code: 
  """simple docstring"""
  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
  
  
  __magic_name__		       =				logging.getLogger(__name__)
  
  require_version('''pytorch_lightning>=1.0.4''')
  
  __magic_name__		       =				{
      '''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
  __magic_name__		       =				{
      '''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
  }
  __magic_name__		       =				sorted(arg_to_scheduler.keys())
  __magic_name__		       =				'''{''' + ''', '''.join(arg_to_scheduler_choices) + '''}'''
  
  
  class    _lowerCAmelCase						(						pl.LightningModule						):
  
  
  
  							def __init__(      self ,						a_ ,						a_=None ,						a_="base" ,						a_=None ,						a_=None ,						a_=None ,						**a_ ,						)							->							int:
  								super().__init__()
  								# TODO: move to self.save_hyperparameters()
  								# self.save_hyperparameters()
  								# can also expand arguments into trainer signature for easier reading
  
  								self.save_hyperparameters(a_   )
  								_UpperCAmelCase =						0
  								_UpperCAmelCase =						Path(self.hparams.output_dir   )
  								_UpperCAmelCase =						self.hparams.cache_dir if self.hparams.cache_dir else None
  								if config is None:
  									_UpperCAmelCase =						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=a_ ,						**a_ ,						)
  								else:
  									_UpperCAmelCase =						config
  
  								_UpperCAmelCase =						("encoder_layerdrop", "decoder_layerdrop", "dropout", "attention_dropout")
  								for p in extra_model_params:
  									if getattr(self.hparams ,						a_ ,						a_   ):
  										assert hasattr(self.config ,						a_   ), f"model config doesn't have a `{p}` attribute"
  										setattr(self.config ,						a_ ,						getattr(self.hparams ,						a_   )   )
  
  								if tokenizer is None:
  									_UpperCAmelCase =						AutoTokenizer.from_pretrained(
  									    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path ,						cache_dir=a_ ,						)
  								else:
  									_UpperCAmelCase =						tokenizer
  								_UpperCAmelCase =						MODEL_MODES[mode]
  								if model is None:
  									_UpperCAmelCase =						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=a_ ,						)
  								else:
  									_UpperCAmelCase =						model
  
  
  
  							def       _a   (      self ,						*a_ ,						**a_   )							->							int:
  								_UpperCAmelCase =						self.model_type.from_pretrained(*a_ ,						**a_   )
  
  
  
  							def       _a   (      self   )							->							Optional[Any]:
  								_UpperCAmelCase =						arg_to_scheduler[self.hparams.lr_scheduler]
  								_UpperCAmelCase =						get_schedule_func(
  								    self.opt ,						num_warmup_steps=self.hparams.warmup_steps ,						num_training_steps=self.total_steps()   )
  								_UpperCAmelCase =						{"scheduler": scheduler, "interval": "step", "frequency": 1}
  								return scheduler
  
  
  
  							def       _a   (      self   )							->							Dict:
  								_UpperCAmelCase =						self.model
  								_UpperCAmelCase =						["bias", "LayerNorm.weight"]
  								_UpperCAmelCase =						[
  								    {
  								        "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:
  									_UpperCAmelCase =						Adafactor(
  									    a_ ,						lr=self.hparams.learning_rate ,						scale_parameter=a_ ,						relative_step=a_   )
  
  								else:
  									_UpperCAmelCase =						AdamW(
  									    a_ ,						lr=self.hparams.learning_rate ,						eps=self.hparams.adam_epsilon   )
  								_UpperCAmelCase =						optimizer
  
  								_UpperCAmelCase =						self.get_lr_scheduler()
  
  								return [optimizer], [scheduler]
  
  
  
  							def       _a   (      self ,						a_ ,						a_   )							->							Union[str, Any]:
  								return self.validation_step(a_ ,						a_   )
  
  
  
  							def       _a   (      self ,						a_   )							->							List[str]:
  								return self.validation_end(a_   )
  
  
  
  							def       _a   (      self   )							->							int:
  								_UpperCAmelCase =						max(1 ,						self.hparams.gpus   )  # TODO: consider num_tpu_cores
  								_UpperCAmelCase =						self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
  								return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs
  
  
  
  							def       _a   (      self ,						a_   )							->							int:
  								if mode == "test":
  									_UpperCAmelCase =						len(self.test_dataloader().dataset   )
  								else:
  									_UpperCAmelCase =						self.get_dataloader("train" ,						self.hparams.train_batch_size ,						shuffle=a_   )
  									_UpperCAmelCase =						len(self.train_dataloader().dataset   )
  
  
  
  							def       _a   (      self ,						a_ ,						a_ ,						a_ = False   )							->							List[Any]:
  								raise NotImplementedError("You must implement this for your task"   )
  
  
  
  							def       _a   (      self   )							->							Union[str, Any]:
  								return self.train_loader
  
  
  
  							def       _a   (      self   )							->							Optional[Any]:
  								return self.get_dataloader("dev" ,						self.hparams.eval_batch_size ,						shuffle=a_   )
  
  
  
  							def       _a   (      self   )							->							Union[str, Any]:
  								return self.get_dataloader("test" ,						self.hparams.eval_batch_size ,						shuffle=a_   )
  
  
  
  							def       _a   (      self ,						a_   )							->							Dict:
  								return os.path.join(
  								    self.hparams.data_dir ,						"cached_{}_{}_{}".format(
  								        a_ ,						list(filter(a_ ,						self.hparams.model_name_or_path.split("/"   )   )   ).pop() ,						str(self.hparams.max_seq_length   ) ,						) ,						)
  
  
  
  							@pl.utilities.rank_zero_only
  							def       _a   (      self ,						a_   )							->							None:
  								_UpperCAmelCase =						self.output_dir.joinpath("best_tfmr"   )
  								_UpperCAmelCase =						self.step_count
  								self.model.save_pretrained(a_   )
  								self.tokenizer.save_pretrained(a_   )
  
  
  
  							@staticmethod
  							def       _a   (      a_ ,						a_   )							->							List[Any]:
  								parser.add_argument(
  								    "--model_name_or_path" ,						default=a_ ,						type=a_ ,						required=a_ ,						help="Path to pretrained model or model identifier from huggingface.co/models" ,						)
  								parser.add_argument(
  								    "--config_name" ,						default="" ,						type=a_ ,						help="Pretrained config name or path if not the same as model_name"   )
  								parser.add_argument(
  								    "--tokenizer_name" ,						default=a_ ,						type=a_ ,						help="Pretrained tokenizer name or path if not the same as model_name" ,						)
  								parser.add_argument(
  								    "--cache_dir" ,						default="" ,						type=a_ ,						help="Where do you want to store the pre-trained models downloaded from huggingface.co" ,						)
  								parser.add_argument(
  								    "--encoder_layerdrop" ,						type=a_ ,						help="Encoder layer dropout probability (Optional). Goes into model.config" ,						)
  								parser.add_argument(
  								    "--decoder_layerdrop" ,						type=a_ ,						help="Decoder layer dropout probability (Optional). Goes into model.config" ,						)
  								parser.add_argument(
  								    "--dropout" ,						type=a_ ,						help="Dropout probability (Optional). Goes into model.config" ,						)
  								parser.add_argument(
  								    "--attention_dropout" ,						type=a_ ,						help="Attention dropout probability (Optional). Goes into model.config" ,						)
  								parser.add_argument("--learning_rate" ,						default=5e-5 ,						type=a_ ,						help="The initial learning rate for Adam."   )
  								parser.add_argument(
  								    "--lr_scheduler" ,						default="linear" ,						choices=a_ ,						metavar=a_ ,						type=a_ ,						help="Learning rate scheduler" ,						)
  								parser.add_argument("--weight_decay" ,						default=0.0 ,						type=a_ ,						help="Weight decay if we apply some."   )
  								parser.add_argument("--adam_epsilon" ,						default=1e-8 ,						type=a_ ,						help="Epsilon for Adam optimizer."   )
  								parser.add_argument("--warmup_steps" ,						default=0 ,						type=a_ ,						help="Linear warmup over warmup_steps."   )
  								parser.add_argument("--num_workers" ,						default=4 ,						type=a_ ,						help="kwarg passed to DataLoader"   )
  								parser.add_argument("--num_train_epochs" ,						dest="max_epochs" ,						default=3 ,						type=a_   )
  								parser.add_argument("--train_batch_size" ,						default=32 ,						type=a_   )
  								parser.add_argument("--eval_batch_size" ,						default=32 ,						type=a_   )
  								parser.add_argument("--adafactor" ,						action="store_true"   )
  
  
  
  
  
  class    _lowerCAmelCase						(						pl.Callback						):
  
  
  
  							def       _a   (      self ,						a_ ,						a_   )							->							Union[str, Any]:
  								_UpperCAmelCase =						trainer.lr_schedulers[0]["scheduler"]
  								_UpperCAmelCase =						{f"lr_group_{i}": lr for i, lr in enumerate(lr_scheduler.get_lr()   )}
  								pl_module.logger.log_metrics(a_   )
  
  
  
  							def       _a   (      self ,						a_ ,						a_   )							->							Tuple:
  								rank_zero_info("***** Validation results *****"   )
  								_UpperCAmelCase =						trainer.callback_metrics
  								# Log results
  								for key in sorted(a_   ):
  									if key not in ["log", "progress_bar"]:
  										rank_zero_info("{} = {}\n".format(a_ ,						str(metrics[key]   )   )   )
  
  
  
  							def       _a   (      self ,						a_ ,						a_   )							->							Any:
  								rank_zero_info("***** Test results *****"   )
  								_UpperCAmelCase =						trainer.callback_metrics
  								# Log and save results to file
  								_UpperCAmelCase =						os.path.join(pl_module.hparams.output_dir ,						"test_results.txt"   )
  								with open(a_ ,						"w"   ) as writer:
  									for key in sorted(a_   ):
  										if key not in ["log", "progress_bar"]:
  											rank_zero_info("{} = {}\n".format(a_ ,						str(metrics[key]   )   )   )
  											writer.write("{} = {}\n".format(a_ ,						str(metrics[key]   )   )   )
  
  
  
  
  
  def     __lowerCamelCase  (		UpperCamelCase__				,     UpperCamelCase__      ):
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  	parser.add_argument(
  	    "--output_dir"				,     default=UpperCamelCase__				,     type=UpperCamelCase__				,     required=UpperCamelCase__				,     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=UpperCamelCase__				,     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=UpperCamelCase__      )
  	parser.add_argument("--max_grad_norm"				,     dest="gradient_clip_val"				,     default=1.0				,     type=UpperCamelCase__				,     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=UpperCamelCase__				,     default=1				,     help="Number of updates steps to accumulate before performing a backward/update pass."				,     )
  	parser.add_argument("--seed"				,     type=UpperCamelCase__				,     default=42				,     help="random seed for initialization"      )
  	parser.add_argument(
  	    "--data_dir"				,     default=UpperCamelCase__				,     type=UpperCamelCase__				,     required=UpperCamelCase__				,     help="The input data dir. Should contain the training files for the CoNLL-2003 NER task."				,     )
  
  
  
  
  def     __lowerCamelCase  (		UpperCamelCase__				,     UpperCamelCase__				,     UpperCamelCase__=None				,     UpperCamelCase__=True				,     UpperCamelCase__=[]				,     UpperCamelCase__=None				,     UpperCamelCase__=None				,     **UpperCamelCase__				,     ):
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  	pl.seed_everything(args.seed      )
  
  	# init model
  	_UpperCAmelCase =						Path(model.hparams.output_dir      )
  	odir.mkdir(exist_ok=UpperCamelCase__      )
  
  	# add custom checkpoints
  	if checkpoint_callback is None:
  		_UpperCAmelCase =						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(UpperCamelCase__      )
  	if logging_callback is None:
  		_UpperCAmelCase =						LoggingCallback()
  
  	_UpperCAmelCase =						{}
  
  	# TODO: remove with PyTorch 1.6 since pl uses native amp
  	if args.fpaa:
  		_UpperCAmelCase =						16
  		_UpperCAmelCase =						args.fpaa_opt_level
  
  	if args.gpus > 1:
  		_UpperCAmelCase =						"ddp"
  
  	_UpperCAmelCase =						args.accumulate_grad_batches
  	_UpperCAmelCase =						extra_train_kwargs.get("accelerator"				,     UpperCamelCase__      )
  	_UpperCAmelCase =						extra_train_kwargs.get("profiler"				,     UpperCamelCase__      )
  
  	_UpperCAmelCase =						pl.Trainer.from_argparse_args(
  	    UpperCamelCase__				,     weights_summary=UpperCamelCase__				,     callbacks=[logging_callback] + extra_callbacks				,     logger=UpperCamelCase__				,     checkpoint_callback=UpperCamelCase__				,     **UpperCamelCase__				,     )
  
  	if args.do_train:
  		trainer.fit(UpperCamelCase__      )
  
  	return trainer
  
  
  
  
style_context: 
  """simple docstring"""
  import heapq
  
  
  
  
  def     __lowerCamelCase  (		UpperCamelCase__				,     UpperCamelCase__				,     UpperCamelCase__      ):
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  	_UpperCAmelCase =						[(0, start)]  # cost from start node,end node
  	_UpperCAmelCase =						set()
  	while heap:
  		((_UpperCAmelCase) ,      (_UpperCAmelCase)) =						heapq.heappop(UpperCamelCase__      )
  		if u in visited:
  			continue
  		visited.add(UpperCamelCase__      )
  		if u == end:
  			return cost
  		for v, c in graph[u]:
  			if v in visited:
  				continue
  			_UpperCAmelCase =						cost + c
  			heapq.heappush(UpperCamelCase__				,     (next_item, v)      )
  	return -1
  
  
  __magic_name__		       =				{
      '''A''': [['''B''', 2], ['''C''', 5]],
      '''B''': [['''A''', 2], ['''D''', 3], ['''E''', 1], ['''F''', 1]],
      '''C''': [['''A''', 5], ['''F''', 3]],
      '''D''': [['''B''', 3]],
      '''E''': [['''B''', 4], ['''F''', 3]],
      '''F''': [['''C''', 3], ['''E''', 3]],
  }
  __magic_name__		       =				{
      '''B''': [['''C''', 1]],
      '''C''': [['''D''', 1]],
      '''D''': [['''F''', 1]],
      '''E''': [['''B''', 1], ['''F''', 3]],
      '''F''': [],
  }
  __magic_name__		       =				{
      '''B''': [['''C''', 1]],
      '''C''': [['''D''', 1]],
      '''D''': [['''F''', 1]],
      '''E''': [['''B''', 1], ['''G''', 2]],
      '''F''': [],
      '''G''': [['''F''', 1]],
  }
  
  __magic_name__		       =				dijkstra(G, '''E''', '''C''')
  print(short_distance)  # E -- 3 --> F -- 3 --> C == 6
  
  __magic_name__		       =				dijkstra(Ga, '''E''', '''F''')
  print(short_distance)  # E -- 3 --> F == 3
  
  __magic_name__		       =				dijkstra(Ga, '''E''', '''F''')
  print(short_distance)  # E -- 2 --> G -- 1 --> F == 3
  
  if __name__ == "__main__":
  			import doctest
  
  			doctest.testmod()
  
  
  
  
  