"""simple docstring"""







import torch.nn as nn

from .imports import is_fpa_available


if is_fpa_available():
     import transformer_engine.pytorch as te



def       A__    (	lowercase_     :	Optional[Any]      , lowercase_     :	Union[str, Any]=True      , lowercase_     :	Union[str, Any]=True      , lowercase_     :	Optional[Any]=True						)  ->							List[str]:
   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       A__    (	lowercase_     :	Optional[Any]						)  ->							Dict:
   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'''




from __future__ import annotations





class        __magic_name__		:







			def __init__(      self							:    List[str]							,     lowercase_							:    int       ):
										lowercase_   :     List[str]									=			vertices
										lowercase_   :     Optional[Any]									=			[[0] * vertices for _ in range(lowercase_       )]







			def       SCREAMING_SNAKE_CASE_	(      self							:    int							,     lowercase_							:    list[int]       ):
										print("""Vertex \t Distance from Source"""       )
										for vertex in range(self.vertices       ):
																	print(lowercase_							,     """\t\t"""							,     distances_from_source[vertex]       )







			def       SCREAMING_SNAKE_CASE_	(      self							:    str							,     lowercase_							:    list[int]							,     lowercase_							:    list[bool]       ):
										lowercase_   :     Dict									=			1E7
										lowercase_   :     int									=			0

										# Search not nearest vertex not in the shortest path tree
										for vertex in range(self.vertices       ):
																	if distances_from_source[vertex] < minimum and visited[vertex] is False:
																								lowercase_   :     Tuple									=			distances_from_source[vertex]
																								lowercase_   :     str									=			vertex
										return min_index







			def       SCREAMING_SNAKE_CASE_	(      self							:    Union[str, Any]							,     lowercase_							:    int       ):
										lowercase_   :     Optional[Any]									=			[int(1E7       )] * self.vertices  # distances from the source
										lowercase_   :     List[Any]									=			0
										lowercase_   :     Any									=			[False] * self.vertices

										for _ in range(self.vertices       ):
																	lowercase_   :     Tuple									=			self.minimum_distance(lowercase_							,     lowercase_       )
																	lowercase_   :     Union[str, Any]									=			True

																	# Update dist value of the adjacent vertices
																	# of the picked vertex only if the current
																	# distance is greater than new distance and
																	# the vertex in not in the shortest path tree
																	for v in range(self.vertices       ):
																								if (
																								    self.graph[u][v] > 0
																								    and visited[v] is False
																								    and distances[v] > distances[u] + self.graph[u][v]
																								):
																															lowercase_   :     Any									=			distances[u] + self.graph[u][v]

										self.print_solution(lowercase_       )


if __name__ == "__main__":
						_lowercase		:					Any													=		Graph(9)
						_lowercase		:					Any													=		[
						    [0, 4, 0, 0, 0, 0, 0, 8, 0],
						    [4, 0, 8, 0, 0, 0, 0, 11, 0],
						    [0, 8, 0, 7, 0, 4, 0, 0, 2],
						    [0, 0, 7, 0, 9, 14, 0, 0, 0],
						    [0, 0, 0, 9, 0, 10, 0, 0, 0],
						    [0, 0, 4, 14, 10, 0, 2, 0, 0],
						    [0, 0, 0, 0, 0, 2, 0, 1, 6],
						    [8, 11, 0, 0, 0, 0, 1, 0, 7],
						    [0, 0, 2, 0, 0, 0, 6, 7, 0],
						]
						graph.dijkstra(0)





