





import timeit

class      A_	:
	def __init__(   self							, _A		):






				'''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		):






				'''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 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__      ,      UpperCamelCase__			)  ->    List[str]:







			'''simple docstring'''

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





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__			)  ->    Tuple:







			'''simple docstring'''

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





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__			)  ->    Union[str, 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 						__SCREAMING_SNAKE_CASE  (    )  ->    Tuple:







			'''simple docstring'''

			UpperCAmelCase       		=    '''
from random import randint
from __main__ import fibonacci_with_matrix_exponentiation
'''
			UpperCAmelCase       		=    '''fibonacci_with_matrix_exponentiation(randint(1,70000), 1, 1)'''
			UpperCAmelCase       		=    timeit.timeit(setup=UpperCamelCase__      ,      stmt=UpperCamelCase__      ,      number=100			)
			print('''With matrix exponentiation the average execution time is '''      ,      exec_time / 100			)
			return exec_time





def 						__SCREAMING_SNAKE_CASE  (    )  ->    List[Any]:







			'''simple docstring'''

			UpperCAmelCase       		=    '''
from random import randint
from __main__ import simple_fibonacci
'''
			UpperCAmelCase       		=    '''simple_fibonacci(randint(1,70000), 1, 1)'''
			UpperCAmelCase       		=    timeit.timeit(setup=UpperCamelCase__      ,      stmt=UpperCamelCase__      ,      number=100			)
			print(
			    '''Without matrix exponentiation the average execution time is '''      ,      exec_time / 100			)
			return exec_time





def 						__SCREAMING_SNAKE_CASE  (    )  ->    Dict:







			'''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,
)


__A    :   Tuple          =       {
    "configuration_mobilevitv2": [
        "MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "MobileViTV2Config",
        "MobileViTV2OnnxConfig",
    ],
}


try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A    :   List[str]          =       [
				    "MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "MobileViTV2ForImageClassification",
				    "MobileViTV2ForSemanticSegmentation",
				    "MobileViTV2Model",
				    "MobileViTV2PreTrainedModel",
				]

if TYPE_CHECKING:
				from .configuration_mobilevitva import (
				    MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    MobileViTVaConfig,
				    MobileViTVaOnnxConfig,
				)

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_mobilevitva import (
								    MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
								    MobileViTVaForImageClassification,
								    MobileViTVaForSemanticSegmentation,
								    MobileViTVaModel,
								    MobileViTVaPreTrainedModel,
								)

else:
				import sys

				__A    :   Tuple          =       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



