
'''simple docstring'''







from typing import TYPE_CHECKING

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


_lowercase				:						Dict												= {
    "configuration_mpnet": ["MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "MPNetConfig"],
    "tokenization_mpnet": ["MPNetTokenizer"],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_lowercase				:						int												= ["MPNetTokenizerFast"]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_lowercase				:						int												= [
							    "MPNET_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "MPNetForMaskedLM",
							    "MPNetForMultipleChoice",
							    "MPNetForQuestionAnswering",
							    "MPNetForSequenceClassification",
							    "MPNetForTokenClassification",
							    "MPNetLayer",
							    "MPNetModel",
							    "MPNetPreTrainedModel",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_lowercase				:						List[Any]												= [
							    "TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "TFMPNetEmbeddings",
							    "TFMPNetForMaskedLM",
							    "TFMPNetForMultipleChoice",
							    "TFMPNetForQuestionAnswering",
							    "TFMPNetForSequenceClassification",
							    "TFMPNetForTokenClassification",
							    "TFMPNetMainLayer",
							    "TFMPNetModel",
							    "TFMPNetPreTrainedModel",
							]


if TYPE_CHECKING:
							from .configuration_mpnet import MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP, MPNetConfig
							from .tokenization_mpnet import MPNetTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_mpnet_fast import MPNetTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_mpnet import (
														    MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
														    MPNetForMaskedLM,
														    MPNetForMultipleChoice,
														    MPNetForQuestionAnswering,
														    MPNetForSequenceClassification,
														    MPNetForTokenClassification,
														    MPNetLayer,
														    MPNetModel,
														    MPNetPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_mpnet import (
														    TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFMPNetEmbeddings,
														    TFMPNetForMaskedLM,
														    TFMPNetForMultipleChoice,
														    TFMPNetForQuestionAnswering,
														    TFMPNetForSequenceClassification,
														    TFMPNetForTokenClassification,
														    TFMPNetMainLayer,
														    TFMPNetModel,
														    TFMPNetPreTrainedModel,
														)

else:
							import sys

							_lowercase				:						Any												= _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





'''simple docstring'''







import math
def       snake_case_	(       __SCREAMING_SNAKE_CASE      :				int							):



						"""simple docstring"""





						if 1 < number < 4:
												# 2 and 3 are primes
												return True
						elif number < 2 or number % 2 == 0 or number % 3 == 0:
												# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
												return False

						# All primes number are in format of 6k +/- 1
						for i in range(5   ,   int(math.sqrt(__SCREAMING_SNAKE_CASE							) + 1							)   ,   6							):
												if number % i == 0 or number % (i + 2) == 0:
																		return False
						return True
def       snake_case_	(       __SCREAMING_SNAKE_CASE      :				int = 2000000							):



						"""simple docstring"""





						return sum(num for num in range(3   ,   __SCREAMING_SNAKE_CASE   ,   2							) if is_prime(__SCREAMING_SNAKE_CASE							)							) + 2 if n > 2 else 0


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



