



"""simple docstring"""
import math
from decimal import Decimal, getcontext
def        A_  (      _lowercase = 4000000     ):







						'''simple docstring'''


						try:
												snake_case_			:str						=      int(_lowercase     )
						except (TypeError, ValueError):
												raise TypeError("""Parameter n must be int or castable to int."""     )
						if n <= 0:
												raise ValueError("""Parameter n must be greater than or equal to one."""     )
						snake_case_			:List[str]						=      100
						snake_case_			:List[str]						=      (Decimal(5     ) ** Decimal(0.5     ) + 1) / Decimal(2     )

						snake_case_			:str						=      (math.floor(math.log(n * (phi + 2), _lowercase     ) - 1     ) // 3) * 3 + 2
						snake_case_			:Tuple						=      Decimal(round(phi ** Decimal(index + 1     )     )     ) / (phi + 2)
						snake_case_			:List[Any]						=      num // 2
						return int(_lowercase     )


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





"""simple docstring"""
from typing import TYPE_CHECKING

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


__a     =    {
    "configuration_xlm_prophetnet": ["XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMProphetNetConfig"],
}

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

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a     =    [
						    "XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "XLMProphetNetDecoder",
						    "XLMProphetNetEncoder",
						    "XLMProphetNetForCausalLM",
						    "XLMProphetNetForConditionalGeneration",
						    "XLMProphetNetModel",
						    "XLMProphetNetPreTrainedModel",
						]


if TYPE_CHECKING:
						from .configuration_xlm_prophetnet import XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMProphetNetConfig

						try:
												if not is_sentencepiece_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_xlm_prophetnet import XLMProphetNetTokenizer

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_xlm_prophetnet import (
												    XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
												    XLMProphetNetDecoder,
												    XLMProphetNetEncoder,
												    XLMProphetNetForCausalLM,
												    XLMProphetNetForConditionalGeneration,
												    XLMProphetNetModel,
												    XLMProphetNetPreTrainedModel,
												)

else:
						import sys

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