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
  
  
  _A							    =	logging.getLogger(__name__)
  
  
  @dataclass
  class 							_lowerCAmelCase		:
  				lowerCamelCase__									=    field(
  				    metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}	)
  				lowerCamelCase__									=    field(
  				    default=UpperCamelCase__  ,   metadata={'help': 'Pretrained config name or path if not the same as model_name'}	)
  				lowerCamelCase__									=    field(
  				    default=UpperCamelCase__  ,   metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}	)
  				lowerCamelCase__									=    field(
  				    default=UpperCamelCase__  ,   metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}  ,   )
  
  
  
  
  
  
  
  @dataclass
  class 							_lowerCAmelCase		:
  				lowerCamelCase__									=    field(
  				    metadata={'help': 'The name of the task to train selected in the list: ' + ', '.join(hans_processors.keys()	)}	)
  				lowerCamelCase__									=    field(
  				    metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'}	)
  				lowerCamelCase__									=    field(
  				    default=1_28  ,   metadata={
  				        'help': (
  				            'The maximum total input sequence length after tokenization. Sequences longer '
  				            'than this will be truncated, sequences shorter will be padded.'
  				        )
  				    }  ,   )
  				lowerCamelCase__									=    field(
  				    default=UpperCamelCase__  ,   metadata={'help': 'Overwrite the cached training and evaluation sets'}	)
  
  
  
  
  
  
  
  def 						lowerCAmelCase_       (	__a )       ->			Dict[str, torch.Tensor]:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							SCREAMING_SNAKE_CASE			: List[Any]	     =default_data_collator(__a )
  							SCREAMING_SNAKE_CASE			: Optional[int]	     =batch.pop('''pairID'''	,	__a )
  							return batch
  def 						lowerCAmelCase_       (	)       ->			Dict:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							SCREAMING_SNAKE_CASE			: Union[str, Any]	     =HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
  							SCREAMING_SNAKE_CASE     ,							SCREAMING_SNAKE_CASE     ,							SCREAMING_SNAKE_CASE			: Optional[int]	     =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:
  														SCREAMING_SNAKE_CASE			: Dict	     =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			: Union[str, Any]	     =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	,	)
  							SCREAMING_SNAKE_CASE			: Union[str, 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	,	)
  							SCREAMING_SNAKE_CASE			: Optional[Any]	     =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
  							SCREAMING_SNAKE_CASE			: List[Any]	     =(
  							    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
  							)
  							SCREAMING_SNAKE_CASE			: Union[str, Any]	     =(
  							    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
  							SCREAMING_SNAKE_CASE			: str	     =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 ***''' )
  
  														SCREAMING_SNAKE_CASE			: Tuple	     =trainer.predict(__a )
  														SCREAMING_SNAKE_CASE			: Union[str, Any]	     =output.predictions
  														SCREAMING_SNAKE_CASE			: List[Any]	     =np.argmax(__a	,	axis=1 )
  
  														SCREAMING_SNAKE_CASE			: List[Any]	     =[ex.pairID for ex in eval_dataset]
  														SCREAMING_SNAKE_CASE			: Union[str, Any]	     =os.path.join(training_args.output_dir	,	'''hans_predictions.txt''' )
  														SCREAMING_SNAKE_CASE			: int	     =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 						lowerCAmelCase_       (	__a )       ->			Dict:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							main()
  
  
  if __name__ == "__main__":
  							main()
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_flax_available,
      is_tf_available,
      is_torch_available,
  )
  
  
  _A							    =	{"""configuration_regnet""": ["""REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RegNetConfig"""]}
  
  try:
  							if not is_torch_available():
  														raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  							pass
  else:
  							_A							    =	[
  							    """REGNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
  							    """RegNetForImageClassification""",
  							    """RegNetModel""",
  							    """RegNetPreTrainedModel""",
  							]
  
  try:
  							if not is_tf_available():
  														raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  							pass
  else:
  							_A							    =	[
  							    """TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
  							    """TFRegNetForImageClassification""",
  							    """TFRegNetModel""",
  							    """TFRegNetPreTrainedModel""",
  							]
  
  try:
  							if not is_flax_available():
  														raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  							pass
  else:
  							_A							    =	[
  							    """FlaxRegNetForImageClassification""",
  							    """FlaxRegNetModel""",
  							    """FlaxRegNetPreTrainedModel""",
  							]
  
  
  if TYPE_CHECKING:
  							from .configuration_regnet import REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP, RegNetConfig
  
  							try:
  														if not is_torch_available():
  																					raise OptionalDependencyNotAvailable()
  							except OptionalDependencyNotAvailable:
  														pass
  							else:
  														from .modeling_regnet import (
  														    REGNET_PRETRAINED_MODEL_ARCHIVE_LIST,
  														    RegNetForImageClassification,
  														    RegNetModel,
  														    RegNetPreTrainedModel,
  														)
  
  							try:
  														if not is_tf_available():
  																					raise OptionalDependencyNotAvailable()
  							except OptionalDependencyNotAvailable:
  														pass
  							else:
  														from .modeling_tf_regnet import (
  														    TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST,
  														    TFRegNetForImageClassification,
  														    TFRegNetModel,
  														    TFRegNetPreTrainedModel,
  														)
  
  							try:
  														if not is_flax_available():
  																					raise OptionalDependencyNotAvailable()
  							except OptionalDependencyNotAvailable:
  														pass
  							else:
  														from .modeling_flax_regnet import (
  														    FlaxRegNetForImageClassification,
  														    FlaxRegNetModel,
  														    FlaxRegNetPreTrainedModel,
  														)
  
  
  else:
  							import sys
  
  							_A							    =	_LazyModule(__name__, globals()["""__file__"""], _import_structure)
  
  
  
  
  
  
  