






'''simple docstring'''
from __future__ import annotations

import typing
from collections.abc import Iterable

import numpy as np

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





def 	_a(					UpperCamelCase__			:  Vector,		UpperCamelCase__			:  Vector							):




      '''simple docstring'''






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





def 	_a(					UpperCamelCase__			:  Vector,		UpperCamelCase__			:  Vector							):




      '''simple docstring'''






      return sum((va - va) ** 2 for va, va in zip(UpperCamelCase__,		UpperCamelCase__							)							) ** (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_0_0_0_0,		globals=globals(),		)							)
        print('''With Numpy'''							)
        print(
            timeit(
                '''euclidean_distance([1, 2, 3], [4, 5, 6])''',		number=1_0_0_0_0,		globals=globals(),		)							)







  benchmark()







'''simple docstring'''
from typing import TYPE_CHECKING

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


a_					=	{
    'configuration_lxmert': ['LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LxmertConfig'],
    'tokenization_lxmert': ['LxmertTokenizer'],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_					=	['LxmertTokenizerFast']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_					=	[
      'LxmertEncoder',
      'LxmertForPreTraining',
      'LxmertForQuestionAnswering',
      'LxmertModel',
      'LxmertPreTrainedModel',
      'LxmertVisualFeatureEncoder',
      'LxmertXLayer',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_					=	[
      'TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TFLxmertForPreTraining',
      'TFLxmertMainLayer',
      'TFLxmertModel',
      'TFLxmertPreTrainedModel',
      'TFLxmertVisualFeatureEncoder',
  ]


if TYPE_CHECKING:
  from .configuration_lxmert import LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP, LxmertConfig
  from .tokenization_lxmert import LxmertTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_lxmert_fast import LxmertTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_lxmert import (
        LxmertEncoder,
        LxmertForPreTraining,
        LxmertForQuestionAnswering,
        LxmertModel,
        LxmertPreTrainedModel,
        LxmertVisualFeatureEncoder,
        LxmertXLayer,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_lxmert import (
        TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFLxmertForPreTraining,
        TFLxmertMainLayer,
        TFLxmertModel,
        TFLxmertPreTrainedModel,
        TFLxmertVisualFeatureEncoder,
    )

else:
  import sys

  a_					=	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)