





"""simple docstring"""





import math
from collections.abc import Iterator
from itertools import takewhile


def       a__					(					_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		if 1 < number < 4:
				# 2 and 3 are primes
				return True
		elif number < 2 or number % 2 == 0 or number % 3 == 0:
				# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
				return False

		# All primes number are in format of 6k +/- 1
		for i in range(5  ,	int(math.sqrt(_SCREAMING_SNAKE_CASE    ) + 1    )  ,	6    ):
				if number % i == 0 or number % (i + 2) == 0:
						return False
		return True


def       a__					(					):




		"""simple docstring"""


		UpperCamelCase   =	2
		while True:
				if is_prime(_SCREAMING_SNAKE_CASE    ):
						yield num
				num += 1


def       a__					(					_SCREAMING_SNAKE_CASE = 2_000_000    ):




		"""simple docstring"""


		return sum(takewhile(lambda _SCREAMING_SNAKE_CASE    : x < n  ,	prime_generator()    )    )


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













"""simple docstring"""





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        _lowerCamelCase      (       unittest.TestCase ):


						@slow
						def     snake_case_   (self		) ->   List[str]:
								UpperCamelCase   =	AutoModelForSeqaSeqLM.from_pretrained("google/mt5-small"   ,							return_dict=__a		).to(__a		)
								UpperCamelCase   =	AutoTokenizer.from_pretrained("google/mt5-small"		)

								UpperCamelCase   =	tokenizer("Hello there"   ,							return_tensors="pt"		).input_ids
								UpperCamelCase   =	tokenizer("Hi I am"   ,							return_tensors="pt"		).input_ids

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

								UpperCamelCase   =	-84.9127
								self.assertTrue(abs(mtf_score - EXPECTED_SCORE		) < 1e-4		)






