

import timeit






class     lowerCamelCase_			:




	def __init__(    self	:    Any				,			__A	:    Dict       ):
		if isinstance(__A				,			__A       ):  # Initializes a matrix identical to the one provided.
			__A   : Optional[int]								= arg
			__A   : Union[str, Any]								= len(__A       )
		else:  # Initializes a square matrix of the given size and set values to zero.
			__A   : int								= arg
			__A   : Tuple								= [[0 for _ in range(self.n       )] for _ in range(self.n       )]




	def __mul__(    self	:    Optional[Any]				,			__A	:    Any       ):
		__A   : Union[str, Any]								= 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    (      a__						:      Dict ,a__						:      Optional[Any]					)				->							str:
	__A   : Optional[Any]								= Matrix([[1, 0], [0, 1]]					)
	while b > 0:
		if b & 1:
			matrix *= a
		a *= a
		b >>= 1
	return matrix
def        __SCREAMING_SNAKE_CASE    (      a__						:      Dict ,a__						:      Union[str, Any] ,a__						:      List[str]					)				->							Union[str, Any]:
	# Trivial Cases
	if n == 1:
		return fa
	elif n == 2:
		return fa
	__A   : str								= Matrix([[1, 1], [1, 0]]					)
	__A   : int								= modular_exponentiation(a__ ,n - 2					)
	return fa * matrix.t[0][0] + fa * matrix.t[0][1]
def        __SCREAMING_SNAKE_CASE    (      a__						:      List[str] ,a__						:      Any ,a__						:      List[str]					)				->							List[Any]:
	# Trivial Cases
	if n == 1:
		return fa
	elif n == 2:
		return fa

	__A   : str								= fa
	__A   : str								= fa
	n -= 2

	while n > 0:
		__A						,							__A   : Tuple								= fn_a + fn_a, fn_a
		n -= 1

	return fn_a
def        __SCREAMING_SNAKE_CASE    (      )				->							Optional[int]:
	__A   : Any								= """
from random import randint
from __main__ import fibonacci_with_matrix_exponentiation
"""
	__A   : Optional[Any]								= """fibonacci_with_matrix_exponentiation(randint(1,70000), 1, 1)"""
	__A   : List[Any]								= timeit.timeit(setup=a__ ,stmt=a__ ,number=100					)
	print("""With matrix exponentiation the average execution time is """ ,exec_time / 100					)
	return exec_time
def        __SCREAMING_SNAKE_CASE    (      )				->							Union[str, Any]:
	__A   : int								= """
from random import randint
from __main__ import simple_fibonacci
"""
	__A   : Optional[Any]								= """simple_fibonacci(randint(1,70000), 1, 1)"""
	__A   : List[Any]								= timeit.timeit(setup=a__ ,stmt=a__ ,number=100					)
	print(
	    """Without matrix exponentiation the average execution time is """ ,exec_time / 100					)
	return exec_time
def        __SCREAMING_SNAKE_CASE    (      )				->							List[Any]:
	matrix_exponentiation_time()
	simple_fibonacci_time()


if __name__ == "__main__":
			main()








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase_       :				Optional[Any]		   =      {
    '''configuration_prophetnet''': ['''PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ProphetNetConfig'''],
    '''tokenization_prophetnet''': ['''ProphetNetTokenizer'''],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCAmelCase_       :				List[str]		   =      [
			    '''PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''ProphetNetDecoder''',
			    '''ProphetNetEncoder''',
			    '''ProphetNetForCausalLM''',
			    '''ProphetNetForConditionalGeneration''',
			    '''ProphetNetModel''',
			    '''ProphetNetPreTrainedModel''',
			]


if TYPE_CHECKING:
			from .configuration_prophetnet import PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ProphetNetConfig
			from .tokenization_prophetnet import ProphetNetTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_prophetnet import (
						    PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
						    ProphetNetDecoder,
						    ProphetNetEncoder,
						    ProphetNetForCausalLM,
						    ProphetNetForConditionalGeneration,
						    ProphetNetModel,
						    ProphetNetPreTrainedModel,
						)

else:
			import sys

			UpperCAmelCase_       :				Any		   =      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





