



"""simple docstring"""
from __future__ import annotations



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

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

		a_     :      Dict 						=     [2]

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

		return primes



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__ = 1_000_000	)			->      int:
		a_     :      Tuple 						=     prime_sieve(SCREAMING_SNAKE_CASE__	)
		a_     :      Dict 						=     0
		a_     :      Union[str, Any] 						=     0

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

						if sol in primes:
								a_     :      int 						=     j - i
								a_     :      List[Any] 						=     sol

		return largest


if __name__ == "__main__":
					print(F"""{solution() = }""")




"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE_				= {
    """configuration_llama""": ["""LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LlamaConfig"""],
}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE_				= ["""LlamaTokenizer"""]

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE_				= ["""LlamaTokenizerFast"""]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE_				= [
					    """LlamaForCausalLM""",
					    """LlamaModel""",
					    """LlamaPreTrainedModel""",
					    """LlamaForSequenceClassification""",
					]


if TYPE_CHECKING:
					from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig

					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_llama import LlamaTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_llama_fast import LlamaTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel


else:
					import sys

					SCREAMING_SNAKE_CASE_				= _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)