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
  
  
  __A  :					List[str]												=	logging.getLogger(__name__)
  
  require_version('pytorch_lightning>=1.0.4')
  
  __A  :					Optional[int]												=	{
      '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
  __A  :					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
  }
  __A  :					Tuple												=	sorted(arg_to_scheduler.keys())
  __A  :					Tuple												=	'{' + ', '.join(arg_to_scheduler_choices) + '}'
  
  
  
  
  
  class     __UpperCamelCase				(     pl.LightningModule		):
  
  
  		def __init__(self				:	str  ,  __SCREAMING_SNAKE_CASE				:	argparse.Namespace  ,  __SCREAMING_SNAKE_CASE				:	Union[str, Any]=None  ,  __SCREAMING_SNAKE_CASE				:	List[str]="base"  ,  __SCREAMING_SNAKE_CASE				:	Any=None  ,  __SCREAMING_SNAKE_CASE				:	int=None  ,  __SCREAMING_SNAKE_CASE				:	Union[str, Any]=None  ,  **__SCREAMING_SNAKE_CASE				:	Dict  ,  ):
  							super().__init__()
  							# TODO: move to self.save_hyperparameters()
  							# self.save_hyperparameters()
  							# can also expand arguments into trainer signature for easier reading
  
  							self.save_hyperparameters(__SCREAMING_SNAKE_CASE)
  							A    =      0
  							A    =      Path(self.hparams.output_dir)
  							A    =      self.hparams.cache_dir if self.hparams.cache_dir else None
  							if config is None:
  												A    =      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=__SCREAMING_SNAKE_CASE  ,  **__SCREAMING_SNAKE_CASE  ,  )
  							else:
  												A    =      config
  
  							A    =      ("encoder_layerdrop", "decoder_layerdrop", "dropout", "attention_dropout")
  							for p in extra_model_params:
  												if getattr(self.hparams  ,  __SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE):
  																	assert hasattr(self.config  ,  __SCREAMING_SNAKE_CASE), F"""model config doesn't have a `{p}` attribute"""
  																	setattr(self.config  ,  __SCREAMING_SNAKE_CASE  ,  getattr(self.hparams  ,  __SCREAMING_SNAKE_CASE))
  
  							if tokenizer is None:
  												A    =      AutoTokenizer.from_pretrained(
  												    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path  ,  cache_dir=__SCREAMING_SNAKE_CASE  ,  )
  							else:
  												A    =      tokenizer
  							A    =      MODEL_MODES[mode]
  							if model is None:
  												A    =      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=__SCREAMING_SNAKE_CASE  ,  )
  							else:
  												A    =      model
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	str  ,  *__SCREAMING_SNAKE_CASE				:	Optional[Any]  ,  **__SCREAMING_SNAKE_CASE				:	Union[str, Any]):
  							A    =      self.model_type.from_pretrained(*__SCREAMING_SNAKE_CASE  ,  **__SCREAMING_SNAKE_CASE)
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	List[str]):
  							A    =      arg_to_scheduler[self.hparams.lr_scheduler]
  							A    =      get_schedule_func(
  							    self.opt  ,  num_warmup_steps=self.hparams.warmup_steps  ,  num_training_steps=self.total_steps())
  							A    =      {"scheduler": scheduler, "interval": "step", "frequency": 1}
  							return scheduler
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	Optional[Any]):
  							A    =      self.model
  							A    =      ["bias", "LayerNorm.weight"]
  							A    =      [
  							    {
  							        "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:
  												A    =      Adafactor(
  												    __SCREAMING_SNAKE_CASE  ,  lr=self.hparams.learning_rate  ,  scale_parameter=__SCREAMING_SNAKE_CASE  ,  relative_step=__SCREAMING_SNAKE_CASE)
  
  							else:
  												A    =      AdamW(
  												    __SCREAMING_SNAKE_CASE  ,  lr=self.hparams.learning_rate  ,  eps=self.hparams.adam_epsilon)
  							A    =      optimizer
  
  							A    =      self.get_lr_scheduler()
  
  							return [optimizer], [scheduler]
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	int  ,  __SCREAMING_SNAKE_CASE				:	Union[str, Any]  ,  __SCREAMING_SNAKE_CASE				:	Tuple):
  							return self.validation_step(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE)
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	Optional[Any]  ,  __SCREAMING_SNAKE_CASE				:	int):
  							return self.validation_end(__SCREAMING_SNAKE_CASE)
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	Dict):
  							A    =      max(1  ,  self.hparams.gpus)  # TODO: consider num_tpu_cores
  							A    =      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				:	Optional[Any]  ,  __SCREAMING_SNAKE_CASE				:	Optional[int]):
  							if mode == "test":
  												A    =      len(self.test_dataloader().dataset)
  							else:
  												A    =      self.get_dataloader("train"  ,  self.hparams.train_batch_size  ,  shuffle=__SCREAMING_SNAKE_CASE)
  												A    =      len(self.train_dataloader().dataset)
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	List[Any]  ,  __SCREAMING_SNAKE_CASE				:	str  ,  __SCREAMING_SNAKE_CASE				:	int  ,  __SCREAMING_SNAKE_CASE				:	bool = False):
  							raise NotImplementedError("You must implement this for your task")
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	List[Any]):
  							return self.train_loader
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	List[Any]):
  							return self.get_dataloader("dev"  ,  self.hparams.eval_batch_size  ,  shuffle=__SCREAMING_SNAKE_CASE)
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	Optional[int]):
  							return self.get_dataloader("test"  ,  self.hparams.eval_batch_size  ,  shuffle=__SCREAMING_SNAKE_CASE)
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	Optional[int]  ,  __SCREAMING_SNAKE_CASE				:	Optional[Any]):
  							return os.path.join(
  							    self.hparams.data_dir  ,  "cached_{}_{}_{}".format(
  							        __SCREAMING_SNAKE_CASE  ,  list(filter(__SCREAMING_SNAKE_CASE  ,  self.hparams.model_name_or_path.split("/"))).pop()  ,  str(self.hparams.max_seq_length)  ,  )  ,  )
  
  
  		@pl.utilities.rank_zero_only
  		def       SCREAMING_SNAKE_CASE__     (self				:	int  ,  __SCREAMING_SNAKE_CASE				:	Dict[str, Any]):
  							A    =      self.output_dir.joinpath("best_tfmr")
  							A    =      self.step_count
  							self.model.save_pretrained(__SCREAMING_SNAKE_CASE)
  							self.tokenizer.save_pretrained(__SCREAMING_SNAKE_CASE)
  
  
  		@staticmethod
  		def       SCREAMING_SNAKE_CASE__     (__SCREAMING_SNAKE_CASE				:	int  ,  __SCREAMING_SNAKE_CASE				:	Any):
  							parser.add_argument(
  							    "--model_name_or_path"  ,  default=__SCREAMING_SNAKE_CASE  ,  type=__SCREAMING_SNAKE_CASE  ,  required=__SCREAMING_SNAKE_CASE  ,  help="Path to pretrained model or model identifier from huggingface.co/models"  ,  )
  							parser.add_argument(
  							    "--config_name"  ,  default=""  ,  type=__SCREAMING_SNAKE_CASE  ,  help="Pretrained config name or path if not the same as model_name")
  							parser.add_argument(
  							    "--tokenizer_name"  ,  default=__SCREAMING_SNAKE_CASE  ,  type=__SCREAMING_SNAKE_CASE  ,  help="Pretrained tokenizer name or path if not the same as model_name"  ,  )
  							parser.add_argument(
  							    "--cache_dir"  ,  default=""  ,  type=__SCREAMING_SNAKE_CASE  ,  help="Where do you want to store the pre-trained models downloaded from huggingface.co"  ,  )
  							parser.add_argument(
  							    "--encoder_layerdrop"  ,  type=__SCREAMING_SNAKE_CASE  ,  help="Encoder layer dropout probability (Optional). Goes into model.config"  ,  )
  							parser.add_argument(
  							    "--decoder_layerdrop"  ,  type=__SCREAMING_SNAKE_CASE  ,  help="Decoder layer dropout probability (Optional). Goes into model.config"  ,  )
  							parser.add_argument(
  							    "--dropout"  ,  type=__SCREAMING_SNAKE_CASE  ,  help="Dropout probability (Optional). Goes into model.config"  ,  )
  							parser.add_argument(
  							    "--attention_dropout"  ,  type=__SCREAMING_SNAKE_CASE  ,  help="Attention dropout probability (Optional). Goes into model.config"  ,  )
  							parser.add_argument("--learning_rate"  ,  default=5E-5  ,  type=__SCREAMING_SNAKE_CASE  ,  help="The initial learning rate for Adam.")
  							parser.add_argument(
  							    "--lr_scheduler"  ,  default="linear"  ,  choices=__SCREAMING_SNAKE_CASE  ,  metavar=__SCREAMING_SNAKE_CASE  ,  type=__SCREAMING_SNAKE_CASE  ,  help="Learning rate scheduler"  ,  )
  							parser.add_argument("--weight_decay"  ,  default=0.0  ,  type=__SCREAMING_SNAKE_CASE  ,  help="Weight decay if we apply some.")
  							parser.add_argument("--adam_epsilon"  ,  default=1E-8  ,  type=__SCREAMING_SNAKE_CASE  ,  help="Epsilon for Adam optimizer.")
  							parser.add_argument("--warmup_steps"  ,  default=0  ,  type=__SCREAMING_SNAKE_CASE  ,  help="Linear warmup over warmup_steps.")
  							parser.add_argument("--num_workers"  ,  default=4  ,  type=__SCREAMING_SNAKE_CASE  ,  help="kwarg passed to DataLoader")
  							parser.add_argument("--num_train_epochs"  ,  dest="max_epochs"  ,  default=3  ,  type=__SCREAMING_SNAKE_CASE)
  							parser.add_argument("--train_batch_size"  ,  default=3_2  ,  type=__SCREAMING_SNAKE_CASE)
  							parser.add_argument("--eval_batch_size"  ,  default=3_2  ,  type=__SCREAMING_SNAKE_CASE)
  							parser.add_argument("--adafactor"  ,  action="store_true")
  
  
  
  
  
  class     __UpperCamelCase				(     pl.Callback		):
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	Optional[Any]  ,  __SCREAMING_SNAKE_CASE				:	Optional[Any]  ,  __SCREAMING_SNAKE_CASE				:	Any):
  							A    =      trainer.lr_schedulers[0]["scheduler"]
  							A    =      {F"""lr_group_{i}""": lr for i, lr in enumerate(lr_scheduler.get_lr())}
  							pl_module.logger.log_metrics(__SCREAMING_SNAKE_CASE)
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	Optional[int]  ,  __SCREAMING_SNAKE_CASE				:	pl.Trainer  ,  __SCREAMING_SNAKE_CASE				:	pl.LightningModule):
  							rank_zero_info("***** Validation results *****")
  							A    =      trainer.callback_metrics
  							# Log results
  							for key in sorted(__SCREAMING_SNAKE_CASE):
  												if key not in ["log", "progress_bar"]:
  																	rank_zero_info("{} = {}\n".format(__SCREAMING_SNAKE_CASE  ,  str(metrics[key])))
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	str  ,  __SCREAMING_SNAKE_CASE				:	pl.Trainer  ,  __SCREAMING_SNAKE_CASE				:	pl.LightningModule):
  							rank_zero_info("***** Test results *****")
  							A    =      trainer.callback_metrics
  							# Log and save results to file
  							A    =      os.path.join(pl_module.hparams.output_dir  ,  "test_results.txt")
  							with open(__SCREAMING_SNAKE_CASE  ,  "w") as writer:
  												for key in sorted(__SCREAMING_SNAKE_CASE):
  																	if key not in ["log", "progress_bar"]:
  																						rank_zero_info("{} = {}\n".format(__SCREAMING_SNAKE_CASE  ,  str(metrics[key])))
  																						writer.write("{} = {}\n".format(__SCREAMING_SNAKE_CASE  ,  str(metrics[key])))
  
  def      __SCREAMING_SNAKE_CASE		(		lowercase__    ,							lowercase__   ):
  					"""simple docstring"""
  					#  To allow all pl args uncomment the following line
  					#  parser = pl.Trainer.add_argparse_args(parser)
  					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      __SCREAMING_SNAKE_CASE		(		lowercase__    ,							lowercase__    ,							lowercase__=None    ,							lowercase__=True    ,							lowercase__=[]    ,							lowercase__=None    ,							lowercase__=None    ,							**lowercase__    ,							):
  					"""simple docstring"""
  					pl.seed_everything(args.seed   )
  
  					# init model
  					A    =      Path(model.hparams.output_dir   )
  					odir.mkdir(exist_ok=lowercase__   )
  
  					# add custom checkpoints
  					if checkpoint_callback is None:
  										A    =      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:
  										A    =      LoggingCallback()
  
  					A    =      {}
  
  					# TODO: remove with PyTorch 1.6 since pl uses native amp
  					if args.fpaa:
  										A    =      16
  										A    =      args.fpaa_opt_level
  
  					if args.gpus > 1:
  										A    =      "ddp"
  
  					A    =      args.accumulate_grad_batches
  					A    =      extra_train_kwargs.get("accelerator"    ,							lowercase__   )
  					A    =      extra_train_kwargs.get("profiler"    ,							lowercase__   )
  
  					A    =      pl.Trainer.from_argparse_args(
  					    lowercase__    ,							weights_summary=lowercase__    ,							callbacks=[logging_callback] + extra_callbacks    ,							logger=lowercase__    ,							checkpoint_callback=lowercase__    ,							**lowercase__    ,							)
  
  					if args.do_train:
  										trainer.fit(lowercase__   )
  
  					return trainer
  
  
  
  
  
  
style_context: 
  
  """simple docstring"""
  
  
  
  
  def      __SCREAMING_SNAKE_CASE		(		lowercase__    ,							lowercase__   ):
  					"""simple docstring"""
  					A    =      1
  					while b > 0:
  										if b & 1:
  															res *= a
  
  										a *= a
  										b >>= 1
  
  					return res
  def      __SCREAMING_SNAKE_CASE		(		lowercase__    ,							lowercase__    ,							lowercase__   ):
  					"""simple docstring"""
  					A    =      1
  					while b > 0:
  										if b & 1:
  															A    =      ((res % c) * (a % c)) % c
  
  										a *= a
  										b >>= 1
  
  					return res
  
  
  
  
  
  