input stringlengths 33 5k | output stringlengths 32 5k |
|---|---|
import numpy as np
import pytest
from fastapi import FastAPI
from httpx import AsyncClient
from docarray import BaseDocument, Image, Text
from docarray.typing import NdArray
@pytest.mark.asyncio
async def test_fast_api():
class Mmdoc(BaseDocument):
img: Image
text: Text
title: str
in... | import numpy as np
import pytest
from fastapi import FastAPI
from httpx import AsyncClient
from docarray import Document, Image, Text
from docarray.typing import NdArray
@pytest.mark.asyncio
async def test_fast_api():
class Mmdoc(Document):
img: Image
text: Text
title: str
input_doc ... |
# 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... |
import os
from llama_index.core.tools.function_tool import FunctionTool
import pytest
from llama_index.core.base.llms.base import BaseLLM
from llama_index.core.base.llms.types import ChatMessage, ImageBlock, MessageRole
from llama_index.llms.gemini import Gemini
from llama_index.llms.gemini.utils import chat_message_t... | import os
from llama_index.core.tools.function_tool import FunctionTool
import pytest
from llama_index.core.base.llms.base import BaseLLM
from llama_index.core.base.llms.types import ChatMessage, ImageBlock, MessageRole
from llama_index.llms.gemini import Gemini
from llama_index.llms.gemini.utils import chat_message_t... |
from llama_index_instrumentation.span.base import BaseSpan # noqa
| from typing import Any, Dict, Optional
from uuid import uuid4
from llama_index.core.bridge.pydantic import BaseModel, Field, ConfigDict
class BaseSpan(BaseModel):
"""Base data class representing a span."""
model_config = ConfigDict(arbitrary_types_allowed=True)
id_: str = Field(default_factory=lambda: st... |
import torch
from docarray.computation.torch_backend import TorchCompBackend
def test_to_device():
t = torch.rand(10, 3)
assert t.device == torch.device('cpu')
t = TorchCompBackend.to_device(t, 'meta')
assert t.device == torch.device('meta')
def test_empty():
tensor = TorchCompBackend.empty((10... | import torch
from docarray.computation.torch_backend import TorchCompBackend
def test_to_device():
t = torch.rand(10, 3)
assert t.device == torch.device('cpu')
t = TorchCompBackend.to_device(t, 'meta')
assert t.device == torch.device('meta')
|
"""DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.quantizers import deserialize
from keras.src.quantizers import get
from keras.src.quantizers import serialize
from keras.src.quantizers.quantizers import AbsMaxQuantizer
from keras.sr... | """DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.quantizers import deserialize
from keras.src.quantizers import get
from keras.src.quantizers import serialize
from keras.src.quantizers.quantizers import AbsMaxQuantizer
from keras.sr... |
"""Mock prompt utils."""
from llama_index.core.prompts.base import PromptTemplate
from llama_index.core.prompts.prompt_type import PromptType
MOCK_SUMMARY_PROMPT_TMPL = "{context_str}\n"
MOCK_SUMMARY_PROMPT = PromptTemplate(
MOCK_SUMMARY_PROMPT_TMPL, prompt_type=PromptType.SUMMARY
)
MOCK_INSERT_PROMPT_TMPL = "{n... | """Mock prompt utils."""
from llama_index.core.prompts.base import PromptTemplate
from llama_index.core.prompts.prompt_type import PromptType
MOCK_SUMMARY_PROMPT_TMPL = "{context_str}\n"
MOCK_SUMMARY_PROMPT = PromptTemplate(
MOCK_SUMMARY_PROMPT_TMPL, prompt_type=PromptType.SUMMARY
)
MOCK_INSERT_PROMPT_TMPL = "{n... |
from __future__ import annotations
import random
import pytest
import torch
from torch.utils.data import ConcatDataset
from sentence_transformers.sampler import NoDuplicatesBatchSampler, ProportionalBatchSampler
from sentence_transformers.util import is_datasets_available
if is_datasets_available():
from datase... | from __future__ import annotations
import random
import pytest
import torch
from datasets import Dataset
from torch.utils.data import ConcatDataset
from sentence_transformers.sampler import NoDuplicatesBatchSampler, ProportionalBatchSampler
@pytest.fixture
def dummy_dataset() -> Dataset:
"""
Dummy dataset ... |
"""
Tests the correct computation of evaluation scores from BinaryClassificationEvaluator
"""
from __future__ import annotations
import csv
import gzip
import os
from pathlib import Path
import pytest
from torch.utils.data import DataLoader
from sentence_transformers import (
InputExample,
SentenceTransform... | """
Tests the correct computation of evaluation scores from BinaryClassificationEvaluator
"""
from __future__ import annotations
import csv
import gzip
import os
from pathlib import Path
from torch.utils.data import DataLoader
from sentence_transformers import (
InputExample,
SentenceTransformer,
evalua... |
from typing import Any, Dict, List, Sequence, Union
from deprecated import deprecated
from llama_index.core.base.llms.types import (
CompletionResponse,
CompletionResponseAsyncGen,
CompletionResponseGen,
MessageRole,
)
from llama_index.core.base.llms.generic_utils import (
chat_response_to_completi... | from typing import Any, Dict, List, Sequence
from llama_index.core.base.llms.types import (
CompletionResponse,
CompletionResponseAsyncGen,
CompletionResponseGen,
MessageRole,
)
from llama_index.core.base.llms.generic_utils import (
chat_response_to_completion_response,
stream_chat_response_to_... |
"""Simple reader that reads weather data from OpenWeatherMap API."""
from typing import List
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document
class WeatherReader(BaseReader):
"""
Weather Reader.
Reads the forecast & current weather of any location using ... | """Simple reader that reads weather data from OpenWeatherMap API."""
from typing import List
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document
class WeatherReader(BaseReader):
"""
Weather Reader.
Reads the forecast & current weather of any location using O... |
# Copyright (c) OpenMMLab. All rights reserved.
from .dist_utils import (DistOptimizerHook, all_reduce_dict, allreduce_grads,
reduce_mean, sync_random_seed)
from .misc import (center_of_mass, filter_scores_and_topk, flip_tensor,
generate_coordinate, mask2ndarray, multi_apply,... | # Copyright (c) OpenMMLab. All rights reserved.
from .dist_utils import (DistOptimizerHook, all_reduce_dict, allreduce_grads,
reduce_mean)
from .misc import (center_of_mass, filter_scores_and_topk, flip_tensor,
generate_coordinate, mask2ndarray, multi_apply,
... |
from langchain_core._api import warn_deprecated
from pydantic.v1.dataclasses import * # noqa: F403
warn_deprecated(
"0.3.0",
removal="1.0.0",
alternative="pydantic.v1 or pydantic",
message=(
"As of langchain-core 0.3.0, LangChain uses pydantic v2 internally. "
"The langchain.pydantic_v... | from langchain_core._api import warn_deprecated
try:
from pydantic.v1.dataclasses import * # noqa: F403
except ImportError:
from pydantic.dataclasses import * # type: ignore # noqa: F403
warn_deprecated(
"0.3.0",
removal="1.0.0",
alternative="pydantic.v1 or pydantic",
message=(
"As o... |
from __future__ import annotations
from typing import Any, Dict, Optional
from docarray import BaseDoc, DocList
from docarray.typing import AnyEmbedding, AnyTensor
class LegacyDocument(BaseDoc):
"""
This Document is the LegacyDocument. It follows the same schema as in DocArray <=0.21.
It can be useful t... | from __future__ import annotations
from typing import Any, Dict, Optional
from docarray import BaseDoc, DocList
from docarray.typing import AnyEmbedding, AnyTensor
class LegacyDocument(BaseDoc):
"""
This Document is the LegacyDocument. It follows the same schema as in DocArray <=0.21.
It can be useful t... |
# Copyright (c) OpenMMLab. All rights reserved.
from .vis_backend import (BaseVisBackend, LocalVisBackend, MLflowVisBackend,
TensorboardVisBackend, WandbVisBackend)
from .visualizer import Visualizer
__all__ = [
'Visualizer', 'BaseVisBackend', 'LocalVisBackend', 'WandbVisBackend',
'Te... | # Copyright (c) OpenMMLab. All rights reserved.
from .vis_backend import (BaseVisBackend, LocalVisBackend,
TensorboardVisBackend, WandbVisBackend)
from .visualizer import Visualizer
__all__ = [
'Visualizer', 'BaseVisBackend', 'LocalVisBackend', 'WandbVisBackend',
'TensorboardVisBacken... |
from docarray.documents.text import TextDoc
def test_text_document_operators():
doc = TextDoc(text='text', url='http://url.com')
assert doc == 'text'
assert doc != 'http://url.com'
doc2 = TextDoc(id=doc.id, text='text', url='http://url.com')
assert doc == doc2
doc3 = TextDoc(id='other-id', ... | from docarray.documents.text import TextDoc
def test_text_document_operators():
doc = TextDoc(text='text', url='url.com')
assert doc == 'text'
assert doc != 'url.com'
doc2 = TextDoc(id=doc.id, text='text', url='url.com')
assert doc == doc2
doc3 = TextDoc(id='other-id', text='text', url='ur... |
#!/usr/bin/env python3
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unles... | #!/usr/bin/env python3
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unles... |
"""Test ChatDeepSeek chat model."""
from typing import Optional, Type
import pytest
from langchain_core.language_models import BaseChatModel
from langchain_core.messages import AIMessageChunk, BaseMessageChunk
from langchain_core.tools import BaseTool
from langchain_tests.integration_tests import ChatModelIntegration... | """Test ChatDeepSeek chat model."""
from typing import Optional, Type
import pytest
from langchain_core.language_models import BaseChatModel
from langchain_core.messages import AIMessageChunk, BaseMessageChunk
from langchain_core.tools import BaseTool
from langchain_tests.integration_tests import ChatModelIntegration... |
from __future__ import annotations
from sentence_transformers.losses.MSELoss import MSELoss
from sentence_transformers.sparse_encoder.SparseEncoder import SparseEncoder
class SparseMSELoss(MSELoss):
def __init__(self, model: SparseEncoder) -> None:
"""
Computes the MSE loss between the computed s... | from __future__ import annotations
from sentence_transformers.losses.MSELoss import MSELoss
from sentence_transformers.sparse_encoder.SparseEncoder import SparseEncoder
class SparseMSELoss(MSELoss):
def __init__(self, model: SparseEncoder) -> None:
"""
# TODO: Update as it's mentionned trainings ... |
from pathlib import Path
import numpy as np
import pytest as pytest
from jina import Document, DocumentArray, Executor
compose_yml = Path(__file__).parent / 'docker-compose.yml'
def test_config():
ex = Executor.load_config(str(Path(__file__).parents[1] / 'config.yml'))
assert ex.port == 6379
@pytest.mark.... | import os
import numpy as np
import pytest as pytest
from jina import Document, DocumentArray
cur_dir = os.path.dirname(os.path.abspath(__file__))
compose_yml = os.path.abspath(os.path.join(cur_dir, 'docker-compose.yml'))
@pytest.mark.parametrize('docker_compose', [compose_yml], indirect=['docker_compose'])
def tes... |
from __future__ import annotations
from sentence_transformers import SentenceTransformer, losses, util
class AnglELoss(losses.CoSENTLoss):
def __init__(self, model: SentenceTransformer, scale: float = 20.0) -> None:
"""
This class implements AnglE (Angle Optimized) loss.
This is a modific... | from __future__ import annotations
from sentence_transformers import SentenceTransformer, losses, util
class AnglELoss(losses.CoSENTLoss):
def __init__(self, model: SentenceTransformer, scale: float = 20.0) -> None:
"""
This class implements AnglE (Angle Optimized) loss.
This is a modific... |
# Copyright (c) OpenMMLab. All rights reserved.
from typing import Optional
import torch.nn as nn
import torch.nn.functional as F
from torch import Tensor
from mmdet.registry import MODELS
from .utils import weighted_loss
@weighted_loss
def knowledge_distillation_kl_div_loss(pred: Tensor,
... | # Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
import torch.nn.functional as F
from mmdet.registry import MODELS
from .utils import weighted_loss
@weighted_loss
def knowledge_distillation_kl_div_loss(pred,
soft_label,
... |
import asyncio
import time
import pytest
from jina import Client, Deployment, Executor, requests
from jina._docarray import Document, DocumentArray
from jina.excepts import BadServer
from jina.helper import random_port
class MyExecutor(Executor):
@requests(on='/hello')
async def task(self, doc: Document, **... | import asyncio
import time
import pytest
from jina import Client, Deployment, Executor, requests
from jina._docarray import Document, DocumentArray
from jina.excepts import BadServer
from jina.helper import random_port
class MyExecutor(Executor):
@requests(on='/hello')
async def task(self, doc: Document, **... |
from ._source_separation_pipeline import (
CONVTASNET_BASE_LIBRI2MIX,
HDEMUCS_HIGH_MUSDB,
HDEMUCS_HIGH_MUSDB_PLUS,
SourceSeparationBundle,
)
from ._squim_pipeline import SQUIM_OBJECTIVE, SQUIM_SUBJECTIVE, SquimObjectiveBundle, SquimSubjectiveBundle
from ._tts import (
TACOTRON2_GRIFFINLIM_CHAR_LJSPE... | from ._source_separation_pipeline import (
CONVTASNET_BASE_LIBRI2MIX,
HDEMUCS_HIGH_MUSDB,
HDEMUCS_HIGH_MUSDB_PLUS,
SourceSeparationBundle,
)
from ._tts import (
TACOTRON2_GRIFFINLIM_CHAR_LJSPEECH,
TACOTRON2_GRIFFINLIM_PHONE_LJSPEECH,
TACOTRON2_WAVERNN_CHAR_LJSPEECH,
TACOTRON2_WAVERNN_PHO... |
"""Tool for the Wikidata API."""
from typing import Optional
from langchain_core.callbacks import CallbackManagerForToolRun
from langchain_core.tools import BaseTool
from langchain_community.utilities.wikidata import WikidataAPIWrapper
class WikidataQueryRun(BaseTool):
"""Tool that searches the Wikidata API.""... | """Tool for the Wikidata API."""
from typing import Optional
from langchain_core.callbacks import CallbackManagerForToolRun
from langchain_core.tools import BaseTool
from langchain_community.utilities.wikidata import WikidataAPIWrapper
class WikidataQueryRun(BaseTool): # type: ignore[override]
"""Tool that se... |
import os
import time
import pytest
from jina.excepts import RuntimeFailToStart
from jina.orchestrate.pods import Pod
from jina.parsers import set_gateway_parser
from jina.serve.runtimes import asyncio as runtime_asyncio
from jina.serve.executors import BaseExecutor
from tests.helper import _generate_pod_args
@pyte... | import os
import time
import pytest
from jina.excepts import RuntimeFailToStart
from jina.orchestrate.pods import Pod
from jina.parsers import set_gateway_parser
from jina.serve.runtimes import asyncio as runtime_asyncio
from jina.serve.executors import BaseExecutor
from tests.helper import _generate_pod_args
@pyte... |
"""DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.models.cloning import clone_model as clone_model
from keras.src.models.model import Model as Model
from keras.src.models.model import model_from_json as model_from_json
from keras.src... | """DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.models.cloning import clone_model
from keras.src.models.model import Model
from keras.src.models.model import model_from_json
from keras.src.models.sequential import Sequential
from k... |
# Copyright (c) OpenMMLab. All rights reserved.
from .interpolation import InterpolateTracklets
from .kalman_filter import KalmanFilter
from .similarity import embed_similarity
__all__ = ['KalmanFilter', 'InterpolateTracklets', 'embed_similarity']
| # Copyright (c) OpenMMLab. All rights reserved.
from .interpolation import InterpolateTracklets
from .kalman_filter import KalmanFilter
__all__ = ['KalmanFilter', 'InterpolateTracklets']
|
from typing import TYPE_CHECKING, Any, Generic, Type, TypeVar, Union
import numpy as np
from docarray.typing.tensor.abstract_tensor import AbstractTensor
from docarray.typing.tensor.ndarray import NdArray
from docarray.utils._internal.misc import is_tf_available, is_torch_available # noqa
torch_available = is_torch... | from typing import Union
from docarray.typing.tensor.ndarray import NdArray
from docarray.utils._internal.misc import is_tf_available, is_torch_available
torch_available = is_torch_available()
if torch_available:
from docarray.typing.tensor.torch_tensor import TorchTensor # noqa: F401
tf_available = is_tf_avai... |
# 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... |
import os
from pathlib import Path
from typing import Any, Callable, Optional, Tuple, Union
from PIL import Image
from .utils import check_integrity, download_and_extract_archive, download_url
from .vision import VisionDataset
class SBU(VisionDataset):
"""`SBU Captioned Photo <http://www.cs.virginia.edu/~vicent... | import os
from typing import Any, Callable, Optional, Tuple
from PIL import Image
from .utils import check_integrity, download_and_extract_archive, download_url
from .vision import VisionDataset
class SBU(VisionDataset):
"""`SBU Captioned Photo <http://www.cs.virginia.edu/~vicente/sbucaptions/>`_ Dataset.
... |
from typing import TYPE_CHECKING, Optional, Type
from langchain_core.callbacks import (
CallbackManagerForToolRun,
)
from langchain_core.tools import BaseTool
from pydantic import BaseModel, Field
if TYPE_CHECKING:
# This is for linting and IDE typehints
import multion
else:
try:
# We do this ... | from typing import TYPE_CHECKING, Optional, Type
from langchain_core.callbacks import (
CallbackManagerForToolRun,
)
from langchain_core.tools import BaseTool
from pydantic import BaseModel, Field
if TYPE_CHECKING:
# This is for linting and IDE typehints
import multion
else:
try:
# We do this ... |
from typing import Any
from langchain_core.memory import BaseMemory
class SimpleMemory(BaseMemory):
"""Simple memory for storing context or other information that shouldn't
ever change between prompts.
"""
memories: dict[str, Any] = dict()
@property
def memory_variables(self) -> list[str]:
... | from typing import Any
from langchain_core.memory import BaseMemory
class SimpleMemory(BaseMemory):
"""Simple memory for storing context or other information that shouldn't
ever change between prompts.
"""
memories: dict[str, Any] = dict()
@property
def memory_variables(self) -> list[str]:
... |
_base_ = './cascade-mask-rcnn_r50_fpn_20e_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='py... | _base_ = './cascade_mask_rcnn_r50_fpn_20e_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
style='py... |
_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... |
"""
This script contains an example how to perform semantic search with Elasticsearch.
You need Elasticsearch up and running locally:
https://www.elastic.co/guide/en/elasticsearch/reference/current/run-elasticsearch-locally.html
Further, you need the Python Elasticsearch Client installed: https://elasticsearch-py.rea... | """
This script contains an example how to perform semantic search with Elasticsearch.
You need Elasticsearch up and running locally:
https://www.elastic.co/guide/en/elasticsearch/reference/current/run-elasticsearch-locally.html
Further, you need the Python Elasticsearch Client installed: https://elasticsearch-py.rea... |
import numpy as np
import torch
from docarray import Document
from docarray.document import AnyDocument
from docarray.typing import AnyUrl, Embedding, ImageUrl, NdArray, TextUrl, TorchTensor
def test_proto_all_types():
class Mymmdoc(Document):
tensor: NdArray
torch_tensor: TorchTensor
emb... | import numpy as np
import torch
from docarray import Document
from docarray.document import AnyDocument
from docarray.typing import AnyUrl, Embedding, ImageUrl, NdArray, TextUrl, TorchTensor
def test_proto_all_types():
class Mymmdoc(Document):
tensor: NdArray
torch_tensor: TorchTensor
emb... |
"""
This file is part of the private API. Please do not use directly these classes as they will be modified on
future versions without warning. The classes should be accessed only via the transforms argument of Weights.
"""
from typing import Optional, Union
import PIL.Image
import torch
from torch import Tensor
fr... | """
This file is part of the private API. Please do not use directly these classes as they will be modified on
future versions without warning. The classes should be accessed only via the transforms argument of Weights.
"""
from typing import List, Optional, Tuple, Union
import PIL.Image
import torch
from torch impor... |
from typing import Any, Dict, Optional, Type
from jina.jaml.parsers.base import BaseLegacyParser
from jina.serve.gateway import BaseGateway
class GatewayLegacyParser(BaseLegacyParser):
"""Legacy parser for gateway."""
def parse(
self,
cls: Type['BaseGateway'],
data: Dict,
run... | from typing import Any, Dict, Optional, Type
from jina.jaml.parsers.base import BaseLegacyParser
from jina.serve.gateway import BaseGateway
class GatewayLegacyParser(BaseLegacyParser):
"""Legacy parser for gateway."""
def parse(
self,
cls: Type['BaseGateway'],
data: Dict,
run... |
__copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
from jina import Flow, Document
from ...laser_encoder import LaserEncoder
def data_generator(num_docs):
for i in range(num_docs):
doc = Document(
text='it is a good day! the dog sits on ... | __copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
from jina import Flow, Document
from jinahub.encoder.laser_encoder import LaserEncoder
def data_generator(num_docs):
for i in range(num_docs):
doc = Document(
text='it is a good day! the... |
from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.llms.loading import load_llm, load_llm_from_config
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handling opti... | from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.llms.loading import load_llm, load_llm_from_config
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handling opti... |
# 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... |
"""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 typing import TYPE_CHECKING
... |
from langchain_core.runnables.configurable import (
DynamicRunnable,
RunnableConfigurableAlternatives,
RunnableConfigurableFields,
StrEnum,
make_options_spec,
)
__all__ = [
"DynamicRunnable",
"RunnableConfigurableAlternatives",
"RunnableConfigurableFields",
"StrEnum",
"make_opti... | from langchain_core.runnables.configurable import (
DynamicRunnable,
RunnableConfigurableAlternatives,
RunnableConfigurableFields,
StrEnum,
make_options_spec,
)
__all__ = [
"DynamicRunnable",
"RunnableConfigurableFields",
"StrEnum",
"RunnableConfigurableAlternatives",
"make_opti... |
# Copyright (c) OpenMMLab. All rights reserved.
from typing import Tuple
from torch import Tensor
from mmdet.registry import MODELS
from .standard_roi_head import StandardRoIHead
@MODELS.register_module()
class DoubleHeadRoIHead(StandardRoIHead):
"""RoI head for `Double Head RCNN <https://arxiv.org/abs/1904.064... | # Copyright (c) OpenMMLab. All rights reserved.
from mmdet.registry import MODELS
from .standard_roi_head import StandardRoIHead
@MODELS.register_module()
class DoubleHeadRoIHead(StandardRoIHead):
"""RoI head for Double Head RCNN.
https://arxiv.org/abs/1904.06493
"""
def __init__(self, reg_roi_scale... |
from __future__ import annotations
from abc import ABC, abstractmethod
from typing import TYPE_CHECKING, Any
from langchain_core.runnables.config import run_in_executor
if TYPE_CHECKING:
from collections.abc import Sequence
from langchain_core.documents import Document
class BaseDocumentTransformer(ABC):
... | from __future__ import annotations
from abc import ABC, abstractmethod
from collections.abc import Sequence
from typing import TYPE_CHECKING, Any
from langchain_core.runnables.config import run_in_executor
if TYPE_CHECKING:
from langchain_core.documents import Document
class BaseDocumentTransformer(ABC):
"... |
"""``langchain-core`` defines the base abstractions for the LangChain ecosystem.
The interfaces for core components like chat models, LLMs, vector stores, retrievers,
and more are defined here. The universal invocation protocol (Runnables) along with
a syntax for combining components (LangChain Expression Language) ar... | """``langchain-core`` defines the base abstractions for the LangChain ecosystem.
The interfaces for core components like chat models, LLMs, vector stores, retrievers,
and more are defined here. The universal invocation protocol (Runnables) along with
a syntax for combining components (LangChain Expression Language) ar... |
from typing import (
TYPE_CHECKING,
Iterable,
)
from docarray.array.memory import DocumentArrayInMemory
if TYPE_CHECKING:
from docarray.document import Document
class MatchArray(DocumentArrayInMemory):
"""
:class:`MatchArray` inherits from :class:`DocumentArray`.
It's a subset of Documents t... | from typing import (
TYPE_CHECKING,
Iterable,
)
from .memory import DocumentArrayInMemory
if TYPE_CHECKING:
from ..document import Document
class MatchArray(DocumentArrayInMemory):
"""
:class:`MatchArray` inherits from :class:`DocumentArray`.
It's a subset of Documents that represents the ma... |
# Copyright (c) OpenMMLab. All rights reserved.
from .data_preprocessor import (BatchFixedSizePad, BatchResize,
BatchSyncRandomResize, BoxInstDataPreprocessor,
DetDataPreprocessor,
MultiBranchDataPreprocessor)
from .track_da... | # Copyright (c) OpenMMLab. All rights reserved.
from .data_preprocessor import (BatchFixedSizePad, BatchResize,
BatchSyncRandomResize, BoxInstDataPreprocessor,
DetDataPreprocessor,
MultiBranchDataPreprocessor)
__all__ = [
... |
import os
from pathlib import Path
from torchaudio.datasets import gtzan
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
"""
mo... | import os
from pathlib import Path
from torchaudio.datasets import gtzan
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
"""
mo... |
# Copyright (c) OpenMMLab. All rights reserved.
from .anchor_free_head import AnchorFreeHead
from .anchor_head import AnchorHead
from .atss_head import ATSSHead
from .autoassign_head import AutoAssignHead
from .cascade_rpn_head import CascadeRPNHead, StageCascadeRPNHead
from .centernet_head import CenterNetHead
from .c... | # Copyright (c) OpenMMLab. All rights reserved.
from .anchor_free_head import AnchorFreeHead
from .anchor_head import AnchorHead
from .atss_head import ATSSHead
from .autoassign_head import AutoAssignHead
from .cascade_rpn_head import CascadeRPNHead, StageCascadeRPNHead
from .centernet_head import CenterNetHead
from .c... |
from __future__ import annotations
import logging
from typing import TYPE_CHECKING, Any
from sentence_transformers.evaluation import TranslationEvaluator
if TYPE_CHECKING:
import numpy as np
from torch import Tensor
from sentence_transformers.sparse_encoder.SparseEncoder import SparseEncoder
logger = ... | from __future__ import annotations
import logging
from typing import TYPE_CHECKING, Any
from sentence_transformers.evaluation import TranslationEvaluator
if TYPE_CHECKING:
import numpy as np
from torch import Tensor
from sentence_transformers.sparse_encoder.SparseEncoder import SparseEncoder
logger = ... |
__copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
from typing import Dict, Optional, Tuple
import numpy as np
import paddlehub as hub
from jina import DocumentArray, Executor, requests
from jina_commons.batching import get_docs_batch_generator
class TextPaddl... | __copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
from typing import Optional, List, Any, Dict, Tuple
import numpy as np
import paddlehub as hub
from jina import Executor, DocumentArray, requests
from jina_commons.batching import get_docs_batch_generator
clas... |
import os
from abc import abstractmethod
from typing import Union
from unittest import mock
import pytest
from langchain_core.tools import BaseTool
from pydantic import SecretStr
from langchain_tests.base import BaseStandardTests
class ToolsTests(BaseStandardTests):
"""
:private:
Base class for testing ... | import os
from abc import abstractmethod
from typing import Tuple, Type, Union
from unittest import mock
import pytest
from langchain_core.tools import BaseTool
from pydantic import SecretStr
from langchain_tests.base import BaseStandardTests
class ToolsTests(BaseStandardTests):
"""
:private:
Base class... |
"""
Borrowed from Langchain's Neo4j graph utility functions.
https://github.com/langchain-ai/langchain/blob/95c3e5f85f8ed8026a11e351b57bfae488d654c4/libs/community/langchain_community/graphs/neo4j_graph.py
"""
from typing import Any
LIST_LIMIT = 128
def clean_string_values(text: str) -> str:
return text.replac... | """Borrowed from Langchain's Neo4j graph utility functions.
https://github.com/langchain-ai/langchain/blob/95c3e5f85f8ed8026a11e351b57bfae488d654c4/libs/community/langchain_community/graphs/neo4j_graph.py
"""
from typing import Any
LIST_LIMIT = 128
def clean_string_values(text: str) -> str:
return text.replace... |
# Copyright (c) OpenMMLab. All rights reserved.
import pickle
from .base import BaseFileHandler
class PickleHandler(BaseFileHandler):
str_like = False
def load_from_fileobj(self, file, **kwargs):
return pickle.load(file, **kwargs)
def load_from_path(self, filepath, **kwargs):
return su... | # Copyright (c) OpenMMLab. All rights reserved.
import pickle
from .base import BaseFileHandler
class PickleHandler(BaseFileHandler):
str_like = False
def load_from_fileobj(self, file, **kwargs):
return pickle.load(file, **kwargs)
def load_from_path(self, filepath, **kwargs):
return su... |
# dataset settings
dataset_type = 'Objects365V2Dataset'
data_root = 'data/Objects365/Obj365_v2/'
# 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/detectio... | # dataset settings
dataset_type = 'Objects365V2Dataset'
data_root = 'data/Objects365/Obj365_v2/'
# file_client_args = dict(
# backend='petrel',
# path_mapping=dict({
# './data/': 's3://openmmlab/datasets/detection/',
# 'data/': 's3://openmmlab/datasets/detection/'
# }))
file_client_args = d... |
_base_ = '../retinanet/retinanet_r50_fpn_1x_coco.py'
# model settings
model = dict(
type='FSAF',
bbox_head=dict(
type='FSAFHead',
num_classes=80,
in_channels=256,
stacked_convs=4,
feat_channels=256,
reg_decoded_bbox=True,
# Only anchor-free branch is imple... | _base_ = '../retinanet/retinanet_r50_fpn_1x_coco.py'
# model settings
model = dict(
type='FSAF',
bbox_head=dict(
type='FSAFHead',
num_classes=80,
in_channels=256,
stacked_convs=4,
feat_channels=256,
reg_decoded_bbox=True,
# Only anchor-free branch is imple... |
import torch
from docarray import BaseDocument
from docarray.typing import TorchEmbedding, TorchTensor
def test_set_torch_tensor():
class MyDocument(BaseDocument):
tensor: TorchTensor
d = MyDocument(tensor=torch.zeros((3, 224, 224)))
assert isinstance(d.tensor, TorchTensor)
assert isinstanc... | import torch
from docarray import Document
from docarray.typing import TorchEmbedding, TorchTensor
def test_set_torch_tensor():
class MyDocument(Document):
tensor: TorchTensor
d = MyDocument(tensor=torch.zeros((3, 224, 224)))
assert isinstance(d.tensor, TorchTensor)
assert isinstance(d.tens... |
__copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
import subprocess
import pytest
from jina import Document, Flow
from ...torch_object_detection_segmenter import TorchObjectDetectionSegmenter
def test_exec():
f = Flow().add(uses=TorchObjectDetectionSegme... | __copyright__ = "Copyright (c) 2020-2021 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
from jina import Document, Flow
from ...torch_object_detection_segmenter import TorchObjectDetectionSegmenter
def test_exec():
f = Flow().add(uses=TorchObjectDetectionSegmenter)
with f:
resp = ... |
import os
import tempfile
import httpx
import pytest
from PIL import Image
from llama_index.core.base.embeddings.base import BaseEmbedding
from llama_index.embeddings.cohere import CohereEmbedding
from llama_index.embeddings.cohere.base import VALID_MODEL_INPUT_TYPES
def test_embedding_class():
emb = CohereEmbed... | import os
import httpx
import pytest
from llama_index.core.base.embeddings.base import BaseEmbedding
from llama_index.embeddings.cohere import CohereEmbedding
def test_embedding_class():
emb = CohereEmbedding(api_key="token")
assert isinstance(emb, BaseEmbedding)
@pytest.mark.skipif(
os.environ.get("C... |
import json
import multiprocessing
import os
import time
import pytest
from jina.helper import random_port
from jina.parsers import set_gateway_parser, set_pod_parser
from jina.serve.runtimes.gateway import GatewayRuntime
from jina.serve.runtimes.worker import WorkerRuntime
from tests.helper import (
_validate_cu... | import json
import multiprocessing
import os
import time
import pytest
from jina.helper import random_port
from jina.parsers import set_gateway_parser, set_pod_parser
from jina.serve.runtimes.gateway import GatewayRuntime
from jina.serve.runtimes.worker import WorkerRuntime
from tests.helper import (
_validate_cu... |
_base_ = './vfnet_r50_fpn_1x_coco.py'
train_pipeline = [
dict(type='LoadImageFromFile', backend_args={{_base_.backend_args}}),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='RandomResize', scale=[(1333, 480), (1333, 960)],
keep_ratio=True),
dict(type='RandomFlip', prob=0.5),
... | _base_ = './vfnet_r50_fpn_1x_coco.py'
train_pipeline = [
dict(
type='LoadImageFromFile',
file_client_args={{_base_.file_client_args}}),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='RandomResize', scale=[(1333, 480), (1333, 960)],
keep_ratio=True),
dict(type='... |
# Copyright (c) OpenMMLab. All rights reserved.
import torch
def mask_matrix_nms(masks,
labels,
scores,
filter_thr=-1,
nms_pre=-1,
max_num=-1,
kernel='gaussian',
sigma=2.0,
... | # Copyright (c) OpenMMLab. All rights reserved.
import torch
def mask_matrix_nms(masks,
labels,
scores,
filter_thr=-1,
nms_pre=-1,
max_num=-1,
kernel='gaussian',
sigma=2.0,
... |
# Copyright (c) OpenMMLab. All rights reserved.
from ..builder import BBOX_CODERS
from ..transforms import bbox2distance, distance2bbox
from .base_bbox_coder import BaseBBoxCoder
@BBOX_CODERS.register_module()
class DistancePointBBoxCoder(BaseBBoxCoder):
"""Distance Point BBox coder.
This coder encodes gt bb... | from ..builder import BBOX_CODERS
from ..transforms import bbox2distance, distance2bbox
from .base_bbox_coder import BaseBBoxCoder
@BBOX_CODERS.register_module()
class DistancePointBBoxCoder(BaseBBoxCoder):
"""Distance Point BBox coder.
This coder encodes gt bboxes (x1, y1, x2, y2) into (top, bottom, left,
... |
_base_ = './retinanet_r50-caffe_fpn_ms-1x_coco.py'
# training schedule for 2x
train_cfg = dict(max_epochs=36)
# learning rate policy
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=36,
... | _base_ = './retinanet_r50_caffe_fpn_mstrain_1x_coco.py'
# training schedule for 2x
train_cfg = dict(max_epochs=36)
# learning rate policy
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=36,... |
from .cmuarctic import CMUARCTIC
from .cmudict import CMUDict
from .commonvoice import COMMONVOICE
from .dr_vctk import DR_VCTK
from .gtzan import GTZAN
from .librimix import LibriMix
from .librispeech import LIBRISPEECH
from .libritts import LIBRITTS
from .ljspeech import LJSPEECH
from .quesst14 import QUESST14
from .... | from .cmuarctic import CMUARCTIC
from .cmudict import CMUDict
from .commonvoice import COMMONVOICE
from .dr_vctk import DR_VCTK
from .gtzan import GTZAN
from .librimix import LibriMix
from .librispeech import LIBRISPEECH
from .libritts import LIBRITTS
from .ljspeech import LJSPEECH
from .speechcommands import SPEECHCOM... |
from torchvision.transforms import InterpolationMode # usort: skip
from ._utils import is_pure_tensor, register_kernel # usort: skip
from ._meta import (
clamp_bounding_boxes,
convert_bounding_box_format,
get_dimensions_image,
get_dimensions_video,
get_dimensions,
get_num_frames_video,
g... | from torchvision.transforms import InterpolationMode # usort: skip
from ._utils import is_pure_tensor, register_kernel # usort: skip
from ._meta import (
clamp_bounding_boxes,
convert_bounding_box_format,
get_dimensions_image,
_get_dimensions_image_pil,
get_dimensions_video,
get_dimensions,
... |
from jina import DocumentArray, Executor, Flow, requests
def test_gateway_metric_labels(monkeypatch_metric_exporter):
collect_metrics, read_metrics = monkeypatch_metric_exporter
class FirstExec(Executor):
@requests()
def meow(self, docs, **kwargs):
return DocumentArray.empty(3)
... | from jina import DocumentArray, Executor, Flow, requests
def test_gateway_metric_labels(monkeypatch_metric_exporter):
collect_metrics, read_metrics = monkeypatch_metric_exporter
class FirstExec(Executor):
@requests()
def meow(self, docs, **kwargs):
return DocumentArray.empty(3)
... |
import os
import warnings
from modulefinder import Module
import torch
from torchvision import datasets, io, models, ops, transforms, utils
from .extension import _HAS_OPS
try:
from .version import __version__ # noqa: F401
except ImportError:
pass
# Check if torchvision is being imported within the root f... | import os
import warnings
from modulefinder import Module
import torch
from torchvision import datasets, io, models, ops, transforms, utils
from .extension import _HAS_OPS
try:
from .version import __version__ # noqa: F401
except ImportError:
pass
# Check if torchvision is being imported within the root f... |
# Copyright (c) OpenMMLab. All rights reserved.
from .fileio import (FileClient, dict_from_file, dump, list_from_file, load,
register_handler)
from .misc import (check_prerequisites, concat_list, deprecated_api_warning,
has_method, import_modules_from_strings, is_list_of,
... | # Copyright (c) OpenMMLab. All rights reserved.
# type: ignore
from .fileio import (FileClient, dict_from_file, dump, list_from_file, load,
register_handler)
from .misc import (check_prerequisites, concat_list, deprecated_api_warning,
has_method, import_modules_from_strings, is_l... |
import numpy as np
import pytest
from keras.src import backend
from keras.src import initializers
from keras.src import layers
from keras.src import models
from keras.src import testing
class SpectralNormalizationTest(testing.TestCase):
@pytest.mark.requires_trainable_backend
def test_basic_spectralnorm(self... | import numpy as np
import pytest
from keras.src import backend
from keras.src import initializers
from keras.src import layers
from keras.src import models
from keras.src import testing
class SpectralNormalizationTest(testing.TestCase):
@pytest.mark.requires_trainable_backend
def test_basic_spectralnorm(self... |
import os
from unittest import TestCase
import cv2
import numpy as np
import torch
from mmengine.data import InstanceData, PixelData
from mmdet.evaluation import INSTANCE_OFFSET
from mmdet.structures import DetDataSample
from mmdet.visualization import DetLocalVisualizer
def _rand_bboxes(num_boxes, h, w):
cx, c... | import os
from unittest import TestCase
import cv2
import numpy as np
import torch
from mmengine.data import InstanceData, PixelData
from mmdet.evaluation import INSTANCE_OFFSET
from mmdet.structures import DetDataSample
from mmdet.visualization import DetLocalVisualizer
def _rand_bboxes(num_boxes, h, w):
cx, c... |
from typing import TypeVar
from docarray.typing.proto_register import _register_proto
from docarray.typing.tensor.image.abstract_image_tensor import AbstractImageTensor
from docarray.typing.tensor.torch_tensor import TorchTensor, metaTorchAndNode
T = TypeVar('T', bound='ImageTorchTensor')
@_register_proto(proto_typ... | from typing import TypeVar
from docarray.typing.proto_register import _register_proto
from docarray.typing.tensor.image.abstract_image_tensor import AbstractImageTensor
from docarray.typing.tensor.torch_tensor import TorchTensor, metaTorchAndNode
T = TypeVar('T', bound='ImageTorchTensor')
@_register_proto(proto_typ... |
from __future__ import annotations
from sentence_transformers.sparse_encoder.evaluation.SparseBinaryClassificationEvaluator import (
SparseBinaryClassificationEvaluator,
)
from sentence_transformers.sparse_encoder.evaluation.SparseEmbeddingSimilarityEvaluator import (
SparseEmbeddingSimilarityEvaluator,
)
from... | from __future__ import annotations
from sentence_transformers.sparse_encoder.evaluation.SparseEmbeddingSimilarityEvaluator import (
SparseEmbeddingSimilarityEvaluator,
)
from sentence_transformers.sparse_encoder.evaluation.SparseInformationRetrievalEvaluator import (
SparseInformationRetrievalEvaluator,
)
__a... |
_base_ = './cascade-mask-rcnn_r50_fpn_ms-3x_coco.py'
model = dict(
# ResNeXt-101-32x8d model trained with Caffe2 at FB,
# so the mean and std need to be changed.
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[103.530, 116.280, 123.675],
std=[57.375, 57.120, 58.395],
... | _base_ = './cascade-mask-rcnn_r50_fpn_ms-3x_coco.py'
model = dict(
# ResNeXt-101-32x8d model trained with Caffe2 at FB,
# so the mean and std need to be changed.
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[103.530, 116.280, 123.675],
std=[57.375, 57.120, 58.395],
... |
from keras.src.backend.config import backend
if backend() == "torch":
# When using the torch backend,
# torch needs to be imported first, otherwise it will segfault
# upon import.
import torch
from keras.src.api_export import keras_export
from keras.src.backend.common.dtypes import result_type
from ke... | from keras.src.backend.config import backend
if backend() == "torch":
# When using the torch backend,
# torch needs to be imported first, otherwise it will segfault
# upon import.
import torch
from keras.src.api_export import keras_export
from keras.src.backend.common.dtypes import result_type
from ke... |
# Copyright (c) OpenMMLab. All rights reserved.
import warnings
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import ConvModule
from mmcv.runner import BaseModule, auto_fp16, force_fp32
from mmdet.models.builder import HEADS, build_loss
@HEADS.register_module()
class FusedSemanticHead(BaseModu... | # Copyright (c) OpenMMLab. All rights reserved.
import warnings
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import ConvModule
from mmcv.runner import BaseModule, auto_fp16, force_fp32
from mmdet.models.builder import HEADS, build_loss
@HEADS.register_module()
class FusedSemanticHead(BaseModu... |
_base_ = './fcos_r50_fpn_gn-head-center-normbbox-centeronreg-giou_8xb8-amp-lsj-200e_coco.py' # noqa
model = dict(
backbone=dict(
depth=18,
init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet18')),
neck=dict(in_channels=[64, 128, 256, 512]))
| _base_ = './fcos_center-normbbox-centeronreg-giou_r50_fpn_gn-head_lsj_200e_8x8_fp16_coco.py' # noqa
model = dict(
backbone=dict(
depth=18,
init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet18')),
neck=dict(in_channels=[64, 128, 256, 512]))
|
# Licensed to the LF AI & Data foundation under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the "License");
# you may not use this fil... | from typing import Any, Dict, List, Optional, Union
from docarray.utils._internal.query_language.lookup import (
LookupLeaf,
LookupNode,
LookupTreeElem,
Q,
)
LOGICAL_OPERATORS: Dict[str, Union[str, bool]] = {
'$and': 'and',
'$or': 'or',
'$not': True,
}
COMPARISON_OPERATORS = {
'$lt': ... |
from __future__ import annotations
try:
from typing import Self
except ImportError:
from typing_extensions import Self
import torch
from torch import nn
from sentence_transformers.models.Module import Module
class CNN(Module):
"""CNN-layer with multiple kernel-sizes over the word embeddings"""
con... | from __future__ import annotations
import json
import os
import torch
from safetensors.torch import load_model as load_safetensors_model
from safetensors.torch import save_model as save_safetensors_model
from torch import nn
class CNN(nn.Module):
"""CNN-layer with multiple kernel-sizes over the word embeddings"... |
# mypy: allow-untyped-defs
import sys
from contextlib import contextmanager
from typing import TYPE_CHECKING
import torch
from torch.backends import (
__allow_nonbracketed_mutation,
_FP32Precision,
_get_fp32_precision_getter,
_set_fp32_precision_setter,
ContextProp,
PropModule,
)
def is_avail... | # mypy: allow-untyped-defs
import sys
from contextlib import contextmanager
from typing import TYPE_CHECKING
import torch
from torch.backends import __allow_nonbracketed_mutation, ContextProp, PropModule
def is_available():
r"""Return whether PyTorch is built with MKL-DNN support."""
return torch._C._has_mkl... |
from llama_index.core.schema import NodeRelationship, RelatedNodeInfo, TextNode
from llama_index.vector_stores.lancedb import LanceDBVectorStore
from llama_index.core import VectorStoreIndex
import lance # noqa: F401
import pytest
import pytest_asyncio
try:
from llama_index.embeddings.huggingface import HuggingF... | from llama_index.core.schema import NodeRelationship, RelatedNodeInfo, TextNode
from llama_index.vector_stores.lancedb import LanceDBVectorStore
from llama_index.core import VectorStoreIndex
import pytest
import pytest_asyncio
try:
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
from lanced... |
"""
Train XGBoost with cat_in_the_dat dataset
=========================================
A simple demo for categorical data support using dataset from Kaggle categorical data
tutorial.
The excellent tutorial is at:
https://www.kaggle.com/shahules/an-overview-of-encoding-techniques
And the data can be found at:
https:... | """
Train XGBoost with cat_in_the_dat dataset
=========================================
A simple demo for categorical data support using dataset from Kaggle categorical data
tutorial.
The excellent tutorial is at:
https://www.kaggle.com/shahules/an-overview-of-encoding-techniques
And the data can be found at:
https:... |
_base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth' # noqa
model = dict(
type... | _base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
pretrained = 'https://download.openmmlab.com/mmclassification/v0/swin-transformer/swin_tiny_224_b16x64_300e_imagenet_20210616_090925-66df6be6.pth'... |
from typing import TYPE_CHECKING, Any, Type, TypeVar, Union
from docarray.base_doc import BaseDoc
from docarray.typing.tensor.tensor import AnyTensor
from docarray.utils._internal.misc import import_library
T = TypeVar('T', bound='VerticesAndFaces')
class VerticesAndFaces(BaseDoc):
"""
Document for handling... | from typing import TYPE_CHECKING, Any, Type, TypeVar, Union
from docarray.base_doc import BaseDoc
from docarray.typing.tensor.tensor import AnyTensor
from docarray.utils._internal.misc import import_library
T = TypeVar('T', bound='VerticesAndFaces')
class VerticesAndFaces(BaseDoc):
"""
Document for handling... |
"""Determination of parameter bounds"""
# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause
from numbers import Real
import numpy as np
from ..preprocessing import LabelBinarizer
from ..utils._param_validation import Interval, StrOptions, validate_params
from ..utils.extmath import safe_s... | """Determination of parameter bounds"""
# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause
from numbers import Real
import numpy as np
from ..preprocessing import LabelBinarizer
from ..utils._param_validation import Interval, StrOptions, validate_params
from ..utils.extmath import safe_s... |
from typing import Dict
MISTRALAI_MODELS: Dict[str, int] = {
"mistral-tiny": 32000,
"mistral-small": 32000,
"mistral-medium": 32000,
"mistral-large": 131000,
"mistral-saba-latest": 32000,
"open-mixtral-8x7b": 32000,
"open-mistral-7b": 32000,
"open-mixtral-8x22b": 64000,
"mistral-sma... | from typing import Dict
MISTRALAI_MODELS: Dict[str, int] = {
"mistral-tiny": 32000,
"mistral-small": 32000,
"mistral-medium": 32000,
"mistral-large": 32000,
"open-mixtral-8x7b": 32000,
"open-mistral-7b": 32000,
"open-mixtral-8x22b": 64000,
"mistral-small-latest": 32000,
"mistral-med... |
"""Argparser module for WorkerRuntime"""
from jina.parsers.helper import KVAppendAction
def mixin_base_runtime_parser(arg_group):
"""Mixing in arguments required by any class that extends :class:`AsynNewLoopRuntime` into the given parser.
:param arg_group: the parser instance to which we add arguments
""... | """Argparser module for WorkerRuntime"""
from jina.parsers.helper import KVAppendAction
def mixin_base_runtime_parser(arg_group):
"""Mixing in arguments required by any class that extends :class:`AsynNewLoopRuntime` into the given parser.
:param arg_group: the parser instance to which we add arguments
""... |
# coding=utf-8
# Copyright 2024 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
"""
Utility that checks that mo... | # coding=utf-8
# Copyright 2024 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
"""
Utility that checks that mo... |
from typing import Any, Iterator, List, Optional
from urllib.parse import urljoin, urlparse
from langchain_core.documents import Document
from langchain_community.document_loaders.web_base import WebBaseLoader
class GitbookLoader(WebBaseLoader):
"""Load `GitBook` data.
1. load from either a single page, or... | from typing import Any, Iterator, List, Optional
from urllib.parse import urljoin, urlparse
from langchain_core.documents import Document
from langchain_community.document_loaders.web_base import WebBaseLoader
class GitbookLoader(WebBaseLoader):
"""Load `GitBook` data.
1. load from either a single page, or... |
"""Test Fireworks LLM."""
from typing import cast
from pydantic import SecretStr
from pytest import CaptureFixture, MonkeyPatch
from langchain_fireworks import Fireworks
def test_fireworks_api_key_is_secret_string() -> None:
"""Test that the API key is stored as a SecretStr."""
llm = Fireworks( # type: ig... | """Test Fireworks LLM"""
from typing import cast
from pydantic import SecretStr
from pytest import CaptureFixture, MonkeyPatch
from langchain_fireworks import Fireworks
def test_fireworks_api_key_is_secret_string() -> None:
"""Test that the API key is stored as a SecretStr."""
llm = Fireworks( # type: ign... |
__version__ = '0.12.2'
import os
from .document import Document
from .array import DocumentArray
from .dataclasses import dataclass, field
if 'DA_NO_RICH_HANDLER' not in os.environ:
from rich.traceback import install
install()
| __version__ = '0.12.1'
import os
from .document import Document
from .array import DocumentArray
from .dataclasses import dataclass, field
if 'DA_NO_RICH_HANDLER' not in os.environ:
from rich.traceback import install
install()
|
"""Standard LangChain interface tests"""
import pytest
from langchain_core.language_models import BaseChatModel
from langchain_core.rate_limiters import InMemoryRateLimiter
from langchain_core.tools import BaseTool
from langchain_tests.integration_tests import (
ChatModelIntegrationTests,
)
from langchain_groq im... | """Standard LangChain interface tests"""
from typing import Type
import pytest
from langchain_core.language_models import BaseChatModel
from langchain_core.rate_limiters import InMemoryRateLimiter
from langchain_core.tools import BaseTool
from langchain_tests.integration_tests import (
ChatModelIntegrationTests,
... |
from typing import Any, Dict, Optional, Union
import PIL.Image
import torch
from torchvision.prototype import features
from torchvision.prototype.transforms import functional as F, Transform
class ConvertBoundingBoxFormat(Transform):
_transformed_types = (features.BoundingBox,)
def __init__(self, format: U... | from typing import Any, Dict, Optional, Union
import PIL.Image
import torch
from torchvision.prototype import features
from torchvision.prototype.transforms import functional as F, Transform
class ConvertBoundingBoxFormat(Transform):
_transformed_types = (features.BoundingBox,)
def __init__(self, format: U... |
"""DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.api.utils import legacy
from keras.src.backend.common.global_state import clear_session
from keras.src.backend.common.keras_tensor import is_keras_tensor
from keras.src.backend.common.var... | """DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.api.utils import legacy
from keras.src.backend.common.global_state import clear_session
from keras.src.backend.common.keras_tensor import is_keras_tensor
from keras.src.backend.common.var... |
from llama_index.core.base.embeddings.base import BaseEmbedding
from llama_index.embeddings.huggingface import (
HuggingFaceEmbedding,
HuggingFaceInferenceAPIEmbedding,
)
def test_huggingfaceembedding_class():
names_of_base_classes = [b.__name__ for b in HuggingFaceEmbedding.__mro__]
assert BaseEmbedd... | from llama_index.core.base.embeddings.base import BaseEmbedding
from llama_index.embeddings.huggingface import (
HuggingFaceEmbedding,
HuggingFaceInferenceAPIEmbedding,
)
import pytest
def test_huggingfaceembedding_class():
names_of_base_classes = [b.__name__ for b in HuggingFaceEmbedding.__mro__]
ass... |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# This software may be used and distributed according to the terms of the GNU General Public License version 3.
from typing import Tuple
import os
import sys
import torch
import fire
import time
import json
from pathlib import Path
from fairscale.nn.model_parallel... | # Copyright (c) Meta Platforms, Inc. and affiliates.
# This software may be used and distributed according to the terms of the GNU General Public License version 3.
from typing import Tuple
import os
import sys
import torch
import fire
import time
import json
from pathlib import Path
from fairscale.nn.model_parallel... |
# 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 .ade20k import (ADE20KInstanceDataset, ADE20KPanopticDataset,
ADE20KSegDataset)
from .base_det_dataset import BaseDetDataset
from .base_semseg_dataset import BaseSegDataset
from .base_video_dataset import BaseVideoDataset
from .cityscapes import ... | # 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... |
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