input stringlengths 33 5k | output stringlengths 32 5k |
|---|---|
import threading
from typing import Callable, ParamSpec, TypeVar
P = ParamSpec("P")
R = TypeVar("R")
def thread_cached(func: Callable[P, R]) -> Callable[P, R]:
thread_local = threading.local()
def wrapper(*args: P.args, **kwargs: P.kwargs) -> R:
cache = getattr(thread_local, "cache", None)
i... | from typing import Callable, TypeVar, ParamSpec
import threading
P = ParamSpec("P")
R = TypeVar("R")
def thread_cached(func: Callable[P, R]) -> Callable[P, R]:
thread_local = threading.local()
def wrapper(*args: P.args, **kwargs: P.kwargs) -> R:
cache = getattr(thread_local, "cache", None)
i... |
from backend.data.block import (
Block,
BlockCategory,
BlockManualWebhookConfig,
BlockOutput,
BlockSchema,
)
from backend.data.model import SchemaField
from backend.integrations.providers import ProviderName
from backend.integrations.webhooks.generic import GenericWebhookType
class GenericWebhookT... | from backend.data.block import (
Block,
BlockCategory,
BlockManualWebhookConfig,
BlockOutput,
BlockSchema,
)
from backend.data.model import SchemaField
from backend.integrations.providers import ProviderName
from backend.integrations.webhooks.generic import GenericWebhookType
class GenericWebhookT... |
import math
import random
class NoDuplicatesDataLoader:
def __init__(self, train_examples, batch_size):
"""
A special data loader to be used with MultipleNegativesRankingLoss.
The data loader ensures that there are no duplicate sentences within the same batch
"""
self.batch... | import random
import math
class NoDuplicatesDataLoader:
def __init__(self, train_examples, batch_size):
"""
A special data loader to be used with MultipleNegativesRankingLoss.
The data loader ensures that there are no duplicate sentences within the same batch
"""
self.batch... |
from docarray.document.any_document import AnyDocument
from docarray.document.document import BaseDocument
__all__ = ['AnyDocument', 'BaseDocument']
| from docarray.document.any_document import AnyDocument
from docarray.document.document import BaseDocument
|
import datetime
from typing import List
import prisma.enums
import pydantic
class Pagination(pydantic.BaseModel):
total_items: int = pydantic.Field(
description="Total number of items.", examples=[42]
)
total_pages: int = pydantic.Field(
description="Total number of pages.", examples=[97]... | import datetime
from typing import List
import prisma.enums
import pydantic
class Pagination(pydantic.BaseModel):
total_items: int = pydantic.Field(
description="Total number of items.", examples=[42]
)
total_pages: int = pydantic.Field(
description="Total number of pages.", examples=[97]... |
from __future__ import annotations
import argparse
import concurrent.futures
import json
import logging
import os
import subprocess
import sys
from enum import Enum
from pathlib import Path
from typing import NamedTuple
REPO_ROOT = Path(__file__).absolute().parents[3]
PYPROJECT = REPO_ROOT / "pyproject.toml"
DICTION... | from __future__ import annotations
import argparse
import concurrent.futures
import json
import logging
import os
import subprocess
import sys
from enum import Enum
from pathlib import Path
from typing import NamedTuple
REPO_ROOT = Path(__file__).absolute().parents[3]
PYPROJECT = REPO_ROOT / "pyproject.toml"
DICTION... |
# Copyright (c) OpenMMLab. All rights reserved.
__version__ = '2.24.0'
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__ = '2.23.0'
short_version = __version__
def parse_version_info(version_str):
version_info = []
for x in version_str.split('.'):
if x.isdigit():
version_info.append(int(x))
elif x.find('rc') != -1:
patch_version... |
"""ChatGPT Plugiun Tool."""
from typing import List, Optional
import requests
from llama_index.core.schema import Document
from llama_index.core.tools.tool_spec.base import BaseToolSpec
from llama_index.tools.openapi.base import OpenAPIToolSpec
class ChatGPTPluginToolSpec(BaseToolSpec):
"""
ChatGPT Plugin T... | """ChatGPT Plugiun Tool."""
from typing import List, Optional
import requests
from llama_index.core.schema import Document
from llama_index.core.tools.tool_spec.base import BaseToolSpec
from llama_index.tools.openapi.base import OpenAPIToolSpec
class ChatGPTPluginToolSpec(BaseToolSpec):
"""
ChatGPT Plugin T... |
from langchain_core.prompts import PromptTemplate
template = """You are a teacher coming up with questions to ask on a quiz.
Given the following document, please generate a question and answer based on that document.
Example Format:
<Begin Document>
...
<End Document>
QUESTION: question here
ANSWER: answer here
Thes... | # flake8: noqa
from langchain.output_parsers.regex import RegexParser
from langchain_core.prompts import PromptTemplate
template = """You are a teacher coming up with questions to ask on a quiz.
Given the following document, please generate a question and answer based on that document.
Example Format:
<Begin Documen... |
import torch
from torch import Tensor
def _box_cxcywh_to_xyxy(boxes: Tensor) -> Tensor:
"""
Converts bounding boxes from (cx, cy, w, h) format to (x1, y1, x2, y2) format.
(cx, cy) refers to center of bounding box
(w, h) are width and height of bounding box
Args:
boxes (Tensor[N, 4]): boxes... | import torch
from torch import Tensor
def _box_cxcywh_to_xyxy(boxes: Tensor) -> Tensor:
"""
Converts bounding boxes from (cx, cy, w, h) format to (x1, y1, x2, y2) format.
(cx, cy) refers to center of bounding box
(w, h) are width and height of bounding box
Args:
boxes (Tensor[N, 4]): boxes... |
from collections import namedtuple
from typing import TYPE_CHECKING, Dict, NamedTuple, Optional
from urllib.parse import urlparse
if TYPE_CHECKING:
from docarray import DocumentArray
_ParsedHost = namedtuple('ParsedHost', 'on host port version scheme')
def _parse_host(host: str) -> NamedTuple:
"""Parse a h... | from collections import namedtuple
from typing import TYPE_CHECKING, Dict, NamedTuple, Optional
from urllib.parse import urlparse
if TYPE_CHECKING:
from ... import DocumentArray
_ParsedHost = namedtuple('ParsedHost', 'on host port version scheme')
def _parse_host(host: str) -> NamedTuple:
"""Parse a host s... |
# Copyright (c) OpenMMLab. All rights reserved.
from typing import Any, Optional, Sequence, Tuple
from mmengine.data import BaseDataSample
from mmengine.registry import HOOKS
from .hook import Hook
DATA_BATCH = Optional[Sequence[Tuple[Any, BaseDataSample]]]
@HOOKS.register_module()
class ParamSchedulerHook(Hook):
... | # Copyright (c) OpenMMLab. All rights reserved.
from typing import Any, Optional, Sequence, Tuple
from mmengine.data import BaseDataSample
from mmengine.registry import HOOKS
from .hook import Hook
@HOOKS.register_module()
class ParamSchedulerHook(Hook):
"""A hook to update some hyper-parameters in optimizer, e.... |
"""
This examples trains BERT (or any other transformer model like RoBERTa, DistilBERT etc.) for the STSbenchmark from scratch.
It uses AdaptiveLayerLoss with the powerful CoSENTLoss to train models that perform well even when removing some layers.
It generates sentence embeddings that can be compared using cosine-simi... | """
This examples trains BERT (or any other transformer model like RoBERTa, DistilBERT etc.) for the STSbenchmark from scratch.
It uses AdaptiveLayerLoss with the powerful CoSENTLoss to train models that perform well even when removing some layers.
It generates sentence embeddings that can be compared using cosine-simi... |
from __future__ import annotations
import logging
from datasets import load_dataset
from sentence_transformers import SparseEncoder, SparseEncoderTrainer, SparseEncoderTrainingArguments
from sentence_transformers.evaluation import SequentialEvaluator, SimilarityFunction
from sentence_transformers.models import Pooli... | from __future__ import annotations
import logging
from datasets import load_dataset
from sentence_transformers import SparseEncoder, SparseEncoderTrainer, SparseEncoderTrainingArguments
from sentence_transformers.evaluation import SequentialEvaluator, SimilarityFunction
from sentence_transformers.models import Pooli... |
"""
Computes embeddings
"""
from __future__ import annotations
import numpy as np
import pytest
from sentence_transformers import SentenceTransformer
@pytest.mark.parametrize("normalize_embeddings", (False, True))
@pytest.mark.parametrize("prompt_name", (None, "retrieval"))
def test_encode_multi_process(
stsb_... | """
Computes embeddings
"""
from __future__ import annotations
import numpy as np
import pytest
from sentence_transformers import SentenceTransformer
@pytest.mark.parametrize("normalize_embeddings", (False, True))
@pytest.mark.parametrize("prompt_name", (None, "retrieval"))
def test_encode_multi_process(
stsb_... |
from __future__ import annotations
from collections.abc import Iterable
import torch
from torch import Tensor, nn
from sentence_transformers import SentenceTransformer
class MSELoss(nn.Module):
def __init__(self, model: SentenceTransformer) -> None:
"""
Computes the MSE loss between the compute... | from __future__ import annotations
from collections.abc import Iterable
import torch
from torch import Tensor, nn
from sentence_transformers import SentenceTransformer
class MSELoss(nn.Module):
def __init__(self, model: SentenceTransformer) -> None:
"""
Computes the MSE loss between the compute... |
from enum import Enum
from typing import Any, Optional
from pydantic import BaseModel
from backend.data.block import BlockInput
class BlockCostType(str, Enum):
RUN = "run" # cost X credits per run
BYTE = "byte" # cost X credits per byte
SECOND = "second" # cost X credits per second
class BlockCost(... | from enum import Enum
from typing import Any, Optional
from pydantic import BaseModel
from backend.data.block import BlockInput
class BlockCostType(str, Enum):
RUN = "run" # cost X credits per run
BYTE = "byte" # cost X credits per byte
SECOND = "second" # cost X credits per second
DOLLAR = "doll... |
# Copyright (c) OpenMMLab. All rights reserved.
import unittest
from unittest import TestCase
import torch
from mmengine.config import ConfigDict
from mmengine.structures import InstanceData
from parameterized import parameterized
from mmdet.models.roi_heads.mask_heads import FCNMaskHead
class TestFCNMaskHead(TestC... | # 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):
... |
import collections
import json
import os
import string
from typing import Iterable, List
from .WordTokenizer import ENGLISH_STOP_WORDS, WordTokenizer
class WhitespaceTokenizer(WordTokenizer):
"""
Simple and fast white-space tokenizer. Splits sentence based on white spaces.
Punctuation are stripped from t... | from typing import List, Iterable
import collections
import string
import os
import json
from .WordTokenizer import WordTokenizer, ENGLISH_STOP_WORDS
class WhitespaceTokenizer(WordTokenizer):
"""
Simple and fast white-space tokenizer. Splits sentence based on white spaces.
Punctuation are stripped from to... |
# ruff: noqa
# Copyright 2020 The HuggingFace Datasets Authors and the TensorFlow Datasets Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICE... | # ruff: noqa
# Copyright 2020 The HuggingFace Datasets Authors and the TensorFlow Datasets Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICE... |
import os
from typing import BinaryIO, Optional, Union
import pyarrow.parquet as pq
from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules import _PACKAGED_DATASETS_MODULES
from ..packaged_modules.parquet.parquet import Parquet
from ..utils import logging
fro... | import os
from typing import BinaryIO, Optional, Union
import pyarrow.parquet as pq
from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules import _PACKAGED_DATASETS_MODULES
from ..packaged_modules.parquet.parquet import Parquet
from ..utils import logging
fro... |
"""DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.applications.mobilenet_v3 import (
decode_predictions as decode_predictions,
)
from keras.src.applications.mobilenet_v3 import (
preprocess_input as preprocess_input,
)
| """DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.applications.mobilenet_v3 import decode_predictions
from keras.src.applications.mobilenet_v3 import preprocess_input
|
import logging
import os
import signal
import sys
from abc import ABC, abstractmethod
from multiprocessing import Process, get_all_start_methods, set_start_method
from typing import Optional
from backend.util.logging import configure_logging
from backend.util.metrics import sentry_init
logger = logging.getLogger(__na... | import logging
import os
import signal
import sys
from abc import ABC, abstractmethod
from multiprocessing import Process, set_start_method
from typing import Optional
from backend.util.logging import configure_logging
from backend.util.metrics import sentry_init
logger = logging.getLogger(__name__)
_SERVICE_NAME = "... |
# Copyright (c) OpenMMLab. All rights reserved.
from .base_boxes import BaseBoxes
from .bbox_overlaps import bbox_overlaps
from .box_type import (autocast_box_type, convert_box_type, get_box_type,
register_box, register_box_converter)
from .horizontal_boxes import HorizontalBoxes
from .transforms... | # Copyright (c) OpenMMLab. All rights reserved.
from .base_boxes import BaseBoxes
from .bbox_overlaps import bbox_overlaps
from .box_type import (convert_box_type, get_box_type, register_box,
register_box_converter)
from .horizontal_boxes import HorizontalBoxes
from .transforms import (bbox2corne... |
# Copyright (c) OpenMMLab. All rights reserved.
"""MMEngine provides 11 root registries to support using modules across
projects.
More datails can be found at
https://mmengine.readthedocs.io/en/latest/tutorials/registry.html.
"""
from .registry import Registry
# manage all kinds of runners like `EpochBasedRunner` an... | # Copyright (c) OpenMMLab. All rights reserved.
"""MMEngine provides 11 root registries to support using modules across
projects.
More datails can be found at
https://mmengine.readthedocs.io/en/latest/tutorials/registry.html.
"""
from .registry import Registry
# manage all kinds of runners like `EpochBasedRunner` an... |
from collections import defaultdict
from typing import TYPE_CHECKING, Optional
from google.protobuf.json_format import MessageToDict
from google.protobuf.struct_pb2 import Struct
from docarray.proto.io.ndarray import flush_ndarray, read_ndarray
from docarray.proto.docarray_pb2 import NdArrayProto, DocumentProto
if T... | from collections import defaultdict
from typing import TYPE_CHECKING, Optional
from google.protobuf.json_format import MessageToDict
from google.protobuf.struct_pb2 import Struct
from .ndarray import flush_ndarray, read_ndarray
from ..docarray_pb2 import NdArrayProto, DocumentProto
if TYPE_CHECKING:
from ... imp... |
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... | from backend.app import run_processes
from backend.executor import DatabaseManager, ExecutionScheduler
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.
"""
... |
from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.agent_toolkits.amadeus.toolkit import AmadeusToolkit
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handling op... | from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.agent_toolkits.amadeus.toolkit import AmadeusToolkit
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handling op... |
"""
ReAct agent.
Simple wrapper around AgentRunner + ReActAgentWorker.
For the legacy implementation see:
```python
from llama_index.core.agent.legacy.react.base import ReActAgent
```
"""
| """ReAct agent.
Simple wrapper around AgentRunner + ReActAgentWorker.
For the legacy implementation see:
```python
from llama_index.core.agent.legacy.react.base import ReActAgent
```
"""
|
# Copyright (c) OpenMMLab. All rights reserved.
__version__ = '3.3.0'
short_version = __version__
def parse_version_info(version_str):
"""Parse a version string into a tuple.
Args:
version_str (str): The version string.
Returns:
tuple[int | str]: The version info, e.g., "1.3.0" is parsed... | # Copyright (c) OpenMMLab. All rights reserved.
__version__ = '3.2.0'
short_version = __version__
def parse_version_info(version_str):
"""Parse a version string into a tuple.
Args:
version_str (str): The version string.
Returns:
tuple[int | str]: The version info, e.g., "1.3.0" is parsed... |
__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 ...vid... | __copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
from pathlib import Path
import pytest
import torch
import numpy as np
import torchvision.models.video as models
from torchvision import transforms
from jina import Document, DocumentArray, Executor
from ...v... |
from llama_index.core.base.embeddings.base import BaseEmbedding
from llama_index.embeddings.text_embeddings_inference import TextEmbeddingsInference
def test_text_inference_embedding_class():
names_of_base_classes = [b.__name__ for b in TextEmbeddingsInference.__mro__]
assert BaseEmbedding.__name__ in names_o... | from llama_index.core.base.embeddings.base import BaseEmbedding
from llama_index.embeddings.text_embeddings_inference import TextEmbeddingsInference
def test_text_inference_embedding_class():
names_of_base_classes = [b.__name__ for b in TextEmbeddingsInference.__mro__]
assert BaseEmbedding.__name__ in names_o... |
# Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
import tempfile
from unittest import TestCase
from unittest.mock import Mock
import torch
import torch.nn as nn
from torch.utils.data import Dataset
from mmengine.hooks import EMAHook
from mmengine.model import ExponentialMovingAverage
from mmengin... | # Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
import tempfile
from unittest import TestCase
from unittest.mock import Mock
import torch
import torch.nn as nn
from torch.utils.data import Dataset
from mmengine.hooks import EMAHook
from mmengine.model import ExponentialMovingAverage
from mmengin... |
import collections
import torch
from torch.utils._ordered_set import OrderedSet
def _end_ptr(tensor: torch.Tensor) -> int:
if tensor.nelement():
stop = tensor.view(-1)[-1].data_ptr() + tensor.element_size()
else:
stop = tensor.data_ptr()
return stop
class TensorProperties:
def __ini... | import collections
import torch
from torch.utils._ordered_set import OrderedSet
def _end_ptr(tensor: torch.Tensor) -> int:
if tensor.nelement():
stop = tensor.view(-1)[-1].data_ptr() + tensor.element_size()
else:
stop = tensor.data_ptr()
return stop
class TensorProperties:
def __ini... |
from typing import Union
from torch import nn
import transformers
import torch
from PIL import Image
class CLIPModel(nn.Module):
def __init__(self, model_name: str = "openai/clip-vit-base-patch32", processor_name=None):
super(CLIPModel, self).__init__()
if processor_name is None:
proc... | from torch import nn
import transformers
import torch
from PIL import Image
class CLIPModel(nn.Module):
def __init__(self, model_name: str = "openai/clip-vit-base-patch32", processor_name=None):
super(CLIPModel, self).__init__()
if processor_name is None:
processor_name = model_name
... |
import sys
import pytest
from llama_index.graph_rag.cognee import CogneeGraphRAG
@pytest.mark.skipif(
sys.version_info < (3, 10), reason="mock strategy requires python3.10 or higher"
)
@pytest.mark.asyncio()
async def test_get_graph_url(monkeypatch):
# Instantiate cognee GraphRAG
cogneeRAG = CogneeGraphR... | import asyncio
import pytest
from llama_index.graph_rag.cognee import CogneeGraphRAG
@pytest.mark.asyncio()
async def test_get_graph_url(monkeypatch):
# Instantiate cognee GraphRAG
cogneeRAG = CogneeGraphRAG(
llm_api_key="",
llm_provider="openai",
llm_model="gpt-4o-mini",
graph... |
# Copyright (c) OpenMMLab. All rights reserved.
from mmdet.registry import MODELS
from .single_stage import SingleStageDetector
@MODELS.register_module()
class FOVEA(SingleStageDetector):
"""Implementation of `FoveaBox <https://arxiv.org/abs/1904.03797>`_"""
def __init__(self,
backbone,
... | # Copyright (c) OpenMMLab. All rights reserved.
from ..builder import DETECTORS
from .single_stage import SingleStageDetector
@DETECTORS.register_module()
class FOVEA(SingleStageDetector):
"""Implementation of `FoveaBox <https://arxiv.org/abs/1904.03797>`_"""
def __init__(self,
backbone,
... |
_base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py',
'./centernet_tta.py'
]
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
# model settings
model = dict(
type='CenterNet',
data_preprocessor=dict(
type='DetDataPrepro... | _base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
# model settings
model = dict(
type='CenterNet',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123... |
"""Run smoke tests"""
import os
from pathlib import Path
import torch
import torchvision
from torchvision.io import read_image
from torchvision.models import resnet50, ResNet50_Weights
SCRIPT_DIR = Path(__file__).parent
def smoke_test_torchvision() -> None:
print(
"Is torchvision useable?",
all... | """Run smoke tests"""
import os
from pathlib import Path
import torch
import torchvision
from torchvision.io import read_image
from torchvision.models import resnet50, ResNet50_Weights
SCRIPT_DIR = Path(__file__).parent
def smoke_test_torchvision() -> None:
print(
"Is torchvision useable?",
all... |
import os
from typing import Optional
import numpy as np
import pytest
import torch
from pydantic import parse_obj_as
from docarray import BaseDocument
from docarray.documents import Audio
from docarray.typing import AudioUrl
from docarray.typing.tensor.audio import AudioNdArray, AudioTorchTensor
from tests import TO... | import os
from typing import Optional
import numpy as np
import pytest
import torch
from pydantic import parse_obj_as
from docarray import BaseDocument
from docarray.documents import Audio
from docarray.typing import AudioUrl
from docarray.typing.tensor.audio import AudioNdArray, AudioTorchTensor
from tests import TO... |
# Copyright (c) OpenMMLab. All rights reserved.
from abc import ABCMeta, abstractmethod
from typing import Dict, List, Tuple, Union
import torch.nn.functional as F
from mmengine.model import BaseModule
from torch import Tensor
from mmdet.registry import MODELS
from mmdet.structures import SampleList
from mmdet.utils ... | # Copyright (c) OpenMMLab. All rights reserved.
from abc import ABCMeta, abstractmethod
from typing import Dict, List, Tuple, Union
import torch.nn.functional as F
from mmengine.model import BaseModule
from torch import Tensor
from mmdet.data_elements import SampleList
from mmdet.registry import MODELS
from mmdet.uti... |
# Copyright (c) OpenMMLab. All rights reserved.
from mmengine.utils.dl_utils import TORCH_VERSION
from mmengine.utils.version_utils import digit_version
from .averaged_model import (BaseAveragedModel, ExponentialMovingAverage,
MomentumAnnealingEMA, StochasticWeightAverage)
from .base_model ... | # Copyright (c) OpenMMLab. All rights reserved.
from mmengine.utils.dl_utils import TORCH_VERSION
from mmengine.utils.version_utils import digit_version
from .averaged_model import (BaseAveragedModel, ExponentialMovingAverage,
MomentumAnnealingEMA, StochasticWeightAverage)
from .base_model ... |
__all__ = ['reduce', 'reduce_all']
from typing import Dict, List, Optional
from docarray import DocList
def reduce(
left: DocList, right: DocList, left_id_map: Optional[Dict] = None
) -> 'DocList':
"""
Reduces left and right DocList into one DocList in-place.
Changes are applied to the left DocList.... | __all__ = ['reduce', 'reduce_all']
from typing import Dict, List, Optional
from docarray import DocList
def reduce(
left: DocList, right: DocList, left_id_map: Optional[Dict] = None
) -> 'DocList':
"""
Reduces left and right DocList into one DocList in-place.
Changes are applied to the left DocList.... |
_base_ = [
'../_base_/models/retinanet_r50_fpn.py', '../_base_/datasets/voc0712.py',
'../_base_/default_runtime.py'
]
model = dict(bbox_head=dict(num_classes=20))
# training schedule, voc dataset is repeated 3 times, in
# `_base_/datasets/voc0712.py`, so the actual epoch = 4 * 3 = 12
max_epochs = 4
train_cfg =... | _base_ = [
'../_base_/models/retinanet_r50_fpn.py', '../_base_/datasets/voc0712.py',
'../_base_/default_runtime.py'
]
model = dict(bbox_head=dict(num_classes=20))
# optimizer
optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
optimizer_config = dict(grad_clip=None)
# learning policy
# actu... |
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 typing
import fastapi
import fastapi.middleware.cors
import fastapi.responses
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.util.settings.Se... |
_base_ = [
'../_base_/models/retinanet_r50_fpn.py', '../_base_/datasets/voc0712.py',
'../_base_/default_runtime.py'
]
model = dict(bbox_head=dict(num_classes=20))
# training schedule, voc dataset is repeated 3 times, in
# `_base_/datasets/voc0712.py`, so the actual epoch = 4 * 3 = 12
max_epochs = 4
train_cfg =... | _base_ = [
'../_base_/models/retinanet_r50_fpn.py', '../_base_/datasets/voc0712.py',
'../_base_/default_runtime.py'
]
model = dict(bbox_head=dict(num_classes=20))
# training schedule, voc dataset is repeated 3 times, in
# `_base_/datasets/voc0712.py`, so the actual epoch = 4 * 3 = 12
max_epochs = 4
train_cfg =... |
"""dad_jokes reader."""
from typing import List
import requests
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document
class DadJokesReader(BaseReader):
"""
Dad jokes reader.
Reads a random dad joke.
"""
def _get_random_dad_joke(self):
respon... | """dad_jokes reader."""
from typing import List
import requests
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document
class DadJokesReader(BaseReader):
"""Dad jokes reader.
Reads a random dad joke.
"""
def _get_random_dad_joke(self):
response = ... |
# Copyright (c) OpenMMLab. All rights reserved.
from mmdet.models.utils import ResLayer, SimplifiedBasicBlock
from mmdet.registry import MODELS
from .fused_semantic_head import FusedSemanticHead
@MODELS.register_module()
class SCNetSemanticHead(FusedSemanticHead):
"""Mask head for `SCNet <https://arxiv.org/abs/20... | # Copyright (c) OpenMMLab. All rights reserved.
from mmdet.models.builder import HEADS
from mmdet.models.utils import ResLayer, SimplifiedBasicBlock
from .fused_semantic_head import FusedSemanticHead
@HEADS.register_module()
class SCNetSemanticHead(FusedSemanticHead):
"""Mask head for `SCNet <https://arxiv.org/ab... |
from __future__ import annotations
from copy import deepcopy
import pytest
from sentence_transformers import SparseEncoder
@pytest.fixture(scope="session")
def _splade_bert_tiny_model() -> SparseEncoder:
model = SparseEncoder("sparse-encoder-testing/splade-bert-tiny-nq")
model.model_card_data.generate_widg... | from __future__ import annotations
from copy import deepcopy
import pytest
from sentence_transformers import SparseEncoder
@pytest.fixture(scope="session")
def _splade_bert_tiny_model() -> SparseEncoder:
model = SparseEncoder("sparse-encoder-testing/splade-bert-tiny-nq")
model.model_card_data.generate_widg... |
_base_ = [
'../_base_/models/mask-rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# TODO: delete custom_imports after mmcls supports auto import
# please install mmcls>=1.0
# import mmcls.models to trigger register_module in mm... | _base_ = [
'../_base_/models/mask-rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# TODO: delete custom_imports after mmcls supports auto import
# please install mmcls>=1.0
# import mmcls.models to trigger register_module in mm... |
from backend.blocks.hubspot._auth import (
HubSpotCredentials,
HubSpotCredentialsField,
HubSpotCredentialsInput,
)
from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
from backend.data.model import SchemaField
from backend.util.request import Requests
class HubSpotCompanyBlock(Bl... | from backend.blocks.hubspot._auth import (
HubSpotCredentials,
HubSpotCredentialsField,
HubSpotCredentialsInput,
)
from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
from backend.data.model import SchemaField
from backend.util.request import Requests
class HubSpotCompanyBlock(Bl... |
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... |
from pathlib import Path
from typing import Dict, List, Optional
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document, ImageDocument
from llama_index.core.utils import infer_torch_device
class ImageCaptionReader(BaseReader):
"""
Image parser.
Caption image us... | from pathlib import Path
from typing import Dict, List, Optional
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document, ImageDocument
from llama_index.core.utils import infer_torch_device
class ImageCaptionReader(BaseReader):
"""Image parser.
Caption image using B... |
"""Standard LangChain interface tests"""
from typing import Optional
from langchain_core.language_models import BaseChatModel
from langchain_core.messages import AIMessageChunk, BaseMessageChunk
from langchain_core.rate_limiters import InMemoryRateLimiter
from langchain_tests.integration_tests import ( # type: ignor... | """Standard LangChain interface tests"""
from typing import Optional
from langchain_core.language_models import BaseChatModel
from langchain_core.messages import AIMessageChunk, BaseMessageChunk
from langchain_core.rate_limiters import InMemoryRateLimiter
from langchain_tests.integration_tests import ( # type: ignor... |
"""Notebook utils."""
from collections import defaultdict
from typing import Any, List, Optional, Tuple
from llama_index.core.evaluation import EvaluationResult
from llama_index.core.evaluation.retrieval.base import RetrievalEvalResult
DEFAULT_METRIC_KEYS = ["hit_rate", "mrr"]
def get_retrieval_results_df(
nam... | """Notebook utils."""
from collections import defaultdict
from typing import Any, List, Optional, Tuple
from llama_index.core.evaluation import EvaluationResult
from llama_index.core.evaluation.retrieval.base import RetrievalEvalResult
DEFAULT_METRIC_KEYS = ["hit_rate", "mrr"]
def get_retrieval_results_df(
nam... |
import functools
import warnings
from collections import defaultdict
from collections.abc import Sequence
from typing import Any, Optional, TypeVar, Union
import torch
from torchvision import tv_tensors
from torchvision.transforms.v2 import Transform
from torchvision.transforms.v2._utils import is_pure_tensor
T = ... | import functools
import warnings
from collections import defaultdict
from typing import Any, Dict, Optional, Sequence, Tuple, Type, TypeVar, Union
import torch
from torchvision import tv_tensors
from torchvision.transforms.v2 import Transform
from torchvision.transforms.v2._utils import is_pure_tensor
T = TypeVar(... |
# Copyright (c) OpenMMLab. All rights reserved.
import copy
import unittest
from unittest import TestCase
import torch
from mmdet.registry import MODELS
from mmdet.testing import demo_mm_inputs, demo_mm_proposals, get_roi_head_cfg
from mmdet.utils import register_all_modules
class TestTridentRoIHead(TestCase):
... | # Copyright (c) OpenMMLab. All rights reserved.
import copy
import unittest
from unittest import TestCase
import torch
from mmdet.registry import MODELS
from mmdet.testing import demo_mm_inputs, demo_mm_proposals, get_roi_head_cfg
from mmdet.utils import register_all_modules
class TestTridentRoIHead(TestCase):
... |
"""This modules defines all kinds of exceptions raised in Jina."""
from typing import Set, Union
import grpc.aio
class BaseJinaException(BaseException):
"""A base class for all exceptions raised by Jina"""
class RuntimeFailToStart(SystemError, BaseJinaException):
"""When pod/deployment is failed to started... | """This modules defines all kinds of exceptions raised in Jina."""
from typing import Set, Union
import grpc.aio
class BaseJinaException(BaseException):
"""A base class for all exceptions raised by Jina"""
class RuntimeFailToStart(SystemError, BaseJinaException):
"""When pod/deployment is failed to started... |
from pathlib import Path
import pytest
from jina import Document, DocumentArray, Executor
from sentencizer import Sentencizer
def test_config():
ex = Executor.load_config(str(Path(__file__).parents[2] / 'config.yml'))
assert ex.min_sent_len == 1
@pytest.mark.parametrize('traversal_paths', [('r',), ('c',)])... | from pathlib import Path
from jina import Document, DocumentArray, Executor
from sentencizer import Sentencizer
def test_config():
ex = Executor.load_config(str(Path(__file__).parents[2] / 'config.yml'))
assert ex.min_sent_len == 1
def test_executor():
ex = Sentencizer()
input = DocumentArray([Docu... |
"""Test volc engine maas LLM model."""
from typing import Generator
from langchain_core.outputs import LLMResult
from pydantic import SecretStr
from pytest import CaptureFixture
from langchain_community.llms.volcengine_maas import (
VolcEngineMaasBase,
VolcEngineMaasLLM,
)
def test_api_key_is_string() -> N... | """Test volc engine maas LLM model."""
from typing import Generator
from langchain_core.outputs import LLMResult
from pydantic import SecretStr
from pytest import CaptureFixture
from langchain_community.llms.volcengine_maas import (
VolcEngineMaasBase,
VolcEngineMaasLLM,
)
def test_api_key_is_string() -> N... |
from langchain_core.runnables.config import (
EmptyDict,
RunnableConfig,
acall_func_with_variable_args,
call_func_with_variable_args,
ensure_config,
get_async_callback_manager_for_config,
get_callback_manager_for_config,
get_config_list,
get_executor_for_config,
merge_configs,
... | from langchain_core.runnables.config import (
EmptyDict,
RunnableConfig,
acall_func_with_variable_args,
call_func_with_variable_args,
ensure_config,
get_async_callback_manager_for_config,
get_callback_manager_for_config,
get_config_list,
get_executor_for_config,
merge_configs,
... |
# Copyright (c) OpenMMLab. All rights reserved.
from .gaussian_target import (gather_feat, gaussian_radius,
gen_gaussian_target, get_local_maximum,
get_topk_from_heatmap, transpose_and_gather_feat)
from .image import imrenormalize
from .make_divisible import m... | # Copyright (c) OpenMMLab. All rights reserved.
from .gaussian_target import (gather_feat, gaussian_radius,
gen_gaussian_target, get_local_maximum,
get_topk_from_heatmap, transpose_and_gather_feat)
from .image import imrenormalize
from .make_divisible import m... |
"""Standard LangChain interface tests"""
from langchain_core.language_models import BaseChatModel
from langchain_tests.unit_tests import ( # type: ignore[import-not-found]
ChatModelUnitTests, # type: ignore[import-not-found]
)
from langchain_xai import ChatXAI
class TestXAIStandard(ChatModelUnitTests):
@p... | """Standard LangChain interface tests"""
from typing import Tuple, Type
from langchain_core.language_models import BaseChatModel
from langchain_tests.unit_tests import ( # type: ignore[import-not-found]
ChatModelUnitTests, # type: ignore[import-not-found]
)
from langchain_xai import ChatXAI
class TestXAIStan... |
from typing import Union
from docarray.typing.tensor.ndarray import NdArray
from docarray.utils.misc import is_tf_available, is_torch_available
torch_available = is_torch_available()
if torch_available:
from docarray.typing.tensor.torch_tensor import TorchTensor # noqa: F401
tf_available = is_tf_available()
if... | from typing import Union
from docarray.typing.tensor.ndarray import NdArray
try:
import torch # noqa: F401
from docarray.typing.tensor.torch_tensor import TorchTensor # noqa: F401
is_torch_available = True
except ImportError:
is_torch_available = False
try:
import tensorflow as tf # type: ig... |
# Copyright (c) OpenMMLab. All rights reserved.
from .approx_max_iou_assigner import ApproxMaxIoUAssigner
from .assign_result import AssignResult
from .atss_assigner import ATSSAssigner
from .base_assigner import BaseAssigner
from .center_region_assigner import CenterRegionAssigner
from .dynamic_soft_label_assigner imp... | # Copyright (c) OpenMMLab. All rights reserved.
from .approx_max_iou_assigner import ApproxMaxIoUAssigner
from .assign_result import AssignResult
from .atss_assigner import ATSSAssigner
from .base_assigner import BaseAssigner
from .center_region_assigner import CenterRegionAssigner
from .dynamic_soft_label_assigner imp... |
import PIL.Image
import torch
from torchvision import datapoints
from torchvision.utils import _log_api_usage_once
from ._utils import _get_kernel, _register_explicit_noop, _register_kernel_internal, is_simple_tensor
@_register_explicit_noop(
PIL.Image.Image, datapoints.Image, datapoints.BoundingBoxes, datapoi... | import torch
from torchvision import datapoints
from torchvision.utils import _log_api_usage_once
from ._utils import is_simple_tensor
def uniform_temporal_subsample_video(video: torch.Tensor, num_samples: int) -> torch.Tensor:
# Reference: https://github.com/facebookresearch/pytorchvideo/blob/a0a131e/pytorchv... |
"""Interface for tools."""
from typing import Optional
from langchain_core.callbacks import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain_core.tools import BaseTool, tool
class InvalidTool(BaseTool): # type: ignore[override]
"""Tool that is run when invalid tool name is ... | """Interface for tools."""
from typing import List, Optional
from langchain_core.callbacks import (
AsyncCallbackManagerForToolRun,
CallbackManagerForToolRun,
)
from langchain_core.tools import BaseTool, tool
class InvalidTool(BaseTool): # type: ignore[override]
"""Tool that is run when invalid tool na... |
"""
This file contains deprecated code that can only be used with the old `model.fit`-style Sentence Transformers v2.X training.
It exists for backwards compatibility with the `model.old_fit` method, but will be removed in a future version.
Nowadays, with Sentence Transformers v3+, it is recommended to use the `Senten... | """
This file contains deprecated code that can only be used with the old `model.fit`-style Sentence Transformers v2.X training.
It exists for backwards compatibility with the `model.old_fit` method, but will be removed in a future version.
Nowadays, with Sentence Transformers v3+, it is recommended to use the `Senten... |
_base_ = ['faster_rcnn_r50_fpn_32x2_1x_openimages.py']
model = dict(
roi_head=dict(bbox_head=dict(num_classes=500)),
test_cfg=dict(rcnn=dict(score_thr=0.01)))
# dataset settings
dataset_type = 'OpenImagesChallengeDataset'
data_root = 'data/OpenImages/'
data = dict(
train=dict(
type=dataset_type,
... | _base_ = ['faster_rcnn_r50_fpn_32x2_1x_openimages.py']
model = dict(
roi_head=dict(bbox_head=dict(num_classes=500)),
test_cfg=dict(rcnn=dict(score_thr=0.01)))
# dataset settings
dataset_type = 'OpenImagesChallengeDataset'
data_root = 'data/OpenImages/'
data = dict(
train=dict(
type=dataset_type,
... |
_base_ = './faster-rcnn_r50_fpn_1x_coco.py'
model = dict(
roi_head=dict(
bbox_head=dict(
reg_decoded_bbox=True,
loss_bbox=dict(type='IoULoss', loss_weight=10.0))))
| _base_ = './faster_rcnn_r50_fpn_1x_coco.py'
model = dict(
roi_head=dict(
bbox_head=dict(
reg_decoded_bbox=True,
loss_bbox=dict(type='IoULoss', loss_weight=10.0))))
|
# This is different from the TTA of official CenterNet.
tta_model = dict(
type='DetTTAModel',
tta_cfg=dict(nms=dict(type='nms', iou_threshold=0.5), max_per_img=100))
tta_pipeline = [
dict(type='LoadImageFromFile', to_float32=True, backend_args=None),
dict(
type='TestTimeAug',
transform... | # This is different from the TTA of official CenterNet.
tta_model = dict(
type='DetTTAModel',
tta_cfg=dict(nms=dict(type='nms', iou_threshold=0.5), max_per_img=100))
tta_pipeline = [
dict(
type='LoadImageFromFile',
to_float32=True,
file_client_args=dict(backend='disk')),
dict(
... |
# 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... | # Copyright (c) OpenMMLab. All rights reserved.
import unittest
from unittest import TestCase
import torch
from parameterized import parameterized
from mmdet import * # noqa
from mmdet.core import DetDataSample
from mmdet.testing import demo_mm_inputs, get_detector_cfg
from mmdet.utils import register_all_modules
... |
"""System message."""
from typing import Any, Literal, Union
from langchain_core.messages.base import BaseMessage, BaseMessageChunk
class SystemMessage(BaseMessage):
"""Message for priming AI behavior.
The system message is usually passed in as the first of a sequence
of input messages.
Example:
... | """System message."""
from typing import Any, Literal, Union
from langchain_core.messages.base import BaseMessage, BaseMessageChunk
class SystemMessage(BaseMessage):
"""Message for priming AI behavior.
The system message is usually passed in as the first of a sequence
of input messages.
Example:
... |
import numpy as np
import pytest
import torch
from pydantic import parse_obj_as
from docarray import BaseDoc
from docarray.documents import ImageDoc
from docarray.typing import ImageBytes
from docarray.utils._internal.misc import is_tf_available
tf_available = is_tf_available()
if tf_available:
import tensorflow ... | import numpy as np
import pytest
import torch
from pydantic import parse_obj_as
from docarray import BaseDoc
from docarray.documents import ImageDoc
from docarray.typing import ImageBytes
from docarray.utils._internal.misc import is_tf_available
tf_available = is_tf_available()
if tf_available:
import tensorflow ... |
"""Test text splitting functionality using NLTK and Spacy based sentence splitters."""
from typing import Any
import nltk
import pytest
from langchain_core.documents import Document
from langchain_text_splitters.nltk import NLTKTextSplitter
from langchain_text_splitters.spacy import SpacyTextSplitter
def setup_mod... | """Test text splitting functionality using NLTK and Spacy based sentence splitters."""
from typing import Any
import nltk
import pytest
from langchain_core.documents import Document
from langchain_text_splitters.nltk import NLTKTextSplitter
from langchain_text_splitters.spacy import SpacyTextSplitter
def setup_mod... |
import os
from typing import Optional
import pytest
from docarray import BaseDoc, DocList
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):
imag... | 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 hashlib
import secrets
from typing import NamedTuple
class APIKeyContainer(NamedTuple):
"""Container for API key parts."""
raw: str
prefix: str
postfix: str
hash: str
class APIKeyManager:
PREFIX: str = "agpt_"
PREFIX_LENGTH: int = 8
POSTFIX_LENGTH: int = 8
def generate_a... | from typing import NamedTuple
import secrets
import hashlib
class APIKeyContainer(NamedTuple):
"""Container for API key parts."""
raw: str
prefix: str
postfix: str
hash: str
class APIKeyManager:
PREFIX: str = "agpt_"
PREFIX_LENGTH: int = 8
POSTFIX_LENGTH: int = 8
def generate_api_... |
import numpy as np
from keras.src import backend
from keras.src import ops
from keras.src import testing
from keras.src.backend.common.stateless_scope import StatelessScope
class TestStatelessScope(testing.TestCase):
def test_basic_flow(self):
var1 = backend.Variable(np.zeros((2,)))
var2 = backen... | import numpy as np
from keras.src import backend
from keras.src import ops
from keras.src import testing
from keras.src.backend.common.stateless_scope import StatelessScope
class TestStatelessScope(testing.TestCase):
def test_basic_flow(self):
var1 = backend.Variable(np.zeros((2,)))
var2 = backen... |
import os
# When using jax.experimental.enable_x64 in unit test, we want to keep the
# default dtype with 32 bits, aligning it with Keras's default.
os.environ["JAX_DEFAULT_DTYPE_BITS"] = "32"
try:
# When using torch and tensorflow, torch needs to be imported first,
# otherwise it will segfault upon import. T... | import os
# When using jax.experimental.enable_x64 in unit test, we want to keep the
# default dtype with 32 bits, aligning it with Keras's default.
os.environ["JAX_DEFAULT_DTYPE_BITS"] = "32"
try:
# When using torch and tensorflow, torch needs to be imported first,
# otherwise it will segfault upon import. T... |
from torchvision.transforms import InterpolationMode # usort: skip
from ._utils import is_simple_tensor # usort: skip
from ._meta import (
clamp_bounding_box,
convert_format_bounding_box,
convert_dtype_image_tensor,
convert_dtype,
convert_dtype_video,
convert_image_dtype,
get_dimensions_... | # TODO: Add _log_api_usage_once() in all mid-level kernels. If they remain not jit-scriptable we can use decorators
from torchvision.transforms import InterpolationMode # usort: skip
from ._utils import is_simple_tensor # usort: skip
from ._meta import (
clamp_bounding_box,
convert_format_bounding_box,
... |
# 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, Union
from langchain_core.utils.json import parse_json_markdown
from typing_extensions import override
from langchain.evaluation.schema import StringEvaluator
class JsonSchemaEvaluator(StringEvaluator):
"""An evaluator that validates a JSON prediction against a JSON schema reference.
... | from typing import Any, Union
from langchain_core.utils.json import parse_json_markdown
from typing_extensions import override
from langchain.evaluation.schema import StringEvaluator
class JsonSchemaEvaluator(StringEvaluator):
"""An evaluator that validates a JSON prediction against a JSON schema reference.
... |
"""**Messages** are objects used in prompts and chat conversations.
**Class hierarchy:**
.. code-block::
BaseMessage --> SystemMessage, AIMessage, HumanMessage, ChatMessage, FunctionMessage, ToolMessage
--> BaseMessageChunk --> SystemMessageChunk, AIMessageChunk, HumanMessageChunk, ChatMessageChu... | """**Messages** are objects used in prompts and chat conversations.
**Class hierarchy:**
.. code-block::
BaseMessage --> SystemMessage, AIMessage, HumanMessage, ChatMessage, FunctionMessage, ToolMessage
--> BaseMessageChunk --> SystemMessageChunk, AIMessageChunk, HumanMessageChunk, ChatMessageChu... |
from typing import Any
from langchain_core.callbacks import (
UsageMetadataCallbackHandler,
get_usage_metadata_callback,
)
from langchain_core.language_models import GenericFakeChatModel
from langchain_core.messages import AIMessage
from langchain_core.messages.ai import (
InputTokenDetails,
OutputToke... | from itertools import cycle
from langchain_core.callbacks import (
UsageMetadataCallbackHandler,
get_usage_metadata_callback,
)
from langchain_core.language_models import GenericFakeChatModel
from langchain_core.messages import AIMessage
from langchain_core.messages.ai import (
InputTokenDetails,
Outpu... |
# Copyright 2025 HiDream-ai Team and 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 r... | from dataclasses import dataclass
from typing import List, Union
import numpy as np
import PIL.Image
from ...utils import BaseOutput
@dataclass
class HiDreamImagePipelineOutput(BaseOutput):
"""
Output class for HiDreamImage pipelines.
Args:
images (`List[PIL.Image.Image]` or `np.ndarray`)
... |
_base_ = './rtmdet_l_8xb32-300e_coco.py'
checkpoint = 'https://download.openmmlab.com/mmdetection/v3.0/rtmdet/cspnext_rsb_pretrain/cspnext-s_imagenet_600e.pth' # noqa
model = dict(
backbone=dict(
deepen_factor=0.33,
widen_factor=0.5,
init_cfg=dict(
type='Pretrained', prefix='bac... | _base_ = './rtmdet_l_8xb32-300e_coco.py'
checkpoint = 'https://download.openmmlab.com/mmdetection/v3.0/rtmdet/cspnext_rsb_pretrain/cspnext-s_imagenet_600e.pth' # noqa
model = dict(
backbone=dict(
deepen_factor=0.33,
widen_factor=0.5,
init_cfg=dict(
type='Pretrained', prefix='bac... |
"""
Separation of concerns:
DataAdapter:
- x, y
- sample_weight
- class_weight
- shuffle
- batch_size
- steps, as it relates to batch_size for array data
EpochIterator:
- whether to yield numpy or tf data
- steps
- most argument validation
Trainer:
- steps_per_execution
... | """
Separation of concerns:
DataAdapter:
- x, y
- sample_weight
- class_weight
- shuffle
- batch_size
- steps, as it relates to batch_size for array data
EpochIterator:
- whether to yield numpy or tf data
- steps
- most argument validation
Trainer:
- steps_per_execution
... |
# Copyright (c) OpenMMLab. All rights reserved.
__version__ = '2.21.0'
short_version = __version__
def parse_version_info(version_str):
version_info = []
for x in version_str.split('.'):
if x.isdigit():
version_info.append(int(x))
elif x.find('rc') != -1:
patch_version... | # Copyright (c) OpenMMLab. All rights reserved.
__version__ = '2.20.0'
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... |
from typing import Union
from langchain_core.agents import AgentAction, AgentFinish
from langchain.agents import AgentOutputParser
class XMLAgentOutputParser(AgentOutputParser):
"""Parses tool invocations and final answers in XML format.
Expects output to be in one of two formats.
If the output signal... | from typing import Union
from langchain_core.agents import AgentAction, AgentFinish
from langchain.agents import AgentOutputParser
class XMLAgentOutputParser(AgentOutputParser):
"""Parses tool invocations and final answers in XML format.
Expects output to be in one of two formats.
If the output signal... |
from llama_index.core import PromptTemplate
ZERO_SHOT_COMPLETION_TEMPLATE = (
"{instruction}\n{label_heading}: {label}\n{text_heading}: {synthetic_text}"
)
zero_shot_completion_template = PromptTemplate(ZERO_SHOT_COMPLETION_TEMPLATE)
SINGLE_EXAMPLE_TEMPLATE = (
"{label_heading}: {example_label}\n{text_heading... | from llama_index.core import PromptTemplate
ZERO_SHOT_COMPLETION_TEMPLATE = (
"{instruction}\n" "{label_heading}: {label}\n{text_heading}: {synthetic_text}"
)
zero_shot_completion_template = PromptTemplate(ZERO_SHOT_COMPLETION_TEMPLATE)
SINGLE_EXAMPLE_TEMPLATE = (
"{label_heading}: {example_label}\n{text_head... |
# 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 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... |
from docarray.typing.proto_register import _register_proto
from docarray.typing.tensor.audio.abstract_audio_tensor import AbstractAudioTensor
from docarray.typing.tensor.ndarray import NdArray
@_register_proto(proto_type_name='audio_ndarray')
class AudioNdArray(AbstractAudioTensor, NdArray):
"""
Subclass of [... | from docarray.typing.proto_register import _register_proto
from docarray.typing.tensor.audio.abstract_audio_tensor import AbstractAudioTensor
from docarray.typing.tensor.ndarray import NdArray
@_register_proto(proto_type_name='audio_ndarray')
class AudioNdArray(AbstractAudioTensor, NdArray):
"""
Subclass of [... |
"""
This file evaluates CrossEncoder on the TREC 2019 Deep Learning (DL) Track: https://arxiv.org/abs/2003.07820
TREC 2019 DL is based on the corpus of MS Marco. MS Marco provides a sparse annotation, i.e., usually only a single
passage is marked as relevant for a given query. Many other highly relevant passages are n... | """
This file evaluates CrossEncoder on the TREC 2019 Deep Learning (DL) Track: https://arxiv.org/abs/2003.07820
TREC 2019 DL is based on the corpus of MS Marco. MS Marco provides a sparse annotation, i.e., usually only a single
passage is marked as relevant for a given query. Many other highly relevant passages are n... |
from typing import Any, Optional, Type, TypeVar, Union
import numpy as np
from docarray.base_document import BaseDocument
from docarray.documents import Audio
from docarray.typing import AnyEmbedding, AnyTensor
from docarray.typing.tensor.abstract_tensor import AbstractTensor
from docarray.typing.tensor.video.video_t... | from typing import Any, Optional, Type, TypeVar, Union
import numpy as np
from docarray.base_document import BaseDocument
from docarray.documents import Audio
from docarray.typing import AnyEmbedding, AnyTensor
from docarray.typing.tensor.abstract_tensor import AbstractTensor
from docarray.typing.tensor.video.video_t... |
from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.agent_toolkits.playwright.toolkit import (
PlayWrightBrowserToolkit,
)
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecat... | from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.agent_toolkits.playwright.toolkit import (
PlayWrightBrowserToolkit,
)
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecat... |
# Copyright (c) OpenMMLab. All rights reserved.
from mmdet.registry import MODELS
from mmdet.utils import ConfigType, OptConfigType, OptMultiConfig
from .single_stage import SingleStageDetector
@MODELS.register_module()
class VFNet(SingleStageDetector):
"""Implementation of `VarifocalNet
(VFNet).<https://arxi... | # Copyright (c) OpenMMLab. All rights reserved.
from mmdet.core import ConfigType, OptConfigType, OptMultiConfig
from mmdet.registry import MODELS
from .single_stage import SingleStageDetector
@MODELS.register_module()
class VFNet(SingleStageDetector):
"""Implementation of `VarifocalNet
(VFNet).<https://arxiv... |
import gzip
import logging
import os
from datetime import datetime
import torch
from sentence_transformers import LoggingHandler, SentenceTransformer, evaluation, losses, models, util
#### Just some code to print debug information to stdout
logging.basicConfig(
format="%(asctime)s - %(message)s", datefmt="%Y-%m-... | from sentence_transformers import SentenceTransformer, LoggingHandler
from sentence_transformers import models, util, evaluation, losses
import logging
import os
import gzip
from datetime import datetime
import torch
#### Just some code to print debug information to stdout
logging.basicConfig(
format="%(asctime)s ... |
"""
This example loads the pre-trained SentenceTransformer model 'nli-distilroberta-base-v2' from Hugging Face.
It then fine-tunes this model for some epochs on the STS benchmark dataset.
Note: In this example, you must specify a SentenceTransformer model.
If you want to fine-tune a huggingface/transformers model like... | """
This example loads the pre-trained SentenceTransformer model 'nli-distilroberta-base-v2' from Hugging Face.
It then fine-tunes this model for some epochs on the STS benchmark dataset.
Note: In this example, you must specify a SentenceTransformer model.
If you want to fine-tune a huggingface/transformers model like... |
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... |
import argparse
import logging
from typing import Optional
import torch
import torchaudio
from torchaudio.models.decoder import ctc_decoder, download_pretrained_files
logger = logging.getLogger(__name__)
def run_inference(args):
# get pretrained wav2vec2.0 model
bundle = getattr(torchaudio.pipelines, args.... | import argparse
import logging
from typing import Optional
import torch
import torchaudio
from torchaudio.prototype.ctc_decoder import download_pretrained_files, lexicon_decoder
logger = logging.getLogger(__name__)
def run_inference(args):
# get pretrained wav2vec2.0 model
bundle = getattr(torchaudio.pipel... |
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