input stringlengths 33 5k | output stringlengths 32 5k |
|---|---|
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appl... | # Copyright 2024 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appl... |
import warnings
from typing import List, Optional
import torchaudio
from . import utils
# TODO: Once legacy global backend is removed, move this to torchaudio.__init__
def _init_backend():
torchaudio.info = utils.get_info_func()
torchaudio.load = utils.get_load_func()
torchaudio.save = utils.get_save_fu... | from .utils import get_info_func, get_load_func, get_save_func
info = get_info_func()
load = get_load_func()
save = get_save_func()
|
from typing import Any, Optional, Type, TypeVar, Union
import numpy as np
from docarray.base_document import BaseDocument
from docarray.typing import AnyEmbedding, AnyTensor, PointCloud3DUrl
from docarray.typing.tensor.abstract_tensor import AbstractTensor
from docarray.utils.misc import is_tf_available, is_torch_ava... | from typing import Any, Optional, Type, TypeVar, Union
import numpy as np
from docarray.base_document import BaseDocument
from docarray.typing import AnyEmbedding, AnyTensor, PointCloud3DUrl
from docarray.typing.tensor.abstract_tensor import AbstractTensor
from docarray.utils.misc import is_torch_available
torch_ava... |
"""
Using rmm on a single node device
=================================
"""
import rmm
from sklearn.datasets import make_classification
import xgboost as xgb
# Initialize RMM pool allocator
rmm.reinitialize(pool_allocator=True)
# Optionally force XGBoost to use RMM for all GPU memory allocation, see ./README.md
# xg... | """
Using rmm on a single node device
=================================
"""
import rmm
from sklearn.datasets import make_classification
import xgboost as xgb
# Initialize RMM pool allocator
rmm.reinitialize(pool_allocator=True)
# Optionally force XGBoost to use RMM for all GPU memory allocation, see ./README.md
# xgb... |
import logging
from datasets import load_dataset
from sentence_transformers import SparseEncoder
from sentence_transformers.sparse_encoder.evaluation import SparseTripletEvaluator
logging.basicConfig(format="%(message)s", level=logging.INFO)
# Load a model
model = SparseEncoder("naver/splade-cocondenser-ensembledis... | import logging
from datasets import load_dataset
from sentence_transformers import SparseEncoder
from sentence_transformers.sparse_encoder.evaluation import SparseTripletEvaluator
logging.basicConfig(format="%(message)s", level=logging.INFO)
# Load a model
model = SparseEncoder("naver/splade-cocondenser-ensembledis... |
from typing import TYPE_CHECKING
from .github import GithubWebhooksManager
from .slant3d import Slant3DWebhooksManager
if TYPE_CHECKING:
from ..providers import ProviderName
from .base import BaseWebhooksManager
# --8<-- [start:WEBHOOK_MANAGERS_BY_NAME]
WEBHOOK_MANAGERS_BY_NAME: dict["ProviderName", type["Ba... | from typing import TYPE_CHECKING
from .github import GithubWebhooksManager
from .slant3d import Slant3DWebhooksManager
if TYPE_CHECKING:
from .base import BaseWebhooksManager
# --8<-- [start:WEBHOOK_MANAGERS_BY_NAME]
WEBHOOK_MANAGERS_BY_NAME: dict[str, type["BaseWebhooksManager"]] = {
handler.PROVIDER_NAME: ... |
from llama_index.vector_stores.redis.base import RedisVectorStore, TokenEscaper
__all__ = ["RedisVectorStore", "TokenEscaper"]
| from llama_index.vector_stores.redis.base import RedisVectorStore
__all__ = ["RedisVectorStore"]
|
from __future__ import annotations
from .MLMTransformer import MLMTransformer
from .SparseAutoEncoder import SparseAutoEncoder
from .SparseStaticEmbedding import SparseStaticEmbedding
from .SpladePooling import SpladePooling
__all__ = ["SparseAutoEncoder", "MLMTransformer", "SpladePooling", "SparseStaticEmbedding"]
| from __future__ import annotations
from .IDF import IDF
from .MLMTransformer import MLMTransformer
from .SparseAutoEncoder import SparseAutoEncoder
from .SpladePooling import SpladePooling
__all__ = ["SparseAutoEncoder", "MLMTransformer", "SpladePooling", "IDF"]
|
from typing import Optional
from docarray import Document, DocumentArray
from pydantic import BaseModel
from uvicorn import Config, Server
from jina import Gateway, __default_host__
from jina.clients.request import request_generator
class DummyResponseModel(BaseModel):
arg1: Optional[str]
arg2: Optional[str... | from typing import Optional
from docarray import Document, DocumentArray
from pydantic import BaseModel
from uvicorn import Config, Server
from jina import Gateway, __default_host__
from jina.clients.request import request_generator
class DummyResponseModel(BaseModel):
arg1: Optional[str]
arg2: Optional[str... |
from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.tools import E2BDataAnalysisTool
from langchain_community.tools.e2b_data_analysis.tool import (
E2BDataAnalysisToolArguments,
UploadedFile,
)
# Create a way to dynam... | from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.tools import E2BDataAnalysisTool
from langchain_community.tools.e2b_data_analysis.tool import (
E2BDataAnalysisToolArguments,
UploadedFile,
)
# Create a way to dynam... |
import csv
import os
from pathlib import Path
from typing import Dict, List, Tuple, Union
import torchaudio
from torch import Tensor
from torch.utils.data import Dataset
def load_commonvoice_item(
line: List[str], header: List[str], path: str, folder_audio: str, ext_audio: str
) -> Tuple[Tensor, int, Dict[str, s... | import csv
import os
from pathlib import Path
from typing import Dict, List, Tuple, Union
import torchaudio
from torch import Tensor
from torch.utils.data import Dataset
def load_commonvoice_item(
line: List[str], header: List[str], path: str, folder_audio: str, ext_audio: str
) -> Tuple[Tensor, int, Dict[str, s... |
# dataset settings
dataset_type = 'OpenImagesDataset'
data_root = 'data/OpenImages/'
# file_client_args = dict(
# backend='petrel',
# path_mapping=dict({
# './data/': 's3://openmmlab/datasets/detection/',
# 'data/': 's3://openmmlab/datasets/detection/'
# }))
file_client_args = dict(backend... | # dataset settings
dataset_type = 'OpenImagesDataset'
data_root = 'data/OpenImages/'
# file_client_args = dict(
# backend='petrel',
# path_mapping=dict({
# './data/': 's3://openmmlab/datasets/detection/',
# 'data/': 's3://openmmlab/datasets/detection/'
# }))
file_client_args = dict(backend... |
# Copyright (c) OpenMMLab. All rights reserved.
import math
import torch
from torch.utils.data import DistributedSampler as _DistributedSampler
from mmdet.core.utils import sync_random_seed
from mmdet.registry import DATA_SAMPLERS
from mmdet.utils import get_device
@DATA_SAMPLERS.register_module()
class Distributed... | # Copyright (c) OpenMMLab. All rights reserved.
import math
import torch
from torch.utils.data import DistributedSampler as _DistributedSampler
from mmdet.core.utils import sync_random_seed
from mmdet.utils import get_device
class DistributedSampler(_DistributedSampler):
def __init__(self,
dat... |
import pytest
from backend.data import db
from backend.executor.scheduler import SchedulerClient
from backend.server.model import CreateGraph
from backend.usecases.sample import create_test_graph, create_test_user
from backend.util.service import get_service_client
from backend.util.test import SpinTestServer
@pytes... | import pytest
from backend.data import db
from backend.executor.scheduler import SchedulerClient
from backend.server.model import CreateGraph
from backend.usecases.sample import create_test_graph, create_test_user
from backend.util.service import get_service_client
from backend.util.test import SpinTestServer
@pytes... |
import multiprocessing
import pytest
from jina import Client
from jina.parsers import set_gateway_parser
from jina.serve.runtimes.asyncio import AsyncNewLoopRuntime
from jina.serve.runtimes.gateway import GatewayRuntime
from jina.serve.runtimes.worker import WorkerRuntime
from tests.helper import _generate_pod_args
... | import multiprocessing
import pytest
from jina import Client
from jina.parsers import set_gateway_parser, set_pod_parser
from jina.serve.runtimes.asyncio import AsyncNewLoopRuntime
from jina.serve.runtimes.gateway import GatewayRuntime
from jina.serve.runtimes.worker import WorkerRuntime
def _create_worker_runtime(... |
from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.chat_message_histories import FileChatMessageHistory
# 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.chat_message_histories import FileChatMessageHistory
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handling op... |
# Copyright (c) OpenMMLab. All rights reserved.
from .onnx_helper import (add_dummy_nms_for_onnx, dynamic_clip_for_onnx,
get_k_for_topk)
from .pytorch2onnx import (build_model_from_cfg,
generate_inputs_and_wrap_model,
preprocess_example_inp... | from .onnx_helper import (add_dummy_nms_for_onnx, dynamic_clip_for_onnx,
get_k_for_topk)
from .pytorch2onnx import (build_model_from_cfg,
generate_inputs_and_wrap_model,
preprocess_example_input)
__all__ = [
'build_model_from_cfg', 'ge... |
__copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import subprocess
from typing import Iterable, Optional
import torch
from jina import DocumentArray, Executor, requests
from jina.logging.logger import JinaLogger
from laserembeddings import Laser
class Laser... | __copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import subprocess
from typing import Iterable, Optional
import torch
from jina import DocumentArray, Executor, requests
from jina.logging.logger import JinaLogger
from laserembeddings import Laser
class Laser... |
from keras.src import backend
def is_in_jax_tracing_scope(x=None):
if backend.backend() == "jax":
if x is None:
x = backend.numpy.ones(())
for c in x.__class__.__mro__:
if c.__name__ == "Tracer" and c.__module__.startswith("jax"):
return True
return Fals... | from keras.src import backend
def is_in_jax_tracing_scope(x=None):
if backend.backend() == "jax":
if x is None:
x = backend.numpy.ones(())
if x.__class__.__name__ == "DynamicJaxprTracer":
return True
return False
|
__copyright__ = "Copyright (c) 2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import os
from typing import Dict
import pytest
import torch
import numpy as np
from torchvision.models.mobilenetv2 import model_urls
from PIL import Image
from jina import DocumentArray, Document
@pytest.fixture(... | __copyright__ = "Copyright (c) 2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import os
from typing import Dict
import pytest
import torch
import numpy as np
from torchvision.models.mobilenetv2 import model_urls
from PIL import Image
from jina import DocumentArray, Document
@pytest.fixture(... |
from ._dsp import (
adsr_envelope,
exp_sigmoid,
extend_pitch,
filter_waveform,
frequency_impulse_response,
oscillator_bank,
sinc_impulse_response,
)
from ._rir import simulate_rir_ism
from .functional import barkscale_fbanks, chroma_filterbank
__all__ = [
"adsr_envelope",
"exp_sigm... | from ._dsp import (
adsr_envelope,
exp_sigmoid,
extend_pitch,
filter_waveform,
frequency_impulse_response,
oscillator_bank,
sinc_impulse_response,
)
from ._rir import simulate_rir_ism
from .functional import barkscale_fbanks
__all__ = [
"adsr_envelope",
"exp_sigmoid",
"barkscal... |
from typing import (
TYPE_CHECKING,
TypeVar,
Sequence,
List,
Dict,
Optional,
)
import numpy as np
from .... import Document, DocumentArray
from ....math import ndarray
from ....math.helper import EPSILON
from ....math.ndarray import to_numpy_array
from ....score import NamedScore
if TYPE_CHEC... | from typing import (
TYPE_CHECKING,
TypeVar,
Sequence,
List,
)
import numpy as np
from .... import Document, DocumentArray
from ....math import ndarray
from ....math.helper import EPSILON
from ....math.ndarray import to_numpy_array
from ....score import NamedScore
if TYPE_CHECKING:
import tensorf... |
from typing import Optional
import numpy as np
import pytest
import torch
from docarray import DocumentArray
from docarray.base_document import BaseDocument
from docarray.typing import NdArray, TorchTensor
@pytest.mark.proto
def test_proto_simple():
class CustomDoc(BaseDocument):
text: str
doc = Cu... | from typing import Optional
import numpy as np
import torch
from docarray import DocumentArray
from docarray.base_document import BaseDocument
from docarray.typing import NdArray, TorchTensor
def test_proto_simple():
class CustomDoc(BaseDocument):
text: str
doc = CustomDoc(text='hello')
Custom... |
# 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... |
import multiprocessing
import time
import grpc
import pytest
import requests
from jina import __version__
from jina.constants import __jina_env__
from jina.proto import jina_pb2, jina_pb2_grpc
from jina.serve.runtimes.asyncio import AsyncNewLoopRuntime
from jina.serve.runtimes.servers import BaseServer
from jina.serv... | import multiprocessing
import time
import grpc
import pytest
import requests
from jina import __version__
from jina.constants import __jina_env__
from jina.proto import jina_pb2, jina_pb2_grpc
from jina.serve.runtimes.asyncio import AsyncNewLoopRuntime
from jina.serve.runtimes.worker import WorkerRuntime
from tests.h... |
# coding=utf-8
# Copyright 2025 The HuggingFace Team Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a clone of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable... | # coding=utf-8
# Copyright 2024 The HuggingFace Team Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a clone of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable... |
# 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.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... |
"""python scripts/update_mypy_ruff.py"""
import glob
import tomllib
from pathlib import Path
import toml
import subprocess
import re
ROOT_DIR = Path(__file__).parents[1]
def main():
for path in glob.glob(str(ROOT_DIR / "libs/**/pyproject.toml"), recursive=True):
if "libs/cli/" in path:
cont... | """python scripts/update_mypy_ruff.py"""
import glob
import tomllib
from pathlib import Path
import toml
import subprocess
import re
ROOT_DIR = Path(__file__).parents[1]
def main():
for path in glob.glob(str(ROOT_DIR / "libs/**/pyproject.toml"), recursive=True):
if "libs/cli/" in path:
cont... |
import os
from functools import lru_cache
from typing import Union
import ffmpeg
import numpy as np
import torch
import torch.nn.functional as F
from .utils import exact_div
# hard-coded audio hyperparameters
SAMPLE_RATE = 16000
N_FFT = 400
N_MELS = 80
HOP_LENGTH = 160
CHUNK_LENGTH = 30
N_SAMPLES = CHUNK_LENGTH * SA... | import os
from functools import lru_cache
from typing import Union
import ffmpeg
import numpy as np
import torch
import torch.nn.functional as F
from .utils import exact_div
# hard-coded audio hyperparameters
SAMPLE_RATE = 16000
N_FFT = 400
N_MELS = 80
HOP_LENGTH = 160
CHUNK_LENGTH = 30
N_SAMPLES = CHUNK_LENGTH * SA... |
from typing import Any, Union
from langchain_core.utils.json import parse_json_markdown
from langchain.evaluation.schema import StringEvaluator
class JsonSchemaEvaluator(StringEvaluator):
"""An evaluator that validates a JSON prediction against a JSON schema reference.
This evaluator checks if a given JSON... | from typing import Any, Union
from langchain_core.utils.json import parse_json_markdown
from langchain.evaluation.schema import StringEvaluator
class JsonSchemaEvaluator(StringEvaluator):
"""An evaluator that validates a JSON prediction against a JSON schema reference.
This evaluator checks if a given JSON... |
_base_ = './mask2former_r50_8xb2-lsj-50e_coco-panoptic.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| _base_ = './mask2former_r50_lsj_8x2_50e_coco-panoptic.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
|
__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.... |
import pytest
from jina import Flow
num_calls = 0
@pytest.fixture(scope='function', autouse=True)
def patched_path_import(mocker):
from jina.importer import _path_import
def _wrapped_path_import(absolute_path: str):
global num_calls
num_calls += 1
assert num_calls < 2
return... | import pytest
from jina import Flow
num_calls = 0
@pytest.fixture(scope='function', autouse=True)
def patched_path_import(mocker):
from jina.importer import _path_import
def _wrapped_path_import(absolute_path: str):
global num_calls
num_calls += 1
assert num_calls < 2
return ... |
from datasets import Dataset
from sentence_transformers.sparse_encoder import SparseEncoder, SparseEncoderTrainer, losses
# Initialize the SPLADE model
model = SparseEncoder("naver/splade-cocondenser-ensembledistil")
guide = SparseEncoder("prithivida/Splade_PP_en_v1")
train_dataset = Dataset.from_dict(
{
... | from datasets import Dataset
from sentence_transformers.sparse_encoder import (
MLMTransformer,
SparseEncoder,
SparseEncoderTrainer,
SparseGISTEmbedLoss,
SpladePooling,
)
# Initialize the SPLADE model
model_name = "naver/splade-cocondenser-ensembledistil"
model = SparseEncoder(
modules=[
... |
import importlib
import pytest
from fastapi.testclient import TestClient
from ...utils import needs_py39
@pytest.fixture(
name="client",
params=[
"tutorial008b",
"tutorial008b_an",
pytest.param("tutorial008b_an_py39", marks=needs_py39),
],
)
def get_client(request: pytest.Fixture... | from fastapi.testclient import TestClient
from docs_src.dependencies.tutorial008b import app
client = TestClient(app)
def test_get_no_item():
response = client.get("/items/foo")
assert response.status_code == 404, response.text
assert response.json() == {"detail": "Item not found"}
def test_owner_erro... |
# Copyright (c) OpenMMLab. All rights reserved.
from mmdet.registry import TASK_UTILS
from mmdet.structures.bbox import (HorizontalBoxes, bbox2distance,
distance2bbox, get_box_tensor)
from .base_bbox_coder import BaseBBoxCoder
@TASK_UTILS.register_module()
class DistancePointBBoxCod... | # Copyright (c) OpenMMLab. All rights reserved.
from mmdet.models.utils.misc import get_box_tensor
from mmdet.registry import TASK_UTILS
from mmdet.structures.bbox import HorizontalBoxes, bbox2distance, distance2bbox
from .base_bbox_coder import BaseBBoxCoder
@TASK_UTILS.register_module()
class DistancePointBBoxCoder... |
import os
from jina import Executor, requests, DocumentArray
import socket
class TestExecutor(Executor):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
from jina.logging.logger import JinaLogger
self.logger = JinaLogger(self.__class__.__name__)
self._name ... | import os
from jina import Executor, requests, DocumentArray
import socket
class TestExecutor(Executor):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
from jina.logging.logger import JinaLogger
self.logger = JinaLogger(self.__class__.__name__)
self._name ... |
from typing import Union, Iterable, Dict, List
import warnings
from docarray.array.storage.base.seqlike import BaseSequenceLikeMixin
from docarray import Document
class SequenceLikeMixin(BaseSequenceLikeMixin):
"""Implement sequence-like methods for DocumentArray with Elastic as storage"""
def __eq__(self, ... | from typing import Union, Iterable, Dict, List
import warnings
from docarray.array.storage.base.seqlike import BaseSequenceLikeMixin
from docarray import Document
class SequenceLikeMixin(BaseSequenceLikeMixin):
"""Implement sequence-like methods for DocumentArray with Elastic as storage"""
def __eq__(self, ... |
"""DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.utils.image_utils import array_to_img as array_to_img
from keras.src.utils.image_utils import img_to_array as img_to_array
from keras.src.utils.image_utils import load_img as load_img... | """DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.utils.image_utils import array_to_img
from keras.src.utils.image_utils import img_to_array
from keras.src.utils.image_utils import load_img
from keras.src.utils.image_utils import sav... |
from typing import Any, Dict, List, Optional, Sequence, Type, Union
import PIL.Image
import torch
from torchvision import datapoints
from torchvision.prototype.datapoints import Label, OneHotLabel
from torchvision.transforms.v2 import functional as F, Transform
from torchvision.transforms.v2._utils import _FillType, ... | from typing import Any, Dict, List, Optional, Sequence, Type, Union
import PIL.Image
import torch
from torchvision import datapoints
from torchvision.prototype.datapoints import Label, OneHotLabel
from torchvision.transforms.v2 import functional as F, Transform
from torchvision.transforms.v2._utils import _FillType, ... |
from functools import lru_cache
import numpy as np
import torch
try:
import triton
import triton.language as tl
except ImportError:
raise RuntimeError("triton import failed; try `pip install --pre triton`")
@triton.jit
def dtw_kernel(
cost, trace, x, x_stride, cost_stride, trace_stride, N, M, BLOCK_... | import math
import numpy as np
import torch
from functools import lru_cache
try:
import triton
import triton.language as tl
except ImportError:
raise RuntimeError("triton import failed; try `pip install --pre triton`")
@triton.jit
def dtw_kernel(cost, trace, x, x_stride, cost_stride, trace_stride, N, M,... |
_base_ = '../_base_/default_runtime.py'
# dataset settings
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
image_size = (1024, 1024)
file_client_args = dict(backend='disk')
# comment out the code below to use different file client
# file_client_args = dict(
# backend='petrel',
# path_mapping=dict({
# ... | _base_ = '../_base_/default_runtime.py'
# dataset settings
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
image_size = (1024, 1024)
file_client_args = dict(backend='disk')
# comment out the code below to use different file client
# file_client_args = dict(
# backend='petrel',
# path_mapping=dict({
# ... |
# 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... |
# Copyright (c) OpenMMLab. All rights reserved.
from .collect_env import collect_env
from .logger import get_root_logger
__all__ = ['get_root_logger', 'collect_env']
| from .collect_env import collect_env
from .logger import get_root_logger
__all__ = ['get_root_logger', 'collect_env']
|
"""Prompts for comparing the outputs of two models for a given question.
This prompt is used to compare two responses and evaluate which one best follows the instructions
and answers the question. The prompt is based on the paper from
Zheng, et. al. https://arxiv.org/abs/2306.05685
""" # noqa: E501
from langchain_co... | """Prompts for comparing the outputs of two models for a given question.
This prompt is used to compare two responses and evaluate which one best follows the instructions
and answers the question. The prompt is based on the paper from
Zheng, et. al. https://arxiv.org/abs/2306.05685
"""
# flake8: noqa
from langchain_c... |
from torchaudio import ( # noqa: F401
_extension,
compliance,
datasets,
functional,
io,
kaldi_io,
models,
pipelines,
sox_effects,
transforms,
utils,
)
from torchaudio.backend import get_audio_backend, list_audio_backends, set_audio_backend
try:
from .version import __ve... | from torchaudio import _extension # noqa: F401
from torchaudio import (
io,
compliance,
datasets,
functional,
models,
pipelines,
kaldi_io,
utils,
sox_effects,
transforms,
)
from torchaudio.backend import (
list_audio_backends,
get_audio_backend,
set_audio_backend,
)
... |
# flake8: 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/LI... | # flake8: 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/LI... |
# Copyright (c) OpenMMLab. All rights reserved.
import warnings
from mmdet.registry import TASK_UTILS
MATCH_COST = TASK_UTILS
def build_match_cost(cfg, default_args=None):
"""Builder of IoU calculator."""
warnings.warn('``build_match_cost`` would be deprecated soon, please use '
'``mmdet.r... | # Copyright (c) OpenMMLab. All rights reserved.
from mmcv.utils import Registry, build_from_cfg
MATCH_COST = Registry('Match Cost')
def build_match_cost(cfg, default_args=None):
"""Builder of IoU calculator."""
return build_from_cfg(cfg, MATCH_COST, default_args)
|
import csv
import os
from pathlib import Path
from typing import Dict, List, Tuple, Union
import torchaudio
from torch import Tensor
from torch.utils.data import Dataset
def load_commonvoice_item(
line: List[str], header: List[str], path: str, folder_audio: str, ext_audio: str
) -> Tuple[Tensor, int, Dict[str, s... | import csv
import os
from pathlib import Path
from typing import List, Dict, Tuple, Union
import torchaudio
from torch import Tensor
from torch.utils.data import Dataset
def load_commonvoice_item(
line: List[str], header: List[str], path: str, folder_audio: str, ext_audio: str
) -> Tuple[Tensor, int, Dict[str, s... |
# flake8: 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/LI... | # flake8: 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/LI... |
# Copyright (c) OpenMMLab. All rights reserved.
import time
import pytest
import mmengine
def test_timer_init():
timer = mmengine.Timer(start=False)
assert not timer.is_running
timer.start()
assert timer.is_running
timer = mmengine.Timer()
assert timer.is_running
def test_timer_run():
... | # Copyright (c) OpenMMLab. All rights reserved.
import time
import pytest
import mmengine
def test_timer_init():
timer = mmengine.Timer(start=False)
assert not timer.is_running
timer.start()
assert timer.is_running
timer = mmengine.Timer()
assert timer.is_running
def test_timer_run():
... |
import os
import pytest
import requests
from jina import Flow
from tests.helper import (
ProcessExecutor,
_validate_custom_gateway_process,
_validate_dummy_custom_gateway_response,
)
from tests.unit.yaml.dummy_gateway import DummyGateway
from tests.unit.yaml.dummy_gateway_get_streamer import DummyGatewayG... | import os
import pytest
import requests
from jina import Flow
from tests.helper import (
ProcessExecutor,
_validate_custom_gateway_process,
_validate_dummy_custom_gateway_response,
)
from tests.unit.yaml.dummy_gateway import DummyGateway
cur_dir = os.path.dirname(os.path.abspath(__file__))
_dummy_gateway... |
from typing import Any, Optional, Type, TypeVar, Union
import numpy as np
from docarray.base_document import BaseDocument
from docarray.typing import AnyEmbedding, ImageBytes, ImageUrl
from docarray.typing.tensor.abstract_tensor import AbstractTensor
from docarray.typing.tensor.image.image_tensor import ImageTensor
... | from typing import Any, Optional, Type, TypeVar, Union
import numpy as np
from docarray.base_document import BaseDocument
from docarray.typing import AnyEmbedding, AnyTensor, ImageUrl
from docarray.typing.tensor.abstract_tensor import AbstractTensor
T = TypeVar('T', bound='Image')
try:
import torch
torch_a... |
"""Dappier Real Time Search tool spec."""
import os
from typing import Optional
from llama_index.core.tools.tool_spec.base import BaseToolSpec
class DappierRealTimeSearchToolSpec(BaseToolSpec):
"""Dappier Real Time Search tool spec."""
spec_functions = ["search_real_time_data", "search_stock_market_data"]
... | """Dappier Real Time Search tool spec."""
import os
from typing import Optional
from llama_index.core.tools.tool_spec.base import BaseToolSpec
class DappierRealTimeSearchToolSpec(BaseToolSpec):
"""Dappier Real Time Search tool spec."""
spec_functions = ["search_real_time_data", "search_stock_market_data"]
... |
__copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import subprocess
import numpy as np
import pytest
from jina import Document, DocumentArray, Flow
from ...audioclip_image import AudioCLIPImageEncoder
@pytest.mark.parametrize("request_size", [1, 10, 50, 100]... | __copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
from typing import Callable
import numpy as np
import pytest
from jina import Document, DocumentArray, Flow
from ...audioclip_image import AudioCLIPImageEncoder
@pytest.mark.parametrize("request_size", [1, 10... |
"""Loads word documents."""
import os
import tempfile
from abc import ABC
from pathlib import Path
from typing import Any, List, Union
from urllib.parse import urlparse
import requests
from langchain_core.documents import Document
from langchain_community.document_loaders.base import BaseLoader
from langchain_commun... | """Loads word documents."""
import os
import tempfile
from abc import ABC
from pathlib import Path
from typing import Any, List, Union
from urllib.parse import urlparse
import requests
from langchain_core.documents import Document
from langchain_community.document_loaders.base import BaseLoader
from langchain_commun... |
from keras.src import activations
from keras.src.api_export import keras_export
from keras.src.layers.layer import Layer
@keras_export("keras.layers.ReLU")
class ReLU(Layer):
"""Rectified Linear Unit activation function layer.
Formula:
``` python
f(x) = max(x,0)
f(x) = max_value if x >= max_value... | from keras.src import activations
from keras.src.api_export import keras_export
from keras.src.layers.layer import Layer
@keras_export("keras.layers.ReLU")
class ReLU(Layer):
"""Rectified Linear Unit activation function layer.
Formula:
``` python
f(x) = max(x,0)
f(x) = max_value if x >= max_value... |
from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.tools.ainetwork.base import AINBaseTool, OperationType
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handling ... | from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.tools.ainetwork.base import AINBaseTool, OperationType
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handling ... |
_base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
'../common/lsj_100e_coco_instance.py'
]
image_size = (1024, 1024)
batch_augments = [dict(type='BatchFixedSizePad', size=image_size)]
norm_cfg = dict(type='SyncBN', requires_grad=True)
# Use MMSyncBN that handles empty tensor in head. It can be changed to
# Sy... | _base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
'../common/lsj_100e_coco_instance.py'
]
image_size = (1024, 1024)
batch_augments = [dict(type='BatchFixedSizePad', size=image_size)]
norm_cfg = dict(type='SyncBN', requires_grad=True)
# Use MMSyncBN that handles empty tensor in head. It can be changed to
# Sy... |
# coding=utf-8
# Copyright 2025 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... | # coding=utf-8
# Copyright 2024 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... |
# 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 2024 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appl... |
from langchain_huggingface.chat_models.huggingface import ( # type: ignore[import-not-found]
TGI_MESSAGE,
TGI_RESPONSE,
ChatHuggingFace,
_convert_dict_to_message,
)
__all__ = ["ChatHuggingFace", "_convert_dict_to_message", "TGI_MESSAGE", "TGI_RESPONSE"]
| from langchain_huggingface.chat_models.huggingface import ( # type: ignore[import-not-found]
TGI_MESSAGE,
TGI_RESPONSE,
ChatHuggingFace,
_convert_message_to_chat_message,
_convert_TGI_message_to_LC_message,
)
__all__ = [
"ChatHuggingFace",
"_convert_message_to_chat_message",
"_convert_... |
"""DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.ops.nn import average_pool
from keras.src.ops.nn import batch_normalization
from keras.src.ops.nn import binary_crossentropy
from keras.src.ops.nn import categorical_crossentropy
from... | """DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.ops.nn import average_pool
from keras.src.ops.nn import batch_normalization
from keras.src.ops.nn import binary_crossentropy
from keras.src.ops.nn import categorical_crossentropy
from... |
_base_ = './fast-rcnn_r50_fpn_1x_coco.py'
train_cfg = dict(max_epochs=24)
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,
milestones=[16, 22],
gamm... | _base_ = './fast-rcnn_r50_fpn_1x_coco.py'
# learning policy
lr_config = dict(step=[16, 22])
runner = dict(type='EpochBasedRunner', max_epochs=24)
|
"""Base tool spec class."""
import asyncio
from inspect import signature
from typing import Any, Awaitable, Callable, Dict, List, Optional, Tuple, Type, Union
from llama_index.core.bridge.pydantic import BaseModel
from llama_index.core.tools.function_tool import FunctionTool
from llama_index.core.tools.types import T... | """Base tool spec class."""
import asyncio
from inspect import signature
from typing import Any, Awaitable, Callable, Dict, List, Optional, Tuple, Type, Union
from llama_index.core.bridge.pydantic import BaseModel
from llama_index.core.tools.function_tool import FunctionTool
from llama_index.core.tools.types import T... |
"""DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.ops.nn import average_pool
from keras.src.ops.nn import batch_normalization
from keras.src.ops.nn import binary_crossentropy
from keras.src.ops.nn import categorical_crossentropy
from... | """DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.ops.nn import average_pool
from keras.src.ops.nn import batch_normalization
from keras.src.ops.nn import binary_crossentropy
from keras.src.ops.nn import categorical_crossentropy
from... |
import inspect
import threading
from typing import Awaitable, Callable, ParamSpec, TypeVar, cast, overload
P = ParamSpec("P")
R = TypeVar("R")
@overload
def thread_cached(func: Callable[P, Awaitable[R]]) -> Callable[P, Awaitable[R]]: ...
@overload
def thread_cached(func: Callable[P, R]) -> Callable[P, R]: ...
de... | import inspect
import threading
from typing import Any, Awaitable, Callable, ParamSpec, TypeVar, cast, overload
P = ParamSpec("P")
R = TypeVar("R")
@overload
def thread_cached(func: Callable[P, Awaitable[R]]) -> Callable[P, Awaitable[R]]: ...
@overload
def thread_cached(func: Callable[P, R]) -> Callable[P, R]: ...... |
import logging
from typing import Any
from autogpt_libs.utils.cache import thread_cached
from backend.data.block import (
Block,
BlockCategory,
BlockInput,
BlockOutput,
BlockSchema,
BlockType,
get_block,
)
from backend.data.execution import ExecutionStatus
from backend.data.model import Sc... | import logging
from typing import Any
from autogpt_libs.utils.cache import thread_cached
from backend.data.block import (
Block,
BlockCategory,
BlockInput,
BlockOutput,
BlockSchema,
BlockType,
get_block,
)
from backend.data.execution import ExecutionStatus
from backend.data.model import Sc... |
# Copyright 2025 Google Brain 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 requ... | # Copyright 2024 Google Brain 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 requ... |
# Copyright (c) OpenMMLab. All rights reserved.
from .accuracy import Accuracy, accuracy
from .ae_loss import AssociativeEmbeddingLoss
from .balanced_l1_loss import BalancedL1Loss, balanced_l1_loss
from .cross_entropy_loss import (CrossEntropyLoss, binary_cross_entropy,
cross_entropy, m... | from .accuracy import Accuracy, accuracy
from .ae_loss import AssociativeEmbeddingLoss
from .balanced_l1_loss import BalancedL1Loss, balanced_l1_loss
from .cross_entropy_loss import (CrossEntropyLoss, binary_cross_entropy,
cross_entropy, mask_cross_entropy)
from .focal_loss import Focal... |
from __future__ import annotations
import logging
from typing import TYPE_CHECKING, Any, Callable
from sentence_transformers.evaluation import RerankingEvaluator
from sentence_transformers.util import cos_sim
if TYPE_CHECKING:
import numpy as np
from torch import Tensor
from sentence_transformers.sparse... | from __future__ import annotations
import logging
from typing import TYPE_CHECKING, Any, Callable
from sentence_transformers.evaluation import RerankingEvaluator
from sentence_transformers.util import cos_sim
if TYPE_CHECKING:
import numpy as np
from torch import Tensor
from sentence_transformers.sparse... |
"""DeepMemory Retrieval Pack."""
from typing import Any, Dict, List, Optional
from llama_index.core.indices.vector_store import VectorStoreIndex
from llama_index.core.llama_pack.base import BaseLlamaPack
from llama_index.core.query_engine import RetrieverQueryEngine
from llama_index.core.schema import TextNode
from l... | """DeepMemory Retrieval Pack."""
from typing import Any, Dict, List, Optional
from llama_index.core.indices.vector_store import VectorStoreIndex
from llama_index.core.llama_pack.base import BaseLlamaPack
from llama_index.core.query_engine import RetrieverQueryEngine
from llama_index.core.schema import TextNode
from ... |
from fastapi import FastAPI
from fastapi.openapi.docs import (
get_redoc_html,
get_swagger_ui_html,
get_swagger_ui_oauth2_redirect_html,
)
app = FastAPI(docs_url=None, redoc_url=None)
@app.get("/docs", include_in_schema=False)
async def custom_swagger_ui_html():
return get_swagger_ui_html(
op... | from fastapi import FastAPI
from fastapi.openapi.docs import (
get_redoc_html,
get_swagger_ui_html,
get_swagger_ui_oauth2_redirect_html,
)
app = FastAPI(docs_url=None, redoc_url=None)
@app.get("/docs", include_in_schema=False)
async def custom_swagger_ui_html():
return get_swagger_ui_html(
op... |
# Copyright (c) OpenMMLab. All rights reserved.
# from mmengine.dist import get_dist_info, all_reduce
from collections import OrderedDict
from typing import Generator, List
from unittest.mock import MagicMock, Mock
import torch
from torch._utils import (_flatten_dense_tensors, _take_tensors,
... | # Copyright (c) OpenMMLab. All rights reserved.
# from mmengine.dist import get_dist_info, all_reduce
from collections import OrderedDict
from typing import Generator, List
from unittest.mock import MagicMock, Mock
import torch
from torch._utils import (_flatten_dense_tensors, _take_tensors,
... |
_base_ = 'ssd300_coco.py'
# model settings
input_size = 512
model = dict(
neck=dict(
out_channels=(512, 1024, 512, 256, 256, 256, 256),
level_strides=(2, 2, 2, 2, 1),
level_paddings=(1, 1, 1, 1, 1),
last_kernel_size=4),
bbox_head=dict(
in_channels=(512, 1024, 512, 256, 2... | _base_ = 'ssd300_coco.py'
# model settings
input_size = 512
model = dict(
neck=dict(
out_channels=(512, 1024, 512, 256, 256, 256, 256),
level_strides=(2, 2, 2, 2, 1),
level_paddings=(1, 1, 1, 1, 1),
last_kernel_size=4),
bbox_head=dict(
in_channels=(512, 1024, 512, 256, 2... |
import subprocess
import pytest
from flair_text import FlairTextEncoder
from jina import Document, DocumentArray, Flow
_EMBEDDING_DIM = 100
@pytest.mark.parametrize('request_size', [1, 10, 50, 100])
def test_integration(request_size: int):
docs = DocumentArray(
[Document(text='just some random text here... | import subprocess
import pytest
from flair_text import FlairTextEncoder
from jina import Document, DocumentArray, Flow
_EMBEDDING_DIM = 100
@pytest.mark.parametrize('request_size', [1, 10, 50, 100])
def test_integration(request_size: int):
docs = DocumentArray(
[Document(text='just some random text here... |
# Copyright (c) OpenMMLab. All rights reserved.
import math
from typing import Sequence, Tuple
import torch
import torch.nn as nn
from mmcv.cnn import ConvModule
from mmengine.model import BaseModule
from mmdet.registry import MODELS
from mmdet.utils import OptMultiConfig
@MODELS.register_module()
class CTResNetNec... | # Copyright (c) OpenMMLab. All rights reserved.
import math
from typing import Sequence, Tuple
import torch
import torch.nn as nn
from mmcv.cnn import ConvModule
from mmengine.model import BaseModule
from mmdet.core.utils import OptMultiConfig
from mmdet.registry import MODELS
@MODELS.register_module()
class CTResN... |
"""
This example uses average word embeddings (for example from GloVe). It adds two fully-connected feed-forward layers (dense layers) to create a Deep Averaging Network (DAN).
If 'glove.6B.300d.txt.gz' does not exist, it tries to download it from our server.
See https://public.ukp.informatik.tu-darmstadt.de/reimers/... | """
This example uses average word embeddings (for example from GloVe). It adds two fully-connected feed-forward layers (dense layers) to create a Deep Averaging Network (DAN).
If 'glove.6B.300d.txt.gz' does not exist, it tries to download it from our server.
See https://public.ukp.informatik.tu-darmstadt.de/reimers/... |
from docarray.documents.text import Text
def test_text_document_operators():
doc = Text(text='text', url='url.com')
assert doc == 'text'
assert doc != 'url.com'
doc2 = Text(id=doc.id, text='text', url='url.com')
assert doc == doc2
doc3 = Text(id='other-id', text='text', url='url.com')
... | from docarray.documents.text import Text
def test_text_document_operators():
doc = Text(text='text', url='url.com')
assert doc == 'text'
assert doc != 'url.com'
doc2 = Text(id=doc.id, text='text', url='url.com')
assert doc == doc2
doc3 = Text(id='other-id', text='text', url='url.com')
... |
_base_ = './retinanet_r50-caffe_fpn_ms-3x_coco.py'
# learning policy
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(
type='Pretrained',
checkpoint='open-mmlab://detectron2/resnet101_caffe')))
| _base_ = './retinanet_r50-caffe_fpn_ms-3x_coco.py'
# learning policy
model = dict(
pretrained='open-mmlab://detectron2/resnet101_caffe',
backbone=dict(depth=101))
|
from llama_index_instrumentation.span import active_span_id
from llama_index_instrumentation.span.base import BaseSpan
from llama_index_instrumentation.span.simple import SimpleSpan
__all__ = ["BaseSpan", "SimpleSpan", "active_span_id"]
| from contextvars import ContextVar
from typing import Optional
from llama_index.core.instrumentation.span.base import BaseSpan
from llama_index.core.instrumentation.span.simple import SimpleSpan
# ContextVar for managing active spans
active_span_id: ContextVar[Optional[str]] = ContextVar("active_span_id", default=Non... |
# 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/datasets/detection/coco/'
# Method 2: Us... | # 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_client_args = dict(backend='disk')
tra... |
# Copyright 2024 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... | # Copyright 2024 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... |
__version__ = '0.30.1'
import logging
from docarray.array import DocList, DocVec
from docarray.base_doc.doc import BaseDoc
__all__ = ['BaseDoc', 'DocList', 'DocVec']
logger = logging.getLogger('docarray')
handler = logging.StreamHandler()
formatter = logging.Formatter("%(levelname)s - %(name)s - %(message)s")
hand... | __version__ = '0.30.0'
import logging
from docarray.array import DocList, DocVec
from docarray.base_doc.doc import BaseDoc
__all__ = ['BaseDoc', 'DocList', 'DocVec']
logger = logging.getLogger('docarray')
handler = logging.StreamHandler()
formatter = logging.Formatter("%(levelname)s - %(name)s - %(message)s")
hand... |
# Copyright (c) OpenMMLab. All rights reserved.
from .accuracy import Accuracy, accuracy
from .ae_loss import AssociativeEmbeddingLoss
from .balanced_l1_loss import BalancedL1Loss, balanced_l1_loss
from .cross_entropy_loss import (CrossEntropyLoss, binary_cross_entropy,
cross_entropy, m... | # Copyright (c) OpenMMLab. All rights reserved.
from .accuracy import Accuracy, accuracy
from .ae_loss import AssociativeEmbeddingLoss
from .balanced_l1_loss import BalancedL1Loss, balanced_l1_loss
from .cross_entropy_loss import (CrossEntropyLoss, binary_cross_entropy,
cross_entropy, m... |
from collections.abc import Sequence
from inspect import signature
from typing import Optional, Union
from langchain_core.callbacks import Callbacks
from langchain_core.documents import (
BaseDocumentCompressor,
BaseDocumentTransformer,
Document,
)
from pydantic import ConfigDict
class DocumentCompressor... | from collections.abc import Sequence
from inspect import signature
from typing import Optional, Union
from langchain_core.callbacks.manager import Callbacks
from langchain_core.documents import (
BaseDocumentCompressor,
BaseDocumentTransformer,
Document,
)
from pydantic import ConfigDict
class DocumentCo... |
from __future__ import annotations
from typing import TYPE_CHECKING, Any, Optional, Tuple, Type
from langchain_core.tools import BaseTool
from langchain_core.utils import guard_import
from pydantic import model_validator
if TYPE_CHECKING:
from playwright.async_api import Browser as AsyncBrowser
from playwrig... | from __future__ import annotations
from typing import TYPE_CHECKING, Any, Optional, Tuple, Type
from langchain_core.tools import BaseTool
from langchain_core.utils import guard_import
from pydantic import model_validator
if TYPE_CHECKING:
from playwright.async_api import Browser as AsyncBrowser
from playwrig... |
import os
from source_separation.utils.dataset import wsj0mix
from torchaudio_unittest.common_utils import get_whitenoise, normalize_wav, save_wav, TempDirMixin, TorchaudioTestCase
_FILENAMES = [
"012c0207_1.9952_01cc0202_-1.9952.wav",
"01co0302_1.63_014c020q_-1.63.wav",
"01do0316_0.24011_205a0104_-0.240... | import os
from source_separation.utils.dataset import wsj0mix
from torchaudio_unittest.common_utils import (
get_whitenoise,
normalize_wav,
save_wav,
TempDirMixin,
TorchaudioTestCase,
)
_FILENAMES = [
"012c0207_1.9952_01cc0202_-1.9952.wav",
"01co0302_1.63_014c020q_-1.63.wav",
"01do031... |
# Owner(s): ["module: dynamo"]
import torch
import torch._dynamo
import torch._dynamo.test_case
@torch._dynamo.config.patch("capture_scalar_outputs", True)
class ViewTests(torch._dynamo.test_case.TestCase):
def test_view_to_2d(self):
@torch.compile(fullgraph=True, backend="eager")
def f(t, _u0):
... | # Owner(s): ["module: dynamo"]
import torch
import torch._dynamo
import torch._dynamo.test_case
@torch._dynamo.config.patch("capture_scalar_outputs", True)
class ViewTests(torch._dynamo.test_case.TestCase):
def test_view_to_2d(self):
@torch.compile(fullgraph=True, backend="eager")
def f(t, _u0):
... |
import pathlib
from argparse import ArgumentParser
import sentencepiece as spm
from lightning import ConformerRNNTModule
from pytorch_lightning import seed_everything, Trainer
from pytorch_lightning.callbacks import LearningRateMonitor, ModelCheckpoint
from pytorch_lightning.plugins import DDPPlugin
from transforms i... | import pathlib
from argparse import ArgumentParser
import sentencepiece as spm
from lightning import ConformerRNNTModule
from pytorch_lightning import seed_everything, Trainer
from pytorch_lightning.callbacks import LearningRateMonitor, ModelCheckpoint
from pytorch_lightning.plugins import DDPPlugin
from transforms i... |
from typing import Any, Optional
from langchain_core.runnables.base import RunnableBindingBase
from langchain_core.runnables.utils import Input, Output
class HubRunnable(RunnableBindingBase[Input, Output]):
"""
An instance of a runnable stored in the LangChain Hub.
"""
owner_repo_commit: str
de... | from typing import Any, Optional
from langchain_core.runnables.base import Input, Output, RunnableBindingBase
class HubRunnable(RunnableBindingBase[Input, Output]):
"""
An instance of a runnable stored in the LangChain Hub.
"""
owner_repo_commit: str
def __init__(
self,
owner_re... |
# Copyright (c) OpenMMLab. All rights reserved.
from .hook import Hook
from .iter_timer_hook import IterTimerHook
from .sampler_seed_hook import DistSamplerSeedHook
from .param_scheduler_hook import ParamSchedulerHook
__all__ = [
'Hook', 'IterTimerHook', 'DistSamplerSeedHook', 'ParamSchedulerHook'
]
| # Copyright (c) OpenMMLab. All rights reserved.
from .hook import Hook
from .iter_timer_hook import IterTimerHook
from .sampler_seed_hook import DistSamplerSeedHook
__all__ = ['Hook', 'IterTimerHook', 'DistSamplerSeedHook']
|
_base_ = './fcos_hrnetv2p-w32-gn-head_ms-640-800-4xb4-2x_coco.py'
model = dict(
backbone=dict(
extra=dict(
stage2=dict(num_channels=(18, 36)),
stage3=dict(num_channels=(18, 36, 72)),
stage4=dict(num_channels=(18, 36, 72, 144))),
init_cfg=dict(
type='Pr... | _base_ = './fcos_hrnetv2p_w32_gn-head_mstrain_640-800_4x4_2x_coco.py'
model = dict(
backbone=dict(
extra=dict(
stage2=dict(num_channels=(18, 36)),
stage3=dict(num_channels=(18, 36, 72)),
stage4=dict(num_channels=(18, 36, 72, 144))),
init_cfg=dict(
type... |
from typing import Dict, Optional, Tuple
import torch
import torchaudio
from torchaudio.backend.common import AudioMetaData
# Note: need to comply TorchScript syntax -- need annotation and no f-string nor global
def _info_audio(
s: torch.classes.torchaudio.ffmpeg_StreamReader,
):
i = s.find_best_audio_stream... | from typing import Dict, Optional, Tuple
import torch
import torchaudio
from torchaudio.backend.common import AudioMetaData
# Note: need to comply TorchScript syntax -- need annotation and no f-string nor global
def _info_audio(
s: torch.classes.torchaudio.ffmpeg_StreamReader,
):
i = s.find_best_audio_stream... |
from pathlib import Path
import pytest
from torchaudio.datasets import dr_vctk
from torchaudio_unittest.common_utils import get_whitenoise, save_wav, TempDirMixin, TorchaudioTestCase
_SUBSETS = ["train", "test"]
_CONDITIONS = ["clean", "device-recorded"]
_SOURCES = ["DR-VCTK_Office1_ClosedWindow", "DR-VCTK_Office1_O... | from pathlib import Path
import pytest
from torchaudio.datasets import dr_vctk
from torchaudio_unittest.common_utils import (
get_whitenoise,
save_wav,
TempDirMixin,
TorchaudioTestCase,
)
_SUBSETS = ["train", "test"]
_CONDITIONS = ["clean", "device-recorded"]
_SOURCES = ["DR-VCTK_Office1_ClosedWindow... |
"""
This is a simple application for sentence embeddings: clustering
Sentences are mapped to sentence embeddings and then agglomerative clustering with a threshold is applied.
"""
from sentence_transformers import SentenceTransformer
from sklearn.cluster import AgglomerativeClustering
embedder = SentenceTransformer(... | """
This is a simple application for sentence embeddings: clustering
Sentences are mapped to sentence embeddings and then agglomerative clustering with a threshold is applied.
"""
from sentence_transformers import SentenceTransformer
from sklearn.cluster import AgglomerativeClustering
import numpy as np
embedder = S... |
from typing import Optional
from docarray import Document, DocumentArray
from pydantic import BaseModel
from uvicorn import Config, Server
from jina import Gateway, __default_host__
from jina.clients.request import request_generator
class DummyResponseModel(BaseModel):
arg1: Optional[str]
arg2: Optional[str... | from typing import Optional
from docarray import Document, DocumentArray
from pydantic import BaseModel
from uvicorn import Config, Server
from jina import Gateway, __default_host__
from jina.clients.request import request_generator
class DummyResponseModel(BaseModel):
arg1: Optional[str]
arg2: Optional[str... |
# 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 TOOD(SingleStageDetector):
r"""Implementation of `TOOD: Task-aligned One-stage Object Det... | # Copyright (c) OpenMMLab. All rights reserved.
from mmdet.registry import MODELS
from .single_stage import SingleStageDetector
@MODELS.register_module()
class TOOD(SingleStageDetector):
r"""Implementation of `TOOD: Task-aligned One-stage Object Detection.
<https://arxiv.org/abs/2108.07755>`_."""
def __i... |
# Copyright (c) OpenMMLab. All rights reserved.
__version__ = '2.25.0'
short_version = __version__
def parse_version_info(version_str):
version_info = []
for x in version_str.split('.'):
if x.isdigit():
version_info.append(int(x))
elif x.find('rc') != -1:
patch_version... | # Copyright (c) OpenMMLab. All rights reserved.
__version__ = '2.24.1'
short_version = __version__
def parse_version_info(version_str):
version_info = []
for x in version_str.split('.'):
if x.isdigit():
version_info.append(int(x))
elif x.find('rc') != -1:
patch_version... |
from contextlib import contextmanager
from threading import Lock
from typing import TYPE_CHECKING, Any
from expiringdict import ExpiringDict
if TYPE_CHECKING:
from redis import Redis
from redis.lock import Lock as RedisLock
class RedisKeyedMutex:
"""
This class provides a mutex that can be locked an... | from contextlib import contextmanager
from threading import Lock
from typing import TYPE_CHECKING, Any
from expiringdict import ExpiringDict
if TYPE_CHECKING:
from redis import Redis
from redis.lock import Lock as RedisLock
class RedisKeyedMutex:
"""
This class provides a mutex that can be locked an... |
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