


'''simple docstring'''



import warnings
from typing import Dict

import numpy as np

from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline


if is_tf_available():
      from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

if is_torch_available():
      from ..models.auto.modeling_auto import MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING



def      UpperCAmelCase__   (      UpperCAmelCase_       :							List[Any]				)	->						Union[str, Any]:
     return 1.0 / (1.0 + np.exp(-_outputs				))



def      UpperCAmelCase__   (      UpperCAmelCase_       :							Any				)	->						str:
     __lowerCamelCase  :      int														=						np.max(_outputs ,       axis=-1 ,       keepdims=UpperCAmelCase_				)
     __lowerCamelCase  :      Dict														=						np.exp(_outputs - maxes				)
     return shifted_exp / shifted_exp.sum(axis=-1 ,       keepdims=UpperCAmelCase_				)





class   UpperCAmelCase_    (_UpperCAmelCase    ):





    """simple docstring"""






    lowerCamelCase    :   int					=     'sigmoid'
    lowerCamelCase    :   List[Any]					=     'softmax'
    lowerCamelCase    :   Optional[Any]					=     'none'







@add_end_docstrings(
    _UpperCAmelCase   ,						r'\n        return_all_scores (`bool`, *optional*, defaults to `False`):\n            Whether to return all prediction scores or just the one of the predicted class.\n        function_to_apply (`str`, *optional*, defaults to `"default"`):\n            The function to apply to the model outputs in order to retrieve the scores. Accepts four different values:\n\n            - `"default"`: if the model has a single label, will apply the sigmoid function on the output. If the model\n              has several labels, will apply the softmax function on the output.\n            - `"sigmoid"`: Applies the sigmoid function on the output.\n            - `"softmax"`: Applies the softmax function on the output.\n            - `"none"`: Does not apply any function on the output.\n    '   ,						)
class   UpperCAmelCase_    (_UpperCAmelCase    ):





    """simple docstring"""






    lowerCamelCase    :   Any					=     False
    lowerCamelCase    :   Any					=     ClassificationFunction.NONE







    def __init__(				self     ,	**SCREAMING_SNAKE_CASE_      )							->	str:
         super().__init__(**SCREAMING_SNAKE_CASE_      )

         self.check_model_type(
             TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
             if self.framework == 'tf'
             else MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING      )







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_=""     ,	**SCREAMING_SNAKE_CASE_      )							->	Optional[int]:
         # Using "" as default argument because we're going to use `top_k=None` in user code to declare
         # "No top_k"
         __lowerCamelCase  :      Optional[Any]														=						tokenizer_kwargs

         __lowerCamelCase  :      Optional[int]														=						{}
         if hasattr(self.model.config     ,	'return_all_scores'      ) and return_all_scores is None:
              __lowerCamelCase  :      List[Any]														=						self.model.config.return_all_scores

         if isinstance(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      ) or top_k is None:
              __lowerCamelCase  :      List[str]														=						top_k
              __lowerCamelCase  :      List[Any]														=						False
         elif return_all_scores is not None:
              warnings.warn(
                  '`return_all_scores` is now deprecated,  if want a similar functionality use `top_k=None` instead of'
                  ' `return_all_scores=True` or `top_k=1` instead of `return_all_scores=False`.'     ,	SCREAMING_SNAKE_CASE_     ,	)
              if return_all_scores:
                   __lowerCamelCase  :      List[str]														=						None
              else:
                   __lowerCamelCase  :      str														=						1

         if isinstance(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      ):
              __lowerCamelCase  :      Union[str, Any]														=						ClassificationFunction[function_to_apply.upper()]

         if function_to_apply is not None:
              __lowerCamelCase  :      Any														=						function_to_apply
         return preprocess_params, {}, postprocess_params







    def __call__(				self     ,	*SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_      )							->	Dict:
         __lowerCamelCase  :      Any														=						super().__call__(*SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_      )
         # TODO try and retrieve it in a nicer way from _sanitize_parameters.
         __lowerCamelCase  :      Dict														=						'top_k' not in kwargs
         if isinstance(args[0]     ,	SCREAMING_SNAKE_CASE_      ) and _legacy:
              # This pipeline is odd, and return a list when single item is run
              return [result]
         else:
              return result







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_      )							->	Dict[str, GenericTensor]:
         __lowerCamelCase  :      int														=						self.framework
         if isinstance(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      ):
              return self.tokenizer(**SCREAMING_SNAKE_CASE_     ,	return_tensors=SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_      )
         elif isinstance(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      ) and len(SCREAMING_SNAKE_CASE_      ) == 1 and isinstance(inputs[0]     ,	SCREAMING_SNAKE_CASE_      ) and len(inputs[0]      ) == 2:
              # It used to be valid to use a list of list of list for text pairs, keeping this path for BC
              return self.tokenizer(
                  text=inputs[0][0]     ,	text_pair=inputs[0][1]     ,	return_tensors=SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_      )
         elif isinstance(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      ):
              # This is likely an invalid usage of the pipeline attempting to pass text pairs.
              raise ValueError(
                  'The pipeline received invalid inputs, if you are trying to send text pairs, you can try to send a'
                  ' dictionary `{"text": "My text", "text_pair": "My pair"}` in order to send a text pair.'      )
         return self.tokenizer(SCREAMING_SNAKE_CASE_     ,	return_tensors=SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_      )







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_      )							->	Optional[int]:
         return self.model(**SCREAMING_SNAKE_CASE_      )







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_=1     ,	SCREAMING_SNAKE_CASE_=True      )							->	Optional[Any]:
         # `_legacy` is used to determine if we're running the naked pipeline and in backward
         # compatibility mode, or if running the pipeline with `pipeline(..., top_k=1)` we're running
         # the more natural result containing the list.
         # Default value before `set_parameters`
         if function_to_apply is None:
              if self.model.config.problem_type == "multi_label_classification" or self.model.config.num_labels == 1:
                   __lowerCamelCase  :      Any														=						ClassificationFunction.SIGMOID
              elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
                   __lowerCamelCase  :      Dict														=						ClassificationFunction.SOFTMAX
              elif hasattr(self.model.config     ,	'function_to_apply'      ) and function_to_apply is None:
                   __lowerCamelCase  :      int														=						self.model.config.function_to_apply
              else:
                   __lowerCamelCase  :      Optional[Any]														=						ClassificationFunction.NONE

         __lowerCamelCase  :      Dict														=						model_outputs['logits'][0]
         __lowerCamelCase  :      Any														=						outputs.numpy()

         if function_to_apply == ClassificationFunction.SIGMOID:
              __lowerCamelCase  :      Optional[Any]														=						sigmoid(SCREAMING_SNAKE_CASE_      )
         elif function_to_apply == ClassificationFunction.SOFTMAX:
              __lowerCamelCase  :      int														=						softmax(SCREAMING_SNAKE_CASE_      )
         elif function_to_apply == ClassificationFunction.NONE:
              __lowerCamelCase  :      List[str]														=						outputs
         else:
              raise ValueError(f'Unrecognized `function_to_apply` argument: {function_to_apply}'      )

         if top_k == 1 and _legacy:
              return {"label": self.model.config.idalabel[scores.argmax().item()], "score": scores.max().item()}

         __lowerCamelCase  :      List[str]														=						[
             {'label': self.model.config.idalabel[i], 'score': score.item()} for i, score in enumerate(SCREAMING_SNAKE_CASE_      )
         ]
         if not _legacy:
              dict_scores.sort(key=lambda SCREAMING_SNAKE_CASE_      : x["score"]     ,	reverse=SCREAMING_SNAKE_CASE_      )
              if top_k is not None:
                   __lowerCamelCase  :      int														=						dict_scores[:top_k]
         return dict_scores










'''simple docstring'''



import sys
from collections import defaultdict





class   UpperCAmelCase_    :





    """simple docstring"""






    def __init__(				self      )							->	int:
         __lowerCamelCase  :      Any														=						[]







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_      )							->	Any:
         return self.node_position[vertex]







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )							->	Tuple:
         __lowerCamelCase  :      Optional[int]														=						pos







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )							->	int:
         if start > size // 2 - 1:
              return
         else:
              if 2 * start + 2 >= size:
                   __lowerCamelCase  :      str														=						2 * start + 1
              else:
                   if heap[2 * start + 1] < heap[2 * start + 2]:
                        __lowerCamelCase  :      Optional[Any]														=						2 * start + 1
                   else:
                        __lowerCamelCase  :      int														=						2 * start + 2
              if heap[smallest_child] < heap[start]:
                   __lowerCamelCase							,			__lowerCamelCase  :      Optional[Any]														=						heap[smallest_child], positions[smallest_child]
                   __lowerCamelCase							,			__lowerCamelCase  :      int														=						(
                       heap[start],
                       positions[start],
                   )
                   __lowerCamelCase							,			__lowerCamelCase  :      str														=						temp, tempa

                   __lowerCamelCase  :      Dict														=						self.get_position(positions[smallest_child]      )
                   self.set_position(
                       positions[smallest_child]     ,	self.get_position(positions[start]      )      )
                   self.set_position(positions[start]     ,	SCREAMING_SNAKE_CASE_      )

                   self.top_to_bottom(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )							->	Any:
         __lowerCamelCase  :      Any														=						position[index]

         while index != 0:
              __lowerCamelCase  :      Union[str, Any]														=						int((index - 2) / 2      ) if index % 2 == 0 else int((index - 1) / 2      )

              if val < heap[parent]:
                   __lowerCamelCase  :      Union[str, Any]														=						heap[parent]
                   __lowerCamelCase  :      Any														=						position[parent]
                   self.set_position(position[parent]     ,	SCREAMING_SNAKE_CASE_      )
              else:
                   __lowerCamelCase  :      Tuple														=						val
                   __lowerCamelCase  :      List[str]														=						temp
                   self.set_position(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )
                   break
              __lowerCamelCase  :      Tuple														=						parent
         else:
              __lowerCamelCase  :      Union[str, Any]														=						val
              __lowerCamelCase  :      Tuple														=						temp
              self.set_position(SCREAMING_SNAKE_CASE_     ,	0      )







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )							->	Optional[int]:
         __lowerCamelCase  :      Optional[int]														=						len(SCREAMING_SNAKE_CASE_      ) // 2 - 1
         for i in range(SCREAMING_SNAKE_CASE_     ,	-1     ,	-1      ):
              self.top_to_bottom(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	len(SCREAMING_SNAKE_CASE_      )     ,	SCREAMING_SNAKE_CASE_      )







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )							->	List[Any]:
         __lowerCamelCase  :      Any														=						positions[0]
         __lowerCamelCase  :      Union[str, Any]														=						sys.maxsize
         self.top_to_bottom(SCREAMING_SNAKE_CASE_     ,	0     ,	len(SCREAMING_SNAKE_CASE_      )     ,	SCREAMING_SNAKE_CASE_      )
         return temp







def      UpperCAmelCase__   (      UpperCAmelCase_       :							Optional[int]				)	->						str:
     __lowerCamelCase  :      List[Any]														=						Heap()

     __lowerCamelCase  :      Optional[int]														=						[0] * len(UpperCAmelCase_				)
     __lowerCamelCase  :      str														=						[-1] * len(UpperCAmelCase_				)  # Neighboring Tree Vertex of selected vertex
     # Minimum Distance of explored vertex with neighboring vertex of partial tree
     # formed in graph
     __lowerCamelCase  :      List[str]														=						[]  # Heap of Distance of vertices from their neighboring vertex
     __lowerCamelCase  :      Tuple														=						[]

     for vertex in range(len(UpperCAmelCase_				)				):
          distance_tv.append(sys.maxsize				)
          positions.append(UpperCAmelCase_				)
          heap.node_position.append(UpperCAmelCase_				)

     __lowerCamelCase  :      Tuple														=						[]
     __lowerCamelCase  :      Dict														=						1
     __lowerCamelCase  :      str														=						sys.maxsize
     for neighbor, distance in adjacency_list[0]:
          __lowerCamelCase  :      Any														=						0
          __lowerCamelCase  :      Any														=						distance
     heap.heapify(UpperCAmelCase_ ,       UpperCAmelCase_				)

     for _ in range(1 ,       len(UpperCAmelCase_				)				):
          __lowerCamelCase  :      List[Any]														=						heap.delete_minimum(UpperCAmelCase_ ,       UpperCAmelCase_				)
          if visited[vertex] == 0:
               tree_edges.append((nbr_tv[vertex], vertex)				)
               __lowerCamelCase  :      Union[str, Any]														=						1
               for neighbor, distance in adjacency_list[vertex]:
                    if (
                        visited[neighbor] == 0
                        and distance < distance_tv[heap.get_position(UpperCAmelCase_				)]
                    ):
                         __lowerCamelCase  :      Dict														=						distance
                         heap.bottom_to_top(
                             UpperCAmelCase_ ,       heap.get_position(UpperCAmelCase_				) ,       UpperCAmelCase_ ,       UpperCAmelCase_				)
                         __lowerCamelCase  :      str														=						vertex
     return tree_edges


if __name__ == "__main__":  # pragma: no cover
      # < --------- Prims Algorithm --------- >
      A__						:   Tuple				=    int(input("""Enter number of edges: """).strip())
      A__						:   str				=    defaultdict(list)
      for _ in range(edges_number):
            A__						:   Optional[int]				=    [int(x) for x in input().strip().split()]
            adjacency_list[edge[0]].append([edge[1], edge[2]])
            adjacency_list[edge[1]].append([edge[0], edge[2]])
      print(prisms_algorithm(adjacency_list))






