






"""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


A_     :					Optional[Any]							=     logging.getLogger(__name__)



@dataclass
class 						a_ :







      '''simple docstring'''

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



@dataclass
class 						a_ :







      '''simple docstring'''

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






def 			lowerCamelCase_    (		_lowerCamelCase							):
 lowerCamelCase__	:     Optional[int]								=		default_data_collator(_lowerCamelCase							)
 lowerCamelCase__	:     Dict								=		batch.pop('pairID'     ,  _lowerCamelCase							)
 return batch






def 			lowerCamelCase_    (		):
 # 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.

 lowerCamelCase__	:     List[Any]								=		HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)							)
 lowerCamelCase__							,	lowerCamelCase__							,	lowerCamelCase__	:     Dict								=		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:
  lowerCamelCase__	:     Union[str, Any]								=		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.

 lowerCamelCase__	:     List[str]								=		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     ,  )
 lowerCamelCase__	:     List[str]								=		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     ,  )
 lowerCamelCase__	:     List[Any]								=		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
 lowerCamelCase__	:     Union[str, 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
 )
 lowerCamelCase__	:     Dict								=		(
     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
 lowerCamelCase__	:     List[str]								=		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 ***'							)

  lowerCamelCase__	:     List[str]								=		trainer.predict(_lowerCamelCase							)
  lowerCamelCase__	:     Dict								=		output.predictions
  lowerCamelCase__	:     List[Any]								=		np.argmax(_lowerCamelCase     ,  axis=1							)

  lowerCamelCase__	:     List[str]								=		[ex.pairID for ex in eval_dataset]
  lowerCamelCase__	:     Dict								=		os.path.join(training_args.output_dir     ,  'hans_predictions.txt'							)
  lowerCamelCase__	:     List[Any]								=		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							):
 # For xla_spawn (TPUs)
 main()


if __name__ == "__main__":
      main()









"""simple docstring"""
from transformers import HfArgumentParser, PyTorchBenchmark, PyTorchBenchmarkArguments






def 			lowerCamelCase_    (		):
 lowerCamelCase__	:     Tuple								=		HfArgumentParser(_lowerCamelCase							)
 try:
  lowerCamelCase__	:     Dict								=		parser.parse_args_into_dataclasses()[0]
 except ValueError as e:
  lowerCamelCase__	:     Optional[int]								=		'Arg --no_{0} is no longer used, please use --no-{0} instead.'
  lowerCamelCase__	:     Optional[Any]								=		' '.join(str(_lowerCamelCase							).split(' '							)[:-1]							)
  lowerCamelCase__	:     int								=		''
  lowerCamelCase__	:     Optional[int]								=		eval(str(_lowerCamelCase							).split(' '							)[-1]							)
  lowerCamelCase__	:     Tuple								=		[]
  for arg in depreciated_args:
   # arg[2:] removes '--'
   if arg[2:] in PyTorchBenchmarkArguments.deprecated_args:
    # arg[5:] removes '--no_'
    full_error_msg += arg_error_msg.format(arg[5:]							)
   else:
    wrong_args.append(_lowerCamelCase							)
  if len(_lowerCamelCase							) > 0:
   lowerCamelCase__	:     Optional[int]								=		full_error_msg + begin_error_msg + str(_lowerCamelCase							)
  raise ValueError(_lowerCamelCase							)

 lowerCamelCase__	:     Any								=		PyTorchBenchmark(args=_lowerCamelCase							)
 benchmark.run()


if __name__ == "__main__":
      main()

