






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

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


lowerCAmelCase__    						=			{
    'configuration_convbert': ['CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ConvBertConfig', 'ConvBertOnnxConfig'],
    'tokenization_convbert': ['ConvBertTokenizer'],
}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase__    						=			['ConvBertTokenizerFast']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase__    						=			[
			    'CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'ConvBertForMaskedLM',
			    'ConvBertForMultipleChoice',
			    'ConvBertForQuestionAnswering',
			    'ConvBertForSequenceClassification',
			    'ConvBertForTokenClassification',
			    'ConvBertLayer',
			    'ConvBertModel',
			    'ConvBertPreTrainedModel',
			    'load_tf_weights_in_convbert',
			]


try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase__    						=			[
			    'TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'TFConvBertForMaskedLM',
			    'TFConvBertForMultipleChoice',
			    'TFConvBertForQuestionAnswering',
			    'TFConvBertForSequenceClassification',
			    'TFConvBertForTokenClassification',
			    'TFConvBertLayer',
			    'TFConvBertModel',
			    'TFConvBertPreTrainedModel',
			]


if TYPE_CHECKING:
			from .configuration_convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertOnnxConfig
			from .tokenization_convbert import ConvBertTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_convbert_fast import ConvBertTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_convbert import (
						    CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    ConvBertForMaskedLM,
						    ConvBertForMultipleChoice,
						    ConvBertForQuestionAnswering,
						    ConvBertForSequenceClassification,
						    ConvBertForTokenClassification,
						    ConvBertLayer,
						    ConvBertModel,
						    ConvBertPreTrainedModel,
						    load_tf_weights_in_convbert,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_convbert import (
						    TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFConvBertForMaskedLM,
						    TFConvBertForMultipleChoice,
						    TFConvBertForQuestionAnswering,
						    TFConvBertForSequenceClassification,
						    TFConvBertForTokenClassification,
						    TFConvBertLayer,
						    TFConvBertModel,
						    TFConvBertPreTrainedModel,
						)


else:
			import sys

			lowerCAmelCase__    						=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)









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

from collections.abc import Generator





def 				lowercase__		(					):
				_SCREAMING_SNAKE_CASE   :		dict[int, int]       		=    {}
				_SCREAMING_SNAKE_CASE   :		List[Any]       		=    2
				while True:
								_SCREAMING_SNAKE_CASE   :		List[Any]       		=    factor_map.pop(lowerCamelCase,						lowerCamelCase       )
								if factor:
												_SCREAMING_SNAKE_CASE   :		str       		=    factor + prime
												while x in factor_map:
																x += factor
												_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    factor
								else:
												_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    prime
												yield prime
								prime += 1





def 				lowercase__		(					lowerCamelCase = 1E10       ):
				_SCREAMING_SNAKE_CASE   :		Dict       		=    sieve()

				_SCREAMING_SNAKE_CASE   :		Dict       		=    1
				while True:
								_SCREAMING_SNAKE_CASE   :		int       		=    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())

