




"""simple docstring"""







from math import factorial

class 				snake_case_			:







   """simple docstring"""




   def __init__(		self       ,					lowerCamelCase_       ,					lowerCamelCase_)				->   str:
       UpperCamelCase              =	real
       if isinstance(lowerCamelCase_       ,					lowerCamelCase_):
           UpperCamelCase              =	[1] * rank
       else:
           UpperCamelCase              =	rank







   def __repr__(		self)				->   Any:
       return (
           F'{self.real}+'
           F'{"+".join(str(lowerCamelCase_)+"E"+str(n+1)for n,dual in enumerate(self.duals))}'
       )







   def   UpperCAmelCase__			(		self)				->   Optional[Any]:
       UpperCamelCase              =	self.duals.copy()
       while cur[-1] == 0:
           cur.pop(-1)
       return Dual(self.real       ,					lowerCamelCase_)







   def __add__(		self       ,					lowerCamelCase_)				->   List[str]:
       if not isinstance(lowerCamelCase_       ,					lowerCamelCase_):
           return Dual(self.real + other       ,					self.duals)
       UpperCamelCase              =	self.duals.copy()
       UpperCamelCase              =	other.duals.copy()
       if len(lowerCamelCase_) > len(lowerCamelCase_):
           o_dual.extend([1] * (len(lowerCamelCase_) - len(lowerCamelCase_)))
       elif len(lowerCamelCase_) < len(lowerCamelCase_):
           s_dual.extend([1] * (len(lowerCamelCase_) - len(lowerCamelCase_)))
       UpperCamelCase              =	[]
       for i in range(len(lowerCamelCase_)):
           new_duals.append(s_dual[i] + o_dual[i])
       return Dual(self.real + other.real       ,					lowerCamelCase_)

   A_				      =       __add__







   def __sub__(		self       ,					lowerCamelCase_)				->   str:
       return self + other * -1







   def __mul__(		self       ,					lowerCamelCase_)				->   Union[str, Any]:
       if not isinstance(lowerCamelCase_       ,					lowerCamelCase_):
           UpperCamelCase              =	[]
           for i in self.duals:
               new_duals.append(i * other)
           return Dual(self.real * other       ,					lowerCamelCase_)
       UpperCamelCase              =	[0] * (len(self.duals) + len(other.duals) + 1)
       for i, item in enumerate(self.duals):
           for j, jtem in enumerate(other.duals):
               new_duals[i + j + 1] += item * jtem
       for k in range(len(self.duals)):
           new_duals[k] += self.duals[k] * other.real
       for index in range(len(other.duals)):
           new_duals[index] += other.duals[index] * self.real
       return Dual(self.real * other.real       ,					lowerCamelCase_)

   A_				      =       __mul__







   def __truediv__(		self       ,					lowerCamelCase_)				->   List[str]:
       if not isinstance(lowerCamelCase_       ,					lowerCamelCase_):
           UpperCamelCase              =	[]
           for i in self.duals:
               new_duals.append(i / other)
           return Dual(self.real / other       ,					lowerCamelCase_)
       raise ValueError







   def __floordiv__(		self       ,					lowerCamelCase_)				->   Optional[Any]:
       if not isinstance(lowerCamelCase_       ,					lowerCamelCase_):
           UpperCamelCase              =	[]
           for i in self.duals:
               new_duals.append(i // other)
           return Dual(self.real // other       ,					lowerCamelCase_)
       raise ValueError







   def __pow__(		self       ,					lowerCamelCase_)				->   str:
       if n < 0 or isinstance(lowerCamelCase_       ,					lowerCamelCase_):
           raise ValueError('''power must be a positive integer''')
       if n == 0:
           return 1
       if n == 1:
           return self
       UpperCamelCase              =	self
       for _ in range(n - 1):
           x *= self
       return x





def      __snake_case (				_lowercase			,_lowercase			,_lowercase    ):


    """simple docstring"""







    if not callable(_lowercase    ):
        raise ValueError('''differentiate() requires a function as input for func'''    )
    if not isinstance(_lowercase			,(float, int)    ):
        raise ValueError('''differentiate() requires a float as input for position'''    )
    if not isinstance(_lowercase			,_lowercase    ):
        raise ValueError('''differentiate() requires an int as input for order'''    )
    UpperCamelCase              =	Dual(_lowercase			,1    )
    UpperCamelCase              =	func(_lowercase    )
    if order == 0:
        return result.real
    return result.duals[order - 1] * factorial(_lowercase    )


if __name__ == "__main__":
  import doctest

  doctest.testmod()





  def      __snake_case (				_lowercase    ):


      """simple docstring"""







      return y**2 * y**4


  print(differentiate(f, 9, 2))



'''simple docstring'''
import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class   lowercase      (				A__   ):

		"""simple docstring"""

		_a       					=							['image_processor', 'tokenizer']
		_a       					=							'ChineseCLIPImageProcessor'
		_a       					=							('BertTokenizer', 'BertTokenizerFast')




		def __init__(						self       ,  UpperCamelCase_=None       ,  UpperCamelCase_=None       ,  **UpperCamelCase_				):

							'''simple docstring'''


							UpperCamelCase__  :List[Any]				   =					None
							if "feature_extractor" in kwargs:
												warnings.warn(
												    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
												    ''' instead.'''       ,  UpperCamelCase_       ,  )
												UpperCamelCase__  :List[Any]				   =					kwargs.pop('''feature_extractor'''				)

							UpperCamelCase__  :List[str]				   =					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__(UpperCamelCase_       ,  UpperCamelCase_				)
							UpperCamelCase__  :Any				   =					self.image_processor




		def __call__(						self       ,  UpperCamelCase_=None       ,  UpperCamelCase_=None       ,  UpperCamelCase_=None       ,  **UpperCamelCase_				):

							'''simple docstring'''


							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:
												UpperCamelCase__  :Tuple				   =					self.tokenizer(UpperCamelCase_       ,  return_tensors=UpperCamelCase_       ,  **UpperCamelCase_				)

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

							if text is not None and images is not None:
												UpperCamelCase__  :Optional[int]				   =					image_features.pixel_values
												return encoding
							elif text is not None:
												return encoding
							else:
												return BatchEncoding(data=dict(**UpperCamelCase_				)       ,  tensor_type=UpperCamelCase_				)




		def        lowerCAmelCase__							(						self       ,  *UpperCamelCase_       ,  **UpperCamelCase_				):

							'''simple docstring'''


							return self.tokenizer.batch_decode(*UpperCamelCase_       ,  **UpperCamelCase_				)




		def        lowerCAmelCase__							(						self       ,  *UpperCamelCase_       ,  **UpperCamelCase_				):

							'''simple docstring'''


							return self.tokenizer.decode(*UpperCamelCase_       ,  **UpperCamelCase_				)




		@property
		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :int				   =					self.tokenizer.model_input_names
							UpperCamelCase__  :Any				   =					self.image_processor.model_input_names
							return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)




		@property
		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


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