



"""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


_UpperCamelCase:				int    							=  logging.getLogger(__name__)


@dataclass
class   a__		:
   _lowerCamelCase												=				field(
       metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}     )
   _lowerCamelCase												=				field(
       default=_SCREAMING_SNAKE_CASE,  metadata={'help': 'Pretrained config name or path if not the same as model_name'}     )
   _lowerCamelCase												=				field(
       default=_SCREAMING_SNAKE_CASE,  metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}     )
   _lowerCamelCase												=				field(
       default=_SCREAMING_SNAKE_CASE,  metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'},  )




@dataclass
class   a__		:
   _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=_SCREAMING_SNAKE_CASE,  metadata={'help': 'Overwrite the cached training and evaluation sets'}     )




def   lowercase__     (			_UpperCAmelCase				)     ->			List[str]:






      '''simple docstring'''


      lowercase  :      List[Any]					=   default_data_collator(_UpperCAmelCase				)
      lowercase  :      str					=   batch.pop('pairID'	,      _UpperCAmelCase				)
      return batch


def   lowercase__     (			)     ->			Dict:






      '''simple docstring'''


      lowercase  :      int					=   HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)				)
      lowercase	,    lowercase	,    lowercase  :      Tuple					=   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'	,      _UpperCAmelCase				)

      # Set seed
      set_seed(training_args.seed				)

      try:
            lowercase  :      Optional[int]					=   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.

      lowercase  :      Union[str, Any]					=   AutoConfig.from_pretrained(
          model_args.config_name if model_args.config_name else model_args.model_name_or_path	,      num_labels=_UpperCAmelCase	,      finetuning_task=data_args.task_name	,      cache_dir=model_args.cache_dir	,      )
      lowercase  :      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	,      )
      lowercase  :      Optional[int]					=   AutoModelForSequenceClassification.from_pretrained(
          model_args.model_name_or_path	,      from_tf=bool('.ckpt' in model_args.model_name_or_path				)	,      config=_UpperCAmelCase	,      cache_dir=model_args.cache_dir	,      )

      # Get datasets
      lowercase  :      str					=   (
          HansDataset(
              data_dir=data_args.data_dir	,      tokenizer=_UpperCAmelCase	,      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
      )
      lowercase  :      Dict					=   (
          HansDataset(
              data_dir=data_args.data_dir	,      tokenizer=_UpperCAmelCase	,      task=data_args.task_name	,      max_seq_length=data_args.max_seq_length	,      overwrite_cache=data_args.overwrite_cache	,      evaluate=_UpperCAmelCase	,      )
          if training_args.do_eval
          else None
      )

      # Initialize our Trainer
      lowercase  :      Union[str, Any]					=   Trainer(
          model=_UpperCAmelCase	,      args=_UpperCAmelCase	,      train_dataset=_UpperCAmelCase	,      eval_dataset=_UpperCAmelCase	,      data_collator=_UpperCAmelCase	,      )

      # 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 ***'				)

            lowercase  :      str					=   trainer.predict(_UpperCAmelCase				)
            lowercase  :      List[Any]					=   output.predictions
            lowercase  :      Tuple					=   np.argmax(_UpperCAmelCase	,      axis=1				)

            lowercase  :      Dict					=   [ex.pairID for ex in eval_dataset]
            lowercase  :      List[str]					=   os.path.join(training_args.output_dir	,      'hans_predictions.txt'				)
            lowercase  :      Union[str, Any]					=   eval_dataset.get_labels()
            if trainer.is_world_master():
                  with open(_UpperCAmelCase	,      'w'				) as writer:
                        writer.write('pairID,gold_label\n'				)
                        for pid, pred in zip(_UpperCAmelCase	,      _UpperCAmelCase				):
                              writer.write('ex' + str(_UpperCAmelCase				) + ',' + label_list[int(_UpperCAmelCase				)] + '\n'				)

            trainer._log(output.metrics				)


def   lowercase__     (			_UpperCAmelCase				)     ->			Any:






      '''simple docstring'''


      main()


if __name__ == "__main__":
     main()





"""simple docstring"""




import re
import tempfile
import unittest
from pathlib import Path

from datasets.utils.metadata import DatasetMetadata


def   lowercase__     (			_UpperCAmelCase				)     ->			str:






      '''simple docstring'''


      lowercase  :      List[str]					=   min(re.search('^ +'	,      _UpperCAmelCase				).end() if t.startswith(' '				) else 0 for t in string.splitlines()				)
      return "\n".join([line[indent_level:] for line in string.splitlines() if indent_level < len(_UpperCAmelCase				)]				)


_UpperCamelCase:				Any    							=  '\\n---\nlanguage:\n- zh\n- en\ntask_ids:\n- sentiment-classification\n---\n# Begin of markdown\n\nSome cool dataset card\n'

_UpperCamelCase:				Dict    							=  '\\n---\n---\n# Begin of markdown\n\nSome cool dataset card\n'


_UpperCamelCase:				Dict    							=  '\\n# Begin of markdown\n\nSome cool dataset card\n'





class   a__		(						unittest.TestCase     ):


   def        lowercase				(							self      :							int			)    ->       Dict:
         with tempfile.TemporaryDirectory() as tmp_dir:
               lowercase  :      Tuple					=   Path(lowerCAmelCase			) / 'README.md'
               with open(lowerCAmelCase,     'w+'			) as readme_file:
                     readme_file.write(lowerCAmelCase			)
               lowercase  :      List[str]					=   DatasetMetadata.from_readme(lowerCAmelCase			)
               self.assertDictEqual(lowerCAmelCase,     {'language': ['zh', 'en'], 'task_ids': ['sentiment-classification']}			)

               with open(lowerCAmelCase,     'w+'			) as readme_file:
                     readme_file.write(lowerCAmelCase			)
               lowercase  :      str					=   DatasetMetadata.from_readme(lowerCAmelCase			)
               self.assertDictEqual(lowerCAmelCase,     {}			)

               with open(lowerCAmelCase,     'w+'			) as readme_file:
                     readme_file.write(lowerCAmelCase			)
               lowercase  :      Optional[Any]					=   DatasetMetadata.from_readme(lowerCAmelCase			)
               self.assertEqual(lowerCAmelCase,     {}			)






   def        lowercase				(							self      :							Optional[Any]			)    ->       List[Any]:
         lowercase  :      Union[str, Any]					=   _dedent(
             '\\n            annotations_creators:\n            - found\n            language_creators:\n            - found\n            language:\n            - en\n            license:\n            - unknown\n            multilinguality:\n            - monolingual\n            pretty_name: Test Dataset\n            size_categories:\n            - 10K<n<100K\n            source_datasets:\n            - extended|other-yahoo-webscope-l6\n            task_categories:\n            - question-answering\n            task_ids:\n            - open-domain-qa\n            '			)
         assert DatasetMetadata.from_yaml_string(lowerCAmelCase			)

         lowercase  :      Any					=   _dedent(
             '\\n            annotations_creators:\n            - found\n            language:\n            - en\n            license:\n            - unknown\n            multilinguality:\n            - monolingual\n            pretty_name: Test Dataset\n            size_categories:\n            - 10K<n<100K\n            source_datasets:\n            - extended|other-yahoo-webscope-l6\n            task_categories:\n            - question-answering\n            task_ids:\n            - open-domain-qa\n            task_ids:\n            - open-domain-qa\n            '			)
         with self.assertRaises(lowerCAmelCase			):
               DatasetMetadata.from_yaml_string(lowerCAmelCase			)

         lowercase  :      int					=   _dedent(
             '\\n            annotations_creators:\n            - found\n            language_creators:\n            - found\n            language:\n            - en\n            license:\n            - unknown\n            multilinguality:\n            - monolingual\n            pretty_name: Test Dataset\n            size_categories:\n            - 10K<n<100K\n            source_datasets:\n            - extended|other-yahoo-webscope-l6\n            task_categories:\n            - text-classification\n            task_ids:\n            - multi-class-classification\n            paperswithcode_id:\n            - squad\n            configs:\n            - en\n            train-eval-index:\n            - config: en\n              task: text-classification\n              task_id: multi_class_classification\n              splits:\n                train_split: train\n                eval_split: test\n              col_mapping:\n                text: text\n                label: target\n              metrics:\n                - type: accuracy\n                  name: Accuracy\n            extra_gated_prompt: |\n              By clicking on “Access repository” below, you also agree to ImageNet Terms of Access:\n              [RESEARCHER_FULLNAME] (the "Researcher") has requested permission to use the ImageNet database (the "Database") at Princeton University and Stanford University. In exchange for such permission, Researcher hereby agrees to the following terms and conditions:\n              1. Researcher shall use the Database only for non-commercial research and educational purposes.\n            extra_gated_fields:\n              Company: text\n              Country: text\n              I agree to use this model for non-commerical use ONLY: checkbox\n            '			)
         assert DatasetMetadata.from_yaml_string(lowerCAmelCase			)
