



'''simple docstring'''




def      a      (			):
			'''simple docstring'''





			for n in range(1						,				1_00_00_00   ):
						yield n * (n + 1) // 2



def      a      (			lowerCamelCase__   ):
			'''simple docstring'''





			A_     :						List[str]     =					1
			A_     :						Optional[int]     =					2
			while i * i <= n:
						A_     :						Any     =					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      a      (			):
			'''simple docstring'''





			return next(i for i in triangle_number_generator() if count_divisors(lowerCamelCase__   ) > 5_00   )


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




'''simple docstring'''




from typing import Dict

from .base import GenericTensor, Pipeline







class      _lowerCAmelCase							(      __UpperCAmelCase			):


	def    _a     (self	,				lowercase=None	,				lowercase=None	,				lowercase=None	,				**lowercase							):
				if tokenize_kwargs is None:
							A_     :						Optional[Any]     =					{}

				if truncation is not None:
							if "truncation" in tokenize_kwargs:
										raise ValueError(
										    """truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)"""							)
							A_     :						str     =					truncation

				A_     :						List[str]     =					tokenize_kwargs

				A_     :						Dict     =					{}
				if return_tensors is not None:
							A_     :						List[Any]     =					return_tensors

				return preprocess_params, {}, postprocess_params


	def    _a     (self	,				lowercase	,				**lowercase							):
				A_     :						Optional[int]     =					self.framework
				A_     :						str     =					self.tokenizer(lowercase	,				return_tensors=lowercase	,				**lowercase							)
				return model_inputs


	def    _a     (self	,				lowercase							):
				A_     :						str     =					self.model(**lowercase							)
				return model_outputs


	def    _a     (self	,				lowercase	,				lowercase=False							):
				# [0] is the first available tensor, logits or last_hidden_state.
				if return_tensors:
							return model_outputs[0]
				if self.framework == "pt":
							return model_outputs[0].tolist()
				elif self.framework == "tf":
							return model_outputs[0].numpy().tolist()


	def __call__(self	,				*lowercase	,				**lowercase							):
				return super().__call__(*lowercase	,				**lowercase							)