





from __future__ import annotations


def 							__snake_case							(    lowerCAmelCase_	)				->						list[int]:
    SCREAMING_SNAKE_CASE__											=			[True] * limit
    SCREAMING_SNAKE_CASE__											=			False
    SCREAMING_SNAKE_CASE__											=			False
    SCREAMING_SNAKE_CASE__											=			True

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

    SCREAMING_SNAKE_CASE__											=			[2]

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

    return primes


def 							__snake_case							(    lowerCAmelCase_ = 1_0_0_0_0_0_0	)				->						int:
    SCREAMING_SNAKE_CASE__											=			prime_sieve(lowerCAmelCase_	)
    SCREAMING_SNAKE_CASE__											=			0
    SCREAMING_SNAKE_CASE__											=			0

    for i in range(len(lowerCAmelCase_	)	):
        for j in range(i + length ,							len(lowerCAmelCase_	)	):
            SCREAMING_SNAKE_CASE__											=			sum(primes[i:j]	)
            if sol >= ceiling:
                break

            if sol in primes:
                SCREAMING_SNAKE_CASE__											=			j - i
                SCREAMING_SNAKE_CASE__											=			sol

    return largest


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 TFXLMRobertaModel
@require_tf
@require_sentencepiece
@require_tokenizers
class 		SCREAMING_SNAKE_CASE_				(     unittest.TestCase ):
				"""simple docstring"""




				@slow
				def        __lowercase	(  self	:Union[str, Any]      ):
											__lowerCamelCase   :   List[Any]       =TFXLMRobertaModel.from_pretrained('''jplu/tf-xlm-roberta-base'''      )

											__lowerCamelCase   :   Dict       ={
											    '''input_ids''': tf.convert_to_tensor([[0, 2646, 1_0269, 83, 9_9942, 2]]    ,     dtype=tf.intaa      ),  # "My dog is cute"
											    '''attention_mask''': tf.convert_to_tensor([[1, 1, 1, 1, 1, 1]]    ,     dtype=tf.intaa      ),
											}

											__lowerCamelCase   :   Optional[int]       =model(__lowercase      )['''last_hidden_state''']
											__lowerCamelCase   :   str       =tf.TensorShape((1, 6, 768)      )
											self.assertEqual(output.shape    ,     __lowercase      )
											# compare the actual values for a slice.
											__lowerCamelCase   :   str       =tf.convert_to_tensor(
											    [
											        [
											            [0.0681762, 0.10894451, 0.06772504],
											            [-0.06423668, 0.02366615, 0.04329344],
											            [-0.06057295, 0.09974135, -0.00070584],
											        ]
											    ]    ,     dtype=tf.floataa    ,     )

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




