import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors

import transformers
from transformers import (
    AutoConfig,
    AutoModelForMultipleChoice,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


_lowerCAmelCase       =		logging.getLogger(__name__)
def 					_snake_case	(  __snake_case  ,     __snake_case						):
					return (preds == labels).mean()
@dataclass
class     lowerCAmelCase_     :
							UpperCAmelCase				   =     field(
							    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} )
							UpperCAmelCase				   =     field(
							    default=__lowercase,  metadata={"help": "Pretrained config name or path if not the same as model_name"} )
							UpperCAmelCase				   =     field(
							    default=__lowercase,  metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"} )
							UpperCAmelCase				   =     field(
							    default=__lowercase,  metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"},  )




@dataclass
class     lowerCAmelCase_     :
							UpperCAmelCase				   =     field(metadata={"help": "The name of the task to train on: " + ", ".join(processors.keys() )} )
							UpperCAmelCase				   =     field(metadata={"help": "Should contain the data files for the task."} )
							UpperCAmelCase				   =     field(
							    default=128,  metadata={
							        "help": (
							            "The maximum total input sequence length after tokenization. Sequences longer "
							            "than this will be truncated, sequences shorter will be padded."
							        )
							    },  )
							UpperCAmelCase				   =     field(
							    default=__lowercase,  metadata={"help": "Overwrite the cached training and evaluation sets"} )




def 					_snake_case	(  ):
					# See all possible arguments in src/transformers/training_args.py
					# or by passing the --help flag to this script.
					# We now keep distinct sets of args, for a cleaner separation of concerns.

					_UpperCamelCase			     =     HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)						)
					_UpperCamelCase     ,    _UpperCamelCase     ,    _UpperCamelCase			     =     parser.parse_args_into_dataclasses()

					if (
					    os.path.exists(training_args.output_dir						)
					    and os.listdir(training_args.output_dir						)
					    and training_args.do_train
					    and not training_args.overwrite_output_dir
					):
										raise ValueError(
										    f"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
										    ''' --overwrite_output_dir to overcome.'''						)

					# Setup logging
					logging.basicConfig(
					    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''  ,     datefmt='''%m/%d/%Y %H:%M:%S'''  ,     level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN  ,     )
					logger.warning(
					    '''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s'''  ,     training_args.local_rank  ,     training_args.device  ,     training_args.n_gpu  ,     bool(training_args.local_rank != -1						)  ,     training_args.fpaa  ,     )
					# Set the verbosity to info of the Transformers logger (on main process only):
					if is_main_process(training_args.local_rank						):
										transformers.utils.logging.set_verbosity_info()
										transformers.utils.logging.enable_default_handler()
										transformers.utils.logging.enable_explicit_format()
					logger.info('''Training/evaluation parameters %s'''  ,     __snake_case						)

					# Set seed
					set_seed(training_args.seed						)

					try:
										_UpperCamelCase			     =     processors[data_args.task_name]()
										_UpperCamelCase			     =     processor.get_labels()
										_UpperCamelCase			     =     len(__snake_case						)
					except KeyError:
										raise ValueError('''Task not found: %s''' % (data_args.task_name)						)

					# Load pretrained model and tokenizer
					#
					# Distributed training:
					# The .from_pretrained methods guarantee that only one local process can concurrently
					# download model & vocab.

					_UpperCamelCase			     =     AutoConfig.from_pretrained(
					    model_args.config_name if model_args.config_name else model_args.model_name_or_path  ,     num_labels=__snake_case  ,     finetuning_task=data_args.task_name  ,     cache_dir=model_args.cache_dir  ,     )
					_UpperCamelCase			     =     AutoTokenizer.from_pretrained(
					    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path  ,     cache_dir=model_args.cache_dir  ,     )
					_UpperCamelCase			     =     AutoModelForMultipleChoice.from_pretrained(
					    model_args.model_name_or_path  ,     from_tf=bool('''.ckpt''' in model_args.model_name_or_path						)  ,     config=__snake_case  ,     cache_dir=model_args.cache_dir  ,     )

					# Get datasets
					_UpperCamelCase			     =     (
					    MultipleChoiceDataset(
					        data_dir=data_args.data_dir  ,     tokenizer=__snake_case  ,     task=data_args.task_name  ,     max_seq_length=data_args.max_seq_length  ,     overwrite_cache=data_args.overwrite_cache  ,     mode=Split.train  ,     )
					    if training_args.do_train
					    else None
					)
					_UpperCamelCase			     =     (
					    MultipleChoiceDataset(
					        data_dir=data_args.data_dir  ,     tokenizer=__snake_case  ,     task=data_args.task_name  ,     max_seq_length=data_args.max_seq_length  ,     overwrite_cache=data_args.overwrite_cache  ,     mode=Split.dev  ,     )
					    if training_args.do_eval
					    else None
					)

					def compute_metrics(__snake_case						) -> Dict:
										_UpperCamelCase			     =     np.argmax(p.predictions  ,     axis=1						)
										return {"acc": simple_accuracy(__snake_case  ,     p.label_ids						)}

					# Data collator
					_UpperCamelCase			     =     DataCollatorWithPadding(__snake_case  ,     pad_to_multiple_of=8						) if training_args.fpaa else None

					# Initialize our Trainer
					_UpperCamelCase			     =     Trainer(
					    model=__snake_case  ,     args=__snake_case  ,     train_dataset=__snake_case  ,     eval_dataset=__snake_case  ,     compute_metrics=__snake_case  ,     data_collator=__snake_case  ,     )

					# Training
					if training_args.do_train:
										trainer.train(
										    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path						) else None						)
										trainer.save_model()
										# For convenience, we also re-save the tokenizer to the same directory,
										# so that you can share your model easily on huggingface.co/models =)
										if trainer.is_world_master():
															tokenizer.save_pretrained(training_args.output_dir						)

    # Evaluation
					_UpperCamelCase			     =     {}
					if training_args.do_eval:
										logger.info('''*** Evaluate ***'''						)

										_UpperCamelCase			     =     trainer.evaluate()

										_UpperCamelCase			     =     os.path.join(training_args.output_dir  ,     '''eval_results.txt'''						)
										if trainer.is_world_master():
															with open(__snake_case  ,     '''w'''						) as writer:
																				logger.info('''***** Eval results *****'''						)
																				for key, value in result.items():
																									logger.info('''  %s = %s'''  ,     __snake_case  ,     __snake_case						)
																									writer.write('''%s = %s\n''' % (key, value)						)

																				results.update(__snake_case						)

					return results
def 					_snake_case	(  __snake_case						):
					# For xla_spawn (TPUs)
					main()


if __name__ == "__main__":
					main()

import sys
from collections import defaultdict
class     lowerCAmelCase_     :







							def __init__(   self      :     Optional[int]		):
												_UpperCamelCase			     =     []







							def  UpperCamelCase_						(   self      :     Any ,			_A      :     str		):
												return self.node_position[vertex]







							def  UpperCamelCase_						(   self      :     Optional[Any] ,			_A      :     List[str] ,			_A      :     Union[str, Any]		):
												_UpperCamelCase			     =     pos







							def  UpperCamelCase_						(   self      :     Any ,			_A      :     List[str] ,			_A      :     int ,			_A      :     Optional[Any] ,			_A      :     Union[str, Any]		):
												if start > size // 2 - 1:
																	return
												else:
																	if 2 * start + 2 >= size:
																						_UpperCamelCase			     =     2 * start + 1
																	else:
																						if heap[2 * start + 1] < heap[2 * start + 2]:
																											_UpperCamelCase			     =     2 * start + 1
																						else:
																											_UpperCamelCase			     =     2 * start + 2
																	if heap[smallest_child] < heap[start]:
																						_UpperCamelCase     ,    _UpperCamelCase			     =     heap[smallest_child], positions[smallest_child]
																						_UpperCamelCase     ,    _UpperCamelCase			     =     (
																						    heap[start],
																						    positions[start],
																						)
																						_UpperCamelCase     ,    _UpperCamelCase			     =     temp, tempa

																						_UpperCamelCase			     =     self.get_position(positions[smallest_child]		)
																						self.set_position(
																						    positions[smallest_child] ,			self.get_position(positions[start]		)		)
																						self.set_position(positions[start] ,			_A		)

																						self.top_to_bottom(_A ,			_A ,			_A ,			_A		)







							def  UpperCamelCase_						(   self      :     List[str] ,			_A      :     Tuple ,			_A      :     Optional[Any] ,			_A      :     int ,			_A      :     Optional[int]		):
												_UpperCamelCase			     =     position[index]

												while index != 0:
																	_UpperCamelCase			     =     int((index - 2) / 2		) if index % 2 == 0 else int((index - 1) / 2		)

																	if val < heap[parent]:
																						_UpperCamelCase			     =     heap[parent]
																						_UpperCamelCase			     =     position[parent]
																						self.set_position(position[parent] ,			_A		)
																	else:
																						_UpperCamelCase			     =     val
																						_UpperCamelCase			     =     temp
																						self.set_position(_A ,			_A		)
																						break
																	_UpperCamelCase			     =     parent
												else:
																	_UpperCamelCase			     =     val
																	_UpperCamelCase			     =     temp
																	self.set_position(_A ,			0		)







							def  UpperCamelCase_						(   self      :     int ,			_A      :     Tuple ,			_A      :     int		):
												_UpperCamelCase			     =     len(_A		) // 2 - 1
												for i in range(_A ,			-1 ,			-1		):
																	self.top_to_bottom(_A ,			_A ,			len(_A		) ,			_A		)







							def  UpperCamelCase_						(   self      :     Any ,			_A      :     int ,			_A      :     List[str]		):
												_UpperCamelCase			     =     positions[0]
												_UpperCamelCase			     =     sys.maxsize
												self.top_to_bottom(_A ,			0 ,			len(_A		) ,			_A		)
												return temp




def 					_snake_case	(  __snake_case						):
					_UpperCamelCase			     =     Heap()

					_UpperCamelCase			     =     [0] * len(__snake_case						)
					_UpperCamelCase			     =     [-1] * len(__snake_case						)  # Neighboring Tree Vertex of selected vertex
					# Minimum Distance of explored vertex with neighboring vertex of partial tree
					# formed in graph
					_UpperCamelCase			     =     []  # Heap of Distance of vertices from their neighboring vertex
					_UpperCamelCase			     =     []

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

					_UpperCamelCase			     =     []
					_UpperCamelCase			     =     1
					_UpperCamelCase			     =     sys.maxsize
					for neighbor, distance in adjacency_list[0]:
										_UpperCamelCase			     =     0
										_UpperCamelCase			     =     distance
					heap.heapify(__snake_case  ,     __snake_case						)

					for _ in range(1  ,     len(__snake_case						)						):
										_UpperCamelCase			     =     heap.delete_minimum(__snake_case  ,     __snake_case						)
										if visited[vertex] == 0:
															tree_edges.append((nbr_tv[vertex], vertex)						)
															_UpperCamelCase			     =     1
															for neighbor, distance in adjacency_list[vertex]:
																				if (
																				    visited[neighbor] == 0
																				    and distance < distance_tv[heap.get_position(__snake_case						)]
																				):
																									_UpperCamelCase			     =     distance
																									heap.bottom_to_top(
																									    __snake_case  ,     heap.get_position(__snake_case						)  ,     __snake_case  ,     __snake_case						)
																									_UpperCamelCase			     =     vertex
					return tree_edges


if __name__ == "__main__":  # pragma: no cover
					# < --------- Prims Algorithm --------- >
					_lowerCAmelCase       =		int(input("Enter number of edges: ").strip())
					_lowerCAmelCase       =		defaultdict(list)
					for _ in range(edges_number):
										_lowerCAmelCase       =		[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))
