



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



def  _lowerCAmelCase     (_lowerCAmelCase):
					return [[int(row == column) for column in range(_lowerCAmelCase)] for row in range(_lowerCAmelCase)]



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



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



def  _lowerCAmelCase     ():
					for ordinal in "0th 1st 2nd 3rd 10th 100th 1000th".split():
										UpperCamelCase_			= 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(_lowerCAmelCase)} and using bruteforce is """
										    f"""{nth_fibonacci_bruteforce(_lowerCAmelCase)}\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_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__SCREAMING_SNAKE_CASE	:							Any           =  {'''configuration_pegasus''': ['''PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PegasusConfig''']}

try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE	:							str           =  ['''PegasusTokenizer''']

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE	:							Tuple           =  ['''PegasusTokenizerFast''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE	:							Dict           =  [
		    '''PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''PegasusForCausalLM''',
		    '''PegasusForConditionalGeneration''',
		    '''PegasusModel''',
		    '''PegasusPreTrainedModel''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE	:							List[str]           =  [
		    '''TFPegasusForConditionalGeneration''',
		    '''TFPegasusModel''',
		    '''TFPegasusPreTrainedModel''',
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE	:							List[str]           =  [
		    '''FlaxPegasusForConditionalGeneration''',
		    '''FlaxPegasusModel''',
		    '''FlaxPegasusPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_pegasus import PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusConfig

		try:
				if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_pegasus import PegasusTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_pegasus_fast import PegasusTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_pegasus import (
				    PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
				    PegasusForCausalLM,
				    PegasusForConditionalGeneration,
				    PegasusModel,
				    PegasusPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_pegasus import TFPegasusForConditionalGeneration, TFPegasusModel, TFPegasusPreTrainedModel

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_pegasus import (
				    FlaxPegasusForConditionalGeneration,
				    FlaxPegasusModel,
				    FlaxPegasusPreTrainedModel,
				)

else:
		import sys

		__SCREAMING_SNAKE_CASE	:							Dict           =  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

