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
  
  
  _UpperCAmelCase   :      Union[str, Any] 						=  logging.getLogger(__name__)
  
  @dataclass
  class 			UpperCAmelCase				:
  
  
  
  
  
  		"""simple docstring"""
  
  
  
  
  
  		A__  :   str          =    field(
  		    metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'} )
  		A__  :   Optional[str]          =    field(
  		    default=a_  ,		metadata={'help': 'Pretrained config name or path if not the same as model_name'} )
  		A__  :   Optional[str]          =    field(
  		    default=a_  ,		metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'} )
  		A__  :   Optional[str]          =    field(
  		    default=a_  ,		metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}  ,		)
  
  
  
  
  
  
  
  @dataclass
  class 			UpperCAmelCase				:
  
  
  
  
  
  		"""simple docstring"""
  
  
  
  
  
  		A__  :   str          =    field(
  		    metadata={'help': 'The name of the task to train selected in the list: ' + ', '.join(hans_processors.keys() )} )
  		A__  :   str          =    field(
  		    metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'} )
  		A__  :   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.'
  		        )
  		    }  ,		)
  		A__  :   bool          =    field(
  		    default=a_  ,		metadata={'help': 'Overwrite the cached training and evaluation sets'} )
  
  
  
  
  
  
  
  def 						snake_case__	(      UpperCamelCase	) ->      Dict[str, torch.Tensor]:
  		_UpperCamelCase       : List[Any]    =      default_data_collator(UpperCamelCase	)
  		_UpperCamelCase       : Dict    =      batch.pop('''pairID'''			,UpperCamelCase	)
  		return batch
  
  
  
  
  
  
  
  def 						snake_case__	(      ) ->      List[Any]:
  		# 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       : List[str]    =      HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)	)
  		_UpperCamelCase,							_UpperCamelCase,							_UpperCamelCase       : Dict    =      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'''			,UpperCamelCase	)
  
  		# Set seed
  		set_seed(training_args.seed	)
  
  		try:
  				_UpperCamelCase       : List[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       : Any    =      AutoConfig.from_pretrained(
  		    model_args.config_name if model_args.config_name else model_args.model_name_or_path			,num_labels=UpperCamelCase			,finetuning_task=data_args.task_name			,cache_dir=model_args.cache_dir			,)
  		_UpperCamelCase       : Optional[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=UpperCamelCase			,cache_dir=model_args.cache_dir			,)
  
  		# Get datasets
  		_UpperCamelCase       : str    =      (
  		    HansDataset(
  		        data_dir=data_args.data_dir			,tokenizer=UpperCamelCase			,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       : int    =      (
  		    HansDataset(
  		        data_dir=data_args.data_dir			,tokenizer=UpperCamelCase			,task=data_args.task_name			,max_seq_length=data_args.max_seq_length			,overwrite_cache=data_args.overwrite_cache			,evaluate=UpperCamelCase			,)
  		    if training_args.do_eval
  		    else None
  		)
  
  		# Initialize our Trainer
  		_UpperCamelCase       : List[Any]    =      Trainer(
  		    model=UpperCamelCase			,args=UpperCamelCase			,train_dataset=UpperCamelCase			,eval_dataset=UpperCamelCase			,data_collator=UpperCamelCase			,)
  
  		# 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       : Tuple    =      trainer.predict(UpperCamelCase	)
  				_UpperCamelCase       : int    =      output.predictions
  				_UpperCamelCase       : List[str]    =      np.argmax(UpperCamelCase			,axis=1	)
  
  				_UpperCamelCase       : Union[str, Any]    =      [ex.pairID for ex in eval_dataset]
  				_UpperCamelCase       : Any    =      os.path.join(training_args.output_dir			,'''hans_predictions.txt'''	)
  				_UpperCamelCase       : int    =      eval_dataset.get_labels()
  				if trainer.is_world_master():
  						with open(UpperCamelCase			,'''w'''	) as writer:
  								writer.write('''pairID,gold_label\n'''	)
  								for pid, pred in zip(UpperCamelCase			,UpperCamelCase	):
  										writer.write('''ex''' + str(UpperCamelCase	) + ''',''' + label_list[int(UpperCamelCase	)] + '''\n'''	)
  
  				trainer._log(output.metrics	)
  
  
  
  
  
  
  
  def 						snake_case__	(      UpperCamelCase	) ->      Optional[Any]:
  		# For xla_spawn (TPUs)
  		main()
  
  
  if __name__ == "__main__":
  	main()
  
  
  
  
  
  
style_context: 
  '''simple docstring'''
  
  _UpperCAmelCase   :      Optional[int] 						=  """
  # Transformers installation
  ! pip install transformers datasets evaluate
  # To install from source instead of the last release, comment the command above and uncomment the following one.
  # ! pip install git+https://github.com/huggingface/transformers.git
  """
  
  _UpperCAmelCase   :      Union[str, Any] 						=  [{"""type""": """code""", """content""": INSTALL_CONTENT}]
  _UpperCAmelCase   :      Union[str, Any] 						=  {
      """{processor_class}""": """FakeProcessorClass""",
      """{model_class}""": """FakeModelClass""",
      """{object_class}""": """FakeObjectClass""",
  }
  
  
  
  
  
  