

def 						UpperCamelCase (lowercase_:			int = 50000000 )					->				int:
	A__							:   List[str]					       =		set()
	A__							:   Dict					       =		int((limit - 24) ** (1 / 2) )

	A__							:   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   ,				lowercase_ ) ) )

	for primea in primes:
		A__							:   Any					       =		primea * primea
		for primea in primes:
			A__							:   Optional[int]					       =		primea * primea * primea
			if square + cube >= limit - 16:
				break
			for primea in primes:
				A__							:   Tuple					       =		primea * primea * primea * primea
				A__							:   Dict					       =		square + cube + tetr
				if total >= limit:
					break
				ret.add(lowercase_ )

	return len(lowercase_ )


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    _a						(unittest.TestCase    ):


					'''simple docstring'''






					@slow
					def   __A      (			self      ):
						A__							:   Optional[Any]					       =		AutoModelForSeqaSeqLM.from_pretrained("""google/mt5-small"""   ,	return_dict=A__      ).to(A__      )
						A__							:   str					       =		AutoTokenizer.from_pretrained("""google/mt5-small"""      )

						A__							:   int					       =		tokenizer("""Hello there"""   ,	return_tensors="""pt"""      ).input_ids
						A__							:   List[Any]					       =		tokenizer("""Hi I am"""   ,	return_tensors="""pt"""      ).input_ids

						A__							:   Union[str, Any]					       =		model(input_ids.to(A__      )   ,	labels=labels.to(A__      )      ).loss
						A__							:   Union[str, Any]					       =		-(labels.shape[-1] * loss.item())

						A__							:   Any					       =		-8_4.9_1_2_7
						self.assertTrue(abs(mtf_score - EXPECTED_SCORE      ) < 1e-4      )



