if TYPE_CHECKING:
import numpy as np
from torch import Tensor
from sentence_transformers.similarity_functions import SimilarityFuncti... | from __future__ import annotations
import logging
from typing import TYPE_CHECKING, Any
from sentence_transformers.evaluation import EmbeddingSimilarityEvaluator
if TYPE_CHECKING:
import numpy as np
from torch import Tensor
from sentence_transformers.sparse_encoder.SparseEncoder import SparseEncoder
l... |
NEWS_DOCS = """API documentation:
Endpoint: https://newsapi.org
Top headlines /v2/top-headlines
This endpoint provides live top and breaking headlines for a country, specific category in a country, single source, or multiple sources. You can also search with keywords. Articles are sorted by the earliest date published... | # flake8: noqa
NEWS_DOCS = """API documentation:
Endpoint: https://newsapi.org
Top headlines /v2/top-headlines
This endpoint provides live top and breaking headlines for a country, specific category in a country, single source, or multiple sources. You can also search with keywords. Articles are sorted by the earliest... |
from jina import DocumentArray, Executor, Flow, requests
def test_gateway_metric_labels(monkeypatch_metric_exporter):
collect_metrics, read_metrics = monkeypatch_metric_exporter
class FirstExec(Executor):
@requests()
def meow(self, docs, **kwargs):
return DocumentArray.empty(3)
... | from jina import DocumentArray, Executor, Flow, requests
def test_gateway_metric_labels(monkeypatch_metric_exporter):
collect_metrics, read_metrics = monkeypatch_metric_exporter
class FirstExec(Executor):
@requests()
def meow(self, docs, **kwargs):
return DocumentArray.empty(3)
... |
# In[1]:
import pandas as pd
# In[2]:
# from https://github.com/pytorch/audio/blob/main/.github/process_commit.py
primary_labels_mapping = {
"BC-breaking": "Backward-incompatible changes",
"deprecation": "Deprecations",
"bug fix": "Bug Fixes",
"new feature": "New Features",
"improvement": "Imp... | # In[1]:
import pandas as pd
# In[2]:
# from https://github.com/pytorch/audio/blob/main/.github/process_commit.py
primary_labels_mapping = {
"BC-breaking": "Backward-incompatible changes",
"deprecation": "Deprecations",
"bug fix": "Bug Fixes",
"new feature": "New Features",
"improvement": "Imp... |
from langchain_core.prompts import __all__
EXPECTED_ALL = [
"AIMessagePromptTemplate",
"BaseChatPromptTemplate",
"BasePromptTemplate",
"ChatMessagePromptTemplate",
"ChatPromptTemplate",
"DictPromptTemplate",
"FewShotPromptTemplate",
"FewShotPromptWithTemplates",
"FewShotChatMessageP... | from langchain_core.prompts import __all__
EXPECTED_ALL = [
"AIMessagePromptTemplate",
"BaseChatPromptTemplate",
"BasePromptTemplate",
"ChatMessagePromptTemplate",
"ChatPromptTemplate",
"FewShotPromptTemplate",
"FewShotPromptWithTemplates",
"FewShotChatMessagePromptTemplate",
"forma... |
"""Pydantic v1 compatibility shim."""
from importlib import metadata
from langchain_core._api.deprecation 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 ... | from importlib import metadata
from langchain_core._api.deprecation 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 ... |
_base_ = [
'./faster_rcnn_r50_dc5.py', './mot_challenge.py',
'../../../configs/_base_/default_runtime.py'
]
model = dict(
type='SELSA',
pretrains=None,
detector=dict(
backbone=dict(depth=18, base_channels=2),
roi_head=dict(
type='SelsaRoIHead',
bbox_head=dict(... | _base_ = [
'./faster_rcnn_r50_dc5.py', './mot_challenge.py',
'../../../configs/_base_/default_runtime.py'
]
model = dict(
type='SELSA',
pretrains=None,
detector=dict(
pretrained='torchvision://resnet101',
backbone=dict(depth=101),
roi_head=dict(
type='SelsaRoIHead... |
_base_ = './faster-rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| _base_ = './faster_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
|
# Copyright (c) OpenMMLab. All rights reserved.
"""MMEngine provides 11 root registries to support using modules across
projects.
More datails can be found at
https://mmengine.readthedocs.io/en/latest/tutorials/registry.html.
"""
from .registry import Registry, build_runner_from_cfg
# manage all kinds of runners lik... | # Copyright (c) OpenMMLab. All rights reserved.
"""MMEngine provides 11 root registries to support using modules across
projects.
More datails can be found at
https://mmengine.readthedocs.io/en/latest/tutorials/registry.html.
"""
from .registry import Registry, build_runner_from_cfg
# manage all kinds of runners lik... |
import copy as cp
from dataclasses import fields
from functools import lru_cache
from typing import TYPE_CHECKING, Optional, Tuple, Dict
from docarray.dataclasses import is_multimodal
from docarray.helper import typename
if TYPE_CHECKING: # pragma: no cover
from docarray.typing import T
@lru_cache()
def _get_f... | import copy as cp
from dataclasses import fields
from functools import lru_cache
from typing import TYPE_CHECKING, Optional, Tuple, Dict
from docarray.dataclasses import is_multimodal
from docarray.helper import typename
if TYPE_CHECKING:
from docarray.typing import T
@lru_cache()
def _get_fields(dc):
retur... |
# 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... | # Copyright (c) OpenMMLab. All rights reserved.
__version__ = '2.21.0'
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.
from mmengine.registry import HOOKS
from .hook import Hook
@HOOKS.register_module()
class DistSamplerSeedHook(Hook):
"""Data-loading sampler for distributed training.
When distributed training, it is only useful in conjunction with
:obj:`EpochBasedRunner`, ... | # Copyright (c) OpenMMLab. All rights reserved.
from mmengine.registry import HOOKS
from .hook import Hook
@HOOKS.register_module()
class DistSamplerSeedHook(Hook):
"""Data-loading sampler for distributed training.
When distributed training, it is only useful in conjunction with
:obj:`EpochBasedRunner`, ... |
#!/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... |
import inspect
import pytest
from datasets.splits import Split, SplitDict, SplitInfo
from datasets.utils.py_utils import asdict
@pytest.mark.parametrize(
"split_dict",
[
SplitDict(),
SplitDict({"train": SplitInfo(name="train", num_bytes=1337, num_examples=42, dataset_name="my_dataset")}),
... | import pytest
from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict
@pytest.mark.parametrize(
"split_dict",
[
SplitDict(),
SplitDict({"train": SplitInfo(name="train", num_bytes=1337, num_examples=42, dataset_name="my_dataset")}),
SplitDict({"train... |
# Copyright (c) OpenMMLab. All rights reserved.
"""Get image metas on a specific dataset.
Here is an example to run this script.
Example:
python tools/misc/get_image_metas.py ${CONFIG} \
--out ${OUTPUT FILE NAME}
"""
import argparse
import csv
import os.path as osp
from multiprocessing import Pool
import mmc... | # Copyright (c) OpenMMLab. All rights reserved.
"""Get test image metas on a specific dataset.
Here is an example to run this script.
Example:
python tools/misc/get_image_metas.py ${CONFIG} \
--out ${OUTPUT FILE NAME}
"""
import argparse
import csv
import os.path as osp
from multiprocessing import Pool
impor... |
"""
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... |
# Copyright (c) OpenMMLab. All rights reserved.
from typing import Optional
import torch
import torch.nn as nn
from mmengine.runner import load_checkpoint
from torch import Tensor
from mmdet.core import ConfigType, OptConfigType, SampleList
from mmdet.registry import MODELS
from ..utils.misc import unpack_gt_instance... | # Copyright (c) OpenMMLab. All rights reserved.
from typing import Optional
import torch
import torch.nn as nn
from mmengine.runner import load_checkpoint
from torch import Tensor
from mmdet.core import ConfigType, OptConfigType, SampleList
from mmdet.registry import MODELS
from .kd_one_stage import KnowledgeDistilla... |
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class HfFileSystem(AbstractFileSystem):
"""Interfa... | from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class HfFileSystem(AbstractFileSystem):
"""Interfa... |
from jina.schemas.helper import _cli_to_schema
from jina_cli.export import api_to_dict
for s in ('flow', 'gateway', 'executor', 'deployment'):
a = _cli_to_schema(api_to_dict(), s)
table = ['| Name | Description | Type | Default |', '|----|----|----|----|']
for k, v in a[f'Jina::{s.capitalize()}']['proper... | from jina.schemas.helper import _cli_to_schema
from jina_cli.export import api_to_dict
for s in ('flow', 'gateway', 'executor'):
a = _cli_to_schema(api_to_dict(), s)
table = ['| Name | Description | Type | Default |', '|----|----|----|----|']
for k, v in a[f'Jina::{s.capitalize()}']['properties'].items()... |
from collections.abc import Mapping
from operator import itemgetter
from typing import Any, Callable, Optional, Union
from langchain_core.messages import BaseMessage
from langchain_core.output_parsers.openai_functions import JsonOutputFunctionsParser
from langchain_core.runnables import RouterRunnable, Runnable
from l... | from collections.abc import Mapping
from operator import itemgetter
from typing import Any, Callable, Optional, Union
from langchain_core.messages import BaseMessage
from langchain_core.output_parsers.openai_functions import JsonOutputFunctionsParser
from langchain_core.runnables import RouterRunnable, Runnable
from l... |
"""
Top-level module of Jina.
The primary function of this module is to import all of the public Jina
interfaces into a single place. The interfaces themselves are located in
sub-modules, as described below.
"""
import os as _os
import platform as _platform
import signal as _signal
import sys as _sys
import warnings... | """
Top-level module of Jina.
The primary function of this module is to import all of the public Jina
interfaces into a single place. The interfaces themselves are located in
sub-modules, as described below.
"""
import os as _os
import platform as _platform
import signal as _signal
import sys as _sys
import warnings... |
from typing import Union
from docarray.typing.tensor.ndarray import NdArray
from docarray.utils._internal.misc import is_tf_available, is_torch_available
torch_available = is_torch_available()
if torch_available:
from docarray.typing.tensor.torch_tensor import TorchTensor # noqa: F401
tf_available = is_tf_avai... | from typing import Union
from docarray.typing.tensor.ndarray import NdArray
from docarray.utils.misc import is_tf_available, is_torch_available
torch_available = is_torch_available()
if torch_available:
from docarray.typing.tensor.torch_tensor import TorchTensor # noqa: F401
tf_available = is_tf_available()
if... |
_base_ = './fcos_r50-caffe_fpn_gn-head_1x_coco.py'
# model settings
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron/resnet101_caffe')))
# dataset settings
train_pipeline = [
dict(type='LoadImageFromFile', back... | _base_ = './fcos_r50-caffe_fpn_gn-head_1x_coco.py'
# model settings
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron/resnet101_caffe')))
# dataset settings
train_pipeline = [
dict(type='LoadImageFromFile', back... |
import numpy as np
from docarray import BaseDoc, DocList
from docarray.typing import NdArray
from pydantic import Field, BaseModel
from jina import Executor, requests
class TextDoc(BaseDoc):
text: str = Field(description="The text of the document", default="")
class EmbeddingResponseModel(TextDoc):
embeddi... | import numpy as np
from docarray import BaseDoc, DocList
from docarray.typing import NdArray
from pydantic import Field
from jina import Executor, requests
class TextDoc(BaseDoc):
text: str = Field(description="The text of the document", default="")
class EmbeddingResponseModel(TextDoc):
embeddings: NdArra... |
__copyright__ = "Copyright (c) 2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
from typing import Optional, List, Union, Dict
import numpy as np
from annoy import AnnoyIndex
from jina import Executor, requests, DocumentArray, Document
from jina_commons import get_logger
from jina_commons.index... | __copyright__ = "Copyright (c) 2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
from typing import Optional, List, Union, Dict
import numpy as np
from annoy import AnnoyIndex
from jina import Executor, requests, DocumentArray, Document
from jina_commons import get_logger
from jina_commons.index... |
"""Test in memory docstore."""
from typing import Any
from langchain.output_parsers.combining import CombiningOutputParser
from langchain.output_parsers.regex import RegexParser
from langchain.output_parsers.structured import ResponseSchema, StructuredOutputParser
DEF_EXPECTED_RESULT = {
"answer": "Paris",
"... | """Test in memory docstore."""
from typing import Any, Dict
from langchain.output_parsers.combining import CombiningOutputParser
from langchain.output_parsers.regex import RegexParser
from langchain.output_parsers.structured import ResponseSchema, StructuredOutputParser
DEF_EXPECTED_RESULT = {
"answer": "Paris",... |
"""Argparser module for Deployment runtimes"""
import argparse
from jina.enums import DeploymentRoleType
from jina.parsers.helper import _SHOW_ALL_ARGS, KVAppendAction, add_arg_group
from jina.parsers.orchestrate.runtimes.remote import _mixin_http_server_parser
def mixin_base_deployment_parser(parser):
"""Add mi... | """Argparser module for Deployment runtimes"""
import argparse
from jina.enums import DeploymentRoleType
from jina.parsers.helper import _SHOW_ALL_ARGS, KVAppendAction, add_arg_group
def mixin_base_deployment_parser(parser):
"""Add mixin arguments required by :class:`BaseDeployment` into the given parser.
:... |
# Copyright (c) OpenMMLab. All rights reserved.
"""Image Demo.
This script adopts a new infenence class, currently supports image path,
np.array and folder input formats, and will support video and webcam
in the future.
Example:
Save visualizations and predictions results::
python demo/image_demo.py demo... | # Copyright (c) OpenMMLab. All rights reserved.
"""Image Demo.
This script adopts a new infenence class, currently supports image path,
np.array and folder input formats, and will support video and webcam
in the future.
Example:
Save visualizations and predictions results::
python demo/image_demo.py demo... |
#!/usr/bin/env python
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LI... | #!/usr/bin/env python
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LI... |
# Copyright (c) OpenMMLab. All rights reserved.
from functools import partial
from typing import Optional
import torch
TORCH_VERSION = torch.__version__
def is_rocm_pytorch() -> bool:
"""Check whether the PyTorch is compiled on ROCm."""
is_rocm = False
if TORCH_VERSION != 'parrots':
try:
... | # Copyright (c) OpenMMLab. All rights reserved.
from functools import partial
from typing import Optional
import torch
TORCH_VERSION = torch.__version__
def is_rocm_pytorch() -> bool:
is_rocm = False
if TORCH_VERSION != 'parrots':
try:
from torch.utils.cpp_extension import ROCM_HOME
... |
from __future__ import annotations
import os
import platform
import tempfile
import pytest
from sentence_transformers import CrossEncoder, SentenceTransformer
from sentence_transformers.models import Pooling, Transformer
from sentence_transformers.util import is_datasets_available
if is_datasets_available():
fr... | import os
import platform
import tempfile
import pytest
from sentence_transformers import CrossEncoder, SentenceTransformer
from sentence_transformers.models import Pooling, Transformer
from sentence_transformers.util import is_datasets_available
if is_datasets_available():
from datasets import DatasetDict, load... |
import pytest
from sklearn.base import BaseEstimator, RegressorMixin, TransformerMixin
from sklearn.utils._tags import get_tags
class NoTagsEstimator:
pass
class ClassifierEstimator:
# This is to test whether not inheriting from mixins works.
_estimator_type = "classifier"
@pytest.mark.parametrize(
... | import pytest
from sklearn.base import BaseEstimator
from sklearn.utils._tags import (
_DEFAULT_TAGS,
_safe_tags,
)
class NoTagsEstimator:
pass
class MoreTagsEstimator:
def _more_tags(self):
return {"allow_nan": True}
@pytest.mark.parametrize(
"estimator, err_msg",
[
(Base... |
# coding=utf-8
# Copyright 2025 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... | # coding=utf-8
# Copyright 2024 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... |
import pytest
from docarray import DocumentArray, Document
from docarray.array.weaviate import DocumentArrayWeaviate
import numpy as np
@pytest.fixture()
def docs():
return DocumentArray([Document(id=f'{i}') for i in range(1, 10)])
@pytest.mark.parametrize(
'to_delete',
[
0,
1,
... | import pytest
from docarray import DocumentArray, Document
from docarray.array.weaviate import DocumentArrayWeaviate
@pytest.fixture()
def docs():
return DocumentArray([Document(id=f'{i}') for i in range(1, 10)])
@pytest.mark.parametrize(
'to_delete',
[
0,
1,
4,
-1,
... |
from abc import ABC
from docarray.array.storage.annlite.backend import BackendMixin, AnnliteConfig
from docarray.array.storage.annlite.find import FindMixin
from docarray.array.storage.annlite.getsetdel import GetSetDelMixin
from docarray.array.storage.annlite.seqlike import SequenceLikeMixin
__all__ = ['StorageMixin... | from abc import ABC
from .backend import BackendMixin, AnnliteConfig
from .find import FindMixin
from .getsetdel import GetSetDelMixin
from .seqlike import SequenceLikeMixin
__all__ = ['StorageMixins', 'AnnliteConfig']
class StorageMixins(FindMixin, BackendMixin, GetSetDelMixin, SequenceLikeMixin, ABC):
...
|
import enum
from collections.abc import Sequence
from typing import TypeVar
T = TypeVar("T", bound=enum.Enum)
class StrEnumMeta(enum.EnumMeta):
auto = enum.auto
def from_str(self: type[T], member: str) -> T: # type: ignore[misc]
try:
return self[member]
except KeyError:
... | import enum
from typing import Sequence, Type, TypeVar
T = TypeVar("T", bound=enum.Enum)
class StrEnumMeta(enum.EnumMeta):
auto = enum.auto
def from_str(self: Type[T], member: str) -> T: # type: ignore[misc]
try:
return self[member]
except KeyError:
# TODO: use `add_... |
# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause
import functools
import warnings
from inspect import signature
__all__ = ["deprecated"]
class deprecated:
"""Decorator to mark a function or class as deprecated.
Issue a warning when the function is called/the class is instantia... | # Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause
import functools
import warnings
from inspect import signature
__all__ = ["deprecated"]
class deprecated:
"""Decorator to mark a function or class as deprecated.
Issue a warning when the function is called/the class is instantia... |
"""
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 re
from collections.abc import Sequence
from typing import Optional
from langchain_core.messages import BaseMessage
def _is_openai_data_block(block: dict) -> bool:
"""Check if the block contains multimodal data in OpenAI Chat Completions format."""
if block.get("type") == "image_url":
if (
... | import re
from collections.abc import Sequence
from typing import Optional
from langchain_core.messages import BaseMessage
def _is_openai_data_block(block: dict) -> bool:
"""Check if the block contains multimodal data in OpenAI Chat Completions format."""
if block.get("type") == "image_url":
if (
... |
"""FastAPI framework, high performance, easy to learn, fast to code, ready for production"""
__version__ = "0.115.7"
from starlette import status as status
from .applications import FastAPI as FastAPI
from .background import BackgroundTasks as BackgroundTasks
from .datastructures import UploadFile as UploadFile
from... | """FastAPI framework, high performance, easy to learn, fast to code, ready for production"""
__version__ = "0.115.6"
from starlette import status as status
from .applications import FastAPI as FastAPI
from .background import BackgroundTasks as BackgroundTasks
from .datastructures import UploadFile as UploadFile
from... |
from torio.io import CodecConfig, StreamingMediaDecoder as StreamReader, StreamingMediaEncoder as StreamWriter
from torchaudio._internal.module_utils import dropping_support
from ._effector import AudioEffector
from ._playback import play_audio as _play_audio
CodecConfig.__init__ = dropping_support(CodecConfig.__init... | from torio.io import CodecConfig, StreamingMediaDecoder as StreamReader, StreamingMediaEncoder as StreamWriter
from ._effector import AudioEffector
from ._playback import play_audio
__all__ = [
"AudioEffector",
"StreamReader",
"StreamWriter",
"CodecConfig",
"play_audio",
]
|
_base_ = './solo_r50_fpn_1x_coco.py'
train_pipeline = [
dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}),
dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
dict(
type='RandomChoiceResize',
scales=[(1333, 800), (1333, 768), (1333, 736), (1333, 704),
... | _base_ = './solo_r50_fpn_1x_coco.py'
train_pipeline = [
dict(
type='LoadImageFromFile',
file_client_args={{_base_.file_client_args}}),
dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
dict(
type='RandomChoiceResize',
scales=[(1333, 800), (1333, 768), (1333, 736)... |
from typing import Any, Dict, List, Optional
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document
class MetalReader(BaseReader):
"""
Metal reader.
Args:
api_key (str): Metal API key.
client_id (str): Metal client ID.
index_id (str): Me... | from typing import Any, Dict, List, Optional
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document
class MetalReader(BaseReader):
"""Metal reader.
Args:
api_key (str): Metal API key.
client_id (str): Metal client ID.
index_id (str): Metal i... |
# 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 torch
from docarray.computation.numpy_backend import NumpyCompBackend
from docarray.computation.torch_backend import TorchCompBackend
np_metrics = NumpyCompBackend.Metrics
torch_metrics = TorchCompBackend.Metrics
def test_cosine_sim_compare():
a = torch.rand(128)
b = torch.rand(128)
torch.testing... |
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... |
from enum import Enum
from typing import Dict, Iterable
import torch.nn.functional as F
from torch import Tensor, nn
from sentence_transformers.SentenceTransformer import SentenceTransformer
class TripletDistanceMetric(Enum):
"""The metric for the triplet loss"""
COSINE = lambda x, y: 1 - F.cosine_similari... | import torch
from torch import nn, Tensor
from typing import Union, Tuple, List, Iterable, Dict
import torch.nn.functional as F
from enum import Enum
from ..SentenceTransformer import SentenceTransformer
class TripletDistanceMetric(Enum):
"""
The metric for the triplet loss
"""
COSINE = lambda x, y: 1 ... |
"""init.py."""
from llama_index.tools.chatgpt_plugin.base import (
ChatGPTPluginToolSpec,
)
__all__ = ["ChatGPTPluginToolSpec"]
| """init.py."""
from llama_index.tools.chatgpt_plugin.base import (
ChatGPTPluginToolSpec,
)
__all__ = ["ChatGPTPluginToolSpec"]
|
_base_ = './queryinst_r50_fpn_ms-480-800-3x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| _base_ = './queryinst_r50_fpn_mstrain_480-800_3x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
|
"""
This file is part of the private API. Please do not use directly these classes as they will be modified on
future versions without warning. The classes should be accessed only via the transforms argument of Weights.
"""
from typing import List, Optional, Tuple, Union
import PIL.Image
import torch
from torch impor... | """
This file is part of the private API. Please do not use directly these classes as they will be modified on
future versions without warning. The classes should be accessed only via the transforms argument of Weights.
"""
from typing import List, Optional, Tuple, Union
import PIL.Image
import torch
from torch impor... |
import pytest
from langchain_core.memory import BaseMemory
from langchain.chains.conversation.memory import (
ConversationBufferMemory,
ConversationBufferWindowMemory,
ConversationSummaryMemory,
)
from langchain.memory import ReadOnlySharedMemory, SimpleMemory
from tests.unit_tests.llms.fake_llm import Fak... | import pytest
from langchain_core.memory import BaseMemory
from langchain.chains.conversation.memory import (
ConversationBufferMemory,
ConversationBufferWindowMemory,
ConversationSummaryMemory,
)
from langchain.memory import ReadOnlySharedMemory, SimpleMemory
from tests.unit_tests.llms.fake_llm import Fak... |
"""
Manages process groups for distributed compilation in TorchDynamo.
This module handles the initialization and management of process groups used for
distributed compilation. Key features:
- Lazy initialization of compilation process groups
- Only creates groups when distributed mode is enabled and available
- Inte... | """
Manages process groups for distributed compilation in TorchDynamo.
This module handles the initialization and management of process groups used for
distributed compilation. Key features:
- Lazy initialization of compilation process groups
- Only creates groups when distributed mode is enabled and available
- Inte... |
import unittest
import torch
from mmengine.structures import PixelData
from mmengine.testing import assert_allclose
from mmdet.models.seg_heads import PanopticFPNHead
from mmdet.structures import DetDataSample
class TestPanopticFPNHead(unittest.TestCase):
def test_init_weights(self):
head = PanopticFPN... | import unittest
import torch
from mmengine.data import PixelData
from mmengine.testing import assert_allclose
from mmdet.models.seg_heads import PanopticFPNHead
from mmdet.structures import DetDataSample
class TestPanopticFPNHead(unittest.TestCase):
def test_init_weights(self):
head = PanopticFPNHead(
... |
import io
from abc import ABC
from docarray.typing.tensor.abstract_tensor import AbstractTensor
class AbstractImageTensor(AbstractTensor, ABC):
def to_bytes(self, format: str = 'PNG') -> bytes:
"""
Convert image tensor to bytes.
:param format: the image format use to store the image, can... | import io
from abc import ABC, abstractmethod
from docarray.typing.tensor.abstract_tensor import AbstractTensor
class AbstractImageTensor(AbstractTensor, ABC):
@abstractmethod
def to_bytes(self, format: str = 'PNG') -> bytes:
"""
Convert image tensor to bytes.
:param format: the imag... |
from typing import Dict, Set
from fastapi import WebSocket
from backend.data.execution import (
ExecutionEventType,
GraphExecutionEvent,
NodeExecutionEvent,
)
from backend.server.model import WSMessage, WSMethod
_EVENT_TYPE_TO_METHOD_MAP: dict[ExecutionEventType, WSMethod] = {
ExecutionEventType.GRAP... | from typing import Dict, Set
from fastapi import WebSocket
from backend.data import execution
from backend.server.model import Methods, WsMessage
class ConnectionManager:
def __init__(self):
self.active_connections: Set[WebSocket] = set()
self.subscriptions: Dict[str, Set[WebSocket]] = {}
a... |
from __future__ import annotations
import os
import pytest
from sentence_transformers import SentenceTransformer
from sentence_transformers.models import Pooling, Transformer
from sentence_transformers.util import is_datasets_available
from tests.utils import SafeTemporaryDirectory
if is_datasets_available():
f... | from __future__ import annotations
import os
import pytest
from sentence_transformers import CrossEncoder, SentenceTransformer
from sentence_transformers.models import Pooling, Transformer
from sentence_transformers.util import is_datasets_available
from tests.utils import SafeTemporaryDirectory
if is_datasets_avai... |
from __future__ import annotations
from typing import Any, Iterable
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__(
self,
mode... | from __future__ import annotations
from typing import Any, Iterable
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__(
self,
mode... |
_base_ = [
'../_base_/models/mask-rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
rpn_head=dict(
anchor_generator=dict(type='LegacyAnchorGenerator', center_offset=0.5),
bbox_coder=dict(type='Le... | _base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
rpn_head=dict(
anchor_generator=dict(type='LegacyAnchorGenerator', center_offset=0.5),
bbox_coder=dict(type='Le... |
import os
import sys
import unittest
ROOT_DIR = os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(ROOT_DIR, "utils"))
import create_dependency_mapping # noqa: E402
# This is equivalent to `all` in the current library state (as of 09/01/2025)
MODEL_ROOT = os.... | import os
import sys
import unittest
ROOT_DIR = os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(ROOT_DIR, "utils"))
import create_dependency_mapping # noqa: E402
# This is equivalent to `all` in the current library state (as of 09/01/2025)
MODEL_ROOT = os.... |
"""Integration test for SerpAPI."""
from langchain_community.utilities import SerpAPIWrapper
def test_call() -> None:
"""Test that call gives the correct answer."""
chain = SerpAPIWrapper()
output = chain.run("What was Obama's first name?")
assert output == "Barack Hussein Obama II"
| """Integration test for SerpAPI."""
from langchain_community.utilities import SerpAPIWrapper
def test_call() -> None:
"""Test that call gives the correct answer."""
chain = SerpAPIWrapper() # type: ignore[call-arg]
output = chain.run("What was Obama's first name?")
assert output == "Barack Hussein O... |
from typing import Dict, List, Optional, Callable
from jina.importer import ImportExtensions
from jina.types.request.data import DataRequest
from jina import DocumentArray
from jina._docarray import docarray_v2
if docarray_v2:
from docarray import DocList
def get_fastapi_app(
request_models_map: Dict,
... | from typing import Dict, List, Optional, Callable
from jina.importer import ImportExtensions
from jina.types.request.data import DataRequest
from jina import DocumentArray
from jina._docarray import docarray_v2
if docarray_v2:
from docarray import DocList
def get_fastapi_app(
request_models_map: Dict,
... |
# mypy: allow-untyped-defs
import torch
def is_available():
r"""Return whether PyTorch is built with MKL support."""
return torch._C.has_mkl
VERBOSE_OFF = 0
VERBOSE_ON = 1
class verbose:
"""
On-demand oneMKL verbosing functionality.
To make it easier to debug performance issues, oneMKL can du... | # mypy: allow-untyped-defs
import torch
def is_available():
r"""Return whether PyTorch is built with MKL support."""
return torch._C.has_mkl
VERBOSE_OFF = 0
VERBOSE_ON = 1
class verbose:
"""
On-demand oneMKL verbosing functionality.
To make it easier to debug performance issues, oneMKL can du... |
__copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import subprocess
import pytest
from jina import Document, DocumentArray, Flow
from laser_encoder import LaserEncoder
def data_generator(num_docs):
for i in range(num_docs):
doc = Document(text='it... | __copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import subprocess
import pytest
from jina import Document, DocumentArray, Flow
from laser_encoder import LaserEncoder
_EMBEDDING_DIM = 1024
@pytest.mark.parametrize('request_size', [1, 10, 50, 100])
def test_... |
_base_ = '../common/lsj-200e_coco-detection.py'
image_size = (1024, 1024)
batch_augments = [dict(type='BatchFixedSizePad', size=image_size)]
model = dict(
type='CenterNet',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
... | _base_ = '../common/lsj_200e_coco_detection.py'
image_size = (1024, 1024)
batch_augments = [dict(type='BatchFixedSizePad', size=image_size)]
model = dict(
type='CenterNet',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
... |
from jina.parsers.helper import add_arg_group
def mixin_head_parser(parser):
"""Mixing in arguments required by head pods and runtimes into the given parser.
:param parser: the parser instance to which we add arguments
"""
gp = add_arg_group(parser, title='Head')
gp.add_argument(
'--comp... | import argparse
from jina.parsers.helper import add_arg_group
def mixin_head_parser(parser):
"""Mixing in arguments required by head pods and runtimes into the given parser.
:param parser: the parser instance to which we add arguments
"""
gp = add_arg_group(parser, title='Head')
gp.add_argument... |
# Licensed to the LF AI & Data foundation under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the "License");
# you may not use this fil... | from docarray import BaseDoc, DocList
def test_instance_and_equivalence():
class MyDoc(BaseDoc):
text: str
docs = DocList[MyDoc]([MyDoc(text='hello')])
assert issubclass(DocList[MyDoc], DocList[MyDoc])
assert issubclass(docs.__class__, DocList[MyDoc])
assert isinstance(docs, DocList[MyD... |
import pytest
import torchaudio
from torchaudio.pipelines import (
HUBERT_ASR_LARGE,
HUBERT_ASR_XLARGE,
HUBERT_BASE,
HUBERT_LARGE,
HUBERT_XLARGE,
VOXPOPULI_ASR_BASE_10K_DE,
VOXPOPULI_ASR_BASE_10K_EN,
VOXPOPULI_ASR_BASE_10K_ES,
VOXPOPULI_ASR_BASE_10K_FR,
VOXPOPULI_ASR_BASE_10K_IT,... | import pytest
import torchaudio
from torchaudio.pipelines import (
WAV2VEC2_BASE,
WAV2VEC2_LARGE,
WAV2VEC2_LARGE_LV60K,
WAV2VEC2_ASR_BASE_10M,
WAV2VEC2_ASR_BASE_100H,
WAV2VEC2_ASR_BASE_960H,
WAV2VEC2_ASR_LARGE_10M,
WAV2VEC2_ASR_LARGE_100H,
WAV2VEC2_ASR_LARGE_960H,
WAV2VEC2_ASR_LA... |
_base_ = 'fcos_r50_caffe_fpn_gn-head_1x_coco.py'
# model setting
model = dict(
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[103.530, 116.280, 123.675],
std=[1.0, 1.0, 1.0],
bgr_to_rgb=False,
pad_size_divisor=32),
backbone=dict(
init_cfg=dict(
... | _base_ = 'fcos_r50_caffe_fpn_gn-head_1x_coco.py'
# model setting
preprocess_cfg = dict(
mean=[103.530, 116.280, 123.675],
std=[1.0, 1.0, 1.0],
to_rgb=False,
pad_size_divisor=32)
model = dict(
preprocess_cfg=preprocess_cfg,
backbone=dict(
init_cfg=dict(
type='Pretrained',
... |
from __future__ import annotations
from collections.abc import Iterable
import torch
import torch.nn as nn
from sentence_transformers.sparse_encoder.losses.ReconstructionLoss import ReconstructionLoss
from sentence_transformers.sparse_encoder.losses.SparseMultipleNegativesRankingLoss import (
SparseMultipleNegat... | from __future__ import annotations
from collections.abc import Iterable
import torch
import torch.nn as nn
from sentence_transformers.sparse_encoder.losses.ReconstructionLoss import ReconstructionLoss
from sentence_transformers.sparse_encoder.losses.SparseMultipleNegativesRankingLoss import (
SparseMultipleNegat... |
from typing import Optional
from docarray.document import BaseDocument
from docarray.typing import TextUrl
from docarray.typing.tensor.embedding import Embedding
class Text(BaseDocument):
"""
Document for handling text.
It can contain a TextUrl (`Text.url`), a str (`Text.text`),
and an Embedding (`Te... | from typing import Optional
from docarray.document import BaseDocument
from docarray.typing.tensor.embedding import Embedding, Tensor
class Text(BaseDocument):
"""
base Document for Text handling
"""
text: str = ''
tensor: Optional[Tensor]
embedding: Optional[Embedding]
|
_base_ = [
'../_base_/models/ssd300.py', '../_base_/datasets/voc0712.py',
'../_base_/default_runtime.py'
]
model = dict(
bbox_head=dict(
num_classes=20, anchor_generator=dict(basesize_ratio_range=(0.2,
0.9))))
# dataset settings
dat... | _base_ = [
'../_base_/models/ssd300.py', '../_base_/datasets/voc0712.py',
'../_base_/default_runtime.py'
]
model = dict(
bbox_head=dict(
num_classes=20, anchor_generator=dict(basesize_ratio_range=(0.2,
0.9))))
# dataset settings
dat... |
import os
from typing import Optional
import numpy as np
import pytest
import torch
from pydantic.tools import parse_obj_as, schema_json_of
from docarray import BaseDoc
from docarray.base_doc.io.json import orjson_dumps
from docarray.typing import AudioBytes, AudioTorchTensor, AudioUrl
from docarray.typing.url.mimety... | from typing import Optional
import numpy as np
import pytest
import torch
from pydantic.tools import parse_obj_as, schema_json_of
from docarray import BaseDoc
from docarray.base_doc.io.json import orjson_dumps
from docarray.typing import AudioBytes, AudioTorchTensor, AudioUrl
from docarray.utils._internal.misc import... |
import logging
from datasets import load_dataset
from sentence_transformers.sparse_encoder import (
MLMTransformer,
SparseBinaryClassificationEvaluator,
SparseEncoder,
SpladePooling,
)
logging.basicConfig(format="%(asctime)s - %(message)s", datefmt="%Y-%m-%d %H:%M:%S", level=logging.INFO)
# Initiali... | from datasets import load_dataset
from sentence_transformers.sparse_encoder import (
MLMTransformer,
SparseBinaryClassificationEvaluator,
SparseEncoder,
SpladePooling,
)
# Initialize the SPLADE model
model_name = "naver/splade-cocondenser-ensembledistil"
model = SparseEncoder(
modules=[
ML... |
import torch
import os
import clip
import numpy as np
from glob import glob
from PIL import Image
from jina import Flow, Document
from ...clip_image import CLIPImageEncoder
cur_dir = os.path.dirname(os.path.abspath(__file__))
def test_clip_data():
docs = []
for file in glob(os.path.join(cur_dir, 'test_data',... | import torch
import os
import clip
import numpy as np
from glob import glob
from PIL import Image
from jina import Flow, Document
from jinahub.encoder.clip_image import CLIPImageEncoder
cur_dir = os.path.dirname(os.path.abspath(__file__))
def test_clip_data():
docs = []
for file in glob(os.path.join(cur_dir,... |
import logging
import os
from argparse import ArgumentParser
import sentencepiece as spm
from average_checkpoints import ensemble
from pytorch_lightning import seed_everything, Trainer
from pytorch_lightning.callbacks import LearningRateMonitor, ModelCheckpoint
from pytorch_lightning.strategies import DDPStrategy
from... | import logging
import os
from argparse import ArgumentParser
import sentencepiece as spm
from average_checkpoints import ensemble
from pytorch_lightning import seed_everything, Trainer
from pytorch_lightning.callbacks import LearningRateMonitor, ModelCheckpoint
from pytorch_lightning.strategies import DDPStrategy
from... |
#! /usr/bin/env python
# -*- coding: utf-8 -*-
# Copyright 2023 Imperial College London (Pingchuan Ma)
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
import torch
import torchaudio
import torchvision
class AVSRDataLoader:
def __init__(self, modality, detector="retinaface", resize=None):
self... | #! /usr/bin/env python
# -*- coding: utf-8 -*-
# Copyright 2023 Imperial College London (Pingchuan Ma)
# Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
import torch
import torchaudio
import torchvision
class AVSRDataLoader:
def __init__(self, modality, detector="retinaface", resize=None):
self... |
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