






def 						_a    (      a :int )							->			list[int]:

 if num <= 0:
  raise ValueError('''Input must be a positive integer''' )

 a		  =      [True] * (num + 1)

 a		  =      2
 while p * p <= num:
  if primes[p]:
   for i in range(p * p	,							num + 1	,							a ):
    a		  =      False
  p += 1

 return [prime for prime in range(2	,							num + 1 ) if primes[prime]]


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     UpperCAmelCase__				   =		int(input("Enter a positive integer: ").strip())
     print(prime_sieve_eratosthenes(user_num))










from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch

from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_torch






@require_torch
@require_tf
class 		lowercase_					(	lowercase   ):






      '''simple docstring'''




      def       __lowerCAmelCase					(      self      :						str  )    ->int:




       """simple docstring"""


       a		  =      SMALL_MODEL_IDENTIFIER
       a		  =      '''pt'''
       a		  =      '''tf'''







      def       __lowerCAmelCase					(      self      :						Optional[Any] ,					__UpperCAmelCase      :						str  )    ->Union[str, Any]:




       """simple docstring"""


       a		  =      AutoModel.from_pretrained(self.test_model  )
       model_pt.save_pretrained(__UpperCAmelCase  )







      def       __lowerCAmelCase					(      self      :						Any ,					__UpperCAmelCase      :						Union[str, Any]  )    ->List[str]:




       """simple docstring"""


       a		  =      TFAutoModel.from_pretrained(self.test_model ,					from_pt=__UpperCAmelCase  )
       model_tf.save_pretrained(__UpperCAmelCase  )







      def       __lowerCAmelCase					(      self      :						Any  )    ->int:




       """simple docstring"""


       a		  =      '''mock_framework'''

       # Framework provided - return whatever the user provides
       a		  =      FeaturesManager.determine_framework(self.test_model ,					__UpperCAmelCase  )
       self.assertEqual(__UpperCAmelCase ,					__UpperCAmelCase  )

       # Local checkpoint and framework provided - return provided framework
       # PyTorch checkpoint
       with TemporaryDirectory() as local_pt_ckpt:
        self._setup_pt_ckpt(__UpperCAmelCase  )
        a		  =      FeaturesManager.determine_framework(__UpperCAmelCase ,					__UpperCAmelCase  )
        self.assertEqual(__UpperCAmelCase ,					__UpperCAmelCase  )

       # TensorFlow checkpoint
       with TemporaryDirectory() as local_tf_ckpt:
        self._setup_tf_ckpt(__UpperCAmelCase  )
        a		  =      FeaturesManager.determine_framework(__UpperCAmelCase ,					__UpperCAmelCase  )
        self.assertEqual(__UpperCAmelCase ,					__UpperCAmelCase  )







      def       __lowerCAmelCase					(      self      :						str  )    ->int:




       """simple docstring"""


       with TemporaryDirectory() as local_pt_ckpt:
        self._setup_pt_ckpt(__UpperCAmelCase  )
        a		  =      FeaturesManager.determine_framework(__UpperCAmelCase  )
        self.assertEqual(__UpperCAmelCase ,					self.framework_pt  )

       # TensorFlow checkpoint
       with TemporaryDirectory() as local_tf_ckpt:
        self._setup_tf_ckpt(__UpperCAmelCase  )
        a		  =      FeaturesManager.determine_framework(__UpperCAmelCase  )
        self.assertEqual(__UpperCAmelCase ,					self.framework_tf  )

       # Invalid local checkpoint
       with TemporaryDirectory() as local_invalid_ckpt:
        with self.assertRaises(__UpperCAmelCase  ):
         a		  =      FeaturesManager.determine_framework(__UpperCAmelCase  )







      def       __lowerCAmelCase					(      self      :						Optional[int]  )    ->List[str]:




       """simple docstring"""


       a		  =      MagicMock(return_value=__UpperCAmelCase  )
       with patch('''transformers.onnx.features.is_tf_available''' ,					__UpperCAmelCase  ):
        a		  =      FeaturesManager.determine_framework(self.test_model  )
        self.assertEqual(__UpperCAmelCase ,					self.framework_pt  )

       # PyTorch not in environment -> use TensorFlow
       a		  =      MagicMock(return_value=__UpperCAmelCase  )
       with patch('''transformers.onnx.features.is_torch_available''' ,					__UpperCAmelCase  ):
        a		  =      FeaturesManager.determine_framework(self.test_model  )
        self.assertEqual(__UpperCAmelCase ,					self.framework_tf  )

       # Both in environment -> use PyTorch
       a		  =      MagicMock(return_value=__UpperCAmelCase  )
       a		  =      MagicMock(return_value=__UpperCAmelCase  )
       with patch('''transformers.onnx.features.is_tf_available''' ,					__UpperCAmelCase  ), patch(
           '''transformers.onnx.features.is_torch_available''' ,					__UpperCAmelCase  ):
        a		  =      FeaturesManager.determine_framework(self.test_model  )
        self.assertEqual(__UpperCAmelCase ,					self.framework_pt  )

       # Both not in environment -> raise error
       a		  =      MagicMock(return_value=__UpperCAmelCase  )
       a		  =      MagicMock(return_value=__UpperCAmelCase  )
       with patch('''transformers.onnx.features.is_tf_available''' ,					__UpperCAmelCase  ), patch(
           '''transformers.onnx.features.is_torch_available''' ,					__UpperCAmelCase  ):
        with self.assertRaises(__UpperCAmelCase  ):
         a		  =      FeaturesManager.determine_framework(self.test_model  )


