


'''simple docstring'''
def 	_A  (       A = 5_0_0_0_0_0_0_0							)  ->				List[Any]:
	lowercase						:		List[Any]   					=		set()
	lowercase						:		Dict   					=		int((limit - 2_4) ** (1 / 2)							)

	lowercase						:		Any   					=		set(range(3							,prime_square_limit + 1							,2							)							)
	primes.add(2							)
	for p in range(3							,prime_square_limit + 1							,2							):
		if p not in primes:
			continue
		primes.difference_update(set(range(p * p							,prime_square_limit + 1							,__a							)							)							)

	for primea in primes:
		lowercase						:		Optional[Any]   					=		primea * primea
		for primea in primes:
			lowercase						:		Optional[Any]   					=		primea * primea * primea
			if square + cube >= limit - 1_6:
				break
			for primea in primes:
				lowercase						:		Union[str, Any]   					=		primea * primea * primea * primea
				lowercase						:		Tuple   					=		square + cube + tetr
				if total >= limit:
					break
				ret.add(__a							)

	return len(__a							)


if __name__ == "__main__":
					print(F'''{solution() = }''')






import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
	from transformers import AutoModelForSeqaSeqLM, AutoTokenizer




@require_torch
@require_sentencepiece
@require_tokenizers
class       lowercase_					(unittest.TestCase   ):





				@slow
				def 						__UpperCamelCase    (				self)  -> Optional[int]:
											a__      =AutoModelForSeqaSeqLM.from_pretrained('google/mt5-small'       ,  return_dict=lowercase_).to(lowercase_)
											a__      =AutoTokenizer.from_pretrained('google/mt5-small')

											a__      =tokenizer('Hello there'       ,  return_tensors='pt').input_ids
											a__      =tokenizer('Hi I am'       ,  return_tensors='pt').input_ids

											a__      =model(input_ids.to(lowercase_)       ,  labels=labels.to(lowercase_)).loss
											a__      =-(labels.shape[-1] * loss.item())

											a__      =-84.91_27
											self.assertTrue(abs(mtf_score - EXPECTED_SCORE) < 1e-4)

