






'''simple docstring'''




# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position

__lowerCamelCase   :					Any   							=					'''2.13.1'''

import platform

import pyarrow
from packaging import version


if version.parse(platform.python_version()) < version.parse('''3.7'''):
		raise ImportWarning(
		    '''To use `datasets`, Python>=3.7 is required, and the current version of Python doesn\'t match this condition.'''
		)

if version.parse(pyarrow.__version__).major < 8:
		raise ImportWarning(
		    '''To use `datasets`, the module `pyarrow>=8.0.0` is required, and the current version of `pyarrow` doesn\'t match this condition.\n'''
		    '''If you are running this in a Google Colab, you should probably just restart the runtime to use the right version of `pyarrow`.'''
		)

del platform
del pyarrow
del version

from .arrow_dataset import Dataset
from .arrow_reader import ReadInstruction
from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
from .combine import concatenate_datasets, interleave_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .download import *
from .features import *
from .fingerprint import disable_caching, enable_caching, is_caching_enabled, set_caching_enabled
from .info import DatasetInfo, MetricInfo
from .inspect import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
    list_datasets,
    list_metrics,
)
from .iterable_dataset import IterableDataset
from .load import load_dataset, load_dataset_builder, load_from_disk, load_metric
from .metric import Metric
from .splits import (
    NamedSplit,
    NamedSplitAll,
    Split,
    SplitBase,
    SplitDict,
    SplitGenerator,
    SplitInfo,
    SubSplitInfo,
    percent,
)
from .tasks import *
from .utils import *
from .utils import logging


# deprecated modules
from datasets import arrow_dataset as _arrow_dataset  # isort:skip
from datasets import utils as _utils  # isort:skip
from datasets.utils import download_manager as _deprecated_download_manager  # isort:skip

__lowerCamelCase   :					Tuple   							=					concatenate_datasets
__lowerCamelCase   :					Optional[Any]   							=					DownloadConfig
__lowerCamelCase   :					str   							=					DownloadManager
__lowerCamelCase   :					Optional[Any]   							=					DownloadMode
__lowerCamelCase   :					Dict   							=					DownloadConfig
__lowerCamelCase   :					Tuple   							=					DownloadMode
__lowerCamelCase   :					int   							=					DownloadManager

del _arrow_dataset, _utils, _deprecated_download_manager












'''simple docstring'''




from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__						)->    float:



			"""simple docstring"""



			return math.sqrt(sum(pow(a - b     ,2						) for a, b in zip(__magic_name__     ,__magic_name__						)						)						)

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__						)->    list[list[list[float] | float]]:



			"""simple docstring"""



			if dataset.ndim != value_array.ndim:
						snake_case_				:					int   =       (
						    "Wrong input data's dimensions... "
						    F'''dataset : {dataset.ndim}, value_array : {value_array.ndim}'''
						)
						raise ValueError(__magic_name__						)

			try:
						if dataset.shape[1] != value_array.shape[1]:
									snake_case_				:					Dict   =       (
									    "Wrong input data's shape... "
									    F'''dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}'''
									)
									raise ValueError(__magic_name__						)
			except IndexError:
						if dataset.ndim != value_array.ndim:
									raise TypeError("Wrong shape"						)

			if dataset.dtype != value_array.dtype:
						snake_case_				:					Dict   =       (
						    "Input data have different datatype... "
						    F'''dataset : {dataset.dtype}, value_array : {value_array.dtype}'''
						)
						raise TypeError(__magic_name__						)

			snake_case_				:					Optional[int]   =       []

			for value in value_array:
						snake_case_				:					List[str]   =       euclidean(__magic_name__     ,dataset[0]						)
						snake_case_				:					int   =       dataset[0].tolist()

						for dataset_value in dataset[1:]:
									snake_case_				:					Optional[Any]   =       euclidean(__magic_name__     ,__magic_name__						)

									if dist > temp_dist:
												snake_case_				:					Tuple   =       temp_dist
												snake_case_				:					Optional[int]   =       dataset_value.tolist()

						answer.append([vector, dist]						)

			return answer

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__						)->    float:



			"""simple docstring"""



			return np.dot(__magic_name__     ,__magic_name__						) / (norm(__magic_name__						) * norm(__magic_name__						))


if __name__ == "__main__":
		import doctest

		doctest.testmod()




