


from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import torch

from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
@dataclass
class  SCREAMING_SNAKE_CASE__  (				lowercase__			):
      snake_case__				:     Union[List[np.ndarray], torch.FloatTensor]


try:
   if not (is_transformers_available() and is_torch_available()):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
   from .pipeline_text_to_video_synth import TextToVideoSDPipeline
   from .pipeline_text_to_video_synth_imgaimg import VideoToVideoSDPipeline  # noqa: F401
   from .pipeline_text_to_video_zero import TextToVideoZeroPipeline








from __future__ import annotations

import typing
from collections.abc import Iterable

import numpy as np

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







def SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_						)   ->       VectorOut:



       """simple docstring"""

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







def SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_						)   ->       VectorOut:



       """simple docstring"""

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


if __name__ == "__main__":







  def SCREAMING_SNAKE_CASE				(       )   ->       None:



         """simple docstring"""

         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()


