





"""simple docstring"""


import math
from collections import OrderedDict

import torch
from packaging import version
from torch import Tensor, nn

from .utils import logging


a__    :   Dict    						= logging.get_logger(__name__)



class    lowercase_ (   nn.Module							):

       def __init__(	self   ):






        '''simple docstring'''

        super().__init__()
        if version.parse(torch.__version__   ) < version.parse("1.12.0"   ):
         raise ImportError(
             F"You are using torch=={torch.__version__}, but torch>=1.12.0 is required to use "
             "PytorchGELUTanh. Please upgrade torch."   )

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE   ):






        '''simple docstring'''

        return nn.functional.gelu(__SCREAMING_SNAKE_CASE      ,    approximate="tanh"   )



class    lowercase_ (   nn.Module							):

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE   ):






        '''simple docstring'''

        return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi   ) * (input + 0.044715 * torch.pow(__SCREAMING_SNAKE_CASE      ,    3.0   ))   ))



class    lowercase_ (   nn.Module							):

       def __init__(	self      ,    __SCREAMING_SNAKE_CASE = False   ):






        '''simple docstring'''

        super().__init__()
        if use_gelu_python:
         snake_case__										= self._gelu_python
        else:
         snake_case__										= nn.functional.gelu

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE   ):






        '''simple docstring'''

        return input * 0.5 * (1.0 + torch.erf(input / math.sqrt(2.0   )   ))

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE   ):






        '''simple docstring'''

        return self.act(__SCREAMING_SNAKE_CASE   )



class    lowercase_ (   nn.Module							):

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE   ):






        '''simple docstring'''

        return 0.5 * input * (1.0 + torch.tanh(input * 0.7978845608 * (1.0 + 0.044715 * input * input)   ))



class    lowercase_ (   nn.Module							):

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE   ):






        '''simple docstring'''

        return input * torch.sigmoid(1.702 * input   )



class    lowercase_ (   nn.Module							):

       def __init__(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE   ):






        '''simple docstring'''

        if min > max:
         raise ValueError(F"min should be < max (got min: {min}, max: {max})"   )

        super().__init__()
        snake_case__										= min
        snake_case__										= max

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE   ):






        '''simple docstring'''

        return torch.clip(gelu(__SCREAMING_SNAKE_CASE   )      ,    self.min      ,    self.max   )



class    lowercase_ (   nn.Module							):

       def __init__(	self   ):






        '''simple docstring'''

        super().__init__()
        snake_case__										= math.sqrt(2 / math.pi   )

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE   ):






        '''simple docstring'''

        return 0.5 * input * (1 + torch.tanh(self.precomputed_constant * (input + 0.044715 * torch.pow(__SCREAMING_SNAKE_CASE      ,    3   ))   ))



class    lowercase_ (   nn.Module							):

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE   ):






        '''simple docstring'''

        return nn.functional.silu(__SCREAMING_SNAKE_CASE   )



class    lowercase_ (   nn.Module							):

       def __init__(	self   ):






        '''simple docstring'''

        super().__init__()
        if version.parse(torch.__version__   ) < version.parse("1.9.0"   ):
         snake_case__										= self._mish_python
        else:
         snake_case__										= nn.functional.mish

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE   ):






        '''simple docstring'''

        return input * torch.tanh(nn.functional.softplus(__SCREAMING_SNAKE_CASE   )   )

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE   ):






        '''simple docstring'''

        return self.act(__SCREAMING_SNAKE_CASE   )



class    lowercase_ (   nn.Module							):

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE   ):






        '''simple docstring'''

        return input



class    lowercase_ (   nn.Module							):

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE=0.707107      ,    __SCREAMING_SNAKE_CASE=0.282095   ):






        '''simple docstring'''

        snake_case__										= (input - mu).div(sigma * math.sqrt(2.0   )   )
        return 0.5 * (1.0 + torch.erf(__SCREAMING_SNAKE_CASE   ))



class    lowercase_ (   nn.Module							):

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE   ):






        '''simple docstring'''

        snake_case__										= nn.functional.relu(__SCREAMING_SNAKE_CASE   )
        snake_case__										= torch.square(__SCREAMING_SNAKE_CASE   )
        return squared



class    lowercase_ (   __lowerCAmelCase							):

       def __getitem__(	self      ,    __SCREAMING_SNAKE_CASE   ):






        '''simple docstring'''

        snake_case__										= super().__getitem__(__SCREAMING_SNAKE_CASE   )
        snake_case__							,      snake_case__										= content if isinstance(__SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE   ) else (content, {})
        return cls(**__SCREAMING_SNAKE_CASE   )
a__    :   Dict    						= {
    """gelu""": GELUActivation,
    """gelu_10""": (ClippedGELUActivation, {"""min""": -1_0, """max""": 1_0}),
    """gelu_fast""": FastGELUActivation,
    """gelu_new""": NewGELUActivation,
    """gelu_python""": (GELUActivation, {"""use_gelu_python""": True}),
    """gelu_pytorch_tanh""": PytorchGELUTanh,
    """gelu_accurate""": AccurateGELUActivation,
    """laplace""": LaplaceActivation,
    """linear""": LinearActivation,
    """mish""": MishActivation,
    """quick_gelu""": QuickGELUActivation,
    """relu""": nn.ReLU,
    """relu2""": ReLUSquaredActivation,
    """relu6""": nn.ReLUa,
    """sigmoid""": nn.Sigmoid,
    """silu""": SiLUActivation,
    """swish""": SiLUActivation,
    """tanh""": nn.Tanh,
}
a__    :   List[Any]    						= ClassInstantier(ACTaCLS)





def 							a__	(     _UpperCAmelCase		)			->     Dict:

 '''simple docstring'''






 if activation_string in ACTaFN:
  return ACTaFN[activation_string]
 else:
  raise KeyError(f"function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys()		)}"		)


# For backwards compatibility with: from activations import gelu_python
a__    :   str    						= get_activation("""gelu_python""")
a__    :   List[Any]    						= get_activation("""gelu_new""")
a__    :   Tuple    						= get_activation("""gelu""")
a__    :   List[str]    						= get_activation("""gelu_fast""")
a__    :   Optional[int]    						= get_activation("""quick_gelu""")
a__    :   List[Any]    						= get_activation("""silu""")
a__    :   Optional[Any]    						= get_activation("""mish""")

a__    :   Any    						= get_activation("""linear""")








"""simple docstring"""


from __future__ import annotations





def 							a__	(     _UpperCAmelCase		,   _UpperCAmelCase		,   _UpperCAmelCase		,   )			->     list[float | int]:

 '''simple docstring'''






 if not all((nth_term, start_term_a, common_ratio_r)		):
  return []
 snake_case__										= []
 snake_case__										= 1
 snake_case__										= common_ratio_r
 for _ in range(int(_UpperCAmelCase		)		):
  if not series:
   series.append(_UpperCAmelCase		)
  else:
   power += 1
   series.append(float(start_term_a * multiple		)		)
   snake_case__										= pow(float(_UpperCAmelCase		)		,   _UpperCAmelCase		)
 return series


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   a__    :   int    						= float(input("""Enter the last number (n term) of the Geometric Series"""))
   a__    :   str    						= float(input("""Enter the starting term (a) of the Geometric Series"""))
   a__    :   str    						= float(
       input("""Enter the common ratio between two terms (r) of the Geometric Series""")
   )
   print("""Formula of Geometric Series => a + ar + ar^2 ... +ar^n""")
   print(geometric_series(nth_term, start_term_a, common_ratio_r))

