





def     A__					(						SCREAMING_SNAKE_CASE_      :			int							,  SCREAMING_SNAKE_CASE_      :			list[tuple[int, int, int]]	)     ->     list[tuple[int, int, int]]:






   """simple docstring"""







   _UpperCAmelCase											=      sorted(a_							,  key=lambda SCREAMING_SNAKE_CASE_	: edge[2]	)

   _UpperCAmelCase											=      list(range(a_	)	)

   def find_parent(SCREAMING_SNAKE_CASE_      :			List[str]	):
      if i != parent[i]:
         _UpperCAmelCase											=      find_parent(parent[i]	)
      return parent[i]

   _UpperCAmelCase											=      0
   _UpperCAmelCase											=      []

   for edge in edges:
      _UpperCAmelCase											=      find_parent(edge[0]	)
      _UpperCAmelCase											=      find_parent(edge[1]	)
      if parent_a != parent_b:
         minimum_spanning_tree_cost += edge[2]
         minimum_spanning_tree.append(a_	)
         _UpperCAmelCase											=      parent_b

   return minimum_spanning_tree


if __name__ == "__main__":  # pragma: no cover
   UpperCAmelCase_					,    UpperCAmelCase_											=  list(map(int, input().strip().split()))
   UpperCAmelCase_											=  []

   for _ in range(num_edges):
      UpperCAmelCase_					,    UpperCAmelCase_					,    UpperCAmelCase_											=  (int(x) for x in input().strip().split())
      edges.append((nodea, nodea, cost))

   kruskal(num_nodes, edges)






from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
   from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


UpperCAmelCase_											=  logging.get_logger(__name__)

UpperCAmelCase_											=  {
    "microsoft/deberta-base": "https://huggingface.co/microsoft/deberta-base/resolve/main/config.json",
    "microsoft/deberta-large": "https://huggingface.co/microsoft/deberta-large/resolve/main/config.json",
    "microsoft/deberta-xlarge": "https://huggingface.co/microsoft/deberta-xlarge/resolve/main/config.json",
    "microsoft/deberta-base-mnli": "https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/config.json",
    "microsoft/deberta-large-mnli": "https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/config.json",
    "microsoft/deberta-xlarge-mnli": "https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/config.json",
}




class  __UpperCamelCase						(       A__						):
     __A  :     Dict            =      """deberta"""





     def __init__(    self							,       _UpperCamelCase=50265							,       _UpperCamelCase=768							,       _UpperCamelCase=12							,       _UpperCamelCase=12							,       _UpperCamelCase=3072							,       _UpperCamelCase="gelu"							,       _UpperCamelCase=0.1							,       _UpperCamelCase=0.1							,       _UpperCamelCase=512							,       _UpperCamelCase=0							,       _UpperCamelCase=0.02							,       _UpperCamelCase=1e-7							,       _UpperCamelCase=False							,       _UpperCamelCase=-1							,       _UpperCamelCase=0							,       _UpperCamelCase=True							,       _UpperCamelCase=None							,       _UpperCamelCase=0							,       _UpperCamelCase="gelu"							,       **_UpperCamelCase							,       ):
        super().__init__(**_UpperCamelCase				)

        _UpperCAmelCase											=      hidden_size
        _UpperCAmelCase											=      num_hidden_layers
        _UpperCAmelCase											=      num_attention_heads
        _UpperCAmelCase											=      intermediate_size
        _UpperCAmelCase											=      hidden_act
        _UpperCAmelCase											=      hidden_dropout_prob
        _UpperCAmelCase											=      attention_probs_dropout_prob
        _UpperCAmelCase											=      max_position_embeddings
        _UpperCAmelCase											=      type_vocab_size
        _UpperCAmelCase											=      initializer_range
        _UpperCAmelCase											=      relative_attention
        _UpperCAmelCase											=      max_relative_positions
        _UpperCAmelCase											=      pad_token_id
        _UpperCAmelCase											=      position_biased_input

        # Backwards compatibility
        if type(_UpperCamelCase				) == str:
           _UpperCAmelCase											=      [x.strip() for x in pos_att_type.lower().split('''|'''				)]

        _UpperCAmelCase											=      pos_att_type
        _UpperCAmelCase											=      vocab_size
        _UpperCAmelCase											=      layer_norm_eps

        _UpperCAmelCase											=      kwargs.get('''pooler_hidden_size'''							,       _UpperCamelCase				)
        _UpperCAmelCase											=      pooler_dropout
        _UpperCAmelCase											=      pooler_hidden_act




class  __UpperCamelCase						(       A__						):





     @property
     def 		UpperCamelCase(    self				):
        if self.task == "multiple-choice":
           _UpperCAmelCase											=      {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
        else:
           _UpperCAmelCase											=      {0: '''batch''', 1: '''sequence'''}
        if self._config.type_vocab_size > 0:
           return OrderedDict(
               [('''input_ids''', dynamic_axis), ('''attention_mask''', dynamic_axis), ('''token_type_ids''', dynamic_axis)]				)
        else:
           return OrderedDict([('''input_ids''', dynamic_axis), ('''attention_mask''', dynamic_axis)]				)





     @property
     def 		UpperCamelCase(    self				):
        return 12





     def 		UpperCamelCase(    self							,       _UpperCamelCase							,       _UpperCamelCase = -1							,       _UpperCamelCase = -1							,       _UpperCamelCase = -1							,       _UpperCamelCase = False							,       _UpperCamelCase = None							,       _UpperCamelCase = 3							,       _UpperCamelCase = 40							,       _UpperCamelCase = 40							,       _UpperCamelCase = None							,       ):
        _UpperCAmelCase											=      super().generate_dummy_inputs(preprocessor=_UpperCamelCase							,       framework=_UpperCamelCase				)
        if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
           del dummy_inputs["token_type_ids"]
        return dummy_inputs