

"""simple docstring"""






import timeit






class       _UpperCAmelCase     :
			def __init__(						self ,						_A       )      ->      List[str]:






				'''simple docstring'''







				if isinstance(lowerCAmelCase__ ,						lowerCAmelCase__       ):  # Initializes a matrix identical to the one provided.
					_UpperCAmelCase      :							Union[str, Any]       =   arg
					_UpperCAmelCase      :							Optional[Any]       =   len(lowerCAmelCase__       )
				else:  # Initializes a square matrix of the given size and set values to zero.
					_UpperCAmelCase      :							List[str]       =   arg
					_UpperCAmelCase      :							Union[str, Any]       =   [[0 for _ in range(self.n       )] for _ in range(self.n       )]
			def __mul__(						self ,						_A       )      ->      str:






				'''simple docstring'''







				_UpperCAmelCase      :							Optional[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  UpperCamelCase   (     _lowerCAmelCase       :  Tuple,					_lowerCAmelCase       :  Tuple	)			->     Optional[Any]:
	_UpperCAmelCase      :							Dict       =   Matrix([[1, 0], [0, 1]]	)
	while b > 0:
		if b & 1:
			matrix *= a
		a *= a
		b >>= 1
	return matrix
def  UpperCamelCase   (     _lowerCAmelCase       :  Optional[int],					_lowerCAmelCase       :  Tuple,					_lowerCAmelCase       :  str	)			->     Optional[Any]:



	if n == 1:
		return fa
	elif n == 2:
		return fa
	_UpperCAmelCase      :							Any       =   Matrix([[1, 1], [1, 0]]	)
	_UpperCAmelCase      :							Dict       =   modular_exponentiation(_lowerCAmelCase,					n - 2	)
	return fa * matrix.t[0][0] + fa * matrix.t[0][1]
def  UpperCamelCase   (     _lowerCAmelCase       :  Any,					_lowerCAmelCase       :  Optional[int],					_lowerCAmelCase       :  Any	)			->     Any:



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

	_UpperCAmelCase      :							List[str]       =   fa
	_UpperCAmelCase      :							int       =   fa
	n -= 2

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

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


if __name__ == "__main__":
						main()








"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase__			:					int							  =					{'''configuration_qdqbert''': ['''QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''QDQBertConfig''']}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__			:					Optional[int]							  =					[
						    '''QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''QDQBertForMaskedLM''',
						    '''QDQBertForMultipleChoice''',
						    '''QDQBertForNextSentencePrediction''',
						    '''QDQBertForQuestionAnswering''',
						    '''QDQBertForSequenceClassification''',
						    '''QDQBertForTokenClassification''',
						    '''QDQBertLayer''',
						    '''QDQBertLMHeadModel''',
						    '''QDQBertModel''',
						    '''QDQBertPreTrainedModel''',
						    '''load_tf_weights_in_qdqbert''',
						]


if TYPE_CHECKING:
						from .configuration_qdqbert import QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, QDQBertConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_qdqbert import (
												    QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    QDQBertForMaskedLM,
												    QDQBertForMultipleChoice,
												    QDQBertForNextSentencePrediction,
												    QDQBertForQuestionAnswering,
												    QDQBertForSequenceClassification,
												    QDQBertForTokenClassification,
												    QDQBertLayer,
												    QDQBertLMHeadModel,
												    QDQBertModel,
												    QDQBertPreTrainedModel,
												    load_tf_weights_in_qdqbert,
												)


else:
						import sys

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





