






import torch.nn as nn

from .imports import is_fpa_available


if is_fpa_available():
						import transformer_engine.pytorch as te




def   lowercase__   (  __snake_case					:							List[str]  , __snake_case					:							List[Any]=True  , __snake_case					:							str=True  , __snake_case					:							Tuple=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(__snake_case  , 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
															UpperCAmelCase_					:   Optional[int]   =		module.bias is not None
															UpperCAmelCase_					:   Optional[int]   =		te.Linear(
															    module.in_features  , module.out_features  , bias=__snake_case  , params_dtype=module.weight.dtype						)
															UpperCAmelCase_					:   List[str]   =		module.weight.data.clone()
															if has_bias:
																				UpperCAmelCase_					:   List[Any]   =		module.bias.data.clone()

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

															setattr(__snake_case  , __snake_case  , __snake_case						)
										elif isinstance(__snake_case  , te.Linear						) and not to_transformer_engine and _convert_linear:
															UpperCAmelCase_					:   int   =		module.bias is not None
															UpperCAmelCase_					:   Tuple   =		nn.Linear(
															    module.in_features  , module.out_features  , bias=__snake_case  , params_dtype=module.weight.dtype						)
															UpperCAmelCase_					:   str   =		module.weight.data.clone()
															if has_bias:
																				UpperCAmelCase_					:   Dict   =		module.bias.data.clone()

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

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






def   lowercase__   (  __snake_case					:							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(__snake_case  , (te.LayerNorm, te.Linear)						):
															return True
					return False














from __future__ import annotations



class 						lowerCamelCase   :







		'''simple docstring'''



		def __init__(							self       ,	_UpperCamelCase = 0     )			->							None:
							UpperCAmelCase_					:   int   =		value
							UpperCAmelCase_					:   Node | None   =		None
							UpperCAmelCase_					:   Node | None   =		None





def   lowercase__   (  __snake_case					:							Node | None  , __snake_case					:							Node | None						):


					'''simple docstring'''


					if treea is None:
										return treea
					if treea is None:
										return treea

					UpperCAmelCase_					:   List[Any]   =		treea.value + treea.value
					UpperCAmelCase_					:   Dict   =		merge_two_binary_trees(treea.left  , treea.left						)
					UpperCAmelCase_					:   str   =		merge_two_binary_trees(treea.right  , treea.right						)
					return treea




def   lowercase__   (  __snake_case					:							Node | None						):


					'''simple docstring'''


					if root:
										print(root.value						)
										print_preorder(root.left						)
										print_preorder(root.right						)


if __name__ == "__main__":
		__UpperCAmelCase										=		Node(1)
		__UpperCAmelCase										=		Node(2)
		__UpperCAmelCase										=		Node(3)
		__UpperCAmelCase										=		Node(4)

		__UpperCAmelCase										=		Node(2)
		__UpperCAmelCase										=		Node(4)
		__UpperCAmelCase										=		Node(6)
		__UpperCAmelCase										=		Node(9)
		__UpperCAmelCase										=		Node(5)

		print('Tree1 is: ')
		print_preorder(treea)
		print('Tree2 is: ')
		print_preorder(treea)
		__UpperCAmelCase										=		merge_two_binary_trees(treea, treea)
		print('Merged Tree is: ')
		print_preorder(merged_tree)






