


def 	lowerCAmelCase__			(      )  ->		Optional[Any]:


							'''simple docstring'''





							for n in range(1      ,  1_0_0_0_0_0_0	):
														yield n * (n + 1) // 2

def 	lowerCAmelCase__			(      a__:		Optional[int]	)  ->		Union[str, Any]:


							'''simple docstring'''





							_UpperCAmelCase					=      1
							_UpperCAmelCase					=      2
							while i * i <= n:
														_UpperCAmelCase					=      0
														while n % i == 0:
																					n //= i
																					multiplicity += 1
														divisors_count *= multiplicity + 1
														i += 1
							if n > 1:
														divisors_count *= 2
							return divisors_count

def 	lowerCAmelCase__			(      )  ->		int:


							'''simple docstring'''





							return next(i for i in triangle_number_generator() if count_divisors(__UpperCamelCase	) > 5_0_0	)


if __name__ == "__main__":
		print(solution())




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 		__a				(    UpperCAmelCase      ):
		_a     :  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
