


from __future__ import annotations

from collections.abc import Generator



def 				UpperCamelCase_(      )    ->						Generator[int, None, None]:
    _lowercase       :							dict[int, int]      		=		{}
    _lowercase       :							Optional[int]      		=		2
    while True:
        _lowercase       :							List[Any]      		=		factor_map.pop(lowerCamelCase_							, lowerCamelCase_ )
        if factor:
            _lowercase       :							Optional[Any]      		=		factor + prime
            while x in factor_map:
                x += factor
            _lowercase       :							Optional[int]      		=		factor
        else:
            _lowercase       :							Union[str, Any]      		=		prime
            yield prime
        prime += 1



def 				UpperCamelCase_(      lowerCamelCase_ = 1e10 )    ->						int:
    _lowercase       :							Optional[int]      		=		sieve()

    _lowercase       :							int      		=		1
    while True:
        _lowercase       :							Any      		=		next(lowerCamelCase_ )
        if (2 * prime * n) > limit:
            return n
        # Ignore the next prime as the reminder will be 2.
        next(lowerCamelCase_ )
        n += 2


if __name__ == "__main__":
 print(solution())






from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE       :      str												= {
    "configuration_llama": ["LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP", "LlamaConfig"],
}

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

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

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE       :      int												= [
     "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       :      Optional[int]												= _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


