

from typing import TYPE_CHECKING

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


lowerCAmelCase_			      =      {
    '''configuration_longformer''': [
        '''LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''LongformerConfig''',
        '''LongformerOnnxConfig''',
    ],
    '''tokenization_longformer''': ['''LongformerTokenizer'''],
}

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

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase_			      =      [
					    '''LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''LongformerForMaskedLM''',
					    '''LongformerForMultipleChoice''',
					    '''LongformerForQuestionAnswering''',
					    '''LongformerForSequenceClassification''',
					    '''LongformerForTokenClassification''',
					    '''LongformerModel''',
					    '''LongformerPreTrainedModel''',
					    '''LongformerSelfAttention''',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase_			      =      [
					    '''TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''TFLongformerForMaskedLM''',
					    '''TFLongformerForMultipleChoice''',
					    '''TFLongformerForQuestionAnswering''',
					    '''TFLongformerForSequenceClassification''',
					    '''TFLongformerForTokenClassification''',
					    '''TFLongformerModel''',
					    '''TFLongformerPreTrainedModel''',
					    '''TFLongformerSelfAttention''',
					]


if TYPE_CHECKING:
					from .configuration_longformer import (
					    LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    LongformerConfig,
					    LongformerOnnxConfig,
					)
					from .tokenization_longformer import LongformerTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_longformer_fast import LongformerTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_longformer import (
										    LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    LongformerForMaskedLM,
										    LongformerForMultipleChoice,
										    LongformerForQuestionAnswering,
										    LongformerForSequenceClassification,
										    LongformerForTokenClassification,
										    LongformerModel,
										    LongformerPreTrainedModel,
										    LongformerSelfAttention,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_longformer import (
										    TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFLongformerForMaskedLM,
										    TFLongformerForMultipleChoice,
										    TFLongformerForQuestionAnswering,
										    TFLongformerForSequenceClassification,
										    TFLongformerForTokenClassification,
										    TFLongformerModel,
										    TFLongformerPreTrainedModel,
										    TFLongformerSelfAttention,
										)

else:
					import sys

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






def      A__       (    SCREAMING_SNAKE_CASE_   :      int = 2_00_00_00						)  ->  int:






   """simple docstring"""







   _UpperCAmelCase           =   [0 for i in range(n + 1						)]
   _UpperCAmelCase           =   1
   _UpperCAmelCase           =   1

   for i in range(2				,	int(n**0.5						) + 1						):
      if primality_list[i] == 0:
         for j in range(i * i				,	n + 1				,	SCREAMING_SNAKE_CASE_						):
            _UpperCAmelCase           =   1
   _UpperCAmelCase           =   0
   for i in range(SCREAMING_SNAKE_CASE_						):
      if primality_list[i] == 0:
         sum_of_primes += i
   return sum_of_primes


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