


"""simple docstring"""






from typing import TYPE_CHECKING

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


UpperCAmelCase    :     List[str]   						=		{
    'configuration_tapas': ['TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TapasConfig'],
    'tokenization_tapas': ['TapasTokenizer'],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase    :     List[Any]   						=		[
							    'TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'TapasForMaskedLM',
							    'TapasForQuestionAnswering',
							    'TapasForSequenceClassification',
							    'TapasModel',
							    'TapasPreTrainedModel',
							    'load_tf_weights_in_tapas',
							]
try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase    :     Union[str, Any]   						=		[
							    'TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'TFTapasForMaskedLM',
							    'TFTapasForQuestionAnswering',
							    'TFTapasForSequenceClassification',
							    'TFTapasModel',
							    'TFTapasPreTrainedModel',
							]


if TYPE_CHECKING:
							from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
							from .tokenization_tapas import TapasTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tapas import (
														    TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TapasForMaskedLM,
														    TapasForQuestionAnswering,
														    TapasForSequenceClassification,
														    TapasModel,
														    TapasPreTrainedModel,
														    load_tf_weights_in_tapas,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_tapas import (
														    TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFTapasForMaskedLM,
														    TFTapasForQuestionAnswering,
														    TFTapasForSequenceClassification,
														    TFTapasModel,
														    TFTapasPreTrainedModel,
														)


else:
							import sys

							UpperCAmelCase    :     Optional[int]   						=		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




"""simple docstring"""






def      lowerCamelCase       (     _UpperCamelCase		:      int = 4_0_0_0_0_0_0							)		->       int:

							'''simple docstring'''



							__UpperCAmelCase   :						int      							=					[0, 1]
							__UpperCAmelCase   :						Optional[Any]      							=					0
							while fib[i] <= n:
														fib.append(fib[i] + fib[i + 1]							)
														if fib[i + 2] > n:
																					break
														i += 1
							__UpperCAmelCase   :						str      							=					0
							for j in range(len(_UpperCamelCase							) - 1							):
														if fib[j] % 2 == 0:
																					total += fib[j]

							return total


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