
"""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	 =							logging.getLogger(__name__)


@dataclass
class A_			:




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




@dataclass
class A_			:




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







def 			__a  (	__lowerCamelCase						):
   UpperCAmelCase_      :      Tuple      =					default_data_collator(__lowerCamelCase						)
   UpperCAmelCase_      :      int      =					batch.pop("pairID",	__lowerCamelCase						)
   return batch







def 			__a  (	):
   # 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.

   UpperCAmelCase_      :      Any      =					HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)						)
   UpperCAmelCase_  ,     UpperCAmelCase_  ,     UpperCAmelCase_      :      Tuple      =					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:
      UpperCAmelCase_      :      Optional[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.

   UpperCAmelCase_      :      Optional[int]      =					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,	)
   UpperCAmelCase_      :      Any      =					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,	)
   UpperCAmelCase_      :      Dict      =					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
   UpperCAmelCase_      :      List[str]      =					(
       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
   )
   UpperCAmelCase_      :      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,	evaluate=__lowerCamelCase,	)
       if training_args.do_eval
       else None
   )

   # Initialize our Trainer
   UpperCAmelCase_      :      Union[str, Any]      =					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 ***"						)

      UpperCAmelCase_      :      str      =					trainer.predict(__lowerCamelCase						)
      UpperCAmelCase_      :      List[str]      =					output.predictions
      UpperCAmelCase_      :      List[Any]      =					np.argmax(__lowerCamelCase,	axis=1						)

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


if __name__ == "__main__":
    main()




"""simple docstring"""





def 			__a  (	__lowerCamelCase = 1000						):
   UpperCAmelCase_      :      List[Any]      =					0
   UpperCAmelCase_      :      List[str]      =					(n - 1) // 3
   total += ((terms) * (6 + (terms - 1) * 3)) // 2  # total of an A.P.
   UpperCAmelCase_      :      Any      =					(n - 1) // 5
   total += ((terms) * (10 + (terms - 1) * 5)) // 2
   UpperCAmelCase_      :      Optional[int]      =					(n - 1) // 15
   total -= ((terms) * (30 + (terms - 1) * 15)) // 2
   return total


if __name__ == "__main__":
    print(f"""{solution() = }""")


