




"""simple docstring"""

import math






def 						_snake_case     (					_snake_case							:					Optional[int]							)    ->					list:






      '''simple docstring'''


      _A					   =   [True] * n
      _A					   =   False
      _A					   =   False
      _A					   =   True

      for i in range(3				,  int(n**0.5 + 1							)				,  2							):
            _A					   =   i * 2
            while index < n:
                  _A					   =   False
                  _A					   =   index + i

      _A					   =   [2]

      for i in range(3				,  __A				,  2							):
            if is_prime[i]:
                  primes.append(__A							)

      return primes






def 						_snake_case     (					_snake_case							:					Optional[Any] = 99_99_66_66_33_33							)    ->					int:






      '''simple docstring'''


      _A					   =   math.floor(math.sqrt(__A							)							) + 1_00
      _A					   =   prime_sieve(__A							)

      _A					   =   0
      _A					   =   0
      _A					   =   primes[prime_index]

      while (last_prime**2) <= limit:
            _A					   =   primes[prime_index + 1]

            _A					   =   last_prime**2
            _A					   =   next_prime**2

            # Get numbers divisible by lps(current)
            _A					   =   lower_bound + last_prime
            while upper_bound > current <= limit:
                  matches_sum += current
                  current += last_prime

            # Reset the upper_bound
            while (upper_bound - next_prime) > limit:
                  upper_bound -= next_prime

            # Add the numbers divisible by ups(current)
            _A					   =   upper_bound - next_prime
            while current > lower_bound:
                  matches_sum += current
                  current -= next_prime

            # Remove the numbers divisible by both ups and lps
            _A					   =   0
            while upper_bound > current <= limit:
                  if current <= lower_bound:
                        # Increment the current number
                        current += last_prime * next_prime
                        continue

                  if current > limit:
                        break

                  # Remove twice since it was added by both ups and lps
                  matches_sum -= current * 2

                  # Increment the current number
                  current += last_prime * next_prime

            # Setup for next pair
            _A					   =   next_prime
            prime_index += 1

      return matches_sum


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



'''simple docstring'''







from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
					import numpy as np
					import tensorflow as tf

					from transformers import TFCamembertModel


@require_tf
@require_sentencepiece
@require_tokenizers
class 						__UpperCAmelCase      (					unittest.TestCase						):


							@slow
							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		TFCamembertModel.from_pretrained('jplu/tf-camembert-base'  )

								_snake_case   						=		tf.convert_to_tensor(
								    [[5, 1_21, 11, 6_60, 16, 7_30, 2_55_43, 1_10, 83, 6]]    ,   dtype=tf.intaa    ,   )  # J'aime le camembert !"

								_snake_case   						=		model(lowerCAmelCase_  )['last_hidden_state']
								_snake_case   						=		tf.TensorShape((1, 10, 7_68)  )
								self.assertEqual(output.shape    ,   lowerCAmelCase_  )
								# compare the actual values for a slice.
								_snake_case   						=		tf.convert_to_tensor(
								    [[[-0.0254, 0.0235, 0.1027], [0.0606, -0.1811, -0.0418], [-0.1561, -0.1127, 0.2687]]]    ,   dtype=tf.floataa    ,   )
								# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
								# camembert.eval()
								# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

								self.assertTrue(np.allclose(output[:, :3, :3].numpy()    ,   expected_slice.numpy()    ,   atol=1E-4  )  )

