id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
6,631 | import sys
import os
import sphinx_rtd_theme
from github_link import make_linkcode_resolve
import sphinx
from packaging.version import Version, parse
def setup(app):
# a copy button to copy snippet of code from the documentation
app.add_js_file("js/copybutton.js")
app.add_css_file("basic.css") | null |
6,632 | import os
import sys
import argparse
import json
import ast
import gc
import psutil
import signal
import pickle
import numpy as np
import warnings
from pathlib import Path
from scipy import stats
from memory_profiler import memory_usage
import benchmarks.trees.train as train
import benchmarks.trees.score as score
from ... | null |
6,633 | import os
import sys
import argparse
import json
import ast
import gc
import psutil
import signal
import pickle
import numpy as np
import warnings
from pathlib import Path
from scipy import stats
from memory_profiler import memory_usage
import benchmarks.trees.train as train
import benchmarks.trees.score as score
from ... | null |
6,634 | import os
import sys
import argparse
import json
import ast
import gc
import psutil
import signal
import pickle
import numpy as np
import warnings
from pathlib import Path
from scipy import stats
from memory_profiler import memory_usage
import benchmarks.trees.train as train
import benchmarks.trees.score as score
from ... | null |
6,635 | import os
import sys
import argparse
import json
import ast
import gc
import psutil
import signal
import pickle
import numpy as np
import warnings
from pathlib import Path
from scipy import stats
from memory_profiler import memory_usage
import benchmarks.trees.train as train
import benchmarks.trees.score as score
from ... | null |
6,636 | import os
import sys
import argparse
import json
import ast
import gc
import psutil
import signal
import pickle
import numpy as np
import warnings
from pathlib import Path
from scipy import stats
from memory_profiler import memory_usage
import benchmarks.trees.train as train
import benchmarks.trees.score as score
from ... | null |
6,637 | import os
from enum import Enum
import pandas as pd
import pickle
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.datasets import load_svmlight_file
from urllib.request import urlretrieve
class LearningTask(Enum):
REGRESSION = 1
CLASSIFICATION = 2
MULTICLASS_CLASSIFICATI... | null |
6,638 | import os
from enum import Enum
import pandas as pd
import pickle
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.datasets import load_svmlight_file
from urllib.request import urlretrieve
class LearningTask(Enum):
class Data:
def __init__(self, X_train, X_test, y_train, y_test,... | null |
6,639 | import os
from enum import Enum
import pandas as pd
import pickle
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.datasets import load_svmlight_file
from urllib.request import urlretrieve
class LearningTask(Enum):
REGRESSION = 1
CLASSIFICATION = 2
MULTICLASS_CLASSIFICATI... | null |
6,640 | import os
from enum import Enum
import pandas as pd
import pickle
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.datasets import load_svmlight_file
from urllib.request import urlretrieve
class LearningTask(Enum):
class Data:
def __init__(self, X_train, X_test, y_train, y_test,... | null |
6,641 | import os
from enum import Enum
import pandas as pd
import pickle
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.datasets import load_svmlight_file
from urllib.request import urlretrieve
class LearningTask(Enum):
REGRESSION = 1
CLASSIFICATION = 2
MULTICLASS_CLASSIFICATI... | null |
6,642 | import os
from enum import Enum
import pandas as pd
import pickle
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.datasets import load_svmlight_file
from urllib.request import urlretrieve
class LearningTask(Enum):
REGRESSION = 1
CLASSIFICATION = 2
MULTICLASS_CLASSIFICATI... | null |
6,643 | import os
from enum import Enum
import pandas as pd
import pickle
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.datasets import load_svmlight_file
from urllib.request import urlretrieve
class LearningTask(Enum):
REGRESSION = 1
CLASSIFICATION = 2
MULTICLASS_CLASSIFICATI... | null |
6,644 | import os
import sys
import argparse
import json
import ast
import psutil
from pathlib import Path
import signal
import time
import numpy as np
import sklearn
import joblib
import torch
from scipy import stats
import gc
import benchmarks.pipelines.score as score
from benchmarks.timer import Timer
def print_sys_info(ar... | null |
6,645 | import os
import sys
import argparse
import json
import ast
import psutil
from pathlib import Path
import signal
import time
import numpy as np
import sklearn
import joblib
import torch
from scipy import stats
import gc
import benchmarks.pipelines.score as score
from benchmarks.timer import Timer
def set_alarm(timeout... | null |
6,646 | import os
import sys
import argparse
import json
import ast
import psutil
from pathlib import Path
import signal
import time
import numpy as np
import sklearn
import joblib
import torch
from scipy import stats
import gc
import benchmarks.pipelines.score as score
from benchmarks.timer import Timer
def signal_handler(sig... | null |
6,647 | import os
import sys
import argparse
import json
import ast
import psutil
from pathlib import Path
import signal
import time
import numpy as np
import sklearn
import joblib
import torch
from scipy import stats
import gc
import benchmarks.pipelines.score as score
from benchmarks.timer import Timer
ROOT_PATH = Path(__fil... | null |
6,648 | from packaging.version import Version, parse
import openml
import sklearn
import operator
import keyword
import re
from pathlib import Path
import numpy as np
import random
import os
import joblib
import warnings
from sklearn.datasets import fetch_openml
from sklearn.metrics import accuracy_score
from sklearn.model_sel... | null |
6,649 | import os
import sys
import argparse
import json
import ast
from pathlib import Path
import psutil
import signal
import numpy as np
import warnings
import gc
from scipy import stats
from memory_profiler import memory_usage
from sklearn.linear_model import LogisticRegression, LogisticRegressionCV
from sklearn.linear_mod... | null |
6,650 | import os
import sys
import argparse
import json
import ast
from pathlib import Path
import psutil
import signal
import numpy as np
import warnings
import gc
from scipy import stats
from memory_profiler import memory_usage
from sklearn.linear_model import LogisticRegression, LogisticRegressionCV
from sklearn.linear_mod... | null |
6,651 | import os
import sys
import argparse
import json
import ast
from pathlib import Path
import psutil
import signal
import numpy as np
import warnings
import gc
from scipy import stats
from memory_profiler import memory_usage
from sklearn.linear_model import LogisticRegression, LogisticRegressionCV
from sklearn.linear_mod... | null |
6,652 | import os
import sys
import argparse
import json
import ast
from pathlib import Path
import psutil
import signal
import numpy as np
import warnings
import gc
from scipy import stats
from memory_profiler import memory_usage
from sklearn.linear_model import LogisticRegression, LogisticRegressionCV
from sklearn.linear_mod... | null |
6,653 | import os
import sys
import argparse
import json
import ast
from pathlib import Path
import psutil
import signal
import numpy as np
import warnings
import gc
from scipy import stats
from memory_profiler import memory_usage
from sklearn.linear_model import LogisticRegression, LogisticRegressionCV
from sklearn.linear_mod... | null |
6,654 | import logging
from mcu import MCU_endstop
class ZCalibrationHelper:
def __init__(self, config):
self.state = None
self.z_endstop = None
self.z_homing = None
self.last_state = False
self.last_z_offset = 0.
self.position_z_endstop = None
self.config = config
... | null |
6,655 | from typing import Callable, Dict, Type, TypeVar
from docarray.typing.abstract_type import AbstractType
_PROTO_TYPE_NAME_TO_CLASS: Dict[str, Type[AbstractType]] = {}
T = TypeVar('T', bound='AbstractType')
The provided code snippet includes necessary dependencies for implementing the `_register_proto` function. Write a... | Register a new type to be used in the protobuf serialization. This will add the type key to the global registry of types key used in the proto serialization and deserialization. This is for internal usage only. --- ```python from docarray.typing.proto_register import register_proto from docarray.typing.abstract_type im... |
6,656 | import copy
import logging
from abc import ABC, abstractmethod
from dataclasses import dataclass, field, replace
from typing import (
TYPE_CHECKING,
Any,
Dict,
Generator,
Generic,
Iterable,
List,
Mapping,
Optional,
Sequence,
Tuple,
Type,
TypeVar,
Union,
cast,
... | null |
6,657 | import copy
import logging
from abc import ABC, abstractmethod
from dataclasses import dataclass, field, replace
from typing import (
TYPE_CHECKING,
Any,
Dict,
Generator,
Generic,
Iterable,
List,
Mapping,
Optional,
Sequence,
Tuple,
Type,
TypeVar,
Union,
cast,
... | null |
6,658 | from typing import Any, Dict, List, Tuple, Type, cast
from docarray import BaseDoc, DocList
from docarray.index.abstract import BaseDocIndex
from docarray.utils.filter import filter_docs
from docarray.utils.find import FindResult
def _collect_query_args(method_name: str): # TODO: use partialmethod instead
def inn... | null |
6,659 | from typing import Any, Dict, List, Tuple, Type, cast
from docarray import BaseDoc, DocList
from docarray.index.abstract import BaseDocIndex
from docarray.utils.filter import filter_docs
from docarray.utils.find import FindResult
class BaseDocIndex(ABC, Generic[TSchema]):
"""Abstract class for all Document Stores"... | Executes all find calls from query first using `doc_index.find()`, and filtering queries after that using DocArray's `filter_docs()`. Text search is not supported. :param doc_index: Document index instance. Either InMemoryExactNNIndex or HnswDocumentIndex. :param query: Dictionary containing search and filtering config... |
6,660 | from typing import TYPE_CHECKING, Any, Dict, Optional, Tuple, Type, TypeVar
from pydantic import create_model
from docarray.utils._internal.pydantic import is_pydantic_v2
if not is_pydantic_v2:
from pydantic import create_model_from_typeddict
else:
def create_model_from_typeddict(*args, **kwargs):
raise... | Create a subclass of BaseDoc based on the fields of a `TypedDict`. This is a wrapper around pydantic's create_model_from_typeddict. --- ```python from typing_extensions import TypedDict from docarray import BaseDoc from docarray.documents import Audio from docarray.documents.helper import create_doc_from_typeddict from... |
6,661 | from typing import TYPE_CHECKING, Any, Dict, Optional, Tuple, Type, TypeVar
from pydantic import create_model
from docarray.utils._internal.pydantic import is_pydantic_v2
from pydantic.config import BaseConfig
from typing_extensions import TypedDict
from docarray import BaseDoc
from docarray.utils._internal._typing imp... | Create a subclass of BaseDoc based on example data given as a dictionary. In case the example contains None as a value, corresponding field will be viewed as the type Any. --- ```python import numpy as np from docarray.documents import ImageDoc from docarray.documents.helper import create_doc_from_dict data_dict = {'im... |
6,662 | from typing import (
Any,
Iterable,
List,
Sequence,
TypeVar,
Union,
cast,
no_type_check,
overload,
)
import numpy as np
from typing_extensions import SupportsIndex
from docarray.utils._internal.misc import (
is_jax_available,
is_tf_available,
is_torch_available,
)
def _i... | null |
6,663 | import base64
import csv
import io
import os
import pathlib
import pickle
from abc import abstractmethod
from contextlib import nullcontext
from io import StringIO, TextIOWrapper
from itertools import compress
from typing import (
TYPE_CHECKING,
Any,
BinaryIO,
ContextManager,
Dict,
Generator,
... | Extract protocol and compression algorithm from a string, use defaults if not found. :param file_path: path of a file. :param default_protocol: default serialization protocol used in case not found. :param default_compress: default compression method used in case not found. Examples: >>> _protocol_and_compress_from_fil... |
6,664 | import base64
import io
import pathlib
from abc import abstractmethod
from contextlib import nullcontext
from typing import (
TYPE_CHECKING,
Any,
Dict,
Generator,
Optional,
Type,
TypeVar,
Union,
cast,
)
import numpy as np
import orjson
from pydantic import parse_obj_as
from docarray.... | null |
6,665 | import base64
import io
import pathlib
from abc import abstractmethod
from contextlib import nullcontext
from typing import (
TYPE_CHECKING,
Any,
Dict,
Generator,
Optional,
Type,
TypeVar,
Union,
cast,
)
import numpy as np
import orjson
from pydantic import parse_obj_as
from docarray.... | null |
6,666 | import base64
import io
import pathlib
from abc import abstractmethod
from contextlib import nullcontext
from typing import (
TYPE_CHECKING,
Any,
Dict,
Generator,
Optional,
Type,
TypeVar,
Union,
cast,
)
import numpy as np
import orjson
from pydantic import parse_obj_as
from docarray.... | null |
6,667 | import base64
import io
import pathlib
from abc import abstractmethod
from contextlib import nullcontext
from typing import (
TYPE_CHECKING,
Any,
Dict,
Generator,
Optional,
Type,
TypeVar,
Union,
cast,
)
import numpy as np
import orjson
from pydantic import parse_obj_as
from docarray.... | null |
6,668 | import base64
import io
import pathlib
from abc import abstractmethod
from contextlib import nullcontext
from typing import (
TYPE_CHECKING,
Any,
Dict,
Generator,
Optional,
Type,
TypeVar,
Union,
cast,
)
import numpy as np
import orjson
from pydantic import parse_obj_as
from docarray.... | null |
6,669 | import base64
import io
import pathlib
from abc import abstractmethod
from contextlib import nullcontext
from typing import (
TYPE_CHECKING,
Any,
Dict,
Generator,
Optional,
Type,
TypeVar,
Union,
cast,
)
import numpy as np
import orjson
from pydantic import parse_obj_as
from docarray.... | null |
6,670 | from abc import abstractmethod
from typing import TYPE_CHECKING, Dict, List, Type, TypeVar
from typing_inspect import get_origin
from docarray.utils._internal._typing import safe_issubclass
def _similar_schemas(model1, model2):
return model1.__annotations__ == model2.__annotations__ | null |
6,671 | import base64
import pickle
from abc import abstractmethod
from typing import (
TYPE_CHECKING,
Any,
Callable,
Dict,
Iterable,
List,
Literal,
Optional,
Tuple,
Type,
TypeVar,
Union,
)
from typing import _GenericAlias as GenericAlias
from typing import get_origin
import nump... | Convert any type to a NodeProto :param value: any object that need to be serialized :return: a NodeProto |
6,672 | from typing import Any, Callable, Dict, Type
import orjson
from docarray.utils._internal.pydantic import is_pydantic_v2
def orjson_dumps(v, *, default=None) -> bytes:
# dumps to bytes using orjson
return orjson.dumps(v, default=_default_orjson, option=orjson.OPT_SERIALIZE_NUMPY)
def orjson_dumps_and_decode(v, ... | null |
6,673 | import glob
import itertools
import os
import re
from types import LambdaType
from typing import (
TYPE_CHECKING,
Any,
Callable,
Dict,
Generator,
List,
Optional,
Type,
Union,
)
import numpy as np
from docarray.utils._internal._typing import safe_issubclass
from docarray.utils._intern... | Check if all access paths ("__"-separated) are valid for a given Document class. |
6,674 | import glob
import itertools
import os
import re
from types import LambdaType
from typing import (
TYPE_CHECKING,
Any,
Callable,
Dict,
Generator,
List,
Optional,
Type,
Union,
)
import numpy as np
from docarray.utils._internal._typing import safe_issubclass
from docarray.utils._intern... | Convert a dict, where the keys are access paths ("__"-separated) to a nested dictionary. --- ```python access_path2val = {'image__url': 'some.png'} assert access_path_dict_to_nested_dict(access_path2val) == { 'image': {'url': 'some.png'} } ``` --- :param access_path2val: dict with access_paths as keys :return: nested d... |
6,675 | import glob
import itertools
import os
import re
from types import LambdaType
from typing import (
TYPE_CHECKING,
Any,
Callable,
Dict,
Generator,
List,
Optional,
Type,
Union,
)
import numpy as np
from docarray.utils._internal._typing import safe_issubclass
from docarray.utils._intern... | Convert a (nested) dict to a Dict[access_path, value]. Access paths are defined as a path of field(s) separated by "__". ```python assert dict_to_access_paths({'image': {'url': 'img.png'}}) == {'image__url', 'img.png'} ``` |
6,676 | import glob
import itertools
import os
import re
from types import LambdaType
from typing import (
TYPE_CHECKING,
Any,
Callable,
Dict,
Generator,
List,
Optional,
Type,
Union,
)
import numpy as np
from docarray.utils._internal._typing import safe_issubclass
from docarray.utils._intern... | Yield file paths described by `patterns`. --- ```python from typing import Optional from docarray import BaseDoc, DocList from docarray.helper import get_paths from docarray.typing import TextUrl, ImageUrl class Banner(BaseDoc): text_url: TextUrl image_url: Optional[ImageUrl] # you can call it in the constructor docs =... |
6,677 | import glob
import itertools
import os
import re
from types import LambdaType
from typing import (
TYPE_CHECKING,
Any,
Callable,
Dict,
Generator,
List,
Optional,
Type,
Union,
)
import numpy as np
from docarray.utils._internal._typing import safe_issubclass
from docarray.utils._intern... | null |
6,678 | import warnings
from typing import Any, List, Optional, Tuple
import numpy as np
from docarray.computation import AbstractComputationalBackend
from docarray.computation.abstract_numpy_based_backend import AbstractNumpyBasedBackend
The provided code snippet includes necessary dependencies for implementing the `_expand_... | Expands arrays that only have one axis, at dim 0. This ensures that all outputs can be treated as matrices, not vectors. :param matrices: Matrices to be expanded :return: List of the input matrices, where single axis matrices are expanded at dim 0. |
6,679 | import warnings
from typing import Any, List, Optional, Tuple
import numpy as np
from docarray.computation import AbstractComputationalBackend
from docarray.computation.abstract_numpy_based_backend import AbstractNumpyBasedBackend
def _expand_if_scalar(arr: np.ndarray) -> np.ndarray:
if len(arr.shape) == 0: # avo... | null |
6,680 | import warnings
from typing import Any, List, Optional, Tuple
import numpy as np
from docarray.computation import AbstractComputationalBackend
from docarray.computation.abstract_numpy_based_backend import AbstractNumpyBasedBackend
def identity(array: np.ndarray) -> np.ndarray:
return array | null |
6,681 | import typing
from typing import TYPE_CHECKING, Callable, List, Optional, Tuple
import numpy as np
from docarray.computation import AbstractComputationalBackend
from docarray.computation.abstract_numpy_based_backend import AbstractNumpyBasedBackend
from docarray.typing import TensorFlowTensor
from docarray.utils._inter... | Unsqueezes tensors that only have one axis, at dim 0. This ensures that all outputs can be treated as matrices, not vectors. :param matrices: Matrices to be unsqueezed :return: List of the input matrices, where single axis matrices are unsqueezed at dim 0. |
6,682 | import typing
from typing import TYPE_CHECKING, Callable, List, Optional, Tuple
import numpy as np
from docarray.computation import AbstractComputationalBackend
from docarray.computation.abstract_numpy_based_backend import AbstractNumpyBasedBackend
from docarray.typing import TensorFlowTensor
from docarray.utils._inter... | Unsqueezes tensor of a scalar, from shape () to shape (1,). :param t: tensor to unsqueeze. :return: unsqueezed tf.Tensor |
6,683 | import typing
from typing import TYPE_CHECKING, Callable, List, Optional, Tuple
import numpy as np
from docarray.computation import AbstractComputationalBackend
from docarray.computation.abstract_numpy_based_backend import AbstractNumpyBasedBackend
from docarray.typing import TensorFlowTensor
from docarray.utils._inter... | null |
6,684 | import typing
from typing import TYPE_CHECKING, Callable, List, Optional, Tuple
import numpy as np
from docarray.computation import AbstractComputationalBackend
from docarray.computation.abstract_numpy_based_backend import AbstractNumpyBasedBackend
from docarray.typing import TensorFlowTensor
from docarray.utils._inter... | null |
6,685 | from typing import TYPE_CHECKING, Any, Callable, List, Optional, Tuple
import numpy as np
from docarray.computation.abstract_comp_backend import AbstractComputationalBackend
from docarray.computation.abstract_numpy_based_backend import AbstractNumpyBasedBackend
from docarray.typing import JaxArray
from docarray.utils._... | Expands arrays that only have one axis, at dim 0. This ensures that all outputs can be treated as matrices, not vectors. :param matrices: Matrices to be expanded :return: List of the input matrices, where single axis matrices are expanded at dim 0. |
6,686 | from typing import TYPE_CHECKING, Any, Callable, List, Optional, Tuple
import numpy as np
from docarray.computation.abstract_comp_backend import AbstractComputationalBackend
from docarray.computation.abstract_numpy_based_backend import AbstractNumpyBasedBackend
from docarray.typing import JaxArray
from docarray.utils._... | null |
6,687 | from typing import TYPE_CHECKING, Any, Callable, List, Optional, Tuple
import numpy as np
from docarray.computation.abstract_comp_backend import AbstractComputationalBackend
from docarray.computation.abstract_numpy_based_backend import AbstractNumpyBasedBackend
from docarray.typing import JaxArray
from docarray.utils._... | null |
6,688 | from typing import TYPE_CHECKING, Any, Callable, List, Optional, Tuple
import numpy as np
from docarray.computation.abstract_comp_backend import AbstractComputationalBackend
from docarray.computation.abstract_numpy_based_backend import AbstractNumpyBasedBackend
from docarray.typing import JaxArray
from docarray.utils._... | null |
6,689 | from typing import TYPE_CHECKING, Any, List, Optional, Tuple, Union
import numpy as np
from docarray.computation.abstract_comp_backend import AbstractComputationalBackend
from docarray.utils._internal.misc import import_library
The provided code snippet includes necessary dependencies for implementing the `_unsqueeze_... | Unsqueezes tensors that only have one axis, at dim 0. This ensures that all outputs can be treated as matrices, not vectors. :param matrices: Matrices to be unsqueezed :return: List of the input matrices, where single axis matrices are unsqueezed at dim 0. |
6,690 | from typing import TYPE_CHECKING, Any, List, Optional, Tuple, Union
import numpy as np
from docarray.computation.abstract_comp_backend import AbstractComputationalBackend
from docarray.utils._internal.misc import import_library
def _unsqueeze_if_scalar(t: torch.Tensor):
if len(t.shape) == 0: # avoid scalar output... | null |
6,691 | from contextlib import nullcontext
from typing import Dict, Iterable, Iterator, NoReturn, Optional, Sequence, Type, TypeVar
from rich import filesize
from typing_extensions import TYPE_CHECKING, Protocol
from docarray.utils._internal.misc import ProtocolType
from docarray.utils._internal.progress_bar import _get_progre... | Get the version of libraries used in Jina and environment variables. :return: Version information and environment variables |
6,692 | from contextlib import nullcontext
from typing import Dict, Iterable, Iterator, NoReturn, Optional, Sequence, Type, TypeVar
from rich import filesize
from typing_extensions import TYPE_CHECKING, Protocol
from docarray.utils._internal.misc import ProtocolType
from docarray.utils._internal.progress_bar import _get_progre... | Get an iterator of batched items from Sequence. |
6,693 | from contextlib import nullcontext
from typing import Dict, Iterable, Iterator, NoReturn, Optional, Sequence, Type, TypeVar
from rich import filesize
from typing_extensions import TYPE_CHECKING, Protocol
from docarray.utils._internal.misc import ProtocolType
from docarray.utils._internal.progress_bar import _get_progre... | Definitely raise an error from a response. |
6,694 | from contextlib import nullcontext
from typing import Dict, Iterable, Iterator, NoReturn, Optional, Sequence, Type, TypeVar
from rich import filesize
from typing_extensions import TYPE_CHECKING, Protocol
from docarray.utils._internal.misc import ProtocolType
from docarray.utils._internal.progress_bar import _get_progre... | null |
6,695 | from contextlib import nullcontext
from typing import Dict, Iterable, Iterator, NoReturn, Optional, Sequence, Type, TypeVar
from rich import filesize
from typing_extensions import TYPE_CHECKING, Protocol
from docarray.utils._internal.misc import ProtocolType
from docarray.utils._internal.progress_bar import _get_progre... | null |
6,696 | from typing import Dict, List, Optional
from docarray import DocList
def reduce(
left: DocList, right: DocList, left_id_map: Optional[Dict] = None
) -> 'DocList':
"""
Reduces left and right DocList into one DocList in-place.
Changes are applied to the left DocList.
Reducing 2 DocLists consists in ad... | Reduces a list of DocLists into one DocList. Changes are applied to the first DocList in-place. The resulting DocList contains Documents of all DocLists. If a Document exists (identified by their ID) in many DocLists, data properties are merged with priority to the left-most DocLists (that is, if a data attribute is se... |
6,697 | from contextlib import nullcontext
from math import ceil
from multiprocessing.pool import Pool, ThreadPool
from typing import Callable, Generator, Optional, TypeVar, Union
from rich.progress import track
from docarray import BaseDoc
from docarray.array.any_array import AnyDocArray
from docarray.helper import _is_lambda... | Return an iterator that applies `func` to every Document in `docs` in parallel, yielding the results. --- ```python from docarray import DocList from docarray.documents import ImageDoc from docarray.utils.map import map_docs def load_url_to_tensor(img: ImageDoc) -> ImageDoc: img.tensor = img.url.load() return img url =... |
6,698 | from contextlib import nullcontext
from math import ceil
from multiprocessing.pool import Pool, ThreadPool
from typing import Callable, Generator, Optional, TypeVar, Union
from rich.progress import track
from docarray import BaseDoc
from docarray.array.any_array import AnyDocArray
from docarray.helper import _is_lambda... | Return an iterator that applies `func` to every **minibatch** of iterable in parallel, yielding the results. Each element in the returned iterator is an `AnyDocArray`. --- ```python from docarray import BaseDoc, DocList from docarray.utils.map import map_docs_batched class MyDoc(BaseDoc): name: str def upper_case_name(... |
6,699 | from typing import IO, TYPE_CHECKING, Callable, Optional
from docarray.utils._internal.misc import import_library
if TYPE_CHECKING:
from docarray.typing.tensor import TensorFlowTensor # noqa: F401
from docarray.typing.tensor import ( # noqa: F401
JaxArray,
JaxArrayEmbedding,
TorchEmb... | null |
6,700 | from typing import IO, TYPE_CHECKING, Callable, Optional
from docarray.utils._internal.misc import import_library
if TYPE_CHECKING:
from docarray.typing.tensor import TensorFlowTensor # noqa: F401
from docarray.typing.tensor import ( # noqa: F401
JaxArray,
JaxArrayEmbedding,
TorchEmb... | null |
6,701 | from typing import IO, TYPE_CHECKING, Callable, Optional
from docarray.utils._internal.misc import import_library
if TYPE_CHECKING:
from docarray.typing.tensor import TensorFlowTensor # noqa: F401
from docarray.typing.tensor import ( # noqa: F401
JaxArray,
JaxArrayEmbedding,
TorchEmb... | null |
6,702 | import os
from functools import lru_cache
from pathlib import Path
The provided code snippet includes necessary dependencies for implementing the `_get_cache_path` function. Write a Python function `def _get_cache_path() -> Path` to solve the following problem:
Get the path to the cache directory. :return: The path to... | Get the path to the cache directory. :return: The path to the cache directory. |
6,703 | import importlib
import os
import re
import types
from typing import Any, Optional, Literal
import numpy as np
def _get_path_from_docarray_root_level(file_path: str) -> str:
path = os.path.dirname(file_path)
rel_path = re.sub('(?s:.*)docarray', 'docarray', path).replace('/', '.')
return rel_path | null |
6,704 | import importlib
import os
import re
import types
from typing import Any, Optional, Literal
import numpy as np
def is_np_int(item: Any) -> bool:
dtype = getattr(item, 'dtype', None)
ndim = getattr(item, 'ndim', None)
if dtype is not None and ndim is not None:
try:
return ndim == 0 and n... | null |
6,705 | import importlib
import os
import re
import types
from typing import Any, Optional, Literal
import numpy as np
The provided code snippet includes necessary dependencies for implementing the `is_notebook` function. Write a Python function `def is_notebook() -> bool` to solve the following problem:
Check if we're runnin... | Check if we're running in a Jupyter notebook, using magic command `get_ipython` that only available in Jupyter. :return: True if run in a Jupyter notebook else False. |
6,706 | import re
from functools import partial
from typing import Any, Callable, Iterator, List, Optional, Sequence, Tuple, Union
PLACEHOLDER_PATTERN = re.compile(r'\{\s*([a-zA-Z0-9_]*)\s*}')
def dunder_get(_dict: Any, key: str) -> Any:
"""Returns value for a specified "dunder separated key"
A "dunder separated key" i... | Checks if key-val pair exists in doc using various lookup types The lookup types are derived from the `key` and then used to check if the lookup holds true for the document:: >>> lookup('text.exact', 'hello', doc) The above will return True if doc.text == 'hello' else False. And >>> lookup('text.exact', '{tags__name}',... |
6,707 | import re
from functools import partial
from typing import Any, Callable, Iterator, List, Optional, Sequence, Tuple, Union
The provided code snippet includes necessary dependencies for implementing the `iff` function. Write a Python function `def iff(precond: Callable, val: Any, f: Callable) -> bool` to solve the foll... | If and only if the precond is True Shortcut function for precond(val) and f(val). It is mainly used to create partial functions for commonly required preconditions :param precond : (function) represents the precondition :param val : (mixed) value to which the functions are applied :param f : (function) the actual funct... |
6,708 | from typing import Any, Dict, List, Optional, Union
from docarray.utils._internal.query_language.lookup import (
LookupLeaf,
LookupNode,
LookupTreeElem,
Q,
)
LOGICAL_OPERATORS: Dict[str, Union[str, bool]] = {
'$and': 'and',
'$or': 'or',
'$not': True,
}
SUPPORTED_OPERATORS = {
**COMPARISO... | null |
6,709 | from typing import Any, ForwardRef, Optional, Union
from typing_extensions import get_origin
from typing_inspect import get_args, is_typevar, is_union_type
def is_type_tensor(type_: Any) -> bool:
"""Return True if type is a type Tensor or an Optional Tensor type."""
from docarray.typing.tensor.abstract_tensor i... | Return True if type is a Union of type Tensors. |
6,710 | from typing import Any, ForwardRef, Optional, Union
from typing_extensions import get_origin
from typing_inspect import get_args, is_typevar, is_union_type
The provided code snippet includes necessary dependencies for implementing the `change_cls_name` function. Write a Python function `def change_cls_name(cls: type, ... | Change the name of a class. :param cls: the class to change the name of :param new_name: the new name :param scope: the scope in which the class is defined |
6,711 | from typing import Any, Dict, List, Optional, Type, Union
from pydantic import BaseModel, create_model
from pydantic.fields import FieldInfo
from docarray.base_doc.doc import BaseDocWithoutId
from docarray import BaseDoc, DocList
from docarray.typing import AnyTensor
from docarray.utils._internal._typing import safe_is... | Take a Pydantic model and cast DocList fields into List fields. This may be necessary due to limitations in Pydantic: https://github.com/docarray/docarray/issues/1521 https://github.com/pydantic/pydantic/issues/1457 --- ```python from docarray import BaseDoc class MyDoc(BaseDoc): tensor: Optional[AnyTensor] url: ImageU... |
6,712 | import argparse
import os
import uuid
from pathlib import Path
import main as detection
import submitit
def parse_args():
detection_parser = detection.get_args_parser()
parser = argparse.ArgumentParser("Submitit for detection", parents=[detection_parser])
parser.add_argument("--ngpus", default=8, type=int,... | null |
6,713 | import argparse
import os
import uuid
from pathlib import Path
import main as detection
import submitit
def get_shared_folder() -> Path:
user = os.getenv("USER")
if Path("/checkpoint/").is_dir():
p = Path(f"/checkpoint/{user}/experiments")
p.mkdir(exist_ok=True)
return p
raise Runtim... | null |
6,714 | import argparse
import datetime
import json
import random
import time
from pathlib import Path
import numpy as np
import torch
from torch.utils.data import DataLoader, DistributedSampler
import datasets
import util.misc as utils
from datasets import build_dataset, get_coco_api_from_dataset
from engine import evaluate, ... | null |
6,715 | import copy
import logging
import numpy as np
import torch
from detectron2.data import detection_utils as utils
from detectron2.data import transforms as T
from detectron2.data.transforms import TransformGen
The provided code snippet includes necessary dependencies for implementing the `build_transform_gen` function. ... | Create a list of :class:`TransformGen` from config. Returns: list[TransformGen] |
6,716 | from detectron2.config import CfgNode as CN
The provided code snippet includes necessary dependencies for implementing the `add_detr_config` function. Write a Python function `def add_detr_config(cfg)` to solve the following problem:
Add config for DETR.
Here is the function:
def add_detr_config(cfg):
"""
Ad... | Add config for DETR. |
6,717 | import os
import sys
import itertools
import time
from typing import Any, Dict, List, Set
import torch
import detectron2.utils.comm as comm
from d2.detr import DetrDatasetMapper, add_detr_config
from detectron2.checkpoint import DetectionCheckpointer
from detectron2.config import get_cfg
from detectron2.data import Met... | Create configs and perform basic setups. |
6,718 | import json
import argparse
import numpy as np
import torch
def parse_args():
parser = argparse.ArgumentParser("D2 model converter")
parser.add_argument("--source_model", default="", type=str, help="Path or url to the DETR model to convert")
parser.add_argument("--output_model", default="", type=str, help... | null |
6,719 | import torch
from models.backbone import Backbone, Joiner
from models.detr import DETR, PostProcess
from models.position_encoding import PositionEmbeddingSine
from models.segmentation import DETRsegm, PostProcessPanoptic
from models.transformer import Transformer
def _make_detr(backbone_name: str, dilation=False, num_c... | DETR R50 with 6 encoder and 6 decoder layers. Achieves 42/62.4 AP/AP50 on COCO val5k. |
6,720 | import torch
from models.backbone import Backbone, Joiner
from models.detr import DETR, PostProcess
from models.position_encoding import PositionEmbeddingSine
from models.segmentation import DETRsegm, PostProcessPanoptic
from models.transformer import Transformer
def _make_detr(backbone_name: str, dilation=False, num_c... | DETR-DC5 R50 with 6 encoder and 6 decoder layers. The last block of ResNet-50 has dilation to increase output resolution. Achieves 43.3/63.1 AP/AP50 on COCO val5k. |
6,721 | import torch
from models.backbone import Backbone, Joiner
from models.detr import DETR, PostProcess
from models.position_encoding import PositionEmbeddingSine
from models.segmentation import DETRsegm, PostProcessPanoptic
from models.transformer import Transformer
def _make_detr(backbone_name: str, dilation=False, num_c... | DETR-DC5 R101 with 6 encoder and 6 decoder layers. Achieves 43.5/63.8 AP/AP50 on COCO val5k. |
6,722 | import torch
from models.backbone import Backbone, Joiner
from models.detr import DETR, PostProcess
from models.position_encoding import PositionEmbeddingSine
from models.segmentation import DETRsegm, PostProcessPanoptic
from models.transformer import Transformer
def _make_detr(backbone_name: str, dilation=False, num_c... | DETR-DC5 R101 with 6 encoder and 6 decoder layers. The last block of ResNet-101 has dilation to increase output resolution. Achieves 44.9/64.7 AP/AP50 on COCO val5k. |
6,723 | import torch
from models.backbone import Backbone, Joiner
from models.detr import DETR, PostProcess
from models.position_encoding import PositionEmbeddingSine
from models.segmentation import DETRsegm, PostProcessPanoptic
from models.transformer import Transformer
def _make_detr(backbone_name: str, dilation=False, num_c... | DETR R50 with 6 encoder and 6 decoder layers. Achieves 43.4 PQ on COCO val5k. threshold is the minimum confidence required for keeping segments in the prediction |
6,724 | import torch
from models.backbone import Backbone, Joiner
from models.detr import DETR, PostProcess
from models.position_encoding import PositionEmbeddingSine
from models.segmentation import DETRsegm, PostProcessPanoptic
from models.transformer import Transformer
def _make_detr(backbone_name: str, dilation=False, num_c... | DETR-DC5 R50 with 6 encoder and 6 decoder layers. The last block of ResNet-50 has dilation to increase output resolution. Achieves 44.6 on COCO val5k. threshold is the minimum confidence required for keeping segments in the prediction |
6,725 | import torch
from models.backbone import Backbone, Joiner
from models.detr import DETR, PostProcess
from models.position_encoding import PositionEmbeddingSine
from models.segmentation import DETRsegm, PostProcessPanoptic
from models.transformer import Transformer
def _make_detr(backbone_name: str, dilation=False, num_c... | DETR-DC5 R101 with 6 encoder and 6 decoder layers. Achieves 45.1 PQ on COCO val5k. threshold is the minimum confidence required for keeping segments in the prediction |
6,726 | import os
import contextlib
import copy
import numpy as np
import torch
from pycocotools.cocoeval import COCOeval
from pycocotools.coco import COCO
import pycocotools.mask as mask_util
from util.misc import all_gather
def convert_to_xywh(boxes):
xmin, ymin, xmax, ymax = boxes.unbind(1)
return torch.stack((xmin... | null |
6,727 | import os
import contextlib
import copy
import numpy as np
import torch
from pycocotools.cocoeval import COCOeval
from pycocotools.coco import COCO
import pycocotools.mask as mask_util
from util.misc import all_gather
def merge(img_ids, eval_imgs):
all_img_ids = all_gather(img_ids)
all_eval_imgs = all_gather(ev... | null |
6,728 | import os
import contextlib
import copy
import numpy as np
import torch
from pycocotools.cocoeval import COCOeval
from pycocotools.coco import COCO
import pycocotools.mask as mask_util
from util.misc import all_gather
The provided code snippet includes necessary dependencies for implementing the `evaluate` function. W... | Run per image evaluation on given images and store results (a list of dict) in self.evalImgs :return: None |
6,729 | import random
import PIL
import torch
import torchvision.transforms as T
import torchvision.transforms.functional as F
from util.box_ops import box_xyxy_to_cxcywh
from util.misc import interpolate
def crop(image, target, region):
cropped_image = F.crop(image, *region)
target = target.copy()
i, j, h, w = r... | null |
6,730 | import random
import PIL
import torch
import torchvision.transforms as T
import torchvision.transforms.functional as F
from util.box_ops import box_xyxy_to_cxcywh
from util.misc import interpolate
def hflip(image, target):
flipped_image = F.hflip(image)
w, h = image.size
target = target.copy()
if "bo... | null |
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