


def 				UpperCamelCase      (			__magic_name__  :							list[list[int]]					,					__magic_name__  :							list[list[int]]  )     ->						list[list[int]]:
		"""simple docstring"""




		lowercase__      =	[]
		lowercase__      =	len(__magic_name__  )
		for i in range(__magic_name__  ):
				lowercase__      =	[]
				for j in range(__magic_name__  ):
						lowercase__      =	0
						for k in range(__magic_name__  ):
								lowercase__      =	val + matrix_a[i][k] * matrix_b[k][j]
						list_a.append(__magic_name__  )
				matrix_c.append(__magic_name__  )
		return matrix_c






def 				UpperCamelCase      (			__magic_name__  :							int  )     ->						list[list[int]]:
		"""simple docstring"""




		return [[int(row == column  ) for column in range(__magic_name__  )] for row in range(__magic_name__  )]






def 				UpperCamelCase      (			__magic_name__  :							int  )     ->						int:
		"""simple docstring"""




		if n <= 1:
				return n
		lowercase__      =	identity(2  )
		lowercase__      =	[[1, 1], [1, 0]]
		lowercase__      =	n - 1
		while n > 0:
				if n % 2 == 1:
						lowercase__      =	multiply(__magic_name__					,					__magic_name__  )
				lowercase__      =	multiply(__magic_name__					,					__magic_name__  )
				lowercase__      =	int(n / 2  )
		return res_matrix[0][0]






def 				UpperCamelCase      (			__magic_name__  :							int  )     ->						int:
		"""simple docstring"""




		if n <= 1:
				return n
		lowercase__      =	0
		lowercase__      =	1
		for _ in range(2					,					n + 1  ):
				lowercase__    ,			lowercase__      =	fiba, fiba + fiba
		return fiba






def 				UpperCamelCase      (			)     ->						None:
		"""simple docstring"""




		for ordinal in "0th 1st 2nd 3rd 10th 100th 1000th".split():
				lowercase__      =	int("""""".join(c for c in ordinal if c in """0123456789"""  )  )  # 1000th --> 1000
				print(
				    f'''{ordinal} fibonacci number using matrix exponentiation is '''
				    f'''{nth_fibonacci_matrix(__magic_name__  )} and using bruteforce is '''
				    f'''{nth_fibonacci_bruteforce(__magic_name__  )}\n'''  )
		# from timeit import timeit
		# print(timeit("nth_fibonacci_matrix(1000000)",
		#              "from main import nth_fibonacci_matrix", number=5))
		# print(timeit("nth_fibonacci_bruteforce(1000000)",
		#              "from main import nth_fibonacci_bruteforce", number=5))
		# 2.3342058970001744
		# 57.256506615000035


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	main()





from typing import TYPE_CHECKING

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


A :	str												=     {
    'configuration_nystromformer': ['NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'NystromformerConfig'],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A :	Optional[int]												=     [
	    'NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'NystromformerForMaskedLM',
	    'NystromformerForMultipleChoice',
	    'NystromformerForQuestionAnswering',
	    'NystromformerForSequenceClassification',
	    'NystromformerForTokenClassification',
	    'NystromformerLayer',
	    'NystromformerModel',
	    'NystromformerPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_nystromformer import NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, NystromformerConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_nystromformer import (
		    NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    NystromformerForMaskedLM,
		    NystromformerForMultipleChoice,
		    NystromformerForQuestionAnswering,
		    NystromformerForSequenceClassification,
		    NystromformerForTokenClassification,
		    NystromformerLayer,
		    NystromformerModel,
		    NystromformerPreTrainedModel,
		)


else:
	import sys

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

