code: 
  import argparse
  import logging
  import os
  from pathlib import Path
  from typing import Any, Dict
  
  import pytorch_lightning as pl
  from pytorch_lightning.utilities import rank_zero_info
  
  from transformers import (
      AdamW,
      AutoConfig,
      AutoModel,
      AutoModelForPreTraining,
      AutoModelForQuestionAnswering,
      AutoModelForSeqaSeqLM,
      AutoModelForSequenceClassification,
      AutoModelForTokenClassification,
      AutoModelWithLMHead,
      AutoTokenizer,
      PretrainedConfig,
      PreTrainedTokenizer,
  )
  from transformers.optimization import (
      Adafactor,
      get_cosine_schedule_with_warmup,
      get_cosine_with_hard_restarts_schedule_with_warmup,
      get_linear_schedule_with_warmup,
      get_polynomial_decay_schedule_with_warmup,
  )
  from transformers.utils.versions import require_version
  
  
  snake_case				:	List[Any]							   =						logging.getLogger(__name__)
  
  require_version("pytorch_lightning>=1.0.4")
  
  snake_case				:	List[str]							   =						{
      "base": AutoModel,
      "sequence-classification": AutoModelForSequenceClassification,
      "question-answering": AutoModelForQuestionAnswering,
      "pretraining": AutoModelForPreTraining,
      "token-classification": AutoModelForTokenClassification,
      "language-modeling": AutoModelWithLMHead,
      "summarization": AutoModelForSeqaSeqLM,
      "translation": AutoModelForSeqaSeqLM,
  }
  
  
  # update this and the import above to support new schedulers from transformers.optimization
  snake_case				:	List[str]							   =						{
      "linear": get_linear_schedule_with_warmup,
      "cosine": get_cosine_schedule_with_warmup,
      "cosine_w_restarts": get_cosine_with_hard_restarts_schedule_with_warmup,
      "polynomial": get_polynomial_decay_schedule_with_warmup,
      # '': get_constant_schedule,             # not supported for now
      # '': get_constant_schedule_with_warmup, # not supported for now
  }
  snake_case				:	Optional[int]							   =						sorted(arg_to_scheduler.keys())
  snake_case				:	Dict							   =						"{" + ", ".join(arg_to_scheduler_choices) + "}"
  
  
  
  
  
  class    _snake_case      (  pl.LightningModule       ):
  
  
  
  
  
  
  
     def __init__(       self		,     _a		,     _a=None		,     _a="base"		,     _a=None		,     _a=None		,     _a=None		,     **_a		,     ):
      super().__init__()
      # TODO: move to self.save_hyperparameters()
      # self.save_hyperparameters()
      # can also expand arguments into trainer signature for easier reading
  
      self.save_hyperparameters(lowercase_	)
      __magic_name__	:	Union[str, Any]			     =  0
      __magic_name__	:	Any			     =  Path(self.hparams.output_dir	)
      __magic_name__	:	Optional[Any]			     =  self.hparams.cache_dir if self.hparams.cache_dir else None
      if config is None:
       __magic_name__	:	List[Any]			     =  AutoConfig.from_pretrained(
           self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path		,     **({"num_labels": num_labels} if num_labels is not None else {})		,     cache_dir=lowercase_		,     **lowercase_		,     )
      else:
       __magic_name__	:	PretrainedConfig			     =  config
  
      __magic_name__	:	Optional[Any]			     =  ('''encoder_layerdrop''', '''decoder_layerdrop''', '''dropout''', '''attention_dropout''')
      for p in extra_model_params:
       if getattr(self.hparams		,     lowercase_		,     lowercase_	):
        assert hasattr(self.config		,     lowercase_	), f'''model config doesn\'t have a `{p}` attribute'''
        setattr(self.config		,     lowercase_		,     getattr(self.hparams		,     lowercase_	)	)
  
      if tokenizer is None:
       __magic_name__	:	List[Any]			     =  AutoTokenizer.from_pretrained(
           self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path		,     cache_dir=lowercase_		,     )
      else:
       __magic_name__	:	PreTrainedTokenizer			     =  tokenizer
      __magic_name__	:	str			     =  MODEL_MODES[mode]
      if model is None:
       __magic_name__	:	Optional[Any]			     =  self.model_type.from_pretrained(
           self.hparams.model_name_or_path		,     from_tf=bool(".ckpt" in self.hparams.model_name_or_path	)		,     config=self.config		,     cache_dir=lowercase_		,     )
      else:
       __magic_name__	:	Optional[int]			     =  model
  
  
  
  
  
  
  
     def     SCREAMING_SNAKE_CASE					(       self		,     *_a		,     **_a	):
      __magic_name__	:	Optional[Any]			     =  self.model_type.from_pretrained(*lowercase_		,     **lowercase_	)
  
  
  
  
  
  
  
     def     SCREAMING_SNAKE_CASE					(       self	):
      __magic_name__	:	List[str]			     =  arg_to_scheduler[self.hparams.lr_scheduler]
      __magic_name__	:	Optional[int]			     =  get_schedule_func(
          self.opt		,     num_warmup_steps=self.hparams.warmup_steps		,     num_training_steps=self.total_steps()	)
      __magic_name__	:	int			     =  {'''scheduler''': scheduler, '''interval''': '''step''', '''frequency''': 1}
      return scheduler
  
  
  
  
  
  
  
     def     SCREAMING_SNAKE_CASE					(       self	):
      __magic_name__	:	Optional[int]			     =  self.model
      __magic_name__	:	List[Any]			     =  ['''bias''', '''LayerNorm.weight''']
      __magic_name__	:	Optional[Any]			     =  [
          {
              '''params''': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay	)],
              '''weight_decay''': self.hparams.weight_decay,
          },
          {
              '''params''': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay	)],
              '''weight_decay''': 0.0,
          },
      ]
      if self.hparams.adafactor:
       __magic_name__	:	Optional[int]			     =  Adafactor(
           lowercase_		,     lr=self.hparams.learning_rate		,     scale_parameter=lowercase_		,     relative_step=lowercase_	)
  
      else:
       __magic_name__	:	Optional[Any]			     =  AdamW(
           lowercase_		,     lr=self.hparams.learning_rate		,     eps=self.hparams.adam_epsilon	)
      __magic_name__	:	Optional[int]			     =  optimizer
  
      __magic_name__	:	Tuple			     =  self.get_lr_scheduler()
  
      return [optimizer], [scheduler]
  
  
  
  
  
  
  
     def     SCREAMING_SNAKE_CASE					(       self		,     _a		,     _a	):
      return self.validation_step(lowercase_		,     lowercase_	)
  
  
  
  
  
  
  
     def     SCREAMING_SNAKE_CASE					(       self		,     _a	):
      return self.validation_end(lowercase_	)
  
  
  
  
  
  
  
     def     SCREAMING_SNAKE_CASE					(       self	):
      __magic_name__	:	List[str]			     =  max(1		,     self.hparams.gpus	)  # TODO: consider num_tpu_cores
      __magic_name__	:	Dict			     =  self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
      return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs
  
  
  
  
  
  
  
     def     SCREAMING_SNAKE_CASE					(       self		,     _a	):
  
      if stage == "test":
       __magic_name__	:	Optional[Any]			     =  len(self.test_dataloader().dataset	)
      else:
       __magic_name__	:	Any			     =  self.get_dataloader("train"		,     self.hparams.train_batch_size		,     shuffle=lowercase_	)
       __magic_name__	:	Union[str, Any]			     =  len(self.train_dataloader().dataset	)
  
  
  
  
  
  
  
     def     SCREAMING_SNAKE_CASE					(       self		,     _a		,     _a		,     _a = False	):
      raise NotImplementedError("You must implement this for your task"	)
  
  
  
  
  
  
  
     def     SCREAMING_SNAKE_CASE					(       self	):
      return self.train_loader
  
  
  
  
  
  
  
     def     SCREAMING_SNAKE_CASE					(       self	):
      return self.get_dataloader("dev"		,     self.hparams.eval_batch_size		,     shuffle=lowercase_	)
  
  
  
  
  
  
  
     def     SCREAMING_SNAKE_CASE					(       self	):
      return self.get_dataloader("test"		,     self.hparams.eval_batch_size		,     shuffle=lowercase_	)
  
  
  
  
  
  
  
     def     SCREAMING_SNAKE_CASE					(       self		,     _a	):
      return os.path.join(
          self.hparams.data_dir		,     "cached_{}_{}_{}".format(
              lowercase_		,     list(filter(lowercase_		,     self.hparams.model_name_or_path.split("/"	)	)	).pop()		,     str(self.hparams.max_seq_length	)		,     )		,     )
  
  
  
  
  
  
  
     @pl.utilities.rank_zero_only
     def     SCREAMING_SNAKE_CASE					(       self		,     _a	):
      __magic_name__	:	Tuple			     =  self.output_dir.joinpath("best_tfmr"	)
      __magic_name__	:	str			     =  self.step_count
      self.model.save_pretrained(lowercase_	)
      self.tokenizer.save_pretrained(lowercase_	)
  
  
  
  
  
  
  
     @staticmethod
     def     SCREAMING_SNAKE_CASE					(       _a		,     _a	):
      parser.add_argument(
          "--model_name_or_path"		,     default=lowercase_		,     type=lowercase_		,     required=lowercase_		,     help="Path to pretrained model or model identifier from huggingface.co/models"		,     )
      parser.add_argument(
          "--config_name"		,     default=""		,     type=lowercase_		,     help="Pretrained config name or path if not the same as model_name"	)
      parser.add_argument(
          "--tokenizer_name"		,     default=lowercase_		,     type=lowercase_		,     help="Pretrained tokenizer name or path if not the same as model_name"		,     )
      parser.add_argument(
          "--cache_dir"		,     default=""		,     type=lowercase_		,     help="Where do you want to store the pre-trained models downloaded from huggingface.co"		,     )
      parser.add_argument(
          "--encoder_layerdrop"		,     type=lowercase_		,     help="Encoder layer dropout probability (Optional). Goes into model.config"		,     )
      parser.add_argument(
          "--decoder_layerdrop"		,     type=lowercase_		,     help="Decoder layer dropout probability (Optional). Goes into model.config"		,     )
      parser.add_argument(
          "--dropout"		,     type=lowercase_		,     help="Dropout probability (Optional). Goes into model.config"		,     )
      parser.add_argument(
          "--attention_dropout"		,     type=lowercase_		,     help="Attention dropout probability (Optional). Goes into model.config"		,     )
      parser.add_argument("--learning_rate"		,     default=5e-5		,     type=lowercase_		,     help="The initial learning rate for Adam."	)
      parser.add_argument(
          "--lr_scheduler"		,     default="linear"		,     choices=lowercase_		,     metavar=lowercase_		,     type=lowercase_		,     help="Learning rate scheduler"		,     )
      parser.add_argument("--weight_decay"		,     default=0.0		,     type=lowercase_		,     help="Weight decay if we apply some."	)
      parser.add_argument("--adam_epsilon"		,     default=1e-8		,     type=lowercase_		,     help="Epsilon for Adam optimizer."	)
      parser.add_argument("--warmup_steps"		,     default=0		,     type=lowercase_		,     help="Linear warmup over warmup_steps."	)
      parser.add_argument("--num_workers"		,     default=4		,     type=lowercase_		,     help="kwarg passed to DataLoader"	)
      parser.add_argument("--num_train_epochs"		,     dest="max_epochs"		,     default=3		,     type=lowercase_	)
      parser.add_argument("--train_batch_size"		,     default=32		,     type=lowercase_	)
      parser.add_argument("--eval_batch_size"		,     default=32		,     type=lowercase_	)
      parser.add_argument("--adafactor"		,     action="store_true"	)
  
  
  
  
  
  
  
  class    _snake_case      (  pl.Callback       ):
  
  
  
  
  
  
  
     def     SCREAMING_SNAKE_CASE					(       self		,     _a		,     _a	):
  
      if (
          trainer.is_global_zero and trainer.global_rank == 0
      ):  # we initialize the retriever only on master worker with RAY. In new pytorch-lightning accelorators are removed.
       pl_module.model.rag.retriever.init_retrieval()  # better to use hook functions.
  
  
  
  
  
  
  
  class    _snake_case      (  pl.Callback       ):
  
  
  
  
  
  
  
     def     SCREAMING_SNAKE_CASE					(       self		,     _a		,     _a	):
      __magic_name__	:	Union[str, Any]			     =  trainer.lr_schedulers[0]['''scheduler''']
      __magic_name__	:	Any			     =  {f'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()	)}
      pl_module.logger.log_metrics(lowercase_	)
  
  
  
  
  
  
  
     def     SCREAMING_SNAKE_CASE					(       self		,     _a		,     _a	):
      rank_zero_info("***** Validation results *****"	)
      __magic_name__	:	Tuple			     =  trainer.callback_metrics
      # Log results
      for key in sorted(lowercase_	):
       if key not in ["log", "progress_bar"]:
        rank_zero_info("{} = {}\n".format(lowercase_		,     str(metrics[key]	)	)	)
  
  
  
  
  
  
  
     def     SCREAMING_SNAKE_CASE					(       self		,     _a		,     _a	):
      rank_zero_info("***** Test results *****"	)
      __magic_name__	:	int			     =  trainer.callback_metrics
      # Log and save results to file
      __magic_name__	:	Dict			     =  os.path.join(pl_module.hparams.output_dir		,     "test_results.txt"	)
      with open(lowercase_		,     "w"	) as writer:
       for key in sorted(lowercase_	):
        if key not in ["log", "progress_bar"]:
         rank_zero_info("{} = {}\n".format(lowercase_		,     str(metrics[key]	)	)	)
         writer.write("{} = {}\n".format(lowercase_		,     str(metrics[key]	)	)	)
  
  
  
  
  
  
  
  def 						lowerCAmelCase_		(      _snake_case   :						Dict   ,     _snake_case   :						int     )       ->	None:
  
  
  
  
  
  
  
   '''simple docstring'''
  
  
   parser.add_argument(
       "--output_dir"   ,     default=lowercase_   ,     type=lowercase_   ,     required=lowercase_   ,     help="The output directory where the model predictions and checkpoints will be written."   ,     )
   parser.add_argument(
       "--fp16"   ,     action="store_true"   ,     help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit"   ,     )
  
   parser.add_argument(
       "--fp16_opt_level"   ,     type=lowercase_   ,     default="O2"   ,     help=(
           "For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\']."
           "See details at https://nvidia.github.io/apex/amp.html"
       )   ,     )
   parser.add_argument("--n_tpu_cores"   ,     dest="tpu_cores"   ,     type=lowercase_     )
   parser.add_argument("--max_grad_norm"   ,     dest="gradient_clip_val"   ,     default=1.0   ,     type=lowercase_   ,     help="Max gradient norm"     )
   parser.add_argument("--do_train"   ,     action="store_true"   ,     help="Whether to run training."     )
   parser.add_argument("--do_predict"   ,     action="store_true"   ,     help="Whether to run predictions on the test set."     )
   parser.add_argument(
       "--gradient_accumulation_steps"   ,     dest="accumulate_grad_batches"   ,     type=lowercase_   ,     default=1   ,     help="Number of updates steps to accumulate before performing a backward/update pass."   ,     )
   parser.add_argument("--seed"   ,     type=lowercase_   ,     default=42   ,     help="random seed for initialization"     )
   parser.add_argument(
       "--data_dir"   ,     default=lowercase_   ,     type=lowercase_   ,     required=lowercase_   ,     help="The input data dir. Should contain the training files for the CoNLL-2003 NER task."   ,     )
  
  
  
  
  
  
  def 						lowerCAmelCase_		(      _snake_case   :						List[Any]   ,     _snake_case   :						Any   ,     _snake_case   :						List[str]=None   ,     _snake_case   :						int=True   ,     _snake_case   :						Optional[Any]=None   ,     _snake_case   :						Optional[int]=[]   ,     _snake_case   :						List[Any]=None   ,     _snake_case   :						Dict=None   ,     **_snake_case   :						List[Any]   ,     )       ->	Optional[Any]:
  
  
  
  
  
  
  
   '''simple docstring'''
  
  
   pl.seed_everything(args.seed     )
  
   # init model
   __magic_name__	:	int			     =  Path(model.hparams.output_dir     )
   odir.mkdir(exist_ok=lowercase_     )
  
   # add custom checkpoints
   if checkpoint_callback is None:
    __magic_name__	:	Union[str, Any]			     =  pl.callbacks.ModelCheckpoint(
        filepath=args.output_dir   ,     prefix="checkpoint"   ,     monitor="val_loss"   ,     mode="min"   ,     save_top_k=1     )
   if early_stopping_callback:
    extra_callbacks.append(lowercase_     )
   if logging_callback is None:
    __magic_name__	:	Optional[int]			     =  LoggingCallback()
  
   __magic_name__	:	Union[str, Any]			     =  {}
  
   # TODO: remove with PyTorch 1.6 since pl uses native amp
   if args.fpaa:
    __magic_name__	:	Any			     =  16
    # train_params["amp_level"] = args.fp16_opt_level
  
   if args.gpus > 1:
    __magic_name__	:	List[str]			     =  '''auto'''  # "ddp"
    __magic_name__	:	Dict			     =  '''ddp'''
  
   __magic_name__	:	Optional[int]			     =  args.accumulate_grad_batches
   __magic_name__	:	Optional[Any]			     =  None  # extra_train_kwargs.get("profiler", None) #get unwanted logs
   __magic_name__	:	Tuple			     =  '''auto'''
  
   __magic_name__	:	int			     =  pl.Trainer.from_argparse_args(
       lowercase_   ,     weights_summary=lowercase_   ,     callbacks=[logging_callback] + extra_callbacks + [checkpoint_callback] + [InitCallback()]   ,     logger=lowercase_   ,     **lowercase_   ,     )
  
   if args.do_train:
    trainer.fit(lowercase_     )
  
   return trainer
  
style_context: 
  def 						lowerCAmelCase_		(      _snake_case   :						int   ,     _snake_case   :						list[tuple[int, int, int]]     )       ->	list[tuple[int, int, int]]:
  
  
  
  
  
  
  
  	'''simple docstring'''
  
  
  	__magic_name__	:	Union[str, Any]			     =  sorted(_snake_case   ,     key=lambda _snake_case     : edge[2]     )
  
  	__magic_name__	:	Union[str, Any]			     =  list(range(_snake_case     )     )
  
  	def find_parent(_snake_case   :						Dict     ):
  		if i != parent[i]:
  			__magic_name__	:	Any			     =  find_parent(parent[i]     )
  		return parent[i]
  
  	__magic_name__	:	List[str]			     =  0
  	__magic_name__	:	int			     =  []
  
  	for edge in edges:
  		__magic_name__	:	Any			     =  find_parent(edge[0]     )
  		__magic_name__	:	int			     =  find_parent(edge[1]     )
  		if parent_a != parent_b:
  			minimum_spanning_tree_cost += edge[2]
  			minimum_spanning_tree.append(_snake_case     )
  			__magic_name__	:	Optional[int]			     =  parent_b
  
  	return minimum_spanning_tree
  
  
  if __name__ == "__main__":  # pragma: no cover
  				snake_case   ,snake_case				:	int							   =						list(map(int, input().strip().split()))
  				snake_case				:	int							   =						[]
  
  				for _ in range(num_edges):
  								snake_case   ,snake_case   ,snake_case				:	Dict							   =						(int(x) for x in input().strip().split())
  								edges.append((nodea, nodea, cost))
  
  				kruskal(num_nodes, edges)
  