input stringlengths 33 5k | output stringlengths 32 5k |
|---|---|
# 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 numpy as np
import pytest
from pydantic import parse_obj_as
from docarray.base_doc.doc import BaseDoc
from docarray.documents import Mesh3D
from tests import TOYDATA_DIR
LOCAL_OBJ_FILE = str(TOYDATA_DIR / 'tetrahedron.obj')
REMOTE_OBJ_FILE = 'https://people.sc.fsu.edu/~jburkardt/data/obj/al.obj'
pytestmark = ... |
from __future__ import annotations
from typing import Literal
from sentence_transformers.losses.GISTEmbedLoss import GISTEmbedLoss
from sentence_transformers.sparse_encoder.SparseEncoder import SparseEncoder
class SparseGISTEmbedLoss(GISTEmbedLoss):
def __init__(
self,
model: SparseEncoder,
... | from __future__ import annotations
from sentence_transformers.losses.GISTEmbedLoss import GISTEmbedLoss
from sentence_transformers.sparse_encoder.SparseEncoder import SparseEncoder
class SparseGISTEmbedLoss(GISTEmbedLoss):
def __init__(
self,
model: SparseEncoder,
guide: SparseEncoder,
... |
from PIL import Image
from sentence_transformers import SentenceTransformer, models, util
###########
image = Image.open("two_dogs_in_snow.jpg")
from transformers import CLIPModel, CLIPProcessor
model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
processor = CLIPProcessor.from_pretrained("openai/clip... | from sentence_transformers import SentenceTransformer, util, models
from PIL import Image
###########
image = Image.open("two_dogs_in_snow.jpg")
from transformers import CLIPProcessor, CLIPModel
model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
processor = CLIPProcessor.from_pretrained("openai/clip... |
from collections import Counter
import pytest
from datasets import Dataset
from sentence_transformers.sampler import GroupByLabelBatchSampler
@pytest.fixture
def dummy_dataset():
"""
Dummy dataset for testing purposes. The dataset looks as follows:
{
"data": [0, 1, 2, ..., 99],
"label_a... | import pytest
from datasets import Dataset
from sentence_transformers.sampler import GroupByLabelBatchSampler
from collections import Counter
@pytest.fixture
def dummy_dataset():
"""
Dummy dataset for testing purposes. The dataset looks as follows:
{
"data": [0, 1, 2, ..., 99],
"label_a":... |
from langchain_core.callbacks.base import BaseCallbackHandler, BaseCallbackManager
def test_remove_handler() -> None:
"""Test removing handler does not raise an error on removal.
An handler can be inheritable or not. This test checks that
removing a handler does not raise an error if the handler
is n... | from langchain_core.callbacks.base import BaseCallbackHandler
from langchain_core.callbacks.manager import BaseCallbackManager
def test_remove_handler() -> None:
"""Test removing handler does not raise an error on removal.
An handler can be inheritable or not. This test checks that
removing a handler doe... |
from collections.abc import Generator
import pytest
from langchain_core.vectorstores import VectorStore
from langchain_tests.integration_tests.vectorstores import VectorStoreIntegrationTests
from langchain_chroma import Chroma
class TestChromaStandard(VectorStoreIntegrationTests):
@pytest.fixture()
def vect... | from collections.abc import Generator
import pytest
from langchain_core.vectorstores import VectorStore
from langchain_tests.integration_tests.vectorstores import VectorStoreIntegrationTests
from langchain_chroma import Chroma
class TestChromaStandard(VectorStoreIntegrationTests):
@pytest.fixture()
def vect... |
from typing import Any, Optional, Sequence, List, cast
from llama_index.core.llms import ChatMessage, ImageBlock, TextBlock
from llama_index.core.base.llms.types import ContentBlock
from llama_index.core.base.llms.generic_utils import image_node_to_image_block
from llama_index.core.schema import ImageDocument, ImageNo... | from typing import Any, Optional, Sequence
from llama_index.core.multi_modal_llms.base import ChatMessage
from llama_index.core.schema import ImageDocument
def generate_gemini_multi_modal_chat_message(
prompt: str,
role: str,
image_documents: Optional[Sequence[ImageDocument]] = None,
**kwargs: Any,
)... |
_base_ = '../faster_rcnn/faster_rcnn_r50_caffe_fpn_1x_coco.py'
model = dict(
rpn_head=dict(
_delete_=True,
type='GARPNHead',
in_channels=256,
feat_channels=256,
approx_anchor_generator=dict(
type='AnchorGenerator',
octave_base_scale=8,
scal... | _base_ = '../faster_rcnn/faster_rcnn_r50_caffe_fpn_1x_coco.py'
model = dict(
rpn_head=dict(
_delete_=True,
type='GARPNHead',
in_channels=256,
feat_channels=256,
approx_anchor_generator=dict(
type='AnchorGenerator',
octave_base_scale=8,
scal... |
# Copyright (c) OpenMMLab. All rights reserved.
from unittest import TestCase
from unittest.mock import Mock
from mmengine.hooks import RuntimeInfoHook
from mmengine.logging import MessageHub
class TestRuntimeInfoHook(TestCase):
def test_before_run(self):
message_hub = MessageHub.get_instance(
... | # Copyright (c) OpenMMLab. All rights reserved.
from unittest import TestCase
from unittest.mock import Mock
from mmengine.hooks import RuntimeInfoHook
from mmengine.logging import MessageHub
class TestRuntimeInfoHook(TestCase):
def test_before_run(self):
message_hub = MessageHub.get_instance(
... |
from torch.fx import Graph, GraphModule, Node
from torch.fx._compatibility import compatibility
from .matcher_utils import InternalMatch, SubgraphMatcher
__all__ = ["SubgraphMatcherWithNameNodeMap"]
def _split_to_graph_and_name_node_map(
gm: GraphModule,
) -> tuple[GraphModule, dict[str, Node]]:
from torch... | from torch.fx import Graph, GraphModule, Node
from torch.fx._compatibility import compatibility
from .matcher_utils import InternalMatch, SubgraphMatcher
__all__ = ["SubgraphMatcherWithNameNodeMap"]
def _split_to_graph_and_name_node_map(
gm: GraphModule,
) -> tuple[GraphModule, dict[str, Node]]:
from torch... |
# TODO: Add _log_api_usage_once() in all mid-level kernels. If they remain not jit-scriptable we can use decorators
from torchvision.transforms import InterpolationMode # usort: skip
from ._utils import is_simple_tensor # usort: skip
from ._meta import (
clamp_bounding_box,
convert_format_bounding_box,
... | # TODO: Add _log_api_usage_once() in all mid-level kernels. If they remain not jit-scriptable we can use decorators
from torchvision.transforms import InterpolationMode # usort: skip
from ._utils import is_simple_tensor # usort: skip
from ._meta import (
clamp_bounding_box,
convert_format_bounding_box,
... |
import numpy as np
import pytest
from absl.testing import parameterized
from keras.src import layers
from keras.src import models
from keras.src import ops
from keras.src import testing
from keras.src.utils import summary_utils
class SummaryUtilsTest(testing.TestCase, parameterized.TestCase):
@parameterized.para... | import numpy as np
import pytest
from absl.testing import parameterized
from keras.src import layers
from keras.src import models
from keras.src import testing
from keras.src.utils import summary_utils
class SummaryUtilsTest(testing.TestCase, parameterized.TestCase):
@parameterized.parameters([("adam",), (None,)... |
import textwrap
import pyarrow as pa
import pytest
from datasets import Features, Value
from datasets.packaged_modules.json.json import Json
@pytest.fixture
def jsonl_file(tmp_path):
filename = tmp_path / "file.jsonl"
data = textwrap.dedent(
"""\
{"col_1": 1, "col_2": 2}
{"col_1": 10... | import textwrap
import pyarrow as pa
import pytest
from datasets.packaged_modules.json.json import Json
@pytest.fixture
def jsonl_file(tmp_path):
filename = tmp_path / "file.jsonl"
data = textwrap.dedent(
"""\
{"col_1": 1, "col_2": 2}
{"col_1": 10, "col_2": 20}
"""
)
... |
import pytest
from langchain_core.agents import AgentAction, AgentFinish
from langchain_core.exceptions import OutputParserException
from langchain.agents.mrkl.output_parser import (
MISSING_ACTION_AFTER_THOUGHT_ERROR_MESSAGE,
MISSING_ACTION_INPUT_AFTER_ACTION_ERROR_MESSAGE,
MRKLOutputParser,
)
mrkl_outpu... | import pytest
from langchain_core.agents import AgentAction, AgentFinish
from langchain_core.exceptions import OutputParserException
from langchain.agents.mrkl.output_parser import (
MISSING_ACTION_AFTER_THOUGHT_ERROR_MESSAGE,
MISSING_ACTION_INPUT_AFTER_ACTION_ERROR_MESSAGE,
MRKLOutputParser,
)
mrkl_outpu... |
import pathlib
from argparse import ArgumentParser
from lightning import ConformerRNNTModule
from pytorch_lightning import seed_everything, Trainer
from pytorch_lightning.callbacks import LearningRateMonitor, ModelCheckpoint
from pytorch_lightning.plugins import DDPPlugin
from transforms import get_data_module
def r... | import pathlib
from argparse import ArgumentParser
from lightning import ConformerRNNTModule, get_data_module
from pytorch_lightning import seed_everything, Trainer
from pytorch_lightning.callbacks import LearningRateMonitor, ModelCheckpoint
from pytorch_lightning.plugins import DDPPlugin
def run_train(args):
se... |
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... |
"""Google PaLM embeddings file."""
import deprecated
from typing import Any, List, Optional
from llama_index.core.base.embeddings.base import (
DEFAULT_EMBED_BATCH_SIZE,
BaseEmbedding,
)
from llama_index.core.bridge.pydantic import PrivateAttr
from llama_index.core.callbacks.base import CallbackManager
impor... | """Google PaLM embeddings file."""
import deprecated
from typing import Any, List, Optional
from llama_index.core.base.embeddings.base import (
DEFAULT_EMBED_BATCH_SIZE,
BaseEmbedding,
)
from llama_index.core.bridge.pydantic import PrivateAttr
from llama_index.core.callbacks.base import CallbackManager
impor... |
import pytest
from llama_index.core.workflow.context import Context
from llama_index.core.workflow.decorators import step
from llama_index.core.workflow.events import Event, StartEvent, StopEvent
from llama_index.core.workflow.retry_policy import ConstantDelayRetryPolicy
from llama_index.core.workflow.workflow import ... | import pytest
from llama_index.core.workflow.context import Context
from llama_index.core.workflow.decorators import step
from llama_index.core.workflow.events import Event, StartEvent, StopEvent
from llama_index.core.workflow.retry_policy import ConstantDelayRetryPolicy
from llama_index.core.workflow.workflow import ... |
#!/usr/bin/env python3
import logging
import pathlib
from argparse import ArgumentParser, RawTextHelpFormatter
import torch
import torchaudio
from torchaudio.prototype.pipelines import EMFORMER_RNNT_BASE_TEDLIUM3
logger = logging.getLogger(__name__)
def compute_word_level_distance(seq1, seq2):
return torchaudi... | #!/usr/bin/env python3
import logging
import pathlib
from argparse import ArgumentParser, RawTextHelpFormatter
import torch
import torchaudio
from torchaudio.prototype.pipelines import EMFORMER_RNNT_BASE_TEDLIUM3
logger = logging.getLogger(__name__)
def compute_word_level_distance(seq1, seq2):
return torchaudi... |
"""AgentQL Web Reader."""
import httpx
from typing import Optional, List
from llama_index.core.readers.base import BasePydanticReader
from llama_index.core.schema import Document
import logging
logging.getLogger("root").setLevel(logging.INFO)
QUERY_DATA_ENDPOINT = "https://api.agentql.com/v1/query-data"
API_TIMEOU... | """AgentQL Web Reader."""
import httpx
from typing import Optional, List
from llama_index.core.readers.base import BasePydanticReader
from llama_index.core.schema import Document
import logging
logging.getLogger("root").setLevel(logging.INFO)
QUERY_DATA_ENDPOINT = "https://api.agentql.com/v1/query-data"
API_TIMEOUT... |
import numpy as np
import pytest
from docarray import DocumentArray
from docarray.document.generators import from_ndarray
from jina import Client, Flow
from jina.excepts import BadClientCallback
def validate(x):
raise NotImplementedError
@pytest.mark.skip(
reason='something wrong with parametrize in the fo... | from typing import Optional
import aiohttp
import numpy as np
import pytest
from docarray import DocumentArray
from docarray.document.generators import from_ndarray
from jina import Client, Flow
from jina.excepts import BadClientCallback
def validate(x):
raise NotImplementedError
@pytest.mark.skip(
reason... |
_base_ = [
'../_base_/models/mask-rcnn_r50_fpn.py',
'../common/lsj-100e_coco-instance.py'
]
image_size = (1024, 1024)
batch_augments = [
dict(type='BatchFixedSizePad', size=image_size, pad_mask=True)
]
norm_cfg = dict(type='SyncBN', requires_grad=True)
# Use MMSyncBN that handles empty tensor in head. It c... | _base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
'../common/lsj_100e_coco_instance.py'
]
image_size = (1024, 1024)
batch_augments = [
dict(type='BatchFixedSizePad', size=image_size, pad_mask=True)
]
norm_cfg = dict(type='SyncBN', requires_grad=True)
# Use MMSyncBN that handles empty tensor in head. It c... |
# dataset settings
dataset_type = 'Objects365V1Dataset'
data_root = 'data/Objects365/Obj365_v1/'
# Example to use different file client
# Method 1: simply set the data root and let the file I/O module
# automatically infer from prefix (not support LMDB and Memcache yet)
# data_root = 's3://openmmlab/datasets/detectio... | # dataset settings
dataset_type = 'Objects365V1Dataset'
data_root = 'data/Objects365/Obj365_v1/'
# file_client_args = dict(
# backend='petrel',
# path_mapping=dict({
# './data/': 's3://openmmlab/datasets/detection/',
# 'data/': 's3://openmmlab/datasets/detection/'
# }))
file_client_args = d... |
from __future__ import annotations
import logging
from typing import TYPE_CHECKING, Any, Literal
from sentence_transformers.evaluation import TripletEvaluator
if TYPE_CHECKING:
import numpy as np
from torch import Tensor
from sentence_transformers.similarity_functions import SimilarityFunction
from ... | from __future__ import annotations
import logging
from typing import TYPE_CHECKING, Any, Literal
from sentence_transformers.evaluation import TripletEvaluator
if TYPE_CHECKING:
import numpy as np
from torch import Tensor
from sentence_transformers.similarity_functions import SimilarityFunction
from ... |
# Copyright (c) OpenMMLab. All rights reserved.
import torch
from mmdet.core import bbox2result
from mmdet.registry import MODELS
from .single_stage import SingleStageDetector
@MODELS.register_module()
class YOLACT(SingleStageDetector):
"""Implementation of `YOLACT <https://arxiv.org/abs/1904.02689>`_"""
de... | # Copyright (c) OpenMMLab. All rights reserved.
import torch
from mmdet.core import bbox2result
from ..builder import DETECTORS, build_head
from .single_stage import SingleStageDetector
@DETECTORS.register_module()
class YOLACT(SingleStageDetector):
"""Implementation of `YOLACT <https://arxiv.org/abs/1904.02689>... |
import time
from datasets import load_dataset
from sentence_transformers import SentenceTransformer
from sentence_transformers.quantization import quantize_embeddings, semantic_search_usearch
# 1. Load the quora corpus with questions
dataset = load_dataset("quora", split="train").map(
lambda batch: {"text": [text... | import time
from sentence_transformers import SentenceTransformer
from sentence_transformers.quantization import quantize_embeddings, semantic_search_usearch
from datasets import load_dataset
# 1. Load the quora corpus with questions
dataset = load_dataset("quora", split="train").map(
lambda batch: {"text": [text ... |
from fastapi.testclient import TestClient
from docs_src.configure_swagger_ui.tutorial001 import app
client = TestClient(app)
def test_swagger_ui():
response = client.get("/docs")
assert response.status_code == 200, response.text
assert '"syntaxHighlight": false' in response.text, (
"syntaxHighli... | from fastapi.testclient import TestClient
from docs_src.configure_swagger_ui.tutorial001 import app
client = TestClient(app)
def test_swagger_ui():
response = client.get("/docs")
assert response.status_code == 200, response.text
assert (
'"syntaxHighlight": false' in response.text
), "syntax... |
import os
import pytest
from jina import Client, Document, Executor, Flow, requests
from jina.helper import random_port
class MyExec(Executor):
def __init__(self, bar: str, bar2: int = 3, **kwargs):
super().__init__(**kwargs)
self.bar = bar
self.bar2 = bar2
@requests(on=['/foo', '/f... | import os
import pytest
from jina import Executor, Client, requests, Flow, Document
exposed_port = 12345
class MyExec(Executor):
def __init__(self, bar: str, bar2: int = 3, **kwargs):
super().__init__(**kwargs)
self.bar = bar
self.bar2 = bar2
@requests(on=['/foo', '/foo2'])
def... |
# Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmengine.utils import digit_version
from torch import Tensor
from mmdet.registry import MODELS
MODELS.register_module('Linear', module=nn.Linear)
@MODELS.register_module(name='NormedLinear')
class... | # Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import Tensor
from mmdet.registry import MODELS
MODELS.register_module('Linear', module=nn.Linear)
@MODELS.register_module(name='NormedLinear')
class NormedLinear(nn.Linear):
"""Normaliz... |
"""
====================
Theil-Sen Regression
====================
Computes a Theil-Sen Regression on a synthetic dataset.
See :ref:`theil_sen_regression` for more information on the regressor.
Compared to the OLS (ordinary least squares) estimator, the Theil-Sen
estimator is robust against outliers. It has a breakd... | """
====================
Theil-Sen Regression
====================
Computes a Theil-Sen Regression on a synthetic dataset.
See :ref:`theil_sen_regression` for more information on the regressor.
Compared to the OLS (ordinary least squares) estimator, the Theil-Sen
estimator is robust against outliers. It has a breakd... |
# Copyright (c) OpenMMLab. All rights reserved.
import logging
from abc import ABCMeta, abstractmethod
from mmengine.logging import print_log
class BaseStorageBackend(metaclass=ABCMeta):
"""Abstract class of storage backends.
All backends need to implement two apis: :meth:`get()` and
:meth:`get_text()`.... | # Copyright (c) OpenMMLab. All rights reserved.
import warnings
from abc import ABCMeta, abstractmethod
class BaseStorageBackend(metaclass=ABCMeta):
"""Abstract class of storage backends.
All backends need to implement two apis: :meth:`get()` and
:meth:`get_text()`.
- :meth:`get()` reads the file as... |
import re
from langchain_core.output_parsers import BaseOutputParser
class BooleanOutputParser(BaseOutputParser[bool]):
"""Parse the output of an LLM call to a boolean."""
true_val: str = "YES"
"""The string value that should be parsed as True."""
false_val: str = "NO"
"""The string value that s... | import re
from langchain_core.output_parsers import BaseOutputParser
class BooleanOutputParser(BaseOutputParser[bool]):
"""Parse the output of an LLM call to a boolean."""
true_val: str = "YES"
"""The string value that should be parsed as True."""
false_val: str = "NO"
"""The string value that s... |
"""Init file."""
from llama_index.readers.remote.base import (
RemoteReader,
)
__all__ = ["RemoteReader"]
| """Init file."""
from llama_index.readers.remote.base import (
RemoteReader,
)
__all__ = ["RemoteReader"]
|
from langchain.output_parsers.regex import RegexParser
def load_output_parser(config: dict) -> dict:
"""Load an output parser.
Args:
config: config dict
Returns:
config dict with output parser loaded
"""
if "output_parsers" in config:
if config["output_parsers"] is not No... | from langchain.output_parsers.regex import RegexParser
def load_output_parser(config: dict) -> dict:
"""Load an output parser.
Args:
config: config dict
Returns:
config dict with output parser loaded
"""
if "output_parsers" in config:
if config["output_parsers"] is not No... |
from __future__ import annotations
import copy
from typing import TYPE_CHECKING, List
import pytest
from langchain_core.documents import Document
from pytest_mock import MockerFixture
from langchain_community.retrievers import ZepRetriever
if TYPE_CHECKING:
from zep_python import MemorySearchResult, ZepClient
... | from __future__ import annotations
import copy
from typing import TYPE_CHECKING, List
import pytest
from langchain_core.documents import Document
from pytest_mock import MockerFixture
from langchain_community.retrievers import ZepRetriever
if TYPE_CHECKING:
from zep_python import MemorySearchResult, ZepClient
... |
import sys
from jina.parsers import set_gateway_parser
from jina.parsers.helper import _set_gateway_uses
from jina.serve.runtimes.gateway import GatewayRuntime
def run(*args, **kwargs):
runtime_cls = GatewayRuntime
print(f' args {args}')
runtime_args = set_gateway_parser().parse_args(args)
print(f' p... | import sys
from jina.serve.runtimes.gateway.grpc import GRPCGatewayRuntime
from jina.serve.runtimes.gateway.http import HTTPGatewayRuntime
from jina.serve.runtimes.gateway.websocket import WebSocketGatewayRuntime
from jina.enums import GatewayProtocolType
from jina.parsers import set_gateway_parser
def run(*args, *... |
# Copyright (c) OpenMMLab. All rights reserved.
from .data_preprocessor import BatchSyncRandomResize, DetDataPreprocessor
__all__ = ['DetDataPreprocessor', 'BatchSyncRandomResize']
| # Copyright (c) OpenMMLab. All rights reserved.
from .data_preprocessor import DetDataPreprocessor
__all__ = ['DetDataPreprocessor']
|
_base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# model settings
model = dict(
bbox_head=dict(
_delete_=True,
type='SABLRetinaHead',
num_classes=80,
in_chann... | _base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# model settings
model = dict(
bbox_head=dict(
_delete_=True,
type='SABLRetinaHead',
num_classes=80,
in_chann... |
from dataclasses import dataclass, field
from typing import Union
from transformers import TrainingArguments as TransformersTrainingArguments
from transformers.utils import ExplicitEnum
class BatchSamplers(ExplicitEnum):
"""
Stores the acceptable string identifiers for batch samplers.
The batch sampler ... | from dataclasses import dataclass, field
from typing import Union
from transformers import TrainingArguments as TransformersTrainingArguments
from transformers.utils import ExplicitEnum
class BatchSamplers(ExplicitEnum):
"""
Stores the acceptable string identifiers for batch samplers.
The batch sampler i... |
# Copyright (c) OpenMMLab. All rights reserved.
__version__ = '2.23.0'
short_version = __version__
def parse_version_info(version_str):
version_info = []
for x in version_str.split('.'):
if x.isdigit():
version_info.append(int(x))
elif x.find('rc') != -1:
patch_version... | # Copyright (c) OpenMMLab. All rights reserved.
__version__ = '2.22.0'
short_version = __version__
def parse_version_info(version_str):
version_info = []
for x in version_str.split('.'):
if x.isdigit():
version_info.append(int(x))
elif x.find('rc') != -1:
patch_version... |
# Copyright (c) OpenMMLab. All rights reserved.
from .base_panoptic_fusion_head import \
BasePanopticFusionHead # noqa: F401,F403
from .heuristic_fusion_head import HeuristicFusionHead # noqa: F401,F403
from .maskformer_fusion_head import MaskFormerFusionHead # noqa: F401,F403
| # Copyright (c) OpenMMLab. All rights reserved.
from .base_panoptic_fusion_head import \
BasePanopticFusionHead # noqa: F401,F403
from .heuristic_fusion_head import HeuristicFusionHead # noqa: F401,F403
|
import json
from jina.logging.logger import JinaLogger
from jina.parsers import set_gateway_parser
from jina.serve.runtimes.gateway.http.app import get_fastapi_app
from jina.serve.streamer import GatewayStreamer
JINA_LOGO_URL = 'https://api.jina.ai/logo/logo-product/jina-core/horizontal-layout/colored/Product%20logo_... | import json
from jina.logging.logger import JinaLogger
from jina.parsers import set_gateway_parser
from jina.serve.runtimes.gateway.http.app import get_fastapi_app
JINA_LOGO_URL = 'https://api.jina.ai/logo/logo-product/jina-core/horizontal-layout/colored/Product%20logo_Core_vertical_colorful%402x-margin.png'
GATEWAY_... |
_base_ = './yolof_r50-c5_8xb8-1x_coco.py'
# We implemented the iter-based config according to the source code.
# COCO dataset has 117266 images after filtering. We use 8 gpu and
# 8 batch size training, so 22500 is equivalent to
# 22500/(117266/(8x8))=12.3 epoch, 15000 is equivalent to 8.2 epoch,
# 20000 is equivalent... | _base_ = './yolof_r50_c5_8x8_1x_coco.py'
# We implemented the iter-based config according to the source code.
# COCO dataset has 117266 images after filtering. We use 8 gpu and
# 8 batch size training, so 22500 is equivalent to
# 22500/(117266/(8x8))=12.3 epoch, 15000 is equivalent to 8.2 epoch,
# 20000 is equivalent ... |
from typing import Optional
from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class TextDatasetReader(AbstractDatasetReader):
def __init__(
self,
path_or_paths: Nest... | from typing import Optional
from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class TextDatasetReader(AbstractDatasetReader):
def __init__(
self,
path_or_paths: Nest... |
from unittest.mock import patch
import pytest
from llama_index.core.readers.base import BaseReader
from llama_index.readers.microsoft_outlook_emails import OutlookEmailReader
def test_class():
names_of_base_classes = [b.__name__ for b in OutlookEmailReader.__mro__]
assert BaseReader.__name__ in names_of_base... | import pytest
from unittest.mock import patch
from llama_index.core.readers.base import BaseReader
from llama_index.readers.outlook_emails import OutlookEmailReader
def test_class():
names_of_base_classes = [b.__name__ for b in OutlookEmailReader.__mro__]
assert BaseReader.__name__ in names_of_base_classes
... |
"""Base classes for chain routing."""
from __future__ import annotations
from abc import ABC
from collections.abc import Mapping
from typing import Any, NamedTuple, Optional
from langchain_core.callbacks import (
AsyncCallbackManagerForChainRun,
CallbackManagerForChainRun,
Callbacks,
)
from pydantic impo... | """Base classes for chain routing."""
from __future__ import annotations
from abc import ABC
from collections.abc import Mapping
from typing import Any, NamedTuple, Optional
from langchain_core.callbacks import (
AsyncCallbackManagerForChainRun,
CallbackManagerForChainRun,
Callbacks,
)
from pydantic impo... |
import unittest
import pytest
import torch
from torchvision.models.maxvit import SwapAxes, WindowDepartition, WindowPartition
class MaxvitTester(unittest.TestCase):
def test_maxvit_window_partition(self):
input_shape = (1, 3, 224, 224)
partition_size = 7
n_partitions = input_shape[3] // ... | import unittest
import pytest
import torch
from torchvision.models.maxvit import SwapAxes, WindowDepartition, WindowPartition
class MaxvitTester(unittest.TestCase):
def test_maxvit_window_partition(self):
input_shape = (1, 3, 224, 224)
partition_size = 7
n_partitions = input_shape[3] // ... |
"""Copyright 2024, XGBoost contributors"""
import pytest
from distributed import Client, Scheduler, Worker
from distributed.utils_test import gen_cluster
import xgboost as xgb
from xgboost import testing as tm
from xgboost.testing.dask import check_external_memory
@pytest.mark.parametrize("is_qdm", [True, False])
@... | from typing import List, cast
import numpy as np
from distributed import Client, Scheduler, Worker, get_worker
from distributed.utils_test import gen_cluster
import xgboost as xgb
from xgboost import testing as tm
from xgboost.compat import concat
def run_external_memory(worker_id: int, n_workers: int, comm_args: d... |
import torch
from parameterized import parameterized
from torchaudio.prototype.models import (
conformer_wav2vec2_base,
conformer_wav2vec2_pretrain_base,
conformer_wav2vec2_pretrain_large,
)
from torchaudio_unittest.common_utils import disabledInCI, nested_params, skipIfNoCuda, torch_script, TorchaudioTestC... | import torch
from parameterized import parameterized
from torchaudio.prototype.models import (
conformer_wav2vec2_base,
conformer_wav2vec2_pretrain_base,
conformer_wav2vec2_pretrain_large,
)
from torchaudio_unittest.common_utils import nested_params, skipIfNoCuda, torch_script, TorchaudioTestCase
class Te... |
_base_ = '../cascade_rcnn/cascade-mask-rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)))
| _base_ = '../cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)))
|
_base_ = [
'../_base_/models/mask-rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
vis_backends = [dict(type='LocalVisBackend'), dict(type='WandBVisBackend')]
visualizer = dict(vis_backends=vis_backends)
# MMEngine support the ... | # TODO: Awaiting refactoring
_base_ = [
'../_base_/models/mask-rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# Set evaluation interval
evaluation = dict(interval=2)
# Set checkpoint interval
checkpoint_config = dict(interval=... |
# 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
from docarray.typing import AnyUrl
def test_set_any_url():
class MyDocument(BaseDoc):
any_url: AnyUrl
d = MyDocument(any_url="https://jina.ai")
assert isinstance(d.any_url, AnyUrl)
assert d.any_url == "https://jina.ai"
|
from __future__ import annotations
import pytest
from sentence_transformers import SparseEncoder
@pytest.fixture()
def splade_bert_tiny_model() -> SparseEncoder:
return SparseEncoder("sparse-encoder-testing/splade-bert-tiny-nq")
@pytest.fixture(scope="session")
def splade_bert_tiny_model_reused() -> SparseEnc... | from __future__ import annotations
import pytest
from sentence_transformers import SparseEncoder
@pytest.fixture()
def splade_bert_tiny_model() -> SparseEncoder:
return SparseEncoder("sparse-encoder-testing/splade-bert-tiny-nq")
@pytest.fixture()
def csr_bert_tiny_model() -> SparseEncoder:
return SparseEn... |
"""DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.quantizers import deserialize
from keras.src.quantizers import get
from keras.src.quantizers import serialize
from keras.src.quantizers.quantizers import AbsMaxQuantizer
from keras.sr... | """DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.quantizers import deserialize
from keras.src.quantizers import get
from keras.src.quantizers import serialize
from keras.src.quantizers.quantizers import AbsMaxQuantizer
from keras.sr... |
"""Top-level imports for LlamaIndex."""
__version__ = "0.12.39"
import logging
from logging import NullHandler
from typing import Callable, Optional
try:
# Force pants to install eval_type_backport on 3.9
import eval_type_backport # noqa # type: ignore
except ImportError:
pass
# response
from llama_in... | """Top-level imports for LlamaIndex."""
__version__ = "0.12.38"
import logging
from logging import NullHandler
from typing import Callable, Optional
try:
# Force pants to install eval_type_backport on 3.9
import eval_type_backport # noqa # type: ignore
except ImportError:
pass
# response
from llama_in... |
"""Module to change the configuration of libsox, which is used by I/O functions like
:py:mod:`~torchaudio.backend.sox_io_backend` and :py:mod:`~torchaudio.sox_effects`.
"""
from typing import Dict, List
import torchaudio
sox_ext = torchaudio._extension.lazy_import_sox_ext()
def set_seed(seed: int):
"""Set libs... | """Module to change the configuration of libsox, which is used by I/O functions like
:py:mod:`~torchaudio.backend.sox_io_backend` and :py:mod:`~torchaudio.sox_effects`.
"""
from typing import Dict, List
import torchaudio
@torchaudio._extension.fail_if_no_sox
def set_seed(seed: int):
"""Set libsox's PRNG
Ar... |
from langchain_core.load.dump import default, dumpd, dumps
__all__ = ["default", "dumpd", "dumps"]
| from langchain_core.load.dump import default, dumpd, dumps
__all__ = ["default", "dumps", "dumpd"]
|
import contextlib
import logging
import typing
import fastapi
import fastapi.responses
import starlette.middleware.cors
import uvicorn
import backend.data.block
import backend.data.db
import backend.data.user
import backend.server.routers.v1
import backend.util.service
import backend.util.settings
settings = backend... | import contextlib
import logging
import typing
import fastapi
import fastapi.responses
import starlette.middleware.cors
import uvicorn
import backend.data.block
import backend.data.db
import backend.data.user
import backend.server.routers.v1
import backend.util.service
import backend.util.settings
settings = backend... |
import unittest
import torch
import torchaudio.prototype.functional as F
from parameterized import parameterized
from torchaudio_unittest.common_utils import skipIfNoRIR, TestBaseMixin, torch_script
class TorchScriptConsistencyTestImpl(TestBaseMixin):
def _assert_consistency(self, func, inputs, shape_only=False)... | import unittest
import torch
import torchaudio.prototype.functional as F
from parameterized import parameterized
from torchaudio_unittest.common_utils import skipIfNoRIR, TestBaseMixin, torch_script
class TorchScriptConsistencyTestImpl(TestBaseMixin):
def _assert_consistency(self, func, inputs, shape_only=False)... |
# This file should NEVER be packaged! This is a hack to make "import keras" from
# the base of the repo just import the source files. We'll keep it for compat.
import os # isort: skip
# Add everything in /api/ to the module search path.
__path__.append(os.path.join(os.path.dirname(__file__), "api")) # noqa: F405
f... | # DO NOT EDIT. Generated by api_gen.sh
from keras.api import DTypePolicy
from keras.api import FloatDTypePolicy
from keras.api import Function
from keras.api import Initializer
from keras.api import Input
from keras.api import InputSpec
from keras.api import KerasTensor
from keras.api import Layer
from keras.api import... |
from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.tools import SlackGetMessage
from langchain_community.tools.slack.get_message import SlackGetMessageSchema
# Create a way to dynamically look up deprecated imports.
# Used to consolidat... | from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.tools import SlackGetMessage
from langchain_community.tools.slack.get_message import SlackGetMessageSchema
# Create a way to dynamically look up deprecated imports.
# Used to consolidat... |
"""
Top-level module of Jina.
The primary function of this module is to import all of the public Jina
interfaces into a single place. The interfaces themselves are located in
sub-modules, as described below.
"""
import os as _os
import platform as _platform
import signal as _signal
import sys as _sys
import warnings... | """
Top-level module of Jina.
The primary function of this module is to import all of the public Jina
interfaces into a single place. The interfaces themselves are located in
sub-modules, as described below.
"""
import os as _os
import platform as _platform
import signal as _signal
import sys as _sys
import warnings... |
# THIS FILE HAS BEEN AUTOGENERATED. To update:
# 1. modify the `_deps` dict in setup.py
# 2. run `make deps_table_update``
deps = {
"Pillow": "Pillow>=10.0.1,<=15.0",
"accelerate": "accelerate>=0.26.0",
"av": "av",
"beautifulsoup4": "beautifulsoup4",
"blobfile": "blobfile",
"codecarbon": "codeca... | # THIS FILE HAS BEEN AUTOGENERATED. To update:
# 1. modify the `_deps` dict in setup.py
# 2. run `make deps_table_update``
deps = {
"Pillow": "Pillow>=10.0.1,<=15.0",
"accelerate": "accelerate>=0.26.0",
"av": "av",
"beautifulsoup4": "beautifulsoup4",
"blobfile": "blobfile",
"codecarbon": "codeca... |
# Copyright (c) OpenMMLab. All rights reserved.
from .backbones import * # noqa: F401,F403
from .builder import (BACKBONES, DETECTORS, HEADS, LOSSES, NECKS,
ROI_EXTRACTORS, SHARED_HEADS, build_backbone,
build_detector, build_head, build_loss, build_neck,
... | # Copyright (c) OpenMMLab. All rights reserved.
from .backbones import * # noqa: F401,F403
from .builder import (BACKBONES, DETECTORS, HEADS, LOSSES, NECKS,
ROI_EXTRACTORS, SHARED_HEADS, build_backbone,
build_detector, build_head, build_loss, build_neck,
... |
import sys
from absl import logging
from keras.src.api_export import keras_export
from keras.src.backend.common import global_state
@keras_export(
[
"keras.config.enable_interactive_logging",
"keras.utils.enable_interactive_logging",
]
)
def enable_interactive_logging():
"""Turn on inter... | import sys
from absl import logging
from keras.src.api_export import keras_export
from keras.src.backend.common import global_state
@keras_export(
[
"keras.config.enable_interactive_logging",
"keras.utils.enable_interactive_logging",
]
)
def enable_interactive_logging():
"""Turn on inter... |
"""Standard LangChain interface tests"""
import pytest
from langchain_core.language_models import BaseChatModel
from langchain_core.tools import BaseTool
from langchain_tests.integration_tests import ( # type: ignore[import-not-found]
ChatModelIntegrationTests, # type: ignore[import-not-found]
)
from langchain_... | """Standard LangChain interface tests"""
from typing import Type
import pytest
from langchain_core.language_models import BaseChatModel
from langchain_core.tools import BaseTool
from langchain_tests.integration_tests import ( # type: ignore[import-not-found]
ChatModelIntegrationTests, # type: ignore[import-not-... |
import textwrap
import pyarrow as pa
import pytest
from datasets import Features, Image
from datasets.packaged_modules.text.text import Text
from ..utils import require_pil
@pytest.fixture
def text_file(tmp_path):
filename = tmp_path / "text.txt"
data = textwrap.dedent(
"""\
Lorem ipsum dol... | import textwrap
import pyarrow as pa
import pytest
from datasets import Features, Image
from datasets.packaged_modules.text.text import Text
from ..utils import require_pil
@pytest.fixture
def text_file(tmp_path):
filename = tmp_path / "text.txt"
data = textwrap.dedent(
"""\
Lorem ipsum dol... |
# Copyright 2024 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/LICENSE-2.0
#
# Unless required by appl... | # Copyright 2024 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/LICENSE-2.0
#
# Unless required by appl... |
from langchain_core.agents import AgentAction
from langchain_core.messages import AIMessage, BaseMessage, HumanMessage
def format_log_to_messages(
intermediate_steps: list[tuple[AgentAction, str]],
template_tool_response: str = "{observation}",
) -> list[BaseMessage]:
"""Construct the scratchpad that lets... | from langchain_core.agents import AgentAction
from langchain_core.messages import AIMessage, BaseMessage, HumanMessage
def format_log_to_messages(
intermediate_steps: list[tuple[AgentAction, str]],
template_tool_response: str = "{observation}",
) -> list[BaseMessage]:
"""Construct the scratchpad that lets... |
# Copyright (c) OpenMMLab. All rights reserved.
import torch
from mmdet.core import bbox2result
from mmdet.registry import MODELS
from ...core.utils import flip_tensor
from .single_stage import SingleStageDetector
@MODELS.register_module()
class CenterNet(SingleStageDetector):
"""Implementation of CenterNet(Obje... | # Copyright (c) OpenMMLab. All rights reserved.
import torch
from mmdet.core import bbox2result
from mmdet.models.builder import DETECTORS
from ...core.utils import flip_tensor
from .single_stage import SingleStageDetector
@DETECTORS.register_module()
class CenterNet(SingleStageDetector):
"""Implementation of Ce... |
# ruff: 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/LICE... | # ruff: 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/LICE... |
"""Init file."""
from llama_index.readers.docstring_walker.base import DocstringWalker
__all__ = ["DocstringWalker"]
| """Init file."""
from llama_index.readers.docstring_walker.base import DocstringWalker
__all__ = ["DocstringWalker"]
|
"""DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.legacy.saving.serialization import (
deserialize_keras_object as deserialize_keras_object,
)
from keras.src.legacy.saving.serialization import (
serialize_keras_object as seri... | """DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.legacy.saving.serialization import deserialize_keras_object
from keras.src.legacy.saving.serialization import serialize_keras_object
|
# Copyright (c) OpenMMLab. All rights reserved.
import datetime
import os.path as osp
import warnings
from typing import Optional
from mmengine.fileio import dump
from mmengine.logging import print_log
from . import root
from .default_scope import DefaultScope
from .registry import Registry
def traverse_registry_tre... | # Copyright (c) OpenMMLab. All rights reserved.
import datetime
import os.path as osp
import warnings
from typing import Optional
from mmengine.fileio import dump
from mmengine.logging import print_log
from . import root
from .default_scope import DefaultScope
from .registry import Registry
def traverse_registry_tre... |
from __future__ import annotations
import logging
import numpy as np
from torch.utils.data import IterableDataset
from sentence_transformers.readers import InputExample
logger = logging.getLogger(__name__)
class SentenceLabelDataset(IterableDataset):
"""
This dataset can be used for some specific Triplet ... | import logging
from typing import List
import numpy as np
from torch.utils.data import IterableDataset
from sentence_transformers.readers import InputExample
logger = logging.getLogger(__name__)
class SentenceLabelDataset(IterableDataset):
"""
This dataset can be used for some specific Triplet Losses like ... |
import pytest
import torch
from mmengine.structures import InstanceData
from mmdet.models.utils import empty_instances, unpack_gt_instances
from mmdet.testing import demo_mm_inputs
def test_parse_gt_instance_info():
packed_inputs = demo_mm_inputs()['data_samples']
batch_gt_instances, batch_gt_instances_ignor... | import pytest
import torch
from mmengine.data import InstanceData
from mmdet.models.utils import empty_instances, unpack_gt_instances
from mmdet.testing import demo_mm_inputs
def test_parse_gt_instance_info():
packed_inputs = demo_mm_inputs()
batch_data_samples = []
for inputs in packed_inputs:
... |
# Copyright (c) OpenMMLab. All rights reserved.
from .coco_api import COCO, COCOeval, COCOPanoptic
__all__ = ['COCO', 'COCOeval', 'COCOPanoptic']
| # Copyright (c) OpenMMLab. All rights reserved.
from .coco_api import COCO, COCOeval, COCOPanoptic
from .panoptic_evaluation import pq_compute_multi_core, pq_compute_single_core
__all__ = [
'COCO', 'COCOeval', 'pq_compute_multi_core', 'pq_compute_single_core',
'COCOPanoptic'
]
|
import pathlib
from typing import Any, Dict, List, Tuple, Union
from torchdata.datapipes.iter import Filter, IterDataPipe, Mapper
from torchvision.prototype.datasets.utils import Dataset, EncodedImage, HttpResource, OnlineResource
from torchvision.prototype.datasets.utils._internal import (
hint_sharding,
hint... | import pathlib
from typing import Any, Dict, List, Tuple, Union
from torchdata.datapipes.iter import Filter, IterDataPipe, Mapper
from torchvision.prototype.datasets.utils import Dataset, HttpResource, OnlineResource
from torchvision.prototype.datasets.utils._internal import (
hint_sharding,
hint_shuffling,
... |
class AudioMetaData:
"""Return type of ``torchaudio.info`` function.
This class is used by :py:mod:`"sox_io" backend<torchaudio.backends.sox_io_backend>` and
:py:mod:`"soundfile" backend<torchaudio.backends.soundfile_backend>`.
:ivar int sample_rate: Sample rate
:ivar int num_frames: The number of... | class AudioMetaData:
"""Return type of ``torchaudio.info`` function.
This class is used by :py:mod:`"sox_io" backend<torchaudio.backends.sox_io_backend>` and
:py:mod:`"soundfile" backend<torchaudio.backends.soundfile_backend>`.
:ivar int sample_rate: Sample rate
:ivar int num_frames: The number of... |
"""
This example uses a simple bag-of-words (BoW) approach. A sentence is mapped
to a sparse vector with e.g. 25,000 dimensions. Optionally, you can also use tf-idf.
To make the model trainable, we add multiple dense layers to create a Deep Averaging Network (DAN).
"""
from torch.utils.data import DataLoader
import m... | """
This example uses a simple bag-of-words (BoW) approach. A sentence is mapped
to a sparse vector with e.g. 25,000 dimensions. Optionally, you can also use tf-idf.
To make the model trainable, we add multiple dense layers to create a Deep Averaging Network (DAN).
"""
from torch.utils.data import DataLoader
import m... |
_base_ = 'faster-rcnn_r50-caffe_fpn_ms-1x_coco.py'
max_iter = 90000
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=max_iter,
by_epoch=False,
milestones=[60000, 80000],
... | _base_ = 'faster-rcnn_r50-caffe_fpn_ms-1x_coco.py'
# learning policy
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=0.001,
step=[60000, 80000])
# Runner type
runner = dict(_delete_=True, type='IterBasedRunner', max_iters=90000)
checkpoint_config = dict(interval=1... |
from __future__ import annotations
from collections.abc import Iterable
import torch.nn as nn
from torch import Tensor
from sentence_transformers.losses.CosineSimilarityLoss import CosineSimilarityLoss
from sentence_transformers.sparse_encoder.SparseEncoder import SparseEncoder
class SparseCosineSimilarityLoss(Cos... | from __future__ import annotations
from collections.abc import Iterable
import torch.nn as nn
from torch import Tensor
from sentence_transformers.losses.CosineSimilarityLoss import CosineSimilarityLoss
from sentence_transformers.sparse_encoder.SparseEncoder import SparseEncoder
class SparseCosineSimilarityLoss(Cos... |
# TODO: Add _log_api_usage_once() in all mid-level kernels. If they remain not jit-scriptable we can use decorators
from torchvision.transforms import InterpolationMode # usort: skip
from ._meta import (
clamp_bounding_box,
convert_format_bounding_box,
convert_color_space_image_tensor,
convert_color_s... | # TODO: Add _log_api_usage_once() in all mid-level kernels. If they remain not jit-scriptable we can use decorators
from torchvision.transforms import InterpolationMode # usort: skip
from ._meta import (
clamp_bounding_box,
convert_format_bounding_box,
convert_color_space_image_tensor,
convert_color_s... |
# Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the most common options. For a full
# list see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
# -- Path setup --------------------------------------------------------------
# If ex... | # Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the most common options. For a full
# list see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
# -- Path setup --------------------------------------------------------------
# If ex... |
"""Module for Jina Requests."""
from typing import (
TYPE_CHECKING,
AsyncIterable,
Dict,
Iterable,
Iterator,
Optional,
Tuple,
Union,
)
from jina.clients.request.helper import _new_data_request, _new_data_request_from_batch
from jina.enums import DataInputType
from jina.helper import ba... | """Module for Jina Requests."""
from typing import (
TYPE_CHECKING,
AsyncIterable,
Dict,
Iterable,
Iterator,
Optional,
Tuple,
Union,
)
from jina.clients.request.helper import _new_data_request, _new_data_request_from_batch
from jina.enums import DataInputType
from jina.helper import ba... |
"""This file only exists to be lazy-imported and avoid V2-related import warnings when just using V1."""
import torch
from torchvision import tv_tensors
from torchvision.transforms import v2
class PadIfSmaller(v2.Transform):
def __init__(self, size, fill=0):
super().__init__()
self.size = size
... | """This file only exists to be lazy-imported and avoid V2-related import warnings when just using V1."""
import torch
from torchvision import datapoints
from torchvision.transforms import v2
class PadIfSmaller(v2.Transform):
def __init__(self, size, fill=0):
super().__init__()
self.size = size
... |
__copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
from typing import Iterable, Optional
import torch
from jina import DocumentArray, Executor, requests
from .audio_clip.model import AudioCLIP
class AudioCLIPTextEncoder(Executor):
"""
Encode text data... | __copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
from typing import Iterable, Optional
import torch
from jina import DocumentArray, Executor, requests
from .audio_clip.model import AudioCLIP
class AudioCLIPTextEncoder(Executor):
"""
Encode text data... |
"""Markdown parser.
Contains parser for md files.
"""
import re
from pathlib import Path
from fsspec import AbstractFileSystem
from fsspec.implementations.local import LocalFileSystem
from typing import Any, Dict, List, Optional, Tuple
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema... | """Markdown parser.
Contains parser for md files.
"""
import re
from pathlib import Path
from fsspec import AbstractFileSystem
from fsspec.implementations.local import LocalFileSystem
from typing import Any, Dict, List, Optional, Tuple
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema... |
# Copyright (c) OpenMMLab. All rights reserved.
from .builder import DATASETS
from .coco import CocoDataset
@DATASETS.register_module()
class DeepFashionDataset(CocoDataset):
CLASSES = ('top', 'skirt', 'leggings', 'dress', 'outer', 'pants', 'bag',
'neckwear', 'headwear', 'eyeglass', 'belt', 'footw... | from .builder import DATASETS
from .coco import CocoDataset
@DATASETS.register_module()
class DeepFashionDataset(CocoDataset):
CLASSES = ('top', 'skirt', 'leggings', 'dress', 'outer', 'pants', 'bag',
'neckwear', 'headwear', 'eyeglass', 'belt', 'footwear', 'hair',
'skin', 'face')
|
import logging
from datasets import load_dataset
from sentence_transformers import SparseEncoder
from sentence_transformers.sparse_encoder.evaluation import SparseBinaryClassificationEvaluator
logging.basicConfig(format="%(message)s", level=logging.INFO)
# Initialize the SPLADE model
model = SparseEncoder("naver/sp... | import logging
from datasets import load_dataset
from sentence_transformers import SparseEncoder
from sentence_transformers.sparse_encoder.evaluation import SparseBinaryClassificationEvaluator
logging.basicConfig(format="%(message)s", level=logging.INFO)
# Initialize the SPLADE model
model = SparseEncoder("naver/sp... |
import logging
import os
from typing import Any, Callable, Optional, Tuple, Union
from llama_index.core.base.llms.generic_utils import get_from_param_or_env
from tenacity import (
before_sleep_log,
retry,
retry_if_exception_type,
stop_after_attempt,
stop_after_delay,
wait_exponential,
wait_... | import logging
import os
from typing import Any, Callable, Optional, Tuple, Union
from llama_index.core.base.llms.generic_utils import get_from_param_or_env
from tenacity import (
before_sleep_log,
retry,
retry_if_exception_type,
stop_after_attempt,
stop_after_delay,
wait_exponential,
wait_... |
__copyright__ = "Copyright (c) 2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
from typing import Dict
import pytest
import numpy as np
from jina import DocumentArray, Document
from ...torch_encoder import ImageTorchEncoder
MODELS_TO_TEST = [
'mobilenet_v2',
'squeezenet1_0',
'a... | __copyright__ = "Copyright (c) 2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
from typing import Dict
import pytest
import numpy as np
from jina import DocumentArray, Document
try:
from torch_encoder import ImageTorchEncoder
except:
from jinahub.image.encoder.torch_encoder import Ima... |
# Copyright (c) OpenMMLab. All rights reserved.
from mmdet.registry import MODELS
from .two_stage import TwoStageDetector
@MODELS.register_module()
class GridRCNN(TwoStageDetector):
"""Grid R-CNN.
This detector is the implementation of:
- Grid R-CNN (https://arxiv.org/abs/1811.12030)
- Grid R-CNN Plu... | # Copyright (c) OpenMMLab. All rights reserved.
from ..builder import DETECTORS
from .two_stage import TwoStageDetector
@DETECTORS.register_module()
class GridRCNN(TwoStageDetector):
"""Grid R-CNN.
This detector is the implementation of:
- Grid R-CNN (https://arxiv.org/abs/1811.12030)
- Grid R-CNN Pl... |
# Copyright (c) OpenMMLab. All rights reserved.
import unittest
from unittest import TestCase
import torch
from parameterized import parameterized
from mmdet import * # noqa
from mmdet.core import DetDataSample
from .utils import demo_mm_inputs, get_detector_cfg
class TestSingleStageDetector(TestCase):
@param... | # Copyright (c) OpenMMLab. All rights reserved.
import unittest
from unittest import TestCase
import torch
from parameterized import parameterized
from mmdet import * # noqa
from mmdet.core import DetDataSample
from .utils import demo_mm_inputs, get_detector_cfg
class TestSingleStageDetector(TestCase):
@param... |
# 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, DistancePointBBoxC... |
from io import BytesIO
from typing import TYPE_CHECKING, Any, List, NamedTuple, Type, TypeVar
import numpy as np
from pydantic import parse_obj_as
from pydantic.validators import bytes_validator
from docarray.typing.abstract_type import AbstractType
from docarray.typing.proto_register import _register_proto
from doca... | from io import BytesIO
from typing import TYPE_CHECKING, Any, NamedTuple, Type, TypeVar
import numpy as np
from pydantic import parse_obj_as
from pydantic.validators import bytes_validator
from docarray.typing.abstract_type import AbstractType
from docarray.typing.proto_register import _register_proto
from docarray.t... |
# Copyright (c) OpenMMLab. All rights reserved.
from .amp_optimizer_wrapper import AmpOptimWrapper
from .builder import (OPTIM_WRAPPER_CONSTRUCTORS, OPTIMIZERS,
build_optim_wrapper)
from .default_constructor import DefaultOptimWrapperConstructor
from .optimizer_wrapper import OptimWrapper
from .op... | # Copyright (c) OpenMMLab. All rights reserved.
from .amp_optimizer_wrapper import AmpOptimWrapper
from .builder import (OPTIM_WRAPPER_CONSTRUCTORS, OPTIMIZERS,
build_optim_wrapper)
from .default_constructor import DefaultOptimWrapperConstructor
from .optimizer_wrapper import OptimWrapper
from .op... |
# 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 keras.src import regularizers
from keras.src.api_export import keras_export
from keras.src.layers.layer import Layer
@keras_export("keras.layers.ActivityRegularization")
class ActivityRegularization(Layer):
"""Layer that applies an update to the cost function based input activity.
Args:
l1: L1 r... | from keras.src import regularizers
from keras.src.api_export import keras_export
from keras.src.layers.layer import Layer
@keras_export("keras.layers.ActivityRegularization")
class ActivityRegularization(Layer):
"""Layer that applies an update to the cost function based input activity.
Args:
l1: L1 r... |
from typing import List, Union
class InputExample:
"""Structure for one input example with texts, the label and a unique id"""
def __init__(self, guid: str = "", texts: List[str] = None, label: Union[int, float] = 0):
"""
Creates one InputExample with the given texts, guid and label
... | from typing import Union, List
class InputExample:
"""
Structure for one input example with texts, the label and a unique id
"""
def __init__(self, guid: str = "", texts: List[str] = None, label: Union[int, float] = 0):
"""
Creates one InputExample with the given texts, guid and label... |
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