code: 
  
  
  
  
  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
  
  
  SCREAMING_SNAKE_CASE__    :    str								=					logging.getLogger(__name__)
  
  
  
  @dataclass
  class 			a_					:
  	A     :   List[Any]						       =   field(
  	    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}    )
  	A     :   List[Any]						       =   field(
  	    default=SCREAMING_SNAKE_CASE__     ,						metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}    )
  	A     :   Union[str, Any]						       =   field(
  	    default=SCREAMING_SNAKE_CASE__     ,						metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}    )
  	A     :   Optional[int]						       =   field(
  	    default=SCREAMING_SNAKE_CASE__     ,						metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}     ,						)
  
  
  
  
  @dataclass
  class 			a_					:
  	A     :   Tuple						       =   field(
  	    metadata={'''help''': '''The name of the task to train selected in the list: ''' + ''', '''.join(hans_processors.keys()    )}    )
  	A     :   Optional[Any]						       =   field(
  	    metadata={'''help''': '''The input data dir. Should contain the .tsv files (or other data files) for the task.'''}    )
  	A     :   str						       =   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     :   Dict						       =   field(
  	    default=SCREAMING_SNAKE_CASE__     ,						metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}    )
  
  
  
  
  def lowercase						(     SCREAMING_SNAKE_CASE   ) ->       Dict[str, torch.Tensor]:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			SCREAMING_SNAKE_CASE_			=	default_data_collator(SCREAMING_SNAKE_CASE   )
  			SCREAMING_SNAKE_CASE_			=	batch.pop('pairID'					,						SCREAMING_SNAKE_CASE   )
  			return batch
  
  def lowercase						(     ) ->       Union[str, Any]:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			SCREAMING_SNAKE_CASE_			=	HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)   )
  			SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	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'					,						SCREAMING_SNAKE_CASE   )
  
  			# Set seed
  			set_seed(training_args.seed   )
  
  			try:
  						SCREAMING_SNAKE_CASE_			=	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.
  
  			SCREAMING_SNAKE_CASE_			=	AutoConfig.from_pretrained(
  			    model_args.config_name if model_args.config_name else model_args.model_name_or_path					,						num_labels=SCREAMING_SNAKE_CASE					,						finetuning_task=data_args.task_name					,						cache_dir=model_args.cache_dir					,						)
  			SCREAMING_SNAKE_CASE_			=	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					,						)
  			SCREAMING_SNAKE_CASE_			=	AutoModelForSequenceClassification.from_pretrained(
  			    model_args.model_name_or_path					,						from_tf=bool('.ckpt' in model_args.model_name_or_path   )					,						config=SCREAMING_SNAKE_CASE					,						cache_dir=model_args.cache_dir					,						)
  
  			# Get datasets
  			SCREAMING_SNAKE_CASE_			=	(
  			    HansDataset(
  			        data_dir=data_args.data_dir					,						tokenizer=SCREAMING_SNAKE_CASE					,						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
  			)
  			SCREAMING_SNAKE_CASE_			=	(
  			    HansDataset(
  			        data_dir=data_args.data_dir					,						tokenizer=SCREAMING_SNAKE_CASE					,						task=data_args.task_name					,						max_seq_length=data_args.max_seq_length					,						overwrite_cache=data_args.overwrite_cache					,						evaluate=SCREAMING_SNAKE_CASE					,						)
  			    if training_args.do_eval
  			    else None
  			)
  
  			# Initialize our Trainer
  			SCREAMING_SNAKE_CASE_			=	Trainer(
  			    model=SCREAMING_SNAKE_CASE					,						args=SCREAMING_SNAKE_CASE					,						train_dataset=SCREAMING_SNAKE_CASE					,						eval_dataset=SCREAMING_SNAKE_CASE					,						data_collator=SCREAMING_SNAKE_CASE					,						)
  
  			# 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 ***'   )
  
  						SCREAMING_SNAKE_CASE_			=	trainer.predict(SCREAMING_SNAKE_CASE   )
  						SCREAMING_SNAKE_CASE_			=	output.predictions
  						SCREAMING_SNAKE_CASE_			=	np.argmax(SCREAMING_SNAKE_CASE					,						axis=1   )
  
  						SCREAMING_SNAKE_CASE_			=	[ex.pairID for ex in eval_dataset]
  						SCREAMING_SNAKE_CASE_			=	os.path.join(training_args.output_dir					,						'hans_predictions.txt'   )
  						SCREAMING_SNAKE_CASE_			=	eval_dataset.get_labels()
  						if trainer.is_world_master():
  									with open(SCREAMING_SNAKE_CASE					,						'w'   ) as writer:
  												writer.write('pairID,gold_label\n'   )
  												for pid, pred in zip(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ):
  															writer.write('ex' + str(SCREAMING_SNAKE_CASE   ) + ',' + label_list[int(SCREAMING_SNAKE_CASE   )] + '\n'   )
  
  						trainer._log(output.metrics   )
  
  def lowercase						(     SCREAMING_SNAKE_CASE   ) ->       List[Any]:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			main()
  
  
  if __name__ == "__main__":
  				main()
  
style_context: 
  
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
  
  
  SCREAMING_SNAKE_CASE__    :    List[Any]								=					{"configuration_detr": ["DETR_PRETRAINED_CONFIG_ARCHIVE_MAP", "DetrConfig", "DetrOnnxConfig"]}
  
  try:
  				if not is_vision_available():
  								raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  				pass
  else:
  				SCREAMING_SNAKE_CASE__    :    int								=					["DetrFeatureExtractor"]
  				SCREAMING_SNAKE_CASE__    :    Any								=					["DetrImageProcessor"]
  
  try:
  				if not is_torch_available():
  								raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  				pass
  else:
  				SCREAMING_SNAKE_CASE__    :    List[Any]								=					[
  				    "DETR_PRETRAINED_MODEL_ARCHIVE_LIST",
  				    "DetrForObjectDetection",
  				    "DetrForSegmentation",
  				    "DetrModel",
  				    "DetrPreTrainedModel",
  				]
  
  
  if TYPE_CHECKING:
  				from .configuration_detr import DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DetrConfig, DetrOnnxConfig
  
  				try:
  								if not is_vision_available():
  												raise OptionalDependencyNotAvailable()
  				except OptionalDependencyNotAvailable:
  								pass
  				else:
  								from .feature_extraction_detr import DetrFeatureExtractor
  								from .image_processing_detr import DetrImageProcessor
  
  				try:
  								if not is_torch_available():
  												raise OptionalDependencyNotAvailable()
  				except OptionalDependencyNotAvailable:
  								pass
  				else:
  								from .modeling_detr import (
  								    DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
  								    DetrForObjectDetection,
  								    DetrForSegmentation,
  								    DetrModel,
  								    DetrPreTrainedModel,
  								)
  
  else:
  				import sys
  
  				SCREAMING_SNAKE_CASE__    :    Tuple								=					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  