





def   _lowercase       (    _UpperCAmelCase      ,   _UpperCAmelCase					)    ->	Any:
				lowerCamelCase									=[]
				lowerCamelCase									=len(lowerCAmelCase__					)
				for i in range(lowerCAmelCase__					):
								lowerCamelCase									=[]
								for j in range(lowerCAmelCase__					):
												lowerCamelCase									=0
												for k in range(lowerCAmelCase__					):
																lowerCamelCase									=val + matrix_a[i][k] * matrix_b[k][j]
												list_a.append(lowerCAmelCase__					)
								matrix_c.append(lowerCAmelCase__					)
				return matrix_c





def   _lowercase       (    _UpperCAmelCase					)    ->	List[str]:
				return [[int(row == column					) for column in range(lowerCAmelCase__					)] for row in range(lowerCAmelCase__					)]





def   _lowercase       (    _UpperCAmelCase					)    ->	Dict:
				if n <= 1:
								return n
				lowerCamelCase									=identity(2					)
				lowerCamelCase									=[[1, 1], [1, 0]]
				lowerCamelCase									=n - 1
				while n > 0:
								if n % 2 == 1:
												lowerCamelCase									=multiply(lowerCAmelCase__      ,   lowerCAmelCase__					)
								lowerCamelCase									=multiply(lowerCAmelCase__      ,   lowerCAmelCase__					)
								lowerCamelCase									=int(n / 2					)
				return res_matrix[0][0]





def   _lowercase       (    _UpperCAmelCase					)    ->	Optional[int]:
				if n <= 1:
								return n
				lowerCamelCase									=0
				lowerCamelCase									=1
				for _ in range(2      ,   n + 1					):
								lowerCamelCase			,	lowerCamelCase									=fiba, fiba + fiba
				return fiba





def   _lowercase       (    )    ->	Any:
				for ordinal in "0th 1st 2nd 3rd 10th 100th 1000th".split():
								lowerCamelCase									=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()







import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin







class 							lowercase						(   SCREAMING_SNAKE_CASE__						):
							lowercase_			:			Dict			   =['''image_processor''', '''tokenizer''']
							lowercase_			:			List[str]			   ='''AutoImageProcessor'''
							lowercase_			:			Optional[Any]			   ='''AutoTokenizer'''




							def __init__(							self  ,A__=None  ,A__=None  ,**A__):
												lowercase		      =		None
												if "feature_extractor" in kwargs:
																	warnings.warn(
																	    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
																	    ''' instead.'''  ,A__  ,)
																	lowercase		      =		kwargs.pop('''feature_extractor''')

												lowercase		      =		image_processor if image_processor is not None else feature_extractor
												if image_processor is None:
																	raise ValueError('''You need to specify an `image_processor`.''')
												if tokenizer is None:
																	raise ValueError('''You need to specify a `tokenizer`.''')

												super().__init__(A__  ,A__)
												lowercase		      =		self.image_processor
												lowercase		      =		False




							def __call__(							self  ,*A__  ,**A__):
												# For backward compatibility
												if self._in_target_context_manager:
																	return self.current_processor(*A__  ,**A__)

												lowercase		      =		kwargs.pop('''images'''  ,A__)
												lowercase		      =		kwargs.pop('''text'''  ,A__)
												if len(A__) > 0:
																	lowercase		      =		args[0]
																	lowercase		      =		args[1:]

												if images is None and text is None:
																	raise ValueError('''You need to specify either an `images` or `text` input to process.''')

												if images is not None:
																	lowercase		      =		self.image_processor(A__  ,*A__  ,**A__)
												if text is not None:
																	lowercase		      =		self.tokenizer(A__  ,**A__)

												if text is None:
																	return inputs
												elif images is None:
																	return encodings
												else:
																	lowercase		      =		encodings['''input_ids''']
																	return inputs




							def 					A__ (							self  ,*A__  ,**A__):
												return self.tokenizer.batch_decode(*A__  ,**A__)




							def 					A__ (							self  ,*A__  ,**A__):
												return self.tokenizer.decode(*A__  ,**A__)




							@contextmanager
							def 					A__ (							self):
												warnings.warn(
												    '''`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '''
												    '''labels by using the argument `text` of the regular `__call__` method (either in the same call as '''
												    '''your images inputs, or in a separate call.''')
												lowercase		      =		True
												lowercase		      =		self.tokenizer
												yield
												lowercase		      =		self.image_processor
												lowercase		      =		False




							@property
							def 					A__ (							self):
												warnings.warn(
												    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''  ,A__  ,)
												return self.image_processor_class





							@property
							def 					A__ (							self):
												warnings.warn(
												    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''  ,A__  ,)
												return self.image_processor




