



from typing import TYPE_CHECKING

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


__SCREAMING_SNAKE_CASE     =	{
    """configuration_bart""": ["""BART_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BartConfig""", """BartOnnxConfig"""],
    """tokenization_bart""": ["""BartTokenizer"""],
}

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

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__SCREAMING_SNAKE_CASE     =	[
							    """BART_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """BartForCausalLM""",
							    """BartForConditionalGeneration""",
							    """BartForQuestionAnswering""",
							    """BartForSequenceClassification""",
							    """BartModel""",
							    """BartPretrainedModel""",
							    """PretrainedBartModel""",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__SCREAMING_SNAKE_CASE     =	[
							    """TFBartForConditionalGeneration""",
							    """TFBartForSequenceClassification""",
							    """TFBartModel""",
							    """TFBartPretrainedModel""",
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__SCREAMING_SNAKE_CASE     =	[
							    """FlaxBartDecoderPreTrainedModel""",
							    """FlaxBartForCausalLM""",
							    """FlaxBartForConditionalGeneration""",
							    """FlaxBartForQuestionAnswering""",
							    """FlaxBartForSequenceClassification""",
							    """FlaxBartModel""",
							    """FlaxBartPreTrainedModel""",
							]

if TYPE_CHECKING:
							from .configuration_bart import BART_PRETRAINED_CONFIG_ARCHIVE_MAP, BartConfig, BartOnnxConfig
							from .tokenization_bart import BartTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_bart_fast import BartTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_bart import (
														    BART_PRETRAINED_MODEL_ARCHIVE_LIST,
														    BartForCausalLM,
														    BartForConditionalGeneration,
														    BartForQuestionAnswering,
														    BartForSequenceClassification,
														    BartModel,
														    BartPretrainedModel,
														    PretrainedBartModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_bart import (
														    TFBartForConditionalGeneration,
														    TFBartForSequenceClassification,
														    TFBartModel,
														    TFBartPretrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_bart import (
														    FlaxBartDecoderPreTrainedModel,
														    FlaxBartForCausalLM,
														    FlaxBartForConditionalGeneration,
														    FlaxBartForQuestionAnswering,
														    FlaxBartForSequenceClassification,
														    FlaxBartModel,
														    FlaxBartPreTrainedModel,
														)

else:
							import sys

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




import math
from decimal import Decimal, getcontext




def 						UpperCAmelCase (    _lowerCamelCase = 400_0000     ):

			try:
						A				:       Any														=    int(_lowerCamelCase     )
			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."     )
			A				:       int														=    100
			A				:       Tuple														=    (Decimal(5     ) ** Decimal(0.5     ) + 1) / Decimal(2     )

			A				:       str														=    (math.floor(math.log(n * (phi + 2)			,     _lowerCamelCase     ) - 1     ) // 3) * 3 + 2
			A				:       Optional[int]														=    Decimal(round(phi ** Decimal(index + 1     )     )     ) / (phi + 2)
			A				:       str														=    num // 2
			return int(_lowerCamelCase     )


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