






'''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


__lowerCAmelCase					   =       logging.getLogger(__name__)




@dataclass
class      __SCREAMING_SNAKE_CASE					:


  """simple docstring"""


  _a     :   str			 =    field(
      metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}					)
  _a     :   Optional[str]			 =    field(
      default=__UpperCAmelCase       ,					metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}					)
  _a     :   Optional[str]			 =    field(
      default=__UpperCAmelCase       ,					metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}					)
  _a     :   Optional[str]			 =    field(
      default=__UpperCAmelCase       ,					metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}       ,					)





@dataclass
class      __SCREAMING_SNAKE_CASE					:


  """simple docstring"""


  _a     :   str			 =    field(
      metadata={'''help''': '''The name of the task to train selected in the list: ''' + ''', '''.join(hans_processors.keys()					)}					)
  _a     :   str			 =    field(
      metadata={'''help''': '''The input data dir. Should contain the .tsv files (or other data files) for the task.'''}					)
  _a     :   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.'''
          )
      }       ,					)
  _a     :   bool			 =    field(
      default=__UpperCAmelCase       ,					metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}					)





def 			__UpperCamelCase				(			lowercase_					:     Any      ):
       """simple docstring"""




       a_	    = default_data_collator(lowercase__      )
       a_	    = batch.pop('pairID'      ,					lowercase__      )
       return batch


def 			__UpperCamelCase				(			):
       """simple docstring"""




       a_	    = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)      )
       a_    ,  a_    ,  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'      ,					lowercase__      )

       # 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=lowercase__      ,					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=lowercase__      ,					cache_dir=model_args.cache_dir      ,					)

       # Get datasets
       a_	    = (
           HansDataset(
               data_dir=data_args.data_dir      ,					tokenizer=lowercase__      ,					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=lowercase__      ,					task=data_args.task_name      ,					max_seq_length=data_args.max_seq_length      ,					overwrite_cache=data_args.overwrite_cache      ,					evaluate=lowercase__      ,					)
           if training_args.do_eval
           else None
       )

       # Initialize our Trainer
       a_	    = Trainer(
           model=lowercase__      ,					args=lowercase__      ,					train_dataset=lowercase__      ,					eval_dataset=lowercase__      ,					data_collator=lowercase__      ,					)

       # 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(lowercase__      )
              a_	    = output.predictions
              a_	    = np.argmax(lowercase__      ,					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(lowercase__      ,					'w'      ) as writer:
                            writer.write('pairID,gold_label\n'      )
                            for pid, pred in zip(lowercase__      ,					lowercase__      ):
                                   writer.write('ex' + str(lowercase__      ) + ',' + label_list[int(lowercase__      )] + '\n'      )

              trainer._log(output.metrics      )


def 			__UpperCamelCase				(			lowercase_					:     Optional[int]      ):
       """simple docstring"""




       main()


if __name__ == "__main__":
  main()












'''simple docstring'''







import re


def 			__UpperCamelCase				(			lowercase_					:     int      ):
       """simple docstring"""




       def docstring_decorator(lowercase_					:     int      ):
              a_	    = fn.__doc__
              a_	    = func_doc.split('\n'      )
              a_	    = 0
              while i < len(lowercase_      ) and re.search(R'^\s*Examples?:\s*$'      ,					lines[i]      ) is None:
                     i += 1
              if i < len(lowercase_      ):
                     a_	    = example_docstring
                     a_	    = '\n'.join(lowercase_      )
              else:
                     raise ValueError(
                         F'The function {fn} should have an empty \'Examples:\' in its docstring as placeholder, '
                         F'current docstring is:\n{func_doc}'      )
              a_	    = func_doc
              return fn

       return docstring_decorator




