






from __future__ import annotations

import typing
from collections.abc import Iterable

import numpy as np

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






def 						a    (		lowerCamelCase_	,     lowerCamelCase_       ):






				'''simple docstring'''
				return np.sqrt(np.sum((np.asarray(__SCREAMING_SNAKE_CASE       ) - np.asarray(__SCREAMING_SNAKE_CASE       )) ** 2       )       )






def 						a    (		lowerCamelCase_	,     lowerCamelCase_       ):






				'''simple docstring'''
				return sum((va - va) ** 2 for va, va in zip(__SCREAMING_SNAKE_CASE	,     __SCREAMING_SNAKE_CASE       )       ) ** (1 / 2)


if __name__ == "__main__":






					def 						a    (		):






									'''simple docstring'''
									from timeit import timeit

									print('''Without Numpy'''       )
									print(
									    timeit(
									        '''euclidean_distance_no_np([1, 2, 3], [4, 5, 6])'''	,     number=1_0000	,     globals=globals()	,     )       )
									print('''With Numpy'''       )
									print(
									    timeit(
									        '''euclidean_distance([1, 2, 3], [4, 5, 6])'''	,     number=1_0000	,     globals=globals()	,     )       )




					benchmark()












from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


A__			:    Any         =				{'configuration_unispeech': ['UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP', 'UniSpeechConfig']}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A__			:    Union[str, Any]         =				[
							    'UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'UniSpeechForCTC',
							    'UniSpeechForPreTraining',
							    'UniSpeechForSequenceClassification',
							    'UniSpeechModel',
							    'UniSpeechPreTrainedModel',
							]

if TYPE_CHECKING:
							from .configuration_unispeech import UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_unispeech import (
														    UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST,
														    UniSpeechForCTC,
														    UniSpeechForPreTraining,
														    UniSpeechForSequenceClassification,
														    UniSpeechModel,
														    UniSpeechPreTrainedModel,
														)

else:
							import sys

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




