





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 		UpperCamelCase__ (      lowerCAmelCase_   ):



     '''simple docstring'''

     __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









def    __lowercase   (      _SCREAMING_SNAKE_CASE = 10	) ->   str:


      '''simple docstring'''


      if not isinstance(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE	) or n < 0:
            raise ValueError("""Invalid input"""	)
      SCREAMING_SNAKE_CASE		=						10**n
      SCREAMING_SNAKE_CASE		=						2_84_33 * (pow(2 ,						7_83_04_57 ,						_SCREAMING_SNAKE_CASE	)) + 1
      return str(number % modulus	)


if __name__ == "__main__":
      from doctest import testmod

      testmod()
      print(F'''{solution(1_0) = }''')


