


'''simple docstring'''



from __future__ import annotations

import typing
from collections.abc import Iterable

import numpy as np

_lowercase      :      Dict            =					typing.Union[Iterable[float], Iterable[int], np.ndarray]  # noqa: UP007
_lowercase      :      Dict            =					typing.Union[np.floataa, int, float]  # noqa: UP007


def 				lowerCamelCase__				(	A       :      Vector				,       A       :      Vector					):




						'''simple docstring'''






						return np.sqrt(np.sum((np.asarray(A					) - np.asarray(A					)) ** 2					)					)


def 				lowerCamelCase__				(	A       :      Vector				,       A       :      Vector					):




						'''simple docstring'''






						return sum((va - va) ** 2 for va, va in zip(A				,       A					)					) ** (1 / 2)


if __name__ == "__main__":


	def 				lowerCamelCase__				(	):




							'''simple docstring'''






							from timeit import timeit

							print('''Without Numpy'''					)
							print(
							    timeit(
							        '''euclidean_distance_no_np([1, 2, 3], [4, 5, 6])'''				,       number=1_00_00				,       globals=globals()				,       )					)
							print('''With Numpy'''					)
							print(
							    timeit(
							        '''euclidean_distance([1, 2, 3], [4, 5, 6])'''				,       number=1_00_00				,       globals=globals()				,       )					)




	benchmark()






'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowercase      :      List[Any]            =					{
    """configuration_x_clip""": [
        """XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """XCLIPConfig""",
        """XCLIPTextConfig""",
        """XCLIPVisionConfig""",
    ],
    """processing_x_clip""": ["""XCLIPProcessor"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowercase      :      List[str]            =					[
	    """XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """XCLIPModel""",
	    """XCLIPPreTrainedModel""",
	    """XCLIPTextModel""",
	    """XCLIPVisionModel""",
	]

if TYPE_CHECKING:
	from .configuration_x_clip import (
	    XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    XCLIPConfig,
	    XCLIPTextConfig,
	    XCLIPVisionConfig,
	)
	from .processing_x_clip import XCLIPProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_x_clip import (
		    XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
		    XCLIPModel,
		    XCLIPPreTrainedModel,
		    XCLIPTextModel,
		    XCLIPVisionModel,
		)

else:
	import sys

	_lowercase      :      List[Any]            =					_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





