
__lowerCAmelCase      :					Dict   	=       {
    '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__   (	A_ ):






  '''simple docstring'''





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

def a__   (	A_,					A_ ):






  '''simple docstring'''





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

def a__   (	A_,					A_ ):






  '''simple docstring'''





  return encrypt(A_,					A_ )

def a__   (	A_,					A_ ):






  '''simple docstring'''





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

def a__   (	A_,					A_ ):






  '''simple docstring'''





  __magic_name__					,   __magic_name__    					=	get_position(A_,					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...
    __lowerCAmelCase      :					Any   	=       input('Enter key: ').strip()
    __lowerCAmelCase      :					Tuple   	=       input('Enter text to encrypt: ').strip()
    __lowerCAmelCase      :					str   	=       encrypt(key, text)

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



import torch.nn as nn

from .imports import is_fpa_available


if is_fpa_available():
    import transformer_engine.pytorch as te

def a__   (	A_,					A_=True,					A_=True,					A_=True ):






  '''simple docstring'''





  if not is_fpa_available():
    raise ImportError("""Using `convert_model` requires transformer_engine to be installed.""" )
  for name, module in model.named_children():
    if isinstance(A_,					nn.Linear ) and to_transformer_engine and _convert_linear:
      # Return early if the linear layer weights are not multiples of 16
      if any(p % 16 != 0 for p in module.weight.shape ):
        return
      __magic_name__    					=	module.bias is not None
      __magic_name__    					=	te.Linear(
          module.in_features,					module.out_features,					bias=A_,					params_dtype=module.weight.dtype )
      __magic_name__    					=	module.weight.data.clone()
      if has_bias:
        __magic_name__    					=	module.bias.data.clone()

      setattr(A_,					A_,					A_ )
    elif isinstance(A_,					nn.LayerNorm ) and to_transformer_engine and _convert_ln:
      __magic_name__    					=	te.LayerNorm(module.normalized_shape[0],					eps=module.eps,					params_dtype=module.weight.dtype )
      __magic_name__    					=	module.weight.data.clone()
      __magic_name__    					=	module.bias.data.clone()

      setattr(A_,					A_,					A_ )
    elif isinstance(A_,					te.Linear ) and not to_transformer_engine and _convert_linear:
      __magic_name__    					=	module.bias is not None
      __magic_name__    					=	nn.Linear(
          module.in_features,					module.out_features,					bias=A_,					params_dtype=module.weight.dtype )
      __magic_name__    					=	module.weight.data.clone()
      if has_bias:
        __magic_name__    					=	module.bias.data.clone()

      setattr(A_,					A_,					A_ )
    elif isinstance(A_,					te.LayerNorm ) and not to_transformer_engine and _convert_ln:
      __magic_name__    					=	nn.LayerNorm(module.normalized_shape[0],					eps=module.eps,					params_dtype=module.weight.dtype )
      __magic_name__    					=	module.weight.data.clone()
      __magic_name__    					=	module.bias.data.clone()

      setattr(A_,					A_,					A_ )
    else:
      convert_model(
          A_,					to_transformer_engine=A_,					_convert_linear=_convert_linear,					_convert_ln=_convert_ln,					)





def a__   (	A_ ):






  '''simple docstring'''





  if not is_fpa_available():
    raise ImportError("""Using `has_transformer_engine_layers` requires transformer_engine to be installed.""" )
  for m in model.modules():
    if isinstance(A_,					(te.LayerNorm, te.Linear) ):
      return True
  return False

