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__		:       Dict   							=							logging.getLogger(__name__)
  
  
  
  
  
  
  
  @dataclass
  class 							UpperCAmelCase_     :
  					__lowerCamelCase										=     field(
  					    metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}		)
  					__lowerCamelCase										=     field(
  					    default=__lowerCamelCase       ,			metadata={'help': 'Pretrained config name or path if not the same as model_name'}		)
  					__lowerCamelCase										=     field(
  					    default=__lowerCamelCase       ,			metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}		)
  					__lowerCamelCase										=     field(
  					    default=__lowerCamelCase       ,			metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}       ,			)
  
  
  
  
  
  
  
  @dataclass
  class 							UpperCAmelCase_     :
  					__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=128       ,			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=__lowerCamelCase       ,			metadata={'help': 'Overwrite the cached training and evaluation sets'}		)
  
  
  
  
  
  def    _lowerCamelCase	(	__lowerCamelCase					)   -> Dict[str, torch.Tensor]:
  
  					'''simple docstring'''
  					UpperCAmelCase__			:     str										= default_data_collator(__lowerCamelCase					)
  					UpperCAmelCase__			:     Optional[int]										= batch.pop("""pairID"""   ,       __lowerCamelCase					)
  					return batch
  
  
  
  
  
  def    _lowerCamelCase	(	)   -> Optional[Any]:
  
  					'''simple docstring'''
  					UpperCAmelCase__			:     List[str]										= HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)					)
  					UpperCAmelCase__			:     Union[str, Any]										= 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__			:     str										= 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[Any]										= 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__			:     List[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__			:     int										= 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[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   ,       )
  					    if training_args.do_train
  					    else None
  					)
  					UpperCAmelCase__			:     Tuple										= (
  					    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__			:     int										= 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__			:     Dict										= trainer.predict(__lowerCamelCase					)
  										UpperCAmelCase__			:     List[str]										= output.predictions
  										UpperCAmelCase__			:     List[str]										= np.argmax(__lowerCamelCase   ,       axis=1					)
  
  										UpperCAmelCase__			:     Dict										= [ex.pairID for ex in eval_dataset]
  										UpperCAmelCase__			:     Dict										= os.path.join(training_args.output_dir   ,       """hans_predictions.txt"""					)
  										UpperCAmelCase__			:     int										= 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    _lowerCamelCase	(	__lowerCamelCase					)   -> Optional[Any]:
  
  					'''simple docstring'''
  					main()
  
  
  if __name__ == "__main__":
  						main()
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
  
  
  SCREAMING_SNAKE_CASE__		:       Any   							=							{"""configuration_dinat""": ["""DINAT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DinatConfig"""]}
  
  
  try:
        if not is_torch_available():
              raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
        pass
  else:
        SCREAMING_SNAKE_CASE__		:       str   							=							[
            """DINAT_PRETRAINED_MODEL_ARCHIVE_LIST""",
            """DinatForImageClassification""",
            """DinatModel""",
            """DinatPreTrainedModel""",
            """DinatBackbone""",
        ]
  
  if TYPE_CHECKING:
        from .configuration_dinat import DINAT_PRETRAINED_CONFIG_ARCHIVE_MAP, DinatConfig
  
        try:
              if not is_torch_available():
                    raise OptionalDependencyNotAvailable()
        except OptionalDependencyNotAvailable:
              pass
        else:
              from .modeling_dinat import (
                  DINAT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  DinatBackbone,
                  DinatForImageClassification,
                  DinatModel,
                  DinatPreTrainedModel,
              )
  
  else:
        import sys
  
        SCREAMING_SNAKE_CASE__		:       Dict   							=							_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
  
  
  
  
  
  
  