input stringlengths 33 5k | output stringlengths 32 5k |
|---|---|
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... |
# Copyright (c) OpenMMLab. All rights reserved.
import unittest
from unittest import TestCase
import torch
from mmengine.config import ConfigDict
from mmengine.data import InstanceData
from parameterized import parameterized
from mmdet.models.roi_heads.mask_heads import FCNMaskHead
class TestFCNMaskHead(TestCase):
... | # Copyright (c) OpenMMLab. All rights reserved.
import unittest
from unittest import TestCase
import torch
from mmengine.config import ConfigDict
from mmengine.data import InstanceData
from parameterized import parameterized
from mmdet.models.roi_heads.mask_heads import FCNMaskHead
class TestFCNMaskHead(TestCase):
... |
from keras.src import activations
from keras.src.api_export import keras_export
from keras.src.layers.layer import Layer
@keras_export("keras.layers.ELU")
class ELU(Layer):
"""Applies an Exponential Linear Unit function to an output.
Formula:
```
f(x) = alpha * (exp(x) - 1.) for x < 0
f(x) = x f... | from keras.src import activations
from keras.src.api_export import keras_export
from keras.src.layers.layer import Layer
@keras_export("keras.layers.ELU")
class ELU(Layer):
"""Applies an Exponential Linear Unit function to an output.
Formula:
```
f(x) = alpha * (exp(x) - 1.) for x < 0
f(x) = x f... |
"""Tests for evaluation metrics."""
from typing import Dict, List
import numpy as np
import pytest
import xgboost as xgb
from xgboost.compat import concat
from xgboost.core import _parse_eval_str
def check_precision_score(tree_method: str) -> None:
"""Test for precision with ranking and classification."""
... | """Tests for evaluation metrics."""
from typing import Dict, List
import numpy as np
import pytest
import xgboost as xgb
from xgboost.compat import concat
from xgboost.core import _parse_eval_str
def check_precision_score(tree_method: str) -> None:
"""Test for precision with ranking and classification."""
d... |
import unittest
import torch
import torchaudio.prototype.functional as F
from torchaudio_unittest.common_utils import nested_params, TestBaseMixin, torch_script
class TorchScriptConsistencyTestImpl(TestBaseMixin):
def _assert_consistency(self, func, inputs, shape_only=False):
inputs_ = []
for i i... | import unittest
import torch
import torchaudio.prototype.functional as F
from torchaudio_unittest.common_utils import nested_params, TestBaseMixin, torch_script
class TorchScriptConsistencyTestImpl(TestBaseMixin):
def _assert_consistency(self, func, inputs, shape_only=False):
inputs_ = []
for i i... |
from typing import Any, Optional, Sequence
from llama_index.core.evaluation.base import BaseEvaluator, EvaluationResult
from llama_index.core.prompts.mixin import PromptDictType, PromptMixinType
from tonic_validate.metrics.answer_similarity_metric import (
AnswerSimilarityMetric,
)
from tonic_validate.services.op... | from typing import Any, Optional, Sequence
from llama_index.core.evaluation.base import BaseEvaluator, EvaluationResult
from llama_index.core.prompts.mixin import PromptDictType, PromptMixinType
from tonic_validate.metrics.answer_similarity_metric import (
AnswerSimilarityMetric,
)
from tonic_validate.services.op... |
from backend.app import run_processes
from backend.executor import DatabaseManager
from backend.notifications.notifications import NotificationManager
from backend.server.rest_api import AgentServer
def main():
"""
Run all the processes required for the AutoGPT-server REST API.
"""
run_processes(
... | from backend.app import run_processes
from backend.executor import DatabaseManager, Scheduler
from backend.notifications.notifications import NotificationManager
from backend.server.rest_api import AgentServer
def main():
"""
Run all the processes required for the AutoGPT-server REST API.
"""
run_proc... |
"""Utilities to render tools."""
from __future__ import annotations
from inspect import signature
from typing import Callable
from langchain_core.tools.base import BaseTool
ToolsRenderer = Callable[[list[BaseTool]], str]
def render_text_description(tools: list[BaseTool]) -> str:
"""Render the tool name and de... | from __future__ import annotations
from inspect import signature
from typing import Callable
from langchain_core.tools.base import BaseTool
ToolsRenderer = Callable[[list[BaseTool]], str]
def render_text_description(tools: list[BaseTool]) -> str:
"""Render the tool name and description in plain text.
Args... |
"""
Top-level module of Jina.
The primary function of this module is to import all of the public Jina
interfaces into a single place. The interfaces themselves are located in
sub-modules, as described below.
"""
import os as _os
import platform as _platform
import signal as _signal
import sys as _sys
import warnings... | """
Top-level module of Jina.
The primary function of this module is to import all of the public Jina
interfaces into a single place. The interfaces themselves are located in
sub-modules, as described below.
"""
import os as _os
import platform as _platform
import signal as _signal
import sys as _sys
import warnings... |
# Copyright (c) OpenMMLab. All rights reserved.
from .cityscapes_metric import CityScapesMetric
from .coco_metric import CocoMetric
from .coco_panoptic_metric import CocoPanopticMetric
from .openimages_metric import OpenImagesMetric
__all__ = [
'CityScapesMetric', 'CocoMetric', 'CocoPanopticMetric', 'OpenImagesMet... | # Copyright (c) OpenMMLab. All rights reserved.
from .cityscapes_metric import CityScapesMetric
from .coco_metric import CocoMetric
from .coco_panoptic_metric import CocoPanopticMetric
__all__ = ['CityScapesMetric', 'CocoMetric', 'CocoPanopticMetric']
|
"""Module contains a few fake embedding models for testing purposes."""
# Please do not add additional fake embedding model implementations here.
import hashlib
from pydantic import BaseModel
from typing_extensions import override
from langchain_core.embeddings import Embeddings
class FakeEmbeddings(Embeddings, Ba... | """Module contains a few fake embedding models for testing purposes."""
# Please do not add additional fake embedding model implementations here.
import hashlib
from pydantic import BaseModel
from langchain_core.embeddings import Embeddings
class FakeEmbeddings(Embeddings, BaseModel):
"""Fake embedding model f... |
import time
import uuid
from contextlib import contextmanager
from pathlib import Path
from typing import Optional
import pytest
import requests
from huggingface_hub.hf_api import HfApi, HfFolder, RepositoryNotFoundError
CI_HUB_USER = "__DUMMY_TRANSFORMERS_USER__"
CI_HUB_USER_FULL_NAME = "Dummy User"
CI_HUB_USER_TOK... | import time
from contextlib import contextmanager
from pathlib import Path
import pytest
import requests
from huggingface_hub.hf_api import HfApi, HfFolder
CI_HUB_USER = "__DUMMY_TRANSFORMERS_USER__"
CI_HUB_USER_FULL_NAME = "Dummy User"
CI_HUB_USER_TOKEN = "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"
CI_HUB_ENDPOINT = "... |
"""Question-answering with sources over an index."""
from typing import Any
from langchain_core.callbacks import (
AsyncCallbackManagerForChainRun,
CallbackManagerForChainRun,
)
from langchain_core.documents import Document
from langchain_core.retrievers import BaseRetriever
from pydantic import Field
from l... | """Question-answering with sources over an index."""
from typing import Any
from langchain_core.callbacks import (
AsyncCallbackManagerForChainRun,
CallbackManagerForChainRun,
)
from langchain_core.documents import Document
from langchain_core.retrievers import BaseRetriever
from pydantic import Field
from l... |
"""
This examples demonstrates the setup for Question-Answer-Retrieval.
You can input a query or a question. The script then uses semantic search
to find relevant passages in Simple English Wikipedia (as it is smaller and fits better in RAM).
As model, we use: nq-distilbert-base-v1
It was trained on the Natural Ques... | """
This examples demonstrates the setup for Question-Answer-Retrieval.
You can input a query or a question. The script then uses semantic search
to find relevant passages in Simple English Wikipedia (as it is smaller and fits better in RAM).
As model, we use: nq-distilbert-base-v1
It was trained on the Natural Ques... |
from jina import Executor, Flow, requests, DocumentArray
def test_gateway_metric_labels(monkeypatch_metric_exporter):
collect_metrics, read_metrics = monkeypatch_metric_exporter
class FirstExec(Executor):
@requests()
def meow(self, docs, **kwargs):
return DocumentArray.empty(3)
... | from jina import Executor, Flow, requests, DocumentArray
def test_gateway_metric_labels(monkeypatch_metric_exporter):
collect_metrics, read_metrics = monkeypatch_metric_exporter
class FirstExec(Executor):
@requests()
def meow(self, docs, **kwargs):
return DocumentArray.empty(3)
... |
import json
import logging
from typing import Any, Optional
from langchain_core.callbacks import CallbackManagerForToolRun
from langchain_community.tools.slack.base import SlackBaseTool
class SlackGetChannel(SlackBaseTool):
"""Tool that gets Slack channel information."""
name: str = "get_channelid_name_dic... | import json
import logging
from typing import Any, Optional
from langchain_core.callbacks import CallbackManagerForToolRun
from langchain_community.tools.slack.base import SlackBaseTool
class SlackGetChannel(SlackBaseTool): # type: ignore[override]
"""Tool that gets Slack channel information."""
name: str... |
from unittest.mock import MagicMock
from llama_index.core.base.llms.base import BaseLLM
from llama_index.core.tools import FunctionTool
from llama_index.llms.oci_genai import OCIGenAI
def test_oci_genai_embedding_class():
names_of_base_classes = [b.__name__ for b in OCIGenAI.__mro__]
assert BaseLLM.__name__ i... | from unittest.mock import MagicMock
from llama_index.core.base.llms.base import BaseLLM
from llama_index.core.tools import FunctionTool
from llama_index.llms.oci_genai import OCIGenAI
def test_oci_genai_embedding_class():
names_of_base_classes = [b.__name__ for b in OCIGenAI.__mro__]
assert BaseLLM.__name__ i... |
# Copyright (c) OpenMMLab. All rights reserved.
"""MMEngine provides 20 root registries to support using modules across
projects.
More datails can be found at
https://mmengine.readthedocs.io/en/latest/advanced_tutorials/registry.html.
"""
from .build_functions import (build_model_from_cfg, build_runner_from_cfg,
... | # Copyright (c) OpenMMLab. All rights reserved.
"""MMEngine provides 20 root registries to support using modules across
projects.
More datails can be found at
https://mmengine.readthedocs.io/en/latest/tutorials/registry.html.
"""
from .build_functions import (build_model_from_cfg, build_runner_from_cfg,
... |
"""Retriever tool."""
from typing import TYPE_CHECKING, Any, List, Optional
from llama_index.core.base.base_retriever import BaseRetriever
if TYPE_CHECKING:
from llama_index.core.langchain_helpers.agents.tools import LlamaIndexTool
from llama_index.core.schema import (
MetadataMode,
Node,
NodeWithSc... | """Retriever tool."""
from typing import TYPE_CHECKING, Any, List, Optional
from llama_index.core.base.base_retriever import BaseRetriever
if TYPE_CHECKING:
from llama_index.core.langchain_helpers.agents.tools import LlamaIndexTool
from llama_index.core.schema import (
MetadataMode,
Node,
NodeWithSc... |
# 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... |
# Copyright 2020 The HuggingFace 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
#
# Unless required by applicable law or agreed to... | # Copyright 2020 The HuggingFace 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
#
# Unless required by applicable law or agreed to... |
from typing import Any, Dict, Union
import torch
from torchvision import transforms as _transforms
from torchvision.prototype import datapoints
from torchvision.prototype.transforms import functional as F, Transform
from .utils import is_simple_tensor
class ConvertBoundingBoxFormat(Transform):
_transformed_typ... | from typing import Any, Dict, Union
import torch
from torchvision import transforms as _transforms
from torchvision.prototype import datapoints
from torchvision.prototype.transforms import functional as F, Transform
from .utils import is_simple_tensor
class ConvertBoundingBoxFormat(Transform):
_transformed_typ... |
import multiprocessing
from concurrent.futures import ThreadPoolExecutor
import pytest
import xgboost as xgb
@pytest.mark.parametrize("verbosity_level", [0, 1, 2, 3])
def test_global_config_verbosity(verbosity_level):
def get_current_verbosity():
return xgb.get_config()["verbosity"]
old_verbosity =... | import multiprocessing
from concurrent.futures import ThreadPoolExecutor
import pytest
import xgboost as xgb
@pytest.mark.parametrize("verbosity_level", [0, 1, 2, 3])
def test_global_config_verbosity(verbosity_level):
def get_current_verbosity():
return xgb.get_config()["verbosity"]
old_verbosity =... |
"""Test embedding model integration."""
from typing import Any
from unittest.mock import patch
from langchain_ollama.embeddings import OllamaEmbeddings
MODEL_NAME = "llama3.1"
def test_initialization() -> None:
"""Test embedding model initialization."""
OllamaEmbeddings(model="llama3", keep_alive=1)
@pat... | """Test embedding model integration."""
from langchain_ollama.embeddings import OllamaEmbeddings
def test_initialization() -> None:
"""Test embedding model initialization."""
OllamaEmbeddings(model="llama3", keep_alive=1)
|
from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.agent_toolkits.ainetwork.toolkit import AINetworkToolkit
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handlin... | from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.agent_toolkits.ainetwork.toolkit import AINetworkToolkit
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handlin... |
from docarray.typing.tensor.embedding.embedding import AnyEmbedding
from docarray.typing.tensor.embedding.ndarray import NdArrayEmbedding
__all__ = ['NdArrayEmbedding', 'AnyEmbedding']
from docarray.utils.misc import is_tf_available, is_torch_available
torch_available = is_torch_available()
if torch_available:
f... | from docarray.typing.tensor.embedding.embedding import AnyEmbedding
from docarray.typing.tensor.embedding.ndarray import NdArrayEmbedding
__all__ = ['NdArrayEmbedding', 'AnyEmbedding']
try:
import torch # noqa: F401
except ImportError:
pass
else:
from docarray.typing.tensor.embedding.torch import TorchEm... |
# Copyright (c) OpenMMLab. All rights reserved.
from .collect_env import collect_env
from .compat_config import compat_cfg
from .dist_utils import (all_reduce_dict, allreduce_grads, reduce_mean,
sync_random_seed)
from .logger import get_caller_name, log_img_scale
from .memory import AvoidCUDAOO... | # Copyright (c) OpenMMLab. All rights reserved.
from .collect_env import collect_env
from .compat_config import compat_cfg
from .dist_utils import (all_reduce_dict, allreduce_grads, reduce_mean,
sync_random_seed)
from .logger import get_caller_name, log_img_scale
from .memory import AvoidCUDAOO... |
# Copyright (c) OpenMMLab. All rights reserved.
import os
import os.path as osp
from typing import Optional, Sequence
from mmengine.dist import is_main_process
from mmengine.evaluator import BaseMetric
from mmengine.fileio import dump
from mmengine.logging import MMLogger
from mmengine.structures import InstanceData
... | # Copyright (c) OpenMMLab. All rights reserved.
import os
import os.path as osp
from typing import Optional, Sequence
from mmengine.dist import is_main_process
from mmengine.evaluator import BaseMetric
from mmengine.fileio import dump
from mmengine.logging import MMLogger
from mmengine.structures import InstanceData
... |
import random
from typing import Optional, TYPE_CHECKING
if TYPE_CHECKING: # pragma: no cover
from docarray.array.document import DocumentArray
class SampleMixin:
"""A mixin that provides search functionality to DocumentArrays"""
def sample(self, k: int, seed: Optional[int] = None) -> 'DocumentArray':
... | import random
from typing import Optional, TYPE_CHECKING
if TYPE_CHECKING:
from docarray.array.document import DocumentArray
class SampleMixin:
"""A mixin that provides search functionality to DocumentArrays"""
def sample(self, k: int, seed: Optional[int] = None) -> 'DocumentArray':
"""random sa... |
from __future__ import annotations
import os
import sys
from typing import Any, BinaryIO, TypeVar
import PIL.Image
import torch
from torchvision.prototype.utils._internal import fromfile, ReadOnlyTensorBuffer
from torchvision.tv_tensors._tv_tensor import TVTensor
D = TypeVar("D", bound="EncodedData")
class Encode... | from __future__ import annotations
import os
import sys
from typing import Any, BinaryIO, Optional, Tuple, Type, TypeVar, Union
import PIL.Image
import torch
from torchvision.prototype.utils._internal import fromfile, ReadOnlyTensorBuffer
from torchvision.tv_tensors._tv_tensor import TVTensor
D = TypeVar("D", bound... |
import logging
import os
import sys
from torchaudio._internal.module_utils import eval_env, fail_with_message, is_module_available, no_op
try:
from .fb import _init_ffmpeg
except ImportError:
from .utils import _init_ffmpeg
from .utils import _check_cuda_version, _fail_since_no_ffmpeg, _init_dll_path, _init_s... | import logging
import os
import sys
from torchaudio._internal.module_utils import fail_with_message, is_module_available, no_op
try:
from .fb import _init_ffmpeg
except ImportError:
from .utils import _init_ffmpeg
from .utils import _check_cuda_version, _fail_since_no_ffmpeg, _init_dll_path, _init_sox, _load_... |
__version__ = '0.14.4'
import os
from docarray.document import Document
from docarray.array import DocumentArray
from docarray.dataclasses import dataclass, field
if 'DA_RICH_HANDLER' in os.environ:
from rich.traceback import install
install()
| __version__ = '0.14.3'
import os
from docarray.document import Document
from docarray.array import DocumentArray
from docarray.dataclasses import dataclass, field
if 'DA_RICH_HANDLER' in os.environ:
from rich.traceback import install
install()
|
from base64 import b64encode
from urllib.parse import urlencode
from backend.data.model import OAuth2Credentials
from backend.util.request import requests
from .base import BaseOAuthHandler
class NotionOAuthHandler(BaseOAuthHandler):
"""
Based on the documentation at https://developers.notion.com/docs/autho... | from base64 import b64encode
from urllib.parse import urlencode
from autogpt_libs.supabase_integration_credentials_store import OAuth2Credentials
from backend.util.request import requests
from .base import BaseOAuthHandler
class NotionOAuthHandler(BaseOAuthHandler):
"""
Based on the documentation at https:... |
_base_ = '../cascade_rcnn/cascade-rcnn_r50_fpn_1x_coco.py'
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
# use ResNeSt img_norm
data_preprocessor=dict(
mean=[123.68, 116.779, 103.939],
std=[58.393, 57.12, 57.375],
bgr_to_rgb=True),
backbone=dict(
type='ResN... | _base_ = '../cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco.py'
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
# use ResNeSt img_norm
data_preprocessor=dict(
mean=[123.68, 116.779, 103.939],
std=[58.393, 57.12, 57.375],
bgr_to_rgb=True),
backbone=dict(
type='ResN... |
import os
import pytest
from llama_index.llms.nvidia import NVIDIA
from typing import Any
from pytest_httpx import HTTPXMock
@pytest.fixture()
def mock_local_models(httpx_mock: HTTPXMock):
mock_response = {
"data": [
{
"id": "model1",
"object": "model",
... | import os
import pytest
from llama_index.llms.nvidia import NVIDIA
from typing import Any
from pytest_httpx import HTTPXMock
@pytest.fixture()
def mock_local_models(httpx_mock: HTTPXMock):
mock_response = {
"data": [
{
"id": "model1",
"object": "model",
... |
"""Test Output parsers."""
import pytest
from llama_index.core.output_parsers.langchain import LangchainOutputParser
try:
import langchain # pants: no-infer-dep
from llama_index.core.bridge.langchain import (
BaseOutputParser as LCOutputParser,
)
from llama_index.core.bridge.langchain import ... | """Test Output parsers."""
import pytest
from llama_index.core.output_parsers.langchain import LangchainOutputParser
try:
import langchain # pants: no-infer-dep
from llama_index.core.bridge.langchain import (
BaseOutputParser as LCOutputParser,
)
from llama_index.core.bridge.langchain import... |
"""This is the langchain_ollama package.
It provides infrastructure for interacting with the Ollama service.
"""
from importlib import metadata
from langchain_ollama.chat_models import ChatOllama
from langchain_ollama.embeddings import OllamaEmbeddings
from langchain_ollama.llms import OllamaLLM
try:
__version_... | """This is the langchain_ollama package.
It provides infrastructure for interacting with the Ollama service.
"""
from importlib import metadata
from langchain_ollama.chat_models import ChatOllama
from langchain_ollama.embeddings import OllamaEmbeddings
from langchain_ollama.llms import OllamaLLM
try:
__version_... |
# 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 .compose import... |
from __future__ import annotations
from enum import Enum
from typing import Any, Optional, Tuple, Union
import torch
from ._datapoint import Datapoint
class BoundingBoxFormat(Enum):
"""[BETA] Coordinate format of a bounding box.
Available formats are
* ``XYXY``
* ``XYWH``
* ``CXCYWH``
"""... | from __future__ import annotations
from enum import Enum
from typing import Any, Optional, Tuple, Union
import torch
from ._datapoint import Datapoint
class BoundingBoxFormat(Enum):
"""[BETA] Coordinate format of a bounding box.
Available formats are
* ``XYXY``
* ``XYWH``
* ``CXCYWH``
"""... |
from typing import Any, Callable
from langchain_core.documents import Document
from langchain.retrievers.multi_vector import MultiVectorRetriever, SearchType
from langchain.storage import InMemoryStore
from tests.unit_tests.indexes.test_indexing import InMemoryVectorStore
class InMemoryVectorstoreWithSearch(InMemor... | from typing import Any, Callable, List, Tuple
from langchain_core.documents import Document
from langchain.retrievers.multi_vector import MultiVectorRetriever, SearchType
from langchain.storage import InMemoryStore
from tests.unit_tests.indexes.test_indexing import InMemoryVectorStore
class InMemoryVectorstoreWithS... |
"""
Top-level module of Jina.
The primary function of this module is to import all of the public Jina
interfaces into a single place. The interfaces themselves are located in
sub-modules, as described below.
"""
import os as _os
import platform as _platform
import signal as _signal
import sys as _sys
import warnings... | """
Top-level module of Jina.
The primary function of this module is to import all of the public Jina
interfaces into a single place. The interfaces themselves are located in
sub-modules, as described below.
"""
import os as _os
import platform as _platform
import signal as _signal
import sys as _sys
import warnings... |
import logging
from sentence_transformers import SparseEncoder
from sentence_transformers.sparse_encoder.evaluation import SparseNanoBEIREvaluator
logging.basicConfig(format="%(message)s", level=logging.INFO)
# Load a model
model = SparseEncoder("naver/splade-cocondenser-ensembledistil")
datasets = ["QuoraRetrieval... | import logging
from sentence_transformers import SparseEncoder
from sentence_transformers.sparse_encoder.evaluation import SparseNanoBEIREvaluator
logging.basicConfig(format="%(message)s", level=logging.INFO)
# Load a model
model = SparseEncoder("naver/splade-cocondenser-ensembledistil")
datasets = ["QuoraRetrieval... |
"""
=============================
Recursive feature elimination
=============================
This example demonstrates how Recursive Feature Elimination
(:class:`~sklearn.feature_selection.RFE`) can be used to determine the
importance of individual pixels for classifying handwritten digits.
:class:`~sklearn.feature_s... | """
=============================
Recursive feature elimination
=============================
This example demonstrates how Recursive Feature Elimination
(:class:`~sklearn.feature_selection.RFE`) can be used to determine the
importance of individual pixels for classifying handwritten digits.
:class:`~sklearn.feature_s... |
# Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
from ..builder import LOSSES
from .utils import weight_reduce_loss
def dice_loss(pred,
target,
weight=None,
eps=1e-3,
reduction='mean',
avg_factor=None):
"""Cal... | # Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
from ..builder import LOSSES
from .utils import weight_reduce_loss
def dice_loss(pred,
target,
weight=None,
eps=1e-3,
reduction='mean',
avg_factor=None):
"""Cal... |
"""Helpers for creating Anthropic API clients.
This module allows for the caching of httpx clients to avoid creating new instances
for each instance of ChatAnthropic.
Logic is largely replicated from anthropic._base_client.
"""
from __future__ import annotations
import asyncio
import os
from functools import lru_ca... | """Helpers for creating Anthropic API clients.
This module allows for the caching of httpx clients to avoid creating new instances
for each instance of ChatAnthropic.
Logic is largely replicated from anthropic._base_client.
"""
import asyncio
import os
from functools import lru_cache
from typing import Any, Optional... |
"""DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.applications.vgg16 import VGG16 as VGG16
from keras.src.applications.vgg16 import (
decode_predictions as decode_predictions,
)
from keras.src.applications.vgg16 import preprocess... | """DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.applications.vgg16 import VGG16
from keras.src.applications.vgg16 import decode_predictions
from keras.src.applications.vgg16 import preprocess_input
|
import argparse
from jina.enums import GatewayProtocolType
from jina.helper import parse_host_scheme
from jina.logging.predefined import default_logger
class NetworkChecker:
"""Check if a Deployment is running or not."""
def __init__(self, args: 'argparse.Namespace'):
"""
Create a new :class... | import argparse
from jina.enums import GatewayProtocolType
from jina.helper import parse_host_scheme
from jina.logging.predefined import default_logger
class NetworkChecker:
"""Check if a BaseDeployment is running or not."""
def __init__(self, args: 'argparse.Namespace'):
"""
Create a new :c... |
# -*- coding: utf-8 -*-
# Generated by the protocol buffer compiler. DO NOT EDIT!
# source: docarray.proto
"""Generated protocol buffer code."""
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_d... | # -*- coding: utf-8 -*-
# Generated by the protocol buffer compiler. DO NOT EDIT!
# source: docarray.proto
"""Generated protocol buffer code."""
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_d... |
"""Test in memory docstore."""
from typing import Any
from langchain.output_parsers.combining import CombiningOutputParser
from langchain.output_parsers.regex import RegexParser
from langchain.output_parsers.structured import ResponseSchema, StructuredOutputParser
DEF_EXPECTED_RESULT = {
"answer": "Paris",
"... | """Test in memory docstore."""
from typing import Any
from langchain.output_parsers.combining import CombiningOutputParser
from langchain.output_parsers.regex import RegexParser
from langchain.output_parsers.structured import ResponseSchema, StructuredOutputParser
DEF_EXPECTED_RESULT = {
"answer": "Paris",
"... |
_base_ = './mask-rcnn_s50_fpn_syncbn-backbone+head_ms-1x_coco.py'
model = dict(
backbone=dict(
stem_channels=128,
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='open-mmlab://resnest101')))
| _base_ = './mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py'
model = dict(
backbone=dict(
stem_channels=128,
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='open-mmlab://resnest101')))
|
# dataset settings
dataset_type = 'RefCocoDataset'
data_root = 'data/coco/'
backend_args = None
test_pipeline = [
dict(type='LoadImageFromFile', backend_args=backend_args),
dict(type='Resize', scale=(1333, 800), keep_ratio=True),
dict(
type='LoadAnnotations',
with_mask=True,
with_b... | # dataset settings
dataset_type = 'RefCOCODataset'
data_root = 'data/refcoco/'
backend_args = None
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='Resize', scale=(1333, 800), keep_ratio=True),
dict(type='RandomFlip', prob=0.5),
dict(
type='PackDetInputs',
meta_keys=('img_... |
from ._conformer_wav2vec2 import (
conformer_wav2vec2_base,
conformer_wav2vec2_model,
conformer_wav2vec2_pretrain_base,
conformer_wav2vec2_pretrain_large,
conformer_wav2vec2_pretrain_model,
ConformerWav2Vec2PretrainModel,
)
from ._emformer_hubert import emformer_hubert_base, emformer_hubert_mode... | from ._conformer_wav2vec2 import conformer_wav2vec2_base, conformer_wav2vec2_model
from ._emformer_hubert import emformer_hubert_base, emformer_hubert_model
from .conv_emformer import ConvEmformer
from .rnnt import conformer_rnnt_base, conformer_rnnt_model
__all__ = [
"conformer_rnnt_base",
"conformer_rnnt_mod... |
import os
import shutil
import pytest
import torch
import torchaudio
class GreedyCTCDecoder(torch.nn.Module):
def __init__(self, labels, blank: int = 0):
super().__init__()
self.blank = blank
self.labels = labels
def forward(self, logits: torch.Tensor) -> str:
"""Given a sequ... | import os
import shutil
import pytest
import torch
import torchaudio
class GreedyCTCDecoder(torch.nn.Module):
def __init__(self, labels, blank: int = 0):
super().__init__()
self.blank = blank
self.labels = labels
def forward(self, logits: torch.Tensor) -> str:
"""Given a sequ... |
# Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
from mmcv.cnn import ConvModule
from mmdet.registry import MODELS
from .anchor_head import AnchorHead
@MODELS.register_module()
class RetinaHead(AnchorHead):
r"""An anchor-based head used in `RetinaNet
<https://arxiv.org/pdf/1708.02002.pdf... | # Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
from mmcv.cnn import ConvModule
from ..builder import HEADS
from .anchor_head import AnchorHead
@HEADS.register_module()
class RetinaHead(AnchorHead):
r"""An anchor-based head used in `RetinaNet
<https://arxiv.org/pdf/1708.02002.pdf>`_.
... |
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... |
# Copyright (c) OpenMMLab. All rights reserved.
from .csp_darknet import CSPDarknet
from .darknet import Darknet
from .detectors_resnet import DetectoRS_ResNet
from .detectors_resnext import DetectoRS_ResNeXt
from .hourglass import HourglassNet
from .hrnet import HRNet
from .mobilenet_v2 import MobileNetV2
from .regnet... | # Copyright (c) OpenMMLab. All rights reserved.
from .csp_darknet import CSPDarknet
from .darknet import Darknet
from .detectors_resnet import DetectoRS_ResNet
from .detectors_resnext import DetectoRS_ResNeXt
from .hourglass import HourglassNet
from .hrnet import HRNet
from .mobilenet_v2 import MobileNetV2
from .regnet... |
from __future__ import annotations
from collections import Counter
import pytest
from sentence_transformers.sampler import GroupByLabelBatchSampler
from sentence_transformers.util import is_datasets_available
if is_datasets_available():
from datasets import Dataset
else:
pytest.skip(
reason='Sentenc... | from __future__ import annotations
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": ... |
# dataset settings
dataset_type = 'CityscapesDataset'
data_root = 'data/cityscapes/'
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
dict(type='RandomResize', scale=[(2048, 800), (2048, 1024)]),
dict(type='RandomFlip', prob=0.5),
dict... | # dataset settings
dataset_type = 'CityscapesDataset'
# TODO remove it after cityscape metric
# data_root = '/mnt/lustre/luochunhua.vendor/openmmlab2.0/data/cityscapes/'
data_root = 'data/cityscapes/'
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True, with_mask=True... |
__copyright__ = "Copyright (c) 2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import pytest
from jina import Document, DocumentArray
from ...match_merger import MatchMerger
@pytest.fixture
def docs_matrix():
return [
DocumentArray(
[
Document(
... | __copyright__ = "Copyright (c) 2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import pytest
from jina import Document, DocumentArray
from ...match_merger import MatchMerger
@pytest.fixture
def docs_matrix():
return [
DocumentArray(
[
Document(
... |
from typing import TYPE_CHECKING, Type, List
if TYPE_CHECKING:
from docarray.document.pydantic_model import PydanticDocumentArray
from docarray.typing import T
from pydantic import BaseModel
class PydanticMixin:
@classmethod
def get_json_schema(cls, indent: int = 2) -> str:
"""Return a J... | from typing import TYPE_CHECKING, Type, List
if TYPE_CHECKING:
from ...document.pydantic_model import PydanticDocumentArray
from ...typing import T
from pydantic import BaseModel
class PydanticMixin:
@classmethod
def get_json_schema(cls, indent: int = 2) -> str:
"""Return a JSON Schema o... |
"""Argparser module for pinging"""
from jina.parsers.base import set_base_parser
def set_new_project_parser(parser=None):
"""Set the parser for `new`
:param parser: an existing parser to build upon
:return: the parser
"""
if not parser:
parser = set_base_parser()
parser.add_argument... | """Argparser module for pinging"""
from jina.parsers.base import set_base_parser
def set_new_project_parser(parser=None):
"""Set the parser for `new`
:param parser: an existing parser to build upon
:return: the parser
"""
if not parser:
parser = set_base_parser()
parser.add_argument... |
# Copyright (c) OpenMMLab. All rights reserved.
import math
from typing import Optional
import torch
import torch.nn as nn
from mmengine.model import ExponentialMovingAverage
from torch import Tensor
from mmdet.registry import MODELS
@MODELS.register_module()
class ExpMomentumEMA(ExponentialMovingAverage):
"""E... | # Copyright (c) OpenMMLab. All rights reserved.
import math
from typing import Optional
import torch
import torch.nn as nn
from mmengine.model import ExponentialMovingAverage
from torch import Tensor
from mmdet.registry import MODELS
@MODELS.register_module()
class ExpMomentumEMA(ExponentialMovingAverage):
"""E... |
# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import mmengine
from mmengine import Config, DictAction
from mmengine.evaluator import Evaluator
from mmengine.registry import init_default_scope
from mmdet.registry import DATASETS
def parse_args():
parser = argparse.ArgumentParser(description='Ev... | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import mmengine
from mmengine import Config, DictAction
from mmengine.evaluator import Evaluator
from mmdet.registry import DATASETS
from mmdet.utils import register_all_modules
def parse_args():
parser = argparse.ArgumentParser(description='Evalua... |
import numpy as np
import pytest
import tempfile
import os
from PIL import Image
from unittest.mock import patch, MagicMock
from llama_index.core.schema import ImageDocument
from llama_index.core.base.llms.types import ChatMessage
from llama_index.core.multi_modal_llms.base import MultiModalLLM
from llama_... | import numpy as np
import pytest
import tempfile
import os
from PIL import Image
from unittest.mock import patch, MagicMock
from llama_index.core.schema import ImageDocument
from llama_index.core.base.llms.types import ChatMessage
from llama_index.core.multi_modal_llms.base import MultiModalLLM
from llama_... |
from __future__ import annotations
from torch import Tensor, nn
from sentence_transformers.cross_encoder import CrossEncoder
class BinaryCrossEntropyLoss(nn.Module):
def __init__(self, model: CrossEncoder, pos_weight: Tensor | None = None, **kwargs) -> None:
super().__init__()
self.model = model... | from __future__ import annotations
from torch import Tensor, nn
from sentence_transformers.cross_encoder import CrossEncoder
# TODO: Bad name, don't 1-1 copy the name from PyTorch
class BinaryCrossEntropyLoss(nn.Module):
def __init__(self, model: CrossEncoder, pos_weight: Tensor | None = None, **kwargs) -> None... |
# Copyright 2020 The HuggingFace 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 applicabl... | # Copyright 2020 The HuggingFace 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 applicabl... |
# Copyright (c) OpenMMLab. All rights reserved.
from unittest.mock import patch
import pytest
from mmengine.device import is_cuda_available
from mmengine.testing import RunnerTestCase
class TestEmptyCacheHook(RunnerTestCase):
@pytest.mark.skipif(
not is_cuda_available(), reason='cuda should be availabl... | # Copyright (c) OpenMMLab. All rights reserved.
from unittest.mock import patch
from mmengine.testing import RunnerTestCase
class TestEmptyCacheHook(RunnerTestCase):
def test_with_runner(self):
with patch('torch.cuda.empty_cache') as mock_empty_cache:
cfg = self.epoch_based_cfg
c... |
from typing import Optional, Union, Callable, Tuple, TYPE_CHECKING, Dict
if TYPE_CHECKING: # pragma: no cover
import numpy as np
from docarray.typing import ArrayType
from docarray import DocumentArray
class MatchMixin:
"""A mixin that provides match functionality to DocumentArrays"""
def match... | from typing import Optional, Union, Callable, Tuple, TYPE_CHECKING, Dict
if TYPE_CHECKING: # pragma: no cover
import numpy as np
from docarray.typing import ArrayType
from docarray import DocumentArray
class MatchMixin:
"""A mixin that provides match functionality to DocumentArrays"""
def match... |
"""Interface with the LangChain Hub."""
from __future__ import annotations
import json
from collections.abc import Sequence
from typing import Any, Optional
from langchain_core.load.dump import dumps
from langchain_core.load.load import loads
from langchain_core.prompts import BasePromptTemplate
def _get_client(
... | """Interface with the LangChain Hub."""
from __future__ import annotations
import json
from collections.abc import Sequence
from typing import Any, Optional
from langchain_core.load.dump import dumps
from langchain_core.load.load import loads
from langchain_core.prompts import BasePromptTemplate
def _get_client(
... |
"""DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras._tf_keras.keras.preprocessing import image as image
from keras._tf_keras.keras.preprocessing import sequence as sequence
from keras._tf_keras.keras.preprocessing import text as text
from ... | """DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.api._tf_keras.keras.preprocessing import image
from keras.api._tf_keras.keras.preprocessing import sequence
from keras.api._tf_keras.keras.preprocessing import text
from keras.src.utils.i... |
"""Tests using Scikit-Learn's bundled estimator_checks."""
from contextlib import contextmanager
import pytest
import sklearn
from packaging.version import parse as parse_version
from sklearn.utils.estimator_checks import parametrize_with_checks
import keras
from keras.src.backend import floatx
from keras.src.backen... | """Tests using Scikit-Learn's bundled estimator_checks."""
from contextlib import contextmanager
import pytest
import keras
from keras.src.backend import floatx
from keras.src.backend import set_floatx
from keras.src.layers import Dense
from keras.src.layers import Input
from keras.src.models import Model
from keras... |
"""Test OllamaLLM llm."""
from langchain_core.runnables import RunnableConfig
from langchain_ollama.llms import OllamaLLM
MODEL_NAME = "llama3.1"
def test_stream() -> None:
"""Test streaming tokens from OpenAI."""
llm = OllamaLLM(model=MODEL_NAME)
for token in llm.stream("I'm Pickle Rick"):
as... | """Test OllamaLLM llm."""
from langchain_ollama.llms import OllamaLLM
MODEL_NAME = "llama3"
def test_stream() -> None:
"""Test streaming tokens from OpenAI."""
llm = OllamaLLM(model=MODEL_NAME)
for token in llm.stream("I'm Pickle Rick"):
assert isinstance(token, str)
async def test_astream() ... |
from docarray.base_doc.any_doc import AnyDoc
from docarray.base_doc.base_node import BaseNode
from docarray.base_doc.doc import BaseDoc
from docarray.utils._internal.misc import (
_get_path_from_docarray_root_level,
import_library,
)
__all__ = ['AnyDoc', 'BaseDoc', 'BaseNode']
def __getattr__(name: str):
... | from docarray.base_doc.any_doc import AnyDoc
from docarray.base_doc.base_node import BaseNode
from docarray.base_doc.doc import BaseDoc
from docarray.utils._internal.misc import (
_get_path_from_docarray_root_level,
import_library,
)
__all__ = ['AnyDoc', 'BaseDoc', 'BaseNode']
def __getattr__(name: str):
... |
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... |
## under jina root dir
# python scripts/get-last-release-note.py
## result in root/tmp.md
with open('CHANGELOG.md', encoding='utf-8') as fp:
n = []
for v in fp:
if v.startswith('## Release Note'):
n.clear()
n.append(v)
with open('tmp.md', 'w', encoding='utf-8') as fp:
fp.writel... | ## under jina root dir
# python scripts/get-last-release-note.py
## result in root/tmp.md
with open('CHANGELOG.md') as fp:
n = []
for v in fp:
if v.startswith('## Release Note'):
n.clear()
n.append(v)
with open('tmp.md', 'w') as fp:
fp.writelines(n)
|
__version__ = '0.15.0'
import os
from docarray.document import Document
from docarray.array import DocumentArray
from docarray.dataclasses import dataclass, field
if 'DA_RICH_HANDLER' in os.environ:
from rich.traceback import install
install()
| __version__ = '0.14.12'
import os
from docarray.document import Document
from docarray.array import DocumentArray
from docarray.dataclasses import dataclass, field
if 'DA_RICH_HANDLER' in os.environ:
from rich.traceback import install
install()
|
from typing import Optional, TYPE_CHECKING, TypeVar, Type, Union, Any
from docarray.typing.proto_register import _register_proto
from docarray.typing.url.any_url import AnyUrl
from docarray.typing.url.filetypes import TEXT_FILE_FORMATS
if TYPE_CHECKING:
from pydantic import BaseConfig
from pydantic.fields imp... | from typing import Optional
from docarray.typing.proto_register import _register_proto
from docarray.typing.url.any_url import AnyUrl
@_register_proto(proto_type_name='text_url')
class TextUrl(AnyUrl):
"""
URL to a text file.
Can be remote (web) URL, or a local file path.
"""
def load(self, char... |
# Copyright (c) OpenMMLab. All rights reserved.
__version__ = '0.8.3'
def parse_version_info(version_str):
"""Parse the version information.
Args:
version_str (str): version string like '0.1.0'.
Returns:
tuple: version information contains major, minor, micro version.
"""
versio... | # Copyright (c) OpenMMLab. All rights reserved.
__version__ = '0.8.2'
def parse_version_info(version_str):
"""Parse the version information.
Args:
version_str (str): version string like '0.1.0'.
Returns:
tuple: version information contains major, minor, micro version.
"""
versio... |
_base_ = './faster-rcnn_r50-caffe_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron2/resnet101_caffe')))
| _base_ = './faster_rcnn_r50_caffe_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron2/resnet101_caffe')))
|
import torch
from torchvision.transforms import autoaugment, transforms
from torchvision.transforms.functional import InterpolationMode
class ClassificationPresetTrain:
def __init__(
self,
*,
crop_size,
mean=(0.485, 0.456, 0.406),
std=(0.229, 0.224, 0.225),
interpol... | import torch
from torchvision.transforms import autoaugment, transforms
from torchvision.transforms.functional import InterpolationMode
class ClassificationPresetTrain:
def __init__(
self,
*,
crop_size,
mean=(0.485, 0.456, 0.406),
std=(0.229, 0.224, 0.225),
interpol... |
# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import os.path as osp
import numpy as np
from mmengine.fileio import dump, load
from mmengine.utils import mkdir_or_exist, track_parallel_progress
prog_description = '''K-Fold coco split.
To split coco data for semi-supervised object detection:
pyth... | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import os.path as osp
import numpy as np
from mmengine.fileio import dump, load
from mmengine.utils import mkdir_or_exist, track_parallel_progress
prog_description = '''K-Fold coco split.
To split coco data for semi-supervised object detection:
pyth... |
__copyright__ = "Copyright (c) 2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import pytest
from jina import Document, DocumentArray, Flow, requests
from jina.executors import BaseExecutor
from ...match_merger import MatchMerger
class MockShard(BaseExecutor):
@requests
def search(sel... | __copyright__ = "Copyright (c) 2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import pytest
from jina import Flow, Document, requests, DocumentArray
from jina.executors import BaseExecutor
from ...match_merger import MatchMerger
class MockShard(BaseExecutor):
@requests
def search(sel... |
__copyright__ = 'Copyright (c) 2021 Jina AI Limited. All rights reserved.'
__license__ = 'Apache-2.0'
import os
import subprocess
from pathlib import Path
import pytest
from jina import Document, DocumentArray
@pytest.fixture(scope='session')
def docker_image_name() -> str:
return Path(__file__).parents[1].stem... | __copyright__ = 'Copyright (c) 2021 Jina AI Limited. All rights reserved.'
__license__ = 'Apache-2.0'
import os
import subprocess
from pathlib import Path
import pytest
from jina import Document, DocumentArray
@pytest.fixture(scope='session')
def build_docker_image() -> str:
img_name = Path(__file__).parents[1]... |
import random
import numpy as np
import pytest
from jina import Document, DocumentArray
from ..catboost_ranker import CatboostRanker
NUM_DOCS = 1000
NUM_MATCHES = 5
@pytest.fixture
def ranker():
return CatboostRanker(
query_features=['brand', 'price'],
match_features=['brand', 'price'],
... | import random
import pytest
import numpy as np
from jina import Document, DocumentArray
from ..catboost_ranker import CatboostRanker
NUM_DOCS = 1000
NUM_MATCHES = 5
@pytest.fixture
def ranker():
return CatboostRanker(
query_features=['brand', 'price'],
match_features=['brand', 'price'],
... |
import pytest
from docarray import DocumentArray, Document
from docarray.array.opensearch import DocumentArrayOpenSearch
from docarray.array.qdrant import DocumentArrayQdrant
from docarray.array.sqlite import DocumentArraySqlite
from docarray.array.annlite import DocumentArrayAnnlite, AnnliteConfig
from docarray.array... | import pytest
from docarray import DocumentArray, Document
from docarray.array.qdrant import DocumentArrayQdrant
from docarray.array.sqlite import DocumentArraySqlite
from docarray.array.annlite import DocumentArrayAnnlite, AnnliteConfig
from docarray.array.storage.qdrant import QdrantConfig
from docarray.array.storag... |
__copyright__ = "Copyright (c) 2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import copy
from typing import Dict
from jina import DocumentArray, Executor, requests
from jinahub.indexers.searcher.FaissSearcher import FaissSearcher
from jinahub.indexers.storage.LMDBStorage import LMDBStorage
... | __copyright__ = "Copyright (c) 2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import copy
from typing import Dict
from jina import DocumentArray, Executor, requests
from jinahub.indexers.searcher.FaissSearcher import FaissSearcher
from jinahub.indexers.storage.LMDBStorage import LMDBStorage
... |
# 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... |
import argparse
import jsonlines
from pycocotools.coco import COCO
from tqdm import tqdm
def _has_only_empty_bbox(anno):
return all(any(o <= 1 for o in obj['bbox'][2:]) for obj in anno)
def has_valid_annotation(anno):
# if it's empty, there is no annotation
if len(anno) == 0:
return False
#... | import argparse
import jsonlines
from pycocotools.coco import COCO
from tqdm import tqdm
def _has_only_empty_bbox(anno):
return all(any(o <= 1 for o in obj['bbox'][2:]) for obj in anno)
def has_valid_annotation(anno):
# if it's empty, there is no annotation
if len(anno) == 0:
return False
#... |
from typing import Dict, Optional, TypeVar
from google.protobuf import json_format
from jina.excepts import BadRequestType
from jina.helper import typename
from jina.proto import jina_pb2
from jina.types.mixin import ProtoTypeMixin
StatusSourceType = TypeVar('StatusSourceType', jina_pb2.StatusProto, str, Dict, bytes... | from typing import Dict, Optional, TypeVar
from google.protobuf import json_format
from jina.excepts import BadRequestType
from jina.helper import typename
from jina.proto import jina_pb2
from jina.types.mixin import ProtoTypeMixin
StatusSourceType = TypeVar('StatusSourceType', jina_pb2.StatusProto, str, Dict, bytes... |
from typing import Any
from langchain_core.exceptions import OutputParserException
from langchain.output_parsers import ResponseSchema, StructuredOutputParser
def test_parse() -> None:
"""Test parsing structured output."""
response_schemas = [
ResponseSchema(name="name", description="desc"),
... | from typing import Any
from langchain_core.exceptions import OutputParserException
from langchain.output_parsers import ResponseSchema, StructuredOutputParser
def test_parse() -> None:
"""Test parsing structured output."""
response_schemas = [
ResponseSchema(name="name", description="desc"),
... |
import random
import asyncio
import time
import aiohttp
import grpc
def _raise_last_attempt(err, attempt):
if isinstance(err, asyncio.CancelledError):
trailing_metadata = grpc.aio.Metadata()
trailing_metadata.add('jina-client-attempts', str(attempt))
raise grpc.aio.AioRpcError(
... | import random
import asyncio
import time
import aiohttp
import grpc
def _raise_last_attempt(err, attempt):
if isinstance(err, asyncio.CancelledError):
trailing_metadata = grpc.aio.Metadata()
trailing_metadata.add('jina-client-attempts', str(attempt))
raise grpc.aio.AioRpcError(
... |
import argparse
import json
import logging
import os
import tarfile
from functools import partial
from multiprocessing import Pool
def create_logger(output_file):
logger = logging.getLogger('grit_logger')
logger.setLevel(logging.INFO) # set logger output level
formatter = logging.Formatter('%(asctime)s -... | import argparse
import json
import logging
import os
import tarfile
from functools import partial
from multiprocessing import Pool
def create_logger(output_file):
logger = logging.getLogger('grit_logger')
logger.setLevel(logging.INFO) # set logger output level
formatter = logging.Formatter('%(asctime)s -... |
import os
from typing import Optional
import pytest
from docarray import BaseDoc, DocArray
from docarray.documents import ImageDoc
from tests import TOYDATA_DIR
@pytest.fixture()
def nested_doc_cls():
class MyDoc(BaseDoc):
count: Optional[int]
text: str
class MyDocNested(MyDoc):
ima... | import os
from typing import Optional
import pytest
from docarray import BaseDocument, DocumentArray
from docarray.documents import ImageDoc
from tests import TOYDATA_DIR
@pytest.fixture()
def nested_doc_cls():
class MyDoc(BaseDocument):
count: Optional[int]
text: str
class MyDocNested(MyDo... |
# mypy: allow-untyped-defs
import torch._C._lazy
def reset():
"""Resets all metric counters."""
torch._C._lazy._reset_metrics()
def counter_names():
"""Retrieves all the currently active counter names."""
return torch._C._lazy._counter_names()
def counter_value(name: str):
"""Return the value ... | # mypy: allow-untyped-defs
import torch._C._lazy
def reset():
"""Resets all metric counters."""
torch._C._lazy._reset_metrics()
def counter_names():
"""Retrieves all the currently active counter names."""
return torch._C._lazy._counter_names()
def counter_value(name: str):
"""Return the value ... |
# Copyright (c) OpenMMLab. All rights reserved.
from .conditional_detr_layers import (ConditionalDetrTransformerDecoder,
ConditionalDetrTransformerDecoderLayer)
from .dab_detr_layers import (DABDetrTransformerDecoder,
DABDetrTransformerDecoderLayer,
... | # Copyright (c) OpenMMLab. All rights reserved.
from .conditional_detr_layers import (ConditionalDetrTransformerDecoder,
ConditionalDetrTransformerDecoderLayer)
from .dab_detr_layers import (DABDetrTransformerDecoder,
DABDetrTransformerDecoderLayer,
... |
from __future__ import annotations
import os
import sys
from typing import Any, BinaryIO, Optional, Tuple, Type, TypeVar, Union
import PIL.Image
import torch
from torchvision.prototype.datapoints._datapoint import Datapoint
from torchvision.prototype.utils._internal import fromfile, ReadOnlyTensorBuffer
D = TypeVar... | from __future__ import annotations
import os
import sys
from typing import Any, BinaryIO, Optional, Tuple, Type, TypeVar, Union
import PIL.Image
import torch
from torchvision.prototype.features._feature import _Feature
from torchvision.prototype.utils._internal import fromfile, ReadOnlyTensorBuffer
D = TypeVar("D",... |
from dataclasses import dataclass
from functools import partial
from typing import Callable
import torch
import torchaudio
from torchaudio.prototype.models import conv_tasnet_base, hdemucs_high
@dataclass
class SourceSeparationBundle:
"""torchaudio.prototype.pipelines.SourceSeparationBundle()
Dataclass tha... | from dataclasses import dataclass
from functools import partial
from typing import Callable
import torch
import torchaudio
from torchaudio.prototype.models import conv_tasnet_base, hdemucs_high
@dataclass
class SourceSeparationBundle:
"""torchaudio.prototype.pipelines.SourceSeparationBundle()
Dataclass tha... |
import tempfile
import unittest
import numpy as np
import pytest
import torch
from diffusers import DiffusionPipeline
from diffusers.models.attention_processor import Attention, AttnAddedKVProcessor
from diffusers.utils.testing_utils import torch_device
class AttnAddedKVProcessorTests(unittest.TestCase):
def ge... | import tempfile
import unittest
import numpy as np
import torch
from diffusers import DiffusionPipeline
from diffusers.models.attention_processor import Attention, AttnAddedKVProcessor
class AttnAddedKVProcessorTests(unittest.TestCase):
def get_constructor_arguments(self, only_cross_attention: bool = False):
... |
__copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import subprocess
import pytest
from executor.audioclip_text import AudioCLIPTextEncoder
from jina import Document, DocumentArray, Flow
_EMBEDDING_DIM = 1024
@pytest.mark.parametrize('request_size', [1, 10, 5... | __copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import subprocess
import pytest
from executor.audioclip_text import AudioCLIPTextEncoder
from jina import Document, DocumentArray, Flow
_EMBEDDING_DIM = 1024
@pytest.mark.parametrize('request_size', [1, 10, 5... |
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... | from typing import TYPE_CHECKING, Any, Dict, 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.url_3d.url_3d import Url3D
if TYPE_CHECKING:
from docarray.doc... |
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