input stringlengths 33 5k | output stringlengths 32 5k |
|---|---|
import itertools
from dataclasses import dataclass
from typing import List, Optional
import pyarrow as pa
import pyarrow.parquet as pq
import datasets
from datasets.table import table_cast
logger = datasets.utils.logging.get_logger(__name__)
@dataclass
class ParquetConfig(datasets.BuilderConfig):
"""BuilderCo... | import itertools
from dataclasses import dataclass
from typing import List, Optional
import pyarrow as pa
import pyarrow.parquet as pq
import datasets
from datasets.table import table_cast
logger = datasets.utils.logging.get_logger(__name__)
@dataclass
class ParquetConfig(datasets.BuilderConfig):
"""BuilderCo... |
import warnings
from sys import platform
from typing import Optional
import torch
import torchaudio
from torchaudio.io import StreamWriter
dict_format = {
torch.uint8: "u8",
torch.int16: "s16",
torch.int32: "s32",
torch.int64: "s64",
torch.float32: "flt",
torch.float64: "dbl",
}
@torchaudio.... | import warnings
from sys import platform
from typing import Optional
import torch
import torchaudio
from torchaudio.io import StreamWriter
dict_format = {
torch.uint8: "u8",
torch.int16: "s16",
torch.int32: "s32",
torch.int64: "s64",
torch.float32: "flt",
torch.float64: "dbl",
}
def play_aud... |
# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import tempfile
from collections import OrderedDict
import torch
from mmengine import Config
def parse_config(config_strings):
temp_file = tempfile.NamedTemporaryFile()
config_path = f'{temp_file.name}.py'
with open(config_path, 'w') as f:
... | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import tempfile
from collections import OrderedDict
import torch
from mmcv import Config
def parse_config(config_strings):
temp_file = tempfile.NamedTemporaryFile()
config_path = f'{temp_file.name}.py'
with open(config_path, 'w') as f:
... |
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 sequence logits over labels, ... | 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 sequence logits over labels, ... |
from dataclasses import dataclass
from typing import List, Optional, Tuple
import torch
from torch import Tensor
from torchaudio._extension import fail_if_no_align
__all__ = []
@fail_if_no_align
def forced_align(
log_probs: Tensor,
targets: Tensor,
input_lengths: Optional[Tensor] = None,
target_leng... | from dataclasses import dataclass
from typing import List, Optional, Tuple
import torch
from torch import Tensor
from torchaudio._extension import fail_if_no_align
__all__ = []
@fail_if_no_align
def forced_align(
log_probs: Tensor,
targets: Tensor,
input_lengths: Optional[Tensor] = None,
target_leng... |
from docarray.typing.url.any_url import AnyUrl
from docarray.typing.url.image_url import ImageUrl
__all__ = ['ImageUrl', 'AnyUrl']
| from .image_url import ImageUrl
__all__ = ['ImageUrl']
|
# Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import mmengine
from mmengine.utils import digit_version
from .version import __version__, version_info
mmcv_minimum_version = '2.0.0rc4'
mmcv_maximum_version = '2.1.0'
mmcv_version = digit_version(mmcv.__version__)
mmengine_minimum_version = '0.7.1'
mmengi... | # Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import mmengine
from mmengine.utils import digit_version
from .version import __version__, version_info
mmcv_minimum_version = '2.0.0rc4'
mmcv_maximum_version = '2.1.0'
mmcv_version = digit_version(mmcv.__version__)
mmengine_minimum_version = '0.6.0'
mmengi... |
"""Map-reduce chain.
Splits up a document, sends the smaller parts to the LLM with one prompt,
then combines the results with another one.
"""
from __future__ import annotations
from collections.abc import Mapping
from typing import Any, Optional
from langchain_core._api import deprecated
from langchain_core.callba... | """Map-reduce chain.
Splits up a document, sends the smaller parts to the LLM with one prompt,
then combines the results with another one.
"""
from __future__ import annotations
from collections.abc import Mapping
from typing import Any, Optional
from langchain_core._api import deprecated
from langchain_core.callba... |
from torchaudio._internal.module_utils import dropping_support
from ._alignment import forced_align as _forced_align, merge_tokens, TokenSpan
from .filtering import (
allpass_biquad,
band_biquad,
bandpass_biquad,
bandreject_biquad,
bass_biquad,
biquad,
contrast,
dcshift,
deemph_biqu... | from torchaudio._internal.module_utils import dropping_support
from ._alignment import forced_align as _forced_align, merge_tokens, TokenSpan
from .filtering import (
allpass_biquad,
band_biquad,
bandpass_biquad,
bandreject_biquad,
bass_biquad,
biquad,
contrast,
dcshift,
deemph_biqu... |
__copyright__ = "Copyright (c) 2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
from typing import Optional, List, Dict
import hnswlib
import numpy as np
from jina import Executor, requests, DocumentArray, Document
from jina_commons import get_logger
from jina_commons.indexers.dump import import... | __copyright__ = "Copyright (c) 2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
from typing import Optional, List, Dict
import hnswlib
import numpy as np
from jina import Executor, requests, DocumentArray, Document
from jina_commons import get_logger
from jina_commons.indexers.dump import import... |
"""Init file of LlamaIndex."""
__version__ = "0.12.31"
import logging
from logging import NullHandler
from typing import Callable, Optional
try:
# Force pants to install eval_type_backport on 3.9
import eval_type_backport # noqa # type: ignore
except ImportError:
pass
# response
from llama_index.core.... | """Init file of LlamaIndex."""
__version__ = "0.12.30"
import logging
from logging import NullHandler
from typing import Callable, Optional
try:
# Force pants to install eval_type_backport on 3.9
import eval_type_backport # noqa # type: ignore
except ImportError:
pass
# response
from llama_index.core.... |
# flake8: noqa
"""Test SQL database wrapper with schema support.
Using DuckDB as SQLite does not support schemas.
"""
import pytest
from sqlalchemy import (
Column,
Integer,
MetaData,
Sequence,
String,
Table,
create_engine,
event,
insert,
schema,
)
import sqlalchemy as sa
fro... | # flake8: noqa
"""Test SQL database wrapper with schema support.
Using DuckDB as SQLite does not support schemas.
"""
import pytest
from sqlalchemy import (
Column,
Integer,
MetaData,
Sequence,
String,
Table,
create_engine,
event,
insert,
schema,
)
import sqlalchemy as sa
fro... |
from llama_index.core.base.llms.types import (
LLMMetadata,
)
from llama_index.core.bridge.pydantic import Field
from llama_index.llms.openai_like.base import OpenAILike
class OPEA(OpenAILike):
"""
Adapter for a OPEA LLM.
Examples:
`pip install llama-index-llms-opea`
```python
... | from llama_index.core.base.llms.types import (
LLMMetadata,
)
from llama_index.core.bridge.pydantic import Field
from llama_index.llms.openai_like.base import OpenAILike
class OPEA(OpenAILike):
"""Adapter for a OPEA LLM.
Examples:
`pip install llama-index-llms-opea`
```python
fro... |
import numpy as np
import pytest
from keras.src import layers
from keras.src import models
from keras.src import testing
class MaskingTest(testing.TestCase):
@pytest.mark.requires_trainable_backend
def test_masking_basics(self):
self.run_layer_test(
layers.Masking,
init_kwargs... | import numpy as np
import pytest
from keras.src import layers
from keras.src import models
from keras.src import testing
class MaskingTest(testing.TestCase):
@pytest.mark.requires_trainable_backend
def test_masking_basics(self):
self.run_layer_test(
layers.Masking,
init_kwargs... |
"""
This file loads sentences from a provided text file. It is expected, that the there is one sentence per line in that text file.
SimCSE will be training using these sentences. Checkpoints are stored every 500 steps to the output folder.
Usage:
python train_simcse_from_file.py path/to/sentences.txt
"""
import gzi... | """
This file loads sentences from a provided text file. It is expected, that the there is one sentence per line in that text file.
SimCSE will be training using these sentences. Checkpoints are stored every 500 steps to the output folder.
Usage:
python train_simcse_from_file.py path/to/sentences.txt
"""
from torch... |
# Copyright (c) OpenMMLab. All rights reserved.
from .cityscapes_metric import CityScapesMetric
from .coco_metric import CocoMetric
from .coco_panoptic_metric import CocoPanopticMetric
from .crowdhuman_metric import CrowdHumanMetric
from .lvis_metric import LVISMetric
from .openimages_metric import OpenImagesMetric
fro... | # Copyright (c) OpenMMLab. All rights reserved.
from .cityscapes_metric import CityScapesMetric
from .coco_metric import CocoMetric
from .coco_panoptic_metric import CocoPanopticMetric
from .lvis_metric import LVISMetric
from .openimages_metric import OpenImagesMetric
from .voc_metric import VOCMetric
__all__ = [
... |
import numpy as np
from docarray import BaseDoc, DocList
from docarray.typing import NdArray
from pydantic import Field
from jina import Executor, requests
class TextDoc(BaseDoc):
text: str = Field(description="The text of the document", default="")
class EmbeddingResponseModel(TextDoc):
embeddings: NdArra... | import numpy as np
from docarray import BaseDoc, DocList
from docarray.typing import NdArray
from pydantic import Field
from jina import Executor, requests
class TextDoc(BaseDoc):
text: str
class EmbeddingResponseModel(BaseDoc):
embeddings: NdArray = Field(description="The embedding of the texts", default=... |
import os
import numpy as np
import pytest
import torch
from pydantic import parse_obj_as
from docarray import BaseDocument
from docarray.typing.tensor.audio.audio_ndarray import AudioNdArray
from docarray.typing.tensor.audio.audio_torch_tensor import AudioTorchTensor
from docarray.utils.misc import is_tf_available
... | import os
import numpy as np
import pytest
import torch
from pydantic import parse_obj_as
from docarray import BaseDocument
from docarray.typing.tensor.audio.audio_ndarray import AudioNdArray
from docarray.typing.tensor.audio.audio_torch_tensor import AudioTorchTensor
from docarray.utils.misc import is_tf_available
... |
_base_ = '../mask_rcnn/mask-rcnn_r50-caffe_fpn_ms-1x_coco.py'
# model settings
model = dict(
type='PointRend',
roi_head=dict(
type='PointRendRoIHead',
mask_roi_extractor=dict(
type='GenericRoIExtractor',
aggregation='concat',
roi_layer=dict(
_d... | _base_ = '../mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain_1x_coco.py'
# model settings
model = dict(
type='PointRend',
roi_head=dict(
type='PointRendRoIHead',
mask_roi_extractor=dict(
type='GenericRoIExtractor',
aggregation='concat',
roi_layer=dict(
... |
import PIL.Image
import pytest
import torch
import torchvision.transforms.v2._utils
from common_utils import DEFAULT_SIZE, make_bounding_boxes, make_detection_masks, make_image
from torchvision import tv_tensors
from torchvision.transforms.v2._utils import has_all, has_any
from torchvision.transforms.v2.functional i... | import PIL.Image
import pytest
import torch
import torchvision.transforms.v2._utils
from common_utils import DEFAULT_SIZE, make_bounding_boxes, make_detection_mask, make_image
from torchvision import tv_tensors
from torchvision.transforms.v2._utils import has_all, has_any
from torchvision.transforms.v2.functional im... |
from .cmuarctic import CMUARCTIC
from .cmudict import CMUDict
from .commonvoice import COMMONVOICE
from .dr_vctk import DR_VCTK
from .fluentcommands import FluentSpeechCommands
from .gtzan import GTZAN
from .iemocap import IEMOCAP
from .librilight_limited import LibriLightLimited
from .librimix import LibriMix
from .li... | from .cmuarctic import CMUARCTIC
from .cmudict import CMUDict
from .commonvoice import COMMONVOICE
from .dr_vctk import DR_VCTK
from .fluentcommands import FluentSpeechCommands
from .gtzan import GTZAN
from .iemocap import IEMOCAP
from .librilight_limited import LibriLightLimited
from .librimix import LibriMix
from .li... |
"""Simple Reader for Memos."""
from typing import Dict, List
from urllib.parse import urljoin
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document
class MemosReader(BaseReader):
"""
Memos reader.
Reads content from an Memos.
"""
def __init__(self, ... | """Simple Reader for Memos."""
from typing import Dict, List
from urllib.parse import urljoin
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document
class MemosReader(BaseReader):
"""Memos reader.
Reads content from an Memos.
"""
def __init__(self, host:... |
#!/usr/bin/env python
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# U... | #!/usr/bin/env python
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LI... |
from typing import TYPE_CHECKING, Any, Callable, List, Optional, Type
from llama_index.core.bridge.pydantic import BaseModel, ConfigDict
from .errors import WorkflowValidationError
from .utils import (
is_free_function,
validate_step_signature,
inspect_signature,
ServiceDefinition,
)
if TYPE_CHECKING... | from typing import TYPE_CHECKING, Any, Callable, List, Optional, Type
from llama_index.core.bridge.pydantic import BaseModel, ConfigDict
from .errors import WorkflowValidationError
from .utils import (
is_free_function,
validate_step_signature,
inspect_signature,
ServiceDefinition,
)
if TYPE_CHECKING... |
"""DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.quantizers import deserialize as deserialize
from keras.src.quantizers import get as get
from keras.src.quantizers import serialize as serialize
from keras.src.quantizers.quantizers i... | """DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.quantizers import deserialize
from keras.src.quantizers import get
from keras.src.quantizers import serialize
from keras.src.quantizers.quantizers import AbsMaxQuantizer
from keras.sr... |
_base_ = '../_base_/default_runtime.py'
# dataset settings
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
# Example to use different file client
# Method 1: simply set the data root and let the file I/O module
# automatically infer from prefix (not support LMDB and Memcache yet)
# data_root = 's3://openmmlab/d... | _base_ = '../_base_/default_runtime.py'
# dataset settings
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
# file_client_args = dict(
# backend='petrel',
# path_mapping=dict({
# './data/': 's3://openmmlab/datasets/detection/',
# 'data/': 's3://openmmlab/datasets/detection/'
# }))
file... |
from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.tools import ClickTool
from langchain_community.tools.playwright.click import ClickToolInput
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for ra... | from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.tools import ClickTool
from langchain_community.tools.playwright.click import ClickToolInput
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for ra... |
"""Retrieval evaluators."""
from typing import List, Optional, Tuple
from llama_index.core.base.base_retriever import BaseRetriever
from llama_index.core.bridge.pydantic import Field, SerializeAsAny
from llama_index.core.evaluation.retrieval.base import (
BaseRetrievalEvaluator,
RetrievalEvalMode,
)
from llam... | """Retrieval evaluators."""
from typing import List, Optional, Tuple
from llama_index.core.base.base_retriever import BaseRetriever
from llama_index.core.bridge.pydantic import Field, SerializeAsAny
from llama_index.core.evaluation.retrieval.base import (
BaseRetrievalEvaluator,
RetrievalEvalMode,
)
from llam... |
from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Optional, Union
import pyarrow as pa
if TYPE_CHECKING:
from .features import FeatureType
@dataclass
class Translation:
"""`Feature` for translations with fixed languages per example.
Here for compatibility with tf... | from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Optional, Union
import pyarrow as pa
if TYPE_CHECKING:
from .features import FeatureType
@dataclass
class Translation:
"""`Feature` for translations with fixed languages per example.
Here for compatibility with tf... |
# coding=utf-8
# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless r... | # coding=utf-8
# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless r... |
# Copyright (c) OpenMMLab. All rights reserved.
__version__ = '3.1.0'
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__ = '3.0.0'
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... |
import random
import numpy as np
import torch
from torchvision import transforms as T
from torchvision.transforms import functional as F
def pad_if_smaller(img, size, fill=0):
min_size = min(img.size)
if min_size < size:
ow, oh = img.size
padh = size - oh if oh < size else 0
padw = si... | import random
import numpy as np
import torch
from torchvision import transforms as T
from torchvision.transforms import functional as F
def pad_if_smaller(img, size, fill=0):
min_size = min(img.size)
if min_size < size:
ow, oh = img.size
padh = size - oh if oh < size else 0
padw = si... |
# Copyright (c) OpenMMLab. All rights reserved.
__version__ = '0.10.6'
def parse_version_info(version_str):
"""Parse the version information.
Args:
version_str (str): version string like '0.1.0'.
Returns:
tuple: version information contains major, minor, micro version.
"""
versi... | # Copyright (c) OpenMMLab. All rights reserved.
__version__ = '0.10.5'
def parse_version_info(version_str):
"""Parse the version information.
Args:
version_str (str): version string like '0.1.0'.
Returns:
tuple: version information contains major, minor, micro version.
"""
versi... |
from __future__ import annotations
import logging
from typing import Literal
import torch
from torch import Tensor
from sentence_transformers.models.InputModule import InputModule
from .tokenizer import WhitespaceTokenizer
logger = logging.getLogger(__name__)
class BoW(InputModule):
"""Implements a Bag-of-Wo... | from __future__ import annotations
import json
import logging
import os
from typing import Literal
import torch
from torch import Tensor, nn
from .tokenizer import WhitespaceTokenizer
logger = logging.getLogger(__name__)
class BoW(nn.Module):
"""Implements a Bag-of-Words (BoW) model to derive sentence embeddi... |
"""Tests for dask shared by different test modules."""
from typing import Literal
import numpy as np
import pandas as pd
from dask import array as da
from dask import dataframe as dd
from distributed import Client
import xgboost as xgb
from xgboost.testing.updater import get_basescore
def check_init_estimation_clf... | """Tests for dask shared by different test modules."""
import numpy as np
import pandas as pd
from dask import array as da
from dask import dataframe as dd
from distributed import Client
import xgboost as xgb
from xgboost.testing.updater import get_basescore
def check_init_estimation_clf(tree_method: str, client: C... |
# Copyright (c) OpenMMLab. All rights reserved.
from unittest.mock import MagicMock, Mock
import torch
from torch import nn
from mmengine.hooks import OptimizerHook
class TestOptimizerHook:
def test_after_train_iter(self):
class Model(nn.Module):
def __init__(self):
super(... | # Copyright (c) OpenMMLab. All rights reserved.
from unittest.mock import MagicMock, Mock
import torch
from torch import nn
from mmengine.hooks import OptimizerHook
class TestOptimizerHook:
def test_after_train_iter(self):
class Model(nn.Module):
def __init__(self):
super(... |
from __future__ import annotations
# TODO: Consider renaming all evaluators to CrossEncoder..., e.g. CrossEncoderNanoBEIREvaluator, CrossEncoderClassificationEvaluator, etc.
from .CEBinaryAccuracyEvaluator import CEBinaryAccuracyEvaluator
from .CEBinaryClassificationEvaluator import CEBinaryClassificationEvaluator
fro... | from __future__ import annotations
from .CEBinaryAccuracyEvaluator import CEBinaryAccuracyEvaluator
from .CEBinaryClassificationEvaluator import CEBinaryClassificationEvaluator
from .CECorrelationEvaluator import CECorrelationEvaluator
from .CEF1Evaluator import CEF1Evaluator
from .CERerankingEvaluator import CERerank... |
# Copyright (c) OpenMMLab. All rights reserved.
from ..builder import DETECTORS
from .single_stage import SingleStageDetector
@DETECTORS.register_module()
class VFNet(SingleStageDetector):
"""Implementation of `VarifocalNet
(VFNet).<https://arxiv.org/abs/2008.13367>`_"""
def __init__(self,
... | from ..builder import DETECTORS
from .single_stage import SingleStageDetector
@DETECTORS.register_module()
class VFNet(SingleStageDetector):
"""Implementation of `VarifocalNet
(VFNet).<https://arxiv.org/abs/2008.13367>`_"""
def __init__(self,
backbone,
neck,
... |
"""LLM Compiler agent pack."""
from typing import Any, Dict, List, Optional
from llama_index.core.agent import AgentRunner
from llama_index.core.callbacks import CallbackManager
from llama_index.core.llama_pack.base import BaseLlamaPack
from llama_index.core.llms.llm import LLM
from llama_index.core.tools.types impor... | """LLM Compiler agent pack."""
from typing import Any, Dict, List, Optional
from llama_index.core.agent import AgentRunner
from llama_index.core.callbacks import CallbackManager
from llama_index.core.llama_pack.base import BaseLlamaPack
from llama_index.core.llms.llm import LLM
from llama_index.core.tools.types impor... |
"""Test PremChat model"""
from typing import cast
import pytest
from langchain_core.messages import AIMessage, HumanMessage, SystemMessage, ToolMessage
from pydantic import SecretStr
from pytest import CaptureFixture
from langchain_community.chat_models import ChatPremAI
from langchain_community.chat_models.premai i... | """Test PremChat model"""
from typing import cast
import pytest
from langchain_core.messages import AIMessage, HumanMessage, SystemMessage, ToolMessage
from pydantic import SecretStr
from pytest import CaptureFixture
from langchain_community.chat_models import ChatPremAI
from langchain_community.chat_models.premai i... |
from __future__ import annotations
try:
from typing import Self
except ImportError:
from typing_extensions import Self
import torch
from torch import Tensor, nn
from sentence_transformers.models.Module import Module
class WeightedLayerPooling(Module):
"""Token embeddings are weighted mean of their diff... | from __future__ import annotations
import json
import os
import torch
from safetensors.torch import load_model as load_safetensors_model
from safetensors.torch import save_model as save_safetensors_model
from torch import Tensor, nn
class WeightedLayerPooling(nn.Module):
"""Token embeddings are weighted mean of... |
import logging
import re
from typing import Any
import uvicorn.config
from colorama import Fore
def remove_color_codes(s: str) -> str:
return re.sub(r"\x1B(?:[@-Z\\-_]|\[[0-?]*[ -/]*[@-~])", "", s)
def fmt_kwargs(kwargs: dict) -> str:
return ", ".join(f"{n}={repr(v)}" for n, v in kwargs.items())
def prin... | import logging
import re
from typing import Any
import uvicorn.config
from colorama import Fore
def remove_color_codes(s: str) -> str:
return re.sub(r"\x1B(?:[@-Z\\-_]|\[[0-?]*[ -/]*[@-~])", "", s)
def fmt_kwargs(kwargs: dict) -> str:
return ", ".join(f"{n}={repr(v)}" for n, v in kwargs.items())
def print... |
import torch
from torchaudio_unittest.common_utils import PytorchTestCase, skipIfNoCuda
from .tacotron2_loss_impl import Tacotron2LossGradcheckTests, Tacotron2LossShapeTests, Tacotron2LossTorchscriptTests
@skipIfNoCuda
class TestTacotron2LossShapeFloat32CUDA(PytorchTestCase, Tacotron2LossShapeTests):
dtype = tor... | import torch
from torchaudio_unittest.common_utils import PytorchTestCase, skipIfNoCuda
from .tacotron2_loss_impl import (
Tacotron2LossGradcheckTests,
Tacotron2LossShapeTests,
Tacotron2LossTorchscriptTests,
)
@skipIfNoCuda
class TestTacotron2LossShapeFloat32CUDA(PytorchTestCase, Tacotron2LossShapeTests)... |
_base_ = '../faster_rcnn/faster-rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
plugins=[
dict(
cfg=dict(
type='GeneralizedAttention',
spatial_range=-1,
num_heads=8,
attention_type='1111',
... | _base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
plugins=[
dict(
cfg=dict(
type='GeneralizedAttention',
spatial_range=-1,
num_heads=8,
attention_type='1111',
... |
from ._dsp import oscillator_bank
from .functional import add_noise, barkscale_fbanks, convolve, fftconvolve
__all__ = [
"add_noise",
"barkscale_fbanks",
"convolve",
"fftconvolve",
"oscillator_bank",
]
| from .functional import add_noise, barkscale_fbanks, convolve, fftconvolve
__all__ = ["add_noise", "barkscale_fbanks", "convolve", "fftconvolve"]
|
_base_ = './mask-rcnn_x101-32x4d_fpn_gn-ws-all_2x_coco.py'
# learning policy
max_epochs = 24
train_cfg = dict(max_epochs=max_epochs)
# learning rate
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
... | _base_ = './mask_rcnn_x101_32x4d_fpn_gn_ws-all_2x_coco.py'
# learning policy
max_epochs = 24
train_cfg = dict(max_epochs=max_epochs)
# learning rate
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
... |
from __future__ import annotations
from collections.abc import Iterable
from torch import Tensor
from sentence_transformers import util
from sentence_transformers.losses.CoSENTLoss import CoSENTLoss
from sentence_transformers.sparse_encoder.SparseEncoder import SparseEncoder
class SparseCoSENTLoss(CoSENTLoss):
... | from __future__ import annotations
from collections.abc import Iterable
from torch import Tensor
from sentence_transformers import util
from sentence_transformers.losses.CoSENTLoss import CoSENTLoss
from sentence_transformers.sparse_encoder.SparseEncoder import SparseEncoder
class SparseCoSENTLoss(CoSENTLoss):
... |
import warnings
from abc import ABC
from typing import Any, Optional
from langchain_core._api import deprecated
from langchain_core.chat_history import (
BaseChatMessageHistory,
InMemoryChatMessageHistory,
)
from langchain_core.memory import BaseMemory
from langchain_core.messages import AIMessage, HumanMessag... | import warnings
from abc import ABC
from typing import Any, Dict, Optional, Tuple
from langchain_core._api import deprecated
from langchain_core.chat_history import (
BaseChatMessageHistory,
InMemoryChatMessageHistory,
)
from langchain_core.memory import BaseMemory
from langchain_core.messages import AIMessage... |
# Copyright 2024 The OpenXLA 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 in ... | # Copyright 2024 The OpenXLA 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 in ... |
import numpy as np
import pytest
import torch
from docarray import BaseDocument
from docarray.base_document import AnyDocument
from docarray.typing import (
AnyEmbedding,
AnyUrl,
ImageUrl,
Mesh3DUrl,
NdArray,
PointCloud3DUrl,
TextUrl,
TorchTensor,
)
@pytest.mark.proto
def test_proto_a... | import numpy as np
import torch
from docarray import BaseDocument
from docarray.base_document import AnyDocument
from docarray.typing import (
AnyEmbedding,
AnyUrl,
ImageUrl,
Mesh3DUrl,
NdArray,
PointCloud3DUrl,
TextUrl,
TorchTensor,
)
def test_proto_all_types():
class Mymmdoc(Bas... |
from typing import Any, Optional, Type, TypeVar, Union
from docarray.base_document import BaseDocument
from docarray.typing import TextUrl
from docarray.typing.tensor.embedding import AnyEmbedding
T = TypeVar('T', bound='Text')
class Text(BaseDocument):
"""
Document for handling text.
It can contain a T... | from typing import Optional
from docarray.base_document import BaseDocument
from docarray.typing import TextUrl
from docarray.typing.tensor.embedding import AnyEmbedding
class Text(BaseDocument):
"""
Document for handling text.
It can contain a TextUrl (`Text.url`), a str (`Text.text`),
and an AnyEmb... |
# Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
from mmcv.cnn import ConvModule
from mmcv.ops import MaskedConv2d
from ..builder import HEADS
from .guided_anchor_head import FeatureAdaption, GuidedAnchorHead
@HEADS.register_module()
class GARetinaHead(GuidedAnchorHead):
"""Guided-Anchor-bas... | import torch.nn as nn
from mmcv.cnn import ConvModule
from mmcv.ops import MaskedConv2d
from ..builder import HEADS
from .guided_anchor_head import FeatureAdaption, GuidedAnchorHead
@HEADS.register_module()
class GARetinaHead(GuidedAnchorHead):
"""Guided-Anchor-based RetinaNet head."""
def __init__(self,
... |
"""Init file of LlamaIndex."""
__version__ = "0.12.11"
import logging
from logging import NullHandler
from typing import Callable, Optional
try:
# Force pants to install eval_type_backport on 3.9
import eval_type_backport # noqa # type: ignore
except ImportError:
pass
# response
from llama_index.core.... | """Init file of LlamaIndex."""
__version__ = "0.12.10"
import logging
from logging import NullHandler
from typing import Callable, Optional
try:
# Force pants to install eval_type_backport on 3.9
import eval_type_backport # noqa # type: ignore
except ImportError:
pass
# response
from llama_index.core.... |
_base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
# file_client_args = dict(
# backend='petrel',
# path_mapping=dict({
# './data/': 's3://openmmlab/datasets/detection/'... | _base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
type='CenterNet',
backbone=dict(
type='ResNet',
depth=18,
norm_eval=False,
norm_cfg=dict(type='BN'),
init_cfg=dict(type='Pretra... |
import PIL.Image
import pytest
import torch
import torchvision.transforms.v2.utils
from common_utils import DEFAULT_SIZE, make_bounding_box, make_detection_mask, make_image
from torchvision import datapoints
from torchvision.transforms.v2.functional import to_pil_image
from torchvision.transforms.v2.utils import has... | import PIL.Image
import pytest
import torch
import torchvision.transforms.v2.utils
from common_utils import DEFAULT_SIZE, make_bounding_box, make_detection_mask, make_image
from torchvision import datapoints
from torchvision.transforms.v2.functional import to_image_pil
from torchvision.transforms.v2.utils import has... |
"""Hubspot reader."""
from typing import List
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document
class HubspotReader(BaseReader):
"""
Hubspot reader. Reads data from a Hubspot account.
Args:
access_token(str): Hubspot API key.
"""
def __i... | """Hubspot reader."""
from typing import List
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document
class HubspotReader(BaseReader):
"""Hubspot reader. Reads data from a Hubspot account.
Args:
access_token(str): Hubspot API key.
"""
def __init__(... |
"""LangChain **Runnable** and the **LangChain Expression Language (LCEL)**.
The LangChain Expression Language (LCEL) offers a declarative method to build
production-grade programs that harness the power of LLMs.
Programs created using LCEL and LangChain Runnables inherently support
synchronous, asynchronous, batch, a... | """LangChain **Runnable** and the **LangChain Expression Language (LCEL)**.
The LangChain Expression Language (LCEL) offers a declarative method to build
production-grade programs that harness the power of LLMs.
Programs created using LCEL and LangChain Runnables inherently support
synchronous, asynchronous, batch, a... |
"""
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... |
_base_ = '../grounding_dino_swin-t_pretrain_obj365.py'
data_root = 'data/cityscapes/'
class_name = ('person', 'rider', 'car', 'truck', 'bus', 'train', 'motorcycle',
'bicycle')
palette = [(220, 20, 60), (255, 0, 0), (0, 0, 142), (0, 0, 70), (0, 60, 100),
(0, 80, 100), (0, 0, 230), (119, 11, 32)... | _base_ = '../grounding_dino_swin-t_pretrain_obj365.py'
data_root = 'data/cityscapes/'
class_name = ('person', 'rider', 'car', 'truck', 'bus', 'train', 'motorcycle',
'bicycle')
palette = [(220, 20, 60), (255, 0, 0), (0, 0, 142), (0, 0, 70), (0, 60, 100),
(0, 80, 100), (0, 0, 230), (119, 11, 32)... |
checkpoint_config = dict(interval=1)
# yapf:disable
log_config = dict(
interval=50,
hooks=[
dict(type='TextLoggerHook'),
# dict(type='TensorboardLoggerHook')
])
# yapf:enable
custom_hooks = [dict(type='NumClassCheckHook')]
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = No... | checkpoint_config = dict(interval=1)
# yapf:disable
log_config = dict(
interval=50,
hooks=[
dict(type='TextLoggerHook'),
# dict(type='TensorboardLoggerHook')
])
# yapf:enable
custom_hooks = [dict(type='NumClassCheckHook')]
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = No... |
_base_ = './yolox_s_8xb8-300e_coco.py'
# model settings
model = dict(
data_preprocessor=dict(batch_augments=[
dict(
type='BatchSyncRandomResize',
random_size_range=(320, 640),
size_divisor=32,
interval=10)
]),
backbone=dict(deepen_factor=0.33, widen_f... | _base_ = './yolox_s_8xb8-300e_coco.py'
# model settings
model = dict(
data_preprocessor=dict(batch_augments=[
dict(
type='BatchSyncRandomResize',
random_size_range=(320, 640),
size_divisor=32,
interval=10)
]),
backbone=dict(deepen_factor=0.33, widen_f... |
import logging
from datasets import load_dataset
from sentence_transformers.sparse_encoder import (
SparseEncoder,
SparseTripletEvaluator,
)
logging.basicConfig(format="%(message)s", level=logging.INFO)
# Load a model
model = SparseEncoder("naver/splade-cocondenser-ensembledistil")
# Load triplets from the... | import logging
from datasets import load_dataset
from sentence_transformers.sparse_encoder import (
MLMTransformer,
SparseEncoder,
SparseTripletEvaluator,
SpladePooling,
)
logging.basicConfig(format="%(asctime)s - %(message)s", datefmt="%Y-%m-%d %H:%M:%S", level=logging.INFO)
# Initialize the SPLADE... |
"""
Visual demo for survival analysis (regression) with Accelerated Failure Time (AFT) model.
=========================================================================================
This demo uses 1D toy data and visualizes how XGBoost fits a tree ensemble. The ensemble
model starts out as a flat line and evolves in... | """
Visual demo for survival analysis (regression) with Accelerated Failure Time (AFT) model.
=========================================================================================
This demo uses 1D toy data and visualizes how XGBoost fits a tree ensemble. The ensemble
model starts out as a flat line and evolves in... |
from typing import Any, Literal
from pydantic import SecretStr
from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
from backend.data.model import (
APIKeyCredentials,
CredentialsField,
CredentialsMetaInput,
SchemaField,
)
from backend.integrations.providers import ProviderNam... | from typing import Any, Literal
from pydantic import SecretStr
from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
from backend.data.model import (
APIKeyCredentials,
CredentialsField,
CredentialsMetaInput,
SchemaField,
)
from backend.integrations.providers import ProviderNam... |
# Owner(s): ["module: inductor"]
import torch
from torch._inductor import config, metrics
from torch._inductor.test_case import run_tests, TestCase
from torch._inductor.utils import collect_defined_kernels
from torch._inductor.wrapper_benchmark import get_kernel_category_by_source_code
from torch.testing._internal.comm... | # Owner(s): ["module: inductor"]
import torch
from torch._inductor import config, metrics
from torch._inductor.test_case import run_tests, TestCase
from torch._inductor.utils import collect_defined_kernels
from torch._inductor.wrapper_benchmark import get_kernel_category_by_source_code
from torch.testing._internal.comm... |
from enum import Enum
# --8<-- [start:ProviderName]
class ProviderName(str, Enum):
ANTHROPIC = "anthropic"
DISCORD = "discord"
D_ID = "d_id"
E2B = "e2b"
EXA = "exa"
FAL = "fal"
GITHUB = "github"
GOOGLE = "google"
GOOGLE_MAPS = "google_maps"
GROQ = "groq"
HUBSPOT = "hubspot"... | from enum import Enum
class ProviderName(str, Enum):
GITHUB = "github"
GOOGLE = "google"
NOTION = "notion"
|
_base_ = './yolov3_d53_8xb8-ms-608-273e_coco.py'
# fp16 settings
optim_wrapper = dict(type='AmpOptimWrapper', loss_scale='dynamic')
| _base_ = './yolov3_d53_mstrain-608_273e_coco.py'
# fp16 settings
optim_wrapper = dict(type='AmpOptimWrapper', loss_scale='dynamic')
|
import random
import pytest
from pathlib import Path
from typing import Dict, Tuple, Callable
import opentelemetry.sdk.metrics.export
import opentelemetry.sdk.metrics.view
from opentelemetry.sdk.metrics.export import (
AggregationTemporality,
MetricExporter,
MetricExportResult,
MetricsData,
Periodic... | import random
import pytest
from pathlib import Path
from typing import Dict, Tuple, Callable
import opentelemetry.sdk.metrics.export
import opentelemetry.sdk.metrics.view
from opentelemetry.sdk.metrics.export import (
AggregationTemporality,
MetricExporter,
MetricExportResult,
MetricsData,
)
class Di... |
# Copyright (c) OpenMMLab. All rights reserved.
from .builder import DATASETS, PIPELINES, build_dataset
from .cityscapes import CityscapesDataset
from .coco import CocoDataset
from .coco_panoptic import CocoPanopticDataset
from .dataset_wrappers import MultiImageMixDataset
from .deepfashion import DeepFashionDataset
fr... | # Copyright (c) OpenMMLab. All rights reserved.
from .builder import DATASETS, PIPELINES, build_dataset
from .cityscapes import CityscapesDataset
from .coco import CocoDataset
from .coco_panoptic import CocoPanopticDataset
from .dataset_wrappers import MultiImageMixDataset
from .deepfashion import DeepFashionDataset
fr... |
"""Linkup tool spec."""
from llama_index.core.tools.tool_spec.base import BaseToolSpec
class LinkupToolSpec(BaseToolSpec):
"""Linkup tool spec."""
spec_functions = [
"search",
]
def __init__(self, api_key: str, depth: str, output_type: str) -> None:
"""Initialize with parameters."""... | """Linkup tool spec."""
from llama_index.core.tools.tool_spec.base import BaseToolSpec
class LinkupToolSpec(BaseToolSpec):
"""Linkup tool spec."""
spec_functions = [
"search",
]
def __init__(self, api_key: str, depth: str, output_type: str) -> None:
"""Initialize with parameters."""... |
"""LLama Kibela Reader."""
from typing import Dict, Generic, List, Optional, TypeVar
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document
from llama_index.core.bridge.pydantic import BaseModel
NodeType = TypeVar("NodeType")
class Edge(BaseModel, Generic[NodeType]):
... | """LLama Kibela Reader."""
from typing import Dict, Generic, List, Optional, TypeVar
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document
from llama_index.core.bridge.pydantic import BaseModel
NodeType = TypeVar("NodeType")
class Edge(BaseModel, Generic[NodeType]):
... |
# 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 (c) OpenMMLab. All rights reserved.
from .det_inferencer import DetInferencer
from .inference import (async_inference_detector, inference_detector,
init_detector)
__all__ = [
'init_detector', 'async_inference_detector', 'inference_detector',
'DetInferencer'
]
| # Copyright (c) OpenMMLab. All rights reserved.
from .inference import (async_inference_detector, inference_detector,
init_detector)
__all__ = [
'init_detector',
'async_inference_detector',
'inference_detector',
]
|
import pytest
from llama_index.voice_agents.openai.types import (
ConversationDeltaEvent,
ConversationDoneEvent,
ConversationSession,
ConversationSessionUpdate,
)
@pytest.fixture()
def session_json() -> dict:
return {
"modalities": ["text", "audio"],
"instructions": ... | import pytest
from llama_index.voice_agents.openai.types import (
ConversationDeltaEvent,
ConversationDoneEvent,
ConversationSession,
ConversationSessionUpdate,
)
@pytest.fixture()
def session_json() -> dict:
return {
"modalities": ["text", "audio"],
"instructions": ... |
_base_ = [
'../_base_/models/ssd300.py', '../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
]
# dataset settings
input_size = 300
train_pipeline = [
dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}),
dict(type='LoadAnnotations... | _base_ = [
'../_base_/models/ssd300.py', '../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
]
# dataset settings
input_size = 300
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(
... |
_base_ = './ms-rcnn_x101-64x4d_fpn_1x_coco.py'
# learning policy
max_epochs = 24
train_cfg = dict(
type='EpochBasedTrainLoop', max_epochs=max_epochs, val_interval=1)
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
... | _base_ = './ms_rcnn_x101_64x4d_fpn_1x_coco.py'
# learning policy
max_epochs = 24
train_cfg = dict(
type='EpochBasedTrainLoop', max_epochs=max_epochs, val_interval=1)
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
... |
from pathlib import Path
from typing import Dict, Tuple, Union
import torchaudio
from torch import Tensor
from torch.hub import download_url_to_file
from torch.utils.data import Dataset
from torchaudio.datasets.utils import extract_archive
_URL = "https://datashare.ed.ac.uk/bitstream/handle/10283/3038/DR-VCTK.zip"
_... | from pathlib import Path
from typing import Dict, Tuple, Union
import torchaudio
from torch import Tensor
from torch.hub import download_url_to_file
from torch.utils.data import Dataset
from torchaudio.datasets.utils import extract_archive
_URL = "https://datashare.ed.ac.uk/bitstream/handle/10283/3038/DR-VCTK.zip"
_... |
_base_ = './fovea_r50_fpn_4xb4-1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| _base_ = './fovea_r50_fpn_4x4_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
|
from typing import Dict, List
import numpy as np
import pytest
from docarray import DocList
from docarray.base_doc import AnyDoc, BaseDoc
from docarray.typing import NdArray
def test_any_doc():
class InnerDocument(BaseDoc):
text: str
tensor: NdArray
class CustomDoc(BaseDoc):
inner: ... | from typing import Dict, List
import numpy as np
import pytest
from orjson import orjson
from docarray import DocList
from docarray.base_doc import AnyDoc, BaseDoc
from docarray.base_doc.io.json import orjson_dumps_and_decode
from docarray.typing import NdArray
from docarray.typing.tensor.abstract_tensor import Abstr... |
_base_ = [
'../_base_/models/cascade-mask-rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
]
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvisio... | _base_ = [
'../_base_/models/cascade-mask-rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
]
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvisio... |
import importlib.util
import warnings
from functools import wraps
from typing import Optional
def is_module_available(*modules: str) -> bool:
r"""Returns if a top-level module with :attr:`name` exists *without**
importing it. This is generally safer than try-catch block around a
`import X`. It avoids thir... | import importlib.util
import warnings
from functools import wraps
from typing import Optional
import torch
def is_module_available(*modules: str) -> bool:
r"""Returns if a top-level module with :attr:`name` exists *without**
importing it. This is generally safer than try-catch block around a
`import X`. ... |
try:
from llama_index.readers.imdb_review.scraper import main_scraper
except ImportError:
from scraper import main_scraper
from typing import List
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document
class IMDBReviews(BaseReader):
def __init__(
self,
... | try:
from llama_index.readers.imdb_review.scraper import main_scraper
except ImportError:
from scraper import main_scraper
from typing import List
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document
class IMDBReviews(BaseReader):
def __init__(
self,
... |
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": ... |
from typing import Any, Dict, Iterator
import torch
from ..utils import _log_api_usage_once
try:
from ._load_gpu_decoder import _HAS_GPU_VIDEO_DECODER
except ModuleNotFoundError:
_HAS_GPU_VIDEO_DECODER = False
from ._video_opt import (
_HAS_CPU_VIDEO_DECODER,
_HAS_VIDEO_OPT,
_probe_video_from_fi... | from typing import Any, Dict, Iterator
import torch
from ..utils import _log_api_usage_once
try:
from ._load_gpu_decoder import _HAS_GPU_VIDEO_DECODER
except ModuleNotFoundError:
_HAS_GPU_VIDEO_DECODER = False
from ._video_opt import (
_HAS_CPU_VIDEO_DECODER,
_HAS_VIDEO_OPT,
_probe_video_from_fi... |
"""Wrapper around in-memory storage."""
from __future__ import annotations
from typing import Any, Dict, List, Literal, Optional
from langchain_core.embeddings import Embeddings
from langchain_community.vectorstores.docarray.base import (
DocArrayIndex,
_check_docarray_import,
)
class DocArrayInMemorySear... | """Wrapper around in-memory storage."""
from __future__ import annotations
from typing import Any, Dict, List, Literal, Optional
from langchain_core.embeddings import Embeddings
from langchain_community.vectorstores.docarray.base import (
DocArrayIndex,
_check_docarray_import,
)
class DocArrayInMemorySear... |
# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appl... | # Copyright 2025 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appl... |
"""
This example trains a SparseEncoder for the Natural Questions (NQ) dataset.
The training script fine-tunes a SparseEncoder using the Splade loss function for retrieval.
It loads a subset of the Natural Questions dataset, splits it into training and evaluation subsets,
and trains the model as a retriever. After trai... | """
This example trains a SparseEncoder for the Natural Questions (NQ) dataset.
The training script fine-tunes a SparseEncoder using the Splade loss function for retrieval.
It loads a subset of the Natural Questions dataset, splits it into training and evaluation subsets,
and trains the model as a retriever. After trai... |
import json
from enum import Enum
from typing import Any
from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
from backend.data.model import SchemaField
from backend.util.request import requests
class HttpMethod(Enum):
GET = "GET"
POST = "POST"
PUT = "PUT"
DELETE = "DELETE"
... | import json
from enum import Enum
import requests
from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
from backend.data.model import SchemaField
class HttpMethod(Enum):
GET = "GET"
POST = "POST"
PUT = "PUT"
DELETE = "DELETE"
PATCH = "PATCH"
OPTIONS = "OPTIONS"
H... |
_base_ = './mask-rcnn_r50-caffe_fpn_ms-poly-1x_coco.py'
train_cfg = dict(max_epochs=36)
# learning rate
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=24,
by_epoch=True,
mil... | _base_ = './mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco.py'
train_cfg = dict(max_epochs=36)
# learning rate
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=24,
by_epoch=True,
... |
"""Base classes for chain routing."""
from __future__ import annotations
from abc import ABC
from collections.abc import Mapping
from typing import Any, NamedTuple, Optional
from langchain_core.callbacks import (
AsyncCallbackManagerForChainRun,
CallbackManagerForChainRun,
Callbacks,
)
from pydantic impo... | """Base classes for chain routing."""
from __future__ import annotations
from abc import ABC
from typing import Any, Dict, List, Mapping, NamedTuple, Optional
from langchain_core.callbacks import (
AsyncCallbackManagerForChainRun,
CallbackManagerForChainRun,
Callbacks,
)
from pydantic import ConfigDict
... |
# Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
from mmcv.runner import BaseModule, auto_fp16
from mmdet.models.builder import HEADS
@HEADS.register_module()
class FeatureRelayHead(BaseModule):
"""Feature Relay Head used in `SCNet <https://arxiv.org/abs/2012.10150>`_.
Args:
in_... | import torch.nn as nn
from mmcv.runner import BaseModule, auto_fp16
from mmdet.models.builder import HEADS
@HEADS.register_module()
class FeatureRelayHead(BaseModule):
"""Feature Relay Head used in `SCNet <https://arxiv.org/abs/2012.10150>`_.
Args:
in_channels (int, optional): number of input channe... |
from typing import Union
import numpy as np
Matrix = Union[list[list[float]], list[np.ndarray], np.ndarray]
def maximal_marginal_relevance(
query_embedding: np.ndarray,
embedding_list: list,
lambda_mult: float = 0.5,
k: int = 4,
) -> list[int]:
"""Calculate maximal marginal relevance."""
if ... | from typing import List, Union
import numpy as np
Matrix = Union[List[List[float]], List[np.ndarray], np.ndarray]
def maximal_marginal_relevance(
query_embedding: np.ndarray,
embedding_list: list,
lambda_mult: float = 0.5,
k: int = 4,
) -> List[int]:
"""Calculate maximal marginal relevance."""
... |
# Copyright (c) OpenMMLab. All rights reserved.
import unittest
import torch
from mmengine.config import Config
from mmdet.models.seg_heads.panoptic_fusion_heads import MaskFormerFusionHead
from mmdet.structures import DetDataSample
class TestMaskFormerFusionHead(unittest.TestCase):
def test_loss(self):
... | # Copyright (c) OpenMMLab. All rights reserved.
import unittest
import torch
from mmengine.config import Config
from mmdet.data_elements import DetDataSample
from mmdet.models.seg_heads.panoptic_fusion_heads import MaskFormerFusionHead
class TestMaskFormerFusionHead(unittest.TestCase):
def test_loss(self):
... |
import torch
from parameterized import parameterized
from torchaudio.prototype.models import squim_objective_base, squim_subjective_base
from torchaudio_unittest.common_utils import skipIfNoCuda, torch_script, TorchaudioTestCase
class TestSquimObjective(TorchaudioTestCase):
def _smoke_test_objective(self, model, ... | import torch
from parameterized import parameterized
from torchaudio.prototype.models import squim_objective_base
from torchaudio_unittest.common_utils import skipIfNoCuda, torch_script, TorchaudioTestCase
class TestSQUIM(TorchaudioTestCase):
def _smoke_test_objective(self, model, device, dtype):
model = ... |
from __future__ import annotations
from .CrossEncoder import CrossEncoder
from .model_card import CrossEncoderModelCardData
from .trainer import CrossEncoderTrainer
from .training_args import CrossEncoderTrainingArguments
__all__ = [
"CrossEncoder",
"CrossEncoderTrainer",
"CrossEncoderTrainingArguments",
... | from __future__ import annotations
from .CrossEncoder import CrossEncoder
__all__ = ["CrossEncoder"]
|
_base_ = '../mask_rcnn/mask-rcnn_r101_fpn_1x_coco.py'
model = dict(
backbone=dict(plugins=[
dict(
cfg=dict(type='ContextBlock', ratio=1. / 16),
stages=(False, True, True, True),
position='after_conv3')
]))
| _base_ = '../mask_rcnn/mask_rcnn_r101_fpn_1x_coco.py'
model = dict(
backbone=dict(plugins=[
dict(
cfg=dict(type='ContextBlock', ratio=1. / 16),
stages=(False, True, True, True),
position='after_conv3')
]))
|
from typing import (
Union,
TYPE_CHECKING,
TypeVar,
Sequence,
Optional,
List,
Dict,
Generator,
Iterable,
Tuple,
ForwardRef,
)
if TYPE_CHECKING: # pragma: no cover
import scipy.sparse
import tensorflow
import torch
import numpy as np
from PIL.Image import... | from typing import (
Union,
TYPE_CHECKING,
TypeVar,
Sequence,
Optional,
List,
Dict,
Generator,
Iterable,
Tuple,
ForwardRef,
)
if TYPE_CHECKING:
import scipy.sparse
import tensorflow
import torch
import numpy as np
from PIL.Image import Image as PILImage
... |
import tracemalloc
from functools import wraps
from docarray import DocList
from docarray.documents import TextDoc
def get_test_da(n: int):
return DocList[TextDoc](gen_text_docs(n))
def gen_text_docs(n: int):
for i in range(n):
yield TextDoc(text=f'text {i}')
def profile_memory(func):
"""Deco... | import tracemalloc
from functools import wraps
from docarray import DocArray
from docarray.documents import TextDoc
def get_test_da(n: int):
return DocArray[TextDoc](gen_text_docs(n))
def gen_text_docs(n: int):
for i in range(n):
yield TextDoc(text=f'text {i}')
def profile_memory(func):
"""De... |
"""Init composability."""
from llama_index.core.composability.base import ComposableGraph
from llama_index.core.composability.joint_qa_summary import (
QASummaryQueryEngineBuilder,
)
__all__ = ["ComposableGraph", "QASummaryQueryEngineBuilder"]
| """Init composability."""
from llama_index.core.composability.base import ComposableGraph
from llama_index.core.composability.joint_qa_summary import (
QASummaryQueryEngineBuilder,
)
__all__ = ["ComposableGraph", "QASummaryQueryEngineBuilder"]
|
# Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
from mmcv.cnn import ConvModule, is_norm
from mmengine.model import caffe2_xavier_init, constant_init, normal_init
from torch.nn import BatchNorm2d
from mmdet.registry import MODELS
class Bottleneck(nn.Module):
"""Bottleneck block for DilatedE... | # Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
from mmcv.cnn import ConvModule, is_norm
from mmengine.model.utils import caffe2_xavier_init, constant_init, normal_init
from torch.nn import BatchNorm2d
from mmdet.registry import MODELS
class Bottleneck(nn.Module):
"""Bottleneck block for Di... |
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