input stringlengths 33 5k | output stringlengths 32 5k |
|---|---|
# Copyright (c) OpenMMLab. All rights reserved.
from .base_loop import BaseLoop
from .checkpoint import (CheckpointLoader, get_deprecated_model_names,
get_external_models, get_mmcls_models, get_state_dict,
get_torchvision_models, load_checkpoint,
... | # Copyright (c) OpenMMLab. All rights reserved.
from .checkpoint import (CheckpointLoader, get_deprecated_model_names,
get_external_models, get_mmcls_models, get_state_dict,
get_torchvision_models, load_checkpoint,
load_state_dict, save_checkpoi... |
"""Embeddings."""
from typing import Any, List
from unittest.mock import patch
from llama_index.core.base.embeddings.base import SimilarityMode, mean_agg
from llama_index.core.embeddings.mock_embed_model import MockEmbedding
def mock_get_text_embedding(text: str) -> List[float]:
"""Mock get text embedding."""
... | """Embeddings."""
from typing import Any, List
from unittest.mock import patch
from llama_index.core.base.embeddings.base import SimilarityMode, mean_agg
from llama_index.core.embeddings.mock_embed_model import MockEmbedding
def mock_get_text_embedding(text: str) -> List[float]:
"""Mock get text embedding."""
... |
# Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmdet.registry import MODELS
MODELS.register_module('Linear', module=nn.Linear)
@MODELS.register_module(name='NormedLinear')
class NormedLinear(nn.Linear):
"""Normalized Linear Layer.
Arg... | # Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import CONV_LAYERS
from .builder import LINEAR_LAYERS
@LINEAR_LAYERS.register_module(name='NormedLinear')
class NormedLinear(nn.Linear):
"""Normalized Linear Layer.
Args:
... |
# Copyright (c) OpenMMLab. All rights reserved.
from .dist import (all_gather_object, all_reduce, all_gather, all_reduce_dict,
collect_results, gather, broadcast, gather_object,
sync_random_seed, broadcast_object_list,
collect_results_cpu, collect_results_gpu)
fr... | # Copyright (c) OpenMMLab. All rights reserved.
from .dist import (all_gather_object, all_reduce, all_gather, all_reduce_dict,
collect_results, gather, broadcast, gather_object,
sync_random_seed, broadcast_object_list,
collect_results_cpu, collect_results_gpu)
fr... |
from docarray.dataclasses.types import dataclass, is_multimodal, field
| from .types import dataclass, is_multimodal, field
|
import os
import subprocess
import sys
import time
def wait_for_postgres(max_retries=5, delay=5):
for _ in range(max_retries):
try:
result = subprocess.run(
[
"docker",
"compose",
"-f",
"docker-comp... | import subprocess
import sys
import time
def wait_for_postgres(max_retries=5, delay=5):
for _ in range(max_retries):
try:
result = subprocess.run(
[
"docker",
"compose",
"-f",
"docker-compose.test.y... |
"""Init file of LlamaIndex."""
__version__ = "0.12.34.post1"
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_index... | """Init file of LlamaIndex."""
__version__ = "0.12.33.post1"
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_index... |
from typing import Callable, Dict, Any, List, Optional, Awaitable
from ag_ui.core import RunAgentInput
from fastapi import APIRouter
from fastapi.responses import StreamingResponse
from llama_index.core.llms.function_calling import FunctionCallingLLM
from llama_index.core.workflow import Workflow
from llama_index.pro... | from typing import Callable, Dict, Any, List, Optional, Awaitable
from ag_ui.core import RunAgentInput
from fastapi import APIRouter
from fastapi.responses import StreamingResponse
from llama_index.core.llms.function_calling import FunctionCallingLLM
from llama_index.core.workflow import Workflow
from llama_index.pro... |
from ._tts import (
TACOTRON2_GRIFFINLIM_CHAR_LJSPEECH,
TACOTRON2_GRIFFINLIM_PHONE_LJSPEECH,
TACOTRON2_WAVERNN_CHAR_LJSPEECH,
TACOTRON2_WAVERNN_PHONE_LJSPEECH,
Tacotron2TTSBundle,
)
from ._wav2vec2.impl import (
HUBERT_ASR_LARGE,
HUBERT_ASR_XLARGE,
HUBERT_BASE,
HUBERT_LARGE,
HUBE... | from ._tts import (
Tacotron2TTSBundle,
TACOTRON2_GRIFFINLIM_CHAR_LJSPEECH,
TACOTRON2_GRIFFINLIM_PHONE_LJSPEECH,
TACOTRON2_WAVERNN_CHAR_LJSPEECH,
TACOTRON2_WAVERNN_PHONE_LJSPEECH,
)
from ._wav2vec2.impl import (
Wav2Vec2Bundle,
Wav2Vec2ASRBundle,
WAV2VEC2_BASE,
WAV2VEC2_LARGE,
WA... |
import os
import pytest
from docarray import Document
from jina import Executor, Flow, requests
class MyExec(Executor):
@requests
def foo(self, docs, **kwargs):
pass
@pytest.fixture
def cert_prefix():
cur_dir = os.path.dirname(os.path.abspath(__file__))
return f'{cur_dir}/../../../unit/ser... | import os
import pytest
from docarray import Document
from jina import Executor, Flow, requests
class MyExec(Executor):
@requests
def foo(self, docs, **kwargs):
pass
@pytest.fixture
def cert_prefix():
cur_dir = os.path.dirname(os.path.abspath(__file__))
return f'{cur_dir}/../../../unit/ser... |
# coding=utf-8
# Copyright 2025 The HuggingFace Inc. team.
#
# 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... | # coding=utf-8
# Copyright 2024 The HuggingFace Inc. team.
#
# 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... |
import os
import urllib
import numpy as np
import PIL
import pytest
from pydantic.tools import parse_obj_as, schema_json_of
from docarray.base_document.io.json import orjson_dumps
from docarray.typing import ImageUrl
CUR_DIR = os.path.dirname(os.path.abspath(__file__))
PATH_TO_IMAGE_DATA = os.path.join(CUR_DIR, '..'... | import os
import urllib
import numpy as np
import PIL
import pytest
from pydantic.tools import parse_obj_as, schema_json_of
from docarray.document.io.json import orjson_dumps
from docarray.typing import ImageUrl
CUR_DIR = os.path.dirname(os.path.abspath(__file__))
PATH_TO_IMAGE_DATA = os.path.join(CUR_DIR, '..', '..... |
"""Testing code shared by other tests."""
# pylint: disable=invalid-name
import collections
import importlib.util
import json
import os
import tempfile
from typing import Any, Callable, Dict, Type
import numpy as np
import xgboost as xgb
from xgboost._typing import ArrayLike
def validate_leaf_output(leaf: np.ndarr... | """Testing code shared by other tests."""
# pylint: disable=invalid-name
import collections
import importlib.util
import json
import os
import tempfile
from typing import Any, Callable, Dict, Type
import numpy as np
import xgboost as xgb
from xgboost._typing import ArrayLike
def validate_leaf_output(leaf: np.ndarr... |
# -*- coding: utf-8 -*-
# Generated by the protocol buffer compiler. DO NOT EDIT!
# source: docarray.proto
"""Generated protocol buffer code."""
from google.protobuf.internal import builder as _builder
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool... | # -*- coding: utf-8 -*-
# Generated by the protocol buffer compiler. DO NOT EDIT!
# source: docarray.proto
"""Generated protocol buffer code."""
from google.protobuf.internal import builder as _builder
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool... |
"""
Quantile Regression
===================
.. versionadded:: 2.0.0
The script is inspired by this awesome example in sklearn:
https://scikit-learn.org/stable/auto_examples/ensemble/plot_gradient_boosting_quantile.html
.. note::
The feature is only supported using the Python, R, and C packages. In addition,... | """
Quantile Regression
===================
.. versionadded:: 2.0.0
The script is inspired by this awesome example in sklearn:
https://scikit-learn.org/stable/auto_examples/ensemble/plot_gradient_boosting_quantile.html
.. note::
The feature is only supported using the Python, R, and C packages. In addition,... |
from langchain_core.messages import AIMessage, ToolCall, ToolMessage
from langchain.agents.format_scratchpad.openai_tools import (
format_to_openai_tool_messages,
)
from langchain.agents.output_parsers.openai_tools import (
parse_ai_message_to_openai_tool_action,
)
def test_calls_convert_agent_action_to_mess... | from langchain_core.messages import AIMessage, ToolCall, ToolMessage
from langchain.agents.format_scratchpad.openai_tools import (
format_to_openai_tool_messages,
)
from langchain.agents.output_parsers.openai_tools import (
parse_ai_message_to_openai_tool_action,
)
def test_calls_convert_agent_action_to_mess... |
# Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
from mmcv.runner import BaseModule, auto_fp16
from mmdet.registry import MODELS
@MODELS.register_module()
class FeatureRelayHead(BaseModule):
"""Feature Relay Head used in `SCNet <https://arxiv.org/abs/2012.10150>`_.
Args:
in_chan... | # Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
from mmcv.runner import BaseModule, auto_fp16
from mmdet.models.builder import HEADS
@HEADS.register_module()
class FeatureRelayHead(BaseModule):
"""Feature Relay Head used in `SCNet <https://arxiv.org/abs/2012.10150>`_.
Args:
in_... |
import asyncio
import copy
from typing import Any, List, TYPE_CHECKING
from jina.serve.runtimes.servers import BaseServer
if TYPE_CHECKING:
from jina.logging.logger import JinaLogger
class CompositeBaseServer(BaseServer):
"""Composite Base Server implementation from which u can inherit a specific custom com... | import asyncio
import copy
from typing import Any, List, TYPE_CHECKING
from jina.serve.runtimes.servers import BaseServer
if TYPE_CHECKING:
from jina.logging.logger import JinaLogger
class CompositeBaseServer(BaseServer):
"""Composite Base Server implementation from which u can inherit a specific custom com... |
# dataset settings
dataset_type = 'CocoPanopticDataset'
data_root = 'data/coco/'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='LoadPanopticAnnotations',
with_bbox=True,
wit... | # dataset settings
dataset_type = 'CocoPanopticDataset'
data_root = 'data/coco/'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='LoadPanopticAnnotations',
with_bbox=True,
wit... |
# Copyright (c) OpenMMLab. All rights reserved.
from typing import Dict, List
import torch
from torch.nn.parallel.distributed import DistributedDataParallel
from mmengine.data import BaseDataElement
from mmengine.optim import OptimWrapper
from mmengine.registry import MODEL_WRAPPERS
from ..utils import detect_anomalo... | # Copyright (c) OpenMMLab. All rights reserved.
from typing import Dict, List
import torch
from torch.nn.parallel.distributed import DistributedDataParallel
from mmengine.data import BaseDataElement
from mmengine.optim import OptimWrapper
from mmengine.registry import MODEL_WRAPPERS
from ..utils import detect_anomalo... |
from typing import Union, Iterable, Dict
import warnings
from ..base.seqlike import BaseSequenceLikeMixin
from .... import Document
class SequenceLikeMixin(BaseSequenceLikeMixin):
"""Implement sequence-like methods for DocumentArray with Elastic as storage"""
def __eq__(self, other):
"""Compare this... | from typing import Union, Iterable, Dict
from ..base.seqlike import BaseSequenceLikeMixin
from .... import Document
class SequenceLikeMixin(BaseSequenceLikeMixin):
"""Implement sequence-like methods for DocumentArray with Elastic as storage"""
def __eq__(self, other):
"""Compare this object to the o... |
# Copyright (c) OpenMMLab. All rights reserved.
from .hub import load_url
from .manager import ManagerMeta, ManagerMixin
from .misc import (check_prerequisites, concat_list, deprecated_api_warning,
has_batch_norm, has_method, import_modules_from_strings,
is_list_of, is_method_overr... | # Copyright (c) OpenMMLab. All rights reserved.
from .hub import load_url
from .manager import ManagerMeta, ManagerMixin
from .misc import (check_prerequisites, concat_list, deprecated_api_warning,
has_batch_norm, has_method, import_modules_from_strings,
is_list_of, is_method_overr... |
import os
from pathlib import Path
import cv2
import pytest
from jina import Document, DocumentArray, Executor
from yolov5_segmenter import YoloV5Segmenter
cur_dir = os.path.dirname(os.path.abspath(__file__))
def test_load():
segmenter = Executor.load_config(str(Path(__file__).parents[2] / 'config.yml'))
as... | import os
from pathlib import Path
import cv2
import pytest
from jina import Document, DocumentArray, Executor
from ...yolov5_segmenter import YoloV5Segmenter
cur_dir = os.path.dirname(os.path.abspath(__file__))
def test_load():
segmenter = Executor.load_config(str(Path(__file__).parents[2] / 'config.yml'))
... |
from os.path import join
from typing import Any, Callable, List, Optional, Tuple
from PIL import Image
from .utils import check_integrity, download_and_extract_archive, list_dir, list_files
from .vision import VisionDataset
class Omniglot(VisionDataset):
"""`Omniglot <https://github.com/brendenlake/omniglot>`_ ... | from os.path import join
from typing import Any, Callable, List, Optional, Tuple
from PIL import Image
from .utils import check_integrity, download_and_extract_archive, list_dir, list_files
from .vision import VisionDataset
class Omniglot(VisionDataset):
"""`Omniglot <https://github.com/brendenlake/omniglot>`_ ... |
from llama_index.core.schema import NodeRelationship, RelatedNodeInfo, TextNode
from llama_index.vector_stores.qdrant import QdrantVectorStore
import qdrant_client
import pytest_asyncio
@pytest_asyncio.fixture
async def vector_store() -> QdrantVectorStore:
client = qdrant_client.QdrantClient(":memory:")
aclie... | from llama_index.core.schema import NodeRelationship, RelatedNodeInfo, TextNode
from llama_index.vector_stores.qdrant import QdrantVectorStore
import qdrant_client
import pytest_asyncio
@pytest_asyncio.fixture
async def vector_store() -> QdrantVectorStore:
client = qdrant_client.QdrantClient(":memory:")
aclie... |
from typing import TYPE_CHECKING, Any, Optional, Type, TypeVar, Union
import numpy as np
from docarray.base_doc import BaseDoc
from docarray.documents.point_cloud.points_and_colors import PointsAndColors
from docarray.typing import AnyEmbedding, PointCloud3DUrl
from docarray.typing.tensor.abstract_tensor import Abstr... | from typing import TYPE_CHECKING, Any, Optional, Type, TypeVar, Union
import numpy as np
from docarray.base_doc import BaseDoc
from docarray.documents.point_cloud.points_and_colors import PointsAndColors
from docarray.typing import AnyEmbedding, PointCloud3DUrl
from docarray.typing.tensor.abstract_tensor import Abstr... |
#!/usr/bin/env python3
# coding=utf-8
# Copyright 2024 The HuggingFace Inc. team.
#
# 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
#
# Unles... | #!/usr/bin/env python3
# coding=utf-8
# Copyright 2024 The HuggingFace Inc. team.
#
# 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
#
# Unles... |
"""
Tool implementations for the Riza (https://riza.io) code interpreter API.
Documentation: https://docs.riza.io
API keys: https://dashboard.riza.io
"""
from typing import Any, Optional, Type
from langchain_core.callbacks import (
CallbackManagerForToolRun,
)
from langchain_core.tools import BaseTool, Tool... | """
Tool implementations for the Riza (https://riza.io) code interpreter API.
Documentation: https://docs.riza.io
API keys: https://dashboard.riza.io
"""
from typing import Any, Optional, Type
from langchain_core.callbacks import (
CallbackManagerForToolRun,
)
from langchain_core.tools import BaseTool, Tool... |
# Copyright (c) OpenMMLab. All rights reserved.
from mmengine.utils.parrots_wrapper import TORCH_VERSION
from mmengine.utils.version_utils import digit_version
from .averaged_model import (ExponentialMovingAverage, MomentumAnnealingEMA,
StochasticWeightAverage)
from .base_model import BaseD... | # 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, ModuleDict, ModuleList, Seq... |
# Copyright (c) OpenMMLab. All rights reserved.
__version__ = '0.10.1'
def parse_version_info(version_str):
"""Parse the version information.
Args:
version_str (str): version string like '0.1.0'.
Returns:
tuple: version information contains major, minor, micro version.
"""
versi... | # Copyright (c) OpenMMLab. All rights reserved.
__version__ = '0.10.0'
def parse_version_info(version_str):
"""Parse the version information.
Args:
version_str (str): version string like '0.1.0'.
Returns:
tuple: version information contains major, minor, micro version.
"""
versi... |
_base_ = [
'../_base_/models/faster-rcnn_r50-caffe-dc5.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# use caffe img_norm
img_norm_cfg = dict(
mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
train_pipeline = [
... | _base_ = [
'../_base_/models/faster_rcnn_r50_caffe_dc5.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# use caffe img_norm
img_norm_cfg = dict(
mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
train_pipeline = [
... |
from __future__ import annotations
from collections.abc import Iterable
import torch
from torch import Tensor, nn
from sentence_transformers import SentenceTransformer, util
class DistillKLDivLoss(nn.Module):
# TODO
def __init__(self, model: SentenceTransformer, similarity_fct=util.pairwise_dot_score) -> ... | from __future__ import annotations
from collections.abc import Iterable
import torch
from torch import Tensor, nn
from sentence_transformers import SentenceTransformer, util
class DistillKLDivLoss(nn.Module):
# TODO
def __init__(self, model: SentenceTransformer, similarity_fct=util.pairwise_dot_score) -> ... |
from dataclasses import dataclass, asdict, field
from typing import (
Union,
Dict,
Optional,
TYPE_CHECKING,
Iterable,
List,
Tuple,
)
import numpy as np
from docarray.array.storage.base.backend import BaseBackendMixin, TypeMap
from docarray.helper import dataclass_from_dict, filter_dict, _s... | from dataclasses import dataclass, asdict, field
from typing import (
Union,
Dict,
Optional,
TYPE_CHECKING,
Iterable,
List,
Tuple,
)
import numpy as np
from docarray.array.storage.base.backend import BaseBackendMixin, TypeMap
from docarray.helper import dataclass_from_dict, filter_dict, _s... |
from langchain_core.prompts import PromptTemplate
prompt_template = """Write a concise summary of the following:
"{text}"
CONCISE SUMMARY:"""
PROMPT = PromptTemplate(template=prompt_template, input_variables=["text"])
| # flake8: noqa
from langchain_core.prompts import PromptTemplate
prompt_template = """Write a concise summary of the following:
"{text}"
CONCISE SUMMARY:"""
PROMPT = PromptTemplate(template=prompt_template, input_variables=["text"])
|
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... | 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... |
from typing import Dict
import torch.nn.functional as F
from torch import Tensor, nn
class Normalize(nn.Module):
"""This layer normalizes embeddings to unit length"""
def __init__(self):
super(Normalize, self).__init__()
def forward(self, features: Dict[str, Tensor]):
features.update({"... | from torch import Tensor
from torch import nn
from typing import Dict
import torch.nn.functional as F
class Normalize(nn.Module):
"""This layer normalizes embeddings to unit length"""
def __init__(self):
super(Normalize, self).__init__()
def forward(self, features: Dict[str, Tensor]):
fe... |
from typing import Dict, Set
from fastapi import WebSocket
from backend.data import execution
from backend.server.model import Methods, WsMessage
class ConnectionManager:
def __init__(self):
self.active_connections: Set[WebSocket] = set()
self.subscriptions: Dict[str, Set[WebSocket]] = {}
a... | from typing import Dict, Set
from fastapi import WebSocket
from backend.data import execution
from backend.server.model import Methods, WsMessage
class ConnectionManager:
def __init__(self):
self.active_connections: Set[WebSocket] = set()
self.subscriptions: Dict[str, Set[WebSocket]] = {}
a... |
# Copyright (c) OpenMMLab. All rights reserved.
import copy
import os.path as osp
import mmcv
import numpy as np
from mmdet.datasets.pipelines import (LoadImageFromFile, LoadImageFromWebcam,
LoadMultiChannelImageFromFiles)
class TestLoading:
@classmethod
def setup_clas... | # Copyright (c) OpenMMLab. All rights reserved.
import copy
import os.path as osp
import mmcv
import numpy as np
from mmdet.datasets.pipelines import (LoadImageFromFile, LoadImageFromWebcam,
LoadMultiChannelImageFromFiles)
class TestLoading:
@classmethod
def setup_clas... |
# Copyright (c) OpenMMLab. All rights reserved.
import os
import os.path as osp
import tempfile
import mmcv
import numpy as np
import pytest
import torch
from mmdet.core import visualization as vis
def test_color():
assert vis.color_val_matplotlib(mmcv.Color.blue) == (0., 0., 1.)
assert vis.color_val_matplo... | # Copyright (c) Open-MMLab. All rights reserved.
import os
import os.path as osp
import tempfile
import mmcv
import numpy as np
import pytest
import torch
from mmdet.core import visualization as vis
def test_color():
assert vis.color_val_matplotlib(mmcv.Color.blue) == (0., 0., 1.)
assert vis.color_val_matpl... |
from __future__ import annotations
from typing import Any, Optional, Union
import torch
from ._datapoint import Datapoint
class Video(Datapoint):
"""[BETA] :class:`torch.Tensor` subclass for videos.
Args:
data (tensor-like): Any data that can be turned into a tensor with :func:`torch.as_tensor`.
... | from __future__ import annotations
from typing import Any, Optional, Union
import torch
from ._datapoint import Datapoint
class Video(Datapoint):
"""[BETA] :class:`torch.Tensor` subclass for videos.
Args:
data (tensor-like): Any data that can be turned into a tensor with :func:`torch.as_tensor`.
... |
# 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
import torch
from pydantic import parse_obj_as
from docarray import BaseDoc
from docarray.documents import PointCloud3D
from docarray.utils._internal.misc import is_tf_available
from tests import TOYDATA_DIR
tf_available = is_tf_available()
if tf_available:
import tensorflow as tf... |
_base_ = '../rpn/rpn_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,
scales_per_octave=3,... | _base_ = '../rpn/rpn_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,
scales_per_octave=3,... |
# coding=utf-8
# Copyright 2025 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... | # coding=utf-8
# Copyright 2024 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... |
import re
from typing import Union
from langchain_core.agents import AgentAction, AgentFinish
from langchain_core.exceptions import OutputParserException
from langchain.agents.agent import AgentOutputParser
from langchain.agents.mrkl.prompt import FORMAT_INSTRUCTIONS
FINAL_ANSWER_ACTION = "Final Answer:"
MISSING_ACT... | import re
from typing import Union
from langchain_core.agents import AgentAction, AgentFinish
from langchain_core.exceptions import OutputParserException
from langchain.agents.agent import AgentOutputParser
from langchain.agents.mrkl.prompt import FORMAT_INSTRUCTIONS
FINAL_ANSWER_ACTION = "Final Answer:"
MISSING_ACT... |
# Copyright (c) OpenMMLab. All rights reserved.
import unittest
from unittest import TestCase
import torch
from parameterized import parameterized
from mmdet import * # noqa
from mmdet.structures import DetDataSample
from mmdet.testing import demo_mm_inputs, get_detector_cfg
from mmdet.utils import register_all_modu... | # Copyright (c) OpenMMLab. All rights reserved.
import unittest
from unittest import TestCase
import torch
from parameterized import parameterized
from mmdet import * # noqa
from mmdet.data_elements import DetDataSample
from mmdet.testing import demo_mm_inputs, get_detector_cfg
from mmdet.utils import register_all_m... |
from __future__ import annotations
from torch import Tensor, nn
from sentence_transformers.cross_encoder import CrossEncoder
from sentence_transformers.util import fullname
class MSELoss(nn.Module):
def __init__(self, model: CrossEncoder, **kwargs) -> None:
super().__init__()
self.model = model
... | from __future__ import annotations
from torch import Tensor, nn
from sentence_transformers.cross_encoder import CrossEncoder
# TODO: This loss hasn't been tested yet
class MSELoss(nn.Module):
def __init__(self, model: CrossEncoder, **kwargs) -> None:
super().__init__()
self.model = model
... |
# Copyright (c) OpenMMLab. All rights reserved.
__version__ = '2.25.0'
short_version = __version__
def parse_version_info(version_str):
version_info = []
for x in version_str.split('.'):
if x.isdigit():
version_info.append(int(x))
elif x.find('rc') != -1:
patch_version... | # Copyright (c) OpenMMLab. All rights reserved.
__version__ = '2.24.1'
short_version = __version__
def parse_version_info(version_str):
version_info = []
for x in version_str.split('.'):
if x.isdigit():
version_info.append(int(x))
elif x.find('rc') != -1:
patch_version... |
_base_ = './faster-rcnn_r50-caffe-dc5_ms-1x_coco.py'
# learning policy
lr_config = dict(step=[28, 34])
runner = dict(type='EpochBasedRunner', max_epochs=36)
| _base_ = './faster_rcnn_r50_caffe_dc5_mstrain_1x_coco.py'
# learning policy
lr_config = dict(step=[28, 34])
runner = dict(type='EpochBasedRunner', max_epochs=36)
|
from unittest.mock import AsyncMock, Mock
import pytest
from llama_index.core.base.embeddings.base import BaseEmbedding
from llama_index.core.callbacks.base import CallbackManager
from llama_index.embeddings.oci_data_science import OCIDataScienceEmbedding
from llama_index.embeddings.oci_data_science.client import Asyn... | from unittest.mock import AsyncMock, Mock
import pytest
from llama_index.core.base.embeddings.base import BaseEmbedding
from llama_index.core.callbacks.base import CallbackManager
from llama_index.embeddings.oci_data_science import OCIDataScienceEmbedding
from llama_index.embeddings.oci_data_science.client import Asyn... |
import asyncio
import logging
import sentry_sdk
from pydantic import SecretStr
from sentry_sdk.integrations.anthropic import AnthropicIntegration
from sentry_sdk.integrations.logging import LoggingIntegration
from backend.util.settings import Settings
def sentry_init():
sentry_dsn = Settings().secrets.sentry_ds... | import logging
import sentry_sdk
from sentry_sdk.integrations.anthropic import AnthropicIntegration
from sentry_sdk.integrations.logging import LoggingIntegration
from backend.util.settings import Settings
def sentry_init():
sentry_dsn = Settings().secrets.sentry_dsn
sentry_sdk.init(
dsn=sentry_dsn,... |
# 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 typing import TYPE_CHECKING, Any, Dict, List, Optional, TypeVar
import numpy as np
from pydantic import parse_obj_as
from docarray.typing.proto_register import _register_proto
from docarray.typing.tensor.ndarray import NdArray
from docarray.typing.url.mimetypes import MESH_EXTRA_EXTENSIONS
from docarray.typing.u... |
import logging
from datasets import load_dataset
from sentence_transformers import SparseEncoder
from sentence_transformers.sparse_encoder.evaluation import SparseEmbeddingSimilarityEvaluator
logging.basicConfig(format="%(message)s", level=logging.INFO)
# Load a model
model = SparseEncoder("naver/splade-cocondenser... | import logging
from datasets import load_dataset
from sentence_transformers.sparse_encoder import (
MLMTransformer,
SparseEmbeddingSimilarityEvaluator,
SparseEncoder,
SpladePooling,
)
logging.basicConfig(format="%(asctime)s - %(message)s", datefmt="%Y-%m-%d %H:%M:%S", level=logging.INFO)
# Initializ... |
# Copyright (c) OpenMMLab. All rights reserved.
import unittest
import torch
from parameterized import parameterized
from mmdet.models import build_detector
from mmdet.structures import DetDataSample
from mmdet.testing._utils import demo_mm_inputs, get_detector_cfg
from mmdet.utils import register_all_modules
class... | # Copyright (c) OpenMMLab. All rights reserved.
import unittest
import torch
from parameterized import parameterized
from mmdet.models import build_detector
from mmdet.structures import DetDataSample
from mmdet.testing._utils import demo_mm_inputs, get_detector_cfg
from mmdet.utils import register_all_modules
class... |
import logging
from datasets import load_dataset
from sentence_transformers import SparseEncoder
from sentence_transformers.sparse_encoder.evaluation import SparseRerankingEvaluator
logging.basicConfig(format="%(message)s", level=logging.INFO)
# Load a model
model = SparseEncoder("naver/splade-cocondenser-ensembled... | import logging
from datasets import load_dataset
from sentence_transformers import SparseEncoder
from sentence_transformers.sparse_encoder.evaluation import SparseRerankingEvaluator
logging.basicConfig(format="%(message)s", level=logging.INFO)
# Load a model
model = SparseEncoder("naver/splade-cocondenser-ensembled... |
# Copyright (c) OpenMMLab. All rights reserved.
import os
import subprocess
import warnings
from packaging.version import parse
def digit_version(version_str: str, length: int = 4):
"""Convert a version string into a tuple of integers.
This method is usually used for comparing two versions. For pre-release
... | # Copyright (c) OpenMMLab. All rights reserved.
import os
import subprocess
import warnings
from packaging.version import parse
def digit_version(version_str: str, length: int = 4):
"""Convert a version string into a tuple of integers.
This method is usually used for comparing two versions. For pre-release
... |
from docarray.typing.proto_register import _register_proto
from docarray.typing.tensor.audio.abstract_audio_tensor import AbstractAudioTensor
from docarray.typing.tensor.torch_tensor import TorchTensor, metaTorchAndNode
@_register_proto(proto_type_name='audio_torch_tensor')
class AudioTorchTensor(AbstractAudioTensor,... | from docarray.typing.proto_register import _register_proto
from docarray.typing.tensor.audio.abstract_audio_tensor import AbstractAudioTensor
from docarray.typing.tensor.torch_tensor import TorchTensor, metaTorchAndNode
@_register_proto(proto_type_name='audio_torch_tensor')
class AudioTorchTensor(AbstractAudioTensor,... |
import copy
from dataclasses import dataclass, field
from pathlib import Path
from typing import Any, Optional, Union
from .. import config
@dataclass
class DownloadConfig:
"""Configuration for our cached path manager.
Attributes:
cache_dir (`str` or `Path`, *optional*):
Specify a cache ... | import copy
from dataclasses import dataclass, field
from pathlib import Path
from typing import Any, Dict, Optional, Union
from .. import config
@dataclass
class DownloadConfig:
"""Configuration for our cached path manager.
Attributes:
cache_dir (`str` or `Path`, *optional*):
Specify a ... |
from jina import Client
from docarray import DocList
from docarray.documents import TextDoc
if __name__ == '__main__':
c = Client(host='grpc://0.0.0.0:54321')
da = c.post(
'/', DocList[TextDoc]([TextDoc(), TextDoc()]), return_type=DocList[TextDoc]
)
print(da.text)
| from jina import Client
from docarray import DocList
from docarray.documents import TextDoc
if __name__ == '__main__':
c = Client(host='grpc://0.0.0.0:54321')
da = c.post('/', DocList[TextDoc]([TextDoc(), TextDoc()]), return_type=DocList[TextDoc])
print(da.text)
|
# mypy: allow-untyped-defs
import sys
from contextlib import contextmanager
from typing import TYPE_CHECKING
import torch
from torch.backends import (
__allow_nonbracketed_mutation,
_FP32Precision,
_get_fp32_precision_getter,
_set_fp32_precision_setter,
ContextProp,
PropModule,
)
def is_avail... | # mypy: allow-untyped-defs
import sys
from contextlib import contextmanager
from typing import TYPE_CHECKING
import torch
from torch.backends import (
__allow_nonbracketed_mutation,
_FP32Precision,
_get_fp32_precision_getter,
_set_fp32_precision_setter,
ContextProp,
PropModule,
)
def is_avail... |
from typing import Tuple
import numpy as np
import pytest
import xgboost as xgb
from xgboost import testing as tm
from xgboost.testing.updater import get_basescore
rng = np.random.RandomState(1994)
class TestEarlyStopping:
@pytest.mark.skipif(**tm.no_sklearn())
def test_early_stopping_nonparallel(self):
... | import numpy as np
import pytest
import xgboost as xgb
from xgboost import testing as tm
from xgboost.testing.updater import get_basescore
rng = np.random.RandomState(1994)
class TestEarlyStopping:
@pytest.mark.skipif(**tm.no_sklearn())
def test_early_stopping_nonparallel(self):
from sklearn.dataset... |
from keras.src import tree
from keras.src.backend import KerasTensor
class SymbolicArguments:
def __init__(self, *args, **kwargs):
self.args = tree.map_structure(lambda x: x, args)
self.kwargs = tree.map_structure(lambda x: x, kwargs)
self._flat_arguments = tree.flatten((self.args, self.kw... | from keras.src import tree
from keras.src.backend import KerasTensor
class SymbolicArguments:
def __init__(self, *args, **kwargs):
self.args = tree.map_structure(lambda x: x, args)
self.kwargs = tree.map_structure(lambda x: x, kwargs)
self._flat_arguments = tree.flatten((self.args, self.kw... |
import numpy as np
import pytest
from docarray import BaseDocument, DocumentArray
from docarray.array.array_stacked import DocumentArrayStacked
from docarray.documents import Image, Text
from docarray.typing import NdArray
@pytest.mark.proto
def test_simple_proto():
class CustomDoc(BaseDocument):
text: s... | import numpy as np
from docarray import BaseDocument, DocumentArray
from docarray.array.array_stacked import DocumentArrayStacked
from docarray.documents import Image, Text
from docarray.typing import NdArray
def test_simple_proto():
class CustomDoc(BaseDocument):
text: str
tensor: NdArray
d... |
"""
A quantized model executes some or all of the operations with integers rather than floating point values. This allows for a more compact models and the use of high performance vectorized operations on many hardware platforms.
As a result, you get about 40% smaller and faster models. The speed-up depends on your CP... | """
A quantized model executes some or all of the operations with integers rather than floating point values. This allows for a more compact models and the use of high performance vectorized operations on many hardware platforms.
As a result, you get about 40% smaller and faster models. The speed-up depends on your CP... |
import os
from jina import Executor, requests
class FilewriterExec(Executor):
@requests
def foo(self, **kwargs):
print(self.workspace)
file = os.path.join(self.workspace, 'out.txt')
with open(file, 'w', encoding='utf-8') as f:
f.write('Filewriter was here')
| import os
from jina import Executor, requests
class FilewriterExec(Executor):
@requests
def foo(self, **kwargs):
print(self.workspace)
file = os.path.join(self.workspace, 'out.txt')
with open(file, 'w') as f:
f.write('Filewriter was here')
|
import pytest
from unittest.mock import Mock, patch, AsyncMock
from llama_index.embeddings.nvidia import NVIDIAEmbedding
class MockEmbeddingResponse:
"""Mock response matching the structure expected by the code."""
def __init__(self):
self.data = [
Mock(embedding=[1.0, 2.0, 3.0], index=0)... | import pytest
import respx
from llama_index.embeddings.nvidia import NVIDIAEmbedding
import re
import httpx
import json
@pytest.fixture()
def mocked_route() -> respx.Route:
all_urls = re.compile(r".*/embeddings")
fake_response = httpx.Response(
200, json={"data": [{"index": 0, "embedding": [1.0, 2.0, ... |
"""
Top-level module of Jina.
The primary function of this module is to import all of the public Jina
interfaces into a single place. The interfaces themselves are located in
sub-modules, as described below.
"""
import os as _os
import platform as _platform
import signal as _signal
import sys as _sys
import warnings... | """
Top-level module of Jina.
The primary function of this module is to import all of the public Jina
interfaces into a single place. The interfaces themselves are located in
sub-modules, as described below.
"""
import os as _os
import platform as _platform
import signal as _signal
import sys as _sys
import warnings... |
from langchain_core.vectorstores import VST, VectorStore, VectorStoreRetriever
__all__ = ["VST", "VectorStore", "VectorStoreRetriever"]
| from langchain_core.vectorstores import VST, VectorStore, VectorStoreRetriever
__all__ = ["VectorStore", "VectorStoreRetriever", "VST"]
|
from abc import abstractmethod
import logging
from typing import Any, Dict, List, Optional
from llama_index.core.graph_stores.types import GraphStore
from .neptune import refresh_schema
logger = logging.getLogger(__name__)
class NeptuneBaseGraphStore(GraphStore):
"""This is an abstract base class that represents... | from abc import abstractmethod
import logging
from typing import Any, Dict, List, Optional
from llama_index.core.graph_stores.types import GraphStore
from .neptune import refresh_schema
logger = logging.getLogger(__name__)
class NeptuneBaseGraphStore(GraphStore):
"""This is an abstract base class that represents... |
from typing import Optional
import numpy as np
import pytest
import torch
from pydantic.tools import parse_obj_as, schema_json_of
from docarray import BaseDocument
from docarray.base_document.io.json import orjson_dumps
from docarray.typing import AudioNdArray, AudioTorchTensor, AudioUrl
from tests import TOYDATA_DIR... | from typing import Optional
import numpy as np
import pytest
import torch
from pydantic.tools import parse_obj_as, schema_json_of
from docarray import BaseDocument
from docarray.document.io.json import orjson_dumps
from docarray.typing import AudioNdArray, AudioTorchTensor, AudioUrl
from tests import TOYDATA_DIR
AUD... |
_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/'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
# In mstrain 3x config, img_scale=[(1333, 640), (1333, 800)],
# multiscale_mode='range'
train_pipeline = [
... |
import importlib
import shutil
import warnings
from typing import List
import fsspec
import fsspec.asyn
from fsspec.implementations.local import LocalFileSystem
from . import compression
COMPRESSION_FILESYSTEMS: list[compression.BaseCompressedFileFileSystem] = [
compression.Bz2FileSystem,
compression.GzipFi... | import importlib
import shutil
import warnings
from typing import List
import fsspec
import fsspec.asyn
from fsspec.implementations.local import LocalFileSystem
from . import compression
COMPRESSION_FILESYSTEMS: List[compression.BaseCompressedFileFileSystem] = [
compression.Bz2FileSystem,
compression.GzipFi... |
import logging
from collections import defaultdict
from typing import Annotated, Any, Dict, List, Optional, Sequence
from fastapi import APIRouter, Body, Depends, HTTPException
from prisma.enums import AgentExecutionStatus, APIKeyPermission
from typing_extensions import TypedDict
import backend.data.block
from backen... | import logging
from collections import defaultdict
from typing import Annotated, Any, Dict, List, Optional, Sequence
from autogpt_libs.utils.cache import thread_cached
from fastapi import APIRouter, Body, Depends, HTTPException
from prisma.enums import AgentExecutionStatus, APIKeyPermission
from typing_extensions impo... |
import pathlib
from argparse import ArgumentParser
from lightning import ConformerRNNTModule, get_data_module
from pytorch_lightning import Trainer, seed_everything
from pytorch_lightning.callbacks import ModelCheckpoint, LearningRateMonitor
from pytorch_lightning.plugins import DDPPlugin
def run_train(args):
se... | import pathlib
from argparse import ArgumentParser
from lightning import ConformerRNNTModule
from pytorch_lightning import Trainer
from pytorch_lightning.callbacks import ModelCheckpoint, LearningRateMonitor
from pytorch_lightning.plugins import DDPPlugin
def run_train(args):
checkpoint_dir = args.exp_dir / "che... |
"""Function components."""
from inspect import signature
from typing import Any, Callable, Dict, Optional, Set, Tuple
from typing_extensions import Annotated
from llama_index.core.base.query_pipeline.query import (
InputKeys,
OutputKeys,
QueryComponent,
)
from llama_index.core.bridge.pydantic import (
... | """Function components."""
from inspect import signature
from typing import Any, Callable, Dict, Optional, Set, Tuple
from typing_extensions import Annotated
from llama_index.core.base.query_pipeline.query import (
InputKeys,
OutputKeys,
QueryComponent,
)
from llama_index.core.bridge.pydantic import (
... |
# Copyright (c) OpenMMLab. All rights reserved.
import math
import torch
from torch.utils.data import DistributedSampler as _DistributedSampler
from mmdet.core.utils import sync_random_seed
class DistributedSampler(_DistributedSampler):
def __init__(self,
dataset,
num_replicas... | # Copyright (c) OpenMMLab. All rights reserved.
import math
import torch
from torch.utils.data import DistributedSampler as _DistributedSampler
class DistributedSampler(_DistributedSampler):
def __init__(self,
dataset,
num_replicas=None,
rank=None,
... |
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... |
# coding: utf-8
"""Helper script for checking versions in the dynamic symbol table.
This script checks that LightGBM library is linked to the appropriate symbol versions.
Linking to newer symbol versions at compile time is problematic because it could result
in built artifacts being unusable on older platforms.
Vers... | # coding: utf-8
"""Helper script for checking versions in the dynamic symbol table.
This script checks that LightGBM library is linked to the appropriate symbol versions.
Linking to newer symbol versions at compile time is problematic because it could result
in built artifacts being unusable on older platforms.
Vers... |
from unittest import mock
from click.testing import CliRunner
from llama_dev.cli import cli
def test_cmd_exec_no_package_no_all_flag():
runner = CliRunner()
result = runner.invoke(cli, ["pkg", "exec", "--cmd", "echo hello"])
assert result.exit_code != 0
assert "Either specify a package name or use th... | from pathlib import Path
from unittest import mock
from click.testing import CliRunner
from llama_dev.cli import cli
def test_cmd_exec_no_package_no_all_flag():
runner = CliRunner()
result = runner.invoke(cli, ["pkg", "exec", "--cmd", "echo hello"])
assert result.exit_code != 0
assert "Either specify... |
from typing import Dict, List, Optional, Callable
from jina.importer import ImportExtensions
from jina.types.request.data import DataRequest
from jina import DocumentArray
from jina._docarray import docarray_v2
if docarray_v2:
from docarray import DocList
def get_fastapi_app(
request_models_map: Dict,
... | from typing import Dict, List, Optional, Callable
from jina.importer import ImportExtensions
from jina.types.request.data import DataRequest
from jina import DocumentArray
from jina._docarray import docarray_v2
if docarray_v2:
from docarray import DocList
def get_fastapi_app(
request_models_map: Dict,
... |
__copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
from pathlib import Path
import numpy as np
import pytest
import torch
import torchvision.models.video as models
from jina import Document, DocumentArray, Executor
from torchvision import transforms
from video_t... | __copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
from pathlib import Path
import numpy as np
import pytest
import torch
import torchvision.models.video as models
from jina import Document, DocumentArray, Executor
from torchvision import transforms
from video_t... |
"""
This is a simple application for sentence embeddings: semantic search
We have a corpus with various sentences. Then, for a given query sentence,
we want to find the most similar sentence in this corpus.
This script outputs for various queries the top 5 most similar sentences in the corpus.
"""
import torch
from... | """
This is a simple application for sentence embeddings: semantic search
We have a corpus with various sentences. Then, for a given query sentence,
we want to find the most similar sentence in this corpus.
This script outputs for various queries the top 5 most similar sentences in the corpus.
"""
from sentence_trans... |
# 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/LICENSE-2.0
#
# U... | # 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/LICENSE-2.0
#
# U... |
from typing import Optional
import pytest
import torch
from docarray import BaseDoc, DocList
from docarray.array.any_array import AnyDocArray
from docarray.documents import TextDoc
from docarray.typing import TorchTensor
num_docs = 5
num_sub_docs = 2
num_sub_sub_docs = 3
@pytest.fixture
def multi_model_docs():
... | from typing import Optional
import pytest
import torch
from docarray import BaseDoc, DocList
from docarray.array.any_array import AnyDocArray
from docarray.documents import TextDoc
from docarray.typing import TorchTensor
num_docs = 5
num_sub_docs = 2
num_sub_sub_docs = 3
@pytest.fixture
def multi_model_docs():
... |
_base_ = './retinanet_r50_caffe_fpn_mstrain_1x_coco.py'
# training schedule for 2x
train_cfg = dict(max_epochs=36)
# learning rate policy
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=36,... | _base_ = './retinanet_r50_caffe_fpn_mstrain_1x_coco.py'
# learning policy
lr_config = dict(step=[28, 34])
runner = dict(type='EpochBasedRunner', max_epochs=36)
|
# Copyright (c) OpenMMLab. All rights reserved.
__version__ = '2.19.0'
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__ = '2.18.1'
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... |
"""
A quantized model executes some or all of the operations with integers rather than floating point values. This allows for a more compact models and the use of high performance vectorized operations on many hardware platforms.
As a result, you get about 40% smaller and faster models. The speed-up depends on your CP... | """
A quantized model executes some or all of the operations with integers rather than floating point values. This allows for a more compact models and the use of high performance vectorized operations on many hardware platforms.
As a result, you get about 40% smaller and faster models. The speed-up depends on your CP... |
# 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/LICENSE-2.0
#
# U... | # 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/LICENSE-2.0
#
# U... |
"""
This example runs a CNN after the word embedding lookup. The output of the CNN is than pooled,
for example with mean-pooling.
"""
import logging
import sys
import traceback
from datetime import datetime
from datasets import load_dataset
from sentence_transformers import SentenceTransformer, losses, models
from... | """
This example runs a CNN after the word embedding lookup. The output of the CNN is than pooled,
for example with mean-pooling.
"""
import logging
import sys
import traceback
from datetime import datetime
from datasets import load_dataset
from sentence_transformers import SentenceTransformer, losses, models
from ... |
# Copyright (c) OpenMMLab. All rights reserved.
import copy
import inspect
from typing import List, Union
import torch
import torch.nn as nn
from mmengine.config import Config, ConfigDict
from mmengine.device import is_npu_available
from mmengine.registry import OPTIM_WRAPPER_CONSTRUCTORS, OPTIMIZERS
from .optimizer_... | # Copyright (c) OpenMMLab. All rights reserved.
import copy
import inspect
from typing import List, Union
import torch
import torch.nn as nn
from mmengine.config import Config, ConfigDict
from mmengine.device import is_npu_available
from mmengine.registry import OPTIM_WRAPPER_CONSTRUCTORS, OPTIMIZERS
from .optimizer_... |
# Copyright (c) OpenMMLab. All rights reserved.
from .augment_wrappers import AutoAugment, RandAugment
from .colorspace import (AutoContrast, Brightness, Color, ColorTransform,
Contrast, Equalize, Invert, Posterize, Sharpness,
Solarize, SolarizeAdd)
from .formatting imp... | # Copyright (c) OpenMMLab. All rights reserved.
from .augment_wrappers import AutoAugment, RandAugment
from .colorspace import (AutoContrast, Brightness, Color, ColorTransform,
Contrast, Equalize, Invert, Posterize, Sharpness,
Solarize, SolarizeAdd)
from .formatting imp... |
from typing import Any # noqa: F401
from docarray.typing.proto_register import _register_proto
from docarray.typing.tensor.embedding.embedding_mixin import EmbeddingMixin
from docarray.typing.tensor.torch_tensor import TorchTensor
torch_base = type(TorchTensor) # type: Any
embedding_base = type(EmbeddingMixin) # t... | from typing import Any # noqa: F401
from docarray.typing.tensor.embedding.embedding_mixin import EmbeddingMixin
from docarray.typing.tensor.torch_tensor import TorchTensor
torch_base = type(TorchTensor) # type: Any
embedding_base = type(EmbeddingMixin) # type: Any
class metaTorchAndEmbedding(torch_base, embeddin... |
from dataclasses import dataclass, field
from typing import Any, Callable, Dict, List
import torch
@dataclass
class SentenceTransformerDataCollator:
"""Collator for a SentenceTransformers model.
This encodes the text columns to {column}_input_ids and {column}_attention_mask columns.
This works with the t... | from dataclasses import dataclass, field
from typing import Any, Callable, Dict, List
import torch
@dataclass
class SentenceTransformerDataCollator:
"""Collator for a SentenceTransformers model.
This encodes the text columns to {column}_input_ids and {column}_attention_mask columns.
This works with the t... |
from typing import Any, Sequence, Union
from deprecated import deprecated
from llama_index.core.base.llms.generic_utils import (
chat_response_to_completion_response,
stream_chat_response_to_completion_response,
astream_chat_response_to_completion_response,
)
from llama_index.core.base.llms.types import (
... | from typing import Any, Optional, Sequence
from pathlib import Path
from llama_index.core.base.llms.generic_utils import (
chat_response_to_completion_response,
stream_chat_response_to_completion_response,
astream_chat_response_to_completion_response,
)
from llama_index.core.base.llms.types import (
Ch... |
# Copyright (c) OpenMMLab. All rights reserved.
from unittest.mock import Mock
from mmengine.hooks import ParamSchedulerHook
class TestParamSchedulerHook:
def test_after_iter(self):
Hook = ParamSchedulerHook()
Runner = Mock()
scheduler = Mock()
scheduler.step = Mock()
sch... | # Copyright (c) OpenMMLab. All rights reserved.
from unittest.mock import Mock
from mmengine.hooks import ParamSchedulerHook
class TestParamSchedulerHook:
def test_after_iter(self):
Hook = ParamSchedulerHook()
Runner = Mock()
scheduler = Mock()
scheduler.step = Mock()
sch... |
"""Ollama specific chat model integration tests"""
from typing import Annotated, Optional
import pytest
from pydantic import BaseModel, Field
from typing_extensions import TypedDict
from langchain_ollama import ChatOllama
DEFAULT_MODEL_NAME = "llama3.1"
@pytest.mark.parametrize(("method"), [("function_calling"), ... | """Ollama specific chat model integration tests"""
from typing import Annotated, Optional
import pytest
from pydantic import BaseModel, Field
from typing_extensions import TypedDict
from langchain_ollama import ChatOllama
@pytest.mark.parametrize(("method"), [("function_calling"), ("json_schema")])
def test_struct... |
import os
from abc import ABC, abstractmethod
from typing import List, Optional
import fsspec
from llama_index.core.data_structs.data_structs import IndexStruct
DEFAULT_PERSIST_DIR = "./storage"
DEFAULT_PERSIST_FNAME = "index_store.json"
DEFAULT_PERSIST_PATH = os.path.join(DEFAULT_PERSIST_DIR, DEFAULT_PERSIST_FNAME)
... | import os
from abc import ABC, abstractmethod
from typing import List, Optional
import fsspec
from llama_index.core.data_structs.data_structs import IndexStruct
DEFAULT_PERSIST_DIR = "./storage"
DEFAULT_PERSIST_FNAME = "index_store.json"
DEFAULT_PERSIST_PATH = os.path.join(DEFAULT_PERSIST_DIR, DEFAULT_PERSIST_FNAME)
... |
from io import BytesIO
from unittest.mock import MagicMock
from llama_index.core.schema import ImageDocument
from llama_index.multi_modal_llms.gemini.utils import (
generate_gemini_multi_modal_chat_message,
)
def test_generate_message_no_image_documents():
result = generate_gemini_multi_modal_chat_message(
... | from unittest.mock import MagicMock
from llama_index.core.schema import ImageDocument
from llama_index.multi_modal_llms.gemini.utils import (
generate_gemini_multi_modal_chat_message,
)
def test_generate_message_no_image_documents():
result = generate_gemini_multi_modal_chat_message(
prompt="Hello", r... |
_base_ = '../mask_rcnn/mask-rcnn_r50_fpn_1x_coco.py'
train_pipeline = [
dict(
type='LoadImageFromFile',
file_client_args={{_base_.file_client_args}}),
dict(
type='InstaBoost',
action_candidate=('normal', 'horizontal', 'skip'),
action_prob=(1, 0, 0),
scale=(0.8, 1... | _base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py'
train_pipeline = [
dict(
type='LoadImageFromFile',
file_client_args={{_base_.file_client_args}}),
dict(
type='InstaBoost',
action_candidate=('normal', 'horizontal', 'skip'),
action_prob=(1, 0, 0),
scale=(0.8, 1... |
# 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... |
import subprocess
import pytest
from jina import Document, DocumentArray, Flow
from text_paddle import TextPaddleEncoder
@pytest.fixture(scope='function')
def flow():
return Flow().add(uses=TextPaddleEncoder)
@pytest.fixture(scope='function')
def content():
return 'hello world'
@pytest.fixture(scope='fun... | import pytest
from jina import Document, DocumentArray, Flow
from text_paddle import TextPaddleEncoder
@pytest.fixture(scope='function')
def flow():
return Flow().add(uses=TextPaddleEncoder)
@pytest.fixture(scope='function')
def content():
return 'hello world'
@pytest.fixture(scope='function')
def documen... |
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