





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__			:    List[str]			      =		logging.getLogger(__name__)





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





@dataclass
class UpperCAmelCase_							:
  __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=_SCREAMING_SNAKE_CASE    ,		metadata={"""help""": """Overwrite the cached training and evaluation sets"""}   )




def     UpperCAmelCase_						(						_UpperCAmelCase       :List[InputFeatures]							)  ->							Dict[str, torch.Tensor]:
     '''simple docstring'''


     A_					      =					default_data_collator(_UpperCAmelCase							)
     A_					      =					batch.pop('''pairID'''     ,	_UpperCAmelCase							)
     return batch

def     UpperCAmelCase_						(						)  ->							Union[str, Any]:
     '''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'''     ,	_UpperCAmelCase							)

     # 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=_UpperCAmelCase     ,	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=_UpperCAmelCase     ,	cache_dir=model_args.cache_dir     ,	)

     # Get datasets
     A_					      =					(
         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
     )
     A_					      =					(
         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
     A_					      =					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 ***'''							)

          A_					      =					trainer.predict(_UpperCAmelCase							)
          A_					      =					output.predictions
          A_					      =					np.argmax(_UpperCAmelCase     ,	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(_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     UpperCAmelCase_						(						_UpperCAmelCase       :Optional[int]							)  ->							Dict:
     '''simple docstring'''


     main()


if __name__ == "__main__":
      main()










from transformers import HfArgumentParser, PyTorchBenchmark, PyTorchBenchmarkArguments

def     UpperCAmelCase_						(						)  ->							Optional[int]:
     '''simple docstring'''


     A_					      =					HfArgumentParser(_UpperCAmelCase							)
     try:
          A_					      =					parser.parse_args_into_dataclasses()[0]
     except ValueError as e:
          A_					      =					'''Arg --no_{0} is no longer used, please use --no-{0} instead.'''
          A_					      =					''' '''.join(str(_UpperCAmelCase							).split(''' '''							)[:-1]							)
          A_					      =					''''''
          A_					      =					eval(str(_UpperCAmelCase							).split(''' '''							)[-1]							)
          A_					      =					[]
          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(_UpperCAmelCase							)
          if len(_UpperCAmelCase							) > 0:
               A_					      =					full_error_msg + begin_error_msg + str(_UpperCAmelCase							)
          raise ValueError(_UpperCAmelCase							)

     A_					      =					PyTorchBenchmark(args=_UpperCAmelCase							)
     benchmark.run()


if __name__ == "__main__":
      main()



