code: 
  """simple docstring"""
  
  
  
  
  
  import logging
  import os
  from dataclasses import dataclass, field
  from typing import Dict, List, Optional
  
  import numpy as np
  import torch
  from utils_hans import HansDataset, InputFeatures, hans_processors, hans_tasks_num_labels
  
  import transformers
  from transformers import (
      AutoConfig,
      AutoModelForSequenceClassification,
      AutoTokenizer,
      HfArgumentParser,
      Trainer,
      TrainingArguments,
      default_data_collator,
      set_seed,
  )
  from transformers.trainer_utils import is_main_process
  
  
  _a	 =							logging.getLogger(__name__)
  
  
  @dataclass
  class A_			:
  
  
  
  
    '''simple docstring'''
    SCREAMING_SNAKE_CASE__    :		str						  =	field(
        metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}   )
    SCREAMING_SNAKE_CASE__    :		Optional[str]						  =	field(
        default=lowercase__       ,metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}   )
    SCREAMING_SNAKE_CASE__    :		Optional[str]						  =	field(
        default=lowercase__       ,metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}   )
    SCREAMING_SNAKE_CASE__    :		Optional[str]						  =	field(
        default=lowercase__       ,metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}       ,)
  
  
  
  
  @dataclass
  class A_			:
  
  
  
  
    '''simple docstring'''
    SCREAMING_SNAKE_CASE__    :		str						  =	field(
        metadata={"""help""": """The name of the task to train selected in the list: """ + """, """.join(hans_processors.keys()   )}   )
    SCREAMING_SNAKE_CASE__    :		str						  =	field(
        metadata={"""help""": """The input data dir. Should contain the .tsv files (or other data files) for the task."""}   )
    SCREAMING_SNAKE_CASE__    :		int						  =	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."""
            )
        }       ,)
    SCREAMING_SNAKE_CASE__    :		bool						  =	field(
        default=lowercase__       ,metadata={"""help""": """Overwrite the cached training and evaluation sets"""}   )
  
  
  
  
  
  
  
  def 			__a  (	__lowerCamelCase						):
     UpperCAmelCase_      :      Tuple      =					default_data_collator(__lowerCamelCase						)
     UpperCAmelCase_      :      int      =					batch.pop("pairID",	__lowerCamelCase						)
     return batch
  
  
  
  
  
  
  
  def 			__a  (	):
     # 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_      :      Any      =					HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)						)
     UpperCAmelCase_  ,     UpperCAmelCase_  ,     UpperCAmelCase_      :      Tuple      =					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",	__lowerCamelCase						)
  
     # Set seed
     set_seed(training_args.seed						)
  
     try:
        UpperCAmelCase_      :      Optional[Any]      =					hans_tasks_num_labels[data_args.task_name]
     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_      :      Optional[int]      =					AutoConfig.from_pretrained(
         model_args.config_name if model_args.config_name else model_args.model_name_or_path,	num_labels=__lowerCamelCase,	finetuning_task=data_args.task_name,	cache_dir=model_args.cache_dir,	)
     UpperCAmelCase_      :      Any      =					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_      :      Dict      =					AutoModelForSequenceClassification.from_pretrained(
         model_args.model_name_or_path,	from_tf=bool(".ckpt" in model_args.model_name_or_path						),	config=__lowerCamelCase,	cache_dir=model_args.cache_dir,	)
  
     # Get datasets
     UpperCAmelCase_      :      List[str]      =					(
         HansDataset(
             data_dir=data_args.data_dir,	tokenizer=__lowerCamelCase,	task=data_args.task_name,	max_seq_length=data_args.max_seq_length,	overwrite_cache=data_args.overwrite_cache,	)
         if training_args.do_train
         else None
     )
     UpperCAmelCase_      :      Any      =					(
         HansDataset(
             data_dir=data_args.data_dir,	tokenizer=__lowerCamelCase,	task=data_args.task_name,	max_seq_length=data_args.max_seq_length,	overwrite_cache=data_args.overwrite_cache,	evaluate=__lowerCamelCase,	)
         if training_args.do_eval
         else None
     )
  
     # Initialize our Trainer
     UpperCAmelCase_      :      Union[str, Any]      =					Trainer(
         model=__lowerCamelCase,	args=__lowerCamelCase,	train_dataset=__lowerCamelCase,	eval_dataset=__lowerCamelCase,	data_collator=__lowerCamelCase,	)
  
     # 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
     if training_args.do_eval:
        logger.info("*** Evaluate ***"						)
  
        UpperCAmelCase_      :      str      =					trainer.predict(__lowerCamelCase						)
        UpperCAmelCase_      :      List[str]      =					output.predictions
        UpperCAmelCase_      :      List[Any]      =					np.argmax(__lowerCamelCase,	axis=1						)
  
        UpperCAmelCase_      :      List[str]      =					[ex.pairID for ex in eval_dataset]
        UpperCAmelCase_      :      Union[str, Any]      =					os.path.join(training_args.output_dir,	"hans_predictions.txt"						)
        UpperCAmelCase_      :      Dict      =					eval_dataset.get_labels()
        if trainer.is_world_master():
           with open(__lowerCamelCase,	"w"						) as writer:
              writer.write("pairID,gold_label\n"						)
              for pid, pred in zip(__lowerCamelCase,	__lowerCamelCase						):
                 writer.write("ex" + str(__lowerCamelCase						) + "," + label_list[int(__lowerCamelCase						)] + "\n"						)
  
        trainer._log(output.metrics						)
  
  
  
  
  
  
  
  def 			__a  (	__lowerCamelCase						):
     # For xla_spawn (TPUs)
     main()
  
  
  if __name__ == "__main__":
      main()
  
  
  
style_context: 
  """simple docstring"""
  
  
  
  
  
  def 			__a  (	__lowerCamelCase = 1000						):
     UpperCAmelCase_      :      List[Any]      =					0
     UpperCAmelCase_      :      List[str]      =					(n - 1) // 3
     total += ((terms) * (6 + (terms - 1) * 3)) // 2  # total of an A.P.
     UpperCAmelCase_      :      Any      =					(n - 1) // 5
     total += ((terms) * (10 + (terms - 1) * 5)) // 2
     UpperCAmelCase_      :      Optional[int]      =					(n - 1) // 15
     total -= ((terms) * (30 + (terms - 1) * 15)) // 2
     return total
  
  
  if __name__ == "__main__":
      print(f"""{solution() = }""")
  
  
  