





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() = }')

UpperCAmelCase_           =				'''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''

UpperCAmelCase_           =				[{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
UpperCAmelCase_           =				{
    '''{processor_class}''': '''FakeProcessorClass''',
    '''{model_class}''': '''FakeModelClass''',
    '''{object_class}''': '''FakeObjectClass''',
}