



"""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() = }""")








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 tensorflow as tf

				from transformers import AutoTokenizer, TFAutoModelForSeqaSeqLM



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





	@slow
	def 						A_(							self      )  ->					str:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						TFAutoModelForSeqaSeqLM.from_pretrained('google/mt5-small'      )
				SCREAMING_SNAKE_CASE_								=						AutoTokenizer.from_pretrained('google/mt5-small'      )

				SCREAMING_SNAKE_CASE_								=						tokenizer('Hello there'      ,     return_tensors='tf'      ).input_ids
				SCREAMING_SNAKE_CASE_								=						tokenizer('Hi I am'      ,     return_tensors='tf'      ).input_ids

				SCREAMING_SNAKE_CASE_								=						model(SCREAMING_SNAKE_CASE      ,     labels=SCREAMING_SNAKE_CASE      ).loss
				SCREAMING_SNAKE_CASE_								=						-tf.math.reduce_mean(SCREAMING_SNAKE_CASE      ).numpy()

				SCREAMING_SNAKE_CASE_								=						-2_1.2_2_8_1_6_8
				self.assertTrue(abs(mtf_score - EXPECTED_SCORE      ) < 2e-4      )
