


def 							UpperCamelCase_( lowerCamelCase_				,					lowerCamelCase_				,					lowerCamelCase_					)       ->		List[str]:
    if n == 0:
        return 1

    elif n % 2 == 1:
        return (binary_exponentiation(lowerCamelCase_				,					n - 1				,					lowerCamelCase_					) * a) % mod

    else:
        _lowercase			:				str 						=						binary_exponentiation(lowerCamelCase_				,					n / 2				,					lowerCamelCase_					)
        return (b * b) % mod


# a prime number
SCREAMING_SNAKE_CASE			: str				   =					701

SCREAMING_SNAKE_CASE			: Optional[int]				   =					1000000000
SCREAMING_SNAKE_CASE			: Optional[int]				   =					10

# using binary exponentiation function, O(log(p)):
print((a / b) % p == (a * binary_exponentiation(b, p - 2, p)) % p)

print((a / b) % p == (a * b ** (p - 2)) % p)





'''simple docstring'''



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 				_snake_case					(   a_			):
  SCREAMING_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





