
import timeit


class 							lowercase   :



		def __init__(					self							, A_			)    ->   List[str]:
				"""simple docstring"""
				if isinstance(A_							, A_			):  # Initializes a matrix identical to the one provided.
						UpperCamelCase            =	arg
						UpperCamelCase            =	len(A_			)
				else:  # Initializes a square matrix of the given size and set values to zero.
						UpperCamelCase            =	arg
						UpperCamelCase            =	[[0 for _ in range(self.n			)] for _ in range(self.n			)]



		def __mul__(					self							, A_			)    ->   Tuple:
				"""simple docstring"""
				UpperCamelCase            =	Matrix(self.n			)
				for i in range(self.n			):
						for j in range(self.n			):
								for k in range(self.n			):
										matrix.t[i][j] += self.t[i][k] * b.t[k][j]
				return matrix






def  A			(   lowercase       ,			lowercase					)  ->    Optional[Any]:
		'''simple docstring'''

		UpperCamelCase            =	Matrix([[1, 0], [0, 1]]					)
		while b > 0:
				if b & 1:
						matrix *= a
				a *= a
				b >>= 1
		return matrix


def  A			(   lowercase       ,			lowercase       ,			lowercase					)  ->    str:
		'''simple docstring'''

		if n == 1:
				return fa
		elif n == 2:
				return fa
		UpperCamelCase            =	Matrix([[1, 1], [1, 0]]					)
		UpperCamelCase            =	modular_exponentiation(lowercase       ,			n - 2					)
		return fa * matrix.t[0][0] + fa * matrix.t[0][1]


def  A			(   lowercase       ,			lowercase       ,			lowercase					)  ->    List[Any]:
		'''simple docstring'''

		if n == 1:
				return fa
		elif n == 2:
				return fa

		UpperCamelCase            =	fa
		UpperCamelCase            =	fa
		n -= 2

		while n > 0:
				UpperCamelCase      ,    UpperCamelCase            =	fn_a + fn_a, fn_a
				n -= 1

		return fn_a


def  A			(   )  ->    List[Any]:
		'''simple docstring'''

		UpperCamelCase            =	'\nfrom random import randint\nfrom __main__ import fibonacci_with_matrix_exponentiation\n'
		UpperCamelCase            =	'fibonacci_with_matrix_exponentiation(randint(1,70000), 1, 1)'
		UpperCamelCase            =	timeit.timeit(setup=lowercase       ,			stmt=lowercase       ,			number=100					)
		print('With matrix exponentiation the average execution time is '       ,			exec_time / 100					)
		return exec_time


def  A			(   )  ->    Tuple:
		'''simple docstring'''

		UpperCamelCase            =	'\nfrom random import randint\nfrom __main__ import simple_fibonacci\n'
		UpperCamelCase            =	'simple_fibonacci(randint(1,70000), 1, 1)'
		UpperCamelCase            =	timeit.timeit(setup=lowercase       ,			stmt=lowercase       ,			number=100					)
		print(
		    'Without matrix exponentiation the average execution time is '       ,			exec_time / 100					)
		return exec_time


def  A			(   )  ->    Tuple:
		'''simple docstring'''

		matrix_exponentiation_time()
		simple_fibonacci_time()


if __name__ == "__main__":
					main()


from typing import TYPE_CHECKING

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


_UpperCAmelCase   :					int    =			{"configuration_levit": ["LEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LevitConfig", "LevitOnnxConfig"]}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCAmelCase   :					Optional[Any]    =			["LevitFeatureExtractor"]
					_UpperCAmelCase   :					Optional[int]    =			["LevitImageProcessor"]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCAmelCase   :					Optional[int]    =			[
					    "LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "LevitForImageClassification",
					    "LevitForImageClassificationWithTeacher",
					    "LevitModel",
					    "LevitPreTrainedModel",
					]


if TYPE_CHECKING:
					from .configuration_levit import LEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, LevitConfig, LevitOnnxConfig

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_levit import LevitFeatureExtractor
										from .image_processing_levit import LevitImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_levit import (
										    LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    LevitForImageClassification,
										    LevitForImageClassificationWithTeacher,
										    LevitModel,
										    LevitPreTrainedModel,
										)
else:
					import sys

					_UpperCAmelCase   :					Optional[Any]    =			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
