"""simple docstring"""






import unittest

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


if is_torch_available():
       import torch

       from transformers import XLMRobertaModel





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

      @slow
      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			XLMRobertaModel.from_pretrained("""xlm-roberta-base"""		)
            lowercase														=			torch.tensor([[0, 581, 1_0269, 83, 9_9942, 136, 6_0742, 23, 70, 8_0583, 1_8276, 2]]		)
            # The dog is cute and lives in the garden house

            lowercase														=			torch.Size((1, 12, 768)		)  # batch_size, sequence_length, embedding_vector_dim
            lowercase														=			torch.tensor(
                [[-0.0_1_0_1, 0.1_2_1_8, -0.0_8_0_3, 0.0_8_0_1, 0.1_3_2_7, 0.0_7_7_6, -0.1_2_1_5, 0.2_3_8_3, 0.3_3_3_8, 0.3_1_0_6, 0.0_3_0_0, 0.0_2_5_2]]		)
            #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
            #  xlmr.eval()
            #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
            with torch.no_grad():
                  lowercase														=			model(__lowerCAmelCase		)["""last_hidden_state"""].detach()
            self.assertEqual(output.shape	, __lowerCAmelCase		)
            # compare the actual values for a slice of last dim
            self.assertTrue(torch.allclose(output[:, :, -1]	, __lowerCAmelCase	, atol=1E-3		)		)







      @slow
      def        A__ (		self		):







            """simple docstring"""



            lowercase														=			XLMRobertaModel.from_pretrained("""xlm-roberta-large"""		)
            lowercase														=			torch.tensor([[0, 581, 1_0269, 83, 9_9942, 136, 6_0742, 23, 70, 8_0583, 1_8276, 2]]		)
            # The dog is cute and lives in the garden house

            lowercase														=			torch.Size((1, 12, 1024)		)  # batch_size, sequence_length, embedding_vector_dim
            lowercase														=			torch.tensor(
                [[-0.0_6_9_9, -0.0_3_1_8, 0.0_7_0_5, -0.1_2_4_1, 0.0_9_9_9, -0.0_5_2_0, 0.1_0_0_4, -0.1_8_3_8, -0.4_7_0_4, 0.1_4_3_7, 0.0_8_2_1, 0.0_1_2_6]]		)
            #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
            #  xlmr.eval()
            #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
            with torch.no_grad():
                  lowercase														=			model(__lowerCAmelCase		)["""last_hidden_state"""].detach()
            self.assertEqual(output.shape	, __lowerCAmelCase		)
            # compare the actual values for a slice of last dim
            self.assertTrue(torch.allclose(output[:, :, -1]	, __lowerCAmelCase	, atol=1E-3		)		)




"""simple docstring"""






from __future__ import annotations

import math


def     UpperCAmelCase__ (       lowerCAmelCase__  :int	)				->					list[int]:
      '''simple docstring'''


      if num <= 0:
            lowercase														=			f'{num}: Invalid input, please enter a positive integer.'
            raise ValueError(lowerCAmelCase__	)

      lowercase														=			[True] * (num + 1)
      lowercase														=			[]
      lowercase														=			2
      lowercase														=			int(math.sqrt(lowerCAmelCase__	)	)

      while start <= end:
            # If start is a prime
            if sieve[start] is True:
                  prime.append(lowerCAmelCase__	)

                  # Set multiples of start be False
                  for i in range(start * start      ,	num + 1      ,	lowerCAmelCase__	):
                        if sieve[i] is True:
                              lowercase														=			False

            start += 1

      for j in range(end + 1      ,	num + 1	):
            if sieve[j] is True:
                  prime.append(lowerCAmelCase__	)

      return prime


if __name__ == "__main__":
       print(prime_sieve(int(input("""Enter a positive integer: """).strip())))



