

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    	=			logging.getLogger(__name__)



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



@dataclass
class 						UpperCAmelCase					:
      UpperCamelCase_    :	Union[str, Any]		   =       field(
          metadata={"help": "The name of the task to train selected in the list: " + ", ".join(hans_processors.keys()  )}  )
      UpperCamelCase_    :	List[str]		   =       field(
          metadata={"help": "The input data dir. Should contain the .tsv files (or other data files) for the task."}  )
      UpperCamelCase_    :	List[Any]		   =       field(
          default=1_2_8     ,							metadata={
              "help": (
                  "The maximum total input sequence length after tokenization. Sequences longer "
                  "than this will be truncated, sequences shorter will be padded."
              )
          }     ,							)
      UpperCamelCase_    :	int		   =       field(
          default=__lowerCamelCase     ,							metadata={"help": "Overwrite the cached training and evaluation sets"}  )



def    snake_case_(_UpperCamelCase  )							->			Dict[str, torch.Tensor]:



  """simple docstring"""






  _snake_case		=							default_data_collator(__lowerCamelCase  )
  _snake_case		=							batch.pop('''pairID'''				, __lowerCamelCase  )
  return batch



def    snake_case_()							->			Optional[Any]:



  """simple docstring"""






  _snake_case		=							HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)  )
  _snake_case		=							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:
    _snake_case		=							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.

  _snake_case		=							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				, )
  _snake_case		=							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				, )
  _snake_case		=							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
  _snake_case		=							(
      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
  )
  _snake_case		=							(
      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
  _snake_case		=							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 ***'''  )

    _snake_case		=							trainer.predict(__lowerCamelCase  )
    _snake_case		=							output.predictions
    _snake_case		=							np.argmax(__lowerCamelCase				, axis=1  )

    _snake_case		=							[ex.pairID for ex in eval_dataset]
    _snake_case		=							os.path.join(training_args.output_dir				, '''hans_predictions.txt'''  )
    _snake_case		=							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    snake_case_(_UpperCamelCase  )							->			Optional[Any]:



  """simple docstring"""






  main()


if __name__ == "__main__":
 main()




from __future__ import annotations



def    snake_case_(_UpperCamelCase  )							->			int | float:



  """simple docstring"""






  if len(_UpperCamelCase  ) == 0:
    raise ValueError('''find_max() arg is an empty sequence'''  )
  _snake_case		=							nums[0]
  for x in nums:
    if x > max_num:
      _snake_case		=							x
  return max_num


if __name__ == "__main__":
 import doctest

 doctest.testmod(verbose=True)

