



"""simple docstring"""






from __future__ import annotations

import math






def 			snake_case_    ( A_   :						int		):


       '''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(A_		) + 1		),							6		):
              if number % i == 0 or number % (i + 2) == 0:
                     return False
       return True


lowerCAmelCase__              =							[num for num in range(3, 100001, 2) if not is_prime(num)]






def 			snake_case_    ( A_   :						int		):


       '''simple docstring'''





       if not isinstance(A_,							A_		):
              raise ValueError('''n must be an integer'''		)
       if n <= 0:
              raise ValueError('''n must be >= 0'''		)

       _lowerCamelCase		:							Dict            =					[]
       for num in range(len(A_		)		):
              _lowerCamelCase		:							Dict            =					0
              while 2 * i * i <= odd_composites[num]:
                     _lowerCamelCase		:							int            =					odd_composites[num] - 2 * i * i
                     if is_prime(A_		):
                            break
                     i += 1
              else:
                     list_nums.append(odd_composites[num]		)
                     if len(A_		) == n:
                            return list_nums

       return []






def 			snake_case_    ( ):


       '''simple docstring'''





       return compute_nums(1		)[0]


if __name__ == "__main__":
   print(F"""{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 lowercase_       (		unittest.TestCase			):







   """simple docstring"""

   @slow
   def 	__UpperCAmelCase   (      self      :			List[str]     )  ->			Any:
        _A    		=  TFCamembertModel.from_pretrained('jplu/tf-camembert-base'     )

        _A    		=  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 !"

        _A    		=  model(UpperCamelCase__     )['last_hidden_state']
        _A    		=  tf.TensorShape((1, 10, 7_68)     )
        self.assertEqual(output.shape, UpperCamelCase__     )
        # compare the actual values for a slice.
        _A    		=  tf.convert_to_tensor(
            [[[-0.0_254, 0.0_235, 0.1_027], [0.0_606, -0.1_811, -0.0_418], [-0.1_561, -0.1_127, 0.2_687]]], 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     )     )



