





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

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


_snake_case	:						str					=					{
    'configuration_mpnet': ['MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MPNetConfig'],
    'tokenization_mpnet': ['MPNetTokenizer'],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_snake_case	:						Union[str, Any]					=					['MPNetTokenizerFast']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_snake_case	:						Any					=					[
		    'MPNET_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'MPNetForMaskedLM',
		    'MPNetForMultipleChoice',
		    'MPNetForQuestionAnswering',
		    'MPNetForSequenceClassification',
		    'MPNetForTokenClassification',
		    'MPNetLayer',
		    'MPNetModel',
		    'MPNetPreTrainedModel',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_snake_case	:						Any					=					[
		    'TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'TFMPNetEmbeddings',
		    'TFMPNetForMaskedLM',
		    'TFMPNetForMultipleChoice',
		    'TFMPNetForQuestionAnswering',
		    'TFMPNetForSequenceClassification',
		    'TFMPNetForTokenClassification',
		    'TFMPNetMainLayer',
		    'TFMPNetModel',
		    'TFMPNetPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_mpnet import MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP, MPNetConfig
		from .tokenization_mpnet import MPNetTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_mpnet_fast import MPNetTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_mpnet import (
				    MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
				    MPNetForMaskedLM,
				    MPNetForMultipleChoice,
				    MPNetForQuestionAnswering,
				    MPNetForSequenceClassification,
				    MPNetForTokenClassification,
				    MPNetLayer,
				    MPNetModel,
				    MPNetPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_mpnet import (
				    TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFMPNetEmbeddings,
				    TFMPNetForMaskedLM,
				    TFMPNetForMultipleChoice,
				    TFMPNetForQuestionAnswering,
				    TFMPNetForSequenceClassification,
				    TFMPNetForTokenClassification,
				    TFMPNetMainLayer,
				    TFMPNetModel,
				    TFMPNetPreTrainedModel,
				)

else:
		import sys

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









"""simple docstring"""
from functools import lru_cache






@lru_cache
def    A__							(     UpperCamelCase			):
							if not isinstance(UpperCamelCase   ,    UpperCamelCase			):
														raise TypeError("k must be an integer."			)
							if k < 0:
														raise ValueError("k integer must be greater or equal to zero."			)
							if k == 0:
														return 0
							elif k == 1:
														return 1
							else:
														return fibonacci(k - 1			) + fibonacci(k - 2			)






def    A__							(     UpperCamelCase   ,    UpperCamelCase			):
							A						=    len(UpperCamelCase			)
							# Find m such that F_m >= n where F_i is the i_th fibonacci number.
							A						=    0
							while True:
														if fibonacci(UpperCamelCase			) >= len_list:
																					A						=    i
																					break
														i += 1
							A						=    0
							while fibb_k > 0:
														A						=    min(
														    offset + fibonacci(fibb_k - 1			)   ,    len_list - 1			)  # Prevent out of range
														A						=    arr[index_k]
														if item_k_1 == val:
																					return index_k
														elif val < item_k_1:
																					fibb_k -= 1
														elif val > item_k_1:
																					offset += fibonacci(fibb_k - 1			)
																					fibb_k -= 2
							else:
														return -1


if __name__ == "__main__":
		import doctest

		doctest.testmod()


