input stringlengths 33 5k | output stringlengths 32 5k |
|---|---|
import json
import re
from typing import TypeVar
import yaml
from langchain_core.exceptions import OutputParserException
from langchain_core.output_parsers import BaseOutputParser
from pydantic import BaseModel, ValidationError
from langchain.output_parsers.format_instructions import YAML_FORMAT_INSTRUCTIONS
T = Typ... | import json
import re
from typing import TypeVar
import yaml
from langchain_core.exceptions import OutputParserException
from langchain_core.output_parsers import BaseOutputParser
from pydantic import BaseModel, ValidationError
from langchain.output_parsers.format_instructions import YAML_FORMAT_INSTRUCTIONS
T = Typ... |
# Copyright (c) Meta Platforms, Inc. and affiliates
from llama_index.llms.meta.base import LlamaLLM
__all__ = ["LlamaLLM"]
| from llama_index.llms.meta.base import LlamaLLM
__all__ = ["LlamaLLM"]
|
from jina import Client, Document, Executor, Flow, requests
def validate_results(results):
req = results[0]
assert len(req.docs) == 1
assert len(req.docs[0].matches) == 5
assert len(req.docs[0].matches[0].matches) == 5
assert len(req.docs[0].matches[-1].matches) == 5
assert len(req.docs[0].mat... | from jina import Client, Document, Executor, Flow, requests
exposed_port = 12345
def validate_results(results):
req = results[0]
assert len(req.docs) == 1
assert len(req.docs[0].matches) == 5
assert len(req.docs[0].matches[0].matches) == 5
assert len(req.docs[0].matches[-1].matches) == 5
asse... |
_base_ = './faster-rcnn_r50-caffe-dc5_ms-1x_coco.py'
# MMEngine support the following two ways, users can choose
# according to convenience
# param_scheduler = [
# dict(
# type='LinearLR', start_factor=0.001, by_epoch=False, begin=0, end=500), # noqa
# dict(
# type='MultiStepLR',
# begi... | _base_ = './faster-rcnn_r50-caffe-dc5_ms-1x_coco.py'
# learning policy
lr_config = dict(step=[28, 34])
runner = dict(type='EpochBasedRunner', max_epochs=36)
|
import logging
from typing import Any
from autogpt_libs.utils.cache import thread_cached
from backend.data.block import (
Block,
BlockCategory,
BlockInput,
BlockOutput,
BlockSchema,
BlockType,
get_block,
)
from backend.data.execution import ExecutionStatus
from backend.data.model import Sc... | import logging
from typing import Any
from autogpt_libs.utils.cache import thread_cached
from backend.data.block import (
Block,
BlockCategory,
BlockInput,
BlockOutput,
BlockSchema,
BlockType,
get_block,
)
from backend.data.execution import ExecutionStatus
from backend.data.model import Sc... |
from typing import Iterable, Dict
from docarray.array.storage.annlite.helper import OffsetMapping
from docarray.array.storage.base.getsetdel import BaseGetSetDelMixin
from docarray.array.storage.base.helper import Offset2ID
from docarray.array.memory import DocumentArrayInMemory
from docarray import Document
class G... | from typing import Iterable, Dict
from .helper import OffsetMapping
from ..base.getsetdel import BaseGetSetDelMixin
from ..base.helper import Offset2ID
from ...memory import DocumentArrayInMemory
from .... import Document
class GetSetDelMixin(BaseGetSetDelMixin):
"""Implement required and derived functions that ... |
_base_ = './mask_rcnn_r101_fpn_1x_coco.py'
preprocess_cfg = dict(
mean=[103.530, 116.280, 123.675],
std=[57.375, 57.120, 58.395],
to_rgb=False,
pad_size_divisor=32)
model = dict(
preprocess_cfg=preprocess_cfg,
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
b... | _base_ = './mask_rcnn_r101_fpn_1x_coco.py'
model = dict(
backbone=dict(
type='ResNeXt',
depth=101,
groups=32,
base_width=8,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=False),
style='pytorch',... |
from enum import Enum
# --8<-- [start:ProviderName]
class ProviderName(str, Enum):
ANTHROPIC = "anthropic"
COMPASS = "compass"
DISCORD = "discord"
D_ID = "d_id"
E2B = "e2b"
EXA = "exa"
FAL = "fal"
GITHUB = "github"
GOOGLE = "google"
GOOGLE_MAPS = "google_maps"
GROQ = "groq"... | from enum import Enum
# --8<-- [start:ProviderName]
class ProviderName(str, Enum):
ANTHROPIC = "anthropic"
COMPASS = "compass"
DISCORD = "discord"
D_ID = "d_id"
E2B = "e2b"
EXA = "exa"
FAL = "fal"
GITHUB = "github"
GOOGLE = "google"
GOOGLE_MAPS = "google_maps"
GROQ = "groq"... |
# mypy: allow-untyped-defs
import functools
from collections.abc import Hashable
from dataclasses import dataclass, fields
from typing import TypeVar
from typing_extensions import dataclass_transform
T = TypeVar("T", bound="_Union")
class _UnionTag(str):
__slots__ = ("_cls",)
_cls: Hashable
@staticmeth... | # mypy: allow-untyped-defs
import functools
from collections.abc import Hashable
from dataclasses import dataclass, fields
from typing import TypeVar
from typing_extensions import dataclass_transform
T = TypeVar("T", bound="_Union")
class _UnionTag(str):
__slots__ = ("_cls",)
_cls: Hashable
@staticmeth... |
"""Common structures for structured indices."""
from dataclasses import dataclass
from typing import Dict, Optional
from dataclasses_json import DataClassJsonMixin
# TODO: migrate this to be a data_struct
@dataclass
class SQLContextContainer(DataClassJsonMixin):
"""
SQLContextContainer.
A container int... | """Common structures for structured indices."""
from dataclasses import dataclass
from typing import Dict, Optional
from dataclasses_json import DataClassJsonMixin
# TODO: migrate this to be a data_struct
@dataclass
class SQLContextContainer(DataClassJsonMixin):
"""
SQLContextContainer.
A container inte... |
# Owner(s): ["module: inductor"]
from unittest.mock import patch
import torch
from torch._inductor import config
from torch._inductor.async_compile import AsyncCompile, shutdown_compile_workers
from torch._inductor.runtime.triton_compat import Config
from torch._inductor.runtime.triton_heuristics import (
generate... | # Owner(s): ["module: inductor"]
import torch
from torch._inductor import config
from torch._inductor.async_compile import AsyncCompile, shutdown_compile_workers
from torch._inductor.test_case import run_tests, TestCase
from torch._inductor.utils import fresh_cache
from torch.testing._internal.common_utils import (
... |
import pytest
from docarray import DocumentArray, Document
from docarray.array.weaviate import DocumentArrayWeaviate
import numpy as np
@pytest.fixture()
def docs():
return DocumentArray([Document(id=f'{i}') for i in range(1, 10)])
@pytest.mark.parametrize(
'to_delete',
[
0,
1,
... | import pytest
from docarray import DocumentArray, Document
from docarray.array.weaviate import DocumentArrayWeaviate
import numpy as np
@pytest.fixture()
def docs():
return DocumentArray([Document(id=f'{i}') for i in range(1, 10)])
@pytest.mark.parametrize(
'to_delete',
[
0,
1,
... |
"""Helper functions for managing the LangChain API.
This module is only relevant for LangChain developers, not for users.
.. warning::
This module and its submodules are for internal use only. Do not use them
in your own code. We may change the API at any time with no warning.
"""
from .deprecation impor... | """Helper functions for managing the LangChain API.
This module is only relevant for LangChain developers, not for users.
.. warning::
This module and its submodules are for internal use only. Do not use them
in your own code. We may change the API at any time with no warning.
"""
from .deprecation impor... |
from typing import TYPE_CHECKING, Any, List, Tuple, Type, TypeVar, Union
import numpy as np
from docarray.typing.proto_register import _register_proto
from docarray.typing.tensor.torch_tensor import TorchTensor, metaTorchAndNode
from docarray.typing.tensor.video.video_tensor_mixin import VideoTensorMixin
T = TypeVar... | from typing import TYPE_CHECKING, Any, List, Tuple, Type, TypeVar, Union
import numpy as np
from docarray.typing.proto_register import _register_proto
from docarray.typing.tensor.torch_tensor import TorchTensor, metaTorchAndNode
from docarray.typing.tensor.video.video_tensor_mixin import VideoTensorMixin
T = TypeVar... |
# Copyright (c) OpenMMLab. All rights reserved.
from .manager import ManagerMeta, ManagerMixin
from .misc import (check_prerequisites, concat_list, deprecated_api_warning,
deprecated_function, has_method,
import_modules_from_strings, is_list_of,
is_method_overrid... | # 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,
... |
_base_ = [
'../_base_/models/mask-rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
backbone=dict(
frozen_stages=0,
norm_cfg=dict(type='SyncBN', requires_grad=True),
norm_eval=False,
... | _base_ = [
'../_base_/models/mask_rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
model = dict(
backbone=dict(
frozen_stages=0,
norm_cfg=dict(type='SyncBN', requires_grad=True),
norm_eval=False,
... |
from torchaudio._internal.module_utils import dropping_support
from ._alignment import forced_align as _forced_align, merge_tokens, TokenSpan
from .filtering import (
allpass_biquad,
band_biquad,
bandpass_biquad,
bandreject_biquad,
bass_biquad,
biquad,
contrast,
dcshift,
deemph_biqu... | from ._alignment import forced_align, merge_tokens, TokenSpan
from .filtering import (
allpass_biquad,
band_biquad,
bandpass_biquad,
bandreject_biquad,
bass_biquad,
biquad,
contrast,
dcshift,
deemph_biquad,
dither,
equalizer_biquad,
filtfilt,
flanger,
gain,
hi... |
import warnings
from typing import Any
from langchain_core.memory import BaseMemory
from pydantic import field_validator
from langchain.memory.chat_memory import BaseChatMemory
class CombinedMemory(BaseMemory):
"""Combining multiple memories' data together."""
memories: list[BaseMemory]
"""For tracking... | import warnings
from typing import Any
from langchain_core.memory import BaseMemory
from pydantic import field_validator
from langchain.memory.chat_memory import BaseChatMemory
class CombinedMemory(BaseMemory):
"""Combining multiple memories' data together."""
memories: list[BaseMemory]
"""For tracking... |
from typing import Dict, Optional, Union
import pytest
from docarray.typing import NdArray, TorchTensor
from docarray.typing.tensor.abstract_tensor import AbstractTensor
from docarray.utils._typing import is_tensor_union, is_type_tensor
from docarray.utils.misc import is_tf_available
tf_available = is_tf_available()... | from typing import Dict, Optional, Union
import pytest
from docarray.typing import NdArray, TorchTensor
from docarray.typing.tensor.abstract_tensor import AbstractTensor
from docarray.utils._typing import is_tensor_union, is_type_tensor
try:
from docarray.typing import TensorFlowTensor
except (ImportError, TypeE... |
# Copyright (c) OpenMMLab. All rights reserved.
import math
import torch.nn as nn
from mmcv.cnn import ConvModule
from mmcv.runner import BaseModule, auto_fp16
from mmdet.registry import MODELS
@MODELS.register_module()
class CTResNetNeck(BaseModule):
"""The neck used in `CenterNet <https://arxiv.org/abs/1904.0... | # Copyright (c) OpenMMLab. All rights reserved.
import math
import torch.nn as nn
from mmcv.cnn import ConvModule
from mmcv.runner import BaseModule, auto_fp16
from mmdet.models.builder import NECKS
@NECKS.register_module()
class CTResNetNeck(BaseModule):
"""The neck used in `CenterNet <https://arxiv.org/abs/19... |
from __future__ import annotations
from .CSRLoss import CSRLoss, CSRReconstructionLoss
from .FlopsLoss import FlopsLoss
from .SparseAnglELoss import SparseAnglELoss
from .SparseCoSENTLoss import SparseCoSENTLoss
from .SparseCosineSimilarityLoss import SparseCosineSimilarityLoss
from .SparseDistillKLDivLoss import Spar... | from __future__ import annotations
from .CSRLoss import CSRLoss, CSRReconstructionLoss
from .FlopsLoss import FlopsLoss
from .SparseAnglELoss import SparseAnglELoss
from .SparseCachedGISTEmbedLoss import SparseCachedGISTEmbedLoss
from .SparseCachedMultipleNegativesRankingLoss import SparseCachedMultipleNegativesRankin... |
"""Callback Handler that writes to a file."""
from __future__ import annotations
from pathlib import Path
from typing import TYPE_CHECKING, Any, Optional, TextIO, cast
from langchain_core.callbacks import BaseCallbackHandler
from langchain_core.utils.input import print_text
if TYPE_CHECKING:
from langchain_core... | """Callback Handler that writes to a file."""
from __future__ import annotations
from typing import TYPE_CHECKING, Any, Optional, TextIO, cast
from langchain_core.callbacks import BaseCallbackHandler
from langchain_core.utils.input import print_text
if TYPE_CHECKING:
from langchain_core.agents import AgentActio... |
__version__ = '0.30.1'
import logging
from docarray.array import DocList, DocVec
from docarray.base_doc.doc import BaseDoc
from docarray.utils._internal.misc import _get_path_from_docarray_root_level
__all__ = ['BaseDoc', 'DocList', 'DocVec']
logger = logging.getLogger('docarray')
handler = logging.StreamHandler()... | __version__ = '0.30.1'
import logging
from docarray.array import DocList, DocVec
from docarray.base_doc.doc import BaseDoc
__all__ = ['BaseDoc', 'DocList', 'DocVec']
logger = logging.getLogger('docarray')
handler = logging.StreamHandler()
formatter = logging.Formatter("%(levelname)s - %(name)s - %(message)s")
hand... |
from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.tools import NavigateTool
from langchain_community.tools.playwright.navigate import NavigateToolInput
# Create a way to dynamically look up deprecated imports.
# Used to consolidate log... | from typing import TYPE_CHECKING, Any
from langchain._api import create_importer
if TYPE_CHECKING:
from langchain_community.tools import NavigateTool
from langchain_community.tools.playwright.navigate import NavigateToolInput
# Create a way to dynamically look up deprecated imports.
# Used to consolidate log... |
"""Dataset Module."""
from llama_index.core.llama_dataset.base import (
BaseLlamaDataExample,
BaseLlamaDataset,
BaseLlamaExamplePrediction,
BaseLlamaPredictionDataset,
CreatedBy,
CreatedByType,
)
from llama_index.core.llama_dataset.download import download_llama_dataset
from llama_index.core.ll... | """ Dataset Module."""
from llama_index.core.llama_dataset.base import (
BaseLlamaDataExample,
BaseLlamaDataset,
BaseLlamaExamplePrediction,
BaseLlamaPredictionDataset,
CreatedBy,
CreatedByType,
)
from llama_index.core.llama_dataset.download import download_llama_dataset
from llama_index.core.l... |
"""[DEPRECATED] Pipeline prompt template."""
from typing import Any
from pydantic import model_validator
from langchain_core._api.deprecation import deprecated
from langchain_core.prompt_values import PromptValue
from langchain_core.prompts.base import BasePromptTemplate
from langchain_core.prompts.chat import BaseC... | """[DEPRECATED] Pipeline prompt template."""
from typing import Any
from pydantic import model_validator
from langchain_core._api.deprecation import deprecated
from langchain_core.prompt_values import PromptValue
from langchain_core.prompts.base import BasePromptTemplate
from langchain_core.prompts.chat import BaseC... |
import itertools
import os.path
import pytest
from docarray import Document, DocumentArray
from jina import Client, Executor, Flow, requests
from jina.helper import random_port
PROTOCOLS = ['grpc', 'http', 'websocket']
cur_dir = os.path.dirname(__file__)
class MyExecutor(Executor):
@requests
def foo(self, ... | import itertools
import os.path
import pytest
from docarray import Document, DocumentArray
from jina import Client, Executor, Flow, requests
from jina.helper import random_port
PROTOCOLS = ['grpc', 'http', 'websocket']
cur_dir = os.path.dirname(__file__)
class MyExecutor(Executor):
@requests
def foo(self, ... |
# mypy: allow-untyped-defs
from contextlib import contextmanager
from typing import NoReturn
try:
from torch._C import _itt
except ImportError:
class _ITTStub:
@staticmethod
def _fail(*args, **kwargs) -> NoReturn:
raise RuntimeError(
"ITT functions not installed. A... | # mypy: allow-untyped-defs
from contextlib import contextmanager
try:
from torch._C import _itt
except ImportError:
class _ITTStub:
@staticmethod
def _fail(*args, **kwargs):
raise RuntimeError(
"ITT functions not installed. Are you sure you have a ITT build?"
... |
from docarray.typing.proto_register import _register_proto
from docarray.typing.tensor.audio.abstract_audio_tensor import AbstractAudioTensor
from docarray.typing.tensor.ndarray import NdArray
@_register_proto(proto_type_name='audio_ndarray')
class AudioNdArray(AbstractAudioTensor, NdArray):
"""
Subclass of N... | from docarray.typing.proto_register import _register_proto
from docarray.typing.tensor.audio.abstract_audio_tensor import AbstractAudioTensor
from docarray.typing.tensor.ndarray import NdArray
@_register_proto(proto_type_name='audio_ndarray')
class AudioNdArray(AbstractAudioTensor, NdArray):
"""
Subclass of N... |
"""
Epub parser.
Contains parsers for epub files.
"""
from pathlib import Path
from typing import Dict, List, Optional
import logging
from fsspec import AbstractFileSystem
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document
logger = logging.getLogger(__name__)
class E... | """Epub parser.
Contains parsers for epub files.
"""
from pathlib import Path
from typing import Dict, List, Optional
import logging
from fsspec import AbstractFileSystem
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document
logger = logging.getLogger(__name__)
class Ep... |
import os
from typing import TYPE_CHECKING, Any, Optional, Type, TypeVar
import orjson
from pydantic import BaseModel, Field
from rich.console import Console
from docarray.base_document.base_node import BaseNode
from docarray.base_document.io.json import orjson_dumps, orjson_dumps_and_decode
from docarray.base_docume... | import os
from typing import Optional, Type
import orjson
from pydantic import BaseModel, Field
from rich.console import Console
from docarray.base_document.base_node import BaseNode
from docarray.base_document.io.json import orjson_dumps, orjson_dumps_and_decode
from docarray.base_document.mixins import IOMixin, Upd... |
import os
import sys
from test_utils import DirectoryExcursion
if len(sys.argv) != 4:
print("Usage: {} [wheel to rename] [commit id] [platform tag]".format(sys.argv[0]))
sys.exit(1)
whl_path = sys.argv[1]
commit_id = sys.argv[2]
platform_tag = sys.argv[3]
dirname, basename = os.path.dirname(whl_path), os.p... | import os
import sys
from contextlib import contextmanager
@contextmanager
def cd(path):
path = os.path.normpath(path)
cwd = os.getcwd()
os.chdir(path)
print("cd " + path)
try:
yield path
finally:
os.chdir(cwd)
if len(sys.argv) != 4:
print('Usage: {} [wheel to rename] [co... |
import importlib
import os
import re
from pathlib import Path
from typing import Type, TypeVar
from backend.data.block import Block
# Dynamically load all modules under backend.blocks
AVAILABLE_MODULES = []
current_dir = Path(__file__).parent
modules = [
str(f.relative_to(current_dir))[:-3].replace(os.path.sep, "... | import importlib
import os
import re
from pathlib import Path
from typing import Type, TypeVar
from backend.data.block import Block
# Dynamically load all modules under backend.blocks
AVAILABLE_MODULES = []
current_dir = Path(__file__).parent
modules = [
str(f.relative_to(current_dir))[:-3].replace(os.path.sep, "... |
from datetime import datetime
from typing import List
from backend.blocks.exa._auth import (
ExaCredentials,
ExaCredentialsField,
ExaCredentialsInput,
)
from backend.blocks.exa.helpers import ContentSettings
from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
from backend.data.mod... | from datetime import datetime
from typing import List
from backend.blocks.exa._auth import (
ExaCredentials,
ExaCredentialsField,
ExaCredentialsInput,
)
from backend.blocks.exa.helpers import ContentSettings
from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
from backend.data.mod... |
try:
import sklearn
except ImportError:
sklearn = None
def _validate_data(estimator, *args, **kwargs):
"""Validate the input data.
wrapper for sklearn.utils.validation.validate_data or
BaseEstimator._validate_data depending on the scikit-learn version.
TODO: remove when minimum scikit-learn ... | import sklearn
from packaging.version import parse as parse_version
from sklearn import get_config
sklearn_version = parse_version(parse_version(sklearn.__version__).base_version)
if sklearn_version < parse_version("1.6"):
def patched_more_tags(estimator, expected_failed_checks):
import copy
fro... |
from typing import Any, Dict, Union
import torch
from torchvision import datapoints, transforms as _transforms
from torchvision.transforms.v2 import functional as F, Transform
from .utils import is_simple_tensor
class ConvertBoundingBoxFormat(Transform):
_transformed_types = (datapoints.BoundingBox,)
def ... | from typing import Any, Dict, Union
import torch
from torchvision import datapoints, transforms as _transforms
from torchvision.transforms.v2 import functional as F, Transform
from .utils import is_simple_tensor
class ConvertBoundingBoxFormat(Transform):
_transformed_types = (datapoints.BoundingBox,)
def ... |
import time
from jina import Flow
from tests.integration.instrumentation import ExecutorTestWithTracing, get_traces
def test_span_order(jaeger_port, otlp_collector, otlp_receiver_port):
f = Flow(
tracing=True,
traces_exporter_host='http://localhost',
traces_exporter_port=otlp_receiver_por... | import time
from jina import Flow
from tests.integration.instrumentation import ExecutorTestWithTracing, get_traces
def test_span_order(jaeger_port, otlp_collector, otlp_receiver_port):
f = Flow(
tracing=True,
traces_exporter_host='http://localhost',
traces_exporter_port=otlp_receiver_por... |
from torch import nn, Tensor
__all__ = [
"Wav2Letter",
]
class Wav2Letter(nn.Module):
r"""Wav2Letter model architecture from *Wav2Letter: an End-to-End ConvNet-based Speech
Recognition System* [:footcite:`collobert2016wav2letter`].
:math:`\text{padding} = \frac{\text{ceil}(\text{kernel} - \text{str... | from torch import Tensor
from torch import nn
__all__ = [
"Wav2Letter",
]
class Wav2Letter(nn.Module):
r"""Wav2Letter model architecture from *Wav2Letter: an End-to-End ConvNet-based Speech
Recognition System* [:footcite:`collobert2016wav2letter`].
:math:`\text{padding} = \frac{\text{ceil}(\text{ke... |
"""
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... |
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 ... |
from backend.data.credit import UsageTransactionMetadata, get_user_credit_model
from backend.data.execution import (
GraphExecution,
NodeExecutionResult,
RedisExecutionEventBus,
create_graph_execution,
get_graph_execution,
get_incomplete_node_executions,
get_latest_node_execution,
get_no... | from backend.data.credit import UsageTransactionMetadata, get_user_credit_model
from backend.data.execution import (
GraphExecutionMeta,
NodeExecutionResult,
RedisExecutionEventBus,
create_graph_execution,
get_incomplete_node_executions,
get_latest_node_execution,
get_node_execution_results,... |
"""Various utilities to help with development."""
# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause
import platform
import warnings
from collections.abc import Sequence
import numpy as np
from ..exceptions import DataConversionWarning
from . import _joblib, metadata_routing
from ._bunch... | """Various utilities to help with development."""
# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause
import platform
import warnings
from collections.abc import Sequence
import numpy as np
from ..exceptions import DataConversionWarning
from . import _joblib, metadata_routing
from ._bunch... |
from langchain_core.example_selectors.semantic_similarity import (
MaxMarginalRelevanceExampleSelector,
SemanticSimilarityExampleSelector,
sorted_values,
)
__all__ = [
"MaxMarginalRelevanceExampleSelector",
"SemanticSimilarityExampleSelector",
"sorted_values",
]
| from langchain_core.example_selectors.semantic_similarity import (
MaxMarginalRelevanceExampleSelector,
SemanticSimilarityExampleSelector,
sorted_values,
)
__all__ = [
"sorted_values",
"SemanticSimilarityExampleSelector",
"MaxMarginalRelevanceExampleSelector",
]
|
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_failure = True
traceba... | 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... |
# Copyright (c) OpenMMLab. All rights reserved.
"""This file holding some environment constant for sharing by other files."""
import os.path as osp
import subprocess
import sys
from collections import OrderedDict, defaultdict
from distutils import errors
import cv2
import numpy as np
import torch
import mmengine
from... | # Copyright (c) OpenMMLab. All rights reserved.
"""This file holding some environment constant for sharing by other files."""
import os.path as osp
import subprocess
import sys
from collections import OrderedDict, defaultdict
import cv2
import numpy as np
import torch
import mmengine
from .parrots_wrapper import TORC... |
# Copyright (c) OpenMMLab. All rights reserved.
from .vis_backend import (BaseVisBackend, ClearMLVisBackend, DVCLiveVisBackend,
LocalVisBackend, MLflowVisBackend, NeptuneVisBackend,
TensorboardVisBackend, WandbVisBackend)
from .visualizer import Visualizer
__all__ = ... | # Copyright (c) OpenMMLab. All rights reserved.
from .vis_backend import (BaseVisBackend, ClearMLVisBackend, LocalVisBackend,
MLflowVisBackend, NeptuneVisBackend,
TensorboardVisBackend, WandbVisBackend)
from .visualizer import Visualizer
__all__ = [
'Visualizer',... |
_base_ = [
'../_base_/default_runtime.py', '../_base_/datasets/coco_detection.py'
]
# model settings
model = dict(
type='CornerNet',
backbone=dict(
type='HourglassNet',
downsample_times=5,
num_stacks=2,
stage_channels=[256, 256, 384, 384, 384, 512],
stage_blocks=[2, ... | _base_ = [
'../_base_/default_runtime.py', '../_base_/datasets/coco_detection.py'
]
# model settings
model = dict(
type='CornerNet',
backbone=dict(
type='HourglassNet',
downsample_times=5,
num_stacks=2,
stage_channels=[256, 256, 384, 384, 384, 512],
stage_blocks=[2, ... |
from __future__ import annotations
from collections.abc import Iterable
from torch import Tensor
from sentence_transformers.losses.TripletLoss import TripletDistanceMetric, TripletLoss
from sentence_transformers.sparse_encoder.SparseEncoder import SparseEncoder
class SparseTripletLoss(TripletLoss):
def __init_... | from __future__ import annotations
from collections.abc import Iterable
from torch import Tensor
from sentence_transformers.losses.TripletLoss import TripletDistanceMetric, TripletLoss
from sentence_transformers.sparse_encoder.SparseEncoder import SparseEncoder
class SparseTripletLoss(TripletLoss):
def __init_... |
_base_ = '../mask_rcnn/mask-rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)))
fp16 = dict(loss_scale=512.)
| _base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py'
model = dict(
backbone=dict(
dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True)))
fp16 = dict(loss_scale=512.)
|
"""Weaviate reader."""
from typing import Any, List, Optional
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document
class WeaviateReader(BaseReader):
"""
Weaviate reader.
Retrieves documents from Weaviate through vector lookup. Allows option
to concatenat... | """Weaviate reader."""
from typing import Any, List, Optional
from llama_index.core.readers.base import BaseReader
from llama_index.core.schema import Document
class WeaviateReader(BaseReader):
"""Weaviate reader.
Retrieves documents from Weaviate through vector lookup. Allows option
to concatenate ret... |
from typing import Sequence, cast
import prisma.enums
import prisma.types
AGENT_NODE_INCLUDE: prisma.types.AgentNodeInclude = {
"Input": True,
"Output": True,
"Webhook": True,
"AgentBlock": True,
}
AGENT_GRAPH_INCLUDE: prisma.types.AgentGraphInclude = {
"Nodes": {"include": AGENT_NODE_INCLUDE}
}
... | from typing import cast
import prisma.enums
import prisma.types
from backend.blocks.io import IO_BLOCK_IDs
AGENT_NODE_INCLUDE: prisma.types.AgentNodeInclude = {
"Input": True,
"Output": True,
"Webhook": True,
"AgentBlock": True,
}
AGENT_GRAPH_INCLUDE: prisma.types.AgentGraphInclude = {
"Nodes": ... |
from typing import Any, List, Optional, Tuple
import numpy as np
import pytest
from docarray import DocList, DocVec
from docarray.base_doc.doc import BaseDoc
from docarray.typing import NdArray
from docarray.utils._internal.pydantic import is_pydantic_v2
def test_base_document_init():
doc = BaseDoc()
asser... | from typing import Any, List, Optional, Tuple
import numpy as np
import pytest
from docarray import DocList, DocVec
from docarray.base_doc.doc import BaseDoc
from docarray.typing import NdArray
from docarray.utils._internal.pydantic import is_pydantic_v2
def test_base_document_init():
doc = BaseDoc()
asser... |
_base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
]
# model settings
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
backbone=dict(
depth=101,
init_c... | _base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
]
# model settings
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
backbone=dict(
depth=101,
init_c... |
_base_ = './solov2_r50_fpn_ms-3x_coco.py'
# model settings
model = dict(
backbone=dict(
depth=101, init_cfg=dict(checkpoint='torchvision://resnet101')))
| _base_ = 'solov2_r50_fpn_mstrain_3x_coco.py'
# model settings
model = dict(
backbone=dict(
depth=101, init_cfg=dict(checkpoint='torchvision://resnet101')))
|
# ReAct agent formatter
import logging
from abc import abstractmethod
from typing import List, Optional, Sequence
from llama_index.core.agent.react.prompts import (
CONTEXT_REACT_CHAT_SYSTEM_HEADER,
REACT_CHAT_SYSTEM_HEADER,
)
from llama_index.core.agent.react.types import (
BaseReasoningStep,
Observa... | # ReAct agent formatter
import logging
from abc import abstractmethod
from typing import List, Optional, Sequence
from llama_index.core.agent.react.prompts import (
CONTEXT_REACT_CHAT_SYSTEM_HEADER,
REACT_CHAT_SYSTEM_HEADER,
)
from llama_index.core.agent.react.types import (
BaseReasoningStep,
Observa... |
import os
import numpy as np
import pytest
import torch
from pydantic import parse_obj_as
from docarray import BaseDoc
from docarray.typing import ImageBytes, ImageNdArray, ImageTensor, ImageTorchTensor
from docarray.utils._internal.misc import is_tf_available
tf_available = is_tf_available()
if tf_available:
im... | import os
import numpy as np
import pytest
import torch
from pydantic import parse_obj_as
from docarray.typing import ImageBytes, ImageNdArray, ImageTorchTensor
from docarray.utils._internal.misc import is_tf_available
tf_available = is_tf_available()
if tf_available:
import tensorflow as tf
from docarray.t... |
_base_ = './yolox_s_8x8_300e_coco.py'
# model settings
model = dict(
random_size_range=(10, 20),
backbone=dict(deepen_factor=0.33, widen_factor=0.375),
neck=dict(in_channels=[96, 192, 384], out_channels=96),
bbox_head=dict(in_channels=96, feat_channels=96))
img_scale = (640, 640) # height, width
tra... | _base_ = './yolox_s_8x8_300e_coco.py'
# model settings
model = dict(
random_size_range=(10, 20),
backbone=dict(deepen_factor=0.33, widen_factor=0.375),
neck=dict(in_channels=[96, 192, 384], out_channels=96),
bbox_head=dict(in_channels=96, feat_channels=96))
img_scale = (640, 640)
train_pipeline = [
... |
from .conv_emformer import ConvEmformer
from .conv_tasnet import conv_tasnet_base
from .rnnt import conformer_rnnt_base, conformer_rnnt_model
__all__ = [
"conformer_rnnt_base",
"conformer_rnnt_model",
"conv_tasnet_base",
"ConvEmformer",
]
| from .conv_emformer import ConvEmformer
from .conv_tasnet import conv_tasnet_base
from .hdemucs import HDemucs, hdemucs_high, hdemucs_low, hdemucs_medium
from .rnnt import conformer_rnnt_base, conformer_rnnt_model
__all__ = [
"conformer_rnnt_base",
"conformer_rnnt_model",
"conv_tasnet_base",
"ConvEmfor... |
from __future__ import annotations
from typing import TYPE_CHECKING, Any
from langchain_core.callbacks import Callbacks
from langchain_core.callbacks.manager import (
AsyncCallbackManager,
AsyncCallbackManagerForChainGroup,
AsyncCallbackManagerForChainRun,
AsyncCallbackManagerForLLMRun,
AsyncCallb... | from __future__ import annotations
from typing import TYPE_CHECKING, Any
from langchain_core.callbacks.manager import (
AsyncCallbackManager,
AsyncCallbackManagerForChainGroup,
AsyncCallbackManagerForChainRun,
AsyncCallbackManagerForLLMRun,
AsyncCallbackManagerForRetrieverRun,
AsyncCallbackMan... |
from sentence_transformers import SentenceTransformer
from contextlib import nullcontext
from sentence_transformers.evaluation import SentenceEvaluator
import logging
import os
import csv
from typing import Dict, List, Optional
logger = logging.getLogger(__name__)
class MSEEvaluator(SentenceEvaluator):
"""
... | from sentence_transformers import SentenceTransformer
from contextlib import nullcontext
from sentence_transformers.evaluation import SentenceEvaluator
import logging
import os
import csv
from typing import List, Optional
logger = logging.getLogger(__name__)
class MSEEvaluator(SentenceEvaluator):
"""
Comput... |
from keras.src import tree
from keras.src.trainers.data_adapters import data_adapter_utils
from keras.src.trainers.data_adapters.data_adapter import DataAdapter
class TFDatasetAdapter(DataAdapter):
"""Adapter that handles `tf.data.Dataset`."""
def __init__(self, dataset, class_weight=None, distribution=None)... | from keras.src import tree
from keras.src.trainers.data_adapters import data_adapter_utils
from keras.src.trainers.data_adapters.data_adapter import DataAdapter
class TFDatasetAdapter(DataAdapter):
"""Adapter that handles `tf.data.Dataset`."""
def __init__(self, dataset, class_weight=None, distribution=None)... |
"""
This examples trains a CrossEncoder for the NLI task. A CrossEncoder takes a sentence pair
as input and outputs a label. Here, it learns to predict the labels: "contradiction": 0, "entailment": 1, "neutral": 2.
It does NOT produce a sentence embedding and does NOT work for individual sentences.
Usage:
python trai... | """
This examples trains a CrossEncoder for the NLI task. A CrossEncoder takes a sentence pair
as input and outputs a label. Here, it learns to predict the labels: "contradiction": 0, "entailment": 1, "neutral": 2.
It does NOT produce a sentence embedding and does NOT work for individual sentences.
Usage:
python trai... |
_base_ = [
'../_base_/models/mask-rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
]
model = dict(
backbone=dict(
frozen_stages=0,
norm_cfg=dict(type='SyncBN', requires_grad=True),
norm_eval=False,
... | _base_ = [
'../_base_/models/mask-rcnn_r50_fpn.py',
'../_base_/datasets/coco_instance.py',
'../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
]
model = dict(
backbone=dict(
frozen_stages=0,
norm_cfg=dict(type='SyncBN', requires_grad=True),
norm_eval=False,
... |
import os
import shutil
import subprocess
import sys
def _get_run_args(print_args: bool = True):
from jina.helper import get_rich_console
from jina.parsers import get_main_parser
console = get_rich_console()
silent_print = {'help', 'hub', 'export', 'auth'}
parser = get_main_parser()
if len(... | import os
import shutil
import subprocess
import sys
def _get_run_args(print_args: bool = True):
from jina.helper import get_rich_console
from jina.parsers import get_main_parser
console = get_rich_console()
silent_print = {'help', 'hub', 'export'}
parser = get_main_parser()
if len(sys.argv... |
# Copyright (c) OpenMMLab. All rights reserved.
"""copy from
https://github.com/ZwwWayne/K-Net/blob/main/knet/det/mask_pseudo_sampler.py."""
import torch
from torch import Tensor
from mmdet.core.bbox.assigners import AssignResult
from .sampling_result import SamplingResult
class MaskSamplingResult(SamplingResult):
... | # Copyright (c) OpenMMLab. All rights reserved.
"""copy from
https://github.com/ZwwWayne/K-Net/blob/main/knet/det/mask_pseudo_sampler.py."""
import torch
from .sampling_result import SamplingResult
class MaskSamplingResult(SamplingResult):
"""Mask sampling result."""
def __init__(self, pos_inds, neg_inds, ... |
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"... | 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"... |
"""DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.datasets.fashion_mnist import load_data as load_data
| """DO NOT EDIT.
This file was autogenerated. Do not edit it by hand,
since your modifications would be overwritten.
"""
from keras.src.datasets.fashion_mnist import load_data
|
# Copyright (c) OpenMMLab. All rights reserved.
from mmdet.core.utils import ConfigType, OptConfigType, OptMultiConfig
from mmdet.registry import MODELS
from .single_stage import SingleStageDetector
@MODELS.register_module()
class RetinaNet(SingleStageDetector):
"""Implementation of `RetinaNet <https://arxiv.org/... | # Copyright (c) OpenMMLab. All rights reserved.
from typing import Optional, Union
from mmengine.config import ConfigDict
from mmdet.registry import MODELS
from .single_stage import SingleStageDetector
@MODELS.register_module()
class RetinaNet(SingleStageDetector):
"""Implementation of `RetinaNet <https://arxiv... |
"""Pydantic v1 compatibility shim."""
from langchain_core._api import warn_deprecated
try:
from pydantic.v1.dataclasses import * # noqa: F403
except ImportError:
from pydantic.dataclasses import * # type: ignore[no-redef] # noqa: F403
warn_deprecated(
"0.3.0",
removal="1.0.0",
alternative="pyda... | """Pydantic v1 compatibility shim."""
from langchain_core._api import warn_deprecated
try:
from pydantic.v1.dataclasses import * # noqa: F403
except ImportError:
from pydantic.dataclasses import * # noqa: F403
warn_deprecated(
"0.3.0",
removal="1.0.0",
alternative="pydantic.v1 or pydantic",
... |
# Copyright (c) OpenMMLab. All rights reserved.
"""Collecting some commonly used type hint in mmdetection."""
from typing import List, Optional, Union
from mmengine.config import ConfigDict
from mmengine.data import InstanceData
from ..bbox.samplers import SamplingResult
from ..data_structures import DetDataSample
#... | # Copyright (c) OpenMMLab. All rights reserved.
"""Collecting some commonly used type hint in mmdetection."""
from typing import List, Optional, Union
from mmengine.config import ConfigDict
from mmengine.data import InstanceData
from ..data_structures import DetDataSample
# Type hint of config data
ConfigType = Uni... |
import numpy as np
from absl.testing import parameterized
from keras.src import backend
from keras.src import testing
from keras.src.utils import backend_utils
class BackendUtilsTest(testing.TestCase):
@parameterized.named_parameters(
("numpy", "numpy"),
("jax", "jax"),
("tensorflow", "te... | import numpy as np
from absl.testing import parameterized
from keras.src import backend
from keras.src import testing
from keras.src.utils import backend_utils
class BackendUtilsTest(testing.TestCase):
@parameterized.named_parameters(
("numpy", "numpy"),
("jax", "jax"),
("tensorflow", "te... |
# Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
import torch.nn.functional as F
from mmcv.cnn import constant_init
from mmdet.models.utils import DyReLU, SELayer
def test_se_layer():
with pytest.raises(AssertionError):
# act_cfg sequence length must equal to 2
SELayer(c... | # Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from mmdet.models.utils import SELayer
def test_se_layer():
with pytest.raises(AssertionError):
# act_cfg sequence length must equal to 2
SELayer(channels=32, act_cfg=(dict(type='ReLU'), ))
with pytest.raises(Assertio... |
_base_ = './solo_r50_fpn_8xb8-lsj-200e_coco.py'
model = dict(
backbone=dict(
depth=18,
init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet18')),
neck=dict(in_channels=[64, 128, 256, 512]))
| _base_ = './solo_r50_fpn_lsj_200e_8x8_coco.py'
model = dict(
backbone=dict(
depth=18,
init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet18')),
neck=dict(in_channels=[64, 128, 256, 512]))
|
# Copyright (c) OpenMMLab. All rights reserved.
from typing import Optional, Sequence, Union
from mmengine.data import BaseDataElement
from mmengine.hooks import Hook
from mmengine.runner import Runner
from mmdet.registry import HOOKS
@HOOKS.register_module()
class MemoryProfilerHook(Hook):
"""Memory profiler h... | # Copyright (c) OpenMMLab. All rights reserved.
from mmcv.runner.hooks import Hook
from mmdet.registry import HOOKS
@HOOKS.register_module()
class MemoryProfilerHook(Hook):
"""Memory profiler hook recording memory information including virtual
memory, swap memory, and the memory of the current process.
... |
from .filtering import (
allpass_biquad,
band_biquad,
bandpass_biquad,
bandreject_biquad,
bass_biquad,
biquad,
contrast,
dcshift,
deemph_biquad,
dither,
equalizer_biquad,
filtfilt,
flanger,
gain,
highpass_biquad,
lfilter,
lowpass_biquad,
overdrive,... | from .filtering import (
allpass_biquad,
band_biquad,
bandpass_biquad,
bandreject_biquad,
bass_biquad,
biquad,
contrast,
dcshift,
deemph_biquad,
dither,
equalizer_biquad,
filtfilt,
flanger,
gain,
highpass_biquad,
lfilter,
lowpass_biquad,
overdrive,... |
_base_ = [
'../_base_/models/faster-rcnn_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
]
| _base_ = [
'../_base_/models/faster_rcnn_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
]
|
import logging
from sentence_transformers import SparseEncoder
from sentence_transformers.sparse_encoder.evaluation import SparseNanoBEIREvaluator
logging.basicConfig(format="%(message)s", level=logging.INFO)
# Load a model
model = SparseEncoder("naver/splade-cocondenser-ensembledistil")
evaluator = SparseNanoBEIR... | import logging
from sentence_transformers import SparseEncoder
from sentence_transformers.sparse_encoder.evaluation import SparseNanoBEIREvaluator
logging.basicConfig(format="%(message)s", level=logging.INFO)
# Load a model
model = SparseEncoder("naver/splade-cocondenser-ensembledistil")
evaluator = SparseNanoBEIR... |
# Copyright (c) OpenMMLab. All rights reserved.
from typing import Sequence
from torch.utils.data import BatchSampler, Sampler
from mmdet.datasets.samplers.track_img_sampler import TrackImgSampler
from mmdet.registry import DATA_SAMPLERS
# TODO: maybe replace with a data_loader wrapper
@DATA_SAMPLERS.register_modul... | # Copyright (c) OpenMMLab. All rights reserved.
from typing import Sequence
from torch.utils.data import BatchSampler, Sampler
from mmdet.registry import DATA_SAMPLERS
# TODO: maybe replace with a data_loader wrapper
@DATA_SAMPLERS.register_module()
class AspectRatioBatchSampler(BatchSampler):
"""A sampler wrap... |
from __future__ import annotations
import re
from typing import Optional
from langchain_core.output_parsers import BaseOutputParser
class RegexDictParser(BaseOutputParser[dict[str, str]]):
"""Parse the output of an LLM call into a Dictionary using a regex."""
regex_pattern: str = r"{}:\s?([^.'\n']*)\.?" #... | from __future__ import annotations
import re
from typing import Dict, Optional
from langchain_core.output_parsers import BaseOutputParser
class RegexDictParser(BaseOutputParser[Dict[str, str]]):
"""Parse the output of an LLM call into a Dictionary using a regex."""
regex_pattern: str = r"{}:\s?([^.'\n']*)\... |
import os
import subprocess
import sys
import pytest
from xgboost import testing as tm
DEMO_DIR = tm.demo_dir(__file__)
PYTHON_DEMO_DIR = os.path.join(DEMO_DIR, "guide-python")
@pytest.mark.skipif(**tm.no_cupy())
def test_data_iterator():
script = os.path.join(PYTHON_DEMO_DIR, "quantile_data_iterator.py")
... | import os
import subprocess
import sys
import pytest
from xgboost import testing as tm
DEMO_DIR = tm.demo_dir(__file__)
PYTHON_DEMO_DIR = os.path.join(DEMO_DIR, "guide-python")
@pytest.mark.skipif(**tm.no_cupy())
def test_data_iterator():
script = os.path.join(PYTHON_DEMO_DIR, "quantile_data_iterator.py")
... |
from __future__ import annotations
import math
import random
class NoDuplicatesDataLoader:
def __init__(self, train_examples, batch_size):
"""
A special data loader to be used with MultipleNegativesRankingLoss.
The data loader ensures that there are no duplicate sentences within the same ... | import math
import random
class NoDuplicatesDataLoader:
def __init__(self, train_examples, batch_size):
"""
A special data loader to be used with MultipleNegativesRankingLoss.
The data loader ensures that there are no duplicate sentences within the same batch
"""
self.batch... |
# Copyright (c) OpenMMLab. All rights reserved.
from typing import Any, Optional, Sequence, Tuple, Union
import torch
from mmengine.data import BaseDataSample
from mmengine.registry import HOOKS
from .hook import Hook
DATA_BATCH = Optional[Sequence[Tuple[Any, BaseDataSample]]]
@HOOKS.register_module()
class EmptyC... | # Copyright (c) OpenMMLab. All rights reserved.
from typing import Any, Optional, Sequence, Tuple, Union
import torch
from mmengine.data import BaseDataSample
from mmengine.registry import HOOKS
from .hook import Hook
DATA_BATCH = Optional[Sequence[Tuple[Any, BaseDataSample]]]
@HOOKS.register_module()
class EmptyC... |
import pytest
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')
@pytest.mark.parametrize(
'array,result... | import pytest
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')
@pytest.mark.parametrize(
'array,result... |
"""Tool for the OpenAI DALLE V1 Image Generation SDK."""
from typing import Optional
from langchain_core.callbacks import CallbackManagerForToolRun
from langchain_core.tools import BaseTool
from langchain_community.utilities.dalle_image_generator import DallEAPIWrapper
class OpenAIDALLEImageGenerationTool(BaseTool... | """Tool for the OpenAI DALLE V1 Image Generation SDK."""
from typing import Optional
from langchain_core.callbacks import CallbackManagerForToolRun
from langchain_core.tools import BaseTool
from langchain_community.utilities.dalle_image_generator import DallEAPIWrapper
class OpenAIDALLEImageGenerationTool(BaseTool... |
# Copyright (c) OpenMMLab. All rights reserved.
from unittest import TestCase
from unittest.mock import Mock, patch
from mmdet.engine.hooks import YOLOXModeSwitchHook
class TestYOLOXModeSwitchHook(TestCase):
@patch('mmdet.engine.hooks.yolox_mode_switch_hook.is_model_wrapper')
def test_is_model_wrapper_and_p... | # Copyright (c) OpenMMLab. All rights reserved.
from unittest import TestCase
from unittest.mock import Mock, patch
from mmdet.engine.hooks import YOLOXModeSwitchHook
class TestYOLOXModeSwitchHook(TestCase):
@patch('mmdet.engine.hooks.yolox_mode_switch_hook.is_model_wrapper')
def test_is_model_wrapper_and_p... |
_base_ = './retinanet_r50_fpn_8xb8-amp-lsj-200e_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
| _base_ = './retinanet_r50_fpn_lsj_200e_8x8_fp16_coco.py'
model = dict(
backbone=dict(
depth=101,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet101')))
|
import pytest
from docarray.utils.misc import is_tf_available
tf_available = is_tf_available()
if tf_available:
import tensorflow as tf
import tensorflow._api.v2.experimental.numpy as tnp
from docarray.computation.tensorflow_backend import TensorFlowCompBackend
from docarray.typing import TensorFlowT... | import pytest
try:
import tensorflow as tf
import tensorflow._api.v2.experimental.numpy as tnp
from docarray.computation.tensorflow_backend import TensorFlowCompBackend
from docarray.typing import TensorFlowTensor
except (ImportError, TypeError):
pass
@pytest.mark.tensorflow
def test_top_k_desce... |
# Copyright (c) OpenMMLab. All rights reserved.
from .atss import ATSS
from .autoassign import AutoAssign
from .base import BaseDetector
from .cascade_rcnn import CascadeRCNN
from .centernet import CenterNet
from .cornernet import CornerNet
from .deformable_detr import DeformableDETR
from .detr import DETR
from .fast_r... | # Copyright (c) OpenMMLab. All rights reserved.
from .atss import ATSS
from .autoassign import AutoAssign
from .base import BaseDetector
from .cascade_rcnn import CascadeRCNN
from .centernet import CenterNet
from .cornernet import CornerNet
from .deformable_detr import DeformableDETR
from .detr import DETR
from .fast_r... |
# Copyright (c) OpenMMLab. All rights reserved.
from unittest.mock import Mock
from mmengine.hooks import SyncBuffersHook
class TestSyncBuffersHook:
def test_sync_buffers_hook(self):
Runner = Mock()
Runner.model = Mock()
Hook = SyncBuffersHook()
Hook._after_epoch(Runner)
| # Copyright (c) OpenMMLab. All rights reserved.
from unittest.mock import Mock
from mmengine.hooks import SyncBuffersHook
class TestSyncBuffersHook:
def test_sync_buffers_hook(self):
Runner = Mock()
Runner.model = Mock()
Hook = SyncBuffersHook()
Hook.after_epoch(Runner)
|
# Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
from mmdet.core.mask import BitmapMasks, PolygonMasks
def _check_fields(results, pipeline_results, keys):
"""Check data in fields from two results are same."""
for key in keys:
if isinstance(results[key], (BitmapMasks, PolygonMasks)):... | # Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
from mmdet.core.mask import BitmapMasks, PolygonMasks
def _check_fields(results, pipeline_results, keys):
"""Check data in fields from two results are same."""
for key in keys:
if isinstance(results[key], (BitmapMasks, PolygonMasks)):... |
_base_ = './retinanet_r50_fpn_crop640-50e_coco.py'
# model settings
model = dict(
# `pad_size_divisor=128` ensures the feature maps sizes
# in `NAS_FPN` won't mismatch.
data_preprocessor=dict(pad_size_divisor=128),
neck=dict(
_delete_=True,
type='NASFPN',
in_channels=[256, 512, ... | _base_ = './retinanet_r50_fpn_crop640_50e_coco.py'
# model settings
model = dict(
# `pad_size_divisor=128` ensures the feature maps sizes
# in `NAS_FPN` won't mismatch.
data_preprocessor=dict(pad_size_divisor=128),
neck=dict(
_delete_=True,
type='NASFPN',
in_channels=[256, 512, ... |
# Copyright (c) OpenMMLab. All rights reserved.
__version__ = '0.7.1'
def parse_version_info(version_str):
"""Parse the version information.
Args:
version_str (str): version string like '0.1.0'.
Returns:
tuple: version information contains major, minor, micro version.
"""
versio... | # Copyright (c) OpenMMLab. All rights reserved.
__version__ = '0.7.0'
def parse_version_info(version_str):
"""Parse the version information.
Args:
version_str (str): version string like '0.1.0'.
Returns:
tuple: version information contains major, minor, micro version.
"""
versio... |
__version__ = '0.13.1'
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()
if 'NO_VERSION_CHECK' not in os.environ:
from .helper import is_latest_versi... | __version__ = '0.13.0'
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()
if 'NO_VERSION_CHECK' not in os.environ:
from .helper import is_latest_versi... |
from typing import TYPE_CHECKING
from ...utils import (
DIFFUSERS_SLOW_IMPORT,
OptionalDependencyNotAvailable,
_LazyModule,
get_objects_from_module,
is_torch_available,
is_transformers_available,
)
_dummy_objects = {}
_import_structure = {}
try:
if not (is_transformers_available() and i... | from typing import TYPE_CHECKING
from ...utils import (
DIFFUSERS_SLOW_IMPORT,
OptionalDependencyNotAvailable,
_LazyModule,
get_objects_from_module,
is_torch_available,
is_transformers_available,
)
_dummy_objects = {}
_import_structure = {}
try:
if not (is_transformers_available() and i... |
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless r... | # coding=utf-8
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless r... |
from __future__ import annotations
from pathlib import Path
from unittest.mock import Mock, PropertyMock
import pytest
import torch
from sentence_transformers import SentenceTransformer
from sentence_transformers.evaluation import InformationRetrievalEvaluator
from sentence_transformers.util import cos_sim
@pytest... | from __future__ import annotations
from unittest.mock import Mock, PropertyMock
import pytest
import torch
from sentence_transformers import SentenceTransformer
from sentence_transformers.evaluation import InformationRetrievalEvaluator
from sentence_transformers.util import cos_sim
@pytest.fixture
def mock_model()... |
# 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 docarray import BaseDoc
from docarray.typing import ImageUrl
def test_set_image_url():
class MyDocument(BaseDoc):
image_url: ImageUrl
d = MyDocument(image_url="https://jina.ai/img.png")
assert isinstance(d.image_url, ImageUrl)
assert d.image_url == "https://jina.ai/img.png"
|
from typing import TYPE_CHECKING
if TYPE_CHECKING:
from docarray import Document
def image_getter(doc: 'Document'):
if doc._metadata['image_type'] == 'uri':
return doc.uri
elif doc._metadata['image_type'] == 'PIL':
from PIL import Image
return Image.fromarray(doc.tensor)
elif... | from typing import TYPE_CHECKING
if TYPE_CHECKING:
from docarray import Document
def image_getter(doc: 'Document'):
if doc._metadata['image_type'] == 'uri':
return doc.uri
elif doc._metadata['image_type'] == 'PIL':
from PIL import Image
return Image.fromarray(doc.tensor)
elif... |
_base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
]
# model settings
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
backbone=dict(
depth=101,
init_c... | _base_ = [
'../_base_/models/retinanet_r50_fpn.py',
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
]
# model settings
norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
model = dict(
backbone=dict(
depth=101,
init_c... |
# 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... |
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