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
  
  
  __SCREAMING_SNAKE_CASE     :   Union[str, Any]					=						logging.getLogger(__name__)
  
  
  
  
  
  @dataclass
  class __snake_case		:
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
  
     lowerCAmelCase_				:   str     	=			field(
         metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}      )
     lowerCAmelCase_				:   Optional[str]     	=			field(
         default=a_				,       metadata={'help': 'Pretrained config name or path if not the same as model_name'}      )
     lowerCAmelCase_				:   Optional[str]     	=			field(
         default=a_				,       metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}      )
     lowerCAmelCase_				:   Optional[str]     	=			field(
         default=a_				,       metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}				,       )
  
  
  
  
  
  
  @dataclass
  class __snake_case		:
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
  
     lowerCAmelCase_				:   str     	=			field(
         metadata={'help': 'The name of the task to train selected in the list: ' + ', '.join(hans_processors.keys()      )}      )
     lowerCAmelCase_				:   str     	=			field(
         metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'}      )
     lowerCAmelCase_				:   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.'
             )
         }				,       )
     lowerCAmelCase_				:   bool     	=			field(
         default=a_				,       metadata={'help': 'Overwrite the cached training and evaluation sets'}      )
  
  
  
  
  
  
  def 						__a      (  a					):
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         _a          =				default_data_collator(a					)
         _a          =				batch.pop("pairID",		a					)
         return batch
  
  
  
  def 						__a      (  ):
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         _a          =				HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)					)
         _a          =				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",		a					)
  
         # Set seed
         set_seed(training_args.seed					)
  
         try:
                _a          =				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.
  
         _a          =				AutoConfig.from_pretrained(
             model_args.config_name if model_args.config_name else model_args.model_name_or_path,		num_labels=a,		finetuning_task=data_args.task_name,		cache_dir=model_args.cache_dir,		)
         _a          =				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,		)
         _a          =				AutoModelForSequenceClassification.from_pretrained(
             model_args.model_name_or_path,		from_tf=bool(".ckpt" in model_args.model_name_or_path					),		config=a,		cache_dir=model_args.cache_dir,		)
  
         # Get datasets
         _a          =				(
             HansDataset(
                 data_dir=data_args.data_dir,		tokenizer=a,		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
         )
         _a          =				(
             HansDataset(
                 data_dir=data_args.data_dir,		tokenizer=a,		task=data_args.task_name,		max_seq_length=data_args.max_seq_length,		overwrite_cache=data_args.overwrite_cache,		evaluate=a,		)
             if training_args.do_eval
             else None
         )
  
         # Initialize our Trainer
         _a          =				Trainer(
             model=a,		args=a,		train_dataset=a,		eval_dataset=a,		data_collator=a,		)
  
         # 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 ***"					)
  
                _a          =				trainer.predict(a					)
                _a          =				output.predictions
                _a          =				np.argmax(a,		axis=1					)
  
                _a          =				[ex.pairID for ex in eval_dataset]
                _a          =				os.path.join(training_args.output_dir,		"hans_predictions.txt"					)
                _a          =				eval_dataset.get_labels()
                if trainer.is_world_master():
                       with open(a,		"w"					) as writer:
                              writer.write("pairID,gold_label\n"					)
                              for pid, pred in zip(a,		a					):
                                     writer.write("ex" + str(a					) + "," + label_list[int(a					)] + "\n"					)
  
                trainer._log(output.metrics					)
  
  
  
  def 						__a      (  a					):
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         main()
  
  
  if __name__ == "__main__":
     main()
  
style_context: 
  
  
  """simple docstring"""
  
  
  
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  
  
  __SCREAMING_SNAKE_CASE					=						logging.get_logger(__name__)
  
  __SCREAMING_SNAKE_CASE					=						{
      """bert-base-uncased""": """https://huggingface.co/bert-base-uncased/resolve/main/config.json""",
      # QDQBERT models can be loaded from any BERT checkpoint, available at https://huggingface.co/models?filter=bert
  }
  
  
  
  
  
  class __snake_case		(					_SCREAMING_SNAKE_CASE      ):
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
  
     lowerCAmelCase_				:   Tuple     	=			'qdqbert'
  
  
  
  
     def __init__(		self							:List[Any]				,  UpperCamelCase__							:List[Any]=30_522				,  UpperCamelCase__							:Optional[int]=768				,  UpperCamelCase__							:str=12				,  UpperCamelCase__							:Tuple=12				,  UpperCamelCase__							:Any=3_072				,  UpperCamelCase__							:Union[str, Any]="gelu"				,  UpperCamelCase__							:Dict=0.1				,  UpperCamelCase__							:Any=0.1				,  UpperCamelCase__							:Dict=512				,  UpperCamelCase__							:Optional[int]=2				,  UpperCamelCase__							:Any=0.02				,  UpperCamelCase__							:Optional[int]=1E-12				,  UpperCamelCase__							:str=True				,  UpperCamelCase__							:str=1				,  UpperCamelCase__							:List[Any]=0				,  UpperCamelCase__							:List[str]=2				,  **UpperCamelCase__							:Optional[Any]				,  ):
            super().__init__(pad_token_id=UpperCamelCase__				,  bos_token_id=UpperCamelCase__				,  eos_token_id=UpperCamelCase__				,  **UpperCamelCase__       )
  
            _a          =				vocab_size
            _a          =				max_position_embeddings
            _a          =				hidden_size
            _a          =				num_hidden_layers
            _a          =				num_attention_heads
            _a          =				intermediate_size
            _a          =				hidden_act
            _a          =				hidden_dropout_prob
            _a          =				attention_probs_dropout_prob
            _a          =				initializer_range
            _a          =				type_vocab_size
            _a          =				layer_norm_eps
            _a          =				use_cache
  