






"""simple docstring"""




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






class  UpperCamelCase    (			snake_case_   ):
 UpperCamelCase      : List[str]    =      ['''image_processor''', '''tokenizer''']
 UpperCamelCase      : List[str]    =      '''EfficientNetImageProcessor'''
 UpperCamelCase      : List[Any]    =      ('''BertTokenizer''', '''BertTokenizerFast''')


 def __init__(  self	:		Optional[int]							,		UpperCAmelCase__	:		List[str]							,		UpperCAmelCase__	:		Tuple							)      -> Dict:
   super().__init__(UpperCAmelCase__							,		UpperCAmelCase__							)


 def __call__(  self	:		List[str]							,		UpperCAmelCase__	:		Tuple=None							,		UpperCAmelCase__	:		List[Any]=None							,		UpperCAmelCase__	:		List[Any]="max_length"							,		UpperCAmelCase__	:		Optional[Any]=64							,		UpperCAmelCase__	:		Any=None							,		**UpperCAmelCase__	:		List[Any]							)      -> List[str]:
   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:
     _a						: List[Any]    =			self.tokenizer(
         UpperCAmelCase__							,		padding=UpperCAmelCase__							,		max_length=UpperCAmelCase__							,		return_tensors=UpperCAmelCase__							,		**UpperCAmelCase__							)

   if images is not None:
     _a						: List[str]    =			self.image_processor(UpperCAmelCase__							,		return_tensors=UpperCAmelCase__							,		**UpperCAmelCase__							)

   if text is not None and images is not None:
     _a						: Union[str, Any]    =			image_features.pixel_values
     return encoding
   elif text is not None:
     return encoding
   else:
     return BatchEncoding(data=dict(**UpperCAmelCase__							)							,		tensor_type=UpperCAmelCase__							)


 def _lowercase			(  self	:		str							,		*UpperCAmelCase__	:		str							,		**UpperCAmelCase__	:		Optional[int]							)      -> int:
   return self.tokenizer.batch_decode(*UpperCAmelCase__							,		**UpperCAmelCase__							)


 def _lowercase			(  self	:		Tuple							,		*UpperCAmelCase__	:		Dict							,		**UpperCAmelCase__	:		str							)      -> List[str]:
   return self.tokenizer.decode(*UpperCAmelCase__							,		**UpperCAmelCase__							)







 @property
 def _lowercase			(  self	:		Dict							)      -> List[str]:
   _a						: Optional[int]    =			self.tokenizer.model_input_names
   _a						: Dict    =			self.image_processor.model_input_names
   return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)









"""simple docstring"""




from __future__ import annotations

import sys

def     lowerCAmelCase__  (  UpperCamelCase__       ):



  '''simple docstring'''
  if n < 0:
    raise ValueError("""Negative arguments are not supported"""       )
  return _fib(UpperCamelCase__       )[0]

def     lowerCAmelCase__  (  UpperCamelCase__       ):



  '''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
  _a   ,						_a						: Dict    =			_fib(n // 2       )
  _a						: Dict    =			a * (b * 2 - a)
  _a						: Optional[Any]    =			a * a + b * b
  return (d, c + d) if n % 2 else (c, d)


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

