input stringlengths 33 5k | output stringlengths 32 5k |
|---|---|
import os
from typing import Dict
DEPLOYMENT_FILES = [
'statefulset-executor',
'deployment-executor',
'deployment-gateway',
'deployment-uses-before',
'deployment-uses-after',
'deployment-uses-before-after',
]
cur_dir = os.path.dirname(__file__)
DEFAULT_RESOURCE_DIR = os.path.join(
cur_dir,... | import os
from typing import Dict
DEPLOYMENT_FILES = [
'statefulset-executor',
'deployment-executor',
'deployment-gateway',
'deployment-uses-before',
'deployment-uses-after',
'deployment-uses-before-after',
]
cur_dir = os.path.dirname(__file__)
DEFAULT_RESOURCE_DIR = os.path.join(
cur_dir,... |
# dataset settings
dataset_type = 'VOCDataset'
data_root = 'data/VOCdevkit/'
# file_client_args = dict(
# backend='petrel',
# path_mapping=dict({
# './data/': 's3://openmmlab/datasets/detection/',
# 'data/': 's3://openmmlab/datasets/detection/'
# }))
file_client_args = dict(backend='disk')
... | # dataset settings
dataset_type = 'VOCDataset'
data_root = 'data/VOCdevkit/'
# file_client_args = dict(
# backend='petrel',
# path_mapping=dict({
# './data/': 's3://openmmlab/datasets/detection/',
# 'data/': 's3://openmmlab/datasets/detection/'
# }))
file_client_args = dict(backend='disk')
... |
import os
from typing import Callable, List
import numpy as np
import pytest
import torch
from jina import Document, DocumentArray
from jinahub.encoder.transform_encoder import TransformerTorchEncoder
cur_dir = os.path.dirname(os.path.abspath(__file__))
def test_compute_tokens():
enc = TransformerTorchEncoder()... | import os
from typing import Callable, List
import numpy as np
import pytest
import torch
from jina import Document, DocumentArray
from jinahub.encoder.transform_encoder import TransformerTorchEncoder
cur_dir = os.path.dirname(os.path.abspath(__file__))
def test_compute_tokens():
enc = TransformerTorchEncoder(b... |
"""A class for JAX specific optimizer logic.
Its purpose is to route around statelessness
requirements in cond ops used for EMA handling
and gradient accumulation handling. We do this
by skipping conditionals entirely.
"""
import jax
from jax import numpy as jnp
from keras.src.optimizers import base_optimizer
clas... | """A class for JAX specific optimizer logic.
Its purpose is to route around statelessness
requirements in cond ops used for EMA handling
and gradient accumulation handling. We do this
by skipping conditionals entirely.
"""
import jax
from jax import numpy as jnp
from keras.src.optimizers import base_optimizer
clas... |
from __future__ import annotations
import logging
from datasets import load_dataset
from sentence_transformers.evaluation import SequentialEvaluator
from sentence_transformers.models import Pooling, Transformer
from sentence_transformers.sparse_encoder import SparseEncoder
from sentence_transformers.sparse_encoder.e... | from __future__ import annotations
import logging
from datasets import load_dataset
from sentence_transformers.evaluation import SequentialEvaluator
from sentence_transformers.models import Pooling, Transformer
from sentence_transformers.sparse_encoder import SparseEncoder
from sentence_transformers.sparse_encoder.e... |
from torchaudio_unittest.common_utils import PytorchTestCase, skipIfNoCuda
from .autograd_test_impl import AutogradTestFloat32, AutogradTestMixin
@skipIfNoCuda
class AutogradCUDATest(AutogradTestMixin, PytorchTestCase):
device = "cuda"
@skipIfNoCuda
class AutogradRNNTCUDATest(AutogradTestFloat32, PytorchTestCa... | from torchaudio_unittest.common_utils import (
PytorchTestCase,
skipIfNoCuda,
)
from .autograd_test_impl import AutogradTestMixin, AutogradTestFloat32
@skipIfNoCuda
class AutogradCUDATest(AutogradTestMixin, PytorchTestCase):
device = "cuda"
@skipIfNoCuda
class AutogradRNNTCUDATest(AutogradTestFloat32, ... |
# Copyright (c) OpenMMLab. All rights reserved.
"""Collecting some commonly used type hint in mmdetection."""
from typing import List, Optional, Union
from mmengine.config import ConfigDict
from mmengine.data import InstanceData
from ..bbox.samplers import SamplingResult
from ..data_structures import DetDataSample
#... | # Copyright (c) OpenMMLab. All rights reserved.
"""Collecting some commonly used type hint in mmdetection."""
from typing import List, Optional, Union
from mmengine.config import ConfigDict
from mmengine.data import InstanceData
from ..bbox.samplers import SamplingResult
from ..data_structures import DetDataSample
#... |
# Copyright (c) OpenMMLab. All rights reserved.
from .assigners import (AssignResult, BaseAssigner, CenterRegionAssigner,
MaxIoUAssigner, RegionAssigner)
from .builder import build_assigner, build_bbox_coder, build_sampler
from .coder import (BaseBBoxCoder, DeltaXYWHBBoxCoder, DistancePointBBoxC... | # Copyright (c) OpenMMLab. All rights reserved.
from .assigners import (AssignResult, BaseAssigner, CenterRegionAssigner,
MaxIoUAssigner, RegionAssigner)
from .builder import build_assigner, build_bbox_coder, build_sampler
from .coder import (BaseBBoxCoder, DeltaXYWHBBoxCoder, PseudoBBoxCoder,
... |
import json
from typing import Any, Type, TypeGuard, TypeVar, overload
import jsonschema
from fastapi.encoders import jsonable_encoder
from pydantic import BaseModel
from .type import type_match
def to_dict(data) -> dict:
if isinstance(data, BaseModel):
data = data.model_dump()
return jsonable_encod... | import json
from typing import Any, Type, TypeGuard, TypeVar, overload
import jsonschema
from fastapi.encoders import jsonable_encoder
from pydantic import BaseModel
from .type import type_match
def to_dict(data) -> dict:
if isinstance(data, BaseModel):
data = data.model_dump()
return jsonable_encod... |
import pytest
import datasets
import datasets.config
# Import fixture modules as plugins
pytest_plugins = ["tests.fixtures.files", "tests.fixtures.hub", "tests.fixtures.fsspec"]
def pytest_collection_modifyitems(config, items):
# Mark tests as "unit" by default if not marked as "integration" (or already marked... | import pytest
import datasets
import datasets.config
# Import fixture modules as plugins
pytest_plugins = ["tests.fixtures.files", "tests.fixtures.hub", "tests.fixtures.fsspec"]
def pytest_collection_modifyitems(config, items):
# Mark tests as "unit" by default if not marked as "integration" (or already marked... |
_base_ = './ga-retinanet_r101-caffe_fpn_1x_coco.py'
train_pipeline = [
dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='RandomResize', scale=[(1333, 480), (1333, 960)],
keep_ratio=True),
dict(type='RandomFlip... | _base_ = './ga-retinanet_r101-caffe_fpn_1x_coco.py'
train_pipeline = [
dict(
type='LoadImageFromFile',
file_client_args={{_base_.file_client_args}}),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='RandomResize', scale=[(1333, 480), (1333, 960)],
keep_ratio=True),
... |
"""Abstract interface for document loader implementations."""
from __future__ import annotations
from abc import ABC, abstractmethod
from typing import TYPE_CHECKING, Optional
from langchain_core.runnables import run_in_executor
if TYPE_CHECKING:
from collections.abc import AsyncIterator, Iterator
from lan... | """Abstract interface for document loader implementations."""
from __future__ import annotations
from abc import ABC, abstractmethod
from typing import TYPE_CHECKING, Optional
from langchain_core.runnables import run_in_executor
if TYPE_CHECKING:
from collections.abc import AsyncIterator, Iterator
from lan... |
"""XGBoost: eXtreme Gradient Boosting library.
Contributors: https://github.com/dmlc/xgboost/blob/master/CONTRIBUTORS.md
"""
from . import tracker # noqa
from . import collective, dask
from .core import (
Booster,
DataIter,
DMatrix,
ExtMemQuantileDMatrix,
QuantileDMatrix,
_py_version,
bui... | """XGBoost: eXtreme Gradient Boosting library.
Contributors: https://github.com/dmlc/xgboost/blob/master/CONTRIBUTORS.md
"""
from . import tracker # noqa
from . import collective, dask
from .core import Booster, DataIter, DMatrix, QuantileDMatrix, _py_version, build_info
from .tracker import RabitTracker # noqa
fro... |
from typing import Any, Callable, Optional, Sequence
from llama_index.core.base.embeddings.base import (
BaseEmbedding,
SimilarityMode,
similarity,
)
from llama_index.core.evaluation.base import BaseEvaluator, EvaluationResult
from llama_index.core.prompts.mixin import PromptDictType
from llama_index.core.... | from typing import Any, Callable, Optional, Sequence
from llama_index.core.base.embeddings.base import (
BaseEmbedding,
SimilarityMode,
similarity,
)
from llama_index.core.evaluation.base import BaseEvaluator, EvaluationResult
from llama_index.core.prompts.mixin import PromptDictType
from llama_index.core.... |
from pathlib import Path
from typing import List
import pytest
from executor.audioclip_text import AudioCLIPTextEncoder
from jina import Document, DocumentArray, Executor
_EMBEDDING_DIM = 1024
@pytest.fixture(scope='module')
def basic_encoder() -> AudioCLIPTextEncoder:
return AudioCLIPTextEncoder(
model... | from pathlib import Path
from typing import List
import pytest
from executor.audioclip_text import AudioCLIPTextEncoder
from jina import Document, DocumentArray, Executor
_EMBEDDING_DIM = 1024
@pytest.fixture(scope='module')
def basic_encoder() -> AudioCLIPTextEncoder:
return AudioCLIPTextEncoder(
model... |
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... | import numpy as np
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------------------------------... |
import os
import sys
import numpy as np
import pytest
import xgboost as xgb
from xgboost import testing as tm
from xgboost.core import DataSplitMode
pytestmark = pytest.mark.skipif(
tm.no_arrow()["condition"] or tm.no_pandas()["condition"],
reason=tm.no_arrow()["reason"] + " or " + tm.no_pandas()["reason"],
... | import os
import sys
import numpy as np
import pytest
import xgboost as xgb
from xgboost import testing as tm
from xgboost.core import DataSplitMode
try:
import pandas as pd
import pyarrow as pa
import pyarrow.csv as pc
except ImportError:
pass
pytestmark = pytest.mark.skipif(
tm.no_arrow()["con... |
from typing import Any, Dict, Optional, Union
import numpy as np
import PIL.Image
import torch
from torchvision import datapoints
from torchvision.transforms.v2 import functional as F, Transform
from torchvision.transforms.v2.utils import is_simple_tensor
class PILToTensor(Transform):
"""[BETA] Convert a PIL I... | from typing import Any, Dict, Optional, Union
import numpy as np
import PIL.Image
import torch
from torchvision import datapoints
from torchvision.transforms.v2 import functional as F, Transform
from torchvision.transforms.v2.utils import is_simple_tensor
class PILToTensor(Transform):
"""[BETA] Convert a PIL I... |
import datetime
import prisma.fields
import prisma.models
import pytest
import backend.server.v2.library.model as library_model
@pytest.mark.asyncio
async def test_agent_preset_from_db():
# Create mock DB agent
db_agent = prisma.models.AgentPreset(
id="test-agent-123",
createdAt=datetime.dat... | import datetime
import prisma.fields
import prisma.models
import pytest
import backend.server.v2.library.model as library_model
from backend.util import json
@pytest.mark.asyncio
async def test_agent_preset_from_db():
# Create mock DB agent
db_agent = prisma.models.AgentPreset(
id="test-agent-123",
... |
# Copyright (c) OpenMMLab. All rights reserved.
from typing import Dict, Optional, Sequence
from ..registry import HOOKS
from ..utils import get_git_hash
from .hook import Hook
DATA_BATCH = Optional[Sequence[dict]]
@HOOKS.register_module()
class RuntimeInfoHook(Hook):
"""A hook that updates runtime information ... | # Copyright (c) OpenMMLab. All rights reserved.
from typing import Dict, Optional, Sequence
from mmengine.registry import HOOKS
from .hook import Hook
DATA_BATCH = Optional[Sequence[dict]]
@HOOKS.register_module()
class RuntimeInfoHook(Hook):
"""A hook that updates runtime information into message hub.
E.g... |
__copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import subprocess
import numpy as np
import pytest
from executor.audioclip_image import AudioCLIPImageEncoder
from jina import Document, DocumentArray, Flow
@pytest.mark.parametrize("request_size", [1, 10, 50,... | __copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import subprocess
import numpy as np
import pytest
from executor.audioclip_image import AudioCLIPImageEncoder
from jina import Document, DocumentArray, Flow
@pytest.mark.parametrize("request_size", [1, 10, 50,... |
# Copyright (c) OpenMMLab. All rights reserved.
from .averaged_model import (ExponentialMovingAverage, MomentumAnnealingEMA,
StochasticWeightAverage)
from .base_model import BaseDataPreprocessor, BaseModel, ImgDataPreprocessor
from .base_module import BaseModule
from .utils import detect_an... | # Copyright (c) OpenMMLab. All rights reserved.
from .averaged_model import (ExponentialMovingAverage, MomentumAnnealingEMA,
StochasticWeightAverage)
from .base_model import BaseDataPreprocessor, BaseModel, ImgDataPreprocessor
from .base_module import BaseModule
from .utils import detect_an... |
_base_ = './fcos_r50_caffe_fpn_gn-head_1x_coco.py'
# model settings
model = dict(
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
bgr_to_rgb=True,
pad_size_divisor=32),
backbone=dict(
type='ResNeXt'... | _base_ = './fcos_r50_caffe_fpn_gn-head_1x_coco.py'
# model settings
preprocess_cfg = dict(
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True,
pad_size_divisor=32)
model = dict(
preprocess_cfg=preprocess_cfg,
backbone=dict(
type='ResNeXt',
depth=101,
... |
import pytest
import datasets
import datasets.config
# Import fixture modules as plugins
pytest_plugins = ["tests.fixtures.files", "tests.fixtures.hub", "tests.fixtures.fsspec"]
def pytest_collection_modifyitems(config, items):
# Mark tests as "unit" by default if not marked as "integration" (or already marked... | import pytest
import datasets
import datasets.config
# Import fixture modules as plugins
pytest_plugins = ["tests.fixtures.files", "tests.fixtures.hub", "tests.fixtures.fsspec"]
def pytest_collection_modifyitems(config, items):
# Mark tests as "unit" by default if not marked as "integration" (or already marked... |
# 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... | # coding=utf-8
# Copyright 2024 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... |
from typing import Union, BinaryIO, TYPE_CHECKING
from docarray.document.mixins.helper import _uri_to_blob, _get_file_context
if TYPE_CHECKING: # pragma: no cover
from docarray.typing import T
class UriFileMixin:
"""Provide helper functions for :class:`Document` to dump content to a file."""
def save_... | from typing import Union, BinaryIO, TYPE_CHECKING
from docarray.document.mixins.helper import _uri_to_blob, _get_file_context
if TYPE_CHECKING:
from docarray.typing import T
class UriFileMixin:
"""Provide helper functions for :class:`Document` to dump content to a file."""
def save_uri_to_file(self: 'T... |
from __future__ import annotations
from typing import Any
import torch
from torch import nn
from transformers import AutoConfig, AutoModelForMaskedLM, AutoTokenizer
class MLMTransformer(nn.Module):
"""A minimal Transformer model that uses MLM (Masked Language Modeling).
This model implements only the essen... | from __future__ import annotations
from typing import Any
import torch
from torch import nn
from transformers import AutoConfig, AutoModelForMaskedLM, AutoTokenizer
class MLMTransformer(nn.Module):
"""A minimal Transformer model that uses MLM (Masked Language Modeling).
This model implements only the essen... |
from . import InputExample
import gzip
import os
class NLIDataReader(object):
"""Reads in the Stanford NLI dataset and the MultiGenre NLI dataset"""
def __init__(self, dataset_folder):
self.dataset_folder = dataset_folder
def get_examples(self, filename, max_examples=0):
"""
data... | from . import InputExample
import gzip
import os
class NLIDataReader(object):
"""
Reads in the Stanford NLI dataset and the MultiGenre NLI dataset
"""
def __init__(self, dataset_folder):
self.dataset_folder = dataset_folder
def get_examples(self, filename, max_examples=0):
"""
... |
import pytest
import torch
from pydantic.tools import parse_obj_as, schema_json_of
from docarray.base_document.io.json import orjson_dumps
from docarray.typing import TorchEmbedding, TorchTensor
def test_proto_tensor():
tensor = parse_obj_as(TorchTensor, torch.zeros(3, 224, 224))
tensor._to_node_protobuf()... | import pytest
import torch
from pydantic.tools import parse_obj_as, schema_json_of
from docarray.base_document.io.json import orjson_dumps
from docarray.typing import TorchEmbedding, TorchTensor
def test_proto_tensor():
tensor = parse_obj_as(TorchTensor, torch.zeros(3, 224, 224))
tensor._to_node_protobuf()... |
_base_ = [
'../_base_/models/cascade-mask-rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
]
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvisio... | _base_ = [
'../_base_/models/cascade-mask-rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
]
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvisio... |
import types
from typing_extensions import TYPE_CHECKING
from docarray.typing.tensor.image.image_ndarray import ImageNdArray
from docarray.typing.tensor.image.image_tensor import ImageTensor
from docarray.utils._internal.misc import (
_get_path_from_docarray_root_level,
import_library,
)
if TYPE_CHECKING:
... | from docarray.typing.tensor.image.image_ndarray import ImageNdArray
from docarray.typing.tensor.image.image_tensor import ImageTensor
__all__ = ['ImageNdArray', 'ImageTensor']
from docarray.utils._internal.misc import is_tf_available, is_torch_available
torch_available = is_torch_available()
if torch_available:
... |
_base_ = [
'../_base_/models/ssd300.py', '../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
]
# dataset settings
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
input_size = 300
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type=... | _base_ = [
'../_base_/models/ssd300.py', '../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
]
# dataset settings
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
input_size = 300
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type=... |
# Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import mmengine
from mmengine.utils import digit_version
from .version import __version__, version_info
mmcv_minimum_version = '2.0.0rc4'
mmcv_maximum_version = '2.2.0'
mmcv_version = digit_version(mmcv.__version__)
mmengine_minimum_version = '0.7.1'
mmengi... | # Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import mmengine
from mmengine.utils import digit_version
from .version import __version__, version_info
mmcv_minimum_version = '2.0.0rc4'
mmcv_maximum_version = '2.1.0'
mmcv_version = digit_version(mmcv.__version__)
mmengine_minimum_version = '0.7.1'
mmengi... |
_base_ = ['./mask2former_swin-b-p4-w12-384_8xb2-lsj-50e_coco-panoptic.py']
pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window12_384_22k.pth' # noqa
model = dict(
backbone=dict(init_cfg=dict(type='Pretrained', checkpoint=pretrained)))
| _base_ = ['./mask2former_swin-b-p4-w12-384_lsj_8x2_50e_coco-panoptic.py']
pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window12_384_22k.pth' # noqa
model = dict(
backbone=dict(init_cfg=dict(type='Pretrained', checkpoint=pretrained)))
|
from jina.serve.runtimes.gateway.http.fastapi import FastAPIBaseGateway # keep import here for backwards compatibility
from jina.serve.runtimes.gateway.gateway import BaseGateway
from jina.serve.runtimes.servers.http import HTTPServer
__all__ = ['HTTPGateway']
class HTTPGateway(HTTPServer, BaseGateway):
"""
... | from jina.serve.runtimes.gateway.http.fastapi import FastAPIBaseGateway
__all__ = ['HTTPGateway']
class HTTPGateway(FastAPIBaseGateway):
"""
:class:`HTTPGateway` is a FastAPIBaseGateway that uses the default FastAPI app
"""
@property
def app(self):
"""Get the default base API app for HTT... |
"""Argparser module for WorkerRuntime"""
from jina.parsers.helper import KVAppendAction, add_arg_group
from jina.parsers.orchestrate.runtimes.runtime import mixin_base_runtime_parser
def mixin_worker_runtime_parser(parser):
"""Mixing in arguments required by :class:`WorkerRuntime` into the given parser.
:par... | """Argparser module for WorkerRuntime"""
from jina import __default_host__, helper
from jina.enums import PollingType
from jina.parsers.helper import KVAppendAction, add_arg_group
from jina.parsers.orchestrate.runtimes.runtime import mixin_base_runtime_parser
def mixin_worker_runtime_parser(parser):
"""Mixing in ... |
from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.tools import O365SendEvent
from langchain_community.tools.office365.send_event import SendEventSchema
# Create a way to dynamically look up deprecated imports.
# Used to consolidate log... | from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.tools import O365SendEvent
from langchain_community.tools.office365.send_event import SendEventSchema
# Create a way to dynamically look up deprecated imports.
# Used to consolidate log... |
_base_ = './yolox_s_8x8_300e_coco.py'
# model settings
model = dict(
data_preprocessor=dict(batch_augments=[
dict(
type='BatchSyncRandomResize',
random_size_range=(320, 640),
size_divisor=32,
interval=10)
]),
backbone=dict(deepen_factor=0.33, widen_fa... | _base_ = './yolox_s_8x8_300e_coco.py'
# model settings
model = dict(
data_preprocessor=dict(batch_augments=[
dict(
type='BatchSyncRandomResize',
random_size_range=(320, 640),
size_divisor=32,
interval=10)
]),
backbone=dict(deepen_factor=0.33, widen_fa... |
# Copyright (c) OpenMMLab. All rights reserved.
from unittest import TestCase
import torch
from mmengine import Config
from mmengine.structures import InstanceData
from mmdet import * # noqa
from mmdet.models.dense_heads import YOLOFHead
class TestYOLOFHead(TestCase):
def test_yolof_head_loss(self):
"... | # Copyright (c) OpenMMLab. All rights reserved.
from unittest import TestCase
import torch
from mmengine import Config
from mmengine.data import InstanceData
from mmdet import * # noqa
from mmdet.models.dense_heads import YOLOFHead
class TestYOLOFHead(TestCase):
def test_yolof_head_loss(self):
"""Test... |
# coding=utf-8
# Copyright 2025 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... | # coding=utf-8
# Copyright 2024 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... |
from enum import Enum
from typing import Any, Optional
from langchain_core.callbacks import (
AsyncCallbackManagerForRetrieverRun,
CallbackManagerForRetrieverRun,
)
from langchain_core.documents import Document
from langchain_core.retrievers import BaseRetriever
from langchain_core.stores import BaseStore, Byt... | from enum import Enum
from typing import Any, Optional
from langchain_core.callbacks import (
AsyncCallbackManagerForRetrieverRun,
CallbackManagerForRetrieverRun,
)
from langchain_core.documents import Document
from langchain_core.retrievers import BaseRetriever
from langchain_core.stores import BaseStore, Byt... |
from typing import Union, Iterable
from docarray.array.storage.base.seqlike import BaseSequenceLikeMixin
from docarray.array.storage.registry import _REGISTRY
from docarray import Document
class SequenceLikeMixin(BaseSequenceLikeMixin):
"""Implement sequence-like methods for DocumentArray with weaviate as storag... | from typing import Union, Iterable
from docarray.array.storage.base.seqlike import BaseSequenceLikeMixin
from docarray.array.storage.registry import _REGISTRY
from docarray import Document
class SequenceLikeMixin(BaseSequenceLikeMixin):
"""Implement sequence-like methods for DocumentArray with weaviate as storag... |
__copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import os
import subprocess
import librosa
import pytest
from executor.vggish import vggish_input
from jina import Document, DocumentArray, Flow
cur_dir = os.path.dirname(os.path.abspath(__file__))
def test_f... | __copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import os
import subprocess
import librosa
import pytest
from jina import Document, DocumentArray, Flow
from ...vggish import vggish_input
cur_dir = os.path.dirname(os.path.abspath(__file__))
def test_flow_f... |
import pytest
@pytest.mark.compile
def test_placeholder() -> None:
"""Used for compiling integration tests without running any real tests."""
| import pytest
@pytest.mark.compile
def test_placeholder() -> None:
"""Used for compiling integration tests without running any real tests."""
pass
|
import warnings
from typing import Optional, Union, TYPE_CHECKING, Callable
import numpy as np
from docarray.score import NamedScore
if TYPE_CHECKING:
from docarray import Document, DocumentArray
class EvaluationMixin:
"""A mixin that provides ranking evaluation functionality to DocumentArrayLike objects""... | import warnings
from typing import Optional, Union, TYPE_CHECKING, Callable
import numpy as np
from ...score import NamedScore
if TYPE_CHECKING:
from ... import Document, DocumentArray
class EvaluationMixin:
"""A mixin that provides ranking evaluation functionality to DocumentArrayLike objects"""
def ... |
import importlib
import os
import re
import types
from typing import Any, Optional
import numpy as np
try:
import torch # noqa: F401
except ImportError:
torch_imported = False
else:
torch_imported = True
try:
import tensorflow as tf # type: ignore # noqa: F401
except (ImportError, TypeError):
... | import importlib
import os
import re
import types
from typing import Any, Optional
import numpy as np
try:
import torch # noqa: F401
except ImportError:
torch_imported = False
else:
torch_imported = True
try:
import tensorflow as tf # type: ignore # noqa: F401
except (ImportError, TypeError):
... |
"""**Utility functions** for LangChain.
These functions do not depend on any other LangChain module.
"""
from importlib import import_module
from typing import TYPE_CHECKING
if TYPE_CHECKING:
# for type checking and IDE support, we include the imports here
# but we don't want to eagerly import them at runtim... | """**Utility functions** for LangChain.
These functions do not depend on any other LangChain module.
"""
from langchain_core.utils import image
from langchain_core.utils.aiter import abatch_iterate
from langchain_core.utils.env import get_from_dict_or_env, get_from_env
from langchain_core.utils.formatting import Stri... |
import multiprocessing
import pytest
from jina import Client
from jina.parsers import set_gateway_parser, set_pod_parser
from jina.serve.runtimes.asyncio import AsyncNewLoopRuntime
from jina.serve.runtimes.gateway import GatewayRuntime
from jina.serve.runtimes.worker import WorkerRuntime
def _create_worker_runtime(... | import multiprocessing
import pytest
from jina import Client
from jina.parsers import set_gateway_parser, set_pod_parser
from jina.serve.runtimes.asyncio import AsyncNewLoopRuntime
from jina.serve.runtimes.gateway.grpc import GRPCGatewayRuntime
from jina.serve.runtimes.gateway.http import HTTPGatewayRuntime
from jina... |
import warnings
from typing import TYPE_CHECKING, Any, Optional, Tuple, Type, TypeVar, Union
import numpy as np
from docarray.typing.proto_register import _register_proto
from docarray.typing.url.any_url import AnyUrl
from docarray.utils._internal.misc import is_notebook
if TYPE_CHECKING:
from pydantic import Ba... | import warnings
from typing import TYPE_CHECKING, Any, Optional, Tuple, Type, TypeVar, Union
import numpy as np
from docarray.typing.proto_register import _register_proto
from docarray.typing.url.any_url import AnyUrl
from docarray.utils.misc import is_notebook
if TYPE_CHECKING:
from pydantic import BaseConfig
... |
_base_ = [
'../_base_/models/rpn_r50_fpn.py', '../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
val_evaluator = dict(metric='proposal_fast')
test_evaluator = val_evaluator
# inference on val dataset and dump the proposals with evaluate metric
# data... | _base_ = [
'../_base_/models/rpn_r50_fpn.py', '../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
val_evaluator = dict(metric='proposal_fast')
test_evaluator = val_evaluator
|
from typing import Literal
from pydantic import SecretStr
from backend.data.model import (
APIKeyCredentials,
CredentialsField,
CredentialsMetaInput,
OAuth2Credentials,
)
from backend.integrations.providers import ProviderName
from backend.util.settings import Secrets
secrets = Secrets()
GITHUB_OAUTH... | from typing import Literal
from pydantic import SecretStr
from backend.data.model import (
APIKeyCredentials,
CredentialsField,
CredentialsMetaInput,
OAuth2Credentials,
)
from backend.util.settings import Secrets
secrets = Secrets()
GITHUB_OAUTH_IS_CONFIGURED = bool(
secrets.github_client_id and ... |
import copy
import importlib
import os
import sys
from keras.src import backend as backend_module
from keras.src.api_export import keras_export
from keras.src.backend.common import global_state
def in_tf_graph():
if global_state.get_global_attribute("in_tf_graph_scope", False):
return True
if "tenso... | import copy
import importlib
import os
import sys
from keras.src import backend as backend_module
from keras.src.api_export import keras_export
from keras.src.backend.common import global_state
def in_tf_graph():
if global_state.get_global_attribute("in_tf_graph_scope", False):
return True
if "tenso... |
import logging
import typing
from autogpt_libs.auth import requires_admin_user
from autogpt_libs.auth.depends import get_user_id
from fastapi import APIRouter, Body, Depends
from prisma import Json
from prisma.enums import CreditTransactionType
from backend.data.credit import admin_get_user_history, get_user_credit_m... | import logging
import typing
from autogpt_libs.auth import requires_admin_user
from autogpt_libs.auth.depends import get_user_id
from fastapi import APIRouter, Body, Depends
from prisma import Json
from prisma.enums import CreditTransactionType
from backend.data.credit import admin_get_user_history, get_user_credit_m... |
import torch
from torchaudio.models import emformer_rnnt_model, RNNTBeamSearch
from torchaudio_unittest.common_utils import TestBaseMixin, torch_script
class RNNTBeamSearchTestImpl(TestBaseMixin):
def _get_input_config(self):
model_config = self._get_model_config()
return {
"batch_size... | import torch
from torchaudio.models import emformer_rnnt_model, RNNTBeamSearch
from torchaudio_unittest.common_utils import TestBaseMixin, torch_script
class RNNTBeamSearchTestImpl(TestBaseMixin):
def _get_input_config(self):
model_config = self._get_model_config()
return {
"batch_size... |
"""Standard LangChain interface tests."""
import pytest
from langchain_core.language_models import BaseChatModel
from langchain_tests.unit_tests import ChatModelUnitTests
from pytest_benchmark.fixture import BenchmarkFixture # type: ignore[import-untyped]
from langchain_anthropic import ChatAnthropic
class TestAnt... | """Standard LangChain interface tests"""
import pytest
from langchain_core.language_models import BaseChatModel
from langchain_tests.unit_tests import ChatModelUnitTests
from pytest_benchmark.fixture import BenchmarkFixture # type: ignore[import-untyped]
from langchain_anthropic import ChatAnthropic
class TestAnth... |
# 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... | # 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 __future__ import annotations
from collections.abc import Iterable
from enum import Enum
from typing import Any
import torch.nn.functional as F
from torch import Tensor, nn
from sentence_transformers.SentenceTransformer import SentenceTransformer
class SiameseDistanceMetric(Enum):
"""The metric for the co... | from __future__ import annotations
from collections.abc import Iterable
from enum import Enum
from typing import Any
import torch.nn.functional as F
from torch import Tensor, nn
from sentence_transformers.SentenceTransformer import SentenceTransformer
class SiameseDistanceMetric(Enum):
"""The metric for the co... |
from langchain_anthropic import __all__
EXPECTED_ALL = [
"ChatAnthropicMessages",
"ChatAnthropic",
"convert_to_anthropic_tool",
"Anthropic",
"AnthropicLLM",
]
def test_all_imports() -> None:
assert sorted(EXPECTED_ALL) == sorted(__all__)
| from langchain_anthropic import __all__
EXPECTED_ALL = ["ChatAnthropicMessages", "ChatAnthropic", "Anthropic", "AnthropicLLM"]
def test_all_imports() -> None:
assert sorted(EXPECTED_ALL) == sorted(__all__)
|
__copyright__ = 'Copyright (c) 2021 Jina AI Limited. All rights reserved.'
__license__ = 'Apache-2.0'
import subprocess
import pytest
from jina import Document, DocumentArray, Flow
from ...transform_encoder import TransformerTorchEncoder
_EMBEDDING_DIM = 768
@pytest.mark.parametrize('request_size', [1, 10, 50, 10... | __copyright__ = 'Copyright (c) 2021 Jina AI Limited. All rights reserved.'
__license__ = 'Apache-2.0'
import subprocess
from typing import Callable, List
import pytest
from jina import DocumentArray, Flow
from ...transform_encoder import TransformerTorchEncoder
@pytest.mark.parametrize('request_size', [1, 10, 50, ... |
# Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
import tempfile
from unittest import TestCase
from unittest.mock import Mock
import torch
import torch.nn as nn
from torch.utils.data import Dataset
from mmengine.hooks import EMAHook
from mmengine.model import ExponentialMovingAverage
from mmengin... | # Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
import tempfile
from unittest import TestCase
from unittest.mock import Mock
import torch
import torch.nn as nn
from torch.utils.data import Dataset
from mmengine.hooks import EMAHook
from mmengine.model import ExponentialMovingAverage
from mmengin... |
from langchain_huggingface.chat_models import (
ChatHuggingFace, # type: ignore[import-not-found]
)
from langchain_huggingface.embeddings import (
HuggingFaceEmbeddings,
HuggingFaceEndpointEmbeddings,
)
from langchain_huggingface.llms import (
HuggingFaceEndpoint,
HuggingFacePipeline,
)
__all__ = ... | from langchain_huggingface.chat_models import (
ChatHuggingFace, # type: ignore[import-not-found]
)
from langchain_huggingface.embeddings import (
HuggingFaceEmbeddings,
HuggingFaceEndpointEmbeddings,
)
from langchain_huggingface.llms import (
HuggingFaceEndpoint,
HuggingFacePipeline,
)
__all__ = ... |
"""
Prompts for implementing Chain of Abstraction.
While official prompts are not given (and the paper finetunes models for the task),
we can take inspiration and use few-shot prompting to generate a prompt for implementing
chain of abstraction in an LLM agent.
"""
REASONING_PROMPT_TEMPALTE = """Generate an abstract ... | """
Prompts for implementing Chain of Abstraction.
While official prompts are not given (and the paper finetunes models for the task),
we can take inspiration and use few-shot prompting to generate a prompt for implementing
chain of abstraction in an LLM agent.
"""
REASONING_PROMPT_TEMPALTE = """Generate an abstract ... |
from __future__ import annotations
try:
from typing import Self
except ImportError:
from typing_extensions import Self
import torch
import transformers
from PIL import Image
from sentence_transformers.models.Asym import InputModule
class CLIPModel(InputModule):
save_in_root: bool = True
def __init... | from __future__ import annotations
import torch
import transformers
from PIL import Image
from torch import nn
class CLIPModel(nn.Module):
save_in_root: bool = True
def __init__(self, model_name: str = "openai/clip-vit-base-patch32", processor_name=None) -> None:
super().__init__()
if proce... |
# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import subprocess
import torch
def parse_args():
parser = argparse.ArgumentParser(
description='Process a checkpoint to be published')
parser.add_argument('in_file', help='input checkpoint filename')
parser.add_argument('out_file', h... | import argparse
import subprocess
import torch
def parse_args():
parser = argparse.ArgumentParser(
description='Process a checkpoint to be published')
parser.add_argument('in_file', help='input checkpoint filename')
parser.add_argument('out_file', help='output checkpoint filename')
args = par... |
from keras.src import activations
from keras.src import backend
from keras.src.api_export import keras_export
from keras.src.layers.layer import Layer
def _large_negative_number(dtype):
"""Return a Large negative number based on dtype."""
if backend.standardize_dtype(dtype) == "float16":
return -3e4
... | from keras.src import activations
from keras.src import backend
from keras.src.api_export import keras_export
from keras.src.layers.layer import Layer
def _large_negative_number(dtype):
"""Return a Large negative number based on dtype."""
if backend.standardize_dtype(dtype) == "float16":
return -3e4
... |
# flake8: noqa
# Copyright 2020 The HuggingFace Datasets Authors and the TensorFlow Datasets Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LI... | # flake8: noqa
# Copyright 2020 The HuggingFace Datasets Authors and the TensorFlow Datasets Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LI... |
# Copyright (c) OpenMMLab. All rights reserved.
from ..builder import HEADS
from .standard_roi_head import StandardRoIHead
@HEADS.register_module()
class DoubleHeadRoIHead(StandardRoIHead):
"""RoI head for Double Head RCNN.
https://arxiv.org/abs/1904.06493
"""
def __init__(self, reg_roi_scale_factor... | from ..builder import HEADS
from .standard_roi_head import StandardRoIHead
@HEADS.register_module()
class DoubleHeadRoIHead(StandardRoIHead):
"""RoI head for Double Head RCNN.
https://arxiv.org/abs/1904.06493
"""
def __init__(self, reg_roi_scale_factor, **kwargs):
super(DoubleHeadRoIHead, se... |
from collections.abc import Sequence
from langchain_core.tools import BaseTool
def validate_tools_single_input(class_name: str, tools: Sequence[BaseTool]) -> None:
"""Validate tools for single input.
Args:
class_name: Name of the class.
tools: List of tools to validate.
Raises:
... | from typing import Sequence
from langchain_core.tools import BaseTool
def validate_tools_single_input(class_name: str, tools: Sequence[BaseTool]) -> None:
"""Validate tools for single input.
Args:
class_name: Name of the class.
tools: List of tools to validate.
Raises:
ValueErro... |
# Copyright (c) OpenMMLab. All rights reserved.
from .backends import (BaseStorageBackend, HTTPBackend, LmdbBackend,
LocalBackend, MemcachedBackend, PetrelBackend,
register_backend)
from .file_client import FileClient, HardDiskBackend
from .handlers import (BaseFileHandler,... | # Copyright (c) OpenMMLab. All rights reserved.
from .file_client import (BaseStorageBackend, FileClient, HardDiskBackend,
HTTPBackend, LmdbBackend, MemcachedBackend,
PetrelBackend)
from .handlers import BaseFileHandler, JsonHandler, PickleHandler, YamlHandler
from .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 string
import random
import pytest
import time
import os
cur_dir = os.path.dirname(os.path.abspath(__file__))
milvus_yml = os.path.abspath(os.path.join(cur_dir, 'docker-compose.yml'))
@pytest.fixture(scope='session', autouse=True)
def start_storage():
os.system(f"docker compose -f {milvus_yml} up -d --r... |
"""This module contains all classes used for composing graphs over indices."""
from llama_index.core.indices.composability.graph import ComposableGraph
__all__ = ["ComposableGraph"]
| """This module contains all classes used for composing graphs over indices."""
from llama_index.core.indices.composability.graph import ComposableGraph
__all__ = ["ComposableGraph"]
|
"""Tests for tf.distribute related functionality under tf implementation."""
import numpy as np
import pytest
import tensorflow as tf
from tensorflow.python.eager import context
from keras.src import backend
from keras.src import layers
from keras.src import models
from keras.src import testing
from keras.src.backend... | """Tests for tf.distribute related functionality under tf implementation."""
import numpy as np
import pytest
import tensorflow as tf
from tensorflow.python.eager import context
from keras.src import backend
from keras.src import layers
from keras.src import models
from keras.src import testing
from keras.src.backend... |
from llama_index.core.memory.chat_memory_buffer import ChatMemoryBuffer
from llama_index.core.memory.chat_summary_memory_buffer import ChatSummaryMemoryBuffer
from llama_index.core.memory.types import BaseMemory
from llama_index.core.memory.vector_memory import VectorMemory
from llama_index.core.memory.simple_composabl... | from llama_index.core.memory.chat_memory_buffer import ChatMemoryBuffer
from llama_index.core.memory.chat_summary_memory_buffer import ChatSummaryMemoryBuffer
from llama_index.core.memory.types import BaseMemory
from llama_index.core.memory.vector_memory import VectorMemory
from llama_index.core.memory.simple_composabl... |
__version__ = '0.14.3'
import os
from docarray.document import Document
from docarray.array import DocumentArray
from docarray.dataclasses import dataclass, field
if 'DA_RICH_HANDLER' in os.environ:
from rich.traceback import install
install()
| __version__ = '0.14.2'
import os
from docarray.document import Document
from docarray.array import DocumentArray
from docarray.dataclasses import dataclass, field
if 'DA_RICH_HANDLER' in os.environ:
from rich.traceback import install
install()
|
_base_ = '../_base_/default_runtime.py'
# dataset settings
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
# file_client_args = dict(
# backend='petrel',
# path_mapping=dict({
# './data/': 's3://openmmlab/datasets/detection/',
# 'data/': 's3://openmmlab/datasets/detection/'
# }))
fil... | _base_ = '../_base_/default_runtime.py'
# dataset settings
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
# file_client_args = dict(
# backend='petrel',
# path_mapping=dict({
# './data/': 's3://openmmlab/datasets/detection/',
# 'data/': 's3://openmmlab/datasets/detection/'
# }))
fil... |
import os
import sys
from typing import Iterator, TYPE_CHECKING
import numpy as np
if TYPE_CHECKING:
from docarray import Document
file_dir = os.path.dirname(__file__)
sys.path.append(os.path.dirname(file_dir))
def random_docs(
num_docs,
chunks_per_doc=5,
embed_dim=10,
jitter=1,
start_id=0,... | import os
import sys
from typing import Iterator, TYPE_CHECKING
import numpy as np
if TYPE_CHECKING:
from jina import Document
file_dir = os.path.dirname(__file__)
sys.path.append(os.path.dirname(file_dir))
def random_docs(
num_docs,
chunks_per_doc=5,
embed_dim=10,
jitter=1,
start_id=0,
... |
import pytest
from docarray import Document, DocumentArray
from jina import Executor, requests
from jina.clients.request import request_generator
from jina.logging.logger import JinaLogger
from jina.parsers import set_pod_parser
from jina.serve.runtimes.request_handlers.data_request_handler import DataRequestHandler
... | import pytest
from docarray import Document, DocumentArray
from jina import Executor, requests
from jina.logging.logger import JinaLogger
from jina.parsers import set_pod_parser
from jina.serve.runtimes.request_handlers.data_request_handler import (
DataRequestHandler,
)
from jina.clients.request import request_ge... |
from keras.src import ops
from keras.src.api_export import keras_export
from keras.src.layers.layer import Layer
@keras_export("keras.layers.UnitNormalization")
class UnitNormalization(Layer):
"""Unit normalization layer.
Normalize a batch of inputs so that each input in the batch has a L2 norm
equal to ... | from keras.src import ops
from keras.src.api_export import keras_export
from keras.src.layers.layer import Layer
@keras_export("keras.layers.UnitNormalization")
class UnitNormalization(Layer):
"""Unit normalization layer.
Normalize a batch of inputs so that each input in the batch has a L2 norm
equal to ... |
import posixpath
from pathlib import Path
import fsspec
import pytest
from fsspec.implementations.local import AbstractFileSystem, LocalFileSystem, stringify_path
class MockFileSystem(AbstractFileSystem):
protocol = "mock"
def __init__(self, *args, local_root_dir, **kwargs):
super().__init__()
... | import posixpath
from pathlib import Path
import fsspec
import pytest
from fsspec.implementations.local import AbstractFileSystem, LocalFileSystem, stringify_path
class MockFileSystem(AbstractFileSystem):
protocol = "mock"
def __init__(self, *args, local_root_dir, **kwargs):
super().__init__()
... |
import json
from pathlib import Path
from typing import Any, Callable, Optional, Tuple, Union
import PIL.Image
from .utils import download_and_extract_archive, verify_str_arg
from .vision import VisionDataset
class Food101(VisionDataset):
"""`The Food-101 Data Set <https://data.vision.ee.ethz.ch/cvl/datasets_ex... | import json
from pathlib import Path
from typing import Any, Callable, Optional, Tuple
import PIL.Image
from .utils import download_and_extract_archive, verify_str_arg
from .vision import VisionDataset
class Food101(VisionDataset):
"""`The Food-101 Data Set <https://data.vision.ee.ethz.ch/cvl/datasets_extra/foo... |
from abc import abstractmethod
from typing import TYPE_CHECKING, Dict, List, Optional, Sequence, TypeVar, Union
from docarray import Document, DocumentArray
from docarray.math import ndarray
from docarray.score import NamedScore
from qdrant_client.http import models
from qdrant_client.http.models.models import Distanc... | from abc import abstractmethod
from typing import TYPE_CHECKING, Dict, List, Optional, Sequence, TypeVar, Union
from docarray import Document, DocumentArray
from docarray.math import ndarray
from docarray.score import NamedScore
from qdrant_client.http import models as rest
from qdrant_client.http.models.models import... |
#!/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... | #!/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/master/configs'
files = sorted(glob.glob('../../configs/*/README.md'))
stats = []
titles = []
num_ckpts = 0
for f in files:
url = osp.dirnam... |
_base_ = './fcos_r50_caffe_fpn_gn-head_1x_coco.py'
# model settings
preprocess_cfg = dict(
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True,
pad_size_divisor=32)
model = dict(
preprocess_cfg=preprocess_cfg,
backbone=dict(
type='ResNeXt',
depth=101,
... | _base_ = './fcos_r50_caffe_fpn_gn-head_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),
norm_eval... |
"""Retrieval evaluators."""
from typing import List, Optional, Tuple
from llama_index.core.base.base_retriever import BaseRetriever
from llama_index.core.bridge.pydantic import Field, SerializeAsAny
from llama_index.core.evaluation.retrieval.base import (
BaseRetrievalEvaluator,
RetrievalEvalMode,
)
from llam... | """Retrieval evaluators."""
from typing import List, Optional, Tuple
from llama_index.core.base.base_retriever import BaseRetriever
from llama_index.core.bridge.pydantic import Field, SerializeAsAny
from llama_index.core.evaluation.retrieval.base import (
BaseRetrievalEvaluator,
RetrievalEvalMode,
)
from llam... |
from typing import TYPE_CHECKING, Dict, List, Optional, Sequence, TypeVar, Union
import numpy as np
from docarray import Document, DocumentArray
from docarray.array.mixins.find import FindMixin as BaseFindMixin
from docarray.math import ndarray
from docarray.math.ndarray import to_numpy_array
from docarray.score impor... | from typing import TYPE_CHECKING, Dict, List, Optional, Sequence, TypeVar, Union
import numpy as np
from docarray import Document, DocumentArray
from docarray.array.mixins.find import FindMixin as BaseFindMixin
from docarray.math import ndarray
from docarray.math.ndarray import to_numpy_array
from docarray.score impor... |
# Copyright (c) OpenMMLab. All rights reserved.
from abc import ABCMeta, abstractmethod
from typing import List, Optional, Tuple
import torch
import torch.nn as nn
from mmcv import ops
from mmengine.model import BaseModule
from torch import Tensor
from mmdet.utils import ConfigType, OptMultiConfig
class BaseRoIExtr... | # Copyright (c) OpenMMLab. All rights reserved.
from abc import ABCMeta, abstractmethod
from typing import List, Optional, Tuple
import torch
import torch.nn as nn
from mmcv import ops
from mmengine.model import BaseModule
from torch import Tensor
from mmdet.utils import ConfigType, OptMultiConfig
class BaseRoIExtr... |
import os
import librosa
from jina import Executor, Document, DocumentArray
from tensorflow.python.framework import ops
from ...vggish import vggish_input
from ...vggish_audio_encoder import VggishAudioEncoder
cur_dir = os.path.dirname(os.path.abspath(__file__))
def test_load():
encoder = Executor.load_config(... | import os
import librosa
from jina import Executor, Document, DocumentArray
from tensorflow.python.framework import ops
from ...vggish import vggish_input
from ...vggish_audio_encoder import VggishAudioEncoder
cur_dir = os.path.dirname(os.path.abspath(__file__))
def test_load():
encoder = Executor.load_config(... |
_base_ = [
'../_base_/models/cascade-rcnn_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
| _base_ = [
'../_base_/models/cascade_rcnn_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
|
"""DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.ops.nn import average_pool
from keras.src.ops.nn import batch_normalization
from keras.src.ops.nn import binary_crossentropy
from keras.src.ops.nn import categorical_crossentropy
from... | """DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.ops.nn import average_pool
from keras.src.ops.nn import batch_normalization
from keras.src.ops.nn import binary_crossentropy
from keras.src.ops.nn import categorical_crossentropy
from... |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# This software may be used and distributed according to the terms of the Llama 2 Community License Agreement.
import fire
from llama import Llama
def main(
ckpt_dir: str,
tokenizer_path: str,
temperature: float = 0.6,
top_p: float = 0.9,
max_... | # Copyright (c) Meta Platforms, Inc. and affiliates.
# This software may be used and distributed according to the terms of the Llama 2 Community License Agreement.
import fire
from llama import Llama
def main(
ckpt_dir: str,
tokenizer_path: str,
temperature: float = 0.6,
top_p: float = 0.9,
max_... |
_base_ = ['./mask2former_r50_lsj_8x2_50e_coco-panoptic.py']
num_things_classes = 80
num_stuff_classes = 0
num_classes = num_things_classes + num_stuff_classes
image_size = (1024, 1024)
batch_augments = [
dict(
type='BatchFixedSizePad',
size=image_size,
img_pad_value=0,
pad_mask=True... | _base_ = ['./mask2former_r50_lsj_8x2_50e_coco-panoptic.py']
num_things_classes = 80
num_stuff_classes = 0
num_classes = num_things_classes + num_stuff_classes
model = dict(
panoptic_head=dict(
num_things_classes=num_things_classes,
num_stuff_classes=num_stuff_classes,
loss_cls=dict(class_wei... |
# Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import torch
import torch.nn as nn
import torch.nn.functional as F
from ..builder import LOSSES
@mmcv.jit(derivate=True, coderize=True)
def ae_loss_per_image(tl_preds, br_preds, match):
"""Associative Embedding Loss in one image.
Associative Embedd... | import mmcv
import torch
import torch.nn as nn
import torch.nn.functional as F
from ..builder import LOSSES
@mmcv.jit(derivate=True, coderize=True)
def ae_loss_per_image(tl_preds, br_preds, match):
"""Associative Embedding Loss in one image.
Associative Embedding Loss including two parts: pull loss and push... |
from typing import Dict, Tuple, Optional, List
import numpy as np
from jina import Executor, DocumentArray, requests, Document
from jina.types.arrays.memmap import DocumentArrayMemmap
class SimpleIndexer(Executor):
"""
A simple indexer that stores all the Document data together,
in a DocumentArrayMemmap ... | from typing import Dict, Tuple, Optional, List
import numpy as np
from jina import Executor, DocumentArray, requests, Document
from jina.types.arrays.memmap import DocumentArrayMemmap
class SimpleIndexer(Executor):
"""
A simple indexer that stores all the Document data together,
in a DocumentArrayMemmap ... |
from typing import Any, ForwardRef, Optional, Union
from typing_extensions import get_origin
from typing_inspect import get_args, is_typevar, is_union_type
from docarray.typing.id import ID
from docarray.typing.tensor.abstract_tensor import AbstractTensor
def is_type_tensor(type_: Any) -> bool:
"""Return True i... | from typing import Any, ForwardRef, Optional
from typing_extensions import get_origin
from typing_inspect import get_args, is_typevar, is_union_type
from docarray.typing.id import ID
from docarray.typing.tensor.abstract_tensor import AbstractTensor
def is_type_tensor(type_: Any) -> bool:
"""Return True if type ... |
_base_ = './mask_rcnn_hrnetv2p_w40_1x_coco.py'
# learning policy
max_epochs = 24
train_cfg = dict(max_epochs=max_epochs)
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=max_epochs,
b... | _base_ = './mask_rcnn_hrnetv2p_w40_1x_coco.py'
# learning policy
lr_config = dict(step=[16, 22])
runner = dict(type='EpochBasedRunner', max_epochs=24)
|
"""
This script contains an example how to perform semantic search with Qdrant.
You need Qdrant up and running locally:
https://qdrant.tech/documentation/quickstart/
Further, you need the Python Qdrant Client installed: https://python-client.qdrant.tech/, e.g.:
```
pip install qdrant-client
```
This script was create... | """
This script contains an example how to perform semantic search with Qdrant.
You need Qdrant up and running locally:
https://qdrant.tech/documentation/quickstart/
Further, you need the Python Qdrant Client installed: https://python-client.qdrant.tech/, e.g.:
```
pip install qdrant-client
```
This script was create... |
"""Kept for backwards compatibility."""
from langchain_text_splitters import (
Language,
RecursiveCharacterTextSplitter,
TextSplitter,
Tokenizer,
TokenTextSplitter,
)
from langchain_text_splitters.base import split_text_on_tokens
from langchain_text_splitters.character import CharacterTextSplitter
... | """Kept for backwards compatibility."""
from langchain_text_splitters import (
Language,
RecursiveCharacterTextSplitter,
TextSplitter,
Tokenizer,
TokenTextSplitter,
)
from langchain_text_splitters.base import split_text_on_tokens
from langchain_text_splitters.character import CharacterTextSplitter
... |
# Copyright (c) OpenMMLab. All rights reserved.
from .registry import Registry, build_from_cfg
from .root import (DATA_SAMPLERS, DATASETS, HOOKS, MODELS,
OPTIMIZER_CONSTRUCTORS, OPTIMIZERS, PARAM_SCHEDULERS,
RUNNER_CONSTRUCTORS, RUNNERS, TASK_UTILS, TRANSFORMS,
W... | # Copyright (c) OpenMMLab. All rights reserved.
from .registry import Registry, build_from_cfg
from .root import (DATA_SAMPLERS, DATASETS, HOOKS, MODELS,
OPTIMIZER_CONSTRUCTORS, OPTIMIZERS, RUNNER_CONSTRUCTORS,
RUNNERS, TASK_UTILS, TRANSFORMS, WEIGHT_INITIALIZERS)
__all__ = [
... |
# Copyright (c) OpenMMLab. All rights reserved.
import math
from typing import Optional
import torch
import torch.nn as nn
from mmengine.model import ExponentialMovingAverage
from torch import Tensor
from mmdet.registry import MODELS
@MODELS.register_module()
class ExpMomentumEMA(ExponentialMovingAverage):
"""E... | # Copyright (c) OpenMMLab. All rights reserved.
import math
from typing import Optional
import torch
import torch.nn as nn
from mmengine.model import ExponentialMovingAverage
from torch import Tensor
from mmdet.registry import MODELS
@MODELS.register_module()
class ExpMomentumEMA(ExponentialMovingAverage):
"""E... |
"""JSON node parser."""
import json
from typing import Any, Dict, Generator, List, Optional, Sequence
from llama_index.core.callbacks.base import CallbackManager
from llama_index.core.node_parser.interface import NodeParser
from llama_index.core.node_parser.node_utils import build_nodes_from_splits
from llama_index.co... | """JSON node parser."""
import json
from typing import Any, Dict, Generator, List, Optional, Sequence
from llama_index.core.callbacks.base import CallbackManager
from llama_index.core.node_parser.interface import NodeParser
from llama_index.core.node_parser.node_utils import build_nodes_from_splits
from llama_index.co... |
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