


import torch.nn as nn

from .imports import is_fpa_available


if is_fpa_available():
						import transformer_engine.pytorch as te



def       lowerCAmelCase			(		lowerCAmelCase_			, lowerCAmelCase_=True			, lowerCAmelCase_=True			, lowerCAmelCase_=True			)->			Union[str, Any]:
				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(lowerCAmelCase_			, 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
												lowerCAmelCase_			:   Optional[int]     					=					module.bias is not None
												lowerCAmelCase_			:   Any     					=					te.Linear(
												    module.in_features			, module.out_features			, bias=lowerCAmelCase_			, params_dtype=module.weight.dtype			)
												lowerCAmelCase_			:   Dict     					=					module.weight.data.clone()
												if has_bias:
																lowerCAmelCase_			:   Any     					=					module.bias.data.clone()

												setattr(lowerCAmelCase_			, lowerCAmelCase_			, lowerCAmelCase_			)
								elif isinstance(lowerCAmelCase_			, nn.LayerNorm			) and to_transformer_engine and _convert_ln:
												lowerCAmelCase_			:   List[str]     					=					te.LayerNorm(module.normalized_shape[0]			, eps=module.eps			, params_dtype=module.weight.dtype			)
												lowerCAmelCase_			:   Union[str, Any]     					=					module.weight.data.clone()
												lowerCAmelCase_			:   List[Any]     					=					module.bias.data.clone()

												setattr(lowerCAmelCase_			, lowerCAmelCase_			, lowerCAmelCase_			)
								elif isinstance(lowerCAmelCase_			, te.Linear			) and not to_transformer_engine and _convert_linear:
												lowerCAmelCase_			:   Dict     					=					module.bias is not None
												lowerCAmelCase_			:   Union[str, Any]     					=					nn.Linear(
												    module.in_features			, module.out_features			, bias=lowerCAmelCase_			, params_dtype=module.weight.dtype			)
												lowerCAmelCase_			:   Optional[Any]     					=					module.weight.data.clone()
												if has_bias:
																lowerCAmelCase_			:   Union[str, Any]     					=					module.bias.data.clone()

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

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




def       lowerCAmelCase			(		lowerCAmelCase_			)->			str:
				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(lowerCAmelCase_			, (te.LayerNorm, te.Linear)			):
												return True
				return False



import numpy as np

_UpperCAmelCase      :	int  =[
    ["""a""", """b""", """c""", """d""", """e"""],
    ["""f""", """g""", """h""", """i""", """k"""],
    ["""l""", """m""", """n""", """o""", """p"""],
    ["""q""", """r""", """s""", """t""", """u"""],
    ["""v""", """w""", """x""", """y""", """z"""],
]







class 					snake_case__:







						'''simple docstring'''




						def __init__(					self				)       ->     None:
										lowerCAmelCase_			:   List[Any]     					=					np.array(__lowercase				)




						def     lowercase_			(					self ,    __lowercase				)       ->     np.ndarray:
										lowerCAmelCase_					,	lowerCAmelCase_			:   List[str]     					=					np.where(letter == self.SQUARE				)
										lowerCAmelCase_			:   Union[str, Any]     					=					np.concatenate([indexa + 1, indexa + 1]				)
										return indexes




						def     lowercase_			(					self ,    __lowercase ,    __lowercase				)       ->     str:
										return self.SQUARE[indexa - 1, indexa - 1]




						def     lowercase_			(					self ,    __lowercase				)       ->     str:
										lowerCAmelCase_			:   Dict     					=					message.lower()
										lowerCAmelCase_			:   Any     					=					message.replace('''j''' ,    '''i'''				)

										lowerCAmelCase_			:   Optional[Any]     					=					''''''
										for letter_index in range(len(__lowercase				)				):
														if message[letter_index] != " ":
																		lowerCAmelCase_			:   Optional[Any]     					=					self.letter_to_numbers(message[letter_index]				)
																		lowerCAmelCase_			:   List[Any]     					=					encoded_message + str(numbers[0]				) + str(numbers[1]				)
														elif message[letter_index] == " ":
																		lowerCAmelCase_			:   Optional[Any]     					=					encoded_message + ''' '''

										return encoded_message




						def     lowercase_			(					self ,    __lowercase				)       ->     str:
										lowerCAmelCase_			:   List[str]     					=					message.replace(''' ''' ,    '''  '''				)
										lowerCAmelCase_			:   Union[str, Any]     					=					''''''
										for numbers_index in range(int(len(__lowercase				) / 2				)				):
														if message[numbers_index * 2] != " ":
																		lowerCAmelCase_			:   Any     					=					message[numbers_index * 2]
																		lowerCAmelCase_			:   Dict     					=					message[numbers_index * 2 + 1]

																		lowerCAmelCase_			:   Dict     					=					self.numbers_to_letter(int(__lowercase				) ,    int(__lowercase				)				)
																		lowerCAmelCase_			:   Tuple     					=					decoded_message + letter
														elif message[numbers_index * 2] == " ":
																		lowerCAmelCase_			:   List[str]     					=					decoded_message + ''' '''

										return decoded_message