






from typing import Mapping

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


__lowerCamelCase						= logging.get_logger(__name__)

class      UpperCamelCase_							(				UpperCamelCase       ):
						lowercase									=  '''mt5'''
						lowercase									=  ['''past_key_values''']




						def __init__(    self		,	lowercase=250_112		,	lowercase=512		,	lowercase=64		,	lowercase=1_024		,	lowercase=8		,	lowercase=None		,	lowercase=6		,	lowercase=32		,	lowercase=128		,	lowercase=0.1		,	lowercase=1e-6		,	lowercase=1.0		,	lowercase="gated-gelu"		,	lowercase=True		,	lowercase=True		,	lowercase="T5Tokenizer"		,	lowercase=False		,	lowercase=0		,	lowercase=1		,	lowercase=0		,	**lowercase		,	)			->						Union[str, Any]:
													super().__init__(
													    is_encoder_decoder=__SCREAMING_SNAKE_CASE		,	tokenizer_class=__SCREAMING_SNAKE_CASE		,	tie_word_embeddings=__SCREAMING_SNAKE_CASE		,	pad_token_id=__SCREAMING_SNAKE_CASE		,	eos_token_id=__SCREAMING_SNAKE_CASE		,	decoder_start_token_id=__SCREAMING_SNAKE_CASE		,	**__SCREAMING_SNAKE_CASE		,	)
													_a  :    Optional[int]			=						vocab_size
													_a  :    Tuple			=						d_model
													_a  :    str			=						d_kv
													_a  :    Dict			=						d_ff
													_a  :    int			=						num_layers
													_a  :    Any			=						(
													    num_decoder_layers if num_decoder_layers is not None else self.num_layers
													)  # default = symmetry
													_a  :    List[Any]			=						num_heads
													_a  :    Any			=						relative_attention_num_buckets
													_a  :    Union[str, Any]			=						relative_attention_max_distance
													_a  :    Any			=						dropout_rate
													_a  :    List[Any]			=						layer_norm_epsilon
													_a  :    Optional[Any]			=						initializer_factor
													_a  :    Optional[int]			=						feed_forward_proj
													_a  :    int			=						use_cache

													_a  :    Optional[int]			=						self.feed_forward_proj.split('''-'''       )
													_a  :    str			=						act_info[-1]
													_a  :    Optional[Any]			=						act_info[0] == 'gated'

													if len(__SCREAMING_SNAKE_CASE       ) > 1 and act_info[0] != "gated" or len(__SCREAMING_SNAKE_CASE       ) > 2:
																				raise ValueError(
																				    F'`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'
																				    '''Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '''
																				    '''\'gated-gelu\' or \'relu\''''       )

													# for backwards compatibility
													if feed_forward_proj == "gated-gelu":
																				_a  :    Tuple			=						'gelu_new'




						@property
						def      snake_case__(    self       )			->						List[Any]:
													return self.d_model




						@property
						def      snake_case__(    self       )			->						Dict:
													return self.num_heads







						@property
						def      snake_case__(    self       )			->						List[str]:
													return self.num_layers

class      UpperCamelCase_							(				UpperCamelCase       ):




						@property
						# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
						def      snake_case__(    self       )			->						int:
													_a  :    Optional[Any]			=						{
													    'input_ids': {0: 'batch', 1: 'encoder_sequence'},
													    'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
													}
													if self.use_past:
																				_a  :    Any			=						'past_encoder_sequence + sequence'
																				_a  :    List[str]			=						{0: 'batch'}
																				_a  :    Optional[int]			=						{0: 'batch', 1: 'past_decoder_sequence + sequence'}
													else:
																				_a  :    Union[str, Any]			=						{0: 'batch', 1: 'decoder_sequence'}
																				_a  :    Optional[Any]			=						{0: 'batch', 1: 'decoder_sequence'}

													if self.use_past:
																				self.fill_with_past_key_values_(__SCREAMING_SNAKE_CASE		,	direction='''inputs'''       )

													return common_inputs




						@property
						# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
						def      snake_case__(    self       )			->						str:
													return 13







						@property
						def      snake_case__(    self       )			->						Optional[int]:
													return 5e-4

'''simple docstring'''

def        SCREAMING_SNAKE_CASE_  (	snake_case_				:	int   ,     snake_case_				:	list[tuple[int, int, int]]							)       ->	list[tuple[int, int, int]]:
 SCREAMING_SNAKE_CASE  :  List[Any]								=		sorted(snake_case_   ,     key=lambda snake_case_							: edge[2]							)

 SCREAMING_SNAKE_CASE  :  str								=		list(range(snake_case_							)							)

 def find_parent(snake_case_				:	Union[str, Any]							):
  if i != parent[i]:
   SCREAMING_SNAKE_CASE  :  int								=		find_parent(parent[i]							)
  return parent[i]

 SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		0
 SCREAMING_SNAKE_CASE  :  Any								=		[]

 for edge in edges:
  SCREAMING_SNAKE_CASE  :  Dict								=		find_parent(edge[0]							)
  SCREAMING_SNAKE_CASE  :  Optional[int]								=		find_parent(edge[1]							)
  if parent_a != parent_b:
   minimum_spanning_tree_cost += edge[2]
   minimum_spanning_tree.append(snake_case_							)
   SCREAMING_SNAKE_CASE  :  Tuple								=		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)


