input stringlengths 33 5k | output stringlengths 32 5k |
|---|---|
# Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
import tempfile
from unittest import TestCase
from unittest.mock import Mock
import torch
import torch.nn as nn
from mmengine.model import BaseModel
from mmengine.optim import OptimWrapper
from mmengine.registry import MODEL_WRAPPERS
from mmengine.r... | # Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
import tempfile
from unittest import TestCase
from unittest.mock import Mock
import torch
import torch.nn as nn
from mmengine.model import BaseModel
from mmengine.optim import OptimWrapper
from mmengine.registry import DATASETS, MODEL_WRAPPERS
from ... |
from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.agent_toolkits.powerbi.prompt import (
POWERBI_CHAT_PREFIX,
POWERBI_CHAT_SUFFIX,
POWERBI_PREFIX,
POWERBI_SUFFIX,
)
# Create a way to dynamically look up ... | from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.agent_toolkits.powerbi.prompt import (
POWERBI_CHAT_PREFIX,
POWERBI_CHAT_SUFFIX,
POWERBI_PREFIX,
POWERBI_SUFFIX,
)
# Create a way to dynamically look up ... |
from __future__ import annotations
import collections
import json
import os
import string
from typing import Iterable
from .WordTokenizer import ENGLISH_STOP_WORDS, WordTokenizer
class WhitespaceTokenizer(WordTokenizer):
"""
Simple and fast white-space tokenizer. Splits sentence based on white spaces.
P... | import collections
import json
import os
import string
from typing import Iterable, List
from .WordTokenizer import ENGLISH_STOP_WORDS, WordTokenizer
class WhitespaceTokenizer(WordTokenizer):
"""
Simple and fast white-space tokenizer. Splits sentence based on white spaces.
Punctuation are stripped from t... |
from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.callbacks.openai_info import OpenAICallbackHandler
# 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.callbacks.openai_info import OpenAICallbackHandler
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for raising deprecation warnings and
# handling opti... |
_base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
# model settings
preprocess_cfg = dict(
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True,
pad_size_divisor=32)
model = dict(
preprocess_cfg=prepr... | _base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
type='TOOD',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=d... |
"""
This is a simple application for sparse encoder: Computing embeddings.
we have multiple sentences and we want to compute their embeddings.
The embeddings are sparse, meaning that most of the values are zero.
The embeddings are stored in a sparse matrix format, which is more efficient for storage and computation.
w... | """
This is a simple application for sparse encoder: Computing embeddings.
we have multiple sentences and we want to compute their embeddings.
The embeddings are sparse, meaning that most of the values are zero.
The embeddings are stored in a sparse matrix format, which is more efficient for storage and computation.
w... |
_base_ = 'faster-rcnn_regnetx-3.2GF_fpn_ms-3x_coco.py'
model = dict(
backbone=dict(
type='RegNet',
arch='regnetx_4.0gf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init_cfg=... | _base_ = 'faster_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py'
model = dict(
backbone=dict(
type='RegNet',
arch='regnetx_4.0gf',
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
init... |
from typing import Any, Dict, Optional, Type
from langchain_core.callbacks import CallbackManagerForToolRun
from langchain_core.language_models import BaseLanguageModel
from pydantic import BaseModel, Field, model_validator
from langchain_community.chat_models import ChatOpenAI
from langchain_community.tools.amadeus.... | from typing import Any, Dict, Optional, Type
from langchain_core.callbacks import CallbackManagerForToolRun
from langchain_core.language_models import BaseLanguageModel
from pydantic import BaseModel, Field, model_validator
from langchain_community.chat_models import ChatOpenAI
from langchain_community.tools.amadeus.... |
"""Standard LangChain interface tests"""
from langchain_core.language_models import BaseChatModel
from langchain_tests.unit_tests import ChatModelUnitTests
from langchain_openai import ChatOpenAI
class TestOpenAIStandard(ChatModelUnitTests):
@property
def chat_model_class(self) -> type[BaseChatModel]:
... | """Standard LangChain interface tests"""
from typing import Tuple, Type
from langchain_core.language_models import BaseChatModel
from langchain_tests.unit_tests import ChatModelUnitTests
from langchain_openai import ChatOpenAI
class TestOpenAIStandard(ChatModelUnitTests):
@property
def chat_model_class(sel... |
from enum import Enum
from langchain_core.exceptions import OutputParserException
from langchain_core.output_parsers import BaseOutputParser
from langchain_core.utils import pre_init
class EnumOutputParser(BaseOutputParser[Enum]):
"""Parse an output that is one of a set of values."""
enum: type[Enum]
""... | from enum import Enum
from langchain_core.exceptions import OutputParserException
from langchain_core.output_parsers import BaseOutputParser
from langchain_core.utils import pre_init
class EnumOutputParser(BaseOutputParser[Enum]):
"""Parse an output that is one of a set of values."""
enum: type[Enum]
""... |
"""Cohere Reranker Finetuning Engine."""
import importlib.util
import os
from typing import Optional
from llama_index.finetuning.types import BaseCohereRerankerFinetuningEngine
from llama_index.postprocessor.cohere_rerank import CohereRerank
class CohereRerankerFinetuneEngine(BaseCohereRerankerFinetuningEngine):
... | """Cohere Reranker Finetuning Engine."""
import importlib.util
import os
from typing import Optional
from llama_index.finetuning.types import BaseCohereRerankerFinetuningEngine
from llama_index.postprocessor.cohere_rerank import CohereRerank
class CohereRerankerFinetuneEngine(BaseCohereRerankerFinetuningEngine):
... |
import logging
from datasets import load_dataset
from sentence_transformers import SparseEncoder
from sentence_transformers.sparse_encoder.evaluation import SparseTripletEvaluator
logging.basicConfig(format="%(message)s", level=logging.INFO)
# Load a model
model = SparseEncoder("naver/splade-cocondenser-ensembledis... | import logging
from datasets import load_dataset
from sentence_transformers import SparseEncoder
from sentence_transformers.sparse_encoder.evaluation import SparseTripletEvaluator
logging.basicConfig(format="%(message)s", level=logging.INFO)
# Load a model
model = SparseEncoder("naver/splade-cocondenser-ensembledis... |
import json
import os
from typing import List
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"""
def ... | import json
import os
from typing import List
import torch
from torch import nn
class CNN(nn.Module):
"""CNN-layer with multiple kernel-sizes over the word embeddings"""
def __init__(
self,
in_word_embedding_dimension: int,
out_channels: int = 256,
kernel_sizes: List[int] = [... |
# Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
import torch
from mmdet.utils.util_random import ensure_rng
def random_boxes(num=1, scale=1, rng=None):
"""Simple version of ``kwimage.Boxes.random``
Returns:
Tensor: shape (n, 4) in x1, y1, x2, y2 format.
References:
ht... | import numpy as np
import torch
from mmdet.utils.util_random import ensure_rng
def random_boxes(num=1, scale=1, rng=None):
"""Simple version of ``kwimage.Boxes.random``
Returns:
Tensor: shape (n, 4) in x1, y1, x2, y2 format.
References:
https://gitlab.kitware.com/computer-vision/kwimage... |
"""
This script contains an example how to perform re-ranking with a Cross-Encoder for semantic search.
First, we use an efficient Bi-Encoder to retrieve similar questions from the Quora Duplicate Questions dataset:
https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs
Then, we re-rank the hits... | """
This script contains an example how to perform re-ranking with a Cross-Encoder for semantic search.
First, we use an efficient Bi-Encoder to retrieve similar questions from the Quora Duplicate Questions dataset:
https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs
Then, we re-rank the hits... |
from typing import Dict, List, Optional, Set, Tuple
import numpy as np
import pytest
import torch
from docarray import DocumentArray
from docarray.base_document import BaseDocument
from docarray.typing import NdArray, TorchTensor
@pytest.mark.proto
def test_proto_simple():
class CustomDoc(BaseDocument):
... | from typing import Optional, Dict, List, Set, Tuple
import numpy as np
import pytest
import torch
from docarray import DocumentArray
from docarray.base_document import BaseDocument
from docarray.typing import NdArray, TorchTensor
@pytest.mark.proto
def test_proto_simple():
class CustomDoc(BaseDocument):
... |
# Owner(s): ["module: dynamo"]
import unittest
import torch
import torch._dynamo.test_case
from torch._dynamo.testing import CompileCounter, EagerAndRecordGraphs, normalize_gm
from torch.testing._internal.common_cuda import TEST_CUDA
from torch.testing._internal.common_utils import TEST_XPU
device_type = (
acc.t... | # Owner(s): ["module: dynamo"]
import unittest
import torch
import torch._dynamo.test_case
from torch._dynamo.testing import CompileCounter, EagerAndRecordGraphs, normalize_gm
from torch.testing._internal.common_cuda import TEST_CUDA
class PythonDispatcherTests(torch._dynamo.test_case.TestCase):
def test_dispatc... |
# Copyright 2020 The HuggingFace Datasets Authors and the TensorFlow Datasets Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# U... | # Copyright 2020 The HuggingFace Datasets Authors and the TensorFlow Datasets Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# U... |
from typing import TYPE_CHECKING
from docarray.array.storage.qdrant.backend import BackendMixin, QdrantConfig
from docarray.array.storage.qdrant.find import FindMixin
from docarray.array.storage.qdrant.getsetdel import GetSetDelMixin
from docarray.array.storage.qdrant.helper import DISTANCES
from docarray.array.storag... | from typing import TYPE_CHECKING
from docarray.array.storage.qdrant.backend import BackendMixin, QdrantConfig
from docarray.array.storage.qdrant.find import FindMixin
from docarray.array.storage.qdrant.getsetdel import GetSetDelMixin
from docarray.array.storage.qdrant.helper import DISTANCES
from docarray.array.storag... |
from __future__ import annotations
from sentence_transformers.sparse_encoder.evaluation.ReciprocalRankFusionEvaluator import (
ReciprocalRankFusionEvaluator,
)
from sentence_transformers.sparse_encoder.evaluation.SparseBinaryClassificationEvaluator import (
SparseBinaryClassificationEvaluator,
)
from sentence_... | from __future__ import annotations
from sentence_transformers.sparse_encoder.evaluation.SparseBinaryClassificationEvaluator import (
SparseBinaryClassificationEvaluator,
)
from sentence_transformers.sparse_encoder.evaluation.SparseEmbeddingSimilarityEvaluator import (
SparseEmbeddingSimilarityEvaluator,
)
from... |
from typing import Any
from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
from backend.data.model import SchemaField
from backend.util.json import json
class StepThroughItemsBlock(Block):
class Input(BlockSchema):
items: list = SchemaField(
advanced=False,
... | from typing import Any
from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
from backend.data.model import SchemaField
from backend.util.json import json
class StepThroughItemsBlock(Block):
class Input(BlockSchema):
items: list = SchemaField(
advanced=False,
... |
# Copyright (c) OpenMMLab. All rights reserved.
from .checkloss_hook import CheckInvalidLossHook
from .memory_profiler_hook import MemoryProfilerHook
from .num_class_check_hook import NumClassCheckHook
from .set_epoch_info_hook import SetEpochInfoHook
from .sync_norm_hook import SyncNormHook
from .visualization_hook im... | # Copyright (c) OpenMMLab. All rights reserved.
from .checkloss_hook import CheckInvalidLossHook
from .memory_profiler_hook import MemoryProfilerHook
from .num_class_check_hook import NumClassCheckHook
from .set_epoch_info_hook import SetEpochInfoHook
from .sync_norm_hook import SyncNormHook
from .yolox_mode_switch_hoo... |
"""DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.initializers import deserialize
from keras.src.initializers import get
from keras.src.initializers import serialize
from keras.src.initializers.constant_initializers import STFT
from ... | """DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.initializers import deserialize
from keras.src.initializers import get
from keras.src.initializers import serialize
from keras.src.initializers.constant_initializers import Constant
f... |
"""Document compressor."""
from __future__ import annotations
from abc import ABC, abstractmethod
from typing import TYPE_CHECKING, Optional
from pydantic import BaseModel
from langchain_core.runnables import run_in_executor
if TYPE_CHECKING:
from collections.abc import Sequence
from langchain_core.callba... | from __future__ import annotations
from abc import ABC, abstractmethod
from typing import TYPE_CHECKING, Optional
from pydantic import BaseModel
from langchain_core.runnables import run_in_executor
if TYPE_CHECKING:
from collections.abc import Sequence
from langchain_core.callbacks import Callbacks
fro... |
"""Init file."""
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 import BrowserbaseWebReader
from llama_index.readers.web.firecrawl_web.base import... | """Init file."""
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 import BrowserbaseWebReader
from llama_index.readers.web.firecrawl_web.base import... |
_base_ = './libra-faster-rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| _base_ = './libra_faster_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
|
_base_ = './mask-rcnn_r50_fpn_seesaw-loss_random-ms-2x_lvis-v1.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| _base_ = './mask_rcnn_r50_fpn_random_seesaw_loss_mstrain_2x_lvis_v1.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
|
from typing import Any, Optional
from typing_extensions import get_origin
from typing_inspect import get_args, is_typevar, is_union_type
from docarray.typing.tensor.abstract_tensor import AbstractTensor
from typing import ForwardRef
def is_type_tensor(type_: Any) -> bool:
"""Return True if type is a type Tensor... | from typing import Any, Optional
from typing_extensions import get_origin
from typing_inspect import get_args, is_typevar, is_union_type
from docarray.typing.tensor.abstract_tensor import AbstractTensor
def is_type_tensor(type_: Any) -> bool:
"""Return True if type is a type Tensor or an Optional Tensor type.""... |
from collections import OrderedDict
import pytest
from docarray.array.chunk import ChunkArray
from jina import Client, Document, DocumentArray, Executor, Flow, requests
from jina.helper import random_port
class DummyExecutor(Executor):
def __init__(self, mode=None, *args, **kwargs):
super().__init__(*ar... | from collections import OrderedDict
import pytest
from docarray.array.chunk import ChunkArray
from jina import Client, Document, DocumentArray, Executor, Flow, requests
from jina.helper import random_port
class DummyExecutor(Executor):
def __init__(self, mode=None, *args, **kwargs):
super().__init__(*ar... |
from __future__ import annotations
from typing import Any, Optional, Union
import torch
from ._datapoint import Datapoint
class Video(Datapoint):
"""[BETA] :class:`torch.Tensor` subclass for videos.
Args:
data (tensor-like): Any data that can be turned into a tensor with :func:`torch.as_tensor`.
... | from __future__ import annotations
from typing import Any, Optional, Union
import torch
from ._datapoint import Datapoint
class Video(Datapoint):
"""[BETA] :class:`torch.Tensor` subclass for videos.
Args:
data (tensor-like): Any data that can be turned into a tensor with :func:`torch.as_tensor`.
... |
from typing import List
from llama_index.core.prompts.base import BasePromptTemplate
def get_empty_prompt_txt(prompt: BasePromptTemplate) -> str:
"""
Get empty prompt text.
Substitute empty strings in parts of the prompt that have
not yet been filled out. Skip variables that have already
been pa... | from typing import List
from llama_index.core.prompts.base import BasePromptTemplate
def get_empty_prompt_txt(prompt: BasePromptTemplate) -> str:
"""
Get empty prompt text.
Substitute empty strings in parts of the prompt that have
not yet been filled out. Skip variables that have already
been pa... |
"""Tests related to the `DataIter` interface."""
from typing import Callable, Optional
import numpy as np
from xgboost import testing as tm
from ..core import DataIter, DMatrix, ExtMemQuantileDMatrix, QuantileDMatrix
def run_mixed_sparsity(device: str) -> None:
"""Check QDM with mixed batches."""
X_0, y_0... | """Tests related to the `DataIter` interface."""
from typing import Callable, Optional
import numpy as np
from xgboost import testing as tm
from ..core import DataIter, ExtMemQuantileDMatrix, QuantileDMatrix
def run_mixed_sparsity(device: str) -> None:
"""Check QDM with mixed batches."""
X_0, y_0, _ = tm.... |
"""Parsing utils to go from string to AgentAction or Agent Finish.
AgentAction means that an action should be taken.
This contains the name of the tool to use, the input to pass to that tool,
and a `log` variable (which contains a log of the agent's thinking).
AgentFinish means that a response should be given.
This c... | """Parsing utils to go from string to AgentAction or Agent Finish.
AgentAction means that an action should be taken.
This contains the name of the tool to use, the input to pass to that tool,
and a `log` variable (which contains a log of the agent's thinking).
AgentFinish means that a response should be given.
This c... |
from llama_index_instrumentation.span_handlers.base import BaseSpanHandler
from llama_index_instrumentation.span_handlers.null import NullSpanHandler
from llama_index_instrumentation.span_handlers.simple import SimpleSpanHandler
__all__ = [
"BaseSpanHandler",
"NullSpanHandler",
"SimpleSpanHandler",
]
| from llama_index.core.instrumentation.span_handlers.base import BaseSpanHandler
from llama_index.core.instrumentation.span_handlers.null import NullSpanHandler
from llama_index.core.instrumentation.span_handlers.simple import SimpleSpanHandler
__all__ = [
"BaseSpanHandler",
"NullSpanHandler",
"SimpleSpanH... |
import importlib
from types import ModuleType
import pytest
from fastapi.exceptions import FastAPIError
from fastapi.testclient import TestClient
from ...utils import needs_py39
@pytest.fixture(
name="mod",
params=[
"tutorial008c",
"tutorial008c_an",
pytest.param("tutorial008c_an_py3... | import pytest
from fastapi.exceptions import FastAPIError
from fastapi.testclient import TestClient
@pytest.fixture(name="client")
def get_client():
from docs_src.dependencies.tutorial008c import app
client = TestClient(app)
return client
def test_get_no_item(client: TestClient):
response = client.... |
# ruff: noqa
__all__ = [
"Audio",
"Array2D",
"Array3D",
"Array4D",
"Array5D",
"ClassLabel",
"Features",
"Sequence",
"Value",
"Image",
"Translation",
"TranslationVariableLanguages",
]
from .audio import Audio
from .features import Array2D, Array3D, Array4D, Array5D, Class... | # flake8: noqa
__all__ = [
"Audio",
"Array2D",
"Array3D",
"Array4D",
"Array5D",
"ClassLabel",
"Features",
"Sequence",
"Value",
"Image",
"Translation",
"TranslationVariableLanguages",
]
from .audio import Audio
from .features import Array2D, Array3D, Array4D, Array5D, Cla... |
import numpy as np
import pytest
from keras.src import layers
from keras.src import models
from keras.src import ops
from keras.src import testing
from keras.src.saving import load_model
class MaskingTest(testing.TestCase):
@pytest.mark.requires_trainable_backend
def test_masking_basics(self):
self.r... | import numpy as np
import pytest
from keras.src import layers
from keras.src import models
from keras.src import testing
class MaskingTest(testing.TestCase):
@pytest.mark.requires_trainable_backend
def test_masking_basics(self):
self.run_layer_test(
layers.Masking,
init_kwargs... |
import gzip
import logging
import os
from datetime import datetime
from torch.utils.data import DataLoader
from sentence_transformers import InputExample, LoggingHandler, SentenceTransformer, evaluation, losses, models, util
#### Just some code to print debug information to stdout
logging.basicConfig(
format="%(... | from sentence_transformers import SentenceTransformer, LoggingHandler, InputExample
from sentence_transformers import models, util, evaluation, losses
import logging
import os
import gzip
from torch.utils.data import DataLoader
from datetime import datetime
#### Just some code to print debug information to stdout
log... |
from typing import List
import pytest
from sqlalchemy import create_engine, text
from llama_index.readers.database import DatabaseReader
from llama_index.core.schema import Document
# --------------------------------------------------------------------------- #
# Fixtures
# -----------------------------------------... | from typing import List
import pytest
from sqlalchemy import create_engine, text
from llama_index.readers.database import DatabaseReader
from llama_index.core.schema import Document
# --------------------------------------------------------------------------- #
# Fixtures
# -----------------------------------------... |
"""
Top-level module of Jina.
The primary function of this module is to import all of the public Jina
interfaces into a single place. The interfaces themselves are located in
sub-modules, as described below.
"""
import os as _os
import platform as _platform
import signal as _signal
import sys as _sys
import warnings... | """
Top-level module of Jina.
The primary function of this module is to import all of the public Jina
interfaces into a single place. The interfaces themselves are located in
sub-modules, as described below.
"""
import os as _os
import platform as _platform
import signal as _signal
import sys as _sys
import warnings... |
"""DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.tree.tree_api import MAP_TO_NONE as MAP_TO_NONE
from keras.src.tree.tree_api import assert_same_paths as assert_same_paths
from keras.src.tree.tree_api import (
assert_same_struct... | """DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.tree.tree_api import MAP_TO_NONE
from keras.src.tree.tree_api import assert_same_paths
from keras.src.tree.tree_api import assert_same_structure
from keras.src.tree.tree_api import fl... |
"""
This is a simple application for sentence embeddings: semantic search
We have a corpus with various sentences. Then, for a given query sentence,
we want to find the most similar sentence in this corpus.
This script outputs for various queries the top 5 most similar sentences in the corpus.
"""
from sentence_tran... | """
This is a simple application for sentence embeddings: semantic search
We have a corpus with various sentences. Then, for a given query sentence,
we want to find the most similar sentence in this corpus.
This script outputs for various queries the top 5 most similar sentences in the corpus.
"""
from sentence_tran... |
import os
from shutil import rmtree
from typing import Callable, Dict, List, Optional
import tqdm
from llama_index.core.base.base_retriever import BaseRetriever
from llama_index.core.postprocessor.types import BaseNodePostprocessor
from llama_index.core.schema import Document, QueryBundle
from llama_index.core.utils i... | import os
from shutil import rmtree
from typing import Callable, Dict, List, Optional
import tqdm
from llama_index.core.base.base_retriever import BaseRetriever
from llama_index.core.postprocessor.types import BaseNodePostprocessor
from llama_index.core.schema import Document, QueryBundle
from llama_index.core.utils i... |
from keras.src.api_export import keras_export
from keras.src.layers.preprocessing.image_preprocessing.base_image_preprocessing_layer import ( # noqa: E501
BaseImagePreprocessingLayer,
)
from keras.src.random.seed_generator import SeedGenerator
@keras_export("keras.layers.RandomContrast")
class RandomContrast(Bas... | from keras.src.api_export import keras_export
from keras.src.layers.preprocessing.image_preprocessing.base_image_preprocessing_layer import ( # noqa: E501
BaseImagePreprocessingLayer,
)
from keras.src.random.seed_generator import SeedGenerator
@keras_export("keras.layers.RandomContrast")
class RandomContrast(Bas... |
import zlib
from typing import Iterator, TextIO
def exact_div(x, y):
assert x % y == 0
return x // y
def str2bool(string):
str2val = {"True": True, "False": False}
if string in str2val:
return str2val[string]
else:
raise ValueError(f"Expected one of {set(str2val.keys())}, got {st... | import zlib
from typing import Iterator, TextIO
def exact_div(x, y):
assert x % y == 0
return x // y
def str2bool(string):
str2val = {"True": True, "False": False}
if string in str2val:
return str2val[string]
else:
raise ValueError(f"Expected one of {set(str2val.keys())}, got {st... |
from typing import List, Optional
from llama_index.core.data_structs.data_structs import IndexStruct
from llama_index.core.storage.index_store.types import BaseIndexStore
from llama_index.core.storage.index_store.utils import (
index_struct_to_json,
json_to_index_struct,
)
from llama_index.core.storage.kvstore... | from typing import List, Optional
from llama_index.core.data_structs.data_structs import IndexStruct
from llama_index.core.storage.index_store.types import BaseIndexStore
from llama_index.core.storage.index_store.utils import (
index_struct_to_json,
json_to_index_struct,
)
from llama_index.core.storage.kvstore... |
from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.graphs.graph_document import (
GraphDocument,
Node,
Relationship,
)
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for rai... | from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.graphs.graph_document import (
GraphDocument,
Node,
Relationship,
)
# Create a way to dynamically look up deprecated imports.
# Used to consolidate logic for rai... |
from typing import List, _LiteralGenericAlias, get_args, Tuple
import kuzu
Triple = Tuple[str, str, str]
def create_fresh_database(db: str) -> None:
"""
Create a new Kùzu database by removing existing database directory and its contents.
"""
import shutil
shutil.rmtree(db, ignore_errors=True)
... | from typing import List, _LiteralGenericAlias, get_args, Tuple
import kuzu
Triple = Tuple[str, str, str]
def create_fresh_database(db: str) -> None:
"""
Create a new Kùzu database by removing existing database directory and its contents.
"""
import shutil
shutil.rmtree(db, ignore_errors=True)
... |
# CREDITS: https://github.com/openai/CLIP
import gzip
import html
from functools import lru_cache
from pathlib import Path
import ftfy
import regex as re
@lru_cache()
def default_bpe():
return str(Path(__file__).parents[2] / '.cache/bpe_simple_vocab_16e6.txt.gz')
@lru_cache()
def bytes_to_unicode():
"""
... | # CREDITS: https://github.com/openai/CLIP
import gzip
import html
import os
from functools import lru_cache
import ftfy
import regex as re
@lru_cache()
def default_bpe():
return os.path.join(os.getcwd(), '.cache', 'bpe_simple_vocab_16e6.txt.gz')
@lru_cache()
def bytes_to_unicode():
"""
Returns list of... |
"""Init file of LlamaIndex."""
__version__ = "0.12.27"
import logging
from logging import NullHandler
from typing import Callable, Optional
try:
# Force pants to install eval_type_backport on 3.9
import eval_type_backport # noqa # type: ignore
except ImportError:
pass
# response
from llama_index.core.... | """Init file of LlamaIndex."""
__version__ = "0.12.26"
import logging
from logging import NullHandler
from typing import Callable, Optional
try:
# Force pants to install eval_type_backport on 3.9
import eval_type_backport # noqa # type: ignore
except ImportError:
pass
# response
from llama_index.core.... |
"""Retriever tool."""
from typing import TYPE_CHECKING, Any, List, Optional
from llama_index.core.base.base_retriever import BaseRetriever
if TYPE_CHECKING:
from llama_index.core.langchain_helpers.agents.tools import LlamaIndexTool
from llama_index.core.schema import (
MetadataMode,
Node,
NodeWithSc... | """Retriever tool."""
from typing import TYPE_CHECKING, Any, List, Optional
from llama_index.core.base.base_retriever import BaseRetriever
if TYPE_CHECKING:
from llama_index.core.langchain_helpers.agents.tools import LlamaIndexTool
from llama_index.core.schema import (
MetadataMode,
Node,
NodeWithSc... |
# Copyright (c) OpenMMLab. All rights reserved.
from .amp import autocast
from .base_loop import BaseLoop
from .checkpoint import (CheckpointLoader, find_latest_checkpoint,
get_deprecated_model_names, get_external_models,
get_mmcls_models, get_state_dict,
... | # Copyright (c) OpenMMLab. All rights reserved.
from .amp import autocast
from .base_loop import BaseLoop
from .checkpoint import (CheckpointLoader, find_latest_checkpoint,
get_deprecated_model_names, get_external_models,
get_mmcls_models, get_state_dict,
... |
_base_ = './cascade-rcnn_hrnetv2p-w32-20e_coco.py'
# model settings
model = dict(
backbone=dict(
extra=dict(
stage2=dict(num_channels=(18, 36)),
stage3=dict(num_channels=(18, 36, 72)),
stage4=dict(num_channels=(18, 36, 72, 144))),
init_cfg=dict(
type='... | _base_ = './cascade_rcnn_hrnetv2p_w32_20e_coco.py'
# model settings
model = dict(
backbone=dict(
extra=dict(
stage2=dict(num_channels=(18, 36)),
stage3=dict(num_channels=(18, 36, 72)),
stage4=dict(num_channels=(18, 36, 72, 144))),
init_cfg=dict(
type='... |
# training schedule for 20e
train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=20, val_interval=1)
val_cfg = dict(type='ValLoop')
test_cfg = dict(type='TestLoop')
# learning rate
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='M... | # training schedule for 20e
train_cfg = dict(by_epoch=True, max_epochs=20)
val_cfg = dict(interval=1)
test_cfg = dict()
# learning rate
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
begin=0,
end=20,
... |
import logging
import traceback
from datasets import load_dataset
from sentence_transformers.cross_encoder import CrossEncoder
from sentence_transformers.cross_encoder.evaluation import CrossEncoderNanoBEIREvaluator
from sentence_transformers.cross_encoder.losses.BinaryCrossEntropyLoss import BinaryCrossEntropyLoss
f... | import logging
import traceback
from datasets import load_dataset
from sentence_transformers.cross_encoder import CrossEncoder
from sentence_transformers.cross_encoder.evaluation import CrossEncoderNanoBEIREvaluator
from sentence_transformers.cross_encoder.losses.BinaryCrossEntropyLoss import BinaryCrossEntropyLoss
f... |
import multiprocessing
import socket
import sys
import time
import numpy as np
import pytest
import xgboost as xgb
from xgboost import RabitTracker, build_info, federated
if sys.platform.startswith("win"):
pytest.skip("Skipping collective tests on Windows", allow_module_level=True)
def run_rabit_worker(rabit_e... | import multiprocessing
import socket
import sys
import time
import numpy as np
import pytest
import xgboost as xgb
from xgboost import RabitTracker, build_info, federated
if sys.platform.startswith("win"):
pytest.skip("Skipping collective tests on Windows", allow_module_level=True)
def run_rabit_worker(rabit_e... |
_base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
teacher_ckpt = 'https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r101_fpn_mstrain_2x_coco/gfl_r101_fpn_mstrain_2x_coco_20200629_200126-dd12f847.pth' # noqa
model = dict(
type='Kn... | _base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
teacher_ckpt = 'https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r101_fpn_mstrain_2x_coco/gfl_r101_fpn_mstrain_2x_coco_20200629_200126-dd12f847.pth' # noqa
model = dict(
type='Kn... |
from datetime import datetime
from typing import Any, List
from backend.blocks.exa._auth import (
ExaCredentials,
ExaCredentialsField,
ExaCredentialsInput,
)
from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
from backend.data.model import SchemaField
from backend.util.request im... | from datetime import datetime
from typing import Any, List
from backend.blocks.exa._auth import (
ExaCredentials,
ExaCredentialsField,
ExaCredentialsInput,
)
from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
from backend.data.model import SchemaField
from backend.util.request im... |
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
pytestmark = pytest.mark.integration
@pytest.mark.parametrize("path", ["paws", "csv"])
def test_inspect_dataset(p... | import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
pytestmark = pytest.mark.integration
@pytest.mark.parametrize("path", ["paws", "csv"])
def test_inspect_dataset(p... |
import sys
import traceback
from importlib.machinery import SourceFileLoader
if __name__ == "__main__":
files = sys.argv[1:]
has_failure = False
for file in files:
try:
SourceFileLoader("x", file).load_module()
except Exception:
has_faillure = True
traceb... | import sys
import traceback
from importlib.machinery import SourceFileLoader
if __name__ == "__main__":
files = sys.argv[1:]
has_failure = False
for file in files:
try:
SourceFileLoader("x", file).load_module()
except Exception:
has_faillure = True
print(... |
# Copyright (c) OpenMMLab. All rights reserved.
import math
from typing import Optional
import torch
import torch.nn as nn
from mmengine.model import ExponentialMovingAverage
from torch import Tensor
from mmdet.registry import MODELS
@MODELS.register_module()
class ExpMomentumEMA(ExponentialMovingAverage):
"""E... | # Copyright (c) OpenMMLab. All rights reserved.
import math
from typing import Optional
import torch
import torch.nn as nn
from mmengine.model import ExponentialMovingAverage
from torch import Tensor
from mmdet.registry import MODELS
@MODELS.register_module()
class ExpMomentumEMA(ExponentialMovingAverage):
"""E... |
import types
from keras.src.activations.activations import celu
from keras.src.activations.activations import elu
from keras.src.activations.activations import exponential
from keras.src.activations.activations import gelu
from keras.src.activations.activations import glu
from keras.src.activations.activations import ... | import types
from keras.src.activations.activations import celu
from keras.src.activations.activations import elu
from keras.src.activations.activations import exponential
from keras.src.activations.activations import gelu
from keras.src.activations.activations import glu
from keras.src.activations.activations import ... |
import numpy as np
import torch
from docarray import Document
from docarray.document import AnyDocument
from docarray.typing import AnyUrl, Embedding, ImageUrl, Tensor, TorchTensor
def test_proto_all_types():
class Mymmdoc(Document):
tensor: Tensor
torch_tensor: TorchTensor
embedding: Emb... | import numpy as np
import torch
from docarray import Document
from docarray.document import AnyDocument
from docarray.typing import AnyUrl, Embedding, ImageUrl, Tensor, TorchTensor
def test_proto_all_types():
class Mymmdoc(Document):
tensor: Tensor
torch_tensor: TorchTensor
embedding: Emb... |
"""Auto Merging Retriever."""
from typing import Any, Dict, List
from llama_index.core import VectorStoreIndex
from llama_index.core.llama_pack.base import BaseLlamaPack
from llama_index.core.node_parser import (
HierarchicalNodeParser,
get_leaf_nodes,
)
from llama_index.core.query_engine import RetrieverQuer... | """Auto Merging Retriever."""
from typing import Any, Dict, List
from llama_index.core import VectorStoreIndex
from llama_index.core.llama_pack.base import BaseLlamaPack
from llama_index.core.node_parser import (
HierarchicalNodeParser,
get_leaf_nodes,
)
from llama_index.core.query_engine import RetrieverQuer... |
# Copyright 2024 The OpenXLA Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in ... | # Copyright 2024 The OpenXLA Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in ... |
"""Run smoke tests"""
import os
import sys
from pathlib import Path
import torch
import torchvision
from torchvision.io import decode_image, decode_jpeg, decode_webp, read_file
from torchvision.models import resnet50, ResNet50_Weights
SCRIPT_DIR = Path(__file__).parent
def smoke_test_torchvision() -> None:
pr... | """Run smoke tests"""
import os
import sys
from pathlib import Path
import torch
import torchvision
from torchvision.io import decode_jpeg, decode_webp, read_file, read_image
from torchvision.models import resnet50, ResNet50_Weights
SCRIPT_DIR = Path(__file__).parent
def smoke_test_torchvision() -> None:
prin... |
_base_ = './ms_rcnn_r101_caffe_fpn_1x_coco.py'
# learning policy
max_epochs = 24
train_cfg = dict(
type='EpochBasedTrainLoop', max_epochs=max_epochs, val_interval=1)
param_scheduler = [
dict(
type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500),
dict(
type='MultiStepLR',
... | _base_ = './ms_rcnn_r101_caffe_fpn_1x_coco.py'
# learning policy
lr_config = dict(step=[16, 22])
runner = dict(type='EpochBasedRunner', max_epochs=24)
|
import pytest
import torchaudio
from torchaudio.pipelines import (
HUBERT_ASR_LARGE,
HUBERT_ASR_XLARGE,
HUBERT_BASE,
HUBERT_LARGE,
HUBERT_XLARGE,
VOXPOPULI_ASR_BASE_10K_DE,
VOXPOPULI_ASR_BASE_10K_EN,
VOXPOPULI_ASR_BASE_10K_ES,
VOXPOPULI_ASR_BASE_10K_FR,
VOXPOPULI_ASR_BASE_10K_IT,... | import pytest
import torchaudio
from torchaudio.pipelines import (
HUBERT_ASR_LARGE,
HUBERT_ASR_XLARGE,
HUBERT_BASE,
HUBERT_LARGE,
HUBERT_XLARGE,
VOXPOPULI_ASR_BASE_10K_DE,
VOXPOPULI_ASR_BASE_10K_EN,
VOXPOPULI_ASR_BASE_10K_ES,
VOXPOPULI_ASR_BASE_10K_FR,
VOXPOPULI_ASR_BASE_10K_IT,... |
_base_ = './sparse_rcnn_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='RandomChoiceResize',
scales=[(480, 1333), (512, 1333), (544, 1333), (576, 1... | _base_ = './sparse_rcnn_r50_fpn_1x_coco.py'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
min_values = (480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
... |
from ._presets import StereoMatching # usort: skip
from ._augment import SimpleCopyPaste
from ._geometry import FixedSizeCrop
from ._misc import PermuteDimensions, TransposeDimensions
from ._type_conversion import LabelToOneHot
| from ._presets import StereoMatching # usort: skip
from ._augment import RandomCutMix, RandomMixUp, SimpleCopyPaste
from ._geometry import FixedSizeCrop
from ._misc import PermuteDimensions, TransposeDimensions
from ._type_conversion import LabelToOneHot
|
"""DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.ops.linalg import cholesky as cholesky
from keras.src.ops.linalg import det as det
from keras.src.ops.linalg import eig as eig
from keras.src.ops.linalg import eigh as eigh
from keras... | """DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.ops.linalg import cholesky
from keras.src.ops.linalg import det
from keras.src.ops.linalg import eig
from keras.src.ops.linalg import eigh
from keras.src.ops.linalg import inv
from ke... |
from docarray.array.array import DocumentArray
__all__ = ['DocumentArray']
| from docarray.array.documentarray import DocumentArray
__all__ = ['DocumentArray']
|
"""This is the langchain_ollama package.
It provides infrastructure for interacting with the Ollama service.
"""
from importlib import metadata
from langchain_ollama.chat_models import ChatOllama
from langchain_ollama.embeddings import OllamaEmbeddings
from langchain_ollama.llms import OllamaLLM
try:
__version_... | """This is the langchain_ollama package.
It provides infrastructure for interacting with the Ollama service.
"""
from importlib import metadata
from langchain_ollama.chat_models import ChatOllama
from langchain_ollama.embeddings import OllamaEmbeddings
from langchain_ollama.llms import OllamaLLM
try:
__version... |
import torch
from torchvision import _BETA_TRANSFORMS_WARNING, _WARN_ABOUT_BETA_TRANSFORMS
from ._bounding_box import BoundingBoxes, BoundingBoxFormat
from ._datapoint import Datapoint
from ._image import Image
from ._mask import Mask
from ._torch_function_helpers import set_return_type
from ._video import Video
if _... | import torch
from torchvision import _BETA_TRANSFORMS_WARNING, _WARN_ABOUT_BETA_TRANSFORMS
from ._bounding_box import BoundingBoxes, BoundingBoxFormat
from ._datapoint import Datapoint
from ._image import Image
from ._mask import Mask
from ._torch_function_helpers import set_return_type
from ._video import Video
if _... |
"""OpenAI-Like embeddings."""
from typing import Any, Dict, Optional
import httpx
from llama_index.core.callbacks.base import CallbackManager
from llama_index.embeddings.openai import OpenAIEmbedding
class OpenAILikeEmbedding(OpenAIEmbedding):
"""
OpenAI-Like class for embeddings.
Args:
model_n... | """OpenAI-Like embeddings."""
from typing import Any, Dict, Optional
import httpx
from llama_index.core.callbacks.base import CallbackManager
from llama_index.embeddings.openai import OpenAIEmbedding
class OpenAILikeEmbedding(OpenAIEmbedding):
"""
OpenAI-Like class for embeddings.
Args:
model_n... |
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... |
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 = 'MyExecutorAfterReload'
@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 = 'MyExecutorAfterReload'
|
import asyncio
from typing import Any, AsyncGenerator, Optional
from llama_index.core.workflow.context import Context
from llama_index.core.workflow.errors import WorkflowDone
from llama_index.core.workflow.events import Event, StopEvent
from .utils import BUSY_WAIT_DELAY
class WorkflowHandler(asyncio.Future):
... | import asyncio
from typing import Any, AsyncGenerator, Optional
from llama_index.core.workflow.context import Context
from llama_index.core.workflow.events import Event, StopEvent
from llama_index.core.workflow.errors import WorkflowDone
class WorkflowHandler(asyncio.Future):
def __init__(
self,
... |
"""
This example uses average word embeddings (for example from GloVe). It adds two fully-connected feed-forward layers (dense layers) to create a Deep Averaging Network (DAN).
If 'glove.6B.300d.txt.gz' does not exist, it tries to download it from our server.
See https://public.ukp.informatik.tu-darmstadt.de/reimers/... | """
This example uses average word embeddings (for example from GloVe). It adds two fully-connected feed-forward layers (dense layers) to create a Deep Averaging Network (DAN).
If 'glove.6B.300d.txt.gz' does not exist, it tries to download it from our server.
See https://public.ukp.informatik.tu-darmstadt.de/reimers/... |
"""DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.tree.tree_api import MAP_TO_NONE as MAP_TO_NONE
from keras.src.tree.tree_api import assert_same_paths as assert_same_paths
from keras.src.tree.tree_api import (
assert_same_struct... | """DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.tree.tree_api import MAP_TO_NONE
from keras.src.tree.tree_api import assert_same_paths
from keras.src.tree.tree_api import assert_same_structure
from keras.src.tree.tree_api import fl... |
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, 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 Any, List, Optional, Union
from llama_index.core.base.llms.types import ChatMessage, ContentBlock, TextBlock
from llama_index.core.bridge.pydantic import Field, field_validator
from llama_index.core.memory.memory import BaseMemoryBlock
class StaticMemoryBlock(BaseMemoryBlock[List[ContentBlock]]):
... | from typing import Any, List, Optional, Union
from llama_index.core.base.llms.types import ChatMessage, ContentBlock, TextBlock
from llama_index.core.bridge.pydantic import Field, field_validator
from llama_index.core.memory.memory import BaseMemoryBlock
class StaticMemoryBlock(BaseMemoryBlock[List[ContentBlock]]):
... |
from argparse import Namespace
from copy import deepcopy
from typing import TYPE_CHECKING, Type
from hubble.executor.helper import is_valid_huburi
from hubble.executor.hubio import HubIO
from jina.enums import PodRoleType
from jina.orchestrate.pods import Pod
from jina.orchestrate.pods.container import ContainerPod
... | from argparse import Namespace
from copy import deepcopy
from typing import TYPE_CHECKING, Type
from hubble.executor.helper import is_valid_huburi
from hubble.executor.hubio import HubIO
from jina.enums import PodRoleType
from jina.orchestrate.pods import Pod
from jina.orchestrate.pods.container import ContainerPod
... |
import torch
from torchaudio_unittest.common_utils import PytorchTestCase
from .tacotron2_loss_impl import Tacotron2LossGradcheckTests, Tacotron2LossShapeTests, Tacotron2LossTorchscriptTests
class TestTacotron2LossShapeFloat32CPU(Tacotron2LossShapeTests, PytorchTestCase):
dtype = torch.float32
device = torch... | import torch
from torchaudio_unittest.common_utils import PytorchTestCase
from .tacotron2_loss_impl import (
Tacotron2LossGradcheckTests,
Tacotron2LossShapeTests,
Tacotron2LossTorchscriptTests,
)
class TestTacotron2LossShapeFloat32CPU(Tacotron2LossShapeTests, PytorchTestCase):
dtype = torch.float32
... |
#!/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... |
# Copyright (c) OpenMMLab. All rights reserved.
from .manager import ManagerMeta, ManagerMixin
from .misc import (check_prerequisites, concat_list, deprecated_api_warning,
has_method, import_modules_from_strings, is_list_of,
is_method_overridden, is_seq_of, is_str, is_tuple_of,
... | # Copyright (c) OpenMMLab. All rights reserved.
from .manager import ManagerMeta, ManagerMixin
from .misc import (check_prerequisites, concat_list, deprecated_api_warning,
has_method, import_modules_from_strings, is_list_of,
is_method_overridden, is_seq_of, is_str, is_tuple_of,
... |
# Copyright (c) OpenMMLab. All rights reserved.
from .gaussian_target import (gather_feat, gaussian_radius,
gen_gaussian_target, get_local_maximum,
get_topk_from_heatmap, transpose_and_gather_feat)
from .image import imrenormalize
from .make_divisible import m... | # Copyright (c) OpenMMLab. All rights reserved.
from .gaussian_target import (gather_feat, gaussian_radius,
gen_gaussian_target, get_local_maximum,
get_topk_from_heatmap, transpose_and_gather_feat)
from .make_divisible import make_divisible
from .misc import (... |
from typing import Any, Optional, Type, TypeVar, Union
from docarray.base_document import BaseDocument
from docarray.typing import TextUrl
from docarray.typing.tensor.embedding import AnyEmbedding
T = TypeVar('T', bound='Text')
class Text(BaseDocument):
"""
Document for handling text.
It can contain a T... | from typing import Any, Optional, Type, TypeVar, Union
from docarray.base_document import BaseDocument
from docarray.typing import TextUrl
from docarray.typing.tensor.embedding import AnyEmbedding
T = TypeVar('T', bound='Text')
class Text(BaseDocument):
"""
Document for handling text.
It can contain a T... |
from __future__ import annotations
import logging
from typing import TYPE_CHECKING, Any, Literal
from sentence_transformers.evaluation import BinaryClassificationEvaluator
if TYPE_CHECKING:
import numpy as np
from torch import Tensor
from sentence_transformers.sparse_encoder.SparseEncoder import SparseE... | from __future__ import annotations
import logging
from typing import TYPE_CHECKING, Any, Literal
from sentence_transformers.evaluation import BinaryClassificationEvaluator
if TYPE_CHECKING:
import numpy as np
from torch import Tensor
from sentence_transformers.sparse_encoder.SparseEncoder import SparseE... |
from typing import Any, Callable, Optional
import torch
from .. import transforms
from .vision import VisionDataset
class FakeData(VisionDataset):
"""A fake dataset that returns randomly generated images and returns them as PIL images
Args:
size (int, optional): Size of the dataset. Default: 1000 i... | from typing import Any, Callable, Optional
import torch
from .. import transforms
from .vision import VisionDataset
class FakeData(VisionDataset):
"""A fake dataset that returns randomly generated images and returns them as PIL images
Args:
size (int, optional): Size of the dataset. Default: 1000 i... |
from typing import Union, Iterable
from docarray.array.storage.base.seqlike import BaseSequenceLikeMixin
from docarray.array.memory import DocumentArrayInMemory
from docarray import Document
class SequenceLikeMixin(BaseSequenceLikeMixin):
"""Implement sequence-like methods"""
def extend(self, values: Iterab... | from typing import Union, Iterable
from docarray.array.storage.base.seqlike import BaseSequenceLikeMixin
from docarray.array.memory import DocumentArrayInMemory
from docarray import Document
class SequenceLikeMixin(BaseSequenceLikeMixin):
"""Implement sequence-like methods"""
def extend(self, values: Iterab... |
from keras.src import ops
from keras.src.api_export import keras_export
from keras.src.layers.merging.base_merge import Merge
@keras_export("keras.layers.Maximum")
class Maximum(Merge):
"""Computes element-wise maximum on a list of inputs.
It takes as input a list of tensors, all of the same shape,
and r... | from keras.src import ops
from keras.src.api_export import keras_export
from keras.src.layers.merging.base_merge import Merge
@keras_export("keras.layers.Maximum")
class Maximum(Merge):
"""Computes element-wise maximum on a list of inputs.
It takes as input a list of tensors, all of the same shape,
and r... |
"""Argparser module for Deployment runtimes"""
import argparse
from jina import helper
from jina.enums import DeploymentRoleType
from jina.parsers.helper import _SHOW_ALL_ARGS, KVAppendAction, add_arg_group
def mixin_base_deployment_parser(parser):
"""Add mixin arguments required by :class:`BaseDeployment` into ... | """Argparser module for Deployment runtimes"""
import argparse
from jina import helper
from jina.enums import DeploymentRoleType
from jina.parsers.helper import _SHOW_ALL_ARGS, KVAppendAction, add_arg_group
def mixin_base_deployment_parser(parser):
"""Add mixin arguments required by :class:`BaseDeployment` into ... |
# 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... |
from torchvision.transforms import AutoAugmentPolicy, InterpolationMode # usort: skip
from . import functional, utils # usort: skip
from ._transform import Transform # usort: skip
from ._augment import Cutmix, Mixup, RandomErasing
from ._auto_augment import AugMix, AutoAugment, RandAugment, TrivialAugmentWide
fro... | from torchvision.transforms import AutoAugmentPolicy, InterpolationMode # usort: skip
from . import functional, utils # usort: skip
from ._transform import Transform # usort: skip
from ._augment import RandomErasing
from ._auto_augment import AugMix, AutoAugment, RandAugment, TrivialAugmentWide
from ._color impor... |
# Copyright 2020 The HuggingFace Datasets Authors and the TensorFlow Datasets Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# U... | # Copyright 2020 The HuggingFace Datasets Authors and the TensorFlow Datasets Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# U... |
# Copyright (c) OpenMMLab. All rights reserved.
from mmdet.registry import MODELS
from mmdet.utils import ConfigType, OptConfigType, OptMultiConfig
from .single_stage import SingleStageDetector
@MODELS.register_module()
class TOOD(SingleStageDetector):
r"""Implementation of `TOOD: Task-aligned One-stage Object De... | # Copyright (c) OpenMMLab. All rights reserved.
from mmdet.core import ConfigType, OptConfigType, OptMultiConfig
from mmdet.registry import MODELS
from .single_stage import SingleStageDetector
@MODELS.register_module()
class TOOD(SingleStageDetector):
r"""Implementation of `TOOD: Task-aligned One-stage Object Det... |
"""Test LLM Bash functionality."""
import os
import sys
from unittest.mock import patch
import pytest
from langchain.chains.llm import LLMChain
from langchain.evaluation.loading import load_evaluator
from langchain.evaluation.qa.eval_chain import (
ContextQAEvalChain,
CotQAEvalChain,
QAEvalChain,
_pa... | """Test LLM Bash functionality."""
import os
import sys
from unittest.mock import patch
import pytest
from langchain.chains.llm import LLMChain
from langchain.evaluation.loading import load_evaluator
from langchain.evaluation.qa.eval_chain import (
ContextQAEvalChain,
CotQAEvalChain,
QAEvalChain,
_pa... |
# Copyright 2020 The HuggingFace Datasets Authors and the TensorFlow Datasets Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# U... | # Copyright 2020 The HuggingFace Datasets Authors and the TensorFlow Datasets Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# U... |
# Copyright (c) OpenMMLab. All rights reserved.
"""Get image shape on CrowdHuman dataset.
Here is an example to run this script.
Example:
python tools/misc/get_crowdhuman_id_hw.py ${CONFIG} \
--dataset ${DATASET_TYPE}
"""
import argparse
import json
import logging
import os.path as osp
from multiprocessing im... | # Copyright (c) OpenMMLab. All rights reserved.
"""Get image shape on CrowdHuman dataset.
Here is an example to run this script.
Example:
python tools/misc/get_crowdhuman_id_hw.py ${CONFIG} \
--dataset ${DATASET_TYPE}
"""
import argparse
import json
import logging
import os.path as osp
from multiprocessing im... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.