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_		:			str										=   logging.getLogger(__name__)
  @dataclass
  class 					A_   :
  
  
      '''simple docstring'''
  
  
  
  
  
  
      a__     							=       field(
          metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}						)
      a__     							=       field(
          default=_a ,     metadata={"help": "Pretrained config name or path if not the same as model_name"}						)
      a__     							=       field(
          default=_a ,     metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}						)
      a__     							=       field(
          default=_a ,     metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"} ,     )
  
  
  
  
  
  
  @dataclass
  class 					A_   :
  
  
      '''simple docstring'''
  
  
  
  
  
  
      a__     							=       field(
          metadata={"help": "The name of the task to train selected in the list: " + ", ".join(hans_processors.keys()						)}						)
      a__     							=       field(
          metadata={"help": "The input data dir. Should contain the .tsv files (or other data files) for the task."}						)
      a__     							=       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."
              )
          } ,     )
      a__     							=       field(
          default=_a ,     metadata={"help": "Overwrite the cached training and evaluation sets"}						)
  
  
  
  
  
  
  def       __a       (   SCREAMING_SNAKE_CASE      )      ->     Dict[str, torch.Tensor]:
  
  
  
  
  
  
      '''simple docstring'''
      __UpperCAmelCase									=			default_data_collator(SCREAMING_SNAKE_CASE      )
      __UpperCAmelCase									=			batch.pop('''pairID'''       ,   SCREAMING_SNAKE_CASE      )
      return batch
  
  
  def       __a       (   )      ->     Dict:
  
  
  
  
  
  
      '''simple docstring'''
      # 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									=			HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)      )
      __UpperCAmelCase					,     __UpperCAmelCase					,     __UpperCAmelCase									=			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:
          __UpperCAmelCase									=			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									=			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       ,   )
      __UpperCAmelCase									=			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									=			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
      __UpperCAmelCase									=			(
          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
      )
      __UpperCAmelCase									=			(
          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
      __UpperCAmelCase									=			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 ***'''      )
  
          __UpperCAmelCase									=			trainer.predict(SCREAMING_SNAKE_CASE      )
          __UpperCAmelCase									=			output.predictions
          __UpperCAmelCase									=			np.argmax(SCREAMING_SNAKE_CASE       ,   axis=1      )
  
          __UpperCAmelCase									=			[ex.pairID for ex in eval_dataset]
          __UpperCAmelCase									=			os.path.join(training_args.output_dir       ,   '''hans_predictions.txt'''      )
          __UpperCAmelCase									=			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       __a       (   SCREAMING_SNAKE_CASE      )      ->     Tuple:
  
  
  
  
  
  
      '''simple docstring'''
      # For xla_spawn (TPUs)
      main()
  
  
  if __name__ == "__main__":
         main()
  
  
  
  
  
style_context: 
  
  import csv
  import json
  import os
  
  import datasets
  
  
  # TODO: Add BibTeX citation
  # Find for instance the citation on arxiv or on the dataset repo/website
  A_		:			Any										=   '\\n@InProceedings{huggingface:dataset,\ntitle = {A great new dataset},\nauthor={huggingface, Inc.\n},\nyear={2020}\n}\n'
  
  # TODO: Add description of the dataset here
  # You can copy an official description
  A_		:			Optional[Any]										=   '\\nThis new dataset is designed to solve this great NLP task and is crafted with a lot of care.\n'
  
  # TODO: Add a link to an official homepage for the dataset here
  A_		:			Dict										=   ''
  
  # TODO: Add the licence for the dataset here if you can find it
  A_		:			Dict										=   ''
  
  # TODO: Add link to the official dataset URLs here
  # The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
  # This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
  A_		:			Union[str, Any]										=   {
      'first_domain': 'https://huggingface.co/great-new-dataset-first_domain.zip',
      'second_domain': 'https://huggingface.co/great-new-dataset-second_domain.zip',
  }
  class 					A_   (      datasets.GeneratorBasedBuilder						):
  
  
      '''simple docstring'''
  
  
  
  
  
  
      a__     							=       datasets.Version("1.1.0"						)
  
      # This is an example of a dataset with multiple configurations.
      # If you don't want/need to define several sub-sets in your dataset,
      # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
  
      # If you need to make complex sub-parts in the datasets with configurable options
      # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
      # BUILDER_CONFIG_CLASS = MyBuilderConfig
  
      # You will be able to load one or the other configurations in the following list with
      # data = datasets.load_dataset('my_dataset', 'first_domain')
      # data = datasets.load_dataset('my_dataset', 'second_domain')
      a__     							=       [
          datasets.BuilderConfig(name="first_domain" ,     version=_a ,     description="This part of my dataset covers a first domain"						),
          datasets.BuilderConfig(name="second_domain" ,     version=_a ,     description="This part of my dataset covers a second domain"						),
      ]
  
      a__     							=       "first_domain"  # It's not mandatory to have a default configuration. Just use one if it make sense.
  
  
      def      lowerCAmelCase_						(self  )     ->      Tuple:
          # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
          if self.config.name == "first_domain":  # This is the name of the configuration selected in BUILDER_CONFIGS above
              __UpperCAmelCase									=			datasets.Features(
                  {
                      '''sentence''': datasets.Value('''string'''  ),
                      '''option1''': datasets.Value('''string'''  ),
                      '''answer''': datasets.Value('''string'''  )
                      # These are the features of your dataset like images, labels ...
                  }  )
          else:  # This is an example to show how to have different features for "first_domain" and "second_domain"
              __UpperCAmelCase									=			datasets.Features(
                  {
                      '''sentence''': datasets.Value('''string'''  ),
                      '''option2''': datasets.Value('''string'''  ),
                      '''second_domain_answer''': datasets.Value('''string'''  )
                      # These are the features of your dataset like images, labels ...
                  }  )
          return datasets.DatasetInfo(
              # This is the description that will appear on the datasets page.
              description=_DESCRIPTION     ,      features=lowercase__     ,      homepage=_HOMEPAGE     ,      license=_LICENSE     ,      citation=_CITATION     ,      )
  
  
      def      lowerCAmelCase_						(self     ,      lowercase__  )     ->      int:
          # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
          # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
  
          # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
          # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
          # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
          __UpperCAmelCase									=			_URLS[self.config.name]
          __UpperCAmelCase									=			dl_manager.download_and_extract(lowercase__  )
          return [
              datasets.SplitGenerator(
                  name=datasets.Split.TRAIN     ,      gen_kwargs={
                      '''filepath''': os.path.join(lowercase__     ,      '''train.jsonl'''  ),
                      '''split''': '''train''',
                  }     ,      ),
              datasets.SplitGenerator(
                  name=datasets.Split.VALIDATION     ,      gen_kwargs={
                      '''filepath''': os.path.join(lowercase__     ,      '''dev.jsonl'''  ),
                      '''split''': '''dev''',
                  }     ,      ),
              datasets.SplitGenerator(
                  name=datasets.Split.TEST     ,      gen_kwargs={
                      '''filepath''': os.path.join(lowercase__     ,      '''test.jsonl'''  ),
                      '''split''': '''test'''
                  }     ,      ),
          ]
  
  
  
  
  
  
      def      lowerCAmelCase_						(self     ,      lowercase__     ,      lowercase__  )     ->      Tuple:
          # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
          # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
          with open(lowercase__     ,      encoding='''utf-8'''  ) as f:
              for key, row in enumerate(lowercase__  ):
                  __UpperCAmelCase									=			json.loads(lowercase__  )
                  if self.config.name == "first_domain":
                      # Yields examples as (key, example) tuples
                      yield key, {
                          "sentence": data["sentence"],
                          "option1": data["option1"],
                          "answer": "" if split == "test" else data["answer"],
                      }
                  else:
                      yield key, {
                          "sentence": data["sentence"],
                          "option2": data["option2"],
                          "second_domain_answer": "" if split == "test" else data["second_domain_answer"],
                      }
  
  
  
  
  