




'''simple docstring'''







import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin


class      _a    (      _lowercase					):
       __a							:	List[str]  							=   ['''image_processor''', '''tokenizer''']
       __a							:	Optional[int]  							=   '''AutoImageProcessor'''
       __a							:	int  							=   '''AutoTokenizer'''
       def __init__(      self		:      List[Any]  ,							lowercase		:      List[str]=None  ,							lowercase		:      int=None  ,							**lowercase		:      Dict							):



           '''simple docstring'''

           UpperCAmelCase     			=						None
           if "feature_extractor" in kwargs:
               warnings.warn(
                   '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
                   ''' instead.'''  ,							_SCREAMING_SNAKE_CASE  ,							)
               UpperCAmelCase     			=						kwargs.pop('''feature_extractor'''							)

           UpperCAmelCase     			=						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__(_SCREAMING_SNAKE_CASE  ,							_SCREAMING_SNAKE_CASE							)
           UpperCAmelCase     			=						self.image_processor
           UpperCAmelCase     			=						False
       def __call__(      self		:      Dict  ,							*lowercase		:      str  ,							**lowercase		:      Optional[Any]							):



           '''simple docstring'''

           if self._in_target_context_manager:
               return self.current_processor(*_SCREAMING_SNAKE_CASE  ,							**_SCREAMING_SNAKE_CASE							)

           UpperCAmelCase     			=						kwargs.pop('''images'''  ,							_SCREAMING_SNAKE_CASE							)
           UpperCAmelCase     			=						kwargs.pop('''text'''  ,							_SCREAMING_SNAKE_CASE							)
           if len(_SCREAMING_SNAKE_CASE							) > 0:
               UpperCAmelCase     			=						args[0]
               UpperCAmelCase     			=						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:
               UpperCAmelCase     			=						self.image_processor(_SCREAMING_SNAKE_CASE  ,							*_SCREAMING_SNAKE_CASE  ,							**_SCREAMING_SNAKE_CASE							)
           if text is not None:
               UpperCAmelCase     			=						self.tokenizer(_SCREAMING_SNAKE_CASE  ,							**_SCREAMING_SNAKE_CASE							)

           if text is None:
               return inputs
           elif images is None:
               return encodings
           else:
               UpperCAmelCase     			=						encodings['''input_ids''']
               return inputs
       def        A (      self		:      Optional[int]  ,							*lowercase		:      Optional[Any]  ,							**lowercase		:      Optional[int]							):



           '''simple docstring'''

           return self.tokenizer.batch_decode(*_SCREAMING_SNAKE_CASE  ,							**_SCREAMING_SNAKE_CASE							)
       def        A (      self		:      str  ,							*lowercase		:      Dict  ,							**lowercase		:      Optional[int]							):



           '''simple docstring'''

           return self.tokenizer.decode(*_SCREAMING_SNAKE_CASE  ,							**_SCREAMING_SNAKE_CASE							)
       @contextmanager
       def        A (      self		:      Optional[Any]							):



           '''simple docstring'''

           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.'''							)
           UpperCAmelCase     			=						True
           UpperCAmelCase     			=						self.tokenizer
           yield
           UpperCAmelCase     			=						self.image_processor
           UpperCAmelCase     			=						False
       @property
       def        A (      self		:      str							):



           '''simple docstring'''

           warnings.warn(
               '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''  ,							_SCREAMING_SNAKE_CASE  ,							)
           return self.image_processor_class







       @property
       def        A (      self		:      List[Any]							):



           '''simple docstring'''

           warnings.warn(
               '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''  ,							_SCREAMING_SNAKE_CASE  ,							)
           return self.image_processor








from __future__ import annotations

import sys






def 	lowerCamelCase_       (			_a			):




		"""simple docstring"""






		if n < 0:
				raise ValueError('''Negative arguments are not supported'''			)
		return _fib(_a			)[0]






def 	lowerCamelCase_       (			_a			):




		"""simple docstring"""






		if n == 0:  # (F(0), F(1))
				return (0, 1)

		# F(2n) = F(n)[2F(n+1) − F(n)]
		# F(2n+1) = F(n+1)^2+F(n)^2
		lowerCAmelCase__	,      lowerCAmelCase__  :     Dict       	=   _fib(n // 2			)
		lowerCAmelCase__  :     Dict       	=   a * (b * 2 - a)
		lowerCAmelCase__  :     Union[str, Any]       	=   a * a + b * b
		return (d, c + d) if n % 2 else (c, d)


if __name__ == "__main__":
		lowerCamelCase		=						int(sys.argv[1])
		print(f'''fibonacci({n}) is {fibonacci(n)}''')




