





'''simple docstring'''


import math
from collections import OrderedDict

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

from .utils import logging


_SCREAMING_SNAKE_CASE			=							logging.get_logger(__name__)

class 				lowerCAmelCase_						(						nn.Module						):




 def __init__(		self						)      ->							Optional[int]:
     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 							_snake_case				(		self     ,       _lowerCAmelCase						)      ->							Tensor:
     return nn.functional.gelu(_lowerCAmelCase     ,       approximate="tanh"						)







class 				lowerCAmelCase_						(						nn.Module						):




 def 							_snake_case				(		self     ,       _lowerCAmelCase						)      ->							Tensor:
     return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi						) * (input + 0.044715 * torch.pow(_lowerCAmelCase     ,       3.0						))						))







class 				lowerCAmelCase_						(						nn.Module						):




 def __init__(		self     ,       _lowerCAmelCase = False						)      ->							Optional[int]:
     super().__init__()
     if use_gelu_python:
         _lowerCAmelCase		     = self._gelu_python
     else:
         _lowerCAmelCase		     = nn.functional.gelu




 def 							_snake_case				(		self     ,       _lowerCAmelCase						)      ->							Tensor:
     return input * 0.5 * (1.0 + torch.erf(input / math.sqrt(2.0						)						))





 def 							_snake_case				(		self     ,       _lowerCAmelCase						)      ->							Tensor:
     return self.act(_lowerCAmelCase						)







class 				lowerCAmelCase_						(						nn.Module						):




 def 							_snake_case				(		self     ,       _lowerCAmelCase						)      ->							Tensor:
     return 0.5 * input * (1.0 + torch.tanh(input * 0.7978845608 * (1.0 + 0.044715 * input * input)						))







class 				lowerCAmelCase_						(						nn.Module						):




 def 							_snake_case				(		self     ,       _lowerCAmelCase						)      ->							Tensor:
     return input * torch.sigmoid(1.702 * input						)







class 				lowerCAmelCase_						(						nn.Module						):




 def __init__(		self     ,       _lowerCAmelCase     ,       _lowerCAmelCase						)      ->							List[Any]:
     if min > max:
         raise ValueError(f'''min should be < max (got min: {min}, max: {max})'''						)

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





 def 							_snake_case				(		self     ,       _lowerCAmelCase						)      ->							Tensor:
     return torch.clip(gelu(_lowerCAmelCase						)     ,       self.min     ,       self.max						)







class 				lowerCAmelCase_						(						nn.Module						):




 def __init__(		self						)      ->							List[str]:
     super().__init__()
     _lowerCAmelCase		     = math.sqrt(2 / math.pi						)





 def 							_snake_case				(		self     ,       _lowerCAmelCase						)      ->							Tensor:
     return 0.5 * input * (1 + torch.tanh(self.precomputed_constant * (input + 0.044715 * torch.pow(_lowerCAmelCase     ,       3						))						))







class 				lowerCAmelCase_						(						nn.Module						):




 def 							_snake_case				(		self     ,       _lowerCAmelCase						)      ->							Tensor:
     return nn.functional.silu(_lowerCAmelCase						)







class 				lowerCAmelCase_						(						nn.Module						):




 def __init__(		self						)      ->							Any:
     super().__init__()
     if version.parse(torch.__version__						) < version.parse("1.9.0"						):
         _lowerCAmelCase		     = self._mish_python
     else:
         _lowerCAmelCase		     = nn.functional.mish




 def 							_snake_case				(		self     ,       _lowerCAmelCase						)      ->							Tensor:
     return input * torch.tanh(nn.functional.softplus(_lowerCAmelCase						)						)





 def 							_snake_case				(		self     ,       _lowerCAmelCase						)      ->							Tensor:
     return self.act(_lowerCAmelCase						)







class 				lowerCAmelCase_						(						nn.Module						):




 def 							_snake_case				(		self     ,       _lowerCAmelCase						)      ->							Tensor:
     return input







class 				lowerCAmelCase_						(						nn.Module						):




 def 							_snake_case				(		self     ,       _lowerCAmelCase     ,       _lowerCAmelCase=0.707107     ,       _lowerCAmelCase=0.282095						)      ->							Union[str, Any]:
     _lowerCAmelCase		     = (input - mu).div(sigma * math.sqrt(2.0						)						)
     return 0.5 * (1.0 + torch.erf(_lowerCAmelCase						))







class 				lowerCAmelCase_						(						nn.Module						):




 def 							_snake_case				(		self     ,       _lowerCAmelCase						)      ->							int:
     _lowerCAmelCase		     = nn.functional.relu(_lowerCAmelCase						)
     _lowerCAmelCase		     = torch.square(_lowerCAmelCase						)
     return squared







class 				lowerCAmelCase_						(						__magic_name__						):




 def __getitem__(		self     ,       _lowerCAmelCase						)      ->							List[Any]:
     _lowerCAmelCase		     = super().__getitem__(_lowerCAmelCase						)
     _lowerCAmelCase     ,     _lowerCAmelCase		     = content if isinstance(_lowerCAmelCase     ,       _lowerCAmelCase						) else (content, {})
     return cls(**_lowerCAmelCase						)







_SCREAMING_SNAKE_CASE			=							{
    "gelu": GELUActivation,
    "gelu_10": (ClippedGELUActivation, {"min": -10, "max": 10}),
    "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,
}
_SCREAMING_SNAKE_CASE			=							ClassInstantier(ACTaCLS)


def 		__a(SCREAMING_SNAKE_CASE_					:   Any      ):



    '''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
_SCREAMING_SNAKE_CASE			=							get_activation("gelu_python")
_SCREAMING_SNAKE_CASE			=							get_activation("gelu_new")
_SCREAMING_SNAKE_CASE			=							get_activation("gelu")
_SCREAMING_SNAKE_CASE			=							get_activation("gelu_fast")
_SCREAMING_SNAKE_CASE			=							get_activation("quick_gelu")
_SCREAMING_SNAKE_CASE			=							get_activation("silu")
_SCREAMING_SNAKE_CASE			=							get_activation("mish")





_SCREAMING_SNAKE_CASE			=							get_activation("linear")









'''simple docstring'''


_SCREAMING_SNAKE_CASE			=							{
    "A": ("ABCDEFGHIJKLM", "NOPQRSTUVWXYZ"),
    "B": ("ABCDEFGHIJKLM", "NOPQRSTUVWXYZ"),
    "C": ("ABCDEFGHIJKLM", "ZNOPQRSTUVWXY"),
    "D": ("ABCDEFGHIJKLM", "ZNOPQRSTUVWXY"),
    "E": ("ABCDEFGHIJKLM", "YZNOPQRSTUVWX"),
    "F": ("ABCDEFGHIJKLM", "YZNOPQRSTUVWX"),
    "G": ("ABCDEFGHIJKLM", "XYZNOPQRSTUVW"),
    "H": ("ABCDEFGHIJKLM", "XYZNOPQRSTUVW"),
    "I": ("ABCDEFGHIJKLM", "WXYZNOPQRSTUV"),
    "J": ("ABCDEFGHIJKLM", "WXYZNOPQRSTUV"),
    "K": ("ABCDEFGHIJKLM", "VWXYZNOPQRSTU"),
    "L": ("ABCDEFGHIJKLM", "VWXYZNOPQRSTU"),
    "M": ("ABCDEFGHIJKLM", "UVWXYZNOPQRST"),
    "N": ("ABCDEFGHIJKLM", "UVWXYZNOPQRST"),
    "O": ("ABCDEFGHIJKLM", "TUVWXYZNOPQRS"),
    "P": ("ABCDEFGHIJKLM", "TUVWXYZNOPQRS"),
    "Q": ("ABCDEFGHIJKLM", "STUVWXYZNOPQR"),
    "R": ("ABCDEFGHIJKLM", "STUVWXYZNOPQR"),
    "S": ("ABCDEFGHIJKLM", "RSTUVWXYZNOPQ"),
    "T": ("ABCDEFGHIJKLM", "RSTUVWXYZNOPQ"),
    "U": ("ABCDEFGHIJKLM", "QRSTUVWXYZNOP"),
    "V": ("ABCDEFGHIJKLM", "QRSTUVWXYZNOP"),
    "W": ("ABCDEFGHIJKLM", "PQRSTUVWXYZNO"),
    "X": ("ABCDEFGHIJKLM", "PQRSTUVWXYZNO"),
    "Y": ("ABCDEFGHIJKLM", "OPQRSTUVWXYZN"),
    "Z": ("ABCDEFGHIJKLM", "OPQRSTUVWXYZN"),
}


def 		__a(SCREAMING_SNAKE_CASE_					:   str      ):



    '''simple docstring'''





    return [alphabet[char] for char in key.upper()]


def 		__a(SCREAMING_SNAKE_CASE_					:   str						,    SCREAMING_SNAKE_CASE_					:   str      ):



    '''simple docstring'''





    _lowerCAmelCase		     = ""
    _lowerCAmelCase		     = 0
    _lowerCAmelCase		     = generate_table(SCREAMING_SNAKE_CASE_      )
    for char in words.upper():
        cipher += get_opponent(table[count]						,    SCREAMING_SNAKE_CASE_      )
        _lowerCAmelCase		     = (count + 1) % len(SCREAMING_SNAKE_CASE_      )
    return cipher


def 		__a(SCREAMING_SNAKE_CASE_					:   str						,    SCREAMING_SNAKE_CASE_					:   str      ):



    '''simple docstring'''





    return encrypt(SCREAMING_SNAKE_CASE_						,    SCREAMING_SNAKE_CASE_      )


def 		__a(SCREAMING_SNAKE_CASE_					:   tuple[str, str]						,    SCREAMING_SNAKE_CASE_					:   str      ):



    '''simple docstring'''





    _lowerCAmelCase		     = 0 if char in table[0] else 1
    _lowerCAmelCase		     = table[row].index(SCREAMING_SNAKE_CASE_      )
    return row, col


def 		__a(SCREAMING_SNAKE_CASE_					:   tuple[str, str]						,    SCREAMING_SNAKE_CASE_					:   str      ):



    '''simple docstring'''





    _lowerCAmelCase     ,     _lowerCAmelCase		     = get_position(SCREAMING_SNAKE_CASE_						,    char.upper()      )
    if row == 1:
        return table[0][col]
    else:
        return table[1][col] if row == 0 else char


if __name__ == "__main__":
       import doctest

       doctest.testmod()  # Fist ensure that all our tests are passing...
       _SCREAMING_SNAKE_CASE			=							input("Enter key: ").strip()
       _SCREAMING_SNAKE_CASE			=							input("Enter text to encrypt: ").strip()
       _SCREAMING_SNAKE_CASE			=							encrypt(key, text)

       print(f'''Encrypted: {cipher_text}''')
       print(f'''Decrypted with key: {decrypt(key, cipher_text)}''')


