import torch.nn as nn

from .imports import is_fpa_available


if is_fpa_available():
      import transformer_engine.pytorch as te

def 				UpperCamelCase					(     lowerCAmelCase_							,       lowerCAmelCase_=True							,       lowerCAmelCase_=True							,       lowerCAmelCase_=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(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
                     _A=						module.bias is not None
                     _A=						te.Linear(
                         module.in_features							,       module.out_features							,       bias=lowerCAmelCase_							,       params_dtype=module.weight.dtype )
                     _A=						module.weight.data.clone()
                     if has_bias:
                            _A=						module.bias.data.clone()

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

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

                     setattr(lowerCAmelCase_							,       lowerCAmelCase_							,       lowerCAmelCase_ )
              elif isinstance(lowerCAmelCase_							,       te.LayerNorm ) and not to_transformer_engine and _convert_ln:
                     _A=						nn.LayerNorm(module.normalized_shape[0]							,       eps=module.eps							,       params_dtype=module.weight.dtype )
                     _A=						module.weight.data.clone()
                     _A=						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 				UpperCamelCase					(     lowerCAmelCase_ )		->    str:




       '''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(lowerCAmelCase_							,       (te.LayerNorm, te.Linear) ):
                     return True
       return False
import heapq

def 				UpperCamelCase					(     lowerCAmelCase_							,       lowerCAmelCase_							,       lowerCAmelCase_ )		->    Dict:




       '''simple docstring'''






       _A=						[(0, start)]  # cost from start node,end node
       _A=						set()
       while heap:
              ((_A),      (_A))=						heapq.heappop(lowerCAmelCase_ )
              if u in visited:
                     continue
              visited.add(lowerCAmelCase_ )
              if u == end:
                     return cost
              for v, c in graph[u]:
                     if v in visited:
                            continue
                     _A=						cost + c
                     heapq.heappush(lowerCAmelCase_							,       (next_item, v) )
       return -1


UpperCAmelCase_    		=				{
    '''A''': [['''B''', 2], ['''C''', 5]],
    '''B''': [['''A''', 2], ['''D''', 3], ['''E''', 1], ['''F''', 1]],
    '''C''': [['''A''', 5], ['''F''', 3]],
    '''D''': [['''B''', 3]],
    '''E''': [['''B''', 4], ['''F''', 3]],
    '''F''': [['''C''', 3], ['''E''', 3]],
}
UpperCAmelCase_    		=				{
    '''B''': [['''C''', 1]],
    '''C''': [['''D''', 1]],
    '''D''': [['''F''', 1]],
    '''E''': [['''B''', 1], ['''F''', 3]],
    '''F''': [],
}
UpperCAmelCase_    		=				{
    '''B''': [['''C''', 1]],
    '''C''': [['''D''', 1]],
    '''D''': [['''F''', 1]],
    '''E''': [['''B''', 1], ['''G''', 2]],
    '''F''': [],
    '''G''': [['''F''', 1]],
}

UpperCAmelCase_    		=				dijkstra(G, '''E''', '''C''')
print(short_distance)  # E -- 3 --> F -- 3 --> C == 6

UpperCAmelCase_    		=				dijkstra(Ga, '''E''', '''F''')
print(short_distance)  # E -- 3 --> F == 3

UpperCAmelCase_    		=				dijkstra(Ga, '''E''', '''F''')
print(short_distance)  # E -- 2 --> G -- 1 --> F == 3

if __name__ == "__main__":
      import doctest

      doctest.testmod()