





from __future__ import annotations

import typing
from collections.abc import Iterable

import numpy as np

__lowerCAmelCase :Union[str, Any]     =    typing.Union[Iterable[float], Iterable[int], np.ndarray]  # noqa: UP007
__lowerCAmelCase :Optional[Any]     =    typing.Union[np.floataa, int, float]  # noqa: UP007







def    A				(							UpperCAmelCase  ,  UpperCAmelCase				):
					return np.sqrt(np.sum((np.asarray(UpperCAmelCase				) - np.asarray(UpperCAmelCase				)) ** 2				)				)







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


if __name__ == "__main__":







							def    A				(							):
												from timeit import timeit

												print("Without Numpy"				)
												print(
												    timeit(
												        "euclidean_distance_no_np([1, 2, 3], [4, 5, 6])"  ,  number=10_000  ,  globals=globals()  ,  )				)
												print("With Numpy"				)
												print(
												    timeit(
												        "euclidean_distance([1, 2, 3], [4, 5, 6])"  ,  number=10_000  ,  globals=globals()  ,  )				)

							benchmark()






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowerCAmelCase :str     =    {
    'configuration_nllb_moe': [
        'NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'NllbMoeConfig',
    ]
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase :Optional[int]     =    [
							    'NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'NllbMoeForConditionalGeneration',
							    'NllbMoeModel',
							    'NllbMoePreTrainedModel',
							    'NllbMoeTop2Router',
							    'NllbMoeSparseMLP',
							]


if TYPE_CHECKING:
							from .configuration_nllb_moe import (
							    NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    NllbMoeConfig,
							)

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_nllb_moe import (
														    NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST,
														    NllbMoeForConditionalGeneration,
														    NllbMoeModel,
														    NllbMoePreTrainedModel,
														    NllbMoeSparseMLP,
														    NllbMoeTopaRouter,
														)


else:
							import sys

							__lowerCAmelCase :int     =    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)