


from typing import Mapping

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


_A							     =       logging.get_logger(__name__)

class 			lowerCamelCase_					(    _SCREAMING_SNAKE_CASE    ):
							_lowerCamelCase				: Optional[Any]       =					"""mt5"""
							_lowerCamelCase				: int       =					["""past_key_values"""]





							def __init__(							self			,       _SCREAMING_SNAKE_CASE=25_0112			,       _SCREAMING_SNAKE_CASE=512			,       _SCREAMING_SNAKE_CASE=64			,       _SCREAMING_SNAKE_CASE=1024			,       _SCREAMING_SNAKE_CASE=8			,       _SCREAMING_SNAKE_CASE=None			,       _SCREAMING_SNAKE_CASE=6			,       _SCREAMING_SNAKE_CASE=32			,       _SCREAMING_SNAKE_CASE=128			,       _SCREAMING_SNAKE_CASE=0.1			,       _SCREAMING_SNAKE_CASE=1E-6			,       _SCREAMING_SNAKE_CASE=1.0			,       _SCREAMING_SNAKE_CASE="gated-gelu"			,       _SCREAMING_SNAKE_CASE=True			,       _SCREAMING_SNAKE_CASE=True			,       _SCREAMING_SNAKE_CASE="T5Tokenizer"			,       _SCREAMING_SNAKE_CASE=False			,       _SCREAMING_SNAKE_CASE=0			,       _SCREAMING_SNAKE_CASE=1			,       _SCREAMING_SNAKE_CASE=0			,       **_SCREAMING_SNAKE_CASE			,       ):
								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_     			=   vocab_size
								a_     			=   d_model
								a_     			=   d_kv
								a_     			=   d_ff
								a_     			=   num_layers
								a_     			=   (
								    num_decoder_layers if num_decoder_layers is not None else self.num_layers
								)  # default = symmetry
								a_     			=   num_heads
								a_     			=   relative_attention_num_buckets
								a_     			=   relative_attention_max_distance
								a_     			=   dropout_rate
								a_     			=   layer_norm_epsilon
								a_     			=   initializer_factor
								a_     			=   feed_forward_proj
								a_     			=   use_cache

								a_     			=   self.feed_forward_proj.split("""-"""    )
								a_     			=   act_info[-1]
								a_     			=   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_     			=   """gelu_new"""





							@property
							def   __magic_name__      (							self    ):
								return self.d_model





							@property
							def   __magic_name__      (							self    ):
								return self.num_heads





							@property
							def   __magic_name__      (							self    ):
								return self.num_layers







class 			lowerCamelCase_					(    _SCREAMING_SNAKE_CASE    ):





							@property
							# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
							def   __magic_name__      (							self    ):
								a_     			=   {
								    """input_ids""": {0: """batch""", 1: """encoder_sequence"""},
								    """attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
								}
								if self.use_past:
									a_     			=   """past_encoder_sequence + sequence"""
									a_     			=   {0: """batch"""}
									a_     			=   {0: """batch""", 1: """past_decoder_sequence + sequence"""}
								else:
									a_     			=   {0: """batch""", 1: """decoder_sequence"""}
									a_     			=   {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   __magic_name__      (							self    ):
								return 13





							@property
							def   __magic_name__      (							self    ):
								return 5E-4



def 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      int     ,					UpperCamelCase		:      list[tuple[int, int, int]]	)     ->	list[tuple[int, int, int]]:



	"""simple docstring"""






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

	a_     			=   list(range(UpperCamelCase	)	)

	def find_parent(UpperCamelCase		:      Any	):
		if i != parent[i]:
			a_     			=   find_parent(parent[i]	)
		return parent[i]

	a_     			=   0
	a_     			=   []

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

	return minimum_spanning_tree


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

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

			kruskal(num_nodes, edges)