




'''simple docstring'''





def 					UpperCamelCase_(     ):

							'''simple docstring'''






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


def 					UpperCamelCase_(     snake_case							:	List[Any]					):

							'''simple docstring'''






							snake_case_      =				1
							snake_case_      =				2
							while i * i <= n:
														snake_case_      =				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 					UpperCamelCase_(     ):

							'''simple docstring'''






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


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










'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_SCREAMING_SNAKE_CASE							:       Optional[int]						=			{
    "configuration_canine": ["CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP", "CanineConfig"],
    "tokenization_canine": ["CanineTokenizer"],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_SCREAMING_SNAKE_CASE							:       Dict						=			[
		    "CANINE_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "CanineForMultipleChoice",
		    "CanineForQuestionAnswering",
		    "CanineForSequenceClassification",
		    "CanineForTokenClassification",
		    "CanineLayer",
		    "CanineModel",
		    "CaninePreTrainedModel",
		    "load_tf_weights_in_canine",
		]


if TYPE_CHECKING:
		from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
		from .tokenization_canine import CanineTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_canine import (
				    CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
				    CanineForMultipleChoice,
				    CanineForQuestionAnswering,
				    CanineForSequenceClassification,
				    CanineForTokenClassification,
				    CanineLayer,
				    CanineModel,
				    CaninePreTrainedModel,
				    load_tf_weights_in_canine,
				)


else:
		import sys

		_SCREAMING_SNAKE_CASE							:       int						=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




