




'''simple docstring'''





def 					UpperCamelCase						(	snake_case						,  snake_case ):




   '''simple docstring'''

   SCREAMING_SNAKE_CASE__      :					str							    =			[]
   SCREAMING_SNAKE_CASE__      :					Dict							    =			len(snake_case )
   for i in range(snake_case ):
      SCREAMING_SNAKE_CASE__      :					int							    =			[]
      for j in range(snake_case ):
         SCREAMING_SNAKE_CASE__      :					int							    =			0
         for k in range(snake_case ):
            SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			val + matrix_a[i][k] * matrix_b[k][j]
         list_a.append(snake_case )
      matrix_c.append(snake_case )
   return matrix_c


def 					UpperCamelCase						(	snake_case ):




   '''simple docstring'''

   return [[int(row == column ) for column in range(snake_case )] for row in range(snake_case )]


def 					UpperCamelCase						(	snake_case ):




   '''simple docstring'''

   if n <= 1:
      return n
   SCREAMING_SNAKE_CASE__      :					int							    =			identity(2 )
   SCREAMING_SNAKE_CASE__      :					Tuple							    =			[[1, 1], [1, 0]]
   SCREAMING_SNAKE_CASE__      :					int							    =			n - 1
   while n > 0:
      if n % 2 == 1:
         SCREAMING_SNAKE_CASE__      :					Dict							    =			multiply(snake_case						,  snake_case )
      SCREAMING_SNAKE_CASE__      :					Tuple							    =			multiply(snake_case						,  snake_case )
      SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			int(n / 2 )
   return res_matrix[0][0]


def 					UpperCamelCase						(	snake_case ):




   '''simple docstring'''

   if n <= 1:
      return n
   SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			0
   SCREAMING_SNAKE_CASE__      :					Dict							    =			1
   for _ in range(2						,  n + 1 ):
      SCREAMING_SNAKE_CASE__    ,					SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			fiba, fiba + fiba
   return fiba


def 					UpperCamelCase						(	):




   '''simple docstring'''

   for ordinal in "0th 1st 2nd 3rd 10th 100th 1000th".split():
      SCREAMING_SNAKE_CASE__      :					List[str]							    =			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(snake_case )} and using bruteforce is """
          f"""{nth_fibonacci_bruteforce(snake_case )}\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()







'''simple docstring'''





from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class     lowercase  ( UpperCAmelCase_):
      __lowercase					=	['''image_processor''', '''tokenizer''']
      __lowercase					=	'''EfficientNetImageProcessor'''
      __lowercase					=	('''BertTokenizer''', '''BertTokenizerFast''')






      def __init__(self			,	a__			,	a__       )							-> Tuple:
         super().__init__(a__			,	a__       )






      def __call__(self			,	a__=None			,	a__=None			,	a__="max_length"			,	a__=6_4			,	a__=None			,	**a__       )							-> Any:
         if text is None and images is None:
            raise ValueError('''You have to specify either text or images. Both cannot be none.'''       )

         if text is not None:
            SCREAMING_SNAKE_CASE__      :					List[Any]							    =			self.tokenizer(
                a__			,	padding=a__			,	max_length=a__			,	return_tensors=a__			,	**a__       )

         if images is not None:
            SCREAMING_SNAKE_CASE__      :					Any							    =			self.image_processor(a__			,	return_tensors=a__			,	**a__       )

         if text is not None and images is not None:
            SCREAMING_SNAKE_CASE__      :					str							    =			image_features.pixel_values
            return encoding
         elif text is not None:
            return encoding
         else:
            return BatchEncoding(data=dict(**a__       )			,	tensor_type=a__       )






      def  A__(self			,	*a__			,	**a__       )							-> str:
         return self.tokenizer.batch_decode(*a__			,	**a__       )






      def  A__(self			,	*a__			,	**a__       )							-> str:
         return self.tokenizer.decode(*a__			,	**a__       )






      @property
      def  A__(self       )							-> Optional[int]:
         SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			self.tokenizer.model_input_names
         SCREAMING_SNAKE_CASE__      :					Tuple							    =			self.image_processor.model_input_names
         return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names       )       )

