




'''simple docstring'''


from typing import TYPE_CHECKING

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


_snake_case      :      int   							=		{
    'configuration_markuplm': ['MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MarkupLMConfig'],
    'feature_extraction_markuplm': ['MarkupLMFeatureExtractor'],
    'processing_markuplm': ['MarkupLMProcessor'],
    'tokenization_markuplm': ['MarkupLMTokenizer'],
}

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_snake_case      :      Any   							=		['MarkupLMTokenizerFast']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_snake_case      :      List[Any]   							=		[
				    'MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'MarkupLMForQuestionAnswering',
				    'MarkupLMForSequenceClassification',
				    'MarkupLMForTokenClassification',
				    'MarkupLMModel',
				    'MarkupLMPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_markuplm import MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP, MarkupLMConfig
				from .feature_extraction_markuplm import MarkupLMFeatureExtractor
				from .processing_markuplm import MarkupLMProcessor
				from .tokenization_markuplm import MarkupLMTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_markuplm_fast import MarkupLMTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_markuplm import (
								    MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST,
								    MarkupLMForQuestionAnswering,
								    MarkupLMForSequenceClassification,
								    MarkupLMForTokenClassification,
								    MarkupLMModel,
								    MarkupLMPreTrainedModel,
								)


else:
				import sys

				_snake_case      :      Optional[Any]   							=		_LazyModule(__name__, globals()['__file__'], _import_structure)



"""simple docstring"""




from functools import lru_cache

@lru_cache
def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_			):
					if not isinstance(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			):
										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   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			):
					SCREAMING_SNAKE_CASE				=	len(SCREAMING_SNAKE_CASE_			)
					# Find m such that F_m >= n where F_i is the i_th fibonacci number.
					SCREAMING_SNAKE_CASE				=	0
					while True:
										if fibonacci(SCREAMING_SNAKE_CASE_			) >= len_list:
															SCREAMING_SNAKE_CASE				=	i
															break
										i += 1
					SCREAMING_SNAKE_CASE				=	0
					while fibb_k > 0:
										SCREAMING_SNAKE_CASE				=	min(
										    offset + fibonacci(fibb_k - 1			),     len_list - 1			)  # Prevent out of range
										SCREAMING_SNAKE_CASE				=	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()
