input stringlengths 33 5k | output stringlengths 32 5k |
|---|---|
import requests
from packaging import version
from typing import Sequence, Union, List, Optional
from llama_index.core.base.llms.types import (
ChatMessage,
ChatResponse,
)
from tgi.types import (
Message,
)
def resolve_tgi_function_call(url: str) -> bool:
url = f"{url}/info"
model_info = dict(req... | import requests
from packaging import version
from typing import Sequence, Union, List, Optional
from llama_index.core.base.llms.types import (
ChatMessage,
ChatResponse,
)
from text_generation.types import (
Message,
)
def resolve_tgi_function_call(url: str) -> bool:
url = f"{url}/info"
model_inf... |
_INITIALIZED = False
_LAZILY_IMPORTED = [
"CTCHypothesis",
"CTCDecoder",
"CTCDecoderLM",
"CTCDecoderLMState",
"ctc_decoder",
"download_pretrained_files",
]
def __getattr__(name: str):
if name in _LAZILY_IMPORTED:
try:
from . import _ctc_decoder
except AttributeE... | _INITIALIZED = False
_LAZILY_IMPORTED = [
"CTCHypothesis",
"CTCDecoder",
"ctc_decoder",
"download_pretrained_files",
]
def __getattr__(name: str):
if name in _LAZILY_IMPORTED:
try:
from . import _ctc_decoder
except AttributeError as err:
raise RuntimeError(
... |
from keras.src import ops
from keras.src.api_export import keras_export
from keras.src.layers.layer import Layer
@keras_export("keras.layers.UnitNormalization")
class UnitNormalization(Layer):
"""Unit normalization layer.
Normalize a batch of inputs so that each input in the batch has a L2 norm
equal to ... | from keras.src import ops
from keras.src.api_export import keras_export
from keras.src.layers.layer import Layer
@keras_export("keras.layers.UnitNormalization")
class UnitNormalization(Layer):
"""Unit normalization layer.
Normalize a batch of inputs so that each input in the batch has a L2 norm
equal to ... |
import os.path
from pathlib import Path
from typing import Any, Callable, Optional, Tuple, Union
import numpy as np
from PIL import Image
from .utils import check_integrity, download_url
from .vision import VisionDataset
class SEMEION(VisionDataset):
r"""`SEMEION <http://archive.ics.uci.edu/ml/datasets/semeion+... | import os.path
from typing import Any, Callable, Optional, Tuple
import numpy as np
from PIL import Image
from .utils import check_integrity, download_url
from .vision import VisionDataset
class SEMEION(VisionDataset):
r"""`SEMEION <http://archive.ics.uci.edu/ml/datasets/semeion+handwritten+digit>`_ Dataset.
... |
import numpy as np
import scipy.signal
from keras.src import backend
from keras.src import initializers
from keras.src import testing
class ConstantInitializersTest(testing.TestCase):
def test_zeros_initializer(self):
shape = (3, 3)
initializer = initializers.Zeros()
values = initializer... | import numpy as np
from keras.src import backend
from keras.src import initializers
from keras.src import testing
class ConstantInitializersTest(testing.TestCase):
def test_zeros_initializer(self):
shape = (3, 3)
initializer = initializers.Zeros()
values = initializer(shape=shape)
... |
from ...utils import is_torch_available
if is_torch_available():
from .auraflow_transformer_2d import AuraFlowTransformer2DModel
from .cogvideox_transformer_3d import CogVideoXTransformer3DModel
from .dit_transformer_2d import DiTTransformer2DModel
from .dual_transformer_2d import DualTransformer2DMod... | from ...utils import is_torch_available
if is_torch_available():
from .auraflow_transformer_2d import AuraFlowTransformer2DModel
from .cogvideox_transformer_3d import CogVideoXTransformer3DModel
from .dit_transformer_2d import DiTTransformer2DModel
from .dual_transformer_2d import DualTransformer2DMod... |
"""Callback Handler streams to stdout on new llm token."""
import sys
from typing import Any, Optional
from langchain_core.callbacks import StreamingStdOutCallbackHandler
DEFAULT_ANSWER_PREFIX_TOKENS = ["Final", "Answer", ":"]
class FinalStreamingStdOutCallbackHandler(StreamingStdOutCallbackHandler):
"""Callba... | """Callback Handler streams to stdout on new llm token."""
import sys
from typing import Any, Optional
from langchain_core.callbacks import StreamingStdOutCallbackHandler
DEFAULT_ANSWER_PREFIX_TOKENS = ["Final", "Answer", ":"]
class FinalStreamingStdOutCallbackHandler(StreamingStdOutCallbackHandler):
"""Callba... |
import time
from jina import Flow
from tests.integration.instrumentation import (
get_exported_jobs,
get_flow_metric_labels,
get_services,
)
def test_docker_instrumentation(
jaeger_port,
otlp_collector,
otlp_receiver_port,
docker_image_name,
docker_image_built,
prometheus_client,
... | import os
import time
import pytest
from jina import Flow
from tests.integration.instrumentation import (
get_exported_jobs,
get_flow_metric_labels,
get_services,
)
def test_docker_instrumentation(
jaeger_port,
otlp_collector,
otlp_receiver_port,
docker_image_name,
docker_image_built... |
import torch
import torchaudio.prototype.functional as F
from torchaudio_unittest.common_utils import nested_params, TorchaudioTestCase
class BatchConsistencyTest(TorchaudioTestCase):
@nested_params(
[F.convolve, F.fftconvolve],
["full", "valid", "same"],
)
def test_convolve(self, fn, mode... | import torch
import torchaudio.prototype.functional as F
from torchaudio_unittest.common_utils import nested_params, TorchaudioTestCase
class BatchConsistencyTest(TorchaudioTestCase):
@nested_params(
[F.convolve, F.fftconvolve],
["full", "valid", "same"],
)
def test_convolve(self, fn, mode... |
from abc import ABC, abstractmethod
from typing import Dict, List
import torch
import torchaudio.functional as F
from torch import Tensor
from torchaudio.functional import TokenSpan
class ITokenizer(ABC):
@abstractmethod
def __call__(self, transcript: List[str]) -> List[List[str]]:
"""Tokenize the gi... | from abc import ABC, abstractmethod
from typing import Dict, List
import torch
import torchaudio.functional as F
from torch import Tensor
from torchaudio.functional import TokenSpan
class ITokenizer(ABC):
@abstractmethod
def __call__(self, transcript: List[str]) -> List[List[str]]:
"""Tokenize the gi... |
from typing import Optional
from llama_index.core.readers.base import BasePydanticReader
from llama_index.core.schema import Document
from zyte_api import ZyteAPI
from zyte_api.utils import USER_AGENT as PYTHON_ZYTE_API_USER_AGENT
class ZyteSerpReader(BasePydanticReader):
"""
Get google search results URLs ... | from typing import Optional
from llama_index.core.readers.base import BasePydanticReader
from llama_index.core.schema import Document
from zyte_api import ZyteAPI
from zyte_api.utils import USER_AGENT as PYTHON_ZYTE_API_USER_AGENT
class ZyteSerpReader(BasePydanticReader):
"""Get google search results URLs for a... |
import itertools
import warnings
from dataclasses import dataclass
from typing import Optional
import pandas as pd
import pyarrow as pa
import datasets
from datasets.table import table_cast
@dataclass
class PandasConfig(datasets.BuilderConfig):
"""BuilderConfig for Pandas."""
features: Optional[datasets.Fe... | import itertools
from dataclasses import dataclass
from typing import Optional
import pandas as pd
import pyarrow as pa
import datasets
from datasets.table import table_cast
@dataclass
class PandasConfig(datasets.BuilderConfig):
"""BuilderConfig for Pandas."""
features: Optional[datasets.Features] = None
... |
from keras.src import backend
from keras.src.api_export import keras_export
from keras.src.dtype_policies import dtype_policy
from keras.src.dtype_policies.dtype_policy import QUANTIZATION_MODES
from keras.src.dtype_policies.dtype_policy import DTypePolicy
from keras.src.dtype_policies.dtype_policy import FloatDTypePol... | from keras.src import backend
from keras.src.api_export import keras_export
from keras.src.dtype_policies import dtype_policy
from keras.src.dtype_policies.dtype_policy import QUANTIZATION_MODES
from keras.src.dtype_policies.dtype_policy import DTypePolicy
from keras.src.dtype_policies.dtype_policy import FloatDTypePol... |
# 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... |
_base_ = './cascade-rcnn_r50_fpn_1x_coco.py'
model = dict(
# use caffe img_norm
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[103.530, 116.280, 123.675],
std=[1.0, 1.0, 1.0],
to_rgb=False,
pad_size_divisor=32),
backbone=dict(
norm_cfg=dict(require... | _base_ = './cascade_rcnn_r50_fpn_1x_coco.py'
model = dict(
# use caffe img_norm
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[103.530, 116.280, 123.675],
std=[1.0, 1.0, 1.0],
to_rgb=False,
pad_size_divisor=32),
backbone=dict(
norm_cfg=dict(require... |
import grpc
from grpc_health.v1 import health, health_pb2, health_pb2_grpc
from grpc_reflection.v1alpha import reflection
from pydantic import BaseModel
from uvicorn import Config, Server
from jina import Gateway, __default_host__
from jina.proto import jina_pb2, jina_pb2_grpc
class DummyResponseModel(BaseModel):
... | import grpc
from grpc_health.v1 import health, health_pb2, health_pb2_grpc
from grpc_reflection.v1alpha import reflection
from pydantic import BaseModel
from uvicorn import Config, Server
from jina import Gateway, __default_host__
from jina.proto import jina_pb2, jina_pb2_grpc
class DummyResponseModel(BaseModel):
... |
from jina import Executor, requests
class MyExecutorToReload1(Executor):
def __init__(self, **kwargs):
super().__init__(**kwargs)
@requests()
def foo(self, docs, **kwargs):
for doc in docs:
doc.text = 'MyExecutorBeforeReload'
@requests(on='/bar')
def bar(self, docs, *... | from jina import Executor, requests
class MyExecutorToReload1(Executor):
def __init__(self, **kwargs):
super().__init__(**kwargs)
@requests()
def foo(self, docs, **kwargs):
for doc in docs:
doc.text = 'MyExecutorBeforeReload'
|
import random
from pathlib import Path
from typing import Callable, Dict, Tuple
import opentelemetry.sdk.metrics.view
import pytest
from opentelemetry.sdk.metrics.export import (
AggregationTemporality,
MetricExporter,
MetricExportResult,
MetricsData,
PeriodicExportingMetricReader,
)
class DirMet... | import random
from pathlib import Path
from typing import Callable, Dict, Tuple
import opentelemetry.sdk.metrics.export
import opentelemetry.sdk.metrics.view
import pytest
from opentelemetry.sdk.metrics.export import (
AggregationTemporality,
MetricExporter,
MetricExportResult,
MetricsData,
Periodi... |
from typing import Any, Optional, Type, TypeVar, Union
import numpy as np
from docarray.base_document import BaseDocument
from docarray.documents import Audio
from docarray.typing import AnyEmbedding, AnyTensor
from docarray.typing.tensor.abstract_tensor import AbstractTensor
from docarray.typing.tensor.video.video_t... | from typing import Any, Optional, Type, TypeVar, Union
import numpy as np
from docarray.base_document import BaseDocument
from docarray.documents import Audio
from docarray.typing import AnyEmbedding, AnyTensor
from docarray.typing.tensor.abstract_tensor import AbstractTensor
from docarray.typing.tensor.video.video_t... |
import os
import pytest
from llama_index.llms.nvidia import NVIDIA
from typing import Any
from pytest_httpx import HTTPXMock
@pytest.fixture()
def mock_local_models(httpx_mock: HTTPXMock):
mock_response = {
"data": [
{
"id": "model1",
"object": "model",
... | import os
import pytest
from llama_index.llms.nvidia import NVIDIA
from typing import Any
from pytest_httpx import HTTPXMock
@pytest.fixture()
def mock_local_models(httpx_mock: HTTPXMock):
mock_response = {
"data": [
{
"id": "model1",
"object": "model",
... |
import glob
import os
import pytest
from jina import Document, Flow
from jina.constants import __uptime__, __windows__
from jina.enums import LogVerbosity
from jina.helper import colored
from jina.logging.logger import JinaLogger
cur_dir = os.path.dirname(os.path.abspath(__file__))
def log(logger: JinaLogger):
... | import glob
import os
from datetime import datetime
import pytest
from jina import Document, Flow, __uptime__, __windows__
from jina.enums import LogVerbosity
from jina.helper import colored
from jina.logging.logger import JinaLogger
cur_dir = os.path.dirname(os.path.abspath(__file__))
def log(logger: JinaLogger):... |
__copyright__ = 'Copyright (c) 2020-2021 Jina AI Limited. All rights reserved.'
__license__ = 'Apache-2.0'
from typing import Any, Iterable, Optional
import librosa as lr
import numpy as np
import torch
from jina import DocumentArray, Executor, requests
from jina.excepts import BadDocType
from .audio_clip.model impo... | __copyright__ = 'Copyright (c) 2020-2021 Jina AI Limited. All rights reserved.'
__license__ = 'Apache-2.0'
from typing import Optional, Iterable, Any
from jina import Executor, DocumentArray, requests
from jina.excepts import BadDocType
import librosa as lr
import numpy as np
import torch
from .audio_clip.model impo... |
_base_ = '../_base_/default_runtime.py'
# dataset settings
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
# file_client_args = dict(
# backend='petrel',
# path_mapping=dict({
# './data/': 's3://openmmlab/datasets/detection/',
# 'data/': 's3://openmmlab/datasets/detection/'
# }))
fil... | _base_ = '../_base_/default_runtime.py'
# dataset settings
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
# file_client_args = dict(
# backend='petrel',
# path_mapping=dict({
# './data/': 's3://openmmlab/datasets/detection/',
# 'data/': 's3://openmmlab/datasets/detection/'
# }))
fil... |
import csv
import os
from pathlib import Path
from typing import 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
_RELEASE_CONFIGS = {
"release1": {
"folder_in_arch... | import csv
import os
from pathlib import Path
from typing import 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
_RELEASE_CONFIGS = {
"release1": {
"folder_in_arch... |
import numpy as np
from docarray import Image
def test_image():
image = Image(url='http://jina.ai')
image.tensor = image.url.load()
assert isinstance(image.tensor, np.ndarray)
| import numpy as np
from docarray import Image
from docarray.typing import Tensor
def test_image():
image = Image(uri='http://jina.ai')
image.tensor = image.uri.load()
assert isinstance(image.tensor, np.ndarray)
|
"""Output parsers using Pydantic."""
import json
from typing import Annotated, Generic, Optional
import pydantic
from pydantic import SkipValidation
from typing_extensions import override
from langchain_core.exceptions import OutputParserException
from langchain_core.output_parsers import JsonOutputParser
from langc... | """Output parsers using Pydantic."""
import json
from typing import Annotated, Generic, Optional
import pydantic
from pydantic import SkipValidation
from typing_extensions import override
from langchain_core.exceptions import OutputParserException
from langchain_core.output_parsers import JsonOutputParser
from langc... |
from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.agent_toolkits.openapi.planner_prompt import (
API_CONTROLLER_PROMPT,
API_CONTROLLER_TOOL_DESCRIPTION,
API_CONTROLLER_TOOL_NAME,
API_ORCHESTRATOR_PROMPT,
... | from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.agent_toolkits.openapi.planner_prompt import (
API_CONTROLLER_PROMPT,
API_CONTROLLER_TOOL_DESCRIPTION,
API_CONTROLLER_TOOL_NAME,
API_ORCHESTRATOR_PROMPT,
... |
from __future__ import annotations
__version__ = "4.2.0.dev0"
__MODEL_HUB_ORGANIZATION__ = "sentence-transformers"
import importlib
import os
import warnings
from sentence_transformers.backend import (
export_dynamic_quantized_onnx_model,
export_optimized_onnx_model,
export_static_quantized_openvino_mode... | from __future__ import annotations
__version__ = "4.2.0.dev0"
__MODEL_HUB_ORGANIZATION__ = "sentence-transformers"
import importlib
import os
from sentence_transformers.backend import (
export_dynamic_quantized_onnx_model,
export_optimized_onnx_model,
export_static_quantized_openvino_model,
)
from senten... |
# Copyright (c) OpenMMLab. All rights reserved.
import torch
from mmcv.runner import force_fp32
from mmdet.registry import MODELS
from .base_roi_extractor import BaseRoIExtractor
@MODELS.register_module()
class SingleRoIExtractor(BaseRoIExtractor):
"""Extract RoI features from a single level feature map.
If... | # Copyright (c) OpenMMLab. All rights reserved.
import torch
from mmcv.runner import force_fp32
from mmdet.models.builder import ROI_EXTRACTORS
from .base_roi_extractor import BaseRoIExtractor
@ROI_EXTRACTORS.register_module()
class SingleRoIExtractor(BaseRoIExtractor):
"""Extract RoI features from a single leve... |
from __future__ import annotations
from pathlib import Path
from unittest.mock import Mock, PropertyMock
import pytest
import torch
from sentence_transformers import SentenceTransformer
from sentence_transformers.evaluation import InformationRetrievalEvaluator
from sentence_transformers.util import cos_sim
@pytest... | from __future__ import annotations
from pathlib import Path
from unittest.mock import Mock, PropertyMock
import pytest
import torch
from sentence_transformers import SentenceTransformer
from sentence_transformers.evaluation import InformationRetrievalEvaluator
from sentence_transformers.util import cos_sim
@pytest... |
# Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
import torch.nn.functional as F
from ..builder import LOSSES
from .utils import weighted_loss
@weighted_loss
def mse_loss(pred, target):
"""Warpper of mse loss."""
return F.mse_loss(pred, target, reduction='none')
@LOSSES.register_module... | import torch.nn as nn
import torch.nn.functional as F
from ..builder import LOSSES
from .utils import weighted_loss
@weighted_loss
def mse_loss(pred, target):
"""Warpper of mse loss."""
return F.mse_loss(pred, target, reduction='none')
@LOSSES.register_module()
class MSELoss(nn.Module):
"""MSELoss.
... |
from __future__ import annotations
import math
from pathlib import Path
import pytest
from tokenizers import Tokenizer
from sentence_transformers import SentenceTransformer
from sentence_transformers.models.StaticEmbedding import StaticEmbedding
try:
import model2vec
except ImportError:
model2vec = None
sk... | from __future__ import annotations
import math
from pathlib import Path
import numpy as np
import pytest
from packaging.version import Version, parse
from tokenizers import Tokenizer
from sentence_transformers import SentenceTransformer
from sentence_transformers.models.StaticEmbedding import StaticEmbedding
try:
... |
import datasets
_DESCRIPTION = """\
"""
_URL = "https://www.gutenberg.org/files/2554/2554-h/2554-h.htm"
_DATA_URL = "https://raw.githubusercontent.com/patrickvonplaten/datasets/master/crime_and_punishment.txt"
class CrimeAndPunish(datasets.GeneratorBasedBuilder):
def _info(self):
return datasets.Datase... | import datasets
_DESCRIPTION = """\
"""
_URL = "https://www.gutenberg.org/files/2554/2554-h/2554-h.htm"
_DATA_URL = "https://raw.githubusercontent.com/patrickvonplaten/datasets/master/crime_and_punishment.txt"
class CrimeAndPunishConfig(datasets.BuilderConfig):
"""BuilderConfig for Crime and Punish."""
de... |
__copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
from typing import Dict, Iterable, Optional
import torch
from jina import DocumentArray, Executor, requests
from sentence_transformers import SentenceTransformer
class TransformerSentenceEncoder(Executor):
... | __copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
from typing import Dict, Iterable, Optional
import torch
from jina import DocumentArray, Executor, requests
from jina_commons.batching import get_docs_batch_generator
from sentence_transformers import SentenceTr... |
# Copyright (c) OpenMMLab. All rights reserved.
from .base_det_dataset import BaseDetDataset
from .cityscapes import CityscapesDataset
from .coco import CocoDataset
from .coco_panoptic import CocoPanopticDataset
from .crowdhuman import CrowdHumanDataset
from .dataset_wrappers import MultiImageMixDataset
from .deepfashi... | # Copyright (c) OpenMMLab. All rights reserved.
from .base_det_dataset import BaseDetDataset
from .cityscapes import CityscapesDataset
from .coco import CocoDataset
from .coco_panoptic import CocoPanopticDataset
from .crowdhuman import CrowdHumanDataset
from .dataset_wrappers import MultiImageMixDataset
from .deepfashi... |
import os
from pathlib import Path
from torchaudio.datasets import cmuarctic
from torchaudio_unittest.common_utils import (
get_whitenoise,
normalize_wav,
save_wav,
TempDirMixin,
TorchaudioTestCase,
)
def get_mock_dataset(root_dir):
"""
root_dir: directory to the mocked dataset
"""
... | import os
from pathlib import Path
from torchaudio.datasets import cmuarctic
from torchaudio_unittest.common_utils import (
TempDirMixin,
TorchaudioTestCase,
get_whitenoise,
save_wav,
normalize_wav,
)
def get_mock_dataset(root_dir):
"""
root_dir: directory to the mocked dataset
"""
... |
from __future__ import annotations
from .model_card import SparseEncoderModelCardData
from .SparseEncoder import SparseEncoder
from .trainer import SparseEncoderTrainer
from .training_args import SparseEncoderTrainingArguments
__all__ = [
"SparseEncoder",
"SparseEncoderTrainer",
"SparseEncoderTrainingArgu... | from __future__ import annotations
from .model_card import SparseEncoderModelCardData
from .SparseEncoder import SparseEncoder
from .trainer import SparseEncoderTrainer
from .training_args import SparseEncoderTrainingArguments
__all__ = [
"SparseEncoder",
"SparseEncoderTrainer",
"SparseEncoderTrainingArgu... |
"""Script to check if python modules can be imported."""
import random
import string
import sys
import traceback
from importlib.machinery import SourceFileLoader
if __name__ == "__main__":
files = sys.argv[1:]
has_failure = False
for file in files:
try:
module_name = "".join(
... | import random
import string
import sys
import traceback
from importlib.machinery import SourceFileLoader
if __name__ == "__main__":
files = sys.argv[1:]
has_failure = False
for file in files:
try:
module_name = "".join(
random.choice(string.ascii_letters)
... |
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
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... |
"""DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.utils.sequence_utils import pad_sequences as pad_sequences
| """DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.utils.sequence_utils import pad_sequences
|
# Copyright (c) OpenMMLab. All rights reserved.
from typing import Union
import torch
from torch import Tensor
from mmdet.registry import TASK_UTILS
from mmdet.structures.bbox import BaseBoxes, HorizontalBoxes, get_box_tensor
from .base_bbox_coder import BaseBBoxCoder
@TASK_UTILS.register_module()
class YOLOBBoxCod... | # Copyright (c) OpenMMLab. All rights reserved.
import torch
from mmdet.registry import TASK_UTILS
from mmdet.structures.bbox import HorizontalBoxes, get_box_tensor
from .base_bbox_coder import BaseBBoxCoder
@TASK_UTILS.register_module()
class YOLOBBoxCoder(BaseBBoxCoder):
"""YOLO BBox coder.
Following `YOL... |
# Copyright (c) OpenMMLab. All rights reserved.
"""MMEngine provides 20 root registries to support using modules across
projects.
More datails can be found at
https://mmengine.readthedocs.io/en/latest/advanced_tutorials/registry.html.
"""
from .build_functions import (build_model_from_cfg, build_runner_from_cfg,
... | # Copyright (c) OpenMMLab. All rights reserved.
"""MMEngine provides 20 root registries to support using modules across
projects.
More datails can be found at
https://mmengine.readthedocs.io/en/latest/advanced_tutorials/registry.html.
"""
from .build_functions import (build_model_from_cfg, build_runner_from_cfg,
... |
from unittest.mock import MagicMock, patch
import pytest
from llama_index.core.llms import ChatMessage, MessageRole
from llama_index.llms.huggingface import HuggingFaceInferenceAPI
STUB_MODEL_NAME = "placeholder_model"
@pytest.fixture(name="hf_inference_api")
def fixture_hf_inference_api() -> HuggingFaceInferenceAP... | from unittest.mock import MagicMock, patch
import pytest
from llama_index.core.llms import ChatMessage, MessageRole
from llama_index.llms.huggingface import HuggingFaceInferenceAPI
STUB_MODEL_NAME = "placeholder_model"
@pytest.fixture(name="hf_inference_api")
def fixture_hf_inference_api() -> HuggingFaceInferenceAP... |
# Copyright (c) OpenMMLab. All rights reserved.
from .default_scope import DefaultScope
from .registry import Registry, build_from_cfg
from .root import (DATA_SAMPLERS, DATASETS, HOOKS, LOG_PROCESSORS, LOOPS,
METRICS, MODEL_WRAPPERS, MODELS, OPTIM_WRAPPER_CONSTRUCTORS,
OPTIM_WRAPPE... | # Copyright (c) OpenMMLab. All rights reserved.
from .default_scope import DefaultScope
from .registry import Registry, build_from_cfg
from .root import (DATA_SAMPLERS, DATASETS, HOOKS, LOG_PROCESSORS, LOOPS,
METRICS, MODEL_WRAPPERS, MODELS, OPTIMIZER_CONSTRUCTORS,
OPTIMIZERS, PARA... |
_base_ = '../mask_rcnn/mask-rcnn_x101-32x4d_fpn_1x_coco.py'
model = dict(
backbone=dict(
norm_cfg=dict(type='SyncBN', requires_grad=True),
norm_eval=False,
plugins=[
dict(
cfg=dict(type='ContextBlock', ratio=1. / 4),
stages=(False, True, True, True... | _base_ = '../mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco.py'
model = dict(
backbone=dict(
norm_cfg=dict(type='SyncBN', requires_grad=True),
norm_eval=False,
plugins=[
dict(
cfg=dict(type='ContextBlock', ratio=1. / 4),
stages=(False, True, True, True... |
# Copyright (c) OpenMMLab. All rights reserved.
from .amp_optimizer_wrapper import AmpOptimWrapper
from .builder import (OPTIM_WRAPPER_CONSTRUCTORS, OPTIMIZERS,
build_optim_wrapper)
from .default_constructor import DefaultOptimWrapperConstructor
from .optimizer_wrapper import OptimWrapper
from .op... | # Copyright (c) OpenMMLab. All rights reserved.
from .builder import OPTIMIZER_CONSTRUCTORS, OPTIMIZERS, build_optimizer
from .default_constructor import DefaultOptimizerConstructor
__all__ = [
'OPTIMIZER_CONSTRUCTORS', 'OPTIMIZERS', 'DefaultOptimizerConstructor',
'build_optimizer'
]
|
_base_ = [
'../_base_/models/faster-rcnn_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
data_preprocessor=dict(
# The mean and std are used in PyCls when training RegNets
mean=[103.53, 116.28, 123.... | _base_ = [
'../_base_/models/faster_rcnn_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
data_preprocessor=dict(
# The mean and std are used in PyCls when training RegNets
mean=[103.53, 116.28, 123.... |
import json
from pathlib import Path
import yaml
from llama_index.core.tools.tool_spec.base import BaseToolSpec
from llama_index.tools.openapi import OpenAPIToolSpec
def test_class():
names_of_base_classes = [b.__name__ for b in OpenAPIToolSpec.__mro__]
assert BaseToolSpec.__name__ in names_of_base_classes
... | from llama_index.core.tools.tool_spec.base import BaseToolSpec
from llama_index.tools.openapi import OpenAPIToolSpec
def test_class():
names_of_base_classes = [b.__name__ for b in OpenAPIToolSpec.__mro__]
assert BaseToolSpec.__name__ in names_of_base_classes
|
from langchain_core.utils.html import (
DEFAULT_LINK_REGEX,
PREFIXES_TO_IGNORE,
PREFIXES_TO_IGNORE_REGEX,
SUFFIXES_TO_IGNORE,
SUFFIXES_TO_IGNORE_REGEX,
extract_sub_links,
find_all_links,
)
__all__ = [
"DEFAULT_LINK_REGEX",
"PREFIXES_TO_IGNORE",
"PREFIXES_TO_IGNORE_REGEX",
"S... | from langchain_core.utils.html import (
DEFAULT_LINK_REGEX,
PREFIXES_TO_IGNORE,
PREFIXES_TO_IGNORE_REGEX,
SUFFIXES_TO_IGNORE,
SUFFIXES_TO_IGNORE_REGEX,
extract_sub_links,
find_all_links,
)
__all__ = [
"PREFIXES_TO_IGNORE",
"SUFFIXES_TO_IGNORE",
"SUFFIXES_TO_IGNORE_REGEX",
"P... |
_base_ = './mask_rcnn_r50_fpn_1x_coco.py'
model = dict(
# use caffe img_norm
data_preprocessor=dict(
mean=[103.530, 116.280, 123.675],
std=[1.0, 1.0, 1.0],
bgr_to_rgb=False),
backbone=dict(
norm_cfg=dict(requires_grad=False),
style='caffe',
init_cfg=dict(
... | _base_ = './mask_rcnn_r50_fpn_1x_coco.py'
data_preprocessor = dict(
mean=[103.530, 116.280, 123.675],
std=[1.0, 1.0, 1.0],
bgr_to_rgb=False,
pad_size_divisor=32)
model = dict(
# use caffe img_norm
data_preprocessor=data_preprocessor,
backbone=dict(
norm_cfg=dict(requires_grad=False),... |
from backend.data.credit import UsageTransactionMetadata, get_user_credit_model
from backend.data.execution import (
ExecutionResult,
RedisExecutionEventBus,
create_graph_execution,
get_execution_results,
get_incomplete_executions,
get_latest_execution,
update_execution_status,
update_ex... | from backend.data.credit import get_user_credit_model
from backend.data.execution import (
ExecutionResult,
NodeExecutionEntry,
RedisExecutionEventBus,
create_graph_execution,
get_execution_results,
get_incomplete_executions,
get_latest_execution,
update_execution_status,
update_exec... |
__copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import subprocess
import pytest
from jina import Document, Flow
def data_generator(num_docs):
for i in range(num_docs):
doc = Document(text='it is a good day! the dog sits on the floor.')
y... | __copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
from jina import Document, Flow
def data_generator(num_docs):
for i in range(num_docs):
doc = Document(text='it is a good day! the dog sits on the floor.')
yield doc
def test_use_in_flow()... |
_base_ = '../cascade_rcnn/cascade-mask-rcnn_r50_fpn_1x_coco.py'
train_pipeline = [
dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}),
dict(
type='InstaBoost',
action_candidate=('normal', 'horizontal', 'skip'),
action_prob=(1, 0, 0),
scale=(0.8, 1.2),
d... | _base_ = '../cascade_rcnn/cascade-mask-rcnn_r50_fpn_1x_coco.py'
train_pipeline = [
dict(
type='LoadImageFromFile',
file_client_args={{_base_.file_client_args}}),
dict(
type='InstaBoost',
action_candidate=('normal', 'horizontal', 'skip'),
action_prob=(1, 0, 0),
sc... |
"""
MangaDex info reader.
Retrieves data about a particular manga by title.
"""
import logging
from typing import List
import requests
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document
logger = logging.getLogger(__name__)
class MangaDexReader(BaseReader):
def __... | """
MangaDex info reader.
Retrieves data about a particular manga by title.
"""
import logging
from typing import List
import requests
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document
logger = logging.getLogger(__name__)
class MangaDexReader(BaseReader):
def __... |
"""Elasticsearch (or Opensearch) reader over REST api.
This only uses the basic search api, so it will work with Elasticsearch and Opensearch.
"""
from typing import Any, List, Optional
from llama_index.core.bridge.pydantic import PrivateAttr
from llama_index.core.readers.base import BasePydanticReader
from llama_... | """Elasticsearch (or Opensearch) reader over REST api.
This only uses the basic search api, so it will work with Elasticsearch and Opensearch.
"""
from typing import Any, List, Optional
from llama_index.core.bridge.pydantic import PrivateAttr
from llama_index.core.readers.base import BasePydanticReader
from llama_... |
"""Init file."""
from llama_index.readers.mangadex.base import MangaDexReader
__all__ = ["MangaDexReader"]
| """Init file."""
from llama_index.readers.mangadex.base import MangaDexReader
__all__ = ["MangaDexReader"]
|
# 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 (c) OpenMMLab. All rights reserved.
import torch
from mmdet.core import build_assigner, build_sampler
def _dummy_bbox_sampling(proposal_list, gt_bboxes, gt_labels):
"""Create sample results that can be passed to BBoxHead.get_targets."""
num_imgs = 1
feat = torch.rand(1, 1, 3, 3)
assign_co... | import torch
from mmdet.core import build_assigner, build_sampler
def _dummy_bbox_sampling(proposal_list, gt_bboxes, gt_labels):
"""Create sample results that can be passed to BBoxHead.get_targets."""
num_imgs = 1
feat = torch.rand(1, 1, 3, 3)
assign_config = dict(
type='MaxIoUAssigner',
... |
_base_ = [
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# model settings
model = dict(
type='SOLOv2',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],... | _base_ = [
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# model settings
model = dict(
type='SOLOv2',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,... |
# Copyright (c) OpenMMLab. All rights reserved.
from mmengine.utils import digit_version
from mmengine.utils.dl_utils import TORCH_VERSION
from .base import BaseStrategy
from .deepspeed import DeepSpeedStrategy
from .distributed import DDPStrategy
from .single_device import SingleDeviceStrategy
__all__ = [
'BaseSt... | # Copyright (c) OpenMMLab. All rights reserved.
from mmengine.utils import digit_version, is_installed
from mmengine.utils.dl_utils import TORCH_VERSION
from .base import BaseStrategy
from .distributed import DDPStrategy
from .single_device import SingleDeviceStrategy
__all__ = ['BaseStrategy', 'DDPStrategy', 'SingleD... |
"""Schema for Blobs and Blob Loaders.
The goal is to facilitate decoupling of content loading from content parsing code.
In addition, content loading code should provide a lazy loading interface by default.
"""
from __future__ import annotations
from abc import ABC, abstractmethod
from typing import TYPE_CHECKING
... | """Schema for Blobs and Blob Loaders.
The goal is to facilitate decoupling of content loading from content parsing code.
In addition, content loading code should provide a lazy loading interface by default.
"""
from __future__ import annotations
from abc import ABC, abstractmethod
from collections.abc import Iterab... |
import hashlib
import secrets
from typing import NamedTuple
class APIKeyContainer(NamedTuple):
"""Container for API key parts."""
raw: str
prefix: str
postfix: str
hash: str
class APIKeyManager:
PREFIX: str = "agpt_"
PREFIX_LENGTH: int = 8
POSTFIX_LENGTH: int = 8
def generate_a... | import hashlib
import secrets
from typing import NamedTuple
class APIKeyContainer(NamedTuple):
"""Container for API key parts."""
raw: str
prefix: str
postfix: str
hash: str
class APIKeyManager:
PREFIX: str = "agpt_"
PREFIX_LENGTH: int = 8
POSTFIX_LENGTH: int = 8
def generate_a... |
from .filtering import (
allpass_biquad,
band_biquad,
bandpass_biquad,
bandreject_biquad,
bass_biquad,
biquad,
contrast,
dcshift,
deemph_biquad,
dither,
equalizer_biquad,
filtfilt,
flanger,
gain,
highpass_biquad,
lfilter,
lowpass_biquad,
overdrive,... | from .filtering import (
allpass_biquad,
band_biquad,
bandpass_biquad,
bandreject_biquad,
bass_biquad,
biquad,
contrast,
dither,
dcshift,
deemph_biquad,
equalizer_biquad,
filtfilt,
flanger,
gain,
highpass_biquad,
lfilter,
lowpass_biquad,
overdrive,... |
from sentence_transformers import models
from sentence_transformers.sparse_encoder import SparseEncoder
from sentence_transformers.sparse_encoder.models import IDF, MLMTransformer, SpladePooling
print("# ------------------------------------------example with v2 distill-----------------------------------------")
doc_en... | from sentence_transformers import models
from sentence_transformers.sparse_encoder import SparseEncoder
from sentence_transformers.sparse_encoder.models import IDF, MLMTransformer, SpladePooling
print("# ------------------------------------------example with v2 distill-----------------------------------------")
doc_en... |
from typing import Optional
from docarray.document import BaseDocument
from docarray.typing.tensor.embedding import Embedding, Tensor
class Text(BaseDocument):
"""
base Document for Text handling
"""
text: str = ''
tensor: Optional[Tensor]
embedding: Optional[Embedding]
| from typing import Optional
from docarray.document import BaseDocument
from docarray.typing.embedding import Embedding, Tensor
class Text(BaseDocument):
"""
base Document for Text handling
"""
text: str = ''
tensor: Optional[Tensor]
embedding: Optional[Embedding]
|
from pathlib import Path
from typing import Any, Callable, Optional, Tuple, Union
import PIL.Image
from .utils import check_integrity, download_and_extract_archive, download_url, verify_str_arg
from .vision import VisionDataset
class Flowers102(VisionDataset):
"""`Oxford 102 Flower <https://www.robots.ox.ac.uk/... | from pathlib import Path
from typing import Any, Callable, Optional, Tuple
import PIL.Image
from .utils import check_integrity, download_and_extract_archive, download_url, verify_str_arg
from .vision import VisionDataset
class Flowers102(VisionDataset):
"""`Oxford 102 Flower <https://www.robots.ox.ac.uk/~vgg/da... |
__version__ = '0.39.0'
import logging
from docarray.array import DocList, DocVec
from docarray.base_doc.doc import BaseDoc
from docarray.utils._internal.misc import _get_path_from_docarray_root_level
__all__ = ['BaseDoc', 'DocList', 'DocVec']
logger = logging.getLogger('docarray')
handler = logging.StreamHandler()... | __version__ = '0.38.1'
import logging
from docarray.array import DocList, DocVec
from docarray.base_doc.doc import BaseDoc
from docarray.utils._internal.misc import _get_path_from_docarray_root_level
__all__ = ['BaseDoc', 'DocList', 'DocVec']
logger = logging.getLogger('docarray')
handler = logging.StreamHandler()... |
import os
from typing import Any
import numpy as np
import pytest
from scipy import sparse
from jina import Document, DocumentArray, Executor, Flow, requests
from tests import validate_callback
cur_dir = os.path.dirname(os.path.abspath(__file__))
TOP_K = 3
@pytest.fixture(scope='function')
def num_docs():
retu... | from typing import Any
import os
import pytest
import numpy as np
from scipy import sparse
from jina import Flow, Document, DocumentArray, requests, Executor
from tests import validate_callback
cur_dir = os.path.dirname(os.path.abspath(__file__))
TOP_K = 3
@pytest.fixture(scope='function')
def num_docs():
ret... |
from typing import Any, ForwardRef, Optional
from typing_extensions import get_origin
from typing_inspect import get_args, is_typevar, is_union_type
from docarray.typing.id import ID
from docarray.typing.tensor.abstract_tensor import AbstractTensor
def is_type_tensor(type_: Any) -> bool:
"""Return True if type ... | from typing import Any, ForwardRef, Optional
from typing_extensions import get_origin
from typing_inspect import get_args, is_typevar, is_union_type
from docarray.typing.id import ID
from docarray.typing.tensor.abstract_tensor import AbstractTensor
def is_type_tensor(type_: Any) -> bool:
"""Return True if type ... |
from typing import Optional, List
import numpy as np
from docarray import BaseDoc, DocList
from docarray.typing import NdArray
from docarray.typing.bytes import ImageBytes
from docarray.typing.url import AnyUrl
from jina import Executor, requests
from pydantic import Field
class TextAndImageDoc(BaseDoc):
text: O... | from typing import Optional
import numpy as np
from docarray import BaseDoc, DocList
from docarray.typing import NdArray
from docarray.typing.bytes import ImageBytes
from docarray.typing.url import AnyUrl
from jina import Executor, requests
from pydantic import Field
class TextAndImageDoc(BaseDoc):
text: Optiona... |
from langchain_core.prompts.prompt import PromptTemplate
_DEFAULT_TEMPLATE = """Question: Who lived longer, Muhammad Ali or Alan Turing?
Are follow up questions needed here: Yes.
Follow up: How old was Muhammad Ali when he died?
Intermediate answer: Muhammad Ali was 74 years old when he died.
Follow up: How old was Al... | # flake8: noqa
from langchain_core.prompts.prompt import PromptTemplate
_DEFAULT_TEMPLATE = """Question: Who lived longer, Muhammad Ali or Alan Turing?
Are follow up questions needed here: Yes.
Follow up: How old was Muhammad Ali when he died?
Intermediate answer: Muhammad Ali was 74 years old when he died.
Follow up:... |
import sqlite3
import warnings
from dataclasses import dataclass, field
from tempfile import NamedTemporaryFile
from typing import Iterable, Dict, Optional, TYPE_CHECKING, Union
from docarray.array.storage.sqlite.helper import initialize_table
from docarray.array.storage.base.backend import BaseBackendMixin
from docar... | import sqlite3
import warnings
from dataclasses import dataclass, field
from tempfile import NamedTemporaryFile
from typing import Iterable, Dict, Optional, TYPE_CHECKING, Union
from docarray.array.storage.sqlite.helper import initialize_table
from docarray.array.storage.base.backend import BaseBackendMixin
from docar... |
import torch
from datasets import Dataset
from sentence_transformers.sparse_encoder import (
MLMTransformer,
SparseEncoder,
SparseEncoderTrainer,
SparseMarginMSELoss,
SpladePooling,
)
# Initialize the SPLADE model
student_model_name = "prithivida/Splade_PP_en_v1"
student_model = SparseEncoder(
... | from datasets import Dataset
from sentence_transformers.sparse_encoder import (
MLMTransformer,
SparseEncoder,
SparseEncoderTrainer,
SparseMarginMSELoss,
SpladePooling,
)
# Initialize the SPLADE model
student_model_name = "prithivida/Splade_PP_en_v1"
student_model = SparseEncoder(
modules=[
... |
import os
from collections import namedtuple
import pytest
from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict
_TestCommandArgs = namedtuple(
"_TestCommandArgs",
[
"dataset",
"name",... | import os
from collections import namedtuple
import pytest
from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict
_TestCommandArgs = namedtuple(
"_TestCommandArgs",
[
"dataset",
"name",... |
from typing import Dict, Optional, Tuple
import numpy as np
import torch
import torchvision.transforms as T
from jina import DocumentArray, Executor, requests
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
class TimmImageEncoder(Execu... | from typing import Dict, Iterable, Optional, Tuple
import numpy as np
import torch
import torchvision.transforms as T
from jina import DocumentArray, Executor, requests
from jina_commons.batching import get_docs_batch_generator
from timm import create_model
from timm.data import resolve_data_config
from timm.data.tran... |
# Copyright (c) OpenMMLab. All rights reserved.
from unittest.mock import Mock
from mmengine.hooks import DistSamplerSeedHook
class TestDistSamplerSeedHook:
def test_before_epoch(self):
hook = DistSamplerSeedHook()
# Test dataset sampler
runner = Mock()
runner.epoch = 1
... | # Copyright (c) OpenMMLab. All rights reserved.
from unittest.mock import Mock
from mmengine.hooks import DistSamplerSeedHook
class TestDistSamplerSeedHook:
def test_before_epoch(self):
hook = DistSamplerSeedHook()
# Test dataset sampler
runner = Mock()
runner.epoch = 1
... |
"""Init file."""
from llama_index.readers.web.agentql_web.base import (
AgentQLWebReader,
)
from llama_index.readers.web.async_web.base import (
AsyncWebPageReader,
)
from llama_index.readers.web.beautiful_soup_web.base import (
BeautifulSoupWebReader,
)
from llama_index.readers.web.browserbase_web.base imp... | """Init file."""
from llama_index.readers.web.agentql_web.base import (
AgentQLWebReader,
)
from llama_index.readers.web.async_web.base import (
AsyncWebPageReader,
)
from llama_index.readers.web.beautiful_soup_web.base import (
BeautifulSoupWebReader,
)
from llama_index.readers.web.browserbase_web.base imp... |
# 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... | # dataset settings
dataset_type = 'CocoDataset'
data_root = 'data/coco/'
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(type='Pad', size_diviso... |
import numpy as np
from docarray import Document
from docarray.typing import Tensor
def test_set_tensor():
class MyDocument(Document):
tensor: Tensor
d = MyDocument(tensor=np.zeros((3, 224, 224)))
assert isinstance(d.tensor, Tensor)
assert isinstance(d.tensor, np.ndarray)
assert (d.tens... | import numpy as np
from docarray.typing import Tensor
from docarray import Document
def test_set_tensor():
class MyDocument(Document):
tensor: Tensor
d = MyDocument(tensor=np.zeros((3, 224, 224)))
assert isinstance(d.tensor, Tensor)
assert isinstance(d.tensor, np.ndarray)
assert (d.ten... |
from docarray.typing.tensor.audio.audio_ndarray import AudioNdArray
__all__ = ['AudioNdArray']
from docarray.utils._internal.misc import is_tf_available, is_torch_available
torch_available = is_torch_available()
if torch_available:
from docarray.typing.tensor.audio.audio_torch_tensor import AudioTorchTensor # n... | from docarray.typing.tensor.audio.audio_ndarray import AudioNdArray
__all__ = ['AudioNdArray']
from docarray.utils.misc import is_tf_available, is_torch_available
torch_available = is_torch_available()
if torch_available:
from docarray.typing.tensor.audio.audio_torch_tensor import AudioTorchTensor # noqa
_... |
# Copyright 2020 The TensorFlow Authors. 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 applica... | # Copyright 2020 The TensorFlow Authors. 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 applica... |
# Copyright (c) OpenMMLab. All rights reserved.
from math import ceil
from unittest import TestCase
import torch
from mmengine import Config
from mmengine.structures import InstanceData
from mmdet.models.dense_heads import FSAFHead
class TestFSAFHead(TestCase):
def test_fsaf_head_loss(self):
"""Tests f... | # Copyright (c) OpenMMLab. All rights reserved.
from math import ceil
from unittest import TestCase
import torch
from mmengine import Config
from mmengine.data import InstanceData
from mmdet.models.dense_heads import FSAFHead
class TestFSAFHead(TestCase):
def test_fsaf_head_loss(self):
"""Tests fsaf he... |
# Copyright (c) OpenMMLab. All rights reserved.
from .auto_augment import (AutoAugment, BrightnessTransform, ColorTransform,
ContrastTransform, EqualizeTransform, Rotate, Shear,
Translate)
from .compose import Compose
from .formatting import (ImageToTensor, PackDetI... | # Copyright (c) OpenMMLab. All rights reserved.
from .auto_augment import (AutoAugment, BrightnessTransform, ColorTransform,
ContrastTransform, EqualizeTransform, Rotate, Shear,
Translate)
from .compose import Compose
from .formatting import (ImageToTensor, PackDetI... |
from docutils import nodes
from docutils.parsers.rst import Directive
class BetaStatus(Directive):
has_content = True
text = "The {api_name} is in Beta stage, and backward compatibility is not guaranteed."
def run(self):
text = self.text.format(api_name=" ".join(self.content))
return [nod... | from docutils import nodes
from docutils.parsers.rst import Directive
class BetaStatus(Directive):
has_content = True
def run(self):
api_name = " ".join(self.content)
text = f"The {api_name} is in Beta stage, and backward compatibility is not guaranteed."
return [nodes.warning("", nod... |
import asyncio
import copy
from typing import Any, List, TYPE_CHECKING
from jina.serve.runtimes.servers import BaseServer
if TYPE_CHECKING:
from jina.logging.logger import JinaLogger
class CompositeBaseServer(BaseServer):
"""Composite Base Server implementation from which u can inherit a specific custom com... | import asyncio
import copy
from typing import Any, List
from jina.serve.runtimes.servers import BaseServer
class CompositeServer(BaseServer):
"""Composite Server implementation"""
def __init__(
self,
**kwargs,
):
"""Initialize the gateway
:param kwargs: keyword ar... |
from __future__ import annotations
from typing import Any, Optional, Union
import PIL.Image
import torch
from ._tv_tensor import TVTensor
class Mask(TVTensor):
""":class:`torch.Tensor` subclass for segmentation and detection masks.
Args:
data (tensor-like, PIL.Image.Image): Any data that can be tu... | from __future__ import annotations
from typing import Any, Optional, Union
import PIL.Image
import torch
from ._tv_tensor import TVTensor
class Mask(TVTensor):
"""[BETA] :class:`torch.Tensor` subclass for segmentation and detection masks.
Args:
data (tensor-like, PIL.Image.Image): Any data that ca... |
import os
import sys
from pathlib import Path
import pytest
from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node
from .utils import execute_subprocess_async, get_torch_dist_unique_port, require_torch
def test_split_dataset_by_node_map_style():
full_ds = Dataset.f... | import os
import sys
from pathlib import Path
import pytest
from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node
from .utils import execute_subprocess_async, get_torch_dist_unique_port, require_torch
def test_split_dataset_by_node_map_style():
full_ds = Dataset.f... |
"""Tool for asking human input."""
from typing import Callable, Optional
from langchain_core.callbacks import CallbackManagerForToolRun
from langchain_core.tools import BaseTool
from pydantic import Field
def _print_func(text: str) -> None:
print("\n") # noqa: T201
print(text) # noqa: T201
class HumanIn... | """Tool for asking human input."""
from typing import Callable, Optional
from langchain_core.callbacks import CallbackManagerForToolRun
from langchain_core.tools import BaseTool
from pydantic import Field
def _print_func(text: str) -> None:
print("\n") # noqa: T201
print(text) # noqa: T201
class HumanIn... |
import sys
from os import path
from setuptools import find_packages
from setuptools import setup
if sys.version_info < (3, 7, 0):
raise OSError(f'DocArray requires Python >=3.7, but yours is {sys.version}')
try:
pkg_name = 'docarray'
libinfo_py = path.join(pkg_name, '__init__.py')
libinfo_content = o... | import sys
from os import path
from setuptools import find_packages
from setuptools import setup
if sys.version_info < (3, 7, 0):
raise OSError(f'DocArray requires Python >=3.7, but yours is {sys.version}')
try:
pkg_name = 'docarray'
libinfo_py = path.join(pkg_name, '__init__.py')
libinfo_content = o... |
import logging
import random
from datasets import load_dataset
from sentence_transformers import SparseEncoder
from sentence_transformers.sparse_encoder.evaluation import SparseInformationRetrievalEvaluator
logging.basicConfig(format="%(message)s", level=logging.INFO)
# Load a model
model = SparseEncoder("naver/spl... | import logging
import random
from datasets import load_dataset
from sentence_transformers import SparseEncoder
from sentence_transformers.sparse_encoder.evaluation import SparseInformationRetrievalEvaluator
logging.basicConfig(format="%(message)s", level=logging.INFO)
# Load a model
model = SparseEncoder("naver/spl... |
__copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import subprocess
import pytest
from executor.audioclip_text import AudioCLIPTextEncoder
from jina import Document, DocumentArray, Flow
_EMBEDDING_DIM = 1024
@pytest.mark.parametrize('request_size', [1, 10, 5... | __copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import subprocess
import pytest
from executor.audioclip_text import AudioCLIPTextEncoder
from jina import Document, DocumentArray, Flow
_EMBEDDING_DIM = 1024
@pytest.mark.parametrize('request_size', [1, 10, 5... |
from __future__ import annotations
import torch
import transformers
from PIL import Image
from torch import nn
class CLIPModel(nn.Module):
save_in_root: bool = True
def __init__(self, model_name: str = "openai/clip-vit-base-patch32", processor_name=None) -> None:
super().__init__()
if proce... | from __future__ import annotations
import torch
import transformers
from PIL import Image
from torch import nn
class CLIPModel(nn.Module):
save_in_root: bool = True
def __init__(self, model_name: str = "openai/clip-vit-base-patch32", processor_name=None) -> None:
super().__init__()
if proce... |
from langchain_core.agents import AgentAction
from langchain.agents.format_scratchpad.xml import format_xml
def test_single_agent_action_observation() -> None:
# Arrange
agent_action = AgentAction(tool="Tool1", tool_input="Input1", log="Log1")
observation = "Observation1"
intermediate_steps = [(agent... | from langchain_core.agents import AgentAction
from langchain.agents.format_scratchpad.xml import format_xml
def test_single_agent_action_observation() -> None:
# Arrange
agent_action = AgentAction(tool="Tool1", tool_input="Input1", log="Log1")
observation = "Observation1"
intermediate_steps = [(agent... |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=True)
class AutomaticSpeechRecognition(TaskTemplate):
task: str = field(default="automatic-speech-recognition", metadata={"include_... | import copy
from dataclasses import dataclass
from typing import ClassVar, Dict
from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=True)
class AutomaticSpeechRecognition(TaskTemplate):
task: str = "automatic-speech-recognition"
input_schema: ClassVar[Features] = Fe... |
_base_ = './rtmdet_l_8xb32-300e_coco.py'
checkpoint = 'https://download.openmmlab.com/mmdetection/v3.0/rtmdet/cspnext_rsb_pretrain/cspnext-s_imagenet_600e.pth' # noqa
model = dict(
backbone=dict(
deepen_factor=0.33,
widen_factor=0.5,
init_cfg=dict(
type='Pretrained', prefix='bac... | _base_ = './rtmdet_l_8xb32-300e_coco.py'
checkpoint = 'TODO:imagenet_pretrain' # noqa
model = dict(
backbone=dict(
deepen_factor=0.33,
widen_factor=0.5,
init_cfg=dict(
type='Pretrained', prefix='backbone.', checkpoint=checkpoint)),
neck=dict(in_channels=[128, 256, 512], out_... |
# Copyright (c) OpenMMLab. All rights reserved.
from argparse import ArgumentParser, Namespace
from pathlib import Path
from tempfile import TemporaryDirectory
from mmengine.config import Config
from mmengine.utils import mkdir_or_exist
try:
from model_archiver.model_packaging import package_model
from model_... | # Copyright (c) OpenMMLab. All rights reserved.
from argparse import ArgumentParser, Namespace
from pathlib import Path
from tempfile import TemporaryDirectory
import mmcv
try:
from model_archiver.model_packaging import package_model
from model_archiver.model_packaging_utils import ModelExportUtils
except Imp... |
from backend.blocks.nvidia._auth import (
NvidiaCredentials,
NvidiaCredentialsField,
NvidiaCredentialsInput,
)
from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
from backend.data.model import SchemaField
from backend.util.request import requests
from backend.util.type import Medi... | from backend.blocks.nvidia._auth import (
NvidiaCredentials,
NvidiaCredentialsField,
NvidiaCredentialsInput,
)
from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
from backend.data.model import SchemaField
from backend.util.request import requests
class NvidiaDeepfakeDetectBlock(... |
_base_ = 'tridentnet_r50-caffe_1x_coco.py'
train_pipeline = [
dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='RandomChoiceResize',
scales=[(1333, 640), (1333, 672), (1333, 704), (1333, 736),
(133... | _base_ = 'tridentnet_r50-caffe_1x_coco.py'
train_pipeline = [
dict(
type='LoadImageFromFile',
file_client_args={{_base_.file_client_args}}),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='RandomChoiceResize',
scales=[(1333, 640), (1333, 672), (1333, 704), (1333, 7... |
# Copyright (c) OpenMMLab. All rights reserved.
"""copy from
https://github.com/ZwwWayne/K-Net/blob/main/knet/det/mask_pseudo_sampler.py."""
import torch
from mmengine.data import InstanceData
from mmdet.core.bbox.assigners import AssignResult
from mmdet.registry import TASK_UTILS
from .base_sampler import BaseSample... | # Copyright (c) OpenMMLab. All rights reserved.
"""copy from
https://github.com/ZwwWayne/K-Net/blob/main/knet/det/mask_pseudo_sampler.py."""
import torch
from mmdet.registry import TASK_UTILS
from .base_sampler import BaseSampler
from .mask_sampling_result import MaskSamplingResult
@TASK_UTILS.register_module()
cla... |
from typing import TYPE_CHECKING, Type, TypeVar
from pydantic import AnyUrl as BaseAnyUrl
from pydantic import errors, parse_obj_as
from docarray.document.base_node import BaseNode
from docarray.proto import NodeProto
if TYPE_CHECKING:
from pydantic.networks import Parts
T = TypeVar('T', bound='AnyUrl')
class... | from typing import Type, TypeVar
from pydantic import AnyUrl as BaseAnyUrl
from pydantic import parse_obj_as
from docarray.document.base_node import BaseNode
from docarray.proto import NodeProto
T = TypeVar('T', bound='AnyUrl')
class AnyUrl(BaseAnyUrl, BaseNode):
def _to_node_protobuf(self) -> NodeProto:
... |
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