


from math import factorial


class      __lowerCAmelCase  :






  """simple docstring"""





  def __init__(  self							:							Dict     ,					_lowerCAmelCase							:							str     ,					_lowerCAmelCase							:							List[Any]	) ->			str:




     """simple docstring"""
     snake_case_            =       real
     if isinstance(_lowerCAmelCase     ,					_lowerCAmelCase	):
        snake_case_            =       [1] * rank
     else:
        snake_case_            =       rank



  def __repr__(  self							:							Any	) ->			Any:




     """simple docstring"""
     return (
         F'''{self.real}+'''
         F'''{'+'.join(str(_lowerCAmelCase	)+'E'+str(n+1	)for n,dual in enumerate(self.duals	)	)}'''
     )



  def        lowerCAmelCase__	(  self							:							Optional[int]	) ->			Dict:




     """simple docstring"""
     snake_case_            =       self.duals.copy()
     while cur[-1] == 0:
        cur.pop(-1	)
     return Dual(self.real     ,					_lowerCAmelCase	)



  def __add__(  self							:							Any     ,					_lowerCAmelCase							:							List[Any]	) ->			Optional[int]:




     """simple docstring"""
     if not isinstance(_lowerCAmelCase     ,					_lowerCAmelCase	):
        return Dual(self.real + other     ,					self.duals	)
     snake_case_            =       self.duals.copy()
     snake_case_            =       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	))	)
     snake_case_            =       []
     for i in range(len(_lowerCAmelCase	)	):
        new_duals.append(s_dual[i] + o_dual[i]	)
     return Dual(self.real + other.real     ,					_lowerCAmelCase	)

  _SCREAMING_SNAKE_CASE		     =							__add__



  def __sub__(  self							:							str     ,					_lowerCAmelCase							:							str	) ->			str:




     """simple docstring"""
     return self + other * -1



  def __mul__(  self							:							Any     ,					_lowerCAmelCase							:							Optional[Any]	) ->			Union[str, Any]:




     """simple docstring"""
     if not isinstance(_lowerCAmelCase     ,					_lowerCAmelCase	):
        snake_case_            =       []
        for i in self.duals:
           new_duals.append(i * other	)
        return Dual(self.real * other     ,					_lowerCAmelCase	)
     snake_case_            =       [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	)

  _SCREAMING_SNAKE_CASE		     =							__mul__



  def __truediv__(  self							:							List[Any]     ,					_lowerCAmelCase							:							Dict	) ->			Optional[int]:




     """simple docstring"""
     if not isinstance(_lowerCAmelCase     ,					_lowerCAmelCase	):
        snake_case_            =       []
        for i in self.duals:
           new_duals.append(i / other	)
        return Dual(self.real / other     ,					_lowerCAmelCase	)
     raise ValueError



  def __floordiv__(  self							:							Tuple     ,					_lowerCAmelCase							:							int	) ->			List[str]:




     """simple docstring"""
     if not isinstance(_lowerCAmelCase     ,					_lowerCAmelCase	):
        snake_case_            =       []
        for i in self.duals:
           new_duals.append(i // other	)
        return Dual(self.real // other     ,					_lowerCAmelCase	)
     raise ValueError



  def __pow__(  self							:							str     ,					_lowerCAmelCase							:							Any	) ->			List[str]:




     """simple docstring"""
     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
     snake_case_            =       self
     for _ in range(n - 1	):
        x *= self
     return x






def       _lowerCAmelCase    (				lowerCAmelCase_				:Dict		,    lowerCAmelCase_				:Optional[int]		,    lowerCAmelCase_				:Optional[int]				)->int:
   '''simple docstring'''






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


if __name__ == "__main__":
   import doctest

   doctest.testmod()



   def       _lowerCAmelCase    (				lowerCAmelCase_				:Optional[Any]				)->List[str]:
      '''simple docstring'''






      return y**2 * y**4



   print(differentiate(f, 9, 2))








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE						:str			=  {'''configuration_unispeech''': ['''UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''UniSpeechConfig''']}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE						:List[str]			=  [
       '''UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''UniSpeechForCTC''',
       '''UniSpeechForPreTraining''',
       '''UniSpeechForSequenceClassification''',
       '''UniSpeechModel''',
       '''UniSpeechPreTrainedModel''',
   ]

if TYPE_CHECKING:
   from .configuration_unispeech import UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_unispeech import (
          UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST,
          UniSpeechForCTC,
          UniSpeechForPreTraining,
          UniSpeechForSequenceClassification,
          UniSpeechModel,
          UniSpeechPreTrainedModel,
      )

else:
   import sys

   SCREAMING_SNAKE_CASE						:List[str]			=  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




