



import torch.nn as nn

from .imports import is_fpa_available


if is_fpa_available():
		import transformer_engine.pytorch as te
def 						snake_case__							(       __lowercase				,  __lowercase=True				,  __lowercase=True				,  __lowercase=True							)		->		Tuple:







						"""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(__lowercase				,  nn.Linear							) and to_transformer_engine and _convert_linear:
																		# Return early if the linear layer weights are not multiples of 16
																		if any(p % 1_6 != 0 for p in module.weight.shape							):
																								return
																		A__						:				Optional[Any]         =			module.bias is not None
																		A__						:				Any         =			te.Linear(
																		    module.in_features				,  module.out_features				,  bias=__lowercase				,  params_dtype=module.weight.dtype							)
																		A__						:				str         =			module.weight.data.clone()
																		if has_bias:
																								A__						:				Optional[Any]         =			module.bias.data.clone()

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

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

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

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




def 						snake_case__							(       __lowercase							)		->		List[Any]:







						"""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(__lowercase				,  (te.LayerNorm, te.Linear)							):
																		return True
						return False




class 				lowerCAmelCase__	:







					def __init__(							self   :  Union[str, Any]):
											A__						:				Optional[int]         =			[0]
											A__						:				Any         =			0







					def 			_lowercase      (							self   :  Optional[int]      ,		_A   :  int):
											A__						:				Optional[Any]         =			self.__heap[i]
											while i // 2 > 0:
																	if self.__heap[i] > self.__heap[i // 2]:
																							A__						:				Union[str, Any]         =			self.__heap[i // 2]
																							A__						:				List[str]         =			temporary
																	i //= 2







					def 			_lowercase      (							self   :  Union[str, Any]      ,		_A   :  int):
											self.__heap.append(_A)
											self.__size += 1
											self.__swap_up(self.__size)







					def 			_lowercase      (							self   :  Any      ,		_A   :  int):
											while self.__size >= 2 * i:
																	if 2 * i + 1 > self.__size:
																							A__						:				str         =			2 * i
																	else:
																							if self.__heap[2 * i] > self.__heap[2 * i + 1]:
																													A__						:				Tuple         =			2 * i
																							else:
																													A__						:				Tuple         =			2 * i + 1
																	A__						:				Any         =			self.__heap[i]
																	if self.__heap[i] < self.__heap[bigger_child]:
																							A__						:				List[str]         =			self.__heap[bigger_child]
																							A__						:				Tuple         =			temporary
																	A__						:				Tuple         =			bigger_child







					def 			_lowercase      (							self   :  int):
											A__						:				str         =			self.__heap[1]
											A__						:				Tuple         =			self.__heap[self.__size]
											self.__size -= 1
											self.__heap.pop()
											self.__swap_down(1)
											return max_value







					@property
					def 			_lowercase      (							self   :  Any):
											return self.__heap[1:]







					def __len__(							self   :  Union[str, Any]):
											return self.__size


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		# create an instance of BinaryHeap
		snake_case     :    List[Any]						=    BinaryHeap()
		binary_heap.insert(6)
		binary_heap.insert(1_0)
		binary_heap.insert(1_5)
		binary_heap.insert(1_2)
		# pop root(max-values because it is max heap)
		print(binary_heap.pop())  # 15
		print(binary_heap.pop())  # 12
		# get the list and size after operations
		print(binary_heap.get_list)
		print(len(binary_heap))