




from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE:    Optional[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:
						SCREAMING_SNAKE_CASE:    List[str]											=   ['''MarkupLMTokenizerFast''']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						SCREAMING_SNAKE_CASE:    str											=   [
						    '''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

						SCREAMING_SNAKE_CASE:    List[Any]											=   _LazyModule(__name__, globals()['''__file__'''], _import_structure)





from __future__ import annotations

import sys


def 				_a       (				lowerCAmelCase   )->  int:
		if n < 0:
				raise ValueError('Negative arguments are not supported'   )
		return _fib(lowerCAmelCase   )[0]


def 				_a       (				lowerCAmelCase   )->  tuple[int, int]:
		if n == 0:  # (F(0), F(1))
				return (0, 1)

		# F(2n) = F(n)[2F(n+1) − F(n)]
		# F(2n+1) = F(n+1)^2+F(n)^2
		SCREAMING_SNAKE_CASE_			,							SCREAMING_SNAKE_CASE_							=       _fib(n // 2   )
		SCREAMING_SNAKE_CASE_							=       a * (b * 2 - a)
		SCREAMING_SNAKE_CASE_							=       a * a + b * b
		return (d, c + d) if n % 2 else (c, d)


if __name__ == "__main__":
						SCREAMING_SNAKE_CASE:    Optional[int]											=   int(sys.argv[1])
						print(f"""fibonacci({n}) is {fibonacci(n)}""")