


'''simple docstring'''

import torch.nn as nn

from .imports import is_fpa_available


if is_fpa_available():
     import transformer_engine.pytorch as te





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

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

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

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

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







def __snake_case (				UpperCAmelCase_						:						List[Any]		):
 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(UpperCAmelCase_ ,    (te.LayerNorm, te.Linear)		):
   return True
 return False







'''simple docstring'''

class 					snake_case   :





  """simple docstring"""




  def __init__(   self      ,  UpperCamelCase					):







   """simple docstring"""
   lowerCamelCase_								=  data
   lowerCamelCase_								=  None
   lowerCamelCase_								=  None





def __snake_case (				UpperCAmelCase_						:						BinaryTreeNode | None ,    UpperCAmelCase_						:						int		):
 if node is None:
  lowerCamelCase_								=  BinaryTreeNode(UpperCAmelCase_		)
  return node

 # binary search tree is not empty,
 # so we will insert it into the tree
 # if new_value is less than value of data in node,
 #  add it to left subtree and proceed recursively
 if new_value < node.data:
  lowerCamelCase_								=  insert(node.left_child ,    UpperCAmelCase_		)
 else:
  # if new_value is greater than value of data in node,
  #  add it to right subtree and proceed recursively
  lowerCamelCase_								=  insert(node.right_child ,    UpperCAmelCase_		)
 return node





def __snake_case (				UpperCAmelCase_						:						None | BinaryTreeNode		):  # if node is None,return
 if node:
  lowerCamelCase_								=  inorder(node.left_child		)
  lowerCamelCase_								=  [*inorder_array, node.data]
  lowerCamelCase_								=  inorder_array + inorder(node.right_child		)
 else:
  lowerCamelCase_								=  []
 return inorder_array





def __snake_case (				):
 lowerCamelCase_								=  insert(UpperCAmelCase_ ,    15		)
 insert(UpperCAmelCase_ ,    10		)
 insert(UpperCAmelCase_ ,    25		)
 insert(UpperCAmelCase_ ,    6		)
 insert(UpperCAmelCase_ ,    14		)
 insert(UpperCAmelCase_ ,    20		)
 insert(UpperCAmelCase_ ,    60		)
 return root





def __snake_case (				):
 # main function
 lowerCamelCase_								=  make_tree()
 print("Printing values of binary search tree in Inorder Traversal."		)
 inorder(UpperCAmelCase_		)


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     main()



