

"""simple docstring"""






def   lowercase_	(    _lowercase       :		int	):




					'''simple docstring'''






					UpperCAmelCase			:   int      =				0
					while num > 0:
										digit_sum += num % 10
										num //= 10
					return digit_sum



def   lowercase_	(    _lowercase       :		int = 1_00	):




					'''simple docstring'''






					UpperCAmelCase			:   Tuple      =				1
					UpperCAmelCase			:   Dict      =				2

					for i in range(2							,      max_n + 1	):
										UpperCAmelCase			:   Union[str, Any]      =				pre_numerator
										UpperCAmelCase			:   Optional[int]      =				2 * i // 3 if i % 3 == 0 else 1
										UpperCAmelCase			:   Tuple      =				cur_numerator
										UpperCAmelCase			:   Optional[int]      =				e_cont * pre_numerator + temp

					return sum_digits(_lowercase	)


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





"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


snake_case_   :   Dict        =       {
    """configuration_mvp""": ["""MVP_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MvpConfig""", """MvpOnnxConfig"""],
    """tokenization_mvp""": ["""MvpTokenizer"""],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case_   :   List[Any]        =       ["""MvpTokenizerFast"""]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case_   :   Any        =       [
	    """MVP_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """MvpForCausalLM""",
	    """MvpForConditionalGeneration""",
	    """MvpForQuestionAnswering""",
	    """MvpForSequenceClassification""",
	    """MvpModel""",
	    """MvpPreTrainedModel""",
	]

if TYPE_CHECKING:
	from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
	from .tokenization_mvp import MvpTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_mvp_fast import MvpTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_mvp import (
		    MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
		    MvpForCausalLM,
		    MvpForConditionalGeneration,
		    MvpForQuestionAnswering,
		    MvpForSequenceClassification,
		    MvpModel,
		    MvpPreTrainedModel,
		)

else:
	import sys

	snake_case_   :   List[str]        =       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


