



"""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:				List[str]    							=  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				)     ->			Dict[str, torch.Tensor]:






      '''simple docstring'''


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


def   lowercase__     (			)     ->			Any:






      '''simple docstring'''


      lowercase  :      int					=   HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)				)
      lowercase	,    lowercase	,    lowercase  :      List[str]					=   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  :      Tuple					=   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  :      int					=   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  :      List[str]					=   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  :      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	,      )
          if training_args.do_train
          else None
      )
      lowercase  :      Union[str, Any]					=   (
          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  :      List[str]					=   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  :      Optional[Any]					=   trainer.predict(_UpperCAmelCase				)
            lowercase  :      Tuple					=   output.predictions
            lowercase  :      Union[str, Any]					=   np.argmax(_UpperCAmelCase	,      axis=1				)

            lowercase  :      Optional[int]					=   [ex.pairID for ex in eval_dataset]
            lowercase  :      Dict					=   os.path.join(training_args.output_dir	,      'hans_predictions.txt'				)
            lowercase  :      List[str]					=   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				)     ->			Dict:






      '''simple docstring'''


      main()


if __name__ == "__main__":
     main()





"""simple docstring"""




from arguments import TokenizerTrainingArguments
from datasets import load_dataset
from tqdm import tqdm

from transformers import AutoTokenizer, HfArgumentParser
from transformers.models.gpta.tokenization_gpta import bytes_to_unicode


def   lowercase__     (			_UpperCAmelCase=10				)     ->			Any:






      '''simple docstring'''


      for _ in tqdm(range(0	,      args.n_examples	,      _UpperCAmelCase				)				):
            yield [next(_UpperCAmelCase				)[args.text_column] for _ in range(_UpperCAmelCase				)]


# Configuration
_UpperCamelCase:				Union[str, Any]    							=  HfArgumentParser(TokenizerTrainingArguments)
_UpperCamelCase:				Dict    							=  parser.parse_args()

# Base tokenizer
_UpperCamelCase:				List[Any]    							=  AutoTokenizer.from_pretrained(args.base_tokenizer)
_UpperCamelCase:				int    							=  list(bytes_to_unicode().values())

# Load dataset
_UpperCamelCase:				Union[str, Any]    							=  load_dataset(args.dataset_name, split='train', streaming=True)
_UpperCamelCase:				Union[str, Any]    							=  iter(dataset)


# Training and saving
_UpperCamelCase:				Union[str, Any]    							=  tokenizer.train_new_from_iterator(
    batch_iterator(), vocab_size=args.vocab_size, initial_alphabet=base_vocab
)





new_tokenizer.save_pretrained(args.tokenizer_name, push_to_hub=args.push_to_hub)
