

from typing import TYPE_CHECKING

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


lowerCAmelCase__						=	{
    """configuration_camembert""": ["""CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CamembertConfig""", """CamembertOnnxConfig"""],
}

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

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

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCAmelCase__						=	[
	    """CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """CamembertForCausalLM""",
	    """CamembertForMaskedLM""",
	    """CamembertForMultipleChoice""",
	    """CamembertForQuestionAnswering""",
	    """CamembertForSequenceClassification""",
	    """CamembertForTokenClassification""",
	    """CamembertModel""",
	    """CamembertPreTrainedModel""",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCAmelCase__						=	[
	    """TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """TFCamembertForCausalLM""",
	    """TFCamembertForMaskedLM""",
	    """TFCamembertForMultipleChoice""",
	    """TFCamembertForQuestionAnswering""",
	    """TFCamembertForSequenceClassification""",
	    """TFCamembertForTokenClassification""",
	    """TFCamembertModel""",
	    """TFCamembertPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig, CamembertOnnxConfig

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_camembert import CamembertTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_camembert_fast import CamembertTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_camembert import (
		    CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    CamembertForCausalLM,
		    CamembertForMaskedLM,
		    CamembertForMultipleChoice,
		    CamembertForQuestionAnswering,
		    CamembertForSequenceClassification,
		    CamembertForTokenClassification,
		    CamembertModel,
		    CamembertPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_camembert import (
		    TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFCamembertForCausalLM,
		    TFCamembertForMaskedLM,
		    TFCamembertForMultipleChoice,
		    TFCamembertForQuestionAnswering,
		    TFCamembertForSequenceClassification,
		    TFCamembertForTokenClassification,
		    TFCamembertModel,
		    TFCamembertPreTrainedModel,
		)

else:
	import sys

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




import math



def    lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							int )     -> bool:




				'''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    lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							int = 2_0_0_0_0_0_0 )     -> int:




				'''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() = }""")

