


def 						_lowerCamelCase		(							SCREAMING_SNAKE_CASE_				: int = 10_00      ):






							"""simple docstring"""



							a_       :   int			=	0
							a_       :   Tuple			=	0
							while 1:
														num += 3
														if num >= n:
																					break
														total += num
														num += 2
														if num >= n:
																					break
														total += num
														num += 1
														if num >= n:
																					break
														total += num
														num += 3
														if num >= n:
																					break
														total += num
														num += 1
														if num >= n:
																					break
														total += num
														num += 2
														if num >= n:
																					break
														total += num
														num += 3
														if num >= n:
																					break
														total += num
							return total


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






from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE : Optional[Any]          =    {
    "configuration_camembert": ["CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "CamembertConfig", "CamembertOnnxConfig"],
}

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

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

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	SCREAMING_SNAKE_CASE : Union[str, Any]          =    [
	    "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:
	SCREAMING_SNAKE_CASE : List[Any]          =    [
	    "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

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


